MegaDepth: Learning Single-View Depth Prediction from Internet Photos

Understand

Single-view depth prediction is a fundamental problem in computer vision.

• Recently, deep learning methods have led to significant progress, but such methods are limited by the available training data.
• Current datasets based on 3D sensors have key limitations, including indoor-only images (NYU), small numbers of training examples (Make3D), and sparse sampling (KITTI).
• We propose to use multi-view Internet photo collections, a virtually unlimited data source, to generate training data via modern structure-from-motion and multi-view stereo (MVS) methods, and present a large depth dataset called MegaDepth based on this idea.