10 Point Cloud Analysis

The Reality Capture API can output a point cloud alongside (or instead of) a mesh. Once you’ve converted it to LAS format, lidR (Roussel et al. 2020) takes over — height statistics, density maps, canopy models, voxel volumes. This is the go-to tool for site survey analysis.

10.1 Getting the Point Cloud

Request rcs format when creating your photoscene:

library(AutoDeskR)

resp    <- getToken(id     = Sys.getenv("client_id"),
                    secret = Sys.getenv("client_secret"),
                    scope  = "data:read data:write")
myToken <- resp$content$access_token

ps <- createPhotoscene(name = "site-2026-q2", token = myToken)
# ... uploadImages(), processPhotoscene(), waitForPhotoscene() ...

result  <- checkPhotoscene(photoscene_id = myPhotosceneId, token = myToken)
rcs_url <- result$content$photoscene$scenelink

download.file(url      = rcs_url,
              destfile = "site-survey.rcs",
              headers  = c(Authorization = paste("Bearer", myToken)))

Warning

lidR reads LAS/LAZ, not RCS. Convert the downloaded .rcs file to LAS using CloudCompare (free and open source) before proceeding. In CloudCompare: File → Open the RCS file, then File → Save As → LAS format.

10.2 Reading the Point Cloud

library(lidR)
pc <- readLAS("site-survey.las")
pc
#> class        : LAS (v1.3 format 1)
#> memory       : 247.8 Mb
#> extent       : 406325.4, 406412.8, 5765842.6, 5765901.3
#> coord. ref.  : WGS 84 / UTM zone 32N
#> area         : 4064.9 units²
#> points       : 3.62 million pts (1 return)

10.3 Height Statistics

Normalise to ground level before computing anything height-related:

pc_norm <- normalize_height(pc, knnidw())

mean(pc_norm$Z)
#> [1] 4.12    # mean height above ground (m)
sd(pc_norm$Z)
#> [1] 2.87

quantile(pc_norm$Z, probs = c(0.25, 0.5, 0.75, 0.95))
#>   25%   50%   75%   95%
#>  1.20  3.44  6.11 11.83

10.4 Height Distribution

Warning: package 'ggplot2' was built under R version 4.4.3

ggplot(data.frame(z = z_sample), aes(x = z)) +
  geom_histogram(binwidth = 0.25, fill = "#367ABF", colour = "white") +
  labs(title = "Point Cloud Height Distribution",
       subtitle = "Normalised above ground level",
       x = "Height (m)", y = "Point count") +
  theme_minimal()

10.5 Canopy Height Model

Rasterise the maximum Z per cell to produce a canopy height model — great for visualising roof heights or vegetation structure across the site:

chm <- rasterize_canopy(pc_norm, res = 0.5, algorithm = p2r())
plot(chm, col = height.colors(50), main = "Canopy Height Model (0.5 m resolution)")

10.6 Point Density Grid

density <- grid_density(pc, res = 1)
plot(density, col = gray.colors(50), main = "Point Density (pts/m²)")

mean(density[], na.rm = TRUE)
#> [1] 891.3   # points per square metre

10.7 Voxel Volume

vox        <- voxelize_points(pc_norm, res = 0.25)
volume_m3  <- nrow(vox) * 0.25^3
cat("Estimated volume:", round(volume_m3, 1), "m³\n")
#> Estimated volume: 1483.2 m³

10.8 Summary Table

library(ggplot2)

metrics <- data.frame(
  metric = c("Total points", "Survey area (m²)", "Mean density (pts/m²)",
             "Mean height (m)", "95th-pct height (m)", "Voxel volume (m³)"),
  value  = c(npoints(pc), area(pc), mean(density[], na.rm = TRUE),
             mean(pc_norm$Z), quantile(pc_norm$Z, 0.95), volume_m3)
)

ggplot(metrics, aes(x = reorder(metric, value), y = value)) +
  geom_col(fill = "#367ABF") +
  coord_flip() +
  labs(title = "Point Cloud Summary Metrics",
       x = NULL, y = "Value") +
  theme_minimal()