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Unsupervised Building Instance Segmentation of Airborne LiDAR Point Clouds for Parallel Reconstruction Analysis

Yongjun Zhang, Wangshan Yang, Xinyi Liu, Yi Wan, Xianzhang Zhu, Yuhui Tan
2021 Remote Sensing  
This paper proposes a novel unsupervised building instance segmentation (UBIS) method of airborne Light Detection and Ranging (LiDAR) point clouds for parallel reconstruction analysis, which combines a  ...  The proposed method first divides building point clouds into building instances by the improved kd tree 2D shared nearest neighbor clustering algorithm (Ikd-2DSNN).  ...  The authors also are grateful to ISPRS for providing the ALS dataset and the Dayton Annotated LiDAR Earth Scan (DALES) dataset.  ... 
doi:10.3390/rs13061136 fatcat:vvpxb6v47vfrphbk776hwzhsru

3D Object Segmentation through Label Diffusion from 2D Images

Brian Wang, Wei-Lun Chao, Yan Wang, Bharath Hariharan, Kilian Weinberger, Mark Campbell
2019 IEEE Robotics and Automation Letters  
In this letter, we present Label Diffusion Lidar Segmentation (LDLS), a novel approach for 3-D point cloud segmentation, which leverages 2-D segmentation of an RGB image from an aligned camera to avoid  ...  We obtain 2-D segmentation predictions by applying Mask-RCNN to the RGB image, and then link this image to a 3-D lidar point cloud by building a graph of connections among 3-D points and 2-D pixels.  ...  APPROACH This section presents our label diffusion method for object instance segmentation in lidar point clouds.  ... 
doi:10.1109/lra.2019.2922582 fatcat:paglaezr45grhfkzsyfvgrznjm

LiDAR-based 4D Panoptic Segmentation via Dynamic Shifting Network [article]

Fangzhou Hong, Hui Zhou, Xinge Zhu, Hongsheng Li, Ziwei Liu
2022 arXiv   pre-print
We observe that commonly-used clustering algorithms are incapable of handling complex autonomous driving scenes with non-uniform point cloud distributions and varying instance sizes.  ...  DS-Net adopts the cylinder convolution that is specifically designed for LiDAR point clouds. 2) Dynamic Shifting for complex point distributions.  ...  of LiDAR point clouds.  ... 
arXiv:2203.07186v1 fatcat:sd7gdh3fozebrcjgg3yxfjwkiy

Slice-Based Instance and Semantic Segmentation for Low-Channel Roadside LiDAR Data

Hui Liu, Ciyun Lin, Dayong Wu, Bowen Gong
2020 Remote Sensing  
The part of the instance segmentation of point cloud is based on the regional growth method, and we proposed a seed point generation method for low-channel LiDAR data.  ...  This paper presents a novel segmentation (slice-based) method for point clouds of roadside LiDAR.  ...  Their comments and suggestions greatly improved the quality of this paper. Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/rs12223830 fatcat:j5qelcpjczco7i3s7h3vrnpj2q

LiDAR-based Panoptic Segmentation via Dynamic Shifting Network [article]

Fangzhou Hong, Hui Zhou, Xinge Zhu, Hongsheng Li, Ziwei Liu
2020 arXiv   pre-print
DS-Net adopts the cylinder convolution that is specifically designed for LiDAR point clouds.  ...  As one of the first endeavors towards this new challenging task, we propose the Dynamic Shifting Network (DS-Net), which serves as an effective panoptic segmentation framework in the point cloud realm.  ...  of LiDAR point clouds.  ... 
arXiv:2011.11964v2 fatcat:cpoofyegezf5xib6ybbi2xtuga

Monocular 3D Object Detection with Pseudo-LiDAR Point Cloud [article]

Xinshuo Weng, Kris Kitani
2019 arXiv   pre-print
Specifically, we perform monocular depth estimation and lift the input image to a point cloud representation, which we call pseudo-LiDAR point cloud.  ...  with its corresponding 2D proposal after projecting onto the image; (2) use the instance mask instead of the bounding box as the representation of 2D proposals, in order to reduce the number of points  ...  ., in the 6th row 1st column and 8th row 2nd column of the Figure 4) .  ... 
arXiv:1903.09847v4 fatcat:vwqdxubedzddnkeuos6eqqgsny

SqueezeSeg: Convolutional Neural Nets with Recurrent CRF for Real-Time Road-Object Segmentation from 3D LiDAR Point Cloud [article]

Bichen Wu, Alvin Wan, Xiangyu Yue, Kurt Keutzer
2017 arXiv   pre-print
In this paper, we address semantic segmentation of road-objects from 3D LiDAR point clouds.  ...  Our CNN model is trained on LiDAR point clouds from the KITTI dataset, and our point-wise segmentation labels are derived from 3D bounding boxes from KITTI.  ...  Semantic segmentation for 3D LiDAR point clouds Previous work saw a wide range of granularity in LiDAR segmentation, handling anything from specific components to the whole pipeline.  ... 
arXiv:1710.07368v1 fatcat:rz46hci6wfgm5alcuivvdifvoi

A Technical Survey and Evaluation of Traditional Point Cloud Clustering Methods for LiDAR Panoptic Segmentation [article]

