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	56.91 144	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	75.41 185	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	70.43 195	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	80.75 183	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	80.75 183	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	74.88 190	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	38.14 282	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	80.30 193	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	72.08 253	44.57 235	71.99 230	75.23 198	51.67 217	67.06 132	82.57 129	54.68 31	77.94 226	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	34.73 307	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	56.16 163	68.59 105	79.55 213	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	56.14 164	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	71.12 187	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	80.30 193	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	55.65 172	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	55.86 166	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	55.86 166	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	56.32 159	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	55.51 175	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	79.35 217	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	50.30 234	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	70.78 189	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	78.95 222	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	69.70 218	87.46 22

EI-MVSNet		69.27 122	68.44 117	71.73 156	74.47 198	49.39 198	75.20 179	78.45 160	59.60 105	69.16 101	76.51 262	51.29 60	82.50 152	59.86 132	71.45 185	83.30 153

IterMVS-LS		69.22 125	68.48 115	71.43 162	74.44 200	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	69.90 213	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	65.87 234	75.22 187	38.56 279	74.66 190	75.08 204	58.90 120	61.79 203	82.63 127	51.18 62	78.07 225	43.63 233	55.87 306	80.99 204

VNet		69.68 113	70.19 87	68.16 205	79.73 101	41.63 258	70.53 244	77.38 177	60.37 80	70.69 68	86.63 64	51.08 63	77.09 236	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	77.58 230	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	55.81 169	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	56.08 165	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	37.75 285	77.57 133	82.11 69	62.03 60	62.65 183	82.48 131	50.57 68	79.46 195	42.91 240	64.01 264	84.79 108

EPNet		73.09 60	72.16 62	75.90 54	75.95 178	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	79.14 207	49.78 184	69.66 219	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	78.30 232

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	76.17 174	41.73 256	70.41 247	76.14 188	53.99 194	65.98 144	83.51 116	49.48 76	76.24 246	48.60 195	73.46 153	84.14 132

v8		70.33 103	69.28 106	73.49 106	73.15 228	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	68.71 226	85.28 91

v17		68.37 142	67.00 148	72.48 140	73.22 224	50.31 172	78.10 117	79.58 131	59.71 100	61.67 205	77.60 244	49.31 78	82.89 137	62.37 107	61.48 284	84.23 125

v18		68.33 143	66.96 149	72.42 146	73.13 232	50.16 181	77.97 122	79.57 133	59.57 109	61.80 202	77.50 249	49.30 79	82.90 136	62.31 109	61.50 282	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	73.15 228	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	70.38 198	85.42 83

v7new		70.66 89	69.70 92	73.53 103	73.15 228	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	70.38 198	85.42 83

v6		70.66 89	69.70 92	73.53 103	73.14 231	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	70.39 197	85.42 83

v16		68.38 141	67.01 147	72.47 144	73.22 224	50.29 173	78.10 117	79.59 129	59.71 100	61.72 204	77.60 244	49.28 84	82.89 137	62.36 108	61.54 281	84.23 125

XXY-MVS		60.68 236	61.67 219	57.70 290	70.43 272	38.45 280	64.19 287	66.47 262	48.05 254	63.22 175	80.86 178	49.28 84	60.47 308	45.25 224	67.28 244	74.19 283

divwei89l23v2f112		70.50 96	69.53 97	73.41 112	72.91 239	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	70.31 203	85.47 77

v1		70.50 96	69.53 97	73.42 111	72.91 239	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	70.30 204	85.47 77

cdsmvs_eth3d_5k		17.50 328	23.34 325	0.00 345	0.00 359	0.00 359	0.00 351	78.63 153	0.00 355	0.00 356	82.18 137	49.25 88	0.00 356	0.00 355	0.00 353	0.00 356

v15		68.22 151	66.81 155	72.47 144	73.25 223	50.40 170	77.92 124	79.60 126	59.77 99	61.28 213	77.52 248	49.25 88	82.77 143	62.16 114	60.51 291	84.24 124

diffmvs		67.72 158	66.73 156	70.70 177	69.74 282	47.69 214	73.33 206	74.74 205	53.30 198	64.51 166	81.80 150	49.25 88	79.02 209	59.15 134	74.75 137	85.39 88

v1141		70.50 96	69.53 97	73.41 112	72.92 238	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	70.30 204	85.46 79

V14		68.25 148	66.82 154	72.52 139	73.33 222	50.53 167	78.02 120	79.60 126	59.83 96	61.16 215	77.57 246	49.19 92	82.77 143	62.18 110	60.50 292	84.26 123

V9		68.27 146	66.84 151	72.56 137	73.39 221	50.63 162	78.10 117	79.60 126	59.94 91	61.05 217	77.62 243	49.18 93	82.77 143	62.17 112	60.48 293	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	55.09 180	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	77.03 164	50.57 164	74.50 192	81.52 78	53.66 196	64.22 171	79.72 205	49.13 95	82.87 139	55.82 146	73.92 146	79.77 221

PVSNet_Blended		68.59 134	67.72 127	71.19 167	77.03 164	50.57 164	72.51 219	81.52 78	51.91 210	64.22 171	77.77 238	49.13 95	82.87 139	55.82 146	79.58 93	80.14 215

v12		68.28 145	66.83 153	72.60 136	73.43 218	50.74 159	78.18 112	79.59 129	60.01 90	60.89 219	77.66 242	49.12 97	82.77 143	62.18 110	60.46 294	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	70.43 195	87.37 25

Baseline_NR-MVSNet		67.05 168	67.56 130	65.50 241	75.65 180	37.70 286	75.42 173	74.65 207	59.90 92	68.14 115	83.15 122	49.12 97	77.20 234	52.23 170	69.78 215	81.60 182

v13		68.29 144	66.84 151	72.63 134	73.50 216	50.83 157	78.25 110	79.58 131	60.05 88	60.76 220	77.68 241	49.11 100	82.77 143	62.17 112	60.45 295	84.30 120

VPNet		67.52 159	68.11 122	65.74 235	79.18 106	36.80 294	72.17 224	72.83 219	62.04 59	67.79 125	85.83 81	48.88 101	76.60 242	51.30 176	72.97 164	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	51.47 223	70.27 74	86.61 65	48.61 104	86.51 54	53.85 159	87.96 24	78.16 234

v148		68.24 150	67.19 145	71.40 163	70.43 272	47.77 213	75.76 169	77.03 181	58.91 119	67.36 129	80.10 197	48.60 105	81.89 160	60.01 129	66.52 247	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	73.43 218	50.28 174	77.94 123	79.65 123	59.88 95	61.11 216	77.55 247	48.25 108	82.75 148	61.88 119	60.85 288	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	75.46 184	50.94 152	73.95 197	81.85 74	41.57 305	62.54 186	78.57 228	47.98 110	85.47 76	52.97 166	82.05 66	75.14 266

