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.

OPU-MVS				79.83 4	87.54 10	60.93 38	87.82 5				89.89 41	67.01 1	90.33 8	73.16 44	91.15 2	88.23 10

SED-MVS		81.56 1	82.30 1	79.32 9	87.77 4	58.90 67	87.82 5	86.78 11	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_ONE					87.77 4	58.90 67		86.78 11	64.20 31	85.97 1	91.34 10	66.87 2	90.78 4

test_241102_TWO								86.73 13	64.18 32	84.26 4	91.84 6	65.19 4	90.83 2	78.63 12	90.70 5	87.65 29

DPE-MVS		80.56 4	80.98 4	79.29 11	87.27 12	60.56 44	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 53

MSP-MVS		80.84 3	81.64 2	78.42 33	87.75 7	59.07 62	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	85.45 103

test0726					87.75 7	59.07 62	87.86 2	86.83 9	64.26 29	84.19 5	91.92 5	64.82 6

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

DVP-MVS		81.06 2	81.40 3	80.02 1	86.21 29	62.73 12	86.09 15	86.83 9	65.51 14	83.81 8	90.51 21	63.71 9	89.23 16	81.51 1	88.44 28	88.09 15

ETH3 D test6400		79.14 10	79.32 9	78.61 29	86.34 26	58.11 79	84.65 32	87.66 4	58.56 129	78.87 20	89.54 46	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 57	60.32 46	83.03 55	85.33 28	62.86 52	80.17 13	90.03 37	61.76 11	88.95 20	74.21 34	88.67 27	88.12 14

CSCG		76.92 37	76.75 36	77.41 49	83.96 64	59.60 52	82.95 56	86.50 15	60.78 86	75.27 36	84.83 114	60.76 12	86.56 71	67.86 71	87.87 42	86.06 79

TSAR-MVS + MP.		78.44 19	78.28 20	78.90 23	84.96 53	61.41 29	84.03 42	83.82 58	59.34 117	79.37 15	89.76 44	59.84 13	87.62 44	76.69 22	86.74 53	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
canonicalmvs		74.67 58	74.98 52	73.71 117	78.94 135	50.56 183	80.23 98	83.87 56	60.30 99	77.15 28	86.56 87	59.65 14	82.00 169	66.01 87	82.12 85	88.58 6

APDe-MVS		80.16 6	80.59 5	78.86 25	86.64 19	60.02 48	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 56

DELS-MVS		74.76 56	74.46 57	75.65 76	77.84 164	52.25 163	75.59 180	84.17 47	63.76 38	73.15 67	82.79 150	59.58 16	86.80 61	67.24 77	86.04 58	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
CNVR-MVS		79.84 8	79.97 8	79.45 7	87.90 2	62.17 20	84.37 34	85.03 33	66.96 5	77.58 27	90.06 35	59.47 17	89.13 18	78.67 9	89.73 16	87.03 49

MCST-MVS		77.48 32	77.45 29	77.54 47	86.67 18	58.36 76	83.22 53	86.93 7	56.91 149	74.91 43	88.19 62	59.15 18	87.68 42	73.67 40	87.45 43	86.57 60

nrg030		72.96 76	73.01 72	72.84 139	75.41 213	50.24 187	80.02 101	82.89 83	58.36 133	74.44 48	86.73 78	58.90 19	80.83 193	65.84 89	74.46 162	87.44 38

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 47	79.00 7	90.37 10	85.26 112

ETH3D cwj APD-0.16		78.02 24	78.13 24	77.71 44	82.10 80	58.65 72	82.72 62	87.55 5	58.33 134	78.05 25	90.06 35	58.35 21	87.65 43	76.15 25	89.86 15	86.82 54

xxxxxxxxxxxxxcwj		78.37 20	78.25 22	78.76 26	86.17 31	61.30 31	83.98 44	79.95 136	59.00 120	79.16 16	90.75 15	57.96 22	87.09 54	77.08 20	90.18 11	87.87 21

SF-MVS		78.82 12	79.22 11	77.60 46	82.88 75	57.83 83	84.99 30	88.13 3	61.86 73	79.16 16	90.75 15	57.96 22	87.09 54	77.08 20	90.18 11	87.87 21

casdiffmvs		74.80 55	74.89 54	74.53 98	75.59 210	50.37 186	78.17 130	85.06 32	62.80 56	74.40 49	87.86 66	57.88 24	83.61 135	69.46 63	82.79 81	89.59 2

ETH3D-3000-0.1		78.58 15	78.91 13	77.61 45	83.06 70	57.86 82	84.14 41	88.31 1	60.37 93	79.14 18	90.35 27	57.76 25	87.00 57	77.16 19	89.90 14	87.97 18

DeepC-MVS	69.38 2	78.56 17	78.14 23	79.83 4	83.60 65	61.62 26	84.17 38	86.85 8	63.23 44	73.84 59	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
baseline		74.61 59	74.70 55	74.34 102	75.70 206	49.99 193	77.54 141	84.63 40	62.73 57	73.98 53	87.79 68	57.67 27	83.82 131	69.49 61	82.74 82	89.20 3

9.14			78.75 15		83.10 69		84.15 39	88.26 2	59.90 105	78.57 23	90.36 26	57.51 28	86.86 60	77.39 15	89.52 21

DPM-MVS		75.47 51	75.00 50	76.88 55	81.38 92	59.16 58	79.94 103	85.71 24	56.59 157	72.46 77	86.76 76	56.89 29	87.86 39	66.36 83	88.91 26	83.64 162

UniMVSNet_NR-MVSNet		71.11 100	71.00 94	71.44 166	79.20 128	44.13 257	76.02 176	82.60 85	66.48 13	68.20 130	84.60 120	56.82 30	82.82 155	54.62 169	70.43 214	87.36 42

SMA-MVS		80.28 5	80.39 6	79.95 3	86.60 20	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

Regformer-1		75.47 51	74.93 53	77.09 54	80.43 107	57.70 86	79.50 113	82.13 90	67.84 1	75.73 35	80.75 198	56.50 32	86.07 80	71.07 56	80.38 101	87.50 35

Effi-MVS+		73.31 73	72.54 76	75.62 77	77.87 163	53.64 139	79.62 111	79.61 142	61.63 75	72.02 82	82.61 155	56.44 33	85.97 86	63.99 105	79.07 123	87.25 44

alignmvs		73.86 69	73.99 61	73.45 126	78.20 153	50.50 184	78.57 123	82.43 86	59.40 115	76.57 29	86.71 80	56.42 34	81.23 184	65.84 89	81.79 87	88.62 4

test_prior3		76.89 39	76.96 34	76.69 58	84.20 62	57.27 91	81.75 77	84.88 36	60.37 93	75.01 39	89.06 52	56.22 35	86.43 76	72.19 48	88.96 24	86.38 62

test_prior2							81.75 77		60.37 93	75.01 39	89.06 52	56.22 35		72.19 48	88.96 24

TSAR-MVS + GP.		74.90 54	74.15 60	77.17 53	82.00 82	58.77 70	81.80 76	78.57 161	58.58 127	74.32 50	84.51 123	55.94 37	87.22 48	67.11 78	84.48 67	85.52 99

testtj		78.47 18	78.43 18	78.61 29	86.82 13	60.67 42	86.07 16	85.38 27	62.12 66	78.65 22	90.29 31	55.76 38	89.31 15	73.55 42	87.22 45	85.84 84

Regformer-2		75.63 50	74.99 51	77.54 47	80.43 107	58.32 77	79.50 113	82.92 80	67.84 1	75.94 32	80.75 198	55.73 39	86.80 61	71.44 55	80.38 101	87.50 35

MVS_Test		72.45 82	72.46 77	72.42 151	74.88 218	48.50 212	76.28 168	83.14 78	59.40 115	72.46 77	84.68 116	55.66 40	81.12 185	65.98 88	79.66 111	87.63 30

APD-MVS		78.02 24	78.04 25	77.98 41	86.44 24	60.81 40	85.52 26	84.36 43	60.61 88	79.05 19	90.30 30	55.54 41	88.32 29	73.48 43	87.03 48	84.83 123
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
NCCC		78.58 15	78.31 19	79.39 8	87.51 11	62.61 16	85.20 29	84.42 42	66.73 9	74.67 47	89.38 49	55.30 42	89.18 17	74.19 35	87.34 44	86.38 62

FIs		70.82 103	71.43 85	68.98 212	78.33 150	38.14 301	76.96 155	83.59 62	61.02 83	67.33 150	86.73 78	55.07 43	81.64 174	54.61 171	79.22 119	87.14 47

SD-MVS		77.70 29	77.62 28	77.93 42	84.47 60	61.88 24	84.55 33	83.87 56	60.37 93	79.89 14	89.38 49	54.97 44	85.58 92	76.12 26	84.94 62	86.33 69
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
CANet		76.46 42	75.93 44	78.06 39	81.29 93	57.53 88	82.35 68	83.31 72	67.78 3	70.09 97	86.34 91	54.92 45	88.90 21	72.68 46	84.55 65	87.76 26

Regformer-3		73.89 68	73.28 71	75.71 74	79.75 120	55.48 124	78.54 125	79.93 137	66.58 11	73.62 61	80.30 206	54.87 46	84.54 115	69.09 65	76.84 148	87.10 48

MP-MVS		78.35 21	78.26 21	78.64 28	86.54 22	63.47 5	86.02 18	83.55 63	63.89 37	73.60 62	90.60 18	54.85 47	86.72 64	77.20 18	88.06 37	85.74 92
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
mvs_anonymous		68.03 161	67.51 147	69.59 203	72.08 257	44.57 255	71.99 238	75.23 214	51.67 222	67.06 153	82.57 156	54.68 48	77.94 237	56.56 152	75.71 157	86.26 75

test12				77.76 43	84.52 59	58.41 75		83.36 70		72.93 71		54.61 49	88.05 34		88.12 36	86.81 55

CS-MVS		74.18 65	73.60 65	75.92 68	78.99 133	52.53 157	80.61 94	85.93 20	61.17 82	71.15 88	79.34 229	54.52 50	88.86 22	66.07 84	84.67 64	86.38 62

FC-MVSNet-test		69.80 122	70.58 100	67.46 226	77.61 174	34.73 321	76.05 174	83.19 76	60.84 84	65.88 174	86.46 88	54.52 50	80.76 197	52.52 186	78.12 134	86.91 50

SteuartSystems-ACMMP		79.48 9	79.31 10	79.98 2	83.01 73	62.18 19	87.60 7	85.83 21	66.69 10	78.03 26	90.98 12	54.26 52	90.06 9	78.42 14	89.02 23	87.69 27
Skip Steuart: Steuart Systems R&D Blog.
segment_acmp												54.23 53

