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Perspective geometry is a fundamental, challenging, and captivating subject within the engineering bachelor's degree program. It holds significant importance in developing graphic skills, analytical abilities, sketching proficiency, and comprehension of drawings. Moreover, it plays a crucial role in organizing the architectural components of a bachelor's thesis. However, the teaching and learning of perspective geometry often reveal various difficulties and shortcomings. Moreover, one of the various systems of symbols and languages created by global cultures is the graphical language, which is an exceptional and unparalleled language for understanding scientific and technical information. This language is considered the oldest international language. Every visual piece of information in various processes of human life has been formed through the graphical language, which is composed of various geometric shapes. In this research, data has been gathered from the perspectives of first-year students from the 2013 batch regarding the difficulties encountered in perspective geometry. A questionnaire was distributed to collect their opinions, and three graphical tasks with different levels of complexity were given to the students. The results obtained from this study indicate that employing suitable teaching methods and providing adequate resources can alleviate most of the learning difficulties associated with graphic skills. Instructors also play a pivotal role in resolving learning obstacles. Therefore, efforts should be made to enhance the quality of teaching graphic subjects by updating the content and curriculum of educational programs and improving teaching methods through the use of technology. In higher education institutions, conditions for fostering students' professional knowledge, enhancing their ability to draw maps and create technical documents can be facilitated through computer graphics education. Updating the teaching methods plays a crucial role in improving the quality of graphic education. The mentioned factors provide a suitable environment for the growth of graphic knowledge and the implementation of projects related to professional subjects. [ABSTRACT FROM AUTHOR]

While perspective is a well-studied topic in art, it is generally taken for granted in images. However, for the recent wave of high-quality image synthesis methods such as latent diffusion models, perspective accuracy is not an explicit requirement. Since these methods are capable of outputting a wide gamut of possible images, it is difficult for these synthesized images to adhere to the principles of linear perspective. We introduce a novel geometric constraint in the training process of generative models to enforce perspective accuracy. We show that outputs of models trained with this constraint both appear more realistic and improve performance of downstream models trained on generated images. Subjective human trials show that images generated with latent diffusion models trained with our constraint are preferred over images from the Stable Diffusion V2 model 70% of the time. SOTA monocular depth estimation models such as DPT and PixelFormer, fine-tuned on our images, outperform the original models trained on real images by up to 7.03% in RMSE and 19.3% in SqRel on the KITTI test set for zero-shot transfer. [ABSTRACT FROM AUTHOR]

Ever since the first image of a coral reef was captured in 1885, people worldwide have been accumulating images of coral reefscapes that document the historic conditions of reefs. However, these innumerable reefscape images suffer from perspective distortion, which reduces the apparent size of distant taxa, rendering the images unusable for quantitative analysis of reef conditions. Here we solve this century-long distortion problem by developing a novel computer-vision algorithm, ReScape, which removes the perspective distortion from reefscape images by transforming them into top-down views, making them usable for quantitative analysis of reef conditions. In doing so, we demonstrate the first-ever ecological application and extension of inverse-perspective mapping—a foundational technique used in the autonomous-driving industry. The ReScape algorithm is composed of seven functions that (1) calibrate the camera lens, (2) remove the inherent lens-induced image distortions, (3) detect the scene's horizon line, (4) remove the camera-roll angle, (5) detect the transformable reef area, (6) detect the scene's perspective geometry, and (7) apply brute-force inverse-perspective mapping. The performance of the ReScape algorithm was evaluated by transforming the perspective of 125 reefscape images. Eighty-five percent of the images had no processing errors and of those, 95% were successfully transformed into top-down views. ReScape was validated by demonstrating that same-length transects, placed increasingly further from the camera, became the same length after transformation. The mission of the ReScape algorithm is to (i) unlock historical information about coral-reef conditions from previously unquantified periods and localities, (ii) enable citizen scientists and recreational photographers to contribute reefscape images to the scientific process, and (iii) provide a new survey technique that can rigorously assess relatively large areas of coral reefs, and other marine and even terrestrial ecosystems, worldwide. To facilitate this mission, we compiled the ReScape algorithm into a free, user-friendly App that does not require any coding experience. Equipped with the ReScape App, scientists can improve the management and prediction of the future of coral reefs by uncovering historical information from reefscape-image archives and by using reefscape images as a new, rapid survey method, opening a new era of coral-reef monitoring. [ABSTRACT FROM AUTHOR]

