HIGHLIGHTS
- who: Matching and collaborators from the School of Mathematics and Statistics, Xi`an Jiaotong University, Xi`an, China have published the paper: Dense Feature Learning and Compact Cost Aggregation for Deep Stereo Matching, in the Journal: (JOURNAL)
- what: The authors propose a novel deep stereo network based on the strategies of dense feature learning and compact cost aggregation namely DFL-CCA-Net. The authors design an efficient compact cost aggregation module to make the updated cost volume more informative, which can largely improve the final disparity regression accuracies. The authors propose an end-to-end . . .
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