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An Efficient Multi-resolution Framework for High Quality Interactive Rendering of Massive Point Clouds using Multi-way kd-Trees

Prashant Goswami, Fatih Erol, Rahul Mukhi, Renato Pajarola, and Enrico Gobbetti

2013

Abstract

We present an efficient technique for out-of-core multi-resolution construction and high quality interactive visualization of massive point clouds. Our approach introduces a novel hierarchical level of detail (LOD) organization based on multi-way kd-trees, which simplifies memory management and allows control over the LOD-tree height. The LOD tree, constructed bottom up using a fast high-quality point simplification method, is fully balanced and contains all uniformly sized nodes. To this end, we introduce and analyze three efficient point simplification approaches that yield a desired number of high-quality output points. For constant rendering performance, we propose an efficient rendering-on-a-budget method with asynchronous data loading, which delivers fully continuous high quality rendering through LOD geo-morphing and deferred blending. Our algorithm is in corporated in a full end-to-end rendering system, which supports both local rendering and cluster-parallel distributed rendering. The method is evaluated on complex models made of hundreds of millions of point samples.

Reference and download information

Prashant Goswami, Fatih Erol, Rahul Mukhi, Renato Pajarola, and Enrico Gobbetti. An Efficient Multi-resolution Framework for High Quality Interactive Rendering of Massive Point Clouds using Multi-way kd-Trees. The Visual Computer, 29(1): 69-83, 2013. http://dx.doi.org/10.1007/s00371-012-0675-2.

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Bibtex citation record

@Article{Goswami:2013:EMF,
    author = {Prashant Goswami and Fatih Erol and Rahul Mukhi and Renato Pajarola and Enrico Gobbetti},
    title = {An Efficient Multi-resolution Framework for High Quality Interactive Rendering of Massive Point Clouds using Multi-way kd-Trees},
    journal = {The Visual Computer},
    volume = {29},
    number = {1},
    pages = {69--83},
    year = {2013},
    keywords = {Point-based rendering; Level-of-detail; Multi-way kd-tree; Entropy-based reduction; k-clustering; Parallel rendering; Geo-morphing},
    issn = {0178-2789},
    abstract = { We present an efficient technique for out-of-core multi-resolution construction and high quality interactive visualization of massive point clouds. Our approach introduces a novel hierarchical level of detail (LOD) organization based on multi-way kd-trees, which simplifies memory management and allows control over the LOD-tree height. The LOD tree, constructed bottom up using a fast high-quality point simplification method, is fully balanced and contains all uniformly sized nodes. To this end, we introduce and analyze three efficient point simplification approaches that yield a desired number of high-quality output points. For constant rendering performance, we propose an efficient rendering-on-a-budget method with asynchronous data loading, which delivers fully continuous high quality rendering through LOD geo-morphing and deferred blending. Our algorithm is in corporated in a full end-to-end rendering system, which supports both local rendering and cluster-parallel distributed rendering. The method is evaluated on complex models made of hundreds of millions of point samples. },
    url = {http://vic.crs4.it/vic/cgi-bin/bib-page.cgi?id='Goswami:2013:EMF'},
}