scholarly journals Fast 3D Semantic Mapping in Road Scenes

2019 ◽  
Vol 9 (4) ◽  
pp. 631 ◽  
Author(s):  
Xuanpeng Li ◽  
Dong Wang ◽  
Huanxuan Ao ◽  
Rachid Belaroussi ◽  
Dominique Gruyer
Keyword(s):  
3D Reconstruction ◽  
Autonomous Navigation ◽  
Point Cloud ◽  
Semantic Information ◽  
Computational Cost ◽  
Semantic Segmentation ◽  
Semantic Mapping ◽  
3D Point Cloud ◽  
Semantic Inference ◽  
Mapping System

Fast 3D reconstruction with semantic information in road scenes is of great requirements for autonomous navigation. It involves issues of geometry and appearance in the field of computer vision. In this work, we propose a fast 3D semantic mapping system based on the monocular vision by fusion of localization, mapping, and scene parsing. From visual sequences, it can estimate the camera pose, calculate the depth, predict the semantic segmentation, and finally realize the 3D semantic mapping. Our system consists of three modules: a parallel visual Simultaneous Localization And Mapping (SLAM) and semantic segmentation module, an incrementally semantic transfer from 2D image to 3D point cloud, and a global optimization based on Conditional Random Field (CRF). It is a heuristic approach that improves the accuracy of the 3D semantic labeling in light of the spatial consistency on each step of 3D reconstruction. In our framework, there is no need to make semantic inference on each frame of sequence, since the 3D point cloud data with semantic information is corresponding to sparse reference frames. It saves on the computational cost and allows our mapping system to perform online. We evaluate the system on road scenes, e.g., KITTI, and observe a significant speed-up in the inference stage by labeling on the 3D point cloud.

scholarly journals Fast 3D Semantic Mapping on Naturalistic Road Scenes

2019 ◽  
Author(s):  
Xuanpeng Li ◽  
Dong Wang ◽  
Huanxuan Ao ◽  
Rachid Belaroussi ◽  
Dominique Gruyer
Keyword(s):  
Autonomous Navigation ◽  
Semantic Information ◽  
Reference Frames ◽  
Computational Cost ◽  
Semantic Segmentation ◽  
Semantic Mapping ◽  
Semantic Inference ◽  
Spatial Consistency ◽  
Mapping System ◽  
Speed Up

Fast 3D reconstruction with semantic information on road scenes is of great requirements for autonomous navigation. It involves issues of geometry and appearance in the field of computer vision. In this work, we propose a method of fast 3D semantic mapping based on the monocular vision. At present, due to the inexpensive price and easy installation, monocular cameras are widely equipped on recent vehicles for the advanced driver assistance and it is possible to acquire semantic information and 3D map. The monocular visual sequence is used to estimate the camera pose, calculate the depth, predict the semantic segmentation, and finally realize the 3D semantic mapping by combination of the techniques of localization, mapping and scene parsing. Our method recovers the 3D semantic mapping by incrementally transferring 2D semantic information to 3D point cloud. And a global optimization is explored to improve the accuracy of the semantic mapping in light of the spatial consistency. In our framework, there is no need to make semantic inference on each frame of the sequence, since the mesh data with semantic information is corresponding to sparse reference frames. It saves amounts of the computational cost and allows our mapping system to perform online. We evaluate the system on naturalistic road scenes, e.g., KITTI and observe a significant speed-up in the inference stage by labeling on the mesh.

scholarly journals Modeling and Processing of Smart Point Clouds of Cultural Relics with Complex Geometries

2021 ◽  
Vol 10 (9) ◽  
pp. 617
Author(s):  
Su Yang ◽  
Miaole Hou ◽  
Ahmed Shaker ◽  
Songnian Li
Keyword(s):  
Point Cloud ◽  
Semantic Information ◽  
Point Clouds ◽  
Information Modeling ◽  
Complex Geometries ◽  
3D Point Cloud ◽  
Point Cloud Data ◽  
Modeling Framework ◽  
Cloud Data ◽  
Cultural Relics

The digital documentation of cultural relics plays an important role in archiving, protection, and management. In the field of cultural heritage, three-dimensional (3D) point cloud data is effective at expressing complex geometric structures and geometric details on the surface of cultural relics, but lacks semantic information. To elaborate the geometric information of cultural relics and add meaningful semantic information, we propose a modeling and processing method of smart point clouds of cultural relics with complex geometries. An information modeling framework for complex geometric cultural relics was designed based on the concept of smart point clouds, in which 3D point cloud data are organized through the time dimension and different spatial scales indicating different geometric details. The proposed model allows smart point clouds or a subset to be linked with semantic information or related documents. As such, this novel information modeling framework can be used to describe rich semantic information and high-level details of geometry. The proposed information model not only expresses the complex geometric structure of the cultural relics and the geometric details on the surface, but also has rich semantic information, and can even be associated with documents. A case study of the Dazu Thousand-Hand Bodhisattva Statue, which is characterized by a variety of complex geometries, reveals that our proposed framework is capable of modeling and processing the statue with excellent applicability and expansibility. This work provides insights into the sustainable development of cultural heritage protection globally.

