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.

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

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

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

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

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

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

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

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
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

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

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

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

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
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

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

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

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

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_prior2							81.75 61		60.37 80	75.01 22	89.06 35	56.22 19		72.19 34	88.96 10

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

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

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

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
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

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

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

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

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

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

mvs_anonymous		68.03 155	67.51 133	69.59 190	72.08 252	44.57 235	71.99 229	75.23 198	51.67 216	67.06 132	82.57 129	54.68 31	77.94 225	56.56 142	75.71 133	86.26 53

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

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

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.
segment_acmp												54.23 35

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

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	79.55 212	53.97 37	84.05 108	53.34 164	77.53 117	85.65 72

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

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

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

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
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

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

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

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

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

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

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	79.35 216	52.75 49	84.89 91	66.46 63	74.23 142	85.83 62

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

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	50.30 233	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
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
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
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

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	78.95 221	52.19 55	84.66 99	65.47 71	73.57 151	85.32 90

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

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

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

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

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	76.51 261	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.
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	55.87 305	80.99 203

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

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

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

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

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

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	64.01 263	84.79 108

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
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

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

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

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	78.30 231

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

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

v8		70.33 103	69.28 106	73.49 106	73.15 227	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

v17		68.37 142	67.00 148	72.48 140	73.22 223	50.31 172	78.10 117	79.58 131	59.71 100	61.67 204	77.60 243	49.31 78	82.89 137	62.37 107	61.48 283	84.23 125

v18		68.33 143	66.96 149	72.42 146	73.13 231	50.16 181	77.97 122	79.57 133	59.57 109	61.80 201	77.50 248	49.30 79	82.90 136	62.31 109	61.50 281	84.20 131

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

v1neww		70.66 89	69.70 92	73.53 103	73.15 227	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	73.15 227	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	73.14 230	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

v16		68.38 141	67.01 147	72.47 144	73.22 223	50.29 173	78.10 117	79.59 129	59.71 100	61.72 203	77.60 243	49.28 84	82.89 137	62.36 108	61.54 280	84.23 125

XXY-MVS		60.68 235	61.67 218	57.70 289	70.43 271	38.45 279	64.19 286	66.47 261	48.05 253	63.22 175	80.86 177	49.28 84	60.47 307	45.25 224	67.28 243	74.19 282

divwei89l23v2f112		70.50 96	69.53 97	73.41 112	72.91 238	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	72.91 238	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

cdsmvs_eth3d_5k		17.50 327	23.34 324	0.00 344	0.00 358	0.00 358	0.00 350	78.63 153	0.00 354	0.00 355	82.18 137	49.25 88	0.00 355	0.00 354	0.00 352	0.00 355

v15		68.22 151	66.81 155	72.47 144	73.25 222	50.40 170	77.92 124	79.60 126	59.77 99	61.28 212	77.52 247	49.25 88	82.77 143	62.16 114	60.51 290	84.24 124

diffmvs		67.72 158	66.73 156	70.70 177	69.74 281	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

v1141		70.50 96	69.53 97	73.41 112	72.92 237	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

V14		68.25 148	66.82 154	72.52 139	73.33 221	50.53 167	78.02 120	79.60 126	59.83 96	61.16 214	77.57 245	49.19 92	82.77 143	62.18 110	60.50 291	84.26 123

V9		68.27 146	66.84 151	72.56 137	73.39 220	50.63 162	78.10 117	79.60 126	59.94 91	61.05 216	77.62 242	49.18 93	82.77 143	62.17 112	60.48 292	84.27 122

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

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	79.77 220

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	77.77 237	49.13 95	82.87 139	55.82 146	79.58 93	80.14 214

v12		68.28 145	66.83 153	72.60 136	73.43 217	50.74 159	78.18 112	79.59 129	60.01 90	60.89 218	77.66 241	49.12 97	82.77 143	62.18 110	60.46 293	84.29 121

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

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

v13		68.29 144	66.84 151	72.63 134	73.50 215	50.83 157	78.25 110	79.58 131	60.05 88	60.76 219	77.68 240	49.11 100	82.77 143	62.17 112	60.45 294	84.30 120

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

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

原ACMM1				74.69 74	85.39 33	59.40 47		83.42 47	51.47 222	70.27 74	86.61 65	48.61 104	86.51 54	53.85 159	87.96 24	78.16 233

v148		68.24 150	67.19 145	71.40 163	70.43 271	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	66.52 246	84.53 114

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

Test By Simon												48.33 107

v11		68.15 154	66.73 156	72.42 146	73.43 217	50.28 174	77.94 123	79.65 123	59.88 95	61.11 215	77.55 246	48.25 108	82.75 148	61.88 119	60.85 287	84.23 125

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

MVS		67.37 160	66.33 166	70.51 179	75.46 183	50.94 152	73.95 197	81.85 74	41.57 304	62.54 186	78.57 227	47.98 110	85.47 76	52.97 166	82.05 66	75.14 265

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	6.49 350	47.95 111	88.01 24	71.55 38	86.74 37	86.37 46

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	78.70 222	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
PAPM		67.92 156	66.69 158	71.63 158	78.09 132	49.02 201	77.09 143	81.24 91	51.04 228	60.91 217	83.98 110	47.71 114	84.99 82	40.81 252	79.32 99	80.90 204

v10		70.21 105	69.02 108	73.81 89	73.51 214	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

v7		70.57 93	69.48 102	73.85 87	73.50 215	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

v1144		70.42 101	69.31 105	73.76 92	73.22 223	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

v2v482		70.50 96	69.45 104	73.66 98	72.62 244	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

pm-mvs1		65.24 193	64.97 183	66.04 228	72.38 247	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	66.54 245	82.24 174

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	49.29 239	61.66 205	80.92 175	47.43 120	76.68 240	44.91 225	71.69 181	81.94 178

