High-res multi-view results

This table lists the benchmark results for the high-res multi-view scenario. The following metrics are evaluated:

Accuracy [%]: The fraction of the reconstruction which is closer to the ground truth than the evaluation threshold distance (*). Larger is better.
Completeness [%]: The fraction of the ground truth which is closer to the reconstruction than the evaluation threshold distance (*). Larger is better.
F₁ score [%]: The harmonic mean of accuracy and completeness, used to rank methods based on both metrics. Larger is better.
Time [s]: The runtime of the method. Smaller is better.

(*) For exact definitions, detailing how potentially incomplete ground truth is taken into account, see our paper.

The datasets are grouped into different categories, and result averages are computed for a category and method if results of the method are available for all datasets within the category. Note that the category "all" includes both the high-res multi-view and the low-res many-view scenarios.

Methods with suffix _ROB may participate in the Robust Vision Challenge.

Click a dataset result cell to show a visualization of the reconstruction. For training datasets, ground truth and accuracy / completeness visualizations are also available. The visualizations may not work with mobile browsers.

Method	all	high-res multi-view	indoor	outdoor	courty.	delive.	electro	facade	kicker	meadow	office	pipes	playgr.	relief	relief.	terrace	terrai.

ACMH+	702.88 1	670.82 93	589.81 93	765.33 94	662.52 90	701.42 87	795.24 91	1698.33 104	598.81 99	226.55 87	403.02 83	187.13 82	786.82 96	724.80 99	646.56 92	422.52 87	866.95 96

PMVS	743.55 2	836.66 98	637.90 95	1068.55 107	1306.50 115	875.12 100	1166.70 113	2629.00 111	512.24 88	196.22 82	282.69 77	237.15 89	597.87 83	842.47 101	765.54 100	514.99 97	950.09 98
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
IB-MVS	750.90 3	552.99 79	511.38 83	601.53 78	606.02 86	735.75 90	688.75 88	1183.86 91	436.09 81	210.63 86	397.40 82	233.85 88	551.05 81	521.77 81	533.78 81	368.88 81	721.05 86
Christian Sormann, Mattia Rossi, Andreas Kuhn and Friedrich Fraundorfer: IB-MVS: An Iterative Algorithm for Deep Multi-View Stereo based on Binary Decisions. BMVC 2021
ACMH	935.68 4	948.23 101	857.00 101	1054.67 106	946.00 102	970.00 104	1125.00 111	2467.00 109	823.00 105	330.00 100	622.00 104	211.00 87	891.00 103	970.00 104	1061.00 111	569.00 100	1342.00 110
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ACMM	1129.18 5	1047.56 108	912.90 102	1204.67 108	1040.21 110	1016.71 111	1105.32 109	2918.67 114	813.91 103	327.22 99	665.34 105	267.72 99	1192.58 112	1091.35 112	991.88 104	643.99 106	1543.40 117
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ACMP	1170.86 6	1091.37 110	943.23 104	1264.21 110	1112.10 112	1138.94 113	1276.90 115	2675.36 113	989.62 108	426.15 110	742.77 108	349.09 112	1454.51 117	1047.36 111	969.90 103	640.22 105	1364.94 112
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
OpenMVS	2049.89 7	1895.92 115	1721.29 116	2099.67 116	2064.00 120	2419.00 124	2138.00 122	4572.00 123	1541.00 122	548.00 113	937.00 113	580.00 118	1983.00 119	1971.00 123	1993.00 121	1293.00 125	2608.00 121

DeepC-MVS_fast	2081.98 8	1311.87 113	1172.50 115	1474.47 113	1411.78 116	1397.23 118	1519.03 118	3401.40 118	1201.29 116	417.90 108	801.95 110	976.25 140	1409.37 115	1114.52 113	1201.12 115	687.33 108	1515.14 116
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
3Dnovator	2220.89 9	2303.77 123	2037.00 125	2615.00 125	2617.00 130	2967.00 131	2616.00 125	5882.00 133	1928.00 128	589.00 115	907.00 112	528.00 117	2443.00 130	2370.00 131	2392.00 132	1543.00 135	3167.00 128

CMPMVS	2507.20 10	2083.60 117	1983.13 121	2200.81 118	2582.70 129	3097.30 132	2332.10 123	4364.50 121	1390.00 118	653.68 118	997.75 115	742.65 128	1880.20 118	2298.80 129	2413.60 133	1391.70 129	2941.80 126
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
DeepPCF-MVS	3033.31 11	2362.79 125	2183.15 127	2572.38 123	2349.28 126	2383.16 123	2740.92 130	5212.97 126	2043.02 129	751.44 121	1466.47 129	1327.42 153	2904.33 133	2573.45 136	2227.77 126	1475.33 130	3260.73 130

3Dnovator+	3173.67 12	2938.77 134	2519.43 130	3428.00 135	3256.00 138	3708.00 141	3443.00 137	7516.00 140	2525.00 136	1121.00 145	972.00 114	925.00 137	3192.00 137	2899.00 139	2897.00 143	2040.00 146	3710.00 139

PCF-MVS	3738.24 13	2129.60 118	1746.89 117	2576.10 124	2258.22 125	2263.67 122	2650.06 129	6361.78 135	1676.15 124	695.66 119	1209.77 123	737.88 125	2237.28 120	2083.02 125	1835.31 117	1253.58 124	2422.45 119
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
COLMAP_ROB	3798.28 14	2690.62 132	2704.29 139	2674.67 126	3138.00 134	3522.00 140	3826.00 144	3219.00 116	2702.00 140	1138.00 148	2260.00 147	1212.00 152	2968.00 136	2936.00 143	2571.00 136	1759.00 144	3727.00 140
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
PLC	4785.50 15	3275.54 141	3030.86 143	3561.00 138	4093.00 147	4596.00 146	5174.00 153	4267.00 120	3570.00 148	1495.00 154	2940.00 150	1594.00 155	3947.00 144	3817.00 148	3343.00 147	2390.00 148	1356.00 111
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
TAPA-MVS	5463.68 16	2764.21 133	2434.28 129	3149.14 134	3645.15 143	3257.60 136	3452.71 138	6526.32 136	2591.85 138	651.36 117	1964.77 145	947.44 138	2924.41 134	2523.36 134	2452.68 134	1694.86 141	3302.28 131
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
DeepC-MVS	5581.15 17	4824.92 147	5020.53 148	4596.70 146	4714.52 150	3205.14 134	3108.50 133	10234.20 151	3823.34 150	1885.23 157	9002.42 154	2045.34 159	4715.23 150	7652.65 152	3993.41 148	2922.51 151	5421.41 148
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
MVE	11086.50 18	13278.69 152	12303.57 153	14416.33 153	9890.00 156	17107.00 159	4634.00 149	59884.00 158	3666.00 149	966.00 129	1792.00 139	896.00 136	5680.00 151	20677.00 157	23030.00 156	5444.00 156	18957.00 154
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
LTVRE_ROB	38377.89 19	49656.08 155	18936.71 155	85495.33 156	306852.00 162	22526.00 161	29206.00 161	112098.00 160	30022.00 157	17348.00 163	2824.00 149	11239.00 163	29457.00 157	20569.00 156	19941.00 155	18011.00 159	25436.00 156
Andreas Kuhn, Heiko HirschmÃ¼ller, Daniel Scharstein, Helmut Mayer: A TV Prior for High-Quality Scalable Multi-View Stereo Reconstruction. International Journal of Computer Vision 2016
FA-MVS(training)		203.85 69	180.15 69	231.50 69	222.82 71	259.22 74	245.99 73	478.61 72	172.32 72	82.03 74	130.35 70	72.14 73	221.74 71	190.35 70	197.39 70	137.78 73	239.26 70

test2506		10000000.00 157	10000000.00 158	10000000.00 158	10000000.00 163	10000000.00 164	10000000.00 164	10000000.00 161	10000000.00 160	10000000.00 165	10000000.00 160	10000000.00 166	10000000.00 160	10000000.00 160	10000000.00 159	10000000.00 162	10000000.00 159

test1111		10000000.00 157	10000000.00 158	10000000.00 158	10000000.00 163	10000000.00 164	10000000.00 164	10000000.00 161	10000000.00 160	10000000.00 165	10000000.00 160	10000000.00 166	10000000.00 160	10000000.00 160	10000000.00 159	10000000.00 162	10000000.00 159

