Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
QUEEN: QUantized Efficient ENcoding of Dynamic Gaussians for Streaming Free-viewpoint Videos
Authors: Sharath Girish, Tianye Li, Amrita Mazumdar, Abhinav Shrivastava, david luebke, Shalini De Mello
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our approach, QUEEN, on two benchmark datasets, containing diverse scenes with large geometric motion and illumination changes. QUEEN outperforms all prior state-of-the-art online FVV methods on all metrics. Notably, for several highly dynamic scenes, it reduces the model size to just 0.7 MB per frame while training in under 5 sec and rendering at 350 FPS. |
| Researcher Affiliation | Collaboration | Sharath Girish University of Maryland EMAIL Tianye Li NVIDIA EMAIL Amrita Mazumdar NVIDIA EMAIL Abhinav Shrivastava University of Maryland EMAIL David Luebke NVIDIA EMAIL Shalini De Mello NVIDIA EMAIL |
| Pseudocode | No | The paper describes methods in text and figures, but does not include a clearly labeled 'Pseudocode' or 'Algorithm' block. |
| Open Source Code | No | We aim to release the code in the future. |
| Open Datasets | Yes | We evaluate our method on two challenging FVV video datasets. (1) Neural 3D Videos (N3DV) [41] consists of six indoor scenes with forward-facing 20-view videos. (2) Immersive Videos [4] consists of seven indoor and outdoor scenes captures with 46 cameras. |
| Dataset Splits | No | The paper states: 'In both datasets, the central view is held out for testing.' and describes training on the remaining views. It does not explicitly define a separate validation dataset split for hyperparameter tuning or early stopping. |
| Hardware Specification | Yes | We train for 500 and 350 epochs for the first time-step, and for 10 and 15 epochs for the subsequent time-steps, for N3DV and Immersive, respectively, on an NVIDIA A100 GPU. |
| Software Dependencies | No | The paper mentions building its implementation on [29] (3D Gaussian Splatting) and using the Adam optimizer [30], but does not provide specific version numbers for these or other software dependencies like Python, PyTorch, or CUDA. |
| Experiment Setup | Yes | We train for 500 and 350 epochs for the first time-step, and for 10 and 15 epochs for the subsequent time-steps, for N3DV and Immersive, respectively... We set the SH degree to 2 for N3DV and 3 for Immersive. We set the score vector threshold td = 0.001 for all experiments... The position residual learning rate is set to 0.00016 for N3DV and 0.0005 for Immersive. Other hyperparameters are provided in Table 11. |