Reconstructing 3D object from a single image (RGB or depth) is a fundamental problem in visual scene understanding and yet remains challenging due to its ill-posed nature and complexity in real-world scenes. To address those challenges, we adopt a primitive-based representation for 3D object, and propose a two-stage graph network for primitive-based 3D object estimation, which consists of a sequential proposal module and a graph reasoning module. Given a 2D image, our proposal module first generates a sequence of 3D primitives from input image with local feature attention. Then the graph reasoning module performs joint reasoning on a primitive graph to capture the global shape context for each primitive. Such a framework is capable of taking into account rich geometry and semantic constraints during 3D structure recovery, producing 3D objects with more coherent structure even under challenging viewing conditions. We train the entire graph neural network in a stage-wise strategy and evaluate it on three benchmarks: Pix3D, ModelNet and NYU Depth V2. Extensive experiments show that our approach outperforms the previous state of the arts with a considerable margin.
PRIMU: Uncertainty Estimation for Novel Views in Gaussian Splatting from Primitive-Based Representations of Error and Coverage
Department of Mathematics（数学系）；University of Wuppertal（乌珀塔尔大学）；Institute of Computer Science（计算机科学研究所）；University of Osnabrück（奥斯纳布吕克大学）；Centre for Robotics（机器人中心）；Queensland University of Technology（昆士兰理工大学）
DPF-Net: Combining Explicit Shape Priors in Deformable Primitive Field for Unsupervised Structural Reconstruction of 3D Objects
National Engineering Laboratory for Brain-inspired Intelligence Technology and Application（脑启发智能技术与应用国家工程实验室）；University of Science and Technology of China（中国科学技术大学）
Progressive Reasoning with Primitive Correction for Compositional Zero-Shot Learning
School of Computer Science and Technology, Beijing Jiaotong University（北京交通大学计算机科学与技术学院）；Key Laboratory of Big Data & Artificial Intelligence in Transportation (Ministry of Education)（交通运输大数据与人工智能教育部重点实验室）
Fit4CAD: A point cloud benchmark for fitting simple geometric primitives in CAD objects
Istituto di Matematica Applicata e Tecnologie Informatiche “E. Magenes”, Consiglio Nazionale delle Ricerche（意大利国家研究委员会“E. Magenes”应用数学与信息技术研究所）；University of Oslo（奥斯陆大学）；ENS Paris-Saclay, Université Paris-Saclay（巴黎萨克雷大学巴黎萨克雷高等师范学院）
ProCC: Progressive Cross-primitive Compatibility for Open-World Compositional Zero-Shot Learning
The Hong Kong Polytechnic University（香港理工大学）；The Hong Kong Polytechnic University Shenzhen Research Institute（香港理工大学深圳研究院）；The Hong Kong University of Science and Technology（香港科技大学）；Huazhong University of Science and Technology（华中科技大学）
Neural Parts: Learning Expressive 3D Shape Abstractions with Invertible Neural Networks
Max Planck Institute for Intelligent Systems（马克斯·普朗克智能系统研究所）；Idiap Research Institute（伊迪亚普研究所）；NVIDIA（英伟达）；University of Toronto（多伦多大学）；Vector Institute（矢量研究所）
Zero-shot point cloud segmentation by transferring geometric primitives
The University of Hong Kong（香港大学）；The Chinese University of Hong Kong（香港中文大学）；Inceptio（嬴彻科技）；ShanghaiTech University（上海科技大学）；Texas A&M University（德州农工大学）
A Mixed-Primitive-based Gaussian Splatting Method for Surface Reconstruction
Lancaster University, United Kingdom（兰卡斯特大学，英国）；The University of Queensland, Australia（昆士兰大学，澳大利亚）；The Hong Kong Polytechnic University, China（香港理工大学，中国）；University at Buffalo, United States of America（布法罗大学，美国）
Unsupervised Primitive Discovery for Improved 3D Generative Modeling
Inception Institute of Artificial Intelligence（起点人工智能研究院）；National University of Defense Technology（国防科技大学）；Australian National University（澳大利亚国立大学）；Sun Yat-sen University（中山大学）；University of Canberra（堪培拉大学）；Data61, Commonwealth Scientific and Industrial Research Organization（联邦科学与工业研究组织Data61）
Envisioning Beyond the Few: Disentangled Semantics and Primitives for Few-Shot Atypical Layout-to-Image Generation
State Key Laboratory of Virtual Reality Technology and Systems（虚拟现实技术与系统国家重点实验室）；School of Computer Science and Engineering（计算机科学与工程学院）；Qingdao Research Institute, Beihang University, China（北京航空航天大学青岛研究所，中国）
University of Tübingen（图宾根大学）；MPI for Informatics（马克斯·普朗克信息学研究所）；University of Surrey（萨里大学）；Wageningen University（瓦赫宁根大学）；MPI for Intelligent Systems（马克斯·普朗克智能系统研究所）
Distortion-Corrected Diffusion MRI Using Rotated-View EPI and Joint Field-Map/Image Estimation with Gaussian Primitives
Chair for AI in Healthcare and Medicine, Technical University of Munich (TUM) and TUM University Hospital, and the School of Computation, Information and Technology, TUM（慕尼黑工业大学(TUM)医疗与医学人工智能教席、TUM大学医院、计算信息与技术学院）；Neuroimaging Technology Research Center, Department of Radiology and Biomedical Imaging, University of California, San Francisco（加州大学旧金山分校放射与生物医学影像系神经影像技术研究中心）；Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences（中国科学院精密测量科学与技术创新研究院）；Department of Radiology, Stanford University（斯坦福大学放射学系）；Munich Center for Machine Learning (MCML)（慕尼黑机器学习中心(MCML)）；Department of Computing, Imperial College London（伦敦帝国理工学院计算系）
Contextual Interaction via Primitive-based Adversarial Training For Compositional Zero-shot Learning
School of Computer Science and Engineering, Nanjing University of Science and Technology（南京理工大学计算机科学与工程学院）；School of Engineering, Newcastle University（纽卡斯尔大学工程学院）；Department of Computer Science, Durham University（杜伦大学计算机科学系）；Harbin Institute of Technology（哈尔滨工业大学）；School of Artificial Intelligence, Nanjing University of Science and Technology（南京理工大学人工智能学院）
Aligning Instance-Semantic Sparse Representation towards Unsupervised Object Segmentation and Shape Abstraction with Repeatable Primitives
School of Big Data and Software Engineering, Chongqing University（重庆大学大数据与软件学院）；Department of Computer Science and Engineering, State University of New York at Buffalo（纽约州立大学布法罗分校计算机科学与工程系）
CADDreamer: CAD Object Generation from Single-view Images
University of Texas at Dallas（德克萨斯大学达拉斯分校）；The University of Hong Kong（香港大学）；Hong Kong University of Science and Technology（香港科技大学）；Nanjing University（南京大学）；ByteDance（字节跳动）；Texas A&M University（德克萨斯农工大学）
