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

2019/06/14 by Hamid Laga, Laga, Hamid · 1 citation
Computer Science · Engineering · #Advanced Vision and Imaging #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #FOS: Electrical engineering #Graphics (cs.GR) #Image Processing Techniques and Applications #Image and Video Processing (eess.IV) #Optical measurement and interference techniques #Robotics (cs.RO) #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.1906.06113

openalex publication_date 2019/06/14 · openalex created_date 2019/06/27 · openalex updated_date 2026/07/28

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.

Citations

• Deep Convolutional Inverse Graphics Network
• Distilling the Knowledge in a Neural Network
• Depth Map Prediction from a Single Image using a Multi-Scale Deep Network
• ShapeNet: An Information-Rich 3D Model Repository
• SfM-Net: Learning of Structure and Motion from Video
• Self-Supervised Learning for Stereo Matching with Self-Improving Ability
• Practical Deep Stereo (PDS): Toward applications-friendly deep stereo\n matching
• SegStereo: Exploiting Semantic Information for Disparity Estimation

Cited by

• M³VSNet: Unsupervised Multi-metric Multi-view Stereo Network

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