Learning to Reconstruct Symmetric Shapes using Planar Parameterization of 3D Surface

Jain, Hardik; Wöllhaf, Manuel; Hellwich, Olaf

doi:10.1109/iccvw.2019.00508

Cited by 2 publications

(1 citation statement)

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“…For the experiments, 25k models are selected with 9.7k vertices and 19.5k faces on average and a standard deviation of 1.4k vertices and 2.8 faces. When models are not genus-0, they are transformed into genus-0 meshes using the technique previously described in [63]. As is common practice, the model vertices are subjected to artificial Gaussian white noise along their normals n v i .…”

Section: Experiments and Discussion A Datasetsmentioning

confidence: 99%

IMD-Net: A Deep Learning-Based Icosahedral Mesh Denoising Network

2022

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In this work, we propose a novel denoising technique, the icosahedral mesh denoising network (IMD-Net) for closed genus-0 meshes. IMD-Net is a deep neural network that produces a denoised mesh in a single end-to-end pass, preserving and emphasizing natural object features in the process. A preprocessing step, exploiting the homeomorphism between genus-0 mesh and sphere, remeshes an irregular mesh using the regular mesh structure of a frequency subdivided icosahedron. Enabled by gauge equivariant convolutional layers arranged in a residual U-net, IMD-Net denoises the remeshing invariant to global mesh transformations as well as local feature constellations and orientations, doing so with a computational complexity of traditional conv2D kernel. The network is equipped with carefully crafted loss function that leverages differences between positional, normal and curvature fields of target and noisy mesh in a numerically stable fashion. In a first, two large shape datasets commonly used in related fields, ABC and ShapeNetCore, are introduced to evaluate mesh denoising. IMD-Net's competitiveness with existing stateof-the-art techniques is established using both metric evaluations and visual inspection of denoised models.

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Section: Experiments and Discussion A Datasetsmentioning

confidence: 99%