Update Blender version 4.x -> 5.0 in rai:dataCollection

croissant.json

@@ -79,7 +79,7 @@
   "isLiveDataset": false,
   "isAccessibleForFree": true,
   "inLanguage": "en",
-  "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 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: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.",