A point cloud is considered a promising 3D representation that has achieved wide applications in several fields. However, quality degradation inevitably occurs during its acquisition and generation, communication and transmission, and rendering and display. Therefore, how to accurately perceive the visual quality of point clouds is a meaningful topic. In this survey, we first introduce the point cloud to emphasize the importance of point cloud quality assessment (PCQA). A review of subjective PCQA is followed, including common point cloud distortions, subjective experimental setups and subjective databases. Then we review and compare objective PCQA methods in terms of model-based and projection-based. Finally, we provide evaluation criteria for objective PCQA methods and compare the performances of various methods across multiple databases. This survey provides an overview of classical methods and recent advances in PCQA.
Point cloud compression is critical to deploy 3D representation of the physical world such as 3D immersive telepresence, autonomous driving, and cultural heritage preservation. However, point cloud data are distributed irregularly and discontinuously in spatial and temporal domains, where redundant unoccupied voxels and weak correlations in 3D space make achieving efficient compression a challenging problem. In this paper, we propose a spatio-temporal context-guided algorithm for lossless point cloud geometry compression. The proposed scheme starts with dividing the point cloud into sliced layers of unit thickness along the longest axis. Then, it introduces a prediction method where both intra-frame and inter-frame point clouds are available, by determining correspondences between adjacent layers and estimating the shortest path using the travelling salesman algorithm. Finally, the few prediction residual is efficiently compressed with optimal context-guided and adaptive fast-mode arithmetic coding techniques. Experiments prove that the proposed method can effectively achieve low bit rate lossless compression of point cloud geometric information, and is suitable for 3D point cloud compression applicable to various types of scenes.
Recent years have witnessed that 3D point cloud compression (PCC) has become a research hotspot both in academia and industry. Especially in industry, the Moving Picture Expert Group (MPEG) has actively initiated the development of PCC standards. One of the adopted frameworks called geometry-based PCC (G-PCC) follows the architecture of coding geometry first and then coding attributes, where the region adaptive hierarchical transform (RAHT) method is introduced for the lossy attribute compression. The upsampled transform domain prediction in RAHT does not sufficiently explore the attribute correlations between neighbor nodes and thus fails to further reduce the attribute redundancy between neighbor nodes. In this paper, we propose a subnode-based prediction method, where the spatial position relationship between neighbor nodes is fully considered and prediction precision is further promoted. We utilize some already-encoded neighbor nodes to facilitate the upsampled transform domain prediction in RAHT by means of a weighted average strategy. Experimental results have illustrated that our proposed attribute compression method shows better rate-distortion (R-D) performance than the latest MPEG G-PCC (both on reference software TMC13-v22.0 and GeS-TM-v2.0).
Light detection and ranging (LiDAR) sensors play a vital role in acquiring 3D point cloud data and extracting valuable information about objects for tasks such as autonomous driving, robotics, and virtual reality (VR). However, the sparse and disordered nature of the 3D point cloud poses significant challenges to feature extraction. Overcoming limitations is critical for 3D point cloud processing. 3D point cloud object detection is a very challenging and crucial task, in which point cloud processing and feature extraction methods play a crucial role and have a significant impact on subsequent object detection performance. In this overview of outstanding work in object detection from the 3D point cloud, we specifically focus on summarizing methods employed in 3D point cloud processing. We introduce the way point clouds are processed in classical 3D object detection algorithms, and their improvements to solve the problems existing in point cloud processing. Different voxelization methods and point cloud sampling strategies will influence the extracted features, thereby impacting the final detection performance.
Point-based rendering is a common method widely used in point cloud rendering. It realizes rendering by turning the points into the base geometry. The critical step in point-based rendering is to set an appropriate rendering radius for the base geometry, usually calculated using the average Euclidean distance of the N nearest neighboring points to the rendered point. This method effectively reduces the appearance of empty spaces between points in rendering. However, it also causes the problem that the rendering radius of outlier points far away from the central region of the point cloud sequence could be large, which impacts the perceptual quality. To solve the above problem, we propose an algorithm for point-based point cloud rendering through outlier detection to optimize the perceptual quality of rendering. The algorithm determines whether the detected points are outliers using a combination of local and global geometric features. For the detected outliers, the minimum radius is used for rendering. We examine the performance of the proposed method in terms of both objective quality and perceptual quality. The experimental results show that the peak signal-to-noise ratio (PSNR) of the point cloud sequences is improved under all geometric quantization, and the PSNR improvement ratio is more evident in dense point clouds. Specifically, the PSNR of the point cloud sequences is improved by 3.6% on average compared with the original algorithm. The proposed method significantly improves the perceptual quality of the rendered point clouds and the results of ablation studies prove the feasibility and effectiveness of the proposed method.
