405 research outputs found
Deformable and articulated 3D reconstruction from monocular video sequences
PhDThis thesis addresses the problem of deformable and articulated structure from motion from
monocular uncalibrated video sequences. Structure from motion is defined as the problem of
recovering information about the 3D structure of scenes imaged by a camera in a video sequence.
Our study aims at the challenging problem of non-rigid shapes (e.g. a beating heart or a smiling
face). Non-rigid structures appear constantly in our everyday life, think of a bicep curling, a
torso twisting or a smiling face. Our research seeks a general method to perform 3D shape
recovery purely from data, without having to rely on a pre-computed model or training data.
Open problems in the field are the difficulty of the non-linear estimation, the lack of a real-time
system, large amounts of missing data in real-world video sequences, measurement noise and
strong deformations. Solving these problems would take us far beyond the current state of the
art in non-rigid structure from motion. This dissertation presents our contributions in the field
of non-rigid structure from motion, detailing a novel algorithm that enforces the exact metric
structure of the problem at each step of the minimisation by projecting the motion matrices
onto the correct deformable or articulated metric motion manifolds respectively. An important
advantage of this new algorithm is its ability to handle missing data which becomes crucial
when dealing with real video sequences. We present a generic bilinear estimation framework,
which improves convergence and makes use of the manifold constraints. Finally, we demonstrate
a sequential, frame-by-frame estimation algorithm, which provides a 3D model and camera
parameters for each video frame, while simultaneously building a model of object deformation
Shape basis interpretation for monocular deformable 3D reconstruction
© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.In this paper, we propose a novel interpretable shape model to encode object non-rigidity. We first use the initial frames of a monocular video to recover a rest shape, used later to compute a dissimilarity measure based on a distance matrix measurement. Spectral analysis is then applied to this matrix to obtain a reduced shape basis, that in contrast to existing approaches, can be physically interpreted. In turn, these pre-computed shape bases are used to linearly span the deformation of a wide variety of objects. We introduce the low-rank basis into a sequential approach to recover both camera motion and non-rigid shape from the monocular video, by simply optimizing the weights of the linear combination using bundle adjustment. Since the number of parameters to optimize per frame is relatively small, specially when physical priors are considered, our approach is fast and can potentially run in real time. Validation is done in a wide variety of real-world objects, undergoing both inextensible and extensible deformations. Our approach achieves remarkable robustness to artifacts such as noisy and missing measurements and shows an improved performance to competing methods.Peer ReviewedPostprint (author's final draft
Deep Structured Layers for Instance-Level Optimization in 2D and 3D Vision
The approach we present in this thesis is that of integrating optimization problems
as layers in deep neural networks. Optimization-based modeling provides an additional set of tools enabling the design of powerful neural networks for a wide
battery of computer vision tasks. This thesis shows formulations and experiments
for vision tasks ranging from image reconstruction to 3D reconstruction.
We first propose an unrolled optimization method with implicit regularization
properties for reconstructing images from noisy camera readings. The method resembles an unrolled majorization minimization framework with convolutional neural networks acting as regularizers. We report state-of-the-art performance in image
reconstruction on both noisy and noise-free evaluation setups across many datasets.
We further focus on the task of monocular 3D reconstruction of articulated objects using video self-supervision. The proposed method uses a structured layer for
accurate object deformation that controls a 3D surface by displacing a small number
of learnable handles. While relying on a small set of training data per category for
self-supervision, the method obtains state-of-the-art reconstruction accuracy with
diverse shapes and viewpoints for multiple articulated objects.
