Survey and Analysis of Collision Detection Algorithms in Blender

Collision detection algorithms are essential in 3D modeling and animation software like Blender to ensure realistic physical interactions, prevent object interpenetration, and support accurate simulations. They enable efficient rendering and real-time feedback by leveraging spatial data structures like Bounding Volume Hierarchies (BVH) to reduce computational overhead. These algorithms play a critical role in character animation, visual effects, particle systems, and game asset validation, enhancing both performance and realism in complex 3D environments. This paper presents a comparative analysis of collision detection techniques in Blender, focusing on the SMPL [3] human model. Traditional bounding box-based methods, such as axis-aligned bounding boxes (AABB) and manually defined collision constraints, are compared against state-of-the-art techniques like Physics-Informed Neural Networks (PINNs), Graph Neural Networks (GNNs), and Temporal Convolutional Networks (TCNs). The evaluation considers performance metrics such as detection accuracy, computational complexity, and flexibility in handling various motion sequences. The study highlights the limitations of traditional methods in dynamic skeletal systems and demonstrates how modern techniques improve anomaly detection and smooth motion correction, particularly in nonstandard formats like SMPL's. pkl motion sequences.