CGPart: A Part Segmentation Dataset Based on 3D Computer Graphics Models

• URL: http://arxiv.org/abs/2103.14098v1
• Date: Thu, 25 Mar 2021 19:34:21 GMT
• Title: CGPart: A Part Segmentation Dataset Based on 3D Computer Graphics Models
• Authors: Qing Liu, Adam Kortylewski, Zhishuai Zhang, Zizhang Li, Mengqi Guo, Qihao Liu, Xiaoding Yuan, Jiteng Mu, Weichao Qiu, Alan Yuille
• Abstract summary: CGPart provides detailed annotations on 3D CAD models, synthetic images, and real test images. CGPart includes $21$ 3D CAD models covering $5$ vehicle categories, each with detailed per-mesh part labeling. We make $168,000$ synthetic images from these CAD models, each with automatically generated part segmentation ground-truth.
• Score: 19.691187561807475
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Part segmentations provide a rich and detailed part-level description of objects, but their annotation requires an enormous amount of work. In this paper, we introduce CGPart, a comprehensive part segmentation dataset that provides detailed annotations on 3D CAD models, synthetic images, and real test images. CGPart includes $21$ 3D CAD models covering $5$ vehicle categories, each with detailed per-mesh part labeling. The average number of parts per category is $24$, which is larger than any existing datasets for part segmentation on vehicle objects. By varying the rendering parameters, we make $168,000$ synthetic images from these CAD models, each with automatically generated part segmentation ground-truth. We also annotate part segmentations on $200$ real images for evaluation purposes. To illustrate the value of CGPart, we apply it to image part segmentation through unsupervised domain adaptation (UDA). We evaluate several baseline methods by adapting top-performing UDA algorithms from related tasks to part segmentation. Moreover, we introduce a new method called Geometric-Matching Guided domain adaptation (GMG), which leverages the spatial object structure to guide the knowledge transfer from the synthetic to the real images. Experimental results demonstrate the advantage of our new algorithm and reveal insights for future improvement. We will release our data and code.

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