Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: KDFNet: Learning Keypoint Distance Field for 6D Object Pose Estimation   We present KDFNet, a novel method for 6D object pose estimation from RGB
images. To handle occlusion, many recent works have proposed to localize 2D
keypoints through pixel-wise voting and solve a Perspective-n-Point (PnP)
problem for pose estimation, which achieves leading performance. However, such
voting process is direction-based and cannot handle long and thin objects where
the direction intersections cannot be robustly found. To address this problem,
we propose a novel continuous representation called Keypoint Distance Field
(KDF) for projected 2D keypoint locations. Formulated as a 2D array, each
element of the KDF stores the 2D Euclidean distance between the corresponding
image pixel and a specified projected 2D keypoint. We use a fully convolutional
neural network to regress the KDF for each keypoint. Using this KDF encoding of
projected object keypoint locations, we propose to use a distance-based voting
scheme to localize the keypoints by calculating circle intersections in a
RANSAC fashion. We validate the design choices of our framework by extensive
ablation experiments. Our proposed method achieves state-of-the-art performance
on Occlusion LINEMOD dataset with an average ADD(-S) accuracy of 50.3% and TOD
dataset mug subset with an average ADD accuracy of 75.72%. Extensive
experiments and visualizations demonstrate that the proposed method is able to
robustly estimate the 6D pose in challenging scenarios including occlusion.

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The categories of this paper are: hep-lat
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The categories of this paper are: solv-int
33
The categories of this paper are: plasm-ph
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The categories of this paper are: math-ph
3
The categories of this paper are: physics
21
The categories of this paper are: adap-org
34
The categories of this paper are: chem-ph
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The categories of this paper are: hep-th
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The categories of this paper are: cond-mat
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The categories of this paper are: supr-con
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The categories of this paper are: nucl-th
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The categories of this paper are: eess
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The categories of this paper are: chao-dyn
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The categories of this paper are: quant-ph
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The categories of this paper are: econ
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The categories of this paper are: nucl-ex