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#3d-models

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papersSEP 12 04:00 UTC

Canonical Inputs Proposed for Neural Networks on CAD Boundary Representations

A new arXiv paper addresses how the same 3D solid can be described by multiple boundary representations (B-reps) in CAD systems, which creates ambiguity for machine learning models. The authors propose learning canonical inputs so that neural networks operate on the underlying solid rather than the particular file encoding. This aims to make predictions consistent regardless of how a model was originally constructed.

cad/neural-networks/3d-models/geometry