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.

HSP-MVS		80.69 1	81.20 1	79.14 8	86.21 17	62.73 12	86.09 8	85.03 14	65.51 15	83.81 1	90.51 11	63.71 2	89.23 6	81.51 1	88.44 12	85.45 79

test_part2					87.58 3	60.47 37				83.42 2

APDe-MVS		80.16 2	80.59 2	78.86 18	86.64 10	60.02 40	88.12 1	86.42 6	62.94 40	82.40 3	92.12 1	59.64 7	89.76 3	78.70 5	88.32 16	86.79 38

DeepPCF-MVS	69.58 1	79.03 6	79.00 6	79.13 9	84.92 42	60.32 38	83.03 37	85.33 11	62.86 43	80.17 4	90.03 21	61.76 3	88.95 10	74.21 19	88.67 11	88.12 7

SD-MVS		77.70 17	77.62 15	77.93 32	84.47 45	61.88 23	84.55 19	83.87 36	60.37 78	79.89 5	89.38 30	54.97 26	85.58 70	76.12 12	84.94 46	86.33 48

TSAR-MVS + MP.		78.44 10	78.28 10	78.90 16	84.96 38	61.41 27	84.03 26	83.82 38	59.34 113	79.37 6	89.76 26	59.84 5	87.62 28	76.69 11	86.74 36	87.68 16

APD-MVS		78.02 13	78.04 13	77.98 31	86.44 14	60.81 34	85.52 15	84.36 22	60.61 73	79.05 7	90.30 16	55.54 23	88.32 16	73.48 28	87.03 31	84.83 104
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
HPM-MVS++		79.88 3	80.14 3	79.10 11	88.17 1	64.80 1	86.59 4	83.70 40	65.37 16	78.78 8	90.64 7	58.63 12	87.24 31	79.00 4	90.37 2	85.26 92

ACMMP_Plus		78.77 7	78.78 7	78.74 20	85.44 30	61.04 31	83.84 28	85.16 12	62.88 42	78.10 9	91.26 3	52.51 49	88.39 13	79.34 3	90.52 1	86.78 39

SteuartSystems-ACMMP		79.48 5	79.31 5	79.98 1	83.01 56	62.18 19	87.60 2	85.83 8	66.69 11	78.03 10	90.98 4	54.26 33	90.06 1	78.42 7	89.02 8	87.69 15
Skip Steuart: Steuart Systems R&D Blog.
CNVR-MVS		79.84 4	79.97 4	79.45 4	87.90 2	62.17 20	84.37 20	85.03 14	66.96 6	77.58 11	90.06 20	59.47 9	89.13 8	78.67 6	89.73 4	87.03 33

canonicalmvs		74.67 45	74.98 39	73.71 95	78.94 110	50.56 165	80.23 79	83.87 36	60.30 82	77.15 12	86.56 66	59.65 6	82.00 157	66.01 64	82.12 64	88.58 4

alignmvs		73.86 53	73.99 46	73.45 107	78.20 127	50.50 167	78.57 101	82.43 65	59.40 111	76.57 13	86.71 59	56.42 17	81.23 170	65.84 66	81.79 66	88.62 2

旧先验2							76.08 160		45.32 268	76.55 14			65.56 285	58.75 133

MP-MVS-pluss		78.35 11	78.46 8	78.03 30	84.96 38	59.52 45	82.93 39	85.39 10	62.15 53	76.41 15	91.51 2	52.47 51	86.78 43	80.66 2	89.64 6	87.80 12
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
Regformer-2		75.63 39	74.99 38	77.54 34	80.43 88	58.32 61	79.50 92	82.92 59	67.84 1	75.94 16	80.75 180	55.73 21	86.80 41	71.44 38	80.38 80	87.50 20

MPTG		77.61 19	77.36 18	78.35 24	86.08 21	63.57 2	83.37 33	80.97 98	65.13 18	75.77 17	90.88 5	48.63 101	86.66 45	77.23 8	88.17 18	84.81 105

MTAPA		76.90 26	76.42 27	78.35 24	86.08 21	63.57 2	74.92 184	80.97 98	65.13 18	75.77 17	90.88 5	48.63 101	86.66 45	77.23 8	88.17 18	84.81 105

Regformer-1		75.47 40	74.93 40	77.09 40	80.43 88	57.70 67	79.50 92	82.13 67	67.84 1	75.73 19	80.75 180	56.50 15	86.07 58	71.07 39	80.38 80	87.50 20

CSCG		76.92 25	76.75 23	77.41 36	83.96 49	59.60 44	82.95 38	86.50 5	60.78 71	75.27 20	84.83 91	60.76 4	86.56 50	67.86 51	87.87 26	86.06 56

test_prior3		76.89 27	76.96 21	76.69 44	84.20 47	57.27 72	81.75 59	84.88 16	60.37 78	75.01 21	89.06 33	56.22 18	86.43 54	72.19 32	88.96 9	86.38 42

test_prior2							81.75 59		60.37 78	75.01 21	89.06 33	56.22 18		72.19 32	88.96 9

TEST9					85.58 28	61.59 25	81.62 63	81.26 89	55.65 169	74.93 23	88.81 39	53.70 41	84.68 95

train_agg		76.27 33	76.15 28	76.64 47	85.58 28	61.59 25	81.62 63	81.26 89	55.86 163	74.93 23	88.81 39	53.70 41	84.68 95	75.24 15	88.33 14	83.65 148

MCST-MVS		77.48 20	77.45 16	77.54 34	86.67 9	58.36 60	83.22 35	86.93 1	56.91 141	74.91 25	88.19 44	59.15 10	87.68 27	73.67 25	87.45 27	86.57 41

test_8					85.40 31	60.96 32	81.54 66	81.18 92	55.86 163	74.81 26	88.80 41	53.70 41	84.45 100

agg_prior1		75.94 36	76.01 31	75.72 57	85.04 35	59.96 41	81.44 67	81.04 95	56.14 161	74.68 27	88.90 37	53.91 37	84.04 107	75.01 17	87.92 25	83.16 159

agg_prior					85.04 35	59.96 41		81.04 95		74.68 27			84.04 107

NCCC		78.58 8	78.31 9	79.39 5	87.51 4	62.61 16	85.20 17	84.42 21	66.73 10	74.67 29	89.38 30	55.30 24	89.18 7	74.19 20	87.34 28	86.38 42

nrg030		72.96 60	73.01 55	72.84 128	75.41 183	50.24 174	80.02 82	82.89 62	58.36 127	74.44 30	86.73 57	58.90 11	80.83 177	65.84 66	74.46 138	87.44 23

TSAR-MVS + GP.		74.90 42	74.15 45	77.17 39	82.00 63	58.77 56	81.80 58	78.57 154	58.58 121	74.32 31	84.51 100	55.94 20	87.22 32	67.11 57	84.48 50	85.52 74

CDPH-MVS		76.31 32	75.67 35	78.22 27	85.35 33	59.14 50	81.31 69	84.02 29	56.32 156	74.05 32	88.98 36	53.34 44	87.92 24	69.23 44	88.42 13	87.59 18

HFP-MVS		78.01 14	77.65 14	79.10 11	86.71 7	62.81 10	86.29 5	84.32 23	62.82 44	73.96 33	90.50 12	53.20 46	88.35 14	74.02 21	87.05 29	86.13 53

#test#		77.83 15	77.41 17	79.10 11	86.71 7	62.81 10	85.69 14	84.32 23	61.61 62	73.96 33	90.50 12	53.20 46	88.35 14	73.68 24	87.05 29	86.13 53

abl_6		74.34 46	73.50 49	76.86 41	82.43 59	60.16 39	83.48 32	81.86 73	58.81 119	73.95 35	89.86 24	41.87 169	86.62 47	67.98 50	81.23 71	83.80 141

Regformer-4		74.25 49	73.48 50	76.57 48	79.75 97	56.54 84	78.54 103	81.49 81	66.93 8	73.90 36	80.30 190	53.84 39	85.98 63	69.76 41	76.84 124	87.17 30

testdata				64.66 246	81.52 68	52.93 129		65.29 268	46.09 261	73.88 37	87.46 50	38.08 209	66.26 281	53.31 163	78.48 110	74.78 268

DeepC-MVS	69.38 2	78.56 9	78.14 12	79.83 2	83.60 50	61.62 24	84.17 24	86.85 2	63.23 35	73.84 38	90.25 18	57.68 13	89.96 2	74.62 18	89.03 7	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
agg_prior3		76.13 34	75.89 34	76.85 42	85.76 24	62.02 21	81.65 61	81.01 97	55.51 172	73.73 39	88.60 43	53.23 45	84.90 88	75.24 15	88.33 14	83.65 148

APD-MVS_3200maxsize		74.96 41	74.39 43	76.67 46	82.20 61	58.24 62	83.67 29	83.29 53	58.41 125	73.71 40	90.14 19	45.62 134	85.99 62	69.64 42	82.85 61	85.78 62

Regformer-3		73.89 52	73.28 54	75.71 58	79.75 97	55.48 104	78.54 103	79.93 119	66.58 12	73.62 41	80.30 190	54.87 28	84.54 98	69.09 45	76.84 124	87.10 32

MP-MVS		78.35 11	78.26 11	78.64 21	86.54 12	63.47 5	86.02 9	83.55 43	63.89 31	73.60 42	90.60 8	54.85 29	86.72 44	77.20 10	88.06 21	85.74 67

ACMMPR		77.71 16	77.23 19	79.16 6	86.75 6	62.93 9	86.29 5	84.24 25	62.82 44	73.55 43	90.56 10	49.80 72	88.24 17	74.02 21	87.03 31	86.32 49

PHI-MVS		75.87 37	75.36 36	77.41 36	80.62 86	55.91 96	84.28 21	85.78 9	56.08 162	73.41 44	86.58 65	50.94 65	88.54 12	70.79 40	89.71 5	87.79 13

region2R		77.67 18	77.18 20	79.15 7	86.76 5	62.95 8	86.29 5	84.16 27	62.81 46	73.30 45	90.58 9	49.90 70	88.21 18	73.78 23	87.03 31	86.29 51

VDD-MVS		72.50 64	72.09 62	73.75 93	81.58 67	49.69 193	77.76 125	77.63 172	63.21 36	73.21 46	89.02 35	42.14 165	83.32 123	61.72 118	82.50 62	88.25 6

DELS-MVS		74.76 43	74.46 42	75.65 60	77.84 138	52.25 140	75.59 168	84.17 26	63.76 32	73.15 47	82.79 121	59.58 8	86.80 41	67.24 56	86.04 42	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
HPM-MVS		77.28 21	76.85 22	78.54 22	85.00 37	60.81 34	82.91 40	85.08 13	62.57 47	73.09 48	89.97 23	50.90 66	87.48 29	75.30 13	86.85 34	87.33 28

