Fringe projection profilometry (FPP) has been widely applied in industrial 3D measurement due to its high precision and non-contact advantages. However, FPP may encounter fringe saturation in high-reflective scenes, consequently impacting phase computation and introducing measurement errors. To address this problem, an efficient exposure fusion method is proposed in this paper. We propose incorporating complementary gray codes into the multi-exposure fusion method to improve measurement efficiency for high-reflective scenes. After obtaining high-quality wrapped phase by the fused phase-shifting patterns, complementary Gray code patterns are used to assist phase unwrapping to achieve 3D measurement. This method takes advantage of the fast projection speed and edge error elimination capability of complementary Gray code patterns, requiring only one set of patterns to complete phase unwrapping. Compared to the common multi-frequency method, our method reduces the number of projected images and exposure time. Experiments are conducted to demonstrate the feasibility and efficiency of the proposed method.
Depth estimation and semantic segmentation are crucial for visual perception and scene understanding. Multi-task learning, which captures shared features across multiple tasks within a scene, is often applied to depth estimation and semantic segmentation tasks to jointly improve accuracy. In this paper, a deformable attention-guided network for multi-task learning is proposed to enhance the accuracy of both depth estimation and semantic segmentation. The primary network architecture consists of a shared encoder, initial pred modules, deformable attention modules and decoders. RGB images are first input into the shared encoder to extract generic representations for different tasks. These shared feature maps are then decoupled into depth, semantic, edge and surface normal features in the initial pred module. At each stage, effective attention is applied to depth and semantic features under the guidance of fusion features in the deformable attention module. The decoder upsamples each deformable attention-enhanced feature map and outputs the final predictions. The proposed model achieves mIoU accuracy of 44.25% and RMSE of 0.5183, outperforming the single task baseline, multi-task baseline and state-of-the-art multi-task learning model.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.