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Given a maze (e.g., in a book of puzzles), you might solve it by drawing out paths with your pencil. But even without a pencil, you might naturally find yourself mentally tracing along various paths. This "mental path tracing" may intuitively seem to depend on your (overt, conscious, voluntary) goal of wanting to get out of the maze, but might it also occur spontaneously—as a result of simply seeing the maze, via a kind of dynamic visual routine? Here, observers simply had to compare the visual properties of two probes presented in a maze. The maze itself was entirely task irrelevant, but we predicted that simply seeing the maze's visual structure would "afford" incidental mental path tracing (à la Gibson). Across four experiments, observers were slower to compare probes that were further from each other along the paths, even when controlling for lower level properties (such as the probes' brute linear separation, ignoring the maze "walls"). These results also generalized beyond mazes to other unfamiliar displays with task-irrelevant circular obstacles. This novel combination of two prominent themes from our field—affordances and visual routines—suggests that at least some visual routines may not require voluntary goals; instead, they may operate in an automatic (incidental, stimulus-driven) fashion, as a part of visual processing itself. Public Significance Statement: What goes on in your mind when you solve a maze, for example in a book of puzzles? Normally, this might involve tracing through the paths with a pencil or finger. But not necessarily: You can also solve a maze just by looking. Here we show that this mental path tracing also occurs when you are not trying to solve such puzzles (and with unfamiliar sorts of mazes, without entrances or exits): Even when just passively viewing such displays, your mind automatically and spontaneously traces the paths between salient points. We demonstrated this by showing that the time it takes to compare two small probes that appear in a maze (which is itself entirely irrelevant to the task) is a function of how long the path between them is (as opposed to the brute linear distance between them). This reveals how even such simple stimuli may engage surprisingly sophisticated dynamic visual processing. [ABSTRACT FROM AUTHOR]

The track tracking effect of intelligent vehicle directly affects the safety of vehicle and passengers. In the process of intelligent vehicle track tracking, the track tracking accuracy is related to many factors such as track curvature, road friction coefficient, and longitudinal speed change. In this paper, a new lateral and longitudinal coupling control algorithm is proposed. Based on the vehicle dynamics model and the optimal control theory, combined with the fuzzy control theory, the Fuzzy Linear Quadratic Regulator (FLQR) lateral optimal controller is designed, and feed-forward control and predictive controller are added. According to the real-time tracking lateral error and fuzzy control algorithm fed back by the system, the weight coefficient of the lateral displacement deviation in the cost function is dynamically adjusted; considering the coupling effect of lateral and longitudinal controllers of vehicle trajectory tracking control, a model predictive control (MPC) longitudinal speed controller is designed based on MPC theory, considering acceleration constraint and acceleration variation constraint, and taking lateral stability as evaluation index. A joint simulation platform is built based on CarSim and Simulink. The simulation results show that the designed lateral and longitudinal coupling controller of FLQR + MPC has better track tracking accuracy and can improve the driving stability of the vehicle; finally, the tracking effect of the designed algorithm is verified by real vehicle experiments. The maximum error of the designed controller algorithm in real vehicle tracking is 0.56 m, and the tracking effect is good. [ABSTRACT FROM AUTHOR]

Obstructive sleep apnea (OSA) is a common sleep breathing disorder that is often accompanied by changes in structural connectivity (SC) and functional connectivity (FC). However, the current understanding of the interaction between SC and FC in OSA is still limited. The aim of this study is to integrate complementary neuroimaging modalities into a unified framework using multi-layer network analysis methods and to reveal their complex interrelationships. We introduce a new graph metric called SC-FC bandwidth, which measures the throughput of SC mediating FC in a multi-layer network. The bandwidth differences between two groups are evaluated using the network-based statistics (NBS) method. Additionally, we traced and analyzed the SC pathways corresponding to the abnormal bandwidth. In both the healthy control and patients with OSA, the majority offunctionally synchronized nodes were connected via SC paths of length 2. With the NBS method, we observed significantly lower bandwidth between the right Posterior cingulate gyrus and right Cuneus, bilateral Middle frontal gyrus, bilateral Gyrus rectus in OSA patients. By tracing the high-proportion SC pathways, it was found that OSA patients typically exhibit a decrease in direct SC-FC, SC-FC triangles, and SC-FC quads intra- and inter-networks. Complex interrelationship changes have been observed between the SC and FC in patients with OSA, which might leads to abnormal information transmission and communication in the brain network. • We have introduced a new graph theory metric called SC-FC bandwidth. • We observed a significant reduction in bandwidth between multiple brain regions in patients with OSA. • Patients with OSA show decreases in direct SC-FC, SC-FC triangles, and SC-FC quads intra- and inter-network. [ABSTRACT FROM AUTHOR]

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IntroductionUltrasound is well-established as an imaging modality for diagnostic and interventional purposes. However, the image quality varies with operator skills as acquiring and interpreting ultrasound images requires extensive training due to the imaging artefacts, the range of acquisition parameters and the variability of patient anatomies. Automating the image acquisition task could improve acquisition reproducibility and quality but training such an algorithm requires large amounts of navigation data, not saved in routine examinations.MethodsWe propose a method to generate large amounts of ultrasound images from other modalities and from arbitrary positions, such that this pipeline can later be used by learning algorithms for navigation. We present a novel simulation pipeline which uses segmentations from other modalities, an optimized volumetric data representation and GPU-accelerated Monte Carlo path tracing to generate view-dependent and patient-specific ultrasound images.ResultsWe extensively validate the correctness of our pipeline with a phantom experiment, where structures' sizes, contrast and speckle noise properties are assessed. Furthermore, we demonstrate its usability to train neural networks for navigation in an echocardiography view classification experiment by generating synthetic images from more than 1,000 patients. Networks pre-trained with our simulations achieve significantly superior performance in settings where large real datasets are not available, especially for under-represented classes.DiscussionThe proposed approach allows for fast and accurate patient-specific ultrasound image generation, and its usability for training networks for navigation-related tasks is demonstrated.

