Dynamic Scene Reconstruction with Sinusoidal Priors

This paper introduces SirenPose, a novel loss function leveraging sinusoidal representation networks and geometric priors for improved dynamic 3D scene reconstruction. The key contribution lies in addressing the challenges of motion modeling accuracy and spatiotemporal consistency in complex scenes, particularly those with rapid motion. The use of physics-inspired constraints and an expanded dataset are notable improvements over existing methods.

• •Proposes SirenPose, a novel loss function for dynamic 3D scene reconstruction.
• •Combines sinusoidal representation networks with geometric priors.
• •Addresses issues of motion accuracy and spatiotemporal consistency.
• •Employs physics-inspired constraints.
• •Utilizes an expanded training dataset.
• •Demonstrates improved performance in handling rapid motion and complex scene changes.