End-to-end weakly-supervised semantic alignment

We tackle the task of semantic alignment where the goal is to compute dense semantic correspondence aligning two images depicting objects of the same category. This is a challenging task due to large intra-class variation, changes in viewpoint and background clutter. We present the following three principal contributions. First, we develop a convolutional neural network architecture for semantic alignment that is trainable in an end-to-end manner from weak image-level supervision in the form of matching image pairs. The outcome is that parameters are learnt from rich appearance variation present in different but semantically related images without the need for tedious manual annotation of correspondences at training time. Second, the main component of this architecture is a differentiable soft inlier scoring module, inspired by the RANSAC inlier scoring procedure, that computes the quality of the alignment based on only geometrically consistent correspondences thereby reducing the effect of background clutter. Third, we demonstrate that the proposed approach achieves state-of-the-art performance on multiple standard benchmarks for semantic alignment.Comment: In 2018 IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2018

Convolutional neural network architecture for geometric matching

We address the problem of determining correspondences between two images in agreement with a geometric model such as an affine or thin-plate spline transformation, and estimating its parameters. The contributions of this work are three-fold. First, we propose a convolutional neural network architecture for geometric matching. The architecture is based on three main components that mimic the standard steps of feature extraction, matching and simultaneous inlier detection and model parameter estimation, while being trainable end-to-end. Second, we demonstrate that the network parameters can be trained from synthetically generated imagery without the need for manual annotation and that our matching layer significantly increases generalization capabilities to never seen before images. Finally, we show that the same model can perform both instance-level and category-level matching giving state-of-the-art results on the challenging Proposal Flow dataset.Comment: In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2017

Neighbourhood Consensus Networks

We address the problem of finding reliable dense correspondences between a pair of images. This is a challenging task due to strong appearance differences between the corresponding scene elements and ambiguities generated by repetitive patterns. The contributions of this work are threefold. First, inspired by the classic idea of disambiguating feature matches using semi-local constraints, we develop an end-to-end trainable convolutional neural network architecture that identifies sets of spatially consistent matches by analyzing neighbourhood consensus patterns in the 4D space of all possible correspondences between a pair of images without the need for a global geometric model. Second, we demonstrate that the model can be trained effectively from weak supervision in the form of matching and non-matching image pairs without the need for costly manual annotation of point to point correspondences. Third, we show the proposed neighbourhood consensus network can be applied to a range of matching tasks including both category- and instance-level matching, obtaining the state-of-the-art results on the PF Pascal dataset and the InLoc indoor visual localization benchmark.Comment: In Proceedings of the 32nd Conference on Neural Information Processing Systems (NeurIPS 2018

D2-Net: A Trainable CNN for Joint Detection and Description of Local Features

In this work we address the problem of finding reliable pixel-level correspondences under difficult imaging conditions. We propose an approach where a single convolutional neural network plays a dual role: It is simultaneously a dense feature descriptor and a feature detector. By postponing the detection to a later stage, the obtained keypoints are more stable than their traditional counterparts based on early detection of low-level structures. We show that this model can be trained using pixel correspondences extracted from readily available large-scale SfM reconstructions, without any further annotations. The proposed method obtains state-of-the-art performance on both the difficult Aachen Day-Night localization dataset and the InLoc indoor localization benchmark, as well as competitive performance on other benchmarks for image matching and 3D reconstruction.Comment: Accepted at CVPR 201

DynamicStereo: consistent dynamic depth from stereo videos

We consider the problem of reconstructing a dynamic scene observed from a stereo camera. Most existing methods for depth from stereo treat different stereo frames independently, leading to temporally inconsistent depth predictions. Temporal consistency is especially important for immersive AR or VR scenarios, where flickering greatly diminishes the user experience. We propose DynamicStereo, a novel transformer-based architecture to estimate disparity for stereo videos. The network learns to pool information from neighboring frames to improve the temporal consistency of its predictions. Our architecture is designed to process stereo videos efficiently through divided attention layers. We also introduce Dynamic Replica, a new benchmark dataset containing synthetic videos of people and animals in scanned environments, which provides complementary training and evaluation data for dynamic stereo closer to real applications than existing datasets. Training with this dataset further improves the quality of predictions of our proposed DynamicStereo as well as prior methods. Finally, it acts as a benchmark for consistent stereo methods

