General Neural Gauge Fields
Authors: Fangneng Zhan, Lingjie Liu, Adam Kortylewski, Christian Theobalt
ICLR 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 4 EXPERIMENTS4.3 EVALUATION RESULTS |
| Researcher Affiliation | Academia | Max Planck Institute for Informatics, 66123, Germany {fzhan,lliu,akortyle,theobalt}@mpi-inf.mpg.de |
| Pseudocode | Yes | The pseudo code of the forward & backward propagation of discrete cases is given in Algorithm 1.Algorithm 1 Pseudo code of forward & backward propagation in learning discrete gauge transformation.Algorithm 2 Pseudo code of differentiable top-k operation. |
| Open Source Code | Yes | We attach the source code of neural gauge fields in the supplementary material. |
| Open Datasets | Yes | All datasets used in our experiments are publicly accessible. |
| Dataset Splits | No | The paper does not explicitly provide specific train/validation/test dataset split percentages or sample counts. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU models, CPU types, or memory specifications used for running experiments. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers (e.g., Python, PyTorch, CUDA versions) that would be needed to replicate the experiment environment. |
| Experiment Setup | Yes | All models are optimized for 150k steps with a batch size of 1024 pixel rays.By default, the codebook has two layers and each layer contains 256 vectors with 128 dimensions. In line with Instant-NGP (M uller et al., 2022), the 3D space is also divided into two-level 3D grids with size 16 16 16 and 32 32 32 for discrete gauge transformation.The gauge network M for learning gauge transformations can be a MLP network or a transformation matrix, depending on the specific downstream application. For the mapping from 3D space to 2D plane to get (view-dependent) textures, the neural field is modeled by a MLP-based network which takes predicted 2D coordinates, i.e., output of the gauge network, and a certain view to predict color and density. For the mapping from 3D space to discrete codebooks, the neural field is modeled by looking up features from the codebook, followed by a small MLP of two layers to predict color and density. |