ELFNet: Evidential Local-global Fusion for Stereo Matching
Jieming Lou Weide Liu Zhuo Chen Fayao Liu Jun Cheng
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.