Datasets:
Formats:
json
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English
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< 1K
Tags:
3d-scene-understanding
monocular-depth-estimation
controlnet
diffusion-models
benchmark
evaluation
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File size: 62,673 Bytes
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"description": "IGF-Bench (Indoor Geometric Fidelity Benchmark) is the first benchmark for evaluating structural-level geometric fidelity of conditionally generated indoor scene images. Built atop 3D-FRONT, it provides 3,600 calibrated views from 300 rooms at three furniture-complexity levels (L0 empty, L1 basic, L2 full) with four camera viewpoints each. Six state-of-the-art Canny-conditioned diffusion models produce 21,600 generated images. Each view is accompanied by ground-truth metric depth (EXR), Canny edge maps, semantic segmentation masks, 3D wireframes, and camera parameters. Monocular depth estimates (DepthPro, with Depth Anything V2 and ZoeDepth ablations) enable four novel geometric fidelity metrics—planarity error (L_plane), orthogonality error (L_ortho), edge straightness (L_edge), and vanishing-point consistency (L_vp)—that capture structural degradation invisible to perceptual scores such as FID and LPIPS.",
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"rai:dataCollection": "Ground-truth scenes are sourced from the 3D-FRONT dataset (Fu et al., 2021), a large-scale repository of professionally designed indoor layouts. 300 rooms were sampled across 7 subtypes (Bedroom 106, MasterBedroom 113, SecondBedroom 16, KidsRoom 12, ElderlyRoom 1, LivingRoom 18, LivingDiningRoom 34) and stratified into three composition groups by dominant type and footprint (Group A 177 rooms / Group B 94 / Group C 29). Selection criteria: complete ceiling-wall-floor enclosure, approximate floor area in 5--50 m^2, at least one window or door opening. Each room was rendered at three furniture-complexity levels (L0 empty, L1 basic, L2 fully furnished) from four calibrated camera viewpoints (placed at bounding-box corners, eye-height 1.43 m, FOV 90 degrees) using Blender 5.0 Cycles with physically based materials, yielding 3,600 ground-truth views at 1024x1024 resolution. Generated images were produced via Canny-conditioned ControlNet for six diffusion models (Stable Diffusion 1.5, SDXL, Stable Diffusion 3.5, Flux.1 Dev, Hunyuan-DiT, Kolors) under uniform settings: seed=42, cn_scale=1.0, Canny thresholds low=100/high=200, empty negative prompt. Depth estimation was performed with Apple DepthPro (primary), with ablation subsets using Depth Anything V2 and ZoeDepth. No crowd-sourcing or human annotation was involved; all labels are derived from the 3D scene geometry.",
"rai:dataCollectionType": "Synthetically rendered from existing 3D assets; no new data collection from human participants.",
"rai:personalSensitiveInformation": "None. The dataset contains no personal data, no human faces, no identifiable information, and no biometric data. All content is synthetically generated from 3D architectural scene models depicting indoor spaces with furniture.",
"rai:dataBiases": "The 3D-FRONT source dataset consists of professionally designed Chinese residential interior layouts. Results may not generalize to: (1) non-Chinese architectural styles or culturally distinct interiors, (2) informal or low-income housing, (3) non-residential spaces (offices, hospitals, retail), (4) outdoor or mixed indoor-outdoor environments. All six generation models are Canny-conditioned; other conditioning types (depth, segmentation, sketch) are not evaluated. A single random seed (42) is used, limiting stochastic diversity analysis.",
