  Meshes are widely used in 3D computer vision and graphics, but their
irregular topology poses challenges in applying them to existing neural network
architectures. Recent advances in mesh neural networks turn to remeshing and
push the boundary of pioneer methods that solely take the raw meshes as input.
Although the remeshing offers a regular topology that significantly facilitates
the design of mesh network architectures, features extracted from such remeshed
proxies may struggle to retain the underlying geometry faithfully, limiting the
subsequent neural network's capacity. To address this issue, we propose
SieveNet, a novel paradigm that takes into account both the regular topology
and the exact geometry. Specifically, this method utilizes structured mesh
topology from remeshing and accurate geometric information from
distortion-aware point sampling on the surface of the original mesh.
Furthermore, our method eliminates the need for hand-crafted feature
engineering and can leverage off-the-shelf network architectures such as the
vision transformer. Comprehensive experimental results on classification and
segmentation tasks well demonstrate the effectiveness and superiority of our
method.
