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Leveraging Confident Points for Accurate Depth Refinement on Embedded Systems.

, , and . CVPR Workshops, page 158-167. Computer Vision Foundation / IEEE, (2019)

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Neural Disparity Refinement for Arbitrary Resolution Stereo., , , , , , and . 3DV, page 207-217. IEEE, (2021)MonoViT: Self-Supervised Monocular Depth Estimation with a Vision Transformer., , , , , , , , and . 3DV, page 668-678. IEEE, (2022)Efficient Confidence Measures for Embedded Stereo., , and . ICIAP (1), volume 10484 of Lecture Notes in Computer Science, page 483-494. Springer, (2017)On the Synergies between Machine Learning and Stereo: a Survey., , , , and . CoRR, (2020)RGB-Multispectral Matching: Dataset, Learning Methodology, Evaluation., , , , , and . CVPR, page 15937-15947. IEEE, (2022)Distilled Semantics for Comprehensive Scene Understanding from Videos., , , , , , and . CVPR, page 4653-4664. Computer Vision Foundation / IEEE, (2020)Leveraging Confident Points for Accurate Depth Refinement on Embedded Systems., , and . CVPR Workshops, page 158-167. Computer Vision Foundation / IEEE, (2019)Real-Time Self-Adaptive Deep Stereo., , , , and . CVPR, page 195-204. Computer Vision Foundation / IEEE, (2019)Learning a General-Purpose Confidence Measure Based on O(1) Features and a Smarter Aggregation Strategy for Semi Global Matching., and . 3DV, page 509-518. IEEE Computer Society, (2016)Lightweight Self-Supervised Depth Estimation with few-beams LiDAR Data., , , and . BMVC, page 356-357. BMVA Press, (2023)