Bibliographic record
Abstract
We aim to add realistic force-feedback to the process of real-time volume removal from a 3D mesh object.We refer to this volume removal as "3D carving".3D carving is particularly applicable to the computer simulation of surgical procedures involving bone reductions that are performed with a motorized burr tool; however the methods and algorithms presented here are generic enough to be used for other purposes, such as modeling, destructible objects & terrains in games, etc.The system represents the volume of the virtual objects using a voxel-set during carving process.A polygonal mesh is created from this voxel-set to display a smoother rendition of the virtual object.Out system also employs the novel Dynamic Ball-Pivoting Algorithm to generate quick mesh updates when voxel-set revisions occur.The advantages of this being that minor changes to the voxel-set only require minor changes to the mesh, whereas the standard Ball-Pivoting Algorithm approach is to perform a global re-meshing.In our haptic carving system interactions with the aforementioned voxel-set can provide output force-feedback to the system operator.A pen-based haptic tool is used to act as a 3D mouse in order to "feel" the surface of the model as well as to remove select volume segments of the object.Two collision detection schemes are presented here which allow users to feel the surface of virtual objects either using the voxels alone or by using supplemental information from the polygonal mesh.When the burr-tool has its "cutting mode" enabled, which sections of an object's volume are to be removed is decided by evaluating that volume's proximity to the center of the burr.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".