Proceedings of the 27th annual conference on Computer graphics and interactive techniques
Robust fusion of irregularly sampled data using adaptive normalized convolution
EURASIP Journal on Applied Signal Processing
Fixing geometric errors on polygonal models: a survey
Journal of Computer Science and Technology
Identifying, visualizing, and comparing regions in irregularly spaced 3D surface data
Computer Vision and Image Understanding
Invariant image reconstruction from irregular samples and hexagonal grid splines
Image and Vision Computing
Fast hole-filling in images via fast comparison of incomplete patches
MRCS'06 Proceedings of the 2006 international conference on Multimedia Content Representation, Classification and Security
Kernel Regression for Image Processing and Reconstruction
IEEE Transactions on Image Processing
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A priority-based method for pixel reconstruction and incremental hole filling in incomplete images and 3D surface data is presented. The method is primarily intended for reconstruction of occluded areas in 3D surfaces and makes use of a novel prioritizing scheme, based on a pixelwise defined confidence measure, that determines the order in which pixels are iteratively reconstructed. The actual reconstruction of individual pixels is performed by interpolation using normalized convolution. The presented approach has been applied to the problem of reconstructing 3D surface data of a rock pile as well as randomly sampled image data. It is concluded that the method is not optimal in the latter case, but the results show an improvement to ordinary normalized convolution when applied to the rock data and are in this case comparable to those obtained from normalized convolution using adaptive neighborhood sizes.