Moving objects with 2D input devices in CAD systems and Desktop Virtual Environments
Bibliographic record
Abstract
Part assembly and scene layout are basic tasks in 3D design in Desktop Virtual Environment (DVE) systems as well as Computer Aided Design (CAD) systems. 2D input devices such as a mouse or a stylus are still the most common input devices for such systems. With such devices, a notably difficult problem is to provide an efficient and predictable object motion in 3D based on their 2D motion. This paper presents a new technique to move objects in CAD/DVE using 2D input devices.The technique presented in this paper utilizes the fact that people easily recognize the depth-order of shapes based on occlusions. In the presented technique, the object position follows the mouse cursor position, while the object slides on various surfaces in the scene. In contrast to existing techniques, the movement surface and the relative object position is determined using the whole area of overlap of the moving object with the static scene. The resulting object movement is visually smooth and predictable, while avoiding undesirable collisions. The proposed technique makes use of the framebuffer for efficiency and runs in real-time. Finally, the evaluation of the new technique with a user study shows that it compares very favorably to conventional techniques.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".