168 research outputs found
A performance analysis of dense stereo correspondence algorithms and error reduction techniques
Abstract: Dense stereo correspondence has been intensely studied and there exists a wide variety of proposed solutions in the literature. Different datasets have been constructed to test stereo algorithms, however, their ground truth formation and scene types vary. In this paper, state-of-the-art algorithms are compared using a number of datasets captured under varied conditions, with accuracy and density metrics forming the basis of a performance evaluation. Pre- and post-processing disparity map error reduction techniques are quantified
Real-time Visual Flow Algorithms for Robotic Applications
Vision offers important sensor cues to modern robotic platforms.
Applications such as control of aerial vehicles, visual servoing,
simultaneous localization and mapping, navigation and more
recently, learning, are examples where visual information is
fundamental to accomplish tasks. However, the use of computer
vision algorithms carries the computational cost of extracting
useful information from the stream of raw pixel data. The most
sophisticated algorithms use complex mathematical formulations
leading typically to computationally expensive, and consequently,
slow implementations. Even with modern computing resources,
high-speed and high-resolution video feed can only be used for
basic image processing operations. For a vision algorithm to be
integrated on a robotic system, the output of the algorithm
should be provided in real time, that is, at least at the same
frequency as the control logic of the robot. With robotic
vehicles becoming more dynamic and ubiquitous, this places higher
requirements to the vision processing pipeline.
This thesis addresses the problem of estimating dense visual flow
information in real time. The contributions of this work are
threefold. First, it introduces a new filtering algorithm for the
estimation of dense optical flow at frame rates as fast as 800 Hz
for 640x480 image resolution. The algorithm follows a
update-prediction architecture to estimate dense optical flow
fields incrementally over time. A fundamental component of the
algorithm is the modeling of the spatio-temporal evolution of the
optical flow field by means of partial differential equations.
Numerical predictors can implement such PDEs to propagate current
estimation of flow forward in time. Experimental validation of
the algorithm is provided using high-speed ground truth image
dataset as well as real-life video data at 300 Hz.
The second contribution is a new type of visual flow named
structure flow. Mathematically, structure flow is the
three-dimensional scene flow scaled by the inverse depth at each
pixel in the image. Intuitively, it is the complete velocity
field associated with image motion, including both optical flow
and scale-change or apparent divergence of the image. Analogously
to optic flow, structure flow provides a robotic vehicle with
perception of the motion of the environment as seen by the
camera. However, structure flow encodes the full 3D image motion
of the scene whereas optic flow only encodes the component on the
image plane. An algorithm to estimate structure flow from image
and depth measurements is proposed based on the same filtering
idea used to estimate optical flow.
The final contribution is the spherepix data structure for
processing spherical images. This data structure is the numerical
back-end used for the real-time implementation of the structure
flow filter. It consists of a set of overlapping patches covering
the surface of the sphere. Each individual patch approximately
holds properties such as orthogonality and equidistance of
points, thus allowing efficient implementations of low-level
classical 2D convolution based image processing routines such as
Gaussian filters and numerical derivatives.
These algorithms are implemented on GPU hardware and can be
integrated to future Robotic Embedded Vision systems to provide
fast visual information to robotic vehicles
INTERMEDIATE VIEW RECONSTRUCTION FOR MULTISCOPIC 3D DISPLAY
This thesis focuses on Intermediate View Reconstruction (IVR) which generates additional images from the available stereo images. The main application of IVR is to generate the content of multiscopic 3D displays, and it can be applied to generate different viewpoints to Free-viewpoint TV (FTV). Although IVR is considered a good approach to generate additional images, there are some problems with the reconstruction process, such as detecting and handling the occlusion areas, preserving the discontinuity at edges, and reducing image artifices through formation of the texture of the intermediate image. The occlusion area is defined as the visibility of such an area in one image and its disappearance in the other one. Solving IVR problems is considered a significant challenge for researchers.
In this thesis, several novel algorithms have been specifically designed to solve IVR challenges by employing them in a highly robust intermediate view reconstruction
algorithm. Computer simulation and experimental results confirm the importance of occluded areas in IVR. Therefore, we propose a novel occlusion detection algorithm and another novel algorithm to Inpaint those areas. Then, these proposed algorithms are employed in a novel occlusion-aware intermediate view reconstruction that finds an intermediate image with a given disparity between two input images. This novelty is addressed by adding occlusion awareness to the reconstruction algorithm and proposing three quality improvement techniques to reduce image artifices: filling the re-sampling holes, removing ghost contours, and handling the disocclusion area.
