An efficient multi-resolution framework for high quality interactive rendering of massive point clouds using multi-way kd-trees |
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Authors: | Prashant Goswami Fatih Erol Rahul Mukhi Renato Pajarola Enrico Gobbetti |
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Affiliation: | 1. Visualization and MultiMedia Lab, University of Zurich, Zurich, Switzerland 3. Department of Informatics, University of Zurich, Zurich, Switzerland 2. CRS4, Pula (CA), Italy
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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 incorporated 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. |
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