DenseSplat: Densifying Gaussian Splatting SLAM with Neural Radiance Prior (2502.09111v1)

Abstract: Gaussian SLAM systems excel in real-time rendering and fine-grained reconstruction compared to NeRF-based systems. However, their reliance on extensive keyframes is impractical for deployment in real-world robotic systems, which typically operate under sparse-view conditions that can result in substantial holes in the map. To address these challenges, we introduce DenseSplat, the first SLAM system that effectively combines the advantages of NeRF and 3DGS. DenseSplat utilizes sparse keyframes and NeRF priors for initializing primitives that densely populate maps and seamlessly fill gaps. It also implements geometry-aware primitive sampling and pruning strategies to manage granularity and enhance rendering efficiency. Moreover, DenseSplat integrates loop closure and bundle adjustment, significantly enhancing frame-to-frame tracking accuracy. Extensive experiments on multiple large-scale datasets demonstrate that DenseSplat achieves superior performance in tracking and mapping compared to current state-of-the-art methods.

• The paper introduces DenseSplat, a novel SLAM system that effectively integrates Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS) to address incomplete mapping issues in sparse-view scenarios.
• DenseSplat employs geometry-aware sampling and pruning to optimize the 3D Gaussian representation and computational efficiency.
• Experimental results show DenseSplat achieves superior tracking accuracy and map reconstruction quality across various datasets, particularly in filling sparse-view gaps.

Overview of "DenseSplat: Densifying Gaussian Splatting SLAM with Neural Radiance Prior"

The paper "DenseSplat: Densifying Gaussian Splatting SLAM with Neural Radiance Prior" proposes a novel SLAM (Simultaneous Localization and Mapping) system named DenseSplat, which is designed to leverage the strengths of both Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS). The paper addresses significant deficiencies in current Gaussian SLAM approaches, particularly in scenarios with sparse viewpoints leading to incomplete map reconstructions. DenseSplat introduces an effective integration strategy that combines NeRF's interpolation capabilities with the explicit representation of Gaussian primitives to enhance both tracking and mapping performance in real-world settings.

Key Contributions

1. NeRF and 3DGS Integration: DenseSplat is the first SLAM system that effectively combines the NeRF model's implicit scene representation capabilities with the explicit, detailed texture representation of 3D Gaussian Splatting. This integration aims to initialize primitive densities effectively, filling gaps in the map that are common in sparse-view conditions.
2. Geometry-aware Sampling: The system implements geometry-aware primitive sampling and pruning strategies. These mechanisms control the granularity of the 3D Gaussian representation and reduce computational overhead by pruning inactive primitives, resulting in a more efficient rendering process without compromising map fidelity.
3. Enhanced Tracking with Loop Closure and Bundle Adjustment: DenseSplat incorporates robust frame-to-frame tracking enhanced by loop closure detection and bundle adjustment (BA). This integration significantly improves map optimization, mitigating accumulative drift errors typical in real-time SLAM deployments.
4. Experimental Validation: The paper validates DenseSplat across multiple large-scale datasets, demonstrating superior performance in both tracking accuracy and map reconstruction quality compared to state-of-the-art SLAM systems. DenseSplat shows noteworthy improvements in filling gaps resulting from unobserved or obstructed scene areas.

Theoretical and Practical Implications

The introduction of DenseSplat signifies an important step forward in the development of SLAM systems capable of operating effectively under conditions of sparse supervision. The utilization of NeRF for scene interpolation enhances the representation of unobserved geometries, leading to more complete and accurate maps, which is crucial for applications in robotics and AR/VR environments. Furthermore, the geometry-aware sampling approach ensures that map density aligns with scene complexity, optimizing computational efficiency.

The integration of loop closure and BA not only addresses fundamental tracking challenges but also increases the robustness of Gaussian splatting in rapidly changing environments by refining pose estimation and map consistency. This capability opens up avenues for deploying SLAM systems in more complex real-world scenarios where seamless and accurate map updates are essential.

Future Directions

Potential future developments of the DenseSplat approach may focus on optimizing its application to mobile platforms, enhancing compatibility with diverse sensor inputs, and further reducing computational overhead to facilitate real-time processing in resource-constrained environments. Investigation into more nuanced submap division and fusion strategies could also prove beneficial, especially in scenarios involving collaborative multi-agent systems. Additionally, further work could explore leveraging learned priors to extend the system's robustness to scenes with limited visual features or challenging lighting conditions.

Overall, the DenseSplat framework stands out as a promising advancement in the field of visual SLAM, offering a robust solution to some of the pressing challenges associated with sparse view tracking and mapping in real-world environments.