Copyright of Journal of Chongqing University of Technology (Natural Science) is the property of Chongqing University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Aiming at the space target detection application of a space-based terahertz (THz) radar, according to the imaging mechanism of broadband THz radars, a THz radar imaging simulation method based on improved path tracing is proposed. Firstly, the characterization method of THz scattering characteristics based on Kirchhoff's approximation method is introduced. The multi-parameter THz bidirectional reflectance distribution function (THz-BRDF) models of aluminum (Al), white-painted Al, and polyimide film at 0.215 THz are fitted according to the theoretical data, with fitting errors below 4%. Then, the THz radar imaging simulation method based on improved path tracing is presented in detail. The simulation method utilizes path tracing to simulate parallelized THz radar echo signal data, considering multi-path energy scattering based on the THz-BRDF model. Finally, we conducted THz radar imaging simulation experiments. The influences in the imaging process of different fly-around motions are analyzed, and a comparison experiment is conducted with the fast-physical optics (FPO) method. The comparative results indicate that the proposed method exhibits richer and more realistic features compared with the FPO method. The simulation experiments results demonstrate that the proposed method can provide a data source for ground algorithm testing of THz radars, particularly in evaluating the target detection and recognition algorithm based on deep learning, providing strong support for the application of space-based THz radars in the future. [ABSTRACT FROM AUTHOR]

We present a straightforward approach for reconstructing 3D celadon models from a single 2D image. The celadon is a historical example of the surface of revolution. Our approach uses a surface of revolution technique to generate the basic shape of the celadon and then applies texture mapping to create a realistic appearance. The process involves detecting the contour and corners of the celadon image, determining an axis of revolution, generating a profile curve, and finally constructing a 3D celadon model. Additionally, we create models as triangular meshes at multiple resolutions, employing a B-spline curve as the profile curve. It enhances the adaptability of the models for various purposes. We render various scenes using a path tracer to assess the suitability of the generated 3D celadon models and generate a VR celadon museum with the models. Overall, our approach offers a simple and efficient solution for reconstructing a 3D celadon model, generating VR content, and demonstrating extensive applicability across numerous disciplines. [ABSTRACT FROM AUTHOR]

Fast holographic generation technology depends on the swift creation of computer-generated holograms (CGHs). Currently, the fast generation method of computer-generated holograms based on ray tracing is still based on non-physical ray tracing methods, making it difficult to accurately simulate and generate computer-generated holograms. There is a scarcity of research on employing path tracing to swiftly generate CGH with a minimal computational workload and reduced computation time. To address this issue, we employed physically-based path tracing technique for realistic rendering and made improvements on the bidirectional scattering distribution function (BSDF) and ray sampling methods. We have developed a path tracing technique based on improved BSDF and single ray sampling to generate CGHs with realism. In comparison to conventional point cloud and ray tracing methods, the path tracing approach employs distinct shading rendering and lighting models to enhance a scene's sense of realism and generate CGHs rapidly. Our proposed technique takes approximately 10 ms to generate CGHs in various scenes, and the reconstructed images featuring photorealistic appearances are produced through optical reconstruction experiments. This novel method offers an efficient solution for generating CGHs and we attempted to generate a sequence using our method, applying our method to rapidly generate CGH videos.

Copyright of Journal of Computer-Aided Design & Computer Graphics / Jisuanji Fuzhu Sheji Yu Tuxingxue Xuebao is the property of Gai Kan Bian Wei Hui and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

With the development in agricultural mechanization and information technology, orchard agricultural machinery is also constantly pursuing the goal of intelligence and efficiency. Fruit picking is the most labor-intensive part of the orchard harvesting process. In order to resolve the problems of high labor intensity, low picking efficiency, and labor shortage when harvesting dwarf high-density orchards, an automatic navigation fruit-picking platform with voice control was developed in the present study. First, the platform utilized a voice-controlled high-level extendable working platform and a fruit-box-lifting device to adapt to varying orchard planting row spacing and enable convenient fruit box loading and unloading. Second, an automatic navigation system, which employed China's Beidou navigation satellite system to acquire positional data and the Stanley algorithm for path-tracking control, was implemented. Third, the fruit-picking platform with automatic navigation system was fabricated and assembled, its outer wheel steering was measured to have a maximum angle of 30.3° and an average minimum turning radius of 4.5 m, meeting the turning radius requirements under orchard conditions. Finally, automatic navigation tests of the developed platform were performed in the orchard conditions. The results indicated that the platform could maintain a straight-line path with a maximum lateral deviation of 101.5 mm and a maximum absolute average deviation of 44.1 mm at 0.4 m/s. Under the U-shaped paths, the measured maximum lateral deviation was 148.6 mm and the maximum absolute average deviation was 57.2 mm. The navigation accuracy was sufficient to meet the requirements for the harvesting platform's routine operation in the orchards. [ABSTRACT FROM AUTHOR]

