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High-precision 3d teeth reconstruction based on five-view intra-oral photos

displays 2025年 87卷

作者： Wang, Yanqi Sun, Xinyue Jia, Jun Jin, Zuolin Ma, Yanning AF Med Univ State Key Lab Oral & Maxillofacial Reconstruct & R Xian 710032 Peoples R China Shanghai Jiao Tong Univ Shanghai 200240 Peoples R China Shanxi Med Univ Sch & Hosp Stomatol Dept Orthodont Taiyuan 030001 Peoples R China

Reconstructing 3d dental model from multi-view intra-oral photos plays an important role in the process of orthodontic treatment. Compared with cone-beam computed tomography (CBCT) or intra-oral scanner (IOS), 3d reconstruction provides a low-cost solution to monitor teeth, which does not require professional devices and operations. This paper introduces an enhanced fully automated framework for 3d tooth reconstruction using five-view intraoral photos, capable of automatically generating the shapes, alignments, and occlusal relationships of both upper and lower teeth. The proposed framework includes three phases. Initially, a parametric dental model based on a statistical shape is built to represent the shape and position of each tooth. Next, in the feature extraction stage, the segment anything model (SAM) is used to accurately detect the tooth boundaries from intra-oral photos, and the single-view depth estimation approach known as depth Anything is used to obtain depth information. And grayscale conversion and normalization processing are performed on the photos to extract luminance information separately in order to deal with the problem of tooth surface reflection. Finally, an iterative reconstruction process in two stages is implemented: the first stage involves alternating between searching for point correspondences and optimizing a composite loss function to align the parameterized tooth model with the predicted contours of teeth;in the second stage, image depth and lightness information are utilized for additional refinement. Extensive experiments are conducted to validate the proposed methods. Compared with existing methods, the proposed method not only qualitatively outperforms in misaligned, missing, or complex occlusion cases, but also quantificationally achieve good RMSd and dice.

关键词： 3d reconstruction Segment anything Orthodontic treatment

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A survey of deep learning-based microscopic cell image understanding

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displays 2025年 87卷

作者： Huo, Yue Lu, Zixuan deng, Zhi Zhang, Feifan Xiong, Junwen Zhang, Peng Huang, Hui Northwestern Polytech Univ Sch Comp Sci Xian Shaanxi Peoples R China Northwestern Polytech Univ Lib Xian Shaanxi Peoples R China

Enhancement and recognition of microscopic cell images are crucial for advancing biomedical research, with deep learning methods rapidly developing for various tasks. deep learning drives the processing of multiple image types, including microscopic cell images. Numerous surveys have focused on clinical images like X-rays or CT scans, or on enhancement and segmentation tasks for typical cells and tissues. This inspired us to conduct a comprehensive survey on deep learning-based image processing of microscopic cell images. After introducing recent imaging techniques in the biomedical field, we classified existing deep learning-based methods into two categories: enhancement and recognition. The enhancement section addresses image denoising and super- resolution of 2d images, while 3d image enhancement methods are further divided into 3d reconstruction and isotropic reconstruction. The recognition section is organized into three main aspects: single cell recognition, organelles and subcellular recognition, and live cell dynamic behavior recognition. Finally, we discuss the unique challenges and potential improvements for deep learning-based methods.

关键词： deep learning Microscopic cell image image enhancement image recognition Survey

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Enhanced detection of power transformer winding faults through 3d FRA signatures and image processing techniques

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ELECTRIC POWER SYSTEMS research 2025年 242卷

作者： Zhao, Xiaozhen Wu, Ge Yang, dingqian Xu, Guanghu Xing, Yunqi Yao, Chenguo Abu-Siada, Ahmed Hebei Univ Technol State Key Lab Reliabil & Intelligence Elect Equip Tianjin 300130 Peoples R China State Grid XinJiang Elect Power Res Inst Urumqi 830011 Peoples R China Chongqing Univ State Key Lab Power Transmiss Equipment Technol Chongqing 400044 Peoples R China Curtin Univ Perth WA 6872 Australia Hebei Univ Technol Key Lab Electromagnet Field & Elect Apparat Reliab Tianjin 300130 Peoples R China

