playground (low-res many-view)

playground (low-res many-view) - Tolerance 2cm

Height profile

Point number: -

Visualizations

Ground Truth (full)
Ground Truth (rig eval)
3Dnovator
3Dnovator - accuracy (63.51%)
3Dnovator - completeness (27.70%)
3Dnovator+
3Dnovator+ - accuracy (61.87%)
3Dnovator+ - completeness (28.26%)
A-TVSNet + Gipuma
A-TVSNet + Gipuma - accuracy (33.28%)
A-TVSNet + Gipuma - completeness (32.41%)
ACMH
ACMH - accuracy (41.67%)
ACMH - completeness (39.63%)
ACMH+
ACMH+ - accuracy (41.33%)
ACMH+ - completeness (42.64%)
ACMM
ACMM - accuracy (46.41%)
ACMM - completeness (41.60%)
ACMP
ACMP - accuracy (41.82%)
ACMP - completeness (43.08%)
BP-MVSNet
BP-MVSNet - accuracy (37.99%)
BP-MVSNet - completeness (31.14%)
CasMVSNet(base)
CasMVSNet(base) - accuracy (58.62%)
CasMVSNet(base) - completeness (19.20%)
CasMVSNet(SR_A)
CasMVSNet(SR_A) - accuracy (50.74%)
CasMVSNet(SR_A) - completeness (20.70%)
CasMVSNet(SR_B)
CasMVSNet(SR_B) - accuracy (50.74%)
CasMVSNet(SR_B) - completeness (20.70%)
CIDER
CIDER - accuracy (36.76%)
CIDER - completeness (29.78%)
CMPMVS
CMPMVS - accuracy (0.00%)
CMPMVS - completeness (0.00%)
COLMAP(base)
COLMAP(base) - accuracy (41.99%)
COLMAP(base) - completeness (38.28%)
COLMAP(SR)
COLMAP(SR) - accuracy (35.80%)
COLMAP(SR) - completeness (50.11%)
COLMAP_ROB
COLMAP_ROB - accuracy (63.74%)
COLMAP_ROB - completeness (21.28%)
DeepC-MVS
DeepC-MVS - accuracy (57.15%)
DeepC-MVS - completeness (40.77%)
DeepC-MVS_fast
DeepC-MVS_fast - accuracy (53.58%)
DeepC-MVS_fast - completeness (43.86%)
DeepPCF-MVS
DeepPCF-MVS - accuracy (50.07%)
DeepPCF-MVS - completeness (44.38%)
dnet
dnet - accuracy (0.00%)
dnet - completeness (0.00%)
DPSNet
DPSNet - accuracy (2.95%)
DPSNet - completeness (4.25%)
example
example - accuracy (6.49%)
example - completeness (8.53%)
GSE
GSE - accuracy (36.75%)
GSE - completeness (34.40%)
hgnet
hgnet - accuracy (2.95%)
hgnet - completeness (4.25%)
IB-MVS
IB-MVS - accuracy (47.55%)
IB-MVS - completeness (30.66%)
LPCS
LPCS - accuracy (40.04%)
LPCS - completeness (32.46%)
LTVRE_ROB
LTVRE_ROB - accuracy (59.24%)
LTVRE_ROB - completeness (27.88%)
MVE
MVE - accuracy (10.73%)
MVE - completeness (9.08%)
OpenMVS
OpenMVS - accuracy (58.26%)
OpenMVS - completeness (25.96%)
PCF-MVS
PCF-MVS - accuracy (41.39%)
PCF-MVS - completeness (46.23%)
PLC
PLC - accuracy (40.30%)
PLC - completeness (36.92%)
PMVS
PMVS - accuracy (39.19%)
PMVS - completeness (3.83%)
TAPA-MVS
TAPA-MVS - accuracy (41.27%)
TAPA-MVS - completeness (38.33%)
TAPA-MVS(SR)
TAPA-MVS(SR) - accuracy (47.40%)
TAPA-MVS(SR) - completeness (40.36%)
unsupervisedMVS_cas
unsupervisedMVS_cas - accuracy (29.49%)
unsupervisedMVS_cas - completeness (18.07%)

Materials

Attributes:
RGB

Attribute Weights

RGB:

Intensity:

Elevation:

Classification:

Return Number:

Source ID:

RGB

Gamma: 1.00

Brightness: 0.00

Contrast: 0.00

Elevation

Elevation range: -0.72 to 9.74

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 Adaptive
Quality Squares

Eye-Dome-Lighting

Enable

Radius:1.4

Strength:1.0

Background

Gradient

Black

White

Tools

Navigation

Speed: 7.2

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:

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Fixed
Attenuated
Adaptive

Squares
Circles
Interpolation

RGB
RGB and Elevation
Elevation
Level of Detail