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

pcd_1.5k_mvsjas		3.92 333	5.23 334	0.00 344	0.00 358	0.00 358	0.00 350	0.00 358	0.00 354	0.00 355	0.00 356	47.05 122	0.00 355	0.00 354	0.00 352	0.00 355

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

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	78.66 223	47.05 122	82.19 156	61.61 121	79.58 93	80.83 205

WTY-MVS		59.75 242	60.39 228	57.85 287	72.32 249	37.83 283	61.05 300	64.18 281	45.95 273	61.91 199	79.11 219	47.01 125	60.88 306	42.50 242	69.49 220	74.83 271

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	80.81 206

v144192		69.71 112	68.51 114	73.33 116	73.10 232	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

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	63.08 271	85.51 76

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

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	63.43 268	85.45 80

V42		68.65 133	67.35 139	72.56 137	68.93 284	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	67.82 238	84.53 114

1112_ss		64.00 204	63.36 196	65.93 231	79.28 104	42.58 251	71.35 233	72.36 222	46.41 266	60.55 221	77.89 234	46.27 132	73.28 262	46.18 209	69.97 210	81.92 179

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

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	63.26 269	85.42 83

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

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	60.88 286	85.00 100

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	78.12 229	45.59 137	82.95 135	43.26 236	75.54 134	74.27 277

HQP2-MVS												45.46 138

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

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
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

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	76.91 251

v1192		69.97 110	68.68 112	73.85 87	73.19 226	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

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_prior6					81.20 79	56.24 90						45.26 144

v1921920		69.47 119	68.17 121	73.36 115	73.06 233	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

test_normal		69.26 123	67.90 125	73.32 117	70.84 268	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

DI_MVS_plusplus_test		69.35 121	68.03 123	73.30 118	71.11 265	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

Vis-MVSNet (Re-imp)		63.69 205	63.88 189	63.14 257	74.75 192	31.04 327	71.16 237	63.64 284	56.32 158	59.80 229	84.99 91	44.51 149	75.46 248	39.12 259	80.62 75	82.92 163

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

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

v748		67.26 162	65.67 172	72.02 151	69.90 279	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	65.26 254	84.12 133

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
旧先验1					83.04 55	53.15 127		67.52 255			87.85 50	44.08 154			80.76 74	78.03 237

Test_1112_low_res		62.32 223	61.77 217	64.00 251	79.08 109	39.53 269	68.17 266	70.17 230	43.25 293	59.03 239	79.90 198	44.08 154	71.24 269	43.79 232	68.42 227	81.25 194

MVSFormer		71.50 81	70.38 85	74.88 71	78.76 115	57.15 80	82.79 43	78.48 158	51.26 225	69.49 92	83.22 119	43.99 156	83.24 127	66.06 64	79.37 96	84.23 125

lupinMVS		69.57 116	68.28 119	73.44 109	78.76 115	57.15 80	76.57 151	73.29 217	46.19 268	69.49 92	82.18 137	43.99 156	79.23 199	64.66 76	79.37 96	83.93 134

v7n		69.01 127	67.36 138	73.98 85	72.51 246	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

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
jason		69.65 114	68.39 118	73.43 110	78.27 127	56.88 82	77.12 142	73.71 215	46.53 264	69.34 96	83.22 119	43.37 160	79.18 200	64.77 75	79.20 101	84.23 125
jason: jason.
v1240		69.24 124	67.91 124	73.25 121	73.02 235	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

LCM-MVSNet-Re		61.88 229	61.35 221	63.46 252	74.58 195	31.48 326	61.42 296	58.14 307	58.71 122	53.02 293	79.55 212	43.07 162	76.80 238	45.69 215	77.96 114	82.11 177

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

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

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

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

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

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	79.30 225

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

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

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

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

CostFormer		64.04 203	62.51 204	68.61 202	71.88 256	45.77 226	71.30 234	70.60 229	47.55 257	64.31 169	76.61 259	41.63 174	79.62 194	49.74 186	69.00 222	80.42 209

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	77.02 248

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	77.09 251	41.56 176	84.02 112	60.60 125	71.09 187	81.53 183

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

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

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

1121		68.53 139	67.16 146	72.63 134	85.64 28	61.14 29	73.95 197	66.46 262	44.61 282	70.28 73	86.68 62	41.42 180	80.78 181	53.62 160	81.79 67	75.97 255

CHOSEN 1792x2688		65.08 196	62.84 201	71.82 154	81.49 71	56.26 89	66.32 273	74.20 211	40.53 310	63.16 177	78.65 224	41.30 181	77.80 227	45.80 214	74.09 143	81.40 186

新几何1				70.76 174	85.66 26	61.13 30		66.43 263	44.68 281	70.29 72	86.64 63	41.29 182	75.23 256	49.72 187	81.75 69	75.93 258

tpmrst		58.24 256	58.70 244	56.84 291	66.97 296	34.32 308	69.57 253	61.14 298	47.17 261	58.58 244	71.60 291	41.28 183	60.41 308	49.20 191	62.84 272	75.78 260

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	68.09 235	78.97 229

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

V4		67.09 166	65.16 180	72.87 127	66.76 300	51.60 148	73.69 203	79.45 138	57.88 132	62.45 190	78.58 226	40.96 186	83.34 123	61.99 117	64.71 257	83.68 145

v52		67.09 166	65.16 180	72.87 127	66.77 299	51.60 148	73.69 203	79.45 138	57.88 132	62.46 189	78.57 227	40.95 187	83.34 123	61.99 117	64.70 259	83.68 145

114514_t		70.83 86	69.56 96	74.64 77	86.21 18	54.63 112	82.34 53	81.81 75	48.22 250	63.01 178	85.83 81	40.92 188	87.10 39	57.91 137	79.79 91	82.18 175

