Multi-Event-Camera Depth Estimation and Outlier Rejection by Refocused Events Fusion

Abstract

Event cameras are bio-inspired sensors that offer advantages over traditional cameras. They operate asynchronously, sampling the scene at microsecond resolution and producing a stream of brightness changes. This unconventional output has sparked novel computer vision methods to unlock the camera's potential. Here, the problem of event-based stereo 3D reconstruction for SLAM is considered. Most event-based stereo methods attempt to exploit the high temporal resolution of the camera and the simultaneity of events across cameras to establish matches and estimate depth. By contrast, this work investigates how to estimate depth without explicit data association by fusing Disparity Space Images (DSIs) originated in efficient monocular methods. Fusion theory is developed and applied to design multi-camera 3D reconstruction algorithms that produce state-of-the-art results, as confirmed by comparisons with four baseline methods and tests on a variety of available datasets.

arXiv comment20 pages, 19 figures, 9 tables

References in corpus (6)

• Event-based Vision: A Survey
• The Event-Camera Dataset and Simulator: Event-based Data for Pose Estimation, Visual Odometry, and SLAM
• The Multi Vehicle Stereo Event Camera Dataset: An Event Camera Dataset for 3D Perception
• Visual-Inertial Mapping with Non-Linear Factor Recovery
• Semi-Dense 3D Reconstruction with a Stereo Event Camera
• Event Collapse in Contrast Maximization Frameworks

Cited by in corpus (9)

• CMax-SLAM: Event-based Rotational-Motion Bundle Adjustment and SLAM System using Contrast Maximization
• Formulating Event-based Image Reconstruction as a Linear Inverse Problem with Deep Regularization using Optical Flow
• ESVO2: Direct Visual-Inertial Odometry with Stereo Event Cameras
• Neuromorphic Perception and Navigation for Mobile Robots: A Review
• Event-based Stereo Depth Estimation: A Survey
• A Fast Geometric Regularizer to Mitigate Event Collapse in the Contrast Maximization Framework
• DH-PTAM: A Deep Hybrid Stereo Events-Frames Parallel Tracking And Mapping System
• ES-PTAM: Event-based Stereo Parallel Tracking and Mapping
• Fourier-based Action Recognition for Wildlife Behavior Quantification with Event Cameras