MVS_111021_HR		74.02 66	73.46 69	75.69 75	83.01 73	60.63 43	77.29 148	78.40 172	61.18 81	70.58 91	85.97 99	54.18 54	84.00 128	67.52 75	82.98 76	82.45 188

Fast-Effi-MVS+		70.28 114	69.12 120	73.73 116	78.50 144	51.50 170	75.01 192	79.46 146	56.16 167	68.59 123	79.55 223	53.97 55	84.05 123	53.34 181	77.53 139	85.65 95

ZNCC-MVS		78.82 12	78.67 16	79.30 10	86.43 25	62.05 21	86.62 9	86.01 19	63.32 42	75.08 38	90.47 25	53.96 56	88.68 23	76.48 23	89.63 20	87.16 46

agg_prior1		75.94 47	76.01 43	75.72 73	85.04 50	59.96 49	81.44 84	81.04 117	56.14 168	74.68 45	88.90 56	53.91 57	84.04 124	75.01 30	87.92 41	83.16 176

UniMVSNet (Re)		70.63 106	70.20 104	71.89 155	78.55 143	45.29 248	75.94 177	82.92 80	63.68 40	68.16 133	83.59 140	53.89 58	83.49 138	53.97 174	71.12 208	86.89 51

Regformer-4		74.25 64	73.48 67	76.57 62	79.75 120	56.54 103	78.54 125	81.49 104	66.93 7	73.90 57	80.30 206	53.84 59	85.98 85	69.76 59	76.84 148	87.17 45

DeepC-MVS_fast	68.24 3	77.25 34	76.63 38	79.12 16	86.15 33	60.86 39	84.71 31	84.85 38	61.98 72	73.06 69	88.88 57	53.72 60	89.06 19	68.27 67	88.04 38	87.42 39
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
TEST9					85.58 43	61.59 27	81.62 80	81.26 111	55.65 179	74.93 41	88.81 58	53.70 61	84.68 112

train_agg		76.27 44	76.15 41	76.64 61	85.58 43	61.59 27	81.62 80	81.26 111	55.86 172	74.93 41	88.81 58	53.70 61	84.68 112	75.24 29	88.33 31	83.65 161

test_8					85.40 46	60.96 37	81.54 83	81.18 114	55.86 172	74.81 44	88.80 60	53.70 61	84.45 117

ETV-MVS		74.46 60	73.84 63	76.33 64	79.27 127	55.24 127	79.22 116	85.00 35	64.97 21	72.65 74	79.46 225	53.65 64	87.87 38	67.45 76	82.91 77	85.89 83

CDPH-MVS		76.31 43	75.67 47	78.22 37	85.35 48	59.14 60	81.31 86	84.02 49	56.32 162	74.05 52	88.98 55	53.34 65	87.92 37	69.23 64	88.42 29	87.59 32

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 54	90.50 22	53.20 66	88.35 27	74.02 36	87.05 46	86.13 76

#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 54	90.50 22	53.20 66	88.35 27	73.68 39	87.05 46	86.13 76

EI-MVSNet-Vis-set		72.42 83	71.59 83	74.91 87	78.47 146	54.02 135	77.05 153	79.33 148	65.03 20	71.68 85	79.35 228	52.75 68	84.89 108	66.46 82	74.23 165	85.83 85

GST-MVS		78.14 23	77.85 26	78.99 22	86.05 37	61.82 25	85.84 19	85.21 29	63.56 41	74.29 51	90.03 37	52.56 69	88.53 25	74.79 31	88.34 30	86.63 59

ACMMP_NAP		78.77 14	78.78 14	78.74 27	85.44 45	61.04 36	83.84 46	85.16 30	62.88 51	78.10 24	91.26 11	52.51 70	88.39 26	79.34 5	90.52 9	86.78 57

PCF-MVS	61.88 8	70.95 101	69.49 113	75.35 81	77.63 169	55.71 117	76.04 175	81.81 97	50.30 240	69.66 108	85.40 111	52.51 70	84.89 108	51.82 191	80.24 105	85.45 103
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
MP-MVS-pluss		78.35 21	78.46 17	78.03 40	84.96 53	59.52 53	82.93 57	85.39 26	62.15 65	76.41 31	91.51 9	52.47 72	86.78 63	80.66 2	89.64 19	87.80 24
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
CLD-MVS		73.33 72	72.68 75	75.29 84	78.82 137	53.33 146	78.23 129	84.79 39	61.30 80	70.41 93	81.04 188	52.41 73	87.12 52	64.61 100	82.49 84	85.41 107
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
NR-MVSNet		69.54 130	68.85 123	71.59 164	78.05 159	43.81 261	74.20 205	80.86 123	65.18 16	62.76 214	84.52 121	52.35 74	83.59 136	50.96 197	70.78 210	87.37 40

EI-MVSNet-UG-set		71.92 90	71.06 93	74.52 99	77.98 161	53.56 141	76.62 160	79.16 149	64.40 27	71.18 87	78.95 233	52.19 75	84.66 114	65.47 93	73.57 174	85.32 109

miper_ehance_all_eth		68.03 161	67.24 159	70.40 189	70.54 275	46.21 237	73.98 208	78.68 159	55.07 191	66.05 169	77.80 249	52.16 76	81.31 181	61.53 128	69.32 235	83.67 158

EIA-MVS		71.78 92	70.60 98	75.30 83	79.85 119	53.54 142	77.27 149	83.26 75	57.92 137	66.49 161	79.39 226	52.07 77	86.69 65	60.05 136	79.14 122	85.66 94

cl_fuxian		68.33 154	67.56 143	70.62 185	70.87 272	46.21 237	74.47 203	78.80 155	56.22 166	66.19 167	78.53 240	51.88 78	81.40 179	62.08 120	69.04 241	84.25 139

PAPM_NR		72.63 79	71.80 81	75.13 85	81.72 85	53.42 145	79.91 105	83.28 74	59.14 119	66.31 166	85.90 100	51.86 79	86.06 81	57.45 149	80.62 95	85.91 82

MG-MVS		73.96 67	73.89 62	74.16 105	85.65 41	49.69 198	81.59 82	81.29 110	61.45 77	71.05 89	88.11 63	51.77 80	87.73 41	61.05 129	83.09 72	85.05 117

EPP-MVSNet		72.16 88	71.31 90	74.71 90	78.68 141	49.70 196	82.10 74	81.65 100	60.40 92	65.94 171	85.84 101	51.74 81	86.37 78	55.93 156	79.55 114	88.07 17

TranMVSNet+NR-MVSNet		70.36 112	70.10 107	71.17 176	78.64 142	42.97 268	76.53 162	81.16 115	66.95 6	68.53 126	85.42 110	51.61 82	83.07 145	52.32 187	69.70 231	87.46 37

diffmvs		70.69 105	70.43 101	71.46 165	69.45 292	48.95 208	72.93 223	78.46 167	57.27 143	71.69 84	83.97 133	51.48 83	77.92 238	70.70 58	77.95 136	87.53 34

EI-MVSNet		69.27 135	68.44 132	71.73 159	74.47 226	49.39 203	75.20 187	78.45 168	59.60 111	69.16 119	76.51 265	51.29 84	82.50 161	59.86 140	71.45 206	83.30 168

IterMVS-LS		69.22 137	68.48 129	71.43 168	74.44 228	49.40 202	76.23 169	77.55 183	59.60 111	65.85 175	81.59 179	51.28 85	81.58 177	59.87 139	69.90 226	83.30 168
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
TransMVSNet (Re)		64.72 210	64.33 198	65.87 249	75.22 215	38.56 298	74.66 200	75.08 220	58.90 123	61.79 230	82.63 154	51.18 86	78.07 236	43.63 254	55.87 314	80.99 213

miper_enhance_ethall		67.11 178	66.09 181	70.17 193	69.21 294	45.98 240	72.85 226	78.41 171	51.38 229	65.65 177	75.98 273	51.17 87	81.25 182	60.82 130	69.32 235	83.29 170

VNet		69.68 126	70.19 105	68.16 221	79.73 123	41.63 280	70.53 257	77.38 187	60.37 93	70.69 90	86.63 83	51.08 88	77.09 249	53.61 179	81.69 91	85.75 91

VPA-MVSNet		69.02 138	69.47 114	67.69 225	77.42 177	41.00 284	74.04 207	79.68 140	60.06 102	69.26 117	84.81 115	51.06 89	77.58 243	54.44 172	74.43 163	84.48 134

PAPR		71.72 94	70.82 96	74.41 101	81.20 97	51.17 171	79.55 112	83.33 71	55.81 175	66.93 155	84.61 119	50.95 90	86.06 81	55.79 159	79.20 120	86.00 80

PHI-MVS		75.87 48	75.36 48	77.41 49	80.62 105	55.91 115	84.28 35	85.78 22	56.08 170	73.41 64	86.58 86	50.94 91	88.54 24	70.79 57	89.71 17	87.79 25

HPM-MVS		77.28 33	76.85 35	78.54 31	85.00 52	60.81 40	82.91 58	85.08 31	62.57 58	73.09 68	89.97 40	50.90 92	87.48 45	75.30 27	86.85 51	87.33 43
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
WR-MVS_H		67.02 180	66.92 163	67.33 229	77.95 162	37.75 304	77.57 139	82.11 92	62.03 71	62.65 217	82.48 159	50.57 93	79.46 212	42.91 261	64.01 278	84.79 126

EPNet		73.09 75	72.16 78	75.90 69	75.95 202	56.28 106	83.05 54	72.39 241	66.53 12	65.27 183	87.00 74	50.40 94	85.47 98	62.48 118	86.32 57	85.94 81
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
WR-MVS		68.47 153	68.47 130	68.44 219	80.20 113	39.84 287	73.75 215	76.07 202	64.68 22	68.11 135	83.63 139	50.39 95	79.14 222	49.78 202	69.66 232	86.34 68

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 65	90.58 19	49.90 96	88.21 31	73.78 38	87.03 48	86.29 74

UA-Net		73.13 74	72.93 73	73.76 113	83.58 66	51.66 169	78.75 118	77.66 181	67.75 4	72.61 75	89.42 47	49.82 97	83.29 140	53.61 179	83.14 71	86.32 71

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 63	90.56 20	49.80 98	88.24 30	74.02 36	87.03 48	86.32 71

API-MVS		72.17 87	71.41 86	74.45 100	81.95 83	57.22 93	84.03 42	80.38 130	59.89 108	68.40 127	82.33 162	49.64 99	87.83 40	51.87 190	84.16 69	78.30 242

ab-mvs		66.65 188	66.42 172	67.37 227	76.17 198	41.73 278	70.41 260	76.14 201	53.99 204	65.98 170	83.51 142	49.48 100	76.24 258	48.60 214	73.46 177	84.14 143

v8		70.33 113	69.28 117	73.49 124	73.15 239	50.22 188	78.62 122	80.78 124	60.79 85	66.45 163	82.11 170	49.35 101	84.98 105	63.58 110	68.71 245	85.28 110

