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	botani.	boulde.	bridge	door	exhibi.	lectur.	living.	lounge	observ.	old co.	statue	terrac.

3Dnovator+	66.72 4	75.84 39	74.57 42	79.66 3	82.40 61	59.92 44	85.83 12	86.32 7	66.92 9	67.80 124	89.24 34	42.03 167	89.38 7	64.07 88	86.50 40	89.69 1

alignmvs		73.86 54	73.99 47	73.45 108	78.20 128	50.50 168	78.57 103	82.43 65	59.40 113	76.57 14	86.71 61	56.42 18	81.23 172	65.84 68	81.79 67	88.62 2

IS-MVSNet		71.57 79	71.00 78	73.27 119	78.86 112	45.63 227	80.22 82	78.69 152	64.14 29	66.46 138	87.36 53	49.30 79	85.60 70	50.26 182	83.71 54	88.59 3

canonicalmvs		74.67 46	74.98 40	73.71 96	78.94 111	50.56 166	80.23 81	83.87 36	60.30 84	77.15 13	86.56 68	59.65 7	82.00 159	66.01 66	82.12 65	88.58 4

VDDNet		71.81 76	71.33 73	73.26 120	82.80 59	47.60 215	78.74 99	75.27 197	59.59 108	72.94 51	89.40 31	41.51 179	83.91 114	58.75 135	82.99 58	88.26 5

VDD-MVS		72.50 65	72.09 63	73.75 94	81.58 68	49.69 194	77.76 127	77.63 172	63.21 38	73.21 47	89.02 37	42.14 166	83.32 125	61.72 120	82.50 63	88.25 6

DeepPCF-MVS	69.58 1	79.03 7	79.00 7	79.13 10	84.92 43	60.32 39	83.03 39	85.33 11	62.86 45	80.17 5	90.03 23	61.76 4	88.95 12	74.21 21	88.67 12	88.12 7

EPP-MVSNet		72.16 73	71.31 74	74.71 73	78.68 118	49.70 192	82.10 58	81.65 77	60.40 79	65.94 145	85.84 80	51.74 58	86.37 58	55.93 145	79.55 95	88.07 8

DELS-MVS		74.76 44	74.46 43	75.65 61	77.84 139	52.25 141	75.59 170	84.17 26	63.76 32	73.15 48	82.79 123	59.58 9	86.80 43	67.24 58	86.04 43	87.89 9
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
DeepC-MVS	69.38 2	78.56 10	78.14 13	79.83 2	83.60 51	61.62 24	84.17 26	86.85 2	63.23 37	73.84 39	90.25 20	57.68 14	89.96 4	74.62 20	89.03 8	87.89 9
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
MVS_0304		76.73 30	76.04 31	78.78 20	81.32 74	58.89 55	82.50 51	84.07 28	67.73 5	72.08 61	87.28 56	49.49 75	89.57 6	73.52 29	86.40 41	87.87 11

MP-MVS-pluss		78.35 12	78.46 9	78.03 31	84.96 39	59.52 46	82.93 41	85.39 10	62.15 55	76.41 16	91.51 2	52.47 52	86.78 45	80.66 2	89.64 7	87.80 12
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
PHI-MVS		75.87 38	75.36 37	77.41 37	80.62 87	55.91 97	84.28 23	85.78 9	56.08 164	73.41 45	86.58 67	50.94 66	88.54 14	70.79 42	89.71 6	87.79 13

CANet		76.46 32	75.93 33	78.06 30	81.29 75	57.53 70	82.35 52	83.31 52	67.78 3	70.09 75	86.34 71	54.92 28	88.90 13	72.68 32	84.55 49	87.76 14

SteuartSystems-ACMMP		79.48 6	79.31 6	79.98 1	83.01 57	62.18 19	87.60 2	85.83 8	66.69 11	78.03 11	90.98 6	54.26 34	90.06 3	78.42 7	89.02 9	87.69 15
Skip Steuart: Steuart Systems R&D Blog.
TSAR-MVS + MP.		78.44 11	78.28 11	78.90 17	84.96 39	61.41 27	84.03 28	83.82 38	59.34 115	79.37 7	89.76 28	59.84 6	87.62 30	76.69 11	86.74 37	87.68 16

MVS_Test		72.45 67	72.46 61	72.42 146	74.88 189	48.50 205	76.28 157	83.14 57	59.40 113	72.46 57	84.68 95	55.66 23	81.12 173	65.98 67	79.66 92	87.63 17

CDPH-MVS		76.31 33	75.67 36	78.22 28	85.35 34	59.14 51	81.31 71	84.02 29	56.32 158	74.05 33	88.98 38	53.34 45	87.92 26	69.23 46	88.42 14	87.59 18

OMC-MVS		71.40 83	70.60 82	73.78 90	76.60 169	53.15 127	79.74 90	79.78 120	58.37 128	68.75 104	86.45 70	45.43 140	80.60 184	62.58 103	77.73 115	87.58 19

Regformer-1		75.47 41	74.93 41	77.09 41	80.43 89	57.70 68	79.50 94	82.13 67	67.84 1	75.73 20	80.75 182	56.50 16	86.07 60	71.07 41	80.38 81	87.50 20

Regformer-2		75.63 40	74.99 39	77.54 35	80.43 89	58.32 62	79.50 94	82.92 59	67.84 1	75.94 17	80.75 182	55.73 22	86.80 43	71.44 40	80.38 81	87.50 20

TranMVSNet+NR-MVSNet		70.36 102	70.10 89	71.17 168	78.64 119	42.97 249	76.53 152	81.16 93	66.95 7	68.53 108	85.42 89	51.61 59	83.07 132	52.32 169	69.70 217	87.46 22

nrg030		72.96 61	73.01 56	72.84 129	75.41 184	50.24 175	80.02 84	82.89 62	58.36 129	74.44 31	86.73 59	58.90 12	80.83 179	65.84 68	74.46 139	87.44 23

DeepC-MVS_fast	68.24 3	77.25 23	76.63 26	79.12 11	86.15 20	60.86 33	84.71 20	84.85 18	61.98 61	73.06 50	88.88 40	53.72 41	89.06 11	68.27 49	88.04 23	87.42 24
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
DU-MVS		70.01 108	69.53 97	71.44 160	78.05 134	44.13 238	75.01 183	81.51 80	64.37 27	68.20 112	84.52 100	49.12 97	82.82 141	54.62 154	70.43 194	87.37 25

NR-MVSNet		69.54 117	68.85 109	71.59 159	78.05 134	43.81 242	74.20 195	80.86 101	65.18 17	62.76 180	84.52 100	52.35 54	83.59 120	50.96 178	70.78 188	87.37 25

UniMVSNet_NR-MVSNet		71.11 84	71.00 78	71.44 160	79.20 105	44.13 238	76.02 166	82.60 64	66.48 14	68.20 112	84.60 99	56.82 15	82.82 141	54.62 154	70.43 194	87.36 27

HPM-MVS		77.28 22	76.85 23	78.54 23	85.00 38	60.81 34	82.91 42	85.08 13	62.57 49	73.09 49	89.97 25	50.90 67	87.48 31	75.30 15	86.85 35	87.33 28

Effi-MVS+		73.31 58	72.54 60	75.62 62	77.87 138	53.64 119	79.62 92	79.61 124	61.63 63	72.02 62	82.61 128	56.44 17	85.97 66	63.99 91	79.07 103	87.25 29

Regformer-4		74.25 50	73.48 51	76.57 49	79.75 98	56.54 85	78.54 105	81.49 81	66.93 8	73.90 37	80.30 192	53.84 40	85.98 65	69.76 43	76.84 125	87.17 30

FIs		70.82 87	71.43 69	68.98 197	78.33 125	38.14 281	76.96 146	83.59 42	61.02 70	67.33 130	86.73 59	55.07 26	81.64 164	54.61 156	79.22 100	87.14 31

Regformer-3		73.89 53	73.28 55	75.71 59	79.75 98	55.48 105	78.54 105	79.93 119	66.58 12	73.62 42	80.30 192	54.87 29	84.54 100	69.09 47	76.84 125	87.10 32

CNVR-MVS		79.84 5	79.97 5	79.45 4	87.90 2	62.17 20	84.37 22	85.03 14	66.96 6	77.58 12	90.06 22	59.47 10	89.13 10	78.67 6	89.73 5	87.03 33

FC-MVSNet-test		69.80 111	70.58 83	67.46 210	77.61 149	34.73 306	76.05 164	83.19 55	60.84 71	65.88 147	86.46 69	54.52 33	80.76 183	52.52 168	78.12 113	86.91 34

UniMVSNet (Re)		70.63 92	70.20 86	71.89 152	78.55 120	45.29 228	75.94 167	82.92 59	63.68 34	68.16 114	83.59 114	53.89 39	83.49 122	53.97 158	71.12 186	86.89 35

LFMVS		71.78 77	71.59 67	72.32 149	83.40 53	46.38 223	79.75 89	71.08 225	64.18 28	72.80 53	88.64 44	42.58 164	83.72 117	57.41 140	84.49 50	86.86 36

test12				77.76 34	84.52 45	58.41 60		83.36 50		72.93 52		54.61 32	88.05 23		88.12 21	86.81 37

APDe-MVS		80.16 3	80.59 3	78.86 19	86.64 11	60.02 41	88.12 1	86.42 6	62.94 42	82.40 4	92.12 1	59.64 8	89.76 5	78.70 5	88.32 17	86.79 38

test_part1								86.64 3				65.59 1			90.06 3	86.78 39

ESAPD		80.72 1	81.17 2	79.38 6	87.58 3	60.47 37	86.37 5	86.64 3	63.49 35	83.42 2	91.40 3	65.59 1	90.90 1	75.98 13	90.06 3	86.78 39

ACMMP_Plus		78.77 8	78.78 8	78.74 21	85.44 31	61.04 31	83.84 30	85.16 12	62.88 44	78.10 10	91.26 5	52.51 50	88.39 15	79.34 3	90.52 1	86.78 39

MCST-MVS		77.48 21	77.45 17	77.54 35	86.67 10	58.36 61	83.22 37	86.93 1	56.91 143	74.91 26	88.19 46	59.15 11	87.68 29	73.67 27	87.45 28	86.57 42

test_prior3		76.89 28	76.96 22	76.69 45	84.20 48	57.27 73	81.75 61	84.88 16	60.37 80	75.01 22	89.06 35	56.22 19	86.43 56	72.19 34	88.96 10	86.38 43

test_prior				76.69 45	84.20 48	57.27 73		84.88 16					86.43 56			86.38 43

NCCC		78.58 9	78.31 10	79.39 5	87.51 5	62.61 16	85.20 19	84.42 21	66.73 10	74.67 30	89.38 32	55.30 25	89.18 9	74.19 22	87.34 29	86.38 43

XVS		77.17 24	76.56 27	79.00 15	86.32 16	62.62 14	85.83 12	83.92 32	64.55 22	72.17 59	90.01 24	47.95 111	88.01 24	71.55 38	86.74 37	86.37 46

