We introduce PartSTAD, a method designed for the task adaptation of 2D-to-3D segmentation lifting. Recent studies have highlighted the advantages of utilizing 2D segmentation models to achieve high-quality 3D segmentation through few-shot adaptation. However, previous approaches have focused on adapting 2D segmentation models for domain shift to rendered images and synthetic text descriptions, rather than optimizing the model specifically for 3D segmentation. Our proposed task adaptation method finetunes a 2D bounding box prediction model with an objective function for 3D segmentation. We introduce weights for 2D bounding boxes for adaptive merging and learn the weights using a small additional neural network. Additionally, we incorporate SAM, a foreground segmentation model on a bounding box, to improve the boundaries of 2D segments and consequently those of 3D segmentation. Our experiments on the PartNet-Mobility dataset show significant improvements with our task adaptation approach, achieving a 7.0%p increase in mIoU and a 5.2%p improvement in mAP_50 for semantic and instance segmentation compared to the SotA few-shot 3D segmentation model.

我们介绍了PartSTAD，一种用于2D到3D分割转换的任务适应方法。我们的实验在PartNet-Mobility数据集上展示出了显著的改进，与当前few-shot 3D分割模型相比，语义和实例分割的mIoU增加了7.0%p，mAP_50提高了5.2%p。

PartSTAD: 二维到三维零部件分割任务适应