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 10	86.21 20	62.73 12	86.09 9	85.03 17	65.51 15	83.81 1	90.51 14	63.71 2	89.23 8	81.51 1	88.44 13	85.45 92

test_part2					87.58 3	60.47 40				83.42 2

v1.0		34.38 334	45.84 314	0.00 359	87.58 3	0.00 373	0.00 365	86.64 3	63.49 36	83.42 2	91.40 5	0.00 375	0.00 369	0.00 368	0.00 366	0.00 369

ESAPD		80.56 2	80.98 2	79.29 7	87.27 6	60.56 39	85.71 17	86.42 6	63.28 37	83.27 4	91.83 3	64.96 1	90.47 1	76.41 13	89.67 6	86.84 42

APDe-MVS		80.16 4	80.59 3	78.86 21	86.64 12	60.02 43	88.12 1	86.42 6	62.94 43	82.40 5	92.12 2	59.64 7	89.76 4	78.70 6	88.32 18	86.79 44

SMA-MVS		80.28 3	80.39 4	79.95 2	86.60 13	61.95 22	86.33 5	85.75 11	62.49 52	82.20 6	92.28 1	56.53 16	89.70 5	79.85 3	91.48 1	88.19 8

DeepPCF-MVS	69.58 1	79.03 8	79.00 8	79.13 11	84.92 46	60.32 41	83.03 41	85.33 13	62.86 46	80.17 7	90.03 24	61.76 3	88.95 12	74.21 22	88.67 12	88.12 9

SD-MVS		77.70 20	77.62 18	77.93 35	84.47 49	61.88 24	84.55 23	83.87 40	60.37 83	79.89 8	89.38 34	54.97 29	85.58 75	76.12 14	84.94 48	86.33 57

TSAR-MVS + MP.		78.44 12	78.28 12	78.90 19	84.96 42	61.41 29	84.03 30	83.82 42	59.34 119	79.37 9	89.76 30	59.84 5	87.62 31	76.69 12	86.74 38	87.68 20

APD-MVS		78.02 16	78.04 15	77.98 34	86.44 17	60.81 36	85.52 19	84.36 26	60.61 78	79.05 10	90.30 19	55.54 26	88.32 19	73.48 31	87.03 33	84.83 116
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
HPM-MVS++		79.88 5	80.14 5	79.10 13	88.17 1	64.80 1	86.59 4	83.70 44	65.37 16	78.78 11	90.64 10	58.63 13	87.24 34	79.00 5	90.37 3	85.26 104

ACMMP_Plus		78.77 9	78.78 9	78.74 23	85.44 34	61.04 33	83.84 32	85.16 15	62.88 45	78.10 12	91.26 6	52.51 54	88.39 16	79.34 4	90.52 2	86.78 45

SteuartSystems-ACMMP		79.48 7	79.31 7	79.98 1	83.01 60	62.18 19	87.60 2	85.83 9	66.69 11	78.03 13	90.98 7	54.26 36	90.06 2	78.42 8	89.02 9	87.69 19
Skip Steuart: Steuart Systems R&D Blog.
CNVR-MVS		79.84 6	79.97 6	79.45 5	87.90 2	62.17 20	84.37 24	85.03 17	66.96 6	77.58 14	90.06 23	59.47 9	89.13 10	78.67 7	89.73 4	87.03 38

canonicalmvs		74.67 49	74.98 42	73.71 102	78.94 119	50.56 172	80.23 85	83.87 40	60.30 88	77.15 15	86.56 71	59.65 6	82.00 169	66.01 71	82.12 66	88.58 5

alignmvs		73.86 58	73.99 50	73.45 114	78.20 138	50.50 174	78.57 108	82.43 70	59.40 117	76.57 16	86.71 64	56.42 19	81.23 182	65.84 73	81.79 68	88.62 3

旧先验2							76.08 170		45.32 289	76.55 17			65.56 307	58.75 139

MP-MVS-pluss		78.35 13	78.46 10	78.03 33	84.96 42	59.52 48	82.93 43	85.39 12	62.15 57	76.41 18	91.51 4	52.47 56	86.78 46	80.66 2	89.64 7	87.80 16
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
casdiffmvs1		75.24 44	74.76 44	76.69 48	80.32 94	55.61 106	82.80 45	83.60 46	52.54 216	76.15 19	86.48 72	59.44 10	85.78 72	69.78 46	81.70 72	88.69 2

Regformer-2		75.63 42	74.99 41	77.54 37	80.43 92	58.32 64	79.50 99	82.92 64	67.84 1	75.94 20	80.75 195	55.73 24	86.80 44	71.44 41	80.38 83	87.50 24

zzz-MVS		77.61 22	77.36 21	78.35 27	86.08 24	63.57 2	83.37 37	80.97 105	65.13 18	75.77 21	90.88 8	48.63 106	86.66 48	77.23 9	88.17 20	84.81 117

MTAPA		76.90 29	76.42 30	78.35 27	86.08 24	63.57 2	74.92 194	80.97 105	65.13 18	75.77 21	90.88 8	48.63 106	86.66 48	77.23 9	88.17 20	84.81 117

Regformer-1		75.47 43	74.93 43	77.09 43	80.43 92	57.70 70	79.50 99	82.13 73	67.84 1	75.73 23	80.75 195	56.50 17	86.07 62	71.07 42	80.38 83	87.50 24

CSCG		76.92 28	76.75 26	77.41 39	83.96 53	59.60 47	82.95 42	86.50 5	60.78 76	75.27 24	84.83 100	60.76 4	86.56 54	67.86 58	87.87 28	86.06 66

test_prior3		76.89 30	76.96 24	76.69 48	84.20 51	57.27 75	81.75 65	84.88 19	60.37 83	75.01 25	89.06 37	56.22 21	86.43 58	72.19 35	88.96 10	86.38 51

test_prior2							81.75 65		60.37 83	75.01 25	89.06 37	56.22 21		72.19 35	88.96 10

TEST9					85.58 32	61.59 27	81.62 69	81.26 95	55.65 179	74.93 27	88.81 43	53.70 44	84.68 103

train_agg		76.27 36	76.15 31	76.64 52	85.58 32	61.59 27	81.62 69	81.26 95	55.86 173	74.93 27	88.81 43	53.70 44	84.68 103	75.24 17	88.33 16	83.65 161

MCST-MVS		77.48 23	77.45 19	77.54 37	86.67 11	58.36 63	83.22 39	86.93 1	56.91 151	74.91 29	88.19 48	59.15 11	87.68 30	73.67 28	87.45 29	86.57 48

test_8					85.40 35	60.96 34	81.54 72	81.18 98	55.86 173	74.81 30	88.80 45	53.70 44	84.45 108

agg_prior1		75.94 39	76.01 34	75.72 62	85.04 39	59.96 44	81.44 73	81.04 101	56.14 171	74.68 31	88.90 41	53.91 40	84.04 115	75.01 19	87.92 27	83.16 172

agg_prior					85.04 39	59.96 44		81.04 101		74.68 31			84.04 115

NCCC		78.58 10	78.31 11	79.39 6	87.51 5	62.61 16	85.20 21	84.42 25	66.73 10	74.67 33	89.38 34	55.30 27	89.18 9	74.19 23	87.34 30	86.38 51

nrg030		72.96 65	73.01 60	72.84 135	75.41 197	50.24 181	80.02 88	82.89 67	58.36 134	74.44 34	86.73 62	58.90 12	80.83 189	65.84 73	74.46 148	87.44 27

TSAR-MVS + GP.		74.90 46	74.15 49	77.17 42	82.00 67	58.77 59	81.80 64	78.57 163	58.58 128	74.32 35	84.51 109	55.94 23	87.22 35	67.11 64	84.48 52	85.52 85

GST-MVS		78.14 15	77.85 16	78.99 18	86.05 26	61.82 25	85.84 12	85.21 14	63.56 35	74.29 36	90.03 24	52.56 53	88.53 15	74.79 20	88.34 15	86.63 47

CDPH-MVS		76.31 35	75.67 38	78.22 30	85.35 37	59.14 53	81.31 75	84.02 33	56.32 166	74.05 37	88.98 40	53.34 47	87.92 27	69.23 50	88.42 14	87.59 22

HFP-MVS		78.01 17	77.65 17	79.10 13	86.71 9	62.81 10	86.29 6	84.32 27	62.82 47	73.96 38	90.50 15	53.20 49	88.35 17	74.02 24	87.05 31	86.13 63

#test#		77.83 18	77.41 20	79.10 13	86.71 9	62.81 10	85.69 18	84.32 27	61.61 66	73.96 38	90.50 15	53.20 49	88.35 17	73.68 27	87.05 31	86.13 63

abl_6		74.34 51	73.50 54	76.86 45	82.43 63	60.16 42	83.48 36	81.86 79	58.81 125	73.95 40	89.86 28	41.87 176	86.62 50	67.98 56	81.23 74	83.80 154

Regformer-4		74.25 54	73.48 55	76.57 53	79.75 105	56.54 87	78.54 110	81.49 87	66.93 8	73.90 41	80.30 205	53.84 42	85.98 67	69.76 47	76.84 134	87.17 35

testdata				64.66 262	81.52 72	52.93 134		65.29 283	46.09 282	73.88 42	87.46 54	38.08 218	66.26 303	53.31 175	78.48 117	74.78 286

DeepC-MVS	69.38 2	78.56 11	78.14 14	79.83 3	83.60 54	61.62 26	84.17 28	86.85 2	63.23 38	73.84 43	90.25 21	57.68 14	89.96 3	74.62 21	89.03 8	87.89 11
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 37	75.89 37	76.85 46	85.76 28	62.02 21	81.65 67	81.01 103	55.51 182	73.73 44	88.60 47	53.23 48	84.90 95	75.24 17	88.33 16	83.65 161

APD-MVS_3200maxsize		74.96 45	74.39 47	76.67 51	82.20 65	58.24 65	83.67 33	83.29 58	58.41 132	73.71 45	90.14 22	45.62 139	85.99 66	69.64 48	82.85 63	85.78 72

Regformer-3		73.89 57	73.28 59	75.71 63	79.75 105	55.48 109	78.54 110	79.93 128	66.58 12	73.62 46	80.30 205	54.87 31	84.54 106	69.09 51	76.84 134	87.10 37

MP-MVS		78.35 13	78.26 13	78.64 24	86.54 15	63.47 5	86.02 11	83.55 48	63.89 31	73.60 47	90.60 11	54.85 32	86.72 47	77.20 11	88.06 23	85.74 77
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
ACMMPR		77.71 19	77.23 22	79.16 8	86.75 8	62.93 9	86.29 6	84.24 29	62.82 47	73.55 48	90.56 13	49.80 78	88.24 20	74.02 24	87.03 33	86.32 59

casdiffmvs		74.55 50	73.78 52	76.87 44	79.00 117	56.18 93	82.36 55	84.45 23	53.88 205	73.46 49	85.76 89	56.38 20	86.59 51	70.70 44	78.04 120	87.83 15

PHI-MVS		75.87 40	75.36 39	77.41 39	80.62 90	55.91 100	84.28 25	85.78 10	56.08 172	73.41 50	86.58 70	50.94 71	88.54 14	70.79 43	89.71 5	87.79 17

region2R		77.67 21	77.18 23	79.15 9	86.76 7	62.95 8	86.29 6	84.16 31	62.81 49	73.30 51	90.58 12	49.90 76	88.21 21	73.78 26	87.03 33	86.29 61

