  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.
