Rotation-Invariant Local-to-Global Representation Learning for 3D Point Cloud
Authors: SEOHYUN KIM, JaeYoo Park, Bohyung Han
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | This section presents the experimental results of our algorithm compared to existing approaches. We demonstrate the effectiveness of our framework via several ablation studies. |
| Researcher Affiliation | Academia | Computer Vision Laboratory & ASRI, Seoul National University {goodbye61, bellos1203, bhhan}@snu.ac.kr |
| Pseudocode | No | No explicit pseudocode or algorithm blocks were found in the paper. |
| Open Source Code | Yes | The source codes are available on our project page1. 1https://cvlab.snu.ac.kr/research/rotation_invariant_l2g/ |
| Open Datasets | Yes | To evaluate the robustness to rotation, we compare the proposed algorithm, RI-GCN, with recent 3D object classification approaches on Model Net40 [26], a widely used benchmark. |
| Dataset Splits | Yes | It consists of CAD models in 40 categories and contains 9,843 and 2,468 shapes for training and testing, respectively. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models) used for experiments were mentioned in the paper. |
| Software Dependencies | No | The paper does not provide specific version numbers for software dependencies like libraries or frameworks. |
| Experiment Setup | No | The paper does not explicitly provide specific hyperparameter values (e.g., learning rate, batch size, number of epochs) or detailed optimizer settings. It mentions following evaluation protocols of previous works but does not list their own setup details. |