ELFNet: Evidential Local-global Fusion for Stereo Matching

• URL: http://arxiv.org/abs/2308.00728v1
• Date: Tue, 1 Aug 2023 15:51:04 GMT
• Title: ELFNet: Evidential Local-global Fusion for Stereo Matching
• Authors: Jieming Lou, Weide Liu, Zhuo Chen, Fayao Liu, and Jun Cheng
• Abstract summary: We introduce the textbfEvidential textbfLocal-global textbfFusion (ELF) framework for stereo matching. It endows both uncertainty estimation and confidence-aware fusion with trustworthy heads.
• Score: 17.675146012208124
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Although existing stereo matching models have achieved continuous improvement, they often face issues related to trustworthiness due to the absence of uncertainty estimation. Additionally, effectively leveraging multi-scale and multi-view knowledge of stereo pairs remains unexplored. In this paper, we introduce the \textbf{E}vidential \textbf{L}ocal-global \textbf{F}usion (ELF) framework for stereo matching, which endows both uncertainty estimation and confidence-aware fusion with trustworthy heads. Instead of predicting the disparity map alone, our model estimates an evidential-based disparity considering both aleatoric and epistemic uncertainties. With the normal inverse-Gamma distribution as a bridge, the proposed framework realizes intra evidential fusion of multi-level predictions and inter evidential fusion between cost-volume-based and transformer-based stereo matching. Extensive experimental results show that the proposed framework exploits multi-view information effectively and achieves state-of-the-art overall performance both on accuracy and cross-domain generalization. The codes are available at https://github.com/jimmy19991222/ELFNet.

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