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..
Convolutional Generation of Textured 3D Meshes
Authors: Dario Pavllo, Graham Spinks, Thomas Hofmann, Marie-Francine Moens, Aurelien Lucchi
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the efficacy of our method on Pascal3D+ Cars and CUB, both in an unconditional setting and in settings where the model is conditioned on class labels, attributes, and text. |
| Researcher Affiliation | Academia | Dario Pavllo Dept. of Computer Science ETH Zurich Graham Spinks Dept. of Computer Science KU Leuven Thomas Hofmann Dept. of Computer Science ETH Zurich Marie-Francine Moens Dept. of Computer Science KU Leuven Aurelien Lucchi Dept. of Computer Science ETH Zurich |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | We release our code and pretrained models at https://github.com/dariopavllo/convmesh. |
| Open Datasets | Yes | We evaluate our method on two datasets with annotated keypoints, and use the implementation of [24] to estimate the pose from keypoints using structure-from-motion. CUB-200-2011 [52] We use the train/test split of [24]... Pascal3D+ (P3D) [57] We use the cars subset... |
| Dataset Splits | No | The paper mentions "train/test split" for CUB and "training images" for P3D, but does not explicitly detail a validation set or its split. |
| Hardware Specification | Yes | We train with a batch size of 50 on a single Pascal GPU, which requires 12 hours. |
| Software Dependencies | No | The paper mentions software like Adam, DIB-R, and Mask R-CNN, but does not provide specific version numbers for them. |
| Experiment Setup | Yes | The model (Fig. 1) is trained for 1000 epochs using Adam [28], with an initial learning rate of 10 4 halved every 250 epochs. We train with a batch size of 50 on a single Pascal GPU, which requires 12 hours. (...) train for 600 epochs with a constant learning rate of 0.0001 for G and 0.0004 for D (two time-scale update rule [19]). We update D twice per G update, and evaluate the model on a running average of G s weights (β = 0.999) as proposed by [64, 25, 26, 4]. (...) For all experiments, we use a total batch size of 32 and we employ synchronized batch normalization across multiple GPUs. |