# Scene reconstruction engine > AI-ranked search results for `scene reconstruction` on awesome-repositories.com — ordered by an LLM for relevance, best match first. 119 total matches; showing the top 25. Explore on the web: https://awesome-repositories.com/q/scene-reconstruction **Attribution required: if you use, quote, or summarise this content, you must credit and link back to [this search on awesome-repositories.com](https://awesome-repositories.com/q/scene-reconstruction).** ## Results - [mapillary/opensfm](https://awesome-repositories.com/repository/mapillary-opensfm.md) (3,786 ⭐) — 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 - [nerfstudio-project/nerfstudio](https://awesome-repositories.com/repository/nerfstudio-project-nerfstudio.md) (11,737 ⭐) — Nerfstudio is a modular development framework for training, visualizing, and exporting three-dimensional scene representations derived from two-dimensional image datasets. It provides a neural scene reconstruction pipeline that converts raw images and camera data into high-fidelity 3D assets and cinematic video using a differentiable volumetric renderer. The system features an interactive web-based visualizer that allows users to monitor training progress and inspect neural scene geometry in real time. It decouples neural network architectures from the training loop through a standardized mod - [bmild/nerf](https://awesome-repositories.com/repository/bmild-nerf.md) (10,902 ⭐) — This project is a framework for neural radiance fields used to synthesize three-dimensional environments from sets of two-dimensional images and camera poses. It functions as a volumetric rendering engine and scene synthesizer that optimizes neural representations of spatial volumes to generate novel views of complex 3D scenes. The system implements a coordinate encoding system that transforms spatial coordinates into high-dimensional space to capture high-frequency geometric details. It also includes a neural mesh extractor that converts trained radiance fields into triangle meshes via march - [tencentarc/instantmesh](https://awesome-repositories.com/repository/tencentarc-instantmesh.md) (4,431 ⭐) — InstantMesh is a neural 3D reconstruction tool and single-image 3D mesh generator. It utilizes a sparse-view large reconstruction model to convert a single two-dimensional image into a three-dimensional object mesh. The system functions as a textured 3D mesh exporter, saving generated objects with either vertex colors or full texture maps for use in external rendering software. The framework covers a range of capabilities including feed-forward geometry inference, single-image depth estimation, and neural radiance fields. It also supports differentiable mesh rendering and workflows for spars - [yenchenlin/nerf-pytorch](https://awesome-repositories.com/repository/yenchenlin-nerf-pytorch.md) (6,037 ⭐) — This project is a PyTorch implementation of a Neural Radiance Field framework. It serves as a 3D scene synthesizer and differentiable volumetric renderer used to train volumetric representations of scenes by predicting color and density for 3D spatial coordinates. The system enables novel view synthesis, allowing for the generation of new images of complex 3D scenes from previously unseen perspectives. It supports 3D scene reconstruction by processing 2D images and camera poses to build a digital volumetric representation of a physical space. The framework includes capabilities for 3D model - [opendronemap/odm](https://awesome-repositories.com/repository/opendronemap-odm.md) (5,853 ⭐) — OpenDroneMap (ODM) is an open-source aerial drone photogrammetry pipeline that converts 2D images into georeferenced 3D models, orthophotos, point clouds, and digital elevation maps. At its core, the OpenDroneMap Processing Engine orchestrates a complete Structure-from-Motion workflow, from feature extraction through dense reconstruction and tiled output generation, purpose-built for transforming drone-captured imagery into geospatial data products. The toolkit distinguishes itself through GPU-accelerated SIFT feature extraction using CUDA-capable NVIDIA graphics cards, roughly doubling proce - [openmvg/openmvg](https://awesome-repositories.com/repository/openmvg-openmvg.md) (6,451 ⭐) — 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 - [colmap/colmap](https://awesome-repositories.com/repository/colmap-colmap.md) (12,014 ⭐) — 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 - [dreamgaussian/dreamgaussian](https://awesome-repositories.com/repository/dreamgaussian-dreamgaussian.md) (4,332 ⭐) — DreamGaussian is a generative system and converter designed to create textured three-dimensional models from text or images using Gaussian Splatting. It functions as a pipeline for transforming two-dimensional inputs into high-fidelity 3D assets. The project provides specific workflows for converting 3D Gaussian point clouds into standard textured mesh formats compatible with external 3D software. It supports the generation of textured meshes from single images via volumetric refinement and UV texture optimization, as well as the creation of 3D models from text prompts through intermediate im - [google-research/multinerf](https://awesome-repositories.com/repository/google-research-multinerf.md) (3,806 ⭐) — 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 - [nvlabs/neuralangelo](https://awesome-repositories.com/repository/nvlabs-neuralangelo.md) (4,602 ⭐) — Neuralangelo is a neural surface reconstruction framework that transforms two-dimensional image sequences and multi-view photography into high-fidelity 3D meshes. It implements a pipeline for training neural radiance fields to represent complex scenes as digital geometry. The project utilizes a signed distance function for surface representation and multi-resolution hash encoding to capture both coarse and fine geometric details. It employs differentiable volume rendering and gradient-based eikonal regularization to ensure the learned distance functions remain physically plausible. The syste - [bytedance-seed/depth-anything-3](https://awesome-repositories.com/repository/bytedance-seed-depth-anything-3.md) (4,412 ⭐) — Depth-Anything-3 is a collection of core model implementations for depth prediction, multi-view geometry estimation, and RGB-D spatial pipelines. It includes a monocular depth estimation model for predicting depth maps