3d-object-detection

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

An extension of Open3D to address 3D Machine Learning tasks

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

The Medical Detection Toolkit contains 2D + 3D implementations of prevalent object detectors such as Mask R-CNN, Retina Net, Retina U-Net, as well as a training and inference framework focused on dealing with medical images.

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

Paper reading notes on Deep Learning and Machine Learning

[CVPR 2022 Oral, Best Student Paper] EPro-PnP: Generalized End-to-End Probabilistic Perspective-n-Points for Monocular Object Pose Estimation

KITTI Object Visualization (Birdview, Volumetric LiDar point cloud )

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

alfred-py: A deep learning utility library for **human**, more detail about the usage of lib to: https://zhuanlan.zhihu.com/p/341446046

Code for a series of work in LiDAR perception, including SST (CVPR 22), FSD (NeurIPS 22), FSD++ (TPAMI 23), FSDv2, and CTRL (ICCV 23, oral).

VoxelNeXt: Fully Sparse VoxelNet for 3D Object Detection and Tracking (CVPR 2023)

Ultralytics YOLOv8, YOLOv9, YOLOv10, YOLOv11, YOLOv12 for ROS 2

SMOKE: Single-Stage Monocular 3D Object Detection via Keypoint Estimation

Official PyTorch implementation of FB-BEV & FB-OCC - Forward-backward view transformation for vision-centric autonomous driving perception

3D Bounding Box Annotation Tool (3D-BAT) Point cloud and Image Labeling

A lightweight tool for labeling 3D bounding boxes in point clouds.

[ICCV 2023] OccNet: Scene as Occupancy

nnDetection is a self-configuring framework for 3D (volumetric) medical object detection which can be applied to new data sets without manual intervention. It includes guides for 12 data sets that were used to develop and evaluate the performance of the proposed method.

3D Object Detection for Autonomous Driving: A Comprehensive Survey (IJCV 2023)