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Improving Robustness of 3D Reconstruction for Sparse Captures and Challenging Environments
Improving Robustness of 3D Reconstruction for Sparse Captures and Challenging Environments
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20260209102835
- ISBN
- 9798314842034
- DDC
- 004
- 저자명
- Kataria, Rajbir.
- 서명/저자
- Improving Robustness of 3D Reconstruction for Sparse Captures and Challenging Environments
- 발행사항
- [Sl] : University of Illinois at Urbana-Champaign, 2023
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2023
- 형태사항
- 76 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 86-11, Section: B.
- 주기사항
- Advisor: Hoiem, Derek.
- 학위논문주기
- Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2023.
- 초록/해제
- 요약The applications of 3D modeling in the world are ubiquitous. For example, the construction industry models ongoing projects to monitor progress. The housing industry uses 360 images to develop 3D floor plans to help customers visualize home interiors. These applications rely on real-world data that poses challenges for 3D modeling systems. In this dissertation, I will discuss the specific challenges that arise during the modeling process, and how we address them. First, images capture the scene of interest, which can be onerous as planned capture paths can result in reconstruction failures or yield incomplete models. Our feature track simulator uses a camera trajectory and scene geometry to evaluate planned paths prior to the collection process. Next, a structure from motion (SfM) system reconstructs the scene, and outputs camera parameters and image poses. Images that contain repeated or duplicate structures present ambiguities and can cause catastrophic failures in reconstruction. Our approach discounts matches on repeated structures and estimates correct poses using a set of reliable images in the resectioning process. Then, a multi-view stereo (MVS) system uses the camera parameters and image poses to generate a dense model. MVS systems require sufficient overlap between images for accurate depth estimation, which is often burdensome and costly. Our solution detects and completes planar surfaces with only one or two views, and circumvents the overlap requirement.
- 일반주제명
- Computer science
- 일반주제명
- Computer engineering
- 키워드
- 3D vision
- 기타저자
- University of Illinois at Urbana-Champaign Computer Science
- 기본자료저록
- Dissertations Abstracts International. 86-11B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a004
■1001 ▼aKataria, Rajbir.
■24510▼aImproving Robustness of 3D Reconstruction for Sparse Captures and Challenging Environments
■260 ▼a[Sl]▼bUniversity of Illinois at Urbana-Champaign▼c2023
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2023
■300 ▼a76 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 86-11, Section: B.
■500 ▼aAdvisor: Hoiem, Derek.
■5021 ▼aThesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2023.
■520 ▼aThe applications of 3D modeling in the world are ubiquitous. For example, the construction industry models ongoing projects to monitor progress. The housing industry uses 360 images to develop 3D floor plans to help customers visualize home interiors. These applications rely on real-world data that poses challenges for 3D modeling systems. In this dissertation, I will discuss the specific challenges that arise during the modeling process, and how we address them. First, images capture the scene of interest, which can be onerous as planned capture paths can result in reconstruction failures or yield incomplete models. Our feature track simulator uses a camera trajectory and scene geometry to evaluate planned paths prior to the collection process. Next, a structure from motion (SfM) system reconstructs the scene, and outputs camera parameters and image poses. Images that contain repeated or duplicate structures present ambiguities and can cause catastrophic failures in reconstruction. Our approach discounts matches on repeated structures and estimates correct poses using a set of reliable images in the resectioning process. Then, a multi-view stereo (MVS) system uses the camera parameters and image poses to generate a dense model. MVS systems require sufficient overlap between images for accurate depth estimation, which is often burdensome and costly. Our solution detects and completes planar surfaces with only one or two views, and circumvents the overlap requirement.
■590 ▼aSchool code: 0090.
■650 4▼aComputer science
■650 4▼aComputer engineering
■653 ▼aStructure from motion
■653 ▼aMulti-view stereo
■653 ▼a3D vision
■653 ▼aConstruction industry
■653 ▼a3D modeling systems
■690 ▼a0984
■690 ▼a0543
■690 ▼a0800
■690 ▼a0464
■71020▼aUniversity of Illinois at Urbana-Champaign▼bComputer Science.
■7730 ▼tDissertations Abstracts International▼g86-11B.
■790 ▼a0090
■791 ▼aPh.D.
■792 ▼a2023
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17365839▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
