Current quality evaluations of 3D Gaussian Splatting typically rely on fixed-interval view sampling (e.g., selecting every 8th view as test view) to split training and test datasets, which may lead to biased assessments of reconstruction quality due to limited spatial coverage and view diversity. To address this issue, we propose a novel view selection strategy based on Farthest Point Sampling (FPS) that optimizes test view selection by maximizing the spatial coverage of camera positions and the diversity of viewing directions. In our experiments, the Baseline refers to the official implementation of the classic 3D Gaussian Splatting method, and our FPS method modifies the train/test split selection strategy within the same framework for fair comparison. We conduct a subjective experiment by rendering Gaussian Splatting results into video sequences and collecting subjective scores from 16 viewers on 10 scenes, including both real-world and synthetic datasets. Experimental results demonstrate that our FPS-based method provides a more comprehensive and reliable evaluation of 3D Gaussian Splatting quality, outperforming the conventional fixed-interval view sampling approach and establishing a robust framework for performance assessment.

Privacy leakage of Internet of Things (IoT) has become a great challenge with the popularity of IoT services through mobile networks, such as smart homes, wearables, and healthcare. While previous work summarized general structures to analyze IoT privacy and provide case studies of specific devices or scenarios, it is still challenging to conduct a comprehensive and systematic quantification study of large-scale IoT privacy leakage in real world. To combine systematic analyses with real-world measurements, we provide a method to quantify IoT privacy leakage on a large-scale mobile network traffic data set containing 47651 IoT devices. We generate privacy fingerprints and attribute them to a privacy quantification framework. The framework is constructed based on the semantics of multiple privacy sensitive markers selected from the traffic along with the involved network entity types in IoT (i.e., user, device, and platform), and the fingerprints are generated from sensitive information extracted in the traffic via their markers. Our quantification shows that IoT users, devices, and platforms have considerable risks, respectively. Moreover, IoT devices have a larger scale of privacy leakage than users and platforms, and they perform different daily patterns on privacy leakage following their working conditions. In addition, we present three case studies on the leakage of location information, application calling, and voice service, which illustrate that a third party can profile a network entity in both cyberspace and physical space.

As 360-degree videos and virtual reality (VR) applications become popular for consumer and enterprise use cases, the desire to enable truly mobile experiences also increases. Delivering 360-degree videos and cloud/edge-based VR applications require ultra-high bandwidth and ultra-low latency[1], challenging to achieve with mobile networks. A common approach to reduce bandwidth is streaming only the field of view (FOV). However, extracting and transmitting the FOV in response to user head motion can add high latency, adversely affecting user experience. In this paper, we propose a predictive adaptive streaming approach, where the predicted view with high predictive probability is adaptively encoded in relatively high quality according to bandwidth conditions and transmitted in advance, leading to a simultaneous reduction in bandwidth and latency. The predictive adaptive streaming method is based on a deep-learning-based viewpoint prediction model we develop, which uses past head motions to predict where a user will be looking in the 360-degree view. Using a very large dataset consisting of head motion traces from over 36,000 viewers for nineteen 360-degree/VR videos, we validate the ability of our predictive adaptive streaming method to offer high-quality view while simultaneously significantly reducing bandwidth.

As virtual reality (VR) applications become popular, the desire to enable high-quality, lightweight and mobile VR leads to various edge/cloud-based techniques. This paper introduces a predictive pre-rendering approach to address the ultra-low latency challenge in edge/cloud-based six Degrees of Freedom (6DoF) VR. Compared to 360-degree videos and 3DoF (head motion only) VR, 6DoF VR supports both head and body motions, thus not only viewing direction, but also viewing position changes. In our approach, the predictive view is rendered in advance based on the predicted viewing direction and position, leading to a reduction in latency. The key to achieving this efficient predictive pre-rendering approach is to predict the head and body motion accurately using past head and body motion traces. We develop a deep learning-based model and validate its ability using a dataset of over 840,000 samples for head and body motion.

In this article, we explore the possibility of enabling cloud-based virtual space applications for better computational scalability and easy access from any end device, including future lightweight wireless head-mounted displays. In particular, we investigate virtual space applications such as virtual classroom and virtual gallery, in which the scenes and activities are rendered in the cloud, with multiple views captured and streamed to each end device. A key challenge is the high bandwidth requirement to stream all the user views, leading to high operational cost and potential large delay in a bandwidth-restricted wireless network. We propose a novel hybrid-cast approach to save bandwidth in a multi-user streaming scenario. We identify and broadcast the common pixels shared by multiple users, while unicasting the residual pixels for each user. We formulate the problem of minimizing the total bitrate needed to transmit the user views using hybrid-casting and describe our approach. A common view extraction approach and a smart grouping algorithm are proposed and developed to achieve our hybrid-cast approach. Simulation results show that the hybrid-cast approach can significantly reduce total bitrate by up to 55% and avoid congestion-related latency, compared to traditional cloud-based approach of transmitting all the views as individual unicast streams, hence addressing the bandwidth challenges of the cloud, with additional benefits in cost and delay.

Triggered by several head-mounted display (HMD) devices that have come to the market recently, such as Oculus Rift, HTC Vive, and Samsung Gear VR, significant interest has developed in virtual reality (VR) systems, experiences and applications. However, the current HMD devices are still very heavy and large, negatively affecting user experience. Moreover, current VR approaches perform rendering locally either on a mobile device tethered to an HMD, or on a computer/console tethered to the HMD. In this paper, we discuss how to enable a truly portable and mobile VR experience, with light weight VR glasses wirelessly connecting with edge/cloud computing devices that perform the rendering remotely. We investigate the challenges associated with enabling the new wireless VR approach with edge/cloud computing with different application scenarios that we implement. Specifically, we analyze the challenging bitrate and latency requirements to enable wireless VR, and investigate several possible solutions.

In this paper, we explore the possibility of enabling cloud-based virtual classroom applications providing the advantages of computational scalability and access from any end device. In particular, we investigate a virtual classroom application in which the classroom including teacher, students and activities are rendered on the cloud, with each student view captured and streamed to students' end devices. By identifying that many student views may share common pixels, we design a novel hyper-cast system so that common pixels can be transmitted by a single broadcast stream while the residual pixels for individual student view can be transmitted by unicast in cellular networks. The hyper-cast approach can significantly reduce total bitrate needed, and therefore decrease cloud cost and cellular bandwidth. Simulation results show that the proposed hyper-cast technique can save up to 44.6% bitrate compared to traditional cloud-based approach.

Facial expression recognition has important practical applications. In this paper, we propose a method based on the combination of optical flow and a deep neural network—stacked sparse autoencoder (SAE). This method classifies facial expressions into six categories (i.e. happiness, sadness, anger, fear, disgust and surprise). In order to extract the representation of facial expressions, we choose the optical flow method because it could analyze video image sequences effectively and reduce the influence of personal appearance difference on facial expression recognition. Then, we train the stacked SAE with the optical flow field as the input to extract high-level features. To achieve classification, we apply a softmax classifier on the top layer of the stacked SAE. This method is applied to the Extended Cohn-Kanade Dataset (CK+). The expression classification result shows that the SAE performances the classification effectively and successfully. Further experiments (transformation and purification) are carried out to illustrate the application of the feature extraction and input reconstruction ability of SAE.