Edge Domain Adaptation for Stereo Matching

LI Xing; FAN Yangyu; GUO Zhe; DUAN Yu; LIU Shiya

doi:10.11999/JEIT231113

Volume 46 Issue 7

Jul. 2024

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Article Navigation > Journal of Electronics & Information Technology > 2024 > 46(7): 2970-2980

LI Xing, FAN Yangyu, GUO Zhe, DUAN Yu, LIU Shiya. Edge Domain Adaptation for Stereo Matching[J]. Journal of Electronics & Information Technology, 2024, 46(7): 2970-2980. doi: 10.11999/JEIT231113

Citation:

LI Xing, FAN Yangyu, GUO Zhe, DUAN Yu, LIU Shiya. Edge Domain Adaptation for Stereo Matching[J]. Journal of Electronics & Information Technology, 2024, 46(7): 2970-2980. doi: 10.11999/JEIT231113

LI Xing, FAN Yangyu, GUO Zhe, DUAN Yu, LIU Shiya. Edge Domain Adaptation for Stereo Matching[J]. Journal of Electronics & Information Technology, 2024, 46(7): 2970-2980. doi: 10.11999/JEIT231113

Citation:

LI Xing, FAN Yangyu, GUO Zhe, DUAN Yu, LIU Shiya. Edge Domain Adaptation for Stereo Matching[J]. Journal of Electronics & Information Technology, 2024, 46(7): 2970-2980. doi: 10.11999/JEIT231113

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Edge Domain Adaptation for Stereo Matching

doi: 10.11999/JEIT231113

LI Xing¹,
FAN Yangyu¹,
GUO Zhe^{1
,
,},
DUAN Yu²,
LIU Shiya³

1.
School of Electronics and Information, Northwestern Polytechnical University, Xi’an 710072, China
2.
School of Computer Science, Northwestern Polytechnical University, Xi’an 710072, China
3.
Content Production Center of Virtual Reality, Beijing 101318, China

Funds: The National Natural Science Foundation of China (62071384), The Key Research and Development Project of Shaanxi Province (2023-YBGY-239), Jiangxi Natural Science Foundations (20224BAB212009)

Received Date: 2023-10-12
Rev Recd Date: 2023-12-28

Available Online: 2024-01-02

Publish Date: 2024-07-29

Abstract

Abstract

The style transfer method, due to its excellent domain adaptation capability, is widely used to alleviate domain gap of computer vision domain. Currently, stereo matching based on style transfer faces the following challenges: (1) The transformed left and right images need to remain matched; (2) The content and spatial information of the transformed images should remain consistent with the original images. To address these challenges, an Edge Domain Adaptation Stereo matching (EDA-Stereo) method is proposed. First, an Edge-guided Generative Adversarial Network (Edge-GAN) is constructed. By incorporating edge cues and synthetic features through the Spatial Feature Transform (SFT) layer. the Edge-GAN guides the generator to produce pseudo-images that retain the structural features of syntheitic domain images. Second, a warping loss is introduced to guarantee the left image to be reconstructed based on the transformed right image to approximate the original left image, preventing mismatches between the transformed left and right images. Finally, a normal loss based stetreo matching network is proposed to capture more geometric details by characterizing local depth variations, thereby improving matching accuracy. By training on synthetic datasets and comparing with various methods on real datasets, results show the effectiveness in mitigating domain gaps. On the KITTI 2012 and KITTI 2015 datasets, the D1 error is 3.9% and 4.8%, respectively, which is a relative reduction of 37% and 26% compared to the state-of-the-art Domain-invariant Stereo Matching Networks (DSM-Net) method.
- Stereo matching,
- Domain adaptation,
- Edge-guided,
- Generative Adversarial Network(GAN)

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References(37)

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