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In this paper we study the problem of object detection for RGB-D images usingsemantically rich image and depth features. We propose a new geocentricembedding for depth images that encodes height above ground and angle withgravity for each pixel in addition to the horizontal disparity. We demonstratethat this geocentric embedding works better than using raw depth images forlearning feature representations with convolutional neural networks. Our finalobject detection system achieves an average precision of 37.3%, which is a 56%relative improvement over existing methods. We then focus on the task ofinstance segmentation where we label pixels belonging to object instances foundby our detector. For this task, we propose a decision forest approach thatclassifies pixels in the detection window as foreground or background using afamily of unary and binary tests that query shape and geocentric pose features.Finally, we use the output from our object detectors in an existing superpixelclassification framework for semantic scene segmentation and achieve a 24%relative improvement over current state-of-the-art for the object categoriesthat we study. We believe advances such as those represented in this paper willfacilitate the use of perception in fields like robotics.

Learning Rich Features from RGB-D Images for Object Detection and Segmentation