ECVR-MVS		10000000.00 157	10000000.00 158	10000000.00 158	10000000.00 163	10000000.00 164	10000000.00 164	10000000.00 161	10000000.00 160	10000000.00 165	10000000.00 160	10000000.00 166	10000000.00 160	10000000.00 160	10000000.00 159	10000000.00 162	10000000.00 159

DVP-MVS++		3961.71 144	3092.38 144	4975.92 147	4850.81 152	5691.32 150	5760.96 154	12294.80 152	2980.85 146	1135.72 147	1840.86 142	949.63 139	4197.95 146	3019.10 146	2873.21 142	1615.27 138	4291.72 145

GeoE		20.05 49	18.83 49	21.47 49	25.60 49	29.60 50	22.50 49	31.20 49	19.10 50	8.30 48	13.50 50	9.40 49	17.80 49	14.10 49	17.50 49	23.40 50	28.60 50

test_method		18.19 48	15.64 48	21.17 48	19.00 48	22.00 49	22.50 49	38.00 50	15.50 49	7.50 47	13.00 49	7.00 48	19.00 50	15.50 50	15.50 48	21.00 49	21.00 49

pmnet_mix02		659.48 92	547.83 85	789.74 95	720.05 92	846.87 99	740.59 89	2153.63 108	456.15 86	191.24 81	336.49 80	200.39 85	511.90 79	608.74 88	634.29 91	421.05 86	751.91 87

RE-MVS-def										610.10 116

SED-MVS		3539.47 142	2909.05 142	4274.95 145	4528.93 149	4986.51 148	5003.31 152	8985.31 147	2904.53 145	1294.24 152	1839.12 141	1003.69 147	4135.17 145	2926.32 142	2897.04 144	1702.75 142	3806.13 142

SF-MVS		0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1

9.14												0.00 1

uanet_test		1000.00 104	1000.00 108	1000.00 102	1000.00 106	1000.00 107	1000.00 105	1000.00 85	1000.00 111	1000.00 134	1000.00 118	1000.00 143	1000.00 107	1000.00 107	1000.00 107	1000.00 115	1000.00 104

ET-MVSNet_ETH3D		10000000.00 157	10000000.00 158	10000000.00 158	10000000.00 163	10000000.00 164	10000000.00 164	10000000.00 161	10000000.00 160	10000000.00 165	10000000.00 160	10000000.00 166	10000000.00 160	10000000.00 160	10000000.00 159	10000000.00 162	10000000.00 159

UniMVSNet_ETH3D		10000000.00 157	10000000.00 158	10000000.00 158	10000000.00 163	10000000.00 164	10000000.00 164	10000000.00 161	10000000.00 160	10000000.00 165	10000000.00 160	10000000.00 166	10000000.00 160	10000000.00 160	10000000.00 159	10000000.00 162	10000000.00 159

EIA-MVS		0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1

ETV-MVS		0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1

CS-MVS		0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1

DVP-MVS		3258.34 140	2529.80 131	4108.31 143	4005.62 146	4693.14 147	4737.88 150	10195.40 150	2408.38 134	941.44 128	1505.82 130	734.32 124	3438.27 142	2449.15 132	2386.04 131	1331.25 127	3531.75 137
Zhenlong Yuan, Jinguo Luo, Fei Shen, Zhaoxin Li, Cong Liu, Tianlu Mao, Zhaoqi Wang: DVP-MVS: Synergize Depth-Edge and Visibility Prior for Multi-View Stereo. AAAI2025
SR-MVS					610.10 87			610.10 74				610.10 119

DPM-MVS		3051.54 135	2661.57 136	3506.50 136	3532.00 141	3332.00 138	3744.00 142	7839.00 143	2772.00 141	985.00 130	1919.00 143	738.00 126	3319.00 138	2901.00 140	2715.00 138	1620.00 139	4254.00 143

thisisatest0530		10000000.00 157	10000000.00 158	10000000.00 158	10000000.00 163	10000000.00 164	10000000.00 164	10000000.00 161	10000000.00 160	10000000.00 165	10000000.00 160	10000000.00 166	10000000.00 160	10000000.00 160	10000000.00 159	10000000.00 162	10000000.00 159

Anonymous202405211			10000000.00 158		10000000.00 163	10000000.00 164	10000000.00 164	10000000.00 161	10000000.00 160		10000000.00 160	10000000.00 166	10000000.00 160	10000000.00 160	10000000.00 159	10000000.00 162	10000000.00 159

DCV-MVSNet		10000000.00 157	10000000.00 158	10000000.00 158	10000000.00 163	10000000.00 164	10000000.00 164	10000000.00 161	10000000.00 160	10000000.00 165	10000000.00 160	10000000.00 166	10000000.00 160	10000000.00 160	10000000.00 159	10000000.00 162	10000000.00 159

tttt0517		10000000.00 157	10000000.00 158	10000000.00 158	10000000.00 163	10000000.00 164	10000000.00 164	10000000.00 161	10000000.00 160	10000000.00 165	10000000.00 160	10000000.00 166	10000000.00 160	10000000.00 160	10000000.00 159	10000000.00 162	10000000.00 159

our_test_3					9999.00 157	99.00 58	99.00 57

thisisatest0515		10000000.00 157	10000000.00 158	10000000.00 158	10000000.00 163	10000000.00 164	10000000.00 164	10000000.00 161	10000000.00 160	10000000.00 165	10000000.00 160	10000000.00 166	10000000.00 160	10000000.00 160	10000000.00 159	10000000.00 162	10000000.00 159

SMA-MVS		0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1
Yufeng Yin; Xiaoyan Liu; Zichao Zhang: SMA-MVS: Segmentation-Guided Multi-Scale Anchor Deformation Patch Multi-View Stereo. IEEE Transactions on Circuits and Systems for Video Technology
DPE-MVS		10000000.00 157	10000000.00 158	10000000.00 158	10000000.00 163	10000000.00 164	10000000.00 164	10000000.00 161	10000000.00 160	10000000.00 165	10000000.00 160	10000000.00 166	10000000.00 160	10000000.00 160	10000000.00 159	10000000.00 162	10000000.00 159
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
thres100view900		10000000.00 157	10000000.00 158	10000000.00 158	10000000.00 163	10000000.00 164	10000000.00 164	10000000.00 161	10000000.00 160	10000000.00 165	10000000.00 160	10000000.00 166	10000000.00 160	10000000.00 160	10000000.00 159	10000000.00 162	10000000.00 159

tfpnnormal		10000000.00 157	10000000.00 158	10000000.00 158	10000000.00 163	10000000.00 164	10000000.00 164	10000000.00 161	10000000.00 160	10000000.00 165	10000000.00 160	10000000.00 166	10000000.00 160	10000000.00 160	10000000.00 159	10000000.00 162	10000000.00 159

tfpn200view9		10000000.00 157	10000000.00 158	10000000.00 158	10000000.00 163	10000000.00 164	10000000.00 164	10000000.00 161	10000000.00 160	10000000.00 165	10000000.00 160	10000000.00 166	10000000.00 160	10000000.00 160	10000000.00 159	10000000.00 162	10000000.00 159

CHOSEN 280x420		2225.10 119	1992.66 122	2496.28 119	2192.10 122	2641.81 126	2621.41 126	5345.34 127	2116.46 130	1002.26 138	1683.84 133	837.43 129	2338.65 122	1920.94 120	2020.28 122	1477.92 131	2727.86 122

CANet		100.44 54	93.07 54	109.04 54	111.90 56	131.06 60	119.40 58	222.42 62	80.48 55	36.93 54	70.80 55	40.92 56	89.90 55	100.59 62	101.42 55	73.68 58	126.25 55

Fast-Effi-MVS+-dtu		0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1

Effi-MVS+-dtu		0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1

CANet_DTU		100.44 54	93.07 54	109.04 54	111.90 56	131.06 60	119.40 58	222.42 62	80.48 55	36.93 54	70.80 55	40.92 56	89.90 55	100.59 62	101.42 55	73.68 58	126.25 55

MVS_0304		100.44 54	93.07 54	109.04 54	111.90 56	131.06 60	119.40 58	222.42 62	80.48 55	36.93 54	70.80 55	40.92 56	89.90 55	100.59 62	101.42 55	73.68 58	126.25 55

