Real-time Volume Preserving Constraints for Volumetric Model on GPU

This paper presents a parallel method for simulating real-time 3D deformable objects using the volume preservation mass-spring system method on tetrahedron meshes. In general, the conventional mass-spring system is manipulated as a force-driven method because it is fast, simple to implement, and the parameters can be controlled. However, the springs in traditional mass-spring system can be excessively elongated which cause severe stability and robustness issues that lead to shape restoring, simulation blow-up, and huge volume loss of the deformable object. In addition, traditional method that uses a serial process of the central processing unit (CPU) to solve the system in every frame cannot handle the complex structure of deformable object in real-time. Therefore, the first order implicit constraint enforcement for a mass-spring model is utilized to achieve accurate visual realism of deformable objects with tough constraint error. In this paper, we applied the distance constraint and volume conservation constraints for each tetrahedron element to improve the stability of deformable object simulation using the mass-spring system and behave the same as its real-world counterparts. To reduce the computational complexity while ensuring stable simulation, we applied a method that utilizes OpenGL compute shader, a part of OpenGL Shading Language (GLSL) that executes on the graphic processing unit (GPU) to solve the numerical problems effectively. We applied the proposed methods to experimental volumetric models, and volume percentages of all objects are compared. The average volume percentages of all models during the simulation using the mass-spring system, distance constraint, and the volume constraint method were 68.21%, 89.64%, and 98.70%, respectively. The proposed approaches are successfully applied to improve the stability of mass-spring system and the performance comparison from our experimental tests also shows that the GPU-based method is faster than CPU-based implementation for all cases.