Robust Multi-Model Fitting through Learning Neighbor Regions

2026-09-14

AI Summary

为解决多模型拟合中的特征利用不足、优化效率低等问题，提出Learning Neighbor Regions框架，通过学习邻域特征和粗到细的策略提高拟合精度与效率。

Abstract

Multi-model fitting involves fitting multiple models accurately in a noisy environment. It is the basis for computer vision tasks such as scene reconstruction and mixed reality. However, its performance is often limited by insufficient feature utilization, inefficient optimization, model overlap, and the non-differentiable pipelines. To overcome these limitations, we introduce a robust coarse-to-fine framework called Learning Neighbor Regions (LNR). Recognizing that substantial computational resources are wasted on numerous bad minimum sets, we propose the coarse-level module. This module utilizes a neural network to extract and analyze geometric feature of both local point-wise relationships and global contextual information in minimum sets, outputting confidence to pre-select a small number of good minimum sets, thereby enhancing overall efficiency before solving hypotheses. To address model overlap, LNR encodes neighbor region features for each hypothesis in its fine-level module. These region features consist of geometric features of neighboring data points, which can be used by multiple regions simultaneously. This design allows the neural network to individually refine and score each hypothesis. Importantly, LNR is trained to learn directly from data point features rather than from the hypothesis parameters, thus avoiding differentiating the sampling process and the model solvers. Extensive experiments on four classic multi-model fitting tasks demonstrate that LNR achieves state-of-the-art performance. The analysis suggests that LNR can be easily adapted to various robust multi-model fitting tasks.

Problem

• Multi-model fitting
• Noisy environment
• Feature utilization
• Optimization
• Model overlap

Innovation

• Learning Neighbor Regions
• coarse-to-fine framework
• geometric feature extraction
• neighbor region features
• differentiation-free learning