# Data-driven quasi-interpolant spline surfaces for point cloud approximation

## TL;DR

This paper introduces a new local surface approximation method called wQISA, optimized for large, noisy point clouds, demonstrating improved prediction and efficiency through extensive real-data comparisons.

## Contribution

The paper presents a novel data-driven implementation of wQISA, enhancing surface approximation for large noisy point clouds with better prediction and computational efficiency.

## Key findings

- wQISA effectively approximates large noisy point clouds
- The method outperforms existing approaches in accuracy
- It demonstrates computational efficiency on real datasets

## Abstract

In this paper we investigate a local surface approximation, the Weighted Quasi Interpolant Spline Approximation (wQISA), specifically designed for large and noisy point clouds. We briefly describe the properties of the wQISA representation and introduce a novel data-driven implementation, which combines prediction capability and complexity efficiency. We provide an extended comparative analysis with other continuous approximations on real data, including different types of surfaces and levels of noise, such as 3D models, terrain data and digital environmental data.