Robust Multi-Model Fitting through Learning Neighbor Regions
作者: Chang Nie, Guangming Wang, Zhe Liu, Hesheng Wang
分类: cs.CV
发布日期: 2026-09-14
💡 一句话要点
提出LNR框架以解决多模型拟合中的噪声问题
🎯 匹配领域: 支柱三：空间感知与语义 (Perception & Semantics)
关键词: 多模型拟合 计算机视觉 鲁棒性 神经网络 几何特征 优化效率 混合现实
📋 核心要点
- 现有多模型拟合方法在噪声环境中表现不佳，主要受限于特征利用不足和优化效率低下。
- 本文提出的LNR框架通过神经网络提取几何特征，预选优质最小集，并编码邻域区域特征以解决模型重叠问题。
- 实验结果显示，LNR在四个经典多模型拟合任务中表现优异，达到了最先进的性能水平。
📝 摘要（中文）
多模型拟合涉及在噪声环境中准确拟合多个模型，是计算机视觉任务（如场景重建和混合现实）的基础。然而，现有方法常因特征利用不足、优化效率低、模型重叠及不可微分管道等问题而受限。为克服这些限制，本文提出了一种名为学习邻域区域（LNR）的鲁棒粗到细框架。该框架通过神经网络提取几何特征，预选出少量优质最小集，从而提高整体效率，并通过编码邻域区域特征解决模型重叠问题。LNR通过直接从数据点特征学习，避免了对采样过程和模型求解器的微分。大量实验表明，LNR在四个经典多模型拟合任务中实现了最先进的性能。
🔬 方法详解
问题定义：本文旨在解决多模型拟合中噪声环境对模型准确性的影响，现有方法在特征利用和优化效率上存在明显不足，导致拟合效果不理想。
核心思路：LNR框架通过粗到细的方式，首先利用神经网络提取几何特征，预选出少量优质的最小集，从而提高计算效率，并在细化阶段编码邻域区域特征以处理模型重叠问题。
技术框架：LNR框架分为两个主要模块：粗级模块和细级模块。粗级模块负责提取和分析局部点关系及全局上下文信息，细级模块则对每个假设进行单独的细化和评分。
关键创新：LNR的核心创新在于其通过神经网络直接从数据点特征学习，而非依赖假设参数，从而避免了对采样过程的微分，提升了模型的鲁棒性和效率。
关键设计：在粗级模块中，网络结构设计用于提取几何特征，损失函数则关注于提高假设的置信度；细级模块则通过邻域区域特征的编码，允许多个区域同时使用，提高了模型的适应性和准确性。
🖼️ 关键图片
📊 实验亮点
在四个经典多模型拟合任务中的实验结果显示，LNR框架相比于现有基线方法，性能提升显著，具体表现为在准确率和效率上均达到最先进水平，展示了其优越性和适应性。
🎯 应用场景
该研究的潜在应用领域包括计算机视觉中的场景重建、混合现实、机器人导航等。通过提高多模型拟合的鲁棒性和效率，LNR框架能够在复杂环境中实现更高精度的模型拟合，具有重要的实际价值和广泛的应用前景。
📄 摘要（原文）
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.