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.

自动驾驶中现有的感知方法无法识别训练数据中未覆盖的未知实体。开放词汇方法能够检测任何目标，但受限于用户指定的查询代表目标类别。我们提出了 AutoVoc3D，一种用于自动目标类别识别和开放式分割的框架。在 nuScenes 中的评估显示了 AutoVoc3D 生成精确的语义类别和准确的逐点分割的能力。此外，我们引入了一种新的度量方法 Text-Point 语义相似性，用于评估文本和点云之间的语义相似性，而不排除新的类别。