EASNet: Searching Elastic and Accurate Network Architecture for Stereo Matching

Qiang Wang, Shaohuai Shi, Kaiyong Zhao, Xiaowen Chu*

*Corresponding author for this work

Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review

3 Citations (Scopus)

Abstract

Recent advanced studies have spent considerable human efforts on optimizing network architectures for stereo matching but hardly achieved both high accuracy and fast inference speed. To ease the workload in network design, neural architecture search (NAS) has been applied with great success to various sparse prediction tasks, such as image classification and object detection. However, existing NAS studies on the dense prediction task, especially stereo matching, still cannot be efficiently and effectively deployed on devices of different computing capability. To this end, we propose to train an elastic and accurate network for stereo matching (EASNet) that supports various 3D architectural settings on devices with different compute capability. Given the deployment latency constraint on the target device, we can quickly extract a sub-network from the full EASNet without additional training while the accuracy of the sub-network can still be maintained. Extensive experiments show that our EASNet outperforms both state-of-the-art human-designed and NAS-based architectures on Scene Flow and MPI Sintel datasets in terms of model accuracy and inference speed. Particularly, deployed on an inference GPU, EASNet achieves a new SOTA 0.73 EPE on the Scene Flow dataset with 100 ms, which is 4.5 × faster than LEAStereo with a better quality model. The codes of EASNet are available at: https://github.com/HKBU-HPML/EASNet.git.

Original languageEnglish
Title of host publicationComputer Vision – ECCV 2022
Subtitle of host publication17th European Conference, Tel Aviv, Israel, October 23–27, 2022, Proceedings, Part XXXII
EditorsShai Avidan, Gabriel Brostow, Moustapha Cissé, Giovanni Maria Farinella, Tal Hassner
PublisherSpringer Cham
Pages437-453
Number of pages17
ISBN (Electronic)9783031198243
ISBN (Print)9783031198236
DOIs
Publication statusPublished - 11 Nov 2022
Event17th European Conference on Computer Vision, ECCV 2022 - Tel Aviv, Israel
Duration: 23 Oct 202227 Oct 2022
https://eccv2022.ecva.net/
https://link.springer.com/conference/eccv
https://link.springer.com/book/10.1007/978-3-031-19769-7

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference17th European Conference on Computer Vision, ECCV 2022
Country/TerritoryIsrael
CityTel Aviv
Period23/10/2227/10/22
Internet address

Scopus Subject Areas

  • Theoretical Computer Science
  • General Computer Science

User-Defined Keywords

  • Neural architecture search
  • Stereo matching

Fingerprint

Dive into the research topics of 'EASNet: Searching Elastic and Accurate Network Architecture for Stereo Matching'. Together they form a unique fingerprint.

Cite this