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	6.49 351	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	54.90 184	68.08 117	78.70 223	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	51.04 229	60.91 218	83.98 110	47.71 114	84.99 82	40.81 252	79.32 99	80.90 205

v10		70.21 105	69.02 108	73.81 89	73.51 215	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	68.86 225	85.09 98

v7		70.57 93	69.48 102	73.85 87	73.50 216	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	70.62 191	85.21 94

v1144		70.42 101	69.31 105	73.76 92	73.22 224	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	70.64 190	85.34 89

v2v482		70.50 96	69.45 104	73.66 98	72.62 245	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	70.17 207	86.09 56

pm-mvs1		65.24 193	64.97 183	66.04 229	72.38 248	39.40 270	72.62 217	75.63 193	55.53 174	62.35 197	83.18 121	47.45 119	76.47 243	49.06 192	66.54 246	82.24 174

HY-MVS	56.14 13	64.55 201	63.89 188	66.55 219	74.73 194	41.02 260	69.96 251	74.43 208	49.29 240	61.66 206	80.92 176	47.43 120	76.68 241	44.91 225	71.69 182	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		3.92 334	5.23 335	0.00 345	0.00 359	0.00 359	0.00 351	0.00 359	0.00 355	0.00 356	0.00 357	47.05 122	0.00 356	0.00 355	0.00 353	0.00 356

PS-MVSNAJss		72.24 70	71.21 75	75.31 67	78.50 121	55.93 96	81.63 64	82.12 68	56.24 162	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	55.04 182	69.88 81	78.66 224	47.05 122	82.19 156	61.61 121	79.58 93	80.83 206

WTY-MVS		59.75 243	60.39 229	57.85 288	72.32 250	37.83 284	61.05 301	64.18 282	45.95 274	61.91 200	79.11 220	47.01 125	60.88 307	42.50 242	69.49 221	74.83 272

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	54.92 183	69.96 80	79.68 206	47.00 126	82.09 158	61.60 122	79.37 96	80.81 207

v144192		69.71 112	68.51 114	73.33 116	73.10 233	50.13 183	77.54 134	80.64 106	56.65 150	68.57 107	80.55 186	46.87 127	84.96 86	62.98 100	69.66 219	84.89 104

PEN-MVS		66.60 177	66.45 161	67.04 214	77.11 162	36.56 296	77.03 145	80.42 114	62.95 41	62.51 188	84.03 109	46.69 128	79.07 208	44.22 226	63.08 272	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	36.33 298	76.83 149	79.52 135	62.45 51	62.54 186	83.47 118	46.32 130	78.37 220	45.47 221	63.43 269	85.45 80

V42		68.65 133	67.35 139	72.56 137	68.93 285	50.18 180	72.90 211	79.47 136	56.92 143	69.45 94	80.26 195	46.29 131	82.99 133	64.07 88	67.82 239	84.53 114

1112_ss		64.00 204	63.36 196	65.93 232	79.28 104	42.58 251	71.35 234	72.36 222	46.41 267	60.55 222	77.89 235	46.27 132	73.28 263	46.18 209	69.97 211	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	36.30 300	76.94 147	79.61 124	62.36 53	62.43 195	83.66 112	45.69 134	78.37 220	45.35 223	63.26 270	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	76.06 177	34.79 305	76.43 154	79.38 140	62.55 50	61.66 206	83.83 111	45.60 136	79.15 206	41.64 250	60.88 287	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	54.51 189	64.85 162	78.12 230	45.59 137	82.95 135	43.26 236	75.54 134	74.27 278

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	76.60 170	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	76.91 252

v1192		69.97 110	68.68 112	73.85 87	73.19 227	50.94 152	77.68 131	81.36 85	57.51 135	68.95 103	80.85 179	45.28 143	85.33 80	62.97 101	70.37 200	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	73.06 234	50.10 184	77.39 136	80.56 110	56.58 156	68.59 105	80.37 189	44.72 146	84.98 84	62.47 106	69.82 214	85.00 100

test_normal		69.26 123	67.90 125	73.32 117	70.84 269	50.38 171	75.30 175	79.17 142	54.23 192	62.00 198	80.61 185	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	71.11 266	50.14 182	75.49 172	79.16 143	54.57 188	62.45 190	80.76 182	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	63.14 258	74.75 193	31.04 328	71.16 238	63.64 285	56.32 159	59.80 230	84.99 91	44.51 149	75.46 249	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	57.03 141	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	52.32 207	65.28 152	81.72 151	44.49 151	77.40 233	42.32 243	78.66 109	82.92 163

v748		67.26 162	65.67 172	72.02 151	69.90 280	49.77 191	76.24 158	79.57 133	58.58 123	60.49 223	80.38 188	44.47 152	82.17 157	56.16 144	65.26 255	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		67.52 256			87.85 50	44.08 154			80.76 74	78.03 238

Test_1112_low_res		62.32 224	61.77 218	64.00 252	79.08 109	39.53 269	68.17 267	70.17 230	43.25 294	59.03 240	79.90 199	44.08 154	71.24 270	43.79 232	68.42 228	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	51.26 226	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	46.19 269	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	72.51 247	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	68.70 227	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	52.15 208	64.72 163	80.23 196	43.56 159	77.10 235	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	46.53 265	69.34 96	83.22 119	43.37 160	79.18 201	64.77 75	79.20 101	84.23 125
jason: jason.
v1240		69.24 124	67.91 124	73.25 121	73.02 236	49.82 189	77.21 141	80.54 111	56.43 158	68.34 111	80.51 187	43.33 161	84.99 82	62.03 116	69.77 217	84.95 103

LCM-MVSNet-Re		61.88 230	61.35 222	63.46 253	74.58 196	31.48 327	61.42 297	58.14 308	58.71 122	53.02 294	79.55 213	43.07 162	76.80 239	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	76.34 245	65.62 70	77.54 116	79.30 226

CPTT-MVS		72.78 62	72.08 64	74.87 72	84.88 44	61.41 27	84.15 27	77.86 167	55.27 177	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	56.62 153	65.04 158	82.70 124	41.85 171	80.33 187	47.18 201	72.76 166	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	56.62 153	65.04 158	82.70 124	41.85 171	80.33 187	47.18 201	72.76 166	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	56.62 153	65.22 157	82.17 139	41.85 171	80.18 190	47.05 204	72.72 169	83.20 156

CostFormer		64.04 203	62.51 204	68.61 202	71.88 257	45.77 226	71.30 235	70.60 229	47.55 258	64.31 169	76.61 260	41.63 174	79.62 194	49.74 186	69.00 223	80.42 210

AdaColmap		69.99 109	68.66 113	73.97 86	84.94 41	57.83 66	82.63 47	78.71 151	56.28 161	64.34 167	84.14 106	41.57 175	87.06 40	46.45 207	78.88 104	77.02 249

Effi-MVS+-dtu		69.64 115	67.53 132	75.95 53	76.10 175	62.29 18	80.20 83	76.06 190	59.83 96	65.26 154	77.09 252	41.56 176	84.02 112	60.60 125	71.09 188	81.53 183

mvs-test1		70.44 100	68.19 120	77.18 39	76.10 175	63.22 6	80.59 79	76.06 190	59.83 96	66.32 141	79.87 200	41.56 176	85.53 73	60.60 125	72.77 165	82.80 167

