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CoFiI2P: Coarse-to-Fine Correspondences for Image-to-Point Cloud Registration
Image-to-point cloud (I2P) registration is a fundamental task in the fields
of robot navigation and mobile mapping. Existing I2P registration works
estimate correspondences at the point-to-pixel level, neglecting the global
alignment. However, I2P matching without high-level guidance from global
constraints may converge to the local optimum easily. To solve the problem,
this paper proposes CoFiI2P, a novel I2P registration network that extracts
correspondences in a coarse-to-fine manner for the global optimal solution.
First, the image and point cloud are fed into a Siamese encoder-decoder network
for hierarchical feature extraction. Then, a coarse-to-fine matching module is
designed to exploit features and establish resilient feature correspondences.
Specifically, in the coarse matching block, a novel I2P transformer module is
employed to capture the homogeneous and heterogeneous global information from
image and point cloud. With the discriminate descriptors, coarse
super-point-to-super-pixel matching pairs are estimated. In the fine matching
module, point-to-pixel pairs are established with the
super-point-to-super-pixel correspondence supervision. Finally, based on
matching pairs, the transform matrix is estimated with the EPnP-RANSAC
algorithm. Extensive experiments conducted on the KITTI dataset have
demonstrated that CoFiI2P achieves a relative rotation error (RRE) of 2.25
degrees and a relative translation error (RTE) of 0.61 meters. These results
represent a significant improvement of 14% in RRE and 52% in RTE compared to
the current state-of-the-art (SOTA) method. The demo video for the experiments
is available at https://youtu.be/TG2GBrJTuW4. The source code will be public at
https://github.com/kang-1-2-3/CoFiI2P.Comment: demo video: https://youtu.be/TG2GBrJTuW4 source code:
https://github.com/kang-1-2-3/CoFiI2
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