Segmentation of unbalanced and in-homogeneous point clouds and its application to 3D scanned trees

Abstract

Segmentation of 3D point clouds is still an open issue in the case of unbalanced and in-homogeneous data-sets. In the application context of the modeling of botanical trees, a fundamental challenge consists in separating the leaves from the wood. Based on deep learning and a class decision process, we propose an innovative method designed to separate leaf points from wood points in terrestrial LiDAR point clouds of trees. Although simple, our approach learns trees characteristic point patterns efficiently and robustly. To train our 3D deep learning model, we constructed a 3D labeled point cloud data-set of different tree species. Experiments show that our 3D deep representation together with our geometric approach leads to significant improvement over the state-of-the-art methods in segmentation task.

Sensor Agnostic Semantic Segmentation of Structurally Diverse and Complex Forest Point Clouds Using Deep Learning

“…[24] achieved a 91% overall accuracy using a technique based upon the random forest technique. [23] used a Pointnet++ inspired approach and claimed an overall accuracy of "close to 90%". [26] tested a variety of approaches, with their best results being on their Carabost dataset.…”

“…In the case of this paper, we are focused on separating parts of a forest into terrain, vegetation, coarse woody debris, and stem categories from a point cloud. There have been many different approaches to the segmentation of forest point clouds [20][21][22][23][24][25][26][27][28][29][30][31][32][33][34] so far. Some approaches use heuristics [20,22,25,28,29] or morphological operations [27], while others use supervised [23,26,[30][31][32][33] or unsupervised [21,34] machine learning techniques.…”

“…With supervised machine learning techniques, the first major challenge to building a model is to obtain or generate sufficient and appropriate training data. Some works in this area [23,33] approach this through the use of artificially generated datasets, which are made from simulating a forest and a terrestrial laser scanning operation to create perfectly labeled point clouds on demand. This approach is certainly logical, as manually labeling point cloud datasets is time-consuming and monotonous while also requiring skilled and attentive operators; however, it can be difficult to generate synthetic datasets with all of the same challenges present within real-world point clouds.…”

“…The segmentation works described above were mostly designed with individual sensing methods in mind, such as TLS [20][21][22][23]27,28,33,34], MLS [25,28], or ALS [26,[28][29][30][31][32], resulting in limited transferability to point clouds captured using other methods with the exception of [28], whose approach was demonstrated on ALS, TLS, and MLS. To move toward the idea of a fully automated and universal forest point cloud processing tool, the goal of our work was to create a workflow capable of automatically segmenting forest point clouds from a variety of sensors with varied point cloud accuracy, density, and mapping challenges.…”

Wood–Leaf Classification of Tree Point Cloud Based on Intensity and Geometric Information

“…Krishna Moorthy et al realized wood-leaf classification using radially bounded nearest neighbors on multiple spatial scales in a machine learning model [47]. Morel et al classified wood points and leaf points based on deep learning and a class decision process [48]. The automation and the efficiency of machine learning methods decreases due to the laborious and time-consuming manual selection of training data for the classifier.…”