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.

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

MP-MVS-pluss		78.35 21	78.46 17	78.03 40	84.96 55	59.52 57	82.93 61	85.39 26	62.15 65	76.41 32	91.51 9	52.47 73	86.78 65	80.66 2	89.64 19	87.80 24
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
SMA-MVS		80.28 5	80.39 6	79.95 3	86.60 21	61.95 22	86.33 11	85.75 23	62.49 60	82.20 12	92.28 1	56.53 31	89.70 12	79.85 3	91.48 1	88.19 12
Yufeng Yin; Xiaoyan Liu; Zichao Zhang: SMA-MVS: Segmentation-Guided Multi-Scale Anchor Deformation Patch Multi-View Stereo. IEEE Transactions on Circuits and Systems for Video Technology
test_0728_THIRD									65.04 19	83.82 6	92.00 3	64.69 8	90.75 5	79.48 4	90.63 6	88.09 15

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

IU-MVS					87.77 4	59.15 63		85.53 25	53.93 210	84.64 3				79.07 6	90.87 3	88.37 7

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

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

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

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

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

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

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

SteuartSystems-ACMMP		79.48 9	79.31 10	79.98 2	83.01 77	62.18 19	87.60 7	85.83 21	66.69 10	78.03 27	90.98 12	54.26 53	90.06 9	78.42 14	89.02 23	87.69 27
Skip Steuart: Steuart Systems R&D Blog.
9.14			78.75 15		83.10 73		84.15 42	88.26 2	59.90 105	78.57 23	90.36 26	57.51 28	86.86 62	77.39 15	89.52 21

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

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

MP-MVS		78.35 21	78.26 21	78.64 28	86.54 23	63.47 5	86.02 18	83.55 63	63.89 37	73.60 64	90.60 18	54.85 48	86.72 66	77.20 18	88.06 37	85.74 93
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
ETH3D-3000-0.1		78.58 15	78.91 13	77.61 45	83.06 74	57.86 86	84.14 44	88.31 1	60.37 93	79.14 18	90.35 27	57.76 25	87.00 59	77.16 19	89.90 14	87.97 18

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

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

TSAR-MVS + MP.		78.44 19	78.28 20	78.90 23	84.96 55	61.41 29	84.03 45	83.82 58	59.34 118	79.37 15	89.76 48	59.84 13	87.62 45	76.69 22	86.74 54	87.68 28
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
ZNCC-MVS		78.82 12	78.67 16	79.30 10	86.43 26	62.05 21	86.62 9	86.01 19	63.32 42	75.08 39	90.47 25	53.96 57	88.68 24	76.48 23	89.63 20	87.16 47

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

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

SD-MVS		77.70 29	77.62 28	77.93 42	84.47 62	61.88 24	84.55 34	83.87 56	60.37 93	79.89 14	89.38 53	54.97 45	85.58 96	76.12 26	84.94 63	86.33 70
Zhenlong Yuan, Jiakai Cao, Zhaoxin Li, Hao Jiang and Zhaoqi Wang: SD-MVS: Segmentation-driven Deformation Multi-View Stereo with Spherical Refinement and EM optimization. AAAI2024
HPM-MVS		77.28 33	76.85 35	78.54 31	85.00 54	60.81 40	82.91 62	85.08 31	62.57 58	73.09 72	89.97 44	50.90 93	87.48 46	75.30 27	86.85 52	87.33 44
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
test9_res														75.28 28	88.31 33	83.81 155

train_agg		76.27 44	76.15 41	76.64 62	85.58 44	61.59 27	81.62 84	81.26 116	55.86 177	74.93 42	88.81 62	53.70 62	84.68 117	75.24 29	88.33 31	83.65 165

agg_prior1		75.94 47	76.01 43	75.72 76	85.04 52	59.96 51	81.44 88	81.04 122	56.14 173	74.68 46	88.90 60	53.91 58	84.04 129	75.01 30	87.92 41	83.16 180

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

DeepC-MVS	69.38 2	78.56 17	78.14 23	79.83 4	83.60 67	61.62 26	84.17 41	86.85 9	63.23 44	73.84 61	90.25 33	57.68 26	89.96 10	74.62 32	89.03 22	87.89 19
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
ETH3 D test6400		79.14 10	79.32 9	78.61 29	86.34 27	58.11 83	84.65 33	87.66 4	58.56 130	78.87 20	89.54 50	63.67 10	89.57 13	74.60 33	89.98 13	88.14 13

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

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

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

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

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

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

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

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

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

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

APD-MVS		78.02 24	78.04 25	77.98 41	86.44 25	60.81 40	85.52 26	84.36 43	60.61 88	79.05 19	90.30 30	55.54 42	88.32 30	73.48 44	87.03 48	84.83 124
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
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

agg_prior2														73.09 46	87.93 40	84.33 138

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

PGM-MVS		76.77 40	76.06 42	78.88 24	86.14 35	62.73 12	82.55 70	83.74 59	61.71 74	72.45 83	90.34 29	48.48 117	88.13 33	72.32 48	86.85 52	85.78 87

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

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

ACMMP		76.02 46	75.33 49	78.07 38	85.20 51	61.91 23	85.49 29	84.44 41	63.04 47	69.80 111	89.74 49	45.43 155	87.16 52	72.01 51	82.87 80	85.14 114
Qingshan Xu, Weihang Kong, Wenbing Tao, Marc Pollefeys: Multi-Scale Geometric Consistency Guided and Planar Prior Assisted Multi-View Stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence
mPP-MVS		76.54 41	75.93 44	78.34 36	86.47 24	63.50 4	85.74 23	82.28 89	62.90 50	71.77 87	90.26 32	46.61 144	86.55 74	71.71 52	85.66 61	84.97 121

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

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

X-MVStestdata		70.21 118	67.28 159	79.00 20	86.32 28	62.62 14	85.83 20	83.92 52	64.55 23	72.17 84	6.49 352	47.95 121	88.01 36	71.55 54	86.74 54	86.37 67

Regformer-2		75.63 50	74.99 51	77.54 47	80.43 111	58.32 81	79.50 117	82.92 80	67.84 1	75.94 33	80.75 203	55.73 40	86.80 63	71.44 56	80.38 105	87.50 35

Regformer-1		75.47 51	74.93 53	77.09 54	80.43 111	57.70 90	79.50 117	82.13 91	67.84 1	75.73 36	80.75 203	56.50 32	86.07 82	71.07 57	80.38 105	87.50 35

PHI-MVS		75.87 48	75.36 48	77.41 49	80.62 109	55.91 119	84.28 38	85.78 22	56.08 175	73.41 66	86.58 90	50.94 92	88.54 25	70.79 58	89.71 17	87.79 25

diffmvs		70.69 108	70.43 105	71.46 168	69.45 297	48.95 213	72.93 227	78.46 172	57.27 148	71.69 88	83.97 138	51.48 84	77.92 243	70.70 59	77.95 140	87.53 34

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

Regformer-4		74.25 66	73.48 70	76.57 64	79.75 124	56.54 107	78.54 129	81.49 106	66.93 7	73.90 59	80.30 211	53.84 60	85.98 87	69.76 61	76.84 152	87.17 46

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

baseline		74.61 60	74.70 56	74.34 105	75.70 211	49.99 198	77.54 145	84.63 40	62.73 57	73.98 55	87.79 72	57.67 27	83.82 136	69.49 63	82.74 83	89.20 3

OPM-MVS		74.73 58	74.25 60	76.19 68	80.81 105	59.01 69	82.60 69	83.64 61	63.74 39	72.52 80	87.49 73	47.18 135	85.88 90	69.47 64	80.78 97	83.66 164

casdiffmvs		74.80 56	74.89 54	74.53 101	75.59 215	50.37 191	78.17 134	85.06 32	62.80 56	74.40 51	87.86 70	57.88 24	83.61 140	69.46 65	82.79 82	89.59 2

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

Regformer-3		73.89 70	73.28 74	75.71 77	79.75 124	55.48 128	78.54 129	79.93 142	66.58 11	73.62 63	80.30 211	54.87 47	84.54 120	69.09 67	76.84 152	87.10 49

CPTT-MVS		72.78 80	72.08 83	74.87 92	84.88 60	61.41 29	84.15 42	77.86 182	55.27 189	67.51 153	88.08 69	41.93 187	81.85 176	69.04 68	80.01 111	81.35 209

DeepC-MVS_fast	68.24 3	77.25 34	76.63 38	79.12 16	86.15 34	60.86 39	84.71 32	84.85 38	61.98 72	73.06 73	88.88 61	53.72 61	89.06 19	68.27 69	88.04 38	87.42 40
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
SR-MVS-dyc-post		74.57 61	73.90 63	76.58 63	83.49 69	59.87 54	84.29 36	81.36 109	58.07 139	73.14 70	90.07 36	44.74 161	85.84 91	68.20 70	81.76 91	84.03 147

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

HQP_MVS		74.31 64	73.73 66	76.06 69	81.41 94	56.31 108	84.22 39	84.01 50	64.52 25	69.27 120	86.10 99	45.26 159	87.21 50	68.16 72	80.58 101	84.65 130

plane_prior5								84.01 50					87.21 50	68.16 72	80.58 101	84.65 130

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

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

LPG-MVS_test		72.74 81	71.74 86	75.76 74	80.22 115	57.51 93	82.55 70	83.40 68	61.32 78	66.67 163	87.33 76	39.15 216	86.59 71	67.70 76	77.30 147	83.19 177

LGP-MVS_train				75.76 74	80.22 115	57.51 93		83.40 68	61.32 78	66.67 163	87.33 76	39.15 216	86.59 71	67.70 76	77.30 147	83.19 177

HPM-MVS_fast		74.30 65	73.46 72	76.80 58	84.45 63	59.04 68	83.65 52	81.05 121	60.15 101	70.43 96	89.84 47	41.09 203	85.59 95	67.61 78	82.90 79	85.77 90

MVS_111021_HR		74.02 68	73.46 72	75.69 78	83.01 77	60.63 44	77.29 152	78.40 177	61.18 81	70.58 95	85.97 103	54.18 55	84.00 133	67.52 79	82.98 77	82.45 192

