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 49	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 34	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 32

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 33	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 32	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 30	83.82 6	92.00 3	64.82 6	90.75 5	78.66 10	90.61 7	85.45 104
Zhenlong Yuan, Jinguo Luo, Fei Shen, Zhaoxin Li, Cong Liu, Tianlu Mao, Zhaoqi Wang: DVP-MVS: Synergize Depth-Edge and Visibility Prior for Multi-View Stereo. AAAI2025
test0726					87.75 7	59.07 66	87.86 2	86.83 10	64.26 30	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 29	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 44	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 67	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 56	73.30 67	90.58 19	49.90 98	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 54	73.55 65	90.56 20	49.80 100	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 54	73.96 56	90.50 22	53.20 68	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 77	73.96 56	90.50 22	53.20 68	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 44	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 50	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 61	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 65	86.57 22	57.99 85	81.15 92	82.96 79	57.03 151	66.78 161	85.56 111	44.50 166	88.11 34	51.77 198	80.23 110	83.10 181

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 38	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 51	71.77 88	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 89	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 43	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 32	87.66 4	58.56 131	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 24	72.17 85	90.01 43	47.95 121	88.01 37	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 24	72.17 85	6.49 357	47.95 121	88.01 37	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
Zhenlong Yuan, Cong Liu, Fei Shen, Zhaoxin Li, Jingguo luo, Tianlu Mao and Zhaoqi Wang: MSP-MVS: Multi-granularity Segmentation Prior Guided Multi-View Stereo. AAAI2025
114514_t		70.83 105	69.56 115	74.64 98	86.21 30	54.63 137	82.34 73	81.81 98	48.22 266	63.01 219	85.83 107	40.92 205	87.10 55	57.91 153	79.79 112	82.18 195

xxxxxxxxxxxxxcwj		78.37 20	78.25 22	78.76 26	86.17 32	61.30 31	83.98 46	79.95 140	59.00 122	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 46	79.66 145	59.00 122

DeepC-MVS_fast	68.24 3	77.25 34	76.63 38	79.12 16	86.15 34	60.86 39	84.71 31	84.85 38	61.98 73	73.06 74	88.88 61	53.72 62	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 75	72.45 84	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 18	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 18	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 42	74.29 53	90.03 41	52.56 71	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 63	72.68 78	90.50 22	48.18 119	87.34 48	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	59.99 105	75.10 38	90.35 27	47.66 125	86.52 75	71.64 53	82.99 75	84.47 135

新几何1				70.76 187	85.66 41	61.13 35		66.43 288	44.68 298	70.29 99	86.64 86	41.29 201	75.23 267	49.72 211	81.75 93	75.93 276

MG-MVS		73.96 70	73.89 65	74.16 109	85.65 42	49.69 204	81.59 86	81.29 115	61.45 78	71.05 94	88.11 67	51.77 82	87.73 43	61.05 135	83.09 73	85.05 118

1121		68.53 155	67.16 165	72.63 148	85.64 43	61.14 34	73.95 213	66.46 287	44.61 299	70.28 100	86.68 85	41.42 199	80.78 201	53.62 183	81.79 89	75.97 274

TEST9					85.58 44	61.59 27	81.62 84	81.26 116	55.65 185	74.93 42	88.81 62	53.70 63	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 63	84.68 117	75.24 29	88.33 31	83.65 165

test1172		75.36 53	74.81 55	77.02 55	85.47 46	60.79 42	83.94 49	81.63 102	59.52 116	74.66 49	90.18 34	44.74 162	85.84 91	70.63 60	82.52 84	84.42 136

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 52	78.10 24	91.26 11	52.51 72	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 63	84.45 122

原ACMM1				74.69 94	85.39 49	59.40 58		83.42 67	51.47 235	70.27 101	86.61 88	48.61 115	86.51 76	53.85 182	87.96 39	78.16 251

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 67	87.92 39	69.23 66	88.42 29	87.59 33

ACMMP		76.02 46	75.33 49	78.07 38	85.20 51	61.91 23	85.49 28	84.44 41	63.04 48	69.80 112	89.74 49	45.43 155	87.16 53	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 75	85.04 52	59.96 51	81.44 88	81.04 122	56.14 173	74.68 46	88.90 60	53.91 59	84.04 129	75.01 30	87.92 41	83.16 180

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 59	73.09 73	89.97 44	50.90 94	87.48 47	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 66	76.41 32	91.51 9	52.47 74	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 44	83.82 58	59.34 119	79.37 15	89.76 48	59.84 13	87.62 46	76.69 22	86.74 54	87.68 29
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	78.71 161	56.28 169	64.34 206	84.14 132	41.57 194	87.06 58	46.45 234	78.88 128	77.02 266

DP-MVS		65.68 202	63.66 212	71.75 161	84.93 58	56.87 106	80.74 96	73.16 243	53.06 217	59.09 260	82.35 166	36.79 244	85.94 89	32.82 318	69.96 228	72.45 310

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 53	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 84	74.87 92	84.88 60	61.41 29	84.15 41	77.86 181	55.27 190	67.51 153	88.08 69	41.93 188	81.85 176	69.04 68	80.01 111	81.35 209

test12				77.76 43	84.52 61	58.41 79		83.36 70		72.93 76		54.61 50	88.05 36		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 33	83.87 56	60.37 94	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 66	73.46 73	76.80 58	84.45 63	59.04 68	83.65 52	81.05 121	60.15 102	70.43 97	89.84 47	41.09 204	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 94	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 87	75.27 37	84.83 119	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 40	86.85 9	63.23 45	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 77	73.76 117	83.58 68	51.66 175	78.75 122	77.66 185	67.75 4	72.61 80	89.42 51	49.82 99	83.29 145	53.61 185	83.14 72	86.32 72

SR-MVS-dyc-post		74.57 62	73.90 64	76.58 63	83.49 69	59.87 54	84.29 35	81.36 109	58.07 139	73.14 70	90.07 36	44.74 162	85.84 91	68.20 70	81.76 91	84.03 146

RE-MVS-def			73.71 68		83.49 69	59.87 54	84.29 35	81.36 109	58.07 139	73.14 70	90.07 36	43.06 178		68.20 70	81.76 91	84.03 146

LFMVS		71.78 95	71.59 87	72.32 155	83.40 71	46.38 239	79.75 112	71.08 255	64.18 33	72.80 77	88.64 65	42.58 181	83.72 137	57.41 156	84.49 67	86.86 53

test222					83.14 72	58.68 75	72.57 234	63.45 304	41.78 319	67.56 152	86.12 99	37.13 239			78.73 133	74.98 287

9.14			78.75 15		83.10 73		84.15 41	88.26 2	59.90 106	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 43	88.31 1	60.37 94	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	53.15 153		67.52 279			87.85 71	44.08 169			80.76 98	78.03 256

MSLP-MVS++		73.77 73	73.47 72	74.66 96	83.02 76	59.29 61	82.30 77	81.88 95	59.34 119	71.59 91	86.83 79	45.94 148	83.65 139	65.09 101	85.22 62	81.06 216

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 28
Skip Steuart: Steuart Systems R&D Blog.
MVS_111021_HR		74.02 69	73.46 73	75.69 78	83.01 77	60.63 44	77.29 152	78.40 176	61.18 82	70.58 96	85.97 104	54.18 55	84.00 133	67.52 79	82.98 77	82.45 192

SF-MVS		78.82 12	79.22 11	77.60 46	82.88 79	57.83 87	84.99 30	88.13 3	61.86 74	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	75.27 217	59.59 115	72.94 75	89.40 52	41.51 198	83.91 134	58.75 151	82.99 75	88.26 8

abl_6		74.34 64	73.50 70	76.86 57	82.43 81	60.16 49	83.48 54	81.86 96	58.81 126	73.95 58	89.86 46	41.87 189	86.62 70	67.98 74	81.23 96	83.80 158

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	42.03 186	89.38 14	64.07 107	86.50 57	89.69 1

APD-MVS_3200maxsize		74.96 54	74.39 60	76.67 61	82.20 83	58.24 82	83.67 51	83.29 73	58.41 133	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 45	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	80.35 136	55.09 195	65.82 182	82.16 173	49.17 107	82.64 164	60.34 140	78.62 135	82.50 191

TSAR-MVS + GP.		74.90 55	74.15 62	77.17 53	82.00 86	58.77 74	81.80 80	78.57 165	58.58 129	74.32 52	84.51 128	55.94 38	87.22 50	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 44	80.38 135	59.89 109	68.40 132	82.33 167	49.64 101	87.83 42	51.87 196	84.16 70	78.30 249

MAR-MVS		71.51 99	70.15 110	75.60 81	81.84 88	59.39 59	81.38 89	82.90 82	54.90 201	68.08 141	78.70 240	47.73 123	85.51 99	51.68 200	84.17 69	81.88 201
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	53.42 150	79.91 109	83.28 74	59.14 121	66.31 172	85.90 105	51.86 81	86.06 83	57.45 155	80.62 99	85.91 83

test_part1		74.74 58	74.42 59	75.70 77	81.69 90	51.26 177	83.98 46	87.05 7	65.31 16	73.10 72	86.20 97	53.94 58	88.06 35	65.32 98	73.17 187	87.77 26

