75,369 research outputs found
Adaptive User Perspective Rendering for Handheld Augmented Reality
Handheld Augmented Reality commonly implements some variant of magic lens
rendering, which turns only a fraction of the user's real environment into AR
while the rest of the environment remains unaffected. Since handheld AR devices
are commonly equipped with video see-through capabilities, AR magic lens
applications often suffer from spatial distortions, because the AR environment
is presented from the perspective of the camera of the mobile device. Recent
approaches counteract this distortion based on estimations of the user's head
position, rendering the scene from the user's perspective. To this end,
approaches usually apply face-tracking algorithms on the front camera of the
mobile device. However, this demands high computational resources and therefore
commonly affects the performance of the application beyond the already high
computational load of AR applications. In this paper, we present a method to
reduce the computational demands for user perspective rendering by applying
lightweight optical flow tracking and an estimation of the user's motion before
head tracking is started. We demonstrate the suitability of our approach for
computationally limited mobile devices and we compare it to device perspective
rendering, to head tracked user perspective rendering, as well as to fixed
point of view user perspective rendering
Morphing a Stereogram into Hologram
This paper develops a simple and fast method to reconstruct reality from
stereoscopic images. We bring together ideas from robust optical flow
techniques, morphing deformations and lightfield 3D rendering in order to
create unsupervised multiview images of a scene. The reconstruction algorithm
provides a good visualization of the virtual 3D imagery behind stereograms upon
display on a headset-free Looking Glass 3D monitor. We discuss the possibility
of applying the method for live 3D streaming optimized via an associated lookup
table.Comment: PDF, 8 pages, 4 Fig
BlinkFlow: A Dataset to Push the Limits of Event-based Optical Flow Estimation
Event cameras provide high temporal precision, low data rates, and high
dynamic range visual perception, which are well-suited for optical flow
estimation. While data-driven optical flow estimation has obtained great
success in RGB cameras, its generalization performance is seriously hindered in
event cameras mainly due to the limited and biased training data. In this
paper, we present a novel simulator, BlinkSim, for the fast generation of
large-scale data for event-based optical flow. BlinkSim consists of a
configurable rendering engine and a flexible engine for event data simulation.
By leveraging the wealth of current 3D assets, the rendering engine enables us
to automatically build up thousands of scenes with different objects, textures,
and motion patterns and render very high-frequency images for realistic event
data simulation. Based on BlinkSim, we construct a large training dataset and
evaluation benchmark BlinkFlow that contains sufficient, diversiform, and
challenging event data with optical flow ground truth. Experiments show that
BlinkFlow improves the generalization performance of state-of-the-art methods
by more than 40% on average and up to 90%. Moreover, we further propose an
Event optical Flow transFormer (E-FlowFormer) architecture. Powered by our
BlinkFlow, E-FlowFormer outperforms the SOTA methods by up to 91% on MVSEC
dataset and 14% on DSEC dataset and presents the best generalization
performance
ADFactory: An Effective Framework for Generalizing Optical Flow with Nerf
A significant challenge facing current optical flow methods is the difficulty
in generalizing them well to the real world. This is mainly due to the high
cost of hand-crafted datasets, and existing self-supervised methods are limited
by indirect loss and occlusions, resulting in fuzzy outcomes. To address this
challenge, we introduce a novel optical flow training framework: automatic data
factory (ADF). ADF only requires RGB images as input to effectively train the
optical flow network on the target data domain. Specifically, we use advanced
Nerf technology to reconstruct scenes from photo groups collected by a
monocular camera, and then calculate optical flow labels between camera pose
pairs based on the rendering results. To eliminate erroneous labels caused by
defects in the scene reconstructed by Nerf, we screened the generated labels
from multiple aspects, such as optical flow matching accuracy, radiation field
confidence, and depth consistency. The filtered labels can be directly used for
network supervision. Experimentally, the generalization ability of ADF on KITTI
surpasses existing self-supervised optical flow and monocular scene flow
algorithms. In addition, ADF achieves impressive results in real-world
zero-point generalization evaluations and surpasses most supervised methods.Comment: 8 page
High-speed Video from Asynchronous Camera Array
This paper presents a method for capturing high-speed video using an
asynchronous camera array. Our method sequentially fires each sensor in a
camera array with a small time offset and assembles captured frames into a
high-speed video according to the time stamps. The resulting video, however,
suffers from parallax jittering caused by the viewpoint difference among
sensors in the camera array. To address this problem, we develop a dedicated
novel view synthesis algorithm that transforms the video frames as if they were
captured by a single reference sensor. Specifically, for any frame from a
non-reference sensor, we find the two temporally neighboring frames captured by
the reference sensor. Using these three frames, we render a new frame with the
same time stamp as the non-reference frame but from the viewpoint of the
reference sensor. Specifically, we segment these frames into super-pixels and
then apply local content-preserving warping to warp them to form the new frame.
We employ a multi-label Markov Random Field method to blend these warped
frames. Our experiments show that our method can produce high-quality and
high-speed video of a wide variety of scenes with large parallax, scene
dynamics, and camera motion and outperforms several baseline and
state-of-the-art approaches.Comment: 10 pages, 82 figures, Published at IEEE WACV 201
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