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.
초록·키워드
Abstract Foveated rendering (FR) technology is designed to improve the efficiency of graphical rendering processes. In rendering, individualized approaches can help to balance users’ experiences of visual quality and saving computational resource. However, previous studies have not rigorously examined it related with the FR techniques. To address this issue, we developed an individualized FR (IFR) method using different central vision sizes and peripheral vision resolutions across individuals in virtual reality. In three user studies with 88 participants who were divided into groups designated as “large central area (LCA)” and “small central area (SCA),” the effects of IFR were compared with those of using the full-resolution condition and the average FR condition. The results indicate that the LCA group experienced higher visual quality under the IFR and full-resolution conditions than under the average FR condition. In contrast, the SCA group exhibited comparable levels of dependent measures between the IFR and average FR conditions, but both were lower than those of the full-resolution condition. We also evaluated the computational benefits of the proposed IFR method, and the results demonstrated the effectiveness of our approach in saving resources across the two groups. Although lower-bound adjustments may be required for some users, our overall results suggest that IFR is a malleable technology for enhancing rendering efficiency in virtual reality.
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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