Research ArticleCOMPUTER SCIENCE

Making data matter: Voxel printing for the digital fabrication of data across scales and domains

See allHide authors and affiliations

Science Advances  30 May 2018:
Vol. 4, no. 5, eaas8652
DOI: 10.1126/sciadv.aas8652
  • Fig. 1 General workflow for the conversion of data sets to 3D-printed data physicalizations.

    For a given composition of data sets (A), a hull is generated first (B). Here, the composition of data sets contains a volumetric (1), point cloud (2), graph (3), and image stack (4) data set. (C) The enclosure, together with the available printer resolution, thus determines the dimension and number of the generated layers. The data set is then processed for each layer (D), according to “Volumes,” “Point clouds,” “Curves and graphs,” and “Image-based” sections, respectively (E), to generate, to generate per-pixel material information. Here, every layer’s pixel contains an associated position and is given the actual data set and additional information governing the desired appearance of the final physical visualization. The material information of each data set is then composited (F) and converted to material-mixing ratios (G). Finally, the material-mixing ratios are dithered to binary bitmap layers (H), one for each material given in the printer.

  • Fig. 2 Variability in optical transparency as a function of transparent to opaque resin mixing ratios.

    (A) A typical single layer of different material-mixing ratios acquired through material dithering. White pixels in the bitmap represent physical material droplets of opaque and transparent material, respectively. Numbers relate to transparent material ratios, and in combination, the two material descriptions result in an opacity gradient. The corresponding 3D-printed objects are shown in (B). Here, it is apparent that visual characteristics are not linearly related to material-mixing ratios. In (C), we show that perceivably separable differences accumulate at mixing ratios of high clear material content and that small changes in additionally deposited opaque material droplets can have a dramatic change in perceived opacity.

  • Fig. 3 Point cloud data processing workflow and representative 3D-printed models from point cloud data sets.

    (A) Initial point cloud data containing point-specific attributes. (B) Determination of containment for the point cloud. (C) The containment, combined with the available printer resolution, determines the dimension and number of the generated layers. (D) The point cloud is processed for each layer. (E) For each pixel within a single layer, the point cloud is queried for nearby points, which are interpolated and rasterized to generate the final material data. (F) Material information is dithered into binary material deposition descriptions. (G) and (H) show representative 3D-printed models from point cloud data sets. (G) The point cloud representing a statue from the Tampak Siring Temple in Bali consists of 3.6 million points and was generated through an automated, cloud-based, photogrammetric processing service (38). The digital elevation model of the moon shown in (H) is represented through a point cloud of 21 million points. The data were captured by NASA’s Lunar Reconnaissance Orbiter, which was launched in 2009 and has since orbited the moon (39).

  • Fig. 4 Volumetric data processing workflow and representative 3D-printed models from volumetric data sets.

    (A) Initial volumetric data from which an external enclosure is generated in (B). (C) Layers are generated and processed in parallel. (D) Here, a voxel intersecting a layer is shown and (E) for each pixel within a given layer, its position information is used to find interpolated values for per-pixel material data from the surrounding voxel. (F) Material information is dithered into binary material deposition descriptions. (G) and (H) show representative 3D-printed models from volumetric data sets. (G) A computational fluid simulation of the chaotic mixing of white and green fluids in a transparent volume. (H) A CT scan of the left hand of a patient with arthritis. The radiodensity information stored in the CT volume is mapped to a material gradient of opaque white and transparent material. White areas represent bone with the highest density and transparent regions represent skin and soft tissue, while semitransparent gradients in between represent lower-density bone, muscles, and tendons. In this example, the transparency was globally adjusted to emphasize the subtle differences in bone mineral density, while the local skin contours define the external hull geometry of the hand.

  • Fig. 5 Curve and graph data processing workflows and their representative 3D-printed models.

    For the input curve or graph data (A), an enclosure is specified (B) from which dimensions and number of printing layers are determined (C). (D) For each pixel in each layer, the closest curve or line segment is queried (E), and properties associated with the curve or line segments are interpolated and rasterized to the layer. (F) Every material information layer is dithered into binary material composition layers, one for each material that is needed to fabricate the input data set. (G) Protein crystal structure of apolipoprotein A-I. The data set consists of 6588 points (representing each atom) and 13,392 line segments, representing the interatomic bonds. (H) White matter tractography data of the human brain, created with the 3D Slicer medical image processing platform (37), visualizing bundles of axons, which connect different regions of the brain. The original data were acquired through diffusion-weighted MRI, where 48 scans are taken for each MRI slice, to capture the diffusion of water molecules in white matter brain tissue, which is visualized as 3595 individual fibers. The fiber data set consists of a total of 291,362 line segments that are colored according to their orientation in 3D space.

  • Fig. 6 Representative 3D-printed models of image-based data.

    (A) In vitro reconstructed living human lung tissue on a microfluidic device, observed through confocal microscopy (29). The cilia, responsible for transporting airway secretions and mucus-trapped particles and pathogens, are colored orange. Goblet cells, responsible for mucus production, are colored cyan. (B) Biopsy from a mouse hippocampus, observed via confocal expansion microscopy (proExM) (30). The 3D print visualizes neuronal cell bodies, axons, and dendrites.

Supplementary Materials

  • Supplementary material for this article is available at http://advances.sciencemag.org/cgi/content/full/4/5/eaas8652/DC1

    Supplementary Information

    fig. S1. White matter tractography data, created with the 3D Slicer medical image processing platform (37).

    fig. S2. Image stack that captures data observed through protein-retention expansion microscopy (30).

    fig. S3. Variability in optical transparency as a function of transparent opaque resin mixing ratios and feature size.

    fig. S4. Transmittance behavior of material samples with different transparent-to-opaque material ratios.

    fig. S5. Two observed visual characteristics that arise from the use of the transparent build material.

    fig. S6. Comparison of 3D renderings to 3D-printed models produced with our method.

    fig. S7. Brief illustration of the conversion of tetrahedral meshes to 3D printable models through our method.

    fig. S8. Elevation map of a portion of the Brooks Range in Northern Alaska.

  • Supplementary Materials

    This PDF file includes:

    • Supplementary Information
    • fig. S1. White matter tractography data, created with the 3D Slicer medical image processing platform (37).
    • fig. S2. Image stack that captures data observed through protein-retention expansion microscopy (30).
    • fig. S3. Variability in optical transparency as a function of transparent opaque resin mixing ratios and feature size.
    • fig. S4. Transmittance behavior of material samples with different transparent-to-opaque material ratios.
    • fig. S5. Two observed visual characteristics that arise from the use of the transparent build material.
    • fig. S6. Comparison of 3D renderings to 3D-printed models produced with our method.
    • fig. S7. Brief illustration of the conversion of tetrahedral meshes to 3D printable models through our method.
    • fig. S8. Elevation map of a portion of the Brooks Range in Northern Alaska.

    Download PDF

    Files in this Data Supplement:

Navigate This Article