This paper proposes a local point cloud map-based Web augmented reality (AR) indoor navigation system solution. By delivering the local point cloud map to the web front end for positioning, the real-time positioning can be implemented only with the help of the computing power of the web front end. In addition, with the characteristics of short time consumption and accurate positioning, an optimization solution to the local point cloud map is proposed, which includes specific measures such as descriptor de-duplicating and outlier removal, thus improving the quality of the point cloud. In this document, interpolation and smoothing effects are introduced for local map positioning, enhancing the anchoring effect and improving the smoothness and appearance of user experience. In small-scale indoor scenarios, the positioning frequency on an iPhone 13 can reach 30 fps, and the positioning precision is within 50 cm. Compared with an existing mainstream visual-based positioning manner for AR navigation, this specification does not rely on any additional sensor or cloud computing device, thereby greatly saving computing resources. It takes a very short time to meet the real-time requirements and provide users with a smooth positioning effect.
Falls are a major cause of disability and even death in the elderly, and fall detection can effectively reduce the damage. Compared with cameras and wearable sensors, Wi-Fi devices can protect user privacy and are inexpensive and easy to deploy. Wi-Fi devices sense user activity by analyzing the channel state information (CSI) of the received signal, which makes fall detection possible. We propose a fall detection system based on commercial Wi-Fi devices which achieves good performance. In the feature extraction stage, we select the discrete wavelet transform (DWT) spectrum as the feature for activity classification, which can balance the temporal and spatial resolution. In the feature classification stage, we design a deep learning model based on convolutional neural networks, which has better performance compared with other traditional machine learning models. Experimental results show our work achieves a false alarm rate of 4.8% and a missed alarm rate of 1.9%.
A holistic analysis of problem and incident tickets in a real production cloud service environment is presented in this paper. By extracting different bags of words, we use principal component analysis (PCA) to examine the clustering characteristics of these tickets. Then K-means and latent Dirichlet allocation (LDA) are applied to show the potential clusters within this Cloud environment. The second part of our study uses a pre-trained bidirectional encoder representation from transformers (BERT) model to classify the tickets, with the goal of predicting the optimal dispatching department for a given ticket. Experimental results show that due to the unique characteristics of ticket description, pre-processing with domain knowledge turns out to be critical in both clustering and classification. Our classification model yields 86% accuracy when predicting the target dispatching department.
Rectifiers with high efficiency and high power density are crucial to the stable and efficient power supply of 5G communication base stations, which deserves in-depth investigation. In general, there are two key problems to be addressed: supporting both alternating current (AC) and direct current (DC) input, and minimizing the common-mode voltage as well as leakage current for safety reasons. In this paper, a hybrid five-level single-phase rectifier is proposed. A five-level topology is adopted in the upper arm, and a half-bridge diode topology is adopted in the lower arm. A dual closed-loop control strategy and a flying capacitor voltage regulation method are designed accordingly so that the compatibility of both AC and DC input is realized with low common voltage and small passive devices. Simulation and experimental results demonstrate the effectiveness and performance of the proposed rectifier.
This paper proposes a design and fine-tuning method for mixed electric and magnetic coupling filters. It derives the quantitative relationship between the coupling coefficients (electric and magnetic coupling, i.e., EC and MC) and the linear coefficients of frequency-dependent coupling for the first time. Different from the parameter extraction technique using the bandpass circuit model, the proposed approach explicitly relatesEC and MC to the coupling matrix model. This paper provides a general theoretic framework for computer-aided design and tuning of a mixed electric and magnetic coupling filter based on coupling matrices. An example of a 7th-order coaxial combline filter design is given in the paper, verifying the practical value of the approach.
With the rapid development of immersive multimedia technologies, 360-degree video services have quickly gained popularity and how to ensure sufficient spatial presence of end users when viewing 360-degree videos becomes a new challenge. In this regard, accurately acquiring users’ sense of spatial presence is of fundamental importance for video service providers to improve their service quality. Unfortunately, there is no efficient evaluation model so far for measuring the sense of spatial presence for 360-degree videos. In this paper, we first design an assessment framework to clarify the influencing factors of spatial presence. Related parameters of 360-degree videos and head-mounted display devices are both considered in this framework. Well-designed subjective experiments are then conducted to investigate the impact of various influencing factors on the sense of presence. Based on the subjective ratings, we propose a spatial presence assessment model that can be easily deployed in 360-degree video applications. To the best of our knowledge, this is the first attempt in literature to establish a quantitative spatial presence assessment model by using technical parameters that are easily extracted. Experimental results demonstrate that the proposed model can reliably predict the sense of spatial presence.