Improved Compressed Sensing Based 3D Soft Tissue Surface Reconstruction

Three-dimensional (3D) medical images are prone to overlap, and there are some problems, such as low detection efficiency and inconsistent with the actual situation. Therefore, a 3D medical image surface reconstruction method based on data mining and machine learning is proposed. The 3D medical images were classified according to different ways, the information frame of 3D medical images was established and the surface overlapping information model of 3D images was given. Based on this information framework, the nonlinear function of overlapping area information of 3D medical images was constructed. The weight of the nonlinear function was used to calculate the input and output results of overlapping area information. Combined with the input mode of 3D medical image information, the error between the information output and the expected output was set. The nonlinear function weight of the overlapping area information of 3D medical images was modified by using the learning rate and the use time of the overlapping area information, and the influence factors of the overlapping information detection were obtained by increasing the situation terms, so as to complete the detection of the surface reconstruction information of 3D medical images. The experimental results show that the information detection results of the proposed method fit well with the actual situation, and the information detection efficiency is high.

Real-time 3D surface reconstruction is a widely interested technique in the manufacturing industry. In this paper, an effective 3D surface reconstruction method using the active stereo with the projection of straight line patterns is proposed. For the purpose of real-time applications, 3D surface reconstruction is achieved by using two simple line projections. A depth-map is obtained by searching the projected lines. The final 3D surface is constructed by applying a spatial Kalman filter to the measured depth-map. Because it uses very simple line projections and searches the robust line patterns, it can be a fast, reliable and cost saving 3D measurement in comparison with the sinusoidal fringes projection methods.KeywordsKalman FilterReference PlaneLine PatternGray CodeHeight EstimateThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Abstract. In this paper, we propose the 3D surface reconstruction scheme using multi-view and multi-date Google Earth (GE) satellite images. Multi-view stereo matching (MVS) scheme is one of the methods for reconstructing dense 3D surface based on multi-view images and corresponding camera pose geometry. If many views are input, MVS can estimate the disparity (depth) by matching pixels. However, the common users are not always possible to obtain both multi-view satellite images and the camera geometry (such as Rational Polynomial Camera) in various earth regions. Instead, the GE provides multi-view and multi-date satellite images of earth regions. Therefore, the goal of the proposed method is to perform a 3D surface reconstruction using the GE satellite image. We suppose that the GE satellite image is a pinhole camera model, and the camera pose geometry is estimated using the perspective projection model (PPM) based structure from the motion (SfM) method. Then the 3D surface is reconstructed and fusion using the MVS method. However, the GE satellite image is a transformed pseudo-orthoimage for integration into the raster image. For this reason, the camera pose geometry is inaccurately computed in the SfM process. Thus, the high-rise structures in the reconstructed 3D surface are distorted (distorted hexahedral 3D space). Importantly, the satellite image is a weak PPM and it can express the orthographic projection model. Therefore, we compute 3D homography for transforming between distorted hexahedral space to orthographic cuboid space. Then, the distorted 3D surface is transformed using a projective reconstruction based on 3D homography. The transformed 3D surface has the correct shape in the orthographic projection model. The advantage of the proposed method is that the 3D surface of various earth regions is reconstructed using simply accessible GE satellite images. And the transformed 3D surface is reconstructed into orthographic projection model space, thus the orthoimage can be generated using projection.

Surface reconstruction is a foundational topic in computer graphics and has gained substantial research interest in recent years. With the emergence of advanced neural radiance fields (NeRFs) and 3D Gaussian splatting (3D GS), numerous innovative many novel algorithms for 3D model surface reconstruction have been developed. The rapid expansion of this field presents challenges in tracking ongoing advancements. This survey aims to present core methodologies for the surface reconstruction of 3D models and establish a structured roadmap that encompasses 3D representations, reconstruction methods, datasets, and related applications. Specifically, we introduce 3D representations using 3D Gaussians as the central framework. Additionally, we provide a comprehensive overview of the rapidly evolving surface reconstruction methods based on 3D Gaussian splatting. We categorize the primary phases of surface reconstruction algorithms for 3D models into scene representation, Gaussian optimization, and surface structure extraction. Finally, we review the available datasets, applications, and challenges and suggest potential future research directions in this domain. Through this survey, we aim to provide valuable resources that support and inspire researchers in the field, fostering advancements in 3D reconstruction technologies.

