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Applications of Sensor Array and Multi-channel Signal Processing

Time Difference of Arrival Estimation from Frequency-sliding Generalized Cross-Correlations Using Convolutional Neural Networks​


The interest in deep learning methods for solving traditional signal processing tasks has been steadily growing in the last years.

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Authors:
Luca Comanducci, Maximo Cobos, Fabio Antonacci, Augusto Sarti
Submitted On:
20 May 2020 - 3:02pm
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Presentation

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[1] Luca Comanducci, Maximo Cobos, Fabio Antonacci, Augusto Sarti, "Time Difference of Arrival Estimation from Frequency-sliding Generalized Cross-Correlations Using Convolutional Neural Networks​", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5419. Accessed: Jul. 04, 2020.
@article{5419-20,
url = {http://sigport.org/5419},
author = {Luca Comanducci; Maximo Cobos; Fabio Antonacci; Augusto Sarti },
publisher = {IEEE SigPort},
title = {Time Difference of Arrival Estimation from Frequency-sliding Generalized Cross-Correlations Using Convolutional Neural Networks​},
year = {2020} }
TY - EJOUR
T1 - Time Difference of Arrival Estimation from Frequency-sliding Generalized Cross-Correlations Using Convolutional Neural Networks​
AU - Luca Comanducci; Maximo Cobos; Fabio Antonacci; Augusto Sarti
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5419
ER -
Luca Comanducci, Maximo Cobos, Fabio Antonacci, Augusto Sarti. (2020). Time Difference of Arrival Estimation from Frequency-sliding Generalized Cross-Correlations Using Convolutional Neural Networks​. IEEE SigPort. http://sigport.org/5419
Luca Comanducci, Maximo Cobos, Fabio Antonacci, Augusto Sarti, 2020. Time Difference of Arrival Estimation from Frequency-sliding Generalized Cross-Correlations Using Convolutional Neural Networks​. Available at: http://sigport.org/5419.
Luca Comanducci, Maximo Cobos, Fabio Antonacci, Augusto Sarti. (2020). "Time Difference of Arrival Estimation from Frequency-sliding Generalized Cross-Correlations Using Convolutional Neural Networks​." Web.
1. Luca Comanducci, Maximo Cobos, Fabio Antonacci, Augusto Sarti. Time Difference of Arrival Estimation from Frequency-sliding Generalized Cross-Correlations Using Convolutional Neural Networks​ [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5419

User Tuneable Sound Zones


Creating sound zones has been an active area of research since it was first introduced. Generally, this can be done either by maximizing an acoustic contrast that represents the acoustic potential energy ratio between the bright and dark zones or by minimizing a reproduction error between the desired and reproduced sound fields. However, the former suffers from severe distortion in the reproduced sound field, whereas the latter suffers from poor acoustic contrast.

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Authors:
Taewoong Lee, Liming Shi, Jesper Kjær Nielsen, Mads Græsbøll Christensen
Submitted On:
14 May 2020 - 4:38am
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slides

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[1] Taewoong Lee, Liming Shi, Jesper Kjær Nielsen, Mads Græsbøll Christensen, "User Tuneable Sound Zones", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5269. Accessed: Jul. 04, 2020.
@article{5269-20,
url = {http://sigport.org/5269},
author = {Taewoong Lee; Liming Shi; Jesper Kjær Nielsen; Mads Græsbøll Christensen },
publisher = {IEEE SigPort},
title = {User Tuneable Sound Zones},
year = {2020} }
TY - EJOUR
T1 - User Tuneable Sound Zones
AU - Taewoong Lee; Liming Shi; Jesper Kjær Nielsen; Mads Græsbøll Christensen
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5269
ER -
Taewoong Lee, Liming Shi, Jesper Kjær Nielsen, Mads Græsbøll Christensen. (2020). User Tuneable Sound Zones. IEEE SigPort. http://sigport.org/5269
Taewoong Lee, Liming Shi, Jesper Kjær Nielsen, Mads Græsbøll Christensen, 2020. User Tuneable Sound Zones. Available at: http://sigport.org/5269.
Taewoong Lee, Liming Shi, Jesper Kjær Nielsen, Mads Græsbøll Christensen. (2020). "User Tuneable Sound Zones." Web.
1. Taewoong Lee, Liming Shi, Jesper Kjær Nielsen, Mads Græsbøll Christensen. User Tuneable Sound Zones [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5269

RAW WAVEFORM BASED END-TO-END DEEP CONVOLUTIONAL NETWORK FOR SPATIAL LOCALIZATION OF MULTIPLE ACOUSTIC SOURCES


In this paper, we present an end-to-end deep convolutional neural network operating on multi-channel raw audio data to localize multiple simultaneously active acoustic sources in space. Previously reported end-to-end deep learning based approaches work well in localizing a single source directly from multi-channel raw-audio, but are not easily extendable to localize multiple sources due to the well known permutation problem.

