cvg/pixel-perfect-sfm

Health v2 · maintenance only

Adoption not part of the score

1484 stars · 153 forks observed · 2026-08-28

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

pixsfm is a Python package with a C++ core that improves Structure-from-Motion and visual localization accuracy by refining keypoints, camera poses, and 3D points via featuremetric alignment of deep features. It integrates with COLMAP and the hloc toolbox to refine existing models or reconstruct new datasets with state-of-the-art image matching.

Use cases

• refine an existing COLMAP 3D model for higher accuracy
• reconstruct a new dataset from images with state-of-the-art feature matching
• localize query images against a 3D scene in challenging conditions
• run keypoint adjustment on a COLMAP database before SfM
• run bundle adjustment to refine 3D points and camera poses
• evaluate refinement quality on the ETH3D benchmark

When to choose

• you need pixel-accurate camera poses and 3D points for SfM or visual localization
• you already use COLMAP or hloc and want to boost reconstruction accuracy
• you work with challenging conditions where standard keypoints underperform
• you need a research-grade pipeline from an award-winning ICCV 2021 paper

When to avoid

• you need a turnkey photogrammetry tool without building COLMAP and Ceres from source
• you work on Windows, which is untested
• you need real-time or lightweight SfM - the refinement is computationally heavy
• your project only needs basic feature matching without refinement

Facets

library · maturity maintenance

computer-vision image-processing machine-learning deep-learning computer-vision python cpp structure-from-motion feature-matching keypoint-refinement bundle-adjustment colmap hloc visual-localization ceres-solver pytorch 3d-vision linux

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markdown · JSON · MCP: product_card(name="cvg/pixel-perfect-sfm")