Modern 3D semantic instance segmentation approaches predominantly rely on specialized
voting mechanisms followed by carefully designed geometric clustering techniques. Building …

We introduce the task of open-vocabulary 3D instance segmentation. Current approaches
for 3D instance segmentation can typically only recognize object categories from a pre …

We study the problem of 3D semantic segmentation from raw point clouds. Unlike existing
methods which primarily rely on a large amount of human annotations for training neural …

Accurate and massive medical image annotation data is crucial for diagnosis, surgical
planning, and deep learning in the development of medical images. However, creating large …

Generalizable 3D part segmentation is important but challenging in vision and robotics.
Training deep models via conventional supervised methods requires large-scale 3D …

Abstract 3D instance segmentation is fundamental to geometric understanding of the world
around us. Existing methods for instance segmentation of 3D scenes rely on supervision …

Abstract We introduce Equivariant Neural Field Expectation Maximization (EFEM), a simple,
effective, and robust geometric algorithm that can segment objects in 3D scenes without …

In the past decade, deep neural networks have achieved significant progress in point cloud
learning. However, collecting large-scale precisely-annotated point clouds is extremely …

Pseudo-labels are widely employed in weakly supervised 3D segmentation tasks where
only sparse ground-truth labels are available for learning. Existing methods often rely on …

Contrastive learning has recently demonstrated great potential for unsupervised pre-training
in 3D scene understanding tasks. However, most existing work randomly selects point …