3D-PhysNet: Learning the Intuitive Physics of Non-Rigid Object Deformations
Authors: Zhihua Wang, Stefano Rosa, Bo Yang, Sen Wang, Niki Trigoni, Andrew Markham
IJCAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results show the viability and the generalisation properties of the proposed architecture. ... 4 Experiments ... 4.3 Results and Discussion |
| Researcher Affiliation | Academia | Zhihua Wang 1, Stefano Rosa 1, Bo Yang1, Sen Wang2, Niki Trigoni1 and Andrew Markham1 1Department of Computer Science, University of Oxford 2School of Engineering and Physical Sciences, Heriot-Watt University {name.surname}@cs.ox.ac.uk, s.wang@hw.ac.uk |
| Pseudocode | No | The paper describes the network architecture and implementation details (Table 1) but does not include any explicitly labeled 'Pseudocode' or 'Algorithm' block. |
| Open Source Code | No | The paper states, 'The full generated datasets will be released for the sake of reproducibility and testing.', but it does not mention releasing the source code for the methodology itself. |
| Open Datasets | Yes | The full generated datasets will be released for the sake of reproducibility and testing. |
| Dataset Splits | No | The paper mentions 'training data' and 'test on unseen values' but does not specify exact training/validation/test dataset splits by percentages or counts. |
| Hardware Specification | Yes | The network was implemented with Tensorflow 1.4 and trained on a single Nvidia Pascal Titan X GPU. |
| Software Dependencies | Yes | The network was implemented with Tensorflow 1.4 |
| Experiment Setup | Yes | The network was trained with a batch size of 8 using the Adam optimizer, with lr = 5e 5, β1 = 0.5, β2 = 0.999,ϵ = 1e 8. λ is set to 10 in Eq. 8. |