DeepC-MVS_fast	68.24 3	77.25 22	76.63 25	79.12 10	86.15 19	60.86 33	84.71 18	84.85 18	61.98 59	73.06 49	88.88 38	53.72 40	89.06 9	68.27 47	88.04 22	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
VDDNet		71.81 75	71.33 72	73.26 119	82.80 58	47.60 214	78.74 97	75.27 197	59.59 106	72.94 50	89.40 29	41.51 178	83.91 112	58.75 133	82.99 57	88.26 5

test12				77.76 33	84.52 44	58.41 59		83.36 50		72.93 51		54.61 31	88.05 21		88.12 20	86.81 37

LFMVS		71.78 76	71.59 66	72.32 148	83.40 52	46.38 222	79.75 87	71.08 225	64.18 28	72.80 52	88.64 42	42.58 163	83.72 115	57.41 138	84.49 49	86.86 36

CP-MVS		77.12 24	76.68 24	78.43 23	86.05 23	63.18 7	87.55 3	83.45 46	62.44 50	72.68 53	90.50 12	48.18 108	87.34 30	73.59 26	85.71 43	84.76 109

UA-Net		73.13 58	72.93 56	73.76 91	83.58 51	51.66 146	78.75 96	77.66 171	67.75 4	72.61 54	89.42 28	49.82 71	83.29 124	53.61 160	83.14 54	86.32 49

OPM-MVS		74.73 44	74.25 44	76.19 50	80.81 82	59.01 52	82.60 46	83.64 41	63.74 33	72.52 55	87.49 49	47.18 120	85.88 66	69.47 43	80.78 72	83.66 147

MVS_Test		72.45 66	72.46 60	72.42 145	74.88 188	48.50 204	76.28 155	83.14 57	59.40 111	72.46 56	84.68 93	55.66 22	81.12 171	65.98 65	79.66 91	87.63 17

PGM-MVS		76.77 28	76.06 29	78.88 17	86.14 20	62.73 12	82.55 47	83.74 39	61.71 60	72.45 57	90.34 15	48.48 105	88.13 19	72.32 31	86.85 34	85.78 62

XVS		77.17 23	76.56 26	79.00 14	86.32 15	62.62 14	85.83 10	83.92 32	64.55 22	72.17 58	90.01 22	47.95 110	88.01 22	71.55 36	86.74 36	86.37 45

X-MVStestdata		70.21 104	67.28 140	79.00 14	86.32 15	62.62 14	85.83 10	83.92 32	64.55 22	72.17 58	6.49 342	47.95 110	88.01 22	71.55 36	86.74 36	86.37 45

MVS_0304		76.73 29	76.04 30	78.78 19	81.32 73	58.89 54	82.50 49	84.07 28	67.73 5	72.08 60	87.28 54	49.49 74	89.57 4	73.52 27	86.40 40	87.87 11

Effi-MVS+		73.31 57	72.54 59	75.62 61	77.87 137	53.64 118	79.62 90	79.61 124	61.63 61	72.02 61	82.61 126	56.44 16	85.97 64	63.99 89	79.07 102	87.25 29

mPP-MVS		76.54 30	75.93 32	78.34 26	86.47 13	63.50 4	85.74 13	82.28 66	62.90 41	71.77 62	90.26 17	46.61 128	86.55 51	71.71 35	85.66 44	84.97 101

EI-MVSNet-Vis-set		72.42 67	71.59 66	74.91 69	78.47 122	54.02 114	77.05 142	79.33 141	65.03 20	71.68 63	79.35 208	52.75 48	84.89 89	66.46 61	74.23 141	85.83 61

MSLP-MVS++		73.77 54	73.47 51	74.66 74	83.02 55	59.29 49	82.30 55	81.88 72	59.34 113	71.59 64	86.83 56	45.94 132	83.65 117	65.09 71	85.22 45	81.06 198

EI-MVSNet-UG-set		71.92 74	71.06 76	74.52 79	77.98 135	53.56 120	76.62 148	79.16 143	64.40 26	71.18 65	78.95 213	52.19 54	84.66 97	65.47 69	73.57 150	85.32 89

MG-MVS		73.96 51	73.89 47	74.16 82	85.65 26	49.69 193	81.59 65	81.29 88	61.45 63	71.05 66	88.11 45	51.77 56	87.73 26	61.05 121	83.09 55	85.05 98

VNet		69.68 112	70.19 86	68.16 204	79.73 100	41.63 257	70.53 241	77.38 177	60.37 78	70.69 67	86.63 62	51.08 62	77.09 233	53.61 160	81.69 70	85.75 66

MVS_111021_HR		74.02 50	73.46 52	75.69 59	83.01 56	60.63 36	77.29 138	78.40 163	61.18 67	70.58 68	85.97 76	54.18 35	84.00 111	67.52 55	82.98 58	82.45 171

HPM-MVS_fast		74.30 48	73.46 52	76.80 43	84.45 46	59.04 51	83.65 30	81.05 94	60.15 84	70.43 69	89.84 25	41.09 184	85.59 69	67.61 54	82.90 59	85.77 64

CLD-MVS		73.33 56	72.68 58	75.29 67	78.82 112	53.33 124	78.23 109	84.79 19	61.30 66	70.41 70	81.04 168	52.41 52	87.12 36	64.61 76	82.49 63	85.41 86
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
新几何1				70.76 173	85.66 25	61.13 30		66.43 263	44.68 273	70.29 71	86.64 61	41.29 181	75.23 248	49.72 185	81.75 68	75.93 253

1121		68.53 138	67.16 145	72.63 133	85.64 27	61.14 29	73.95 195	66.46 262	44.61 274	70.28 72	86.68 60	41.42 179	80.78 179	53.62 158	81.79 66	75.97 250

原ACMM1				74.69 73	85.39 32	59.40 46		83.42 47	51.47 214	70.27 73	86.61 63	48.61 103	86.51 52	53.85 157	87.96 23	78.16 230

CANet		76.46 31	75.93 32	78.06 29	81.29 74	57.53 69	82.35 50	83.31 52	67.78 3	70.09 74	86.34 69	54.92 27	88.90 11	72.68 30	84.55 48	87.76 14

xiu_mvs_v1_base_debu		68.58 134	67.28 140	72.48 139	78.19 128	57.19 76	75.28 174	75.09 201	51.61 209	70.04 75	81.41 162	32.79 259	79.02 206	63.81 91	77.31 117	81.22 194

xiu_mvs_v1_base		68.58 134	67.28 140	72.48 139	78.19 128	57.19 76	75.28 174	75.09 201	51.61 209	70.04 75	81.41 162	32.79 259	79.02 206	63.81 91	77.31 117	81.22 194

xiu_mvs_v1_base_debi		68.58 134	67.28 140	72.48 139	78.19 128	57.19 76	75.28 174	75.09 201	51.61 209	70.04 75	81.41 162	32.79 259	79.02 206	63.81 91	77.31 117	81.22 194

PS-MVSNAJss		72.24 69	71.21 74	75.31 66	78.50 120	55.93 95	81.63 62	82.12 68	56.24 159	70.02 78	85.68 82	47.05 121	84.34 102	65.27 70	74.41 140	85.67 69

xiu_mvs_v2_base		70.52 93	69.75 89	72.84 128	81.21 77	55.63 101	75.11 179	78.92 147	54.92 180	69.96 79	79.68 203	47.00 125	82.09 156	61.60 120	79.37 95	80.81 203

PS-MVSNAJ		70.51 94	69.70 91	72.93 124	81.52 68	55.79 97	74.92 184	79.00 146	55.04 179	69.88 80	78.66 215	47.05 121	82.19 154	61.61 119	79.58 92	80.83 202

v1neww		70.66 88	69.70 91	73.53 102	73.15 220	50.22 175	78.11 112	80.68 103	59.65 100	69.83 81	81.67 150	49.29 80	84.96 84	64.55 77	70.38 196	85.42 82

v7new		70.66 88	69.70 91	73.53 102	73.15 220	50.22 175	78.11 112	80.68 103	59.65 100	69.83 81	81.67 150	49.29 80	84.96 84	64.55 77	70.38 196	85.42 82

v6		70.66 88	69.70 91	73.53 102	73.14 223	50.21 178	78.11 112	80.67 105	59.65 100	69.82 83	81.65 152	49.29 80	84.96 84	64.55 77	70.39 195	85.42 82

ACMMP		76.02 35	75.33 37	78.07 28	85.20 34	61.91 22	85.49 16	84.44 20	63.04 38	69.80 84	89.74 27	45.43 139	87.16 35	72.01 34	82.87 60	85.14 94
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
PCF-MVS	61.88 8	70.95 84	69.49 100	75.35 65	77.63 143	55.71 98	76.04 163	81.81 75	50.30 225	69.66 85	85.40 88	52.51 49	84.89 89	51.82 170	80.24 84	85.45 79
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
v7		70.57 92	69.48 101	73.85 86	73.50 208	50.92 153	78.27 107	81.43 82	58.93 116	69.61 86	81.49 158	47.56 115	85.43 76	63.94 90	70.62 189	85.21 93

v1141		70.50 95	69.53 96	73.41 111	72.92 230	50.00 185	77.69 126	80.60 107	59.50 108	69.60 87	81.43 159	49.24 90	84.77 92	64.48 81	70.30 202	85.46 78

divwei89l23v2f112		70.50 95	69.53 96	73.41 111	72.91 231	50.00 185	77.69 126	80.59 108	59.50 108	69.60 87	81.43 159	49.26 85	84.77 92	64.48 81	70.31 201	85.47 76

v1		70.50 95	69.53 96	73.42 110	72.91 231	50.00 185	77.69 126	80.59 108	59.50 108	69.59 89	81.42 161	49.26 85	84.77 92	64.49 80	70.30 202	85.47 76

v2v482		70.50 95	69.45 103	73.66 97	72.62 237	50.03 184	77.58 130	80.51 112	59.90 90	69.52 90	82.14 139	47.53 116	84.88 91	65.07 72	70.17 205	86.09 55

MVSFormer		71.50 80	70.38 84	74.88 70	78.76 114	57.15 79	82.79 41	78.48 158	51.26 217	69.49 91	83.22 117	43.99 155	83.24 125	66.06 62	79.37 95	84.23 124

lupinMVS		69.57 115	68.28 118	73.44 108	78.76 114	57.15 79	76.57 149	73.29 217	46.19 260	69.49 91	82.18 135	43.99 155	79.23 197	64.66 74	79.37 95	83.93 133

