Deng, H;
Liao, Q;
Lu, Z;
Xue, J-H;
(2021)
Parallax Contextual Representations For Stereo Matching.
In:
Proceedings of the IEEE International Conference on Image Processing (ICIP) 2021.
(pp. pp. 3193-3197).
Institute of Electrical and Electronics Engineers (IEEE)
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Abstract
In this work, we study the context aggregation in stereo matching from a new parallax perspective. Unlike previous works, we propose to characterize and augment a pixel with its parallax contextual representation (PCR), which has not been explored before. We also propose a new concept called disparity prototype to describe the overall representation of a disparity plane. Our proposed PCR module consists of three steps: 1) divide disparity planes for a rough estimation of disparity; 2) estimate the disparity prototypes for each disparity plane; 3) derive PCR-augmented representations with disparity prototypes. Extensive experiments on various datasets using different networks validate the effectiveness of our proposal.
| Type: | Proceedings paper |
|---|---|
| Title: | Parallax Contextual Representations For Stereo Matching |
| Event: | 2021 IEEE International Conference on Image Processing (ICIP) |
| Location: | Anchorage (AK), USA |
| Dates: | 19th-22th September 2021 |
| ISBN-13: | 978-1-6654-4115-5 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/icip42928.2021.9506747 |
| Publisher version: | https://doi.org/10.1109/ICIP42928.2021.9506747 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions. |
| Keywords: | Stereo matching, parallax contextual representation, disparity prototype, disparity plane |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10133571 |
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