Semantic segmentation of point cloud data using raw laser scanner measurements and deep neural networks

Deep learning methods based on convolutional neural networks have shown to give excellent results in semantic segmentation of images, but the inherent irregularity of point cloud data complicates their usage in semantically segmenting 3D laser scanning data. To overcome this problem, point cloud networks particularly specialized for the purpose have been implemented since 2017 but finding the most appropriate way to semantically segment point clouds is still an open research question. In this study we attempted semantic segmentation of point cloud data with convolutional neural networks by using only the raw measurements provided by a multiple echo detection capable profiling laser scanner. We formatted the measurements to a series of 2D rasters, where each raster contains the measurements (range, reflectance, echo deviation) of a single scanner mirror rotation to be able to use the rich research done on semantic segmentation of 2D images with convolutional neural networks. Similar approach for profiling laser scanner in forest context has never been proposed before. A boreal forest in Evo region near Hämeenlinna in Finland was used as experimental study area. The data was collected with FGI Akhka-R3 backpack laser scanning system, georeferenced and then manually labelled to ground, understorey, tree trunk and foliage classes for training and evaluation purposes. The labelled points were then transformed back to 2D rasters and used for training three different neural network architectures. Further, the same georeferenced data in point cloud format was used for training the state-of-the-art point cloud semantic segmentation network RandLA-Net and the results were compared with those of our method. Our best semantic segmentation network reached the mean Intersection-over-Union value of 80.1% and it is comparable to the 80.6% reached by the point cloud -based RandLA-Net. The numerical results and visual analysis of the resulting point clouds show that our method is a valid way of doing semantic segmentation of point clouds at least in the forest context. The labelled datasets were also released to the research community.