发表机构
Shanghai Jiao Tong University; Cambridge University（上海交通大学; 剑桥大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对多模型拟合中特征利用不足、优化低效、模型重叠和非可微流程问题，提出学习邻域区域（LNR）粗到细框架，通过神经网络预选优质最小集并编码邻域区域特征，实现最先进性能。
AI 中文摘要
多模型拟合涉及在噪声环境中准确拟合多个模型。它是场景重建和混合现实等计算机视觉任务的基础。然而，其性能常常受到特征利用不足、优化效率低下、模型重叠以及非可微流程的限制。为了克服这些限制，我们引入了一种称为学习邻域区域（LNR）的鲁棒从粗到细框架。认识到大量计算资源被浪费在众多不良最小集上，我们提出了粗粒度模块。该模块利用神经网络提取和分析最小集中局部点关系与全局上下文信息的几何特征，输出置信度以预选少量优质最小集，从而在求解假设之前提高整体效率。为解决模型重叠问题，LNR在其细粒度模块中为每个假设编码邻域区域特征。这些区域特征由相邻数据点的几何特征组成，可同时被多个区域使用。这种设计使得神经网络能够单独细化并评分每个假设。重要的是，LNR被训练为直接从数据点特征学习，而非从假设参数学习，从而避免了对采样过程和模型求解器进行微分。在四个经典多模型拟合任务上的大量实验表明，LNR达到了最先进的性能。分析表明，LNR可以轻松适应各种鲁棒多模型拟合任务。
英文摘要
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.
