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Starred repositories
DeepI2P: Image-to-Point Cloud Registration via Deep Classification. CVPR 2021
[PyTorch] Official implementation of CVPR2021 paper "PointDSC: Robust Point Cloud Registration using Deep Spatial Consistency". https://arxiv.org/abs/2103.05465
End-to-End 3D Point Cloud Learning for Registration Task Using Virtual Correspondences (IROS 2020)
Web site of the Computational Optimal Transport book
Apollo notes (Apollo学习笔记) - Apollo learning notes for beginners.
PointASNL: Robust Point Clouds Processing using Nonlocal Neural Networks with Adaptive Sampling （CVPR 2020）
LEARNING LATENT PERMUTATIONS WITH GUMBEL-SINKHORN NETWORKS IMPLEMENTATION WITH PYTORCH
Automatic Differentiated Extended Kalman Filter (ADEKF) - This is a generic EKF Implementation that uses automatic differentiation to get rid of the need to define Jacobians.
Efficient 3D Point Cloud Feature Learning for Large-Scale Place Recognition (EPC-Net)
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"
This is the clean implementation of PointNetLK with pretrained models. (A 3D point cloud registration network)
[CVPR 2021] SpinNet: Learning a General Surface Descriptor for 3D Point Cloud Registration
Differentiable convex optimization layers
Compute descriptors for 3D point cloud registration using a multi scale sparse voxel architecture
Project page of the paper "Learning general and distinctive 3D local deep descriptors for point cloud registration" published in IEEE T-PAMI
Papers and Datasets about Point Cloud.
Code for ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks
MinkLoc++: Lidar and Monocular Image Fusion for Place Recognition
[MICRO'23, MLSys'22] TorchSparse: Efficient Training and Inference Framework for Sparse Convolution on GPUs.
Discrete Rotation Equivariance for Point Cloud Recognition. ICRA 2019
Hierarchical Neural Architecture Searchfor Deep Stereo Matching (NeurIPS 2020)
This github is a supplementary material including data, code, trained model and demo for the paper "NDT-Transformer: Large-Scale 3D Point Cloud Localisation using the Normal Distribution Transform …
[DEPRECATED] Self-Supervised Extrinsic Calibration using 3D Spatial Transformer Networks
Automatic Extrinsic Calibration Method for LiDAR and Camera Sensor Setups. ROS Package.
CalibRCNN: Calibrating Camera and LiDAR by Recurrent Convolutional Neural Network and Geometric Constraints
Vector Neurons: A General Framework for SO(3)-Equivariant Networks
[CVPR'21] PU-GCN: Point Cloud Upsampling using Graph Convolutional Networks