Skeleton-bridged Point Completion: From Global Inference to Local Adjustment
Authors: Yinyu Nie, Yiqun Lin, Xiaoguang Han, Shihui Guo, Jian Chang, Shuguang Cui, Jian.J Zhang
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The qualitative and quantitative experiments on both point cloud and mesh completion show that our approach outperforms the existing methods on various object categories. |
| Researcher Affiliation | Academia | Yinyu Nie1,2, Yiqun Lin2 Xiaoguang Han2, Shihui Guo3 Jian Chang1 Shuguang Cui2 Jian Jun Zhang1 1Bournemouth University 2SRIBD, CUHKSZ 3Xiamen University |
| Pseudocode | No | The paper describes the network architecture and modules in text and figures, but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not include any statement or link indicating that the source code for the methodology is openly available. |
| Open Datasets | Yes | Datasets. Two datasets are used for our training. 1) Shape Net-Skeleton [31] for skeleton extraction, and 2) Shape Net Core [4] for surface completion. |
| Dataset Splits | Yes | We adopt the train/validation/test split from [39] with five categories (i.e., chair, airplane, rifle, lamp, table) and 15,338 models in total. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers. |
| Experiment Setup | Yes | We adopt the batch size at 16 and the learning rate at 1e-3, which decreases by the scale of 0.5 if there is no loss drop within five epochs. 200 epochs are used in total. The weights used in the loss functions are: λk, λs = 1, λm = 0.1, λn = 0.001, λr = 0.1, λG = 0.01. |