This study aims to compare a relatively novel three-dimensional rendering called Path Tracing (PT) to the Volume Rendering technique (VR) in the post-surgical assessment of head and neck oncologic surgery followed by bone flap reconstruction. This retrospective study included 39 oncologic patients who underwent head and neck surgery with free bone flap reconstructions. All exams were acquired using a 64 Multi-Detector CT (MDCT). PT and VR images were created on a dedicated workstation. Five readers, with different expertise in bone flap reconstructive surgery, independently reviewed the images (two radiologists, one head and neck surgeon and two otorhinolaryngologists, respectively). Every observer evaluated the images according to a 5-point Likert scale. The parameters assessed were image quality, anatomical accuracy, bone flap evaluation, and metal artefact. Mean and median values for all the parameters across the observer were calculated. The scores of both reconstruction methods were compared using a Wilcoxon matched-pairs signed rank test. Inter-reader agreement was calculated using Spearman's rank correlation coefficient. PT was considered significantly superior to VR 3D reconstructions by all readers (p < 0.05). Inter-reader agreement was moderate to strong across four out of five readers. The agreement was stronger with PT images compared to VR images. In conclusion, PT reconstructions are significantly better than VR ones. Although they did not modify patient outcomes, they may improve the post-surgical evaluation of bone-free flap reconstructions following major head and neck surgery. [ABSTRACT FROM AUTHOR]

We propose an adaptive sampling and reconstruction method for offline Monte Carlo rendering. Our method produces sampling maps constrained by a user-defined budget that minimize the expected future denoising error. Compared to other state-of-the-art methods, which produce the necessary training data on the fly by composing pre-rendered images, our method samples from analytic noise distributions instead. These distributions are compact and closely approximate the pixel value distributions stemming from Monte Carlo rendering. Our method can efficiently sample training data by leveraging only a few per-pixel statistics of the target distribution, which provides several benefits over the current state of the art. Most notably, our analytic distributions' modeling accuracy and sampling efficiency increase with sample count, essential for high-quality offline rendering. Although our distributions are approximate, our method supports joint end-to-end training of the sampling and denoising networks. Finally, we propose the addition of a global summary module to our architecture that accumulates valuable information from image regions outside of the network's receptive field. This information discourages sub-optimal decisions based on local information. Our evaluation against other state-of-the-art neural sampling methods demonstrates denoising quality and data efficiency improvements. [ABSTRACT FROM AUTHOR]

Aiming at the space target detection application of a space-based terahertz (THz) radar, according to the imaging mechanism of broadband THz radars, a THz radar imaging simulation method based on improved path tracing is proposed. Firstly, the characterization method of THz scattering characteristics based on Kirchhoff’s approximation method is introduced. The multi-parameter THz bidirectional reflectance distribution function (THz-BRDF) models of aluminum (Al), white-painted Al, and polyimide film at 0.215 THz are fitted according to the theoretical data, with fitting errors below 4%. Then, the THz radar imaging simulation method based on improved path tracing is presented in detail. The simulation method utilizes path tracing to simulate parallelized THz radar echo signal data, considering multi-path energy scattering based on the THz-BRDF model. Finally, we conducted THz radar imaging simulation experiments. The influences in the imaging process of different fly-around motions are analyzed, and a comparison experiment is conducted with the fast-physical optics (FPO) method. The comparative results indicate that the proposed method exhibits richer and more realistic features compared with the FPO method. The simulation experiments results demonstrate that the proposed method can provide a data source for ground algorithm testing of THz radars, particularly in evaluating the target detection and recognition algorithm based on deep learning, providing strong support for the application of space-based THz radars in the future.

We present a straightforward approach for reconstructing 3D celadon models from a single 2D image. The celadon is a historical example of the surface of revolution. Our approach uses a surface of revolution technique to generate the basic shape of the celadon and then applies texture mapping to create a realistic appearance. The process involves detecting the contour and corners of the celadon image, determining an axis of revolution, generating a profile curve, and finally constructing a 3D celadon model. Additionally, we create models as triangular meshes at multiple resolutions, employing a B-spline curve as the profile curve. It enhances the adaptability of the models for various purposes. We render various scenes using a path tracer to assess the suitability of the generated 3D celadon models and generate a VR celadon museum with the models. Overall, our approach offers a simple and efficient solution for reconstructing a 3D celadon model, generating VR content, and demonstrating extensive applicability across numerous disciplines.

As scenes become ever more complex and real-time applications embrace ray tracing, path sampling algorithms that maximize quality at low sample counts become vital. Recent resampling algorithms building on Talbot et al.'s [2005] resampled importance sampling (RIS) reuse paths spatiotemporally to render surprisingly complex light transport with a few samples per pixel. These reservoir-based spatiotemporal importance resamplers (ReSTIR) and their underlying RIS theory make various assumptions, including sample independence. But sample reuse introduces correlation, so ReSTIR-style iterative reuse loses most convergence guarantees that RIS theoretically provides. We introduce generalized resampled importance sampling (GRIS) to extend the theory, allowing RIS on correlated samples, with unknown PDFs and taken from varied domains. This solidifies the theoretical foundation, allowing us to derive variance bounds and convergence conditions in ReSTIR-based samplers. It also guides practical algorithm design and enables advanced path reuse between pixels via complex shift mappings. We show a path-traced resampler (ReSTIR PT) running interactively on complex scenes, capturing many-bounce diffuse and specular lighting while shading just one path per pixel. With our new theoretical foundation, we can also modify the algorithm to guarantee convergence for offline renderers. [ABSTRACT FROM AUTHOR]

