In order to enhance the normal-guided sampling technique for handling complex geometric structures and noisy input data, several strategies can be implemented:
Adaptive Sampling Density: Implementing an adaptive sampling density based on the local curvature of the surface can help in capturing intricate details of complex geometries. By increasing the sampling density in regions with high curvature, the method can better represent the surface.
Noise Filtering: Incorporating noise filtering techniques, such as outlier removal or denoising algorithms, before sampling along the normals can help in reducing the impact of noise on the sampled distance values. This can improve the accuracy of the signed distance field estimation.
Multi-Scale Sampling: Utilizing a multi-scale sampling approach where points are sampled at different scales along the normal direction can provide a more comprehensive representation of the surface geometry. This can help in capturing both fine details and overall shape characteristics.
Surface Feature Detection: Integrating surface feature detection algorithms to identify key geometric features, such as edges or corners, and adapt the sampling strategy accordingly. This can ensure that important structural elements are accurately represented in the signed distance field.
Robust Truncation Handling: Implementing robust truncation handling mechanisms to address the challenges posed by complex structures and noisy data. By dynamically adjusting the truncation interval based on local surface properties, the method can better handle varying geometric complexities.