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.

APDe-MVS		89.15 5	89.63 5	87.73 27	94.49 18	71.69 55	93.83 2	93.96 14	75.70 85	91.06 12	96.03 1	76.84 12	97.03 12	89.09 3	95.65 28	94.47 23

SMA-MVS		89.08 6	89.23 6	88.61 3	94.25 28	73.73 8	92.40 20	93.63 20	74.77 102	92.29 6	95.97 2	74.28 31	97.24 8	88.58 10	96.91 1	94.87 9
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
test0726					95.27 5	71.25 59	93.60 4	94.11 6	77.33 45	92.81 3	95.79 3	80.98 7

DVP-MVS		89.60 2	90.35 2	87.33 42	95.27 5	71.25 59	93.49 7	92.73 58	77.33 45	92.12 8	95.78 4	80.98 7	97.40 4	89.08 4	96.41 8	93.33 75

test_0728_THIRD									78.38 32	92.12 8	95.78 4	81.46 5	97.40 4	89.42 2	96.57 5	94.67 16

SED-MVS		90.08 1	90.85 1	87.77 23	95.30 2	70.98 65	93.57 5	94.06 10	77.24 47	93.10 1	95.72 6	82.99 1	97.44 2	89.07 6	96.63 2	94.88 7

test_241102_TWO								94.06 10	77.24 47	92.78 4	95.72 6	81.26 6	97.44 2	89.07 6	96.58 4	94.26 31

DPE-MVS		89.48 4	89.98 3	88.01 12	94.80 9	72.69 30	91.59 40	94.10 8	75.90 81	92.29 6	95.66 8	81.67 4	97.38 6	87.44 17	96.34 11	93.95 44

test_241102_ONE					95.30 2	70.98 65		94.06 10	77.17 50	93.10 1	95.39 9	82.99 1	97.27 7

MP-MVS-pluss		87.67 21	87.72 21	87.54 36	93.64 45	72.04 49	89.80 80	93.50 24	75.17 96	86.34 36	95.29 10	70.86 57	96.00 50	88.78 9	96.04 12	94.58 19
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
xxxxxxxxxxxxxcwj		87.88 18	87.92 18	87.77 23	93.80 39	72.35 42	90.47 62	89.69 162	74.31 111	89.16 15	95.10 11	75.65 18	96.19 42	87.07 18	96.01 13	94.79 11

SF-MVS		88.46 10	88.74 10	87.64 35	92.78 64	71.95 50	92.40 20	94.74 2	75.71 83	89.16 15	95.10 11	75.65 18	96.19 42	87.07 18	96.01 13	94.79 11

ACMMP_NAP		88.05 15	88.08 16	87.94 15	93.70 42	73.05 21	90.86 52	93.59 21	76.27 77	88.14 22	95.09 13	71.06 56	96.67 25	87.67 13	96.37 10	94.09 36

zzz-MVS		87.53 23	87.41 26	87.90 19	94.18 32	74.25 3	90.23 69	92.02 87	79.45 19	85.88 38	94.80 14	68.07 81	96.21 40	86.69 21	95.34 32	93.23 78

MTAPA		87.23 32	87.00 33	87.90 19	94.18 32	74.25 3	86.58 176	92.02 87	79.45 19	85.88 38	94.80 14	68.07 81	96.21 40	86.69 21	95.34 32	93.23 78

SteuartSystems-ACMMP		88.72 9	88.86 9	88.32 6	92.14 75	72.96 24	93.73 3	93.67 19	80.19 14	88.10 23	94.80 14	73.76 36	97.11 10	87.51 15	95.82 20	94.90 6
Skip Steuart: Steuart Systems R&D Blog.
9.14			88.26 14		92.84 63		91.52 43	94.75 1	73.93 121	88.57 20	94.67 17	75.57 20	95.79 56	86.77 20	95.76 24

SR-MVS		86.73 38	86.67 39	86.91 49	94.11 35	72.11 48	92.37 24	92.56 66	74.50 107	86.84 33	94.65 18	67.31 90	95.77 57	84.80 34	92.85 68	92.84 94

ETH3D-3000-0.1		88.09 12	88.29 13	87.50 38	92.76 65	71.89 53	91.43 44	94.70 3	74.47 108	88.86 18	94.61 19	75.23 21	95.84 54	86.62 23	95.92 17	94.78 13

test1172		86.20 49	86.22 45	86.12 67	93.95 37	69.89 90	91.79 39	92.28 75	75.07 97	86.40 35	94.58 20	65.00 114	95.56 62	84.34 41	92.60 72	92.90 92

region2R		87.42 27	87.20 31	88.09 10	94.63 12	73.55 11	93.03 12	93.12 38	76.73 65	84.45 62	94.52 21	69.09 76	96.70 23	84.37 40	94.83 46	94.03 39

ACMMPR		87.44 25	87.23 30	88.08 11	94.64 11	73.59 10	93.04 10	93.20 34	76.78 62	84.66 59	94.52 21	68.81 79	96.65 26	84.53 37	94.90 41	94.00 42

APD-MVS		87.44 25	87.52 23	87.19 44	94.24 29	72.39 40	91.86 37	92.83 54	73.01 139	88.58 19	94.52 21	73.36 37	96.49 34	84.26 42	95.01 38	92.70 96
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
APD-MVS_3200maxsize		85.97 50	85.88 51	86.22 64	92.69 67	69.53 97	91.93 34	92.99 45	73.54 129	85.94 37	94.51 24	65.80 106	95.61 60	83.04 57	92.51 74	93.53 69

CP-MVS		87.11 34	86.92 35	87.68 34	94.20 31	73.86 6	93.98 1	92.82 57	76.62 67	83.68 75	94.46 25	67.93 83	95.95 52	84.20 44	94.39 55	93.23 78

SR-MVS-dyc-post		85.77 53	85.61 54	86.23 63	93.06 57	70.63 76	91.88 35	92.27 76	73.53 130	85.69 42	94.45 26	65.00 114	95.56 62	82.75 60	91.87 77	92.50 103

RE-MVS-def			85.48 55		93.06 57	70.63 76	91.88 35	92.27 76	73.53 130	85.69 42	94.45 26	63.87 121		82.75 60	91.87 77	92.50 103

HFP-MVS		87.58 22	87.47 24	87.94 15	94.58 14	73.54 13	93.04 10	93.24 32	76.78 62	84.91 52	94.44 28	70.78 58	96.61 29	84.53 37	94.89 42	93.66 57

#test#		87.33 30	87.13 32	87.94 15	94.58 14	73.54 13	92.34 25	93.24 32	75.23 93	84.91 52	94.44 28	70.78 58	96.61 29	83.75 49	94.89 42	93.66 57

testtj		87.78 19	87.78 20	87.77 23	94.55 16	72.47 37	92.23 29	93.49 25	74.75 103	88.33 21	94.43 30	73.27 39	97.02 13	84.18 45	94.84 44	93.82 52

PGM-MVS		86.68 40	86.27 44	87.90 19	94.22 30	73.38 17	90.22 71	93.04 39	75.53 87	83.86 72	94.42 31	67.87 85	96.64 27	82.70 62	94.57 51	93.66 57

MP-MVS		87.71 20	87.64 22	87.93 18	94.36 26	73.88 5	92.71 19	92.65 63	77.57 38	83.84 73	94.40 32	72.24 47	96.28 38	85.65 25	95.30 36	93.62 64
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
ZNCC-MVS		87.94 17	87.85 19	88.20 9	94.39 24	73.33 18	93.03 12	93.81 17	76.81 60	85.24 47	94.32 33	71.76 51	96.93 15	85.53 26	95.79 21	94.32 28

ETH3D cwj APD-0.16		87.31 31	87.27 27	87.44 40	91.60 82	72.45 39	90.02 74	94.37 4	71.76 153	87.28 30	94.27 34	75.18 22	96.08 46	85.16 27	95.77 22	93.80 55

HPM-MVS++		89.02 7	89.15 7	88.63 2	95.01 8	76.03 1	92.38 23	92.85 53	80.26 13	87.78 26	94.27 34	75.89 16	96.81 19	87.45 16	96.44 7	93.05 86

mPP-MVS		86.67 41	86.32 43	87.72 29	94.41 22	73.55 11	92.74 17	92.22 80	76.87 59	82.81 87	94.25 36	66.44 97	96.24 39	82.88 59	94.28 58	93.38 72

DeepC-MVS	79.81 2	87.08 36	86.88 37	87.69 33	91.16 86	72.32 44	90.31 67	93.94 15	77.12 52	82.82 86	94.23 37	72.13 49	97.09 11	84.83 33	95.37 31	93.65 62
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
XVS		87.18 33	86.91 36	88.00 13	94.42 20	73.33 18	92.78 15	92.99 45	79.14 21	83.67 76	94.17 38	67.45 88	96.60 31	83.06 55	94.50 52	94.07 37

MSP-MVS		89.51 3	89.91 4	88.30 7	94.28 27	73.46 16	92.90 14	94.11 6	80.27 12	91.35 11	94.16 39	78.35 10	96.77 20	89.59 1	94.22 60	94.67 16

abl_6		85.23 62	84.95 66	86.07 68	92.23 74	70.48 80	90.80 54	92.08 85	73.51 132	85.26 46	94.16 39	62.75 137	95.92 53	82.46 65	91.30 86	91.81 125

DeepPCF-MVS	80.84 1	88.10 11	88.56 11	86.73 53	92.24 73	69.03 105	89.57 86	93.39 30	77.53 42	89.79 14	94.12 41	78.98 9	96.58 33	85.66 24	95.72 25	94.58 19

HPM-MVS_fast		85.35 61	84.95 66	86.57 58	93.69 43	70.58 79	92.15 32	91.62 106	73.89 122	82.67 89	94.09 42	62.60 138	95.54 65	80.93 73	92.93 66	93.57 66

ZD-MVS					94.38 25	72.22 45		92.67 60	70.98 167	87.75 27	94.07 43	74.01 35	96.70 23	84.66 36	94.84 44

CNVR-MVS		88.93 8	89.13 8	88.33 5	94.77 10	73.82 7	90.51 59	93.00 43	80.90 9	88.06 24	94.06 44	76.43 13	96.84 17	88.48 11	95.99 15	94.34 27

OPU-MVS				89.06 1	94.62 13	75.42 2	93.57 5				94.02 45	82.45 3	96.87 16	83.77 48	96.48 6	94.88 7

SD-MVS		88.06 13	88.50 12	86.71 54	92.60 71	72.71 28	91.81 38	93.19 35	77.87 33	90.32 13	94.00 46	74.83 24	93.78 138	87.63 14	94.27 59	93.65 62
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
GST-MVS		87.42 27	87.26 28	87.89 22	94.12 34	72.97 23	92.39 22	93.43 28	76.89 58	84.68 58	93.99 47	70.67 61	96.82 18	84.18 45	95.01 38	93.90 47

