A stereo matching network with a cascade spatial pyramid pooling (CSPP) substructure

Ting Tang; Zongqing Lu; Qingmin Liao

doi:10.1117/12.2539613

14 August 2019 A stereo matching network with a cascade spatial pyramid pooling (CSPP) substructure

Ting Tang, Zongqing Lu, Qingmin Liao

Proceedings Volume 11179, Eleventh International Conference on Digital Image Processing (ICDIP 2019); 111793S (2019) https://doi.org/10.1117/12.2539613
Event: Eleventh International Conference on Digital Image Processing (ICDIP 2019), 2019, Guangzhou, China

Abstract

We propose a novel end-to-end supervised convolutional neural network(CNN) to compute disparity from a pair of stereo images. To solve the current problem of computing the high-quality disparity in ill-areas, our cascade spatial pyramid pooling (CSPP) substructure is able to gather global context information by aggregating the context information in different positions and different feature block scales from coarse to fine. We also introduce a warp layer, the right feature map is warped with the previously predicted disparity, and then is compared with the left feature map to form a cost volume. We learn the disparity from the cost volume with different level features information. We evaluate our method on three stereo datasets, and results show our method has advantages in textured areas, target edge areas and efficiency. We also achieve a high ranking performance.

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Ting Tang, Zongqing Lu, and Qingmin Liao "A stereo matching network with a cascade spatial pyramid pooling (CSPP) substructure", Proc. SPIE 11179, Eleventh International Conference on Digital Image Processing (ICDIP 2019), 111793S (14 August 2019); https://doi.org/10.1117/12.2539613

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KEYWORDS

Convolution

RGB color model

Data modeling

Performance modeling

Computer programming

Network architectures

3D modeling

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