--- license: cc-by-nc-sa-4.0 language: - en pretty_name: IGF-Bench (Indoor Geometric Fidelity Benchmark) tags: - 3d-scene-understanding - monocular-depth-estimation - controlnet - diffusion-models - benchmark - evaluation - indoor-scenes - geometric-fidelity - 3d-front - vanishing-points - structural-evaluation task_categories: - depth-estimation - image-to-image - text-to-image size_categories: - 10K **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 images** from six state-of-the-art ControlNet models, plus **25,200 monocular-depth estimates**, all evaluated with four complementary geometric metrics: **planarity (`L_plane`)**, **orthogonality (`L_ortho`)**, **edge alignment (`L_edge`)**, and **vanishing-point consistency (`L_vp`)**. | Quick stats | Value | |:---|:---| | Calibrated GT views | 3,600 (300 rooms × 3 complexity levels × 4 viewpoints) | | Paired generated images | 21,600 (6 ControlNet models, all conditioned on identical Canny maps) | | Paired depth estimates | 25,200 (DepthPro on all GT + generated; DAv2 / ZoeDepth subsets) | | Camera FOV | 90° | | Render resolution | 1024×1024 | | Total size | ≈ 219 GB | | License | CC BY-NC-SA 4.0 (data) + Apache 2.0 (code) | | Code repo | | | Paper | NeurIPS 2026 E&D Track (under review) | --- ## Table of Contents 1. [What's in this Repository](#whats-in-this-repository) 2. [Headline Findings (verified against this release)](#headline-findings) 3. [Quick Start](#quick-start) 4. [Directory Structure](#directory-structure) 5. [Reproducing Each Paper Experiment](#reproducing-each-paper-experiment) 6. [Pre-Computed Result JSONs (no compute needed)](#pre-computed-result-jsons) 7. [Schema Reference](#schema-reference) 8. [License Chain](#license-chain) 9. [Citation](#citation) --- ## What's in this Repository This HuggingFace dataset repository ships **everything needed to evaluate or extend IGF-Bench**: - ✅ **Ground-truth 3D-FRONT renders** with calibrated cameras (RGB + EXR depth + 7-class semantic mask + Canny edge map) at 3 complexity levels (`L0_empty` / `L1_basic` / `L2_full`). - ✅ **Paired generated images** from six ControlNet pipelines (SD 1.5, SDXL 1.0, SD 3.5 Large, Flux.1 Dev, Hunyuan-DiT, Kolors) under a uniform protocol (Canny conditioning, seed=42, no negative prompts, `cn_scale=1.0`). - ✅ **Per-view depth estimates** from DepthPro on all 25,200 images, plus DAv2 and ZoeDepth on the 200-view ablation subset (App C.1). - ✅ **Pre-computed evaluation JSONs** that already populate every table and figure in the paper (`evaluation/`). - ✅ **A trained LoRA adapter** (`experiments/finetune/dav2_lora_adapter/`) that demonstrates IGF-Bench is usable as supervision for fine-tuning a pretrained MDE (App C.8). - ✅ **A 200-view MDE consistency subset** with DAv2 + ZoeDepth depth pre-computed for paper App C.1 (`tab:mde_ablation`). - ✅ A formal **Datasheet for Datasets** (`DATASHEET.md`) and **Croissant 1.0 metadata** (`croissant.json`). --- ## Headline Findings > Each finding here is reproducible from the JSONs shipped under `evaluation/` — no recompute required. 1. **Perceptual quality ≠ geometric fidelity.** Models with comparable FID (e.g., SDXL FID = 64.2 vs. Flux.1 FID = 56.3) can still differ by ≈2× in `ΔL_ortho` (Flux.1 0.032 vs. SDXL 0.061). Perceptual metrics alone miss structural failures. 2. **Conditioning architecture matters.** Flux.1's channel-concatenation conditioning achieves the lowest `ΔL_ortho` (0.032), substantially better than the residual-injection ControlNet variants used by the other five models. 3. **Edge alignment is the universal bottleneck.