  Open-world 3D part segmentation is pivotal in diverse applications such as
robotics and AR/VR. Traditional supervised methods often grapple with limited
3D data availability and struggle to generalize to unseen object categories.
PartSLIP, a recent advancement, has made significant strides in zero- and
few-shot 3D part segmentation. This is achieved by harnessing the capabilities
of the 2D open-vocabulary detection module, GLIP, and introducing a heuristic
method for converting and lifting multi-view 2D bounding box predictions into
3D segmentation masks. In this paper, we introduce PartSLIP++, an enhanced
version designed to overcome the limitations of its predecessor. Our approach
incorporates two major improvements. First, we utilize a pre-trained 2D
segmentation model, SAM, to produce pixel-wise 2D segmentations, yielding more
precise and accurate annotations than the 2D bounding boxes used in PartSLIP.
Second, PartSLIP++ replaces the heuristic 3D conversion process with an
innovative modified Expectation-Maximization algorithm. This algorithm
conceptualizes 3D instance segmentation as unobserved latent variables, and
then iteratively refines them through an alternating process of 2D-3D matching
and optimization with gradient descent. Through extensive evaluations, we show
that PartSLIP++ demonstrates better performance over PartSLIP in both low-shot
3D semantic and instance-based object part segmentation tasks. Code released at
https://github.com/zyc00/PartSLIP2.
