Updated on 2024/11/07

Information

 

写真a

 
NAKASHIMA KAZUTO
 
Organization
Faculty of Information Science and Electrical Engineering Associate Professor
School of Engineering Department of Electrical Engineering and Computer Science(Concurrent)
Graduate School of Information Science and Electrical Engineering Department of Information Science and Technology(Concurrent)
Title
Associate Professor
Contact information
メールアドレス
Profile
コンピュータビジョン・機械学習の研究に従事
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Degree

  • Ph.D.

Research Interests・Research Keywords

  • Research theme:Restoration and domain adaptation on 3D LiDAR data

    Keyword:3D LiDAR, Deep Learning

    Research period: 2020.4

  • Research theme:Outdoor scene understanding using 3D LiDAR sensors

    Keyword:3D LiDAR, Deep Learning

    Research period: 2016.10 - 2020.12

  • Research theme:Visual lifelogging for human-robot symbiosis space

    Keyword:human-robot symbiosis space, visual lifelogging, deep learning

    Research period: 2016.4 - 2020.12

Awards

  • Contribution Award

    2019.11   Joint Workshop on Machine Perception and Robotics (MPR)  

  • Best Poster Presentation Award

    2018.10   Joint Workshop on Machine Perception and Robotics (MPR)  

  • 学生奨励賞

    2017.8   画像の認識・理解シンポジウム (MIRU)  

  • Best Service Robotics Paper Award Finalist

    2017.5   IEEE International Conference on Robotics and Automation (ICRA)  

Papers

  • Learning Viewpoint-Invariant Features for LiDAR-Based Gait Recognition Reviewed International journal

    Jeongho Ahn, Kazuto Nakashima, Koki Yoshino, Yumi Iwashita, Ryo Kurazume

    IEEE Access   2023.11

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    Language:English   Publishing type:Research paper (scientific journal)  

  • Lifelogging Caption Generation via Fourth-Person Vision in a Human-Robot Symbiotic Environment Reviewed International journal

    Kazuto Nakashima, Yumi Iwashita, Ryo Kurazume

    ROBOMECH Journal   2020.9

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    Language:English   Publishing type:Research paper (scientific journal)  

  • Virtual IR Sensing for Planetary Rovers: Improved Terrain Classification and Thermal Inertia Estimation Reviewed International journal

    Yumi Iwashita, Kazuto Nakashima, Joseph Gatto, Shoya Higa, Norris Khoo, Ryo Kurazume, Adrian Stoica

    IEEE Robotics and Automation Letters (RA-L)   2020.8

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    Language:English   Publishing type:Research paper (scientific journal)  

  • Fukuoka Datasets for Place Categorization Reviewed International journal

    Oscar Martinez Mozos, Kazuto Nakashima, Hojung Jung, Yumi Iwashita, Ryo Kurazume

    International Journal of Robotics Research (IJRR)   2019.3

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    Language:English   Publishing type:Research paper (scientific journal)  

  • Learning Geometric and Photometric Features from Panoramic LiDAR Scans for Outdoor Place Categorization Reviewed International journal

    Kazuto Nakashima, Hojung Jung, Yuki Oto, Yumi Iwashita, Ryo Kurazume, Oscar Martinez Mozos

    Advanced Robotics (AR)   2018.7

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    Language:English   Publishing type:Research paper (scientific journal)  

  • RGB-Based Gait Recognition With Disentangled Gait Feature Swapping

    Yoshino, K; Nakashima, K; Ahn, J; Iwashita, Y; Kurazume, R

    IEEE ACCESS   12   115515 - 115531   2024   ISSN:2169-3536

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    Publisher:IEEE Access  

    Gait recognition enables the non-contact identification of individuals from a distance based on their walking patterns and body shapes. For vision-based gait recognition, covariates (e.g., clothing, baggage and background) can negatively impact identification. As a result, many existing studies extract gait features from silhouettes or skeletal information obtained through preprocessing, rather than directly from RGB image sequences. In contrast to preprocessing which relies on the fitting accuracy of models trained on different tasks, disentangled representation learning (DRL) is drawing attention as a method for directly extracting gait features from RGB image sequences. However, DRL learns to extract features of the target attribute from the differences among multiple inputs with various attributes, which means its separation performance depends on the variation and amount of the training data. In this study, aiming to enhance the variation and quantity of each subject's videos, we propose a novel data augmentation pipeline by feature swapping for RGB-based gait recognition. To expand the variety of training data, features of posture and covariates separated through DRL are paired with features extracted from different individuals, which enables the generation of images of subjects with new attributes. Dynamic gait features are extracted through temporal modeling from pose features of each frame, not only from real images but also from generated ones. The experiments demonstrate that the proposed pipeline increases both the quality of generated images and the identification accuracy. The proposed method also outperforms the RGB-based state-of-the-art method in most settings.