We finally address the shortcomings of the previous method that revolve
around regressing the camera pose using multiple hypotheses. We propose a method
that recovers a 3D shape from a 2D image by relying solely on 3D-2D correspondences regressed from a convolutional neural network. These correspondences are
used in conjunction with an optimization problem to estimate per sample the camera pose and deformation. We quantitatively show the effectiveness of the proposed
method on self-supervised 3D reconstruction on multiple categories without the need for multiple hypotheses
Real Time Sequential Non Rigid Structure from motion using a single camera
En la actualidad las aplicaciones que basan su funcionamiento en una correcta localización y reconstrucción dentro de un entorno real en 3D han experimentado un gran interés en los últimos años, tanto por la comunidad investigadora como por la industrial. Estas aplicaciones varían desde la realidad aumentada, la robótica, la simulación, los videojuegos, etc. Dependiendo de la aplicación y del nivel de detalle de la reconstrucción, se emplean diversos dispositivos, algunos específicos, más complejos y caros como las cámaras estéreo, cámara y profundidad (RGBD) con Luz estructurada y Time of Flight (ToF), así como láser y otros más avanzados. Para aplicaciones sencillas es suficiente con dispositivos de uso común, como los smartphones, en los que aplicando técnicas de visión artificial, se pueden obtener modelos 3D del entorno para, en el caso de la realidad aumentada, mostrar información aumentada en la ubicación seleccionada.En robótica, la localización y generación simultáneas de un mapa del entorno en 3D es una tarea fundamental para conseguir la navegación autónoma. Este problema se conoce en el estado del arte como Simultaneous Localization And Mapping (SLAM) o Structure from Motion (SfM). Para la aplicación de estas técnicas, el objeto no ha de cambiar su forma a lo largo del tiempo. La reconstrucción es unívoca salvo factor de escala en captura monocular sin referencia. Si la condición de rigidez no se cumple, es porque la forma del objeto cambia a lo largo del tiempo. El problema sería equivalente a realizar una reconstrucción por fotograma, lo cual no se puede hacer de manera directa, puesto que diferentes formas, combinadas con diferentes poses de cámara pueden dar proyecciones similares. Es por esto que el campo de la reconstrucción de objetos deformables es todavía un área en desarrollo. Los métodos de SfM se han adaptado aplicando modelos físicos, restricciones temporales, espaciales, geométricas o de otros tipos para reducir la ambigüedad en las soluciones, naciendo así las técnicas conocidas como Non-Rigid SfM (NRSfM).En esta tesis se propone partir de una técnica de reconstrucción rígida bien conocida en el estado del arte como es PTAM (Parallel Tracking and Mapping) y adaptarla para incluir técnicas de NRSfM, basadas en modelo de bases lineales para estimar las deformaciones del objeto modelado dinámicamente y aplicar restricciones temporales y espaciales para mejorar las reconstrucciones, además de ir adaptándose a cambios de deformación que se presenten en la secuencia. Para ello, hay que realizar cambios de manera que cada uno de sus hilos de ejecución procesen datos no rígidos.El hilo encargado del seguimiento ya realizaba seguimiento basado en un mapa de puntos 3D, proporcionado a priori. La modificación más importante aquí es la integración de un modelo de deformación lineal para que se realice el cálculo de la deformación del objeto en tiempo real, asumiendo fijas las formas básicas de deformación. El cálculo de la pose de la cámara está basado en el sistema de estimación rígido, por lo que la estimación de pose y coeficientes de deformación se hace de manera alternada usando el algoritmo E-M (Expectation-Maximization). También, se imponen restricciones temporales y de forma para restringir las ambigüedades inherentes en las soluciones y mejorar la calidad de la estimación 3D.Respecto al hilo que gestiona el mapa, se actualiza en función del tiempo para que sea capaz de mejorar las bases de deformación cuando éstas no son capaces de explicar las formas que se ven en las imágenes actuales. Para ello, se sustituye la optimización de modelo rígido incluida en este hilo por un método de procesamiento exhaustivo NRSfM, para mejorar las bases acorde a las imágenes con gran error de reconstrucción desde el hilo de seguimiento. Con esto, el modelo se consigue adaptar a nuevas deformaciones, permitiendo al sistema evolucionar y ser estable a largo plazo.A diferencia de una gran parte de los métodos de la literatura, el sistema propuesto aborda el problema de la proyección perspectiva de forma nativa, minimizando los problemas de ambigüedad y de distancia al objeto existente en la proyección ortográfica. El sistema propuesto maneja centenares de puntos y está preparado para cumplir con restricciones de tiempo real para su aplicación en sistemas con recursos hardware limitados
Local Deformation Modelling for Non-Rigid Structure from Motion
PhDReconstructing the 3D geometry of scenes based on monocular image sequences is
a long-standing problem in computer vision. Structure from motion (SfM) aims at a
data-driven approach without requiring a priori models of the scene. When the scene is
rigid, SfM is a well understood problem with solutions widely used in industry. However,
if the scene is non-rigid, monocular reconstruction without additional information
is an ill-posed problem and no satisfactory solution has yet been found.
Current non-rigid SfM (NRSfM) methods typically aim at modelling deformable
motion globally. Additionally, most of these methods focus on cases where deformable
motion is seen as small variations from a mean shape. In turn, these methods fail at
reconstructing highly deformable objects such as a flag waving in the wind. Additionally,
reconstructions typically consist of low detail, sparse point-cloud representation
of objects.
In this thesis we aim at reconstructing highly deformable surfaces by modelling
them locally. In line with a recent trend in NRSfM, we propose a piecewise approach
which reconstructs local overlapping regions independently. These reconstructions are
merged into a global object by imposing 3D consistency of the overlapping regions.