ETV-MVS		74.46 62	73.84 65	76.33 67	79.27 131	55.24 131	79.22 120	85.00 35	64.97 21	72.65 78	79.46 230	53.65 65	87.87 39	67.45 80	82.91 78	85.89 84

DELS-MVS		74.76 57	74.46 58	75.65 79	77.84 169	52.25 168	75.59 184	84.17 47	63.76 38	73.15 69	82.79 155	59.58 16	86.80 63	67.24 81	86.04 59	87.89 19
Christian Sormann, Emanuele Santellani, Mattia Rossi, Andreas Kuhn, Friedrich Fraundorfer: DELS-MVS: Deep Epipolar Line Search for Multi-View Stereo. Winter Conference on Applications of Computer Vision (WACV), 2023
TSAR-MVS + GP.		74.90 55	74.15 61	77.17 53	82.00 86	58.77 74	81.80 80	78.57 166	58.58 128	74.32 52	84.51 127	55.94 38	87.22 49	67.11 82	84.48 68	85.52 100

BP-MVS														67.04 83

HQP-MVS		73.45 73	72.80 77	75.40 83	80.66 106	54.94 133	82.31 74	83.90 54	62.10 67	67.85 144	85.54 112	45.46 153	86.93 60	67.04 83	80.35 107	84.32 139

ACMP	63.53 6	72.30 87	71.20 96	75.59 82	80.28 113	57.54 91	82.74 65	82.84 84	60.58 89	65.24 192	86.18 97	39.25 214	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
EI-MVSNet-Vis-set		72.42 86	71.59 87	74.91 90	78.47 150	54.02 140	77.05 157	79.33 153	65.03 20	71.68 89	79.35 233	52.75 69	84.89 113	66.46 86	74.23 169	85.83 86

DPM-MVS		75.47 51	75.00 50	76.88 56	81.38 96	59.16 62	79.94 107	85.71 24	56.59 162	72.46 81	86.76 80	56.89 29	87.86 40	66.36 87	88.91 26	83.64 166

CS-MVS		74.18 67	73.60 68	75.92 71	78.99 137	52.53 162	80.61 98	85.93 20	61.17 82	71.15 92	79.34 234	54.52 51	88.86 22	66.07 88	84.67 65	86.38 63

MVSFormer		71.50 100	70.38 107	74.88 91	78.76 142	57.15 102	82.79 63	78.48 170	51.26 237	69.49 115	83.22 150	43.99 170	83.24 146	66.06 89	79.37 119	84.23 142

test_djsdf		69.45 136	67.74 143	74.58 100	74.57 230	54.92 135	82.79 63	78.48 170	51.26 237	65.41 186	83.49 148	38.37 223	83.24 146	66.06 89	69.25 243	85.56 99

canonicalmvs		74.67 59	74.98 52	73.71 120	78.94 139	50.56 188	80.23 102	83.87 56	60.30 99	77.15 29	86.56 91	59.65 14	82.00 174	66.01 91	82.12 87	88.58 6

MVS_Test		72.45 85	72.46 80	72.42 154	74.88 223	48.50 217	76.28 172	83.14 78	59.40 116	72.46 81	84.68 120	55.66 41	81.12 190	65.98 92	79.66 115	87.63 30

alignmvs		73.86 71	73.99 62	73.45 129	78.20 158	50.50 189	78.57 127	82.43 87	59.40 116	76.57 30	86.71 84	56.42 34	81.23 189	65.84 93	81.79 89	88.62 4

nrg030		72.96 79	73.01 75	72.84 142	75.41 218	50.24 192	80.02 105	82.89 83	58.36 135	74.44 50	86.73 82	58.90 19	80.83 198	65.84 93	74.46 166	87.44 39

RRT_MVS		68.77 147	66.71 169	74.95 89	75.93 208	58.55 78	80.50 100	75.84 210	56.09 174	68.17 137	83.74 142	28.50 306	82.98 152	65.67 95	65.91 270	83.33 171

MVS_111021_LR		69.50 134	68.78 129	71.65 165	78.38 151	59.33 60	74.82 201	70.11 257	58.08 138	67.83 148	84.68 120	41.96 186	76.34 262	65.62 96	77.54 142	79.30 240

EI-MVSNet-UG-set		71.92 93	71.06 97	74.52 102	77.98 166	53.56 146	76.62 164	79.16 154	64.40 27	71.18 91	78.95 238	52.19 76	84.66 119	65.47 97	73.57 178	85.32 110

PS-MVSNAJss		72.24 88	71.21 95	75.31 85	78.50 148	55.93 118	81.63 83	82.12 92	56.24 170	70.02 105	85.68 109	47.05 137	84.34 124	65.27 98	74.41 168	85.67 94

MSLP-MVS++		73.77 72	73.47 71	74.66 96	83.02 76	59.29 61	82.30 77	81.88 95	59.34 118	71.59 90	86.83 79	45.94 148	83.65 139	65.09 99	85.22 62	81.06 216

v2v482		70.50 112	69.45 119	73.66 122	72.62 254	50.03 197	77.58 142	80.51 133	59.90 105	69.52 114	82.14 174	47.53 128	84.88 115	65.07 100	70.17 225	86.09 79

jason		69.65 130	68.39 137	73.43 131	78.27 157	56.88 104	77.12 155	73.71 238	46.53 279	69.34 119	83.22 150	43.37 174	79.18 222	64.77 101	79.20 124	84.23 142
jason: jason.
anonymousdsp		67.00 184	64.82 201	73.57 126	70.09 288	56.13 113	76.35 170	77.35 193	48.43 262	64.99 198	80.84 201	33.01 271	80.34 207	64.66 102	67.64 261	84.23 142

lupinMVS		69.57 132	68.28 138	73.44 130	78.76 142	57.15 102	76.57 165	73.29 241	46.19 282	69.49 115	82.18 170	43.99 170	79.23 221	64.66 102	79.37 119	83.93 150

CLD-MVS		73.33 74	72.68 78	75.29 87	78.82 141	53.33 151	78.23 133	84.79 39	61.30 80	70.41 97	81.04 193	52.41 74	87.12 54	64.61 104	82.49 86	85.41 108
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
test_part1		73.17 76	72.05 84	76.54 65	78.34 154	54.99 132	85.51 28	86.99 7	58.40 133	69.77 112	84.50 128	48.78 112	87.16 52	64.60 105	71.53 209	87.48 37

testing_2		66.02 199	63.77 209	72.76 146	66.03 320	50.48 190	72.93 227	80.36 136	54.41 207	54.25 297	76.76 266	30.89 289	83.16 149	64.19 106	74.08 171	84.65 130

V42		68.65 149	67.35 157	72.56 149	68.93 301	50.18 194	72.90 229	79.47 150	56.92 153	69.45 117	80.26 213	46.29 146	82.99 151	64.07 107	67.82 259	84.53 133

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

v1144		70.42 114	69.31 120	73.76 116	73.22 242	50.64 185	77.83 138	81.43 107	58.58 128	69.40 118	81.16 190	47.53 128	85.29 107	64.01 109	70.64 216	85.34 109

Effi-MVS+		73.31 75	72.54 79	75.62 80	77.87 168	53.64 144	79.62 115	79.61 147	61.63 75	72.02 86	82.61 160	56.44 33	85.97 88	63.99 110	79.07 127	87.25 45

xiu_mvs_v1_base_debu		68.58 151	67.28 159	72.48 151	78.19 159	57.19 99	75.28 188	75.09 222	51.61 228	70.04 102	81.41 186	32.79 274	79.02 229	63.81 111	77.31 144	81.22 211

xiu_mvs_v1_base		68.58 151	67.28 159	72.48 151	78.19 159	57.19 99	75.28 188	75.09 222	51.61 228	70.04 102	81.41 186	32.79 274	79.02 229	63.81 111	77.31 144	81.22 211

xiu_mvs_v1_base_debi		68.58 151	67.28 159	72.48 151	78.19 159	57.19 99	75.28 188	75.09 222	51.61 228	70.04 102	81.41 186	32.79 274	79.02 229	63.81 111	77.31 144	81.22 211

RRT_test8_iter05		68.17 163	66.86 168	72.07 157	75.81 209	46.33 239	76.41 169	81.81 98	56.43 165	66.52 165	81.30 189	31.90 286	84.25 125	63.77 114	67.83 258	85.64 97

v8		70.33 116	69.28 121	73.49 127	73.15 244	50.22 193	78.62 126	80.78 129	60.79 85	66.45 168	82.11 175	49.35 102	84.98 110	63.58 115	68.71 250	85.28 111

jajsoiax		68.25 159	66.45 173	73.66 122	75.62 213	55.49 127	80.82 94	78.51 169	52.33 223	64.33 205	84.11 133	28.28 307	81.81 178	63.48 116	70.62 217	83.67 162

mvs_tets		68.18 161	66.36 178	73.63 125	75.61 214	55.35 130	80.77 95	78.56 167	52.48 222	64.27 207	84.10 134	27.45 313	81.84 177	63.45 117	70.56 218	83.69 161

v144192		69.71 126	68.51 132	73.33 134	73.10 245	50.13 195	77.54 145	80.64 130	56.65 156	68.57 130	80.55 205	46.87 142	84.96 112	62.98 118	69.66 237	84.89 123

v1192		69.97 123	68.68 130	73.85 112	73.19 243	50.94 178	77.68 141	81.36 109	57.51 146	68.95 126	80.85 200	45.28 158	85.33 106	62.97 119	70.37 221	85.27 112

v10		70.21 118	69.02 125	73.81 113	73.51 241	50.92 180	78.74 123	81.39 108	60.05 103	66.39 169	81.83 178	47.58 127	85.41 105	62.80 120	68.86 249	85.09 117

OMC-MVS		71.40 102	70.60 102	73.78 114	76.60 197	53.15 153	79.74 113	79.78 143	58.37 134	68.75 127	86.45 93	45.43 155	80.60 203	62.58 121	77.73 141	87.58 33

