917 research outputs found
Learning shape correspondence with anisotropic convolutional neural networks
Establishing correspondence between shapes is a fundamental problem in
geometry processing, arising in a wide variety of applications. The problem is
especially difficult in the setting of non-isometric deformations, as well as
in the presence of topological noise and missing parts, mainly due to the
limited capability to model such deformations axiomatically. Several recent
works showed that invariance to complex shape transformations can be learned
from examples. In this paper, we introduce an intrinsic convolutional neural
network architecture based on anisotropic diffusion kernels, which we term
Anisotropic Convolutional Neural Network (ACNN). In our construction, we
generalize convolutions to non-Euclidean domains by constructing a set of
oriented anisotropic diffusion kernels, creating in this way a local intrinsic
polar representation of the data (`patch'), which is then correlated with a
filter. Several cascades of such filters, linear, and non-linear operators are
stacked to form a deep neural network whose parameters are learned by
minimizing a task-specific cost. We use ACNNs to effectively learn intrinsic
dense correspondences between deformable shapes in very challenging settings,
achieving state-of-the-art results on some of the most difficult recent
correspondence benchmarks
Semantic Cross-View Matching
Matching cross-view images is challenging because the appearance and
viewpoints are significantly different. While low-level features based on
gradient orientations or filter responses can drastically vary with such
changes in viewpoint, semantic information of images however shows an invariant
characteristic in this respect. Consequently, semantically labeled regions can
be used for performing cross-view matching. In this paper, we therefore explore
this idea and propose an automatic method for detecting and representing the
semantic information of an RGB image with the goal of performing cross-view
matching with a (non-RGB) geographic information system (GIS). A segmented
image forms the input to our system with segments assigned to semantic concepts
such as traffic signs, lakes, roads, foliage, etc. We design a descriptor to
robustly capture both, the presence of semantic concepts and the spatial layout
of those segments. Pairwise distances between the descriptors extracted from
the GIS map and the query image are then used to generate a shortlist of the
most promising locations with similar semantic concepts in a consistent spatial
layout. An experimental evaluation with challenging query images and a large
urban area shows promising results
Geometric and Photometric Data Fusion in Non-Rigid Shape Analysis
In this paper, we explore the use of the diffusion geometry framework for the fusion of geometric and photometric information in local and global shape descriptors. Our construction is based on the definition of a diffusion process on the shape manifold embedded into a high-dimensional space where the embedding coordinates represent the photometric information. Experimental results show that such data fusion is useful in coping with different challenges of shape analysis where pure geometric and pure photometric methods fai
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