IS-MVSNet		71.57 95	71.00 94	73.27 132	78.86 136	45.63 246	80.22 99	78.69 158	64.14 35	66.46 162	87.36 71	49.30 102	85.60 90	50.26 201	83.71 70	88.59 5

XXY-MVS		60.68 246	61.67 228	57.70 295	70.43 277	38.45 299	64.19 295	66.47 276	48.05 262	63.22 209	80.86 194	49.28 103	60.47 314	45.25 244	67.28 258	74.19 290

cdsmvs_eth3d_5k		17.50 319	23.34 318	0.00 337	0.00 356	0.00 356	0.00 348	78.63 160	0.00 352	0.00 353	82.18 165	49.25 104	0.00 352	0.00 351	0.00 349	0.00 350

PVSNet_Blended_VisFu		71.45 98	70.39 102	74.65 94	82.01 81	58.82 69	79.93 104	80.35 132	55.09 189	65.82 176	82.16 168	49.17 105	82.64 159	60.34 134	78.62 131	82.50 187

PVSNet_BlendedMVS		68.56 151	67.72 140	71.07 179	77.03 184	50.57 181	74.50 202	81.52 101	53.66 208	64.22 204	79.72 219	49.13 106	82.87 151	55.82 157	73.92 169	79.77 231

PVSNet_Blended		68.59 147	67.72 140	71.19 175	77.03 184	50.57 181	72.51 231	81.52 101	51.91 221	64.22 204	77.77 251	49.13 106	82.87 151	55.82 157	79.58 112	80.14 224

DU-MVS		70.01 118	69.53 112	71.44 166	78.05 159	44.13 257	75.01 192	81.51 103	64.37 28	68.20 130	84.52 121	49.12 108	82.82 155	54.62 169	70.43 214	87.37 40

Baseline_NR-MVSNet		67.05 179	67.56 143	65.50 252	75.65 207	37.70 305	75.42 182	74.65 223	59.90 105	68.14 134	83.15 148	49.12 108	77.20 247	52.23 188	69.78 228	81.60 199

VPNet		67.52 169	68.11 137	65.74 250	79.18 129	36.80 309	72.17 236	72.83 239	62.04 70	67.79 145	85.83 102	48.88 110	76.60 254	51.30 195	72.97 186	83.81 151

zzz-MVS		77.61 31	77.36 31	78.35 34	86.08 35	63.57 2	83.37 51	80.97 120	65.13 17	75.77 33	90.88 13	48.63 111	86.66 66	77.23 16	88.17 34	84.81 124

MTAPA		76.90 38	76.42 40	78.35 34	86.08 35	63.57 2	74.92 195	80.97 120	65.13 17	75.77 33	90.88 13	48.63 111	86.66 66	77.23 16	88.17 34	84.81 124

原ACMM1				74.69 91	85.39 47	59.40 54		83.42 67	51.47 228	70.27 96	86.61 84	48.61 113	86.51 74	53.85 176	87.96 39	78.16 244

v148		68.24 157	67.19 160	71.40 169	70.43 277	47.77 221	75.76 179	77.03 192	58.91 122	67.36 149	80.10 211	48.60 114	81.89 170	60.01 137	66.52 262	84.53 132

PGM-MVS		76.77 40	76.06 42	78.88 24	86.14 34	62.73 12	82.55 66	83.74 59	61.71 74	72.45 79	90.34 29	48.48 115	88.13 32	72.32 47	86.85 51	85.78 86

Test By Simon												48.33 116

CP-MVS		77.12 36	76.68 37	78.43 32	86.05 37	63.18 7	87.55 8	83.45 66	62.44 62	72.68 73	90.50 22	48.18 117	87.34 46	73.59 41	85.71 59	84.76 128

MVS		67.37 171	66.33 175	70.51 188	75.46 212	50.94 173	73.95 209	81.85 96	41.57 313	62.54 220	78.57 239	47.98 118	85.47 98	52.97 184	82.05 86	75.14 276

XVS		77.17 35	76.56 39	79.00 20	86.32 27	62.62 14	85.83 20	83.92 52	64.55 23	72.17 80	90.01 39	47.95 119	88.01 35	71.55 53	86.74 53	86.37 66

X-MVStestdata		70.21 115	67.28 155	79.00 20	86.32 27	62.62 14	85.83 20	83.92 52	64.55 23	72.17 80	6.49 347	47.95 119	88.01 35	71.55 53	86.74 53	86.37 66

MAR-MVS		71.51 96	70.15 106	75.60 78	81.84 84	59.39 55	81.38 85	82.90 82	54.90 195	68.08 136	78.70 234	47.73 121	85.51 95	51.68 194	84.17 68	81.88 197
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		67.92 164	66.69 166	71.63 163	78.09 157	49.02 206	77.09 152	81.24 113	51.04 234	60.91 236	83.98 132	47.71 122	84.99 103	40.81 273	79.32 118	80.90 214

SR-MVS		76.13 45	75.70 46	77.40 51	85.87 39	61.20 33	85.52 26	82.19 89	59.99 104	75.10 37	90.35 27	47.66 123	86.52 73	71.64 52	82.99 74	84.47 135

cl-mvsnet2		67.47 170	66.45 169	70.54 187	69.85 288	46.49 232	73.85 213	77.35 188	55.07 191	65.51 179	77.92 245	47.64 124	81.10 186	61.58 127	69.32 235	84.01 145

v10		70.21 115	69.02 121	73.81 110	73.51 236	50.92 175	78.74 119	81.39 106	60.05 103	66.39 164	81.83 173	47.58 125	85.41 100	62.80 115	68.86 244	85.09 116

v1144		70.42 111	69.31 116	73.76 113	73.22 237	50.64 180	77.83 134	81.43 105	58.58 127	69.40 113	81.16 185	47.53 126	85.29 102	64.01 104	70.64 211	85.34 108

v2v482		70.50 109	69.45 115	73.66 119	72.62 249	50.03 192	77.58 138	80.51 128	59.90 105	69.52 109	82.14 169	47.53 126	84.88 110	65.07 96	70.17 220	86.09 78

pm-mvs1		65.24 206	64.97 196	66.04 244	72.38 253	39.40 292	72.62 229	75.63 208	55.53 181	62.35 226	83.18 147	47.45 128	76.47 255	49.06 211	66.54 261	82.24 190

HY-MVS	56.14 13	64.55 214	63.89 201	66.55 235	74.73 222	41.02 282	69.96 263	74.43 224	49.29 248	61.66 231	80.92 192	47.43 129	76.68 253	44.91 245	71.69 202	81.94 195

cl-mvsnet_		67.18 175	66.26 179	69.94 196	70.20 280	45.74 242	73.30 218	76.83 195	55.10 187	65.27 183	79.57 222	47.39 130	80.53 199	59.41 144	69.22 239	83.53 164

cl-mvsnet1		67.18 175	66.26 179	69.94 196	70.20 280	45.74 242	73.29 219	76.83 195	55.10 187	65.27 183	79.58 221	47.38 131	80.53 199	59.43 143	69.22 239	83.54 163

eth_miper_zixun_eth		67.63 167	66.28 178	71.67 161	71.60 264	48.33 214	73.68 216	77.88 176	55.80 176	65.91 172	78.62 238	47.35 132	82.88 150	59.45 142	66.25 263	83.81 151

OPM-MVS		74.73 57	74.25 59	76.19 65	80.81 101	59.01 65	82.60 65	83.64 61	63.74 39	72.52 76	87.49 69	47.18 133	85.88 88	69.47 62	80.78 93	83.66 160

baseline1		63.81 218	63.87 203	63.62 264	76.29 196	36.36 312	71.78 241	67.29 272	56.05 171	64.23 203	82.95 149	47.11 134	74.41 265	47.30 221	61.85 293	80.10 225

pcd_1.5k_mvsjas		3.92 325	5.23 327	0.00 337	0.00 356	0.00 356	0.00 348	0.00 356	0.00 352	0.00 353	0.00 353	47.05 135	0.00 352	0.00 351	0.00 349	0.00 350

PS-MVSNAJss		72.24 85	71.21 91	75.31 82	78.50 144	55.93 114	81.63 79	82.12 91	56.24 165	70.02 101	85.68 105	47.05 135	84.34 119	65.27 94	74.41 164	85.67 93

PS-MVSNAJ		70.51 108	69.70 110	72.93 137	81.52 87	55.79 116	74.92 195	79.00 151	55.04 193	69.88 105	78.66 235	47.05 135	82.19 166	61.61 125	79.58 112	80.83 215

WTY-MVS		59.75 252	60.39 240	57.85 293	72.32 255	37.83 303	61.05 309	64.18 291	45.95 282	61.91 228	79.11 232	47.01 138	60.88 313	42.50 263	69.49 234	74.83 282

xiu_mvs_v2_base		70.52 107	69.75 108	72.84 139	81.21 96	55.63 120	75.11 189	78.92 152	54.92 194	69.96 104	79.68 220	47.00 139	82.09 168	61.60 126	79.37 115	80.81 216

v144192		69.71 123	68.51 128	73.33 131	73.10 240	50.13 190	77.54 141	80.64 125	56.65 151	68.57 125	80.55 200	46.87 140	84.96 107	62.98 113	69.66 232	84.89 122

PEN-MVS		66.60 189	66.45 169	67.04 230	77.11 182	36.56 311	77.03 154	80.42 129	62.95 48	62.51 222	84.03 130	46.69 141	79.07 223	44.22 246	63.08 286	85.51 100

mPP-MVS		76.54 41	75.93 44	78.34 36	86.47 23	63.50 4	85.74 23	82.28 88	62.90 50	71.77 83	90.26 32	46.61 142	86.55 72	71.71 51	85.66 60	84.97 120

CP-MVSNet		66.49 192	66.41 173	66.72 232	77.67 168	36.33 314	76.83 159	79.52 144	62.45 61	62.54 220	83.47 144	46.32 143	78.37 231	45.47 241	63.43 283	85.45 103

V42		68.65 146	67.35 153	72.56 146	68.93 296	50.18 189	72.90 225	79.47 145	56.92 148	69.45 112	80.26 208	46.29 144	82.99 146	64.07 102	67.82 254	84.53 132

1112_ss		64.00 217	63.36 211	65.93 247	79.28 126	42.58 270	71.35 244	72.36 242	46.41 275	60.55 238	77.89 247	46.27 145	73.28 268	46.18 230	69.97 223	81.92 196

MSLP-MVS++		73.77 70	73.47 68	74.66 93	83.02 72	59.29 57	82.30 73	81.88 94	59.34 117	71.59 86	86.83 75	45.94 146	83.65 134	65.09 95	85.22 61	81.06 212

