3D-FRONT: 3D Furnished Rooms with layOuts and semaNTics

By Huan Fu, Bowen Cai, Lin Gao, Ling-Xiao Zhang, Cao Li, Zengqi Xun, Chengyue Sun, Yiyun Fei, Yu Zheng, Ying Li and 10 more. 2020

472 citations recorded by the source corpus.

We introduce 3D-FRONT (3D Furnished Rooms with layOuts and semaNTics), a new, large-scale, and comprehensive repository of synthetic indoor scenes highlighted by professionally designed layouts and a large number of rooms populated by high-quality textured 3D models with style compatibility. From layout semantics down to texture details of individual objects, our dataset is freely available to the academic community and beyond. Currently, 3D-FRONT contains 6,813 CAD houses, where 18,968 rooms diversely furnished by 3D objects, far surpassing all publicly available scene datasets. The 13,151 furniture objects all come with high-quality textures. While the floorplans and layout designs (i.e., furniture arrangements) are directly sourced from professional creations, the interior designs in terms of furniture styles, color, and textures have been carefully curated based on a recommender system we develop to attain consistent styles as expert designs. Furthermore, we release Trescope, a light-weight rendering tool, to support benchmark rendering of 2D images and annotations from 3D-FRONT. We demonstrate two applications, interior scene synthesis and texture synthesis, that are especially tailored to the strengths of our new dataset.

Subfields: 3D Vision & Pose

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Citations in the corpus

Hunyuan3D 2.0: Scaling Diffusion Models for High Resolution Textured 3D Assets Generation (2025, unknown): “From 3Dscanrep [18, 44, 93] to ShapeNet [8], the growth of 3D datasets has been gradual [85, 123, 25, 77, 103, 21, 17].”
PointOdyssey: A Large-Scale Synthetic Dataset for Long-Term Point Tracking (2023, methodology): “We manually build twenty 3D indoor scenes to replicate specific 3D environments from our motion capture datasets [67, 66], matching the scene layouts and furniture as closely as possible, sourcing furniture assets from Blenderkit [1] and 3D-FRONT [21, 22].”
What's Behind the Couch? Directed Ray Distance Functions (DRDF) for 3D Scene Reconstruction (2021, methodology): “99% of ScanNet rays have 1 or 2 hits, while≥24% of Matterport3D [5] and 3DFront [17] rays have more than 2 hits.”
LayoutGPT: Compositional Visual Planning and Generation with Large Language Models (2023, methodology): “Datasets & Benchmarks For indoor scene synthesis, we use an updated version of the 3D-FRONT dataset [12, 13] following ATISS [38].”
Habitat-Matterport 3D Dataset (HM3D): 1000 Large-scale 3D Environments for Embodied AI (2021, background): “There are also datasets of building-scale synthetic scenes [11–13].”
iGibson, a Simulation Environment for Interactive Tasks in Large Realistic Scenes (2020, methodology): “Additionally, we provide compatibility with CubiCasa5K [23] and 3D-Front [24] repositories of home scenes.”
iGibson 1.0: A Simulation Environment for Interactive Tasks in Large Realistic Scenes (2020, methodology): “Casa5K [23] and 3D-Front [24] repositories of home scenes.”
ProcTHOR: Large-Scale Embodied AI Using Procedural Generation (2022, methodology): “In this work, we found the procedural generation approaches to be more reliable and flexible than the ones based on deep learning when adapting it to our custom object database and when generating more complex houses that were out of the distribution of static house datasets [18, 57, 34].”