charlesq34/frustum-pointnets

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

1668 stars · 530 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

Official TensorFlow code release for the CVPR 2018 paper 'Frustum PointNets for 3D Object Detection from RGB-D Data' by Stanford and Nuro researchers. It combines 2D object detectors with PointNet/PointNet++ networks to perform 3D instance segmentation and amodal 3D bounding box estimation directly on point clouds.

Use cases

• detect 3d objects from rgb-d data
• run 3d object detection on kitti benchmark
• train frustum pointnet on sunrgbd dataset
• segment 3d instances in point clouds
• estimate amodal 3d bounding boxes from lidar point clouds
• reproduce cvpr 2018 3d detection research results

When to choose

• you need a proven 3D object detection pipeline for RGB-D or LiDAR data
• you want to reproduce or build on the Frustum PointNets paper
• you work with KITTI or SUN RGB-D datasets and TensorFlow
• you need direct point-cloud processing without voxelization

When to avoid

• you need a maintained production system with recent framework support
• you prefer PyTorch over TensorFlow 1.x
• you need state-of-the-art 3D detection beyond 2018-era methods
• you want plug-and-play inference without research setup effort

Facets

library · maturity maintenance

machine-learning deep-learning computer-vision image-processing computer-vision robotics autonomous-vehicles deep-learning artificial-intelligence python 3d-object-detection point-cloud pointnet rgb-d kitti sunrgbd cvpr-2018 tensorflow instance-segmentation research-code linux gpu

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