Stars
Code, dataset and models for our CVPR 2022 publication "Text2Pos"
[CVPR2024] DiffLoc: Diffusion Model for Outdoor LiDAR Localization
Parameter-Efficient Fine-Tuning in Spectral Domain for Point Cloud Learning
[RA-L] BoW3D: Bag of Words for Real-Time Loop Closing in 3D LiDAR SLAM.
PyTorch code for training LCDNet for loop closure detection in LiDAR SLAM. http://rl.uni-freiburg.de/research/lidar-slam-lc
OverlapNet - Loop Closing for 3D LiDAR-based SLAM (chen2020rss)
[TRO] Fast and Accurate Deep Loop Closing and Relocalization for Reliable LiDAR SLAM
[CVPR 2024] Scalable 3D Registration via Truncated Entry-wise Absolute Residuals
Source code of CVPR 2024 paper 'FastMAC: Stochastic Spectral Sampling of Correspondence Graph'
A PyTorch implementation of Dynamic Graph CNN for Learning on Point Clouds (DGCNN)
Code for PCAN: 3D Attention Map Learning Using Contextual Information for Point Cloud Based Retrieval
Iterative Feedback Network for Unsupervised Point Cloud Registration (RAL 2024)
🏞️ [IEEE ICRA2023] The official repository for paper "Wild-Places: A Large-Scale Dataset for Lidar Place Recognition in Unstructured Natural Environments" To appear in 2023 IEEE International Confe…
[CVPR 24] Extend Your Own Correspondences: Unsupervised Distant Point Cloud Registration by Progressive Distance Extension
A simple method for finding the extrinsic calibration between a 3D lidar and a 6-dof pose sensor
M2DGR： a Multi-modal and Multi-scenario Dataset for Ground Robots(RA-L2021 & ICRA2022)
Differentiable Scan Context with Orientation
DH3D: Deep Hierarchical 3D Descriptors for Robust Large-Scale 6DOF Relocalization
MinkLoc3D: Point Cloud Based Large-Scale Place Recognition
EgoNN: Egocentric Neural Network for Point Cloud Based 6DoF Relocalization at the City Scale
Laser Odometry and Mapping (Loam) is a realtime method for state estimation and mapping using a 3D lidar.
This repository is the implementation of our CVPR 2020 work: "Feature-metric Registration: A Fast Semi-supervised Approach for Robust Point Cloud Registration without Correspondences"
[IEEE RAL 2022] UGMM: Unsupervised Point Cloud Registration by Learning Unified Gaussian Mixture Models