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MSECNet: Accurate and Robust Normal Estimation for 3D Point Clouds by Multi-Scale Edge Conditioning

Autor: Xiu, Haoyi, Liu, Xin, Wang, Weimin, Kim, Kyoung-Sook, Matsuoka, Masashi

Estimating surface normals from 3D point clouds is critical for various applications, including surface reconstruction and rendering. While existing methods for normal estimation perform well in regions where normals change slowly, they tend to fail

Externí odkaz: http://arxiv.org/abs/2308.02237

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Diffusion Unit: Interpretable Edge Enhancement and Suppression Learning for 3D Point Cloud Segmentation

Autor: Xiu, Haoyi, Liu, Xin, Wang, Weimin, Kim, Kyoung-Sook, Shinohara, Takayuki, Chang, Qiong, Matsuoka, Masashi

3D point clouds are discrete samples of continuous surfaces which can be used for various applications. However, the lack of true connectivity information, i.e., edge information, makes point cloud recognition challenging. Recent edge-aware methods i

Externí odkaz: http://arxiv.org/abs/2209.09483

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Enhancing Local Geometry Learning for 3D Point Cloud via Decoupling Convolution

Autor: Xiu, Haoyi, Liu, Xin, Wang, Weimin, Kim, Kyoung-Sook, Shinohara, Takayuki, Chang, Qiong, Matsuoka, Masashi

Modeling the local surface geometry is challenging in 3D point cloud understanding due to the lack of connectivity information. Most prior works model local geometry using various convolution operations. We observe that the convolution can be equival

Externí odkaz: http://arxiv.org/abs/2207.01181

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Enhancing Local Feature Learning Using Diffusion for 3D Point Cloud Understanding

Autor: Xiu, Haoyi, Liu, Xin, Wang, Weimin, Kim, Kyoung-Sook, Shinohara, Takayuki, Chang, Qiong, Matsuoka, Masashi

Learning point clouds is challenging due to the lack of connectivity information, i.e., edges. Although existing edge-aware methods can improve the performance by modeling edges, how edges contribute to the improvement is unclear. In this study, we p

Externí odkaz: http://arxiv.org/abs/2207.01174

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Enhancing Local Feature Learning for 3D Point Cloud Processing using Unary-Pairwise Attention

Autor: Xiu, Haoyi, Liu, Xin, Wang, Weimin, Kim, Kyoung-Sook, Shinohara, Takayuki, Chang, Qiong, Matsuoka, Masashi

We present a simple but effective attention named the unary-pairwise attention (UPA) for modeling the relationship between 3D point clouds. Our idea is motivated by the analysis that the standard self-attention (SA) that operates globally tends to pr

Externí odkaz: http://arxiv.org/abs/2203.00172

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