ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., II-3, 17-23, 2014
https://doi.org/10.5194/isprsannals-II-3-17-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.
 
07 Aug 2014
Projective Texturing Uncertain Geometry: silhouette-aware box-filtered blending using integral radial images
M. Brédif Université Paris-Est, IGN, SRIG, MATIS, 73 avenue de Paris, 94160 Saint Mandé, France
Keywords: Projective texturing, Uncertainty, Image based rendering Abstract. Projective texturing is a commonly used image based rendering technique that enables the synthesis of novel views from the blended reprojection of nearby views on a coarse geometry proxy approximating the scene. When scene geometry is inexact, aliasing artefacts occur. This introduces disturbing artefacts in applications such as street-level immersive navigation in mobile mapping imagery, since a pixel-accurate modelling of the scene geometry and all its details is most of the time out of question. The filtered blending approach applies the necessary 1D low-pass filtering on the projective texture to trade out the aliasing artefacts at the cost of some radial blurring. This paper proposes extensions of the filtered blending approach. Firstly, we introduce Integral Radial Images that enable constant time radial box filtering and show how they can be used to apply box-filtered blending in constant time independently of the amount of depth uncertainty. Secondly, we show a very efficient application of filtered blending where the scene geometry is only given by a loose depth interval prior rather than an actual geometry proxy. Thirdly, we propose a silhouette-aware extension of the box-filtered blending that not only account for uncertain depth along the viewing ray but also for uncertain silhouettes that have to be blurred as well.
Conference paper (PDF, 11532 KB)


Citation: Brédif, M.: Projective Texturing Uncertain Geometry: silhouette-aware box-filtered blending using integral radial images, ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., II-3, 17-23, https://doi.org/10.5194/isprsannals-II-3-17-2014, 2014.

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