scholarly journals FROM POINT CLOUDS TO 3D ISOVISTS IN INDOOR ENVIRONMENTS

2018 ◽  
Vol XLII-4 ◽  
pp. 149-154 ◽  
Author(s):  
L. Díaz-Vilariño ◽  
L. González-deSantos ◽  
E. Verbree ◽  
G. Michailidou ◽  
S. Zlatanova
Keyword(s):  
Point Cloud ◽  
Eye Gaze ◽  
Point Clouds ◽  
Observation Point ◽  
Line Of Sight ◽  
Indoor Environments ◽  
Spatial Behaviour ◽  
Visibility Analysis ◽  
Spatial Properties ◽  
Tracing Algorithm

<p><strong>Abstract.</strong> Visibility is a common measure to describe the spatial properties of an environment related to the spatial behaviour. Isovists represent the space that can be seen from one observation point, and they are used to analyse the existence of obstacles affecting or blocking intervisibility in an area. Although point clouds depict the as-built reality in a very detailed and accurate way, literature addressing the analysis of visibility in 3D, and more specifically the usage of point clouds to visibility analysis, is rather limited. In this paper, a methodology to evaluate visibility from point clouds in indoor environments is proposed, resulting in the creation of 3D isovists. Point cloud is firstly discretized in a voxel-based structure and voxels are labelled into ‘exterior’, ‘occupied’, ‘visible’ and ‘occluded’ based on an occupancy followed by a visibility analysis performed from a ray-tracing algorithm. 3D Isovists are created from the boundary of visible voxels from an observer position and considering as input parameters the visual angle, maximum line of sight, and eye gaze direction.</p>

scholarly journals Fast and Robust Flight Altitude Estimation of Multirotor UAVs in Dynamic Unstructured Environments Using 3D Point Cloud Sensors

Aerospace ◽  
2018 ◽  
Vol 5 (3) ◽  
pp. 94 ◽  
Author(s):  
Hriday Bavle ◽  
Jose Sanchez-Lopez ◽  
Paloma Puente ◽  
Alejandro Rodriguez-Ramos ◽  
Carlos Sampedro ◽  
...  
Keyword(s):  
Point Cloud ◽  
Point Clouds ◽  
Estimation Algorithm ◽  
Indoor Environments ◽  
3D Point Cloud ◽  
Flight Altitude ◽  
Cloud Data ◽  
3D Point Clouds ◽  
Laser Altimeters ◽  
Altitude Estimation

This paper presents a fast and robust approach for estimating the flight altitude of multirotor Unmanned Aerial Vehicles (UAVs) using 3D point cloud sensors in cluttered, unstructured, and dynamic indoor environments. The objective is to present a flight altitude estimation algorithm, replacing the conventional sensors such as laser altimeters, barometers, or accelerometers, which have several limitations when used individually. Our proposed algorithm includes two stages: in the first stage, a fast clustering of the measured 3D point cloud data is performed, along with the segmentation of the clustered data into horizontal planes. In the second stage, these segmented horizontal planes are mapped based on the vertical distance with respect to the point cloud sensor frame of reference, in order to provide a robust flight altitude estimation even in presence of several static as well as dynamic ground obstacles. We validate our approach using the IROS 2011 Kinect dataset available in the literature, estimating the altitude of the RGB-D camera using the provided 3D point clouds. We further validate our approach using a point cloud sensor on board a UAV, by means of several autonomous real flights, closing its altitude control loop using the flight altitude estimated by our proposed method, in presence of several different static as well as dynamic ground obstacles. In addition, the implementation of our approach has been integrated in our open-source software framework for aerial robotics called Aerostack.

scholarly journals INDOOR PHOTOGRAMMETRY AIDED WITH UWB NAVIGATION

2018 ◽  
Vol XLII-2 ◽  
pp. 683-690 ◽  
Author(s):  
A. Masiero ◽  
F. Fissore ◽  
A. Guarnieri ◽  
A. Vettore
Keyword(s):  
Point Cloud ◽  
Inertial Sensors ◽  
Point Clouds ◽  
External Information ◽  
Indoor Environments ◽  
Metric Reconstruction ◽  
North East ◽  
3D Point Clouds ◽  
Significant Interest ◽  
The Subject

The subject of photogrammetric surveying with mobile devices, in particular smartphones, is becoming of significant interest in the research community. Nowadays, the process of providing 3D point clouds with photogrammetric procedures is well known. However, external information is still typically needed in order to move from the point cloud obtained from images to a 3D metric reconstruction. This paper investigates the integration of information provided by an UWB positioning system with visual based reconstruction to produce a metric reconstruction. Furthermore, the orientation (with respect to North-East directions) of the obtained model is assessed thanks to the use of inertial sensors included in the considered UWB devices. Results of this integration are shown on two case studies in indoor environments.

