Using Signed Distance Fields to Achieve Temporal Compression of Mesh-Based Volumetric Video
Bibliographic record
Abstract
Volumetric Video is an exciting medium that enables visualization of 3D time-varying data, often as sequence of Point Clouds or Textured Meshes. Despite over a decade of focus, there still exist several problems that make it challenging to employ. This paper focuses on one aspect of deploying Volumetric Video — data compression. Reducing the data storage size is necessary for streaming applications due to the large size of sequences consisting of even a few seconds of footage. This work explores the use of temporal compression of time-varying textured meshes using Signed Distance Fields and compare with the state-of-the art techniques. The goal is to produce a novel compression method to maximize a size-to-error compression ratio while maintaining a decompression speed of 30 frames per second. The results reflect that as file sizes decrease, the proposed method’s error increases at a slower rate than the state-of-the-art.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".