Multiview Aggregation for Learning Category-Specific Shape Reconstruction
Authors: Srinath Sridhar, Davis Rempe, Julien Valentin, Bouaziz Sofien, Leonidas J. Guibas
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments show that our approach is able to produce dense 3D reconstructions of objects that improve in quality as more views are added. |
| Researcher Affiliation | Collaboration | Srinath Sridhar1 Davis Rempe1 Julien Valentin2 Sofien Bouaziz2 Leonidas J. Guibas1,3 1Stanford University 2Google Inc. 3Facebook AI Research |
| Pseudocode | No | The paper describes its architecture and method through text and figures (e.g., Figure 4 illustrating the network), but it does not include formal pseudocode or algorithm blocks. |
| Open Source Code | No | The paper mentions a project website 'geometry.stanford.edu/projects/xnocs' but does not explicitly state that source code for the described methodology is released or provide a direct link to a code repository. |
| Open Datasets | Yes | We generated our own dataset, called Shape Net COCO, consisting of object instances from 3 categories commonly used in related work: cars, chairs, and airplanes. We use thousands of instances from the Shape Net [5] repository and additionally augment backgrounds with randomly chosen COCO images [24]. This dataset is harder than previously proposed datasets because of random backgrounds, and widely varying camera distances. To facilitate comparisons with previous work [6, 17], we also generated a simpler dataset, called Shape Net Plain, with white backgrounds and 5 views per object following the camera placement procedure of [17]. |
| Dataset Splits | No | The paper states, 'We follow the train/test protocol of [36].' However, it does not explicitly provide the percentages or specific counts for training, validation, and test splits within the paper itself. |
| Hardware Specification | No | The paper does not explicitly describe the hardware used for running the experiments, such as specific GPU or CPU models. |
| Software Dependencies | No | The paper mentions using an 'encoder-decoder architecture based on Seg Net [3]' and the 'Adam optimizer' but does not provide specific version numbers for any software dependencies, libraries, or frameworks. |
| Experiment Setup | Yes | Unless otherwise specified, we use a batch size of 1 (multiview) or 2 (single-view), a learning rate of 0.0001, and the Adam optimizer. |