HPM-MVS		87.11 34	86.98 34	87.50 38	93.88 38	72.16 46	92.19 30	93.33 31	76.07 80	83.81 74	93.95 48	69.77 70	96.01 49	85.15 28	94.66 48	94.32 28
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
TSAR-MVS + MP.		88.02 16	88.11 15	87.72 29	93.68 44	72.13 47	91.41 45	92.35 73	74.62 106	88.90 17	93.85 49	75.75 17	96.00 50	87.80 12	94.63 49	95.04 3
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
ACMMP		85.89 52	85.39 57	87.38 41	93.59 46	72.63 32	92.74 17	93.18 36	76.78 62	80.73 112	93.82 50	64.33 117	96.29 37	82.67 63	90.69 91	93.23 78
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
test_prior3		86.73 38	86.86 38	86.33 60	92.61 69	69.59 95	88.85 104	92.97 48	75.41 89	84.91 52	93.54 51	74.28 31	95.48 66	83.31 50	95.86 18	93.91 45

test_prior2							88.85 104		75.41 89	84.91 52	93.54 51	74.28 31		83.31 50	95.86 18

ETH3 D test6400		87.50 24	87.44 25	87.70 32	93.71 41	71.75 54	90.62 57	94.05 13	70.80 169	87.59 29	93.51 53	77.57 11	96.63 28	83.31 50	95.77 22	94.72 15

VDDNet		81.52 108	80.67 111	84.05 120	90.44 98	64.13 199	89.73 83	85.91 241	71.11 164	83.18 80	93.48 54	50.54 256	93.49 153	73.40 139	88.25 121	94.54 22

CDPH-MVS		85.76 54	85.29 61	87.17 45	93.49 48	71.08 63	88.58 116	92.42 71	68.32 219	84.61 60	93.48 54	72.32 46	96.15 45	79.00 86	95.43 30	94.28 30

NCCC		88.06 13	88.01 17	88.24 8	94.41 22	73.62 9	91.22 49	92.83 54	81.50 6	85.79 41	93.47 56	73.02 42	97.00 14	84.90 30	94.94 40	94.10 35

3Dnovator+	77.84 4	85.48 57	84.47 71	88.51 4	91.08 87	73.49 15	93.18 9	93.78 18	80.79 10	76.66 174	93.37 57	60.40 179	96.75 22	77.20 105	93.73 63	95.29 2

DeepC-MVS_fast	79.65 3	86.91 37	86.62 40	87.76 26	93.52 47	72.37 41	91.26 46	93.04 39	76.62 67	84.22 67	93.36 58	71.44 54	96.76 21	80.82 75	95.33 34	94.16 33
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
VDD-MVS		83.01 88	82.36 90	84.96 89	91.02 89	66.40 157	88.91 101	88.11 204	77.57 38	84.39 65	93.29 59	52.19 233	93.91 133	77.05 107	88.70 115	94.57 21

UA-Net		85.08 66	84.96 65	85.45 74	92.07 76	68.07 132	89.78 81	90.86 130	82.48 2	84.60 61	93.20 60	69.35 73	95.22 79	71.39 155	90.88 90	93.07 85

agg_prior1		86.22 48	86.09 50	86.62 56	92.85 61	71.94 51	88.59 115	91.78 102	68.96 209	84.41 63	93.18 61	74.94 23	94.93 91	84.75 35	95.33 34	93.01 89

TEST9					93.26 51	72.96 24	88.75 108	91.89 96	68.44 218	85.00 50	93.10 62	74.36 30	95.41 71

train_agg		86.43 44	86.20 46	87.13 46	93.26 51	72.96 24	88.75 108	91.89 96	68.69 214	85.00 50	93.10 62	74.43 27	95.41 71	84.97 29	95.71 26	93.02 88

test_8					93.13 53	72.57 34	88.68 113	91.84 99	68.69 214	84.87 56	93.10 62	74.43 27	95.16 81

LFMVS		81.82 103	81.23 104	83.57 133	91.89 79	63.43 215	89.84 77	81.85 287	77.04 55	83.21 79	93.10 62	52.26 232	93.43 156	71.98 150	89.95 103	93.85 49

旧先验1					91.96 77	65.79 168		86.37 235			93.08 66	69.31 75			92.74 69	88.74 231

testdata				79.97 226	90.90 91	64.21 197		84.71 249	59.27 304	85.40 44	92.91 67	62.02 151	89.08 264	68.95 178	91.37 84	86.63 275

MCST-MVS		87.37 29	87.25 29	87.73 27	94.53 17	72.46 38	89.82 78	93.82 16	73.07 137	84.86 57	92.89 68	76.22 14	96.33 36	84.89 32	95.13 37	94.40 24

Vis-MVSNet		83.46 79	82.80 85	85.43 75	90.25 101	68.74 115	90.30 68	90.13 150	76.33 76	80.87 111	92.89 68	61.00 168	94.20 118	72.45 149	90.97 88	93.35 74
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
CPTT-MVS		83.73 74	83.33 77	84.92 92	93.28 50	70.86 72	92.09 33	90.38 140	68.75 213	79.57 119	92.83 70	60.60 175	93.04 174	80.92 74	91.56 82	90.86 150

3Dnovator	76.31 5	83.38 82	82.31 91	86.59 57	87.94 174	72.94 27	90.64 56	92.14 84	77.21 49	75.47 198	92.83 70	58.56 186	94.72 103	73.24 142	92.71 70	92.13 117

MSLP-MVS++		85.43 59	85.76 53	84.45 104	91.93 78	70.24 81	90.71 55	92.86 52	77.46 44	84.22 67	92.81 72	67.16 92	92.94 176	80.36 79	94.35 57	90.16 175

MG-MVS		83.41 80	83.45 75	83.28 141	92.74 66	62.28 233	88.17 133	89.50 166	75.22 94	81.49 102	92.74 73	66.75 93	95.11 83	72.85 145	91.58 81	92.45 106

casdiffmvs		85.11 65	85.14 62	85.01 87	87.20 197	65.77 169	87.75 143	92.83 54	77.84 34	84.36 66	92.38 74	72.15 48	93.93 132	81.27 71	90.48 94	95.33 1

baseline		84.93 67	84.98 64	84.80 96	87.30 195	65.39 176	87.30 154	92.88 51	77.62 36	84.04 71	92.26 75	71.81 50	93.96 126	81.31 70	90.30 96	95.03 4

QAPM		80.88 117	79.50 133	85.03 86	88.01 173	68.97 109	91.59 40	92.00 90	66.63 235	75.15 212	92.16 76	57.70 191	95.45 68	63.52 217	88.76 114	90.66 156

IS-MVSNet		83.15 84	82.81 84	84.18 115	89.94 108	63.30 217	91.59 40	88.46 201	79.04 25	79.49 120	92.16 76	65.10 111	94.28 112	67.71 185	91.86 79	94.95 5

1121		80.84 119	79.77 126	84.05 120	93.11 55	70.78 73	84.66 219	85.42 244	57.37 317	81.76 101	92.02 78	63.41 124	94.12 122	67.28 190	92.93 66	87.26 260

新几何1				83.42 136	93.13 53	70.71 74		85.48 243	57.43 316	81.80 98	91.98 79	63.28 126	92.27 195	64.60 214	92.99 65	87.27 259

OpenMVS	72.83 10	79.77 147	78.33 160	84.09 118	85.17 226	69.91 88	90.57 58	90.97 126	66.70 231	72.17 244	91.91 80	54.70 214	93.96 126	61.81 236	90.95 89	88.41 239

PHI-MVS		86.43 44	86.17 48	87.24 43	90.88 92	70.96 67	92.27 28	94.07 9	72.45 142	85.22 48	91.90 81	69.47 72	96.42 35	83.28 53	95.94 16	94.35 26

VNet		82.21 95	82.41 88	81.62 190	90.82 93	60.93 247	84.47 225	89.78 158	76.36 75	84.07 70	91.88 82	64.71 116	90.26 244	70.68 160	88.89 111	93.66 57

OPM-MVS		83.50 78	82.95 82	85.14 83	88.79 149	70.95 68	89.13 97	91.52 109	77.55 41	80.96 110	91.75 83	60.71 171	94.50 108	79.67 85	86.51 144	89.97 191

XVG-OURS-SEG-HR		80.81 122	79.76 127	83.96 127	85.60 220	68.78 112	83.54 248	90.50 137	70.66 174	76.71 172	91.66 84	60.69 172	91.26 225	76.94 109	81.58 198	91.83 123

EPNet		83.72 75	82.92 83	86.14 66	84.22 241	69.48 98	91.05 51	85.27 245	81.30 7	76.83 168	91.65 85	66.09 101	95.56 62	76.00 117	93.85 62	93.38 72
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
OMC-MVS		82.69 90	81.97 97	84.85 93	88.75 151	67.42 141	87.98 136	90.87 129	74.92 100	79.72 118	91.65 85	62.19 148	93.96 126	75.26 124	86.42 145	93.16 83

test222					91.50 83	68.26 128	84.16 234	83.20 274	54.63 328	79.74 117	91.63 87	58.97 184			91.42 83	86.77 271

MVS_111021_HR		85.14 64	84.75 68	86.32 62	91.65 81	72.70 29	85.98 191	90.33 144	76.11 79	82.08 93	91.61 88	71.36 55	94.17 121	81.02 72	92.58 73	92.08 118

原ACMM1				84.35 109	93.01 59	68.79 111		92.44 68	63.96 267	81.09 108	91.57 89	66.06 102	95.45 68	67.19 193	94.82 47	88.81 228

LPG-MVS_test		82.08 97	81.27 103	84.50 102	89.23 132	68.76 113	90.22 71	91.94 94	75.37 91	76.64 175	91.51 90	54.29 217	94.91 93	78.44 92	83.78 169	89.83 196

LGP-MVS_train				84.50 102	89.23 132	68.76 113		91.94 94	75.37 91	76.64 175	91.51 90	54.29 217	94.91 93	78.44 92	83.78 169	89.83 196

XVG-OURS		80.41 135	79.23 140	83.97 126	85.64 219	69.02 106	83.03 255	90.39 139	71.09 165	77.63 153	91.49 92	54.62 216	91.35 223	75.71 118	83.47 176	91.54 130

alignmvs		85.48 57	85.32 59	85.96 70	89.51 118	69.47 99	89.74 82	92.47 67	76.17 78	87.73 28	91.46 93	70.32 64	93.78 138	81.51 68	88.95 110	94.63 18

CANet		86.45 43	86.10 49	87.51 37	90.09 104	70.94 69	89.70 84	92.59 65	81.78 4	81.32 103	91.43 94	70.34 63	97.23 9	84.26 42	93.36 64	94.37 25

nrg030		83.88 72	83.53 74	84.96 89	86.77 205	69.28 104	90.46 64	92.67 60	74.79 101	82.95 82	91.33 95	72.70 43	93.09 170	80.79 77	79.28 227	92.50 103

canonicalmvs		85.91 51	85.87 52	86.04 69	89.84 110	69.44 103	90.45 65	93.00 43	76.70 66	88.01 25	91.23 96	73.28 38	93.91 133	81.50 69	88.80 113	94.77 14

DPM-MVS		84.93 67	84.29 72	86.84 50	90.20 102	73.04 22	87.12 158	93.04 39	69.80 188	82.85 85	91.22 97	73.06 41	96.02 48	76.72 112	94.63 49	91.46 135

