Survey on Semantic Stereo Matching / Semantic Depth Estimation
- URL: http://arxiv.org/abs/2109.10123v1
- Date: Tue, 21 Sep 2021 12:11:56 GMT
- Title: Survey on Semantic Stereo Matching / Semantic Depth Estimation
- Authors: Viny Saajan Victor and Peter Neigel
- Abstract summary: Finding pixel correspondences in non-textured, occluded and reflective areas is the major challenge in stereo matching.
Deep neural network architectures have been proposed to leverage the advantages of semantic segmentation in stereo matching.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Stereo matching is one of the widely used techniques for inferring depth from
stereo images owing to its robustness and speed. It has become one of the major
topics of research since it finds its applications in autonomous driving,
robotic navigation, 3D reconstruction, and many other fields. Finding pixel
correspondences in non-textured, occluded and reflective areas is the major
challenge in stereo matching. Recent developments have shown that semantic cues
from image segmentation can be used to improve the results of stereo matching.
Many deep neural network architectures have been proposed to leverage the
advantages of semantic segmentation in stereo matching. This paper aims to give
a comparison among the state of art networks both in terms of accuracy and in
terms of speed which are of higher importance in real-time applications.
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