Abstract
Point cloud classification is quite challenging due to the influence of noise, occlusion, and the variety of types and sizes of objects. Currently, most methods mainly focus on subjectively designing and extracting features. However, the features rely on prior knowledge, and it is also difficult to accurately characterize the complex objects of point clouds. In this paper, we propose a concise multi-scale convolutional network (MSNet) for adaptive and robust point cloud classification. Both the local feature and global context are incorporated for this purpose. First, around each point, the spatial contexts of different sizes are partitioned as voxels of different scales. A voxel-based MSNet is then simultaneously applied at multiple scales to adaptively learn the discriminative local features. The class probability of a point cloud is predicted by fusing the features together across multiple scales. Finally, the predicted class probabilities of MSNet are optimized globally using the conditional random field (CRF) with a spatial consistency constraint. The proposed method was tested with data sets of mobile laser scanning (MLS), terrestrial laser scanning (TLS), and airborne laser scanning (ALS) point clouds. The experimental results show that the proposed method was able to achieve appreciable classification accuracies of 83.18%, 98.24%, and 97.02% on the MLS, TLS, and ALS data sets, respectively. The results also demonstrate that the proposed network has a strong generalization capability for classifying different kinds of point clouds under complex urban environments.
Highlights
Point clouds are widely available due to the progressive development of various laser sensors and dense image matching techniques
To automatically understand point clouds across different scales for discriminative feature learning, the context of a point cloud is analyzed by multi-scale voxels that are centered at the point, which allows the network to closely observe at a fine scale, and a consider rough view at a coarse scale
Both mobile laser scanning (MLS) and airborne laser scanning (ALS) point clouds were used to evaluate the proposed method, and included objects of different sizes and scanning densities. They were acquired from the same area of Wuhan University (WHU), China, and are available at https://github.com/wleigithub/WHU_pointcloud_dataset
Summary
Point clouds are widely available due to the progressive development of various laser sensors and dense image matching techniques. The efficient classification of point clouds is one of the fundamental problems in scene understanding for three-dimensional (3D) digital cities, intelligent robots, and unmanned vehicles. Classifying point clouds under complex urban environments is not a trivial task, since they are usually noisy, sparse, and unorganized [1]. The density of point clouds varies with the sampling intervals and ranges of laser scanners. Severe occlusions between objects during scanning can lead to incomplete coverage of object surfaces. These problems present challenges for point cloud classification
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