In intelligent shield tunneling, the accuracy of dump
volume measurements is compromised by distortions in the raw point cloud data
of the dump carriages. To address this issue, a model that utilizes a deep
convolutional selfcoding network for selfadaptive distortionremoval in dump
carriages is developed. This model combines point cloud processing with deeplearning techniques. Initially, surround box filtering and antidistanceweighted interpolation are applied
for the data refinement. Subsequently, the actual size of the slag soil
carriage is utilized to establish the ideal output of the network, transforming
the point cloud into pseudosymbols using grayscale
values to create a network dataset. Furthermore, the dump carriage selfcoding network based on a traditional convolutional selfcoding framework is constructed. This involves an integrated network
design with stacked convolutional layers and autoencoders to enhance the
network depth to achieve superior feature representation. Finally, the method
is tested using shield tunneling data. The experimental results indicate that
the proposed method significantly outperforms traditional selfcoding networks and geometric constraint correction methods in terms
of time efficiency. With a peak signaltonoise ratio of 29.73 and a structural similarity index of 0.86, the
proposed approach demonstrates its superiority and validity. This advancement
not only enhances the utility of dump carriage point cloud data, but also lays
a theoretical foundation for applying point cloud distortionremoval technology in 3D reconstruction and engineering projects.
ZHAO Shuanfeng
,
WANG Shuaijun
,
LI Yang
,
WU Yuyao
,
WANG Mengwei
.
SelfCorrecting Point Cloud for Dump
Carriage Based on Deep
Convolutional SelfCoding Network
[J]. Tunnel Construction, 2023
, 43(12)
: 1996
-2006
.
DOI: 10.3973/j.issn.2096-4498.2023.12.002