VDD-MVS		72.50 69	72.09 67	73.75 100	81.58 71	49.69 200	77.76 133	77.63 181	63.21 39	73.21 52	89.02 39	42.14 172	83.32 131	61.72 125	82.50 64	88.25 7

DELS-MVS		74.76 47	74.46 46	75.65 65	77.84 149	52.25 145	75.59 178	84.17 30	63.76 32	73.15 53	82.79 134	59.58 8	86.80 44	67.24 63	86.04 44	87.89 11
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 24	76.85 25	78.54 25	85.00 41	60.81 36	82.91 44	85.08 16	62.57 50	73.09 54	89.97 27	50.90 72	87.48 32	75.30 15	86.85 36	87.33 33
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
DeepC-MVS_fast	68.24 3	77.25 25	76.63 28	79.12 12	86.15 22	60.86 35	84.71 22	84.85 21	61.98 63	73.06 55	88.88 42	53.72 43	89.06 11	68.27 53	88.04 24	87.42 28
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 80	71.33 78	73.26 126	82.80 62	47.60 225	78.74 104	75.27 208	59.59 112	72.94 56	89.40 33	41.51 185	83.91 120	58.75 139	82.99 59	88.26 6

test12				77.76 36	84.52 48	58.41 62		83.36 55		72.93 57		54.61 34	88.05 24		88.12 22	86.81 43

LFMVS		71.78 81	71.59 71	72.32 157	83.40 56	46.38 234	79.75 94	71.08 239	64.18 28	72.80 58	88.64 46	42.58 168	83.72 123	57.41 144	84.49 51	86.86 41

CP-MVS		77.12 27	76.68 27	78.43 26	86.05 26	63.18 7	87.55 3	83.45 51	62.44 54	72.68 59	90.50 15	48.18 113	87.34 33	73.59 29	85.71 45	84.76 121

UA-Net		73.13 63	72.93 61	73.76 98	83.58 55	51.66 151	78.75 103	77.66 180	67.75 4	72.61 60	89.42 32	49.82 77	83.29 132	53.61 172	83.14 56	86.32 59

OPM-MVS		74.73 48	74.25 48	76.19 55	80.81 86	59.01 55	82.60 51	83.64 45	63.74 33	72.52 61	87.49 53	47.18 125	85.88 70	69.47 49	80.78 75	83.66 160

MVS_Test		72.45 71	72.46 65	72.42 154	74.88 202	48.50 214	76.28 165	83.14 62	59.40 117	72.46 62	84.68 102	55.66 25	81.12 183	65.98 72	79.66 96	87.63 21

PGM-MVS		76.77 31	76.06 32	78.88 20	86.14 23	62.73 12	82.55 52	83.74 43	61.71 64	72.45 63	90.34 18	48.48 110	88.13 22	72.32 34	86.85 36	85.78 72

XVS		77.17 26	76.56 29	79.00 16	86.32 18	62.62 14	85.83 13	83.92 36	64.55 22	72.17 64	90.01 26	47.95 115	88.01 25	71.55 39	86.74 38	86.37 54

X-MVStestdata		70.21 111	67.28 151	79.00 16	86.32 18	62.62 14	85.83 13	83.92 36	64.55 22	72.17 64	6.49 365	47.95 115	88.01 25	71.55 39	86.74 38	86.37 54

MVS_0304		76.73 32	76.04 33	78.78 22	81.32 77	58.89 57	82.50 54	84.07 32	67.73 5	72.08 66	87.28 58	49.49 80	89.57 6	73.52 30	86.40 42	87.87 13

Effi-MVS+		73.31 62	72.54 64	75.62 66	77.87 148	53.64 123	79.62 97	79.61 133	61.63 65	72.02 67	82.61 139	56.44 18	85.97 68	63.99 96	79.07 108	87.25 34

mPP-MVS		76.54 33	75.93 35	78.34 29	86.47 16	63.50 4	85.74 16	82.28 72	62.90 44	71.77 68	90.26 20	46.61 133	86.55 55	71.71 38	85.66 46	84.97 113

EI-MVSNet-Vis-set		72.42 72	71.59 71	74.91 74	78.47 131	54.02 119	77.05 151	79.33 150	65.03 20	71.68 69	79.35 229	52.75 51	84.89 96	66.46 68	74.23 151	85.83 71

MSLP-MVS++		73.77 59	73.47 56	74.66 79	83.02 59	59.29 52	82.30 61	81.88 78	59.34 119	71.59 70	86.83 60	45.94 137	83.65 125	65.09 78	85.22 47	81.06 211

EI-MVSNet-UG-set		71.92 79	71.06 82	74.52 84	77.98 146	53.56 125	76.62 158	79.16 152	64.40 26	71.18 71	78.95 234	52.19 59	84.66 105	65.47 76	73.57 160	85.32 101

diffmvs1		71.56 84	71.40 75	72.04 159	71.20 279	48.71 212	74.80 197	77.10 190	60.84 73	71.10 72	85.28 97	52.65 52	80.01 202	70.26 45	79.35 103	87.40 29

MG-MVS		73.96 56	73.89 51	74.16 89	85.65 30	49.69 200	81.59 71	81.29 94	61.45 67	71.05 73	88.11 49	51.77 61	87.73 29	61.05 128	83.09 57	85.05 110

VNet		69.68 122	70.19 92	68.16 218	79.73 108	41.63 273	70.53 256	77.38 186	60.37 83	70.69 74	86.63 67	51.08 68	77.09 248	53.61 172	81.69 73	85.75 76

MVS_111021_HR		74.02 55	73.46 57	75.69 64	83.01 60	60.63 38	77.29 147	78.40 172	61.18 71	70.58 75	85.97 83	54.18 38	84.00 119	67.52 62	82.98 60	82.45 184

HPM-MVS_fast		74.30 53	73.46 57	76.80 47	84.45 50	59.04 54	83.65 34	81.05 100	60.15 90	70.43 76	89.84 29	41.09 191	85.59 74	67.61 61	82.90 61	85.77 74

CLD-MVS		73.33 61	72.68 63	75.29 72	78.82 121	53.33 129	78.23 116	84.79 22	61.30 70	70.41 77	81.04 182	52.41 57	87.12 39	64.61 83	82.49 65	85.41 99
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				70.76 187	85.66 29	61.13 32		66.43 277	44.68 294	70.29 78	86.64 66	41.29 188	75.23 268	49.72 198	81.75 70	75.93 271

1121		68.53 149	67.16 156	72.63 141	85.64 31	61.14 31	73.95 208	66.46 276	44.61 295	70.28 79	86.68 65	41.42 186	80.78 191	53.62 170	81.79 68	75.97 269

原ACMM1				74.69 78	85.39 36	59.40 49		83.42 52	51.47 235	70.27 80	86.61 68	48.61 108	86.51 56	53.85 169	87.96 25	78.16 246

CANet		76.46 34	75.93 35	78.06 32	81.29 78	57.53 72	82.35 56	83.31 57	67.78 3	70.09 81	86.34 76	54.92 30	88.90 13	72.68 33	84.55 50	87.76 18

xiu_mvs_v1_base_debu		68.58 145	67.28 151	72.48 148	78.19 139	57.19 79	75.28 184	75.09 212	51.61 230	70.04 82	81.41 176	32.79 279	79.02 222	63.81 98	77.31 126	81.22 207

xiu_mvs_v1_base		68.58 145	67.28 151	72.48 148	78.19 139	57.19 79	75.28 184	75.09 212	51.61 230	70.04 82	81.41 176	32.79 279	79.02 222	63.81 98	77.31 126	81.22 207

xiu_mvs_v1_base_debi		68.58 145	67.28 151	72.48 148	78.19 139	57.19 79	75.28 184	75.09 212	51.61 230	70.04 82	81.41 176	32.79 279	79.02 222	63.81 98	77.31 126	81.22 207

PS-MVSNAJss		72.24 74	71.21 80	75.31 71	78.50 129	55.93 99	81.63 68	82.12 74	56.24 169	70.02 85	85.68 90	47.05 126	84.34 110	65.27 77	74.41 150	85.67 79

0601test		69.69 120	69.13 113	71.36 175	78.37 133	45.74 238	74.71 199	80.20 125	57.91 137	70.01 86	83.83 119	42.44 169	82.87 145	54.97 159	79.72 94	85.48 87

Anonymous20240521		69.69 120	69.13 113	71.36 175	78.37 133	45.74 238	74.71 199	80.20 125	57.91 137	70.01 86	83.83 119	42.44 169	82.87 145	54.97 159	79.72 94	85.48 87

xiu_mvs_v2_base		70.52 99	69.75 96	72.84 135	81.21 81	55.63 105	75.11 189	78.92 156	54.92 191	69.96 88	79.68 218	47.00 130	82.09 168	61.60 127	79.37 100	80.81 219

Anonymous20240529		69.91 117	69.02 116	72.56 144	80.19 100	47.65 224	77.56 140	80.99 104	55.45 184	69.88 89	86.76 61	39.24 205	82.18 166	54.04 167	77.10 131	87.85 14

PS-MVSNAJ		70.51 100	69.70 98	72.93 131	81.52 72	55.79 101	74.92 194	79.00 155	55.04 190	69.88 89	78.66 236	47.05 126	82.19 165	61.61 126	79.58 97	80.83 218

v1neww		70.66 94	69.70 98	73.53 109	73.15 241	50.22 182	78.11 119	80.68 110	59.65 106	69.83 91	81.67 164	49.29 86	84.96 91	64.55 84	70.38 209	85.42 95

v7new		70.66 94	69.70 98	73.53 109	73.15 241	50.22 182	78.11 119	80.68 110	59.65 106	69.83 91	81.67 164	49.29 86	84.96 91	64.55 84	70.38 209	85.42 95

v6		70.66 94	69.70 98	73.53 109	73.14 244	50.21 185	78.11 119	80.67 112	59.65 106	69.82 93	81.65 166	49.29 86	84.96 91	64.55 84	70.39 208	85.42 95

ACMMP		76.02 38	75.33 40	78.07 31	85.20 38	61.91 23	85.49 20	84.44 24	63.04 41	69.80 94	89.74 31	45.43 144	87.16 38	72.01 37	82.87 62	85.14 106
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 90	69.49 107	75.35 70	77.63 154	55.71 102	76.04 173	81.81 81	50.30 246	69.66 95	85.40 96	52.51 54	84.89 96	51.82 183	80.24 87	85.45 92
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
v7		70.57 98	69.48 108	73.85 93	73.50 229	50.92 159	78.27 114	81.43 88	58.93 122	69.61 96	81.49 172	47.56 120	85.43 83	63.94 97	70.62 202	85.21 105

v1141		70.50 101	69.53 103	73.41 118	72.92 251	50.00 192	77.69 134	80.60 114	59.50 114	69.60 97	81.43 173	49.24 95	84.77 100	64.48 88	70.30 215	85.46 91

divwei89l23v2f112		70.50 101	69.53 103	73.41 118	72.91 252	50.00 192	77.69 134	80.59 115	59.50 114	69.60 97	81.43 173	49.26 91	84.77 100	64.48 88	70.31 214	85.47 89

v1		70.50 101	69.53 103	73.42 117	72.91 252	50.00 192	77.69 134	80.59 115	59.50 114	69.59 99	81.42 175	49.26 91	84.77 100	64.49 87	70.30 215	85.47 89

v2v482		70.50 101	69.45 110	73.66 104	72.62 258	50.03 191	77.58 138	80.51 119	59.90 96	69.52 100	82.14 153	47.53 121	84.88 98	65.07 79	70.17 218	86.09 65