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Authors:
Harshavardhan Sundar, Weiran Wang, Ming Sun, Chao Wang
Submitted On:
3 May 2020 - 3:51pm
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Raw Waveform based MSL

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[1] Harshavardhan Sundar, Weiran Wang, Ming Sun, Chao Wang, "RAW WAVEFORM BASED END-TO-END DEEP CONVOLUTIONAL NETWORK FOR SPATIAL LOCALIZATION OF MULTIPLE ACOUSTIC SOURCES", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5118. Accessed: Jul. 04, 2020.
@article{5118-20,
url = {http://sigport.org/5118},
author = {Harshavardhan Sundar; Weiran Wang; Ming Sun; Chao Wang },
publisher = {IEEE SigPort},
title = {RAW WAVEFORM BASED END-TO-END DEEP CONVOLUTIONAL NETWORK FOR SPATIAL LOCALIZATION OF MULTIPLE ACOUSTIC SOURCES},
year = {2020} }
TY - EJOUR
T1 - RAW WAVEFORM BASED END-TO-END DEEP CONVOLUTIONAL NETWORK FOR SPATIAL LOCALIZATION OF MULTIPLE ACOUSTIC SOURCES
AU - Harshavardhan Sundar; Weiran Wang; Ming Sun; Chao Wang
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5118
ER -
Harshavardhan Sundar, Weiran Wang, Ming Sun, Chao Wang. (2020). RAW WAVEFORM BASED END-TO-END DEEP CONVOLUTIONAL NETWORK FOR SPATIAL LOCALIZATION OF MULTIPLE ACOUSTIC SOURCES. IEEE SigPort. http://sigport.org/5118
Harshavardhan Sundar, Weiran Wang, Ming Sun, Chao Wang, 2020. RAW WAVEFORM BASED END-TO-END DEEP CONVOLUTIONAL NETWORK FOR SPATIAL LOCALIZATION OF MULTIPLE ACOUSTIC SOURCES. Available at: http://sigport.org/5118.
Harshavardhan Sundar, Weiran Wang, Ming Sun, Chao Wang. (2020). "RAW WAVEFORM BASED END-TO-END DEEP CONVOLUTIONAL NETWORK FOR SPATIAL LOCALIZATION OF MULTIPLE ACOUSTIC SOURCES." Web.
1. Harshavardhan Sundar, Weiran Wang, Ming Sun, Chao Wang. RAW WAVEFORM BASED END-TO-END DEEP CONVOLUTIONAL NETWORK FOR SPATIAL LOCALIZATION OF MULTIPLE ACOUSTIC SOURCES [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5118

LEAK DETECTION OF SPACECRAFT IN ORBIT USING ULTRASONIC SENSOR ARRAY BY LAMB WAVES


With the increasing of human space activities, the number of space debris has increased dramatically, the possibility that spacecraft in orbit is impacted by space debris is growing. It is important to detect and locate the gas leak accurately and timely. In this paper, a leak detection method using ultrasonic sensor array is proposed. Firstly, the ultrasonic sensor array is used to detect the leak acoustic signal which propagates as Lamb wave through spacecraft structure. Then we apply beam forming algorithm to determine the direction of the leak source.

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Authors:
Lei Qi, Lichen Sun, Donghui Meng, Rongxin Yan, Wei Sun
Submitted On:
23 February 2020 - 3:59am
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LEAK DETECTION OF SPACECRAFT IN ORBIT USING ULTRASONIC SENSOR ARRAY BY LAMB WAVES.doc

