Event-based Stereo Depth Estimation from Ego-motion using Ray Density Fusion

TL;DR

This paper introduces a novel stereo depth estimation method using event cameras that fuses back-projected ray densities without explicit data association, demonstrating effectiveness on egocentric head-mounted data.

Contribution

It proposes a new depth estimation approach from stereo event cameras that avoids explicit data association by leveraging ray density fusion, suitable for egocentric scenarios.

Findings

Effective depth estimation on head-mounted data

No explicit data association needed

High dynamic range and low latency performance

Abstract

Event cameras are bio-inspired sensors that mimic the human retina by responding to brightness changes in the scene. They generate asynchronous spike-based outputs at microsecond resolution, providing advantages over traditional cameras like high dynamic range, low motion blur and power efficiency. 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 from stereo event cameras without explicit data association by fusing back-projected ray densities, and demonstrates its effectiveness on head-mounted camera data, which is recorded in an egocentric fashion. Code and video are available at https://github.com/tub-rip/dvs_mcemvs