"rai:dataLimitations": "Stable Diffusion 1.5 generates at 512x512 resolution while the other five models generate at 1024x1024; cross-resolution comparison is noted as a limitation. Single seed (42) for all models. Monocular depth estimation is inherently scale-ambiguous and may introduce systematic errors. Synthetic ground truth may not capture all real-world material and lighting complexity. Only Canny edge conditioning is evaluated. Semantic masks are derived from render passes and may have minor aliasing artifacts at object boundaries.",
"rai:dataUseCases": "Primary: benchmarking geometric fidelity of conditional image generation models for indoor scenes. Secondary: evaluating monocular depth estimation robustness across rendered vs. generated image domains; analysing ControlNet conditioning mechanism differences (channel concatenation vs. residual injection); architectural visualization quality assessment; 3D-aware image generation research; indoor scene understanding. Discouraged: do not use for generating photorealistic deceptive architectural renderings without explicit disclosure (the paper Section 6 notes that IGF-Bench could in principle facilitate such misuse, as with all generative-image benchmarks); do not use as the sole quality signal for production-grade 3D reconstruction without combining with held-out real-world data; do not use to evaluate generation outside the indoor-scene domain (the benchmark is restricted to enclosed indoor rooms).",
"rai:dataSocialImpact": "This benchmark advances evaluation methodology for generative models in architecture and interior design. Potential positive impacts include improved quality control for AI-generated architectural imagery and more reliable synthetic data for downstream 3D reconstruction. Potential negative impacts are limited because the dataset contains only synthetic indoor scenes with no personal or sensitive content. Users should be aware that high-fidelity generated images could be misused for deceptive architectural renderings; the geometric metrics introduced here can serve as detection tools for such misuse.",
"rai:annotationsPerItem": "Each of the 3,600 ground-truth views directory contains 9 files: (1) RGB render (rgb_textured.png, 1024x1024 8-bit), (2) metric depth map (depth.exr, OpenEXR 32-bit float, in metres; canonical source), (3) 8-bit depth visualisation (depth.png, QA only), (4) NumPy float32 cache of depth.exr (depth_gt.npy, ~4 MB; for fast loading without an EXR decoder during LoRA training), (5) Canny edge map (canny.png, thresholds 100/200), (6) semantic segmentation mask with class IDs (semantic_id.png, 0=background, 1=floor, 2=ceiling, 3=wall, 4=opening, 5=structural element, 6=furniture), (7) colour-mapped semantic visualisation (semantic_mask.png, QA only), (8) 3D wireframe overlay (wireframe_3d.png, QA only), (9) camera parameters (camera.json: position, target, FOV=90, bounding box). Each of the 21,600 generated images has a corresponding DepthPro depth estimate (NPY) under depth_results/gen/<model>/depthpro/. Ablation subsets include Depth Anything V2 and ZoeDepth estimates.",
