by jsendak | Sep 14, 2024 | Computer Science
arXiv:2409.07759v1 Announce Type: new
Abstract: Recent advances in 3D Gaussian Splatting (3DGS) have garnered significant attention in computer vision and computer graphics due to its high rendering speed and remarkable quality. While extant research has endeavored to extend the application of 3DGS from static to dynamic scenes, such efforts have been consistently impeded by excessive model sizes, constraints on video duration, and content deviation. These limitations significantly compromise the streamability of dynamic 3D Gaussian models, thereby restricting their utility in downstream applications, including volumetric video, autonomous vehicle, and immersive technologies such as virtual, augmented, and mixed reality.
This paper introduces SwinGS, a novel framework for training, delivering, and rendering volumetric video in a real-time streaming fashion. To address the aforementioned challenges and enhance streamability, SwinGS integrates spacetime Gaussian with Markov Chain Monte Carlo (MCMC) to adapt the model to fit various 3D scenes across frames, in the meantime employing a sliding window captures Gaussian snapshots for each frame in an accumulative way. We implement a prototype of SwinGS and demonstrate its streamability across various datasets and scenes. Additionally, we develop an interactive WebGL viewer enabling real-time volumetric video playback on most devices with modern browsers, including smartphones and tablets. Experimental results show that SwinGS reduces transmission costs by 83.6% compared to previous work with ignorable compromise in PSNR. Moreover, SwinGS easily scales to long video sequences without compromising quality.
Recent advances in 3D Gaussian Splatting (3DGS) have been revolutionizing the fields of computer vision and computer graphics. The high rendering speed and remarkable quality of 3DGS have made it a popular choice for various applications. However, the application of 3DGS to dynamic scenes has been limited due to challenges such as excessive model sizes, constraints on video duration, and content deviation.
In this paper, the authors introduce SwinGS, a novel framework that solves these challenges and enables real-time streaming of volumetric videos. SwinGS combines spacetime Gaussian with Markov Chain Monte Carlo (MCMC) to adapt the model to fit different 3D scenes across frames. It also uses a sliding window to capture Gaussian snapshots for each frame, accumulating them in a way that enhances streamability.
The multi-disciplinary nature of this framework is worth highlighting. It integrates techniques from computer vision, computer graphics, and probabilistic modeling. The use of MCMC enhances the adaptability of the model, making it suitable for a wide range of dynamic scenes. Additionally, the implementation of a WebGL viewer allows for real-time playback of volumetric videos on various devices.
From the perspective of multimedia information systems, SwinGS offers significant advancements. The ability to stream volumetric videos in real-time opens up possibilities for various applications, such as volumetric video communication, autonomous vehicles, and immersive technologies like virtual, augmented, and mixed reality. These applications heavily rely on the efficient rendering and delivery of multimedia content, and SwinGS addresses this need.
This research also has implications for animations, artificial reality, augmented reality, and virtual realities. The ability to accurately render dynamic scenes in real-time is crucial for creating realistic virtual environments. SwinGS reduces transmission costs compared to previous methods, making it more feasible for applications that require large-scale deployment of volumetric videos. The scalability of SwinGS to long video sequences without compromising quality is crucial for creating immersive experiences that are not limited by the duration of the content.
Overall, SwinGS is a significant contribution to the field of multimedia information systems and related disciplines. Its integration of techniques from various domains, coupled with its ability to address the limitations of previous methods, makes it a promising framework for real-time streaming of volumetric videos in many applications.
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by jsendak | Sep 14, 2024 | Computer Science
Building elastic and scalable edge resources is crucial for the successful implementation of platform-based smart city services. These services rely on edge computing to deliver low-latency applications, but the limited resources of edge devices have always been a challenge. A single edge device simply cannot handle the complex computations required by a smart city, which is why there is a growing need for the large-scale deployment of edge devices from different service providers to build a comprehensive edge resource platform.
However, selecting computing power from different service providers poses a game-theoretic problem. In order to incentivize service providers to actively contribute their resources and facilitate collaborative computing power with low-latency, a game-theoretic deep learning model is introduced. This model aims to help reach a consensus among service providers on task scheduling and resource provisioning.
