Interacted Planes Reveal 3D Line Mapping
This work addresses the problem of creating compact and structured 3D visual representations for man-made environments, offering incremental improvements in line mapping and visual localization.
The paper tackles 3D line mapping from multi-view RGB images by introducing LiP-Map, a joint optimization framework that models line and planar primitives, resulting in improved accuracy and completeness over state-of-the-art methods on over 100 scenes, with reconstructions typically taking 3 to 5 minutes per scene.
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.