Decoupling Bidirectional Geometric Representations of 4D cost volume with 2D convolution

• URL: http://arxiv.org/abs/2509.02415v1
• Date: Tue, 02 Sep 2025 15:21:49 GMT
• Title: Decoupling Bidirectional Geometric Representations of 4D cost volume with 2D convolution
• Authors: Xiaobao Wei, Changyong Shu, Zhaokun Yue, Chang Huang, Weiwei Liu, Shuai Yang, Lirong Yang, Peng Gao, Wenbin Zhang, Gaochao Zhu, Chengxiang Wang,
• Abstract summary: We present a deployment-friendly 4D cost aggregation network DBStereo.<n>It is based on pure 2D convolutions and achieves real-time performance and impressive accuracy simultaneously.
• Score: 40.103929972279126
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: High-performance real-time stereo matching methods invariably rely on 3D regularization of the cost volume, which is unfriendly to mobile devices. And 2D regularization based methods struggle in ill-posed regions. In this paper, we present a deployment-friendly 4D cost aggregation network DBStereo, which is based on pure 2D convolutions. Specifically, we first provide a thorough analysis of the decoupling characteristics of 4D cost volume. And design a lightweight bidirectional geometry aggregation block to capture spatial and disparity representation respectively. Through decoupled learning, our approach achieves real-time performance and impressive accuracy simultaneously. Extensive experiments demonstrate that our proposed DBStereo outperforms all existing aggregation-based methods in both inference time and accuracy, even surpassing the iterative-based method IGEV-Stereo. Our study break the empirical design of using 3D convolutions for 4D cost volume and provides a simple yet strong baseline of the proposed decouple aggregation paradigm for further study. Code will be available at (\href{https://github.com/happydummy/DBStereo}{https://github.com/happydummy/DBStereo}) soon.

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