SweepLSD: A One-Pass, O(width)-Memory Line Segment Detector with an Integer-Only Streaming Core and a Real-Time FPGA Realization
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Abstract (arXiv)
We present SweepLSD, a line segment detector that reads the image exactly once and emits each segment within a few rows of its last pixel passing the scan line. Every stage, including connected-component labeling and the final line test, processes the image as a row stream: intermediate memory is O(width) rather than O(pixels), and the per-pixel core is integer-only. We give the first complete description of the algorithm, designed in the author's 2014 master's thesis but never published, together with an open-source C++17 implementation and an FPGA realization -- held bit-exact against the software in its hardware configuration -- detecting segments in live 1080p30 video on 2009-era silicon without frame buffer or external memory. On structure-rich public 4K photographs downscaled to Full-HD, one CPU thread detects segments in ~11 ms -- 4.6x/5.2x/25x faster than the original authors' implementations of ELSED, EDLines, and LSD -- with the tightest frame-time distribution and the best per-segment direction accuracy of the four detectors, and curve rejection by design, while trailing ELSED in F-score on synthetic ground truth. A Manhattan-frame vanishing-point study on York Urban and NYU-VP scores every detector under a selection/evaluation-separated best-estimator-per-detector protocol, under which SweepLSD leads on NYU-VP by ~0.3 degrees and trails by 0.1 degrees on York Urban, with the fastest end-to-end pipeline of the four detectors on both. A single-frame camera-attitude application, evaluated on synthetic scenes with exact ground truth and on EuRoC and TUM-VI, matches the baselines' accuracy at a fraction of their memory, and drives a 4K horizon lock to 0.06 degrees median attitude error at 32 ms median per frame.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 18 of its 43 references that are in the map. First posted on arXiv 22 Aug 2026.
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18 of 43 references.
References · 18
- 2018The TUM VI Benchmark for Evaluating Visual-Inertial Odometrycited by 99
- 2021Line Segment Detection Using Transformers without Edgescited by 73
- 2019End-to-End Wireframe Parsingcited by 72
- 2020Learning to Parse Wireframes in Images of Man-Made Environmentscited by 71
- 2020Holistically-Attracted Wireframe Parsingcited by 64
- 2022DeepLSD: Line Segment Detection and Refinement with Deep Image Gradientscited by 60
- 2017PL-SLAM: a Stereo SLAM System through the Combination of Points and Line Segmentscited by 59
- 2021SOLD2: Self-supervised Occlusion-aware Line Description and Detectioncited by 47
- 2019A Comprehensive Benchmark for Single Image Compression Artifacts Reductioncited by 33
- 2021Towards Light-weight and Real-time Line Segment Detectioncited by 33
- 2020CONSAC: Robust Multi-Model Fitting by Conditional Sample Consensuscited by 30
- 2022Holistically-Attracted Wireframe Parsing: From Supervised to Self-Supervised Learningcited by 26
6 more
- 2021ELSED: Enhanced Line SEgment Drawingcited by 23
- 2023A Comprehensive Review of Image Line Segment Detection and Description: Taxonomies, Comparisons, and Challengescited by 6
- 2024DT-LSD: Deformable Transformer-based Line Segment Detectioncited by 6
- 2025ScaleLSD: Scalable Deep Line Segment Detection Streamlinedcited by 5
- 2026A Low-Latency ASIC Architecture for Real-Time Line Segment Detectioncited by 2
- 2026MiLSD: A Micro Line-Segment Detector for Resource-Constrained Devicescited by 1
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