We propose our own local model – the Quadratic Deformation model – and show
how patch division and reconstruction can be formulated in a principled approach by
alternating at minimizing a single geometric cost – the image re-projection error of
the reconstruction. Moreover, we extend our approach to dense NRSfM, where reconstructions
are preformed at the pixel level, improving the detail of state of the art
reconstructions.
Finally we show how our principled approach can be used to perform simultaneous
segmentation and reconstruction of articulated motion, recovering meaningful
segments which provide a coarse 3D skeleton of the object.Fundacao para a Ciencia e a Tecnologia (FCT)
under Doctoral Grant SFRH/BD/70312/2010; European Research Council
under ERC Starting Grant agreement 204871-HUMANI
Progress in industrial photogrammetry by means of markerless solutions
174 p.La siguiente tesis está enfocada al desarrollo y uso avanzado de metodologías fotogramétrica sin dianas en aplicaciones industriales. La fotogrametría es una técnica de medición óptica 3D que engloba múltiples configuraciones y aproximaciones. En este estudio se han desarrollado procedimientos de medición, modelos y estrategias de procesamiento de imagen que van más allá que la fotogrametría convencional y buscan el emplear soluciones de otros campos de la visión artificial en aplicaciones industriales. Mientras que la fotogrametría industrial requiere emplear dianas artificiales para definir los puntos o elementos de interés, esta tesis contempla la reducción e incluso la eliminación de las dianas tanto pasivas como activas como alternativas prácticas. La mayoría de los sistemas de medida utilizan las dianas tanto para definir los puntos de control, relacionar las distintas perspectivas, obtener precisión, así como para automatizar las medidas. Aunque en muchas situaciones el empleo de dianas no sea restrictivo existen aplicaciones industriales donde su empleo condiciona y restringe considerablemente los procedimientos de medida empleados en la inspección. Un claro ejemplo es la verificación y control de calidad de piezas seriadas, o la medición y seguimiento de elementos prismáticos relacionados con un sistema de referencia determinado. Es en este punto donde la fotogrametría sin dianas puede combinarse o complementarse con soluciones tradicionales para tratar de mejorar las prestaciones actuales
Applications in Monocular Computer Vision using Geometry and Learning : Map Merging, 3D Reconstruction and Detection of Geometric Primitives
As the dream of autonomous vehicles moving around in our world comes closer, the problem of robust localization and mapping is essential to solve. In this inherently structured and geometric problem we also want the agents to learn from experience in a data driven fashion. How the modern Neural Network models can be combined with Structure from Motion (SfM) is an interesting research question and this thesis studies some related problems in 3D reconstruction, feature detection, SfM and map merging.In Paper I we study how a Bayesian Neural Network (BNN) performs in Semantic Scene Completion, where the task is to predict a semantic 3D voxel grid for the Field of View of a single RGBD image. We propose an extended task and evaluate the benefits of the BNN when encountering new classes at inference time. It is shown that the BNN outperforms the deterministic baseline.Papers II-III are about detection of points, lines and planes defining a Room Layout in an RGB image. Due to the repeated textures and homogeneous colours of indoor surfaces it is not ideal to only use point features for Structure from Motion. The idea is to complement the point features by detecting a Wireframe – a connected set of line segments – which marks the intersection of planes in the Room Layout. Paper II concerns a task for detecting a Semantic Room Wireframe and implements a Neural Network model utilizing a Graph Convolutional Network module. The experiments show that the method is more flexible than previous Room Layout Estimation methods and perform better than previous Wireframe Parsing methods. Paper III takes the task closer to Room Layout Estimation by detecting a connected set of semantic polygons in an RGB image. The end-to-end trainable model is a combination of a Wireframe Parsing model and a Heterogeneous Graph Neural Network. We show promising results by outperforming state of the art models for Room Layout Estimation using synthetic Wireframe detections. However, the joint Wireframe and Polygon detector requires further research to compete with the state of the art models.In Paper IV we propose minimal solvers for SfM with parallel cylinders. The problem may be reduced to estimating circles in 2D and the paper contributes with theory for the twoview relative motion and two-circle relative structure problem. Fast solvers are derived and experiments show good performance in both simulation and on real data.Papers V-VII cover the task of map merging. That is, given a set of individually optimized point clouds with camera poses from a SfM pipeline, how can the solutions be effectively merged without completely resolving the Structure from Motion problem? Papers V-VI introduce an effective method for merging and shows the effectiveness through experiments of real and simulated data. Paper VII considers the matching problem for point clouds and proposes minimal solvers that allows for deformation ofeach point cloud. Experiments show that the method robustly matches point clouds with drift in the SfM solution
- …