Yiming Zhao, Xiao Zhang, Xinming Huang
2021 arXiv   pre-print
In contrast to popular end-to-end deep learning solutions, we propose a hybrid method with an existing semantic segmentation network to extract semantic information and a traditional LiDAR point cloud  ...  To our best knowledge, we are the first to attempt the point cloud panoptic segmentation with clustering algorithms.  ...  On the segmentation of 3d lidar point clouds.  ... 
arXiv:2108.09522v1 fatcat:qzp2igtte5bbbm3bgousm5wisa

Analyzing General-Purpose Deep-Learning Detection and Segmentation Models with Images from a Lidar as a Camera Sensor [article]

Yu Xianjia, Sahar Salimpour, Jorge Peña Queralta, Tomi Westerlund
2022 arXiv   pre-print
Rather than processing the three-dimensional point cloud data, this is, to the best of our knowledge, the first work to focus on low-resolution images with 360\textdegree field of view obtained with lidar  ...  This work explores the potential of general-purpose DL perception algorithms, specifically detection and segmentation neural networks, for processing image-like outputs of advanced lidar sensors.  ...  However, while lidar odometry, localization and mapping are at the pinnacle of autonomous technology [9] , the processing of point cloud data for object detection or semantic scene segmentation is not  ... 
arXiv:2203.04064v1 fatcat:wve3nbfw6naqdccpa5sho3rdma

How to Build a Curb Dataset with LiDAR Data for Autonomous Driving [article]

Dongfeng Bai, Tongtong Cao, Jingming Guo, Bingbing Liu
2021 arXiv   pre-print
During the long period of time before wide application of Deep Neural Network (DNN) with point clouds, LiDAR-based curb detection methods are based on hand-crafted features, which suffer from poor detection  ...  Recently, DNN-based dynamic object detection using LiDAR data has become prevalent, while few works pay attention to curb detection with a DNN approach due to lack of labeled data.  ...  Semantic Segmentation with Curbs Semantic segmentation of LiDAR data is a point-wise classification task.  ... 
arXiv:2110.03968v1 fatcat:h7mzha23fbaq5bw57hns6pw27y

Fast Object Classification and Meaningful Data Representation of Segmented Lidar Instances [article]

Lukas Hahn and Frederik Hasecke and Anton Kummert
2020 arXiv   pre-print
We apply our method on the tasks of object detection and semantic segmentation, as well as the relatively new challenge of panoptic segmentation.  ...  We show how our approach uses segmented object instances to extract important features, enabling a computationally efficient batch-wise classification.  ...  We see promising results in recent development of methods for real-time object instance segmentation of Lidar point clouds [10] , [11] .  ... 
arXiv:2006.10011v1 fatcat:mogze4xvrrh3jfnmlcxpytpkae

Input-Output Balanced Framework for Long-tailed LiDAR Semantic Segmentation [article]

Peishan Cong, Xinge Zhu, Yuexin Ma
2021 arXiv   pre-print
A thorough and holistic scene understanding is crucial for autonomous vehicles, where LiDAR semantic segmentation plays an indispensable role.  ...  Specifically, for the input space, we synthesize these tailed instances from mesh models and well simulate the position and density distribution of LiDAR scan, which enhances the input data balance and  ...  RELATED WORK LiDAR point cloud segmentation Different from indoor-scene point cloud segmentation [10, 11, 12] , outdoor LiDAR point cloud segmentation has much more challenges because of the varying  ... 
arXiv:2103.14269v1 fatcat:y6abrhoxlzdbzozf7cvlpwoebq

Ground-distance segmentation of 3D LiDAR point cloud toward autonomous driving

Jian Wu, Qingxiong Yang
2020 APSIPA Transactions on Signal and Information Processing  
This paper is focusing on semantic segmentation of the sparse point clouds obtained from 32-channel LiDAR sensor with deep neural networks.  ...  In this paper, we study the semantic segmentation of 3D LiDAR point cloud data in urban environments for autonomous driving, and a method utilizing the surface information of the ground plane was proposed  ...  segmentation of 3d lidar point cloud toward autonomous driving several super points.  ... 
doi:10.1017/atsip.2020.21 fatcat:ihfiaqckmfaghoxreojsgvoorm

SemanticPOSS: A Point Cloud Dataset with Large Quantity of Dynamic Instances [article]

Yancheng Pan, Biao Gao, Jilin Mei, Sibo Geng, Chengkun Li, Huijing Zhao
2020 arXiv   pre-print
In this paper, we propose the SemanticPOSS dataset, which contains 2988 various and complicated LiDAR scans with large quantity of dynamic instances.  ...  However, the present datasets for 3D semantic segmentation are lack of point-wise annotation, diversiform scenes and dynamic objects.  ...  (a) LiDAR points of only the static objects are visualized. (b) LiDAR points of the dynamic objects are highlighted. (c) Instances of the dynamic objects in a single LiDAR scan.  ... 
arXiv:2002.09147v1 fatcat:744ww2ccrrhu3i3tpxcaixprhi

FLIC: Fast Lidar Image Clustering [article]

Frederik Hasecke and Lukas Hahn and Anton Kummert
2020 arXiv   pre-print
In this work, we propose an algorithmic approach for real-time instance segmentation of Lidar sensor data.  ...  However, due to the potentially large amount of Lidar points per scan, tailored algorithms are required to identify objects (e.g. pedestrians or vehicles) with high precision in a very short time.  ...  This work presents a Lidar point cloud segmentation approach which provides (1) a high level of accuracy in point cloud segmentation, while (2) being able to run in real time, faster than usual sensor  ... 
arXiv:2003.00575v2 fatcat:tyxceijke5ehzoo6ixsfyu6kwi
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