SEMANTIC SEGMENTATION OF TERRESTRIAL LIDAR DATA USING CO-REGISTERED RGB DATA

SEMANTIC SEGMENTATION OF TERRESTRIAL LIDAR DATA USING CO-REGISTERED RGB DATA

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This paper proposes a semantic segmentation pipeline for terrestrial laser scanning data.We achieve this by combining co-registered RGB and 3D point cloud information.Semantic segmentation is performed by applying a pre-trained off-the-shelf 2D convolutional neural network over a set of projected images extracted from Day cream a panoramic photograph.

This allows the network to exploit the visual image features that are learnt in a state-of-the-art segmentation models trained on very large datasets.The study focuses on the adoption of the Vitamin A spherical information from the laser capture and assessing the results using image classification metrics.The obtained results demonstrate that the approach is a promising alternative for asset identification in laser scanning data.

We demonstrate comparable performance with spherical machine learning frameworks, however, avoid both the labelling and training efforts required with such approaches.