X-MVStestdata		70.21 105	67.28 141	79.00 15	86.32 16	62.62 14	85.83 12	83.92 32	64.55 22	72.17 59	6.49 349	47.95 111	88.01 24	71.55 38	86.74 37	86.37 46

WR-MVS		68.47 140	68.47 116	68.44 204	80.20 94	39.84 265	73.75 202	76.07 189	64.68 21	68.11 116	83.63 113	50.39 70	79.14 206	49.78 184	69.66 218	86.34 48

SD-MVS		77.70 18	77.62 16	77.93 33	84.47 46	61.88 23	84.55 21	83.87 36	60.37 80	79.89 6	89.38 32	54.97 27	85.58 72	76.12 12	84.94 47	86.33 49

UA-Net		73.13 59	72.93 57	73.76 92	83.58 52	51.66 147	78.75 98	77.66 171	67.75 4	72.61 55	89.42 30	49.82 72	83.29 126	53.61 162	83.14 55	86.32 50

ACMMPR		77.71 17	77.23 20	79.16 7	86.75 7	62.93 9	86.29 7	84.24 25	62.82 46	73.55 44	90.56 12	49.80 73	88.24 19	74.02 23	87.03 32	86.32 50

region2R		77.67 19	77.18 21	79.15 8	86.76 6	62.95 8	86.29 7	84.16 27	62.81 48	73.30 46	90.58 11	49.90 71	88.21 20	73.78 25	87.03 32	86.29 52

mvs_anonymous		68.03 155	67.51 133	69.59 190	72.08 251	44.57 235	71.99 229	75.23 198	51.67 215	67.06 132	82.57 129	54.68 31	77.94 225	56.56 142	75.71 133	86.26 53

HFP-MVS		78.01 15	77.65 15	79.10 12	86.71 8	62.81 10	86.29 7	84.32 23	62.82 46	73.96 34	90.50 14	53.20 47	88.35 16	74.02 23	87.05 30	86.13 54

#test#		77.83 16	77.41 18	79.10 12	86.71 8	62.81 10	85.69 16	84.32 23	61.61 64	73.96 34	90.50 14	53.20 47	88.35 16	73.68 26	87.05 30	86.13 54

v2v482		70.50 96	69.45 104	73.66 98	72.62 243	50.03 185	77.58 132	80.51 112	59.90 92	69.52 91	82.14 141	47.53 117	84.88 93	65.07 74	70.17 206	86.09 56

CSCG		76.92 26	76.75 24	77.41 37	83.96 50	59.60 45	82.95 40	86.50 5	60.78 73	75.27 21	84.83 93	60.76 5	86.56 52	67.86 53	87.87 27	86.06 57

PAPR		71.72 78	70.82 80	74.41 82	81.20 79	51.17 151	79.55 93	83.33 51	55.81 168	66.93 134	84.61 98	50.95 65	86.06 61	55.79 148	79.20 101	86.00 58

EPNet		73.09 60	72.16 62	75.90 54	75.95 177	56.28 88	83.05 38	72.39 221	66.53 13	65.27 153	87.00 57	50.40 69	85.47 76	62.48 105	86.32 42	85.94 59
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
PAPM_NR		72.63 64	71.80 65	75.13 69	81.72 67	53.42 124	79.91 86	83.28 54	59.14 117	66.31 142	85.90 79	51.86 56	86.06 61	57.45 139	80.62 75	85.91 60

Test4		67.77 157	65.97 169	73.19 122	68.64 284	50.58 163	74.80 189	80.48 113	54.13 192	59.11 237	79.07 219	33.89 255	83.12 131	63.61 96	79.98 88	85.87 61

EI-MVSNet-Vis-set		72.42 68	71.59 67	74.91 70	78.47 123	54.02 115	77.05 144	79.33 141	65.03 20	71.68 64	79.35 215	52.75 49	84.89 91	66.46 63	74.23 142	85.83 62

APD-MVS_3200maxsize		74.96 42	74.39 44	76.67 47	82.20 62	58.24 63	83.67 31	83.29 53	58.41 127	73.71 41	90.14 21	45.62 135	85.99 64	69.64 44	82.85 62	85.78 63

PGM-MVS		76.77 29	76.06 30	78.88 18	86.14 21	62.73 12	82.55 49	83.74 39	61.71 62	72.45 58	90.34 17	48.48 106	88.13 21	72.32 33	86.85 35	85.78 63

HPM-MVS_fast		74.30 49	73.46 53	76.80 44	84.45 47	59.04 52	83.65 32	81.05 94	60.15 86	70.43 70	89.84 27	41.09 185	85.59 71	67.61 56	82.90 60	85.77 65

Vis-MVSNet		72.18 71	71.37 72	74.61 78	81.29 75	55.41 106	80.90 74	78.28 165	60.73 74	69.23 100	88.09 48	44.36 153	82.65 150	57.68 138	81.75 69	85.77 65
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
VNet		69.68 113	70.19 87	68.16 205	79.73 101	41.63 258	70.53 243	77.38 177	60.37 80	70.69 68	86.63 64	51.08 63	77.09 235	53.61 162	81.69 71	85.75 67

MP-MVS		78.35 12	78.26 12	78.64 22	86.54 13	63.47 5	86.02 11	83.55 43	63.89 31	73.60 43	90.60 10	54.85 30	86.72 46	77.20 10	88.06 22	85.74 68

test_normal		69.26 123	67.90 125	73.32 117	70.84 267	50.38 171	75.30 175	79.17 142	54.23 191	62.00 197	80.61 184	44.69 147	83.89 115	64.33 85	79.95 89	85.69 69

PS-MVSNAJss		72.24 70	71.21 75	75.31 67	78.50 121	55.93 96	81.63 64	82.12 68	56.24 161	70.02 79	85.68 84	47.05 122	84.34 104	65.27 72	74.41 141	85.67 70

pcd1.5k->3k		30.06 319	30.56 320	28.55 334	78.81 114	0.00 357	0.00 349	82.07 70	0.00 353	0.00 354	0.00 355	39.61 194	0.00 354	0.00 353	74.56 138	85.66 71

Fast-Effi-MVS+		70.28 104	69.12 107	73.73 95	78.50 121	51.50 150	75.01 183	79.46 137	56.16 162	68.59 105	79.55 211	53.97 37	84.05 108	53.34 164	77.53 117	85.65 72

test_djsdf		69.45 120	67.74 126	74.58 79	74.57 196	54.92 110	82.79 43	78.48 158	51.26 224	65.41 151	83.49 117	38.37 205	83.24 127	66.06 64	69.25 221	85.56 73

DI_MVS_plusplus_test		69.35 121	68.03 123	73.30 118	71.11 264	50.14 182	75.49 172	79.16 143	54.57 187	62.45 190	80.76 181	44.67 148	84.20 105	64.23 86	79.81 90	85.54 74

TSAR-MVS + GP.		74.90 43	74.15 46	77.17 40	82.00 64	58.77 57	81.80 60	78.57 154	58.58 123	74.32 32	84.51 102	55.94 21	87.22 34	67.11 59	84.48 51	85.52 75

PEN-MVS		66.60 177	66.45 161	67.04 214	77.11 161	36.56 295	77.03 145	80.42 114	62.95 41	62.51 188	84.03 109	46.69 128	79.07 207	44.22 226	63.08 270	85.51 76

divwei89l23v2f112		70.50 96	69.53 97	73.41 112	72.91 237	50.00 186	77.69 128	80.59 108	59.50 110	69.60 88	81.43 161	49.26 86	84.77 94	64.48 83	70.31 202	85.47 77

v1		70.50 96	69.53 97	73.42 111	72.91 237	50.00 186	77.69 128	80.59 108	59.50 110	69.59 90	81.42 163	49.26 86	84.77 94	64.49 82	70.30 203	85.47 77

v1141		70.50 96	69.53 97	73.41 112	72.92 236	50.00 186	77.69 128	80.60 107	59.50 110	69.60 88	81.43 161	49.24 91	84.77 94	64.48 83	70.30 203	85.46 79

HSP-MVS		80.69 2	81.20 1	79.14 9	86.21 18	62.73 12	86.09 10	85.03 14	65.51 15	83.81 1	90.51 13	63.71 3	89.23 8	81.51 1	88.44 13	85.45 80

CP-MVSNet		66.49 180	66.41 164	66.72 216	77.67 143	36.33 297	76.83 149	79.52 135	62.45 51	62.54 186	83.47 118	46.32 130	78.37 219	45.47 221	63.43 267	85.45 80

PCF-MVS	61.88 8	70.95 85	69.49 101	75.35 66	77.63 144	55.71 99	76.04 165	81.81 75	50.30 232	69.66 86	85.40 90	52.51 50	84.89 91	51.82 172	80.24 85	85.45 80
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
v1neww		70.66 89	69.70 92	73.53 103	73.15 226	50.22 176	78.11 114	80.68 103	59.65 102	69.83 82	81.67 152	49.29 81	84.96 86	64.55 79	70.38 197	85.42 83

v7new		70.66 89	69.70 92	73.53 103	73.15 226	50.22 176	78.11 114	80.68 103	59.65 102	69.83 82	81.67 152	49.29 81	84.96 86	64.55 79	70.38 197	85.42 83

v6		70.66 89	69.70 92	73.53 103	73.14 229	50.21 179	78.11 114	80.67 105	59.65 102	69.82 84	81.65 154	49.29 81	84.96 86	64.55 79	70.39 196	85.42 83

PS-CasMVS		66.42 181	66.32 167	66.70 218	77.60 150	36.30 299	76.94 147	79.61 124	62.36 53	62.43 194	83.66 112	45.69 134	78.37 219	45.35 223	63.26 268	85.42 83

CLD-MVS		73.33 57	72.68 59	75.29 68	78.82 113	53.33 125	78.23 111	84.79 19	61.30 68	70.41 71	81.04 170	52.41 53	87.12 38	64.61 78	82.49 64	85.41 87
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
diffmvs		67.72 158	66.73 156	70.70 177	69.74 280	47.69 214	73.33 206	74.74 205	53.30 197	64.51 166	81.80 150	49.25 88	79.02 208	59.15 134	74.75 137	85.39 88

v1144		70.42 101	69.31 105	73.76 92	73.22 222	50.64 161	77.83 125	81.43 82	58.58 123	69.40 95	81.16 167	47.53 117	85.29 81	64.01 90	70.64 189	85.34 89

EI-MVSNet-UG-set		71.92 75	71.06 77	74.52 80	77.98 136	53.56 121	76.62 150	79.16 143	64.40 26	71.18 66	78.95 220	52.19 55	84.66 99	65.47 71	73.57 151	85.32 90

v8		70.33 103	69.28 106	73.49 106	73.15 226	50.22 176	78.62 102	80.78 102	60.79 72	66.45 139	82.11 142	49.35 77	84.98 84	63.58 97	68.71 225	85.28 91

v1192		69.97 110	68.68 112	73.85 87	73.19 225	50.94 152	77.68 131	81.36 85	57.51 135	68.95 103	80.85 178	45.28 143	85.33 80	62.97 101	70.37 199	85.27 92