MSP-MVS		3241.28 139	2825.26 141	3726.65 141	3945.31 145	4305.03 144	4267.51 148	6962.25 138	2896.37 144	1068.15 143	1824.25 140	1061.55 150	4307.30 147	3013.40 145	3171.41 145	1809.38 145	3504.79 136
Zhenlong Yuan, Cong Liu, Fei Shen, Zhaoxin Li, Jingguo luo, Tianlu Mao and Zhaoqi Wang: MSP-MVS: Multi-granularity Segmentation Prior Guided Multi-View Stereo. AAAI2025
IterMVS-SCA-FT		10000000.00 157	10000000.00 158	10000000.00 158	10000000.00 163	10000000.00 164	10000000.00 164	10000000.00 161	10000000.00 160	10000000.00 165	10000000.00 160	10000000.00 166	10000000.00 160	10000000.00 160	10000000.00 159	10000000.00 162	10000000.00 159

TSAR-MVS + MP.		4216.58 145	4674.68 147	3682.13 140	3752.63 144	4315.48 145	4264.77 147	7782.51 142	2583.81 137	1125.68 146	16722.30 157	882.60 134	3589.10 143	2523.80 135	2486.45 135	1578.08 137	3208.34 129
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
OPM-MVS		3051.54 135	2661.57 136	3506.50 136	3532.00 141	3332.00 138	3744.00 142	7839.00 143	2772.00 141	985.00 130	1919.00 143	738.00 126	3319.00 138	2901.00 140	2715.00 138	1620.00 139	4254.00 143
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
ACMMP_NAP		119.64 65	106.58 58	134.88 65	123.82 60	127.44 59	145.38 62	298.43 70	116.16 69	43.72 59	77.82 59	29.30 54	131.24 66	117.82 69	106.21 59	66.69 57	171.29 69

ambc			999.00 106		999.00 104	999.00 105	999.00 103		999.00 109	999.00 132	999.00 116	999.00 141	999.00 105	999.00 105	999.00 105	999.00 113	999.00 102

CS-MVS-test		0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1

Effi-MVS+		0.53 26	0.53 26	0.53 26	0.53 26	0.53 26	0.53 26	0.53 26	0.53 26	0.53 26	0.53 26	0.53 27	0.53 26	0.53 26	0.53 26	0.53 26	0.53 26

new-patchmatchnet		712.65 94	632.59 94	806.05 98	787.79 100	936.25 103	882.47 100	1795.72 105	528.60 90	244.29 88	471.90 92	257.36 91	681.47 87	705.16 98	659.05 97	444.55 88	869.82 97

pmmvs6		10000000.00 157	10000000.00 158	10000000.00 158	10000000.00 163	10000000.00 164	10000000.00 164	10000000.00 161	10000000.00 160	10000000.00 165	10000000.00 160	10000000.00 166	10000000.00 160	10000000.00 160	10000000.00 159	10000000.00 162	10000000.00 159

pmmvs5		10000000.00 157	10000000.00 158	10000000.00 158	10000000.00 163	10000000.00 164	10000000.00 164	10000000.00 161	10000000.00 160	10000000.00 165	10000000.00 160	10000000.00 166	10000000.00 160	10000000.00 160	10000000.00 159	10000000.00 162	10000000.00 159

Fast-Effi-MVS+		0.41 25	0.41 25	0.41 25	0.41 25	0.41 25	0.41 25	0.41 25	0.41 25	0.41 25	0.41 25	0.41 26	0.41 25	0.41 25	0.41 25	0.41 25	0.41 25

Anonymous20231211		10000000.00 157	10000000.00 158	10000000.00 158	10000000.00 163	10000000.00 164	10000000.00 164	10000000.00 161	10000000.00 160	10000000.00 165	10000000.00 160	10000000.00 166	10000000.00 160	10000000.00 160	10000000.00 159	10000000.00 162	10000000.00 159

pmmvs-eth3d		10000000.00 157	10000000.00 158	10000000.00 158	10000000.00 163	10000000.00 164	10000000.00 164	10000000.00 161	10000000.00 160	10000000.00 165	10000000.00 160	10000000.00 166	10000000.00 160	10000000.00 160	10000000.00 159	10000000.00 162	10000000.00 159

GG-mvs-BLEND		557.53 81	503.49 79	620.58 81	485.90 78	370.41 76	267.30 75	710.53 76	454.42 82	785.72 122	491.23 95	326.12 104	881.92 99	538.27 84	650.05 93	592.08 101	693.94 81

Anonymous20231206		10000000.00 157	10000000.00 158	10000000.00 158	10000000.00 163	10000000.00 164	10000000.00 164	10000000.00 161	10000000.00 160	10000000.00 165	10000000.00 160	10000000.00 166	10000000.00 160	10000000.00 160	10000000.00 159	10000000.00 162	10000000.00 159

MTAPA										41.98 58		28.43 53

MTMP										10000000.00 165		31.71 55

gm-plane-assit		551.84 78	503.49 79	608.24 80	411.90 74	370.41 76	267.30 75	710.53 76	454.42 82	785.72 122	491.23 95	326.12 104	881.92 99	538.27 84	650.05 93	592.08 101	693.94 81

train_agg		0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1

gg-mvs-nofinetune		557.53 81	503.49 79	620.58 81	485.90 78	370.41 76	267.30 75	710.53 76	454.42 82	785.72 122	491.23 95	326.12 104	881.92 99	538.27 84	650.05 93	592.08 101	693.94 81

SCA		10000000.00 157	10000000.00 158	10000000.00 158	10000000.00 163	10000000.00 164	10000000.00 164	10000000.00 161	10000000.00 160	10000000.00 165	10000000.00 160	10000000.00 166	10000000.00 160	10000000.00 160	10000000.00 159	10000000.00 162	10000000.00 159

MS-PatchMatch		438.85 73	388.71 73	497.33 74	479.00 77	527.00 82	558.00 83	988.00 82	379.00 77	174.00 80	278.00 76	162.00 80	494.00 78	418.00 74	412.00 73	291.00 76	545.00 77

Patchmatch-RL test							10000000.00 164

tmp_tt				299.50 71	365.00 73	54.00 55	649.00 86	241.00 66	159.00 70	204.00 84	479.00 93	308.00 102	243.00 72	246.00 72	82.00 54	95.00 72

canonicalmvs		10000000.00 157	10000000.00 158	10000000.00 158	10000000.00 163	10000000.00 164	10000000.00 164	10000000.00 161	10000000.00 160	10000000.00 165	10000000.00 160	10000000.00 166	10000000.00 160	10000000.00 160	10000000.00 159	10000000.00 162	10000000.00 159

anonymousdsp		10000000.00 157	10000000.00 158	10000000.00 158	10000000.00 163	10000000.00 164	10000000.00 164	10000000.00 161	10000000.00 160	10000000.00 165	10000000.00 160	10000000.00 166	10000000.00 160	10000000.00 160	10000000.00 159	10000000.00 162	10000000.00 159

v144192		0.40 13	0.40 13	0.40 13	0.40 13	0.40 13	0.40 13	0.40 13	0.40 13	0.40 13	0.40 13	0.40 14	0.40 13	0.40 13	0.40 13	0.40 13	0.40 13

v1921920		0.40 13	0.40 13	0.40 13	0.40 13	0.40 13	0.40 13	0.40 13	0.40 13	0.40 13	0.40 13	0.40 14	0.40 13	0.40 13	0.40 13	0.40 13	0.40 13

FC-MVSNet-train		10000000.00 157	10000000.00 158	10000000.00 158	10000000.00 163	10000000.00 164	10000000.00 164	10000000.00 161	10000000.00 160	10000000.00 165	10000000.00 160	10000000.00 166	10000000.00 160	10000000.00 160	10000000.00 159	10000000.00 162	10000000.00 159

UA-Net		10000000.00 157	10000000.00 158	10000000.00 158	10000000.00 163	10000000.00 164	10000000.00 164	10000000.00 161	10000000.00 160	10000000.00 165	10000000.00 160	10000000.00 166	10000000.00 160	10000000.00 160	10000000.00 159	10000000.00 162	10000000.00 159

v1192		0.40 13	0.40 13	0.40 13	0.40 13	0.40 13	0.40 13	0.40 13	0.40 13	0.40 13	0.40 13	0.40 14	0.40 13	0.40 13	0.40 13	0.40 13	0.40 13

