ELFNet: Evidential Local-global Fusion for Stereo Matching

Jieming Lou, Weide Liu, Zhuo Chen, Fayao Liu, Jun Cheng; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2023, pp. 17784-17793


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 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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[pdf] [arXiv]
@InProceedings{Lou_2023_ICCV, author = {Lou, Jieming and Liu, Weide and Chen, Zhuo and Liu, Fayao and Cheng, Jun}, title = {ELFNet: Evidential Local-global Fusion for Stereo Matching}, booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)}, month = {October}, year = {2023}, pages = {17784-17793} }