1,979 research outputs found
Towards High-Frequency Tracking and Fast Edge-Aware Optimization
This dissertation advances the state of the art for AR/VR tracking systems by
increasing the tracking frequency by orders of magnitude and proposes an
efficient algorithm for the problem of edge-aware optimization.
AR/VR is a natural way of interacting with computers, where the physical and
digital worlds coexist. We are on the cusp of a radical change in how humans
perform and interact with computing. Humans are sensitive to small
misalignments between the real and the virtual world, and tracking at
kilo-Hertz frequencies becomes essential. Current vision-based systems fall
short, as their tracking frequency is implicitly limited by the frame-rate of
the camera. This thesis presents a prototype system which can track at orders
of magnitude higher than the state-of-the-art methods using multiple commodity
cameras. The proposed system exploits characteristics of the camera
traditionally considered as flaws, namely rolling shutter and radial
distortion. The experimental evaluation shows the effectiveness of the method
for various degrees of motion.
Furthermore, edge-aware optimization is an indispensable tool in the computer
vision arsenal for accurate filtering of depth-data and image-based rendering,
which is increasingly being used for content creation and geometry processing
for AR/VR. As applications increasingly demand higher resolution and speed,
there exists a need to develop methods that scale accordingly. This
dissertation proposes such an edge-aware optimization framework which is
efficient, accurate, and algorithmically scales well, all of which are much
desirable traits not found jointly in the state of the art. The experiments
show the effectiveness of the framework in a multitude of computer vision tasks
such as computational photography and stereo.Comment: PhD thesi
USB-NeRF: Unrolling Shutter Bundle Adjusted Neural Radiance Fields
Neural Radiance Fields (NeRF) has received much attention recently due to its
impressive capability to represent 3D scene and synthesize novel view images.
Existing works usually assume that the input images are captured by a global
shutter camera. Thus, rolling shutter (RS) images cannot be trivially applied
to an off-the-shelf NeRF algorithm for novel view synthesis. Rolling shutter
effect would also affect the accuracy of the camera pose estimation (e.g. via
COLMAP), which further prevents the success of NeRF algorithm with RS images.
In this paper, we propose Unrolling Shutter Bundle Adjusted Neural Radiance
Fields (USB-NeRF). USB-NeRF is able to correct rolling shutter distortions and
recover accurate camera motion trajectory simultaneously under the framework of
NeRF, by modeling the physical image formation process of a RS camera.
Experimental results demonstrate that USB-NeRF achieves better performance
compared to prior works, in terms of RS effect removal, novel view image
synthesis as well as camera motion estimation. Furthermore, our algorithm can
also be used to recover high-fidelity high frame-rate global shutter video from
a sequence of RS images
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