Acta Geodaetica et Cartographica Sinica >

2017 , Vol. 46 >Issue 9: 1193 - 1201

DOI: https://doi.org/10.11947/j.AGCS.2017.20160636

GPU Parallel Bundle Block Adjustment

  • ZHENG Maoteng ,
  • ZHOU Shunping ,
  • XIONG Xiaodong ,
  • ZHU Junfeng
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  • 1. National Engineering Research Center for Geographic Information System, China University of Geosciences, Wuhan 430074, China;
    2. Smart Mapping Technology Lnc., Beijing 100830, China

Received date: 2016-12-22

  Revised date: 2017-08-14

  Online published: 2017-10-12

Supported by

The National Natural Science Foundation of China (No. 41601502);The China Postdoctoral Science Foundation (No. 2015M572224);The Fundamental Research Funds for the Central Universities(Nos. CUG160838;CUG170664)

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Abstract

To deal with massive data in photogrammetry, we introduce the GPU parallel computing technology. The preconditioned conjugate gradient and inexact Newton method are also applied to decrease the iteration times while solving the normal equation. A brand new workflow of bundle adjustment is developed to utilize GPU parallel computing technology. Our method can avoid the storage and inversion of the big normal matrix, and compute the normal matrix in real time. The proposed method can not only largely decrease the memory requirement of normal matrix, but also largely improve the efficiency of bundle adjustment. It also achieves the same accuracy as the conventional method. Preliminary experiment results show that the bundle adjustment of a dataset with about 4500 images and 9 million image points can be done in only 1.5 minutes while achieving sub-pixel accuracy.

Key words： GPU parallel computing; bundle adjustment; preconditioned conjugate gradient; inexact Newton method; big data

Cite this article

ZHENG Maoteng , ZHOU Shunping , XIONG Xiaodong , ZHU Junfeng . GPU Parallel Bundle Block Adjustment[J]. Acta Geodaetica et Cartographica Sinica, 2017 , 46(9) : 1193 -1201 . DOI: 10.11947/j.AGCS.2017.20160636

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