105 research outputs found
Autocalibration with the Minimum Number of Cameras with Known Pixel Shape
In 3D reconstruction, the recovery of the calibration parameters of the
cameras is paramount since it provides metric information about the observed
scene, e.g., measures of angles and ratios of distances. Autocalibration
enables the estimation of the camera parameters without using a calibration
device, but by enforcing simple constraints on the camera parameters. In the
absence of information about the internal camera parameters such as the focal
length and the principal point, the knowledge of the camera pixel shape is
usually the only available constraint. Given a projective reconstruction of a
rigid scene, we address the problem of the autocalibration of a minimal set of
cameras with known pixel shape and otherwise arbitrarily varying intrinsic and
extrinsic parameters. We propose an algorithm that only requires 5 cameras (the
theoretical minimum), thus halving the number of cameras required by previous
algorithms based on the same constraint. To this purpose, we introduce as our
basic geometric tool the six-line conic variety (SLCV), consisting in the set
of planes intersecting six given lines of 3D space in points of a conic. We
show that the set of solutions of the Euclidean upgrading problem for three
cameras with known pixel shape can be parameterized in a computationally
efficient way. This parameterization is then used to solve autocalibration from
five or more cameras, reducing the three-dimensional search space to a
two-dimensional one. We provide experiments with real images showing the good
performance of the technique.Comment: 19 pages, 14 figures, 7 tables, J. Math. Imaging Vi
Data Fusion of Objects Using Techniques Such as Laser Scanning, Structured Light and Photogrammetry for Cultural Heritage Applications
In this paper we present a semi-automatic 2D-3D local registration pipeline
capable of coloring 3D models obtained from 3D scanners by using uncalibrated
images. The proposed pipeline exploits the Structure from Motion (SfM)
technique in order to reconstruct a sparse representation of the 3D object and
obtain the camera parameters from image feature matches. We then coarsely
register the reconstructed 3D model to the scanned one through the Scale
Iterative Closest Point (SICP) algorithm. SICP provides the global scale,
rotation and translation parameters, using minimal manual user intervention. In
the final processing stage, a local registration refinement algorithm optimizes
the color projection of the aligned photos on the 3D object removing the
blurring/ghosting artefacts introduced due to small inaccuracies during the
registration. The proposed pipeline is capable of handling real world cases
with a range of characteristics from objects with low level geometric features
to complex ones
From Multiview Image Curves to 3D Drawings
Reconstructing 3D scenes from multiple views has made impressive strides in
recent years, chiefly by correlating isolated feature points, intensity
patterns, or curvilinear structures. In the general setting - without
controlled acquisition, abundant texture, curves and surfaces following
specific models or limiting scene complexity - most methods produce unorganized
point clouds, meshes, or voxel representations, with some exceptions producing
unorganized clouds of 3D curve fragments. Ideally, many applications require
structured representations of curves, surfaces and their spatial relationships.
This paper presents a step in this direction by formulating an approach that
combines 2D image curves into a collection of 3D curves, with topological
connectivity between them represented as a 3D graph. This results in a 3D
drawing, which is complementary to surface representations in the same sense as
a 3D scaffold complements a tent taut over it. We evaluate our results against
truth on synthetic and real datasets.Comment: Expanded ECCV 2016 version with tweaked figures and including an
overview of the supplementary material available at
multiview-3d-drawing.sourceforge.ne
A Self-calibration Algorithm Based on a Unified Framework for Constraints on Multiple Views
Towards High-resolution Imaging from Underwater Vehicles
Large area mapping at high resolution underwater continues to be constrained by sensor-level environmental constraints and the mismatch between available navigation and sensor accuracy. In this paper, advances are presented that exploit aspects of the sensing modality, and consistency and redundancy within local sensor measurements to build high-resolution optical and acoustic maps that are a consistent representation of the environment. This work is presented in the context of real-world data acquired using autonomous underwater vehicles (AUVs) and remotely operated vehicles (ROVs) working in diverse applications including shallow water coral reef surveys with the Seabed AUV, a forensic survey of the RMS Titanic in the North Atlantic at a depth of 4100 m using the Hercules ROV, and a survey of the TAG hydrothermal vent area in the mid-Atlantic at a depth of 3600 m using the Jason II ROV. Specifically, the focus is on the related problems of structure from motion from underwater optical imagery assuming pose instrumented calibrated cameras. General wide baseline solutions are presented for these problems based on the extension of techniques from the simultaneous localization and mapping (SLAM), photogrammetric and the computer vision communities. It is also examined how such techniques can be extended for the very different sensing modality and scale associated with multi-beam bathymetric mapping. For both the optical and acoustic mapping cases it is also shown how the consistency in mapping can be used not only for better global mapping, but also to refine navigation estimates.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/86051/1/hsingh-21.pd
Model-free Consensus Maximization for Non-Rigid Shapes
Many computer vision methods use consensus maximization to relate