Abstract   This paper presents a novel method for 3D camera calibration. Calculation of the focal length and the optical center of the camera are the main objectives of this research work. The proposed technique requires a single image having two vanishing points. A rectangular prism is employed as the calibration target to generate vanishing points. The special arrangement of the calibration object adds more accuracy in finding the intrinsic parameters. Based on the geometry of the perspective distortion of the edges of the prisms from the image, vanishing points are found. There on, fixing up the picture plane followed by fixing up of the station point is carried out based on the relations that are formulated. Experimental results of our method are likened with Zhang’s method. Results are tabulated to show the accuracy of the proposed approach. [ABSTRACT FROM AUTHOR]

Along with other types of calculus, multiplicative calculus brings an entirely new perspective. Geometry now has a new field as a result of this new understanding. In this study, multiplicative differential geometry was used to explore peculiar surfaces. Multiplicative quaternions are also used to depict surfaces. Additionally, multiplicative differential geometry was used to generate the accretive surface subject, which is a developing subject. The derived surfaces' perspective silhouette curve equation is provided. The Bishop multiplicative frame was also established and applied when expressing surfaces along with these. Finally, the surfaces and perspective silhouette curves were visualized using Maple, and the equations were obtained. [ABSTRACT FROM AUTHOR]

Achieving precise 6 degrees of freedom (6D) pose estimation of rigid objects from color images is a critical challenge with wide-ranging applications in robotics and close-contact aircraft operations. This study investigates key techniques in the application of YOLOv5 object detection convolutional neural network (CNN) for 6D pose localization of aircraft using only color imagery. Traditional object detection labeling methods suffer from inaccuracies due to perspective geometry and being limited to visible key points. This research demonstrates that with precise labeling, a CNN can predict object features with near-pixel accuracy, effectively learning the distinct appearance of the object due to perspective distortion with a pinhole camera. Additionally, we highlight the crucial role of knowledge about occluded features. Training the CNN with such knowledge slightly reduces pixel precision, but enables the prediction of 3 times more features, including those that are not initially visible, resulting in an overall better performing 6D system. Notably, we reveal that the data augmentation technique of scale can interfere with pixel precision when used during training. These findings are crucial for the entire system, which leverages the Solve Perspective-N-Point (Solve-PnP) algorithm, achieving 6D pose accuracy within 1 ∘ and 7 cm at distances ranging from 7.5 to 35 m from the camera. Moreover, this solution operates in real-time, achieving sub-10ms processing times on a desktop PC. [ABSTRACT FROM AUTHOR]

An innovative electro-optical navigation system called Optical Navigation System (OptoNav) for Autonomous Landing consisting of a machine vision camera, and a set of co-registered structured light beacons is proposed. OptoNav is used to directly sense the altitude and two orientation states of an air vehicle with respect to the flight path direction. Motivated by autonomous landing applications involving fixed-wing aircraft, the machine vision camera is used to process the images of the runway so as to establish a moving coordinate system affixed to the runway, making use of the perspective geometry of the camera system. Three or more structured light emitters are used to make direct measurements of the range using the geometry of the perspective projection model by making use of the co-registration of the camera with respect to the structured light emitting sources. A unique formulation of the minimal relative motion states is used to directly estimate the three states of altitude, pitch and roll angle of the airframe with respect to the moving coordinates affixed to the runway. Laboratory experiments are used to validate the utility of the OptoNav sensor system. [ABSTRACT FROM AUTHOR]

Image stitching for two images without a global transformation between them is notoriously difficult. In this paper, noticing the importance of semantic planar structures under perspective geometry, we propose a new image stitching method which stitches images by allowing for the alignment of a set of matched dominant semantic planar regions. Clearly different from previous methods resorting to plane segmentation, the key to our approach is to utilize rich semantic information directly from RGB images to extract semantic planar image regions with a deep Convolutional Neural Network (CNN). We specifically design a module implementing our newly proposed clustering loss to make full use of existing semantic segmentation networks to accommodate region segmentation. To train the network, a dataset for semantic planar region segmentation is constructed. With the prior of semantic planar region, a set of local transformation models can be obtained by constraining matched regions, enabling more precise alignment in the overlapping area. We also use this prior to estimate a transformation field over the whole image. The final mosaic is obtained by mesh-based optimization which maintains high alignment accuracy and relaxes similarity transformation at the same time. Extensive experiments with both qualitative and quantitative comparisons show that our method can deal with different situations and outperforms the state-of-the-arts on challenging scenes. [ABSTRACT FROM AUTHOR]

