Shape-Pose Ambiguity in Learning 3D Reconstruction from Images
Authors: Yunjie Wu, Zhengxing Sun, Youcheng Song, Yunhan Sun, YiJie Zhong, Jinlong Shi2978-2985
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Through experiments on synthetic and real image datasets, we demonstrate that our method can perform comparably to existing methods while not requiring any extra pose-aware annotations, making it more applicable and adaptable. We conduct comprehensive experiments on public datasets, including both synthetic and real images. |
| Researcher Affiliation | Academia | Yunjie Wu,1 Zhengxing Sun,1 Youcheng Song,1 Yunhan Sun,1 Yi Jie Zhong,1 Jinlong Shi2 1 State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, P R China 2 Department of Computer, Jiangsu University of Science and Technology, Zhengjiang, P R China szx@nju.edu.cn |
| Pseudocode | No | The paper describes its method in detail using text and diagrams, but it does not include any pseudocode or algorithm blocks. |
| Open Source Code | Yes | We provide more details in the appendix and the code in: https://github.com/Jiejiang Wu/Shape-Pose-Ambiguity. |
| Open Datasets | Yes | Datasets We test our method on two public datasets. The first is the Shape Net s rendered images (Kato, Ushiku, and Harada 2018). ... The second is CUB-200-2011 (Wah et al. 2011). It contains images of 200 species of birds. |
| Dataset Splits | Yes | We use the provided train-test split. For both two datasets, we use no extra annotations except the silhouettes for training. ... We use an 8:2 train-test split. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. It only mentions general settings like 'The batch size is 32'. |
| Software Dependencies | No | The paper mentions using a 'differentiable renderer (Liu et al. 2019)' and 'resnet-18 (He et al. 2016)', but it does not provide specific version numbers for these or any other software dependencies. |
| Experiment Setup | Yes | Each shape has 642 vertices, the same as VPL (Kato and Harada 2019). ... The batch size is 32, and the learning rate is 1e-4. We provide more details in the appendix and the code in: https://github.com/Jiejiang Wu/Shape-Pose-Ambiguity. ... In practice, the λ is set to 5e-4, and the μ is set to 5e-3 to make all loss terms in the close magnitude. |