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.

IU-MVS					99.03 16	85.34 50		96.86 44	92.05 16	98.74 1				98.15 4	98.97 17	99.42 13

PC_three_1452									91.12 22	98.33 2	98.42 28	92.51 2	99.81 21	98.96 2	99.37 1	99.70 3

SMA-MVS		94.70 17	94.68 17	94.76 25	98.02 70	85.94 38	97.47 87	96.77 55	85.32 123	97.92 3	98.70 18	83.09 52	99.84 12	95.79 30	99.08 10	98.49 52
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
SED-MVS		95.88 5	96.22 4	94.87 22	99.03 16	85.03 62	99.12 6	96.78 49	88.72 56	97.79 4	98.91 3	88.48 17	99.82 18	98.15 4	98.97 17	99.74 1

test_241102_ONE					99.03 16	85.03 62		96.78 49	88.72 56	97.79 4	98.90 6	88.48 17	99.82 18

DVP-MVS++		96.05 4	96.41 3	94.96 21	99.05 10	85.34 50	98.13 39	96.77 55	88.38 63	97.70 6	98.77 12	92.06 3	99.84 12	97.47 14	99.37 1	99.70 3

test_241102_TWO								96.78 49	88.72 56	97.70 6	98.91 3	87.86 21	99.82 18	98.15 4	99.00 15	99.47 9

patch_mono-2		95.14 12	96.08 7	92.33 115	98.44 48	77.84 235	98.43 27	97.21 20	92.58 11	97.68 8	97.65 78	86.88 26	99.83 16	98.25 3	97.60 74	99.33 17

test0726					99.05 10	85.18 55	99.11 8	96.78 49	88.75 54	97.65 9	98.91 3	87.69 22

TSAR-MVS + MP.		94.79 16	95.17 14	93.64 60	97.66 82	84.10 79	95.85 203	96.42 106	91.26 21	97.49 10	96.80 117	86.50 28	98.49 132	95.54 34	99.03 13	98.33 59
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
MSP-MVS		95.62 7	96.54 1	92.86 95	98.31 55	80.10 169	97.42 95	96.78 49	92.20 14	97.11 11	98.29 31	93.46 1	99.10 100	96.01 26	99.30 5	99.38 14
Zhenlong Yuan, Cong Liu, Fei Shen, Zhaoxin Li, Jingguo luo, Tianlu Mao and Zhaoqi Wang: MSP-MVS: Multi-granularity Segmentation Prior Guided Multi-View Stereo. AAAI2025
CNVR-MVS		96.30 1	96.54 1	95.55 14	99.31 5	87.69 21	99.06 9	97.12 24	94.66 3	96.79 12	98.78 11	86.42 29	99.95 3	97.59 13	99.18 7	99.00 28

DVP-MVS		95.58 8	95.91 9	94.57 29	99.05 10	85.18 55	99.06 9	96.46 101	88.75 54	96.69 13	98.76 14	87.69 22	99.76 25	97.90 9	98.85 22	98.77 35
Zhenlong Yuan, Jinguo Luo, Fei Shen, Zhaoxin Li, Cong Liu, Tianlu Mao, Zhaoqi Wang: DVP-MVS: Synergize Depth-Edge and Visibility Prior for Multi-View Stereo. AAAI2025
test_0728_THIRD									88.38 63	96.69 13	98.76 14	89.64 13	99.76 25	97.47 14	98.84 24	99.38 14

SD-MVS		94.84 15	95.02 15	94.29 37	97.87 76	84.61 71	97.76 65	96.19 130	89.59 42	96.66 15	98.17 39	84.33 38	99.60 51	96.09 24	98.50 41	98.66 43
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
test_one_0601					98.91 20	84.56 72		96.70 65	88.06 69	96.57 16	98.77 12	88.04 20

DPE-MVS		95.32 10	95.55 10	94.64 28	98.79 25	84.87 67	97.77 61	96.74 60	86.11 105	96.54 17	98.89 7	88.39 19	99.74 33	97.67 12	99.05 12	99.31 19
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
DPM-MVS		96.21 2	95.53 11	98.26 1	96.26 114	95.09 1	99.15 4	96.98 32	93.39 9	96.45 18	98.79 10	90.17 10	99.99 1	89.33 117	99.25 6	99.70 3

PS-MVSNAJ		94.17 27	93.52 41	96.10 8	95.65 130	92.35 2	98.21 34	95.79 153	92.42 13	96.24 19	98.18 35	71.04 203	99.17 93	96.77 21	97.39 82	96.79 161

旧先验2							96.97 133		74.06 307	96.10 20			97.76 159	88.38 127

test_part2					98.90 21	85.14 61				96.07 21

xiu_mvs_v2_base		93.92 35	93.26 45	95.91 10	95.07 147	92.02 6	98.19 35	95.68 158	92.06 15	96.01 22	98.14 40	70.83 206	98.96 108	96.74 22	96.57 102	96.76 164

ETH3 D test6400		95.56 9	95.41 13	96.00 9	99.02 19	89.42 9	98.75 17	96.80 48	87.28 86	95.88 23	98.95 2	85.92 31	99.41 67	97.15 18	98.95 20	99.18 25

HPM-MVS++		95.32 10	95.48 12	94.85 23	98.62 38	86.04 35	97.81 59	96.93 38	92.45 12	95.69 24	98.50 24	85.38 32	99.85 10	94.75 43	99.18 7	98.65 44

NCCC		95.63 6	95.94 8	94.69 27	99.21 7	85.15 60	99.16 3	96.96 35	94.11 6	95.59 25	98.64 21	85.07 34	99.91 4	95.61 33	99.10 9	99.00 28

ETH3D-3000-0.1		94.43 21	94.42 24	94.45 31	97.78 77	85.78 41	97.98 49	96.53 93	85.29 126	95.45 26	98.81 8	83.36 47	99.38 69	96.07 25	98.53 37	98.19 72

EPNet		94.06 32	94.15 30	93.76 53	97.27 101	84.35 74	98.29 31	97.64 13	94.57 4	95.36 27	96.88 112	79.96 77	99.12 99	91.30 86	96.11 106	97.82 108
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CANet		94.89 14	94.64 18	95.63 12	97.55 88	88.12 15	99.06 9	96.39 113	94.07 7	95.34 28	97.80 69	76.83 121	99.87 8	97.08 19	97.64 73	98.89 31

TEST9					98.64 35	83.71 86	97.82 57	96.65 74	84.29 154	95.16 29	98.09 46	84.39 37	99.36 75

train_agg		94.28 24	94.45 22	93.74 54	98.64 35	83.71 86	97.82 57	96.65 74	84.50 146	95.16 29	98.09 46	84.33 38	99.36 75	95.91 29	98.96 19	98.16 75

test_8					98.63 37	83.64 89	97.81 59	96.63 79	84.50 146	95.10 31	98.11 45	84.33 38	99.23 81

DeepPCF-MVS	89.82 1	94.61 18	96.17 5	89.91 196	97.09 104	70.21 324	98.99 14	96.69 68	95.57 1	95.08 32	99.23 1	86.40 30	99.87 8	97.84 11	98.66 34	99.65 6

xxxxxxxxxxxxxcwj		94.38 22	94.62 19	93.68 58	98.24 58	83.34 94	98.61 23	92.69 298	91.32 19	95.07 33	98.74 16	82.93 53	99.38 69	95.42 36	98.51 38	98.32 60

SF-MVS		94.17 27	94.05 32	94.55 30	97.56 87	85.95 36	97.73 67	96.43 105	84.02 159	95.07 33	98.74 16	82.93 53	99.38 69	95.42 36	98.51 38	98.32 60

APDe-MVS		94.56 19	94.75 16	93.96 48	98.84 24	83.40 93	98.04 47	96.41 107	85.79 113	95.00 35	98.28 32	84.32 41	99.18 92	97.35 16	98.77 29	99.28 20

MVSFormer		91.36 87	90.57 91	93.73 56	93.00 205	88.08 16	94.80 241	94.48 223	80.74 221	94.90 36	97.13 103	78.84 89	95.10 291	83.77 163	97.46 77	98.02 87

lupinMVS		93.87 38	93.58 40	94.75 26	93.00 205	88.08 16	99.15 4	95.50 168	91.03 24	94.90 36	97.66 74	78.84 89	97.56 166	94.64 46	97.46 77	98.62 46

CS-MVS-test		92.98 49	93.67 37	90.90 163	96.52 110	76.87 253	98.68 18	94.73 208	90.36 34	94.84 38	97.89 62	77.94 102	97.15 195	94.28 50	97.80 70	98.70 42

9.14			94.26 29		98.10 66		98.14 36	96.52 94	84.74 138	94.83 39	98.80 9	82.80 56	99.37 73	95.95 28	98.42 46

testdata				90.13 187	95.92 123	74.17 290		96.49 100	73.49 312	94.82 40	97.99 55	78.80 91	97.93 150	83.53 172	97.52 76	98.29 65

testtj		94.09 31	94.08 31	94.09 45	99.28 6	83.32 96	97.59 77	96.61 80	83.60 175	94.77 41	98.46 26	82.72 57	99.64 47	95.29 38	98.42 46	99.32 18

APD-MVS		93.61 39	93.59 39	93.69 57	98.76 26	83.26 97	97.21 105	96.09 135	82.41 198	94.65 42	98.21 34	81.96 62	98.81 119	94.65 45	98.36 54	99.01 27
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
test_prior3		94.03 33	94.34 26	93.09 84	98.68 29	81.91 121	98.37 29	96.40 110	86.08 107	94.57 43	98.02 52	83.14 49	99.06 102	95.05 40	98.79 27	98.29 65

test_prior2							98.37 29		86.08 107	94.57 43	98.02 52	83.14 49		95.05 40	98.79 27

CS-MVS		92.73 56	93.48 42	90.48 176	96.27 113	75.93 272	98.55 25	94.93 195	89.32 46	94.54 45	97.67 73	78.91 88	97.02 199	93.80 53	97.32 84	98.49 52

FOURS1					98.51 44	78.01 227	98.13 39	96.21 127	83.04 185	94.39 46

ACMMP_NAP		93.46 41	93.23 46	94.17 42	97.16 102	84.28 77	96.82 142	96.65 74	86.24 103	94.27 47	97.99 55	77.94 102	99.83 16	93.39 60	98.57 36	98.39 58

agg_prior1		94.10 30	94.31 27	93.48 70	98.59 39	83.13 99	97.77 61	96.56 89	84.38 150	94.19 48	98.13 41	84.66 36	99.16 94	95.74 31	98.74 31	98.15 77

agg_prior					98.59 39	83.13 99		96.56 89		94.19 48			99.16 94

SteuartSystems-ACMMP		94.13 29	94.44 23	93.20 79	95.41 136	81.35 138	99.02 13	96.59 85	89.50 43	94.18 50	98.36 30	83.68 46	99.45 65	94.77 42	98.45 44	98.81 34
Skip Steuart: Steuart Systems R&D Blog.
ETH3D cwj APD-0.16		93.91 37	93.76 35	94.36 34	96.70 108	85.74 42	97.22 102	96.41 107	83.94 162	94.13 51	98.69 20	83.13 51	99.37 73	95.25 39	98.39 51	97.97 97

PHI-MVS		93.59 40	93.63 38	93.48 70	98.05 69	81.76 129	98.64 21	97.13 23	82.60 196	94.09 52	98.49 25	80.35 70	99.85 10	94.74 44	98.62 35	98.83 33

TSAR-MVS + GP.		94.35 23	94.50 20	93.89 49	97.38 98	83.04 102	98.10 41	95.29 182	91.57 17	93.81 53	97.45 87	86.64 27	99.43 66	96.28 23	94.01 130	99.20 23

CANet_DTU		90.98 94	90.04 103	93.83 51	94.76 156	86.23 33	96.32 177	93.12 291	93.11 10	93.71 54	96.82 116	63.08 250	99.48 63	84.29 155	95.12 120	95.77 188