Anonymous202405211		78.25 181	77.01 189	81.99 184	91.03 88	60.67 251	84.77 217	83.90 260	70.65 175	80.00 116	91.20 98	41.08 317	91.43 221	65.21 208	85.26 155	93.85 49

Anonymous20240529		80.19 141	78.89 147	84.10 117	90.60 95	64.75 187	88.95 100	90.90 128	65.97 243	80.59 113	91.17 99	49.97 261	93.73 144	69.16 176	82.70 188	93.81 53

EPP-MVSNet		83.40 81	83.02 81	84.57 100	90.13 103	64.47 193	92.32 26	90.73 131	74.45 110	79.35 122	91.10 100	69.05 78	95.12 82	72.78 146	87.22 133	94.13 34

TAPA-MVS	73.13 9	79.15 161	77.94 167	82.79 169	89.59 114	62.99 226	88.16 134	91.51 110	65.77 244	77.14 165	91.09 101	60.91 169	93.21 161	50.26 303	87.05 135	92.17 116
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
CSCG		86.41 46	86.19 47	87.07 47	92.91 60	72.48 36	90.81 53	93.56 22	73.95 119	83.16 81	91.07 102	75.94 15	95.19 80	79.94 83	94.38 56	93.55 67

FIs		82.07 98	82.42 87	81.04 208	88.80 148	58.34 271	88.26 130	93.49 25	76.93 57	78.47 136	91.04 103	69.92 68	92.34 194	69.87 169	84.97 157	92.44 107

MVS_111021_LR		82.61 92	82.11 92	84.11 116	88.82 146	71.58 56	85.15 209	86.16 238	74.69 104	80.47 114	91.04 103	62.29 145	90.55 242	80.33 80	90.08 101	90.20 174

DP-MVS Recon		83.11 86	82.09 93	86.15 65	94.44 19	70.92 71	88.79 106	92.20 81	70.53 176	79.17 123	91.03 105	64.12 119	96.03 47	68.39 182	90.14 99	91.50 132

HQP_MVS		83.64 76	83.14 78	85.14 83	90.08 105	68.71 117	91.25 47	92.44 68	79.12 23	78.92 127	91.00 106	60.42 177	95.38 73	78.71 89	86.32 146	91.33 137

plane_prior4											91.00 106

FC-MVSNet-test		81.52 108	82.02 95	80.03 225	88.42 161	55.97 307	87.95 138	93.42 29	77.10 53	77.38 157	90.98 108	69.96 67	91.79 211	68.46 181	84.50 162	92.33 108

Vis-MVSNet (Re-imp)		78.36 180	78.45 154	78.07 257	88.64 153	51.78 325	86.70 173	79.63 308	74.14 117	75.11 213	90.83 109	61.29 162	89.75 252	58.10 268	91.60 80	92.69 98

114514_t		80.68 128	79.51 132	84.20 114	94.09 36	67.27 145	89.64 85	91.11 124	58.75 309	74.08 226	90.72 110	58.10 188	95.04 88	69.70 170	89.42 108	90.30 171

PAPM_NR		83.02 87	82.41 88	84.82 94	92.47 72	66.37 158	87.93 140	91.80 100	73.82 123	77.32 159	90.66 111	67.90 84	94.90 95	70.37 163	89.48 107	93.19 82

LS3D		76.95 210	74.82 222	83.37 139	90.45 97	67.36 144	89.15 96	86.94 227	61.87 285	69.52 272	90.61 112	51.71 244	94.53 106	46.38 323	86.71 141	88.21 241

VPNet		78.69 172	78.66 150	78.76 246	88.31 164	55.72 309	84.45 228	86.63 231	76.79 61	78.26 140	90.55 113	59.30 182	89.70 254	66.63 197	77.05 244	90.88 149

UniMVSNet_ETH3D		79.10 163	78.24 162	81.70 189	86.85 202	60.24 257	87.28 155	88.79 191	74.25 114	76.84 167	90.53 114	49.48 267	91.56 217	67.98 183	82.15 192	93.29 76

ACMP	74.13 6	81.51 110	80.57 112	84.36 108	89.42 120	68.69 120	89.97 76	91.50 113	74.46 109	75.04 216	90.41 115	53.82 222	94.54 105	77.56 101	82.91 183	89.86 195
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
PCF-MVS	73.52 7	80.38 136	78.84 148	85.01 87	87.71 182	68.99 108	83.65 243	91.46 114	63.00 272	77.77 151	90.28 116	66.10 100	95.09 87	61.40 239	88.22 122	90.94 148
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
NP-MVS					89.62 113	68.32 126					90.24 117

HQP-MVS		82.61 92	82.02 95	84.37 107	89.33 124	66.98 149	89.17 92	92.19 82	76.41 70	77.23 162	90.23 118	60.17 180	95.11 83	77.47 102	85.99 151	91.03 144

PS-MVSNAJss		82.07 98	81.31 102	84.34 110	86.51 209	67.27 145	89.27 90	91.51 110	71.75 154	79.37 121	90.22 119	63.15 131	94.27 113	77.69 100	82.36 191	91.49 133

TSAR-MVS + GP.		85.71 55	85.33 58	86.84 50	91.34 84	72.50 35	89.07 98	87.28 224	76.41 70	85.80 40	90.22 119	74.15 34	95.37 76	81.82 67	91.88 76	92.65 100

Regformer-1		86.41 46	86.33 42	86.64 55	89.33 124	70.93 70	88.43 118	91.39 115	82.14 3	86.65 34	90.09 121	74.39 29	95.01 89	83.97 47	90.63 92	93.97 43

Regformer-2		86.63 42	86.53 41	86.95 48	89.33 124	71.24 62	88.43 118	92.05 86	82.50 1	86.88 32	90.09 121	74.45 26	95.61 60	84.38 39	90.63 92	94.01 41

Regformer-3		85.23 62	85.07 63	85.70 72	88.95 141	69.01 107	88.29 128	89.91 156	80.95 8	85.01 49	90.01 123	72.45 45	94.19 119	82.50 64	87.57 125	93.90 47

Regformer-4		85.68 56	85.45 56	86.35 59	88.95 141	69.67 94	88.29 128	91.29 117	81.73 5	85.36 45	90.01 123	72.62 44	95.35 77	83.28 53	87.57 125	94.03 39

TranMVSNet+NR-MVSNet		80.84 119	80.31 118	82.42 177	87.85 176	62.33 231	87.74 144	91.33 116	80.55 11	77.99 147	89.86 125	65.23 110	92.62 183	67.05 195	75.24 278	92.30 110

diffmvs		82.10 96	81.88 98	82.76 172	83.00 267	63.78 205	83.68 242	89.76 159	72.94 140	82.02 94	89.85 126	65.96 105	90.79 238	82.38 66	87.30 132	93.71 56

BH-RMVSNet		79.61 149	78.44 155	83.14 149	89.38 123	65.93 164	84.95 214	87.15 225	73.56 128	78.19 142	89.79 127	56.67 203	93.36 157	59.53 253	86.74 140	90.13 177

AdaColmap		80.58 132	79.42 134	84.06 119	93.09 56	68.91 110	89.36 88	88.97 187	69.27 198	75.70 195	89.69 128	57.20 199	95.77 57	63.06 223	88.41 120	87.50 254

ACMM	73.20 8	80.78 127	79.84 125	83.58 132	89.31 129	68.37 125	89.99 75	91.60 107	70.28 180	77.25 160	89.66 129	53.37 225	93.53 152	74.24 130	82.85 184	88.85 226
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CNLPA		78.08 187	76.79 196	81.97 185	90.40 99	71.07 64	87.59 147	84.55 252	66.03 242	72.38 242	89.64 130	57.56 193	86.04 293	59.61 252	83.35 177	88.79 229

test_yl		81.17 113	80.47 115	83.24 144	89.13 136	63.62 206	86.21 186	89.95 154	72.43 145	81.78 99	89.61 131	57.50 194	93.58 147	70.75 158	86.90 137	92.52 101

DCV-MVSNet		81.17 113	80.47 115	83.24 144	89.13 136	63.62 206	86.21 186	89.95 154	72.43 145	81.78 99	89.61 131	57.50 194	93.58 147	70.75 158	86.90 137	92.52 101

EI-MVSNet-Vis-set		84.19 71	83.81 73	85.31 77	88.18 166	67.85 135	87.66 145	89.73 161	80.05 16	82.95 82	89.59 133	70.74 60	94.82 99	80.66 78	84.72 160	93.28 77

PAPR		81.66 106	80.89 109	83.99 125	90.27 100	64.00 200	86.76 172	91.77 104	68.84 212	77.13 166	89.50 134	67.63 86	94.88 97	67.55 187	88.52 118	93.09 84

jajsoiax		79.29 159	77.96 166	83.27 142	84.68 235	66.57 155	89.25 91	90.16 149	69.20 202	75.46 200	89.49 135	45.75 293	93.13 168	76.84 110	80.80 206	90.11 179

MVSFormer		82.85 89	82.05 94	85.24 80	87.35 190	70.21 82	90.50 60	90.38 140	68.55 216	81.32 103	89.47 136	61.68 153	93.46 154	78.98 87	90.26 97	92.05 119

jason		81.39 111	80.29 119	84.70 98	86.63 208	69.90 89	85.95 192	86.77 229	63.24 269	81.07 109	89.47 136	61.08 167	92.15 200	78.33 95	90.07 102	92.05 119
jason: jason.
mvs_tets		79.13 162	77.77 174	83.22 146	84.70 234	66.37 158	89.17 92	90.19 148	69.38 196	75.40 203	89.46 138	44.17 301	93.15 166	76.78 111	80.70 208	90.14 176

UGNet		80.83 121	79.59 131	84.54 101	88.04 171	68.09 131	89.42 87	88.16 203	76.95 56	76.22 184	89.46 138	49.30 270	93.94 129	68.48 180	90.31 95	91.60 128
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
VPA-MVSNet		80.60 130	80.55 113	80.76 213	88.07 170	60.80 250	86.86 166	91.58 108	75.67 86	80.24 115	89.45 140	63.34 125	90.25 245	70.51 162	79.22 228	91.23 140

MVS_Test		83.15 84	83.06 80	83.41 138	86.86 201	63.21 219	86.11 189	92.00 90	74.31 111	82.87 84	89.44 141	70.03 66	93.21 161	77.39 104	88.50 119	93.81 53

EI-MVSNet-UG-set		83.81 73	83.38 76	85.09 85	87.87 175	67.53 139	87.44 151	89.66 163	79.74 18	82.23 92	89.41 142	70.24 65	94.74 102	79.95 82	83.92 168	92.99 90

RPSCF		73.23 253	71.46 254	78.54 250	82.50 279	59.85 259	82.18 261	82.84 278	58.96 306	71.15 254	89.41 142	45.48 295	84.77 301	58.82 261	71.83 303	91.02 146

RRT_MVS		79.88 146	78.38 157	84.38 106	85.42 223	70.60 78	88.71 112	88.75 196	72.30 147	78.83 129	89.14 144	44.44 299	92.18 199	78.50 91	79.33 226	90.35 169

