Towards Efficient Implementation of an Octree for a Large 3D Point Cloud

The present study introduces an efficient algorithm to construct a file-based octree for a large 3D point cloud. However, the algorithm was very slow compared with a memory-based approach, and got even worse when using a 3D point cloud scanned in longish objects like tunnels and corridors. The defects were addressed by implementing a semi-isometric octree group. The approach implements several semi-isometric octrees in a group, which tightly covers the 3D point cloud, though each octree along with its leaf node still maintains an isometric shape. The proposed approach was tested using three 3D point clouds captured in a long tunnel and a short tunnel by a terrestrial laser scanner, and in an urban area by an airborne laser scanner. The experimental results showed that the performance of the semi-isometric approach was not worse than a memory-based approach, and quite a lot better than a file-based one. Thus, it was proven that the proposed semi-isometric approach achieves a good balance between query performance and memory efficiency. In conclusion, if given enough main memory and using a moderately sized 3D point cloud, a memory-based approach is preferable. When the 3D point cloud is larger than the main memory, a file-based approach seems to be the inevitable choice, however, the semi-isometric approach is the better option.

Download Full-text

Accuracy Assessment of Cultural Heritage Models Extracting 3D Point Cloud Geometric Features with RPAS SfM-MVS and TLS Techniques

Drones ◽

10.3390/drones5040145 ◽

2021 ◽

Vol 5 (4) ◽

pp. 145

Author(s):

Alessandra Capolupo

Keyword(s):

Cultural Heritage ◽

Point Cloud ◽

Accuracy Assessment ◽

Laser Scanner ◽

Point Clouds ◽

Geometric Features ◽

3D Point Cloud ◽

3D Point Clouds ◽

Research Activities ◽

The Impact

A proper classification of 3D point clouds allows fully exploiting data potentiality in assessing and preserving cultural heritage. Point cloud classification workflow is commonly based on the selection and extraction of respective geometric features. Although several research activities have investigated the impact of geometric features on classification outcomes accuracy, only a few works focused on their accuracy and reliability. This paper investigates the accuracy of 3D point cloud geometric features through a statistical analysis based on their corresponding eigenvalues and covariance with the aim of exploiting their effectiveness for cultural heritage classification. The proposed approach was separately applied on two high-quality 3D point clouds of the All Saints’ Monastery of Cuti (Bari, Southern Italy), generated using two competing survey techniques: Remotely Piloted Aircraft System (RPAS) Structure from Motion (SfM) and Multi View Stereo (MVS) techniques and Terrestrial Laser Scanner (TLS). Point cloud compatibility was guaranteed through re-alignment and co-registration of data. The geometric features accuracy obtained by adopting the RPAS digital photogrammetric and TLS models was consequently analyzed and presented. Lastly, a discussion on convergences and divergences of these results is also provided.

Download Full-text

A TWO-LEVEL APPROACH FOR THE CROWD-BASED COLLECTION OF VEHICLES FROM 3D POINT CLOUDS

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-annals-v-4-2021-97-2021 ◽

2021 ◽

Vol V-4-2021 ◽

pp. 97-104

Author(s):

V. Walter ◽

M. Kölle ◽

D. Collmar ◽

Y. Zhang

Keyword(s):

Point Cloud ◽

Point Clouds ◽

3D Point Cloud ◽

3D Point Clouds ◽

Bounding Box ◽

Geometric Quality ◽

Bounding Boxes ◽

The Individual ◽

Better Than

Abstract. In this article, we present a two-level approach for the crowd-based collection of vehicles from 3D point clouds. In the first level, the crowdworkers are asked to identify the coarse positions of vehicles in 2D rasterized shadings that were derived from the 3D point cloud. In order to increase the quality of the results, we utilize the wisdom of the crowd principle which says that averaging multiple estimates of a group of individuals provides an outcome that is often better than most of the underlying estimates or even better than the best estimate. For this, each crowd job is duplicated 10 times and the multiple results are integrated with a DBSCAN cluster algorithm. In the second level, we use the integrated results as pre-information for extracting small subsets of the 3D point cloud that are then presented to crowdworkers for approximating the included vehicle by means of a Minimum Bounding Box (MBB). Again, the crowd jobs are duplicated 10 times and an average bounding box is calculated from the individual bounding boxes. We will discuss the quality of the results of both steps and show that the wisdom of the crowd significantly improves the completeness as well as the geometric quality. With a tenfold acquisition, we have achieve a completeness of 93.3 percent and a geometric deviation of less than 1 m for 95 percent of the collected vehicles.

