Pinned Repositories
A-LOAM
Advanced implementation of LOAM
awesome-radar-perception
A curated list of radar datasets, detection, tracking and fusion
baiduyun
油猴脚本 直接下载百度网盘和百度网盘分享的文件,直链下载超级加速
EigenExamples
Common Eigen examples for robotics applications
kalman
Header-only C++11 Kalman Filtering Library (EKF, UKF) based on Eigen3
kitti_lidar_camera
KiTTI LiDAR-Camera Fusion, implemented using kitti_ros.
multi_lidar_calibration
Multi lidar calibration tool from autoware, using NDT algorithm, need a approximate initial transformation
Multitarget-tracker
Hungarian algorithm + Kalman filter multitarget tracker implementation.
MVision
机器人视觉 移动机器人 VS-SLAM ORB-SLAM2 深度学习目标检测 yolov3 行为检测 opencv PCL 机器学习 无人驾驶
pcl_tutorials_code
官网基础基础教程代码
TmacTmac1992's Repositories
TmacTmac1992/awesome-radar-perception
A curated list of radar datasets, detection, tracking and fusion
TmacTmac1992/4D-PLS
4D Panoptic Lidar Segmentation
TmacTmac1992/awesome-lane-detection
A paper list of lane detection.
TmacTmac1992/Awesome-LiDAR-Camera-Calibration
A Collection of LiDAR-Camera-Calibration Papers, Toolboxes and Notes
TmacTmac1992/awesome-productivity-cn
绝妙的个人生产力（Awesome Productivity 中文版）
TmacTmac1992/Complex-YOLOv4-Pytorch
The PyTorch Implementation based on YOLOv4 of the paper: "Complex-YOLO: Real-time 3D Object Detection on Point Clouds"
TmacTmac1992/CV_interviews_Q-A
CV算法岗知识点及面试问答汇总，主要分为计算机视觉、机器学习、图像处理和 C++基础四大块，一起努力向offers发起冲击！
TmacTmac1992/CVPR2021-Papers-with-Code
CVPR 2021 论文和开源项目合集
TmacTmac1992/Detection-PyTorch-Notebook
代码 -《深度学习之PyTorch物体检测实战》
TmacTmac1992/EagerMOT
Official code for "EagerMOT: 3D Multi-Object Tracking via Sensor Fusion" [ICRA 2021]
TmacTmac1992/ImmortalTracker
TmacTmac1992/Learning-Deep-Learning
Paper reading notes on Deep Learning and Machine Learning
TmacTmac1992/leetcode-master
LeetCode 刷题攻略：200道经典题目刷题顺序，共60w字的详细图解，视频难点剖析，50余张思维导图，支持C++，Java，Python，Go，JavaScript等多语言版本，从此算法学习不再迷茫！🔥🔥 来看看，你会发现相见恨晚！🚀
TmacTmac1992/LiDAR-MOS
Moving Object Segmentation in 3D LiDAR Data: A Learning-based Approach Exploiting Sequential Data (RAL/IROS 2021)
TmacTmac1992/lidar-with-velocity
TmacTmac1992/netron
Visualizer for neural network, deep learning, and machine learning models
TmacTmac1992/ONCE_Benchmark
One Million Scenes for Autonomous Driving
TmacTmac1992/pillar-motion
Self-Supervised Pillar Motion Learning for Autonomous Driving (CVPR 2021)
TmacTmac1992/PMF
Perception-aware multi-sensor fusion for 3D LiDAR semantic segmentation (ICCV 2021)
TmacTmac1992/point-cloud-utils
A Python library for common tasks on 3D point clouds
TmacTmac1992/point2seq
TmacTmac1992/PointPillars_MultiHead_40FPS
A REAL-TIME 3D detection network [Pointpillars] compiled by CUDA/TensorRT/C++.
TmacTmac1992/PV-SSD
The proposed approach enhances the CenterPoint baseline with a multimodal fusion mechanism. First, inspired by PointPainting, an off-the-shelf Mask-RCNN model trained from nuImages is employed to generate 2D object mask information based on the camera images. Furthermore, the Cylinder3D is also adopted to produce the 3D semantic information of the input LiDAR point cloud. Then, an improved version of CenterPoint takes the painted points(with 2D instance segmentation and 3D semantic segmentation) as inputs for accurate object detection. Specifically, we replace the RPN module in CenterPoint with modified Spatial-Semantic Feature Aggregation(SSFA) to well address multi-class detection. A simple pseudo labeling technique is also integrated in a semi-supervised learning manner. In addition, the Test Time Augmentation(TTA) strategy including multiple flip and rotation operations is applied during the inference time. Finally, the detections generated from multiple voxel resolutions (0.05m to 0.125m) are assembled with 3D Weighted Bounding Box Fusion(WBF) technique to produce the final results.
TmacTmac1992/Rotated_IoU
Differentiable IoU of rotated bounding boxes using Pytorch
TmacTmac1992/SFA3D
Super Fast and Accurate 3D Object Detection based on 3D LiDAR Point Clouds (The PyTorch implementation)
TmacTmac1992/SimpleTrack
TmacTmac1992/simtrack
Exploring Simple 3D Multi-Object Tracking for Autonomous Driving (ICCV 2021)
TmacTmac1992/XUAN-Bike
TmacTmac1992/YOLOP
You Only Look Once for Panopitic Driving Perception.（https://arxiv.org/abs/2108.11250）
TmacTmac1992/yolov7
🔥🔥🔥🔥 YOLO with Transformers and Instance Segmentation, with TensorRT acceleration! 🔥🔥🔥