HyFIR lowres test		65.67 187	63.01 199	73.67 97	79.97 97	55.65 101	69.07 257	75.52 194	42.68 298	63.53 173	77.95 231	40.43 189	81.64 164	46.01 212	71.91 179	83.73 143

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

MVP-Stereo		65.41 191	63.80 190	70.22 181	77.62 148	55.53 103	76.30 156	78.53 156	50.59 232	56.47 265	78.65 224	39.84 191	82.68 149	44.10 229	72.12 178	72.44 296
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
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	80.20 212

pmmvs6		63.69 205	62.82 202	66.27 224	70.63 269	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	64.35 262	82.53 169

pcd1.5k->3k		30.06 320	30.56 321	28.55 335	78.81 114	0.00 358	0.00 350	82.07 70	0.00 354	0.00 355	0.00 356	39.61 194	0.00 355	0.00 354	74.56 138	85.66 71

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

Fast-Effi-MVS+-dtu		67.37 160	65.33 177	73.48 107	72.94 236	57.78 67	77.47 135	76.88 182	57.60 134	61.97 198	76.85 255	39.31 196	80.49 185	54.72 153	70.28 205	82.17 176

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
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

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	50.64 231	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
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	79.97 215

sss		56.17 269	56.57 259	54.96 298	66.93 297	36.32 298	57.94 308	61.69 297	41.67 302	58.64 243	75.32 272	38.72 202	56.25 326	42.04 245	66.19 247	72.31 300

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	80.30 211
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
MVSTER		67.16 165	65.58 174	71.88 153	70.37 273	49.70 192	70.25 248	78.45 160	51.52 220	69.16 101	80.37 188	38.45 204	82.50 152	60.19 128	71.46 183	83.44 151

test_djsdf		69.45 120	67.74 126	74.58 79	74.57 196	54.92 110	82.79 43	78.48 158	51.26 225	65.41 151	83.49 117	38.37 205	83.24 127	66.06 64	69.25 221	85.56 73

tpm2		62.07 226	60.10 230	67.99 206	72.79 241	43.86 241	71.05 238	66.85 260	43.14 295	62.77 179	75.39 271	38.32 206	80.80 180	41.69 247	68.88 223	79.32 224

tpm cat1		59.25 250	56.95 257	66.15 226	72.19 250	46.96 219	68.09 267	65.76 264	40.03 313	57.81 256	70.56 298	38.32 206	74.51 260	38.26 262	61.50 281	77.00 249

CNLPA		65.43 190	64.02 187	69.68 188	78.73 117	58.07 64	77.82 126	70.71 228	51.49 221	61.57 207	83.58 115	38.23 208	70.82 270	43.90 230	70.10 208	80.16 213

1314		64.61 200	63.21 197	68.80 199	71.87 257	47.46 216	73.95 197	78.39 164	42.88 297	59.97 225	76.60 260	38.11 209	79.39 197	54.84 152	72.32 175	79.55 221

testdata				64.66 247	81.52 69	52.93 130		65.29 268	46.09 269	73.88 38	87.46 52	38.08 210	66.26 289	53.31 165	78.48 111	74.78 273

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

tpmp4_e23		62.71 220	60.13 229	70.45 180	73.40 219	48.39 206	72.82 212	69.49 239	44.88 278	59.91 226	74.99 273	37.79 212	81.47 169	40.22 254	67.71 240	81.48 184

EPNet_dtu		61.90 227	61.97 212	61.68 265	72.89 240	39.78 266	75.85 168	65.62 265	55.09 179	54.56 280	79.36 215	37.59 213	67.02 285	39.80 258	76.95 123	78.25 232
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
Patchmatch-test1		59.75 242	58.00 251	64.98 246	74.14 210	48.06 209	63.35 288	63.23 287	49.13 241	59.33 235	71.46 292	37.45 214	69.59 275	41.39 251	62.57 274	77.30 242

IterMVS		62.79 215	61.27 222	67.35 212	69.37 282	52.04 145	71.17 236	68.24 250	52.63 203	59.82 228	76.91 254	37.32 215	72.36 265	52.80 167	63.19 270	77.66 238
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
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	79.83 217

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

tpm		57.34 262	58.16 248	54.86 299	71.80 258	34.77 305	67.47 269	56.04 319	48.20 251	60.10 224	76.92 253	37.17 218	53.41 334	40.76 253	65.01 255	76.40 254

test222					83.14 54	58.68 58	72.57 217	63.45 285	41.78 300	67.56 127	86.12 74	37.13 219			78.73 108	74.98 269

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	76.99 252	37.07 220	78.96 213	31.28 299	73.28 156	77.06 247

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

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	79.83 217

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

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

PatchFormer-LS_test		62.20 224	60.59 227	67.04 214	72.18 251	46.82 221	70.36 247	68.62 248	51.92 208	59.19 236	70.23 300	36.86 228	75.07 257	50.23 183	65.68 251	79.23 226

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	72.45 295

semantic-postprocess				65.40 241	71.99 254	50.80 158		69.63 236	45.71 275	60.61 220	77.93 232	36.56 230	65.99 291	55.67 149	63.50 267	79.42 223

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

pmmvs4		61.48 232	59.39 232	67.76 208	71.57 259	53.86 117	71.42 232	65.34 267	44.20 286	59.46 231	77.92 233	35.90 232	74.71 259	43.87 231	64.87 256	74.71 274

CR-MVSNet		59.91 240	57.90 252	65.96 229	69.96 277	52.07 143	65.31 280	63.15 288	42.48 299	59.36 232	74.84 274	35.83 233	70.75 271	45.50 219	64.65 260	75.06 266

