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Sharpen&Bend: recovering curved sharp edges in triangle meshes produced by feature-insensitive sampling

机译:锐化与弯曲:恢复特征不敏感采样所产生的三角形网格中的弯曲锐利边缘

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Various acquisition, analysis, visualization, and compression approaches sample surfaces of 3D shapes in a uniform fashion without any attempt to align the samples with sharp edges or to adapt the sampling density to the surface curvature. Consequently, triangle meshes that interpolate these samples usually chamfer sharp features and exhibit a relatively large error in their vicinity. We present two new filters that improve the quality of these resampled models. EdgeSharpener restores the sharp edges by splitting the chamfer edges and forcing the new vertices to lie on intersections of planes extending the smooth surfaces incident upon these chamfers. Bender refines the resulting triangle mesh using an interpolating subdivision scheme that preserves the sharpness of the recovered sharp edges while bending their polyline approximations into smooth curves. A combined Sharpen&Bend postprocessing significantly reduces the error produced by feature-insensitive sampling processes. For example, we have observed that the mean-squared distortion introduced by the SwingWrapper remeshing-based compressor can often be reduced by 80 percent executing EdgeSharpener alone after decompression. For models with curved regions, this error may be further reduced by an additional 60 percent if we follow the EdgeSharpening phase by Bender.
机译:各种采集,分析,可视化和压缩均以统一的方式处理3D形状的样本表面,而无需尝试将样本与锋利的边缘对齐或使采样密度适应表面曲率。因此,内插这些样本的三角形网格通常会倒角出明显的特征,并在其附近显示出较大的误差。我们提出了两个新的过滤器,可以提高这些重采样模型的质量。 EdgeSharpener通过分割倒角边缘并迫使新的顶点位于平面的相交点上,从而恢复了锐利的边缘,这些平面扩展了入射在这些倒角上的平滑表面。 Bender使用插值细分方案细化所得的三角形网格,该方案可保留恢复的尖锐边缘的清晰度,同时将其折线近似值弯曲为平滑曲线。结合的Sharpen&Bend后处理可大大减少因特征不敏感的采样过程而产生的错误。例如,我们已经观察到,基于SwingWrapper重网格的压缩器引入的均方失真通常可以在解压缩后单独执行EdgeSharpener时减少80%。对于具有弯曲区域的模型,如果我们按照Bender的EdgeSharpening阶段操作,此错误可能会进一步减少60%。

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