A Benchmark for Heterogeneous Stereo Deblurring with Physically- and Epipolar-constrained Cross Attention

Modern stereo-capable smartphones enable immersive XRcontent capture. However, hardware heterogeneity across camera mod-ules often causes severe asymmetric blur artifacts. Existing methods andbenchmarks largely assume homogeneous stereo setups and therefore donot explicitly address such asymmetric degradation. To bridge this gap,we present a dedicated framework for heterogeneous stereo deblurring.First, we introduce the heterogeneous stereo deblurring (HSD) dataset,constructed from real smartphone stereo captures via multi-frame inte-gration. Second, we propose physically- and epipolar-constrained crossattention (PECA), a lightweight module that restricts cross-view match-ing to an epipolar search window bounded by an optics-derived dis-parity upper bound. By enforcing physically valid disparity constraints,PECA enables efficient and reliable cross-view feature fusion. Moreover,our confidence-weighted attention with residual fusion emphasizes cross-guided deblurring when correspondences are reliable, while naturallyfalling back to self-deblurring in occluded or unreliable regions. PECAis architecture-agnostic and consistently improves CNN-, Transformer-,and NAFNet-based baselines. Extensive experiments on HSD show thatPECA-enhanced models achieve improved restoration performance withfavorable efficiency. The dataset and source code are publicly released athttps://github.com/shinhoju/PECA.