Patchmtry		57.16 263	56.47 260	59.23 278	69.17 283	34.58 307	62.98 289	63.15 288	44.53 283	56.83 262	74.84 274	35.83 233	68.71 279	40.03 256	60.91 285	74.39 276

conf0.01		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

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

thresconf0.02		59.40 246	58.81 236	61.17 271	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	74.25 278

tfpn_n400		59.40 246	58.81 236	61.17 271	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	74.25 278

tfpnconf		59.40 246	58.81 236	61.17 271	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	74.25 278

tfpnview11		59.40 246	58.81 236	61.17 271	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	74.25 278

RPMNet		58.70 253	56.29 263	65.96 229	69.96 277	52.07 143	65.31 280	62.15 295	43.20 294	59.36 232	70.15 302	35.37 241	70.75 271	36.42 275	64.65 260	75.06 266

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	35.25 242	77.41 231	59.88 130	78.76 107	85.14 95

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	35.00 243	84.36 103	26.41 326	64.71 257	71.15 308

tfpn_ndepth		59.57 245	59.02 235	61.23 270	73.81 211	35.60 301	69.40 255	65.59 266	50.96 229	57.96 255	77.72 238	34.81 244	75.91 247	30.36 302	70.57 192	72.18 301

sam_mvs1												34.74 245				78.05 235

tfpn1000		59.24 251	58.70 244	60.86 276	73.75 212	33.99 310	68.86 258	63.98 282	51.25 227	57.29 260	79.51 214	34.58 246	75.26 255	29.08 318	69.99 209	73.32 287

pmmvs5		56.47 265	55.68 266	58.86 281	61.41 322	36.71 294	66.37 272	62.75 291	40.38 311	53.70 287	76.62 258	34.56 247	67.05 284	40.02 257	65.27 253	72.83 290

patchmatchnet-post											64.03 319	34.50 248	74.27 261

PatchmatchNet		59.84 241	58.24 247	64.65 248	73.05 234	46.70 222	69.42 254	62.18 294	47.55 257	58.88 240	71.96 290	34.49 249	69.16 277	42.99 239	63.60 266	78.07 234
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
Patchmatch-test		49.08 294	48.28 294	51.50 312	64.40 312	30.85 328	45.68 334	48.46 338	35.60 325	46.10 316	72.10 289	34.47 250	46.37 340	27.08 324	60.65 289	77.27 244

MS-PatchMatch		62.42 222	61.46 220	65.31 243	75.21 187	52.10 142	72.05 228	74.05 212	46.41 266	57.42 259	74.36 278	34.35 251	77.57 230	45.62 217	73.67 148	66.26 319

tpmvs		58.47 254	56.95 257	63.03 259	70.20 274	41.21 259	67.90 268	67.23 258	49.62 238	54.73 278	70.84 296	34.14 252	76.24 245	36.64 272	61.29 284	71.64 304

PMMVS		53.96 278	53.26 282	56.04 293	62.60 318	50.92 154	61.17 299	56.09 318	32.81 329	53.51 291	66.84 312	34.04 253	59.93 310	44.14 228	68.18 234	57.27 334

Patchmatch-RL test		58.16 257	55.49 267	66.15 226	67.92 292	48.89 202	60.66 301	51.07 332	47.86 255	59.36 232	62.71 325	34.02 254	72.27 266	56.41 143	59.40 297	77.30 242

test_post											3.55 352	33.90 255	66.52 287

Test4		67.77 157	65.97 169	73.19 122	68.64 285	50.58 163	74.80 189	80.48 113	54.13 192	59.11 237	79.07 220	33.89 256	83.12 131	63.61 96	79.98 88	85.87 61

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	48.51 246	55.71 268	81.89 148	33.71 257	79.71 192	41.66 248	70.37 199	77.58 239
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
GA-MVS		65.53 189	63.70 192	71.02 172	70.87 267	48.10 208	70.48 244	74.40 209	56.69 148	64.70 164	76.77 256	33.66 258	81.10 174	55.42 151	70.32 201	83.87 138

LS3D		64.71 198	62.50 205	71.34 164	79.72 102	55.71 99	79.82 87	74.72 206	48.50 247	56.62 263	84.62 97	33.59 259	82.34 155	29.65 310	75.23 135	75.97 255

sam_mvs												33.43 260

PatchT		53.17 284	53.44 281	52.33 310	68.29 290	25.34 341	58.21 307	54.41 325	44.46 284	54.56 280	69.05 305	33.32 261	60.94 305	36.93 268	61.76 279	70.73 310

test20.03		53.87 280	54.02 277	53.41 305	61.47 321	28.11 333	61.30 297	59.21 303	51.34 224	52.09 295	77.43 249	33.29 262	58.55 314	29.76 309	60.27 295	73.58 286

anonymousdsp		67.00 170	64.82 184	73.57 102	70.09 275	56.13 91	76.35 155	77.35 178	48.43 248	64.99 161	80.84 179	33.01 263	80.34 186	64.66 76	67.64 241	84.23 125

MDTV_nov1_ep13_2view						25.89 338	61.22 298		40.10 312	51.10 298		32.97 264		38.49 261		78.61 230

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	73.73 282	32.86 265	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
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	51.61 217	70.04 76	81.41 164	32.79 266	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	51.61 217	70.04 76	81.41 164	32.79 266	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	51.61 217	70.04 76	81.41 164	32.79 266	79.02 208	63.81 93	77.31 118	81.22 195

Anonymous20231206		55.10 276	55.30 268	54.48 301	69.81 280	33.94 311	62.91 290	62.13 296	41.08 305	55.18 275	75.65 269	32.75 269	56.59 323	30.32 303	67.86 237	72.91 289

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	32.71 270	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
test-LLR		58.15 258	58.13 250	58.22 284	68.57 286	44.80 231	65.46 277	57.92 308	50.08 235	55.44 271	69.82 303	32.62 271	57.44 317	49.66 188	73.62 149	72.41 297

test0.0.03 1		53.32 283	53.59 280	52.50 309	62.81 317	29.45 330	59.51 303	54.11 327	50.08 235	54.40 282	74.31 279	32.62 271	55.92 328	30.50 301	63.95 264	72.15 303

