4,958 research outputs found
Unsupervised Learning of Depth and Ego-Motion from Cylindrical Panoramic Video
We introduce a convolutional neural network model for unsupervised learning
of depth and ego-motion from cylindrical panoramic video. Panoramic depth
estimation is an important technology for applications such as virtual reality,
3D modeling, and autonomous robotic navigation. In contrast to previous
approaches for applying convolutional neural networks to panoramic imagery, we
use the cylindrical panoramic projection which allows for the use of the
traditional CNN layers such as convolutional filters and max pooling without
modification. Our evaluation of synthetic and real data shows that unsupervised
learning of depth and ego-motion on cylindrical panoramic images can produce
high-quality depth maps and that an increased field-of-view improves ego-motion
estimation accuracy. We also introduce Headcam, a novel dataset of panoramic
video collected from a helmet-mounted camera while biking in an urban setting.Comment: Accepted to IEEE AIVR 201
360MonoDepth: High-Resolution 360° Monocular Depth Estimation
360{\deg} cameras can capture complete environments in a single shot, which
makes 360{\deg} imagery alluring in many computer vision tasks. However,
monocular depth estimation remains a challenge for 360{\deg} data, particularly
for high resolutions like 2K (2048x1024) and beyond that are important for
novel-view synthesis and virtual reality applications. Current CNN-based
methods do not support such high resolutions due to limited GPU memory. In this
work, we propose a flexible framework for monocular depth estimation from
high-resolution 360{\deg} images using tangent images. We project the 360{\deg}
input image onto a set of tangent planes that produce perspective views, which
are suitable for the latest, most accurate state-of-the-art perspective
monocular depth estimators. To achieve globally consistent disparity estimates,
we recombine the individual depth estimates using deformable multi-scale
alignment followed by gradient-domain blending. The result is a dense,
high-resolution 360{\deg} depth map with a high level of detail, also for
outdoor scenes which are not supported by existing methods. Our source code and
data are available at https://manurare.github.io/360monodepth/.Comment: CVPR 2022. Project page: https://manurare.github.io/360monodepth
360° Optical Flow using Tangent Images
Omnidirectional 360° images have found many promising and exciting applications in computer vision, robotics and other fields, thanks to their increasing affordability, portability and their 360° field of view. The most common format for storing, processing and visualising 360° images is equirectangular projection (ERP). However, the distortion introduced by the nonlinear mapping from 360° images to ERP images is still a barrier that holds back ERP images from being used as easily as conventional perspective images. This is especially relevant when estimating 360° optical flow, as the distortions need to be mitigated appropriately. In this paper, we propose a 360° optical flow method based on tangent images. Our method leverages gnomonic projection to locally convert ERP images to perspective images, and uniformly samples the ERP image by projection to a cubemap and regular icosahedron faces, to incrementally refine the estimated 360° flow fields even in the presence of large rotations. Our experiments demonstrate the benefits of our proposed method both quantitatively and qualitatively
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