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
This paper proposes a method for more efficiently reconstructing high-resolution 3D spaces by combining drone (UAV) and NeRF technologies. Two drone flights were conducted over an area of approximately 2.8 km² targeting a building complex located in Beijing's central business district, collecting approximately 4,000 high-resolution images and utilizing accurate location data to construct reference data. Accuracy, processing time, frames per second, visual quality, and 3D accuracy were evaluated, and RMSE was reduced from 0.023m to 0.017m by considering urban characteristics such as traffic congestion, low light, and high-density architectural environments. In addition, the 3D environment reconstruction time was reduced to 36.5 seconds, achieving real-time performance of 15 FPS. Therefore, this study improved the efficiency of NeRF through flight path optimization and image preprocessing standardization, and demonstrated that more accurate spatial representation is possible in real time in NeRF-based 3D reconstruction. It also proposed future improvement directions utilizing multi-sensor fusion and lightweight technology to overcome the limitations of large-scale and real-time processing.
본문·목차
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