Multi-Resolution Modeling and Locally Refined Collision Detection for Haptic Interaction
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The computational cost of a collision detection (CD) algorithm on polygonal surfaces depends highly on the complexity of the models. A novel "locally refined" approach is introduced in this paper for fast CD in haptic rendering applications, e.g. haptic surgery and haptic sculpture simulations. Exact interference detections are performed on proposed locally refined meshes, which are in multiresolution representation. The meshes are generated using mesh simplification and space partition. A new BVH algorithm called "active bounding tree", or AB-tree, handling collision queries is introduced. At runtime the meshes are dynamically refined to higher resolution in areas that are most likely to collide with other objects. The algorithms are successfully demonstrated in an interactive haptic environment. Compared to existing CD algorithms on single resolution models, noticeable performance improvement has been observed in terms of the precision of collision queries, frame rate, and memory usage.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it