V42		68.65 132	67.35 138	72.56 136	68.93 277	50.18 179	72.90 209	79.47 136	56.92 140	69.45 93	80.26 192	46.29 130	82.99 131	64.07 86	67.82 231	84.53 113

v1144		70.42 100	69.31 104	73.76 91	73.22 216	50.64 160	77.83 123	81.43 82	58.58 121	69.40 94	81.16 165	47.53 116	85.29 79	64.01 88	70.64 188	85.34 88

jason		69.65 113	68.39 117	73.43 109	78.27 126	56.88 81	77.12 140	73.71 215	46.53 256	69.34 95	83.22 117	43.37 159	79.18 198	64.77 73	79.20 100	84.23 124
jason: jason.
HQP_MVS		74.31 47	73.73 48	76.06 51	81.41 71	56.31 85	84.22 22	84.01 30	64.52 24	69.27 96	86.10 73	45.26 143	87.21 33	68.16 48	80.58 76	84.65 110

plane_prior3						56.09 91			63.92 30	69.27 96

VPA-MVSNet		69.02 125	69.47 102	67.69 208	77.42 151	41.00 261	74.04 194	79.68 122	60.06 85	69.26 98	84.81 92	51.06 63	77.58 227	54.44 155	74.43 139	84.48 115

Vis-MVSNet		72.18 70	71.37 71	74.61 77	81.29 74	55.41 105	80.90 72	78.28 165	60.73 72	69.23 99	88.09 46	44.36 152	82.65 148	57.68 136	81.75 68	85.77 64
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
EI-MVSNet		69.27 121	68.44 116	71.73 155	74.47 196	49.39 197	75.20 177	78.45 160	59.60 103	69.16 100	76.51 253	51.29 59	82.50 150	59.86 130	71.45 183	83.30 152

MVSTER		67.16 164	65.58 173	71.88 152	70.37 266	49.70 191	70.25 246	78.45 160	51.52 212	69.16 100	80.37 186	38.45 203	82.50 150	60.19 126	71.46 182	83.44 150

v1192		69.97 109	68.68 111	73.85 86	73.19 219	50.94 151	77.68 129	81.36 85	57.51 133	68.95 102	80.85 176	45.28 142	85.33 78	62.97 99	70.37 198	85.27 91

OMC-MVS		71.40 82	70.60 81	73.78 89	76.60 168	53.15 126	79.74 88	79.78 120	58.37 126	68.75 103	86.45 68	45.43 139	80.60 182	62.58 101	77.73 114	87.58 19

Fast-Effi-MVS+		70.28 103	69.12 106	73.73 94	78.50 120	51.50 149	75.01 181	79.46 137	56.16 160	68.59 104	79.55 204	53.97 36	84.05 106	53.34 162	77.53 116	85.65 71

v1921920		69.47 118	68.17 120	73.36 114	73.06 226	50.10 183	77.39 134	80.56 110	56.58 153	68.59 104	80.37 186	44.72 145	84.98 82	62.47 104	69.82 212	85.00 99

v144192		69.71 111	68.51 113	73.33 115	73.10 225	50.13 182	77.54 132	80.64 106	56.65 147	68.57 106	80.55 183	46.87 126	84.96 84	62.98 98	69.66 217	84.89 103

TranMVSNet+NR-MVSNet		70.36 101	70.10 88	71.17 167	78.64 118	42.97 248	76.53 150	81.16 93	66.95 7	68.53 107	85.42 87	51.61 58	83.07 130	52.32 167	69.70 216	87.46 22

API-MVS		72.17 71	71.41 69	74.45 80	81.95 64	57.22 74	84.03 26	80.38 115	59.89 92	68.40 108	82.33 132	49.64 73	87.83 25	51.87 169	84.16 52	78.30 228

BH-RMVSNet		68.81 128	67.42 134	72.97 123	80.11 94	52.53 135	74.26 192	76.29 186	58.48 124	68.38 109	84.20 103	42.59 162	83.83 114	46.53 204	75.91 130	82.56 167

v1240		69.24 123	67.91 123	73.25 120	73.02 228	49.82 188	77.21 139	80.54 111	56.43 155	68.34 110	80.51 184	43.33 160	84.99 80	62.03 114	69.77 215	84.95 102

UniMVSNet_NR-MVSNet		71.11 83	71.00 77	71.44 159	79.20 104	44.13 237	76.02 164	82.60 64	66.48 14	68.20 111	84.60 97	56.82 14	82.82 139	54.62 152	70.43 193	87.36 27

DU-MVS		70.01 107	69.53 96	71.44 159	78.05 133	44.13 237	75.01 181	81.51 80	64.37 27	68.20 111	84.52 98	49.12 96	82.82 139	54.62 152	70.43 193	87.37 25

UniMVSNet (Re)		70.63 91	70.20 85	71.89 151	78.55 119	45.29 227	75.94 165	82.92 59	63.68 34	68.16 113	83.59 112	53.89 38	83.49 120	53.97 156	71.12 185	86.89 35

Baseline_NR-MVSNet		67.05 167	67.56 129	65.50 239	75.65 178	37.70 284	75.42 171	74.65 207	59.90 90	68.14 114	83.15 120	49.12 96	77.20 231	52.23 168	69.78 213	81.60 181

WR-MVS		68.47 139	68.47 115	68.44 203	80.20 93	39.84 264	73.75 200	76.07 189	64.68 21	68.11 115	83.63 111	50.39 69	79.14 204	49.78 182	69.66 217	86.34 47

MAR-MVS		71.51 79	70.15 87	75.60 62	81.84 65	59.39 47	81.38 68	82.90 61	54.90 181	68.08 116	78.70 214	47.73 112	85.51 73	51.68 173	84.17 51	81.88 179
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
TR-MVS		66.59 178	65.07 181	71.17 167	79.18 105	49.63 195	73.48 203	75.20 199	52.95 197	67.90 117	80.33 189	39.81 191	83.68 116	43.20 235	73.56 151	80.20 209

HQP-NCC					80.66 83		82.31 52		62.10 54	67.85 118

ACMP_Plane					80.66 83		82.31 52		62.10 54	67.85 118

HQP4-MVS										67.85 118			86.93 39			84.32 117

HQP-MVS		73.45 55	72.80 57	75.40 64	80.66 83	54.94 107	82.31 52	83.90 34	62.10 54	67.85 118	85.54 85	45.46 137	86.93 39	67.04 58	80.35 82	84.32 117

MVS_111021_LR		69.50 117	68.78 110	71.65 156	78.38 123	59.33 48	74.82 186	70.11 231	58.08 129	67.83 122	84.68 93	41.96 167	76.34 242	65.62 68	77.54 115	79.30 222

3Dnovator+	66.72 4	75.84 38	74.57 41	79.66 3	82.40 60	59.92 43	85.83 10	86.32 7	66.92 9	67.80 123	89.24 32	42.03 166	89.38 5	64.07 86	86.50 39	89.69 1

VPNet		67.52 158	68.11 121	65.74 233	79.18 105	36.80 292	72.17 221	72.83 219	62.04 57	67.79 124	85.83 79	48.88 100	76.60 239	51.30 174	72.97 162	83.81 138

XVG-OURS		68.76 131	67.37 136	72.90 125	74.32 200	57.22 74	70.09 247	78.81 149	55.24 175	67.79 124	85.81 81	36.54 230	78.28 219	62.04 113	75.74 131	83.19 156

test222					83.14 53	58.68 57	72.57 215	63.45 279	41.78 292	67.56 126	86.12 72	37.13 218			78.73 107	74.98 264

CPTT-MVS		72.78 61	72.08 63	74.87 71	84.88 43	61.41 27	84.15 25	77.86 167	55.27 174	67.51 127	88.08 47	41.93 168	81.85 159	69.04 46	80.01 86	81.35 192

v148		68.24 149	67.19 144	71.40 162	70.43 264	47.77 212	75.76 167	77.03 181	58.91 117	67.36 128	80.10 194	48.60 104	81.89 158	60.01 127	66.52 239	84.53 113

FIs		70.82 86	71.43 68	68.98 196	78.33 124	38.14 280	76.96 144	83.59 42	61.02 68	67.33 129	86.73 57	55.07 25	81.64 162	54.61 154	79.22 99	87.14 31

ACMM	61.98 7	70.80 87	69.73 90	74.02 83	80.59 87	58.59 58	82.68 44	82.02 71	55.46 173	67.18 130	84.39 102	38.51 202	83.17 127	60.65 122	76.10 129	80.30 208
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
mvs_anonymous		68.03 154	67.51 132	69.59 189	72.08 245	44.57 234	71.99 227	75.23 198	51.67 208	67.06 131	82.57 127	54.68 30	77.94 223	56.56 140	75.71 132	86.26 52

XVG-OURS-SEG-HR		68.81 128	67.47 133	72.82 130	74.40 199	56.87 82	70.59 240	79.04 145	54.77 182	66.99 132	86.01 75	39.57 194	78.21 220	62.54 102	73.33 154	83.37 151

PAPR		71.72 77	70.82 79	74.41 81	81.20 78	51.17 150	79.55 91	83.33 51	55.81 166	66.93 133	84.61 96	50.95 64	86.06 59	55.79 146	79.20 100	86.00 57

DP-MVS Recon		72.15 73	70.73 80	76.40 49	86.57 11	57.99 64	81.15 71	82.96 58	57.03 138	66.78 134	85.56 83	44.50 149	88.11 20	51.77 171	80.23 85	83.10 160

LPG-MVS_test		72.74 62	71.74 65	75.76 55	80.22 91	57.51 70	82.55 47	83.40 48	61.32 64	66.67 135	87.33 52	39.15 197	86.59 48	67.70 52	77.30 120	83.19 156

LGP-MVS_train				75.76 55	80.22 91	57.51 70		83.40 48	61.32 64	66.67 135	87.33 52	39.15 197	86.59 48	67.70 52	77.30 120	83.19 156

IS-MVSNet		71.57 78	71.00 77	73.27 118	78.86 111	45.63 226	80.22 80	78.69 152	64.14 29	66.46 137	87.36 51	49.30 78	85.60 68	50.26 180	83.71 53	88.59 3

v8		70.33 102	69.28 105	73.49 105	73.15 220	50.22 175	78.62 100	80.78 102	60.79 70	66.45 138	82.11 140	49.35 76	84.98 82	63.58 95	68.71 224	85.28 90

v10		70.21 104	69.02 107	73.81 88	73.51 207	50.92 153	78.74 97	81.39 84	60.05 86	66.39 139	81.83 147	47.58 114	85.41 77	62.80 100	68.86 223	85.09 97

mvs-test1		70.44 99	68.19 119	77.18 38	76.10 173	63.22 6	80.59 77	76.06 190	59.83 94	66.32 140	79.87 197	41.56 175	85.53 71	60.60 123	72.77 163	82.80 166

PAPM_NR		72.63 63	71.80 64	75.13 68	81.72 66	53.42 123	79.91 84	83.28 54	59.14 115	66.31 141	85.90 77	51.86 55	86.06 59	57.45 137	80.62 74	85.91 59

