1,011 research outputs found
A Minimalist Approach to Type-Agnostic Detection of Quadrics in Point Clouds
This paper proposes a segmentation-free, automatic and efficient procedure to
detect general geometric quadric forms in point clouds, where clutter and
occlusions are inevitable. Our everyday world is dominated by man-made objects
which are designed using 3D primitives (such as planes, cones, spheres,
cylinders, etc.). These objects are also omnipresent in industrial
environments. This gives rise to the possibility of abstracting 3D scenes
through primitives, thereby positions these geometric forms as an integral part
of perception and high level 3D scene understanding.
As opposed to state-of-the-art, where a tailored algorithm treats each
primitive type separately, we propose to encapsulate all types in a single
robust detection procedure. At the center of our approach lies a closed form 3D
quadric fit, operating in both primal & dual spaces and requiring as low as 4
oriented-points. Around this fit, we design a novel, local null-space voting
strategy to reduce the 4-point case to 3. Voting is coupled with the famous
RANSAC and makes our algorithm orders of magnitude faster than its conventional
counterparts. This is the first method capable of performing a generic
cross-type multi-object primitive detection in difficult scenes. Results on
synthetic and real datasets support the validity of our method.Comment: Accepted for publication at CVPR 201
Rotational Subgroup Voting and Pose Clustering for Robust 3D Object Recognition
It is possible to associate a highly constrained subset of relative 6 DoF
poses between two 3D shapes, as long as the local surface orientation, the
normal vector, is available at every surface point. Local shape features can be
used to find putative point correspondences between the models due to their
ability to handle noisy and incomplete data. However, this correspondence set
is usually contaminated by outliers in practical scenarios, which has led to
many past contributions based on robust detectors such as the Hough transform
or RANSAC. The key insight of our work is that a single correspondence between
oriented points on the two models is constrained to cast votes in a 1 DoF
rotational subgroup of the full group of poses, SE(3). Kernel density
estimation allows combining the set of votes efficiently to determine a full 6
DoF candidate pose between the models. This modal pose with the highest density
is stable under challenging conditions, such as noise, clutter, and occlusions,
and provides the output estimate of our method.
We first analyze the robustness of our method in relation to noise and show
that it handles high outlier rates much better than RANSAC for the task of 6
DoF pose estimation. We then apply our method to four state of the art data
sets for 3D object recognition that contain occluded and cluttered scenes. Our
method achieves perfect recall on two LIDAR data sets and outperforms competing
methods on two RGB-D data sets, thus setting a new standard for general 3D
object recognition using point cloud data.Comment: Accepted for International Conference on Computer Vision (ICCV), 201
GASP : Geometric Association with Surface Patches
A fundamental challenge to sensory processing tasks in perception and
robotics is the problem of obtaining data associations across views. We present
a robust solution for ascertaining potentially dense surface patch (superpixel)
associations, requiring just range information. Our approach involves
decomposition of a view into regularized surface patches. We represent them as
sequences expressing geometry invariantly over their superpixel neighborhoods,
as uniquely consistent partial orderings. We match these representations
through an optimal sequence comparison metric based on the Damerau-Levenshtein
distance - enabling robust association with quadratic complexity (in contrast
to hitherto employed joint matching formulations which are NP-complete). The
approach is able to perform under wide baselines, heavy rotations, partial
overlaps, significant occlusions and sensor noise.
The technique does not require any priors -- motion or otherwise, and does
not make restrictive assumptions on scene structure and sensor movement. It
does not require appearance -- is hence more widely applicable than appearance
reliant methods, and invulnerable to related ambiguities such as textureless or
aliased content. We present promising qualitative and quantitative results
under diverse settings, along with comparatives with popular approaches based
on range as well as RGB-D data.Comment: International Conference on 3D Vision, 201
Furniture models learned from the WWW: using web catalogs to locate and categorize unknown furniture pieces in 3D laser scans
In this article, we investigate how autonomous robots can exploit the high quality information already available from the WWW concerning 3-D models of office furniture. Apart from the hobbyist effort in Google 3-D Warehouse, many companies providing office furnishings already have the models for considerable portions of the objects found in our workplaces and homes. In particular, we present an approach that allows a robot to learn generic models of typical office furniture using examples found in the Web. These generic models are then used by the robot to locate and categorize unknown furniture in real indoor environments
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