Sensors (Dec 2023)

Unsupervised Stereo Matching with Surface Normal Assistance for Indoor Depth Estimation

  • Xiule Fan,
  • Ali Jahani Amiri,
  • Baris Fidan,
  • Soo Jeon

DOI
https://doi.org/10.3390/s23249850
Journal volume & issue
Vol. 23, no. 24
p. 9850

Abstract

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To obtain more accurate depth information with stereo cameras, various learning-based stereo-matching algorithms have been developed recently. These algorithms, however, are significantly affected by textureless regions in indoor applications. To address this problem, we propose a new deep-neural-network-based data-driven stereo-matching scheme that utilizes the surface normal. The proposed scheme includes a neural network and a two-stage training strategy. The neural network involves a feature-extraction module, a normal-estimation branch, and a disparity-estimation branch. The training processes of the feature-extraction module and the normal-estimation branch are supervised while the training of the disparity-estimation branch is performed unsupervised. Experimental results indicate that the proposed scheme is capable of estimating the surface normal accurately in textureless regions, leading to improvement in the disparity-estimation accuracy and stereo-matching quality in indoor applications involving such textureless regions.

Keywords