Efficient Surfel Fusion Using Normalised Information Distance

TL;DR

This paper introduces a novel surfel fusion method that uses Normalised Information Distance to reduce measurement requirements, enhancing efficiency and accuracy in dense 3D mapping.

Contribution

It presents a new technique leveraging Normalised Information Distance for selective frame fusion, improving efficiency in surfel-based 3D reconstruction.

Findings

Significant reduction in measurements needed for accurate mapping

Improved computational efficiency over ElasticFusion

Maintains high reconstruction accuracy with fewer frames

Abstract

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.