VNet		92.11 70	91.22 82	94.79 24	96.91 105	86.98 26	97.91 52	97.96 9	86.38 102	93.65 55	95.74 135	70.16 211	98.95 111	93.39 60	88.87 174	98.43 56

ZD-MVS					99.09 9	83.22 98		96.60 84	82.88 190	93.61 56	98.06 51	82.93 53	99.14 96	95.51 35	98.49 42

xiu_mvs_v1_base_debu		90.54 104	89.54 114	93.55 65	92.31 219	87.58 22	96.99 128	94.87 199	87.23 88	93.27 57	97.56 82	57.43 289	98.32 139	92.72 72	93.46 138	94.74 207

xiu_mvs_v1_base		90.54 104	89.54 114	93.55 65	92.31 219	87.58 22	96.99 128	94.87 199	87.23 88	93.27 57	97.56 82	57.43 289	98.32 139	92.72 72	93.46 138	94.74 207

xiu_mvs_v1_base_debi		90.54 104	89.54 114	93.55 65	92.31 219	87.58 22	96.99 128	94.87 199	87.23 88	93.27 57	97.56 82	57.43 289	98.32 139	92.72 72	93.46 138	94.74 207

CDPH-MVS		93.12 45	92.91 51	93.74 54	98.65 34	83.88 81	97.67 72	96.26 123	83.00 187	93.22 60	98.24 33	81.31 63	99.21 85	89.12 118	98.74 31	98.14 78

ETV-MVS		92.72 58	92.87 52	92.28 118	94.54 161	81.89 123	97.98 49	95.21 185	89.77 41	93.11 61	96.83 114	77.23 117	97.50 173	95.74 31	95.38 115	97.44 134

MSLP-MVS++		94.28 24	94.39 25	93.97 47	98.30 56	84.06 80	98.64 21	96.93 38	90.71 27	93.08 62	98.70 18	79.98 76	99.21 85	94.12 51	99.07 11	98.63 45

alignmvs		92.97 50	92.26 65	95.12 18	95.54 133	87.77 19	98.67 19	96.38 114	88.04 70	93.01 63	97.45 87	79.20 85	98.60 126	93.25 65	88.76 175	98.99 30

canonicalmvs		92.27 68	91.22 82	95.41 15	95.80 126	88.31 13	97.09 122	94.64 216	88.49 61	92.99 64	97.31 94	72.68 185	98.57 128	93.38 62	88.58 177	99.36 16

DROMVSNet		91.73 75	92.11 69	90.58 172	93.54 188	77.77 237	98.07 44	94.40 229	87.44 82	92.99 64	97.11 105	74.59 166	96.87 208	93.75 54	97.08 89	97.11 150

jason		92.73 56	92.23 66	94.21 41	90.50 265	87.30 25	98.65 20	95.09 188	90.61 28	92.76 66	97.13 103	75.28 155	97.30 184	93.32 63	96.75 101	98.02 87
jason: jason.
Regformer-1		94.00 34	94.04 33	93.87 50	98.41 49	84.29 76	97.43 93	97.04 28	89.50 43	92.75 67	98.13 41	82.60 59	99.26 80	93.55 58	96.99 91	98.06 84

Regformer-2		93.92 35	94.01 34	93.67 59	98.41 49	83.75 85	97.43 93	97.00 30	89.43 45	92.69 68	98.13 41	82.48 60	99.22 83	93.51 59	96.99 91	98.04 85

test12				94.25 38	98.34 53	85.55 47		96.35 117		92.36 69		80.84 65	99.22 83		98.31 56	97.98 94

MG-MVS		94.25 26	93.72 36	95.85 11	99.38 3	89.35 11	97.98 49	98.09 8	89.99 37	92.34 70	96.97 109	81.30 64	98.99 106	88.54 123	98.88 21	99.20 23

h-mvs33		89.30 126	88.95 124	90.36 180	95.07 147	76.04 266	96.96 134	97.11 25	90.39 32	92.22 71	95.10 159	74.70 162	98.86 116	93.14 67	65.89 332	96.16 180

hse-mvs2		88.22 155	88.21 132	88.25 227	93.54 188	73.41 293	95.41 218	95.89 147	90.39 32	92.22 71	94.22 176	74.70 162	96.66 219	93.14 67	64.37 337	94.69 211

MCST-MVS		96.17 3	96.12 6	96.32 7	99.42 2	89.36 10	98.94 15	97.10 26	95.17 2	92.11 73	98.46 26	87.33 24	99.97 2	97.21 17	99.31 4	99.63 7

SR-MVS		92.16 69	92.27 64	91.83 136	98.37 52	78.41 213	96.67 154	95.76 154	82.19 202	91.97 74	98.07 50	76.44 127	98.64 123	93.71 55	97.27 85	98.45 55

region2R		92.72 58	92.70 56	92.79 98	98.68 29	80.53 159	97.53 82	96.51 95	85.22 127	91.94 75	97.98 57	77.26 113	99.67 45	90.83 93	98.37 53	98.18 73

Effi-MVS+		90.70 100	89.90 108	93.09 84	93.61 185	83.48 91	95.20 226	92.79 296	83.22 179	91.82 76	95.70 137	71.82 194	97.48 175	91.25 87	93.67 135	98.32 60

HFP-MVS		92.89 51	92.86 53	92.98 89	98.71 27	81.12 141	97.58 78	96.70 65	85.20 129	91.75 77	97.97 59	78.47 94	99.71 37	90.95 89	98.41 48	98.12 80

#test#		92.99 48	92.99 49	92.98 89	98.71 27	81.12 141	97.77 61	96.70 65	85.75 114	91.75 77	97.97 59	78.47 94	99.71 37	91.36 85	98.41 48	98.12 80

DeepC-MVS_fast	89.06 2	94.48 20	94.30 28	95.02 19	98.86 23	85.68 45	98.06 45	96.64 77	93.64 8	91.74 79	98.54 22	80.17 75	99.90 5	92.28 78	98.75 30	99.49 8
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
test1172		91.64 79	92.00 71	90.54 174	98.20 62	74.48 287	96.45 166	95.65 159	81.97 207	91.63 80	98.02 52	75.76 140	98.61 124	93.16 66	97.17 87	98.52 51

ACMMPR		92.69 60	92.67 57	92.75 99	98.66 32	80.57 155	97.58 78	96.69 68	85.20 129	91.57 81	97.92 61	77.01 118	99.67 45	90.95 89	98.41 48	98.00 92

DELS-MVS		94.98 13	94.49 21	96.44 6	96.42 111	90.59 7	99.21 2	97.02 29	94.40 5	91.46 82	97.08 106	83.32 48	99.69 41	92.83 71	98.70 33	99.04 26
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
XVS		92.69 60	92.71 54	92.63 105	98.52 42	80.29 162	97.37 98	96.44 103	87.04 93	91.38 83	97.83 68	77.24 115	99.59 52	90.46 99	98.07 62	98.02 87

X-MVStestdata		86.26 183	84.14 201	92.63 105	98.52 42	80.29 162	97.37 98	96.44 103	87.04 93	91.38 83	20.73 383	77.24 115	99.59 52	90.46 99	98.07 62	98.02 87

1121		90.66 101	89.82 110	93.16 81	97.39 95	81.71 132	93.33 272	96.66 73	74.45 304	91.38 83	97.55 85	79.27 82	99.52 58	79.95 199	98.43 45	98.26 69

PMMVS		89.46 123	89.92 107	88.06 231	94.64 157	69.57 331	96.22 182	94.95 194	87.27 87	91.37 86	96.54 124	65.88 232	97.39 179	88.54 123	93.89 132	97.23 146

Regformer-3		93.19 43	93.19 47	93.19 80	98.10 66	83.01 103	97.08 124	96.98 32	88.98 51	91.35 87	97.89 62	80.80 66	99.23 81	92.30 77	95.20 117	97.32 140

dcpmvs_2		93.10 46	93.46 43	92.02 128	97.77 78	79.73 179	94.82 239	93.86 254	86.91 95	91.33 88	96.76 118	85.20 33	98.06 147	96.90 20	97.60 74	98.27 68

原ACMM1				91.22 154	97.77 78	78.10 225		96.61 80	81.05 216	91.28 89	97.42 91	77.92 104	98.98 107	79.85 202	98.51 38	96.59 168

Regformer-4		93.06 47	93.12 48	92.89 94	98.10 66	82.20 117	97.08 124	96.92 40	88.87 53	91.23 90	97.89 62	80.57 69	99.19 90	92.21 79	95.20 117	97.29 144

新几何1				93.12 82	97.44 91	81.60 135		96.71 64	74.54 303	91.22 91	97.57 81	79.13 86	99.51 61	77.40 226	98.46 43	98.26 69

UA-Net		88.92 133	88.48 130	90.24 184	94.06 176	77.18 250	93.04 281	94.66 213	87.39 84	91.09 92	93.89 185	74.92 160	98.18 146	75.83 242	91.43 157	95.35 198

ZNCC-MVS		92.75 53	92.60 59	93.23 78	98.24 58	81.82 127	97.63 73	96.50 97	85.00 134	91.05 93	97.74 71	78.38 96	99.80 24	90.48 98	98.34 55	98.07 83

APD-MVS_3200maxsize		91.23 91	91.35 81	90.89 164	97.89 74	76.35 262	96.30 178	95.52 167	79.82 244	91.03 94	97.88 65	74.70 162	98.54 129	92.11 81	96.89 95	97.77 111

GST-MVS		92.43 67	92.22 67	93.04 87	98.17 63	81.64 134	97.40 97	96.38 114	84.71 140	90.90 95	97.40 92	77.55 110	99.76 25	89.75 111	97.74 71	97.72 114

PGM-MVS		91.93 72	91.80 74	92.32 117	98.27 57	79.74 178	95.28 221	97.27 18	83.83 167	90.89 96	97.78 70	76.12 134	99.56 56	88.82 121	97.93 68	97.66 119

SR-MVS-dyc-post		91.29 89	91.45 80	90.80 166	97.76 80	76.03 267	96.20 185	95.44 172	80.56 226	90.72 97	97.84 66	75.76 140	98.61 124	91.99 82	96.79 99	97.75 112

RE-MVS-def			91.18 85		97.76 80	76.03 267	96.20 185	95.44 172	80.56 226	90.72 97	97.84 66	73.36 180		91.99 82	96.79 99	97.75 112

MP-MVS		92.61 63	92.67 57	92.42 112	98.13 65	79.73 179	97.33 100	96.20 128	85.63 116	90.53 99	97.66 74	78.14 100	99.70 40	92.12 80	98.30 57	97.85 105
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
HY-MVS	84.06 6	91.63 80	90.37 96	95.39 16	96.12 118	88.25 14	90.22 310	97.58 14	88.33 65	90.50 100	91.96 206	79.26 83	99.06 102	90.29 105	89.07 171	98.88 32

CP-MVS		92.54 65	92.60 59	92.34 114	98.50 45	79.90 172	98.40 28	96.40 110	84.75 137	90.48 101	98.09 46	77.40 112	99.21 85	91.15 88	98.23 59	97.92 100

diffmvs		91.17 92	90.74 90	92.44 111	93.11 204	82.50 111	96.25 181	93.62 269	87.79 76	90.40 102	95.93 132	73.44 179	97.42 177	93.62 57	92.55 145	97.41 136

MVS_Test		90.29 110	89.18 119	93.62 62	95.23 140	84.93 65	94.41 247	94.66 213	84.31 152	90.37 103	91.02 221	75.13 157	97.82 157	83.11 177	94.42 126	98.12 80

zzz-MVS		92.74 54	92.71 54	92.86 95	97.90 72	80.85 148	96.47 163	96.33 118	87.92 72	90.20 104	98.18 35	76.71 124	99.76 25	92.57 75	98.09 60	97.96 98

