arXiv Computer Vision
Aug 21
Unified and Efficient Point-Line Local Features
arXiv:2608. 19894v1 Announce Type: new Abstract: Multi-view computer vision pipelines typically rely on accurate sparse keypoints and robust descriptors.
Adaptive Dual-Constrained Line Aggregation for Cross-Paradigm Line Segment Detection
The paper introduces Adaptive Dual-Constrained Line Aggregation (ADLA), a framework that extracts line segments across multiple detection paradigms by aggregating pixels from an edge strength map under orientation coherence and bounded orthogonal distance constraints. ADLA dynamically updates line centroids and orientations, incorporates edge strength into various stages, and reduces parameter tuning. Experiments on generic, wireframe, and Manhattan line segment datasets show strong performance, achieving F^H scores of 0.8665, 0.8720, and 0.7297 respectively.
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arXiv AI
Jul 9
MiLSD: A Micro Line-Segment Detector for Resource-Constrained Devices
arXiv:2607. 06600v1 Announce Type: cross Abstract: Line segment detection is a key building block in visual SLAM, 3D reconstruction, and industrial inspection.
arXiv Computer Vision
Aug 25
SweepLSD: A One-Pass, O(width)-Memory Line Segment Detector with an Integer-Only Streaming Core and a Real-Time FPGA Realization
arXiv:2608.22086v1 Announce Type: cross Abstract: 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...
arXiv AI
Aug 11
Structure-Enhanced Features and Quality-Aware Dynamic Anchor Scoring for Robust Lane Detection
arXiv:2608. 09610v1 Announce Type: cross Abstract: Lane detection requires recovering thin, elongated, and frequently occluded lane structures under challenging driving conditions.
arXiv Computer Vision
Sep 1
RealOOB: A Definition-Consistent Real-World Oriented Occlusion Boundary Benchmark
arXiv:2608.30820v1 Announce Type: new Abstract: Occlusion boundaries (OBs) are pixel-level image boundaries corresponding to surface visibility discontinuities caused by occlusion. Through precise bo...
arXiv Computer Vision
Sep 23
NPLSD: Accelerating Line-Segment Detection on NPU Microcontrollers
The paper introduces NPLSD, a pair of line‑segment detectors optimized for the Neural‑ART NPU on STM32N6 microcontrollers. NPLSD‑H retains a convolutional backbone and replaces transformer components with a fully‑convolutional head, while NPLSD‑M adapts a lightweight trunk to the NPU’s supported operators. Trained on ImageNet and ShanghaiTech Wireframe, the models achieve competitive accuracy with 2.63 M and 0.62 M parameters, respectively, and ablation studies show initialization contributes significantly to performance.
arXiv Computer Vision
Sep 15
Pixel-wise Planarity for High-Precision Monocular Plane Segmentation
arXiv:2609.13246v1 Announce Type: new Abstract: Plane segmentation from a single RGB image remains challenging due to imprecise region grouping and geometrically inconsistent supervision, often leadi...
arXiv Machine Learning
Jul 15
Rough Path Signature-Guided Geometry Augmentation for Few-Shot Industrial Surface Defect Detection
arXiv:2607. 12245v1 Announce Type: cross Abstract: Few-shot industrial defect detection remains difficult for standard supervised detectors, which achieve poor performance on boundary-dominated industrial defects.
arXiv Computer Vision
Sep 21
ALINA: Advanced Line Identification and Notation Algorithm
arXiv:2406.08775v2 Announce Type: replace Abstract: Labels are the cornerstone of supervised machine learning algorithms. Most visual recognition methods are fully supervised, using bounding boxes or...
arXiv Computer Vision
Oct 12
A Real-World CAD Segmentation Benchmark: Comparing Rule-Based and Learning-Based Approaches through Hole Detection
The paper introduces a real‑world CAD segmentation benchmark focused on fastener‑relevant hole detection, a task that demands human annotation and presents higher geometric complexity than typical synthetic datasets. It presents two rule‑based methods—a graph‑wire‑search and a multi‑view approach—and trains learning‑based models (UV‑Net and AAGNet) on a newly annotated engineering CAD dataset. Extensive experiments evaluate parameter tuning, cross‑domain generalisation, and performance across dataset sizes and IoU thresholds, revealing that rule‑based methods generalise without training data while learning‑based methods excel with sufficient annotations but are less robust to domain shift. "whyItMatters":"The study provides a realistic benchmark and actionable insights into the trade‑offs between data requirements and robustness for CAD segmentation, informing future research and practical applications in engineering tasks."
Hugging Face Trending Papers
Aug 22
SweepLSD: A One-Pass, O(width)-Memory Line Segment Detector with an Integer-Only Streaming Core and a Real-Time FPGA Realization
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-compon...
arXiv Computer Vision
Oct 22
LLMSeg: Training-Free Text-Prompted Image Segmentation with B-Splines
arXiv:2610.29409v1 Announce Type: new Abstract: Promptable segmentation models such as SAM 3 achieve remarkable open-vocabulary performance but require dedicated architectures, large-scale annotation...