110 citations · 452 across the 37 of their papers we have counts for
10 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…
Garment4D: Garment Reconstruction from Point Cloud Sequences
Fangzhou Hong, Liang Pan, Zhongang Cai +1
Learning to reconstruct 3D garments is important for dressing 3D human bodies of different shapes in different poses. Previous works typically rely on 2D images as input, which how…
A Conditional Point Diffusion-Refinement Paradigm for 3D Point Cloud Completion
Zhaoyang Lyu, Zhifeng Kong, Xudong Xu +2
3D point cloud is an important 3D representation for capturing real world 3D objects. However, real-scanned 3D point clouds are often incomplete, and it is important to recover com…
PTTR: Relational 3D Point Cloud Object Tracking with Transformer
Changqing Zhou, Zhipeng Luo, Yueru Luo +5
In a point cloud sequence, 3D object tracking aims to predict the location and orientation of an object in the current search point cloud given a template point cloud. Motivated by…
Density-aware Chamfer Distance as a Comprehensive Metric for Point Cloud Completion
Tong Wu, Liang Pan, Junzhe Zhang +3
Chamfer Distance (CD) and Earth Mover's Distance (EMD) are two broadly adopted metrics for measuring the similarity between two point sets. However, CD is usually insensitive to mi…
Robust Partial-to-Partial Point Cloud Registration in a Full Range
Liang Pan, Zhongang Cai, Ziwei Liu
Point cloud registration for 3D objects is a challenging task due to sparse and noisy measurements, incomplete observations and large transformations. In this work, we propose \tex…
TAda! Temporally-Adaptive Convolutions for Video Understanding
Ziyuan Huang, Shiwei Zhang, Liang Pan +4
Spatial convolutions are widely used in numerous deep video models. It fundamentally assumes spatio-temporal invariance, i.e., using shared weights for every location in different…
CSG-Stump: A Learning Friendly CSG-Like Representation for Interpretable Shape Parsing
Daxuan Ren, Jianmin Zheng, Jianfei Cai +8
Generating an interpretable and compact representation of 3D shapes from point clouds is an important and challenging problem. This paper presents CSG-Stump Net, an unsupervised en…
Unsupervised 3D Shape Completion through GAN Inversion
Junzhe Zhang, Xinyi Chen, Zhongang Cai +6
Most 3D shape completion approaches rely heavily on partial-complete shape pairs and learn in a fully supervised manner. Despite their impressive performances on in-domain data, wh…
Variational Relational Point Completion Network
Liang Pan, Xinyi Chen, Zhongang Cai +4
Real-scanned point clouds are often incomplete due to viewpoint, occlusion, and noise. Existing point cloud completion methods tend to generate global shape skeletons and hence lac…