Evaluating the Quality of Semantic Segmented 3D Point Clouds

Abstract

Recently, 3D point clouds have become a quasi-standard for digitization. Point cloud processing remains a challenge due to the complex and unstructured nature of point clouds. Currently, most automatic point cloud segmentation methods are data-based and gain knowledge from manually segmented ground truth (GT) point clouds. The creation of GT point clouds by capturing data with an optical sensor and then performing a manual or semi-automatic segmentation is a less studied research field. Usually, GT point clouds are semantically segmented only once and considered to be free of semantic errors. In this work, it is shown that this assumption has no overall validity if the reality is to be represented by a semantic point cloud. Our quality model has been developed to describe and evaluate semantic GT point clouds and their manual creation processes. It is applied on our dataset and publicly available point cloud datasets. Furthermore, we believe that this quality model contributes to the objective evaluation and comparability of data-based segmentation algorithms.

Evaluation of Class Distribution and Class Combinations on Semantic Segmentation of 3D Point Clouds With PointNet

“…The semantic segmentation of all defined classes is usually achieved in one step. Reference [86] observed that the class definition and the class content have an influence on the semantic segmentation result. As an alternative to the application-oriented class definition, an algorithmoriented class definition is also possible.…”

Keep the Human in the Loop: Arguments for Human Assistance in the Synthesis of Simulation Data for Robot Training

“…However, these can exhibit shortcomings due to their type, the complexity of the scene, or the algorithms employed in the reconstruction process. The sensor's data quality can differ, depending on the sensor's resolution and precision, affecting the accuracy and reliability of the recordings [38]. For instance, scanning object surfaces with optical sensors can introduce artifacts, which are often a consequence of reflection, the distance at which the acquisition is made, and varying environmental conditions [38].…”

“…The sensor's data quality can differ, depending on the sensor's resolution and precision, affecting the accuracy and reliability of the recordings [38]. For instance, scanning object surfaces with optical sensors can introduce artifacts, which are often a consequence of reflection, the distance at which the acquisition is made, and varying environmental conditions [38]. In addition, the complexity of scenes, especially those with occlusions, reflective surfaces, and transparent objects, necessitates acquiring data from multiple viewpoints to ensure comprehensive capturing of all elements.…”

“…During segmentation, all data points belonging to an object must be identified and labeled. Segmentation algorithms based on traditional and machine learning (ML) algorithms can produce high-quality results [38] but also inaccurate and unreliable results in many cases. Noisy segmentations result in coarse boundaries between entities in the scenes, and in some cases, identify ghost objects that are not present in the scene.…”

“…Humans excel at object recognition [38], which makes them valuable for improving the computed virtual representations of recordings using their scene understanding [29,42]. To enhance the recorded virtual scans, they could specify data points, extend planar surfaces, insert single or clusters of data, draw missing objects, or add objects from collections.…”