We lifted 9 functions out of this paper's own repositories and ran 8 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 |
|---|---|---|
| yilinliu77/nvdnet | canonical | 8 of 9 |
| Function | Status | Where it lives |
|---|---|---|
| BCE_loss | Ran | yilinliu77/nvdnet/python/model.py pointer only (licence: GPL-3.0) · get_code("69084fe2b219a2df") |
| de_normalize_angles | Ran | yilinliu77/nvdnet/python/model.py pointer only (licence: GPL-3.0) · get_code("941d9dfeb8e835c5") |
| de_normalize_udf | Ran | yilinliu77/nvdnet/python/abc_hdf5_dataset.py pointer only (licence: GPL-3.0) · get_code("d0974891578ca9b1") |
| focal_loss | Ran | yilinliu77/nvdnet/python/model.py pointer only (licence: GPL-3.0) · get_code("b20bcef51505bcf4") |
| normalize_points | Ran | yilinliu77/nvdnet/python/abc_hdf5_dataset.py pointer only (licence: GPL-3.0) · get_code("53d7273ca10fa7f4") |
| profile_time | Ran | yilinliu77/nvdnet/python/common_utils.py pointer only (licence: GPL-3.0) · get_code("eeab5803faa431b3") |
| to_homogeneous | Ran | yilinliu77/nvdnet/python/common_utils.py pointer only (licence: GPL-3.0) · get_code("b82f0f667d7d6081") |
| to_homogeneous_vector | Ran | yilinliu77/nvdnet/python/common_utils.py pointer only (licence: GPL-3.0) · get_code("9698a6496704e632") |
| de_normalize_angles | Not yet run | yilinliu77/nvdnet/python/abc_hdf5_dataset.py pointer only (licence: GPL-3.0) · get_code("5a2aea8cd9c5f477") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
We introduce a novel method for acquiring boundary representations (B-Reps) of 3D CAD models which involves a two-step process: it first applies a spatial partitioning, referred to as the ``split``, followed by a ``fit`` operation to derive a single primitive within each partition. Specifically, our partitioning aims to produce the classical Voronoi diagram of the set of ground-truth (GT) B-Rep primitives. In contrast to prior B-Rep constructions which were bottom-up, either via direct primitive fitting or point clustering, our Split-and-Fit approach is top-down and structure-aware, since a Voronoi partition explicitly reveals both the number of and the connections between the primitives. We design a neural network to predict the Voronoi diagram from an input point cloud or distance field via a binary classification. We show that our network, coined NVD-Net for neural Voronoi diagrams, can effectively learn Voronoi partitions for CAD models from training data and exhibits superior generalization capabilities. Extensive experiments and evaluation demonstrate that the resulting B-Reps, consisting of parametric surfaces, curves, and vertices, are more plausible than those obtained by existing alternatives, with significant improvements in reconstruction quality. Code will be released on https://github.com/yilinliu77/NVDNet.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2406.05261")
get_code_for_paper("2406.05261")
have("2406.05261")
Connect an agent — have() is free.