MTAPA		92.45 66	92.31 63	92.86 95	97.90 72	80.85 148	92.88 285	96.33 118	87.92 72	90.20 104	98.18 35	76.71 124	99.76 25	92.57 75	98.09 60	97.96 98

test_yl		91.46 84	90.53 92	94.24 39	97.41 93	85.18 55	98.08 42	97.72 10	80.94 217	89.85 106	96.14 128	75.61 142	98.81 119	90.42 103	88.56 178	98.74 36

DCV-MVSNet		91.46 84	90.53 92	94.24 39	97.41 93	85.18 55	98.08 42	97.72 10	80.94 217	89.85 106	96.14 128	75.61 142	98.81 119	90.42 103	88.56 178	98.74 36

WTY-MVS		92.65 62	91.68 76	95.56 13	96.00 121	88.90 12	98.23 33	97.65 12	88.57 59	89.82 108	97.22 100	79.29 81	99.06 102	89.57 113	88.73 176	98.73 40

MVS_111021_HR		93.41 42	93.39 44	93.47 73	97.34 99	82.83 105	97.56 80	98.27 6	89.16 49	89.71 109	97.14 102	79.77 78	99.56 56	93.65 56	97.94 66	98.02 87

sss		90.87 98	89.96 105	93.60 63	94.15 173	83.84 84	97.14 115	98.13 7	85.93 111	89.68 110	96.09 130	71.67 195	99.30 77	87.69 131	89.16 170	97.66 119

test222					96.15 117	78.41 213	95.87 201	96.46 101	71.97 324	89.66 111	97.45 87	76.33 131			98.24 58	98.30 64

LFMVS		89.27 127	87.64 143	94.16 44	97.16 102	85.52 48	97.18 109	94.66 213	79.17 258	89.63 112	96.57 123	55.35 306	98.22 143	89.52 115	89.54 167	98.74 36

CostFormer		89.08 129	88.39 131	91.15 156	93.13 202	79.15 194	88.61 321	96.11 134	83.14 181	89.58 113	86.93 280	83.83 45	96.87 208	88.22 129	85.92 200	97.42 135

PVSNet_BlendedMVS		90.05 113	89.96 105	90.33 182	97.47 89	83.86 82	98.02 48	96.73 61	87.98 71	89.53 114	89.61 242	76.42 128	99.57 54	94.29 48	79.59 243	87.57 311

PVSNet_Blended		93.13 44	92.98 50	93.57 64	97.47 89	83.86 82	99.32 1	96.73 61	91.02 25	89.53 114	96.21 127	76.42 128	99.57 54	94.29 48	95.81 113	97.29 144

HPM-MVS		91.62 81	91.53 79	91.89 132	97.88 75	79.22 191	96.99 128	95.73 156	82.07 204	89.50 116	97.19 101	75.59 144	98.93 114	90.91 91	97.94 66	97.54 126
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
abl_6		89.80 116	89.71 113	90.07 188	96.53 109	75.52 276	94.48 244	95.04 191	81.12 215	89.22 117	97.00 108	68.83 215	98.96 108	89.86 108	95.27 116	95.73 189

EI-MVSNet-Vis-set		91.84 74	91.77 75	92.04 127	97.60 84	81.17 140	96.61 155	96.87 42	88.20 67	89.19 118	97.55 85	78.69 93	99.14 96	90.29 105	90.94 160	95.80 187

MP-MVS-pluss		92.58 64	92.35 62	93.29 75	97.30 100	82.53 109	96.44 168	96.04 140	84.68 141	89.12 119	98.37 29	77.48 111	99.74 33	93.31 64	98.38 52	97.59 125
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
VDD-MVS		88.28 153	87.02 163	92.06 126	95.09 145	80.18 168	97.55 81	94.45 227	83.09 183	89.10 120	95.92 134	47.97 329	98.49 132	93.08 70	86.91 190	97.52 130

baseline		90.76 99	90.10 102	92.74 100	92.90 209	82.56 108	94.60 243	94.56 221	87.69 79	89.06 121	95.67 139	73.76 174	97.51 172	90.43 102	92.23 151	98.16 75

EIA-MVS		91.73 75	92.05 70	90.78 168	94.52 162	76.40 261	98.06 45	95.34 180	89.19 48	88.90 122	97.28 98	77.56 109	97.73 160	90.77 94	96.86 98	98.20 71

HPM-MVS_fast		90.38 109	90.17 101	91.03 159	97.61 83	77.35 246	97.15 114	95.48 169	79.51 250	88.79 123	96.90 110	71.64 197	98.81 119	87.01 139	97.44 79	96.94 154

PAPM		92.87 52	92.40 61	94.30 36	92.25 226	87.85 18	96.40 172	96.38 114	91.07 23	88.72 124	96.90 110	82.11 61	97.37 181	90.05 107	97.70 72	97.67 118

MVS_111021_LR		91.60 82	91.64 78	91.47 147	95.74 127	78.79 204	96.15 187	96.77 55	88.49 61	88.64 125	97.07 107	72.33 188	99.19 90	93.13 69	96.48 103	96.43 172

casdiffmvs		90.95 96	90.39 94	92.63 105	92.82 210	82.53 109	96.83 141	94.47 225	87.69 79	88.47 126	95.56 143	74.04 171	97.54 170	90.90 92	92.74 143	97.83 107

mPP-MVS		91.88 73	91.82 73	92.07 125	98.38 51	78.63 207	97.29 101	96.09 135	85.12 131	88.45 127	97.66 74	75.53 145	99.68 43	89.83 109	98.02 65	97.88 101

PAPR		92.74 54	92.17 68	94.45 31	98.89 22	84.87 67	97.20 107	96.20 128	87.73 78	88.40 128	98.12 44	78.71 92	99.76 25	87.99 130	96.28 104	98.74 36

tpmrst		88.36 150	87.38 153	91.31 149	94.36 168	79.92 171	87.32 331	95.26 184	85.32 123	88.34 129	86.13 296	80.60 68	96.70 216	83.78 162	85.34 208	97.30 143

GG-mvs-BLEND				93.49 69	94.94 151	86.26 32	81.62 353	97.00 30		88.32 130	94.30 174	91.23 5	96.21 232	88.49 125	97.43 80	98.00 92

EI-MVSNet-UG-set		91.35 88	91.22 82	91.73 138	97.39 95	80.68 152	96.47 163	96.83 45	87.92 72	88.30 131	97.36 93	77.84 105	99.13 98	89.43 116	89.45 168	95.37 197

MAR-MVS		90.63 102	90.22 98	91.86 133	98.47 47	78.20 223	97.18 109	96.61 80	83.87 166	88.18 132	98.18 35	68.71 216	99.75 31	83.66 168	97.15 88	97.63 122
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
DP-MVS Recon		91.72 77	90.85 87	94.34 35	99.50 1	85.00 64	98.51 26	95.96 143	80.57 225	88.08 133	97.63 80	76.84 120	99.89 7	85.67 145	94.88 121	98.13 79

VDDNet		86.44 180	84.51 192	92.22 120	91.56 246	81.83 126	97.10 121	94.64 216	69.50 336	87.84 134	95.19 152	48.01 328	97.92 155	89.82 110	86.92 189	96.89 158

UGNet		87.73 163	86.55 168	91.27 152	95.16 144	79.11 195	96.35 175	96.23 125	88.14 68	87.83 135	90.48 230	50.65 319	99.09 101	80.13 198	94.03 128	95.60 192
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
test2506		90.96 95	90.39 94	92.65 104	93.54 188	82.46 112	96.37 173	97.35 16	86.78 99	87.55 136	95.25 147	77.83 106	97.50 173	84.07 157	94.80 122	97.98 94

tpm2		87.35 168	86.26 169	90.62 171	92.93 208	78.67 206	88.06 326	95.99 141	79.33 253	87.40 137	86.43 291	80.28 72	96.40 224	80.23 196	85.73 204	96.79 161

CPTT-MVS		89.72 118	89.87 109	89.29 206	98.33 54	73.30 296	97.70 69	95.35 179	75.68 294	87.40 137	97.44 90	70.43 208	98.25 142	89.56 114	96.90 94	96.33 177

gg-mvs-nofinetune		85.48 196	82.90 218	93.24 77	94.51 165	85.82 40	79.22 357	96.97 34	61.19 358	87.33 139	53.01 373	90.58 6	96.07 234	86.07 143	97.23 86	97.81 109

CHOSEN 280x420		91.71 78	91.85 72	91.29 151	94.94 151	82.69 106	87.89 327	96.17 131	85.94 110	87.27 140	94.31 173	90.27 9	95.65 263	94.04 52	95.86 111	95.53 194

EPNet_dtu		87.65 165	87.89 137	86.93 257	94.57 159	71.37 318	96.72 149	96.50 97	88.56 60	87.12 141	95.02 161	75.91 138	94.01 314	66.62 300	90.00 164	95.42 196
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
Vis-MVSNet		88.67 141	87.82 139	91.24 153	92.68 211	78.82 201	96.95 135	93.85 255	87.55 81	87.07 142	95.13 157	63.43 248	97.21 189	77.58 223	96.15 105	97.70 117
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
thisisatest0515		90.95 96	90.26 97	93.01 88	94.03 179	84.27 78	97.91 52	96.67 70	83.18 180	86.87 143	95.51 144	88.66 16	97.85 156	80.46 192	89.01 172	96.92 157

TESTMET0.1,1		89.83 115	89.34 117	91.31 149	92.54 217	80.19 167	97.11 118	96.57 87	86.15 104	86.85 144	91.83 210	79.32 80	96.95 202	81.30 186	92.35 149	96.77 163

PVSNet_Blended_VisFu		91.24 90	90.77 89	92.66 103	95.09 145	82.40 113	97.77 61	95.87 150	88.26 66	86.39 145	93.94 184	76.77 122	99.27 78	88.80 122	94.00 131	96.31 178

API-MVS		90.18 111	88.97 122	93.80 52	98.66 32	82.95 104	97.50 86	95.63 162	75.16 298	86.31 146	97.69 72	72.49 186	99.90 5	81.26 187	96.07 107	98.56 48

test-LLR		88.48 146	87.98 136	89.98 192	92.26 224	77.23 248	97.11 118	95.96 143	83.76 170	86.30 147	91.38 214	72.30 189	96.78 214	80.82 189	91.92 153	95.94 184

test-mter		88.95 131	88.60 128	89.98 192	92.26 224	77.23 248	97.11 118	95.96 143	85.32 123	86.30 147	91.38 214	76.37 130	96.78 214	80.82 189	91.92 153	95.94 184

PAPM_NR		91.46 84	90.82 88	93.37 74	98.50 45	81.81 128	95.03 235	96.13 132	84.65 142	86.10 149	97.65 78	79.24 84	99.75 31	83.20 175	96.88 96	98.56 48

FA-MVS(test-final)		87.71 164	86.23 170	92.17 122	94.19 172	80.55 156	87.16 333	96.07 138	82.12 203	85.98 150	88.35 258	72.04 193	98.49 132	80.26 195	89.87 165	97.48 133

MDTV_nov1_ep13_2view						81.74 130	86.80 335		80.65 223	85.65 151		74.26 168		76.52 234		96.98 153

ECVR-MVS		88.35 151	87.25 155	91.65 140	93.54 188	79.40 186	96.56 159	90.78 325	86.78 99	85.57 152	95.25 147	57.25 293	97.56 166	84.73 153	94.80 122	97.98 94

AUN-MVS		86.25 184	85.57 174	88.26 226	93.57 187	73.38 294	95.45 216	95.88 148	83.94 162	85.47 153	94.21 177	73.70 177	96.67 218	83.54 171	64.41 336	94.73 210

PVSNet	82.34 9	89.02 130	87.79 140	92.71 102	95.49 134	81.50 136	97.70 69	97.29 17	87.76 77	85.47 153	95.12 158	56.90 295	98.90 115	80.33 193	94.02 129	97.71 116

EPP-MVSNet		89.76 117	89.72 112	89.87 197	93.78 181	76.02 269	97.22 102	96.51 95	79.35 252	85.11 155	95.01 162	84.82 35	97.10 197	87.46 134	88.21 182	96.50 170

test1111		88.11 156	87.04 162	91.35 148	93.15 200	78.79 204	96.57 157	90.78 325	86.88 97	85.04 156	95.20 151	57.23 294	97.39 179	83.88 160	94.59 124	97.87 103

