This paper addresses the problem of mirror surface reconstruction, and proposes a solution based on observing the reflections of a moving reference plane on the mirror surface. Unlike previous approaches which require tedious calibration, our method can recover the camera intrinsics, the poses of the reference plane, as well as the mirror surface from the observed reflections of the reference plane under at least three unknown distinct poses. We first show that the 3D poses of the reference plane can be estimated from the reflection correspondences established between the images and the reference plane. We then form a bunch of 3D lines from the reflection correspondences, and derive an analytical solution to recover the line projection matrix. We transform the line projection matrix to its equivalent camera projection matrix, and propose a cross-ratio based formulation to optimize the camera projection matrix by minimizing reprojection errors. The mirror surface is then reconstructed based on the optimized cross-ratio constraint. Experimental results on both synthetic and real data are presented, which demonstrate the feasibility and accuracy of our method.

This paper addresses the problem of mirror surface reconstruction, and a solution based on observing the reflections of a moving reference plane on the mirror surface is proposed. Unlike previous approaches which require tedious work to calibrate the camera, our method can recover both the camera intrinsics and extrinsics together with the mirror surface from reflections of the reference plane under at least three unknown distinct poses. Our previous work has demonstrated that 3D poses of the reference plane can be registered in a common coordinate system using reflection correspondences established across images. This leads to a bunch of registered 3D lines formed from the reflection correspondences. Given these lines, we first derive an analytical solution to recover the camera projection matrix through estimating the line projection matrix. We then optimize the camera projection matrix by minimizing reprojection errors computed based on a cross-ratio formulation. The mirror surface is finally reconstructed based on the optimized cross-ratio constraint. Experimental results on both synthetic and real data are presented, which demonstrate the feasibility and accuracy of our method.

In this paper, we present a novel interactive image segmentation technique that automatically learns segmentation parameters tailored for each and every image. Unlike existing work, our method does not require any offline parameter tuning or training stage, and is capable of determining image-specific parameters according to some simple user interactions with the target image. We formulate the segmentation problem as an inference of a conditional random field (CRF) over a segmentation mask and the target image, and parametrize this CRF by different weights (e.g., color, texture and smoothing). The weight parameters are learned via an energy margin maximization, which is solved using a constraint approximation scheme and the cutting plane method. Experimental results show that our method, by learning image-specific parameters automatically, outperforms other state-of-the-art interactive image segmentation techniques.

This paper introduces a novel method for 3D reconstruction from spherical mirrors in a single view. Traditional reconstruction algorithms either assume both intrinsic and extrinsic parameters of the cameras being known precisely and rely on multiple view information to recover 3D objects, or use reflections of the 3D objects on spherical mirrors with known radii in a single calibrated view. It will be shown in this paper that 3D objects can be recovered up to a scale using an unknown spherical mirror in a single view. Experimental results on both synthetic and real data show the practicality of the proposed reconstruction method.

This paper introduces a novel method for reconstructing human eyes and visual display from reflections on the cornea. This problem is difficult because the camera is not directly facing the display, but instead captures the eyes of a person in front of the display. Reconstruction of eyes and display is useful for point-of-gaze estimation, which can be approximated from the 3D positions of the iris and display. It is shown that iris boundaries (limbus) and display reflections in a single intrinsically calibrated image provide enough information for such an estimation. The proposed method assumes a simplified geometric eyeball model with certain anatomical constants which are used to reconstruct the eye. A noise performance analysis shows the sensitivity of the proposed method to imperfect data. Experiments on various subjects show that it is possible to determine the approximate area of gaze on a display from a single image.

This paper deals with the problems of scene illumination estimation and shape recovery from an image sequence of a smooth textureless object. A novel method that exploits the surface points estimated from the silhouettes for recovering the scene illumination is introduced. Those surface points are acquired by a dual space approach and filtered according to their rank errors. Selected surface points allow a direct closed-form solution of illumination. In the mesh evolution step, an algorithm for optimizing the visual hull mesh is developed. It evolves the mesh by iteratively estimating both the surface normal and depth that maximize the photometric consistency across the sequence. Compared with previous work which optimizes the mesh by estimating the surface normal only, the proposed method shows better convergence and can recover better surface details, especially when concavities are deep and sharp. 1