Single-Shot Colored Speckle Pattern for High Accuracy Depth Sensing

The light field camera provides a robust way to capture both spatial and angular information within a single shot. One of its important applications is in 3D depth sensing, which can extract depth information from the acquired scene. However, conventional light field cameras suffer from shallow depth of field (DoF). Here, a vari-focal light field camera (VF-LFC) with an extended DoF is newly proposed for mid-range 3D depth sensing applications. As a main lens of the system, a vari-focal lens with four different focal lengths is adopted to extend the DoF up to ~15 m. The focal length of the micro-lens array (MLA) is optimized by considering the DoF both in the image plane and in the object plane for each focal length. By dividing measurement regions with each focal length, depth estimation with high reliability is available within the entire DoF. The proposed VF-LFC is evaluated by the disparity data extracted from images with different distances. Moreover, the depth measurement in an outdoor environment demonstrates that our VF-LFC could be applied in various fields such as delivery robots, autonomous vehicles, and remote sensing drones.

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706 High Accuracy Analysis Method by Only two Pictures for Electric Speckle Pattern Interferometry

The Proceedings of Conference of Kansai Branch ◽

10.1299/jsmekansai.2013.88._7-6_ ◽

2013 ◽

Vol 2013.88 (0) ◽

pp. _7-6_

Author(s):

Takuya INOUE ◽

Yasuhiko ARAI

Keyword(s):

Speckle Pattern ◽

High Accuracy ◽

Accuracy Analysis ◽

Analysis Method

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Study on the Double Pulse Laser Dynamic Electronic Speckle Measurement Based on FPGA

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.900.617 ◽

2014 ◽

Vol 900 ◽

pp. 617-622

Author(s):

Fu Sheng Yu ◽

Teng Fei Li ◽

Yan Chao Wu ◽

Zhong Guo Sun ◽

Sheng Jiang Yin

Keyword(s):

Stress Distribution ◽

High Speed ◽

Surface Deformation ◽

Speckle Pattern ◽

Dynamic Measurement ◽

High Accuracy ◽

Laser Speckle ◽

Tool Surface ◽

Milling Tools ◽

Intermittent Cutting

Speckle pattern interferometry can be used to measure he displacement, strain and vibration, surface deformation and surface roughness. And dynamic laser speckle measurement with high accuracy has been widely used in measurement of surface deformation. Tool breakage is the main bottleneck of high-speed intermittent cutting development, therefore, obtaining stress distribution of milling tools is a base of improving the tool design and tool life. Using a speckle measurement method of double pulsed digital based on FPGA, which involves the laser cutter, tools and CCD, transforms the high-speed dynamic measurement to quasi-static measurement. As a result, we can get two speckle images of front and back milling cutter surface and calculate the deformation ,strain and stress distribution of the tool surface with analysis.

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Noninvasive object imaging with single-shot low-resolution speckle pattern through strongly-scattering turbid layers

2019 International Conference on Optical Instruments and Technology: Optoelectronic Imaging/Spectroscopy and Signal Processing Technology ◽

10.1117/12.2542166 ◽

2020 ◽

Author(s):

Yingbo Wang ◽

Jie Cao ◽

Qun Hao ◽

Chengqiang Xu ◽

Mingyuan Tang

Keyword(s):

Speckle Pattern ◽

Single Shot ◽

Low Resolution ◽

Object Imaging

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Single-Shot Structured Light Sensor for 3D Dense and Dynamic Reconstruction

Sensors ◽

10.3390/s20041094 ◽

2020 ◽

Vol 20 (4) ◽

pp. 1094 ◽

Cited By ~ 4

Author(s):

Feifei Gu ◽

Zhan Song ◽

Zilong Zhao

Keyword(s):

