A Survey on Deep Learning Architectures for Image-based Depth Reconstruction

Abstract

Estimating depth from RGB images is a long-standing ill-posed problem, which has been explored for decades by the computer vision, graphics, and machine learning communities. In this article, we provide a comprehensive survey of the recent developments in this field. We will focus on the works which use deep learning techniques to estimate depth from one or multiple images. Deep learning, coupled with the availability of large training datasets, have revolutionized the way the depth reconstruction problem is being approached by the research community. In this article, we survey more than 100 key contributions that appeared in the past five years, summarize the most commonly used pipelines, and discuss their benefits and limitations. In retrospect of what has been achieved so far, we also conjecture what the future may hold for learning-based depth reconstruction research.

References in corpus (16)

• Distilling the Knowledge in a Neural Network
• Efficient Inference in Fully Connected CRFs with Gaussian Edge Potentials
• Depth Map Prediction from a Single Image using a Multi-Scale Deep Network
• Learning Depth from Single Monocular Images Using Deep Convolutional Neural Fields
• Computing the Stereo Matching Cost with a Convolutional Neural Network
• DeMoN: Depth and Motion Network for Learning Monocular Stereo
• Deep Convolutional Inverse Graphics Network
• SfM-Net: Learning of Structure and Motion from Video
• Weakly-supervised Disentangling with Recurrent Transformations for 3D View Synthesis
• Self-Supervised Learning for Stereo Matching with Self-Improving Ability
• Estimating Depth from RGB and Sparse Sensing
• Look Wider to Match Image Patches with Convolutional Neural Networks
• Practical Deep Stereo (PDS): Toward applications-friendly deep stereo matching
• Numerical Inversion of SRNF Maps for Elastic Shape Analysis of Genus-Zero Surfaces
• Recurrent Neural Network for Learning DenseDepth and Ego-Motion from Video
• SegStereo: Exploiting Semantic Information for Disparity Estimation

Cited by in corpus (4)

• Image-based 3D Object Reconstruction: State-of-the-Art and Trends in the Deep Learning Era
• M^3VSNet: Unsupervised Multi-metric Multi-view Stereo Network
• Towards Real-Time Monocular Depth Estimation for Robotics: A Survey
• Black-box Adversarial Attacks on Monocular Depth Estimation Using Evolutionary Multi-objective Optimization