Dynamic Vision Optical Flow-Guided Gaussian Splatting for Dynamic Scene Reconstruction

Deformation-based dynamic Gaussian Splatting models every point of a monocular video with a single deformation field, whether the point is static, part of a rigid object, or part of a fluid. We found that such deformation fields cause rapidly moving objects to lose pattern details or become blurred, rather than exhibiting a clear motion trajectory. Existing event-based methods such as E-4DGS and Event-boosted D3DGS utilize events for photometric loss, which requires multiple rendering passes during training, whereas we use an event camera to decompose the scene. We estimate dynamic vision optical flow (DVOF) from events recorded by a dynamic vision sensor using contrast maximization, and derive an observability matrix from the contrast curvature to make the aperture problem explicit. Patches are grouped by whether a single affine motion explains them along their observable directions, and the groups are linked into tracks over time. This divides the scene into static, rigid, and free regions. Gaussians are assigned to the regions by rendering-weight voting, and consistency-checked spatial completion recovers event-sparse object interiors. Static Gaussians optimize time-invariant geometry and appearance, rigid ones follow explicit rigid trajectories supervised by the flow, and free ones keep a deformation field. On real RGB-event sequences, our method improves quality of full image and dynamic region over deformation-based and event-based methods, and improves the consistency of image-plane motion between rendered and observed sequences.