FC-MVSNet-test		10000000.00 157	10000000.00 158	10000000.00 158	10000000.00 163	10000000.00 164	10000000.00 164	10000000.00 161	10000000.00 160	10000000.00 165	10000000.00 160	10000000.00 166	10000000.00 160	10000000.00 160	10000000.00 159	10000000.00 162	10000000.00 159

v1144		0.40 13	0.40 13	0.40 13	0.40 13	0.40 13	0.40 13	0.40 13	0.40 13	0.40 13	0.40 13	0.40 14	0.40 13	0.40 13	0.40 13	0.40 13	0.40 13

sosnet-low-res		10000000.00 157	10000000.00 158	10000000.00 158	10000000.00 163	10000000.00 164	10000000.00 164	10000000.00 161	10000000.00 160	10000000.00 165	10000000.00 160	10000000.00 166	10000000.00 160	10000000.00 160	10000000.00 159	10000000.00 162	10000000.00 159

HFP-MVS		0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1

v148		0.40 13	0.40 13	0.40 13	0.40 13	0.40 13	0.40 13	0.40 13	0.40 13	0.40 13	0.40 13	0.40 14	0.40 13	0.40 13	0.40 13	0.40 13	0.40 13

sosnet		1000.00 104	1000.00 108	1000.00 102	1000.00 106	1000.00 107	1000.00 105	1000.00 85	1000.00 111	1000.00 134	1000.00 118	1000.00 143	1000.00 107	1000.00 107	1000.00 107	1000.00 115	1000.00 104

v7n		0.40 13	0.40 13	0.40 13	0.40 13	0.40 13	0.40 13	0.40 13	0.40 13	0.40 13	0.40 13	0.40 14	0.40 13	0.40 13	0.40 13	0.40 13	0.40 13

DI_MVS_plusplus_trai		10000000.00 157	10000000.00 158	10000000.00 158	10000000.00 163	10000000.00 164	10000000.00 164	10000000.00 161	10000000.00 160	10000000.00 165	10000000.00 160	10000000.00 166	10000000.00 160	10000000.00 160	10000000.00 159	10000000.00 162	10000000.00 159

HPM-MVS++		10000000.00 157	10000000.00 158	10000000.00 158	10000000.00 163	10000000.00 164	10000000.00 164	10000000.00 161	10000000.00 160	10000000.00 165	10000000.00 160	10000000.00 166	10000000.00 160	10000000.00 160	10000000.00 159	10000000.00 162	10000000.00 159

XVS					52.20 51	52.20 52	52.20 52			52.20 60		52.20 70				52.20 54

v1240		0.40 13	0.40 13	0.40 13	0.40 13	0.40 13	0.40 13	0.40 13	0.40 13	0.40 13	0.40 13	0.40 14	0.40 13	0.40 13	0.40 13	0.40 13	0.40 13

pm-mvs1		10000000.00 157	10000000.00 158	10000000.00 158	10000000.00 163	10000000.00 164	10000000.00 164	10000000.00 161	10000000.00 160	10000000.00 165	10000000.00 160	10000000.00 166	10000000.00 160	10000000.00 160	10000000.00 159	10000000.00 162	10000000.00 159

X-MVStestdata					52.20 51	52.20 52	52.20 52			52.20 60		52.20 70				52.20 54

X-MVS		52.20 51	52.20 51	52.20 51	52.20 51	52.20 52	52.20 52	52.20 51	52.20 52	52.20 60	52.20 52	52.20 70	52.20 54	52.20 52	52.20 51	52.20 54	52.20 54

v8		0.40 13	0.40 13	0.40 13	0.40 13	0.40 13	0.40 13	0.40 13	0.40 13	0.40 13	0.40 13	0.40 14	0.40 13	0.40 13	0.40 13	0.40 13	0.40 13

v10		0.40 13	0.40 13	0.40 13	0.40 13	0.40 13	0.40 13	0.40 13	0.40 13	0.40 13	0.40 13	0.40 14	0.40 13	0.40 13	0.40 13	0.40 13	0.40 13

v2v482		0.40 13	0.40 13	0.40 13	0.40 13	0.40 13	0.40 13	0.40 13	0.40 13	0.40 13	0.40 13	0.40 14	0.40 13	0.40 13	0.40 13	0.40 13	0.40 13

V42		0.40 13	0.40 13	0.40 13	0.40 13	0.40 13	0.40 13	0.40 13	0.40 13	0.40 13	0.40 13	0.40 14	0.40 13	0.40 13	0.40 13	0.40 13	0.40 13

SD-MVS		2324.36 124	1910.97 119	2806.65 131	2945.31 133	3305.03 137	3267.51 134	5962.25 134	1896.37 127	848.15 127	1224.25 124	661.55 122	2707.30 132	1913.40 119	1871.41 118	1109.38 120	2504.79 120
Zhenlong Yuan, Jiakai Cao, Zhaoxin Li, Hao Jiang and Zhaoqi Wang: SD-MVS: Segmentation-driven Deformation Multi-View Stereo with Spherical Refinement and EM optimization. AAAI2024
GA-MVS		0.40 13	0.40 13	0.40 13	0.40 13	0.40 13	0.40 13	0.40 13	0.40 13	0.40 13	0.40 13	0.40 14	0.40 13	0.40 13	0.40 13	0.40 13	0.40 13

MSLP-MVS++		993.91 102	732.21 98	1299.24 111	1631.22 118	1345.26 116	1480.05 117	2524.20 110	735.65 101	463.57 112	499.89 99	302.78 101	1292.66 113	675.99 96	612.79 83	403.71 85	953.11 100

APDe-MVS		10000000.00 157	10000000.00 158	10000000.00 158	10000000.00 163	10000000.00 164	10000000.00 164	10000000.00 161	10000000.00 160	10000000.00 165	10000000.00 160	10000000.00 166	10000000.00 160	10000000.00 160	10000000.00 159	10000000.00 162	10000000.00 159

TSAR-MVS + COLMAP		6088.23 148	5315.97 149	6989.21 149	6522.51 153	8025.15 155	7938.31 156	15276.20 153	5401.97 153	1914.31 158	3802.65 152	1979.48 158	6704.11 152	5217.07 150	5137.16 150	3579.82 153	7648.29 150

CVMVSNet		1.42 30	1.42 30	1.42 30	1.42 30	1.42 31	1.42 31	1.42 31	1.42 31	1.42 31	1.42 31	1.42 32	1.42 31	1.42 31	1.42 30	1.42 31	1.42 31

TSAR-MVS + ACMM		3184.92 137	2679.45 138	3774.64 142	3302.51 139	4168.07 143	4256.74 146	8947.31 146	2634.53 139	1026.25 142	1723.72 138	1043.87 149	3387.29 140	2794.29 137	2687.41 137	1727.73 143	3704.25 138

pmmvs4		10000000.00 157	10000000.00 158	10000000.00 158	10000000.00 163	10000000.00 164	10000000.00 164	10000000.00 161	10000000.00 160	10000000.00 165	10000000.00 160	10000000.00 166	10000000.00 160	10000000.00 160	10000000.00 159	10000000.00 162	10000000.00 159

EU-MVSNet		1.42 30	1.42 30	1.42 30	1.42 30	1.42 31	1.42 31	1.42 31	1.42 31	1.42 31	1.42 31	1.42 32	1.42 31	1.42 31	1.42 30	1.42 31	1.42 31

test-LLR		10000000.00 157	10000000.00 158	10000000.00 158	10000000.00 163	10000000.00 164	10000000.00 164	10000000.00 161	10000000.00 160	10000000.00 165	10000000.00 160	10000000.00 166	10000000.00 160	10000000.00 160	10000000.00 159	10000000.00 162	10000000.00 159

TESTMET0.1,1		10000000.00 157	10000000.00 158	10000000.00 158	10000000.00 163	10000000.00 164	10000000.00 164	10000000.00 161	10000000.00 160	10000000.00 165	10000000.00 160	10000000.00 166	10000000.00 160	10000000.00 160	10000000.00 159	10000000.00 162	10000000.00 159

test-mter		10000000.00 157	10000000.00 158	10000000.00 158	10000000.00 163	10000000.00 164	10000000.00 164	10000000.00 161	10000000.00 160	10000000.00 165	10000000.00 160	10000000.00 166	10000000.00 160	10000000.00 160	10000000.00 159	10000000.00 162	10000000.00 159

