OAAFormer: Robust and Efficient Point Cloud Registration Through Overlapping-Aware Attention in Transformer

• URL: http://arxiv.org/abs/2310.09817v1
• Date: Sun, 15 Oct 2023 12:27:18 GMT
• Title: OAAFormer: Robust and Efficient Point Cloud Registration Through Overlapping-Aware Attention in Transformer
• Authors: Junjie Gao, Qiujie Dong, Ruian Wang, Shuangmin Chen, Shiqing Xin, Changhe Tu, Wenping Wang
• Abstract summary: coarse-to-fine feature matching paradigm has received substantial attention in the domain of point cloud registration. We introduce a soft matching mechanism, facilitating the propagation of potentially valuable correspondences from coarse to fine levels. Our approach leads to a substantial increase of about 7% in the inlier ratio, as well as an enhancement of 2-4% in registration recall.
• Score: 37.41780280364752
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In the domain of point cloud registration, the coarse-to-fine feature matching paradigm has received substantial attention owing to its impressive performance. This paradigm involves a two-step process: first, the extraction of multi-level features, and subsequently, the propagation of correspondences from coarse to fine levels. Nonetheless, this paradigm exhibits two notable limitations.Firstly, the utilization of the Dual Softmax operation has the potential to promote one-to-one correspondences between superpoints, inadvertently excluding valuable correspondences. This propensity arises from the fact that a source superpoint typically maintains associations with multiple target superpoints. Secondly, it is imperative to closely examine the overlapping areas between point clouds, as only correspondences within these regions decisively determine the actual transformation. Based on these considerations, we propose {\em OAAFormer} to enhance correspondence quality. On one hand, we introduce a soft matching mechanism, facilitating the propagation of potentially valuable correspondences from coarse to fine levels. Additionally, we integrate an overlapping region detection module to minimize mismatches to the greatest extent possible. Furthermore, we introduce a region-wise attention module with linear complexity during the fine-level matching phase, designed to enhance the discriminative capabilities of the extracted features. Tests on the challenging 3DLoMatch benchmark demonstrate that our approach leads to a substantial increase of about 7\% in the inlier ratio, as well as an enhancement of 2-4\% in registration recall. =

Related papers

• Fully-Geometric Cross-Attention for Point Cloud Registration [51.865371511201765]
  Point cloud registration approaches often fail when the overlap between point clouds is low due to noisy point correspondences. This work introduces a novel cross-attention mechanism tailored for Transformer-based architectures that tackles this problem. We integrate the Gromov-Wasserstein distance into the cross-attention formulation to jointly compute distances between points across different point clouds. At the point level, we also devise a self-attention mechanism that aggregates the local geometric structure information into point features for fine matching.
  arXiv  Detail & Related papers  (2025-02-12T10:44:36Z)
• PSReg: Prior-guided Sparse Mixture of Experts for Point Cloud Registration [33.41608015231465]
  We propose a prior-guided SMoE-based registration method to improve the feature distinctiveness by dispatching the potential correspondences to the same experts. Our experiments demonstrate the effectiveness of our method, achieving state-of-the-art registration recall (95.7%/79.3%) on the 3DMatch/3DLoMatch benchmark.
  arXiv  Detail & Related papers  (2025-01-14T00:30:22Z)
• A Consistency-Aware Spot-Guided Transformer for Versatile and Hierarchical Point Cloud Registration [9.609585217048664]
  We develop a consistency-aware spot-guided Transformer (CAST) CAST incorporates a spot-guided cross-attention module to avoid interfering with irrelevant areas. A lightweight fine matching module for both sparse keypoints and dense features can estimate the transformation accurately.
  arXiv  Detail & Related papers  (2024-10-14T08:48:25Z)
• Global Attention-Guided Dual-Domain Point Cloud Feature Learning for Classification and Segmentation [21.421806351869552]
  We propose a Global Attention-guided Dual-domain Feature Learning network (GAD) to address the above-mentioned issues. We first devise the Contextual Position-enhanced Transformer (CPT) module, which is armed with an improved global attention mechanism. Then, the Dual-domain K-nearest neighbor Feature Fusion (DKFF) is cascaded to conduct effective feature aggregation.
  arXiv  Detail & Related papers  (2024-07-12T05:19:19Z)
• Unifying Feature and Cost Aggregation with Transformers for Semantic and Visual Correspondence [51.54175067684008]
  This paper introduces a Transformer-based integrative feature and cost aggregation network designed for dense matching tasks. We first show that feature aggregation and cost aggregation exhibit distinct characteristics and reveal the potential for substantial benefits stemming from the judicious use of both aggregation processes. Our framework is evaluated on standard benchmarks for semantic matching, and also applied to geometric matching, where we show that our approach achieves significant improvements compared to existing methods.
  arXiv  Detail & Related papers  (2024-03-17T07:02:55Z)
• Overlap Bias Matching is Necessary for Point Cloud Registration [21.584033532099134]
  Overlap between point clouds to be registered may be relatively small. We propose an unsupervised network Overlap Bias Matching Network (OBMNet) for partial point cloud registration.
  arXiv  Detail & Related papers  (2023-08-18T07:47:22Z)
• Object Segmentation by Mining Cross-Modal Semantics [68.88086621181628]
  We propose a novel approach by mining the Cross-Modal Semantics to guide the fusion and decoding of multimodal features. Specifically, we propose a novel network, termed XMSNet, consisting of (1) all-round attentive fusion (AF), (2) coarse-to-fine decoder (CFD), and (3) cross-layer self-supervision.
  arXiv  Detail & Related papers  (2023-05-17T14:30:11Z)
• Part-guided Relational Transformers for Fine-grained Visual Recognition [59.20531172172135]
  We propose a framework to learn the discriminative part features and explore correlations with a feature transformation module. Our proposed approach does not rely on additional part branches and reaches state-the-of-art performance on 3-of-the-level object recognition.
  arXiv  Detail & Related papers  (2022-12-28T03:45:56Z)
• Coarse to Fine: Domain Adaptive Crowd Counting via Adversarial Scoring Network [58.05473757538834]
  This paper proposes a novel adversarial scoring network (ASNet) to bridge the gap across domains from coarse to fine granularity. Three sets of migration experiments show that the proposed methods achieve state-of-the-art counting performance.
  arXiv  Detail & Related papers  (2021-07-27T14:47:24Z)
• FINet: Dual Branches Feature Interaction for Partial-to-Partial Point Cloud Registration [31.014309817116175]
  We present FINet, a feature interaction-based structure with the capability to enable and strengthen the information associating between the inputs at multiple stages. Experiments demonstrate that our method performs higher precision and robustness compared to the state-of-the-art traditional and learning-based methods.
  arXiv  Detail & Related papers  (2021-06-07T10:15:02Z)

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.