20,423 research outputs found
Video Frame Interpolation via Adaptive Separable Convolution
Standard video frame interpolation methods first estimate optical flow
between input frames and then synthesize an intermediate frame guided by
motion. Recent approaches merge these two steps into a single convolution
process by convolving input frames with spatially adaptive kernels that account
for motion and re-sampling simultaneously. These methods require large kernels
to handle large motion, which limits the number of pixels whose kernels can be
estimated at once due to the large memory demand. To address this problem, this
paper formulates frame interpolation as local separable convolution over input
frames using pairs of 1D kernels. Compared to regular 2D kernels, the 1D
kernels require significantly fewer parameters to be estimated. Our method
develops a deep fully convolutional neural network that takes two input frames
and estimates pairs of 1D kernels for all pixels simultaneously. Since our
method is able to estimate kernels and synthesizes the whole video frame at
once, it allows for the incorporation of perceptual loss to train the neural
network to produce visually pleasing frames. This deep neural network is
trained end-to-end using widely available video data without any human
annotation. Both qualitative and quantitative experiments show that our method
provides a practical solution to high-quality video frame interpolation.Comment: ICCV 2017, http://graphics.cs.pdx.edu/project/sepconv
Wireless Software Synchronization of Multiple Distributed Cameras
We present a method for precisely time-synchronizing the capture of image
sequences from a collection of smartphone cameras connected over WiFi. Our
method is entirely software-based, has only modest hardware requirements, and
achieves an accuracy of less than 250 microseconds on unmodified commodity
hardware. It does not use image content and synchronizes cameras prior to
capture. The algorithm operates in two stages. In the first stage, we designate
one device as the leader and synchronize each client device's clock to it by
estimating network delay. Once clocks are synchronized, the second stage
initiates continuous image streaming, estimates the relative phase of image
timestamps between each client and the leader, and shifts the streams into
alignment. We quantitatively validate our results on a multi-camera rig imaging
a high-precision LED array and qualitatively demonstrate significant
improvements to multi-view stereo depth estimation and stitching of dynamic
scenes. We release as open source 'libsoftwaresync', an Android implementation
of our system, to inspire new types of collective capture applications.Comment: Main: 9 pages, 10 figures. Supplemental: 3 pages, 5 figure
- …