+
−
⇧
i
D
T
forest (low-res many-view) - Tolerance 2cm
Height profile
Point number
:
-
Visualizations
Ground Truth (full)
Ground Truth (rig eval)
3Dnovator
3Dnovator - accuracy (71.81%)
3Dnovator - completeness (68.61%)
3Dnovator+
3Dnovator+ - accuracy (71.19%)
3Dnovator+ - completeness (69.91%)
A-TVSNet + Gipuma
A-TVSNet + Gipuma - accuracy (45.44%)
A-TVSNet + Gipuma - completeness (70.65%)
ACMH
ACMH - accuracy (46.62%)
ACMH - completeness (74.13%)
ACMH+
ACMH+ - accuracy (45.83%)
ACMH+ - completeness (74.64%)
ACMM
ACMM - accuracy (51.32%)
ACMM - completeness (72.84%)
ACMP
ACMP - accuracy (48.91%)
ACMP - completeness (73.09%)
BP-MVSNet
BP-MVSNet - accuracy (57.05%)
BP-MVSNet - completeness (70.42%)
CasMVSNet(base)
CasMVSNet(base) - accuracy (73.58%)
CasMVSNet(base) - completeness (61.20%)
CasMVSNet(SR_A)
CasMVSNet(SR_A) - accuracy (87.07%)
CasMVSNet(SR_A) - completeness (40.45%)
CasMVSNet(SR_B)
CasMVSNet(SR_B) - accuracy (71.26%)
CasMVSNet(SR_B) - completeness (63.99%)
CIDER
CIDER - accuracy (58.74%)
CIDER - completeness (70.37%)
CMPMVS
CMPMVS - accuracy (1.66%)
CMPMVS - completeness (0.11%)
COLMAP(base)
COLMAP(base) - accuracy (53.68%)
COLMAP(base) - completeness (70.11%)
COLMAP(SR)
COLMAP(SR) - accuracy (52.60%)
COLMAP(SR) - completeness (80.86%)
COLMAP_ROB
COLMAP_ROB - accuracy (60.77%)
COLMAP_ROB - completeness (62.93%)
DeepC-MVS
DeepC-MVS - accuracy (63.91%)
DeepC-MVS - completeness (72.80%)
DeepC-MVS_fast
DeepC-MVS_fast - accuracy (66.58%)
DeepC-MVS_fast - completeness (74.28%)
DeepPCF-MVS
DeepPCF-MVS - accuracy (64.65%)
DeepPCF-MVS - completeness (73.53%)
dnet
dnet - accuracy (0.00%)
dnet - completeness (0.00%)
DPSNet
DPSNet - accuracy (20.25%)
DPSNet - completeness (16.09%)
example
example - accuracy (22.01%)
example - completeness (22.42%)
GSE
GSE - accuracy (45.62%)
GSE - completeness (67.12%)
hgnet
hgnet - accuracy (20.25%)
hgnet - completeness (16.09%)
IB-MVS
IB-MVS - accuracy (67.25%)
IB-MVS - completeness (70.50%)
LPCS
LPCS - accuracy (46.89%)
LPCS - completeness (65.27%)
LTVRE_ROB
LTVRE_ROB - accuracy (80.00%)
LTVRE_ROB - completeness (50.93%)
MVE
MVE - accuracy (6.32%)
MVE - completeness (14.89%)
OpenMVS
OpenMVS - accuracy (63.63%)
OpenMVS - completeness (67.68%)
PCF-MVS
PCF-MVS - accuracy (62.25%)
PCF-MVS - completeness (73.10%)
PLC
PLC - accuracy (48.97%)
PLC - completeness (68.03%)
PMVS
PMVS - accuracy (59.11%)
PMVS - completeness (16.70%)
TAPA-MVS
TAPA-MVS - accuracy (55.89%)
TAPA-MVS - completeness (71.36%)
TAPA-MVS(SR)
TAPA-MVS(SR) - accuracy (58.15%)
TAPA-MVS(SR) - completeness (74.10%)
unsupervisedMVS_cas
unsupervisedMVS_cas - accuracy (32.37%)
unsupervisedMVS_cas - completeness (54.34%)
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
:
0.00 to 1.00
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
:
0.4
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