Physical light transport (PLT) algorithms can represent the wave nature of light globally in a scene, and are consistent with Maxwell's theory of electromagnetism. As such, they are able to reproduce the wave-interference and diffraction effects of real physical optics. However, the recent works that have proposed PLT are too expensive to apply to real-world scenes with complex geometry and materials. To address this problem, we propose a novel framework for physical light transport based on several key ideas that actually makes PLT practical for complex scenes. First, we restrict the spatial coherence shape of light to an anisotropic Gaussian and justify this restriction with general arguments based on entropy. This restriction serves to simplify the rest of the derivations, without practical loss of generality. To describe partially-coherent light, we present new rendering primitives that generalize the radiometric radiance and irradiance, and are based on the well-known Stokes parameters. We are able to represent light of arbitrary spectral content and states of polarization, and with any coherence volume and anisotropy. We also present the wave BSDF to accurately render diffractions and wave-interference effects. Furthermore, we present an approach to importance sample this wave BSDF to facilitate bi-directional path tracing, which has been previously impossible. We show good agreement with state-of-the-art methods, but unlike them we are able to render complex scenes where all the materials are new, coherence-aware physical optics materials, and with performance approaching that of "classical" rendering methods. [ABSTRACT FROM AUTHOR]

Many applications ranging from quasi-Monte Carlo integration over optimal control to neural networks benefit from high-dimensional, highly uniform samples. In the case of computer graphics, and more particularly in rendering, despite the need for uniformity, several sub-problems expose a low-dimensional structure. In this context, mastering sampling uniformity over projections while preserving high-dimensional uniformity has been intrinsically challenging. This difficulty may explain the relatively small number of mathematical constructions for such samplers. We propose a novel approach by showing that uniformity constraints can be expressed as an integer linear program that results in a sampler with the desired properties. As it turns out, complex constraints are easy to describe by means of stratification and sequence properties of digital nets. Formalized using generator matrix determinants, our new MatBuilder software solves the set of constraints by iterating the linear integer program solver in a greedy fashion to compute a problem-specific set of generator matrices that can be used as a drop-in replacement in the popular digital net samplers. The samplers created by MatBuilder achieve the uniformity of classic low discrepancy sequences. More importantly, we demonstrate the benefit of the unprecedented versatility of our constraint approach with respect to low-dimensional problem structure for several applications. [ABSTRACT FROM AUTHOR]

Evaluating the transmittance between two points along a ray is a key component in solving the light transport through heterogeneous participating media and entails computing an intractable exponential of the integrated medium's extinction coefficient. While algorithms for estimating this transmittance exist, there is a lack of theoretical knowledge about their behaviour, which also prevent new theoretically sound algorithms from being developed. For this purpose, we introduce a new class of unbiased transmittance estimators based on random sampling or truncation of a Taylor expansion of the exponential function. In contrast to classical tracking algorithms, these estimators are non-analogous to the physical light transport process and directly sample the underlying extinction function without performing incremental advancement. We present several versions of the new class of estimators, based on either importance sampling or Russian roulette to provide finite unbiased estimators of the infinite Taylor series expansion. We also show that the well known ratio tracking algorithm can be seen as a special case of the new class of estimators. Lastly, we conduct performance evaluations on both the central processing unit (CPU) and the graphics processing unit (GPU), and the results demonstrate that the new algorithms outperform traditional algorithms for heterogeneous mediums. [ABSTRACT FROM AUTHOR]

Mobile Ad-hoc network (MANET) is an infrastructure less network where communication takes place between collections of mobile hosts. There are several threats to adhoc network termed popularly as active and passive attacks. Collaborative attack is a new generation threat for ad-hoc networks. In collaboration Black hole attacks, multiple attackers synchronize their actions against some network to disturb the genuine communication. This paper proposes a new technique called Optimal Trust Based Hybrid Collaborative Path Tracing System for defending Black hole Attack in MANET by finding out dubious path information from black hole nodes. Also, a Swarm based optimal trust inference model is introduced to compute the trust of each node and therefore, the data encrypted using AES algorithm can be forwarded through these trusted nodes. Experimental results assessed using NS2 simulator show that in the presence of malicious nodes, the proposed system works better than the existing techniques in terms of routing overhead and packet delivery ratio. [ABSTRACT FROM AUTHOR]

Copyright of Coal Science & Technology (0253-2336) is the property of Coal Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

We propose a path-guiding algorithm to be incorporated into the wavefront style of path tracers (WFPTs). As WFPTs are primarily implemented on graphics processing units (GPUs), the proposed method aims to leverage the capabilities of the GPUs and reduce the hierarchical data structure and memory usage typically required for such techniques. To achieve this, our algorithm only stores the radiant exitance on a single global sparse voxel octree (SVO) data structure. Probability density functions required to guide the rays are generated on-the-fly using this data structure. The proposed approach reduces the scene-related persistent memory requirements compared to other path-guiding techniques while producing similar or better results depending on scene characteristics. To our knowledge, our algorithm is the first one that incorporates path guiding into a WFPT. [Display omitted] • Path guiding method that is designed for wavefront path tracing. • Utilizing on-the-fly generated incident radiant exitance. • Hardware-accelerated approximate cone tracing to query the radiant exitance. • GPU-oriented parallel product path guiding via warp-level intrinsics. • A heuristic that optimally combines the proposed method's samples. [ABSTRACT FROM AUTHOR]