XVG-OURS-SEG-HR		68.81 144	67.47 152	72.82 144	74.40 234	56.87 105	70.59 260	79.04 155	54.77 201	66.99 159	86.01 102	39.57 211	78.21 239	62.54 122	73.33 183	83.37 170

EPNet		73.09 78	72.16 81	75.90 72	75.95 207	56.28 110	83.05 58	72.39 246	66.53 12	65.27 188	87.00 78	50.40 95	85.47 102	62.48 123	86.32 58	85.94 82
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
v1921920		69.47 135	68.17 140	73.36 133	73.06 246	50.10 196	77.39 148	80.56 131	56.58 163	68.59 128	80.37 207	44.72 163	84.98 110	62.47 124	69.82 232	85.00 119

cl_fuxian		68.33 157	67.56 147	70.62 189	70.87 277	46.21 242	74.47 207	78.80 160	56.22 171	66.19 172	78.53 245	51.88 79	81.40 184	62.08 125	69.04 246	84.25 141

XVG-OURS		68.76 148	67.37 155	72.90 141	74.32 235	57.22 97	70.09 266	78.81 159	55.24 190	67.79 150	85.81 108	36.54 243	78.28 238	62.04 126	75.74 160	83.19 177

v1240		69.24 139	67.91 142	73.25 137	73.02 248	49.82 199	77.21 154	80.54 132	56.43 165	68.34 134	80.51 206	43.33 175	84.99 108	62.03 127	69.77 235	84.95 122

ET-MVSNet_ETH3D		67.96 166	65.72 190	74.68 95	76.67 195	55.62 125	75.11 193	74.74 226	52.91 217	60.03 246	80.12 215	33.68 264	82.64 164	61.86 128	76.34 157	85.78 87

VDD-MVS		72.50 83	72.09 82	73.75 118	81.58 90	49.69 203	77.76 140	77.63 187	63.21 45	73.21 68	89.02 58	42.14 184	83.32 144	61.72 129	82.50 85	88.25 9

PS-MVSNAJ		70.51 111	69.70 114	72.93 140	81.52 91	55.79 120	74.92 199	79.00 156	55.04 198	69.88 109	78.66 240	47.05 137	82.19 171	61.61 130	79.58 116	80.83 219

xiu_mvs_v2_base		70.52 110	69.75 112	72.84 142	81.21 100	55.63 124	75.11 193	78.92 157	54.92 199	69.96 108	79.68 225	47.00 141	82.09 173	61.60 131	79.37 119	80.81 220

cl-mvsnet2		67.47 173	66.45 173	70.54 191	69.85 293	46.49 237	73.85 217	77.35 193	55.07 196	65.51 184	77.92 250	47.64 126	81.10 191	61.58 132	69.32 240	84.01 149

miper_ehance_all_eth		68.03 164	67.24 163	70.40 193	70.54 280	46.21 242	73.98 212	78.68 164	55.07 196	66.05 174	77.80 254	52.16 77	81.31 186	61.53 133	69.32 240	83.67 162

MG-MVS		73.96 69	73.89 64	74.16 108	85.65 42	49.69 203	81.59 86	81.29 115	61.45 77	71.05 93	88.11 67	51.77 81	87.73 42	61.05 134	83.09 73	85.05 118

miper_enhance_ethall		67.11 181	66.09 185	70.17 197	69.21 299	45.98 245	72.85 230	78.41 176	51.38 234	65.65 182	75.98 278	51.17 88	81.25 187	60.82 135	69.32 240	83.29 174

ACMM	61.98 7	70.80 107	69.73 113	74.02 109	80.59 110	58.59 77	82.68 67	82.02 94	55.46 187	67.18 157	84.39 130	38.51 221	83.17 148	60.65 136	76.10 158	80.30 225
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
Effi-MVS+-dtu		69.64 131	67.53 150	75.95 70	76.10 204	62.29 18	80.20 104	76.06 208	59.83 109	65.26 191	77.09 260	41.56 194	84.02 132	60.60 137	71.09 214	81.53 204

mvs-test1		70.44 113	68.19 139	77.18 52	76.10 204	63.22 6	80.59 99	76.06 208	59.83 109	66.32 170	79.87 219	41.56 194	85.53 97	60.60 137	72.77 192	82.80 187

PVSNet_Blended_VisFu		71.45 101	70.39 106	74.65 97	82.01 85	58.82 73	79.93 108	80.35 137	55.09 194	65.82 181	82.16 173	49.17 106	82.64 164	60.34 139	78.62 135	82.50 191

MVSTER		67.16 180	65.58 193	71.88 159	70.37 284	49.70 201	70.25 265	78.45 173	51.52 231	69.16 124	80.37 207	38.45 222	82.50 166	60.19 140	71.46 210	83.44 169

EIA-MVS		71.78 95	70.60 102	75.30 86	79.85 123	53.54 147	77.27 153	83.26 75	57.92 142	66.49 166	79.39 231	52.07 78	86.69 67	60.05 141	79.14 126	85.66 95

v148		68.24 160	67.19 164	71.40 172	70.43 282	47.77 226	75.76 183	77.03 197	58.91 123	67.36 154	80.10 216	48.60 116	81.89 175	60.01 142	66.52 267	84.53 133

CANet_DTU		68.18 161	67.71 146	69.59 207	74.83 224	46.24 241	78.66 125	76.85 199	59.60 111	63.45 213	82.09 176	35.25 248	77.41 250	59.88 143	78.76 132	85.14 114

IterMVS-LS		69.22 140	68.48 133	71.43 171	74.44 233	49.40 207	76.23 173	77.55 188	59.60 111	65.85 180	81.59 184	51.28 86	81.58 182	59.87 144	69.90 231	83.30 172
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
EI-MVSNet		69.27 138	68.44 136	71.73 162	74.47 231	49.39 208	75.20 191	78.45 173	59.60 111	69.16 124	76.51 270	51.29 85	82.50 166	59.86 145	71.45 211	83.30 172

3Dnovator	64.47 5	72.49 84	71.39 91	75.79 73	77.70 171	58.99 70	80.66 97	83.15 77	62.24 64	65.46 185	86.59 89	42.38 183	85.52 98	59.59 146	84.72 64	82.85 186

eth_miper_zixun_eth		67.63 170	66.28 182	71.67 164	71.60 269	48.33 219	73.68 220	77.88 181	55.80 181	65.91 177	78.62 243	47.35 134	82.88 155	59.45 147	66.25 268	83.81 155

cl-mvsnet1		67.18 178	66.26 183	69.94 200	70.20 285	45.74 247	73.29 223	76.83 200	55.10 192	65.27 188	79.58 226	47.38 133	80.53 204	59.43 148	69.22 244	83.54 167

cl-mvsnet_		67.18 178	66.26 183	69.94 200	70.20 285	45.74 247	73.30 222	76.83 200	55.10 192	65.27 188	79.57 227	47.39 132	80.53 204	59.41 149	69.22 244	83.53 168

旧先验2							76.08 176		45.32 289	76.55 31			65.56 305	58.75 150

VDDNet		71.81 94	71.33 93	73.26 136	82.80 80	47.60 229	78.74 123	75.27 218	59.59 114	72.94 74	89.40 52	41.51 197	83.91 134	58.75 150	82.99 75	88.26 8

114514_t		70.83 105	69.56 115	74.64 98	86.21 30	54.63 137	82.34 73	81.81 98	48.22 264	63.01 217	85.83 106	40.92 204	87.10 55	57.91 152	79.79 112	82.18 195

Vis-MVSNet		72.18 89	71.37 92	74.61 99	81.29 97	55.41 129	80.90 93	78.28 179	60.73 87	69.23 123	88.09 68	44.36 167	82.65 163	57.68 153	81.75 93	85.77 90
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
PAPM_NR		72.63 82	71.80 85	75.13 88	81.72 89	53.42 150	79.91 109	83.28 74	59.14 120	66.31 171	85.90 104	51.86 80	86.06 83	57.45 154	80.62 99	85.91 83

LFMVS		71.78 95	71.59 87	72.32 155	83.40 71	46.38 238	79.75 112	71.08 251	64.18 32	72.80 76	88.64 65	42.58 180	83.72 137	57.41 155	84.49 67	86.86 53

v7n		69.01 142	67.36 156	73.98 110	72.51 257	52.65 159	78.54 129	81.30 114	60.26 100	62.67 221	81.62 181	43.61 172	84.49 121	57.01 156	68.70 251	84.79 127

mvs_anonymous		68.03 164	67.51 151	69.59 207	72.08 262	44.57 260	71.99 242	75.23 219	51.67 227	67.06 158	82.57 161	54.68 49	77.94 242	56.56 157	75.71 161	86.26 76

Patchmatch-RL test		58.16 264	55.49 276	66.15 245	67.92 307	48.89 214	60.66 314	51.07 336	47.86 269	59.36 253	62.71 335	34.02 261	72.27 278	56.41 158	59.40 309	77.30 258

miper_lstm_enhance		62.03 240	60.88 242	65.49 256	66.71 314	46.25 240	56.29 326	75.70 212	50.68 241	61.27 239	75.48 283	40.21 206	68.03 293	56.31 159	65.25 276	82.18 195

thisisatest0530		67.92 167	65.78 189	74.33 106	76.29 201	51.03 177	76.89 161	74.25 232	53.67 212	65.59 183	81.76 179	35.15 249	85.50 100	55.94 160	72.47 197	86.47 62

EPP-MVSNet		72.16 91	71.31 94	74.71 93	78.68 145	49.70 201	82.10 78	81.65 101	60.40 92	65.94 176	85.84 105	51.74 82	86.37 80	55.93 161	79.55 118	88.07 17

PVSNet_BlendedMVS		68.56 154	67.72 144	71.07 182	77.03 189	50.57 186	74.50 206	81.52 103	53.66 213	64.22 209	79.72 224	49.13 107	82.87 156	55.82 162	73.92 173	79.77 235

PVSNet_Blended		68.59 150	67.72 144	71.19 178	77.03 189	50.57 186	72.51 235	81.52 103	51.91 226	64.22 209	77.77 256	49.13 107	82.87 156	55.82 162	79.58 116	80.14 228

