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Towards Learning Monocular 3D Object Localization From 2D Labels using the Physical Laws of Motion
We present a novel method for precise 3D object localization in single images
from a single calibrated camera using only 2D labels. No expensive 3D labels
are needed. Thus, instead of using 3D labels, our model is trained with
easy-to-annotate 2D labels along with the physical knowledge of the object's
motion. Given this information, the model can infer the latent third dimension,
even though it has never seen this information during training. Our method is
evaluated on both synthetic and real-world datasets, and we are able to achieve
a mean distance error of just 6 cm in our experiments on real data. The results
indicate the method's potential as a step towards learning 3D object location
estimation, where collecting 3D data for training is not feasible
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