Traditional centralized resource management approaches prove to be inefficient and lack credibility. This is where the introduction of blockchain technology comes into play, offering a decentralized and secure solution for resource trading and scheduling. By leveraging blockchain technology, a contribution-based proof mechanism is proposed to ensure the low-latency service of edge computing.
The deep learning model at the core of this approach consists of dual encoders and a single decoder. The Graph Neural Network (GNN) encoder processes structured decision action data, while the Recurrent Neural Network (RNN) encoder handles time-series task scheduling data. Through extensive experiments, it has been demonstrated that this model can reduce latency by a significant 584% when compared to the current state-of-the-art.
Expert Insights
This article addresses a critical challenge in the implementation of smart city services: the need for scalable and elastic edge resources. It is important to note that edge computing plays a crucial role in enabling low-latency applications, ensuring that smart city services can be delivered efficiently.
The proposed game-theoretic deep learning model showcases the potential of using advanced technology to address the resource limitations of edge computing. By incentivizing service providers to actively contribute their resources, this model enables efficient task scheduling and resource provisioning. Additionally, the introduction of blockchain technology adds a layer of trust and decentralization to the system, allowing for secure resource trading.
The combination of dual encoders, with the GNN encoder processing decision action data and the RNN encoder handling task scheduling data, allows for a comprehensive approach to resource management. This model has demonstrated impressive results, significantly reducing latency compared to current state-of-the-art solutions.
Looking ahead, this research opens up new possibilities for the development and deployment of smart city services. The game-theoretic approach, coupled with deep learning techniques and blockchain technology, has the potential to revolutionize how edge resources are utilized. As smart cities continue to evolve and grow, it will be essential to have efficient and scalable edge resources in place to support the increasing demand for low-latency applications and services.
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by jsendak | Sep 13, 2024 | Computer Science
arXiv:2409.06709v1 Announce Type: new
Abstract: Audio-Visual Source Localization (AVSL) aims to localize the source of sound within a video. In this paper, we identify a significant issue in existing benchmarks: the sounding objects are often easily recognized based solely on visual cues, which we refer to as visual bias. Such biases hinder these benchmarks from effectively evaluating AVSL models. To further validate our hypothesis regarding visual biases, we examine two representative AVSL benchmarks, VGG-SS and EpicSounding-Object, where the vision-only models outperform all audiovisual baselines. Our findings suggest that existing AVSL benchmarks need further refinement to facilitate audio-visual learning.
Audio-Visual Source Localization: Challenges and Opportunities
Audio-Visual Source Localization (AVSL) is an emerging field that aims to accurately determine the location of sound sources within a video. This has several applications in multimedia information systems, animations, artificial reality, augmented reality, and virtual realities. AVSL has the potential to enhance the user experience in these domains by providing more immersive and interactive audiovisual content.
In this paper, the authors identify a significant issue in existing AVSL benchmarks – visual bias. They point out that in many benchmarks, sounding objects can be easily recognized based solely on visual cues. This visual bias undermines the evaluation of AVSL models, as they don’t effectively capture the audio-visual learning capabilities. To demonstrate this, the authors analyze two representative AVSL benchmarks, VGG-SS and EpicSounding-Object, where vision-only models outperform all audiovisual baselines.
This research highlights the need for refinement in existing AVSL benchmarks to promote accurate audio-visual learning. It emphasizes the multi-disciplinary nature of AVSL, requiring the integration of computer vision and audio processing techniques. By tackling the issue of visual bias, researchers can develop more robust AVSL models that are capable of accurately localizing sound sources in videos.
In the wider field of multimedia information systems, AVSL has the potential to revolutionize the way we interact with audiovisual content. By accurately localizing sound sources, multimedia systems can provide a more immersive experience by adapting the audio output based on the user’s perspective and position relative to the source. This can greatly enhance virtual reality and augmented reality applications by creating a more realistic and interactive audiovisual environment.
Moreover, AVSL can contribute to the advancement of animations and artificial reality. By accurately localizing sound sources, animators can synchronize audio and visual elements more precisely, resulting in a more immersive and engaging animated experience. In artificial reality applications, AVSL can add another layer of realism by accurately reproducing spatial audio cues, making artificial environments indistinguishable from real ones.
