Sparse to Dense Scene Flow Estimation from Light Fields

International audienceThe paper addresses the problem of scene flow estimation from sparsely sampled video light fields. The scene flow estimation method is based on an affine model in the 4D ray space that allows us to estimate a dense flow from sparse estimates in 4D clusters. A dataset of synthetic video light fields created for assessing scene flow estimation techniques is also described. Experiments show that the proposed method gives error rates on the optical flow components that are comparable to those obtained with state of the art optical flow estimation methods, while computing a more accurate disparity variation when compared with prior scene flow estimation techniques

Enhanced processing methods for light field imaging

The light field camera provides rich textural and geometric information, but it is still challenging to use it efficiently and accurately to solve computer vision problems. Light field image processing is divided into multiple levels. First, low-level processing technology mainly includes the acquisition of light field images and their preprocessing. Second, the middle-level process consists of the depth estimation, light field encoding, and the extraction of cues from the light field. Third, high-level processing involves 3D reconstruction, target recognition, visual odometry, image reconstruction, and other advanced applications. We propose a series of improved algorithms for each of these levels. The light field signal contains rich angular information. By contrast, traditional computer vision methods, as used for 2D images, often cannot make full use of the high-frequency part of the light field angular information. We propose a fast pre-estimation algorithm to enhance the light field feature to improve its speed and accuracy when keeping full use of the angular information.Light field filtering and refocusing are essential cues in light field signal processing. Modern frequency domain filtering technology and wavelet technology have effectively improved light field filtering accuracy but may fail at object edges. We adapted the sub-window filtering with the light field to improve the reconstruction of object edges. Light field images can analyze the effects of scattering and refraction phenomena, and there are still insufficient metrics to evaluate the results. Therefore, we propose a physical rendering-based light field dataset that simulates the distorted light field image through a transparent medium, such as atmospheric turbulence or water surface. The neural network is an essential method to process complex light field data. We propose an efficient 3D convolutional autoencoder network for the light field structure. This network overcomes the severe distortion caused by high-intensity turbulence with limited angular resolution and solves the difficulty of pixel matching between distorted images. This work emphasizes the application and usefulness of light field imaging in computer vision whilst improving light field image processing speed and accuracy through signal processing, computer graphics, computer vision, and artificial neural networks