Recurrent Neural Network for (Un-)supervised Learning of Monocular VideoVisual Odometry and Depth

Abstract

Deep learning-based, single-view depth estimation methods have recently shown highly promising results. However, such methods ignore one of the most important features for determining depth in the human vision system, which is motion. We propose a learning-based, multi-view dense depth map and odometry estimation method that uses Recurrent Neural Networks (RNN) and trains utilizing multi-view image reprojection and forward-backward flow-consistency losses. Our model can be trained in a supervised or even unsupervised mode. It is designed for depth and visual odometry estimation from video where the input frames are temporally correlated. However, it also generalizes to single-view depth estimation. Our method produces superior results to the state-of-the-art approaches for single-view and multi-view learning-based depth estimation on the KITTI driving dataset.

References in corpus (2)

• Very Deep Convolutional Networks for Large-Scale Image Recognition
• DeepVO: Towards End-to-End Visual Odometry with Deep Recurrent Convolutional Neural Networks

Cited by in corpus (10)

• A Survey on Deep Learning for Localization and Mapping: Towards the Age of Spatial Machine Intelligence
• Forget About the LiDAR: Self-Supervised Depth Estimators with MED Probability Volumes
• Deep Learning based Monocular Depth Prediction: Datasets, Methods and Applications
• VOLDOR-SLAM: For the Times When Feature-Based or Direct Methods Are Not Good Enough
• Learning Monocular Visual Odometry via Self-Supervised Long-Term Modeling
• Is Depth Really Necessary for Salient Object Detection?
• VOLDOR: Visual Odometry from Log-logistic Dense Optical flow Residuals
• Deep 3D Pan via adaptive "t-shaped" convolutions with global and local adaptive dilations
• Transformer Guided Geometry Model for Flow-Based Unsupervised Visual Odometry
• Semantics-Driven Unsupervised Learning for Monocular Depth and Ego-Motion Estimation