MUTE-SLAM: Real-Time Neural SLAM with Multiple Tri-Plane Hash Representations

Core Concepts

Efficiently tracking camera positions and building scalable multi-map representations using tri-plane hash-encodings for real-time performance and high-fidelity surface reconstruction.

Abstract

Introduces MUTE-SLAM for real-time RGB-D SLAM
Utilizes tri-plane hash-encodings for efficient scene representation
Dynamically allocates sub-maps for new regions
Ensures global consistency through optimization strategy
Demonstrates state-of-the-art surface reconstruction quality and tracking performance

Stats

MUTE-SLAM delivers state-of-the-art surface reconstruction quality and competitive tracking performance across diverse indoor settings.
The local map’s size is determined by extending the cuboid vicinity of the current camera position.
The encoder interpolates the point features bilinearly by querying the nearest four vertices from each level of the hash table.

Quotes

"Our proposed MUTE-SLAM overcomes this by introducing a multi-map-based scene representation."
"We represent each sub-map with a tri-plane hash-encoding which allows for fast convergence and detailed surface reconstruction."

Key Insights Distilled From

MUTE-SLAM

by Yifan Yan,Ru... at arxiv.org 03-27-2024

https://arxiv.org/pdf/2403.17765.pdf

Deeper Inquiries

How does MUTE-SLAM handle illumination changes and inaccurate depth measurements?

MUTE-SLAM handles illumination changes and inaccurate depth measurements by relying on the valid observation of RGB-D sensors. While illumination changes can affect the quality of the captured data, the system processes the RGB-D stream effectively to track camera poses and build a scalable multi-map representation for indoor environments. By dynamically allocating sub-maps for new regions and utilizing a tri-plane hash-encoding method, MUTE-SLAM can adapt to varying lighting conditions and mitigate the impact of inaccurate depth measurements. This approach ensures that the system can continue to track camera positions and incrementally build accurate 3D maps despite challenges posed by changes in illumination and depth inaccuracies.

What are the limitations of randomly sampling from all historical keyframes for global bundle adjustment?

One limitation of randomly sampling from all historical keyframes for global bundle adjustment is the potential for insufficient optimization in less frequently observed regions. When sampling uniformly from all historical keyframes, there is a risk that certain areas of the scene may not receive adequate optimization during the global bundle adjustment process. This can lead to suboptimal results in these less frequently observed regions, impacting the overall reconstruction quality in those areas. Additionally, random sampling may not prioritize keyframes that are most informative or relevant for global consistency, potentially affecting the accuracy of the optimization process.

How does the tri-plane hash-encoding approach compare to grid hash-encoding in terms of performance and memory usage?

The tri-plane hash-encoding approach in MUTE-SLAM offers several advantages over grid hash-encoding in terms of performance and memory usage. By representing each sub-map with tri-plane hash-encoding, MUTE-SLAM efficiently reduces hash collisions and trainable parameters, leading to high-fidelity surface reconstruction and low memory consumption. Compared to grid hash-encoding, the tri-plane approach results in fewer hash table queries and collisions, enhancing the system's performance. Additionally, the tri-plane hash-encoding method preserves scene details accurately while minimizing memory usage, making it a more efficient and effective choice for dense SLAM systems like MUTE-SLAM.