通过平面与线条的交互建模，实现高效高精度3D线映射。
Interacted Planes Reveal 3D Line Mapping
- 提出线与平面联合优化框架，显式建模可学习的线和面原型。
- 在多个数据集上优于当前最优方法，单场景重建仅需3-5分钟。
- 适用于需要结构化重建的室内场景，对视觉定位也有显著提升。
从多视角RGB图像进行3D线映射能提供紧凑且结构化的场景表征。本文从物理与拓扑角度出发：3D线最自然地表现为有限3D平面块的边界。我们提出LiP-Map框架，一种线-平面联合优化方法，显式建模可学习的线与平面原型。该耦合机制在保持强效率的同时实现高精度、高细节的3D线映射（通常每场景3至5分钟完成）。LiP-Map首次将平面拓扑融入3D线映射，不依赖成对共面约束，而是显式构建平面与线原型间的交互，为人工环境中的结构化重建提供了原则性路径。在超过100个来自ScanNetV2、ScanNet++、Hypersim、7Scenes和Tanks&Temple的场景中，该方法在准确率与完整性上均超越现有最优技术。此外，其在线映射质量之外，显著提升了线辅助视觉定位性能，在7Scenes上表现优异。代码已开源：https://github.com/calmke/LiPMAP。
原文摘要 · Abstract (English)
3D line mapping from multi-view RGB images provides a compact and structured visual representation of scenes. We study the problem from a physical and topological perspective: a 3D line most naturally emerges as the edge of a finite 3D planar patch. We present LiP-Map, a line-plane joint optimization framework that explicitly models learnable line and planar primitives. This coupling enables accurate and detailed 3D line mapping while maintaining strong efficiency (typically completing a reconstruction in 3 to 5 minutes per scene). LiP-Map pioneers the integration of planar topology into 3D line mapping, not by imposing pairwise coplanarity constraints but by explicitly constructing interactions between plane and line primitives, thus offering a principled route toward structured reconstruction in man-made environments. On more than 100 scenes from ScanNetV2, ScanNet++, Hypersim, 7Scenes, and Tanks\&Temple, LiP-Map improves both accuracy and completeness over state-of-the-art methods. Beyond line mapping quality, LiP-Map significantly advances line-assisted visual localization, establishing strong performance on 7Scenes. Our code is released at https://github.com/calmke/LiPMAP for reproducible research.
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