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..
Normal-GS: 3D Gaussian Splatting with Normal-Involved Rendering
Authors: Meng Wei, Qianyi Wu, Jianmin Zheng, Hamid Rezatofighi, Jianfei Cai
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments demonstrate that Normal-GS achieves near state-of-the-art visual quality while obtaining accurate surface normals and preserving real-time rendering performance. |
| Researcher Affiliation | Academia | 1Monash Univeristy 2Nanyang Technological University EMAIL {ASJMZheng}@ntu.edu.sg |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | We will release our code after publication. |
| Open Datasets | Yes | We followed the original 3DGS [2] methodology and used the Ne RF Synthetic [1], Mip-Ne RF 360 [26], Tank and Temple [54], and Deep Blending [55] datasets to demonstrate the performance of our method. |
| Dataset Splits | No | The paper mentions training and testing splits: 'we selected every 8th image for testing and used the remaining images for training,' but does not explicitly describe a separate validation split. |
| Hardware Specification | Yes | We tested our method and the baseline methods using their original released implementations with default hyperparameters on an NVIDIA RTX 3090 GPU with 24 GB of memory. |
| Software Dependencies | No | We implemented our method in Python using the Py Torch framework [56]. Specific version numbers for Python, PyTorch, or other libraries are not provided. |
| Experiment Setup | Yes | We trained our models for 30k iterations, following the settings of baseline methods. Consistent with [2, 6], we set λvol = 0.001. For the depth-normal loss, we used λN = 0.01. Because the depth and normals were inaccurate at the start of training, we added the depth-normal loss after training for 5k iterations. ... Our loss is defined as L = LP +λvol Lvol +λN LN , with λN = 0.01 and λvol = 0.001. The photometric loss, as defined in [2], is Lp = (1 λSSIM)L1 +λSSIMLD-SSIM, with λSSIM = 0.2. |