Haoyi Xiu 研究室

主宰者：Haoyi Xiu

東京工業大学

AI 要約（直近 5 年の研究成果）

要約はまだ生成されていません。

※ AI（Claude）が、公開されている論文要旨から研究の問い・手法・主要な発見を事実情報として抽出・再構成して自動生成しています。誤りを含む可能性があるため、正確性は研究室公式情報でご確認ください。

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研究成果（33 件）

[2025] Advancing ALS Applications with Large-Scale Pre-Training: Framework, Dataset, and Downstream Assessment
DOI: https://doi.org/10.3390/rs17111859
[2025] Exploring the Potential of Pre-Trained Language Models in Long-Term Semantic Scene Change Prediction Using Variable Scene Graphs
DOI: https://doi.org/10.1145/3746252.3760897
[2025] Customizing Masked Autoencoders for Self-Supervised Learning on Airborne Lidar Point Clouds
DOI: https://doi.org/10.1109/igarss55030.2025.11243149
[2025] Evaluating Segmentation Performance of Learnable Resized Models on Small Features from Sparse Point Cloud-Derived Imagery
DOI: https://doi.org/10.1109/igarss55030.2025.11242434
[2025] DiffRPTraj: Diffusion-Based Realistic Pedestrian Trajectory Generation
DOI: https://doi.org/10.1109/igarss55030.2025.11242860
[2024] Learnable Resized and Laplacian-Filtered U-Net: Better Road Marking Extraction and Classification on Sparse-Point-Cloud-Derived Imagery
DOI: https://doi.org/10.3390/rs16234592
[2023] Spatial Roughness Learning with Neural Networks for Collapsed Building Detection Using Post-Earthquake 3D Point Clouds
[2023] 広域的なハザードマップ基礎情報のための深層学習による旧版地図からの集落・水域の自動抽出
[2023] Diffusion unit: Interpretable edge enhancement and suppression learning for 3D point cloud segmentation
[2023] MSECNet: Accurate and Robust Normal Estimation for 3D Point Clouds by Multi-Scale Edge Conditioning
DOI: https://doi.org/10.1145/3581783.3613762

続きを表示（残り 23 件）

[2023] Accurate and Robust Normal Estimation for 3D PointClouds by Multi-Scale Edge Conditioning
[2023] Diffusion unit: Interpretable edge enhancement and suppression learning for 3D point cloud segmentation
DOI: https://doi.org/10.1016/j.neucom.2023.126780
[2023] Backscattering Characteristics of SAR Images in Damaged Buildings Due to the 2016 Kumamoto Earthquake
DOI: https://doi.org/10.3390/rs15082181
[2023] Backscattering Characteristics of SAR Images in Damaged Buildings due to the 2016 Kumamoto Earthquake
[2023] Optimizing Local Feature Representations of 3D Point Clouds with Anisotropic Edge Modeling
[2023] DS-Net: A dedicated approach for collapsed building detection from post-event airborne point clouds
[2023] Diffusion Unit: Interpretable Edge Enhancement and Suppression Learning for 3D Point Cloud Segmentation
DOI: https://doi.org/10.2139/ssrn.4346396
[2022] U-Netを用いた旧版地図からの水域の抽出
[2022] DS-Net: A dedicated approach for collapsed building detection from post-event airborne point clouds
DOI: https://doi.org/10.1016/j.jag.2022.103150
[2022] Detection of Residential and Water Areas in Historical Topographic Maps of Japan using U-Net
[2021] Image to Point Cloud Translation Using Conditional Generative Adversarial Network for Airborne LiDAR Data
[2021] Enhancing Local Feature Learning for 3D Point Cloud Processing using Unary-Pairwise Attention
[2021] U-Netを用いた旧版地図からの市街地抽出
[2021] Statistical analysis and modeling to examine the exterior and interior building damage pertaining to the 2016 Kumamoto earthquake
DOI: https://doi.org/10.1177/87552930211035408
[2021] 3D Point Cloud Generation Using Adversarial Training for Large-Scale Outdoor Scene
DOI: https://doi.org/10.1109/igarss47720.2021.9554523
[2021] 3D Point Cloud Generation Using Adversarial Training for Large Scale Outdoor Scene
[2021] IMAGE TO POINT CLOUD TRANSLATION USING CONDITIONAL GENERATIVE ADVERSARIAL NETWORK FOR AIRBORNE LIDAR DATA
DOI: https://doi.org/10.5194/isprs-annals-v-2-2021-169-2021
[2021] Point2color: 3D Point Cloud Colorization Using a Conditional Generative Network and Differentiable Rendering for Airborne LiDAR
DOI: https://doi.org/10.1109/cvprw53098.2021.00117
[2021] Point2color: 3D Point Cloud Colorization Using a Conditional Generative Network and Differentiable Rendering for Airborne LiDAR
[2021] Point2Wave: 3-D Point Cloud to Waveform Translation Using a Conditional Generative Adversarial Network With Dual Discriminators
DOI: https://doi.org/10.1109/jstars.2021.3124610
[2021] Statistical Analysis and Modeling to Examine the Exterior and Interior Building Damage Pertaining to the 2016 Kumamoto Earthquake
[2021] Enhancing Local Feature Learning for 3D Point Cloud Processing using Unary-Pairwise Attention
DOI: https://doi.org/10.5244/c.35.190
[2021] Point2Wave: 3-D Point Cloud to Waveform Translation Using a Conditional Generative Adversarial Network With Dual Discriminators

科研費（0 件）

まだデータがありません（KAKEN 取り込み後に表示）。

所属学会・役職（0 件）

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AI 要約（直近 5 年の研究成果）

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関連研究室(8 件)

研究成果（33 件）

科研費（0 件）

所属学会・役職（0 件）