MVSFormer		71.50 86	70.38 90	74.88 75	78.76 123	57.15 82	82.79 46	78.48 167	51.26 238	69.49 101	83.22 130	43.99 160	83.24 133	66.06 69	79.37 100	84.23 136

lupinMVS		69.57 125	68.28 129	73.44 115	78.76 123	57.15 82	76.57 159	73.29 229	46.19 281	69.49 101	82.18 149	43.99 160	79.23 212	64.66 81	79.37 100	83.93 146

V42		68.65 143	67.35 149	72.56 144	68.93 301	50.18 186	72.90 221	79.47 145	56.92 150	69.45 103	80.26 207	46.29 135	82.99 139	64.07 93	67.82 251	84.53 125

v1144		70.42 106	69.31 111	73.76 98	73.22 237	50.64 167	77.83 131	81.43 88	58.58 128	69.40 104	81.16 179	47.53 121	85.29 86	64.01 95	70.64 201	85.34 100

jason		69.65 123	68.39 128	73.43 116	78.27 137	56.88 84	77.12 149	73.71 227	46.53 278	69.34 105	83.22 130	43.37 164	79.18 214	64.77 80	79.20 106	84.23 136
jason: jason.
HQP_MVS		74.31 52	73.73 53	76.06 56	81.41 75	56.31 88	84.22 26	84.01 34	64.52 24	69.27 106	86.10 80	45.26 148	87.21 36	68.16 54	80.58 79	84.65 122

plane_prior3						56.09 95			63.92 30	69.27 106

VPA-MVSNet		69.02 136	69.47 109	67.69 222	77.42 162	41.00 277	74.04 207	79.68 131	60.06 91	69.26 108	84.81 101	51.06 69	77.58 242	54.44 166	74.43 149	84.48 127

Vis-MVSNet		72.18 75	71.37 77	74.61 82	81.29 78	55.41 110	80.90 78	78.28 174	60.73 77	69.23 109	88.09 50	44.36 157	82.65 158	57.68 142	81.75 70	85.77 74
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
EI-MVSNet		69.27 132	68.44 127	71.73 165	74.47 210	49.39 205	75.20 187	78.45 169	59.60 109	69.16 110	76.51 274	51.29 64	82.50 160	59.86 137	71.45 196	83.30 165

MVSTER		67.16 176	65.58 186	71.88 162	70.37 289	49.70 198	70.25 261	78.45 169	51.52 233	69.16 110	80.37 201	38.45 212	82.50 160	60.19 133	71.46 195	83.44 163

v1192		69.97 116	68.68 121	73.85 93	73.19 240	50.94 157	77.68 137	81.36 91	57.51 142	68.95 112	80.85 191	45.28 147	85.33 85	62.97 106	70.37 211	85.27 103

OMC-MVS		71.40 88	70.60 87	73.78 96	76.60 181	53.15 131	79.74 95	79.78 129	58.37 133	68.75 113	86.45 74	45.43 144	80.60 194	62.58 108	77.73 122	87.58 23

Fast-Effi-MVS+		70.28 110	69.12 115	73.73 101	78.50 129	51.50 154	75.01 191	79.46 146	56.16 170	68.59 114	79.55 225	53.97 39	84.05 114	53.34 174	77.53 124	85.65 81

v1921920		69.47 128	68.17 131	73.36 121	73.06 247	50.10 190	77.39 143	80.56 117	56.58 163	68.59 114	80.37 201	44.72 150	84.98 89	62.47 111	69.82 226	85.00 111

v144192		69.71 119	68.51 123	73.33 122	73.10 246	50.13 189	77.54 141	80.64 113	56.65 157	68.57 116	80.55 198	46.87 131	84.96 91	62.98 105	69.66 231	84.89 115

TranMVSNet+NR-MVSNet		70.36 107	70.10 94	71.17 181	78.64 127	42.97 263	76.53 160	81.16 99	66.95 7	68.53 117	85.42 95	51.61 63	83.07 138	52.32 180	69.70 230	87.46 26

API-MVS		72.17 76	71.41 74	74.45 85	81.95 68	57.22 77	84.03 30	80.38 122	59.89 98	68.40 118	82.33 146	49.64 79	87.83 28	51.87 182	84.16 54	78.30 244

BH-RMVSNet		68.81 139	67.42 145	72.97 130	80.11 102	52.53 140	74.26 205	76.29 197	58.48 131	68.38 119	84.20 112	42.59 167	83.83 122	46.53 218	75.91 140	82.56 180

v1240		69.24 134	67.91 134	73.25 127	73.02 249	49.82 195	77.21 148	80.54 118	56.43 165	68.34 120	80.51 199	43.33 165	84.99 87	62.03 121	69.77 229	84.95 114

UniMVSNet_NR-MVSNet		71.11 89	71.00 83	71.44 171	79.20 112	44.13 252	76.02 174	82.60 69	66.48 14	68.20 121	84.60 106	56.82 15	82.82 149	54.62 163	70.43 206	87.36 32

DU-MVS		70.01 114	69.53 103	71.44 171	78.05 144	44.13 252	75.01 191	81.51 86	64.37 27	68.20 121	84.52 107	49.12 101	82.82 149	54.62 163	70.43 206	87.37 30

UniMVSNet (Re)		70.63 97	70.20 91	71.89 161	78.55 128	45.29 242	75.94 175	82.92 64	63.68 34	68.16 123	83.59 125	53.89 41	83.49 128	53.97 168	71.12 198	86.89 40

Baseline_NR-MVSNet		67.05 179	67.56 140	65.50 255	75.65 192	37.70 301	75.42 181	74.65 217	59.90 96	68.14 124	83.15 133	49.12 101	77.20 246	52.23 181	69.78 227	81.60 194

WR-MVS		68.47 150	68.47 125	68.44 217	80.20 99	39.84 280	73.75 213	76.07 200	64.68 21	68.11 125	83.63 124	50.39 75	79.14 220	49.78 195	69.66 231	86.34 56

MAR-MVS		71.51 85	70.15 93	75.60 67	81.84 69	59.39 50	81.38 74	82.90 66	54.90 192	68.08 126	78.70 235	47.73 117	85.51 78	51.68 186	84.17 53	81.88 192
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
Anonymous202405211		66.84 184	65.99 178	69.40 206	80.19 100	42.21 267	71.11 250	71.31 238	58.80 126	67.90 127	86.39 75	29.83 301	79.65 205	49.60 201	78.78 112	86.33 57

TR-MVS		66.59 191	65.07 194	71.17 181	79.18 113	49.63 202	73.48 216	75.20 210	52.95 211	67.90 127	80.33 204	39.81 198	83.68 124	43.20 249	73.56 161	80.20 225

HQP-NCC					80.66 87		82.31 58		62.10 58	67.85 129

ACMP_Plane					80.66 87		82.31 58		62.10 58	67.85 129

HQP4-MVS										67.85 129			86.93 42			84.32 129

HQP-MVS		73.45 60	72.80 62	75.40 69	80.66 87	54.94 112	82.31 58	83.90 38	62.10 58	67.85 129	85.54 93	45.46 142	86.93 42	67.04 65	80.35 85	84.32 129

MVS_111021_LR		69.50 127	68.78 120	71.65 167	78.38 132	59.33 51	74.82 196	70.11 245	58.08 136	67.83 133	84.68 102	41.96 174	76.34 256	65.62 75	77.54 123	79.30 238

3Dnovator+	66.72 4	75.84 41	74.57 45	79.66 4	82.40 64	59.92 46	85.83 13	86.32 8	66.92 9	67.80 134	89.24 36	42.03 173	89.38 7	64.07 93	86.50 41	89.69 1

VPNet		67.52 170	68.11 132	65.74 249	79.18 113	36.80 309	72.17 234	72.83 232	62.04 61	67.79 135	85.83 86	48.88 105	76.60 253	51.30 187	72.97 173	83.81 151

XVG-OURS		68.76 142	67.37 147	72.90 132	74.32 214	57.22 77	70.09 262	78.81 158	55.24 186	67.79 135	85.81 88	36.54 240	78.28 234	62.04 120	75.74 141	83.19 169

diffmvs		70.36 107	69.99 95	71.46 170	70.48 285	48.19 217	74.59 203	76.30 196	60.36 87	67.75 137	83.81 122	51.22 66	79.77 203	67.92 57	77.50 125	86.42 50

test222					83.14 57	58.68 60	72.57 228	63.45 300	41.78 315	67.56 138	86.12 79	37.13 227			78.73 114	74.98 282

CPTT-MVS		72.78 66	72.08 68	74.87 76	84.88 47	61.41 29	84.15 29	77.86 176	55.27 185	67.51 139	88.08 51	41.93 175	81.85 171	69.04 52	80.01 89	81.35 205

v148		68.24 160	67.19 155	71.40 174	70.43 287	47.77 223	75.76 177	77.03 191	58.91 123	67.36 140	80.10 209	48.60 109	81.89 170	60.01 134	66.52 259	84.53 125

FIs		70.82 92	71.43 73	68.98 210	78.33 135	38.14 297	76.96 153	83.59 47	61.02 72	67.33 141	86.73 62	55.07 28	81.64 174	54.61 165	79.22 105	87.14 36

Anonymous20231211		69.28 131	68.47 125	71.73 165	80.28 95	47.18 229	79.98 89	82.37 71	54.61 194	67.24 142	84.01 117	39.43 202	82.41 163	55.45 157	72.83 174	85.62 82

ACMM	61.98 7	70.80 93	69.73 97	74.02 90	80.59 91	58.59 61	82.68 49	82.02 77	55.46 183	67.18 143	84.39 111	38.51 211	83.17 135	60.65 129	76.10 139	80.30 224
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
mvs_anonymous		68.03 165	67.51 143	69.59 202	72.08 266	44.57 249	71.99 240	75.23 209	51.67 229	67.06 144	82.57 140	54.68 33	77.94 238	56.56 146	75.71 142	86.26 62

XVG-OURS-SEG-HR		68.81 139	67.47 144	72.82 137	74.40 213	56.87 85	70.59 255	79.04 154	54.77 193	66.99 145	86.01 82	39.57 201	78.21 235	62.54 109	73.33 164	83.37 164

PAPR		71.72 82	70.82 85	74.41 86	81.20 82	51.17 155	79.55 98	83.33 56	55.81 176	66.93 146	84.61 105	50.95 70	86.06 63	55.79 153	79.20 106	86.00 67

DP-MVS Recon		72.15 78	70.73 86	76.40 54	86.57 14	57.99 67	81.15 77	82.96 63	57.03 148	66.78 147	85.56 91	44.50 154	88.11 23	51.77 184	80.23 88	83.10 173

LPG-MVS_test		72.74 67	71.74 70	75.76 60	80.22 97	57.51 73	82.55 52	83.40 53	61.32 68	66.67 148	87.33 56	39.15 206	86.59 51	67.70 59	77.30 129	83.19 169

LGP-MVS_train				75.76 60	80.22 97	57.51 73		83.40 53	61.32 68	66.67 148	87.33 56	39.15 206	86.59 51	67.70 59	77.30 129	83.19 169