FE-MVS		86.06 186	84.15 200	91.78 137	94.33 169	79.81 173	84.58 346	96.61 80	76.69 289	85.00 157	87.38 271	70.71 207	98.37 138	70.39 284	91.70 156	97.17 149

OMC-MVS		88.80 138	88.16 134	90.72 169	95.30 139	77.92 232	94.81 240	94.51 222	86.80 98	84.97 158	96.85 113	67.53 221	98.60 126	85.08 149	87.62 185	95.63 191

CHOSEN 1792x2688		91.07 93	90.21 99	93.64 60	95.18 143	83.53 90	96.26 180	96.13 132	88.92 52	84.90 159	93.10 195	72.86 183	99.62 50	88.86 120	95.67 114	97.79 110

thres200		88.92 133	87.65 142	92.73 101	96.30 112	85.62 46	97.85 55	98.86 1	84.38 150	84.82 160	93.99 183	75.12 158	98.01 148	70.86 281	86.67 191	94.56 212

MDTV_nov1_ep13			83.69 204		94.09 175	81.01 143	86.78 336	96.09 135	83.81 168	84.75 161	84.32 319	74.44 167	96.54 220	63.88 314	85.07 209

CDS-MVSNet		89.50 122	88.96 123	91.14 157	91.94 242	80.93 146	97.09 122	95.81 152	84.26 155	84.72 162	94.20 178	80.31 71	95.64 264	83.37 174	88.96 173	96.85 160
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
ACMMP		90.39 107	89.97 104	91.64 141	97.58 86	78.21 222	96.78 145	96.72 63	84.73 139	84.72 162	97.23 99	71.22 200	99.63 49	88.37 128	92.41 148	97.08 152
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
CSCG		92.02 71	91.65 77	93.12 82	98.53 41	80.59 154	97.47 87	97.18 22	77.06 287	84.64 164	97.98 57	83.98 43	99.52 58	90.72 95	97.33 83	99.23 22

ab-mvs		87.08 169	84.94 187	93.48 70	93.34 196	83.67 88	88.82 318	95.70 157	81.18 214	84.55 165	90.14 238	62.72 251	98.94 113	85.49 147	82.54 229	97.85 105

mvs-test1		86.83 174	87.17 157	85.81 274	91.96 239	65.24 344	97.90 54	93.34 282	85.57 117	84.51 166	95.14 156	61.99 258	97.19 191	83.55 169	90.55 162	95.00 202

EPMVS		87.47 167	85.90 173	92.18 121	95.41 136	82.26 116	87.00 334	96.28 122	85.88 112	84.23 167	85.57 302	75.07 159	96.26 229	71.14 279	92.50 146	98.03 86

Anonymous202405211		84.41 213	81.93 232	91.85 135	96.78 107	78.41 213	97.44 89	91.34 315	70.29 332	84.06 168	94.26 175	41.09 351	98.96 108	79.46 204	82.65 228	98.17 74

HyFIR lowres test		89.36 124	88.60 128	91.63 143	94.91 153	80.76 151	95.60 211	95.53 165	82.56 197	84.03 169	91.24 218	78.03 101	96.81 212	87.07 138	88.41 180	97.32 140

tfpn200view9		88.48 146	87.15 158	92.47 109	96.21 115	85.30 53	97.44 89	98.85 2	83.37 177	83.99 170	93.82 186	75.36 152	97.93 150	69.04 289	86.24 197	94.17 214

thres400		88.42 149	87.15 158	92.23 119	96.21 115	85.30 53	97.44 89	98.85 2	83.37 177	83.99 170	93.82 186	75.36 152	97.93 150	69.04 289	86.24 197	93.45 227

tpm		85.55 194	84.47 195	88.80 215	90.19 270	75.39 278	88.79 319	94.69 209	84.83 136	83.96 172	85.21 308	78.22 99	94.68 302	76.32 238	78.02 260	96.34 175

Fast-Effi-MVS+		87.93 161	86.94 165	90.92 162	94.04 177	79.16 193	98.26 32	93.72 265	81.29 213	83.94 173	92.90 196	69.83 212	96.68 217	76.70 232	91.74 155	96.93 155

XVG-OURS-SEG-HR		85.74 192	85.16 183	87.49 245	90.22 269	71.45 317	91.29 303	94.09 244	81.37 212	83.90 174	95.22 149	60.30 268	97.53 171	85.58 146	84.42 212	93.50 225

thisisatest0530		89.65 119	89.02 121	91.53 145	93.46 194	80.78 150	96.52 160	96.67 70	81.69 210	83.79 175	94.90 164	88.85 15	97.68 161	77.80 217	87.49 188	96.14 181

DeepC-MVS	86.58 3	91.53 83	91.06 86	92.94 92	94.52 162	81.89 123	95.95 195	95.98 142	90.76 26	83.76 176	96.76 118	73.24 181	99.71 37	91.67 84	96.96 93	97.22 147
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
IS-MVSNet		88.67 141	88.16 134	90.20 186	93.61 185	76.86 254	96.77 147	93.07 292	84.02 159	83.62 177	95.60 142	74.69 165	96.24 231	78.43 216	93.66 136	97.49 132

thres100view900		88.30 152	86.95 164	92.33 115	96.10 119	84.90 66	97.14 115	98.85 2	82.69 194	83.41 178	93.66 189	75.43 149	97.93 150	69.04 289	86.24 197	94.17 214

thres600view7		88.06 157	86.70 167	92.15 123	96.10 119	85.17 59	97.14 115	98.85 2	82.70 193	83.41 178	93.66 189	75.43 149	97.82 157	67.13 298	85.88 201	93.45 227

XVG-OURS		85.18 199	84.38 196	87.59 240	90.42 267	71.73 314	91.06 306	94.07 245	82.00 206	83.29 180	95.08 160	56.42 300	97.55 168	83.70 167	83.42 217	93.49 226

Vis-MVSNet (Re-imp)		88.88 135	88.87 126	88.91 212	93.89 180	74.43 288	96.93 137	94.19 237	84.39 149	83.22 181	95.67 139	78.24 98	94.70 301	78.88 212	94.40 127	97.61 124

TAMVS		88.48 146	87.79 140	90.56 173	91.09 254	79.18 192	96.45 166	95.88 148	83.64 173	83.12 182	93.33 191	75.94 137	95.74 259	82.40 180	88.27 181	96.75 165

baseline1		88.85 136	87.49 149	92.93 93	95.21 142	86.85 28	95.47 215	94.61 218	87.29 85	83.11 183	94.99 163	80.70 67	96.89 206	82.28 181	73.72 274	95.05 201

AdaColmap		88.81 137	87.61 146	92.39 113	99.33 4	79.95 170	96.70 153	95.58 163	77.51 279	83.05 184	96.69 122	61.90 261	99.72 36	84.29 155	93.47 137	97.50 131

PatchmatchNet		86.83 174	85.12 184	91.95 130	94.12 174	82.27 115	86.55 338	95.64 161	84.59 144	82.98 185	84.99 314	77.26 113	95.96 243	68.61 293	91.34 158	97.64 121
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
SCA		85.63 193	83.64 207	91.60 144	92.30 222	81.86 125	92.88 285	95.56 164	84.85 135	82.52 186	85.12 312	58.04 284	95.39 274	73.89 259	87.58 187	97.54 126

114514_t		88.79 139	87.57 147	92.45 110	98.21 61	81.74 130	96.99 128	95.45 171	75.16 298	82.48 187	95.69 138	68.59 217	98.50 131	80.33 193	95.18 119	97.10 151

PatchT		79.75 273	76.85 283	88.42 220	89.55 281	75.49 277	77.37 363	94.61 218	63.07 349	82.46 188	73.32 361	75.52 146	93.41 324	51.36 356	84.43 211	96.36 173

TR-MVS		86.30 182	84.93 188	90.42 177	94.63 158	77.58 241	96.57 157	93.82 256	80.30 234	82.42 189	95.16 154	58.74 279	97.55 168	74.88 249	87.82 184	96.13 182

HQP-NCC					92.08 233		97.63 73		90.52 29	82.30 190

ACMP_Plane					92.08 233		97.63 73		90.52 29	82.30 190

HQP4-MVS										82.30 190			97.32 182			91.13 234

HQP-MVS		87.91 162	87.55 148	88.98 211	92.08 233	78.48 209	97.63 73	94.80 204	90.52 29	82.30 190	94.56 169	65.40 236	97.32 182	87.67 132	83.01 221	91.13 234

CR-MVSNet		83.53 225	81.36 241	90.06 189	90.16 271	79.75 176	79.02 359	91.12 317	84.24 156	82.27 194	80.35 341	75.45 147	93.67 320	63.37 318	86.25 195	96.75 165

RPMNet		79.85 272	75.92 290	91.64 141	90.16 271	79.75 176	79.02 359	95.44 172	58.43 367	82.27 194	72.55 363	73.03 182	98.41 137	46.10 367	86.25 195	96.75 165

CVMVSNet		84.83 205	85.57 174	82.63 317	91.55 247	60.38 358	95.13 229	95.03 192	80.60 224	82.10 196	94.71 166	66.40 231	90.19 353	74.30 256	90.32 163	97.31 142

iter_conf_final		89.51 121	89.21 118	90.39 178	95.60 131	84.44 73	97.22 102	89.09 338	89.11 50	82.07 197	92.80 197	87.03 25	96.03 235	89.10 119	80.89 233	90.70 239

PLC	83.97 7	88.00 159	87.38 153	89.83 199	98.02 70	76.46 259	97.16 113	94.43 228	79.26 257	81.98 198	96.28 126	69.36 213	99.27 78	77.71 221	92.25 150	93.77 222
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
JIA-IIPM		79.00 281	77.20 279	84.40 298	89.74 278	64.06 348	75.30 366	95.44 172	62.15 352	81.90 199	59.08 371	78.92 87	95.59 268	66.51 303	85.78 203	93.54 224

Anonymous20240529		83.15 232	80.60 251	90.80 166	95.74 127	78.27 217	96.81 143	94.92 196	60.10 363	81.89 200	92.54 201	45.82 336	98.82 118	79.25 208	78.32 258	95.31 199

tttt0517		88.57 145	88.19 133	89.71 203	93.00 205	75.99 270	95.67 208	96.67 70	80.78 220	81.82 201	94.40 172	88.97 14	97.58 165	76.05 240	86.31 194	95.57 193

BH-RMVSNet		86.84 173	85.28 179	91.49 146	95.35 138	80.26 165	96.95 135	92.21 302	82.86 191	81.77 202	95.46 145	59.34 275	97.64 162	69.79 287	93.81 134	96.57 169

iter_conf05		90.14 112	89.79 111	91.17 155	95.85 125	86.93 27	97.68 71	88.67 345	89.93 38	81.73 203	92.80 197	90.37 8	96.03 235	90.44 101	80.65 236	90.56 241

HQP_MVS		87.50 166	87.09 161	88.74 216	91.86 243	77.96 229	97.18 109	94.69 209	89.89 39	81.33 204	94.15 179	64.77 242	97.30 184	87.08 136	82.82 225	90.96 236

plane_prior3						77.75 238			90.17 36	81.33 204

VPA-MVSNet		85.32 197	83.83 203	89.77 202	90.25 268	82.63 107	96.36 174	97.07 27	83.03 186	81.21 206	89.02 248	61.58 262	96.31 228	85.02 151	70.95 288	90.36 244

GeoE		86.36 181	85.20 180	89.83 199	93.17 199	76.13 264	97.53 82	92.11 303	79.58 249	80.99 207	94.01 182	66.60 230	96.17 233	73.48 263	89.30 169	97.20 148

GA-MVS		85.79 191	84.04 202	91.02 160	89.47 283	80.27 164	96.90 138	94.84 202	85.57 117	80.88 208	89.08 246	56.56 299	96.47 223	77.72 220	85.35 207	96.34 175

