TY - GEN
T1 - Densely Connecting Depth Maps for Monocular Depth Estimation
AU - Zhang, Jinqing
AU - Yue, Haosong
AU - Wu, Xingming
AU - Chen, Weihai
AU - Wen, Changyun
N1 - Publisher Copyright:
© 2020, Springer Nature Switzerland AG.
PY - 2020
Y1 - 2020
N2 - Predicting depth map from a single RGB image is beneficial for many three-dimensional applications. Although recent monocular depth estimation methods have achieved impressive accuracy, the preference on high-level features or low-level features prevents them from balancing sharpness and fidelity of depth maps. In this work, we propose a dense connection mechanism that connects diverse sub-depth maps produced by the sub-predictors to the final depth map to contribute information from features at different levels. Besides, two kinds of diversity enhancement devices are proposed to increase the number and diversity of the sub-depth maps collected by the dense connection mechanism. Experimental results on KITTI and NYU Depth V2 datasets shows that, by fusing the dense connection mechanism and diversity enhancement devices, our proposed method achieves state-of-the-art accuracy and predicts sharp depth maps that restore reliable object structures.
AB - Predicting depth map from a single RGB image is beneficial for many three-dimensional applications. Although recent monocular depth estimation methods have achieved impressive accuracy, the preference on high-level features or low-level features prevents them from balancing sharpness and fidelity of depth maps. In this work, we propose a dense connection mechanism that connects diverse sub-depth maps produced by the sub-predictors to the final depth map to contribute information from features at different levels. Besides, two kinds of diversity enhancement devices are proposed to increase the number and diversity of the sub-depth maps collected by the dense connection mechanism. Experimental results on KITTI and NYU Depth V2 datasets shows that, by fusing the dense connection mechanism and diversity enhancement devices, our proposed method achieves state-of-the-art accuracy and predicts sharp depth maps that restore reliable object structures.
KW - Dense connection mechanism
KW - Diversity enhancement device
KW - Monocular depth estimation
UR - https://www.scopus.com/pages/publications/85101768627
U2 - 10.1007/978-3-030-66823-5_9
DO - 10.1007/978-3-030-66823-5_9
M3 - 会议稿件
AN - SCOPUS:85101768627
SN - 9783030668228
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 149
EP - 165
BT - Computer Vision – ECCV 2020 Workshops, Proceedings
A2 - Bartoli, Adrien
A2 - Fusiello, Andrea
PB - Springer Science and Business Media Deutschland GmbH
T2 - Workshops held at the 16th European Conference on Computer Vision, ECCV 2020
Y2 - 23 August 2020 through 28 August 2020
ER -