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..
A Statistical Manifold Framework for Point Cloud Data
Authors: Yonghyeon Lee, Seungyeon Kim, Jinwon Choi, Frank Park
ICML 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments with large-scale standard benchmark point cloud data show greatly improved classification accuracy visa-vis existing methods. |
| Researcher Affiliation | Collaboration | 1Department of Mechanical Engineering, Seoul National University, Seoul, South Korea 2Kakao Enterprise, Seongnam, Kyonggi-do, South Korea 3Saige Research, Seoul, South Korea. |
| Pseudocode | No | The paper describes methods and formulations in text and mathematical equations, but does not include any dedicated pseudocode blocks or algorithm listings. |
| Open Source Code | Yes | Code is available at https://github.com/seungyeon-k/SMFpublic. |
| Open Datasets | Yes | Experiments are carried out with both synthetic and standard benchmark datasets (Shape Net (Chang et al., 2015), Model Net (Wu et al., 2015)). |
| Dataset Splits | Yes | In Section 4.1.1, we use a dataset consisting of cones, cylinders, and ellipsoids, which are split into training/validation/test sets of size 3196/800/804. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU or CPU models used for experiments. |
| Software Dependencies | No | The paper mentions software components like 'ADAM' and 'DGCNN' and refers to their use, but does not specify version numbers for these or any other software dependencies like Python, PyTorch/TensorFlow, or CUDA. |
| Experiment Setup | Yes | To train the networks, we use ADAM with a learning rate of 0.001 and batch size of 16; the total number of the epochs is 500. The mean value of MEDs of the dataset is 0.0339, and we use the bandwidth value k to 0.5. We use Chamfer distance as the reconstruction loss; for regularization figures, the regularization term with the version of Equation (12) is multiplied by a coefficient λ = 107 with the info-Riemannian metric and by a coefficient λ = 1 with the Euclidean metric and added to the reconstruction loss term for each metric case. The value of η is set to be 0.0. |