VDD-MVS		72.50 83	72.09 83	73.75 119	81.58 91	49.69 204	77.76 140	77.63 186	63.21 46	73.21 68	89.02 58	42.14 185	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 92	55.79 121	74.92 199	79.00 155	55.04 199	69.88 110	78.66 241	47.05 137	82.19 171	61.61 131	79.58 116	80.83 219

testdata				64.66 263	81.52 92	52.93 156		65.29 294	46.09 288	73.88 60	87.46 74	38.08 229	66.26 307	53.31 188	78.48 136	74.78 291

CHOSEN 1792x2688		65.08 213	62.84 221	71.82 160	81.49 94	56.26 112	66.32 286	74.20 234	40.53 327	63.16 218	78.65 242	41.30 200	77.80 246	45.80 240	74.09 170	81.40 206

HQP_MVS		74.31 65	73.73 67	76.06 68	81.41 95	56.31 109	84.22 38	84.01 50	64.52 26	69.27 120	86.10 100	45.26 159	87.21 51	68.16 72	80.58 101	84.65 130

plane_prior7					81.41 95	55.96 118

DPM-MVS		75.47 51	75.00 50	76.88 56	81.38 97	59.16 62	79.94 107	85.71 24	56.59 162	72.46 82	86.76 80	56.89 29	87.86 41	66.36 87	88.91 26	83.64 166

CANet		76.46 42	75.93 44	78.06 39	81.29 98	57.53 92	82.35 72	83.31 72	67.78 3	70.09 102	86.34 95	54.92 46	88.90 21	72.68 47	84.55 66	87.76 27

Vis-MVSNet		72.18 89	71.37 92	74.61 99	81.29 98	55.41 130	80.90 93	78.28 178	60.73 88	69.23 123	88.09 68	44.36 168	82.65 163	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					81.27 100

xiu_mvs_v2_base		70.52 110	69.75 112	72.84 143	81.21 101	55.63 125	75.11 193	78.92 156	54.92 200	69.96 109	79.68 226	47.00 141	82.09 173	61.60 132	79.37 119	80.81 220

plane_prior6					81.20 102	56.24 113						45.26 159

PAPR		71.72 97	70.82 100	74.41 105	81.20 102	51.17 178	79.55 116	83.33 71	55.81 180	66.93 160	84.61 124	50.95 92	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	81.04 104	54.87 136	78.57 127	77.47 188	48.51 262	55.71 283	81.89 177	33.71 265	79.71 214	41.66 276	70.37 220	77.58 258
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
NP-MVS					80.98 105	56.05 117					85.54 113

OPM-MVS		74.73 59	74.25 61	76.19 67	80.81 106	59.01 69	82.60 69	83.64 61	63.74 40	72.52 81	87.49 73	47.18 135	85.88 90	69.47 64	80.78 97	83.66 164

HQP-NCC					80.66 107		82.31 74		62.10 68	67.85 144

ACMP_Plane					80.66 107		82.31 74		62.10 68	67.85 144

HQP-MVS		73.45 74	72.80 78	75.40 83	80.66 107	54.94 133	82.31 74	83.90 54	62.10 68	67.85 144	85.54 113	45.46 153	86.93 60	67.04 83	80.35 107	84.32 138

PHI-MVS		75.87 48	75.36 48	77.41 49	80.62 110	55.91 120	84.28 37	85.78 22	56.08 175	73.41 66	86.58 90	50.94 93	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	80.59 111	58.59 77	82.68 67	82.02 94	55.46 188	67.18 157	84.39 130	38.51 222	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	80.43 112	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 36

Regformer-2		75.63 50	74.99 51	77.54 47	80.43 112	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 36

Anonymous20231211		69.28 137	68.47 134	71.73 162	80.28 114	47.18 234	79.98 106	82.37 88	54.61 203	67.24 156	84.01 136	39.43 213	82.41 169	55.45 169	72.83 191	85.62 98

ACMP	63.53 6	72.30 87	71.20 96	75.59 82	80.28 114	57.54 91	82.74 65	82.84 84	60.58 90	65.24 193	86.18 98	39.25 215	86.03 85	66.95 85	76.79 154	83.22 175
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 73	80.22 116	57.51 93	82.55 70	83.40 68	61.32 79	66.67 164	87.33 76	39.15 217	86.59 71	67.70 76	77.30 147	83.19 177

LGP-MVS_train				75.76 73	80.22 116	57.51 93		83.40 68	61.32 79	66.67 164	87.33 76	39.15 217	86.59 71	67.70 76	77.30 147	83.19 177

WR-MVS		68.47 156	68.47 134	68.44 224	80.20 118	39.84 293	73.75 220	76.07 206	64.68 23	68.11 140	83.63 144	50.39 97	79.14 228	49.78 208	69.66 236	86.34 69

Anonymous20240529		69.91 124	69.02 125	72.56 149	80.19 119	47.65 228	77.56 144	80.99 124	55.45 189	69.88 110	86.76 80	39.24 216	82.18 172	54.04 179	77.10 149	87.85 23

Anonymous202405211		66.84 188	65.99 187	69.40 212	80.19 119	42.21 279	71.11 256	71.31 254	58.80 127	67.90 142	86.39 94	29.83 300	79.65 215	49.60 214	78.78 131	86.33 70

BH-RMVSNet		68.81 144	67.42 153	72.97 140	80.11 121	52.53 162	74.26 208	76.29 203	58.48 132	68.38 133	84.20 131	42.59 180	83.83 135	46.53 233	75.91 159	82.56 188

test_0402		63.25 229	61.01 242	69.96 200	80.00 122	54.37 139	76.86 162	72.02 250	54.58 205	58.71 263	80.79 203	35.00 253	84.36 123	26.41 344	64.71 280	71.15 322

HyFIR lowres test		65.67 203	63.01 219	73.67 122	79.97 123	55.65 124	69.07 275	75.52 214	42.68 317	63.53 214	77.95 249	40.43 206	81.64 180	46.01 238	71.91 204	83.73 160

EIA-MVS		71.78 95	70.60 102	75.30 86	79.85 124	53.54 147	77.27 153	83.26 75	57.92 142	66.49 167	79.39 232	52.07 79	86.69 67	60.05 142	79.14 126	85.66 95

Regformer-3		73.89 71	73.28 75	75.71 76	79.75 125	55.48 129	78.54 129	79.93 141	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 67	73.48 71	76.57 64	79.75 125	56.54 108	78.54 129	81.49 106	66.93 7	73.90 59	80.30 212	53.84 61	85.98 87	69.76 61	76.84 152	87.17 46

BH-untuned		68.27 159	67.29 158	71.21 177	79.74 127	53.22 152	76.06 177	77.46 190	57.19 149	66.10 174	81.61 183	45.37 157	83.50 142	45.42 248	76.68 156	76.91 270

VNet		69.68 129	70.19 109	68.16 226	79.73 128	41.63 286	70.53 262	77.38 191	60.37 94	70.69 95	86.63 87	51.08 90	77.09 255	53.61 185	81.69 95	85.75 92

LS3D		64.71 215	62.50 225	71.34 175	79.72 129	55.71 122	79.82 110	74.72 227	48.50 263	56.62 278	84.62 123	33.59 268	82.34 170	29.65 336	75.23 164	75.97 274

AUN-MVS		68.45 157	66.41 177	74.57 101	79.53 130	57.08 104	73.93 216	75.23 218	54.44 208	66.69 163	81.85 178	37.10 240	82.89 154	62.07 126	66.84 265	83.75 159

BH-w/o		66.85 187	65.83 189	69.90 204	79.29 131	52.46 165	74.66 204	76.65 201	54.51 207	64.85 201	78.12 247	45.59 152	82.95 153	43.26 263	75.54 162	74.27 296

1112_ss		64.00 221	63.36 216	65.93 251	79.28 132	42.58 276	71.35 249	72.36 249	46.41 285	60.55 245	77.89 253	46.27 147	73.28 274	46.18 236	69.97 227	81.92 200

ETV-MVS		74.46 63	73.84 66	76.33 66	79.27 133	55.24 132	79.22 120	85.00 35	64.97 22	72.65 79	79.46 231	53.65 66	87.87 40	67.45 80	82.91 78	85.89 84

UniMVSNet_NR-MVSNet		71.11 103	71.00 98	71.44 169	79.20 134	44.13 263	76.02 180	82.60 86	66.48 13	68.20 135	84.60 125	56.82 30	82.82 160	54.62 175	70.43 218	87.36 43

VPNet		67.52 173	68.11 141	65.74 254	79.18 135	36.80 318	72.17 240	72.83 245	62.04 71	67.79 150	85.83 107	48.88 112	76.60 260	51.30 201	72.97 190	83.81 154

TR-MVS		66.59 195	65.07 200	71.17 180	79.18 135	49.63 206	73.48 222	75.20 220	52.95 218	67.90 142	80.33 211	39.81 210	83.68 138	43.20 264	73.56 178	80.20 227