    DOI: 10.1109/ACCESS.2024.3445415

    Web of Science

    Scopus

  • Development of a Retrofit Backhoe Teleoperation System Using Cat Command.

    Koshi Shibata, Yuki Nishiura, Yusuke Tamaishi, Kohei Matsumoto, Kazuto Nakashima, Ryo Kurazume

    SII   1486 - 1491   2024   ISBN:9798350312072

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    Publishing type:Research paper (international conference proceedings)  

    DOI: 10.1109/SII58957.2024.10417625

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    Other Link: https://dblp.uni-trier.de/db/conf/sii/sii2024.html#ShibataNTMNK24

  • LiDAR Data Synthesis with Denoising Diffusion Probabilistic Models.

    Kazuto Nakashima, Ryo Kurazume

    ICRA   14724 - 14731   2024   ISSN:10504729 ISBN:9798350384574

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    Publishing type:Research paper (international conference proceedings)  

    DOI: 10.1109/ICRA57147.2024.10611480

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    Other Link: https://dblp.uni-trier.de/db/conf/icra/icra2024.html#NakashimaK24

  • Analysis of Force Applied to Horizontal and Vertical Handrails with Impaired Motor Function.

    Ryoya Kihara, Qi An, Kensuke Takita, Shu Ishiguro, Kazuto Nakashima, Ryo Kurazume

    IEEE/SICE International Symposium on System Integration(SII)   1 - 6   2023   ISSN:2474-2317 ISBN:979-8-3503-9868-7

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    Publishing type:Research paper (international conference proceedings)   Publisher:IEEE  

    DOI: 10.1109/SII55687.2023.10039452

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    Other Link: https://dblp.uni-trier.de/db/conf/sii/sii2023.html#KiharaATINK23

  • Evaluation of ground stiffness using multiple accelerometers on the ground during compaction by vibratory rollers

    Tamaishi Y., Fukuda K., Nakashima K., Maeda R., Matsumoto K., Kurazume R.

    Proceedings of the International Symposium on Automation and Robotics in Construction   262 - 269   2023   ISBN:9780645832204

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    Publisher:Proceedings of the International Symposium on Automation and Robotics in Construction  

    Soil compaction is one of the most important basic elements in construction work because it directly affects the quality of structures. Compaction work using vibratory rollers is generally applied to strengthen ground stiffness, and the method that focuses on the number of compaction cycles is widely used to manage the ground stiffness by vibratory rollers. In contrast to this method, the continuous compaction control (CCC) using accelerometers installed on the vibratory rollers has been proposed as a quantitative evaluation method more suited to actual ground conditions. This method quantifies the distortion rate of the acceleration waveform of the vibratory roller. However, this method based on acceleration response has problems in measurement discrimination accuracy and sensor durability because the accelerometer is installed on the vibration roller, which is the source of vibration. In this paper, we propose a new ground stiffness evaluation method using multiple accelerometers installed on the ground surface. The proposed method measures the acceleration response during compaction work by vibratory rollers using multiple accelerometers on the ground surface. Experiments show the proposed method has a higher discrimination than the conventional methods.

    DOI: 10.22260/ISARC2023/0037

    Scopus

  • Generative Range Imaging for Learning Scene Priors of 3D LiDAR Data.

    Kazuto Nakashima, Yumi Iwashita, Ryo Kurazume

    IEEE/CVF Winter Conference on Applications of Computer Vision(WACV)   1256 - 1266   2023   ISSN:2472-6737 ISBN:978-1-6654-9346-8

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    Publishing type:Research paper (international conference proceedings)   Publisher:IEEE  

    DOI: 10.1109/WACV56688.2023.00131

    Web of Science

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    Other Link: https://dblp.uni-trier.de/db/conf/wacv/wacv2023.html#NakashimaIK23

  • Learning Viewpoint-Invariant Features for LiDAR-Based Gait Recognition.

