Objective

2

The deformation body simulation technology has been continuously developed in recent years and has been widely investigated in virtual simulation. It has been widely used in animation

design

and games. In the computer virtual environment

rapid and accurate self-collision detection can immensely enhance the realistic sense of deformation body simulation. A cloth is a representative deformable body composed of a large number of geometric elements

which is characterized by thin and soft

and easy to deform

squeeze

and wrinkle. Therefore

fabric self-collision should be detected and penetration should be prevented to ensure the reality of fabric simulation. Data analysis shows that self-collision detection takes 60% 80% of the time in the simulated scene of a deformed body. Self-collision detection of cloth frequently consumes considerable time resources. For the deformation model without thickness

such as cloth

whether the fabric itself has a collision is difficult to determine through traditional collision detection in real time. Therefore

self-collision detection is a cutting-edge problem in cloth simulation. The self-collision detection effect of a deformed body

such as cloth

largely determines the authenticity of virtual simulation. Real-time is a difficult point in self-collision detection. Self-collision detection consumes considerable time because the cloth model consists of many primitives. Most existing self-collision detection algorithms focus on improving the test speed. The traditional self-collision detection algorithm uses bounding box test

which is extremely large and complex. It cannot meet the accuracy and real-time requirements at the same time. In other words

the pursuit of accuracy will prolong the time consumed by calculation

and the pursuit of real-time will reduce the detection accuracy. The above conditions will make the simulation effect insufficiently real. A previous work combined the normal cone test with traditional collision detection method

reduced the computation of intersection detection

and improved the detection speed to improve the speed of self-collision detection without affecting the detection accuracy. The calculation of the intersection test of the primitives is reduced by constructing a hybrid hierarchy composed of various bounding boxes to improve the fit of the bounding box. However

the improved algorithm cannot meet the real-time and accuracy requirements. This paper proposes a method combining the aixe align bounding box (AABB)-circular hybrid hierarchy of deep neural networks to solve the above problems.

Method

2

This paper proposes an AABB-circular hybrid hierarchical bounding box that integrates deep neural networks. The working principle is described as follows. First

an AABB-circular hybrid hierarchical bounding box tree is built for the clothing model. The AABB hierarchical boundary box tree is constructed for the model

and the three vertices of the triangle are used to calculate the circular boundary frame of the leaf nodes. Second

a bounding volume test tree (BVTT) is generated in accordance with the AABB-circular hierarchical bounding box. Third

the corresponding normal cone is calculated for the nodes in the constructed BVTT tree. Fourth

a bounding box test is performed first

followed by a normal cone test

and multiple pairs of potential collision primitives are outputted. Finally

multiple pairs of collision triangles are outputted by performing a primitive intersection test on the potential collision primitive.

Result

2

Experimental results shows that the proposed method achieves a balance between accuracy and time consumption on the premise of finding the optimal number of hidden layers in the deep neural network and the optimal number of nodes in each layer. The AABB-oriented bounding box(OBB) algorithm takes 19% to 33% less time than the classic self-collision detection algorithm. We compared the proposed method with the AABB-OBB method and three classical methods in terms of time consumption of updating

detecting

and primitive testing to verify the advantages of the proposed method in computation time. Experimental results show that the proposed method consumes a small amount of update time

optimizes the speed of primitive crossover testing

and reduces the overall time consumption. We compared the proposed method with the AABB-OBB method and three classic methods in the primitive intersection test to verify its superiority in terms of fit. The proposed method has the least number of pattern intersection tests and the best fit.

Conclusion

2

An AABB-circular hybrid hierarchical self-collision detection method based on deep neural network is proposed. The AABB and the round form a hybrid layer bounding box to perform self-collision detection on the cloth. The intersection of the circular bounding box is detected using a deep neural network. Compared with the classic AABB-OBB bounding box and the classic three bounding boxes

the accuracy and timeliness of the AABB-circular hybrid hierarchical bounding box with fusion deep neural network are verified from multiple indicators.