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<p>Point cloud registration is a fundamental task in 3D computer vision, with applications spanning robotics, autonomous driving, and augmented reality. While recent advances have improved the extraction of transformation-invariant (TI) features in point clouds, their potential in point registration remains underutilized due to the prevalence of high outlier correspondences. This paper introduces a novel two-channel model that leverages both transformation-variant (TV) and transformation-invariant (TI) features to enhance point cloud registration. Our approach employs a coarse-to-fine feature extraction framework, where a dual-feature attention module utilizes both TI and TV features to refine representations at the coarse level before projecting them to the fine level for accurate correspondence estimation. Extensive experiments on the 3DMatch and KITTI datasets demonstrate the effectiveness of our method, achieving an inlier ratio of 74.6% and a feature matching recall of 98.3% on 3DMatch, and a relative rotation error of 0.24<InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(^{\circ }\)</EquationSource> <EquationSource Format="MATHML"><math> <mmultiscripts> <mrow /> <mrow /> <mo>∘</mo> </mmultiscripts> </math></EquationSource> </InlineEquation> and a relative translation error of 6.6 cm on KITTI. These results highlight the potential of our dual-feature approach in improving point cloud registration accuracy across diverse environments. Our code is available at <a href="http://www.github.com/saifabel/DualFeatAtt">http://www.github.com/saifabel/DualFeatAtt</a>.</p>

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Dual-feature attention for robust point cloud registration: integrating transformation-variant and invariant features

  • 期刊论文

  • 发表日期：

    2026年5月7日

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  • Saifullahi Aminu Bello,
  • Ali Aljofey,
  • Jian Lu,
  • Chen Xu,
  • Yuru Zou

摘要

Point cloud registration is a fundamental task in 3D computer vision, with applications spanning robotics, autonomous driving, and augmented reality. While recent advances have improved the extraction of transformation-invariant (TI) features in point clouds, their potential in point registration remains underutilized due to the prevalence of high outlier correspondences. This paper introduces a novel two-channel model that leverages both transformation-variant (TV) and transformation-invariant (TI) features to enhance point cloud registration. Our approach employs a coarse-to-fine feature extraction framework, where a dual-feature attention module utilizes both TI and TV features to refine representations at the coarse level before projecting them to the fine level for accurate correspondence estimation. Extensive experiments on the 3DMatch and KITTI datasets demonstrate the effectiveness of our method, achieving an inlier ratio of 74.6% and a feature matching recall of 98.3% on 3DMatch, and a relative rotation error of 0.24 \(^{\circ }\) ∘ and a relative translation error of 6.6 cm on KITTI. These results highlight the potential of our dual-feature approach in improving point cloud registration accuracy across diverse environments. Our code is available at http://www.github.com/saifabel/DualFeatAtt.