awesome-repositories.com
Blog
MCP
awesome-repositories.com

Discover the best open-source repositories with AI-powered search.

ExploreCurated searchesOpen-source alternativesSelf-hosted softwareBlogSitemap
ProjectMCP serverAboutHow we rankPress
LegalPrivacyTerms
© 2026 Bringes Technology SRL·VAT RO45896025·hello@awesome-repositories.com
cdcseacave avatar

cdcseacave/openMVS

0
View on GitHub↗

AI search

Explore more awesome repositories

Describe what you need in plain English — the AI ranks thousands of curated open-source projects by relevance.

Start searching with AI
cdcseacave.github.io
↗

OpenMVS

openMVS is a multi-view stereo library and photogrammetry pipeline used for 3D scene reconstruction. It transforms Structure from Motion data—specifically camera poses and sparse point clouds—into detailed 3D models consisting of dense point clouds and textured meshes.

The project provides a sequence of processing stages to densify point clouds, generate 3D surface meshes, and apply photorealistic textures. It uses multi-view texture blending to map accurate colors onto reconstructed geometry and employs iterative refinement to optimize mesh details.

The system includes capabilities for importing external Structure from Motion scenes, defining regions of interest via bounding-box filtering, and visualizing 3D scenes. It also supports extracting keyframes from video and exporting geometry to standard formats such as PLY, OBJ, and GLB.

Features

  • Neural Scene Reconstructions - Creates detailed 3D models of real-world environments from multi-view image datasets.
  • 3D Reconstruction Pipelines - Provides a complete pipeline for converting sets of 2D photographs into accurate 3D digital surface meshes.
  • Dense Reconstruction Backends - Generates dense 3D point clouds from sparse scenes using Patch-Match or Semi-Global Matching algorithms.
  • Multi-View Stereo Generators - Utilizes multiple calibrated views of a scene to generate dense 3D geometry.
4,021 stars·978 forks·C++·AGPL-3.0·44 views
  • Point-to-Mesh Conversions - Converts dense point clouds into continuous surface meshes with iterative geometry refinement.
  • Point Cloud Surface Extraction - Extracts 3D surface meshes that best fit the geometry of an input dense point cloud.
  • Point Cloud Generation - Generates high-density 3D point clouds from sparse data using Patch-Match or Semi-Global Matching.
  • 3D Mesh Generators - Programmatically creates 3D polygonal meshes from input point clouds and refines them for structural detail.
  • 3D Mesh Reconstruction Tools - Provides a complete toolset for converting sparse point clouds into refined 3D surface meshes.
  • 3D Mesh Texturing Tools - Implements a process for blending pixel data from multiple camera views to texture 3D geometry.
  • Point Cloud Generators - Generates detailed 3D point clouds from raw image frames using depth map estimation.
  • Photogrammetric Texture Mapping - Implements automatic mapping of colors from calibrated photographs onto reconstructed 3D meshes.
  • Constrained Delaunay Triangulations - Implements constrained Delaunay triangulations to generate continuous 3D surface meshes from dense point clouds.
  • Geometric Error Minimization - Optimizes surface geometry using gradient-descent to minimize the distance between the mesh and the dense point cloud.
  • Iterative Geometry Refinement - Employs iterative geometry refinement to improve the fidelity of 3D meshes through optimization and smoothing.
  • Textured Mesh Optimizations - Computes sharp and accurate color textures to map onto 3D mesh surfaces via joint optimization.
  • Photorealistic Mesh Texturing - Maps original image data onto 3D meshes to create realistic colored surfaces.
  • Multi-Texture Blending - Implements multi-view texture blending to map accurate colors onto reconstructed geometry using weighted pixel data.
  • Image-to-3D Texture Engines - Provides a pipeline to transfer 2D image textures onto reconstructed 3D meshes for photorealistic surfaces.
  • Point Cloud Densification - Converts sparse point clouds into complete and accurate dense point clouds for detailed scene representation.
  • Semi-Global Matching - Computes depth maps using Semi-Global Matching to optimize global energy functions across image pixels.
  • Stereo Depth Estimation - Estimates depth maps using Patch-Match stereo matching to find the best geometric fit across multiple images.
  • Structure from Motion Pipelines - Implements a full pipeline for calculating camera poses and sparse point clouds from unordered photos.
  • Mesh - Computes and applies accurate textures to color 3D mesh surfaces based on input images.
  • 3D Scene Importers - Provides utilities to import camera poses and sparse point clouds from external SfM solvers into its project format.
  • 3D Scene Visualization - Provides an interactive viewer for rendering project files, depth maps, and standard 3D geometry.
  • Camera Pose Estimators - Calculates camera orientations and positions from unordered images using pinhole or spherical camera models.
  • Mesh Detail Reconstruction - Provides capabilities to recover and enhance fine geometric details on reconstructed 3D meshes.
  • Geometry Data Exporters - Writes reconstructed point clouds and meshes to standard formats including PLY, OBJ, and GLB.
  • Photogrammetry Data Pipelines - Ships a workflow for converting raw image captures into structured poses and point clouds for 3D modeling.
  • Video Frame Extraction - Extracts stable and well-spaced keyframes from video or spherical footage for use in 3D reconstruction.
  • Pinhole Camera Models - Utilizes pinhole camera models to project 3D world coordinates onto 2D image planes for triangulation.
  • Structure From Motion - Library for multi-view stereo reconstruction.
  • Multi View Stereo - Library for dense point cloud and mesh generation.
  • Star history