We compared the proposed algorithms to the previously well-known algorithms on each field qualitatively and quantitatively. The obtained results show that our algorithms are superior to the previous well-known algorithms. The performance of the proposed reconstruction algorithm is tested under 13 real images and 13 synthetic images. Moreover, analysis of a human-trial experiment conducted with 21 participants confirmed that the reconstructed images from our proposed algorithm have very high quality compared with the reconstructed images from the other existing algorithms
Combining Features and Semantics for Low-level Computer Vision
Visual perception of depth and motion plays a significant role in understanding and navigating the environment.
Reconstructing outdoor scenes in 3D and estimating the motion from video cameras are of utmost importance for applications like autonomous driving.
The corresponding problems in computer vision have witnessed tremendous progress over the last decades, yet some aspects still remain challenging today. Striking examples are reflecting and textureless surfaces or large motions which cannot be easily recovered using traditional local methods. Further challenges include occlusions, large distortions and difficult lighting conditions. In this thesis, we propose to overcome these challenges by modeling non-local interactions leveraging semantics and contextual information.
Firstly, for binocular stereo estimation, we propose to regularize over larger areas on the image using object-category specific disparity proposals which we sample using inverse graphics techniques based on a sparse disparity estimate and a semantic segmentation of the image. The disparity proposals encode the fact that objects of certain categories are not arbitrarily shaped but typically exhibit regular structures. We integrate them as non-local regularizer for the challenging object class 'car' into a superpixel-based graphical model and demonstrate its benefits especially in reflective regions.
Secondly, for 3D reconstruction, we leverage the fact that the larger the reconstructed area, the more likely objects of similar type and shape will occur in the scene. This is particularly true for outdoor scenes where buildings and vehicles often suffer from missing texture or reflections, but share similarity in 3D shape. We take advantage of this shape similarity by localizing objects using detectors and jointly reconstructing them while learning a volumetric model of their shape. This allows to reduce noise while completing missing surfaces as objects of similar shape benefit from all observations for the respective category. Evaluations with respect to LIDAR ground-truth on a novel challenging suburban dataset show the advantages of modeling structural dependencies between objects.
Finally, motivated by the success of deep learning techniques in matching problems, we present a method for learning context-aware features for solving optical flow using discrete optimization. Towards this goal, we present an efficient way of training a context network with a large receptive field size on top of a local network using dilated convolutions on patches. We perform feature matching by comparing each pixel in the reference image to every pixel in the target image, utilizing fast GPU matrix multiplication. The matching cost volume from the network's output forms the data term for discrete MAP inference in a pairwise Markov random field. Extensive evaluations reveal the importance of context for feature matching.Die visuelle Wahrnehmung von Tiefe und Bewegung spielt eine wichtige Rolle bei dem VerstĂ€ndnis und der Navigation in unserer Umwelt. Die 3D Rekonstruktion von Szenen im Freien und die SchĂ€tzung der Bewegung von Videokameras sind von gröĂter Bedeutung fĂŒr Anwendungen, wie das autonome Fahren.
Die Erforschung der entsprechenden Probleme des maschinellen Sehens hat in den letzten Jahrzehnten enorme Fortschritte gemacht, jedoch bleiben einige Aspekte heute noch ungelöst. Beispiele hierfĂŒr sind reflektierende und texturlose OberflĂ€chen oder groĂe Bewegungen, bei denen herkömmliche lokale Methoden hĂ€ufig scheitern. Weitere Herausforderungen sind niedrige Bildraten, Verdeckungen, groĂe Verzerrungen und schwierige LichtverhĂ€ltnisse. In dieser Arbeit schlagen wir vor nicht-lokale Interaktionen zu modellieren, die semantische und kontextbezogene Informationen nutzen, um diese Herausforderungen zu meistern.
FĂŒr die binokulare Stereo SchĂ€tzung schlagen wir zuallererst vor zusammenhĂ€ngende Bereiche mit objektklassen-spezifischen DisparitĂ€ts VorschlĂ€gen zu regularisieren, die wir mit inversen Grafik Techniken auf der Grundlage einer spĂ€rlichen DisparitĂ€tsschĂ€tzung und semantischen Segmentierung des Bildes erhalten. Die DisparitĂ€ts VorschlĂ€ge kodieren die Tatsache, dass die GegenstĂ€nde bestimmter Kategorien nicht willkĂŒrlich geformt sind, sondern typischerweise regelmĂ€Ăige Strukturen aufweisen. Wir integrieren sie fĂŒr die komplexe Objektklasse 'Auto' in Form eines nicht-lokalen Regularisierungsterm in ein Superpixel-basiertes grafisches Modell und zeigen die Vorteile vor allem in reflektierenden Bereichen.
