Vox-Fusion++: Voxel-based Neural Implicit Dense Tracking and Mapping with Multi-maps

Alapfogalmak

Vox-Fusion++ introduces a robust dense tracking and mapping system that fuses neural implicit representations with traditional volumetric fusion techniques for real-world scenarios.

Kivonat

Vox-Fusion++ combines neural implicit surface representation with an octree-based structure for scene division.
The system utilizes a high-performance multi-process framework for real-time performance.
Multi-maps are employed to handle large-scale scenes, reducing pose drift and removing duplicate geometry.
Extensive evaluations show superior reconstruction quality and accuracy compared to previous methods.
The system is applicable in augmented reality and collaborative mapping applications.

Statisztikák

Our method outperforms previous methods in terms of reconstruction quality and accuracy across various scenarios.

Idézetek

"Our approach extends the applicability of implicit mapping systems to real-world scenarios."
"Our method ensures suitability for applications with stringent time constraints."

Főbb Kivonatok

Vox-Fusion++

by Hongjia Zhai... : arxiv.org 03-20-2024

https://arxiv.org/pdf/2403.12536.pdf

Mélyebb kérdések

How can the use of multi-maps improve the efficiency of dense tracking and mapping systems

Multi-maps can improve the efficiency of dense tracking and mapping systems in several ways. Firstly, by dividing large-scale scenes into smaller maps, the computational complexity is reduced as each map only focuses on a specific region. This allows for more efficient processing and optimization within each map, leading to faster reconstruction times. Secondly, multi-maps enable incremental mapping where new maps are created as needed based on scene exploration, preventing memory overflow and ensuring optimal resource utilization. Additionally, loop closure detection and hierarchical pose optimization between different maps help reduce long-term pose drift and eliminate duplicate geometry, resulting in more accurate reconstructions without sacrificing efficiency.

What are the limitations of pre-trained networks in neural implicit SLAM approaches

Pre-trained networks have limitations in neural implicit SLAM approaches due to their struggle with generalization to different scene types. These networks may not adapt well to diverse environments without prior knowledge or training data bias, impacting the accuracy and coherency of the reconstructed surfaces. Furthermore, relying on pre-trained networks can hinder the ability to achieve a consistent global representation of the scene since they often focus on local latent codes rather than capturing holistic scene features accurately. This limitation can lead to subpar surface reconstruction quality and difficulty in handling novel view synthesis tasks effectively.

How does the dynamic voxel management strategy contribute to efficient scene reconstruction

The dynamic voxel management strategy contributes significantly to efficient scene reconstruction by enabling adaptive voxel allocation based on new observations during exploration. By dynamically creating voxels only when new observations are made, unnecessary computations for empty spaces are avoided, reducing memory consumption and optimizing resource usage efficiently. The use of Morton encoding for voxel coordinates further enhances efficiency by providing a compact representation that facilitates rapid retrieval of indexed embeddings from sparse voxels. This strategy ensures that resources are allocated judiciously where needed most while maintaining computational speed and accuracy in reconstructing large indoor scenes.