# Patchwork: Concentric Zone-based Region-wise Ground Segmentation with Ground Likelihood Estimation Using a 3D LiDAR Sensor

arXiv: https://arxiv.org/abs/2108.05560 · HTML: https://mlsanity.com/paper/2108.05560/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 26 references that are in the map.
- First posted on arXiv 12 Aug 2021.

## Builds on (most cited first, 3 of 3)

- [Identifying Unknown Instances for Autonomous Driving](https://mlsanity.com/paper/1910.11296/) (2019)
- [YOLO3D: End-to-end real-time 3D Oriented Object Bounding Box Detection from LiDAR Point Cloud](https://mlsanity.com/paper/1808.02350/) (2018)
- [ERASOR: Egocentric Ratio of Pseudo Occupancy-based Dynamic Object Removal for Static 3D Point Cloud Map Building](https://mlsanity.com/paper/2103.04316/) (2021)

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

- [Patchwork++: Fast and Robust Ground Segmentation Solving Partial Under-Segmentation Using 3D Point Cloud](https://mlsanity.com/paper/2207.11919/) (2022)
- [NeRF-LOAM: Neural Implicit Representation for Large-Scale Incremental LiDAR Odometry and Mapping](https://mlsanity.com/paper/2303.10709/) (2023)
- [4D Contrastive Superflows are Dense 3D Representation Learners](https://mlsanity.com/paper/2407.06190/) (2024)
- [BEVContrast: Self-Supervision in BEV Space for Automotive Lidar Point Clouds](https://mlsanity.com/paper/2310.17281/) (2023)
- [A Single Correspondence Is Enough: Robust Global Registration to Avoid Degeneracy in Urban Environments](https://mlsanity.com/paper/2203.06612/) (2022)
- [KISS-Matcher: Fast and Robust Point Cloud Registration Revisited](https://mlsanity.com/paper/2409.15615/) (2024)
- [GroundGrid:LiDAR Point Cloud Ground Segmentation and Terrain Estimation](https://mlsanity.com/paper/2405.15664/) (2024)
- [BUFFER-X: Towards Zero-Shot Point Cloud Registration in Diverse Scenes](https://mlsanity.com/paper/2503.07940/) (2025)
- [Real-Aug: Realistic Scene Synthesis for LiDAR Augmentation in 3D Object Detection](https://mlsanity.com/paper/2305.12853/) (2023)
- [DreamWaQ++: Obstacle-Aware Quadrupedal Locomotion With Resilient Multi-Modal Reinforcement Learning](https://mlsanity.com/paper/2409.19709/) (2024)
- [UNIT: Unsupervised Online Instance Segmentation through Time](https://mlsanity.com/paper/2409.07887/) (2024)
- [Enhanced Spatiotemporal Consistency for Image-to-LiDAR Data Pretraining](https://mlsanity.com/paper/2503.19912/) (2025)
- [TerraSeg: Self-Supervised Ground Segmentation for Any LiDAR](https://mlsanity.com/paper/2603.27344/) (2026)
- [PaGO-LOAM: Robust Ground-Optimized LiDAR Odometry](https://mlsanity.com/paper/2206.00266/) (2022)
- [A Ground Segmentation Method Based on Point Cloud Map for Unstructured Roads](https://mlsanity.com/paper/2309.08164/) (2023)
- [Differentiable SLAM Helps Deep Learning-based LiDAR Perception Tasks](https://mlsanity.com/paper/2309.09206/) (2023)
- [Outlier-Robust Long-Term Robotic Mapping Leveraging Ground Segmentation](https://mlsanity.com/paper/2405.11176/) (2024)
- [Can We Remove the Ground? Obstacle-aware Point Cloud Compression for Remote Object Detection](https://mlsanity.com/paper/2410.00582/) (2024)
- [Toward Zero-Shot Point Cloud Registration Across Diverse Scales, Scenes, and Sensor Setups](https://mlsanity.com/paper/2601.02759/) (2026)
- [FugSeg: Fast Uncertainty-aware Ground Segmentation for 3D Point Cloud](https://mlsanity.com/paper/2605.08952/) (2026)
- [A Segment-Wise Gaussian Process-Based Ground Segmentation With Local Smoothness Estimation](https://mlsanity.com/paper/2210.10515/) (2022)
- [A Spatiotemporal Correspondence Approach to Unsupervised LiDAR Segmentation with Traffic Applications](https://mlsanity.com/paper/2308.12433/) (2023)
- [Accelerating Point Cloud Ground Segmentation: From Mechanical to Solid-State Lidars](https://mlsanity.com/paper/2408.10404/) (2024)
- [Unsupervised Online 3D Instance Segmentation with Synthetic Sequences and Dynamic Loss](https://mlsanity.com/paper/2509.23194/) (2025)
- [OT-Drive: Out-of-Distribution Off-Road Traversable Area Segmentation via Optimal Transport](https://mlsanity.com/paper/2601.09952/) (2026)
- [LOGOS: LiDAR-Only Gaussian Elevation Splatting for Unified Tiny Obstacle Segmentation](https://mlsanity.com/paper/2606.21527/) (2026)
- [PixelPilot: Scalable Vision-Language-Action Models for End-to-End Autonomous Driving](https://mlsanity.com/paper/2607.04637/) (2026)
- [Hydra++: Real-Time Hierarchical 3D Scene Graph Construction With Object-Level Shape Estimation](https://mlsanity.com/paper/2607.09455/) (2026)
- [ACZ-GSeg: Adaptive Concentric Zone-based Two-stage Ground Segmentation for LiDAR Point Clouds](https://mlsanity.com/paper/2607.12110/) (2026)

## Abstract (arXiv)

Ground segmentation is crucial for terrestrial mobile platforms to perform navigation or neighboring object recognition. Unfortunately, the ground is not flat, as it features steep slopes; bumpy roads; or objects, such as curbs, flower beds, and so forth. To tackle the problem, this paper presents a novel ground segmentation method called textitPatchwork, which is robust for addressing the under-segmentation problem and operates at more than 40 Hz. In this paper, a point cloud is encoded into a Concentric Zone Model-based representation to assign an appropriate density of cloud points among bins in a way that is not computationally complex. This is followed by Region-wise Ground Plane Fitting, which is performed to estimate the partial ground for each bin. Finally, Ground Likelihood Estimation is introduced to dramatically reduce false positives. As experimentally verified on SemanticKITTI and rough terrain datasets, our proposed method yields promising performance compared with the state-of-the-art methods, showing faster speed compared with existing plane fitting--based methods. Code is available: https://github.com/LimHyungTae/patchwork