Binocular 3D reconstruction for wood ice cream sticks via speckle projection and SAM2 mask extraction

Abstract Natural wooden cream sticks exhibit diverse depth defects on their surfaces, presenting significant challenges for automatic detection. Current inspection technique mainly relies on manual means, which cannot provide quantitative evaluation and significantly limits production efficiency. Realizing in situ , online, and intelligent detection of surface depth defects in ice cream sticks is crucial for enhancing automation and ensuring consistent product quality. Through considering the surface and geometric features of ice cream sticks, the paper proposes a binocular three-dimensional (3D) reconstruction method for ice cream sticks, which incorporates speckle projection and Segment Anything Model v2(SAM2)-based target mask extraction. By performing automatic mask generation, candidate region selection, and refined mask extraction, the region of interest (ROI) of target is obtained from binocular speckle images. A colour-gradient weighted stereo matching algorithm combined with guided filtering generates high-quality disparity maps, and achieves a matching efficiency improvement exceeding 45 times. The 3D reconstruction experiment of a textureless step-shaped standard block verifies that the proposed method can achieve the measurement accuracy of about 0.05 mm. For various defective ice cream sticks, the reconstructed results compared with manual measurements show a maximum error not exceeding 3.4%. The proposed method provides an efficient and reliable 3D reconstruction solution for the in situ , non-destructive detection of depth defects in ice cream sticks, demonstrating practical value and promising engineering applicability.