Augmented Reality
Point cloud streaming has recently attracted research attention as it has the potential to provide six degrees of freedom movement, which is essential for truly immersive media. The transmission of point clouds requires high-bandwidth connections, and adaptive streaming is a promising solution to cope with fluctuating bandwidth conditions. Thus, understanding the impact of different factors in adaptive streaming on the Quality of Experience (QoE) becomes fundamental. Point clouds have been evaluated in Virtual Reality (VR), where viewers are completely immersed in a virtual environment.
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A promising technique to realize augmented reality on future light-weight glasses is to offload computationally extensive rendering tasks to the cloud. This however places considerable demands on the network as well as the air interface with respect to latency, reliability and throughput. For evaluation of these architectures and for traffic modelling, a dataset is provided, which contains realistic payloads of cloud-rendered augmented reality in form of video files.
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Nextmed project is a software platform for the segmentation and visualization of medical images. It consist on a series of different automatic segmentation algorithms for different anatomical structures and a platform for the visualization of the results as 3D models.
This dataset contains the .obj and .nrrd files that correspond to the results of applying our automatic lung segmentation algorithm to the LIDC-IDRI dataset.
This dataset relates to 718 of the 1012 LIDC-IDRI scans.
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