2,181 research outputs found
Deformable Shape Completion with Graph Convolutional Autoencoders
The availability of affordable and portable depth sensors has made scanning
objects and people simpler than ever. However, dealing with occlusions and
missing parts is still a significant challenge. The problem of reconstructing a
(possibly non-rigidly moving) 3D object from a single or multiple partial scans
has received increasing attention in recent years. In this work, we propose a
novel learning-based method for the completion of partial shapes. Unlike the
majority of existing approaches, our method focuses on objects that can undergo
non-rigid deformations. The core of our method is a variational autoencoder
with graph convolutional operations that learns a latent space for complete
realistic shapes. At inference, we optimize to find the representation in this
latent space that best fits the generated shape to the known partial input. The
completed shape exhibits a realistic appearance on the unknown part. We show
promising results towards the completion of synthetic and real scans of human
body and face meshes exhibiting different styles of articulation and
partiality.Comment: CVPR 201
Proposal Flow: Semantic Correspondences from Object Proposals
Finding image correspondences remains a challenging problem in the presence
of intra-class variations and large changes in scene layout. Semantic flow
methods are designed to handle images depicting different instances of the same
object or scene category. We introduce a novel approach to semantic flow,
dubbed proposal flow, that establishes reliable correspondences using object
proposals. Unlike prevailing semantic flow approaches that operate on pixels or
regularly sampled local regions, proposal flow benefits from the
characteristics of modern object proposals, that exhibit high repeatability at
multiple scales, and can take advantage of both local and geometric consistency
constraints among proposals. We also show that the corresponding sparse
proposal flow can effectively be transformed into a conventional dense flow
field. We introduce two new challenging datasets that can be used to evaluate
both general semantic flow techniques and region-based approaches such as
proposal flow. We use these benchmarks to compare different matching
algorithms, object proposals, and region features within proposal flow, to the
state of the art in semantic flow. This comparison, along with experiments on
standard datasets, demonstrates that proposal flow significantly outperforms
existing semantic flow methods in various settings.Comment: arXiv admin note: text overlap with arXiv:1511.0506
SFNet: Learning Object-aware Semantic Correspondence
We address the problem of semantic correspondence, that is, establishing a
dense flow field between images depicting different instances of the same
object or scene category. We propose to use images annotated with binary
foreground masks and subjected to synthetic geometric deformations to train a
convolutional neural network (CNN) for this task. Using these masks as part of
the supervisory signal offers a good compromise between semantic flow methods,
where the amount of training data is limited by the cost of manually selecting
point correspondences, and semantic alignment ones, where the regression of a
single global geometric transformation between images may be sensitive to
image-specific details such as background clutter. We propose a new CNN
architecture, dubbed SFNet, which implements this idea. It leverages a new and
differentiable version of the argmax function for end-to-end training, with a
loss that combines mask and flow consistency with smoothness terms.
Experimental results demonstrate the effectiveness of our approach, which
significantly outperforms the state of the art on standard benchmarks.Comment: cvpr 2019 oral pape
SuperPoint: Self-Supervised Interest Point Detection and Description
This paper presents a self-supervised framework for training interest point
detectors and descriptors suitable for a large number of multiple-view geometry
problems in computer vision. As opposed to patch-based neural networks, our
fully-convolutional model operates on full-sized images and jointly computes
pixel-level interest point locations and associated descriptors in one forward
pass. We introduce Homographic Adaptation, a multi-scale, multi-homography
approach for boosting interest point detection repeatability and performing
cross-domain adaptation (e.g., synthetic-to-real). Our model, when trained on
the MS-COCO generic image dataset using Homographic Adaptation, is able to
repeatedly detect a much richer set of interest points than the initial
pre-adapted deep model and any other traditional corner detector. The final
system gives rise to state-of-the-art homography estimation results on HPatches
when compared to LIFT, SIFT and ORB.Comment: Camera-ready version for CVPR 2018 Deep Learning for Visual SLAM
Workshop (DL4VSLAM2018
WarpNet: Weakly Supervised Matching for Single-view Reconstruction
We present an approach to matching images of objects in fine-grained datasets
without using part annotations, with an application to the challenging problem
of weakly supervised single-view reconstruction. This is in contrast to prior
works that require part annotations, since matching objects across class and
pose variations is challenging with appearance features alone. We overcome this
challenge through a novel deep learning architecture, WarpNet, that aligns an
object in one image with a different object in another. We exploit the
structure of the fine-grained dataset to create artificial data for training
this network in an unsupervised-discriminative learning approach. The output of
the network acts as a spatial prior that allows generalization at test time to
match real images across variations in appearance, viewpoint and articulation.
On the CUB-200-2011 dataset of bird categories, we improve the AP over an
appearance-only network by 13.6%. We further demonstrate that our WarpNet
matches, together with the structure of fine-grained datasets, allow
single-view reconstructions with quality comparable to using annotated point
correspondences.Comment: to appear in IEEE Conference on Computer Vision and Pattern
Recognition (CVPR) 201
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