UniMVSNet_NR-MVSNet		81.88 101	81.54 101	82.92 160	88.46 159	63.46 213	87.13 157	92.37 72	80.19 14	78.38 137	89.14 144	71.66 53	93.05 172	70.05 166	76.46 253	92.25 112

tttt0517		79.40 156	77.91 168	83.90 129	88.10 169	63.84 203	88.37 125	84.05 258	71.45 161	76.78 170	89.12 146	49.93 264	94.89 96	70.18 165	83.18 180	92.96 91

DU-MVS		81.12 115	80.52 114	82.90 161	87.80 178	63.46 213	87.02 161	91.87 98	79.01 26	78.38 137	89.07 147	65.02 112	93.05 172	70.05 166	76.46 253	92.20 114

NR-MVSNet		80.23 139	79.38 136	82.78 170	87.80 178	63.34 216	86.31 183	91.09 125	79.01 26	72.17 244	89.07 147	67.20 91	92.81 182	66.08 202	75.65 264	92.20 114

DELS-MVS		85.41 60	85.30 60	85.77 71	88.49 157	67.93 134	85.52 206	93.44 27	78.70 28	83.63 78	89.03 149	74.57 25	95.71 59	80.26 81	94.04 61	93.66 57
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
baseline1		76.98 209	76.75 199	77.66 262	88.13 167	55.66 310	85.12 210	81.89 285	73.04 138	76.79 169	88.90 150	62.43 143	87.78 282	63.30 221	71.18 307	89.55 205

DP-MVS		76.78 212	74.57 224	83.42 136	93.29 49	69.46 101	88.55 117	83.70 262	63.98 266	70.20 260	88.89 151	54.01 221	94.80 100	46.66 320	81.88 196	86.01 285

ab-mvs		79.51 151	78.97 145	81.14 205	88.46 159	60.91 248	83.84 240	89.24 175	70.36 178	79.03 124	88.87 152	63.23 129	90.21 246	65.12 209	82.57 189	92.28 111

PEN-MVS		77.73 196	77.69 178	77.84 259	87.07 200	53.91 318	87.91 141	91.18 121	77.56 40	73.14 233	88.82 153	61.23 163	89.17 262	59.95 249	72.37 298	90.43 166

test_djsdf		80.30 138	79.32 138	83.27 142	83.98 246	65.37 177	90.50 60	90.38 140	68.55 216	76.19 185	88.70 154	56.44 204	93.46 154	78.98 87	80.14 217	90.97 147

PAPM		77.68 199	76.40 205	81.51 193	87.29 196	61.85 238	83.78 241	89.59 164	64.74 256	71.23 252	88.70 154	62.59 139	93.66 146	52.66 293	87.03 136	89.01 218

DTE-MVSNet		76.99 208	76.80 195	77.54 266	86.24 211	53.06 322	87.52 148	90.66 132	77.08 54	72.50 239	88.67 156	60.48 176	89.52 256	57.33 275	70.74 309	90.05 186

PS-CasMVS		78.01 191	78.09 164	77.77 261	87.71 182	54.39 316	88.02 135	91.22 119	77.50 43	73.26 231	88.64 157	60.73 170	88.41 275	61.88 234	73.88 290	90.53 163

cdsmvs_eth3d_5k		19.96 322	26.61 324	0.00 340	0.00 361	0.00 362	0.00 352	89.26 174	0.00 357	0.00 358	88.61 158	61.62 155	0.00 358	0.00 356	0.00 356	0.00 354

lupinMVS		81.39 111	80.27 120	84.76 97	87.35 190	70.21 82	85.55 202	86.41 233	62.85 275	81.32 103	88.61 158	61.68 153	92.24 197	78.41 94	90.26 97	91.83 123

F-COLMAP		76.38 220	74.33 229	82.50 176	89.28 130	66.95 152	88.41 121	89.03 182	64.05 264	66.83 294	88.61 158	46.78 283	92.89 177	57.48 272	78.55 229	87.67 249

mvs_anonymous		79.42 155	79.11 142	80.34 220	84.45 238	57.97 277	82.59 257	87.62 217	67.40 225	76.17 188	88.56 161	68.47 80	89.59 255	70.65 161	86.05 150	93.47 70

CP-MVSNet		78.22 182	78.34 159	77.84 259	87.83 177	54.54 314	87.94 139	91.17 122	77.65 35	73.48 229	88.49 162	62.24 147	88.43 274	62.19 230	74.07 286	90.55 162

PVSNet_Blended_VisFu		82.62 91	81.83 99	84.96 89	90.80 94	69.76 92	88.74 110	91.70 105	69.39 195	78.96 125	88.46 163	65.47 108	94.87 98	74.42 127	88.57 116	90.24 173

CANet_DTU		80.61 129	79.87 124	82.83 164	85.60 220	63.17 222	87.36 152	88.65 197	76.37 74	75.88 192	88.44 164	53.51 224	93.07 171	73.30 140	89.74 105	92.25 112

PLC	70.83 11	78.05 189	76.37 206	83.08 152	91.88 80	67.80 136	88.19 132	89.46 167	64.33 262	69.87 269	88.38 165	53.66 223	93.58 147	58.86 260	82.73 186	87.86 246
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
WR-MVS		79.49 152	79.22 141	80.27 222	88.79 149	58.35 270	85.06 211	88.61 199	78.56 29	77.65 152	88.34 166	63.81 123	90.66 241	64.98 211	77.22 242	91.80 126

XXY-MVS		75.41 233	75.56 211	74.96 288	83.59 252	57.82 281	80.59 275	83.87 261	66.54 236	74.93 218	88.31 167	63.24 128	80.09 319	62.16 231	76.85 248	86.97 267

Effi-MVS+		83.62 77	83.08 79	85.24 80	88.38 162	67.45 140	88.89 102	89.15 178	75.50 88	82.27 91	88.28 168	69.61 71	94.45 109	77.81 99	87.84 123	93.84 51

API-MVS		81.99 100	81.23 104	84.26 113	90.94 90	70.18 87	91.10 50	89.32 170	71.51 160	78.66 132	88.28 168	65.26 109	95.10 86	64.74 213	91.23 87	87.51 253

thisisatest0530		79.40 156	77.76 175	84.31 111	87.69 184	65.10 183	87.36 152	84.26 256	70.04 183	77.42 156	88.26 170	49.94 262	94.79 101	70.20 164	84.70 161	93.03 87

xiu_mvs_v1_base_debu		80.80 124	79.72 128	84.03 122	87.35 190	70.19 84	85.56 199	88.77 192	69.06 205	81.83 95	88.16 171	50.91 250	92.85 178	78.29 96	87.56 127	89.06 213

xiu_mvs_v1_base		80.80 124	79.72 128	84.03 122	87.35 190	70.19 84	85.56 199	88.77 192	69.06 205	81.83 95	88.16 171	50.91 250	92.85 178	78.29 96	87.56 127	89.06 213

xiu_mvs_v1_base_debi		80.80 124	79.72 128	84.03 122	87.35 190	70.19 84	85.56 199	88.77 192	69.06 205	81.83 95	88.16 171	50.91 250	92.85 178	78.29 96	87.56 127	89.06 213

UniMVSNet (Re)		81.60 107	81.11 106	83.09 151	88.38 162	64.41 194	87.60 146	93.02 42	78.42 31	78.56 133	88.16 171	69.78 69	93.26 160	69.58 172	76.49 252	91.60 128

Anonymous20231211		78.97 167	77.69 178	82.81 166	90.54 96	64.29 196	90.11 73	91.51 110	65.01 254	76.16 189	88.13 175	50.56 255	93.03 175	69.68 171	77.56 239	91.11 143

pm-mvs1		77.25 206	76.68 201	78.93 244	84.22 241	58.62 269	86.41 180	88.36 202	71.37 162	73.31 230	88.01 176	61.22 164	89.15 263	64.24 215	73.01 295	89.03 217

LTVRE_ROB	69.57 13	76.25 221	74.54 226	81.41 195	88.60 154	64.38 195	79.24 287	89.12 181	70.76 172	69.79 271	87.86 177	49.09 272	93.20 163	56.21 281	80.16 215	86.65 274
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
test_part1		80.58 132	78.97 145	85.40 76	86.75 206	69.46 101	92.32 26	93.13 37	66.72 230	76.67 173	87.81 178	56.73 202	95.01 89	75.34 123	75.27 276	91.73 127

WTY-MVS		75.65 229	75.68 210	75.57 283	86.40 210	56.82 293	77.92 301	82.40 281	65.10 251	76.18 186	87.72 179	63.13 134	80.90 316	60.31 247	81.96 194	89.00 220

TAMVS		78.89 169	77.51 181	83.03 155	87.80 178	67.79 137	84.72 218	85.05 248	67.63 221	76.75 171	87.70 180	62.25 146	90.82 237	58.53 264	87.13 134	90.49 164

BH-untuned		79.47 153	78.60 151	82.05 182	89.19 134	65.91 165	86.07 190	88.52 200	72.18 148	75.42 202	87.69 181	61.15 165	93.54 151	60.38 246	86.83 139	86.70 273

COLMAP_ROB	66.92 17	73.01 255	70.41 265	80.81 212	87.13 199	65.63 170	88.30 127	84.19 257	62.96 273	63.80 315	87.69 181	38.04 328	92.56 186	46.66 320	74.91 280	84.24 303
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
OurMVSNet-221017-0		74.26 240	72.42 247	79.80 230	83.76 250	59.59 262	85.92 194	86.64 230	66.39 237	66.96 291	87.58 183	39.46 322	91.60 215	65.76 205	69.27 313	88.22 240

Baseline_NR-MVSNet		78.15 186	78.33 160	77.61 264	85.79 216	56.21 305	86.78 170	85.76 242	73.60 127	77.93 148	87.57 184	65.02 112	88.99 265	67.14 194	75.33 273	87.63 250

WR-MVS_H		78.51 176	78.49 153	78.56 249	88.02 172	56.38 302	88.43 118	92.67 60	77.14 51	73.89 227	87.55 185	66.25 99	89.24 261	58.92 259	73.55 293	90.06 185

EI-MVSNet		80.52 134	79.98 122	82.12 180	84.28 239	63.19 221	86.41 180	88.95 188	74.18 116	78.69 130	87.54 186	66.62 94	92.43 189	72.57 148	80.57 210	90.74 154

CVMVSNet		72.99 256	72.58 245	74.25 295	84.28 239	50.85 331	86.41 180	83.45 269	44.56 338	73.23 232	87.54 186	49.38 268	85.70 295	65.90 203	78.44 232	86.19 280

ACMH+	68.96 14	76.01 224	74.01 231	82.03 183	88.60 154	65.31 178	88.86 103	87.55 218	70.25 181	67.75 283	87.47 188	41.27 315	93.19 164	58.37 265	75.94 261	87.60 251

RRT_test8_iter05		78.38 179	77.40 182	81.34 199	86.00 214	58.86 266	86.55 178	91.26 118	72.13 151	75.91 190	87.42 189	44.97 296	93.73 144	77.02 108	75.30 274	91.45 136

