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 6	80.14 6	79.10 16	88.17 1	64.80 1	86.59 9	83.70 59	65.37 15	78.78 20	90.64 16	58.63 19	87.24 46	79.00 7	90.37 9	85.26 111

CNVR-MVS		79.84 7	79.97 7	79.45 7	87.90 2	62.17 20	84.37 33	85.03 32	66.96 5	77.58 26	90.06 34	59.47 16	89.13 17	78.67 9	89.73 15	87.03 48

test_0728_SECOND				79.19 11	87.82 3	59.11 61	87.85 3	87.15 6					90.84 1	78.66 10	90.61 6	87.62 30

IU-MVS					87.77 4	59.15 59		85.53 24	53.93 204	84.64 2				79.07 6	90.87 3	88.37 7

test_241102_ONE					87.77 4	58.90 67		86.78 11	64.20 31	85.97 1	91.34 9	66.87 2	90.78 3

MSP-MVS		80.84 2	81.64 1	78.42 32	87.75 6	59.07 62	87.85 3	85.03 32	64.26 29	83.82 5	92.00 3	64.82 5	90.75 4	78.66 10	90.61 6	85.45 102

test0726					87.75 6	59.07 62	87.86 2	86.83 9	64.26 29	84.19 4	91.92 5	64.82 5

test_part2					87.58 8	60.47 45				83.42 8

OPU-MVS				79.83 4	87.54 9	60.93 38	87.82 5				89.89 40	67.01 1	90.33 7	73.16 43	91.15 2	88.23 10

NCCC		78.58 14	78.31 18	79.39 8	87.51 10	62.61 16	85.20 28	84.42 41	66.73 9	74.67 46	89.38 48	55.30 41	89.18 16	74.19 34	87.34 43	86.38 61

DPE-MVS		80.56 3	80.98 3	79.29 10	87.27 11	60.56 44	85.71 23	86.42 15	63.28 42	83.27 9	91.83 7	64.96 4	90.47 6	76.41 23	89.67 17	86.84 52

testtj		78.47 17	78.43 17	78.61 28	86.82 12	60.67 42	86.07 15	85.38 26	62.12 65	78.65 21	90.29 30	55.76 37	89.31 14	73.55 41	87.22 44	85.84 83

region2R		77.67 29	77.18 32	79.15 13	86.76 13	62.95 8	86.29 11	84.16 47	62.81 54	73.30 64	90.58 18	49.90 95	88.21 30	73.78 37	87.03 47	86.29 73

ACMMPR		77.71 27	77.23 31	79.16 12	86.75 14	62.93 9	86.29 11	84.24 45	62.82 52	73.55 62	90.56 19	49.80 97	88.24 29	74.02 35	87.03 47	86.32 70

HFP-MVS		78.01 25	77.65 26	79.10 16	86.71 15	62.81 10	86.29 11	84.32 43	62.82 52	73.96 53	90.50 21	53.20 65	88.35 26	74.02 35	87.05 45	86.13 75

#test#		77.83 26	77.41 29	79.10 16	86.71 15	62.81 10	85.69 24	84.32 43	61.61 75	73.96 53	90.50 21	53.20 65	88.35 26	73.68 38	87.05 45	86.13 75

MCST-MVS		77.48 31	77.45 28	77.54 46	86.67 17	58.36 75	83.22 52	86.93 7	56.91 148	74.91 42	88.19 61	59.15 17	87.68 41	73.67 39	87.45 42	86.57 59

APDe-MVS		80.16 5	80.59 4	78.86 24	86.64 18	60.02 48	88.12 1	86.42 15	62.94 48	82.40 10	92.12 2	59.64 14	89.76 10	78.70 8	88.32 31	86.79 55

SMA-MVS		80.28 4	80.39 5	79.95 3	86.60 19	61.95 22	86.33 10	85.75 22	62.49 59	82.20 11	92.28 1	56.53 30	89.70 11	79.85 3	91.48 1	88.19 11

DP-MVS Recon		72.15 88	70.73 96	76.40 62	86.57 20	57.99 80	81.15 87	82.96 78	57.03 145	66.78 155	85.56 105	44.50 160	88.11 32	51.77 191	80.23 105	83.10 176

MP-MVS		78.35 19	78.26 20	78.64 27	86.54 21	63.47 5	86.02 17	83.55 62	63.89 36	73.60 61	90.60 17	54.85 46	86.72 63	77.20 17	88.06 36	85.74 91
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
mPP-MVS		76.54 40	75.93 43	78.34 35	86.47 22	63.50 4	85.74 22	82.28 87	62.90 49	71.77 82	90.26 31	46.61 141	86.55 71	71.71 50	85.66 59	84.97 119

APD-MVS		78.02 23	78.04 24	77.98 40	86.44 23	60.81 40	85.52 25	84.36 42	60.61 87	79.05 18	90.30 29	55.54 40	88.32 28	73.48 42	87.03 47	84.83 122
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
ZNCC-MVS		78.82 11	78.67 15	79.30 9	86.43 24	62.05 21	86.62 8	86.01 18	63.32 41	75.08 37	90.47 24	53.96 55	88.68 22	76.48 22	89.63 19	87.16 45

ETH3 D test6400		79.14 9	79.32 8	78.61 28	86.34 25	58.11 78	84.65 31	87.66 4	58.56 128	78.87 19	89.54 45	63.67 9	89.57 12	74.60 32	89.98 12	88.14 12

XVS		77.17 34	76.56 38	79.00 19	86.32 26	62.62 14	85.83 19	83.92 51	64.55 23	72.17 79	90.01 38	47.95 118	88.01 34	71.55 52	86.74 52	86.37 65

X-MVStestdata		70.21 114	67.28 154	79.00 19	86.32 26	62.62 14	85.83 19	83.92 51	64.55 23	72.17 79	6.49 346	47.95 118	88.01 34	71.55 52	86.74 52	86.37 65

DVP-MVS		81.06 1	81.40 2	80.02 1	86.21 28	62.73 12	86.09 14	86.83 9	65.51 14	83.81 7	90.51 20	63.71 8	89.23 15	81.51 1	88.44 27	88.09 14

114514_t		70.83 101	69.56 110	74.64 94	86.21 28	54.63 131	82.34 68	81.81 96	48.22 258	63.01 211	85.83 101	40.92 198	87.10 52	57.91 146	79.79 107	82.18 190

xxxxxxxxxxxxxcwj		78.35 19	78.22 21	78.76 25	86.17 30	61.30 31	83.98 43	79.66 139	59.00 119	79.16 15	90.75 14	57.96 21	87.09 53	77.08 19	90.18 10	87.87 20

save fliter					86.17 30	61.30 31	83.98 43	79.66 139	59.00 119

DeepC-MVS_fast	68.24 3	77.25 33	76.63 37	79.12 15	86.15 32	60.86 39	84.71 30	84.85 37	61.98 71	73.06 68	88.88 56	53.72 59	89.06 18	68.27 66	88.04 37	87.42 38
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 39	76.06 41	78.88 23	86.14 33	62.73 12	82.55 65	83.74 58	61.71 73	72.45 78	90.34 28	48.48 114	88.13 31	72.32 46	86.85 50	85.78 85

zzz-MVS		77.61 30	77.36 30	78.35 33	86.08 34	63.57 2	83.37 50	80.97 119	65.13 17	75.77 32	90.88 12	48.63 110	86.66 65	77.23 15	88.17 33	84.81 123

MTAPA		76.90 37	76.42 39	78.35 33	86.08 34	63.57 2	74.92 194	80.97 119	65.13 17	75.77 32	90.88 12	48.63 110	86.66 65	77.23 15	88.17 33	84.81 123

GST-MVS		78.14 22	77.85 25	78.99 21	86.05 36	61.82 25	85.84 18	85.21 28	63.56 40	74.29 50	90.03 36	52.56 68	88.53 24	74.79 30	88.34 29	86.63 58

CP-MVS		77.12 35	76.68 36	78.43 31	86.05 36	63.18 7	87.55 7	83.45 65	62.44 61	72.68 72	90.50 21	48.18 116	87.34 45	73.59 40	85.71 58	84.76 127

SR-MVS		76.13 44	75.70 45	77.40 50	85.87 38	61.20 33	85.52 25	82.19 88	59.99 103	75.10 36	90.35 26	47.66 122	86.52 72	71.64 51	82.99 73	84.47 134

新几何1				70.76 181	85.66 39	61.13 35		66.43 277	44.68 287	70.29 93	86.64 81	41.29 194	75.23 260	49.72 204	81.75 88	75.93 268

MG-MVS		73.96 66	73.89 61	74.16 104	85.65 40	49.69 197	81.59 81	81.29 109	61.45 76	71.05 88	88.11 62	51.77 79	87.73 40	61.05 128	83.09 71	85.05 116

1121		68.53 151	67.16 160	72.63 144	85.64 41	61.14 34	73.95 208	66.46 276	44.61 288	70.28 94	86.68 80	41.42 192	80.78 194	53.62 176	81.79 86	75.97 266

TEST9					85.58 42	61.59 27	81.62 79	81.26 110	55.65 178	74.93 40	88.81 57	53.70 60	84.68 111

train_agg		76.27 43	76.15 40	76.64 60	85.58 42	61.59 27	81.62 79	81.26 110	55.86 171	74.93 40	88.81 57	53.70 60	84.68 111	75.24 28	88.33 30	83.65 160

ACMMP_NAP		78.77 13	78.78 13	78.74 26	85.44 44	61.04 36	83.84 45	85.16 29	62.88 50	78.10 23	91.26 10	52.51 69	88.39 25	79.34 5	90.52 8	86.78 56

test_8					85.40 45	60.96 37	81.54 82	81.18 113	55.86 171	74.81 43	88.80 59	53.70 60	84.45 116

原ACMM1				74.69 90	85.39 46	59.40 54		83.42 66	51.47 227	70.27 95	86.61 83	48.61 112	86.51 73	53.85 175	87.96 38	78.16 243

CDPH-MVS		76.31 42	75.67 46	78.22 36	85.35 47	59.14 60	81.31 85	84.02 48	56.32 161	74.05 51	88.98 54	53.34 64	87.92 36	69.23 63	88.42 28	87.59 31