HPM-MVS++		79.88 4	80.14 4	79.10 12	88.17 1	64.80 1	86.59 4	83.70 40	65.37 16	78.78 9	90.64 9	58.63 13	87.24 33	79.00 4	90.37 2	85.26 93

v7		70.57 93	69.48 102	73.85 87	73.50 214	50.92 154	78.27 109	81.43 82	58.93 118	69.61 87	81.49 160	47.56 116	85.43 78	63.94 92	70.62 190	85.21 94

CANet_DTU		68.18 152	67.71 129	69.59 190	74.83 190	46.24 224	78.66 101	76.85 183	59.60 105	63.45 174	82.09 143	35.25 241	77.41 231	59.88 130	78.76 107	85.14 95

ACMMP		76.02 36	75.33 38	78.07 29	85.20 35	61.91 22	85.49 18	84.44 20	63.04 40	69.80 85	89.74 29	45.43 140	87.16 37	72.01 36	82.87 61	85.14 95
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
TAPA-MVS	59.36 10	66.60 177	65.20 179	70.81 173	76.63 168	48.75 203	76.52 153	80.04 118	50.64 230	65.24 155	84.93 92	39.15 198	78.54 214	36.77 269	76.88 124	85.14 95
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
v10		70.21 105	69.02 108	73.81 89	73.51 213	50.92 154	78.74 99	81.39 84	60.05 88	66.39 140	81.83 149	47.58 115	85.41 79	62.80 102	68.86 224	85.09 98

MG-MVS		73.96 52	73.89 48	74.16 83	85.65 27	49.69 194	81.59 67	81.29 88	61.45 65	71.05 67	88.11 47	51.77 57	87.73 28	61.05 123	83.09 56	85.05 99

v1921920		69.47 119	68.17 121	73.36 115	73.06 232	50.10 184	77.39 136	80.56 110	56.58 155	68.59 105	80.37 188	44.72 146	84.98 84	62.47 106	69.82 213	85.00 100

DTE-MVSNet		65.58 188	65.34 176	66.31 221	76.06 176	34.79 304	76.43 154	79.38 140	62.55 50	61.66 205	83.83 111	45.60 136	79.15 205	41.64 250	60.88 285	85.00 100

mPP-MVS		76.54 31	75.93 33	78.34 27	86.47 14	63.50 4	85.74 15	82.28 66	62.90 43	71.77 63	90.26 19	46.61 129	86.55 53	71.71 37	85.66 45	84.97 102

v1240		69.24 124	67.91 124	73.25 121	73.02 234	49.82 189	77.21 141	80.54 111	56.43 157	68.34 111	80.51 186	43.33 161	84.99 82	62.03 116	69.77 216	84.95 103

v144192		69.71 112	68.51 114	73.33 116	73.10 231	50.13 183	77.54 134	80.64 106	56.65 149	68.57 107	80.55 185	46.87 127	84.96 86	62.98 100	69.66 218	84.89 104

APD-MVS		78.02 14	78.04 14	77.98 32	86.44 15	60.81 34	85.52 17	84.36 22	60.61 75	79.05 8	90.30 18	55.54 24	88.32 18	73.48 30	87.03 32	84.83 105
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
MPTG		77.61 20	77.36 19	78.35 25	86.08 22	63.57 2	83.37 35	80.97 98	65.13 18	75.77 18	90.88 7	48.63 102	86.66 47	77.23 8	88.17 19	84.81 106

MTAPA		76.90 27	76.42 28	78.35 25	86.08 22	63.57 2	74.92 186	80.97 98	65.13 18	75.77 18	90.88 7	48.63 102	86.66 47	77.23 8	88.17 19	84.81 106

v7n		69.01 127	67.36 138	73.98 85	72.51 245	52.65 133	78.54 105	81.30 87	60.26 85	62.67 182	81.62 155	43.61 158	84.49 101	57.01 141	68.70 226	84.79 108

WR-MVS_H		67.02 169	66.92 150	67.33 213	77.95 137	37.75 284	77.57 133	82.11 69	62.03 60	62.65 183	82.48 131	50.57 68	79.46 195	42.91 240	64.01 262	84.79 108

CP-MVS		77.12 25	76.68 25	78.43 24	86.05 24	63.18 7	87.55 3	83.45 46	62.44 52	72.68 54	90.50 14	48.18 109	87.34 32	73.59 28	85.71 44	84.76 110

testing_2		66.02 184	63.77 191	72.76 132	66.03 304	50.48 169	72.93 210	80.36 116	54.41 189	54.25 283	76.76 256	30.89 278	83.16 130	64.19 87	74.08 144	84.65 111

HQP_MVS		74.31 48	73.73 49	76.06 52	81.41 72	56.31 86	84.22 24	84.01 30	64.52 24	69.27 97	86.10 75	45.26 144	87.21 35	68.16 50	80.58 77	84.65 111

plane_prior5								84.01 30					87.21 35	68.16 50	80.58 77	84.65 111

v148		68.24 150	67.19 145	71.40 163	70.43 270	47.77 213	75.76 169	77.03 181	58.91 119	67.36 129	80.10 196	48.60 105	81.89 160	60.01 129	66.52 245	84.53 114

V42		68.65 133	67.35 139	72.56 137	68.93 283	50.18 180	72.90 211	79.47 136	56.92 142	69.45 94	80.26 194	46.29 131	82.99 133	64.07 88	67.82 237	84.53 114

VPA-MVSNet		69.02 126	69.47 103	67.69 209	77.42 152	41.00 262	74.04 196	79.68 122	60.06 87	69.26 99	84.81 94	51.06 64	77.58 229	54.44 157	74.43 140	84.48 116

agg_prior2														73.09 31	87.93 25	84.33 117

HQP4-MVS										67.85 119			86.93 41			84.32 118

HQP-MVS		73.45 56	72.80 58	75.40 65	80.66 84	54.94 108	82.31 54	83.90 34	62.10 56	67.85 119	85.54 87	45.46 138	86.93 41	67.04 60	80.35 83	84.32 118

v13		68.29 144	66.84 151	72.63 134	73.50 214	50.83 157	78.25 110	79.58 131	60.05 88	60.76 219	77.68 239	49.11 100	82.77 143	62.17 112	60.45 293	84.30 120

v12		68.28 145	66.83 153	72.60 136	73.43 216	50.74 159	78.18 112	79.59 129	60.01 90	60.89 218	77.66 240	49.12 97	82.77 143	62.18 110	60.46 292	84.29 121

V9		68.27 146	66.84 151	72.56 137	73.39 219	50.63 162	78.10 117	79.60 126	59.94 91	61.05 216	77.62 241	49.18 93	82.77 143	62.17 112	60.48 291	84.27 122

V14		68.25 148	66.82 154	72.52 139	73.33 220	50.53 167	78.02 120	79.60 126	59.83 96	61.16 214	77.57 244	49.19 92	82.77 143	62.18 110	60.50 290	84.26 123

v15		68.22 151	66.81 155	72.47 144	73.25 221	50.40 170	77.92 124	79.60 126	59.77 99	61.28 212	77.52 246	49.25 88	82.77 143	62.16 114	60.51 289	84.24 124

anonymousdsp		67.00 170	64.82 184	73.57 102	70.09 274	56.13 91	76.35 155	77.35 178	48.43 247	64.99 161	80.84 179	33.01 262	80.34 186	64.66 76	67.64 240	84.23 125

v17		68.37 142	67.00 148	72.48 140	73.22 222	50.31 172	78.10 117	79.58 131	59.71 100	61.67 204	77.60 242	49.31 78	82.89 137	62.37 107	61.48 282	84.23 125

v16		68.38 141	67.01 147	72.47 144	73.22 222	50.29 173	78.10 117	79.59 129	59.71 100	61.72 203	77.60 242	49.28 84	82.89 137	62.36 108	61.54 279	84.23 125

v11		68.15 154	66.73 156	72.42 146	73.43 216	50.28 174	77.94 123	79.65 123	59.88 95	61.11 215	77.55 245	48.25 108	82.75 148	61.88 119	60.85 286	84.23 125

MVSFormer		71.50 81	70.38 85	74.88 71	78.76 115	57.15 80	82.79 43	78.48 158	51.26 224	69.49 92	83.22 119	43.99 156	83.24 127	66.06 64	79.37 96	84.23 125

jason		69.65 114	68.39 118	73.43 110	78.27 127	56.88 82	77.12 142	73.71 215	46.53 263	69.34 96	83.22 119	43.37 160	79.18 200	64.77 75	79.20 101	84.23 125
jason: jason.
v18		68.33 143	66.96 149	72.42 146	73.13 230	50.16 181	77.97 122	79.57 133	59.57 109	61.80 201	77.50 247	49.30 79	82.90 136	62.31 109	61.50 280	84.20 131

ab-mvs		66.65 176	66.42 163	67.37 211	76.17 173	41.73 256	70.41 246	76.14 188	53.99 193	65.98 144	83.51 116	49.48 76	76.24 245	48.60 195	73.46 153	84.14 132

v748		67.26 162	65.67 172	72.02 151	69.90 278	49.77 191	76.24 158	79.57 133	58.58 123	60.49 222	80.38 187	44.47 152	82.17 157	56.16 144	65.26 253	84.12 133

lupinMVS		69.57 116	68.28 119	73.44 109	78.76 115	57.15 80	76.57 151	73.29 217	46.19 267	69.49 92	82.18 137	43.99 156	79.23 199	64.66 76	79.37 96	83.93 134

GBi-Net		67.21 163	66.55 159	69.19 194	77.63 144	43.33 245	77.31 137	77.83 168	56.62 152	65.04 158	82.70 124	41.85 171	80.33 187	47.18 201	72.76 165	83.92 135

test1		67.21 163	66.55 159	69.19 194	77.63 144	43.33 245	77.31 137	77.83 168	56.62 152	65.04 158	82.70 124	41.85 171	80.33 187	47.18 201	72.76 165	83.92 135

FMVSNet1		66.70 175	65.87 170	69.19 194	77.49 151	43.33 245	77.31 137	77.83 168	56.45 156	64.60 165	82.70 124	38.08 210	80.33 187	46.08 211	72.31 176	83.92 135

GA-MVS		65.53 189	63.70 192	71.02 172	70.87 266	48.10 208	70.48 244	74.40 209	56.69 148	64.70 164	76.77 255	33.66 257	81.10 174	55.42 151	70.32 201	83.87 138

test9_res														75.28 16	88.31 18	83.81 139

VPNet		67.52 159	68.11 122	65.74 234	79.18 106	36.80 293	72.17 223	72.83 219	62.04 59	67.79 125	85.83 81	48.88 101	76.60 241	51.30 176	72.97 163	83.81 139