"rai:dataReleaseMaintenancePlan": "Maintained for at least 5 years on HuggingFace (igfbench-neurips2026/IGF-Bench) and on the anonymous code mirror at anonymous.4open.science/r/IGF-Bench-Code (camera-ready: GitHub). Bug reports and questions are received via the HuggingFace dataset card discussion tab and via GitHub Issues on the code repository. Versioning follows semantic versioning conventions: minor metadata or documentation fixes ship as 1.x patch releases, schema-breaking changes (e.g. new room types, additional generation models, additional metrics) bump the major version, and older versions remain accessible on HuggingFace as immutable revisions. No deprecation is planned within the 5-year maintenance window.",
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"@id": "calibrated-views-canny",
"name": "calibrated-views-canny",
"description": "3,600 calibrated GT views indexed by (room_id, level, view) with the corresponding canny.png payload. Each row is one of the 300 rooms x 3 complexity levels x 4 viewpoints = 3,600 per-view files.",
"field": [
{
"@type": "cr:Field",
"@id": "calibrated-views-canny/room_id",
"name": "room_id",
"description": "3D-FRONT room identifier (UUID + RoomType-SceneID), extracted from the file path.",
"dataType": "sc:Text",
"source": {
"fileSet": {
"@id": "gt-canny-files"
},
"extract": {
"fileProperty": "fullpath"
},
"transform": {
"regex": "^(?:.*/)?renders_textured/([^/]+)/L[012]_[a-z]+/view_[0-3]/canny\\.png$"
}
}
},
{
"@type": "cr:Field",
"@id": "calibrated-views-canny/level",
"name": "level",
"description": "Furniture complexity level: L0_empty (bare room), L1_basic (key furniture only), or L2_full (fully furnished). Extracted from the file path.",
"dataType": "sc:Text",
"source": {
"fileSet": {
"@id": "gt-canny-files"
},
"extract": {
"fileProperty": "fullpath"
},
"transform": {
"regex": "^(?:.*/)?renders_textured/[^/]+/(L[012]_[a-z]+)/view_[0-3]/canny\\.png$"
}
}
},
{
"@type": "cr:Field",
"@id": "calibrated-views-canny/view",
"name": "view",
"description": "Camera viewpoint index (view_0 through view_3, placed at room bounding-box corners at 1.43 m eye height with FOV 90 degrees). Extracted from the file path.",
"dataType": "sc:Text",
"source": {
"fileSet": {
"@id": "gt-canny-files"
},
"extract": {
"fileProperty": "fullpath"
},
"transform": {
"regex": "^(?:.*/)?renders_textured/[^/]+/L[012]_[a-z]+/(view_[0-3])/canny\\.png$"
}
}
},
{
"@type": "cr:Field",
"@id": "calibrated-views-canny/canny",
"name": "canny",
"description": "Canny edge map (PNG; thresholds low=100/high=200). Used as the uniform ControlNet conditioning input for all six generation pipelines.",
"dataType": "sc:ImageObject",
"source": {
"fileSet": {
"@id": "gt-canny-files"
}
}
}
]
},
{
"@type": "cr:RecordSet",
"@id": "calibrated-views-semantic-id",
"name": "calibrated-views-semantic-id",
"description": "3,600 calibrated GT views indexed by (room_id, level, view) with the corresponding semantic_id.png payload. Each row is one of the 300 rooms x 3 complexity levels x 4 viewpoints = 3,600 per-view files.",
"field": [
{
"@type": "cr:Field",
"@id": "calibrated-views-semantic-id/room_id",
"name": "room_id",
"description": "3D-FRONT room identifier (UUID + RoomType-SceneID), extracted from the file path.",
"dataType": "sc:Text",
"source": {
"fileSet": {
"@id": "gt-semantic-id-files"
},
"extract": {
"fileProperty": "fullpath"
},
"transform": {
"regex": "^(?:.*/)?renders_textured/([^/]+)/L[012]_[a-z]+/view_[0-3]/semantic_id\\.png$"
}
}
},
{
"@type": "cr:Field",
"@id": "calibrated-views-semantic-id/level",
"name": "level",