1112_ss		88.60 144	87.47 151	92.00 129	93.21 197	80.97 145	96.47 163	92.46 300	83.64 173	80.86 209	97.30 96	80.24 73	97.62 163	77.60 222	85.49 205	97.40 137

dp		84.30 215	82.31 227	90.28 183	94.24 171	77.97 228	86.57 337	95.53 165	79.94 243	80.75 210	85.16 310	71.49 199	96.39 225	63.73 315	83.36 218	96.48 171

Test_1112_low_res		88.03 158	86.73 166	91.94 131	93.15 200	80.88 147	96.44 168	92.41 301	83.59 176	80.74 211	91.16 219	80.18 74	97.59 164	77.48 225	85.40 206	97.36 139

cascas		86.50 179	84.48 194	92.55 108	92.64 215	85.95 36	97.04 127	95.07 190	75.32 296	80.50 212	91.02 221	54.33 313	97.98 149	86.79 141	87.62 185	93.71 223

TAPA-MVS	81.61 12	85.02 202	83.67 205	89.06 208	96.79 106	73.27 298	95.92 197	94.79 206	74.81 301	80.47 213	96.83 114	71.07 202	98.19 145	49.82 361	92.57 144	95.71 190
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
OPM-MVS		85.84 189	85.10 185	88.06 231	88.34 294	77.83 236	95.72 206	94.20 236	87.89 75	80.45 214	94.05 181	58.57 280	97.26 188	83.88 160	82.76 227	89.09 275
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
nrg030		86.79 176	85.43 176	90.87 165	88.76 288	85.34 50	97.06 126	94.33 231	84.31 152	80.45 214	91.98 205	72.36 187	96.36 226	88.48 126	71.13 286	90.93 238

EI-MVSNet		85.80 190	85.20 180	87.59 240	91.55 247	77.41 244	95.13 229	95.36 177	80.43 231	80.33 216	94.71 166	73.72 175	95.97 240	76.96 230	78.64 252	89.39 263

MVSTER		89.25 128	88.92 125	90.24 184	95.98 122	84.66 70	96.79 144	95.36 177	87.19 91	80.33 216	90.61 229	90.02 12	95.97 240	85.38 148	78.64 252	90.09 254

ADS-MVSNet2		79.57 275	77.53 277	85.71 277	93.78 181	72.13 305	79.48 355	86.11 356	73.09 315	80.14 218	79.99 344	62.15 255	90.14 354	59.49 330	83.52 215	94.85 204

ADS-MVSNet		81.26 261	78.36 271	89.96 194	93.78 181	79.78 174	79.48 355	93.60 270	73.09 315	80.14 218	79.99 344	62.15 255	95.24 283	59.49 330	83.52 215	94.85 204

baseline2		90.39 107	90.21 99	90.93 161	90.86 259	80.99 144	95.20 226	97.41 15	86.03 109	80.07 220	94.61 168	90.58 6	97.47 176	87.29 135	89.86 166	94.35 213

Effi-MVS+-dtu		84.61 209	84.90 189	83.72 306	91.96 239	63.14 351	94.95 236	93.34 282	85.57 117	79.79 221	87.12 277	61.99 258	95.61 267	83.55 169	85.83 202	92.41 230

VPNet		84.69 208	82.92 217	90.01 190	89.01 287	83.45 92	96.71 151	95.46 170	85.71 115	79.65 222	92.18 204	56.66 298	96.01 239	83.05 178	67.84 319	90.56 241

CLD-MVS		87.97 160	87.48 150	89.44 204	92.16 231	80.54 158	98.14 36	94.92 196	91.41 18	79.43 223	95.40 146	62.34 253	97.27 187	90.60 97	82.90 224	90.50 243
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
IB-MVS	85.34 4	88.67 141	87.14 160	93.26 76	93.12 203	84.32 75	98.76 16	97.27 18	87.19 91	79.36 224	90.45 231	83.92 44	98.53 130	84.41 154	69.79 299	96.93 155
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		85.00 203	83.66 206	89.02 210	95.86 124	74.55 286	92.49 289	93.60 270	79.30 255	79.29 225	91.47 212	58.53 281	98.45 135	70.22 285	92.17 152	94.07 218

CNLPA		86.96 170	85.37 178	91.72 139	97.59 85	79.34 189	97.21 105	91.05 320	74.22 305	78.90 226	96.75 120	67.21 225	98.95 111	74.68 251	90.77 161	96.88 159

MVS		90.60 103	88.64 127	96.50 5	94.25 170	90.53 8	93.33 272	97.21 20	77.59 278	78.88 227	97.31 94	71.52 198	99.69 41	89.60 112	98.03 64	99.27 21

mvs_anonymous		88.68 140	87.62 145	91.86 133	94.80 155	81.69 133	93.53 268	94.92 196	82.03 205	78.87 228	90.43 232	75.77 139	95.34 277	85.04 150	93.16 141	98.55 50

MVS_0304		78.43 283	76.70 284	83.60 308	88.22 296	69.81 327	92.91 284	95.10 187	72.32 322	78.71 229	80.29 343	33.78 362	93.37 325	68.77 292	80.23 238	87.63 308

mvsmamba		85.17 200	84.54 191	87.05 255	87.94 299	75.11 281	96.22 182	87.79 348	86.91 95	78.55 230	91.77 211	64.93 241	95.91 247	86.94 140	79.80 239	90.12 251

tpm cat1		83.63 224	81.38 240	90.39 178	93.53 193	78.19 224	85.56 344	95.09 188	70.78 330	78.51 231	83.28 327	74.80 161	97.03 198	66.77 299	84.05 213	95.95 183

UniMVSNet (Re)		85.31 198	84.23 198	88.55 219	89.75 276	80.55 156	96.72 149	96.89 41	85.42 121	78.40 232	88.93 249	75.38 151	95.52 271	78.58 214	68.02 316	89.57 262

FIs		86.73 178	86.10 171	88.61 218	90.05 273	80.21 166	96.14 188	96.95 36	85.56 120	78.37 233	92.30 202	76.73 123	95.28 281	79.51 203	79.27 246	90.35 245

BH-w/o		88.24 154	87.47 151	90.54 174	95.03 150	78.54 208	97.41 96	93.82 256	84.08 157	78.23 234	94.51 171	69.34 214	97.21 189	80.21 197	94.58 125	95.87 186

UniMVSNet_NR-MVSNet		85.49 195	84.59 190	88.21 229	89.44 284	79.36 187	96.71 151	96.41 107	85.22 127	78.11 235	90.98 223	76.97 119	95.14 288	79.14 209	68.30 313	90.12 251

DU-MVS		84.57 210	83.33 213	88.28 225	88.76 288	79.36 187	96.43 170	95.41 176	85.42 121	78.11 235	90.82 225	67.61 219	95.14 288	79.14 209	68.30 313	90.33 246

miper_enhance_ethall		85.95 188	85.20 180	88.19 230	94.85 154	79.76 175	96.00 192	94.06 246	82.98 188	77.74 237	88.76 251	79.42 79	95.46 273	80.58 191	72.42 281	89.36 268

v1144		82.90 238	81.27 242	87.78 236	86.29 314	79.07 198	96.14 188	93.93 249	80.05 240	77.38 238	86.80 282	65.50 234	95.93 246	75.21 247	70.13 294	88.33 296

FC-MVSNet-test		85.96 187	85.39 177	87.66 238	89.38 285	78.02 226	95.65 210	96.87 42	85.12 131	77.34 239	91.94 208	76.28 132	94.74 300	77.09 227	78.82 250	90.21 249

v2v482		83.46 226	81.86 233	88.25 227	86.19 316	79.65 181	96.34 176	94.02 247	81.56 211	77.32 240	88.23 260	65.62 233	96.03 235	77.77 218	69.72 301	89.09 275

Baseline_NR-MVSNet		81.22 262	80.07 259	84.68 290	85.32 331	75.12 280	96.48 162	88.80 341	76.24 292	77.28 241	86.40 292	67.61 219	94.39 308	75.73 244	66.73 330	84.54 342

V42		83.04 235	81.53 238	87.57 242	86.27 315	79.09 197	95.87 201	94.11 242	80.35 233	77.22 242	86.79 283	65.32 238	96.02 238	77.74 219	70.14 293	87.61 310

v144192		82.43 244	80.73 248	87.54 243	85.81 323	78.22 219	95.98 193	93.78 261	79.09 260	77.11 243	86.49 287	64.66 244	95.91 247	74.20 257	69.42 302	88.49 290

ACMM	80.70 13	83.72 223	82.85 219	86.31 267	91.19 252	72.12 306	95.88 200	94.29 233	80.44 229	77.02 244	91.96 206	55.24 307	97.14 196	79.30 207	80.38 237	89.67 261
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
v1192		82.31 248	80.55 252	87.60 239	85.94 320	78.47 212	95.85 203	93.80 259	79.33 253	76.97 245	86.51 286	63.33 249	95.87 249	73.11 264	70.13 294	88.46 292

PCF-MVS	84.09 5	86.77 177	85.00 186	92.08 124	92.06 236	83.07 101	92.14 293	94.47 225	79.63 248	76.90 246	94.78 165	71.15 201	99.20 89	72.87 265	91.05 159	93.98 219
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
cl22		85.11 201	84.17 199	87.92 233	95.06 149	78.82 201	95.51 213	94.22 235	79.74 246	76.77 247	87.92 265	75.96 136	95.68 260	79.93 201	72.42 281	89.27 269

v1921920		82.02 252	80.23 256	87.41 246	85.62 325	77.92 232	95.79 205	93.69 266	78.86 265	76.67 248	86.44 289	62.50 252	95.83 251	72.69 266	69.77 300	88.47 291

WR-MVS		84.32 214	82.96 216	88.41 221	89.38 285	80.32 161	96.59 156	96.25 124	83.97 161	76.63 249	90.36 233	67.53 221	94.86 298	75.82 243	70.09 297	90.06 256

BH-untuned		86.95 171	85.94 172	89.99 191	94.52 162	77.46 243	96.78 145	93.37 281	81.80 208	76.62 250	93.81 188	66.64 229	97.02 199	76.06 239	93.88 133	95.48 195

v1240		81.70 255	79.83 263	87.30 250	85.50 326	77.70 240	95.48 214	93.44 275	78.46 270	76.53 251	86.44 289	60.85 265	95.84 250	71.59 273	70.17 292	88.35 295

bld_raw_dy_0_64		82.13 250	80.76 247	86.24 269	85.78 324	75.03 282	94.40 250	82.62 368	83.12 182	76.46 252	90.96 224	53.83 315	94.55 304	81.04 188	78.60 255	89.14 273

PS-MVSNAJss		84.91 204	84.30 197	86.74 258	85.89 322	74.40 289	94.95 236	94.16 239	83.93 164	76.45 253	90.11 239	71.04 203	95.77 254	83.16 176	79.02 249	90.06 256

miper_ehance_all_eth		84.57 210	83.60 209	87.50 244	92.64 215	78.25 218	95.40 219	93.47 274	79.28 256	76.41 254	87.64 268	76.53 126	95.24 283	78.58 214	72.42 281	89.01 280

LPG-MVS_test		84.20 216	83.49 211	86.33 264	90.88 257	73.06 299	95.28 221	94.13 240	82.20 200	76.31 255	93.20 192	54.83 311	96.95 202	83.72 165	80.83 234	88.98 281

LGP-MVS_train				86.33 264	90.88 257	73.06 299		94.13 240	82.20 200	76.31 255	93.20 192	54.83 311	96.95 202	83.72 165	80.83 234	88.98 281

F-COLMAP		84.50 212	83.44 212	87.67 237	95.22 141	72.22 303	95.95 195	93.78 261	75.74 293	76.30 257	95.18 153	59.50 273	98.45 135	72.67 267	86.59 193	92.35 231

tpmvs		83.04 235	80.77 246	89.84 198	95.43 135	77.96 229	85.59 343	95.32 181	75.31 297	76.27 258	83.70 324	73.89 172	97.41 178	59.53 329	81.93 231	94.14 216