Pantomime

2021 ◽  
Vol 5 (1) ◽  
pp. 1-27
Author(s):  
Sameera Palipana ◽  
Dariush Salami ◽  
Luis A. Leiva ◽  
Stephan Sigg
Keyword(s):  
Point Cloud ◽  
Continuous Wave ◽  
Spatial Information ◽  
Point Clouds ◽  
Recognition System ◽  
Indoor Environments ◽  
Unique Region ◽  
Temporal Properties ◽  
3D Point Clouds ◽  
Wave Radar

We introduce Pantomime, a novel mid-air gesture recognition system exploiting spatio-temporal properties of millimeter-wave radio frequency (RF) signals. Pantomime is positioned in a unique region of the RF landscape: mid-resolution mid-range high-frequency sensing, which makes it ideal for motion gesture interaction. We configure a commercial frequency-modulated continuous-wave radar device to promote spatial information over the temporal resolution by means of sparse 3D point clouds and contribute a deep learning architecture that directly consumes the point cloud, enabling real-time performance with low computational demands. Pantomime achieves 95% accuracy and 99% AUC in a challenging set of 21 gestures articulated by 41 participants in two indoor environments, outperforming four state-of-the-art 3D point cloud recognizers. We further analyze the effect of the environment in 5 different indoor environments, the effect of articulation speed, angle, and the distance of the person up to 5m. We have publicly made available the collected mmWave gesture dataset consisting of nearly 22,000 gesture instances along with our radar sensor configuration, trained models, and source code for reproducibility. We conclude that pantomime is resilient to various input conditions and that it may enable novel applications in industrial, vehicular, and smart home scenarios.

scholarly journals 3D Modelling of Interior Spaces: Learning the Language of Indoor Architecture

2014 ◽  
Vol XL-5 ◽  
pp. 321-326 ◽  
Author(s):  
K. Khoshelham ◽  
L. Díaz-Vilariño
Keyword(s):  
Point Cloud ◽  
Point Clouds ◽  
3D Models ◽  
3D Modelling ◽  
Shape Grammar ◽  
Real Point ◽  
Indoor Environments ◽  
Interior Spaces ◽  
Grammar Rules

3D models of indoor environments are important in many applications, but they usually exist only for newly constructed buildings. Automated approaches to modelling indoor environments from imagery and/or point clouds can make the process easier, faster and cheaper. We present an approach to 3D indoor modelling based on a shape grammar. We demonstrate that interior spaces can be modelled by iteratively placing, connecting and merging cuboid shapes. We also show that the parameters and sequence of grammar rules can be learned automatically from a point cloud. Experiments with simulated and real point clouds show promising results, and indicate the potential of the method in 3D modelling of large indoor environments.

scholarly journals Detail Preserved Surface Reconstruction from Point Cloud

Sensors ◽  
2019 ◽  
Vol 19 (6) ◽  
pp. 1278 ◽  
Author(s):  
Yang Zhou ◽  
Shuhan Shen ◽  
Zhanyi Hu
Keyword(s):  
Total Energy ◽  
Surface Reconstruction ◽  
Point Cloud ◽  
State Of The Art ◽  
Point Clouds ◽  
Experimental Result ◽  
Line Of Sight ◽  
Noise Filtering ◽  
Energy Term ◽  
Object Edge

In this paper, we put forward a new method for surface reconstruction from image-based point clouds. In particular, we introduce a new visibility model for each line of sight to preserve scene details without decreasing the noise filtering ability. To make the proposed method suitable for point clouds with heavy noise, we introduce a new likelihood energy term to the total energy of the binary labeling problem of Delaunay tetrahedra, and we give its s-t graph implementation. Besides, we further improve the performance of the proposed method with the dense visibility technique, which helps to keep the object edge sharp. The experimental result shows that the proposed method rivalled the state-of-the-art methods in terms of accuracy and completeness, and performed better with reference to detail preservation.

scholarly journals GEOMETRICAL NETWORK MODEL GENERATION USING POINT CLOUD DATA FOR INDOOR NAVIGATION

2018 ◽  
Vol IV-4 ◽  
pp. 141-146 ◽  
Author(s):  
M. Nakagawa ◽  
R. Nozaki
Keyword(s):  
Network Model ◽  
Point Cloud ◽  
Optimal Path ◽  
Three Dimensional ◽  
Point Clouds ◽  
Indoor Navigation ◽  
Model Generation ◽  
Indoor Environments ◽  
Point Cloud Data ◽  
Cloud Data

<p><strong>Abstract.</strong> Three-dimensional indoor navigation requires various functions, such as the shortest path retrieval, obstacle avoidance, and secure path retrieval, for optimal path finding using a geometrical network model. Although the geometrical network model can be prepared manually, the model should be automatically generated using images and point clouds to represent changing indoor environments. Thus, we propose a methodology for generating a geometrical network model for indoor navigation using point clouds through object classification, navigable area estimation, and navigable path estimation. Our proposed methodology was evaluated through experiments using the benchmark of the International Society for Photogrammetry and Remote Sensing for indoor modeling. In our experiments, we confirmed that our methodology can generate a geometrical network model automatically.</p>

scholarly journals Visibility analysis of point cloud in close range photogrammetry