UGNet		68.81 129	67.39 136	73.06 123	78.33 125	54.47 113	79.77 88	75.40 196	60.45 78	63.22 175	84.40 103	32.71 269	80.91 178	51.71 174	80.56 79	83.81 139
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
abl_6		74.34 47	73.50 50	76.86 42	82.43 60	60.16 40	83.48 34	81.86 73	58.81 121	73.95 36	89.86 26	41.87 170	86.62 49	67.98 52	81.23 72	83.80 142

HyFIR lowres test		65.67 187	63.01 199	73.67 97	79.97 97	55.65 101	69.07 257	75.52 194	42.68 297	63.53 173	77.95 230	40.43 189	81.64 164	46.01 212	71.91 179	83.73 143

mvs_tets		68.18 152	66.36 165	73.63 101	75.61 181	55.35 107	80.77 76	78.56 155	52.48 204	64.27 170	84.10 108	27.45 299	81.84 162	63.45 99	70.56 193	83.69 144

v52		67.09 166	65.16 180	72.87 127	66.77 298	51.60 148	73.69 203	79.45 138	57.88 132	62.46 189	78.57 226	40.95 187	83.34 123	61.99 117	64.70 258	83.68 145

V4		67.09 166	65.16 180	72.87 127	66.76 299	51.60 148	73.69 203	79.45 138	57.88 132	62.45 190	78.58 225	40.96 186	83.34 123	61.99 117	64.71 256	83.68 145

jajsoiax		68.25 148	66.45 161	73.66 98	75.62 180	55.49 104	80.82 75	78.51 157	52.33 205	64.33 168	84.11 107	28.28 293	81.81 163	63.48 98	70.62 190	83.67 147

OPM-MVS		74.73 45	74.25 45	76.19 51	80.81 83	59.01 53	82.60 48	83.64 41	63.74 33	72.52 56	87.49 51	47.18 121	85.88 68	69.47 45	80.78 73	83.66 148

train_agg		76.27 34	76.15 29	76.64 48	85.58 29	61.59 25	81.62 65	81.26 89	55.86 165	74.93 24	88.81 41	53.70 42	84.68 97	75.24 17	88.33 15	83.65 149

agg_prior3		76.13 35	75.89 35	76.85 43	85.76 25	62.02 21	81.65 63	81.01 97	55.51 174	73.73 40	88.60 45	53.23 46	84.90 90	75.24 17	88.33 15	83.65 149

MVSTER		67.16 165	65.58 174	71.88 153	70.37 272	49.70 192	70.25 248	78.45 160	51.52 219	69.16 101	80.37 188	38.45 204	82.50 152	60.19 128	71.46 183	83.44 151

XVG-OURS-SEG-HR		68.81 129	67.47 134	72.82 131	74.40 200	56.87 83	70.59 242	79.04 145	54.77 184	66.99 133	86.01 77	39.57 195	78.21 222	62.54 104	73.33 155	83.37 152

EI-MVSNet		69.27 122	68.44 117	71.73 156	74.47 197	49.39 198	75.20 179	78.45 160	59.60 105	69.16 101	76.51 260	51.29 60	82.50 152	59.86 132	71.45 184	83.30 153

IterMVS-LS		69.22 125	68.48 115	71.43 162	74.44 199	49.40 197	76.23 159	77.55 173	59.60 105	65.85 148	81.59 158	51.28 61	81.58 167	59.87 131	69.90 212	83.30 153
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
ACMP	63.53 6	72.30 69	71.20 76	75.59 64	80.28 91	57.54 69	82.74 45	82.84 63	60.58 76	65.24 155	86.18 73	39.25 197	86.03 63	66.95 62	76.79 127	83.22 155
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
FMVSNet2		66.93 171	66.31 168	68.79 200	77.63 144	42.98 248	76.11 161	77.47 174	56.62 152	65.22 157	82.17 139	41.85 171	80.18 190	47.05 204	72.72 168	83.20 156

XVG-OURS		68.76 132	67.37 137	72.90 126	74.32 201	57.22 75	70.09 249	78.81 149	55.24 177	67.79 125	85.81 83	36.54 231	78.28 221	62.04 115	75.74 132	83.19 157

LPG-MVS_test		72.74 63	71.74 66	75.76 56	80.22 92	57.51 71	82.55 49	83.40 48	61.32 66	66.67 136	87.33 54	39.15 198	86.59 50	67.70 54	77.30 121	83.19 157

LGP-MVS_train				75.76 56	80.22 92	57.51 71		83.40 48	61.32 66	66.67 136	87.33 54	39.15 198	86.59 50	67.70 54	77.30 121	83.19 157

agg_prior1		75.94 37	76.01 32	75.72 58	85.04 36	59.96 42	81.44 69	81.04 95	56.14 163	74.68 28	88.90 39	53.91 38	84.04 109	75.01 19	87.92 26	83.16 160

DP-MVS Recon		72.15 74	70.73 81	76.40 50	86.57 12	57.99 65	81.15 73	82.96 58	57.03 140	66.78 135	85.56 85	44.50 150	88.11 22	51.77 173	80.23 86	83.10 161

CDS-MVSNet		66.80 173	65.37 175	71.10 170	78.98 110	53.13 129	73.27 207	71.07 226	52.15 207	64.72 163	80.23 195	43.56 159	77.10 234	45.48 220	78.88 104	83.05 162
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
TAMVS		66.78 174	65.27 178	71.33 165	79.16 108	53.67 118	73.84 201	69.59 237	52.32 206	65.28 152	81.72 151	44.49 151	77.40 232	42.32 243	78.66 109	82.92 163

Vis-MVSNet (Re-imp)		63.69 205	63.88 189	63.14 256	74.75 192	31.04 326	71.16 237	63.64 283	56.32 158	59.80 229	84.99 91	44.51 149	75.46 248	39.12 259	80.62 75	82.92 163

FMVSNet3		66.32 182	65.61 173	68.46 203	76.48 172	42.34 252	74.98 185	77.15 180	55.83 167	65.04 158	81.16 167	39.91 190	80.14 191	47.18 201	72.76 165	82.90 165

3Dnovator	64.47 5	72.49 66	71.39 71	75.79 55	77.70 141	58.99 54	80.66 78	83.15 56	62.24 54	65.46 150	86.59 66	42.38 165	85.52 74	59.59 133	84.72 48	82.85 166

mvs-test1		70.44 100	68.19 120	77.18 39	76.10 174	63.22 6	80.59 79	76.06 190	59.83 96	66.32 141	79.87 199	41.56 176	85.53 73	60.60 125	72.77 164	82.80 167

BH-RMVSNet		68.81 129	67.42 135	72.97 124	80.11 95	52.53 136	74.26 194	76.29 186	58.48 126	68.38 110	84.20 105	42.59 163	83.83 116	46.53 206	75.91 131	82.56 168

pmmvs6		63.69 205	62.82 202	66.27 224	70.63 268	39.27 271	73.13 208	75.47 195	52.69 202	59.75 230	82.30 135	39.71 193	77.03 236	47.40 200	64.35 261	82.53 169

cascas		65.98 185	63.42 195	73.64 100	77.26 159	52.58 135	72.26 222	77.21 179	48.56 244	61.21 213	74.60 276	32.57 273	85.82 69	50.38 181	76.75 128	82.52 170

PVSNet_Blended_VisFu		71.45 82	70.39 84	74.65 76	82.01 63	58.82 56	79.93 85	80.35 117	55.09 179	65.82 149	82.16 140	49.17 94	82.64 151	60.34 127	78.62 110	82.50 171

MVS_111021_HR		74.02 51	73.46 53	75.69 60	83.01 57	60.63 36	77.29 140	78.40 163	61.18 69	70.58 69	85.97 78	54.18 36	84.00 113	67.52 57	82.98 59	82.45 172

RPSCF		55.80 271	54.22 275	60.53 276	65.13 308	42.91 250	64.30 284	57.62 309	36.84 322	58.05 248	82.28 136	28.01 294	56.24 326	37.14 267	58.61 299	82.44 173

pm-mvs1		65.24 193	64.97 183	66.04 228	72.38 246	39.40 270	72.62 216	75.63 193	55.53 173	62.35 196	83.18 121	47.45 119	76.47 242	49.06 192	66.54 244	82.24 174

114514_t		70.83 86	69.56 96	74.64 77	86.21 18	54.63 112	82.34 53	81.81 75	48.22 249	63.01 178	85.83 81	40.92 188	87.10 39	57.91 137	79.79 91	82.18 175

Fast-Effi-MVS+-dtu		67.37 160	65.33 177	73.48 107	72.94 235	57.78 67	77.47 135	76.88 182	57.60 134	61.97 198	76.85 254	39.31 196	80.49 185	54.72 153	70.28 205	82.17 176

LCM-MVSNet-Re		61.88 229	61.35 221	63.46 252	74.58 195	31.48 325	61.42 295	58.14 306	58.71 122	53.02 292	79.55 211	43.07 162	76.80 238	45.69 215	77.96 114	82.11 177

HY-MVS	56.14 13	64.55 201	63.89 188	66.55 219	74.73 193	41.02 260	69.96 250	74.43 208	49.29 238	61.66 205	80.92 175	47.43 120	76.68 240	44.91 225	71.69 181	81.94 178

1112_ss		64.00 204	63.36 196	65.93 231	79.28 104	42.58 251	71.35 233	72.36 222	46.41 265	60.55 221	77.89 233	46.27 132	73.28 261	46.18 209	69.97 210	81.92 179

K. test v3		60.47 236	57.11 253	70.56 178	73.74 212	48.22 207	75.10 182	62.55 291	58.27 130	53.62 288	76.31 262	27.81 296	81.59 166	47.42 199	39.18 334	81.88 180

MAR-MVS		71.51 80	70.15 88	75.60 63	81.84 66	59.39 48	81.38 70	82.90 61	54.90 183	68.08 117	78.70 221	47.73 113	85.51 75	51.68 175	84.17 52	81.88 180
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
Baseline_NR-MVSNet		67.05 168	67.56 130	65.50 240	75.65 179	37.70 285	75.42 173	74.65 207	59.90 92	68.14 115	83.15 122	49.12 97	77.20 233	52.23 170	69.78 214	81.60 182

Effi-MVS+-dtu		69.64 115	67.53 132	75.95 53	76.10 174	62.29 18	80.20 83	76.06 190	59.83 96	65.26 154	77.09 250	41.56 176	84.02 112	60.60 125	71.09 187	81.53 183

tpmp4_e23		62.71 220	60.13 229	70.45 180	73.40 218	48.39 206	72.82 212	69.49 239	44.88 277	59.91 226	74.99 272	37.79 212	81.47 169	40.22 254	67.71 239	81.48 184

QAPM		70.05 107	68.81 110	73.78 90	76.54 171	53.43 123	83.23 36	83.48 44	52.89 200	65.90 146	86.29 72	41.55 178	86.49 55	51.01 177	78.40 112	81.42 185