PAPR		71.72 97	70.82 100	74.41 104	81.20 101	51.17 176	79.55 116	83.33 71	55.81 180	66.93 160	84.61 123	50.95 91	86.06 83	55.79 164	79.20 124	86.00 81

tttt0517		67.83 169	65.66 191	74.33 106	76.69 194	50.82 182	77.86 137	73.99 235	54.54 205	64.64 202	82.53 163	35.06 250	85.50 100	55.71 165	69.91 230	86.67 59

IterMVS-SCA-FT		62.49 233	61.52 234	65.40 257	71.99 264	50.80 183	71.15 254	69.63 261	45.71 288	60.61 242	77.93 249	37.45 232	65.99 303	55.67 166	63.50 287	79.42 238

XVG-ACMP-BASELINE		64.36 218	62.23 227	70.74 187	72.35 259	52.45 166	70.80 259	78.45 173	53.84 211	59.87 248	81.10 192	16.24 340	79.32 220	55.64 167	71.76 205	80.47 222

Anonymous20231211		69.28 137	68.47 134	71.73 162	80.28 113	47.18 233	79.98 106	82.37 88	54.61 202	67.24 156	84.01 136	39.43 212	82.41 169	55.45 168	72.83 191	85.62 98

GA-MVS		65.53 205	63.70 210	71.02 183	70.87 277	48.10 221	70.48 262	74.40 230	56.69 155	64.70 201	76.77 265	33.66 265	81.10 191	55.42 169	70.32 223	83.87 154

test_yl		69.69 127	69.13 122	71.36 173	78.37 152	45.74 247	74.71 202	80.20 138	57.91 143	70.01 106	83.83 139	42.44 181	82.87 156	54.97 170	79.72 113	85.48 102

DCV-MVSNet		69.69 127	69.13 122	71.36 173	78.37 152	45.74 247	74.71 202	80.20 138	57.91 143	70.01 106	83.83 139	42.44 181	82.87 156	54.97 170	79.72 113	85.48 102

1314		64.61 216	63.21 216	68.80 218	71.87 267	47.46 230	73.95 213	78.39 178	42.88 311	59.97 247	76.60 269	38.11 227	79.39 219	54.84 172	72.32 200	79.55 236

Fast-Effi-MVS+-dtu		67.37 174	65.33 196	73.48 128	72.94 249	57.78 89	77.47 147	76.88 198	57.60 145	61.97 232	76.85 264	39.31 213	80.49 206	54.72 173	70.28 224	82.17 197

UniMVSNet_NR-MVSNet		71.11 103	71.00 98	71.44 169	79.20 132	44.13 262	76.02 180	82.60 86	66.48 13	68.20 135	84.60 124	56.82 30	82.82 160	54.62 174	70.43 219	87.36 43

DU-MVS		70.01 121	69.53 116	71.44 169	78.05 164	44.13 262	75.01 196	81.51 105	64.37 28	68.20 135	84.52 125	49.12 109	82.82 160	54.62 174	70.43 219	87.37 41

FIs		70.82 106	71.43 89	68.98 216	78.33 155	38.14 306	76.96 159	83.59 62	61.02 83	67.33 155	86.73 82	55.07 44	81.64 179	54.61 176	79.22 123	87.14 48

VPA-MVSNet		69.02 141	69.47 118	67.69 229	77.42 182	41.00 289	74.04 211	79.68 145	60.06 102	69.26 122	84.81 119	51.06 90	77.58 248	54.44 177	74.43 167	84.48 135

Anonymous20240529		69.91 124	69.02 125	72.56 149	80.19 118	47.65 227	77.56 144	80.99 124	55.45 188	69.88 109	86.76 80	39.24 215	82.18 172	54.04 178	77.10 149	87.85 23

UniMVSNet (Re)		70.63 109	70.20 108	71.89 158	78.55 147	45.29 253	75.94 181	82.92 80	63.68 40	68.16 138	83.59 145	53.89 59	83.49 143	53.97 179	71.12 213	86.89 52

D2MVS		62.30 237	60.29 245	68.34 224	66.46 316	48.42 218	65.70 287	73.42 239	47.71 270	58.16 267	75.02 286	30.51 291	77.71 246	53.96 180	71.68 207	78.90 244

原ACMM1				74.69 94	85.39 49	59.40 58		83.42 67	51.47 233	70.27 100	86.61 88	48.61 115	86.51 76	53.85 181	87.96 39	78.16 248

无先验							79.66 114	74.30 231	48.40 263				80.78 200	53.62 182		79.03 242

1121		68.53 155	67.16 165	72.63 148	85.64 43	61.14 34	73.95 213	66.46 282	44.61 294	70.28 99	86.68 85	41.42 198	80.78 200	53.62 182	81.79 89	75.97 271

UA-Net		73.13 77	72.93 76	73.76 116	83.58 68	51.66 174	78.75 122	77.66 186	67.75 4	72.61 79	89.42 51	49.82 98	83.29 145	53.61 184	83.14 72	86.32 72

VNet		69.68 129	70.19 109	68.16 225	79.73 127	41.63 285	70.53 261	77.38 192	60.37 93	70.69 94	86.63 87	51.08 89	77.09 254	53.61 184	81.69 95	85.75 92

Fast-Effi-MVS+		70.28 117	69.12 124	73.73 119	78.50 148	51.50 175	75.01 196	79.46 151	56.16 172	68.59 128	79.55 228	53.97 56	84.05 128	53.34 186	77.53 143	85.65 96

testdata				64.66 262	81.52 91	52.93 156		65.29 289	46.09 283	73.88 60	87.46 74	38.08 228	66.26 302	53.31 187	78.48 136	74.78 288

thisisatest0515		65.83 201	63.50 213	72.82 144	73.75 239	49.50 206	71.32 249	73.12 243	49.39 252	63.82 211	76.50 272	34.95 252	84.84 116	53.20 188	75.49 163	84.13 146

MVS		67.37 174	66.33 179	70.51 192	75.46 217	50.94 178	73.95 213	81.85 97	41.57 318	62.54 225	78.57 244	47.98 120	85.47 102	52.97 189	82.05 88	75.14 280

IterMVS		62.79 232	61.27 237	67.35 232	69.37 298	52.04 172	71.17 252	68.24 273	52.63 221	59.82 249	76.91 263	37.32 234	72.36 276	52.80 190	63.19 290	77.66 254
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
FC-MVSNet-test		69.80 125	70.58 104	67.46 230	77.61 179	34.73 326	76.05 178	83.19 76	60.84 84	65.88 179	86.46 92	54.52 51	80.76 202	52.52 191	78.12 138	86.91 51

TranMVSNet+NR-MVSNet		70.36 115	70.10 111	71.17 179	78.64 146	42.97 273	76.53 166	81.16 120	66.95 6	68.53 131	85.42 114	51.61 83	83.07 150	52.32 192	69.70 236	87.46 38

Baseline_NR-MVSNet		67.05 182	67.56 147	65.50 255	75.65 212	37.70 310	75.42 186	74.65 228	59.90 105	68.14 139	83.15 153	49.12 109	77.20 252	52.23 193	69.78 233	81.60 203

UniMVSNet_ETH3D		67.60 171	67.07 166	69.18 215	77.39 183	42.29 277	74.18 210	75.59 214	60.37 93	66.77 162	86.06 101	37.64 230	78.93 234	52.16 194	73.49 180	86.32 72

API-MVS		72.17 90	71.41 90	74.45 103	81.95 87	57.22 97	84.03 45	80.38 135	59.89 108	68.40 132	82.33 167	49.64 100	87.83 41	51.87 195	84.16 70	78.30 246

PCF-MVS	61.88 8	70.95 104	69.49 117	75.35 84	77.63 174	55.71 121	76.04 179	81.81 98	50.30 245	69.66 113	85.40 115	52.51 71	84.89 113	51.82 196	80.24 109	85.45 104
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
DP-MVS Recon		72.15 92	70.73 101	76.40 66	86.57 22	57.99 85	81.15 92	82.96 79	57.03 151	66.78 161	85.56 110	44.50 165	88.11 34	51.77 197	80.23 110	83.10 181

UGNet		68.81 144	67.39 154	73.06 138	78.33 155	54.47 138	79.77 111	75.40 217	60.45 91	63.22 214	84.40 129	32.71 278	80.91 197	51.71 198	80.56 103	83.81 155
Wanjuan Su, Qingshan Xu, Wenbing Tao: Uncertainty-guided Multi-view Stereo Network for Depth Estimation. IEEE Transactions on Circuits and Systems for Video Technology, 2022
MAR-MVS		71.51 99	70.15 110	75.60 81	81.84 88	59.39 59	81.38 89	82.90 82	54.90 200	68.08 141	78.70 239	47.73 123	85.51 99	51.68 199	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
VPNet		67.52 172	68.11 141	65.74 253	79.18 133	36.80 314	72.17 240	72.83 244	62.04 70	67.79 150	85.83 106	48.88 111	76.60 259	51.30 200	72.97 190	83.81 155

QAPM		70.05 120	68.81 128	73.78 114	76.54 199	53.43 149	83.23 56	83.48 64	52.89 218	65.90 178	86.29 96	41.55 196	86.49 77	51.01 201	78.40 137	81.42 205

NR-MVSNet		69.54 133	68.85 127	71.59 167	78.05 164	43.81 266	74.20 209	80.86 128	65.18 16	62.76 219	84.52 125	52.35 75	83.59 141	50.96 202	70.78 215	87.37 41

IB-MVS	56.42 12	65.40 208	62.73 222	73.40 132	74.89 222	52.78 158	73.09 226	75.13 221	55.69 183	58.48 265	73.73 296	32.86 273	86.32 81	50.63 203	70.11 226	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
PM-MVS		52.33 294	50.19 299	58.75 291	62.10 333	45.14 254	65.75 286	40.38 348	43.60 304	53.52 304	72.65 299	9.16 351	65.87 304	50.41 204	54.18 324	65.24 332

cascas		65.98 200	63.42 214	73.64 124	77.26 185	52.58 161	72.26 239	77.21 195	48.56 259	61.21 240	74.60 290	32.57 282	85.82 93	50.38 205	76.75 155	82.52 190

