  Image-to-point cloud registration seeks to estimate their relative camera
pose, which remains an open question due to the data modality gaps. The recent
matching-based methods tend to tackle this by building 2D-3D correspondences.
In this paper, we reveal the information loss inherent in these methods and
propose a matching-free paradigm, named MaFreeI2P. Our key insight is to
actively retrieve the camera pose in SE(3) space by contrasting the geometric
features between the point cloud and the query image. To achieve this, we first
sample a set of candidate camera poses and construct their cost volume using
the cross-modal features. Superior to matching, cost volume can preserve more
information and its feature similarity implicitly reflects the confidence level
of the sampled poses. Afterwards, we employ a convolutional network to
adaptively formulate a similarity assessment function, where the input cost
volume is further improved by filtering and pose-based weighting. Finally, we
update the camera pose based on the similarity scores, and adopt a heuristic
strategy to iteratively shrink the pose sampling space for convergence. Our
MaFreeI2P achieves a very competitive registration accuracy and recall on the
KITTI-Odometry and Apollo-DaoxiangLake datasets.
