Neighborhood Filters and the Recovery of 3D Information

Abstract : Following their success in image processing (see Chapter Local Smoothing Neighborhood Filters), neighborhood filters have been extended to 3D surface processing. This adaptation is not straightforward. It has led to several variants for surfaces depending on whether the surface is defined as a mesh, or as a raw data point set. The image gray level in the bilateral similarity measure is replaced by a geometric information such as the normal or the curvature. The first section of this chapter reviews the variants of 3D mesh bilateral filters and compares them to the simplest possible isotropic filter, the mean curvature motion.In a second part, this chapter reviews applications of the bilateral filter to a data composed of a sparse depth map (or of depth cues) and of the image on which they have been computed. Such sparse depth cues can be obtained by stereovision or by psychophysical techniques. The underlying assumption to these applications is that pixels with similar intensity around a region are likely to have similar depths. Therefore, when diffusing depth information with a bilateral filter based on locality and color similarity, the discontinuities in depth are assured to be consistent with the color discontinuities, which is generally a desirable property. In the reviewed applications, this ends up with the reconstruction of a dense perceptual depth map from the joint data of an image and of depth cues.
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Contributor : Julie Digne <>
Submitted on : Monday, December 7, 2015 - 9:52:29 AM
Last modification on : Saturday, June 1, 2019 - 11:18:15 AM

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Julie Digne, Mariella Dimiccoli, Neus Sabater, Philippe Salembier. Neighborhood Filters and the Recovery of 3D Information. Otmar Scherzer. Handbook of Mathematical Methods in Imaging (2nd Edition), Springer, pp.1645-1673, 2015, 978-1-4939-0789-2. ⟨10.1007/978-1-4939-0790-8_27⟩. ⟨http://link.springer.com/referenceworkentry/10.1007%2F978-1-4939-0790-8_27#⟩. ⟨hal-01238753⟩

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