Human Identification Using Automotive Radar

In this study, we perform human identification using accumulated radar point clouds in an outdoor scene. We employ PointNet as classification network and explore the impact of adding radars' non-spatial features as input, namely doppler velocity and radar cross section (RCS). Furthermore, we encode time as an additional time identity dimension...

Analyzing deformation of buildings using LiDAR point clouds obtained by a Mobile Laser Scanning System

Unequal deformation of the soil can cause deformation or damage to buildings, like tilted facades or cracks in walls. This research investigates how deformation of a building can be analyzed using Light Detection And Ranging (LiDAR) data. Cyclomedia captures LiDAR data yearly in the Netherlands making it possible to analyze either one or...

DeltaConv: Anisotropic Operators for Geometric Deep Learning on Point Clouds

Learning from 3D point-cloud data has rapidly gained momentum, motivated by the success of deep learning on images and the increased availability of 3D~data. In this paper, we aim to construct anisotropic convolution layers that work directly on the surface derived from a point cloud. This is challenging because of the lack of a global...

PCSERVE-ND-Pointclouds Retrieval over the Web

We investigate how PCServe, a web service for disseminating massive point clouds, performs for read-only access (i.e. a visualization application). PCServe is backed by a database model based on Space Filling Curves. By adding a virtual hierarchy of blocks to the database, we can support different visualization applications for retrieval of...

Clearance measurement validation for highway infrastructure with use of lidar point clouds

This paper introduces a method to automatically estimate vertical and horizontal clearances of highway viaducts and gantries from Mobile Laser Scanner (MLS) point clouds. It is essential to have accurate data on the vertical and horizontal clearances of overhead infrastructure objects along the highway. Accurate clearance data is used for...

3D Road Boundary Mapping of MLS Point Clouds

Roads in modern cities facilitate different types of users, including car drivers, cyclists, and pedestrians. These different users often have a designated section of the road to operate on. Road management, e.g., by municipalities, needs to take this sectioning into account, preferably in an efficient way. Mobile laser scanning (MLS) point...

A comparative study of point clouds semantic segmentation using three different neural networks on the railway station dataset

Point cloud data have rich semantic representations and can benefit various applications towards a digital twin. However, they are unordered and anisotropically distributed, thus being unsuitable for a typical Convolutional Neural Networks (CNN) to handle. With the advance of deep learning, several neural networks claim to have solved the...

HistSFC: Optimization for nD massive spatial points querying

Space Filling Curve (SFC) mapping-based clustering and indexing works effectively for point clouds management and querying. It maps both points and queries into a one-dimensional SFC space so that B+- tree could be utilized. Based on the basic structure, this paper develops a generic HistSFC approach which utilizes a histogram tree recording...

An optimized SFC approach for nD window querying on point clouds

Dramatically increasing collection of point clouds raises an essential demand for highly efficient data management. It can also facilitate modern applications such as robotics and virtual reality. Extensive studies have been performed on point data management and querying, but most of them concentrate on low dimensional spaces. High...

Semantic Segmentation of Large-scale Urban Scenes from Point Clouds

Deep learning methods have been demonstrated to be promising in semantic segmentation of point clouds. Existing works focus on extracting informative local features based on individual points and their local neighborhood. They lack consideration of the general structures and latent contextual relations of underlying shapes among points. To this...

Semi-automatic fold detection in geological outcrops using Laser Scanning data: Applied to a limestone-marl sequences in the Vocontian Basin, France

Geological surveying is a common practice performed by geologists nowadays. Determining layer orientation parameters and identifying folds are examples of features needed in order to create a geological map or model of the (sub) surface. Surveying is expensive and time consuming, therefore such survey is recently being investigated and...

Classification of large scale outdoor point clouds using convolutional neural networks

There is a paradigm shift from two- to three-dimensional data, from maps to information dense models. Self-driving cars, digitization of historic buildings or maintenance of highway infrastructure are a small selection of many applications that use laser scanning to acquire three-dimensional data of our physical surroundings. Most of these...

Chronocity: Technical Report Towards an Open Point Cloud Map supporting on-the-fly change detection

We are now gradually entering the era of big data - maybe a bit too much of a buzzword, but it is not lied. Technology is evolving fast, enabling faster and more efficient data acquisition, storage, retrieval and processing. Point cloud datasets are such a type which relies on large files and lots of processing power. The rather fast evolutions...

Comparison of ZEB1 and Leica C10 indoor laser scanning point clouds

We present a comparison of point cloud generation and quality of data acquired by Zebedee (Zeb1) and Leica C10 devices which are used in the same building interior. Both sensor devices come with different practical and technical advantages. As it could be expected, these advantages come with some drawbacks. Therefore, depending on the...

Quality assessment and comparison of smartphone, airborne and leica c10 laser scanner based point clouds

3D urban models are valuable for urban map generation, environment monitoring, safety planning and educational purposes. For 3D measurement of urban structures, generally airborne laser scanning sensors or multi-view satellite images are used as a data source. However, close-range sensors (such as terrestrial laser scanners) and low cost cameras...

A semi-automatic procedure for texturing of laser scanning point clouds with google streetview images

We introduce a method to texture 3D urban models with photographs that even works for Google Streetview images and can be done with currently available free software. This allows realistic texturing, even when it is not possible or cost-effective to (re)visit a scanned site to take textured scans or photographs. Mapping a photograph onto a 3D...

Features of Point Clouds and Functionalities of Processing Software

To be useful, the millions or even billions of 3D points generated by a variety of active and passive sensors need to be stored, organised, combined, geo-referenced, measured, analysed and distributed within organisations or outward. Initially, the data is unorganised; software has been designed, developed and put on the market place to organise...