Noncontact optical imaging is frequently used in the inspection and metrology of stationary objects, including in particular the reconstruction of the 3-D surface profile. A technique, known as phase-measuring profilometry, involves projecting a sinusoidal pattern and then inferring the height of various points on the object by measuring the resulting phase changes at the respective locations. However, this method cannot be directly applied to systems involving moving objects, as the translation and the perspective geometry effect manifest as errors in the height calculations. In this paper, we report on an imaging and numerical surface profilometry with error compensation technology (INSPECT) measurement system that is tailored for moving objects. We model the imaging system that considers the nonlinear perspective geometry effect, and simplify to a first-order equation using Taylor series expansion. With this, we generalize the conventional phase shift algorithm, and develop the optimization procedures that can compute the height information effectively. We apply this technology to the INSPECT measurement system in semiconductor manufacturing and show significant improvement in accuracy and robustness. [ABSTRACT FROM PUBLISHER]

Leibniz's manuscripts on perspective geometry remained unpublished and unknown until very recently. Among them, Scientia perspectiva stands out as the most complex and the most original. In this paper, we offer a thorough analysis of this manuscript, showing how Leibniz moves from perspective concepts fairly common at that time to a completely new idea of the practice that could have affected its entire history. This new science represents not only Leibniz's unique contribution to the development of perspective but also casts a new light on his own notion of space and geometry and their philosophical grounding. [ABSTRACT FROM AUTHOR]

Quantitative description of perspective geometries is a challenging task due to the complexities of geometric shapes. In this paper, we address this gap by proposing a new methodology based on variational autoencoders (VAE) to derive low‐dimensional and exploitable parameters of the perspective road geometry. First, road perspective images were generated based on different alignment scenarios. Then, a VAE was built to create a regularized and exploitable latent space from the data. The latent space is a compressed representation of perspective geometry, from which six latent parameters were derived. Without prior expert knowledge, four of the latent parameters were found to represent distinctive attributes of the geometry, such as visual curvature, slope, sight distance, and curve direction. The latent parameters provided quantitative measurements of how the design scheme looks like in perspective view. It was found that a road with low accident rate has low values for codes 4 and 5, high values for code 3, and low variance for codes 3 and 6. The trained VAE model also ensured accurate generation of the perspective images by decoding the latent parameters. Overall, this research advances the understanding of road design by considering the driver's perception. [ABSTRACT FROM AUTHOR]

The hand–eye measurements have recently been proven to be very efficient for spacecraft attitude determination relative to an ellipsoidal asteroid. However, the recent method does not guarantee full attitude observability for all conditions. This article refines this problem by taking the vector observations into account so that the accuracy and robustness of the spacecraft attitude estimation can be improved. The vector observations come from many sources including visual perspective geometry, optical navigation, and point clouds that frequently occur in aerospace electronic systems. Completely closed-form solutions along with their uncertainty descriptions are presented for the proposed problem. Experiments using our simulated dataset and real-world spacecraft measurements from National Aeronautics and Space Administration's Dawn spacecraft verify the effectiveness and superiority of the derived solution. [ABSTRACT FROM AUTHOR]

Nowadays, robots are indispensable in industry, especially logistics industry, to replace human employees performing heavy lifting tasks. Introducing robots prevents musculoskeletal disorders that are common in ageing workforce. We designed and implemented a dual arm robot to grasp cardboard boxes of different dimensions using a hydrid force/position control. In a first step, the position of the cardboard was estimated using markers and ARtags, and an integrated camera. However this solution showed some limitation, because it is not possible to place ARtag on every cardboard of a logistic warehouse. In this paper, we propose a new method to estimate one cardboard box position based on vision without the need of markers at all. Our method explores the advantages of the RGBD integrated camera through the use of strong features and perspective geometry. Our method is very adequate to the case of one cardboard box due to the simplicity of its geometric shape. The experiments show that our method is fast, robust and precise, and of course is better suited to the logistics warehouse environment than the marker estimation procedure for palletization applications. [ABSTRACT FROM AUTHOR]

