DSCNet: lightweight and efficient self-supervised network via depthwise separable cross convolution blocks for speckle image matching

Abstract

Speckle structured light has become a research hotspot due to its ability to acquire target three-dimensional information with single image projection in recent years. To address the challenges of a low number of extracted speckle feature points, high mismatch rate and poor real-time performance in traditional algorithms, as well as the obstacle of requiring expensive annotation data in deep learning-based methods, a lightweight and efficient self-supervised convolutional neural network (CNN) is proposed to achieve high-precision and rapid matching of speckle images. First, to efficiently utilize the speckle projection information, a feature extraction backbone based on the depthwise separable cross convolution blocks is proposed. Second, in the feature detection module, a softargmax detection head is designed to refine the coordinates of speckle feature points to sub-pixel accuracy. In the feature description module, a coarse-to-fine module is presented to further refine matching accuracy. Third, we adopt strategies of transfer learning and self-supervised learning to improve the generalization and feature representation capabilities of the model. Data augmentation and real-time training techniques are used to improve the robustness of the model. The experimental results show that the proposed method achieves a mean matching accuracy of 91.62% for speckle feature points on the pilot’s helmet, with mere 0.95% mismatch rate. The full model runs at 42ms for a speckle image pair on an RTX 3060.