QAPM		70.05 107	68.81 110	73.78 90	76.54 172	53.43 123	83.23 36	83.48 44	52.89 201	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	66.46 263	44.61 283	70.28 73	86.68 62	41.42 180	80.78 181	53.62 160	81.79 67	75.97 256

CHOSEN 1792x2688		65.08 196	62.84 201	71.82 154	81.49 71	56.26 89	66.32 274	74.20 211	40.53 311	63.16 177	78.65 225	41.30 181	77.80 228	45.80 214	74.09 143	81.40 186

新几何1				70.76 174	85.66 26	61.13 30		66.43 264	44.68 282	70.29 72	86.64 63	41.29 182	75.23 257	49.72 187	81.75 69	75.93 259

tpmrst		58.24 257	58.70 245	56.84 292	66.97 297	34.32 309	69.57 254	61.14 299	47.17 262	58.58 245	71.60 292	41.28 183	60.41 309	49.20 191	62.84 273	75.78 261

tfpnnormal		62.47 222	61.63 220	64.99 246	74.81 192	39.01 272	71.22 236	73.72 214	55.22 179	60.21 224	80.09 198	41.26 184	76.98 238	30.02 305	68.09 236	78.97 230

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	66.76 301	51.60 148	73.69 203	79.45 138	57.88 132	62.45 190	78.58 227	40.96 186	83.34 123	61.99 117	64.71 258	83.68 145

v52		67.09 166	65.16 180	72.87 127	66.77 300	51.60 148	73.69 203	79.45 138	57.88 132	62.46 189	78.57 228	40.95 187	83.34 123	61.99 117	64.70 260	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	48.22 251	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	69.07 258	75.52 194	42.68 299	63.53 173	77.95 232	40.43 189	81.64 164	46.01 212	71.91 180	83.73 143

FMVSNet3		66.32 182	65.61 173	68.46 203	76.48 173	42.34 252	74.98 185	77.15 180	55.83 168	65.04 158	81.16 167	39.91 190	80.14 191	47.18 201	72.76 166	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	50.59 233	56.47 266	78.65 225	39.84 191	82.68 149	44.10 229	72.12 179	72.44 297
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	52.95 200	67.90 118	80.33 192	39.81 192	83.68 118	43.20 237	73.56 152	80.20 213

pmmvs6		63.69 205	62.82 202	66.27 225	70.63 270	39.27 271	73.13 208	75.47 195	52.69 203	59.75 231	82.30 135	39.71 193	77.03 237	47.40 200	64.35 263	82.53 169

pcd1.5k->3k		30.06 321	30.56 322	28.55 336	78.81 114	0.00 359	0.00 351	82.07 70	0.00 355	0.00 356	0.00 357	39.61 194	0.00 356	0.00 355	74.56 138	85.66 71

XVG-OURS-SEG-HR		68.81 129	67.47 134	72.82 131	74.40 201	56.87 83	70.59 243	79.04 145	54.77 185	66.99 133	86.01 77	39.57 195	78.21 223	62.54 104	73.33 155	83.37 152

Fast-Effi-MVS+-dtu		67.37 160	65.33 177	73.48 107	72.94 237	57.78 67	77.47 135	76.88 182	57.60 134	61.97 199	76.85 256	39.31 196	80.49 185	54.72 153	70.28 206	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	76.63 169	48.75 203	76.52 153	80.04 118	50.64 232	65.24 155	84.93 92	39.15 198	78.54 215	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	74.26 203	53.99 116	81.21 72	81.34 86	52.70 202	62.75 181	85.55 86	38.86 201	84.14 107	48.41 197	83.01 57	79.97 216

sss		56.17 270	56.57 260	54.96 299	66.93 298	36.32 299	57.94 309	61.69 298	41.67 303	58.64 244	75.32 273	38.72 202	56.25 327	42.04 245	66.19 248	72.31 301

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	55.46 176	67.18 131	84.39 104	38.51 203	83.17 129	60.65 124	76.10 130	80.30 212
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	70.37 274	49.70 192	70.25 249	78.45 160	51.52 221	69.16 101	80.37 189	38.45 204	82.50 152	60.19 128	71.46 184	83.44 151

test_djsdf		69.45 120	67.74 126	74.58 79	74.57 197	54.92 110	82.79 43	78.48 158	51.26 226	65.41 151	83.49 117	38.37 205	83.24 127	66.06 64	69.25 222	85.56 73

tpm2		62.07 227	60.10 231	67.99 206	72.79 242	43.86 241	71.05 239	66.85 261	43.14 296	62.77 179	75.39 272	38.32 206	80.80 180	41.69 247	68.88 224	79.32 225

tpm cat1		59.25 251	56.95 258	66.15 227	72.19 251	46.96 219	68.09 268	65.76 265	40.03 314	57.81 257	70.56 299	38.32 206	74.51 261	38.26 262	61.50 282	77.00 250

CNLPA		65.43 190	64.02 187	69.68 188	78.73 117	58.07 64	77.82 126	70.71 228	51.49 222	61.57 208	83.58 115	38.23 208	70.82 271	43.90 230	70.10 209	80.16 214

1314		64.61 200	63.21 197	68.80 199	71.87 258	47.46 216	73.95 197	78.39 164	42.88 298	59.97 226	76.60 261	38.11 209	79.39 197	54.84 152	72.32 176	79.55 222

testdata				64.66 248	81.52 69	52.93 130		65.29 269	46.09 270	73.88 38	87.46 52	38.08 210	66.26 290	53.31 165	78.48 111	74.78 274

FMVSNet1		66.70 175	65.87 170	69.19 194	77.49 151	43.33 245	77.31 137	77.83 168	56.45 157	64.60 165	82.70 124	38.08 210	80.33 187	46.08 211	72.31 177	83.92 135

tpmp4_e23		62.71 221	60.13 230	70.45 180	73.40 220	48.39 206	72.82 212	69.49 239	44.88 279	59.91 227	74.99 274	37.79 212	81.47 169	40.22 254	67.71 241	81.48 184

EPNet_dtu		61.90 228	61.97 213	61.68 266	72.89 241	39.78 266	75.85 168	65.62 266	55.09 180	54.56 281	79.36 216	37.59 213	67.02 286	39.80 258	76.95 123	78.25 233
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
Patchmatch-test1		59.75 243	58.00 252	64.98 247	74.14 211	48.06 209	63.35 289	63.23 288	49.13 242	59.33 236	71.46 293	37.45 214	69.59 276	41.39 251	62.57 275	77.30 243