CHOSEN 1792x2688		65.08 196	62.84 201	71.82 154	81.49 71	56.26 89	66.32 272	74.20 211	40.53 309	63.16 177	78.65 223	41.30 181	77.80 227	45.80 214	74.09 143	81.40 186

view600		62.77 216	61.84 213	65.55 236	77.28 155	36.87 289	72.15 224	67.78 251	56.79 144	61.46 208	81.92 144	36.88 224	78.42 215	29.86 305	72.46 169	81.36 187

view800		62.77 216	61.84 213	65.55 236	77.28 155	36.87 289	72.15 224	67.78 251	56.79 144	61.46 208	81.92 144	36.88 224	78.42 215	29.86 305	72.46 169	81.36 187

conf0.05thres1000		62.77 216	61.84 213	65.55 236	77.28 155	36.87 289	72.15 224	67.78 251	56.79 144	61.46 208	81.92 144	36.88 224	78.42 215	29.86 305	72.46 169	81.36 187

tfpn		62.77 216	61.84 213	65.55 236	77.28 155	36.87 289	72.15 224	67.78 251	56.79 144	61.46 208	81.92 144	36.88 224	78.42 215	29.86 305	72.46 169	81.36 187

thres600view7		63.30 209	62.27 208	66.41 220	77.18 160	38.87 273	72.35 220	69.11 244	56.98 141	62.37 195	80.96 174	37.01 221	79.00 212	31.43 298	73.05 162	81.36 187

thres400		63.31 208	62.18 210	66.72 216	76.85 166	39.62 267	71.96 230	69.44 240	56.63 150	62.61 184	79.83 200	37.18 216	79.17 201	31.84 290	73.25 157	81.36 187

CPTT-MVS		72.78 62	72.08 64	74.87 72	84.88 44	61.41 27	84.15 27	77.86 167	55.27 176	67.51 128	88.08 49	41.93 169	81.85 161	69.04 48	80.01 87	81.35 193

Test_1112_low_res		62.32 223	61.77 217	64.00 251	79.08 109	39.53 269	68.17 265	70.17 230	43.25 292	59.03 239	79.90 198	44.08 154	71.24 268	43.79 232	68.42 227	81.25 194

xiu_mvs_v1_base_debu		68.58 135	67.28 141	72.48 140	78.19 129	57.19 77	75.28 176	75.09 201	51.61 216	70.04 76	81.41 164	32.79 265	79.02 208	63.81 93	77.31 118	81.22 195

xiu_mvs_v1_base		68.58 135	67.28 141	72.48 140	78.19 129	57.19 77	75.28 176	75.09 201	51.61 216	70.04 76	81.41 164	32.79 265	79.02 208	63.81 93	77.31 118	81.22 195

xiu_mvs_v1_base_debi		68.58 135	67.28 141	72.48 140	78.19 129	57.19 77	75.28 176	75.09 201	51.61 216	70.04 76	81.41 164	32.79 265	79.02 208	63.81 93	77.31 118	81.22 195

IB-MVS	56.42 12	65.40 192	62.73 203	73.40 114	74.89 188	52.78 132	73.09 209	75.13 200	55.69 170	58.48 246	73.73 281	32.86 264	86.32 59	50.63 179	70.11 207	81.10 198
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
MSLP-MVS++		73.77 55	73.47 52	74.66 75	83.02 56	59.29 50	82.30 57	81.88 72	59.34 115	71.59 65	86.83 58	45.94 133	83.65 119	65.09 73	85.22 46	81.06 199

conf0.002		59.97 238	58.81 236	63.42 253	74.15 204	33.83 312	68.32 260	64.22 275	51.79 210	58.04 249	79.57 206	35.41 235	75.41 249	29.57 311	68.26 228	81.03 200

conf200view11		63.38 207	62.41 206	66.29 223	77.31 153	38.66 276	72.65 213	69.11 244	57.07 138	62.45 190	81.03 171	37.01 221	79.17 201	31.84 290	73.25 157	81.03 200

TransMVSNet (Re)		64.72 197	64.33 185	65.87 233	75.22 186	38.56 278	74.66 190	75.08 204	58.90 120	61.79 202	82.63 127	51.18 62	78.07 224	43.63 233	55.87 304	80.99 202

PAPM		67.92 156	66.69 158	71.63 158	78.09 132	49.02 201	77.09 143	81.24 91	51.04 227	60.91 217	83.98 110	47.71 114	84.99 82	40.81 252	79.32 99	80.90 203

PS-MVSNAJ		70.51 95	69.70 92	72.93 125	81.52 69	55.79 98	74.92 186	79.00 146	55.04 181	69.88 81	78.66 222	47.05 122	82.19 156	61.61 121	79.58 93	80.83 204

xiu_mvs_v2_base		70.52 94	69.75 90	72.84 129	81.21 78	55.63 102	75.11 181	78.92 147	54.92 182	69.96 80	79.68 205	47.00 126	82.09 158	61.60 122	79.37 96	80.81 205

lessismore_v0				69.91 186	71.42 261	47.80 211		50.90 332		50.39 302	75.56 269	27.43 300	81.33 170	45.91 213	34.10 337	80.59 206

XVG-ACMP-BASELINE		64.36 202	62.23 209	70.74 175	72.35 247	52.45 139	70.80 241	78.45 160	53.84 194	59.87 227	81.10 169	16.24 328	79.32 198	55.64 150	71.76 180	80.47 207

CostFormer		64.04 203	62.51 204	68.61 202	71.88 255	45.77 226	71.30 234	70.60 229	47.55 256	64.31 169	76.61 258	41.63 174	79.62 194	49.74 186	69.00 222	80.42 208

SixPastTwentyTwo		61.65 231	58.80 242	70.20 183	75.80 178	47.22 218	75.59 170	69.68 235	54.61 185	54.11 284	79.26 216	27.07 302	82.96 134	43.27 235	49.79 322	80.41 209

ACMM	61.98 7	70.80 88	69.73 91	74.02 84	80.59 88	58.59 59	82.68 46	82.02 71	55.46 175	67.18 131	84.39 104	38.51 203	83.17 129	60.65 124	76.10 130	80.30 210
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
TR-MVS		66.59 179	65.07 182	71.17 168	79.18 106	49.63 196	73.48 205	75.20 199	52.95 199	67.90 118	80.33 191	39.81 192	83.68 118	43.20 237	73.56 152	80.20 211

CNLPA		65.43 190	64.02 187	69.68 188	78.73 117	58.07 64	77.82 126	70.71 228	51.49 220	61.57 207	83.58 115	38.23 208	70.82 269	43.90 230	70.10 208	80.16 212

PVSNet_Blended		68.59 134	67.72 127	71.19 167	77.03 163	50.57 164	72.51 218	81.52 78	51.91 209	64.22 171	77.77 236	49.13 95	82.87 139	55.82 146	79.58 93	80.14 213

OpenMVS	61.03 9	68.85 128	67.56 130	72.70 133	74.26 202	53.99 116	81.21 72	81.34 86	52.70 201	62.75 181	85.55 86	38.86 201	84.14 107	48.41 197	83.01 57	79.97 214

ACMH+	57.40 11	66.12 183	64.06 186	72.30 150	77.79 140	52.83 131	80.39 80	78.03 166	57.30 136	57.47 257	82.55 130	27.68 297	84.17 106	45.54 218	69.78 214	79.90 215

thres100view900		63.28 210	62.41 206	65.89 232	77.31 153	38.66 276	72.65 213	69.11 244	57.07 138	62.45 190	81.03 171	37.01 221	79.17 201	31.84 290	73.25 157	79.83 216

tfpn200view9		63.18 212	62.18 210	66.21 225	76.85 166	39.62 267	71.96 230	69.44 240	56.63 150	62.61 184	79.83 200	37.18 216	79.17 201	31.84 290	73.25 157	79.83 216

DWT-MVSNet_test		61.90 227	59.93 231	67.83 207	71.98 254	46.09 225	71.03 239	69.71 233	50.09 233	58.51 245	70.62 296	30.21 283	77.63 228	49.28 190	67.91 235	79.78 218

PVSNet_BlendedMVS		68.56 138	67.72 127	71.07 171	77.03 163	50.57 164	74.50 192	81.52 78	53.66 195	64.22 171	79.72 204	49.13 95	82.87 139	55.82 146	73.92 146	79.77 219

1314		64.61 200	63.21 197	68.80 199	71.87 256	47.46 216	73.95 197	78.39 164	42.88 296	59.97 225	76.60 259	38.11 209	79.39 197	54.84 152	72.32 175	79.55 220

OurMVSNet-221017-0		61.37 233	58.63 245	69.61 189	72.05 252	48.06 209	73.93 200	72.51 220	47.23 259	54.74 276	80.92 175	21.49 322	81.24 171	48.57 196	56.22 303	79.53 221

semantic-postprocess				65.40 241	71.99 253	50.80 158		69.63 236	45.71 274	60.61 220	77.93 231	36.56 230	65.99 290	55.67 149	63.50 266	79.42 222

tpm2		62.07 226	60.10 230	67.99 206	72.79 240	43.86 241	71.05 238	66.85 260	43.14 294	62.77 179	75.39 270	38.32 206	80.80 180	41.69 247	68.88 223	79.32 223

MVS_111021_LR		69.50 118	68.78 111	71.65 157	78.38 124	59.33 49	74.82 188	70.11 231	58.08 131	67.83 123	84.68 95	41.96 168	76.34 244	65.62 70	77.54 116	79.30 224

PatchFormer-LS_test		62.20 224	60.59 227	67.04 214	72.18 250	46.82 221	70.36 247	68.62 248	51.92 208	59.19 236	70.23 299	36.86 228	75.07 256	50.23 183	65.68 250	79.23 225

ITE_SJBPF				62.09 263	66.16 302	44.55 236		64.32 274	47.36 258	55.31 272	80.34 190	19.27 324	62.68 300	36.29 276	62.39 275	79.04 226

无先验							79.66 91	74.30 210	48.40 248				80.78 181	53.62 160		79.03 227

tfpnnormal		62.47 221	61.63 219	64.99 245	74.81 191	39.01 272	71.22 235	73.72 214	55.22 178	60.21 223	80.09 197	41.26 184	76.98 237	30.02 304	68.09 234	78.97 228

MDTV_nov1_ep13_2view						25.89 337	61.22 297		40.10 311	51.10 297		32.97 263		38.49 261		78.61 229

API-MVS		72.17 72	71.41 70	74.45 81	81.95 65	57.22 75	84.03 28	80.38 115	59.89 94	68.40 109	82.33 134	49.64 74	87.83 27	51.87 171	84.16 53	78.30 230

EPNet_dtu		61.90 227	61.97 212	61.68 264	72.89 239	39.78 266	75.85 168	65.62 265	55.09 179	54.56 279	79.36 214	37.59 213	67.02 284	39.80 258	76.95 123	78.25 231
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
原ACMM1				74.69 74	85.39 33	59.40 47		83.42 47	51.47 221	70.27 74	86.61 65	48.61 104	86.51 54	53.85 159	87.96 24	78.16 232

