Multi-instance robust fitting for non-classical geometric models

• URL: http://arxiv.org/abs/2602.05602v1
• Date: Thu, 05 Feb 2026 12:38:38 GMT
• Title: Multi-instance robust fitting for non-classical geometric models
• Authors: Zongliang Zhang, Shuxiang Li, Xingwang Huang, Zongyue Wang,
• Abstract summary: This paper aims to reconstruct multiple instances of non-classical models from noisy data.<n>We propose a novel estimator based on the model-to-data error, capable of handling outliers without a predefined error threshold.<n>The effectiveness of our method are demonstrated through experimental results on various non-classical models.
• Score: 2.0914225508247415
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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