Research Papers
OpenLiDARMap presents a GNSS-free mapping framework that combines sparse public map priors with LiDAR data through scan-to-map and scan-to-scan alignment. This approach achieves georeferenced and drift-free point cloud maps. Key
MedSAM2 introduces a robust foundation model for promptable segmentation in 3D medical images and temporal video data, built by fine-tuning SAM2.1 on a large-scale curated medical dataset. Key Highlights: Resources
GenZ-ICP introduces an innovative iterative Closest Point (ICP) method that enhances LiDAR-based pose estimation by adaptively integrating point-to-plane and point-to-point error metrics, ensuring robust performance across diverse and degenerative environments.
Imperial College London unveils MASt3R-SLAM: a cutting-edge monocular dense SLAM system built on the revolutionary MASt3R two-view 3D reconstruction prior, delivering unmatched real-time accuracy and global consistency. Key Highlights: MASt3R-SLAM
Fast3R breaks the pairwise bottleneck in multi-view 3D reconstruction. Building on DUSt3R, it introduces a transformer-based architecture that directly regresses dense 3D pointmaps from unposed, unordered RGB images-processing 1000+ views
AirSLAM introduces a hybrid visual SLAM approach that integrates deep learning for feature detection with traditional backend optimization. Key highlights: Resource Links Related articles from LearnOpenCV:
