3D object detection

BEVFormer is a perception framework that transforms multi-camera images into bird's-eye-view representations for autonomous driving. It functions as a multi-camera vision pipeline that integrates multiple camera streams into a single unified spatial perspective to facilitate environmental understanding. The system implements a transformer-based architecture that employs query-based feature extraction and spatiotemporal networks to aggregate spatial image features and temporal historical data. It uses recurrent temporal accumulation to maintain a persistent memory of the scene across consecuti

PCDet is a LiDAR 3D object detection toolbox and point cloud processing library built on the PyTorch deep learning framework. It provides a system for identifying and locating three-dimensional objects within point cloud data. The project utilizes a data-model separation pattern to decouple dataset loading logic from the core detection pipeline. It features a multi-sensor fusion pipeline that combines data from multiple sensors into a shared spatial view and a distributed GPU training system to scale workloads across multiple graphics processors. The toolkit covers several capability areas,

MMDetection3D is an open-source toolbox for 3D perception, providing a unified framework for detecting and segmenting objects in three-dimensional environments. It supports a range of core tasks including monocular 3D object detection from single camera images, LiDAR-based 3D object detection from raw point clouds, and multi-modal fusion that combines camera images with LiDAR data. The toolbox also covers point cloud semantic segmentation, assigning class labels to every point in a scan for scene understanding. The project distinguishes itself through a config-driven pipeline that orchestrate

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

Deep Hough Voting for 3D Object Detection in Point Clouds

OpenPCDet is a PyTorch deep learning library and toolbox for LiDAR 3D object detection. It functions as a point cloud processing framework designed to develop, train, and evaluate machine learning models that identify and locate objects in three dimensional space. The project includes a GPU-accelerated geometry engine for high-performance implementation of 3D intersection over union and rotated non-maximum suppression. It also provides a distributed model training tool to scale the training and testing of detection models across multiple GPUs and computing nodes. The framework covers point c

Ultralytics is a comprehensive computer vision framework designed for training, validating, and deploying deep learning models across a wide range of visual recognition tasks. It provides a unified interface for core operations including object detection, instance segmentation, pose estimation, and image classification. By utilizing a modular architecture, the platform allows users to swap model components to balance inference speed and accuracy requirements for diverse applications. The framework distinguishes itself through its support for real-time processing and flexible deployment. It in

Code release for the paper PointRCNN:3D Object Proposal Generation and Detection from Point Cloud, CVPR 2019.

SECOND for KITTI/NuScenes object detection

Group-Free 3D Object Detection via Transformers