  3D object detection is a key module for safety-critical robotics applications
such as autonomous driving. For these applications, we care most about how the
detections affect the ego-agent's behavior and safety (the egocentric
perspective). Intuitively, we seek more accurate descriptions of object
geometry when it's more likely to interfere with the ego-agent's motion
trajectory. However, current detection metrics, based on box
Intersection-over-Union (IoU), are object-centric and aren't designed to
capture the spatio-temporal relationship between objects and the ego-agent. To
address this issue, we propose a new egocentric measure to evaluate 3D object
detection, namely Support Distance Error (SDE). Our analysis based on SDE
reveals that the egocentric detection quality is bounded by the coarse geometry
of the bounding boxes. Given the insight that SDE would benefit from more
accurate geometry descriptions, we propose to represent objects as amodal
contours, specifically amodal star-shaped polygons, and devise a simple model,
StarPoly, to predict such contours. Our experiments on the large-scale Waymo
Open Dataset show that SDE better reflects the impact of detection quality on
the ego-agent's safety compared to IoU; and the estimated contours from
StarPoly consistently improve the egocentric detection quality over recent 3D
object detectors.
