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..
General Neural Gauge Fields
Authors: Fangneng Zhan, Lingjie Liu, Adam Kortylewski, Christian Theobalt
ICLR 2023 | Venue PDF | 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 EMAIL |
| 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. |