PS-CasMVS		66.42 193	66.32 176	66.70 234	77.60 175	36.30 316	76.94 156	79.61 142	62.36 63	62.43 224	83.66 138	45.69 147	78.37 231	45.35 243	63.26 284	85.42 106

APD-MVS_3200maxsize		74.96 53	74.39 58	76.67 60	82.20 79	58.24 78	83.67 47	83.29 73	58.41 131	73.71 60	90.14 34	45.62 148	85.99 84	69.64 60	82.85 80	85.78 86

DTE-MVSNet		65.58 201	65.34 191	66.31 237	76.06 201	34.79 319	76.43 164	79.38 147	62.55 59	61.66 231	83.83 134	45.60 149	79.15 221	41.64 272	60.88 299	85.00 118

BH-w/o		66.85 183	65.83 184	69.90 199	79.29 125	52.46 160	74.66 200	76.65 197	54.51 201	64.85 194	78.12 241	45.59 150	82.95 149	43.26 257	75.54 158	74.27 289

HQP2-MVS												45.46 151

HQP-MVS		73.45 71	72.80 74	75.40 80	80.66 102	54.94 128	82.31 70	83.90 54	62.10 67	67.85 139	85.54 108	45.46 151	86.93 58	67.04 79	80.35 103	84.32 137

ACMMP		76.02 46	75.33 49	78.07 38	85.20 49	61.91 23	85.49 28	84.44 41	63.04 47	69.80 107	89.74 45	45.43 153	87.16 51	72.01 50	82.87 79	85.14 113
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
OMC-MVS		71.40 99	70.60 98	73.78 111	76.60 192	53.15 148	79.74 109	79.78 138	58.37 132	68.75 122	86.45 89	45.43 153	80.60 198	62.58 116	77.73 137	87.58 33

BH-untuned		68.27 155	67.29 154	71.21 174	79.74 122	53.22 147	76.06 173	77.46 186	57.19 144	66.10 168	81.61 177	45.37 155	83.50 137	45.42 242	76.68 152	76.91 263

v1192		69.97 120	68.68 126	73.85 109	73.19 238	50.94 173	77.68 137	81.36 107	57.51 141	68.95 121	80.85 195	45.28 156	85.33 101	62.97 114	70.37 216	85.27 111

HQP_MVS		74.31 62	73.73 64	76.06 66	81.41 90	56.31 104	84.22 36	84.01 50	64.52 25	69.27 115	86.10 95	45.26 157	87.21 49	68.16 68	80.58 97	84.65 129

plane_prior6					81.20 97	56.24 108						45.26 157

v1921920		69.47 132	68.17 136	73.36 130	73.06 241	50.10 191	77.39 144	80.56 126	56.58 158	68.59 123	80.37 202	44.72 159	84.98 105	62.47 119	69.82 227	85.00 118

Vis-MVSNet (Re-imp)		63.69 219	63.88 202	63.14 268	74.75 221	31.04 334	71.16 249	63.64 293	56.32 162	59.80 245	84.99 112	44.51 160	75.46 260	39.12 279	80.62 95	82.92 179

DP-MVS Recon		72.15 89	70.73 97	76.40 63	86.57 21	57.99 81	81.15 88	82.96 79	57.03 146	66.78 156	85.56 106	44.50 161	88.11 33	51.77 192	80.23 106	83.10 177

TAMVS		66.78 186	65.27 193	71.33 173	79.16 131	53.67 138	73.84 214	69.59 257	52.32 219	65.28 182	81.72 175	44.49 162	77.40 246	42.32 264	78.66 130	82.92 179

Vis-MVSNet		72.18 86	71.37 88	74.61 96	81.29 93	55.41 125	80.90 89	78.28 174	60.73 87	69.23 118	88.09 64	44.36 163	82.65 158	57.68 148	81.75 89	85.77 89
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
旧先验1					83.04 71	53.15 148		67.52 269			87.85 67	44.08 164			80.76 94	78.03 249

Test_1112_low_res		62.32 233	61.77 227	64.00 263	79.08 132	39.53 291	68.17 272	70.17 251	43.25 302	59.03 253	79.90 213	44.08 164	71.24 276	43.79 253	68.42 248	81.25 206

MVSFormer		71.50 97	70.38 103	74.88 88	78.76 138	57.15 98	82.79 59	78.48 165	51.26 232	69.49 110	83.22 145	43.99 166	83.24 141	66.06 85	79.37 115	84.23 140

lupinMVS		69.57 129	68.28 134	73.44 127	78.76 138	57.15 98	76.57 161	73.29 236	46.19 277	69.49 110	82.18 165	43.99 166	79.23 216	64.66 98	79.37 115	83.93 146

v7n		69.01 139	67.36 152	73.98 107	72.51 252	52.65 154	78.54 125	81.30 109	60.26 100	62.67 216	81.62 176	43.61 168	84.49 116	57.01 151	68.70 246	84.79 126

CDS-MVSNet		66.80 185	65.37 190	71.10 178	78.98 134	53.13 150	73.27 220	71.07 247	52.15 220	64.72 195	80.23 209	43.56 169	77.10 248	45.48 240	78.88 124	83.05 178
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
jason		69.65 127	68.39 133	73.43 128	78.27 152	56.88 100	77.12 151	73.71 233	46.53 274	69.34 114	83.22 145	43.37 170	79.18 217	64.77 97	79.20 120	84.23 140
jason: jason.
v1240		69.24 136	67.91 138	73.25 134	73.02 243	49.82 194	77.21 150	80.54 127	56.43 160	68.34 129	80.51 201	43.33 171	84.99 103	62.03 122	69.77 230	84.95 121

LCM-MVSNet-Re		61.88 240	61.35 232	63.46 265	74.58 224	31.48 333	61.42 305	58.14 315	58.71 126	53.02 303	79.55 223	43.07 172	76.80 252	45.69 235	77.96 135	82.11 194

baseline2		63.42 221	61.26 234	69.89 200	72.55 251	47.62 223	71.54 242	68.38 267	50.11 241	54.82 284	75.55 277	43.06 173	80.96 189	48.13 217	67.16 259	81.11 210

BH-RMVSNet		68.81 141	67.42 149	72.97 136	80.11 116	52.53 157	74.26 204	76.29 199	58.48 130	68.38 128	84.20 126	42.59 174	83.83 130	46.53 227	75.91 155	82.56 184

LFMVS		71.78 92	71.59 83	72.32 152	83.40 67	46.38 233	79.75 108	71.08 246	64.18 32	72.80 72	88.64 61	42.58 175	83.72 132	57.41 150	84.49 66	86.86 52

test_yl		69.69 124	69.13 118	71.36 170	78.37 148	45.74 242	74.71 198	80.20 133	57.91 138	70.01 102	83.83 134	42.44 176	82.87 151	54.97 165	79.72 109	85.48 101

DCV-MVSNet		69.69 124	69.13 118	71.36 170	78.37 148	45.74 242	74.71 198	80.20 133	57.91 138	70.01 102	83.83 134	42.44 176	82.87 151	54.97 165	79.72 109	85.48 101

3Dnovator	64.47 5	72.49 81	71.39 87	75.79 70	77.70 166	58.99 66	80.66 93	83.15 77	62.24 64	65.46 180	86.59 85	42.38 178	85.52 94	59.59 141	84.72 63	82.85 182

VDD-MVS		72.50 80	72.09 79	73.75 115	81.58 86	49.69 198	77.76 136	77.63 182	63.21 45	73.21 66	89.02 54	42.14 179	83.32 139	61.72 124	82.50 83	88.25 9

3Dnovator+	66.72 4	75.84 49	74.57 56	79.66 6	82.40 78	59.92 51	85.83 20	86.32 18	66.92 8	67.80 144	89.24 51	42.03 180	89.38 14	64.07 102	86.50 56	89.69 1

MVS_111021_LR		69.50 131	68.78 125	71.65 162	78.38 147	59.33 56	74.82 197	70.11 252	58.08 136	67.83 143	84.68 116	41.96 181	76.34 257	65.62 92	77.54 138	79.30 236

CPTT-MVS		72.78 77	72.08 80	74.87 89	84.88 58	61.41 29	84.15 39	77.86 177	55.27 184	67.51 148	88.08 65	41.93 182	81.85 171	69.04 66	80.01 107	81.35 205

abl_6		74.34 61	73.50 66	76.86 56	82.43 77	60.16 47	83.48 50	81.86 95	58.81 124	73.95 56	89.86 42	41.87 183	86.62 68	67.98 70	81.23 92	83.80 155

GBi-Net		67.21 173	66.55 167	69.19 208	77.63 169	43.33 264	77.31 145	77.83 178	56.62 154	65.04 190	82.70 151	41.85 184	80.33 203	47.18 222	72.76 189	83.92 147

test1		67.21 173	66.55 167	69.19 208	77.63 169	43.33 264	77.31 145	77.83 178	56.62 154	65.04 190	82.70 151	41.85 184	80.33 203	47.18 222	72.76 189	83.92 147

FMVSNet2		66.93 182	66.31 177	68.79 215	77.63 169	42.98 267	76.11 171	77.47 184	56.62 154	65.22 189	82.17 167	41.85 184	80.18 206	47.05 225	72.72 192	83.20 172

CostFormer		64.04 216	62.51 219	68.61 217	71.88 261	45.77 241	71.30 246	70.60 250	47.55 267	64.31 201	76.61 263	41.63 187	79.62 211	49.74 204	69.00 242	80.42 219

AdaColmap		69.99 119	68.66 127	73.97 108	84.94 55	57.83 83	82.63 64	78.71 157	56.28 164	64.34 199	84.14 127	41.57 188	87.06 56	46.45 228	78.88 124	77.02 259

Effi-MVS+-dtu		69.64 128	67.53 146	75.95 67	76.10 199	62.29 18	80.20 100	76.06 203	59.83 109	65.26 186	77.09 255	41.56 189	84.02 127	60.60 132	71.09 209	81.53 200

mvs-test1		70.44 110	68.19 135	77.18 52	76.10 199	63.22 6	80.59 95	76.06 203	59.83 109	66.32 165	79.87 214	41.56 189	85.53 93	60.60 132	72.77 188	82.80 183

QAPM		70.05 117	68.81 124	73.78 111	76.54 194	53.43 144	83.23 52	83.48 64	52.89 213	65.90 173	86.29 92	41.55 191	86.49 75	51.01 196	78.40 133	81.42 201

VDDNet		71.81 91	71.33 89	73.26 133	82.80 76	47.60 224	78.74 119	75.27 213	59.59 114	72.94 70	89.40 48	41.51 192	83.91 129	58.75 145	82.99 74	88.26 8

