NeuralIndicator: Implicit Surface Reconstruction from Neural Indicator Priors
Authors: Shi-Sheng Huang, Guo Chen, Chen Li Heng, Hua Huang
ICML 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive evaluations on synthetic and real-scan datasets show that our approach consistently outperforms previous approaches, especially when point clouds are incomplete and/or noisy with complex topology structure. |
| Researcher Affiliation | Academia | 1School of Artificial Intelligence, Beijing Normal University, Beijing, China. |
| Pseudocode | No | The paper describes methods and processes in textual form and through mathematical equations, but it does not include structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper includes footnotes referencing supplementary materials for results and computational cost analysis, but it does not provide an explicit statement or link for open-source code availability for the methodology described. |
| Open Datasets | Yes | We use five public point cloud datasets, including both synthetic datasets (ABC (Koch et al., 2019), FAMOUS (Erler et al., 2020), Reconbench (Berger et al., 2013), Thingi10K (Zhou & Jacobson, 2016)) and real-scan dataset (DPoint (Wu et al., 2015) dataset)... |
| Dataset Splits | No | The paper mentions using specific test subsets from public datasets for evaluation but does not explicitly provide details on training or validation splits with percentages, counts, or a clear splitting methodology for those subsets. |
| Hardware Specification | Yes | It takes about one hour for our system to finish 10, 000 training epochs on a desktop computer with an NVIDIA Ge Force RTX 3060 12G GPU. |
| Software Dependencies | No | The paper does not explicitly provide specific version numbers for software dependencies used in the experiments. |
| Experiment Setup | Yes | Specifically, we set voxel resolution as 32, 64, 128 and 256 voxels in the unit box, and perform the surface reconstruction with such four system variants (called SIFG-32, SIFG-64, SIFG-128 and SIFG-256 respectively) on the Test dataset. |