playground (low-res many-view)

playground (low-res many-view) - Tolerance 1cm

Ground Truth (full)
Ground Truth (rig eval)
3Dnovator
3Dnovator - accuracy (47.20%)
3Dnovator - completeness (19.48%)
3Dnovator+
3Dnovator+ - accuracy (46.99%)
3Dnovator+ - completeness (19.10%)
A-TVSNet + Gipuma
A-TVSNet + Gipuma - accuracy (20.90%)
A-TVSNet + Gipuma - completeness (19.28%)
ACMH
ACMH - accuracy (27.53%)
ACMH - completeness (27.77%)
ACMH+
ACMH+ - accuracy (26.98%)
ACMH+ - completeness (28.39%)
ACMM
ACMM - accuracy (32.21%)
ACMM - completeness (27.97%)
ACMP
ACMP - accuracy (28.36%)
ACMP - completeness (28.28%)
BP-MVSNet
BP-MVSNet - accuracy (24.94%)
BP-MVSNet - completeness (22.04%)
CasMVSNet(base)
CasMVSNet(base) - accuracy (41.61%)
CasMVSNet(base) - completeness (15.17%)
CasMVSNet(SR_A)
CasMVSNet(SR_A) - accuracy (35.27%)
CasMVSNet(SR_A) - completeness (16.52%)
CasMVSNet(SR_B)
CasMVSNet(SR_B) - accuracy (35.27%)
CasMVSNet(SR_B) - completeness (16.52%)
CIDER
CIDER - accuracy (23.57%)
CIDER - completeness (18.72%)
CMPMVS
CMPMVS - accuracy (0.00%)
CMPMVS - completeness (0.00%)
COLMAP(base)
COLMAP(base) - accuracy (27.50%)
COLMAP(base) - completeness (20.07%)
COLMAP(SR)
COLMAP(SR) - accuracy (23.11%)
COLMAP(SR) - completeness (30.70%)
COLMAP_ROB
COLMAP_ROB - accuracy (46.83%)
COLMAP_ROB - completeness (10.90%)
DeepC-MVS
DeepC-MVS - accuracy (41.12%)
DeepC-MVS - completeness (26.58%)
DeepC-MVS_fast
DeepC-MVS_fast - accuracy (38.29%)
DeepC-MVS_fast - completeness (27.41%)
DeepPCF-MVS
DeepPCF-MVS - accuracy (35.41%)
DeepPCF-MVS - completeness (26.81%)
dnet
dnet - accuracy (0.00%)
dnet - completeness (0.00%)
DPSNet
DPSNet - accuracy (1.57%)
DPSNet - completeness (1.09%)
example
example - accuracy (3.67%)
example - completeness (3.97%)
GSE
GSE - accuracy (22.61%)
GSE - completeness (18.45%)
hgnet
hgnet - accuracy (1.57%)
hgnet - completeness (1.09%)
IB-MVS
IB-MVS - accuracy (33.16%)
IB-MVS - completeness (21.79%)
LPCS
LPCS - accuracy (24.38%)
LPCS - completeness (18.12%)
LTVRE_ROB
LTVRE_ROB - accuracy (42.95%)
LTVRE_ROB - completeness (13.65%)
MVE
MVE - accuracy (5.92%)
MVE - completeness (4.12%)
OpenMVS
OpenMVS - accuracy (42.47%)
OpenMVS - completeness (17.65%)
PCF-MVS
PCF-MVS - accuracy (27.61%)
PCF-MVS - completeness (27.37%)
PLC
PLC - accuracy (26.03%)
PLC - completeness (18.93%)
PMVS
PMVS - accuracy (30.62%)
PMVS - completeness (1.25%)
TAPA-MVS
TAPA-MVS - accuracy (27.25%)
TAPA-MVS - completeness (21.47%)
TAPA-MVS(SR)
TAPA-MVS(SR) - accuracy (31.96%)
TAPA-MVS(SR) - completeness (27.92%)
unsupervisedMVS_cas
unsupervisedMVS_cas - accuracy (19.34%)
unsupervisedMVS_cas - completeness (13.99%)

Attributes:
RGB

Attribute Weights

RGB:

Intensity:

Elevation:

Classification:

Return Number:

Source ID:

RGB

Gamma: 1.00

Brightness: 0.00

Contrast: 0.00

Elevation

: 0.00 to 1.00

Transition

transition:

Intensity

Range: 0 to 300

Gamma: 1.00

Brightness: 0.00

Contrast: 0.00

:1,000,000
:1.00
:60
:1.00
Point Sizing Adaptive
Squares

Eye-Dome-Lighting

:1.4

:1.0

Background

Gradient

Black

White

Navigation

: 0.4

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:

loading 1 / 10

Fixed
Attenuated
Adaptive

Squares
Circles
Interpolation

RGB
RGB and Elevation
Elevation
Level of Detail