1121		68.53 152	67.16 161	72.63 145	85.64 42	61.14 34	73.95 209	66.46 277	44.61 289	70.28 95	86.68 81	41.42 193	80.78 195	53.62 177	81.79 87	75.97 267

CHOSEN 1792x2688		65.08 209	62.84 216	71.82 157	81.49 89	56.26 107	66.32 280	74.20 228	40.53 317	63.16 211	78.65 236	41.30 194	77.80 240	45.80 234	74.09 166	81.40 202

新几何1				70.76 182	85.66 40	61.13 35		66.43 278	44.68 288	70.29 94	86.64 82	41.29 195	75.23 261	49.72 205	81.75 89	75.93 269

tpmrst		58.24 260	58.70 249	56.84 296	66.97 306	34.32 323	69.57 266	61.14 307	47.17 271	58.58 258	71.60 300	41.28 196	60.41 315	49.20 210	62.84 287	75.78 271

tfpnnormal		62.47 231	61.63 229	64.99 258	74.81 220	39.01 294	71.22 247	73.72 232	55.22 186	60.21 239	80.09 212	41.26 197	76.98 251	30.02 324	68.09 250	78.97 239

HPM-MVS_fast		74.30 63	73.46 69	76.80 57	84.45 61	59.04 64	83.65 48	81.05 116	60.15 101	70.43 92	89.84 43	41.09 198	85.59 91	67.61 74	82.90 78	85.77 89

114514_t		70.83 102	69.56 111	74.64 95	86.21 29	54.63 132	82.34 69	81.81 97	48.22 259	63.01 212	85.83 102	40.92 199	87.10 53	57.91 147	79.79 108	82.18 191

HyFIR lowres test		65.67 200	63.01 214	73.67 118	79.97 118	55.65 119	69.07 270	75.52 210	42.68 307	63.53 207	77.95 243	40.43 200	81.64 174	46.01 232	71.91 200	83.73 156

miper_lstm_enhance		62.03 237	60.88 238	65.49 253	66.71 309	46.25 235	56.29 322	75.70 207	50.68 236	61.27 234	75.48 278	40.21 201	68.03 289	56.31 154	65.25 271	82.18 191

FMVSNet3		66.32 194	65.61 188	68.46 218	76.48 195	42.34 271	74.98 194	77.15 191	55.83 174	65.04 190	81.16 185	39.91 202	80.14 207	47.18 222	72.76 189	82.90 181

MVP-Stereo		65.41 204	63.80 204	70.22 190	77.62 173	55.53 122	76.30 167	78.53 163	50.59 239	56.47 272	78.65 236	39.84 203	82.68 157	44.10 250	72.12 199	72.44 304
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
TR-MVS		66.59 191	65.07 195	71.17 176	79.18 129	49.63 200	73.48 217	75.20 215	52.95 211	67.90 137	80.33 205	39.81 204	83.68 133	43.20 258	73.56 175	80.20 222

pmmvs6		63.69 219	62.82 217	66.27 239	70.63 274	39.27 293	73.13 221	75.47 211	52.69 215	59.75 246	82.30 163	39.71 205	77.03 250	47.40 220	64.35 277	82.53 185

XVG-OURS-SEG-HR		68.81 141	67.47 148	72.82 141	74.40 229	56.87 101	70.59 256	79.04 150	54.77 196	66.99 154	86.01 98	39.57 206	78.21 234	62.54 117	73.33 179	83.37 166

Anonymous20231211		69.28 134	68.47 130	71.73 159	80.28 109	47.18 228	79.98 102	82.37 87	54.61 197	67.24 151	84.01 131	39.43 207	82.41 164	55.45 163	72.83 187	85.62 97

Fast-Effi-MVS+-dtu		67.37 171	65.33 192	73.48 125	72.94 244	57.78 85	77.47 143	76.88 193	57.60 140	61.97 227	76.85 259	39.31 208	80.49 201	54.72 168	70.28 219	82.17 193

ACMP	63.53 6	72.30 84	71.20 92	75.59 79	80.28 109	57.54 87	82.74 61	82.84 84	60.58 89	65.24 187	86.18 93	39.25 209	86.03 83	66.95 81	76.79 150	83.22 171
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
Anonymous20240529		69.91 121	69.02 121	72.56 146	80.19 114	47.65 222	77.56 140	80.99 119	55.45 183	69.88 105	86.76 76	39.24 210	82.18 167	54.04 173	77.10 145	87.85 23

LPG-MVS_test		72.74 78	71.74 82	75.76 71	80.22 111	57.51 89	82.55 66	83.40 68	61.32 78	66.67 158	87.33 72	39.15 211	86.59 69	67.70 72	77.30 143	83.19 173

LGP-MVS_train				75.76 71	80.22 111	57.51 89		83.40 68	61.32 78	66.67 158	87.33 72	39.15 211	86.59 69	67.70 72	77.30 143	83.19 173

TAPA-MVS	59.36 10	66.60 189	65.20 194	70.81 181	76.63 191	48.75 210	76.52 163	80.04 135	50.64 238	65.24 187	84.93 113	39.15 211	78.54 230	36.77 290	76.88 147	85.14 113
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
OpenMVS	61.03 9	68.85 140	67.56 143	72.70 144	74.26 231	53.99 136	81.21 87	81.34 108	52.70 214	62.75 215	85.55 107	38.86 214	84.14 122	48.41 216	83.01 73	79.97 226

sss		56.17 275	56.57 264	54.96 302	66.93 307	36.32 315	57.94 317	61.69 305	41.67 311	58.64 257	75.32 280	38.72 215	56.25 329	42.04 267	66.19 264	72.31 308

ACMM	61.98 7	70.80 104	69.73 109	74.02 106	80.59 106	58.59 73	82.68 63	82.02 93	55.46 182	67.18 152	84.39 125	38.51 216	83.17 143	60.65 131	76.10 154	80.30 221
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
MVSTER		67.16 177	65.58 189	71.88 156	70.37 279	49.70 196	70.25 261	78.45 168	51.52 226	69.16 119	80.37 202	38.45 217	82.50 161	60.19 135	71.46 205	83.44 165

test_djsdf		69.45 133	67.74 139	74.58 97	74.57 225	54.92 130	82.79 59	78.48 165	51.26 232	65.41 181	83.49 143	38.37 218	83.24 141	66.06 85	69.25 238	85.56 98

tpm2		62.07 236	60.10 242	67.99 222	72.79 246	43.86 260	71.05 252	66.85 275	43.14 304	62.77 213	75.39 279	38.32 219	80.80 194	41.69 269	68.88 243	79.32 235

tpm cat1		59.25 254	56.95 262	66.15 241	72.19 256	46.96 229	68.09 273	65.76 280	40.03 320	57.81 264	70.56 307	38.32 219	74.51 264	38.26 283	61.50 296	77.00 260

CNLPA		65.43 203	64.02 200	69.68 201	78.73 140	58.07 80	77.82 135	70.71 249	51.49 227	61.57 233	83.58 141	38.23 221	70.82 277	43.90 251	70.10 222	80.16 223

1314		64.61 213	63.21 212	68.80 214	71.87 262	47.46 225	73.95 209	78.39 173	42.88 306	59.97 242	76.60 264	38.11 222	79.39 214	54.84 167	72.32 196	79.55 232

testdata				64.66 259	81.52 87	52.93 151		65.29 284	46.09 278	73.88 58	87.46 70	38.08 223	66.26 298	53.31 182	78.48 132	74.78 284

FMVSNet1		66.70 187	65.87 183	69.19 208	77.49 176	43.33 264	77.31 145	77.83 178	56.45 159	64.60 198	82.70 151	38.08 223	80.33 203	46.08 231	72.31 197	83.92 147

UniMVSNet_ETH3D		67.60 168	67.07 162	69.18 211	77.39 178	42.29 272	74.18 206	75.59 209	60.37 93	66.77 157	86.06 97	37.64 225	78.93 229	52.16 189	73.49 176	86.32 71

EPNet_dtu		61.90 238	61.97 226	61.68 277	72.89 245	39.78 288	75.85 178	65.62 281	55.09 189	54.56 288	79.36 227	37.59 226	67.02 294	39.80 278	76.95 146	78.25 243
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
IterMVS-SCA-FT		62.49 230	61.52 230	65.40 254	71.99 259	50.80 178	71.15 250	69.63 256	45.71 283	60.61 237	77.93 244	37.45 227	65.99 299	55.67 161	63.50 282	79.42 234

SCA		60.49 247	58.38 251	66.80 231	74.14 233	48.06 217	63.35 297	63.23 296	49.13 250	59.33 251	72.10 297	37.45 227	74.27 266	44.17 247	62.57 289	78.05 246

IterMVS		62.79 229	61.27 233	67.35 228	69.37 293	52.04 167	71.17 248	68.24 268	52.63 216	59.82 244	76.91 258	37.32 229	72.36 271	52.80 185	63.19 285	77.66 250
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
tfpn200view9		63.18 226	62.18 224	66.21 240	76.85 187	39.62 289	71.96 239	69.44 259	56.63 152	62.61 218	79.83 215	37.18 230	79.17 218	31.84 312	73.25 181	79.83 228

thres400		63.31 222	62.18 224	66.72 232	76.85 187	39.62 289	71.96 239	69.44 259	56.63 152	62.61 218	79.83 215	37.18 230	79.17 218	31.84 312	73.25 181	81.36 203

tpm		57.34 267	58.16 253	54.86 303	71.80 263	34.77 320	67.47 276	56.04 325	48.20 260	60.10 240	76.92 257	37.17 232	53.41 336	40.76 274	65.01 272	76.40 266

test222					83.14 68	58.68 71	72.57 230	63.45 294	41.78 309	67.56 147	86.12 94	37.13 233			78.73 129	74.98 280

thres200		62.20 235	61.16 236	65.34 255	75.38 214	39.99 286	69.60 265	69.29 261	55.64 180	61.87 229	76.99 256	37.07 234	78.96 228	31.28 320	73.28 180	77.06 258

thres100view900		63.28 224	62.41 221	65.89 248	77.31 179	38.66 297	72.65 227	69.11 263	57.07 145	62.45 223	81.03 189	37.01 235	79.17 218	31.84 312	73.25 181	79.83 228

thres600view7		63.30 223	62.27 222	66.41 236	77.18 181	38.87 295	72.35 233	69.11 263	56.98 147	62.37 225	80.96 191	37.01 235	79.00 227	31.43 319	73.05 185	81.36 203

DP-MVS		65.68 199	63.66 207	71.75 158	84.93 56	56.87 101	80.74 92	73.16 237	53.06 210	59.09 252	82.35 161	36.79 237	85.94 87	32.82 308	69.96 224	72.45 303

