Pith. sign in

REVIEW 3 cited by

ABO: Dataset and Benchmarks for Real-World 3D Object Understanding

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2110.06199 v2 pith:R4UCZSJ5 submitted 2021-10-12 cs.CV cs.AIcs.GR

ABO: Dataset and Benchmarks for Real-World 3D Object Understanding

Jasmine Collins , Shubham Goel , Kenan Deng , Achleshwar Luthra , Leon Xu , Erhan Gundogdu , Xi Zhang , Tomas F. Yago Vicente
show 4 more authors
Thomas Dideriksen Himanshu Arora Matthieu Guillaumin Jitendra Malik
This is my paper
classification cs.CV cs.AIcs.GR
keywords objectbenchmarksdatasetobjectsrealreal-worldunderstandingamazon
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
0 comments
read the original abstract

We introduce Amazon Berkeley Objects (ABO), a new large-scale dataset designed to help bridge the gap between real and virtual 3D worlds. ABO contains product catalog images, metadata, and artist-created 3D models with complex geometries and physically-based materials that correspond to real, household objects. We derive challenging benchmarks that exploit the unique properties of ABO and measure the current limits of the state-of-the-art on three open problems for real-world 3D object understanding: single-view 3D reconstruction, material estimation, and cross-domain multi-view object retrieval.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 3 Pith papers

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

  1. Arbor: Explicit Geometric Conditioning for Controllable 3D Asset Generation

    cs.CV 2026-06 unverdicted novelty 7.0

    Arbor attaches constraint mesh tokens to a frozen text-to-3D denoiser to enable controllable generation obeying hull, avoidance, and touch constraints.

  2. SiPhy: Single-Image Physical Property Reasoning

    cs.CV 2026-07 conditional novelty 5.0

    A single-image vision-language pipeline reports state-of-the-art mass, density, and stiffness predictions by combining CLIP features, a fine-tuned VLM, and depth-adaptive pseudo-voxel sampling.

  3. Data Pyramid for Embodied Manipulation

    cs.RO 2026-07 conditional novelty 3.0

    Embodied training data form a five-layer pyramid—real-robot, UMI, ego/exo, simulation, general V–L—ordered by the trade-off between scale and robot alignment, and model capabilities track how those layers are mixed.