3,843 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
Extraction robuste de primitives géométriques 3D dans un nuage de points et alignement basé sur les primitives
Dans ce projet, nous Ă©tudions les problĂšmes de rĂ©tro-ingĂ©nierie et de contrĂŽle de la qualitĂ© qui jouent un rĂŽle important dans la fabrication industrielle. La rĂ©tro-ingĂ©nierie tente de reconstruire un modĂšle 3D Ă partir de nuages de points, qui sâapparente au problĂšme de la reconstruction de la surface 3D. Le contrĂŽle de la qualitĂ© est un processus dans lequel la qualitĂ© de tous les facteurs impliquĂ©s dans la production est abordĂ©e. En fait, les systĂšmes ci-dessus nĂ©cessitent beaucoup dâintervention de la part dâun utilisateur expĂ©rimentĂ©, rĂ©sultat souhaitĂ© est encore loin soit une automatisation complĂšte du processus. Par consĂ©quent, de nombreux dĂ©fis doivent encore ĂȘtre abordĂ©s pour atteindre ce rĂ©sultat hautement souhaitable en production automatisĂ©e. La premiĂšre question abordĂ©e dans la thĂšse consiste Ă extraire les primitives gĂ©omĂ©triques 3D Ă partir de nuages de points. Un cadre complet pour extraire plusieurs types de primitives Ă partir de donnĂ©es 3D est proposĂ©. En particulier, une nouvelle mĂ©thode de validation est proposĂ©e pour Ă©valuer la qualitĂ© des primitives extraites. Ă la fin, toutes les primitives prĂ©sentes dans le nuage de points sont extraites avec les points de donnĂ©es associĂ©s et leurs paramĂštres descriptifs. Ces rĂ©sultats pourraient ĂȘtre utilisĂ©s dans diverses applications telles que la reconstruction de scĂšnes on dâĂ©difices, la gĂ©omĂ©trie constructive et etc. La seconde question traiĂ©e dans ce travail porte sur lâalignement de deux ensembles de donnĂ©es 3D Ă lâaide de primitives gĂ©omĂ©triques, qui sont considĂ©rĂ©es comme un nouveau descripteur robuste. LâidĂ©e dâutiliser les primitives pour lâalignement arrive Ă surmonter plusieurs dĂ©fis rencontrĂ©s par les mĂ©thodes dâalignement existantes. Ce problĂšme dâalignement est une Ă©tape essentielle dans la modĂ©lisation 3D, la mise en registre, la rĂ©cupĂ©ration de modĂšles. Enfin, nous proposons Ă©galement une mĂ©thode automatique pour extraire les discontinutĂ©s Ă partir de donnĂ©es 3D dâobjets manufacturĂ©s. En intĂ©grant ces discontinutĂ©s au problĂšme dâalignement, il est possible dâĂ©tablir automatiquement les correspondances entre primitives en utilisant lâappariement de graphes relationnels avec attributs. Nous avons expĂ©rimentĂ© tous les algorithmes proposĂ©s sur diffĂ©rents jeux de donnĂ©es synthĂ©tiques et rĂ©elles. Ces algorithmes ont non seulement rĂ©ussi Ă accomplir leur tĂąches avec succĂšs mais se sont aussi avĂ©rĂ©s supĂ©rieus aux mĂ©thodes proposĂ©es dans la literature. Les rĂ©sultats prĂ©sentĂ©s dans le thĂšse pourraient sâavĂ©rĂ©r utilises Ă plusieurs applications.In this research project, we address reverse engineering and quality control problems that play significant roles in industrial manufacturing. Reverse engineering attempts to rebuild a 3D model from the scanned data captured from a object, which is the problem similar to 3D surface reconstruction. Quality control is a process in which the quality of all factors involved in production is monitored and revised. In fact, the above systems currently require significant intervention from experienced users, and are thus still far from being fully automated. Therefore, many challenges still need to be addressed to achieve the desired performance for automated production. The first proposition of this thesis is to extract 3D geometric primitives from point clouds for reverse engineering and surface reconstruction. A complete framework to extract multiple types of primitives from 3D data is proposed. In particular, a novel validation method is also proposed to assess the quality of the extracted primitives. At the end, all primitives present in the point cloud are extracted with their associated data points and descriptive parameters. These results could be used in various applications such as scene and building reconstruction, constructive solid geometry, etc. The second proposition of the thesis is to align two 3D datasets using the extracted geometric primitives, which is introduced as a novel and robust descriptor. The idea of using primitives for alignment is addressed several challenges faced by existing registration methods. This alignment problem is an essential step in 3D modeling, registration and model retrieval. Finally, an automatic method to extract sharp features from 3D data of man-made objects is also proposed. By integrating the extracted sharp features into the alignment framework, it is possible implement automatic assignment of primitive correspondences using attribute relational graph matching. Each primitive is considered as a node of the graph and an attribute relational graph is created to provide a structural and relational description between primitives. We have experimented all the proposed algorithms on different synthetic and real scanned datasets. Our algorithms not only are successful in completing their tasks with good results but also outperform other methods. We believe that the contribution of them could be useful in many applications