IterMVS		62.79 216	61.27 223	67.35 212	69.37 283	52.04 145	71.17 237	68.24 251	52.63 204	59.82 229	76.91 255	37.32 215	72.36 266	52.80 167	63.19 271	77.66 239
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
tfpn200view9		63.18 213	62.18 211	66.21 226	76.85 167	39.62 267	71.96 231	69.44 240	56.63 151	62.61 184	79.83 201	37.18 216	79.17 202	31.84 290	73.25 157	79.83 218

thres400		63.31 209	62.18 211	66.72 216	76.85 167	39.62 267	71.96 231	69.44 240	56.63 151	62.61 184	79.83 201	37.18 216	79.17 202	31.84 290	73.25 157	81.36 187

tpm		57.34 263	58.16 249	54.86 300	71.80 259	34.77 306	67.47 270	56.04 320	48.20 252	60.10 225	76.92 254	37.17 218	53.41 335	40.76 253	65.01 256	76.40 255

test222					83.14 54	58.68 58	72.57 218	63.45 286	41.78 301	67.56 127	86.12 74	37.13 219			78.73 108	74.98 270

thres200		62.20 225	61.16 225	65.34 243	75.38 186	39.99 264	69.60 253	69.29 242	55.64 173	61.87 201	76.99 253	37.07 220	78.96 214	31.28 300	73.28 156	77.06 248

tfpn111		63.33 208	62.34 208	66.30 222	77.31 153	38.66 276	72.65 213	69.11 244	57.07 138	62.45 190	81.03 171	37.01 221	79.23 199	31.38 299	73.09 162	81.03 200

conf200view11		63.38 207	62.41 206	66.29 224	77.31 153	38.66 276	72.65 213	69.11 244	57.07 138	62.45 190	81.03 171	37.01 221	79.17 202	31.84 290	73.25 157	81.03 200

thres100view900		63.28 211	62.41 206	65.89 233	77.31 153	38.66 276	72.65 213	69.11 244	57.07 138	62.45 190	81.03 171	37.01 221	79.17 202	31.84 290	73.25 157	79.83 218

thres600view7		63.30 210	62.27 209	66.41 220	77.18 161	38.87 273	72.35 221	69.11 244	56.98 142	62.37 196	80.96 175	37.01 221	79.00 213	31.43 298	73.05 163	81.36 187

view600		62.77 217	61.84 214	65.55 237	77.28 156	36.87 290	72.15 225	67.78 252	56.79 145	61.46 209	81.92 144	36.88 225	78.42 216	29.86 306	72.46 170	81.36 187

view800		62.77 217	61.84 214	65.55 237	77.28 156	36.87 290	72.15 225	67.78 252	56.79 145	61.46 209	81.92 144	36.88 225	78.42 216	29.86 306	72.46 170	81.36 187

conf0.05thres1000		62.77 217	61.84 214	65.55 237	77.28 156	36.87 290	72.15 225	67.78 252	56.79 145	61.46 209	81.92 144	36.88 225	78.42 216	29.86 306	72.46 170	81.36 187

tfpn		62.77 217	61.84 214	65.55 237	77.28 156	36.87 290	72.15 225	67.78 252	56.79 145	61.46 209	81.92 144	36.88 225	78.42 216	29.86 306	72.46 170	81.36 187

PatchFormer-LS_test		62.20 225	60.59 228	67.04 214	72.18 252	46.82 221	70.36 248	68.62 249	51.92 209	59.19 237	70.23 301	36.86 229	75.07 258	50.23 183	65.68 252	79.23 227

DP-MVS		65.68 186	63.66 193	71.75 155	84.93 42	56.87 83	80.74 77	73.16 218	53.06 199	59.09 239	82.35 133	36.79 230	85.94 67	32.82 286	69.96 212	72.45 296

semantic-postprocess				65.40 242	71.99 255	50.80 158		69.63 236	45.71 276	60.61 221	77.93 233	36.56 231	65.99 292	55.67 149	63.50 268	79.42 224

XVG-OURS		68.76 132	67.37 137	72.90 126	74.32 202	57.22 75	70.09 250	78.81 149	55.24 178	67.79 125	85.81 83	36.54 232	78.28 222	62.04 115	75.74 132	83.19 157

pmmvs4		61.48 233	59.39 233	67.76 208	71.57 260	53.86 117	71.42 233	65.34 268	44.20 287	59.46 232	77.92 234	35.90 233	74.71 260	43.87 231	64.87 257	74.71 275

CR-MVSNet		59.91 241	57.90 253	65.96 230	69.96 278	52.07 143	65.31 281	63.15 289	42.48 300	59.36 233	74.84 275	35.83 234	70.75 272	45.50 219	64.65 261	75.06 267

Patchmtry		57.16 264	56.47 261	59.23 279	69.17 284	34.58 308	62.98 290	63.15 289	44.53 284	56.83 263	74.84 275	35.83 234	68.71 280	40.03 256	60.91 286	74.39 277

conf0.01		59.97 239	58.81 237	63.42 254	74.15 205	33.83 313	68.32 261	64.22 276	51.79 211	58.04 250	79.57 207	35.41 236	75.41 250	29.57 312	68.26 229	81.03 200

conf0.002		59.97 239	58.81 237	63.42 254	74.15 205	33.83 313	68.32 261	64.22 276	51.79 211	58.04 250	79.57 207	35.41 236	75.41 250	29.57 312	68.26 229	81.03 200

thresconf0.02		59.40 247	58.81 237	61.17 272	74.15 205	33.83 313	68.32 261	64.22 276	51.79 211	58.04 250	79.57 207	35.41 236	75.41 250	29.57 312	68.26 229	74.25 279

tfpn_n400		59.40 247	58.81 237	61.17 272	74.15 205	33.83 313	68.32 261	64.22 276	51.79 211	58.04 250	79.57 207	35.41 236	75.41 250	29.57 312	68.26 229	74.25 279

tfpnconf		59.40 247	58.81 237	61.17 272	74.15 205	33.83 313	68.32 261	64.22 276	51.79 211	58.04 250	79.57 207	35.41 236	75.41 250	29.57 312	68.26 229	74.25 279

tfpnview11		59.40 247	58.81 237	61.17 272	74.15 205	33.83 313	68.32 261	64.22 276	51.79 211	58.04 250	79.57 207	35.41 236	75.41 250	29.57 312	68.26 229	74.25 279

RPMNet		58.70 254	56.29 264	65.96 230	69.96 278	52.07 143	65.31 281	62.15 296	43.20 295	59.36 233	70.15 303	35.37 242	70.75 272	36.42 275	64.65 261	75.06 267

