On application of spatial decomposition method for large data sets indexing.
The paper is dedicated to theoretical research of spatial indexing methods in conformity to three dimensional scenes arising in CAD/CAM systems, robotics, virtual and augmented reality applications. Special attention is given to the decomposition methods based on regular dynamic octrees. A particular version of the octrees is described and investigated to satisfy to the efficiency requirements for typical spatial queries in complex dynamic scenes with the extended borders objects. To perform the needed complexity analysis, the developed octree structure is analyzed against typical spatial queries like collision detection, frustum culling. To obtain more relevant estimates of the complexity on average rather than known pessimistic estimates in worst case, model scene datasets have been introduced. They assume uniform distribution of equal size objects over the scene volume and, being parametric, allow simulation of wide range of industry meaningful scenes. Some auxiliary lemmas about octree depth and distribution of the scene objects over octree levels have been proven based on probabilistic analysis. Final theorem statements provides for the complexity estimates of query evaluation for the model scenes. The obtained results show that asymptotic complexity grows with the object sizes and the scene occupancy. However, the estimates on average improve known results in worst case and can be considered as a theoretical background to introduce the investigated indexing structures to practical applications.
Proceedings of the Institute for System Programming, vol. 25, 2013, pp. 131-166.
ISSN 2220-6426 (Online), ISSN 2079-8156 (Print).
DOI: 10.15514/ISPRAS-2013-25-8Full text of the paper in pdf (in Russian) Back to the contents of the volume