Denser is Better:cost distribution super-resolution network for more accurate sub-pixel disparity

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.