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Archive of posts filed under the Reconstruction category.

3D Reconstruction by Gluing Pair-wise Euclidean Reconstructions

or   “How to Achieve a Good Reconstruction from Bad Images” 3DPVT’06 Demo Daniel Martinec, Tomáš Pajdla, Jana Kostková, and Radim Šára Given a set of unorganized images taken from unknown viewpoints and directions, the proposed method [1] estimates camera positions. Such automatic calibration makes it possible to compute an accurate 3D model of the object. […]

3D Reconstruction by Fitting Low-rank Matrices with Missing Data

Daniel Martinec, Tomáš Pajdla, Jana Kostková, and Radim Šára Given a set of unorganized images taken from unknown viewpoints and directions, the proposed method [1] estimates camera positions. Such automatic calibration makes it possible to compute an accurate 3D model of the object. The only assumption is a sufficient overlap between some image pairs.

Multi-Camera Self-Calibration

Tomas Svoboda, Daniel Martinec, Tomas Pajdla

Projective 3D-reconstruction from Perspective Images with Occlusions and Outliers

Daniel Martinec Why is the task important? Perspective projection, occlusions and outliers are often present. Many images are often available -> a technique treating all data uniformly is needed.

Corridor – Projective Reconstruction Based on Cake Configuration with Panoramic Reference View

Martin Urban, Tomas Pajdla, Tomas Werner, Vaclav HlavacIn this experiment, an image from a panoramic catadioptric camera was used as reference view. The other seven views were taken using a standard digital photo-camera. The resolution of the catadioptric camera was 1000×1000 pixels and of the photo-camera 1200×1600 pixels.