Multi-instance robust fitting for non-classical geometric models

TL;DR

This paper introduces a novel multi-instance robust fitting method for non-classical geometric models, capable of handling outliers and reconstructing multiple instances from noisy data using a non-differentiable estimator and a meta-heuristic optimizer.

Contribution

It presents a new estimator based on model-to-data error for non-classical models and employs a meta-heuristic optimizer to find global solutions, addressing outliers without predefined thresholds.

Findings

Effective in reconstructing multiple non-classical model instances

Handles outliers without predefined error thresholds

Demonstrates robustness on various non-classical models

Abstract

Most existing robust fitting methods are designed for classical models, such as lines, circles, and planes. In contrast, fewer methods have been developed to robustly handle non-classical models, such as spiral curves, procedural character models, and free-form surfaces. Furthermore, existing methods primarily focus on reconstructing a single instance of a non-classical model. This paper aims to reconstruct multiple instances of non-classical models from noisy data. We formulate this multi-instance fitting task as an optimization problem, which comprises an estimator and an optimizer. Specifically, we propose a novel estimator based on the model-to-data error, capable of handling outliers without a predefined error threshold. Since the proposed estimator is non-differentiable with respect to the model parameters, we employ a meta-heuristic algorithm as the optimizer to seek the global optimum. The effectiveness of our method are demonstrated through experimental results on various non-classical models. The code is available at https://github.com/zhangzongliang/fitting.