Event3DGS: Event-Based 3D Gaussian Splatting for High-Speed Robot Egomotion

TL;DR

Event3DGS introduces an event-based 3D Gaussian Splatting framework that leverages event camera data to achieve high-fidelity 3D reconstruction during high-speed egomotion, outperforming existing methods in quality and efficiency.

Contribution

The paper presents a novel event-based 3D Gaussian Splatting method that significantly improves reconstruction quality and reduces computational costs in high-speed egomotion scenarios.

Findings

Reconstructs high-fidelity 3D structure under high-speed egomotion.

Achieves +3dB improvement in reconstruction quality.

Reduces computational costs by 95%.

Abstract

By combining differentiable rendering with explicit point-based scene representations, 3D Gaussian Splatting (3DGS) has demonstrated breakthrough 3D reconstruction capabilities. However, to date 3DGS has had limited impact on robotics, where high-speed egomotion is pervasive: Egomotion introduces motion blur and leads to artifacts in existing frame-based 3DGS reconstruction methods. To address this challenge, we introduce Event3DGS, an {\em event-based} 3DGS framework. By exploiting the exceptional temporal resolution of event cameras, Event3GDS can reconstruct high-fidelity 3D structure and appearance under high-speed egomotion. Extensive experiments on multiple synthetic and real-world datasets demonstrate the superiority of Event3DGS compared with existing event-based dense 3D scene reconstruction frameworks; Event3DGS substantially improves reconstruction quality (+3dB) while reducing computational costs by 95\%. Our framework also allows one to incorporate a few motion-blurred frame-based measurements into the reconstruction process to further improve appearance fidelity without loss of structural accuracy.