2024
PARE-Net: Position-Aware Rotation-Equivariant Networks for Robust Point Cloud Registration
ECCV 2024poster
"Learning rotation-invariant distinctive features is a fundamental requirement for point cloud registration. Existing methods often use rotation-sensitive networks to extract features, while employing rotation augmentation to learn an approximate invariant mapping rudely. This makes networks fragile…