Comprehensive Framework for Evaluation of Stereo Correspondence Solutions in Immersive Augmented and Virtual Realities
Bibliographic record
Abstract
In this article, a comprehensive approach for the evaluation of hardware and software solutions to support stereo vision and depth-dependent interactions based on the specific requirements of the human visual system within the context of augmented reality applications is presented. To evaluate stereo correspondence solutions in software, we present an evaluation model that integrates existing metrics of stereo correspondence algorithms with additional metrics that consider human factors that are relevant in the context of outdoor augmented reality systems. Our model provides modified metrics of stereoacuity, average outliers, disparity error, and processing time. These metrics have been modified to provide more relevant information with respect to the target application. We illustrate how this model can be used to evaluate two stereo correspondence methods: the OpenCV implementation of the semi-global block matching, also known as SGBM, which is a modified version of the semi-global matching by Hirschmuller; and ADCensusB, our implementation of ADCensus, by Mei et al.. To test these methods, we use a sample of fifty-two image pairs selected from the Kitti stereo dataset, which depicts many situations typical of outdoor scenery. Further on, we present an analysis of the effect and the trade-off of the post processing steps in the stereo algorithms between the accuracy of the results and performance. Experimental results show that our proposed model can provide a more detailed evaluation of both algorithms. To evaluate the hardware solutions, we use the characteristics of the human visual system as a baseline to characterize the state-of-the-art in equipment designed to support interactions within immersive augmented and virtual reality systems. The analysis suggests that current hardware developments have not yet reached the point where their characteristics adequately match the capabilities of the human visual system and serves as a reference point as to what are the desirable characteristics of such systems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.028 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".