# facebookresearch/House3D

a Realistic and Rich 3D Environment

Repository: https://github.com/facebookresearch/House3D
Canonical: https://ross.abutalabs.com/products/house3d
Language: C++
License: Apache-2.0
License Family: permissive
Archived: true
Last push: 2020-07-06T04:41:49+00:00

## Health v2 (maintenance only)
Score: 10/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 8, longevity 100
- inputs: {"age_days": 3199, "days_push": 2249, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: archived
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1200, forks 185 (observed 2026-08-28T04:03:58.032899+00:00)

## What it is
House3D is a virtual 3D environment of over 45k fully annotated indoor scenes from the SUNCG dataset, built for training embodied AI agents. It provides fast rendering of RGB, depth, segmentation, and top-down map observations, making it suitable for large-scale reinforcement learning research.

## Use cases
- train reinforcement learning agents for indoor navigation
- research embodied question answering in 3D environments
- simulate agents navigating to rooms by high-level concepts
- generate RGB, depth, and segmentation observations from 3D indoor scenes
- benchmark generalization of navigation models across environments

## When to choose
- you need a large-scale, annotated 3D indoor environment for embodied AI or RL research
- you want fast rendering (thousands of FPS) for large-scale training
- you need multi-modal observations like depth, segmentation, and top-down maps

## When to avoid
- you need outdoor or non-indoor environments
- you need actively maintained software with recent updates and support
- you need photorealistic rendering beyond SUNCG scene quality

## Facets
- artifact type: library
- maturity: maintenance
- function: simulation, machine-learning, reinforcement-learning, computer-vision, graphics
- domain: artificial-intelligence, reinforcement-learning, computer-vision, simulation
- platform: cpp, python
- tags: 3d-environment, embodied-ai, indoor-scenes, suncg, visual-navigation, reinforcement-learning-environment, research, linux

## Member repositories
- facebookresearch/House3D (main) score 10

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:58.032899+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 2026-08-30T06:20:49.775147+00:00, confidence not recorded.
  - readme: https://github.com/facebookresearch/House3D (fetched 2026-08-28T04:03:58.032899+00:00, sha 75160ee9c327)
- Data as of 2026-08-30T08:39:29.467469+00:00.