BH-untuned		68.27 145	67.29 139	71.21 165	79.74 99	53.22 125	76.06 161	77.46 176	57.19 135	66.10 142	81.61 154	45.37 141	83.50 119	45.42 220	76.68 128	76.91 246

ab-mvs		66.65 175	66.42 162	67.37 210	76.17 172	41.73 255	70.41 244	76.14 188	53.99 191	65.98 143	83.51 114	49.48 75	76.24 243	48.60 193	73.46 152	84.14 131

EPP-MVSNet		72.16 72	71.31 73	74.71 72	78.68 117	49.70 191	82.10 56	81.65 77	60.40 77	65.94 144	85.84 78	51.74 57	86.37 56	55.93 143	79.55 94	88.07 8

QAPM		70.05 106	68.81 109	73.78 89	76.54 170	53.43 122	83.23 34	83.48 44	52.89 198	65.90 145	86.29 70	41.55 177	86.49 53	51.01 175	78.40 111	81.42 184

FC-MVSNet-test		69.80 110	70.58 82	67.46 209	77.61 148	34.73 305	76.05 162	83.19 55	60.84 69	65.88 146	86.46 67	54.52 32	80.76 181	52.52 166	78.12 112	86.91 34

IterMVS-LS		69.22 124	68.48 114	71.43 161	74.44 198	49.40 196	76.23 157	77.55 173	59.60 103	65.85 147	81.59 156	51.28 60	81.58 165	59.87 129	69.90 211	83.30 152
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
PVSNet_Blended_VisFu		71.45 81	70.39 83	74.65 75	82.01 62	58.82 55	79.93 83	80.35 117	55.09 177	65.82 148	82.16 138	49.17 93	82.64 149	60.34 125	78.62 109	82.50 170

3Dnovator	64.47 5	72.49 65	71.39 70	75.79 54	77.70 140	58.99 53	80.66 76	83.15 56	62.24 52	65.46 149	86.59 64	42.38 164	85.52 72	59.59 131	84.72 47	82.85 165

test_djsdf		69.45 119	67.74 125	74.58 78	74.57 195	54.92 109	82.79 41	78.48 158	51.26 217	65.41 150	83.49 115	38.37 204	83.24 125	66.06 62	69.25 220	85.56 72

TAMVS		66.78 173	65.27 177	71.33 164	79.16 107	53.67 117	73.84 199	69.59 237	52.32 204	65.28 151	81.72 149	44.49 150	77.40 230	42.32 241	78.66 108	82.92 162

EPNet		73.09 59	72.16 61	75.90 53	75.95 176	56.28 87	83.05 36	72.39 221	66.53 13	65.27 152	87.00 55	50.40 68	85.47 74	62.48 103	86.32 41	85.94 58
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
Effi-MVS+-dtu		69.64 114	67.53 131	75.95 52	76.10 173	62.29 18	80.20 81	76.06 190	59.83 94	65.26 153	77.09 243	41.56 175	84.02 110	60.60 123	71.09 186	81.53 182

ACMP	63.53 6	72.30 68	71.20 75	75.59 63	80.28 90	57.54 68	82.74 43	82.84 63	60.58 74	65.24 154	86.18 71	39.25 196	86.03 61	66.95 60	76.79 126	83.22 154
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
TAPA-MVS	59.36 10	66.60 176	65.20 178	70.81 172	76.63 167	48.75 202	76.52 151	80.04 118	50.64 223	65.24 154	84.93 90	39.15 197	78.54 212	36.77 267	76.88 123	85.14 94
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
FMVSNet2		66.93 170	66.31 167	68.79 199	77.63 143	42.98 247	76.11 159	77.47 174	56.62 150	65.22 156	82.17 137	41.85 170	80.18 188	47.05 202	72.72 167	83.20 155

GBi-Net		67.21 162	66.55 158	69.19 193	77.63 143	43.33 244	77.31 135	77.83 168	56.62 150	65.04 157	82.70 122	41.85 170	80.33 185	47.18 199	72.76 164	83.92 134

test1		67.21 162	66.55 158	69.19 193	77.63 143	43.33 244	77.31 135	77.83 168	56.62 150	65.04 157	82.70 122	41.85 170	80.33 185	47.18 199	72.76 164	83.92 134

FMVSNet3		66.32 181	65.61 172	68.46 202	76.48 171	42.34 251	74.98 183	77.15 180	55.83 165	65.04 157	81.16 165	39.91 189	80.14 189	47.18 199	72.76 164	82.90 164

anonymousdsp		67.00 169	64.82 183	73.57 101	70.09 268	56.13 90	76.35 153	77.35 178	48.43 240	64.99 160	80.84 177	33.01 256	80.34 184	64.66 74	67.64 234	84.23 124

BH-w/o		66.85 171	65.83 170	69.90 186	79.29 102	52.46 137	74.66 188	76.65 184	54.51 186	64.85 161	78.12 221	45.59 136	82.95 133	43.26 234	75.54 133	74.27 272

CDS-MVSNet		66.80 172	65.37 174	71.10 169	78.98 109	53.13 128	73.27 205	71.07 226	52.15 205	64.72 162	80.23 193	43.56 158	77.10 232	45.48 218	78.88 103	83.05 161
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
GA-MVS		65.53 188	63.70 191	71.02 171	70.87 260	48.10 207	70.48 242	74.40 209	56.69 146	64.70 163	76.77 248	33.66 251	81.10 172	55.42 149	70.32 200	83.87 137

FMVSNet1		66.70 174	65.87 169	69.19 193	77.49 150	43.33 244	77.31 135	77.83 168	56.45 154	64.60 164	82.70 122	38.08 209	80.33 185	46.08 209	72.31 175	83.92 134

diffmvs		67.72 157	66.73 155	70.70 176	69.74 274	47.69 213	73.33 204	74.74 205	53.30 195	64.51 165	81.80 148	49.25 87	79.02 206	59.15 132	74.75 136	85.39 87

AdaColmap		69.99 108	68.66 112	73.97 85	84.94 40	57.83 65	82.63 45	78.71 151	56.28 158	64.34 166	84.14 104	41.57 174	87.06 38	46.45 205	78.88 103	77.02 243

jajsoiax		68.25 147	66.45 160	73.66 97	75.62 179	55.49 103	80.82 73	78.51 157	52.33 203	64.33 167	84.11 105	28.28 287	81.81 161	63.48 96	70.62 189	83.67 146

CostFormer		64.04 202	62.51 203	68.61 201	71.88 249	45.77 225	71.30 232	70.60 229	47.55 249	64.31 168	76.61 251	41.63 173	79.62 192	49.74 184	69.00 221	80.42 206

mvs_tets		68.18 151	66.36 164	73.63 100	75.61 180	55.35 106	80.77 74	78.56 155	52.48 202	64.27 169	84.10 106	27.45 293	81.84 160	63.45 97	70.56 192	83.69 143

PVSNet_BlendedMVS		68.56 137	67.72 126	71.07 170	77.03 162	50.57 163	74.50 190	81.52 78	53.66 193	64.22 170	79.72 202	49.13 94	82.87 137	55.82 144	73.92 145	79.77 217

PVSNet_Blended		68.59 133	67.72 126	71.19 166	77.03 162	50.57 163	72.51 216	81.52 78	51.91 207	64.22 170	77.77 229	49.13 94	82.87 137	55.82 144	79.58 92	80.14 211

HyFIR lowres test		65.67 186	63.01 198	73.67 96	79.97 96	55.65 100	69.07 255	75.52 194	42.68 290	63.53 172	77.95 223	40.43 188	81.64 162	46.01 210	71.91 178	83.73 142

CANet_DTU		68.18 151	67.71 128	69.59 189	74.83 189	46.24 223	78.66 99	76.85 183	59.60 103	63.45 173	82.09 141	35.25 235	77.41 229	59.88 128	78.76 106	85.14 94

UGNet		68.81 128	67.39 135	73.06 122	78.33 124	54.47 112	79.77 86	75.40 196	60.45 76	63.22 174	84.40 101	32.71 263	80.91 176	51.71 172	80.56 78	83.81 138
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
XXY-MVS		60.68 234	61.67 217	57.70 282	70.43 264	38.45 278	64.19 278	66.47 261	48.05 245	63.22 174	80.86 175	49.28 83	60.47 299	45.25 222	67.28 236	74.19 273

CHOSEN 1792x2688		65.08 195	62.84 200	71.82 153	81.49 70	56.26 88	66.32 265	74.20 211	40.53 302	63.16 176	78.65 216	41.30 180	77.80 225	45.80 212	74.09 142	81.40 185

114514_t		70.83 85	69.56 95	74.64 76	86.21 17	54.63 111	82.34 51	81.81 75	48.22 242	63.01 177	85.83 79	40.92 187	87.10 37	57.91 135	79.79 90	82.18 174

tpm2		62.07 225	60.10 229	67.99 205	72.79 234	43.86 240	71.05 236	66.85 260	43.14 287	62.77 178	75.39 263	38.32 205	80.80 178	41.69 245	68.88 222	79.32 221

NR-MVSNet		69.54 116	68.85 108	71.59 158	78.05 133	43.81 241	74.20 193	80.86 101	65.18 17	62.76 179	84.52 98	52.35 53	83.59 118	50.96 176	70.78 187	87.37 25

OpenMVS	61.03 9	68.85 127	67.56 129	72.70 132	74.26 201	53.99 115	81.21 70	81.34 86	52.70 199	62.75 180	85.55 84	38.86 200	84.14 105	48.41 195	83.01 56	79.97 212

v7n		69.01 126	67.36 137	73.98 84	72.51 239	52.65 132	78.54 103	81.30 87	60.26 83	62.67 181	81.62 153	43.61 157	84.49 99	57.01 139	68.70 225	84.79 107

WR-MVS_H		67.02 168	66.92 149	67.33 212	77.95 136	37.75 283	77.57 131	82.11 69	62.03 58	62.65 182	82.48 129	50.57 67	79.46 193	42.91 238	64.01 256	84.79 107

tfpn200view9		63.18 211	62.18 209	66.21 224	76.85 165	39.62 266	71.96 228	69.44 240	56.63 148	62.61 183	79.83 198	37.18 215	79.17 199	31.84 288	73.25 156	79.83 214

thres400		63.31 207	62.18 209	66.72 215	76.85 165	39.62 266	71.96 228	69.44 240	56.63 148	62.61 183	79.83 198	37.18 215	79.17 199	31.84 288	73.25 156	81.36 186

MVS		67.37 159	66.33 165	70.51 178	75.46 182	50.94 151	73.95 195	81.85 74	41.57 296	62.54 185	78.57 219	47.98 109	85.47 74	52.97 164	82.05 65	75.14 260

