Structured3D: A Large Photo-realistic Dataset for Structured 3D Modeling
arXiv:1908.00222
Abstract
Recently, there has been growing interest in developing learning-based methods to detect and utilize salient semi-global or global structures, such as junctions, lines, planes, cuboids, smooth surfaces, and all types of symmetries, for 3D scene modeling and understanding. However, the ground truth annotations are often obtained via human labor, which is particularly challenging and inefficient for such tasks due to the large number of 3D structure instances (e.g., line segments) and other factors such as viewpoints and occlusions. In this paper, we present a new synthetic dataset, Structured3D, with the aim of providing large-scale photo-realistic images with rich 3D structure annotations for a wide spectrum of structured 3D modeling tasks. We take advantage of the availability of professional interior designs and automatically extract 3D structures from them. We generate high-quality images with an industry-leading rendering engine. We use our synthetic dataset in combination with real images to train deep networks for room layout estimation and demonstrate improved performance on benchmark datasets.
arXiv commentAccepted to ECCV 2020. Project website: https://structured3d-dataset.org
References in corpus (5)
- Joint 2D-3D-Semantic Data for Indoor Scene Understanding
- ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes
- Matterport3D: Learning from RGB-D Data in Indoor Environments
- Deep Cuboid Detection: Beyond 2D Bounding Boxes
- DuLa-Net: A Dual-Projection Network for Estimating Room Layouts from a Single RGB Panorama
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- Fast 3D Indoor Scene Synthesis with Discrete and Exact Layout Pattern Extraction
- Monte Carlo Scene Search for 3D Scene Understanding
- Towards Unsupervised Learning of Generative Models for 3D Controllable Image Synthesis
- End-to-End Optimization of Scene Layout
- VersatileGait: A Large-Scale Synthetic Gait Dataset Towards in-the-Wild Simulation
- Indoor Panorama Planar 3D Reconstruction via Divide and Conquer