866 research outputs found
Graph-Based Classification of Omnidirectional Images
Omnidirectional cameras are widely used in such areas as robotics and virtual
reality as they provide a wide field of view. Their images are often processed
with classical methods, which might unfortunately lead to non-optimal solutions
as these methods are designed for planar images that have different geometrical
properties than omnidirectional ones. In this paper we study image
classification task by taking into account the specific geometry of
omnidirectional cameras with graph-based representations. In particular, we
extend deep learning architectures to data on graphs; we propose a principled
way of graph construction such that convolutional filters respond similarly for
the same pattern on different positions of the image regardless of lens
distortions. Our experiments show that the proposed method outperforms current
techniques for the omnidirectional image classification problem
Omnidirectional Stereo Vision for Autonomous Vehicles
Environment perception with cameras is an important requirement for many applications for autonomous vehicles and robots. This work presents a stereoscopic omnidirectional camera system for autonomous vehicles which resolves the problem of a limited field of view and provides a 360° panoramic view of the environment. We present a new projection model for these cameras and show that the camera setup overcomes major drawbacks of traditional perspective cameras in many applications
3D Scene Geometry Estimation from 360 Imagery: A Survey
This paper provides a comprehensive survey on pioneer and state-of-the-art 3D
scene geometry estimation methodologies based on single, two, or multiple
images captured under the omnidirectional optics. We first revisit the basic
concepts of the spherical camera model, and review the most common acquisition
technologies and representation formats suitable for omnidirectional (also
called 360, spherical or panoramic) images and videos. We then survey
monocular layout and depth inference approaches, highlighting the recent
advances in learning-based solutions suited for spherical data. The classical
stereo matching is then revised on the spherical domain, where methodologies
for detecting and describing sparse and dense features become crucial. The
stereo matching concepts are then extrapolated for multiple view camera setups,
categorizing them among light fields, multi-view stereo, and structure from
motion (or visual simultaneous localization and mapping). We also compile and
discuss commonly adopted datasets and figures of merit indicated for each
purpose and list recent results for completeness. We conclude this paper by
pointing out current and future trends.Comment: Published in ACM Computing Survey
Occlusion Handling using Semantic Segmentation and Visibility-Based Rendering for Mixed Reality
Real-time occlusion handling is a major problem in outdoor mixed reality
system because it requires great computational cost mainly due to the
complexity of the scene. Using only segmentation, it is difficult to accurately
render a virtual object occluded by complex objects such as trees, bushes etc.
In this paper, we propose a novel occlusion handling method for real-time,
outdoor, and omni-directional mixed reality system using only the information
from a monocular image sequence. We first present a semantic segmentation
scheme for predicting the amount of visibility for different type of objects in
the scene. We also simultaneously calculate a foreground probability map using
depth estimation derived from optical flow. Finally, we combine the
segmentation result and the probability map to render the computer generated
object and the real scene using a visibility-based rendering method. Our
results show great improvement in handling occlusions compared to existing
blending based methods
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