We introduce the task of open-vocabulary 3D instance segmentation. Traditional
approaches for 3D instance segmentation largely rely on existing 3D annotated datasets …

We introduce Open3DIS a novel solution designed to tackle the problem of Open-
Vocabulary Instance Segmentation within 3D scenes. Objects within 3D environments …

Developing robot perception systems for recognizing objects in the real world requires
computer vision algorithms to be carefully scrutinized with respect to the expected operating …

To effectively employ robots in real-life scenarios, comprehending information about the
surrounding objects is crucial for achieving dexterous manipulations. Since vision-based …

Despite a growing number of datasets being collected for training 3D object detection
models, significant human effort is still required to annotate 3D boxes on LiDAR scans. To …

Since real-world training datasets cannot properly sample the long tail of the underlying data
distribution, corner cases and rare out-of-domain samples can severely hinder the …

Recent advances of 3D acquisition devices have enabled large-scale acquisition of 3D
scene data. Such data, if completely and well annotated, can serve as useful ingredients for …

A considerable amount of research is concerned with the challenging task of estimating
three-dimensional (3D) pose and size for multi-object indoor scene configurations. Many …

Open-vocabulary 3D instance segmentation transcends traditional closed-vocabulary
methods by enabling the identification of both previously seen and unseen objects in real …

Current 3D segmentation methods heavily rely on large-scale point-cloud datasets, which
are notoriously laborious to annotate. Few attempts have been made to circumvent the need …