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.
논문 기본 정보
- 저자정보
피인용 0
초록·키워드
High-fidelity 3D reconstruction is essential for the preservation, restoration, and digital archiving of cultural assets. However, state-of-the-art methods such as Neural Radiance Fields (NeRFs) and 3D Gaussian Splatting (3DGS) are sensitive to background elements, which can degrade the quality of the results. This challenge is particularly pronounced for cultural assets with intricate patterns and material properties, where inaccurate background segmentation can lead to significant artifacts. This study aims to systematically analyze the impact of background segmentation precision on the quality of 3D reconstruction. To this end, we develop an Iterative Segmentation technique to generate masks with varying accuracies by progressive refinement. By applying these intermediate masks to the 3DGS reconstruction at each step, we quantitatively demonstrate the overall improvement in the reconstruction quality as the segmentation accuracy increases. Furthermore, using the final refined mask as a baseline, we conduct a controlled study to compare the distinct effects of intentional “under-segmentation” (background leakage) and “over-segmentation” (object erosion) errors on the geometric and photometric fidelities of the 3D model. This study provides a quantitative analysis of the effect of different segmentation error types on the reconstruction of cultural assets, underscoring the critical need for precise foreground isolation to achieve high-fidelity results.
본문·목차
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
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UCI(KEPA) : I410-151-26-02-094399269
