Transform-Aware Sparse Voxel Directed Acyclic Graphs

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.