Multi-View Silhouette and Depth Decomposition for High Resolution 3D Object Representation
Authors: Edward Smith, Scott Fujimoto, David Meger
NeurIPS 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our work on multiple experiments concerning high-resolution 3D objects, and show our system is capable of accurately predicting novel objects at resolutions as large as 512 512 512 the highest resolution reported for this task. We achieve state-of-the-art performance on 3D object reconstruction from RGB images on the Shape Net dataset, and further demonstrate the first effective 3D super-resolution method. |
| Researcher Affiliation | Academia | Edward Smith Mc Gill University edward.smith@mail.mcgill.ca Scott Fujimoto Mc Gill University scott.fujimoto@mail.mcgill.ca David Meger Mc Gill University dmeger@cim.mcgill.ca |
| Pseudocode | No | The paper does not contain pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | Yes | In order to ensure reproducible experimental comparison, code for our system has been made publicly available on a Git Hub repository1. 1https://github.com/Edward Smith1884/Multi-View-Silhouette-and-Depth-Decomposition-for-High Resolution-3D-Object-Representation |
| Open Datasets | Yes | We evaluate our method s ability to perform 3D object reconstruction on the the Shape Net dataset [1]. [1] Angel X Chang, Thomas Funkhouser, Leonidas Guibas, Pat Hanrahan, Qixing Huang, Zimo Li, Silvio Savarese, Manolis Savva, Shuran Song, Hao Su, et al. Shapenet: An information-rich 3d model repository. ar Xiv preprint ar Xiv:1512.03012, 2015. |
| Dataset Splits | Yes | Data is split into training, validation, and test set using a ratio of 70:10:20 respectively. |
| Hardware Specification | Yes | Given the efficiency of our method, each experiment was run on a single NVIDIA Titan X GPU in the order of hours. |
| Software Dependencies | No | The paper mentions 'Exact network architectures and training regime are provided in the supplementary material' but does not list specific software dependencies with version numbers in the main text. |
| Experiment Setup | No | The paper states 'Exact network architectures and training regime are provided in the supplementary material' but does not list specific hyperparameter values or concrete training configurations in the main text. |