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.

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_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

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

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

IU-MVS					95.30 2	71.25 59		92.95 50	66.81 228	92.39 5				88.94 8	96.63 2	94.85 10

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
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

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

test_part2					95.06 7	72.65 31				91.80 10

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

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

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
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

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

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
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

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
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

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

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

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.
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

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

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

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

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

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

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

旧先验2							86.56 177		58.10 312	87.04 31			88.98 267	74.07 132

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

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

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

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

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
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

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

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

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

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

testdata				79.97 227	90.90 91	64.21 198		84.71 250	59.27 305	85.40 44	92.91 67	62.02 151	89.08 265	68.95 179	91.37 84	86.63 276

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

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

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

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

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

TEST9					93.26 51	72.96 24	88.75 108	91.89 96	68.44 219	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	68.69 215	85.00 50	93.10 62	74.43 27	95.41 71	84.97 29	95.71 26	93.02 88

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

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

test_8					93.13 53	72.57 34	88.68 113	91.84 99	68.69 215	84.87 56	93.10 62	74.43 27	95.16 81

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

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

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

CDPH-MVS		85.76 54	85.29 61	87.17 45	93.49 48	71.08 63	88.58 116	92.42 71	68.32 220	84.61 60	93.48 54	72.32 46	96.15 45	79.00 86	95.43 30	94.28 30

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	71.39 156	90.88 90	93.07 85

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

agg_prior1		86.22 48	86.09 50	86.62 56	92.85 61	71.94 51	88.59 115	91.78 102	68.96 210	84.41 63	93.18 61	74.94 23	94.93 91	84.75 35	95.33 34	93.01 89

agg_prior					92.85 61	71.94 51		91.78 102		84.41 63			94.93 91

VDD-MVS		83.01 88	82.36 90	84.96 89	91.02 89	66.40 158	88.91 101	88.11 204	77.57 38	84.39 65	93.29 59	52.19 234	93.91 133	77.05 107	88.70 115	94.57 21

casdiffmvs		85.11 65	85.14 62	85.01 87	87.20 198	65.77 170	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

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	92.94 177	80.36 79	94.35 57	90.16 176

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
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	85.71 237	69.32 74	95.38 73	80.82 75	91.37 84	92.72 95

VNet		82.21 95	82.41 88	81.62 191	90.82 93	60.93 248	84.47 226	89.78 158	76.36 75	84.07 70	91.88 82	64.71 116	90.26 245	70.68 161	88.89 111	93.66 57

baseline		84.93 67	84.98 64	84.80 96	87.30 196	65.39 177	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

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.
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
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

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

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	12.47 353	67.45 88	96.60 31	83.06 55	94.50 52	94.07 37

DELS-MVS		85.41 60	85.30 60	85.77 71	88.49 158	67.93 134	85.52 207	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
LFMVS		81.82 103	81.23 104	83.57 134	91.89 79	63.43 216	89.84 77	81.85 288	77.04 55	83.21 79	93.10 62	52.26 233	93.43 157	71.98 151	89.95 103	93.85 49

VDDNet		81.52 108	80.67 111	84.05 120	90.44 98	64.13 200	89.73 83	85.91 242	71.11 164	83.18 80	93.48 54	50.54 257	93.49 153	73.40 140	88.25 121	94.54 22

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

nrg030		83.88 72	83.53 74	84.96 89	86.77 206	69.28 104	90.46 64	92.67 60	74.79 101	82.95 82	91.33 95	72.70 43	93.09 171	80.79 77	79.28 227	92.50 103

EI-MVSNet-Vis-set		84.19 71	83.81 73	85.31 77	88.18 167	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

MVS_Test		83.15 84	83.06 80	83.41 139	86.86 202	63.21 220	86.11 189	92.00 90	74.31 111	82.87 84	89.44 141	70.03 66	93.21 162	77.39 104	88.50 119	93.81 53

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

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
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

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

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

CS-MVS		84.76 70	84.61 70	85.22 82	89.66 112	66.43 157	90.23 69	93.56 22	76.52 69	82.59 90	85.93 232	70.41 62	95.80 55	79.93 84	92.68 71	93.42 71

Effi-MVS+		83.62 77	83.08 79	85.24 80	88.38 163	67.45 141	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

EI-MVSNet-UG-set		83.81 73	83.38 76	85.09 85	87.87 176	67.53 140	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

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

diffmvs		82.10 96	81.88 98	82.76 173	83.00 268	63.78 206	83.68 243	89.76 159	72.94 140	82.02 94	89.85 126	65.96 105	90.79 239	82.38 66	87.30 132	93.71 56

xiu_mvs_v1_base_debu		80.80 124	79.72 128	84.03 123	87.35 191	70.19 84	85.56 200	88.77 192	69.06 206	81.83 95	88.16 171	50.91 251	92.85 179	78.29 96	87.56 127	89.06 214

xiu_mvs_v1_base		80.80 124	79.72 128	84.03 123	87.35 191	70.19 84	85.56 200	88.77 192	69.06 206	81.83 95	88.16 171	50.91 251	92.85 179	78.29 96	87.56 127	89.06 214

xiu_mvs_v1_base_debi		80.80 124	79.72 128	84.03 123	87.35 191	70.19 84	85.56 200	88.77 192	69.06 206	81.83 95	88.16 171	50.91 251	92.85 179	78.29 96	87.56 127	89.06 214

新几何1				83.42 137	93.13 53	70.71 74		85.48 244	57.43 317	81.80 98	91.98 79	63.28 126	92.27 196	64.60 215	92.99 65	87.27 260

test_yl		81.17 113	80.47 115	83.24 145	89.13 136	63.62 207	86.21 186	89.95 154	72.43 145	81.78 99	89.61 131	57.50 194	93.58 147	70.75 159	86.90 137	92.52 101

DCV-MVSNet		81.17 113	80.47 115	83.24 145	89.13 136	63.62 207	86.21 186	89.95 154	72.43 145	81.78 99	89.61 131	57.50 194	93.58 147	70.75 159	86.90 137	92.52 101

1121		80.84 119	79.77 126	84.05 120	93.11 55	70.78 73	84.66 220	85.42 245	57.37 318	81.76 101	92.02 78	63.41 124	94.12 122	67.28 191	92.93 66	87.26 261

MG-MVS		83.41 80	83.45 75	83.28 142	92.74 66	62.28 234	88.17 133	89.50 166	75.22 94	81.49 102	92.74 73	66.75 93	95.11 83	72.85 146	91.58 81	92.45 106

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

MVSFormer		82.85 89	82.05 94	85.24 80	87.35 191	70.21 82	90.50 60	90.38 140	68.55 217	81.32 103	89.47 136	61.68 153	93.46 155	78.98 87	90.26 97	92.05 119

lupinMVS		81.39 111	80.27 120	84.76 97	87.35 191	70.21 82	85.55 203	86.41 234	62.85 276	81.32 103	88.61 158	61.68 153	92.24 198	78.41 94	90.26 97	91.83 123

xiu_mvs_v2_base		81.69 104	81.05 107	83.60 132	89.15 135	68.03 133	84.46 228	90.02 152	70.67 173	81.30 106	86.53 222	63.17 130	94.19 119	75.60 121	88.54 117	88.57 236

PS-MVSNAJ		81.69 104	81.02 108	83.70 131	89.51 118	68.21 130	84.28 234	90.09 151	70.79 170	81.26 107	85.62 241	63.15 131	94.29 111	75.62 120	88.87 112	88.59 235

原ACMM1				84.35 109	93.01 59	68.79 111		92.44 68	63.96 268	81.09 108	91.57 89	66.06 102	95.45 68	67.19 194	94.82 47	88.81 229

jason		81.39 111	80.29 119	84.70 98	86.63 209	69.90 89	85.95 192	86.77 230	63.24 270	81.07 109	89.47 136	61.08 167	92.15 201	78.33 95	90.07 102	92.05 119
jason: jason.
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	89.97 192

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	72.45 150	90.97 88	93.35 74
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
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
Anonymous20240529		80.19 141	78.89 147	84.10 117	90.60 95	64.75 188	88.95 100	90.90 128	65.97 244	80.59 113	91.17 99	49.97 262	93.73 144	69.16 177	82.70 188	93.81 53

MVS_111021_LR		82.61 92	82.11 92	84.11 116	88.82 146	71.58 56	85.15 210	86.16 239	74.69 104	80.47 114	91.04 103	62.29 145	90.55 243	80.33 80	90.08 101	90.20 175

