10,868 research outputs found
Robust 2D-3D alignment based on geometrical consistency
This paper presents a new registration algorithm of a 2D image and a 3D geometrical model, which is robust for initial registration errors, for reconstructing a realistic 3D model of indoor scene settings. One of the typical tech-niques of pose estimation of a 3D model in a 2D image is the method based on the correspondences between 2D pho-tometrical edges and 3D geometrical edges projected on the 2D image. However, for indoor settings, features extracted on the 2D image and jump edges of the geometrical model, which can be extracted robustly, are limited. Therefore, it is difficult to find corresponding edges between the 2D image and the 3D model correctly. For this reason, in most cases, the relative position has to be manually set close to correct position beforehand. To overcome this problem, in the pro-posed method, firstly the relative pose is roughly estimated by utilizing geometrical consistencies of back-projected 2D photometrical edges on a 3D model. Next, the edge-based method is applied for the precise pose estimation after the above estimation procedure is converged. The performance of the proposed method is successfully demonstrated with some experiments using simulated models of indoor scene settings and actual environments measured by range and image sensors. 1
Automating joiners
Pictures taken from different view points cannot be stitched into a geometrically consistent mosaic, unless the structure of the scene is very special. However, geometrical consistency is not the only criterion for success: incorporating multiple view points into the same picture may produce compelling and informative representations. A multi viewpoint form of visual expression that has recently become highly popular is that of joiners (a term coined by artist David Hockney). Joiners are compositions where photographs are layered on a 2D canvas, with some photographs occluding others and boundaries fully visible.
Composing joiners is currently a tedious manual process, especially when a great number of photographs is involved. We are thus interested in automating their construction. Our approach is based on optimizing a cost function encouraging image-to-image consistency which is measured on point-features and along picture boundaries. The optimization looks for consistency in the 2D composition rather than 3D geometrical scene consistency and explicitly considers occlusion between pictures. We illustrate our ideas with a number of experiments on collections of images of objects, people, and outdoor scenes
Multi-View Image Compositions
The geometry of single-viewpoint panoramas is well understood: multiple pictures taken from the same viewpoint may be stitched together into a consistent panorama mosaic. By contrast, when the point of view changes or when the scene changes (e.g., due to objects moving) no consistent mosaic may be obtained, unless the structure of the scene is very special.
Artists have explored this problem and demonstrated that geometrical consistency is not the only criterion for success: incorporating multiple view points in space and time into the same panorama may produce compelling and
informative pictures. We explore this avenue and suggest an approach to automating the construction of mosaics from images taken from multiple view points into a single panorama. Rather than looking at 3D scene consistency we look at image consistency. Our approach is based on optimizing a cost function that keeps into account image-to-image consistency which is measured on point-features and along picture boundaries. The optimization explicitly considers occlusion between pictures.
We illustrate our ideas with a number of experiments on collections of images of objects and outdoor scenes
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
3D Registration of Aerial and Ground Robots for Disaster Response: An Evaluation of Features, Descriptors, and Transformation Estimation
Global registration of heterogeneous ground and aerial mapping data is a
challenging task. This is especially difficult in disaster response scenarios
when we have no prior information on the environment and cannot assume the
regular order of man-made environments or meaningful semantic cues. In this
work we extensively evaluate different approaches to globally register UGV
generated 3D point-cloud data from LiDAR sensors with UAV generated point-cloud
maps from vision sensors. The approaches are realizations of different
selections for: a) local features: key-points or segments; b) descriptors:
FPFH, SHOT, or ESF; and c) transformation estimations: RANSAC or FGR.
Additionally, we compare the results against standard approaches like applying
ICP after a good prior transformation has been given. The evaluation criteria
include the distance which a UGV needs to travel to successfully localize, the
registration error, and the computational cost. In this context, we report our
findings on effectively performing the task on two new Search and Rescue
datasets. Our results have the potential to help the community take informed
decisions when registering point-cloud maps from ground robots to those from
aerial robots.Comment: Awarded Best Paper at the 15th IEEE International Symposium on
Safety, Security, and Rescue Robotics 2017 (SSRR 2017
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