CLIP-Mono3D: End-to-End Open-Vocabulary Monocular 3D Object Detection via Semantic-Geometric Similarity

Open-vocabulary 3D object detection (OV-3DOD) is crucial for real-world perception, yet existing monocular methods are often limited by predefined categories or heavy reliance on external 2D detectors. In this paper, we propose CLIP-Mono3D, an end-to-end one-stage transformer framework that directly integrates vision-language semantics into monocular 3D detection. By leveraging CLIP-derived semantic priors and grounding object queries in semantically salient regions, our model achieves robust zero-shot generalization to novel categories without requiring auxiliary 2D detectors. Furthermore, we introduce OV-KITTI, a large-scale benchmark extending KITTI with 40 new categories and over 7,000 annotated 3D bounding boxes. Extensive experiments on OV-KITTI, KITTI, and Argoverse demonstrate that CLIP-Mono3D achieves competitive performance in open-vocabulary scenarios.