Patchwork++: Fast and Robust Ground Segmentation Solving Partial Under-Segmentation Using 3D Point Cloud

• URL: http://arxiv.org/abs/2207.11919v1
• Date: Mon, 25 Jul 2022 06:09:02 GMT
• Title: Patchwork++: Fast and Robust Ground Segmentation Solving Partial Under-Segmentation Using 3D Point Cloud
• Authors: Seungjae Lee, Hyungtae Lim, and Hyun Myung
• Abstract summary: Some ground segmentation methods require fine-tuning of parameters depending on the surroundings. A partial under-segmentation problem can still emerge, which implies ground segmentation failures in some regions. We present reflected noise removal (RNR) to eliminate virtual noise points efficiently based on the 3D LiDAR model.
• Score: 7.111443975103329
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: In the field of 3D perception using 3D LiDAR sensors, ground segmentation is an essential task for various purposes, such as traversable area detection and object recognition. Under these circumstances, several ground segmentation methods have been proposed. However, some limitations are still encountered. First, some ground segmentation methods require fine-tuning of parameters depending on the surroundings, which is excessively laborious and time-consuming. Moreover, even if the parameters are well adjusted, a partial under-segmentation problem can still emerge, which implies ground segmentation failures in some regions. Finally, ground segmentation methods typically fail to estimate an appropriate ground plane when the ground is above another structure, such as a retaining wall. To address these problems, we propose a robust ground segmentation method called Patchwork++, an extension of Patchwork. Patchwork++ exploits adaptive ground likelihood estimation (A-GLE) to calculate appropriate parameters adaptively based on the previous ground segmentation results. Moreover, temporal ground revert (TGR) alleviates a partial under-segmentation problem by using the temporary ground property. Also, region-wise vertical plane fitting (R-VPF) is introduced to segment the ground plane properly even if the ground is elevated with different layers. Finally, we present reflected noise removal (RNR) to eliminate virtual noise points efficiently based on the 3D LiDAR reflection model. We demonstrate the qualitative and quantitative evaluations using a SemanticKITTI dataset. Our code is available at https://github.com/url-kaist/patchwork-plusplus

Related papers

• Gaussian Splatting with Localized Points Management [52.009874685460694]
  Localized Point Management (LPM) is capable of identifying those error-contributing zones in the highest demand for both point addition and geometry calibration. LPM applies point densification in the identified zone, whilst resetting the opacity of those points residing in front of these regions so that a new opportunity is created to correct ill-conditioned points. Notably, LPM improves both vanilla 3DGS and SpaceTimeGS to achieve state-of-the-art rendering quality while retaining real-time speeds.
  arXiv  Detail & Related papers  (2024-06-06T16:55:07Z)
• GroundGrid:LiDAR Point Cloud Ground Segmentation and Terrain Estimation [0.7373617024876724]
  We propose our system GroundGrid which relies on 2D elevation maps to solve the terrain estimation and point cloud ground segmentation problems. We evaluate the ground segmentation and terrain estimation performance of GroundGrid and compare it to other state-of-the-art methods. The results show that GroundGrid is capable of outperforming other state-of-the-art systems with an average IoU of 94.78% while maintaining a high run-time performance of 171Hz.
  arXiv  Detail & Related papers  (2024-05-24T16:02:44Z)
• A Ground Segmentation Method Based on Point Cloud Map for Unstructured Roads [10.367730390937622]
  A ground segmentation method based on point cloud map is proposed, which involves three parts: region of interest extraction, point cloud registration and background subtraction. Experimental results show that the correct segmentation rate of ground points is 99.95%, and the running time is 26ms. The proposed method is practically applied to unstructured road scenarios represented by open pit mines.
  arXiv  Detail & Related papers  (2023-09-15T05:23:16Z)
• Flattening-Net: Deep Regular 2D Representation for 3D Point Cloud Analysis [66.49788145564004]
  We present an unsupervised deep neural architecture called Flattening-Net to represent irregular 3D point clouds of arbitrary geometry and topology. Our methods perform favorably against the current state-of-the-art competitors.
  arXiv  Detail & Related papers  (2022-12-17T15:05:25Z)
• Ground Plane Matters: Picking Up Ground Plane Prior in Monocular 3D Object Detection [92.75961303269548]
  The ground plane prior is a very informative geometry clue in monocular 3D object detection (M3OD) We propose a Ground Plane Enhanced Network (GPENet) which resolves both issues at one go. Our GPENet can outperform other methods and achieve state-of-the-art performance, well demonstrating the effectiveness and the superiority of the proposed approach.
  arXiv  Detail & Related papers  (2022-11-03T02:21:35Z)
• A Segment-Wise Gaussian Process-Based Ground Segmentation With Local Smoothness Estimation [3.42658286826597]
  The precise and informative model of the ground and the surface ahead is crucial for navigation and obstacle avoidance. In bumpy and rough scenes the functional relationship of the surface-related features may vary in different areas of the ground. The segment-wise GP-based ground segmentation method with local smoothness estimation is proposed.
  arXiv  Detail & Related papers  (2022-10-19T12:42:21Z)
• Neural 3D Scene Reconstruction with the Manhattan-world Assumption [58.90559966227361]
  This paper addresses the challenge of reconstructing 3D indoor scenes from multi-view images. Planar constraints can be conveniently integrated into the recent implicit neural representation-based reconstruction methods. The proposed method outperforms previous methods by a large margin on 3D reconstruction quality.
  arXiv  Detail & Related papers  (2022-05-05T17:59:55Z)
• Patchwork: Concentric Zone-based Region-wise Ground Segmentation with Ground Likelihood Estimation Using a 3D LiDAR Sensor [0.1657441317977376]
  Ground segmentation is crucial for terrestrial mobile platforms to perform navigation or neighboring object recognition. This paper presents a novel ground segmentation method called textitPatchwork, which is robust for addressing the under-segmentation problem.
  arXiv  Detail & Related papers  (2021-08-12T06:52:10Z)
• PointFlow: Flowing Semantics Through Points for Aerial Image Segmentation [96.76882806139251]
  We propose a point-wise affinity propagation module based on the Feature Pyramid Network (FPN) framework, named PointFlow. Rather than dense affinity learning, a sparse affinity map is generated upon selected points between the adjacent features. Experimental results on three different aerial segmentation datasets suggest that the proposed method is more effective and efficient than state-of-the-art general semantic segmentation methods.
  arXiv  Detail & Related papers  (2021-03-11T09:42:32Z)
• A Fast Point Cloud Ground Segmentation Approach Based on Coarse-To-Fine Markov Random Field [0.32546166337127946]
  A fast point cloud ground segmentation approach based on a coarse-to-fine Markov random field (MRF) method is proposed. Experiments on datasets showed that our method improves on other algorithms in terms of ground segmentation accuracy.
  arXiv  Detail & Related papers  (2020-11-26T06:07:24Z)
• Refined Plane Segmentation for Cuboid-Shaped Objects by Leveraging Edge Detection [63.942632088208505]
  We propose a post-processing algorithm to align the segmented plane masks with edges detected in the image. This allows us to increase the accuracy of state-of-the-art approaches, while limiting ourselves to cuboid-shaped objects.
  arXiv  Detail & Related papers  (2020-03-28T18:51:43Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.