Results 251 to 260 of about 381,180 (306)

SuperPC: A Single Diffusion Model for Point Cloud Completion, Upsampling, Denoising, and Colorization

Computer Vision and Pattern Recognition
Point cloud (PC) processing tasks—such as completion, up-sampling, denoising, and colorization—are crucial in applications like autonomous driving and 3D reconstruction. Despite substantial advancements, prior approaches often address each of these tasks
Yi Du, Zhipeng Zhao, Shaoshu Su, Sharath Golluri, Haoze Zheng, Runmao Yao, Chen Wang   +6 more
semanticscholar   +1 more source

A point contextual transformer network for point cloud completion

Expert Systems with Applications
Siyi Leng, Zhenxin Zhang, Liqiang Zhang
openaire   +3 more sources

PointSea: Point Cloud Completion via Self-structure Augmentation

International Journal of Computer Vision
Point cloud completion is a fundamental yet not well-solved problem in 3D vision. Current approaches often rely on 3D coordinate information and/or additional data (e.g., images and scanning viewpoints) to fill in missing parts.
Zhe Zhu, Honghua Chen, Xing He, Mingqiang Wei   +3 more
semanticscholar   +1 more source

Parametric Point Cloud Completion for Polygonal Surface Reconstruction

Computer Vision and Pattern Recognition
Existing polygonal surface reconstruction methods heavily depend on input completeness and struggle with incomplete point clouds. We argue that while current point cloud completion techniques may recover missing points, they are not optimized for ...
Zhaiyu Chen, Yuqing Wang, Liangliang Nan, Xiao Xiang Zhu   +3 more
semanticscholar   +1 more source

Complete 3D surface reconstruction from unstructured point cloud

Journal of Mechanical Science and Technology, 2005
In this study, a complete 3D surface reconstruction method is proposed based on the concept that the vertices of surface model can be completely matched to the unstructured point cloud. In order to generate the initial mesh model from the point cloud, the mesh subdivision of bounding box and shrink-wrapping algorithm are introduced.
Rixie Li, Seokil Kim
openaire   +1 more source

Multimodality Consistency for Point Cloud Completion via Differentiable Rendering

IEEE Transactions on Artificial Intelligence
Point cloud completion aims to acquire complete and high-fidelity point clouds from partial and low-quality point clouds, which are used in remote sensing applications.
Ben Fei, Yixuan Li, Weidong Yang, Wen-Ming Chen, Zhijun Li   +4 more
semanticscholar   +1 more source

RLGrid: Reinforcement Learning Controlled Grid Deformation for Coarse-to-Fine Point Cloud Completion

IEEE transactions on multimedia
Many point cloud completion methods typically rely on two steps: coarse generation and 2D grid deformed fine output. However, in the fine generation, the expansion range (2D grid scale) required by each point cloud sample may be vastly different.
Shanshan Li, Pan Gao, Xiaoyang Tan, Wei Xiang   +3 more
semanticscholar   +1 more source

SK-TreePCN: Skeleton-Embedded Transformer Model for Point Cloud Completion of Individual Trees from Simulated to Real Data

Remote Sensing
Tree structural information is essential for studying forest ecosystem functions, driving mechanisms, and global change response mechanisms. Although current terrestrial laser scanning (TLS) can acquire high-precision 3D structural information of forests,
Haifeng Xu, Yongjian Huai, Xun Zhao, Qingkuo Meng, Xiaoying Nie, Bowen Li, Hao Lu   +6 more
semanticscholar   +1 more source

DC-PCN: Point Cloud Completion Network with Dual-Codebook Guided Quantization

AAAI Conference on Artificial Intelligence
Point cloud completion aims to reconstruct complete 3D shapes from partial 3D point clouds. With advancements in deep learning techniques, various methods for point cloud completion have been developed.
Qiuxia Wu, Haiyan Huang, Kunming Su, Zhiyong Wang, Kun Hu   +4 more
semanticscholar   +1 more source

Multi-Modal Point Cloud Completion with Interleaved Attention Enhanced Transformer

International Joint Conference on Artificial Intelligence
Multi-modal point cloud completion, which utilizes a complete image and a partial point cloud as input, is a crucial task in 3D computer vision. Previous methods commonly employ a cross-attention mechanism to fuse point clouds and images.
Chenghao Fang, Jianqing Liang, Jiye Liang, Hangkun Wang, Kaixuan Yao, Feilong Cao   +5 more
semanticscholar   +1 more source