308,236 research outputs found
Deep Shape Matching
We cast shape matching as metric learning with convolutional networks. We
break the end-to-end process of image representation into two parts. Firstly,
well established efficient methods are chosen to turn the images into edge
maps. Secondly, the network is trained with edge maps of landmark images, which
are automatically obtained by a structure-from-motion pipeline. The learned
representation is evaluated on a range of different tasks, providing
improvements on challenging cases of domain generalization, generic
sketch-based image retrieval or its fine-grained counterpart. In contrast to
other methods that learn a different model per task, object category, or
domain, we use the same network throughout all our experiments, achieving
state-of-the-art results in multiple benchmarks.Comment: ECCV 201
Joint Symmetry Detection and Shape Matching for Non-Rigid Point Cloud
Despite the success of deep functional maps in non-rigid 3D shape matching,
there exists no learning framework that models both self-symmetry and shape
matching simultaneously. This is despite the fact that errors due to symmetry
mismatch are a major challenge in non-rigid shape matching. In this paper, we
propose a novel framework that simultaneously learns both self symmetry as well
as a pairwise map between a pair of shapes. Our key idea is to couple a self
symmetry map and a pairwise map through a regularization term that provides a
joint constraint on both of them, thereby, leading to more accurate maps. We
validate our method on several benchmarks where it outperforms many competitive
baselines on both tasks.Comment: Under Review. arXiv admin note: substantial text overlap with
arXiv:2110.0299
Geometrically Consistent Partial Shape Matching
Finding correspondences between 3D shapes is a crucial problem in computer
vision and graphics, which is for example relevant for tasks like shape
interpolation, pose transfer, or texture transfer. An often neglected but
essential property of matchings is geometric consistency, which means that
neighboring triangles in one shape are consistently matched to neighboring
triangles in the other shape. Moreover, while in practice one often has only
access to partial observations of a 3D shape (e.g. due to occlusion, or
scanning artifacts), there do not exist any methods that directly address
geometrically consistent partial shape matching. In this work we fill this gap
by proposing to integrate state-of-the-art deep shape features into a novel
integer linear programming partial shape matching formulation. Our optimization
yields a globally optimal solution on low resolution shapes, which we then
refine using a coarse-to-fine scheme. We show that our method can find more
reliable results on partial shapes in comparison to existing geometrically
consistent algorithms (for which one first has to fill missing parts with a
dummy geometry). Moreover, our matchings are substantially smoother than
learning-based state-of-the-art shape matching methods
A Survey on Joint Object Detection and Pose Estimation using Monocular Vision
In this survey we present a complete landscape of joint object detection and
pose estimation methods that use monocular vision. Descriptions of traditional
approaches that involve descriptors or models and various estimation methods
have been provided. These descriptors or models include chordiograms,
shape-aware deformable parts model, bag of boundaries, distance transform
templates, natural 3D markers and facet features whereas the estimation methods
include iterative clustering estimation, probabilistic networks and iterative
genetic matching. Hybrid approaches that use handcrafted feature extraction
followed by estimation by deep learning methods have been outlined. We have
investigated and compared, wherever possible, pure deep learning based
approaches (single stage and multi stage) for this problem. Comprehensive
details of the various accuracy measures and metrics have been illustrated. For
the purpose of giving a clear overview, the characteristics of relevant
datasets are discussed. The trends that prevailed from the infancy of this
problem until now have also been highlighted.Comment: Accepted at the International Joint Conference on Computer Vision and
Pattern Recognition (CCVPR) 201
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