Overall, the identification of visual bias in existing AVSL benchmarks underscores the importance of refining these benchmarks to promote accurate audio-visual learning. This research highlights the interdisciplinary nature of AVSL and its applications in multimedia information systems, animations, artificial reality, augmented reality, and virtual realities. By addressing these challenges, researchers can unlock the full potential of AVSL and revolutionize the way we perceive and interact with audiovisual content.
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by jsendak | Sep 13, 2024 | Computer Science
Hyperspectral images (HSIs) provide rich spectral information, making them valuable in various visual tasks. However, obtaining high-resolution HSIs is a challenge due to limitations in physical imaging. This article introduces a novel HSI super-resolution (HSI-SR) model that addresses this challenge by fusing a low-resolution HSI (LR-HSI) with a high-resolution multispectral image (HR-MSI) to generate a high-resolution HSI (HR-HSI).
KAN-Fusion: Enhancing Spatial Information Integration
The key aspect of the proposed HSI-SR model is the fusion module called KAN-Fusion, inspired by Kolmogorov-Arnold Networks (KANs). KANs are known for their effectiveness in incorporating spatial information. By leveraging KANs, the fusion module allows for the efficient integration of spatial information from the HR-MSI.
KAN-CAB: Spectral Channel Attention for Feature Extraction
Another essential component of the HSI-SR model is the KAN Channel Attention Block (KAN-CAB), which incorporates a spectral channel attention mechanism. This module enhances the fine-grained adjustment ability of deep networks, enabling them to accurately capture the details of spectral sequences and spatial textures.
Overcoming Curse of Dimensionality
One advantage of the KAN-CAB module is its ability to effectively address the Curse of Dimensionality (COD) in hyperspectral data. COD refers to the challenge of dealing with high-dimensional data, which can negatively impact the performance of deep networks. By integrating channel attention with KANs, KAN-CAB mitigates COD, enabling improved performance in HSI-SR tasks.
Superior Performance
The proposed HSR-KAN model outperforms current state-of-the-art HSI-SR methods in both qualitative and quantitative assessments. Extensive experiments validate its superior performance and demonstrate its ability to generate high-resolution HSIs with enhanced details.
Overall, the combination of KAN-Fusion for spatial information integration and KAN-CAB for spectral channel attention makes the HSI-SR model a promising approach for enhancing the resolution of hyperspectral images. Further research and exploration of this model may lead to advancements in various applications that rely on high-resolution HSIs.
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by jsendak | Sep 12, 2024 | Computer Science
arXiv:2409.06051v1 Announce Type: new
Abstract: Due to the soaring popularity of video applications and the consequent rise in video traffic on the Internet, technologies like HTTP Adaptive Streaming (HAS) are crucial for delivering high Quality of Experience (QoE) to consumers. HAS technology enables video players on consumer devices to enhance viewer engagement by dynamically adapting video content quality based on network conditions. This is especially relevant for consumer electronics as it ensures an optimized viewing experience across a variety of devices, from smartphones to smart TVs. This paper introduces REVISION, an efficient roadmap designed to enhance adaptive video streaming, a core feature of modern consumer electronics. The REVISION optimization triangle highlights three essential aspects for improving streaming: Objective, Input Space, and Action Domain. Additionally, REVISION proposes a novel layer-based architecture tailored to refine video streaming systems, comprising Application, Control and Management, and Resource layers. Each layer is designed to optimize different components of the streaming process, which is directly linked to the performance and efficiency of consumer devices. By adopting the principles of the REVISION, manufacturers and developers can significantly improve the streaming capabilities of consumer electronics, thereby enriching the consumer’s multimedia experience and accommodating the increasing demand for high-quality, real-time video content. This approach addresses the complexities of today’s diverse video streaming ecosystem and paves the way for future advancements in consumer technology.
Enhancing Adaptive Video Streaming: The REVISION Approach
The growing popularity of video applications and the subsequent increase in internet video traffic have made technologies like HTTP Adaptive Streaming (HAS) vital for delivering a high Quality of Experience (QoE) to consumers. This not only ensures viewer engagement but also optimizes the streaming experience across various consumer devices.
The REVISION approach, introduced in this paper, offers a roadmap to enhance adaptive video streaming, which is a core feature of modern consumer electronics. The authors emphasize three crucial aspects for improving streaming: Objective, Input Space, and Action Domain. By focusing on these elements, manufacturers and developers can develop efficient strategies to dynamically adapt video content quality based on network conditions, resulting in an optimized viewing experience.
