Dynamic Adaptive Point Cloud Streaming

High-quality point clouds have recently gained interest as an emerging form of representing immersive 3D graphics. Unfortunately, these 3D media are bulky and severely bandwidth intensive, which makes it difficult for streaming to resource-limited and mobile devices. This has called researchers to propose efficient and adaptive approaches for streaming of high-quality point clouds. In this paper, we run a pilot study towards dynamic adaptive point cloud streaming, and extend the concept of dynamic adaptive streaming over HTTP (DASH) towards DASH-PC, a dynamic adaptive bandwidth-efficient and view-aware point cloud streaming system. DASH-PC can tackle the huge bandwidth demands of dense point cloud streaming while at the same time can semantically link to human visual acuity to maintain high visual quality when needed. In order to describe the various quality representations, we propose multiple thinning approaches to spatially sub-sample point clouds in the 3D space, and design a DASH Media Presentation Description manifest specific for point cloud streaming. Our initial evaluations show that we can achieve significant bandwidth and performance improvement on dense point cloud streaming with minor negative quality impacts compared to the baseline scenario when no adaptations is applied.Comment: 6 pages, 23rd ACM Packet Video (PV'18) Workshop, June 12--15, 2018, Amsterdam, Netherland

Objective and subjective QoE evaluation for adaptive point cloud streaming

Volumetric media has the potential to provide the six degrees of freedom (6DoF) required by truly immersive media. However, achieving 6DoF requires ultra-high bandwidth transmissions, which real-world wide area networks cannot provide today. Therefore, recent efforts have started to target efficient delivery of volumetric media, using a combination of compression and adaptive streaming techniques. It remains, however, unclear how the effects of such techniques on the user perceived quality can be accurately evaluated. In this paper, we present the results of an extensive objective and subjective quality of experience (QoE) evaluation of volumetric 6DoF streaming. We use PCC-DASH, a standards-compliant means for HTTP adaptive streaming of scenes comprising multiple dynamic point cloud objects. By means of a thorough analysis, we investigate the perceived quality impact of the available bandwidth, rate adaptation algorithm, viewport prediction strategy and user's motion within the scene. We determine which of these aspects has more impact on the user's QoE, and to what extent subjective and objective assessments are aligned

Towards QoE-Driven Optimization of Multi-Dimensional Content Streaming

Whereas adaptive video streaming for 2D video is well established and frequently used in streaming services, adaptation for emerging higher-dimensional content, such as point clouds, is still a research issue. Moreover, how to optimize resource usage in streaming services that support multiple content types of different dimensions and levels of interactivity has so far not been sufficiently studied. Learning-based approaches aim to optimize the streaming experience according to user needs. They predict quality metrics and try to find system parameters maximizing them given the current network conditions. With this paper, we show how to approach content and network adaption driven by Quality of Experience (QoE) for multi-dimensional content. We describe components required to create a system adapting multiple streams of different content types simultaneously, identify research gaps and propose potential next steps

Impact of Point Clouds transmission and compression errors on the user experience

Il lavoro di ricerca alla base della tesi aveva come obiettivo preparare e condurre esperimenti sull'impatto di trasmissione e codifica di point cloud sulla qualità dell'esperienza degli utenti, analizzare i dati ricavati da questi ultimi alla ricerca di intuizioni utili

User Pattern Exploration in Immersive Applications.

openPoint clouds have become essential for different industries that require 3D modelling of objects or environments. Thus exploration of point clouds has become imperative and subjective evaluation is often used to understand how humans perceive, interpret, and interact with the point cloud data. This interaction as an outcome naturally involves physical mobility around the object. Hence, human trajectory plays a crucial role in accessing and analysing point cloud models. When exploring a 3D point cloud model, users navigate through the dataset and view different parts of it from different angles and perspectives in order to fully understand and interpret the data. This physical movement pattern around a scene results in individualistic camera paths, taking into account the same dataset for all the users. Therefore, by aggregating all camera paths and extrapolating a mean trajectory, it's possible to formulate a collective reference path for generating a more comprehensive 2D video that can then be used for further subjective assessment and analysis. This further analysis highlights precise visual evaluation and gives insights into the completeness of the integral data.Point clouds have become essential for different industries that require 3D modelling of objects or environments. Thus exploration of point clouds has become imperative and subjective evaluation is often used to understand how humans perceive, interpret, and interact with the point cloud data. This interaction as an outcome naturally involves physical mobility around the object. Hence, human trajectory plays a crucial role in accessing and analysing point cloud models. When exploring a 3D point cloud model, users navigate through the dataset and view different parts of it from different angles and perspectives in order to fully understand and interpret the data. This physical movement pattern around a scene results in individualistic camera paths, taking into account the same dataset for all the users. Therefore, by aggregating all camera paths and extrapolating a mean trajectory, it's possible to formulate a collective reference path for generating a more comprehensive 2D video that can then be used for further subjective assessment and analysis. This further analysis highlights precise visual evaluation and gives insights into the completeness of the integral data

User centered adaptive streaming of dynamic point clouds with low complexity tiling

In recent years, the development of devices for acquisition and rendering of 3D contents have facilitated the diffusion of immersive virtual reality experiences. In particular, the point cloud representation has emerged as a popular format for volumetric photorealistic reconstructions of dynamic real world objects, due to its simplicity and versatility. To optimize the delivery of the large amount of data needed to provide these experiences, adaptive streaming over HTTP is a promising solution. In order to ensure the best quality of experience within the bandwidth constraints, adaptive streaming is combined with tiling to optimize the quality of what is being visualized by the user at a given moment; as such, it has been successfully used in the past for omnidirectional contents. However, its adoption to the point cloud streaming scenario has only been studied to optimize multi-object delivery. In this paper, we present a low-complexity tiling approach to perform adaptive streaming of point cloud content. Tiles are defined by segmenting each point cloud object in several parts, which are then independently encoded. In order to evaluate the approach, we first collect real navigation paths, obtained through a user study in 6 degrees of freedom with 26 participants. The variation in movements and interaction behaviour among users indicate that a user-centered adaptive delivery could lead to sensible gains in terms of perceived quality. Evaluation of the performance of the proposed tiling approach against state of the art solutions for point cloud compression, performed on the collected navigation paths, confirms that considerable gains can be obtained by exploiting user-adaptive streaming, achieving bitrate gains up to 57% with respect to a non-adaptive approach with the same codec. Moreover, we demonstrate that the selection of navigation data has an impact on the relative objective scores