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Predicting Sharp and Accurate Occlusion Boundaries in Monocular Depth Estimation Using Displacement Fields
Current methods for depth map prediction from monocular images tend to p...
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Relighting Humans: Occlusion-Aware Inverse Rendering for Full-Body Human Images
Relighting of human images has various applications in image synthesis. ...
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Occlusion-Aware Depth Estimation with Adaptive Normal Constraints
We present a new learning-based method for multi-frame depth estimation ...
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Edge-Guided Occlusion Fading Reduction for a Light-Weighted Self-Supervised Monocular Depth Estimation
Self-supervised monocular depth estimation methods generally suffer the ...
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DDNet: Dual-path Decoder Network for Occlusion Relationship Reasoning
Occlusion relationship reasoning based on convolution neural networks co...
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Occlusion-shared and Feature-separated Network for Occlusion Relationship Reasoning
Occlusion relationship reasoning demands closed contour to express the o...
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Multimodal In-bed Pose and Shape Estimation under the Blankets
Humans spend vast hours in bed – about one-third of the lifetime on aver...
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Pixel-Pair Occlusion Relationship Map(P2ORM): Formulation, Inference Application
We formalize concepts around geometric occlusion in 2D images (i.e., ignoring semantics), and propose a novel unified formulation of both occlusion boundaries and occlusion orientations via a pixel-pair occlusion relation. The former provides a way to generate large-scale accurate occlusion datasets while, based on the latter, we propose a novel method for task-independent pixel-level occlusion relationship estimation from single images. Experiments on a variety of datasets demonstrate that our method outperforms existing ones on this task. To further illustrate the value of our formulation, we also propose a new depth map refinement method that consistently improve the performance of state-of-the-art monocular depth estimation methods. Our code and data are available at http://imagine.enpc.fr/ qiux/P2ORM/.
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