We present MemoVision, a digital catalog system that captures semantic, spatial, temporal and interaction information as users move around physical environments using client devices such as smart glasses. The system utilizes open-vocabulary semantic segmentation and 3D scans to store objects-of-interest with comprehensive semantic, spatial, temporal and interaction labels. Our demonstration shows multimodal information query and retrieval capabilities, supporting specific queries about object locations, temporal events and user interactions including eye gaze and hand poses, enabling more contextualized responses compared to current multimodal large language models.

In this paper, we present the development of an automated visual inspection system designed to detect defects on the upper surface of an aircraft airframe. Specifically, the system employs a multi-camera PTZ (Pan-Tilt-Zoom) set-up to capture and process images from designated regions. Custom developed software manages path planning and camera localization, while a hybrid-AI framework is integrated to identify various defect types, including missing and damaged components. The demonstration highlights the system’s detection capabilities and prototype functionalities using a large aircraft model, supported by a user interface to monitor progress and visualize results. To help validate this work, performance evaluations were conducted using selected multimodal and object detection models.