PatchmatchNet		59.84 240	58.24 246	64.65 248	73.05 233	46.70 222	69.42 254	62.18 293	47.55 256	58.88 240	71.96 289	34.49 248	69.16 276	42.99 239	63.60 265	78.07 233
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
GSMVS																78.05 234

sam_mvs1												34.74 244				78.05 234

旧先验1					83.04 55	53.15 127		67.52 255			87.85 50	44.08 154			80.76 74	78.03 236

IterMVS		62.79 215	61.27 222	67.35 212	69.37 281	52.04 145	71.17 236	68.24 250	52.63 203	59.82 228	76.91 253	37.32 215	72.36 264	52.80 167	63.19 269	77.66 237
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
PLC	56.13 14	65.09 195	63.21 197	70.72 176	81.04 81	54.87 111	78.57 103	77.47 174	48.51 245	55.71 267	81.89 148	33.71 256	79.71 192	41.66 248	70.37 199	77.58 238
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
LTVRE_ROB	55.42 16	63.15 213	61.23 223	68.92 198	76.57 170	47.80 211	59.92 301	76.39 185	54.35 190	58.67 242	82.46 132	29.44 288	81.49 168	42.12 244	71.14 185	77.46 239
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
ambc				65.13 244	63.72 314	37.07 287	47.66 331	78.78 150		54.37 282	71.42 292	11.24 340	80.94 176	45.64 216	53.85 312	77.38 240

Patchmatch-test1		59.75 241	58.00 250	64.98 246	74.14 209	48.06 209	63.35 287	63.23 286	49.13 240	59.33 235	71.46 291	37.45 214	69.59 274	41.39 251	62.57 273	77.30 241

Patchmatch-RL test		58.16 256	55.49 266	66.15 226	67.92 291	48.89 202	60.66 300	51.07 331	47.86 254	59.36 232	62.71 324	34.02 253	72.27 265	56.41 143	59.40 296	77.30 241

Patchmatch-test		49.08 293	48.28 293	51.50 311	64.40 311	30.85 327	45.68 333	48.46 337	35.60 324	46.10 315	72.10 288	34.47 249	46.37 339	27.08 323	60.65 288	77.27 243

MIMVSNet1		55.17 274	54.31 273	57.77 287	70.03 275	32.01 323	65.68 274	64.81 270	49.19 239	46.75 312	76.00 264	25.53 311	64.04 295	28.65 319	62.13 276	77.26 244

ACMH	55.70 15	65.20 194	63.57 194	70.07 184	78.07 133	52.01 146	79.48 96	79.69 121	55.75 169	56.59 263	80.98 173	27.12 301	80.94 176	42.90 241	71.58 182	77.25 245
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
thres200		62.20 224	61.16 224	65.34 242	75.38 185	39.99 264	69.60 252	69.29 242	55.64 172	61.87 200	76.99 251	37.07 220	78.96 213	31.28 299	73.28 156	77.06 246

AdaColmap		69.99 109	68.66 113	73.97 86	84.94 41	57.83 66	82.63 47	78.71 151	56.28 160	64.34 167	84.14 106	41.57 175	87.06 40	46.45 207	78.88 104	77.02 247

tpm cat1		59.25 249	56.95 256	66.15 226	72.19 249	46.96 219	68.09 266	65.76 264	40.03 312	57.81 255	70.56 297	38.32 206	74.51 259	38.26 262	61.50 280	77.00 248

F-COLMAP		63.05 214	60.87 226	69.58 192	76.99 165	53.63 120	78.12 113	76.16 187	47.97 253	52.41 293	81.61 156	27.87 295	78.11 223	40.07 255	66.66 243	77.00 248

BH-untuned		68.27 146	67.29 140	71.21 166	79.74 100	53.22 126	76.06 163	77.46 176	57.19 137	66.10 143	81.61 156	45.37 142	83.50 121	45.42 222	76.68 129	76.91 250

AllTest		57.08 263	54.65 270	64.39 249	71.44 259	49.03 199	69.92 251	67.30 256	45.97 270	47.16 309	79.77 202	17.47 325	67.56 281	33.65 283	59.16 297	76.57 251

TestCases				64.39 249	71.44 259	49.03 199		67.30 256	45.97 270	47.16 309	79.77 202	17.47 325	67.56 281	33.65 283	59.16 297	76.57 251

tpm		57.34 261	58.16 247	54.86 298	71.80 257	34.77 305	67.47 268	56.04 318	48.20 250	60.10 224	76.92 252	37.17 218	53.41 333	40.76 253	65.01 254	76.40 253

Anonymous20231211		55.92 269	53.63 278	62.77 259	68.22 290	35.56 302	74.48 193	69.89 232	46.42 264	49.07 305	73.45 283	21.13 323	76.77 239	28.74 318	51.30 318	75.97 254

1121		68.53 139	67.16 146	72.63 134	85.64 28	61.14 29	73.95 197	66.46 262	44.61 281	70.28 73	86.68 62	41.42 180	80.78 181	53.62 160	81.79 67	75.97 254

LS3D		64.71 198	62.50 205	71.34 164	79.72 102	55.71 99	79.82 87	74.72 206	48.50 246	56.62 262	84.62 97	33.59 258	82.34 155	29.65 310	75.23 135	75.97 254

新几何1				70.76 174	85.66 26	61.13 30		66.43 263	44.68 280	70.29 72	86.64 63	41.29 182	75.23 255	49.72 187	81.75 69	75.93 257

CVMVSNet		59.63 243	59.14 234	61.08 274	74.47 197	38.84 274	75.20 179	68.74 247	31.15 331	58.24 247	76.51 260	32.39 274	68.58 279	49.77 185	65.84 248	75.81 258

tpmrst		58.24 255	58.70 243	56.84 290	66.97 295	34.32 308	69.57 253	61.14 297	47.17 260	58.58 244	71.60 290	41.28 183	60.41 307	49.20 191	62.84 271	75.78 259

EPMVS		53.96 277	53.69 277	54.79 299	66.12 303	31.96 324	62.34 292	49.05 334	44.42 284	55.54 268	71.33 293	30.22 282	56.70 320	41.65 249	62.54 274	75.71 260

FMVSNet5		55.86 270	54.93 268	58.66 282	71.05 265	36.35 296	64.18 286	62.48 292	46.76 262	50.66 301	74.73 275	25.80 309	64.04 295	33.11 285	65.57 251	75.59 261

PVSNet	50.76 19	58.40 254	57.39 252	61.42 266	75.53 182	44.04 240	61.43 294	63.45 284	47.04 261	56.91 260	73.61 282	27.00 303	64.76 293	39.12 259	72.40 173	75.47 262

MIMVSNet		57.35 260	57.07 254	58.22 283	74.21 203	37.18 286	62.46 290	60.88 298	48.88 242	55.29 273	75.99 266	31.68 276	62.04 302	31.87 289	72.35 174	75.43 263

MVS		67.37 160	66.33 166	70.51 179	75.46 183	50.94 152	73.95 197	81.85 74	41.57 303	62.54 186	78.57 226	47.98 110	85.47 76	52.97 166	82.05 66	75.14 264

EU-MVSNet		55.61 272	54.41 272	59.19 278	65.41 307	33.42 318	72.44 219	71.91 224	28.81 333	51.27 296	73.87 280	24.76 314	69.08 277	43.04 238	58.20 300	75.06 265

CR-MVSNet		59.91 239	57.90 251	65.96 229	69.96 276	52.07 143	65.31 279	63.15 287	42.48 298	59.36 232	74.84 273	35.83 233	70.75 270	45.50 219	64.65 259	75.06 265

RPMNet		58.70 252	56.29 262	65.96 229	69.96 276	52.07 143	65.31 279	62.15 294	43.20 293	59.36 232	70.15 301	35.37 240	70.75 270	36.42 275	64.65 259	75.06 265

test222					83.14 54	58.68 58	72.57 217	63.45 284	41.78 299	67.56 127	86.12 74	37.13 219			78.73 108	74.98 268

MSDG		61.81 230	59.23 233	69.55 193	72.64 242	52.63 134	70.45 245	75.81 192	51.38 222	53.70 286	76.11 263	29.52 286	81.08 175	37.70 264	65.79 249	74.93 269

WTY-MVS		59.75 241	60.39 228	57.85 286	72.32 248	37.83 283	61.05 299	64.18 280	45.95 272	61.91 199	79.11 218	47.01 125	60.88 305	42.50 242	69.49 220	74.83 270

gg-mvs-nofinetune		57.86 258	56.43 260	62.18 262	72.62 243	35.35 303	66.57 269	56.33 315	50.65 229	57.64 256	57.10 331	30.65 279	76.36 243	37.38 266	78.88 104	74.82 271

testdata				64.66 247	81.52 69	52.93 130		65.29 268	46.09 268	73.88 38	87.46 52	38.08 210	66.26 288	53.31 165	78.48 111	74.78 272

pmmvs4		61.48 232	59.39 232	67.76 208	71.57 258	53.86 117	71.42 232	65.34 267	44.20 285	59.46 231	77.92 232	35.90 232	74.71 258	43.87 231	64.87 255	74.71 273

new-patchmatchnet		47.56 297	47.73 295	47.06 318	58.81 332	9.37 351	48.78 329	59.21 302	43.28 291	44.22 319	68.66 305	25.67 310	57.20 319	31.57 297	49.35 325	74.62 274

Patchmtry		57.16 262	56.47 259	59.23 277	69.17 282	34.58 307	62.98 288	63.15 287	44.53 282	56.83 261	74.84 273	35.83 233	68.71 278	40.03 256	60.91 284	74.39 275

BH-w/o		66.85 172	65.83 171	69.90 187	79.29 103	52.46 138	74.66 190	76.65 184	54.51 188	64.85 162	78.12 228	45.59 137	82.95 135	43.26 236	75.54 134	74.27 276

thresconf0.02		59.40 245	58.81 236	61.17 270	74.15 204	33.83 312	68.32 260	64.22 275	51.79 210	58.04 249	79.57 206	35.41 235	75.41 249	29.57 311	68.26 228	74.25 277

tfpn_n400		59.40 245	58.81 236	61.17 270	74.15 204	33.83 312	68.32 260	64.22 275	51.79 210	58.04 249	79.57 206	35.41 235	75.41 249	29.57 311	68.26 228	74.25 277

tfpnconf		59.40 245	58.81 236	61.17 270	74.15 204	33.83 312	68.32 260	64.22 275	51.79 210	58.04 249	79.57 206	35.41 235	75.41 249	29.57 311	68.26 228	74.25 277

tfpnview11		59.40 245	58.81 236	61.17 270	74.15 204	33.83 312	68.32 260	64.22 275	51.79 210	58.04 249	79.57 206	35.41 235	75.41 249	29.57 311	68.26 228	74.25 277

XXY-MVS		60.68 235	61.67 218	57.70 288	70.43 270	38.45 279	64.19 285	66.47 261	48.05 252	63.22 175	80.86 177	49.28 84	60.47 306	45.25 224	67.28 242	74.19 281

