Pose Determination from Multi-View Image using Deep Learning

Sun, Shantong; Liu, Rongke; Pan, Yu; Du, Qiuchen; Su, Han

doi:10.1109/iwcmc.2019.8766635

Cited by 2 publications

(1 citation statement)

References 9 publications

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“…Zhang et al [36] proposed a method for measuring the distance of a given target using deep learning and binocular vision methods, where target detection network methodology and geometric measurement theory were combined to obtain 3D target information. Sun et al [37] designed a CNN and established a multiview system for high-precision attitude determination, which effectively mitigates the lack of reliable visual features in visual measurement. Yang et al [38] established a binocular stereo vision system combined with online deep learning technology for semantic segmentation and ultimately generated an outdoor large-scale 3D dense semantic map.…”

Section: Introductionmentioning

confidence: 99%

Vision‐Based Three‐Dimensional Reconstruction and Monitoring of Large‐Scale Steel Tubular Structures

Tang

Chen

Lin

et al. 2020

Advances in Civil Engineering

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A four-ocular vision system is proposed for the three-dimensional (3D) reconstruction of large-scale concrete-filled steel tube (CFST) under complex testing conditions. These measurements are vitally important for evaluating the seismic performance and 3D deformation of large-scale specimens. A four-ocular vision system is constructed to sample the large-scale CFST; then point cloud acquisition, point cloud filtering, and point cloud stitching algorithms are applied to obtain a 3D point cloud of the specimen surface. A point cloud correction algorithm based on geometric features and a deep learning algorithm are utilized, respectively, to correct the coordinates of the stitched point cloud. This enhances the vision measurement accuracy in complex environments and therefore yields a higher-accuracy 3D model for the purposes of real-time complex surface monitoring. The performance indicators of the two algorithms are evaluated on actual tasks. The cross-sectional diameters at specific heights in the reconstructed models are calculated and compared against laser rangefinder data to test the performance of the proposed algorithms. A visual tracking test on a CFST under cyclic loading shows that the reconstructed output well reflects the complex 3D surface after correction and meets the requirements for dynamic monitoring. The proposed methodology is applicable to complex environments featuring dynamic movement, mechanical vibration, and continuously changing features.

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Section: Introductionmentioning

confidence: 99%