CP-MVSNet		66.49 179	66.41 163	66.72 215	77.67 142	36.33 296	76.83 147	79.52 135	62.45 49	62.54 185	83.47 116	46.32 129	78.37 217	45.47 219	63.43 261	85.45 79

PEN-MVS		66.60 176	66.45 160	67.04 213	77.11 160	36.56 294	77.03 143	80.42 114	62.95 39	62.51 187	84.03 107	46.69 127	79.07 205	44.22 224	63.08 264	85.51 75

v52		67.09 165	65.16 179	72.87 126	66.77 292	51.60 147	73.69 201	79.45 138	57.88 130	62.46 188	78.57 219	40.95 186	83.34 121	61.99 115	64.70 252	83.68 144

conf200view11		63.38 206	62.41 205	66.29 222	77.31 152	38.66 275	72.65 211	69.11 244	57.07 136	62.45 189	81.03 169	37.01 220	79.17 199	31.84 288	73.25 156	81.03 199

thres100view900		63.28 209	62.41 205	65.89 231	77.31 152	38.66 275	72.65 211	69.11 244	57.07 136	62.45 189	81.03 169	37.01 220	79.17 199	31.84 288	73.25 156	79.83 214

V4		67.09 165	65.16 179	72.87 126	66.76 293	51.60 147	73.69 201	79.45 138	57.88 130	62.45 189	78.58 218	40.96 185	83.34 121	61.99 115	64.71 250	83.68 144

DI_MVS_plusplus_test		69.35 120	68.03 122	73.30 117	71.11 258	50.14 181	75.49 170	79.16 143	54.57 185	62.45 189	80.76 179	44.67 147	84.20 103	64.23 84	79.81 89	85.54 73

PS-CasMVS		66.42 180	66.32 166	66.70 217	77.60 149	36.30 298	76.94 145	79.61 124	62.36 51	62.43 193	83.66 110	45.69 133	78.37 217	45.35 221	63.26 262	85.42 82

thres600view7		63.30 208	62.27 207	66.41 219	77.18 159	38.87 272	72.35 218	69.11 244	56.98 139	62.37 194	80.96 172	37.01 220	79.00 210	31.43 296	73.05 161	81.36 186

pm-mvs1		65.24 192	64.97 182	66.04 227	72.38 240	39.40 269	72.62 214	75.63 193	55.53 171	62.35 195	83.18 119	47.45 118	76.47 240	49.06 190	66.54 238	82.24 173

test_normal		69.26 122	67.90 124	73.32 116	70.84 261	50.38 170	75.30 173	79.17 142	54.23 189	62.00 196	80.61 182	44.69 146	83.89 113	64.33 83	79.95 88	85.69 68

Fast-Effi-MVS+-dtu		67.37 159	65.33 176	73.48 106	72.94 229	57.78 66	77.47 133	76.88 182	57.60 132	61.97 197	76.85 247	39.31 195	80.49 183	54.72 151	70.28 204	82.17 175

WTY-MVS		59.75 239	60.39 227	57.85 280	72.32 242	37.83 282	61.05 292	64.18 275	45.95 265	61.91 198	79.11 211	47.01 124	60.88 298	42.50 240	69.49 219	74.83 266

thres200		62.20 223	61.16 223	65.34 241	75.38 184	39.99 263	69.60 250	69.29 242	55.64 170	61.87 199	76.99 244	37.07 219	78.96 211	31.28 297	73.28 155	77.06 242

v18		68.33 142	66.96 148	72.42 145	73.13 224	50.16 180	77.97 120	79.57 133	59.57 107	61.80 200	77.50 240	49.30 78	82.90 134	62.31 107	61.50 274	84.20 130

TransMVSNet (Re)		64.72 196	64.33 184	65.87 232	75.22 185	38.56 277	74.66 188	75.08 204	58.90 118	61.79 201	82.63 125	51.18 61	78.07 222	43.63 231	55.87 298	80.99 200

v16		68.38 140	67.01 146	72.47 143	73.22 216	50.29 172	78.10 115	79.59 129	59.71 98	61.72 202	77.60 235	49.28 83	82.89 135	62.36 106	61.54 273	84.23 124

v17		68.37 141	67.00 147	72.48 139	73.22 216	50.31 171	78.10 115	79.58 131	59.71 98	61.67 203	77.60 235	49.31 77	82.89 135	62.37 105	61.48 276	84.23 124

DTE-MVSNet		65.58 187	65.34 175	66.31 220	76.06 175	34.79 303	76.43 152	79.38 140	62.55 48	61.66 204	83.83 109	45.60 135	79.15 203	41.64 248	60.88 279	85.00 99

HY-MVS	56.14 13	64.55 200	63.89 187	66.55 218	74.73 192	41.02 259	69.96 248	74.43 208	49.29 231	61.66 204	80.92 173	47.43 119	76.68 238	44.91 223	71.69 180	81.94 177

CNLPA		65.43 189	64.02 186	69.68 187	78.73 116	58.07 63	77.82 124	70.71 228	51.49 213	61.57 206	83.58 113	38.23 207	70.82 262	43.90 228	70.10 207	80.16 210

view600		62.77 215	61.84 212	65.55 235	77.28 154	36.87 288	72.15 222	67.78 251	56.79 142	61.46 207	81.92 142	36.88 223	78.42 213	29.86 303	72.46 168	81.36 186

view800		62.77 215	61.84 212	65.55 235	77.28 154	36.87 288	72.15 222	67.78 251	56.79 142	61.46 207	81.92 142	36.88 223	78.42 213	29.86 303	72.46 168	81.36 186

conf0.05thres1000		62.77 215	61.84 212	65.55 235	77.28 154	36.87 288	72.15 222	67.78 251	56.79 142	61.46 207	81.92 142	36.88 223	78.42 213	29.86 303	72.46 168	81.36 186

tfpn		62.77 215	61.84 212	65.55 235	77.28 154	36.87 288	72.15 222	67.78 251	56.79 142	61.46 207	81.92 142	36.88 223	78.42 213	29.86 303	72.46 168	81.36 186

v15		68.22 150	66.81 154	72.47 143	73.25 215	50.40 169	77.92 122	79.60 126	59.77 97	61.28 211	77.52 239	49.25 87	82.77 141	62.16 112	60.51 283	84.24 123

cascas		65.98 184	63.42 194	73.64 99	77.26 158	52.58 134	72.26 220	77.21 179	48.56 237	61.21 212	74.60 269	32.57 267	85.82 67	50.38 179	76.75 127	82.52 169

V14		68.25 147	66.82 153	72.52 138	73.33 214	50.53 166	78.02 118	79.60 126	59.83 94	61.16 213	77.57 237	49.19 91	82.77 141	62.18 108	60.50 284	84.26 122

v11		68.15 153	66.73 155	72.42 145	73.43 210	50.28 173	77.94 121	79.65 123	59.88 93	61.11 214	77.55 238	48.25 107	82.75 146	61.88 117	60.85 280	84.23 124

V9		68.27 145	66.84 150	72.56 136	73.39 213	50.63 161	78.10 115	79.60 126	59.94 89	61.05 215	77.62 234	49.18 92	82.77 141	62.17 110	60.48 285	84.27 121

PAPM		67.92 155	66.69 157	71.63 157	78.09 131	49.02 200	77.09 141	81.24 91	51.04 220	60.91 216	83.98 108	47.71 113	84.99 80	40.81 250	79.32 98	80.90 201

v12		68.28 144	66.83 152	72.60 135	73.43 210	50.74 158	78.18 110	79.59 129	60.01 88	60.89 217	77.66 233	49.12 96	82.77 141	62.18 108	60.46 286	84.29 120

v13		68.29 143	66.84 150	72.63 133	73.50 208	50.83 156	78.25 108	79.58 131	60.05 86	60.76 218	77.68 232	49.11 99	82.77 141	62.17 110	60.45 287	84.30 119

semantic-postprocess				65.40 240	71.99 247	50.80 157		69.63 236	45.71 267	60.61 219	77.93 224	36.56 229	65.99 283	55.67 147	63.50 260	79.42 220

1112_ss		64.00 203	63.36 195	65.93 230	79.28 103	42.58 250	71.35 231	72.36 222	46.41 258	60.55 220	77.89 226	46.27 131	73.28 254	46.18 207	69.97 209	81.92 178

v748		67.26 161	65.67 171	72.02 150	69.90 272	49.77 190	76.24 156	79.57 133	58.58 121	60.49 221	80.38 185	44.47 151	82.17 155	56.16 142	65.26 247	84.12 132

tfpnnormal		62.47 220	61.63 218	64.99 244	74.81 190	39.01 271	71.22 233	73.72 214	55.22 176	60.21 222	80.09 195	41.26 183	76.98 235	30.02 302	68.09 228	78.97 226

tpm		57.34 255	58.16 241	54.86 292	71.80 251	34.77 304	67.47 261	56.04 313	48.20 243	60.10 223	76.92 245	37.17 217	53.41 326	40.76 251	65.01 248	76.40 249

1314		64.61 199	63.21 196	68.80 198	71.87 250	47.46 215	73.95 195	78.39 164	42.88 289	59.97 224	76.60 252	38.11 208	79.39 195	54.84 150	72.32 174	79.55 218

tpmp4_e23		62.71 219	60.13 228	70.45 179	73.40 212	48.39 205	72.82 210	69.49 239	44.88 270	59.91 225	74.99 265	37.79 211	81.47 167	40.22 252	67.71 233	81.48 183

XVG-ACMP-BASELINE		64.36 201	62.23 208	70.74 174	72.35 241	52.45 138	70.80 239	78.45 160	53.84 192	59.87 226	81.10 167	16.24 322	79.32 196	55.64 148	71.76 179	80.47 205

IterMVS		62.79 214	61.27 221	67.35 211	69.37 275	52.04 144	71.17 234	68.24 250	52.63 201	59.82 227	76.91 246	37.32 214	72.36 257	52.80 165	63.19 263	77.66 233
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Vis-MVSNet (Re-imp)		63.69 204	63.88 188	63.14 254	74.75 191	31.04 320	71.16 235	63.64 278	56.32 156	59.80 228	84.99 89	44.51 148	75.46 246	39.12 257	80.62 74	82.92 162

pmmvs6		63.69 204	62.82 201	66.27 223	70.63 262	39.27 270	73.13 206	75.47 195	52.69 200	59.75 229	82.30 133	39.71 192	77.03 234	47.40 198	64.35 255	82.53 168

pmmvs4		61.48 231	59.39 231	67.76 207	71.57 252	53.86 116	71.42 230	65.34 267	44.20 278	59.46 230	77.92 225	35.90 231	74.71 251	43.87 229	64.87 249	74.71 269