ACMMP		76.02 45	75.33 48	78.07 37	85.20 48	61.91 23	85.49 27	84.44 40	63.04 46	69.80 106	89.74 44	45.43 152	87.16 50	72.01 49	82.87 78	85.14 112
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 46	76.01 42	75.72 72	85.04 49	59.96 49	81.44 83	81.04 116	56.14 167	74.68 44	88.90 55	53.91 56	84.04 123	75.01 29	87.92 40	83.16 175

agg_prior					85.04 49	59.96 49		81.04 116		74.68 44			84.04 123

HPM-MVS		77.28 32	76.85 34	78.54 30	85.00 51	60.81 40	82.91 57	85.08 30	62.57 57	73.09 67	89.97 39	50.90 91	87.48 44	75.30 26	86.85 50	87.33 42
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
MP-MVS-pluss		78.35 19	78.46 16	78.03 39	84.96 52	59.52 53	82.93 56	85.39 25	62.15 64	76.41 30	91.51 8	52.47 71	86.78 62	80.66 2	89.64 18	87.80 23
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
TSAR-MVS + MP.		78.44 18	78.28 19	78.90 22	84.96 52	61.41 29	84.03 41	83.82 57	59.34 116	79.37 14	89.76 43	59.84 12	87.62 43	76.69 21	86.74 52	87.68 27
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
AdaColmap		69.99 118	68.66 126	73.97 107	84.94 54	57.83 82	82.63 63	78.71 156	56.28 163	64.34 198	84.14 126	41.57 187	87.06 55	46.45 227	78.88 123	77.02 258

DP-MVS		65.68 198	63.66 206	71.75 157	84.93 55	56.87 100	80.74 91	73.16 236	53.06 209	59.09 251	82.35 160	36.79 236	85.94 86	32.82 307	69.96 223	72.45 302

DeepPCF-MVS	69.58 1	79.03 10	79.00 11	79.13 14	84.92 56	60.32 46	83.03 54	85.33 27	62.86 51	80.17 12	90.03 36	61.76 10	88.95 19	74.21 33	88.67 26	88.12 13

CPTT-MVS		72.78 76	72.08 79	74.87 88	84.88 57	61.41 29	84.15 38	77.86 176	55.27 183	67.51 147	88.08 64	41.93 181	81.85 170	69.04 65	80.01 106	81.35 204

test12				77.76 42	84.52 58	58.41 74		83.36 69		72.93 70		54.61 48	88.05 33		88.12 35	86.81 54

SD-MVS		77.70 28	77.62 27	77.93 41	84.47 59	61.88 24	84.55 32	83.87 55	60.37 92	79.89 13	89.38 48	54.97 43	85.58 91	76.12 25	84.94 61	86.33 68
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 62	73.46 68	76.80 56	84.45 60	59.04 64	83.65 47	81.05 115	60.15 100	70.43 91	89.84 42	41.09 197	85.59 90	67.61 73	82.90 77	85.77 88

test_prior3		76.89 38	76.96 33	76.69 57	84.20 61	57.27 90	81.75 76	84.88 35	60.37 92	75.01 38	89.06 51	56.22 34	86.43 75	72.19 47	88.96 23	86.38 61

test_prior				76.69 57	84.20 61	57.27 90		84.88 35					86.43 75			86.38 61

CSCG		76.92 36	76.75 35	77.41 48	83.96 63	59.60 52	82.95 55	86.50 14	60.78 85	75.27 35	84.83 113	60.76 11	86.56 70	67.86 70	87.87 41	86.06 78

DeepC-MVS	69.38 2	78.56 16	78.14 22	79.83 4	83.60 64	61.62 26	84.17 37	86.85 8	63.23 43	73.84 58	90.25 32	57.68 25	89.96 9	74.62 31	89.03 21	87.89 18
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 73	72.93 72	73.76 112	83.58 65	51.66 168	78.75 117	77.66 180	67.75 4	72.61 74	89.42 46	49.82 96	83.29 139	53.61 178	83.14 70	86.32 70

LFMVS		71.78 91	71.59 82	72.32 151	83.40 66	46.38 232	79.75 107	71.08 245	64.18 32	72.80 71	88.64 60	42.58 174	83.72 131	57.41 149	84.49 65	86.86 51

test222					83.14 67	58.68 70	72.57 229	63.45 293	41.78 308	67.56 146	86.12 93	37.13 232			78.73 128	74.98 279

9.14			78.75 14		83.10 68		84.15 38	88.26 2	59.90 104	78.57 22	90.36 25	57.51 27	86.86 59	77.39 14	89.52 20

ETH3D-3000-0.1		78.58 14	78.91 12	77.61 44	83.06 69	57.86 81	84.14 40	88.31 1	60.37 92	79.14 17	90.35 26	57.76 24	87.00 56	77.16 18	89.90 13	87.97 17

旧先验1					83.04 70	53.15 147		67.52 268			87.85 66	44.08 163			80.76 93	78.03 248

MSLP-MVS++		73.77 69	73.47 67	74.66 92	83.02 71	59.29 57	82.30 72	81.88 93	59.34 116	71.59 85	86.83 74	45.94 145	83.65 133	65.09 94	85.22 60	81.06 211

SteuartSystems-ACMMP		79.48 8	79.31 9	79.98 2	83.01 72	62.18 19	87.60 6	85.83 20	66.69 10	78.03 25	90.98 11	54.26 51	90.06 8	78.42 13	89.02 22	87.69 26
Skip Steuart: Steuart Systems R&D Blog.
MVS_111021_HR		74.02 65	73.46 68	75.69 74	83.01 72	60.63 43	77.29 147	78.40 171	61.18 80	70.58 90	85.97 98	54.18 53	84.00 127	67.52 74	82.98 75	82.45 187

SF-MVS		78.82 11	79.22 10	77.60 45	82.88 74	57.83 82	84.99 29	88.13 3	61.86 72	79.16 15	90.75 14	57.96 21	87.09 53	77.08 19	90.18 10	87.87 20

VDDNet		71.81 90	71.33 88	73.26 132	82.80 75	47.60 223	78.74 118	75.27 212	59.59 113	72.94 69	89.40 47	41.51 191	83.91 128	58.75 144	82.99 73	88.26 8

abl_6		74.34 60	73.50 65	76.86 55	82.43 76	60.16 47	83.48 49	81.86 94	58.81 123	73.95 55	89.86 41	41.87 182	86.62 67	67.98 69	81.23 91	83.80 154

3Dnovator+	66.72 4	75.84 48	74.57 55	79.66 6	82.40 77	59.92 51	85.83 19	86.32 17	66.92 8	67.80 143	89.24 50	42.03 179	89.38 13	64.07 101	86.50 55	89.69 1

APD-MVS_3200maxsize		74.96 52	74.39 57	76.67 59	82.20 78	58.24 77	83.67 46	83.29 72	58.41 130	73.71 59	90.14 33	45.62 147	85.99 83	69.64 59	82.85 79	85.78 85

ETH3D cwj APD-0.16		78.02 23	78.13 23	77.71 43	82.10 79	58.65 71	82.72 61	87.55 5	58.33 133	78.05 24	90.06 34	58.35 20	87.65 42	76.15 24	89.86 14	86.82 53

PVSNet_Blended_VisFu		71.45 97	70.39 101	74.65 93	82.01 80	58.82 68	79.93 103	80.35 131	55.09 188	65.82 175	82.16 167	49.17 104	82.64 158	60.34 133	78.62 130	82.50 186

TSAR-MVS + GP.		74.90 53	74.15 59	77.17 52	82.00 81	58.77 69	81.80 75	78.57 160	58.58 126	74.32 49	84.51 122	55.94 36	87.22 47	67.11 77	84.48 66	85.52 98

API-MVS		72.17 86	71.41 85	74.45 99	81.95 82	57.22 92	84.03 41	80.38 129	59.89 107	68.40 126	82.33 161	49.64 98	87.83 39	51.87 189	84.16 68	78.30 241

MAR-MVS		71.51 95	70.15 105	75.60 77	81.84 83	59.39 55	81.38 84	82.90 81	54.90 194	68.08 135	78.70 233	47.73 120	85.51 94	51.68 193	84.17 67	81.88 196
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 78	71.80 80	75.13 84	81.72 84	53.42 144	79.91 104	83.28 73	59.14 118	66.31 165	85.90 99	51.86 78	86.06 80	57.45 148	80.62 94	85.91 81

VDD-MVS		72.50 79	72.09 78	73.75 114	81.58 85	49.69 197	77.76 135	77.63 181	63.21 44	73.21 65	89.02 53	42.14 178	83.32 138	61.72 123	82.50 82	88.25 9

PS-MVSNAJ		70.51 107	69.70 109	72.93 136	81.52 86	55.79 115	74.92 194	79.00 150	55.04 192	69.88 104	78.66 234	47.05 134	82.19 165	61.61 124	79.58 111	80.83 214

testdata				64.66 258	81.52 86	52.93 150		65.29 283	46.09 277	73.88 57	87.46 69	38.08 222	66.26 297	53.31 181	78.48 131	74.78 283

CHOSEN 1792x2688		65.08 208	62.84 215	71.82 156	81.49 88	56.26 106	66.32 279	74.20 227	40.53 316	63.16 210	78.65 235	41.30 193	77.80 239	45.80 233	74.09 165	81.40 201

HQP_MVS		74.31 61	73.73 63	76.06 65	81.41 89	56.31 103	84.22 35	84.01 49	64.52 25	69.27 114	86.10 94	45.26 156	87.21 48	68.16 67	80.58 96	84.65 128

plane_prior7					81.41 89	55.96 112

DPM-MVS		75.47 50	75.00 49	76.88 54	81.38 91	59.16 58	79.94 102	85.71 23	56.59 156	72.46 76	86.76 75	56.89 28	87.86 38	66.36 82	88.91 25	83.64 161

CANet		76.46 41	75.93 43	78.06 38	81.29 92	57.53 87	82.35 67	83.31 71	67.78 3	70.09 96	86.34 90	54.92 44	88.90 20	72.68 45	84.55 64	87.76 25

Vis-MVSNet		72.18 85	71.37 87	74.61 95	81.29 92	55.41 124	80.90 88	78.28 173	60.73 86	69.23 117	88.09 63	44.36 162	82.65 157	57.68 147	81.75 88	85.77 88
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
plane_prior1					81.27 94

xiu_mvs_v2_base		70.52 106	69.75 107	72.84 138	81.21 95	55.63 119	75.11 188	78.92 151	54.92 193	69.96 103	79.68 219	47.00 138	82.09 167	61.60 125	79.37 114	80.81 215

plane_prior6					81.20 96	56.24 107						45.26 156

PAPR		71.72 93	70.82 95	74.41 100	81.20 96	51.17 170	79.55 111	83.33 70	55.81 174	66.93 154	84.61 118	50.95 89	86.06 80	55.79 158	79.20 119	86.00 79

PLC	56.13 14	65.09 207	63.21 211	70.72 183	81.04 98	54.87 130	78.57 122	77.47 183	48.51 254	55.71 274	81.89 171	33.71 257	79.71 207	41.66 269	70.37 215	77.58 250
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
NP-MVS					80.98 99	56.05 111					85.54 107

OPM-MVS		74.73 56	74.25 58	76.19 64	80.81 100	59.01 65	82.60 64	83.64 60	63.74 38	72.52 75	87.49 68	47.18 132	85.88 87	69.47 61	80.78 92	83.66 159

HQP-NCC					80.66 101		82.31 69		62.10 66	67.85 138

ACMP_Plane					80.66 101		82.31 69		62.10 66	67.85 138

HQP-MVS		73.45 70	72.80 73	75.40 79	80.66 101	54.94 127	82.31 69	83.90 53	62.10 66	67.85 138	85.54 107	45.46 150	86.93 57	67.04 78	80.35 102	84.32 136

