Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Neural Unsigned Distance Fields for Implicit Function Learning
Authors: Julian Chibane, Mohamad Aymen mir, Gerard Pons-Moll
NeurIPS 2020 | Venue PDF | LLM Run Details | Input Tokens: 20,948 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,934 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on Shape Net [13] show that NDF, while simple, is the state-of-the art, and allows to reconstruct shapes with inner structures, such as the chairs inside a bus. |
| Researcher Affiliation | Academia | Julian Chibane Aymen Mir Gerard Pons-Moll Max Planck Institute for Informatics, Saarland Informatics Campus, Germany EMAIL |
| Pseudocode | Yes | Algorithm 1: NDF: Dense PCs |
| Open Source Code | Yes | To encourage further research in this new direction, we make code and model publicly available at https://virtualhumans.mpi-inf.mpg.de/ndf/. |
| Open Datasets | Yes | Experiments on Shape Net [13] show that NDF, while simple, is the state-of-the art, and allows to reconstruct shapes with inner structures, such as the chairs inside a bus. [...] Angel X Chang, Thomas Funkhouser, Leonidas Guibas, Pat Hanrahan, Qixing Huang, Zimo Li, Silvio Savarese, Manolis Savva, Shuran Song, Hao Su, Jianxiong Xiao, Li Yi, and Fisher Yu. Shapenet: An information-rich 3d model repository. ar Xiv preprint ar Xiv:1512.03012, 2015. |
| Dataset Splits | Yes | We use a 80/20 random train and test split. |
| Hardware Specification | Yes | Each projection takes around 3.7 seconds for a 1 million points on a Tesla V100. |
| Software Dependencies | No | The paper does not list specific software dependencies (e.g., libraries, frameworks) with their version numbers. |
| Experiment Setup | Yes | clamping the maximal regressed distance to a value δ > 0 concentrates the model capacity to represent the vicinity of the surface accurately. Larger δ values increase the convergence of our visualization Algorithms 1 and 2. We find a good trade-offwith δ = 10cm. [...] To address 1), we found that projecting a point with Eq. 2 multiple times (we use 5 in our experiments) with unit normalized gradient yields accurate surface predictions. |