IS-MVSNet		71.57 98	71.00 98	73.27 135	78.86 140	45.63 251	80.22 103	78.69 163	64.14 35	66.46 167	87.36 75	49.30 103	85.60 94	50.26 206	83.71 71	88.59 5

WR-MVS		68.47 156	68.47 134	68.44 223	80.20 117	39.84 292	73.75 219	76.07 207	64.68 22	68.11 140	83.63 144	50.39 96	79.14 227	49.78 207	69.66 237	86.34 69

CVMVSNet		59.63 257	59.14 250	61.08 285	74.47 231	38.84 301	75.20 191	68.74 270	31.15 337	58.24 266	76.51 270	32.39 283	68.58 291	49.77 208	65.84 272	75.81 274

CostFormer		64.04 219	62.51 223	68.61 221	71.88 266	45.77 246	71.30 250	70.60 255	47.55 272	64.31 206	76.61 268	41.63 192	79.62 216	49.74 209	69.00 247	80.42 223

新几何1				70.76 186	85.66 41	61.13 35		66.43 283	44.68 293	70.29 98	86.64 86	41.29 200	75.23 266	49.72 210	81.75 93	75.93 273

test-LLR		58.15 265	58.13 260	58.22 293	68.57 302	44.80 256	65.46 290	57.92 320	50.08 248	55.44 283	69.82 316	32.62 279	57.44 327	49.66 211	73.62 176	72.41 309

test-mter		56.42 275	55.82 274	58.22 293	68.57 302	44.80 256	65.46 290	57.92 320	39.94 326	55.44 283	69.82 316	21.92 335	57.44 327	49.66 211	73.62 176	72.41 309

Anonymous202405211		66.84 187	65.99 186	69.40 211	80.19 118	42.21 278	71.11 255	71.31 250	58.80 126	67.90 142	86.39 94	29.83 298	79.65 214	49.60 213	78.78 131	86.33 70

DWT-MVSNet_test		61.90 241	59.93 247	67.83 227	71.98 265	46.09 244	71.03 257	69.71 258	50.09 247	58.51 264	70.62 311	30.21 295	77.63 247	49.28 214	67.91 256	79.78 234

tpmrst		58.24 263	58.70 253	56.84 299	66.97 311	34.32 328	69.57 270	61.14 311	47.17 276	58.58 263	71.60 305	41.28 201	60.41 319	49.20 215	62.84 292	75.78 275

pm-mvs1		65.24 209	64.97 200	66.04 248	72.38 258	39.40 297	72.62 233	75.63 213	55.53 186	62.35 231	83.18 152	47.45 130	76.47 260	49.06 216	66.54 266	82.24 194

gm-plane-assit					71.40 274	41.72 284			48.85 258		73.31 298		82.48 168	48.90 217

CMPMVS	42.80 21	57.81 268	55.97 273	63.32 269	60.98 339	47.38 231	64.66 297	69.50 263	32.06 336	46.83 327	77.80 254	29.50 300	71.36 280	48.68 218	73.75 174	71.21 318
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
ab-mvs		66.65 191	66.42 176	67.37 231	76.17 203	41.73 283	70.41 264	76.14 206	53.99 209	65.98 175	83.51 147	49.48 101	76.24 263	48.60 219	73.46 181	84.14 145

OurMVSNet-221017-0		61.37 248	58.63 254	69.61 206	72.05 263	48.06 222	73.93 216	72.51 245	47.23 275	54.74 290	80.92 197	21.49 336	81.24 188	48.57 220	56.22 318	79.53 237

OpenMVS	61.03 9	68.85 143	67.56 147	72.70 147	74.26 236	53.99 141	81.21 91	81.34 113	52.70 219	62.75 220	85.55 111	38.86 219	84.14 127	48.41 221	83.01 74	79.97 230

baseline2		63.42 224	61.26 238	69.89 204	72.55 256	47.62 228	71.54 246	68.38 272	50.11 246	54.82 289	75.55 282	43.06 177	80.96 194	48.13 222	67.16 264	81.11 214

TESTMET0.1,1		55.28 282	54.90 280	56.42 300	66.56 315	43.67 267	65.46 290	56.27 327	39.18 328	53.83 300	67.44 324	24.21 330	55.46 337	48.04 223	73.11 188	70.13 322

K. test v3		60.47 252	57.11 264	70.56 190	73.74 240	48.22 220	75.10 195	62.55 305	58.27 137	53.62 303	76.31 273	27.81 310	81.59 181	47.42 224	39.18 342	81.88 201

pmmvs6		63.69 222	62.82 221	66.27 243	70.63 279	39.27 298	73.13 225	75.47 216	52.69 220	59.75 251	82.30 168	39.71 210	77.03 255	47.40 225	64.35 282	82.53 189

baseline1		63.81 221	63.87 207	63.62 267	76.29 201	36.36 317	71.78 245	67.29 277	56.05 176	64.23 208	82.95 154	47.11 136	74.41 270	47.30 226	61.85 298	80.10 229

GBi-Net		67.21 176	66.55 171	69.19 212	77.63 174	43.33 269	77.31 149	77.83 183	56.62 159	65.04 195	82.70 156	41.85 189	80.33 208	47.18 227	72.76 193	83.92 151

test1		67.21 176	66.55 171	69.19 212	77.63 174	43.33 269	77.31 149	77.83 183	56.62 159	65.04 195	82.70 156	41.85 189	80.33 208	47.18 227	72.76 193	83.92 151

FMVSNet3		66.32 197	65.61 192	68.46 222	76.48 200	42.34 276	74.98 198	77.15 196	55.83 179	65.04 195	81.16 190	39.91 207	80.14 212	47.18 227	72.76 193	82.90 185

FMVSNet2		66.93 185	66.31 181	68.79 219	77.63 174	42.98 272	76.11 175	77.47 189	56.62 159	65.22 194	82.17 172	41.85 189	80.18 211	47.05 230	72.72 196	83.20 176

testdata2													72.18 279	46.95 231

BH-RMVSNet		68.81 144	67.42 153	72.97 139	80.11 120	52.53 162	74.26 208	76.29 204	58.48 131	68.38 133	84.20 131	42.59 179	83.83 135	46.53 232	75.91 159	82.56 188

AdaColmap		69.99 122	68.66 131	73.97 111	84.94 57	57.83 87	82.63 68	78.71 162	56.28 169	64.34 204	84.14 132	41.57 193	87.06 58	46.45 233	78.88 128	77.02 263

EG-PatchMatch MVS		64.71 214	62.87 219	70.22 194	77.68 172	53.48 148	77.99 136	78.82 158	53.37 214	56.03 279	77.41 259	24.75 329	84.04 129	46.37 234	73.42 182	73.14 300

1112_ss		64.00 220	63.36 215	65.93 250	79.28 130	42.58 275	71.35 248	72.36 247	46.41 280	60.55 243	77.89 252	46.27 147	73.28 273	46.18 235	69.97 228	81.92 200

FMVSNet1		66.70 190	65.87 187	69.19 212	77.49 181	43.33 269	77.31 149	77.83 183	56.45 164	64.60 203	82.70 156	38.08 228	80.33 208	46.08 236	72.31 201	83.92 151

HyFIR lowres test		65.67 203	63.01 218	73.67 121	79.97 122	55.65 123	69.07 274	75.52 215	42.68 312	63.53 212	77.95 248	40.43 205	81.64 179	46.01 237	71.91 204	83.73 160

lessismore_v0				69.91 202	71.42 273	47.80 224		50.90 337		50.39 319	75.56 281	27.43 314	81.33 185	45.91 238	34.10 344	80.59 221

CHOSEN 1792x2688		65.08 212	62.84 220	71.82 160	81.49 93	56.26 111	66.32 284	74.20 233	40.53 322	63.16 216	78.65 241	41.30 199	77.80 245	45.80 239	74.09 170	81.40 206

LCM-MVSNet-Re		61.88 243	61.35 236	63.46 268	74.58 229	31.48 338	61.42 309	58.14 319	58.71 127	53.02 308	79.55 228	43.07 176	76.80 257	45.69 240	77.96 139	82.11 198

ambc				65.13 260	63.72 330	37.07 312	47.66 339	78.78 161		54.37 296	71.42 306	11.24 347	80.94 195	45.64 241	53.85 326	77.38 257

MS-PatchMatch		62.42 235	61.46 235	65.31 259	75.21 221	52.10 169	72.05 241	74.05 234	46.41 280	57.42 272	74.36 291	34.35 258	77.57 249	45.62 242	73.67 175	66.26 330

ACMH+	57.40 11	66.12 198	64.06 203	72.30 156	77.79 170	52.83 157	80.39 101	78.03 180	57.30 147	57.47 271	82.55 162	27.68 311	84.17 126	45.54 243	69.78 233	79.90 231

CR-MVSNet		59.91 254	57.90 261	65.96 249	69.96 291	52.07 170	65.31 293	63.15 302	42.48 313	59.36 253	74.84 287	35.83 245	70.75 283	45.50 244	64.65 280	75.06 281

CDS-MVSNet		66.80 188	65.37 194	71.10 181	78.98 138	53.13 155	73.27 224	71.07 252	52.15 225	64.72 200	80.23 214	43.56 173	77.10 253	45.48 245	78.88 128	83.05 182
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
CP-MVSNet		66.49 195	66.41 177	66.72 236	77.67 173	36.33 319	76.83 163	79.52 149	62.45 61	62.54 225	83.47 149	46.32 145	78.37 236	45.47 246	63.43 288	85.45 104

BH-untuned		68.27 158	67.29 158	71.21 177	79.74 126	53.22 152	76.06 177	77.46 191	57.19 149	66.10 173	81.61 182	45.37 157	83.50 142	45.42 247	76.68 156	76.91 267

