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..
Spiking NeRF: Representing the Real-World Geometry by a Discontinuous Representation
Authors: Zhanfeng Liao, Yan Liu, Qian Zheng, Gang Pan
AAAI 2024 | Venue PDF | 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 EMAIL |
| 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. |