Towards Real-Time Monocular Depth Estimation for Robotics: A Survey
arXiv:2111.08600
Abstract
As an essential component for many autonomous driving and robotic activities such as ego-motion estimation, obstacle avoidance and scene understanding, monocular depth estimation (MDE) has attracted great attention from the computer vision and robotics communities. Over the past decades, a large number of methods have been developed. To the best of our knowledge, however, there is not a comprehensive survey of MDE. This paper aims to bridge this gap by reviewing 197 relevant articles published between 1970 and 2021. In particular, we provide a comprehensive survey of MDE covering various methods, introduce the popular performance evaluation metrics and summarize publically available datasets. We also summarize available open-source implementations of some representative methods and compare their performances. Furthermore, we review the application of MDE in some important robotic tasks. Finally, we conclude this paper by presenting some promising directions for future research. This survey is expected to assist readers to navigate this research field.
References in corpus (13)
- An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
- Depth Map Prediction from a Single Image using a Multi-Scale Deep Network
- AdaBins: Depth Estimation using Adaptive Bins
- DepthTransfer: Depth Extraction from Video Using Non-parametric Sampling
- Monocular Depth Estimation: A Survey
- On Deep Learning Techniques to Boost Monocular Depth Estimation for Autonomous Navigation
- MiniNet: An extremely lightweight convolutional neural network for real-time unsupervised monocular depth estimation
- A Survey on Deep Learning Architectures for Image-based Depth Reconstruction
- Virtual KITTI 2
- Hybridnet for depth estimation and semantic segmentation
- DepthNet Nano: A Highly Compact Self-Normalizing Neural Network for Monocular Depth Estimation
- Robust Vision-based Obstacle Avoidance for Micro Aerial Vehicles in Dynamic Environments
- Edge-Guided Occlusion Fading Reduction for a Light-Weighted Self-Supervised Monocular Depth Estimation