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.

HPM-MVS++		79.88 7	80.14 7	79.10 17	88.17 1	64.80 1	86.59 10	83.70 60	65.37 15	78.78 21	90.64 17	58.63 20	87.24 48	79.00 7	90.37 10	85.26 113

CNVR-MVS		79.84 8	79.97 8	79.45 7	87.90 2	62.17 20	84.37 35	85.03 33	66.96 5	77.58 28	90.06 38	59.47 17	89.13 18	78.67 9	89.73 16	87.03 50

test_0728_SECOND				79.19 12	87.82 3	59.11 65	87.85 3	87.15 6					90.84 1	78.66 10	90.61 7	87.62 31

SED-MVS		81.56 1	82.30 1	79.32 9	87.77 4	58.90 71	87.82 5	86.78 12	64.18 32	85.97 1	91.84 6	66.87 2	90.83 2	78.63 12	90.87 3	88.23 10

IU-MVS					87.77 4	59.15 63		85.53 25	53.93 211	84.64 3				79.07 6	90.87 3	88.37 7

test_241102_ONE					87.77 4	58.90 71		86.78 12	64.20 31	85.97 1	91.34 10	66.87 2	90.78 4

DVP-MVS		80.84 3	81.64 2	78.42 33	87.75 7	59.07 66	87.85 3	85.03 33	64.26 29	83.82 6	92.00 3	64.82 6	90.75 5	78.66 10	90.61 7	85.45 104

test0726					87.75 7	59.07 66	87.86 2	86.83 10	64.26 29	84.19 5	91.92 5	64.82 6

test_part2					87.58 9	60.47 46				83.42 9

OPU-MVS				79.83 4	87.54 10	60.93 38	87.82 5				89.89 45	67.01 1	90.33 8	73.16 45	91.15 2	88.23 10

NCCC		78.58 15	78.31 19	79.39 8	87.51 11	62.61 16	85.20 30	84.42 42	66.73 9	74.67 48	89.38 53	55.30 43	89.18 17	74.19 35	87.34 44	86.38 63

DPE-MVS		80.56 4	80.98 4	79.29 11	87.27 12	60.56 45	85.71 24	86.42 16	63.28 43	83.27 10	91.83 8	64.96 5	90.47 7	76.41 24	89.67 18	86.84 54

testtj		78.47 18	78.43 18	78.61 29	86.82 13	60.67 43	86.07 16	85.38 27	62.12 66	78.65 22	90.29 31	55.76 39	89.31 15	73.55 43	87.22 45	85.84 85

region2R		77.67 30	77.18 33	79.15 14	86.76 14	62.95 8	86.29 12	84.16 48	62.81 55	73.30 67	90.58 19	49.90 97	88.21 32	73.78 39	87.03 48	86.29 75

ACMMPR		77.71 28	77.23 32	79.16 13	86.75 15	62.93 9	86.29 12	84.24 46	62.82 53	73.55 65	90.56 20	49.80 99	88.24 31	74.02 37	87.03 48	86.32 72

HFP-MVS		78.01 26	77.65 27	79.10 17	86.71 16	62.81 10	86.29 12	84.32 44	62.82 53	73.96 56	90.50 22	53.20 67	88.35 28	74.02 37	87.05 46	86.13 77

#test#		77.83 27	77.41 30	79.10 17	86.71 16	62.81 10	85.69 25	84.32 44	61.61 76	73.96 56	90.50 22	53.20 67	88.35 28	73.68 40	87.05 46	86.13 77

MCST-MVS		77.48 32	77.45 29	77.54 47	86.67 18	58.36 80	83.22 57	86.93 8	56.91 154	74.91 44	88.19 66	59.15 18	87.68 43	73.67 41	87.45 43	86.57 61

ZD-MVS					86.64 19	60.38 47		82.70 85	57.95 141	78.10 24	90.06 38	56.12 37	88.84 23	74.05 36	87.00 51

APDe-MVS		80.16 6	80.59 5	78.86 25	86.64 19	60.02 50	88.12 1	86.42 16	62.94 49	82.40 11	92.12 2	59.64 15	89.76 11	78.70 8	88.32 32	86.79 57

SMA-MVS		80.28 5	80.39 6	79.95 3	86.60 21	61.95 22	86.33 11	85.75 23	62.49 60	82.20 12	92.28 1	56.53 31	89.70 12	79.85 3	91.48 1	88.19 12
Yufeng Yin; Xiaoyan Liu; Zichao Zhang: SMA-MVS: Segmentation-Guided Multi-Scale Anchor Deformation Patch Multi-View Stereo. IEEE Transactions on Circuits and Systems for Video Technology
DP-MVS Recon		72.15 92	70.73 101	76.40 66	86.57 22	57.99 85	81.15 92	82.96 79	57.03 151	66.78 161	85.56 110	44.50 165	88.11 34	51.77 198	80.23 110	83.10 182

MP-MVS		78.35 21	78.26 21	78.64 28	86.54 23	63.47 5	86.02 18	83.55 63	63.89 37	73.60 64	90.60 18	54.85 48	86.72 66	77.20 18	88.06 37	85.74 93
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
mPP-MVS		76.54 41	75.93 44	78.34 36	86.47 24	63.50 4	85.74 23	82.28 89	62.90 50	71.77 87	90.26 32	46.61 144	86.55 74	71.71 52	85.66 61	84.97 121

APD-MVS		78.02 24	78.04 25	77.98 41	86.44 25	60.81 40	85.52 26	84.36 43	60.61 88	79.05 19	90.30 30	55.54 42	88.32 30	73.48 44	87.03 48	84.83 124
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
ZNCC-MVS		78.82 12	78.67 16	79.30 10	86.43 26	62.05 21	86.62 9	86.01 19	63.32 42	75.08 39	90.47 25	53.96 57	88.68 24	76.48 23	89.63 20	87.16 47

ETH3 D test6400		79.14 10	79.32 9	78.61 29	86.34 27	58.11 83	84.65 33	87.66 4	58.56 130	78.87 20	89.54 50	63.67 10	89.57 13	74.60 33	89.98 13	88.14 13

XVS		77.17 35	76.56 39	79.00 20	86.32 28	62.62 14	85.83 20	83.92 52	64.55 23	72.17 84	90.01 43	47.95 121	88.01 36	71.55 54	86.74 54	86.37 67

X-MVStestdata		70.21 118	67.28 159	79.00 20	86.32 28	62.62 14	85.83 20	83.92 52	64.55 23	72.17 84	6.49 354	47.95 121	88.01 36	71.55 54	86.74 54	86.37 67

MSP-MVS		81.06 2	81.40 3	80.02 1	86.21 30	62.73 12	86.09 15	86.83 10	65.51 14	83.81 8	90.51 21	63.71 9	89.23 16	81.51 1	88.44 28	88.09 15

114514_t		70.83 105	69.56 115	74.64 98	86.21 30	54.63 138	82.34 73	81.81 98	48.22 265	63.01 218	85.83 106	40.92 204	87.10 55	57.91 153	79.79 112	82.18 196

xxxxxxxxxxxxxcwj		78.37 20	78.25 22	78.76 26	86.17 32	61.30 31	83.98 47	79.95 141	59.00 121	79.16 16	90.75 15	57.96 22	87.09 56	77.08 20	90.18 11	87.87 21

save fliter					86.17 32	61.30 31	83.98 47	79.66 146	59.00 121

DeepC-MVS_fast	68.24 3	77.25 34	76.63 38	79.12 16	86.15 34	60.86 39	84.71 32	84.85 38	61.98 72	73.06 73	88.88 61	53.72 61	89.06 19	68.27 69	88.04 38	87.42 40
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
PGM-MVS		76.77 40	76.06 42	78.88 24	86.14 35	62.73 12	82.55 70	83.74 59	61.71 74	72.45 83	90.34 29	48.48 117	88.13 33	72.32 48	86.85 52	85.78 87

zzz-MVS		77.61 31	77.36 31	78.35 34	86.08 36	63.57 2	83.37 55	80.97 125	65.13 17	75.77 34	90.88 13	48.63 113	86.66 68	77.23 16	88.17 34	84.81 125

MTAPA		76.90 38	76.42 40	78.35 34	86.08 36	63.57 2	74.92 199	80.97 125	65.13 17	75.77 34	90.88 13	48.63 113	86.66 68	77.23 16	88.17 34	84.81 125

GST-MVS		78.14 23	77.85 26	78.99 22	86.05 38	61.82 25	85.84 19	85.21 29	63.56 41	74.29 53	90.03 41	52.56 70	88.53 26	74.79 31	88.34 30	86.63 60

CP-MVS		77.12 36	76.68 37	78.43 32	86.05 38	63.18 7	87.55 8	83.45 66	62.44 62	72.68 77	90.50 22	48.18 119	87.34 47	73.59 42	85.71 60	84.76 129

SR-MVS		76.13 45	75.70 46	77.40 51	85.87 40	61.20 33	85.52 26	82.19 90	59.99 104	75.10 38	90.35 27	47.66 125	86.52 75	71.64 53	82.99 75	84.47 136

新几何1				70.76 187	85.66 41	61.13 35		66.43 285	44.68 295	70.29 98	86.64 86	41.29 200	75.23 267	49.72 211	81.75 93	75.93 275

MG-MVS		73.96 69	73.89 64	74.16 109	85.65 42	49.69 204	81.59 86	81.29 115	61.45 77	71.05 93	88.11 67	51.77 81	87.73 42	61.05 135	83.09 73	85.05 118

1121		68.53 155	67.16 165	72.63 149	85.64 43	61.14 34	73.95 213	66.46 284	44.61 296	70.28 99	86.68 85	41.42 198	80.78 201	53.62 183	81.79 89	75.97 273

TEST9					85.58 44	61.59 27	81.62 84	81.26 116	55.65 184	74.93 42	88.81 62	53.70 62	84.68 117

train_agg		76.27 44	76.15 41	76.64 62	85.58 44	61.59 27	81.62 84	81.26 116	55.86 177	74.93 42	88.81 62	53.70 62	84.68 117	75.24 29	88.33 31	83.65 166

test1172		75.36 53	74.81 55	77.02 55	85.47 46	60.79 42	83.94 49	81.63 102	59.52 115	74.66 49	90.18 34	44.74 161	85.84 91	70.63 60	82.52 84	84.42 137

ACMMP_NAP		78.77 14	78.78 14	78.74 27	85.44 47	61.04 36	83.84 50	85.16 30	62.88 51	78.10 24	91.26 11	52.51 71	88.39 27	79.34 5	90.52 9	86.78 58

test_8					85.40 48	60.96 37	81.54 87	81.18 119	55.86 177	74.81 45	88.80 64	53.70 62	84.45 122

原ACMM1				74.69 94	85.39 49	59.40 58		83.42 67	51.47 234	70.27 100	86.61 88	48.61 115	86.51 76	53.85 182	87.96 39	78.16 250

CDPH-MVS		76.31 43	75.67 47	78.22 37	85.35 50	59.14 64	81.31 90	84.02 49	56.32 167	74.05 54	88.98 59	53.34 66	87.92 38	69.23 66	88.42 29	87.59 32

