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A Joint 2D-3D Complementary Network for Stereo Matching
Article

Optimizing 3D Convolution Kernels on Stereo Matching for Resource Efficient Computations

Division of Electrical Engineering and Computer Science, Kanazawa University, Kanazawa 920-1192, Japan
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Author to whom correspondence should be addressed.
Academic Editors: Adrian Burlacu and Enric Cervera
Sensors 2021, 21(20), 6808; https://doi.org/10.3390/s21206808
Received: 16 September 2021 / Revised: 4 October 2021 / Accepted: 8 October 2021 / Published: 13 October 2021
Despite recent stereo matching algorithms achieving significant results on public benchmarks, the problem of requiring heavy computation remains unsolved. Most works focus on designing an architecture to reduce the computational complexity, while we take aim at optimizing 3D convolution kernels on the Pyramid Stereo Matching Network (PSMNet) for solving the problem. In this paper, we design a series of comparative experiments exploring the performance of well-known convolution kernels on PSMNet. Our model saves the computational complexity from 256.66 G MAdd (Multiply-Add operations) to 69.03 G MAdd (198.47 G MAdd to 10.84 G MAdd for only considering 3D convolutional neural networks) without losing accuracy. On Scene Flow and KITTI 2015 datasets, our model achieves results comparable to the state-of-the-art with a low computational cost. View Full-Text
Keywords: stereo matching; lightweight 3D kernels; 3D channel-wise attention; network design; 3D vision stereo matching; lightweight 3D kernels; 3D channel-wise attention; network design; 3D vision
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MDPI and ACS Style

Xiao, J.; Ma, D.; Yamane, S. Optimizing 3D Convolution Kernels on Stereo Matching for Resource Efficient Computations. Sensors 2021, 21, 6808. https://doi.org/10.3390/s21206808

AMA Style

Xiao J, Ma D, Yamane S. Optimizing 3D Convolution Kernels on Stereo Matching for Resource Efficient Computations. Sensors. 2021; 21(20):6808. https://doi.org/10.3390/s21206808

Chicago/Turabian Style

Xiao, Jianqiang, Dianbo Ma, and Satoshi Yamane. 2021. "Optimizing 3D Convolution Kernels on Stereo Matching for Resource Efficient Computations" Sensors 21, no. 20: 6808. https://doi.org/10.3390/s21206808

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