End-to-end depth from motion with stabilized monocular videos

Abstract

We propose a depth map inference system from monocular videos based on a novel dataset for navigation that mimics aerial footage from gimbal stabilized monocular camera in rigid scenes. Unlike most navigation datasets, the lack of rotation implies an easier structure from motion problem which can be leveraged for different kinds of tasks such as depth inference and obstacle avoidance. We also propose an architecture for end-to-end depth inference with a fully convolutional network. Results show that although tied to camera inner parameters, the problem is locally solvable and leads to good quality depth prediction.

References in corpus (10)

• Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
• Fully Convolutional Networks for Semantic Segmentation
• A Large Dataset to Train Convolutional Networks for Disparity, Optical Flow, and Scene Flow Estimation
• Empirical Evaluation of Rectified Activations in Convolutional Network
• Depth Map Prediction from a Single Image using a Multi-Scale Deep Network
• Computing the Stereo Matching Cost with a Convolutional Neural Network
• Unsupervised CNN for Single View Depth Estimation: Geometry to the Rescue
• Virtual Worlds as Proxy for Multi-Object Tracking Analysis
• Unsupervised Learning of Depth and Ego-Motion from Video
• Unsupervised learning of depth and motion

Cited by in corpus (2)

• Learning structure-from-motion from motion
• Does it work outside this benchmark? Introducing the Rigid Depth Constructor tool, depth validation dataset construction in rigid scenes for the masses