IS-MVSNet		71.57 83	71.00 83	73.27 125	78.86 120	45.63 240	80.22 86	78.69 161	64.14 29	66.46 150	87.36 55	49.30 84	85.60 73	50.26 193	83.71 55	88.59 4

v8		70.33 109	69.28 112	73.49 112	73.15 241	50.22 182	78.62 107	80.78 109	60.79 75	66.45 151	82.11 154	49.35 82	84.98 89	63.58 102	68.71 238	85.28 102

v10		70.21 111	69.02 116	73.81 95	73.51 228	50.92 159	78.74 104	81.39 90	60.05 92	66.39 152	81.83 161	47.58 119	85.41 84	62.80 107	68.86 237	85.09 109

mvs-test1		70.44 105	68.19 130	77.18 41	76.10 187	63.22 6	80.59 83	76.06 201	59.83 100	66.32 153	79.87 212	41.56 182	85.53 76	60.60 130	72.77 175	82.80 179

PAPM_NR		72.63 68	71.80 69	75.13 73	81.72 70	53.42 128	79.91 91	83.28 59	59.14 121	66.31 154	85.90 84	51.86 60	86.06 63	57.45 143	80.62 77	85.91 69

BH-untuned		68.27 156	67.29 150	71.21 179	79.74 107	53.22 130	76.06 171	77.46 185	57.19 144	66.10 155	81.61 168	45.37 146	83.50 127	45.42 234	76.68 138	76.91 265

ab-mvs		66.65 188	66.42 172	67.37 224	76.17 186	41.73 271	70.41 259	76.14 199	53.99 204	65.98 156	83.51 127	49.48 81	76.24 257	48.60 207	73.46 162	84.14 143

EPP-MVSNet		72.16 77	71.31 79	74.71 77	78.68 126	49.70 198	82.10 62	81.65 83	60.40 82	65.94 157	85.84 85	51.74 62	86.37 60	55.93 150	79.55 99	88.07 10

QAPM		70.05 113	68.81 119	73.78 96	76.54 183	53.43 127	83.23 38	83.48 49	52.89 212	65.90 158	86.29 77	41.55 184	86.49 57	51.01 188	78.40 118	81.42 197

FC-MVSNet-test		69.80 118	70.58 88	67.46 223	77.61 159	34.73 321	76.05 172	83.19 60	60.84 73	65.88 159	86.46 73	54.52 35	80.76 193	52.52 179	78.12 119	86.91 39

IterMVS-LS		69.22 135	68.48 124	71.43 173	74.44 212	49.40 204	76.23 167	77.55 182	59.60 109	65.85 160	81.59 170	51.28 65	81.58 177	59.87 136	69.90 225	83.30 165
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
PVSNet_Blended_VisFu		71.45 87	70.39 89	74.65 80	82.01 66	58.82 58	79.93 90	80.35 124	55.09 188	65.82 161	82.16 152	49.17 98	82.64 159	60.34 132	78.62 116	82.50 183

thisisatest0530		67.92 166	65.78 182	74.33 87	76.29 185	51.03 156	76.89 155	74.25 221	53.67 207	65.59 162	81.76 162	35.15 252	85.50 79	55.94 149	72.47 180	86.47 49

3Dnovator	64.47 5	72.49 70	71.39 76	75.79 59	77.70 151	58.99 56	80.66 82	83.15 61	62.24 56	65.46 163	86.59 69	42.38 171	85.52 77	59.59 138	84.72 49	82.85 178

test_djsdf		69.45 129	67.74 136	74.58 83	74.57 209	54.92 114	82.79 46	78.48 167	51.26 238	65.41 164	83.49 128	38.37 213	83.24 133	66.06 69	69.25 234	85.56 83

TAMVS		66.78 186	65.27 190	71.33 178	79.16 115	53.67 122	73.84 212	69.59 250	52.32 219	65.28 165	81.72 163	44.49 155	77.40 245	42.32 255	78.66 115	82.92 175

EPNet		73.09 64	72.16 66	75.90 58	75.95 190	56.28 90	83.05 40	72.39 234	66.53 13	65.27 166	87.00 59	50.40 74	85.47 81	62.48 110	86.32 43	85.94 68
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
Effi-MVS+-dtu		69.64 124	67.53 142	75.95 57	76.10 187	62.29 18	80.20 87	76.06 201	59.83 100	65.26 167	77.09 264	41.56 182	84.02 118	60.60 130	71.09 199	81.53 195

ACMP	63.53 6	72.30 73	71.20 81	75.59 68	80.28 95	57.54 71	82.74 48	82.84 68	60.58 79	65.24 168	86.18 78	39.25 204	86.03 65	66.95 67	76.79 136	83.22 167
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
TAPA-MVS	59.36 10	66.60 189	65.20 191	70.81 186	76.63 180	48.75 211	76.52 161	80.04 127	50.64 244	65.24 168	84.93 99	39.15 206	78.54 227	36.77 282	76.88 133	85.14 106
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
FMVSNet2		66.93 182	66.31 177	68.79 213	77.63 154	42.98 262	76.11 169	77.47 183	56.62 160	65.22 170	82.17 151	41.85 177	80.18 200	47.05 216	72.72 179	83.20 168

GBi-Net		67.21 174	66.55 168	69.19 207	77.63 154	43.33 259	77.31 144	77.83 177	56.62 160	65.04 171	82.70 135	41.85 177	80.33 197	47.18 213	72.76 176	83.92 147

test1		67.21 174	66.55 168	69.19 207	77.63 154	43.33 259	77.31 144	77.83 177	56.62 160	65.04 171	82.70 135	41.85 177	80.33 197	47.18 213	72.76 176	83.92 147

FMVSNet3		66.32 194	65.61 185	68.46 216	76.48 184	42.34 266	74.98 193	77.15 189	55.83 175	65.04 171	81.16 179	39.91 196	80.14 201	47.18 213	72.76 176	82.90 177

anonymousdsp		67.00 181	64.82 196	73.57 108	70.09 291	56.13 94	76.35 163	77.35 187	48.43 262	64.99 174	80.84 192	33.01 276	80.34 196	64.66 81	67.64 254	84.23 136

BH-w/o		66.85 183	65.83 181	69.90 199	79.29 110	52.46 142	74.66 201	76.65 194	54.51 199	64.85 175	78.12 242	45.59 141	82.95 141	43.26 248	75.54 143	74.27 291

CDS-MVSNet		66.80 185	65.37 187	71.10 183	78.98 118	53.13 133	73.27 217	71.07 240	52.15 220	64.72 176	80.23 208	43.56 163	77.10 247	45.48 232	78.88 109	83.05 174
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
GA-MVS		65.53 202	63.70 204	71.02 185	70.87 282	48.10 218	70.48 257	74.40 219	56.69 156	64.70 177	76.77 269	33.66 270	81.10 184	55.42 158	70.32 213	83.87 150

tttt0517		67.83 168	65.66 184	74.33 87	76.69 179	50.82 163	77.86 130	73.99 224	54.54 198	64.64 178	82.53 142	35.06 253	85.50 79	55.71 154	69.91 224	86.67 46

FMVSNet1		66.70 187	65.87 180	69.19 207	77.49 161	43.33 259	77.31 144	77.83 177	56.45 164	64.60 179	82.70 135	38.08 218	80.33 197	46.08 223	72.31 188	83.92 147

AdaColmap		69.99 115	68.66 122	73.97 92	84.94 44	57.83 68	82.63 50	78.71 160	56.28 168	64.34 180	84.14 113	41.57 181	87.06 41	46.45 219	78.88 109	77.02 261

jajsoiax		68.25 158	66.45 170	73.66 104	75.62 193	55.49 108	80.82 79	78.51 166	52.33 218	64.33 181	84.11 114	28.28 309	81.81 173	63.48 103	70.62 202	83.67 159

CostFormer		64.04 216	62.51 217	68.61 215	71.88 270	45.77 237	71.30 246	70.60 243	47.55 271	64.31 182	76.61 272	41.63 180	79.62 207	49.74 197	69.00 235	80.42 222

mvs_tets		68.18 162	66.36 174	73.63 107	75.61 194	55.35 111	80.77 80	78.56 164	52.48 217	64.27 183	84.10 115	27.45 315	81.84 172	63.45 104	70.56 205	83.69 156

PVSNet_BlendedMVS		68.56 148	67.72 137	71.07 184	77.03 174	50.57 170	74.50 204	81.52 84	53.66 208	64.22 184	79.72 217	49.13 99	82.87 145	55.82 151	73.92 155	79.77 233

PVSNet_Blended		68.59 144	67.72 137	71.19 180	77.03 174	50.57 170	72.51 229	81.52 84	51.91 222	64.22 184	77.77 250	49.13 99	82.87 145	55.82 151	79.58 97	80.14 227

thisisatest0515		65.83 198	63.50 207	72.82 137	73.75 225	49.50 203	71.32 245	73.12 231	49.39 252	63.82 186	76.50 276	34.95 255	84.84 99	53.20 176	75.49 144	84.13 144

HyFIR lowres test		65.67 200	63.01 212	73.67 103	79.97 104	55.65 104	69.07 270	75.52 205	42.68 313	63.53 187	77.95 244	40.43 195	81.64 174	46.01 224	71.91 191	83.73 155

CANet_DTU		68.18 162	67.71 139	69.59 202	74.83 203	46.24 235	78.66 106	76.85 193	59.60 109	63.45 188	82.09 155	35.25 251	77.41 244	59.88 135	78.76 113	85.14 106

UGNet		68.81 139	67.39 146	73.06 129	78.33 135	54.47 117	79.77 93	75.40 207	60.45 81	63.22 189	84.40 110	32.71 283	80.91 188	51.71 185	80.56 81	83.81 151
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		60.68 249	61.67 232	57.70 304	70.43 287	38.45 295	64.19 301	66.47 275	48.05 267	63.22 189	80.86 190	49.28 89	60.47 321	45.25 236	67.28 256	74.19 296

CHOSEN 1792x2688		65.08 209	62.84 214	71.82 163	81.49 74	56.26 91	66.32 287	74.20 222	40.53 325	63.16 191	78.65 237	41.30 187	77.80 240	45.80 226	74.09 152	81.40 198

114514_t		70.83 91	69.56 102	74.64 81	86.21 20	54.63 116	82.34 57	81.81 81	48.22 264	63.01 192	85.83 86	40.92 194	87.10 40	57.91 141	79.79 93	82.18 187

tpm2		62.07 240	60.10 244	67.99 219	72.79 255	43.86 255	71.05 251	66.85 274	43.14 310	62.77 193	75.39 285	38.32 214	80.80 190	41.69 259	68.88 236	79.32 237

NR-MVSNet		69.54 126	68.85 118	71.59 169	78.05 144	43.81 256	74.20 206	80.86 108	65.18 17	62.76 194	84.52 107	52.35 58	83.59 126	50.96 189	70.78 200	87.37 30

OpenMVS	61.03 9	68.85 138	67.56 140	72.70 140	74.26 215	53.99 120	81.21 76	81.34 92	52.70 213	62.75 195	85.55 92	38.86 209	84.14 113	48.41 209	83.01 58	79.97 228

v7n		69.01 137	67.36 148	73.98 91	72.51 260	52.65 137	78.54 110	81.30 93	60.26 89	62.67 196	81.62 167	43.61 162	84.49 107	57.01 145	68.70 239	84.79 119

