Terrain Diffusion Network: Climatic-Aware Terrain Generation with Geological Sketch Guidance
Authors: Zexin Hu, Kun Hu, Clinton Mo, Lei Pan, Zhiyong Wang
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Comprehensive experiments on a new dataset constructed from NASA Topology Images clearly demonstrate the effectiveness of our proposed method, achieving the state-of-the-art performance. |
| Researcher Affiliation | Academia | 1The University of Sydney 2Civil Aviation Flight University of China |
| Pseudocode | No | The paper describes its methodology in narrative text and through a diagram (Fig. 2) but does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our code is available at https://github.com/TDNResearch/TDN. |
| Open Datasets | Yes | A new dataset is constructed from NASA Topology Images (Allen 2005) to evaluate the effectiveness of the proposed TDN. |
| Dataset Splits | Yes | We obtained 10,446 samples for training and 2,611 for testing. |
| Hardware Specification | Yes | We used one NVIDIA RTX 3090 GPU card to train our model. |
| Software Dependencies | No | The paper mentions 'Pysheds (Bartos 2020) packages' but does not provide specific version numbers for this or any other software libraries or dependencies required to replicate the experiments. |
| Experiment Setup | Yes | The U-Net like synthesisers in TDN comprise 3 downsampling blocks, 3 upsampling blocks, and a middle block with 8-head selfattention mechanisms employed for embedding the diffusion step t. With a total of 1.216 billion parameters, the model s training is conducted with a learning rate of 1.0e05 and a batch size of 6. During inference, TDN takes a set of user sketches as the input and iteratively generates a noise-free terrain latent representation. In total, 36 steps are taken to derive the final latent representation. |