서브메뉴
검색
Recovering and Creating 3D Experiences From Casual Data
Recovering and Creating 3D Experiences From Casual Data
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20260202103605
- ISBN
- 9798288863585
- DDC
- 004
- 서명/저자
- Recovering and Creating 3D Experiences From Casual Data
- 발행사항
- [Sl] : University of California, Berkeley, 2025
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2025
- 형태사항
- 137 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 87-01, Section: B.
- 주기사항
- Advisor: Kanazawa, Angjoo.
- 학위논문주기
- Thesis (Ph.D.)--University of California, Berkeley, 2025.
- 초록/해제
- 요약In today's world, we are surrounded by casually captured visual data-photos and videos from our phones, TV shows, cartoons, social media clips, and more. These formats depict rich, physical 3D spatiotemporal worlds, even though, when you look closely, the frames themselves are often not necessarily geometrically consistent or explicitly 3D. Yet, as viewers, we effortlessly perceive the underlying structure, intuitively reconstructing the spaces and stories they represent. We could even imagine what might lie in the space behind the camera, where the photographer is standing. Why is it that humans can so easily make sense of these visual experiences, while machines still struggle to recover or create 3D from such data? This thesis aims to bridge that gap, bringing the human-like ability to recover and create 3D experiences from casual data to machines. We develop new methods that robustly reconstruct 3D environments from unstructured, in-the-wild imagery, such as videos you took with your smartphone, and introduce generative techniques to complete missing regions and hallucinate plausible content where data is sparse or absent. Our work advances the state of the art in neural rendering, scene completion, and generative modeling, with contributions including open-source frameworks, new methods for artifact removal and generative scene completion, and the first large-scale 3D reconstruction of television shows and hand-drawn cartoons. By bridging the gap between 3D reconstruction and generation, this thesis explores new possibilities for experiencing and understanding the visual world-no matter how casual or unconventional the data may be.
- 일반주제명
- Computer science
- 일반주제명
- Information technology
- 키워드
- Casual data
- 키워드
- TV shows
- 기타저자
- University of California, Berkeley Computer Science
- 기본자료저록
- Dissertations Abstracts International. 87-01B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
008260126s2025 us c eng d■001000017357826
■00520260202103605
■006m o d
■007cr#unu||||||||
■020 ▼a9798288863585
■035 ▼a(MiAaPQ)AAI32042691
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a004
■1001 ▼aWeber, Ethan John.
■24510▼aRecovering and Creating 3D Experiences From Casual Data
■260 ▼a[Sl]▼bUniversity of California, Berkeley▼c2025
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2025
■300 ▼a137 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 87-01, Section: B.
■500 ▼aAdvisor: Kanazawa, Angjoo.
■5021 ▼aThesis (Ph.D.)--University of California, Berkeley, 2025.
■520 ▼aIn today's world, we are surrounded by casually captured visual data-photos and videos from our phones, TV shows, cartoons, social media clips, and more. These formats depict rich, physical 3D spatiotemporal worlds, even though, when you look closely, the frames themselves are often not necessarily geometrically consistent or explicitly 3D. Yet, as viewers, we effortlessly perceive the underlying structure, intuitively reconstructing the spaces and stories they represent. We could even imagine what might lie in the space behind the camera, where the photographer is standing. Why is it that humans can so easily make sense of these visual experiences, while machines still struggle to recover or create 3D from such data? This thesis aims to bridge that gap, bringing the human-like ability to recover and create 3D experiences from casual data to machines. We develop new methods that robustly reconstruct 3D environments from unstructured, in-the-wild imagery, such as videos you took with your smartphone, and introduce generative techniques to complete missing regions and hallucinate plausible content where data is sparse or absent. Our work advances the state of the art in neural rendering, scene completion, and generative modeling, with contributions including open-source frameworks, new methods for artifact removal and generative scene completion, and the first large-scale 3D reconstruction of television shows and hand-drawn cartoons. By bridging the gap between 3D reconstruction and generation, this thesis explores new possibilities for experiencing and understanding the visual world-no matter how casual or unconventional the data may be.
■590 ▼aSchool code: 0028.
■650 4▼aComputer science
■650 4▼aInformation technology
■653 ▼a3D reconstruction
■653 ▼aCasual data
■653 ▼aTV shows
■653 ▼aHand-drawn cartoons
■690 ▼a0800
■690 ▼a0984
■690 ▼a0489
■71020▼aUniversity of California, Berkeley▼bComputer Science.
■7730 ▼tDissertations Abstracts International▼g87-01B.
■790 ▼a0028
■791 ▼aPh.D.
■792 ▼a2025
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17357826▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
