Weakly Supervised Semantic Segmentation for Large-Scale Point Cloud
Authors: Yachao Zhang, Zonghao Li, Yuan Xie, Yanyun Qu, Cuihua Li, Tao Mei3421-3429
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our method is evaluated on large-scale point cloud datasets with different scenarios including indoor and outdoor. The experimental results show the large gain against existing weakly supervised methods and comparable results to fully supervised methods. |
| Researcher Affiliation | Collaboration | Yachao Zhang1, Zonghao Li1, Yuan Xie2 , Yanyun Qu1 , Cuihua Li1, Tao Mei3 1 School of Informatics, Xiamen University, Fujian, China 2 School of Computer Science and Technology, East China Normal University, Shanghai, China 3 JD AI Research, Beijing, China |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | Yes | We choose Scan Net (Dai et al. 2017) as the pre-training dataset...we experiment on the indoor scene dataset S3DIS (Armeni et al. 2016) and Scan Netv2 (Dai et al. 2017) and outdoor dataset Semantic3D (Hackel et al. 2017). |
| Dataset Splits | Yes | We conduct a pilot study to understand the effectiveness and show the m Io U curve of our method and baseline on validation set. |
| Hardware Specification | Yes | The two networks can be trained built upon Tensor Flow with a single NVIDIA Titan RTX. |
| Software Dependencies | No | The paper mentions 'Tensor Flow' but does not provide a specific version number or other software dependencies with versions. |
| Experiment Setup | Yes | We use Adam optimizer with default parameters. The number of neighboring points K is 16. Both the self-supervised pretext task and weakly semantic segmentation network are trained 80 epochs. |