Extraction of cylinders and cones from minimal point sets
We propose new algebraic methods for extracting cylinders and cones from
minimal point sets, including oriented points. More precisely, we are
interested in computing efficiently cylinders through a set of three points,
one of them being oriented, or through a set of five simple points. We are also
interested in computing efficiently cones through a set of two oriented points,
through a set of four points, one of them being oriented, or through a set of
six points. For these different interpolation problems, we give optimal bounds
on the number of solutions. Moreover, we describe algebraic methods targeted to
solve these problems efficiently
Cylinders extraction in non-oriented point clouds as a clustering problem
Finding geometric primitives in 3D point clouds is a fundamental task in many engineering applications such as robotics, autonomous-vehicles and automated industrial inspection. Among all solid shapes, cylinders are frequently found in a variety of scenes, comprising natural or man-made objects. Despite their ubiquitous presence, automated extraction and fitting can become challenging if performed âin-the-wildâ, when the number of primitives is unknown or the point cloud is noisy and not oriented. In this paper we pose the problem of extracting multiple cylinders in a scene by means of a Game-Theoretic inlier selection process exploiting the geometrical relations between pairs of axis candidates. First, we formulate the similarity between two possible cylinders considering the rigid motion aligning the two axes to the same line. This motion is represented with a unitary dual-quaternion so that the distance between two cylinders is induced by the length of the shortest geodesic path in SE(3). Then, a Game-Theoretical process exploits such similarity function to extract sets of primitives maximizing their inner mutual consensus. The outcome of the evolutionary process consists in a probability distribution over the sets of candidates (ie axes), which in turn is used to directly estimate the final cylinder parameters. An extensive experimental section shows that the proposed algorithm offers a high resilience to noise, since the process inherently discards inconsistent data. Compared to other methods, it does not need point normals and does not require a fine tuning of multiple parameters
Robust Detection of Non-overlapping Ellipses from Points with Applications to Circular Target Extraction in Images and Cylinder Detection in Point Clouds
This manuscript provides a collection of new methods for the automated
detection of non-overlapping ellipses from edge points. The methods introduce
new developments in: (i) robust Monte Carlo-based ellipse fitting to
2-dimensional (2D) points in the presence of outliers; (ii) detection of
non-overlapping ellipse from 2D edge points; and (iii) extraction of cylinder
from 3D point clouds. The proposed methods were thoroughly compared with
established state-of-the-art methods, using simulated and real-world datasets,
through the design of four sets of original experiments. It was found that the
proposed robust ellipse detection was superior to four reliable robust methods,
including the popular least median of squares, in both simulated and real-world
datasets. The proposed process for detecting non-overlapping ellipses achieved
F-measure of 99.3% on real images, compared to F-measures of 42.4%, 65.6%, and
59.2%, obtained using the methods of Fornaciari, Patraucean, and Panagiotakis,
respectively. The proposed cylinder extraction method identified all detectable
mechanical pipes in two real-world point clouds, obtained under laboratory, and
industrial construction site conditions. The results of this investigation show
promise for the application of the proposed methods for automatic extraction of
circular targets from images and pipes from point clouds
Algorithms for fitting cylindrical objects to sparse range point clouds for rapid workspace modeling
3-D Hand Pose Estimation from Kinect's Point Cloud Using Appearance Matching
We present a novel appearance-based approach for pose estimation of a human
hand using the point clouds provided by the low-cost Microsoft Kinect sensor.
