73,201 research outputs found
Accurate Optical Flow via Direct Cost Volume Processing
We present an optical flow estimation approach that operates on the full
four-dimensional cost volume. This direct approach shares the structural
benefits of leading stereo matching pipelines, which are known to yield high
accuracy. To this day, such approaches have been considered impractical due to
the size of the cost volume. We show that the full four-dimensional cost volume
can be constructed in a fraction of a second due to its regularity. We then
exploit this regularity further by adapting semi-global matching to the
four-dimensional setting. This yields a pipeline that achieves significantly
higher accuracy than state-of-the-art optical flow methods while being faster
than most. Our approach outperforms all published general-purpose optical flow
methods on both Sintel and KITTI 2015 benchmarks.Comment: Published at the Conference on Computer Vision and Pattern
Recognition (CVPR 2017
DCTM: Discrete-Continuous Transformation Matching for Semantic Flow
Techniques for dense semantic correspondence have provided limited ability to
deal with the geometric variations that commonly exist between semantically
similar images. While variations due to scale and rotation have been examined,
there lack practical solutions for more complex deformations such as affine
transformations because of the tremendous size of the associated solution
space. To address this problem, we present a discrete-continuous transformation
matching (DCTM) framework where dense affine transformation fields are inferred
through a discrete label optimization in which the labels are iteratively
updated via continuous regularization. In this way, our approach draws
solutions from the continuous space of affine transformations in a manner that
can be computed efficiently through constant-time edge-aware filtering and a
proposed affine-varying CNN-based descriptor. Experimental results show that
this model outperforms the state-of-the-art methods for dense semantic
correspondence on various benchmarks
Dramatic Shape Sensitivity of Directional Emission Patterns from Similarly Deformed Cylindrical Polymer Lasers
Recent experiments on similarly shaped polymer micro-cavity lasers show a
dramatic difference in the far-field emission patterns. We show for different
deformations of the ellipse, quadrupole and hexadecapole that the large
differences in the far-field emission patterns is explained by the differing
ray dynamics corresponding to each shape. Analyzing the differences in the
appropriate phase space for ray motion, it is shown that the differing
geometries of the unstable manifolds of periodic orbits are the decisive
factors in determining the far-field pattern. Surprisingly, we find that
strongly chaotic ray dynamics is compatible with highly directional emission in
the far-field.Comment: 14 pages, 16 figures (eps), RevTeX 4, submitted to JOSA
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
LookUP: Vision-Only Real-Time Precise Underground Localisation for Autonomous Mining Vehicles
A key capability for autonomous underground mining vehicles is real-time
accurate localisation. While significant progress has been made, currently
deployed systems have several limitations ranging from dependence on costly
additional infrastructure to failure of both visual and range sensor-based
techniques in highly aliased or visually challenging environments. In our
previous work, we presented a lightweight coarse vision-based localisation
system that could map and then localise to within a few metres in an
underground mining environment. However, this level of precision is
insufficient for providing a cheaper, more reliable vision-based automation
alternative to current range sensor-based systems. Here we present a new
precision localisation system dubbed "LookUP", which learns a
neural-network-based pixel sampling strategy for estimating homographies based
on ceiling-facing cameras without requiring any manual labelling. This new
system runs in real time on limited computation resource and is demonstrated on
two different underground mine sites, achieving real time performance at ~5
frames per second and a much improved average localisation error of ~1.2 metre.Comment: 7 pages, 7 figures, accepted for IEEE ICRA 201
Proposal Flow
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 proposal flow can effectively be
transformed into a conventional dense flow field. We introduce a new dataset
that can be used to evaluate both general semantic flow techniques and
region-based approaches such as proposal flow. We use this benchmark 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
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