delivery_area (low-res many-view)

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

Height profile

Point number: -

Visualizations

Ground Truth (full)
Ground Truth (rig eval)
3Dnovator
3Dnovator - accuracy (54.58%)
3Dnovator - completeness (39.13%)
3Dnovator+
3Dnovator+ - accuracy (57.83%)
3Dnovator+ - completeness (39.47%)
A-TVSNet + Gipuma
A-TVSNet + Gipuma - accuracy (23.11%)
A-TVSNet + Gipuma - completeness (42.17%)
ACMH
ACMH - accuracy (38.05%)
ACMH - completeness (38.88%)
ACMH+
ACMH+ - accuracy (40.39%)
ACMH+ - completeness (49.95%)
ACMM
ACMM - accuracy (33.39%)
ACMM - completeness (45.88%)
ACMP
ACMP - accuracy (41.23%)
ACMP - completeness (55.49%)
BP-MVSNet
BP-MVSNet - accuracy (32.80%)
BP-MVSNet - completeness (51.59%)
CasMVSNet(base)
CasMVSNet(base) - accuracy (37.99%)
CasMVSNet(base) - completeness (32.16%)
CasMVSNet(SR_A)
CasMVSNet(SR_A) - accuracy (34.02%)
CasMVSNet(SR_A) - completeness (32.09%)
CasMVSNet(SR_B)
CasMVSNet(SR_B) - accuracy (34.02%)
CasMVSNet(SR_B) - completeness (32.09%)
CIDER
CIDER - accuracy (23.77%)
CIDER - completeness (35.72%)
CMPMVS
CMPMVS - accuracy (0.00%)
CMPMVS - completeness (0.00%)
COLMAP(base)
COLMAP(base) - accuracy (45.13%)
COLMAP(base) - completeness (42.96%)
COLMAP(SR)
COLMAP(SR) - accuracy (40.55%)
COLMAP(SR) - completeness (55.29%)
COLMAP_ROB
COLMAP_ROB - accuracy (58.83%)
COLMAP_ROB - completeness (27.30%)
DeepC-MVS
DeepC-MVS - accuracy (52.36%)
DeepC-MVS - completeness (54.09%)
DeepC-MVS_fast
DeepC-MVS_fast - accuracy (55.68%)
DeepC-MVS_fast - completeness (51.78%)
DeepPCF-MVS
DeepPCF-MVS - accuracy (53.86%)
DeepPCF-MVS - completeness (53.52%)
dnet
dnet - accuracy (0.00%)
dnet - completeness (0.00%)
DPSNet
DPSNet - accuracy (4.69%)
DPSNet - completeness (9.40%)
example
example - accuracy (5.50%)
example - completeness (6.63%)
GSE
GSE - accuracy (47.61%)
GSE - completeness (46.56%)
hgnet
hgnet - accuracy (4.69%)
hgnet - completeness (9.40%)
IB-MVS
IB-MVS - accuracy (39.73%)
IB-MVS - completeness (40.95%)
LPCS
LPCS - accuracy (49.50%)
LPCS - completeness (38.19%)
LTVRE_ROB
LTVRE_ROB - accuracy (62.97%)
LTVRE_ROB - completeness (29.11%)
MVE
MVE - accuracy (3.37%)
MVE - completeness (11.64%)
OpenMVS
OpenMVS - accuracy (51.05%)
OpenMVS - completeness (39.95%)
PCF-MVS
PCF-MVS - accuracy (46.44%)
PCF-MVS - completeness (51.00%)
PLC
PLC - accuracy (41.31%)
PLC - completeness (42.15%)
PMVS
PMVS - accuracy (39.36%)
PMVS - completeness (3.15%)
TAPA-MVS
TAPA-MVS - accuracy (33.25%)
TAPA-MVS - completeness (55.62%)
TAPA-MVS(SR)
TAPA-MVS(SR) - accuracy (38.52%)
TAPA-MVS(SR) - completeness (49.38%)
unsupervisedMVS_cas
unsupervisedMVS_cas - accuracy (17.77%)
unsupervisedMVS_cas - completeness (28.19%)

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: -12.28 to 29.11

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: 35.6

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