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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 36 / No. 2: AAAI-22 Technical Tracks 2

Local Similarity Pattern and Cost Self-Reassembling for Deep Stereo Matching Networks

February 1, 2023

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Authors

Biyang Liu

College of Information Science & Electronic Engineering, Zhejiang University ZJU-League Research & Development Center


Huimin Yu

College of Information Science & Electronic Engineering, Zhejiang University ZJU-League Research & Development Center State Key Lab of CAD&CG, Zhejiang University Zhejiang Provincial Key Laboratory of Information Processing, Communication and Networking


Yangqi Long

College of Information Science & Electronic Engineering, Zhejiang University ZJU-League Research & Development Center


DOI:

10.1609/aaai.v36i2.20056


Abstract:

Although convolutional neural network based stereo matching architectures have made impressive achievements, there are still some limitations: 1) Convolutional Feature (CF) tends to capture appearance information, which is inadequate for accurate matching. 2) Due to the static filters, current convolution based disparity refinement modules often produce over-smooth results. In this paper, we present two schemes to address these issues, where some traditional wisdoms are integrated. Firstly, we introduce a pairwise feature for deep stereo matching networks, named LSP (Local Similarity Pattern). Through explicitly revealing the neighbor relationships, LSP contains rich structural information, which can be leveraged to aid CF for more discriminative feature description. Secondly, we design a dynamic self-reassembling refinement strategy and apply it to the cost distribution and the disparity map respectively. The former could be equipped with the unimodal distribution constraint to alleviate the over-smoothing problem, and the latter is more practical. The effectiveness of the proposed methods is demonstrated via incorporating them into two well-known basic architectures, GwcNet and GANet-deep. Experimental results on the SceneFlow and KITTI benchmarks show that our modules significantly improve the performance of the model. Code is available at https://github.com/SpadeLiu/Lac-GwcNet.

Topics: AAAI

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HOW TO CITE:

Biyang Liu||Huimin Yu||Yangqi Long Local Similarity Pattern and Cost Self-Reassembling for Deep Stereo Matching Networks Proceedings of the AAAI Conference on Artificial Intelligence (2022) 1647-1655.

Biyang Liu||Huimin Yu||Yangqi Long Local Similarity Pattern and Cost Self-Reassembling for Deep Stereo Matching Networks AAAI 2022, 1647-1655.

Biyang Liu||Huimin Yu||Yangqi Long (2022). Local Similarity Pattern and Cost Self-Reassembling for Deep Stereo Matching Networks. Proceedings of the AAAI Conference on Artificial Intelligence, 1647-1655.

Biyang Liu||Huimin Yu||Yangqi Long. Local Similarity Pattern and Cost Self-Reassembling for Deep Stereo Matching Networks. Proceedings of the AAAI Conference on Artificial Intelligence 2022 p.1647-1655.

Biyang Liu||Huimin Yu||Yangqi Long. 2022. Local Similarity Pattern and Cost Self-Reassembling for Deep Stereo Matching Networks. "Proceedings of the AAAI Conference on Artificial Intelligence". 1647-1655.

Biyang Liu||Huimin Yu||Yangqi Long. (2022) "Local Similarity Pattern and Cost Self-Reassembling for Deep Stereo Matching Networks", Proceedings of the AAAI Conference on Artificial Intelligence, p.1647-1655

Biyang Liu||Huimin Yu||Yangqi Long, "Local Similarity Pattern and Cost Self-Reassembling for Deep Stereo Matching Networks", AAAI, p.1647-1655, 2022.

Biyang Liu||Huimin Yu||Yangqi Long. "Local Similarity Pattern and Cost Self-Reassembling for Deep Stereo Matching Networks". Proceedings of the AAAI Conference on Artificial Intelligence, 2022, p.1647-1655.

Biyang Liu||Huimin Yu||Yangqi Long. "Local Similarity Pattern and Cost Self-Reassembling for Deep Stereo Matching Networks". Proceedings of the AAAI Conference on Artificial Intelligence, (2022): 1647-1655.

Biyang Liu||Huimin Yu||Yangqi Long. Local Similarity Pattern and Cost Self-Reassembling for Deep Stereo Matching Networks. AAAI[Internet]. 2022[cited 2023]; 1647-1655.


ISSN: 2374-3468


Published by AAAI Press, Palo Alto, California USA
Copyright 2022, Association for the Advancement of
Artificial Intelligence 1900 Embarcadero Road, Suite
101, Palo Alto, California 94303 All Rights Reserved

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