FS-I2P:A Hierarchical Focus-Sweep Registration Network with Dynamically Allocated Depth

• URL: http://arxiv.org/abs/2605.07607v1
• Date: Fri, 08 May 2026 11:33:18 GMT
• Title: FS-I2P:A Hierarchical Focus-Sweep Registration Network with Dynamically Allocated Depth
• Abstract summary: Image-to-point cloud registration is often challenged by viewpoint changes, cross-modal discrepancies, and repetitive textures.<n>Recent detection-free methods alleviate this issue by leveraging multi-scale features and transformer-based interactions.<n>Inspired by human behavior, we propose a Focus--Sweep'' paradigm to enhance multi-level cross-modal feature association.
• Score: 23.96591843663943
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Image-to-point cloud registration is often challenged by viewpoint changes, cross-modal discrepancies, and repetitive textures, which induce scale ambiguity and consequently lead to erroneous correspondences. Recent detection-free methods alleviate this issue by leveraging multi-scale features and transformer-based interactions. However, they still suffer from attention drift across layers and intra-scale inconsistencies, hindering precise registration. Inspired by human behavior, we propose a ``Focus--Sweep'' paradigm and develop a Hierarchical Focus--Sweep Interaction Module within an SSM-based framework to enhance multi-level cross-modal feature association. In addition, we introduce a Dynamic Layer Allocation Strategy that adaptively determines the iteration depth to better exploit geometric constraints and improve matching robustness. Extensive experiments and ablations on two benchmarks, RGB-D Scenes V2 and 7-Scenes, demonstrate that our approach achieves state-of-the-art performance.

Related papers

• Hierarchical Consistency Learning for Test-time Adaptation in Camouflage Perception [50.278200968044665]
  Camouflaged object detection (COD) aims to localize targets that exhibit minimal perceptual differences from backgrounds through physical attributes.<n>Existing methods, constrained by the static train-then-freeze paradigm, suffer from domain rigidity and dependency annotation.<n>We propose the hierarchical consistency learning framework, which integrates test-time adaptation for dynamic representation recalibration.
  arXiv  Detail & Related papers  (2026-05-25T09:57:46Z)
• UniV2D: Bridging Visual Restoration and Semantic Perception for Underwater Salient Object Detection [71.83097731030254]
  We propose a Unified Vision-to-Detection Network (UniV2D) to jointly optimize visual restoration and salient object detection.<n>UniV2D introduces a semantic-driven learning paradigm: high-level saliency semantics actively guide the restoration process.<n>It significantly outperforms state-of-the-art methods in both quantitative and qualitative evaluations.
  arXiv  Detail & Related papers  (2026-05-08T02:31:45Z)
• Graph-Based Uncertainty Modeling and Multimodal Fusion for Salient Object Detection [12.743278093269325]
  We propose a dynamic uncertainty propagation and multimodal collaborative reasoning network (DUP-MCRNet)<n>DUGC is designed to propagate uncertainty between layers through a sparse graph constructed based on spatial semantic distance.<n>MCF uses learnable modality gating weights to weightedly fuse the attention maps of RGB, depth, and edge features.
  arXiv  Detail & Related papers  (2025-08-28T04:31:48Z)
• YOLO-FDA: Integrating Hierarchical Attention and Detail Enhancement for Surface Defect Detection [0.32634122554914]
  YOLO-FDA is a novel YOLO-based detection framework that integrates fine-grained detail enhancement and attention-guided feature fusion.<n>We show that YOLO-FDA consistently outperforms existing state-of-the-art methods in terms of both accuracy and robustness across diverse types of defects and scales.
  arXiv  Detail & Related papers  (2025-06-26T10:32:37Z)
• Multi-Modality Driven LoRA for Adverse Condition Depth Estimation [61.525312117638116]
  We propose Multi-Modality Driven LoRA (MMD-LoRA) for Adverse Condition Depth Estimation.<n>It consists of two core components: Prompt Driven Domain Alignment (PDDA) and Visual-Text Consistent Contrastive Learning (VTCCL)<n>It achieves state-of-the-art performance on the nuScenes and Oxford RobotCar datasets.
  arXiv  Detail & Related papers  (2024-12-28T14:23:58Z)
• Divide-and-Conquer: Confluent Triple-Flow Network for RGB-T Salient Object Detection [70.84835546732738]
  RGB-Thermal Salient Object Detection aims to pinpoint prominent objects within aligned pairs of visible and thermal infrared images.<n>Traditional encoder-decoder architectures may not have adequately considered the robustness against noise originating from defective modalities.<n>We propose the ConTriNet, a robust Confluent Triple-Flow Network employing a Divide-and-Conquer strategy.
  arXiv  Detail & Related papers  (2024-12-02T14:44:39Z)
• HiH: A Multi-modal Hierarchy in Hierarchy Network for Unconstrained Gait Recognition [3.431054404120758]
  We present a multi-modal Hierarchy in Hierarchy network (HiH) that integrates silhouette and pose sequences for robust gait recognition. HiH features a main branch that utilizes Hierarchical Gait Decomposer modules for depth-wise and intra-module hierarchical examination of general gait patterns from silhouette data. An auxiliary branch, based on 2D joint sequences, enriches the spatial and temporal aspects of gait analysis.
  arXiv  Detail & Related papers  (2023-11-19T03:25:14Z)
• SAAN: Similarity-aware attention flow network for change detection with VHR remote sensing images [41.27207121222832]
  Change detection (CD) is a fundamental and important task for monitoring the land surface dynamics in the earth observation field. These CD methods typically extract bi-temporal image features using a weight-sharing Siamese encoder network and identify change regions using a decoder network. We propose a novel similarity-aware attention flow network (SAAN) to achieve effective change detection.
  arXiv  Detail & Related papers  (2023-08-28T13:35:07Z)
• 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)
• High-resolution Depth Maps Imaging via Attention-based Hierarchical Multi-modal Fusion [84.24973877109181]
  We propose a novel attention-based hierarchical multi-modal fusion network for guided DSR. We show that our approach outperforms state-of-the-art methods in terms of reconstruction accuracy, running speed and memory efficiency.
  arXiv  Detail & Related papers  (2021-04-04T03:28:33Z)
• Deep Semantic Matching with Foreground Detection and Cycle-Consistency [103.22976097225457]
  We address weakly supervised semantic matching based on a deep network. We explicitly estimate the foreground regions to suppress the effect of background clutter. We develop cycle-consistent losses to enforce the predicted transformations across multiple images to be geometrically plausible and consistent.
  arXiv  Detail & Related papers  (2020-03-31T22:38:09Z)

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.