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..
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 | Venue PDF | 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 EMAIL |
| 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 ๏ฌrst 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 ฮป is set to 5e-4, and the ฮผ is set to 5e-3 to make all loss terms in the close magnitude. |