# DeepPCO: End-to-End Point Cloud Odometry through Deep Parallel Neural Network

arXiv: https://arxiv.org/abs/1910.11088 · HTML: https://mlsanity.com/paper/1910.11088/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Oct 2019.

## Built on by (most cited first, 14 of 14)

- [NeRF-LOAM: Neural Implicit Representation for Large-Scale Incremental LiDAR Odometry and Mapping](https://mlsanity.com/paper/2303.10709/) (2023)
- [DVLO: Deep Visual-LiDAR Odometry with Local-to-Global Feature Fusion and Bi-Directional Structure Alignment](https://mlsanity.com/paper/2403.18274/) (2024)
- [Efficient 3D Deep LiDAR Odometry](https://mlsanity.com/paper/2111.02135/) (2021)
- [4DRVO-Net: Deep 4D Radar-Visual Odometry Using Multi-Modal and Multi-Scale Adaptive Fusion](https://mlsanity.com/paper/2308.06573/) (2023)
- [UnDeepLIO: Unsupervised Deep Lidar-Inertial Odometry](https://mlsanity.com/paper/2109.01533/) (2021)
- [GreenPCO: An Unsupervised Lightweight Point Cloud Odometry Method](https://mlsanity.com/paper/2112.04054/) (2021)
- [Unsupervised Learning of 3D Scene Flow with 3D Odometry Assistance](https://mlsanity.com/paper/2209.04945/) (2022)
- [FusionLoc: Camera-2D LiDAR Fusion Using Multi-Head Self-Attention for End-to-End Serving Robot Relocalization](https://mlsanity.com/paper/2303.06872/) (2023)
- [UnPWC-SVDLO: Multi-SVD on PointPWC for Unsupervised Lidar Odometry](https://mlsanity.com/paper/2205.08150/) (2022)
- [Neural Implicit Representation for Highly Dynamic LiDAR Mapping and Odometry](https://mlsanity.com/paper/2409.17729/) (2024)
- [D3DLO: Deep 3D LiDAR Odometry](https://mlsanity.com/paper/2101.12242/) (2021)
- [Deep Weakly Supervised Positioning](https://mlsanity.com/paper/2104.04866/) (2021)
- [LoRCoN-LO: Long-term Recurrent Convolutional Network-based LiDAR Odometry](https://mlsanity.com/paper/2303.11853/) (2023)
- [Efficient Active Training for Deep LiDAR Odometry](https://mlsanity.com/paper/2509.03211/) (2025)

## Abstract (arXiv)

Odometry is of key importance for localization in the absence of a map. There is considerable work in the area of visual odometry (VO), and recent advances in deep learning have brought novel approaches to VO, which directly learn salient features from raw images. These learning-based approaches have led to more accurate and robust VO systems. However, they have not been well applied to point cloud data yet. In this work, we investigate how to exploit deep learning to estimate point cloud odometry (PCO), which may serve as a critical component in point cloud-based downstream tasks or learning-based systems. Specifically, we propose a novel end-to-end deep parallel neural network called DeepPCO, which can estimate the 6-DOF poses using consecutive point clouds. It consists of two parallel sub-networks to estimate 3-D translation and orientation respectively rather than a single neural network. We validate our approach on KITTI Visual Odometry/SLAM benchmark dataset with different baselines. Experiments demonstrate that the proposed approach achieves good performance in terms of pose accuracy.