PS-CasMVS		66.42 196	66.32 180	66.70 238	77.60 180	36.30 321	76.94 160	79.61 147	62.36 63	62.43 229	83.66 143	45.69 149	78.37 236	45.35 248	63.26 289	85.42 107

XXY-MVS		60.68 250	61.67 232	57.70 298	70.43 282	38.45 304	64.19 299	66.47 281	48.05 267	63.22 214	80.86 199	49.28 104	60.47 318	45.25 249	67.28 263	74.19 294

HY-MVS	56.14 13	64.55 217	63.89 205	66.55 239	74.73 227	41.02 287	69.96 267	74.43 229	49.29 253	61.66 236	80.92 197	47.43 131	76.68 258	44.91 250	71.69 206	81.94 199

PEN-MVS		66.60 192	66.45 173	67.04 234	77.11 187	36.56 316	77.03 158	80.42 134	62.95 48	62.51 227	84.03 135	46.69 143	79.07 228	44.22 251	63.08 291	85.51 101

test_post1							68.67 275				3.64 353	32.39 283	69.49 287	44.17 252

SCA		60.49 251	58.38 256	66.80 235	74.14 238	48.06 222	63.35 301	63.23 301	49.13 255	59.33 256	72.10 302	37.45 232	74.27 271	44.17 252	62.57 294	78.05 250

PMMVS		53.96 286	53.26 292	56.04 301	62.60 332	50.92 180	61.17 312	56.09 328	32.81 335	53.51 305	66.84 326	34.04 260	59.93 321	44.14 254	68.18 254	57.27 338

MVP-Stereo		65.41 207	63.80 208	70.22 194	77.62 178	55.53 126	76.30 171	78.53 168	50.59 244	56.47 277	78.65 241	39.84 208	82.68 162	44.10 255	72.12 203	72.44 308
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
CNLPA		65.43 206	64.02 204	69.68 205	78.73 144	58.07 84	77.82 139	70.71 254	51.49 232	61.57 238	83.58 146	38.23 226	70.82 282	43.90 256	70.10 227	80.16 227

pmmvs4		61.48 247	59.39 248	67.76 228	71.57 270	53.86 142	71.42 247	65.34 288	44.20 299	59.46 252	77.92 250	35.90 244	74.71 268	43.87 257	64.87 278	74.71 289

Test_1112_low_res		62.32 236	61.77 231	64.00 266	79.08 136	39.53 296	68.17 276	70.17 256	43.25 307	59.03 258	79.90 218	44.08 168	71.24 281	43.79 258	68.42 253	81.25 210

TransMVSNet (Re)		64.72 213	64.33 202	65.87 252	75.22 220	38.56 303	74.66 204	75.08 225	58.90 124	61.79 235	82.63 159	51.18 87	78.07 241	43.63 259	55.87 319	80.99 217

pmmvs-eth3d		58.81 259	56.31 272	66.30 242	67.61 308	52.42 167	72.30 238	64.76 292	43.55 305	54.94 288	74.19 293	28.95 303	72.60 275	43.31 260	57.21 314	73.88 297

SixPastTwentyTwo		61.65 245	58.80 252	70.20 196	75.80 210	47.22 232	75.59 184	69.68 260	54.61 202	54.11 298	79.26 235	27.07 316	82.96 153	43.27 261	49.79 333	80.41 224

BH-w/o		66.85 186	65.83 188	69.90 203	79.29 129	52.46 165	74.66 204	76.65 202	54.51 206	64.85 199	78.12 246	45.59 152	82.95 154	43.26 262	75.54 162	74.27 293

TR-MVS		66.59 194	65.07 199	71.17 179	79.18 133	49.63 205	73.48 221	75.20 220	52.95 216	67.90 142	80.33 210	39.81 209	83.68 138	43.20 263	73.56 179	80.20 226

EU-MVSNet		55.61 281	54.41 284	59.19 288	65.41 323	33.42 332	72.44 236	71.91 249	28.81 339	51.27 312	73.87 295	24.76 328	69.08 289	43.04 264	58.20 313	75.06 281

PatchmatchNet		59.84 255	58.24 257	64.65 263	73.05 247	46.70 236	69.42 271	62.18 307	47.55 272	58.88 259	71.96 304	34.49 256	69.16 288	42.99 265	63.60 286	78.07 249
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
WR-MVS_H		67.02 183	66.92 167	67.33 233	77.95 167	37.75 309	77.57 143	82.11 93	62.03 71	62.65 222	82.48 164	50.57 94	79.46 217	42.91 266	64.01 283	84.79 127

ACMH	55.70 15	65.20 210	63.57 212	70.07 198	78.07 163	52.01 173	79.48 119	79.69 144	55.75 182	56.59 276	80.98 195	27.12 315	80.94 195	42.90 267	71.58 208	77.25 261
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
WTY-MVS		59.75 256	60.39 244	57.85 296	72.32 260	37.83 308	61.05 313	64.18 296	45.95 287	61.91 233	79.11 237	47.01 140	60.88 317	42.50 268	69.49 239	74.83 286

TAMVS		66.78 189	65.27 197	71.33 176	79.16 135	53.67 143	73.84 218	69.59 262	52.32 224	65.28 187	81.72 180	44.49 166	77.40 251	42.32 269	78.66 134	82.92 183

MVS_0304		58.51 260	57.36 263	61.96 279	70.04 289	41.83 281	69.40 272	65.46 287	50.73 240	53.30 307	74.06 294	22.65 332	70.18 286	42.16 270	68.44 252	73.86 298

LTVRE_ROB	55.42 16	63.15 230	61.23 239	68.92 217	76.57 198	47.80 224	59.92 315	76.39 203	54.35 208	58.67 261	82.46 165	29.44 301	81.49 183	42.12 271	71.14 212	77.46 256
Andreas Kuhn, Heiko HirschmÃ¼ller, Daniel Scharstein, Helmut Mayer: A TV Prior for High-Quality Scalable Multi-View Stereo Reconstruction. International Journal of Computer Vision 2016
sss		56.17 278	56.57 269	54.96 305	66.93 312	36.32 320	57.94 321	61.69 309	41.67 316	58.64 262	75.32 285	38.72 220	56.25 333	42.04 272	66.19 269	72.31 312

UnsupCasMVSNet_eth		53.16 293	52.47 293	55.23 304	59.45 342	33.39 333	59.43 317	69.13 267	45.98 284	50.35 320	72.32 301	29.30 302	58.26 325	42.02 273	44.30 339	74.05 295

tpm2		62.07 239	60.10 246	67.99 226	72.79 251	43.86 265	71.05 256	66.85 280	43.14 309	62.77 218	75.39 284	38.32 224	80.80 199	41.69 274	68.88 248	79.32 239

PLC	56.13 14	65.09 211	63.21 216	70.72 188	81.04 103	54.87 136	78.57 127	77.47 189	48.51 260	55.71 280	81.89 177	33.71 263	79.71 213	41.66 275	70.37 221	77.58 255
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
EPMVS		53.96 286	53.69 289	54.79 307	66.12 319	31.96 337	62.34 306	49.05 339	44.42 298	55.54 281	71.33 307	30.22 294	56.70 330	41.65 276	62.54 295	75.71 276

DTE-MVSNet		65.58 204	65.34 195	66.31 241	76.06 206	34.79 324	76.43 168	79.38 152	62.55 59	61.66 236	83.83 139	45.60 151	79.15 226	41.64 277	60.88 304	85.00 119

PAPM		67.92 167	66.69 170	71.63 166	78.09 162	49.02 211	77.09 156	81.24 118	51.04 239	60.91 241	83.98 137	47.71 124	84.99 108	40.81 278	79.32 122	80.90 218

tpm		57.34 270	58.16 258	54.86 306	71.80 268	34.77 325	67.47 280	56.04 329	48.20 265	60.10 245	76.92 262	37.17 237	53.41 340	40.76 279	65.01 277	76.40 270

F-COLMAP		63.05 231	60.87 243	69.58 209	76.99 191	53.63 145	78.12 135	76.16 205	47.97 268	52.41 309	81.61 182	27.87 309	78.11 240	40.07 280	66.66 265	77.00 264

Patchmtry		57.16 271	56.47 270	59.23 287	69.17 300	34.58 327	62.98 302	63.15 302	44.53 295	56.83 274	74.84 287	35.83 245	68.71 290	40.03 281	60.91 303	74.39 292

pmmvs5		56.47 274	55.68 275	58.86 290	61.41 336	36.71 315	66.37 283	62.75 304	40.38 323	53.70 301	76.62 267	34.56 254	67.05 297	40.02 282	65.27 275	72.83 302

EPNet_dtu		61.90 241	61.97 230	61.68 280	72.89 250	39.78 293	75.85 182	65.62 286	55.09 194	54.56 293	79.36 232	37.59 231	67.02 298	39.80 283	76.95 150	78.25 247
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
Vis-MVSNet (Re-imp)		63.69 222	63.88 206	63.14 271	74.75 226	31.04 339	71.16 253	63.64 298	56.32 167	59.80 250	84.99 116	44.51 164	75.46 265	39.12 284	80.62 99	82.92 183

PVSNet	50.76 19	58.40 262	57.39 262	61.42 282	75.53 216	44.04 264	61.43 308	63.45 299	47.04 277	56.91 273	73.61 297	27.00 317	64.76 306	39.12 284	72.40 198	75.47 278

MDTV_nov1_ep13_2view						25.89 346	61.22 311		40.10 324	51.10 313		32.97 272		38.49 286		78.61 245

our_test_3		56.49 273	54.42 283	62.68 275	69.51 295	45.48 252	66.08 285	61.49 310	44.11 302	50.73 317	69.60 318	33.05 270	68.15 292	38.38 287	56.86 315	74.40 291

tpm cat1		59.25 258	56.95 267	66.15 245	72.19 261	46.96 234	68.09 277	65.76 285	40.03 325	57.81 269	70.56 312	38.32 224	74.51 269	38.26 288	61.50 301	77.00 264

USDC		56.35 276	54.24 286	62.69 274	64.74 325	40.31 290	65.05 295	73.83 236	43.93 303	47.58 323	77.71 257	15.36 342	75.05 267	38.19 289	61.81 299	72.70 303

