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
In recent years, large datasets have been provided for DL (Deep Learning) to train the neural network in calculating camera poses and object depths from images. The datasets were typically obtained using RGB-D (Red, Green, Blue, and Depth) cameras, however, which have limitations in capturing depths in many cases. To overcome the limitations of RGB-D cameras, many DL approaches have been proposed to overcome the problems. However, the accuracy potentials of pose and depth calculated by DL approaches have not been studied well. Thus, this study selected the ScanNet dataset for the experiment and presents a method to evaluate the accuracy of pose and depth that can be obtained using point matching and homography. From the pairs of matching points in two views, their fundamental matrix, essential matrix, and camera matrix were reconstructed and then the camera pose and depth were calculated. The experimental results show that the relative rotation angles were calculated accurately by using homography and the translation angles were calculated less accurately than the rotation angles. In addition, the experiments show that the depths of matching points could be calculated with reasonable accuracy by using the camera matrix constructed by matching.
본문·목차
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