6,879 research outputs found
Source coding for transmission of reconstructed dynamic geometry: a rate-distortion-complexity analysis of different approaches
Live 3D reconstruction of a human as a 3D mesh with commodity electronics is becoming a reality. Immersive applications (i.e. cloud gaming, tele-presence) benefit from effective transmission of such content over a bandwidth limited link. In this paper we outline different approaches for compressing live reconstructed mesh geometry based on distributing mesh reconstruction functions between sender and receiver. We evaluate rate-performance-complexity of different configurations. First, we investigate 3D mesh compression methods (i.e. dynamic/static) from MPEG-4. Second, we evaluate the option of using octree based point cloud compression and receiver side surface reconstruction
Low-latency Cloud-based Volumetric Video Streaming Using Head Motion Prediction
Volumetric video is an emerging key technology for immersive representation
of 3D spaces and objects. Rendering volumetric video requires lots of
computational power which is challenging especially for mobile devices. To
mitigate this, we developed a streaming system that renders a 2D view from the
volumetric video at a cloud server and streams a 2D video stream to the client.
However, such network-based processing increases the motion-to-photon (M2P)
latency due to the additional network and processing delays. In order to
compensate the added latency, prediction of the future user pose is necessary.
We developed a head motion prediction model and investigated its potential to
reduce the M2P latency for different look-ahead times. Our results show that
the presented model reduces the rendering errors caused by the M2P latency
compared to a baseline system in which no prediction is performed.Comment: 7 pages, 4 figure
From Capture to Display: A Survey on Volumetric Video
Volumetric video, which offers immersive viewing experiences, is gaining
increasing prominence. With its six degrees of freedom, it provides viewers
with greater immersion and interactivity compared to traditional videos.
Despite their potential, volumetric video services poses significant
challenges. This survey conducts a comprehensive review of the existing
literature on volumetric video. We firstly provide a general framework of
volumetric video services, followed by a discussion on prerequisites for
volumetric video, encompassing representations, open datasets, and quality
assessment metrics. Then we delve into the current methodologies for each stage
of the volumetric video service pipeline, detailing capturing, compression,
transmission, rendering, and display techniques. Lastly, we explore various
applications enabled by this pioneering technology and we present an array of
research challenges and opportunities in the domain of volumetric video
services. This survey aspires to provide a holistic understanding of this
burgeoning field and shed light on potential future research trajectories,
aiming to bring the vision of volumetric video to fruition.Comment: Submitte
Geometric Prior Based Deep Human Point Cloud Geometry Compression
The emergence of digital avatars has raised an exponential increase in the
demand for human point clouds with realistic and intricate details. The
compression of such data becomes challenging with overwhelming data amounts
comprising millions of points. Herein, we leverage the human geometric prior in
geometry redundancy removal of point clouds, greatly promoting the compression
performance. More specifically, the prior provides topological constraints as
geometry initialization, allowing adaptive adjustments with a compact parameter
set that could be represented with only a few bits. Therefore, we can envisage
high-resolution human point clouds as a combination of geometric priors and
structural deviations. The priors could first be derived with an aligned point
cloud, and subsequently the difference of features is compressed into a compact
latent code. The proposed framework can operate in a play-and-plug fashion with
existing learning based point cloud compression methods. Extensive experimental
results show that our approach significantly improves the compression
performance without deteriorating the quality, demonstrating its promise in a
variety of applications
Lossless Compression of Neuromorphic Vision Sensor Data Based on Point Cloud Representation
Visual information varying over time is typically captured by cameras that acquire data via images (frames) equally spaced in time. Using a different approach, Neuromorphic Vision Sensors (NVSs) are emerging visual capturing devices that only acquire information when changes occur in the scene. This results in major advantages in terms of low power consumption, wide dynamic range, high temporal resolution, and lower data rates than conventional video. Although the acquisition strategy already results in much lower data rates than conventional video, such data can be further compressed. To this end, in this paper we propose a lossless compression strategy based on point cloud compression, inspired by the observation that, by appropriately reporting NVS data in a tridimensional space, we have a point cloud representation of NVS data. The proposed strategy outperforms the benchmark strategies resulting in a compression ratio up to 30% higher for the considered
Network streaming and compression for mixed reality tele-immersion
Bulterman, D.C.A. [Promotor]Cesar, P.S. [Copromotor
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