NeRF-SLAM: Real-Time Dense Monocular SLAM with Neural Radiance Fields (2210.13641v1)

Abstract: We propose a novel geometric and photometric 3D mapping pipeline for accurate and real-time scene reconstruction from monocular images. To achieve this, we leverage recent advances in dense monocular SLAM and real-time hierarchical volumetric neural radiance fields. Our insight is that dense monocular SLAM provides the right information to fit a neural radiance field of the scene in real-time, by providing accurate pose estimates and depth-maps with associated uncertainty. With our proposed uncertainty-based depth loss, we achieve not only good photometric accuracy, but also great geometric accuracy. In fact, our proposed pipeline achieves better geometric and photometric accuracy than competing approaches (up to 179% better PSNR and 86% better L1 depth), while working in real-time and using only monocular images.

• The paper introduces a system that combines dense monocular SLAM with neural radiance fields to produce real-time, accurate 3D reconstructions.
• The approach leverages an uncertainty-based depth loss to significantly enhance both geometric and photometric fidelity, achieving up to 179% PSNR improvement and 86% L1 depth accuracy gains.
• The results open up practical applications in robotics, augmented reality, and gaming by enabling cost-effective, lightweight, and robust monocular 3D mapping.

NeRF-SLAM: Real-Time Dense Monocular SLAM with Neural Radiance Fields

Introduction

The paper presents a novel method titled "NeRF-SLAM" for real-time dense monocular Simultaneous Localization and Mapping (SLAM) using Neural Radiance Fields (NeRF). This approach targets the challenging task of constructing accurate 3D scene reconstructions using only monocular images, a task traditionally dominated by technologies relying on more complex sensors such as LiDAR or RGB-D cameras. By leveraging recent advancements in dense monocular SLAM and neural radiance fields, the authors propose a system capable of generating geometrically and photometrically accurate 3D maps efficiently in real-time scenarios.

Methodology

The key innovation lies in integrating dense monocular SLAM with a neural radiance field pipeline. Dense monocular SLAM is used to provide accurate camera pose estimates and depth maps, along with uncertainty estimates which are crucial for fitting neural radiance fields effectively. The paper introduces an uncertainty-based depth loss to enhance accuracy, ensuring both photometric and geometric fidelity in the reconstructions.

The proposed system executes this by:

1. Utilizing dense monocular SLAM frameworks to extract accurate pose and depth information.
2. Applying this data to fit a neural radiance field representation of the scene on-the-fly, providing real-time capability.
3. Employing an innovative depth loss function that accounts for depth uncertainty, improving the robustness and precision of the generated 3D maps.

Numerical Results

The results show that the proposed method achieves superior performance compared to other techniques in terms of geometric and photometric accuracy. Specifically, the approach can yield up to 179% improvement in Peak Signal-to-Noise Ratio (PSNR) and up to 86% enhancement in L1 depth accuracy over competing methods, while maintaining real-time processing capabilities. This underscores the effectiveness of combining depth uncertainty into the loss function, allowing for the reduction of errors typically associated with raw depth map utilization.

Implications and Future Directions

Practically, this development opens up new opportunities in fields such as robotics, gaming, and augmented reality, where lightweight and cost-effective 3D mapping solutions are desirable. Theoretically, it contributes to the understanding of how neural radiance fields can be adapted for real-time applications, a significant step forward in the deployment of neural representations in dynamic environments.

Future research could explore the expansion of this approach to incorporate additional data sources beyond monocular images, potentially improving robustness in more diverse operational environments. Another promising direction is enhancing the scalability of the solution to handle larger and more complex scenes. Additionally, the integration of semantic understanding through neural representations could further enrich the capability of such systems, adding another layer of information to the already robust geometric and photometric mapping.

Conclusion

NeRF-SLAM exemplifies a significant advancement in real-time monocular SLAM by effectively integrating state-of-the-art neural rendering techniques with established SLAM methodologies. This paper highlights a robust framework capable of producing accurate 3D scene reconstructions, catalyzing future research and application developments in various domains reliant on real-time 3D perception.