Systems and methods for depth estimation using convolutional spatial propagation networks

Abstract

Presented are systems and methods for improving speed and quality of real-time per-pixel depth estimation of scene layouts from a single image by using a 3D end-to-end Convolutional Spatial Propagation Network (CSPN). An efficient linear propagation model performs propagation using a recurrent convolutional operation. The affinity among neighboring pixels may be learned through a deep convolutional neural network (CNN). The CSPN may be applied to two depth estimation tasks, given a single image: (1) to refine the depth output of existing methods, and (2) to convert sparse depth samples to a dense depth map, e.g., by embedding the depth samples within the propagation procedure. For stereo depth estimation, the 3D CPSN is applied to stereo matching by adding a diffusion dimension over discrete disparity space and feature scale space. This aids the recovered stereo depth to generate more details and to avoid error matching from noisy appearance caused by sunlight, shadow, and similar effects.