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..
Geometry Cloak: Preventing TGS-based 3D Reconstruction from Copyrighted Images
Authors: Qi Song, Ziyuan Luo, Ka Chun Cheung, Simon See, Renjie Wan
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments have verified the effectiveness of our geometry cloak. Our project is available at https://qsong2001.github.io/geometry_cloak. |
| Researcher Affiliation | Collaboration | 1Department of Computer Science, Hong Kong Baptist University 2NVIDIA AI Technology Center, NVIDIA |
| Pseudocode | Yes | Algorithm 1: Optimizing Geometry Cloak with view-specific PGD |
| Open Source Code | Yes | Our project is available at https://qsong2001.github.io/geometry_cloak. |
| Open Datasets | Yes | To evaluate the performance of our method, we conduct experiments on the Google Scanned Objects (GSO) [7] and Omni Object3D (Omni3D) [45] datasets. |
| Dataset Splits | No | The paper mentions selecting images from datasets for evaluation but does not specify explicit training, validation, or test dataset splits (e.g., percentages or counts). |
| Hardware Specification | Yes | Our method uses the Py Torch framework on a single NVIDIA V100 GPU. |
| Software Dependencies | No | The paper mentions 'PyTorch framework' but does not specify a version number for it or any other software dependencies. |
| Experiment Setup | Yes | The input image I(0) = I is initialized, and iteratively updated for N = 100 steps with a step size of ฮฑ = 0.001. The loss is defined as the Chamfer Distance [1] between the predicted point cloud P and the target point cloud หPtar. The updated image I(i) is clipped to the valid range [0, 1], and after N = 100 iterations, the final image หI = I(N) are obtained as the geometry cloaked output. |