WR-MVS_H		67.02 180	66.92 160	67.33 226	77.95 147	37.75 300	77.57 139	82.11 75	62.03 62	62.65 197	82.48 143	50.57 73	79.46 208	42.91 252	64.01 276	84.79 119

tfpn200view9		63.18 226	62.18 224	66.21 239	76.85 177	39.62 282	71.96 241	69.44 253	56.63 158	62.61 198	79.83 213	37.18 224	79.17 215	31.84 304	73.25 166	79.83 230

thres400		63.31 222	62.18 224	66.72 229	76.85 177	39.62 282	71.96 241	69.44 253	56.63 158	62.61 198	79.83 213	37.18 224	79.17 215	31.84 304	73.25 166	81.36 199

MVS		67.37 171	66.33 175	70.51 191	75.46 196	50.94 157	73.95 208	81.85 80	41.57 319	62.54 200	78.57 240	47.98 114	85.47 81	52.97 177	82.05 67	75.14 278

CP-MVSNet		66.49 192	66.41 173	66.72 229	77.67 153	36.33 313	76.83 157	79.52 144	62.45 53	62.54 200	83.47 129	46.32 134	78.37 232	45.47 233	63.43 281	85.45 92

PEN-MVS		66.60 189	66.45 170	67.04 227	77.11 172	36.56 311	77.03 152	80.42 121	62.95 42	62.51 202	84.03 116	46.69 132	79.07 221	44.22 238	63.08 284	85.51 86

v52		67.09 177	65.16 192	72.87 133	66.77 315	51.60 152	73.69 214	79.45 147	57.88 139	62.46 203	78.57 240	40.95 193	83.34 129	61.99 122	64.70 272	83.68 157

tfpn111		63.33 221	62.34 221	66.30 235	77.31 163	38.66 291	72.65 223	69.11 257	57.07 145	62.45 204	81.03 183	37.01 229	79.23 212	31.38 313	73.09 171	81.03 212

conf200view11		63.38 220	62.41 219	66.29 237	77.31 163	38.66 291	72.65 223	69.11 257	57.07 145	62.45 204	81.03 183	37.01 229	79.17 215	31.84 304	73.25 166	81.03 212

thres100view900		63.28 224	62.41 219	65.89 247	77.31 163	38.66 291	72.65 223	69.11 257	57.07 145	62.45 204	81.03 183	37.01 229	79.17 215	31.84 304	73.25 166	79.83 230

V4		67.09 177	65.16 192	72.87 133	66.76 316	51.60 152	73.69 214	79.45 147	57.88 139	62.45 204	78.58 239	40.96 192	83.34 129	61.99 122	64.71 270	83.68 157

DI_MVS_plusplus_test		69.35 130	68.03 133	73.30 124	71.11 280	50.14 188	75.49 180	79.16 152	54.57 197	62.45 204	80.76 194	44.67 152	84.20 111	64.23 91	79.81 92	85.54 84

PS-CasMVS		66.42 193	66.32 176	66.70 231	77.60 160	36.30 315	76.94 154	79.61 133	62.36 55	62.43 209	83.66 123	45.69 138	78.37 232	45.35 235	63.26 282	85.42 95

thres600view7		63.30 223	62.27 222	66.41 233	77.18 171	38.87 288	72.35 231	69.11 257	56.98 149	62.37 210	80.96 187	37.01 229	79.00 225	31.43 312	73.05 172	81.36 199

pm-mvs1		65.24 206	64.97 195	66.04 243	72.38 261	39.40 285	72.62 227	75.63 204	55.53 181	62.35 211	83.18 132	47.45 123	76.47 254	49.06 204	66.54 258	82.24 186

test_normal		69.26 133	67.90 135	73.32 123	70.84 283	50.38 177	75.30 183	79.17 151	54.23 202	62.00 212	80.61 197	44.69 151	83.89 121	64.33 90	79.95 91	85.69 78

Fast-Effi-MVS+-dtu		67.37 171	65.33 189	73.48 113	72.94 250	57.78 69	77.47 142	76.88 192	57.60 141	61.97 213	76.85 268	39.31 203	80.49 195	54.72 162	70.28 217	82.17 188

WTY-MVS		59.75 256	60.39 242	57.85 302	72.32 263	37.83 299	61.05 315	64.18 296	45.95 286	61.91 214	79.11 232	47.01 129	60.88 320	42.50 254	69.49 233	74.83 284

thres200		62.20 238	61.16 238	65.34 257	75.38 198	39.99 279	69.60 265	69.29 255	55.64 180	61.87 215	76.99 265	37.07 228	78.96 226	31.28 314	73.28 165	77.06 260

v18		68.33 153	66.96 159	72.42 154	73.13 245	50.16 187	77.97 127	79.57 142	59.57 113	61.80 216	77.50 261	49.30 84	82.90 142	62.31 114	61.50 294	84.20 142

TransMVSNet (Re)		64.72 210	64.33 197	65.87 248	75.22 199	38.56 294	74.66 201	75.08 215	58.90 124	61.79 217	82.63 138	51.18 67	78.07 237	43.63 245	55.87 320	80.99 216

v16		68.38 151	67.01 157	72.47 152	73.22 237	50.29 179	78.10 122	79.59 138	59.71 104	61.72 218	77.60 256	49.28 89	82.89 143	62.36 113	61.54 293	84.23 136

v17		68.37 152	67.00 158	72.48 148	73.22 237	50.31 178	78.10 122	79.58 140	59.71 104	61.67 219	77.60 256	49.31 83	82.89 143	62.37 112	61.48 296	84.23 136

DTE-MVSNet		65.58 201	65.34 188	66.31 234	76.06 189	34.79 319	76.43 162	79.38 149	62.55 51	61.66 220	83.83 119	45.60 140	79.15 219	41.64 262	60.88 299	85.00 111

HY-MVS	56.14 13	64.55 214	63.89 200	66.55 232	74.73 206	41.02 275	69.96 263	74.43 218	49.29 253	61.66 220	80.92 188	47.43 124	76.68 252	44.91 237	71.69 193	81.94 190

CNLPA		65.43 203	64.02 199	69.68 200	78.73 125	58.07 66	77.82 132	70.71 242	51.49 234	61.57 222	83.58 126	38.23 216	70.82 283	43.90 242	70.10 220	80.16 226

view600		62.77 230	61.84 227	65.55 251	77.28 166	36.87 305	72.15 235	67.78 265	56.79 152	61.46 223	81.92 156	36.88 233	78.42 228	29.86 320	72.46 181	81.36 199

view800		62.77 230	61.84 227	65.55 251	77.28 166	36.87 305	72.15 235	67.78 265	56.79 152	61.46 223	81.92 156	36.88 233	78.42 228	29.86 320	72.46 181	81.36 199

conf0.05thres1000		62.77 230	61.84 227	65.55 251	77.28 166	36.87 305	72.15 235	67.78 265	56.79 152	61.46 223	81.92 156	36.88 233	78.42 228	29.86 320	72.46 181	81.36 199

tfpn		62.77 230	61.84 227	65.55 251	77.28 166	36.87 305	72.15 235	67.78 265	56.79 152	61.46 223	81.92 156	36.88 233	78.42 228	29.86 320	72.46 181	81.36 199

v15		68.22 161	66.81 165	72.47 152	73.25 236	50.40 176	77.92 129	79.60 135	59.77 103	61.28 227	77.52 260	49.25 93	82.77 151	62.16 119	60.51 303	84.24 135

cascas		65.98 197	63.42 208	73.64 106	77.26 170	52.58 139	72.26 233	77.21 188	48.56 259	61.21 228	74.60 291	32.57 287	85.82 71	50.38 192	76.75 137	82.52 182

V14		68.25 158	66.82 164	72.52 147	73.33 235	50.53 173	78.02 125	79.60 135	59.83 100	61.16 229	77.57 258	49.19 96	82.77 151	62.18 115	60.50 304	84.26 134

v11		68.15 164	66.73 166	72.42 154	73.43 231	50.28 180	77.94 128	79.65 132	59.88 99	61.11 230	77.55 259	48.25 112	82.75 156	61.88 124	60.85 300	84.23 136

V9		68.27 156	66.84 161	72.56 144	73.39 234	50.63 168	78.10 122	79.60 135	59.94 95	61.05 231	77.62 255	49.18 97	82.77 151	62.17 117	60.48 305	84.27 133

PAPM		67.92 166	66.69 167	71.63 168	78.09 142	49.02 208	77.09 150	81.24 97	51.04 241	60.91 232	83.98 118	47.71 118	84.99 87	40.81 264	79.32 104	80.90 217

v12		68.28 155	66.83 163	72.60 143	73.43 231	50.74 165	78.18 117	79.59 138	60.01 94	60.89 233	77.66 254	49.12 101	82.77 151	62.18 115	60.46 306	84.29 132

v13		68.29 154	66.84 161	72.63 141	73.50 229	50.83 162	78.25 115	79.58 140	60.05 92	60.76 234	77.68 253	49.11 104	82.77 151	62.17 117	60.45 307	84.30 131

semantic-postprocess				65.40 256	71.99 268	50.80 164		69.63 249	45.71 288	60.61 235	77.93 245	36.56 239	65.99 305	55.67 155	63.50 280	79.42 236

1112_ss		64.00 217	63.36 209	65.93 246	79.28 111	42.58 265	71.35 244	72.36 235	46.41 279	60.55 236	77.89 247	46.27 136	73.28 274	46.18 221	69.97 222	81.92 191

v748		67.26 173	65.67 183	72.02 160	69.90 295	49.77 197	76.24 166	79.57 142	58.58 128	60.49 237	80.38 200	44.47 156	82.17 167	56.16 148	65.26 267	84.12 145

tfpnnormal		62.47 235	61.63 233	64.99 260	74.81 204	39.01 287	71.22 247	73.72 226	55.22 187	60.21 238	80.09 210	41.26 190	76.98 250	30.02 319	68.09 248	78.97 242

tpm		57.34 277	58.16 262	54.86 314	71.80 272	34.77 320	67.47 283	56.04 335	48.20 265	60.10 239	76.92 266	37.17 226	53.41 348	40.76 265	65.01 268	76.40 268

1314		64.61 213	63.21 210	68.80 212	71.87 271	47.46 226	73.95 208	78.39 173	42.88 312	59.97 240	76.60 273	38.11 217	79.39 210	54.84 161	72.32 187	79.55 234

tpmp4_e23		62.71 234	60.13 243	70.45 192	73.40 233	48.39 215	72.82 222	69.49 252	44.88 291	59.91 241	74.99 287	37.79 220	81.47 179	40.22 266	67.71 253	81.48 196

XVG-ACMP-BASELINE		64.36 215	62.23 223	70.74 188	72.35 262	52.45 143	70.80 254	78.45 169	53.84 206	59.87 242	81.10 181	16.24 343	79.32 211	55.64 156	71.76 192	80.47 221

IterMVS		62.79 229	61.27 236	67.35 225	69.37 299	52.04 149	71.17 248	68.24 264	52.63 215	59.82 243	76.91 267	37.32 223	72.36 277	52.80 178	63.19 283	77.66 251
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Vis-MVSNet (Re-imp)		63.69 218	63.88 201	63.14 272	74.75 205	31.04 342	71.16 249	63.64 299	56.32 166	59.80 244	84.99 98	44.51 153	75.46 260	39.12 271	80.62 77	82.92 175

pmmvs6		63.69 218	62.82 215	66.27 238	70.63 284	39.27 286	73.13 218	75.47 206	52.69 214	59.75 245	82.30 147	39.71 199	77.03 249	47.40 212	64.35 275	82.53 181

pmmvs4		61.48 246	59.39 246	67.76 221	71.57 273	53.86 121	71.42 243	65.34 282	44.20 300	59.46 246	77.92 246	35.90 241	74.71 271	43.87 243	64.87 269	74.71 287