This paper presents a single-camera trilateration scheme which estimates the instantaneous 3D pose of a regular forward-looking camera from a single image of landmarks at known positions. Derived on the basis of the classical pinhole camera model and principles of perspective geometry, the proposed algorithm estimates the camera position and orientation successively. It provides a convenient self-localization tool for mobile robots and vehicles equipped with onboard cameras. Performance analysis has been conducted through extensive simulations with representative examples, which provides an insight into how the input errors and the geometric arrangement of the camera and landmarks affect the performance of the proposed algorithm. The effectiveness of the proposed algorithm has been further verified through an experiment. [ABSTRACT FROM AUTHOR]

Biological visual systems rely on pose estimation of 3D objects to navigate and interact with their environment, but the neural mechanisms and computations for inferring 3D poses from 2D retinal images are only partially understood, especially where stereo information is missing. We previously presented evidence that humans infer the poses of 3D objects lying centered on the ground by using the geometrical back-transform from retinal images to viewer-centered world coordinates. This model explained the almost veridical estimation of poses in real scenes and the illusory rotation of poses in obliquely viewed pictures, which includes the "pointing out of the picture" phenomenon. Here we test this model for more varied configurations and find that it needs to be augmented. Five observers estimated poses of sloped, elevated, or off-center 3D sticks in each of 16 different poses displayed on a monitor in frontal and oblique views. Pose estimates in scenes and pictures showed remarkable accuracy and agreement between observers, but with a systematic fronto-parallel bias for oblique poses similar to the ground condition. The retinal projection of the pose of an object sloped wrt the ground depends on the slope. We show that observers' estimates can be explained by the back-transform derived for close to the correct slope. The back-transform explanation also applies to obliquely viewed pictures and to off-center objects and elevated objects, making it more likely that observers use internalized perspective geometry to make 3D pose inferences while actively incorporating inferences about other aspects of object placement. [ABSTRACT FROM AUTHOR]

Humans inherently perceive 3D scenes using prior knowledge and visual perception, but 3D reconstruction in computer graphics is challenging due to complex object geometries, noisy backgrounds, and occlusions, leading to high time and space complexity. To addresses these challenges, this study introduces 3DRecNet, a compact 3D reconstruction architecture optimized for both efficiency and accuracy through five key modules. The first module, the Human-Inspired Memory Network (HIMNet), is designed for initial point cloud estimation, assisting in identifying and localizing objects in occluded and complex regions while preserving critical spatial information. Next, separate image and 3D encoders perform feature extraction from input images and initial point clouds. These features are combined using a dual attention-based feature fusion module, which emphasizes features from the image branch over those from the 3D encoding branch. This approach ensures independence from proposals at inference time and filters out irrelevant information, leading to more accurate and detailed reconstructions. Finally, a Decoder Branch transforms the fused features into a 3D representation. The integration of attention-based fusion with the memory network in 3DRecNet significantly enhances the overall reconstruction process. Experimental results on the benchmark datasets, such as ShapeNet, ObjectNet3D, and Pix3D, demonstrate that 3DRecNet outperforms existing methods. [ABSTRACT FROM AUTHOR]

The current highway abnormal event detection efficiency is low, the leakage rate is high, real-time poor, and so on, this paper proposed highway abnormal parking detection method based on space-time relationship. Firstly, it used the traffic flow frequency to quantify the split image and removed the smaller connectivity domain to extract the road surface information, then used the perspective geometry of the pinhole camera to normalize the same scale. It used YOLOv4 network to obtain the exact position of the vehicle target, and established the space-time information matrix. To detect abnormal region and the detection results can output by the update of the space-time matrix. The experimental results show that the accuracy of this method is 95% in long-distance scenes and 93% in crowded scenes. [ABSTRACT FROM AUTHOR]

