An integrated framework for 3 d modeling, object detection, and pose estimation from point-clouds

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AN INTEGRATED FRAMEWORK FOR 3-D MODELING, OBJECT
DETECTION, AND POSE ESTIMATION FROM POINT-CLOUDS
By
A
PROJECT REPORT
Submitted to the Department of electronics &communication Engineering in the
FACULTY OF ENGINEERING & TECHNOLOGY
In partial fulfillment of the requirements for the award of the degree
Of
MASTER OF TECHNOLOGY
IN
ELECTRONICS &COMMUNICATION ENGINEERING
APRIL 2016

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CERTIFICATE
Certified that this project report titled “An Integrated Framework for 3-D Modeling, Object
Detection, and Pose Estimation From Point-Clouds” is the bonafide work of Mr.
_____________Who carried out the research under my supervision Certified further, that to the
best of my knowledge the work reported herein does not form part of any other project report or
dissertation on the basis of which a degree or award was conferred on an earlier occasion on this
or any other candidate.
Signature of the Guide Signature of the H.O.D
Name Name

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DECLARATION
I hereby declare that the project work entitled “An Integrated Framework for 3-D Modeling,
Object Detection, and Pose Estimation From Point-Clouds” Submitted to
BHARATHIDASAN UNIVERSITY in partial fulfillment of the requirement for the award of the
Degree of MASTER OF APPLIED ELECTRONICS is a record of original work done by me the
guidance of Prof.A.Vinayagam M.Sc., M.Phil., M.E., to the best of my knowledge, the work
reported here is not a part of any other thesis or work on the basis of which a degree or award was
conferred on an earlier occasion to me or any other candidate.
(Student Name)
(Reg.No)
Place:
Date:

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ACKNOWLEDGEMENT
I am extremely glad to present my project “An Integrated Framework for 3-D Modeling, Object
Detection, and Pose Estimation From Point-Clouds” which is a part of my curriculum of third
semester Master of Science in Computer science. I take this opportunity to express my sincere
gratitude to those who helped me in bringing out this project work.
I would like to express my Director,Dr. K. ANANDAN, M.A.(Eco.), M.Ed., M.Phil.,(Edn.),
PGDCA., CGT., M.A.(Psy.)of who had given me an opportunity to undertake this project.
I am highly indebted to Co-OrdinatorProf. Muniappan Department of Physics and thank from
my deep heart for her valuable comments I received through my project.
I wish to express my deep sense of gratitude to my guide
Prof. A.Vinayagam M.Sc., M.Phil., M.E., for her immense help and encouragement for
successful completion of this project.
I also express my sincere thanks to the all the staff members of Computer science for their kind
advice.
And last, but not the least, I express my deep gratitude to my parents and friends for their
encouragement and support throughout the project.

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ABSTRACT:
3-D modeling, object detection, and pose estimation are three of the most challenging tasks
in the area of 3-D computer vision. This paper presents a novel algorithm to perform these tasks
simultaneously from unordered point-clouds. Given a set of input point-clouds in the presence of
clutter and occlusion, an initial model is first constructed by performing pair-wise registration
between any two point-clouds. The resulting model is then updated from the remaining point-
clouds using a novel model growing technique. Once the final model is reconstructed, the instances
of the object are detected and the poses of its instances in the scenes are estimated. This algorithm
is automatic, model free, and does not rely on any prior information about the objects in the scene.
The algorithm was comprehensively tested on the University of Western Australia data set.
Experimental results show that our algorithm achieved accurate modeling, detection, and pose
estimation performance

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INTRODUCTION:
With the rapid development of 3-D surface measurement techniques point-clouds are now
readily available and popular. The availability of low-cost point-clouds and powerful computing
devices is inspiring ample research in several areas including measurement, computer vision, and
computer graphics. Among these, 3-D modeling and 3-D object recognition are two of the major
fundamental problems. The task of 3-D modeling is to align the point-clouds which are measured
at different viewpoints, and merge these point-clouds to obtain a complete model of an object
Meanwhile, the aim of 3-D object recognition is to correctly estimate the identities and poses
(locations and orientations) of these objects in a scene. 3-D object modeling, detection, and
recognition have a number of applications including scene measurement, autonomous mapping,
city planning, reverse engineering, remote sensing, industrial inspection, and biometrics .
Most existing 3-D object recognition algorithms follow a model-based paradigm.During
the offline preprocessing phase, 3-D models of objects of interest are first constructed and stored
in a library along with a set of suitably extracted features. During the online recognition phase,
features are extracted from a point-cloud of the scene and matched against these model features to
recognize potential objects Several features have been proposed to enhance the performance of 3-
D object recognition, including point signatures,spin image,3-D tensor,exponential map rotational
projection statistics (RoPS), signature of histograms of orientations (SHOT) and tri-spin image
(TriSI) However, these 3-D object recognition algorithms require prior 3-D models of objects.
They are therefore, unable to recognize unknown objects in a scene.
To perform 3-D object modeling/recognition, multiple point-clouds must be registered in
a common coordinate basis . A complete registration process usually consists of two steps: coarse
and fine registrations. Coarse registration can be performed by either manual alignment, motion
tracking, or local feature matching . Local feature matching-based algorithms automatically extract
corresponding points from any two (pairwise registration) or multiple point-clouds and coarsely
register them by minimizing the distance between these points. Due to the nature that they are
automatic, flexible, and cheap, local feature matchingbased algorithms have been intensively
studied. Once these point-clouds are coarsely registered, a fine registration algorithm is applied to
iteratively refine the initial coarse registration. Examples of fine registration algorithms include
the iterative closest point (ICP) algorithm, Chen and Medioni’s algorithm, and the signed distance