ACMMP		76.02 46	75.33 49	78.07 38	85.20 51	61.91 23	85.49 29	84.44 41	63.04 47	69.80 111	89.74 49	45.43 155	87.16 52	72.01 51	82.87 80	85.14 114
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
agg_prior1		75.94 47	76.01 43	75.72 76	85.04 52	59.96 51	81.44 88	81.04 122	56.14 173	74.68 46	88.90 60	53.91 58	84.04 129	75.01 30	87.92 41	83.16 181

agg_prior					85.04 52	59.96 51		81.04 122		74.68 46			84.04 129

HPM-MVS		77.28 33	76.85 35	78.54 31	85.00 54	60.81 40	82.91 62	85.08 31	62.57 58	73.09 72	89.97 44	50.90 93	87.48 46	75.30 27	86.85 52	87.33 44
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
MP-MVS-pluss		78.35 21	78.46 17	78.03 40	84.96 55	59.52 57	82.93 61	85.39 26	62.15 65	76.41 32	91.51 9	52.47 73	86.78 65	80.66 2	89.64 19	87.80 24
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
TSAR-MVS + MP.		78.44 19	78.28 20	78.90 23	84.96 55	61.41 29	84.03 45	83.82 58	59.34 118	79.37 15	89.76 48	59.84 13	87.62 45	76.69 22	86.74 54	87.68 28
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
AdaColmap		69.99 122	68.66 131	73.97 112	84.94 57	57.83 87	82.63 68	78.71 162	56.28 169	64.34 205	84.14 132	41.57 193	87.06 58	46.45 234	78.88 128	77.02 265

DP-MVS		65.68 203	63.66 212	71.75 162	84.93 58	56.87 106	80.74 96	73.16 244	53.06 216	59.09 258	82.35 166	36.79 243	85.94 89	32.82 315	69.96 229	72.45 309

DeepPCF-MVS	69.58 1	79.03 11	79.00 12	79.13 15	84.92 59	60.32 48	83.03 59	85.33 28	62.86 52	80.17 13	90.03 41	61.76 11	88.95 20	74.21 34	88.67 27	88.12 14

CPTT-MVS		72.78 80	72.08 83	74.87 92	84.88 60	61.41 29	84.15 42	77.86 182	55.27 189	67.51 153	88.08 69	41.93 187	81.85 177	69.04 68	80.01 111	81.35 210

test12				77.76 43	84.52 61	58.41 79		83.36 70		72.93 75		54.61 50	88.05 35		88.12 36	86.81 56

SD-MVS		77.70 29	77.62 28	77.93 42	84.47 62	61.88 24	84.55 34	83.87 56	60.37 93	79.89 14	89.38 53	54.97 45	85.58 96	76.12 26	84.94 63	86.33 70
Zhenlong Yuan, Jiakai Cao, Zhaoxin Li, Hao Jiang and Zhaoqi Wang: SD-MVS: Segmentation-driven Deformation Multi-View Stereo with Spherical Refinement and EM optimization. AAAI2024
HPM-MVS_fast		74.30 65	73.46 72	76.80 58	84.45 63	59.04 68	83.65 52	81.05 121	60.15 101	70.43 96	89.84 47	41.09 203	85.59 95	67.61 78	82.90 79	85.77 90

test_prior3		76.89 39	76.96 34	76.69 59	84.20 64	57.27 95	81.75 81	84.88 36	60.37 93	75.01 40	89.06 56	56.22 35	86.43 78	72.19 49	88.96 24	86.38 63

test_prior				76.69 59	84.20 64	57.27 95		84.88 36					86.43 78			86.38 63

CSCG		76.92 37	76.75 36	77.41 49	83.96 66	59.60 56	82.95 60	86.50 15	60.78 86	75.27 37	84.83 118	60.76 12	86.56 73	67.86 75	87.87 42	86.06 80

DeepC-MVS	69.38 2	78.56 17	78.14 23	79.83 4	83.60 67	61.62 26	84.17 41	86.85 9	63.23 44	73.84 61	90.25 33	57.68 26	89.96 10	74.62 32	89.03 22	87.89 19
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
UA-Net		73.13 77	72.93 76	73.76 117	83.58 68	51.66 175	78.75 122	77.66 186	67.75 4	72.61 79	89.42 51	49.82 98	83.29 145	53.61 185	83.14 72	86.32 72

SR-MVS-dyc-post		74.57 61	73.90 63	76.58 63	83.49 69	59.87 54	84.29 36	81.36 109	58.07 139	73.14 70	90.07 36	44.74 161	85.84 91	68.20 70	81.76 91	84.03 147

RE-MVS-def			73.71 67		83.49 69	59.87 54	84.29 36	81.36 109	58.07 139	73.14 70	90.07 36	43.06 177		68.20 70	81.76 91	84.03 147

LFMVS		71.78 95	71.59 87	72.32 156	83.40 71	46.38 239	79.75 112	71.08 253	64.18 32	72.80 76	88.64 65	42.58 180	83.72 137	57.41 156	84.49 67	86.86 53

test222					83.14 72	58.68 75	72.57 235	63.45 301	41.78 316	67.56 152	86.12 98	37.13 238			78.73 133	74.98 286

9.14			78.75 15		83.10 73		84.15 42	88.26 2	59.90 105	78.57 23	90.36 26	57.51 28	86.86 62	77.39 15	89.52 21

ETH3D-3000-0.1		78.58 15	78.91 13	77.61 45	83.06 74	57.86 86	84.14 44	88.31 1	60.37 93	79.14 18	90.35 27	57.76 25	87.00 59	77.16 19	89.90 14	87.97 18

旧先验1					83.04 75	53.15 154		67.52 276			87.85 71	44.08 168			80.76 98	78.03 255

MSLP-MVS++		73.77 72	73.47 71	74.66 96	83.02 76	59.29 61	82.30 77	81.88 95	59.34 118	71.59 90	86.83 79	45.94 148	83.65 139	65.09 99	85.22 62	81.06 217

SteuartSystems-ACMMP		79.48 9	79.31 10	79.98 2	83.01 77	62.18 19	87.60 7	85.83 21	66.69 10	78.03 27	90.98 12	54.26 53	90.06 9	78.42 14	89.02 23	87.69 27
Skip Steuart: Steuart Systems R&D Blog.
MVS_111021_HR		74.02 68	73.46 72	75.69 78	83.01 77	60.63 44	77.29 152	78.40 177	61.18 81	70.58 95	85.97 103	54.18 55	84.00 133	67.52 79	82.98 77	82.45 193

SF-MVS		78.82 12	79.22 11	77.60 46	82.88 79	57.83 87	84.99 31	88.13 3	61.86 73	79.16 16	90.75 15	57.96 22	87.09 56	77.08 20	90.18 11	87.87 21

VDDNet		71.81 94	71.33 93	73.26 137	82.80 80	47.60 230	78.74 123	75.27 218	59.59 114	72.94 74	89.40 52	41.51 197	83.91 134	58.75 151	82.99 75	88.26 8

abl_6		74.34 63	73.50 69	76.86 57	82.43 81	60.16 49	83.48 54	81.86 96	58.81 125	73.95 58	89.86 46	41.87 188	86.62 70	67.98 74	81.23 96	83.80 159

3Dnovator+	66.72 4	75.84 49	74.57 57	79.66 6	82.40 82	59.92 53	85.83 20	86.32 18	66.92 8	67.80 149	89.24 55	42.03 185	89.38 14	64.07 107	86.50 57	89.69 1

APD-MVS_3200maxsize		74.96 54	74.39 59	76.67 61	82.20 83	58.24 82	83.67 51	83.29 73	58.41 132	73.71 62	90.14 35	45.62 150	85.99 86	69.64 62	82.85 81	85.78 87

ETH3D cwj APD-0.16		78.02 24	78.13 24	77.71 44	82.10 84	58.65 76	82.72 66	87.55 5	58.33 136	78.05 26	90.06 38	58.35 21	87.65 44	76.15 25	89.86 15	86.82 55

PVSNet_Blended_VisFu		71.45 101	70.39 106	74.65 97	82.01 85	58.82 73	79.93 108	80.35 137	55.09 194	65.82 182	82.16 173	49.17 106	82.64 165	60.34 140	78.62 135	82.50 192

TSAR-MVS + GP.		74.90 55	74.15 61	77.17 53	82.00 86	58.77 74	81.80 80	78.57 166	58.58 128	74.32 52	84.51 127	55.94 38	87.22 49	67.11 82	84.48 68	85.52 100

API-MVS		72.17 90	71.41 90	74.45 104	81.95 87	57.22 97	84.03 45	80.38 135	59.89 108	68.40 132	82.33 167	49.64 100	87.83 41	51.87 196	84.16 70	78.30 248

MAR-MVS		71.51 99	70.15 110	75.60 81	81.84 88	59.39 59	81.38 89	82.90 82	54.90 200	68.08 141	78.70 240	47.73 123	85.51 99	51.68 200	84.17 69	81.88 202
Zhenyu Xu, Yiguang Liu, Xuelei Shi, Ying Wang, Yunan Zheng: MARMVS: Matching Ambiguity Reduced Multiple View Stereo for Efficient Large Scale Scene Reconstruction. CVPR 2020
PAPM_NR		72.63 82	71.80 85	75.13 88	81.72 89	53.42 151	79.91 109	83.28 74	59.14 120	66.31 172	85.90 104	51.86 80	86.06 83	57.45 155	80.62 99	85.91 83

VDD-MVS		72.50 83	72.09 82	73.75 119	81.58 90	49.69 204	77.76 140	77.63 187	63.21 45	73.21 68	89.02 58	42.14 184	83.32 144	61.72 130	82.50 85	88.25 9

PS-MVSNAJ		70.51 111	69.70 114	72.93 141	81.52 91	55.79 121	74.92 199	79.00 156	55.04 198	69.88 109	78.66 241	47.05 137	82.19 172	61.61 131	79.58 116	80.83 220

testdata				64.66 263	81.52 91	52.93 157		65.29 291	46.09 285	73.88 60	87.46 74	38.08 228	66.26 304	53.31 188	78.48 136	74.78 290

CHOSEN 1792x2688		65.08 213	62.84 221	71.82 161	81.49 93	56.26 112	66.32 285	74.20 235	40.53 324	63.16 217	78.65 242	41.30 199	77.80 246	45.80 240	74.09 170	81.40 207

HQP_MVS		74.31 64	73.73 66	76.06 69	81.41 94	56.31 109	84.22 39	84.01 50	64.52 25	69.27 120	86.10 99	45.26 159	87.21 50	68.16 72	80.58 101	84.65 130

plane_prior7					81.41 94	55.96 118

DPM-MVS		75.47 51	75.00 50	76.88 56	81.38 96	59.16 62	79.94 107	85.71 24	56.59 162	72.46 81	86.76 80	56.89 29	87.86 40	66.36 87	88.91 26	83.64 167

CANet		76.46 42	75.93 44	78.06 39	81.29 97	57.53 92	82.35 72	83.31 72	67.78 3	70.09 101	86.34 95	54.92 46	88.90 21	72.68 47	84.55 66	87.76 26

