Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Generative Category-level Object Pose Estimation via Diffusion Models
Authors: Jiyao Zhang, Mingdong Wu, Hao Dong
NeurIPS 2023 | Venue PDF | 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. |