Content-aware Warping for View Synthesis

• URL: http://arxiv.org/abs/2201.09023v1
• Date: Sat, 22 Jan 2022 11:35:05 GMT
• Title: Content-aware Warping for View Synthesis
• Authors: Mantang Guo, Jing Jin, Hui Liu, Junhui Hou, Huanqiang Zeng, Jiwen Lu
• Abstract summary: We propose content-aware warping, which adaptively learns the weights for pixels of a relatively large neighborhood from their contextual information via a lightweight neural network. Based on this learnable warping module, we propose a new end-to-end learning-based framework for novel view synthesis from two source views. Experimental results on structured light field datasets with wide baselines and unstructured multi-view datasets show that the proposed method significantly outperforms state-of-the-art methods both quantitatively and visually.
• Score: 110.54435867693203
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Existing image-based rendering methods usually adopt depth-based image warping operation to synthesize novel views. In this paper, we reason the essential limitations of the traditional warping operation to be the limited neighborhood and only distance-based interpolation weights. To this end, we propose content-aware warping, which adaptively learns the interpolation weights for pixels of a relatively large neighborhood from their contextual information via a lightweight neural network. Based on this learnable warping module, we propose a new end-to-end learning-based framework for novel view synthesis from two input source views, in which two additional modules, namely confidence-based blending and feature-assistant spatial refinement, are naturally proposed to handle the occlusion issue and capture the spatial correlation among pixels of the synthesized view, respectively. Besides, we also propose a weight-smoothness loss term to regularize the network. Experimental results on structured light field datasets with wide baselines and unstructured multi-view datasets show that the proposed method significantly outperforms state-of-the-art methods both quantitatively and visually. The source code will be publicly available at https://github.com/MantangGuo/CW4VS.

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