    Jeongho Ahn, Kazuto Nakashima, Koki Yoshino, Yumi Iwashita, Ryo Kurazume

    IEEE Access   11   129749 - 129762   2023   ISSN:2169-3536

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    Publishing type:Research paper (scientific journal)  

    DOI: 10.1109/ACCESS.2023.3333037

    Web of Science

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  • Development of Distributed Sensor Pods for Evaluation of Compacted Ground

    FUKUDA Kentaro, NAKASHIMA Kazuto, KURAZUME Ryo

    The Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec)   2022 ( 0 )   1A1-E04   2022   eISSN:24243124

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    Language:Japanese   Publisher:The Japan Society of Mechanical Engineers  

    <p>In this study, we develop a sensor terminal with multiple and various sensors named sensor pod, which collects various environmental information at a construction site. The sensor pod is equipped with a 3D-LiDAR and a vibration sensor, which can be used to predict the surrounding hazards and evaluate the ground stiffness. In this paper, we introduce a method of evaluating ground stiffness using the waveform distortion of multi-point synchronized vibration data obtained by the distributed sensor pods.</p>

    DOI: 10.1299/jsmermd.2022.1a1-e04

    CiNii Research

  • 2V-Gait: Gait Recognition using 3D LiDAR Robust to Changes in Walking Direction and Measurement Distance.

    Jeongho Ahn, Kazuto Nakashima, Koki Yoshino, Yumi Iwashita, Ryo Kurazume

    IEEE/SICE International Symposium on System Integration(SII)   602 - 607   2022   ISSN:2474-2317 ISBN:978-1-6654-4540-5

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    Publishing type:Research paper (international conference proceedings)   Publisher:IEEE  

    DOI: 10.1109/SII52469.2022.9708899

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    Other Link: https://dblp.uni-trier.de/db/conf/sii/sii2022.html#AhnNYIK22

  • Gait Recognition using Identity-Aware Adversarial Data Augmentation.

    Koki Yoshino, Kazuto Nakashima, Jeongho Ahn, Yumi Iwashita, Ryo Kurazume

    IEEE/SICE International Symposium on System Integration(SII)   596 - 601   2022   ISSN:2474-2317 ISBN:978-1-6654-4540-5

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    Publishing type:Research paper (international conference proceedings)   Publisher:IEEE  

    DOI: 10.1109/SII52469.2022.9708776

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    Other Link: https://dblp.uni-trier.de/db/conf/sii/sii2022.html#YoshinoNAIK22

  • Understanding Humanitude Care for Sit-to-stand Motion by Wearable Sensors.

    Qi An, Akito Tanaka, Kazuto Nakashima, Hidenobu Sumioka, Masahiro Shiomi, Ryo Kurazume

    IEEE International Conference on Systems, Man, and Cybernetics(SMC)   2022-October   1874 - 1879   2022   ISSN:1062922X ISBN:9781665452588

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    Publishing type:Research paper (international conference proceedings)   Publisher:IEEE  

    DOI: 10.1109/SMC53654.2022.9945156

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    Other Link: https://dblp.uni-trier.de/db/conf/smc/smc2022.html#AnTNSSK22

  • Development of Retrofit Type Backhoe Remote Control System

    NISHIURA Yuki, NAKASHIMA Kazuto, KURAZUME Ryo

    The Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec)   2022 ( 0 )   1P1-C07   2022   eISSN:24243124

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    Language:Japanese   Publisher:The Japan Society of Mechanical Engineers  

    <p>This paper presents a retrofit backhoe remote control system that is inexpensive, compact, and easy to install. The system consists of a remote control system using a teleoperation system embedded by a construction machinery manufacturer and a small robot arm, and a remote sensing system using a multi-core microcomputer.</p>

    DOI: 10.1299/jsmermd.2022.1p1-c07

    CiNii Research

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Presentations

  • Leaning to Drop Points for LiDAR Scan Synthesis International conference

    Kazuto Nakashima, Ryo Kurazume

    IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)  2021.9 

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    Language:English  

    Country:Czech Republic  

  • Generative Range Imaging for Learning Scene Priors of 3D LiDAR Data International conference