With the development in agricultural mechanization and information technology, orchard agricultural machinery is also constantly pursuing the goal of intelligence and efficiency. Fruit picking is the most labor-intensive part of the orchard harvesting process. In order to resolve the problems of high labor intensity, low picking efficiency, and labor shortage when harvesting dwarf high-density orchards, an automatic navigation fruit-picking platform with voice control was developed in the present study. First, the platform utilized a voice-controlled high-level extendable working platform and a fruit-box-lifting device to adapt to varying orchard planting row spacing and enable convenient fruit box loading and unloading. Second, an automatic navigation system, which employed China’s Beidou navigation satellite system to acquire positional data and the Stanley algorithm for path-tracking control, was implemented. Third, the fruit-picking platform with automatic navigation system was fabricated and assembled, its outer wheel steering was measured to have a maximum angle of 30.3° and an average minimum turning radius of 4.5 m, meeting the turning radius requirements under orchard conditions. Finally, automatic navigation tests of the developed platform were performed in the orchard conditions. The results indicated that the platform could maintain a straight-line path with a maximum lateral deviation of 101.5 mm and a maximum absolute average deviation of 44.1 mm at 0.4 m/s. Under the U-shaped paths, the measured maximum lateral deviation was 148.6 mm and the maximum absolute average deviation was 57.2 mm. The navigation accuracy was sufficient to meet the requirements for the harvesting platform’s routine operation in the orchards.

Rendering photo‐realistic images using Monte Carlo path tracing often requires sampling a large number of paths to reach acceptable levels of noise. This is particularly the case when rendering participating media, that complexify light paths with multiple scattering events. Our goal is to accelerate the rendering of heterogeneous participating media by exploiting redundancy across views, for instance when rendering animated camera paths, motion blur in consecutive frames or multi‐view images such as lenticular or light‐field images. This poses a challenge as existing methods for sharing light paths across views cannot handle heterogeneous participating media and classical estimators are not optimal in this context. We address these issues by proposing three key ideas. First, we propose new volume shift mappings to transform light paths from one view to another within the recently introduced null‐scattering framework, taking into account changes in density along the transformed path. Second, we generate a shared path suffix that best contributes to a subset of views, thus effectively reducing variance. Third, we introduce the multiple weighted importance sampling estimator that benefits from multiple importance sampling for combining sampling strategies, and from weighted importance sampling for reducing the variance due to non contributing strategies. We observed significant reuse when views largely overlap, with no visible bias and reduced variance compared to regular path tracing at equal time. Our method further readily integrates into existing volumetric path tracing pipelines. [ABSTRACT FROM AUTHOR]

Monte Carlo integration is an established method in simulating light transport. However, due to its stochastic nature, it requires copious amounts of samples to eliminate the estimation error, and variance reduction techniques such as importance sampling are used to improve the convergence speed as a remedy. Path guiding is a class of adaptive importance sampling methods devised specifically for light transport simulation, which demonstrates significant improvements over classical sampling techniques. Yet most of the previous path guiding methods only account for the radiance term, omitting the bidirectional scattering distribution function (BSDF) term. This results in suboptimal sample quality for non-diffuse light transport. This paper presents a path guiding method which guides paths according to the full product of the BSDF and radiance terms in light transport equation. Extending a spatio-directional tree based path guiding method, we use a semi-adaptive grid-quadtree hybrid subdividing the directional domain in a spatial subdomain to learn and represent the radiance field. With the help of BSDF lookup tables used to accelerate BSDF evaluations, this grid-quadtree allows us to efficiently construct approximate product distributions and guide paths according to the product of incident radiance and cosine-weighted BSDF at each path vertex. The resulting method is relatively simple to implement and enables more robust sampling on scenes with many glossy surfaces. [Display omitted] • A modified spatio-directional tree that captures the radiance field in a scene. • The modifications enable a product importance sampling scheme. • Efficiency improvements through compressed BSDF LUTs and SIMD vectorization. • Improved sample quality in scenes with many glossy surfaces. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer-Aided Design & Computer Graphics / Jisuanji Fuzhu Sheji Yu Tuxingxue Xuebao is the property of Gai Kan Bian Wei Hui and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Rendering quality is largely influenced by the samplers used in Monte Carlo integration. Important factors include sample uniformity (e.g., low discrepancy) in the high-dimensional integration domain, sample uniformity in lower-dimensional projections, and lack of dominant structures that could result in aliasing artifacts. A widely used and successful construction is the Sobol' sequence that guarantees good high-dimensional uniformity and consequently results in faster convergence of quasi-Monte Carlo integration. We show that this sequence exhibits low uniformity and dominant structures in low-dimensional projections. These structures impair quality in the context of rendering, as they precisely occur in the 2-dimensional projections used for sampling light sources, reflectance functions, or the camera lens or sensor. We propose a new cascaded construction, which, despite dropping the sequential aspect of Sobol' samples, produces point sets exhibiting provably perfect dyadic partitioning (and therefore, excellent uniformity) in consecutive 2-dimensional projections, while preserving good high-dimensional uniformity. By optimizing the initialization parameters and performing Owen scrambling at finer levels of binary representations, we further improve over Sobol's integration convergence rate. Our method does not incur any overhead as compared to the generation of the Sobol' sequence, is compatible with Owen scrambling and can be used in rendering applications. [ABSTRACT FROM AUTHOR]

