Abstract. The OpenMVG C++ library provides a vast collection of multipleview geometry tools and algorithms to spread the usage of computer vision and structure-from-motion techniques. Close to the state-of-the-art in its domain, it provides an easy access to common tools used in 3D reconstruction from images. Following the credo "Keep it simple, keep it maintainable" the library is designed as a modular collection of algorithms, libraries and binaries that can be used independently or as bricks to build larger systems. Thanks to its strict test driven development, the library is packaged with unit-test code samples that make the library easy to learn, modify and use. Since its first release in 2013 under the MPL2 license, OpenMVG has gathered an active community of users and contributors from many fields, spanning hobbyists, students, computer vision experts, and industry members.

Many rendering systems rely on spherical harmonics or wavelets that provide a mean to

Biomedical image mosaicking is a trending topic. It consists in computing a single large image from multiple observations, and becomes a challenging task when said observations barely overlap or are subject to illumination changes, poor resolution, blur and either highly textured or predominantly homogeneous content. Because such challenges are common in biomedical images, classical keypoint/featurebased methods perform poorly.In this paper, we propose a new framework based on pairwise template matching coupled with a constrained, confidence-driven global optimization strategy to solve the issue of microscopic biomedical image mosaicking. First we provide experimental results that show significant improvement on a subjective level. Then we describe a new validation strategy for objective assessment, which shows improvement as well.

Face landmarking, defined as the detection of fiducial points on faces, has received a lot of attention over the last two decades within the computer vision community. While research literature documents major advances using state-of-art deep convolutional neural networks, earlier cascaded regression tree-based approaches remain a relevant alternative for low-cost, low-power embedded systems. Yet, from a practical point of view, their implementation and parametrization can be a difficult and tedious process. In this paper, we provide the readers with insights and advice on how to design a successful face landmarking system using a cascade of regression trees.