  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.
