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..
Approximately Piecewise E(3) Equivariant Point Networks
Authors: Matan Atzmon, Jiahui Huang, Francis Williams, Or Litany
ICLR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our empirical results demonstrate the advantage of integrating piecewise E(3) symmetry into network design, showing a distinct improvement in generalization accuracy compared to prior works for both classification and segmentation tasks. |
| Researcher Affiliation | Collaboration | Matan Atzmon 1 Jiahui Huang 1 Francis Williams 1 Or Litany1 2 1 NVIDIA 2 Technion EMAIL |
| Pseudocode | Yes | Algorithm 1 Q prediction |
| Open Source Code | No | The paper does not contain an explicit statement about releasing source code or provide a link to a code repository for the described methodology. |
| Open Datasets | Yes | We conducted experiments using datasets comprising of (i) articulated objects consisting of human subjects performing various sequence movements (Bogo et al., 2017), and (ii) real-world room scans of furniture-type objects (Huang et al., 2021a). |
| Dataset Splits | Yes | The first consists of a random (90%/10%) train/test split of 41,461 human models from the SMPL dataset (Loper et al., 2015) consisting of 10 different human subjects as in (Huang et al., 2021b). |
| Hardware Specification | Yes | Training was done on a single Nvidia V-100 GPU |
| Software Dependencies | No | The paper mentions 'using PYTORCH deep learning framework (Paszke et al., 2019)' but does not specify a version number for PyTorch or any other software dependency. |
| Experiment Setup | Yes | We set σl = (0.002,0.005,0.008,0.1). The number of iterations for the EM was 16. We trained our networks using the ADAM (Kingma & Ba, 2014) optimizer, setting the batch size to 8. We set a fixed learning rate of 0.001. All models were trained for 3000 epochs. |