Vox-Fusion++: Voxel-based Neural Implicit Dense Tracking and Mapping
  with Multi-maps
Hongjia Zhai Hai Li Xingrui Yang Gan Huang Yuhang Ming Hujun Bao Guofeng Zhang
In this paper, we introduce Vox-Fusion++, a multi-maps-based robust dense
tracking and mapping system that seamlessly fuses neural implicit
representations with traditional volumetric fusion techniques. Building upon
the concept of implicit mapping and positioning systems, our approach extends
its applicability to real-world scenarios. Our system employs a voxel-based
neural implicit surface representation, enabling efficient encoding and
optimization of the scene within each voxel. To handle diverse environments
without prior knowledge, we incorporate an octree-based structure for scene
division and dynamic expansion. To achieve real-time performance, we propose a
high-performance multi-process framework. This ensures the system's suitability
for applications with stringent time constraints. Additionally, we adopt the
idea of multi-maps to handle large-scale scenes, and leverage loop detection
and hierarchical pose optimization strategies to reduce long-term pose drift
and remove duplicate geometry. Through comprehensive evaluations, we
demonstrate that our method outperforms previous methods in terms of
reconstruction quality and accuracy across various scenarios. We also show that
our Vox-Fusion++ can be used in augmented reality and collaborative mapping
applications. Our source code will be publicly available at
\url{https://github.com/zju3dv/Vox-Fusion_Plus_Plus}