Exploring explicit models for geometric point cloud learning
department or school: Science, Faculty of
department or school: Computer Science, Department of
data provider: DSpace
degree campus: UBCV
creator: Sun, Weiwei
issued: 2024-10-09T21:31:19Z
issued: 2024
related degree: Doctor of Philosophy - PhD
degree grantor: University of British Columbia
description: We are interested in processing point clouds -- a set of unordered points -- specifically in Euclidean space, such as 3D point cloud acquired from a range sensor (LiDAR) or 4D correspondence cloud in stereo matching task. Point clouds play an increasingly essential role in many tasks due to prevalence they hold. However, it is notoriously challenging to process point clouds with deep neural networks because of their irregular data structure, the difficulty in encoding contextual information from nearby points, and the large compute requirement that is typically required. This thesis addresses these challenges by enforcing intermediate features or model parameters to carry specific meanings such as attention and poses, leading to explicit representation. The meanings of explicit representation allow for traditional ways of manipulating features in order to solve target tasks. We refer to these architectures with explicit representations as explicit models. Explicit models largely improve performances without massively scaling up training data or model size because the explicit representation directly injects the prior knowledge needed by target tasks into neural networks without any learning. We explore explicit models for point cloud learning to perform robust estimation, stereo matching, segmentation, reconstruction and neural rendering. The thesis is organized into four chapters: 1, ACNe: An optimization-inspired network architecture that allows learning with point clouds contaminated with an abundance of outliers. 2, Canonical Capsules: An equivariant latent representation that consists of pose and pose-invariant features, enabling point cloud auto-encoding in unaligned datasets. 3, NeuralBF: A novel 3D instance proposal generation inspired by traditional bilateral filtering for top-down instance segmentation for 3D point clouds. 4, PointNeRF++: A multi-scale, point-based NeRF architecture, allowing seamless integration of point-based representation with Neural R…
aggregated CHO: https://circle.library.ubc.ca/rest/handle/2429/89368
note: Exploring Explicit Models forGeometric Point Cloud LearningbyWeiwei SunA THESIS SUBMITTED IN PARTIAL FULFILLMENT OFTHE REQUIREMENTS FOR THE DEGREE OFDOCTOR OF PHILOSOPHYinThe Faculty of Graduate and Postdoctoral Studies(Computer Science)THE UNIVERSITY OF BRITISH COLUMBIA(Vancouver)October 2024© Weiwei Sun 2024The following individuals certify that they have read, and recommend tothe Faculty of Graduate and Postdoctoral Studies for acceptance, the thesisentitled:Exploring Explicit Models for Geometric Point Cloud Learningsubmitted by Weiwei Sun in partial fulfillment of the requirements for thedegree of Doctor of Philosophy in Computer Science.Examining Committee:Kwang Moo Yi, Assistant Professor, Department of Computer Science, TheUniversity of British ColumbiaSupervisorAndrea Tagliasacchi, Associate Professor, Department of Computer Science,Simon Fraser UniversityCo-SupervisorIan Mitchell, Professor, Department of Computer Science, The Universityof British ColumbiaUniversity ExaminerNicholas Coops, Professor, Department of Forest Resources Management,The University of British ColumbiaUniversity ExaminerAndrew Markham, Professor, Department of Computer Science, Universityof OxfordExternal ExaminerAdditional Supervisory Committee Members:Helge Rhodin, Assistant Professor, Department of Computer Science, TheUniversity of British ColumbiaSupervisory Committee MemberiiAbstractWe are interested in processing point clouds – a set of unordered points –specifically in Euclidean space, such as 3D point cloud acquired from a rangesensor (LiDAR) or 4D correspondence cloud in stereo matching task. Pointclouds play an increasingly essential role in many tasks due to prevalencethey hold. However, it is notoriously challenging to process point cloudswith deep neural networks because of their irregular data structure, thedifficulty in encoding contextual information from nearby points, and thelarge compute requirement that is typically required.This thesis addresses these challenge…
has type: Thesis/Dissertation
date issued: 2024-11
is shown at: 10.14288/1.0445525
language: eng
degree discipline: Computer Science
provider: Vancouver : University of British Columbia Library
publisher: University of British Columbia
rights: Attribution-NonCommercial-NoDerivatives 4.0 International
rights URI: http://creativecommons.org/licenses/by-nc-nd/4.0/
scholar level: Graduate
contributor: Yi, Kwang Moo
contributor: Tagliasacchi, Andrea
type: Text
identifier URI: http://hdl.handle.net/2429/89368