Point Cloud Semantic Scene Completion from RGB-D Images
Authors: Shoulong Zhang, Shuai Li, Aimin Hao, Hong Qin3385-3393
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments validate that our new method achieves the stateof-the-art performance, in contrast with the current methods applied to our dataset. |
| Researcher Affiliation | Collaboration | 1Beihang University, Beijing, China 2Peng Cheng Laboratory, Shenzhen, China 3Stony Brook University (SUNY), Stony Brook, USA |
| Pseudocode | Yes | Algorithm 1: Main Steps of PCSSC-Net Training. |
| Open Source Code | No | The paper does not provide a direct link or explicit statement about releasing the source code for the described methodology. |
| Open Datasets | Yes | we synthesized a new dataset with 12 layouts of the Scene Net RGB-D dataset (Mc Cormac et al. 2017) and 263 typical indoor object models of the Shape Net dataset (Chang et al. 2015) with realistic textures. |
| Dataset Splits | No | We divide our dataset into 1520 and 392 scenes for the training and testing purpose. A separate validation split is not explicitly defined. |
| Hardware Specification | Yes | We trained our model on Nvidia RTX 2080Ti GPU for roughly 70 hours with a batch size of 8. |
| Software Dependencies | No | The paper states 'Our model is implemented in Py Torch' but does not specify its version number or versions for any other software dependencies. |
| Experiment Setup | Yes | The initial learning rate is set to 0.001, and the decay rate is 0.7 for every ten epochs. For the parameters in the loss, we set α = 0.005, β = 1.5 and γ = 0 for the first 10 training hours. Then, we set α = 0.015, β = 1.5, and γ = 1.5 for geometry refinement based on their semantic classes. The CD weight for the chair class is set to 0.01 and 1 for other classes. |