Transform-Aware Sparse Voxel Directed Acyclic Graphs
M.L. Molenaar (TU Delft - Computer Graphics and Visualisation)
E. Eisemann (TU Delft - Computer Graphics and Visualisation)
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Abstract
Sparse Voxel Directed Acyclic Graphs (SVDAGs) have proven to be an efficient data structure for storing sparse binary voxel scenes. The SVDAG exploits repeating geometric patterns; which can be improved when considering mirror symmetries. We extend the previous work by providing a generalized framework to efficiently involve additional types of transformations and propose a novel translation matching for even more geometry reuse. Our new data structure is stored using a novel pointer encoding scheme to achieve a practical reduction in memory usage.
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