Marching cubes: A high resolution 3D surface construction algorithm
SIGGRAPH '87 Proceedings of the 14th annual conference on Computer graphics and interactive techniques
Algorithms in C
Three-dimensional computer vision: a geometric viewpoint
Three-dimensional computer vision: a geometric viewpoint
Zippered polygon meshes from range images
SIGGRAPH '94 Proceedings of the 21st annual conference on Computer graphics and interactive techniques
Arithmetic coding for data compression
Communications of the ACM
Multiresolution analysis of arbitrary meshes
SIGGRAPH '95 Proceedings of the 22nd annual conference on Computer graphics and interactive techniques
Geometric compression through topological surgery
ACM Transactions on Graphics (TOG)
The digital Michelangelo project: 3D scanning of large statues
Proceedings of the 27th annual conference on Computer graphics and interactive techniques
QSplat: a multiresolution point rendering system for large meshes
Proceedings of the 27th annual conference on Computer graphics and interactive techniques
Progressive lossless compression of arbitrary simplicial complexes
Proceedings of the 29th annual conference on Computer graphics and interactive techniques
Efficient high quality rendering of point sampled geometry
EGRW '02 Proceedings of the 13th Eurographics workshop on Rendering
Progressive point set surfaces
ACM Transactions on Graphics (TOG)
Progressive compression of point-sampled models
SPBG'04 Proceedings of the First Eurographics conference on Point-Based Graphics
Compression of point-based 3D models by shape-adaptive wavelet coding of multi-height fields
SPBG'04 Proceedings of the First Eurographics conference on Point-Based Graphics
Dynamic compression of curve-based point cloud
PSIVT'11 Proceedings of the 5th Pacific Rim conference on Advances in Image and Video Technology - Volume Part II
Octree-based point-cloud compression
SPBG'06 Proceedings of the 3rd Eurographics / IEEE VGTC conference on Point-Based Graphics
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We present a simple technique for single-rate compression of point clouds sampled from a surface, based on a spanning tree of the points. Unlike previous methods, we predict future vertices using both a linear predictor, which uses the previous edge as a predictor for the current edge, and lateral predictors that rotate the previous edge 90° left or right about an estimated normal.By careful construction of the spanning tree and choice of prediction rules, our method improves upon existing compression rates when applied to regularly sampled point sets, such as those produced by laser range scanning or uniform tesselation of higher-order surfaces. For less regular sets of points, the compression rate is still generally within 1.5 bits per point of other compression algorithms.