LayoutMP3D: Layout Annotation of Matterport3D
arXiv:2003.13516
Abstract
Inferring the information of 3D layout from a single equirectangular panorama is crucial for numerous applications of virtual reality or robotics (e.g., scene understanding and navigation). To achieve this, several datasets are collected for the task of 360 layout estimation. To facilitate the learning algorithms for autonomous systems in indoor scenarios, we consider the Matterport3D dataset with their originally provided depth map ground truths and further release our annotations for layout ground truths from a subset of Matterport3D. As Matterport3D contains accurate depth ground truths from time-of-flight (ToF) sensors, our dataset provides both the layout and depth information, which enables the opportunity to explore the environment by integrating both cues.
arXiv commentAnnotation is available at https://github.com/fuenwang/LayoutMP3D
References in corpus (7)
- Joint 2D-3D-Semantic Data for Indoor Scene Understanding
- Matterport3D: Learning from RGB-D Data in Indoor Environments
- Spherical CNNs
- MINOS: Multimodal Indoor Simulator for Navigation in Complex Environments
- Learning Spherical Convolution for Fast Features from 360° Imagery
- Self-Supervised Learning of Depth and Camera Motion from 360° Videos
- 360SD-Net: 360° Stereo Depth Estimation with Learnable Cost Volume
Cited by in corpus (4)
- Manhattan Room Layout Reconstruction from a Single 360 image: A Comparative Study of State-of-the-art Methods
- HoHoNet: 360 Indoor Holistic Understanding with Latent Horizontal Features
- LED2-Net: Monocular 360 Layout Estimation via Differentiable Depth Rendering
- Monocular Spherical Depth Estimation with Explicitly Connected Weak Layout Cues