Yunbo Wang, Xiaokang Yang, Yumeng He
We lifted 13 functions out of this paper's own repositories and ran 6 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 |
|---|---|---|
| ymhe12/GS-Phong | canonical | 6 of 13 |
| Function | Status | Where it lives |
|---|---|---|
| gaussian | Ran | ymhe12/GS-Phong/utils/loss_utils.py pointer only (licence: NONE) · get_code("c56b7ef16f309a45") |
| l1_loss | Ran | ymhe12/GS-Phong/utils/loss_utils.py pointer only (licence: NONE) · get_code("ac0e42d6fbcfbbe6") |
| l2_loss | Ran | ymhe12/GS-Phong/utils/loss_utils.py pointer only (licence: NONE) · get_code("8c3b0f873ba11813") |
| normalize_activation | Ran | ymhe12/GS-Phong/lpipsPyTorch/modules/utils.py pointer only (licence: NONE) · get_code("1dab900b2adbe38e") |
| readImages | Ran | ymhe12/GS-Phong/metrics.py pointer only (licence: NONE) · get_code("cdd00787894554b5") |
| read_next_bytes | Ran | ymhe12/GS-Phong/utils/read_write_model.py pointer only (licence: NONE) · get_code("56858e04e6fdb2ff") |
| get_network | Not yet run | ymhe12/GS-Phong/lpipsPyTorch/modules/networks.py pointer only (licence: NONE) · get_code("07bd0da29c4dc7bb") |
| get_state_dict | Not yet run | ymhe12/GS-Phong/lpipsPyTorch/modules/utils.py pointer only (licence: NONE) · get_code("b06f27c08cf5d0ca") |
| prepare_output_and_logger | Not yet run | ymhe12/GS-Phong/train_meta.py pointer only (licence: NONE) · get_code("43887f53e9b99271") |
| qvec2rotmat | Not yet run | ymhe12/GS-Phong/scene/colmap_loader.py pointer only (licence: NONE) · get_code("6ce64cf0fbcd6bb1") |
| read_cameras_binary | Not yet run | ymhe12/GS-Phong/utils/read_write_model.py pointer only (licence: NONE) · get_code("a10bf79d34938dbd") |
| read_cameras_text | Not yet run | ymhe12/GS-Phong/utils/read_write_model.py pointer only (licence: NONE) · get_code("c424ebf4a6c32cbd") |
| rotmat2qvec | Not yet run | ymhe12/GS-Phong/scene/colmap_loader.py pointer only (licence: NONE) · get_code("659bc4e7e63ed8f9") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Out-of-distribution (OOD) 3D relighting requires novel view synthesis under unseen lighting conditions that differ significantly from the observed images. Existing relighting methods, which assume consistent light source distributions between training and testing, often degrade in OOD scenarios. We introduce MetaGS to tackle this challenge from two perspectives. First, we propose a meta-learning approach to train 3D Gaussian splatting, which explicitly promotes learning generalizable Gaussian geometries and appearance attributes across diverse lighting conditions, even with biased training data. Second, we embed fundamental physical priors from the Blinn-Phong reflection model into Gaussian splatting, which enhances the decoupling of shading components and leads to more accurate 3D scene reconstruction. Results on both synthetic and real-world datasets demonstrate the effectiveness of MetaGS in challenging OOD relighting tasks, supporting efficient point-light relighting and generalizing well to unseen environment lighting maps.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2405.20791")
get_code_for_paper("2405.20791")
have("2405.20791")
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