Patchmatch-RL test		58.16 250	55.49 260	66.15 225	67.92 285	48.89 201	60.66 293	51.07 326	47.86 247	59.36 231	62.71 317	34.02 247	72.27 258	56.41 141	59.40 290	77.30 237

CR-MVSNet		59.91 237	57.90 245	65.96 228	69.96 270	52.07 142	65.31 272	63.15 282	42.48 291	59.36 231	74.84 266	35.83 232	70.75 263	45.50 217	64.65 253	75.06 261

RPMNet		58.70 246	56.29 256	65.96 228	69.96 270	52.07 142	65.31 272	62.15 289	43.20 286	59.36 231	70.15 294	35.37 234	70.75 263	36.42 273	64.65 253	75.06 261

Patchmatch-test1		59.75 239	58.00 244	64.98 245	74.14 203	48.06 208	63.35 280	63.23 281	49.13 233	59.33 234	71.46 284	37.45 213	69.59 267	41.39 249	62.57 267	77.30 237

PatchFormer-LS_test		62.20 223	60.59 226	67.04 213	72.18 244	46.82 220	70.36 245	68.62 248	51.92 206	59.19 235	70.23 292	36.86 227	75.07 249	50.23 181	65.68 244	79.23 223

Test4		67.77 156	65.97 168	73.19 121	68.64 278	50.58 162	74.80 187	80.48 113	54.13 190	59.11 236	79.07 212	33.89 249	83.12 129	63.61 94	79.98 87	85.87 60

DP-MVS		65.68 185	63.66 192	71.75 154	84.93 41	56.87 82	80.74 75	73.16 218	53.06 196	59.09 237	82.35 131	36.79 228	85.94 65	32.82 284	69.96 210	72.45 286

Test_1112_low_res		62.32 222	61.77 216	64.00 250	79.08 108	39.53 268	68.17 258	70.17 230	43.25 285	59.03 238	79.90 196	44.08 153	71.24 261	43.79 230	68.42 226	81.25 193

PatchmatchNet		59.84 238	58.24 240	64.65 247	73.05 227	46.70 221	69.42 252	62.18 288	47.55 249	58.88 239	71.96 282	34.49 242	69.16 269	42.99 237	63.60 259	78.07 231
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
test_0402		63.25 210	61.01 224	69.96 184	80.00 95	54.37 113	76.86 146	72.02 223	54.58 184	58.71 240	80.79 178	35.00 236	84.36 101	26.41 318	64.71 250	71.15 299

LTVRE_ROB	55.42 16	63.15 212	61.23 222	68.92 197	76.57 169	47.80 210	59.92 294	76.39 185	54.35 188	58.67 241	82.46 130	29.44 282	81.49 166	42.12 242	71.14 184	77.46 235
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
sss		56.17 262	56.57 252	54.96 291	66.93 290	36.32 297	57.94 300	61.69 291	41.67 294	58.64 242	75.32 264	38.72 201	56.25 318	42.04 243	66.19 240	72.31 291

tpmrst		58.24 249	58.70 237	56.84 284	66.97 289	34.32 307	69.57 251	61.14 292	47.17 253	58.58 243	71.60 283	41.28 182	60.41 300	49.20 189	62.84 265	75.78 255

DWT-MVSNet_test		61.90 226	59.93 230	67.83 206	71.98 248	46.09 224	71.03 237	69.71 233	50.09 226	58.51 244	70.62 289	30.21 277	77.63 226	49.28 188	67.91 229	79.78 216

IB-MVS	56.42 12	65.40 191	62.73 202	73.40 113	74.89 187	52.78 131	73.09 207	75.13 200	55.69 168	58.48 245	73.73 274	32.86 258	86.32 57	50.63 177	70.11 206	81.10 197
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
CVMVSNet		59.63 241	59.14 233	61.08 268	74.47 196	38.84 273	75.20 177	68.74 247	31.15 324	58.24 246	76.51 253	32.39 268	68.58 272	49.77 183	65.84 242	75.81 254

RPSCF		55.80 265	54.22 269	60.53 270	65.13 302	42.91 249	64.30 277	57.62 304	36.84 315	58.05 247	82.28 134	28.01 288	56.24 319	37.14 265	58.61 293	82.44 172

tfpn_ndepth		59.57 242	59.02 234	61.23 267	73.81 204	35.60 300	69.40 253	65.59 266	50.96 221	57.96 248	77.72 230	34.81 237	75.91 245	30.36 300	70.57 191	72.18 292

tpm cat1		59.25 243	56.95 250	66.15 225	72.19 243	46.96 218	68.09 259	65.76 264	40.03 305	57.81 249	70.56 290	38.32 205	74.51 252	38.26 260	61.50 274	77.00 244

gg-mvs-nofinetune		57.86 252	56.43 254	62.18 260	72.62 237	35.35 302	66.57 262	56.33 310	50.65 222	57.64 250	57.10 324	30.65 273	76.36 241	37.38 264	78.88 103	74.82 267

ACMH+	57.40 11	66.12 182	64.06 185	72.30 149	77.79 139	52.83 130	80.39 78	78.03 166	57.30 134	57.47 251	82.55 128	27.68 291	84.17 104	45.54 216	69.78 213	79.90 213

MS-PatchMatch		62.42 221	61.46 219	65.31 242	75.21 186	52.10 141	72.05 226	74.05 212	46.41 258	57.42 252	74.36 270	34.35 244	77.57 228	45.62 215	73.67 147	66.26 310

tfpn1000		59.24 244	58.70 237	60.86 269	73.75 205	33.99 309	68.86 256	63.98 276	51.25 219	57.29 253	79.51 206	34.58 239	75.26 247	29.08 310	69.99 208	73.32 278

PVSNet	50.76 19	58.40 248	57.39 246	61.42 264	75.53 181	44.04 239	61.43 287	63.45 279	47.04 254	56.91 254	73.61 275	27.00 297	64.76 286	39.12 257	72.40 172	75.47 258

Patchmtry		57.16 256	56.47 253	59.23 271	69.17 276	34.58 306	62.98 281	63.15 282	44.53 275	56.83 255	74.84 266	35.83 232	68.71 271	40.03 254	60.91 278	74.39 271

LS3D		64.71 197	62.50 204	71.34 163	79.72 101	55.71 98	79.82 85	74.72 206	48.50 239	56.62 256	84.62 95	33.59 252	82.34 153	29.65 308	75.23 134	75.97 250

ACMH	55.70 15	65.20 193	63.57 193	70.07 183	78.07 132	52.01 145	79.48 94	79.69 121	55.75 167	56.59 257	80.98 171	27.12 295	80.94 174	42.90 239	71.58 181	77.25 241
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
MVP-Stereo		65.41 190	63.80 189	70.22 180	77.62 147	55.53 102	76.30 154	78.53 156	50.59 224	56.47 258	78.65 216	39.84 190	82.68 147	44.10 227	72.12 177	72.44 287
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
OpenMVS_ROB	52.78 18	60.03 236	58.14 242	65.69 234	70.47 263	44.82 229	75.33 172	70.86 227	45.04 269	56.06 259	76.00 257	26.89 298	79.65 191	35.36 276	67.29 235	72.60 283

EG-PatchMatch MVS		64.71 197	62.87 199	70.22 180	77.68 141	53.48 121	77.99 119	78.82 148	53.37 194	56.03 260	77.41 242	24.75 309	84.04 107	46.37 206	73.42 153	73.14 279

PLC	56.13 14	65.09 194	63.21 196	70.72 175	81.04 80	54.87 110	78.57 101	77.47 174	48.51 238	55.71 261	81.89 146	33.71 250	79.71 190	41.66 246	70.37 198	77.58 234
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
EPMVS		53.96 271	53.69 271	54.79 293	66.12 297	31.96 318	62.34 285	49.05 329	44.42 277	55.54 262	71.33 286	30.22 276	56.70 313	41.65 247	62.54 268	75.71 256

MDTV_nov1_ep13			57.00 249		72.73 235	38.26 279	65.02 275	64.73 272	44.74 272	55.46 263	72.48 279	32.61 266	70.47 265	37.47 263	67.75 232

test-LLR		58.15 251	58.13 243	58.22 277	68.57 279	44.80 230	65.46 269	57.92 302	50.08 227	55.44 264	69.82 295	32.62 264	57.44 309	49.66 186	73.62 148	72.41 288

test-mter		56.42 259	55.82 258	58.22 277	68.57 279	44.80 230	65.46 269	57.92 302	39.94 306	55.44 264	69.82 295	21.92 315	57.44 309	49.66 186	73.62 148	72.41 288

ITE_SJBPF				62.09 261	66.16 296	44.55 235		64.32 274	47.36 251	55.31 266	80.34 188	19.27 318	62.68 293	36.29 274	62.39 269	79.04 224

MIMVSNet		57.35 254	57.07 248	58.22 277	74.21 202	37.18 285	62.46 283	60.88 293	48.88 235	55.29 267	75.99 259	31.68 270	62.04 295	31.87 287	72.35 173	75.43 259

Anonymous20231206		55.10 269	55.30 261	54.48 294	69.81 273	33.94 310	62.91 282	62.13 290	41.08 297	55.18 268	75.65 261	32.75 262	56.59 315	30.32 301	67.86 230	72.91 280

pmmvs-eth3d		58.81 245	56.31 255	66.30 221	67.61 286	52.42 139	72.30 219	64.76 271	43.55 283	54.94 269	74.19 272	28.95 284	72.60 256	43.31 232	57.21 295	73.88 276

OurMVSNet-221017-0		61.37 232	58.63 239	69.61 188	72.05 246	48.06 208	73.93 198	72.51 220	47.23 252	54.74 270	80.92 173	21.49 316	81.24 169	48.57 194	56.22 297	79.53 219

GG-mvs-BLEND				62.34 259	71.36 257	37.04 287	69.20 254	57.33 305		54.73 271	65.48 310	30.37 274	77.82 224	34.82 277	74.93 135	72.17 293

tpmvs		58.47 247	56.95 250	63.03 256	70.20 267	41.21 258	67.90 260	67.23 258	49.62 230	54.73 271	70.84 288	34.14 245	76.24 243	36.64 270	61.29 277	71.64 295

