Fusing Structure from Motion and Simulation-Augmented Pose Regression from Optical Flow for Challenging Indoor Environments. (arXiv:2304.07250v1 [cs.CV])

The localization of objects is a crucial task in various applications such as
robotics, virtual and augmented reality, and the transportation of goods in
warehouses. Recent advances in deep learning have enabled the localization
using monocular visual cameras. While structure from motion (SfM) predicts the
absolute pose from a point cloud, absolute pose regression (APR) methods learn
a semantic understanding of the environment through neural networks. However,
both fields face challenges caused by the environment such as motion blur,
lighting …

applications arxiv augmented reality cameras challenges cloud deep learning environment environments face flow learn lighting localization networks neural networks objects optical flow patterns reality regression robotics semantic simulation transportation understanding virtual warehouses