# DeepMapping: Unsupervised Map Estimation From Multiple Point Clouds

arXiv: https://arxiv.org/abs/1811.11397 · HTML: https://mlsanity.com/paper/1811.11397/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Nov 2018.

## Built on by (most cited first, 20 of 20)

- [Neural Scene Flow Prior](https://mlsanity.com/paper/2111.01253/) (2021)
- [R-PointHop: A Green, Accurate, and Unsupervised Point Cloud Registration Method](https://mlsanity.com/paper/2103.08129/) (2021)
- [DeepMapping2: Self-Supervised Large-Scale LiDAR Map Optimization](https://mlsanity.com/paper/2212.06331/) (2022)
- [TALoS: Enhancing Semantic Scene Completion via Test-time Adaptation on the Line of Sight](https://mlsanity.com/paper/2410.15674/) (2024)
- [Robust Partial-to-Partial Point Cloud Registration in a Full Range](https://mlsanity.com/paper/2111.15606/) (2021)
- [VRNet: Learning the Rectified Virtual Corresponding Points for 3D Point Cloud Registration](https://mlsanity.com/paper/2203.13241/) (2022)
- [City-scale Scene Change Detection using Point Clouds](https://mlsanity.com/paper/2103.14314/) (2021)
- [End-to-end Learning the Partial Permutation Matrix for Robust 3D Point Cloud Registration](https://mlsanity.com/paper/2110.15250/) (2021)
- [COTReg:Coupled Optimal Transport based Point Cloud Registration](https://mlsanity.com/paper/2112.14381/) (2021)
- [Cross-Modal Information-Guided Network using Contrastive Learning for Point Cloud Registration](https://mlsanity.com/paper/2311.01202/) (2023)
- [A Representation Separation Perspective to Correspondences-free Unsupervised 3D Point Cloud Registration](https://mlsanity.com/paper/2203.13239/) (2022)
- [Learning a Task-specific Descriptor for Robust Matching of 3D Point Clouds](https://mlsanity.com/paper/2210.14899/) (2022)
- [3D Registration in 30 Years: A Survey](https://mlsanity.com/paper/2412.13735/) (2024)
- [Exploring Easy Boosts for Lidar Semantic Scene Completion](https://mlsanity.com/paper/2606.03992/) (2026)
- [Iterative Feedback Network for Unsupervised Point Cloud Registration](https://mlsanity.com/paper/2401.04357/) (2024)
- [Efficient and Distributed Large-Scale 3D Map Registration using Tomographic Features](https://mlsanity.com/paper/2406.19461/) (2024)
- [Multiview Point Cloud Registration via Optimization in an Autoencoder Latent Space](https://mlsanity.com/paper/2504.21467/) (2025)
- [Deep Weakly Supervised Positioning](https://mlsanity.com/paper/2104.04866/) (2021)
- [Crowd Source Scene Change Detection and Local Map Update](https://mlsanity.com/paper/2203.05205/) (2022)
- [Spectral Consistency-Guided Multiview Point Cloud Registration for Low-Overlap Scenes](https://mlsanity.com/paper/2609.12417/) (2026)

## Abstract (arXiv)

We propose DeepMapping, a novel registration framework using deep neural networks (DNNs) as auxiliary functions to align multiple point clouds from scratch to a globally consistent frame. We use DNNs to model the highly non-convex mapping process that traditionally involves hand-crafted data association, sensor pose initialization, and global refinement. Our key novelty is that "training" these DNNs with properly defined unsupervised losses is equivalent to solving the underlying registration problem, but less sensitive to good initialization than ICP. Our framework contains two DNNs: a localization network that estimates the poses for input point clouds, and a map network that models the scene structure by estimating the occupancy status of global coordinates. This allows us to convert the registration problem to a binary occupancy classification, which can be solved efficiently using gradient-based optimization. We further show that DeepMapping can be readily extended to address the problem of Lidar SLAM by imposing geometric constraints between consecutive point clouds. Experiments are conducted on both simulated and real datasets. Qualitative and quantitative comparisons demonstrate that DeepMapping often enables more robust and accurate global registration of multiple point clouds than existing techniques. Our code is available at https://ai4ce.github.io/DeepMapping/.