# 3D object detection

> AI-ranked search results for `3d object detection` on awesome-repositories.com — ordered by an LLM for relevance, best match first. 118 total matches; showing the top 10.

Explore on the web: https://awesome-repositories.com/q/3d-object-detection

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## Results

- [fundamentalvision/bevformer](https://awesome-repositories.com/repository/fundamentalvision-bevformer.md) (4,519 ⭐) — 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
- [sshaoshuai/pcdet](https://awesome-repositories.com/repository/sshaoshuai-pcdet.md) (5,621 ⭐) — 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,
- [open-mmlab/mmdetection3d](https://awesome-repositories.com/repository/open-mmlab-mmdetection3d.md) (6,273 ⭐) — 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
- [maudzung/super-fast-accurate-3d-object-detection](https://awesome-repositories.com/repository/maudzung-super-fast-accurate-3d-object-detection.md) (1,125 ⭐) — Super Fast and Accurate 3D Object Detection based on 3D LiDAR Point Clouds (The PyTorch implementation)
- [facebookresearch/votenet](https://awesome-repositories.com/repository/facebookresearch-votenet.md) (1,760 ⭐) — Deep Hough Voting for 3D Object Detection in Point Clouds
- [open-mmlab/openpcdet](https://awesome-repositories.com/repository/open-mmlab-openpcdet.md) (5,621 ⭐) — 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/ultralytics](https://awesome-repositories.com/repository/ultralytics-ultralytics.md) (58,468 ⭐) — 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
- [sshaoshuai/pointrcnn](https://awesome-repositories.com/repository/sshaoshuai-pointrcnn.md) (1,818 ⭐) — Code release for the paper PointRCNN:3D Object Proposal Generation and Detection from Point Cloud, CVPR 2019.
- [traveller59/second.pytorch](https://awesome-repositories.com/repository/traveller59-second-pytorch.md) (1,774 ⭐) — SECOND for KITTI/NuScenes object detection
- [zeliu98/group-free-3d](https://awesome-repositories.com/repository/zeliu98-group-free-3d.md) (255 ⭐) — Group-Free 3D Object Detection via Transformers