TY - GEN
T1 - Layered RGBD scene flow estimation with global non-rigid local rigid assumption
AU - Li, Xiuxiu
AU - Liu, Yanjuan
AU - Jin, Haiyan
AU - Cai, Lei
AU - Zheng, Jiangbin
N1 - Publisher Copyright:
© Springer Nature Switzerland AG 2020.
PY - 2020
Y1 - 2020
N2 - RGBD scene flow has attracted increasing attention in the computer vision community with the popularity of depth sensor. To accurately estimate three-dimensional motion of object, a layered scene flow estimation with global non-rigid, local rigid motion assumption is presented in this paper. Firstly, depth image is inpainted based on RGB image due to original depth image contains noises. Secondly, depth image is layered according to K-means clustering algorithm, which can quickly and simply layer the depth image. Thirdly, scene flow is estimated based on the assumption we proposed. Finally, experiments are implemented on RGBD tracking dataset and deformable 3D reconstruction dataset, and the analysis of quantitative indicators, RMS (Root Mean Square error) and AAE (Average Angular Error). The results show that the proposed method can distinguish moving regions from the static background better, and more accurately estimate the motion information of the scene by comparing with the global rigid, local non-rigid assumption.
AB - RGBD scene flow has attracted increasing attention in the computer vision community with the popularity of depth sensor. To accurately estimate three-dimensional motion of object, a layered scene flow estimation with global non-rigid, local rigid motion assumption is presented in this paper. Firstly, depth image is inpainted based on RGB image due to original depth image contains noises. Secondly, depth image is layered according to K-means clustering algorithm, which can quickly and simply layer the depth image. Thirdly, scene flow is estimated based on the assumption we proposed. Finally, experiments are implemented on RGBD tracking dataset and deformable 3D reconstruction dataset, and the analysis of quantitative indicators, RMS (Root Mean Square error) and AAE (Average Angular Error). The results show that the proposed method can distinguish moving regions from the static background better, and more accurately estimate the motion information of the scene by comparing with the global rigid, local non-rigid assumption.
KW - Global non-rigid
KW - Local rigid
KW - RGBD image
KW - Scene flow
UR - http://www.scopus.com/inward/record.url?scp=85080934047&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-39431-8_21
DO - 10.1007/978-3-030-39431-8_21
M3 - 会议稿件
AN - SCOPUS:85080934047
SN - 9783030394301
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 224
EP - 232
BT - Advances in Brain Inspired Cognitive Systems - 10th International Conference, BICS 2019, Proceedings
A2 - Ren, Jinchang
A2 - Hussain, Amir
A2 - Zhao, Huimin
A2 - Cai, Jun
A2 - Chen, Rongjun
A2 - Xiao, Yinyin
A2 - Huang, Kaizhu
A2 - Zheng, Jiangbin
PB - Springer
T2 - 10th International Conference on Brain Inspired Cognitive Systems, BICS 2019
Y2 - 13 July 2019 through 14 July 2019
ER -