MDTV_nov1_ep13			57.00 256		72.73 242	38.26 280	65.02 283	64.73 272	44.74 280	55.46 270	72.48 287	32.61 273	70.47 273	37.47 265	67.75 239

cascas		65.98 185	63.42 195	73.64 100	77.26 159	52.58 135	72.26 222	77.21 179	48.56 245	61.21 213	74.60 277	32.57 274	85.82 69	50.38 181	76.75 128	82.52 170

test_post1							68.67 259				3.64 351	32.39 275	69.49 276	44.17 227

CVMVSNet		59.63 244	59.14 234	61.08 275	74.47 197	38.84 274	75.20 179	68.74 247	31.15 332	58.24 247	76.51 261	32.39 275	68.58 280	49.77 185	65.84 249	75.81 259

MIMVSNet		57.35 261	57.07 255	58.22 284	74.21 203	37.18 286	62.46 291	60.88 299	48.88 243	55.29 274	75.99 267	31.68 277	62.04 303	31.87 289	72.35 174	75.43 264

PVSNet_0	43.31 20	47.46 299	45.64 299	52.92 307	67.60 294	44.65 233	54.06 317	54.64 323	41.59 303	46.15 314	58.75 331	30.99 278	58.66 313	32.18 287	24.81 341	55.46 335

testing_2		66.02 184	63.77 191	72.76 132	66.03 305	50.48 169	72.93 210	80.36 116	54.41 189	54.25 284	76.76 257	30.89 279	83.16 130	64.19 87	74.08 144	84.65 111

gg-mvs-nofinetune		57.86 259	56.43 261	62.18 263	72.62 244	35.35 303	66.57 270	56.33 316	50.65 230	57.64 257	57.10 332	30.65 280	76.36 243	37.38 266	78.88 104	74.82 272

GG-mvs-BLEND				62.34 262	71.36 264	37.04 288	69.20 256	57.33 311		54.73 278	65.48 318	30.37 281	77.82 226	34.82 279	74.93 136	72.17 302

MDA-MVSNet-bldmvs		53.87 280	50.81 287	63.05 258	66.25 302	48.58 204	56.93 311	63.82 283	48.09 252	41.22 328	70.48 299	30.34 282	68.00 281	34.24 281	45.92 330	72.57 293

EPMVS		53.96 278	53.69 278	54.79 300	66.12 304	31.96 325	62.34 293	49.05 335	44.42 285	55.54 269	71.33 294	30.22 283	56.70 321	41.65 249	62.54 275	75.71 261

DWT-MVSNet_test		61.90 227	59.93 231	67.83 207	71.98 255	46.09 225	71.03 239	69.71 233	50.09 234	58.51 245	70.62 297	30.21 284	77.63 228	49.28 190	67.91 236	79.78 219

YYNet1		50.73 291	48.96 290	56.03 294	61.10 324	41.78 255	51.94 324	56.44 315	40.94 307	44.84 317	67.80 309	30.08 285	55.08 332	36.77 269	50.71 320	71.22 306

MDA-MVSNet_test_wron		50.71 292	48.95 291	56.00 295	61.17 323	41.84 254	51.90 325	56.45 314	40.96 306	44.79 318	67.84 308	30.04 286	55.07 333	36.71 271	50.69 321	71.11 309

MSDG		61.81 230	59.23 233	69.55 193	72.64 243	52.63 134	70.45 245	75.81 192	51.38 223	53.70 287	76.11 264	29.52 287	81.08 175	37.70 264	65.79 250	74.93 270

CMPMVS	42.80 21	57.81 260	55.97 264	63.32 255	60.98 325	47.38 217	64.66 284	69.50 238	32.06 331	46.83 312	77.80 236	29.50 288	71.36 268	48.68 194	73.75 147	71.21 307
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
LTVRE_ROB	55.42 16	63.15 213	61.23 223	68.92 198	76.57 170	47.80 211	59.92 302	76.39 185	54.35 190	58.67 242	82.46 132	29.44 289	81.49 168	42.12 244	71.14 185	77.46 240
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
UnsupCasMVSNet_eth		53.16 285	52.47 283	55.23 296	59.45 332	33.39 320	59.43 304	69.13 243	45.98 270	50.35 304	72.32 288	29.30 290	58.26 315	42.02 246	44.30 332	74.05 283

CHOSEN 280x420		47.83 297	46.36 298	52.24 311	67.37 295	49.78 190	38.91 342	43.11 344	35.00 326	43.27 325	63.30 324	28.95 291	49.19 339	36.53 273	60.80 288	57.76 333

pmmvs-eth3d		58.81 252	56.31 262	66.30 222	67.61 293	52.42 140	72.30 221	64.76 271	43.55 291	54.94 276	74.19 280	28.95 291	72.60 264	43.31 234	57.21 302	73.88 285

dp		51.89 288	51.60 286	52.77 308	68.44 289	32.45 322	62.36 292	54.57 324	44.16 287	49.31 305	67.91 307	28.87 293	56.61 322	33.89 282	54.89 308	69.24 316

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	28.28 294	81.81 163	63.48 98	70.62 190	83.67 147

RPSCF		55.80 272	54.22 276	60.53 277	65.13 309	42.91 250	64.30 285	57.62 310	36.84 323	58.05 248	82.28 136	28.01 295	56.24 327	37.14 267	58.61 300	82.44 173

F-COLMAP		63.05 214	60.87 226	69.58 192	76.99 165	53.63 120	78.12 113	76.16 187	47.97 254	52.41 294	81.61 156	27.87 296	78.11 223	40.07 255	66.66 244	77.00 249

K. test v3		60.47 236	57.11 254	70.56 178	73.74 213	48.22 207	75.10 182	62.55 292	58.27 130	53.62 289	76.31 263	27.81 297	81.59 166	47.42 199	39.18 335	81.88 180

