Accurate ground truth annotations are critical to supervised learning and evaluating the performance of autonomous vehicle systems. These vehicles are typically equipped with active sensors, such as LiDAR, which scan the environment in predefined patterns. 3D box annotation based on data from such sensors is challenging in dynamic scenarios, where objects are observed at different timestamps, hence different positions. Without proper handling of this phenomenon, systematic errors are prone to being introduced in the box annotations. Our work is the first to discover such annotation errors in widely used, publicly available datasets. Through our novel offline estimation method, we correct the annotations so that they follow physically feasible trajectories and achieve spatial and temporal consistency with the sensor data. For the first time, we define metrics for this problem; and we evaluate our method on the Argoverse 2, MAN TruckScenes, and our proprietary datasets. Our approach increases the quality of box annotations by more than 17% in these datasets. Furthermore, we quantify the annotation errors in them and find that the original annotations are misplaced by up to 2.5 m, with highly dynamic objects being the most affected. Finally, we test the impact of the errors in benchmarking and find that the impact is larger than the improvements that state-of-the-art methods typically achieve with respect to the previous state-of-the-art methods; showing that accurate annotations are essential for correct interpretation of performance. Our code is available at https://github.com/alexandre-justo-miro/annotation-correction-3D-boxes.

翻译：精确的地面真值标注对于监督学习和评估自动驾驶系统性能至关重要。这些车辆通常配备主动传感器（如激光雷达），这些传感器以预定义模式扫描环境。基于此类传感器数据的三维边界框标注在动态场景中具有挑战性，因为物体在不同时间戳被观测到，从而处于不同位置。若未妥善处理此现象，边界框标注极易引入系统性误差。我们的工作首次在广泛使用的公开数据集中发现了此类标注误差。通过新颖的离线估计方法，我们校正了标注结果，使其遵循物理上可行的轨迹，并与传感器数据保持时空一致性。我们首次为此问题定义了量化指标，并在Argoverse 2、MAN TruckScenes及我们的专有数据集上评估了所提方法。我们的方法将这些数据集的边界框标注质量提升了超过17%。此外，我们量化了其中的标注误差，发现原始标注的位移最大可达2.5米，其中高动态物体受影响最为显著。最后，我们测试了误差对基准测试的影响，发现其影响程度超过了当前最先进方法相较于先前最优方法通常实现的改进幅度，这表明精确的标注对于正确解读性能表现至关重要。我们的代码发布于https://github.com/alexandre-justo-miro/annotation-correction-3D-boxes。