Zweitens nutzen wir fĂŒr die 3D-Rekonstruktion die Tatsache, dass mit der GröĂe der rekonstruierten FlĂ€che auch die Wahrscheinlichkeit steigt, Objekte von Ă€hnlicher Art und Form in der Szene zu enthalten. Dies gilt besonders fĂŒr Szenen im Freien, in denen GebĂ€ude und Fahrzeuge oft vorkommen, die unter fehlender Textur oder Reflexionen leiden aber Ă€hnlichkeit in der Form aufweisen. Wir nutzen diese Ă€hnlichkeiten zur Lokalisierung von Objekten mit Detektoren und zur gemeinsamen Rekonstruktion indem ein volumetrisches Modell ihrer Form erlernt wird. Dies ermöglicht auftretendes Rauschen zu reduzieren, wĂ€hrend fehlende FlĂ€chen vervollstĂ€ndigt werden, da Objekte Ă€hnlicher Form von allen Beobachtungen der jeweiligen Kategorie profitieren. Die Evaluierung auf einem neuen, herausfordernden vorstĂ€dtischen Datensatz in Anbetracht von LIDAR-Entfernungsdaten zeigt die Vorteile der Modellierung von strukturellen AbhĂ€ngigkeiten zwischen Objekten.
Zuletzt, motiviert durch den Erfolg von Deep Learning Techniken bei der Mustererkennung, prĂ€sentieren wir eine Methode zum Erlernen von kontextbezogenen Merkmalen zur Lösung des optischen Flusses mittels diskreter Optimierung. Dazu stellen wir eine effiziente Methode vor um zusĂ€tzlich zu einem Lokalen Netzwerk ein Kontext-Netzwerk zu erlernen, das mit Hilfe von erweiterter Faltung auf Patches ein groĂes rezeptives Feld besitzt. FĂŒr das Feature Matching vergleichen wir mit schnellen GPU-Matrixmultiplikation jedes Pixel im Referenzbild mit jedem Pixel im Zielbild. Das aus dem Netzwerk resultierende Matching Kostenvolumen bildet den Datenterm fĂŒr eine diskrete MAP Inferenz in einem paarweisen Markov Random Field. Eine umfangreiche Evaluierung zeigt die Relevanz des Kontextes fĂŒr das Feature Matching
From small to large baseline multiview stereo : dealing with blur, clutter and occlusions
This thesis addresses the problem of reconstructing the three-dimensional
(3D) digital model of a scene from a collection of two-dimensional (2D)
images taken from it. To address this fundamental computer vision
problem, we propose three algorithms. They are the main contributions
of this thesis.
First, we solve multiview stereo with the o -axis aperture camera.
This system has a very small baseline as images are captured from
viewpoints close to each other. The key idea is to change the size or
the 3D location of the aperture of the camera so as to extract selected
portions of the scene. Our imaging model takes both defocus and
stereo information into account and allows to solve shape reconstruction
and image restoration in one go. The o -axis aperture camera can
be used in a small-scale space where the camera motion is constrained
by the surrounding environment, such as in 3D endoscopy.
Second, to solve multiview stereo with large baseline, we present a
framework that poses the problem of recovering a 3D surface in the
scene as a regularized minimal partition problem of a visibility function.
The formulation is convex and hence guarantees that the solution
converges to the global minimum. Our formulation is robust
to view-varying extensive occlusions, clutter and image noise. At
any stage during the estimation process the method does not rely on
the visual hull, 2D silhouettes, approximate depth maps, or knowing
which views are dependent(i.e., overlapping) and which are independent(
i.e., non overlapping). Furthermore, the degenerate solution, the
null surface, is not included as a global solution in this formulation.
One limitation of this algorithm is that its computation complexity
grows with the number of views that we combine simultaneously. To
address this limitation, we propose a third formulation. In this formulation,
the visibility functions are integrated within a narrow band
around the estimated surface by setting weights to each point along
optical rays.
This thesis presents technical descriptions for each algorithm and detailed
analyses to show how these algorithms improve existing reconstruction
techniques
Convolutional Neural Operators for robust and accurate learning of PDEs
Although very successfully used in conventional machine learning, convolution
based neural network architectures -- believed to be inconsistent in function
space -- have been largely ignored in the context of learning solution
operators of PDEs. Here, we present novel adaptations for convolutional neural
networks to demonstrate that they are indeed able to process functions as
inputs and outputs. The resulting architecture, termed as convolutional neural
operators (CNOs), is designed specifically to preserve its underlying
continuous nature, even when implemented in a discretized form on a computer.
We prove a universality theorem to show that CNOs can approximate operators
arising in PDEs to desired accuracy. CNOs are tested on a novel suite of
benchmarks, encompassing a diverse set of PDEs with possibly multi-scale
solutions and are observed to significantly outperform baselines, paving the
way for an alternative framework for robust and accurate operator learning
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