CoFiI2P: Coarse-to-Fine Correspondences for Image-to-Point Cloud Registration
arxiv(2023)
摘要
Image-to-point cloud (I2P) registration is a fundamental task for robots and
autonomous vehicles to achieve cross-modality data fusion and localization.
Existing I2P registration methods estimate correspondences at the point/pixel
level, often overlooking global alignment. However, I2P matching can easily
converge to a local optimum when performed without high-level guidance from
global constraints. To address this issue, this paper introduces CoFiI2P, a
novel I2P registration network that extracts correspondences in a
coarse-to-fine manner to achieve the globally optimal solution. First, the
image and point cloud data are processed through a Siamese encoder-decoder
network for hierarchical feature extraction. Second, a coarse-to-fine matching
module is designed to leverage these features and establish robust feature
correspondences. Specifically, In the coarse matching phase, a novel I2P
transformer module is employed to capture both homogeneous and heterogeneous
global information from the image and point cloud data. This enables the
estimation of coarse super-point/super-pixel matching pairs with discriminative
descriptors. In the fine matching module, point/pixel pairs are established
with the guidance of super-point/super-pixel correspondences. Finally, based on
matching pairs, the transform matrix is estimated with the EPnP-RANSAC
algorithm. Extensive experiments conducted on the KITTI dataset demonstrate
that CoFiI2P achieves impressive results, with a relative rotation error (RRE)
of 1.14 degrees and a relative translation error (RTE) of 0.29 meters. These
results represent a significant improvement of 84
compared to the current state-of-the-art (SOTA) method. The project page is
available at .
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