: This paper proposes a deep neural architecture for piece-wise planar depthmap reconstruction from a single RGB image. From left to right, an input image, a piece-wise planar segmentation, a reconstructed depthmap, and a texture-mapped 3D model. 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 largescale 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. The depth value calculation requires camera intrinsic parameters, which can be estimated via vanishing point analysis, for example. In our experiments, intrinsics are given for each image through the database information.
With the recent resurgence of deep learning methods, an end-to-end deep neural network (DNN) have been proposed to obtain single view 3D reconstruction REF .
PlaneNet: Piece-Wise Planar Reconstruction from a Single RGB Image
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
Computer Science