Enabling Distance-Aware Real-Time Volumetric Video Streaming
Bibliographic record
Abstract
Live, real-time volumetric video streaming enables immersive remote communication by transmitting 3D representations of participants, but faces significant bandwidth and computational challenges on consumer hardware. We introduce a distance-aware volumetric video streaming method that prunes point cloud data in real time based on both real-world and virtual viewing distances before any conventional compression or tiling is applied. Our prototype, RealityStream, uses a single Kinect v1 for capture, referencing a precomputed VMAF-driven distance table to guide adaptive downsampling for transmission. This effectively reduces bandwidth demand by up to 65% while maintaining acceptable visual fidelity. Implemented on a commodity laptop and streamed to a standalone VR headset, RealityStream is shown to be practical, achieving end-to-end latencies of around 75 ms. These findings highlight that taking into account both real and virtual distance is a low-complexity approach for efficient, real-time volumetric video capture and streaming. RealityStream will benefit existing volumetric streaming platforms, making real-time 3D communication more efficient over networks.
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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 | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".