Depth from Combining Defocus and Correspondence Using Light-Field Cameras
01 Dec 2013-pp 673-680
TL;DR: A novel simple and principled algorithm is presented that computes dense depth estimation by combining both defocus and correspondence depth cues, and shows how to combine the two cues into a high quality depth map, suitable for computer vision applications such as matting, full control of depth-of-field, and surface reconstruction.
Abstract: Light-field cameras have recently become available to the consumer market. An array of micro-lenses captures enough information that one can refocus images after acquisition, as well as shift one's viewpoint within the sub-apertures of the main lens, effectively obtaining multiple views. Thus, depth cues from both defocus and correspondence are available simultaneously in a single capture. Previously, defocus could be achieved only through multiple image exposures focused at different depths, while correspondence cues needed multiple exposures at different viewpoints or multiple cameras, moreover, both cues could not easily be obtained together. In this paper, we present a novel simple and principled algorithm that computes dense depth estimation by combining both defocus and correspondence depth cues. We analyze the x-u 2D epipolar image (EPI), where by convention we assume the spatial x coordinate is horizontal and the angular u coordinate is vertical (our final algorithm uses the full 4D EPI). We show that defocus depth cues are obtained by computing the horizontal (spatial) variance after vertical (angular) integration, and correspondence depth cues by computing the vertical (angular) variance. We then show how to combine the two cues into a high quality depth map, suitable for computer vision applications such as matting, full control of depth-of-field, and surface reconstruction.
Citations
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21 Jul 2013
TL;DR: This paper proposes an algorithm that leverages coherence in massive light fields by breaking with a number of established practices in image-based reconstruction, and introduces a sparse representation and a propagation scheme for reliable depth estimates which make the algorithm particularly effective for 3D input.
Abstract: This paper describes a method for scene reconstruction of complex, detailed environments from 3D light fields. Densely sampled light fields in the order of 109 light rays allow us to capture the real world in unparalleled detail, but efficiently processing this amount of data to generate an equally detailed reconstruction represents a significant challenge to existing algorithms. We propose an algorithm that leverages coherence in massive light fields by breaking with a number of established practices in image-based reconstruction. Our algorithm first computes reliable depth estimates specifically around object boundaries instead of interior regions, by operating on individual light rays instead of image patches. More homogeneous interior regions are then processed in a fine-to-coarse procedure rather than the standard coarse-to-fine approaches. At no point in our method is any form of global optimization performed. This allows our algorithm to retain precise object contours while still ensuring smooth reconstructions in less detailed areas. While the core reconstruction method handles general unstructured input, we also introduce a sparse representation and a propagation scheme for reliable depth estimates which make our algorithm particularly effective for 3D input, enabling fast and memory efficient processing of "Gigaray light fields" on a standard GPU. We show dense 3D reconstructions of highly detailed scenes, enabling applications such as automatic segmentation and image-based rendering, and provide an extensive evaluation and comparison to existing image-based reconstruction techniques.
449 citations
07 Jun 2015
TL;DR: This paper introduces an algorithm that accurately estimates depth maps using a lenslet light field camera and estimates the multi-view stereo correspondences with sub-pixel accuracy using the cost volume using the phase shift theorem.
Abstract: This paper introduces an algorithm that accurately estimates depth maps using a lenslet light field camera. The proposed algorithm estimates the multi-view stereo correspondences with sub-pixel accuracy using the cost volume. The foundation for constructing accurate costs is threefold. First, the sub-aperture images are displaced using the phase shift theorem. Second, the gradient costs are adaptively aggregated using the angular coordinates of the light field. Third, the feature correspondences between the sub-aperture images are used as additional constraints. With the cost volume, the multi-label optimization propagates and corrects the depth map in the weak texture regions. Finally, the local depth map is iteratively refined through fitting the local quadratic function to estimate a non-discrete depth map. Because micro-lens images contain unexpected distortions, a method is also proposed that corrects this error. The effectiveness of the proposed algorithm is demonstrated through challenging real world examples and including comparisons with the performance of advanced depth estimation algorithms.
436 citations
Cites methods from "Depth from Combining Defocus and Co..."
...Second, the gradient costs are adaptively aggregated using the angular coordinates of the light field....
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11 Nov 2016
TL;DR: In this paper, a learning-based approach is proposed to synthesize new views from a sparse set of input views using two sequential convolutional neural networks to model disparity and color estimation components and train both networks simultaneously by minimizing the error between the synthesized and ground truth images.
Abstract: With the introduction of consumer light field cameras, light field imaging has recently become widespread. However, there is an inherent trade-off between the angular and spatial resolution, and thus, these cameras often sparsely sample in either spatial or angular domain. In this paper, we use machine learning to mitigate this trade-off. Specifically, we propose a novel learning-based approach to synthesize new views from a sparse set of input views. We build upon existing view synthesis techniques and break down the process into disparity and color estimation components. We use two sequential convolutional neural networks to model these two components and train both networks simultaneously by minimizing the error between the synthesized and ground truth images. We show the performance of our approach using only four corner sub-aperture views from the light fields captured by the Lytro Illum camera. Experimental results show that our approach synthesizes high-quality images that are superior to the state-of-the-art techniques on a variety of challenging real-world scenes. We believe our method could potentially decrease the required angular resolution of consumer light field cameras, which allows their spatial resolution to increase.
