An improved deep learning-based algorithm for 3D reconstruction of vacuum arcs

2021 ◽  
Vol 92 (12) ◽  
pp. 123509
Author(s):  
Zhenxing Wang ◽  
Yangbo Pan ◽  
Wei Zhang ◽  
Haomin Li ◽  
Yingsan Geng ◽  
...  
Keyword(s):  
Deep Learning ◽  
3D Reconstruction

3D reconstruction of synapses with deep learning based on EM Images

2017 ◽  
Author(s):  
Chi Xiao ◽  
Qiang Rao ◽  
Dandan Zhang ◽  
Xi Chen ◽  
Hua Han ◽  
...  
Keyword(s):  
Deep Learning ◽  
3D Reconstruction

scholarly journals A 3D reconstruction method of image sequence based on deep learning

2020 ◽  
Vol 1550 ◽  
pp. 032051
Author(s):  
Yun-peng Liu ◽  
Xing-peng Yan ◽  
Ning Wang ◽  
Xin Zhang ◽  
Zhe Li
Keyword(s):  
Deep Learning ◽  
3D Reconstruction ◽  
Image Sequence ◽  
Reconstruction Method

Structural light 3D reconstruction algorithm based on deep learning

2019 ◽  
Author(s):  
Haibin Niu ◽  
Limin Hu ◽  
Shi Yan ◽  
Lei Ning ◽  
Yang Yang ◽  
...  
Keyword(s):  
Deep Learning ◽  
3D Reconstruction ◽  
Reconstruction Algorithm

3D reconstruction using deep learning: a survey

2020 ◽  
Vol 20 (4) ◽  
pp. 389-413
Author(s):  
Yiwei Jin ◽  
Diqiong Jiang ◽  
Ming Cai
Keyword(s):  
Deep Learning ◽  
3D Reconstruction

scholarly journals Full 3D layout reconstruction from one single 360º image

2018 ◽  
Vol 6 ◽  
Author(s):  
Clara Fernández Labrador ◽  
Alejandro Pérez Yus ◽  
Gonzalo López Nicolás ◽  
José Jesús Guerrero Campo
Keyword(s):  
Deep Learning ◽  
3D Reconstruction ◽  
3D Layout ◽  
Actual Shape

We propose an entire pipeline which receives as input a 360º panorama and returns a closed, 3D reconstruction of the room faithful to its actual shape. We exploit deep learning combined with geometry to obtain structural lines, and thus structural corners, from which we generate final layout models assuming Manhattan world.

scholarly journals Deep Learning-Supported Cytoarchitectonic Mapping of the Human Lateral Geniculate Body in the BigBrain

2021 ◽  
pp. 22-32
Author(s):  
Andrea Brandstetter ◽  
Najoua Bolakhrif ◽  
Christian Schiffer ◽  
Timo Dickscheid ◽  
Hartmut Mohlberg ◽  
...  
Keyword(s):  
Deep Learning ◽  
High Resolution ◽  
Visual System ◽  
3D Reconstruction ◽  
Learning Algorithm ◽  
Lateral Geniculate Body ◽  
Isotropic Resolution ◽  
Lateral Geniculate ◽  
Resolution Model ◽  
Relay Nucleus

AbstractThe human lateral geniculate body (LGB) with its six sickle shaped layers (lam) represents the principal thalamic relay nucleus for the visual system. Cytoarchitectonic analysis serves as the groundtruth for multimodal approaches and studies exploring its function. This technique, however, requires experienced knowledge about human neuroanatomy and is costly in terms of time. Here we mapped the six layers of the LGB manually in serial, histological sections of the BigBrain, a high-resolution model of the human brain, whereby their extent was manually labeled in every 30th section in both hemispheres. These maps were then used to train a deep learning algorithm in order to predict the borders on sections in-between these sections. These delineations needed to be performed in 1 µm scans of the tissue sections, for which no exact cross-section alignment is available. Due to the size and number of analyzed sections, this requires to employ high-performance computing. Based on the serial section delineations, high-resolution 3D reconstruction was performed at 20 µm isotropic resolution of the BigBrain model. The 3D reconstruction shows the shape of the human LGB and its sublayers for the first time at cellular precision. It represents a use case to study other complex structures, to visualize their shape and relationship to neighboring structures. Finally, our results could provide reference data of the LGB for modeling and simulation to investigate the dynamics of signal transduction in the visual system.

scholarly journals DEEP LEARNING FOR 3D RECONSTRUCTION OF THE MARTIAN SURFACE USING MONOCULAR IMAGES: A FIRST GLANCE

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 1111-1116
Author(s):  
Z. Chen ◽  
B. Wu ◽  
W. C. Liu
Keyword(s):  
Deep Learning ◽  
3D Reconstruction ◽  
Experimental Results ◽  
Training Data ◽  
Network System ◽  
Stereo Images ◽  
Martian Surface ◽  
Preliminary Results ◽  
Low Level ◽  
Traditional Technology

Abstract. The paper presents our efforts on CNN-based 3D reconstruction of the Martian surface using monocular images. The Viking colorized global mosaic and Mar Express HRSC blended DEM are used as training data. An encoder-decoder network system is employed in the framework. The encoder section extracts features from the images, which includes convolution layers and reduction layers. The decoder section consists of deconvolution layers and is to integrate features and convert the images to desired DEMs. In addition, skip connection between encoder and decoder section is applied, which offers more low-level features for the decoder section to improve its performance. Monocular Context Camera (CTX) images are used to test and verify the performance of the proposed CNN-based approach. Experimental results show promising performances of the proposed approach. Features in images are well utilized, and topographical details in images are successfully recovered in the DEMs. In most cases, the geometric accuracies of the generated DEMs are comparable to those generated by the traditional technology of photogrammetry using stereo images. The preliminary results show that the proposed CNN-based approach has great potential for 3D reconstruction of the Martian surface.

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