scholarly journals Automated calibration of multi-camera-projector structured light systems for volumetric high-speed 3D surface reconstructions

2018 ◽  
Vol 26 (25) ◽  
pp. 33278 ◽  
Author(s):  
Marc E. Deetjen ◽  
David Lentink
Author(s):  
S. Nietiedt ◽  
P. Kalinowski ◽  
H. Hastedt ◽  
T. Luhmann

Abstract. In the last few years, photogrammetric methods for 3D surface reconstruction at close range have increased significantly in importance. On the one hand, this is due to the increased performance of the systems and on the other hand to the improved quality (accuracy, completeness) of the created point clouds. In order to verify the accuracy of various area probing methods, the German VDI guideline 2634 part 2 and 3 is applied. However, the high-precision test reference objects existing so far consist of diffuse textureless surfaces, so that passive methods, like image matching, cannot be compared with active methods (e.g. structured light systems). In order to make this possible, a certified textured dumbbell with an accuracy of better than 10 μm is presented in this paper, with the aim to examine the suitability of the textured dumbbell artefact for close-range photogrammetric 3D surface reconstruction. Furthermore, the accuracy level of a structured light system, Structure from Motion (SfM) and Multi-View Stereo Method (MVS) is verified and compared with each other.


2021 ◽  
Vol 21 (2) ◽  
pp. 1799-1808
Author(s):  
Guijin Wang ◽  
Chenchen Feng ◽  
Xiaowei Hu ◽  
Huazhong Yang

Author(s):  
Yakun Ju ◽  
Kin-Man Lam ◽  
Yang Chen ◽  
Lin Qi ◽  
Junyu Dong

We present an attention-weighted loss in a photometric stereo neural network to improve 3D surface recovery accuracy in complex-structured areas, such as edges and crinkles, where existing learning-based methods often failed. Instead of using a uniform penalty for all pixels, our method employs the attention-weighted loss learned in a self-supervise manner for each pixel, avoiding blurry reconstruction result in such difficult regions. The network first estimates a surface normal map and an adaptive attention map, and then the latter is used to calculate a pixel-wise attention-weighted loss that focuses on complex regions. In these regions, the attention-weighted loss applies higher weights of the detail-preserving gradient loss to produce clear surface reconstructions. Experiments on real datasets show that our approach significantly outperforms traditional photometric stereo algorithms and state-of-the-art learning-based methods.


Author(s):  
Martin Landmann ◽  
Stefan Heist ◽  
Patrick Dietrich ◽  
Peter Lutzke ◽  
Ingo Gebhart ◽  
...  

Author(s):  
Marc-Antoine Drouin ◽  
Guy Godin ◽  
Michel Picard ◽  
Jonathan Boisvert ◽  
Louis-Guy Dicaire

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