terrains (low-res many-view)

terrains (low-res many-view) - Tolerance 5cm

Height profile

Point number: -

Visualizations

Ground Truth (full)
Ground Truth (rig eval)
3Dnovator
3Dnovator - accuracy (98.11%)
3Dnovator - completeness (66.77%)
3Dnovator+
3Dnovator+ - accuracy (97.79%)
3Dnovator+ - completeness (65.84%)
A-TVSNet + Gipuma
A-TVSNet + Gipuma - accuracy (96.99%)
A-TVSNet + Gipuma - completeness (59.45%)
ACMH
ACMH - accuracy (93.54%)
ACMH - completeness (68.45%)
ACMH+
ACMH+ - accuracy (91.27%)
ACMH+ - completeness (70.23%)
ACMM
ACMM - accuracy (92.93%)
ACMM - completeness (73.84%)
ACMP
ACMP - accuracy (93.60%)
ACMP - completeness (72.64%)
BP-MVSNet
BP-MVSNet - accuracy (94.03%)
BP-MVSNet - completeness (66.71%)
CasMVSNet(base)
CasMVSNet(base) - accuracy (92.77%)
CasMVSNet(base) - completeness (59.42%)
CasMVSNet(SR_A)
CasMVSNet(SR_A) - accuracy (94.11%)
CasMVSNet(SR_A) - completeness (59.09%)
CasMVSNet(SR_B)
CasMVSNet(SR_B) - accuracy (94.11%)
CasMVSNet(SR_B) - completeness (59.09%)
CIDER
CIDER - accuracy (92.63%)
CIDER - completeness (52.38%)
CMPMVS
CMPMVS - accuracy (83.04%)
CMPMVS - completeness (42.82%)
COLMAP(base)
COLMAP(base) - accuracy (91.65%)
COLMAP(base) - completeness (70.21%)
COLMAP(SR)
COLMAP(SR) - accuracy (93.90%)
COLMAP(SR) - completeness (67.74%)
COLMAP_ROB
COLMAP_ROB - accuracy (97.47%)
COLMAP_ROB - completeness (61.97%)
DeepC-MVS
DeepC-MVS - accuracy (96.47%)
DeepC-MVS - completeness (72.36%)
DeepC-MVS_fast
DeepC-MVS_fast - accuracy (96.32%)
DeepC-MVS_fast - completeness (72.44%)
DeepPCF-MVS
DeepPCF-MVS - accuracy (95.74%)
DeepPCF-MVS - completeness (76.39%)
dnet
dnet - accuracy (0.00%)
dnet - completeness (0.00%)
DPSNet
DPSNet - accuracy (90.65%)
DPSNet - completeness (45.54%)
example
example - accuracy (88.24%)
example - completeness (56.87%)
GSE
GSE - accuracy (96.97%)
GSE - completeness (68.42%)
hgnet
hgnet - accuracy (90.65%)
hgnet - completeness (45.54%)
IB-MVS
IB-MVS - accuracy (97.96%)
IB-MVS - completeness (66.40%)
LPCS
LPCS - accuracy (96.33%)
LPCS - completeness (65.83%)
LTVRE_ROB
LTVRE_ROB - accuracy (98.09%)
LTVRE_ROB - completeness (65.43%)
MVE
MVE - accuracy (64.91%)
MVE - completeness (54.88%)
OpenMVS
OpenMVS - accuracy (97.67%)
OpenMVS - completeness (64.69%)
PCF-MVS
PCF-MVS - accuracy (90.16%)
PCF-MVS - completeness (69.34%)
PLC
PLC - accuracy (91.31%)
PLC - completeness (70.56%)
PMVS
PMVS - accuracy (89.52%)
PMVS - completeness (57.21%)
TAPA-MVS
TAPA-MVS - accuracy (95.63%)
TAPA-MVS - completeness (71.63%)
TAPA-MVS(SR)
TAPA-MVS(SR) - accuracy (97.71%)
TAPA-MVS(SR) - completeness (66.48%)
unsupervisedMVS_cas
unsupervisedMVS_cas - accuracy (61.34%)
unsupervisedMVS_cas - completeness (58.08%)

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: 0.00 to 1.00

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: 0.4

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