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A Large Dataset of Object Scans

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arxiv 1602.02481 v3 pith:RDKRUJDE submitted 2016-02-08 cs.CV cs.GR

Sungjoon Choi , Qian-Yi Zhou , Stephen Miller , Vladlen Koltun This is my paper · ORCID
classification cs.CVcs.GR
keywords datasetlargeobjectsscansdataobjectoperatorsthem
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We have created a dataset of more than ten thousand 3D scans of real objects. To create the dataset, we recruited 70 operators, equipped them with consumer-grade mobile 3D scanning setups, and paid them to scan objects in their environments. The operators scanned objects of their choosing, outside the laboratory and without direct supervision by computer vision professionals. The result is a large and diverse collection of object scans: from shoes, mugs, and toys to grand pianos, construction vehicles, and large outdoor sculptures. We worked with an attorney to ensure that data acquisition did not violate privacy constraints. The acquired data was irrevocably placed in the public domain and is available freely at http://redwood-data.org/3dscan .

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Forward citations

Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Points as Tori: Fast Pointwise Signed Distance for Point Clouds

    cs.GR 2026-07 conditional novelty 7.0 of 10

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  2. Revisiting Point Cloud Classification: A New Benchmark Dataset and Classification Model on Real-World Data

    cs.CV 2019-08 conditional novelty 7.0 of 10

    Real-world scanned point clouds are far harder to classify than CAD models, and a new benchmark with a background-aware network quantifies and narrows that gap.

  3. A Holistic Method for Superquadric Fitting Using Unsupervised Clustering Analysis

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    Introduces a clustering-based optimization technique for fitting superquadrics to point clouds that handles noise, outliers, and deformations with closed-form solutions and convergence proofs.

  4. VIRTUS-FPP: Virtual Sensor Modeling for Fringe Projection Profilometry in NVIDIA Isaac Sim

    eess.IV 2025-09 conditional novelty 4.0 of 10

    VIRTUS-FPP implements a complete virtual fringe projection profilometry pipeline in NVIDIA Isaac Sim and claims sub-millimeter reconstruction plus digital-twin fidelity to a physical scanner.

  5. A Cookbook of 3D Vision: Data, Learning Paradigms, and Application

    cs.CV 2026-06 unverdicted novelty 3.0 of 10

    A survey paper that constructs a taxonomy linking 3D geometric representations, acquisition, datasets, supervision regimes, and applications in reconstruction, generation, and 4D modeling.

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