A dual-cue network for multispectral photometric stereo
作者：
Highlights：
• A novel dual-cue fused network is proposed for surface normal recovering, which exploits specular highlights, shadows and interreflections appearing in local image patches, meanwhile maintaining high-frequency details.
• Compared to previous multispectral photometric stereo algorithms, the proposed method requires no extra information and breaks the limitation of Lambertian surfaces.
• The Dual-cue fused network outperforms existing approaches in robustness under complex illumination.
摘要
•A novel dual-cue fused network is proposed for surface normal recovering, which exploits specular highlights, shadows and interreflections appearing in local image patches, meanwhile maintaining high-frequency details.•Compared to previous multispectral photometric stereo algorithms, the proposed method requires no extra information and breaks the limitation of Lambertian surfaces.•The Dual-cue fused network outperforms existing approaches in robustness under complex illumination.
论文关键词：Multispectral photometric stereo,Normal estimation,Deep neural networks,Networks fusion
论文评审过程：Received 10 February 2019, Revised 10 December 2019, Accepted 12 December 2019, Available online 12 December 2019, Version of Record 19 December 2019.
论文官网地址：https://doi.org/10.1016/j.patcog.2019.107162
