PIANO: A Parametric Hand Bone Model from Magnetic Resonance Imaging
Authors: Yuwei Li, Minye Wu, Yuyao Zhang, Lan Xu, Jingyi Yu
IJCAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this paper, we present PIANO, the first parametric bone model of human hands from MRI data. Our PIANO model is biologically correct, simple to animate, and differentiable, achieving more anatomically precise modeling of the inner hand kinematic structure in a data-driven manner than the traditional hand models based on the outer surface only. Furthermore, our PIANO model can be applied in neural network layers to enable training with a fine-grained semantic loss, which opens up the new task of data-driven fine-grained hand bone anatomic and semantic understanding from MRI or even RGB images. We make our model publicly available. |
| Researcher Affiliation | Academia | Yuwei Li1,2,3 , Minye Wu1,2,3 , Yuyao Zhang1 , Lan Xu1 and Jingyi Yu1 1Shanghai Engineering Research Center of Intelligent Vision and Imaging, School of Information Science and Technology, Shanghai Tech University 2Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences 3University of Chinese Academy of Sciences |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | We make our model publicly available. 1https://reyuwei.github.io/proj/piano.html |
| Open Datasets | Yes | To this end, we first collect a new large-scale dataset of high-quality MRI hand scans using a clinical 3T scanner and an efficient simplified molding procedure from [Wang et al., 2019], which consists of 200 scans of 35 subjects in up to 50 poses with rich and accurate 3D joint and bone segmentation annotations from experienced radiologists. ... We make available our PIANO model and the new dataset of 200 MRI hand scans of 35 subjects with various poses and rich annotations1. |
| Dataset Splits | No | For the MRI-based tasks, we separate our MRI dataset into 140 training volumes and 60 testing volumes, where testing volumes contain unseen hand shapes and poses. A dedicated validation set split is not explicitly mentioned. |
| Hardware Specification | No | The paper specifies the MRI scanner used for data acquisition: 'The scanning is performed with a 3.0 Tesla MR scanner of United Imaging (u MR780, Shanghai)'. However, it does not provide specific details about the hardware (e.g., GPU models, CPU types) used for training or running the experiments. |
| Software Dependencies | No | The paper mentions 'Py Torch auto differentiation' and 'Limited-memory BFGS optimizer' but does not provide specific version numbers for these or other software dependencies. |
| Experiment Setup | Yes | To build PIANO, we set λreg, λe and λj to 5.0, 1.0 and 100.0 respectively and iterate through the whole process of registration and parameter training with Limited-memory BFGS optimizer and Py Torch auto differentiation. |