Liu等人[20]采用光谱聚类的方法来生成超级点。这些方法没有考虑三维点云的颜色信息,其中一些类别的物体与周围的物体(如窗户和木板)只有颜色上的不同。而且最小化全局能量函数是很耗时的。Landrieu等人[8]将超点的生成制定为一个深度度量学习问题。但是这种划分方法需要三维点云的语义信息。 针对上述问题,我们提出了...
the problem that some existing semantic segmentation networks for 3D point clouds generally have poor performance on small objects, a Spatial Eight-Quadrant Kernel Convolution (SEQKC) algorithm is proposed to enhance the ability of the network for extracting fine-grained features from 3D po...
Towards Semantic Segmentation of Urban-Scale 3D Point Clouds: A Dataset, Benchmarks and Challengesarxiv.org/abs/2009.03137 Code: GitHub - QingyongHu/SensatUrban: Urban-scale point cloud dataset (CVPR 2021)github.com/QingyongHu/SensatUrban Motivations: 将深度学习在3D领域的潜力释放出来的前提...
Point attention network for semantic segmentation of 3D point clouds Mingtao Fenga, Liang Zhangb, Xuefei Linc, Syed Zulqarnain Gilanid and AjmalMiand* 年份:2020 期刊:Pattern Recognition IF:7.196 1、创新 1)通过attention机制实现LAE-Convs 学习丰富的局部信息 2)point-wise spatial attention module学...
ACSeg: Adaptive Conceptualization for Unsupervised Semantic Segmentation Out-of-Candidate Rectification for Weakly Supervised Semantic Segmentation GrowSP: Unsupervised Semantic Segmentation of 3D Point Clouds Single Domain Generalization for LiDAR Semantic Segmentation ...
OpenPointClass - Fast Semantic Segmentation of 3D Point Clouds A fast, memory efficient free and open source point cloud classifier. It generates an AI model from a set of input point clouds that have been labeled and can subsequently use that model to classify new datasets. On the default ...
3D point cloud semantic segmentation is a challenging topic in the computer vision field. Most of the existing methods in literature require a large amount of fully labeled training data, but it is extremely time-consuming to obtain these training data by manually labeling massive point clouds. Ad...
In this paper, we present an efficient semantic segmentation framework for indoor scenes operating on 3D point clouds. We use the results of a Random Forest Classifier to initialize the unary potentials of a densely interconnected Conditional Random Field, for which we learn the parameters...
1. Introduction 3D semantic segmentation of LiDAR point clouds has played a key role in scene understanding, facilitating applications such as autonomous driving [6, 23, 26, 28, 46, 60, 63] and robotics [3, 38, 51, 52]. However, many contemporary meth- ods require rela...
Semantic Segmentation on Benchmarks 在本节中,作者评估了RandLA-Net在三个大型公共数据集上的语义分割:室外Semantic3D和SemanticKITTI,以及室内S3DIS。 1)Evaluation on Semantic3D Semantic3D数据集包含15个用于训练的点云和15个用于测试的点云,每个点云有108个点,在真实的3D空间中覆盖160 x 240 x 30立方米。原...