VPA-MVSNet		80.60 130	80.55 113	80.76 214	88.07 171	60.80 251	86.86 166	91.58 108	75.67 86	80.24 115	89.45 140	63.34 125	90.25 246	70.51 163	79.22 228	91.23 140

Anonymous202405211		78.25 182	77.01 190	81.99 185	91.03 88	60.67 252	84.77 218	83.90 261	70.65 175	80.00 116	91.20 98	41.08 318	91.43 222	65.21 209	85.26 155	93.85 49

test222					91.50 83	68.26 128	84.16 235	83.20 275	54.63 329	79.74 117	91.63 87	58.97 184			91.42 83	86.77 272

OMC-MVS		82.69 90	81.97 97	84.85 93	88.75 151	67.42 142	87.98 136	90.87 129	74.92 100	79.72 118	91.65 85	62.19 148	93.96 126	75.26 125	86.42 145	93.16 83

CPTT-MVS		83.73 74	83.33 77	84.92 92	93.28 50	70.86 72	92.09 33	90.38 140	68.75 214	79.57 119	92.83 70	60.60 175	93.04 175	80.92 74	91.56 82	90.86 151

IS-MVSNet		83.15 84	82.81 84	84.18 115	89.94 108	63.30 218	91.59 40	88.46 201	79.04 25	79.49 120	92.16 76	65.10 111	94.28 112	67.71 186	91.86 79	94.95 5

PS-MVSNAJss		82.07 98	81.31 102	84.34 110	86.51 210	67.27 146	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

EPP-MVSNet		83.40 81	83.02 81	84.57 100	90.13 103	64.47 194	92.32 26	90.73 131	74.45 110	79.35 122	91.10 100	69.05 78	95.12 82	72.78 147	87.22 133	94.13 34

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	68.39 183	90.14 99	91.50 132

ab-mvs		79.51 151	78.97 145	81.14 206	88.46 160	60.91 249	83.84 241	89.24 175	70.36 178	79.03 124	88.87 152	63.23 129	90.21 247	65.12 210	82.57 189	92.28 111

EIA-MVS		83.31 83	82.80 85	84.82 94	89.59 114	65.59 172	88.21 131	92.68 59	74.66 105	78.96 125	86.42 224	69.06 77	95.26 78	75.54 122	90.09 100	93.62 64

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	74.42 128	88.57 116	90.24 174

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_prior3						68.60 122			78.44 30	78.92 127

RRT_MVS		79.88 146	78.38 157	84.38 106	85.42 224	70.60 78	88.71 112	88.75 196	72.30 147	78.83 129	89.14 144	44.44 300	92.18 200	78.50 91	79.33 226	90.35 170

EI-MVSNet		80.52 134	79.98 122	82.12 181	84.28 240	63.19 222	86.41 180	88.95 188	74.18 116	78.69 130	87.54 187	66.62 94	92.43 190	72.57 149	80.57 210	90.74 155

MVSTER		79.01 166	77.88 170	82.38 179	83.07 265	64.80 187	84.08 240	88.95 188	69.01 209	78.69 130	87.17 199	54.70 214	92.43 190	74.69 127	80.57 210	89.89 195

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	64.74 214	91.23 87	87.51 254

UniMVSNet (Re)		81.60 107	81.11 106	83.09 152	88.38 163	64.41 195	87.60 146	93.02 42	78.42 31	78.56 133	88.16 171	69.78 69	93.26 161	69.58 173	76.49 252	91.60 128

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	86.21 228	62.36 144	94.52 107	65.36 208	92.05 75	89.77 200
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
Fast-Effi-MVS+		80.81 122	79.92 123	83.47 135	88.85 143	64.51 191	85.53 205	89.39 168	70.79 170	78.49 135	85.06 253	67.54 87	93.58 147	67.03 197	86.58 142	92.32 109

FIs		82.07 98	82.42 87	81.04 209	88.80 148	58.34 272	88.26 130	93.49 25	76.93 57	78.47 136	91.04 103	69.92 68	92.34 195	69.87 170	84.97 157	92.44 107

UniMVSNet_NR-MVSNet		81.88 101	81.54 101	82.92 161	88.46 160	63.46 214	87.13 157	92.37 72	80.19 14	78.38 137	89.14 144	71.66 53	93.05 173	70.05 167	76.46 253	92.25 112

DU-MVS		81.12 115	80.52 114	82.90 162	87.80 179	63.46 214	87.02 161	91.87 98	79.01 26	78.38 137	89.07 147	65.02 112	93.05 173	70.05 167	76.46 253	92.20 114

CLD-MVS		82.31 94	81.65 100	84.29 112	88.47 159	67.73 138	85.81 198	92.35 73	75.78 82	78.33 139	86.58 219	64.01 120	94.35 110	76.05 116	87.48 130	90.79 152
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
VPNet		78.69 173	78.66 150	78.76 247	88.31 165	55.72 310	84.45 229	86.63 232	76.79 61	78.26 140	90.55 113	59.30 182	89.70 255	66.63 198	77.05 244	90.88 150

V42		79.38 158	78.24 162	82.83 165	81.10 302	65.50 174	85.55 203	89.82 157	71.57 159	78.21 141	86.12 230	60.66 173	93.18 166	75.64 119	75.46 269	89.81 199

BH-RMVSNet		79.61 149	78.44 155	83.14 150	89.38 123	65.93 165	84.95 215	87.15 226	73.56 128	78.19 142	89.79 127	56.67 203	93.36 158	59.53 254	86.74 140	90.13 178

v2v482		80.23 139	79.29 139	83.05 155	83.62 252	64.14 199	87.04 160	89.97 153	73.61 126	78.18 143	87.22 196	61.10 166	93.82 136	76.11 115	76.78 250	91.18 141

PVSNet_BlendedMVS		80.60 130	80.02 121	82.36 180	88.85 143	65.40 175	86.16 188	92.00 90	69.34 197	78.11 144	86.09 231	66.02 103	94.27 113	71.52 153	82.06 193	87.39 256

PVSNet_Blended		80.98 116	80.34 117	82.90 162	88.85 143	65.40 175	84.43 230	92.00 90	67.62 223	78.11 144	85.05 254	66.02 103	94.27 113	71.52 153	89.50 106	89.01 219

v1144		80.03 143	79.03 143	83.01 157	83.78 250	64.51 191	87.11 159	90.57 135	71.96 152	78.08 146	86.20 229	61.41 158	93.94 129	74.93 126	77.23 241	90.60 160

TranMVSNet+NR-MVSNet		80.84 119	80.31 118	82.42 178	87.85 177	62.33 232	87.74 144	91.33 116	80.55 11	77.99 147	89.86 125	65.23 110	92.62 184	67.05 196	75.24 278	92.30 110

Baseline_NR-MVSNet		78.15 187	78.33 160	77.61 265	85.79 217	56.21 306	86.78 170	85.76 243	73.60 127	77.93 148	87.57 185	65.02 112	88.99 266	67.14 195	75.33 273	87.63 251

TR-MVS		77.44 203	76.18 208	81.20 204	88.24 166	63.24 219	84.61 224	86.40 235	67.55 224	77.81 149	86.48 223	54.10 219	93.15 167	57.75 272	82.72 187	87.20 262

v1192		79.59 150	78.43 156	83.07 154	83.55 254	64.52 190	86.93 164	90.58 134	70.83 168	77.78 150	85.90 233	59.15 183	93.94 129	73.96 133	77.19 243	90.76 153

PCF-MVS	73.52 7	80.38 136	78.84 148	85.01 87	87.71 183	68.99 108	83.65 244	91.46 114	63.00 273	77.77 151	90.28 116	66.10 100	95.09 87	61.40 240	88.22 122	90.94 149
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
WR-MVS		79.49 152	79.22 141	80.27 223	88.79 149	58.35 271	85.06 212	88.61 199	78.56 29	77.65 152	88.34 166	63.81 123	90.66 242	64.98 212	77.22 242	91.80 126

XVG-OURS		80.41 135	79.23 140	83.97 127	85.64 220	69.02 106	83.03 256	90.39 139	71.09 165	77.63 153	91.49 92	54.62 216	91.35 224	75.71 118	83.47 176	91.54 130

v144192		79.47 153	78.37 158	82.78 171	83.35 256	63.96 202	86.96 162	90.36 143	69.99 184	77.50 154	85.67 239	60.66 173	93.77 140	74.27 130	76.58 251	90.62 158

v1921920		79.22 160	78.03 165	82.80 168	83.30 258	63.94 203	86.80 168	90.33 144	69.91 186	77.48 155	85.53 242	58.44 187	93.75 142	73.60 135	76.85 248	90.71 156

thisisatest0530		79.40 156	77.76 175	84.31 111	87.69 185	65.10 184	87.36 152	84.26 257	70.04 183	77.42 156	88.26 170	49.94 263	94.79 101	70.20 165	84.70 161	93.03 87

