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..
Polyhedral Complex Derivation from Piecewise Trilinear Networks
Authors: Jin-Hwa Kim
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We empirically validate correctness and parsimony through chamfer distance and efficiency, and angular distance, while examining the correlation between the eikonal loss and the planarity of the hypersurfaces. and 6 Experiment |
| Researcher Affiliation | Collaboration | Jin-Hwa Kim NAVER AI Lab & SNU AIIS Republic of Korea EMAIL |
| Pseudocode | Yes | Algorithm 1 Polyhedral Complex Derivation |
| Open Source Code | Yes | The code is available at https://github.com/naver-ai/tropical-nerf.pytorch. |
| Open Datasets | Yes | Table 1 and Figure 4 show the results for the Standford 3D Scanning repository [31]. and The Stanford 3D Scanining repository can be freely download via http://graphics.stanford.edu/ data/3Dscanrep/. |
| Dataset Splits | No | The paper does not explicitly specify train/validation/test splits for the neural network training. |
| Hardware Specification | Yes | We conducted all experiments with an NVIDIA V100 32GB. |
| Software Dependencies | Yes | We use the official Python package of tinycudnn 4 for the Hash Grid module. |
| Experiment Setup | Yes | We used the number of layers L of 3 and hidden size H of 16 for the networks, and ϵ of 1e-4 for the sign-vectors (ref. Definition 3.4). The weight for the eikonal loss is 1e-2. For Hash Grid, the resolution levels of 4, feature size of 2, base resolution Nmin of 2, and max resolution Nmax of 32 by default. |