CANet_DTU		68.18 152	67.71 129	69.59 190	74.83 191	46.24 224	78.66 101	76.85 183	59.60 105	63.45 174	82.09 143	35.25 243	77.41 232	59.88 130	78.76 107	85.14 95

test_0402		63.25 212	61.01 226	69.96 185	80.00 96	54.37 114	76.86 148	72.02 223	54.58 187	58.71 242	80.79 181	35.00 244	84.36 103	26.41 327	64.71 258	71.15 309

tfpn_ndepth		59.57 246	59.02 236	61.23 271	73.81 212	35.60 302	69.40 256	65.59 267	50.96 230	57.96 256	77.72 239	34.81 245	75.91 248	30.36 303	70.57 193	72.18 302

sam_mvs1												34.74 246				78.05 236

tfpn1000		59.24 252	58.70 245	60.86 277	73.75 213	33.99 311	68.86 259	63.98 283	51.25 228	57.29 261	79.51 215	34.58 247	75.26 256	29.08 319	69.99 210	73.32 288

pmmvs5		56.47 266	55.68 267	58.86 282	61.41 323	36.71 295	66.37 273	62.75 292	40.38 312	53.70 288	76.62 259	34.56 248	67.05 285	40.02 257	65.27 254	72.83 291

patchmatchnet-post											64.03 320	34.50 249	74.27 262

PatchmatchNet		59.84 242	58.24 248	64.65 249	73.05 235	46.70 222	69.42 255	62.18 295	47.55 258	58.88 241	71.96 291	34.49 250	69.16 278	42.99 239	63.60 267	78.07 235
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
Patchmatch-test		49.08 295	48.28 295	51.50 313	64.40 313	30.85 329	45.68 335	48.46 339	35.60 326	46.10 317	72.10 290	34.47 251	46.37 341	27.08 325	60.65 290	77.27 245

MS-PatchMatch		62.42 223	61.46 221	65.31 244	75.21 188	52.10 142	72.05 229	74.05 212	46.41 267	57.42 260	74.36 279	34.35 252	77.57 231	45.62 217	73.67 148	66.26 320

tpmvs		58.47 255	56.95 258	63.03 260	70.20 275	41.21 259	67.90 269	67.23 259	49.62 239	54.73 279	70.84 297	34.14 253	76.24 246	36.64 272	61.29 285	71.64 305

PMMVS		53.96 279	53.26 283	56.04 294	62.60 319	50.92 154	61.17 300	56.09 319	32.81 330	53.51 292	66.84 313	34.04 254	59.93 311	44.14 228	68.18 235	57.27 335

Patchmatch-RL test		58.16 258	55.49 268	66.15 227	67.92 293	48.89 202	60.66 302	51.07 333	47.86 256	59.36 233	62.71 326	34.02 255	72.27 267	56.41 143	59.40 298	77.30 243

test_post											3.55 353	33.90 256	66.52 288

Test4		67.77 157	65.97 169	73.19 122	68.64 286	50.58 163	74.80 189	80.48 113	54.13 193	59.11 238	79.07 221	33.89 257	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	48.51 247	55.71 269	81.89 148	33.71 258	79.71 192	41.66 248	70.37 200	77.58 240
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	70.87 268	48.10 208	70.48 245	74.40 209	56.69 149	64.70 164	76.77 257	33.66 259	81.10 174	55.42 151	70.32 202	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	48.50 248	56.62 264	84.62 97	33.59 260	82.34 155	29.65 311	75.23 135	75.97 256

sam_mvs												33.43 261

PatchT		53.17 285	53.44 282	52.33 311	68.29 291	25.34 342	58.21 308	54.41 326	44.46 285	54.56 281	69.05 306	33.32 262	60.94 306	36.93 268	61.76 280	70.73 311

test20.03		53.87 281	54.02 278	53.41 306	61.47 322	28.11 334	61.30 298	59.21 304	51.34 225	52.09 296	77.43 250	33.29 263	58.55 315	29.76 310	60.27 296	73.58 287

anonymousdsp		67.00 170	64.82 184	73.57 102	70.09 276	56.13 91	76.35 155	77.35 178	48.43 249	64.99 161	80.84 180	33.01 264	80.34 186	64.66 76	67.64 242	84.23 125

MDTV_nov1_ep13_2view						25.89 339	61.22 299		40.10 313	51.10 299		32.97 265		38.49 261		78.61 231

IB-MVS	56.42 12	65.40 192	62.73 203	73.40 114	74.89 189	52.78 132	73.09 209	75.13 200	55.69 171	58.48 247	73.73 283	32.86 266	86.32 59	50.63 179	70.11 208	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	51.61 218	70.04 76	81.41 164	32.79 267	79.02 209	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	51.61 218	70.04 76	81.41 164	32.79 267	79.02 209	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	51.61 218	70.04 76	81.41 164	32.79 267	79.02 209	63.81 93	77.31 118	81.22 195

Anonymous20231206		55.10 277	55.30 269	54.48 302	69.81 281	33.94 312	62.91 291	62.13 297	41.08 306	55.18 276	75.65 270	32.75 270	56.59 324	30.32 304	67.86 238	72.91 290

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	32.71 271	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		58.15 259	58.13 251	58.22 285	68.57 287	44.80 231	65.46 278	57.92 309	50.08 236	55.44 272	69.82 304	32.62 272	57.44 318	49.66 188	73.62 149	72.41 298

test0.0.03 1		53.32 284	53.59 281	52.50 310	62.81 318	29.45 331	59.51 304	54.11 328	50.08 236	54.40 283	74.31 280	32.62 272	55.92 329	30.50 302	63.95 265	72.15 304

MDTV_nov1_ep13			57.00 257		72.73 243	38.26 281	65.02 284	64.73 273	44.74 281	55.46 271	72.48 288	32.61 274	70.47 274	37.47 265	67.75 240

cascas		65.98 185	63.42 195	73.64 100	77.26 160	52.58 135	72.26 223	77.21 179	48.56 246	61.21 214	74.60 278	32.57 275	85.82 69	50.38 181	76.75 128	82.52 170

test_post1							68.67 260				3.64 352	32.39 276	69.49 277	44.17 227

CVMVSNet		59.63 245	59.14 235	61.08 276	74.47 198	38.84 274	75.20 179	68.74 248	31.15 333	58.24 248	76.51 262	32.39 276	68.58 281	49.77 185	65.84 250	75.81 260

MIMVSNet		57.35 262	57.07 256	58.22 285	74.21 204	37.18 287	62.46 292	60.88 300	48.88 244	55.29 275	75.99 268	31.68 278	62.04 304	31.87 289	72.35 175	75.43 265

PVSNet_0	43.31 20	47.46 300	45.64 300	52.92 308	67.60 295	44.65 233	54.06 318	54.64 324	41.59 304	46.15 315	58.75 332	30.99 279	58.66 314	32.18 287	24.81 342	55.46 336

testing_2		66.02 184	63.77 191	72.76 132	66.03 306	50.48 169	72.93 210	80.36 116	54.41 190	54.25 285	76.76 258	30.89 280	83.16 130	64.19 87	74.08 144	84.65 111

gg-mvs-nofinetune		57.86 260	56.43 262	62.18 264	72.62 245	35.35 304	66.57 271	56.33 317	50.65 231	57.64 258	57.10 333	30.65 281	76.36 244	37.38 266	78.88 104	74.82 273

GG-mvs-BLEND				62.34 263	71.36 265	37.04 289	69.20 257	57.33 312		54.73 279	65.48 319	30.37 282	77.82 227	34.82 279	74.93 136	72.17 303