FC-MVSNet-test		81.52 108	82.02 95	80.03 226	88.42 162	55.97 308	87.95 138	93.42 29	77.10 53	77.38 157	90.98 108	69.96 67	91.79 212	68.46 182	84.50 162	92.33 108

v1240		78.99 167	77.78 173	82.64 174	83.21 260	63.54 211	86.62 175	90.30 146	69.74 192	77.33 158	85.68 238	57.04 200	93.76 141	73.13 144	76.92 245	90.62 158

PAPM_NR		83.02 87	82.41 88	84.82 94	92.47 72	66.37 159	87.93 140	91.80 100	73.82 123	77.32 159	90.66 111	67.90 84	94.90 95	70.37 164	89.48 107	93.19 82

ACMM	73.20 8	80.78 127	79.84 125	83.58 133	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	74.24 131	82.85 184	88.85 227
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
HQP4-MVS										77.24 161			95.11 83			91.03 145

AUN-MVS		79.21 161	77.60 180	84.05 120	88.71 152	67.61 139	85.84 196	87.26 225	69.08 205	77.23 162	88.14 175	53.20 227	93.47 154	75.50 123	73.45 294	91.06 144

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

HQP-MVS		82.61 92	82.02 95	84.37 107	89.33 124	66.98 150	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	91.03 145

TAPA-MVS	73.13 9	79.15 162	77.94 167	82.79 170	89.59 114	62.99 227	88.16 134	91.51 110	65.77 245	77.14 166	91.09 101	60.91 169	93.21 162	50.26 304	87.05 135	92.17 116
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
PAPR		81.66 106	80.89 109	83.99 126	90.27 100	64.00 201	86.76 172	91.77 104	68.84 213	77.13 167	89.50 134	67.63 86	94.88 97	67.55 188	88.52 118	93.09 84

UniMVSNet_ETH3D		79.10 164	78.24 162	81.70 190	86.85 203	60.24 258	87.28 155	88.79 191	74.25 114	76.84 168	90.53 114	49.48 268	91.56 218	67.98 184	82.15 192	93.29 76

EPNet		83.72 75	82.92 83	86.14 66	84.22 242	69.48 98	91.05 51	85.27 246	81.30 7	76.83 169	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
baseline1		76.98 210	76.75 200	77.66 263	88.13 168	55.66 311	85.12 211	81.89 286	73.04 138	76.79 170	88.90 150	62.43 143	87.78 283	63.30 222	71.18 308	89.55 206

tttt0517		79.40 156	77.91 168	83.90 130	88.10 170	63.84 204	88.37 125	84.05 259	71.45 161	76.78 171	89.12 146	49.93 265	94.89 96	70.18 166	83.18 180	92.96 91

TAMVS		78.89 170	77.51 182	83.03 156	87.80 179	67.79 137	84.72 219	85.05 249	67.63 222	76.75 172	87.70 181	62.25 146	90.82 238	58.53 265	87.13 134	90.49 165

XVG-OURS-SEG-HR		80.81 122	79.76 127	83.96 128	85.60 221	68.78 112	83.54 249	90.50 137	70.66 174	76.71 173	91.66 84	60.69 172	91.26 226	76.94 109	81.58 198	91.83 123

test_part1		80.58 132	78.97 145	85.40 76	86.75 207	69.46 101	92.32 26	93.13 37	66.72 231	76.67 174	87.81 179	56.73 202	95.01 89	75.34 124	75.27 276	91.73 127

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	76.66 175	93.37 57	60.40 179	96.75 22	77.20 105	93.73 63	95.29 2

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	76.64 176	91.51 90	54.29 217	94.91 93	78.44 92	83.78 169	89.83 197

LGP-MVS_train				84.50 102	89.23 132	68.76 113		91.94 94	75.37 91	76.64 176	91.51 90	54.29 217	94.91 93	78.44 92	83.78 169	89.83 197

tfpn200view9		76.42 219	75.37 218	79.55 238	89.13 136	57.65 284	85.17 208	83.60 264	73.41 133	76.45 178	86.39 225	52.12 235	91.95 207	48.33 312	83.75 171	89.07 212

thres400		76.50 216	75.37 218	79.86 229	89.13 136	57.65 284	85.17 208	83.60 264	73.41 133	76.45 178	86.39 225	52.12 235	91.95 207	48.33 312	83.75 171	90.00 188

HyFIR lowres test		77.53 202	75.40 216	83.94 129	89.59 114	66.62 154	80.36 277	88.64 198	56.29 324	76.45 178	85.17 250	57.64 192	93.28 160	61.34 242	83.10 182	91.91 121

mvs-test1		80.88 117	79.40 135	85.29 78	85.13 230	69.75 93	89.28 89	88.10 205	74.99 98	76.44 181	86.72 208	57.27 197	94.26 117	73.53 136	83.18 180	91.87 122

CDS-MVSNet		79.07 165	77.70 177	83.17 149	87.60 186	68.23 129	84.40 232	86.20 238	67.49 225	76.36 182	86.54 221	61.54 156	90.79 239	61.86 236	87.33 131	90.49 165
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
thres100view900		76.50 216	75.55 213	79.33 239	89.52 117	56.99 292	85.83 197	83.23 273	73.94 120	76.32 183	87.12 200	51.89 242	91.95 207	48.33 312	83.75 171	89.07 212

thres600view7		76.50 216	75.44 214	79.68 233	89.40 121	57.16 289	85.53 205	83.23 273	73.79 124	76.26 184	87.09 201	51.89 242	91.89 210	48.05 317	83.72 174	90.00 188

UGNet		80.83 121	79.59 131	84.54 101	88.04 172	68.09 131	89.42 87	88.16 203	76.95 56	76.22 185	89.46 138	49.30 271	93.94 129	68.48 181	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
test_djsdf		80.30 138	79.32 138	83.27 143	83.98 247	65.37 178	90.50 60	90.38 140	68.55 217	76.19 186	88.70 154	56.44 204	93.46 155	78.98 87	80.14 217	90.97 148

v148		78.72 172	77.80 172	81.47 195	82.73 275	61.96 238	86.30 184	88.08 207	73.26 135	76.18 187	85.47 244	62.46 142	92.36 194	71.92 152	73.82 291	90.09 182

WTY-MVS		75.65 230	75.68 211	75.57 284	86.40 211	56.82 294	77.92 302	82.40 282	65.10 252	76.18 187	87.72 180	63.13 134	80.90 317	60.31 248	81.96 194	89.00 221

mvs_anonymous		79.42 155	79.11 142	80.34 221	84.45 239	57.97 278	82.59 258	87.62 217	67.40 226	76.17 189	88.56 161	68.47 80	89.59 256	70.65 162	86.05 150	93.47 70

Anonymous20231211		78.97 168	77.69 178	82.81 167	90.54 96	64.29 197	90.11 73	91.51 110	65.01 255	76.16 190	88.13 176	50.56 256	93.03 176	69.68 172	77.56 239	91.11 143

thisisatest0515		77.33 206	75.38 217	83.18 148	85.27 226	63.80 205	82.11 263	83.27 272	65.06 253	75.91 191	83.84 267	49.54 267	94.27 113	67.24 193	86.19 148	91.48 134

RRT_test8_iter05		78.38 180	77.40 183	81.34 200	86.00 215	58.86 267	86.55 178	91.26 118	72.13 151	75.91 191	87.42 190	44.97 297	93.73 144	77.02 108	75.30 274	91.45 136

CANet_DTU		80.61 129	79.87 124	82.83 165	85.60 221	63.17 223	87.36 152	88.65 197	76.37 74	75.88 193	88.44 164	53.51 224	93.07 172	73.30 141	89.74 105	92.25 112

thres200		75.55 231	74.47 228	78.82 246	87.78 182	57.85 281	83.07 255	83.51 267	72.44 144	75.84 194	84.42 259	52.08 237	91.75 213	47.41 319	83.64 175	86.86 270

CHOSEN 1792x2688		77.63 201	75.69 210	83.44 136	89.98 107	68.58 123	78.70 295	87.50 220	56.38 323	75.80 195	86.84 204	58.67 185	91.40 223	61.58 239	85.75 154	90.34 171

