Gitzhao's repositories
awesome-rgbd-datasets
This repository contains information for the paper "A Survey on RGB-D Datasets" and is a collaborative initiative to update the datasets list faster.
ldvo
Lightweight Dense Visual Odometry - a header-only C++ library for real-time dense RGB-D odometry.
CodeSLAM
PyTorch implementation of the conditional variational autoencoder (CVAE) from CodeSLAM
Reconstruction_Pipeline
3D reconstruction with L515, VINS-RGBD and voxblox
co-fusion
Co-Fusion: Real-time Segmentation, Tracking and Fusion of Multiple Objects
UCAS_Course_2019
**科学院大学2019-2020课程（秋季，春季，夏季）
void-dataset
Visual Odometry with Inertial and Depth (VOID) dataset
AVP-SLAM-PLUS-PLUS
An implementation of AVP-SLAM with loop closure and graph optimization.
VINS-Mono-Android
录制Android手机的图像和IMU数据，标定并运行VINS-Mono
FastFlowNet
A lightweight deep network for fast and accurate optical flow estimation.
MSDM-SLAM
Metric-Semantic Dense Mapping and SLAM system
depth-image-completion
Use morphological and filter operators to inpaint (complete) a depth image
ORB_SLAM3_Monodepth
ORB-SLAM3-Monodepth is an extended version of ORB-SLAM3 that utilizes a deep monocular depth estimation network
MyG2O
This is an math lib for factor graph Bundle Adjustment, including point-, line-, plane- reprojection factors etc.
MiDaS-cpp
PyTorch C++ implementation for single-image relative depth estimation (MiDaS)
aerial-depth-completion
Aerial Single-View Depth Completion with Image-Guided Uncertainty Estimation (RA-L/ICRA 2020)
dsm
Direct Sparse Mapping
awesome-slam-datasets
SLAM常见数据集汇总
line_matching
A KLT-based Line Segment Matching Algorithm.
Kimera
Index repo for Kimera code
KinectFusion
Re-implementation of the paper KinectFusion: Real-Time Dense Surface Mapping and Tracking
open_vins_rgbd
An open source platform for visual-inertial navigation research.
Map-based-Visual-Localization
A general framework for map-based visual localization. It contains 1) Map Generation which support traditional features or deeplearning features. 2) Hierarchical-Localizationvisual in visual(points or line) map. 3)Fusion framework with IMU, wheel odom and GPS sensors.
CV_Notes
「3D视觉(三维重建、SLAM、AR/VR) + 传统图像处理 + 计算机视觉(偏AI) 」重要知识点和面试问题。