Pith Number

pith:NCIPT62R

pith:2021:NCIPT62RAWCW7N2FGHZVNSKEGH

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Habitat-Matterport 3D Dataset (HM3D): 1000 Large-scale 3D Environments for Embodied AI

Aaron Gokaslan, Alex Clegg, Andrew Westbury, Angel X. Chang, Dhruv Batra, Eric Undersander, Erik Wijmans, John Turner, Manolis Savva, Oleksandr Maksymets, Santhosh K. Ramakrishnan, Wojciech Galuba, Yili Zhao

HM3D dataset of 1000 real indoor 3D scenes produces PointGoal navigation agents that achieve top performance on HM3D, Gibson, and MP3D evaluations.

arxiv:2109.08238 v1 · 2021-09-16 · cs.CV · cs.AI

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2 Internet Archive

3 Author claim open · sign in to claim

4 Citations open

5 Replications open

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The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

HM3D is 'pareto optimal' in the sense that agents trained to perform PointGoal navigation on HM3D achieve the highest performance regardless of whether they are evaluated on HM3D, Gibson, or MP3D.

C2weakest assumption

The assumption that the reported performance gains are primarily attributable to the dataset's scale, completeness, and visual fidelity rather than differences in training procedures or evaluation protocols.

C3one line summary

HM3D offers 1000 building-scale 3D environments that are larger and higher-fidelity than existing datasets, enabling better-performing embodied AI agents for tasks like PointGoal navigation.

References

46 extracted · 46 resolved · 6 Pith anchors

[1] SceneNN: A scene meshes dataset with annotations 2016

[2] ScanNet: Richly-annotated 3D reconstructions of indoor scenes 2017

[3] Joint 2d-3d-semantic data for indoor scene understanding 2017 · arXiv:1702.01105

[4] Matterport3D: Learning from RGB-D data in indoor environments 2017

[5] Zamir, Zhi-Yang He, Alexander Sax, Jitendra Malik, and Silvio Savarese 2018

Formal links

2 machine-checked theorem links

Cited by

44 papers in Pith

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A Deployable Embodied Vision-Language Navigation System with Hierarchical Cognition and Context-Aware Exploration

When Robots Do the Chores: A Benchmark and Agent for Long-Horizon Household Task Execution

Receipt and verification

First computed	2026-05-17T23:39:22.207461Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

6890f9fb5105856fb74531f356c94431f93bbe0d890b3f99a356cc95c76b3af0

Aliases

arxiv: 2109.08238 · arxiv_version: 2109.08238v1 · doi: 10.48550/arxiv.2109.08238 · pith_short_12: NCIPT62RAWCW · pith_short_16: NCIPT62RAWCW7N2F · pith_short_8: NCIPT62R

Agent API

Resolver JSON Graph JSON Events JSON Schema Signing key

Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/NCIPT62RAWCW7N2FGHZVNSKEGH \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 6890f9fb5105856fb74531f356c94431f93bbe0d890b3f99a356cc95c76b3af0

Canonical record JSON

{
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    "abstract_canon_sha256": "c1b8bccd81c267a3d91499d8cdf238a95099495019fcfba75b5fc265ce9b3eb9",
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      "cs.AI"
    ],
    "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/",
    "primary_cat": "cs.CV",
    "submitted_at": "2021-09-16T22:01:24Z",
    "title_canon_sha256": "c73c1f5f04e172cf3716c3e7a6812c660d72e56af413c77d1c1c460ce9c1c8b2"
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  "source": {
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    "kind": "arxiv",
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}