Supervoxel-Based Instance Segmentation of Pole-Like Facilities From Mobile Laser Scanning Data Using Pyramid Cascaded Fisher Vector Modeling

Abstract

Efficient and automatic object recognition in road scenes plays an essential role in smart city applications such as autonomous driving and intelligent infrastructure. As an important component of road scenes, pole-like facilities (PLFs) have been challenging to recognize high-definition road mapping. To achieve the automatic recognition of PLFs from cluttered mobile laser scanning (MLS) data, a novel instance segmentation method is proposed. First, candidate poles are detected by a supervoxel-based histogram analysis from partitioned off-ground point clouds. Then, instance segmentation of PLFs is achieved through a constrained hierarchical region-growing algorithm based on voxelized point clouds. A pyramid cascaded Fisher vector (FV) model and a random forest (RF) classifier are applied to classify the delineated pole-like road facilities into six predefined categories: trees, traffic signs, traffic lights, lamps, bare poles, and other objects. The proposed method is tested on three datasets collected in street scenes with different types of road facilities and point densities. Results demonstrate that our method can effectively achieve instance segmentation of PLFs in complex road environments. The proposed method outperformed state of the art for PLF detection in correctness (93.5%), completeness (95.3%), and quality (88.81%). Besides, the proposed method achieved satisfactory results for instance-level semantic segmentation with an average F1 score of 90.2%, demonstrating the effectiveness of geometric information enhancement in the designed FV coding approach.