Figure 5.
The flow diagram presents a 3 D reconstruction training pipeline that integrates captured intensity frames and captured event frames. Captured intensity frames pass through an intensity correction stage and are processed by 3 D reconstruction algorithms such as N e R F and 3 D G S to generate a synthesized 3 D scene. The synthesized scene is rendered to produce intensity outputs and a simulated event stream. The simulated event stream contributes to event loss, while rendered intensity contributes to intensity loss. Captured event frames are also converted from event to intensity representations to support intensity loss computation. The pipeline combines rendering, simulation, and dual loss supervision to guide 3 D reconstruction learning.Typical pipeline for 3D reconstruction using event cameras. The process begins by leveraging the event data to enhance or restore degraded intensity frames. These restored frames provide a more reliable foundation for subsequent processing. Once the intensity information is refined, a 3D reconstruction algorithm is applied to generate a spatial representation of the scene. During the reconstruction phase, novel views of the scene are rendered to simulate different perspectives. The system uses both intensity loss and event loss as guiding signals to optimize the reconstruction quality, ensuring that the output is consistent with both the original intensity frames and the event stream