EVER: Edge-Assisted Auto-Verification for Mobile MR-Aided Operation

TL;DR

EVER is an edge-assisted system that enables fast, accurate auto-verification of user actions in mobile MR applications by comparing virtual and physical objects efficiently, ensuring real-time feedback with minimal energy use.

Contribution

The paper introduces EVER, a novel edge-assisted auto-verification system that improves accuracy and speed in mobile MR operations by leveraging segmentation, rendering, and IoU metrics.

Findings

Achieves over 90% verification accuracy

Verifies actions within 100 milliseconds

Consumes minimal additional energy

Abstract

Mixed Reality (MR)-aided operation overlays digital objects on the physical world to provide a more immersive and intuitive operation process. A primary challenge is the precise and fast auto-verification of whether the user follows MR guidance by comparing frames before and after each operation. The pre-operation frame includes virtual guiding objects, while the post-operation frame contains physical counterparts. Existing approaches fall short of accounting for the discrepancies between physical and virtual objects due to imperfect 3D modeling or lighting estimation. In this paper, we propose EVER: an edge-assisted auto-verification system for mobile MR-aided operations. Unlike traditional frame-based similarity comparisons, EVER leverages the segmentation model and rendering pipeline adapted to the unique attributes of frames with physical pieces and those with their virtual counterparts; it adopts a threshold-based strategy using Intersection over Union (IoU) metrics for accurate auto-verification. To ensure fast auto-verification and low energy consumption, EVER offloads compute-intensive tasks to an edge server. Through comprehensive evaluations of public datasets and custom datasets with practical implementation, EVER achieves over 90% verification accuracy within 100 milliseconds (significantly faster than average human reaction time of approximately 273 milliseconds), while consuming only minimal additional computational resources and energy compared to a system without auto-verification.