terrains (low-res many-view)

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

Height profile

Point number: -

Visualizations

Ground Truth (full)
Ground Truth (rig eval)
3Dnovator
3Dnovator - accuracy (93.85%)
3Dnovator - completeness (61.92%)
3Dnovator+
3Dnovator+ - accuracy (92.23%)
3Dnovator+ - completeness (60.19%)
A-TVSNet + Gipuma
A-TVSNet + Gipuma - accuracy (85.79%)
A-TVSNet + Gipuma - completeness (52.24%)
ACMH
ACMH - accuracy (78.18%)
ACMH - completeness (60.88%)
ACMH+
ACMH+ - accuracy (75.84%)
ACMH+ - completeness (63.53%)
ACMM
ACMM - accuracy (78.11%)
ACMM - completeness (65.26%)
ACMP
ACMP - accuracy (78.40%)
ACMP - completeness (65.61%)
BP-MVSNet
BP-MVSNet - accuracy (76.92%)
BP-MVSNet - completeness (59.68%)
CasMVSNet(base)
CasMVSNet(base) - accuracy (76.74%)
CasMVSNet(base) - completeness (51.07%)
CasMVSNet(SR_A)
CasMVSNet(SR_A) - accuracy (82.22%)
CasMVSNet(SR_A) - completeness (51.26%)
CasMVSNet(SR_B)
CasMVSNet(SR_B) - accuracy (82.22%)
CasMVSNet(SR_B) - completeness (51.26%)
CIDER
CIDER - accuracy (78.52%)
CIDER - completeness (41.69%)
CMPMVS
CMPMVS - accuracy (71.48%)
CMPMVS - completeness (35.53%)
COLMAP(base)
COLMAP(base) - accuracy (75.86%)
COLMAP(base) - completeness (62.07%)
COLMAP(SR)
COLMAP(SR) - accuracy (77.70%)
COLMAP(SR) - completeness (59.52%)
COLMAP_ROB
COLMAP_ROB - accuracy (88.59%)
COLMAP_ROB - completeness (52.88%)
DeepC-MVS
DeepC-MVS - accuracy (86.38%)
DeepC-MVS - completeness (67.02%)
DeepC-MVS_fast
DeepC-MVS_fast - accuracy (87.35%)
DeepC-MVS_fast - completeness (65.54%)
DeepPCF-MVS
DeepPCF-MVS - accuracy (86.72%)
DeepPCF-MVS - completeness (72.14%)
dnet
dnet - accuracy (0.00%)
dnet - completeness (0.00%)
DPSNet
DPSNet - accuracy (65.18%)
DPSNet - completeness (31.54%)
example
example - accuracy (61.74%)
example - completeness (44.13%)
GSE
GSE - accuracy (83.44%)
GSE - completeness (60.32%)
hgnet
hgnet - accuracy (65.18%)
hgnet - completeness (31.54%)
IB-MVS
IB-MVS - accuracy (90.30%)
IB-MVS - completeness (59.20%)
LPCS
LPCS - accuracy (78.09%)
LPCS - completeness (59.33%)
LTVRE_ROB
LTVRE_ROB - accuracy (93.50%)
LTVRE_ROB - completeness (58.99%)
MVE
MVE - accuracy (34.53%)
MVE - completeness (47.38%)
OpenMVS
OpenMVS - accuracy (92.45%)
OpenMVS - completeness (59.93%)
PCF-MVS
PCF-MVS - accuracy (78.67%)
PCF-MVS - completeness (64.20%)
PLC
PLC - accuracy (75.12%)
PLC - completeness (62.80%)
PMVS
PMVS - accuracy (84.08%)
PMVS - completeness (46.40%)
TAPA-MVS
TAPA-MVS - accuracy (85.06%)
TAPA-MVS - completeness (66.74%)
TAPA-MVS(SR)
TAPA-MVS(SR) - accuracy (85.06%)
TAPA-MVS(SR) - completeness (59.89%)
unsupervisedMVS_cas
unsupervisedMVS_cas - accuracy (36.73%)
unsupervisedMVS_cas - completeness (47.06%)

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: -0.86 to 4.24

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: 3.5

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