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	57.47 258	82.55 130	27.68 298	84.17 106	45.54 218	69.78 214	79.90 216

UnsupCasMVSNet_bld		50.07 293	48.87 292	53.66 303	60.97 326	33.67 318	57.62 309	64.56 273	39.47 315	47.38 309	64.02 321	27.47 299	59.32 311	34.69 280	43.68 333	67.98 318

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	27.45 300	81.84 162	63.45 99	70.56 193	83.69 144

lessismore_v0				69.91 186	71.42 262	47.80 211		50.90 333		50.39 303	75.56 270	27.43 301	81.33 170	45.91 213	34.10 338	80.59 207

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	56.59 264	80.98 173	27.12 302	80.94 176	42.90 241	71.58 182	77.25 246
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
SixPastTwentyTwo		61.65 231	58.80 243	70.20 183	75.80 178	47.22 218	75.59 170	69.68 235	54.61 185	54.11 285	79.26 217	27.07 303	82.96 134	43.27 235	49.79 323	80.41 210

PVSNet	50.76 19	58.40 255	57.39 253	61.42 267	75.53 182	44.04 240	61.43 295	63.45 285	47.04 262	56.91 261	73.61 283	27.00 304	64.76 294	39.12 259	72.40 173	75.47 263

OpenMVS_ROB	52.78 18	60.03 237	58.14 249	65.69 235	70.47 270	44.82 230	75.33 174	70.86 227	45.04 277	56.06 266	76.00 265	26.89 305	79.65 193	35.36 278	67.29 242	72.60 292

ADS-MVSNet2		51.33 290	48.76 293	59.07 280	66.02 306	44.60 234	50.90 326	59.76 302	36.90 321	50.74 300	66.18 316	26.38 306	63.11 298	27.17 322	54.76 309	69.50 313

ADS-MVSNet		48.48 296	47.77 295	50.63 313	66.02 306	29.92 329	50.90 326	50.87 334	36.90 321	50.74 300	66.18 316	26.38 306	52.47 336	27.17 322	54.76 309	69.50 313

N_pmnet		39.35 313	40.28 310	36.54 330	63.76 314	1.62 356	49.37 329	0.76 357	34.62 327	43.61 324	66.38 315	26.25 308	42.57 346	26.02 328	51.77 316	65.44 320

MVS-HIRNet		45.52 302	44.48 304	48.65 317	68.49 288	34.05 309	59.41 305	44.50 343	27.03 336	37.96 334	50.47 339	26.16 309	64.10 295	26.74 325	59.52 296	47.82 338

FMVSNet5		55.86 271	54.93 269	58.66 283	71.05 266	36.35 296	64.18 287	62.48 293	46.76 263	50.66 302	74.73 276	25.80 310	64.04 296	33.11 285	65.57 252	75.59 262

new-patchmatchnet		47.56 298	47.73 296	47.06 319	58.81 333	9.37 352	48.78 330	59.21 303	43.28 292	44.22 320	68.66 306	25.67 311	57.20 320	31.57 297	49.35 326	74.62 275

MIMVSNet1		55.17 275	54.31 274	57.77 288	70.03 276	32.01 324	65.68 275	64.81 270	49.19 240	46.75 313	76.00 265	25.53 312	64.04 296	28.65 320	62.13 277	77.26 245

PatchMatch-RL		56.25 268	54.55 272	61.32 269	77.06 162	56.07 93	65.57 276	54.10 328	44.13 288	53.49 292	71.27 295	25.20 313	66.78 286	36.52 274	63.66 265	61.12 328

JIA-IIPM		51.56 289	47.68 297	63.21 256	64.61 311	50.73 160	47.71 331	58.77 305	42.90 296	48.46 307	51.72 336	24.97 314	70.24 274	36.06 277	53.89 312	68.64 317

EU-MVSNet		55.61 273	54.41 273	59.19 279	65.41 308	33.42 319	72.44 219	71.91 224	28.81 334	51.27 297	73.87 281	24.76 315	69.08 278	43.04 238	58.20 301	75.06 266

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	56.03 267	77.41 250	24.75 316	84.04 109	46.37 208	73.42 154	73.14 288

TESTMET0.1,1		55.28 274	54.90 270	56.42 292	66.56 301	43.67 243	65.46 277	56.27 317	39.18 316	53.83 286	67.44 310	24.21 317	55.46 331	48.04 198	73.11 161	70.13 311

LP		48.51 295	45.51 300	57.52 290	62.86 316	44.53 237	52.38 323	59.84 301	38.11 318	42.81 326	61.02 326	23.23 318	63.02 299	24.10 329	45.24 331	65.02 322

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	49.92 237	50.96 299	81.51 159	23.06 319	79.40 196	31.63 295	65.85 248	74.01 284
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
testgi		51.90 287	52.37 284	50.51 314	60.39 328	23.55 344	58.42 306	58.15 306	49.03 242	51.83 296	79.21 218	22.39 320	55.59 329	29.24 317	62.64 273	72.40 299

DSMNet-mixed		39.30 314	38.72 313	41.03 327	51.22 340	19.66 346	45.53 335	31.35 350	15.83 346	39.80 333	67.42 311	22.19 321	45.13 343	22.43 330	52.69 314	58.31 332

test-mter		56.42 266	55.82 265	58.22 284	68.57 286	44.80 231	65.46 277	57.92 308	39.94 314	55.44 271	69.82 303	21.92 322	57.44 317	49.66 188	73.62 149	72.41 297

OurMVSNet-221017-0		61.37 233	58.63 246	69.61 189	72.05 253	48.06 209	73.93 200	72.51 220	47.23 260	54.74 277	80.92 175	21.49 323	81.24 171	48.57 196	56.22 304	79.53 222

