Roadside perception datasets are typically constructed via cooperative labeling between synchronized vehicle and roadside frame pairs. However, real deployment often requires annotation of roadside-only data due to hardware and privacy constraints. Even human experts struggle to produce accurate labels without vehicle-side data (image, LIDAR), which not only increases annotation difficulty and cost, but also reveals a fundamental learnability problem: many roadside-only scenes contain distant, blurred, or occluded objects whose 3D properties are ambiguous from a single view and can only be reliably annotated by cross-checking paired vehicle--roadside frames. We refer to such cases as inherently ambiguous samples. To reduce wasted annotation effort on inherently ambiguous samples while still obtaining high-performing models, we turn to active learning. This work focuses on active learning for roadside monocular 3D object detection and proposes a learnability-driven framework that selects scenes which are both informative and reliably labelable, suppressing inherently ambiguous samples while ensuring coverage. Experiments demonstrate that our method, LH3D, achieves 86.06%, 67.32%, and 78.67% of full-performance for vehicles, pedestrians, and cyclists respectively, using only 25% of the annotation budget on DAIR-V2X-I, significantly outperforming uncertainty-based baselines. This confirms that learnability, not uncertainty, matters for roadside 3D perception.

翻译：路边感知数据集通常通过同步车辆与路边帧对之间的协同标注构建。然而，由于硬件和隐私限制，实际部署常需对仅含路边视角的数据进行标注。即使人类专家在缺乏车辆端数据（图像、激光雷达）的情况下也难以生成准确标签，这不仅增加了标注难度与成本，更揭示了一个根本性的可学习性问题：许多仅含路边视角的场景中存在遥远、模糊或被遮挡的物体，其三维属性在单视角下具有歧义，仅能通过交叉比对配对的车辆-路边帧进行可靠标注。我们将此类样本称为本质模糊样本。为减少在本质模糊样本上浪费标注资源，同时仍能获得高性能模型，我们转向主动学习。本研究聚焦于路边单目三维物体检测的主动学习，提出一种可学习性驱动的框架，该框架选择既信息丰富又可可靠标注的场景，在抑制本质模糊样本的同时确保覆盖度。实验表明，我们的方法LH3D在DAIR-V2X-I数据集上仅使用25%的标注预算，即可分别达到车辆、行人和骑行者全性能的86.06%、67.32%和78.67%，显著优于基于不确定性的基线方法。这证实了对于路边三维感知而言，关键因素是可学习性而非不确定性。