[TPAMI 2023] RoReg: Pairwise Point Cloud Registration with Oriented Descriptors and Local Rotations

[ACM MM 2022] You Only Hypothesize Once: Point Cloud Registration with Rotation-equivariant Descriptors

PyTorch implementations of CNNs and domain-bridging algorithms used in baseline studies of SPEED+ dataset

Official implementation of `Splatter Image: Ultra-Fast Single-View 3D Reconstruction' CVPR 2024

Official code release for Doppler ICP

This is the official PyTorch implementation of our paper "Density-preserving Deep Point Cloud Compression" (CVPR 2022).

[CVPR 2024 Highlight] FoundationPose: Unified 6D Pose Estimation and Tracking of Novel Objects

A comprehensive list of Implicit Representations, NeRF and 3D Gaussian Splatting papers relating to SLAM/Robotics domain, including papers, videos, codes, and related websites

Wrapper over the excellent and fast miniply to read PLY files

SE(3) Diffusion Model-based Point Cloud Registration for Robust 6D Object Pose Estimation (Accepted by NeurIPS-2023)

"Structure-Aware Sparse-View X-ray 3D Reconstruction" (CVPR 2024)

[CVPR 2024] SiTH: Single-view Textured Human Reconstruction with Image-Conditioned Diffusion

Project page of ref^2-nerf

RNb-NeuS: Reflectance and Normal-based Multi-view 3D Reconstruction.

DUSt3R: Geometric 3D Vision Made Easy

Original reference implementation of "GES : Generalized Exponential Splatting for Efficient Radiance Field Rendering" [CVPR 2024]

Large Gaussian Reconstruction Model for Efficient 3D Reconstruction and Generation

Papers and Datasets about Point Cloud.

[MVA 24] Addressing the generalization of 3D registration methods with a featureless baseline and an unbiased benchmark

Leveraging Inlier Correspondences Proportion for Point Cloud Registration. https://arxiv.org/abs/2201.12094.

Source code of CVPR 2023 paper

Rotation-Invariant Transformer for Point Cloud Matching

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

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

3DMatch - a 3D ConvNet-based local geometric descriptor for aligning 3D meshes and point clouds.

A list of papers and datasets about point cloud analysis (processing)

[CVPR2020] Learning multiview 3D point cloud registration

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

Create your own point cloud registration dataset from 3DMatch