基于深度相机的大场景三维重建

刘东生; 陈建林; 费点; 张之江

doi:10.3788/OPE.20202801.0234

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基于深度相机的大场景三维重建

信息科学 | 更新时间：2020-08-13

- 基于深度相机的大场景三维重建
- Three-dimensional reconstruction of large-scale scene based on depth camera
- 光学精密工程 2020年28卷第1期页码：234-243
- 作者机构：
  
  上海大学特种光纤与光接入网重点实验室，上海 200444
- 作者简介：
  
  [ "刘东生 (1994-), 男，湖北十堰人，硕士研究生，2017年于上海大学获得学士学位，主要从事计算机视觉与三维重建方面的研究。E-mail:liudongsheng_10@163.com" ]
  [ "张之江 (1968-)，男，上海人，博士，教授，博士生导师，1991年于哈尔滨科技大学获得工学学士学位，1996年、1999年于哈尔滨工业大学分别获工学硕士和工学博士学位，主要从事图像处理、视觉检测、数字全息和三维显示等方面的研究。E-mail: zjzhang@shu.edu.cn" ]
- 基金信息：
  
  国家自然科学基金面上项目(61572307)
- DOI：10.3788/OPE.20202801.0234
  中图分类号： TN249
- 收稿日期：2019-07-01，
  
  录用日期：2019-8-27，
  
  纸质出版日期：2020-01-25
- 稿件说明：
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刘东生, 陈建林, 费点, 等. 基于深度相机的大场景三维重建[J]. 光学精密工程, 2020,28(1):234-243.

Dong-sheng LIU, Jian-lin CHEN, Dian FEI, et al. Three-dimensional reconstruction of large-scale scene based on depth camera[J]. Optics and precision engineering, 2020, 28(1): 234-243.
刘东生, 陈建林, 费点, 等. 基于深度相机的大场景三维重建[J]. 光学精密工程, 2020,28(1):234-243. DOI： 10.3788/OPE.20202801.0234.

Dong-sheng LIU, Jian-lin CHEN, Dian FEI, et al. Three-dimensional reconstruction of large-scale scene based on depth camera[J]. Optics and precision engineering, 2020, 28(1): 234-243. DOI： 10.3788/OPE.20202801.0234.

摘要

针对大场景三维重建中，由位姿估计的累积误差而导致的相机漂移和重建模型质量低的问题，提出了减少累积误差的方法。首先，基于由最新K对深度和彩色图像融合的模型，最小化输入RGB-D图像的几何误差和亮度误差来跟踪相机。然后，若相机位置与当前子网格的中心点距离大于给定阈值时，则将子网格平移体素单元整数倍的距离，基于新建的子网格继续跟踪相机并重建局部场景模型。最后，在子网格间以迭代步长式的方法寻找对应表面点，以对应点间的欧氏距离与亮度误差为约束，优化全局相机轨迹。基于数据集的实验结果表明

相机位姿估计精度比主流方法提升14.1%，全局轨迹优化精度提升8%。对于自采数据，本文设计的系统可减少位姿估计中的累积误差、重建高质量的场景模型。

Abstract

Aiming at the problem of camera drift and low-quality reconstruction caused by accumulated errors in pose estimation for large-scale reconstruction

we proposed a method to reduce accumulated errors. First

based on the model fused by the latest

depth and color images

the geometric and photometric error of the input RGB-D image were minimized to track the camera. Then

if the distance between the camera position and the subvolume center was more than a given threshold

the subvolume was shifted by multiples of voxel size

the camera was continuously tracked and the local scene model was reconstructed based on the newly created subvolume. Finally

the corresponding surface points were searched by the iterative step method between subvolumes

and the global camera trajectory was optimized by Euclidean distance and photometric error between the correspondence. The experimental results based on the dataset show that the camera pose estimation accuracy is improved by 14.1% than the current method

and the global trajectory optimization accuracy is improved by 8%. The system designed in this paper can also reduce the accumulated errors in pose estimation and reconstruct a high-quality scene model for self-collected data.

关键词

Keywords

references

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