Download Full-text

Comparison of Depth Camera and Terrestrial Laser Scanner in Monitoring Structural Deflections

Sensors ◽

10.3390/s21010201 ◽

2020 ◽

Vol 21 (1) ◽

pp. 201

Author(s):

Michael Bekele Maru ◽

Donghwan Lee ◽

Kassahun Demissie Tola ◽

Seunghee Park

Keyword(s):

Optical Sensors ◽

Point Cloud ◽

Three Dimensional ◽

Laser Scanner ◽

Point Clouds ◽

Depth Camera ◽

Terrestrial Laser Scanner ◽

Cloud Data ◽

3D Point Clouds ◽

3D Information

Modeling a structure in the virtual world using three-dimensional (3D) information enhances our understanding, while also aiding in the visualization, of how a structure reacts to any disturbance. Generally, 3D point clouds are used for determining structural behavioral changes. Light detection and ranging (LiDAR) is one of the crucial ways by which a 3D point cloud dataset can be generated. Additionally, 3D cameras are commonly used to develop a point cloud containing many points on the external surface of an object around it. The main objective of this study was to compare the performance of optical sensors, namely a depth camera (DC) and terrestrial laser scanner (TLS) in estimating structural deflection. We also utilized bilateral filtering techniques, which are commonly used in image processing, on the point cloud data for enhancing their accuracy and increasing the application prospects of these sensors in structure health monitoring. The results from these sensors were validated by comparing them with the outputs from a linear variable differential transformer sensor, which was mounted on the beam during an indoor experiment. The results showed that the datasets obtained from both the sensors were acceptable for nominal deflections of 3 mm and above because the error range was less than ±10%. However, the result obtained from the TLS were better than those obtained from the DC.

Download Full-text

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

Aerospace ◽

10.3390/aerospace5030094 ◽

2018 ◽

Vol 5 (3) ◽

pp. 94 ◽

Cited By ~ 5

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.

Download Full-text

Multi-view attention-convolution pooling network for 3D point cloud classification

Applied Intelligence ◽

10.1007/s10489-021-02840-2 ◽

2021 ◽

Author(s):

Wenju Wang ◽

Tao Wang ◽

Yu Cai

Keyword(s):

Point Cloud ◽

Point Clouds ◽

Information Loss ◽

Feature Representation ◽

3D Point Cloud ◽

Current Output ◽

3D Point Clouds ◽

Cloud Classification ◽

Feature Information ◽

Point Cloud Classification

AbstractClassifying 3D point clouds is an important and challenging task in computer vision. Currently, classification methods using multiple views lose characteristic or detail information during the representation or processing of views. For this reason, we propose a multi-view attention-convolution pooling network framework for 3D point cloud classification tasks. This framework uses Res2Net to extract the features from multiple 2D views. Our attention-convolution pooling method finds more useful information in the input data related to the current output, effectively solving the problem of feature information loss caused by feature representation and the detail information loss during dimensionality reduction. Finally, we obtain the probability distribution of the model to be classified using a full connection layer and the softmax function. The experimental results show that our framework achieves higher classification accuracy and better performance than other contemporary methods using the ModelNet40 dataset.

Download Full-text

IMAGE TO POINT CLOUD TRANSLATION USING CONDITIONAL GENERATIVE ADVERSARIAL NETWORK FOR AIRBORNE LIDAR DATA

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-annals-v-2-2021-169-2021 ◽

2021 ◽

Vol V-2-2021 ◽

pp. 169-174

Author(s):

T. Shinohara ◽

H. Xiu ◽

M. Matsuoka

Keyword(s):

Point Cloud ◽

Large Scale ◽

Point Clouds ◽

Airborne Lidar ◽

Real Point ◽

3D Point Cloud ◽

Generative Adversarial Network ◽

Adversarial Network ◽

3D Point Clouds ◽

Latent Features

Abstract. This study introduces a novel image to a 3D point-cloud translation method with a conditional generative adversarial network that creates a large-scale 3D point cloud. This can generate supervised point clouds observed via airborne LiDAR from aerial images. The network is composed of an encoder to produce latent features of input images, generator to translate latent features to fake point clouds, and discriminator to classify false or real point clouds. The encoder is a pre-trained ResNet; to overcome the difficulty of generating 3D point clouds in an outdoor scene, we use a FoldingNet with features from ResNet. After a fixed number of iterations, our generator can produce fake point clouds that correspond to the input image. Experimental results show that our network can learn and generate certain point clouds using the data from the 2018 IEEE GRSS Data Fusion Contest.

