4 citations · 6 across the 2 of their papers we have counts for
6 papers · 1 filter
Self-supervised Learning for Pre-Training 3D Point Clouds: A Survey
Ben Fei, Weidong Yang, Liwen Liu +4
Point cloud data has been extensively studied due to its compact form and flexibility in representing complex 3D structures. The ability of point cloud data to accurately capture a…
IterativePFN: True Iterative Point Cloud Filtering
Dasith de Silva Edirimuni, Xuequan Lu, Zhiwen Shao +3
The quality of point clouds is often limited by noise introduced during their capture process. Consequently, a fundamental 3D vision task is the removal of noise, known as point cl…
2S-UDF: A Novel Two-stage UDF Learning Method for Robust Non-watertight Model Reconstruction from Multi-view Images
Junkai Deng, Fei Hou, Xuhui Chen +2
Recently, building on the foundation of neural radiance field, various techniques have emerged to learn unsigned distance fields (UDF) to reconstruct 3D non-watertight models from…
IDEA-Net: Dynamic 3D Point Cloud Interpolation via Deep Embedding Alignment
Yiming Zeng, Yue Qian, Qijian Zhang +3
This paper investigates the problem of temporally interpolating dynamic 3D point clouds with large non-rigid deformation. We formulate the problem as estimation of point-wise traje…
Multi Point-Voxel Convolution (MPVConv) for Deep Learning on Point Clouds
Wei Zhou, Xin Cao, Xiaodan Zhang +3
The existing 3D deep learning methods adopt either individual point-based features or local-neighboring voxel-based features, and demonstrate great potential for processing 3D data…
Multi Voxel-Point Neurons Convolution (MVPConv) for Fast and Accurate 3D Deep Learning
Wei Zhou, Xin Cao, Xiaodan Zhang +3
We present a new convolutional neural network, called Multi Voxel-Point Neurons Convolution (MVPConv), for fast and accurate 3D deep learning. The previous works adopt either indiv…