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It is difficult to estimate the effective rotation velocity for non‐triaxial stabilized space targets, which leads to deviations in the estimation of imaging plane vectors and azimuth resolution. This makes it hard to reconstruct the three‐dimensional (3D) of space targets based on inverse synthetic aperture radar (ISAR) image sequence. To solve this problem, a joint estimation method of imaging plane vector and 3D structure based on ISAR image sequences is proposed. First, a compact form of imaging plane vector is defined. Then, the 3D structure of a target is characterized by a fully connected deep network. The volume rendering for ISAR images is redesigned with the analysis of projection formula for ISAR imaging. The network is trained using the discrepancy between the modulus of rendered and observed ISAR images in a self‐supervised manner without 3D supervision. And the 3D structure can be obtained by inquiring all points in the 3D space. Therefore, it can optimize imaging planes and produce more complete and accurate results for complex space targets. Adequate simulation experiments verify its superiority. [ABSTRACT FROM AUTHOR]

The existing three-dimensional (3D) x-ray reconstruction methods for lung cancer tissue reconstruct the investigated objects based on a series of two-dimensional (2D) image sections and a chosen 3D reconstruction algorithm. However, because these procedures apply the same segmentation method for all 2D image sections, they may not achieve the optimal segmentation for each section. As a result, the reconstructed 3D images have limited spatial resolution. Furthermore, the existing 3D reconstruction method is time-consuming and results in a limited time resolution. This research presents an innovation of 3D reconstruction by reformulating two main components of the method. First, a validity index for fuzzy clustering is used to obtain the optimal segmentations of any 2D x-ray image. The process is realized by automatically determining the optimal number of clusters for the image. Second, unlike the existing 3D reconstruction methods, a fast-FCM algorithm is used to speed up the 2D image segmenting process, thereby raising the time resolution of the 3D reconstruction process. With the aid of commonly used VTK software, the proposed method has been used to visualize four classes of typical lung cancer tissues: adenocarcinoma, large cell carcinoma, small cell carcinoma, and squamous cell carcinoma. Experimental results validate the effectiveness and efficiency of the proposed algorithm. Thus, the method contributes a useful tool for x-ray-based 3D image reconstruction. [ABSTRACT FROM AUTHOR]

In this paper, a novel depth extraction method based on the regional feature point is proposed to enhance the estimation accuracy of the depth and is used to reconstruct 3D scene. Specifically, each elemental image is segmented into many regions firstly and the depth of the object point corresponding to each feature point in each region is extracted, then the depth corresponding to all feature points can be calculated to express the depth corresponding to all object points, which can enhance the accuracy by solving the wrong matching problem in weak texture regions and reduce the amount of calculation. To segment each elemental image properly, we first preprocess each elemental image and segment each elemental image into many regions, then merge some similar regions. Some computational reconstruction experiments are carried to verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This work provides an analysis of the applicability of two image-based three-dimensional (3D) reconstruction techniques-(1) four-dimensional augmented reality () 3D reconstruction software and (2) phototourism applications-for remote site construction project planning. Remote sites present uncertainty to construction project planning through access, material and resource availability, potential lack of technical expertise on site, and environmental considerations. Both and phototourism have the potential to minimize remote site uncertainty by using available camera technology to digitally capture existing site conditions for the development of accurate 3D modeling for construction project planning. For this study, the researchers (1) analyzed the applicability of 3D reconstruction and phototourism software applications to remote construction planning; (2) determined the appropriateness and robustness of various levels of camera technology for capturing existing conditions on remote sites; and (3) evaluated the impact of image resolution and the method of capture on 3D image-based software-all key facets to usability and access. The researchers used three cameras of various performance capabilities, i.e., a 35-mm disposable box camera, a 5.7-17.1-mm (35 mm equivalent: 37-111 mm) low-level digital camera, and a high-level digital single-lens reflex camera. The following paper outlines a methodology for testing these camera technologies and discusses their appropriateness for reducing barriers to the application of image-based reconstruction techniques for remote site construction planning. This work offers the conclusion that ordinary camera technology is an appropriate option for capturing existing conditions in remote sites (usability), while more advanced camera technology offers better total system benefits (access) of the image-based 3D reconstruction platform. offers a more robust system for modeling remote sites; however, both and phototourism have the potential to generate useful computer models using catalogs of digital images to enable better planning and execution of construction projects in remote areas. [ABSTRACT FROM AUTHOR]

Although three-dimensional (3D) internal log images are useful to wood scientists for assessing internal log features, little work has been carried out on this subject. The aim of this paper was to demonstrate how two-dimensional (2D) log images may be combined to create 3D images of internal log features. The selected log features include heartwood, sapwood, bark, and knots in a sugar maple log and a black spruce log. The 2D cross-sectional images of the logs were acquired through computed tomography (CT) scanning. For each log, 72 successive 2D cross-sectional CT images of the log were employed to create the corresponding 3D internal log images based on the marching cubes algorithm. The results suggest that the proposed method can produce clear 3D images for each selected internal log features in sugar maple and black spruce. The accuracy analysis of the 3D reconstruction is still a huge challenge and should be fully investigated in future work. [ABSTRACT FROM AUTHOR]

Simple Summary: We report the first clinical use of Endosight, a new guidance system for percutaneous interventional procedures based on augmented reality, to guide percutaneous thermal ablations. The new system was demonstrated to be precise and reliable, with a targeting accuracy of 3.4 mm. Clinically acceptable, rapid setup and procedural times can be achieved. Background: Over the last two decades, augmented reality (AR) has been used as a visualization tool in many medical fields in order to increase precision, limit the radiation dose, and decrease the variability among operators. Here, we report the first in vivo study of a novel AR system for the guidance of percutaneous interventional oncology procedures. Methods: Eight patients with 15 liver tumors (0.7–3.0 cm, mean 1.56 + 0.55) underwent percutaneous thermal ablations using AR guidance (i.e., the Endosight system). Prior to the intervention, the patients were evaluated with US and CT. The targeted nodules were segmented and three-dimensionally (3D) reconstructed from CT images, and the probe trajectory to the target was defined. The procedures were guided solely by AR, with the position of the probe tip was subsequently confirmed by conventional imaging. The primary endpoints were the targeting accuracy, the system setup time, and targeting time (i.e., from the target visualization to the correct needle insertion). The technical success was also evaluated and validated by co-registration software. Upon completion, the operators were assessed for cybersickness or other symptoms related to the use of AR. Results: Rapid system setup and procedural targeting times were noted (mean 14.3 min; 12.0–17.2 min; 4.3 min, 3.2–5.7 min, mean, respectively). The high targeting accuracy (3.4 mm; 2.6–4.2 mm, mean) was accompanied by technical success in all 15 lesions (i.e., the complete ablation of the tumor and 13/15 lesions with a >90% 5-mm periablational margin). No intra/periprocedural complications or operator cybersickness were observed. Conclusions: AR guidance is highly accurate, and allows for the confident performance of percutaneous thermal ablations. [ABSTRACT FROM AUTHOR]