Dataset · Hugging Face

igfbench-neurips2026/IGF-Bench

Name: IGF-Bench
Creator: igfbench-neurips2026
License: cc-by-nc-sa-4.0
Keywords: task_categories:depth-estimation, task_categories:image-to-image, task_categories:text-to-image, language:en, license:cc-by-nc-sa-4.0, size_categories:n<1K, format:json, modality:image, modality:tabular, modality:text, library:datasets, library:pandas, library:polars, library:mlcroissant, region:us

IGF-Bench: Indoor Geometric Fidelity Benchmark Anonymous mirror for NeurIPS 2026 Evaluations and Datasets Track double-blind review. The de-anonymised author/maintainer information will replace this header at camera-ready. IGF-Bench is the first benchmark for evaluating structural-level geometric fidelity of conditionally generated indoor scene images, going beyond perceptual metrics like FID and LPIPS. It pairs 3,600 calibrated synthetic ground-truth views with 21,600 generated… See the full description on the dataset page: https://huggingface.co/datasets/igfbench-neurips2026/IGF-Bench.

View on Hugging FaceReport an issue

Author / organization	igfbench-neurips2026
License (as declared)	cc-by-nc-sa-4.0
Downloads	915 (as reported by Hugging Face)
Likes	0
Created	2 May 2026
Last updated at source	3 May 2026

3d-scene-understandingmonocular-depth-estimationcontrolnetdiffusion-modelsbenchmarkevaluationindoor-scenesgeometric-fidelity3d-frontvanishing-pointsstructural-evaluation

Embed this entry

<iframe src="https://silytics.si/embed/item/cd2414f1-be14-4a05-9845-f80eee3ba2c3" width="420" height="190" style="border:0" loading="lazy" title="Silytics entry"></iframe>

Free, no tracking. More about feeds and embeds.

Silytics shows public metadata from Hugging Face and links to the original. Information may be incomplete or out of date. Last refreshed 16 October 2026. Licenses and terms of the original apply to the work itself. Not financial, legal or professional advice.