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Found 3 papers, 3 papers with code
Two-in-One Depth: Bridging the Gap Between Monocular and Binocular Self-supervised Depth Estimation
Monocular and binocular self-supervised depth estimations are two important and related tasks in computer vision, which aim to predict scene depths from single images and stereo image pairs respectively.
Self-distilled Feature Aggregation for Self-supervised Monocular Depth Estimation
Addressing this problem, we propose the Self-Distilled Feature Aggregation (SDFA) module for simultaneously aggregating a pair of low-scale and high-scale features and maintaining their contextual consistency.
Learning Occlusion-Aware Coarse-to-Fine Depth Map for Self-supervised Monocular Depth Estimation
In spite of recent efforts in this field, how to learn accurate scene depths and alleviate the negative influence of occlusions for self-supervised depth estimation, still remains an open problem.
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