Pith Number

pith:PRRIYH5H

pith:2021:PRRIYH5HWHYI4NRKDLGTZEH65O

not attested not anchored not stored refs resolved

ARKitScenes: A Diverse Real-World Dataset For 3D Indoor Scene Understanding Using Mobile RGB-D Data

Afshin Dehghan, Arik Schwartz, Brandon Joffe, Daniel Kurz, Elad Shulman, Gilad Baruch, Peter Fu, Tal Dimry, Thomas Gebauer, Yuri Feigin, Zhuoyuan Chen

ARKitScenes is the largest indoor RGB-D dataset captured with widely available mobile LiDAR sensors and includes laser-scanned depth plus manual 3D bounding box labels.

arxiv:2111.08897 v3 · 2021-11-17 · cs.CV · cs.AI

Open paper page JSON Open Graph Bundle Merged state Verified badge What is a Pith Number?

Add to your LaTeX paper

\usepackage{pith}
\pithnumber{PRRIYH5HWHYI4NRKDLGTZEH65O}

Prints a linked badge after your title and injects PDF metadata. Compiles on arXiv. Learn more · Embed verified badge

Record completeness

1 Bitcoin timestamp

2 Internet Archive

3 Author claim open · sign in to claim

4 Citations open

5 Replications open

✓ Portable graph bundle live · download bundle · merged state

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

ARKitScenes is not only the first RGB-D dataset captured with a now widely available depth sensor, but to our best knowledge, it also is the largest indoor scene understanding data released.

C2weakest assumption

That the mobile RGB-D captures, laser-scanned depth maps, and manual 3D bounding box labels are sufficiently accurate and representative of real-world indoor scenes to push state-of-the-art methods on the two downstream tasks.

C3one line summary

ARKitScenes is the largest real-world indoor RGB-D dataset captured with mobile LiDAR, including high-resolution depth maps and 3D furniture bounding box annotations for advancing object detection and depth upsampling.

References

46 extracted · 46 resolved · 2 Pith anchors

[1] 3d-sis: 3d semantic instance segmentation of rgb-d scans 2019

[2] Gspn: Generative shape proposal network for 3d instance segmentation in point cloud 2019

[3] Sgpn: Similarity group proposal network for 3d point cloud instance segmentation 2018

[4] Deep hough voting for 3d object detection in point clouds 2019

[5] Qi, Xinlei Chen, and Leonidas J 2020

Formal links

2 machine-checked theorem links

Cited by

38 papers in Pith

SplatWeaver: Learning to Allocate Gaussian Primitives for Generalizable Novel View Synthesis

IVGT: Implicit Visual Geometry Transformer for Neural Scene Representation

VGenST-Bench: A Benchmark for Spatio-Temporal Reasoning via Active Video Synthesis

ForeSplat: Optimization-Aware Foresight for Feed-Forward 3D Gaussian Splatting

JoyAI-Image: Awaking Spatial Intelligence in Unified Multimodal Understanding and Generation

Receipt and verification

First computed	2026-05-17T23:38:52.743728Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

7c628c1fa7b1f08e362a1acd3c90feeb8e2077a23376125208c7e9d086d0d3ae

Aliases

arxiv: 2111.08897 · arxiv_version: 2111.08897v3 · doi: 10.48550/arxiv.2111.08897 · pith_short_12: PRRIYH5HWHYI · pith_short_16: PRRIYH5HWHYI4NRK · pith_short_8: PRRIYH5H

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/PRRIYH5HWHYI4NRKDLGTZEH65O \
  | 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: 7c628c1fa7b1f08e362a1acd3c90feeb8e2077a23376125208c7e9d086d0d3ae

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "32764f7a0c144c7bd2dab1ad2004ee0ea600580838bd74cdfe964d9fa0b5599c",
    "cross_cats_sorted": [
      "cs.AI"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.CV",
    "submitted_at": "2021-11-17T04:27:01Z",
    "title_canon_sha256": "1d9c2faad2ac5b1cb29b77b3c1a171793a4e2c0591f490e1224e248e99d896f1"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2111.08897",
    "kind": "arxiv",
    "version": 3
  }
}