5 research outputs found
3D Pose Regression using Convolutional Neural Networks
3D pose estimation is a key component of many important computer vision tasks
such as autonomous navigation and 3D scene understanding. Most state-of-the-art
approaches to 3D pose estimation solve this problem as a pose-classification
problem in which the pose space is discretized into bins and a CNN classifier
is used to predict a pose bin. We argue that the 3D pose space is continuous
and propose to solve the pose estimation problem in a CNN regression framework
with a suitable representation, data augmentation and loss function that
captures the geometry of the pose space. Experiments on PASCAL3D+ show that the
proposed 3D pose regression approach achieves competitive performance compared
to the state-of-the-art
Convolutional Networks for Object Category and 3D Pose Estimation from 2D Images
Current CNN-based algorithms for recovering the 3D pose of an object in an
image assume knowledge about both the object category and its 2D localization
in the image. In this paper, we relax one of these constraints and propose to
solve the task of joint object category and 3D pose estimation from an image
assuming known 2D localization. We design a new architecture for this task
composed of a feature network that is shared between subtasks, an object
categorization network built on top of the feature network, and a collection of
category dependent pose regression networks. We also introduce suitable loss
functions and a training method for the new architecture. Experiments on the
challenging PASCAL3D+ dataset show state-of-the-art performance in the joint
categorization and pose estimation task. Moreover, our performance on the joint
task is comparable to the performance of state-of-the-art methods on the
simpler 3D pose estimation with known object category task
RotationNet: Joint Object Categorization and Pose Estimation Using Multiviews from Unsupervised Viewpoints
We propose a Convolutional Neural Network (CNN)-based model "RotationNet,"
which takes multi-view images of an object as input and jointly estimates its
pose and object category. Unlike previous approaches that use known viewpoint
labels for training, our method treats the viewpoint labels as latent
variables, which are learned in an unsupervised manner during the training
using an unaligned object dataset. RotationNet is designed to use only a
partial set of multi-view images for inference, and this property makes it
useful in practical scenarios where only partial views are available. Moreover,
our pose alignment strategy enables one to obtain view-specific feature
representations shared across classes, which is important to maintain high
accuracy in both object categorization and pose estimation. Effectiveness of
RotationNet is demonstrated by its superior performance to the state-of-the-art
methods of 3D object classification on 10- and 40-class ModelNet datasets. We
also show that RotationNet, even trained without known poses, achieves the
state-of-the-art performance on an object pose estimation dataset. The code is
available on https://github.com/kanezaki/rotationnetComment: 24 pages, 23 figures. Accepted to CVPR 201
Learning visual representations with deep neural networks for intelligent transportation systems problems
Esta tesis se centra en dos grandes problemas en el área de los sistemas de transportes inteligentes (STI): el conteo de vehÃculos en escenas de congestión de tráfico; y la detección y estimación del punto de vista, de forma simultánea, de los objetos en una escena.
Respecto al problema del conteo, este trabajo se centra primero en el diseño de arquitecturas de redes neuronales profundas que tengan la capacidad de aprender representaciones multi-escala profundas, capaces de estimar de forma precisa la cuenta de objetos, mediante mapas de densidad. Se trata también el problema de la escala de los objetos introducida por la gran perspectiva tÃpicamente presente en el área de recuento de objetos. Además, con el éxito de las redes hourglass profundas en el campo del conteo de objetos, este trabajo propone un nuevo tipo de red hourglass profunda con conexiones de corto circuito auto-gestionadas. Los modelos propuestos se evalúan en las bases de datos públicas más utilizadas y logran los resultados iguales o superiores al estado del arte en el momento en que fueron publicadas.
Para la segunda parte, se realiza un estudio comparativo completo del problema de detección de objetos y la estimación de la pose de forma simultánea. Se expone el compromiso existente entre la localización del objeto y la estimación de su pose. Un detector necesita idealmente una representación que sea invariable al punto de vista, mientras que un estimador de poses necesita ser discriminatorio. Por lo tanto, se proponen tres nuevas arquitecturas de redes neurales profundas en las que el problema de la detección de objetos y la estimación de la pose se van desacoplando progresivamente. Además, se aborda la cuestión de si la pose debe expresarse como un valor discreto o continuo. A pesar de ofrecer un rendimiento similar, los resultados muestran que los enfoques continuos son más sensibles al sesgo del punto de vista principal de la categorÃa del objeto. Se realiza un análisis comparativo detallado en las dos bases de datos principales, es decir, PASCAL3D+ y ObjectNet3D. Se logran resultados competitivos con todos los modelos propuestos en ambos conjuntos de datos