# LCD: Learned Cross-Domain Descriptors for 2D-3D Matching

arXiv: https://arxiv.org/abs/1911.09326 · HTML: https://mlsanity.com/paper/1911.09326/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Nov 2019.

## Built on by (most cited first, 29 of 29)

- [DeepI2P: Image-to-Point Cloud Registration via Deep Classification](https://mlsanity.com/paper/2104.03501/) (2021)
- [Point-set Distances for Learning Representations of 3D Point Clouds](https://mlsanity.com/paper/2102.04014/) (2021)
- [P2-Net: Joint Description and Detection of Local Features for Pixel and Point Matching](https://mlsanity.com/paper/2103.01055/) (2021)
- [FreeReg: Image-to-Point Cloud Registration Leveraging Pretrained Diffusion Models and Monocular Depth Estimators](https://mlsanity.com/paper/2310.03420/) (2023)
- [ASFM-Net: Asymmetrical Siamese Feature Matching Network for Point Completion](https://mlsanity.com/paper/2104.09587/) (2021)
- [2D3D-MATR: 2D-3D Matching Transformer for Detection-free Registration between Images and Point Clouds](https://mlsanity.com/paper/2308.05667/) (2023)
- [Looking 3D: Anomaly Detection with 2D-3D Alignment](https://mlsanity.com/paper/2406.19393/) (2024)
- [E2PNet: Event to Point Cloud Registration with Spatio-Temporal Representation Learning](https://mlsanity.com/paper/2311.18433/) (2023)
- [EP2P-Loc: End-to-End 3D Point to 2D Pixel Localization for Large-Scale Visual Localization](https://mlsanity.com/paper/2309.07471/) (2023)
- [Self-Supervised Learning with Multi-View Rendering for 3D Point Cloud Analysis](https://mlsanity.com/paper/2210.15904/) (2022)
- [Diff2I2P: Differentiable Image-to-Point Cloud Registration with Diffusion Prior](https://mlsanity.com/paper/2507.06651/) (2025)
- [Self-Attention Amortized Distributional Projection Optimization for Sliced Wasserstein Point-Cloud Reconstruction](https://mlsanity.com/paper/2301.04791/) (2023)
- [VOLoc: Visual Place Recognition by Querying Compressed Lidar Map](https://mlsanity.com/paper/2402.15961/) (2024)
- [Contrastive Learning of Features between Images and LiDAR](https://mlsanity.com/paper/2206.12071/) (2022)
- [Category-Agnostic 6D Pose Estimation with Conditional Neural Processes](https://mlsanity.com/paper/2206.07162/) (2022)
- [EdgeRegNet: Edge Feature-based Multimodal Registration Network between Images and LiDAR Point Clouds](https://mlsanity.com/paper/2503.15284/) (2025)
- [LiDAR Point--to--point Correspondences for Rigorous Registration of Kinematic Scanning in Dynamic Networks](https://mlsanity.com/paper/2201.00596/) (2022)
- [TrafficLoc: Localizing Traffic Surveillance Cameras in 3D Scenes](https://mlsanity.com/paper/2412.10308/) (2024)
- [Cross-modal feature fusion for robust point cloud registration with ambiguous geometry](https://mlsanity.com/paper/2505.13088/) (2025)
- [Zero in on Shape: A Generic 2D-3D Instance Similarity Metric learned from Synthetic Data](https://mlsanity.com/paper/2108.04091/) (2021)
- [EEPNet: Efficient Edge Pixel-based Matching Network for Cross-Modal Dynamic Registration between LiDAR and Camera](https://mlsanity.com/paper/2409.19305/) (2024)
- [OmniEvent: Unified Event Representation Learning](https://mlsanity.com/paper/2508.01842/) (2025)
- [Hg-I2P: Bridging Modalities for Generalizable Image-to-Point-Cloud Registration via Heterogeneous Graphs](https://mlsanity.com/paper/2603.27969/) (2026)
- [TFCT-I2P: Three stream fusion network with color aware transformer for image-to-point cloud registration](https://mlsanity.com/paper/2410.00360/) (2024)
- [EEPNet-V2: Patch-to-Pixel Solution for Efficient Cross-Modal Registration between LiDAR Point Cloud and Camera Image](https://mlsanity.com/paper/2503.15285/) (2025)
- [CRISTAL: Real-time Camera Registration in Static LiDAR Scans using Neural Rendering](https://mlsanity.com/paper/2511.16349/) (2025)
- [LEAR: Learning Edge-Aware Representations for Event-to-LiDAR Localization](https://mlsanity.com/paper/2603.01839/) (2026)
- [Angle-I2P: Angle-Consistent-Aware Hierarchical Attention for Cross-Modality Outlier Rejection](https://mlsanity.com/paper/2605.04541/) (2026)
- [Cross-Coordinate Correspondence Pruning for Image-to-Point Cloud Registration](https://mlsanity.com/paper/2607.17200/) (2026)

## Abstract (arXiv)

In this work, we present a novel method to learn a local cross-domain descriptor for 2D image and 3D point cloud matching. Our proposed method is a dual auto-encoder neural network that maps 2D and 3D input into a shared latent space representation. We show that such local cross-domain descriptors in the shared embedding are more discriminative than those obtained from individual training in 2D and 3D domains. To facilitate the training process, we built a new dataset by collecting ≈ 1.4 millions of 2D-3D correspondences with various lighting conditions and settings from publicly available RGB-D scenes. Our descriptor is evaluated in three main experiments: 2D-3D matching, cross-domain retrieval, and sparse-to-dense depth estimation. Experimental results confirm the robustness of our approach as well as its competitive performance not only in solving cross-domain tasks but also in being able to generalize to solve sole 2D and 3D tasks. Our dataset and code are released publicly at urlhttps://hkust-vgd.github.io/lcd.