Spiking NeRF: Representing the Real-World Geometry by a Discontinuous Representation
Authors: Zhanfeng Liao, Yan Liu, Qian Zheng, Gang Pan
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments, Experimental Settings, Datasets. We evaluate our methods in 8 scenes from the Blender dataset (Mildenhall et al. 2021) and 6 scenes from Dex-Ne RF (Ichnowski et al. 2021). Metrics. Following most 3D reconstruction methods, we measure the reconstruction quality with the Chamfer distances, Overall Performance, Ablation Study. |
| Researcher Affiliation | Academia | Zhanfeng Liao, Yan Liu, Qian Zheng*, Gang Pan* Zhejiang University {zhanfengliao, qianzheng, rliuyan2, gpan}@zju.edu.cn |
| Pseudocode | No | The paper describes methods using equations and prose, but does not include a clearly labeled "Pseudocode" or "Algorithm" block. |
| Open Source Code | Yes | The source code and the supplementary material are available at https://github.com/liaozhanfeng/Spiking-Ne RF. |
| Open Datasets | Yes | We evaluate our methods in 8 scenes from the Blender dataset (Mildenhall et al. 2021) and 6 scenes from Dex-Ne RF (Ichnowski et al. 2021). Following most 3D reconstruction methods (Wang et al. 2021; Wang, Skorokhodov, and Wonka 2022, 2023; Oechsle, Peng, and Geiger 2021), we also evaluate our methods in 15 scenes from the DTU dataset (Jensen et al. 2014). |
| Dataset Splits | No | The paper mentions the datasets used (Blender, Dex-Ne RF, DTU) but does not provide specific train/validation/test split percentages, sample counts, or explicit references to predefined splits with details. It only cites the datasets. |
| Hardware Specification | Yes | We sample 1024 rays per batch and train our model for 400k iterations for 10 hours on a single NVIDIA RTX3090 GPU. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions) required to reproduce the experiments. |
| Experiment Setup | Yes | We sample 1024 rays per batch and train our model for 400k iterations for 10 hours on a single NVIDIA RTX3090 GPU. Our network architecture and initialization scheme are similar to those of Ne RF (Mildenhall et al. 2021)... We set the initial value of r and k to 100 and 1... we set λ1 to 0.15 at first and continue to increase λ1 as the training process progresses. To preserve true high-frequency geometric information, we initially set the λ2 to 0.0001 and gradually decrease the proportion of Lg in L as the training progresses by decreasing λ2. |