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DL3DV-10K/Dataset

3D Vision Dataset for Novel View Synthesis

GraphCanon updated Sep 23, 2026 · GitHub synced Sep 23, 2026

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674 stars17 forksLast push Feb 10, 2026 HTML Other

Decision brief

DL3DV-10K is a 3D Vision dataset for deep learning research in novel view synthesis using PyTorch.

Good fit when

  • Use when working on projects focused specifically on 3D vision, reconstruction, and novel view synthesis where you require large-scale datasets.
  • Suitable if your project benefits from the terms of use that are tailored for DL3DV-10K, facilitating research in deep learning with PyTorch.

Avoid when

  • Not recommended if your project or methodology does not align with the specific Terms of Use provided by DL3DV-10K.
  • Avoid using this dataset if you are working on a framework other than PyTorch, as it's optimized for and primarily documented within that context.
Hosting:
unknown - DL3DV-10K hosts its 3D vision dataset for research purposes primarily.

Observed Jul 17, 2026 · Source: enrich:decision_facts

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Maintenance and security

Full trust report
Maintenance
Slowing (225d since push)
As of Sep 23, 2026
Provenance
Not a fork · Organization account
As of Sep 23, 2026
Security (OSV)
No lockfile
As of Jul 11, 2026

Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.

Install

git clone https://github.com/DL3DV-10K/Dataset

Similar tools

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Evidence and technical details

Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.

Overview

DL3DV-10K is a dataset released under specific terms of use for research in 3D models, reconstruction and novel view synthesis using deep learning techniques with PyTorch.

Capability facts

Languages
html

Source: github.language · Sep 23, 2026

Categories

Tags

README

License DL3DV 10K is released under the DL3DV 10K Terms of Use. The DL3DV 10K Terms of Use , disclaimer, and the copy of the license of the license are available in this repository. Copyright (c) 2023

For agents

This page has a .md twin and JSON over the API.

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