Yong Liu, Yiyi Liao, Zizhang Li, Xiaoyang Lyu, Mengmeng Wang, Yuanyuan Ding
We lifted 7 functions out of this paper's own repositories and ran 4 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 |
|---|---|---|
| kyleleey/RICO | — | 2 of 5 |
| copy not recorded | — | 2 of 2 |
| Function | Status | Where it lives |
|---|---|---|
| alpha_to_w | Ran | kyleleey/RICO/code/model/network_rico.py code served (permissive licence) · get_code("71a7649f2baba014") |
| cdf_Phi_s | Ran | this paper's copy was not recorded; identical code first harvested from ventusff/neurecon pointer only · get_code("967d31403271a227") |
| sample_pdf | Ran | kyleleey/RICO/code/model/network_rico.py code served (permissive licence) · get_code("acf7388f597b707b") |
| sdf_to_alpha | Ran | this paper's copy was not recorded; identical code first harvested from ventusff/neurecon pointer only · get_code("7bf297619343c144") |
| RICONetwork | Not yet run | kyleleey/RICO/code/model/network_rico.py code served (permissive licence) · get_code("802dcfdaf2b02962") |
| RenderingNetwork | Not yet run | kyleleey/RICO/code/model/network_rico.py code served (permissive licence) · get_code("0953b4aa75c07444") |
| SemImplicitNetwork | Not yet run | kyleleey/RICO/code/model/network_rico.py code served (permissive licence) · get_code("50f059ada318d29a") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Recently, neural implicit surfaces have become popular for multi-view reconstruction. To facilitate practical applications like scene editing and manipulation, some works extend the framework with semantic masks input for the object-compositional reconstruction rather than the holistic perspective. Though achieving plausible disentanglement, the performance drops significantly when processing the indoor scenes where objects are usually partially observed. We propose RICO to address this by regularizing the unobservable regions for indoor compositional reconstruction. Our key idea is to first regularize the smoothness of the occluded background, which then in turn guides the foreground object reconstruction in unobservable regions based on the object-background relationship. Particularly, we regularize the geometry smoothness of occluded background patches. With the improved background surface, the signed distance function and the reversedly rendered depth of objects can be optimized to bound them within the background range. Extensive experiments show our method outperforms other methods on synthetic and real-world indoor scenes and prove the effectiveness of proposed regularizations. The code is available at https://github.com/kyleleey/RICO
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2303.08605")
get_code_for_paper("2303.08605")
have("2303.08605")
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