Normal improvement for point rendering

TL;DR: It is argued that for point rendering, removing noise from normals is more important than removes noise from geometry, because normals have a greater impact on the model's perceived quality.

Abstract: Models created from 3D scanners are becoming more prevalent as the demand for realistic geometry grows and scanners become more common. Unfortunately, scanned models are invariably noisy. This noise corrupts both samples' positions and normals. Our proposed method for improving normals is derived from a feature-preserving geometry filter. Many such filters are available, most operating on models represented as triangle meshes. We argue that for point rendering, removing noise from normals is more important than removing noise from geometry, because normals have a greater impact on the model's perceived quality. Two approaches for smoothing point models have been proposed. Point set surfaces estimate smoothed normals and geometry by least-squares fitting to locally weighted neighborhoods. The spectral processing method creates a local height field, which is then filtered and resampled. The former method is not feature preserving, while the latter requires resampling to a regular grid, which can degrade features. Our method is novel in that it preserves features and doesn't require resampling.