A Fully Convolutional Network for Semantic Labeling of 3D Point Clouds

Abstract

When classifying point clouds, a large amount of time is devoted to the process of engineering a reliable set of features which are then passed to a classifier of choice. Generally, such features - usually derived from the 3D-covariance matrix - are computed using the surrounding neighborhood of points. While these features capture local information, the process is usually time-consuming, and requires the application at multiple scales combined with contextual methods in order to adequately describe the diversity of objects within a scene. In this paper we present a 1D-fully convolutional network that consumes terrain-normalized points directly with the corresponding spectral data,if available, to generate point-wise labeling while implicitly learning contextual features in an end-to-end fashion. Our method uses only the 3D-coordinates and three corresponding spectral features for each point. Spectral features may either be extracted from 2D-georeferenced images, as shown here for Light Detection and Ranging (LiDAR) point clouds, or extracted directly for passive-derived point clouds,i.e. from muliple-view imagery. We train our network by splitting the data into square regions, and use a pooling layer that respects the permutation-invariance of the input points. Evaluated using the ISPRS 3D Semantic Labeling Contest, our method scored second place with an overall accuracy of 81.6%. We ranked third place with a mean F1-score of 63.32%, surpassing the F1-score of the method with highest accuracy by 1.69%. In addition to labeling 3D-point clouds, we also show that our method can be easily extended to 2D-semantic segmentation tasks, with promising initial results.

References in corpus (3)

• Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
• PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
• Understanding Neural Networks Through Deep Visualization

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• Deep Learning-based 3D Point Cloud Classification: A Systematic Survey and Outlook
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• Beyond single receptive field: A receptive field fusion-and-stratification network for airborne laser scanning point cloud classification
• Density-Aware Convolutional Networks with Context Encoding for Airborne LiDAR Point Cloud Classification
• Addressing Overfitting on Pointcloud Classification using Atrous XCRF
• Tree Annotations in LiDAR Data Using Point Densities and Convolutional Neural Networks
• DAPnet: A Double Self-attention Convolutional Network for Point Cloud Semantic Labeling
• Airborne LiDAR Point Cloud Classification with Graph Attention Convolution Neural Network
• LGENet: Local and Global Encoder Network for Semantic Segmentation of Airborne Laser Scanning Point Clouds
• GraNet: Global Relation-aware Attentional Network for ALS Point Cloud Classification