TransMVSNet (Re)		75.39 234	74.56 225	77.86 258	85.50 222	57.10 290	86.78 170	86.09 240	72.17 149	71.53 250	87.34 190	63.01 135	89.31 260	56.84 278	61.83 329	87.17 262

GBi-Net		78.40 177	77.40 182	81.40 196	87.60 185	63.01 223	88.39 122	89.28 171	71.63 156	75.34 205	87.28 191	54.80 210	91.11 228	62.72 224	79.57 220	90.09 181

test1		78.40 177	77.40 182	81.40 196	87.60 185	63.01 223	88.39 122	89.28 171	71.63 156	75.34 205	87.28 191	54.80 210	91.11 228	62.72 224	79.57 220	90.09 181

FMVSNet2		78.20 184	77.21 186	81.20 203	87.60 185	62.89 227	87.47 150	89.02 183	71.63 156	75.29 209	87.28 191	54.80 210	91.10 231	62.38 228	79.38 224	89.61 203

FMVSNet1		77.44 202	76.12 208	81.40 196	86.81 204	63.01 223	88.39 122	89.28 171	70.49 177	74.39 223	87.28 191	49.06 273	91.11 228	60.91 243	78.52 230	90.09 181

v2v482		80.23 139	79.29 139	83.05 154	83.62 251	64.14 198	87.04 160	89.97 153	73.61 126	78.18 143	87.22 195	61.10 166	93.82 136	76.11 115	76.78 250	91.18 141

ITE_SJBPF				78.22 254	81.77 289	60.57 252		83.30 270	69.25 199	67.54 285	87.20 196	36.33 333	87.28 286	54.34 286	74.62 283	86.80 270

anonymousdsp		78.60 174	77.15 187	82.98 158	80.51 307	67.08 147	87.24 156	89.53 165	65.66 246	75.16 211	87.19 197	52.52 227	92.25 196	77.17 106	79.34 225	89.61 203

MVSTER		79.01 165	77.88 170	82.38 178	83.07 264	64.80 186	84.08 239	88.95 188	69.01 208	78.69 130	87.17 198	54.70 214	92.43 189	74.69 126	80.57 210	89.89 194

thres100view900		76.50 215	75.55 212	79.33 238	89.52 117	56.99 291	85.83 196	83.23 272	73.94 120	76.32 182	87.12 199	51.89 241	91.95 206	48.33 311	83.75 171	89.07 211

thres600view7		76.50 215	75.44 213	79.68 232	89.40 121	57.16 288	85.53 204	83.23 272	73.79 124	76.26 183	87.09 200	51.89 241	91.89 209	48.05 316	83.72 174	90.00 187

XVG-ACMP-BASELINE		76.11 223	74.27 230	81.62 190	83.20 260	64.67 188	83.60 246	89.75 160	69.75 190	71.85 247	87.09 200	32.78 338	92.11 201	69.99 168	80.43 213	88.09 242

HY-MVS	69.67 12	77.95 192	77.15 187	80.36 219	87.57 189	60.21 258	83.37 250	87.78 215	66.11 239	75.37 204	87.06 202	63.27 127	90.48 243	61.38 240	82.43 190	90.40 168

CHOSEN 1792x2688		77.63 200	75.69 209	83.44 135	89.98 107	68.58 123	78.70 294	87.50 220	56.38 322	75.80 194	86.84 203	58.67 185	91.40 222	61.58 238	85.75 154	90.34 170

v8		79.97 145	79.02 144	82.80 167	84.09 243	64.50 192	87.96 137	90.29 147	74.13 118	75.24 210	86.81 204	62.88 136	93.89 135	74.39 128	75.40 271	90.00 187

AllTest		70.96 268	68.09 280	79.58 235	85.15 227	63.62 206	84.58 224	79.83 306	62.31 281	60.32 325	86.73 205	32.02 339	88.96 268	50.28 301	71.57 305	86.15 281

TestCases				79.58 235	85.15 227	63.62 206		79.83 306	62.31 281	60.32 325	86.73 205	32.02 339	88.96 268	50.28 301	71.57 305	86.15 281

mvs-test1		80.88 117	79.40 135	85.29 78	85.13 229	69.75 93	89.28 89	88.10 205	74.99 98	76.44 180	86.72 207	57.27 197	94.26 117	73.53 135	83.18 180	91.87 122

LCM-MVSNet-Re		77.05 207	76.94 192	77.36 267	87.20 197	51.60 326	80.06 279	80.46 300	75.20 95	67.69 284	86.72 207	62.48 141	88.98 266	63.44 219	89.25 109	91.51 131

1112_ss		77.40 204	76.43 204	80.32 221	89.11 140	60.41 256	83.65 243	87.72 216	62.13 283	73.05 234	86.72 207	62.58 140	89.97 249	62.11 233	80.80 206	90.59 161

ab-mvs-re		7.23 325	9.64 328	0.00 340	0.00 361	0.00 362	0.00 352	0.00 362	0.00 357	0.00 358	86.72 207	0.00 363	0.00 358	0.00 356	0.00 356	0.00 354

IterMVS-LS		80.06 142	79.38 136	82.11 181	85.89 215	63.20 220	86.79 169	89.34 169	74.19 115	75.45 201	86.72 207	66.62 94	92.39 191	72.58 147	76.86 247	90.75 153
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
ACMH	67.68 16	75.89 225	73.93 232	81.77 188	88.71 152	66.61 154	88.62 114	89.01 184	69.81 187	66.78 295	86.70 212	41.95 314	91.51 220	55.64 282	78.14 235	87.17 262
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
Test_1112_low_res		76.40 219	75.44 213	79.27 239	89.28 130	58.09 273	81.69 266	87.07 226	59.53 302	72.48 240	86.67 213	61.30 161	89.33 259	60.81 245	80.15 216	90.41 167

FMVSNet3		77.88 194	76.85 194	80.97 209	86.84 203	62.36 230	86.52 179	88.77 192	71.13 163	75.34 205	86.66 214	54.07 220	91.10 231	62.72 224	79.57 220	89.45 206

pmmvs6		74.69 237	73.39 237	78.61 248	81.38 296	57.48 286	86.64 174	87.95 210	64.99 255	70.18 261	86.61 215	50.43 257	89.52 256	62.12 232	70.18 311	88.83 227

ET-MVSNet_ETH3D		78.63 173	76.63 202	84.64 99	86.73 207	69.47 99	85.01 212	84.61 251	69.54 193	66.51 298	86.59 216	50.16 259	91.75 212	76.26 114	84.24 166	92.69 98

testgi		66.67 295	66.53 293	67.08 321	75.62 335	41.69 346	75.93 308	76.50 322	66.11 239	65.20 308	86.59 216	35.72 335	74.71 339	43.71 330	73.38 294	84.84 297

CLD-MVS		82.31 94	81.65 100	84.29 112	88.47 158	67.73 138	85.81 197	92.35 73	75.78 82	78.33 139	86.58 218	64.01 120	94.35 110	76.05 116	87.48 130	90.79 151
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
v10		79.74 148	78.67 149	82.97 159	84.06 244	64.95 184	87.88 142	90.62 133	73.11 136	75.11 213	86.56 219	61.46 157	94.05 125	73.68 133	75.55 266	89.90 193

CDS-MVSNet		79.07 164	77.70 177	83.17 148	87.60 185	68.23 129	84.40 231	86.20 237	67.49 224	76.36 181	86.54 220	61.54 156	90.79 238	61.86 235	87.33 131	90.49 164
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
xiu_mvs_v2_base		81.69 104	81.05 107	83.60 131	89.15 135	68.03 133	84.46 227	90.02 152	70.67 173	81.30 106	86.53 221	63.17 130	94.19 119	75.60 121	88.54 117	88.57 235

TR-MVS		77.44 202	76.18 207	81.20 203	88.24 165	63.24 218	84.61 223	86.40 234	67.55 223	77.81 149	86.48 222	54.10 219	93.15 166	57.75 271	82.72 187	87.20 261

EIA-MVS		83.31 83	82.80 85	84.82 94	89.59 114	65.59 171	88.21 131	92.68 59	74.66 105	78.96 125	86.42 223	69.06 77	95.26 78	75.54 122	90.09 100	93.62 64

tfpn200view9		76.42 218	75.37 217	79.55 237	89.13 136	57.65 283	85.17 207	83.60 263	73.41 133	76.45 177	86.39 224	52.12 234	91.95 206	48.33 311	83.75 171	89.07 211

thres400		76.50 215	75.37 217	79.86 228	89.13 136	57.65 283	85.17 207	83.60 263	73.41 133	76.45 177	86.39 224	52.12 234	91.95 206	48.33 311	83.75 171	90.00 187

v7n		78.97 167	77.58 180	83.14 149	83.45 254	65.51 172	88.32 126	91.21 120	73.69 125	72.41 241	86.32 226	57.93 189	93.81 137	69.18 175	75.65 264	90.11 179

MAR-MVS		81.84 102	80.70 110	85.27 79	91.32 85	71.53 57	89.82 78	90.92 127	69.77 189	78.50 134	86.21 227	62.36 144	94.52 107	65.36 207	92.05 75	89.77 199
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
v1144		80.03 143	79.03 143	83.01 156	83.78 249	64.51 190	87.11 159	90.57 135	71.96 152	78.08 146	86.20 228	61.41 158	93.94 129	74.93 125	77.23 241	90.60 159

V42		79.38 158	78.24 162	82.83 164	81.10 301	65.50 173	85.55 202	89.82 157	71.57 159	78.21 141	86.12 229	60.66 173	93.18 165	75.64 119	75.46 269	89.81 198

PVSNet_BlendedMVS		80.60 130	80.02 121	82.36 179	88.85 143	65.40 174	86.16 188	92.00 90	69.34 197	78.11 144	86.09 230	66.02 103	94.27 113	71.52 152	82.06 193	87.39 255

CS-MVS		84.76 70	84.61 70	85.22 82	89.66 112	66.43 156	90.23 69	93.56 22	76.52 69	82.59 90	85.93 231	70.41 62	95.80 55	79.93 84	92.68 71	93.42 71

v1192		79.59 150	78.43 156	83.07 153	83.55 253	64.52 189	86.93 164	90.58 134	70.83 168	77.78 150	85.90 232	59.15 183	93.94 129	73.96 132	77.19 243	90.76 152

SixPastTwentyTwo		73.37 249	71.26 258	79.70 231	85.08 231	57.89 279	85.57 198	83.56 265	71.03 166	65.66 302	85.88 233	42.10 312	92.57 185	59.11 257	63.34 328	88.65 233

EPNet_dtu		75.46 231	74.86 221	77.23 271	82.57 278	54.60 313	86.89 165	83.09 275	71.64 155	66.25 300	85.86 234	55.99 205	88.04 279	54.92 284	86.55 143	89.05 216
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
sss		73.60 247	73.64 236	73.51 298	82.80 272	55.01 312	76.12 307	81.69 288	62.47 280	74.68 221	85.85 235	57.32 196	78.11 326	60.86 244	80.93 203	87.39 255

