TT-TSDF: Memory-Efficient TSDF with Low-Rank Tensor Train Decomposition

In this paper we apply the low-rank Tensor Train decomposition for compression and operations on 3D objects and scenes represented by volumetric distance functions. Our study shows that not only it allows for a very efficient compression of the high-resolution TSDF maps (up to three orders of magnitude of the original memory footprint at resolution of 512
³
), but also allows to perform TSDF-Fusion directly in the low-rank form. This can potentially enable much more efficient 3D mapping on low-power mobile and consumer robot platforms.