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1. SAUSA: Securing Access, Usage, and Storage of 3D Point CloudData by a Blockchain-Based Authentication Network

   https://doi.org/10.3390/fi14120354  

   Xu, Ronghua; Chen, Yu; Chen, Genshe; Blasch, Erik (December 2022, Future Internet)

   The rapid development of three-dimensional (3D) acquisition technology based on 3D sensors provides a large volume of data, which are often represented in the form of point clouds. Point cloud representation can preserve the original geometric information along with associated attributes in a 3D space. Therefore, it has been widely adopted in many scene-understanding-related applications such as virtual reality (VR) and autonomous driving. However, the massive amount of point cloud data aggregated from distributed 3D sensors also poses challenges for secure data collection, management, storage, and sharing. Thanks to the characteristics of decentralization and security, Blockchain has great potential to improve point cloud services and enhance security and privacy preservation. Inspired by the rationales behind the software-defined network (SDN) technology, this paper envisions SAUSA, a Blockchain-based authentication network that is capable of recording, tracking, and auditing the access, usage, and storage of 3D point cloud datasets in their life-cycle in a decentralized manner. SAUSA adopts an SDN-inspired point cloud service architecture, which allows for efficient data processing and delivery to satisfy diverse quality-of-service (QoS) requirements. A Blockchain-based authentication framework is proposed to ensure security and privacy preservation in point cloud data acquisition, storage, and analytics. Leveraging smart contracts for digitizing access control policies and point cloud data on the Blockchain, data owners have full control of their 3D sensors and point clouds. In addition, anyone can verify the authenticity and integrity of point clouds in use without relying on a third party. Moreover, SAUSA integrates a decentralized storage platform to store encrypted point clouds while recording references of raw data on the distributed ledger. Such a hybrid on-chain and off-chain storage strategy not only improves robustness and availability, but also ensures privacy preservation for sensitive information in point cloud applications. A proof-of-concept prototype is implemented and tested on a physical network. The experimental evaluation validates the feasibility and effectiveness of the proposed SAUSA solution. 

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2. A Cloud 3D Dataset and Application-Specific Learned Image Compression in Cloud 3D

   Liu, Tianyi; He, Sen; Jayakumar, Vinodh K; Wang, Wei (October 2022, European Conference on Computer Vision (ECCV))

   In Cloud 3D, such as Cloud Gaming and Cloud Virtual Reality (VR), image frames are rendered and compressed (encoded) in the cloud, and sent to the clients for users to view. For low latency and high image quality, fast, high compression rate, and high-quality image compression techniques are preferable. This paper explores computation time reduction techniques for learned image compression to make it more suitable for cloud 3D. More specifically, we employed slim (low-complexity) and application-specific AI models to reduce the computation time without degrading image quality. Our approach is based on two key insights: (1) as the frames generated by a 3D application are highly homogeneous, application-specific compression models can improve the rate-distortion performance over a general model; (2) many computer-generated frames from 3D applications are less complex than natural photos, which makes it feasible to reduce the model complexity to accelerate compression computation. We evaluated our models on six gaming image datasets. The results show that our approach has similar rate-distortion performance as a state-of-the-art learned image compression algorithm, while obtaining about 5x to 9x speedup and reducing the compression time to be less than 1 s (0.74s), bringing learned image compression closer to being viable for cloud 3D. Code is available at https://github.com/cloud-graphics-rendering/AppSpecificLIC. 

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3. VEMIC: View-aware Entropy model for Multi-view Image Compression

   Jabbireddy, Susmija; Soselia, Davit; Ehrlich, Max; Metzler, Chris; Varshney, Amitabh (November 2024, 35th British Machine Vision Conference 2024, BMVC 2024, The British Machine Vision Association and Society for Pattern Recognition)

   With the ever-increasing amount of 3D data being captured and processed, multi-view image compression is essential to various applications, including virtual reality and 3D modeling. Despite the considerable success of learning-based compression models on single images, limited progress has been made in multi-view image compression. In this paper, we propose an efficient approach to multi-view image compression by leveraging the redundant information across different viewpoints without explicitly using warping operations or camera parameters. Our method builds upon the recent advancements in Multi-Reference Entropy Models (MEM), which were initially proposed to capture correlations within an image. We extend the MEM models to employ cross-view correlations in addition to within-image correlations. Specifically, we generate latent representations for each view independently and integrate a cross-view context module within the entropy model. The estimation of entropy parameters for each view follows an autoregressive technique, leveraging correlations with the previous views. We show that adding this view context module further enhances the compression performance when jointly trained with the autoencoder. Experimental results demonstrate superior performance compared to both traditional and learning-based multi-view compression methods. 

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4. VRFromX: From Scanned Reality to Interactive Virtual Experience with Human-in-the-Loop

   https://doi.org/10.1145/3411763.3451747  

   Ipsita, Ananya; Li, Hao; Duan, Runlin; Cao, Yuanzhi; Chidambaram, Subramanian; Liu, Min; Ramani, Karthik (May 2021, Conference on Human Factors in Computing Systems (CHI EA '21))

   null (Ed.)

   There is an increasing trend of Virtual-Reality (VR) applications found in education, entertainment, and industry. Many of them utilize real world tools, environments, and interactions as bases for creation. However, creating such applications is tedious, fragmented, and involves expertise in authoring VR using programming and 3D-modelling softwares. This hinders VR adoption by decoupling subject matter experts from the actual process of authoring while increasing cost and time. We present VRFromX, an in-situ Do-It-Yourself (DIY) platform for content creation in VR that allows users to create interactive virtual experiences. Using our system, users can select region(s) of interest (ROI) in scanned point cloud or sketch in mid-air using a brush tool to retrieve virtual models and then attach behavioral properties to them. We ran an exploratory study to evaluate usability of VRFromX and the results demonstrate feasibility of the framework as an authoring tool. Finally, we implemented three possible use-cases to showcase potential applications. 

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5. Point Cloud Attribute Compression Via Chroma Subsampling

   https://doi.org/10.1109/ICASSP43922.2022.9746352  

   Sridhara, Shashank N.; Pavez, Eduardo; Ortega, Antonio; Watanabe, Ryosuke; Nonaka, Keisuke (May 2022, 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP))

   We introduce chroma subsampling for 3D point cloud attribute compression by proposing a novel technique to sample points irregularly placed in 3D space. While most current video compression standards use chroma subsampling, these chroma subsampling methods cannot be directly applied to 3D point clouds, given their irregularity and sparsity. In this work, we develop a framework to incorporate chroma subsampling into geometry-based point cloud encoders, such as region adaptive hierarchical transform (RAHT) and region adaptive graph Fourier transform (RAGFT). We propose different sampling patterns on a regular 3D grid to sample the points at different rates. We use a simple graph-based nearest neighbor interpolation technique to reconstruct the full resolution point cloud at the decoder end. Experimental results demonstrate that our proposed method provides significant coding gains with negligible impact on the reconstruction quality. For some sequences, we observe a bitrate reduction of 10-15% under the Bjontegaard metric. More generally, perceptual masking makes it possible to achieve larger bitrate reductions without visible changes in quality. 

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