Semi-Dense 3D Reconstruction with a Stereo Event Camera

Code

• github.com/HKUST-Aerial-Robotics/ESVO
  none★ 493
• github.com/nail-hnu/esvo2
  none★ 127
• github.com/gogojjh/ESVO_extension
  none★ 65
• github.com/nail-hnu/esvio_aa
  none★ 27

Abstract

Event cameras are bio-inspired sensors that offer several advantages, such as low latency, high-speed and high dynamic range, to tackle challenging scenarios in computer vision. This paper presents a solution to the problem of 3D reconstruction from data captured by a stereo event-camera rig moving in a static scene, such as in the context of stereo Simultaneous Localization and Mapping. The proposed method consists of the optimization of an energy function designed to exploit small-baseline spatio-temporal consistency of events triggered across both stereo image planes. To improve the density of the reconstruction and to reduce the uncertainty of the estimation, a probabilistic depth-fusion strategy is also developed. The resulting method has no special requirements on either the motion of the stereo event-camera rig or on prior knowledge about the scene. Experiments demonstrate our method can deal with both texture-rich scenes as well as sparse scenes, outperforming state-of-the-art stereo methods based on event data image representations.

Tasks

Reproductions