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slambook2

edition 2 of the slambook

smpl

Pytorch implementation of human body SMPL model, support GPU training, batch processing and joint regression.

smpl2bvh

This repository contains an example script to convert from a SMPL model to a bvh file.

smpl_tutorial

stereo_ptam

Python implementation of SLAM algorithm Stereo-PTAM

structure-from-motion

A simplified version of incremental structure from motion to reconstruct the 3D structure of a scene from a sequence of 2D images

sugar

Official PyTorch implementation of SuGaR: Surface-Aligned Gaussian Splatting for Efficient 3D Mesh Reconstruction and High-Quality Mesh Rendering

super-colmap

SuperPoint replace the sift in colmap framework

tinyrenderer

A brief computer graphics / rendering course

transformer-in-computer-vision

A paper list of some recent Transformer-based CV works.

trianflow

Code release for "Towards Better Generalization: Joint Depth-Pose Learning without PoseNet".

udacity_lidar_object_detection

unifuse-unidirectional-fusion

visuallocalizationbenchmark

visualodometry

Implementation in TensorFlow 2.2

vo-slam-review

SLAM is mainly divided into two parts: the front end and the back end. The front end is the visual odometer (VO), which roughly estimates the motion of the camera based on the information of adjacent images and provides a good initial value for the back end.The implementation methods of VO can be divided into two categories according to whether features are extracted or not: feature point-based methods, and direct methods without feature points. VO based on feature points is stable and insensitive to illumination and dynamic objects