PlaneNet: Piece-wise Planar Reconstruction from a Single RGB Image
arXiv:1804.06278
Abstract
This paper proposes a deep neural network (DNN) for piece-wise planar depthmap reconstruction from a single RGB image. While DNNs have brought remarkable progress to single-image depth prediction, piece-wise planar depthmap reconstruction requires a structured geometry representation, and has been a difficult task to master even for DNNs. The proposed end-to-end DNN learns to directly infer a set of plane parameters and corresponding plane segmentation masks from a single RGB image. We have generated more than 50,000 piece-wise planar depthmaps for training and testing from ScanNet, a large-scale RGBD video database. Our qualitative and quantitative evaluations demonstrate that the proposed approach outperforms baseline methods in terms of both plane segmentation and depth estimation accuracy. To the best of our knowledge, this paper presents the first end-to-end neural architecture for piece-wise planar reconstruction from a single RGB image. Code and data are available at https://github.com/art-programmer/PlaneNet.
arXiv commentCVPR 2018
References in corpus (11)
- Adam: A Method for Stochastic Optimization
- TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
- Deep Residual Learning for Image Recognition
- Efficient Inference in Fully Connected CRFs with Gaussian Edge Potentials
- Depth Map Prediction from a Single Image using a Multi-Scale Deep Network
- ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes
- Pyramid Scene Parsing Network
- Dilated Residual Networks
- Predicting Depth, Surface Normals and Semantic Labels with a Common Multi-Scale Convolutional Architecture
- A Point Set Generation Network for 3D Object Reconstruction from a Single Image
- Depth from a Single Image by Harmonizing Overcomplete Local Network Predictions