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Real-time 3D mapping is a critical component in many important applicationstoday including robotics, AR/VR, and 3D visualization. 3D mapping involvescontinuously fusing depth maps obtained from depth sensors in phones, robots,and autonomous vehicles into a single 3D representative model of the scene.Many important applications, e.g., global path planning and trajectorygeneration in micro aerial vehicles, require the construction of large maps athigh resolutions. In this work, we identify mapping, i.e., construction andupdates of 3D maps to be a critical bottleneck in these applications. Thememory required and access times of these maps limit the size of theenvironment and the resolution with which the environment can be feasiblymapped, especially in resource constrained environments such as autonomousrobot platforms and portable devices. To address this challenge, we proposeVoxelCache: a hardware-software technique to accelerate map data access timesin 3D mapping applications. We observe that mapping applications typicallyaccess voxels in the map that are spatially co-located to each other. Weleverage this temporal locality in voxel accesses to cache indices to blocks ofvoxels to enable quick lookup and avoid expensive access times. We evaluateVoxelCache on popularly used mapping and reconstruction applications on bothGPUs and CPUs. We demonstrate an average speedup of 1.47X (up to 1.66X) and1.79X (up to 1.91X) on CPUs and GPUs respectively.

VoxelCache: Accelerating Online Mapping in Robotics and 3D Reconstruction Tasks