PHI-MVS		75.87 47	75.36 47	77.41 48	80.62 104	55.91 114	84.28 34	85.78 21	56.08 169	73.41 63	86.58 85	50.94 90	88.54 23	70.79 56	89.71 16	87.79 24

ACMM	61.98 7	70.80 103	69.73 108	74.02 105	80.59 105	58.59 72	82.68 62	82.02 92	55.46 181	67.18 151	84.39 124	38.51 215	83.17 142	60.65 130	76.10 153	80.30 220
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
Regformer-1		75.47 50	74.93 52	77.09 53	80.43 106	57.70 85	79.50 112	82.13 89	67.84 1	75.73 34	80.75 197	56.50 31	86.07 79	71.07 55	80.38 100	87.50 34

Regformer-2		75.63 49	74.99 50	77.54 46	80.43 106	58.32 76	79.50 112	82.92 79	67.84 1	75.94 31	80.75 197	55.73 38	86.80 60	71.44 54	80.38 100	87.50 34

Anonymous20231211		69.28 133	68.47 129	71.73 158	80.28 108	47.18 227	79.98 101	82.37 86	54.61 196	67.24 150	84.01 130	39.43 206	82.41 163	55.45 162	72.83 186	85.62 96

ACMP	63.53 6	72.30 83	71.20 91	75.59 78	80.28 108	57.54 86	82.74 60	82.84 83	60.58 88	65.24 186	86.18 92	39.25 208	86.03 82	66.95 80	76.79 149	83.22 170
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
LPG-MVS_test		72.74 77	71.74 81	75.76 70	80.22 110	57.51 88	82.55 65	83.40 67	61.32 77	66.67 157	87.33 71	39.15 210	86.59 68	67.70 71	77.30 142	83.19 172

LGP-MVS_train				75.76 70	80.22 110	57.51 88		83.40 67	61.32 77	66.67 157	87.33 71	39.15 210	86.59 68	67.70 71	77.30 142	83.19 172

WR-MVS		68.47 152	68.47 129	68.44 218	80.20 112	39.84 286	73.75 214	76.07 201	64.68 22	68.11 134	83.63 138	50.39 94	79.14 221	49.78 201	69.66 231	86.34 67

Anonymous20240529		69.91 120	69.02 120	72.56 145	80.19 113	47.65 221	77.56 139	80.99 118	55.45 182	69.88 104	86.76 75	39.24 209	82.18 166	54.04 172	77.10 144	87.85 22

Anonymous202405211		66.84 183	65.99 181	69.40 206	80.19 113	42.21 272	71.11 250	71.31 244	58.80 124	67.90 136	86.39 89	29.83 292	79.65 208	49.60 207	78.78 126	86.33 68

BH-RMVSNet		68.81 140	67.42 148	72.97 135	80.11 115	52.53 156	74.26 203	76.29 198	58.48 129	68.38 127	84.20 125	42.59 173	83.83 129	46.53 226	75.91 154	82.56 183

test_0402		63.25 224	61.01 236	69.96 194	80.00 116	54.37 133	76.86 157	72.02 242	54.58 198	58.71 254	80.79 196	35.00 245	84.36 117	26.41 333	64.71 273	71.15 314

HyFIR lowres test		65.67 199	63.01 213	73.67 117	79.97 117	55.65 118	69.07 269	75.52 209	42.68 306	63.53 206	77.95 242	40.43 199	81.64 173	46.01 231	71.91 199	83.73 155

EIA-MVS		71.78 91	70.60 97	75.30 82	79.85 118	53.54 141	77.27 148	83.26 74	57.92 136	66.49 160	79.39 225	52.07 76	86.69 64	60.05 135	79.14 121	85.66 93

Regformer-3		73.89 67	73.28 70	75.71 73	79.75 119	55.48 123	78.54 124	79.93 135	66.58 11	73.62 60	80.30 205	54.87 45	84.54 114	69.09 64	76.84 147	87.10 47

Regformer-4		74.25 63	73.48 66	76.57 61	79.75 119	56.54 102	78.54 124	81.49 103	66.93 7	73.90 56	80.30 205	53.84 58	85.98 84	69.76 58	76.84 147	87.17 44

BH-untuned		68.27 154	67.29 153	71.21 173	79.74 121	53.22 146	76.06 172	77.46 185	57.19 143	66.10 167	81.61 176	45.37 154	83.50 136	45.42 241	76.68 151	76.91 262

VNet		69.68 125	70.19 104	68.16 220	79.73 122	41.63 279	70.53 256	77.38 186	60.37 92	70.69 89	86.63 82	51.08 87	77.09 248	53.61 178	81.69 90	85.75 90

LS3D		64.71 210	62.50 219	71.34 171	79.72 123	55.71 116	79.82 105	74.72 221	48.50 255	56.62 269	84.62 117	33.59 260	82.34 164	29.65 325	75.23 159	75.97 266

BH-w/o		66.85 182	65.83 183	69.90 198	79.29 124	52.46 159	74.66 199	76.65 196	54.51 200	64.85 193	78.12 240	45.59 149	82.95 148	43.26 256	75.54 157	74.27 288

1112_ss		64.00 216	63.36 210	65.93 246	79.28 125	42.58 269	71.35 243	72.36 241	46.41 274	60.55 237	77.89 246	46.27 144	73.28 267	46.18 229	69.97 222	81.92 195

ETV-MVS		74.46 59	73.84 62	76.33 63	79.27 126	55.24 126	79.22 115	85.00 34	64.97 21	72.65 73	79.46 224	53.65 63	87.87 37	67.45 75	82.91 76	85.89 82

UniMVSNet_NR-MVSNet		71.11 99	71.00 93	71.44 165	79.20 127	44.13 256	76.02 175	82.60 84	66.48 13	68.20 129	84.60 119	56.82 29	82.82 154	54.62 168	70.43 213	87.36 41

VPNet		67.52 168	68.11 136	65.74 249	79.18 128	36.80 308	72.17 235	72.83 238	62.04 69	67.79 144	85.83 101	48.88 109	76.60 253	51.30 194	72.97 185	83.81 150

TR-MVS		66.59 190	65.07 194	71.17 175	79.18 128	49.63 199	73.48 216	75.20 214	52.95 210	67.90 136	80.33 204	39.81 203	83.68 132	43.20 257	73.56 174	80.20 221

TAMVS		66.78 185	65.27 192	71.33 172	79.16 130	53.67 137	73.84 213	69.59 256	52.32 218	65.28 181	81.72 174	44.49 161	77.40 245	42.32 263	78.66 129	82.92 178

Test_1112_low_res		62.32 232	61.77 226	64.00 262	79.08 131	39.53 290	68.17 271	70.17 250	43.25 301	59.03 252	79.90 212	44.08 163	71.24 275	43.79 252	68.42 247	81.25 205

CS-MVS		74.18 64	73.60 64	75.92 67	78.99 132	52.53 156	80.61 93	85.93 19	61.17 81	71.15 87	79.34 228	54.52 49	88.86 21	66.07 83	84.67 63	86.38 61

CDS-MVSNet		66.80 184	65.37 189	71.10 177	78.98 133	53.13 149	73.27 219	71.07 246	52.15 219	64.72 194	80.23 208	43.56 168	77.10 247	45.48 239	78.88 123	83.05 177
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
canonicalmvs		74.67 57	74.98 51	73.71 116	78.94 134	50.56 182	80.23 97	83.87 55	60.30 98	77.15 27	86.56 86	59.65 13	82.00 168	66.01 86	82.12 84	88.58 6

IS-MVSNet		71.57 94	71.00 93	73.27 131	78.86 135	45.63 245	80.22 98	78.69 157	64.14 34	66.46 161	87.36 70	49.30 101	85.60 89	50.26 200	83.71 69	88.59 5

CLD-MVS		73.33 71	72.68 74	75.29 83	78.82 136	53.33 145	78.23 128	84.79 38	61.30 79	70.41 92	81.04 187	52.41 72	87.12 51	64.61 99	82.49 83	85.41 106
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 96	70.38 102	74.88 87	78.76 137	57.15 97	82.79 58	78.48 164	51.26 231	69.49 109	83.22 144	43.99 165	83.24 140	66.06 84	79.37 114	84.23 139

lupinMVS		69.57 128	68.28 133	73.44 126	78.76 137	57.15 97	76.57 160	73.29 235	46.19 276	69.49 109	82.18 164	43.99 165	79.23 215	64.66 97	79.37 114	83.93 145

CNLPA		65.43 202	64.02 199	69.68 200	78.73 139	58.07 79	77.82 134	70.71 248	51.49 226	61.57 232	83.58 140	38.23 220	70.82 276	43.90 250	70.10 221	80.16 222

EPP-MVSNet		72.16 87	71.31 89	74.71 89	78.68 140	49.70 195	82.10 73	81.65 99	60.40 91	65.94 170	85.84 100	51.74 80	86.37 77	55.93 155	79.55 113	88.07 16

TranMVSNet+NR-MVSNet		70.36 111	70.10 106	71.17 175	78.64 141	42.97 267	76.53 161	81.16 114	66.95 6	68.53 125	85.42 109	51.61 81	83.07 144	52.32 186	69.70 230	87.46 36

UniMVSNet (Re)		70.63 105	70.20 103	71.89 154	78.55 142	45.29 247	75.94 176	82.92 79	63.68 39	68.16 132	83.59 139	53.89 57	83.49 137	53.97 173	71.12 207	86.89 50

Fast-Effi-MVS+		70.28 113	69.12 119	73.73 115	78.50 143	51.50 169	75.01 191	79.46 145	56.16 166	68.59 122	79.55 222	53.97 54	84.05 122	53.34 180	77.53 138	85.65 94

PS-MVSNAJss		72.24 84	71.21 90	75.31 81	78.50 143	55.93 113	81.63 78	82.12 90	56.24 164	70.02 100	85.68 104	47.05 134	84.34 118	65.27 93	74.41 163	85.67 92

EI-MVSNet-Vis-set		72.42 82	71.59 82	74.91 86	78.47 145	54.02 134	77.05 152	79.33 147	65.03 20	71.68 84	79.35 227	52.75 67	84.89 107	66.46 81	74.23 164	85.83 84

MVS_111021_LR		69.50 130	68.78 124	71.65 161	78.38 146	59.33 56	74.82 196	70.11 251	58.08 135	67.83 142	84.68 115	41.96 180	76.34 256	65.62 91	77.54 137	79.30 235

test_yl		69.69 123	69.13 117	71.36 169	78.37 147	45.74 241	74.71 197	80.20 132	57.91 137	70.01 101	83.83 133	42.44 175	82.87 150	54.97 164	79.72 108	85.48 100

