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The Volumetric Accelerator (VOLA) format 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, Deep Learning and Convolutional Neural Network (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 for network transmission. 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. Four real world Light Detection and Ranging (LiDAR) datasets are converted and produced data an order of magnitude smaller than the current binary standard for point cloud data. Additionally, a new metric based on a neighborhood lookup is developed that measures an accurate resolution for a point cloud

VOLA: A Compact Volumetric Format for 3D Mapping and Embedded Systems