Vis-MVSNet		72.18 89	71.37 92	74.61 99	81.29 97	55.41 130	80.90 93	78.28 179	60.73 87	69.23 123	88.09 68	44.36 167	82.65 164	57.68 154	81.75 93	85.77 90
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
plane_prior1					81.27 99

xiu_mvs_v2_base		70.52 110	69.75 112	72.84 143	81.21 100	55.63 125	75.11 193	78.92 157	54.92 199	69.96 108	79.68 226	47.00 141	82.09 174	61.60 132	79.37 119	80.81 221

plane_prior6					81.20 101	56.24 113						45.26 159

PAPR		71.72 97	70.82 100	74.41 105	81.20 101	51.17 177	79.55 116	83.33 71	55.81 180	66.93 160	84.61 123	50.95 91	86.06 83	55.79 165	79.20 124	86.00 81

PLC	56.13 14	65.09 212	63.21 217	70.72 189	81.04 103	54.87 137	78.57 127	77.47 189	48.51 261	55.71 281	81.89 177	33.71 264	79.71 214	41.66 276	70.37 221	77.58 257
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
NP-MVS					80.98 104	56.05 117					85.54 112

OPM-MVS		74.73 58	74.25 60	76.19 68	80.81 105	59.01 69	82.60 69	83.64 61	63.74 39	72.52 80	87.49 73	47.18 135	85.88 90	69.47 64	80.78 97	83.66 165

HQP-NCC					80.66 106		82.31 74		62.10 67	67.85 144

ACMP_Plane					80.66 106		82.31 74		62.10 67	67.85 144

HQP-MVS		73.45 73	72.80 77	75.40 83	80.66 106	54.94 134	82.31 74	83.90 54	62.10 67	67.85 144	85.54 112	45.46 153	86.93 60	67.04 83	80.35 107	84.32 139

PHI-MVS		75.87 48	75.36 48	77.41 49	80.62 109	55.91 120	84.28 38	85.78 22	56.08 175	73.41 66	86.58 90	50.94 92	88.54 25	70.79 58	89.71 17	87.79 25

ACMM	61.98 7	70.80 107	69.73 113	74.02 110	80.59 110	58.59 77	82.68 67	82.02 94	55.46 187	67.18 157	84.39 130	38.51 221	83.17 148	60.65 137	76.10 158	80.30 226
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
Regformer-1		75.47 51	74.93 53	77.09 54	80.43 111	57.70 90	79.50 117	82.13 91	67.84 1	75.73 36	80.75 204	56.50 32	86.07 82	71.07 57	80.38 105	87.50 35

Regformer-2		75.63 50	74.99 51	77.54 47	80.43 111	58.32 81	79.50 117	82.92 80	67.84 1	75.94 33	80.75 204	55.73 40	86.80 63	71.44 56	80.38 105	87.50 35

Anonymous20231211		69.28 137	68.47 134	71.73 163	80.28 113	47.18 234	79.98 106	82.37 88	54.61 202	67.24 156	84.01 136	39.43 212	82.41 170	55.45 169	72.83 191	85.62 98

ACMP	63.53 6	72.30 87	71.20 96	75.59 82	80.28 113	57.54 91	82.74 65	82.84 84	60.58 89	65.24 193	86.18 97	39.25 214	86.03 85	66.95 85	76.79 154	83.22 176
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
LPG-MVS_test		72.74 81	71.74 86	75.76 74	80.22 115	57.51 93	82.55 70	83.40 68	61.32 78	66.67 164	87.33 76	39.15 216	86.59 71	67.70 76	77.30 147	83.19 178

LGP-MVS_train				75.76 74	80.22 115	57.51 93		83.40 68	61.32 78	66.67 164	87.33 76	39.15 216	86.59 71	67.70 76	77.30 147	83.19 178

WR-MVS		68.47 156	68.47 134	68.44 224	80.20 117	39.84 293	73.75 220	76.07 207	64.68 22	68.11 140	83.63 144	50.39 96	79.14 228	49.78 208	69.66 237	86.34 69

Anonymous20240529		69.91 124	69.02 125	72.56 150	80.19 118	47.65 228	77.56 144	80.99 124	55.45 188	69.88 109	86.76 80	39.24 215	82.18 173	54.04 179	77.10 149	87.85 23

Anonymous202405211		66.84 188	65.99 187	69.40 212	80.19 118	42.21 279	71.11 256	71.31 252	58.80 126	67.90 142	86.39 94	29.83 299	79.65 215	49.60 214	78.78 131	86.33 70

BH-RMVSNet		68.81 144	67.42 153	72.97 140	80.11 120	52.53 163	74.26 208	76.29 204	58.48 131	68.38 133	84.20 131	42.59 179	83.83 135	46.53 233	75.91 159	82.56 189

test_0402		63.25 229	61.01 242	69.96 200	80.00 121	54.37 140	76.86 162	72.02 250	54.58 204	58.71 261	80.79 203	35.00 252	84.36 123	26.41 341	64.71 280	71.15 321

HyFIR lowres test		65.67 204	63.01 219	73.67 122	79.97 122	55.65 124	69.07 275	75.52 215	42.68 314	63.53 213	77.95 249	40.43 205	81.64 180	46.01 238	71.91 204	83.73 161

EIA-MVS		71.78 95	70.60 102	75.30 86	79.85 123	53.54 148	77.27 153	83.26 75	57.92 142	66.49 167	79.39 232	52.07 78	86.69 67	60.05 142	79.14 126	85.66 95

Regformer-3		73.89 70	73.28 74	75.71 77	79.75 124	55.48 129	78.54 129	79.93 142	66.58 11	73.62 63	80.30 212	54.87 47	84.54 120	69.09 67	76.84 152	87.10 49

Regformer-4		74.25 66	73.48 70	76.57 64	79.75 124	56.54 108	78.54 129	81.49 106	66.93 7	73.90 59	80.30 212	53.84 60	85.98 87	69.76 61	76.84 152	87.17 46

BH-untuned		68.27 159	67.29 158	71.21 178	79.74 126	53.22 153	76.06 177	77.46 191	57.19 149	66.10 174	81.61 183	45.37 157	83.50 142	45.42 248	76.68 156	76.91 269

VNet		69.68 129	70.19 109	68.16 226	79.73 127	41.63 286	70.53 262	77.38 192	60.37 93	70.69 94	86.63 87	51.08 89	77.09 255	53.61 185	81.69 95	85.75 92

LS3D		64.71 215	62.50 225	71.34 176	79.72 128	55.71 122	79.82 110	74.72 228	48.50 262	56.62 276	84.62 122	33.59 267	82.34 171	29.65 333	75.23 164	75.97 273

AUN-MVS		68.45 157	66.41 177	74.57 101	79.53 129	57.08 104	73.93 216	75.23 219	54.44 207	66.69 163	81.85 178	37.10 239	82.89 155	62.07 126	66.84 265	83.75 160

BH-w/o		66.85 187	65.83 189	69.90 204	79.29 130	52.46 166	74.66 204	76.65 202	54.51 206	64.85 200	78.12 247	45.59 152	82.95 154	43.26 263	75.54 162	74.27 295

1112_ss		64.00 221	63.36 216	65.93 251	79.28 131	42.58 276	71.35 249	72.36 249	46.41 282	60.55 244	77.89 253	46.27 147	73.28 274	46.18 236	69.97 228	81.92 201

ETV-MVS		74.46 62	73.84 65	76.33 67	79.27 132	55.24 132	79.22 120	85.00 35	64.97 21	72.65 78	79.46 231	53.65 65	87.87 39	67.45 80	82.91 78	85.89 84

UniMVSNet_NR-MVSNet		71.11 103	71.00 98	71.44 170	79.20 133	44.13 263	76.02 180	82.60 86	66.48 13	68.20 135	84.60 124	56.82 30	82.82 161	54.62 175	70.43 219	87.36 43

VPNet		67.52 173	68.11 141	65.74 254	79.18 134	36.80 315	72.17 241	72.83 246	62.04 70	67.79 150	85.83 106	48.88 111	76.60 260	51.30 201	72.97 190	83.81 155

TR-MVS		66.59 195	65.07 200	71.17 180	79.18 134	49.63 206	73.48 222	75.20 221	52.95 217	67.90 142	80.33 211	39.81 209	83.68 138	43.20 264	73.56 179	80.20 227

TAMVS		66.78 190	65.27 198	71.33 177	79.16 136	53.67 144	73.84 219	69.59 264	52.32 225	65.28 188	81.72 181	44.49 166	77.40 252	42.32 270	78.66 134	82.92 184

Test_1112_low_res		62.32 237	61.77 232	64.00 267	79.08 137	39.53 297	68.17 277	70.17 258	43.25 309	59.03 259	79.90 219	44.08 168	71.24 282	43.79 259	68.42 253	81.25 211

CS-MVS		74.18 67	73.60 68	75.92 71	78.99 138	52.53 163	80.61 98	85.93 20	61.17 82	71.15 92	79.34 235	54.52 51	88.86 22	66.07 88	84.67 65	86.38 63

CDS-MVSNet		66.80 189	65.37 195	71.10 182	78.98 139	53.13 156	73.27 225	71.07 254	52.15 226	64.72 201	80.23 215	43.56 173	77.10 254	45.48 246	78.88 128	83.05 183
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
canonicalmvs		74.67 59	74.98 52	73.71 121	78.94 140	50.56 189	80.23 102	83.87 56	60.30 99	77.15 29	86.56 91	59.65 14	82.00 175	66.01 91	82.12 87	88.58 6

IS-MVSNet		71.57 98	71.00 98	73.27 136	78.86 141	45.63 252	80.22 103	78.69 163	64.14 35	66.46 168	87.36 75	49.30 103	85.60 94	50.26 207	83.71 71	88.59 5

CLD-MVS		73.33 74	72.68 78	75.29 87	78.82 142	53.33 152	78.23 133	84.79 39	61.30 80	70.41 97	81.04 194	52.41 74	87.12 54	64.61 104	82.49 86	85.41 108
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
MVSFormer		71.50 100	70.38 107	74.88 91	78.76 143	57.15 102	82.79 63	78.48 170	51.26 238	69.49 115	83.22 150	43.99 170	83.24 146	66.06 89	79.37 119	84.23 142

lupinMVS		69.57 132	68.28 138	73.44 131	78.76 143	57.15 102	76.57 165	73.29 243	46.19 284	69.49 115	82.18 170	43.99 170	79.23 222	64.66 102	79.37 119	83.93 150

CNLPA		65.43 207	64.02 205	69.68 206	78.73 145	58.07 84	77.82 139	70.71 256	51.49 233	61.57 239	83.58 146	38.23 226	70.82 283	43.90 257	70.10 227	80.16 228

EPP-MVSNet		72.16 91	71.31 94	74.71 93	78.68 146	49.70 202	82.10 78	81.65 101	60.40 92	65.94 177	85.84 105	51.74 82	86.37 80	55.93 162	79.55 118	88.07 17

TranMVSNet+NR-MVSNet		70.36 115	70.10 111	71.17 180	78.64 147	42.97 274	76.53 166	81.16 120	66.95 6	68.53 131	85.42 114	51.61 83	83.07 150	52.32 193	69.70 236	87.46 38

UniMVSNet (Re)		70.63 109	70.20 108	71.89 159	78.55 148	45.29 254	75.94 181	82.92 80	63.68 40	68.16 138	83.59 145	53.89 59	83.49 143	53.97 180	71.12 213	86.89 52

