We introduce and test the performance of two sampling methods that utilize distance distributions of laser point clouds in terrestrial and mobile laser scanning geometries. The methods are leveled histogram sampling and inversely weighted distance sampling. The methods aim to reduce a significant portion of the laser point cloud data while retaining most characteristics of the full point cloud....

Point cloud segmentation and classification is currently a research highlight. Methods in this field create labelled data, where each point has additional class information. Current approaches are to generate a graph on the basis of all points in the point cloud, calculate or learn descriptors and train a matcher for the descriptor to the corresponding classes. Since these approaches need to lo...

Thermal processing of the key lime juice leads to the inactivation of pectin methylesterase (PME) and the degradation of ascorbic acid (AA). These changes affect directly the cloud stability and color of the juice. In this study, an artificial neural network (ANN) model was applied for designing and developing an intelligent system for prediction of the thermal processing effects on the physico...

We present a constraint-based haptic rendering algorithm for arbitrary point cloud data. With the recent proliferation of low-cost range sensors, 3D point cloud data is readily available at high update rates. Challenges in haptic rendering of this data arise due to noise and poorly defined surface composition. We propose that point cloud data can be rendered as an implicit surface, which can be...

With the rapid development of remote sensing technology, three dimensional coordinates and intensity information of observation target can be obtained directly from LIDAR system. It is one of the important issues in the study on LIDAR data processing that how to extract the object from the point cloud data captured by laser scanning effectively without the help of spectral information as well a...

The paper proposes a set of techniques for improving the quality of MLS surfaces reconstructed from point clouds that are composed by the union of many scanned range maps. The main idea of those techniques is that the range-map structure should be exploited during the reconstruction process and not lost in the uniform point soup that is usually fed into reconstruction algorithms; on this purpos...

This technical report presents a normal estimation method for point clouds with low sampling density and sharp features. To achieve the best trade-off between quality and performance, normal of a point on smooth regions is estimated using a isotropic neighborhood with constant size; but normal of a point near features are evaluated with an anisotropic neighborhood from which the local tangent p...

Building on the work of Martinetz, Schulten and de Silva, Carlsson, we introduce a 2-parameter family of witness complexes and algorithms for constructing them. This family can be used to determine the gross topology of point cloud data in R or other metric spaces. The 2-parameter family is sensitive to differences in sampling density and thus amenable to detecting patterns within the data set....

Surface reconstruction has long been targeted at scan data. With the rise of multi-view acquisition, existing surface reconstruction techniques often turn out to be ill adapted to the highly irregular sampling and multilayered aspect of such data. In this paper, a novel surface reconstruction technique is developed to address these new challenges by means of an advancing front guided by a spher...

Many objects can be represented as sets of multidimensional points. A common approach to learning from these point sets is to assume that each set is an i.i.d. sample from an unknown underlying distribution, and then estimate the similarities between these distributions. In realistic situations, however, the point sets are often subject to sampling biases due to variable or inconsistent observa...