PotholeGuard: A Pothole Detection Approach by Point Cloud Semantic Segmentation

TL;DR

The paper tackles robust pothole detection from sparse 3D point clouds by introducing PotholeGuard, a SCF-Net–based architecture that enhances local-global feature representation through a Feature Augmenter and a dedicated PotholeGuard Module. These components enable better handling of density variations and complex pothole geometry, aided by a lightweight KNN-based refinement and shared MLP pooling. Empirical results on public datasets show strong performance, with state-of-the-art Overall Accuracy and mIoU on S3DIS and competitive results on ScanObjectNN, along with ablations demonstrating the value of feature augmentation. The work offers a practical pathway to reliable 3D pothole segmentation, with direct implications for autonomous inspection, road maintenance, and safety applications.

Abstract

Pothole detection is crucial for road safety and maintenance, traditionally relying on 2D image segmentation. However, existing 3D Semantic Pothole Segmentation research often overlooks point cloud sparsity, leading to suboptimal local feature capture and segmentation accuracy. Our research presents an innovative point cloud-based pothole segmentation architecture. Our model efficiently identifies hidden features and uses a feedback mechanism to enhance local characteristics, improving feature presentation. We introduce a local relationship learning module to understand local shape relationships, enhancing structural insights. Additionally, we propose a lightweight adaptive structure for refining local point features using the K nearest neighbor algorithm, addressing point cloud density differences and domain selection. Shared MLP Pooling is integrated to learn deep aggregation features, facilitating semantic data exploration and segmentation guidance. Extensive experiments on three public datasets confirm PotholeGuard's superior performance over state-of-the-art methods. Our approach offers a promising solution for robust and accurate 3D pothole segmentation, with applications in road maintenance and safety.

Figures (7)

• Figure 1: Feature Augmenter Module
• Figure 2: PotholeGuard Module
• Figure 3: Architecture of the Network
• Figure 4: Segmentation Results for a Pothole with Dense Point Populations
• Figure 5: Segregation Outcome on Sparse Point Populations within a Pothole