# 3DRegNet: A Deep Neural Network for 3D Point Registration

## TL;DR

3DRegNet is a deep learning model designed for efficient and accurate 3D scan registration, utilizing correspondence classification, regression, and refinement to outperform existing methods on challenging datasets.

## Contribution

The paper introduces 3DRegNet, a novel deep neural network architecture that improves 3D point registration accuracy and speed through correspondence classification, regression, and a refinement network.

## Key findings

- Outperforms existing methods on challenging datasets
- Achieves higher registration accuracy
- Provides faster registration process

## Abstract

We present 3DRegNet, a novel deep learning architecture for the registration of 3D scans. Given a set of 3D point correspondences, we build a deep neural network to address the following two challenges: (i) classification of the point correspondences into inliers/outliers, and (ii) regression of the motion parameters that align the scans into a common reference frame. With regard to regression, we present two alternative approaches: (i) a Deep Neural Network (DNN) registration and (ii) a Procrustes approach using SVD to estimate the transformation. Our correspondence-based approach achieves a higher speedup compared to competing baselines. We further propose the use of a refinement network, which consists of a smaller 3DRegNet as a refinement to improve the accuracy of the registration. Extensive experiments on two challenging datasets demonstrate that we outperform other methods and achieve state-of-the-art results. The code is available.