Abstract Ever since the first image of a coral reef was captured in 1885, people worldwide have been accumulating images of coral reefscapes that document the historic conditions of reefs. However, these innumerable reefscape images suffer from perspective distortion, which reduces the apparent size of distant taxa, rendering the images unusable for quantitative analysis of reef conditions. Here we solve this century-long distortion problem by developing a novel computer-vision algorithm, ReScape, which removes the perspective distortion from reefscape images by transforming them into top-down views, making them usable for quantitative analysis of reef conditions. In doing so, we demonstrate the first-ever ecological application and extension of inverse-perspective mapping—a foundational technique used in the autonomous-driving industry. The ReScape algorithm is composed of seven functions that (1) calibrate the camera lens, (2) remove the inherent lens-induced image distortions, (3) detect the scene’s horizon line, (4) remove the camera-roll angle, (5) detect the transformable reef area, (6) detect the scene’s perspective geometry, and (7) apply brute-force inverse-perspective mapping. The performance of the ReScape algorithm was evaluated by transforming the perspective of 125 reefscape images. Eighty-five percent of the images had no processing errors and of those, 95% were successfully transformed into top-down views. ReScape was validated by demonstrating that same-length transects, placed increasingly further from the camera, became the same length after transformation. The mission of the ReScape algorithm is to (i) unlock historical information about coral-reef conditions from previously unquantified periods and localities, (ii) enable citizen scientists and recreational photographers to contribute reefscape images to the scientific process, and (iii) provide a new survey technique that can rigorously assess relatively large areas of coral reefs, and other marine and even terrestrial ecosystems, worldwide. To facilitate this mission, we compiled the ReScape algorithm into a free, user-friendly App that does not require any coding experience. Equipped with the ReScape App, scientists can improve the management and prediction of the future of coral reefs by uncovering historical information from reefscape-image archives and by using reefscape images as a new, rapid survey method, opening a new era of coral-reef monitoring.

Discontinuity is a key element used by geoscientists and civil engineers to characterize rock masses. The traditional approach to detecting and measuring rock discontinuity relies on fieldwork, which poses dangers to human life. Photogrammetric pattern recognition and 3D measurement techniques offer new possibilities without direct contact with rock masses. This study proposes a new approach to detect discontinuities using close-range photogrammetric techniques and convolutional neural networks (CNNs) trained on a small amount of data. Investigations were conducted on basalts in Bala, Ankara, Türkiye. A total of 34 multi-view images were collected with a remotely piloted aircraft system (RPAS), and discontinuity lines were manually delineated on a point cloud generated from these images. The lines were back-projected onto the raw images to increase the amount of data, a process we call multi-view (3D) augmentation. We further evaluated radiometric and geometric augmentation methods, the contribution of multi-view augmentation to the proposed model, and the transfer learning performance of six different CNN architectures. The highest performance was achieved with U-Net + SE-ResNeXt-50 with an F1-score of 90.6%. The CNN model trained from scratch with local features also yielded a similar F1-score (91.7%), which is the highest performance reported in the literature. [ABSTRACT FROM AUTHOR]

The use of Augmented Reality technology in the pharmaceutical sector, particularly in pharmacies, has introduced significant innovations in providing drug information to consumers. AR applications can help healthcare workers and patients better understand drug use through three-dimensional visualization superimposed on the real environment. Observations at Sultan Abdul Aziz Syah Peureulak Hospital indicate that 70% of patients still experience confusion regarding the drug information provided by pharmacy staff. This difficulty is caused by the complexity of the information, lack of personal interaction, or low health literacy. To address this problem, the Android-based Mediscan application was developed using AR technology. This application utilizes Unity and Vuforia Engine software to present drug information visually in card form via the smart device screen. Users can point the camera at a particular drug and receive complete information regarding general indications, dosage, drug class, contraindications, and side effects. This research aims to develop the Mediscan application, calculate the level of patient understanding, measure consumer satisfaction, and evaluate the impact of using AR in presenting drug information. The System Development Life Cycle methodology with the Waterfall model was used in this research. The results show that AR technology can be implemented in Android-based applications to present drug information visually and interactively. The majority of patients feel more informed and gain a better understanding of the medication they are taking. AR technology in the Mediscan application improves user experience, supports the education and training of medical personnel and patients, and enhances the quality of healthcare services in hospitals. This application also makes it easier for medical personnel and pharmacists to convey drug information more effectively and efficiently. [ABSTRACT FROM AUTHOR]

