Bibliographic record
Abstract
Despite the emergence of highly realistic computer-generated images, line-drawing images are still a common practice in showing the shapes and movements of three-dimensional objects. It is especially true when rendering time is critical in interactive applications such as the modeling and testing stage of computer aided design/manufacturing, computer animation, and virtual reality. Hence much effort has been devoted to provide sufficient information of the displayed objects with the least amount of time. While the techniques that determine visible surfaces in an image-space have the advantages on rendering speed and processable shapes, those that decide visible lines or line segments in an object-space are more suitable for showing hidden lines. The P-buffer algorithm introduced in this paper is a method for rendering line-drawing images with dashed hidden-lines. Being an image-space method, this algorithm preserves the low computational cost and works on a wide range of object shapes; as an extension to the Z-buffer algorithm it, moreover, discloses hidden surfaces by showing them with dashed lines. After a discussion on rendering techniques of line-drawing images, this paper presents this algorithm with pseudocode in C++ programming language and shows some experimental results as well. This image-space algorithm can be used as a compromise approach that reveals the concealed information of hidden-surface-removed views for time-critical rendering.
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.
How this classification was reachedexpand
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".