A novel reconstruction algorithm is presented to address the noise artifacts of path tracing. SURE (Stein's unbiased risk estimator) is adopted to estimate the noise level per pixel that guides adaptive sampling process. Modified MLPs (multilayer perceptron) network is used to predict the optimal reconstruction parameters. In sampling stage, coarse samples are firstly generated. Then each noise level is estimated with SURE. Additional samples are distributed to the pixels with high noise level. Next, we extract a few features from the results of adaptive sampling used for the subsequent reconstruction stage. In reconstruction stage, modified MLPs network is adopted to model a complex relationship between extracted features and optimal reconstruction parameters. An anisotropic filter is used to reconstruct the final images with the parameters predicted by neural networks. Compared to the state-of-the-art methods, experiment results demonstrate that our algorithm performs better than other methods in numerical error and visual image quality.

Path tracing is a commonly used but computationally highly expensive stochastic ray tracing method for rendering photorealistic visual content. Combined with a real-time constraint, for example in stereoscopic virtual/augmented reality applications, it typically limits us to rendering at most a few samples per pixel, yielding very noisy results. However, the spatial and temporal redundancies are commonly utilized by reprojecting existing samples between different viewpoints and frames, thus cheaply improving the quality. We provide new insights to the quality benefits of reprojection by systematically evaluating the effective quality of spatiotemporally reprojected stereoscopic path traced data. We show that spatiotemporal reprojection increases the quality of 1 sample per pixel (spp) data by almost a factor of 25 on average, in terms of the effective spp count of the result. Since we are able to reproject 94-98% of the samples, only the remaining 2-6% of the samples in the target frame need to be path traced. We also evaluate how the quality improvement gained through spatiotemporal reprojection scales as the number of input samples per pixel increases, showing that the highest gains are achieved at the lowest input spp counts. Finally, we show how blending existing path traced data and stereoscopically reprojected data further improves the quality of spatiotemporal reprojection, on average yielding a 47% higher effective spp than without blending.

This study aims to compare a relatively novel three-dimensional rendering called Path Tracing (PT) to the Volume Rendering technique (VR) in the post-surgical assessment of head and neck oncologic surgery followed by bone flap reconstruction. This retrospective study included 39 oncologic patients who underwent head and neck surgery with free bone flap reconstructions. All exams were acquired using a 64 Multi-Detector CT (MDCT). PT and VR images were created on a dedicated workstation. Five readers, with different expertise in bone flap reconstructive surgery, independently reviewed the images (two radiologists, one head and neck surgeon and two otorhinolaryngologists, respectively). Every observer evaluated the images according to a 5-point Likert scale. The parameters assessed were image quality, anatomical accuracy, bone flap evaluation, and metal artefact. Mean and median values for all the parameters across the observer were calculated. The scores of both reconstruction methods were compared using a Wilcoxon matched-pairs signed rank test. Inter-reader agreement was calculated using Spearman’s rank correlation coefficient. PT was considered significantly superior to VR 3D reconstructions by all readers (p < 0.05). Inter-reader agreement was moderate to strong across four out of five readers. The agreement was stronger with PT images compared to VR images. In conclusion, PT reconstructions are significantly better than VR ones. Although they did not modify patient outcomes, they may improve the post-surgical evaluation of bone-free flap reconstructions following major head and neck surgery.

Introduction: Ultrasound is well-established as an imaging modality for diagnostic and interventional purposes. However, the image quality varies with operator skills as acquiring and interpreting ultrasound images requires extensive training due to the imaging artefacts, the range of acquisition parameters and the variability of patient anatomies. Automating the image acquisition task could improve acquisition reproducibility and quality but training such an algorithm requires large amounts of navigation data, not saved in routine examinations., Methods: We propose a method to generate large amounts of ultrasound images from other modalities and from arbitrary positions, such that this pipeline can later be used by learning algorithms for navigation. We present a novel simulation pipeline which uses segmentations from other modalities, an optimized volumetric data representation and GPU-accelerated Monte Carlo path tracing to generate view-dependent and patient-specific ultrasound images., Results: We extensively validate the correctness of our pipeline with a phantom experiment, where structures' sizes, contrast and speckle noise properties are assessed. Furthermore, we demonstrate its usability to train neural networks for navigation in an echocardiography view classification experiment by generating synthetic images from more than 1,000 patients. Networks pre-trained with our simulations achieve significantly superior performance in settings where large real datasets are not available, especially for under-represented classes., Discussion: The proposed approach allows for fast and accurate patient-specific ultrasound image generation, and its usability for training networks for navigation-related tasks is demonstrated., Competing Interests: PK, KP, VS, Y-HK and FCG are employed by Siemens Healthineers. RL and TM were employed by Siemens Healthineers. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (© 2024 Amadou, Peralta, Dryburgh, Klein, Petkov, Housden, Singh, Liao, Kim, Ghesu, Mansi, Rajani, Young and Rhode.)