EPNet_dtu		61.90 226	61.97 211	61.68 262	72.89 233	39.78 265	75.85 166	65.62 265	55.09 177	54.56 273	79.36 207	37.59 212	67.02 277	39.80 256	76.95 122	78.25 229
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
PatchT		53.17 277	53.44 274	52.33 303	68.29 283	25.34 334	58.21 299	54.41 319	44.46 276	54.56 273	69.05 297	33.32 254	60.94 297	36.93 266	61.76 272	70.73 301

test0.0.03 1		53.32 276	53.59 273	52.50 302	62.81 310	29.45 323	59.51 295	54.11 321	50.08 227	54.40 275	74.31 271	32.62 264	55.92 320	30.50 299	63.95 257	72.15 294

ambc				65.13 243	63.72 308	37.07 286	47.66 324	78.78 150		54.37 276	71.42 285	11.24 334	80.94 174	45.64 214	53.85 306	77.38 236

testing_2		66.02 183	63.77 190	72.76 131	66.03 298	50.48 168	72.93 208	80.36 116	54.41 187	54.25 277	76.76 249	30.89 272	83.16 128	64.19 85	74.08 143	84.65 110

SixPastTwentyTwo		61.65 230	58.80 236	70.20 182	75.80 177	47.22 217	75.59 168	69.68 235	54.61 183	54.11 278	79.26 209	27.07 296	82.96 132	43.27 233	49.79 316	80.41 207

TESTMET0.1,1		55.28 267	54.90 263	56.42 285	66.56 294	43.67 242	65.46 269	56.27 311	39.18 308	53.83 279	67.44 302	24.21 310	55.46 323	48.04 196	73.11 160	70.13 302

pmmvs5		56.47 258	55.68 259	58.86 274	61.41 315	36.71 293	66.37 264	62.75 285	40.38 303	53.70 280	76.62 250	34.56 240	67.05 276	40.02 255	65.27 246	72.83 281

MSDG		61.81 229	59.23 232	69.55 192	72.64 236	52.63 133	70.45 243	75.81 192	51.38 215	53.70 280	76.11 256	29.52 280	81.08 173	37.70 262	65.79 243	74.93 265

K. test v3		60.47 235	57.11 247	70.56 177	73.74 206	48.22 206	75.10 180	62.55 286	58.27 128	53.62 282	76.31 255	27.81 290	81.59 164	47.42 197	39.18 328	81.88 179

PM-MVS		52.33 279	50.19 282	58.75 275	62.10 312	45.14 228	65.75 266	40.38 339	43.60 282	53.52 283	72.65 278	9.16 338	65.87 284	50.41 178	54.18 304	65.24 312

PMMVS		53.96 271	53.26 275	56.04 286	62.60 311	50.92 153	61.17 291	56.09 312	32.81 321	53.51 284	66.84 304	34.04 246	59.93 302	44.14 226	68.18 227	57.27 325

PatchMatch-RL		56.25 261	54.55 265	61.32 266	77.06 161	56.07 92	65.57 268	54.10 322	44.13 280	53.49 285	71.27 287	25.20 306	66.78 278	36.52 272	63.66 258	61.12 319

LCM-MVSNet-Re		61.88 228	61.35 220	63.46 251	74.58 194	31.48 319	61.42 288	58.14 301	58.71 120	53.02 286	79.55 204	43.07 161	76.80 236	45.69 213	77.96 113	82.11 176

F-COLMAP		63.05 213	60.87 225	69.58 191	76.99 164	53.63 119	78.12 111	76.16 187	47.97 246	52.41 287	81.61 154	27.87 289	78.11 221	40.07 253	66.66 237	77.00 244

test20.03		53.87 273	54.02 270	53.41 298	61.47 314	28.11 326	61.30 289	59.21 297	51.34 216	52.09 288	77.43 241	33.29 255	58.55 306	29.76 307	60.27 288	73.58 277

testgi		51.90 280	52.37 277	50.51 307	60.39 321	23.55 337	58.42 298	58.15 300	49.03 234	51.83 289	79.21 210	22.39 313	55.59 321	29.24 309	62.64 266	72.40 290

EU-MVSNet		55.61 266	54.41 266	59.19 272	65.41 301	33.42 312	72.44 217	71.91 224	28.81 326	51.27 290	73.87 273	24.76 308	69.08 270	43.04 236	58.20 294	75.06 261

MDTV_nov1_ep13_2view						25.89 331	61.22 290		40.10 304	51.10 291		32.97 257		38.49 259		78.61 227

COLMAP_ROB	52.97 17	61.27 233	58.81 235	68.64 200	74.63 193	52.51 136	78.42 106	73.30 216	49.92 229	50.96 292	81.51 157	23.06 312	79.40 194	31.63 293	65.85 241	74.01 275
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
ADS-MVSNet2		51.33 283	48.76 286	59.07 273	66.02 299	44.60 233	50.90 318	59.76 296	36.90 313	50.74 293	66.18 308	26.38 299	63.11 290	27.17 314	54.76 302	69.50 304

ADS-MVSNet		48.48 289	47.77 288	50.63 306	66.02 299	29.92 322	50.90 318	50.87 328	36.90 313	50.74 293	66.18 308	26.38 299	52.47 328	27.17 314	54.76 302	69.50 304

FMVSNet5		55.86 264	54.93 262	58.66 276	71.05 259	36.35 295	64.18 279	62.48 287	46.76 255	50.66 295	74.73 268	25.80 303	64.04 288	33.11 283	65.57 245	75.59 257

lessismore_v0				69.91 185	71.42 255	47.80 210		50.90 327		50.39 296	75.56 262	27.43 294	81.33 168	45.91 211	34.10 331	80.59 204

UnsupCasMVSNet_eth		53.16 278	52.47 276	55.23 289	59.45 325	33.39 313	59.43 296	69.13 243	45.98 262	50.35 297	72.32 280	29.30 283	58.26 307	42.02 244	44.30 325	74.05 274

dp		51.89 281	51.60 279	52.77 301	68.44 282	32.45 315	62.36 284	54.57 318	44.16 279	49.31 298	67.91 299	28.87 286	56.61 314	33.89 280	54.89 301	69.24 307

Anonymous20231211		55.92 263	53.63 272	62.77 257	68.22 284	35.56 301	74.48 191	69.89 232	46.42 257	49.07 299	73.45 276	21.13 317	76.77 237	28.74 311	51.30 312	75.97 250

JIA-IIPM		51.56 282	47.68 290	63.21 253	64.61 304	50.73 159	47.71 323	58.77 299	42.90 288	48.46 300	51.72 328	24.97 307	70.24 266	36.06 275	53.89 305	68.64 308

USDC		56.35 260	54.24 268	62.69 258	64.74 303	40.31 262	65.05 274	73.83 213	43.93 281	47.58 301	77.71 231	15.36 324	75.05 250	38.19 261	61.81 271	72.70 282

UnsupCasMVSNet_bld		50.07 286	48.87 285	53.66 296	60.97 319	33.67 311	57.62 301	64.56 273	39.47 307	47.38 302	64.02 313	27.47 292	59.32 303	34.69 278	43.68 326	67.98 309

AllTest		57.08 257	54.65 264	64.39 248	71.44 253	49.03 198	69.92 249	67.30 256	45.97 263	47.16 303	79.77 200	17.47 319	67.56 274	33.65 281	59.16 291	76.57 247

TestCases				64.39 248	71.44 253	49.03 198		67.30 256	45.97 263	47.16 303	79.77 200	17.47 319	67.56 274	33.65 281	59.16 291	76.57 247

CMPMVS	42.80 21	57.81 253	55.97 257	63.32 252	60.98 318	47.38 216	64.66 276	69.50 238	32.06 323	46.83 305	77.80 228	29.50 281	71.36 260	48.68 192	73.75 146	71.21 298
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
MIMVSNet1		55.17 268	54.31 267	57.77 281	70.03 269	32.01 317	65.68 267	64.81 270	49.19 232	46.75 306	76.00 257	25.53 305	64.04 288	28.65 312	62.13 270	77.26 240

PVSNet_0	43.31 20	47.46 292	45.64 292	52.92 300	67.60 287	44.65 232	54.06 309	54.64 317	41.59 295	46.15 307	58.75 323	30.99 271	58.66 305	32.18 285	24.81 334	55.46 326

test2356		45.61 294	44.66 296	48.47 311	60.15 322	28.08 327	52.44 314	52.83 325	38.01 311	46.13 308	60.98 319	15.08 325	55.54 322	20.43 330	55.85 299	61.78 317

Patchmatch-test		49.08 287	48.28 287	51.50 305	64.40 305	30.85 321	45.68 326	48.46 332	35.60 317	46.10 309	72.10 281	34.47 243	46.37 332	27.08 316	60.65 282	77.27 239

YYNet1		50.73 284	48.96 283	56.03 287	61.10 317	41.78 254	51.94 316	56.44 309	40.94 299	44.84 310	67.80 301	30.08 278	55.08 324	36.77 267	50.71 313	71.22 297

MDA-MVSNet_test_wron		50.71 285	48.95 284	56.00 288	61.17 316	41.84 253	51.90 317	56.45 308	40.96 298	44.79 311	67.84 300	30.04 279	55.07 325	36.71 269	50.69 314	71.11 300

TDRefinement		53.44 275	50.72 281	61.60 263	64.31 306	46.96 218	70.89 238	65.27 269	41.78 292	44.61 312	77.98 222	11.52 333	66.36 280	28.57 313	51.59 310	71.49 296

new-patchmatchnet		47.56 291	47.73 289	47.06 312	58.81 326	9.37 345	48.78 322	59.21 297	43.28 284	44.22 313	68.66 298	25.67 304	57.20 312	31.57 295	49.35 319	74.62 270

1111		44.40 298	45.00 295	42.61 319	57.55 328	17.33 342	53.82 312	57.05 306	40.78 300	44.11 314	66.57 305	13.37 327	45.77 333	22.15 323	49.58 317	64.73 314

.test1245		34.88 310	39.49 305	21.04 331	57.55 328	17.33 342	53.82 312	57.05 306	40.78 300	44.11 314	66.57 305	13.37 327	45.77 333	22.15 323	0.00 345	0.03 344

testus		44.59 297	43.87 299	46.76 313	59.85 324	24.65 335	53.86 310	55.82 314	36.26 316	43.97 316	63.42 314	8.39 339	53.14 327	20.70 329	52.52 308	62.51 315

N_pmnet		39.35 306	40.28 303	36.54 323	63.76 307	1.62 349	49.37 321	0.76 351	34.62 319	43.61 317	66.38 307	26.25 301	42.57 338	26.02 320	51.77 309	65.44 311

CHOSEN 280x420		47.83 290	46.36 291	52.24 304	67.37 288	49.78 189	38.91 334	43.11 338	35.00 318	43.27 318	63.30 316	28.95 284	49.19 331	36.53 271	60.80 281	57.76 324

LP		48.51 288	45.51 293	57.52 283	62.86 309	44.53 236	52.38 315	59.84 295	38.11 310	42.81 319	61.02 318	23.23 311	63.02 291	24.10 321	45.24 324	65.02 313

TinyColmap		54.14 270	51.72 278	61.40 265	66.84 291	41.97 252	66.52 263	68.51 249	44.81 271	42.69 320	75.77 260	11.66 332	72.94 255	31.96 286	56.77 296	69.27 306

