Stars
Highly recommended resources for SLAM newbies (Lecture, Reviewed paper, Books, Tutorial, etc)
A curated list of awesome computer vision resources
CVPR'21 "Multi-view 3D Reconstruction of a Texture-less Smooth Surface of Unknown Generic Reflectance"
KISS-Matcher: Fast, Robust, and Scalable Registration + ROS2 SLAM examples
Pixel-Perfect Structure-from-Motion with Featuremetric Refinement (ICCV 2021, Best Student Paper Award)
InstantMesh: Efficient 3D Mesh Generation from a Single Image with Sparse-view Large Reconstruction Models
Master the command line, in one page
PointNet and PointNet++ implemented by pytorch (pure python) and on ModelNet, ShapeNet and S3DIS.
A PyTorch Implementation of Pointnet++.
Fully Convolutional Geometric Features: Fast and accurate 3D features for registration and correspondence.
VQMT: Video Quality Measurement Tool. Fast implementations of the following objective image quality metrics: PSNR, SSIM, MS-SSIM, VIFp, PSNR-HVS and PSNR-HVS-M.
SC-Depth (V1, V2, and V3) for Unsupervised Monocular Depth Estimation Webpage:https://jiawangbian.github.io/sc_depth_pl/
This repository contains several baseline algorithms for cross-source point cloud registration.
This is a complete package of recent deep learning methods for 3D point clouds in pytorch (with pretrained models).
[TensorFlow] Official implementation of CVPR'20 oral paper - D3Feat: Joint Learning of Dense Detection and Description of 3D Local Features https://arxiv.org/abs/2003.03164
A curated list of point cloud registration.
[CVPR 2021, Oral] PREDATOR: Registration of 3D Point Clouds with Low Overlap.
[PyTorch] Official implementation of CVPR2021 paper "PointDSC: Robust Point Cloud Registration using Deep Spatial Consistency". https://arxiv.org/abs/2103.05465
CVPR Paper - "3D Point Cloud Registration for Localization using a Deep Neural Network Auto-Encoder" - Partial Implementation Code
A simple and efficient 3D line detection algorithm for large scale unorganized point cloud
Python runtime for CloudCompare