Evaluating 2D and 3D-Aware Vision Foundation Models for Vehicle Attribute Recognition
Vehicle attribute recognition is an important task in intelligent transportation systems, particularly when Automatic License Plate Recognition (ALPR) is unavailable or unreliable. Although vision foundation models have shown strong transferability across domains, their effectiveness for fine-grained vehicle classification remains underexplored. Moreover, given the inherently three-dimensional structure of vehicles, it is unclear whether emerging 3D-aware foundation models offer advantages over standard 2D architectures. This paper presents an empirical benchmark of 14 state-of-the-art 2D and 3D-aware vision foundation models. Using the challenging real-world UFPR-VeSV dataset, we evaluat...
| Authors | Alexandre V. Delazeri, Gabriel E. Lima, Eduil Nascimento et al. |
|---|---|
| Primary category | cs.CV |
| Published | 30 August 2026 |
| Last updated at source | 30 August 2026 |
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