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Siyu Ren - IEEE Xplore Author Profile
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Siyu Ren

Publications
6
Citations
67
Publications by Year
20202025
Co-Authors:
Show All Co-Authors (11)
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Siyu Ren

Affiliation

Department of Computer Science
City University of Hong Kong
Kowloon Tong, Hong Kong
School of Precision Instruments and Opto-Electronic Engineering
Tianjin University
Tianjin, China

Publication Topics

Biography

Siyu Ren received the B.S. degree from Tianjin University, Tianjin, China, in 2018, where he is currently pursuing the Ph.D. degree. His research interests include computer vision and deep learning.(Based on document published on 2 April 2020).
Publications
6
Citations
67
Publications by Year
20202025
Co-Authors:
Show All Co-Authors (11)
Author's Published Works

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DDM: A Metric for Comparing 3D Shapes Using Directional Distance Fields

Siyu Ren;Junhui Hou;Xiaodong Chen;Hongkai Xiong;Wenping Wang

IEEE Transactions on Pattern Analysis and Machine Intelligence
Year: 2025 | Early Access Article |
Qualifying the discrepancy between 3D geometric models, which could be represented with either point clouds or triangle meshes, is a pivotal issue with board applications. Existing methods mainly focus on directly establishing the correspondence between two models and then aggregating point-wise distance between corresponding points, resulting in them being either inefficient or ineffective. In th...Show More

DDM: A Metric for Comparing 3D Shapes Using Directional Distance Fields

Siyu Ren;Junhui Hou;Xiaodong Chen;Hongkai Xiong;Wenping Wang

Year: 2025 | Early Access Article |

Dynamic 3D Point Cloud Sequences as 2D Videos

Yiming Zeng;Junhui Hou;Qijian Zhang;Siyu Ren;Wenping Wang

IEEE Transactions on Pattern Analysis and Machine Intelligence
Year: 2024 | Volume: 46, Issue: 12 | Journal Article |
Dynamic 3D point cloud sequences serve as one of the most common and practical representation modalities of dynamic real-world environments. However, their unstructured nature in both spatial and temporal domains poses significant challenges to effective and efficient processing. Existing deep point cloud sequence modeling approaches imitate the mature 2D video learning mechanisms by developing co...Show More

Dynamic 3D Point Cloud Sequences as 2D Videos

Yiming Zeng;Junhui Hou;Qijian Zhang;Siyu Ren;Wenping Wang

Year: 2024 | Volume: 46, Issue: 12 | Journal Article |

GeoUDF: Surface Reconstruction from 3D Point Clouds via Geometry-guided Distance Representation

Siyu Ren;Junhui Hou;Xiaodong Chen;Ying He;Wenping Wang

2023 IEEE/CVF International Conference on Computer Vision (ICCV)
Year: 2023 | Conference Paper |
Cited by: Papers (13)
We present a learning-based method, namely GeoUDF, to tackle the long-standing and challenging problem of reconstructing a discrete surface from a sparse point cloud. To be specific, we propose a geometry-guided learning method for UDF and its gradient estimation that explicitly formulates the unsigned distance of a query point as the learnable affine averaging of its distances to the tangent plan...Show More

GeoUDF: Surface Reconstruction from 3D Point Clouds via Geometry-guided Distance Representation

Siyu Ren;Junhui Hou;Xiaodong Chen;Ying He;Wenping Wang

Year: 2023 | Conference Paper |

CorrI2P: Deep Image-to-Point Cloud Registration via Dense Correspondence

Siyu Ren;Yiming Zeng;Junhui Hou;Xiaodong Chen

IEEE Transactions on Circuits and Systems for Video Technology
Year: 2023 | Volume: 33, Issue: 3 | Journal Article |
Cited by: Papers (37)
Motivated by the intuition that the critical step of localizing a 2D image in the corresponding 3D point cloud is establishing 2D-3D correspondence between them, we propose the first feature-based dense correspondence framework for addressing the challenging problem of 2D image-to-3D point cloud registration, dubbed CorrI2P. CorrI2P is mainly composed of three modules, i.e., feature embedding, sym...Show More

CorrI2P: Deep Image-to-Point Cloud Registration via Dense Correspondence

Siyu Ren;Yiming Zeng;Junhui Hou;Xiaodong Chen

Year: 2023 | Volume: 33, Issue: 3 | Journal Article |

Local Foreground Removal Disocclusion Filling Method for View Synthesis

Haitao Liang;Xiaodong Chen;Huaiyuan Xu;Siyu Ren;Huaiyu Cai;Yi Wang

IEEE Access
Year: 2020 | Volume: 8 | Journal Article |
Cited by: Papers (6)
View synthesis is an effective method to generate the contents of multiple views based on a limited number of reference views, which can be used in 2D to 3D conversion, free viewpoint video and multiview video rendering. Depth-image-based rendering (DIBR) is a practical technique to generate virtual view by using a 2D reference view and its depth image. However, a critical problem in DIBR process ...Show More

Local Foreground Removal Disocclusion Filling Method for View Synthesis

Haitao Liang;Xiaodong Chen;Huaiyuan Xu;Siyu Ren;Huaiyu Cai;Yi Wang

Year: 2020 | Volume: 8 | Journal Article |

CrossPatch-Based Rolling Label Expansion for Dense Stereo Matching

Huaiyuan Xu;Xiaodong Chen;Haitao Liang;Siyu Ren;Yi Wang;Huaiyu Cai

IEEE Access
Year: 2020 | Volume: 8 | Journal Article |
Cited by: Papers (11)
We present a novel algorithm called crosspatch-based rolling label expansion for accurate stereo matching. This optimization-based approach can effectively estimate the 3D label of each pixel from huge and infinite label space and then generate a continuous disparity map. The algorithm has two obvious characteristics when compared with the traditional label expansion algorithms. The first feature ...Show More

CrossPatch-Based Rolling Label Expansion for Dense Stereo Matching

Huaiyuan Xu;Xiaodong Chen;Haitao Liang;Siyu Ren;Yi Wang;Huaiyu Cai

Year: 2020 | Volume: 8 | Journal Article |

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