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MP-SfM redefines classical Structure-from-Motion by tightly integrating monocular depth and surface normal priors into incremental SfM, enabling robust 3D reconstruction from sparse, unstructured image collections. Key Highlights: Resources Paper: https://arxiv.org/abs/2504.20040Github:
Imagine this! A video of a world leader giving a speech they never actually delivered, or a celebrity appearing to endorse a product they’ve never even heard of. These aren’t
NormalCrafter introduces a novel approach for surface normal estimation in videos, leveraging diffusion priors to achieve high spatial fidelity and temporal consistency over arbitrary-length sequences. Key Highlights: Project Related articles
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
Computer Vision and Deep Learning are the superstars of today’s AI universe, fueling everything from cars that drive themselves to medical tools smart enough to spot issues even seasoned doctors
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
Computer vision is one of artificial intelligence’s most dynamic and rapidly advancing areas, enabling machines to interpret and understand the visual world. From self-driving cars that detect and avoid pedestrians
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:
Imagine you’re using an AI writing assistant to draft an email. It’s excellent at creating clear and concise text, but you need the email to reference specific updates from a
