Fast Omnidirectional Depth Densification

Abstract

Omnidirectional cameras are commonly equipped with fish-eye lenses to capture 360-degree visual information, and severe spherical projective distortion occurs when a 360-degree image is stored as a two-dimensional image array. As a consequence, traditional depth estimation methods are not directly applicable to omnidirectional cameras. Dense depth estimation for omnidirectional imaging has been achieved by applying several offline processes, such as patch-matching, optical flow, and convolutional propagation filtering, resulting in additional heavy computation. No dense depth estimation for real-time applications is available yet. In response, we propose an efficient depth densification method designed for omnidirectional imaging to achieve 360-degree dense depth video with an omnidirectional camera. First, we compute the sparse depth estimates using a conventional simultaneous localization and mapping (SLAM) method, and then use these estimates as input to a depth densification method. We propose a novel densification method using the spherical pull-push method by devising a joint spherical pyramid for color and depth, based on multi-level icosahedron subdivision surfaces. This allows us to propagate the sparse depth continuously over 360-degree angles efficiently in an edge-aware manner. The results demonstrate that our real-time densification method is comparable to state-of-the-art offline methods in terms of per-pixel depth accuracy. Combining our depth densification with a conventional SLAM allows us to capture real-time 360-degree RGB-D video with a single omnidirectional camera.