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pith:2019:UU7EXWUW64DLAQWOEM3G6CFCHI
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nuScenes: A multimodal dataset for autonomous driving

Alex H. Lang, Anush Krishnan, Giancarlo Baldan, Holger Caesar, Oscar Beijbom, Qiang Xu, Sourabh Vora, Varun Bankiti, Venice Erin Liong, Yu Pan

nuScenes supplies 1000 annotated scenes with a full suite of cameras, lidar and radar to train and evaluate 3D detection and tracking for autonomous driving.

arxiv:1903.11027 v5 · 2019-03-26 · cs.LG · cs.CV · cs.RO · stat.ML

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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

nuScenes comprises 1000 scenes, each 20s long and fully annotated with 3D bounding boxes for 23 classes and 8 attributes. It has 7x as many annotations and 100x as many images as the pioneering KITTI dataset.

C2weakest assumption

The assumption that the 3D annotations and sensor calibrations are sufficiently accurate and representative of real-world autonomous-driving conditions to serve as a reliable training and evaluation benchmark.

C3one line summary

nuScenes provides the first public autonomous-driving dataset that includes synchronized 360-degree data from cameras, radars, and lidar together with 3D bounding-box annotations across 1000 scenes.

References

90 extracted · 90 resolved · 6 Pith anchors

[1] Vehicle and guard rail detection using radar and vision data fusion 2007
[2] Exploiting 3d semantic scene priors for online traffic light interpreta- tion 2015
[3] Three decades of driver assistance systems: Review and future per- spectives 2014
[4] Multiple object tracking performance metrics and evaluation in a smart room environment 2006
[5] Monoloco: Monocular 3d pedestrian localization and uncer- tainty estimation 2019

Formal links

2 machine-checked theorem links

Cited by

21 papers in Pith

How much real data do we actually need: Analyzing object detection performance using synthetic and real data
Radar-Guided Polynomial Fitting for Metric Depth Estimation
From Spherical to Gaussian: A Comparative Analysis of Point Cloud Cropping Strategies in Large-Scale 3D Environments
Structured Labeling Enables Faster Vision-Language Models for End-to-End Autonomous Driving
Unleashing the Potential of Diffusion Models for End-to-End Autonomous Driving
Receipt and verification
First computed 2026-05-17T23:38:13.991051Z
Builder pith-number-builder-2026-05-17-v1
Signature Pith Ed25519 (pith-v1-2026-05) · public key
Schema pith-number/v1.0

Canonical hash

a53e4bda96f706b042ce23366f08a23a06cb062305d787af8243a34b34f59574

Aliases

arxiv: 1903.11027 · arxiv_version: 1903.11027v5 · doi: 10.48550/arxiv.1903.11027 · pith_short_12: UU7EXWUW64DL · pith_short_16: UU7EXWUW64DLAQWO · pith_short_8: UU7EXWUW
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/UU7EXWUW64DLAQWOEM3G6CFCHI \
  | 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: a53e4bda96f706b042ce23366f08a23a06cb062305d787af8243a34b34f59574
Canonical record JSON 
{
  "metadata": {
    "abstract_canon_sha256": "711012bff0871dcbe8a460f262d8920228652facccaf6163ca36391a8336abfb",
    "cross_cats_sorted": [
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      "cs.RO",
      "stat.ML"
    ],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "cs.LG",
    "submitted_at": "2019-03-26T17:19:56Z",
    "title_canon_sha256": "ba3a786ba489b9250d19493e51d3b8ce69b1edfe59adbe651c8dbf6d332e8be8"
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  "source": {
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    "kind": "arxiv",
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  }
}