74 citations · 520 across the 46 of their papers we have counts for
9 papers · 1 filter
Neural Gradient Learning and Optimization for Oriented Point Normal Estimation
Qing Li, Huifang Feng, Kanle Shi +3
We propose Neural Gradient Learning (NGL), a deep learning approach to learn gradient vectors with consistent orientation from 3D point clouds for normal estimation. It has excelle…
GridPull: Towards Scalability in Learning Implicit Representations from 3D Point Clouds
Chao Chen, Yu-Shen Liu, Zhizhong Han
Learning implicit representations has been a widely used solution for surface reconstruction from 3D point clouds. The latest methods infer a distance or occupancy field by overfit…
Learning a More Continuous Zero Level Set in Unsigned Distance Fields through Level Set Projection
Junsheng Zhou, Baorui Ma, Shujuan Li +2
Latest methods represent shapes with open surfaces using unsigned distance functions (UDFs). They train neural networks to learn UDFs and reconstruct surfaces with the gradients ar…
Retro-FPN: Retrospective Feature Pyramid Network for Point Cloud Semantic Segmentation
Peng Xiang, Xin Wen, Yu-Shen Liu +3
Learning per-point semantic features from the hierarchical feature pyramid is essential for point cloud semantic segmentation. However, most previous methods suffered from ambiguou…
Learning Signed Distance Functions from Noisy 3D Point Clouds via Noise to Noise Mapping
Baorui Ma, Yu-Shen Liu, Zhizhong Han
Learning signed distance functions (SDFs) from 3D point clouds is an important task in 3D computer vision. However, without ground truth signed distances, point normals or clean po…
Towards Better Gradient Consistency for Neural Signed Distance Functions via Level Set Alignment
Baorui Ma, Junsheng Zhou, Yu-Shen Liu +1
Neural signed distance functions (SDFs) have shown remarkable capability in representing geometry with details. However, without signed distance supervision, it is still a challeng…
D-Net: Learning for Distinctive Point Clouds by Self-Attentive Point Searching and Learnable Feature Fusion
Xinhai Liu, Zhizhong Han, Sanghuk Lee +2
Learning and selecting important points on a point cloud is crucial for point cloud understanding in various applications. Most of early methods selected the important points on 3D…
Robust Multiview Point Cloud Registration with Reliable Pose Graph Initialization and History Reweighting
Haiping Wang, Yuan Liu, Zhen Dong +4
In this paper, we present a new method for the multiview registration of point cloud. Previous multiview registration methods rely on exhaustive pairwise registration to construct…
Unsupervised Inference of Signed Distance Functions from Single Sparse Point Clouds without Learning Priors
Chao Chen, Yu-Shen Liu, Zhizhong Han
It is vital to infer signed distance functions (SDFs) from 3D point clouds. The latest methods rely on generalizing the priors learned from large scale supervision. However, the le…