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

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 18,797 rooms diversely furnished by 3D objects, far surpassing all publicly available scene datasets. In addition, the 7,302 furniture objects all come with high-quality textures. While the floorplans and layout designs 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. The project page is at: this https URL.

[1]  Roberto Cipolla,et al.  Understanding RealWorld Indoor Scenes with Synthetic Data , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[2]  Duc Thanh Nguyen,et al.  SceneNN: A Scene Meshes Dataset with aNNotations , 2016, 2016 Fourth International Conference on 3D Vision (3DV).

[3]  Ryan Shaun Joazeiro de Baker,et al.  Case studies in the use of ROC curve analysis for sensor-based estimates in human computer interaction , 2005, Graphics Interface.

[4]  Wenbin Li,et al.  InteriorNet: Mega-scale Multi-sensor Photo-realistic Indoor Scenes Dataset , 2018, BMVC.

[5]  Andrew Owens,et al.  SUN3D: A Database of Big Spaces Reconstructed Using SfM and Object Labels , 2013, 2013 IEEE International Conference on Computer Vision.

[6]  Vladimir G. Kim,et al.  Learning to Generate Textures on 3D Meshes , 2019, CVPR Workshops.

[7]  Angel X. Chang,et al.  Deep convolutional priors for indoor scene synthesis , 2018, ACM Trans. Graph..

[8]  Ming-Wei Chang,et al.  BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding , 2019, NAACL.

[9]  Silvio Savarese,et al.  3D Semantic Parsing of Large-Scale Indoor Spaces , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[10]  Matthias Nießner,et al.  PiGraphs: learning interaction snapshots from observations , 2016, ACM Trans. Graph..

[11]  Michael Goesele,et al.  The Replica Dataset: A Digital Replica of Indoor Spaces , 2019, ArXiv.

[12]  Lin Gao,et al.  TM-NET , 2020, ACM Trans. Graph..

[13]  Max Welling,et al.  Semi-Supervised Classification with Graph Convolutional Networks , 2016, ICLR.

[14]  Thorsten Joachims,et al.  Semantic Labeling of 3D Point Clouds for Indoor Scenes , 2011, NIPS.

[15]  Stefan Leutenegger,et al.  SceneNet RGB-D: Can 5M Synthetic Images Beat Generic ImageNet Pre-training on Indoor Segmentation? , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[16]  Leonidas J. Guibas,et al.  ShapeNet: An Information-Rich 3D Model Repository , 2015, ArXiv.

[17]  Lukasz Kaiser,et al.  Attention is All you Need , 2017, NIPS.

[18]  Wolfram Burgard,et al.  A benchmark for the evaluation of RGB-D SLAM systems , 2012, 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[19]  Zihan Zhou,et al.  Structured3D: A Large Photo-realistic Dataset for Structured 3D Modeling , 2019, ECCV.

[20]  Chenfanfu Jiang,et al.  Fast and Scalable Position-Based Layout Synthesis , 2018, IEEE Transactions on Visualization and Computer Graphics.

[21]  Matthias Nießner,et al.  ScanNet: Richly-Annotated 3D Reconstructions of Indoor Scenes , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[22]  Mark Sandler,et al.  MobileNetV2: Inverted Residuals and Linear Bottlenecks , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[23]  David Vázquez,et al.  Context-Aware Visual Compatibility Prediction , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[24]  Mike Roberts,et al.  Hypersim: A Photorealistic Synthetic Dataset for Holistic Indoor Scene Understanding , 2020, ArXiv.

[25]  Silvio Savarese,et al.  Joint 2D-3D-Semantic Data for Indoor Scene Understanding , 2017, ArXiv.

[26]  Kalyan Sunkavalli,et al.  OpenRooms: An End-to-End Open Framework for Photorealistic Indoor Scene Datasets , 2020, ArXiv.

[27]  Matthias Nießner,et al.  Scan2CAD: Learning CAD Model Alignment in RGB-D Scans , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[28]  Jitendra Malik,et al.  Gibson Env: Real-World Perception for Embodied Agents , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[29]  Alexei A. Efros,et al.  The Unreasonable Effectiveness of Deep Features as a Perceptual Metric , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[30]  Derek Hoiem,et al.  Indoor Segmentation and Support Inference from RGBD Images , 2012, ECCV.

[31]  Kai Wang,et al.  Fast and Flexible Indoor Scene Synthesis via Deep Convolutional Generative Models , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[32]  Chongyang Ma,et al.  Deep Generative Modeling for Scene Synthesis via Hybrid Representations , 2018, ACM Trans. Graph..

[33]  Matthias Nießner,et al.  Matterport3D: Learning from RGB-D Data in Indoor Environments , 2017, 2017 International Conference on 3D Vision (3DV).

[34]  Angel X. Chang,et al.  PlanIT , 2019, ACM Transactions on Graphics.

[35]  J. Friedman Greedy function approximation: A gradient boosting machine. , 2001 .

[36]  Pat Hanrahan,et al.  Example-based synthesis of 3D object arrangements , 2012, ACM Trans. Graph..

[37]  Thomas A. Funkhouser,et al.  Semantic Scene Completion from a Single Depth Image , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[38]  Ricardo Martin-Brualla,et al.  GeLaTO: Generative Latent Textured Objects , 2020, ECCV.

[39]  Jianxiong Xiao,et al.  SUN RGB-D: A RGB-D scene understanding benchmark suite , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[40]  José García Rodríguez,et al.  The RobotriX: An Extremely Photorealistic and Very-Large-Scale Indoor Dataset of Sequences with Robot Trajectories and Interactions , 2018, 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[41]  Ersin Yumer,et al.  Physically-Based Rendering for Indoor Scene Understanding Using Convolutional Neural Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[42]  Daniel Cohen-Or,et al.  GRAINS , 2018, ACM Trans. Graph..

[43]  Lin Gao,et al.  3D-FUTURE: 3D Furniture Shape with TextURE , 2020, International Journal of Computer Vision.