    Kazuto Nakashima, Yumi Iwashita, Ryo Kurazume

    IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)  2023.1 

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    Language:English  

    Country:United States  

    Repository Public URL: https://hdl.handle.net/2324/7232999

  • LiDAR Data Synthesis with Denoising Diffusion Probabilistic Models International conference

    Kazuto Nakashima, Ryo Kurazume

    IEEE International Conference on Robotics and Automation (ICRA)  2024.5 

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    Language:English   Presentation type:Oral presentation (general)  

    Country:Japan  

  • Fourth-Person Sensing for Pro-active Services International conference

    Yumi Iwashita, Kazuto Nakashima, Yoonseok Pyo, Ryo Kurazume

    International Conference on Emerging Security Technologies (EST)  2014.9 

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    Language:English   Presentation type:Oral presentation (general)  

    Country:Spain  

  • Fourth-Person Sensing for a Service Robot International conference

    Kazuto Nakashima, Yumi Iwashita, Pyo Yoonseok, Asamichi Takamine, Ryo Kurazume

    IEEE Sensors  2015.11 

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    Language:English   Presentation type:Oral presentation (general)  

    Country:Korea, Republic of  

  • Automatic Houseware Registration System for Informationally-Structured Environment International conference

    Kazuto Nakashima, Julien Girard, Yumi Iwashita, Ryo Kurazume

    IEEE/SICE International Symposium on System Integration (SII)  2016.12 

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    Language:English   Presentation type:Oral presentation (general)  

    Country:Japan  

  • Feasibility Study of IoRT Platform "Big Sensor Box" International conference

    Ryo Kurazume, Yoonseok Pyo, Kazuto Nakashima, Akihiro Kawamura, Tokuo Tsuji

    IEEE International Conference on Robotics and Automation (ICRA)  2017.5 

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    Language:English   Presentation type:Oral presentation (general)  

    Country:Singapore  

  • Previewed Reality: Near-Future Perception System International conference

    Yuta Horikawa, Asuka Egashira, Kazuto Nakashima, Akihiro Kawamura, Ryo Kurazume

    IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)  2017.9 

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    Language:English   Presentation type:Oral presentation (general)  

    Country:Canada  

  • Recognizing Outdoor Scenes by Convolutional Features of Omni-Directional LiDAR Scans International conference

    Kazuto Nakashima, Seungwoo Nham, Hojung Jung, Yumi Iwashita, Ryo Kurazume, Oscar M Mozos

    IEEE/SICE International Symposium on System Integration (SII)  2017.12 

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    Language:English   Presentation type:Oral presentation (general)  

    Country:Taiwan, Province of China  

  • Virtual Sensors Determined Through Machine Learning International conference

    Yumi Iwashita, Adrian Stoica, Kazuto Nakashima, Ryo Kurazume, Jim Torresen

    World Automation Congress (WAC)  2018.6 

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    Language:English   Presentation type:Oral presentation (general)  

    Country:United States  

  • Fourth-person Captioning: Describing Daily Events by Uni-supervised and Tri-regularized Training International conference

    Kazuto Nakashima, Yumi Iwashita, Akihiro Kawamura, Ryo Kurazume

    IEEE International Conference on Systems, Man, and Cybernetics (SMC)  2018.10 

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    Language:English   Presentation type:Oral presentation (general)  

    Country:Japan  

  • TU-Net and TDeepLab: Deep Learning-based Terrain Classification Robust to Illumination Changes, Combining Visible and Thermal Imagery International conference

    Yumi Iwashita, Kazuto Nakashima, Adrian Stoica, Ryo Kurazume

    IEEE Conference on Multimedia Information Processing and Retrieval (MIPR)  2019.3 

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    Language:English   Presentation type:Oral presentation (general)  

    Country:United States  

  • MU-Net: Deep Learning-Based Thermal IR Image Estimation From RGB Image International conference

    Yumi Iwashita, Kazuto Nakashima, Sir Rafol, Adrian Stoica, Ryo Kurazume

    IEEE/CVF Computer Vision and Pattern Recognition Conference Workshops (CVPRW)  2019.6 

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    Language:English   Presentation type:Oral presentation (general)  

