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Joint 3D Layout and Depth Prediction from a Single Indoor Panorama Image

Conference paper
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Part of the Lecture Notes in Computer Science book series (LNCS, volume 12361)

Abstract

In this paper, we propose a method which jointly learns the layout prediction and depth estimation from a single indoor panorama image. Previous methods have considered layout prediction and depth estimation from a single panorama image separately. However, these two tasks are tightly intertwined. Leveraging the layout depth map as an intermediate representation, our proposed method outperforms existing methods for both panorama layout prediction and depth estimation. Experiments on the challenging real-world dataset of Stanford 2D–3D demonstrate that our approach obtains superior performance for both the layout prediction tasks (3D IoU: \(85.81\%\) v.s. \(79.79\%\)) and the depth estimation (Abs Rel: 0.068 v.s. 0.079).

Keywords

Indoor panorama image Layout prediction Depth estimation Layout depth map 

Supplementary material

504471_1_En_39_MOESM1_ESM.pdf (27.6 mb)
Supplementary material 1 (pdf 28241 KB)

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Computer Vision LaboratoryUniversity of AmsterdamAmsterdamThe Netherlands
  2. 2.3DUniversumAmsterdamThe Netherlands

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