"description": "Furniture complexity level: L0_empty (bare room), L1_basic (key furniture only), or L2_full (fully furnished). Extracted from the file path.",
"dataType": "sc:Text",
"source": {
"fileSet": {
"@id": "gt-semantic-id-files"
},
"extract": {
"fileProperty": "fullpath"
},
"transform": {
"regex": "^(?:.*/)?renders_textured/[^/]+/(L[012]_[a-z]+)/view_[0-3]/semantic_id\\.png$"
}
}
},
{
"@type": "cr:Field",
"@id": "calibrated-views-semantic-id/view",
"name": "view",
"description": "Camera viewpoint index (view_0 through view_3, placed at room bounding-box corners at 1.43 m eye height with FOV 90 degrees). Extracted from the file path.",
"dataType": "sc:Text",
"source": {
"fileSet": {
"@id": "gt-semantic-id-files"
},
"extract": {
"fileProperty": "fullpath"
},
"transform": {
"regex": "^(?:.*/)?renders_textured/[^/]+/L[012]_[a-z]+/(view_[0-3])/semantic_id\\.png$"
}
}
},
{
"@type": "cr:Field",
"@id": "calibrated-views-semantic-id/semantic_id",
"name": "semantic_id",
"description": "Semantic class label map (PNG; pixel value = class ID with 0=background, 1=floor, 2=ceiling, 3=wall, 4=opening, 5=structural element, 6=furniture). Primary planar classes 1/2/3 are used for L_plane and L_ortho computation; furniture (class 6) is excluded.",
"dataType": "sc:ImageObject",
"source": {
"fileSet": {
"@id": "gt-semantic-id-files"
}
}
}
]
},
{
"@type": "cr:RecordSet",
"@id": "calibrated-views-semantic-rgb",
"name": "calibrated-views-semantic-rgb",
"description": "3,600 calibrated GT views indexed by (room_id, level, view) with the corresponding semantic_mask.png payload. Each row is one of the 300 rooms x 3 complexity levels x 4 viewpoints = 3,600 per-view files.",
"field": [
{
"@type": "cr:Field",
"@id": "calibrated-views-semantic-rgb/room_id",
"name": "room_id",
"description": "3D-FRONT room identifier (UUID + RoomType-SceneID), extracted from the file path.",
"dataType": "sc:Text",
"source": {
"fileSet": {
"@id": "gt-semantic-mask-files"
},
"extract": {
"fileProperty": "fullpath"
},
"transform": {
"regex": "^(?:.*/)?renders_textured/([^/]+)/L[012]_[a-z]+/view_[0-3]/semantic_mask\\.png$"
}
}
},
{
"@type": "cr:Field",
"@id": "calibrated-views-semantic-rgb/level",
"name": "level",
"description": "Furniture complexity level: L0_empty (bare room), L1_basic (key furniture only), or L2_full (fully furnished). Extracted from the file path.",
"dataType": "sc:Text",
"source": {
"fileSet": {
"@id": "gt-semantic-mask-files"
},
"extract": {
"fileProperty": "fullpath"
},
"transform": {
"regex": "^(?:.*/)?renders_textured/[^/]+/(L[012]_[a-z]+)/view_[0-3]/semantic_mask\\.png$"
}
}
},
{
"@type": "cr:Field",
"@id": "calibrated-views-semantic-rgb/view",
"name": "view",
"description": "Camera viewpoint index (view_0 through view_3, placed at room bounding-box corners at 1.43 m eye height with FOV 90 degrees). Extracted from the file path.",
"dataType": "sc:Text",
"source": {
"fileSet": {
"@id": "gt-semantic-mask-files"
},
"extract": {
"fileProperty": "fullpath"
},
"transform": {
"regex": "^(?:.*/)?renders_textured/[^/]+/L[012]_[a-z]+/(view_[0-3])/semantic_mask\\.png$"
}
}
},
{
"@type": "cr:Field",
"@id": "calibrated-views-semantic-rgb/semantic_mask",
"name": "semantic_mask",
"description": "Colour-mapped visualisation of semantic_id.png (RGB PNG, QA only). Not used by any evaluation script.",
"dataType": "sc:ImageObject",
"source": {
"fileSet": {
"@id": "gt-semantic-mask-files"
}
}
}
]
},
{
"@type": "cr:RecordSet",
"@id": "calibrated-views-wireframe",
"name": "calibrated-views-wireframe",
