← 研踪 PaperTrailarXiv:2611.04102 · v1
Is This 3D Asset Usable? Beyond the Beauty Render for Asset Curation
首次提交 2026-11-03 08:24
版本更新 2026-11-03 08:24
雷达观测 2026-11-04 23:40
摘要
Large 3D repositories make it easy to collect assets, but difficult to determine whether they can be reused reliably across scene assembly, editing, rendering, game production, and training-data construction. An asset can look plausible in RGB while hiding disconnected geometry, problematic UV organization, or materials that fail to resolve correctly after import. We introduce Diagnostic Rendered Evidence (DRE): aligned beauty, wireframe, and UV-seam-plus-wireframe views scored by pass-specific frozen ViT classifiers and combined through late fusion. On 1,234 human labeled TexVerse assets, nested grouped evaluation achieves 86.36% balanced accuracy and 88.32% bad-asset recall. On a separate, balanced 764 external reference test, DRE achieved 88.9% balanced reference-label accuracy. Our method requires only an imported asset and renderer-derived evidence, without repository identity, creator, popularity, text, or source metadata at inference. Our results support cross-corpus portability and show that purpose built diagnostic views expose defects that just the beauty views can miss. Webpage: https://rbz-99.github.io/is-this-3d-asset-usable/
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v1 · 2026-11-03 08:24 · 最新观测版本
Is This 3D Asset Usable? Beyond the Beauty Render for Asset Curation
Large 3D repositories make it easy to collect assets, but difficult to determine whether they can be reused reliably across scene assembly, editing, rendering, game production, and training-data construction. An asset can look plausible in RGB while hiding disconnected geometry, problematic UV organization, or materials that fail to resolve correctly after import. We introduce Diagnostic Rendered Evidence (DRE): aligned beauty, wireframe, and UV-seam-plus-wireframe views scored by pass-specific frozen ViT classifiers and combined through late fusion. On 1,234 human labeled TexVerse assets, nested grouped evaluation achieves 86.36% balanced accuracy and 88.32% bad-asset recall. On a separate, balanced 764 external reference test, DRE achieved 88.9% balanced reference-label accuracy. Our method requires only an imported asset and renderer-derived evidence, without repository identity, creator, popularity, text, or source metadata at inference. Our results support cross-corpus portability and show that purpose built diagnostic views expose defects that just the beauty views can miss. Webpage: https://rbz-99.github.io/is-this-3d-asset-usable/
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