Regressing 3D rotations of objects from 2D images is a crucial yet challenging task, with broad applications in autonomous driving, virtual reality, and robotic control. This paper proposes a hardness-aware curriculum learning framework for semi-supervised rotation regression that dynamically selects pseudo-labeled samples based on their difficulty, progressing from easy to complex examples. The approach addresses limitations of rigid entropy-based pseudo-label filtering by introducing both multi-stage and adaptive curriculum strategies to effectively distinguish between reliable and unreliable unlabeled samples.