RoGS: Large Scale Road Surface Reconstruction based on 2D Gaussian Splatting
(2405.14342)Abstract
Road surface reconstruction plays a crucial role in autonomous driving, which can be used for road lane perception and autolabeling tasks. Recently, mesh-based road surface reconstruction algorithms show promising reconstruction results. However, these mesh-based methods suffer from slow speed and poor rendering quality. In contrast, the 3D Gaussian Splatting (3DGS) shows superior rendering speed and quality. Although 3DGS employs explicit Gaussian spheres to represent the scene, it lacks the ability to directly represent the geometric information of the scene. To address this limitation, we propose a novel large-scale road surface reconstruction approach based on 2D Gaussian Splatting (2DGS), named RoGS. The geometric shape of the road is explicitly represented using 2D Gaussian surfels, where each surfel stores color, semantics, and geometric information. Compared to Gaussian spheres, the Gaussian surfels aligns more closely with the physical reality of the road. Distinct from previous initialization methods that rely on point clouds for Gaussian spheres, we introduce a trajectory-based initialization for Gaussian surfels. Thanks to the explicit representation of the Gaussian surfels and a good initialization, our method achieves a significant acceleration while improving reconstruction quality. We achieve excellent results in reconstruction of roads surfaces in a variety of challenging real-world scenes.
Overview
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RoGS, a novel method using 2D Gaussian Splatting, significantly improves the speed and accuracy of road surface reconstructions for autonomous driving applications.
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The method addresses the shortcomings of traditional mesh-based and implicit 3D reconstruction methods, particularly in sparse-feature areas such as roads.
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RoGS leverages vehicle trajectory data for initialization, enhancing the starting point for optimization and achieving impressive results on datasets like KITTI and NuScenes.
Efficient Road Surface Reconstruction with RoGS
Overview
Road surface reconstruction is crucial for autonomous driving, enabling better road lane perception and auto-labeling tasks. Traditional methods like mesh-based algorithms show promise but fall short in speed and rendering quality. Enter RoGS, an innovative approach using 2D Gaussian Splatting (2DGS) to achieve faster and more accurate road surface reconstructions.
Why Road Surface Reconstruction Matters
Reconstructing road surfaces from video data aids in creating high-definition maps and offers valuable annotations for training autonomous driving systems. It plays a significant role in understanding road regions, lane lines, and road markings. Traditional methods face challenges in sparse-feature areas like roads, leading to incomplete reconstructions with holes and noise.
Traditional Approaches and Their Limitations
Conventional methods fall into two categories:
- Classical 3D Reconstruction: Techniques like Structure-from-Motion (SfM) and Multi-View Stereo (MVS) generate sparse or semi-dense point clouds. These approaches struggle with road surfaces due to sparse texture features, often producing incomplete reconstructions.
- Implicit 3D Reconstruction: Methods such as Neural Radiance Fields (NeRF) offer impressive rendering effects but struggle with geometric accuracy, especially in large-scale scenes. Additionally, these methods can be time-consuming and computationally intensive.
The RoGS Approach
RoGS introduces a new perspective by using 2D Gaussian surfels. Each surfel stores not just geometric data but also color and semantic information. This explicit representation aligns more closely with the physical reality of roads and offers several advantages:
- Speed: RoGS achieves significant speedups compared to traditional mesh-based methods.
- Quality: The 2D Gaussian surfels provide better accuracy in representing road geometry.
Data Initialization
RoGS leverages vehicle trajectory data for initialization. By aligning surfel coordinates with the vehicle trajectory, the method ensures a more accurate starting point for optimization. This trajectory-based initialization is more effective than random or point-cloud based approaches.
Results and Performance
The authors tested RoGS on the KITTI and NuScenes datasets, achieving impressive results:
- Speed: RoGS exhibits a 15.84× speedup compared to traditional methods like RoMe.
- Accuracy: With LiDAR data for supervision, RoGS shows a 17.62% reduction in elevation error, surpassing RoMe in both PSNR and mIoU metrics.
Implications and Future Prospects
The implications of this research are twofold:
- Practical: Faster and more accurate road reconstructions mean better training data for autonomous vehicles and more reliable maps.
- Theoretical: The combination of explicit geometric representations with trajectory-based initialization offers a new avenue for improving 3D scene reconstruction techniques.
Looking forward, this approach could benefit various applications beyond autonomous driving, such as urban planning and augmented reality. Future developments might focus on refining the initialization process and further improving the robustness of the method against less accurate trajectory data.
Conclusion
RoGS represents a significant step forward in road surface reconstruction. By leveraging 2D Gaussian surfels and trajectory-based initialization, this method not only speeds up the process but also enhances the accuracy of the reconstructions. These advancements pave the way for more efficient and reliable autonomous driving technologies.