XVG-OURS		68.76 145	67.37 151	72.90 138	74.32 230	57.22 93	70.09 262	78.81 154	55.24 185	67.79 145	85.81 104	36.54 238	78.28 233	62.04 121	75.74 156	83.19 173

pmmvs4		61.48 243	59.39 244	67.76 224	71.57 265	53.86 137	71.42 243	65.34 283	44.20 294	59.46 247	77.92 245	35.90 239	74.71 263	43.87 252	64.87 273	74.71 285

CR-MVSNet		59.91 250	57.90 256	65.96 245	69.96 286	52.07 165	65.31 289	63.15 297	42.48 308	59.36 248	74.84 282	35.83 240	70.75 278	45.50 239	64.65 275	75.06 277

Patchmtry		57.16 268	56.47 265	59.23 284	69.17 295	34.58 322	62.98 298	63.15 297	44.53 290	56.83 269	74.84 282	35.83 240	68.71 286	40.03 276	60.91 298	74.39 288

RPMNet		58.70 256	56.29 268	65.96 245	69.96 286	52.07 165	65.31 289	62.15 303	43.20 303	59.36 248	70.15 310	35.37 242	70.75 278	36.42 297	64.65 275	75.06 277

CANet_DTU		68.18 158	67.71 142	69.59 203	74.83 219	46.24 236	78.66 121	76.85 194	59.60 111	63.45 208	82.09 171	35.25 243	77.41 245	59.88 138	78.76 128	85.14 113

thisisatest0530		67.92 164	65.78 185	74.33 103	76.29 196	51.03 172	76.89 157	74.25 227	53.67 207	65.59 178	81.76 174	35.15 244	85.50 96	55.94 155	72.47 193	86.47 61

tttt0517		67.83 166	65.66 187	74.33 103	76.69 189	50.82 177	77.86 133	73.99 230	54.54 200	64.64 197	82.53 158	35.06 245	85.50 96	55.71 160	69.91 225	86.67 58

test_0402		63.25 225	61.01 237	69.96 195	80.00 117	54.37 134	76.86 158	72.02 243	54.58 199	58.71 255	80.79 197	35.00 246	84.36 118	26.41 334	64.71 274	71.15 315

thisisatest0515		65.83 198	63.50 209	72.82 141	73.75 234	49.50 201	71.32 245	73.12 238	49.39 247	63.82 206	76.50 267	34.95 247	84.84 111	53.20 183	75.49 159	84.13 144

sam_mvs1												34.74 248				78.05 246

pmmvs5		56.47 271	55.68 271	58.86 287	61.41 331	36.71 310	66.37 279	62.75 299	40.38 318	53.70 296	76.62 262	34.56 249	67.05 293	40.02 277	65.27 270	72.83 298

patchmatchnet-post											64.03 326	34.50 250	74.27 266

PatchmatchNet		59.84 251	58.24 252	64.65 260	73.05 242	46.70 231	69.42 267	62.18 302	47.55 267	58.88 254	71.96 299	34.49 251	69.16 284	42.99 260	63.60 281	78.07 245
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
Patchmatch-test		49.08 299	48.28 300	51.50 316	64.40 322	30.85 335	45.68 336	48.46 338	35.60 327	46.10 325	72.10 297	34.47 252	46.37 340	27.08 332	60.65 301	77.27 255

MS-PatchMatch		62.42 232	61.46 231	65.31 256	75.21 216	52.10 164	72.05 237	74.05 229	46.41 275	57.42 267	74.36 286	34.35 253	77.57 244	45.62 237	73.67 171	66.26 326

tpmvs		58.47 258	56.95 262	63.03 270	70.20 280	41.21 281	67.90 275	67.23 273	49.62 246	54.73 286	70.84 304	34.14 254	76.24 258	36.64 294	61.29 297	71.64 311

PMMVS		53.96 283	53.26 288	56.04 298	62.60 327	50.92 175	61.17 308	56.09 324	32.81 330	53.51 300	66.84 321	34.04 255	59.93 317	44.14 249	68.18 249	57.27 334

Patchmatch-RL test		58.16 261	55.49 272	66.15 241	67.92 302	48.89 209	60.66 310	51.07 332	47.86 264	59.36 248	62.71 330	34.02 256	72.27 273	56.41 153	59.40 304	77.30 254

test_post											3.55 349	33.90 257	66.52 296

PLC	56.13 14	65.09 208	63.21 212	70.72 184	81.04 99	54.87 131	78.57 123	77.47 184	48.51 255	55.71 275	81.89 172	33.71 258	79.71 208	41.66 270	70.37 216	77.58 251
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
ET-MVSNet_ETH3D		67.96 163	65.72 186	74.68 92	76.67 190	55.62 121	75.11 189	74.74 221	52.91 212	60.03 241	80.12 210	33.68 259	82.64 159	61.86 123	76.34 153	85.78 86

GA-MVS		65.53 202	63.70 206	71.02 180	70.87 272	48.10 216	70.48 258	74.40 225	56.69 150	64.70 196	76.77 260	33.66 260	81.10 186	55.42 164	70.32 218	83.87 150

LS3D		64.71 211	62.50 220	71.34 172	79.72 124	55.71 117	79.82 106	74.72 222	48.50 256	56.62 270	84.62 118	33.59 261	82.34 165	29.65 326	75.23 160	75.97 267

sam_mvs												33.43 262

PatchT		53.17 289	53.44 287	52.33 314	68.29 301	25.34 343	58.21 316	54.41 328	44.46 292	54.56 288	69.05 314	33.32 263	60.94 312	36.93 289	61.76 295	70.73 317

test20.03		53.87 285	54.02 284	53.41 309	61.47 330	28.11 339	61.30 306	59.21 311	51.34 231	52.09 305	77.43 253	33.29 264	58.55 320	29.76 325	60.27 302	73.58 295

our_test_3		56.49 270	54.42 279	62.68 272	69.51 290	45.48 247	66.08 281	61.49 306	44.11 297	50.73 312	69.60 313	33.05 265	68.15 288	38.38 282	56.86 310	74.40 287

anonymousdsp		67.00 181	64.82 197	73.57 123	70.09 283	56.13 109	76.35 166	77.35 188	48.43 257	64.99 193	80.84 196	33.01 266	80.34 202	64.66 98	67.64 256	84.23 140

MDTV_nov1_ep13_2view						25.89 341	61.22 307		40.10 319	51.10 308		32.97 267		38.49 281		78.61 241

IB-MVS	56.42 12	65.40 205	62.73 218	73.40 129	74.89 217	52.78 153	73.09 222	75.13 216	55.69 178	58.48 260	73.73 291	32.86 268	86.32 79	50.63 198	70.11 221	81.10 211
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
xiu_mvs_v1_base_debu		68.58 148	67.28 155	72.48 148	78.19 154	57.19 95	75.28 184	75.09 217	51.61 223	70.04 98	81.41 181	32.79 269	79.02 224	63.81 106	77.31 140	81.22 207

xiu_mvs_v1_base		68.58 148	67.28 155	72.48 148	78.19 154	57.19 95	75.28 184	75.09 217	51.61 223	70.04 98	81.41 181	32.79 269	79.02 224	63.81 106	77.31 140	81.22 207

xiu_mvs_v1_base_debi		68.58 148	67.28 155	72.48 148	78.19 154	57.19 95	75.28 184	75.09 217	51.61 223	70.04 98	81.41 181	32.79 269	79.02 224	63.81 106	77.31 140	81.22 207

Anonymous20231206		55.10 281	55.30 274	54.48 305	69.81 289	33.94 325	62.91 299	62.13 304	41.08 314	55.18 282	75.65 275	32.75 272	56.59 328	30.32 323	67.86 252	72.91 297

UGNet		68.81 141	67.39 150	73.06 135	78.33 150	54.47 133	79.77 107	75.40 212	60.45 91	63.22 209	84.40 124	32.71 273	80.91 192	51.71 193	80.56 99	83.81 151
Wanjuan Su, Qingshan Xu, Wenbing Tao: Uncertainty-guided Multi-view Stereo Network for Depth Estimation. IEEE Transactions on Circuits and Systems for Video Technology, 2022
test-LLR		58.15 262	58.13 255	58.22 290	68.57 297	44.80 251	65.46 286	57.92 316	50.08 243	55.44 278	69.82 311	32.62 274	57.44 323	49.66 206	73.62 172	72.41 305

test0.0.03 1		53.32 288	53.59 286	52.50 313	62.81 326	29.45 337	59.51 312	54.11 329	50.08 243	54.40 290	74.31 287	32.62 274	55.92 331	30.50 322	63.95 279	72.15 310

MDTV_nov1_ep13			57.00 261		72.73 247	38.26 300	65.02 292	64.73 288	44.74 287	55.46 277	72.48 295	32.61 276	70.47 280	37.47 286	67.75 255

cascas		65.98 197	63.42 210	73.64 121	77.26 180	52.58 156	72.26 235	77.21 190	48.56 254	61.21 235	74.60 285	32.57 277	85.82 89	50.38 200	76.75 151	82.52 186

test_post1							68.67 271				3.64 348	32.39 278	69.49 283	44.17 247

CVMVSNet		59.63 253	59.14 246	61.08 282	74.47 226	38.84 296	75.20 187	68.74 265	31.15 332	58.24 261	76.51 265	32.39 278	68.58 287	49.77 203	65.84 267	75.81 270

ppachtmachnet_test		58.06 263	55.38 273	66.10 243	69.51 290	48.99 207	68.01 274	66.13 279	44.50 291	54.05 294	70.74 305	32.09 280	72.34 272	36.68 293	56.71 312	76.99 262

RRT_test8_iter05		68.17 160	66.86 164	72.07 154	75.81 204	46.33 234	76.41 165	81.81 97	56.43 160	66.52 160	81.30 184	31.90 281	84.25 120	63.77 109	67.83 253	85.64 96

MIMVSNet		57.35 266	57.07 260	58.22 290	74.21 232	37.18 306	62.46 300	60.88 308	48.88 252	55.29 281	75.99 272	31.68 282	62.04 310	31.87 311	72.35 195	75.43 275

PVSNet_0	43.31 20	47.46 303	45.64 305	52.92 311	67.60 304	44.65 253	54.06 325	54.64 326	41.59 312	46.15 324	58.75 332	30.99 283	58.66 319	32.18 309	24.81 340	55.46 335

testing_2		66.02 196	63.77 205	72.76 143	66.03 315	50.48 185	72.93 223	80.36 131	54.41 202	54.25 292	76.76 261	30.89 284	83.16 144	64.19 101	74.08 167	84.65 129

gg-mvs-nofinetune		57.86 264	56.43 266	62.18 274	72.62 249	35.35 318	66.57 277	56.33 322	50.65 237	57.64 265	57.10 333	30.65 285	76.36 256	37.38 287	78.88 124	74.82 283

