  We study the 3D object understanding task for manipulating everyday objects
with different material properties (diffuse, specular, transparent and mixed).
Existing monocular and RGB-D methods suffer from scale ambiguity due to missing
or imprecise depth measurements. We present CODERS, a one-stage approach for
Category-level Object Detection, pose Estimation and Reconstruction from Stereo
images. The base of our pipeline is an implicit stereo matching module that
combines stereo image features with 3D position information. Concatenating this
presented module and the following transform-decoder architecture leads to
end-to-end learning of multiple tasks required by robot manipulation. Our
approach significantly outperforms all competing methods in the public TOD
dataset. Furthermore, trained on simulated data, CODERS generalize well to
unseen category-level object instances in real-world robot manipulation
experiments. Our dataset, code, and demos will be available on our project
page.