    Country:United States  

  • 2V-Gait: Gait Recognition Using 3D LiDAR Robust to Changes in Walking Direction and Measurement Distance International conference

    Jeongho Ahn, Kazuto Nakashima, Koki Yoshino, Yumi Iwashita, Ryo Kurazume

    IEEE/SICE International Symposium on System Integration (SII)  2022.1 

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    Language:English   Presentation type:Oral presentation (general)  

    Country:Norway  

  • Gait Recognition using Identity-Aware Adversarial Data Augmentation International conference

    Koki Yoshino, Kazuto Nakashima, Jeongho Ahn, Yumi Iwashita, Ryo Kurazume

    IEEE/SICE International Symposium on System Integration (SII)  2022.1 

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    Language:English   Presentation type:Oral presentation (general)  

    Country:Norway  

  • Understanding Humanitude Care for Sit-To-Stand Motion by Wearable Sensors International conference

    Qi An, Akito Tanaka, Kazuto Nakashima, Hidenobu Sumioka, Masahiro Shiomi, Ryo Kurazume

    IEEE International Conference on Systems, Man, and Cybernetics (SMC)  2022.10 

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    Language:English   Presentation type:Oral presentation (general)  

    Country:Czech Republic  

  • Analysis of Force Applied to Horizontal and Vertical Handrails with Impaired Motor Function International conference

    Ryoya Kihara, Qi An, Kensuke Takita, Shu Ishiguro, Kazuto Nakashima, Ryo Kurazume

    IEEE/SICE International Symposium on System Integration (SII)  2023.1 

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    Language:English   Presentation type:Oral presentation (general)  

    Country:United States  

  • Evaluation of Ground Stiffness using Multiple Accelerometers on the Ground during Compaction by Vibratory Rollers International conference

    Yusuke Tamaishi, Kentaro Fukuda, Kazuto Nakashima, Ryuichi Maeda, Kohei Matsumoto, Ryo Kurazume

    International Symposium on Automation and Robotics in Construction (ISARC)  2023.7 

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    Language:English   Presentation type:Oral presentation (general)  

    Country:India  

  • ROS2-TMS for Construction: CPS platform for earthwork sites International conference

    Ryuichi Maeda, Kohei Matsumoto, Tomoya Kouno, Tomoya Itsuka, Kazuto Nakashima, Yusuke Tamaishi, Ryo Kurazume

    International Symposium on Artificial Life and Robotics (AROB)  2024.1 

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    Language:English   Presentation type:Oral presentation (invited, special)  

    Country:Japan  

  • Development of a Retrofit Backhoe Teleoperation System using Cat Command International conference

    Koshi Shibata, Yuki Nishiura, Yusuke Tamaishi, Kohei Matsumoto, Kazuto Nakashima, Ryo Kurazume

    IEEE/SICE International Symposium on System Integration (SII)  2024.1 

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    Language:English   Presentation type:Oral presentation (general)  

    Country:Viet Nam  

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MISC

  • Fast LiDAR Upsampling using Conditional Diffusion Models

    Sander Elias Magnussen Helgesen, Kazuto Nakashima, Jim Tørresen, Ryo Kurazume

    CoRR   abs/2405.04889   2024.5

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    The search for refining 3D LiDAR data has attracted growing interest
    motivated by recent techniques such as supervised learning or generative
    model-based methods. Existing approaches have shown the possibilities for using
    diffusion models to generate refined LiDAR data with high fidelity, although
    the performance and speed of such methods have been limited. These limitations
    make it difficult to execute in real-time, causing the approaches to struggle
    in real-world tasks such as autonomous navigation and human-robot interaction.
    In this work, we introduce a novel approach based on conditional diffusion
    models for fast and high-quality sparse-to-dense upsampling of 3D scene point
    clouds through an image representation. Our method employs denoising diffusion
    probabilistic models trained with conditional inpainting masks, which have been
    shown to give high performance on image completion tasks. We introduce a series
    of experiments, including multiple datasets, sampling steps, and conditional
    masks. This paper illustrates that our method outperforms the baselines in
    sampling speed and quality on upsampling tasks using the KITTI-360 dataset.
    Furthermore, we illustrate the generalization ability of our approach by
    simultaneously training on real-world and synthetic datasets, introducing
    variance in quality and environments.

