Feature-preserving optimization for noisy mesh using joint bilateral filter and constrained Laplacian smoothing

• We propose a joint bilateral filter to remove noises on a mesh while preserving its features.
• We develop a constrained Laplacian smoothing scheme by adding two kinds of constraints.
• By integrating the two steps, our method can effectively remove noises while maintain features.

Advanced 3D optical and laser scanners can generate mesh models with high-resolution details, while inevitably introducing noises from various sources and mesh irregularity due to inconsistent sampling. Noises and irregularity of a scanned model prohibit its use in practical applications where high quality models are required. However, optimizing a noisy mesh while preserving its geometric features is a challenging task. We present a robust two-step approach to meet the challenges of noisy mesh optimization. In the first step, we propose a joint bilateral filter to remove noises on a mesh while maintaining its volume and preserving its features. In the second step, we develop a constrained Laplacian smoothing scheme by adding two kinds of constraints into the original Laplacian equation. As most noises have been removed in the first step, we can easily detect feature edges from the model and add them as constraints in the Laplacian smoothing. As a result, the constrained scheme can simultaneously preserve sharp features and avoid volume shrinkage during mesh smoothing. By integrating these two steps, our approach can effectively remove noises, maintain features, improve regularity for a noisy mesh, as well as avoid side-effects such as volume shrinkage. Extensive qualitative and quantitative experiments have been performed on meshes with synthetic and raw noises to demonstrate the feasibility and effectiveness of our approach.