Self-Supervised Learning of Depth and Motion Under Photometric Inconsistency
arXiv:1909.09115
Abstract
The self-supervised learning of depth and pose from monocular sequences provides an attractive solution by using the photometric consistency of nearby frames as it depends much less on the ground-truth data. In this paper, we address the issue when previous assumptions of the self-supervised approaches are violated due to the dynamic nature of real-world scenes. Different from handling the noise as uncertainty, our key idea is to incorporate more robust geometric quantities and enforce internal consistency in the temporal image sequence. As demonstrated on commonly used benchmark datasets, the proposed method substantially improves the state-of-the-art methods on both depth and relative pose estimation for monocular image sequences, without adding inference overhead.
arXiv commentInternational Conference on Computer Vision (ICCV) Workshop 2019
References in corpus (7)
- Adam: A Method for Stochastic Optimization
- Depth Map Prediction from a Single Image using a Multi-Scale Deep Network
- Learning Depth from Single Monocular Images Using Deep Convolutional Neural Fields
- DeMoN: Depth and Motion Network for Learning Monocular Stereo
- DepthTransfer: Depth Extraction from Video Using Non-parametric Sampling
- SfM-Net: Learning of Structure and Motion from Video
- Unsupervised CNN for Single View Depth Estimation: Geometry to the Rescue