Next Article in Journal
Dynamic Consolidation Measurements in a Well Field Using Fiber Bragg Grating Sensors
Previous Article in Journal
A Physical Model-Based Observer Framework for Nonlinear Constrained State Estimation Applied to Battery State Estimation
Open AccessArticle

Fast and Accurate 3D Measurement Based on Light-Field Camera and Deep Learning

1
School of Mechanical Engineering, Shanghai Jiao Tong University, 800 Dongchuan Rd., Shanghai 200240, China
2
VOMMA (Shanghai) Technology Co., Ltd, Shanghai 200240, China
3
School of Computer Science, University of Manchester, Kilburn Building, Manchester M13 9PL, UK
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(20), 4399; https://doi.org/10.3390/s19204399
Received: 4 August 2019 / Revised: 24 September 2019 / Accepted: 24 September 2019 / Published: 11 October 2019
(This article belongs to the Section Optical Sensors)
The precise combination of image sensor and micro-lens array enables light-field cameras to record both angular and spatial information of incoming light, therefore, one can calculate disparity and depth from one single light-field image captured by one single light-field camera. In turn, 3D models of the recorded objects can be recovered, which means a 3D measurement system can be built using a light-field camera. However, reflective and texture-less areas in light-field images have complicated conditions, making it hard to correctly calculate disparity with existing algorithms. To tackle this problem, we introduce a novel end-to-end network VommaNet to retrieve multi-scale features from reflective and texture-less regions for accurate disparity estimation. Meanwhile, our network has achieved similar or better performance in other regions for both synthetic light-field images and real-world data compared to the state-of-the-art algorithms. View Full-Text
Keywords: light-field imaging; depth estimation; texture-less and reflective areas light-field imaging; depth estimation; texture-less and reflective areas
Show Figures

Figure 1

MDPI and ACS Style

Ma, H.; Qian, Z.; Mu, T.; Shi, S. Fast and Accurate 3D Measurement Based on Light-Field Camera and Deep Learning. Sensors 2019, 19, 4399.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop