ELFNet: Evidential Local-global Fusion for Stereo Matching

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ELFNet: Evidential Local-global Fusion for Stereo Matching
ELFNet: Evidential Local-global Fusion for Stereo Matching
Journal Title:
International Conference on Computer Vision
Publication Date:
04 October 2023
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 Evidential Local-global Fusion (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 uncertainty. 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.
License type:
Publisher Copyright
Funding Info:
This research / project is supported by the A*STAR - MTC Programmatic
Grant Reference no. : M23L7b0021
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