  On the journey to enable robots to interact with the real world where humans,
animals, and unpredictable elements are acting as independent agents; it is
crucial for robots to have the capability to detect dynamic objects. In this
paper, we argue that the detection of dynamic objects can be solved by
computing the spatiotemporal normals of a point cloud. In our experiments, we
demonstrate that this simple method can be used robustly for LiDAR and depth
cameras with performances similar to the state of the art while offering a
significantly simpler method.
