$\mathrm{SE}(3)$-Equivariant Attention Networks for Shape Reconstruction in Function Space
Authors: Evangelos Chatzipantazis, Stefanos Pertigkiozoglou, Edgar Dobriban, Kostas Daniilidis
ICLR 2023 | Conference PDF | Archive PDF | Plain Text | 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 {vaghat,pstefano}@seas.upenn.edu Edgar Dobriban University of Pennsylvania dobriban@wharton.upenn.edu Kostas Daniilidis University of Pennsylvania kostas@cis.upenn.edu |
| 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. |