A Statistical Manifold Framework for Point Cloud Data
Authors: Yonghyeon Lee, Seungyeon Kim, Jinwon Choi, Frank Park
ICML 2022 | Conference PDF | Archive PDF | Plain Text | 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. |