Recent years have witnessed significant progress in 3D hand mesh recovery. Nevertheless, because of the intrinsic 2D-to-3D ambiguity, recovering camera-space 3D information from a single RGB image remains challenging. To tackle this problem, we divide camera-space mesh recovery into two sub-tasks, i.e., root-relative mesh recovery and root recovery. First, joint landmarks and silhouette are extracted from a single input image to provide 2D cues for the 3D tasks. In the root-relative mesh recovery task, we exploit semantic relations among joints to generate a 3D mesh from the extracted 2D cues. Such generated 3D mesh coordinates are expressed relative to a root position, i.e., wrist of the hand. In the root recovery task, the root position is registered to the camera space by aligning the generated 3D mesh back to 2D cues, thereby completing cameraspace 3D mesh recovery. Our pipeline is novel in that (1) it explicitly makes use of known semantic relations among joints and (2) it exploits 1D projections of the silhouette and mesh to achieve robust registration. Extensive experiments on popular datasets such as FreiHAND, RHD, and Human3.6M demonstrate that our approach achieves stateof-the-art performance on both root-relative mesh recovery and root recovery. Our code is publicly available at https://github.com/SeanChenxy/HandMesh.
In this paper a fast triangular mesh based registration method is proposed. Having Template and Reference images as inputs, the template image is triangulated using a content adaptive mesh generation algorithm. Considering the pixel values at mesh nodes, interpolated using spline interpolation method for both of the images, the energy functional needed for image registration is minimized. The minimization process was achieved using a mesh based discretization of the distance measure and regularization term which resulted in a sparse system of linear equations, which due to the smaller size in …
Research institute for signals, systems and computational intelligence, sinc(i), FICH-UNL/CONICET（信号、系统与计算智能研究所 sinc(i)，FICH-UNL/CONICET）；University of Oxford（牛津大学）；TheraPanacea；CentraleSupelec（中央理工-高等电力学院）；Universite Paris-Saclay（巴黎-萨克雷大学）
Deformable registration has been one of the pillars of biomedical image computing. Conventional approaches refer to the definition of a similarity criterion that, once endowed with a deformation model and a smoothness constraint, determines the optimal transformation to align two given images. The definition of this metric function is among the most critical aspects of the registration process. We argue that incorporating semantic information (in the form of anatomical segmentation maps) into the registration process will further improve the accuracy of the results. In this paper, we propose a…
Aachen Institute for Advanced Study in Computational Engineering Science (AICES), RWTH Aachen University（亚琛工业大学计算工程科学高级研究院（AICES））；Peter L. Reichertz Institute for Medical Informatics, University of Braunschweig – Institute of Technology and Hannover Medical School（布伦瑞克工业大学与汉诺威医学院彼得·L·赖歇茨医学信息学研究所）
Region of interest (ROI) alignment in medical images plays a crucial role in diagnostics, procedure planning, treatment, and follow-up. Frequently, a model is represented as triangulated mesh while the patient data is provided from CAT scanners as pixel or voxel data. Previously, we presented a 2D method for curve-to-pixel registration. This paper contributes (i) a general mesh-to-raster (M2R) framework to register ROIs in multi-modal images; (ii) a 3D surface-to-voxel application, and (iii) a comprehensive quantitative evaluation in 2D using ground truth provided by the simultaneous truth and…
University of Electronic Science and Technology of China（电子科技大学）；Megvii Technology（旷视科技）
Image alignment by mesh warps, such as meshflow, is a fundamental task which has been widely applied in various vision applications(e.g., multi-frame HDR/denoising, video stabilization). Traditional mesh warp methods detect and match image features, where the quality of alignment highly depends on the quality of image features. However, the image features are not robust in occurrence of low-texture and low-light scenes. Deep homography methods, on the other hand, are free from such problem by learning deep features for robust performance. However, a homography is limited to plane motions. In t…
ETH Zürich（苏黎世联邦理工学院）；Max Planck Institute for Intelligent Systems Tübingen（马克斯·普朗克智能系统研究所）；University of Tübingen（蒂宾根大学）
Registering point clouds of dressed humans to parametric human models is a challenging task in computer vision. Traditional approaches often rely on heavily engineered pipelines that require accurate manual initialization of human poses and tedious post-processing. More recently, learning-based methods are proposed in hope to automate this process. We observe that pose initialization is key to accurate registration but existing methods often fail to provide accurate pose initialization. One major obstacle is that, regressing joint rotations from point clouds or images of humans is still very c…
Leiden University Medical Center（莱顿大学医学中心）；Centrum Wiskunde & Informatica（荷兰数学与计算机科学研究院）
Reliably and physically accurately transferring information between images through deformable image registration with large anatomical differences is an open challenge in medical image analysis. Most existing methods have two key shortcomings: first, they require extensive up-front parameter tuning to each specific registration problem, and second, they have difficulty capturing large deformations and content mismatches between images. There have however been developments that have laid the foundation for potential solutions to both shortcomings. Towards the first shortcoming, a multi-objectiv…
