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3D Object Reconstruction on ShapeNet (test)

0.7419Mean IoUPixel2Mesh
0.3218440.4308970.539950.649003Dec 2, 2016Jul 6, 2017Feb 7, 2018Sep 12, 2018Apr 16, 2019Nov 18, 2019Jun 22, 2020

Evaluation results

MethodLinks
Pixel2MeshThreshold=2*tau2018.040.74190.81380.71860.77190.84150.70420.67010.6150.65610.83470.69830.7920.82860.6999—————————————————————
Pix2Vox++/ANumber of views=20 views, Resolution=32^32020.060.719————————————————————————————————46.2—
Pix2Vox++/ANumber of views=16 views, Resolution=32^32020.060.718————————————————————————————————46.1—
Pix2Vox++/ANumber of views=12 views, Resolution=32^32020.060.717————————————————————————————————46—
Pix2Vox++/ANumber of views=8 views, Resolution=32^32020.060.715————————————————————————————————45.9—
Pix2Vox++/ANumber of views=5 views, Resolution=32^32020.060.711————————————————————————————————45.8—
Pix2Vox++/ANumber of views=4 views, Resolution=32^32020.060.708————————————————————————————————45.7—
Pix2Vox++/ANumber of views=3 views, Resolution=32^32020.060.704————————————————————————————————45.5—
PSGThreshold=2*tau2018.040.69780.81220.69170.67030.77790.6370.63640.58840.56790.82650.62950.7310.79630.7063—————————————————————
Pix2Vox++/ANumber of views=2 views, Resolution=32^32020.060.695————————————————————————————————45.2—
Pix2Vox++/FNumber of views=20 views, Resolution=32^32020.060.694————————————————————————————————45.1—
AttSetsNumber of views=20 views, Resolution=32^32020.060.693————————————————————————————————44.8—
Pix2Vox++/FNumber of views=16 views, Resolution=32^32020.060.693————————————————————————————————45—
AttSetsNumber of views=16 views, Resolution=32^32020.060.692————————————————————————————————44.7—
Pix2Vox++/FNumber of views=12 views, Resolution=32^32020.060.692————————————————————————————————44.9—
Pix2Vox++/FNumber of views=8 views, Resolution=32^32020.060.69————————————————————————————————44.6—
AttSetsNumber of views=12 views, Resolution=32^32020.060.688————————————————————————————————44.5—
2D-3D self-consistency framework (Sup)Training data=2D mask & voxel, Label percentage=100%, Single/multi views=S2018.110.687———0.8010.572————————0.688————————————————————
AttSetsNumber of views=8 views, Resolution=32^32020.060.685————————————————————————————————44.4—
Pix2Vox++/FNumber of views=5 views, Resolution=32^32020.060.685————————————————————————————————44—
Pix2Vox++/FNumber of views=4 views, Resolution=32^32020.060.682————————————————————————————————43.7—
Pix2Vox++/FNumber of views=3 views, Resolution=32^32020.060.678————————————————————————————————43.2—
AttSetsNumber of views=5 views, Resolution=32^32020.060.677————————————————————————————————43.2—
AttSetsNumber of views=4 views, Resolution=32^32020.060.675————————————————————————————————43—
GEOMetricsMethod Type=Graph-encoded Mesh, Average Vertices=574.062019.010.67370.890.72110.59520.74640.56610.5950.58650.49530.88360.59540.66330.73650.6832—————————————————————
Pix2Vox++/AResolution=32^3, Input Size=224x2242020.060.67—0.6080.7990.8580.581—0.4570.721——0.620.8090.6030.674———————————————0.5480.6170.725——
AttSetsNumber of views=3 views, Resolution=32^32020.060.67————————————————————————————————42.6—
Pix2Vox++/ANumber of views=1 view, Resolution=32^32020.060.67————————————————————————————————43.6—
Pix2Vox++/FNumber of views=2 views, Resolution=32^32020.060.669————————————————————————————————42.2—
MVD2018.020.66390.87340.69920.65870.67690.62570.57480.48370.53880.78120.53660.68060.860.6407—————————————————————
