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Abstract

We propose a novel approach to jointly perform 3D object retrieval and pose
estimation from monocular images.In order to make the method robust to real
world scene variations in the images, e.g. texture, lighting and background,we
learn an embedding space from 3D data that only includes the relevant
information, namely the shape and pose.Our method can then be trained for
robustness under real world scene variations without having to render a large
training set simulating these variations. Our learned embedding explicitly
disentangles a shape vector and a pose vector, which alleviates both pose bias
for 3D shape retrieval and categorical bias for pose estimation. Having the
learned disentangled embedding, we train a CNN to map the images to the
embedding space, and then retrieve the closest 3D shape from the database and
estimate the 6D pose of the object using the embedding vectors. Our method
achieves 10.8 median error for pose estimation and 0.514 top-1-accuracy for
category agnostic 3D object retrieval on the Pascal3D+ dataset. It therefore
outperforms the previous state-of-the-art methods on both tasks.