Jian Zhang, Jie Chen, Jiarui Meng, Qiankun Gao, Chengxiang Wen
We lifted 3 functions out of this paper's own repositories and ran 1 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| gqk/hicom | — | 1 of 3 |
| Function | Status | Where it lives |
|---|---|---|
| Grid | Ran | gqk/hicom/pipeline/hicom/model/deformation.py pointer only (licence: NOASSERTION) · get_code("ca66ee414c168052") |
| DeformConfig | Not yet run | gqk/hicom/pipeline/hicom/model/deformation.py pointer only (licence: NOASSERTION) · get_code("c5e56f86ed839f09") |
| Deformation | Not yet run | gqk/hicom/pipeline/hicom/model/deformation.py pointer only (licence: NOASSERTION) · get_code("f8abe2b60b7ece72") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Training Time (seconds) 100 200 300 Rendering Speed (fps) StreamRF 3DGStream HiCoM (ours) HiCoM-P4 (ours) Figure 1: The proposed HiCoM framework for streamable dynamic scene reconstruction achieves competitive rendering quality with significantly shorter training time, faster rendering speed, and substantially reduced storage and transmission requirements. The left figures show results of our HiCoM on N3DV [1] and Meet Room [2] datasets, where "Res" indicates video resolution. The right figure is tested on the N3DV [1] dataset, where the radius of the circle corresponds to the average storage per frame and the method in the top left corner demonstrates the best performance.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2411.07541")
get_code_for_paper("2411.07541")
have("2411.07541")
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