Fast 3D vision with robust structured light coding

2009 ◽  
Author(s):  
Chadi Albitar ◽  
Pierre Graebling ◽  
Christophe Doignon
Keyword(s):  
Structured Light ◽  
3D Vision

Real-Time Sensing of Sag Geometry During GTA Welding

1997 ◽  
Vol 119 (2) ◽  
pp. 151-160 ◽  
Author(s):  
Y. M. Zhang ◽  
R. Kovacevic
Keyword(s):  
Real Time ◽  
Vision System ◽  
Structured Light ◽  
Gta Welding ◽  
Seam Tracking ◽  
Image Processing Algorithm ◽  
3D Vision ◽  
Full Penetration ◽  
Laser Stripe ◽  
Penetration Control

Seam tracking and weld penetration control are two fundamental issues in automated welding. Although the seam tracking technique has matured, the latter still remains a unique unsolved problem. It was found that the full penetration status during GTA welding can be determined with sufficient accuracy using the sag depression. To achieve a new full penetration sensing technique, a structured-light 3D vision system is developed to extract the sag geometry behind the pool. The laser stripe, which is the intersection of the structured-light and weldment, is thinned and then used to acquire the sag geometry. To reduce possible control delay, a small distance is selected between the pool rear and laser stripe. An adaptive dynamic search for rapid thinning of the stripe and the maximum principle of slope difference for unbiased recognition of sag border were proposed to develop an effective real-time image processing algorithm for sag geometry acquisition. Experiments have shown that the proposed sensor and image algorithm can provide reliable feedback information of sag geometry for the full penetration control system.

Real-Time Projection Mapping Using High-Frame-Rate Structured Light 3D Vision

2015 ◽  
Vol 8 (4) ◽  
pp. 265-272 ◽  
Author(s):  
Jun CHEN ◽  
Takashi YAMAMOTO ◽  
Tadayoshi AOYAMA ◽  
Takeshi TAKAKI ◽  
Idaku ISHII
Keyword(s):  
Real Time ◽  
Structured Light ◽  
Frame Rate ◽  
3D Vision ◽  
Projection Mapping ◽  
High Frame Rate ◽  
Time Projection

scholarly journals A Structured Light RGB-D Camera System for Accurate Depth Measurement

2018 ◽  
Vol 2018 ◽  
pp. 1-7 ◽  
Author(s):  
Van Luan Tran ◽  
Huei-Yung Lin
Keyword(s):  
Template Matching ◽  
Structured Light ◽  
Gray Code ◽  
Object Surface ◽  
Camera System ◽  
3D Vision ◽  
Depth Measurement ◽  
Sensing System ◽  
Extrinsic Parameters ◽  
Intrinsic And Extrinsic Parameters

The ability to reliably measure the depth of the object surface is very important in a range of high-value industries. With the development of 3D vision techniques, RGB-D cameras have been widely used to perform the 6D pose estimation of target objects for a robotic manipulator. Many applications require accurate shape measurements of the objects for 3D template matching. In this work, we develop an RGB-D camera based on the structured light technique with gray-code coding. The intrinsic and extrinsic parameters of the camera system are determined by a calibration process. 3D reconstruction of the object surface is based on the ray triangulation principle. We construct an RGB-D sensing system with an industrial camera and a digital light projector. In the experiments, real-world objects are used to test the feasibility of the proposed technique. The evaluation carried out using planar objects has demonstrated the accuracy of our RGB-D depth measurement system.

scholarly journals Indoor Key Point Reconstruction Based on Laser Illumination and Omnidirectional Vision

2020 ◽  
Vol 24 (7) ◽  
pp. 864-871
Author(s):  
Yang Qi ◽  
◽  
Yuan Li
Keyword(s):  
Vision System ◽  
Structured Light ◽  
Three Dimensional ◽  
Mathematical Representation ◽  
Omnidirectional Vision ◽  
Wide Field ◽  
3D Vision ◽  
Imaging Model ◽  
Key Points ◽  
Indoor Scenes

Efficient and precise three-dimensional (3D) measurement is an important issue in the field of machine vision. In this paper, a measurement method for indoor key points is proposed with structured lights and omnidirectional vision system and the system can achieve the wide field of view and accurate results. In this paper, the process of obtaining indoor key points is as follows: Firstly, through the analysis of the system imaging model, an omnidirectional vision system based on structured light is constructed. Secondly, the full convolution neural network is used to estimate the scene for the dataset. Then, according to the geometric relationship between the scenery point and its reference point in structured light, for obtaining the 3D coordinates of the unstructured light point is presented. Finally, combining the full convolution network model and the structured light 3D vision model, the 3D mathematical representation of the key points of the indoor scene frame is completed. The experimental results proved that the proposed method can accurately reconstruct indoor scenes, and the measurement error is about 2%.

