Full Transformer Framework for Robust Point Cloud Registration with Deep Information Interaction

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Deep Learning DIT
Overview

Full Transformer Framework for Robust Point Cloud Registration with Deep Information Interaction. arxiv

DIT-architecture

This repository contains python scripts for training and testing [Deep Interaction Transformer (DIT)]

Deep Interaction Transformer (DIT) is a full Transformer framework for point cloud registration, which achieves superior performance compared with current state-of-the-art learning-based methods in accuracy and robustness. DIT consists of the following three main modules:

  • a Point Cloud Structure Extractor for modeling global relation.
  • a Point Feature Transformer for improving the discrimination of features.
  • a GMCCE for correspondence confidence evaluation.

Code coming soon

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Comments
  • Is it suitable for other datasets?

    Is it suitable for other datasets?

    Thanks for your great work！It is an inspiring paper. But it is not experimented on the registration dataset 3DMatch/3DLoMatch.

    I noticed that there is no downsampling process in the feature extraction stage，I wonder if DIT is not suitable for registration tasks with low overlap rate because of this problem.

    opened by Ruye-aa 3
Owner
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