RoMe: Towards Large Scale Road Surface Reconstruction via Mesh Representation (2306.11368v4)

Abstract: In autonomous driving applications, accurate and efficient road surface reconstruction is paramount. This paper introduces RoMe, a novel framework designed for the robust reconstruction of large-scale road surfaces. Leveraging a unique mesh representation, RoMe ensures that the reconstructed road surfaces are accurate and seamlessly aligned with semantics. To address challenges in computational efficiency, we propose a waypoint sampling strategy, enabling RoMe to reconstruct vast environments by focusing on sub-areas and subsequently merging them. Furthermore, we incorporate an extrinsic optimization module to enhance the robustness against inaccuracies in extrinsic calibration. Our extensive evaluations of both public datasets and wild data underscore RoMe's superiority in terms of speed, accuracy, and robustness. For instance, it costs only 2 GPU hours to recover a road surface of 600*600 square meters from thousands of images. Notably, RoMe's capability extends beyond mere reconstruction, offering significant value for autolabeling tasks in autonomous driving applications. All related data and code are available at https://github.com/DRosemei/RoMe.

• The paper presents a novel mesh framework that reconstructs large-scale road surfaces with enhanced speed, precision, and robustness.
• The methodology integrates camera pose estimation with semantic segmentation and waypoint sampling to enable efficient parallel processing and robust optimization.
• Empirical evaluations on KITTI and nuScenes demonstrate a 600x600 m² reconstruction in two hours on a single GPU with a twofold efficiency improvement.

Evaluation of RoMe: Large-Scale Road Surface Reconstruction via Mesh Representation

The paper "RoMe: Towards Large Scale Road Surface Reconstruction via Mesh Representation" introduces a novel framework aimed at robustly reconstructing large-scale road surfaces by leveraging a unique mesh representation. This approach, called RoMe, is designed to address critical challenges in the domain of autonomous driving, focusing on the enhancement of computational efficiency and accuracy in road surface recognition. The proposed method demonstrates significant advancements, particularly in the realms of speed, accuracy, and robustness when compared to existing methods.

Framework and Methodology

RoMe employs a systematic framework to achieve detailed 3D road surface reconstruction solely from image sequences. This framework comprises three primary components: waypoint sampling, mesh initialization, and optimization. By focusing on these, RoMe facilitates the efficient reconstruction of extensive environments, which is critical for real-time applications in autonomous driving.

1. Mesh Initialization: The process begins with the estimation of camera poses using either ORB-SLAM2 or COLMAP, followed by semantic segmentation via Mask2Former. This initialization creates a mesh with vertices that encapsulate essential attributes such as elevation, color, and semantics.
2. Waypoint Sampling: To tackle large-scale surface reconstruction, RoMe implements a waypoint sampling strategy. This approach divides the road surface into sub-areas, allowing for parallel processing which significantly enhances computational efficiency.
3. Optimization: Across this comprehensive process, RoMe utilizes an extrinsic optimization module to refine camera settings and improve robustness against extrinsic calibration inaccuracies. Mesh optimization focuses on aligning rendering in terms of color and semantics, ensuring fidelity in the final representation.

Performance and Evaluation

Empirical evaluations conducted on datasets like KITTI and nuScenes reveal that RoMe significantly outperforms traditional methods in terms of speed and precision, recovering a 600x600 square meter area within just two hours on a single GPU. Notably, the implementation of waypoint sampling demonstrated a twofold increase in efficiency, reducing both training time and GPU memory usage.

The findings underscore the capability of RoMe not only in reconstructing high-fidelity road surfaces but also in automated labeling, which presents valuable implications for further advancements in autonomous driving applications. The paper's numerical results solidify the proposed method's superiority, showcasing enhanced alignment between semantic and color renderings in bird's-eye views despite variations in environment and camera settings.

Implications and Future Directions

The implications of RoMe extend beyond robust road surface reconstruction. Its efficient 3D reconstruction capability provides a foundation for seamlessly projecting auto-labels onto source images, which holds significant potential for advancing automated labeling technologies essential to autonomous systems. Future endeavors could explore integrating RoMe with additional data acquisition tools, such as integrating LiDAR, to bolster its applicability across more diverse environments. Furthermore, extending the framework's applicability in environments with challenging lighting conditions, such as nighttime or adverse weather, could further broaden RoMe's utility in real-world scenarios.

In summary, RoMe demonstrates a marked improvement in large-scale road surface reconstruction, setting a new standard within the autonomous driving sector. Its methodological innovations in mesh representation and waypoint sampling, coupled with strategic optimization techniques, position it as a robust tool for achieving real-time, precise road surface reconstruction in extensive environments.