Streaming Radiance Fields for 3D Video Synthesis
Authors: Lingzhi LI, Zhen Shen, Zhongshu Wang, Li Shen, Ping Tan
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on challenging video sequences demonstrate that our approach is capable of achieving a training speed of 15 seconds per-frame with competitive rendering quality, which attains 1000 speedup over the state-of-the-art implicit methods. |
| Researcher Affiliation | Industry | Lingzhi Li Alibaba Group llz273714@alibaba-inc.com Zhen Shen Alibaba Group zackary.sz@alibaba-inc.com Zhongshu Wang Alibaba Group zhongshu.wzs@alibaba-inc.com Li Shen Alibaba Group lshen.lsh@gmail.com Ping Tan Alibaba Group pingtan@sfu.ca |
| Pseudocode | No | The paper does not contain any blocks explicitly labeled 'Pseudocode' or 'Algorithm'. |
| Open Source Code | Yes | Code is available at https://github.com/Algo Hunt/Stream RF. |
| Open Datasets | Yes | Neural 3D Video (N3DV) Dataset [10]... Following the setting in the original paper [10]... [10] is 'Tianye Li, Mira Slavcheva, Michael Zollhoefer, Simon Green, Christoph Lassner, Changil Kim, Tanner Schmidt, Steven Lovegrove, Michael Goesele, Richard Newcombe, et al. Neural 3d video synthesis from multi-view video. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5521–5531, 2022.' (from bibliography). |
| Dataset Splits | No | The paper describes train/test splits for the datasets but does not explicitly mention a validation split, nor does it provide percentages or sample counts for the splits. |
| Hardware Specification | Yes | All the results are recorded with our 3090 GPU except the results of N3DV are referred to the numbers in the original paper. |
| Software Dependencies | No | The paper mentions 'RMSProp' and 'ZLIB' but does not provide specific version numbers for these software components. For instance, 'ZLIB' is cited with a year '2017' but no precise version number. |
| Experiment Setup | Yes | The total training takes 128K iterations with a batch size of 5K rays. We adopt the RMSProp [19] optimizer with a decay parameter of 0.95 for optimization. We adopt a slighter larger TV penalty to mitigate foggy issues, where λTV is set to 5 × 10−4 for opacity and 5 × 10−3 for color features. |