Official respository for "Band-limited Coordinate Networks for Multiscale Scene Representation" | CVPR 2022

PointNet and PointNet++ implemented by pytorch (pure python) and on ModelNet, ShapeNet and S3DIS.

Code for "DiGS: Divergence guided shape implicit neural representation for unoriented point clouds" (CVPR2022)

[T-PAMI'22] [Jittor & Pytorch] Multiway Non-rigid Point Cloud Registration via Learned Functional Map Synchronization

SAL: Sign Agnostic Learning of Shapes From Raw Data

Sign Agnostic Learning with Derivatives

Sample code and documentation for using the Microsoft HoloLens 2 for Computer Vision research.

Neural Unsigned Distance Fields (NDF) - Codebase

Official implementation of "Implicit Neural Representations with Periodic Activation Functions"

A deep learning library to enable rapid prototyping

Implicit Geometric Regularization for Learning Shapes

A python module for scientific analysis of 3D data based on VTK and Numpy

Data, tools, and documentation of the Fusion 360 Gallery Dataset

Learning Implicit Surfaces from Point Clouds (ECCV 2020)

Animation engine for explanatory math videos

3d plotting for Python in the Jupyter notebook based on IPython widgets using WebGL

Repository containing common 3D test models in original format with original source if known and obj mesh

A set of examples around pytorch in Vision, Text, Reinforcement Learning, etc.

Code repository for our paper: Deep Iterative Surface Normal Estimation

IKEA Furniture Assembly Environment for Long-Horizon Complex Manipulation Tasks

A PyTorch implementation of EfficientNet

Deep Declarative Networks

CGAL bindings using SWIG

Pytorch implementation of PCPNet

Kd-Networks

🚕 Fast and robust clustering of point clouds generated with a Velodyne sensor.

Collection of generative models, e.g. GAN, VAE in Pytorch and Tensorflow.

Code for ``A Point Set Generation Network for 3D Object Reconstruction from a Single Image''

pytorch implementation for "PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation" https://arxiv.org/abs/1612.00593