Stereo Matching ◽

Speckle Pattern ◽

Structured Light ◽

Optical Element ◽

Depth Map ◽

Point Clouds ◽

Calibration Method ◽

Acquisition Time ◽

Single Shot ◽

Dynamic Reconstruction

Structured light (SL) has a trade-off between acquisition time and spatial resolution. Temporally coded SL can produce a 3D reconstruction with high density, yet it is not applicable to dynamic reconstruction. On the contrary, spatially coded SL works with a single shot, but it can only achieve sparse reconstruction. This paper aims to achieve accurate 3D dense and dynamic reconstruction at the same time. A speckle-based SL sensor is presented, which consists of two cameras and a diffractive optical element (DOE) projector. The two cameras record images synchronously. First, a speckle pattern was elaborately designed and projected. Second, a high-accuracy calibration method was proposed to calibrate the system; meanwhile, the stereo images were accurately aligned by developing an optimized epipolar rectification algorithm. Then, an improved semi-global matching (SGM) algorithm was proposed to improve the correctness of the stereo matching, through which a high-quality depth map was achieved. Finally, dense point clouds could be recovered from the depth map. The DOE projector was designed with a size of 8 mm × 8 mm. The baseline between stereo cameras was controlled to be below 50 mm. Experimental results validated the effectiveness of the proposed algorithm. Compared with some other single-shot 3D systems, our system displayed a better performance. At close range, such as 0.4 m, our system could achieve submillimeter accuracy.

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Compact single-shot metalens depth sensors inspired by eyes of jumping spiders

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1912154116 ◽

2019 ◽

Vol 116 (46) ◽

pp. 22959-22965 ◽

Cited By ~ 10

Author(s):

Qi Guo ◽

Zhujun Shi ◽

Yao-Wei Huang ◽

Emma Alexander ◽

Cheng-Wei Qiu ◽

...

Keyword(s):

Depth Perception ◽

Incident Light ◽

Floating Point ◽

Single Shot ◽

Efficient Computation ◽

Depth Sensor ◽

Depth Sensing ◽

Multiple Images ◽

Jumping Spiders ◽

Depth Sensors

Jumping spiders (Salticidae) rely on accurate depth perception for predation and navigation. They accomplish depth perception, despite their tiny brains, by using specialized optics. Each principal eye includes a multitiered retina that simultaneously receives multiple images with different amounts of defocus, and from these images, distance is decoded with relatively little computation. We introduce a compact depth sensor that is inspired by the jumping spider. It combines metalens optics, which modifies the phase of incident light at a subwavelength scale, with efficient computations to measure depth from image defocus. Instead of using a multitiered retina to transduce multiple simultaneous images, the sensor uses a metalens to split the light that passes through an aperture and concurrently form 2 differently defocused images at distinct regions of a single planar photosensor. We demonstrate a system that deploys a 3-mm-diameter metalens to measure depth over a 10-cm distance range, using fewer than 700 floating point operations per output pixel. Compared with previous passive depth sensors, our metalens depth sensor is compact, single-shot, and requires a small amount of computation. This integration of nanophotonics and efficient computation brings artificial depth sensing closer to being feasible on millimeter-scale, microwatts platforms such as microrobots and microsensor networks.

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Exploring RGBDepth Fusion for Real-Time Object Detection

Sensors ◽

10.3390/s19040866 ◽

2019 ◽

Vol 19 (4) ◽

pp. 866 ◽

Cited By ~ 8

Author(s):

Tanguy Ophoff ◽

Kristof Van Beeck ◽

Toon Goedemé

Keyword(s):

Object Detection ◽

Real Time ◽

Network Architecture ◽

Depth Information ◽

Special Focus ◽

Single Shot ◽

Depth Sensing ◽

Real Time Processing ◽

Depth Cameras ◽

Optimal Fusion

In this paper, we investigate whether fusing depth information on top of normal RGB data for camera-based object detection can help to increase the performance of current state-of-the-art single-shot detection networks. Indeed, depth sensing is easily acquired using depth cameras such as a Kinect or stereo setups. We investigate the optimal manner to perform this sensor fusion with a special focus on lightweight single-pass convolutional neural network (CNN) architectures, enabling real-time processing on limited hardware. For this, we implement a network architecture allowing us to parameterize at which network layer both information sources are fused together. We performed exhaustive experiments to determine the optimal fusion point in the network, from which we can conclude that fusing towards the mid to late layers provides the best results. Our best fusion models significantly outperform the baseline RGB network in both accuracy and localization of the detections.

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