A study of level-of-detail in haptic rendering
Bibliographic record
Abstract
This paper presents an initial study of an approach to reduce computational overhead in haptic rendering of physically based models. Haptic rendering refers to the notion of adding physical properties and behavior, specifically a sense of touch or force feedback, to models of objects. In this way, a user through a haptic feedback device can feel interaction forces while visually observing the objects. Physically based modeling is particularly important when representing deformable objects. In this paper, an approach based on a mass-spring damper system is used in modeling deformable objects. Deformation due to interaction forces is obtained by solving a set of differential equations, a process that is in general computationally demanding. To reduce this demand, the notion of level-of-detail in haptic rendering is introduced. Here the interplay between the graphical mesh and the haptic mesh as a function of various levels of subdivision is studied. The approach we describe is to adjust model parameters such that the user feels the same reaction force for a given deformation, regardless of the level of local subdivision.A preliminary user study with simple objects suggests there can be a local subdivision threshold such that the user cannot distinguish between global subdivision and the local subdivision introduced by the level-of-detail algorithm. This conclusion is beneficial for haptic rendering of deformable objects. Similar conclusions were obtained for haptic rendering of rigid objects. These results can be used as a guideline for other approaches to modeling deformable objects, such as finite element representations.
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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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".