AttSetsNumber of views=2 views, Resolution=32^32020.060.662————————————————————————————————41.8—
IM-NetResolution=32^3, Input Size=224x2242020.060.659—0.5640.680.7560.644—0.4330.683——0.6210.7620.6070.702———————————————0.5850.7230.694——
SoftRas (full)Learning paradigm=unsupervised, Supervision setting=silhouette and shading2019.040.6464—0.5429—0.78760.5470.62980.4580.68070.67020.7220.53250.81270.61450.6670.7382———————————————————
Pix2Vox++/FResolution=32^3, Input Size=224x2242020.060.645—0.5440.7820.8410.548—0.4480.721——0.6090.7820.5830.607———————————————0.5290.5940.696——
Pix2Vox++/FNumber of views=1 view, Resolution=32^32020.060.645————————————————————————————————39.4—
AttSetsResolution=32^3, Input Size=224x2242020.060.642—0.5520.7830.8440.559—0.4450.721——0.590.7430.6010.594———————————————0.5650.6010.703——
AttSetsNumber of views=1 view, Resolution=32^32020.060.642————————————————————————————————39.5—
Point Set Generation NetworkNumber of views=1 view2016.120.640.6010.550.7710.8310.5440.5520.4620.7370.6040.7080.6060.7490.611—————————————————————
3D-R2N2Number of views=12 views, Resolution=32^32020.060.636————————————————————————————————38.2—
3D-R2N2Number of views=16 views, Resolution=32^32020.060.636————————————————————————————————38.2—
3D-R2N2Number of views=20 views, Resolution=32^32020.060.636————————————————————————————————38.3—
MatryoshkaResolution=32^3, Input Size=224x2242020.060.635—0.5770.7760.850.547—0.4080.701——0.5730.7560.5910.647———————————————0.5320.6160.681——
3D-R2N2Number of views=8 views, Resolution=32^32020.060.635————————————————————————————————38.3—
3D-R2N2Number of views=5 views, Resolution=32^32020.060.634————————————————————————————————38.2—
3D-R2N2Number of views=5 views2016.120.6310.5610.5270.7720.8360.550.5650.4210.7170.60.7060.580.7540.61—————————————————————
OccNetResolution=32^3, Input Size=224x2242020.060.626—0.5970.6740.6710.583—0.4740.655——0.6590.7940.5790.532———————————————0.6510.6560.669——
3D-R2N2Number of views=4 views, Resolution=32^32020.060.625————————————————————————————————37.8—
2D-3D self-consistency framework (Semi)Training data=2D mask & voxel, Label percentage=15%, Single/multi views=S2018.110.624———0.7530.523————————0.596————————————————————
SoftRas (sil.)Learning paradigm=unsupervised, Supervision setting=silhouette only2019.040.6234—0.508—0.76970.5270.61560.46280.66540.68110.68780.44870.78950.59530.64190.7116———————————————————
3D-R2N2Number of views=3 views2016.120.6170.5490.5020.7630.8290.5330.5450.4150.7080.5930.690.5640.7320.596—————————————————————
3D-R2N2Number of views=3 views, Resolution=32^32020.060.617————————————————————————————————37.2—
DIB-RInput Image Resolution=64 x 64, Voxelization Volume=32^32019.080.612—0.498—0.7880.5270.5880.4030.7260.5610.6770.5080.7430.6090.570.76375.755.652.253.644.746.445.938.88243.151.573.363.255.8—————
3D-R2N2Number of views=2 views, Resolution=32^32020.060.603————————————————————————————————36.8—
NMRLearning paradigm=unsupervised2019.040.6015—0.4998—0.70950.4990.58310.41260.65360.63220.67350.48290.77770.56450.61720.7143———————————————————
Pixel2MeshThreshold=tau2018.040.59720.71120.57570.60390.67860.54380.51390.48150.48840.7320.5190.6630.70240.5512—————————————————————
Pixel2Mesh2018.020.59720.71120.57570.60390.67860.54380.51390.48150.48840.7320.5190.6630.70240.5512—————————————————————
Pixel2MeshMethod Type=Mesh-based, Vertices=24662019.010.59720.71120.57570.60390.67860.54380.51390.48150.48840.7320.5190.6630.70240.5512—————————————————————
