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On evidence gathering in 3D point clouds of static and moving objects

On evidence gathering in 3D point clouds of static and moving objects
On evidence gathering in 3D point clouds of static and moving objects
The recent and considerable progress in 3D sensing technologies mandates the development of efficient algorithms to process the sensed data. Many of these algorithms are based on computing and matching of 3D feature descriptors in order to estimate point correspondences between 3D datasets.

The dependency on 3D feature description and computation can be a significant limitation to many 3D perception tasks; the fact that there are a variety of criteria used to describe 3D features, such as surface normals and curvature, makes feature-based approaches sensitive to noise and occlusion. In many cases, such as smooth surfaces, computation of feature descriptors can be non-informative. Moreover, the process of computing and matching features requires more computational overhead than using points directly.

On the other hand, there has not been much focus on employing evidence gathering frameworks to obtain solutions for 3D perception problems. Evidence gathering approaches, which use data directly, have proved to provide robust performance against noise and occlusion. More importantly, evidence gathering approaches do not require initialisation or training, and avoid the need to solve the correspondence problem.

The capability to detect, extract and reconstruct 3D bjects without relying on feature matching and estimating correspondences between 3D datasets has not been thoroughly investigated, and is certainly desirable and has many practical applications.

In this thesis we present theoretical formulations and practical solutions to 3D perceptual tasks, that are based on evidence gathering. We propose a new 3D reconstruction algorithm for rotating objects that is based on motion-compensated temporal accumulation. We also propose two fast and robust Hough Transform based algorithms for 3D static parametric object detection and 3D moving parametric object extraction.

Furthermore, we introduce two algorithms for 3D motion parameter estimation that are based on Reuleaux's and Chasles' kinematic theorems. The proposed algorithms estimate 3D motion parameters directly from the data by exploiting the geometry of rigid transformation. Moreover, they provide an alternative to the both local and global feature description and matching pipelines commonly used by numerous 3D object recognition and registration algorithms.

Our objective is to provide new means for understanding static and dynamic scenes, captured by new 3D sensing technologies as we believe that these technologies will be dominant in the perception field as they are going under rapid development. We provide alternative solutions to commonly used feature based approaches by using new evidence gathering based methods for the processing of 3D range data.
Abuzaina, Anas
8e37a6fc-d659-45a0-8b8b-e885f385ece5
Abuzaina, Anas
8e37a6fc-d659-45a0-8b8b-e885f385ece5
Nixon, Mark
2b5b9804-5a81-462a-82e6-92ee5fa74e12

Abuzaina, Anas (2015) On evidence gathering in 3D point clouds of static and moving objects. University of Southampton, Physical Sciences and Engineering, Doctoral Thesis, 184pp.

Record type: Thesis (Doctoral)

Abstract

The recent and considerable progress in 3D sensing technologies mandates the development of efficient algorithms to process the sensed data. Many of these algorithms are based on computing and matching of 3D feature descriptors in order to estimate point correspondences between 3D datasets.

The dependency on 3D feature description and computation can be a significant limitation to many 3D perception tasks; the fact that there are a variety of criteria used to describe 3D features, such as surface normals and curvature, makes feature-based approaches sensitive to noise and occlusion. In many cases, such as smooth surfaces, computation of feature descriptors can be non-informative. Moreover, the process of computing and matching features requires more computational overhead than using points directly.

On the other hand, there has not been much focus on employing evidence gathering frameworks to obtain solutions for 3D perception problems. Evidence gathering approaches, which use data directly, have proved to provide robust performance against noise and occlusion. More importantly, evidence gathering approaches do not require initialisation or training, and avoid the need to solve the correspondence problem.

The capability to detect, extract and reconstruct 3D bjects without relying on feature matching and estimating correspondences between 3D datasets has not been thoroughly investigated, and is certainly desirable and has many practical applications.

In this thesis we present theoretical formulations and practical solutions to 3D perceptual tasks, that are based on evidence gathering. We propose a new 3D reconstruction algorithm for rotating objects that is based on motion-compensated temporal accumulation. We also propose two fast and robust Hough Transform based algorithms for 3D static parametric object detection and 3D moving parametric object extraction.

Furthermore, we introduce two algorithms for 3D motion parameter estimation that are based on Reuleaux's and Chasles' kinematic theorems. The proposed algorithms estimate 3D motion parameters directly from the data by exploiting the geometry of rigid transformation. Moreover, they provide an alternative to the both local and global feature description and matching pipelines commonly used by numerous 3D object recognition and registration algorithms.

Our objective is to provide new means for understanding static and dynamic scenes, captured by new 3D sensing technologies as we believe that these technologies will be dominant in the perception field as they are going under rapid development. We provide alternative solutions to commonly used feature based approaches by using new evidence gathering based methods for the processing of 3D range data.

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More information

Published date: June 2015
Organisations: University of Southampton, Vision, Learning and Control

Identifiers

Local EPrints ID: 381290
URI: https://eprints.soton.ac.uk/id/eprint/381290
PURE UUID: 5f77ca8b-b89f-4e8b-8855-945d2118a9f8
ORCID for Mark Nixon: ORCID iD orcid.org/0000-0002-9174-5934

Catalogue record

Date deposited: 13 Oct 2015 13:54
Last modified: 06 Jun 2018 13:18

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