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

Autor: Lin, Fangzhou, Liu, Haotian, Zhou, Haoying, Hou, Songlin, Yamada, Kazunori D, Fischer, Gregory S., Li, Yanhua, Zhang, Haichong K., Zhang, Ziming

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, heavi

Externí odkaz: http://arxiv.org/abs/2409.06171

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DiffCD: A Symmetric Differentiable Chamfer Distance for Neural Implicit Surface Fitting

Autor: Härenstam-Nielsen, Linus, Sang, Lu, Saroha, Abhishek, Araslanov, Nikita, Cremers, Daniel

Neural implicit surfaces can be used to recover accurate 3D geometry from imperfect point clouds. In this work, we show that state-of-the-art techniques work by minimizing an approximation of a one-sided Chamfer distance. This shape metric is not sym

Externí odkaz: http://arxiv.org/abs/2407.17058

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Akademický článek

Influence of diamond layer chamfer parameters on performance of PDC cutters

Autor: Suhui ZHANG, Chuanliu WANG, Geng LI

Publikováno v: Jin'gangshi yu moliao moju gongcheng, Vol 44, Iss 4, Pp 470-475 (2024)

Objectives: The precise control of diamond layer chamfer parameters has a complex influence on the comprehensive performance of PDC cutters. This control not only helps to expand ideas in PDC cutter design but also improves the overall efficiency of

Externí odkaz: https://doaj.org/article/1a7caf16851d474a96b8ab902e4b741c

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Akademický článek

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Akademický článek

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Learnable Chamfer Distance for Point Cloud Reconstruction

Autor: Huang, Tianxin, Liu, Qingyao, Zhao, Xiangrui, Chen, Jun, Liu, Yong

As point clouds are 3D signals with permutation invariance, most existing works train their reconstruction networks by measuring shape differences with the average point-to-point distance between point clouds matched with predefined rules. However, t

Externí odkaz: http://arxiv.org/abs/2312.16582

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CMG-Net: Robust Normal Estimation for Point Clouds via Chamfer Normal Distance and Multi-scale Geometry

Autor: Wu, Yingrui, Zhao, Mingyang, Li, Keqiang, Quan, Weize, Yu, Tianqi, Yang, Jianfeng, Jia, Xiaohong, Yan, Dong-Ming

This work presents an accurate and robust method for estimating normals from point clouds. In contrast to predecessor approaches that minimize the deviations between the annotated and the predicted normals directly, leading to direction inconsistency

Externí odkaz: http://arxiv.org/abs/2312.09154

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