While the cubic B-Spline representation offers a smooth and differentiable way to model camera motion in SE(3) space, it has certain limitations. One potential limitation is its reliance on a fixed number of control points, which may not adequately capture highly dynamic or erratic camera movements. If the camera motion is too complex, the cubic B-Spline may struggle to represent the trajectory accurately, leading to artifacts in the synthesized images.
Another limitation is that cubic B-Splines can introduce oversmoothing, which may not be suitable for scenes with rapid changes in motion or abrupt camera movements. This could result in a loss of detail in the reconstructed scene, particularly in scenarios where precise motion tracking is critical.
To address these limitations, alternative representations could be explored. For instance, using piecewise linear splines or higher-order splines could provide more flexibility in capturing complex trajectories. Additionally, employing learned representations, such as neural networks that predict camera poses based on the event stream, could allow for more adaptive modeling of camera motion. These networks could be trained on diverse motion patterns, enabling them to generalize better to various scenarios.