ACMMPR		1265.65 112	1083.59 113	1478.06 114	1441.06 117	1438.61 119	1540.68 119	3284.05 117	1219.68 117	458.81 111	803.22 111	346.91 111	1421.66 116	1126.11 114	1150.51 113	722.08 110	1500.09 115

testgi		10000000.00 157	10000000.00 158	10000000.00 158	10000000.00 163	10000000.00 164	10000000.00 164	10000000.00 161	10000000.00 160	10000000.00 165	10000000.00 160	10000000.00 166	10000000.00 160	10000000.00 160	10000000.00 159	10000000.00 162	10000000.00 159

test20.03		10000000.00 157	10000000.00 158	10000000.00 158	10000000.00 163	10000000.00 164	10000000.00 164	10000000.00 161	10000000.00 160	10000000.00 165	10000000.00 160	10000000.00 166	10000000.00 160	10000000.00 160	10000000.00 159	10000000.00 162	10000000.00 159

thres600view7		10000000.00 157	10000000.00 158	10000000.00 158	10000000.00 163	10000000.00 164	10000000.00 164	10000000.00 161	10000000.00 160	10000000.00 165	10000000.00 160	10000000.00 166	10000000.00 160	10000000.00 160	10000000.00 159	10000000.00 162	10000000.00 159

ADS-MVSNet		452.36 74	406.02 74	506.42 77	534.76 83	628.22 85	552.79 82	1009.99 89	333.98 73	149.26 76	246.67 73	149.01 78	447.03 76	465.35 78	501.18 78	344.69 80	517.75 75

MP-MVS		10000000.00 157	10000000.00 158	10000000.00 158	10000000.00 163	10000000.00 164	10000000.00 164	10000000.00 161	10000000.00 160	10000000.00 165	10000000.00 160	10000000.00 166	10000000.00 160	10000000.00 160	10000000.00 159	10000000.00 162	10000000.00 159
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
testmvs		10000000.00 157	10000000.00 158	10000000.00 158	10000000.00 163	10000000.00 164	10000000.00 164	10000000.00 161	10000000.00 160	10000000.00 165	10000000.00 160	10000000.00 166	10000000.00 160	10000000.00 160	10000000.00 159	10000000.00 162	10000000.00 159

thres400		10000000.00 157	10000000.00 158	10000000.00 158	10000000.00 163	10000000.00 164	10000000.00 164	10000000.00 161	10000000.00 160	10000000.00 165	10000000.00 160	10000000.00 166	10000000.00 160	10000000.00 160	10000000.00 159	10000000.00 162	10000000.00 159

test123		10000000.00 157	10000000.00 158	10000000.00 158	10000000.00 163	10000000.00 164	10000000.00 164	10000000.00 161	10000000.00 160	10000000.00 165	10000000.00 160	10000000.00 166	10000000.00 160	10000000.00 160	10000000.00 159	10000000.00 162	10000000.00 159

thres200		10000000.00 157	10000000.00 158	10000000.00 158	10000000.00 163	10000000.00 164	10000000.00 164	10000000.00 161	10000000.00 160	10000000.00 165	10000000.00 160	10000000.00 166	10000000.00 160	10000000.00 160	10000000.00 159	10000000.00 162	10000000.00 159

test0.0.03 1		10000000.00 157	10000000.00 158	10000000.00 158	10000000.00 163	10000000.00 164	10000000.00 164	10000000.00 161	10000000.00 160	10000000.00 165	10000000.00 160	10000000.00 166	10000000.00 160	10000000.00 160	10000000.00 159	10000000.00 162	10000000.00 159

pmmvs3		10000000.00 157	10000000.00 158	10000000.00 158	10000000.00 163	10000000.00 164	10000000.00 164	10000000.00 161	10000000.00 160	10000000.00 165	10000000.00 160	10000000.00 166	10000000.00 160	10000000.00 160	10000000.00 159	10000000.00 162	10000000.00 159

EMVS		10000000.00 157	10000000.00 158	10000000.00 158	10000000.00 163	10000000.00 164	10000000.00 164	10000000.00 161	10000000.00 160	10000000.00 165	10000000.00 160	10000000.00 166	10000000.00 160	10000000.00 160	10000000.00 159	10000000.00 162	10000000.00 159

E-PMN		10000000.00 157	10000000.00 158	10000000.00 158	10000000.00 163	10000000.00 164	10000000.00 164	10000000.00 161	10000000.00 160	10000000.00 165	10000000.00 160	10000000.00 166	10000000.00 160	10000000.00 160	10000000.00 159	10000000.00 162	10000000.00 159

PGM-MVS		10000000.00 157	10000000.00 158	10000000.00 158	10000000.00 163	10000000.00 164	10000000.00 164	10000000.00 161	10000000.00 160	10000000.00 165	10000000.00 160	10000000.00 166	10000000.00 160	10000000.00 160	10000000.00 159	10000000.00 162	10000000.00 159

MCST-MVS		10000000.00 157	10000000.00 158	10000000.00 158	10000000.00 163	10000000.00 164	10000000.00 164	10000000.00 161	10000000.00 160	10000000.00 165	10000000.00 160	10000000.00 166	10000000.00 160	10000000.00 160	10000000.00 159	10000000.00 162	10000000.00 159

MVS_Test		654.54 85	583.51 86	737.40 87	721.80 93	822.10 92	808.60 92	1494.00 96	542.20 91	259.90 89	441.60 84	257.70 92	689.10 88	620.80 89	617.70 84	451.00 89	782.50 88

MDA-MVSNet-bldmvs		1.20 29	1.20 29	1.20 29	1.20 29	1.20 30	1.20 30	1.20 30	1.20 30	1.20 30	1.20 30	1.20 31	1.20 30	1.20 30	1.20 29	1.20 30	1.20 30

CDPH-MVS		6.66 45	6.66 45	6.66 45	6.66 45	6.66 46	6.66 46	6.66 46	6.66 46	6.66 46	6.66 46	6.66 47	6.66 46	6.66 46	6.66 45	6.66 46	6.66 46

casdiffmvs		654.54 85	583.51 86	737.40 87	721.80 93	822.10 92	808.60 92	1494.00 96	542.20 91	259.90 89	441.60 84	257.70 92	689.10 88	620.80 89	617.70 84	451.00 89	782.50 88
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
diffmvs		654.54 85	583.51 86	737.40 87	721.80 93	822.10 92	808.60 92	1494.00 96	542.20 91	259.90 89	441.60 84	257.70 92	689.10 88	620.80 89	617.70 84	451.00 89	782.50 88
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
baseline2		654.54 85	583.51 86	737.40 87	721.80 93	822.10 92	808.60 92	1494.00 96	542.20 91	259.90 89	441.60 84	257.70 92	689.10 88	620.80 89	617.70 84	451.00 89	782.50 88

baseline1		654.54 85	583.51 86	737.40 87	721.80 93	822.10 92	808.60 92	1494.00 96	542.20 91	259.90 89	441.60 84	257.70 92	689.10 88	620.80 89	617.70 84	451.00 89	782.50 88

PMMVS2		10000000.00 157	10000000.00 158	10000000.00 158	10000000.00 163	10000000.00 164	10000000.00 164	10000000.00 161	10000000.00 160	10000000.00 165	10000000.00 160	10000000.00 166	10000000.00 160	10000000.00 160	10000000.00 159	10000000.00 162	10000000.00 159

PM-MVS		10000000.00 157	10000000.00 158	10000000.00 158	10000000.00 163	10000000.00 164	10000000.00 164	10000000.00 161	10000000.00 160	10000000.00 165	10000000.00 160	10000000.00 166	10000000.00 160	10000000.00 160	10000000.00 159	10000000.00 162	10000000.00 159

PS-CasMVS		1.42 30	1.42 30	1.42 30	1.42 30	1.42 31	1.42 31	1.42 31	1.42 31	1.42 31	1.42 31	1.42 32	1.42 31	1.42 31	1.42 30	1.42 31	1.42 31

UniMVSNet_NR-MVSNet		1.42 30	1.42 30	1.42 30	1.42 30	1.42 31	1.42 31	1.42 31	1.42 31	1.42 31	1.42 31	1.42 32	1.42 31	1.42 31	1.42 30	1.42 31	1.42 31

PEN-MVS		1.42 30	1.42 30	1.42 30	1.42 30	1.42 31	1.42 31	1.42 31	1.42 31	1.42 31	1.42 31	1.42 32	1.42 31	1.42 31	1.42 30	1.42 31	1.42 31

TransMVSNet (Re)		1.42 30	1.42 30	1.42 30	1.42 30	1.42 31	1.42 31	1.42 31	1.42 31	1.42 31	1.42 31	1.42 32	1.42 31	1.42 31	1.42 30	1.42 31	1.42 31

