Pcct: A Point Cloud Classification Tool to Create 3d Training Data to Adjust and Develop 3d Convnet

Abstract

Point clouds give a very detailed and sometimes very accurate representation of the geometry of captured objects. In surveying, point clouds captured with laser scanners or camera systems are an intermediate result that must be processed further. Often the point cloud has to be divided into regions of similar types (object classes) for the next process steps. These classifications are very time-consuming and cost-intensive compared to acquisition. In order to automate this process step, conventional neural networks (ConvNet), which take over the classification task, are investigated in detail. In addition to the network architecture, the classification performance of a ConvNet depends on the training data with which the task is learned. This paper presents and evaluates the point clould classification tool (PCCT) developed at HCU Hamburg. With the PCCT, large point cloud collections can be semi-automatically classified. Furthermore, the influence of erroneous points in three-dimensional point clouds is investigated. The network architecture PointNet is used for this investigation.

Evaluating the Quality of Semantic Segmented 3D Point Clouds

“…In addition, they are categorized according to the central functions for the segmentation. Recap [71] x 3D 3D CloudCompare [59] x x 3D 3D SemanticKITTI [13] x x 3D PCCT [72] x 2D…”

“…The point cloud classification tool (PCCT) [72] is a tool for the semantic segmentation of (primarily) static panoramic scans. Point clouds are projected into 2D space for classification.…”

The Motor Action Analysis Based on Deep Learning

“…is to normalize the probability distribution so that the sum of all probabilities is one. us, the probability that softmax classifies x into category j can be expressed as [16][17][18] p y (i)…”