MeshCNN

MeshCNN is a specialized deep learning framework that introduces a paradigm shift in how neural networks process 3D data. Unlike traditional 3D models that convert meshes into voxels or point clouds, MeshCNN operates directly on the mesh manifold. It treats mesh edges as the primary units of data, analogous to pixels in an image. The architecture utilizes a unique 'Edge Convolution' layer that aggregates information from the four adjacent edges of the two triangles sharing an edge. This ensures that the network is inherently invariant to rigid transformations like rotation and translation. Furthermore, MeshCNN introduces a specialized 'Mesh Pooling' operation via edge collapse, which allows the network to learn which parts of the topology are redundant and can be simplified, mirroring the pooling layers in standard 2D CNNs. As of 2026, it remains a foundational framework for researchers and engineers in CAD/CAM, medical imaging, and digital twin development, offering high precision in mesh segmentation and classification tasks where topological connectivity is crucial for accuracy.