DTE-MVSNet		1.42 30	1.42 30	1.42 30	1.42 30	1.42 31	1.42 31	1.42 31	1.42 31	1.42 31	1.42 31	1.42 32	1.42 31	1.42 31	1.42 30	1.42 31	1.42 31

DU-MVS		1.42 30	1.42 30	1.42 30	1.42 30	1.42 31	1.42 31	1.42 31	1.42 31	1.42 31	1.42 31	1.42 32	1.42 31	1.42 31	1.42 30	1.42 31	1.42 31

UniMVSNet (Re)		1.42 30	1.42 30	1.42 30	1.42 30	1.42 31	1.42 31	1.42 31	1.42 31	1.42 31	1.42 31	1.42 32	1.42 31	1.42 31	1.42 30	1.42 31	1.42 31

CP-MVSNet		1.42 30	1.42 30	1.42 30	1.42 30	1.42 31	1.42 31	1.42 31	1.42 31	1.42 31	1.42 31	1.42 32	1.42 31	1.42 31	1.42 30	1.42 31	1.42 31

WR-MVS_H		1.42 30	1.42 30	1.42 30	1.42 30	1.42 31	1.42 31	1.42 31	1.42 31	1.42 31	1.42 31	1.42 32	1.42 31	1.42 31	1.42 30	1.42 31	1.42 31

WR-MVS		1.42 30	1.42 30	1.42 30	1.42 30	1.42 31	1.42 31	1.42 31	1.42 31	1.42 31	1.42 31	1.42 32	1.42 31	1.42 31	1.42 30	1.42 31	1.42 31

NR-MVSNet		1.42 30	1.42 30	1.42 30	1.42 30	1.42 31	1.42 31	1.42 31	1.42 31	1.42 31	1.42 31	1.42 32	1.42 31	1.42 31	1.42 30	1.42 31	1.42 31

Baseline_NR-MVSNet		1.42 30	1.42 30	1.42 30	1.42 30	1.42 31	1.42 31	1.42 31	1.42 31	1.42 31	1.42 31	1.42 32	1.42 31	1.42 31	1.42 30	1.42 31	1.42 31

TranMVSNet+NR-MVSNet		1.42 30	1.42 30	1.42 30	1.42 30	1.42 31	1.42 31	1.42 31	1.42 31	1.42 31	1.42 31	1.42 32	1.42 31	1.42 31	1.42 30	1.42 31	1.42 31

TSAR-MVS + GP.		1239.27 111	1094.74 114	1407.89 112	1220.99 114	1845.47 121	1680.98 120	3191.81 115	1098.41 115	362.98 101	679.12 106	339.31 109	1323.06 114	1173.24 115	1093.83 112	667.49 107	1433.78 113

mPP-MVS					10000000.00 163							10000000.00 166

SixPastTwentyTwo		210.90 70	189.75 70	235.58 70	225.41 72	284.88 75	267.17 74	485.88 73	164.28 71	76.51 73	137.62 71	76.69 74	214.49 70	206.28 71	208.00 71	144.02 74	250.52 71

casdiffmvs_mvg		654.54 85	583.51 86	737.40 87	721.80 93	822.10 92	808.60 92	1494.00 96	542.20 91	259.90 89	441.60 84	257.70 92	689.10 88	620.80 89	617.70 84	451.00 89	782.50 88
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
LGP-MVS_train		10000000.00 157	10000000.00 158	10000000.00 158	10000000.00 163	10000000.00 164	10000000.00 164	10000000.00 161	10000000.00 160	10000000.00 165	10000000.00 160	10000000.00 166	10000000.00 160	10000000.00 160	10000000.00 159	10000000.00 162	10000000.00 159

baseline		654.54 85	583.51 86	737.40 87	721.80 93	822.10 92	808.60 92	1494.00 96	542.20 91	259.90 89	441.60 84	257.70 92	689.10 88	620.80 89	617.70 84	451.00 89	782.50 88

EPNet_dtu		10000000.00 157	10000000.00 158	10000000.00 158	10000000.00 163	10000000.00 164	10000000.00 164	10000000.00 161	10000000.00 160	10000000.00 165	10000000.00 160	10000000.00 166	10000000.00 160	10000000.00 160	10000000.00 159	10000000.00 162	10000000.00 159
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CHOSEN 1792x2688		2225.10 119	1992.66 122	2496.28 119	2192.10 122	2641.81 126	2621.41 126	5345.34 127	2116.46 130	1002.26 138	1683.84 133	837.43 129	2338.65 122	1920.94 120	2020.28 122	1477.92 131	2727.86 122

EPNet		10000000.00 157	10000000.00 158	10000000.00 158	10000000.00 163	10000000.00 164	10000000.00 164	10000000.00 161	10000000.00 160	10000000.00 165	10000000.00 160	10000000.00 166	10000000.00 160	10000000.00 160	10000000.00 159	10000000.00 162	10000000.00 159
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
APD-MVS		1000000000.00 221	1000000000.00 223	1000000000.00 222	1000000000.00 229	1000000000.00 229	1000000000.00 230	1000000000.00 226	1000000000.00 226	1000000000.00 230	1000000000.00 225	1000000000.00 232	1000000000.00 225	1000000000.00 225	1000000000.00 224	1000000000.00 227	1000000000.00 224
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
CNVR-MVS		10000000.00 157	10000000.00 158	10000000.00 158	10000000.00 163	10000000.00 164	10000000.00 164	10000000.00 161	10000000.00 160	10000000.00 165	10000000.00 160	10000000.00 166	10000000.00 160	10000000.00 160	10000000.00 159	10000000.00 162	10000000.00 159

NCCC		10000000.00 157	10000000.00 158	10000000.00 158	10000000.00 163	10000000.00 164	10000000.00 164	10000000.00 161	10000000.00 160	10000000.00 165	10000000.00 160	10000000.00 166	10000000.00 160	10000000.00 160	10000000.00 159	10000000.00 162	10000000.00 159

CP-MVS		10000000.00 157	10000000.00 158	10000000.00 158	10000000.00 163	10000000.00 164	10000000.00 164	10000000.00 161	10000000.00 160	10000000.00 165	10000000.00 160	10000000.00 166	10000000.00 160	10000000.00 160	10000000.00 159	10000000.00 162	10000000.00 159

NP-MVS									10000000.00 160

EG-PatchMatch MVS		402.98 72	364.36 72	448.05 73	435.50 76	473.00 81	489.60 80	910.20 81	338.60 75	127.30 75	205.10 72	123.50 77	434.60 74	450.70 77	447.70 75	291.10 77	511.90 74

tpm cat1		2652.13 128	2606.66 133	2705.18 127	3151.86 135	6902.39 152	3612.17 139	5463.78 130	1457.76 119	391.31 103	1358.61 125	446.44 114	2360.09 125	2296.48 126	2290.90 128	1251.84 121	3494.04 133

SteuartSystems-ACMMP		2615.00 127	2242.57 128	3049.50 133	2828.00 132	2801.00 130	3323.00 136	6648.00 137	2491.00 135	1023.00 141	1605.00 131	697.00 123	2926.00 135	2502.00 133	2279.00 127	1549.00 136	3323.00 132
Skip Steuart: Steuart Systems R&D Blog.
CostFormer		2652.13 128	2606.66 133	2705.18 127	3151.86 135	6902.39 152	3612.17 139	5463.78 130	1457.76 119	391.31 103	1358.61 125	446.44 114	2360.09 125	2296.48 126	2290.90 128	1251.84 121	3494.04 133

CR-MVSNet		557.53 81	503.49 79	620.58 81	485.90 78	370.41 76	267.30 75	710.53 76	454.42 82	785.72 122	491.23 95	326.12 104	881.92 99	538.27 84	650.05 93	592.08 101	693.94 81

Patchmtry						1825.90 120	885.90 101	1405.90 95		1285.90 150

PatchT		878.42 100	785.05 100	987.36 100	1050.05 111	922.05 102	1111.05 110	677.05 75	950.05 107	1285.90 150	750.05 109	640.05 121	850.05 97	950.05 102	950.05 102	950.05 112	333.05 72

tpmrst		2688.71 131	2604.46 132	2787.00 130	2472.96 127	3190.36 133	3313.69 135	5003.06 125	1837.26 126	1097.38 144	1721.89 137	1012.20 148	2415.09 129	2970.27 144	2736.80 140	2419.79 149	4762.44 146

tpm		2652.13 128	2606.66 133	2705.18 127	3151.86 135	6902.39 152	3612.17 139	5463.78 130	1457.76 119	391.31 103	1358.61 125	446.44 114	2360.09 125	2296.48 126	2290.90 128	1251.84 121	3494.04 133

