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Scene Flow Estimation Based on Adaptive Anisotropic Total Variation Flow-Driven Method

Author

Listed:
  • Xuezhi Xiang
  • Rongfang Zhang
  • Mingliang Zhai
  • Deguang Xiao
  • Erwei Bai

Abstract

Scene flow estimation based on disparity and optical flow is a challenging task. We present a novel method based on adaptive anisotropic total variation flow-driven method for scene flow estimation from a calibrated stereo image sequence. The basic idea is that diffusion of flow field in different directions has different rates, which can be used to calculate total variation and anisotropic diffusion automatically. Brightness consistency and gradient consistency constraint are employed to establish the data term, and adaptive anisotropic flow-driven penalty constraint is employed to establish the smoothness term. Similar to the optical flow estimation, there are also large displacement problems in the estimation of the scene flow, which is solved by introducing a hierarchical computing optimization. The proposed method is verified by using the synthetic dataset and the real scene image sequences. The experimental results show the effectiveness of the proposed algorithm.

Suggested Citation

  • Xuezhi Xiang & Rongfang Zhang & Mingliang Zhai & Deguang Xiao & Erwei Bai, 2018. "Scene Flow Estimation Based on Adaptive Anisotropic Total Variation Flow-Driven Method," Mathematical Problems in Engineering, Hindawi, vol. 2018, pages 1-10, May.
  • Handle: RePEc:hin:jnlmpe:4908273
    DOI: 10.1155/2018/4908273
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