A Simple and Effective Fusion Approach for Multi-frame Optical Flow Estimation

被引:0
|
作者
Ren, Zhile [1 ]
Gallo, Orazio [2 ]
Sun, Deqing [2 ]
Yang, Ming-Hsuan [3 ]
Sudderth, Erik B. [4 ]
Kautz, Jan [2 ]
机构
[1] Brown Univ, Providence, RI 02912 USA
[2] NVIDIA, Santa Clara, CA 95051 USA
[3] UC Merced, Merced, CA USA
[4] UC Irvine, Irvine, CA USA
关键词
Multi-frame optical flow; Temporal optical flow fusion;
D O I
10.1007/978-3-030-11024-6_53
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
To date, top-performing optical flow estimation methods only take pairs of consecutive frames into account. While elegant and appealing, the idea of using more than two frames has not yet produced state-of-the-art results. We present a simple, yet effective fusion approach for multi-frame optical flow that benefits from longer-term temporal cues. Our method first warps the optical flow from previous frames to the current, thereby yielding multiple plausible estimates. It then fuses the complementary information carried by these estimates into a new optical flow field. At the time of writing, our method ranks first among published results in the MPI Sintel and KITTI 2015 benchmarks.
引用
收藏
页码:706 / 710
页数:5
相关论文
共 50 条
  • [31] Multi-Frame Correspondence Estimation Using Subspace Constraints
    Michal Irani
    International Journal of Computer Vision, 2002, 48 : 173 - 194
  • [32] Constrained, globally optimal, multi-frame motion estimation
    Farsiu, Sina
    Elad, Michael
    Milanfar, Peyman
    2005 IEEE/SP 13TH WORKSHOP ON STATISTICAL SIGNAL PROCESSING (SSP), VOLS 1 AND 2, 2005, : 1313 - 1318
  • [33] Multi-frame correspondence estimation using subspace constraints
    Irani, M
    INTERNATIONAL JOURNAL OF COMPUTER VISION, 2002, 48 (03) : 173 - 194
  • [34] Multi-Frame Self-Supervised Depth Estimation with Multi-Scale Feature Fusion in Dynamic Scenes
    Zhong, Jiquan
    Huang, Xiaolin
    Yu, Xiao
    PROCEEDINGS OF THE 31ST ACM INTERNATIONAL CONFERENCE ON MULTIMEDIA, MM 2023, 2023, : 2553 - 2563
  • [35] Face recognition by multi-frame fusion of rotating heads in videos
    Canavan, Shaun J.
    Kozak, Michael P.
    Zhang, Yong
    Sullins, John R.
    Shreve, Matthew A.
    Goldgof, Dmitry B.
    2007 FIRST IEEE INTERNATIONAL CONFERENCE ON BIOMETRICS: THEORY, APPLICATIONS AND SYSTEMS, 2007, : 162 - +
  • [36] Application of multi-frame approach in single-frame blind deconvolution
    Shi, Dongfeng
    Fan, Chengyu
    Shen, Hong
    Zhang, Pengfei
    Zhang, Jinghui
    Qiao, Chunhong
    Wang, Yingjian
    OPTICS COMMUNICATIONS, 2012, 285 (24) : 4937 - 4940
  • [37] Multi-frame fusion of undersampled 3D imagery
    Cain, Stephen C.
    UNCONVENTIONAL IMAGING AND WAVEFRONT SENSING 2012, 2012, 8520
  • [38] Effective nonlinear approach for optical flow estimation
    Kim, JD
    Kim, J
    SIGNAL PROCESSING, 2001, 81 (10) : 2249 - 2252
  • [39] SceneFlowFields++: Multi-frame Matching, Visibility Prediction, and Robust Interpolation for Scene Flow Estimation
    René Schuster
    Oliver Wasenmüller
    Christian Unger
    Georg Kuschk
    Didier Stricker
    International Journal of Computer Vision, 2020, 128 : 527 - 546
  • [40] ALTERNATING MINIMIZATION APPROACH FOR MULTI-FRAME IMAGE RECONSTRUCTION
    Cho, Fang Hwan
    Ramani, Sathish
    Fessler, Jeffrey A.
    2012 IEEE STATISTICAL SIGNAL PROCESSING WORKSHOP (SSP), 2012, : 225 - 228