MSDG		61.81 244	59.23 249	69.55 210	72.64 253	52.63 160	70.45 263	75.81 211	51.38 234	53.70 301	76.11 274	29.52 299	81.08 193	37.70 290	65.79 273	74.93 285

MDTV_nov1_ep13			57.00 266		72.73 252	38.26 305	65.02 296	64.73 293	44.74 292	55.46 282	72.48 300	32.61 281	70.47 284	37.47 291	67.75 260

gg-mvs-nofinetune		57.86 267	56.43 271	62.18 277	72.62 254	35.35 323	66.57 281	56.33 326	50.65 242	57.64 270	57.10 338	30.65 290	76.36 261	37.38 292	78.88 128	74.82 287

RPSCF		55.80 280	54.22 287	60.53 286	65.13 324	42.91 274	64.30 298	57.62 322	36.84 331	58.05 268	82.28 169	28.01 308	56.24 334	37.14 293	58.61 312	82.44 193

PatchT		53.17 292	53.44 291	52.33 317	68.29 306	25.34 348	58.21 320	54.41 332	44.46 297	54.56 293	69.05 319	33.32 268	60.94 316	36.93 294	61.76 300	70.73 321

YYNet1		50.73 299	48.96 300	56.03 302	61.10 338	41.78 282	51.94 331	56.44 325	40.94 321	44.84 331	67.80 323	30.08 296	55.08 338	36.77 295	50.71 331	71.22 317

TAPA-MVS	59.36 10	66.60 192	65.20 198	70.81 185	76.63 196	48.75 215	76.52 167	80.04 140	50.64 243	65.24 192	84.93 117	39.15 216	78.54 235	36.77 295	76.88 151	85.14 114
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
MDA-MVSNet_test_wron		50.71 300	48.95 301	56.00 303	61.17 337	41.84 280	51.90 332	56.45 324	40.96 320	44.79 332	67.84 322	30.04 297	55.07 339	36.71 297	50.69 332	71.11 320

ppachtmachnet_test		58.06 266	55.38 277	66.10 247	69.51 295	48.99 212	68.01 278	66.13 284	44.50 296	54.05 299	70.74 310	32.09 285	72.34 277	36.68 298	56.71 317	76.99 266

tpmvs		58.47 261	56.95 267	63.03 273	70.20 285	41.21 286	67.90 279	67.23 278	49.62 251	54.73 291	70.84 309	34.14 259	76.24 263	36.64 299	61.29 302	71.64 315

CHOSEN 280x420		47.83 304	46.36 308	52.24 318	67.37 310	49.78 200	38.91 346	43.11 347	35.00 333	43.27 336	63.30 334	28.95 303	49.19 343	36.53 300	60.80 305	57.76 337

PatchMatch-RL		56.25 277	54.55 282	61.32 284	77.06 188	56.07 115	65.57 289	54.10 334	44.13 301	53.49 306	71.27 308	25.20 326	66.78 299	36.52 301	63.66 285	61.12 333

RPMNet		61.53 246	58.42 255	70.86 184	69.96 291	52.07 170	65.31 293	81.36 109	43.20 308	59.36 253	70.15 315	35.37 247	85.47 102	36.42 302	64.65 280	75.06 281

ITE_SJBPF				62.09 278	66.16 318	44.55 261		64.32 295	47.36 274	55.31 285	80.34 209	19.27 337	62.68 312	36.29 303	62.39 296	79.04 241

JIA-IIPM		51.56 297	47.68 307	63.21 270	64.61 326	50.73 184	47.71 338	58.77 317	42.90 310	48.46 322	51.72 341	24.97 327	70.24 285	36.06 304	53.89 325	68.64 328

OpenMVS_ROB	52.78 18	60.03 253	58.14 259	65.69 254	70.47 281	44.82 255	75.33 187	70.86 253	45.04 290	56.06 278	76.00 275	26.89 318	79.65 214	35.36 305	67.29 262	72.60 304

GG-mvs-BLEND				62.34 276	71.36 275	37.04 313	69.20 273	57.33 323		54.73 291	65.48 330	30.37 292	77.82 244	34.82 306	74.93 165	72.17 313

UnsupCasMVSNet_bld		50.07 301	48.87 302	53.66 310	60.97 340	33.67 331	57.62 322	64.56 294	39.47 327	47.38 324	64.02 333	27.47 312	59.32 322	34.69 307	43.68 340	67.98 329

MDA-MVSNet-bldmvs		53.87 288	50.81 297	63.05 272	66.25 317	48.58 216	56.93 324	63.82 297	48.09 266	41.22 338	70.48 313	30.34 293	68.00 294	34.24 308	45.92 338	72.57 305

dp		51.89 296	51.60 296	52.77 315	68.44 305	32.45 335	62.36 305	54.57 331	44.16 300	49.31 321	67.91 321	28.87 305	56.61 331	33.89 309	54.89 321	69.24 327

AllTest		57.08 272	54.65 281	64.39 264	71.44 271	49.03 209	69.92 268	67.30 275	45.97 285	47.16 325	79.77 222	17.47 338	67.56 295	33.65 310	59.16 310	76.57 268

TestCases				64.39 264	71.44 271	49.03 209		67.30 275	45.97 285	47.16 325	79.77 222	17.47 338	67.56 295	33.65 310	59.16 310	76.57 268

FMVSNet5		55.86 279	54.93 279	58.66 292	71.05 276	36.35 318	64.18 300	62.48 306	46.76 278	50.66 318	74.73 289	25.80 323	64.04 308	33.11 312	65.57 274	75.59 277

DP-MVS		65.68 202	63.66 211	71.75 161	84.93 58	56.87 105	80.74 96	73.16 242	53.06 215	59.09 257	82.35 166	36.79 242	85.94 89	32.82 313	69.96 229	72.45 307

PVSNet_0	43.31 20	47.46 306	45.64 309	52.92 314	67.60 309	44.65 258	54.06 329	54.64 330	41.59 317	46.15 329	58.75 337	30.99 288	58.66 323	32.18 314	24.81 345	55.46 339

TinyColmap		54.14 285	51.72 295	61.40 283	66.84 313	41.97 279	66.52 282	68.51 271	44.81 291	42.69 337	75.77 279	11.66 345	72.94 274	31.96 315	56.77 316	69.27 326

MIMVSNet		57.35 269	57.07 265	58.22 293	74.21 237	37.18 311	62.46 304	60.88 312	48.88 257	55.29 286	75.99 277	31.68 287	62.04 314	31.87 316	72.35 199	75.43 279

thres100view900		63.28 227	62.41 225	65.89 251	77.31 184	38.66 302	72.65 231	69.11 268	57.07 150	62.45 228	81.03 194	37.01 240	79.17 223	31.84 317	73.25 185	79.83 232

tfpn200view9		63.18 229	62.18 228	66.21 244	76.85 192	39.62 294	71.96 243	69.44 264	56.63 157	62.61 223	79.83 220	37.18 235	79.17 223	31.84 317	73.25 185	79.83 232

thres400		63.31 225	62.18 228	66.72 236	76.85 192	39.62 294	71.96 243	69.44 264	56.63 157	62.61 223	79.83 220	37.18 235	79.17 223	31.84 317	73.25 185	81.36 207

pmmvs3		44.92 308	41.95 312	53.86 309	52.58 346	43.55 268	62.11 307	46.90 345	26.05 343	40.63 339	60.19 336	11.08 348	57.91 326	31.83 320	46.15 337	60.11 334

LF4IMVS		42.95 309	42.26 311	45.04 325	48.30 348	32.50 334	54.80 327	48.49 341	28.03 340	40.51 340	70.16 314	9.24 350	43.89 346	31.63 321	49.18 335	58.72 335

COLMAP_ROB	52.97 17	61.27 249	58.81 251	68.64 220	74.63 228	52.51 164	78.42 132	73.30 240	49.92 250	50.96 314	81.51 185	23.06 331	79.40 218	31.63 321	65.85 271	74.01 296
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
new-patchmatchnet		47.56 305	47.73 306	47.06 323	58.81 343	9.37 356	48.78 337	59.21 315	43.28 306	44.22 334	68.66 320	25.67 324	57.20 329	31.57 323	49.35 334	74.62 290

thres600view7		63.30 226	62.27 226	66.41 240	77.18 186	38.87 300	72.35 237	69.11 268	56.98 152	62.37 230	80.96 196	37.01 240	79.00 232	31.43 324	73.05 189	81.36 207

thres200		62.20 238	61.16 240	65.34 258	75.38 219	39.99 291	69.60 269	69.29 266	55.64 185	61.87 234	76.99 261	37.07 239	78.96 233	31.28 325	73.28 184	77.06 262

LCM-MVSNet		40.30 312	35.88 317	53.57 311	42.24 350	29.15 343	45.21 342	60.53 313	22.23 347	28.02 345	50.98 343	3.72 356	61.78 315	31.22 326	38.76 343	69.78 323

test0.0.03 1		53.32 291	53.59 290	52.50 316	62.81 331	29.45 342	59.51 316	54.11 333	50.08 248	54.40 295	74.31 292	32.62 279	55.92 335	30.50 327	63.95 284	72.15 314

Anonymous20231206		55.10 284	55.30 278	54.48 308	69.81 294	33.94 330	62.91 303	62.13 308	41.08 319	55.18 287	75.65 280	32.75 277	56.59 332	30.32 328	67.86 257	72.91 301

tfpnnormal		62.47 234	61.63 233	64.99 261	74.81 225	39.01 299	71.22 251	73.72 237	55.22 191	60.21 244	80.09 217	41.26 202	76.98 256	30.02 329	68.09 255	78.97 243

test20.03		53.87 288	54.02 288	53.41 312	61.47 335	28.11 344	61.30 310	59.21 315	51.34 236	52.09 310	77.43 258	33.29 269	58.55 324	29.76 330	60.27 307	73.58 299

