MVDiffusion: Enabling Holistic Multi-view Image Generation with Correspondence-Aware Diffusion, NeurIPS 2023 (spotlight)

UniPAD: A Universal Pre-training Paradigm for Autonomous Driving (CVPR 2024)

Pointcept: Perceive the world with sparse points, a codebase for point cloud perception research. Latest works: Utonia, Concerto (NeurIPS'25), Sonata (CVPR'25 Highlight), PTv3 (CVPR'24 Oral)

[CVPR'24 Oral] Official repository of Point Transformer V3 (PTv3)

Photogrammetry Guide. Photogrammetry is widely used for Aerial surveying, Agriculture, Architecture, 3D Games, Robotics, Archaeology, Construction, Emergency management, and Medical.

A project page template for academic papers. Demo at https://eliahuhorwitz.github.io/Academic-project-page-template/

[ICCV 23] Density-invariant Features for Distant Point Cloud Registration

Benchmarking and Analyzing Point Cloud Perception Robustness under Corruptions

A list of papers about point cloud based place recognition, also known as loop closure detection in SLAM (processing)

OpenPCDet Toolbox for LiDAR-based 3D Object Detection.

Deep Hough Voting for 3D Object Detection in Point Clouds

An extension of Open3D to address 3D Machine Learning tasks

3D point cloud datasets in HDF5 format, containing uniformly sampled 2048 points per shape.

Constrained optimization toolkit for PyTorch

A benchmark for point clouds registration algorithms

3D plotting and mesh analysis through a streamlined interface for the Visualization Toolkit (VTK)

[ NeurIPS 2021 ] Pytorch implementation for "Density-aware Chamfer Distance as a Comprehensive Metric for Point Cloud Completion"

Pytorch framework for doing deep learning on point clouds.

PyTorch3D is FAIR's library of reusable components for deep learning with 3D data

[CVPR 2021] SpinNet: Learning a General Surface Descriptor for 3D Point Cloud Registration

Fully Convolutional Geometric Features: Fast and accurate 3D features for registration and correspondence.

Compute descriptors for 3D point cloud registration using a multi scale sparse voxel architecture

PyTorch implementation of Unsupervised Deep Homography: https://arxiv.org/abs/1709.03966

Content-Aware Unsupervised Deep Homography Estimation

Unsupervised Deep Homography: A Fast and Robust Homography Estimation Model

Riemannian Adaptive Optimization Methods with pytorch optim

This is a complete package of recent deep learning methods for 3D point clouds in pytorch (with pretrained models).

Operations for CNN on manifolds.

Computations and statistics on manifolds with geometric structures.