NICE-SLAM: Neural Implicit Scalable Encoding for SLAM (2112.12130v2)

Abstract: Neural implicit representations have recently shown encouraging results in various domains, including promising progress in simultaneous localization and mapping (SLAM). Nevertheless, existing methods produce over-smoothed scene reconstructions and have difficulty scaling up to large scenes. These limitations are mainly due to their simple fully-connected network architecture that does not incorporate local information in the observations. In this paper, we present NICE-SLAM, a dense SLAM system that incorporates multi-level local information by introducing a hierarchical scene representation. Optimizing this representation with pre-trained geometric priors enables detailed reconstruction on large indoor scenes. Compared to recent neural implicit SLAM systems, our approach is more scalable, efficient, and robust. Experiments on five challenging datasets demonstrate competitive results of NICE-SLAM in both mapping and tracking quality. Project page: https://pengsongyou.github.io/nice-slam

Overview of NICE-SLAM: Neural Implicit Scalable Encoding for SLAM

The paper introduces NICE-SLAM, a dense SLAM system designed to incorporate multi-level local information through a hierarchical scene representation. The framework is developed to address the constraints faced by existing neural implicit SLAM systems, particularly the issues of scalability, efficiency, and robust reconstruction of complex indoor scenes. NICE-SLAM leverages geometric priors and hierarchical feature grids to optimize scene representation, thereby enhancing both mapping and tracking capabilities.

Key Contributions

1. Hierarchical Representation: The core innovation of NICE-SLAM lies in its hierarchical feature grids that represent scene geometry and appearance at different spatial resolutions. This design allows the system to encapsulate detailed geometric information effectively.
2. Efficient Scene Encoding: By using pretrained decoders for different spatial resolutions, NICE-SLAM maintains a balance between detail richness and computational efficiency. These decoders integrate pre-learned inductive biases, stabilizing optimization and ensuring consistent geometry.
3. Local Updates: The architecture facilitates local updates to the scene representation, enhancing scalability and reducing computational overhead. This mechanism allows NICE-SLAM to manage large-scale scenes effectively compared to global neural scene encodings.
4. Experimental Validation: Extensive testing across five challenging datasets demonstrates the superior performance of NICE-SLAM in both tracking and mapping. The system shows significant improvements over previous methods, particularly in scenes of varying sizes and complexities.
5. Handling Dynamics and Large Scenes: NICE-SLAM includes strategies for robustness against dynamic objects and unforeseen scene regions, making it particularly suitable for real-world applications such as indoor robotics and autonomous navigation.

Implications and Future Directions

The implications of NICE-SLAM are substantial in the domain of 3D computer vision, especially within real-time applications where the existing SLAM systems struggle with scaling or dynamic environments. The integration of hierarchical feature grids and pretrained decoders opens new avenues for more resilient and capable visual SLAM systems. Practically, NICE-SLAM can be instrumental in scenarios requiring detailed and reliable environmental mapping, including autonomous vehicle navigation and complex virtual reality setups.

Looking forward, further refinement could explore enhancing the predictive capabilities of the hierarchical representation. The incremental updating mechanism established here could be expanded to incorporate more sophisticated machine learning models, potentially improving performance in dynamically changing or previously unobserved regions. Moreover, exploring loop closure strategies could be a promising direction to enhance system reliability and accuracy over extended operational durations.

Conclusion

NICE-SLAM represents a robust advancement in neural implicit SLAM systems, providing a pathway to more scalable, efficient, and accurate real-time dense mapping solutions. Its architecture effectively addresses the limitations of current methodologies, laying groundwork for future research to further enhance the scalability and robustness of SLAM systems in diverse and dynamic environments. The strong results across various datasets underscore the system's potential to influence 3D vision applications significantly.