融合DNN与AABB—圆形包围盒自碰撞检测

Self-collision detection algorithm based on fused DNN and AABB-circular bounding box

目的为了解决自碰撞检测剔除率低和检测速度慢的问题,提出一种AABB(aixe align bounding box)—圆形包围盒树结构和具有二分类功能的深度神经网络(deep neural network,DNN)加速包围盒相交检测的方法。方法对变形体构建AABB—圆形包围盒树,即对内部节点构建AABB包围盒,对叶子节点构建圆形包围盒。根据AABB—圆形包围盒生成包围盒测试树(bounding volume test tree,BVTT),采用深度神经网络优化BVTT的包围盒相交测试和法向锥测试,输出碰撞三角形对。结果在确定最优隐含层数和每层最优节点数保证深度神经网络达到最佳准确率的情况下,实验结果表明,在没有自碰撞的情况下,本文方法与AABB-OBB方法、经典包围盒方法耗时相同,但在自碰撞足够多的模拟场景中,融合深度神经网络的AABB-圆形包围盒方法比AABB-OBB(oriented bounding box)方法和经典的包围盒方法速度更快,整体耗时缩短了21%~37%。同时,对5种方法的更新率、检测效率和图元相交测试时间进行实验对比,发现本文方法比AABB-OBB方法和经典的方法具有更好的贴合性和更快的相交测试速度。结论本文方法相对于AABB-OBB方法、经典包围盒方法的测试速度更快,不仅提高了自碰撞检测高层剔除率,同时降低了模拟整体耗时,更适用于实时变形体自碰撞检测领域。

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