Generative Category-level Object Pose Estimation via Diffusion Models
Authors: Jiyao Zhang, Mingdong Wu, Hao Dong
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 4 Experiments, 4.1 Experimental Setup, 4.2 Comparison with State-of-the-Art Methods, 4.3 Ablation Studies |
| Researcher Affiliation | Academia | 1 Center on Frontiers of Computing Studies, School of Computer Science, Peking University 2 Beijing Academy of Artificial Intelligence 3 National Key Laboratory for Multimedia Information Processing, School of Computer Science, Peking University |
| Pseudocode | Yes | Algorithm 1 Our Object Pose Tracking Framework |
| Open Source Code | No | Our checkpoints and demonstrations can be found at https://sites.google.com/view/genpose. This link leads to a project demonstration page, not a specific code repository. |
| Open Datasets | Yes | Our method is trained and evaluated on two common category-level object pose estimation datasets, namely CAMERA and REAL275 [4] |
| Dataset Splits | No | The paper mentions training and test sets but does not explicitly detail a separate validation split. 'CAMERA dataset... consists of 275K training images and 25K test images.' and 'REAL275 dataset... consists of 7 scenes for training with 4.3K images and 6 scenes for testing with 2.75K images.' |
| Hardware Specification | Yes | All experiments were conducted on a single RTX3090 with a batch size of 192. |
| Software Dependencies | No | All the experiments are implemented using Py Torch [44]. No specific version numbers for PyTorch or other software dependencies are provided. |
| Experiment Setup | Yes | The input pointcloud consisted of 1024 points. During the training phase, we used the same data augmentation techniques as FS-Net [6], which are widely adopted in category-level object pose estimation tasks. All experiments were conducted on a single RTX3090 with a batch size of 192. |