Approximately Piecewise E(3) Equivariant Point Networks
Authors: Matan Atzmon, Jiahui Huang, Francis Williams, Or Litany
ICLR 2024 | Conference PDF | Archive PDF | Plain Text | 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 {matzmon,jiahuih,fwilliams,olitany}@nvidia.com |
| 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. |