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Poster
GPS-Net: Graph-based Photometric Stereo Network
Zhuokun Yao · Kun Li · Ying Fu · Haofeng Hu · Boxin Shi

Tue Dec 08 09:00 AM -- 11:00 AM (PST) @ Poster Session 1 #399
in Poster Session 2 »

Learning-based photometric stereo methods predict the surface normal either in a per-pixel or an all-pixel manner. Per-pixel methods explore the inter-image intensity variation of each pixel but ignore features from the intra-image spatial domain. All-pixel methods explore the intra-image intensity variation of each input image but pay less attention to the inter-image lighting variation. In this paper, we present a Graph-based Photometric Stereo Network, which unifies per-pixel and all-pixel processings to explore both inter-image and intra-image information. For per-pixel operation, we propose the Unstructured Feature Extraction Layer to connect an arbitrary number of input image-light pairs into graph structures, and introduce Structure-aware Graph Convolution filters to balance the input data by appropriately weighting shadows and specular highlights. For all-pixel operation, we propose the Normal Regression Network to make efficient use of the intra-image spatial information for predicting a surface normal map with rich details. Experimental results on the real-world benchmark show that our method achieves excellent performance under both sparse and dense lighting distributions.

Keywords: Computer Vision · Algorithms · Adversarial Learning · Algorithms -> Classification; Algorithms -> Large Scale Learning; Applications

Author Information

Zhuokun Yao (Tianjin University)
Kun Li (Tianjin University)
Ying Fu (Beijing Institute of Technology)
Haofeng Hu (Tianjin University)
Boxin Shi (Peking University)

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