Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Geometry Processing with Neural Fields
Authors: Guandao Yang, Serge Belongie, Bharath Hariharan, Vladlen Koltun
NeurIPS 2021 | Venue PDF | 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. |