+
−
⇧
i
D
T
forest (low-res many-view) - Tolerance 1cm
Height profile
Point number
:
-
Visualizations
Ground Truth (full)
Ground Truth (rig eval)
3Dnovator
3Dnovator - accuracy (53.59%)
3Dnovator - completeness (55.50%)
3Dnovator+
3Dnovator+ - accuracy (54.75%)
3Dnovator+ - completeness (56.01%)
A-TVSNet + Gipuma
A-TVSNet + Gipuma - accuracy (30.01%)
A-TVSNet + Gipuma - completeness (52.47%)
ACMH
ACMH - accuracy (31.37%)
ACMH - completeness (62.67%)
ACMH+
ACMH+ - accuracy (31.24%)
ACMH+ - completeness (60.45%)
ACMM
ACMM - accuracy (38.58%)
ACMM - completeness (58.55%)
ACMP
ACMP - accuracy (36.21%)
ACMP - completeness (60.42%)
BP-MVSNet
BP-MVSNet - accuracy (40.66%)
BP-MVSNet - completeness (63.02%)
CasMVSNet(base)
CasMVSNet(base) - accuracy (54.40%)
CasMVSNet(base) - completeness (53.88%)
CasMVSNet(SR_A)
CasMVSNet(SR_A) - accuracy (68.60%)
CasMVSNet(SR_A) - completeness (31.51%)
CasMVSNet(SR_B)
CasMVSNet(SR_B) - accuracy (52.90%)
CasMVSNet(SR_B) - completeness (57.42%)
CIDER
CIDER - accuracy (41.33%)
CIDER - completeness (52.69%)
CMPMVS
CMPMVS - accuracy (1.25%)
CMPMVS - completeness (0.09%)
COLMAP(base)
COLMAP(base) - accuracy (38.35%)
COLMAP(base) - completeness (43.92%)
COLMAP(SR)
COLMAP(SR) - accuracy (38.16%)
COLMAP(SR) - completeness (65.41%)
COLMAP_ROB
COLMAP_ROB - accuracy (43.64%)
COLMAP_ROB - completeness (36.90%)
DeepC-MVS
DeepC-MVS - accuracy (50.78%)
DeepC-MVS - completeness (56.08%)
DeepC-MVS_fast
DeepC-MVS_fast - accuracy (53.14%)
DeepC-MVS_fast - completeness (57.24%)
DeepPCF-MVS
DeepPCF-MVS - accuracy (51.70%)
DeepPCF-MVS - completeness (52.59%)
dnet
dnet - accuracy (0.00%)
dnet - completeness (0.00%)
DPSNet
DPSNet - accuracy (11.72%)
DPSNet - completeness (4.42%)
example
example - accuracy (13.29%)
example - completeness (9.20%)
GSE
GSE - accuracy (29.83%)
GSE - completeness (42.34%)
hgnet
hgnet - accuracy (11.72%)
hgnet - completeness (4.42%)
IB-MVS
IB-MVS - accuracy (50.61%)
IB-MVS - completeness (62.12%)
LPCS
LPCS - accuracy (30.42%)
LPCS - completeness (40.15%)
LTVRE_ROB
LTVRE_ROB - accuracy (63.94%)
LTVRE_ROB - completeness (29.17%)
MVE
MVE - accuracy (3.56%)
MVE - completeness (5.03%)
OpenMVS
OpenMVS - accuracy (45.41%)
OpenMVS - completeness (53.95%)
PCF-MVS
PCF-MVS - accuracy (45.76%)
PCF-MVS - completeness (47.93%)
PLC
PLC - accuracy (33.68%)
PLC - completeness (37.68%)
PMVS
PMVS - accuracy (42.55%)
PMVS - completeness (4.14%)
TAPA-MVS
TAPA-MVS - accuracy (38.98%)
TAPA-MVS - completeness (54.71%)
TAPA-MVS(SR)
TAPA-MVS(SR) - accuracy (41.79%)
TAPA-MVS(SR) - completeness (64.25%)
unsupervisedMVS_cas
unsupervisedMVS_cas - accuracy (22.57%)
unsupervisedMVS_cas - completeness (47.65%)
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