3D-PAQA

3D-PAQA — Preference-Aligned 3D Quality Assessment Preference-aligned perceptual quality labels for 240,636 Objaverse assets, rated on six perceptual criteria. The goal of 3D-PAQA is to move beyond synthetic-distortion 3D-QA benchmarks and provide human-preference-aligned quality scores for real, human-created 3D assets, at a scale usable for training and benchmarking automatic quality evaluators. Drawn from a 264,966-asset Objaverse corpus. train.csv — 216,540 labeled assets… See the full description on the dataset page:

Source: Hugging Face Hub (JiHyuk-Byun/3D-PAQA). Metadata imported from the dataset’s Hub tags.