3D cameras have emerged as a critical source of information for applications in robotics and autonomous driving. These cameras provide robots with the ability to capture and utilize point clouds, enabling them to navigate their surroundings and avoid collisions with other objects. However, current standard camera evaluation metrics often fail to consider the specific application context. These metrics typically focus on measures like Chamfer distance (CD) or Earth Mover's Distance (EMD), which may not directly translate to performance in real-world scenarios. To address this limitation, we propose a novel metric for point cloud evaluation, specifically designed to assess the suitability of 3D cameras for the critical task of collision avoidance. This metric incorporates application-specific considerations and provides a more accurate measure of a camera's effectiveness in ensuring safe robot navigation.

翻译：3D相机已成为机器人和自动驾驶应用中关键的信息源。此类相机使机器人能够捕获并利用点云数据，从而在环境中导航并避免与其他物体发生碰撞。然而，当前标准的相机评估指标往往忽略了具体的应用场景。这些指标通常聚焦于如Chamfer距离或Earth Mover's距离等度量，但此类度量可能无法直接转化为实际场景中的性能表现。为解决这一局限，我们提出了一种面向点云评估的新型度量，该度量专为评估3D相机在碰撞规避这一关键任务中的适用性而设计。该度量融入了应用场景特异性考量，能够更准确地衡量相机在保障机器人安全导航方面的效能。