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Approximating Human-Level 3D Visual Inferences With Deep Neural Networks

Open Mind(2025)10.1162/opmi_a_00189Source: DataRank Database

Approximating Human-Level 3D Visual Inferences With Deep Neural Networks is a research paper published in Open Mind (2025). On theSindex it has a DataRank of 0. It has been cited 2 times.

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0DataRank · unranked

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Open Access2 citations

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Linked data & code

Approaching human 3D shape perception with neurally mappable models · arXiv

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datarank_citation_only_1hop_v6· scope data_onlyMethodology

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Enrichment:Funding (5 grants)Impact (FWCI 1.49)OA: goldTopics (18)Linked Datasets (1)DataCite Reuse (1)IDs (PubMed, PMC, OpenAlex)

FAIR Checklist

Context only (not used in score)

Findable (2/2)

Has DOI
Indexed in repositories

Accessible (1/2)

Open Access

Interoperable (2/2)

DataCite relations
Linked datasets

Reusable (0/3)

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Authors (7)

Tyler Bonnen ORCID,Yoni Friedman ORCID,Ayush Tewari ORCID,Vincent Sitzmann ORCID,Joshua B. Tenenbaum ORCID

Live enrichment

PubMed:40013087 PMC:PMC11864798 OpenAlex:https://openalex.org/W4407906759

National Institutes of Health

Grant: DP1HD091947

Office of Naval Research MURI

Grant: PO #BB01540322

Center for Brains, Minds, and Machines (CBMM) funded by NSF STC

Grant: CCF-1231216

NINDS NIH HHS

Grant: F99 NS125816

NEI NIH HHS

Grant: K00 EY037496

FWCI

1.49

Citation Percentile

0.8%

Citation Trend

2026

goldLicense: cc-by

journalhttps://direct.mit.edu/opmi/article-pdf/doi/10.1162/opmi_a_00189/2505089/opmi_a_00189.pdf

publisherhttps://direct.mit.edu/opmi/article-pdf/doi/10.1162/opmi_a_00189/2505089/opmi_a_00189.pdf

journalhttps://doi.org/10.1162/opmi_a_00189

repositoryhttps://pubmed.ncbi.nlm.nih.gov/40013087

repositoryhttps://dspace.mit.edu/handle/1721.1/170587

Fields of Study

Visual perception and processing mechanisms3D Shape Modeling and AnalysisAdvanced Vision and Imaging

Keywords

Computer scienceArtificial intelligenceObject (grammar)Pairwise comparisonSet (abstract data type)AutoencoderRepresentation (politics)Cognitive neuroscience of visual object recognitionArtificial neural networkMachine learningPattern recognition (psychology)PsychophysicsNeural FieldsDeep Neural Networks3D Shape Perception

Approaching human 3D shape perception with neurally mappable models

10.48550/arxiv.2308.11300arXiv