mileyan/pseudo_lidar

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

1005 stars · 216 forks observed · 2026-09-03

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

Research code implementing Pseudo-LiDAR, a CVPR 2019 method that converts image-based depth maps into pseudo-LiDAR point clouds for 3D object detection in autonomous driving. It includes training scripts for stereo depth estimation and 3D detectors on the KITTI benchmark, plus point cloud visualization notebooks.

Use cases

• detect 3D objects from stereo camera images
• convert depth maps to point clouds
• train 3D object detection on KITTI
• replicate pseudo-lidar paper results
• visualize lidar point clouds from depth estimation
• avoid expensive lidar sensors for autonomous driving perception

When to choose

• you need image-based 3D object detection without LiDAR hardware
• you want to reproduce or build on the CVPR 2019 pseudo-lidar paper
• you're working with KITTI stereo depth estimation pipelines

When to avoid

• you need a production-ready, actively maintained detection system
• you want a plug-and-play library with a stable API
• you need real-time detection on embedded hardware

Facets

library · maturity maintenance

computer-vision machine-learning deep-learning image-processing autonomous-vehicles computer-vision deep-learning machine-learning python 3d-object-detection depth-estimation lidar kitti stereo-vision research-code cvpr-2019 linux gpu

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