Object Speed Control with a Signed Distance Field for Distant Mid-Air Object Manipulation in Virtual Reality
Bibliographic record
Abstract
In Virtual Reality (VR) applications, interacting with distant objects relies heavily on mid-air object manipulation. Yet, the inherent distance between the user and the object often restricts movement precision. This paper introduces the Signed Distance Field (SDF) method for mid-air object manipulation and combines it with the ray casting interaction technique to investigate its effect on user performance and user experience. To increase movement accuracy, we leverage the speed-accuracy trade-off to dynamically adjust object manipulation speed based on the SDF algorithm’s output. Our study with 18 participants examines the effects of SDF across three different tasks with different complexity. Our results showed that ray casting with SDF reduces the number of errors in complex tasks without slowing down the participants and improves the user experience. We hope that our proposed assistive system, designed for tasks and applications, can be used as an interaction technique to enable more accurate manipulation of distant objects in fields like surgical planning, architecture, and games.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".