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Denoising 3D Models with Attributes using Soft Thresholding

Abstract : Recent advances in scanning and acquisition technologies allow the construction of complex models from real world scenes. However, the data of those models are generally corrupted by measurement errors. This paper describes an efficient single pass algorithm for denoising irregular meshes of scanned 3D model surfaces. In this algorithm, the frequency content of the model is assessed by a multiresolution analysis that requires only 1-ring neighbourhood without any particular parameterization of the model faces. Denoising is achieved by applying the soft thresholding method to the detail coefficients given by the multiresolution analysis. Our method is suitable for irregular meshes with appearance attributes such as normal vectors and colors. Some results of real world scene models denoised with the proposed algorithm are given to demonstrate its efficiency.
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Contributor : Michaël Roy <>
Submitted on : Tuesday, December 18, 2018 - 4:15:46 PM
Last modification on : Friday, July 17, 2020 - 2:54:11 PM
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  • HAL Id : hal-01959311, version 1


Michael Roy, Sebti Foufou, Frederic Truchetet. Denoising 3D Models with Attributes using Soft Thresholding. SPIE Optics East Conference on Wavelet Applications in Industrial Processing, Oct 2004, Philadelphia, United States. pp.139-147. ⟨hal-01959311⟩



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