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fields-based algorithm. These existing algorithms were proposed to perform matching either
between point-clouds of isolated scenes (in the case of 3-D modeling), or between a cluttered scene
and an isolated model (in the case of 3-D object recognition). To the best of our knowledge, there
is limited research in the literature that covers the matching between two cluttered scenes for the
modeling of isolated 3-D objects (rather than 3-D scenes). For more details on 3-D object
recognition algorithms, the reader is referred to a comprehensive and contemporary survey.
This paper is motivated by this research niche to detect unknown objects without any prior
information, and to model these objects from a set of cluttered scenes. In this paper, 3-D object
detection and modeling is performed based on the observation that, an object may appear in
different scenes due to the movement of the object or sensor. It is therefore, possible to detect,
segment, and reconstruct objects that appear multiple times in a set of point-clouds. A system with
such capability has several applications. For example, a robot with a 3-D sensor can automatically
detect unknown objects and hand them in for labeling, to reduce the labor-consuming object
labeling work. It can also acquire a data set of 3-D models of objects in a room by roaming around,
without isolately placing each object in a controlled environment (e.g., a turntable with a clear
background). Moreover, the surge of low-cost 3-D scanners with an increasingly higher resolution
(e.g., the new Microsoft Kinect) will allow the use of the proposed framework with many practical
applications.
Due to the presence of clutter and occlusion, together with the inability to provide an exact
definition of what constitute an object, it is very challenging to automatically detect unknown
objects in point-clouds. This paper proposes an integrated framework for 3-D modeling, object
detection, and pose estimation from point-clouds. It first registers two cluttered scenes which have
an overlapping surface area to build an initial model. The model is then updated by iteratively
registering the model with the remaining unchecked pointclouds, and finally reconstructed by
confidence thresholding. Consequently, the objects corresponding to this model can be detected
and segmented from these point-clouds at the same time. The contributions of this paper are
threefold. First, it performs 3-D object detection from point-clouds without any prior information
(e.g., models). Second, it constructs 3-D models of multiple unknown objects from a set of
cluttered point-clouds. Third, it performs modeling, detection, and pose estimation of unknown 3-
D objects simultaneously.

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The rest of this paper is organized as follows. Section II describes an overview of the
proposed algorithm. Section III introduces the model initialization technique. Section IV describes
the model growing technique. Section V presents the modeling, detection, and pose estimation
technique. Section VI presents the experimental results and analysis. Section VII gives an
insightful discussion. Section VIII concludes this paper.

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CONCLUSION:
In this paper, we presented a novel algorithm to model and detect 3-D objects, and to
simultaneously estimate their poses from point-clouds. The algorithm consists of three main
modules: model initialization, model growing, and modeling, detection, and pose estimation.
Model initialization is performed by surface registration between any two pointclouds. The highly
descriptive RoPS features and the 1-point RANSAC algorithm are used to achieve surface
registration. Model growing is then performed by surface registration between the model and the
unchecked point-clouds. During the process of model growing, a model update technique and a
confidence scoring strategy are proposed. Finally, a final model is constructed by confidence
thresholding and outlier cleaning. Meanwhile,
The points in a point-cloud which can be registered well with the final model are detected
as an instance of the object, and the pose of the object instance is estimated. The algorithm does
not rely on any prior information and is automatic. Extensive experiments were conducted on the
popular UWA data set. The performance of the proposed algorithm was tested in terms of modeling
accuracy, detection rate, and pose estimation accuracy. Experimental results show that our
algorithm can detect objects with a high detection rate. It can also build models and estimate their
poses very accurately. Moreover, the proposed algorithm was compared with the state-of-the-art
(i.e., the SHOT and spin image-based) algorithms. Experimental results show that our algorithm
achieves the best results.

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An integrated framework for 3 d modeling, object detection, and pose estimation from point-clouds