9 research outputs found
Cavlectometry: Towards Holistic Reconstruction of Large Mirror Objects
We introduce a method based on the deflectometry principle for the
reconstruction of specular objects exhibiting significant size and geometric
complexity. A key feature of our approach is the deployment of an Automatic
Virtual Environment (CAVE) as pattern generator. To unfold the full power of
this extraordinary experimental setup, an optical encoding scheme is developed
which accounts for the distinctive topology of the CAVE. Furthermore, we devise
an algorithm for detecting the object of interest in raw deflectometric images.
The segmented foreground is used for single-view reconstruction, the background
for estimation of the camera pose, necessary for calibrating the sensor system.
Experiments suggest a significant gain of coverage in single measurements
compared to previous methods. To facilitate research on specular surface
reconstruction, we will make our data set publicly available
Real-World Normal Map Capture for Nearly Flat Reflective Surfaces
Although specular objects have gained interest in recent
years, virtually no approaches exist for markerless reconstruction
of reflective scenes in the wild. In this work, we
present a practical approach to capturing normal maps in
real-world scenes using video only. We focus on nearly planar
surfaces such as windows, facades from glass or metal,
or frames, screens and other indoor objects and show how
normal maps of these can be obtained without the use of an
artificial calibration object. Rather, we track the reflections
of real-world straight lines, while moving with a hand-held
or vehicle-mounted camera in front of the object. In contrast
to error-prone local edge tracking, we obtain the reflections
by a robust, global segmentation technique of an
ortho-rectified 3D video cube that also naturally allows efficient
user interaction. Then, at each point of the reflective
surface, the resulting 2D-curve to 3D-line correspondence
provides a novel quadratic constraint on the local surface
normal. This allows to globally solve for the shape by integrability
and smoothness constraints and easily supports
the usage of multiple lines. We demonstrate the technique
on several objects and facades
Reconstructing mass-conserved water surfaces using shape from shading and optical flow
This paper introduces a method for reconstructing water from real video footage. Using a single input video, the proposed method produces a more informative reconstruction from a wider range of possible scenes than the current state of the art. The key is the combination of vision algorithms and physics laws. Shape from shading is used to capture the change of the water's surface, from which a vertical velocity gradient field is calculated. Such a gradient field is used to constrain the tracking of horizontal velocities by minimizing an energy function as a weighted combination of mass-conservation and intensity-conservation. Hence the final reconstruction contains a dense velocity field that is incompressible in 3D. The proposed method is efficient and performs consistently well across water of different types
Recovering Specular Surfaces Using Curved Line Images
International audienceWe present a new shape-from-distortion framework for recovering specular (reflective/refractive) surfaces. While most existing approaches rely on accurate correspondences between 2D pixels and 3D points, we focus on analyzing the curved images of 3D lines which we call curved line images or CLIs. Our approach models CLIs of local reflections or refractions using the recently proposed general linear cameras (GLCs). We first characterize all possible CLIs in a GLC. We show that a 3D line will appear as a conic in any GLC. For a fixed GLC, the conic type is invariant to the position and orientation of the line and is determined by the GLC parameters. Furthermore, CLIs under single reflection/refraction can only be lines or hyperbolas. Based on our new theory, we develop efficient algorithms to use multiple CLIs to recover the GLC camera parameters. We then apply the curvature-GLC theory to derive the Gaussian and mean curvatures from the GLC intrinsics. This leads to a complete distortion-based reconstruction framework. Unlike conventional correspondence-based approaches that are sensitive to image distortions, our approach benefits from the CLI distortions. Finally, we demonstrate applying our framework for recovering curvature fields on both synthetic and real specular surfaces