TAMVS		66.78 190	65.27 198	71.33 176	79.16 137	53.67 143	73.84 219	69.59 267	52.32 226	65.28 188	81.72 181	44.49 167	77.40 252	42.32 270	78.66 134	82.92 183

Test_1112_low_res		62.32 237	61.77 232	64.00 267	79.08 138	39.53 297	68.17 277	70.17 261	43.25 312	59.03 261	79.90 219	44.08 169	71.24 283	43.79 259	68.42 253	81.25 210

CS-MVS		74.18 68	73.60 69	75.92 70	78.99 139	52.53 162	80.61 98	85.93 20	61.17 83	71.15 93	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	78.98 140	53.13 155	73.27 225	71.07 256	52.15 227	64.72 202	80.23 215	43.56 174	77.10 254	45.48 246	78.88 128	83.05 182
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
canonicalmvs		74.67 60	74.98 52	73.71 121	78.94 141	50.56 190	80.23 102	83.87 56	60.30 100	77.15 29	86.56 91	59.65 14	82.00 174	66.01 91	82.12 87	88.58 6

IS-MVSNet		71.57 98	71.00 98	73.27 136	78.86 142	45.63 252	80.22 103	78.69 162	64.14 36	66.46 168	87.36 75	49.30 104	85.60 94	50.26 207	83.71 71	88.59 5

CLD-MVS		73.33 75	72.68 79	75.29 87	78.82 143	53.33 151	78.23 133	84.79 39	61.30 81	70.41 98	81.04 194	52.41 75	87.12 54	64.61 106	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	78.76 144	57.15 102	82.79 63	78.48 169	51.26 239	69.49 115	83.22 150	43.99 171	83.24 146	66.06 89	79.37 119	84.23 141

lupinMVS		69.57 132	68.28 138	73.44 131	78.76 144	57.15 102	76.57 165	73.29 242	46.19 287	69.49 115	82.18 170	43.99 171	79.23 222	64.66 104	79.37 119	83.93 149

CNLPA		65.43 207	64.02 205	69.68 206	78.73 146	58.07 84	77.82 139	70.71 258	51.49 234	61.57 240	83.58 146	38.23 227	70.82 284	43.90 257	70.10 226	80.16 228

EPP-MVSNet		72.16 91	71.31 94	74.71 93	78.68 147	49.70 202	82.10 78	81.65 101	60.40 93	65.94 177	85.84 106	51.74 83	86.37 80	55.93 162	79.55 118	88.07 17

TranMVSNet+NR-MVSNet		70.36 115	70.10 111	71.17 180	78.64 148	42.97 274	76.53 166	81.16 120	66.95 6	68.53 131	85.42 115	51.61 84	83.07 149	52.32 193	69.70 235	87.46 38

UniMVSNet (Re)		70.63 109	70.20 108	71.89 158	78.55 149	45.29 254	75.94 181	82.92 80	63.68 41	68.16 138	83.59 145	53.89 60	83.49 143	53.97 180	71.12 212	86.89 52

Fast-Effi-MVS+		70.28 117	69.12 124	73.73 120	78.50 150	51.50 176	75.01 196	79.46 150	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	78.50 150	55.93 119	81.63 83	82.12 92	56.24 170	70.02 106	85.68 110	47.05 137	84.34 124	65.27 99	74.41 168	85.67 94

EI-MVSNet-Vis-set		72.42 86	71.59 87	74.91 90	78.47 152	54.02 140	77.05 157	79.33 152	65.03 21	71.68 90	79.35 234	52.75 70	84.89 113	66.46 86	74.23 169	85.83 86

MVS_111021_LR		69.50 134	68.78 129	71.65 165	78.38 153	59.33 60	74.82 201	70.11 262	58.08 138	67.83 148	84.68 121	41.96 187	76.34 263	65.62 96	77.54 142	79.30 243

test_yl		69.69 127	69.13 122	71.36 173	78.37 154	45.74 248	74.71 202	80.20 137	57.91 143	70.01 107	83.83 139	42.44 182	82.87 156	54.97 171	79.72 113	85.48 102

DCV-MVSNet		69.69 127	69.13 122	71.36 173	78.37 154	45.74 248	74.71 202	80.20 137	57.91 143	70.01 107	83.83 139	42.44 182	82.87 156	54.97 171	79.72 113	85.48 102

FIs		70.82 106	71.43 89	68.98 217	78.33 156	38.14 307	76.96 159	83.59 62	61.02 84	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	54.47 138	79.77 111	75.40 216	60.45 92	63.22 216	84.40 129	32.71 280	80.91 198	51.71 199	80.56 103	83.81 154
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	73.71 239	46.53 284	69.34 119	83.22 150	43.37 175	79.18 223	64.77 103	79.20 124	84.23 141
jason: jason.
alignmvs		73.86 72	73.99 63	73.45 130	78.20 159	50.50 191	78.57 127	82.43 87	59.40 117	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	72.48 151	78.19 160	57.19 99	75.28 188	75.09 222	51.61 230	70.04 103	81.41 187	32.79 276	79.02 230	63.81 111	77.31 144	81.22 211

xiu_mvs_v1_base		68.58 151	67.28 159	72.48 151	78.19 160	57.19 99	75.28 188	75.09 222	51.61 230	70.04 103	81.41 187	32.79 276	79.02 230	63.81 111	77.31 144	81.22 211

xiu_mvs_v1_base_debi		68.58 151	67.28 159	72.48 151	78.19 160	57.19 99	75.28 188	75.09 222	51.61 230	70.04 103	81.41 187	32.79 276	79.02 230	63.81 111	77.31 144	81.22 211

PAPM		67.92 168	66.69 170	71.63 166	78.09 163	49.02 212	77.09 156	81.24 118	51.04 241	60.91 243	83.98 137	47.71 124	84.99 108	40.81 279	79.32 122	80.90 218

ACMH	55.70 15	65.20 211	63.57 213	70.07 199	78.07 164	52.01 173	79.48 119	79.69 143	55.75 182	56.59 279	80.98 196	27.12 318	80.94 196	42.90 268	71.58 208	77.25 264
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
DU-MVS		70.01 121	69.53 116	71.44 169	78.05 165	44.13 263	75.01 196	81.51 105	64.37 29	68.20 135	84.52 126	49.12 110	82.82 160	54.62 175	70.43 218	87.37 41

NR-MVSNet		69.54 133	68.85 127	71.59 167	78.05 165	43.81 267	74.20 209	80.86 128	65.18 17	62.76 221	84.52 126	52.35 76	83.59 141	50.96 203	70.78 214	87.37 41

EI-MVSNet-UG-set		71.92 93	71.06 97	74.52 103	77.98 167	53.56 146	76.62 164	79.16 153	64.40 28	71.18 92	78.95 239	52.19 77	84.66 119	65.47 97	73.57 177	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 72	62.65 224	82.48 164	50.57 95	79.46 218	42.91 267	64.01 284	84.79 127

Effi-MVS+		73.31 76	72.54 80	75.62 80	77.87 169	53.64 144	79.62 115	79.61 146	61.63 76	72.02 87	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	52.25 168	75.59 184	84.17 47	63.76 39	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	72.30 156	77.79 171	52.83 157	80.39 101	78.03 179	57.30 147	57.47 274	82.55 162	27.68 314	84.17 126	45.54 244	69.78 232	79.90 233

3Dnovator	64.47 5	72.49 84	71.39 91	75.79 72	77.70 172	58.99 70	80.66 97	83.15 77	62.24 65	65.46 186	86.59 89	42.38 184	85.52 98	59.59 147	84.72 64	82.85 186

EG-PatchMatch MVS		64.71 215	62.87 220	70.22 195	77.68 173	53.48 148	77.99 136	78.82 157	53.37 215	56.03 282	77.41 260	24.75 332	84.04 129	46.37 235	73.42 181	73.14 303

CP-MVSNet		66.49 196	66.41 177	66.72 237	77.67 174	36.33 323	76.83 163	79.52 148	62.45 62	62.54 227	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	77.83 182	56.62 159	65.04 197	82.70 156	41.85 190	80.33 209	47.18 228	72.76 193	83.92 150

test1		67.21 177	66.55 171	69.19 213	77.63 175	43.33 270	77.31 149	77.83 182	56.62 159	65.04 197	82.70 156	41.85 190	80.33 209	47.18 228	72.76 193	83.92 150

FMVSNet2		66.93 186	66.31 182	68.79 220	77.63 175	42.98 273	76.11 175	77.47 188	56.62 159	65.22 195	82.17 172	41.85 190	80.18 212	47.05 231	72.72 196	83.20 176

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	50.30 247	69.66 113	85.40 116	52.51 72	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	78.53 167	50.59 246	56.47 280	78.65 242	39.84 209	82.68 162	44.10 256	72.12 203	72.44 311
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	34.73 330	76.05 178	83.19 76	60.84 85	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	36.30 325	76.94 160	79.61 146	62.36 64	62.43 231	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	77.83 182	56.45 164	64.60 205	82.70 156	38.08 229	80.33 209	46.08 237	72.31 201	83.92 150

