Geometry Cloak: Preventing TGS-based 3D Reconstruction from Copyrighted Images
Authors: Qi Song, Ziyuan Luo, Ka Chun Cheung, Simon See, Renjie Wan
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | 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 α = 0.001. The loss is defined as the Chamfer Distance [1] between the predicted point cloud P and the target point cloud ˆPtar. The updated image I(i) is clipped to the valid range [0, 1], and after N = 100 iterations, the final image ˆI = I(N) are obtained as the geometry cloaked output. |