# DeepCLR: Correspondence-Less Architecture for Deep End-to-End Point Cloud Registration

arXiv: https://arxiv.org/abs/2007.11255 · HTML: https://mlsanity.com/paper/2007.11255/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Jul 2020.

## Built on by (most cited first, 7 of 7)

- [Zero NeRF: Registration with Zero Overlap](https://mlsanity.com/paper/2211.12544/) (2022)
- [Robust-DefReg: A Robust Deformable Point Cloud Registration Method based on Graph Convolutional Neural Networks](https://mlsanity.com/paper/2306.04701/) (2023)
- [DSLO: Deep Sequence LiDAR Odometry Based on Inconsistent Spatio-temporal Propagation](https://mlsanity.com/paper/2409.00744/) (2024)
- [DefTransNet: A Transformer-based Method for Non-Rigid Point Cloud Registration in the Simulation of Soft Tissue Deformation](https://mlsanity.com/paper/2502.06336/) (2025)
- [D3DLO: Deep 3D LiDAR Odometry](https://mlsanity.com/paper/2101.12242/) (2021)
- [Automated Automotive Radar Calibration With Intelligent Vehicles](https://mlsanity.com/paper/2306.13323/) (2023)
- [SynBench: A Synthetic Benchmark for Non-rigid 3D Point Cloud Registration](https://mlsanity.com/paper/2409.14474/) (2024)

## Abstract (arXiv)

This work addresses the problem of point cloud registration using deep neural networks. We propose an approach to predict the alignment between two point clouds with overlapping data content, but displaced origins. Such point clouds originate, for example, from consecutive measurements of a LiDAR mounted on a moving platform. The main difficulty in deep registration of raw point clouds is the fusion of template and source point cloud. Our proposed architecture applies flow embedding to tackle this problem, which generates features that describe the motion of each template point. These features are then used to predict the alignment in an end-to-end fashion without extracting explicit point correspondences between both input clouds. We rely on the KITTI odometry and ModelNet40 datasets for evaluating our method on various point distributions. Our approach achieves state-of-the-art accuracy and the lowest run-time of the compared methods.