VPA-MVSNet		69.02 141	69.47 118	67.69 230	77.42 183	41.00 290	74.04 211	79.68 144	60.06 103	69.26 122	84.81 120	51.06 91	77.58 249	54.44 178	74.43 167	84.48 134

UniMVSNet_ETH3D		67.60 172	67.07 166	69.18 216	77.39 184	42.29 278	74.18 210	75.59 213	60.37 94	66.77 162	86.06 102	37.64 231	78.93 235	52.16 195	73.49 179	86.32 72

thres100view900		63.28 228	62.41 226	65.89 252	77.31 185	38.66 303	72.65 231	69.11 273	57.07 150	62.45 230	81.03 195	37.01 242	79.17 224	31.84 322	73.25 184	79.83 235

cascas		65.98 200	63.42 215	73.64 125	77.26 186	52.58 161	72.26 239	77.21 194	48.56 261	61.21 242	74.60 292	32.57 284	85.82 93	50.38 206	76.75 155	82.52 190

thres600view7		63.30 227	62.27 227	66.41 241	77.18 187	38.87 301	72.35 237	69.11 273	56.98 152	62.37 232	80.96 197	37.01 242	79.00 233	31.43 329	73.05 189	81.36 207

PEN-MVS		66.60 193	66.45 173	67.04 235	77.11 188	36.56 320	77.03 158	80.42 134	62.95 49	62.51 229	84.03 135	46.69 143	79.07 229	44.22 252	63.08 292	85.51 101

PatchMatch-RL		56.25 279	54.55 284	61.32 286	77.06 189	56.07 116	65.57 291	54.10 339	44.13 306	53.49 310	71.27 310	25.20 329	66.78 304	36.52 304	63.66 286	61.12 338

PVSNet_BlendedMVS		68.56 154	67.72 144	71.07 183	77.03 190	50.57 188	74.50 206	81.52 103	53.66 214	64.22 211	79.72 225	49.13 108	82.87 156	55.82 163	73.92 172	79.77 238

PVSNet_Blended		68.59 150	67.72 144	71.19 178	77.03 190	50.57 188	72.51 235	81.52 103	51.91 228	64.22 211	77.77 257	49.13 108	82.87 156	55.82 163	79.58 116	80.14 229

F-COLMAP		63.05 232	60.87 245	69.58 210	76.99 192	53.63 145	78.12 135	76.16 204	47.97 270	52.41 313	81.61 183	27.87 312	78.11 241	40.07 282	66.66 266	77.00 267

tfpn200view9		63.18 230	62.18 229	66.21 245	76.85 193	39.62 295	71.96 243	69.44 269	56.63 157	62.61 225	79.83 221	37.18 236	79.17 224	31.84 322	73.25 184	79.83 235

thres400		63.31 226	62.18 229	66.72 237	76.85 193	39.62 295	71.96 243	69.44 269	56.63 157	62.61 225	79.83 221	37.18 236	79.17 224	31.84 322	73.25 184	81.36 207

tttt0517		67.83 170	65.66 192	74.33 107	76.69 195	50.82 184	77.86 137	73.99 236	54.54 206	64.64 204	82.53 163	35.06 252	85.50 100	55.71 166	69.91 229	86.67 59

ET-MVSNet_ETH3D		67.96 167	65.72 191	74.68 95	76.67 196	55.62 126	75.11 193	74.74 226	52.91 219	60.03 248	80.12 216	33.68 266	82.64 164	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	80.04 139	50.64 245	65.24 193	84.93 118	39.15 217	78.54 236	36.77 298	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	53.15 153	79.74 113	79.78 142	58.37 134	68.75 127	86.45 93	45.43 155	80.60 204	62.58 121	77.73 141	87.58 34

LTVRE_ROB	55.42 16	63.15 231	61.23 240	68.92 218	76.57 199	47.80 225	59.92 317	76.39 202	54.35 209	58.67 264	82.46 165	29.44 303	81.49 184	42.12 272	71.14 211	77.46 259
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	53.43 149	83.23 56	83.48 64	52.89 220	65.90 179	86.29 96	41.55 197	86.49 77	51.01 202	78.40 137	81.42 205

FMVSNet3		66.32 198	65.61 193	68.46 223	76.48 201	42.34 277	74.98 198	77.15 195	55.83 179	65.04 197	81.16 191	39.91 208	80.14 213	47.18 228	72.76 193	82.90 185

thisisatest0530		67.92 168	65.78 190	74.33 107	76.29 202	51.03 179	76.89 161	74.25 233	53.67 213	65.59 184	81.76 180	35.15 251	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	36.36 321	71.78 245	67.29 282	56.05 176	64.23 210	82.95 154	47.11 136	74.41 271	47.30 227	61.85 300	80.10 230

ab-mvs		66.65 192	66.42 176	67.37 232	76.17 204	41.73 284	70.41 265	76.14 205	53.99 210	65.98 176	83.51 147	49.48 102	76.24 264	48.60 220	73.46 180	84.14 144

Effi-MVS+-dtu		69.64 131	67.53 150	75.95 69	76.10 205	62.29 18	80.20 104	76.06 207	59.83 110	65.26 192	77.09 261	41.56 195	84.02 132	60.60 138	71.09 213	81.53 204

mvs-test1		70.44 113	68.19 139	77.18 52	76.10 205	63.22 6	80.59 99	76.06 207	59.83 110	66.32 171	79.87 220	41.56 195	85.53 97	60.60 138	72.77 192	82.80 187

DTE-MVSNet		65.58 204	65.34 196	66.31 242	76.06 207	34.79 328	76.43 168	79.38 151	62.55 60	61.66 238	83.83 139	45.60 151	79.15 227	41.64 278	60.88 306	85.00 119

EPNet		73.09 78	72.16 82	75.90 71	75.95 208	56.28 111	83.05 58	72.39 248	66.53 12	65.27 189	87.00 78	50.40 96	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	75.84 209	56.09 174	68.17 137	83.74 142	28.50 308	82.98 151	65.67 95	65.91 271	83.33 171

RRT_test8_iter05		68.17 164	66.86 168	72.07 157	75.81 210	46.33 240	76.41 169	81.81 98	56.43 165	66.52 166	81.30 190	31.90 288	84.25 125	63.77 114	67.83 258	85.64 97

SixPastTwentyTwo		61.65 246	58.80 254	70.20 197	75.80 211	47.22 233	75.59 184	69.68 265	54.61 203	54.11 302	79.26 236	27.07 319	82.96 152	43.27 262	49.79 336	80.41 225

baseline		74.61 61	74.70 56	74.34 106	75.70 212	49.99 199	77.54 145	84.63 40	62.73 58	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	74.65 228	59.90 106	68.14 139	83.15 153	49.12 110	77.20 253	52.23 194	69.78 232	81.60 203

jajsoiax		68.25 160	66.45 173	73.66 123	75.62 214	55.49 128	80.82 94	78.51 168	52.33 225	64.33 207	84.11 133	28.28 310	81.81 178	63.48 116	70.62 216	83.67 162

mvs_tets		68.18 162	66.36 179	73.63 126	75.61 215	55.35 131	80.77 95	78.56 166	52.48 224	64.27 209	84.10 134	27.45 316	81.84 177	63.45 117	70.56 217	83.69 161

casdiffmvs		74.80 56	74.89 54	74.53 102	75.59 216	50.37 192	78.17 134	85.06 32	62.80 57	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	58.40 264	57.39 264	61.42 284	75.53 217	44.04 265	61.43 310	63.45 304	47.04 282	56.91 276	73.61 299	27.00 320	64.76 311	39.12 287	72.40 198	75.47 281

MVS		67.37 175	66.33 180	70.51 193	75.46 218	50.94 180	73.95 213	81.85 97	41.57 323	62.54 227	78.57 245	47.98 120	85.47 102	52.97 190	82.05 88	75.14 283

nrg030		72.96 79	73.01 76	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	69.29 271	55.64 186	61.87 236	76.99 262	37.07 241	78.96 234	31.28 330	73.28 183	77.06 265

TransMVSNet (Re)		64.72 214	64.33 203	65.87 253	75.22 221	38.56 304	74.66 204	75.08 225	58.90 125	61.79 237	82.63 159	51.18 88	78.07 242	43.63 260	55.87 322	80.99 217

MS-PatchMatch		62.42 236	61.46 236	65.31 260	75.21 222	52.10 169	72.05 241	74.05 235	46.41 285	57.42 275	74.36 293	34.35 260	77.57 250	45.62 243	73.67 174	66.26 335

IB-MVS	56.42 12	65.40 209	62.73 223	73.40 133	74.89 223	52.78 158	73.09 227	75.13 221	55.69 183	58.48 268	73.73 298	32.86 275	86.32 81	50.63 204	70.11 225	81.10 215
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 81	72.42 154	74.88 224	48.50 218	76.28 172	83.14 78	59.40 117	72.46 82	84.68 121	55.66 41	81.12 191	65.98 92	79.66 115	87.63 31

CANet_DTU		68.18 162	67.71 146	69.59 208	74.83 225	46.24 242	78.66 125	76.85 198	59.60 112	63.45 215	82.09 176	35.25 250	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	73.72 238	55.22 192	60.21 246	80.09 218	41.26 203	76.98 257	30.02 334	68.09 255	78.97 246

