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. The source code is available at https://github.com/intrinsic-ai/collision-avoidance-metric.

翻译：三维相机已成为机器人与自动驾驶领域应用的关键信息来源。这类相机使机器人能够获取并利用点云数据，从而实现在环境中自主导航并规避与其他物体的碰撞。然而，当前标准的相机评估指标往往未能充分考虑具体应用场景。这些指标通常侧重于倒角距离或推土机距离等度量方式，而这些度量可能无法直接反映实际场景中的性能表现。为克服这一局限，我们提出了一种新颖的点云评估指标，专门用于评估三维相机在碰撞避免这一关键任务中的适用性。该指标融合了应用场景的具体考量，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。相关源代码已发布于 https://github.com/intrinsic-ai/collision-avoidance-metric。