784 research outputs found
Painting-to-3D Model Alignment Via Discriminative Visual Elements
International audienceThis paper describes a technique that can reliably align arbitrary 2D depictions of an architectural site, including drawings, paintings and historical photographs, with a 3D model of the site. This is a tremendously difficult task as the appearance and scene structure in the 2D depictions can be very different from the appearance and geometry of the 3D model, e.g., due to the specific rendering style, drawing error, age, lighting or change of seasons. In addition, we face a hard search problem: the number of possible alignments of the painting to a large 3D model, such as a partial reconstruction of a city, is huge. To address these issues, we develop a new compact representation of complex 3D scenes. The 3D model of the scene is represented by a small set of discriminative visual elements that are automatically learnt from rendered views. Similar to object detection, the set of visual elements, as well as the weights of individual features for each element, are learnt in a discriminative fashion. We show that the learnt visual elements are reliably matched in 2D depictions of the scene despite large variations in rendering style (e.g. watercolor, sketch, historical photograph) and structural changes (e.g. missing scene parts, large occluders) of the scene. We demonstrate an application of the proposed approach to automatic re-photography to find an approximate viewpoint of historical paintings and photographs with respect to a 3D model of the site. The proposed alignment procedure is validated via a human user study on a new database of paintings and sketches spanning several sites. The results demonstrate that our algorithm produces significantly better alignments than several baseline methods
Where was this picture painted ? - Localizing paintings by alignment to 3D models
National audienceCet article présente une technique qui peut de manière fiable aligner une représentation non photo-réaliste d'un site architectural, tel un dessin ou une peinture, avec un model 3D du site. Pour ce faire, nous représentons le model 3D par un ensemble d'éléments discriminatifs qui sont automatiquement découverts dans des vues du modèle. Nous montrons que les éléments trouvés sont reliés de manière robuste aux changements de style (aquarelle, croquis, photographies anciennes) et aux différences structurelles. D'avantage de détails sur notre méthode et une évaluation plus détaillée est disponible [1]
See the Difference: Direct Pre-Image Reconstruction and Pose Estimation by Differentiating HOG
The Histogram of Oriented Gradient (HOG) descriptor has led to many advances
in computer vision over the last decade and is still part of many state of the
art approaches. We realize that the associated feature computation is piecewise
differentiable and therefore many pipelines which build on HOG can be made
differentiable. This lends to advanced introspection as well as opportunities
for end-to-end optimization. We present our implementation of HOG based
on the auto-differentiation toolbox Chumpy and show applications to pre-image
visualization and pose estimation which extends the existing differentiable
renderer OpenDR pipeline. Both applications improve on the respective
state-of-the-art HOG approaches
Mid-level Deep Pattern Mining
Mid-level visual element discovery aims to find clusters of image patches
that are both representative and discriminative. In this work, we study this
problem from the prospective of pattern mining while relying on the recently
popularized Convolutional Neural Networks (CNNs). Specifically, we find that
for an image patch, activations extracted from the first fully-connected layer
of CNNs have two appealing properties which enable its seamless integration
with pattern mining. Patterns are then discovered from a large number of CNN
activations of image patches through the well-known association rule mining.
When we retrieve and visualize image patches with the same pattern,
surprisingly, they are not only visually similar but also semantically
consistent. We apply our approach to scene and object classification tasks, and
demonstrate that our approach outperforms all previous works on mid-level
visual element discovery by a sizeable margin with far fewer elements being
used. Our approach also outperforms or matches recent works using CNN for these
tasks. Source code of the complete system is available online.Comment: Published in Proc. IEEE Conf. Computer Vision and Pattern Recognition
201
Matterport3D: Learning from RGB-D Data in Indoor Environments
Access to large, diverse RGB-D datasets is critical for training RGB-D scene
understanding algorithms. However, existing datasets still cover only a limited
number of views or a restricted scale of spaces. In this paper, we introduce
Matterport3D, a large-scale RGB-D dataset containing 10,800 panoramic views
from 194,400 RGB-D images of 90 building-scale scenes. Annotations are provided
with surface reconstructions, camera poses, and 2D and 3D semantic
segmentations. The precise global alignment and comprehensive, diverse
panoramic set of views over entire buildings enable a variety of supervised and
self-supervised computer vision tasks, including keypoint matching, view
overlap prediction, normal prediction from color, semantic segmentation, and
region classification
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
Understanding deep features with computer-generated imagery
We introduce an approach for analyzing the variation of features generated by
convolutional neural networks (CNNs) with respect to scene factors that occur
in natural images. Such factors may include object style, 3D viewpoint, color,
and scene lighting configuration. Our approach analyzes CNN feature responses
corresponding to different scene factors by controlling for them via rendering
using a large database of 3D CAD models. The rendered images are presented to a
trained CNN and responses for different layers are studied with respect to the
input scene factors. We perform a decomposition of the responses based on
knowledge of the input scene factors and analyze the resulting components. In
particular, we quantify their relative importance in the CNN responses and
visualize them using principal component analysis. We show qualitative and
quantitative results of our study on three CNNs trained on large image
datasets: AlexNet, Places, and Oxford VGG. We observe important differences
across the networks and CNN layers for different scene factors and object
categories. Finally, we demonstrate that our analysis based on
computer-generated imagery translates to the network representation of natural
images
Computational Emotion Analysis From Images: Recent Advances and Future Directions
Emotions are usually evoked in humans by images. Recently, extensive research
efforts have been dedicated to understanding the emotions of images. In this
chapter, we aim to introduce image emotion analysis (IEA) from a computational
perspective with the focus on summarizing recent advances and suggesting future
directions. We begin with commonly used emotion representation models from
psychology. We then define the key computational problems that the researchers
have been trying to solve and provide supervised frameworks that are generally
used for different IEA tasks. After the introduction of major challenges in
IEA, we present some representative methods on emotion feature extraction,
supervised classifier learning, and domain adaptation. Furthermore, we
introduce available datasets for evaluation and summarize some main results.
Finally, we discuss some open questions and future directions that researchers
can pursue.Comment: Accepted chapter in the book "Human Perception of Visual Information
Psychological and Computational Perspective
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