mlsanityData to 14 Oct 2026

Panoramic Depth Estimation via Supervised and Unsupervised Learning in Indoor Scenes

arXiv 2108.08076 ↗First posted 18 Aug 2021cs.CV cs.ROcited by 2

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Abstract (arXiv)

Depth estimation, as a necessary clue to convert 2D images into the 3D space, has been applied in many machine vision areas. However, to achieve an entire surrounding 360-degree geometric sensing, traditional stereo matching algorithms for depth estimation are limited due to large noise, low accuracy, and strict requirements for multi-camera calibration. In this work, for a unified surrounding perception, we introduce panoramic images to obtain larger field of view. We extend PADENet first appeared in our previous conference work for outdoor scene understanding, to perform panoramic monocular depth estimation with a focus for indoor scenes. At the same time, we improve the training process of the neural network adapted to the characteristics of panoramic images. In addition, we fuse traditional stereo matching algorithm with deep learning methods and further improve the accuracy of depth predictions. With a comprehensive variety of experiments, this research demonstrates the effectiveness of our schemes aiming for indoor scene perception.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 32 of its 47 references that are in the map. First posted on arXiv 18 Aug 2021.

Builds on

32 of 47 references.

References · 32

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20 more
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Cited by year

Citing papers per year: 2022: 2.

2 papers.

  1. 2022Review on Panoramic Imaging and Its Applications in Scene Understandingcited by 43
  2. 2022Multi-Modal Masked Pre-Training for Monocular Panoramic Depth Completioncited by 23