Physically accurate rendering often calls for taking the wave nature of light into consideration. In computer graphics, this is done almost exclusively locally, i.e. on a micrometre scale where the diffractive phenomena arise. However, the statistical properties of light, that dictate its coherence characteristics and its capacity to give rise to wave interference effects, evolve globally: these properties change on, e.g., interaction with a surface, diffusion by participating media and simply by propagation. In this paper, we derive the first global light transport framework that is able to account for these properties of light and, therefore, is fully consistent with Maxwell's electromagnetic theory. We show that our framework is a generalization of the classical, radiometry-based light transport---prominent in computer graphics---and retains some of its attractive properties. Finally, as a proof of concept, we apply the presented framework to a few practical problems in rendering and validate against well-studied methods in optics. [ABSTRACT FROM AUTHOR]

We present a real-time neural radiance caching method for path-traced global illumination. Our system is designed to handle fully dynamic scenes, and makes no assumptions about the lighting, geometry, and materials. The data-driven nature of our approach sidesteps many difficulties of caching algorithms, such as locating, interpolating, and updating cache points. Since pretraining neural networks to handle novel, dynamic scenes is a formidable generalization challenge, we do away with pretraining and instead achieve generalization via adaptation, i.e. we opt for training the radiance cache while rendering. We employ self-training to provide low-noise training targets and simulate infinite-bounce transport by merely iterating few-bounce training updates. The updates and cache queries incur a mild overhead---about 2.6ms on full HD resolution---thanks to a streaming implementation of the neural network that fully exploits modern hardware. We demonstrate significant noise reduction at the cost of little induced bias, and report state-of-the-art, real-time performance on a number of challenging scenarios. [ABSTRACT FROM AUTHOR]

• The variation of the PEP film transmittance with the angle is studied. • Greenhouse light environment simulation software was developed based on the principle of light path tracing. • Greenhouse light environment simulation software has a wide range of applications. • Optimization of the greenhouse structure based on the light environment. The interior light environment in greenhouses affects the growth of crops. Researchers have extensively studied improvements in greenhouse light environments. In this article, computer technology was used based on light path tracing and the Monte Carlo method, and a light environment simulation model that is applicable to various greenhouses was developed. Two types of greenhouses with complex roofs were simulated: three-span double-layer film greenhouses and multispan solar energy greenhouses. Experiments were performed to verify that the method could simulate the light environment distribution in greenhouses with various structures. The accuracy of the simulated average solar radiation intensity in greenhouses was greater than 85%, and the simulation correlation coefficient exceeded 0.90. Finally, by adjusting the ratio of rise to span of the greenhouse roof to optimize the greenhouse light environment, the greenhouse light distribution optimization results were found to be consistent between the two types of greenhouses, which verifies the effectiveness of the optimization. At three time points (9:00, 12:00, and 15:00), the horizontal section of the crop canopy (1.5 m) was simulated to construct a light distribution cloud map, and the point cloud data were statistically analyzed. The results show that the influence of the ratio of rise to span on the light distribution uniformity was more significant than that of the total radiation intensity. With an increasing the ratio of rise to span of the three-span double-film and multispan solar energy greenhouses, the total radiation intensity first increased and then decreased, and the light distribution uniformity showed a decreasing trend. When the ratio was 0.05, the total radiation intensity was high. Compared with that in the test greenhouse, the light intensity maximum increased by 6.95% and 6.64%, respectively, and the light distribution uniformity maximum increased by 14.49% and 14.74%, respectively. Combining the two factors of total radiation intensity and light distribution uniformity, the ratio of rise to span of these two types of greenhouses was maintained at 0.05. This design could ensure a greenhouse light distribution uniformity of approximately 90% based on the acquisition of the optimal amount of light energy, which is conducive for establishing a better greenhouse light environment. A statistical analysis of the total daily solar radiation throughout the winter season indicated that the application of the optimization scheme increased the total radiation in the three-span double-film and multispan solar energy greenhouse by 5.59% and 10.49%, respectively. The above results verified the effectiveness of the optimization. The greenhouse lighting simulation method based on optical path tracking is fast and accurate and could be used in greenhouse design and optimization. [ABSTRACT FROM AUTHOR]

Path tracing can generate realistic images based on virtual 3D scene models, but the images are prone to be noisy. To solve this problem, we developed a novel denoising algorithm framework. Firstly, according to the relative mean square error of the noisy pixels, we introduced a progressive adaptive sampling strategy to optimize the distribution of samples. Next, to enhance the quality of the final reconstructed images, we designed an improved bilateral filtering algorithm with use of the gradient feature to obtain the noise-free images. Experimental results demonstrate that our framework outperforms the state-of-the-art path tracing denoising methods in terms of the visual quality, numerical error , and time cost. [ABSTRACT FROM AUTHOR]

• Introduction of Riemannian global illumination. • A path tracer algorithm to approximate the solution of the Riemannian global illumination. • A map of the algorithm to the GPU, using the RTX platform. • We apply the Non-Euclidean path tracer to render "Photorealistic" inside views of the 3D flat torus, Poincaré sphere, and the hyperbolic mirrored dodecahedron. These are examples of the Euclidean, spherical, and hyperbolic spaces: the Thurston classical geometries. This paper presents a novel path tracer algorithm for immersive visualization of Riemannian manifolds. To do this, we introduce Riemannian illumination, a generalization of classical Computer Graphics illumination models. In this context, global light transport is expressed by extending the rendering equation to Riemannian manifolds. Using Monte Carlo integration to solve this equation results in the novel path tracer for Non-Euclidean spaces. We discuss its basic principles, as well as the general CPU algorithm. Additionally, we discuss in detail how to implement a GPU version, using the RTX pipeline. Finally, we apply the algorithm to render "photorealistic" inside views of the flat torus, Poincaré sphere, and the hyperbolic mirrored dodecahedron. These are examples of Euclidean, spherical, and hyperbolic spaces: the Thurston classical geometries. [ABSTRACT FROM AUTHOR]

