Geometry Processing with Neural Fields
Authors: Guandao Yang, Serge Belongie, Bharath Hariharan, Vladlen Koltun
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results show that our methods are on par with the well-established mesh-based methods without committing to a particular surface discretization. |
| Researcher Affiliation | Collaboration | Guandao Yang Cornell University Serge Belongie University of Copenhagen Bharath Hariharan Cornell University Vladlen Koltun Intel Labs |
| Pseudocode | No | The paper does not contain any figures, blocks, or sections explicitly labeled 'Pseudocode' or 'Algorithm'. |
| Open Source Code | Yes | Code is available at https://github.com/stevenygd/NFGP. |
| Open Datasets | Yes | We follow prior works [20, 76] to use Armadillo [40] and a sphere with one half of it corrupted by Gaussian noise. To create neural fields from these meshes, we follow the procedure of Park et al. [56] to compute ground-truth SDF for locations sampled within [ 1, 1]3. |
| Dataset Splits | No | The paper does not explicitly provide specific train/validation/test dataset splits (e.g., percentages, sample counts, or predefined split references) for its experiments. |
| Hardware Specification | Yes | Our deformation method right now requires a Titan X GPU with 12GB memory to train for 10 hours. Our smoothing and sharpening method takes about 10 minutes on the same GPU. |
| Software Dependencies | No | The paper mentions algorithms, optimizers (Adam), and methods (SIREN, Marching Cubes) but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | No | The paper states 'Hyperparameters are provided in the supplement', but does not list specific hyperparameter values or detailed training configurations in the main text. |