Fast-Effi-MVS+		70.28 117	69.12 124	73.73 120	78.50 149	51.50 176	75.01 196	79.46 151	56.16 172	68.59 128	79.55 229	53.97 56	84.05 128	53.34 187	77.53 143	85.65 96

PS-MVSNAJss		72.24 88	71.21 95	75.31 85	78.50 149	55.93 119	81.63 83	82.12 92	56.24 170	70.02 105	85.68 109	47.05 137	84.34 124	65.27 98	74.41 168	85.67 94

EI-MVSNet-Vis-set		72.42 86	71.59 87	74.91 90	78.47 151	54.02 141	77.05 157	79.33 153	65.03 20	71.68 89	79.35 234	52.75 69	84.89 113	66.46 86	74.23 169	85.83 86

MVS_111021_LR		69.50 134	68.78 129	71.65 166	78.38 152	59.33 60	74.82 201	70.11 259	58.08 138	67.83 148	84.68 120	41.96 186	76.34 263	65.62 96	77.54 142	79.30 242

test_yl		69.69 127	69.13 122	71.36 174	78.37 153	45.74 248	74.71 202	80.20 138	57.91 143	70.01 106	83.83 139	42.44 181	82.87 157	54.97 171	79.72 113	85.48 102

DCV-MVSNet		69.69 127	69.13 122	71.36 174	78.37 153	45.74 248	74.71 202	80.20 138	57.91 143	70.01 106	83.83 139	42.44 181	82.87 157	54.97 171	79.72 113	85.48 102

test_part1		73.17 76	72.05 84	76.54 65	78.34 155	54.99 133	85.51 28	86.99 7	58.40 133	69.77 112	84.50 128	48.78 112	87.16 52	64.60 105	71.53 209	87.48 37

FIs		70.82 106	71.43 89	68.98 217	78.33 156	38.14 307	76.96 159	83.59 62	61.02 83	67.33 155	86.73 82	55.07 44	81.64 180	54.61 177	79.22 123	87.14 48

UGNet		68.81 144	67.39 154	73.06 139	78.33 156	54.47 139	79.77 111	75.40 217	60.45 91	63.22 215	84.40 129	32.71 279	80.91 198	51.71 199	80.56 103	83.81 155
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
jason		69.65 130	68.39 137	73.43 132	78.27 158	56.88 105	77.12 155	73.71 240	46.53 281	69.34 119	83.22 150	43.37 174	79.18 223	64.77 101	79.20 124	84.23 142
jason: jason.
alignmvs		73.86 71	73.99 62	73.45 130	78.20 159	50.50 190	78.57 127	82.43 87	59.40 116	76.57 30	86.71 84	56.42 34	81.23 190	65.84 93	81.79 89	88.62 4

xiu_mvs_v1_base_debu		68.58 151	67.28 159	72.48 152	78.19 160	57.19 99	75.28 188	75.09 223	51.61 229	70.04 102	81.41 187	32.79 275	79.02 230	63.81 111	77.31 144	81.22 212

xiu_mvs_v1_base		68.58 151	67.28 159	72.48 152	78.19 160	57.19 99	75.28 188	75.09 223	51.61 229	70.04 102	81.41 187	32.79 275	79.02 230	63.81 111	77.31 144	81.22 212

xiu_mvs_v1_base_debi		68.58 151	67.28 159	72.48 152	78.19 160	57.19 99	75.28 188	75.09 223	51.61 229	70.04 102	81.41 187	32.79 275	79.02 230	63.81 111	77.31 144	81.22 212

PAPM		67.92 168	66.69 170	71.63 167	78.09 163	49.02 212	77.09 156	81.24 118	51.04 240	60.91 242	83.98 137	47.71 124	84.99 108	40.81 279	79.32 122	80.90 219

ACMH	55.70 15	65.20 211	63.57 213	70.07 199	78.07 164	52.01 174	79.48 119	79.69 144	55.75 182	56.59 277	80.98 196	27.12 317	80.94 196	42.90 268	71.58 208	77.25 263
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
DU-MVS		70.01 121	69.53 116	71.44 170	78.05 165	44.13 263	75.01 196	81.51 105	64.37 28	68.20 135	84.52 125	49.12 109	82.82 161	54.62 175	70.43 219	87.37 41

NR-MVSNet		69.54 133	68.85 127	71.59 168	78.05 165	43.81 267	74.20 209	80.86 128	65.18 16	62.76 220	84.52 125	52.35 75	83.59 141	50.96 203	70.78 215	87.37 41

EI-MVSNet-UG-set		71.92 93	71.06 97	74.52 103	77.98 167	53.56 147	76.62 164	79.16 154	64.40 27	71.18 91	78.95 239	52.19 76	84.66 119	65.47 97	73.57 178	85.32 110

WR-MVS_H		67.02 184	66.92 167	67.33 234	77.95 168	37.75 310	77.57 143	82.11 93	62.03 71	62.65 223	82.48 164	50.57 94	79.46 218	42.91 267	64.01 284	84.79 127

Effi-MVS+		73.31 75	72.54 79	75.62 80	77.87 169	53.64 145	79.62 115	79.61 147	61.63 75	72.02 86	82.61 160	56.44 33	85.97 88	63.99 110	79.07 127	87.25 45

DELS-MVS		74.76 57	74.46 58	75.65 79	77.84 170	52.25 169	75.59 184	84.17 47	63.76 38	73.15 69	82.79 155	59.58 16	86.80 63	67.24 81	86.04 59	87.89 19
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
ACMH+	57.40 11	66.12 199	64.06 204	72.30 157	77.79 171	52.83 158	80.39 101	78.03 180	57.30 147	57.47 272	82.55 162	27.68 313	84.17 126	45.54 244	69.78 233	79.90 232

3Dnovator	64.47 5	72.49 84	71.39 91	75.79 73	77.70 172	58.99 70	80.66 97	83.15 77	62.24 64	65.46 186	86.59 89	42.38 183	85.52 98	59.59 147	84.72 64	82.85 187

EG-PatchMatch MVS		64.71 215	62.87 220	70.22 195	77.68 173	53.48 149	77.99 136	78.82 158	53.37 215	56.03 280	77.41 260	24.75 331	84.04 129	46.37 235	73.42 182	73.14 302

CP-MVSNet		66.49 196	66.41 177	66.72 237	77.67 174	36.33 320	76.83 163	79.52 149	62.45 61	62.54 226	83.47 149	46.32 145	78.37 237	45.47 247	63.43 289	85.45 104

GBi-Net		67.21 177	66.55 171	69.19 213	77.63 175	43.33 270	77.31 149	77.83 183	56.62 159	65.04 196	82.70 156	41.85 189	80.33 209	47.18 228	72.76 193	83.92 151

test1		67.21 177	66.55 171	69.19 213	77.63 175	43.33 270	77.31 149	77.83 183	56.62 159	65.04 196	82.70 156	41.85 189	80.33 209	47.18 228	72.76 193	83.92 151

FMVSNet2		66.93 186	66.31 182	68.79 220	77.63 175	42.98 273	76.11 175	77.47 189	56.62 159	65.22 195	82.17 172	41.85 189	80.18 212	47.05 231	72.72 196	83.20 177

PCF-MVS	61.88 8	70.95 104	69.49 117	75.35 84	77.63 175	55.71 122	76.04 179	81.81 98	50.30 246	69.66 113	85.40 115	52.51 71	84.89 113	51.82 197	80.24 109	85.45 104
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
MVP-Stereo		65.41 208	63.80 209	70.22 195	77.62 179	55.53 127	76.30 171	78.53 168	50.59 245	56.47 278	78.65 242	39.84 208	82.68 163	44.10 256	72.12 203	72.44 310
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
FC-MVSNet-test		69.80 125	70.58 104	67.46 231	77.61 180	34.73 327	76.05 178	83.19 76	60.84 84	65.88 180	86.46 92	54.52 51	80.76 203	52.52 192	78.12 138	86.91 51

PS-CasMVS		66.42 197	66.32 181	66.70 239	77.60 181	36.30 322	76.94 160	79.61 147	62.36 63	62.43 230	83.66 143	45.69 149	78.37 237	45.35 249	63.26 290	85.42 107

FMVSNet1		66.70 191	65.87 188	69.19 213	77.49 182	43.33 270	77.31 149	77.83 183	56.45 164	64.60 204	82.70 156	38.08 228	80.33 209	46.08 237	72.31 201	83.92 151

VPA-MVSNet		69.02 141	69.47 118	67.69 230	77.42 183	41.00 290	74.04 211	79.68 145	60.06 102	69.26 122	84.81 119	51.06 90	77.58 249	54.44 178	74.43 167	84.48 135

UniMVSNet_ETH3D		67.60 172	67.07 166	69.18 216	77.39 184	42.29 278	74.18 210	75.59 214	60.37 93	66.77 162	86.06 101	37.64 230	78.93 235	52.16 195	73.49 180	86.32 72

thres100view900		63.28 228	62.41 226	65.89 252	77.31 185	38.66 303	72.65 232	69.11 270	57.07 150	62.45 229	81.03 195	37.01 241	79.17 224	31.84 319	73.25 185	79.83 234

cascas		65.98 201	63.42 215	73.64 125	77.26 186	52.58 162	72.26 240	77.21 195	48.56 260	61.21 241	74.60 291	32.57 283	85.82 93	50.38 206	76.75 155	82.52 191

thres600view7		63.30 227	62.27 227	66.41 241	77.18 187	38.87 301	72.35 238	69.11 270	56.98 152	62.37 231	80.96 197	37.01 241	79.00 233	31.43 326	73.05 189	81.36 208

PEN-MVS		66.60 193	66.45 173	67.04 235	77.11 188	36.56 317	77.03 158	80.42 134	62.95 48	62.51 228	84.03 135	46.69 143	79.07 229	44.22 252	63.08 292	85.51 101

PatchMatch-RL		56.25 278	54.55 283	61.32 285	77.06 189	56.07 116	65.57 290	54.10 336	44.13 303	53.49 307	71.27 309	25.20 328	66.78 301	36.52 303	63.66 286	61.12 335

PVSNet_BlendedMVS		68.56 154	67.72 144	71.07 183	77.03 190	50.57 187	74.50 206	81.52 103	53.66 214	64.22 210	79.72 225	49.13 107	82.87 157	55.82 163	73.92 173	79.77 237

PVSNet_Blended		68.59 150	67.72 144	71.19 179	77.03 190	50.57 187	72.51 236	81.52 103	51.91 227	64.22 210	77.77 257	49.13 107	82.87 157	55.82 163	79.58 116	80.14 229

F-COLMAP		63.05 232	60.87 244	69.58 210	76.99 192	53.63 146	78.12 135	76.16 205	47.97 269	52.41 310	81.61 183	27.87 311	78.11 241	40.07 282	66.66 266	77.00 266

tfpn200view9		63.18 230	62.18 229	66.21 245	76.85 193	39.62 295	71.96 244	69.44 266	56.63 157	62.61 224	79.83 221	37.18 235	79.17 224	31.84 319	73.25 185	79.83 234

thres400		63.31 226	62.18 229	66.72 237	76.85 193	39.62 295	71.96 244	69.44 266	56.63 157	62.61 224	79.83 221	37.18 235	79.17 224	31.84 319	73.25 185	81.36 208

tttt0517		67.83 170	65.66 192	74.33 107	76.69 195	50.82 183	77.86 137	73.99 237	54.54 205	64.64 203	82.53 163	35.06 251	85.50 100	55.71 166	69.91 230	86.67 59