OGNResolution=32^3, Input Size=224x2242020.060.596—0.4810.7290.8280.483—0.3980.637——0.5360.7020.6320.587———————————————0.5020.5930.646——
SoftRas-MeshInput Image Resolution=64 x 64, Voxelization Volume=32^32019.080.593—0.449—0.7710.4970.5470.3910.6840.620.6360.4530.7550.5890.5840.73671.949.941.551.140.841.739.129.882.839.337.168.655.449.9—————
voxelLearning paradigm=unsupervised2019.040.5736—0.4924—0.71230.44940.53950.42230.58680.59870.62210.49380.75040.55070.55560.6823———————————————————
N3MRInput Image Resolution=64 x 64, Voxelization Volume=32^32019.080.57—0.457—0.7130.4140.5550.3670.6740.5570.6020.3910.7620.5940.5850.74180.655.346.353.339.143.846.43583.639.246.974.266.954.7—————
DRCTraining data=2D mask & pose, Label percentage=100%, Single/multi views=M2018.110.562———0.6570.493————————0.535————————————————————
3D-R2N2Number of views=1 view2016.120.560.5130.4210.7160.7980.4660.4680.3810.6620.5440.6280.5130.6610.513—————————————————————
3D-R2N2Resolution=32^3, Input Size=224x2242020.060.56—0.4210.7160.7980.466—0.3810.662——0.5130.6610.5130.513———————————————0.4680.5440.628——
3D-R2N2Number of views=1 view, Resolution=32^32020.060.56————————————————————————————————35.1—
Pixel2MeshResolution=32^3, Input Size=224x2242020.060.552—0.3790.7320.670.484—0.3990.672——0.5360.7620.4710.508———————————————0.5820.4680.622——
3D-R2N2Threshold=2*tau2018.040.54620.63230.48890.64830.54840.5520.48230.44370.57860.46870.53420.59490.60880.5219—————————————————————
MVCTraining data=2D mask, Label percentage=100%, Single/multi views=M2018.110.51———0.6390.396————————0.494————————————————————
PSGThreshold=tau2018.040.48580.6820.49290.39930.5070.4160.40530.4140.32610.69960.36590.53440.55950.5128—————————————————————
PSG2018.020.48580.6820.49290.39930.5070.4160.40530.4140.32610.69960.36590.53440.55950.5128—————————————————————
PSGMethod Type=Point cloud-based2019.010.48580.6820.49290.39930.5070.4160.40530.4140.32610.69960.36590.53440.55950.5128—————————————————————
N3MRThreshold=2*tau2018.040.47720.77150.49580.35160.53930.44590.42760.39410.3220.63280.3990.41730.41830.5885—————————————————————
retrievalLearning paradigm=unsupervised2019.040.4766—0.4875—0.65190.35120.39580.29050.460.51330.53140.30970.66960.40780.55640.5713———————————————————
3D-R2N2Threshold=tau2018.040.39010.41460.34090.49880.3780.40220.34380.32350.4530.28340.40010.43790.42310.371—————————————————————
3D-R2N22018.020.39010.41460.34090.49880.3780.40220.34380.32350.4530.28340.40010.43790.42310.371—————————————————————
3D-R2N2Method Type=Voxel-based2019.010.39010.41460.34090.49880.3780.40220.34380.32350.4530.28340.40010.43790.42310.371—————————————————————
AtlasNetResolution=32^3, Input Size=224x2242020.060.352—0.4310.2570.2820.328—0.2610.296——0.3010.5430.3550.493———————————————0.4570.5730.354——
N3MRThreshold=tau2018.040.3380.6210.35840.21040.36660.30250.28770.27970.19460.52220.25040.2840.27960.4371—————————————————————
N3MR2018.020.3380.6210.35840.21040.36660.30250.28770.27970.19460.52220.25040.2840.27960.4371—————————————————————
N3MRMethod Type=Mesh-based, Vertices=6422019.010.3380.6210.35840.21040.36660.30250.28770.27970.19460.52220.25040.2840.27960.4371—————————————————————
AtlasNet2020.07—————————————————————————————————25.22
CT2020.07—————————————————————————————————35.949
Matryoshka2020.07—————————————————————————————————21.70
OGN2020.07—————————————————————————————————26.41
Retrieval2020.07—————————————————————————————————23.60
Retrieval (oracle)2020.07—————————————————————————————————293