MDA-MVSNet-bldmvs		53.87 281	50.81 288	63.05 259	66.25 303	48.58 204	56.93 312	63.82 284	48.09 253	41.22 329	70.48 300	30.34 283	68.00 282	34.24 281	45.92 331	72.57 294

EPMVS		53.96 279	53.69 279	54.79 301	66.12 305	31.96 326	62.34 294	49.05 336	44.42 286	55.54 270	71.33 295	30.22 284	56.70 322	41.65 249	62.54 276	75.71 262

DWT-MVSNet_test		61.90 228	59.93 232	67.83 207	71.98 256	46.09 225	71.03 240	69.71 233	50.09 235	58.51 246	70.62 298	30.21 285	77.63 229	49.28 190	67.91 237	79.78 220

YYNet1		50.73 292	48.96 291	56.03 295	61.10 325	41.78 255	51.94 325	56.44 316	40.94 308	44.84 318	67.80 310	30.08 286	55.08 333	36.77 269	50.71 321	71.22 307

MDA-MVSNet_test_wron		50.71 293	48.95 292	56.00 296	61.17 324	41.84 254	51.90 326	56.45 315	40.96 307	44.79 319	67.84 309	30.04 287	55.07 334	36.71 271	50.69 322	71.11 310

MSDG		61.81 231	59.23 234	69.55 193	72.64 244	52.63 134	70.45 246	75.81 192	51.38 224	53.70 288	76.11 265	29.52 288	81.08 175	37.70 264	65.79 251	74.93 271

CMPMVS	42.80 21	57.81 261	55.97 265	63.32 256	60.98 326	47.38 217	64.66 285	69.50 238	32.06 332	46.83 313	77.80 237	29.50 289	71.36 269	48.68 194	73.75 147	71.21 308
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
LTVRE_ROB	55.42 16	63.15 214	61.23 224	68.92 198	76.57 171	47.80 211	59.92 303	76.39 185	54.35 191	58.67 243	82.46 132	29.44 290	81.49 168	42.12 244	71.14 186	77.46 241
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		53.16 286	52.47 284	55.23 297	59.45 333	33.39 321	59.43 305	69.13 243	45.98 271	50.35 305	72.32 289	29.30 291	58.26 316	42.02 246	44.30 333	74.05 284

CHOSEN 280x420		47.83 298	46.36 299	52.24 312	67.37 296	49.78 190	38.91 343	43.11 345	35.00 327	43.27 326	63.30 325	28.95 292	49.19 340	36.53 273	60.80 289	57.76 334

pmmvs-eth3d		58.81 253	56.31 263	66.30 222	67.61 294	52.42 140	72.30 222	64.76 272	43.55 292	54.94 277	74.19 281	28.95 292	72.60 265	43.31 234	57.21 303	73.88 286

dp		51.89 289	51.60 287	52.77 309	68.44 290	32.45 323	62.36 293	54.57 325	44.16 288	49.31 306	67.91 308	28.87 294	56.61 323	33.89 282	54.89 309	69.24 317

jajsoiax		68.25 148	66.45 161	73.66 98	75.62 181	55.49 104	80.82 75	78.51 157	52.33 206	64.33 168	84.11 107	28.28 295	81.81 163	63.48 98	70.62 191	83.67 147

RPSCF		55.80 273	54.22 277	60.53 278	65.13 310	42.91 250	64.30 286	57.62 311	36.84 324	58.05 249	82.28 136	28.01 296	56.24 328	37.14 267	58.61 301	82.44 173

F-COLMAP		63.05 215	60.87 227	69.58 192	76.99 166	53.63 120	78.12 113	76.16 187	47.97 255	52.41 295	81.61 156	27.87 297	78.11 224	40.07 255	66.66 245	77.00 250

K. test v3		60.47 237	57.11 255	70.56 178	73.74 214	48.22 207	75.10 182	62.55 293	58.27 130	53.62 290	76.31 264	27.81 298	81.59 166	47.42 199	39.18 336	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	57.47 259	82.55 130	27.68 299	84.17 106	45.54 218	69.78 215	79.90 217

UnsupCasMVSNet_bld		50.07 294	48.87 293	53.66 304	60.97 327	33.67 319	57.62 310	64.56 274	39.47 316	47.38 310	64.02 322	27.47 300	59.32 312	34.69 280	43.68 334	67.98 319

mvs_tets		68.18 152	66.36 165	73.63 101	75.61 182	55.35 107	80.77 76	78.56 155	52.48 205	64.27 170	84.10 108	27.45 301	81.84 162	63.45 99	70.56 194	83.69 144

lessismore_v0				69.91 186	71.42 263	47.80 211		50.90 334		50.39 304	75.56 271	27.43 302	81.33 170	45.91 213	34.10 339	80.59 208

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	55.75 170	56.59 265	80.98 174	27.12 303	80.94 176	42.90 241	71.58 183	77.25 247
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
SixPastTwentyTwo		61.65 232	58.80 244	70.20 183	75.80 179	47.22 218	75.59 170	69.68 235	54.61 186	54.11 286	79.26 218	27.07 304	82.96 134	43.27 235	49.79 324	80.41 211

PVSNet	50.76 19	58.40 256	57.39 254	61.42 268	75.53 183	44.04 240	61.43 296	63.45 286	47.04 263	56.91 262	73.61 284	27.00 305	64.76 295	39.12 259	72.40 174	75.47 264

OpenMVS_ROB	52.78 18	60.03 238	58.14 250	65.69 236	70.47 271	44.82 230	75.33 174	70.86 227	45.04 278	56.06 267	76.00 266	26.89 306	79.65 193	35.36 278	67.29 243	72.60 293

ADS-MVSNet2		51.33 291	48.76 294	59.07 281	66.02 307	44.60 234	50.90 327	59.76 303	36.90 322	50.74 301	66.18 317	26.38 307	63.11 299	27.17 323	54.76 310	69.50 314

ADS-MVSNet		48.48 297	47.77 296	50.63 314	66.02 307	29.92 330	50.90 327	50.87 335	36.90 322	50.74 301	66.18 317	26.38 307	52.47 337	27.17 323	54.76 310	69.50 314

N_pmnet		39.35 314	40.28 311	36.54 331	63.76 315	1.62 357	49.37 330	0.76 358	34.62 328	43.61 325	66.38 316	26.25 309	42.57 347	26.02 329	51.77 317	65.44 321

MVS-HIRNet		45.52 303	44.48 305	48.65 318	68.49 289	34.05 310	59.41 306	44.50 344	27.03 337	37.96 335	50.47 340	26.16 310	64.10 296	26.74 326	59.52 297	47.82 339

FMVSNet5		55.86 272	54.93 270	58.66 284	71.05 267	36.35 297	64.18 288	62.48 294	46.76 264	50.66 303	74.73 277	25.80 311	64.04 297	33.11 285	65.57 253	75.59 263

new-patchmatchnet		47.56 299	47.73 297	47.06 320	58.81 334	9.37 353	48.78 331	59.21 304	43.28 293	44.22 321	68.66 307	25.67 312	57.20 321	31.57 297	49.35 327	74.62 276

