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A Benchmark Dataset and Evaluation Methodology for Video Object Segmentation

This work presents a new benchmark dataset and evaluation methodology for the area of video object segmentation, named DAVIS (Densely Annotated VIdeo Segmentation), and provides a comprehensive analysis of several state-of-the-art segmentation approaches using three complementary metrics.
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Saliency filters: Contrast based filtering for salient region detection

A conceptually clear and intuitive algorithm for contrast-based saliency estimation that outperforms all state-of-the-art approaches and can be formulated in a unified way using high-dimensional Gaussian filters.
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The 2017 DAVIS Challenge on Video Object Segmentation

The scope of the benchmark, the main characteristics of the dataset, the evaluation metrics of the competition, and a detailed analysis of the results of the participants to the challenge are described.
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Learning Video Object Segmentation from Static Images

It is demonstrated that highly accurate object segmentation in videos can be enabled by using a convolutional neural network (convnet) trained with static images only, and a combination of offline and online learning strategies are used.
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Bilateral Space Video Segmentation

A new energy on the vertices of a regularly sampled spatiotemporal bilateral grid is designed, which can be solved efficiently using a standard graph cut label assignment, and implicitly approximates long-range, spatio-temporal connections between pixels while still containing only a small number of variables and only local graph edges.
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Scene reconstruction from high spatio-angular resolution light fields

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.
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Nonlinear disparity mapping for stereoscopic 3D

The most important perceptual aspects of stereo vision are discussed and their implications for stereoscopic content creation are formalized into a set of basic disparity mapping operators that enable us to control and retarget the depth of a stereoscopic scene in a nonlinear and locally adaptive fashion.
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A system for retargeting of streaming video

A novel, integrated system for content-aware video retargeting which combines key frame based constraint editing with numerous automatic algorithms for video analysis and seamlessly integrates into postproduction and computes the reformatting in real-time.
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Fully Connected Object Proposals for Video Segmentation

A novel approach to video segmentation using multiple object proposals that combines appearance with long-range point tracks to ensure robustness with respect to fast motion and occlusions over longer video sequences is presented.
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High-quality surface splatting on today's GPUs

This paper presents an approach that achieves a quality comparable to the original EWA splatting at a rate of more than 20M elliptical splats per second, and provides per-pixel Phong shading even for dynamically changing geometries and high-quality anti-aliased rendering by employing a screen-space pre-filter in addition to the object-space reconstruction filter.
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