ET-MVSNet_ETH3D		67.96 167	65.72 191	74.68 95	76.67 196	55.62 126	75.11 193	74.74 227	52.91 218	60.03 247	80.12 216	33.68 265	82.64 165	61.86 129	76.34 157	85.78 87

TAPA-MVS	59.36 10	66.60 193	65.20 199	70.81 186	76.63 197	48.75 216	76.52 167	80.04 140	50.64 244	65.24 193	84.93 117	39.15 216	78.54 236	36.77 297	76.88 151	85.14 114
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
OMC-MVS		71.40 102	70.60 102	73.78 115	76.60 198	53.15 154	79.74 113	79.78 143	58.37 134	68.75 127	86.45 93	45.43 155	80.60 204	62.58 121	77.73 141	87.58 33

LTVRE_ROB	55.42 16	63.15 231	61.23 240	68.92 218	76.57 199	47.80 225	59.92 316	76.39 203	54.35 209	58.67 262	82.46 165	29.44 302	81.49 184	42.12 272	71.14 212	77.46 258
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
QAPM		70.05 120	68.81 128	73.78 115	76.54 200	53.43 150	83.23 56	83.48 64	52.89 219	65.90 179	86.29 96	41.55 196	86.49 77	51.01 202	78.40 137	81.42 206

FMVSNet3		66.32 198	65.61 193	68.46 223	76.48 201	42.34 277	74.98 198	77.15 196	55.83 179	65.04 196	81.16 191	39.91 207	80.14 213	47.18 228	72.76 193	82.90 186

thisisatest0530		67.92 168	65.78 190	74.33 107	76.29 202	51.03 178	76.89 161	74.25 234	53.67 213	65.59 184	81.76 180	35.15 250	85.50 100	55.94 161	72.47 197	86.47 62

baseline1		63.81 222	63.87 208	63.62 268	76.29 202	36.36 318	71.78 246	67.29 279	56.05 176	64.23 209	82.95 154	47.11 136	74.41 271	47.30 227	61.85 299	80.10 230

ab-mvs		66.65 192	66.42 176	67.37 232	76.17 204	41.73 284	70.41 265	76.14 206	53.99 210	65.98 176	83.51 147	49.48 101	76.24 264	48.60 220	73.46 181	84.14 145

Effi-MVS+-dtu		69.64 131	67.53 150	75.95 70	76.10 205	62.29 18	80.20 104	76.06 208	59.83 109	65.26 192	77.09 261	41.56 194	84.02 132	60.60 138	71.09 214	81.53 205

mvs-test1		70.44 113	68.19 139	77.18 52	76.10 205	63.22 6	80.59 99	76.06 208	59.83 109	66.32 171	79.87 220	41.56 194	85.53 97	60.60 138	72.77 192	82.80 188

DTE-MVSNet		65.58 205	65.34 196	66.31 242	76.06 207	34.79 325	76.43 168	79.38 152	62.55 59	61.66 237	83.83 139	45.60 151	79.15 227	41.64 278	60.88 305	85.00 119

EPNet		73.09 78	72.16 81	75.90 72	75.95 208	56.28 111	83.05 58	72.39 248	66.53 12	65.27 189	87.00 78	50.40 95	85.47 102	62.48 123	86.32 58	85.94 82
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
RRT_MVS		68.77 147	66.71 169	74.95 89	75.93 209	58.55 78	80.50 100	75.84 210	56.09 174	68.17 137	83.74 142	28.50 307	82.98 152	65.67 95	65.91 271	83.33 172

RRT_test8_iter05		68.17 164	66.86 168	72.07 158	75.81 210	46.33 240	76.41 169	81.81 98	56.43 165	66.52 166	81.30 190	31.90 287	84.25 125	63.77 114	67.83 258	85.64 97

SixPastTwentyTwo		61.65 246	58.80 253	70.20 197	75.80 211	47.22 233	75.59 184	69.68 262	54.61 202	54.11 299	79.26 236	27.07 318	82.96 153	43.27 262	49.79 335	80.41 225

baseline		74.61 60	74.70 56	74.34 106	75.70 212	49.99 199	77.54 145	84.63 40	62.73 57	73.98 55	87.79 72	57.67 27	83.82 136	69.49 63	82.74 83	89.20 3

Baseline_NR-MVSNet		67.05 183	67.56 147	65.50 256	75.65 213	37.70 311	75.42 186	74.65 229	59.90 105	68.14 139	83.15 153	49.12 109	77.20 253	52.23 194	69.78 233	81.60 204

jajsoiax		68.25 160	66.45 173	73.66 123	75.62 214	55.49 128	80.82 94	78.51 169	52.33 224	64.33 206	84.11 133	28.28 309	81.81 179	63.48 116	70.62 217	83.67 163

mvs_tets		68.18 162	66.36 179	73.63 126	75.61 215	55.35 131	80.77 95	78.56 167	52.48 223	64.27 208	84.10 134	27.45 315	81.84 178	63.45 117	70.56 218	83.69 162

casdiffmvs		74.80 56	74.89 54	74.53 102	75.59 216	50.37 192	78.17 134	85.06 32	62.80 56	74.40 51	87.86 70	57.88 24	83.61 140	69.46 65	82.79 82	89.59 2

PVSNet	50.76 19	58.40 263	57.39 263	61.42 283	75.53 217	44.04 265	61.43 309	63.45 301	47.04 279	56.91 274	73.61 298	27.00 319	64.76 308	39.12 286	72.40 198	75.47 280

MVS		67.37 175	66.33 180	70.51 193	75.46 218	50.94 179	73.95 213	81.85 97	41.57 320	62.54 226	78.57 245	47.98 120	85.47 102	52.97 190	82.05 88	75.14 282

nrg030		72.96 79	73.01 75	72.84 143	75.41 219	50.24 193	80.02 105	82.89 83	58.36 135	74.44 50	86.73 82	58.90 19	80.83 199	65.84 93	74.46 166	87.44 39

thres200		62.20 239	61.16 241	65.34 259	75.38 220	39.99 292	69.60 270	69.29 268	55.64 185	61.87 235	76.99 262	37.07 240	78.96 234	31.28 327	73.28 184	77.06 264

TransMVSNet (Re)		64.72 214	64.33 203	65.87 253	75.22 221	38.56 304	74.66 204	75.08 226	58.90 124	61.79 236	82.63 159	51.18 87	78.07 242	43.63 260	55.87 321	80.99 218

MS-PatchMatch		62.42 236	61.46 236	65.31 260	75.21 222	52.10 170	72.05 242	74.05 236	46.41 282	57.42 273	74.36 292	34.35 259	77.57 250	45.62 243	73.67 175	66.26 332

IB-MVS	56.42 12	65.40 209	62.73 223	73.40 133	74.89 223	52.78 159	73.09 227	75.13 222	55.69 183	58.48 266	73.73 297	32.86 274	86.32 81	50.63 204	70.11 226	81.10 216
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
MVS_Test		72.45 85	72.46 80	72.42 155	74.88 224	48.50 218	76.28 172	83.14 78	59.40 116	72.46 81	84.68 120	55.66 41	81.12 191	65.98 92	79.66 115	87.63 30

CANet_DTU		68.18 162	67.71 146	69.59 208	74.83 225	46.24 242	78.66 125	76.85 199	59.60 111	63.45 214	82.09 176	35.25 249	77.41 251	59.88 144	78.76 132	85.14 114

tfpnnormal		62.47 235	61.63 234	64.99 262	74.81 226	39.01 300	71.22 252	73.72 239	55.22 191	60.21 245	80.09 218	41.26 202	76.98 257	30.02 331	68.09 255	78.97 245

Vis-MVSNet (Re-imp)		63.69 223	63.88 207	63.14 272	74.75 227	31.04 340	71.16 254	63.64 300	56.32 167	59.80 251	84.99 116	44.51 164	75.46 266	39.12 286	80.62 99	82.92 184

HY-MVS	56.14 13	64.55 218	63.89 206	66.55 240	74.73 228	41.02 288	69.96 268	74.43 230	49.29 254	61.66 237	80.92 198	47.43 131	76.68 259	44.91 251	71.69 206	81.94 200

COLMAP_ROB	52.97 17	61.27 250	58.81 252	68.64 221	74.63 229	52.51 165	78.42 132	73.30 242	49.92 251	50.96 315	81.51 186	23.06 333	79.40 219	31.63 323	65.85 272	74.01 298
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
LCM-MVSNet-Re		61.88 244	61.35 237	63.46 269	74.58 230	31.48 339	61.42 310	58.14 321	58.71 127	53.02 309	79.55 229	43.07 176	76.80 258	45.69 241	77.96 139	82.11 199

test_djsdf		69.45 136	67.74 143	74.58 100	74.57 231	54.92 136	82.79 63	78.48 170	51.26 238	65.41 187	83.49 148	38.37 223	83.24 146	66.06 89	69.25 243	85.56 99

EI-MVSNet		69.27 138	68.44 136	71.73 163	74.47 232	49.39 209	75.20 191	78.45 173	59.60 111	69.16 124	76.51 271	51.29 85	82.50 167	59.86 146	71.45 211	83.30 173

CVMVSNet		59.63 258	59.14 251	61.08 286	74.47 232	38.84 302	75.20 191	68.74 272	31.15 339	58.24 267	76.51 271	32.39 284	68.58 293	49.77 209	65.84 273	75.81 276

IterMVS-LS		69.22 140	68.48 133	71.43 172	74.44 234	49.40 208	76.23 173	77.55 188	59.60 111	65.85 181	81.59 185	51.28 86	81.58 183	59.87 145	69.90 231	83.30 173
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
XVG-OURS-SEG-HR		68.81 144	67.47 152	72.82 145	74.40 235	56.87 106	70.59 261	79.04 155	54.77 201	66.99 159	86.01 102	39.57 211	78.21 240	62.54 122	73.33 183	83.37 171

XVG-OURS		68.76 148	67.37 155	72.90 142	74.32 236	57.22 97	70.09 267	78.81 159	55.24 190	67.79 150	85.81 108	36.54 244	78.28 239	62.04 127	75.74 160	83.19 178

OpenMVS	61.03 9	68.85 143	67.56 147	72.70 148	74.26 237	53.99 142	81.21 91	81.34 113	52.70 220	62.75 221	85.55 111	38.86 219	84.14 127	48.41 222	83.01 74	79.97 231

MIMVSNet		57.35 270	57.07 266	58.22 295	74.21 238	37.18 312	62.46 305	60.88 314	48.88 258	55.29 287	75.99 278	31.68 288	62.04 316	31.87 318	72.35 199	75.43 281

SCA		60.49 252	58.38 257	66.80 236	74.14 239	48.06 223	63.35 302	63.23 303	49.13 256	59.33 257	72.10 303	37.45 232	74.27 272	44.17 253	62.57 295	78.05 252

thisisatest0515		65.83 202	63.50 214	72.82 145	73.75 240	49.50 207	71.32 250	73.12 245	49.39 253	63.82 212	76.50 273	34.95 253	84.84 116	53.20 189	75.49 163	84.13 146