"description": "3,600 calibrated GT views indexed by (room_id, level, view) with the corresponding wireframe_3d.png payload. Each row is one of the 300 rooms x 3 complexity levels x 4 viewpoints = 3,600 per-view files.",
"field": [
{
"@type": "cr:Field",
"@id": "calibrated-views-wireframe/room_id",
"name": "room_id",
"description": "3D-FRONT room identifier (UUID + RoomType-SceneID), extracted from the file path.",
"dataType": "sc:Text",
"source": {
"fileSet": {
"@id": "gt-wireframe-files"
},
"extract": {
"fileProperty": "fullpath"
},
"transform": {
"regex": "^(?:.*/)?renders_textured/([^/]+)/L[012]_[a-z]+/view_[0-3]/wireframe_3d\\.png$"
}
}
},
{
"@type": "cr:Field",
"@id": "calibrated-views-wireframe/level",
"name": "level",
"description": "Furniture complexity level: L0_empty (bare room), L1_basic (key furniture only), or L2_full (fully furnished). Extracted from the file path.",
"dataType": "sc:Text",
"source": {
"fileSet": {
"@id": "gt-wireframe-files"
},
"extract": {
"fileProperty": "fullpath"
},
"transform": {
"regex": "^(?:.*/)?renders_textured/[^/]+/(L[012]_[a-z]+)/view_[0-3]/wireframe_3d\\.png$"
}
}
},
{
"@type": "cr:Field",
"@id": "calibrated-views-wireframe/view",
"name": "view",
"description": "Camera viewpoint index (view_0 through view_3, placed at room bounding-box corners at 1.43 m eye height with FOV 90 degrees). Extracted from the file path.",
"dataType": "sc:Text",
"source": {
"fileSet": {
"@id": "gt-wireframe-files"
},
"extract": {
"fileProperty": "fullpath"
},
"transform": {
"regex": "^(?:.*/)?renders_textured/[^/]+/L[012]_[a-z]+/(view_[0-3])/wireframe_3d\\.png$"
}
}
},
{
"@type": "cr:Field",
"@id": "calibrated-views-wireframe/wireframe_3d",
"name": "wireframe_3d",
"description": "3D wireframe overlay render (PNG, QA only). Provided for visual debugging of camera calibration; not used by IGF metrics.",
"dataType": "sc:ImageObject",
"source": {
"fileSet": {
"@id": "gt-wireframe-files"
}
}
}
]
},
{
"@type": "cr:RecordSet",
"@id": "calibrated-views-camera",
"name": "calibrated-views-camera",
"description": "3,600 calibrated GT views indexed by (room_id, level, view) with the corresponding camera.json payload. Each row is one of the 300 rooms x 3 complexity levels x 4 viewpoints = 3,600 per-view files.",
"field": [
{
"@type": "cr:Field",
"@id": "calibrated-views-camera/room_id",
"name": "room_id",
"description": "3D-FRONT room identifier (UUID + RoomType-SceneID), extracted from the file path.",
"dataType": "sc:Text",
"source": {
"fileSet": {
"@id": "gt-camera-files"
},
"extract": {
"fileProperty": "fullpath"
},
"transform": {
"regex": "^(?:.*/)?renders_textured/([^/]+)/L[012]_[a-z]+/view_[0-3]/camera\\.json$"
}
}
},
{
"@type": "cr:Field",
"@id": "calibrated-views-camera/level",
"name": "level",
"description": "Furniture complexity level: L0_empty (bare room), L1_basic (key furniture only), or L2_full (fully furnished). Extracted from the file path.",
"dataType": "sc:Text",
"source": {
"fileSet": {
"@id": "gt-camera-files"
},
"extract": {
"fileProperty": "fullpath"
},
"transform": {
"regex": "^(?:.*/)?renders_textured/[^/]+/(L[012]_[a-z]+)/view_[0-3]/camera\\.json$"
}
}
},
{
"@type": "cr:Field",
"@id": "calibrated-views-camera/view",
"name": "view",
"description": "Camera viewpoint index (view_0 through view_3, placed at room bounding-box corners at 1.43 m eye height with FOV 90 degrees). Extracted from the file path.",
"dataType": "sc:Text",
"source": {
"fileSet": {
"@id": "gt-camera-files"
},
"extract": {
"fileProperty": "fullpath"