D2MVS		62.30 234	60.29 241	68.34 220	66.46 311	48.42 213	65.70 283	73.42 234	47.71 265	58.16 262	75.02 281	30.51 286	77.71 241	53.96 175	71.68 203	78.90 240

GG-mvs-BLEND				62.34 273	71.36 270	37.04 308	69.20 269	57.33 319		54.73 286	65.48 325	30.37 287	77.82 239	34.82 301	74.93 161	72.17 309

MDA-MVSNet-bldmvs		53.87 285	50.81 293	63.05 269	66.25 312	48.58 211	56.93 320	63.82 292	48.09 261	41.22 333	70.48 308	30.34 288	68.00 290	34.24 303	45.92 333	72.57 301

EPMVS		53.96 283	53.69 285	54.79 304	66.12 314	31.96 332	62.34 302	49.05 335	44.42 293	55.54 276	71.33 302	30.22 289	56.70 326	41.65 271	62.54 290	75.71 272

DWT-MVSNet_test		61.90 238	59.93 243	67.83 223	71.98 260	46.09 239	71.03 253	69.71 253	50.09 242	58.51 259	70.62 306	30.21 290	77.63 242	49.28 209	67.91 251	79.78 230

YYNet1		50.73 296	48.96 296	56.03 299	61.10 333	41.78 277	51.94 327	56.44 321	40.94 316	44.84 326	67.80 318	30.08 291	55.08 334	36.77 290	50.71 326	71.22 313

MDA-MVSNet_test_wron		50.71 297	48.95 297	56.00 300	61.17 332	41.84 275	51.90 328	56.45 320	40.96 315	44.79 327	67.84 317	30.04 292	55.07 335	36.71 292	50.69 327	71.11 316

Anonymous202405211		66.84 184	65.99 182	69.40 207	80.19 114	42.21 273	71.11 251	71.31 245	58.80 125	67.90 137	86.39 90	29.83 293	79.65 209	49.60 208	78.78 127	86.33 69

MSDG		61.81 241	59.23 245	69.55 206	72.64 248	52.63 155	70.45 259	75.81 206	51.38 229	53.70 296	76.11 269	29.52 294	81.08 188	37.70 285	65.79 268	74.93 281

CMPMVS	42.80 21	57.81 265	55.97 269	63.32 266	60.98 334	47.38 226	64.66 293	69.50 258	32.06 331	46.83 322	77.80 249	29.50 295	71.36 275	48.68 213	73.75 170	71.21 314
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
LTVRE_ROB	55.42 16	63.15 227	61.23 235	68.92 213	76.57 193	47.80 219	59.92 311	76.39 198	54.35 203	58.67 256	82.46 160	29.44 296	81.49 178	42.12 266	71.14 207	77.46 252
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
UnsupCasMVSNet_eth		53.16 290	52.47 289	55.23 301	59.45 337	33.39 328	59.43 313	69.13 262	45.98 279	50.35 315	72.32 296	29.30 297	58.26 321	42.02 268	44.30 334	74.05 291

CHOSEN 280x420		47.83 301	46.36 304	52.24 315	67.37 305	49.78 195	38.91 342	43.11 343	35.00 328	43.27 331	63.30 329	28.95 298	49.19 339	36.53 295	60.80 300	57.76 333

pmmvs-eth3d		58.81 255	56.31 267	66.30 238	67.61 303	52.42 162	72.30 234	64.76 287	43.55 300	54.94 283	74.19 288	28.95 298	72.60 270	43.31 255	57.21 309	73.88 293

dp		51.89 293	51.60 292	52.77 312	68.44 300	32.45 330	62.36 301	54.57 327	44.16 295	49.31 316	67.91 316	28.87 300	56.61 327	33.89 304	54.89 316	69.24 323

RRT_MVS		68.77 144	66.71 165	74.95 86	75.93 203	58.55 74	80.50 96	75.84 205	56.09 169	68.17 132	83.74 137	28.50 301	82.98 147	65.67 91	65.91 265	83.33 167

jajsoiax		68.25 156	66.45 169	73.66 119	75.62 208	55.49 123	80.82 90	78.51 164	52.33 218	64.33 200	84.11 128	28.28 302	81.81 173	63.48 111	70.62 212	83.67 158

RPSCF		55.80 277	54.22 283	60.53 283	65.13 319	42.91 269	64.30 294	57.62 318	36.84 326	58.05 263	82.28 164	28.01 303	56.24 330	37.14 288	58.61 307	82.44 189

F-COLMAP		63.05 228	60.87 239	69.58 205	76.99 186	53.63 140	78.12 131	76.16 200	47.97 263	52.41 304	81.61 177	27.87 304	78.11 235	40.07 275	66.66 260	77.00 260

K. test v3		60.47 248	57.11 259	70.56 186	73.74 235	48.22 215	75.10 191	62.55 300	58.27 135	53.62 298	76.31 268	27.81 305	81.59 176	47.42 219	39.18 337	81.88 197

ACMH+	57.40 11	66.12 195	64.06 199	72.30 153	77.79 165	52.83 152	80.39 97	78.03 175	57.30 142	57.47 266	82.55 157	27.68 306	84.17 121	45.54 238	69.78 228	79.90 227

UnsupCasMVSNet_bld		50.07 298	48.87 298	53.66 307	60.97 335	33.67 326	57.62 318	64.56 289	39.47 322	47.38 319	64.02 328	27.47 307	59.32 318	34.69 302	43.68 335	67.98 325

mvs_tets		68.18 158	66.36 174	73.63 122	75.61 209	55.35 126	80.77 91	78.56 162	52.48 217	64.27 202	84.10 129	27.45 308	81.84 172	63.45 112	70.56 213	83.69 157

lessismore_v0				69.91 198	71.42 268	47.80 219		50.90 333		50.39 314	75.56 276	27.43 309	81.33 180	45.91 233	34.10 339	80.59 217

ACMH	55.70 15	65.20 207	63.57 208	70.07 194	78.07 158	52.01 168	79.48 115	79.69 139	55.75 177	56.59 271	80.98 190	27.12 310	80.94 190	42.90 262	71.58 204	77.25 257
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
SixPastTwentyTwo		61.65 242	58.80 248	70.20 192	75.80 205	47.22 227	75.59 180	69.68 255	54.61 197	54.11 293	79.26 230	27.07 311	82.96 148	43.27 256	49.79 328	80.41 220

PVSNet	50.76 19	58.40 259	57.39 257	61.42 279	75.53 211	44.04 259	61.43 304	63.45 294	47.04 272	56.91 268	73.61 292	27.00 312	64.76 302	39.12 279	72.40 194	75.47 274

OpenMVS_ROB	52.78 18	60.03 249	58.14 254	65.69 251	70.47 276	44.82 250	75.33 183	70.86 248	45.04 285	56.06 273	76.00 270	26.89 313	79.65 209	35.36 300	67.29 257	72.60 300

ADS-MVSNet2		51.33 295	48.76 299	59.07 286	66.02 316	44.60 254	50.90 329	59.76 310	36.90 324	50.74 310	66.18 323	26.38 314	63.11 306	27.17 330	54.76 317	69.50 320

ADS-MVSNet		48.48 300	47.77 301	50.63 317	66.02 316	29.92 336	50.90 329	50.87 334	36.90 324	50.74 310	66.18 323	26.38 314	52.47 337	27.17 330	54.76 317	69.50 320

N_pmnet		39.35 310	40.28 310	36.54 326	63.76 324	1.62 354	49.37 332	0.76 355	34.62 329	43.61 330	66.38 322	26.25 316	42.57 343	26.02 335	51.77 323	65.44 327

MVS-HIRNet		45.52 304	44.48 306	48.65 319	68.49 299	34.05 324	59.41 314	44.50 342	27.03 336	37.96 337	50.47 339	26.16 317	64.10 303	26.74 333	59.52 303	47.82 337

FMVSNet5		55.86 276	54.93 275	58.66 289	71.05 271	36.35 313	64.18 296	62.48 301	46.76 273	50.66 313	74.73 284	25.80 318	64.04 304	33.11 307	65.57 269	75.59 273

new-patchmatchnet		47.56 302	47.73 302	47.06 320	58.81 338	9.37 351	48.78 333	59.21 311	43.28 301	44.22 329	68.66 315	25.67 319	57.20 325	31.57 318	49.35 329	74.62 286

MIMVSNet1		55.17 280	54.31 281	57.77 294	70.03 285	32.01 331	65.68 284	64.81 286	49.19 249	46.75 323	76.00 270	25.53 320	64.04 304	28.65 328	62.13 292	77.26 256

PatchMatch-RL		56.25 274	54.55 278	61.32 281	77.06 183	56.07 111	65.57 285	54.10 330	44.13 296	53.49 301	71.27 303	25.20 321	66.78 295	36.52 296	63.66 280	61.12 329

JIA-IIPM		51.56 294	47.68 303	63.21 267	64.61 321	50.73 179	47.71 334	58.77 313	42.90 305	48.46 317	51.72 336	24.97 322	70.24 281	36.06 299	53.89 320	68.64 324

EU-MVSNet		55.61 278	54.41 280	59.19 285	65.41 318	33.42 327	72.44 232	71.91 244	28.81 334	51.27 307	73.87 290	24.76 323	69.08 285	43.04 259	58.20 308	75.06 277

EG-PatchMatch MVS		64.71 211	62.87 215	70.22 190	77.68 167	53.48 143	77.99 132	78.82 153	53.37 209	56.03 274	77.41 254	24.75 324	84.04 124	46.37 229	73.42 178	73.14 296

TESTMET0.1,1		55.28 279	54.90 276	56.42 297	66.56 310	43.67 262	65.46 286	56.27 323	39.18 323	53.83 295	67.44 319	24.21 325	55.46 333	48.04 218	73.11 184	70.13 318

COLMAP_ROB	52.97 17	61.27 245	58.81 247	68.64 216	74.63 223	52.51 159	78.42 128	73.30 235	49.92 245	50.96 309	81.51 180	23.06 326	79.40 213	31.63 316	65.85 266	74.01 292
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
MVS_0304		58.51 257	57.36 258	61.96 276	70.04 284	41.83 276	69.40 268	65.46 282	50.73 235	53.30 302	74.06 289	22.65 327	70.18 282	42.16 265	68.44 247	73.86 294

testgi		51.90 292	52.37 290	50.51 318	60.39 336	23.55 345	58.42 315	58.15 314	49.03 251	51.83 306	79.21 231	22.39 328	55.59 332	29.24 327	62.64 288	72.40 307