Download Full-text

An Improved Method for Restoring the Shape of 3D Point Cloud Surfaces

International Journal of Synthetic Emotions ◽

10.4018/ijse.2018070103 ◽

2018 ◽

Vol 9 (2) ◽

pp. 37-53

Author(s):

Sinh Van Nguyen ◽

Ha Manh Tran ◽

Minh Khai Tran

Keyword(s):

Geometric Modeling ◽

Point Cloud ◽

Point Clouds ◽

Tangent Plane ◽

Surface Patch ◽

3D Point Cloud ◽

Improved Method ◽

Initial Shape ◽

Cloud Data ◽

3D Point Clouds

Building 3D objects or reconstructing their surfaces from 3D point cloud data are researched activities in the field of geometric modeling and computer graphics. In the recent years, they are also studied and used in some fields such as: graph models and simulation; image processing or restoration of digital heritages. This article presents an improved method for restoring the shape of 3D point cloud surfaces. The method is a combination of creating a Bezier surface patch and computing tangent plane of 3D points to fill holes on a surface of 3D point clouds. This method is described as follows: at first, a boundary for each hole on the surface is identified. The holes are then filled by computing Bezier curves of surface patches to find missing points. After that, the holes are refined based on two steps (rough and elaborate) to adjust the inserted points and preserve the local curvature of the holes. The contribution of the proposed method has been shown in processing time and the novelty of combined computation in this method has preserved the initial shape of the surface

Download Full-text

Point Siamese Network for Person Tracking Using 3D Point Clouds

Sensors ◽

10.3390/s20010143 ◽

2019 ◽

Vol 20 (1) ◽

pp. 143

Author(s):

Yubo Cui ◽

Zheng Fang ◽

Sifan Zhou

Keyword(s):

Feature Extraction ◽

Point Cloud ◽

Unscented Kalman Filter ◽

Point Clouds ◽

Target Person ◽

Bayesian Filtering ◽

3D Point Cloud ◽

Person Tracking ◽

Siamese Network ◽

3D Point Clouds

Person tracking is an important issue in both computer vision and robotics. However, most existing person tracking methods using 3D point cloud are based on the Bayesian Filtering framework which are not robust in challenging scenes. In contrast with the filtering methods, in this paper, we propose a neural network to cope with person tracking using only 3D point cloud, named Point Siamese Network (PSN). PSN consists of two input branches named template and search, respectively. After finding the target person (by reading the label or using a detector), we get the inputs of the two branches and create feature spaces for them using feature extraction network. Meanwhile, a similarity map based on the feature space is proposed between them. We can obtain the target person from the map. Furthermore, we add an attention module to the template branch to guide feature extraction. To evaluate the performance of the proposed method, we compare it with the Unscented Kalman Filter (UKF) on 3 custom labeled challenging scenes and the KITTI dataset. The experimental results show that the proposed method performs better than UKF in robustness and accuracy and has a real-time speed. In addition, we publicly release our collected dataset and the labeled sequences to the research community.

Download Full-text

6-DOF Localization for a Mobile Robot Using Outdoor 3D Point Clouds

Journal of Robotics and Mechatronics ◽

10.20965/jrm.2010.p0158 ◽

2010 ◽

Vol 22 (2) ◽

pp. 158-166 ◽

Cited By ~ 7

Author(s):

Taro Suzuki ◽

◽

Yoshiharu Amano ◽

Takumi Hashizume

Keyword(s):

Mobile Robot ◽

Point Cloud ◽

Field Experiments ◽

Point Clouds ◽

Measurement Model ◽

3D Point Cloud ◽

Point Cloud Data ◽

Cloud Data ◽

Continuous Contact ◽

3D Point Clouds

This paper describes outdoor localization for a mobile robot using a laser scanner and three-dimensional (3D) point cloud data. A Mobile Mapping System (MMS) measures outdoor 3D point clouds easily and precisely. The full six-dimensional state of a mobile robot is estimated combining dead reckoning and 3D point cloud data. Two-dimensional (2D) position and orientation are extended to 3D using 3D point clouds assuming that the mobile robot remains in continuous contact with the road surface. Our approach applies a particle filter to correct position error in the laser measurement model in 3D point cloud space. Field experiments were conducted to evaluate the accuracy of our proposal. As the result of the experiment, it was confirmed that a localization precision of 0.2 m (RMS) is possible using our proposal.

Download Full-text