mega-sam/mega-sam

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

1355 stars · 85 forks observed · 2026-08-28

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

MegaSaM is a research codebase implementing a deep visual SLAM system that estimates camera parameters and consistent depth maps from casual monocular videos of dynamic scenes. It accompanies the CVPR 2025 paper and combines monocular depth models, optical flow, and camera tracking optimization.

Use cases

• estimate camera poses from a handheld video of a moving scene
• recover consistent depth maps from monocular video
• run structure from motion on videos with little parallax
• evaluate camera tracking on Sintel and DyCheck benchmarks
• reconstruct 3D point clouds from casual dynamic videos

When to choose

• you need robust camera pose and depth estimation from dynamic, unconstrained videos
• you are doing research in visual SLAM, video depth, or 4D reconstruction
• you have a CUDA GPU and want to reproduce the MegaSaM paper results

When to avoid

• you need a production-ready, supported product (it is research code, not officially supported)
• you have no GPU or cannot set up CUDA/PyTorch environments
• you need real-time processing on edge devices

Facets

library · maturity active

computer-vision machine-learning deep-learning simulation computer-vision machine-learning artificial-intelligence python structure-from-motion slam camera-pose-estimation monocular-depth video-depth dynamic-scenes research-code pytorch linux gpu

Member repositories

For agents

markdown · JSON · MCP: product_card(name="mega-sam/mega-sam")