DESARROLLO DE MODELOS DISCRETOS APLICADOS AL ESTUDIO DEL COMPORTAMIENTO EN FRACTURA DE MATERIALES COMPUESTOS

En el presente trabajo se propone estudiar el comportamiento en fractura de los materiales compuestos. Las ventajas que presentan los materiales compuestos, frente a los materiales tradicionales, ha permitido extender su uso a una amplia variedad de industrias, donde se incluye la automotor, militar, aeroespacial y naval, caracterizándose por la tendencia a ir sustituyendo componentes estructurales fabricados con materiales tradicionales. Por estos motivos, es importante poder desarrollar herramientas que permitan estudiar el comportamiento mecánico de estos materiales, y así poder optimizar el diseño de los componentes estructurales. Como es sabido, el avance de las herramientas computacionales ha dado lugar al desarrollo de modelos numéricos que permiten estudiar problemas complejos, que mediante técnicas analíticas serían inabordables. El método numérico más utilizado en el campo de la mecánica de sólidos y del análisis estructural, es el Método de los Elementos Finitos (MEF). Este método ha probado ser una herramienta muy fiable en muchas áreas, sin embargo, tiene algunas limitaciones en el análisis de problemas de fractura donde las trayectorias de las fisuras son, a priori, desconocidas. Se han propuesto otros modelos denominados Métodos sin Malla, como puede ser el Método de Galerkin sin Elementos, el Método del Punto Material, entre muchos otros. Todos estos métodos se caracterizan por intentar solventar las dificultades que se presentan cuando el método numérico requiere de una malla. Sin lugar a duda, estos nuevos modelos han logrado grandes avances en el análisis de problemas de fractura. Sin embargo, estas modificaciones han dado lugar a formulaciones cada vez más complejas, que en muchos casos se ha visto traducido en un aumento del coste computacional. En el afán por desarrollar modelos numéricos más simples, que sean capaces de predecir trayectorias de fisuras complejas, han surgido los denominados modelos discretos. La facilidad que presentan estas formulaciones ha promovido, el desarrollo de numerosos métodos, en los cuales la diferencia más relevante que existe entre ellos es la manera de relacionar las fuerzas de interacción con las propiedades macroscópicas del material. Las ventajas que presentan estos modelos discretos, sumado al interés por desarrollar modelos que permitan predecir el comportamiento de los materiales avanzados, ha estimulado el desarrollo de métodos alternativos capaces de modelar materiales que presentan algún tipo de anisotropía, como los materiales compuestos. Los modelos discretos aplicados a materiales compuesto, desarrollados hasta el momento, presentan ciertas limitaciones, como por ejemplo el hecho de que son formulaciones bidimensionales que impiden el estudio de problemas de impacto en la dirección perpendicular al plano. En este trabajo se propone seguir avanzando en el desarrollo de modelos numéricos que permitan estudiar el comportamiento de materiales compuestos, frente a problemas de propagación y estabilidad de fisuras, intentando solventando las limitaciones presentes en los modelos actuales. Para esto se propone utilizar la metodología propuesta en el trabajo llevado a cabo por Wang et al. (2009) para materiales isótropos, adaptándola para materiales ortótropos. Primero proponiendo un modelo bidimensional, para posteriormente realizar un modelo tridimensional que permita estudiar problemas más complejos, así como también modelar estructuras tipo sándwich

Conversión de residuos sólidos urbanos en energía

Given Uruguay’s energetic situation, where its main energy sources are from hydroelectric and thermal power from fossil fuels, it is important to consider other energy sources such as Energy from Waste. Waste to Energy (WTE) or Energy from Waste is a Municipal Solid Waste (MSW) management system, which results in an appropriate and sustainable use of the waste which cannot be efficiently recycled or reused. This articleoverviews the different WTE alternatives and proposes a concrete small-scale application for the city of Paysandú.Ante la situación energética del Uruguay, donde sus principales fuentes energéticas son a través de centrales hidráulicas y centrales térmicas a partir de combustibles fósiles, es importante considerar otras fuentes de energía como ser el recurso energético de los residuos. Recuperación de Energía de los Residuos o Waste-to-Energy (WTE), por su sigla en inglés, es un sistema de gestión para la disposición de los residuos sólidos urbanos (MSW, Municipal Solid Waste) que permite un uso adecuado y sostenible de los residuos que no pueden ser eficientemente reciclados o reutilizados. El presente artículo presenta las diferentes alternativas existentes para generar energía a partir de residuos y propone un caso concreto de aplicación de pequeña escala, utilizando un Sistema de Oxidación por Batch para la ciudad de Paysandú, Uruguay

Common Pets in 3D: Dynamic New-View Synthesis of Real-Life Deformable Categories

Obtaining photorealistic reconstructions of objects from sparse views is inherently ambiguous and can only be achieved by learning suitable reconstruction priors. Earlier works on sparse rigid object reconstruction successfully learned such priors from large datasets such as CO3D. In this paper, we extend this approach to dynamic objects. We use cats and dogs as a representative example and introduce Common Pets in 3D (CoP3D), a collection of crowd-sourced videos showing around 4,200 distinct pets. CoP3D is one of the first large-scale datasets for benchmarking non-rigid 3D reconstruction "in the wild". We also propose Tracker-NeRF, a method for learning 4D reconstruction from our dataset. At test time, given a small number of video frames of an unseen object, Tracker-NeRF predicts the trajectories of its 3D points and generates new views, interpolating viewpoint and time. Results on CoP3D reveal significantly better non-rigid new-view synthesis performance than existing baselines

The psychological benefits and efficacy of computer-assisted training on competency enhancement in adults with intellectual disability. A systematic review

The use of modern technologies as instructional tools is becoming increasingly prevalent in both general and special education. This systematic review examines the effects of computer-assisted and digital training on competency enhancement for adults with intellectual disabilities. As opposed to mere knowledge, “competency” was defined as the ability to apply and employ acquired knowledge to carry out tasks and solve problems in professional, social, and personal life domains. The PRISMA procedure was used to search for records dealing with competency enhancement using computerized training for intellectually disabled adults. Twenty-two articles met the inclusion criteria, showing that contemporary technology, computer-based tools, and digital instruments can positively affect the quality of life of adults with intellectual disabilities, and enhance their personal, professional, and social competencies. Ultimately, fostering computer-based technology to enhance competencies in adults with intellectual disabilities appears to be very promising, in that it allows these individuals to better integrate into society and live more independently, autonomously, and effectively

Desarrollo de modelos discretos aplicados al estudio del comportamiento en fractura de materiales compuestos

En el presente trabajo se propone estudiar el comportamiento en fractura de los materiales compuestos. Las ventajas que presentan los materiales compuestos, frente a los materiales tradicionales, ha permitido extender su uso a una amplia variedad de industrias, donde se incluye la automotor, militar, aeroespacial y naval, caracterizándose por la tendencia a ir sustituyendo componentes estructurales fabricados con materiales tradicionales. Por estos motivos, es importante poder desarrollar herramientas que permitan estudiar el comportamiento mecánico de estos materiales, y así poder optimizar el diseño de los componentes estructurales.Universidad Nacional de La Plat