Anonymous20231211		55.92 270	53.63 279	62.77 260	68.22 291	35.56 302	74.48 193	69.89 232	46.42 265	49.07 306	73.45 284	21.13 324	76.77 239	28.74 319	51.30 319	75.97 255

ITE_SJBPF				62.09 264	66.16 303	44.55 236		64.32 274	47.36 259	55.31 273	80.34 190	19.27 325	62.68 301	36.29 276	62.39 276	79.04 227

AllTest		57.08 264	54.65 271	64.39 249	71.44 260	49.03 199	69.92 251	67.30 256	45.97 271	47.16 310	79.77 202	17.47 326	67.56 282	33.65 283	59.16 298	76.57 252

TestCases				64.39 249	71.44 260	49.03 199		67.30 256	45.97 271	47.16 310	79.77 202	17.47 326	67.56 282	33.65 283	59.16 298	76.57 252

testpf		44.11 306	45.40 301	40.26 328	60.52 327	27.34 335	33.26 344	54.33 326	45.87 274	41.08 329	60.26 328	16.46 328	59.14 312	46.09 210	50.68 322	34.31 344

XVG-ACMP-BASELINE		64.36 202	62.23 209	70.74 175	72.35 248	52.45 139	70.80 241	78.45 160	53.84 194	59.87 227	81.10 169	16.24 329	79.32 198	55.64 150	71.76 180	80.47 208

tmp_tt		9.43 329	11.14 330	4.30 341	2.38 355	4.40 355	13.62 349	16.08 354	0.39 351	15.89 346	13.06 349	15.80 330	5.54 354	12.63 345	10.46 350	2.95 350

USDC		56.35 267	54.24 275	62.69 261	64.74 310	40.31 263	65.05 282	73.83 213	43.93 289	47.58 308	77.71 239	15.36 331	75.05 258	38.19 263	61.81 278	72.70 291

test2356		45.61 301	44.66 303	48.47 318	60.15 329	28.08 334	52.44 322	52.83 331	38.01 319	46.13 315	60.98 327	15.08 332	55.54 330	20.43 338	55.85 306	61.78 326

test1235678		45.66 300	44.46 305	49.26 315	59.88 330	28.68 332	56.36 313	55.54 322	39.12 317	40.89 330	63.40 323	14.41 333	57.32 319	21.05 335	49.47 325	61.78 326

1111		44.40 305	45.00 302	42.61 326	57.55 335	17.33 349	53.82 320	57.05 312	40.78 308	44.11 321	66.57 313	13.37 334	45.77 341	22.15 331	49.58 324	64.73 323

.test1245		34.88 317	39.49 312	21.04 338	57.55 335	17.33 349	53.82 320	57.05 312	40.78 308	44.11 321	66.57 313	13.37 334	45.77 341	22.15 331	0.00 352	0.03 353

ANet_high		41.38 310	37.47 314	53.11 306	39.73 349	24.45 343	56.94 310	69.69 234	47.65 256	26.04 340	52.32 335	12.44 336	62.38 302	21.80 334	10.61 349	72.49 294

no-one		40.85 311	36.09 315	55.14 297	48.55 342	38.72 275	42.15 340	62.92 290	34.60 328	23.55 341	49.74 340	12.21 337	66.16 290	26.27 327	24.84 340	60.54 329

FPMVS		42.18 309	41.11 309	45.39 321	58.03 334	41.01 261	49.50 328	53.81 329	30.07 333	33.71 335	64.03 319	11.69 338	52.08 337	14.01 344	55.11 307	43.09 341

TinyColmap		54.14 277	51.72 285	61.40 268	66.84 298	41.97 253	66.52 271	68.51 249	44.81 279	42.69 327	75.77 268	11.66 339	72.94 263	31.96 288	56.77 303	69.27 315

TDRefinement		53.44 282	50.72 288	61.60 266	64.31 313	46.96 219	70.89 240	65.27 269	41.78 300	44.61 319	77.98 230	11.52 340	66.36 288	28.57 321	51.59 317	71.49 305

ambc				65.13 244	63.72 315	37.07 287	47.66 332	78.78 150		54.37 283	71.42 293	11.24 341	80.94 176	45.64 216	53.85 313	77.38 241

pmmvs3		44.92 303	41.95 308	53.86 302	52.58 339	43.55 244	62.11 294	46.90 342	26.05 338	40.63 331	60.19 329	11.08 342	57.91 316	31.83 294	46.15 329	60.11 330

new_pmnet		34.13 319	34.29 318	33.64 331	52.63 338	18.23 348	44.43 338	33.90 348	22.81 341	30.89 337	53.18 334	10.48 343	35.72 350	20.77 336	39.51 334	46.98 339

LF4IMVS		42.95 307	42.26 307	45.04 322	48.30 343	32.50 321	54.80 315	48.49 337	28.03 335	40.51 332	70.16 301	9.24 344	43.89 344	31.63 295	49.18 327	58.72 331

PM-MVS		52.33 286	50.19 289	58.75 282	62.10 319	45.14 229	65.75 274	40.38 345	43.60 290	53.52 290	72.65 286	9.16 345	65.87 292	50.41 180	54.18 311	65.24 321

testus		44.59 304	43.87 306	46.76 320	59.85 331	24.65 342	53.86 318	55.82 320	36.26 324	43.97 323	63.42 322	8.39 346	53.14 335	20.70 337	52.52 315	62.51 324

testmv		42.25 308	40.11 311	48.66 316	53.23 337	27.02 336	56.62 312	55.74 321	37.25 320	33.10 336	59.52 330	7.78 347	56.58 324	19.61 339	38.13 337	62.40 325

