Kazunori D Yamada — accepted AI-conference papers

2024

Loss Distillation via Gradient Matching for Point Cloud Completion with Weighted Chamfer Distance

Fangzhou Lin, Haotian Liu, Haoying Zhou, Songlin Hou, Kazunori D Yamada, Gregory S. Fischer +3

IROS 2024poster

3D point clouds enhanced the robot’s ability to perceive the geometrical information of the environments, making it possible for many downstream tasks such as grasp pose detection and scene understanding. The performance of these tasks, though, heavily relies on the quality of data input, as incompl…

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2023

Hyperbolic Chamfer Distance for Point Cloud Completion

Fangzhou Lin, Yun Yue, Songlin Hou, Xuechu Yu, Yajun Xu, Kazunori D Yamada +1

ICCV 2023poster

Chamfer distance (CD) is a standard metric to measure the shape dissimilarity between point clouds in point cloud completion, as well as a loss function for (deep) learning. However, it is well known that CD is vulnerable to outliers, leading to the drift towards suboptimal models. In contrast to th…

Cited by 43 PDF ↗Scholar ↗