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Yuefan Shen

4 accepted papers

2024

KeypointDETR: An End-to-End 3D Keypoint Detector

ECCV 2024oral

"3D keypoint detection plays a pivotal role in 3D shape analysis. The majority of prevalent methods depend on producing a shared heatmap. This approach necessitates subsequent post-processing techniques such as clustering or non-maximum suppression (NMS) to pinpoint keypoints within high-confidence…

2024

MonoHair: High-Fidelity Hair Modeling from a Monocular Video

CVPR 2024poster

Undoubtedly high-fidelity 3D hair is crucial for achieving realism artistic expression and immersion in computer graphics. While existing 3D hair modeling methods have achieved impressive performance the challenge of achieving high-quality hair reconstruction persists: they either require strict cap…

2022

DCL: Differential Contrastive Learning for Geometry-Aware Depth Synthesis

RA-L 2022

We describe a method for unpaired realistic depth synthesis that learns diverse variations from the real-world depth scans and ensures geometric consistency between the synthetic and synthesized depth. The synthesized realistic depth can then be used to train task-specific networks facilitating labe

Cited by 8SourcecodeScholar
2022

Domain Adaptation on Point Clouds via Geometry-Aware Implicits

CVPR 2022poster

As a popular geometric representation, point clouds have attracted much attention in 3D vision, leading to many applications in autonomous driving and robotics. One important yet unsolved issue for learning on point cloud is that point clouds of the same object can have significant geometric variati…

Cited by 64PDFcodeScholar