DCV-MVSNet		69.69 123	69.13 117	71.36 169	78.37 147	45.74 241	74.71 197	80.20 132	57.91 137	70.01 101	83.83 133	42.44 175	82.87 150	54.97 164	79.72 108	85.48 100

FIs		70.82 102	71.43 84	68.98 211	78.33 149	38.14 300	76.96 154	83.59 61	61.02 82	67.33 149	86.73 77	55.07 42	81.64 173	54.61 170	79.22 118	87.14 46

UGNet		68.81 140	67.39 149	73.06 134	78.33 149	54.47 132	79.77 106	75.40 211	60.45 90	63.22 208	84.40 123	32.71 272	80.91 191	51.71 192	80.56 98	83.81 150
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		69.65 126	68.39 132	73.43 127	78.27 151	56.88 99	77.12 150	73.71 232	46.53 273	69.34 113	83.22 144	43.37 169	79.18 216	64.77 96	79.20 119	84.23 139
jason: jason.
alignmvs		73.86 68	73.99 60	73.45 125	78.20 152	50.50 183	78.57 122	82.43 85	59.40 114	76.57 28	86.71 79	56.42 33	81.23 183	65.84 88	81.79 86	88.62 4

xiu_mvs_v1_base_debu		68.58 147	67.28 154	72.48 147	78.19 153	57.19 94	75.28 183	75.09 216	51.61 222	70.04 97	81.41 180	32.79 268	79.02 223	63.81 105	77.31 139	81.22 206

xiu_mvs_v1_base		68.58 147	67.28 154	72.48 147	78.19 153	57.19 94	75.28 183	75.09 216	51.61 222	70.04 97	81.41 180	32.79 268	79.02 223	63.81 105	77.31 139	81.22 206

xiu_mvs_v1_base_debi		68.58 147	67.28 154	72.48 147	78.19 153	57.19 94	75.28 183	75.09 216	51.61 222	70.04 97	81.41 180	32.79 268	79.02 223	63.81 105	77.31 139	81.22 206

PAPM		67.92 163	66.69 165	71.63 162	78.09 156	49.02 205	77.09 151	81.24 112	51.04 233	60.91 235	83.98 131	47.71 121	84.99 102	40.81 272	79.32 117	80.90 213

ACMH	55.70 15	65.20 206	63.57 207	70.07 193	78.07 157	52.01 167	79.48 114	79.69 137	55.75 176	56.59 270	80.98 189	27.12 309	80.94 189	42.90 261	71.58 203	77.25 256
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
DU-MVS		70.01 117	69.53 111	71.44 165	78.05 158	44.13 256	75.01 191	81.51 102	64.37 28	68.20 129	84.52 120	49.12 107	82.82 154	54.62 168	70.43 213	87.37 39

NR-MVSNet		69.54 129	68.85 122	71.59 163	78.05 158	43.81 260	74.20 204	80.86 122	65.18 16	62.76 213	84.52 120	52.35 73	83.59 135	50.96 196	70.78 209	87.37 39

EI-MVSNet-UG-set		71.92 89	71.06 92	74.52 98	77.98 160	53.56 140	76.62 159	79.16 148	64.40 27	71.18 86	78.95 232	52.19 74	84.66 113	65.47 92	73.57 173	85.32 108

WR-MVS_H		67.02 179	66.92 162	67.33 228	77.95 161	37.75 303	77.57 138	82.11 91	62.03 70	62.65 216	82.48 158	50.57 92	79.46 211	42.91 260	64.01 277	84.79 125

Effi-MVS+		73.31 72	72.54 75	75.62 76	77.87 162	53.64 138	79.62 110	79.61 141	61.63 74	72.02 81	82.61 154	56.44 32	85.97 85	63.99 104	79.07 122	87.25 43

DELS-MVS		74.76 55	74.46 56	75.65 75	77.84 163	52.25 162	75.59 179	84.17 46	63.76 37	73.15 66	82.79 149	59.58 15	86.80 60	67.24 76	86.04 57	87.89 18
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	66.12 194	64.06 198	72.30 152	77.79 164	52.83 151	80.39 96	78.03 174	57.30 141	57.47 265	82.55 156	27.68 305	84.17 120	45.54 237	69.78 227	79.90 226

3Dnovator	64.47 5	72.49 80	71.39 86	75.79 69	77.70 165	58.99 66	80.66 92	83.15 76	62.24 63	65.46 179	86.59 84	42.38 177	85.52 93	59.59 140	84.72 62	82.85 181

EG-PatchMatch MVS		64.71 210	62.87 214	70.22 189	77.68 166	53.48 142	77.99 131	78.82 152	53.37 208	56.03 273	77.41 253	24.75 323	84.04 123	46.37 228	73.42 177	73.14 295

CP-MVSNet		66.49 191	66.41 172	66.72 231	77.67 167	36.33 313	76.83 158	79.52 143	62.45 60	62.54 219	83.47 143	46.32 142	78.37 230	45.47 240	63.43 282	85.45 102

GBi-Net		67.21 172	66.55 166	69.19 207	77.63 168	43.33 263	77.31 144	77.83 177	56.62 153	65.04 189	82.70 150	41.85 183	80.33 202	47.18 221	72.76 188	83.92 146

test1		67.21 172	66.55 166	69.19 207	77.63 168	43.33 263	77.31 144	77.83 177	56.62 153	65.04 189	82.70 150	41.85 183	80.33 202	47.18 221	72.76 188	83.92 146

FMVSNet2		66.93 181	66.31 176	68.79 214	77.63 168	42.98 266	76.11 170	77.47 183	56.62 153	65.22 188	82.17 166	41.85 183	80.18 205	47.05 224	72.72 191	83.20 171

PCF-MVS	61.88 8	70.95 100	69.49 112	75.35 80	77.63 168	55.71 116	76.04 174	81.81 96	50.30 239	69.66 107	85.40 110	52.51 69	84.89 107	51.82 190	80.24 104	85.45 102
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
MVP-Stereo		65.41 203	63.80 203	70.22 189	77.62 172	55.53 121	76.30 166	78.53 162	50.59 238	56.47 271	78.65 235	39.84 202	82.68 156	44.10 249	72.12 198	72.44 303
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
FC-MVSNet-test		69.80 121	70.58 99	67.46 225	77.61 173	34.73 320	76.05 173	83.19 75	60.84 83	65.88 173	86.46 87	54.52 49	80.76 196	52.52 185	78.12 133	86.91 49

PS-CasMVS		66.42 192	66.32 175	66.70 233	77.60 174	36.30 315	76.94 155	79.61 141	62.36 62	62.43 223	83.66 137	45.69 146	78.37 230	45.35 242	63.26 283	85.42 105

FMVSNet1		66.70 186	65.87 182	69.19 207	77.49 175	43.33 263	77.31 144	77.83 177	56.45 158	64.60 197	82.70 150	38.08 222	80.33 202	46.08 230	72.31 196	83.92 146

VPA-MVSNet		69.02 137	69.47 113	67.69 224	77.42 176	41.00 283	74.04 206	79.68 138	60.06 101	69.26 116	84.81 114	51.06 88	77.58 242	54.44 171	74.43 162	84.48 133

UniMVSNet_ETH3D		67.60 167	67.07 161	69.18 210	77.39 177	42.29 271	74.18 205	75.59 208	60.37 92	66.77 156	86.06 96	37.64 224	78.93 228	52.16 188	73.49 175	86.32 70

thres100view900		63.28 223	62.41 220	65.89 247	77.31 178	38.66 296	72.65 226	69.11 262	57.07 144	62.45 222	81.03 188	37.01 234	79.17 217	31.84 311	73.25 180	79.83 227

cascas		65.98 196	63.42 209	73.64 120	77.26 179	52.58 155	72.26 234	77.21 189	48.56 253	61.21 234	74.60 284	32.57 276	85.82 88	50.38 199	76.75 150	82.52 185

thres600view7		63.30 222	62.27 221	66.41 235	77.18 180	38.87 294	72.35 232	69.11 262	56.98 146	62.37 224	80.96 190	37.01 234	79.00 226	31.43 318	73.05 184	81.36 202

PEN-MVS		66.60 188	66.45 168	67.04 229	77.11 181	36.56 310	77.03 153	80.42 128	62.95 47	62.51 221	84.03 129	46.69 140	79.07 222	44.22 245	63.08 285	85.51 99

PatchMatch-RL		56.25 273	54.55 277	61.32 280	77.06 182	56.07 110	65.57 284	54.10 329	44.13 295	53.49 300	71.27 302	25.20 320	66.78 294	36.52 295	63.66 279	61.12 328

PVSNet_BlendedMVS		68.56 150	67.72 139	71.07 178	77.03 183	50.57 180	74.50 201	81.52 100	53.66 207	64.22 203	79.72 218	49.13 105	82.87 150	55.82 156	73.92 168	79.77 230

PVSNet_Blended		68.59 146	67.72 139	71.19 174	77.03 183	50.57 180	72.51 230	81.52 100	51.91 220	64.22 203	77.77 250	49.13 105	82.87 150	55.82 156	79.58 111	80.14 223

F-COLMAP		63.05 227	60.87 238	69.58 204	76.99 185	53.63 139	78.12 130	76.16 199	47.97 262	52.41 303	81.61 176	27.87 303	78.11 234	40.07 274	66.66 259	77.00 259

tfpn200view9		63.18 225	62.18 223	66.21 239	76.85 186	39.62 288	71.96 238	69.44 258	56.63 151	62.61 217	79.83 214	37.18 229	79.17 217	31.84 311	73.25 180	79.83 227

thres400		63.31 221	62.18 223	66.72 231	76.85 186	39.62 288	71.96 238	69.44 258	56.63 151	62.61 217	79.83 214	37.18 229	79.17 217	31.84 311	73.25 180	81.36 202

tttt0517		67.83 165	65.66 186	74.33 102	76.69 188	50.82 176	77.86 132	73.99 229	54.54 199	64.64 196	82.53 157	35.06 244	85.50 95	55.71 159	69.91 224	86.67 57

ET-MVSNet_ETH3D		67.96 162	65.72 185	74.68 91	76.67 189	55.62 120	75.11 188	74.74 220	52.91 211	60.03 240	80.12 209	33.68 258	82.64 158	61.86 122	76.34 152	85.78 85

TAPA-MVS	59.36 10	66.60 188	65.20 193	70.81 180	76.63 190	48.75 209	76.52 162	80.04 134	50.64 237	65.24 186	84.93 112	39.15 210	78.54 229	36.77 289	76.88 146	85.14 112
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
OMC-MVS		71.40 98	70.60 97	73.78 110	76.60 191	53.15 147	79.74 108	79.78 136	58.37 131	68.75 121	86.45 88	45.43 152	80.60 197	62.58 115	77.73 136	87.58 32