MDA-MVSNet-bldmvs		53.87 273	50.81 280	63.05 255	66.25 295	48.58 203	56.93 303	63.82 277	48.09 244	41.22 321	70.48 291	30.34 275	68.00 273	34.24 279	45.92 323	72.57 284

testpf		44.11 299	45.40 294	40.26 321	60.52 320	27.34 328	33.26 336	54.33 320	45.87 266	41.08 322	60.26 320	16.46 321	59.14 304	46.09 208	50.68 315	34.31 335

test1235678		45.66 293	44.46 298	49.26 308	59.88 323	28.68 325	56.36 305	55.54 316	39.12 309	40.89 323	63.40 315	14.41 326	57.32 311	21.05 327	49.47 318	61.78 317

pmmvs3		44.92 296	41.95 301	53.86 295	52.58 332	43.55 243	62.11 286	46.90 336	26.05 330	40.63 324	60.19 321	11.08 335	57.91 308	31.83 292	46.15 322	60.11 321

LF4IMVS		42.95 300	42.26 300	45.04 315	48.30 336	32.50 314	54.80 307	48.49 331	28.03 327	40.51 325	70.16 293	9.24 337	43.89 336	31.63 293	49.18 320	58.72 322

DSMNet-mixed		39.30 307	38.72 306	41.03 320	51.22 333	19.66 339	45.53 327	31.35 344	15.83 338	39.80 326	67.42 303	22.19 314	45.13 335	22.43 322	52.69 307	58.31 323

MVS-HIRNet		45.52 295	44.48 297	48.65 310	68.49 281	34.05 308	59.41 297	44.50 337	27.03 328	37.96 327	50.47 331	26.16 302	64.10 287	26.74 317	59.52 289	47.82 329

FPMVS		42.18 302	41.11 302	45.39 314	58.03 327	41.01 260	49.50 320	53.81 323	30.07 325	33.71 328	64.03 311	11.69 331	52.08 329	14.01 336	55.11 300	43.09 332

testmv		42.25 301	40.11 304	48.66 309	53.23 330	27.02 329	56.62 304	55.74 315	37.25 312	33.10 329	59.52 322	7.78 340	56.58 316	19.61 331	38.13 330	62.40 316

new_pmnet		34.13 312	34.29 311	33.64 324	52.63 331	18.23 341	44.43 330	33.90 342	22.81 333	30.89 330	53.18 326	10.48 336	35.72 342	20.77 328	39.51 327	46.98 330

test12356		36.16 309	35.94 309	36.83 322	50.82 334	8.52 346	44.84 329	53.49 324	32.72 322	30.11 331	55.08 325	7.11 342	49.47 330	16.60 333	32.68 332	52.50 327

LCM-MVSNet		40.30 305	35.88 310	53.57 297	42.24 339	29.15 324	45.21 328	60.53 294	22.23 334	28.02 332	50.98 330	3.72 346	61.78 296	31.22 298	38.76 329	69.78 303

ANet_high		41.38 303	37.47 307	53.11 299	39.73 342	24.45 336	56.94 302	69.69 234	47.65 248	26.04 333	52.32 327	12.44 329	62.38 294	21.80 326	10.61 342	72.49 285

no-one		40.85 304	36.09 308	55.14 290	48.55 335	38.72 274	42.15 332	62.92 284	34.60 320	23.55 334	49.74 332	12.21 330	66.16 282	26.27 319	24.84 333	60.54 320

PMVS	28.69 22	36.22 308	33.29 312	45.02 316	36.82 344	35.98 299	54.68 308	48.74 330	26.31 329	21.02 335	51.61 329	2.88 348	60.10 301	9.99 340	47.58 321	38.99 334
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
PMMVS2		27.40 316	25.91 316	31.87 327	39.46 343	6.57 347	31.17 337	28.52 345	23.96 331	20.45 336	48.94 333	4.20 345	37.94 341	16.51 334	19.97 335	51.09 328

Gipuma		34.77 311	31.91 313	43.33 318	62.05 313	37.87 281	20.39 339	67.03 259	23.23 332	18.41 337	25.84 337	4.24 344	62.73 292	14.71 335	51.32 311	29.38 337
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PNet_i23d		27.88 315	25.99 315	33.55 325	47.54 337	25.89 331	47.24 325	32.91 343	21.44 335	15.90 338	38.09 334	0.85 350	42.76 337	16.90 332	13.03 340	32.00 336

tmp_tt		9.43 322	11.14 323	4.30 334	2.38 348	4.40 348	13.62 341	16.08 348	0.39 343	15.89 339	13.06 341	15.80 323	5.54 346	12.63 337	10.46 343	2.95 341

MVE	17.77 23	21.41 319	17.77 321	32.34 326	34.34 346	25.44 333	16.11 340	24.11 346	11.19 340	13.22 340	31.92 335	1.58 349	30.95 343	10.47 338	17.03 336	40.62 333
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
DeepMVS_CX				12.03 333	17.97 347	10.91 344		10.60 349	7.46 341	11.07 341	28.36 336	3.28 347	11.29 345	8.01 342	9.74 344	13.89 340

E-PMN		23.77 317	22.73 318	26.90 329	42.02 340	20.67 338	42.66 331	35.70 340	17.43 336	10.28 342	25.05 338	6.42 343	42.39 339	10.28 339	14.71 337	17.63 338

EMVS		22.97 318	21.84 320	26.36 330	40.20 341	19.53 340	41.95 333	34.64 341	17.09 337	9.73 343	22.83 340	7.29 341	42.22 340	9.18 341	13.66 339	17.32 339

wuykxyi23d		28.12 314	22.54 319	44.87 317	34.97 345	32.11 316	37.96 335	47.31 334	13.32 339	9.29 344	23.72 339	0.45 351	56.58 316	21.85 325	13.98 338	45.93 331

wuyk23d		13.32 321	12.52 322	15.71 332	47.54 337	26.27 330	31.06 338	1.98 350	4.93 342	5.18 345	1.94 345	0.45 351	18.54 344	6.81 343	12.83 341	2.33 342

testmvs		4.52 325	6.03 326	0.01 336	0.01 349	0.00 351	53.86 310	0.00 352	0.01 344	0.04 346	0.27 346	0.00 354	0.00 347	0.04 344	0.00 345	0.03 344

test123		4.73 324	6.30 325	0.02 335	0.01 349	0.01 350	56.36 305	0.00 352	0.01 344	0.04 346	0.21 347	0.01 353	0.00 347	0.03 345	0.00 345	0.04 343

cdsmvs_eth3d_5k		17.50 320	23.34 317	0.00 337	0.00 351	0.00 351	0.00 342	78.63 153	0.00 346	0.00 348	82.18 135	49.25 87	0.00 347	0.00 346	0.00 345	0.00 346

pcd_1.5k_mvsjas		3.92 326	5.23 327	0.00 337	0.00 351	0.00 351	0.00 342	0.00 352	0.00 346	0.00 348	0.00 348	47.05 121	0.00 347	0.00 346	0.00 345	0.00 346

pcd1.5k->3k		30.06 313	30.56 314	28.55 328	78.81 113	0.00 351	0.00 342	82.07 70	0.00 346	0.00 348	0.00 348	39.61 193	0.00 347	0.00 346	74.56 137	85.66 70

sosnet-low-res		0.00 327	0.00 328	0.00 337	0.00 351	0.00 351	0.00 342	0.00 352	0.00 346	0.00 348	0.00 348	0.00 354	0.00 347	0.00 346	0.00 345	0.00 346

sosnet		0.00 327	0.00 328	0.00 337	0.00 351	0.00 351	0.00 342	0.00 352	0.00 346	0.00 348	0.00 348	0.00 354	0.00 347	0.00 346	0.00 345	0.00 346

uncertanet		0.00 327	0.00 328	0.00 337	0.00 351	0.00 351	0.00 342	0.00 352	0.00 346	0.00 348	0.00 348	0.00 354	0.00 347	0.00 346	0.00 345	0.00 346

Regformer		0.00 327	0.00 328	0.00 337	0.00 351	0.00 351	0.00 342	0.00 352	0.00 346	0.00 348	0.00 348	0.00 354	0.00 347	0.00 346	0.00 345	0.00 346

ab-mvs-re		6.49 323	8.65 324	0.00 337	0.00 351	0.00 351	0.00 342	0.00 352	0.00 346	0.00 348	77.89 226	0.00 354	0.00 347	0.00 346	0.00 345	0.00 346

uanet		0.00 327	0.00 328	0.00 337	0.00 351	0.00 351	0.00 342	0.00 352	0.00 346	0.00 348	0.00 348	0.00 354	0.00 347	0.00 346	0.00 345	0.00 346

test_part1								86.64 4				65.59 1			90.06 3	86.78 39

test11111								86.76 3

sam_mvs1												34.74 238

sam_mvs												33.43 253

MTGPA								80.97 98

test_post1							68.67 257				3.64 343	32.39 268	69.49 268	44.17 225

test_post											3.55 344	33.90 248	66.52 279

patchmatchnet-post											64.03 311	34.50 241	74.27 253

MTMP								17.08 347

gm-plane-assit					71.40 256	41.72 256			48.85 236		73.31 277		82.48 152	48.90 191

test9_res														75.28 14	88.31 17	83.81 138

agg_prior2														73.09 29	87.93 24	84.33 116

test_prior4						62.51 17	82.08 57

test_prior				76.69 44	84.20 47	57.27 72		84.88 16					86.43 54			86.38 42

新几何2							76.12 158

旧先验1					83.04 54	53.15 126		67.52 255			87.85 48	44.08 153			80.76 73	78.03 232

无先验							79.66 89	74.30 210	48.40 241				80.78 179	53.62 158		79.03 225

原ACMM2							79.02 95

testdata2													72.18 259	46.95 203

segment_acmp												54.23 34

testdata1							72.65 211		60.50 75

plane_prior7					81.41 71	55.96 94

plane_prior6					81.20 78	56.24 89						45.26 143

plane_prior5								84.01 30					87.21 33	68.16 48	80.58 76	84.65 110

plane_prior4											86.10 73

plane_prior2							84.22 22		64.52 24

plane_prior1					81.27 76

plane_prior						56.31 85	83.58 31		63.19 37						80.48 79

n2								0.00 352

nn								0.00 352

door-mid								47.19 335

test11								83.47 45

door								47.60 333

HQP5-MVS						54.94 107

BP-MVS														67.04 58

HQP3-MVS								83.90 34							80.35 82

HQP2-MVS												45.46 137

NP-MVS					80.98 81	56.05 93					85.54 85

ACMMP++_ref															74.07 144

ACMMP++															72.16 176

Test By Simon												48.33 106

High-resolution multi-view benchmark