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[1] Lei Qi, Lichen Sun, Donghui Meng, Rongxin Yan, Wei Sun, "LEAK DETECTION OF SPACECRAFT IN ORBIT USING ULTRASONIC SENSOR ARRAY BY LAMB WAVES", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/4993. Accessed: Jul. 04, 2020.
@article{4993-20,
url = {http://sigport.org/4993},
author = { Lei Qi; Lichen Sun; Donghui Meng; Rongxin Yan; Wei Sun },
publisher = {IEEE SigPort},
title = {LEAK DETECTION OF SPACECRAFT IN ORBIT USING ULTRASONIC SENSOR ARRAY BY LAMB WAVES},
year = {2020} }
TY - EJOUR
T1 - LEAK DETECTION OF SPACECRAFT IN ORBIT USING ULTRASONIC SENSOR ARRAY BY LAMB WAVES
AU - Lei Qi; Lichen Sun; Donghui Meng; Rongxin Yan; Wei Sun
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/4993
ER -
Lei Qi, Lichen Sun, Donghui Meng, Rongxin Yan, Wei Sun. (2020). LEAK DETECTION OF SPACECRAFT IN ORBIT USING ULTRASONIC SENSOR ARRAY BY LAMB WAVES. IEEE SigPort. http://sigport.org/4993
Lei Qi, Lichen Sun, Donghui Meng, Rongxin Yan, Wei Sun, 2020. LEAK DETECTION OF SPACECRAFT IN ORBIT USING ULTRASONIC SENSOR ARRAY BY LAMB WAVES. Available at: http://sigport.org/4993.
Lei Qi, Lichen Sun, Donghui Meng, Rongxin Yan, Wei Sun. (2020). "LEAK DETECTION OF SPACECRAFT IN ORBIT USING ULTRASONIC SENSOR ARRAY BY LAMB WAVES." Web.
1. Lei Qi, Lichen Sun, Donghui Meng, Rongxin Yan, Wei Sun. LEAK DETECTION OF SPACECRAFT IN ORBIT USING ULTRASONIC SENSOR ARRAY BY LAMB WAVES [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/4993

Performance Bounds for Displaced Sensor Automotive Radar Imaging


In automotive radar imaging, displaced sensors offer improvement in localization accuracy by jointly processing the data acquired from multiple radar units, each of which may have limited individual resources. In this paper, we derive performance bounds on the estimation error of target parameters processed by displaced sensors that correspond to several independent radars mounted at different locations on the same vehicle. Unlike previous studies, we do not assume a very accurate time synchronization among the sensors.

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15 February 2020 - 1:31am
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ICASSP_2020_displacedSensors (10).pdf

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[1] , "Performance Bounds for Displaced Sensor Automotive Radar Imaging", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/4990. Accessed: Jul. 04, 2020.
@article{4990-20,
url = {http://sigport.org/4990},
author = { },
publisher = {IEEE SigPort},
title = {Performance Bounds for Displaced Sensor Automotive Radar Imaging},
year = {2020} }
TY - EJOUR
T1 - Performance Bounds for Displaced Sensor Automotive Radar Imaging
AU -
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/4990
ER -
. (2020). Performance Bounds for Displaced Sensor Automotive Radar Imaging. IEEE SigPort. http://sigport.org/4990
, 2020. Performance Bounds for Displaced Sensor Automotive Radar Imaging. Available at: http://sigport.org/4990.
. (2020). "Performance Bounds for Displaced Sensor Automotive Radar Imaging." Web.
1. . Performance Bounds for Displaced Sensor Automotive Radar Imaging [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/4990

Simplified 2D mmWave Near-Field Imaging


In this tutorial, simplified signal processing techniques for near-field 2-D image formation is introduced and the specifications of the recorded SAR data samples are detailed.

The source code and example data set can be accessed via the following links:

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Authors:
Murat Torlak
Submitted On:
27 November 2019 - 1:29am
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SAR_Imaging_Tutorial.pdf

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[1] Murat Torlak, "Simplified 2D mmWave Near-Field Imaging", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4962. Accessed: Jul. 04, 2020.
@article{4962-19,
url = {http://sigport.org/4962},
author = {Murat Torlak },
publisher = {IEEE SigPort},
title = {Simplified 2D mmWave Near-Field Imaging},
year = {2019} }
TY - EJOUR
T1 - Simplified 2D mmWave Near-Field Imaging
AU - Murat Torlak
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4962
ER -
Murat Torlak. (2019). Simplified 2D mmWave Near-Field Imaging. IEEE SigPort. http://sigport.org/4962
Murat Torlak, 2019. Simplified 2D mmWave Near-Field Imaging. Available at: http://sigport.org/4962.
Murat Torlak. (2019). "Simplified 2D mmWave Near-Field Imaging." Web.
1. Murat Torlak. Simplified 2D mmWave Near-Field Imaging [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4962

3-D MIMO-SAR Imaging Using Multi-Chip Cascaded Millimeter-Wave Sensors


Integration of multi-chip cascaded multiple-input multiple-output (MIMO) millimeter-wave (mmWave) sensors with synthetic aperture radar (SAR) imaging will enable cost-effective and scalable solutions for a variety of applications including security, automotive, and surveillance. In this paper, the first three-dimensional (3-D) holographic MIMO-SAR imaging system using cascaded mmWave sensors is designed and implemented. The challenges imposed by the use of cascaded mmWave sensors in high-resolution MIMO-SAR imaging systems are discussed.

