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Distinguishing between dynamic foreground objects and a mostly static background is a fundamental problem in many computer vision and computer graphics tasks. This paper presents a novel online video background identification method with the assistance of inertial measurement unit (IMU). Based on the fact that the background motion of a video essentially reflects the 3D camera motion, we leverage IMU data to realize a robust camera motion estimation for identifying background feature points by only investigating a few historical frames. We observe that the displacement of the 2D projection of a scene point caused by camera rotation is depth-invariant, and the rotation estimation by using IMU data can be quite accurate. We thus propose to analyze 2D feature points by decomposing the 2D motion into two components: rotation projection and translation projection. In our method, after establishing the 3D camera rotations, we generate the depth-relevant 2D feature point movement induced by the camera 3D translation. Then, by examining the disparity between inter-frame offset and the projection of estimated 3D camera motion, we can identify the background feature points. In the experiments, our online method is able to run at 30FPS with only 1 frame latency and outperforms state-of-the-art background identification and other relevant methods. Our method directly leads to a better camera motion estimation, which is beneficial to many applications like online video stabilization, SLAM, image stitching, etc. [ABSTRACT FROM AUTHOR]

Inferring the scene illumination from a single image is an essential yet challenging task in computer vision and computer graphics. Existing works estimate lighting by regressing representative illumination parameters or generating illumination maps directly. However, these methods often suffer from poor accuracy and generalization. This paper presents Geometric Mover’s Light (GMLight), a lighting estimation framework that employs a regression network and a generative projector for effective illumination estimation. We parameterize illumination scenes in terms of the geometric light distribution, light intensity, ambient term, and auxiliary depth, which can be estimated by a regression network. Inspired by the earth mover’s distance, we design a novel geometric mover’s loss to guide the accurate regression of light distribution parameters. With the estimated light parameters, the generative projector synthesizes panoramic illumination maps with realistic appearance and high-frequency details. Extensive experiments show that GMLight achieves accurate illumination estimation and superior fidelity in relighting for 3D object insertion. The codes are available at https://github.com/fnzhan/Illumination-Estimation [ABSTRACT FROM AUTHOR]