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Abstract. We present a novel method of geometric calibration of microlens-based light-field cameras. Accurate geometric calibration is a basis of various applications. Instead of using sub-aperture images, we utilize raw images directly for calibration. We select proper regions in raw images and extract line features from micro-lens images in those regions. For the whole process, we formulate a new projection model of micro-lens-based light-field cameras. It is transformed into a linear form using line features. We compute an initial solution of both intrinsic and extrinsic parameters by a linear computation, and refine it via a non-linear optimization. Experimental results show the accuracy of the correspondences between rays and pixels in raw images, estimated by the proposed method.

We present a novel method for the geometric calibration of micro-lens-based light field cameras. Accurate geometric calibration is the basis of various applications. Instead of using sub-aperture images, we directly utilize raw images for calibration. We select appropriate regions in raw images and extract line features from micro-lens images in those regions. For the entire process, we formulate a new projection model of a micro-lens-based light field camera, which contains a smaller number of parameters than previous models. The model is transformed into a linear form using line features. We compute the initial solution of both the intrinsic and the extrinsic parameters by a linear computation and refine them via non-linear optimization. Experimental results demonstrate the accuracy of the correspondences between rays and pixels in raw images, as estimated by the proposed method.

Preserving a heritage as a digital archive is as important as preserving its physical structure. The digital preservation is essential for massive heritages which are often defenceless against various types of destruction and require frequent restorations. However, capturing heritages gets exceedingly harder as their scale grows. In this paper, we present a novel approach to reconstruct a massive-scale structure using a hand-held fusion sensor system. The approach includes new methods on calibration, motion estimation, and accumulated error reduction. The proposed sensor system consists of four cameras and two 2D laser scanners to obtain a wide field-of-view. A new calibration method successfully achieves a much lower reprojection error compared to the previous method. A motion estimation method provides accurate and robust relative poses by fully utilizing plenty observations. At the last stage, the accumulated error reduction removes the drift occurred over tens of thousands frames by adopting weak GPS prior and loop closing. Therefore the system is able to capture and geo-register large heritage architectures of square kilometers size. Furthermore, because no assumption or restriction is made, the user can freely move the system and can control the level of detail of the digital heritage without any effort. To demonstrate the performance, we have captured several important Korean heritages including Gyeongbok-Gung, the royal palace of Korea. The experimental result shows that the estimated route fits Google's satellite image and DGPS data while the detailed appearances of representative constructions are captured and preserved well.

One of the core applications of light field imaging is depth estimation. To acquire a depth map, existing approaches apply a single photo-consistency measure to an entire light field. However, this is not an optimal choice because of the non-uniform light field degradations produced by limitations in the hardware design. In this paper, we introduce a pipeline that automatically determines the best configuration for photo-consistency measurement, which leads to the most reliable depth label from the light field. We analyzed the practical factors affecting degradation in lenslet light field cameras, and designed a learning based framework that can retrieve the best cost measure and optimal depth label. To enhance the reliability of our method, we augmented an existing light field benchmark to simulate realistic source dependent noise, aberrations, and vignetting artifacts. The augmented dataset was used for the training and validation of the proposed approach. Our method was competitive with several state-of-the-art methods for the benchmark and real-world light field datasets.

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