Category-level Object Detection, Pose Estimation and Reconstruction from Stereo Images
This work addresses the challenge of scale ambiguity in 3D object understanding for robot manipulation, particularly with everyday objects of varying material properties, representing a novel method for a known bottleneck.
The paper tackles the problem of 3D object understanding for robot manipulation by introducing CODERS, a one-stage approach for category-level object detection, pose estimation, and reconstruction from stereo images, which significantly outperforms competing methods on the TOD dataset and generalizes well to real-world robot manipulation experiments.
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.