  The structured light (SL)-based 3D measurement techniques with deep learning
have been widely studied, among which speckle projection profilometry (SPP) and
fringe projection profilometry (FPP) 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 consist 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
improves performance at object boundaries. Extensive experiments demonstrate
that our method can reduce fringe order ambiguity while producing high-accuracy
results on a self-made dataset. We also show that the proposed architecture
reveals the potential in infrared-visible image fusion task.