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	75.70 196	89.69 128	57.20 199	95.77 57	63.06 224	88.41 120	87.50 255

cl_fuxian		78.75 171	77.91 168	81.26 202	82.89 272	61.56 243	84.09 239	89.13 180	69.97 185	75.56 197	84.29 261	66.36 98	92.09 203	73.47 139	75.48 268	90.12 179

miper_ehance_all_eth		78.59 176	77.76 175	81.08 208	82.66 277	61.56 243	83.65 244	89.15 178	68.87 212	75.55 198	83.79 269	66.49 96	92.03 204	73.25 142	76.39 255	89.64 203

miper_enhance_ethall		77.87 196	76.86 194	80.92 211	81.65 291	61.38 245	82.68 257	88.98 185	65.52 249	75.47 199	82.30 286	65.76 107	92.00 206	72.95 145	76.39 255	89.39 208

3Dnovator	76.31 5	83.38 82	82.31 91	86.59 57	87.94 175	72.94 27	90.64 56	92.14 84	77.21 49	75.47 199	92.83 70	58.56 186	94.72 103	73.24 143	92.71 70	92.13 117

jajsoiax		79.29 159	77.96 166	83.27 143	84.68 236	66.57 156	89.25 91	90.16 149	69.20 202	75.46 201	89.49 135	45.75 294	93.13 169	76.84 110	80.80 206	90.11 180

IterMVS-LS		80.06 142	79.38 136	82.11 182	85.89 216	63.20 221	86.79 169	89.34 169	74.19 115	75.45 202	86.72 208	66.62 94	92.39 192	72.58 148	76.86 247	90.75 154
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
BH-untuned		79.47 153	78.60 151	82.05 183	89.19 134	65.91 166	86.07 190	88.52 200	72.18 148	75.42 203	87.69 182	61.15 165	93.54 151	60.38 247	86.83 139	86.70 274

mvs_tets		79.13 163	77.77 174	83.22 147	84.70 235	66.37 159	89.17 92	90.19 148	69.38 196	75.40 204	89.46 138	44.17 302	93.15 167	76.78 111	80.70 208	90.14 177

HY-MVS	69.67 12	77.95 193	77.15 188	80.36 220	87.57 190	60.21 259	83.37 251	87.78 215	66.11 240	75.37 205	87.06 203	63.27 127	90.48 244	61.38 241	82.43 190	90.40 169

GBi-Net		78.40 178	77.40 183	81.40 197	87.60 186	63.01 224	88.39 122	89.28 171	71.63 156	75.34 206	87.28 192	54.80 210	91.11 229	62.72 225	79.57 220	90.09 182

test1		78.40 178	77.40 183	81.40 197	87.60 186	63.01 224	88.39 122	89.28 171	71.63 156	75.34 206	87.28 192	54.80 210	91.11 229	62.72 225	79.57 220	90.09 182

FMVSNet3		77.88 195	76.85 195	80.97 210	86.84 204	62.36 231	86.52 179	88.77 192	71.13 163	75.34 206	86.66 215	54.07 220	91.10 232	62.72 225	79.57 220	89.45 207

CostFormer		75.24 236	73.90 234	79.27 240	82.65 278	58.27 273	80.80 272	82.73 280	61.57 287	75.33 209	83.13 276	55.52 206	91.07 235	64.98 212	78.34 234	88.45 238

FMVSNet2		78.20 185	77.21 187	81.20 204	87.60 186	62.89 228	87.47 150	89.02 183	71.63 156	75.29 210	87.28 192	54.80 210	91.10 232	62.38 229	79.38 224	89.61 204

v8		79.97 145	79.02 144	82.80 168	84.09 244	64.50 193	87.96 137	90.29 147	74.13 118	75.24 211	86.81 205	62.88 136	93.89 135	74.39 129	75.40 271	90.00 188

anonymousdsp		78.60 175	77.15 188	82.98 159	80.51 308	67.08 148	87.24 156	89.53 165	65.66 247	75.16 212	87.19 198	52.52 228	92.25 197	77.17 106	79.34 225	89.61 204

QAPM		80.88 117	79.50 133	85.03 86	88.01 174	68.97 109	91.59 40	92.00 90	66.63 236	75.15 213	92.16 76	57.70 191	95.45 68	63.52 218	88.76 114	90.66 157

v10		79.74 148	78.67 149	82.97 160	84.06 245	64.95 185	87.88 142	90.62 133	73.11 136	75.11 214	86.56 220	61.46 157	94.05 125	73.68 134	75.55 266	89.90 194

Vis-MVSNet (Re-imp)		78.36 181	78.45 154	78.07 258	88.64 154	51.78 326	86.70 173	79.63 309	74.14 117	75.11 214	90.83 109	61.29 162	89.75 253	58.10 269	91.60 80	92.69 98

cl-mvsnet2		78.07 189	77.01 190	81.23 203	82.37 284	61.83 240	83.55 248	87.98 209	68.96 210	75.06 216	83.87 265	61.40 159	91.88 211	73.53 136	76.39 255	89.98 191

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	75.04 217	90.41 115	53.82 222	94.54 105	77.56 101	82.91 183	89.86 196
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
Effi-MVS+-dtu		80.03 143	78.57 152	84.42 105	85.13 230	68.74 115	88.77 107	88.10 205	74.99 98	74.97 218	83.49 273	57.27 197	93.36 158	73.53 136	80.88 204	91.18 141

XXY-MVS		75.41 234	75.56 212	74.96 289	83.59 253	57.82 282	80.59 276	83.87 262	66.54 237	74.93 219	88.31 167	63.24 128	80.09 320	62.16 232	76.85 248	86.97 268

eth_miper_zixun_eth		77.92 194	76.69 201	81.61 193	83.00 268	61.98 237	83.15 252	89.20 177	69.52 194	74.86 220	84.35 260	61.76 152	92.56 187	71.50 155	72.89 297	90.28 173

GA-MVS		76.87 212	75.17 221	81.97 186	82.75 274	62.58 229	81.44 271	86.35 237	72.16 150	74.74 221	82.89 278	46.20 289	92.02 205	68.85 180	81.09 202	91.30 139

sss		73.60 248	73.64 237	73.51 299	82.80 273	55.01 313	76.12 308	81.69 289	62.47 281	74.68 222	85.85 236	57.32 196	78.11 327	60.86 245	80.93 203	87.39 256

BH-w/o		78.21 184	77.33 186	80.84 212	88.81 147	65.13 183	84.87 216	87.85 214	69.75 190	74.52 223	84.74 257	61.34 160	93.11 170	58.24 268	85.84 153	84.27 303

FMVSNet1		77.44 203	76.12 209	81.40 197	86.81 205	63.01 224	88.39 122	89.28 171	70.49 177	74.39 224	87.28 192	49.06 274	91.11 229	60.91 244	78.52 230	90.09 182

cl-mvsnet_		77.72 198	76.76 198	80.58 216	82.49 281	60.48 255	83.09 253	87.87 212	69.22 200	74.38 225	85.22 249	62.10 149	91.53 219	71.09 157	75.41 270	89.73 202

cl-mvsnet1		77.72 198	76.76 198	80.58 216	82.48 282	60.48 255	83.09 253	87.86 213	69.22 200	74.38 225	85.24 248	62.10 149	91.53 219	71.09 157	75.40 271	89.74 201

114514_t		80.68 128	79.51 132	84.20 114	94.09 36	67.27 146	89.64 85	91.11 124	58.75 310	74.08 227	90.72 110	58.10 188	95.04 88	69.70 171	89.42 108	90.30 172

WR-MVS_H		78.51 177	78.49 153	78.56 250	88.02 173	56.38 303	88.43 118	92.67 60	77.14 51	73.89 228	87.55 186	66.25 99	89.24 262	58.92 260	73.55 293	90.06 186

tpm2		73.26 253	71.46 255	78.63 248	83.34 257	56.71 297	80.65 275	80.40 302	56.63 322	73.55 229	82.02 290	51.80 244	91.24 227	56.35 281	78.42 233	87.95 244

CP-MVSNet		78.22 183	78.34 159	77.84 260	87.83 178	54.54 315	87.94 139	91.17 122	77.65 35	73.48 230	88.49 162	62.24 147	88.43 275	62.19 231	74.07 286	90.55 163

pm-mvs1		77.25 207	76.68 202	78.93 245	84.22 242	58.62 270	86.41 180	88.36 202	71.37 162	73.31 231	88.01 177	61.22 164	89.15 264	64.24 216	73.01 296	89.03 218

PS-CasMVS		78.01 192	78.09 164	77.77 262	87.71 183	54.39 317	88.02 135	91.22 119	77.50 43	73.26 232	88.64 157	60.73 170	88.41 276	61.88 235	73.88 290	90.53 164