ETV-MVS		84.90 69	84.67 69	85.59 73	89.39 122	68.66 121	88.74 110	92.64 64	79.97 17	84.10 69	85.71 236	69.32 74	95.38 73	80.82 75	91.37 84	92.72 95

v1240		78.99 166	77.78 173	82.64 173	83.21 259	63.54 210	86.62 175	90.30 146	69.74 192	77.33 158	85.68 237	57.04 200	93.76 141	73.13 143	76.92 245	90.62 157

v144192		79.47 153	78.37 158	82.78 170	83.35 255	63.96 201	86.96 162	90.36 143	69.99 184	77.50 154	85.67 238	60.66 173	93.77 140	74.27 129	76.58 251	90.62 157

tfpnnormal		74.39 238	73.16 241	78.08 256	86.10 213	58.05 274	84.65 222	87.53 219	70.32 179	71.22 253	85.63 239	54.97 209	89.86 250	43.03 332	75.02 279	86.32 277

PS-MVSNAJ		81.69 104	81.02 108	83.70 130	89.51 118	68.21 130	84.28 233	90.09 151	70.79 170	81.26 107	85.62 240	63.15 131	94.29 111	75.62 120	88.87 112	88.59 234

v1921920		79.22 160	78.03 165	82.80 167	83.30 257	63.94 202	86.80 168	90.33 144	69.91 186	77.48 155	85.53 241	58.44 187	93.75 142	73.60 134	76.85 248	90.71 155

test_0402		72.79 258	70.44 264	79.84 229	88.13 167	65.99 163	85.93 193	84.29 254	65.57 247	67.40 288	85.49 242	46.92 282	92.61 184	35.88 341	74.38 285	80.94 325

v148		78.72 171	77.80 172	81.47 194	82.73 274	61.96 237	86.30 184	88.08 207	73.26 135	76.18 186	85.47 243	62.46 142	92.36 193	71.92 151	73.82 291	90.09 181

USDC		70.33 274	68.37 275	76.21 278	80.60 305	56.23 304	79.19 289	86.49 232	60.89 290	61.29 321	85.47 243	31.78 341	89.47 258	53.37 290	76.21 259	82.94 318

MVP-Stereo		76.12 222	74.46 228	81.13 206	85.37 224	69.79 91	84.42 230	87.95 210	65.03 253	67.46 286	85.33 245	53.28 226	91.73 214	58.01 269	83.27 178	81.85 322
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
MVS		78.19 185	76.99 191	81.78 187	85.66 218	66.99 148	84.66 219	90.47 138	55.08 327	72.02 246	85.27 246	63.83 122	94.11 124	66.10 201	89.80 104	84.24 303

cl-mvsnet1		77.72 197	76.76 197	80.58 215	82.48 281	60.48 254	83.09 252	87.86 213	69.22 200	74.38 224	85.24 247	62.10 149	91.53 218	71.09 156	75.40 271	89.74 200

cl-mvsnet_		77.72 197	76.76 197	80.58 215	82.49 280	60.48 254	83.09 252	87.87 212	69.22 200	74.38 224	85.22 248	62.10 149	91.53 218	71.09 156	75.41 270	89.73 201

HyFIR lowres test		77.53 201	75.40 215	83.94 128	89.59 114	66.62 153	80.36 276	88.64 198	56.29 323	76.45 177	85.17 249	57.64 192	93.28 159	61.34 241	83.10 182	91.91 121

pmmvs4		74.03 244	71.91 250	80.39 218	81.96 287	68.32 126	81.45 269	82.14 283	59.32 303	69.87 269	85.13 250	52.40 230	88.13 278	60.21 248	74.74 282	84.73 299

TDRefinement		67.49 289	64.34 298	76.92 273	73.47 340	61.07 246	84.86 216	82.98 276	59.77 299	58.30 331	85.13 250	26.06 343	87.89 280	47.92 317	60.59 333	81.81 323

Fast-Effi-MVS+		80.81 122	79.92 123	83.47 134	88.85 143	64.51 190	85.53 204	89.39 168	70.79 170	78.49 135	85.06 252	67.54 87	93.58 147	67.03 196	86.58 142	92.32 109

PVSNet_Blended		80.98 116	80.34 117	82.90 161	88.85 143	65.40 174	84.43 229	92.00 90	67.62 222	78.11 144	85.05 253	66.02 103	94.27 113	71.52 152	89.50 106	89.01 218

CMPMVS	51.72 21	70.19 276	68.16 278	76.28 277	73.15 342	57.55 285	79.47 285	83.92 259	48.02 337	56.48 336	84.81 254	43.13 305	86.42 291	62.67 227	81.81 197	84.89 296
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
EU-MVSNet		68.53 286	67.61 288	71.31 308	78.51 324	47.01 340	84.47 225	84.27 255	42.27 339	66.44 299	84.79 255	40.44 320	83.76 304	58.76 262	68.54 318	83.17 312

BH-w/o		78.21 183	77.33 185	80.84 211	88.81 147	65.13 182	84.87 215	87.85 214	69.75 190	74.52 222	84.74 256	61.34 160	93.11 169	58.24 267	85.84 153	84.27 302

pmmvs5		71.55 264	70.20 267	75.61 282	77.83 325	56.39 301	81.74 265	80.89 292	57.76 313	67.46 286	84.49 257	49.26 271	85.32 299	57.08 277	75.29 275	85.11 295

thres200		75.55 230	74.47 227	78.82 245	87.78 181	57.85 280	83.07 254	83.51 266	72.44 144	75.84 193	84.42 258	52.08 236	91.75 212	47.41 318	83.64 175	86.86 269

eth_miper_zixun_eth		77.92 193	76.69 200	81.61 192	83.00 267	61.98 236	83.15 251	89.20 177	69.52 194	74.86 219	84.35 259	61.76 152	92.56 186	71.50 154	72.89 296	90.28 172

cl_fuxian		78.75 170	77.91 168	81.26 201	82.89 271	61.56 242	84.09 238	89.13 180	69.97 185	75.56 196	84.29 260	66.36 98	92.09 202	73.47 138	75.48 268	90.12 178

Fast-Effi-MVS+-dtu		78.02 190	76.49 203	82.62 174	83.16 263	66.96 151	86.94 163	87.45 222	72.45 142	71.49 251	84.17 261	54.79 213	91.58 216	67.61 186	80.31 214	89.30 209

IterMVS-SCA-FT		75.43 232	73.87 234	80.11 224	82.69 275	64.85 185	81.57 268	83.47 268	69.16 203	70.49 257	84.15 262	51.95 239	88.15 277	69.23 174	72.14 301	87.34 257

1314		76.53 214	75.30 219	80.21 223	83.93 247	62.32 232	84.66 219	88.81 190	60.23 295	70.16 263	84.07 263	55.30 208	90.73 240	67.37 189	83.21 179	87.59 252

cl-mvsnet2		78.07 188	77.01 189	81.23 202	82.37 283	61.83 239	83.55 247	87.98 209	68.96 209	75.06 215	83.87 264	61.40 159	91.88 210	73.53 135	76.39 255	89.98 190

EG-PatchMatch MVS		74.04 243	71.82 252	80.71 214	84.92 232	67.42 141	85.86 195	88.08 207	66.04 241	64.22 312	83.85 265	35.10 336	92.56 186	57.44 273	80.83 205	82.16 321

thisisatest0515		77.33 205	75.38 216	83.18 147	85.27 225	63.80 204	82.11 262	83.27 271	65.06 252	75.91 190	83.84 266	49.54 266	94.27 113	67.24 192	86.19 148	91.48 134

test20.03		67.45 290	66.95 291	68.94 315	75.48 336	44.84 342	77.50 302	77.67 316	66.66 232	63.01 317	83.80 267	47.02 281	78.40 324	42.53 334	68.86 317	83.58 309

miper_ehance_all_eth		78.59 175	77.76 175	81.08 207	82.66 276	61.56 242	83.65 243	89.15 178	68.87 211	75.55 197	83.79 268	66.49 96	92.03 203	73.25 141	76.39 255	89.64 202

MSDG		73.36 251	70.99 259	80.49 217	84.51 237	65.80 167	80.71 273	86.13 239	65.70 245	65.46 303	83.74 269	44.60 297	90.91 236	51.13 298	76.89 246	84.74 298

IterMVS		74.29 239	72.94 243	78.35 253	81.53 293	63.49 212	81.58 267	82.49 280	68.06 220	69.99 266	83.69 270	51.66 245	85.54 296	65.85 204	71.64 304	86.01 285
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
tpm		72.37 262	71.71 253	74.35 294	82.19 285	52.00 323	79.22 288	77.29 319	64.56 258	72.95 235	83.68 271	51.35 246	83.26 309	58.33 266	75.80 262	87.81 247

Effi-MVS+-dtu		80.03 143	78.57 152	84.42 105	85.13 229	68.74 115	88.77 107	88.10 205	74.99 98	74.97 217	83.49 272	57.27 197	93.36 157	73.53 135	80.88 204	91.18 141

baseline2		75.70 228	73.83 235	81.30 200	83.26 258	61.79 240	82.57 258	80.65 296	66.81 227	66.88 292	83.42 273	57.86 190	92.19 198	63.47 218	79.57 220	89.91 192

TinyColmap		67.30 292	64.81 296	74.76 291	81.92 288	56.68 297	80.29 278	81.49 290	60.33 293	56.27 337	83.22 274	24.77 344	87.66 283	45.52 326	69.47 312	79.95 329

CostFormer		75.24 235	73.90 233	79.27 239	82.65 277	58.27 272	80.80 271	82.73 279	61.57 286	75.33 208	83.13 275	55.52 206	91.07 234	64.98 211	78.34 234	88.45 237

miper_lstm_enhance		74.11 242	73.11 242	77.13 272	80.11 310	59.62 261	72.23 323	86.92 228	66.76 229	70.40 258	82.92 276	56.93 201	82.92 310	69.06 177	72.63 297	88.87 225

GA-MVS		76.87 211	75.17 220	81.97 185	82.75 273	62.58 228	81.44 270	86.35 236	72.16 150	74.74 220	82.89 277	46.20 288	92.02 204	68.85 179	81.09 202	91.30 139

K. test v3		71.19 266	68.51 274	79.21 241	83.04 266	57.78 282	84.35 232	76.91 321	72.90 141	62.99 318	82.86 278	39.27 323	91.09 233	61.65 237	52.66 341	88.75 230

MS-PatchMatch		73.83 245	72.67 244	77.30 269	83.87 248	66.02 162	81.82 263	84.66 250	61.37 289	68.61 279	82.82 279	47.29 279	88.21 276	59.27 254	84.32 165	77.68 334

lessismore_v0				78.97 243	81.01 302	57.15 289		65.99 344		61.16 322	82.82 279	39.12 324	91.34 224	59.67 251	46.92 344	88.43 238

D2MVS		74.82 236	73.21 240	79.64 234	79.81 314	62.56 229	80.34 277	87.35 223	64.37 261	68.86 276	82.66 281	46.37 285	90.10 248	67.91 184	81.24 201	86.25 278

