Deep simulations have gained widespread attention owing to their excellent acceleration performances.However,these methods cannot provide effective collision detection and response strategies.We propose a deep interac...Deep simulations have gained widespread attention owing to their excellent acceleration performances.However,these methods cannot provide effective collision detection and response strategies.We propose a deep interac-tive physical simulation framework that can effectively address tool-object collisions.The framework can predict the dynamic information by considering the collision state.In particular,the graph neural network is chosen as the base model,and a collision-aware recursive regression module is introduced to update the network parameters recursively using interpenetration distances calculated from the vertex-face and edge-edge tests.Additionally,a novel self-supervised collision term is introduced to provide a more compact collision response.This study extensively evaluates the proposed method and shows that it effectively reduces interpenetration artifacts while ensuring high simulation efficiency.展开更多
Previous collision detection methods for virtual disassembly mainly detect collisions at discrete time intervals and use oriented bounding boxes to speed up the process. However, these discrete methods cannot guarante...Previous collision detection methods for virtual disassembly mainly detect collisions at discrete time intervals and use oriented bounding boxes to speed up the process. However, these discrete methods cannot guarantee no penetration occurs when the components move. Meanwhile, because some of the components are embedded into each other, these components cannot be separated in the subsequent process. To solve these problems, we propose an approach for real-time collision handling by utilizing the computational power of modern GPUs. First we present a novel GPU-based collision handling framework for virtual disassembly. Second we use a collision-streams based continuous collision detection to guarantee no collision missed. Finally we introduce a triangle intersection detection algorithm to solve the problem that collision cannot be detected when the components are embedded into each other at the initial configuration. The experimental results show that our method can improve the overall performance of collision detection and achieve real-time simulation.展开更多
基金This project was funded by Natural Science Foundation of Guangdong Province,No.2020B010165004。
文摘Deep simulations have gained widespread attention owing to their excellent acceleration performances.However,these methods cannot provide effective collision detection and response strategies.We propose a deep interac-tive physical simulation framework that can effectively address tool-object collisions.The framework can predict the dynamic information by considering the collision state.In particular,the graph neural network is chosen as the base model,and a collision-aware recursive regression module is introduced to update the network parameters recursively using interpenetration distances calculated from the vertex-face and edge-edge tests.Additionally,a novel self-supervised collision term is introduced to provide a more compact collision response.This study extensively evaluates the proposed method and shows that it effectively reduces interpenetration artifacts while ensuring high simulation efficiency.
基金This work was supported by the National Natural Science Foundation of China under Grant No. 61472111, the Zhejiang Provincial Natural Science Foundation of China under Grant No. LQ13F020016, the Foundation of Zhejiang Educational Committee under Grant No. Y201224034, and the Scientific Research Start Foundation of Hangzhou Dianzi University under Grant No. KYS225613032.
文摘Previous collision detection methods for virtual disassembly mainly detect collisions at discrete time intervals and use oriented bounding boxes to speed up the process. However, these discrete methods cannot guarantee no penetration occurs when the components move. Meanwhile, because some of the components are embedded into each other, these components cannot be separated in the subsequent process. To solve these problems, we propose an approach for real-time collision handling by utilizing the computational power of modern GPUs. First we present a novel GPU-based collision handling framework for virtual disassembly. Second we use a collision-streams based continuous collision detection to guarantee no collision missed. Finally we introduce a triangle intersection detection algorithm to solve the problem that collision cannot be detected when the components are embedded into each other at the initial configuration. The experimental results show that our method can improve the overall performance of collision detection and achieve real-time simulation.
