  Monocular 3D object detection (Mono 3Det) aims to identify 3D objects from a
single RGB image. However, existing methods often assume training and test data
follow the same distribution, which may not hold in real-world test scenarios.
To address the out-of-distribution (OOD) problems, we explore a new adaptation
paradigm for Mono 3Det, termed Fully Test-time Adaptation. It aims to adapt a
well-trained model to unlabeled test data by handling potential data
distribution shifts at test time without access to training data and test
labels. However, applying this paradigm in Mono 3Det poses significant
challenges due to OOD test data causing a remarkable decline in object
detection scores. This decline conflicts with the pre-defined score thresholds
of existing detection methods, leading to severe object omissions (i.e., rare
positive detections and many false negatives). Consequently, the limited
positive detection and plenty of noisy predictions cause test-time adaptation
to fail in Mono 3Det. To handle this problem, we propose a novel Monocular
Test-Time Adaptation (MonoTTA) method, based on two new strategies. 1)
Reliability-driven adaptation: we empirically find that high-score objects are
still reliable and the optimization of high-score objects can enhance
confidence across all detections. Thus, we devise a self-adaptive strategy to
identify reliable objects for model adaptation, which discovers potential
objects and alleviates omissions. 2) Noise-guard adaptation: since high-score
objects may be scarce, we develop a negative regularization term to exploit the
numerous low-score objects via negative learning, preventing overfitting to
noise and trivial solutions. Experimental results show that MonoTTA brings
significant performance gains for Mono 3Det models in OOD test scenarios,
approximately 190% gains by average on KITTI and 198% gains on nuScenes.