Patchmatch-RL test		58.16 271	55.49 281	66.15 240	67.92 308	48.89 210	60.66 316	51.07 348	47.86 269	59.36 247	62.71 340	34.02 266	72.27 279	56.41 147	59.40 310	77.30 255

CR-MVSNet		59.91 254	57.90 266	65.96 244	69.96 293	52.07 147	65.31 295	63.15 303	42.48 314	59.36 247	74.84 288	35.83 242	70.75 284	45.50 231	64.65 273	75.06 279

RPMNet		58.70 267	56.29 277	65.96 244	69.96 293	52.07 147	65.31 295	62.15 310	43.20 309	59.36 247	70.15 316	35.37 250	70.75 284	36.42 289	64.65 273	75.06 279

Patchmatch-test1		59.75 256	58.00 265	64.98 261	74.14 223	48.06 219	63.35 303	63.23 302	49.13 255	59.33 250	71.46 305	37.45 222	69.59 288	41.39 263	62.57 287	77.30 255

PatchFormer-LS_test		62.20 238	60.59 241	67.04 227	72.18 265	46.82 232	70.36 260	68.62 262	51.92 221	59.19 251	70.23 314	36.86 237	75.07 269	50.23 194	65.68 264	79.23 239

Test4		67.77 169	65.97 179	73.19 128	68.64 302	50.58 169	74.80 197	80.48 120	54.13 203	59.11 252	79.07 233	33.89 268	83.12 137	63.61 101	79.98 90	85.87 70

DP-MVS		65.68 199	63.66 205	71.75 164	84.93 45	56.87 85	80.74 81	73.16 230	53.06 210	59.09 253	82.35 145	36.79 238	85.94 69	32.82 300	69.96 223	72.45 309

Test_1112_low_res		62.32 237	61.77 231	64.00 266	79.08 116	39.53 284	68.17 279	70.17 244	43.25 308	59.03 254	79.90 211	44.08 158	71.24 282	43.79 244	68.42 240	81.25 206

PatchmatchNet		59.84 255	58.24 261	64.65 263	73.05 248	46.70 233	69.42 267	62.18 309	47.55 271	58.88 255	71.96 303	34.49 261	69.16 290	42.99 251	63.60 279	78.07 247
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
test_0402		63.25 225	61.01 239	69.96 197	80.00 103	54.37 118	76.86 156	72.02 236	54.58 196	58.71 256	80.79 193	35.00 254	84.36 109	26.41 340	64.71 270	71.15 322

LTVRE_ROB	55.42 16	63.15 227	61.23 237	68.92 211	76.57 182	47.80 221	59.92 317	76.39 195	54.35 201	58.67 257	82.46 144	29.44 304	81.49 178	42.12 256	71.14 197	77.46 253
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		56.17 285	56.57 273	54.96 313	66.93 313	36.32 314	57.94 323	61.69 312	41.67 317	58.64 258	75.32 286	38.72 210	56.25 340	42.04 257	66.19 260	72.31 314

tpmrst		58.24 270	58.70 258	56.84 306	66.97 312	34.32 323	69.57 266	61.14 314	47.17 275	58.58 259	71.60 304	41.28 189	60.41 322	49.20 203	62.84 285	75.78 273

DWT-MVSNet_test		61.90 241	59.93 245	67.83 220	71.98 269	46.09 236	71.03 252	69.71 246	50.09 247	58.51 260	70.62 311	30.21 298	77.63 241	49.28 202	67.91 249	79.78 232

IB-MVS	56.42 12	65.40 205	62.73 216	73.40 120	74.89 201	52.78 136	73.09 219	75.13 211	55.69 178	58.48 261	73.73 296	32.86 278	86.32 61	50.63 190	70.11 219	81.10 210
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		59.63 258	59.14 248	61.08 290	74.47 210	38.84 289	75.20 187	68.74 261	31.15 347	58.24 262	76.51 274	32.39 288	68.58 293	49.77 196	65.84 262	75.81 272

RPSCF		55.80 287	54.22 292	60.53 292	65.13 325	42.91 264	64.30 300	57.62 326	36.84 338	58.05 263	82.28 148	28.01 310	56.24 341	37.14 280	58.61 313	82.44 185

conf0.01		59.97 252	58.81 250	63.42 268	74.15 217	33.83 327	68.32 273	64.22 290	51.79 223	58.04 264	79.57 219	35.41 244	75.41 261	29.57 326	68.26 241	81.03 212

conf0.002		59.97 252	58.81 250	63.42 268	74.15 217	33.83 327	68.32 273	64.22 290	51.79 223	58.04 264	79.57 219	35.41 244	75.41 261	29.57 326	68.26 241	81.03 212

thresconf0.02		59.40 260	58.81 250	61.17 286	74.15 217	33.83 327	68.32 273	64.22 290	51.79 223	58.04 264	79.57 219	35.41 244	75.41 261	29.57 326	68.26 241	74.25 292

tfpn_n400		59.40 260	58.81 250	61.17 286	74.15 217	33.83 327	68.32 273	64.22 290	51.79 223	58.04 264	79.57 219	35.41 244	75.41 261	29.57 326	68.26 241	74.25 292

tfpnconf		59.40 260	58.81 250	61.17 286	74.15 217	33.83 327	68.32 273	64.22 290	51.79 223	58.04 264	79.57 219	35.41 244	75.41 261	29.57 326	68.26 241	74.25 292

tfpnview11		59.40 260	58.81 250	61.17 286	74.15 217	33.83 327	68.32 273	64.22 290	51.79 223	58.04 264	79.57 219	35.41 244	75.41 261	29.57 326	68.26 241	74.25 292

tfpn_ndepth		59.57 259	59.02 249	61.23 285	73.81 224	35.60 317	69.40 268	65.59 281	50.96 242	57.96 270	77.72 251	34.81 256	75.91 259	30.36 317	70.57 204	72.18 315

tpm cat1		59.25 264	56.95 271	66.15 240	72.19 264	46.96 230	68.09 280	65.76 279	40.03 328	57.81 271	70.56 312	38.32 214	74.51 272	38.26 275	61.50 294	77.00 262

gg-mvs-nofinetune		57.86 274	56.43 275	62.18 278	72.62 258	35.35 318	66.57 284	56.33 332	50.65 243	57.64 272	57.10 347	30.65 294	76.36 255	37.38 279	78.88 109	74.82 285

ACMH+	57.40 11	66.12 195	64.06 198	72.30 158	77.79 150	52.83 135	80.39 84	78.03 175	57.30 143	57.47 273	82.55 141	27.68 313	84.17 112	45.54 230	69.78 227	79.90 229

MS-PatchMatch		62.42 236	61.46 234	65.31 258	75.21 200	52.10 146	72.05 239	74.05 223	46.41 279	57.42 274	74.36 292	34.35 263	77.57 243	45.62 229	73.67 157	66.26 333

tfpn1000		59.24 265	58.70 258	60.86 291	73.75 225	33.99 325	68.86 271	63.98 297	51.25 240	57.29 275	79.51 227	34.58 258	75.26 267	29.08 333	69.99 221	73.32 301

PVSNet	50.76 19	58.40 269	57.39 267	61.42 282	75.53 195	44.04 254	61.43 310	63.45 300	47.04 276	56.91 276	73.61 297	27.00 319	64.76 308	39.12 271	72.40 185	75.47 276

Patchmtry		57.16 278	56.47 274	59.23 293	69.17 300	34.58 322	62.98 304	63.15 303	44.53 296	56.83 277	74.84 288	35.83 242	68.71 292	40.03 268	60.91 298	74.39 290

LS3D		64.71 211	62.50 218	71.34 177	79.72 109	55.71 102	79.82 92	74.72 216	48.50 261	56.62 278	84.62 104	33.59 271	82.34 164	29.65 325	75.23 145	75.97 269

ACMH	55.70 15	65.20 207	63.57 206	70.07 196	78.07 143	52.01 150	79.48 101	79.69 130	55.75 177	56.59 279	80.98 186	27.12 317	80.94 186	42.90 253	71.58 194	77.25 259
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
MVP-Stereo		65.41 204	63.80 202	70.22 193	77.62 158	55.53 107	76.30 164	78.53 165	50.59 245	56.47 280	78.65 237	39.84 197	82.68 157	44.10 241	72.12 190	72.44 310
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
OpenMVS_ROB	52.78 18	60.03 251	58.14 263	65.69 250	70.47 286	44.82 244	75.33 182	70.86 241	45.04 290	56.06 281	76.00 279	26.89 320	79.65 205	35.36 292	67.29 255	72.60 306

EG-PatchMatch MVS		64.71 211	62.87 213	70.22 193	77.68 152	53.48 126	77.99 126	78.82 157	53.37 209	56.03 282	77.41 263	24.75 331	84.04 115	46.37 220	73.42 163	73.14 302

PLC	56.13 14	65.09 208	63.21 210	70.72 189	81.04 84	54.87 115	78.57 108	77.47 183	48.51 260	55.71 283	81.89 160	33.71 269	79.71 204	41.66 260	70.37 211	77.58 252
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
EPMVS		53.96 293	53.69 294	54.79 315	66.12 320	31.96 340	62.34 308	49.05 351	44.42 299	55.54 284	71.33 307	30.22 297	56.70 335	41.65 261	62.54 288	75.71 274

MDTV_nov1_ep13			57.00 270		72.73 256	38.26 296	65.02 298	64.73 287	44.74 293	55.46 285	72.48 300	32.61 286	70.47 286	37.47 278	67.75 252

test-LLR		58.15 272	58.13 264	58.22 299	68.57 303	44.80 245	65.46 292	57.92 324	50.08 248	55.44 286	69.82 317	32.62 284	57.44 331	49.66 199	73.62 158	72.41 311

test-mter		56.42 282	55.82 279	58.22 299	68.57 303	44.80 245	65.46 292	57.92 324	39.94 329	55.44 286	69.82 317	21.92 337	57.44 331	49.66 199	73.62 158	72.41 311

ITE_SJBPF				62.09 279	66.16 319	44.55 250		64.32 289	47.36 273	55.31 288	80.34 203	19.27 339	62.68 315	36.29 290	62.39 289	79.04 240

MIMVSNet		57.35 276	57.07 269	58.22 299	74.21 216	37.18 302	62.46 306	60.88 315	48.88 257	55.29 289	75.99 281	31.68 291	62.04 317	31.87 303	72.35 186	75.43 277

Anonymous20231206		55.10 291	55.30 283	54.48 316	69.81 296	33.94 326	62.91 305	62.13 311	41.08 320	55.18 290	75.65 283	32.75 282	56.59 337	30.32 318	67.86 250	72.91 303

pmmvs-eth3d		58.81 266	56.31 276	66.30 235	67.61 309	52.42 144	72.30 232	64.76 286	43.55 306	54.94 291	74.19 294	28.95 306	72.60 276	43.31 246	57.21 315	73.88 299

OurMVSNet-221017-0		61.37 247	58.63 260	69.61 201	72.05 267	48.06 219	73.93 211	72.51 233	47.23 274	54.74 292	80.92 188	21.49 338	81.24 181	48.57 208	56.22 319	79.53 235

