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..
Rethinking the compositionality of point clouds through regularization in the hyperbolic space
Authors: Antonio Montanaro, Diego Valsesia, Enrico Magli
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We study the performance of our regularizer Hy Co Re on the synthetic dataset Model Net40 [27] (12,331 objects with 1024 points, 40 classes) and on the real dataset Scan Object NN [28] (15,000 objects with 1024 points, 15 classes). |
| Researcher Affiliation | Academia | Antonio Montanaro Politecnico di Torino, Italy EMAIL Diego Valsesia Politecnico di Torino, Italy EMAIL Enrico Magli Politecnico di Torino, Italy EMAIL |
| Pseudocode | No | The paper does not contain any clearly labeled pseudocode or algorithm blocks. Methods are described in prose. |
| Open Source Code | Yes | Code of the project: https://github.com/diegovalsesia/HyCoRe |
| Open Datasets | Yes | We study the performance of our regularizer Hy Co Re on the synthetic dataset Model Net40 [27] (12,331 objects with 1024 points, 40 classes) and on the real dataset Scan Object NN [28] (15,000 objects with 1024 points, 15 classes). We use standard datasets and splits well known by the literature and report all values of the hyperparameters for our tests. |
| Dataset Splits | Yes | We use standard datasets and splits well known by the literature and report all values of the hyperparameters for our tests. |
| Hardware Specification | Yes | Models are trained on an Nvidia A6000 GPU. |
| Software Dependencies | No | The paper states: |
| Experiment Setup | Yes | We use f = 256 features to be comparable to the official implementations in the Euclidean space, then we test the model over different embedding dimensions in the ablation study. Moreover, we set α = β = 0.01, γ = 1000 and δ = 4. For the number of points of each part N , we select a random number between 200 and 600, and for the whole object a random number between 800 and 1024 to ensure better flexibility of the learned to model to part sizes. We train the models using Riemannian SGD optimization. |