🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.

Python package for the evaluation of odometry and SLAM

Pointcept: a codebase for point cloud perception research. Latest works: PTv3 (CVPR'24 Oral), PPT (CVPR'24), OA-CNNs (CVPR'24), MSC (CVPR'23)

Semantic and Instance Segmentation of LiDAR point clouds for autonomous driving

[CVPR2022] Geometric Transformer for Fast and Robust Point Cloud Registration

OverlapNet - Loop Closing for 3D LiDAR-based SLAM (chen2020rss)

A 3D computer vision development toolkit based on PaddlePaddle. It supports point-cloud object detection, segmentation, and monocular 3D object detection models.

[NeurIPS'22] An official PyTorch implementation of PTv2.

[ICCV 2023] Robo3D: Towards Robust and Reliable 3D Perception against Corruptions

Livox open source detection algorithm

PyTorch code for training LCDNet for loop closure detection in LiDAR SLAM. http://rl.uni-freiburg.de/research/lidar-slam-lc

[IEEE T-RO 2023] Source code of RING and RING++ for loop closure detection in LiDAR SLAM.

SC2-PCR: A Second Order Spatial Compatibility for Efficient and Robust Point Cloud Registration (CVPR 2022)

Benchmark for lidar and visual place recognition

[TIE 2022] SeqOT: A Spatial-Temporal Transformer Network for Place Recognition Using Sequential LiDAR Data.

[ICCV 2021] HRegNet: A Hierarchical Network for Large-scale Outdoor LiDAR Point Cloud Registration

MinkLoc3Dv2: Improving Point Cloud Based Place Recognition with Ranking-based Loss and Large Batch Training

LiDAR-Based Deep Loop Closure Detection and Registration using Panoptic Attention

Correspondence-Free Point Cloud Registration with SO(3)-Equivariant Implicit Shape Representations

Toolbox for quantitative trajectory evaluation of VO/VIO

Supplementary material to our submitted paper in the IEEE Robotics and Automation Letters (RAL)