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- W4386160316 abstract "The low-quality cost distribution obtained by simple upsampling leads to disparity maps with many outliers and low sub-pixel accuracy. We propose the Cost Distribution Super-Resolution Network (CDSRNet), which directly extracts high-resolution cost distribution from the low-resolution 4D cost volume.The similarity extraction module of CDSRNet decomposes the task of estimating high-resolution cost distribution into multiple subtasks and completes each subtask by a specific translation block, ensuring high discrimination of the predicted cost distribution. The inter-block aggregation module aggregates information from other subtasks, obtaining a voting volume with global information for correcting errors in the current subtask. Experiment results demonstrate that the proposed method significantly reduces the ratio of outliers and improves the sub-pixel accuracy." @default.
- W4386160316 created "2023-08-26" @default.
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- W4386160316 date "2023-07-01" @default.
- W4386160316 modified "2023-09-23" @default.
- W4386160316 title "Denser is Better:cost distribution super-resolution network for more accurate sub-pixel disparity" @default.
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- W4386160316 doi "https://doi.org/10.1109/icme55011.2023.00087" @default.
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