CVMVSNet		72.99 257	72.58 246	74.25 296	84.28 240	50.85 332	86.41 180	83.45 270	44.56 339	73.23 233	87.54 187	49.38 269	85.70 296	65.90 204	78.44 232	86.19 281

PEN-MVS		77.73 197	77.69 178	77.84 260	87.07 201	53.91 319	87.91 141	91.18 121	77.56 40	73.14 234	88.82 153	61.23 163	89.17 263	59.95 250	72.37 299	90.43 167

1112_ss		77.40 205	76.43 205	80.32 222	89.11 140	60.41 257	83.65 244	87.72 216	62.13 284	73.05 235	86.72 208	62.58 140	89.97 250	62.11 234	80.80 206	90.59 162

tpm		72.37 263	71.71 254	74.35 295	82.19 286	52.00 324	79.22 289	77.29 320	64.56 259	72.95 236	83.68 272	51.35 247	83.26 310	58.33 267	75.80 262	87.81 248

cascas		76.72 214	74.64 224	82.99 158	85.78 218	65.88 167	82.33 261	89.21 176	60.85 292	72.74 237	81.02 297	47.28 281	93.75 142	67.48 189	85.02 156	89.34 209

CR-MVSNet		73.37 250	71.27 258	79.67 234	81.32 300	65.19 181	75.92 310	80.30 303	59.92 299	72.73 238	81.19 293	52.50 229	86.69 289	59.84 251	77.71 236	87.11 266

RPMNet		73.51 249	70.49 264	82.58 176	81.32 300	65.19 181	75.92 310	92.27 76	57.60 316	72.73 238	76.45 327	52.30 232	95.43 70	48.14 316	77.71 236	87.11 266

DTE-MVSNet		76.99 209	76.80 196	77.54 267	86.24 212	53.06 323	87.52 148	90.66 132	77.08 54	72.50 240	88.67 156	60.48 176	89.52 257	57.33 276	70.74 310	90.05 187

Test_1112_low_res		76.40 220	75.44 214	79.27 240	89.28 130	58.09 274	81.69 267	87.07 227	59.53 303	72.48 241	86.67 214	61.30 161	89.33 260	60.81 246	80.15 216	90.41 168

v7n		78.97 168	77.58 181	83.14 150	83.45 255	65.51 173	88.32 126	91.21 120	73.69 125	72.41 242	86.32 227	57.93 189	93.81 137	69.18 176	75.65 264	90.11 180

SCA		74.22 242	72.33 249	79.91 228	84.05 246	62.17 235	79.96 282	79.29 311	66.30 239	72.38 243	80.13 305	51.95 240	88.60 273	59.25 256	77.67 238	88.96 223

CNLPA		78.08 188	76.79 197	81.97 186	90.40 99	71.07 64	87.59 147	84.55 253	66.03 243	72.38 243	89.64 130	57.56 193	86.04 294	59.61 253	83.35 177	88.79 230

NR-MVSNet		80.23 139	79.38 136	82.78 171	87.80 179	63.34 217	86.31 183	91.09 125	79.01 26	72.17 245	89.07 147	67.20 91	92.81 183	66.08 203	75.65 264	92.20 114

OpenMVS	72.83 10	79.77 147	78.33 160	84.09 118	85.17 227	69.91 88	90.57 58	90.97 126	66.70 232	72.17 245	91.91 80	54.70 214	93.96 126	61.81 237	90.95 89	88.41 240

MVS		78.19 186	76.99 192	81.78 188	85.66 219	66.99 149	84.66 220	90.47 138	55.08 328	72.02 247	85.27 247	63.83 122	94.11 124	66.10 202	89.80 104	84.24 304

XVG-ACMP-BASELINE		76.11 224	74.27 231	81.62 191	83.20 261	64.67 189	83.60 247	89.75 160	69.75 190	71.85 248	87.09 201	32.78 339	92.11 202	69.99 169	80.43 213	88.09 243

PatchmatchNet		73.12 255	71.33 257	78.49 253	83.18 262	60.85 250	79.63 284	78.57 313	64.13 264	71.73 249	79.81 310	51.20 249	85.97 295	57.40 275	76.36 258	88.66 233
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
tpmrst		72.39 261	72.13 250	73.18 301	80.54 307	49.91 335	79.91 283	79.08 312	63.11 271	71.69 250	79.95 307	55.32 207	82.77 312	65.66 207	73.89 289	86.87 269

TransMVSNet (Re)		75.39 235	74.56 226	77.86 259	85.50 223	57.10 291	86.78 170	86.09 241	72.17 149	71.53 251	87.34 191	63.01 135	89.31 261	56.84 279	61.83 330	87.17 263

Fast-Effi-MVS+-dtu		78.02 191	76.49 204	82.62 175	83.16 264	66.96 152	86.94 163	87.45 222	72.45 142	71.49 252	84.17 262	54.79 213	91.58 217	67.61 187	80.31 214	89.30 210

PAPM		77.68 200	76.40 206	81.51 194	87.29 197	61.85 239	83.78 242	89.59 164	64.74 257	71.23 253	88.70 154	62.59 139	93.66 146	52.66 294	87.03 136	89.01 219

tfpnnormal		74.39 239	73.16 242	78.08 257	86.10 214	58.05 275	84.65 223	87.53 219	70.32 179	71.22 254	85.63 240	54.97 209	89.86 251	43.03 333	75.02 279	86.32 278

RPSCF		73.23 254	71.46 255	78.54 251	82.50 280	59.85 260	82.18 262	82.84 279	58.96 307	71.15 255	89.41 142	45.48 296	84.77 302	58.82 262	71.83 304	91.02 147

DWT-MVSNet_test		73.70 247	71.86 252	79.21 242	82.91 271	58.94 266	82.34 260	82.17 283	65.21 250	71.05 256	78.31 316	44.21 301	90.17 248	63.29 223	77.28 240	88.53 237

PatchT		68.46 288	67.85 284	70.29 312	80.70 305	43.93 344	72.47 323	74.88 327	60.15 297	70.55 257	76.57 326	49.94 263	81.59 315	50.58 300	74.83 281	85.34 292

IterMVS-SCA-FT		75.43 233	73.87 235	80.11 225	82.69 276	64.85 186	81.57 269	83.47 269	69.16 203	70.49 258	84.15 263	51.95 240	88.15 278	69.23 175	72.14 302	87.34 258

miper_lstm_enhance		74.11 243	73.11 243	77.13 273	80.11 311	59.62 262	72.23 324	86.92 229	66.76 230	70.40 259	82.92 277	56.93 201	82.92 311	69.06 178	72.63 298	88.87 226

gg-mvs-nofinetune		69.95 279	67.96 282	75.94 280	83.07 265	54.51 316	77.23 305	70.29 337	63.11 271	70.32 260	62.33 340	43.62 304	88.69 272	53.88 289	87.76 124	84.62 302

DP-MVS		76.78 213	74.57 225	83.42 137	93.29 49	69.46 101	88.55 117	83.70 263	63.98 267	70.20 261	88.89 151	54.01 221	94.80 100	46.66 321	81.88 196	86.01 286

pmmvs6		74.69 238	73.39 238	78.61 249	81.38 297	57.48 287	86.64 174	87.95 210	64.99 256	70.18 262	86.61 216	50.43 258	89.52 257	62.12 233	70.18 312	88.83 228

PVSNet	64.34 18	72.08 264	70.87 263	75.69 282	86.21 213	56.44 301	74.37 320	80.73 296	62.06 285	70.17 263	82.23 288	42.86 308	83.31 309	54.77 286	84.45 164	87.32 259

1314		76.53 215	75.30 220	80.21 224	83.93 248	62.32 233	84.66 220	88.81 190	60.23 296	70.16 264	84.07 264	55.30 208	90.73 241	67.37 190	83.21 179	87.59 253

Patchmtry		70.74 270	69.16 272	75.49 286	80.72 304	54.07 318	74.94 319	80.30 303	58.34 311	70.01 265	81.19 293	52.50 229	86.54 290	53.37 291	71.09 309	85.87 289

EPMVS		69.02 284	68.16 279	71.59 304	79.61 319	49.80 337	77.40 304	66.93 344	62.82 277	70.01 265	79.05 312	45.79 292	77.86 329	56.58 280	75.26 277	87.13 265