3Dnovator	82.32 10	89.33 125	87.64 143	94.42 33	93.73 184	85.70 44	97.73 67	96.75 59	86.73 101	76.21 259	95.93 132	62.17 254	99.68 43	81.67 185	97.81 69	97.88 101

TranMVSNet+NR-MVSNet		83.24 231	81.71 235	87.83 234	87.71 302	78.81 203	96.13 190	94.82 203	84.52 145	76.18 260	90.78 227	64.07 245	94.60 303	74.60 254	66.59 331	90.09 254

c3_l		83.80 221	82.65 223	87.25 251	92.10 232	77.74 239	95.25 224	93.04 293	78.58 268	76.01 261	87.21 276	75.25 156	95.11 290	77.54 224	68.89 307	88.91 286

1314		88.94 132	87.20 156	94.17 42	93.21 197	85.73 43	93.33 272	96.64 77	82.89 189	75.98 262	96.36 125	66.83 228	99.39 68	83.52 173	96.02 109	97.39 138

test_part1		84.72 206	82.85 219	90.34 181	95.73 129	84.79 69	96.75 148	94.10 243	79.05 264	75.97 263	89.51 243	67.69 218	95.94 244	79.34 205	67.50 322	90.30 248

Fast-Effi-MVS+-dtu		83.33 228	82.60 224	85.50 281	89.55 281	69.38 332	96.09 191	91.38 312	82.30 199	75.96 264	91.41 213	56.71 296	95.58 269	75.13 248	84.90 210	91.54 232

XXY-MVS		83.84 220	82.00 231	89.35 205	87.13 307	81.38 137	95.72 206	94.26 234	80.15 238	75.92 265	90.63 228	61.96 260	96.52 221	78.98 211	73.28 279	90.14 250

RRT_MVS		83.88 219	83.27 214	85.71 277	87.53 305	72.12 306	95.35 220	94.33 231	83.81 168	75.86 266	91.28 217	60.55 266	95.09 293	83.93 159	76.76 263	89.90 259

GBi-Net		82.42 245	80.43 254	88.39 222	92.66 212	81.95 118	94.30 253	93.38 278	79.06 261	75.82 267	85.66 298	56.38 301	93.84 316	71.23 276	75.38 268	89.38 265

test1		82.42 245	80.43 254	88.39 222	92.66 212	81.95 118	94.30 253	93.38 278	79.06 261	75.82 267	85.66 298	56.38 301	93.84 316	71.23 276	75.38 268	89.38 265

FMVSNet3		84.71 207	82.71 222	90.70 170	94.55 160	87.71 20	95.92 197	94.67 212	81.73 209	75.82 267	88.08 263	66.99 226	94.47 306	71.23 276	75.38 268	89.91 258

eth_miper_zixun_eth		83.12 233	82.01 230	86.47 263	91.85 245	74.80 283	94.33 251	93.18 288	79.11 259	75.74 270	87.25 275	72.71 184	95.32 279	76.78 231	67.13 326	89.27 269

IterMVS-LS		83.93 218	82.80 221	87.31 249	91.46 250	77.39 245	95.66 209	93.43 276	80.44 229	75.51 271	87.26 274	73.72 175	95.16 287	76.99 228	70.72 290	89.39 263
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
3Dnovator+	82.88 8	89.63 120	87.85 138	94.99 20	94.49 166	86.76 30	97.84 56	95.74 155	86.10 106	75.47 272	96.02 131	65.00 240	99.51 61	82.91 179	97.07 90	98.72 41

test_djsdf		83.00 237	82.45 226	84.64 292	84.07 342	69.78 328	94.80 241	94.48 223	80.74 221	75.41 273	87.70 267	61.32 264	95.10 291	83.77 163	79.76 240	89.04 278

v148		82.41 247	80.89 244	86.99 256	86.18 317	76.81 255	96.27 179	93.82 256	80.49 228	75.28 274	86.11 297	67.32 224	95.75 256	75.48 245	67.03 328	88.42 294

QAPM		86.88 172	84.51 192	93.98 46	94.04 177	85.89 39	97.19 108	96.05 139	73.62 309	75.12 275	95.62 141	62.02 257	99.74 33	70.88 280	96.06 108	96.30 179

UniMVSNet_ETH3D		80.86 266	78.75 270	87.22 252	86.31 313	72.02 308	91.95 294	93.76 264	73.51 310	75.06 276	90.16 237	43.04 345	95.66 261	76.37 237	78.55 256	93.98 219

cl____		83.27 229	82.12 228	86.74 258	92.20 227	75.95 271	95.11 231	93.27 285	78.44 271	74.82 277	87.02 279	74.19 169	95.19 285	74.67 252	69.32 303	89.09 275

DIV-MVS_self_test		83.27 229	82.12 228	86.74 258	92.19 228	75.92 273	95.11 231	93.26 286	78.44 271	74.81 278	87.08 278	74.19 169	95.19 285	74.66 253	69.30 304	89.11 274

FMVSNet2		82.79 239	80.44 253	89.83 199	92.66 212	85.43 49	95.42 217	94.35 230	79.06 261	74.46 279	87.28 272	56.38 301	94.31 309	69.72 288	74.68 271	89.76 260

MIMVSNet		79.18 280	75.99 289	88.72 217	87.37 306	80.66 153	79.96 354	91.82 307	77.38 281	74.33 280	81.87 333	41.78 348	90.74 349	66.36 305	83.10 220	94.76 206

RPSCF		77.73 290	76.63 285	81.06 325	88.66 292	55.76 367	87.77 328	87.88 347	64.82 348	74.14 281	92.79 199	49.22 325	96.81 212	67.47 297	76.88 262	90.62 240

ACMP	81.66 11	84.00 217	83.22 215	86.33 264	91.53 249	72.95 301	95.91 199	93.79 260	83.70 172	73.79 282	92.22 203	54.31 314	96.89 206	83.98 158	79.74 242	89.16 272
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
pmmvs5		81.34 260	79.54 264	86.73 261	85.02 333	76.91 252	96.22 182	91.65 310	77.65 277	73.55 283	88.61 253	55.70 304	94.43 307	74.12 258	73.35 278	88.86 287

jajsoiax		82.12 251	81.15 243	85.03 286	84.19 340	70.70 320	94.22 257	93.95 248	83.07 184	73.48 284	89.75 240	49.66 324	95.37 276	82.24 182	79.76 240	89.02 279

mvs_tets		81.74 254	80.71 249	84.84 287	84.22 339	70.29 323	93.91 261	93.78 261	82.77 192	73.37 285	89.46 244	47.36 333	95.31 280	81.99 183	79.55 245	88.92 285

pmmvs4		82.54 243	80.79 245	87.79 235	86.11 318	80.49 160	93.55 267	93.18 288	77.29 282	73.35 286	89.40 245	65.26 239	95.05 295	75.32 246	73.61 275	87.83 304

LS3D		82.22 249	79.94 262	89.06 208	97.43 92	74.06 292	93.20 279	92.05 304	61.90 353	73.33 287	95.21 150	59.35 274	99.21 85	54.54 349	92.48 147	93.90 221

v10		81.43 259	79.53 265	87.11 253	86.38 311	78.87 200	94.31 252	93.43 276	77.88 274	73.24 288	85.26 306	65.44 235	95.75 256	72.14 270	67.71 320	86.72 322

v8		81.88 253	80.06 260	87.32 248	86.63 310	79.04 199	94.41 247	93.65 268	78.77 266	73.19 289	85.57 302	66.87 227	95.81 252	73.84 261	67.61 321	87.11 318

test0.0.03 1		82.79 239	82.48 225	83.74 305	86.81 309	72.22 303	96.52 160	95.03 192	83.76 170	73.00 290	93.20 192	72.30 189	88.88 356	64.15 313	77.52 261	90.12 251

anonymousdsp		80.98 265	79.97 261	84.01 300	81.73 348	70.44 322	92.49 289	93.58 272	77.10 286	72.98 291	86.31 293	57.58 288	94.90 296	79.32 206	78.63 254	86.69 323

XVG-ACMP-BASELINE		79.38 278	77.90 275	83.81 302	84.98 334	67.14 341	89.03 317	93.18 288	80.26 237	72.87 292	88.15 262	38.55 354	96.26 229	76.05 240	78.05 259	88.02 301

WR-MVS_H		81.02 263	80.09 257	83.79 303	88.08 298	71.26 319	94.46 245	96.54 91	80.08 239	72.81 293	86.82 281	70.36 209	92.65 329	64.18 312	67.50 322	87.46 315

OpenMVS	79.58 14	86.09 185	83.62 208	93.50 68	90.95 256	86.71 31	97.44 89	95.83 151	75.35 295	72.64 294	95.72 136	57.42 292	99.64 47	71.41 274	95.85 112	94.13 217

Anonymous20231211		79.72 274	77.19 280	87.33 247	95.59 132	77.16 251	95.18 228	94.18 238	59.31 365	72.57 295	86.20 295	47.89 330	95.66 261	74.53 255	69.24 305	89.18 271

CP-MVSNet		81.01 264	80.08 258	83.79 303	87.91 300	70.51 321	94.29 256	95.65 159	80.83 219	72.54 296	88.84 250	63.71 246	92.32 332	68.58 294	68.36 312	88.55 289

miper_lstm_enhance		81.66 257	80.66 250	84.67 291	91.19 252	71.97 310	91.94 295	93.19 287	77.86 275	72.27 297	85.26 306	73.46 178	93.42 323	73.71 262	67.05 327	88.61 288

PS-CasMVS		80.27 270	79.18 266	83.52 310	87.56 304	69.88 326	94.08 259	95.29 182	80.27 236	72.08 298	88.51 257	59.22 277	92.23 334	67.49 296	68.15 315	88.45 293

FMVSNet1		79.50 276	76.54 286	88.39 222	88.47 293	81.95 118	94.30 253	93.38 278	73.14 314	72.04 299	85.66 298	43.86 339	93.84 316	65.48 307	72.53 280	89.38 265

PEN-MVS		79.47 277	78.26 273	83.08 313	86.36 312	68.58 334	93.85 262	94.77 207	79.76 245	71.37 300	88.55 254	59.79 269	92.46 330	64.50 311	65.40 333	88.19 298

Patchmtry		77.36 294	74.59 299	85.67 279	89.75 276	75.75 275	77.85 362	91.12 317	60.28 361	71.23 301	80.35 341	75.45 147	93.56 322	57.94 335	67.34 325	87.68 307

IterMVS		80.67 267	79.16 267	85.20 284	89.79 275	76.08 265	92.97 283	91.86 306	80.28 235	71.20 302	85.14 311	57.93 287	91.34 343	72.52 268	70.74 289	88.18 299
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
DP-MVS		81.47 258	78.28 272	91.04 158	98.14 64	78.48 209	95.09 234	86.97 350	61.14 359	71.12 303	92.78 200	59.59 271	99.38 69	53.11 353	86.61 192	95.27 200

IterMVS-SCA-FT		80.51 269	79.10 268	84.73 289	89.63 280	74.66 284	92.98 282	91.81 308	80.05 240	71.06 304	85.18 309	58.04 284	91.40 342	72.48 269	70.70 291	88.12 300

v7n		79.32 279	77.34 278	85.28 283	84.05 343	72.89 302	93.38 270	93.87 253	75.02 300	70.68 305	84.37 318	59.58 272	95.62 266	67.60 295	67.50 322	87.32 317

MS-PatchMatch		83.05 234	81.82 234	86.72 262	89.64 279	79.10 196	94.88 238	94.59 220	79.70 247	70.67 306	89.65 241	50.43 321	96.82 211	70.82 283	95.99 110	84.25 345

