Semantic scene understanding on mobile device with illumination invariance for the visually impaired

2019 ◽  
Author(s):  
Chengyou Xu ◽  
Kaiwei Wang ◽  
Kailun Yang ◽  
Ruiqi Cheng ◽  
Jian Bai
Keyword(s):  
Mobile Device ◽  
Visually Impaired ◽  
Scene Understanding ◽  
Illumination Invariance ◽  
Semantic Scene

scholarly journals MLRSNet: A multi-label high spatial resolution remote sensing dataset for semantic scene understanding

2020 ◽  
Vol 169 ◽  
pp. 337-350
Author(s):  
Xiaoman Qi ◽  
Panpan Zhu ◽  
Yuebin Wang ◽  
Liqiang Zhang ◽  
Junhuan Peng ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Scene Understanding ◽  
Semantic Scene

scholarly journals PDDL Planning with Natural Language-Based Scene Understanding for UAV-UGV Cooperation

2019 ◽  
Vol 9 (18) ◽  
pp. 3789 ◽  
Author(s):  
Jiyoun Moon ◽  
Beom-Hee Lee
Keyword(s):  
Deep Learning ◽  
Natural Language ◽  
Knowledge Acquisition ◽  
Scene Understanding ◽  
Semantic Knowledge ◽  
Ground Vehicles ◽  
Learning Techniques ◽  
Recent Developments ◽  
Heterogeneous Robots ◽  
Semantic Scene

Natural-language-based scene understanding can enable heterogeneous robots to cooperate efficiently in large and unconstructed environments. However, studies on symbolic planning rarely consider the semantic knowledge acquisition problem associated with the surrounding environments. Further, recent developments in deep learning methods show outstanding performance for semantic scene understanding using natural language. In this paper, a cooperation framework that connects deep learning techniques and a symbolic planner for heterogeneous robots is proposed. The framework is largely composed of the scene understanding engine, planning agent, and knowledge engine. We employ neural networks for natural-language-based scene understanding to share environmental information among robots. We then generate a sequence of actions for each robot using a planning domain definition language planner. JENA-TDB is used for knowledge acquisition storage. The proposed method is validated using simulation results obtained from one unmanned aerial and three ground vehicles.

scholarly journals Scene Understanding for the Visually Impaired Using Visual Sonification by Visual Feature Analysis and Auditory Signatures

2012 ◽  
Vol 12 (9) ◽  
pp. 804-804
Author(s):  
J. Clemons ◽  
Y. Bao ◽  
M. Bagra ◽  
T. Austin ◽  
S. Savarese
Keyword(s):  
Visually Impaired ◽  
Scene Understanding ◽  
Feature Analysis ◽  
Visual Feature

scholarly journals SEMANTIC SCENE UNDERSTANDING FOR THE AUTONOMOUS PLATFORM

2020 ◽  
Vol XLIII-B2-2020 ◽  
pp. 637-644
Author(s):  
B. Vishnyakov ◽  
Y. Blokhinov ◽  
I. Sgibnev ◽  
V. Sheverdin ◽  
A. Sorokin ◽  
...  
Keyword(s):  
Data Collection ◽  
Autonomous Vehicles ◽  
Three Dimensional ◽  
Scene Understanding ◽  
Semantic Segmentation ◽  
Lidar Data ◽  
Point Cloud Segmentation ◽  
Detection Point ◽  
Sensor Platform ◽  
Semantic Scene

Abstract. In this paper we describe a new multi-sensor platform for data collection and algorithm testing. We propose a couple of methods for solution of semantic scene understanding problem for land autonomous vehicles. We describe our approaches for automatic camera and LiDAR calibration; three-dimensional scene reconstruction and odometry calculation; semantic segmentation that provides obstacle recognition and underlying surface classification; object detection; point cloud segmentation. Also, we describe our virtual simulation complex based on Unreal Engine, that can be used for both data collection and algorithm testing. We collected a large database of field and virtual data: more than 1,000,000 real images with corresponding LiDAR data and more than 3,500,000 simulated images with corresponding LiDAR data. All proposed methods were implemented and tested on our autonomous platform; accuracy estimates were obtained on the collected database.

scholarly journals SemanticKITTI: A Dataset for Semantic Scene Understanding of LiDAR Sequences

2019 ◽  
Author(s):  
Jens Behley ◽  
Martin Garbade ◽  
Andres Milioto ◽  
Jan Quenzel ◽  
Sven Behnke ◽  
...  
Keyword(s):  
Scene Understanding ◽  
Semantic Scene

Indoor Semantic Scene Understanding Using 2D-3D Fusion

2021 ◽  
Author(s):  
Muraleekrishna Gopinathan ◽  
Giang Truong ◽  
Jumana Abu-Khalaf
Keyword(s):  
Scene Understanding ◽  
Semantic Scene

Towards 3D LiDAR-based semantic scene understanding of 3D point cloud sequences: The SemanticKITTI Dataset

2021 ◽  
pp. 027836492110067
Author(s):  
Jens Behley ◽  
Martin Garbade ◽  
Andres Milioto ◽  
Jan Quenzel ◽  
Sven Behnke ◽  
...  
Keyword(s):  
Point Cloud ◽  
Scene Understanding ◽  
Semantic Segmentation ◽  
Point Clouds ◽  
Lessons Learned ◽  
Multiple Point ◽  
3D Point Cloud ◽  
Core Capability ◽  
Semantic Scene ◽  
3D Lidar

A holistic semantic scene understanding exploiting all available sensor modalities is a core capability to master self-driving in complex everyday traffic. To this end, we present the SemanticKITTI dataset that provides point-wise semantic annotations of Velodyne HDL-64E point clouds of the KITTI Odometry Benchmark. Together with the data, we also published three benchmark tasks for semantic scene understanding covering different aspects of semantic scene understanding: (1) semantic segmentation for point-wise classification using single or multiple point clouds as input; (2) semantic scene completion for predictive reasoning on the semantics and occluded regions; and (3) panoptic segmentation combining point-wise classification and assigning individual instance identities to separate objects of the same class. In this article, we provide details on our dataset showing an unprecedented number of fully annotated point cloud sequences, more information on our labeling process to efficiently annotate such a vast amount of point clouds, and lessons learned in this process. The dataset and resources are available at http://www.semantic-kitti.org .

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