K. test v3		60.47 253	57.11 265	70.56 191	73.74 241	48.22 221	75.10 195	62.55 307	58.27 137	53.62 304	76.31 274	27.81 312	81.59 182	47.42 225	39.18 344	81.88 202

v10		70.21 118	69.02 125	73.81 114	73.51 242	50.92 181	78.74 123	81.39 108	60.05 103	66.39 170	81.83 179	47.58 127	85.41 105	62.80 120	68.86 249	85.09 117

v1144		70.42 114	69.31 120	73.76 117	73.22 243	50.64 186	77.83 138	81.43 107	58.58 128	69.40 118	81.16 191	47.53 128	85.29 107	64.01 109	70.64 216	85.34 109

v1192		69.97 123	68.68 130	73.85 113	73.19 244	50.94 179	77.68 141	81.36 109	57.51 146	68.95 126	80.85 201	45.28 158	85.33 106	62.97 119	70.37 221	85.27 112

v8		70.33 116	69.28 121	73.49 128	73.15 245	50.22 194	78.62 126	80.78 129	60.79 85	66.45 169	82.11 175	49.35 102	84.98 110	63.58 115	68.71 250	85.28 111

v144192		69.71 126	68.51 132	73.33 135	73.10 246	50.13 196	77.54 145	80.64 130	56.65 156	68.57 130	80.55 206	46.87 142	84.96 112	62.98 118	69.66 237	84.89 123

v1921920		69.47 135	68.17 140	73.36 134	73.06 247	50.10 197	77.39 148	80.56 131	56.58 163	68.59 128	80.37 208	44.72 163	84.98 110	62.47 124	69.82 232	85.00 119

PatchmatchNet		59.84 256	58.24 258	64.65 264	73.05 248	46.70 237	69.42 272	62.18 309	47.55 273	58.88 260	71.96 305	34.49 257	69.16 290	42.99 266	63.60 287	78.07 251
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
v1240		69.24 139	67.91 142	73.25 138	73.02 249	49.82 200	77.21 154	80.54 132	56.43 165	68.34 134	80.51 207	43.33 175	84.99 108	62.03 128	69.77 235	84.95 122

Fast-Effi-MVS+-dtu		67.37 175	65.33 197	73.48 129	72.94 250	57.78 89	77.47 147	76.88 198	57.60 145	61.97 233	76.85 265	39.31 213	80.49 207	54.72 174	70.28 224	82.17 198

EPNet_dtu		61.90 242	61.97 231	61.68 281	72.89 251	39.78 294	75.85 182	65.62 288	55.09 194	54.56 294	79.36 233	37.59 231	67.02 300	39.80 285	76.95 150	78.25 249
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
tpm2		62.07 240	60.10 247	67.99 227	72.79 252	43.86 266	71.05 257	66.85 282	43.14 311	62.77 219	75.39 285	38.32 224	80.80 200	41.69 275	68.88 248	79.32 241

MDTV_nov1_ep13			57.00 267		72.73 253	38.26 306	65.02 297	64.73 295	44.74 294	55.46 283	72.48 301	32.61 282	70.47 285	37.47 293	67.75 260

MSDG		61.81 245	59.23 250	69.55 211	72.64 254	52.63 161	70.45 264	75.81 211	51.38 235	53.70 302	76.11 275	29.52 300	81.08 194	37.70 292	65.79 274	74.93 287

gg-mvs-nofinetune		57.86 268	56.43 272	62.18 278	72.62 255	35.35 324	66.57 282	56.33 328	50.65 243	57.64 271	57.10 340	30.65 291	76.36 262	37.38 294	78.88 128	74.82 289

v2v482		70.50 112	69.45 119	73.66 123	72.62 255	50.03 198	77.58 142	80.51 133	59.90 105	69.52 114	82.14 174	47.53 128	84.88 115	65.07 100	70.17 225	86.09 79

baseline2		63.42 225	61.26 239	69.89 205	72.55 257	47.62 229	71.54 247	68.38 274	50.11 247	54.82 290	75.55 283	43.06 177	80.96 195	48.13 223	67.16 264	81.11 215

v7n		69.01 142	67.36 156	73.98 111	72.51 258	52.65 160	78.54 129	81.30 114	60.26 100	62.67 222	81.62 182	43.61 172	84.49 121	57.01 157	68.70 251	84.79 127

pm-mvs1		65.24 210	64.97 201	66.04 249	72.38 259	39.40 298	72.62 234	75.63 213	55.53 186	62.35 232	83.18 152	47.45 130	76.47 261	49.06 217	66.54 267	82.24 195

XVG-ACMP-BASELINE		64.36 219	62.23 228	70.74 188	72.35 260	52.45 167	70.80 260	78.45 173	53.84 212	59.87 249	81.10 193	16.24 342	79.32 221	55.64 168	71.76 205	80.47 223

WTY-MVS		59.75 257	60.39 245	57.85 298	72.32 261	37.83 309	61.05 314	64.18 298	45.95 289	61.91 234	79.11 238	47.01 140	60.88 319	42.50 269	69.49 239	74.83 288

tpm cat1		59.25 259	56.95 268	66.15 246	72.19 262	46.96 235	68.09 278	65.76 287	40.03 327	57.81 270	70.56 313	38.32 224	74.51 270	38.26 290	61.50 302	77.00 266

mvs_anonymous		68.03 165	67.51 151	69.59 208	72.08 263	44.57 261	71.99 243	75.23 219	51.67 228	67.06 158	82.57 161	54.68 49	77.94 243	56.56 158	75.71 161	86.26 76

OurMVSNet-221017-0		61.37 249	58.63 255	69.61 207	72.05 264	48.06 223	73.93 216	72.51 247	47.23 277	54.74 291	80.92 198	21.49 338	81.24 189	48.57 221	56.22 320	79.53 239

IterMVS-SCA-FT		62.49 234	61.52 235	65.40 258	71.99 265	50.80 184	71.15 255	69.63 263	45.71 290	60.61 243	77.93 250	37.45 232	65.99 305	55.67 167	63.50 288	79.42 240

DWT-MVSNet_test		61.90 242	59.93 248	67.83 228	71.98 266	46.09 245	71.03 258	69.71 260	50.09 248	58.51 265	70.62 312	30.21 296	77.63 248	49.28 215	67.91 256	79.78 236

CostFormer		64.04 220	62.51 224	68.61 222	71.88 267	45.77 247	71.30 251	70.60 257	47.55 273	64.31 207	76.61 269	41.63 192	79.62 217	49.74 210	69.00 247	80.42 224

1314		64.61 217	63.21 217	68.80 219	71.87 268	47.46 231	73.95 213	78.39 178	42.88 313	59.97 248	76.60 270	38.11 227	79.39 220	54.84 173	72.32 200	79.55 238

tpm		57.34 271	58.16 259	54.86 308	71.80 269	34.77 326	67.47 281	56.04 331	48.20 266	60.10 246	76.92 263	37.17 237	53.41 342	40.76 280	65.01 278	76.40 272

eth_miper_zixun_eth		67.63 171	66.28 183	71.67 165	71.60 270	48.33 220	73.68 221	77.88 181	55.80 181	65.91 178	78.62 244	47.35 134	82.88 156	59.45 148	66.25 269	83.81 155

pmmvs4		61.48 248	59.39 249	67.76 229	71.57 271	53.86 143	71.42 248	65.34 290	44.20 301	59.46 253	77.92 251	35.90 245	74.71 269	43.87 258	64.87 279	74.71 291

AllTest		57.08 273	54.65 282	64.39 265	71.44 272	49.03 210	69.92 269	67.30 277	45.97 287	47.16 327	79.77 223	17.47 340	67.56 297	33.65 312	59.16 311	76.57 270

TestCases				64.39 265	71.44 272	49.03 210		67.30 277	45.97 287	47.16 327	79.77 223	17.47 340	67.56 297	33.65 312	59.16 311	76.57 270

lessismore_v0				69.91 203	71.42 274	47.80 225		50.90 339		50.39 321	75.56 282	27.43 316	81.33 186	45.91 239	34.10 346	80.59 222

gm-plane-assit					71.40 275	41.72 285			48.85 259		73.31 299		82.48 169	48.90 218

GG-mvs-BLEND				62.34 277	71.36 276	37.04 314	69.20 274	57.33 325		54.73 292	65.48 332	30.37 293	77.82 245	34.82 308	74.93 165	72.17 315

FMVSNet5		55.86 280	54.93 280	58.66 294	71.05 277	36.35 319	64.18 301	62.48 308	46.76 280	50.66 320	74.73 290	25.80 325	64.04 310	33.11 314	65.57 275	75.59 279

cl_fuxian		68.33 158	67.56 147	70.62 190	70.87 278	46.21 243	74.47 207	78.80 160	56.22 171	66.19 173	78.53 246	51.88 79	81.40 185	62.08 125	69.04 246	84.25 141

GA-MVS		65.53 206	63.70 211	71.02 184	70.87 278	48.10 222	70.48 263	74.40 231	56.69 155	64.70 202	76.77 266	33.66 266	81.10 192	55.42 170	70.32 223	83.87 154

pmmvs6		63.69 223	62.82 222	66.27 244	70.63 280	39.27 299	73.13 226	75.47 216	52.69 221	59.75 252	82.30 168	39.71 210	77.03 256	47.40 226	64.35 283	82.53 190

miper_ehance_all_eth		68.03 165	67.24 163	70.40 194	70.54 281	46.21 243	73.98 212	78.68 164	55.07 196	66.05 175	77.80 255	52.16 77	81.31 187	61.53 134	69.32 240	83.67 163

OpenMVS_ROB	52.78 18	60.03 254	58.14 260	65.69 255	70.47 282	44.82 256	75.33 187	70.86 255	45.04 292	56.06 279	76.00 276	26.89 320	79.65 215	35.36 307	67.29 262	72.60 306

v148		68.24 161	67.19 164	71.40 173	70.43 283	47.77 227	75.76 183	77.03 197	58.91 123	67.36 154	80.10 217	48.60 116	81.89 176	60.01 143	66.52 268	84.53 133

XXY-MVS		60.68 251	61.67 233	57.70 300	70.43 283	38.45 305	64.19 300	66.47 283	48.05 268	63.22 215	80.86 200	49.28 104	60.47 320	45.25 250	67.28 263	74.19 296

MVSTER		67.16 181	65.58 194	71.88 160	70.37 285	49.70 202	70.25 266	78.45 173	51.52 232	69.16 124	80.37 208	38.45 222	82.50 167	60.19 141	71.46 210	83.44 170

cl-mvsnet_		67.18 179	66.26 184	69.94 201	70.20 286	45.74 248	73.30 223	76.83 200	55.10 192	65.27 189	79.57 228	47.39 132	80.53 205	59.41 150	69.22 244	83.53 169

cl-mvsnet1		67.18 179	66.26 184	69.94 201	70.20 286	45.74 248	73.29 224	76.83 200	55.10 192	65.27 189	79.58 227	47.38 133	80.53 205	59.43 149	69.22 244	83.54 168

tpmvs		58.47 262	56.95 268	63.03 274	70.20 286	41.21 287	67.90 280	67.23 280	49.62 252	54.73 292	70.84 310	34.14 260	76.24 264	36.64 301	61.29 303	71.64 317

anonymousdsp		67.00 185	64.82 202	73.57 127	70.09 289	56.13 114	76.35 170	77.35 193	48.43 263	64.99 199	80.84 202	33.01 272	80.34 208	64.66 102	67.64 261	84.23 142

