HI-SLAM2: Geometry-Aware Gaussian SLAM for Fast Monocular Scene Reconstruction (2411.17982v2)

Abstract: We present HI-SLAM2, a geometry-aware Gaussian SLAM system that achieves fast and accurate monocular scene reconstruction using only RGB input. Existing Neural SLAM or 3DGS-based SLAM methods often trade off between rendering quality and geometry accuracy, our research demonstrates that both can be achieved simultaneously with RGB input alone. The key idea of our approach is to enhance the ability for geometry estimation by combining easy-to-obtain monocular priors with learning-based dense SLAM, and then using 3D Gaussian splatting as our core map representation to efficiently model the scene. Upon loop closure, our method ensures on-the-fly global consistency through efficient pose graph bundle adjustment and instant map updates by explicitly deforming the 3D Gaussian units based on anchored keyframe updates. Furthermore, we introduce a grid-based scale alignment strategy to maintain improved scale consistency in prior depths for finer depth details. Through extensive experiments on Replica, ScanNet, and ScanNet++, we demonstrate significant improvements over existing Neural SLAM methods and even surpass RGB-D-based methods in both reconstruction and rendering quality. The project page and source code will be made available at https://hi-slam2.github.io/.

• The paper introduces a geometry-aware SLAM framework that uses 3D Gaussian Splatting for high-quality monocular scene reconstruction.
• It employs efficient mapping, loop closure via Pose Graph Bundle Adjustment, and grid-based scale alignment to ensure consistent global maps.
• Experimental results on datasets like Replica and ScanNet validate improved camera tracking accuracy and high-fidelity rendering over traditional methods.

Essay: HI-SLAM2: Geometry-Aware Gaussian SLAM for Fast Monocular Scene Reconstruction

The paper "HI-SLAM2: Geometry-Aware Gaussian SLAM for Fast Monocular Scene Reconstruction" introduces a novel monocular Simultaneous Localization and Mapping (SLAM) system that approaches the complexity associated with dense 3D scene reconstruction using only RGB input data. The system, HI-SLAM2, addresses limitations in contemporary RGB and RGB-D-based SLAM implementations by integrating advanced geometry estimation techniques and computational strategies to ensure efficient yet precise scene reconstruction.

HI-SLAM2 differentiates itself by solving the inherent trade-offs between rendering quality and geometric accuracy prevalent in traditional SLAM systems. This is achieved through the utilization of 3D Gaussian Splatting (3DGS), a compact map representation allowing for efficient modeling of monocular scenes. Unlike Neural SLAM systems which often face difficulties in balancing these aspects, HI-SLAM2 demonstrates the ability to harness the full potential of monocular input, providing high-resolution geometry and appearance reconstruction.

Key Contributions

The primary contributions of HI-SLAM2 lie in its innovative integration of several components that advance state-of-the-art monocular SLAM. These include:

1. Geometry-Aware Framework: By employing geometry priors and learning-based dense SLAM methods, HI-SLAM2 significantly enhances its depth estimation accuracy, a crucial component for effective 3D reconstruction. The paper demonstrates that monocular scenes can indeed achieve global topological consistency akin to RGB-D inputs by leveraging geometry-aware strategies.
2. Efficient Mapping and Loop Closure: The system utilizes an online loop closure mechanism achieved via Pose Graph Bundle Adjustment (PGBA) and continuous map updates through efficient deformation of 3D Gaussian units. This ensures HI-SLAM2 maintains both map consistency and fidelity in real-time operations.
3. Innovative Depth and Scale Alignment: A notable methodological advancement is the implementation of a grid-based scale alignment strategy. This technique rectifies the scale inconsistencies typically present in monocular depth prediction, improving the overall depth accuracy significantly over methods like HI-SLAM and other neural monocular SLAM approaches.
4. High-Fidelity Mapping: The integration of normal priors into the 3DGS framework significantly enhances the surface reconstruction, particularly in challenging low-texture areas. This aligns with the system's emphasis on obtaining both geometrically accurate and visually realistic renderings of complex scenes.

Experimental Validation

The system's efficacy is validated against multiple datasets, including Replica, ScanNet, and ScanNet++, where HI-SLAM2 consistently displayed superior performance in camera tracking accuracy compared to existing RGB-only and RGB-D methods. The paper provides extensive quantitative evidence of HI-SLAM2's improved reconstruction quality, particularly in geometry accuracy and map completeness, as well as photometric fidelity when rendering reconstructed scenes.

HI-SLAM2 advances in achieving seamless large-scale mapping without compromising computational efficiency were corroborated by runtime analyses demonstrating effective map management and pruning strategies. This affirmed the system's superiority in managing large environments while providing real-time performance and robust mapping capabilities.

Implications and Speculations

The research presented in HI-SLAM2 is significant for fields requiring lightweight and cost-effective monocular SLAM solutions, including autonomous navigation and augmented reality applications. Future work could explore the adaptability of HI-SLAM2 in dynamic or outdoor environments, and the potential integration with complementary sensor systems to further bolster its robustness and versatility.

In conclusion, HI-SLAM2 provides a valuable contribution to dense visual SLAM research, substantiating the viability of monocular systems in achieving comprehensive and detailed scene reconstructions. It effectively sets a precedent for future work aiming to streamline monocular SLAM systems with a focus on both efficiency and accuracy, widening the scope of potential real-world applications.