KeningChai's repositories

awesome-radar-perception

A curated list of radar datasets, detection, tracking and fusion

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4D-PLS

4D Panoptic Lidar Segmentation

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awesome-lane-detection

A paper list of lane detection.

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Awesome-LiDAR-Camera-Calibration

A Collection of LiDAR-Camera-Calibration Papers, Toolboxes and Notes

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awesome-productivity-cn

绝妙的个人生产力(Awesome Productivity 中文版)

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Complex-YOLOv4-Pytorch

The PyTorch Implementation based on YOLOv4 of the paper: "Complex-YOLO: Real-time 3D Object Detection on Point Clouds"

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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!

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CVPR2021-Papers-with-Code

CVPR 2021 论文和开源项目合集

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Detection-PyTorch-Notebook

代码 -《深度学习之PyTorch物体检测实战》

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EagerMOT

Official code for "EagerMOT: 3D Multi-Object Tracking via Sensor Fusion" [ICRA 2021]

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Learning-Deep-Learning

Paper reading notes on Deep Learning and Machine Learning

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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!🚀

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LiDAR-MOS

Moving Object Segmentation in 3D LiDAR Data: A Learning-based Approach Exploiting Sequential Data (RAL/IROS 2021)

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netron

Visualizer for neural network, deep learning, and machine learning models

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ONCE_Benchmark

One Million Scenes for Autonomous Driving

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pillar-motion

Self-Supervised Pillar Motion Learning for Autonomous Driving (CVPR 2021)

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PMF

Perception-aware multi-sensor fusion for 3D LiDAR semantic segmentation (ICCV 2021)

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point-cloud-utils

A Python library for common tasks on 3D point clouds

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PointPillars_MultiHead_40FPS

A REAL-TIME 3D detection network [Pointpillars] compiled by CUDA/TensorRT/C++.

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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.

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Rotated_IoU

Differentiable IoU of rotated bounding boxes using Pytorch

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SFA3D

Super Fast and Accurate 3D Object Detection based on 3D LiDAR Point Clouds (The PyTorch implementation)

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simtrack

Exploring Simple 3D Multi-Object Tracking for Autonomous Driving (ICCV 2021)

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YOLOP

You Only Look Once for Panopitic Driving Perception.(https://arxiv.org/abs/2108.11250)

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yolov7

🔥🔥🔥🔥 YOLO with Transformers and Instance Segmentation, with TensorRT acceleration! 🔥🔥🔥

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