GraftNet: Towards Domain Generalized Stereo Matching with a Broad-Spectrum and Task-Oriented Feature

• URL: http://arxiv.org/abs/2204.00179v1
• Date: Fri, 1 Apr 2022 03:10:04 GMT
• Title: GraftNet: Towards Domain Generalized Stereo Matching with a Broad-Spectrum and Task-Oriented Feature
• Authors: Biyang Liu, Huimin Yu, Guodong Qi
• Abstract summary: We propose to leverage the feature of a model trained on large-scale datasets to deal with the domain shift. With the cosine similarity based cost volume as a bridge, the feature will be grafted to an ordinary cost aggregation module. Experiments show that the model generalization ability can be improved significantly with this broad-spectrum and task-oriented feature.
• Score: 2.610470075814367
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Although supervised deep stereo matching networks have made impressive achievements, the poor generalization ability caused by the domain gap prevents them from being applied to real-life scenarios. In this paper, we propose to leverage the feature of a model trained on large-scale datasets to deal with the domain shift since it has seen various styles of images. With the cosine similarity based cost volume as a bridge, the feature will be grafted to an ordinary cost aggregation module. Despite the broad-spectrum representation, such a low-level feature contains much general information which is not aimed at stereo matching. To recover more task-specific information, the grafted feature is further input into a shallow network to be transformed before calculating the cost. Extensive experiments show that the model generalization ability can be improved significantly with this broad-spectrum and task-oriented feature. Specifically, based on two well-known architectures PSMNet and GANet, our methods are superior to other robust algorithms when transferring from SceneFlow to KITTI 2015, KITTI 2012, and Middlebury. Code is available at https://github.com/SpadeLiu/Graft-PSMNet.

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