Vis-MVSNet (Re-imp)		63.69 223	63.88 207	63.14 273	74.75 227	31.04 343	71.16 254	63.64 303	56.32 167	59.80 253	84.99 117	44.51 165	75.46 266	39.12 287	80.62 99	82.92 183

HY-MVS	56.14 13	64.55 218	63.89 206	66.55 240	74.73 228	41.02 288	69.96 268	74.43 229	49.29 255	61.66 238	80.92 198	47.43 131	76.68 259	44.91 251	71.69 206	81.94 199

COLMAP_ROB	52.97 17	61.27 251	58.81 253	68.64 221	74.63 229	52.51 164	78.42 132	73.30 241	49.92 252	50.96 318	81.51 186	23.06 334	79.40 219	31.63 326	65.85 272	74.01 299
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	31.48 342	61.42 311	58.14 324	58.71 128	53.02 312	79.55 229	43.07 177	76.80 258	45.69 241	77.96 139	82.11 198

test_djsdf		69.45 136	67.74 143	74.58 100	74.57 231	54.92 135	82.79 63	78.48 169	51.26 239	65.41 187	83.49 148	38.37 224	83.24 146	66.06 89	69.25 242	85.56 99

EI-MVSNet		69.27 138	68.44 136	71.73 162	74.47 232	49.39 209	75.20 191	78.45 172	59.60 112	69.16 124	76.51 271	51.29 86	82.50 166	59.86 146	71.45 210	83.30 172

CVMVSNet		59.63 259	59.14 252	61.08 287	74.47 232	38.84 302	75.20 191	68.74 275	31.15 342	58.24 269	76.51 271	32.39 285	68.58 296	49.77 209	65.84 273	75.81 277

IterMVS-LS		69.22 140	68.48 133	71.43 171	74.44 234	49.40 208	76.23 173	77.55 187	59.60 112	65.85 181	81.59 185	51.28 87	81.58 183	59.87 145	69.90 230	83.30 172
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	79.04 154	54.77 202	66.99 159	86.01 103	39.57 212	78.21 240	62.54 122	73.33 182	83.37 170

XVG-OURS		68.76 148	67.37 155	72.90 142	74.32 236	57.22 97	70.09 267	78.81 158	55.24 191	67.79 150	85.81 109	36.54 245	78.28 239	62.04 127	75.74 160	83.19 177

OpenMVS	61.03 9	68.85 143	67.56 147	72.70 147	74.26 237	53.99 141	81.21 91	81.34 113	52.70 221	62.75 222	85.55 112	38.86 220	84.14 127	48.41 222	83.01 74	79.97 232

MIMVSNet		57.35 271	57.07 267	58.22 298	74.21 238	37.18 312	62.46 306	60.88 317	48.88 259	55.29 289	75.99 279	31.68 289	62.04 319	31.87 321	72.35 199	75.43 282

SCA		60.49 253	58.38 258	66.80 236	74.14 239	48.06 223	63.35 303	63.23 306	49.13 257	59.33 259	72.10 304	37.45 233	74.27 272	44.17 253	62.57 295	78.05 253

thisisatest0515		65.83 201	63.50 214	72.82 145	73.75 240	49.50 207	71.32 250	73.12 244	49.39 254	63.82 213	76.50 273	34.95 254	84.84 116	53.20 189	75.49 163	84.13 145

K. test v3		60.47 254	57.11 266	70.56 191	73.74 241	48.22 221	75.10 195	62.55 310	58.27 137	53.62 307	76.31 275	27.81 313	81.59 182	47.42 225	39.18 347	81.88 201

v10		70.21 118	69.02 125	73.81 114	73.51 242	50.92 182	78.74 123	81.39 108	60.05 104	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	50.64 187	77.83 138	81.43 107	58.58 129	69.40 118	81.16 191	47.53 128	85.29 107	64.01 109	70.64 215	85.34 109

v1192		69.97 123	68.68 130	73.85 113	73.19 244	50.94 180	77.68 141	81.36 109	57.51 146	68.95 126	80.85 201	45.28 158	85.33 106	62.97 119	70.37 220	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 86	66.45 169	82.11 175	49.35 103	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	69.66 236	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	44.72 164	84.98 110	62.47 124	69.82 231	85.00 119

PatchmatchNet		59.84 257	58.24 259	64.65 264	73.05 248	46.70 237	69.42 272	62.18 312	47.55 274	58.88 262	71.96 306	34.49 258	69.16 293	42.99 266	63.60 287	78.07 252
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	43.33 176	84.99 108	62.03 128	69.77 234	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	76.88 197	57.60 145	61.97 234	76.85 265	39.31 214	80.49 207	54.72 174	70.28 223	82.17 197

EPNet_dtu		61.90 242	61.97 231	61.68 282	72.89 251	39.78 294	75.85 182	65.62 291	55.09 195	54.56 298	79.36 233	37.59 232	67.02 303	39.80 285	76.95 150	78.25 250
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
tpm2		62.07 240	60.10 248	67.99 227	72.79 252	43.86 266	71.05 257	66.85 285	43.14 314	62.77 220	75.39 286	38.32 225	80.80 200	41.69 275	68.88 248	79.32 242

MDTV_nov1_ep13			57.00 268		72.73 253	38.26 306	65.02 298	64.73 298	44.74 297	55.46 285	72.48 302	32.61 283	70.47 286	37.47 294	67.75 260

MSDG		61.81 245	59.23 251	69.55 211	72.64 254	52.63 160	70.45 264	75.81 210	51.38 236	53.70 305	76.11 276	29.52 301	81.08 194	37.70 293	65.79 274	74.93 288

gg-mvs-nofinetune		57.86 269	56.43 273	62.18 279	72.62 255	35.35 327	66.57 283	56.33 331	50.65 244	57.64 273	57.10 343	30.65 292	76.36 262	37.38 295	78.88 128	74.82 290

v2v482		70.50 112	69.45 119	73.66 123	72.62 255	50.03 198	77.58 142	80.51 133	59.90 106	69.52 114	82.14 174	47.53 128	84.88 115	65.07 102	70.17 224	86.09 79

baseline2		63.42 225	61.26 239	69.89 205	72.55 257	47.62 229	71.54 246	68.38 277	50.11 248	54.82 294	75.55 284	43.06 178	80.96 195	48.13 223	67.16 264	81.11 214

v7n		69.01 142	67.36 156	73.98 111	72.51 258	52.65 159	78.54 129	81.30 114	60.26 101	62.67 223	81.62 182	43.61 173	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	72.62 233	75.63 212	55.53 187	62.35 233	83.18 152	47.45 130	76.47 261	49.06 217	66.54 267	82.24 194

XVG-ACMP-BASELINE		64.36 219	62.23 228	70.74 188	72.35 260	52.45 166	70.80 260	78.45 172	53.84 212	59.87 251	81.10 193	16.24 345	79.32 221	55.64 168	71.76 205	80.47 223

WTY-MVS		59.75 258	60.39 246	57.85 301	72.32 261	37.83 309	61.05 315	64.18 301	45.95 292	61.91 235	79.11 238	47.01 140	60.88 322	42.50 269	69.49 238	74.83 289

tpm cat1		59.25 260	56.95 269	66.15 246	72.19 262	46.96 235	68.09 278	65.76 290	40.03 330	57.81 272	70.56 314	38.32 225	74.51 270	38.26 291	61.50 303	77.00 267

mvs_anonymous		68.03 165	67.51 151	69.59 208	72.08 263	44.57 261	71.99 242	75.23 218	51.67 229	67.06 158	82.57 161	54.68 49	77.94 243	56.56 158	75.71 161	86.26 76

OurMVSNet-221017-0		61.37 250	58.63 256	69.61 207	72.05 264	48.06 223	73.93 216	72.51 247	47.23 280	54.74 295	80.92 198	21.49 341	81.24 189	48.57 221	56.22 321	79.53 240

IterMVS-SCA-FT		62.49 234	61.52 235	65.40 258	71.99 265	50.80 185	71.15 255	69.63 266	45.71 293	60.61 244	77.93 250	37.45 233	65.99 308	55.67 167	63.50 288	79.42 241

DWT-MVSNet_test		61.90 242	59.93 249	67.83 228	71.98 266	46.09 245	71.03 258	69.71 263	50.09 249	58.51 267	70.62 313	30.21 297	77.63 248	49.28 215	67.91 256	79.78 237

CostFormer		64.04 220	62.51 224	68.61 222	71.88 267	45.77 247	71.30 251	70.60 259	47.55 274	64.31 208	76.61 269	41.63 193	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	78.39 177	42.88 316	59.97 249	76.60 270	38.11 228	79.39 220	54.84 173	72.32 200	79.55 239

tpm		57.34 272	58.16 260	54.86 311	71.80 269	34.77 329	67.47 282	56.04 334	48.20 267	60.10 247	76.92 263	37.17 238	53.41 345	40.76 280	65.01 278	76.40 273

eth_miper_zixun_eth		67.63 171	66.28 183	71.67 164	71.60 270	48.33 220	73.68 221	77.88 180	55.80 181	65.91 178	78.62 244	47.35 134	82.88 155	59.45 148	66.25 269	83.81 154

pmmvs4		61.48 249	59.39 250	67.76 229	71.57 271	53.86 142	71.42 247	65.34 293	44.20 304	59.46 255	77.92 251	35.90 246	74.71 269	43.87 258	64.87 279	74.71 292

