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

Paper Details

Date Published: 14 August 2019
PDF: 7 pages
Proc. SPIE 11179, Eleventh International Conference on Digital Image Processing (ICDIP 2019), 111793S (14 August 2019); doi: 10.1117/12.2539613
Show Author Affiliations
Ting Tang, Tsinghua Univ. (China)
Zongqing Lu, Tsinghua Univ. (China)
Qingmin Liao, Tsinghua Univ. (China)


Published in SPIE Proceedings Vol. 11179:
Eleventh International Conference on Digital Image Processing (ICDIP 2019)
Jenq-Neng Hwang; Xudong Jiang, Editor(s)

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