measurements containing outliers with the correct transformation model. In the
context of rigid shapes, this is typically done using Random Sampling and
Consensus (RANSAC) by estimating an analytical model that agrees with the
largest number of measurements (inliers). However, small parameter models may
not be always available. In this paper, we formulate the model-free consensus
maximization as an Integer Program in a graph using `rules' on measurements. We
then provide a method to solve it optimally using the Branch and Bound (BnB)
paradigm. We focus its application on non-rigid shapes, where we apply the
method to remove outlier 3D correspondences and achieve performance superior to
the state of the art. Our method works with outlier ratio as high as 80\%. We
further derive a similar formulation for 3D template to image matching,
achieving similar or better performance compared to the state of the art.Comment: ECCV1
Naturalised Vitis Rootstocks in Europe and Consequences to Native Wild Grapevine
The genus Vitis is represented by several coexisting species in Europe. Our study focuses on naturalised rootstocks that originate in viticulture. The consequences of their presence to the landscape and to native European species (Vitis vinifera ssp. silvestris) are evaluated. This study compares ecological traits (seven qualitative and quantitative descriptors) and the genetic diversity (10 SSR markers) of populations of naturalised rootstocks and native wild grapevines. 18 large naturalised rootstock populations were studied in the Rhône watershed. Wild European grapevines are present in four main habitats (screes, alluvial forests, hedges, and streamside hedges). In contrast, naturalised rootstock populations are mainly located in alluvial forests, but they clearly take advantage of alluvial system dynamics and connectivity at the landscape level. These latter populations appear to reproduce sexually, and show a higher genetic diversity than Vitis vinifera ssp. silvestris. The regrouping of naturalised rootstocks in interconnected populations tends to create active hybrid swarms of rootstocks. The rootstocks show characters of invasive plants. The spread of naturalised rootstocks in the environment, the acceleration of the decline of the European wild grapevine, and the propagation of genes of viticultural interest in natural populations are potential consequences that should be kept in mind when undertaking appropriate management measures
Foundation characteristics of edible Musa triploids revealed from allelic distribution of SSR markers
Background and Aims The production of triploid banana and plantain (Musa spp.) cultivars with improved characteristics (e.g. greater disease resistance or higher yield), while still preserving the main features of current popular cultivars (e.g. taste and cooking quality), remains a major challenge for Musa breeders. In this regard, breeders require a sound knowledge of the lineage of the current sterile triploid cultivars, to select diploid parents that are able to transmit desirable traits, together with a breeding strategy ensuring final triploidization and sterility. Highly polymorphic single sequence repeats (SSRs) are valuable markers for investigating phylogenetic relationships. Methods Here, the allelic distribution of each of 22 SSR loci across 561 Musa accessions is analysed. Key Results and ConclusionsWe determine the closest diploid progenitors of the triploid 'Cavendish' and 'Gros Michel' subgroups, valuable information for breeding programmes. Nevertheless, in establishing the likely monoclonal origin of the main edible triploid banana subgroups (i.e. 'Cavendish', 'Plantain' and 'Mutika- Lujugira'), we postulated that the huge phenotypic diversity observed within these subgroups did not result from gamete recombination, but rather from epigenetic regulations. This emphasizes the need to investigate the regulatory mechanisms of genome expression on a unique model in the plant kingdom. We also propose experimental standards to compare additional and independent genotyping data for reference. (Résumé d'auteur
Imposing Semi-Local Geometric Constraints for Accurate Correspondences Selection in Structure from Motion: A Game-Theoretic Perspective
Most Structure from Motion pipelines are based on the iterative refinement of an initial batch of feature correspondences. Typically this is performed by selecting a set of match candidates based on their photometric similarity; an initial estimate of camera intrinsic and extrinsic parameters is then computed by minimizing the reprojection error. Finally, outliers in the initial correspondences are filtered by enforcing some global geometric property such as the epipolar constraint. In the literature many different approaches have been proposed to deal with each of these three steps, but almost invariably they separate the first inlier selection step, which is based only on local image properties, from the enforcement of global geometric consistency. Unfortunately, these two steps are not independent since outliers can lead to inaccurate parameter estimation or even prevent convergence, leading to the well known sensitivity of all filtering approaches to the number of outliers, especially in the presence of structured noise, which can arise, for example, when the images present several repeated patterns. In this paper we introduce a novel stereo correspondence selection scheme that casts the problem into a Game-Theoretic framework in order to guide the inlier selection towards a consistent subset of correspondences. This is done by enforcing geometric constraints that do not depend on full knowledge of the motion parameters but rather on some semi-local property that can be estimated from the local appearance of the image features. The practical effectiveness of the proposed approach is confirmed by an extensive set of experiments and comparisons with state-of-the-art techniques
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