GG-mvs-BLEND				62.34 277	71.36 278	37.04 304	69.20 269	57.33 327		54.73 293	65.48 333	30.37 295	77.82 239	34.82 293	74.93 146	72.17 316

tpmvs		58.47 268	56.95 271	63.03 274	70.20 290	41.21 274	67.90 282	67.23 272	49.62 251	54.73 293	70.84 309	34.14 264	76.24 257	36.64 286	61.29 297	71.64 318

EPNet_dtu		61.90 241	61.97 226	61.68 280	72.89 254	39.78 281	75.85 176	65.62 280	55.09 188	54.56 295	79.36 228	37.59 221	67.02 299	39.80 270	76.95 132	78.25 245
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
PatchT		53.17 299	53.44 296	52.33 325	68.29 307	25.34 356	58.21 322	54.41 341	44.46 298	54.56 295	69.05 320	33.32 273	60.94 319	36.93 281	61.76 292	70.73 324

test0.0.03 1		53.32 298	53.59 295	52.50 324	62.81 333	29.45 345	59.51 318	54.11 343	50.08 248	54.40 297	74.31 293	32.62 284	55.92 342	30.50 316	63.95 277	72.15 317

ambc				65.13 259	63.72 331	37.07 303	47.66 347	78.78 159		54.37 298	71.42 306	11.24 355	80.94 186	45.64 228	53.85 328	77.38 254

testing_2		66.02 196	63.77 203	72.76 139	66.03 321	50.48 175	72.93 220	80.36 123	54.41 200	54.25 299	76.76 270	30.89 293	83.16 136	64.19 92	74.08 153	84.65 122

SixPastTwentyTwo		61.65 245	58.80 257	70.20 195	75.80 191	47.22 228	75.59 178	69.68 248	54.61 194	54.11 300	79.26 230	27.07 318	82.96 140	43.27 247	49.79 337	80.41 223

ppachtmachnet_test		58.06 273	55.38 282	66.10 242	69.51 297	48.99 209	68.01 281	66.13 278	44.50 297	54.05 301	70.74 310	32.09 290	72.34 278	36.68 285	56.71 318	76.99 264

TESTMET0.1,1		55.28 289	54.90 285	56.42 307	66.56 317	43.67 257	65.46 292	56.27 333	39.18 331	53.83 302	67.44 325	24.21 332	55.46 345	48.04 210	73.11 170	70.13 325

pmmvs5		56.47 281	55.68 280	58.86 296	61.41 338	36.71 310	66.37 286	62.75 306	40.38 326	53.70 303	76.62 271	34.56 259	67.05 298	40.02 269	65.27 266	72.83 304

MSDG		61.81 244	59.23 247	69.55 205	72.64 257	52.63 138	70.45 258	75.81 203	51.38 236	53.70 303	76.11 278	29.52 302	81.08 185	37.70 277	65.79 263	74.93 283

K. test v3		60.47 250	57.11 268	70.56 190	73.74 227	48.22 216	75.10 190	62.55 307	58.27 135	53.62 305	76.31 277	27.81 312	81.59 176	47.42 211	39.18 349	81.88 192

PM-MVS		52.33 301	50.19 304	58.75 297	62.10 335	45.14 243	65.75 289	40.38 361	43.60 305	53.52 306	72.65 299	9.16 359	65.87 306	50.41 191	54.18 326	65.24 335

PMMVS		53.96 293	53.26 297	56.04 308	62.60 334	50.92 159	61.17 314	56.09 334	32.81 344	53.51 307	66.84 327	34.04 265	59.93 324	44.14 240	68.18 247	57.27 348

PatchMatch-RL		56.25 284	54.55 287	61.32 284	77.06 173	56.07 96	65.57 291	54.10 344	44.13 302	53.49 308	71.27 308	25.20 328	66.78 300	36.52 288	63.66 278	61.12 342

LCM-MVSNet-Re		61.88 243	61.35 235	63.46 267	74.58 208	31.48 341	61.42 311	58.14 323	58.71 127	53.02 309	79.55 225	43.07 166	76.80 251	45.69 227	77.96 121	82.11 189

F-COLMAP		63.05 228	60.87 240	69.58 204	76.99 176	53.63 124	78.12 118	76.16 198	47.97 268	52.41 310	81.61 168	27.87 311	78.11 236	40.07 267	66.66 257	77.00 262

test20.03		53.87 295	54.02 293	53.41 320	61.47 337	28.11 348	61.30 312	59.21 319	51.34 237	52.09 311	77.43 262	33.29 274	58.55 328	29.76 324	60.27 308	73.58 300

testgi		51.90 302	52.37 299	50.51 329	60.39 344	23.55 359	58.42 321	58.15 322	49.03 256	51.83 312	79.21 231	22.39 335	55.59 343	29.24 332	62.64 286	72.40 313

EU-MVSNet		55.61 288	54.41 289	59.19 294	65.41 324	33.42 334	72.44 230	71.91 237	28.81 349	51.27 313	73.87 295	24.76 330	69.08 291	43.04 250	58.20 314	75.06 279

MDTV_nov1_ep13_2view						25.89 353	61.22 313		40.10 327	51.10 314		32.97 277		38.49 273		78.61 243

COLMAP_ROB	52.97 17	61.27 248	58.81 250	68.64 214	74.63 207	52.51 141	78.42 113	73.30 228	49.92 250	50.96 315	81.51 171	23.06 334	79.40 209	31.63 309	65.85 261	74.01 298
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
ADS-MVSNet2		51.33 305	48.76 308	59.07 295	66.02 322	44.60 248	50.90 341	59.76 318	36.90 336	50.74 316	66.18 331	26.38 321	63.11 312	27.17 336	54.76 324	69.50 327

ADS-MVSNet		48.48 311	47.77 310	50.63 328	66.02 322	29.92 344	50.90 341	50.87 350	36.90 336	50.74 316	66.18 331	26.38 321	52.47 350	27.17 336	54.76 324	69.50 327

our_test_3		56.49 280	54.42 288	62.68 276	69.51 297	45.48 241	66.08 288	61.49 313	44.11 303	50.73 318	69.60 319	33.05 275	68.15 294	38.38 274	56.86 316	74.40 289

FMVSNet5		55.86 286	54.93 284	58.66 298	71.05 281	36.35 312	64.18 302	62.48 308	46.76 277	50.66 319	74.73 290	25.80 325	64.04 310	33.11 299	65.57 265	75.59 275

lessismore_v0				69.91 198	71.42 276	47.80 221		50.90 349		50.39 320	75.56 284	27.43 316	81.33 180	45.91 225	34.10 352	80.59 220

UnsupCasMVSNet_eth		53.16 300	52.47 298	55.23 311	59.45 348	33.39 335	59.43 319	69.13 256	45.98 283	50.35 321	72.32 301	29.30 305	58.26 329	42.02 258	44.30 346	74.05 297

dp		51.89 303	51.60 301	52.77 323	68.44 306	32.45 337	62.36 307	54.57 340	44.16 301	49.31 322	67.91 322	28.87 308	56.61 336	33.89 296	54.89 323	69.24 330

JIA-IIPM		51.56 304	47.68 312	63.21 271	64.61 327	50.73 166	47.71 346	58.77 321	42.90 311	48.46 323	51.72 351	24.97 329	70.24 287	36.06 291	53.89 327	68.64 331

USDC		56.35 283	54.24 291	62.69 275	64.74 326	40.31 278	65.05 297	73.83 225	43.93 304	47.58 324	77.71 252	15.36 345	75.05 270	38.19 276	61.81 291	72.70 305

UnsupCasMVSNet_bld		50.07 308	48.87 307	53.66 318	60.97 342	33.67 333	57.62 324	64.56 288	39.47 330	47.38 325	64.02 336	27.47 314	59.32 325	34.69 294	43.68 347	67.98 332

AllTest		57.08 279	54.65 286	64.39 264	71.44 274	49.03 206	69.92 264	67.30 270	45.97 284	47.16 326	79.77 215	17.47 340	67.56 296	33.65 297	59.16 311	76.57 266

TestCases				64.39 264	71.44 274	49.03 206		67.30 270	45.97 284	47.16 326	79.77 215	17.47 340	67.56 296	33.65 297	59.16 311	76.57 266

CMPMVS	42.80 21	57.81 275	55.97 278	63.32 270	60.98 341	47.38 227	64.66 299	69.50 251	32.06 346	46.83 328	77.80 249	29.50 303	71.36 281	48.68 206	73.75 156	71.21 321
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
MIMVSNet1		55.17 290	54.31 290	57.77 303	70.03 292	32.01 339	65.68 290	64.81 285	49.19 254	46.75 329	76.00 279	25.53 327	64.04 310	28.65 334	62.13 290	77.26 258

PVSNet_0	43.31 20	47.46 314	45.64 315	52.92 322	67.60 310	44.65 247	54.06 332	54.64 339	41.59 318	46.15 330	58.75 346	30.99 292	58.66 327	32.18 301	24.81 355	55.46 349

test2356		45.61 316	44.66 319	48.47 333	60.15 345	28.08 349	52.44 337	52.83 347	38.01 334	46.13 331	60.98 342	15.08 346	55.54 344	20.43 352	55.85 321	61.78 340

Patchmatch-test		49.08 309	48.28 309	51.50 327	64.40 328	30.85 343	45.68 349	48.46 354	35.60 340	46.10 332	72.10 302	34.47 262	46.37 354	27.08 338	60.65 302	77.27 257

YYNet1		50.73 306	48.96 305	56.03 309	61.10 340	41.78 270	51.94 339	56.44 331	40.94 322	44.84 333	67.80 324	30.08 299	55.08 346	36.77 282	50.71 334	71.22 320

MDA-MVSNet_test_wron		50.71 307	48.95 306	56.00 310	61.17 339	41.84 269	51.90 340	56.45 330	40.96 321	44.79 334	67.84 323	30.04 300	55.07 347	36.71 284	50.69 335	71.11 323

TDRefinement		53.44 297	50.72 303	61.60 281	64.31 329	46.96 230	70.89 253	65.27 284	41.78 315	44.61 335	77.98 243	11.52 354	66.36 302	28.57 335	51.59 332	71.49 319

new-patchmatchnet		47.56 313	47.73 311	47.06 334	58.81 349	9.37 367	48.78 345	59.21 319	43.28 307	44.22 336	68.66 321	25.67 326	57.20 334	31.57 311	49.35 340	74.62 288

1111		44.40 320	45.00 318	42.61 341	57.55 351	17.33 364	53.82 335	57.05 328	40.78 323	44.11 337	66.57 328	13.37 348	45.77 355	22.15 345	49.58 338	64.73 337

.test1245		34.88 332	39.49 328	21.04 353	57.55 351	17.33 364	53.82 335	57.05 328	40.78 323	44.11 337	66.57 328	13.37 348	45.77 355	22.15 345	0.00 366	0.03 367

testus		44.59 319	43.87 322	46.76 335	59.85 347	24.65 357	53.86 333	55.82 336	36.26 339	43.97 339	63.42 337	8.39 360	53.14 349	20.70 351	52.52 330	62.51 338

N_pmnet		39.35 328	40.28 326	36.54 345	63.76 330	1.62 371	49.37 344	0.76 373	34.62 342	43.61 340	66.38 330	26.25 323	42.57 360	26.02 342	51.77 331	65.44 334