MVS_0304		58.51 261	57.36 264	61.96 280	70.04 290	41.83 282	69.40 273	65.46 289	50.73 241	53.30 308	74.06 295	22.65 334	70.18 288	42.16 271	68.44 252	73.86 300

MIMVSNet1		55.17 285	54.31 286	57.77 299	70.03 291	32.01 337	65.68 289	64.81 293	49.19 255	46.75 330	76.00 276	25.53 327	64.04 310	28.65 335	62.13 298	77.26 262

CR-MVSNet		59.91 255	57.90 262	65.96 250	69.96 292	52.07 171	65.31 294	63.15 304	42.48 315	59.36 254	74.84 288	35.83 246	70.75 284	45.50 245	64.65 281	75.06 283

RPMNet		61.53 247	58.42 256	70.86 185	69.96 292	52.07 171	65.31 294	81.36 109	43.20 310	59.36 254	70.15 316	35.37 248	85.47 102	36.42 304	64.65 281	75.06 283

cl-mvsnet2		67.47 174	66.45 173	70.54 192	69.85 294	46.49 238	73.85 218	77.35 193	55.07 196	65.51 185	77.92 251	47.64 126	81.10 192	61.58 133	69.32 240	84.01 149

Anonymous20231206		55.10 286	55.30 279	54.48 310	69.81 295	33.94 331	62.91 304	62.13 310	41.08 321	55.18 288	75.65 281	32.75 278	56.59 334	30.32 330	67.86 257	72.91 303

our_test_3		56.49 274	54.42 284	62.68 276	69.51 296	45.48 253	66.08 286	61.49 312	44.11 304	50.73 319	69.60 320	33.05 271	68.15 294	38.38 289	56.86 317	74.40 293

ppachtmachnet_test		58.06 267	55.38 278	66.10 248	69.51 296	48.99 213	68.01 279	66.13 286	44.50 298	54.05 300	70.74 311	32.09 286	72.34 278	36.68 300	56.71 319	76.99 268

diffmvs		70.69 108	70.43 105	71.46 169	69.45 298	48.95 214	72.93 228	78.46 172	57.27 148	71.69 88	83.97 138	51.48 84	77.92 244	70.70 59	77.95 140	87.53 34

IterMVS		62.79 233	61.27 238	67.35 233	69.37 299	52.04 173	71.17 253	68.24 275	52.63 222	59.82 250	76.91 264	37.32 234	72.36 277	52.80 191	63.19 291	77.66 256
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
miper_enhance_ethall		67.11 182	66.09 186	70.17 198	69.21 300	45.98 246	72.85 231	78.41 176	51.38 235	65.65 183	75.98 279	51.17 88	81.25 188	60.82 136	69.32 240	83.29 175

Patchmtry		57.16 272	56.47 271	59.23 288	69.17 301	34.58 328	62.98 303	63.15 304	44.53 297	56.83 275	74.84 288	35.83 246	68.71 292	40.03 283	60.91 304	74.39 294

V42		68.65 149	67.35 157	72.56 150	68.93 302	50.18 195	72.90 230	79.47 150	56.92 153	69.45 117	80.26 214	46.29 146	82.99 151	64.07 107	67.82 259	84.53 133

test-LLR		58.15 266	58.13 261	58.22 295	68.57 303	44.80 257	65.46 291	57.92 322	50.08 249	55.44 284	69.82 318	32.62 280	57.44 329	49.66 212	73.62 176	72.41 311

test-mter		56.42 276	55.82 275	58.22 295	68.57 303	44.80 257	65.46 291	57.92 322	39.94 328	55.44 284	69.82 318	21.92 337	57.44 329	49.66 212	73.62 176	72.41 311

MVS-HIRNet		45.52 309	44.48 312	48.65 324	68.49 305	34.05 330	59.41 319	44.50 348	27.03 343	37.96 344	50.47 346	26.16 324	64.10 309	26.74 340	59.52 309	47.82 343

dp		51.89 298	51.60 298	52.77 317	68.44 306	32.45 336	62.36 306	54.57 333	44.16 302	49.31 323	67.91 323	28.87 306	56.61 333	33.89 311	54.89 323	69.24 329

PatchT		53.17 294	53.44 293	52.33 319	68.29 307	25.34 350	58.21 321	54.41 334	44.46 299	54.56 294	69.05 321	33.32 269	60.94 318	36.93 296	61.76 301	70.73 323

Patchmatch-RL test		58.16 265	55.49 277	66.15 246	67.92 308	48.89 215	60.66 315	51.07 338	47.86 270	59.36 254	62.71 337	34.02 262	72.27 279	56.41 159	59.40 310	77.30 260

pmmvs-eth3d		58.81 260	56.31 273	66.30 243	67.61 309	52.42 168	72.30 239	64.76 294	43.55 307	54.94 289	74.19 294	28.95 304	72.60 276	43.31 261	57.21 316	73.88 299

PVSNet_0	43.31 20	47.46 308	45.64 311	52.92 316	67.60 310	44.65 259	54.06 331	54.64 332	41.59 319	46.15 331	58.75 339	30.99 289	58.66 325	32.18 316	24.81 347	55.46 341

CHOSEN 280x420		47.83 306	46.36 310	52.24 320	67.37 311	49.78 201	38.91 348	43.11 349	35.00 335	43.27 338	63.30 336	28.95 304	49.19 345	36.53 302	60.80 306	57.76 339

tpmrst		58.24 264	58.70 254	56.84 301	66.97 312	34.32 329	69.57 271	61.14 313	47.17 278	58.58 264	71.60 306	41.28 201	60.41 321	49.20 216	62.84 293	75.78 277

sss		56.17 279	56.57 270	54.96 307	66.93 313	36.32 321	57.94 322	61.69 311	41.67 318	58.64 263	75.32 286	38.72 220	56.25 335	42.04 273	66.19 270	72.31 314

TinyColmap		54.14 287	51.72 297	61.40 284	66.84 314	41.97 280	66.52 283	68.51 273	44.81 293	42.69 339	75.77 280	11.66 347	72.94 275	31.96 317	56.77 318	69.27 328

miper_lstm_enhance		62.03 241	60.88 243	65.49 257	66.71 315	46.25 241	56.29 328	75.70 212	50.68 242	61.27 240	75.48 284	40.21 206	68.03 295	56.31 160	65.25 277	82.18 196

TESTMET0.1,1		55.28 283	54.90 281	56.42 302	66.56 316	43.67 268	65.46 291	56.27 329	39.18 330	53.83 301	67.44 326	24.21 332	55.46 339	48.04 224	73.11 188	70.13 324

D2MVS		62.30 238	60.29 246	68.34 225	66.46 317	48.42 219	65.70 288	73.42 241	47.71 271	58.16 268	75.02 287	30.51 292	77.71 247	53.96 181	71.68 207	78.90 246

MDA-MVSNet-bldmvs		53.87 290	50.81 299	63.05 273	66.25 318	48.58 217	56.93 326	63.82 299	48.09 267	41.22 340	70.48 314	30.34 294	68.00 296	34.24 310	45.92 340	72.57 307

ITE_SJBPF				62.09 279	66.16 319	44.55 262		64.32 297	47.36 276	55.31 286	80.34 210	19.27 339	62.68 314	36.29 305	62.39 297	79.04 243

EPMVS		53.96 288	53.69 291	54.79 309	66.12 320	31.96 338	62.34 307	49.05 341	44.42 300	55.54 282	71.33 308	30.22 295	56.70 332	41.65 277	62.54 296	75.71 278

testing_2		66.02 200	63.77 210	72.76 147	66.03 321	50.48 191	72.93 228	80.36 136	54.41 208	54.25 298	76.76 267	30.89 290	83.16 149	64.19 106	74.08 171	84.65 130

ADS-MVSNet2		51.33 300	48.76 305	59.07 291	66.02 322	44.60 260	50.90 335	59.76 316	36.90 331	50.74 317	66.18 330	26.38 321	63.11 312	27.17 337	54.76 324	69.50 326

ADS-MVSNet		48.48 305	47.77 307	50.63 322	66.02 322	29.92 342	50.90 335	50.87 340	36.90 331	50.74 317	66.18 330	26.38 321	52.47 343	27.17 337	54.76 324	69.50 326

EU-MVSNet		55.61 282	54.41 285	59.19 290	65.41 324	33.42 333	72.44 237	71.91 251	28.81 341	51.27 313	73.87 296	24.76 330	69.08 291	43.04 265	58.20 314	75.06 283

RPSCF		55.80 281	54.22 288	60.53 287	65.13 325	42.91 275	64.30 299	57.62 324	36.84 333	58.05 269	82.28 169	28.01 310	56.24 336	37.14 295	58.61 313	82.44 194

USDC		56.35 277	54.24 287	62.69 275	64.74 326	40.31 291	65.05 296	73.83 238	43.93 305	47.58 325	77.71 258	15.36 344	75.05 268	38.19 291	61.81 300	72.70 305

JIA-IIPM		51.56 299	47.68 309	63.21 271	64.61 327	50.73 185	47.71 340	58.77 319	42.90 312	48.46 324	51.72 343	24.97 329	70.24 287	36.06 306	53.89 327	68.64 330

Patchmatch-test		49.08 304	48.28 306	51.50 321	64.40 328	30.85 341	45.68 342	48.46 344	35.60 334	46.10 332	72.10 303	34.47 258	46.37 346	27.08 339	60.65 307	77.27 261

TDRefinement		53.44 292	50.72 300	61.60 282	64.31 329	46.96 235	70.89 259	65.27 292	41.78 316	44.61 335	77.98 248	11.52 348	66.36 303	28.57 336	51.59 331	71.49 318

N_pmnet		39.35 315	40.28 316	36.54 331	63.76 330	1.62 361	49.37 338	0.76 361	34.62 336	43.61 337	66.38 329	26.25 323	42.57 349	26.02 342	51.77 330	65.44 333

ambc				65.13 261	63.72 331	37.07 313	47.66 341	78.78 161		54.37 297	71.42 307	11.24 349	80.94 196	45.64 242	53.85 328	77.38 259

test0.0.03 1		53.32 293	53.59 292	52.50 318	62.81 332	29.45 343	59.51 317	54.11 335	50.08 249	54.40 296	74.31 293	32.62 280	55.92 337	30.50 329	63.95 285	72.15 316

PMMVS		53.96 288	53.26 294	56.04 303	62.60 333	50.92 181	61.17 313	56.09 330	32.81 337	53.51 306	66.84 328	34.04 261	59.93 323	44.14 255	68.18 254	57.27 340

PM-MVS		52.33 296	50.19 301	58.75 293	62.10 334	45.14 255	65.75 287	40.38 350	43.60 306	53.52 305	72.65 300	9.16 353	65.87 306	50.41 205	54.18 326	65.24 334