},
"transform": {
"regex": "^(?:.*/)?renders_textured/[^/]+/L[012]_[a-z]+/(view_[0-3])/camera\\.json$"
}
}
},
{
"@type": "cr:Field",
"@id": "calibrated-views-camera/camera",
"name": "camera",
"description": "Per-view camera intrinsics and extrinsics (JSON: position, target, FOV=90, bounding box). Required to project depth into 3D point clouds for L_plane / L_ortho computation.",
"dataType": "sc:URL",
"source": {
"fileSet": {
"@id": "gt-camera-files"
}
}
}
]
},
{
"@type": "cr:RecordSet",
"@id": "generated-images",
"name": "generated-images",
"description": "21,600 generated images produced by six Canny-conditioned diffusion models (6 models x 3,600 views).",
"field": [
{
"@type": "cr:Field",
"@id": "generated-images/room_id",
"name": "room_id",
"description": "3D-FRONT room identifier, matching calibrated-views/room_id",
"dataType": "sc:Text"
},
{
"@type": "cr:Field",
"@id": "generated-images/level",
"name": "level",
"description": "Furniture complexity level (L0_empty, L1_basic, L2_full)",
"dataType": "sc:Text"
},
{
"@type": "cr:Field",
"@id": "generated-images/view",
"name": "view",
"description": "Camera viewpoint index (view_0 through view_3)",
"dataType": "sc:Text"
},
{
"@type": "cr:Field",
"@id": "generated-images/model",
"name": "model",
"description": "Generation model identifier: sd15 (Stable Diffusion 1.5, 512x512), sdxl (SDXL, 1024x1024), sd35 (Stable Diffusion 3.5, 1024x1024), flux1 (Flux.1 Dev, 1024x1024), hunyuan (Hunyuan-DiT, 1024x1024), kolors (Kolors, 1024x1024)",
"dataType": "sc:Text"
},
{
"@type": "cr:Field",
"@id": "generated-images/image",
"name": "image",
"description": "Canny-conditioned generated image (PNG; 512x512 for sd15, 1024x1024 for all others; seed=42, cn_scale=1.0, empty negative prompt)",
"dataType": "sc:ImageObject",
"source": {
"fileSet": {
"@id": "gen-sd15-files"
}
}
}
]
},
{
"@type": "cr:RecordSet",
"@id": "depth-estimates",
"name": "depth-estimates",
"description": "Monocular depth estimates for ground-truth renders and generated images. Primary estimator: DepthPro (25,200 files). Ablation estimators: Depth Anything V2 and ZoeDepth (subsets).",
"field": [
{
"@type": "cr:Field",
"@id": "depth-estimates/room_id",
"name": "room_id",
"description": "3D-FRONT room identifier",
"dataType": "sc:Text"
},
{
"@type": "cr:Field",
"@id": "depth-estimates/level",
"name": "level",
"description": "Furniture complexity level",
"dataType": "sc:Text"
},
{
"@type": "cr:Field",
"@id": "depth-estimates/view",
"name": "view",
"description": "Camera viewpoint index",
"dataType": "sc:Text"
},
{
"@type": "cr:Field",
"@id": "depth-estimates/image_source",
"name": "image_source",
"description": "Whether depth was estimated from a ground-truth render (gt) or a generated image (gen)",
"dataType": "sc:Text"
},
{
"@type": "cr:Field",
"@id": "depth-estimates/gen_model",
"name": "gen_model",
"description": "Generation model name (sd15, sdxl, sd35, flux1, hunyuan, kolors); only populated when image_source=gen",
"dataType": "sc:Text"
},
{
"@type": "cr:Field",
"@id": "depth-estimates/mde_method",
"name": "mde_method",
"description": "Monocular depth estimation method: depthpro (primary), dav2 (Depth Anything V2, ablation), zoedepth (ZoeDepth, ablation)",
"dataType": "sc:Text"
},
{
"@type": "cr:Field",
"@id": "depth-estimates/depth_npy",
"name": "depth_npy",
"description": "Estimated depth map stored as a NumPy array (.npy, float32)",
"dataType": "sc:URL",
"source": {
"fileSet": {
"@id": "depth-gen-depthpro-files"
}
}
}
]
}
]
}
|