## 2 Related Work
[29] proposed a Multi-view Convolutional Neural Network (MVCNN) architecture to maximize the multi-view features of point clouds into global descriptors that can be used to identify 3D shapes to complete point cloud classification. [PERSON] et al. [30] first used a network of relationships to leverage the interrelationships of a set of views (region-region relationship or view-view relationship) and then aggregated these views to classify point clouds. Moreover, there are other deep learning methods that use point-based strategies to directly perform semantic segmentation on a point cloud and process and manipulate the point cloud through the relationship between the kernel points and local points, defined in the point cloud, to realize the feature extraction and learning of point cloud data. Examples include Point Cloud Neural Networks (PointNet [31], PointNet++ [32]), Kernel Point Convolution (KPConv) [33], RandLA-Net [34], and Dynamic Graph Convolutional Neural Networks (DGCNNs) [35]. Most of these methods are point-based strategies that directly use point clouds for feature extraction and have achieved promising results in relatively straightforward and structured road extraction scenarios. However, while the accuracy has been improved, a deep learning-based method requires a large sample set and time for training, which reduces the efficiency of the road surface point cloud extraction [36]. Additionally, there is a lack of relevant datasets and experiments established for proving the effectiveness and robustness of these algorithms for the extraction of unstructured road surfaces in complex scenarios. Among these approaches, [PERSON] et al. [37] proposed a deep learning framework for the extraction of off-highway drivable areas for the application of 3D point clouds in autonomous driving. To reduce the need for the manual labeling of data when the neural network processes data, they used weakly supervised or semi-supervised training methods and successfully improved the efficiency of the algorithm. However, the road boundary was not accurately segmented, and the robustness of the algorithm must be improved. In summary, the above methods usually require the collection and processing of additional point cloud data information, such as the surface condition of the road, geometry of the road, point cloud density, reflection intensity of the laser, and GPS time. Owing to the large topographic fluctuations in China, the longitudinal elevation of an expressway changes significantly. Therefore, the previously used method was not applicable. To overcome these limitations, a new method is proposed that combines a supervoxel model, vehicle trajectory data, and region growing segmentation technology to improve the robustness and efficiency of highway road surface extraction. This study proposes a road surface extraction method based on a supervoxel-assisted region growing algorithm to solve the aforementioned challenges, reduce data processing complexity, and efficiently and accurately extract road surfaces from highway MLS point cloud data. This method combines the supervoxel data model with region growing segmentation to accelerate road surface extraction and optimize the quality and precision of road extraction results. Subsequently, the proposed method was experimentally validated using measured data from two typical highway scenarios: flat and sloped. The results are compared with the extraction results of three other traditional segmentation algorithms: Euclidean clustering, Random Sample Consensus (RANSAC), and traditional region growing algorithms. The remainder of this study is structured as follows. Section 2 provides an overview of the work related on road surface extraction with MLS point clouds. Section 3 describes the proposed method for extracting road surfaces in detail. Section 4 describes the parameterization and analysis of the proposed method. The results of the experiment and related discussions are presented in Section 5. Section 6 concludes the study with a summary of the findings.