Gipuma		34.77 318	31.91 322	43.33 329	62.05 335	37.87 308	20.39 351	67.03 281	23.23 347	18.41 351	25.84 350	4.24 356	62.73 313	14.71 347	51.32 332	29.38 348
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
test20.03		53.87 290	54.02 289	53.41 314	61.47 336	28.11 345	61.30 311	59.21 317	51.34 237	52.09 311	77.43 259	33.29 270	58.55 326	29.76 332	60.27 308	73.58 301

pmmvs5		56.47 275	55.68 276	58.86 292	61.41 337	36.71 316	66.37 284	62.75 306	40.38 325	53.70 302	76.62 268	34.56 255	67.05 299	40.02 284	65.27 276	72.83 304

MDA-MVSNet_test_wron		50.71 302	48.95 303	56.00 305	61.17 338	41.84 281	51.90 334	56.45 326	40.96 322	44.79 334	67.84 324	30.04 298	55.07 341	36.71 299	50.69 334	71.11 322

YYNet1		50.73 301	48.96 302	56.03 304	61.10 339	41.78 283	51.94 333	56.44 327	40.94 323	44.84 333	67.80 325	30.08 297	55.08 340	36.77 297	50.71 333	71.22 319

CMPMVS	42.80 21	57.81 269	55.97 274	63.32 270	60.98 340	47.38 232	64.66 298	69.50 265	32.06 338	46.83 329	77.80 255	29.50 301	71.36 281	48.68 219	73.75 174	71.21 320
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
UnsupCasMVSNet_bld		50.07 303	48.87 304	53.66 312	60.97 341	33.67 332	57.62 324	64.56 296	39.47 329	47.38 326	64.02 335	27.47 314	59.32 324	34.69 309	43.68 342	67.98 331

testgi		51.90 297	52.37 296	50.51 323	60.39 342	23.55 352	58.42 320	58.15 320	49.03 257	51.83 312	79.21 237	22.39 335	55.59 338	29.24 334	62.64 294	72.40 313

UnsupCasMVSNet_eth		53.16 295	52.47 295	55.23 306	59.45 343	33.39 334	59.43 318	69.13 269	45.98 286	50.35 322	72.32 302	29.30 303	58.26 327	42.02 274	44.30 341	74.05 297

new-patchmatchnet		47.56 307	47.73 308	47.06 325	58.81 344	9.37 358	48.78 339	59.21 317	43.28 308	44.22 336	68.66 322	25.67 326	57.20 331	31.57 325	49.35 336	74.62 292

FPMVS		42.18 312	41.11 315	45.39 326	58.03 345	41.01 289	49.50 337	53.81 337	30.07 340	33.71 345	64.03 333	11.69 346	52.08 344	14.01 348	55.11 322	43.09 345

CL-MVSNet_2432*1600		55.22 284	53.89 290	59.21 289	57.80 346	27.47 346	57.75 323	74.32 232	47.38 275	50.90 316	70.00 317	28.45 308	70.30 286	40.44 281	57.92 315	79.87 233

new_pmnet		34.13 319	34.29 320	33.64 332	52.63 347	18.23 356	44.43 345	33.90 353	22.81 348	30.89 346	53.18 341	10.48 351	35.72 353	20.77 345	39.51 343	46.98 344

pmmvs3		44.92 310	41.95 314	53.86 311	52.58 348	43.55 269	62.11 308	46.90 347	26.05 345	40.63 341	60.19 338	11.08 350	57.91 328	31.83 322	46.15 339	60.11 336

DSMNet-mixed		39.30 316	38.72 317	41.03 330	51.22 349	19.66 354	45.53 343	31.35 354	15.83 352	39.80 343	67.42 327	22.19 336	45.13 347	22.43 343	52.69 329	58.31 338

LF4IMVS		42.95 311	42.26 313	45.04 327	48.30 350	32.50 335	54.80 329	48.49 343	28.03 342	40.51 342	70.16 315	9.24 352	43.89 348	31.63 323	49.18 337	58.72 337

wuyk23d		13.32 325	12.52 328	15.71 337	47.54 351	26.27 347	31.06 350	1.98 360	4.93 355	5.18 357	1.94 357	0.45 362	18.54 355	6.81 355	12.83 352	2.33 353

LCM-MVSNet		40.30 314	35.88 319	53.57 313	42.24 352	29.15 344	45.21 344	60.53 315	22.23 349	28.02 347	50.98 345	3.72 358	61.78 317	31.22 328	38.76 345	69.78 325

E-PMN		23.77 321	22.73 325	26.90 335	42.02 353	20.67 353	42.66 346	35.70 351	17.43 350	10.28 355	25.05 351	6.42 355	42.39 350	10.28 351	14.71 350	17.63 349

EMVS		22.97 322	21.84 326	26.36 336	40.20 354	19.53 355	41.95 347	34.64 352	17.09 351	9.73 356	22.83 352	7.29 354	42.22 351	9.18 353	13.66 351	17.32 350

ANet_high		41.38 313	37.47 318	53.11 315	39.73 355	24.45 351	56.94 325	69.69 261	47.65 272	26.04 348	52.32 342	12.44 345	62.38 315	21.80 344	10.61 353	72.49 308

PMMVS2		27.40 320	25.91 323	31.87 334	39.46 356	6.57 359	31.17 349	28.52 355	23.96 346	20.45 350	48.94 347	4.20 357	37.94 352	16.51 346	19.97 348	51.09 342

PMVS	28.69 22	36.22 317	33.29 321	45.02 328	36.82 357	35.98 323	54.68 330	48.74 342	26.31 344	21.02 349	51.61 344	2.88 360	60.10 322	9.99 352	47.58 338	38.99 347
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MVE	17.77 23	21.41 323	17.77 327	32.34 333	34.34 358	25.44 349	16.11 352	24.11 356	11.19 353	13.22 353	31.92 348	1.58 361	30.95 354	10.47 350	17.03 349	40.62 346
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
DeepMVS_CX				12.03 338	17.97 359	10.91 357		10.60 359	7.46 354	11.07 354	28.36 349	3.28 359	11.29 356	8.01 354	9.74 355	13.89 351

tmp_tt		9.43 326	11.14 329	4.30 339	2.38 360	4.40 360	13.62 353	16.08 358	0.39 356	15.89 352	13.06 353	15.80 343	5.54 357	12.63 349	10.46 354	2.95 352

testmvs		4.52 329	6.03 332	0.01 341	0.01 361	0.00 363	53.86 332	0.00 362	0.01 357	0.04 358	0.27 358	0.00 364	0.00 358	0.04 356	0.00 356	0.03 355

test123		4.73 328	6.30 331	0.02 340	0.01 361	0.01 362	56.36 327	0.00 362	0.01 357	0.04 358	0.21 359	0.01 363	0.00 358	0.03 357	0.00 356	0.04 354

uanet_test		0.00 331	0.00 334	0.00 342	0.00 363	0.00 363	0.00 354	0.00 362	0.00 359	0.00 360	0.00 360	0.00 364	0.00 358	0.00 358	0.00 356	0.00 356

cdsmvs_eth3d_5k		17.50 324	23.34 324	0.00 342	0.00 363	0.00 363	0.00 354	78.63 165	0.00 359	0.00 360	82.18 170	49.25 105	0.00 358	0.00 358	0.00 356	0.00 356

pcd_1.5k_mvsjas		3.92 330	5.23 333	0.00 342	0.00 363	0.00 363	0.00 354	0.00 362	0.00 359	0.00 360	0.00 360	47.05 137	0.00 358	0.00 358	0.00 356	0.00 356

sosnet-low-res		0.00 331	0.00 334	0.00 342	0.00 363	0.00 363	0.00 354	0.00 362	0.00 359	0.00 360	0.00 360	0.00 364	0.00 358	0.00 358	0.00 356	0.00 356

sosnet		0.00 331	0.00 334	0.00 342	0.00 363	0.00 363	0.00 354	0.00 362	0.00 359	0.00 360	0.00 360	0.00 364	0.00 358	0.00 358	0.00 356	0.00 356

uncertanet		0.00 331	0.00 334	0.00 342	0.00 363	0.00 363	0.00 354	0.00 362	0.00 359	0.00 360	0.00 360	0.00 364	0.00 358	0.00 358	0.00 356	0.00 356

Regformer		0.00 331	0.00 334	0.00 342	0.00 363	0.00 363	0.00 354	0.00 362	0.00 359	0.00 360	0.00 360	0.00 364	0.00 358	0.00 358	0.00 356	0.00 356

ab-mvs-re		6.49 327	8.65 330	0.00 342	0.00 363	0.00 363	0.00 354	0.00 362	0.00 359	0.00 360	77.89 253	0.00 364	0.00 358	0.00 358	0.00 356	0.00 356

uanet		0.00 331	0.00 334	0.00 342	0.00 363	0.00 363	0.00 354	0.00 362	0.00 359	0.00 360	0.00 360	0.00 364	0.00 358	0.00 358	0.00 356	0.00 356

test_241102_TWO								86.73 14	64.18 32	84.26 4	91.84 6	65.19 4	90.83 2	78.63 12	90.70 5	87.65 29

test_0728_THIRD									65.04 19	83.82 6	92.00 3	64.69 8	90.75 5	79.48 4	90.63 6	88.09 15

GSMVS																78.05 252

sam_mvs1												34.74 254				78.05 252

sam_mvs												33.43 268

MTGPA								80.97 125

test_post1							68.67 276				3.64 355	32.39 284	69.49 289	44.17 253

test_post											3.55 356	33.90 263	66.52 302

patchmatchnet-post											64.03 333	34.50 256	74.27 272

MTMP							86.03 17	17.08 357

test9_res														75.28 28	88.31 33	83.81 155

agg_prior2														73.09 46	87.93 40	84.33 138

test_prior4						62.51 17	82.08 79

test_prior2							81.75 81		60.37 93	75.01 40	89.06 56	56.22 35		72.19 49	88.96 24

旧先验2							76.08 176		45.32 291	76.55 31			65.56 307	58.75 151

新几何2							76.12 174

无先验							79.66 114	74.30 233	48.40 264				80.78 201	53.62 183		79.03 244

原ACMM2							79.02 121

testdata2													72.18 280	46.95 232

segment_acmp												54.23 54

testdata1							72.65 232		60.50 90

plane_prior5								84.01 50					87.21 50	68.16 72	80.58 101	84.65 130

plane_prior4											86.10 99

plane_prior3						56.09 115			63.92 36	69.27 120

plane_prior2							84.22 39		64.52 25

plane_prior						56.31 109	83.58 53		63.19 46						80.48 104

n2								0.00 362

nn								0.00 362

door-mid								47.19 346

test11								83.47 65

door								47.60 345

HQP5-MVS						54.94 134

BP-MVS														67.04 83

HQP4-MVS										67.85 144			86.93 60			84.32 139

HQP3-MVS								83.90 54							80.35 107

HQP2-MVS												45.46 153

MDTV_nov1_ep13_2view						25.89 348	61.22 312		40.10 326	51.10 314		32.97 273		38.49 288		78.61 247

ACMMP++_ref															74.07 172

ACMMP++															72.16 202

Test By Simon												48.33 118

High-resolution multi-view benchmark