A Kaleidoscopic Approach to Surround Geometry and Reflectance Acquisition
We describe a system for acquiring reflectance fields of objects without moving parts and without a massively parallel hardware setup. Our system consists of a set of planar mirrors which serve to multiply a single camera and a single projector into a multitude of virtual counterparts. Using this arrangement, we can acquire reflectance fields with an average angular sampling rate of about 120+ view/light pairs per surface point. The mirror system allows for freely programmable illumination with full directional coverage. We employ this setup to realize a 3D acquisition system that employs structured illumination to capture the unknown object geometry, in addition to dense reflectance sampling. On the software side, we combine state-of-the-art 3D reconstruction algorithms with a reflectance sharing technique based on non-negative matrix factorization in order to reconstruct a joint model of geometry and reflectance. We demonstrate for a number of test scenes that the kaleidoscopic approach can acquire complex reflectance properties faithfully. The main limitation is that the multiplexing approach limits the attainable spatial resolution, trading it off for improved directional coverage.
Three-Dimensional Kaleidoscopic ImagingIlya Reshetouski, Alkhazur Manakov, Hans-Peter Seidel, and Ivo Ihrke
CVPR 2011 (oral)
We introduce three-dimensional kaleidoscopic imaging, a promising alternative for recording multi-view imagery.
The main limitation of multi-view reconstruction techniques is the limited number of views that are available from multi-camera systems, especially for dynamic scenes.
Our new system is based on imaging an object inside a kaleidoscopic mirror system. We show that this approach can generate a large number of high-quality views well distributed over the hemisphere surrounding the object in a single shot. In comparison to existing multi-view systems, our method offers a number of advantages: it is possible to operate with a single camera, the individual views are perfectly synchronized, and they have the same radiometric and colorimetric properties.
We describe the setup both theoretically, and provide methods for a practical implementation. Enabling interfacing to standard multi-view algorithms for further processing is an important goal of our techniques.
Example of labeling process:
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|Labeling of views|
Supplemental materials [pdf]
Labeling data example (with MatLab loader) [zip]
Labeling of the dynamic scene example: Input movie [mpg], Segmentation movie [mpg], Labeling movie [mpg]