DELS-MVS		458.84 76	423.31 76	500.30 75	511.99 81	616.59 84	578.86 84	991.10 83	355.79 76	158.99 77	319.09 78	190.62 83	453.64 77	437.73 76	439.52 74	307.20 78	603.81 78
Christian Sormann, Emanuele Santellani, Mattia Rossi, Andreas Kuhn, Friedrich Fraundorfer: DELS-MVS: Deep Epipolar Line Search for Multi-View Stereo. Winter Conference on Applications of Computer Vision (WACV), 2023
RPMNet		858.01 99	961.99 105	736.69 86	550.05 84	1120.24 112	1212.05 114	463.05 71	485.90 87	550.05 114	580.05 102	890.05 135	559.05 82	1485.90 116	1185.90 114	1085.90 119	985.90 101

MVSTER		834.63 97	737.03 99	948.49 99	988.86 103	1140.51 114	1129.34 112	1832.80 107	682.42 100	319.01 98	550.98 101	311.35 103	872.51 98	763.95 100	757.83 99	548.41 98	952.20 99

CPTT-MVS		10000000.00 157	10000000.00 158	10000000.00 158	10000000.00 163	10000000.00 164	10000000.00 164	10000000.00 161	10000000.00 160	10000000.00 165	10000000.00 160	10000000.00 166	10000000.00 160	10000000.00 160	10000000.00 159	10000000.00 162	10000000.00 159

GBi-Net		39.84 50	35.75 50	44.63 50	43.04 50	49.88 51	51.06 51	86.51 52	35.34 51	17.20 51	29.83 51	15.85 52	43.43 53	35.35 51	35.70 50	26.51 53	48.28 53

PVSNet_Blended_VisFu		129.22 66	115.76 66	144.92 66	140.60 68	162.80 71	166.50 70	281.20 67	114.70 59	55.50 63	96.20 60	51.80 67	140.60 67	114.70 66	114.70 67	85.10 62	155.40 59

PVSNet_BlendedMVS		129.22 66	115.76 66	144.92 66	140.60 68	162.80 71	166.50 70	281.20 67	114.70 59	55.50 63	96.20 60	51.80 67	140.60 67	114.70 66	114.70 67	85.10 62	155.40 59

PVSNet_Blended		129.22 66	115.76 66	144.92 66	140.60 68	162.80 71	166.50 70	281.20 67	114.70 59	55.50 63	96.20 60	51.80 67	140.60 67	114.70 66	114.70 67	85.10 62	155.40 59

FMVSNet5		116.86 58	110.47 59	124.32 58	131.47 61	157.41 64	150.67 63	199.64 55	115.30 62	56.61 66	97.70 63	51.77 60	117.26 59	77.26 53	113.14 60	90.25 65	160.68 62

test1		116.86 58	110.47 59	124.32 58	131.47 61	157.41 64	150.67 63	199.64 55	115.30 62	56.61 66	97.70 63	51.77 60	117.26 59	77.26 53	113.14 60	90.25 65	160.68 62

new_pmnet		75.00 52	74.67 52	75.39 52	85.42 54	96.03 56	98.77 55	173.23 53	69.57 53	36.19 52	56.93 53	117.43 75	32.98 51	85.38 60	68.41 52	25.74 51	28.95 51

FMVSNet3		116.86 58	110.47 59	124.32 58	131.47 61	157.41 64	150.67 63	199.64 55	115.30 62	56.61 66	97.70 63	51.77 60	117.26 59	77.26 53	113.14 60	90.25 65	160.68 62

dps		534.60 77	472.40 77	607.16 79	690.38 91	718.85 88	636.03 85	1231.66 92	422.74 79	198.65 83	325.68 79	194.97 84	512.90 80	509.81 80	513.65 79	373.33 82	621.11 79

FMVSNet2		116.86 58	110.47 59	124.32 58	131.47 61	157.41 64	150.67 63	199.64 55	115.30 62	56.61 66	97.70 63	51.77 60	117.26 59	77.26 53	113.14 60	90.25 65	160.68 62

FMVSNet1		116.86 58	110.47 59	124.32 58	131.47 61	157.41 64	150.67 63	199.64 55	115.30 62	56.61 66	97.70 63	51.77 60	117.26 59	77.26 53	113.14 60	90.25 65	160.68 62

N_pmnet		75.00 52	74.67 52	75.39 52	85.42 54	96.03 56	98.77 55	173.23 53	69.57 53	36.19 52	56.93 53	117.43 75	32.98 51	85.38 60	68.41 52	25.74 51	28.95 51

UGNet		100.44 54	93.07 54	109.04 54	111.90 56	131.06 60	119.40 58	222.42 62	80.48 55	36.93 54	70.80 55	40.92 56	89.90 55	100.59 62	101.42 55	73.68 58	126.25 55
Wanjuan Su, Qingshan Xu, Wenbing Tao: Uncertainty-guided Multi-view Stereo Network for Depth Estimation. IEEE Transactions on Circuits and Systems for Video Technology, 2022
DROMVSNet		0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1

MDTV_nov1_ep13_2view		9.99 46	9.99 46	9.99 46	9.99 46	9.99 47	9.99 47	9.99 47	9.99 47	9.99 49	9.99 47	9.99 50	9.99 47	9.99 47	9.99 46	9.99 47	9.99 47

MDTV_nov1_ep13		9.99 46	9.99 46	9.99 46	9.99 46	9.99 47	9.99 47	9.99 47	9.99 47	9.99 49	9.99 47	9.99 50	9.99 47	9.99 47	9.99 46	9.99 47	9.99 47

MIMVSNet1		116.86 58	110.47 59	124.32 58	131.47 61	157.41 64	150.67 63	199.64 55	115.30 62	56.61 66	97.70 63	51.77 60	117.26 59	77.26 53	113.14 60	90.25 65	160.68 62

MIMVSNet		116.86 58	110.47 59	124.32 58	131.47 61	157.41 64	150.67 63	199.64 55	115.30 62	56.61 66	97.70 63	51.77 60	117.26 59	77.26 53	113.14 60	90.25 65	160.68 62

IterMVS-LS		714.97 95	638.00 96	804.77 97	793.60 101	914.20 101	886.50 102	1612.70 103	588.30 98	285.30 97	491.20 94	278.40 100	759.20 95	677.40 97	681.30 98	491.30 96	835.20 95
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
CDS-MVSNet		0.88 27	0.88 27	0.88 27	0.88 27	0.88 27	0.88 27	0.88 27	0.88 27	0.88 27	0.88 27	0.88 28	0.88 27	0.88 27	0.88 27	0.88 27	0.88 27
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
IterMVS		554.60 80	488.63 78	631.57 84	636.90 88	700.80 86	674.70 87	1271.10 94	430.50 80	207.10 85	370.50 81	202.70 86	611.80 84	534.20 83	545.30 82	387.80 83	636.40 80
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
MVS_111021_LR		1055.24 109	916.36 103	1217.27 109	1129.93 113	1268.26 115	1313.88 116	2635.76 112	923.19 106	396.92 106	704.88 107	327.66 108	1136.32 111	965.38 103	946.95 101	690.83 109	1278.20 109

HQP-MVS		10000000.00 157	10000000.00 158	10000000.00 158	10000000.00 163	10000000.00 164	10000000.00 164	10000000.00 161	10000000.00 160	10000000.00 165	10000000.00 160	10000000.00 166	10000000.00 160	10000000.00 160	10000000.00 159	10000000.00 162	10000000.00 159

QAPM		0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1

Vis-MVSNet		2390.77 126	1976.57 120	2874.00 132	2644.00 131	3248.00 135	3040.00 132	7371.00 139	1627.00 123	421.00 109	1200.00 122	622.00 120	2251.00 121	2326.00 130	2052.00 125	1517.00 134	2761.00 125
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
MVS-HIRNet		1975.98 116	1797.02 118	2184.76 117	2111.07 121	2443.03 125	2503.43 124	4063.71 119	1684.46 125	711.34 120	1456.05 128	848.70 133	2396.26 128	1900.93 118	1915.01 119	1322.75 126	2330.99 118

