scholarly journals A comprehensive study of the rate-distortion performance in MPEG point cloud compression

2019 ◽  
Vol 8 ◽  
Author(s):  
Evangelos Alexiou ◽  
Irene Viola ◽  
Tomás M. Borges ◽  
Tiago A. Fonseca ◽  
Ricardo L. de Queiroz ◽  
...  
Keyword(s):  
Point Cloud ◽  
State Of The Art ◽  
Subjective Evaluation ◽  
Rate Distortion ◽  
Point Clouds ◽  
Current Approach ◽  
User Engagement ◽  
Viable Solution ◽  
Wide Range ◽  
Objective Quality

Abstract Recent trends in multimedia technologies indicate the need for richer imaging modalities to increase user engagement with the content. Among other alternatives, point clouds denote a viable solution that offers an immersive content representation, as witnessed by current activities in JPEG and MPEG standardization committees. As a result of such efforts, MPEG is at the final stages of drafting an emerging standard for point cloud compression, which we consider as the state-of-the-art. In this study, the entire set of encoders that have been developed in the MPEG committee are assessed through an extensive and rigorous analysis of quality. We initially focus on the assessment of encoding configurations that have been defined by experts in MPEG for their core experiments. Then, two additional experiments are designed and carried to address some of the identified limitations of current approach. As part of the study, state-of-the-art objective quality metrics are benchmarked to assess their capability to predict visual quality of point clouds under a wide range of radically different compression artifacts. To carry the subjective evaluation experiments, a web-based renderer is developed and described. The subjective and objective quality scores along with the rendering software are made publicly available, to facilitate and promote research on the field.

A novel point cloud quality assessment metric based on perceptual color distance patterns

2021 ◽  
Vol 2021 (9) ◽  
pp. 256-1-256-11
Author(s):  
Rafael Diniz ◽  
Pedro Garcia Freitas ◽  
Mylène Farias
Keyword(s):  
Quality Assessment ◽  
Point Cloud ◽  
State Of The Art ◽  
Perceptual Quality ◽  
Great Effort ◽  
Robust Performance ◽  
Wide Range ◽  
Objective Quality ◽  
Novel Method

In recent years, PCs have become very popular for a wide range of applications, such as immersive virtual reality scenarios. As a consequence, in the last couple of years, there has been a great effort to develop novel acquisition, representation, compression, and transmission solutions for PC contents in the research community. In particular, the development of objective quality assessment methods that are able to predict the perceptual quality of PCs. In this paper, we present an effective novel method for assessing the quality of PCs, which is based on descriptors that extract perceptual color distance-based texture information of PC contents, called Perceptual Color Distance Patterns (PCDP). In this framework, the statistics of the extracted information are used to model the PC visual quality. Experimental results show that the proposed framework exhibit good and robust performance when compared with several state-of-the-art point cloud quality assessment (PCQA) methods.

scholarly journals Object Recognition, Segmentation, and Classification of Mobile Laser Scanning Point Clouds: A State of the Art Review

Sensors ◽  
2019 ◽  
Vol 19 (4) ◽  
pp. 810 ◽  
Author(s):  
Erzhuo Che ◽  
Jaehoon Jung ◽  
Michael Olsen
Keyword(s):  
Feature Extraction ◽  
Object Recognition ◽  
Data Processing ◽  
Point Cloud ◽  
Laser Scanning ◽  
State Of The Art ◽  
Point Clouds ◽  
Wide Range ◽  
Benchmark Datasets ◽  
The Impact

Mobile Laser Scanning (MLS) is a versatile remote sensing technology based on Light Detection and Ranging (lidar) technology that has been utilized for a wide range of applications. Several previous reviews focused on applications or characteristics of these systems exist in the literature, however, reviews of the many innovative data processing strategies described in the literature have not been conducted in sufficient depth. To this end, we review and summarize the state of the art for MLS data processing approaches, including feature extraction, segmentation, object recognition, and classification. In this review, we first discuss the impact of the scene type to the development of an MLS data processing method. Then, where appropriate, we describe relevant generalized algorithms for feature extraction and segmentation that are applicable to and implemented in many processing approaches. The methods for object recognition and point cloud classification are further reviewed including both the general concepts as well as technical details. In addition, available benchmark datasets for object recognition and classification are summarized. Further, the current limitations and challenges that a significant portion of point cloud processing techniques face are discussed. This review concludes with our future outlook of the trends and opportunities of MLS data processing algorithms and applications.

