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The digital Michelangelo project: 3D scanning of large statues
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Spectral compression of mesh geometry
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Surfels: surface elements as rendering primitives
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QSplat: a multiresolution point rendering system for large meshes
Proceedings of the 27th annual conference on Computer graphics and interactive techniques
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Progressive decomposition of point clouds without local planes
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Post-processing of scanned 3D surface data
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Octree-based point-cloud compression
SPBG'06 Proceedings of the 3rd Eurographics / IEEE VGTC conference on Point-Based Graphics
Multi-resolutive sparse approximations of d-dimensional data
Computer Vision and Image Understanding
Feature line extraction from unorganized noisy point clouds using truncated Fourier series
The Visual Computer: International Journal of Computer Graphics
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We present a new framework for processing point-sampled objects using spectral methods. By establishing a concept of local frequencies on geometry, we introduce a versatile spectral representation that provides a rich repository of signal processing algorithms. Based on an adaptive tesselation of the model surface into regularly resampled displacement fields, our method computes a set of windowed Fourier transforms creating a spectral decomposition of the model. Direct analysis and manipulation of the spectral coefficients supports effective filtering, resampling, power spectrum analysis and local error control. Our algorithms operate directly on points and normals, requiring no vertex connectivity information. They are computationally efficient, robust and amenable to hardware acceleration. We demonstrate the performance of our framework on a selection of example applications including noise removal, enhancement, restoration and subsampling.