**You Only Hypothesize Once** is a local descriptor-based framework for the registration of two unaligned point clouds. The proposed descriptor achieves the rotation invariance by recent technologies of group equivariant feature learning, which brings more robustness to point density and noise. The descriptor in YOHO also has a rotation-equivariant part, which enables the estimation the registration from just one correspondence hypothesis.

The Lion optimizer is discovered by symbolic program search. It is more memory-efficient than most adaptive optimizers as it only needs to momentum. The update of Lion is produced by the sign function.

Lion

YOHO

You Only Hypothesize Once: Point Cloud Registration with Rotation-equivariant Descriptors

**Effective Squeeze-and-Excitation Block** is an image model block based on squeeze-and-excitation, the difference being that one less FC layer is used. The authors note the SE module has a limitation: channel information loss due to dimension reduction. For avoiding high model complexity burden, two FC layers of the SE module need to reduce channel dimension. Specifically, while the first FC layer reduces input feature channels $C$ to $C/r$ using reduction ratio $r$, the second FC layer expands the reduced channels to original channel size $C$. As a result, this channel dimension reduction causes channel information loss. Therefore, effective SE (eSE) uses only one FC layer with $C$ channels instead of two FCs without channel dimension reduction, which maintains channel information.

Source	You Only Hypothesize Once: Point Cloud Registration with Rotation-equivariant Descriptors
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com