EMVS		22.97 325	21.84 327	26.36 337	40.20 348	19.53 347	41.95 341	34.64 347	17.09 345	9.73 350	22.83 348	7.29 348	42.22 348	9.18 349	13.66 346	17.32 348

test12356		36.16 316	35.94 316	36.83 329	50.82 341	8.52 353	44.84 337	53.49 330	32.72 330	30.11 338	55.08 333	7.11 349	49.47 338	16.60 341	32.68 339	52.50 336

E-PMN		23.77 324	22.73 325	26.90 336	42.02 347	20.67 345	42.66 339	35.70 346	17.43 344	10.28 349	25.05 346	6.42 350	42.39 347	10.28 347	14.71 344	17.63 347

Gipuma		34.77 318	31.91 320	43.33 325	62.05 320	37.87 282	20.39 347	67.03 259	23.23 340	18.41 344	25.84 345	4.24 351	62.73 300	14.71 343	51.32 318	29.38 346
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PMMVS2		27.40 323	25.91 323	31.87 334	39.46 350	6.57 354	31.17 345	28.52 351	23.96 339	20.45 343	48.94 341	4.20 352	37.94 349	16.51 342	19.97 342	51.09 337

LCM-MVSNet		40.30 312	35.88 317	53.57 304	42.24 346	29.15 331	45.21 336	60.53 300	22.23 342	28.02 339	50.98 338	3.72 353	61.78 304	31.22 300	38.76 336	69.78 312

DeepMVS_CX				12.03 340	17.97 354	10.91 351		10.60 355	7.46 349	11.07 348	28.36 344	3.28 354	11.29 353	8.01 350	9.74 351	13.89 349

PMVS	28.69 22	36.22 315	33.29 319	45.02 323	36.82 351	35.98 300	54.68 316	48.74 336	26.31 337	21.02 342	51.61 337	2.88 355	60.10 309	9.99 348	47.58 328	38.99 343
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MVE	17.77 23	21.41 326	17.77 328	32.34 333	34.34 353	25.44 340	16.11 348	24.11 352	11.19 348	13.22 347	31.92 343	1.58 356	30.95 351	10.47 346	17.03 343	40.62 342
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
PNet_i23d		27.88 322	25.99 322	33.55 332	47.54 344	25.89 338	47.24 333	32.91 349	21.44 343	15.90 345	38.09 342	0.85 357	42.76 345	16.90 340	13.03 347	32.00 345

wuyk23d		13.32 328	12.52 329	15.71 339	47.54 344	26.27 337	31.06 346	1.98 356	4.93 350	5.18 352	1.94 353	0.45 358	18.54 352	6.81 351	12.83 348	2.33 351

wuykxyi23d		28.12 321	22.54 326	44.87 324	34.97 352	32.11 323	37.96 343	47.31 340	13.32 347	9.29 351	23.72 347	0.45 358	56.58 324	21.85 333	13.98 345	45.93 340

test123		4.73 331	6.30 332	0.02 342	0.01 356	0.01 357	56.36 313	0.00 358	0.01 352	0.04 353	0.21 355	0.01 360	0.00 355	0.03 353	0.00 352	0.04 352

sosnet-low-res		0.00 334	0.00 335	0.00 344	0.00 358	0.00 358	0.00 350	0.00 358	0.00 354	0.00 355	0.00 356	0.00 361	0.00 355	0.00 354	0.00 352	0.00 355

sosnet		0.00 334	0.00 335	0.00 344	0.00 358	0.00 358	0.00 350	0.00 358	0.00 354	0.00 355	0.00 356	0.00 361	0.00 355	0.00 354	0.00 352	0.00 355

uncertanet		0.00 334	0.00 335	0.00 344	0.00 358	0.00 358	0.00 350	0.00 358	0.00 354	0.00 355	0.00 356	0.00 361	0.00 355	0.00 354	0.00 352	0.00 355

Regformer		0.00 334	0.00 335	0.00 344	0.00 358	0.00 358	0.00 350	0.00 358	0.00 354	0.00 355	0.00 356	0.00 361	0.00 355	0.00 354	0.00 352	0.00 355

testmvs		4.52 332	6.03 333	0.01 343	0.01 356	0.00 358	53.86 318	0.00 358	0.01 352	0.04 353	0.27 354	0.00 361	0.00 355	0.04 352	0.00 352	0.03 353

ab-mvs-re		6.49 330	8.65 331	0.00 344	0.00 358	0.00 358	0.00 350	0.00 358	0.00 354	0.00 355	77.89 234	0.00 361	0.00 355	0.00 354	0.00 352	0.00 355

uanet		0.00 334	0.00 335	0.00 344	0.00 358	0.00 358	0.00 350	0.00 358	0.00 354	0.00 355	0.00 356	0.00 361	0.00 355	0.00 354	0.00 352	0.00 355

GSMVS																78.05 235

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

MTGPA								80.97 98

MTMP								17.08 353

gm-plane-assit					71.40 263	41.72 257			48.85 244		73.31 285		82.48 154	48.90 193

test9_res														75.28 16	88.31 18	83.81 139

agg_prior2														73.09 31	87.93 25	84.33 117

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

旧先验2							76.08 162		45.32 276	76.55 15			65.56 293	58.75 135

新几何2							76.12 160

无先验							79.66 91	74.30 210	48.40 249				80.78 181	53.62 160		79.03 228

原ACMM2							79.02 97

testdata2													72.18 267	46.95 205

testdata1							72.65 213		60.50 77

plane_prior7					81.41 72	55.96 95

plane_prior5								84.01 30					87.21 35	68.16 50	80.58 77	84.65 111

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								0.00 358

nn								0.00 358

door-mid								47.19 341

test11								83.47 45

door								47.60 339

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

HQP4-MVS										67.85 119			86.93 41			84.32 118

HQP3-MVS								83.90 34							80.35 83

NP-MVS					80.98 82	56.05 94					85.54 87

ACMMP++_ref															74.07 145

ACMMP++															72.16 177

High-resolution multi-view benchmark