Abstract
3D mesh data acquisition is often afflicted by undesirable measurement noise. Such noise has an aversive impact to further processing and also to human perception, and hence plays a pivotal role in mesh processing. We present here a fast saliency-based algorithm that can reduce the noise while preserving the finer details of the original object. In order to capture the object features at multiple scales, our mesh denoising algorithm estimates the mesh saliency from Gaussian weighted curvatures for vertices at fine and coarse scales. The proposed algorithm finds wide application in digitization of archaeological artifacts, such as statues and sculptures, where it is of paramount importance to capture the 3D surface with all its details as accurately as possible. We have tested the algorithm on several datasets, and the results exhibit its speed and efficiency.
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Dutta, S., Banerjee, S., Biswas, P.K., Bhowmick, P. (2015). Mesh Denoising Using Multi-scale Curvature-Based Saliency. In: Jawahar, C., Shan, S. (eds) Computer Vision - ACCV 2014 Workshops. ACCV 2014. Lecture Notes in Computer Science(), vol 9009. Springer, Cham. https://doi.org/10.1007/978-3-319-16631-5_37
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DOI: https://doi.org/10.1007/978-3-319-16631-5_37
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