Applications of VOLA Format for 3D Data Knowledge Discovery
VOLA is a compact data structure that unifies computer vision and 3D rendering and allows for the rapid calculation of connected components, per-voxel census/accounting, CNN inference, path planning and obstacle avoidance. Using a hierarchical bit array format allows it to run efficiently on embedded systems and maximize the level of data compression. The proposed format allows massive scale volumetric data to be used in embedded applications where it would be inconceivable to utilize point-clouds due to memory constraints. Furthermore, geographical and qualitative data is embedded in the file structure to allow it to be used in place of standard point cloud formats.
This work examines the reduction in file size when encoding 3D data using the VOLA format and finds that it is an order of magnitude smaller than the current binary standard for point cloud data.
VOLA is unique in that it combines the benefits of partitioning algorithms with a minimal voxel format. It hierarchically encodes 3D data using modular arithmetic and bit counting operations applied to a bit array. The simplicity of this approach means that it is highly compact and can be run on hardware with simple instruction sets. The choice of a 64 bit integer as the minimum unit of computation means that modern processor operations are already optimized to handle the format. While octree formats either need to be fully dense to be serialized or require bespoke serialization code for sparse data, the VOLA bit array is immediately readable without header information.