A Two-Step Classification Approach to Distinguishing Similar Objects in Mobile Lidar Point Clouds

Abstract

ABSTRACT:Nowadays, lidar is widely used in cultural heritage documentation, urban modeling, and driverless car technology for its fast and accurate 3D scanning ability. However, full exploitation of the potential of point cloud data for efficient and automatic object recognition remains elusive. Recently, feature-based methods have become very popular in object recognition on account of their good performance in capturing object details. Compared with global features describing the whole shape of the object, local features recording the fractional details are more discriminative and are applicable for object classes with considerable similarity. In this paper, we propose a two-step classification approach based on point feature histograms and the bag-of-features method for automatic recognition of similar objects in mobile lidar point clouds. Lamp post, street light and traffic sign are grouped as one category in the first-step classification for their inter similarity compared with tree and vehicle. A finer classification of the lamp post, street light and traffic sign based on the result of the first-step classification is implemented in the second step. The proposed two-step classification approach is shown to yield a considerable improvement over the conventional one-step classification approach.

A multiclass TrAdaBoost transfer learning algorithm for the classification of mobile lidar data

“…However, since such information may not always be available, the proposed framework is based on 3D point coordinates only. We follow a segment-based approach, where we first extract 3D point segments representing potential objects from the raw point clouds, and then label these segments by applying a classification method (He et al, 2017;Khoshelham et al, 2013). To achieve a complete segmentation, we follow the pipeline proposed by Golovinskiy et al (2009).…”

Deep Learning for Semantic Segmentation of 3d Point Cloud

“…This approach is also based on a manual segmentation method. A two-step classification approach based on point feature histograms and the bag-of-features method for automatic recognition of similar objects in mobile lidar point clouds was proposed by (He et al, 2017). The proposed two-step classification approach has the aim to considerably improve the conventional onestep classification approach with insufficient and unbalanced training data.…”