# Dynamic Object Comprehension: A Framework For Evaluating Artificial Visual Perception

Source: [Dynamic Object Comprehension: A Framework For Evaluating Artificial Visual Perception](<https://www.sota2.com/research/paper/68fa6bdb1e209131cea30a8e>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-28T13:56:51.976Z

Authors: Scott Y.L. Chin, Bradley R. Quinton

Published: 2022-02-17T07:49:49.000Z

## Abstract

Augmented and Mixed Reality are emerging as likely successors to the mobile internet. However, many technical challenges remain. One of the key requirements of these systems is the ability to create a continuity between physical and virtual worlds, with the user's visual perception as the primary interface medium. Building this continuity requires the system to develop a visual understanding of the physical world. While there has been significant recent progress in computer vision and AI techniques such as image classification and object detection, success in these areas has not yet led to the visual perception required for these critical MR and AR applications. A significant issue is that current evaluation criteria are insufficient for these applications. To motivate and evaluate progress in this emerging area, there is a need for new metrics. In this paper we outline limitations of current evaluation criteria and propose new criteria.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2202.08490>)

[PDF](<https://arxiv.org/pdf/2202.08490.pdf>)