592 research outputs found

    Accurate Light Field Depth Estimation with Superpixel Regularization over Partially Occluded Regions

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    Depth estimation is a fundamental problem for light field photography applications. Numerous methods have been proposed in recent years, which either focus on crafting cost terms for more robust matching, or on analyzing the geometry of scene structures embedded in the epipolar-plane images. Significant improvements have been made in terms of overall depth estimation error; however, current state-of-the-art methods still show limitations in handling intricate occluding structures and complex scenes with multiple occlusions. To address these challenging issues, we propose a very effective depth estimation framework which focuses on regularizing the initial label confidence map and edge strength weights. Specifically, we first detect partially occluded boundary regions (POBR) via superpixel based regularization. Series of shrinkage/reinforcement operations are then applied on the label confidence map and edge strength weights over the POBR. We show that after weight manipulations, even a low-complexity weighted least squares model can produce much better depth estimation than state-of-the-art methods in terms of average disparity error rate, occlusion boundary precision-recall rate, and the preservation of intricate visual features

    Temporally coherent 4D reconstruction of complex dynamic scenes

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    This paper presents an approach for reconstruction of 4D temporally coherent models of complex dynamic scenes. No prior knowledge is required of scene structure or camera calibration allowing reconstruction from multiple moving cameras. Sparse-to-dense temporal correspondence is integrated with joint multi-view segmentation and reconstruction to obtain a complete 4D representation of static and dynamic objects. Temporal coherence is exploited to overcome visual ambiguities resulting in improved reconstruction of complex scenes. Robust joint segmentation and reconstruction of dynamic objects is achieved by introducing a geodesic star convexity constraint. Comparative evaluation is performed on a variety of unstructured indoor and outdoor dynamic scenes with hand-held cameras and multiple people. This demonstrates reconstruction of complete temporally coherent 4D scene models with improved nonrigid object segmentation and shape reconstruction.Comment: To appear in The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2016 . Video available at: https://www.youtube.com/watch?v=bm_P13_-Ds

    Real-Time Occlusion Handling in Augmented Reality Based on an Object Tracking Approach

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    To produce a realistic augmentation in Augmented Reality, the correct relative positions of real objects and virtual objects are very important. In this paper, we propose a novel real-time occlusion handling method based on an object tracking approach. Our method is divided into three steps: selection of the occluding object, object tracking and occlusion handling. The user selects the occluding object using an interactive segmentation method. The contour of the selected object is then tracked in the subsequent frames in real-time. In the occlusion handling step, all the pixels on the tracked object are redrawn on the unprocessed augmented image to produce a new synthesized image in which the relative position between the real and virtual object is correct. The proposed method has several advantages. First, it is robust and stable, since it remains effective when the camera is moved through large changes of viewing angles and volumes or when the object and the background have similar colors. Second, it is fast, since the real object can be tracked in real-time. Last, a smoothing technique provides seamless merging between the augmented and virtual object. Several experiments are provided to validate the performance of the proposed method

    Generating depth maps from stereo image pairs

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    Perception of depth and motion from ambiguous binocular information

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    AbstractThe visual system can determine motion and depth from ambiguous information contained in images projected onto both retinas over space and time. The key to the way the system overcomes such ambiguity lies in dependency among multiple cues—such as spatial displacement over time, binocular disparity, and interocular time delay—which might be established based on prior knowledge or experience, and stored in spatiotemporal response characteristics of neurons at an early cortical stage. We conducted a psychophysical investigation of whether a single ambiguous cue (specifically, interocular time delay) permits depth discrimination and motion perception. Data from this investigation are consistent with the predictions derived from the response profiles of V1 neurons, which show interdependency in their responses to each cue, indicating that spatial and temporal information is jointly encoded in early vision

    The perceptual consequences and neural basis of monocular occlusions

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    Occluded areas are abundant in natural scenes and play an important role in stereopsis. However, due to the treatment of occlusions as noise by early researchers of stereopsis, this field of study has not seen much development until the last two decades. Consequently, many aspects of depth perception from occlusions are not well understood. The goal of this thesis was to study several such aspects in order to advance the current understanding of monocular occlusions and their neural underpinnings. The psychophysical and computational studies described in this thesis have demonstrated that: 1) occlusions play an important role in defining the shape and depth of occluding surfaces, 2) depth signals from monocular occlusions and disparity interact in complex ways, 3) there is a single mechanism underlying depth perception from monocular occlusions and 4) this mechanism is likely to rely on monocular occlusion geometry. A unified theory of depth computation from monocular occlusions and disparity was proposed based on these findings. A biologically-plausible computational model based on this theory produced results close to observer percepts for a variety of monocular occlusion phenomena

    Dense Wide-Baseline Stereo with Varying Illumination and its Application to Face Recognition

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    We study the problem of dense wide baseline stereo with varying illumination. We are motivated by the problem of face recognition across pose. Stereo matching allows us to compare face images based on physically valid, dense correspondences. We show that the stereo matching cost provides a very robust measure of the similarity of faces that is insensitive to pose variations. We build on the observation that most illumination insensitive local comparisons require the use of relatively large windows. The size of these windows is affected by foreshortening. If we do not account for this effect, we incur misalignments that are systematic and significant and are exacerbated by wide baseline conditions. We present a general formulation of dense wide baseline stereo with varying illumination and provide two methods to solve them. The first method is based on dynamic programming (DP) and fully accounts for the effect of slant. The second method is based on graph cuts (GC) and fully accounts for the effect of both slant and tilt. The GC method finds a global solution using the unary function from the general formulation and a novel smoothness term that encodes surface orientation. Our experiments show that DP dense wide baseline stereo achieves superior performance compared to existing methods in face recognition across pose. The experiments with the GC method show that accounting for both slant and tilt can improve performance in situations with wide baselines and lighting variation. Our formulation can be applied to other more sophisticated window based image comparison methods for stereo

    Filling-in the Forms: Surface and Boundary Interactions in Visual Cortex

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    Defense Advanced Research Projects Agency and the Office of Naval Research (NOOOI4-95-l-0409); Office of Naval Research (NOOO14-95-1-0657)

    Fast and Accurate Depth Estimation from Sparse Light Fields

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    We present a fast and accurate method for dense depth reconstruction from sparsely sampled light fields obtained using a synchronized camera array. In our method, the source images are over-segmented into non-overlapping compact superpixels that are used as basic data units for depth estimation and refinement. Superpixel representation provides a desirable reduction in the computational cost while preserving the image geometry with respect to the object contours. Each superpixel is modeled as a plane in the image space, allowing depth values to vary smoothly within the superpixel area. Initial depth maps, which are obtained by plane sweeping, are iteratively refined by propagating good correspondences within an image. To ensure the fast convergence of the iterative optimization process, we employ a highly parallel propagation scheme that operates on all the superpixels of all the images at once, making full use of the parallel graphics hardware. A few optimization iterations of the energy function incorporating superpixel-wise smoothness and geometric consistency constraints allows to recover depth with high accuracy in textured and textureless regions as well as areas with occlusions, producing dense globally consistent depth maps. We demonstrate that while the depth reconstruction takes about a second per full high-definition view, the accuracy of the obtained depth maps is comparable with the state-of-the-art results.Comment: 15 pages, 15 figure
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