Rollings of reductive homogeneous spaces are investigated. More precisely, for a reductive homogeneous space G/H with reductive decomposition g = h ⊕ m , we consider rollings of m over G/H without slip and without twist, where G/H is equipped with an invariant covariant derivative. To this end, an intrinsic point of view is taken, meaning that a rolling is a curve in the configuration space Q which is tangent to a certain distribution. By considering an H-principal fiber bundle π ¯ : Q ¯ → Q over the configuration space equipped with a suitable principal connection, rollings of m over G/H can be expressed in terms of horizontally lifted curves on Q ¯ . The total space of π ¯ : Q ¯ → Q is a product of Lie groups. In particular, for a given control curve, this point of view allows for characterizing rollings of m over G/H as solutions of an explicit, time-variant ordinary differential equation (ODE) on Q ¯ , the so-called kinematic equation. An explicit solution for the associated initial value problem is obtained for rollings with respect to the canonical invariant covariant derivative of first and second kind if the development curve in G/H is the projection of a one-parameter subgroup in G. Lie groups and Stiefel manifolds are discussed as examples. [ABSTRACT FROM AUTHOR]

The paper proposes a methodology for the use of the oblique sections of images from large-format photogrammetric cameras, by exploiting the effect of the central perspective geometry in the lateral parts of the nadir images ("semi-oblique" images). The point of origin of the investigation was the execution of a photogrammetric flight over Norcia (Italy), which was seriously damaged after the earthquake of 30/10/2016. Contrary to the original plan of oblique acquisitions, the flight was executed on 15/11/2017 using an UltraCam Eagle camera with focal length 80 mm, and combining two flight plans, rotated by 90° ("crisscross" flight). The images (GSD 5 cm) were used to extract a 2.5D DSM cloud, sampled to a XY-grid size of 2 GSD, a 3D point clouds with a mean spatial resolution of 1 GSD and a 3D mesh model at a resolution of 10 cm of the historic centre of Norcia for a quantitative assessment of the damages. From the acquired nadir images the "semi-oblique" images (forward, backward, left and right views) could be extracted and processed in a modified version of GEOBLY software for measurements and restitution purposes. The potential of such semi-oblique image acquisitions from nadir-view cameras is hereafter shown and commented. [ABSTRACT FROM AUTHOR]

Many countries use traffic enforcement camera to monitor the speed limit and capture over speed violations. The main objective of such a system is to enforce the speed limits which results in the reduction of number of accidents, fatalities, and serious injuries. Traditionally, the task is carried out manually by the enforcement agencies with the help of specialized hardware such as radar and camera. To automate the process, an efficient and robust solution is needed. Vehicle detection, tracking and speed estimation are the main tasks in an automated system which are not trivial. In this paper, we address the problem of vehicle detection, tracking, and speed estimation using a single fixed camera. A background subtraction method based on the Gaussian Mixture Model (GMM) is employed to detect vehicles because of its capability in dealing with complex backgrounds and variations in the appearance due to illumination and scale. Next, the detected vehicles are tracked in each frame by using the Kalman Filter. Finally, an estimate the speed of each vehicle is determined by using the perspective geometry model. The complete system is tested at our university campus and the results are promising. [ABSTRACT FROM AUTHOR]

Electromagnetic (EM) composites have stimulated tremendous fundamental and practical interests owing to their flexible electromagnetic properties and extensive potential engineering applications. Hence, it is necessary to systematically understand the physical mechanisms and design principles controlling EM composites. In this Tutorial, we first provide an overview of the basic theory of electromagnetism about electromagnetic constitutive parameters that can represent the electromagnetic properties of materials. We show how this corpus allows a consistent construction of effective medium theories and allows for numerical simulation of EM composites to deal with structure–property relationships. We then discuss the influence of spatial dispersion of shaped inclusions in the material medium on the EM properties of composites, which has not been systematically illustrated in the context of this interdisciplinary topic. Next, artificial composites or metamaterials with peculiar properties not readily available in nature are highlighted with particular emphasis on the control of the EM interaction with composites. We conclude by discussing appropriate methods of electromagnetic measurement and practical aspects for implementing composites for specific applications are described. Overall, this Tutorial will serve the purpose of introducing the basics and applications of electromagnetic composites to newcomers in this field. It is also anticipated that researchers from different backgrounds including materials science, optics, and electrical engineering can communicate to each other with the same language when dealing with this interdisciplinary subject and further push forward this advancement from fundamental science to technological applications. [ABSTRACT FROM AUTHOR]

