# 2D3D-MatchNet: Learning to Match Keypoints Across 2D Image and 3D Point Cloud

arXiv: https://arxiv.org/abs/1904.09742 · HTML: https://mlsanity.com/paper/1904.09742/

## Key facts

- Cited by 62 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Apr 2019.

## Built on by (most cited first, 30 of 62)

- [DeepI2P: Image-to-Point Cloud Registration via Deep Classification](https://mlsanity.com/paper/2104.03501/) (2021)
- [CorrI2P: Deep Image-to-Point Cloud Registration via Dense Correspondence](https://mlsanity.com/paper/2207.05483/) (2022)
- [P2-Net: Joint Description and Detection of Local Features for Pixel and Point Matching](https://mlsanity.com/paper/2103.01055/) (2021)
- [Differentiable Registration of Images and LiDAR Point Clouds with VoxelPoint-to-Pixel Matching](https://mlsanity.com/paper/2312.04060/) (2023)
- [FreeReg: Image-to-Point Cloud Registration Leveraging Pretrained Diffusion Models and Monocular Depth Estimators](https://mlsanity.com/paper/2310.03420/) (2023)
- [2D3D-MATR: 2D-3D Matching Transformer for Detection-free Registration between Images and Point Clouds](https://mlsanity.com/paper/2308.05667/) (2023)
- [LIP-Loc: LiDAR Image Pretraining for Cross-Modal Localization](https://mlsanity.com/paper/2312.16648/) (2023)
- [(LC)2: LiDAR-Camera Loop Constraints For Cross-Modal Place Recognition](https://mlsanity.com/paper/2304.08660/) (2023)
- [I2P-Rec: Recognizing Images on Large-scale Point Cloud Maps through Bird's Eye View Projections](https://mlsanity.com/paper/2303.01043/) (2023)
- [3DAxiesPrompts: Unleashing the 3D Spatial Task Capabilities of GPT-4V](https://mlsanity.com/paper/2312.09738/) (2023)
- [CMRNext: Camera to LiDAR Matching in the Wild for Localization and Extrinsic Calibration](https://mlsanity.com/paper/2402.00129/) (2024)
- [i3dLoc: Image-to-range Cross-domain Localization Robust to Inconsistent Environmental Conditions](https://mlsanity.com/paper/2105.12883/) (2021)
- [Deep Learning for Visual Localization and Mapping: A Survey](https://mlsanity.com/paper/2308.14039/) (2023)
- [Bridge 2D-3D: Uncertainty-aware Hierarchical Registration Network with Domain Alignment](https://mlsanity.com/paper/2504.01641/) (2025)
- [Looking 3D: Anomaly Detection with 2D-3D Alignment](https://mlsanity.com/paper/2406.19393/) (2024)
- [CoFiI2P: Coarse-to-Fine Correspondences for Image-to-Point Cloud Registration](https://mlsanity.com/paper/2309.14660/) (2023)
- [E2PNet: Event to Point Cloud Registration with Spatio-Temporal Representation Learning](https://mlsanity.com/paper/2311.18433/) (2023)
- [End-to-end 2D-3D Registration between Image and LiDAR Point Cloud for Vehicle Localization](https://mlsanity.com/paper/2306.11346/) (2023)
- [EP2P-Loc: End-to-End 3D Point to 2D Pixel Localization for Large-Scale Visual Localization](https://mlsanity.com/paper/2309.07471/) (2023)
- [Radar-to-Lidar: Heterogeneous Place Recognition via Joint Learning](https://mlsanity.com/paper/2102.04960/) (2021)
- [CA-I2P: Channel-Adaptive Registration Network with Global Optimal Selection](https://mlsanity.com/paper/2506.21364/) (2025)
- [Diff2I2P: Differentiable Image-to-Point Cloud Registration with Diffusion Prior](https://mlsanity.com/paper/2507.06651/) (2025)
- [VOLoc: Visual Place Recognition by Querying Compressed Lidar Map](https://mlsanity.com/paper/2402.15961/) (2024)
- [Extrinsic Camera Calibration with Semantic Segmentation](https://mlsanity.com/paper/2208.03949/) (2022)
- [Adaptive Agent Selection and Interaction Network for Image-to-point cloud Registration](https://mlsanity.com/paper/2511.05965/) (2025)
- [Contrastive Learning of Features between Images and LiDAR](https://mlsanity.com/paper/2206.12071/) (2022)
- [VXP: Voxel-Cross-Pixel Large-scale Image-LiDAR Place Recognition](https://mlsanity.com/paper/2403.14594/) (2024)
- [ModaLink: Unifying Modalities for Efficient Image-to-PointCloud Place Recognition](https://mlsanity.com/paper/2403.18762/) (2024)
- [Robust-DefReg: A Robust Deformable Point Cloud Registration Method based on Graph Convolutional Neural Networks](https://mlsanity.com/paper/2306.04701/) (2023)
- [Quantity-Aware Coarse-to-Fine Correspondence for Image-to-Point Cloud Registration](https://mlsanity.com/paper/2307.07142/) (2023)

## Abstract (arXiv)

Large-scale point cloud generated from 3D sensors is more accurate than its image-based counterpart. However, it is seldom used in visual pose estimation due to the difficulty in obtaining 2D-3D image to point cloud correspondences. In this paper, we propose the 2D3D-MatchNet - an end-to-end deep network architecture to jointly learn the descriptors for 2D and 3D keypoint from image and point cloud, respectively. As a result, we are able to directly match and establish 2D-3D correspondences from the query image and 3D point cloud reference map for visual pose estimation. We create our Oxford 2D-3D Patches dataset from the Oxford Robotcar dataset with the ground truth camera poses and 2D-3D image to point cloud correspondences for training and testing the deep network. Experimental results verify the feasibility of our approach.