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..
GramGAN: Deep 3D Texture Synthesis From 2D Exemplars
Authors: Tiziano Portenier, Siavash Arjomand Bigdeli, Orcun Goksel
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Quantitative and qualitative evaluations on a diverse set of exemplars motivate our design decisions and show that our system performs superior to previous state of the art. Finally, we conduct a user study that confirms the benefits of our framework. |
| Researcher Affiliation | Collaboration | Tiziano Portenier1, Siavash Bigdeli2, Orcun Goksel1 1: Computer-assisted Applications in Medicine, ETH Zurich, Switzerland 2: Swiss Center for Electronics and Microtechnology, Switzerland |
| Pseudocode | No | No explicit pseudocode or algorithm blocks are provided. |
| Open Source Code | No | The paper does not provide any explicit statement about releasing source code or a link to a code repository for their methodology. |
| Open Datasets | No | For this purpose we trained both models on a texture dataset consisting of 100 stone texture exemplars collected online. No specific link, DOI, or formal citation is provided for this dataset. |
| Dataset Splits | No | The paper mentions training on a dataset and evaluating on '22 reference input patches not seen during training' but does not specify explicit train/validation/test dataset splits with percentages or counts. |
| Hardware Specification | Yes | Training our model on a single exemplar takes a few hours on a single NVIDIA 2080ti GPU |
| Software Dependencies | No | The paper mentions using 'Adam optimizer' but does not provide specific version numbers for any software dependencies or libraries. |
| Experiment Setup | Yes | We train all models using Adam optimizer [28] and we use (equalized [29]) learning rates of 2 10 3 for D and 5 10 4 for G (E, Q, and S). In each training iteration both G and D are updated once and sequentially. In LD we set λ = 10. An important factor is the choice of the hyperparameters α and β in LG. Although various settings produce plausible outputs, we achieved best results when setting α = 0.1 and β = 1. In the single exemplar setting we set n = 16 and our conditional models use n = 32 noise frequencies. We train on texture patches of size 1282 and use noise instances of resolution 643n. |