Comparative Study of 3D Point Cloud Compression Methods

Bui, Mai; Chang, Lin-Ching; Liu, Hang; Zhao, Qi; Chen, Genshe

doi:10.1109/bigdata52589.2021.9671822

Cited by 8 publications

(4 citation statements)

References 18 publications

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“…In addition, an assessment of the wearing comfort psychological scale can also be performed. Using the Likert scale (1,3,5,7,9), the scale vocabulary is defined as "very uncomfortable" (1), "discomfort" (3), "moderate" (5), "comfortable" (7), and "very comfortable" (9). The Likert scale is a psychological scale often used in psychological questionnaires.…”

Section: Evaluation Using Running-in Degreementioning

confidence: 99%

“…Thus, deep learning models for image recognition require additional processing for point cloud data. Methods such as reordering disordered data, performing data augmentation with all permutations, using recurrent neural networks (RNNs), and using asymmetric functions ensure permutation invariance when processing point cloud data [5,6]. Additionally, invariance should also apply to rotation and translation.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Curvature Generation Based on Weight-Updated Boosting Using Shoe Last Point-Cloud Measurements

Wang

Dey³

et al. 2023

Preprint

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Section: Evaluation Using Running-in Degreementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Curvature Generation Based on Weight-Updated Boosting Using Shoe Last Point-Cloud Measurements

Wang

Dey³

et al. 2023

Preprint

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“…Draco [38] is a software library (by Google) that aims to compress 3D geometric meshes and point clouds to enhance the storage and transmission of 3D graphics. Draco continuously splits the point cloud from the center utilizing the concept of KD tree formation, while also modifying the axes on each direction, followed by the application of entropy encoding techniques to compress the data [39]. Deep learning-based point cloud compression methods utilize neural networks to learn the underlying patterns and structures in the point cloud data, enabling them to efficiently represent the data with fewer bits.…”

Section: A Sourcementioning

confidence: 99%

Wireless communication research challenges for Extended Reality (XR)

Akyildiz¹,

Guo²

2022

ITU J-FET

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Extended Reality (XR) is an umbrella term that includes Augmented Reality (AR), Mixed Reality (MR), and Virtual Reality (VR). XR has a tremendous market size and will profoundly transform our lives by changing the way we interact with the physical world. However, existing XR devices are mainly tethered by cables which limit users' mobility and Quality-of-Experience (QoE). Wireless XR leverages existing and future wireless technologies, such as 5G, 6G, and Wi-Fi, to remove cables that are tethered to the head-mounted devices. Such changes can free users and enable a plethora of applications. High-quality ultimate XR requires an uncompressed data rate up to 2.3 Tbps with an end-to-end latency lower than 10 ms. Although 5G has significantly improved data rates and reduced latency, it still cannot meet such high requirements. This paper provides a roadmap towards wireless ultimate XR. The basics, existing products, and use cases of AR, MR, and VR are reviewed, upon which technical requirements and bottlenecks of realizing ultimate XR using wireless technologies are identified. Challenges of utilizing 6G wireless systems and the next generation Wi-Fi systems and future research directions are provided.

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“…Nevertheless, the massive amount of point cloud data aggregated from distributed 3D sensors also poses challenges for securing data collection, management, storage, and sharing. By using signal processing or neural network techniques, several efficient point cloud compression (PCC) methods [3] have been proposed to reduce the bandwidth of wireless networks or storage space of 3D point cloud raw data. However, there are still a lot of efforts to be made to achieve efficient end-to-end data delivery and optimal storage management.…”

Section: Introductionmentioning

confidence: 99%

SAUSA: Securing Access, Usage, and Storage of 3D Point Clouds Data by a Blockchain-based Authentication Network

Xu¹,

Chen²,

Chen³

et al. 2022

Preprint

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The rapid development of three-dimensional (3D) acquisition technology based on 3D sensors provides a large volume of data, which is 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 nature, Blockchain has a great potential to improve point cloud services and enhance security and privacy preservation. Inspired by the rationales behind 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 data sets in their life-cycle in a decentralized manner. SAUSA adopts an SDN-enabled 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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Comparative Study of 3D Point Cloud Compression Methods

Cited by 8 publications

References 18 publications

Curvature Generation Based on Weight-Updated Boosting Using Shoe Last Point-Cloud Measurements

Curvature Generation Based on Weight-Updated Boosting Using Shoe Last Point-Cloud Measurements

Wireless communication research challenges for Extended Reality (XR)

SAUSA: Securing Access, Usage, and Storage of 3D Point Clouds Data by a Blockchain-based Authentication Network

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