3
The display capabilities of the system have been
expanded to include several different shapes that have trad-
eoffs in terms of both point density and fidelity. Each of
these display variants can be generated directly from the
sensor data and evaluated as one of the following:
• Triangulated mesh, where the point-cloud is taken
and the gd3 algorithm from the point- cloud library
[26] is used to generate a tessellation,
• Square surface patch, where the point cloud is packed
into an Octree that tracks a Gaussian representation
of the points measured in each voxel. A square patch
is then generated that represents the average surface
for that voxel,
• Triangle surface patch, where the point cloud is
packed into an Octree that tracks a Gaussian rep-
resentation of the points measured in each voxel. A
triangular patch is then generated that represents the
average surface for that voxel,
• Cube occupancy grid, where the point cloud is
packed into an Octree, and the voxels that contain
points are extracted and displayed to the user,
• Cuboid surface representation, where the point cloud
is packed into an Octree that tracks a 3D Gaussian
representation which is converted into a rotated and
scaled cuboidal shape,
• Ellipsoid surface representation, where the point
cloud is packed into an Octree that tracks a 3D
Gaussian representation which is converted into an
ellipsoid according to the standard deviations in each
direction for display.
For all images in Figure 1 the number of vertices and
triangles with 2D performance of each display type is given
in Table 1.
After choosing the best performing visualization
method in terms of agility and refresh rate, the final run
snapshots are given in Figure 2, which shows the user per-
spective. It should be noted that the user experience in a
dark environment is more crisp than the figures can capture.
As a result, users can successfully walk through the dark
tunnels, navigate and detect other persons with the given
surface representation, depth coloring and thermal mapping.
(a)
(b)
(c)
Figure 1. Visualization options of (a)single color mapping,
(b) thermal mapping, (c) depth mapping
Figure 2. Example snapshot of the enhancement
The display capabilities of the system have been
expanded to include several different shapes that have trad-
eoffs in terms of both point density and fidelity. Each of
these display variants can be generated directly from the
sensor data and evaluated as one of the following:
• Triangulated mesh, where the point-cloud is taken
and the gd3 algorithm from the point- cloud library
[26] is used to generate a tessellation,
• Square surface patch, where the point cloud is packed
into an Octree that tracks a Gaussian representation
of the points measured in each voxel. A square patch
is then generated that represents the average surface
for that voxel,
• Triangle surface patch, where the point cloud is
packed into an Octree that tracks a Gaussian rep-
resentation of the points measured in each voxel. A
triangular patch is then generated that represents the
average surface for that voxel,
• Cube occupancy grid, where the point cloud is
packed into an Octree, and the voxels that contain
points are extracted and displayed to the user,
• Cuboid surface representation, where the point cloud
is packed into an Octree that tracks a 3D Gaussian
representation which is converted into a rotated and
scaled cuboidal shape,
• Ellipsoid surface representation, where the point
cloud is packed into an Octree that tracks a 3D
Gaussian representation which is converted into an
ellipsoid according to the standard deviations in each
direction for display.
For all images in Figure 1 the number of vertices and
triangles with 2D performance of each display type is given
in Table 1.
After choosing the best performing visualization
method in terms of agility and refresh rate, the final run
snapshots are given in Figure 2, which shows the user per-
spective. It should be noted that the user experience in a
dark environment is more crisp than the figures can capture.
As a result, users can successfully walk through the dark
tunnels, navigate and detect other persons with the given
surface representation, depth coloring and thermal mapping.
(a)
(b)
(c)
Figure 1. Visualization options of (a)single color mapping,
(b) thermal mapping, (c) depth mapping
Figure 2. Example snapshot of the enhancement