PyNancy's starred repositories
ML-From-Scratch
Machine Learning From Scratch. Bare bones NumPy implementations of machine learning models and algorithms with a focus on accessibility. Aims to cover everything from linear regression to deep learning.
Swin-Transformer
This is an official implementation for "Swin Transformer: Hierarchical Vision Transformer using Shifted Windows".
CloudCompare
CloudCompare main repository
Recent_SLAM_Research
Track Advancement of SLAM 跟踪SLAM前沿动态【2021 version】業務調整，暫停更新
hdl_graph_slam
3D LIDAR-based Graph SLAM
second.pytorch
SECOND for KITTI/NuScenes object detection
pcl-learning
🔥PCL（Point Cloud Library）点云库学习记录
libpointmatcher
An Iterative Closest Point (ICP) library for 2D and 3D mapping in Robotics
torchsparse
[MICRO'23, MLSys'22] TorchSparse: Efficient Training and Inference Framework for Sparse Convolution on GPUs.
transfuser
[PAMI'23] TransFuser: Imitation with Transformer-Based Sensor Fusion for Autonomous Driving; [CVPR'21] Multi-Modal Fusion Transformer for End-to-End Autonomous Driving
rpg_trajectory_evaluation
Toolbox for quantitative trajectory evaluation of VO/VIO
lio-mapping
Implementation of Tightly Coupled 3D Lidar Inertial Odometry and Mapping (LIO-mapping)
CenterFusion
CenterFusion: Center-based Radar and Camera Fusion for 3D Object Detection
AVP-SLAM-SIM
A basic implementation(not official code) of AVP-SLAM(IROS 2020) in simulation. https://arxiv.org/abs/2007.01813
slambench1
SLAMBench is an open source tool designed to assist in the development of simultaneous localisation and mapping (SLAM) algorithms, and evaluation of platforms for implementing those algorithms. It runs on the Linux operating system, and has been used on X86 and ARM along with various GPUs, from high end to mobile.
3DLine-SLAM
3DLines-SLAM: A Monocular Vision Semi-Dense 3D Reconstruction Based on ORB-SLAM Abstract-Producing high-quality 3D maps and calculating more accurate camera pose has always been the goal of SLAM technology. The requirements of SLAM technology such as real-time, low computational cost, and low hardware cost are contradictory to the above objectives. For the issues listed above, we propose a novel semi-dense reconstruction algorithm based on the monocular ORB-SLAM system by matching the line segment features extracted from keyframes. Specifically, we build upon ORB-SLAM, the system first provides a set of keyframes and their corresponding camera poses and a series of map points in real-time. Then we use our developed a keyframe re-culling algorithm to culling redundant keyframes. Then an improved line segment extraction method is used to extract line segments in each keyframe. Finally, we use purely geometric constraints to generates accurate 3D scene model by matching 2D line segments from different keyframes. We thoroughly evaluate and in-depth analysis of our approach, the results show our system runs steadily and reliably. Not only the whole system has strong robustness, but also it can quickly generate an accurate 3d model online with low computational costs.