document: Reconstructing surfaces by volumetric regularization using radial basis functions We present a new method of surface reconstruction that generates smooth and seamless models from sparse , noisy , nonuniform , and low resolution range data . Data acquisition techniques from computer vision , such as stereo range images and space carving , produce 3D point sets that are imprecise and nonuniform when compared to laser or optical range scanners . Traditional reconstruction algorithms designed for dense and precise data do not produce smooth reconstructions when applied to vision-based data sets . Our method constructs a 3D implicit surface , formulated as a sum of weighted radial basis functions . We achieve three primary advantages over existing algorithms : -LRB- 1 -RRB- the implicit functions we construct estimate the surface well in regions where there is little data , -LRB- 2 -RRB- the reconstructed surface is insensitive to noise in data acquisition because we can allow the surface to approximate , rather than exactly interpolate , the data , and -LRB- 3 -RRB- the reconstructed surface is locally detailed , yet globally smooth , because we use radial basis functions that achieve multiple orders of smoothness
doc_bio_tags: O O O B I O B I I O O O O O O O O O O O O O O O O O O O O O O B I I I O B I I O B I O O O B I I O B I O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O
extractive_keyphrases: volumetric regularization radial basis functions low resolution range data data acquisition techniques computer vision stereo range images space carving 3d point sets 3d implicit surface
abstractive_keyphrases: surfaces reconstruction sparse range data noisy data nonuniform data vision-based data sets weighted radial basis functions