Existing techniques for monocular 3D detection have a serious restriction. They tend to perform well only on a limited set of benchmarks, faring well either on ego-centric car views or on traffic camera views, but rarely on both. To encourage progress, this work advocates for an extended evaluation of 3D detection frameworks across different camera perspectives. We make two key contributions. First, we introduce the CARLA Drone dataset, CDrone. Simulating drone views, it substantially expands the diversity of camera perspectives in existing benchmarks. Despite its synthetic nature, CDrone represents a real-world challenge. To show this, we confirm that previous techniques struggle to perform well both on CDrone and a real-world 3D drone dataset. Second, we develop an effective data augmentation pipeline called GroundMix. Its distinguishing element is the use of the ground for creating 3D-consistent augmentation of a training image. GroundMix significantly boosts the detection accuracy of a lightweight one-stage detector. In our expanded evaluation, we achieve the average precision on par with or substantially higher than the previous state of the art across all tested datasets.

本研究解决了单目3D检测技术在不同相机视角下表现不佳的问题，推出了CARLA无人机数据集（CDrone），扩展了现有基准的相机视角多样性。通过引入GroundMix数据增强方式，显著提高了轻量级单阶段检测器的准确性，实验结果表明在所有测试数据集上的平均精度达到或超过之前的最佳水平。

CARLA无人机：从不同视角进行单目3D物体检测