GNeSF: Generalizable Neural Semantic Fields
Authors: Hanlin Chen, Chen Li, Mengqi Guo, Zhiwen Yan, Gim Hee Lee
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results show that our approach achieves comparable performance with scene-specific approaches. More importantly, our approach can even outperform existing strong supervisionbased approaches with only 2D annotations. 5 Experiments We evaluate our method on the tasks of semantic view synthesis in Sec. 5.1.1 and 3D semantic segmentation in Sec. 5.1.2. Additionally, we validate the effectiveness of the proposed modules in Sec. 5.2. |
| Researcher Affiliation | Academia | Hanlin Chen Chen Li Mengqi Guo Zhiwen Yan Gim Hee Lee Department of Computer Science, National University of Singapore {hanlin.chen, gimhee.lee}@comp.nus.edu.sg |
| Pseudocode | No | No pseudocode or algorithm blocks are explicitly presented in the paper. |
| Open Source Code | Yes | Our source code is available at: https://github.com/HLin Chen/GNe SF. |
| Open Datasets | Yes | We perform the experiments on two indoor datasets: Scan Net (V2) [13] and Replica [38]. |
| Dataset Splits | Yes | We follow [31] and [40] on the three training/validation/test splits commonly used in previous works. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running experiments. |
| Software Dependencies | No | The paper mentions software components like Mask2Former and Swin-Transformer but does not provide specific version numbers for these or other software dependencies. |
| Experiment Setup | No | The paper does not explicitly state specific hyperparameters (e.g., learning rate, batch size, epochs) or detailed training configurations in the main text. |