HyFIR lowres test		2225.10 119	1992.66 122	2496.28 119	2192.10 122	2641.81 126	2621.41 126	5345.34 127	2116.46 130	1002.26 138	1683.84 133	837.43 129	2338.65 122	1920.94 120	2020.28 122	1477.92 131	2727.86 122

EPMVS		999.00 103	999.00 106	999.00 101	999.00 104	999.00 105	999.00 103	999.00 84	999.00 109	999.00 132	999.00 116	999.00 141	999.00 105	999.00 105	999.00 105	999.00 113	999.00 102

TAMVS		0.88 27	0.88 27	0.88 27	0.88 27	0.88 27	0.88 27	0.88 27	0.88 27	0.88 27	0.88 27	0.88 28	0.88 27	0.88 27	0.88 27	0.88 27	0.88 27

IS_MVSNet		10000000.00 157	10000000.00 158	10000000.00 158	10000000.00 163	10000000.00 164	10000000.00 164	10000000.00 161	10000000.00 160	10000000.00 165	10000000.00 160	10000000.00 166	10000000.00 160	10000000.00 160	10000000.00 159	10000000.00 162	10000000.00 159

RPSCF		376.53 71	341.80 71	417.05 72	418.60 75	457.01 80	467.46 79	825.25 80	336.12 74	159.32 78	260.40 74	151.13 79	386.94 73	364.25 73	352.10 72	244.72 75	471.62 73

Vis-MVSNet (Re-imp)		10000000.00 157	10000000.00 158	10000000.00 158	10000000.00 163	10000000.00 164	10000000.00 164	10000000.00 161	10000000.00 160	10000000.00 165	10000000.00 160	10000000.00 166	10000000.00 160	10000000.00 160	10000000.00 159	10000000.00 162	10000000.00 159

MVS_111021_HR		3192.50 138	2773.91 140	3680.86 139	3515.58 140	3815.81 142	4020.82 145	7766.11 141	2775.23 143	1300.96 153	2221.19 146	1117.25 151	3420.01 141	2873.98 138	2835.60 141	2061.69 147	3778.31 141

CSCG		727.26 96	663.25 97	801.94 96	590.65 85	811.25 91	854.12 99	1805.32 106	816.43 104	388.25 102	612.42 103	345.37 110	621.61 85	507.46 79	471.55 77	551.65 99	1078.31 108

PatchMatch-RL		10000000.00 157	10000000.00 158	10000000.00 158	10000000.00 163	10000000.00 164	10000000.00 164	10000000.00 161	10000000.00 160	10000000.00 165	10000000.00 160	10000000.00 166	10000000.00 160	10000000.00 160	10000000.00 159	10000000.00 162	10000000.00 159

TDRefinement		10000000.00 157	10000000.00 158	10000000.00 158	10000000.00 163	10000000.00 164	10000000.00 164	10000000.00 161	10000000.00 160	10000000.00 165	10000000.00 160	10000000.00 166	10000000.00 160	10000000.00 160	10000000.00 159	10000000.00 162	10000000.00 159

USDC		1000.00 104	1000.00 108	1000.00 102	1000.00 106	1000.00 107	1000.00 105	1000.00 85	1000.00 111	1000.00 134	1000.00 118	1000.00 143	1000.00 107	1000.00 107	1000.00 107	1000.00 115	1000.00 104

EPP-MVSNet		10000000.00 157	10000000.00 158	10000000.00 158	10000000.00 163	10000000.00 164	10000000.00 164	10000000.00 161	10000000.00 160	10000000.00 165	10000000.00 160	10000000.00 166	10000000.00 160	10000000.00 160	10000000.00 159	10000000.00 162	10000000.00 159

PMMVS		10000000.00 157	10000000.00 158	10000000.00 158	10000000.00 163	10000000.00 164	10000000.00 164	10000000.00 161	10000000.00 160	10000000.00 165	10000000.00 160	10000000.00 166	10000000.00 160	10000000.00 160	10000000.00 159	10000000.00 162	10000000.00 159

ACMMP		9999.00 151	9999.00 152	9999.00 152	9999.00 157	9999.00 158	9999.00 159	9999.00 149	9999.00 155	9999.00 161	9999.00 155	9999.00 162	9999.00 155	9999.00 154	9999.00 153	9999.00 157	9999.00 153
Qingshan Xu, Weihang Kong, Wenbing Tao, Marc Pollefeys: Multi-Scale Geometric Consistency Guided and Planar Prior Assisted Multi-View Stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence
CNLPA		17763.08 153	15907.71 154	19927.67 154	19334.00 159	22387.00 160	22896.00 160	38668.00 156	15772.00 156	7632.00 160	13229.00 156	7123.00 161	19334.00 156	15773.00 155	15700.00 154	11702.00 158	21370.00 155

PatchmatchNet		452.63 75	410.20 75	502.13 76	520.70 82	614.40 83	546.00 81	1018.80 90	379.00 77	167.50 79	277.10 75	169.40 81	437.10 75	437.10 75	457.50 76	322.70 79	536.90 76
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
PHI-MVS		99999.00 156	99999.00 157	99999.00 157	99999.00 161	99999.00 163	99999.00 163	99999.00 159	99999.00 159	99999.00 164	99999.00 159	99999.00 165	99999.00 159	99999.00 159	99999.00 158	99999.00 161	99999.00 158

OMC-MVS		4439.92 146	3906.78 146	5061.92 148	4748.70 151	5679.38 149	5855.03 155	9923.00 148	4023.36 151	1740.95 156	1693.78 136	1693.78 157	4658.45 149	4254.77 149	4135.74 149	3445.36 152	5866.63 149

AdaColmap		2288.90 122	2080.73 126	2531.77 122	2485.98 128	2757.87 129	2974.47 131	4914.62 124	2255.54 133	804.63 126	1611.14 132	842.30 132	2657.83 131	2056.92 124	1960.62 120	1353.09 128	3080.73 127

DeepMVS_CX						1.00 29	1.00 29	1.00 29	1.00 29	1.00 29	1.00 29	1.00 30	1.00 29	1.00 29		1.00 29	1.00 29

TinyColmap		1000.00 104	1000.00 108	1000.00 102	1000.00 106	1000.00 107	1000.00 105	1000.00 85	1000.00 111	1000.00 134	1000.00 118	1000.00 143	1000.00 107	1000.00 107	1000.00 107	1000.00 115	1000.00 104

MAR-MVS		3961.62 143	3726.55 145	4235.86 144	4107.62 148	6522.83 151	4862.93 151	7956.64 145	3380.98 147	1665.13 155	3231.54 151	1650.34 156	4357.29 148	3198.61 147	3209.42 146	2465.52 150	4892.15 147
Zhenyu Xu, Yiguang Liu, Xuelei Shi, Ying Wang, Yunan Zheng: MARMVS: Matching Ambiguity Reduced Multiple View Stereo for Efficient Large Scale Scene Reconstruction. CVPR 2020
MSDG		31705.85 154	29167.57 156	34667.17 155	44079.00 160	28314.00 162	44778.00 162	43648.00 157	31045.00 158	14849.00 162	26179.00 158	13752.00 164	38088.00 158	30859.00 158	30835.00 157	22561.00 160	43189.00 157

LS3D		7481.83 150	6596.02 151	8515.28 151	9871.71 155	9844.03 157	9225.16 158	18308.40 155	5364.90 152	1267.41 149	2381.61 148	1512.79 154	7225.72 153	8875.69 153	9516.28 152	5193.26 155	8676.83 151

CLD-MVS		6986.85 149	6222.57 150	7878.50 150	7707.00 154	8884.00 156	9025.00 157	15607.00 154	6148.00 154	2571.00 159	4822.00 153	2082.00 160	7875.00 154	6389.00 151	6449.00 151	4486.00 154	8784.00 152
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
FPMVS		1356.65 114	1072.60 112	1688.04 115	1840.79 119	1397.01 117	1793.24 121	4397.86 122	804.66 102	408.78 107	538.65 100	407.36 113	894.84 104	1587.73 117	1286.38 116	792.73 111	1486.39 114

Gipuma		587.77 84	529.57 84	655.67 85	637.00 89	733.00 89	749.00 90	1245.00 93	525.00 89	269.00 96	445.00 91	253.00 90	637.00 86	525.00 82	525.00 80	397.00 84	701.00 85
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015

High-resolution multi-view benchmark