Double-Shot 3D Shape Measurement with a Dual-Branch Network for Structured Light Projection Profilometry

TL;DR

This paper introduces PDCNet, a dual-branch neural network combining CNN and Transformer architectures with an attention module, to improve 3D shape measurement accuracy and reduce ambiguity in structured light profilometry.

Contribution

The paper presents a novel dual-branch CNN-Transformer network with a double-stream attention module for enhanced 3D shape measurement using structured light.

Findings

Reduces fringe order ambiguity in 3D measurement.

Achieves high accuracy on self-made datasets.

Improves boundary reconstruction with adaptive mixture density head.

Abstract

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.