datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
3D-Front3d-front-rgb3dfront_render_views3d-front-arDepR-3D-FRONT
Dataset for DepR: Depth Guided Single-view Scene Reconstruction with Instance-level Diffusion
Project Page
|
arXiv
File Structure
File
Optional
Description
pickled_data
Raw data (images, etc.) from InstPIFu
instpifu_mask
Instance masks from InstPIFu
metadata
JSONL metadata for scenes
panoptic
Panoptic segmentation maps we rendered
depth
✅
Estimated depth with Depth Pro
grounded_sam
✅
Estimated segmentation with Grounded SAM… See the full description on the dataset page: https://huggingface.co/datasets/zx1239856/DepR-3D-FRONT.3D-Front
3D-Front (MIDI-3D)
Github | Project Page | Paper | Original Dataset
1. Dataset Introduction
TL;DR: This dataset processes 3D-Front into organized 3d scenes paired with rendered multi-view images and surfaces, which are used in MIDI-3D. Each scene contains:
3D models (.glb)
Point cloud (.npy)
Rendered multi-view images in RGB, depth, normal, with camera information
2. Data Extraction
sudo apt-get install git-lfs
git lfs install
git clone… See the full description on the dataset page: https://huggingface.co/datasets/huanngzh/3D-Front.3dfront-render-views3dfront-render-diffuse3dfront_render3d-front-inst-woRGB3d-front-indoor-renders
Indoor Scene Renders from 3D-FRONT / 3D-FUTURE
20,240 photo-realistic indoor scene renders with instance segmentation, 6DoF
object poses, camera intrinsics and depth — rendered from the 3D-FRONT scene
layouts and 3D-FUTURE furniture models.
This is not a copy of the original 3D-FUTURE render set. It is a separate
render set built from the same assets. See Differences from the original below.
Why this exists
The 3D-FUTURE technical report describes 20,240 rendered… See the full description on the dataset page: https://huggingface.co/datasets/Spatial1ntelligence/3d-front-indoor-renders.SSR-3DFRONT
SSR-3DFRONT: Structured Scene Representation for 3D Indoor Scenes
This dataset provides a processed version of the 3D-FRONT dataset with structured scene representations for text-driven 3D indoor scene synthesis and editing.
Mor information about ReSpace: http://respace.mnbucher.com
For detailed usage instructions, training details, and examples, see the associated repository: https://github.com/GradientSpaces/respace
Our model weights for SG-LLM:… See the full description on the dataset page: https://huggingface.co/datasets/gradient-spaces/SSR-3DFRONT.3D-Front
3D-Front (MIDI-3D)
Github | Project Page | Paper | Original Dataset
1. Dataset Introduction
TL;DR: This dataset processes 3D-Front into organized 3d scenes paired with rendered multi-view images and surfaces, which are used in MIDI-3D. Each scene contains:
3D models (.glb)
Point cloud (.npy)
Rendered multi-view images in RGB, depth, normal, with camera information
2. Data Extraction
sudo apt-get install git-lfs
git lfs install
git clone… See the full description on the dataset page: https://huggingface.co/datasets/Sherioc/3D-Front.3D-Front
3D-Front (MIDI-3D)
Github | Project Page | Paper | Original Dataset
1. Dataset Introduction
TL;DR: This dataset processes 3D-Front into organized 3d scenes paired with rendered multi-view images and surfaces, which are used in MIDI-3D. Each scene contains:
3D models (.glb)
Point cloud (.npy)
Rendered multi-view images in RGB, depth, normal, with camera information
2. Data Extraction
sudo apt-get install git-lfs
git lfs install
git clone… See the full description on the dataset page: https://huggingface.co/datasets/1-star-2/3D-Front.3d-front-ar-packed
PixARMesh Training Dataset
Project Page | Paper | GitHub
This repository contains the training dataset for PixARMesh, a method to autoregressively reconstruct complete 3D indoor scene meshes directly from a single RGB image. Unlike prior methods that rely on implicit signed distance fields, PixARMesh jointly predicts object layout and geometry within a unified model, producing coherent and artist-ready meshes in a single forward pass.
Dataset Preparation
According… See the full description on the dataset page: https://huggingface.co/datasets/zx1239856/3d-front-ar-packed.3d-front-code
3D-Front-Code
RoomScript v4 Blender object programs, room-layout renders, code-only wall
architecture, and asset Blender artifacts derived from 3D-FRONT scene evidence.
Contents
13,917 object assets (reference and v4/best) in data/assets/*.tar
21,202 rooms (v4/best and code-only v4_wall/best) in data/rooms/*.tar
searchable JSONL indexes under metadata/
Each tar contains multiple samples while preserving the original
data/front_object_code/by_asset/... or… See the full description on the dataset page: https://huggingface.co/datasets/KevinFan111/3d-front-code.seen2scene-FRONT-3D
Seen2Scene — 3D-FRONT Sample Scenes
A 1000-scene sample of the processed 3D-FRONT data used to train and evaluate
Seen2Scene. This is enough to run the completion and
generation inference commands end to end without regenerating the TSDF fusion pipeline yourself.
The full processed dataset is 4.65 TB across 6,804 scenes; this sample is 720 GB (15%).
What the scenes are
Each scene is a synthetic indoor 3D-FRONT layout that has been rendered to depth and fused… See the full description on the dataset page: https://huggingface.co/datasets/MQ66/seen2scene-FRONT-3D.3D-Front
3D-Front (MIDI-3D)
Github | Project Page | Paper | Original Dataset
1. Dataset Introduction
TL;DR: This dataset processes 3D-Front into organized 3d scenes paired with rendered multi-view images and surfaces, which are used in MIDI-3D. Each scene contains:
3D models (.glb)
Point cloud (.npy)
Rendered multi-view images in RGB, depth, normal, with camera information
2. Data Extraction
sudo apt-get install git-lfs
git lfs install
git clone… See the full description on the dataset page: https://huggingface.co/datasets/mingjiexie/3D-Front.3d-front-mapper3dfront3dfront_eval_18scenes3D-Front
3D-Front (MIDI-3D)
Github | Project Page | Paper | Original Dataset
1. Dataset Introduction
TL;DR: This dataset processes 3D-Front into organized 3d scenes paired with rendered multi-view images and surfaces, which are used in MIDI-3D. Each scene contains:
3D models (.glb)
Point cloud (.npy)
Rendered multi-view images in RGB, depth, normal, with camera information
2. Data Extraction
sudo apt-get install git-lfs
git lfs install
git clone… See the full description on the dataset page: https://huggingface.co/datasets/VanJerryu/3D-Front.3D-Front
3D-Front (MIDI-3D)
Github | Project Page | Paper | Original Dataset
1. Dataset Introduction
TL;DR: This dataset processes 3D-Front into organized 3d scenes paired with rendered multi-view images and surfaces, which are used in MIDI-3D. Each scene contains:
3D models (.glb)
Point cloud (.npy)
Rendered multi-view images in RGB, depth, normal, with camera information
2. Data Extraction
sudo apt-get install git-lfs
git lfs install
git clone… See the full description on the dataset page: https://huggingface.co/datasets/Rizki-firman/3D-Front.3D-Front-Scene3DFront-eval3DFront