Anonymous20231206		68.60 284	67.80 285	71.02 309	80.23 309	50.75 332	78.30 298	80.47 299	56.79 320	66.11 301	82.63 282	46.35 286	78.95 322	43.62 331	75.70 263	83.36 311

MIMVSNet		70.69 270	69.30 269	74.88 289	84.52 236	56.35 303	75.87 311	79.42 309	64.59 257	67.76 282	82.41 283	41.10 316	81.54 315	46.64 322	81.34 199	86.75 272

OpenMVS_ROB	64.09 19	70.56 272	68.19 277	77.65 263	80.26 308	59.41 264	85.01 212	82.96 277	58.76 308	65.43 304	82.33 284	37.63 330	91.23 227	45.34 328	76.03 260	82.32 319

miper_enhance_ethall		77.87 195	76.86 193	80.92 210	81.65 290	61.38 244	82.68 256	88.98 185	65.52 248	75.47 198	82.30 285	65.76 107	92.00 205	72.95 144	76.39 255	89.39 207

test0.0.03 1		68.00 288	67.69 287	68.90 316	77.55 326	47.43 338	75.70 312	72.95 333	66.66 232	66.56 296	82.29 286	48.06 276	75.87 335	44.97 329	74.51 284	83.41 310

PVSNet	64.34 18	72.08 263	70.87 262	75.69 281	86.21 212	56.44 300	74.37 319	80.73 295	62.06 284	70.17 262	82.23 287	42.86 307	83.31 308	54.77 285	84.45 164	87.32 258

MIMVSNet1		68.58 285	66.78 292	73.98 297	80.07 311	51.82 324	80.77 272	84.37 253	64.40 260	59.75 328	82.16 288	36.47 332	83.63 306	42.73 333	70.33 310	86.48 276

tpm2		73.26 252	71.46 254	78.63 247	83.34 256	56.71 296	80.65 274	80.40 301	56.63 321	73.55 228	82.02 289	51.80 243	91.24 226	56.35 280	78.42 233	87.95 243

PatchMatch-RL		72.38 261	70.90 260	76.80 275	88.60 154	67.38 143	79.53 284	76.17 323	62.75 277	69.36 274	82.00 290	45.51 294	84.89 300	53.62 289	80.58 209	78.12 333

FMVSNet5		69.50 280	67.96 281	74.15 296	82.97 269	55.35 311	80.01 280	82.12 284	62.56 279	63.02 316	81.53 291	36.92 331	81.92 313	48.42 310	74.06 287	85.17 294

CR-MVSNet		73.37 249	71.27 257	79.67 233	81.32 299	65.19 180	75.92 309	80.30 302	59.92 298	72.73 237	81.19 292	52.50 228	86.69 288	59.84 250	77.71 236	87.11 265

Patchmtry		70.74 269	69.16 271	75.49 285	80.72 303	54.07 317	74.94 318	80.30 302	58.34 310	70.01 264	81.19 292	52.50 228	86.54 289	53.37 290	71.09 308	85.87 288

testing_2		75.73 227	73.34 239	82.89 163	77.37 328	65.22 179	84.10 237	90.54 136	69.09 204	60.46 324	81.15 294	40.48 319	92.84 181	76.36 113	80.54 212	90.60 159

IB-MVS	68.01 15	75.85 226	73.36 238	83.31 140	84.76 233	66.03 161	83.38 249	85.06 247	70.21 182	69.40 273	81.05 295	45.76 292	94.66 104	65.10 210	75.49 267	89.25 210
Christian Sormann, Mattia Rossi, Andreas Kuhn and Friedrich Fraundorfer: IB-MVS: An Iterative Algorithm for Deep Multi-View Stereo based on Binary Decisions. BMVC 2021
cascas		76.72 213	74.64 223	82.99 157	85.78 217	65.88 166	82.33 260	89.21 176	60.85 291	72.74 236	81.02 296	47.28 280	93.75 142	67.48 188	85.02 156	89.34 208

LF4IMVS		64.02 304	62.19 307	69.50 314	70.90 344	53.29 321	76.13 306	77.18 320	52.65 332	58.59 329	80.98 297	23.55 345	76.52 332	53.06 292	66.66 321	78.68 332

gm-plane-assit					81.40 295	53.83 319			62.72 278		80.94 298		92.39 191	63.40 220

UnsupCasMVSNet_eth		67.33 291	65.99 294	71.37 305	73.48 339	51.47 328	75.16 314	85.19 246	65.20 250	60.78 323	80.93 299	42.35 309	77.20 330	57.12 276	53.69 340	85.44 290

MVS_0304		72.48 259	70.89 261	77.24 270	82.20 284	59.68 260	84.11 236	83.49 267	67.10 226	66.87 293	80.59 300	35.00 337	87.40 284	59.07 258	79.58 219	84.63 300

MDTV_nov1_ep13			69.97 268		83.18 261	53.48 320	77.10 305	80.18 305	60.45 292	69.33 275	80.44 301	48.89 274	86.90 287	51.60 296	78.51 231

pmmvs-eth3d		70.50 273	67.83 284	78.52 251	77.37 328	66.18 160	81.82 263	81.51 289	58.90 307	63.90 314	80.42 302	42.69 308	86.28 292	58.56 263	65.30 325	83.11 314

PM-MVS		66.41 297	64.14 299	73.20 299	73.92 337	56.45 299	78.97 291	64.96 347	63.88 268	64.72 309	80.24 303	19.84 348	83.44 307	66.24 198	64.52 327	79.71 330

SCA		74.22 241	72.33 248	79.91 227	84.05 245	62.17 234	79.96 281	79.29 310	66.30 238	72.38 242	80.13 304	51.95 239	88.60 272	59.25 255	77.67 238	88.96 222

Patchmatch-test		64.82 302	63.24 302	69.57 313	79.42 320	49.82 335	63.49 342	69.05 341	51.98 334	59.95 327	80.13 304	50.91 250	70.98 344	40.66 337	73.57 292	87.90 245

tpmrst		72.39 260	72.13 249	73.18 300	80.54 306	49.91 334	79.91 282	79.08 311	63.11 270	71.69 249	79.95 306	55.32 207	82.77 311	65.66 206	73.89 289	86.87 268

DSMNet-mixed		57.77 310	56.90 312	60.38 325	67.70 346	35.61 349	69.18 331	53.97 350	32.30 348	57.49 333	79.88 307	40.39 321	68.57 346	38.78 339	72.37 298	76.97 335

MDA-MVSNet-bldmvs		66.68 294	63.66 300	75.75 280	79.28 321	60.56 253	73.92 320	78.35 313	64.43 259	50.13 342	79.87 308	44.02 302	83.67 305	46.10 324	56.86 336	83.03 316

PatchmatchNet		73.12 254	71.33 256	78.49 252	83.18 261	60.85 249	79.63 283	78.57 312	64.13 263	71.73 248	79.81 309	51.20 248	85.97 294	57.40 274	76.36 258	88.66 232
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
ppachtmachnet_test		70.04 277	67.34 289	78.14 255	79.80 315	61.13 245	79.19 289	80.59 297	59.16 305	65.27 305	79.29 310	46.75 284	87.29 285	49.33 307	66.72 320	86.00 287

EPMVS		69.02 283	68.16 278	71.59 303	79.61 318	49.80 336	77.40 303	66.93 343	62.82 276	70.01 264	79.05 311	45.79 291	77.86 328	56.58 279	75.26 277	87.13 264

PMMVS		69.34 281	68.67 273	71.35 307	75.67 334	62.03 235	75.17 313	73.46 331	50.00 336	68.68 277	79.05 311	52.07 237	78.13 325	61.16 242	82.77 185	73.90 337

test-LLR		72.94 257	72.43 246	74.48 292	81.35 297	58.04 275	78.38 295	77.46 317	66.66 232	69.95 267	79.00 313	48.06 276	79.24 320	66.13 199	84.83 158	86.15 281

test-mter		71.41 265	70.39 266	74.48 292	81.35 297	58.04 275	78.38 295	77.46 317	60.32 294	69.95 267	79.00 313	36.08 334	79.24 320	66.13 199	84.83 158	86.15 281

DWT-MVSNet_test		73.70 246	71.86 251	79.21 241	82.91 270	58.94 265	82.34 259	82.17 282	65.21 249	71.05 255	78.31 315	44.21 300	90.17 247	63.29 222	77.28 240	88.53 236

tpmvs		71.09 267	69.29 270	76.49 276	82.04 286	56.04 306	78.92 292	81.37 291	64.05 264	67.18 290	78.28 316	49.74 265	89.77 251	49.67 306	72.37 298	83.67 308

our_test_3		69.14 282	67.00 290	75.57 283	79.80 315	58.80 267	77.96 300	77.81 315	59.55 301	62.90 319	78.25 317	47.43 278	83.97 303	51.71 295	67.58 319	83.93 307

MDA-MVSNet_test_wron		65.03 300	62.92 303	71.37 305	75.93 332	56.73 294	69.09 334	74.73 328	57.28 318	54.03 339	77.89 318	45.88 289	74.39 341	49.89 305	61.55 330	82.99 317

YYNet1		65.03 300	62.91 304	71.38 304	75.85 333	56.60 298	69.12 333	74.66 330	57.28 318	54.12 338	77.87 319	45.85 290	74.48 340	49.95 304	61.52 331	83.05 315

ambc				75.24 287	73.16 341	50.51 333	63.05 343	87.47 221		64.28 311	77.81 320	17.80 349	89.73 253	57.88 270	60.64 332	85.49 289

tpm cat1		70.57 271	68.31 276	77.35 268	82.41 282	57.95 278	78.08 299	80.22 304	52.04 333	68.54 280	77.66 321	52.00 238	87.84 281	51.77 294	72.07 302	86.25 278

dp		66.80 293	65.43 295	70.90 310	79.74 317	48.82 337	75.12 316	74.77 327	59.61 300	64.08 313	77.23 322	42.89 306	80.72 317	48.86 309	66.58 322	83.16 313

TESTMET0.1,1		69.89 279	69.00 272	72.55 301	79.27 322	56.85 292	78.38 295	74.71 329	57.64 314	68.09 281	77.19 323	37.75 329	76.70 331	63.92 216	84.09 167	84.10 306

CHOSEN 280x420		66.51 296	64.71 297	71.90 302	81.45 294	63.52 211	57.98 344	68.95 342	53.57 329	62.59 320	76.70 324	46.22 287	75.29 338	55.25 283	79.68 218	76.88 336

PatchT		68.46 287	67.85 283	70.29 311	80.70 304	43.93 343	72.47 322	74.88 326	60.15 296	70.55 256	76.57 325	49.94 262	81.59 314	50.58 299	74.83 281	85.34 291

RPMNet		73.51 248	70.49 263	82.58 175	81.32 299	65.19 180	75.92 309	92.27 76	57.60 315	72.73 237	76.45 326	52.30 231	95.43 70	48.14 315	77.71 236	87.11 265

ADS-MVSNet2		66.20 299	63.33 301	74.82 290	79.92 312	58.75 268	67.55 336	75.19 325	53.37 330	65.25 306	75.86 327	42.32 310	80.53 318	41.57 335	68.91 315	85.18 292

