Dark-EvGS: Event Camera as an Eye for Radiance Field in the Dark

TL;DR

Dark-EvGS leverages event cameras and 3D Gaussian Splatting to reconstruct bright, high-quality views in low-light environments, overcoming noise and color inconsistency issues.

Contribution

It introduces the first event-assisted 3D Gaussian Splatting framework with triplet supervision and color matching for low-light radiance field reconstruction.

Findings

Achieves superior low-light radiance field reconstruction results.

Introduces a new real-captured dataset for event-guided bright frame synthesis.

Outperforms existing methods in challenging low-light scenarios.

Abstract

In low-light environments, conventional cameras often struggle to capture clear multi-view images of objects due to dynamic range limitations and motion blur caused by long exposure. Event cameras, with their high-dynamic range and high-speed properties, have the potential to mitigate these issues. Additionally, 3D Gaussian Splatting (GS) enables radiance field reconstruction, facilitating bright frame synthesis from multiple viewpoints in low-light conditions. However, naively using an event-assisted 3D GS approach still faced challenges because, in low light, events are noisy, frames lack quality, and the color tone may be inconsistent. To address these issues, we propose Dark-EvGS, the first event-assisted 3D GS framework that enables the reconstruction of bright frames from arbitrary viewpoints along the camera trajectory. Triplet-level supervision is proposed to gain holistic knowledge, granular details, and sharp scene rendering. The color tone matching block is proposed to guarantee the color consistency of the rendered frames. Furthermore, we introduce the first real-captured dataset for the event-guided bright frame synthesis task via 3D GS-based radiance field reconstruction. Experiments demonstrate that our method achieves better results than existing methods, conquering radiance field reconstruction under challenging low-light conditions. The code and sample data are included in the supplementary material.