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Authors:
Dan Wang, Murat Torlak
Submitted On:
27 November 2019 - 1:14am
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3-D MIMO-SAR Imaging Using Multi-Chip Cascaded Millimeter-Wave Sensors.pdf

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[1] Dan Wang, Murat Torlak, "3-D MIMO-SAR Imaging Using Multi-Chip Cascaded Millimeter-Wave Sensors", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4947. Accessed: Jul. 04, 2020.
@article{4947-19,
url = {http://sigport.org/4947},
author = {Dan Wang; Murat Torlak },
publisher = {IEEE SigPort},
title = {3-D MIMO-SAR Imaging Using Multi-Chip Cascaded Millimeter-Wave Sensors},
year = {2019} }
TY - EJOUR
T1 - 3-D MIMO-SAR Imaging Using Multi-Chip Cascaded Millimeter-Wave Sensors
AU - Dan Wang; Murat Torlak
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4947
ER -
Dan Wang, Murat Torlak. (2019). 3-D MIMO-SAR Imaging Using Multi-Chip Cascaded Millimeter-Wave Sensors. IEEE SigPort. http://sigport.org/4947
Dan Wang, Murat Torlak, 2019. 3-D MIMO-SAR Imaging Using Multi-Chip Cascaded Millimeter-Wave Sensors. Available at: http://sigport.org/4947.
Dan Wang, Murat Torlak. (2019). "3-D MIMO-SAR Imaging Using Multi-Chip Cascaded Millimeter-Wave Sensors." Web.
1. Dan Wang, Murat Torlak. 3-D MIMO-SAR Imaging Using Multi-Chip Cascaded Millimeter-Wave Sensors [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4947

Exploiting Structural Information in Camera Aided Radar Parameter Estimation


The sparse nature of the ranging and spatial angle
parameter space has been exploited by many radar parameter
estimation algorithms in literature. We note that real world
reflections are not sporadically sparse in the parameter space and
typically exhibit smooth variation effects with non-zero entries
occurring in clusters. In this paper, we explicitly model this
additional structural information into our estimation algorithm
and propose a non-convex regularization of the linear observation

Paper Details

Authors:
Khurram Usman Mazher, Ramakrishna Sai Annaluru, Amine Mezghani, Robert Heath
Submitted On:
11 November 2019 - 11:28am
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Exploiting Structural Information in Camera Aided Radar Parameter Estimation.pdf

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[1] Khurram Usman Mazher, Ramakrishna Sai Annaluru, Amine Mezghani, Robert Heath , "Exploiting Structural Information in Camera Aided Radar Parameter Estimation", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4945. Accessed: Jul. 04, 2020.
@article{4945-19,
url = {http://sigport.org/4945},
author = {Khurram Usman Mazher; Ramakrishna Sai Annaluru; Amine Mezghani; Robert Heath },
publisher = {IEEE SigPort},
title = {Exploiting Structural Information in Camera Aided Radar Parameter Estimation},
year = {2019} }
TY - EJOUR
T1 - Exploiting Structural Information in Camera Aided Radar Parameter Estimation
AU - Khurram Usman Mazher; Ramakrishna Sai Annaluru; Amine Mezghani; Robert Heath
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4945
ER -
Khurram Usman Mazher, Ramakrishna Sai Annaluru, Amine Mezghani, Robert Heath . (2019). Exploiting Structural Information in Camera Aided Radar Parameter Estimation. IEEE SigPort. http://sigport.org/4945
Khurram Usman Mazher, Ramakrishna Sai Annaluru, Amine Mezghani, Robert Heath , 2019. Exploiting Structural Information in Camera Aided Radar Parameter Estimation. Available at: http://sigport.org/4945.
Khurram Usman Mazher, Ramakrishna Sai Annaluru, Amine Mezghani, Robert Heath . (2019). "Exploiting Structural Information in Camera Aided Radar Parameter Estimation." Web.
1. Khurram Usman Mazher, Ramakrishna Sai Annaluru, Amine Mezghani, Robert Heath . Exploiting Structural Information in Camera Aided Radar Parameter Estimation [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4945

Compressive Super-Pixel LiDAR for High-Framerate 3D Depth Imaging


We propose a new sampling and reconstruction framework for full frame depth imaging using synchronised, programmable laser diode and photon detector arrays. By adopting a measurement scheme that probes the environment with sparse, pseudo-random patterns, our method enables eyesafe LiDAR operation, while guaranteeing fast reconstruction of

