2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

Imaging Without Borders

2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

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Paper Detail

Paper ID3D-3.4
Paper Title SEPARABLE CONVOLUTIONS FOR OPTIMIZING 3D STEREO NETWORKS
Authors Rafia Rahim, Faranak Shamsafar, Andreas Zell, University of Tuebingen, Germany
Session3D-3: Stereoscopic and multiview processing
LocationArea J
Session Time:Wednesday, 22 September, 14:30 - 16:00
Presentation Time:Wednesday, 22 September, 14:30 - 16:00
Presentation Poster
Topic Three-Dimensional Image and Video Processing: Stereoscopic and multiview processing and display
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract Deep learning based 3D stereo networks give superior performance compared to 2D networks and conventional stereo methods. However, this improvement in the performance comes at the cost of increased computational complexity, thus making these networks non-practical for the real-world applications. Specifically, these networks use 3D convolutions as a major work horse to refine and regress disparities. In this work first, we show that these 3D convolutions in stereo networks consume up to 94% of overall network operations and act as a major bottleneck. Next, we propose a set of “plug-&-run” separable convolutions to reduce the number of parameters and operations. When integrated with the existing state of the art stereo networks, these convolutions lead up to 7× reduction in number of operations and up to 3.5× reduction in parameters without compromising their performance. In fact these convolutions lead to improvement in their performance in the majority of cases.