BeSlower

996.ICU

Repo for counting stars and contributing. Press F to pay respect to glorious developers.

mmdetection

OpenMMLab Detection Toolbox and Benchmark

netron

Visualizer for neural network, deep learning and machine learning models

vscode-leetcode

Solve LeetCode problems in VS Code

awesome-self-supervised-learning

A curated list of awesome self-supervised methods

mmdetection3d

OpenMMLab's next-generation platform for general 3D object detection.

OpenPCDet

OpenPCDet Toolbox for LiDAR-based 3D Object Detection.

shareOI

算法竞赛课件分享

Awesome-Visual-Transformer

Collect some papers about transformer with vision. Awesome Transformer with Computer Vision (CV)

simpledet

A Simple and Versatile Framework for Object Detection and Instance Recognition

CenterPoint

PointRCNN

PointRCNN: 3D Object Proposal Generation and Detection from Point Cloud, CVPR 2019.

votenet

Deep Hough Voting for 3D Object Detection in Point Clouds

Det3D

World's first general purpose 3D object detection codebse.

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。

lit

Literature for the self-taught AI practitioner! 📚

second.pytorch

PointPillars for KITTI object detection

kitti2bag

Convert KITTI dataset to ROS bag file the easy way!

MOTR

[ECCV2022] MOTR: End-to-End Multiple-Object Tracking with TRansformer

ttfnet

autoware_ai_perception

nutonomy_pointpillars

Convert pointpillars Pytorch Model To ONNX for TensorRT Inference

nuscenes-devkit

Devkit for the public 2019 Lyft Level 5 AV Dataset (fork of https://github.com/nutonomy/nuscenes-devkit)

ml-suite

Getting Started with Xilinx ML Suite

apollo_perception_ros

Object detection / tracking / fusion based on Apollo r3.0.0 perception module in ROS

kitti-object-eval-python

Fast kitti object detection eval in python(finish eval in less than 10 second)

pillar-od

Mono3DPLiDAR

Official PyTorch Implementation for "Monocular 3D Object Detection with Pseudo-LiDAR Point Cloud", ICCVW 2019

kitti_pretrained_point_pillars

mvg_slides

lecture notes of Multiple View Geometry by Prof. Daniel Cremers at Technische Universität München