This survey reviews advances in 3D object detection approaches for autonomous driving. A brief introduction to 2D object detection is first discussed and drawbacks of the existing methodologies are identified for highly dynamic environments. Subsequently, this paper reviews the state-of-the-art 3D object detection techniques that utilizes monocular and stereo vision for reliable detection in urban settings. Based on depth inference basis, learning schemes, and internal representation, this work presents a method taxonomy of three classes: model-based and geometrically constrained approaches, end-to-end learning methodologies, and hybrid methods. There is highlighted segment for current trend of multi-view detectors as end-to-end methods due to their boosted robustness. Detectors from the last two kinds were specially selected to exploit the autonomous driving context in terms of geometry, scene content and instances distribution. To prove the effectiveness of each method, 3D object detection datasets for autonomous vehicles are described with their unique features, e. g., varying weather conditions, multi-modality, multi camera perspective and their respective metrics associated to different difficulty categories. In addition, we included multi-modal visual datasets, i. e., V2X that may tackle the problems of single-view occlusion. Finally, the current research trends in object detection are summarized, followed by a discussion on possible scope for future research in this domain. [ABSTRACT FROM AUTHOR]

Epipolar images can reduce conjugate points searching from a two-dimensional to a one-dimensional space, which significantly improves the robustness and efficiency of dense matching. However, two images matched the lack of redundancy and suffered from occlusions. The third image could introduce two more stereos and improve the accuracy and completeness of the three-dimension reconstruction, which were adopted by spaceborne three linear cameras (TLCs), the ALOS PRSIM and ZY-3. In this study, we propose a new triplet epipolar images generation method for spaceborne TLCs. Triplet epipolar images were defined as any two of the three epipolar image pairs. The triplet epipolar geometry requires the imaging rays of any conjugate point to be coplanar. For high-resolution satellite images, parallel projection can be used to approximate the imaging geometry of satellite images. Therefore, a coplanar principal optical axis is a fundamental requirement for spaceborne TLCs. We propose a general workflow to generate triplet epipolar images (TEIs), which includes free-net bundle adjustment of TLCs, building a triplet epipolar geometry, correcting the y-parallaxes, and generating a rational function model (RFM) for TEIs. The ZY3-02 satellite images were used to validate the proposed method. The root means square error (RMSEs) of the free-net bundle adjustment in the image space was 0.185 pixels, which proved the fine intrinsic accuracy. After compensation, the RMSEs of the y parallaxes of the three epipolar image pairs were 0.295, 0.310, and 0.370 pixels. Owing to the simple geometry of the TEIs, the RMSEs of the RFM replacements were within 0.001 pixels. [ABSTRACT FROM AUTHOR]

The demands for stereoscopic images are rapidly growing in recent years. However, complicated system installation and expensive devices are usually required for those applications. Also, the restrictions of visual angles and the problem of slowly displaying the contents still exist. Furthermore, the traditional applications of anamorphosis are confined to fixed viewing positions with static content. Motivated by solving those problems, this paper presents an imaging system using one projector and perspective projection to generate static and dynamic anamorphic images. Then 3D images can be superimposed and displayed above two orthogonal planes. Furthermore, not only the images can be watched at a fixed position, but the function of demonstrating anamorphic images to viewers standing at different positions can also be achieved. Experimental results have shown that the proposed algorithms can successfully fulfil those two requirements. By using perspective projection to produce static and dynamic anamorphic images, this system can diversify the usage of stereoscopic images, provide high-quality images, and enhance the 3D visual effect. Also, those functions can increase the system variety in different applications of object demonstration. [ABSTRACT FROM AUTHOR]

Structural geometry constraints, such as perpendicularity, parallelism and coplanarity, are widely existing in man-made scene, especially in Manhattan scene. By fully exploiting these structural properties, we propose a monocular visual-inertial odometry (VIO) using point and line features with structural constraints. First, a coarse-to-fine vanishing points estimation method with line segment consistency verification is presented to classify lines into structural and non-structural lines accurately with less computation cost. Then, to get precise estimation of camera pose and the position of 3D landmarks, a cost function which combines structural line constraints with feature reprojection residual and inertial measurement unit residual is minimized under a sliding window framework. For geometric representation of lines, Plücker coordinates and orthonormal representation are utilized for 3D line transformation and non-linear optimization respectively. Sufficient evaluations are conducted using two public datasets to verify that the proposed system can effectively enhance the localization accuracy and robustness than other existing state-of-the-art VIO systems with acceptable time consumption. [ABSTRACT FROM AUTHOR]

