Starred repositories
Implementations of different algorithms for building Euclidean minimum spanning tree in k-dimensional space.
PU-Net: Point Cloud Upsampling Network, CVPR, 2018 (https://arxiv.org/abs/1801.06761)
Learning Normal Orientation For Point Clouds [SIGGRAPH 2021]
PyTorch implementation for paper Neural Marching Cubes.
Code for SIGGRAPH 2022 paper: ComplexGen: CAD Reconstruction by B-Rep Chain Complex Generation
Code of RFEPS: Reconstructing Feature-line Equipped Polygonal Surface. ACM Transactions on Graphics, SIGGRAPH Asia 2022 Paper.
Papers and Datasets about Point Cloud.
A lightweight, easy-to-use, and efficient library for processing and rendering 3D data (C++ & Python)
Some simple Blender scripts for rendering paper figures
Tangent Convolutions for Dense Prediction in 3D
Source code for "PointTriNet: Learned Triangulation of 3D Point Sets", by Nicholas Sharp and Maks Ovsjanikov at ECCV 2020
3D surface reconstruction software: from point clouds to triangle meshes.
厦门大学每日健康打卡程序,在网站上即可实现打卡,支持失败自动重试,支持邮件通知功能。
Learning Implicit Surfaces from Point Clouds (ECCV 2020)
Code for fast approximate generalized winding number (solid angle) computation for triangle soups
A working copy of the code from "A Benchmark for Surface Reconstruction" by Berger et. Al
Mesh Reconstruction assignment for Geometry Processing course
Reconstruct Watertight Meshes from Point Clouds [SIGGRAPH 2020]
Efficient Wasserstein Barycenter in MATLAB (for "Fast Discrete Distribution Clustering Using Wasserstein Barycenter with Sparse Support" TSP)