An Octree-Based Approach towards Efficient Variational Range Data Fusion
This work addresses computational bottlenecks in 3D reconstruction for applications like robotics or computer vision, though it appears incremental as it builds on existing Octree and variational methods.
The paper tackles the problem of expensive memory consumption and runtime in volume-based reconstruction for sparse geometric structures by presenting an efficient variational range data fusion approach using Octree-based truncated signed distance fields, achieving suitability in performance and geometric accuracy across various datasets.
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.