cdcseacave/openMVS
open Multi-View Stereo reconstruction library
What it solves
OpenMVS addresses the final stage of the photogrammetry pipeline. While other tools can recover camera poses and sparse point-clouds, OpenMVS focuses on transforming those sparse inputs into a high-quality, textured 3D mesh of a scene.
How it works
The library takes a set of camera poses and a sparse point-cloud as input. It then applies a series of algorithms to process the data through four main stages:
- Dense point-cloud reconstruction: Creating a more complete and accurate point-cloud.
- Mesh reconstruction: Estimating a surface mesh that best fits the point-cloud.
- Mesh refinement: Recovering fine details of the surface.
- Mesh texturing: Applying a sharp, accurate texture to color the final 3D model.
Who it’s for
It is designed for computer-vision scientists and the Multi-View Stereo (MVS) reconstruction community.
Highlights
- Provides a complete set of algorithms for the end-to-end surface recovery process.
- Supports dense point-cloud generation, mesh estimation, and refinement.
- Includes tools for computing sharp textures for 3D meshes.
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