On the Importance of Stereo for Accurate Depth Estimation: An Efficient Semi-Supervised Deep Neural Network Approach
arXiv:1803.09719
Abstract
We revisit the problem of visual depth estimation in the context of autonomous vehicles. Despite the progress on monocular depth estimation in recent years, we show that the gap between monocular and stereo depth accuracy remains largea particularly relevant result due to the prevalent reliance upon monocular cameras by vehicles that are expected to be self-driving. We argue that the challenges of removing this gap are significant, owing to fundamental limitations of monocular vision. As a result, we focus our efforts on depth estimation by stereo. We propose a novel semi-supervised learning approach to training a deep stereo neural network, along with a novel architecture containing a machine-learned argmax layer and a custom runtime (that will be shared publicly) that enables a smaller version of our stereo DNN to run on an embedded GPU. Competitive results are shown on the KITTI 2015 stereo dataset. We also evaluate the recent progress of stereo algorithms by measuring the impact upon accuracy of various design criteria.
arXiv commentCVPR 2018 Workshop on Autonomous Driving. For video, see https://youtu.be/0FPQdVOYoAU
References in corpus (8)
- Depth Map Prediction from a Single Image using a Multi-Scale Deep Network
- Learning Depth from Single Monocular Images Using Deep Convolutional Neural Fields
- Unsupervised CNN for Single View Depth Estimation: Geometry to the Rescue
- End-to-End Learning of Geometry and Context for Deep Stereo Regression
- Self-Supervised Learning for Stereo Matching with Self-Improving Ability
- Cascade Residual Learning: A Two-stage Convolutional Neural Network for Stereo Matching
- Good Colour Maps: How to Design Them
- Learning for Disparity Estimation through Feature Constancy