+
−
⇧
i
D
T
delivery_area (low-res many-view) - Tolerance 1cm
Height profile
Point number
:
-
Visualizations
Ground Truth (full)
Ground Truth (rig eval)
3Dnovator
3Dnovator - accuracy (35.46%)
3Dnovator - completeness (23.82%)
3Dnovator+
3Dnovator+ - accuracy (38.70%)
3Dnovator+ - completeness (21.49%)
A-TVSNet + Gipuma
A-TVSNet + Gipuma - accuracy (13.24%)
A-TVSNet + Gipuma - completeness (19.10%)
ACMH
ACMH - accuracy (23.56%)
ACMH - completeness (21.70%)
ACMH+
ACMH+ - accuracy (25.44%)
ACMH+ - completeness (29.83%)
ACMM
ACMM - accuracy (20.25%)
ACMM - completeness (25.37%)
ACMP
ACMP - accuracy (26.25%)
ACMP - completeness (35.74%)
BP-MVSNet
BP-MVSNet - accuracy (19.06%)
BP-MVSNet - completeness (36.41%)
CasMVSNet(base)
CasMVSNet(base) - accuracy (22.95%)
CasMVSNet(base) - completeness (24.03%)
CasMVSNet(SR_A)
CasMVSNet(SR_A) - accuracy (20.41%)
CasMVSNet(SR_A) - completeness (24.93%)
CasMVSNet(SR_B)
CasMVSNet(SR_B) - accuracy (20.41%)
CasMVSNet(SR_B) - completeness (24.93%)
CIDER
CIDER - accuracy (13.14%)
CIDER - completeness (13.56%)
CMPMVS
CMPMVS - accuracy (0.00%)
CMPMVS - completeness (0.00%)
COLMAP(base)
COLMAP(base) - accuracy (29.06%)
COLMAP(base) - completeness (18.89%)
COLMAP(SR)
COLMAP(SR) - accuracy (25.54%)
COLMAP(SR) - completeness (33.64%)
COLMAP_ROB
COLMAP_ROB - accuracy (42.68%)
COLMAP_ROB - completeness (10.04%)
DeepC-MVS
DeepC-MVS - accuracy (34.67%)
DeepC-MVS - completeness (30.52%)
DeepC-MVS_fast
DeepC-MVS_fast - accuracy (37.36%)
DeepC-MVS_fast - completeness (29.91%)
DeepPCF-MVS
DeepPCF-MVS - accuracy (35.78%)
DeepPCF-MVS - completeness (28.07%)
dnet
dnet - accuracy (0.00%)
dnet - completeness (0.00%)
DPSNet
DPSNet - accuracy (2.48%)
DPSNet - completeness (2.78%)
example
example - accuracy (2.96%)
example - completeness (2.25%)
GSE
GSE - accuracy (30.01%)
GSE - completeness (28.18%)
hgnet
hgnet - accuracy (2.48%)
hgnet - completeness (2.78%)
IB-MVS
IB-MVS - accuracy (23.68%)
IB-MVS - completeness (25.03%)
LPCS
LPCS - accuracy (30.96%)
LPCS - completeness (24.11%)
LTVRE_ROB
LTVRE_ROB - accuracy (43.90%)
LTVRE_ROB - completeness (12.48%)
MVE
MVE - accuracy (1.73%)
MVE - completeness (2.93%)
OpenMVS
OpenMVS - accuracy (32.86%)
OpenMVS - completeness (25.18%)
PCF-MVS
PCF-MVS - accuracy (30.95%)
PCF-MVS - completeness (27.84%)
PLC
PLC - accuracy (26.36%)
PLC - completeness (17.76%)
PMVS
PMVS - accuracy (29.43%)
PMVS - completeness (0.86%)
TAPA-MVS
TAPA-MVS - accuracy (20.36%)
TAPA-MVS - completeness (25.77%)
TAPA-MVS(SR)
TAPA-MVS(SR) - accuracy (23.97%)
TAPA-MVS(SR) - completeness (33.62%)
unsupervisedMVS_cas
unsupervisedMVS_cas - accuracy (9.61%)
unsupervisedMVS_cas - completeness (18.47%)
Materials
Attributes:
RGB
RGB and Elevation
Elevation
Level of Detail
RGB
Attribute Weights
RGB:
Intensity:
Elevation:
Classification:
Return Number:
Source ID:
RGB
Gamma:
1.00
Brightness:
0.00
Contrast:
0.00
Elevation
Elevation range
:
-19.74 to 69.16
Transition
transition:
Intensity
Range:
0 to 300
Gamma:
1.00
Brightness:
0.00
Contrast:
0.00
Appearance
Point budget
:
1,000,000
Point size
:
1.00
Field of view
:
60
Opacity
:
1.00
Point sizing
Fixed
Attenuated
Adaptive
Adaptive
Quality
Squares
Circles
Interpolation
Squares
Eye-Dome-Lighting
Enable
Radius
:
1.4
Strength
:
1.0
Background
Gradient
Black
White
Tools
Navigation
Speed
:
61.6
Measurements
About this viewer
Potree
is a viewer for large point cloud / LIDAR data sets, developed at the Vienna University of Technology.
(github)
Author:
Markus Schütz
License:
FreeBSD (2-clause BSD)
Libraries:
three.js
Jquery
laszip
Plas.io (laslaz)
OpenLayers3
proj4js
tween
i18next
Donators:
rapidlasso
georepublic
sitn
Veesus
sigeom sa
Credits:
Michael Wimmer
&
Claus Scheiblauer
TU Wien, Insitute of Computer Graphics and Algorithms
Harvest4D
rapidlasso
georepublic
Howard Butler, Uday Verma, Connor Manning
Cloud Compare
sitn
loading 1 / 10
Fixed
Attenuated
Adaptive
Squares
Circles
Interpolation
RGB
RGB and Elevation
Elevation
Level of Detail