IterMVS		74.29 240	72.94 244	78.35 254	81.53 294	63.49 213	81.58 268	82.49 281	68.06 221	69.99 267	83.69 271	51.66 246	85.54 297	65.85 205	71.64 305	86.01 286
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
test-LLR		72.94 258	72.43 247	74.48 293	81.35 298	58.04 276	78.38 296	77.46 318	66.66 233	69.95 268	79.00 314	48.06 277	79.24 321	66.13 200	84.83 158	86.15 282

test-mter		71.41 266	70.39 267	74.48 293	81.35 298	58.04 276	78.38 296	77.46 318	60.32 295	69.95 268	79.00 314	36.08 335	79.24 321	66.13 200	84.83 158	86.15 282

pmmvs4		74.03 245	71.91 251	80.39 219	81.96 288	68.32 126	81.45 270	82.14 284	59.32 304	69.87 270	85.13 251	52.40 231	88.13 279	60.21 249	74.74 282	84.73 300

PLC	70.83 11	78.05 190	76.37 207	83.08 153	91.88 80	67.80 136	88.19 132	89.46 167	64.33 263	69.87 270	88.38 165	53.66 223	93.58 147	58.86 261	82.73 186	87.86 247
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
LTVRE_ROB	69.57 13	76.25 222	74.54 227	81.41 196	88.60 155	64.38 196	79.24 288	89.12 181	70.76 172	69.79 272	87.86 178	49.09 273	93.20 164	56.21 282	80.16 215	86.65 275
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
LS3D		76.95 211	74.82 223	83.37 140	90.45 97	67.36 145	89.15 96	86.94 228	61.87 286	69.52 273	90.61 112	51.71 245	94.53 106	46.38 324	86.71 141	88.21 242

IB-MVS	68.01 15	75.85 227	73.36 239	83.31 141	84.76 234	66.03 162	83.38 250	85.06 248	70.21 182	69.40 274	81.05 296	45.76 293	94.66 104	65.10 211	75.49 267	89.25 211
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
PatchMatch-RL		72.38 262	70.90 261	76.80 276	88.60 155	67.38 144	79.53 285	76.17 324	62.75 278	69.36 275	82.00 291	45.51 295	84.89 301	53.62 290	80.58 209	78.12 334

MDTV_nov1_ep13			69.97 269		83.18 262	53.48 321	77.10 306	80.18 306	60.45 293	69.33 276	80.44 302	48.89 275	86.90 288	51.60 297	78.51 231

D2MVS		74.82 237	73.21 241	79.64 235	79.81 315	62.56 230	80.34 278	87.35 223	64.37 262	68.86 277	82.66 282	46.37 286	90.10 249	67.91 185	81.24 201	86.25 279

PMMVS		69.34 282	68.67 274	71.35 308	75.67 335	62.03 236	75.17 314	73.46 332	50.00 337	68.68 278	79.05 312	52.07 238	78.13 326	61.16 243	82.77 185	73.90 338

Patchmatch-RL test		70.24 276	67.78 287	77.61 265	77.43 328	59.57 264	71.16 326	70.33 336	62.94 275	68.65 279	72.77 334	50.62 255	85.49 298	69.58 173	66.58 323	87.77 249

MS-PatchMatch		73.83 246	72.67 245	77.30 270	83.87 249	66.02 163	81.82 264	84.66 251	61.37 290	68.61 280	82.82 280	47.29 280	88.21 277	59.27 255	84.32 165	77.68 335

tpm cat1		70.57 272	68.31 277	77.35 269	82.41 283	57.95 279	78.08 300	80.22 305	52.04 334	68.54 281	77.66 322	52.00 239	87.84 282	51.77 295	72.07 303	86.25 279

TESTMET0.1,1		69.89 280	69.00 273	72.55 302	79.27 323	56.85 293	78.38 296	74.71 330	57.64 315	68.09 282	77.19 324	37.75 330	76.70 332	63.92 217	84.09 167	84.10 307

MIMVSNet		70.69 271	69.30 270	74.88 290	84.52 237	56.35 304	75.87 312	79.42 310	64.59 258	67.76 283	82.41 284	41.10 317	81.54 316	46.64 323	81.34 199	86.75 273

ACMH+	68.96 14	76.01 225	74.01 232	82.03 184	88.60 155	65.31 179	88.86 103	87.55 218	70.25 181	67.75 284	87.47 189	41.27 316	93.19 165	58.37 266	75.94 261	87.60 252

LCM-MVSNet-Re		77.05 208	76.94 193	77.36 268	87.20 198	51.60 327	80.06 280	80.46 301	75.20 95	67.69 285	86.72 208	62.48 141	88.98 267	63.44 220	89.25 109	91.51 131

ITE_SJBPF				78.22 255	81.77 290	60.57 253		83.30 271	69.25 199	67.54 286	87.20 197	36.33 334	87.28 287	54.34 287	74.62 283	86.80 271

pmmvs5		71.55 265	70.20 268	75.61 283	77.83 326	56.39 302	81.74 266	80.89 293	57.76 314	67.46 287	84.49 258	49.26 272	85.32 300	57.08 278	75.29 275	85.11 296

MVP-Stereo		76.12 223	74.46 229	81.13 207	85.37 225	69.79 91	84.42 231	87.95 210	65.03 254	67.46 287	85.33 246	53.28 226	91.73 215	58.01 270	83.27 178	81.85 323
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
test_0402		72.79 259	70.44 265	79.84 230	88.13 168	65.99 164	85.93 193	84.29 255	65.57 248	67.40 289	85.49 243	46.92 283	92.61 185	35.88 342	74.38 285	80.94 326

GG-mvs-BLEND				75.38 287	81.59 293	55.80 309	79.32 287	69.63 339		67.19 290	73.67 333	43.24 305	88.90 271	50.41 301	84.50 162	81.45 325

tpmvs		71.09 268	69.29 271	76.49 277	82.04 287	56.04 307	78.92 293	81.37 292	64.05 265	67.18 291	78.28 317	49.74 266	89.77 252	49.67 307	72.37 299	83.67 309

OurMVSNet-221017-0		74.26 241	72.42 248	79.80 231	83.76 251	59.59 263	85.92 194	86.64 231	66.39 238	66.96 292	87.58 184	39.46 323	91.60 216	65.76 206	69.27 314	88.22 241

baseline2		75.70 229	73.83 236	81.30 201	83.26 259	61.79 241	82.57 259	80.65 297	66.81 228	66.88 293	83.42 274	57.86 190	92.19 199	63.47 219	79.57 220	89.91 193

MVS_0304		72.48 260	70.89 262	77.24 271	82.20 285	59.68 261	84.11 237	83.49 268	67.10 227	66.87 294	80.59 301	35.00 338	87.40 285	59.07 259	79.58 219	84.63 301

F-COLMAP		76.38 221	74.33 230	82.50 177	89.28 130	66.95 153	88.41 121	89.03 182	64.05 265	66.83 295	88.61 158	46.78 284	92.89 178	57.48 273	78.55 229	87.67 250

ACMH	67.68 16	75.89 226	73.93 233	81.77 189	88.71 152	66.61 155	88.62 114	89.01 184	69.81 187	66.78 296	86.70 213	41.95 315	91.51 221	55.64 283	78.14 235	87.17 263
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
test0.0.03 1		68.00 289	67.69 288	68.90 317	77.55 327	47.43 339	75.70 313	72.95 334	66.66 233	66.56 297	82.29 287	48.06 277	75.87 336	44.97 330	74.51 284	83.41 311

MDTV_nov1_ep13_2view						37.79 349	75.16 315		55.10 327	66.53 298		49.34 270		53.98 288		87.94 245

ET-MVSNet_ETH3D		78.63 174	76.63 203	84.64 99	86.73 208	69.47 99	85.01 213	84.61 252	69.54 193	66.51 299	86.59 217	50.16 260	91.75 213	76.26 114	84.24 166	92.69 98

EU-MVSNet		68.53 287	67.61 289	71.31 309	78.51 325	47.01 341	84.47 226	84.27 256	42.27 340	66.44 300	84.79 256	40.44 321	83.76 305	58.76 263	68.54 319	83.17 313

EPNet_dtu		75.46 232	74.86 222	77.23 272	82.57 279	54.60 314	86.89 165	83.09 276	71.64 155	66.25 301	85.86 235	55.99 205	88.04 280	54.92 285	86.55 143	89.05 217
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
Anonymous20231206		68.60 285	67.80 286	71.02 310	80.23 310	50.75 333	78.30 299	80.47 300	56.79 321	66.11 302	82.63 283	46.35 287	78.95 323	43.62 332	75.70 263	83.36 312