from single images or video, and a 3D Gaussian splatting generator that predicts parameters to synthesize high-fidelity novel views of a scene. The project provides a multi-view geometry estimator for calculating spatially consistent depth and camera poses across synchronized visual inputs. It also functions as a visual SLAM enhancement tool designed to reduce drift and improve - [ashawkey/stable-dreamfusion](https://awesome-repositories.com/repository/ashawkey-stable-dreamfusion.md) (8,841 ⭐) — This project is a diffusion-based 3D generator and image-to-3D reconstruction system. It translates natural language descriptions or two-dimensional images into three-dimensional assets using neural radiance fields and diffusion models. The system utilizes score-distillation sampling and diffusion-based guidance to refine 3D shapes without requiring 3D training data. It includes specialized tools for transforming neural representations into exportable meshes with texture and material data, as well as a pipeline for iterative optimization of geometry and textures. The project covers a broad r - [hustvl/4dgaussians](https://awesome-repositories.com/repository/hustvl-4dgaussians.md) (3,783 ⭐) — 4DGaussians is a research library and neural rendering engine designed for reconstructing and rendering dynamic three-dimensional scenes. It represents moving environments as a collection of Gaussian primitives that evolve in position and appearance over a temporal dimension. The framework utilizes neural deformation fields to predict spatial offsets and rotations for static point representations, simulating complex motion over time. It further employs temporal basis decomposition to encode motion trajectories into learned functions, compressing dynamic scene data while maintaining smooth tra - [cdcseacave/openmvs](https://awesome-repositories.com/repository/cdcseacave-openmvs.md) (4,021 ⭐) — 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 impo - [nerfstudio-project/gsplat](https://awesome-repositories.com/repository/nerfstudio-project-gsplat.md) (4,528 ⭐) — gsplat is a high-performance differentiable rasterization engine for 3D Gaussian splatting, designed for real-time novel view synthesis from 2D images. It provides a complete pipeline for reconstructing 3D scenes by optimizing differentiable Gaussian representations, training models from COLMAP-processed captures or proprietary device files, and generating new viewpoints through a CUDA-accelerated rendering backend. The framework distinguishes itself through memory-optimized CUDA kernels that reduce training memory usage by up to 4x compared to standard implementations while matching publishe - [nvlabs/instant-ngp](https://awesome-repositories.com/repository/nvlabs-instant-ngp.md) (17,278 ⭐) — Instant-ngp is a high-performance neural graphics engine and toolkit designed for 3D reconstruction and the rendering of neural radiance fields. It provides an integrated framework for generating photorealistic volumetric representations from sets of two-dimensional images by optimizing continuous neural scene models. The project distinguishes itself through a focus on rapid training and real-time inference, achieved by mapping spatial coordinates into compact feature grids. By utilizing multiresolution hash encoding and fused processing kernels, the system minimizes computational overhead an - [alicevision/meshroom](https://awesome-repositories.com/repository/alicevision-meshroom.md) (12,562 ⭐) — Meshroom is a node-based photogrammetry software designed to transform collections of two-dimensional images into three-dimensional models and scene geometry. It provides a visual interface for constructing and managing modular data pipelines, allowing users to automate complex computer vision tasks such as feature extraction, depth map estimation, and mesh generation. The software distinguishes itself through a distributed computational framework that dispatches resource-intensive tasks across local hardware or remote render farms. By utilizing a directed acyclic graph execution model, it en - [xxlong0/wonder3d](https://awesome-repositories.com/repository/xxlong0-wonder3d.md) (5,388 ⭐) — Wonder3D is a diffusion-based system for single image 3D reconstruction. It generates high-detail 3D meshes from a single input image by producing consistent multi-view normal maps and color images. The pipeline functions as a multi-view normal map generator and a textured mesh extractor. It uses cross-domain multi-view synthesis to create view-dependent maps, which are then converted into 3D geometry through radiance fusion and memory-efficient surface reconstruction. The project covers 3D mesh generation, multi-view generation, and textured 3D modeling. It also includes capabilities for tr - [facebookresearch/map-anything](https://awesome-repositories.com/repository/facebookresearch-map-anything.md) (2,915 ⭐) — Map-anything is a 3D scene reconstruction framework and neural geometry estimator designed to transform two-dimensional images into metric three-dimensional spatial representations using feed-forward neural networks. It provides a specialized toolkit for predicting camera intrinsics and ray directions from single images without requiring external geometric metadata. The project includes a 3D model benchmarking suite that utilizes a unified model wrapper to standardize outputs from diverse reconstruction models. This allows for consistent evaluation and accuracy measurement across various spat - [totoro97/neus](https://awesome-repositories.com/repository/totoro97-neus.md) (1,774 ⭐) — Code release for NeuS - [ventusff/neurecon](https://awesome-repositories.com/repository/ventusff-neurecon.md) (861 ⭐) — Multi-view 3D reconstruction using neural rendering. Unofficial implementation of UNISURF, VolSDF, NeuS and more. - [xmeng525/neat](https://awesome-repositories.com/repository/xmeng525-neat.md) (165 ⭐) — This is the official repo for the implementation of NeAT: Learning Neural Implicit Surfaces with Arbitrary Topologies from Multi-view Images. - [apchenstu/tensorf](https://awesome-repositories.com/repository/apchenstu-tensorf.md) (1,237 ⭐) — [ECCV 2022] Tensorial Radiance Fields, a novel approach to model and reconstruct radiance fields - [facebookresearch/ridgesfm](https://awesome-repositories.com/repository/facebookresearch-ridgesfm.md) (113 ⭐) — Benjamin Graham, David Novotny 3DV 2020