LTVRE_ROB	55.42 16	63.15 226	61.23 234	68.92 212	76.57 192	47.80 218	59.92 310	76.39 197	54.35 202	58.67 255	82.46 159	29.44 295	81.49 177	42.12 265	71.14 206	77.46 251
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		70.05 116	68.81 123	73.78 110	76.54 193	53.43 143	83.23 51	83.48 63	52.89 212	65.90 172	86.29 91	41.55 190	86.49 74	51.01 195	78.40 132	81.42 200

FMVSNet3		66.32 193	65.61 187	68.46 217	76.48 194	42.34 270	74.98 193	77.15 190	55.83 173	65.04 189	81.16 184	39.91 201	80.14 206	47.18 221	72.76 188	82.90 180

thisisatest0530		67.92 163	65.78 184	74.33 102	76.29 195	51.03 171	76.89 156	74.25 226	53.67 206	65.59 177	81.76 173	35.15 243	85.50 95	55.94 154	72.47 192	86.47 60

baseline1		63.81 217	63.87 202	63.62 263	76.29 195	36.36 311	71.78 240	67.29 271	56.05 170	64.23 202	82.95 148	47.11 133	74.41 264	47.30 220	61.85 292	80.10 224

ab-mvs		66.65 187	66.42 171	67.37 226	76.17 197	41.73 277	70.41 259	76.14 200	53.99 203	65.98 169	83.51 141	49.48 99	76.24 257	48.60 213	73.46 176	84.14 142

Effi-MVS+-dtu		69.64 127	67.53 145	75.95 66	76.10 198	62.29 18	80.20 99	76.06 202	59.83 108	65.26 185	77.09 254	41.56 188	84.02 126	60.60 131	71.09 208	81.53 199

mvs-test1		70.44 109	68.19 134	77.18 51	76.10 198	63.22 6	80.59 94	76.06 202	59.83 108	66.32 164	79.87 213	41.56 188	85.53 92	60.60 131	72.77 187	82.80 182

DTE-MVSNet		65.58 200	65.34 190	66.31 236	76.06 200	34.79 318	76.43 163	79.38 146	62.55 58	61.66 230	83.83 133	45.60 148	79.15 220	41.64 271	60.88 298	85.00 117

EPNet		73.09 74	72.16 77	75.90 68	75.95 201	56.28 105	83.05 53	72.39 240	66.53 12	65.27 182	87.00 73	50.40 93	85.47 97	62.48 117	86.32 56	85.94 80
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
RRT_MVS		68.77 143	66.71 164	74.95 85	75.93 202	58.55 73	80.50 95	75.84 204	56.09 168	68.17 131	83.74 136	28.50 300	82.98 146	65.67 90	65.91 264	83.33 166

RRT_test8_iter05		68.17 159	66.86 163	72.07 153	75.81 203	46.33 233	76.41 164	81.81 96	56.43 159	66.52 159	81.30 183	31.90 280	84.25 119	63.77 108	67.83 252	85.64 95

SixPastTwentyTwo		61.65 241	58.80 247	70.20 191	75.80 204	47.22 226	75.59 179	69.68 254	54.61 196	54.11 292	79.26 229	27.07 310	82.96 147	43.27 255	49.79 327	80.41 219

baseline		74.61 58	74.70 54	74.34 101	75.70 205	49.99 192	77.54 140	84.63 39	62.73 56	73.98 52	87.79 67	57.67 26	83.82 130	69.49 60	82.74 81	89.20 3

Baseline_NR-MVSNet		67.05 178	67.56 142	65.50 251	75.65 206	37.70 304	75.42 181	74.65 222	59.90 104	68.14 133	83.15 147	49.12 107	77.20 246	52.23 187	69.78 227	81.60 198

jajsoiax		68.25 155	66.45 168	73.66 118	75.62 207	55.49 122	80.82 89	78.51 163	52.33 217	64.33 199	84.11 127	28.28 301	81.81 172	63.48 110	70.62 211	83.67 157

mvs_tets		68.18 157	66.36 173	73.63 121	75.61 208	55.35 125	80.77 90	78.56 161	52.48 216	64.27 201	84.10 128	27.45 307	81.84 171	63.45 111	70.56 212	83.69 156

casdiffmvs		74.80 54	74.89 53	74.53 97	75.59 209	50.37 185	78.17 129	85.06 31	62.80 55	74.40 48	87.86 65	57.88 23	83.61 134	69.46 62	82.79 80	89.59 2

PVSNet	50.76 19	58.40 258	57.39 256	61.42 278	75.53 210	44.04 258	61.43 303	63.45 293	47.04 271	56.91 267	73.61 291	27.00 311	64.76 301	39.12 278	72.40 193	75.47 273

MVS		67.37 170	66.33 174	70.51 187	75.46 211	50.94 172	73.95 208	81.85 95	41.57 312	62.54 219	78.57 238	47.98 117	85.47 97	52.97 183	82.05 85	75.14 275

nrg030		72.96 75	73.01 71	72.84 138	75.41 212	50.24 186	80.02 100	82.89 82	58.36 132	74.44 47	86.73 77	58.90 18	80.83 192	65.84 88	74.46 161	87.44 37

thres200		62.20 234	61.16 235	65.34 254	75.38 213	39.99 285	69.60 264	69.29 260	55.64 179	61.87 228	76.99 255	37.07 233	78.96 227	31.28 319	73.28 179	77.06 257

TransMVSNet (Re)		64.72 209	64.33 197	65.87 248	75.22 214	38.56 297	74.66 199	75.08 219	58.90 122	61.79 229	82.63 153	51.18 85	78.07 235	43.63 253	55.87 313	80.99 212

MS-PatchMatch		62.42 231	61.46 230	65.31 255	75.21 215	52.10 163	72.05 236	74.05 228	46.41 274	57.42 266	74.36 285	34.35 252	77.57 243	45.62 236	73.67 170	66.26 325

IB-MVS	56.42 12	65.40 204	62.73 217	73.40 128	74.89 216	52.78 152	73.09 221	75.13 215	55.69 177	58.48 259	73.73 290	32.86 267	86.32 78	50.63 197	70.11 220	81.10 210
Christian Sormann, Mattia Rossi, Andreas Kuhn and Friedrich Fraundorfer: IB-MVS: An Iterative Algorithm for Deep Multi-View Stereo based on Binary Decisions. BMVC 2021
MVS_Test		72.45 81	72.46 76	72.42 150	74.88 217	48.50 211	76.28 167	83.14 77	59.40 114	72.46 76	84.68 115	55.66 39	81.12 184	65.98 87	79.66 110	87.63 29

CANet_DTU		68.18 157	67.71 141	69.59 202	74.83 218	46.24 235	78.66 120	76.85 193	59.60 110	63.45 207	82.09 170	35.25 242	77.41 244	59.88 137	78.76 127	85.14 112

tfpnnormal		62.47 230	61.63 228	64.99 257	74.81 219	39.01 293	71.22 246	73.72 231	55.22 185	60.21 238	80.09 211	41.26 196	76.98 250	30.02 323	68.09 249	78.97 238

Vis-MVSNet (Re-imp)		63.69 218	63.88 201	63.14 267	74.75 220	31.04 333	71.16 248	63.64 292	56.32 161	59.80 244	84.99 111	44.51 159	75.46 259	39.12 278	80.62 94	82.92 178

HY-MVS	56.14 13	64.55 213	63.89 200	66.55 234	74.73 221	41.02 281	69.96 262	74.43 223	49.29 247	61.66 230	80.92 191	47.43 128	76.68 252	44.91 244	71.69 201	81.94 194

COLMAP_ROB	52.97 17	61.27 244	58.81 246	68.64 215	74.63 222	52.51 158	78.42 127	73.30 234	49.92 244	50.96 308	81.51 179	23.06 325	79.40 212	31.63 315	65.85 265	74.01 291
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
LCM-MVSNet-Re		61.88 239	61.35 231	63.46 264	74.58 223	31.48 332	61.42 304	58.14 314	58.71 125	53.02 302	79.55 222	43.07 171	76.80 251	45.69 234	77.96 134	82.11 193

test_djsdf		69.45 132	67.74 138	74.58 96	74.57 224	54.92 129	82.79 58	78.48 164	51.26 231	65.41 180	83.49 142	38.37 217	83.24 140	66.06 84	69.25 237	85.56 97

EI-MVSNet		69.27 134	68.44 131	71.73 158	74.47 225	49.39 202	75.20 186	78.45 167	59.60 110	69.16 118	76.51 264	51.29 83	82.50 160	59.86 139	71.45 205	83.30 167

CVMVSNet		59.63 252	59.14 245	61.08 281	74.47 225	38.84 295	75.20 186	68.74 264	31.15 331	58.24 260	76.51 264	32.39 277	68.58 286	49.77 202	65.84 266	75.81 269

IterMVS-LS		69.22 136	68.48 128	71.43 167	74.44 227	49.40 201	76.23 168	77.55 182	59.60 110	65.85 174	81.59 178	51.28 84	81.58 176	59.87 138	69.90 225	83.30 167
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
XVG-OURS-SEG-HR		68.81 140	67.47 147	72.82 140	74.40 228	56.87 100	70.59 255	79.04 149	54.77 195	66.99 153	86.01 97	39.57 205	78.21 233	62.54 116	73.33 178	83.37 165

XVG-OURS		68.76 144	67.37 150	72.90 137	74.32 229	57.22 92	70.09 261	78.81 153	55.24 184	67.79 144	85.81 103	36.54 237	78.28 232	62.04 120	75.74 155	83.19 172

OpenMVS	61.03 9	68.85 139	67.56 142	72.70 143	74.26 230	53.99 135	81.21 86	81.34 107	52.70 213	62.75 214	85.55 106	38.86 213	84.14 121	48.41 215	83.01 72	79.97 225

MIMVSNet		57.35 265	57.07 259	58.22 289	74.21 231	37.18 305	62.46 299	60.88 307	48.88 251	55.29 280	75.99 271	31.68 281	62.04 309	31.87 310	72.35 194	75.43 274

SCA		60.49 246	58.38 250	66.80 230	74.14 232	48.06 216	63.35 296	63.23 295	49.13 249	59.33 250	72.10 296	37.45 226	74.27 265	44.17 246	62.57 288	78.05 245

thisisatest0515		65.83 197	63.50 208	72.82 140	73.75 233	49.50 200	71.32 244	73.12 237	49.39 246	63.82 205	76.50 266	34.95 246	84.84 110	53.20 182	75.49 158	84.13 143

