Smoothing by Example: Mesh Denoising by Averaging with Similarity-Based Weights

Yoshizawa, Shin; Belyaev, Alexander; Seidel, Hans‐Peter

doi:10.1109/smi.2006.38

Cited by 56 publications

(59 citation statements)

References 24 publications

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“…This is done using adaptive filtering algorithms, which smooth (or denoise) the surface, while keeping the features intact or enhancing them (Eigensatz et al, 2008;Fleishman et al, 2003;Kolomenkin et al, 2011;Yoshizawa et al, 2006). The techniques differ in the criteria (cost function) they attempt to satisfy (minimize).…”

Section: Shape Enhancementmentioning

confidence: 99%

Computer-based, automatic recording and illustration of complex archaeological artifacts

Gilboa

Tal

Shimshoni

et al. 2013

Journal of Archaeological Science

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We report on the development of a computerized automatic system to illustrate complex archaeological objects. The illustrations are based on 3D scans of the artifacts. The 3D models can be automatically translated, by new algorithms specifically designed for this purpose, into 3D or 2D line drawings; into colored images that emphasize the salient shape attributes of the artifacts and of the 3D designs on them; and to images that enhance faint/eroded designs that are otherwise difficult to discern. These illustrations are intended to replace traditional, manual drawings, which are very expensive to produce and not accurate enough. Our illustrations also provide a better visualization tool than the 3D models themselves. Though 3D scanning already improves the visibility of objects and their features, it does not suffice for rapid visual recognition. Our system generates efficient, objective, accurate and simplified representations of complex objects and the designs on them from any number of required views.

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Section: Shape Enhancementmentioning

confidence: 99%

Computer-based, automatic recording and illustration of complex archaeological artifacts

Gilboa

Tal

Shimshoni

et al. 2013

Journal of Archaeological Science

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“…Recently, several approaches for feature-preserving denoising have been proposed based on geometry diffusion [Desbrun et al 1999], projections [Fleischman et al 2005], the bilateral filter [Jones et al 2003] [Fleishman et al 2003], and its extension to non-local means [Yoshizawa et al 2006]. In this section, we will show that, since the Gaussian kd-tree does not place any structural constraints on input data, it can be used for filtering of geometry.…”

Section: Geometry Filteringmentioning

confidence: 99%

“…The filtered vertex is then moved along its normal by the averaged height computed by the bilateral filter. The approach of [Fleishman et al 2003] has been extended to non-local means in [Yoshizawa et al 2006] by adding a geometric descriptor to each vertex in the mesh.…”

Section: Geometry Filteringmentioning

confidence: 99%

Gaussian KD-trees for fast high-dimensional filtering

Adams

Gelfand

Dolson

et al. 2009

ACM SIGGRAPH 2009 Papers

104

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Figure 1: The Gaussian kd-tree accelerates a broad class of non-linear filters, including the bilateral (left), non-local means (middle), and a novel non-local means for geometry (right). AbstractWe propose a method for accelerating a broad class of non-linear filters that includes the bilateral, non-local means, and other related filters. These filters can all be expressed in a similar way: First, assign each value to be filtered a position in some vector space. Then, replace every value with a weighted linear combination of all values, with weights determined by a Gaussian function of distance between the positions. If the values are pixel colors and the positions are (x, y) coordinates, this describes a Gaussian blur. If the positions are instead (x, y, r, g, b) coordinates in a five-dimensional space-color volume, this describes a bilateral filter. If we instead set the positions to local patches of color around the associated pixel, this describes non-local means. We describe a Monte-Carlo kdtree sampling algorithm that efficiently computes any filter that can be expressed in this way, along with a GPU implementation of this technique. We use this algorithm to implement an accelerated bilateral filter that respects full 3D color distance; accelerated non-local means on single images, volumes, and unaligned bursts of images for denoising; and a fast adaptation of non-local means to geometry. If we have n values to filter, and each is assigned a position in a d-dimensional space, then our space complexity is O(dn) and our time complexity is O(dn log n), whereas existing methods are typically either exponential in d or quadratic in n.

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“…Existing approaches for adaptive filtering on meshes operate either on the mesh vertices [4,8,31], the mesh normals [18,24], or the curvatures [6]. The techniques differ in the energy functional they attempt to minimize.…”

Section: Application: Surface Enhancement and Smoothingmentioning

confidence: 99%

Prominent field for shape processing of archaeological artifacts

Kolomenkin

Shimshoni

Tal

2009

2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops

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Archaeological artifacts are an essential element of archaeological research. They provide evidence of the past and enable archaeologists to obtain qualified conclusion. Nowadays, many artifacts are scanned by 3D scanners. While convenient in many aspects, the 3D representation is often unsuitable for further analysis, due to flaws in the scanning process or defects in the original artifacts. We propose a new approach for automatic processing of scanned artifacts. It is based on the definition of a new direction field on surfaces (a normalized vector field), termed the prominent field. The prominent field is oriented with respect to the prominent feature curves of the surface. We demonstrate the applicability of the prominent field in two applications. The first is surface enhancement of archaeological artifacts, which helps enhance eroded features and remove scanning noise. The second is artificial coloring that can replace manual artifact illustration in archaeological reports.

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Smoothing by Example: Mesh Denoising by Averaging with Similarity-Based Weights

Cited by 56 publications

References 24 publications

Computer-based, automatic recording and illustration of complex archaeological artifacts

Computer-based, automatic recording and illustration of complex archaeological artifacts

Gaussian KD-trees for fast high-dimensional filtering

Prominent field for shape processing of archaeological artifacts

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