74 citations · 520 across the 46 of their papers we have counts for
46 papers
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…
NeAF: Learning Neural Angle Fields for Point Normal Estimation
Shujuan Li, Junsheng Zhou, Baorui Ma +2
Normal estimation for unstructured point clouds is an important task in 3D computer vision. Current methods achieve encouraging results by mapping local patches to normal vectors o…
HSurf-Net: Normal Estimation for 3D Point Clouds by Learning Hyper Surfaces
Qing Li, Yu-Shen Liu, Jin-San Cheng +3
We propose a novel normal estimation method called HSurf-Net, which can accurately predict normals from point clouds with noise and density variations. Previous methods focus on le…
CAP-UDF: Learning Unsigned Distance Functions Progressively from Raw Point Clouds with Consistency-Aware Field Optimization
Junsheng Zhou, Baorui Ma, Shujuan Li +3
Surface reconstruction for point clouds is an important task in 3D computer vision. Most of the latest methods resolve this problem by learning signed distance functions from point…
Fast Learning Radiance Fields by Shooting Much Fewer Rays
Wenyuan Zhang, Ruofan Xing, Yunfan Zeng +3
Learning radiance fields has shown remarkable results for novel view synthesis. The learning procedure usually costs lots of time, which motivates the latest methods to speed up th…
Latent Partition Implicit with Surface Codes for 3D Representation
Chao Chen, Yu-Shen Liu, Zhizhong Han
Deep implicit functions have shown remarkable shape modeling ability in various 3D computer vision tasks. One drawback is that it is hard for them to represent a 3D shape as multip…
Surface Reconstruction from Point Clouds by Learning Predictive Context Priors
Baorui Ma, Yu-Shen Liu, Matthias Zwicker +1
Surface reconstruction from point clouds is vital for 3D computer vision. State-of-the-art methods leverage large datasets to first learn local context priors that are represented…
Reconstructing Surfaces for Sparse Point Clouds with On-Surface Priors
Baorui Ma, Yu-Shen Liu, Zhizhong Han
It is an important task to reconstruct surfaces from 3D point clouds. Current methods are able to reconstruct surfaces by learning Signed Distance Functions (SDFs) from single poin…
Learning Deep Implicit Functions for 3D Shapes with Dynamic Code Clouds
Tianyang Li, Xin Wen, Yu-Shen Liu +2
Deep Implicit Function (DIF) has gained popularity as an efficient 3D shape representation. To capture geometry details, current methods usually learn DIF using local latent codes,…
3D Shape Reconstruction from 2D Images with Disentangled Attribute Flow
Xin Wen, Junsheng Zhou, Yu-Shen Liu +2
Reconstructing 3D shape from a single 2D image is a challenging task, which needs to estimate the detailed 3D structures based on the semantic attributes from 2D image. So far, mos…
3D-OAE: Occlusion Auto-Encoders for Self-Supervised Learning on Point Clouds
Junsheng Zhou, Xin Wen, Baorui Ma +4
The manual annotation for large-scale point clouds is still tedious and unavailable for many harsh real-world tasks. Self-supervised learning, which is used on raw and unlabeled da…
PMP-Net++: Point Cloud Completion by Transformer-Enhanced Multi-step Point Moving Paths
Xin Wen, Peng Xiang, Zhizhong Han +4
Point cloud completion concerns to predict missing part for incomplete 3D shapes. A common strategy is to generate complete shape according to incomplete input. However, unordered…
Snowflake Point Deconvolution for Point Cloud Completion and Generation with Skip-Transformer
Peng Xiang, Xin Wen, Yu-Shen Liu +4
Most existing point cloud completion methods suffer from the discrete nature of point clouds and the unstructured prediction of points in local regions, which makes it difficult to…
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…