AllTest		57.08 274	54.65 283	64.39 265	71.44 272	49.03 210	69.92 269	67.30 280	45.97 290	47.16 330	79.77 223	17.47 343	67.56 300	33.65 315	59.16 312	76.57 271

TestCases				64.39 265	71.44 272	49.03 210		67.30 280	45.97 290	47.16 330	79.77 223	17.47 343	67.56 300	33.65 315	59.16 312	76.57 271

lessismore_v0				69.91 203	71.42 274	47.80 225		50.90 342		50.39 324	75.56 283	27.43 317	81.33 186	45.91 239	34.10 349	80.59 222

gm-plane-assit					71.40 275	41.72 285			48.85 260		73.31 300		82.48 168	48.90 218

GG-mvs-BLEND				62.34 278	71.36 276	37.04 316	69.20 274	57.33 328		54.73 296	65.48 335	30.37 294	77.82 245	34.82 311	74.93 165	72.17 316

FMVSNet5		55.86 281	54.93 281	58.66 297	71.05 277	36.35 322	64.18 302	62.48 311	46.76 283	50.66 323	74.73 291	25.80 326	64.04 313	33.11 317	65.57 275	75.59 280

cl_fuxian		68.33 158	67.56 147	70.62 190	70.87 278	46.21 243	74.47 207	78.80 159	56.22 171	66.19 173	78.53 246	51.88 80	81.40 185	62.08 125	69.04 245	84.25 140

GA-MVS		65.53 206	63.70 210	71.02 184	70.87 278	48.10 222	70.48 263	74.40 230	56.69 155	64.70 203	76.77 266	33.66 267	81.10 192	55.42 170	70.32 222	83.87 153

bset_n11_16_dypcd		65.57 205	63.69 211	71.19 178	70.84 280	51.79 174	71.37 248	70.48 260	53.33 216	65.19 196	76.41 274	31.46 290	81.76 179	65.12 100	69.04 245	80.01 231

pmmvs6		63.69 223	62.82 222	66.27 244	70.63 281	39.27 299	73.13 226	75.47 215	52.69 222	59.75 254	82.30 168	39.71 211	77.03 256	47.40 226	64.35 283	82.53 189

miper_ehance_all_eth		68.03 165	67.24 163	70.40 194	70.54 282	46.21 243	73.98 212	78.68 163	55.07 197	66.05 175	77.80 255	52.16 78	81.31 187	61.53 134	69.32 239	83.67 162

OpenMVS_ROB	52.78 18	60.03 255	58.14 261	65.69 255	70.47 283	44.82 256	75.33 187	70.86 257	45.04 295	56.06 281	76.00 277	26.89 321	79.65 215	35.36 310	67.29 262	72.60 307

v148		68.24 161	67.19 164	71.40 172	70.43 284	47.77 227	75.76 183	77.03 196	58.91 124	67.36 154	80.10 217	48.60 116	81.89 175	60.01 143	66.52 268	84.53 132

XXY-MVS		60.68 252	61.67 233	57.70 303	70.43 284	38.45 305	64.19 301	66.47 286	48.05 269	63.22 216	80.86 200	49.28 105	60.47 323	45.25 250	67.28 263	74.19 297

MVSTER		67.16 181	65.58 194	71.88 159	70.37 286	49.70 202	70.25 266	78.45 172	51.52 233	69.16 124	80.37 208	38.45 223	82.50 166	60.19 141	71.46 209	83.44 169

cl-mvsnet_		67.18 179	66.26 184	69.94 201	70.20 287	45.74 248	73.30 223	76.83 199	55.10 193	65.27 189	79.57 228	47.39 132	80.53 205	59.41 150	69.22 243	83.53 168

cl-mvsnet1		67.18 179	66.26 184	69.94 201	70.20 287	45.74 248	73.29 224	76.83 199	55.10 193	65.27 189	79.58 227	47.38 133	80.53 205	59.43 149	69.22 243	83.54 167

tpmvs		58.47 263	56.95 269	63.03 275	70.20 287	41.21 287	67.90 280	67.23 283	49.62 253	54.73 296	70.84 311	34.14 261	76.24 264	36.64 302	61.29 304	71.64 318

anonymousdsp		67.00 185	64.82 202	73.57 127	70.09 290	56.13 114	76.35 170	77.35 192	48.43 264	64.99 200	80.84 202	33.01 273	80.34 208	64.66 104	67.64 261	84.23 141

MVS_0304		58.51 262	57.36 265	61.96 281	70.04 291	41.83 282	69.40 273	65.46 292	50.73 242	53.30 311	74.06 296	22.65 335	70.18 291	42.16 271	68.44 252	73.86 301

MIMVSNet1		55.17 286	54.31 287	57.77 302	70.03 292	32.01 340	65.68 290	64.81 296	49.19 256	46.75 333	76.00 277	25.53 328	64.04 313	28.65 338	62.13 298	77.26 263

CR-MVSNet		59.91 256	57.90 263	65.96 250	69.96 293	52.07 170	65.31 295	63.15 307	42.48 318	59.36 256	74.84 289	35.83 247	70.75 285	45.50 245	64.65 281	75.06 284

RPMNet		61.53 247	58.42 257	70.86 185	69.96 293	52.07 170	65.31 295	81.36 109	43.20 313	59.36 256	70.15 319	35.37 249	85.47 102	36.42 305	64.65 281	75.06 284

cl-mvsnet2		67.47 174	66.45 173	70.54 192	69.85 295	46.49 238	73.85 218	77.35 192	55.07 197	65.51 185	77.92 251	47.64 126	81.10 192	61.58 133	69.32 239	84.01 148

Anonymous20231206		55.10 287	55.30 280	54.48 313	69.81 296	33.94 334	62.91 305	62.13 313	41.08 324	55.18 290	75.65 282	32.75 279	56.59 337	30.32 333	67.86 257	72.91 304

our_test_3		56.49 275	54.42 285	62.68 277	69.51 297	45.48 253	66.08 287	61.49 315	44.11 307	50.73 322	69.60 323	33.05 272	68.15 297	38.38 290	56.86 318	74.40 294

ppachtmachnet_test		58.06 268	55.38 279	66.10 248	69.51 297	48.99 213	68.01 279	66.13 289	44.50 301	54.05 303	70.74 312	32.09 287	72.34 278	36.68 301	56.71 320	76.99 269

diffmvs		70.69 108	70.43 105	71.46 168	69.45 299	48.95 214	72.93 228	78.46 171	57.27 148	71.69 89	83.97 138	51.48 85	77.92 244	70.70 59	77.95 140	87.53 35

IterMVS		62.79 233	61.27 238	67.35 233	69.37 300	52.04 172	71.17 253	68.24 278	52.63 223	59.82 252	76.91 264	37.32 235	72.36 277	52.80 191	63.19 291	77.66 257
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	69.21 301	45.98 246	72.85 230	78.41 175	51.38 236	65.65 183	75.98 280	51.17 89	81.25 188	60.82 136	69.32 239	83.29 174

Patchmtry		57.16 273	56.47 272	59.23 291	69.17 302	34.58 331	62.98 304	63.15 307	44.53 300	56.83 277	74.84 289	35.83 247	68.71 295	40.03 283	60.91 305	74.39 295

CL-MVSNet_2432*1600		61.53 247	60.94 243	63.30 271	68.95 303	36.93 317	67.60 281	72.80 246	55.67 184	59.95 250	76.63 267	45.01 161	72.22 280	39.74 286	62.09 299	80.74 221

V42		68.65 149	67.35 157	72.56 149	68.93 304	50.18 195	72.90 229	79.47 149	56.92 153	69.45 117	80.26 214	46.29 146	82.99 150	64.07 107	67.82 259	84.53 132

test-LLR		58.15 267	58.13 262	58.22 298	68.57 305	44.80 257	65.46 292	57.92 325	50.08 250	55.44 286	69.82 321	32.62 281	57.44 332	49.66 212	73.62 175	72.41 312

test-mter		56.42 277	55.82 276	58.22 298	68.57 305	44.80 257	65.46 292	57.92 325	39.94 331	55.44 286	69.82 321	21.92 338	57.44 332	49.66 212	73.62 175	72.41 312

MVS-HIRNet		45.52 312	44.48 315	48.65 327	68.49 307	34.05 333	59.41 320	44.50 351	27.03 346	37.96 347	50.47 349	26.16 325	64.10 312	26.74 343	59.52 310	47.82 346

dp		51.89 301	51.60 299	52.77 320	68.44 308	32.45 339	62.36 307	54.57 336	44.16 305	49.31 326	67.91 326	28.87 307	56.61 336	33.89 314	54.89 324	69.24 332