DTE-MVSNet		78.37 284	77.06 281	82.32 320	85.22 332	67.17 340	93.40 269	93.66 267	78.71 267	70.53 307	88.29 259	59.06 278	92.23 334	61.38 325	63.28 342	87.56 312

pm-mvs1		80.05 271	78.02 274	86.15 270	85.42 327	75.81 274	95.11 231	92.69 298	77.13 284	70.36 308	87.43 270	58.44 282	95.27 282	71.36 275	64.25 338	87.36 316

D2MVS		82.67 241	81.55 237	86.04 272	87.77 301	76.47 258	95.21 225	96.58 86	82.66 195	70.26 309	85.46 305	60.39 267	95.80 253	76.40 236	79.18 247	85.83 335

PVSNet_0	77.72 15	81.70 255	78.95 269	89.94 195	90.77 262	76.72 257	95.96 194	96.95 36	85.01 133	70.24 310	88.53 256	52.32 316	98.20 144	86.68 142	44.08 371	94.89 203

CL-MVSNet_self_test		75.81 303	74.14 305	80.83 327	78.33 358	67.79 337	94.22 257	93.52 273	77.28 283	69.82 311	81.54 335	61.47 263	89.22 355	57.59 338	53.51 357	85.48 337

tfpnnormal		78.14 286	75.42 292	86.31 267	88.33 295	79.24 190	94.41 247	96.22 126	73.51 310	69.81 312	85.52 304	55.43 305	95.75 256	47.65 365	67.86 318	83.95 348

EU-MVSNet		76.92 298	76.95 282	76.83 339	84.10 341	54.73 369	91.77 298	92.71 297	72.74 318	69.57 313	88.69 252	58.03 286	87.43 362	64.91 310	70.00 298	88.33 296

ITE_SJBPF				82.38 318	87.00 308	65.59 343		89.55 333	79.99 242	69.37 314	91.30 216	41.60 350	95.33 278	62.86 320	74.63 272	86.24 328

DSMNet-mixed		73.13 315	72.45 311	75.19 345	77.51 361	46.82 374	85.09 345	82.01 369	67.61 343	69.27 315	81.33 336	50.89 318	86.28 364	54.54 349	83.80 214	92.46 229

MVP-Stereo		82.65 242	81.67 236	85.59 280	86.10 319	78.29 216	93.33 272	92.82 295	77.75 276	69.17 316	87.98 264	59.28 276	95.76 255	71.77 271	96.88 96	82.73 353
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
MSDG		80.62 268	77.77 276	89.14 207	93.43 195	77.24 247	91.89 296	90.18 329	69.86 335	68.02 317	91.94 208	52.21 317	98.84 117	59.32 332	83.12 219	91.35 233

NR-MVSNet		83.35 227	81.52 239	88.84 213	88.76 288	81.31 139	94.45 246	95.16 186	84.65 142	67.81 318	90.82 225	70.36 209	94.87 297	74.75 250	66.89 329	90.33 246

TransMVSNet (Re)		76.94 297	74.38 301	84.62 293	85.92 321	75.25 279	95.28 221	89.18 337	73.88 308	67.22 319	86.46 288	59.64 270	94.10 312	59.24 333	52.57 361	84.50 343

Anonymous20231206		75.29 306	73.64 307	80.22 329	80.75 349	63.38 350	93.36 271	90.71 327	73.09 315	67.12 320	83.70 324	50.33 322	90.85 348	53.63 352	70.10 296	86.44 325

ppachtmachnet_test		77.19 295	74.22 303	86.13 271	85.39 328	78.22 219	93.98 260	91.36 314	71.74 326	67.11 321	84.87 315	56.67 297	93.37 325	52.21 354	64.59 335	86.80 321

KD-MVS_2432*1600		77.63 291	74.92 296	85.77 275	90.86 259	79.44 184	88.08 324	93.92 250	76.26 290	67.05 322	82.78 329	72.15 191	91.92 337	61.53 322	41.62 374	85.94 333

miper_refine_blended		77.63 291	74.92 296	85.77 275	90.86 259	79.44 184	88.08 324	93.92 250	76.26 290	67.05 322	82.78 329	72.15 191	91.92 337	61.53 322	41.62 374	85.94 333

Patchmatch-test		78.25 285	74.72 298	88.83 214	91.20 251	74.10 291	73.91 369	88.70 344	59.89 364	66.82 324	85.12 312	78.38 96	94.54 305	48.84 363	79.58 244	97.86 104

FMVS2		69.56 325	69.19 325	70.67 348	69.01 372	47.05 373	90.87 307	86.81 352	71.31 329	66.79 325	77.15 352	16.40 376	83.17 370	81.84 184	62.51 344	81.79 361

LTVRE_ROB	73.68 18	77.99 287	75.74 291	84.74 288	90.45 266	72.02 308	86.41 339	91.12 317	72.57 320	66.63 326	87.27 273	54.95 310	96.98 201	56.29 344	75.98 264	85.21 339
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
OurMVSNet-221017-0		77.18 296	76.06 288	80.55 328	83.78 344	60.00 360	90.35 309	91.05 320	77.01 288	66.62 327	87.92 265	47.73 331	94.03 313	71.63 272	68.44 311	87.62 309

testgi		74.88 308	73.40 308	79.32 333	80.13 353	61.75 354	93.21 278	86.64 354	79.49 251	66.56 328	91.06 220	35.51 360	88.67 357	56.79 343	71.25 285	87.56 312

LCM-MVSNet-Re		83.75 222	83.54 210	84.39 299	93.54 188	64.14 347	92.51 288	84.03 363	83.90 165	66.14 329	86.59 285	67.36 223	92.68 328	84.89 152	92.87 142	96.35 174

pmmvs6		74.65 309	71.67 313	83.60 308	79.13 356	69.94 325	93.31 276	90.88 324	61.05 360	65.83 330	84.15 321	43.43 341	94.83 299	66.62 300	60.63 347	86.02 332

our_test_3		77.90 289	75.37 293	85.48 282	85.39 328	76.74 256	93.63 264	91.67 309	73.39 313	65.72 331	84.65 317	58.20 283	93.13 327	57.82 336	67.87 317	86.57 324

COLMAP_ROB	73.24 19	75.74 304	73.00 310	83.94 301	92.38 218	69.08 333	91.85 297	86.93 351	61.48 356	65.32 332	90.27 234	42.27 347	96.93 205	50.91 358	75.63 267	85.80 336
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
FMVSNet5		76.46 300	74.16 304	83.35 312	90.05 273	76.17 263	89.58 313	89.85 331	71.39 328	65.29 333	80.42 340	50.61 320	87.70 361	61.05 327	69.24 305	86.18 329

ACMH+	76.62 16	77.47 293	74.94 295	85.05 285	91.07 255	71.58 316	93.26 277	90.01 330	71.80 325	64.76 334	88.55 254	41.62 349	96.48 222	62.35 321	71.00 287	87.09 319

Patchmatch-RL test		76.65 299	74.01 306	84.55 294	77.37 362	64.23 346	78.49 361	82.84 367	78.48 269	64.63 335	73.40 360	76.05 135	91.70 341	76.99 228	57.84 351	97.72 114

SixPastTwentyTwo		76.04 301	74.32 302	81.22 324	84.54 336	61.43 357	91.16 304	89.30 336	77.89 273	64.04 336	86.31 293	48.23 326	94.29 310	63.54 317	63.84 340	87.93 303

AllTest		75.92 302	73.06 309	84.47 295	92.18 229	67.29 338	91.07 305	84.43 361	67.63 339	63.48 337	90.18 235	38.20 355	97.16 192	57.04 340	73.37 276	88.97 283

TestCases				84.47 295	92.18 229	67.29 338		84.43 361	67.63 339	63.48 337	90.18 235	38.20 355	97.16 192	57.04 340	73.37 276	88.97 283

ACMH	75.40 17	77.99 287	74.96 294	87.10 254	90.67 263	76.41 260	93.19 280	91.64 311	72.47 321	63.44 339	87.61 269	43.34 342	97.16 192	58.34 334	73.94 273	87.72 305
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ET-MVSNet_ETH3D		90.01 114	89.03 120	92.95 91	94.38 167	86.77 29	98.14 36	96.31 121	89.30 47	63.33 340	96.72 121	90.09 11	93.63 321	90.70 96	82.29 230	98.46 54

USDC		78.65 282	76.25 287	85.85 273	87.58 303	74.60 285	89.58 313	90.58 328	84.05 158	63.13 341	88.23 260	40.69 353	96.86 210	66.57 302	75.81 266	86.09 331

LF4IMVS		72.36 319	70.82 316	76.95 338	79.18 355	56.33 364	86.12 340	86.11 356	69.30 337	63.06 342	86.66 284	33.03 364	92.25 333	65.33 308	68.64 309	82.28 357

KD-MVS_self_test		70.97 324	69.31 324	75.95 344	76.24 368	55.39 368	87.45 329	90.94 323	70.20 333	62.96 343	77.48 351	44.01 338	88.09 358	61.25 326	53.26 358	84.37 344

Anonymous20240521		72.06 321	69.91 321	78.50 335	77.11 363	61.67 356	91.62 302	90.97 322	65.52 346	62.37 344	79.05 347	36.32 357	90.96 347	57.75 337	68.52 310	82.87 350

test_0402		72.68 317	69.54 323	82.09 321	88.67 291	71.81 313	92.72 287	86.77 353	61.52 355	62.21 345	83.91 322	43.22 343	93.76 319	34.60 372	72.23 284	80.72 363

OpenMVS_ROB	68.52 20	73.02 316	69.57 322	83.37 311	80.54 352	71.82 312	93.60 266	88.22 346	62.37 351	61.98 346	83.15 328	35.31 361	95.47 272	45.08 368	75.88 265	82.82 351

MVS-HIRNet		71.36 323	67.00 328	84.46 297	90.58 264	69.74 329	79.15 358	87.74 349	46.09 370	61.96 347	50.50 374	45.14 337	95.64 264	53.74 351	88.11 183	88.00 302

test20.03		72.36 319	71.15 315	75.98 343	77.79 359	59.16 362	92.40 291	89.35 335	74.09 306	61.50 348	84.32 319	48.09 327	85.54 367	50.63 359	62.15 345	83.24 349

mvsany_test		67.19 331	65.34 333	72.72 347	63.08 376	48.57 372	83.12 350	78.09 375	72.07 323	61.21 349	77.11 353	22.94 371	87.78 360	78.59 213	51.88 362	81.80 360

PM-MVS		69.32 327	66.93 329	76.49 340	73.60 370	55.84 366	85.91 341	79.32 374	74.72 302	61.09 350	78.18 349	21.76 372	91.10 346	70.86 281	56.90 353	82.51 354

TDRefinement		69.20 328	65.78 332	79.48 332	66.04 375	62.21 353	88.21 323	86.12 355	62.92 350	61.03 351	85.61 301	33.23 363	94.16 311	55.82 347	53.02 359	82.08 358

ambc				76.02 342	68.11 373	51.43 370	64.97 374	89.59 332		60.49 352	74.49 357	17.17 375	92.46 330	61.50 324	52.85 360	84.17 346

pmmvs-eth3d		73.59 311	70.66 317	82.38 318	76.40 366	73.38 294	89.39 316	89.43 334	72.69 319	60.34 353	77.79 350	46.43 335	91.26 345	66.42 304	57.06 352	82.51 354

K. test v3		73.62 310	71.59 314	79.69 331	82.98 346	59.85 361	90.85 308	88.83 340	77.13 284	58.90 354	82.11 331	43.62 340	91.72 340	65.83 306	54.10 356	87.50 314

EG-PatchMatch MVS		74.92 307	72.02 312	83.62 307	83.76 345	73.28 297	93.62 265	92.04 305	68.57 338	58.88 355	83.80 323	31.87 366	95.57 270	56.97 342	78.67 251	82.00 359

lessismore_v0				79.98 330	80.59 351	58.34 363		80.87 370		58.49 356	83.46 326	43.10 344	93.89 315	63.11 319	48.68 364	87.72 305

