Stereo matching with local cost volume refinement network

Mingzhu Wan; Lingbao Kong

doi:10.1117/12.2653259

9 January 2023 Stereo matching with local cost volume refinement network

Mingzhu Wan, Lingbao Kong

Proceedings Volume 12507, Advanced Optical Manufacturing Technologies and Applications 2022; and 2nd International Forum of Young Scientists on Advanced Optical Manufacturing (AOMTA and YSAOM 2022); 1250709 (2023) https://doi.org/10.1117/12.2653259
Event: 2nd International Forum of Young Scientists on Advanced Optical Manufacturing (AOMTA and YSAOM 2022), 2022, Changchun, China

Abstract

Deep learning methods have been widely used to complete the task of stereo matching in recent years, which is the key step in machine vision measurement. State-of-the-art methods are three-dimensional (3D) end-to-end networks that forms a cost volume by concatenating extracted features and processes it with 3D modules. Despite the strong performance in terms of accuracy, 3D networks mostly have high computational cost, heavy memory storge and long run-time. In this paper proposed Local Cost Volume Refinement Network (LCRN), which is a two-dimensional (2D) end-to-end network composed of feature extraction, disparity initialization, disparity refinement and disparity mergence module. LCRN initializes disparity maps by using correlation layer and residual blocks, and refines them by using local cost volumes, residual blocks and disparity regression. Local cost volumes are constructed by warping right features and giving a small disparity shift. To verify the effectiveness of LCRN, the network was pre-trained on SceneFlow dataset and fine-tuned on ROBI dataset. The network is evaluated on the test set of ROBI for robotic bin-picking. Experimental results show that LCRN maintains a competitive accuracy while having fast run-time and requiring less memory storage.

Citation Download Citation

Mingzhu Wan and Lingbao Kong "Stereo matching with local cost volume refinement network", Proc. SPIE 12507, Advanced Optical Manufacturing Technologies and Applications 2022; and 2nd International Forum of Young Scientists on Advanced Optical Manufacturing (AOMTA and YSAOM 2022), 1250709 (9 January 2023); https://doi.org/10.1117/12.2653259

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KEYWORDS

Feature extraction

Robotics

3D metrology

Machine vision

3D modeling

Computer vision technology

Data modeling

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