K. test v3		60.47 247	57.11 258	70.56 185	73.74 234	48.22 214	75.10 190	62.55 299	58.27 134	53.62 297	76.31 267	27.81 304	81.59 175	47.42 218	39.18 336	81.88 196

v10		70.21 114	69.02 120	73.81 109	73.51 235	50.92 174	78.74 118	81.39 105	60.05 102	66.39 163	81.83 172	47.58 124	85.41 99	62.80 114	68.86 243	85.09 115

v1144		70.42 110	69.31 115	73.76 112	73.22 236	50.64 179	77.83 133	81.43 104	58.58 126	69.40 112	81.16 184	47.53 125	85.29 101	64.01 103	70.64 210	85.34 107

v1192		69.97 119	68.68 125	73.85 108	73.19 237	50.94 172	77.68 136	81.36 106	57.51 140	68.95 120	80.85 194	45.28 155	85.33 100	62.97 113	70.37 215	85.27 110

v8		70.33 112	69.28 116	73.49 123	73.15 238	50.22 187	78.62 121	80.78 123	60.79 84	66.45 162	82.11 169	49.35 100	84.98 104	63.58 109	68.71 244	85.28 109

v144192		69.71 122	68.51 127	73.33 130	73.10 239	50.13 189	77.54 140	80.64 124	56.65 150	68.57 124	80.55 199	46.87 139	84.96 106	62.98 112	69.66 231	84.89 121

v1921920		69.47 131	68.17 135	73.36 129	73.06 240	50.10 190	77.39 143	80.56 125	56.58 157	68.59 122	80.37 201	44.72 158	84.98 104	62.47 118	69.82 226	85.00 117

PatchmatchNet		59.84 250	58.24 251	64.65 259	73.05 241	46.70 230	69.42 266	62.18 301	47.55 266	58.88 253	71.96 298	34.49 250	69.16 283	42.99 259	63.60 280	78.07 244
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
v1240		69.24 135	67.91 137	73.25 133	73.02 242	49.82 193	77.21 149	80.54 126	56.43 159	68.34 128	80.51 200	43.33 170	84.99 102	62.03 121	69.77 229	84.95 120

Fast-Effi-MVS+-dtu		67.37 170	65.33 191	73.48 124	72.94 243	57.78 84	77.47 142	76.88 192	57.60 139	61.97 226	76.85 258	39.31 207	80.49 200	54.72 167	70.28 218	82.17 192

EPNet_dtu		61.90 237	61.97 225	61.68 276	72.89 244	39.78 287	75.85 177	65.62 280	55.09 188	54.56 287	79.36 226	37.59 225	67.02 293	39.80 277	76.95 145	78.25 242
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
tpm2		62.07 235	60.10 241	67.99 221	72.79 245	43.86 259	71.05 251	66.85 274	43.14 303	62.77 212	75.39 278	38.32 218	80.80 193	41.69 268	68.88 242	79.32 234

MDTV_nov1_ep13			57.00 260		72.73 246	38.26 299	65.02 291	64.73 287	44.74 286	55.46 276	72.48 294	32.61 275	70.47 279	37.47 285	67.75 254

MSDG		61.81 240	59.23 244	69.55 205	72.64 247	52.63 154	70.45 258	75.81 205	51.38 228	53.70 295	76.11 268	29.52 293	81.08 187	37.70 284	65.79 267	74.93 280

gg-mvs-nofinetune		57.86 263	56.43 265	62.18 273	72.62 248	35.35 317	66.57 276	56.33 321	50.65 236	57.64 264	57.10 332	30.65 284	76.36 255	37.38 286	78.88 123	74.82 282

v2v482		70.50 108	69.45 114	73.66 118	72.62 248	50.03 191	77.58 137	80.51 127	59.90 104	69.52 108	82.14 168	47.53 125	84.88 109	65.07 95	70.17 219	86.09 77

baseline2		63.42 220	61.26 233	69.89 199	72.55 250	47.62 222	71.54 241	68.38 266	50.11 240	54.82 283	75.55 276	43.06 172	80.96 188	48.13 216	67.16 258	81.11 209

v7n		69.01 138	67.36 151	73.98 106	72.51 251	52.65 153	78.54 124	81.30 108	60.26 99	62.67 215	81.62 175	43.61 167	84.49 115	57.01 150	68.70 245	84.79 125

pm-mvs1		65.24 205	64.97 195	66.04 243	72.38 252	39.40 291	72.62 228	75.63 207	55.53 180	62.35 225	83.18 146	47.45 127	76.47 254	49.06 210	66.54 260	82.24 189

XVG-ACMP-BASELINE		64.36 214	62.23 222	70.74 182	72.35 253	52.45 160	70.80 254	78.45 167	53.84 205	59.87 242	81.10 186	16.24 334	79.32 214	55.64 161	71.76 200	80.47 217

WTY-MVS		59.75 251	60.39 239	57.85 292	72.32 254	37.83 302	61.05 308	64.18 290	45.95 281	61.91 227	79.11 231	47.01 137	60.88 312	42.50 262	69.49 233	74.83 281

tpm cat1		59.25 253	56.95 261	66.15 240	72.19 255	46.96 228	68.09 272	65.76 279	40.03 319	57.81 263	70.56 306	38.32 218	74.51 263	38.26 282	61.50 295	77.00 259

mvs_anonymous		68.03 160	67.51 146	69.59 202	72.08 256	44.57 254	71.99 237	75.23 213	51.67 221	67.06 152	82.57 155	54.68 47	77.94 236	56.56 151	75.71 156	86.26 74

OurMVSNet-221017-0		61.37 243	58.63 249	69.61 201	72.05 257	48.06 216	73.93 211	72.51 239	47.23 269	54.74 284	80.92 191	21.49 330	81.24 182	48.57 214	56.22 312	79.53 232

IterMVS-SCA-FT		62.49 229	61.52 229	65.40 253	71.99 258	50.80 177	71.15 249	69.63 255	45.71 282	60.61 236	77.93 243	37.45 226	65.99 298	55.67 160	63.50 281	79.42 233

DWT-MVSNet_test		61.90 237	59.93 242	67.83 222	71.98 259	46.09 238	71.03 252	69.71 252	50.09 241	58.51 258	70.62 305	30.21 289	77.63 241	49.28 208	67.91 250	79.78 229

CostFormer		64.04 215	62.51 218	68.61 216	71.88 260	45.77 240	71.30 245	70.60 249	47.55 266	64.31 200	76.61 262	41.63 186	79.62 210	49.74 203	69.00 241	80.42 218

1314		64.61 212	63.21 211	68.80 213	71.87 261	47.46 224	73.95 208	78.39 172	42.88 305	59.97 241	76.60 263	38.11 221	79.39 213	54.84 166	72.32 195	79.55 231

tpm		57.34 266	58.16 252	54.86 302	71.80 262	34.77 319	67.47 275	56.04 324	48.20 259	60.10 239	76.92 256	37.17 231	53.41 335	40.76 273	65.01 271	76.40 265

eth_miper_zixun_eth		67.63 166	66.28 177	71.67 160	71.60 263	48.33 213	73.68 215	77.88 175	55.80 175	65.91 171	78.62 237	47.35 131	82.88 149	59.45 141	66.25 262	83.81 150

pmmvs4		61.48 242	59.39 243	67.76 223	71.57 264	53.86 136	71.42 242	65.34 282	44.20 293	59.46 246	77.92 244	35.90 238	74.71 262	43.87 251	64.87 272	74.71 284

AllTest		57.08 268	54.65 276	64.39 260	71.44 265	49.03 203	69.92 263	67.30 269	45.97 279	47.16 319	79.77 216	17.47 332	67.56 290	33.65 304	59.16 304	76.57 263

TestCases				64.39 260	71.44 265	49.03 203		67.30 269	45.97 279	47.16 319	79.77 216	17.47 332	67.56 290	33.65 304	59.16 304	76.57 263

lessismore_v0				69.91 197	71.42 267	47.80 218		50.90 332		50.39 313	75.56 275	27.43 308	81.33 179	45.91 232	34.10 338	80.59 216

gm-plane-assit					71.40 268	41.72 278			48.85 252		73.31 292		82.48 162	48.90 211

GG-mvs-BLEND				62.34 272	71.36 269	37.04 307	69.20 268	57.33 318		54.73 285	65.48 324	30.37 286	77.82 238	34.82 300	74.93 160	72.17 308

FMVSNet5		55.86 275	54.93 274	58.66 288	71.05 270	36.35 312	64.18 295	62.48 300	46.76 272	50.66 312	74.73 283	25.80 317	64.04 303	33.11 306	65.57 268	75.59 272

cl_fuxian		68.33 153	67.56 142	70.62 184	70.87 271	46.21 236	74.47 202	78.80 154	56.22 165	66.19 166	78.53 239	51.88 77	81.40 178	62.08 119	69.04 240	84.25 138

GA-MVS		65.53 201	63.70 205	71.02 179	70.87 271	48.10 215	70.48 257	74.40 224	56.69 149	64.70 195	76.77 259	33.66 259	81.10 185	55.42 163	70.32 217	83.87 149

pmmvs6		63.69 218	62.82 216	66.27 238	70.63 273	39.27 292	73.13 220	75.47 210	52.69 214	59.75 245	82.30 162	39.71 204	77.03 249	47.40 219	64.35 276	82.53 184

miper_ehance_all_eth		68.03 160	67.24 158	70.40 188	70.54 274	46.21 236	73.98 207	78.68 158	55.07 190	66.05 168	77.80 248	52.16 75	81.31 180	61.53 127	69.32 234	83.67 157

OpenMVS_ROB	52.78 18	60.03 248	58.14 253	65.69 250	70.47 275	44.82 249	75.33 182	70.86 247	45.04 284	56.06 272	76.00 269	26.89 312	79.65 208	35.36 299	67.29 256	72.60 299

v148		68.24 156	67.19 159	71.40 168	70.43 276	47.77 220	75.76 178	77.03 191	58.91 121	67.36 148	80.10 210	48.60 113	81.89 169	60.01 136	66.52 261	84.53 131

XXY-MVS		60.68 245	61.67 227	57.70 294	70.43 276	38.45 298	64.19 294	66.47 275	48.05 261	63.22 208	80.86 193	49.28 102	60.47 313	45.25 243	67.28 257	74.19 289

MVSTER		67.16 176	65.58 188	71.88 155	70.37 278	49.70 195	70.25 260	78.45 167	51.52 225	69.16 118	80.37 201	38.45 216	82.50 160	60.19 134	71.46 204	83.44 164

cl-mvsnet_		67.18 174	66.26 178	69.94 195	70.20 279	45.74 241	73.30 217	76.83 194	55.10 186	65.27 182	79.57 221	47.39 129	80.53 198	59.41 143	69.22 238	83.53 163

cl-mvsnet1		67.18 174	66.26 178	69.94 195	70.20 279	45.74 241	73.29 218	76.83 194	55.10 186	65.27 182	79.58 220	47.38 130	80.53 198	59.43 142	69.22 238	83.54 162

tpmvs		58.47 257	56.95 261	63.03 269	70.20 279	41.21 280	67.90 274	67.23 272	49.62 245	54.73 285	70.84 303	34.14 253	76.24 257	36.64 293	61.29 296	71.64 310

anonymousdsp		67.00 180	64.82 196	73.57 122	70.09 282	56.13 108	76.35 165	77.35 187	48.43 256	64.99 192	80.84 195	33.01 265	80.34 201	64.66 97	67.64 255	84.23 139