Both the free-hand case, in which the hand is isolated from the surrounding
environment, and the hand-object case, in which the different types of
interactions are classified, have been considered. The hand-object case is
clearly the most challenging task having to deal with multiple tracks. The
approach proposed here belongs to the class of partial pose estimation where
the estimated pose in a frame is used for the initialization of the next one.
The pose estimation is obtained by applying a modified version of the Iterative
Closest Point (ICP) algorithm to synthetic models to obtain the rigid
transformation that aligns each model with respect to the input data. The
proposed framework uses a "pure" point cloud as provided by the Kinect sensor
without any other information such as RGB values or normal vector components.
For this reason, the proposed method can also be applied to data obtained from
other types of depth sensor, or RGB-D camera
Fast Cylinder and Plane Extraction from Depth Cameras for Visual Odometry
This paper presents CAPE, a method to extract planes and cylinder segments
from organized point clouds, which processes 640x480 depth images on a single
CPU core at an average of 300 Hz, by operating on a grid of planar cells.
While, compared to state-of-the-art plane extraction, the latency of CAPE is
more consistent and 4-10 times faster, depending on the scene, we also
demonstrate empirically that applying CAPE to visual odometry can improve
trajectory estimation on scenes made of cylindrical surfaces (e.g. tunnels),
whereas using a plane extraction approach that is not curve-aware deteriorates
performance on these scenes. To use these geometric primitives in visual
odometry, we propose extending a probabilistic RGB-D odometry framework based
on points, lines and planes to cylinder primitives. Following this framework,
CAPE runs on fused depth maps and the parameters of cylinders are modelled
probabilistically to account for uncertainty and weight accordingly the pose
optimization residuals.Comment: Accepted to IROS 201
Tree biomass equations from terrestrial LiDAR : a case study in Guyana
Large uncertainties in tree and forest carbon estimates weaken national efforts to accurately estimate aboveground biomass (AGB) for their national monitoring, measurement, reporting and verification system. Allometric equations to estimate biomass have improved, but remain limited. They rely on destructive sampling; large trees are under-represented in the data used to create them; and they cannot always be applied to different regions. These factors lead to uncertainties and systematic errors in biomass estimations. We developed allometric models to estimate tree AGB in Guyana. These models were based on tree attributes (diameter, height, crown diameter) obtained from terrestrial laser scanning (TLS) point clouds from 72 tropical trees and wood density. We validated our methods and models with data from 26 additional destructively harvested trees. We found that our best TLS-derived allometric models included crown diameter, provided more accurate AGB estimates (R-2 = 0.92-0.93) than traditional pantropical models (R-2 = 0.85-0.89), and were especially accurate for large trees (diameter > 70 cm). The assessed pantropical models underestimated AGB by 4 to 13%. Nevertheless, one pantropical model (Chave et al. 2005 without height) consistently performed best among the pantropical models tested (R-2 = 0.89) and predicted AGB accurately across all size classes-which but for this could not be known without destructive or TLS-derived validation data. Our methods also demonstrate that tree height is difficult to measure in situ, and the inclusion of height in allometric models consistently worsened AGB estimates. We determined that TLS-derived AGB estimates were unbiased. Our approach advances methods to be able to develop, test, and choose allometric models without the need to harvest trees
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