Fig 1.
Overview of our digitization processes.
Input CT volume is binarized to generate 3D model (A). Input photographs are also binarized (B). We estimate camera position relative to 3D model by using binarized photographs (C) and back project all photographs onto 3D model to obtain texture atlases (D). All texture atlases are merged to generate single texture (E).
Fig 2.
We used μRay8700 (a). We place insect directly on stage (b) and fix flower by using plastic tube (c). Obtained CT volumes are visualized in (d) and (e).
Fig 3.
We place specimen at center of light diffuser box (a) and take multiple photographs (b, c).
Fig 4.
We extracted foreground from input CT volume (a) to obtain surface model (b). We segmented surface into near-flat regions (b) and flattened each region into 2D texture domain (c).
Fig 5.
Representation of camera position.
Fig 6.
Difference metric of two binary images.
Fig 7.
Camera rotations represented with δx, δy, and δz correspond to rotation, horizontal translation, and vertical translation of rendered image, respectively (c). In Eq (4), we rotate and translate rendered image (b) to fit it to photograph (d).
Fig 8.
Each photograph (A and B) generates texture atlas (IA and IB). We stitch two textures with indistinct seam to obtain one texture (IC). To obtain clearer texture, we consider both texture color and projected normal direction ( and
visualizes used texture with different color (red for A and green for B).
Fig 9.
Graph-cut textures to stitch two textures with indistinctive seam.
Fig 10.
Blurred textures caused by back-projection.
Fig 11.
Patch-based texture synthesis to recover missing area.
For black circular area (a) (artificially generated for explanation), we fill it in by copying boundary pixels (b), smoothen it (c) and perform texture synthesis to refine it (d).
Fig 12.
Evaluation of our camera estimation by using artificial data set.
This figure shows average, median, and maximum camera estimation errors and cost values of our technique for artificially generated photographs.
Fig 13.
3D models generated with our technique.
Fig 14.
Detailed data for models in Fig 13.
First row shows number of photographs used to reconstruct models in Fig 13. Second and third rows show average and median of cost values for camera position estimation in Eq (3). Notice that photographs of which matching cost were greater than or equal to 0.7 were not used for texture generation and not counted in this table.
Fig 15.
Our browsing system allows user to draw cut stroke (left) to generate cross section on which X-ray CT image is visualized (right).
Fig 16.
Capabilities of existing 3D digitization methods.
Green triangle indicates limitations in reconstructing shapes and textures in occluded areas.
Fig 17.
Photographs of highly symmetric objects.
Our camera estimation fails for photographs (a, b) since their silhouettes match multiple viewpoints of target objects. It works well for photograph (c) with silhouette that matches unique viewpoint of target.