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

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In this paper we study the problem of object detection for
RGB-D images using semantically rich image and depth features.We pro-
pose a new geocentric embedding for depth images that encodes height
above ground and angle with gravity for each pixel in addition to the hor-
izontal disparity. We demonstrate that this geocentric embedding works
better than using raw depth images for learning feature representations
with convolutional neural networks. Our nal object detection system
achieves an average precision of 37.3%, which is a 56% relative improve-
ment over existing methods. We then focus on the task of instance seg-
mentation where we label pixels belonging to object instances found by
our detector. For this task, we propose a decision forest approach that
classies pixels in the detection window as foreground or background us-
ing a family of unary and binary tests that query shape and geocentric
pose features. Finally, we use the output from our object detectors in an
existing superpixel classication framework for semantic scene segmenta-
tion and achieve a 24% relative improvement over current state-of-the-art
for the object categories that we study.We believe advances such as those
represented in this paper will facilitate the use of perception in elds like
robotics.
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