Position-Aware Guided Point Cloud Completion with CLIP Model
Authors: Feng Zhou, Qi Zhang, Ju Dai, Lei Li, Qing Fan, Junliang Xing
AAAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive quantitative and qualitative experiments demonstrate that our method outperforms state-of-the-art point cloud completion methods. ... Experiments Datasets and Evaluation Metrics ... Ablation Study |
| Researcher Affiliation | Collaboration | 1North China University of Technology, Beijing, China 2Peng Cheng Laboratory, Shenzhen, China 3University of Copenhagen, Copenhagen, Denmark 4University of Washington, Washington, USA 5Skywork AI, Beijing, China 6Tsinghua University, Beijing, China |
| Pseudocode | No | The paper describes methods and processes in paragraph form and through architectural diagrams but does not contain explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain an explicit statement about releasing source code or provide any links to code repositories. |
| Open Datasets | Yes | PCN: The PCN dataset (Yuan et al. 2018) is a subset of Shape Net dataset (Chang et al. 2015)... MVP: The MVP dataset consists of 16 categories of high-quality pairs of partial and complete point clouds for training and testing... KITTI dataset (Geiger et al. 2013) |
| Dataset Splits | Yes | The dataset is partitioned similarly to PCN to ensure a fair comparison of our method with other methods. Concurrently, following prior work, the sampled points are down-sampled to a standardized size of 2,048 points for training purposes. |
| Hardware Specification | Yes | The text generation is implemented on a single NVIDIA RTX 4090. |
| Software Dependencies | No | The paper mentions models like VIT-16 and CLIP and notes that experiments are conducted under unified settings, but it does not specify software dependencies with version numbers (e.g., Python, PyTorch, CUDA versions). |
| Experiment Setup | No | The paper states, 'All experiments are conducted under unified settings on the PCN dataset,' and describes a detail of the position-aware module: 'We randomly select one block in each training iteration to learn its parameters while setting the others to a default value of 1.' However, it lacks specific hyperparameters such as learning rate, batch size, or optimizer settings. |