    Star history chart for cdcseacave/openmvsStar history chart for cdcseacave/openmvs

    How this analysis was created: This summary and feature list are AI-generated from collected project material and can contain mistakes. Stars, license and language are imported from GitHub. Inclusion does not mean that we have tested or audited this project. Check the source documentation for any feature you depend on. Learn more on our About page.

    Frequently asked questions

    What does cdcseacave/openmvs do?

    openMVS is a multi-view stereo library and photogrammetry pipeline used for 3D scene reconstruction. It transforms Structure from Motion data—specifically camera poses and sparse point clouds—into detailed 3D models consisting of dense point clouds and textured meshes.

    What are the main features of cdcseacave/openmvs?

    The main features of cdcseacave/openmvs are: Neural Scene Reconstructions, 3D Reconstruction Pipelines, Dense Reconstruction Backends, Multi-View Stereo Generators, Point-to-Mesh Conversions, Point Cloud Surface Extraction, Point Cloud Generation, 3D Mesh Generators.

    Which projects share features with cdcseacave/openmvs?

    Projects with overlapping indexed features include: colmap/colmap — COLMAP is a 3D scene reconstruction suite and C++ geometry library that implements a full structure-from-motion… mapillary/opensfm — OpenSfM is a computer vision library and structure-from-motion pipeline designed to reconstruct three-dimensional… openmvg/openmvg — openMVG is a computer vision geometry library and toolkit for multiple view geometry. It serves as a framework for… google-research/multinerf — MultiNeRF is a 3D scene reconstruction suite and framework for training Neural Radiance Fields to synthesize novel… opendronemap/opendronemap — A command line toolkit to generate maps, point clouds, 3D models and DEMs from drone, balloon or kite images. 📷. openmoonray/openmoonray — OpenMoonray is a production-grade physically based rendering system and path-tracing engine. It simulates the physical…

    Projects sharing features with OpenMVS

    These projects share indexed features with OpenMVS. Shared tags can include platform or build tooling; verify the primary use case before treating a result as a replacement.
    • colmap/colmapcolmap avatar

      colmap/colmap

      12,014View on GitHub↗

      COLMAP is a 3D scene reconstruction suite and C++ geometry library that implements a full structure-from-motion pipeline. It functions as a GPU-accelerated photogrammetry tool and multi-view stereo framework designed to produce dense 3D geometry and watertight meshes from collections of 2D images. The project distinguishes itself through hardware-accelerated feature extraction and a modular camera modeling system that supports perspective, fisheye, and equirectangular lens types. It employs vocabulary tree image retrieval to efficiently identify similar images in large datasets and provides P

      C++
      View on GitHub↗12,014
    • mapillary/opensfmmapillary avatar

      mapillary/OpenSfM

      3,786View on GitHub↗

      OpenSfM is a computer vision library and structure-from-motion pipeline designed to reconstruct three-dimensional scenes and camera trajectories from overlapping images. It functions as a 3D reconstruction engine and photogrammetry toolkit, utilizing automated feature-based image matching and incremental bundle adjustment to derive spatial geometry. The system distinguishes itself as a geospatial alignment tool, integrating GPS and inertial sensor data to align reconstructed 3D models with real-world geographic coordinates. It employs a hybrid Python and C++ execution model to manage large-sc

      Python
      View on GitHub↗3,786
    • openmvg/openmvgopenMVG avatar

      openMVG/openMVG

      6,451View on GitHub↗

      openMVG is a computer vision geometry library and toolkit for multiple view geometry. It serves as a framework for structure from motion and 3D scene reconstruction, providing the tools necessary to recover 3D point clouds and camera poses from collections of 2D images. The library implements both global and incremental structure-from-motion pipelines. It uses geometric algorithms to calculate camera pose estimation and image localization, employing Levenberg-Marquardt bundle adjustment to refine 3D coordinates and camera parameters by minimizing reprojection error. The project covers a broa

      C++
      View on GitHub↗6,451
    • google-research/multinerfgoogle-research avatar

      google-research/multinerf

      3,806View on GitHub↗

      MultiNeRF is a 3D scene reconstruction suite and framework for training Neural Radiance Fields to synthesize novel views from sets of 2D images. It provides a system for generating new perspectives of a scene by optimizing a neural network based on images and camera poses. The toolkit includes research implementations such as Mip-NeRF 360 and Ref-NeRF for high-fidelity volumetric rendering. It features a structure-from-motion pipeline to calculate camera positions and orientations from image datasets to prepare data for training. The project covers a full workflow for volumetric rendering, i

      Pythonnerfneural-radiance-fields
      View on GitHub↗3,806
    Compare all 30 related projects→