LightGlueStick: a Fast and Robust Glue for Joint Point-Line Matching

• URL: http://arxiv.org/abs/2510.16438v1
• Date: Sat, 18 Oct 2025 10:20:14 GMT
• Title: LightGlueStick: a Fast and Robust Glue for Joint Point-Line Matching
• Authors: Aidyn Ubingazhibov, Rémi Pautrat, Iago Suárez, Shaohui Liu, Marc Pollefeys, Viktor Larsson,
• Abstract summary: LightGlueStick is a lightweight matcher for points and line segments.<n>We show that LightGlueStick establishes a new state-of-the-art across different benchmarks.
• Score: 70.96686341680648
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Lines and points are complementary local features, whose combination has proven effective for applications such as SLAM and Structure-from-Motion. The backbone of these pipelines are the local feature matchers, establishing correspondences across images. Traditionally, point and line matching have been treated as independent tasks. Recently, GlueStick proposed a GNN-based network that simultaneously operates on points and lines to establish matches. While running a single joint matching reduced the overall computational complexity, the heavy architecture prevented real-time applications or deployment to edge devices. Inspired by recent progress in point matching, we propose LightGlueStick, a lightweight matcher for points and line segments. The key novel component in our architecture is the Attentional Line Message Passing (ALMP), which explicitly exposes the connectivity of the lines to the network, allowing for efficient communication between nodes. In thorough experiments we show that LightGlueStick establishes a new state-of-the-art across different benchmarks. The code is available at https://github.com/aubingazhib/LightGlueStick.

Related papers

• Co-PLNet: A Collaborative Point-Line Network for Prompt-Guided Wireframe Parsing [5.175452394220525]
  Existing methods predict lines and junctions separately and reconcile them post-hoc, causing mismatches and reduced robustness.<n>We present Co-PLNet, a point-line collaborative framework that exchanges spatial cues between the two tasks.<n>Experiments on Wireframe and YorkUrban show consistent improvements in accuracy and robustness, together with favorable real-time efficiency.
  arXiv  Detail & Related papers  (2026-01-26T08:16:02Z)
• PVINet: Point-Voxel Interlaced Network for Point Cloud Compression [83.74785652597248]
  In point cloud compression, the quality of a reconstructed point cloud relies on both the global structure and the local context.<n>We propose a point-voxel interlaced network (PVINet), which captures global structural features and local contextual features in parallel.<n>PVINet delivers competitive performance compared to state-of-the-art methods.
  arXiv  Detail & Related papers  (2025-09-01T03:37:32Z)
• ReHub: Linear Complexity Graph Transformers with Adaptive Hub-Spoke Reassignment [22.479739056274937]
  We present ReHub, a graph transformer architecture that achieves linear complexity through an efficient reassignment technique.<n>ReHub draws inspiration from the airline industry's hub-and-spoke model, where flights are assigned to a fixed number of virtual nodes.<n>Our experiments on LRGB indicate a consistent improvement in results over the base method, Neural Atoms, while maintaining a linear complexity.
  arXiv  Detail & Related papers  (2024-12-02T14:10:23Z)
• DHGCN: Dynamic Hop Graph Convolution Network for Self-Supervised Point Cloud Learning [23.048005152646592]
  We propose the Dynamic Hop Graph Convolution Network (DHGCN) for explicitly learning the contextual relationships between point parts. We devise a novel self-supervised part-level hop distance reconstruction task and design a novel loss function accordingly to facilitate training. The proposed DHGCN is a plug-and-play module that is compatible with point-based backbone networks.
  arXiv  Detail & Related papers  (2024-01-05T02:54:23Z)
• SegNetr: Rethinking the local-global interactions and skip connections in U-shaped networks [1.121518046252855]
  U-shaped networks have dominated the field of medical image segmentation due to their simple and easily tuned structure. We introduce a novel SegNetr block that can perform local-global interactions dynamically at any stage and with only linear complexity. We validate the effectiveness of SegNetr on four mainstream medical image segmentation datasets, with 59% and 76% fewer parameters and GFLOPs than vanilla U-Net.
  arXiv  Detail & Related papers  (2023-07-06T12:39:06Z)
• IDLS: Inverse Depth Line based Visual-Inertial SLAM [9.38589798999922]
  Inverse Depth Line SLAM (IDLS) is proposed to track the line features in SLAM in an accurate and efficient way. IDLS is extensively evaluated in multiple perceptually-challenging datasets.
  arXiv  Detail & Related papers  (2023-04-23T20:53:05Z)
• GlueStick: Robust Image Matching by Sticking Points and Lines Together [64.18659491529382]
  This paper introduces a new matching paradigm, where points, lines and descriptors are unified into a single wireframe structure. We show that our strategy outperforms that of other matching approaches independently.
  arXiv  Detail & Related papers  (2023-04-04T17:58:14Z)
• SOLD2: Self-supervised Occlusion-aware Line Description and Detection [95.8719432775724]
  We introduce the first joint detection and description of line segments in a single deep network. Our method does not require any annotated line labels and can therefore generalize to any dataset. We evaluate our approach against previous line detection and description methods on several multi-view datasets.
  arXiv  Detail & Related papers  (2021-04-07T19:27:17Z)
• SOE-Net: A Self-Attention and Orientation Encoding Network for Point Cloud based Place Recognition [50.9889997200743]
  We tackle the problem of place recognition from point cloud data with a self-attention and orientation encoding network (SOE-Net) SOE-Net fully explores the relationship between points and incorporates long-range context into point-wise local descriptors. Experiments on various benchmark datasets demonstrate superior performance of the proposed network over the current state-of-the-art approaches.
  arXiv  Detail & Related papers  (2020-11-24T22:28:25Z)
• Graph Neural Networks with Composite Kernels [60.81504431653264]
  We re-interpret node aggregation from the perspective of kernel weighting. We present a framework to consider feature similarity in an aggregation scheme. We propose feature aggregation as the composition of the original neighbor-based kernel and a learnable kernel to encode feature similarities in a feature space.
  arXiv  Detail & Related papers  (2020-05-16T04:44:29Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.