Paper Info
Title
Two-Stage Adaptive Object Scene Flow Using Hybrid Cnn-Crf Model
Abstract
Scene flow estimation based on stereo sequences is a comprehensive task relevant to disparity and optical flow. Some existing methods are time-consuming and often fail in the presence of reflective surfaces. In this paper, we propose a two-stage adaptive object scene flow estimation method using a hybrid CNN-CRF model (ACOSF), which benefits from high-quality features and the structured modelling capability. Meanwhile, in order to balance the computational efficiency and accuracy, we employ adaptive iteration for energy function optimization, which is flexible and efficient for various scenes. Besides, we utilize high-quality pixel selection to reduce the computation time with only a slight decrease in accuracy. Our method achieves competitive results with the state-of-the-art, which ranks second on the challenging KITTI 2015 scene flow benchmark.
Year
DOI
Venue
2020
10.1109/ICPR48806.2021.9413289
2020 25TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR)
DocType
ISSN
Citations 
Conference
1051-4651
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
Congcong Li100.34
Haoyu Ma212.37
QM346472.05