Power transformers may experience various winding deformations because of short circuit faults and other external factors. Frequency response analysis (FRA) technique has been the most popular method for detecting such winding deformations. However, current practices primarily focus on the magnitude response, often neglecting the phase response, which holds vital information. Furthermore, the identification and quantification of internal transformer faults largely depend on hands-on experience, leading to inconsistent interpretations of similar FRA signatures. currently, there is no universally accepted standard for FRA interpretation. In this regard, digital image processing (dIP) technique is employed on a new three-dimensional (3d) FRA signature. The proposed new 3d signature integrates the frequency, magnitude, and phase angle of the conventional FRA signature into one 3d plot that is manipulated using dIP code to extract unique texture analysis features. These features are used to classify various faults through clustering them in a ternary diagram. The proposed technique is validated through extensive experimental analysis on a custom-made three-phase transformer. Results attest the robustness of the proposed technique.

关键词： digital image processing Fault diagnosis Frequency response analysis Power transformers Ternary diagram

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Feature-Centered First Order Structure Tensor Scale-Space in 2d and 3d

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IEEE ACCESS 2025年 13卷 9766-9779页

作者： Pieta, Pawel Tomasz dahl, Anders Bjorholm Frisvad, Jeppe Revall Bigdeli, Siavash Arjomand Christensen, Anders Nymark Tech Univ Denmark Dept Appl Math & Comp Sci DK-2800 Lyngby Denmark

The structure tensor method is often used for 2d and 3d analysis of imaged structures, but its results are in many cases very dependent on the user's choice of method parameters. We simplify this parameter choice in first order structure tensor scale-space by directly connecting the width of the derivative filter to the size of image features. By introducing a ring-filter step, we substitute the Gaussian integration/smoothing with a method that more accurately shifts the derivative filter response from feature edges to their center. We further demonstrate how extracted structural measures can be used to correct known inaccuracies in the scale map, resulting in a reliable representation of the feature sizes both in 2d and 3d. Compared to the traditional first order structure tensor, or previous structure tensor scale-space approaches, our solution is much more accurate and can serve as an out-of-the-box method for extracting a wide range of structural parameters with minimal user input.

关键词： Tensors Filtering theory Three-dimensional displays information filters image edge detection Feature extraction Standards Smoothing methods Shape Hands 3d image processing scale-space structural analysis structure tensor

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ACF-Net: Asymmetric Cascade Fusion for 3d detection With LidAR Point Clouds and images

IEEE TRANSACTIONS ON INTELLIGENT VEHICLES

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IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 2024年第2期9卷 3360-3371页

作者： Tian, Yonglin Zhang, Xianjing Wang, Xiao Xu, Jintao Wang, Jiangong Ai, Rui Gu, Weihao ding, Weiping Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Haomo Technol Co Ltd AI Ctr Beijing 100192 Peoples R China Anhui Univ Sch Artificial Intelligence Hefei 230031 Peoples R China Haomo Technol Co Ltd Beijing 100192 Peoples R China Nantong Univ Sch Informat Sci & Technol Nantong 226019 Peoples R China

The recognition and utilization of complementary information arising from modality-intrinsic properties play crucial roles in multimodal 3d detection. However, most of the current approaches for fusion-based 3d detection follow symmetrical fusion paradigms and adopt early fusion, middle fusion as well as late fusion styles, which ignore the unequal status of data with different modalities. In this paper, according to the timing of fusion, we adopt an asymmetric cascade fusion network to exploit both the structural information from point clouds and the complementary semantic information from images. A multi-stage cascade design of 3d object detection is proposed to iteratively refine predictions and several late image features (comprised of detection clues, segmentation clues, and deep features from encoders) are incorporated into different stages of the LidAR branch to maintain the integrity of image features and enable deep multimodal interactions. Besides, to mitigate the effects of the down-sampling of voxelized features and possible mismatching of multimodal data, we propose proxy-based cross-modality sampling to utilize the high-density point clouds coordinates and develop an image degeneration process to simulate the noise in cross-modality matching for robust training. Extensive experiments are conducted on KITTI and Waymo Open dataset, which validate the effectiveness of the proposed method.

