- > 연구성과 > 국외논문
공지사항
논문명 |
Switchable-Encoder-Based Self-Supervised Learning Framework for Monocular Depth and Pose Estimation |
논문종류 |
SCI |
저자 |
Junoh Kim, Rui Gao, Jisun Park, Jinsoo Yoon and Kyungeun Cho |
Impact Factor |
5.0 |
게재학술지명 |
Remote Sensing |
게재일 |
2023.12 |
Monocular depth prediction research is essential for expanding meaning from 2D to
3D. Recent studies have focused on the application of a newly proposed encoder; however, the
development within the self-supervised learning framework remains unexplored, an aspect critical
for advancing foundational models of 3D semantic interpretation. Addressing the dynamic nature
of encoder-based research, especially in performance evaluations for feature extraction and pretrained models, this research proposes the switchable encoder learning framework (SELF). SELF
enhances versatility by enabling the seamless integration of diverse encoders in a self-supervised
learning context for depth prediction. This integration is realized through the direct transfer of feature
information from the encoder and by standardizing the input structure of the decoder to accommodate
various encoder architectures. Furthermore, the framework is extended and incorporated into an
adaptable decoder for depth prediction and camera pose learning, employing standard loss functions.
Comparative experiments with previous frameworks using the same encoder reveal that SELF
achieves a 7% reduction in parameters while enhancing performance. Remarkably, substituting
newly proposed algorithms in place of an encoder improves the outcomes as well as significantly
decreases the number of parameters by 23%. The experimental findings highlight the ability of SELF
to broaden depth factors, such as depth consistency. This framework facilitates the objective selection
of algorithms as a backbone for extended research in monocular depth prediction. |
|