SixPastTwentyTwo		73.37 250	71.26 259	79.70 232	85.08 232	57.89 280	85.57 199	83.56 266	71.03 166	65.66 303	85.88 234	42.10 313	92.57 186	59.11 258	63.34 329	88.65 234

MSDG		73.36 252	70.99 260	80.49 218	84.51 238	65.80 168	80.71 274	86.13 240	65.70 246	65.46 304	83.74 270	44.60 298	90.91 237	51.13 299	76.89 246	84.74 299

OpenMVS_ROB	64.09 19	70.56 273	68.19 278	77.65 264	80.26 309	59.41 265	85.01 213	82.96 278	58.76 309	65.43 305	82.33 285	37.63 331	91.23 228	45.34 329	76.03 260	82.32 320

ppachtmachnet_test		70.04 278	67.34 290	78.14 256	79.80 316	61.13 246	79.19 290	80.59 298	59.16 306	65.27 306	79.29 311	46.75 285	87.29 286	49.33 308	66.72 321	86.00 288

ADS-MVSNet2		66.20 300	63.33 302	74.82 291	79.92 313	58.75 269	67.55 337	75.19 326	53.37 331	65.25 307	75.86 328	42.32 311	80.53 319	41.57 336	68.91 316	85.18 293

ADS-MVSNet		64.36 304	62.88 306	68.78 319	79.92 313	47.17 340	67.55 337	71.18 335	53.37 331	65.25 307	75.86 328	42.32 311	73.99 343	41.57 336	68.91 316	85.18 293

testgi		66.67 296	66.53 294	67.08 322	75.62 336	41.69 347	75.93 309	76.50 323	66.11 240	65.20 309	86.59 217	35.72 336	74.71 340	43.71 331	73.38 295	84.84 298

PM-MVS		66.41 298	64.14 300	73.20 300	73.92 338	56.45 300	78.97 292	64.96 348	63.88 269	64.72 310	80.24 304	19.84 349	83.44 308	66.24 199	64.52 328	79.71 331

JIA-IIPM		66.32 299	62.82 307	76.82 275	77.09 331	61.72 242	65.34 340	75.38 325	58.04 313	64.51 311	62.32 341	42.05 314	86.51 291	51.45 298	69.22 315	82.21 321

ambc				75.24 288	73.16 342	50.51 334	63.05 344	87.47 221		64.28 312	77.81 321	17.80 350	89.73 254	57.88 271	60.64 333	85.49 290

EG-PatchMatch MVS		74.04 244	71.82 253	80.71 215	84.92 233	67.42 142	85.86 195	88.08 207	66.04 242	64.22 313	83.85 266	35.10 337	92.56 187	57.44 274	80.83 205	82.16 322

dp		66.80 294	65.43 296	70.90 311	79.74 318	48.82 338	75.12 317	74.77 328	59.61 301	64.08 314	77.23 323	42.89 307	80.72 318	48.86 310	66.58 323	83.16 314

pmmvs-eth3d		70.50 274	67.83 285	78.52 252	77.37 329	66.18 161	81.82 264	81.51 290	58.90 308	63.90 315	80.42 303	42.69 309	86.28 293	58.56 264	65.30 326	83.11 315

COLMAP_ROB	66.92 17	73.01 256	70.41 266	80.81 213	87.13 200	65.63 171	88.30 127	84.19 258	62.96 274	63.80 316	87.69 182	38.04 329	92.56 187	46.66 321	74.91 280	84.24 304
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
FMVSNet5		69.50 281	67.96 282	74.15 297	82.97 270	55.35 312	80.01 281	82.12 285	62.56 280	63.02 317	81.53 292	36.92 332	81.92 314	48.42 311	74.06 287	85.17 295

test20.03		67.45 291	66.95 292	68.94 316	75.48 337	44.84 343	77.50 303	77.67 317	66.66 233	63.01 318	83.80 268	47.02 282	78.40 325	42.53 335	68.86 318	83.58 310

K. test v3		71.19 267	68.51 275	79.21 242	83.04 267	57.78 283	84.35 233	76.91 322	72.90 141	62.99 319	82.86 279	39.27 324	91.09 234	61.65 238	52.66 342	88.75 231

our_test_3		69.14 283	67.00 291	75.57 284	79.80 316	58.80 268	77.96 301	77.81 316	59.55 302	62.90 320	78.25 318	47.43 279	83.97 304	51.71 296	67.58 320	83.93 308

CHOSEN 280x420		66.51 297	64.71 298	71.90 303	81.45 295	63.52 212	57.98 345	68.95 343	53.57 330	62.59 321	76.70 325	46.22 288	75.29 339	55.25 284	79.68 218	76.88 337

USDC		70.33 275	68.37 276	76.21 279	80.60 306	56.23 305	79.19 290	86.49 233	60.89 291	61.29 322	85.47 244	31.78 342	89.47 259	53.37 291	76.21 259	82.94 319

lessismore_v0				78.97 244	81.01 303	57.15 290		65.99 345		61.16 323	82.82 280	39.12 325	91.34 225	59.67 252	46.92 345	88.43 239

UnsupCasMVSNet_eth		67.33 292	65.99 295	71.37 306	73.48 340	51.47 329	75.16 315	85.19 247	65.20 251	60.78 324	80.93 300	42.35 310	77.20 331	57.12 277	53.69 341	85.44 291

testing_2		75.73 228	73.34 240	82.89 164	77.37 329	65.22 180	84.10 238	90.54 136	69.09 204	60.46 325	81.15 295	40.48 320	92.84 182	76.36 113	80.54 212	90.60 160

AllTest		70.96 269	68.09 281	79.58 236	85.15 228	63.62 207	84.58 225	79.83 307	62.31 282	60.32 326	86.73 206	32.02 340	88.96 269	50.28 302	71.57 306	86.15 282

TestCases				79.58 236	85.15 228	63.62 207		79.83 307	62.31 282	60.32 326	86.73 206	32.02 340	88.96 269	50.28 302	71.57 306	86.15 282

Patchmatch-test		64.82 303	63.24 303	69.57 314	79.42 321	49.82 336	63.49 343	69.05 342	51.98 335	59.95 328	80.13 305	50.91 251	70.98 345	40.66 338	73.57 292	87.90 246

MIMVSNet1		68.58 286	66.78 293	73.98 298	80.07 312	51.82 325	80.77 273	84.37 254	64.40 261	59.75 329	82.16 289	36.47 333	83.63 307	42.73 334	70.33 311	86.48 277

LF4IMVS		64.02 305	62.19 308	69.50 315	70.90 345	53.29 322	76.13 307	77.18 321	52.65 333	58.59 330	80.98 298	23.55 346	76.52 333	53.06 293	66.66 322	78.68 333

PVSNet_0	57.27 20	61.67 308	59.27 311	68.85 318	79.61 319	57.44 288	68.01 336	73.44 333	55.93 325	58.54 331	70.41 337	44.58 299	77.55 330	47.01 320	35.91 346	71.55 340

TDRefinement		67.49 290	64.34 299	76.92 274	73.47 341	61.07 247	84.86 217	82.98 277	59.77 300	58.30 332	85.13 251	26.06 344	87.89 281	47.92 318	60.59 334	81.81 324

UnsupCasMVSNet_bld		63.70 306	61.53 310	70.21 313	73.69 339	51.39 330	72.82 322	81.89 286	55.63 326	57.81 333	71.80 336	38.67 326	78.61 324	49.26 309	52.21 343	80.63 327

DSMNet-mixed		57.77 311	56.90 313	60.38 326	67.70 347	35.61 350	69.18 332	53.97 351	32.30 349	57.49 334	79.88 308	40.39 322	68.57 347	38.78 340	72.37 299	76.97 336

N_pmnet		52.79 314	53.26 315	51.40 331	78.99 324	7.68 360	69.52 330	3.89 359	51.63 336	57.01 335	74.98 331	40.83 319	65.96 348	37.78 341	64.67 327	80.56 329

new-patchmatchnet		61.73 307	61.73 309	61.70 325	72.74 344	24.50 357	69.16 333	78.03 315	61.40 288	56.72 336	75.53 330	38.42 327	76.48 334	45.95 326	57.67 336	84.13 306

CMPMVS	51.72 21	70.19 277	68.16 279	76.28 278	73.15 343	57.55 286	79.47 286	83.92 260	48.02 338	56.48 337	84.81 255	43.13 306	86.42 292	62.67 228	81.81 197	84.89 297
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
TinyColmap		67.30 293	64.81 297	74.76 292	81.92 289	56.68 298	80.29 279	81.49 291	60.33 294	56.27 338	83.22 275	24.77 345	87.66 284	45.52 327	69.47 313	79.95 330

