3D scanning as a technique to digitize objects in reality and create their 3D
models, is used in many fields and areas. Though the quality of 3D scans
depends on the technical characteristics of the 3D scanner, the common drawback
is the smoothing of fine details, or the edges of an object. We introduce
SepicNet, a novel deep network for the detection and parametrization of sharp
edges in 3D shapes as primitive curves. To make the network end-to-end
trainable, we formulate the curve fitting in a differentiable manner. We
develop an adaptive point cloud sampling technique that captures the sharp
features better than uniform sampling. The experiments were conducted on a
newly introduced large-scale dataset of 50k 3D scans, where the sharp edge
annotations were extracted from their parametric CAD models, and demonstrate
significant improvement over state-of-the-art methods.

介绍了一种新的深度网络 SepicNet 用于检测和参数化 3D 物体的棱角，包括一种自适应点云采样技术，通过大规模数据集的实验表明该方法的边缘检测效果优于现有技术。

SepicNet：基于曲线参数推断的三维形状锐边恢复

SepicNet: Sharp Edges Recovery by Parametric Inference of Curves in 3D  Shapes

Recent success in the field of single image super-resolution (SISR) is
achieved by optimizing deep convolutional neural networks (CNNs) in the image
space with the L1 or L2 loss. However, when trained with these loss functions,
models usually fail to recover sharp edges present in the high-resolution (HR)
images for the reason that the model tends to give a statistical average of
potential HR solutions. During our research, we observe that gradient maps of
images generated by the models trained with the L1 or L2 loss have
significantly lower variance than the gradient maps of the original
high-resolution images. In this work, we propose to alleviate the above issue
by introducing a structure-enhancing loss function, coined Gradient Variance
(GV) loss, and generate textures with perceptual-pleasant details.
Specifically, during the training of the model, we extract patches from the
gradient maps of the target and generated output, calculate the variance of
each patch and form variance maps for these two images. Further, we minimize
the distance between the computed variance maps to enforce the model to produce
high variance gradient maps that will lead to the generation of high-resolution
images with sharper edges. Experimental results show that the GV loss can
significantly improve both Structure Similarity (SSIM) and peak signal-to-noise
ratio (PSNR) performance of existing image super-resolution (SR) deep learning
models.

本文提出了一种名为 Gradient Variance (GV) loss 的结构增强的损失函数，用于训练模型生成高分辨率图像，以解决使用 L1 或 L2 损失函数时模型无法恢复高分辨率图像中锐利边缘的问题。实验结果表明，GV 损失可以显著提高现有图像超分辨率深度学习模型的结构相似性 (SSIM) 和峰值信噪比 (PSNR) 性能。