LS3D		64.71 214	62.50 224	71.34 175	79.72 128	55.71 121	79.82 110	74.72 227	48.50 261	56.62 275	84.62 122	33.59 266	82.34 170	29.65 331	75.23 164	75.97 271

testgi		51.90 295	52.37 294	50.51 321	60.39 341	23.55 350	58.42 319	58.15 318	49.03 256	51.83 311	79.21 236	22.39 333	55.59 336	29.24 332	62.64 293	72.40 311

MIMVSNet1		55.17 283	54.31 285	57.77 297	70.03 290	32.01 336	65.68 288	64.81 291	49.19 254	46.75 328	76.00 275	25.53 325	64.04 308	28.65 333	62.13 297	77.26 260

TDRefinement		53.44 290	50.72 298	61.60 281	64.31 328	46.96 234	70.89 258	65.27 290	41.78 314	44.61 333	77.98 247	11.52 346	66.36 301	28.57 334	51.59 329	71.49 316

ADS-MVSNet2		51.33 298	48.76 303	59.07 289	66.02 321	44.60 259	50.90 333	59.76 314	36.90 329	50.74 315	66.18 328	26.38 319	63.11 310	27.17 335	54.76 322	69.50 324

ADS-MVSNet		48.48 303	47.77 305	50.63 320	66.02 321	29.92 341	50.90 333	50.87 338	36.90 329	50.74 315	66.18 328	26.38 319	52.47 341	27.17 335	54.76 322	69.50 324

Patchmatch-test		49.08 302	48.28 304	51.50 319	64.40 327	30.85 340	45.68 340	48.46 342	35.60 332	46.10 330	72.10 302	34.47 257	46.37 344	27.08 337	60.65 306	77.27 259

MVS-HIRNet		45.52 307	44.48 310	48.65 322	68.49 304	34.05 329	59.41 318	44.50 346	27.03 341	37.96 342	50.47 344	26.16 322	64.10 307	26.74 338	59.52 308	47.82 341

test_0402		63.25 228	61.01 241	69.96 199	80.00 121	54.37 139	76.86 162	72.02 248	54.58 204	58.71 260	80.79 202	35.00 251	84.36 123	26.41 339	64.71 279	71.15 319

N_pmnet		39.35 313	40.28 314	36.54 329	63.76 329	1.62 359	49.37 336	0.76 359	34.62 334	43.61 335	66.38 327	26.25 321	42.57 347	26.02 340	51.77 328	65.44 331

DSMNet-mixed		39.30 314	38.72 315	41.03 328	51.22 347	19.66 352	45.53 341	31.35 352	15.83 350	39.80 341	67.42 325	22.19 334	45.13 345	22.43 341	52.69 327	58.31 336

ANet_high		41.38 311	37.47 316	53.11 313	39.73 353	24.45 349	56.94 323	69.69 259	47.65 271	26.04 346	52.32 340	12.44 343	62.38 313	21.80 342	10.61 351	72.49 306

new_pmnet		34.13 317	34.29 318	33.64 330	52.63 345	18.23 354	44.43 343	33.90 351	22.81 346	30.89 344	53.18 339	10.48 349	35.72 351	20.77 343	39.51 341	46.98 342

PMMVS2		27.40 318	25.91 321	31.87 332	39.46 354	6.57 357	31.17 347	28.52 353	23.96 344	20.45 348	48.94 345	4.20 355	37.94 350	16.51 344	19.97 346	51.09 340

Gipuma		34.77 316	31.91 320	43.33 327	62.05 334	37.87 307	20.39 349	67.03 279	23.23 345	18.41 349	25.84 348	4.24 354	62.73 311	14.71 345	51.32 330	29.38 346
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
FPMVS		42.18 310	41.11 313	45.39 324	58.03 344	41.01 288	49.50 335	53.81 335	30.07 338	33.71 343	64.03 331	11.69 344	52.08 342	14.01 346	55.11 320	43.09 343

tmp_tt		9.43 324	11.14 327	4.30 337	2.38 358	4.40 358	13.62 351	16.08 356	0.39 354	15.89 350	13.06 351	15.80 341	5.54 355	12.63 347	10.46 352	2.95 350

MVE	17.77 23	21.41 321	17.77 325	32.34 331	34.34 356	25.44 347	16.11 350	24.11 354	11.19 351	13.22 351	31.92 346	1.58 359	30.95 352	10.47 348	17.03 347	40.62 344
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
E-PMN		23.77 319	22.73 323	26.90 333	42.02 351	20.67 351	42.66 344	35.70 349	17.43 348	10.28 353	25.05 349	6.42 353	42.39 348	10.28 349	14.71 348	17.63 347

PMVS	28.69 22	36.22 315	33.29 319	45.02 326	36.82 355	35.98 322	54.68 328	48.74 340	26.31 342	21.02 347	51.61 342	2.88 358	60.10 320	9.99 350	47.58 336	38.99 345
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
EMVS		22.97 320	21.84 324	26.36 334	40.20 352	19.53 353	41.95 345	34.64 350	17.09 349	9.73 354	22.83 350	7.29 352	42.22 349	9.18 351	13.66 349	17.32 348

DeepMVS_CX				12.03 336	17.97 357	10.91 355		10.60 357	7.46 352	11.07 352	28.36 347	3.28 357	11.29 354	8.01 352	9.74 353	13.89 349

wuyk23d		13.32 323	12.52 326	15.71 335	47.54 349	26.27 345	31.06 348	1.98 358	4.93 353	5.18 355	1.94 355	0.45 360	18.54 353	6.81 353	12.83 350	2.33 351

testmvs		4.52 327	6.03 330	0.01 339	0.01 359	0.00 361	53.86 330	0.00 360	0.01 355	0.04 356	0.27 356	0.00 362	0.00 356	0.04 354	0.00 354	0.03 353

test123		4.73 326	6.30 329	0.02 338	0.01 359	0.01 360	56.36 325	0.00 360	0.01 355	0.04 356	0.21 357	0.01 361	0.00 356	0.03 355	0.00 354	0.04 352

uanet_test		0.00 329	0.00 332	0.00 340	0.00 361	0.00 361	0.00 352	0.00 360	0.00 357	0.00 358	0.00 358	0.00 362	0.00 356	0.00 356	0.00 354	0.00 354

cdsmvs_eth3d_5k		17.50 322	23.34 322	0.00 340	0.00 361	0.00 361	0.00 352	78.63 165	0.00 357	0.00 358	82.18 170	49.25 105	0.00 356	0.00 356	0.00 354	0.00 354

pcd_1.5k_mvsjas		3.92 328	5.23 331	0.00 340	0.00 361	0.00 361	0.00 352	0.00 360	0.00 357	0.00 358	0.00 358	47.05 137	0.00 356	0.00 356	0.00 354	0.00 354

sosnet-low-res		0.00 329	0.00 332	0.00 340	0.00 361	0.00 361	0.00 352	0.00 360	0.00 357	0.00 358	0.00 358	0.00 362	0.00 356	0.00 356	0.00 354	0.00 354

sosnet		0.00 329	0.00 332	0.00 340	0.00 361	0.00 361	0.00 352	0.00 360	0.00 357	0.00 358	0.00 358	0.00 362	0.00 356	0.00 356	0.00 354	0.00 354

uncertanet		0.00 329	0.00 332	0.00 340	0.00 361	0.00 361	0.00 352	0.00 360	0.00 357	0.00 358	0.00 358	0.00 362	0.00 356	0.00 356	0.00 354	0.00 354

Regformer		0.00 329	0.00 332	0.00 340	0.00 361	0.00 361	0.00 352	0.00 360	0.00 357	0.00 358	0.00 358	0.00 362	0.00 356	0.00 356	0.00 354	0.00 354

ab-mvs-re		6.49 325	8.65 328	0.00 340	0.00 361	0.00 361	0.00 352	0.00 360	0.00 357	0.00 358	77.89 252	0.00 362	0.00 356	0.00 356	0.00 354	0.00 354

uanet		0.00 329	0.00 332	0.00 340	0.00 361	0.00 361	0.00 352	0.00 360	0.00 357	0.00 358	0.00 358	0.00 362	0.00 356	0.00 356	0.00 354	0.00 354

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

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

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

GSMVS																78.05 250

test_part2					87.58 9	60.47 46				83.42 9

sam_mvs1												34.74 253				78.05 250

sam_mvs												33.43 267

MTGPA								80.97 125

test_post											3.55 354	33.90 262	66.52 300

patchmatchnet-post											64.03 331	34.50 255	74.27 271

MTMP							86.03 17	17.08 355

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

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

agg_prior					85.04 52	59.96 51		81.04 122		74.68 46			84.04 129

test_prior4						62.51 17	82.08 79

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

新几何2							76.12 174

旧先验1					83.04 75	53.15 153		67.52 274			87.85 71	44.08 168			80.76 98	78.03 253

原ACMM2							79.02 121

test222					83.14 72	58.68 75	72.57 234	63.45 299	41.78 314	67.56 152	86.12 98	37.13 238			78.73 133	74.98 284

segment_acmp												54.23 54

testdata1							72.65 231		60.50 90

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

plane_prior7					81.41 94	55.96 117

plane_prior6					81.20 101	56.24 112						45.26 159

plane_prior4											86.10 99

plane_prior3						56.09 114			63.92 36	69.27 120

plane_prior2							84.22 39		64.52 25

plane_prior1					81.27 99

plane_prior						56.31 108	83.58 53		63.19 46						80.48 104

n2								0.00 360

nn								0.00 360

door-mid								47.19 344

test11								83.47 65

door								47.60 343

HQP5-MVS						54.94 133

HQP-NCC					80.66 106		82.31 74		62.10 67	67.85 144

ACMP_Plane					80.66 106		82.31 74		62.10 67	67.85 144

HQP4-MVS										67.85 144			86.93 60			84.32 139

HQP3-MVS								83.90 54							80.35 107

HQP2-MVS												45.46 153

NP-MVS					80.98 104	56.05 116					85.54 112

ACMMP++_ref															74.07 172

ACMMP++															72.16 202

Test By Simon												48.33 118

High-resolution multi-view benchmark