The rapid integration of Augmented Reality (AR) and Virtual Reality (VR) into consumer and industrial applications has created an urgent need for efficient object detection systems capable of handling real-time data under challenging conditions, including occlusion. Conventional object detection frameworks, particularly those based on deep learning architectures such as R-CNN and YOLO, often struggle with occluded environments due to their reliance on complete scene information and high computational demands. These limitations are exacerbated in resource-constrained platforms like mobile AR/VR devices and head-mounted displays. Neuromorphic computing, inspired by the biological brain and implemented using Spiking Neural Networks (SNNs), offers a promising alternative through its event-driven processing and low-power characteristics. This paper reviews and evaluates state-of-the-art approaches that integrate neuromorphic methods for occlusion-aware object detection in AR/VR environments. Two prominent strategies are examined: one converting ANN-based YOLO frameworks into SNN-compatible models with channel-wise normalization, and another incorporating Mask R-CNN for image segmentation prior to SNN-based detection. Experimental results from benchmark datasets demonstrate that SNN-based models not only improve detection accuracy under occlusion (up to 98.60% on YOLO-V3-Tiny datasets) but also reduce computational overhead, making them suitable for real-time deployment. This review highlights the emerging role of neuromorphic computing in enhancing perception for immersive systems and sets the foundation for future developments in AR/VR vision technologies.