    DOI: 10.48550/arXiv.2405.04889

    arXiv

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    Other Link: http://arxiv.org/pdf/2405.04889v2

  • LiDAR Data Synthesis with Denoising Diffusion Probabilistic Models.

    Kazuto Nakashima, Ryo Kurazume

    CoRR   abs/2309.09256   2023.9

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    Generative modeling of 3D LiDAR data is an emerging task with promising
    applications for autonomous mobile robots, such as scalable simulation, scene
    manipulation, and sparse-to-dense completion of LiDAR point clouds. Existing
    approaches have shown the feasibility of image-based LiDAR data generation
    using deep generative models while still struggling with the fidelity of
    generated data and training instability. In this work, we present R2DM, a novel
    generative model for LiDAR data that can generate diverse and high-fidelity 3D
    scene point clouds based on the image representation of range and reflectance
    intensity. Our method is based on the denoising diffusion probabilistic models
    (DDPMs), which have demonstrated impressive results among generative model
    frameworks and have been significantly progressing in recent years. To
    effectively train DDPMs on the LiDAR domain, we first conduct an in-depth
    analysis regarding data representation, training objective, and spatial
    inductive bias. Based on our designed model R2DM, we also introduce a flexible
    LiDAR completion pipeline using the powerful properties of DDPMs. We
    demonstrate that our method outperforms the baselines on the generation task of
    KITTI-360 and KITTI-Raw datasets and the upsampling task of KITTI-360 datasets.
    Our code and pre-trained weights will be available at
    https://github.com/kazuto1011/r2dm.

    DOI: 10.48550/arXiv.2309.09256

    arXiv

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    Other Link: http://arxiv.org/pdf/2309.09256v1

  • Development of Distributed Sensor Pods for Evaluation of Ground Stiffness and Safety Management at Civil Engineering Fields

    福田健太郎, 中嶋一斗, 玉石祐介, 玉石祐介, 前田龍一, 松本耕平, 倉爪亮

    ロボティクスシンポジア予稿集   28th   2023   ISSN:1881-7300

  • Generative Range Imaging for Learning Scene Priors of 3D LiDAR Data.

    Kazuto Nakashima, Yumi Iwashita, Ryo Kurazume

    CoRR   abs/2210.11750   2022.10

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    3D LiDAR sensors are indispensable for the robust vision of autonomous mobile
    robots. However, deploying LiDAR-based perception algorithms often fails due to
    a domain gap from the training environment, such as inconsistent angular
    resolution and missing properties. Existing studies have tackled the issue by
    learning inter-domain mapping, while the transferability is constrained by the
    training configuration and the training is susceptible to peculiar lossy noises
    called ray-drop. To address the issue, this paper proposes a generative model
    of LiDAR range images applicable to the data-level domain transfer. Motivated
    by the fact that LiDAR measurement is based on point-by-point range imaging, we
    train an implicit image representation-based generative adversarial networks
    along with a differentiable ray-drop effect. We demonstrate the fidelity and
    diversity of our model in comparison with the point-based and image-based
    state-of-the-art generative models. We also showcase upsampling and restoration
    applications. Furthermore, we introduce a Sim2Real application for LiDAR
    semantic segmentation. We demonstrate that our method is effective as a
    realistic ray-drop simulator and outperforms state-of-the-art methods.

    DOI: 10.48550/arXiv.2210.11750

    arXiv

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    Other Link: http://arxiv.org/pdf/2210.11750v1

  • Deep Generative Modeling of 3D LiDAR Data with Implicit Representation

    中嶋一斗, 岩下友美, 倉爪亮

    ロボティクスシンポジア予稿集   27th   2022   ISSN:1881-7300

  • 3D LiDARセンサの点群投影方式による計測距離と歩行方向に対する歩容認証の頑健性評価

    安正鎬, 中嶋一斗, 吉野弘毅, 岩下友美, 岩下友美, 倉爪亮

    日本ロボット学会学術講演会予稿集(CD-ROM)   40th   2022

  • Development of Retrofit Type Backhoe Remote Control System

    西浦悠生, 中嶋一斗, 倉爪亮

    日本機械学会ロボティクス・メカトロニクス講演会講演論文集(CD-ROM)   2022   2022   ISSN:2424-3124

  • A Method for Evaluating Ground Stiffness Based on Waveform Distortion of Multipoint Synchronous Vibration Data