scholarly journals AUTOMATED DETECTION AND CLOSING OF HOLES IN AERIAL POINT CLOUDS USING AN UAS

2017 ◽  
Vol XLII-2/W6 ◽  
pp. 101-107
Author(s):  
T. Fiolka ◽  
F. Rouatbi ◽  
D. Bender
Keyword(s):  
Point Cloud ◽  
Ground Plane ◽  
Point Clouds ◽  
Current Approach ◽  
Automated Detection ◽  
Lidar System ◽  
3D Point Clouds ◽  
Terrain Models ◽  
3D Terrain ◽  
Flight Route

3D terrain models are an important instrument in areas like geology, agriculture and reconnaissance. Using an automated UAS with a line-based LiDAR can create terrain models fast and easily even from large areas. But the resulting point cloud may contain holes and therefore be incomplete. This might happen due to occlusions, a missed flight route due to wind or simply as a result of changes in the ground height which would alter the swath of the LiDAR system. This paper proposes a method to detect holes in 3D point clouds generated during the flight and adjust the course in order to close them. First, a grid-based search for holes in the horizontal ground plane is performed. Then a check for vertical holes mainly created by buildings walls is done. Due to occlusions and steep LiDAR angles, closing the vertical gaps may be difficult or even impossible. Therefore, the current approach deals with holes in the ground plane and only marks the vertical holes in such a way that the operator can decide on further actions regarding them. The aim is to efficiently create point clouds which can be used for the generation of complete 3D terrain models.

scholarly journals Evaluation of Audio Compression Artifacts

10.14311/906 ◽  
2007 ◽  
Vol 47 (1) ◽  
Author(s):  
M. Herrera Martinez
Keyword(s):  
State Of The Art ◽  
Subjective Evaluation ◽  
Audio Coding ◽  
Coding System ◽  
Audio Compression ◽  
Coding Systems ◽  
Wide Range ◽  
Different Types ◽  
Processing Techniques ◽  
Signal Processing Techniques

This paper deals with subjective evaluation of audio-coding systems. From this evaluation, it is found that, depending on the type of signal and the algorithm of the audio-coding system, different types of audible errors arise. These errors are called coding artifacts. Although three kinds of artifacts are perceivable in the auditory domain, the author proposes that in the coding domain there is only one common cause for the appearance of the artifact, inefficient tracking of transient-stochastic signals. For this purpose, state-of-the art audio coding systems use a wide range of signal processing techniques, including application of the wavelet transform, which is described here. 

scholarly journals Combined Use of Terrestrial Laser Scanning and UAV Photogrammetry in Mapping Alpine Terrain

2019 ◽  
Vol 11 (18) ◽  
pp. 2154 ◽  
Author(s):  
Ján Šašak ◽  
Michal Gallay ◽  
Ján Kaňuk ◽  
Jaroslav Hofierka ◽  
Jozef Minár
Keyword(s):  
High Resolution ◽  
Point Cloud ◽  
Laser Scanning ◽  
Terrestrial Laser Scanning ◽  
Point Clouds ◽  
Mutual Orientation ◽  
Close Range Photogrammetry ◽  
Combined Use ◽  
Wide Range ◽  
Close Range

Airborne and terrestrial laser scanning and close-range photogrammetry are frequently used for very high-resolution mapping of land surface. These techniques require a good strategy of mapping to provide full visibility of all areas otherwise the resulting data will contain areas with no data (data shadows). Especially, deglaciated rugged alpine terrain with abundant large boulders, vertical rock faces and polished roche-moutones surfaces complicated by poor accessibility for terrestrial mapping are still a challenge. In this paper, we present a novel methodological approach based on a combined use of terrestrial laser scanning (TLS) and close-range photogrammetry from an unmanned aerial vehicle (UAV) for generating a high-resolution point cloud and digital elevation model (DEM) of a complex alpine terrain. The approach is demonstrated using a small study area in the upper part of a deglaciated valley in the Tatry Mountains, Slovakia. The more accurate TLS point cloud was supplemented by the UAV point cloud in areas with insufficient TLS data coverage. The accuracy of the iterative closest point adjustment of the UAV and TLS point clouds was in the order of several centimeters but standard deviation of the mutual orientation of TLS scans was in the order of millimeters. The generated high-resolution DEM was compared to SRTM DEM, TanDEM-X and national DMR3 DEM products confirming an excellent applicability in a wide range of geomorphologic applications.

scholarly journals TIF-Reg: Point Cloud Registration with Transform-Invariant Features in SE(3)

