KEYWORDS: Panoramic photography, Data acquisition, Optical spheres, Video processing, Virtual reality, Cameras, Imaging systems, Data processing, Video
We propose a new method of rendering panoramic light field within a certain range, which provides an effective way for the acquisition of virtual reality data. Different from existing panoramic light field reconstruction algorithms, we propose the concept of the ray sphere to render a panoramic light field. Using the internal and external parameters of the cameras in the acquisition system, all the light field data are uniformly expressed in the ray sphere. The constructed ray sphere enables the rendering of a panoramic light field of any view point within a 3DOF+ space, which can not be achieved with correlation methods. In addition, we design and build an acquisition system to capture real scenes to verify the effectiveness of our method. Experimental results show that our method can get panoramic light field rendering of any view point on the horizontal plane within half the radius of the acquisition system, and can effectively process the light field video data.
Light field planar homography is essential for light field camera calibration and light field raw data rectification. However, most previous researches assume light field camera as pinhole camera array and deduce the planar homography matrix of sub-aperture image, which is analogous to traditional method. In this paper, we regard light field as a whole and present a novel light field planar homography matrix based on multi-projection-center (MPC) model. The projections of point, line and conic are exploited based on light field planar homography. In addition, a camera calibration method and a homography estimation are proposed to verify the light field planar homography. Experimental results on light field datasets have verified the performance of the proposed methods.
Circular Light fields imaging is based on images taken on a regular circle with an equal space. Orientation information in epipolar plane images (EPIs) reveals strong depth clue for 3D reconstruction task. However, EPIs in Circular Light fields show a slightly distorted sinusoidal trajectory in 3D space. Rather than analyzing such spiral line on 2D image processing method, we present an algorithm based on 3D formula. By applying 3D Canny into densely sampled Circular Light fields, we can obtain a 3D point cloud in the image cube. Furthermore, we utilize structure tensor to analyze the disparity information in such 3D data. Finally, we build two Hough spaces to reconstruct depth information and obtain an accurate 3D object. Compared with state-of-the-art image-based 3D reconstruction methods, experiment results show our method can obtain improved reconstruction quality on synthetic data.
Pose estimation is the key step of simultaneous localization and mapping (SLAM). The relationship between the rays captured by multiple light field cameras can provide more constraints for pose estimation. In this paper, we propose a novel light field SLAM (LF-SLAM) based on ray-space projection model, including visual odometry, optimization, loop closing and mapping. Unlike traditional SLAM, which estimates pose based on point-point correspondence, we firstly utilize ray-space features to initialize camera motion based on light field fundamental matrix. In addition, a ray-ray cost function is presented to optimize camera pose and 3D points. Finally, we exhibit the motion map and 3D reconstruction results from a moving light field camera. Experimental results have verified the effectiveness and robustness of the proposed method.
The surface camera (SCam) of light fields gathers angular sample rays passing through a 3D point. The consistency of SCams is evaluated to estimate the depth map of scene. But the consistency is affected by several limitations such as occlusions or non-Lambertian surfaces. To solve those limitations, the SCam is partitioned into two segments that one of them could satisfy the consistency constraint. The segmentation pattern of SCam is highly related to the texture of spatial patch, so we enforce a mask matching to describe the shape correlation between segments of SCam and spatial patch. To further address the ambiguity in textureless region, a global method with pixel-wise plane label is presented. Plane label inference at each pixel can recover not only depth value but also local geometry structure, that is suitable for light fields with sub-pixel disparities and continuous view variation. Our method is evaluated on public light field datasets and outperforms the state-of-the-art.
The multi-view light fields (MVLF) provide new solutions to the existing problems in monocular light field, such as the limited field of view. However as key steps in MVLF, the calibration and registration have been limited studied. In this paper, we propose a method to calibrate the camera and register different LFs without the checkboard at the same time, which we call the self-calibrating method. We model the LF structure as a 5-parameter two-parallel-plane (2PP) model, then represent the associations between rays and reconstructed points as a 3D projective transformation. With the constraints of ray-ray correspondences in different LFs, the parameters can be solved with a linear initialization and a nonlinear refinement. The result in real scene and 3D point clouds registration error of MVLF in simulated data verify the high performance of the proposed model.
Light field cameras have been rapidly developed and are likely to appear in mobile devices in near future. It is essential to develop efficient and robust depth estimation algorithm for mobile applications. However, existing methods are either slow or lack of adaptability to occlusion such that they are not suitable to mobile computing platform. In this paper, we present the generalized EPI representation in light field and formulate it using two linear functions. By combining it with the light field occlusion theory, a highly efficient and anti-occlusion depth estimation algorithm is proposed. Our algorithm outperforms the previous local method, especially in occlusion areas. Experimental results on public light field datasets have demonstrated the effectiveness and efficiency of the proposed algorithm.
We propose a novel multiple object tracking algorithm in a particle filter framework, where the input is a set of candidate
regions obtained from Robust Principle Component Analysis (RPCA) in each frame, and the goals is to recover
trajectories of objects over time. Our method adapts to the changing appearance of objects, due to occlusion, illumination
changes and large pose variations, by incorporating a l1 minimization-based appearance model into the Maximize A
Posterior (MAP) inference. Though L1 trackers have showed impressive tracking accuracy, they are computationally
demanding for multiple object tracking. Conventional data association methods using simple nonparametric appearance
model, such as histogram-based descriptor, may suffer from drastic changing object appearance. The robust tracking
performance of our approach has been validated with a comprehensive evaluation involving several challenging
sequences and state-of-the-art multiple object trackers.
In this paper, we propose an approach based on Kolmogorov Complexity (KC) measuie for determining script classes in mixed Chinese (complex characters)/English document images. This approach, which mainly consists of two steps: document image preprocessing and KC measure, can successfully separate Chinese text lines from English ones. Our approach is robust and reliable in handling document images of different appearances and densities, and various fonts, sizes and styles of characters used in documents. Experimental results on a set of 40 text line images (20 English text lines and 20 Complex Chinese text lines) from various document images show that 100% correct classification rate can be achieved.
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