3D Scene Collision DetectionTechnology Based on Convolutional Neural Network

Aiming at the slow collision detection speed and poor accuracy in complex virtual scenes, a new collision detection technology based on convolutional neural network (CNN) is proposed. Using the powerful feature extraction and recognition ability of CNN, we directly learn the relevant features of collision detection from 3D scene data, avoiding the complex geometric calculation and preprocessing steps in traditional methods. The designed network uses 3D convolution layer and pooling layer to effectively process 3D spatial information, and optimize the computational efficiency and parameter number of the model through deeply separable convolution and bottleneck modules. The introduction of multiscale feature-fusion techniques enhances the robustness of the model to scale changes and incorporates attention mechanisms to highlight potential collision regions. Experimental results on multiple standard 3D datasets show that the proposed method outperforms existing collision detection algorithms in terms of both detection speed and accuracy. Especially when dealing with complex scenes and dynamic environments, it shows higher detection sensitivity and lower false alarm rate. It provides a new technical path for the field of 3D scene collision detection, and also provides important technical support for security monitoring and interaction in applications such as intelligent systems and virtual reality.