** All six models show 68%–89% relative `L_edge` degradation versus GT — even the best model fails on fine geometric structure. 4. **Cleanliness of synthetic GT.** DepthPro AbsRel on IGF-Bench GT is 0.056, vs. 0.084 on NYU-v2 (real Kinect) — the lower MDE-noise floor makes IGF-Bench's relative-degradation (`ΔL_*`) design more sensitive to generation-induced artefacts. 5. **`L_ortho` works as a label-free quality signal.** Picking the per-view best of 5 random Flux.1 seeds by minimum `L_ortho` cuts `L_ortho` by 35.1% relative to the best fixed seed (App C.7.1). 6. **The synthetic supervision transfers (in-domain).** Fine-tuning DepthAnything V2-Small with a 1.75%-parameter LoRA adapter improves in-domain AbsRel from 1.044 to 0.081 (−92%) on the 3D-FRONT held-out test set (App C.8). --- ## Quick Start ### Option A — Verify the paper without downloading bulk data The full evaluation already shipped in `evaluation/` is < 50 MB. You can verify every paper number by reading the JSONs directly: ```python import json from huggingface_hub import hf_hub_download # Example: verify the GT L_plane = 0.056 number fp = hf_hub_download( repo_id="igfbench-neurips2026/IGF-Bench", filename="evaluation/igf_summary.json", repo_type="dataset", ) data = json.load(open(fp)) gt = next(d for d in data if d["model"] == "GT_baseline") print(round(gt["l_plane_residual_mean"], 3)) # → 0.056 ``` ### Option B — Download everything and re-run ```bash pip install huggingface_hub huggingface-cli download igfbench-neurips2026/IGF-Bench --repo-type dataset \ --local-dir ./igf-bench-data export IGF_BENCH_ROOT=$(pwd)/igf-bench-data ``` Then clone the code at and run: ```bash python scripts/evaluate_igf.py \ --renders_root $IGF_BENCH_ROOT/renders_textured \ --generated_root $IGF_BENCH_ROOT/generated \ --depth_root $IGF_BENCH_ROOT/depth_results \ --output_summary igf_summary.json ``` This reproduces the full Table 3 from the paper. Runtime ≈ 6 hours on a single RTX 4090; 99% of the time is spent in I/O reading per-view depth NPYs. ### Option C — Download only what you need for one row of the main table For example, to recompute only the **Flux.1** row of Table 3 (≈ 70 GB): ```bash huggingface-cli download igfbench-neurips2026/IGF-Bench --repo-type dataset \ --local-dir ./igf-bench-data \ --include "renders_textured/*" \ "generated/flux1_canny/*" \ "depth_results/gt/depthpro/*" \ "depth_results/gen/flux1/depthpro/*" ``` --- ## Directory Structure ``` igfbench-neurips2026/IGF-Bench/ ├── README.md ← this file ├── DATASHEET.md ← formal Datasheets-for-Datasets record ├── croissant.json ← MLCommons Croissant 1.0 metadata ├── dataset_card.md ← short HF dataset card (4.4 KB summary) ├── LICENSE ← CC BY-NC-SA 4.0 ├── selected_rooms.json ← canonical 300-room list ├── room_statistics.json ← summary statistics │ ├── renders_textured/ ≈ 36 GB — Ground-truth rendered views │ └── {scene_id}_{room_type}-{room_id}/ │ └── {L0_empty,L1_basic,L2_full}/ │ └── view_{0,1,2,3}/ │ ├── rgb_textured.png RGB render │ ├── depth.exr metric depth, OpenEXR float32 │ ├── depth.png 8-bit depth visualisation (QA only) │ ├── depth_gt.npy NumPy float32 cache of depth.exr (LoRA training I/O) │ ├── canny.png Canny edges (thresholds 100/200) │ ├── semantic_id.png 7-class IDs (0..6) │ ├── semantic_mask.png colour-mapped semantic (QA only) │ ├── wireframe_3d.png 3D wireframe overlay (QA only) │ └── camera.json intrinsics + extrinsics │ ├── generated/ ≈ 36 GB — Generated images per model │ │ │ │ --- Main protocol: 6 models × 3,600 views = 21,600 PNG (Table 3) --- │ ├── sd15_canny/ SD 1.5 native 512² (300 rooms × 3 levels × 4 views) │ ├── sdxl_canny/ SDXL 1.0 │ ├── sd35_canny/ SD 3.5 Large │ ├── flux1_canny/ Flux.1 Dev │ ├── hunyuan_canny/ Hunyuan-DiT │ ├── kolors_canny/ Kolors │ │ │ │ --- Ablation: negative prompt, App C.3 (3,552 paired views) --- │ ├── sd15_canny_with_neg/sd15_canny/ SD 1.5 with the legacy negative prompt │ ├── sdxl_canny_with_neg/sdxl_canny/ SDXL with the legacy negative prompt │ │ │ │ --- Ablation: resolution, App C.4 (3,552 paired views) --- │ ├── sd15_upsampled/sd15_canny/ SD 1.5 outputs bicubic-upsampled to 1024² │ │ │ │ --- Ablation: seed sensitivity (App C.6) + quality gating (App C.7), 200 views each --- │ └── ablation/ │ ├── flux1_seed123/ App C.6 / C.7 — Flux.1 with seed=123 │ ├── flux1_seed456/ App C.6 / C.7 — Flux.1 with seed=456 │ ├── flux1_seed789/ App C.7 only — Flux.1 with seed=789 │ ├── flux1_seed1024/ App C.7 only — Flux.1 with seed=1024 │ ├── sdxl_w050/ App C.7 — SDXL with cn_scale=0.50 │ ├── sdxl_w075/ App C.7 — SDXL with cn_scale=0.75 │ ├── sdxl_w125/ App C.7 — SDXL with cn_scale=1.25 │ └── sdxl_w150/ App C.7 — SDXL with cn_scale=1.50 │ (each leaf directory contains the same {room}/{level}/view_{0..3}.png structure) │ ├── depth_results/ ≈ 150 GB — Per-MDE depth estimates (NPY) │ ├── gt/ Ground-truth-render depth (3 MDE backbones) │ │ ├── depthpro/ 3,600 NPY (full set) │ │ ├── dav2/ ablation 200-view subset (App C.1) │ │ └── zoedepth/ ablation 200-view subset (App C.1) │ │ │ └── gen/ Generated-image depth │ │ │ │ --- Main protocol: 6 models × 3,600 = 21,600 NPY --- │ ├── sd15/depthpro/ 3,600 NPY │ ├── sdxl/depthpro/ 3,600 NPY (also dav2/, zoedepth/ for App C.1 200-view subset) │ ├── sd35/depthpro/ 3,600 NPY │ ├── flux1/depthpro/ 3,600 NPY (also dav2/, zoedepth/ for App C.1 200-view subset) │ ├── hunyuan/depthpro/ 3,600 NPY │ ├── kolors/depthpro/ 3,600 NPY │ │ │ │ --- Ablation depth maps --- │ ├── sd15_neg/depthpro/ 3,552 NPY (App C.3 negative prompt) │ ├── sdxl_neg/depthpro/ 3,552 NPY (App C.3 negative prompt) │ ├── sd15_upsampled/depthpro/ 3,552 NPY (App C.4 resolution) │ ├── flux1_seed123/depthpro/ 200 NPY (App C.6 / C.7 seed=123) │ ├── flux1_seed456/depthpro/ 200 NPY (App C.6 / C.7 seed=456) │ ├── flux1_seed789/depthpro/ 200 NPY (App C.7 seed=789) │ ├── flux1_seed1024/depthpro/ 200 NPY (App C.7 seed=1024) │ ├── sdxl_w050/depthpro/ 200 NPY (App C.7 cn_scale=0.50) │ ├── sdxl_w075/depthpro/ 200 NPY (App C.7 cn_scale=0.75) │ ├── sdxl_w125/depthpro/ 200 NPY (App C.7 cn_scale=1.25) │ └── sdxl_w150/depthpro/ 200 NPY (App C.7 cn_scale=1.50) │ (each: {room}/{level}/{view}.npy) │ ├── evaluation/ ≈ 41 MB — Pre-computed metric outputs │ ├── igf_summary.json per-model aggregated (Table 3 source) │ ├── igf_results.json per-view detailed (≈ 25 MB) │ ├── error_decomposition.json App C.2 │ ├── mde_ablation_*.json App C.1 │ ├── neg_prompt_ablation.json App C.3 │ ├── n1_resolution_ablation.json App C.4 │ ├── n3_lvp_improved.json App C.5 │ ├── seed_ablation.json App C.6 │ ├── wilcoxon.json per-pair Wilcoxon + Bonferroni (paper Table 3) │ ├── anova.json two-way Type II ANOVA (paper §4.3) │ └── fid_lpips.json perceptual baselines │ └── experiments/finetune/ └── dav2_lora_adapter/ ★ 1.8 MB — Trained LoRA (App C.8) ├── adapter_config.json peft config: r=16, α=32, Q/K/V, dropout=0.05 ├── adapter_model.safetensors 442,368 trainable params (1.75%) └── README.md load instructions ``` --- ## Reproducing Each Paper Experiment | Paper Section | Required HF subsets | Expected runtime | Code entrypoint | |:---|:---|:---:|:---| | **§4 / Table 3** main IGF metrics | `renders_textured/`, `generated/{all}/`, `depth_results/{gt,gen}/depthpro/` | ~6 h | `scripts/evaluate_igf.py` | | **App C.1** MDE robustness | + `depth_results/{gt,gen}/{dav2,zoedepth}/` (200 views) | ~30 min | `scripts/evaluate_mde_ablation.py` | | **App C.2** Error decomposition | already in `evaluation/error_decomposition.json` | <1 min | `scripts/error_decomposition.py` | | **App C.3** Negative-prompt | needs the legacy generations (archived under `generated/sd15_canny_with_neg/sd15_canny/` and `generated/ablation/sdxl_with_neg/sdxl_canny/` on HF) | ~3 h | `scripts/evaluate_neg_prompt.py` | | **App C.4** Resolution confound | `generated/sd15_canny/` + local bicubic upsample to 1024² | ~1 h | `scripts/run_n1_resolution_ablation.py` | | **App C.5** VP 2D | `renders_textured/` + `depth_results/{gt,gen}/depthpro/` | ~30 min | `scripts/run_n3_lvp_improved.py` | | **App C.6** Seed sensitivity | `generated/ablation/flux1_seed{123,456}/` (already on HF) | ~30 min | `scripts/evaluate_seed_ablation.py` | | **App C.7** Quality gating | `generated/ablation/flux1_seed{123,456,789,1024}/` (seed=42 reuses `generated/flux1_canny/`) + `generated/ablation/sdxl_w{050,075,125,150}/` (cn=1.0 reuses `generated/sdxl_canny/`) | ~30 min | `experiments/analyze_solutions.py` | | **App C.8** LoRA fine-tune | `renders_textured/` + base DAv2-S model | ~1 h training + ~5 min eval | `experiments/finetune/{create_split,train_single_gpu,eval_nyu}.py` (training wrapper); pre-trained adapter at `experiments/finetune/dav2_lora_adapter/` | | **App F.1** Cross-dataset | NYU-v2 + iBims-1 from official sources (NOT redistributed) | ~30 min | `scripts/evaluate_cross_dataset.py` | | **App F.2** Complexity-MDE | already in main 219 GB | ~5 min | included in `evaluate_igf.py` | | **App F.3** 3D reconstruction | `renders_textured/` (30 rooms L2_full) + 3D-FRONT meshes | ~1 h | `experiments/run_appendix_F_experiments.py --section F3_reconstruction` | | **App F.4** MDE domain gap | + NYU-v2 from official source | ~30 min | `experiments/run_appendix_F_experiments.py --section F4_mde_domain` | --- ## Pre-Computed Result JSONs > If you only need to **verify** the paper's numbers (no recompute), the JSONs > below are sufficient. | Paper item | JSON path on HF | Key | |:---|:---|:---| | Table 3 (main) | `evaluation/igf_summary.json` | per-model aggregated means | | Tab error_decomp | `evaluation/error_decomposition.json` | per-model AbsRel decomposition | | Tab mde_ablation | `evaluation/mde_ablation_summary.json` | SDXL/Flux × 3 MDE backbones | | Tab neg_prompt | `evaluation/neg_prompt_ablation.json` | per-model w/ vs w/o | | Tab n1_resolution | `evaluation/n1_resolution_ablation.json` | SD 1.5 native vs upsampled | | Tab n3_lvp_2d | `evaluation/n3_lvp_improved.json` | per-model 2D L_vp | | Tab seed_ablation | `evaluation/seed_ablation.json` | Flux.1 across 3 seeds | | Wilcoxon p-values | `evaluation/wilcoxon.json` | per-pair Bonferroni-corrected | | FID + LPIPS | `evaluation/fid_lpips.json` | per-model perceptual baselines | App F downstream JSONs are released with the paper supplement () under `experiments/results/`. --- ## Schema Reference ### `selected_rooms.json` ```json { "rooms": [ { "scene_id": "00110bde-f580-40be-b8bb-88715b338a2a", "room_id": "Bedroom-43072", "room_type": "Bedroom", "space_type": "Bedroom", "area_m2": 12.3 }, ... ] } ``` The on-disk directory name for each room is `{scene_id}_{room_id}`. ### `evaluation/igf_summary.json` A list of dicts, one per model and one for `GT_baseline`: ```json [ { "model": "GT_baseline", "n_samples": 