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..
$\mathrm{SE}(3)$-Equivariant Attention Networks for Shape Reconstruction in Function Space
Authors: Evangelos Chatzipantazis, Stefanos Pertigkiozoglou, Edgar Dobriban, Kostas Daniilidis
ICLR 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We perform experiments with surface reconstruction from unoriented sparse and noisy input point clouds. |
| Researcher Affiliation | Academia | Evangelos Chatzipantazis , Stefanos Pertigkiozoglou University of Pennsylvania EMAIL Edgar Dobriban University of Pennsylvania EMAIL Kostas Daniilidis University of Pennsylvania EMAIL |
| Pseudocode | No | The paper describes algorithms and methods in prose and mathematical equations but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide an explicit statement or link for open-source code availability. |
| Open Datasets | Yes | We train our model on the Shape Net (Chang et al., 2015) subset constructed in Choy et al. (2016). |
| Dataset Splits | No | The paper does not explicitly provide training/validation/test dataset splits (e.g., percentages, sample counts, or specific split files) for reproducibility, beyond mentioning training and testing on ShapeNet subsets. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper mentions the use of the Adam optimizer and Marching Cubes algorithm but does not specify software dependencies with version numbers (e.g., Python, PyTorch, specific library versions). |
| Experiment Setup | Yes | We use the Adam (Kingma & Ba, 2015) optimizer with learning rate that starts at 2 10 4 and linearly decreases to reach the value of 10 5. We train for 200,000 iterations using a batch size of 64. |