Abstract
Original language | English |
---|---|
Pages (from-to) | 389-399 |
Number of pages | 11 |
Journal | Photogrammetric engineering & remote sensing |
Volume | 85 |
Issue number | 5 |
DOIs | |
Publication status | Published - 2019 |
Fingerprint
Cite this
Self-Supervised Convolutional Neural Networks for Plant Reconstruction Using Stereo Imagery. / Xia, Yuanxin; d'Angelo, Pablo; Tian, Jiaojiao; Fraundorfer, Friedrich; Reinartz, Peter.
In: Photogrammetric engineering & remote sensing, Vol. 85, No. 5, 2019, p. 389-399.Research output: Contribution to journal › Article › Research › peer-review
}
TY - JOUR
T1 - Self-Supervised Convolutional Neural Networks for Plant Reconstruction Using Stereo Imagery
AU - Xia, Yuanxin
AU - d'Angelo, Pablo
AU - Tian, Jiaojiao
AU - Fraundorfer, Friedrich
AU - Reinartz, Peter
PY - 2019
Y1 - 2019
N2 - Stereo matching can provide complete and dense three-dimensional reconstruction to study plant growth. Recently, high-quality stereo matching results were achieved combining Semi-Global Matching (SGM) with deep learning. However, due to a lack of suitable training data, this technique is not readily applicable for plant reconstruction. We propose a self-supervised Matching Cost with a Convolutional Neural Network (MC-CNN) scheme to calculate matching cost and test it for plant reconstruction. The MC-CNN network is retrained using the initial matching results obtained from the standard MC-CNN weights. For the experiment, closerange photogrammetric imagery of an in-house plant is used. The results show that the performance of self-supervised MC-CNN is superior to the Census algorithm and comparable to MC-CNN trained by a Light Detection and Ranging point cloud. Another experiment is performed …
AB - Stereo matching can provide complete and dense three-dimensional reconstruction to study plant growth. Recently, high-quality stereo matching results were achieved combining Semi-Global Matching (SGM) with deep learning. However, due to a lack of suitable training data, this technique is not readily applicable for plant reconstruction. We propose a self-supervised Matching Cost with a Convolutional Neural Network (MC-CNN) scheme to calculate matching cost and test it for plant reconstruction. The MC-CNN network is retrained using the initial matching results obtained from the standard MC-CNN weights. For the experiment, closerange photogrammetric imagery of an in-house plant is used. The results show that the performance of self-supervised MC-CNN is superior to the Census algorithm and comparable to MC-CNN trained by a Light Detection and Ranging point cloud. Another experiment is performed …
U2 - 10.14358/PERS.85.5.389
DO - 10.14358/PERS.85.5.389
M3 - Article
VL - 85
SP - 389
EP - 399
JO - Photogrammetric engineering & remote sensing
JF - Photogrammetric engineering & remote sensing
SN - 0099-1112
IS - 5
ER -