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Interacted Planes Reveal 3D Line Mapping

About

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.

Zeran Ke, Bin Tan, Gui-Song Xia, Yujun Shen, Nan Xue• 2026

Related benchmarks

TaskDatasetResultRank
Visual Localization7Scenes Stairs
Median Translation Error (cm)3.9
25
Visual Localization7Scenes Heads
Median Translation Error (cm)1
25
Visual Localization7Scenes (Office)
Median Translation Error (cm)2.7
25
Visual Localization7Scenes Fire
Median Translation Error (cm)1.9
25
Visual Localization7Scenes Chess
Median Translation Error (cm)1.9
25
Visual Localization7Scenes RedKitchen
Median Translation Error (cm)3.7
25
Visual Localization7Scenes Pumpkin
Median Translation Error (cm)3.5
25
3D line mappingScanNetV2 15 (50 scenes)
Recall@56.74
16
3D line mappingScanNet++ 16 (30 scenes)
Recall@514.31
16
3D line mappingHypersim 17 (First 10 scenes)
R@1396.3
16
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