Galleries, libraries, archives, and museums are nowadays striving to implement innovative approaches to adequately use and distribute the wealth of knowledge found in cultural heritage. A range of technologies can be used to enhance the viewing experience for visitors and boost the expertise of museologists, art historians, scholars, and audience members. The present work aims to provide an overview of current methods and most pertinent studies addressing the use of the innovative technologies for enhancing the fruition of artifacts in traditional museums in an effort to improve the public experience and education. For all the technologies discussed, the paper focuses on the main results obtained in literature and on their possible implementation in the museal context. The overview demonstrates the liveliness of the world of research in the field of technologies for the digital development of museums and how many technologies commonly used in industry are increasingly finding their way into the cultural sphere. [ABSTRACT FROM AUTHOR]

A good understanding of geometrical concepts as well as a broad familiarity with objects lead to excellent human perception of moving objects. The human ability to detect and segment moving objects works in the presence of multiple objects, complex background geometry, motion of the observer and even camouflage. How we perceive moving objects so reliably is a longstanding research question in computer vision and borrows findings from related areas such as psychology, cognitive science and physics. One approach to the problem is to teach a deep network to model all of these effects. This is in contrast with the strategy used by human vision, where cognitive processes and body design are tightly coupled and each is responsible for certain aspects of correctly identifying moving objects. Similarly, from the computer vision perspective there is evidence that classical, geometry-based techniques are better suited to the "motion-based" parts of the problem, while deep networks are more suitable for modeling appearance. In this work, we argue that the coupling of camera rotation and camera translation can create complex motion fields that are difficult for a deep network to untangle directly. We present a novel probabilistic model to estimate the camera's rotation given the motion field. We then rectify the flow field to obtain a rotation-compensated motion field for subsequent segmentation. This strategy of first estimating camera motion, and then allowing a network to learn the remaining parts of the problem, yields improved results on the widely used DAVIS benchmark as well as the more recent motion segmentation data set MoCA (Moving Camouflaged Animals). [ABSTRACT FROM AUTHOR]

Luneburg's model has been the reference for experimental studies of visual space for almost seventy years. His claim for a curved visual space has been a source of inspiration for visual scientists as well as philosophers. The conclusion of many experimental studies has been that Luneburg's model does not describe visual space in various tasks and conditions. Remarkably, no alternative model has been suggested. The current study explores perspective transformations of Euclidean space as a model for visual space. Computations show that the geometry of perspective spaces is considerably different from that of Euclidean space. Collinearity but not parallelism is preserved in perspective space and angles are not invariant under translation and rotation. Similar relationships have shown to be properties of visual space. Alley experiments performed early in the nineteenth century have been instrumental in hypothesizing curved visual spaces. Alleys were computed in perspective space and compared with reconstructed alleys of Blumenfeld. Parallel alleys were accurately described by perspective geometry. Accurate distance alleys were derived from parallel alleys by adjusting the interstimulus distances according to the size-distance invariance hypothesis. Agreement between computed and experimental alleys and accommodation of experimental results that rejected Luneburg's model show that perspective space is an appropriate model for how we perceive orientations and angles. The model is also appropriate for perceived distance ratios between stimuli but fails to predict perceived distances. [ABSTRACT FROM AUTHOR]

There is a widespread belief in art praxis that linear perspective is only a geometric approximation to the 'true' properties of perspective as experienced in the perception of the world, which are thought to involve some form of curvilinear perspective. The origins of that belief are examined from Roman times to the present, with a focus on the generation of perspective curvature by the active viewer as a means of elucidating the underlying perceptual principles involved. It is concluded that the only valid form of perspective for the flat canvas is linear perspective, and that it is valid only for a viewing location at the geometric center of projection for which the picture was constructed. Viewing from any other location (particularly in the case of wide-field images viewed from greater than the required distance) generates perceived distortions that have often been misinterpreted to imply that linear perspective geometry is inadequate and that some form of curvilinear perspective would be more representative. However, as long as it is viewed with one eye from the center of projection, the perceptual experience of accurate linear perspective is of a full, explorable 3D space, in contrast to any other form of perspective convention. [ABSTRACT FROM AUTHOR]

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