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Authors:
Brian Stewart, Joao F.C. Mota, Andrew M. Wallace
Submitted On:
8 November 2019 - 6:40am
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SuperPixel LiDAR GlobalSIP19 print.pdf

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[1] Brian Stewart, Joao F.C. Mota, Andrew M. Wallace, "Compressive Super-Pixel LiDAR for High-Framerate 3D Depth Imaging", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4930. Accessed: Jul. 04, 2020.
@article{4930-19,
url = {http://sigport.org/4930},
author = {Brian Stewart; Joao F.C. Mota; Andrew M. Wallace },
publisher = {IEEE SigPort},
title = {Compressive Super-Pixel LiDAR for High-Framerate 3D Depth Imaging},
year = {2019} }
TY - EJOUR
T1 - Compressive Super-Pixel LiDAR for High-Framerate 3D Depth Imaging
AU - Brian Stewart; Joao F.C. Mota; Andrew M. Wallace
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4930
ER -
Brian Stewart, Joao F.C. Mota, Andrew M. Wallace. (2019). Compressive Super-Pixel LiDAR for High-Framerate 3D Depth Imaging. IEEE SigPort. http://sigport.org/4930
Brian Stewart, Joao F.C. Mota, Andrew M. Wallace, 2019. Compressive Super-Pixel LiDAR for High-Framerate 3D Depth Imaging. Available at: http://sigport.org/4930.
Brian Stewart, Joao F.C. Mota, Andrew M. Wallace. (2019). "Compressive Super-Pixel LiDAR for High-Framerate 3D Depth Imaging." Web.
1. Brian Stewart, Joao F.C. Mota, Andrew M. Wallace. Compressive Super-Pixel LiDAR for High-Framerate 3D Depth Imaging [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4930

KPSNET: KEYPOINT DETECTION AND FEATURE EXTRACTION FOR POINT CLOUD REGISTRATION


This paper presents the KPSNet, a KeyPoint Siamese Network to simultaneously learn task-desirable keypoint detector and feature extractor. The keypoint detector is optimized to predict a score vector, which signifies the probability of each candidate being a keypoint. The feature extractor is optimized to learn robust features of keypoints by exploiting the correspondence between the keypoints generated from two inputs, respectively. For training, the KPSNet does not require to manually annotate keypoints and local patches pairwise.

Paper Details

Authors:
Xiaoshui Huang, Jian Zhang, Lingxiang Yao, Qiang Wu
Submitted On:
16 September 2019 - 9:58pm
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ICIP.pdf

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[1] Xiaoshui Huang, Jian Zhang, Lingxiang Yao, Qiang Wu, "KPSNET: KEYPOINT DETECTION AND FEATURE EXTRACTION FOR POINT CLOUD REGISTRATION", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4648. Accessed: Jul. 04, 2020.
@article{4648-19,
url = {http://sigport.org/4648},
author = {Xiaoshui Huang; Jian Zhang; Lingxiang Yao; Qiang Wu },
publisher = {IEEE SigPort},
title = {KPSNET: KEYPOINT DETECTION AND FEATURE EXTRACTION FOR POINT CLOUD REGISTRATION},
year = {2019} }
TY - EJOUR
T1 - KPSNET: KEYPOINT DETECTION AND FEATURE EXTRACTION FOR POINT CLOUD REGISTRATION
AU - Xiaoshui Huang; Jian Zhang; Lingxiang Yao; Qiang Wu
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4648
ER -
Xiaoshui Huang, Jian Zhang, Lingxiang Yao, Qiang Wu. (2019). KPSNET: KEYPOINT DETECTION AND FEATURE EXTRACTION FOR POINT CLOUD REGISTRATION. IEEE SigPort. http://sigport.org/4648
Xiaoshui Huang, Jian Zhang, Lingxiang Yao, Qiang Wu, 2019. KPSNET: KEYPOINT DETECTION AND FEATURE EXTRACTION FOR POINT CLOUD REGISTRATION. Available at: http://sigport.org/4648.
Xiaoshui Huang, Jian Zhang, Lingxiang Yao, Qiang Wu. (2019). "KPSNET: KEYPOINT DETECTION AND FEATURE EXTRACTION FOR POINT CLOUD REGISTRATION." Web.
1. Xiaoshui Huang, Jian Zhang, Lingxiang Yao, Qiang Wu. KPSNET: KEYPOINT DETECTION AND FEATURE EXTRACTION FOR POINT CLOUD REGISTRATION [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4648

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