niessner/Matterport resource

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

1245 stars · 156 forks observed · 2026-08-28

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

Matterport3D is a large-scale RGB-D dataset of 90 building-scale indoor scenes with 194,400 RGB-D images, panoramic views, textured 3D meshes, camera poses, and 2D/3D semantic annotations. The repository also provides code, loaders, and benchmark tasks for scene understanding research.

Use cases

• train RGB-D scene understanding models
• semantic segmentation of indoor 3D scenes
• keypoint matching and view overlap prediction benchmarks
• surface normal estimation from color images
• 3D reconstruction research with aligned RGB-D data
• region type classification and semantic voxel labeling

When to choose

• you need large-scale indoor RGB-D data with 3D semantic annotations
• you are benchmarking scene understanding or 3D vision algorithms
• you need globally aligned panoramic views of entire buildings

When to avoid

• you need outdoor or non-building-scale scenes
• you cannot sign the institutional Terms of Use agreement required for data access
• you need a lightweight dataset for quick prototyping

Facets

dataset · maturity stable

computer-vision image-processing machine-learning data-science computer-vision machine-learning deep-learning artificial-intelligence python cpp cross-platform rgb-d 3d-reconstruction semantic-segmentation indoor-scenes benchmark point-clouds meshes scene-understanding

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For agents

markdown · JSON · MCP: product_card(name="niessner/Matterport")