374 research outputs found
Living in a Material World: Learning Material Properties from Full-Waveform Flash Lidar Data for Semantic Segmentation
Advances in lidar technology have made the collection of 3D point clouds fast
and easy. While most lidar sensors return per-point intensity (or reflectance)
values along with range measurements, flash lidar sensors are able to provide
information about the shape of the return pulse. The shape of the return
waveform is affected by many factors, including the distance that the light
pulse travels and the angle of incidence with a surface. Importantly, the shape
of the return waveform also depends on the material properties of the
reflecting surface. In this paper, we investigate whether the material type or
class can be determined from the full-waveform response. First, as a proof of
concept, we demonstrate that the extra information about material class, if
known accurately, can improve performance on scene understanding tasks such as
semantic segmentation. Next, we learn two different full-waveform material
classifiers: a random forest classifier and a temporal convolutional neural
network (TCN) classifier. We find that, in some cases, material types can be
distinguished, and that the TCN generally performs better across a wider range
of materials. However, factors such as angle of incidence, material colour, and
material similarity may hinder overall performance.Comment: In Proceedings of the Conference on Robots and Vision (CRV'23),
Montreal, Canada, Jun. 6-8, 202
Efficient large-scale airborne LiDAR data classification via fully convolutional network
Nowadays, we are witnessing an increasing availability of large-scale airborne LiDAR (Light Detection and Ranging) data, that greatly improve our knowledge of urban areas and natural environment. In order to extract useful information from these massive point clouds, appropriate data processing is required, including point cloud classification. In this paper we present a deep learning method to efficiently perform the classification of large-scale LiDAR data, ensuring a good trade-off between speed and accuracy. The algorithm employs the projection of the point cloud into a two-dimensional image, where every pixel stores height, intensity, and echo information of the point falling in the pixel. The image is then segmented by a Fully Convolutional Network (FCN), assigning a label to each pixel and, consequently, to the corresponding point. In particular, the proposed approach is applied to process a dataset of 7700\u2009km2 that covers the entire Friuli Venezia Giulia region (Italy), allowing to distinguish among five classes (i ground, vegetation, roof, overground and power line/i), with an overall accuracy of 92.9%
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