We introduce a method based on the deflectometry principle for the reconstruction of specular objects exhibiting significant size and geometric complexity. A key feature of our approach is the deployment of an Automatic Virtual Environment (CAVE) as pattern generator. To unfold the full power of this extraordinary experimental setup, an optical encoding scheme is developed which accounts for the distinctive topology of the CAVE. Furthermore, we devise an algorithm for detecting the object of interest in raw deflectometric images. The segmented foreground is used for single-view reconstruction, the background for estimation of the camera pose, necessary for calibrating the sensor system. Experiments suggest a significant gain of coverage in single measurements compared to previous methods. To facilitate research on specular surface reconstruction, we will make our data set publicly available.
Controllable illuminants were introduced to boost reliability and density of correspondences: The very first active deflectometry setup -- due to @cite_38 -- contained an array of light-emitting diodes (LEDs) which could be switched on sequentially to detect them in the camera image. The acquisition time can be reduced by showing binary codes in parallel @cite_25 . Both papers address the ambiguity by assuming quasi-parallel illumination. A large body of literature on the subject exists in the field of optical metrology starting with @cite_39 that suggests improvements if the LED array is replaced by a commodity computer monitor. @cite_10 describe the theoretic limits of the method in -vol -ving phase-shifted sine patterns as codes. Light map measurements can be further enhanced by color displays @cite_13 . An active variant of Savarese's method is presented in @cite_7 .
@cite_38 @cite_7 @cite_10 @cite_39 @cite_13 @cite_25
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Over the last decade, several techniques have been developed for looking around the corner by exploiting the round-trip travel time of photons. Typically, these techniques necessitate the collection of a large number of measurements with varying virtual source and virtual detector locations. This data is then processed by a reconstruction algorithm to estimate the hidden scene. As a consequence, even when the region of interest in the hidden volume is small and limited, the acquisition time needed is large as the entire dataset has to be acquired and then processed.In this paper, we present the first example of scanning based non-line-of-sight imaging technique. The key idea is that if the virtual sources (pulsed sources) on the wall are delayed using a quadratic delay profile (much like the quadratic phase of a focusing lens), then these pulses arrive at the same instant at a single point in the hidden volume – the point being scanned. On the imaging side, applying quadratic delays to the virtual detectors before integration on a single gated detector allows us to ‘focus’ and scan each point in the hidden volume. By changing the quadratic delay profiles, we can focus light at different points in the hidden volume. This provides the first example of scanning based non-line-of-sight imaging, allowing us to focus our measurements only in the region of interest. We derive the theoretical underpinnings of ‘temporal focusing’, show compelling simulations of performance analysis, build a hardware prototype system and demonstrate real results. Abstract Structured-light projection methods are facing two remaining challenges. One is a high-speed optical metrology on objects with chromatic surfaces and the other one is avoiding phase errors caused by height steps or spatially isolated surfaces. To overcome them, this paper provides an effective profilometry with a single-shot image by employing the HSI color model to compose the colorful pattern. Three sinusoidal fringes with different phase steps are encoded in RGB channels respectively. The hue component of a deformed pattern is applied to reconstruct the 3D shape of an object. The saturation and the intensity are utilized to correct the hue demodulation. Besides, an effective color calibration procedure is developed to compensate the hue error. Experimental results verify the feasibility of the developed method. This paper presents a deep-learning (DL) based approach to the design of multi-colored visible light communication (VLC) systems where RGB light-emitting diode (LED) lamps accomplish multi-dimensional color modulation under color and illuminance requirements. It is aimed to identify a pair of multi-color modulation transmitter and receiver leading to efficient symbol recovery performance. To this end, an autoencoder (AE), an unsupervised deep learning technique, is adopted to train the end-to-end symbol recovery process that includes the VLC transceiver pair and a channel layer characterizing the optical channel along with additional LED intensity control features. As a result, the VLC transmitter and receiver are jointly designed and optimized. Intensive numerical results demonstrate that the learned VLC system outperforms existing techniques in terms of the average symbol error probability. This framework sheds light on the viability of DL techniques in the optical communication system design. A novel architecture for performing hue-saturation-value (HSV) domain enhancement of digital color images with non-uniform lighting conditions is proposed in this paper for video streaming applications. The approach promotes log-domain computation to eliminate all multiplications, divisions and exponentiations utilizing the effective logarithmic estimation modules. An optimized quadrant symmetric architecture is incorporated into the design of homomorphic filter for the enhancement of intensity value. Efficient modules are also presented for conversion between RGB and HSV color spaces. The design is able to bring out details hidden in shadow regions of the image. It is capable of producing 187.86 million outputs per second (MOPs) on Xilinx's Virtex II XC2V2000-4ff896 field programmable gate array (FPGA) at a clock frequency of 187.86 MHz. It can process over 179.1 (1024 X 1024) frames per second and consumes approximately 70.7 and 76.8 less hardware resource with 127 and 280 performance boost when compared to the designs with machine learning algorithm [10], and with separated dynamic and contrast enhancements [11], respectively.