MVS_0304		58.51 256	57.36 257	61.96 275	70.04 283	41.83 275	69.40 267	65.46 281	50.73 234	53.30 301	74.06 288	22.65 326	70.18 281	42.16 264	68.44 246	73.86 293

MIMVSNet1		55.17 279	54.31 280	57.77 293	70.03 284	32.01 330	65.68 283	64.81 285	49.19 248	46.75 322	76.00 269	25.53 319	64.04 303	28.65 327	62.13 291	77.26 255

CR-MVSNet		59.91 249	57.90 255	65.96 244	69.96 285	52.07 164	65.31 288	63.15 296	42.48 307	59.36 247	74.84 281	35.83 239	70.75 277	45.50 238	64.65 274	75.06 276

RPMNet		58.70 255	56.29 267	65.96 244	69.96 285	52.07 164	65.31 288	62.15 302	43.20 302	59.36 247	70.15 309	35.37 241	70.75 277	36.42 296	64.65 274	75.06 276

cl-mvsnet2		67.47 169	66.45 168	70.54 186	69.85 287	46.49 231	73.85 212	77.35 187	55.07 190	65.51 178	77.92 244	47.64 123	81.10 185	61.58 126	69.32 234	84.01 144

Anonymous20231206		55.10 280	55.30 273	54.48 304	69.81 288	33.94 324	62.91 298	62.13 303	41.08 313	55.18 281	75.65 274	32.75 271	56.59 327	30.32 322	67.86 251	72.91 296

our_test_3		56.49 269	54.42 278	62.68 271	69.51 289	45.48 246	66.08 280	61.49 305	44.11 296	50.73 311	69.60 312	33.05 264	68.15 287	38.38 281	56.86 309	74.40 286

ppachtmachnet_test		58.06 262	55.38 272	66.10 242	69.51 289	48.99 206	68.01 273	66.13 278	44.50 290	54.05 293	70.74 304	32.09 279	72.34 271	36.68 292	56.71 311	76.99 261

diffmvs		70.69 104	70.43 100	71.46 164	69.45 291	48.95 207	72.93 222	78.46 166	57.27 142	71.69 83	83.97 132	51.48 82	77.92 237	70.70 57	77.95 135	87.53 33

IterMVS		62.79 228	61.27 232	67.35 227	69.37 292	52.04 166	71.17 247	68.24 267	52.63 215	59.82 243	76.91 257	37.32 228	72.36 270	52.80 184	63.19 284	77.66 249
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
miper_enhance_ethall		67.11 177	66.09 180	70.17 192	69.21 293	45.98 239	72.85 225	78.41 170	51.38 228	65.65 176	75.98 272	51.17 86	81.25 181	60.82 129	69.32 234	83.29 169

Patchmtry		57.16 267	56.47 264	59.23 283	69.17 294	34.58 321	62.98 297	63.15 296	44.53 289	56.83 268	74.84 281	35.83 239	68.71 285	40.03 275	60.91 297	74.39 287

V42		68.65 145	67.35 152	72.56 145	68.93 295	50.18 188	72.90 224	79.47 144	56.92 147	69.45 111	80.26 207	46.29 143	82.99 145	64.07 101	67.82 253	84.53 131

test-LLR		58.15 261	58.13 254	58.22 289	68.57 296	44.80 250	65.46 285	57.92 315	50.08 242	55.44 277	69.82 310	32.62 273	57.44 322	49.66 205	73.62 171	72.41 304

test-mter		56.42 271	55.82 269	58.22 289	68.57 296	44.80 250	65.46 285	57.92 315	39.94 320	55.44 277	69.82 310	21.92 329	57.44 322	49.66 205	73.62 171	72.41 304

MVS-HIRNet		45.52 303	44.48 305	48.65 318	68.49 298	34.05 323	59.41 313	44.50 341	27.03 335	37.96 336	50.47 338	26.16 316	64.10 302	26.74 332	59.52 302	47.82 336

dp		51.89 292	51.60 291	52.77 311	68.44 299	32.45 329	62.36 300	54.57 326	44.16 294	49.31 315	67.91 315	28.87 299	56.61 326	33.89 303	54.89 315	69.24 322

PatchT		53.17 288	53.44 286	52.33 313	68.29 300	25.34 342	58.21 315	54.41 327	44.46 291	54.56 287	69.05 313	33.32 262	60.94 311	36.93 288	61.76 294	70.73 316

Patchmatch-RL test		58.16 260	55.49 271	66.15 240	67.92 301	48.89 208	60.66 309	51.07 331	47.86 263	59.36 247	62.71 329	34.02 255	72.27 272	56.41 152	59.40 303	77.30 253

pmmvs-eth3d		58.81 254	56.31 266	66.30 237	67.61 302	52.42 161	72.30 233	64.76 286	43.55 299	54.94 282	74.19 287	28.95 297	72.60 269	43.31 254	57.21 308	73.88 292

PVSNet_0	43.31 20	47.46 302	45.64 304	52.92 310	67.60 303	44.65 252	54.06 324	54.64 325	41.59 311	46.15 323	58.75 331	30.99 282	58.66 318	32.18 308	24.81 339	55.46 334

CHOSEN 280x420		47.83 300	46.36 303	52.24 314	67.37 304	49.78 194	38.91 341	43.11 342	35.00 327	43.27 330	63.30 328	28.95 297	49.19 338	36.53 294	60.80 299	57.76 332

tpmrst		58.24 259	58.70 248	56.84 295	66.97 305	34.32 322	69.57 265	61.14 306	47.17 270	58.58 257	71.60 299	41.28 195	60.41 314	49.20 209	62.84 286	75.78 270

sss		56.17 274	56.57 263	54.96 301	66.93 306	36.32 314	57.94 316	61.69 304	41.67 310	58.64 256	75.32 279	38.72 214	56.25 328	42.04 266	66.19 263	72.31 307

TinyColmap		54.14 281	51.72 290	61.40 279	66.84 307	41.97 273	66.52 277	68.51 265	44.81 285	42.69 331	75.77 273	11.66 339	72.94 268	31.96 309	56.77 310	69.27 321

miper_lstm_enhance		62.03 236	60.88 237	65.49 252	66.71 308	46.25 234	56.29 321	75.70 206	50.68 235	61.27 233	75.48 277	40.21 200	68.03 288	56.31 153	65.25 270	82.18 190

TESTMET0.1,1		55.28 278	54.90 275	56.42 296	66.56 309	43.67 261	65.46 285	56.27 322	39.18 322	53.83 294	67.44 318	24.21 324	55.46 332	48.04 217	73.11 183	70.13 317

D2MVS		62.30 233	60.29 240	68.34 219	66.46 310	48.42 212	65.70 282	73.42 233	47.71 264	58.16 261	75.02 280	30.51 285	77.71 240	53.96 174	71.68 202	78.90 239

MDA-MVSNet-bldmvs		53.87 284	50.81 292	63.05 268	66.25 311	48.58 210	56.93 319	63.82 291	48.09 260	41.22 332	70.48 307	30.34 287	68.00 289	34.24 302	45.92 332	72.57 300

ITE_SJBPF				62.09 274	66.16 312	44.55 255		64.32 289	47.36 268	55.31 279	80.34 203	19.27 331	62.68 307	36.29 297	62.39 290	79.04 236

EPMVS		53.96 282	53.69 284	54.79 303	66.12 313	31.96 331	62.34 301	49.05 334	44.42 292	55.54 275	71.33 301	30.22 288	56.70 325	41.65 270	62.54 289	75.71 271

testing_2		66.02 195	63.77 204	72.76 142	66.03 314	50.48 184	72.93 222	80.36 130	54.41 201	54.25 291	76.76 260	30.89 283	83.16 143	64.19 100	74.08 166	84.65 128

ADS-MVSNet2		51.33 294	48.76 298	59.07 285	66.02 315	44.60 253	50.90 328	59.76 309	36.90 323	50.74 309	66.18 322	26.38 313	63.11 305	27.17 329	54.76 316	69.50 319

ADS-MVSNet		48.48 299	47.77 300	50.63 316	66.02 315	29.92 335	50.90 328	50.87 333	36.90 323	50.74 309	66.18 322	26.38 313	52.47 336	27.17 329	54.76 316	69.50 319

EU-MVSNet		55.61 277	54.41 279	59.19 284	65.41 317	33.42 326	72.44 231	71.91 243	28.81 333	51.27 306	73.87 289	24.76 322	69.08 284	43.04 258	58.20 307	75.06 276

RPSCF		55.80 276	54.22 282	60.53 282	65.13 318	42.91 268	64.30 293	57.62 317	36.84 325	58.05 262	82.28 163	28.01 302	56.24 329	37.14 287	58.61 306	82.44 188

USDC		56.35 272	54.24 281	62.69 270	64.74 319	40.31 284	65.05 290	73.83 230	43.93 297	47.58 317	77.71 251	15.36 336	75.05 261	38.19 283	61.81 293	72.70 298

JIA-IIPM		51.56 293	47.68 302	63.21 266	64.61 320	50.73 178	47.71 333	58.77 312	42.90 304	48.46 316	51.72 335	24.97 321	70.24 280	36.06 298	53.89 319	68.64 323

Patchmatch-test		49.08 298	48.28 299	51.50 315	64.40 321	30.85 334	45.68 335	48.46 337	35.60 326	46.10 324	72.10 296	34.47 251	46.37 339	27.08 331	60.65 300	77.27 254

TDRefinement		53.44 286	50.72 293	61.60 277	64.31 322	46.96 228	70.89 253	65.27 284	41.78 308	44.61 327	77.98 241	11.52 340	66.36 296	28.57 328	51.59 323	71.49 311

N_pmnet		39.35 309	40.28 309	36.54 325	63.76 323	1.62 353	49.37 331	0.76 354	34.62 328	43.61 329	66.38 321	26.25 315	42.57 342	26.02 334	51.77 322	65.44 326

ambc				65.13 256	63.72 324	37.07 306	47.66 334	78.78 155		54.37 290	71.42 300	11.24 341	80.94 189	45.64 235	53.85 320	77.38 252

test0.0.03 1		53.32 287	53.59 285	52.50 312	62.81 325	29.45 336	59.51 311	54.11 328	50.08 242	54.40 289	74.31 286	32.62 273	55.92 330	30.50 321	63.95 278	72.15 309

PMMVS		53.96 282	53.26 287	56.04 297	62.60 326	50.92 174	61.17 307	56.09 323	32.81 329	53.51 299	66.84 320	34.04 254	59.93 316	44.14 248	68.18 248	57.27 333

PM-MVS		52.33 290	50.19 294	58.75 287	62.10 327	45.14 248	65.75 281	40.38 343	43.60 298	53.52 298	72.65 293	9.16 345	65.87 299	50.41 198	54.18 318	65.24 327

