GCD-Sampling: A General Cross-scale Decoupled Sampling for Point Cloud
Authors: Tao Dai, Yanzi Wang, Jianyu Xiong, Yaohua Zha, Shu-Tao Xia, Zexuan Zhu
AAAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on different architectures demonstrate the effectiveness of our method over other existing adaptive sampling methods. ... To validate the effectiveness of GCD-sampling, we employ four representative networks with hierarchical architectures as baseline models, including Point Net++ (Qi et al. 2017b), Point CNN (Li et al. 2018), Point MLP (Ma et al. 2022), and Point Ne Xt (Qian et al. 2022). |
| Researcher Affiliation | Academia | 1College of Computer Science and Software Engineering, Shenzhen University 2National Engineering Laboratory for Big Data System Computing Technology, Shenzhen University 3Tsinghua Shenzhen International Graduate School, Tsinghua University 4Shenzhen City Key Laboratory of Embedded System Design, Shenzhen, China EMAIL, EMAIL, EMAIL |
| Pseudocode | No | The paper describes the methodology in detail using text and diagrams, but it does not contain any explicitly labeled 'Pseudocode' or 'Algorithm' blocks. |
| Open Source Code | Yes | Code https://github.com/Yanzi-Wang/GCD-Sampling A-General-Cross-scale-Decoupled-Sampling-for-Point Cloud |
| Open Datasets | Yes | We evaluate our model on the datasets Model Net40 (Wu et al. 2014) and Scan Object NN (Uy et al. 2019) for classification. ... We assess our model using the Shape Net Part dataset citeyi2016scalable for the point cloud part segmentation task. |
| Dataset Splits | No | The paper mentions the use of Model Net40, Scan Object NN, and Shape Net Part datasets for experiments but does not provide specific details on how these datasets were split into training, validation, or test sets (e.g., percentages, sample counts, or explicit references to predefined splits). |
| Hardware Specification | Yes | These experiments are tested on one NVIDIA TITAN X (Pascal) GPU and 16 cores Intel Xeon CPU E5-2697 v4 @ 2.30GHz CPU. |
| Software Dependencies | No | The paper mentions various network architectures used (e.g., Point Net++, Point CNN, Point MLP, Point Ne Xt) but does not provide specific software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | No | The networks are trained from scratch, with the same number of epochs, learning rate, optimizer, and other hyperparameters as the baseline experiments without embedding GCD-sampling. |