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We present a novel method for precise 3D object localization in single imagesfrom a single calibrated camera using only 2D labels. No expensive 3D labelsare needed. Thus, instead of using 3D labels, our model is trained witheasy-to-annotate 2D labels along with the physical knowledge of the object'smotion. Given this information, the model can infer the latent third dimension,even though it has never seen this information during training. Our method isevaluated on both synthetic and real-world datasets, and we are able to achievea mean distance error of just 6 cm in our experiments on real data. The resultsindicate the method's potential as a step towards learning 3D object locationestimation, where collecting 3D data for training is not feasible.

Towards Learning Monocular 3D Object Localization From 2D Labels using the Physical Laws of Motion