  In this work, we present a dense tracking and mapping system named
Vox-Fusion, which seamlessly fuses neural implicit representations with
traditional volumetric fusion methods. Our approach is inspired by the recently
developed implicit mapping and positioning system and further extends the idea
so that it can be freely applied to practical scenarios. Specifically, we
leverage a voxel-based neural implicit surface representation to encode and
optimize the scene inside each voxel. Furthermore, we adopt an octree-based
structure to divide the scene and support dynamic expansion, enabling our
system to track and map arbitrary scenes without knowing the environment like
in previous works. Moreover, we proposed a high-performance multi-process
framework to speed up the method, thus supporting some applications that
require real-time performance. The evaluation results show that our methods can
achieve better accuracy and completeness than previous methods. We also show
that our Vox-Fusion can be used in augmented reality and virtual reality
applications. Our source code is publicly available at
https://github.com/zju3dv/Vox-Fusion.
