Multidimensional epistemologies based on 3D data capture can provide both micro and macro context for 2D data abstractions by layering abstract data in a phenomenological context. However, 3D multimedia software often focuses too much on visual effects and not enough on embedded analytical tools, resulting in a disconnect between multidimensional data and software. To address this, 3D visualization pipelines need to evolve to facilitate post-phenomenological learning where users absorb information without consciously noticing, moving beyond a steep learning curve and limited technical capabilities.

1. Multidimensional Epistemologies
vs
Multidimensional Software:
The Disconnect

2. What is “data”?

3. Traditionally data = 2D abstractions
• Abstracted Observations
• ABCDEFGHIJKLMNOPQRSTUVWXYZ (English descendent of Phoencian
alphabet)
• 12345678910 (decimal system)
• Lines & Shapes that we’re trained to recognize & extrapolate
• Confluence visualizations thereof:

4. 3D Data Capture = 3D Multimedia
• Phenomenological
Observations
• (=Perceptions influenced
by our own biological
senses)
• Can provide micro AND
macro context for
abstractions
• In so doing becomes a
post-phenomenological
experience

5. Layered Data
on a
Digital Scaffold
= layered abstract
data in a
phenomenological
context

6. Example:
A piece of Raw 2D,
multispectral data

7. The same piece of data in 3D context =

8. In the context of Human-Technology
Relations Theories*, a 3D epistemology =
• = MicroPerception AND MacroPerception
• = An Alterity Relation created via a Hermeneutic and/or Fusion
Relation that is simultaneously an Immersive Background Relation
that is so addictive its an Augmentation Relation
• (the most important part of that is that everything is ultimately a Background
Relation)
*(see previous Human Technology Relations Powerpoint for breakdowns of these vocabularies)

9. But 3D multimedia in 2D = a level of needless abstraction
• Messes with scale and base perception
• Hinders absorption/phenomenological and therefore hinders post-
phenomenological experience of concept

10. 3D visualization software often so focused on
a wow factor carrying them through
• Not enough
• Needs to move through embedded analytical annotation systems
• So that it can reach a place of post-phenomenological background
tech absorption ---so that we don’t notice how or what we’re
learning, we just now (= evolving multimedia epistemologies)

11. 3D Visualization software pipeline issues
(on top of prime phenom disconnect = a steep learning curve to use + limited tech paradigm not equipped to handle data scale)

12. With some of that in mind---let’s play with
some data interfaces
• Easy: pull 2D Earth Observation Data from open archive, process into viz media (ArcGIS or
Planet)
• Easy: pull 3D model from 3D model aggregate through viz/print pipeline (Sketchfab
MeshMixer  TinkerineSuite)
• Medium: 2D + 3D integrated mapping in geographic information systems (Esri CityEngine(if we
had a license)
• Medium: pull processed 3D model from open archive into viewer and create viz media gif
(AWS QT Modeller or CloudCompare Giphy+----where/how to share = additional pipeline issue at scicomm end)
• Hard: pull unprocessed 3D model from open archive into viewer and push it through viz
to 3D printing pipeline
(AWS or similar - CloudCompare -3DsMax Meshmixer TinkerineSuite etc)
• Extra Hard: pull raw dataset, co-register, create 3D
(scanner/archive Faro Scene or Leica Cyclone output
• Still Impossible: Merge 2D EOS Data set with 3D multiscale data set