Mode-GS: Monocular Depth Guided Anchored 3D Gaussian Splatting for Robust Ground-View Scene Rendering (2410.04646v1)

Abstract: We present a novel-view rendering algorithm, Mode-GS, for ground-robot trajectory datasets. Our approach is based on using anchored Gaussian splats, which are designed to overcome the limitations of existing 3D Gaussian splatting algorithms. Prior neural rendering methods suffer from severe splat drift due to scene complexity and insufficient multi-view observation, and can fail to fix splats on the true geometry in ground-robot datasets. Our method integrates pixel-aligned anchors from monocular depths and generates Gaussian splats around these anchors using residual-form Gaussian decoders. To address the inherent scale ambiguity of monocular depth, we parameterize anchors with per-view depth-scales and employ scale-consistent depth loss for online scale calibration. Our method results in improved rendering performance, based on PSNR, SSIM, and LPIPS metrics, in ground scenes with free trajectory patterns, and achieves state-of-the-art rendering performance on the R3LIVE odometry dataset and the Tanks and Temples dataset.

• The paper presents a novel approach that uses monocular depth to initialize anchored Gaussian splats, eliminating dependence on traditional SfM or LiDAR data.
• It incorporates a residual-form Gaussian decoder that refines splat attributes for enhanced training efficiency and rendering robustness.
• Empirical results show that Mode-GS achieves better ground-view rendering quality on challenging datasets, outperforming conventional methods in PSNR, SSIM, and LPIPS.

Overview of Mode-GS: Monocular Depth Guided Anchored 3D Gaussian Splatting

The paper presents a novel view rendering algorithm named Mode-GS, focused on improving neural rendering capabilities for ground-robot trajectory datasets. The core innovation in Mode-GS lies in its utilization of anchored Gaussian splats derived from monocular depth estimates. This approach addresses specific challenges inherent in ground-view scenes, including scale ambiguity and the need for accurate pixel poses, by integrating these components into a cohesive rendering framework.

Key Contributions

1. Monocular Depth Integration:

The paper introduces a mechanism to initialize anchored Gaussian splats using monocular depth networks. This approach eliminates the dependency on initial structure-from-motion (SfM) or LiDAR point clouds. The authors employ an anchor depth-scale parameterization strategy complemented by a scale-consistent depth loss, enabling effective online depth-scale calibration.

2. Residual-Form Gaussian Decoders:

A novel decoding mechanism is proposed where each anchor can generate multiple Gaussian splats by incorporating residuals into nominal Gaussian attributes (position, color, opacity, and scaling). This residual-based structure allows direct initialization of splat attributes, enhancing both the training efficiency and robustness of the representation.

3. Robust Rendering Framework:

Mode-GS is tailored for ground-view robot datasets with free trajectories, demonstrating its utility on datasets like R³LIVE and Tanks and Temples. The rendering benefits are highlighted through improvements measured by standard metrics such as PSNR, SSIM, and LPIPS.

Numerical Results and Comparisons

The authors provide rigorous empirical evaluations of Mode-GS against contemporary 3D Gaussian Splatting (3DGS) variants across complex real-world datasets. The results reveal consistent improvements in rendering quality, particularly in scenarios lacking multi-view observational data, as often seen in ground-robot datasets. Mode-GS outperforms alternatives by leveraging its monocular depth-guided approach to deliver enhanced performance without reliance on dense input point clouds.

Implications and Future Directions

Practical Implications:

Mode-GS's ability to function effectively without dense point-cloud input makes it highly practical for applications in robotics where obtaining such data is challenging. The method's anchored splat handling also offers robustness against inaccuracies in sensor-derived positional data, a common issue in dynamic robot navigation scenarios.

Theoretical Implications:

The methodology paves the way for integrating neural rendering with less intrusive data acquisition techniques, potentially influencing further research in monocular depth utilization within neural rendering paradigms. Furthermore, the residual-form decoder contributes to discussions on efficient attribute initialization within machine learning models, especially in 3D representations.

Speculative Future Developments:

Future research could explore hybrid models that incorporate the Mode-GS strategy with more traditional non-anchored splapping techniques to maximize performance in densely captured datasets. Another potential avenue lies in adapting the approach for augmented reality (AR) applications, where reliable real-time rendering is critical.

In conclusion, the Mode-GS algorithm represents a significant step forward in rendering technology for robotic applications by addressing challenges unique to ground-view datasets and showcasing the feasibility of integrating monocular depth data into neural rendering processes.