Polyhedral Complex Derivation from Piecewise Trilinear Networks
Authors: Jin-Hwa Kim
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | 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 j1nhwa.kim@navercorp.com |
| 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. |