EasyCalib: Online Spatio-Temporal Radar-Camera Calibration from Object Observations

Radar and cameras are widely paired in vehicles, roadside infrastructure, and robotics, yet online calibration from routine object observations remains difficult. Camera detection centers and radar returns may describe different physical points; measurement errors differ, and object motion couples pose with timing. We present EasyCalib, which estimates six spatial perturbations and an added radar timestamp offset from observations accumulated over time. Its XYT representation encodes planar position and reported time while retaining visual height as an attribute. A coarse pose estimate is refined through rigid-transform composition, then soft candidate association estimates the offset after spatial compensation. In one forward pass, EasyCalib outputs 3D translation, 3D rotation, and the offset without supplied point correspondences or a pre-estimated delay. We evaluate pose perturbations, offset ranges, training continuations, and perception frontends on an internal nuScenes split with disjoint recording logs. At the widest R4–D4 setting ($\pm 10$ m per translation axis, $\pm 30^{\circ }$ per rotation axis, and $\pm 1$ s added offset), mean per-axis translation and rotation MAEs are 51.89 cm and $0.950^{\circ }$, and added-offset MAE is 238.45 ms. Spatial refinement reduces translation and rotation errors by 10.5% and 23.1%; offset error is 52.3% below a zero-offset control.