3600, "l_plane_residual_mean": 0.056, "l_ortho_mean": 0.166, "l_edge_f1_mean": 0.606, "l_vp_deg_mean": 1.79, "blender_abs_rel_mean": 0.0, "depthpro_abs_rel_mean": 0.056, ... }, { "model": "sdxl", "n_samples": 3600, "delta_l_plane_mean": 0.052, "delta_l_ortho_mean": 0.061, "delta_l_edge_mean": -0.413, ... }, ... ] ``` ### `evaluation/igf_results.json` A dict keyed by model (`gt_baseline`, `sd15`, …, `kolors`), each holding a list of 3,600 per-view records: ```json { "gt_baseline": [ { "room": "00110bde-..._Bedroom-43072", "level": "L0_empty", "view": "view_0", "l_plane_residual": 0.045, "l_plane_inlier_ratio": 0.84, "l_ortho": 0.130, "n_normals": 5, "l_edge_f1": 0.612, "l_edge_precision": 0.59, "l_edge_recall": 0.64, "l_vp_deg": 0.41, "depthpro_abs_rel": 0.052, "depthpro_rmse": 0.118, "depthpro_si_rmse": 0.041, "depthpro_delta125": 0.984, ... }, ... ], "sdxl": [...], ... } ``` ### `experiments/finetune/dav2_lora_adapter/adapter_config.json` ```json { "base_model_name_or_path": "depth-anything/Depth-Anything-V2-Metric-Indoor-Small-hf", "peft_type": "LORA", "r": 16, "lora_alpha": 32, "lora_dropout": 0.05, "target_modules": ["query", "key", "value"], "bias": "none" } ``` Loadable via `peft.PeftModel.from_pretrained(base, adapter_dir)`. --- ## License Chain ``` IGF-Bench dataset ├── License: CC BY-NC-SA 4.0 │ • Inherits NC clause from upstream 3D-FRONT (Alibaba Tianchi NC license). │ • Attribution required, share-alike, non-commercial use only. │ ├── Code (separate): Apache 2.0 │ • Located in the supplementary code repo, not on this dataset HF repo. │ ├── Per-asset upstream model licenses (binding for downstream redistribution): │ ├── SD 1.5 → CreativeML Open RAIL-M │ ├── SDXL 1.0 → CreativeML Open RAIL++-M │ ├── SD 3.5 Large → Stability AI Community License (NC if revenue ≤ $1M/yr) │ ├── Flux.1 Dev → FLUX.1 [dev] Non-Commercial License │ ├── Hunyuan-DiT → Tencent Hunyuan Community License │ ├── Kolors → Apache 2.0 + Kwai commercial-registration requirement │ ├── DepthPro → Apple Sample Code License (apple-amlr; output redistribution │ │ in a "gray area" — see DATASHEET §6 for full disclosure) │ ├── Depth-Anything V2-Small (metric-indoor) → Apache 2.0 │ └── ZoeDepth → MIT │ └── Eval-only datasets (NOT redistributed by us; obtain from the official source): ├── NYU-v2 → https://cs.nyu.edu/~silberman/datasets/nyu_depth_v2.html └── iBims-1 → https://www.cvg.cit.tum.de/data/datasets/ibims1 ``` For the full per-asset table see [`DATASHEET.md`](DATASHEET.md) §6 (Distribution). The `dav2_lora_adapter/` weights inherit CC BY-NC-SA 4.0 from the 3D-FRONT supervision data, even though the base DAv2-S model is Apache 2.0. --- ## Maintenance The authors commit to maintaining IGF-Bench for **at least 5 years post-publication**, including: - Hosting on HuggingFace with versioned releases (`v1.0.0`, `v1.1.0`, …); - Bug fixes via the GitHub Issues tracker (URL pending de-anonymisation); - Adding new generation models as they become available; - Periodic re-evaluation when major MDE backbones are released. Older versions remain accessible on HuggingFace forever (see the `refs/convert/parquet/` history). --- ## Citation ```bibtex @inproceedings{igfbench2026, title = {IGF-Bench: Evaluating Geometric Fidelity of Conditional Image Generation Beyond Perceptual Metrics}, author = {Anonymous}, booktitle = {Advances in Neural Information Processing Systems (Datasets and Benchmarks Track)}, year = {2026} } ``` When citing the upstream 3D-FRONT scenes, please also cite Fu et al., *3D-FRONT: 3D Furnished Rooms with layOuts and semaNTics* (ICCV 2021).