HF-NeuS: Improved Surface Reconstruction Using High-Frequency Details
Authors: Yiqun Wang, Ivan Skorokhodov, Peter Wonka
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 4 Experiments, Table 1: Quantitative results on the DTU dataset. |
| Researcher Affiliation | Academia | Yiqun Wang KAUST Ivan Skorokhodov KAUST Peter Wonka KAUST |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code available at https://github.com/yiqun-wang/HFS. |
| Open Datasets | Yes | We conduct experiments on the DTU dataset [12]. We further choose 9 challenging scenes from other datasets: 6 scenes from the Ne RF-synthetic dataset [19] and 3 scenes from Blended MVS [31](CC-4 License). |
| Dataset Splits | No | The paper mentions using standard datasets like DTU, Ne RF-synthetic, and Blended MVS and following previous work's evaluation protocols, but it does not explicitly provide specific training, validation, and test dataset split percentages or counts. |
| Hardware Specification | Yes | We use Adam with learning rate 5e-4 for the network training using NVIDIA TITAN A100 40GB graphics cards. |
| Software Dependencies | No | The paper mentions using MLPs and the Adam optimizer but does not provide specific version numbers for any software libraries or dependencies used for implementation. |
| Experiment Setup | Yes | We use MLPs to model two signed distance functions fb and fd. Each MLP consists of 8 layers. We use Adam with learning rate 5e-4 for the network training... For adaptive sampling, we first uniformly sample 64 points on the ray... We set L = 16 for the parameter of the frequency band of positional encoding. |