Region-based 3D Mesh Compression Using an Efficient Neighborhood-based Segmentation
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
Due to the popularity of polygonal models in Virtual Reality applications, three-dimensional (3D) mesh compression and segmentation are two active areas of 3D object modeling. Most existing 3D compression algorithms compress the whole object to reduce the local storage requirement and the delays in transmitting objects over the Internet. However, in some interactive applications, the client may be interested in particular section(s) of the object. The server needs to segment the object into parts and send them individually or sequentially. This paper presents a segmentation-based 3D mesh compression scheme that can meet this requirement. We propose an efficient eXtended Multi-Ring neighborhood- (XMR) based 3D mesh segmentation algorithm that decomposes the object into meaningful regions. We then compress them separately and put them into one stream. The common boundary triangles that will be used for sticking the regions together are processed and appended to the end of the stream. This is referred to as a region-conquer-and-stitch scheme.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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