Traditional camera 3D object detectors are typically trained to recognize a
predefined set of known object classes. In real-world scenarios, these
detectors may encounter unknown objects outside the training categories and
fail to identify them correctly. To address this gap, we present OS-Det3D
(Open-set Camera 3D Object Detection), a two-stage training framework enhancing
the ability of camera 3D detectors to identify both known and unknown objects.
The framework involves our proposed 3D Object Discovery Network (ODN3D), which
is specifically trained using geometric cues such as the location and scale of
3D boxes to discover general 3D objects. ODN3D is trained in a class-agnostic
manner, and the provided 3D object region proposals inherently come with data
noise. To boost accuracy in identifying unknown objects, we introduce a Joint
Objectness Selection (JOS) module. JOS selects the pseudo ground truth for
unknown objects from the 3D object region proposals of ODN3D by combining the
ODN3D objectness and camera feature attention objectness. Experiments on the
nuScenes and KITTI datasets demonstrate the effectiveness of our framework in
enabling camera 3D detectors to successfully identify unknown objects while
also improving their performance on known objects.

通过提出的 OS-Det3D 框架，利用特定训练的 3D 物体发现网络（ODN3D）和联合目标性选择（JOS）模块，提高相机 3D 检测器在鉴别已知和未知对象方面的能力，并在 nuScenes 和 KITTI 数据集上进行实验证明了该框架的有效性。