ADS-MVSNet		64.36 303	62.88 305	68.78 318	79.92 312	47.17 339	67.55 336	71.18 334	53.37 330	65.25 306	75.86 327	42.32 310	73.99 342	41.57 335	68.91 315	85.18 292

new-patchmatchnet		61.73 306	61.73 308	61.70 324	72.74 343	24.50 356	69.16 332	78.03 314	61.40 287	56.72 335	75.53 329	38.42 326	76.48 333	45.95 325	57.67 335	84.13 305

N_pmnet		52.79 313	53.26 314	51.40 330	78.99 323	7.68 359	69.52 329	3.89 358	51.63 335	57.01 334	74.98 330	40.83 318	65.96 347	37.78 340	64.67 326	80.56 328

patchmatchnet-post											74.00 331	51.12 249	88.60 272

GG-mvs-BLEND				75.38 286	81.59 292	55.80 308	79.32 286	69.63 338		67.19 289	73.67 332	43.24 304	88.90 270	50.41 300	84.50 162	81.45 324

Patchmatch-RL test		70.24 275	67.78 286	77.61 264	77.43 327	59.57 263	71.16 325	70.33 335	62.94 274	68.65 278	72.77 333	50.62 254	85.49 297	69.58 172	66.58 322	87.77 248

FPMVS		53.68 312	51.64 315	59.81 326	65.08 347	51.03 330	69.48 330	69.58 339	41.46 340	40.67 344	72.32 334	16.46 351	70.00 345	24.24 346	65.42 324	58.40 343

UnsupCasMVSNet_bld		63.70 305	61.53 309	70.21 312	73.69 338	51.39 329	72.82 321	81.89 285	55.63 325	57.81 332	71.80 335	38.67 325	78.61 323	49.26 308	52.21 342	80.63 326

PVSNet_0	57.27 20	61.67 307	59.27 310	68.85 317	79.61 318	57.44 287	68.01 335	73.44 332	55.93 324	58.54 330	70.41 336	44.58 298	77.55 329	47.01 319	35.91 345	71.55 339

pmmvs3		57.79 309	54.26 313	68.37 319	64.02 348	56.72 295	75.12 316	65.17 345	40.20 341	52.93 340	69.86 337	20.36 347	75.48 337	45.45 327	55.25 339	72.90 338

new_pmnet		50.91 314	50.29 316	52.78 329	68.58 345	34.94 351	63.71 341	56.63 349	39.73 342	44.95 343	65.47 338	21.93 346	58.48 348	34.98 342	56.62 337	64.92 341

gg-mvs-nofinetune		69.95 278	67.96 281	75.94 279	83.07 264	54.51 315	77.23 304	70.29 336	63.11 270	70.32 259	62.33 339	43.62 303	88.69 271	53.88 288	87.76 124	84.62 301

JIA-IIPM		66.32 298	62.82 306	76.82 274	77.09 330	61.72 241	65.34 339	75.38 324	58.04 312	64.51 310	62.32 340	42.05 313	86.51 290	51.45 297	69.22 314	82.21 320

LCM-MVSNet		54.25 311	49.68 317	67.97 320	53.73 351	45.28 341	66.85 338	80.78 294	35.96 345	39.45 345	62.23 341	8.70 356	78.06 327	48.24 314	51.20 343	80.57 327

PMMVS2		40.82 318	38.86 321	46.69 331	53.84 350	16.45 357	48.61 347	49.92 351	37.49 344	31.67 346	60.97 342	8.14 357	56.42 349	28.42 344	30.72 346	67.19 340

MVS-HIRNet		59.14 308	57.67 311	63.57 323	81.65 290	43.50 344	71.73 324	65.06 346	39.59 343	51.43 341	57.73 343	38.34 327	82.58 312	39.53 338	73.95 288	64.62 342

ANet_high		50.57 315	46.10 318	63.99 322	48.67 354	39.13 347	70.99 327	80.85 293	61.39 288	31.18 347	57.70 344	17.02 350	73.65 343	31.22 343	15.89 351	79.18 331

PMVS	37.38 22	44.16 317	40.28 320	55.82 327	40.82 356	42.54 345	65.12 340	63.99 348	34.43 346	24.48 349	57.12 345	3.92 358	76.17 334	17.10 349	55.52 338	48.75 344
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
DeepMVS_CX				27.40 335	40.17 357	26.90 354		24.59 357	17.44 352	23.95 350	48.61 346	9.77 354	26.48 353	18.06 348	24.47 347	28.83 347

MVE	26.22 23	30.37 321	25.89 325	43.81 332	44.55 355	35.46 350	28.87 351	39.07 354	18.20 351	18.58 352	40.18 347	2.68 359	47.37 352	17.07 350	23.78 348	48.60 345
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
Gipuma		45.18 316	41.86 319	55.16 328	77.03 331	51.52 327	32.50 350	80.52 298	32.46 347	27.12 348	35.02 348	9.52 355	75.50 336	22.31 347	60.21 334	38.45 346
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
E-PMN		31.77 319	30.64 322	35.15 333	52.87 352	27.67 353	57.09 345	47.86 352	24.64 349	16.40 353	33.05 349	11.23 353	54.90 350	14.46 351	18.15 349	22.87 348

EMVS		30.81 320	29.65 323	34.27 334	50.96 353	25.95 355	56.58 346	46.80 353	24.01 350	15.53 354	30.68 350	12.47 352	54.43 351	12.81 352	17.05 350	22.43 349

tmp_tt		18.61 323	21.40 326	10.23 337	4.82 358	10.11 358	34.70 349	30.74 356	1.48 354	23.91 351	26.07 351	28.42 342	13.41 355	27.12 345	15.35 352	7.17 350

X-MVStestdata		80.37 137	77.83 171	88.00 13	94.42 20	73.33 18	92.78 15	92.99 45	79.14 21	83.67 76	12.47 352	67.45 88	96.60 31	83.06 55	94.50 52	94.07 37

test_post											5.46 353	50.36 258	84.24 302

test_post1							78.90 293				5.43 354	48.81 275	85.44 298	59.25 255

wuyk23d		16.82 324	15.94 327	19.46 336	58.74 349	31.45 352	39.22 348	3.74 359	6.84 353	6.04 355	2.70 355	1.27 360	24.29 354	10.54 353	14.40 353	2.63 351

testmvs		6.04 327	8.02 330	0.10 339	0.08 359	0.03 361	69.74 328	0.04 360	0.05 355	0.31 356	1.68 356	0.02 362	0.04 356	0.24 354	0.02 354	0.25 353

test123		6.12 326	8.11 329	0.14 338	0.06 360	0.09 360	71.05 326	0.03 361	0.04 356	0.25 357	1.30 357	0.05 361	0.03 357	0.21 355	0.01 355	0.29 352

uanet_test		0.00 329	0.00 332	0.00 340	0.00 361	0.00 362	0.00 352	0.00 362	0.00 357	0.00 358	0.00 358	0.00 363	0.00 358	0.00 356	0.00 356	0.00 354

pcd_1.5k_mvsjas		5.26 328	7.02 331	0.00 340	0.00 361	0.00 362	0.00 352	0.00 362	0.00 357	0.00 358	0.00 358	63.15 131	0.00 358	0.00 356	0.00 356	0.00 354

sosnet-low-res		0.00 329	0.00 332	0.00 340	0.00 361	0.00 362	0.00 352	0.00 362	0.00 357	0.00 358	0.00 358	0.00 363	0.00 358	0.00 356	0.00 356	0.00 354

sosnet		0.00 329	0.00 332	0.00 340	0.00 361	0.00 362	0.00 352	0.00 362	0.00 357	0.00 358	0.00 358	0.00 363	0.00 358	0.00 356	0.00 356	0.00 354

uncertanet		0.00 329	0.00 332	0.00 340	0.00 361	0.00 362	0.00 352	0.00 362	0.00 357	0.00 358	0.00 358	0.00 363	0.00 358	0.00 356	0.00 356	0.00 354

Regformer		0.00 329	0.00 332	0.00 340	0.00 361	0.00 362	0.00 352	0.00 362	0.00 357	0.00 358	0.00 358	0.00 363	0.00 358	0.00 356	0.00 356	0.00 354

uanet		0.00 329	0.00 332	0.00 340	0.00 361	0.00 362	0.00 352	0.00 362	0.00 357	0.00 358	0.00 358	0.00 363	0.00 358	0.00 356	0.00 356	0.00 354

IU-MVS					95.30 2	71.25 59		92.95 50	66.81 227	92.39 5				88.94 8	96.63 2	94.85 10

save fliter					93.80 39	72.35 42	90.47 62	91.17 122	74.31 111

test_0728_SECOND				87.71 31	95.34 1	71.43 58	93.49 7	94.23 5					97.49 1	89.08 4	96.41 8	94.21 32

GSMVS																88.96 222

test_part2					95.06 7	72.65 31				91.80 10

sam_mvs1												51.32 247				88.96 222

sam_mvs												50.01 260

MTGPA								92.02 87

MTMP							92.18 31	32.83 355

test9_res														84.90 30	95.70 27	92.87 93

agg_prior2														82.91 58	95.45 29	92.70 96

agg_prior					92.85 61	71.94 51		91.78 102		84.41 63			94.93 91

test_prior4						72.60 33	89.01 99

test_prior				86.33 60	92.61 69	69.59 95		92.97 48					95.48 66			93.91 45

旧先验2							86.56 177		58.10 311	87.04 31			88.98 266	74.07 131

新几何2							86.29 185

无先验							87.48 149	88.98 185	60.00 297				94.12 122	67.28 190		88.97 221

原ACMM2							86.86 166

testdata2													91.01 235	62.37 229

segment_acmp												73.08 40

testdata1							84.14 235		75.71 83

test12				86.80 52	92.63 68	70.70 75		91.79 101		82.71 88		71.67 52	96.16 44		94.50 52	93.54 68

plane_prior7					90.08 105	68.51 124

plane_prior6					89.84 110	68.70 119						60.42 177

plane_prior5								92.44 68					95.38 73	78.71 89	86.32 146	91.33 137

plane_prior3						68.60 122			78.44 30	78.92 127

plane_prior2							91.25 47		79.12 23

plane_prior1					89.90 109

plane_prior						68.71 117	90.38 66		77.62 36						86.16 149

n2								0.00 362

nn								0.00 362

door-mid								69.98 337

test11								92.23 79

door								69.44 340

HQP5-MVS						66.98 149

HQP-NCC					89.33 124		89.17 92		76.41 70	77.23 162

ACMP_Plane					89.33 124		89.17 92		76.41 70	77.23 162

BP-MVS														77.47 102

HQP4-MVS										77.24 161			95.11 83			91.03 144

HQP3-MVS								92.19 82							85.99 151

HQP2-MVS												60.17 180

MDTV_nov1_ep13_2view						37.79 348	75.16 314		55.10 326	66.53 297		49.34 269		53.98 287		87.94 244

ACMMP++_ref															81.95 195

ACMMP++															81.25 200

Test By Simon												64.33 117

High-resolution multi-view benchmark