4,917 research outputs found
In-process surface profile assessment of rotary machined timber using a dynamic photometric stereo technique
Machining operations have advanced in speed and there is an increasing demand for higher quality surface finish. It is
therefore necessary to develop real-time surface inspection techniques which will provide sensory information for controlling
the machining processes. This paper describes a practical method for real-time analysis of planed wood using the
photometric stereo technique. Earlier research has shown that the technique is very effective in assessing surface waviness
on static wood samples. In this paper, the photometric stereo method is extended to real industrial applications
where samples are subjected to rapid movements. Surface profiles extracted from the dynamic photometric stereo
method are compared with those from the static measurements and the results show that there is a high correlation
between the two methods
Photometric reconstruction of a dynamic textured surface from just one color image acquisition
http://www.opticsinfobase.org/josaa/abstract.cfm?msid=85528 This article has been selected for inclusion in the Virtual Journal for Biomedical Optics (Vol. 3, Iss. 4)International audienceTextured surface analysis is essential for many applications. We present a three-dimensional recovery approach for real textured surfaces based on photometric stereo. The aim is to be able to measure the textured surfaces with a high degree of accuracy. For this, we use a color digital sensor and principles of color photometric stereo. This method uses a single color image, instead of a sequence of gray-scale images, to recover the surface of the three dimensions. It can thus be integrated into dynamic systems where there is significant relative motion between the object and the camera. To evaluate the performances of our method, we compare it on real textured surfaces to traditional photometric stereo using three images. We show thus that it is possible to have similar results with just one color image
Driven to Distraction: Self-Supervised Distractor Learning for Robust Monocular Visual Odometry in Urban Environments
We present a self-supervised approach to ignoring "distractors" in camera
images for the purposes of robustly estimating vehicle motion in cluttered
urban environments. We leverage offline multi-session mapping approaches to
automatically generate a per-pixel ephemerality mask and depth map for each
input image, which we use to train a deep convolutional network. At run-time we
use the predicted ephemerality and depth as an input to a monocular visual
odometry (VO) pipeline, using either sparse features or dense photometric
matching. Our approach yields metric-scale VO using only a single camera and
can recover the correct egomotion even when 90% of the image is obscured by
dynamic, independently moving objects. We evaluate our robust VO methods on
more than 400km of driving from the Oxford RobotCar Dataset and demonstrate
reduced odometry drift and significantly improved egomotion estimation in the
presence of large moving vehicles in urban traffic.Comment: International Conference on Robotics and Automation (ICRA), 2018.
Video summary: http://youtu.be/ebIrBn_nc-
Event Fusion Photometric Stereo Network
We present a novel method to estimate the surface normal of an object in an
ambient light environment using RGB and event cameras. Modern photometric
stereo methods rely on an RGB camera, mainly in a dark room, to avoid ambient
illumination. To alleviate the limitations of the darkroom environment and to
use essential light information, we employ an event camera with a high dynamic
range and low latency. This is the first study that uses an event camera for
the photometric stereo task, which works on continuous light sources and
ambient light environment. In this work, we also curate a novel photometric
stereo dataset that is constructed by capturing objects with event and RGB
cameras under numerous ambient lights environment. Additionally, we propose a
novel framework named Event Fusion Photometric Stereo Network~(EFPS-Net), which
estimates the surface normals of an object using both RGB frames and event
signals. Our proposed method interpolates event observation maps that generate
light information with sparse event signals to acquire fluent light
information. Subsequently, the event-interpolated observation maps are fused
with the RGB observation maps. Our numerous experiments showed that EFPS-Net
outperforms state-of-the-art methods on a dataset captured in the real world
where ambient lights exist. Consequently, we demonstrate that incorporating
additional modalities with EFPS-Net alleviates the limitations that occurred
from ambient illumination.Comment: 33 pages, 11 figure
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