Stereo Confidence Estimation via Locally Adaptive Fusion and Knowledge Distillation
- 주제(키워드) deep learning , knowledge distillation , stereo confidence estimation , Stereo matching
- 관리정보기술 faculty
- 등재 SCIE, SCOPUS
- 발행기관 IEEE Computer Society
- 발행년도 2023
- 총서유형 Journal
- URI http://www.dcollection.net/handler/ewha/000000208843
- 본문언어 영어
- Published As https://doi.org/10.1109/TPAMI.2022.3207286
- PubMed 36112555
초록/요약
Stereo confidence estimation aims to estimate the reliability of the estimated disparity by stereo matching. Different from the previous methods that exploit the limited input modality, we present a novel method that estimates confidence map of an initial disparity by making full use of tri-modal input, including matching cost, disparity, and color image through deep networks. The proposed network, termed as Locally Adaptive Fusion Networks (LAF-Net), learns locally-varying attention and scale maps to fuse the tri-modal confidence features. Moreover, we propose a knowledge distillation framework to learn more compact confidence estimation networks as student networks. By transferring the knowledge from LAF-Net as teacher networks, the student networks that solely take as input a disparity can achieve comparable performance. To transfer more informative knowledge, we also propose a module to learn the locally-varying temperature in a softmax function. We further extend this framework to a multiview scenario. Experimental results show that LAF-Net and its variations outperform the state-of-the-art stereo confidence methods on various benchmarks. © 1979-2012 IEEE.
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