DROID-Splat: Combining end-to-end SLAM with 3D Gaussian Splatting (2411.17660v2)

Abstract: Recent progress in scene synthesis makes standalone SLAM systems purely based on optimizing hyperprimitives with a Rendering objective possible. However, the tracking performance still lacks behind traditional and end-to-end SLAM systems. An optimal trade-off between robustness, speed and accuracy has not yet been reached, especially for monocular video. In this paper, we introduce a SLAM system based on an end-to-end Tracker and extend it with a Renderer based on recent 3D Gaussian Splatting techniques. Our framework \textbf{DroidSplat} achieves both SotA tracking and rendering results on common SLAM benchmarks. We implemented multiple building blocks of modern SLAM systems to run in parallel, allowing for fast inference on common consumer GPU's. Recent progress in monocular depth prediction and camera calibration allows our system to achieve strong results even on in-the-wild data without known camera intrinsics. Code will be available at \url{https://github.com/ChenHoy/DROID-Splat}.

• The paper introduces DROID-Splat, a method that combines dense SLAM tracking with 3D Gaussian Splatting to enhance reconstruction accuracy and visual realism.
• It employs parallel feature processing, dense optical flow, and efficient loop closure to achieve real-time performance across varied input types including monocular and RGB-D data.
• Empirical evaluations show superior tracking and rendering performance on standard SLAM benchmarks, highlighting improvements in metrics like ATE RMSE and PSNR.

Analysis of DROID-Splat: Integrating End-to-End SLAM with 3D Gaussian Splatting

The paper presents DROID-Splat, a sophisticated integration of end-to-end Simultaneous Localization and Mapping (SLAM) with 3D Gaussian Splatting for enhanced tracking and rendering capabilities in challenging scenarios, particularly with monocular video data. This paper advances the field of computer vision by addressing prevailing gaps in photorealistic scene reconstruction and offering a practical solution for in-the-wild data with unknown camera intrinsics.

Methodological Insights

DROID-Splat employs a dense, end-to-end tracking system, based on the DROID-SLAM framework, and augments it with a renderer that leverages 3D Gaussian Splatting techniques. The design facilitates a balance between robustness, speed, and accuracy across various SLAM benchmarks, signaling a significant methodological shift toward more integrated SLAM systems. Key innovations include:

• Dense Representation: The paper highlights the importance of dense optical flow tracking, combined with photo-realistic rendering objectives, to enhance the precision and realism of reconstructed scenes.
• Feature Paralleling and Loop Closure: By executing multiple SLAM system components in parallel, the framework optimizes the hardware utilization, achieving real-time rendering of scenes while managing common computational constraints.
• Universal Input Configuration: The system's adaptability to various input configurations, including monocular and RGB-D data, is enhanced by contemporary monocular depth prediction techniques and efficient camera calibration processes.

Empirical Validation

The empirical evaluation demonstrates DROID-Splat's superiority in both qualitative and quantitative terms across industry-standard SLAM benchmarks. Key performance outcomes include:

• Benchmark Superiority: DROID-Splat outperforms existing SLAM systems, such as traditional feature-based and newer differentiable rendering models, in terms of both accuracy of tracking (low ATE RMSE values) and rendering quality (high PSNR, low LPIPS).
• Robust in-the-Wild Performance: Its design permits effective handling of monocular video collections from unknown environments, validated by strong results even in settings with unknown camera intrinsics.

Implications and Future Directions

The integration of SLAM systems with 3D Gaussian Splatting paves the way for more nuanced scene understanding and navigation applications, particularly in autonomous vehicles and augmented reality sectors. The flexibility and regime independence that DROID-Splat offers are critical for applications requiring adaptive models capable of generalization across different environments without significant overhead or manual adjustments.

Moving forward, the implications of this research suggest several avenues for development:

• Scalability: Further exploration into scaling the approach for larger, more complex environments could yield enhancements in the deployment of SLAM systems in real-world applications.
• Cross-disciplinary Integration: As SLAM technologies increasingly intersect with domains like AI-based perception and geomatics, further interdisciplinary methodologies could fortify system robustness and flexibility.
• Differentiable SLAM Systems: Continued development of differentiable approaches that blend traditional optimization techniques with learned models may substantially enhance performance metrics and computational efficiency.

In summary, DROID-Splat exemplifies a methodical blend of dense SLAM tracking and photorealistic rendering, offering not just a state-of-the-art solution for current challenges but also laying a foundation for progressive research and application in mobile autonomy and immersive media pipelines.