Abstract
In the stereo matching task, matching cost aggregation is crucial in both traditional methods and deep neural network models in order to accurately estimate disparities. We propose two novel neural net layers, aimed at capturing local and the whole-image cost dependencies respectively. The first is a semi-global aggregation layer which is a differentiable approximation of the semi-global matching, the second is the local guided aggregation layer which follows a traditional cost filtering strategy to refine thin structures. These two layers can be used to replace the widely used 3D convolutional layer which is computationally costly and memory-consuming as it has cubic computational/memory complexity. In the experiments, we show that nets with a two-layer guided aggregation block easily outperform the state-of-the-art GC-Net which has nineteen 3D convolutional layers. We also train a deep guided aggregation network (GA-Net) which gets better accuracies than state-of-the-art methods on both Scene Flow dataset and KITTI benchmarks.
Original language | English |
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Title of host publication | Proceedings - 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2019 |
Pages | 185-194 |
Number of pages | 10 |
ISBN (Electronic) | 9781728132938 |
DOIs | |
State | Published - Jun 2019 |
Event | 32nd IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2019 - Long Beach, United States Duration: Jun 16 2019 → Jun 20 2019 |
Publication series
Name | Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition |
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Volume | 2019-June |
ISSN (Print) | 1063-6919 |
Conference
Conference | 32nd IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2019 |
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Country/Territory | United States |
City | Long Beach |
Period | 6/16/19 → 6/20/19 |
Bibliographical note
Publisher Copyright:© 2019 IEEE.
Keywords
- 3D from Multiview and Sensors
- Deep Learning
ASJC Scopus subject areas
- Software
- Computer Vision and Pattern Recognition