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: Monocular Depth + Surface Normal Fusion – Augments traditional SfM with priors from off-the-shelf deep networks (e.g., Metric3D-v2, DSINE), eliminating the need for three-view overlap. Two-View Track Reconstruction – Enables…
Image filtering is one of the fundamental building blocks of computer vision. Whether you’re smoothing an image to remove noise or enhancing features for better detection, filtering techniques based on convolution are everywhere. In this article, we’ll explore image filtering using convolution — understanding the mathematics behind it, and seeing how it’s practically implemented in…
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 simple photo edits—deepfakes are hyper-realistic, AI-generated videos, images, or audio clips that manipulate reality in ways previously reserved for science fiction. For example, a recent deepfake…
Color spaces are fundamental to how digital images are represented and processed in computer vision. While the RGB (Red, Green, Blue) is the most commonly used, OpenCV supports several other color spaces like HSV (Hue, Saturation, Value), LAB (Lightness, A, B), and YCrCb (Luminance, Chroma Red, Chroma Blue), each offering unique advantages for different image…
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: Video Diffusion Model Repurposing – Adapts Stable Video Diffusion (SVD) for normal map prediction, maintaining temporal structure instead of RGB generation. Semantic Feature Regularization (SFR) – Aligns intermediate…
Image thresholding is one of the most essential and widely used techniques in image processing and computer vision. It transforms a grayscale image into a binary image by setting pixel values to either a maximum or minimum based on a defined threshold. This simple yet powerful method is commonly used in applications such as object…
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 Highlights Dual ICP-Based Matching Strategy – Integrates scan-to-scan and scan-to-map ICP matching using robust kernels (Tukey & Cauchy) for drift mitigation and local consistency across LiDAR…
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 might miss. But let’s face it: navigating the ocean of online tutorials, endless research papers, and the latest “must-try” frameworks can make even the most…
Image annotation is a crucial step in computer vision that involves adding meaningful information—such as shapes, labels, or markers—to an image. This process is widely used in applications like object detection, image labeling, dataset preparation, and visual storytelling. Whether it’s drawing a bounding box around a face or labeling specific objects in a scene, annotation…
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 to smartphone apps that instantly translate text, the power of computer vision drives countless everyday technologies. In this blog, we’ll explore five practical and impactful…