Sensors ◽  
2021 ◽  
Vol 21 (17) ◽  
pp. 5778
Author(s):  
Baifan Chen ◽  
Hong Chen ◽  
Baojun Song ◽  
Grace Gong
Keyword(s):  
Feature Extraction ◽  
Point Cloud ◽  
Point Clouds ◽  
Rigid Transformation ◽  
Point Cloud Registration ◽  
Registration Method ◽  
Invariant Features ◽  
Invariant Feature ◽  
Deep Feature ◽  
Wide Range

Three-dimensional point cloud registration (PCReg) has a wide range of applications in computer vision, 3D reconstruction and medical fields. Although numerous advances have been achieved in the field of point cloud registration in recent years, large-scale rigid transformation is a problem that most algorithms still cannot effectively handle. To solve this problem, we propose a point cloud registration method based on learning and transform-invariant features (TIF-Reg). Our algorithm includes four modules, which are the transform-invariant feature extraction module, deep feature embedding module, corresponding point generation module and decoupled singular value decomposition (SVD) module. In the transform-invariant feature extraction module, we design TIF in SE(3) (which means the 3D rigid transformation space) which contains a triangular feature and local density feature for points. It fully exploits the transformation invariance of point clouds, making the algorithm highly robust to rigid transformation. The deep feature embedding module embeds TIF into a high-dimension space using a deep neural network, further improving the expression ability of features. The corresponding point cloud is generated using an attention mechanism in the corresponding point generation module, and the final transformation for registration is calculated in the decoupled SVD module. In an experiment, we first train and evaluate the TIF-Reg method on the ModelNet40 dataset. The results show that our method keeps the root mean squared error (RMSE) of rotation within 0.5∘ and the RMSE of translation error close to 0 m, even when the rotation is up to [−180∘, 180∘] or the translation is up to [−20 m, 20 m]. We also test the generalization of our method on the TUM3D dataset using the model trained on Modelnet40. The results show that our method’s errors are close to the experimental results on Modelnet40, which verifies the good generalization ability of our method. All experiments prove that the proposed method is superior to state-of-the-art PCReg algorithms in terms of accuracy and complexity.

scholarly journals FAST PROBABILISTIC FUSION OF 3D POINT CLOUDS VIA OCCUPANCY GRIDS FOR SCENE CLASSIFICATION

2016 ◽  
Vol III-3 ◽  
pp. 325-332
Author(s):  
Andreas Kuhn ◽  
Hai Huang ◽  
Martin Drauschke ◽  
Helmut Mayer
Keyword(s):  
High Resolution ◽  
Point Cloud ◽  
State Of The Art ◽  
Point Clouds ◽  
Automatic Generation ◽  
Scene Classification ◽  
Semantic Classification ◽  
3D Point Clouds ◽  
Occupancy Grids ◽  
High Scalability

High resolution consumer cameras on Unmanned Aerial Vehicles (UAVs) allow for cheap acquisition of highly detailed images, e.g., of urban regions. Via image registration by means of Structure from Motion (SfM) and Multi View Stereo (MVS) the automatic generation of huge amounts of 3D points with a relative accuracy in the centimeter range is possible. Applications such as semantic classification have a need for accurate 3D point clouds, but do not benefit from an extremely high resolution/density. In this paper, we, therefore, propose a fast fusion of high resolution 3D point clouds based on occupancy grids. The result is used for semantic classification. In contrast to state-of-the-art classification methods, we accept a certain percentage of outliers, arguing that they can be considered in the classification process when a per point belief is determined in the fusion process. To this end, we employ an octree-based fusion which allows for the derivation of outlier probabilities. The probabilities give a belief for every 3D point, which is essential for the semantic classification to consider measurement noise. For an example point cloud with half a billion 3D points (cf. Figure 1), we show that our method can reduce runtime as well as improve classification accuracy and offers high scalability for large datasets.

scholarly journals Classification of Photogrammetric and Airborne LiDAR Point Clouds Using Machine Learning Algorithms

Drones ◽  
2021 ◽  
Vol 5 (4) ◽  
pp. 104
Author(s):  
Zaide Duran ◽  
Kubra Ozcan ◽  
Muhammed Enes Atik
Keyword(s):  
Machine Learning ◽  
Point Cloud ◽  
Point Clouds ◽  
Machine Learning Algorithms ◽  
Airborne Lidar ◽  
Aerial Photogrammetry ◽  
Feature Spaces ◽  
Wide Range ◽  
Extract Information