PatchT		53.17 297	53.44 294	52.33 322	68.29 309	25.34 353	58.21 322	54.41 337	44.46 302	54.56 298	69.05 324	33.32 270	60.94 321	36.93 297	61.76 302	70.73 324

Patchmatch-RL test		58.16 266	55.49 278	66.15 246	67.92 310	48.89 215	60.66 316	51.07 341	47.86 271	59.36 256	62.71 340	34.02 263	72.27 279	56.41 159	59.40 311	77.30 261

pmmvs-eth3d		58.81 261	56.31 274	66.30 243	67.61 311	52.42 167	72.30 238	64.76 297	43.55 310	54.94 293	74.19 295	28.95 305	72.60 276	43.31 261	57.21 317	73.88 300

PVSNet_0	43.31 20	47.46 311	45.64 314	52.92 319	67.60 312	44.65 259	54.06 334	54.64 335	41.59 322	46.15 334	58.75 342	30.99 291	58.66 328	32.18 319	24.81 350	55.46 344

CHOSEN 280x420		47.83 309	46.36 313	52.24 323	67.37 313	49.78 201	38.91 351	43.11 352	35.00 338	43.27 341	63.30 339	28.95 305	49.19 348	36.53 303	60.80 307	57.76 342

tpmrst		58.24 265	58.70 255	56.84 304	66.97 314	34.32 332	69.57 271	61.14 316	47.17 281	58.58 266	71.60 307	41.28 202	60.41 324	49.20 216	62.84 293	75.78 278

sss		56.17 280	56.57 271	54.96 310	66.93 315	36.32 324	57.94 323	61.69 314	41.67 321	58.64 265	75.32 287	38.72 221	56.25 338	42.04 273	66.19 270	72.31 315

TinyColmap		54.14 288	51.72 298	61.40 285	66.84 316	41.97 280	66.52 284	68.51 276	44.81 296	42.69 342	75.77 281	11.66 350	72.94 275	31.96 320	56.77 319	69.27 331

miper_lstm_enhance		62.03 241	60.88 244	65.49 257	66.71 317	46.25 241	56.29 329	75.70 211	50.68 243	61.27 241	75.48 285	40.21 207	68.03 298	56.31 160	65.25 277	82.18 195

TESTMET0.1,1		55.28 284	54.90 282	56.42 305	66.56 318	43.67 268	65.46 292	56.27 332	39.18 333	53.83 304	67.44 329	24.21 333	55.46 342	48.04 224	73.11 188	70.13 327

D2MVS		62.30 238	60.29 247	68.34 225	66.46 319	48.42 219	65.70 289	73.42 240	47.71 272	58.16 270	75.02 288	30.51 293	77.71 247	53.96 181	71.68 207	78.90 247

MDA-MVSNet-bldmvs		53.87 291	50.81 302	63.05 274	66.25 320	48.58 217	56.93 327	63.82 302	48.09 268	41.22 343	70.48 317	30.34 295	68.00 299	34.24 313	45.92 341	72.57 308

ITE_SJBPF				62.09 280	66.16 321	44.55 262		64.32 300	47.36 277	55.31 288	80.34 210	19.27 342	62.68 317	36.29 306	62.39 297	79.04 244

EPMVS		53.96 289	53.69 292	54.79 312	66.12 322	31.96 341	62.34 308	49.05 344	44.42 303	55.54 284	71.33 309	30.22 296	56.70 335	41.65 277	62.54 296	75.71 279

ADS-MVSNet2		51.33 303	48.76 308	59.07 294	66.02 323	44.60 260	50.90 338	59.76 319	36.90 334	50.74 320	66.18 333	26.38 322	63.11 315	27.17 340	54.76 325	69.50 329

ADS-MVSNet		48.48 308	47.77 310	50.63 325	66.02 323	29.92 345	50.90 338	50.87 343	36.90 334	50.74 320	66.18 333	26.38 322	52.47 346	27.17 340	54.76 325	69.50 329

EU-MVSNet		55.61 283	54.41 286	59.19 293	65.41 325	33.42 336	72.44 236	71.91 251	28.81 344	51.27 316	73.87 297	24.76 331	69.08 294	43.04 265	58.20 315	75.06 284

RPSCF		55.80 282	54.22 289	60.53 288	65.13 326	42.91 275	64.30 300	57.62 327	36.84 336	58.05 271	82.28 169	28.01 311	56.24 339	37.14 296	58.61 314	82.44 193

USDC		56.35 278	54.24 288	62.69 276	64.74 327	40.31 291	65.05 297	73.83 237	43.93 308	47.58 328	77.71 258	15.36 347	75.05 268	38.19 292	61.81 301	72.70 306

JIA-IIPM		51.56 302	47.68 312	63.21 272	64.61 328	50.73 186	47.71 343	58.77 322	42.90 315	48.46 327	51.72 346	24.97 330	70.24 290	36.06 307	53.89 328	68.64 333

Patchmatch-test		49.08 307	48.28 309	51.50 324	64.40 329	30.85 344	45.68 345	48.46 347	35.60 337	46.10 335	72.10 304	34.47 259	46.37 349	27.08 342	60.65 308	77.27 262

TDRefinement		53.44 295	50.72 303	61.60 283	64.31 330	46.96 235	70.89 259	65.27 295	41.78 319	44.61 338	77.98 248	11.52 351	66.36 306	28.57 339	51.59 332	71.49 319

N_pmnet		39.35 318	40.28 319	36.54 334	63.76 331	1.62 364	49.37 341	0.76 364	34.62 339	43.61 340	66.38 332	26.25 324	42.57 352	26.02 345	51.77 331	65.44 336

ambc				65.13 261	63.72 332	37.07 315	47.66 344	78.78 160		54.37 301	71.42 308	11.24 352	80.94 196	45.64 242	53.85 329	77.38 260

KD-MVS_2432*1600		53.45 293	51.50 300	59.30 289	62.82 333	37.14 313	55.33 330	71.79 252	47.34 278	55.09 291	70.52 315	21.91 339	70.45 287	35.72 308	42.97 344	70.31 325

miper_refine_blended		53.45 293	51.50 300	59.30 289	62.82 333	37.14 313	55.33 330	71.79 252	47.34 278	55.09 291	70.52 315	21.91 339	70.45 287	35.72 308	42.97 344	70.31 325

test0.0.03 1		53.32 296	53.59 293	52.50 321	62.81 335	29.45 346	59.51 318	54.11 338	50.08 250	54.40 300	74.31 294	32.62 281	55.92 340	30.50 332	63.95 285	72.15 317

PMMVS		53.96 289	53.26 295	56.04 306	62.60 336	50.92 182	61.17 314	56.09 333	32.81 340	53.51 309	66.84 331	34.04 262	59.93 326	44.14 255	68.18 254	57.27 343

PM-MVS		52.33 299	50.19 304	58.75 296	62.10 337	45.14 255	65.75 288	40.38 353	43.60 309	53.52 308	72.65 301	9.16 356	65.87 309	50.41 205	54.18 327	65.24 337

Gipuma		34.77 321	31.91 325	43.33 332	62.05 338	37.87 308	20.39 354	67.03 284	23.23 350	18.41 354	25.84 353	4.24 359	62.73 316	14.71 350	51.32 333	29.38 351
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
test20.03		53.87 291	54.02 290	53.41 317	61.47 339	28.11 348	61.30 312	59.21 320	51.34 238	52.09 314	77.43 259	33.29 271	58.55 329	29.76 335	60.27 309	73.58 302

pmmvs5		56.47 276	55.68 277	58.86 295	61.41 340	36.71 319	66.37 285	62.75 309	40.38 328	53.70 305	76.62 268	34.56 256	67.05 302	40.02 284	65.27 276	72.83 305

MDA-MVSNet_test_wron		50.71 305	48.95 306	56.00 308	61.17 341	41.84 281	51.90 337	56.45 329	40.96 325	44.79 337	67.84 327	30.04 299	55.07 344	36.71 300	50.69 335	71.11 323

YYNet1		50.73 304	48.96 305	56.03 307	61.10 342	41.78 283	51.94 336	56.44 330	40.94 326	44.84 336	67.80 328	30.08 298	55.08 343	36.77 298	50.71 334	71.22 320

CMPMVS	42.80 21	57.81 270	55.97 275	63.32 270	60.98 343	47.38 232	64.66 299	69.50 268	32.06 341	46.83 332	77.80 255	29.50 302	71.36 282	48.68 219	73.75 173	71.21 321
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
UnsupCasMVSNet_bld		50.07 306	48.87 307	53.66 315	60.97 344	33.67 335	57.62 325	64.56 299	39.47 332	47.38 329	64.02 338	27.47 315	59.32 327	34.69 312	43.68 343	67.98 334

testgi		51.90 300	52.37 297	50.51 326	60.39 345	23.55 355	58.42 321	58.15 323	49.03 258	51.83 315	79.21 237	22.39 336	55.59 341	29.24 337	62.64 294	72.40 314