CHOSEN 280x420		47.83 312	46.36 313	52.24 326	67.37 311	49.78 196	38.91 357	43.11 360	35.00 341	43.27 341	63.30 339	28.95 306	49.19 353	36.53 287	60.80 301	57.76 347

LP		48.51 310	45.51 316	57.52 305	62.86 332	44.53 251	52.38 338	59.84 317	38.11 333	42.81 342	61.02 341	23.23 333	63.02 313	24.10 343	45.24 345	65.02 336

TinyColmap		54.14 292	51.72 300	61.40 283	66.84 314	41.97 268	66.52 285	68.51 263	44.81 292	42.69 343	75.77 282	11.66 353	72.94 275	31.96 302	56.77 317	69.27 329

MDA-MVSNet-bldmvs		53.87 295	50.81 302	63.05 273	66.25 318	48.58 213	56.93 326	63.82 298	48.09 266	41.22 344	70.48 313	30.34 296	68.00 295	34.24 295	45.92 344	72.57 307

testpf		44.11 321	45.40 317	40.26 343	60.52 343	27.34 350	33.26 359	54.33 342	45.87 287	41.08 345	60.26 343	16.46 342	59.14 326	46.09 222	50.68 336	34.31 358

test1235678		45.66 315	44.46 321	49.26 330	59.88 346	28.68 347	56.36 328	55.54 338	39.12 332	40.89 346	63.40 338	14.41 347	57.32 333	21.05 349	49.47 339	61.78 340

pmmvs3		44.92 318	41.95 324	53.86 317	52.58 355	43.55 258	62.11 309	46.90 358	26.05 353	40.63 347	60.19 344	11.08 356	57.91 330	31.83 308	46.15 343	60.11 344

LF4IMVS		42.95 322	42.26 323	45.04 337	48.30 359	32.50 336	54.80 330	48.49 353	28.03 350	40.51 348	70.16 315	9.24 358	43.89 358	31.63 309	49.18 341	58.72 345

DSMNet-mixed		39.30 329	38.72 329	41.03 342	51.22 356	19.66 361	45.53 350	31.35 366	15.83 361	39.80 349	67.42 326	22.19 336	45.13 357	22.43 344	52.69 329	58.31 346

MVS-HIRNet		45.52 317	44.48 320	48.65 332	68.49 305	34.05 324	59.41 320	44.50 359	27.03 351	37.96 350	50.47 354	26.16 324	64.10 309	26.74 339	59.52 309	47.82 352

FPMVS		42.18 324	41.11 325	45.39 336	58.03 350	41.01 276	49.50 343	53.81 345	30.07 348	33.71 351	64.03 334	11.69 352	52.08 351	14.01 358	55.11 322	43.09 355

testmv		42.25 323	40.11 327	48.66 331	53.23 353	27.02 351	56.62 327	55.74 337	37.25 335	33.10 352	59.52 345	7.78 361	56.58 338	19.61 353	38.13 351	62.40 339

new_pmnet		34.13 335	34.29 334	33.64 346	52.63 354	18.23 363	44.43 353	33.90 364	22.81 356	30.89 353	53.18 349	10.48 357	35.72 364	20.77 350	39.51 348	46.98 353

test12356		36.16 331	35.94 332	36.83 344	50.82 357	8.52 368	44.84 352	53.49 346	32.72 345	30.11 354	55.08 348	7.11 363	49.47 352	16.60 355	32.68 353	52.50 350

LCM-MVSNet		40.30 327	35.88 333	53.57 319	42.24 362	29.15 346	45.21 351	60.53 316	22.23 357	28.02 355	50.98 353	3.72 367	61.78 318	31.22 315	38.76 350	69.78 326

ANet_high		41.38 325	37.47 330	53.11 321	39.73 365	24.45 358	56.94 325	69.69 247	47.65 270	26.04 356	52.32 350	12.44 350	62.38 316	21.80 348	10.61 363	72.49 308

no-one		40.85 326	36.09 331	55.14 312	48.55 358	38.72 290	42.15 355	62.92 305	34.60 343	23.55 357	49.74 355	12.21 351	66.16 304	26.27 341	24.84 354	60.54 343

PMVS	28.69 22	36.22 330	33.29 335	45.02 338	36.82 367	35.98 316	54.68 331	48.74 352	26.31 352	21.02 358	51.61 352	2.88 369	60.10 323	9.99 362	47.58 342	38.99 357
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
PMMVS2		27.40 339	25.91 339	31.87 349	39.46 366	6.57 369	31.17 360	28.52 367	23.96 354	20.45 359	48.94 356	4.20 366	37.94 363	16.51 356	19.97 356	51.09 351

Gipuma		34.77 333	31.91 336	43.33 340	62.05 336	37.87 298	20.39 362	67.03 273	23.23 355	18.41 360	25.84 360	4.24 365	62.73 314	14.71 357	51.32 333	29.38 360
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PNet_i23d		27.88 338	25.99 338	33.55 347	47.54 360	25.89 353	47.24 348	32.91 365	21.44 358	15.90 361	38.09 357	0.85 371	42.76 359	16.90 354	13.03 361	32.00 359

tmp_tt		9.43 345	11.14 346	4.30 356	2.38 371	4.40 370	13.62 364	16.08 370	0.39 366	15.89 362	13.06 364	15.80 344	5.54 368	12.63 359	10.46 364	2.95 364

MVE	17.77 23	21.41 342	17.77 344	32.34 348	34.34 369	25.44 355	16.11 363	24.11 368	11.19 363	13.22 363	31.92 358	1.58 370	30.95 365	10.47 360	17.03 357	40.62 356
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
DeepMVS_CX				12.03 355	17.97 370	10.91 366		10.60 371	7.46 364	11.07 364	28.36 359	3.28 368	11.29 367	8.01 364	9.74 365	13.89 363

E-PMN		23.77 340	22.73 341	26.90 351	42.02 363	20.67 360	42.66 354	35.70 362	17.43 359	10.28 365	25.05 361	6.42 364	42.39 361	10.28 361	14.71 358	17.63 361

EMVS		22.97 341	21.84 343	26.36 352	40.20 364	19.53 362	41.95 356	34.64 363	17.09 360	9.73 366	22.83 363	7.29 362	42.22 362	9.18 363	13.66 360	17.32 362

wuykxyi23d		28.12 337	22.54 342	44.87 339	34.97 368	32.11 338	37.96 358	47.31 356	13.32 362	9.29 367	23.72 362	0.45 372	56.58 338	21.85 347	13.98 359	45.93 354

wuyk23d		13.32 344	12.52 345	15.71 354	47.54 360	26.27 352	31.06 361	1.98 372	4.93 365	5.18 368	1.94 368	0.45 372	18.54 366	6.81 365	12.83 362	2.33 365

testmvs		4.52 348	6.03 349	0.01 358	0.01 372	0.00 373	53.86 333	0.00 374	0.01 367	0.04 369	0.27 369	0.00 375	0.00 369	0.04 366	0.00 366	0.03 367

test123		4.73 347	6.30 348	0.02 357	0.01 372	0.01 372	56.36 328	0.00 374	0.01 367	0.04 369	0.21 370	0.01 374	0.00 369	0.03 367	0.00 366	0.04 366

cdsmvs_eth3d_5k		17.50 343	23.34 340	0.00 359	0.00 374	0.00 373	0.00 365	78.63 162	0.00 369	0.00 371	82.18 149	49.25 93	0.00 369	0.00 368	0.00 366	0.00 369

pcd_1.5k_mvsjas		3.92 349	5.23 350	0.00 359	0.00 374	0.00 373	0.00 365	0.00 374	0.00 369	0.00 371	0.00 371	47.05 126	0.00 369	0.00 368	0.00 366	0.00 369

pcd1.5k->3k		30.06 336	30.56 337	28.55 350	78.81 122	0.00 373	0.00 365	82.07 76	0.00 369	0.00 371	0.00 371	39.61 200	0.00 369	0.00 368	74.56 147	85.66 80

sosnet-low-res		0.00 350	0.00 351	0.00 359	0.00 374	0.00 373	0.00 365	0.00 374	0.00 369	0.00 371	0.00 371	0.00 375	0.00 369	0.00 368	0.00 366	0.00 369

sosnet		0.00 350	0.00 351	0.00 359	0.00 374	0.00 373	0.00 365	0.00 374	0.00 369	0.00 371	0.00 371	0.00 375	0.00 369	0.00 368	0.00 366	0.00 369

uncertanet		0.00 350	0.00 351	0.00 359	0.00 374	0.00 373	0.00 365	0.00 374	0.00 369	0.00 371	0.00 371	0.00 375	0.00 369	0.00 368	0.00 366	0.00 369

Regformer		0.00 350	0.00 351	0.00 359	0.00 374	0.00 373	0.00 365	0.00 374	0.00 369	0.00 371	0.00 371	0.00 375	0.00 369	0.00 368	0.00 366	0.00 369

ab-mvs-re		6.49 346	8.65 347	0.00 359	0.00 374	0.00 373	0.00 365	0.00 374	0.00 369	0.00 371	77.89 247	0.00 375	0.00 369	0.00 368	0.00 366	0.00 369

uanet		0.00 350	0.00 351	0.00 359	0.00 374	0.00 373	0.00 365	0.00 374	0.00 369	0.00 371	0.00 371	0.00 375	0.00 369	0.00 368	0.00 366	0.00 369

GSMVS																78.05 248

test_part1				0.00 359		0.00 373	0.00 365	86.64 3				0.00 375	0.00 369	0.00 368	0.00 366	0.00 369

sam_mvs1												34.74 257				78.05 248

sam_mvs												33.43 272

MTGPA								80.97 105

test_post1							68.67 272				3.64 366	32.39 288	69.49 289	44.17 239

test_post											3.55 367	33.90 267	66.52 301

patchmatchnet-post											64.03 334	34.50 260	74.27 273

MTMP							86.03 10	17.08 369

gm-plane-assit					71.40 277	41.72 272			48.85 258		73.31 298		82.48 162	48.90 205

test9_res														75.28 16	88.31 19	83.81 151

agg_prior2														73.09 32	87.93 26	84.33 128

test_prior4						62.51 17	82.08 63

test_prior				76.69 48	84.20 51	57.27 75		84.88 19					86.43 58			86.38 51

新几何2							76.12 168

旧先验1					83.04 58	53.15 131		67.52 269			87.85 52	44.08 158			80.76 76	78.03 250

无先验							79.66 96	74.30 220	48.40 263				80.78 191	53.62 170		79.03 241

原ACMM2							79.02 102

testdata2													72.18 280	46.95 217

segment_acmp												54.23 37

testdata1							72.65 223		60.50 80

plane_prior7					81.41 75	55.96 98

plane_prior6					81.20 82	56.24 92						45.26 148

plane_prior5								84.01 34					87.21 36	68.16 54	80.58 79	84.65 122

plane_prior4											86.10 80

plane_prior2							84.22 26		64.52 24

plane_prior1					81.27 80

plane_prior						56.31 88	83.58 35		63.19 40						80.48 82

n2								0.00 374

nn								0.00 374

door-mid								47.19 357

test11								83.47 50

door								47.60 355

HQP5-MVS						54.94 112

BP-MVS														67.04 65

HQP3-MVS								83.90 38							80.35 85

HQP2-MVS												45.46 142

NP-MVS					80.98 85	56.05 97					85.54 93

ACMMP++_ref															74.07 154

ACMMP++															72.16 189

Test By Simon												48.33 111

High-resolution multi-view benchmark