435 citations
TL;DR: This paper proposes a novel learning-based approach to synthesize new views from a sparse set of input views that could potentially decrease the required angular resolution of consumer light field cameras, which allows their spatial resolution to increase.
Abstract: With the introduction of consumer light field cameras, light field imaging has recently become widespread. However, there is an inherent trade-off between the angular and spatial resolution, and thus, these cameras often sparsely sample in either spatial or angular domain. In this paper, we use machine learning to mitigate this trade-off. Specifically, we propose a novel learning-based approach to synthesize new views from a sparse set of input views. We build upon existing view synthesis techniques and break down the process into disparity and color estimation components. We use two sequential convolutional neural networks to model these two components and train both networks simultaneously by minimizing the error between the synthesized and ground truth images. We show the performance of our approach using only four corner sub-aperture views from the light fields captured by the Lytro Illum camera. Experimental results show that our approach synthesizes high-quality images that are superior to the state-of-the-art techniques on a variety of challenging real-world scenes. We believe our method could potentially decrease the required angular resolution of consumer light field cameras, which allows their spatial resolution to increase.
427 citations
Cites methods from "Depth from Combining Defocus and Co..."
...Since our disparity estimation CNN is trained to directly minimize the synthesis error, our estimated disparities are more suitable for this application than existing disparity estimation techniques [Wanner and Goldluecke 2012; Tao et al. 2013; Wang et al. 2015; Jeon et al. 2015] (see Fig....
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...Therefore, existing techniques [Tao et al. 2013; Wang et al. 2015] typically select the disparity level that has the minimum standard deviation and maximum mean contrast....
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20 Nov 2016
TL;DR: In computer vision communities such as stereo, optical flow, or visual tracking, commonly accepted and widely used benchmarks have enabled objective comparison and boosted scientific progress.
Abstract: In computer vision communities such as stereo, optical flow, or visual tracking, commonly accepted and widely used benchmarks have enabled objective comparison and boosted scientific progress.
414 citations
Cites background from "Depth from Combining Defocus and Co..."
...For this reason, recent papers often resort to showing qualitative results on real-world datasets to showcase their improved results [2,6,7], but performance is very difficult to judge without ground truth....
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TL;DR: This paper has designed a stand-alone, flexible C++ implementation that enables the evaluation of individual components and that can easily be extended to include new algorithms.
Abstract: Stereo matching is one of the most active research areas in computer vision. While a large number of algorithms for stereo correspondence have been developed, relatively little work has been done on characterizing their performance. In this paper, we present a taxonomy of dense, two-frame stereo methods designed to assess the different components and design decisions made in individual stereo algorithms. Using this taxonomy, we compare existing stereo methods and present experiments evaluating the performance of many different variants. In order to establish a common software platform and a collection of data sets for easy evaluation, we have designed a stand-alone, flexible C++ implementation that enables the evaluation of individual components and that can be easily extended to include new algorithms. We have also produced several new multiframe stereo data sets with ground truth, and are making both the code and data sets available on the Web.
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01 Aug 1996
TL;DR: This paper describes a sampled representation for light fields that allows for both efficient creation and display of inward and outward looking views, and describes a compression system that is able to compress the light fields generated by more than a factor of 100:1 with very little loss of fidelity.
Abstract: A number of techniques have been proposed for flying through scenes by redisplaying previously rendered or digitized views. Techniques have also been proposed for interpolating between views by warping input images, using depth information or correspondences between multiple images. In this paper, we describe a simple and robust method for generating new views from arbitrary camera positions without depth information or feature matching, simply by combining and resampling the available images. The key to this technique lies in interpreting the input images as 2D slices of a 4D function the light field. This function completely characterizes the flow of light through unobstructed space in a static scene with fixed illumination. We describe a sampled representation for light fields that allows for both efficient creation and display of inward and outward looking views. We hav e created light fields from large arrays of both rendered and digitized images. The latter are acquired using a video camera mounted on a computer-controlled gantry. Once a light field has been created, new views may be constructed in real time by extracting slices in appropriate directions. Since the success of the method depends on having a high sample rate, we describe a compression system that is able to compress the light fields we have generated by more than a factor of 100:1 with very little loss of fidelity. We also address the issues of antialiasing during creation, and resampling during slice extraction. CR Categories: I.3.2 [Computer Graphics]: Picture/Image Generation — Digitizing and scanning, Viewing algorithms; I.4.2 [Computer Graphics]: Compression — Approximate methods Additional keywords: image-based rendering, light field, holographic stereogram, vector quantization, epipolar analysis
4,426 citations
"Depth from Combining Defocus and Co..." refers background in this paper
...Perwass and Wietzke [23] propose correspondence techniques to estimate depth, while others [1, 15] have proposed using contrast measurements....
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...Light-fields [6, 15] can be used to refocus images [21]....
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