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..
HF-NeuS: Improved Surface Reconstruction Using High-Frequency Details
Authors: Yiqun Wang, Ivan Skorokhodov, Peter Wonka
NeurIPS 2022 | Venue PDF | 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. |