UnsupCasMVSNet_eth		53.16 284	52.47 282	55.23 295	59.45 331	33.39 319	59.43 303	69.13 243	45.98 269	50.35 303	72.32 287	29.30 289	58.26 314	42.02 246	44.30 331	74.05 282

COLMAP_ROB	52.97 17	61.27 234	58.81 236	68.64 201	74.63 194	52.51 137	78.42 108	73.30 216	49.92 236	50.96 298	81.51 159	23.06 318	79.40 196	31.63 295	65.85 247	74.01 283
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
pmmvs-eth3d		58.81 251	56.31 261	66.30 222	67.61 292	52.42 140	72.30 221	64.76 271	43.55 290	54.94 275	74.19 279	28.95 290	72.60 263	43.31 234	57.21 301	73.88 284

test20.03		53.87 279	54.02 276	53.41 304	61.47 320	28.11 332	61.30 296	59.21 302	51.34 223	52.09 294	77.43 248	33.29 261	58.55 313	29.76 309	60.27 294	73.58 285

tfpn1000		59.24 250	58.70 243	60.86 275	73.75 211	33.99 310	68.86 258	63.98 281	51.25 226	57.29 259	79.51 213	34.58 245	75.26 254	29.08 317	69.99 209	73.32 286

EG-PatchMatch MVS		64.71 198	62.87 200	70.22 181	77.68 142	53.48 122	77.99 121	78.82 148	53.37 196	56.03 266	77.41 249	24.75 315	84.04 109	46.37 208	73.42 154	73.14 287

Anonymous20231206		55.10 275	55.30 267	54.48 300	69.81 279	33.94 311	62.91 289	62.13 295	41.08 304	55.18 274	75.65 268	32.75 268	56.59 322	30.32 303	67.86 236	72.91 288

pmmvs5		56.47 264	55.68 265	58.86 280	61.41 321	36.71 294	66.37 271	62.75 290	40.38 310	53.70 286	76.62 257	34.56 246	67.05 283	40.02 257	65.27 252	72.83 289

USDC		56.35 266	54.24 274	62.69 260	64.74 309	40.31 263	65.05 281	73.83 213	43.93 288	47.58 307	77.71 238	15.36 330	75.05 257	38.19 263	61.81 277	72.70 290

OpenMVS_ROB	52.78 18	60.03 237	58.14 248	65.69 235	70.47 269	44.82 230	75.33 174	70.86 227	45.04 276	56.06 265	76.00 264	26.89 304	79.65 193	35.36 278	67.29 241	72.60 291

MDA-MVSNet-bldmvs		53.87 279	50.81 286	63.05 257	66.25 301	48.58 204	56.93 310	63.82 282	48.09 251	41.22 327	70.48 298	30.34 281	68.00 280	34.24 281	45.92 329	72.57 292

ANet_high		41.38 309	37.47 313	53.11 305	39.73 348	24.45 342	56.94 309	69.69 234	47.65 255	26.04 339	52.32 334	12.44 335	62.38 301	21.80 333	10.61 348	72.49 293

DP-MVS		65.68 186	63.66 193	71.75 155	84.93 42	56.87 83	80.74 77	73.16 218	53.06 198	59.09 238	82.35 133	36.79 229	85.94 67	32.82 286	69.96 211	72.45 294

MVP-Stereo		65.41 191	63.80 190	70.22 181	77.62 148	55.53 103	76.30 156	78.53 156	50.59 231	56.47 264	78.65 223	39.84 191	82.68 149	44.10 229	72.12 178	72.44 295
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
test-LLR		58.15 257	58.13 249	58.22 283	68.57 285	44.80 231	65.46 276	57.92 307	50.08 234	55.44 270	69.82 302	32.62 270	57.44 316	49.66 188	73.62 149	72.41 296

test-mter		56.42 265	55.82 264	58.22 283	68.57 285	44.80 231	65.46 276	57.92 307	39.94 313	55.44 270	69.82 302	21.92 321	57.44 316	49.66 188	73.62 149	72.41 296

testgi		51.90 286	52.37 283	50.51 313	60.39 327	23.55 343	58.42 305	58.15 305	49.03 241	51.83 295	79.21 217	22.39 319	55.59 328	29.24 316	62.64 272	72.40 298

sss		56.17 268	56.57 258	54.96 297	66.93 296	36.32 298	57.94 307	61.69 296	41.67 301	58.64 243	75.32 271	38.72 202	56.25 325	42.04 245	66.19 246	72.31 299

tfpn_ndepth		59.57 244	59.02 235	61.23 269	73.81 210	35.60 301	69.40 255	65.59 266	50.96 228	57.96 254	77.72 237	34.81 243	75.91 247	30.36 302	70.57 192	72.18 300

GG-mvs-BLEND				62.34 261	71.36 263	37.04 288	69.20 256	57.33 310		54.73 277	65.48 317	30.37 280	77.82 226	34.82 279	74.93 136	72.17 301

test0.0.03 1		53.32 282	53.59 279	52.50 308	62.81 316	29.45 329	59.51 302	54.11 326	50.08 234	54.40 281	74.31 278	32.62 270	55.92 327	30.50 301	63.95 263	72.15 302

tpmvs		58.47 253	56.95 256	63.03 258	70.20 273	41.21 259	67.90 267	67.23 258	49.62 237	54.73 277	70.84 295	34.14 251	76.24 245	36.64 272	61.29 283	71.64 303

TDRefinement		53.44 281	50.72 287	61.60 265	64.31 312	46.96 219	70.89 240	65.27 269	41.78 299	44.61 318	77.98 229	11.52 339	66.36 287	28.57 320	51.59 316	71.49 304

YYNet1		50.73 290	48.96 289	56.03 293	61.10 323	41.78 255	51.94 323	56.44 314	40.94 306	44.84 316	67.80 308	30.08 284	55.08 331	36.77 269	50.71 319	71.22 305

CMPMVS	42.80 21	57.81 259	55.97 263	63.32 254	60.98 324	47.38 217	64.66 283	69.50 238	32.06 330	46.83 311	77.80 235	29.50 287	71.36 267	48.68 194	73.75 147	71.21 306
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
test_0402		63.25 211	61.01 225	69.96 185	80.00 96	54.37 114	76.86 148	72.02 223	54.58 186	58.71 241	80.79 180	35.00 242	84.36 103	26.41 325	64.71 256	71.15 307

MDA-MVSNet_test_wron		50.71 291	48.95 290	56.00 294	61.17 322	41.84 254	51.90 324	56.45 313	40.96 305	44.79 317	67.84 307	30.04 285	55.07 332	36.71 271	50.69 320	71.11 308

PatchT		53.17 283	53.44 280	52.33 309	68.29 289	25.34 340	58.21 306	54.41 324	44.46 283	54.56 279	69.05 304	33.32 260	60.94 304	36.93 268	61.76 278	70.73 309

TESTMET0.1,1		55.28 273	54.90 269	56.42 291	66.56 300	43.67 243	65.46 276	56.27 316	39.18 315	53.83 285	67.44 309	24.21 316	55.46 330	48.04 198	73.11 161	70.13 310

LCM-MVSNet		40.30 311	35.88 316	53.57 303	42.24 345	29.15 330	45.21 335	60.53 299	22.23 341	28.02 338	50.98 337	3.72 352	61.78 303	31.22 300	38.76 335	69.78 311

ADS-MVSNet2		51.33 289	48.76 292	59.07 279	66.02 305	44.60 234	50.90 325	59.76 301	36.90 320	50.74 299	66.18 315	26.38 305	63.11 297	27.17 321	54.76 308	69.50 312

ADS-MVSNet		48.48 295	47.77 294	50.63 312	66.02 305	29.92 328	50.90 325	50.87 333	36.90 320	50.74 299	66.18 315	26.38 305	52.47 335	27.17 321	54.76 308	69.50 312

TinyColmap		54.14 276	51.72 284	61.40 267	66.84 297	41.97 253	66.52 270	68.51 249	44.81 278	42.69 326	75.77 267	11.66 338	72.94 262	31.96 288	56.77 302	69.27 314

dp		51.89 287	51.60 285	52.77 307	68.44 288	32.45 321	62.36 291	54.57 323	44.16 286	49.31 304	67.91 306	28.87 292	56.61 321	33.89 282	54.89 307	69.24 315

JIA-IIPM		51.56 288	47.68 296	63.21 255	64.61 310	50.73 160	47.71 330	58.77 304	42.90 295	48.46 306	51.72 335	24.97 313	70.24 273	36.06 277	53.89 311	68.64 316

UnsupCasMVSNet_bld		50.07 292	48.87 291	53.66 302	60.97 325	33.67 317	57.62 308	64.56 273	39.47 314	47.38 308	64.02 320	27.47 298	59.32 310	34.69 280	43.68 332	67.98 317

MS-PatchMatch		62.42 222	61.46 220	65.31 243	75.21 187	52.10 142	72.05 228	74.05 212	46.41 265	57.42 258	74.36 277	34.35 250	77.57 230	45.62 217	73.67 148	66.26 318

N_pmnet		39.35 312	40.28 309	36.54 329	63.76 313	1.62 355	49.37 328	0.76 356	34.62 326	43.61 323	66.38 314	26.25 307	42.57 345	26.02 327	51.77 315	65.44 319

PM-MVS		52.33 285	50.19 288	58.75 281	62.10 318	45.14 229	65.75 273	40.38 344	43.60 289	53.52 289	72.65 285	9.16 344	65.87 291	50.41 180	54.18 310	65.24 320

LP		48.51 294	45.51 299	57.52 289	62.86 315	44.53 237	52.38 322	59.84 300	38.11 317	42.81 325	61.02 325	23.23 317	63.02 298	24.10 328	45.24 330	65.02 321

1111		44.40 304	45.00 301	42.61 325	57.55 334	17.33 348	53.82 319	57.05 311	40.78 307	44.11 320	66.57 312	13.37 333	45.77 340	22.15 330	49.58 323	64.73 322

testus		44.59 303	43.87 305	46.76 319	59.85 330	24.65 341	53.86 317	55.82 319	36.26 323	43.97 322	63.42 321	8.39 345	53.14 334	20.70 336	52.52 314	62.51 323

testmv		42.25 307	40.11 310	48.66 315	53.23 336	27.02 335	56.62 311	55.74 320	37.25 319	33.10 335	59.52 329	7.78 346	56.58 323	19.61 338	38.13 336	62.40 324

test2356		45.61 300	44.66 302	48.47 317	60.15 328	28.08 333	52.44 321	52.83 330	38.01 318	46.13 314	60.98 326	15.08 331	55.54 329	20.43 337	55.85 305	61.78 325

test1235678		45.66 299	44.46 304	49.26 314	59.88 329	28.68 331	56.36 312	55.54 321	39.12 316	40.89 329	63.40 322	14.41 332	57.32 318	21.05 334	49.47 324	61.78 325