DSMNet-mixed		39.30 311	38.72 311	41.03 325	51.22 342	19.66 347	45.53 337	31.35 348	15.83 345	39.80 336	67.42 320	22.19 329	45.13 341	22.43 336	52.69 322	58.31 332

test-mter		56.42 272	55.82 270	58.22 290	68.57 297	44.80 251	65.46 286	57.92 316	39.94 321	55.44 278	69.82 311	21.92 330	57.44 323	49.66 206	73.62 172	72.41 305

OurMVSNet-221017-0		61.37 244	58.63 250	69.61 202	72.05 258	48.06 217	73.93 212	72.51 240	47.23 270	54.74 285	80.92 192	21.49 331	81.24 183	48.57 215	56.22 313	79.53 233

ITE_SJBPF				62.09 275	66.16 313	44.55 256		64.32 290	47.36 269	55.31 280	80.34 204	19.27 332	62.68 308	36.29 298	62.39 291	79.04 237

AllTest		57.08 269	54.65 277	64.39 261	71.44 266	49.03 204	69.92 264	67.30 270	45.97 280	47.16 320	79.77 217	17.47 333	67.56 291	33.65 305	59.16 305	76.57 264

TestCases				64.39 261	71.44 266	49.03 204		67.30 270	45.97 280	47.16 320	79.77 217	17.47 333	67.56 291	33.65 305	59.16 305	76.57 264

XVG-ACMP-BASELINE		64.36 215	62.23 223	70.74 183	72.35 254	52.45 161	70.80 255	78.45 168	53.84 206	59.87 243	81.10 187	16.24 335	79.32 215	55.64 162	71.76 201	80.47 218

tmp_tt		9.43 321	11.14 323	4.30 334	2.38 353	4.40 353	13.62 347	16.08 352	0.39 349	15.89 345	13.06 346	15.80 336	5.54 351	12.63 342	10.46 347	2.95 346

USDC		56.35 273	54.24 282	62.69 271	64.74 320	40.31 285	65.05 291	73.83 231	43.93 298	47.58 318	77.71 252	15.36 337	75.05 262	38.19 284	61.81 294	72.70 299

ANet_high		41.38 308	37.47 312	53.11 310	39.73 348	24.45 344	56.94 319	69.69 254	47.65 266	26.04 341	52.32 335	12.44 338	62.38 309	21.80 337	10.61 346	72.49 302

FPMVS		42.18 307	41.11 309	45.39 321	58.03 339	41.01 283	49.50 331	53.81 331	30.07 333	33.71 338	64.03 326	11.69 339	52.08 338	14.01 341	55.11 315	43.09 339

TinyColmap		54.14 282	51.72 291	61.40 280	66.84 308	41.97 274	66.52 278	68.51 266	44.81 286	42.69 332	75.77 274	11.66 340	72.94 269	31.96 310	56.77 311	69.27 322

TDRefinement		53.44 287	50.72 294	61.60 278	64.31 323	46.96 229	70.89 254	65.27 285	41.78 309	44.61 328	77.98 242	11.52 341	66.36 297	28.57 329	51.59 324	71.49 312

ambc				65.13 257	63.72 325	37.07 307	47.66 335	78.78 156		54.37 291	71.42 301	11.24 342	80.94 190	45.64 236	53.85 321	77.38 253

pmmvs3		44.92 305	41.95 308	53.86 306	52.58 341	43.55 263	62.11 303	46.90 341	26.05 338	40.63 334	60.19 331	11.08 343	57.91 322	31.83 315	46.15 332	60.11 330

new_pmnet		34.13 314	34.29 314	33.64 327	52.63 340	18.23 349	44.43 339	33.90 347	22.81 341	30.89 339	53.18 334	10.48 344	35.72 347	20.77 338	39.51 336	46.98 338

LF4IMVS		42.95 306	42.26 307	45.04 322	48.30 343	32.50 329	54.80 323	48.49 337	28.03 335	40.51 335	70.16 309	9.24 345	43.89 342	31.63 316	49.18 330	58.72 331

PM-MVS		52.33 291	50.19 295	58.75 288	62.10 328	45.14 249	65.75 282	40.38 344	43.60 299	53.52 299	72.65 294	9.16 346	65.87 300	50.41 199	54.18 319	65.24 328

EMVS		22.97 317	21.84 320	26.36 331	40.20 347	19.53 348	41.95 341	34.64 346	17.09 344	9.73 349	22.83 345	7.29 347	42.22 345	9.18 346	13.66 344	17.32 344

E-PMN		23.77 316	22.73 319	26.90 330	42.02 346	20.67 346	42.66 340	35.70 345	17.43 343	10.28 348	25.05 344	6.42 348	42.39 344	10.28 344	14.71 343	17.63 343

Gipuma		34.77 313	31.91 316	43.33 324	62.05 329	37.87 302	20.39 345	67.03 274	23.23 340	18.41 344	25.84 343	4.24 349	62.73 307	14.71 340	51.32 325	29.38 342
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PMMVS2		27.40 315	25.91 317	31.87 329	39.46 349	6.57 352	31.17 343	28.52 349	23.96 339	20.45 343	48.94 340	4.20 350	37.94 346	16.51 339	19.97 341	51.09 336

LCM-MVSNet		40.30 309	35.88 313	53.57 308	42.24 345	29.15 338	45.21 338	60.53 309	22.23 342	28.02 340	50.98 338	3.72 351	61.78 311	31.22 321	38.76 338	69.78 319

DeepMVS_CX				12.03 333	17.97 352	10.91 350		10.60 353	7.46 347	11.07 347	28.36 342	3.28 352	11.29 350	8.01 347	9.74 348	13.89 345

PMVS	28.69 22	36.22 312	33.29 315	45.02 323	36.82 350	35.98 317	54.68 324	48.74 336	26.31 337	21.02 342	51.61 337	2.88 353	60.10 316	9.99 345	47.58 331	38.99 341
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MVE	17.77 23	21.41 318	17.77 321	32.34 328	34.34 351	25.44 342	16.11 346	24.11 350	11.19 346	13.22 346	31.92 341	1.58 354	30.95 348	10.47 343	17.03 342	40.62 340
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
wuyk23d		13.32 320	12.52 322	15.71 332	47.54 344	26.27 340	31.06 344	1.98 354	4.93 348	5.18 350	1.94 350	0.45 355	18.54 349	6.81 348	12.83 345	2.33 347

test123		4.73 323	6.30 325	0.02 335	0.01 354	0.01 355	56.36 321	0.00 356	0.01 350	0.04 351	0.21 352	0.01 356	0.00 352	0.03 350	0.00 349	0.04 348

uanet_test		0.00 326	0.00 328	0.00 337	0.00 356	0.00 356	0.00 348	0.00 356	0.00 352	0.00 353	0.00 353	0.00 357	0.00 352	0.00 351	0.00 349	0.00 350

test_part1				0.00 337		0.00 356	0.00 348	86.64 14				0.00 357	0.00 352	0.00 351	0.00 349	0.00 350

sosnet-low-res		0.00 326	0.00 328	0.00 337	0.00 356	0.00 356	0.00 348	0.00 356	0.00 352	0.00 353	0.00 353	0.00 357	0.00 352	0.00 351	0.00 349	0.00 350

sosnet		0.00 326	0.00 328	0.00 337	0.00 356	0.00 356	0.00 348	0.00 356	0.00 352	0.00 353	0.00 353	0.00 357	0.00 352	0.00 351	0.00 349	0.00 350

uncertanet		0.00 326	0.00 328	0.00 337	0.00 356	0.00 356	0.00 348	0.00 356	0.00 352	0.00 353	0.00 353	0.00 357	0.00 352	0.00 351	0.00 349	0.00 350

Regformer		0.00 326	0.00 328	0.00 337	0.00 356	0.00 356	0.00 348	0.00 356	0.00 352	0.00 353	0.00 353	0.00 357	0.00 352	0.00 351	0.00 349	0.00 350

testmvs		4.52 324	6.03 326	0.01 336	0.01 354	0.00 356	53.86 326	0.00 356	0.01 350	0.04 351	0.27 351	0.00 357	0.00 352	0.04 349	0.00 349	0.03 349

ab-mvs-re		6.49 322	8.65 324	0.00 337	0.00 356	0.00 356	0.00 348	0.00 356	0.00 352	0.00 353	77.89 247	0.00 357	0.00 352	0.00 351	0.00 349	0.00 350

uanet		0.00 326	0.00 328	0.00 337	0.00 356	0.00 356	0.00 348	0.00 356	0.00 352	0.00 353	0.00 353	0.00 357	0.00 352	0.00 351	0.00 349	0.00 350

IU-MVS					87.77 4	59.15 59		85.53 25	53.93 205	84.64 3				79.07 6	90.87 3	88.37 7

save fliter					86.17 31	61.30 31	83.98 44	79.66 141	59.00 120

test_0728_SECOND				79.19 12	87.82 3	59.11 61	87.85 3	87.15 6					90.84 1	78.66 10	90.61 7	87.62 31

GSMVS																78.05 246

test_part2					87.58 9	60.47 45				83.42 9

MTGPA								80.97 120

MTMP							86.03 17	17.08 351

gm-plane-assit					71.40 269	41.72 279			48.85 253		73.31 293		82.48 163	48.90 212

test9_res														75.28 28	88.31 33	83.81 151

agg_prior2														73.09 45	87.93 40	84.33 136

agg_prior					85.04 50	59.96 49		81.04 117		74.68 45			84.04 124

test_prior4						62.51 17	82.08 75

test_prior				76.69 58	84.20 62	57.27 91		84.88 36					86.43 76			86.38 62

旧先验2							76.08 172		45.32 284	76.55 30			65.56 301	58.75 145

新几何2							76.12 170

无先验							79.66 110	74.30 226	48.40 258				80.78 195	53.62 177		79.03 238

原ACMM2							79.02 117

testdata2													72.18 274	46.95 226

testdata1							72.65 227		60.50 90

plane_prior7					81.41 90	55.96 113

plane_prior5								84.01 50					87.21 49	68.16 68	80.58 97	84.65 129

plane_prior4											86.10 95

plane_prior3						56.09 110			63.92 36	69.27 115

plane_prior2							84.22 36		64.52 25

plane_prior1					81.27 95

plane_prior						56.31 104	83.58 49		63.19 46						80.48 100

n2								0.00 356

nn								0.00 356

door-mid								47.19 340

test11								83.47 65

door								47.60 339

HQP5-MVS						54.94 128

HQP-NCC					80.66 102		82.31 70		62.10 67	67.85 139

ACMP_Plane					80.66 102		82.31 70		62.10 67	67.85 139

BP-MVS														67.04 79

HQP4-MVS										67.85 139			86.93 58			84.32 137

HQP3-MVS								83.90 54							80.35 103

NP-MVS					80.98 100	56.05 112					85.54 108

ACMMP++_ref															74.07 168

ACMMP++															72.16 198

High-resolution multi-view benchmark