基于金字塔块匹配的双目场景流估计

陈震; 倪晶晶; 张聪炫; 葛利跃; 王梓歌

doi:10.12263/DZXB.20210385

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基于金字塔块匹配的双目场景流估计

学术论文 | 更新时间：2025-12-08

- 基于金字塔块匹配的双目场景流估计
- Binocular Scene Flow Estimation Based on Pyramid Block Matching
- 电子学报 2022年50卷第9期页码：2164-2171
- 作者机构：
  
  1.南昌航空大学无损检测技术教育部重点实验室，江西南昌 330063
  2.南昌航空大学信息工程学院，江西南昌 330063
- 作者简介：
  
  [ "陈　震　男，1969年11月出生于江西省九江市. 博士，教授. 主要研究方向为图像理解与测量.E-mail: dr_chenzhen@163.com" ]
  [ "倪晶晶　女， 1993年09月出生于安徽省桐城市. 硕士研究生. 主要研究方向为图像检测与智能识别.E-mail: njjplhello@163.com" ]
  [ "张聪炫　男， 1984年07月出生于河南省焦作市. 博士，教授. 主要研究方向为图像处理与计算机视觉." ]
  [ "葛利跃　男， 1992年10月出生于安徽省蚌埠市. 硕士，助理实验师. 主要研究方向为图像处理与计算机视觉.E-mail: lygeah@163.com" ]
  [ "王梓歌　女， 1998年04月出生于河北省晋州市. 硕士研究生. 主要研究方向为图像检测与智能识别．E-mail: wangzggg@163.com" ]
- 基金信息：
  
  国家重点研发计划(2020YFC2003800);国家自然科学基金(61866026;61866025);江西省优势科技创新团队计划(20165BCB19007);江西省技术创新引导类计划项目(20212AEI91005);江西省自然科学基金重点项目(20202ACB214007);航空科学基金(2018ZC56008);中国博士后科学基金(2019M650894);江西省教育厅科学技术研究项目(GJJ210910);江西省图像处理与模式识别重点实验室开放基金资助(ET202104413)
- DOI：10.12263/DZXB.20210385
  中图分类号： TP391
- 收稿：2021-03-22，
  
  修回：2022-05-04，
  
  纸质出版：2022-09-25
- 稿件说明：
移动端阅览
陈震,倪晶晶,张聪炫等.基于金字塔块匹配的双目场景流估计[J].电子学报,2022,50(09):2164-2171.

CHEN Zhen,NI Jing-jing,ZHANG Cong-xuan,et al.Binocular Scene Flow Estimation Based on Pyramid Block Matching[J].ACTA ELECTRONICA SINICA,2022,50(09):2164-2171.
陈震,倪晶晶,张聪炫等.基于金字塔块匹配的双目场景流估计[J].电子学报,2022,50(09):2164-2171. DOI： 10.12263/DZXB.20210385.

CHEN Zhen,NI Jing-jing,ZHANG Cong-xuan,et al.Binocular Scene Flow Estimation Based on Pyramid Block Matching[J].ACTA ELECTRONICA SINICA,2022,50(09):2164-2171. DOI： 10.12263/DZXB.20210385.

摘要

针对现有双目场景流计算方法在大位移、运动遮挡及光照变化等复杂场景下场景流估计的准确性与鲁棒性问题，提出一种基于金字塔块匹配的双目场景流计算方法.首先对双目图像序列进行超像素分割和视差估计，得到图像初始分割结果和视差信息，然后建立基于金字塔块匹配的运动模型并采用Ransac随机一致性算法拟合刚性运动模型和最小化重投影算法估计对象运动参数.最后，本文将金字塔块匹配结果作为约束项，联合对象运动参数和超像素平面参数构建基于金字塔块匹配的双目场景流估计能量函数模型，通过最小化能量函数得到最终场景流.实验分别采用KITTI2015（Karlsruhe Institute of Technology and Toyota technological Institute 2015）和MPI-Sintel（Max-Planck Institute and Sintel）数据集测试图像对本文方法和具有代表性场景流算法进行综合对比分析，结果表明本文方法相对于其他对比方法有效提高大位移、运动遮挡以及光照变化情况下场景流估计精度和鲁棒性.

Abstract

Aiming at the accuracy and robustness of existing binocular scene flow calculation methods in complex scenes such as large displacement

motion occlusion and illumination changes

this paper proposes a binocular scene flow estimation method based on pyramid block matching. Firstly

we apply the superpixel segmentation and disparity estimation to the binocular image sequence to obtain the initial image segmentation results and disparity information. Secondly

we establish a motion model based on pyramid block matching. Then we fit the rigid motion model by using Ransac stochastic consensus algorithm and estimate the object motion parameters by minimizing the reprojection algorithm. Finally

this paper takes the matching result of the pyramid block as a constraint item

then we construct a binocular scene flow estimation energy function model based on the pyramid block matching by combines the object motion parameters and the superpixel plane parameters

and obtains the final scene flow by minimizing the energy function. The image sequences provided by the KITTI2015(Karlsruhe Institute of Technology and Toyota Technological Institute 2015) and MPI-Sintel(Max-Planck Institute and Sintel) databases were adopted to compare and analyze the proposed method in this paper and the existing representative scene flow method. The experimental results show that compared with other comparison methods

the method in this paper has high accuracy and robustness of scene flow estimatin

especially in large displacement

motion occlusion and lighting changes.

关键词

Keywords

references

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