Improvement of 3D Modeling Efficiency and Accuracy of Earthwork Site by Noise Processing Using Deep Learning and Structure from Motion

Author(s):  
Nobuyoshi Yabuki ◽  
Yukako Sakamoto ◽  
Tomohiro Fukuda
2021 ◽  
Author(s):  
Francesco Banterle ◽  
Rui Gong ◽  
Massimiliano Corsini ◽  
Fabio Ganovelli ◽  
Luc Van Gool ◽  
...  

2021 ◽  
Vol 11 (6) ◽  
pp. 574
Author(s):  
Jungirl Seok ◽  
Sungmin Yoon ◽  
Chang-Hwan Ryu ◽  
Seok-ki Kim ◽  
Junsun Ryu ◽  
...  

The aim of this study was to evaluate the usefulness of a personalized 3D-printed thyroid model that characterizes a patient’s individual thyroid lesion. The randomized controlled prospective clinical trial (KCT0005069) was designed. Fifty-three of these patients undergoing thyroid surgery were randomly assigned to two groups: with or without a 3D-printed model of their thyroid lesion when obtaining informed consent. We used a U-Net-based deep learning architecture and a mesh-type 3D modeling technique to fabricate the personalized 3D model. The mean 3D printing time was 258.9 min, and the mean price for production was USD 4.23 for each patient. The size, location, and anatomical relationship of the tumor and thyroid gland could be effectively presented using the mesh-type 3D modeling technique. The group provided with personalized 3D-printed models showed significant improvement in all four categories (general knowledge, benefits and risks of surgery, and satisfaction; all p < 0.05). All patients received a personalized 3D model after surgery and found it helpful to understand the disease, operation, and possible complications and their overall satisfaction (all p < 0.05). In conclusion, the personalized 3D-printed thyroid model may be an effective tool for improving a patient’s understanding and satisfaction during the informed consent process.


2021 ◽  
Vol 13 (16) ◽  
pp. 3103
Author(s):  
Xuyuan Yang ◽  
Guang Jiang

In recent years, there has been a growing demand for 3D reconstructions of tunnel pits, underground pipe networks, and building interiors. For such scenarios, weak textures, repeated textures, or even no textures are common. To reconstruct these scenes, we propose covering the lighting sources with films of spark patterns to “add” textures to the scenes. We use a calibrated camera to take pictures from multiple views and then utilize structure from motion (SFM) and multi-view stereo (MVS) algorithms to carry out a high-precision 3D reconstruction. To improve the effectiveness of our reconstruction, we combine deep learning algorithms with traditional methods to extract and match feature points. Our experiments have verified the feasibility and efficiency of the proposed method.


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