A Serial Perspective on Photometric Stereo of Filtering and Serializing Spatial Information

In this paper, we introduce a novel method of Filtering and Serializing Spatial Information to tackle uncalibrated photometric stereo tasks, termed FSSI-PS. Photometric stereo aims to recover surface normals from images with varying lighting and is crucial for tasks like 3D reconstruction and defect detection. Current methods in complex surface reconstruction are costly and inaccurate due to redundant feature representations from GCN or Transformer modules, caused by the weak global information extraction capability of GCNs or the large computational cost of Transformers. Furthermore, the trainset's lack of richness in texture complexity makes reconstruction more difficult. We address these issues by optimizing feature maps and dataset richness through serializing and filtering. First, we use Mamba-RNN to optimize feature representation by directly fusing feature maps, which reduces redundancy and uses minimal computational resources. Specifically, we treat input spatial information as a sequence and serialize it by sorting. Furthermore, we introduce the Mean Angular Variation metric to assess reconstruction difficulty by measuring texture complexity. It classifies PS-Sculpture and PS-Blobby into three categories: Difficult, Normal, and Simple. We use this to construct DNS-S+B, a photometric stereo training set with rich complexity levels. Our method is compared with state-of-the-art methods on the DiLiGenT and LUCES benchmarks to highlight effectiveness.