An efficient speckle matching network based on deep learning

Speckle projection profilometry (SPP), as an efficient 3D measurement method based on structured light projection, projects the speckle pattern based on spatial encoding onto the measured scene to enhance its texture, thereby improving the accuracy of single-shot 3D measurement. However, the traditional stereo matching method in SPP compromises the measurement accuracy in order to ensure the robustness of 3D measurement. At present, some speckle matching methods based on deep learning have been proposed to obtain high-precision and dense disparity maps, but at the cost of expensive computational overhead, which occupies a lot of memory resources and reduces the running speed. Different from existing networks, this paper proposes a lightweight end-to-end stereo matching network by combining attention mechanism, spatial pyramid pooling module (SPPM), and multi-scale feature fusion, which achieves single-shot 3D measurement with competitive accuracy while running at 170 ms.