YYNet1		65.03 301	62.91 305	71.38 305	75.85 334	56.60 299	69.12 334	74.66 331	57.28 319	54.12 339	77.87 320	45.85 291	74.48 341	49.95 305	61.52 332	83.05 316

MDA-MVSNet_test_wron		65.03 301	62.92 304	71.37 306	75.93 333	56.73 295	69.09 335	74.73 329	57.28 319	54.03 340	77.89 319	45.88 290	74.39 342	49.89 306	61.55 331	82.99 318

pmmvs3		57.79 310	54.26 314	68.37 320	64.02 349	56.72 296	75.12 317	65.17 346	40.20 342	52.93 341	69.86 338	20.36 348	75.48 338	45.45 328	55.25 340	72.90 339

MVS-HIRNet		59.14 309	57.67 312	63.57 324	81.65 291	43.50 345	71.73 325	65.06 347	39.59 344	51.43 342	57.73 344	38.34 328	82.58 313	39.53 339	73.95 288	64.62 343

MDA-MVSNet-bldmvs		66.68 295	63.66 301	75.75 281	79.28 322	60.56 254	73.92 321	78.35 314	64.43 260	50.13 343	79.87 309	44.02 303	83.67 306	46.10 325	56.86 337	83.03 317

new_pmnet		50.91 315	50.29 317	52.78 330	68.58 346	34.94 352	63.71 342	56.63 350	39.73 343	44.95 344	65.47 339	21.93 347	58.48 349	34.98 343	56.62 338	64.92 342

FPMVS		53.68 313	51.64 316	59.81 327	65.08 348	51.03 331	69.48 331	69.58 340	41.46 341	40.67 345	72.32 335	16.46 352	70.00 346	24.24 347	65.42 325	58.40 344

LCM-MVSNet		54.25 312	49.68 318	67.97 321	53.73 352	45.28 342	66.85 339	80.78 295	35.96 346	39.45 346	62.23 342	8.70 357	78.06 328	48.24 315	51.20 344	80.57 328

PMMVS2		40.82 319	38.86 322	46.69 332	53.84 351	16.45 358	48.61 348	49.92 352	37.49 345	31.67 347	60.97 343	8.14 358	56.42 350	28.42 345	30.72 347	67.19 341

ANet_high		50.57 316	46.10 319	63.99 323	48.67 355	39.13 348	70.99 328	80.85 294	61.39 289	31.18 348	57.70 345	17.02 351	73.65 344	31.22 344	15.89 352	79.18 332

Gipuma		45.18 317	41.86 320	55.16 329	77.03 332	51.52 328	32.50 351	80.52 299	32.46 348	27.12 349	35.02 349	9.52 356	75.50 337	22.31 348	60.21 335	38.45 347
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PMVS	37.38 22	44.16 318	40.28 321	55.82 328	40.82 357	42.54 346	65.12 341	63.99 349	34.43 347	24.48 350	57.12 346	3.92 359	76.17 335	17.10 350	55.52 339	48.75 345
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
DeepMVS_CX				27.40 336	40.17 358	26.90 355		24.59 358	17.44 353	23.95 351	48.61 347	9.77 355	26.48 354	18.06 349	24.47 348	28.83 348

tmp_tt		18.61 324	21.40 327	10.23 338	4.82 359	10.11 359	34.70 350	30.74 357	1.48 355	23.91 352	26.07 352	28.42 343	13.41 356	27.12 346	15.35 353	7.17 351

MVE	26.22 23	30.37 322	25.89 326	43.81 333	44.55 356	35.46 351	28.87 352	39.07 355	18.20 352	18.58 353	40.18 348	2.68 360	47.37 353	17.07 351	23.78 349	48.60 346
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
E-PMN		31.77 320	30.64 323	35.15 334	52.87 353	27.67 354	57.09 346	47.86 353	24.64 350	16.40 354	33.05 350	11.23 354	54.90 351	14.46 352	18.15 350	22.87 349

EMVS		30.81 321	29.65 324	34.27 335	50.96 354	25.95 356	56.58 347	46.80 354	24.01 351	15.53 355	30.68 351	12.47 353	54.43 352	12.81 353	17.05 351	22.43 350

wuyk23d		16.82 325	15.94 328	19.46 337	58.74 350	31.45 353	39.22 349	3.74 360	6.84 354	6.04 356	2.70 356	1.27 361	24.29 355	10.54 354	14.40 354	2.63 352

testmvs		6.04 328	8.02 331	0.10 340	0.08 360	0.03 362	69.74 329	0.04 361	0.05 356	0.31 357	1.68 357	0.02 363	0.04 357	0.24 355	0.02 355	0.25 354

test123		6.12 327	8.11 330	0.14 339	0.06 361	0.09 361	71.05 327	0.03 362	0.04 357	0.25 358	1.30 358	0.05 362	0.03 358	0.21 356	0.01 356	0.29 353

uanet_test		0.00 330	0.00 333	0.00 341	0.00 362	0.00 363	0.00 353	0.00 363	0.00 358	0.00 359	0.00 359	0.00 364	0.00 359	0.00 357	0.00 357	0.00 355

cdsmvs_eth3d_5k		19.96 323	26.61 325	0.00 341	0.00 362	0.00 363	0.00 353	89.26 174	0.00 358	0.00 359	88.61 158	61.62 155	0.00 359	0.00 357	0.00 357	0.00 355

pcd_1.5k_mvsjas		5.26 329	7.02 332	0.00 341	0.00 362	0.00 363	0.00 353	0.00 363	0.00 358	0.00 359	0.00 359	63.15 131	0.00 359	0.00 357	0.00 357	0.00 355

sosnet-low-res		0.00 330	0.00 333	0.00 341	0.00 362	0.00 363	0.00 353	0.00 363	0.00 358	0.00 359	0.00 359	0.00 364	0.00 359	0.00 357	0.00 357	0.00 355

sosnet		0.00 330	0.00 333	0.00 341	0.00 362	0.00 363	0.00 353	0.00 363	0.00 358	0.00 359	0.00 359	0.00 364	0.00 359	0.00 357	0.00 357	0.00 355

uncertanet		0.00 330	0.00 333	0.00 341	0.00 362	0.00 363	0.00 353	0.00 363	0.00 358	0.00 359	0.00 359	0.00 364	0.00 359	0.00 357	0.00 357	0.00 355

Regformer		0.00 330	0.00 333	0.00 341	0.00 362	0.00 363	0.00 353	0.00 363	0.00 358	0.00 359	0.00 359	0.00 364	0.00 359	0.00 357	0.00 357	0.00 355

ab-mvs-re		7.23 326	9.64 329	0.00 341	0.00 362	0.00 363	0.00 353	0.00 363	0.00 358	0.00 359	86.72 208	0.00 364	0.00 359	0.00 357	0.00 357	0.00 355

uanet		0.00 330	0.00 333	0.00 341	0.00 362	0.00 363	0.00 353	0.00 363	0.00 358	0.00 359	0.00 359	0.00 364	0.00 359	0.00 357	0.00 357	0.00 355

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

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																88.96 223

sam_mvs1												51.32 248				88.96 223

sam_mvs												50.01 261

MTGPA								92.02 87

test_post1							78.90 294				5.43 355	48.81 276	85.44 299	59.25 256

test_post											5.46 354	50.36 259	84.24 303

patchmatchnet-post											74.00 332	51.12 250	88.60 273

MTMP							92.18 31	32.83 356

gm-plane-assit					81.40 296	53.83 320			62.72 279		80.94 299		92.39 192	63.40 221

test9_res														84.90 30	95.70 27	92.87 93

agg_prior2														82.91 58	95.45 29	92.70 96

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

新几何2							86.29 185

旧先验1					91.96 77	65.79 169		86.37 236			93.08 66	69.31 75			92.74 69	88.74 232

无先验							87.48 149	88.98 185	60.00 298				94.12 122	67.28 191		88.97 222

原ACMM2							86.86 166

testdata2													91.01 236	62.37 230

segment_acmp												73.08 40

testdata1							84.14 236		75.71 83

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_prior4											91.00 106

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								0.00 363

nn								0.00 363

door-mid								69.98 338

test11								92.23 79

door								69.44 341

HQP5-MVS						66.98 150

BP-MVS														77.47 102

HQP3-MVS								92.19 82							85.99 151

HQP2-MVS												60.17 180

NP-MVS					89.62 113	68.32 126					90.24 117

ACMMP++_ref															81.95 195

ACMMP++															81.25 200

Test By Simon												64.33 117

High-resolution multi-view benchmark