ELSR: Efficient Line Segment Reconstruction with Planes and Points Guidance

IEEE Conference on Computer Vision and Pattern Recognition(2022)

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摘要
Three-dimensional (3D) line segments are helpful for scene reconstruction. Most of the existing 3D-line-segment reconstruction algorithms deal with two views or dozens of small-size images; while in practice there are usually hundreds or thousands of large-size images. In this paper, we propose an efficient line segment reconstruction method called ELSR 1 1 Available at https://skyearth.org/publication/project/ELSR. ELSR exploits scene planes that are commonly seen in city scenes and sparse 3D points that can be acquired easily from the structure-from-motion (SfM) approach. For two views, ELSR efficiently finds the local scene plane to guide the line matching and exploits sparse 3D points to accelerate and constrain the matching. To reconstruct a 3D line segment with multiple views, ELSR utilizes an efficient abstraction approach that selects representative 3D lines based on their spatial consistence. Our experiments demonstrated that ELSR had a higher accuracy and efficiency than the existing methods. Moreover, our results showed that ELSR could reconstruct 3D lines efficiently for large and complex scenes that contain thousands of large-size images.
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关键词
3D from multi-view and sensors, Computer vision theory, Explainable computer vision, Low-level vision, Photogrammetry and remote sensing
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