We lifted 2 functions out of this paper's own repositories and ran 2 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 |
|---|---|---|
| fraunhoferhhi/d-neus | canonical | 2 of 2 |
| Function | Status | Where it lives |
|---|---|---|
| extract_fields | Ran | fraunhoferhhi/d-neus/models/renderer.py pointer only (licence: NOASSERTION) · get_code("1f2fc3e977362a4d") |
| find_surface_points | Ran | fraunhoferhhi/d-neus/models/renderer.py pointer only (licence: NOASSERTION) · get_code("26ab2193b537d970") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Recent works on implicit neural representations have made significant strides. Learning implicit neural surfaces using volume rendering has gained popularity in multi-view reconstruction without 3D supervision. However, accurately recovering fine details is still challenging, due to the underlying ambiguity of geometry and appearance representation. In this paper, we present D-NeuS, a volume rendering-base neural implicit surface reconstruction method capable to recover fine geometry details, which extends NeuS by two additional loss functions targeting enhanced reconstruction quality. First, we encourage the rendered surface points from alpha compositing to have zero signed distance values, alleviating the geometry bias arising from transforming SDF to density for volume rendering. Second, we impose multi-view feature consistency on the surface points, derived by interpolating SDF zero-crossings from sampled points along rays. Extensive quantitative and qualitative results demonstrate that our method reconstructs high-accuracy surfaces with details, and outperforms the state of the art.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2211.11320")
get_code_for_paper("2211.11320")
have("2211.11320")
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