Normal-GS: 3D Gaussian Splatting with Normal-Involved Rendering
Authors: Meng Wei, Qianyi Wu, Jianmin Zheng, Hamid Rezatofighi, Jianfei Cai
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | 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 {meng.wei,qianyi.wu,hamid.rezatofighi,jianfei.cai}@monash.edu {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. |