    福田健太郎, 中嶋一斗, 倉爪亮

    建設ロボットシンポジウム論文集(CD-ROM)   20th   2022

  • 歩容特徴の抽出精度向上のための異なる人物間の特徴交換を用いた歩容認証

    吉野弘毅, 中嶋一斗, 安正鎬, 岩下友美, 岩下友美, 倉爪亮

    日本ロボット学会学術講演会予稿集(CD-ROM)   40th   2022

  • Development of Distributed Sensor Pods for Evaluation of Compacted Ground-Quantification of Ground Stiffness Based on Waveform Distortion of Multipoint Synchronous Vibration Data-

    福田健太郎, 中嶋一斗, 倉爪亮

    日本機械学会ロボティクス・メカトロニクス講演会講演論文集(CD-ROM)   2022   2022   ISSN:2424-3124

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Professional Memberships

  • The Robotics Society of Japan

  • IEEE Robotics and Automation Society (RAS)

  • IEEE

Academic Activities

  • 会場担当

    第31回インテリジェント・システム・シンポジウム (FAN 2023)  2023.9

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    Type:Competition, symposium, etc. 

  • Screening of academic papers

    Role(s): Peer review

    2023

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    Type:Peer review 

    Number of peer-reviewed articles in foreign language journals:5

    Proceedings of International Conference Number of peer-reviewed papers:6

    Proceedings of domestic conference Number of peer-reviewed papers:1

  • Screening of academic papers

    Role(s): Peer review

    2022

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    Type:Peer review 

    Number of peer-reviewed articles in foreign language journals:3

    Number of peer-reviewed articles in Japanese journals:1

    Proceedings of International Conference Number of peer-reviewed papers:2

  • Screening of academic papers

    Role(s): Peer review

    2021

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    Type:Peer review 

    Number of peer-reviewed articles in foreign language journals:2

    Proceedings of International Conference Number of peer-reviewed papers:1

  • Screening of academic papers

    Role(s): Peer review

    2020

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    Type:Peer review 

    Proceedings of International Conference Number of peer-reviewed papers:1

  • Screening of academic papers

    Role(s): Peer review

    2019

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    Type:Peer review 

    Proceedings of International Conference Number of peer-reviewed papers:1

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Research Projects

  • 深層生成モデルに基づく写実的なLiDARシミュレータの開発

    2023.4 - 2026.3

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    Authorship:Principal investigator 

  • 深層生成モデルに基づく写実的なLiDARシミュレータの開発

    Grant number:23K16974  2023 - 2025

    日本学術振興会  科学研究費助成事業  若手研究

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    Authorship:Principal investigator  Grant type:Scientific research funding

  • 深層生成モデリングを介した3D LiDARの反射特性学習とSim2Real応用

    2022

    研究スタートプログラム

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    Authorship:Principal investigator  Grant type:On-campus funds, funds, etc.

  • 海洋破砕プラスチックごみ回収ロボットシステムに関する研究開発

    2020.4 - 2025.3

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    Authorship:Coinvestigator(s) 

  • 海洋破砕プラスチックごみ回収ロボットシステムに関する研究開発

    Grant number:20H00230  2020 - 2024

    日本学術振興会  科学研究費助成事業  基盤研究(A)

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    Authorship:Coinvestigator(s)  Grant type:Scientific research funding

  • 複数人称視点に基づく知能化空間の時空間記述とシーン再構成

    2019 - 2020

    日本学術振興会  特別研究員

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    Authorship:Principal investigator  Grant type:Joint research

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Class subject

  • プログラミング演習

    2024.6 - 2024.8   Summer quarter

  • 電気情報工学実験

    2023.10 - 2024.3   Second semester

Media Coverage

  • 九大、センサーポッドで地盤転圧の仕上がり評価 施工の無駄低減

    日刊工業新聞  2022.6

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    九大、センサーポッドで地盤転圧の仕上がり評価 施工の無駄低減

Travel Abroad

  • 2017.10 - 2017.12

    Staying countory name 1:United States   Staying institution name 1:NASA Jet Propulsion Laboratory, California Institute of Technology