Shape from polarization based on sparse self-attention
Accurate estimation of surface normal plays a vital role in various computer vision tasks.Physi-cally-based shape from polarization methods have limitations restricting their applications.Conversely,learning-based shape from polarization methods outperform physical methods in both accuracy and applica-bility.To further improve the accuracy of shape from polarization and make it applicable to a broader range of practical tasks,we proposed a novel method.First,we introduced a new polarization information repre-sentation combining Stokes vectors,enhancing the model's ability to extract polarization physical prior in-formation.Then,we integrated a bi-level routing sparse self-attention mechanism to improve the model's perception of global contextual information,enabling better disambiguation of local polarization informa-tion.Testing on the DeepSfP dataset and out test data,experimental results demonstrate our proposed method achieves an average angular error of 13.37° on the DeepSfP dataset,outperforming existing meth-ods in all tested metrics including accuracy and angular error.This indicates a significant improvement in normal estimation effectiveness with our proposed method.By introducing a novel polarization information representation and sparse self-attention mechanism,our approach enhances the accuracy and applicability of polar surface normal estimation,providing stronger support for practical task applications.
polarization information representationsparse self-attention mechanismshape from polar-ization
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