MIMVSNet1		55.17 276	54.31 275	57.77 289	70.03 277	32.01 325	65.68 276	64.81 271	49.19 241	46.75 314	76.00 266	25.53 313	64.04 297	28.65 321	62.13 278	77.26 246

PatchMatch-RL		56.25 269	54.55 273	61.32 270	77.06 163	56.07 93	65.57 277	54.10 329	44.13 289	53.49 293	71.27 296	25.20 314	66.78 287	36.52 274	63.66 266	61.12 329

JIA-IIPM		51.56 290	47.68 298	63.21 257	64.61 312	50.73 160	47.71 332	58.77 306	42.90 297	48.46 308	51.72 337	24.97 315	70.24 275	36.06 277	53.89 313	68.64 318

EU-MVSNet		55.61 274	54.41 274	59.19 280	65.41 309	33.42 320	72.44 220	71.91 224	28.81 335	51.27 298	73.87 282	24.76 316	69.08 279	43.04 238	58.20 302	75.06 267

EG-PatchMatch MVS		64.71 198	62.87 200	70.22 181	77.68 142	53.48 122	77.99 121	78.82 148	53.37 197	56.03 268	77.41 251	24.75 317	84.04 109	46.37 208	73.42 154	73.14 289

TESTMET0.1,1		55.28 275	54.90 271	56.42 293	66.56 302	43.67 243	65.46 278	56.27 318	39.18 317	53.83 287	67.44 311	24.21 318	55.46 332	48.04 198	73.11 161	70.13 312

LP		48.51 296	45.51 301	57.52 291	62.86 317	44.53 237	52.38 324	59.84 302	38.11 319	42.81 327	61.02 327	23.23 319	63.02 300	24.10 330	45.24 332	65.02 323

COLMAP_ROB	52.97 17	61.27 235	58.81 237	68.64 201	74.63 195	52.51 137	78.42 108	73.30 216	49.92 238	50.96 300	81.51 159	23.06 320	79.40 196	31.63 295	65.85 249	74.01 285
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
testgi		51.90 288	52.37 285	50.51 315	60.39 329	23.55 345	58.42 307	58.15 307	49.03 243	51.83 297	79.21 219	22.39 321	55.59 330	29.24 318	62.64 274	72.40 300

DSMNet-mixed		39.30 315	38.72 314	41.03 328	51.22 341	19.66 347	45.53 336	31.35 351	15.83 347	39.80 334	67.42 312	22.19 322	45.13 344	22.43 331	52.69 315	58.31 333

test-mter		56.42 267	55.82 266	58.22 285	68.57 287	44.80 231	65.46 278	57.92 309	39.94 315	55.44 272	69.82 304	21.92 323	57.44 318	49.66 188	73.62 149	72.41 298

OurMVSNet-221017-0		61.37 234	58.63 247	69.61 189	72.05 254	48.06 209	73.93 200	72.51 220	47.23 261	54.74 278	80.92 176	21.49 324	81.24 171	48.57 196	56.22 305	79.53 223

Anonymous20231211		55.92 271	53.63 280	62.77 261	68.22 292	35.56 303	74.48 193	69.89 232	46.42 266	49.07 307	73.45 285	21.13 325	76.77 240	28.74 320	51.30 320	75.97 256

ITE_SJBPF				62.09 265	66.16 304	44.55 236		64.32 275	47.36 260	55.31 274	80.34 191	19.27 326	62.68 302	36.29 276	62.39 277	79.04 228

AllTest		57.08 265	54.65 272	64.39 250	71.44 261	49.03 199	69.92 252	67.30 257	45.97 272	47.16 311	79.77 203	17.47 327	67.56 283	33.65 283	59.16 299	76.57 253

TestCases				64.39 250	71.44 261	49.03 199		67.30 257	45.97 272	47.16 311	79.77 203	17.47 327	67.56 283	33.65 283	59.16 299	76.57 253

testpf		44.11 307	45.40 302	40.26 329	60.52 328	27.34 336	33.26 345	54.33 327	45.87 275	41.08 330	60.26 329	16.46 329	59.14 313	46.09 210	50.68 323	34.31 345

XVG-ACMP-BASELINE		64.36 202	62.23 210	70.74 175	72.35 249	52.45 139	70.80 242	78.45 160	53.84 195	59.87 228	81.10 169	16.24 330	79.32 198	55.64 150	71.76 181	80.47 209

tmp_tt		9.43 330	11.14 331	4.30 342	2.38 356	4.40 356	13.62 350	16.08 355	0.39 352	15.89 347	13.06 350	15.80 331	5.54 355	12.63 346	10.46 351	2.95 351

USDC		56.35 268	54.24 276	62.69 262	64.74 311	40.31 263	65.05 283	73.83 213	43.93 290	47.58 309	77.71 240	15.36 332	75.05 259	38.19 263	61.81 279	72.70 292

test2356		45.61 302	44.66 304	48.47 319	60.15 330	28.08 335	52.44 323	52.83 332	38.01 320	46.13 316	60.98 328	15.08 333	55.54 331	20.43 339	55.85 307	61.78 327

test1235678		45.66 301	44.46 306	49.26 316	59.88 331	28.68 333	56.36 314	55.54 323	39.12 318	40.89 331	63.40 324	14.41 334	57.32 320	21.05 336	49.47 326	61.78 327

1111		44.40 306	45.00 303	42.61 327	57.55 336	17.33 350	53.82 321	57.05 313	40.78 309	44.11 322	66.57 314	13.37 335	45.77 342	22.15 332	49.58 325	64.73 324

.test1245		34.88 318	39.49 313	21.04 339	57.55 336	17.33 350	53.82 321	57.05 313	40.78 309	44.11 322	66.57 314	13.37 335	45.77 342	22.15 332	0.00 353	0.03 354

ANet_high		41.38 311	37.47 315	53.11 307	39.73 350	24.45 344	56.94 311	69.69 234	47.65 257	26.04 341	52.32 336	12.44 337	62.38 303	21.80 335	10.61 350	72.49 295

no-one		40.85 312	36.09 316	55.14 298	48.55 343	38.72 275	42.15 341	62.92 291	34.60 329	23.55 342	49.74 341	12.21 338	66.16 291	26.27 328	24.84 341	60.54 330

FPMVS		42.18 310	41.11 310	45.39 322	58.03 335	41.01 261	49.50 329	53.81 330	30.07 334	33.71 336	64.03 320	11.69 339	52.08 338	14.01 345	55.11 308	43.09 342

TinyColmap		54.14 278	51.72 286	61.40 269	66.84 299	41.97 253	66.52 272	68.51 250	44.81 280	42.69 328	75.77 269	11.66 340	72.94 264	31.96 288	56.77 304	69.27 316