One of the challenging problems in geometric modeling and computer graphics is the construction of realistic human facial geometry. Such geometry are essential for a wide range of applications, such as 3D face recognition, virtual reality applications, facial expression simulation and computer based plastic surgery application. This paper addresses a method for the construction of 3D geometry of human faces based on the use of Elliptic Partial Differential Equations (PDE). Here the geometry corresponding to a human face is treated as a set of surface patches, whereby each surface patch is represented using four boundary curves in the 3-space that formulate the appropriate boundary conditions for the chosen PDE. These boundary curves are extracted automatically using 3D data of human faces obtained using a 3D scanner. The solution of the PDE generates a continuous single surface patch describing the geometry of the original scanned data. In this study, through a number of experimental verifications we have shown the efficiency of the PDE based method for 3D facial surface reconstruction using scan data. In addition to this, we also show that our approach provides an efficient way of facial representation using a small set of parameters that could be utilized for efficient facial data storage and verification purposes.

To evaluate whether a computer vision model can utilize monocular endoscopic images of the middle ear to generate three-dimensional (3D) surface reconstructions that register to ground-truth (GT) imaging with clinically acceptable accuracy for surgical navigation. Eight temporal bone phantoms derived from the publicly available OpenEar data set were 3D-printed. Middle ear endoscopy was performed in each model using a standard monocular otologic endoscope, and a single endoscopic image captured of the mesotympanum. Images were processed with a monocular surface reconstruction model to generate predicted 3D surface reconstructions. Reconstructions were registered to GT meshes using natural anatomic landmarks. Surface reconstruction error (SRE) was quantified using the mean modified Hausdorff distance, and target registration error (TRE) was calculated using withheld test fiducials. Mean SRE across models was 0.58 ± 0.10mm, with 81.6% of points within 1mm of GT surfaces. Mean TRE was 0.96 ± 0.17mm. Simple 2D, monocular endoscopic images can be converted into high-fidelity 3D surface reconstructions of middle ear anatomy, which can be registered to ground-truth imaging with submillimeter registration accuracy. These findings establish the preclinical feasibility of monocular, endoscope-centric navigation for transcanal endoscopic ear surgery.

IntroductionThe American Society of Gastroenterology Endoscopy led Preservation and Incorporation of Valuable Endoscopic Innovations initiative has identified real time polyp diagnosis as one of the next major technology-driven changes in...

In this study, we propose a three-dimensional (3D) surface temperature measurement method based on the principle of stereoscopic 3D reconstruction and the dependence of phosphorescence lifetime on temperature. A 385-nm UV(Ultraviolet) light was used as the excitation light, and two high-speed cameras were used as the detectors. The phosphor MFG (Mg4FGeO6: Mn4+) was mixed with the binder HPC and sprayed onto the tested 3D surface. The natural texture generated by the surface roughness of the phosphor coating was used as a feature for cross-correlation calculations. The digital image correlation (DIC) algorithm was used to match these feature positions in the phosphorescent images from the two cameras. The effects of the excitation angle and detecting angle were analyzed. The results indicate that the temperature measurement based on phosphorescent lifetime was not affected by the excitation and detecting angle. The method was validated on a turbine blade as an example of a 3D surface to demonstrate the capability. A comparison of the measurement results with the thermocouples proves that the current method can successfully measure the temperature on 3D surfaces with a maximum difference of 1.63°C. The spatial accuracy of the method was obtained by comparing with the measurement results of a 3D scanner, which shows that the maximum absolute error of the 3D reconstruction was 0.350 mm. The current study proposes a promising 3D surface temperature measurement method, which is expected to be widely used in gas turbine blades, Internal Combustion (IC) engine cylinders, complex curved heat exchangers, and other fields due to its non-contact measurement, low susceptibility to infrared radiation interference, high measurement accuracy, and ability to withstand harsh environments.

