Unsupervised High-Resolution Depth Learning From Videos With Dual Networks
arXiv:1910.08897
Abstract
Unsupervised depth learning takes the appearance difference between a target view and a view synthesized from its adjacent frame as supervisory signal. Since the supervisory signal only comes from images themselves, the resolution of training data significantly impacts the performance. High-resolution images contain more fine-grained details and provide more accurate supervisory signal. However, due to the limitation of memory and computation power, the original images are typically down-sampled during training, which suffers heavy loss of details and disparity accuracy. In order to fully explore the information contained in high-resolution data, we propose a simple yet effective dual networks architecture, which can directly take high-resolution images as input and generate high-resolution and high-accuracy depth map efficiently. We also propose a Self-assembled Attention (SA-Attention) module to handle low-texture region. The evaluation on the benchmark KITTI and Make3D datasets demonstrates that our method achieves state-of-the-art results in the monocular depth estimation task.
Accepted by ICCV2019
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Cited by in corpus (4)
- HR-Depth: High Resolution Self-Supervised Monocular Depth Estimation
- Self-Supervised Joint Learning Framework of Depth Estimation via Implicit Cues
- Advancing Self-supervised Monocular Depth Learning with Sparse LiDAR
- Net: Semantic-Aware Self-supervised Depth Estimation with Monocular Videos and Synthetic Data