PointLIE: Locally Invertible Embedding for Point Cloud Sampling and Recovery
Authors: Weibing Zhao, Xu Yan, Jiantao Gao, Ruimao Zhang, Jiayan Zhang, Zhen Li, Song Wu, Shuguang Cui
IJCAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments demonstrate that Point LIE outperforms the state-of-the-art point cloud sampling and upsampling methods both quantitatively and qualitatively. To quantitatively evaluate the performance of different methods, three commonly-used metrics are adopted, i.e., Chamfer distance (CD), Hausdorff distance (HD) and point-to-surface distance (P2F). |
| Researcher Affiliation | Academia | Weibing Zhao1,2 , Xu Yan1,2 , Jiantao Gao2,3, Ruimao Zhang1,2, Jiayan Zhang1, Zhen Li1,2 , Song Wu4, Shuguang Cui1,2 1 The Chinese University of Hong Kong, Shenzhen 2 Shenzhen Research Institute of Big Data 3 Shanghai University 4 Shenzhen Luohu Hospital {weibingzhao@link., xuyan1@link., lizhen@}cuhk.edu.cn |
| Pseudocode | No | The paper describes the model architecture and processes using figures and mathematical equations (e.g., Eq. 3, 4, 5, 6) but does not provide structured pseudocode or an algorithm block. |
| Open Source Code | No | No explicit statement about open-sourcing code or a link to a code repository is provided. |
| Open Datasets | Yes | To fully evaluate the proposed Point LIE, we compared our method with the state-of-the-art methods on PU-147 [Li et al., 2019] dataset, which follows the official split of 120/27 for our training and testing sets. |
| Dataset Splits | No | The paper mentions 'PU-147 [Li et al., 2019] dataset, which follows the official split of 120/27 for our training and testing sets.' but does not explicitly detail the exact percentages or counts for training, validation, and test splits, nor does it explicitly mention a validation set. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory, or cloud instances) used for running experiments are mentioned in the paper. |
| Software Dependencies | No | No specific software dependencies with version numbers (e.g., Python, PyTorch, CUDA, or library versions) are mentioned in the paper. |
| Experiment Setup | Yes | Under the premise of balancing efficiency and effectiveness, we set PI block number M as 8 in the 4 scale task, and M as 4 in the rest 8 and 16 tasks. Furthermore, we set k as 3 to ensure that the information in the discarded points can be sufficiently preserved. |