Learning When to Correct: Selective Calibration for Monocular Kinematics

Fri, 01 Jan 2027 00:00:00 +0900

This work, first-authored by undergraduate researcher Jun Ho Lee, has been accepted to the WACV 2027 Main Conference (Application Track). The authors are Jun Ho Lee, Dongjun Kang, and Kyungsu Lee.

The proposed selective calibration method learns whether to preserve an existing monocular kinematic estimate or adopt an ExtraTrees correction. It achieves a macro MAE of 5.94° and reduces preservation failure at a 5° tolerance from 34.7% to 16.5%.

Selective Calibration Framework

Figure 2 from the paper. Subject-disjoint training and forest consensus features support benefit prediction, allowing reference-free correction decisions at inference.

Dongjun Kang | MacsLAB

Learning When to Correct: Selective Calibration for Monocular Kinematics

Selective Calibration Framework