Imperial College London（帝国理工学院）；TheraPanacea；University of Oxford（牛津大学）；CentraleSupelec（中央高等电力学院）；INRIA（法国国家信息与自动化研究所）；Universite Paris-Saclay（巴黎萨克雷大学）
We propose a novel weakly supervised discriminative algorithm for learning context specific registration metrics as a linear combination of conventional similarity measures. Conventional metrics have been extensively used over the past two decades and therefore both their strengths and limitations are known. The challenge is to find the optimal relative weighting (or parameters) of different metrics forming the similarity measure of the registration algorithm. Hand-tuning these parameters would result in sub optimal solutions and quickly become infeasible as the number of metrics increases. Fu…
Max Planck Institute for Informatics（马克斯·普朗克信息学研究所）；Saarland University（萨尔大学）；Google Research（谷歌研究院）
We address the problem of fitting 3D human models to 3D scans of dressed humans. Classical methods optimize both the data-to-model correspondences and the human model parameters (pose and shape), but are reliable only when initialized close to the solution. Some methods initialize the optimization based on fully supervised correspondence predictors, which is not differentiable end-to-end, and can only process a single scan at a time. Our main contribution is LoopReg, an end-to-end learning framework to register a corpus of scans to a common 3D human model. The key idea is to create a self-supe…
Graduate School of Informatics, Kyoto University（京都大学信息学研究科）；Graduate School of Medicine, Kyoto University（京都大学医学研究科）
Organ shape reconstruction based on a single-projection image during treatment has wide clinical scope, e.g., in image-guided radiotherapy and surgical guidance. We propose an image-to-graph convolutional network that achieves deformable registration of a 3D organ mesh for a single-viewpoint 2D projection image. This framework enables simultaneous training of two types of transformation: from the 2D projection image to a displacement map, and from the sampled per-vertex feature to a 3D displacement that satisfies the geometrical constraint of the mesh structure. Assuming application to radiati…
Registration-Free Learnable Multi-View Capture of Faces in Dense Semantic Correspondence
Institute of Robotics, Athena Research Center（机器人研究所，雅典研究中心）；School of ECE, NTUA（电子工程学院，NTUA）；HERON – Hellenic Robotics Center of Excellence（希腊机器人 excellence 中心）；MPI for Intelligent Systems（智能系统研究所）；ETH Zurich（苏黎世联邦理工学院）；Google（谷歌）
Multi-Expert Learning Framework with the State Space Model for Optical and SAR Image Registration
Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China（教育部智能感知与图像理解重点实验室）；Hangzhou Institute of Technology, Xidian University（西安电子科技大学杭州学院）；School of Computer Science, Xidian University（西安电子科技大学计算机学院）；Department of Automation, Tsinghua University（清华大学自动化系）
Fraunhofer IOSB（弗劳恩霍夫IOSB研究所）；Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）
The comparison of current image data with existing 3D model data of a scene provides an efficient method to keep models up to date. In order to transfer information between 2D and 3D data, a preliminary co-registration is necessary. In this paper, we present a concept to automatically co-register aerial imagery and untextured 3D model data. To refine a given initial camera pose, our algorithm computes dense correspondence fields using SIFT flow between gradient representations of the model and camera image, from which 2D-3D correspondences are obtained. These correspondences are then used in a…
Recent works on 3D semantic segmentation propose to exploit the synergy between images and point clouds by processing each modality with a dedicated network and projecting learned 2D features onto 3D points. Merging large-scale point clouds and images raises several challenges, such as constructing a mapping between points and pixels, and aggregating features between multiple views. Current methods require mesh reconstruction or specialized sensors to recover occlusions, and use heuristics to select and aggregate available images. In contrast, we propose an end-to-end trainable multi-view aggr…
UV Gaussians: Joint Learning of Mesh Deformation and Gaussian Textures for Human Avatar Modeling
Tsinghua Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院）；Tencent AI Lab（腾讯AI实验室）；The Hong Kong University of Science and Technology（香港科技大学）
Hong Kong University of Science and Technology（香港科技大学）；Tencent Lightspeed & Quantum Studios（腾讯光子工作室群）；City University of Hong Kong（香港城市大学）
In recent years, sparse voxel-based methods have become the state-of-the-arts for 3D semantic segmentation of indoor scenes, thanks to the powerful 3D CNNs. Nevertheless, being oblivious to the underlying geometry, voxel-based methods suffer from ambiguous features on spatially close objects and struggle with handling complex and irregular geometries due to the lack of geodesic information. In view of this, we present Voxel-Mesh Network (VMNet), a novel 3D deep architecture that operates on the voxel and mesh representations leveraging both the Euclidean and geodesic information. Intuitively, …
MRASfM: Multi-Camera Reconstruction and Aggregation through Structure-from-Motion in Driving Scenes
Department of Automation, Key Laboratory of System Control and Information Processing of Ministry of Education, Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education, Shanghai Engineering Research Center of Intelligent Control and Management, Shanghai Jiao Tong University（自动化系、教育部系统控制与信息处理重点实验室、教育部海洋智能装备与系统重点实验室、上海智能控制与管理工程研究中心、上海交通大学）；The Advanced Robotics Research Center, Artificial Intelligence Research Institute and School of Information and Control Engineering, China University of Mining and Technology（先进机器人研究中心、人工智能研究院和信息与控制工程学院、中国矿业大学）
