Matthew Bolitho, Ph.D.


Parallel Poisson Surface Reconstruction

Matthew Bolitho, Michael Kazhdan, Randal Burns and Hugues Hoppe, International Symposium on Visual Computing (2009)
In this work we describe a parallel implementation of the Poisson Surface Reconstruction algorithm based on multigrid domain decomposition. We compare implementations using di.erent models of data-sharing between processors and show that a parallel implementation with distributed memory provides the best scalability. Using our method, we are able to parallelize the reconstruction of models from one billion data points on twelve processors across three machines, providing a nine-fold speedup in running time without sacrificing reconstruction accuracy.
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Multilevel Streaming for Out-of-Core Surface Reconstruction

Matthew Bolitho, Michael Kazhdan, Randal Burns and Hugues Hoppe, Eurographics Symposium on Geometry Processing (2007)
Reconstruction of surfaces from huge collections of scanned points often requires out-of-core techniques, and most such techniques involve local computations that are not resilient to data errors. We show that a Poisson-based reconstruction scheme, which considers all points in a global analysis, can be performed efficiently in limited memory using a streaming framework. Specifically, we introduce a multilevel streaming representation, which enables efficient traversal of a sparse octree by concurrently advancing through multiple streams, one per octree level. Remarkably, for our reconstruction application, a sufficiently accurate solution to the global linear system is obtained using a single iteration of cascadic multigrid, which can be evaluated within a single multi-stream pass. We demonstrate scalable performance on several large datasets.
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Poisson Surface Reconstruction

Michael Kazhdan, Matthew Bolitho and Hugues Hoppe. Eurographics Symposium on Geometry Processing (2006)
We show that surface reconstruction from oriented points can be cast as a spatial Poisson problem. This Poisson formulation considers all the points at once, without resorting to heuristic spatial partitioning or blending, and is therefore highly resilient to data noise. Unlike radial basis function schemes, our Poisson approach allows a hierarchy of locally supported basis functions, and therefore the solution reduces to a well conditioned sparse linear system. We describe a spatially adaptive multiscale algorithm whose time and space complexities are proportional to the size of the reconstructed model. Experimenting with publicly available scan data, we demonstrate reconstruction of surfaces with greater detail than previously achievable.
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A Relational Debugging Engine for the Graphics Pipeline

Nathaniel Duca, Krzysztof Niski, Jonathan Bilodeau, Matthew Bolitho, Yuan Chen and Jonathan Cohen. ACM Transactions on Graphics (Proceedings of SIGGRAPH 2005)
We present a new, unifed approach to debugging graphics software. We propose a representation of all graphics state over the course of program execution as a relational database, and produce a query-based framework for extracting, manipulating, and visualizing data from all stages of the graphics pipeline. Using an SQL-based query language, the programmer can establish functional relationships among all the data, linking OpenGL state to primitives to vertices to fragments to pixels. Based on the Chromium library, our approach requires no modifcation to or recompilation of the program to be debugged, and forms a superset of many existing techniques for debugging graphics software.
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