scholarly journals Study of the Effect of Exploiting 3D Semantic Segmentation in LiDAR Odometry

2020 ◽  
Vol 10 (16) ◽  
pp. 5657
Author(s):  
Francisco Miguel Moreno ◽  
Carlos Guindel ◽  
José María Armingol ◽  
Fernando García
Keyword(s):  
Point Cloud ◽  
Semantic Information ◽  
Semantic Segmentation ◽  
Raw Data ◽  
Special Relevance ◽  
Original Point

This paper presents a study of how the performance of LiDAR odometry is affected by the preprocessing of the point cloud through the use of 3D semantic segmentation. The study analyzed the estimated trajectories when the semantic information is exploited to filter the original raw data. Different filtering configurations were tested: raw (original point cloud), dynamic (dynamic obstacles are removed from the point cloud), dynamic vehicles (vehicles are removed), far (distant points are removed), ground (the points belonging to the ground are removed) and structure (only structures and objects are kept in the point cloud). The experiments were performed using the KITTI and SemanticKITTI datasets, which feature different scenarios that allowed identifying the implications and relevance of each element of the environment in LiDAR odometry algorithms. The conclusions obtained from this work are of special relevance for improving the efficiency of LiDAR odometry algorithms in all kinds of scenarios.

scholarly journals Feature Extraction from 3D Point Cloud Data Based on Discrete Curves

2013 ◽  
Vol 2013 ◽  
pp. 1-19 ◽  
Author(s):  
Yi An ◽  
Zhuohan Li ◽  
Cheng Shao
Keyword(s):  
Feature Extraction ◽  
Autonomous Navigation ◽  
Point Cloud ◽  
Geometric Features ◽  
3D Point Cloud ◽  
Point Cloud Data ◽  
Principal Curvatures ◽  
Cloud Data ◽  
Discrete Curves ◽  
Threshold Setting

Reliable feature extraction from 3D point cloud data is an important problem in many application domains, such as reverse engineering, object recognition, industrial inspection, and autonomous navigation. In this paper, a novel method is proposed for extracting the geometric features from 3D point cloud data based on discrete curves. We extract the discrete curves from 3D point cloud data and research the behaviors of chord lengths, angle variations, and principal curvatures at the geometric features in the discrete curves. Then, the corresponding similarity indicators are defined. Based on the similarity indicators, the geometric features can be extracted from the discrete curves, which are also the geometric features of 3D point cloud data. The threshold values of the similarity indicators are taken from[0,1], which characterize the relative relationship and make the threshold setting easier and more reasonable. The experimental results demonstrate that the proposed method is efficient and reliable.

scholarly journals ROAD SIGNS DETECTION AND RECOGNITION UTILIZING IMAGES AND 3D POINT CLOUD ACQUIRED BY MOBILE MAPPING SYSTEM

2016 ◽  
Vol XLI-B1 ◽  
pp. 669-673
Author(s):  
Y. H. Li ◽  
T. Shinohara ◽  
T. Satoh ◽  
K. Tachibana
Keyword(s):  
Template Matching ◽  
Point Cloud ◽  
3D Point Cloud ◽  
Mobile Mapping ◽  
Position Information ◽  
Road Signs ◽  
Road Sign ◽  
Mapping System ◽  
Mobile Mapping System ◽  
Detection And Recognition

High-definition and highly accurate road maps are necessary for the realization of automated driving, and road signs are among the most important element in the road map. Therefore, a technique is necessary which can acquire information about all kinds of road signs automatically and efficiently. Due to the continuous technical advancement of Mobile Mapping System (MMS), it has become possible to acquire large number of images and 3d point cloud efficiently with highly precise position information. In this paper, we present an automatic road sign detection and recognition approach utilizing both images and 3D point cloud acquired by MMS. The proposed approach consists of three stages: 1) detection of road signs from images based on their color and shape features using object based image analysis method, 2) filtering out of over detected candidates utilizing size and position information estimated from 3D point cloud, region of candidates and camera information, and 3) road sign recognition using template matching method after shape normalization. The effectiveness of proposed approach was evaluated by testing dataset, acquired from more than 180 km of different types of roads in Japan. The results show a very high success in detection and recognition of road signs, even under the challenging conditions such as discoloration, deformation and in spite of partial occlusions.

Sign in / Sign up

Export Citation Format

Share Document