PotholeGuard: A Pothole Detection Approach by Point Cloud Semantic
  Segmentation
Sahil Nawale Dhruv Khut Daksh Dave Gauransh Sawhney Pushkar Aggrawal Dr. Kailas Devadakar
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.