# 3D Local Features for Direct Pairwise Registration

## TL;DR

This paper introduces a data-driven, direct pairwise registration method for point clouds that leverages local features and pose-specific descriptors to improve accuracy and speed, outperforming existing techniques.

## Contribution

It develops a novel pose-variant descriptor and a relative pose estimation network, eliminating local reference frames and enhancing registration performance.

## Key findings

- Outperforms state-of-the-art in real datasets
- Achieves faster registration with better accuracy
- Utilizes local pose information for improved generalization

## Abstract

We present a novel, data driven approach for solving the problem of registration of two point cloud scans. Our approach is direct in the sense that a single pair of corresponding local patches already provides the necessary transformation cue for the global registration. To achieve that, we first endow the state of the art PPF-FoldNet auto-encoder (AE) with a pose-variant sibling, where the discrepancy between the two leads to pose-specific descriptors. Based upon this, we introduce RelativeNet, a relative pose estimation network to assign correspondence-specific orientations to the keypoints, eliminating any local reference frame computations. Finally, we devise a simple yet effective hypothesize-and-verify algorithm to quickly use the predictions and align two point sets. Our extensive quantitative and qualitative experiments suggests that our approach outperforms the state of the art in challenging real datasets of pairwise registration and that augmenting the keypoints with local pose information leads to better generalization and a dramatic speed-up.