Fast Geometric Surface based Segmentation of Point Cloud from Lidar Data
- URL: http://arxiv.org/abs/2005.02704v1
- Date: Wed, 6 May 2020 10:17:16 GMT
- Title: Fast Geometric Surface based Segmentation of Point Cloud from Lidar Data
- Authors: Aritra Mukherjee, Sourya Dipta Das, Jasorsi Ghosh, Ananda S.
Chowdhury, Sanjoy Kumar Saha
- Abstract summary: LIDAR provides a fast and accurate 3D point cloud map of the environment which helps in map building.
In this paper, a methodology is presented to generate the segmented surfaces in real time and these can be used in modeling the 3D objects.
- Score: 15.882128188732016
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Mapping the environment has been an important task for robot navigation and
Simultaneous Localization And Mapping (SLAM). LIDAR provides a fast and
accurate 3D point cloud map of the environment which helps in map building.
However, processing millions of points in the point cloud becomes a
computationally expensive task. In this paper, a methodology is presented to
generate the segmented surfaces in real time and these can be used in modeling
the 3D objects. At first an algorithm is proposed for efficient map building
from single shot data of spinning Lidar. It is based on fast meshing and
sub-sampling. It exploits the physical design and the working principle of the
spinning Lidar sensor. The generated mesh surfaces are then segmented by
estimating the normal and considering their homogeneity. The segmented surfaces
can be used as proposals for predicting geometrically accurate model of objects
in the robots activity environment. The proposed methodology is compared with
some popular point cloud segmentation methods to highlight the efficacy in
terms of accuracy and speed.
Related papers
- Exploiting Local Features and Range Images for Small Data Real-Time Point Cloud Semantic Segmentation [4.02235104503587]
In this paper, we harness the information from the three-dimensional representation to proficiently capture local features.
A GPU-based KDTree allows for rapid building, querying, and enhancing projection with straightforward operations.
We show that a reduced version of our model not only demonstrates strong competitiveness against full-scale state-of-the-art models but also operates in real-time.
arXiv Detail & Related papers (2024-10-14T13:49:05Z) - PointRegGPT: Boosting 3D Point Cloud Registration using Generative Point-Cloud Pairs for Training [90.06520673092702]
We present PointRegGPT, boosting 3D point cloud registration using generative point-cloud pairs for training.
To our knowledge, this is the first generative approach that explores realistic data generation for indoor point cloud registration.
arXiv Detail & Related papers (2024-07-19T06:29:57Z) - PointOcc: Cylindrical Tri-Perspective View for Point-based 3D Semantic
Occupancy Prediction [72.75478398447396]
We propose a cylindrical tri-perspective view to represent point clouds effectively and comprehensively.
Considering the distance distribution of LiDAR point clouds, we construct the tri-perspective view in the cylindrical coordinate system.
We employ spatial group pooling to maintain structural details during projection and adopt 2D backbones to efficiently process each TPV plane.
arXiv Detail & Related papers (2023-08-31T17:57:17Z) - Quadric Representations for LiDAR Odometry, Mapping and Localization [93.24140840537912]
Current LiDAR odometry, mapping and localization methods leverage point-wise representations of 3D scenes.
We propose a novel method of describing scenes using quadric surfaces, which are far more compact representations of 3D objects.
Our method maintains low latency and memory utility while achieving competitive, and even superior, accuracy.
arXiv Detail & Related papers (2023-04-27T13:52:01Z) - Object-level 3D Semantic Mapping using a Network of Smart Edge Sensors [25.393382192511716]
We extend a multi-view 3D semantic mapping system consisting of a network of distributed edge sensors with object-level information.
Our method is evaluated on the public Behave dataset where it shows pose estimation within a few centimeters and in real-world experiments with the sensor network in a challenging lab environment.
arXiv Detail & Related papers (2022-11-21T11:13:08Z) - GPCO: An Unsupervised Green Point Cloud Odometry Method [64.86292006892093]
A lightweight point cloud odometry solution is proposed and named the green point cloud odometry (GPCO) method.
GPCO is an unsupervised learning method that predicts object motion by matching features of consecutive point cloud scans.
It is observed that GPCO outperforms benchmarking deep learning methods in accuracy while it has a significantly smaller model size and less training time.
arXiv Detail & Related papers (2021-12-08T00:24:03Z) - Learning Semantic Segmentation of Large-Scale Point Clouds with Random
Sampling [52.464516118826765]
We introduce RandLA-Net, an efficient and lightweight neural architecture to infer per-point semantics for large-scale point clouds.
The key to our approach is to use random point sampling instead of more complex point selection approaches.
Our RandLA-Net can process 1 million points in a single pass up to 200x faster than existing approaches.
arXiv Detail & Related papers (2021-07-06T05:08:34Z) - 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) - Semantic Segmentation of Surface from Lidar Point Cloud [15.882128188732016]
The Lidar sensor can produce near accurate 3D map of the environment in the format of point cloud, in real time.
The data is adequate for extracting information related to SLAM, but processing millions of points in the point cloud is computationally quite expensive.
The methodology presented proposes a fast algorithm that can be used to extract semantically labelled surface segments from the cloud, in real time.
arXiv Detail & Related papers (2020-09-13T13:06:26Z) - Local Grid Rendering Networks for 3D Object Detection in Point Clouds [98.02655863113154]
CNNs are powerful but it would be computationally costly to directly apply convolutions on point data after voxelizing the entire point clouds to a dense regular 3D grid.
We propose a novel and principled Local Grid Rendering (LGR) operation to render the small neighborhood of a subset of input points into a low-resolution 3D grid independently.
We validate LGR-Net for 3D object detection on the challenging ScanNet and SUN RGB-D datasets.
arXiv Detail & Related papers (2020-07-04T13:57:43Z)
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.