Computer vision recognition of stem and calyx in apples using near-infrared linear-array structured light and 3D reconstruction

Utility of 3D Surface Laser Scanning in Brachytherapy of Lip Cancer

In 3D surface reconstruction, the sampling and quantization in the process of digitization limits the surface details obtained by the visual sensor. Obtaining a fine-grained 3D surface at lower cost remains a difficult problem. In our work, we have introduced a new method for enhancing 3D surface details reconstructed from single normal map without the support of hardware and reflection models. Using the new measurement of angle profile to infer microstructures hidden from existing surfaces. In addition, discrete geometry processing (DGP) can be used to further improve inference results. Numerous simulation results show that our method has achieved significant improvements on both subjective visual evaluation and objective quality metrics.

3D surface profilometry based on 2D S-Transform method with optimized window

In this paper, the 3D surface reconstruction method of black melon seeds based on photometric stereo was studied, which was then used to identify the wrinkled seeds. After the image capturing system being set up, the system accuracy was calibrated by reconstructing a standard hemispherical object. Then, the whole 3D surface of a seed was built by reconstructing its central and periphery region with PS separately for the two regions are of great colour differences. Finally, experiments were done to show that cross surface area of the reconstructed surface can be used as wrinkle identification parameter with the identification threshold of 9. Under this threshold more than 90% of wrinkled seeds could be successfully identified while only 5% of normal seeds being identified as wrinkled.

3D map reconstruction plays a critical role in fields such as autonomous driving, urban planning, and virtual reality. Using 2D images from real-world settings, multi-view image-based 3D map reconstruction offers a practical solution across these sectors. Specifically, generating 3D maps from realistic drone-view images has broad applicability. However, direct reconstruction from drone-view images is challenging due to extreme viewing angles and unstable image conditions like motion blur or varying light. Additionally, simultaneously generating and utilizing 3D maps from drone-captured images presents significant challenges, including fragmented sub-tasks such as 3D point cloud reconstruction, surface reconstruction, and shape fitting. To address these challenges, we introduce the 3D Scene Abstraction, which seamlessly integrates image matching, 3D reconstruction, 3D semantic segmentation, clustering, and shape fitting to produce 3D maps from RGB images. Our approach enhances the robustness of 3D map generation by merging 2D semantic information with 3D point clouds, ensuring reliable results under diverse drone-captured image conditions. For efficient data representation, we employ multiple primitives based on prior knowledge of various building shapes, improving both map formation performance and computational efficiency. Our approach not only enhances the effectiveness of 3D map reconstruction but also reduces the amount of data required to represent the scene, thereby improving overall efficiency and practical usability. These contributions address practical challenges in the field and lay the foundation for future advancements in 3D map reconstruction technologies.

Three-dimensional (3D) reconstruction and measurement are popular techniques in precision manufacturing processes. In this manuscript, a single image 3D reconstruction method is proposed based on a novel monocular vision system, which includes a three-level charge coupled device (3-CCD) camera and a ring structured multi-color light emitting diode (LED) illumination. Firstly, a procedure for the calibration of the illumination’s parameters, including LEDs’ mounted angles, distribution density and incident angles, is proposed. Secondly, the incident light information, the color distribution information and gray level information are extracted from the acquired image, and the 3D reconstruction model is built based on the camera imaging model. Thirdly, the surface height information of the detected object within the field of view is computed based on the built model. The proposed method aims at solving the uncertainty and the slow convergence issues arising in 3D surface topography reconstruction using current shape-from-shading (SFS) methods. Three-dimensional reconstruction experimental tests are carried out on convex, concave, angular surfaces and on a mobile subscriber identification module (SIM) card slot, showing relative errors less than 3.6%, respectively. Advantages of the proposed method include a reduced time for 3D surface reconstruction compared to other methods, demonstrating good suitability of the proposed method in reconstructing surface 3D morphology.