443 research outputs found
Photometric Depth Super-Resolution
This study explores the use of photometric techniques (shape-from-shading and
uncalibrated photometric stereo) for upsampling the low-resolution depth map
from an RGB-D sensor to the higher resolution of the companion RGB image. A
single-shot variational approach is first put forward, which is effective as
long as the target's reflectance is piecewise-constant. It is then shown that
this dependency upon a specific reflectance model can be relaxed by focusing on
a specific class of objects (e.g., faces), and delegate reflectance estimation
to a deep neural network. A multi-shot strategy based on randomly varying
lighting conditions is eventually discussed. It requires no training or prior
on the reflectance, yet this comes at the price of a dedicated acquisition
setup. Both quantitative and qualitative evaluations illustrate the
effectiveness of the proposed methods on synthetic and real-world scenarios.Comment: IEEE Transactions on Pattern Analysis and Machine Intelligence
(T-PAMI), 2019. First three authors contribute equall
Depth Super-Resolution Meets Uncalibrated Photometric Stereo
A novel depth super-resolution approach for RGB-D sensors is presented. It
disambiguates depth super-resolution through high-resolution photometric clues
and, symmetrically, it disambiguates uncalibrated photometric stereo through
low-resolution depth cues. To this end, an RGB-D sequence is acquired from the
same viewing angle, while illuminating the scene from various uncalibrated
directions. This sequence is handled by a variational framework which fits
high-resolution shape and reflectance, as well as lighting, to both the
low-resolution depth measurements and the high-resolution RGB ones. The key
novelty consists in a new PDE-based photometric stereo regularizer which
implicitly ensures surface regularity. This allows to carry out depth
super-resolution in a purely data-driven manner, without the need for any
ad-hoc prior or material calibration. Real-world experiments are carried out
using an out-of-the-box RGB-D sensor and a hand-held LED light source.Comment: International Conference on Computer Vision (ICCV) Workshop, 201
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
Artificial intelligence for advanced manufacturing quality
100 p.This Thesis addresses the challenge of AI-based image quality control systems applied to manufacturing industry, aiming to improve this field through the use of advanced techniques for data acquisition and processing, in order to obtain robust, reliable and optimal systems. This Thesis presents contributions onthe use of complex data acquisition techniques, the application and design of specialised neural networks for the defect detection, and the integration and validation of these systems in production processes. It has been developed in the context of several applied research projects that provided a practical feedback of the usefulness of the proposed computational advances as well as real life data for experimental validation
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