Drone-assisted Road Gaussian Splatting with Cross-view Uncertainty (2408.15242v1)

Abstract: Robust and realistic rendering for large-scale road scenes is essential in autonomous driving simulation. Recently, 3D Gaussian Splatting (3D-GS) has made groundbreaking progress in neural rendering, but the general fidelity of large-scale road scene renderings is often limited by the input imagery, which usually has a narrow field of view and focuses mainly on the street-level local area. Intuitively, the data from the drone's perspective can provide a complementary viewpoint for the data from the ground vehicle's perspective, enhancing the completeness of scene reconstruction and rendering. However, training naively with aerial and ground images, which exhibit large view disparity, poses a significant convergence challenge for 3D-GS, and does not demonstrate remarkable improvements in performance on road views. In order to enhance the novel view synthesis of road views and to effectively use the aerial information, we design an uncertainty-aware training method that allows aerial images to assist in the synthesis of areas where ground images have poor learning outcomes instead of weighting all pixels equally in 3D-GS training like prior work did. We are the first to introduce the cross-view uncertainty to 3D-GS by matching the car-view ensemble-based rendering uncertainty to aerial images, weighting the contribution of each pixel to the training process. Additionally, to systematically quantify evaluation metrics, we assemble a high-quality synthesized dataset comprising both aerial and ground images for road scenes.

• The paper introduces a novel uncertainty-aware training paradigm that fuses drone and ground views to overcome convergence challenges in 3D Gaussian Splatting.
• It constructs a high-quality dataset by combining aerial and ground images, enabling realistic road scene synthesis for autonomous driving simulations.
• Extensive experiments on NYC and SF datasets demonstrate significant improvements in PSNR, SSIM, and LPIPS over conventional rendering methods.

Drone-assisted Road Gaussian Splatting with Cross-view Uncertainty

This paper presents a novel approach to enhance the fidelity of large-scale road scene renderings for autonomous driving simulations by integrating drone-assisted views. Traditional methods primarily leverage car-view images which, due to their limited field of view, often fail to capture the necessary detail and context for accurate scene reconstruction. The proposed method incorporates images from drones to provide a comprehensive global view of road scenes, addressing the limitations of current ground-based data.

The paper introduces a new paradigm that fundamentally builds upon the principles of 3D Gaussian Splatting (3D-GS). The authors identify a significant challenge: While joint training with aerial and ground images intuitively seems beneficial, it often fails to improve, and can even degrade, the rendering performance for road scenes. This issue arises due to the substantial view disparity between aerial and ground perspectives, which poses significant convergence challenges for 3D-GS. To overcome this, the authors design an uncertainty-aware training method, introducing the concept of cross-view uncertainty to effectively leverage aerial information where ground images are insufficient.

Key Contributions

1. Problem Formalization and Dataset Creation:
   • The paper formalizes the problem of drone-assisted road scene synthesis and constructs a high-quality synthesized dataset. This dataset contains both aerial and ground images with viewpoints simulated to reflect real-world behavior patterns of drones and vehicles using tools like AirSim and Unreal Engine.
2. Uncertainty-aware Training Paradigm:
   • The authors propose a new training strategy by introducing cross-view uncertainty, which they quantify using an ensemble-based method. Rather than weighting all pixels equally, this approach weights the contribution of each pixel based on the rendering uncertainty from ground views projected to aerial perspectives.
   • This method guides the 3D Gaussians to focus on areas where ground data alone performs poorly, thus significantly enhancing the rendering quality of the synthesized road scenes.
3. Extensive Experimental Evaluation:
   • The paper demonstrates the efficacy of their method through rigorous experiments on two high-fidelity synthesized datasets representing New York City and San Francisco. The proposed method outperforms several baselines including Nerfacto, 3D-GS, Mip-Splatting, and Scaffold-GS.
   • Metrics such as PSNR, SSIM, and LPIPS are used to evaluate the performance, showing significant improvements in reconstructing held-out test views as well as during view shifting and rotation scenarios.

Results and Implications

The results of the research show that the proposed uncertainty-aware training method yields substantial improvements in rendering fidelity. Specifically, on held-out test sets, the method achieves substantial gains in PSNR (0.68 for NYC and 0.41 for SF on average) compared to the state-of-the-art methods. Metrics like SSIM and LPIPS also exhibit notable enhancements, underscoring the robustness of the method.

Through detailed analysis, the authors demonstrate that their approach mitigates the inherent challenges posed by the substantial disparity between aerial and ground views. By effectively utilizing the global context provided by aerial images, their method addresses the key limitations of prior work where aerial imagery was incorporated naively without effective weighting.

Future Directions

The proposed method opens several avenues for future research and development. Practically, it can significantly improve the quality and reliability of autonomous driving simulations, leading to more robust and safer real-world deployments. The concept of cross-view uncertainty itself can be extended and refined, potentially applying the principles to other domains requiring multi-view synthesis and blending of disparate data sources.

From a theoretical standpoint, the integration of uncertain and complementary view perspectives into neural rendering frameworks can spur further advancements in neural scene representations. Future research could explore deeper integrations with other forms of spatial information and leverage more sophisticated machine learning models to further enhance training efficiency and synthesis fidelity.

In summary, this paper demonstrates a thoughtful and innovative approach to improving autonomous driving simulations through improved neural rendering techniques. It sets a new benchmark in the field, showcasing the potential of integrating aerial views via uncertainty-aware methodologies to achieve highly realistic rendering of road scenes. This work stands as a significant contribution with practical implications for autonomous driving and broader applications in digital cities and virtual reality.