Point Clouds
Brendan Harmon
Point Clouds
Sets of x, y, and z coordinates
Point Cloud Data Structure
X |
Y |
Z |
R |
G |
B |
-1.38539982 |
-2.95930004 |
-8.80980003 |
53 |
41 |
45 |
-1.08609962 |
-2.85930014 |
-8.68869996 |
74 |
45 |
40 |
-1.30480003 |
-2.65850019 |
-8.70860004 |
16 |
8 |
7 |
-0.80039978 |
-2.58699989 |
-8.75689995 |
161 |
133 |
126 |
-0.82590008 |
-2.53049994 |
-8.19280005 |
151 |
137 |
136 |
-0.35680008 |
-2.37580013 |
-8.59280002 |
18 |
17 |
18 |
-0.35239983 |
-2.40630007 |
-8.21350002 |
123 |
62 |
48 |
-0.69639969 |
-2.26340008 |
-8.67620003 |
26 |
24 |
22 |
-0.70779991 |
-2.26650000 |
-8.07910001 |
49 |
57 |
69 |
-0.20359993 |
-2.04750013 |
-8.65110004 |
42 |
41 |
49 |
0.29399967 |
-1.95119953 |
-8.60870004 |
53 |
65 |
91 |
0.37310028 |
-1.43799973 |
-8.64540005 |
122 |
89 |
76 |
-0.21290016 |
-2.03420019 |
-8.16050005 |
19 |
13 |
11 |
0.31190014 |
-1.92490005 |
-8.18309999 |
89 |
50 |
37 |
Applications
- Map sites
- Document cultural heritage
- Study landscape dynamics
- Creatively model designs
Cloudism
Designers adopting cloudism will step into
an overwhelmingly convincing simulacra of physical reality, space and time;
this will enhance their understanding of site,
and yield a stronger awareness of ambient aspects and cues.
Girot 2019
Medium
- Point Clouds: Algorithmically mediated, indexical encodings
of space and color at a moment in time
Semiotics
- Iconic: Look like what they represent
- Indexical: Physically and causally connected to what they represent
- Symbolic: Encoded as data and rendered as signs given meaning by convention
Characteristics
- Algorithmic: Transcribed from photons into bits
- Hyperreal: Displaces reality with authoritative data
- Contingent Indices: Depend on what captured
- Contingent Symbols: Depend on digital processing
- Interactive: Not just an image, but rather data
- Abstract: Pointillist in its incompleteness
Scans
Method |
Feature |
Scans |
Resolution |
Points |
Matrice |
Landscape |
1 |
2cm |
360,445,708 |
Faro Focus |
Interior |
94 |
1/16 |
771,951,841 |
|
Dining Room |
7 |
1/4 |
268,464,534 |
|
Façade |
27 |
1/8 |
55,847,608 |
|
Rockery |
26 |
1/8 |
47,414,057 |
|
Milk House |
12 |
1/8 |
58,657,059 |
Both |
Gazebo |
1 + 6 |
1/8 |
41,176,644 |
|
Allée |
1 + 130 |
1/8 |
> 1 billion |
Documentary
- Evocative
- Complexity
- Temporality
- Spatial narratives
- Emotional narratives
Hilltop Arboretum
LSU Landscape Architecture
Fieldwork
- Ground control points
- Soil cores
- Biomass quadrats
Biomass
$$ B = \frac{V \rho_s + V \rho_r}{x y} $$
- $B$ = biomass density $(kg / m^{2})$
- $V$ = volume $(m^3)$
- $\rho_s$ = shoot bulk density $(kg / m^{3})$
- $\rho_r$ = root bulk density $(kg / m^{3})$
- $x$ = east-west resolution $(m)$
- $y$ = north-south resolution $(m)$
Carbon
$$ C = B C_f x y $$
- $C$ = carbon $(kg C)$
- $B$ = biomass density $(kg / m^{2})$
- $Cf$ = carbon fraction $(C$%$)$
- $x$ = east-west resolution $(m)$
- $y$ = north-south resolution $(m)$
Spatiotemporal Phenomena
- Landscape structure
- Photosynthetic activity
- Growth and decay of biomass
- Carbon cycling
Landscape Structure
- Indexicality and iconicity of photography
- Analytic quality of architectural drawings
- Depth and interactivity of 3-dimensional models
- Unique level of detail
Data Science
- Quantitative data as scatterplot
Plots
Robotics