Current state-of-the-art point cloud data management (PCDM) systems rely on a variety of
parallel architectures and diverse data models. The main objective of these implementations …

Dealing with coloured point cloud acquired from terrestrial laser scanner, this paper
identifies remaining challenges for a new data structure: the smart point cloud. This concept …

Discrete spatial datasets known as point clouds often lay the groundwork for decision-
making applications. Eg, we can use such data as a reference for autonomous cars and …

3D point cloud data describes our physical world spatially. Knowledge discovery processes
including semantic segmentation and classification are a great way to complement this …

In the context of collaborative robotics, distributed situation awareness is essential for
supporting collective intelligence in teams of robots and human agents where it can be used …

The widespread use of LiDAR technologies has led to an ever-increasing volume of
captured data that pose a continuous challenge for its storage and organization, so that it …

The importance of being able to separate the semantics from the actual (X, Y, Z) coordinates
in a point cloud has been actively brought up in recent research. However, there is still no …

In the context of collaborative robotics, distributed situation awareness is essential for
supporting collective intelligence in teams of robots and human agents where it can be used …

We investigate how PCServe, a web service for disseminating massive point clouds,
performs for read-only access (ie a visualization application). PCServe is backed by a …

Point cloud usage has increased over the years. The development of low-cost sensors
makes it now possible to acquire frequent point cloud measurements on a short time period …