Achieving pixel-level segmentation with low computational cost using multimodal data remains a key challenge in crack segmentation tasks. Existing methods lack the capability for adaptive perception and efficient interactive fusion of cross-modal features. To address these challenges, we propose a Lightweight Adaptive Cue-Aware Vision Mamba network (LIDAR), which efficiently perceives and integrates morphological and textural cues from different modalities under multimodal crack scenarios, generating clear pixel-level crack segmentation maps. Specifically, LIDAR is composed of a Lightweight Adaptive Cue-Aware Visual State Space module (LacaVSS) and a Lightweight Dual Domain Dynamic Collaborative Fusion module (LD3CF). LacaVSS adaptively models crack cues through the proposed mask-guided Efficient Dynamic Guided Scanning Strategy (EDG-SS), while LD3CF leverages an Adaptive Frequency Domain Perceptron (AFDP) and a dual-pooling fusion strategy to effectively capture spatial and frequency-domain cues across modalities. Moreover, we design a Lightweight Dynamically Modulated Multi-Kernel convolution (LDMK) to perceive complex morphological structures with minimal computational overhead, replacing most convolutional operations in LIDAR. Experiments on three datasets demonstrate that our method outperforms other state-of-the-art (SOTA) methods. On the light-field depth dataset, our method achieves 0.8204 in F1 and 0.8465 in mIoU with only 5.35M parameters. Code and datasets are available at https://github.com/Karl1109/LIDAR-Mamba.
Topology-aware Mamba for Crack Segmentation in Structures
School of Computer Science and Engineering, Jiangsu University of Science and Technology（江苏科技大学计算机科学与工程学院）；School of Electronic and Informatics Engineering, Jiangsu University（江苏大学电子与信息工程学院）；School of Civil and Environmental Engineering, Oklahoma State University（俄克拉荷马州立大学土木与环境工程学院）
Unleashing the Potential of Mamba: Boosting a LiDAR 3D Sparse Detector by Using Cross-Model Knowledge Distillation
East China University of Science and Technology（华东理工大学）；University of Sydney（悉尼大学）；College of Computer Science and Artificial Intelligence, Fudan University（复旦大学计算机科学技术学院）
WeatherGen: A Unified Diverse Weather Generator for LiDAR Point Clouds via Spider Mamba Diffusion
PCA Lab, Nanjing University of Science and Technology（南京理工大学光电信息科学与工程实验室）；School of Automation, Southeast University（东南大学自动化学院）；State Key Laboratory for Novel Software Technology, Nanjing University（南京大学软件新技术国家重点实验室）；School of Intelligence Science and Technology, Nanjing University（南京大学智能科学与技术学院）
3D Modeling and Automated Measurement of Concrete Cracks via Segment Anything Refinement and Visual Inertial LiDAR Fusion
School of Civil Engineering（土木工程学院）；Central South University（中南大学）；Hunan Provincial Key Laboratory for Disaster Prevention and Mitigation of Rail Transit Engineering Structures（湖南省铁路工程结构灾害预防与 mitigation 工程结构重点实验室）；Nvidia；School of Computing（计算学院）；Newcastle University（新castle大学）
MambaMOS: LiDAR-based 3D Moving Object Segmentation with Motion-aware State Space Model
College of Computer Science and Electronic Engineering, Hunan University（湖南大学计算机与通信工程学院）；College of Optical Science and Engineering, Zhejiang University（浙江大学光学工程学院）；School of Robotics, Hunan University（湖南大学机器人学院）；School of Electronics and Information Engineering, South China Normal University（华南师范大学电子与信息工程学院）
MambaFusion: Height-Fidelity Dense Global Fusion for Multi-modal 3D Object Detection
State Key Laboratory of Multimodal Artificial Intelligence Systems (MAIS), CASIA（多模态人工智能系统国家重点实验室（MAIS），中国科学院自动化所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；School of Artificial Intelligence, Shanghai Jiao Tong University（上海交通大学人工智能学院）；Anyverse Intelligence；Beijing Key Laboratory of Super Intelligent Security of Multi-Modal Information（北京多模态信息超级智能安全重点实验室）；School of Information Science and Technology, ShanghaiTech University（上海科技大学信息科学与技术学院）
ForestMamba: Sparse Mamba with Geometry-guided Queries for 3D Forest Point Cloud Segmentation
Nagoya University（名古屋大学）；RIKEN Seika（日本理化学研究所Seika研究中心）；University of California, Los Angeles（加州大学洛杉矶分校）；University of Twente（埃因霍温理工大学）；Ritsumeikan University（立命馆大学）
An Efficient LiDAR-Camera Fusion Network for Multi-Class 3D Dynamic Object Detection and Trajectory Prediction
Institute of Medical Robotics and Department of Automation, Shanghai Jiao Tong University, Key Laboratory of System Control and Information Processing, Ministry of Education（医学机器人研究所和自动化系，上海交通大学，系统控制与信息处理重点实验室，教育部）
