Abstract

3D Object recognition is one of the most important factors behind Augmented Reality (AR) applications to provide immersive and interactive experiences. They optimize recognition accuracy and responsiveness despite varying lighting conditions, motion blur, occlusions, and time constraints. We propose a viewing synthesis and performance training framework for adaptive AR object detection. They would generate the tests, and as per simulated and real-world contexts, they would check the smooth recognition in action-oriented dynamic environments. Performance optimization may encompass model compression, edge computing, adaptive learning, hardware acceleration and energy-aware computing to maximize real-time processing with retained accuracy. The study highlights how AR applications can leverage AI-enabled adaptive learning and self-optimizing. The advancements in federated learning, multi-sensor fusion and 5G illuminating AR frameworks will usher in seamless navigation experiences across various fields (education, retail, geolocation) and enable real-time object recognition capability.