TDRefinement		53.44 283	50.72 289	61.60 267	64.31 314	46.96 219	70.89 241	65.27 270	41.78 301	44.61 320	77.98 231	11.52 341	66.36 289	28.57 322	51.59 318	71.49 306

ambc				65.13 245	63.72 316	37.07 288	47.66 333	78.78 150		54.37 284	71.42 294	11.24 342	80.94 176	45.64 216	53.85 314	77.38 242

pmmvs3		44.92 304	41.95 309	53.86 303	52.58 340	43.55 244	62.11 295	46.90 343	26.05 339	40.63 332	60.19 330	11.08 343	57.91 317	31.83 294	46.15 330	60.11 331

new_pmnet		34.13 320	34.29 319	33.64 332	52.63 339	18.23 349	44.43 339	33.90 349	22.81 342	30.89 338	53.18 335	10.48 344	35.72 351	20.77 337	39.51 335	46.98 340

LF4IMVS		42.95 308	42.26 308	45.04 323	48.30 344	32.50 322	54.80 316	48.49 338	28.03 336	40.51 333	70.16 302	9.24 345	43.89 345	31.63 295	49.18 328	58.72 332

PM-MVS		52.33 287	50.19 290	58.75 283	62.10 320	45.14 229	65.75 275	40.38 346	43.60 291	53.52 291	72.65 287	9.16 346	65.87 293	50.41 180	54.18 312	65.24 322

testus		44.59 305	43.87 307	46.76 321	59.85 332	24.65 343	53.86 319	55.82 321	36.26 325	43.97 324	63.42 323	8.39 347	53.14 336	20.70 338	52.52 316	62.51 325

testmv		42.25 309	40.11 312	48.66 317	53.23 338	27.02 337	56.62 313	55.74 322	37.25 321	33.10 337	59.52 331	7.78 348	56.58 325	19.61 340	38.13 338	62.40 326

EMVS		22.97 326	21.84 328	26.36 338	40.20 349	19.53 348	41.95 342	34.64 348	17.09 346	9.73 351	22.83 349	7.29 349	42.22 349	9.18 350	13.66 347	17.32 349

test12356		36.16 317	35.94 317	36.83 330	50.82 342	8.52 354	44.84 338	53.49 331	32.72 331	30.11 339	55.08 334	7.11 350	49.47 339	16.60 342	32.68 340	52.50 337

E-PMN		23.77 325	22.73 326	26.90 337	42.02 348	20.67 346	42.66 340	35.70 347	17.43 345	10.28 350	25.05 347	6.42 351	42.39 348	10.28 348	14.71 345	17.63 348

Gipuma		34.77 319	31.91 321	43.33 326	62.05 321	37.87 283	20.39 348	67.03 260	23.23 341	18.41 345	25.84 346	4.24 352	62.73 301	14.71 344	51.32 319	29.38 347
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PMMVS2		27.40 324	25.91 324	31.87 335	39.46 351	6.57 355	31.17 346	28.52 352	23.96 340	20.45 344	48.94 342	4.20 353	37.94 350	16.51 343	19.97 343	51.09 338

LCM-MVSNet		40.30 313	35.88 318	53.57 305	42.24 347	29.15 332	45.21 337	60.53 301	22.23 343	28.02 340	50.98 339	3.72 354	61.78 305	31.22 301	38.76 337	69.78 313

DeepMVS_CX				12.03 341	17.97 355	10.91 352		10.60 356	7.46 350	11.07 349	28.36 345	3.28 355	11.29 354	8.01 351	9.74 352	13.89 350

PMVS	28.69 22	36.22 316	33.29 320	45.02 324	36.82 352	35.98 301	54.68 317	48.74 337	26.31 338	21.02 343	51.61 338	2.88 356	60.10 310	9.99 349	47.58 329	38.99 344
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MVE	17.77 23	21.41 327	17.77 329	32.34 334	34.34 354	25.44 341	16.11 349	24.11 353	11.19 349	13.22 348	31.92 344	1.58 357	30.95 352	10.47 347	17.03 344	40.62 343
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
PNet_i23d		27.88 323	25.99 323	33.55 333	47.54 345	25.89 339	47.24 334	32.91 350	21.44 344	15.90 346	38.09 343	0.85 358	42.76 346	16.90 341	13.03 348	32.00 346

wuyk23d		13.32 329	12.52 330	15.71 340	47.54 345	26.27 338	31.06 347	1.98 357	4.93 351	5.18 353	1.94 354	0.45 359	18.54 353	6.81 352	12.83 349	2.33 352

wuykxyi23d		28.12 322	22.54 327	44.87 325	34.97 353	32.11 324	37.96 344	47.31 341	13.32 348	9.29 352	23.72 348	0.45 359	56.58 325	21.85 334	13.98 346	45.93 341

test123		4.73 332	6.30 333	0.02 343	0.01 357	0.01 358	56.36 314	0.00 359	0.01 353	0.04 354	0.21 356	0.01 361	0.00 356	0.03 354	0.00 353	0.04 353

sosnet-low-res		0.00 335	0.00 336	0.00 345	0.00 359	0.00 359	0.00 351	0.00 359	0.00 355	0.00 356	0.00 357	0.00 362	0.00 356	0.00 355	0.00 353	0.00 356

sosnet		0.00 335	0.00 336	0.00 345	0.00 359	0.00 359	0.00 351	0.00 359	0.00 355	0.00 356	0.00 357	0.00 362	0.00 356	0.00 355	0.00 353	0.00 356

uncertanet		0.00 335	0.00 336	0.00 345	0.00 359	0.00 359	0.00 351	0.00 359	0.00 355	0.00 356	0.00 357	0.00 362	0.00 356	0.00 355	0.00 353	0.00 356

Regformer		0.00 335	0.00 336	0.00 345	0.00 359	0.00 359	0.00 351	0.00 359	0.00 355	0.00 356	0.00 357	0.00 362	0.00 356	0.00 355	0.00 353	0.00 356

testmvs		4.52 333	6.03 334	0.01 344	0.01 357	0.00 359	53.86 319	0.00 359	0.01 353	0.04 354	0.27 355	0.00 362	0.00 356	0.04 353	0.00 353	0.03 354

ab-mvs-re		6.49 331	8.65 332	0.00 345	0.00 359	0.00 359	0.00 351	0.00 359	0.00 355	0.00 356	77.89 235	0.00 362	0.00 356	0.00 355	0.00 353	0.00 356

uanet		0.00 335	0.00 336	0.00 345	0.00 359	0.00 359	0.00 351	0.00 359	0.00 355	0.00 356	0.00 357	0.00 362	0.00 356	0.00 355	0.00 353	0.00 356

GSMVS																78.05 236

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								17.08 354

gm-plane-assit					71.40 264	41.72 257			48.85 245		73.31 286		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		45.32 277	76.55 15			65.56 294	58.75 135

新几何2							76.12 160

无先验							79.66 91	74.30 210	48.40 250				80.78 181	53.62 160		79.03 229

原ACMM2							79.02 97

testdata2													72.18 268	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								0.00 359

nn								0.00 359

door-mid								47.19 342

test11								83.47 45

door								47.60 340

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++															72.16 178

High-resolution multi-view benchmark