9,112 research outputs found
Micro Fourier Transform Profilometry (FTP): 3D shape measurement at 10,000 frames per second
Recent advances in imaging sensors and digital light projection technology
have facilitated a rapid progress in 3D optical sensing, enabling 3D surfaces
of complex-shaped objects to be captured with improved resolution and accuracy.
However, due to the large number of projection patterns required for phase
recovery and disambiguation, the maximum fame rates of current 3D shape
measurement techniques are still limited to the range of hundreds of frames per
second (fps). Here, we demonstrate a new 3D dynamic imaging technique, Micro
Fourier Transform Profilometry (FTP), which can capture 3D surfaces of
transient events at up to 10,000 fps based on our newly developed high-speed
fringe projection system. Compared with existing techniques, FTP has the
prominent advantage of recovering an accurate, unambiguous, and dense 3D point
cloud with only two projected patterns. Furthermore, the phase information is
encoded within a single high-frequency fringe image, thereby allowing
motion-artifact-free reconstruction of transient events with temporal
resolution of 50 microseconds. To show FTP's broad utility, we use it to
reconstruct 3D videos of 4 transient scenes: vibrating cantilevers, rotating
fan blades, bullet fired from a toy gun, and balloon's explosion triggered by a
flying dart, which were previously difficult or even unable to be captured with
conventional approaches.Comment: This manuscript was originally submitted on 30th January 1
Kinect Range Sensing: Structured-Light versus Time-of-Flight Kinect
Recently, the new Kinect One has been issued by Microsoft, providing the next
generation of real-time range sensing devices based on the Time-of-Flight (ToF)
principle. As the first Kinect version was using a structured light approach,
one would expect various differences in the characteristics of the range data
delivered by both devices. This paper presents a detailed and in-depth
comparison between both devices. In order to conduct the comparison, we propose
a framework of seven different experimental setups, which is a generic basis
for evaluating range cameras such as Kinect. The experiments have been designed
with the goal to capture individual effects of the Kinect devices as isolatedly
as possible and in a way, that they can also be adopted, in order to apply them
to any other range sensing device. The overall goal of this paper is to provide
a solid insight into the pros and cons of either device. Thus, scientists that
are interested in using Kinect range sensing cameras in their specific
application scenario can directly assess the expected, specific benefits and
potential problem of either device.Comment: 58 pages, 23 figures. Accepted for publication in Computer Vision and
Image Understanding (CVIU
Temporal shape super-resolution by intra-frame motion encoding using high-fps structured light
One of the solutions of depth imaging of moving scene is to project a static
pattern on the object and use just a single image for reconstruction. However,
if the motion of the object is too fast with respect to the exposure time of
the image sensor, patterns on the captured image are blurred and reconstruction
fails. In this paper, we impose multiple projection patterns into each single
captured image to realize temporal super resolution of the depth image
sequences. With our method, multiple patterns are projected onto the object
with higher fps than possible with a camera. In this case, the observed pattern
varies depending on the depth and motion of the object, so we can extract
temporal information of the scene from each single image. The decoding process
is realized using a learning-based approach where no geometric calibration is
needed. Experiments confirm the effectiveness of our method where sequential
shapes are reconstructed from a single image. Both quantitative evaluations and
comparisons with recent techniques were also conducted.Comment: 9 pages, Published at the International Conference on Computer Vision
(ICCV 2017
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