# ToniRV/NeRF-SLAM

NeRF-SLAM: Real-Time Dense Monocular SLAM with Neural Radiance Fields. https://arxiv.org/abs/2210.13641   +   Sigma-Fusion: Probabilistic Volumetric Fusion for Dense Monocular SLAM  https://arxiv.org/abs/2210.01276

Repository: https://github.com/ToniRV/NeRF-SLAM
Canonical: https://ross.abutalabs.com/products/nerf-slam
Language: Python
License: BSD-2-Clause
License Family: permissive
Topics: nerf, slam
Last push: 2024-03-19T04:00:27+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 99
- inputs: {"age_days": 1397, "days_push": 897, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1266, forks 155 (observed 2026-08-28T04:04:11.086712+00:00)

## What it is
NeRF-SLAM is a real-time dense monocular SLAM system that combines neural radiance fields (Instant-NGP) with probabilistic volumetric fusion (Sigma-Fusion) for dense 3D scene reconstruction. It is the research code accompanying two MIT papers, built in Python with C++ components (GTSAM, instant-ngp).

## Use cases
- run real-time dense monocular SLAM with neural radiance fields
- reconstruct 3D scenes from monocular camera video
- research NeRF-based SLAM systems
- fuse depth and pose estimates into a volumetric map
- evaluate SLAM on Replica dataset scenes
- build dense 3D maps for robotics

## When to choose
- you need real-time dense monocular SLAM with NeRF-based rendering
- you want to reproduce or extend the NeRF-SLAM or Sigma-Fusion papers
- you have a CUDA GPU and want state-of-the-art dense 3D reconstruction from RGB video

## When to avoid
- you need a production-ready SLAM solution with support and documentation
- you lack a CUDA-capable GPU or don't want to compile C++ dependencies like GTSAM and instant-ngp
- you need lightweight or CPU-only SLAM
- you want a simple point-cloud SLAM without neural rendering

## Facets
- artifact type: library
- maturity: active
- function: computer-vision, machine-learning, simulation, graphics
- domain: robotics, computer-vision, deep-learning, autonomous-vehicles
- platform: python, cpp
- tags: nerf, slam, monocular-slam, volumetric-fusion, instant-ngp, gtsam, research-code, 3d-reconstruction, linux, gpu

## Member repositories
- ToniRV/NeRF-SLAM (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:11.086712+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T05:03:45.804085+00:00, confidence not recorded.
  - readme: https://github.com/ToniRV/NeRF-SLAM (fetched 2026-08-28T04:04:11.086712+00:00, sha 2b04caa35264)
- Data as of 2026-08-30T08:39:29.467469+00:00.