electro (low-res many-view)

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

Ground Truth (full)
Ground Truth (rig eval)
3Dnovator
3Dnovator - accuracy (56.87%)
3Dnovator - completeness (37.03%)
3Dnovator+
3Dnovator+ - accuracy (63.14%)
3Dnovator+ - completeness (35.52%)
A-TVSNet + Gipuma
A-TVSNet + Gipuma - accuracy (22.77%)
A-TVSNet + Gipuma - completeness (30.61%)
ACMH
ACMH - accuracy (32.42%)
ACMH - completeness (42.80%)
ACMH+
ACMH+ - accuracy (36.06%)
ACMH+ - completeness (46.18%)
ACMM
ACMM - accuracy (46.96%)
ACMM - completeness (43.20%)
ACMP
ACMP - accuracy (41.19%)
ACMP - completeness (49.62%)
BP-MVSNet
BP-MVSNet - accuracy (25.53%)
BP-MVSNet - completeness (51.51%)
CasMVSNet(base)
CasMVSNet(base) - accuracy (44.59%)
CasMVSNet(base) - completeness (37.15%)
CasMVSNet(SR_A)
CasMVSNet(SR_A) - accuracy (39.77%)
CasMVSNet(SR_A) - completeness (10.61%)
CasMVSNet(SR_B)
CasMVSNet(SR_B) - accuracy (45.85%)
CasMVSNet(SR_B) - completeness (41.34%)
CIDER
CIDER - accuracy (27.01%)
CIDER - completeness (28.04%)
CMPMVS
CMPMVS - accuracy (0.00%)
CMPMVS - completeness (0.00%)
COLMAP(base)
COLMAP(base) - accuracy (39.58%)
COLMAP(base) - completeness (29.06%)
COLMAP(SR)
COLMAP(SR) - accuracy (35.05%)
COLMAP(SR) - completeness (42.84%)
COLMAP_ROB
COLMAP_ROB - accuracy (55.93%)
COLMAP_ROB - completeness (19.30%)
DeepC-MVS
DeepC-MVS - accuracy (54.24%)
DeepC-MVS - completeness (40.64%)
DeepC-MVS_fast
DeepC-MVS_fast - accuracy (57.35%)
DeepC-MVS_fast - completeness (40.32%)
DeepPCF-MVS
DeepPCF-MVS - accuracy (54.41%)
DeepPCF-MVS - completeness (38.72%)
dnet
dnet - accuracy (0.00%)
dnet - completeness (0.00%)
DPSNet
DPSNet - accuracy (4.33%)
DPSNet - completeness (1.60%)
example
example - accuracy (2.82%)
example - completeness (1.90%)
GSE
GSE - accuracy (29.24%)
GSE - completeness (26.11%)
hgnet
hgnet - accuracy (4.33%)
hgnet - completeness (1.60%)
IB-MVS
IB-MVS - accuracy (41.33%)
IB-MVS - completeness (48.21%)
LPCS
LPCS - accuracy (36.01%)
LPCS - completeness (28.43%)
LTVRE_ROB
LTVRE_ROB - accuracy (62.86%)
LTVRE_ROB - completeness (25.75%)
MVE
MVE - accuracy (6.20%)
MVE - completeness (9.30%)
OpenMVS
OpenMVS - accuracy (52.12%)
OpenMVS - completeness (35.46%)
PCF-MVS
PCF-MVS - accuracy (40.73%)
PCF-MVS - completeness (34.98%)
PLC
PLC - accuracy (35.26%)
PLC - completeness (26.38%)
PMVS
PMVS - accuracy (23.33%)
PMVS - completeness (1.25%)
TAPA-MVS
TAPA-MVS - accuracy (50.57%)
TAPA-MVS - completeness (26.04%)
TAPA-MVS(SR)
TAPA-MVS(SR) - accuracy (38.71%)
TAPA-MVS(SR) - completeness (45.57%)
unsupervisedMVS_cas
unsupervisedMVS_cas - accuracy (13.06%)
unsupervisedMVS_cas - completeness (26.45%)

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