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Attention-embedding mesh saliency

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Abstract

Recently, the learning method is gradually penetrating into the field of 3D saliency, but the ground truth annotation is too insufficient to directly train a 3D saliency network. Here, we propose a novel attention-embedding strategy for 3D saliency estimation by directly applying the attention embedding scheme to 3D mesh. With this method, the network is trained in a weakly supervised manner, requiring no saliency annotations but generalizing well on different categories of objects, such as animals, furniture, cars and people. Experimental results show that our approach is comparable with existing state-of-the-art methods. We also apply saliency results to mesh simplification. Evaluations on simplified models show that the visually significant parts can be retained during saliency-aware simplification.

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Acknowledgements

We thank all the anonymous reviewers for their valuable comments. This work was supported by the National Key Research and Development Program of China (2020YFB1712401, 2018YFC0824402).

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  1. School of Cyber Science and Engineering, Zhengzhou University, Zhengzhou, 450002, China

    Cheng-ming Liu, Wan-na Luan, Rong-hua Fu, Hai-bo Pang & Ying-hao Li

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Liu, Cm., Luan, Wn., Fu, Rh. et al. Attention-embedding mesh saliency. Vis Comput 39, 1783–1795 (2023). https://doi.org/10.1007/s00371-022-02444-y

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