TARS: Temporal-Spatial Adaptation for Volumetric Video Streaming
Bibliographic record
Abstract
Volumetric video streaming, which presents dynamic 3D objects captured from multiple angles, significantly increases video data size and complexity compared to traditional 2D video, posing major challenges for efficient transmission. Current encoding methods either introduce excessive latency (projectionbased encoding) or inefficiency in bandwidth usage (direct 3D encoding). To address these limitations, this paper introduces TARS, a temporal-spatial adaptive streaming solution that exploits inter-frame correlations in dynamic point-cloud videos. TARS employs a Field-of-View (FoV)-aware approach to intelligently avoid the retransmission of redundant regions across consecutive frames, leveraging a specialized Point Cloud Structural Similarity metric for precise similarity assessment. Experimental results demonstrate that TARS effectively achieves bandwidth savings of up to 66 % while maintaining visual fidelity and achieving minimal quality degradation-with a Mean Square Error (MSE) as low as 0.145 under conservative settings. Additionally, the approach significantly optimizes decoding efficiency, resulting in up to 2.42 times increase in decoding frame rates compared to transmitting and decoding videos with independently coded frames (e.g., the default Draco coding), highlighting its suitability for real-time and bandwidth-constrained volumetric streaming scenarios.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".