关键词： Three-dimensional displays Feature extraction Point cloud compression Laser radar Object detection Timing Fuses 3d detection autonomous driving asymmetric fusion cascade fusion multimodal fusion

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LEAPSE: Learning Environment Affordances for 3d Human Pose and Shape Estimation

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IEEE TRANSACTIONS ON image processing 2024年 33卷 3285-3300页

作者： Tian, Fangzheng Kim, Sungchan Jeonbuk Natl Univ Dept Comp Sci & Artificial Intelligence Jeonju Si 54896 South Korea Jeonbuk Natl Univ Ctr Adv Image Informat Technol Jeonju Si 54896 South Korea

We live in a 3d world where people interact with each other in the environment. Learning 3d posed humans therefore requires us to perceive and interpret these interactions. This paper proposes LEAPSE, a novel method that learns salient instance affordances for estimating a posed body from a single RGB image in a non-parametric manner. Existing methods mostly ignore the environment and estimate the human body independently from the surroundings. We capture the influences of non-contact and contact instances on a posed body as an adequate representation of the "environment affordances". The proposed method learns the global relationships between 3d joints, body mesh vertices, and salient instances as environment affordances on the human body. LEAPSE achieved state-of-the-art results on the 3dPW dataset with many affordance instances, and also demonstrated excellent performance on Human3.6M dataset. We further demonstrate the benefit of our method by showing that the performance of existing weak models can be significantly improved when combined with our environment affordance module.

关键词： Three-dimensional displays Affordances Shape Estimation image reconstruction Transformers Task analysis 3d human pose and shape estimation environment affordances non-parametric

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MonodFM: density Field Modeling-Based End-to-End Monocular 3d Object detection

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IEEE ACCESS 2025年 13卷 74015-74031页

作者： Liu, Gang Huang, Xinrui Xie, Xiaoxiao Hubei Univ Technol Sch Comp Sci Wuhan 430068 Peoples R China

Monocular 3d object detection aims to infer the 3d properties of objects from a single RGB image. Existing methods primarily rely on planar features to estimate 3d information directly. However, the limited 3d information available in 2d images often results in suboptimal detection accuracy. To address this challenge, we propose MonodFM, an end-to-end monocular 3d object detection method based on density field modeling. By modeling the density field from the features of a single image, MonodFM enables a more accurate transition from 2d to 3d representations, improving 3d attribute prediction accuracy. Unlike traditional depth map methods, which are limited to visible regions, MonodFM infers geometric information from occluded regions by predicting the scene's density field. Moreover, compared with more complex approaches like Neural Radiance Fields (NeRF), MonodFM provides a streamlined and efficient prediction process. Experiments conducted on the KITTI dataset show that MonodFM achieves AP3d of (25.13, 16.61, 13.82) and APBEV of (32.61, 22.14, 18.71) on the KITTI benchmark for the Car category under three difficulty settings (easy, moderate, and hard), achieving competitive performance. Ablation studies further validate the effectiveness of each component. As a result, MonodFM offers an effective approach to monocular 3d object detection, demonstrating strong performance.

关键词： Three-dimensional displays Solid modeling Object detection Computational modeling Neural radiance field Accuracy Feature extraction depth measurement Point cloud compression Real-time systems 3d reconstruction density field modeling end-to-end detection monocular 3d object detection occlusion handling

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Eigenpose: Occlusion-Robust 3d Human Mesh Reconstruction

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IEEE TRANSACTIONS ON image processing 2025年 34卷 2379-2391页

作者： Gwon, Mi-Gyeong Um, Gi-Mun Cheong, Won-Sik Kim, Wonjun Konkuk Univ Dept Elect & Elect Engn Seoul 05029 South Korea Elect & Telecommun Res Inst Immers Media Res Sect Daejeon 34129 South Korea

A new approach for occlusion-robust 3d human mesh reconstruction from a single image is introduced in this paper. Since occlusion has emerged as a major problem to be resolved in this field, there have been meaningful efforts to deal with various types of occlusions (e.g., person-to-person occlusion, person-to-object occlusion, self-occlusion, etc.). Although many recent studies have shown the remarkable progress, previous regression-based methods still have respective limitations to handle occlusion problems due to the lack of the appearance information. To address this problem, we propose a novel method for human mesh reconstruction based on the pose-relevant subspace analysis. Specifically, we first generate a set of eigenvectors, so-called eigenposes, by conducting the singular value decomposition (SVd) of the pose matrix, which contains diverse poses sampled from the training set. These eigenposes are then linearly combined to construct a target body pose according to fusing coefficients, which are learned through the proposed network. Such combination of principal body postures (i.e., eigenposes) in a global manner gives a great help to cope with partial ambiguities by occlusions. Furthermore, we also propose to exploit a joint injection module that efficiently incorporates the spatial information of visible joints into the encoded feature during the estimation process of fusing coefficients. Experimental results on benchmark datasets demonstrate the ability of the proposed method to robustly reconstruct the human mesh under various occlusions occurring in real-world scenarios. The code and model are publicly available at: https://***/dCVL-3d/Eigenpose_release.

