Design of Interactive 3D Virtual System for Products Based on Machine Vision

With the rapid development of virtual reality (VR) and augmented reality (AR) technologies, interactive 3D virtual systems have been widely used in fields such as industrial design, medical simulation, and education and training. However, existing 3D virtual interaction systems still have shortcomings in multimodal interaction collaboration and adaptability to application scenarios. This article studies a visual perception based design framework that utilizes machine vision technology to achieve multimodal interactions such as gesture recognition, posture perception, and eye tracking. By combining convolutional neural networks (CNN), 3D scene modeling, and real-time rendering technology, the naturalness of interactions and the real-time feedback capability of the system are improved. The system architecture consists of three modules: visual perception, virtual environment, and user interaction, supporting users to manipulate objects in the roaming scene through natural actions. To validate the system, the study compared visual perception with traditional interaction methods through experiments, collected data on user response time, accuracy, and satisfaction, and analyzed the stability and feedback fluency of the system. The results show that compared with traditional interaction methods, machine vision based 3D virtual systems have task completion times within 10 seconds and system stability exceeding 90%. The experimental results show that the 3D virtual interaction system based on visual perception has significant advantages in improving interaction efficiency, user immersion, and multimodal interaction coordination. This study provides new ideas for future applications in fields such as education, entertainment, and industrial design.