S3-SLAM: Sparse Tri-plane Encoding for Neural Implicit SLAM (2404.18284v1)

Abstract: With the emergence of Neural Radiance Fields (NeRF), neural implicit representations have gained widespread applications across various domains, including simultaneous localization and mapping. However, current neural implicit SLAM faces a challenging trade-off problem between performance and the number of parameters. To address this problem, we propose sparse tri-plane encoding, which efficiently achieves scene reconstruction at resolutions up to 512 using only 2~4% of the commonly used tri-plane parameters (reduced from 100MB to 2~4MB). On this basis, we design S3-SLAM to achieve rapid and high-quality tracking and mapping through sparsifying plane parameters and integrating orthogonal features of tri-plane. Furthermore, we develop hierarchical bundle adjustment to achieve globally consistent geometric structures and reconstruct high-resolution appearance. Experimental results demonstrate that our approach achieves competitive tracking and scene reconstruction with minimal parameters on three datasets. Source code will soon be available.

• The paper introduces a sparse tri-plane encoding technique that drastically reduces model parameters while preserving high-fidelity scene reconstructions.
• It employs hash-grid based 2D planes and hierarchical bundle adjustment to efficiently capture both local and global scene details.
• Experimental results on Replica, ScanNet, and TUM RGB-D datasets demonstrate superior tracking and reconstruction performance using only 2-4% of conventional parameters.

Sparse Tri-plane Encoding for Neural Implicit SLAM

Introduction

Simultaneous Localization and Mapping (SLAM) research has witnessed massive advancements with the incorporation of deep learning. Within this domain, leveraging neural implicit representations such as Neural Radiance Fields (NeRF) has enhanced the capability to reconstruct high-resolution scenes using Multi-Layer Perceptrons (MLPs). However, as we scale these technologies, issues related to computational overhead and memory consumption become prominent. The recently documented paper on S3-SLAM introduces an innovative sparse tri-plane encoding approach that drastically cuts parameter number and improves reconstruction performance. This methodology not only maintains detailed scene information but also dramatically reduces the storage requirements.

Sparse Tri-plane Encoding Concept

The core of the proposed S3-SLAM approach is its innovative sparse tri-plane encoding. Traditional methods often face a rapid escalation in parameter counts when scaling up, but S3-SLAM manages to counteract this with its unique encoding strategy:

• Application of hash-grids: By replacing regular dense tri-plane grids with sparse 2D hash-grid planes, the model uses a hash function to categorize and store vertex information efficiently.
• Orthogonal Plane Projection: S3-SLAM maintains three orthogonal projection planes which significantly condense the spatial data, helping preserve major geometric features while reducing redundancy.
• Multi-resolution Handling: Each level of resolution in the projection planes is managed using hash tables, allowing the method to adapt effectively to various granularities of scene detail.

Hierarchical Bundle Adjustment (HBA)

Conventional bundle adjustment techniques often only consider local consistencies, thereby struggling to manage global scene dynamics. S3-SLAM introduces a hierarchical bundle adjustment strategy that oversees both local details and global geometrical structures. This bifocal approach involves prioritizing keyframes that contribute significantly to the overall structural understanding while relegating others for less computational attention. This nuanced method of adjustment ensures that the SLAM system remains robust across varying scene scales and complexities.

Experimental Results and Observations

The experimental review covers datasets including Replica, ScanNet, and TUM RGB-D, showing that:

• S3-SLAM significantly surpasses baseline methods in terms of memory efficiency, often only utilizing 2-4% of the parameters typically used by other methods.
• The system achieves competitive, and often superior, tracking and reconstruction performance across all tested scenarios.

Theoretical and Practical Implications

• Theory: The development of sparse tri-plane encoding reinforces the notion that effective dimensionality reduction and sparse representations are crucial for scaling SLAM technologies.
• Practical: With its reduced computational overhead, S3-SLAM is particularly beneficial for real-time applications such as robotics and augmented reality, which require rapid, high-fidelity environmental mapping.

Future Directions

The future of neural implicit SLAM looks toward enhancing local update mechanisms to further tackle the issue of memory-induced forgetting, a common problem in continuous learning paradigms. Additionally, adapting these SLAM systems for larger, more dynamic environments represents a natural progression for this line of research.

Conclusion

S3-SLAM sets a new standard in the neural implicit SLAM field by introducing a formidable combination of sparse tri-plane encoding and hierarchical bundle adjustment. This method not only maintains high fidelity in scene reconstruction but also promises substantial reductions in memory usage and computational demand, paving the way for more efficient and scalable SLAM systems in the future.