With the development of photogrammetry technologies, point clouds have found a wide range of use in academic and commercial areas. This situation has made it essential to extract information from point clouds. In particular, artificial intelligence applications have been used to extract information from point clouds to complex structures. Point cloud classification is also one of the leading areas where these applications are used. In this study, the classification of point clouds obtained by aerial photogrammetry and Light Detection and Ranging (LiDAR) technology belonging to the same region is performed by using machine learning. For this purpose, nine popular machine learning methods have been used. Geometric features obtained from point clouds were used for the feature spaces created for classification. Color information is also added to these in the photogrammetric point cloud. According to the LiDAR point cloud results, the highest overall accuracies were obtained as 0.96 with the Multilayer Perceptron (MLP) method. The lowest overall accuracies were obtained as 0.50 with the AdaBoost method. The method with the highest overall accuracy was achieved with the MLP (0.90) method. The lowest overall accuracy method is the GNB method with 0.25 overall accuracy.

scholarly journals INDOOR MAPPING OF A COMPLEX CULTURAL HERITAGE SCENE USING TLS AND HMLS LASER SCANNING

2021 ◽  
Vol XLIII-B2-2021 ◽  
pp. 605-612
Author(s):  
V. E. Oniga ◽  
A. I. Breaban ◽  
E. I. Alexe ◽  
C. Văsii
Keyword(s):  
Standard Deviation ◽  
Cultural Heritage ◽  
Long Range ◽  
Interior Design ◽  
Point Cloud ◽  
Laser Scanning ◽  
Laser Scanner ◽  
Point Clouds ◽  
Acquisition Time ◽  
Wide Range

Abstract. Indoor mapping and modelling is an important research subject with application in a wide range of domains including interior design, real estate, cultural heritage conservation and restoration. There are multiple sensors applicable for 3D indoor modelling, but the laser scanning technique is frequently used because of the acquisition time, detailed information and accuracy. In this paper, the efficiency of the Maptek I-Site 8820 terrestrial scanner, which is a long-range laser scanner and the accuracy of a HMLS point cloud acquired with a mobile scanner, namely GeoSlam Zeb Horizon were tested for indoor mapping. Aula Magna “Carmen Silva” of the “Gheorghe Asachi” Technical University of Iasi is studied in the current paper since the auditorium interior creates a distinct environment that combines complex geometric structures with architectural lighting and for preserving its great cultural value, the monument has a national historical significance. The registration process of the TLS point clouds was done using two methods: a semi-automatic one with artificial targets and a completely automatic one, based on Iterative Closest Point (ICP) algorithm. The resulted TLS point cloud was analysed in relation to the HMLS point cloud by computing the M3C2 (Multiscale Model to Model Cloud Comparison), obtaining a standard deviation of 2.1 cm and by investigating the Hausdorff distances from which resulted a standard deviation (σ) of 1.6 cm. Cross-sections have been extracted from the HMLS and TLS point clouds and after comparing the sections, 80% of the sigma values are less or equal to 1 cm. The results show high potential of using HMLS and also a long-range laser scanner for 3D modelling of complex scenes, the occlusion effect in the case of TLS being only 5% of the scanned area.

scholarly journals Pointwise Rotation-Invariant Network with Adaptive Sampling and 3D Spherical Voxel Convolution

2020 ◽  
Vol 34 (07) ◽  
pp. 12717-12724
Author(s):  
Yang You ◽  
Yujing Lou ◽  
Qi Liu ◽  
Yu-Wing Tai ◽  
Lizhuang Ma ◽  
...  
Keyword(s):  
Adaptive Sampling ◽  
Point Cloud ◽  
Data Augmentation ◽  
Feature Matching ◽  
State Of The Art ◽  
Point Clouds ◽  
Rotation Invariant ◽  
Learning Framework ◽  
Point Set ◽  
Part Segmentation

Point cloud analysis without pose priors is very challenging in real applications, as the orientations of point clouds are often unknown. In this paper, we propose a brand new point-set learning framework PRIN, namely, Pointwise Rotation-Invariant Network, focusing on rotation-invariant feature extraction in point clouds analysis. We construct spherical signals by Density Aware Adaptive Sampling to deal with distorted point distributions in spherical space. In addition, we propose Spherical Voxel Convolution and Point Re-sampling to extract rotation-invariant features for each point. Our network can be applied to tasks ranging from object classification, part segmentation, to 3D feature matching and label alignment. We show that, on the dataset with randomly rotated point clouds, PRIN demonstrates better performance than state-of-the-art methods without any data augmentation. We also provide theoretical analysis for the rotation-invariance achieved by our methods.

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