# Probabilistic Point Cloud Modeling via Self-Organizing Gaussian Mixture Models

arXiv: https://arxiv.org/abs/2302.00047 · HTML: https://mlsanity.com/paper/2302.00047/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 46 references that are in the map.
- First posted on arXiv 31 Jan 2023.

## Builds on (most cited first, 9 of 9)

- [Neural Point-Based Graphics](https://mlsanity.com/paper/1906.08240/) (2019)
- [Voxblox: Incremental 3D Euclidean Signed Distance Fields for On-Board MAV Planning](https://mlsanity.com/paper/1611.03631/) (2016)
- [UFOMap: An Efficient Probabilistic 3D Mapping Framework That Embraces the Unknown](https://mlsanity.com/paper/2003.04749/) (2020)
- [MeanShift++: Extremely Fast Mode-Seeking With Applications to Segmentation and Object Tracking](https://mlsanity.com/paper/2104.00303/) (2021)
- [Multi-Resolution 3D Mapping with Explicit Free Space Representation for Fast and Accurate Mobile Robot Motion Planning](https://mlsanity.com/paper/2010.07929/) (2020)
- [Autonomous Cave Surveying with an Aerial Robot](https://mlsanity.com/paper/2003.13883/) (2020)
- [Rapid and High-Fidelity Subsurface Exploration with Multiple Aerial Robots](https://mlsanity.com/paper/2012.10788/) (2020)
- [Dense Incremental Metric-Semantic Mapping for Multi-Agent Systems via Sparse Gaussian Process Regression](https://mlsanity.com/paper/2103.16170/) (2021)
- [Online Learning of a Probabilistic and Adaptive Scene Representation](https://mlsanity.com/paper/2103.16832/) (2021)

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

- [SplaTAM: Splat, Track & Map 3D Gaussians for Dense RGB-D SLAM](https://mlsanity.com/paper/2312.02126/) (2023)
- [Flexible Techniques for Differentiable Rendering with 3D Gaussians](https://mlsanity.com/paper/2308.14737/) (2023)
- [Incremental Multimodal Surface Mapping via Self-Organizing Gaussian Mixture Models](https://mlsanity.com/paper/2309.10900/) (2023)
- [Distance and Collision Probability Estimation from Gaussian Surface Models](https://mlsanity.com/paper/2402.00186/) (2024)
- [PUGS: Perceptual Uncertainty for Grasp Selection in Underwater Environments](https://mlsanity.com/paper/2502.09824/) (2025)
- [GI-SLAM: Gaussian-Inertial SLAM](https://mlsanity.com/paper/2503.18275/) (2025)
- [GIRA: Gaussian Mixture Models for Inference and Robot Autonomy](https://mlsanity.com/paper/2307.00071/) (2023)

## Abstract (arXiv)

This letter presents a continuous probabilistic modeling methodology for spatial point cloud data using finite Gaussian Mixture Models (GMMs) where the number of components are adapted based on the scene complexity. Few hierarchical and adaptive methods have been proposed to address the challenge of balancing model fidelity with size. Instead, state-of-the-art mapping approaches require tuning parameters for specific use cases, but do not generalize across diverse environments. To address this gap, we utilize a self-organizing principle from information-theoretic learning to automatically adapt the complexity of the GMM model based on the relevant information in the sensor data. The approach is evaluated against existing point cloud modeling techniques on real-world data with varying degrees of scene complexity.