Ev-GS: Event-based Gaussian splatting for Efficient and Accurate Radiance Field Rendering
This addresses efficient novel view synthesis for computational neuromorphic imaging, though it appears incremental as it adapts an existing technique (Gaussian splatting) to event-based data.
The paper tackled the problem of slow and computationally heavy event-based radiance field rendering by introducing Ev-GS, a method that uses 3D Gaussian splatting from a monocular event camera, resulting in reduced blurring, improved visual quality, and competitive reconstruction with reduced computing occupancy.
Computational neuromorphic imaging (CNI) with event cameras offers advantages such as minimal motion blur and enhanced dynamic range, compared to conventional frame-based methods. Existing event-based radiance field rendering methods are built on neural radiance field, which is computationally heavy and slow in reconstruction speed. Motivated by the two aspects, we introduce Ev-GS, the first CNI-informed scheme to infer 3D Gaussian splatting from a monocular event camera, enabling efficient novel view synthesis. Leveraging 3D Gaussians with pure event-based supervision, Ev-GS overcomes challenges such as the detection of fast-moving objects and insufficient lighting. Experimental results show that Ev-GS outperforms the method that takes frame-based signals as input by rendering realistic views with reduced blurring and improved visual quality. Moreover, it demonstrates competitive reconstruction quality and reduced computing occupancy compared to existing methods, which paves the way to a highly efficient CNI approach for signal processing.