Aligning Gradient and Hessian for Neural Signed Distance Function
Authors: Ruian Wang, Zixiong Wang, Yunxiao Zhang, Shuangmin Chen, Shiqing Xin, Changhe Tu, Wenping Wang
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experimental results demonstrate its ability to accurately recover the underlying shape while effectively suppressing the presence of ghost geometry. |
| Researcher Affiliation | Academia | Ruian Wang Shandong University wra.time@gmail.com; Zixiong Wang Nankai University zixiong_wang@outlook.com; Yunxiao Zhang Shandong University zhangyunxiaox@gmail.com; Shuangmin Chen Qingdao University of Science and Technology csmqq@163.com; Shiqing Xin Shan Dong University xinshiqing@sdu.edu.cn; Changhe Tu Shandong University chtu@sdu.edu.cn; Wenping Wang TEXAS A&M UNIVERSITY wenping@cs.hku.hk |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks, nor does it include a figure or section explicitly labeled 'Pseudocode' or 'Algorithm'. |
| Open Source Code | No | The paper does not include an unambiguous statement about releasing code for the work described, nor does it provide a direct link to a source-code repository. |
| Open Datasets | Yes | The Surface Reconstruction Benchmark (SRB) [49]... The Shape Net dataset [13]... The ABC dataset [24]... Thingi10K [57]... 3D Scene dataset [56]... The D-Faust [7] dataset... |
| Dataset Splits | No | The paper mentions splitting for training and testing on datasets like ShapeNet and D-Faust (e.g., '6K scans are used for training and 2K scans for testing' for D-Faust), but it does not explicitly provide details about a separate validation set split or its size/methodology. |
| Hardware Specification | No | The paper does not provide specific hardware details such as exact GPU/CPU models, processor types, or memory amounts used for running its experiments. It only mentions general experimental setups without hardware specifications. |
| Software Dependencies | No | The paper mentions leveraging 'SIREN [40]' and 'Neural-Pull [5]' but does not provide specific version numbers for these or any other software dependencies, such as programming languages or libraries. |
| Experiment Setup | Yes | we set δ = 10 by default. At the same time, we set the default value of α to 6. More details can be checked in our supplementary material. |