Gipuma		34.77 312	31.91 315	43.33 323	62.05 328	37.87 301	20.39 344	67.03 273	23.23 339	18.41 343	25.84 342	4.24 348	62.73 306	14.71 339	51.32 324	29.38 341
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
test20.03		53.87 284	54.02 283	53.41 308	61.47 329	28.11 338	61.30 305	59.21 310	51.34 230	52.09 304	77.43 252	33.29 263	58.55 319	29.76 324	60.27 301	73.58 294

pmmvs5		56.47 270	55.68 270	58.86 286	61.41 330	36.71 309	66.37 278	62.75 298	40.38 317	53.70 295	76.62 261	34.56 248	67.05 292	40.02 276	65.27 269	72.83 297

MDA-MVSNet_test_wron		50.71 296	48.95 296	56.00 299	61.17 331	41.84 274	51.90 327	56.45 319	40.96 314	44.79 326	67.84 316	30.04 291	55.07 334	36.71 291	50.69 326	71.11 315

YYNet1		50.73 295	48.96 295	56.03 298	61.10 332	41.78 276	51.94 326	56.44 320	40.94 315	44.84 325	67.80 317	30.08 290	55.08 333	36.77 289	50.71 325	71.22 312

CMPMVS	42.80 21	57.81 264	55.97 268	63.32 265	60.98 333	47.38 225	64.66 292	69.50 257	32.06 330	46.83 321	77.80 248	29.50 294	71.36 274	48.68 212	73.75 169	71.21 313
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
UnsupCasMVSNet_bld		50.07 297	48.87 297	53.66 306	60.97 334	33.67 325	57.62 317	64.56 288	39.47 321	47.38 318	64.02 327	27.47 306	59.32 317	34.69 301	43.68 334	67.98 324

testgi		51.90 291	52.37 289	50.51 317	60.39 335	23.55 344	58.42 314	58.15 313	49.03 250	51.83 305	79.21 230	22.39 327	55.59 331	29.24 326	62.64 287	72.40 306

UnsupCasMVSNet_eth		53.16 289	52.47 288	55.23 300	59.45 336	33.39 327	59.43 312	69.13 261	45.98 278	50.35 314	72.32 295	29.30 296	58.26 320	42.02 267	44.30 333	74.05 290

new-patchmatchnet		47.56 301	47.73 301	47.06 319	58.81 337	9.37 350	48.78 332	59.21 310	43.28 300	44.22 328	68.66 314	25.67 318	57.20 324	31.57 317	49.35 328	74.62 285

FPMVS		42.18 306	41.11 308	45.39 320	58.03 338	41.01 282	49.50 330	53.81 330	30.07 332	33.71 337	64.03 325	11.69 338	52.08 337	14.01 340	55.11 314	43.09 338

new_pmnet		34.13 313	34.29 313	33.64 326	52.63 339	18.23 348	44.43 338	33.90 346	22.81 340	30.89 338	53.18 333	10.48 343	35.72 346	20.77 337	39.51 335	46.98 337

pmmvs3		44.92 304	41.95 307	53.86 305	52.58 340	43.55 262	62.11 302	46.90 340	26.05 337	40.63 333	60.19 330	11.08 342	57.91 321	31.83 314	46.15 331	60.11 329

DSMNet-mixed		39.30 310	38.72 310	41.03 324	51.22 341	19.66 346	45.53 336	31.35 347	15.83 344	39.80 335	67.42 319	22.19 328	45.13 340	22.43 335	52.69 321	58.31 331

LF4IMVS		42.95 305	42.26 306	45.04 321	48.30 342	32.50 328	54.80 322	48.49 336	28.03 334	40.51 334	70.16 308	9.24 344	43.89 341	31.63 315	49.18 329	58.72 330

wuyk23d		13.32 319	12.52 321	15.71 331	47.54 343	26.27 339	31.06 343	1.98 353	4.93 347	5.18 349	1.94 349	0.45 354	18.54 348	6.81 347	12.83 344	2.33 346

LCM-MVSNet		40.30 308	35.88 312	53.57 307	42.24 344	29.15 337	45.21 337	60.53 308	22.23 341	28.02 339	50.98 337	3.72 350	61.78 310	31.22 320	38.76 337	69.78 318

E-PMN		23.77 315	22.73 318	26.90 329	42.02 345	20.67 345	42.66 339	35.70 344	17.43 342	10.28 347	25.05 343	6.42 347	42.39 343	10.28 343	14.71 342	17.63 342

EMVS		22.97 316	21.84 319	26.36 330	40.20 346	19.53 347	41.95 340	34.64 345	17.09 343	9.73 348	22.83 344	7.29 346	42.22 344	9.18 345	13.66 343	17.32 343

ANet_high		41.38 307	37.47 311	53.11 309	39.73 347	24.45 343	56.94 318	69.69 253	47.65 265	26.04 340	52.32 334	12.44 337	62.38 308	21.80 336	10.61 345	72.49 301

PMMVS2		27.40 314	25.91 316	31.87 328	39.46 348	6.57 351	31.17 342	28.52 348	23.96 338	20.45 342	48.94 339	4.20 349	37.94 345	16.51 338	19.97 340	51.09 335

PMVS	28.69 22	36.22 311	33.29 314	45.02 322	36.82 349	35.98 316	54.68 323	48.74 335	26.31 336	21.02 341	51.61 336	2.88 352	60.10 315	9.99 344	47.58 330	38.99 340
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MVE	17.77 23	21.41 317	17.77 320	32.34 327	34.34 350	25.44 341	16.11 345	24.11 349	11.19 345	13.22 345	31.92 340	1.58 353	30.95 347	10.47 342	17.03 341	40.62 339
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
DeepMVS_CX				12.03 332	17.97 351	10.91 349		10.60 352	7.46 346	11.07 346	28.36 341	3.28 351	11.29 349	8.01 346	9.74 347	13.89 344

tmp_tt		9.43 320	11.14 322	4.30 333	2.38 352	4.40 352	13.62 346	16.08 351	0.39 348	15.89 344	13.06 345	15.80 335	5.54 350	12.63 341	10.46 346	2.95 345

testmvs		4.52 323	6.03 325	0.01 335	0.01 353	0.00 355	53.86 325	0.00 355	0.01 349	0.04 350	0.27 350	0.00 356	0.00 351	0.04 348	0.00 348	0.03 348

test123		4.73 322	6.30 324	0.02 334	0.01 353	0.01 354	56.36 320	0.00 355	0.01 349	0.04 350	0.21 351	0.01 355	0.00 351	0.03 349	0.00 348	0.04 347

uanet_test		0.00 325	0.00 327	0.00 336	0.00 355	0.00 355	0.00 347	0.00 355	0.00 351	0.00 352	0.00 352	0.00 356	0.00 351	0.00 350	0.00 348	0.00 349

cdsmvs_eth3d_5k		17.50 318	23.34 317	0.00 336	0.00 355	0.00 355	0.00 347	78.63 159	0.00 351	0.00 352	82.18 164	49.25 103	0.00 351	0.00 350	0.00 348	0.00 349

pcd_1.5k_mvsjas		3.92 324	5.23 326	0.00 336	0.00 355	0.00 355	0.00 347	0.00 355	0.00 351	0.00 352	0.00 352	47.05 134	0.00 351	0.00 350	0.00 348	0.00 349

sosnet-low-res		0.00 325	0.00 327	0.00 336	0.00 355	0.00 355	0.00 347	0.00 355	0.00 351	0.00 352	0.00 352	0.00 356	0.00 351	0.00 350	0.00 348	0.00 349

sosnet		0.00 325	0.00 327	0.00 336	0.00 355	0.00 355	0.00 347	0.00 355	0.00 351	0.00 352	0.00 352	0.00 356	0.00 351	0.00 350	0.00 348	0.00 349

uncertanet		0.00 325	0.00 327	0.00 336	0.00 355	0.00 355	0.00 347	0.00 355	0.00 351	0.00 352	0.00 352	0.00 356	0.00 351	0.00 350	0.00 348	0.00 349

Regformer		0.00 325	0.00 327	0.00 336	0.00 355	0.00 355	0.00 347	0.00 355	0.00 351	0.00 352	0.00 352	0.00 356	0.00 351	0.00 350	0.00 348	0.00 349

ab-mvs-re		6.49 321	8.65 323	0.00 336	0.00 355	0.00 355	0.00 347	0.00 355	0.00 351	0.00 352	77.89 246	0.00 356	0.00 351	0.00 350	0.00 348	0.00 349

uanet		0.00 325	0.00 327	0.00 336	0.00 355	0.00 355	0.00 347	0.00 355	0.00 351	0.00 352	0.00 352	0.00 356	0.00 351	0.00 350	0.00 348	0.00 349

test_241102_TWO								86.73 12	64.18 32	84.26 3	91.84 6	65.19 3	90.83 2	78.63 12	90.70 4	87.65 28

test_0728_THIRD									65.04 19	83.82 5	92.00 3	64.69 7	90.75 4	79.48 4	90.63 5	88.09 14

GSMVS																78.05 245

test_part1				0.00 336		0.00 355	0.00 347	86.64 13				0.00 356	0.00 351	0.00 350	0.00 348	0.00 349

sam_mvs1												34.74 247				78.05 245

sam_mvs												33.43 261

MTGPA								80.97 119

test_post1							68.67 270				3.64 347	32.39 277	69.49 282	44.17 246

test_post											3.55 348	33.90 256	66.52 295

patchmatchnet-post											64.03 325	34.50 249	74.27 265

MTMP							86.03 16	17.08 350

test9_res														75.28 27	88.31 32	83.81 150

agg_prior2														73.09 44	87.93 39	84.33 135

test_prior4						62.51 17	82.08 74

test_prior2							81.75 76		60.37 92	75.01 38	89.06 51	56.22 34		72.19 47	88.96 23

旧先验2							76.08 171		45.32 283	76.55 29			65.56 300	58.75 144

新几何2							76.12 169

无先验							79.66 109	74.30 225	48.40 257				80.78 194	53.62 176		79.03 237

原ACMM2							79.02 116

testdata2													72.18 273	46.95 225

segment_acmp												54.23 52

testdata1							72.65 226		60.50 89

plane_prior5								84.01 49					87.21 48	68.16 67	80.58 96	84.65 128

plane_prior4											86.10 94

plane_prior3						56.09 109			63.92 35	69.27 114

plane_prior2							84.22 35		64.52 25

plane_prior						56.31 103	83.58 48		63.19 45						80.48 99

n2								0.00 355

nn								0.00 355

door-mid								47.19 339

test11								83.47 64

door								47.60 338

HQP5-MVS						54.94 127

BP-MVS														67.04 78

HQP4-MVS										67.85 138			86.93 57			84.32 136

HQP3-MVS								83.90 53							80.35 102

HQP2-MVS												45.46 150

MDTV_nov1_ep13_2view						25.89 340	61.22 306		40.10 318	51.10 307		32.97 266		38.49 280		78.61 240

ACMMP++_ref															74.07 167

ACMMP++															72.16 197

Test By Simon												48.33 115

High-resolution multi-view benchmark