3-Step-Calibration of 3D Vision Measurement System Based-on Structured Light

2014 ◽  
Vol 8 (3) ◽  
pp. 484-489 ◽  
Author(s):  
Rui-Yin Tang ◽  
◽  
Zhou-Mo Zeng ◽  
Chang-Ku Sun ◽  
Peng Wang ◽  
...  
Keyword(s):  
Measurement System ◽  
Measurement Accuracy ◽  
Structured Light ◽  
Hard Disk ◽  
Disk Surface ◽  
Measurement Model ◽  
3D Vision ◽  
Vision Measurement ◽  
Light Measurement ◽  
Key Steps

In structured light 3D vision measurement system, calibration tasks are key steps. Aiming at a special application of line structured light measurement, namely computer hard-disk surface planeness measurement at a precision equipment manufacturing company in Singapore, and combining with the structured light measurement model, determined three calibration tasks of the system. The three calibrating tasks concluded: calibrating the camera parameters; calibrating the light plane pose and calibrating the movement pose. At the same time, according to the three calibration results, measured the computer hard disk, and reconstructed the 3D model of the computer hard disk. The experimental results show that, the whole system of three calibration process is simple and reliable, the method does not need any auxiliary adjustment and realize the measurement accuracy about 0.023 mm. The work laid the better foundation for hard disk planeness vision measurement.

High-Frame-Rate Structured Light 3-D Vision for Fast Moving Objects

2014 ◽  
Vol 26 (3) ◽  
pp. 311-320 ◽  
Author(s):  
Yongjiu Liu ◽  
◽  
Hao Gao ◽  
Qingyi Gu ◽  
Tadayoshi Aoyama ◽  
...  
Keyword(s):  
Fast Moving ◽  
Moving Objects ◽  
Vision System ◽  
Structured Light ◽  
Gray Code ◽  
Frame Rate ◽  
3D Vision ◽  
High Frame Rate ◽  
Depth Images ◽  
Structured Light Vision

<div class=""abs_img""><img src=""[disp_template_path]/JRM/abst-image/00260003/04.jpg"" width=""300"" />HFR 3D vision system</span></div> This paper presents a fast motion-compensated structured-light vision system that realizes 3-D shape measurement at 500 fps using a high-frame-rate camera-projector system. Multiple light patterns with an 8-bit gray code, are projected on the measured scene at 1000 fps, and are processed in real time for generating 512 × 512 depth images at 500 fps by using the parallel processing of a motion-compensated structured-light method on a GPU board. Several experiments were performed on fast-moving 3-D objects using the proposed method. </span>

3D Double-Vision Inspection Based on Structured Light

2003 ◽  
Vol 125 (3) ◽  
pp. 617-623 ◽  
Author(s):  
Guangjun Zhang ◽  
Zhenzhong Wei ◽  
Xin Li
Keyword(s):  
Neural Network ◽  
Rbf Neural Network ◽  
Structured Light ◽  
Inspection System ◽  
Double Vision ◽  
3D Vision ◽  
Vision Inspection ◽  
Blind Area ◽  
World Coordinate System ◽  
Setting Error

3D double-vision inspection is very necessary. It has a larger field of view, and can solve the problem of “blind area” for 3D measurement, as proposed by 3D single-vision inspection. At the beginning of this paper, the principle of structured-light based 3D vision inspection is introduced. Then, a method of gaining calibration points for 3D double-vision inspection system is proposed in detail. In order to gain calibration points with high precision, a double-directional photoelectric aiming device is designed as well, and a method for compensating the position-setting error of the aiming device is described. The coordinates of all calibration points are precisely unified in a world coordinate system. The application of RBF (radial basis function) neural network in establishing the inspection model of structured-light based 3D vision is described in detail. Finally, with the use of the calibration points, the inspection model of 3D double-vision based on RBF neural network is successfully established. The model’s training accuracy is 0.078 mm, and the testing accuracy is 0.084 mm.

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