Furthermore, the paper presents a layer-based architecture proposed by REVISION, comprising the Application, Control and Management, and Resource layers. This architecture allows for refining different components of the streaming process to enhance the performance and efficiency of consumer devices. Each layer is dedicated to optimizing specific aspects of the streaming system, such as content delivery, control algorithms, and resource allocation.
What sets this approach apart is its multi-disciplinary nature, integrating concepts from multimedia information systems, animations, artificial reality, augmented reality, and virtual realities. By considering these diverse fields, REVISION acknowledges the complexity of the current video streaming ecosystem and offers a comprehensive solution that not only caters to present demands but also anticipates future advancements in consumer technology.
Implications and Future Scope
The REVISION approach has significant implications for both manufacturers and consumers. Manufacturers can leverage these principles to enhance the streaming capabilities of consumer electronics, resulting in a richer multimedia experience for users. Additionally, the optimization triangle and layer-based architecture can serve as a foundation for further research and development in the field of adaptive video streaming.
Looking ahead, there are several areas where further exploration is warranted. One such area is the integration of artificial intelligence (AI) techniques into the streaming process. AI can play a pivotal role in analyzing network conditions, predicting user preferences, and dynamically adapting video content in real-time. Moreover, exploring the application of REVISION principles in emerging technologies, like virtual and augmented reality, can unlock new possibilities for immersive and interactive video experiences.
In conclusion, the REVISION approach offers a comprehensive roadmap for enhancing adaptive video streaming, catering to the demands of today’s diverse video streaming ecosystem. By optimizing the core elements and leveraging a layer-based architecture, this approach enables manufacturers to deliver high-quality, real-time video content across a range of consumer devices. With further advancements in AI and the integration of emerging technologies, the future holds exciting possibilities for the evolution of adaptive video streaming and its impact on the consumer multimedia experience.
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by jsendak | Sep 12, 2024 | Computer Science
Expert Commentary: Overcoming Challenges in Novel View Synthesis with Lantent-SpecGS
In the field of computer graphics, the 3D Gaussian Splatting (3D-GS) method has been widely recognized for its success in real-time rendering of high-quality novel views. However, as highlighted in this recent research, there are still some challenges that need to be addressed in order to achieve even better results.
An important limitation of the 3D-GS method is its inability to effectively model specular reflections and handle anisotropic appearance components, especially under complex lighting conditions. This means that the rendered images may not accurately capture the complex interplay of light and materials, leading to less realistic results. Additionally, the use of spherical harmonic for color representation in 3D-GS has its own limitations, particularly when dealing with scenes that have a high level of complexity.
To overcome these challenges, the authors propose a novel approach called Lantent-SpecGS. This approach introduces a universal latent neural descriptor within each 3D Gaussian, enabling a more effective representation of 3D feature fields that include both appearance and geometry. By incorporating a latent neural descriptor, the authors aim to enhance the ability of the model to capture intricate details and accurately represent the visual characteristics of the scene.
In addition to the latent neural descriptor, Lantent-SpecGS also incorporates two parallel Convolutional Neural Networks (CNNs) that are specifically designed to decode the splatting feature maps into diffuse color and specular color separately. This separation allows for better control and manipulation of the different components of the rendered image, resulting in improved visual quality. Furthermore, the authors introduce a learned mask that accounts for the viewpoint, enabling the merging of the diffuse and specular colors to produce the final rendered image.
The experimental results presented in the research paper demonstrate the effectiveness of the proposed Lantent-SpecGS method. It achieves competitive performance in novel view synthesis and expands the capabilities of the 3D-GS method to handle complex scenarios with specular reflections. These results indicate that the introduction of the latent neural descriptor and the use of parallel CNNs can significantly enhance the rendering capabilities of the 3D-GS method.
In conclusion, the Lantent-SpecGS method represents a valuable contribution to the field of novel view synthesis. By overcoming the limitations of the existing 3D-GS method, it enables more accurate and realistic rendering of complex scenes with specular reflections. The incorporation of a latent neural descriptor and the parallel CNNs demonstrates the potential for further advancements in this area. Future research could explore the application of Lantent-SpecGS to other computer graphics tasks and investigate its performance under different lighting conditions and scene complexities.
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