  We present a new technique that achieves a significant reduction in the
quantity of measurements required for a fusion based dense 3D mapping system to
converge to an accurate, de-noised surface reconstruction. This is achieved
through the use of a Normalised Information Distance metric, that computes the
novelty of the information contained in each incoming frame with respect to the
reconstruction, and avoids fusing those frames that exceed a redundancy
threshold. This provides a principled approach for opitmising the trade-off
between surface reconstruction accuracy and the computational cost of
processing frames. The technique builds upon the ElasticFusion (EF) algorithm
where we report results of the technique's scalability and the accuracy of the
resultant maps by applying it to both the ICL-NUIM and TUM RGB-D datasets.
These results demonstrate the capabilities of the approach in performing
accurate surface reconstructions whilst utilising a fraction of the frames when
compared to the original EF algorithm.
