Double-Shot 3D Shape Measurement with a Dual-Branch Network for Structured Light Projection Profilometry
This work addresses measurement efficiency and accuracy issues in 3D shape reconstruction for applications like industrial inspection or computer vision, representing an incremental improvement over existing single-pattern methods.
The paper tackles the problem of fringe order ambiguity and poor reconstruction accuracy in structured light-based 3D shape measurement by proposing a dual-branch CNN-Transformer network (PDCNet) with a double-stream attention aggregation module and an adaptive mixture density head, resulting in reduced ambiguity and high-accuracy outcomes on self-made datasets.
The structured light (SL)-based three-dimensional (3D) measurement techniques with deep learning have been widely studied to improve measurement efficiency, among which fringe projection profilometry (FPP) and speckle projection profilometry (SPP) are two popular methods. However, they generally use a single projection pattern for reconstruction, resulting in fringe order ambiguity or poor reconstruction accuracy. To alleviate these problems, we propose a parallel dual-branch Convolutional Neural Network (CNN)-Transformer network (PDCNet), to take advantage of convolutional operations and self-attention mechanisms for processing different SL modalities. Within PDCNet, a Transformer branch is used to capture global perception in the fringe images, while a CNN branch is designed to collect local details in the speckle images. To fully integrate complementary features, we design a double-stream attention aggregation module (DAAM) that consists of a parallel attention subnetwork for aggregating multi-scale spatial structure information. This module can dynamically retain local and global representations to the maximum extent. Moreover, an adaptive mixture density head with bimodal Gaussian distribution is proposed for learning a representation that is precise near discontinuities. Compared to the standard disparity regression strategy, this adaptive mixture head can effectively improve performance at object boundaries. Extensive experiments demonstrate that our method can reduce fringe order ambiguity while producing high-accuracy results on self-made datasets.