74 citations · 520 across the 46 of their papers we have counts for
6 papers · 1 filter
Multi-View Partial (MVP) Point Cloud Challenge 2021 on Completion and Registration: Methods and Results
Liang Pan, Tong Wu, Zhongang Cai +26
As real-scanned point clouds are mostly partial due to occlusions and viewpoints, reconstructing complete 3D shapes based on incomplete observations becomes a fundamental problem f…
KTNet: Knowledge Transfer for Unpaired 3D Shape Completion
Zhen Cao, Wenxiao Zhang, Xin Wen +4
Unpaired 3D object completion aims to predict a complete 3D shape from an incomplete input without knowing the correspondence between the complete and incomplete shapes. In this pa…
Unsupervised Learning of Fine Structure Generation for 3D Point Clouds by 2D Projection Matching
Chen Chao, Zhizhong Han, Yu-Shen Liu +1
Learning to generate 3D point clouds without 3D supervision is an important but challenging problem. Current solutions leverage various differentiable renderers to project the gene…
Hierarchical View Predictor: Unsupervised 3D Global Feature Learning through Hierarchical Prediction among Unordered Views
Zhizhong Han, Xiyang Wang, Yu-Shen Liu +1
Unsupervised learning of global features for 3D shape analysis is an important research challenge because it avoids manual effort for supervised information collection. In this pap…
SnowflakeNet: Point Cloud Completion by Snowflake Point Deconvolution with Skip-Transformer
Peng Xiang, Xin Wen, Yu-Shen Liu +4
Point cloud completion aims to predict a complete shape in high accuracy from its partial observation. However, previous methods usually suffered from discrete nature of point clou…
Cycle4Completion: Unpaired Point Cloud Completion using Cycle Transformation with Missing Region Coding
Xin Wen, Zhizhong Han, Yan-Pei Cao +3
In this paper, we present a novel unpaired point cloud completion network, named Cycle4Completion, to infer the complete geometries from a partial 3D object. Previous unpaired comp…