An Octree-Based Approach towards Efficient Variational Range Data Fusion

TL;DR

This paper introduces an efficient variational range data fusion method using Octree structures to reduce memory and computation costs while maintaining accuracy in volumetric reconstructions.

Contribution

The paper proposes a novel Octree-based minimization approach for range data fusion that dynamically adjusts structure during optimization to improve efficiency.

Findings

Achieves faster processing with lower memory usage.

Maintains high geometric accuracy in reconstructions.

Demonstrates effectiveness on various datasets.

Abstract

Volume-based reconstruction is usually expensive both in terms of memory consumption and runtime. Especially for sparse geometric structures, volumetric representations produce a huge computational overhead. We present an efficient way to fuse range data via a variational Octree-based minimization approach by taking the actual range data geometry into account. We transform the data into Octree-based truncated signed distance fields and show how the optimization can be conducted on the newly created structures. The main challenge is to uphold speed and a low memory footprint without sacrificing the solutions' accuracy during optimization. We explain how to dynamically adjust the optimizer's geometric structure via joining/splitting of Octree nodes and how to define the operators. We evaluate on various datasets and outline the suitability in terms of performance and geometric accuracy.