The path tracking of sandblasting robot for wind turbine blade after path planning is likely to be influenced by environmental factors and path parameters. To reveal the tracking control coupling relationship between the actual and theoretical positions of wind turbine blade during sandblasting in a noise environment, this study proposed a fast filtering and position tracking control strategy based on the adaptive recursive least squares(RLS) algorithm. By adjusting the weighted value of the RLS algorithm, this study developed a mathematical model of sandblasting path tracking control for wind turbine blade based on a variable weight RLS algorithm combined with blade surface morphology and interference noise characteristics. Different interference noises and path conditions were analyzed according to the tracking error between the real-time and theoretical positions of the nozzle. Moreover, experiments were conducted to verify the accuracy of the model. For the tracking theoretical path of the robot during sandblasting, the response time of the fast filtering and position tracking control strategy based on the RLS algorithm was proportional to the rate of curvature change of the theoretical path. Result demonstrate that the response time of the suggested algorithm to the straight and curve paths is less than 2.0 s. Compared with the traditional PID control algorithm, the proposed algorithm has a higher response rate, a control accuracy error under 10 mm, and a maximum response error below 5 mm/s. These qualities meet the field requirements of sand blasting for wind turbine blade. This study also provides reference for evaluating the performance of a wind turbine blade. [ABSTRACT FROM AUTHOR]

Monte‐Carlo rendering requires determining the visibility between scene points as the most common and compute intense operation to establish paths between camera and light source. Unfortunately, many tests reveal occlusions and the corresponding paths do not contribute to the final image. In this work, we present next event estimation++ (NEE++): a visibility mapping technique to perform visibility tests in a more informed way by caching voxel to voxel visibility probabilities. We show two scenarios: Russian roulette style rejection of visibility tests and direct importance sampling of the visibility. We show applications to next event estimation and light sampling in a uni‐directional path tracer, and light‐subpath sampling in Bi‐Directional Path Tracing. The technique is simple to implement, easy to add to existing rendering systems, and comes at almost no cost, as the required information can be directly extracted from the rendering process itself. It discards up to 80% of visibility tests on average, while reducing variance by ∼20% compared to other state‐of‐the‐art light sampling techniques with the same number of samples. It gracefully handles complex scenes with efficiency similar to Metropolis light transport techniques but with a more uniform convergence. [ABSTRACT FROM AUTHOR]

We propose neural control variates (NCV) for unbiased variance reduction in parametric Monte Carlo integration. So far, the core challenge of applying the method of control variates has been finding a good approximation of the integrand that is cheap to integrate. We show that a set of neural networks can face that challenge: a normalizing flow that approximates the shape of the integrand and another neural network that infers the solution of the integral equation. We also propose to leverage a neural importance sampler to estimate the difference between the original integrand and the learned control variate. To optimize the resulting parametric estimator, we derive a theoretically optimal, variance-minimizing loss function, and propose an alternative, composite loss for stable online training in practice. When applied to light transport simulation, neural control variates are capable of matching the state-of-the-art performance of other unbiased approaches, while providing means to develop more performant, practical solutions. Specifically, we show that the learned light-field approximation is of sufficient quality for high-order bounces, allowing us to omit the error correction and thereby dramatically reduce the noise at the cost of negligible visible bias. [ABSTRACT FROM AUTHOR]

Forward method to establish parametric scattering center models is highly efficient for radar feature extraction of target. Nevertheless, scattering center extraction especially for multiple reflections is still time‐consuming due to path tracing, ray clustering, and parameter determination from electrically large and complex targets. As the multiple reflection items add, the time consumption of forward parametric scattering center modeling method increases dramatically, which can hardly meet the practical requirements. In this work, we propose a graphics processing unit‐accelerated forward parametric scattering center modeling method together with stackless KD‐Tree traversal technique that accelerates path tracing, clustering, and parameter determination to extract parametric scattering centers. Concurrently, the parameters of the scattering centers arising from triple (usually the maximum number of reflections) or above reflection items are forward determined for obtaining feature information as much as possible from complex target. Finally, the reconstructed radar characteristics from parametric models have been verified by comparison with exactly simulated results. Meanwhile, those examples using this improved method have achieved great speedup. [ABSTRACT FROM AUTHOR]

NMPC was widely applied to the path tracking control of vehicles. However, the impacts of prediction horizon and velocity on the performances of path tracking control were not considered in reported researches. Thus, the relationship among prediction horizon, velocity and the performances of path tracking control was analyzed. Through the cubic polynomial fitting, the control laws of the optimal prediction horizon and reference velocity were obtained, which may guarantee the lateral error of path tracking less than 0.1 m. Then, the NMPC controller for path tracking control was improved, and the performances of the NMPC controller were verified by simulation. The simulation results show that, for the improved NMPC controller, the literal error is within 0.092 8 m and the heading error is within 0,072 4 rad. Compared with the traditional NMPC controller, the improved NMPC controller reduces the maximum lateral error by more than 4.267 1 m and reduces the maximum heading error by more than 0. 392 7 rad, and the performances of path tracing control are improved. [ABSTRACT FROM AUTHOR]