2014 ◽  
Vol II-5 ◽  
pp. 9-16 ◽  
Author(s):  
B. Alsadik ◽  
M. Gerke ◽  
G. Vosselman
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Three Dimensional ◽  
Point Clouds ◽  
Visibility Analysis ◽  
Close Range Photogrammetry ◽  
Visible Points ◽  
Dense Point ◽  
Close Range ◽  
Mathematical Techniques

The ongoing development of advanced techniques in photogrammetry, computer vision (CV), robotics and laser scanning to efficiently acquire three dimensional geometric data offer new possibilities for many applications. The output of these techniques in the digital form is often a sparse or dense point cloud describing the 3D shape of an object. Viewing these point clouds in a computerized digital environment holds a difficulty in displaying the visible points of the object from a given viewpoint rather than the hidden points. This visibility problem is a major computer graphics topic and has been solved previously by using different mathematical techniques. However, to our knowledge, there is no study of presenting the different visibility analysis methods of point clouds from a photogrammetric viewpoint. The visibility approaches, which are surface based or voxel based, and the hidden point removal (HPR) will be presented. Three different problems in close range photogrammetry are presented: camera network design, guidance with synthetic images and the gap detection in a point cloud. The latter one introduces also a new concept of gap classification. Every problem utilizes a different visibility technique to show the valuable effect of visibility analysis on the final solution.

scholarly journals TRANSFER LEARNING FOR INDOOR OBJECT CLASSIFICATION: FROM IMAGES TO POINT CLOUDS

2020 ◽  
Vol V-4-2020 ◽  
pp. 65-70
Author(s):  
J. Balado ◽  
L. Díaz-Vilariño ◽  
E. Verbree ◽  
P. Arias
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Point Clouds ◽  
Indoor Environments ◽  
Vector Data ◽  
Training Set ◽  
Network Training ◽  
Building Models ◽  
Great Relevance ◽  
Online Sources

Abstract. Indoor furniture is of great relevance to building occupants in everyday life. Furniture occupies space in the building, gives comfort, establishes order in rooms and locates services and activities. Furniture is not always static; the rooms can be reorganized according to the needs. Keeping the building models up to date with the current furniture is key to work with indoor environments. Laser scanning technology can acquire indoor environments in a fast and precise way, and recent artificial intelligence techniques can classify correctly the objects that contain. The objective of this work is to study how to minimize the use of point cloud samples in Neural Network training, tedious to label, and replace them with images obtained from online sources. For this, point clouds are converted to images by means of rotations and projections. The conversion of a 3D vector data to a 2D raster allows the use of Convolutional Neural Networks, the achievement of several images for each acquired point cloud object and the combination with images obtained from online sources, such as Google Images. The images have been distributed among the validation and testing training sets following different percentages. The results show that, although point cloud images cannot be completely dispensed within the training set, only 10% of these achieve high accuracy in the classification.

scholarly journals TOWARDS AUTOMATIC RECONSTRUCTION OF INDOOR SCENES FROM INCOMPLETE POINT CLOUDS: DOOR AND WINDOW DETECTION AND REGULARIZATION

2018 ◽  
Vol XLII-4 ◽  
pp. 507-514 ◽  
Author(s):  
M. Previtali ◽  
L. Díaz-Vilariño ◽  
M. Scaioni
Keyword(s):  
Data Quality ◽  
Point Cloud ◽  
Point Clouds ◽  
3D Models ◽  
Visibility Analysis ◽  
Automated Generation ◽  
Indoor Scenes ◽  
Complex Building ◽  
Source Of Information ◽  
Regular Patterns

<p><strong>Abstract.</strong> In the last years, point clouds have become the main source of information for building modelling. Although a considerable amount of methodologies addressing the automated generation of 3D models from point clouds have been developed, indoor modelling is still a challenging task due to complex building layouts and the high presence of severe clutters and occlusions. Most of methodologies are highly dependent on data quality, often producing irregular and non-consistent models. Although manmade environments generally exhibit some regularities, they are not commonly considered. This paper presents an optimization-based approach for detecting regularities (i.e., same shape, same alignment and same spacing) in building indoor features. The methodology starts from the detection of openings based on a voxel-based visibility analysis to distinguish ‘occluded’ from ‘empty’ regions in wall surfaces. The extraction of regular patterns in windows is addressed from studying the point cloud from an outdoor perspective. The layout is regularized by minimizing deformations while respecting the detected constraints. The methodology applies for elements placed in the same plane.</p>

Sign in / Sign up

Export Citation Format

Share Document