3,765 research outputs found
FrameNet: Learning Local Canonical Frames of 3D Surfaces from a Single RGB Image
In this work, we introduce the novel problem of identifying dense canonical
3D coordinate frames from a single RGB image. We observe that each pixel in an
image corresponds to a surface in the underlying 3D geometry, where a canonical
frame can be identified as represented by three orthogonal axes, one along its
normal direction and two in its tangent plane. We propose an algorithm to
predict these axes from RGB. Our first insight is that canonical frames
computed automatically with recently introduced direction field synthesis
methods can provide training data for the task. Our second insight is that
networks designed for surface normal prediction provide better results when
trained jointly to predict canonical frames, and even better when trained to
also predict 2D projections of canonical frames. We conjecture this is because
projections of canonical tangent directions often align with local gradients in
images, and because those directions are tightly linked to 3D canonical frames
through projective geometry and orthogonality constraints. In our experiments,
we find that our method predicts 3D canonical frames that can be used in
applications ranging from surface normal estimation, feature matching, and
augmented reality
Human Perambulation as a Self Calibrating Biometric
This paper introduces a novel method of single camera gait reconstruction which is independent of the walking direction and of the camera parameters. Recognizing people by gait has unique advantages with respect to other biometric techniques: the identification of the walking subject is completely unobtrusive and the identification can be achieved at distance. Recently much research has been conducted into the recognition of frontoparallel gait. The proposed method relies on the very nature of walking to achieve the independence from walking direction. Three major assumptions have been done: human gait is cyclic; the distances between the bone joints are invariant during the execution of the movement; and the articulated leg motion is approximately planar, since almost all of the perceived motion is contained within a single limb swing plane. The method has been tested on several subjects walking freely along six different directions in a small enclosed area. The results show that recognition can be achieved without calibration and without dependence on view direction. The obtained results are particularly encouraging for future system development and for its application in real surveillance scenarios
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
Plane extraction for indoor place recognition
In this paper, we present an image based plane extraction
method well suited for real-time operations. Our approach exploits the
assumption that the surrounding scene is mainly composed by planes
disposed in known directions. Planes are detected from a single image
exploiting a voting scheme that takes into account the vanishing lines.
Then, candidate planes are validated and merged using a region grow-
ing based approach to detect in real-time planes inside an unknown in-
door environment. Using the related plane homographies is possible to
remove the perspective distortion, enabling standard place recognition
algorithms to work in an invariant point of view setup. Quantitative Ex-
periments performed with real world images show the effectiveness of our
approach compared with a very popular method
Contact Representations of Graphs in 3D
We study contact representations of graphs in which vertices are represented
by axis-aligned polyhedra in 3D and edges are realized by non-zero area common
boundaries between corresponding polyhedra. We show that for every 3-connected
planar graph, there exists a simultaneous representation of the graph and its
dual with 3D boxes. We give a linear-time algorithm for constructing such a
representation. This result extends the existing primal-dual contact
representations of planar graphs in 2D using circles and triangles. While
contact graphs in 2D directly correspond to planar graphs, we next study
representations of non-planar graphs in 3D. In particular we consider
representations of optimal 1-planar graphs. A graph is 1-planar if there exists
a drawing in the plane where each edge is crossed at most once, and an optimal
n-vertex 1-planar graph has the maximum (4n - 8) number of edges. We describe a
linear-time algorithm for representing optimal 1-planar graphs without
separating 4-cycles with 3D boxes. However, not every optimal 1-planar graph
admits a representation with boxes. Hence, we consider contact representations
with the next simplest axis-aligned 3D object, L-shaped polyhedra. We provide a
quadratic-time algorithm for representing optimal 1-planar graph with L-shaped
polyhedra
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