Over the past decade deep learning has driven progress in 2D image understanding.
Despite these advancements, techniques for automatic 3D sensed data understanding, such …

The area of surface reconstruction has seen substantial progress in the past two decades.
The traditional problem addressed by surface reconstruction is to recover the digital …

The area of surface reconstruction has seen substantial progress in the past two decades.
The traditional problem addressed by surface reconstruction is to recover the digital …

Abstract 3D scene analysis in terms of automatically assigning 3D points a respective
semantic label has become a topic of great importance in photogrammetry, remote sensing …

We describe an effective and efficient method for point-wise semantic classification of 3D
point clouds. The method can handle unstructured and inhomogeneous point clouds such …

Despite the success of deep learning on supervised point cloud semantic segmentation,
obtaining large-scale point-by-point manual annotations is still a significant challenge. To …

3D object recognition in cluttered scenes is a rapidly growing research area. Based on the
used types of features, 3D object recognition methods can broadly be divided into two …

There is a growing need for tools automating the processing of as-built 3D laser scanned
data, and more particularly the comparison of this as-built data with planned works. This …

Local spatio-temporal interest points (STIPs) and the resulting features from RGB videos
have been proven successful at activity recognition that can handle cluttered backgrounds …

In this paper we investigate the usage of persistent point feature histograms for the problem
of aligning point cloud data views into a consistent global model. Given a collection of noisy …