关键词： image reconstruction Three-dimensional displays Heating systems Training Reliability Transformers Solid modeling Shape Computer architecture Benchmark testing Occlusion-robust 3d human mesh reconstruction pose-relevant subspace analysis joint injection module

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TC-SfM: Robust Track-Community-Based Structure-From-Motion

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IEEE TRANSACTIONS ON image processing 2024年 33卷 1534-1548页

作者： Wang, Lei Ge, Linlin Luo, Shan Yan, Zihan Cui, Zhaopeng Feng, Jieqing Zhejiang Univ State Key Lab CAD&CG Hangzhou 310058 Peoples R China

Structure-from-Motion (SfM) aims to recover 3d scene structures and camera poses based on the correspondences between input images, and thus the ambiguity caused by duplicate structures (i.e., different structures with strong visual resemblance) always results in incorrect camera poses and 3d structures. To deal with the ambiguity, most existing studies resort to additional constraint information or implicit inference by analyzing two-view geometries or feature points. In this paper, we propose to exploit high-level information in the scene, i.e., the spatial contextual information of local regions, to guide the reconstruction. Specifically, a novel structure is proposed, namely, track-community, in which each community consists of a group of tracks and represents a local segment in the scene. A community detection algorithm is performed on the track-graph to partition the scene into segments. Then, the potential ambiguous segments are detected by analyzing the neighborhood of tracks and corrected by checking the pose consistency. Finally, we perform partial reconstruction on each segment and align them with a novel bidirectional consistency cost function which considers both 3d-3d correspondences and pairwise relative camera poses. Experimental results demonstrate that our approach can robustly alleviate reconstruction failure resulting from visually indistinguishable structures and accurately merge the partial reconstructions.

关键词： image reconstruction Cameras Three-dimensional displays image segmentation Pipelines image edge detection Visualization Structure-from-motion image-based reconstruction ambiguous structures track-community

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An Embeddable Implicit IUVd Representation for Part-Based 3d Human Surface Reconstruction

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IEEE TRANSACTIONS ON image processing 2024年 33卷 4334-4347页

作者： Li, Baoxing deng, Yong Yang, Yehui Zhao, Xu Shanghai Jiao Tong Univ Dept Automat Shanghai 200240 Peoples R China

To reconstruct a 3d human surface from a single image, it is crucial to simultaneously consider human pose, shape, and clothing details. Recent approaches have combined parametric body models (such as SMPL), which capture body pose and shape priors, with neural implicit functions that flexibly learn clothing details. However, this combined representation introduces additional computation, e.g. signed distance calculation in 3d body feature extraction, leading to redundancy in the implicit query-and-infer process and failing to preserve the underlying body shape prior. To address these issues, we propose a novel IUVd-Feedback representation, consisting of an IUVd occupancy function and a feedback query algorithm. This representation replaces the time-consuming signed distance calculation with a simple linear transformation in the IUVd space, leveraging the SMPL UV maps. Additionally, it reduces redundant query points through a feedback mechanism, leading to more reasonable 3d body features and more effective query points, thereby preserving the parametric body prior. Moreover, the IUVd-Feedback representation can be embedded into any existing implicit human reconstruction pipeline without requiring modifications to the trained neural networks. Experiments on the THuman2.0 dataset demonstrate that the proposed IUVd-Feedback representation improves the robustness of results and achieves three times faster acceleration in the query-and-infer process. Furthermore, this representation holds potential for generative applications by leveraging its inherent semantic information from the parametric body model.

关键词： Surface reconstruction image reconstruction Surface treatment Three-dimensional displays Shape Clothing Redundancy 3d human surface reconstruction implicit representation UV map human body prior acceleration

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