Dynamic 3D point cloud streaming

Wu, Cheng-Hao; Li, Xiner; Rajesh, Rahul; Ooi, Wei Tsang; Hsu, Cheng-Hsin

doi:10.1145/3458306.3458876

Cited by 9 publications

(5 citation statements)

References 26 publications

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“…On the other hand, some of the results of our work can be compared with those listed in [ 17 ], which also studies and discusses the effects of simulated packet losses on dynamic 3D point cloud streaming. In this article, objective metrics were calculated for several dynamic point clouds distorted with packet losses in different channels of the V-PCC bitstream.…”

Section: Discussionmentioning

confidence: 99%

“…Using the MPEG V-PCC standard codec, article [ 17 ] presents a study on the effects of simulated packet losses on dynamic 3D point cloud streaming. The authors showcased the distortions that occur when several channels of the V-PCC bitstream are lost, with the loss of occupancy and geometry data having the greatest negative effects on the quality.…”

Section: Related Workmentioning

confidence: 99%

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Subjective Quality Assessment of V-PCC-Compressed Dynamic Point Clouds Degraded by Packet Losses

Dumic

Cruz

2023

Sensors

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This article describes an empirical exploration on the effect of information loss affecting compressed representations of dynamic point clouds on the subjective quality of the reconstructed point clouds. The study involved compressing a set of test dynamic point clouds using the MPEG V-PCC (Video-based Point Cloud Compression) codec at 5 different levels of compression and applying simulated packet losses with three packet loss rates (0.5%, 1% and 2%) to the V-PCC sub-bitstreams prior to decoding and reconstructing the dynamic point clouds. The recovered dynamic point clouds qualities were then assessed by human observers in experiments conducted at two research laboratories in Croatia and Portugal, to collect MOS (Mean Opinion Score) values. These scores were subject to a set of statistical analyses to measure the degree of correlation of the data from the two laboratories, as well as the degree of correlation between the MOS values and a selection of objective quality measures, while taking into account compression level and packet loss rates. The subjective quality measures considered, all of the full-reference type, included point cloud specific measures, as well as others adapted from image and video quality measures. In the case of image-based quality measures, FSIM (Feature Similarity index), MSE (Mean Squared Error), and SSIM (Structural Similarity index) yielded the highest correlation with subjective scores in both laboratories, while PCQM (Point Cloud Quality Metric) showed the highest correlation among all point cloud-specific objective measures. The study showed that even 0.5% packet loss rates reduce the decoded point clouds subjective quality by more than 1 to 1.5 MOS scale units, pointing out the need to adequately protect the bitstreams against losses. The results also showed that the degradations in V-PCC occupancy and geometry sub-bitstreams have significantly higher (negative) impact on decoded point cloud subjective quality than degradations of the attribute sub-bitstream.

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Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Subjective Quality Assessment of V-PCC-Compressed Dynamic Point Clouds Degraded by Packet Losses

Dumic

Cruz

2023

Sensors

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“…Zheng et al [25] present a novel ShiftTile-Tracking (STC) streaming system, which crops and transmits video by tracking the FoV movement of users. Wu et al [26] propose a dual-queue streaming framework to enable the Deep Reinforcement Learning (DRL) agent to determine and change the tile download order without incurring overhead. Madarasingha and Thilakarathna [27] present a computational geometric approach-based adaptive tiling mechanism, which can take visual attention information as the input and provide a suitable non-overlapping variable size tile cover on the frame.…”

Section: A Adaptive 360 • Video Streamingmentioning

confidence: 99%

VRCT: A Viewport Reconstruction-Based 360° Video Caching Solution for Tile-Adaptive Streaming

et al. 2023

IEEE Trans. on Broadcast.

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video streaming demands higher bandwidth and lower latency than conventional videos. Some solutions employ tile-adaptive 360 • video streaming and edge caching mechanisms to improve the quality of their content delivery. However, it is hard to cache large amounts of popular content with limited cache capacity. Reconstruction technologies, which have been widely adopted for images and conventional videos, can potentially reconstruct complete tile-based viewports from partial observation for 360 • videos, thus further relieving the pressure on the caching. However, it is challenging to design a flexible and efficient caching solution that supports viewport reconstruction for 360 • videos. In this paper, we propose a Viewport Reconstructionbased 360 • video Caching solution for Tile-adaptive streaming (VRCT). To enhance viewers' quality of experience (QoE), a QoE-driven reconstruction trigger scheme is designed to determine whether to perform reconstruction or not based on current cache information and network conditions. To make efficient use of the cache space and facilitate the viewport reconstruction, a heuristic-based solution, named aggregation-based cache replacement scheme, is proposed to improve the probability of viewport reconstruction by carefully selecting which tiles to be stored in the given limited space. Through comprehensive experiments with a real head movement dataset, we show that the proposed VRCT

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“…Dynamic point clouds are composed of a sequence of static three-dimensional point clouds, each representing a collection of sparsely sampled points taken from the continuous surfaces of objects and scenes. This unique structure serves as a powerful model for rendering realistic static and dynamic 3D objects 4,[8][9][10] . The versatility of dynamic point clouds finds application in a broad spectrum of practical domains, encompassing geographic information systems, cultural heritage preservation, immersive telepresence, telehealth, and enhanced accessibility for individuals with disabilities.…”

Section: Introductionmentioning

confidence: 99%

“…Category 2 encompasses dynamic point clouds characterized by sequences involving human subjects. Lastly, Category 3 is reserved for dynamically acquired point clouds, a prime example being LiDAR point clouds [6][7][8][9] . Notably, recent advancements have given rise to two standardized approaches within the Moving Picture Experts Group: video-based point cloud compression (V-PCC) and geometry-based point cloud compression (G-PCC) 1,2,4 .…”

Section: Introductionmentioning

confidence: 99%

Improved video-based point cloud compression via segmentation

Tohidi,

Paul,

Ulhaq

et al. 2024

Preprint

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Point cloud is a representation of objects or scenes utilising unordered points comprising 3D positions and attributes. The ability of point clouds to mimic natural forms has gained significant attention from diverse applied fields, such as virtual realityand augmented reality. However, the point cloud, especially the dynamic one, must be compressed efficiently due to its hugedata volume. The latest video-based point cloud compression (V-PCC) standard for dynamic point clouds divides the 3D pointcloud into many patches using computationally expensive normal estimation, segmentation, and refinement. The patches areprojected onto a 2D plane to apply existing video coding techniques. This process often results in losing proximity informationand some original points. This loss induces artefacts that adversely affect user perception. The proposed method segments dynamic point clouds based on shape similarity and occlusion before patch generation. This segmentation strategy helps tomaintain the proximity of the points and retain more original points by exploiting density and occlusion of the points . The experimental results establish that the proposed method significantly outperforms the V-PCC standard and other relevant methods in terms of rate-distortion performance and subjective quality testing for both geometric and texture data of several benchmark video sequences.

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Dynamic 3D point cloud streaming

Cited by 9 publications

References 26 publications

Subjective Quality Assessment of V-PCC-Compressed Dynamic Point Clouds Degraded by Packet Losses

Subjective Quality Assessment of V-PCC-Compressed Dynamic Point Clouds Degraded by Packet Losses

VRCT: A Viewport Reconstruction-Based 360° Video Caching Solution for Tile-Adaptive Streaming

Improved video-based point cloud compression via segmentation

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