  Existing perception methods for autonomous driving fall short of recognizing
unknown entities not covered in the training data. Open-vocabulary methods
offer promising capabilities in detecting any object but are limited by
user-specified queries representing target classes. We propose AutoVoc3D, a
framework for automatic object class recognition and open-ended segmentation.
Evaluation on nuScenes showcases AutoVoc3D's ability to generate precise
semantic classes and accurate point-wise segmentation. Moreover, we introduce
Text-Point Semantic Similarity, a new metric to assess the semantic similarity
between text and point cloud without eliminating novel classes.