UnsupCasMVSNet_eth		53.16 298	52.47 296	55.23 309	59.45 346	33.39 337	59.43 319	69.13 272	45.98 289	50.35 325	72.32 303	29.30 304	58.26 330	42.02 274	44.30 342	74.05 298

new-patchmatchnet		47.56 310	47.73 311	47.06 328	58.81 347	9.37 361	48.78 342	59.21 320	43.28 311	44.22 339	68.66 325	25.67 327	57.20 334	31.57 328	49.35 337	74.62 293

FPMVS		42.18 315	41.11 318	45.39 329	58.03 348	41.01 289	49.50 340	53.81 340	30.07 343	33.71 348	64.03 336	11.69 349	52.08 347	14.01 351	55.11 323	43.09 348

DIV-MVS_2432*1600		55.22 285	53.89 291	59.21 292	57.80 349	27.47 349	57.75 324	74.32 231	47.38 276	50.90 319	70.00 320	28.45 309	70.30 289	40.44 281	57.92 316	79.87 234

new_pmnet		34.13 322	34.29 323	33.64 335	52.63 350	18.23 359	44.43 348	33.90 356	22.81 351	30.89 349	53.18 344	10.48 354	35.72 356	20.77 348	39.51 346	46.98 347

pmmvs3		44.92 313	41.95 317	53.86 314	52.58 351	43.55 269	62.11 309	46.90 350	26.05 348	40.63 344	60.19 341	11.08 353	57.91 331	31.83 325	46.15 340	60.11 339

DSMNet-mixed		39.30 319	38.72 320	41.03 333	51.22 352	19.66 357	45.53 346	31.35 357	15.83 355	39.80 346	67.42 330	22.19 337	45.13 350	22.43 346	52.69 330	58.31 341

LF4IMVS		42.95 314	42.26 316	45.04 330	48.30 353	32.50 338	54.80 332	48.49 346	28.03 345	40.51 345	70.16 318	9.24 355	43.89 351	31.63 326	49.18 338	58.72 340

wuyk23d		13.32 328	12.52 331	15.71 340	47.54 354	26.27 350	31.06 353	1.98 363	4.93 358	5.18 360	1.94 360	0.45 365	18.54 358	6.81 358	12.83 355	2.33 356

LCM-MVSNet		40.30 317	35.88 322	53.57 316	42.24 355	29.15 347	45.21 347	60.53 318	22.23 352	28.02 350	50.98 348	3.72 361	61.78 320	31.22 331	38.76 348	69.78 328

E-PMN		23.77 324	22.73 328	26.90 338	42.02 356	20.67 356	42.66 349	35.70 354	17.43 353	10.28 358	25.05 354	6.42 358	42.39 353	10.28 354	14.71 353	17.63 352

EMVS		22.97 325	21.84 329	26.36 339	40.20 357	19.53 358	41.95 350	34.64 355	17.09 354	9.73 359	22.83 355	7.29 357	42.22 354	9.18 356	13.66 354	17.32 353

ANet_high		41.38 316	37.47 321	53.11 318	39.73 358	24.45 354	56.94 326	69.69 264	47.65 273	26.04 351	52.32 345	12.44 348	62.38 318	21.80 347	10.61 356	72.49 309

PMMVS2		27.40 323	25.91 326	31.87 337	39.46 359	6.57 362	31.17 352	28.52 358	23.96 349	20.45 353	48.94 350	4.20 360	37.94 355	16.51 349	19.97 351	51.09 345

PMVS	28.69 22	36.22 320	33.29 324	45.02 331	36.82 360	35.98 326	54.68 333	48.74 345	26.31 347	21.02 352	51.61 347	2.88 363	60.10 325	9.99 355	47.58 339	38.99 350
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MVE	17.77 23	21.41 326	17.77 330	32.34 336	34.34 361	25.44 352	16.11 355	24.11 359	11.19 356	13.22 356	31.92 351	1.58 364	30.95 357	10.47 353	17.03 352	40.62 349
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
DeepMVS_CX				12.03 341	17.97 362	10.91 360		10.60 362	7.46 357	11.07 357	28.36 352	3.28 362	11.29 359	8.01 357	9.74 358	13.89 354

tmp_tt		9.43 329	11.14 332	4.30 342	2.38 363	4.40 363	13.62 356	16.08 361	0.39 359	15.89 355	13.06 356	15.80 346	5.54 360	12.63 352	10.46 357	2.95 355

testmvs		4.52 332	6.03 335	0.01 344	0.01 364	0.00 366	53.86 335	0.00 365	0.01 360	0.04 361	0.27 361	0.00 367	0.00 361	0.04 359	0.00 359	0.03 358

test123		4.73 331	6.30 334	0.02 343	0.01 364	0.01 365	56.36 328	0.00 365	0.01 360	0.04 361	0.21 362	0.01 366	0.00 361	0.03 360	0.00 359	0.04 357

uanet_test		0.00 334	0.00 337	0.00 345	0.00 366	0.00 366	0.00 357	0.00 365	0.00 362	0.00 363	0.00 363	0.00 367	0.00 361	0.00 361	0.00 359	0.00 359

cdsmvs_eth3d_5k		17.50 327	23.34 327	0.00 345	0.00 366	0.00 366	0.00 357	78.63 164	0.00 362	0.00 363	82.18 170	49.25 106	0.00 361	0.00 361	0.00 359	0.00 359

pcd_1.5k_mvsjas		3.92 333	5.23 336	0.00 345	0.00 366	0.00 366	0.00 357	0.00 365	0.00 362	0.00 363	0.00 363	47.05 137	0.00 361	0.00 361	0.00 359	0.00 359

sosnet-low-res		0.00 334	0.00 337	0.00 345	0.00 366	0.00 366	0.00 357	0.00 365	0.00 362	0.00 363	0.00 363	0.00 367	0.00 361	0.00 361	0.00 359	0.00 359

sosnet		0.00 334	0.00 337	0.00 345	0.00 366	0.00 366	0.00 357	0.00 365	0.00 362	0.00 363	0.00 363	0.00 367	0.00 361	0.00 361	0.00 359	0.00 359

uncertanet		0.00 334	0.00 337	0.00 345	0.00 366	0.00 366	0.00 357	0.00 365	0.00 362	0.00 363	0.00 363	0.00 367	0.00 361	0.00 361	0.00 359	0.00 359

Regformer		0.00 334	0.00 337	0.00 345	0.00 366	0.00 366	0.00 357	0.00 365	0.00 362	0.00 363	0.00 363	0.00 367	0.00 361	0.00 361	0.00 359	0.00 359

ab-mvs-re		6.49 330	8.65 333	0.00 345	0.00 366	0.00 366	0.00 357	0.00 365	0.00 362	0.00 363	77.89 253	0.00 367	0.00 361	0.00 361	0.00 359	0.00 359

uanet		0.00 334	0.00 337	0.00 345	0.00 366	0.00 366	0.00 357	0.00 365	0.00 362	0.00 363	0.00 363	0.00 367	0.00 361	0.00 361	0.00 359	0.00 359

test_241102_TWO								86.73 14	64.18 33	84.26 4	91.84 6	65.19 4	90.83 2	78.63 12	90.70 5	87.65 30

test_0728_THIRD									65.04 20	83.82 6	92.00 3	64.69 8	90.75 5	79.48 4	90.63 6	88.09 15

GSMVS																78.05 253

sam_mvs1												34.74 255				78.05 253

sam_mvs												33.43 269

MTGPA								80.97 125

test_post1							68.67 276				3.64 358	32.39 285	69.49 292	44.17 253

test_post											3.55 359	33.90 264	66.52 305

patchmatchnet-post											64.03 336	34.50 257	74.27 272

MTMP							86.03 17	17.08 360

test9_res														75.28 28	88.31 33	83.81 154

agg_prior2														73.09 46	87.93 40	84.33 137

test_prior4						62.51 17	82.08 79

test_prior2							81.75 81		60.37 94	75.01 40	89.06 56	56.22 35		72.19 49	88.96 24

旧先验2							76.08 176		45.32 294	76.55 31			65.56 310	58.75 151

新几何2							76.12 174

无先验							79.66 114	74.30 232	48.40 265				80.78 201	53.62 183		79.03 245

原ACMM2							79.02 121

testdata2													72.18 281	46.95 232

segment_acmp												54.23 54

testdata1							72.65 231		60.50 91

plane_prior5								84.01 50					87.21 51	68.16 72	80.58 101	84.65 130

plane_prior4											86.10 100

plane_prior3						56.09 115			63.92 37	69.27 120

plane_prior2							84.22 38		64.52 26

plane_prior						56.31 109	83.58 53		63.19 47						80.48 104

n2								0.00 365

nn								0.00 365

door-mid								47.19 349

test11								83.47 65

door								47.60 348

HQP5-MVS						54.94 133

BP-MVS														67.04 83

HQP4-MVS										67.85 144			86.93 60			84.32 138

HQP3-MVS								83.90 54							80.35 107

HQP2-MVS												45.46 153

MDTV_nov1_ep13_2view						25.89 351	61.22 313		40.10 329	51.10 317		32.97 274		38.49 289		78.61 248

ACMMP++_ref															74.07 171

ACMMP++															72.16 202

Test By Simon												48.33 118

High-resolution multi-view benchmark