PatchMatch-RL		56.25 267	54.55 271	61.32 268	77.06 162	56.07 93	65.57 275	54.10 327	44.13 287	53.49 291	71.27 294	25.20 312	66.78 285	36.52 274	63.66 264	61.12 327

no-one		40.85 310	36.09 314	55.14 296	48.55 341	38.72 275	42.15 339	62.92 289	34.60 327	23.55 340	49.74 339	12.21 336	66.16 289	26.27 326	24.84 339	60.54 328

pmmvs3		44.92 302	41.95 307	53.86 301	52.58 338	43.55 244	62.11 293	46.90 341	26.05 337	40.63 330	60.19 328	11.08 341	57.91 315	31.83 294	46.15 328	60.11 329

LF4IMVS		42.95 306	42.26 306	45.04 321	48.30 342	32.50 320	54.80 314	48.49 336	28.03 334	40.51 331	70.16 300	9.24 343	43.89 343	31.63 295	49.18 326	58.72 330

DSMNet-mixed		39.30 313	38.72 312	41.03 326	51.22 339	19.66 345	45.53 334	31.35 349	15.83 345	39.80 332	67.42 310	22.19 320	45.13 342	22.43 329	52.69 313	58.31 331

CHOSEN 280x420		47.83 296	46.36 297	52.24 310	67.37 294	49.78 190	38.91 341	43.11 343	35.00 325	43.27 324	63.30 323	28.95 290	49.19 338	36.53 273	60.80 287	57.76 332

PMMVS		53.96 277	53.26 281	56.04 292	62.60 317	50.92 154	61.17 298	56.09 317	32.81 328	53.51 290	66.84 311	34.04 252	59.93 309	44.14 228	68.18 233	57.27 333

PVSNet_0	43.31 20	47.46 298	45.64 298	52.92 306	67.60 293	44.65 233	54.06 316	54.64 322	41.59 302	46.15 313	58.75 330	30.99 277	58.66 312	32.18 287	24.81 340	55.46 334

test12356		36.16 315	35.94 315	36.83 328	50.82 340	8.52 352	44.84 336	53.49 329	32.72 329	30.11 337	55.08 332	7.11 348	49.47 337	16.60 340	32.68 338	52.50 335

PMMVS2		27.40 322	25.91 322	31.87 333	39.46 349	6.57 353	31.17 344	28.52 350	23.96 338	20.45 342	48.94 340	4.20 351	37.94 348	16.51 341	19.97 341	51.09 336

MVS-HIRNet		45.52 301	44.48 303	48.65 316	68.49 287	34.05 309	59.41 304	44.50 342	27.03 335	37.96 333	50.47 338	26.16 308	64.10 294	26.74 324	59.52 295	47.82 337

new_pmnet		34.13 318	34.29 317	33.64 330	52.63 337	18.23 347	44.43 337	33.90 347	22.81 340	30.89 336	53.18 333	10.48 342	35.72 349	20.77 335	39.51 333	46.98 338

wuykxyi23d		28.12 320	22.54 325	44.87 323	34.97 351	32.11 322	37.96 342	47.31 339	13.32 346	9.29 350	23.72 346	0.45 357	56.58 323	21.85 332	13.98 344	45.93 339

FPMVS		42.18 308	41.11 308	45.39 320	58.03 333	41.01 261	49.50 327	53.81 328	30.07 332	33.71 334	64.03 318	11.69 337	52.08 336	14.01 343	55.11 306	43.09 340

MVE	17.77 23	21.41 325	17.77 327	32.34 332	34.34 352	25.44 339	16.11 347	24.11 351	11.19 347	13.22 346	31.92 342	1.58 355	30.95 350	10.47 345	17.03 342	40.62 341
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
PMVS	28.69 22	36.22 314	33.29 318	45.02 322	36.82 350	35.98 300	54.68 315	48.74 335	26.31 336	21.02 341	51.61 336	2.88 354	60.10 308	9.99 347	47.58 327	38.99 342
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
testpf		44.11 305	45.40 300	40.26 327	60.52 326	27.34 334	33.26 343	54.33 325	45.87 273	41.08 328	60.26 327	16.46 327	59.14 311	46.09 210	50.68 321	34.31 343

PNet_i23d		27.88 321	25.99 321	33.55 331	47.54 343	25.89 337	47.24 332	32.91 348	21.44 342	15.90 344	38.09 341	0.85 356	42.76 344	16.90 339	13.03 346	32.00 344

Gipuma		34.77 317	31.91 319	43.33 324	62.05 319	37.87 282	20.39 346	67.03 259	23.23 339	18.41 343	25.84 344	4.24 350	62.73 299	14.71 342	51.32 317	29.38 345
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
E-PMN		23.77 323	22.73 324	26.90 335	42.02 346	20.67 344	42.66 338	35.70 345	17.43 343	10.28 348	25.05 345	6.42 349	42.39 346	10.28 346	14.71 343	17.63 346

EMVS		22.97 324	21.84 326	26.36 336	40.20 347	19.53 346	41.95 340	34.64 346	17.09 344	9.73 349	22.83 347	7.29 347	42.22 347	9.18 348	13.66 345	17.32 347

DeepMVS_CX				12.03 339	17.97 353	10.91 350		10.60 354	7.46 348	11.07 347	28.36 343	3.28 353	11.29 352	8.01 349	9.74 350	13.89 348

tmp_tt		9.43 328	11.14 329	4.30 340	2.38 354	4.40 354	13.62 348	16.08 353	0.39 350	15.89 345	13.06 348	15.80 329	5.54 353	12.63 344	10.46 349	2.95 349

wuyk23d		13.32 327	12.52 328	15.71 338	47.54 343	26.27 336	31.06 345	1.98 355	4.93 349	5.18 351	1.94 352	0.45 357	18.54 351	6.81 350	12.83 347	2.33 350

test123		4.73 330	6.30 331	0.02 341	0.01 355	0.01 356	56.36 312	0.00 357	0.01 351	0.04 352	0.21 354	0.01 359	0.00 354	0.03 352	0.00 351	0.04 351

.test1245		34.88 316	39.49 311	21.04 337	57.55 334	17.33 348	53.82 319	57.05 311	40.78 307	44.11 320	66.57 312	13.37 333	45.77 340	22.15 330	0.00 351	0.03 352

testmvs		4.52 331	6.03 332	0.01 342	0.01 355	0.00 357	53.86 317	0.00 357	0.01 351	0.04 352	0.27 353	0.00 360	0.00 354	0.04 351	0.00 351	0.03 352

cdsmvs_eth3d_5k		17.50 326	23.34 323	0.00 343	0.00 357	0.00 357	0.00 349	78.63 153	0.00 353	0.00 354	82.18 137	49.25 88	0.00 354	0.00 353	0.00 351	0.00 354

pcd_1.5k_mvsjas		3.92 332	5.23 333	0.00 343	0.00 357	0.00 357	0.00 349	0.00 357	0.00 353	0.00 354	0.00 355	47.05 122	0.00 354	0.00 353	0.00 351	0.00 354

sosnet-low-res		0.00 333	0.00 334	0.00 343	0.00 357	0.00 357	0.00 349	0.00 357	0.00 353	0.00 354	0.00 355	0.00 360	0.00 354	0.00 353	0.00 351	0.00 354

sosnet		0.00 333	0.00 334	0.00 343	0.00 357	0.00 357	0.00 349	0.00 357	0.00 353	0.00 354	0.00 355	0.00 360	0.00 354	0.00 353	0.00 351	0.00 354

uncertanet		0.00 333	0.00 334	0.00 343	0.00 357	0.00 357	0.00 349	0.00 357	0.00 353	0.00 354	0.00 355	0.00 360	0.00 354	0.00 353	0.00 351	0.00 354

Regformer		0.00 333	0.00 334	0.00 343	0.00 357	0.00 357	0.00 349	0.00 357	0.00 353	0.00 354	0.00 355	0.00 360	0.00 354	0.00 353	0.00 351	0.00 354

ab-mvs-re		6.49 329	8.65 330	0.00 343	0.00 357	0.00 357	0.00 349	0.00 357	0.00 353	0.00 354	77.89 233	0.00 360	0.00 354	0.00 353	0.00 351	0.00 354

uanet		0.00 333	0.00 334	0.00 343	0.00 357	0.00 357	0.00 349	0.00 357	0.00 353	0.00 354	0.00 355	0.00 360	0.00 354	0.00 353	0.00 351	0.00 354

test_part3							86.37 5		63.49 35		91.40 3		90.90 1	75.98 13

test_part2					87.58 3	60.47 37				83.42 2

sam_mvs												33.43 259

MTGPA								80.97 98

test_post1							68.67 259				3.64 350	32.39 274	69.49 275	44.17 227

test_post											3.55 351	33.90 254	66.52 286

patchmatchnet-post											64.03 318	34.50 247	74.27 260

MTMP								17.08 352

gm-plane-assit					71.40 262	41.72 257			48.85 243		73.31 284		82.48 154	48.90 193

TEST9					85.58 29	61.59 25	81.62 65	81.26 89	55.65 171	74.93 24	88.81 41	53.70 42	84.68 97

test_8					85.40 32	60.96 32	81.54 68	81.18 92	55.86 165	74.81 27	88.80 43	53.70 42	84.45 102

agg_prior					85.04 36	59.96 42		81.04 95		74.68 28			84.04 109

test_prior4						62.51 17	82.08 59

test_prior2							81.75 61		60.37 80	75.01 22	89.06 35	56.22 19		72.19 34	88.96 10

旧先验2							76.08 162		45.32 275	76.55 15			65.56 292	58.75 135

新几何2							76.12 160

原ACMM2							79.02 97

testdata2													72.18 266	46.95 205

segment_acmp												54.23 35

testdata1							72.65 213		60.50 77

plane_prior7					81.41 72	55.96 95

plane_prior6					81.20 79	56.24 90						45.26 144

plane_prior4											86.10 75

plane_prior3						56.09 92			63.92 30	69.27 97

plane_prior2							84.22 24		64.52 24

plane_prior1					81.27 77

plane_prior						56.31 86	83.58 33		63.19 39						80.48 80

n2								0.00 357

nn								0.00 357

door-mid								47.19 340

test11								83.47 45

door								47.60 338

HQP5-MVS						54.94 108

HQP-NCC					80.66 84		82.31 54		62.10 56	67.85 119

ACMP_Plane					80.66 84		82.31 54		62.10 56	67.85 119

BP-MVS														67.04 60

HQP3-MVS								83.90 34							80.35 83

HQP2-MVS												45.46 138

NP-MVS					80.98 82	56.05 94					85.54 87

MDTV_nov1_ep13			57.00 255		72.73 241	38.26 280	65.02 282	64.73 272	44.74 279	55.46 269	72.48 286	32.61 272	70.47 272	37.47 265	67.75 238

ACMMP++_ref															74.07 145

ACMMP++															72.16 177

Test By Simon												48.33 107

High-resolution multi-view benchmark