3,360 research outputs found
Spherical clustering of users navigating 360{\deg} content
In Virtual Reality (VR) applications, understanding how users explore the
omnidirectional content is important to optimize content creation, to develop
user-centric services, or even to detect disorders in medical applications.
Clustering users based on their common navigation patterns is a first direction
to understand users behaviour. However, classical clustering techniques fail in
identifying these common paths, since they are usually focused on minimizing a
simple distance metric. In this paper, we argue that minimizing the distance
metric does not necessarily guarantee to identify users that experience similar
navigation path in the VR domain. Therefore, we propose a graph-based method to
identify clusters of users who are attending the same portion of the spherical
content over time. The proposed solution takes into account the spherical
geometry of the content and aims at clustering users based on the actual
overlap of displayed content among users. Our method is tested on real VR user
navigation patterns. Results show that our solution leads to clusters in which
at least 85% of the content displayed by one user is shared among the other
users belonging to the same cluster.Comment: 5 pages, conference (Published in: ICASSP 2019 - 2019 IEEE
International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Calibration by correlation using metric embedding from non-metric similarities
This paper presents a new intrinsic calibration method that allows us to calibrate a generic single-view point camera just
by waving it around. From the video sequence obtained while the camera undergoes random motion, we compute the pairwise time
correlation of the luminance signal for a subset of the pixels. We show that, if the camera undergoes a random uniform motion, then
the pairwise correlation of any pixels pair is a function of the distance between the pixel directions on the visual sphere. This leads to
formalizing calibration as a problem of metric embedding from non-metric measurements: we want to find the disposition of pixels on
the visual sphere from similarities that are an unknown function of the distances. This problem is a generalization of multidimensional
scaling (MDS) that has so far resisted a comprehensive observability analysis (can we reconstruct a metrically accurate embedding?)
and a solid generic solution (how to do so?). We show that the observability depends both on the local geometric properties (curvature)
as well as on the global topological properties (connectedness) of the target manifold. We show that, in contrast to the Euclidean case,
on the sphere we can recover the scale of the points distribution, therefore obtaining a metrically accurate solution from non-metric
measurements. We describe an algorithm that is robust across manifolds and can recover a metrically accurate solution when the metric
information is observable. We demonstrate the performance of the algorithm for several cameras (pin-hole, fish-eye, omnidirectional),
and we obtain results comparable to calibration using classical methods. Additional synthetic benchmarks show that the algorithm
performs as theoretically predicted for all corner cases of the observability analysis
A Comprehensive Comparison of Projections in Omnidirectional Super-Resolution
Super-Resolution (SR) has gained increasing research attention over the past
few years. With the development of Deep Neural Networks (DNNs), many
super-resolution methods based on DNNs have been proposed. Although most of
these methods are aimed at ordinary frames, there are few works on
super-resolution of omnidirectional frames. In these works, omnidirectional
frames are projected from the 3D sphere to a 2D plane by Equi-Rectangular
Projection (ERP). Although ERP has been widely used for projection, it has
severe projection distortion near poles. Current DNN-based SR methods use 2D
convolution modules, which is more suitable for the regular grid. In this
paper, we find that different projection methods have great impact on the
performance of DNNs. To study this problem, a comprehensive comparison of
projections in omnidirectional super-resolution is conducted. We compare the SR
results of different projection methods. Experimental results show that
Equi-Angular cube map projection (EAC), which has minimal distortion, achieves
the best result in terms of WS-PSNR compared with other projections. Code and
data will be released.Comment: Accepted to ICASSP202
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