인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
피인용 0
초록·키워드
Multiview videos provide useful visual information for reconstructing indoor scenes containing moving human objects as explicit 3D data. In this study, we propose a 3D Gaussian Splatting (3DGS)-based point cloud generation pipeline for a fixed multiview GoPro setup. The proposed pipeline generates frame-wise xyz-red–green–blue (rgb) point cloud sequences that can be used as an input format for downstream point cloud processing systems. Camera parameters are estimated from the first synchronized multiview frame using COLMAP and reused for all frames according to the fixed-camera assumption. A 3DGS representation was optimized for each frame, and an initial point cloud was generated by converting Gaussian center positions and zeroth-order spherical harmonics color coefficients into the xyz-rgb PLY format. However, this Gaussian-center-based conversion does not fully utilize the opacity, scale, and rotation parameters, which can result in sparse human object point clouds. To mitigate this limitation, we introduced a Gaussian primitive-aware point cloud upsampling module that selects reliable Gaussian primitives and generates additional support points along the principal axes of the corresponding Gaussian ellipsoids. The generated point clouds were evaluated using projection-based human-region metrics. Experimental results on an indoor multiview GoPro dataset show that the proposed pipeline generates practical 3D human point cloud sequences and that the Gaussian primitive-aware upsampling module improves projected human-region coverage and rgb reconstruction quality.
본문·목차
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
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