ithink3iam's repositories
3dv_tutorial
An Invitation to 3D Vision: A Tutorial for Everyone
PARE
Code for ICCV2021 paper PARE: Part Attention Regressor for 3D Human Body Estimation
layout-parser
A Unified Toolkit for Deep Learning Based Document Image Analysis
PyTorch-Vision-Transformer
PyTorch implementation of the vision transformer
EPro-PnP
[CVPR 2022 Oral] EPro-PnP: Generalized End-to-End Probabilistic Perspective-n-Points for Monocular Object Pose Estimation
small-baseline-camera-tracking
A dataset to facilitate the research of Structure-from-Motion (SfM) for movie and TV shows.
DF-VO
Depth and Flow for Visual Odometry
smplx
SMPL-X
MANO
A PyTorch Implementation of MANO hand model.
multiviewDataset
multiview dataset
AI-research-tools
:hammer:AI 方向好用的科研工具
A2J
Code for paper "A2J: Anchor-to-Joint Regression Network for 3D Articulated Pose Estimation from a Single Depth Image". ICCV2019
DefSLAM
DefSLAM: Tracking and Mapping of Deforming Scenes from Monocular Sequences. J.Lamarca, S. Parashar, A. Bartoli & J.M.M. Montiel
SLAM-BOOK
这是一本关于SLAM的书稿，希望能清楚的介绍SLAM系统中的使用的几何方法和深度学习方法。书稿最后应该会达到200页左右，书稿每章对应的代码也会被整理出来。
Machine-Learning-Notes
白板推导系列课程笔记 初版
simple-faster-rcnn-pytorch
A simplified implemention of Faster R-CNN that replicate performance from origin paper
ee227c.github.io
EE227C (Spring 2018) Course page
manopth
MANO layer for PyTorch, generating hand meshes as a differentiable layer
tsdf-fusion-python
Python code to fuse multiple RGB-D images into a TSDF voxel volume.
semidense-lines
Incremental 3D Line Segment Extraction for Surface Reconstruction from Semi-dense SLAM
PICMake
你还在学CMake的过程中毫无头绪吗？你还在为复杂程序库依赖发愁吗？你是否觉得原生CMake的编写冗余而低效？那就快来学习和使用PICMake吧！只需要一行，无论是可执行，动态库还是静态库，轻松搞定！同时高效支持多目标，复杂库的编译安装，从此告别大量冗余CMake代码，专注开发核心应用程序，编译不再愁！ 例如下面是使用PICMake编译一个依赖OpenGL的可执行文件，只需要一行！
HandAugment
HandAugment: A Simple Data Augmentation Method for Depth-Based 3D Hand Pose Estimation
DepthFusion
Fuse multiple depth frames into a point cloud
GSLAM
A General Simultaneous Localization and Mapping Framework which supports feature based or direct method and different sensors including monocular camera, RGB-D sensors or any other input types can be handled.
V2V-PoseNet-pytorch
PyTorch implementation of V2V-PoseNet with IntegralPose/PoseFix loss