3D Line Mapping Revisited (2303.17504v1)

Abstract: In contrast to sparse keypoints, a handful of line segments can concisely encode the high-level scene layout, as they often delineate the main structural elements. In addition to offering strong geometric cues, they are also omnipresent in urban landscapes and indoor scenes. Despite their apparent advantages, current line-based reconstruction methods are far behind their point-based counterparts. In this paper we aim to close the gap by introducing LIMAP, a library for 3D line mapping that robustly and efficiently creates 3D line maps from multi-view imagery. This is achieved through revisiting the degeneracy problem of line triangulation, carefully crafted scoring and track building, and exploiting structural priors such as line coincidence, parallelism, and orthogonality. Our code integrates seamlessly with existing point-based Structure-from-Motion methods and can leverage their 3D points to further improve the line reconstruction. Furthermore, as a byproduct, the method is able to recover 3D association graphs between lines and points / vanishing points (VPs). In thorough experiments, we show that LIMAP significantly outperforms existing approaches for 3D line mapping. Our robust 3D line maps also open up new research directions. We show two example applications: visual localization and bundle adjustment, where integrating lines alongside points yields the best results. Code is available at https://github.com/cvg/limap.

Overview of "3D Line Mapping Revisited"

This paper addresses the gap between line-based and point-based 3D reconstruction methods by introducing LIMAP, a robust library for 3D line mapping from multi-view imagery. The focus is on leveraging the high-level structural insights provided by line segments, which are ubiquitous in both urban and indoor scenes. The proposed methodology re-examines the inherent challenges of line triangulation and efficiently exploits structural priors such as line coincidence, parallelism, and orthogonality.

Key Contributions

LIMAP makes several significant contributions to the field of 3D line mapping:

1. Enhanced Proposal Generation: The system integrates advanced line triangulation techniques, leveraging associated features like points and vanishing points (VPs) to create stable 3D line segment proposals.
2. Innovative Scoring and Tracking: The paper introduces novel scale-invariant scoring methods, such as perspective distance and InnerSeg distance, that are specifically designed to handle varying endpoint configurations and line segment lengths for more reliable track building.
3. Joint Line and Structure Optimization: A non-linear refinement phase jointly optimizes 3D lines alongside existing 3D points and VPs, incorporating additional structural priors as soft constraints.
4. Robust 3D Line Reconstruction: Extensive experiments demonstrate that LIMAP significantly outperforms existing methods in terms of length recall, precision, and association robustness, even when tested on large datasets with hundreds of images.
5. Practical Applications: The system’s output facilitates advancements in visual localization and bundle adjustment in SfM. Integrating line features alongside points enhances localization accuracy and camera pose optimization.

Numerical Results

• The experiments show that LIMAP consistently achieves higher length recall and precision across various thresholds compared to existing approaches, such as L3D++ and ELSR.
• It also supports extensive track association, which is critical for downstream applications such as visual localization.

Implications and Future Directions

The introduction of LIMAP marks a promising step toward utilizing structural line features in 3D mapping. This robust integration into existing point-based Structure-from-Motion frameworks encourages a more holistic approach to scene understanding and mapping. The research suggests several avenues for future exploration:

• Incremental Mapping: Integrating LIMAP into real-time or incremental reconstruction systems could significantly enhance their robustness and accuracy, particularly in dynamic environments.
• Enhanced Detection and Matching: Further research into line detection and matching can provide additional gains in robustness and accuracy, potentially simplifying the current extensive geometric verification process.
• Applications in Scene Understanding: Exploiting the structural priors can open up new directions in 3D understanding tasks, such as object recognition in cluttered scenes or semantic segmentation.

Conclusion

The paper successfully revisits 3D line reconstruction and proposes a scalable, robust solution that bridges the gap with point-based methods. By providing a comprehensive system with readily available code, the authors enable other researchers to build upon their work, fostering future innovations in the field of 3D computer vision.