N_pmnet		61.30 335	60.20 338	64.60 353	84.32 338	17.00 390	91.67 301	10.98 389	61.77 354	58.45 357	78.55 348	49.89 323	91.83 339	42.27 370	63.94 339	84.97 340

TinyColmap		72.41 318	68.99 326	82.68 316	88.11 297	69.59 330	88.41 322	85.20 358	65.55 345	57.91 358	84.82 316	30.80 368	95.94 244	51.38 355	68.70 308	82.49 356

UnsupCasMVSNet_eth		73.25 314	70.57 318	81.30 323	77.53 360	66.33 342	87.24 332	93.89 252	80.38 232	57.90 359	81.59 334	42.91 346	90.56 350	65.18 309	48.51 365	87.01 320

MIMVSNet1		69.44 326	66.65 330	77.84 336	76.48 365	62.84 352	87.42 330	88.97 339	66.96 344	57.75 360	79.72 346	32.77 365	85.83 366	46.32 366	63.42 341	84.85 341

pmmvs3		65.75 333	62.18 336	76.45 341	67.12 374	64.54 345	88.68 320	85.05 359	54.77 369	57.54 361	73.79 358	29.40 369	86.21 365	55.49 348	47.77 367	78.62 365

FMVS		64.01 334	62.13 337	69.65 349	63.00 377	45.30 377	83.66 349	80.68 371	61.30 357	55.70 362	72.62 362	14.23 378	84.64 368	69.84 286	58.11 350	79.00 364

new-patchmatchnet		68.85 329	65.93 331	77.61 337	73.57 371	63.94 349	90.11 311	88.73 343	71.62 327	55.08 363	73.60 359	40.84 352	87.22 363	51.35 357	48.49 366	81.67 362

UnsupCasMVSNet_bld		68.60 330	64.50 334	80.92 326	74.63 369	67.80 336	83.97 347	92.94 294	65.12 347	54.63 364	68.23 367	35.97 358	92.17 336	60.13 328	44.83 369	82.78 352

CMPMVS	54.94 21	75.71 305	74.56 300	79.17 334	79.69 354	55.98 365	89.59 312	93.30 284	60.28 361	53.85 365	89.07 247	47.68 332	96.33 227	76.55 233	81.02 232	85.22 338
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
new_pmnet		66.18 332	63.18 335	75.18 346	76.27 367	61.74 355	83.79 348	84.66 360	56.64 368	51.57 366	71.85 366	31.29 367	87.93 359	49.98 360	62.55 343	75.86 367

test_method		56.77 336	54.53 339	63.49 355	76.49 364	40.70 380	75.68 365	74.24 377	19.47 380	48.73 367	71.89 365	19.31 373	65.80 380	57.46 339	47.51 368	83.97 347

YYNet1		73.53 313	70.43 319	82.85 315	84.52 337	71.73 314	91.69 300	91.37 313	67.63 339	46.79 368	81.21 337	55.04 309	90.43 351	55.93 345	59.70 349	86.38 326

MDA-MVSNet_test_wron		73.54 312	70.43 319	82.86 314	84.55 335	71.85 311	91.74 299	91.32 316	67.63 339	46.73 369	81.09 338	55.11 308	90.42 352	55.91 346	59.76 348	86.31 327

MDA-MVSNet-bldmvs		71.45 322	67.94 327	81.98 322	85.33 330	68.50 335	92.35 292	88.76 342	70.40 331	42.99 370	81.96 332	46.57 334	91.31 344	48.75 364	54.39 355	86.11 330

DeepMVS_CX				64.06 354	78.53 357	43.26 378		68.11 381	69.94 334	38.55 371	76.14 355	18.53 374	79.34 371	43.72 369	41.62 374	69.57 370

LCM-MVSNet		52.52 338	48.24 341	65.35 351	47.63 385	41.45 379	72.55 370	83.62 365	31.75 373	37.66 372	57.92 372	9.19 384	76.76 373	49.26 362	44.60 370	77.84 366

FPMVS		55.09 337	52.93 340	61.57 356	55.98 378	40.51 381	83.11 351	83.41 366	37.61 372	34.95 373	71.95 364	14.40 377	76.95 372	29.81 373	65.16 334	67.25 371

PMMVS2		50.90 340	46.31 343	64.67 352	55.53 379	46.67 375	77.30 364	71.02 379	40.89 371	34.16 374	59.32 370	9.83 383	76.14 375	40.09 371	28.63 377	71.21 368

FMVS1		45.70 342	42.41 344	55.58 358	53.29 382	40.02 382	68.96 372	62.67 383	27.45 375	29.85 375	61.58 368	5.98 385	73.83 377	28.49 376	43.46 372	52.90 373

APD_test		45.70 342	42.41 344	55.58 358	53.29 382	40.02 382	68.96 372	62.67 383	27.45 375	29.85 375	61.58 368	5.98 385	73.83 377	28.49 376	43.46 372	52.90 373

tmp_tt		41.54 345	41.93 347	40.38 363	20.10 389	26.84 386	61.93 375	59.09 385	14.81 382	28.51 377	80.58 339	35.53 359	48.33 384	63.70 316	13.11 381	45.96 377

Gipuma		45.11 344	42.05 346	54.30 360	80.69 350	51.30 371	35.80 378	83.81 364	28.13 374	27.94 378	34.53 378	11.41 382	76.70 374	21.45 378	54.65 354	34.90 378
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
ANet_high		46.22 341	41.28 348	61.04 357	39.91 387	46.25 376	70.59 371	76.18 376	58.87 366	23.09 379	48.00 376	12.58 380	66.54 379	28.65 375	13.62 380	70.35 369

MVE	35.65 22	33.85 347	29.49 352	46.92 362	41.86 386	36.28 384	50.45 377	56.52 386	18.75 381	18.28 380	37.84 377	2.41 388	58.41 381	18.71 379	20.62 378	46.06 376
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
PMVS	34.80 23	39.19 346	35.53 349	50.18 361	29.72 388	30.30 385	59.60 376	66.20 382	26.06 377	17.91 381	49.53 375	3.12 387	74.09 376	18.19 380	49.40 363	46.14 375
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
E-PMN		32.70 348	32.39 350	33.65 364	53.35 381	25.70 387	74.07 368	53.33 387	21.08 378	17.17 382	33.63 380	11.85 381	54.84 382	12.98 381	14.04 379	20.42 379

EMVS		31.70 349	31.45 351	32.48 365	50.72 384	23.95 388	74.78 367	52.30 388	20.36 379	16.08 383	31.48 381	12.80 379	53.60 383	11.39 382	13.10 382	19.88 380

wuyk23d		14.10 351	13.89 354	14.72 366	55.23 380	22.91 389	33.83 379	3.56 390	4.94 383	4.11 384	2.28 386	2.06 389	19.66 385	10.23 383	8.74 383	1.59 383

testmvs		9.92 352	12.94 355	0.84 368	0.65 390	0.29 392	93.78 263	0.39 391	0.42 384	2.85 385	15.84 384	0.17 391	0.30 387	2.18 384	0.21 384	1.91 382

test123		9.07 353	11.73 356	1.11 367	0.50 391	0.77 391	89.44 315	0.20 392	0.34 385	2.15 386	10.72 385	0.34 390	0.32 386	1.79 385	0.08 385	2.23 381

EGC-MVSNET		52.46 339	47.56 342	67.15 350	81.98 347	60.11 359	82.54 352	72.44 378	0.11 386	0.70 387	74.59 356	25.11 370	83.26 369	29.04 374	61.51 346	58.09 372

test_blank		0.00 356	0.00 359	0.00 369	0.00 392	0.00 393	0.00 380	0.00 393	0.00 387	0.00 388	0.00 387	0.00 392	0.00 388	0.00 386	0.00 386	0.00 384

uanet_test		0.00 356	0.00 359	0.00 369	0.00 392	0.00 393	0.00 380	0.00 393	0.00 387	0.00 388	0.00 387	0.00 392	0.00 388	0.00 386	0.00 386	0.00 384

DCPMVS		0.00 356	0.00 359	0.00 369	0.00 392	0.00 393	0.00 380	0.00 393	0.00 387	0.00 388	0.00 387	0.00 392	0.00 388	0.00 386	0.00 386	0.00 384

cdsmvs_eth3d_5k		21.43 350	28.57 353	0.00 369	0.00 392	0.00 393	0.00 380	95.93 146	0.00 387	0.00 388	97.66 74	63.57 247	0.00 388	0.00 386	0.00 386	0.00 384

pcd_1.5k_mvsjas		5.92 355	7.89 358	0.00 369	0.00 392	0.00 393	0.00 380	0.00 393	0.00 387	0.00 388	0.00 387	71.04 203	0.00 388	0.00 386	0.00 386	0.00 384

sosnet-low-res		0.00 356	0.00 359	0.00 369	0.00 392	0.00 393	0.00 380	0.00 393	0.00 387	0.00 388	0.00 387	0.00 392	0.00 388	0.00 386	0.00 386	0.00 384

sosnet		0.00 356	0.00 359	0.00 369	0.00 392	0.00 393	0.00 380	0.00 393	0.00 387	0.00 388	0.00 387	0.00 392	0.00 388	0.00 386	0.00 386	0.00 384

uncertanet		0.00 356	0.00 359	0.00 369	0.00 392	0.00 393	0.00 380	0.00 393	0.00 387	0.00 388	0.00 387	0.00 392	0.00 388	0.00 386	0.00 386	0.00 384

Regformer		0.00 356	0.00 359	0.00 369	0.00 392	0.00 393	0.00 380	0.00 393	0.00 387	0.00 388	0.00 387	0.00 392	0.00 388	0.00 386	0.00 386	0.00 384

ab-mvs-re		8.11 354	10.81 357	0.00 369	0.00 392	0.00 393	0.00 380	0.00 393	0.00 387	0.00 388	97.30 96	0.00 392	0.00 388	0.00 386	0.00 386	0.00 384

uanet		0.00 356	0.00 359	0.00 369	0.00 392	0.00 393	0.00 380	0.00 393	0.00 387	0.00 388	0.00 387	0.00 392	0.00 388	0.00 386	0.00 386	0.00 384

MSC_two_6792asdad				97.14 3	99.05 10	92.19 4		96.83 45					99.81 21	98.08 7	98.81 25	99.43 11

No_MVS				97.14 3	99.05 10	92.19 4		96.83 45					99.81 21	98.08 7	98.81 25	99.43 11

eth-test2					0.00 392

eth-test					0.00 392

OPU-MVS				97.30 2	99.19 8	92.31 3	99.12 6				98.54 22	92.06 3	99.84 12	99.11 1	99.37 1	99.74 1

save fliter					98.24 58	83.34 94	98.61 23	96.57 87	91.32 19

test_0728_SECOND				95.14 17	99.04 15	86.14 34	99.06 9	96.77 55					99.84 12	97.90 9	98.85 22	99.45 10

GSMVS																97.54 126

sam_mvs1												77.59 108				97.54 126

sam_mvs												75.35 154

MTGPA								96.33 118

test_post1							85.88 342				30.24 382	73.77 173	95.07 294	73.89 259

test_post											33.80 379	76.17 133	95.97 240

patchmatchnet-post											77.09 354	77.78 107	95.39 274

MTMP							97.53 82	68.16 380

gm-plane-assit					92.27 223	79.64 182			84.47 148		95.15 155		97.93 150	85.81 144

test9_res														96.00 27	99.03 13	98.31 63

agg_prior2														94.30 47	99.00 15	98.57 47

test_prior4						82.34 114	97.75 66

test_prior				93.09 84	98.68 29	81.91 121		96.40 110					99.06 102			98.29 65

新几何2							96.42 171

旧先验1					97.39 95	79.58 183		96.54 91			98.08 49	84.00 42			97.42 81	97.62 123

无先验							96.87 139	96.78 49	77.39 280				99.52 58	79.95 199		98.43 56

原ACMM2							96.84 140

testdata2													99.48 63	76.45 235

segment_acmp												82.69 58

testdata1							95.57 212		87.44 82

plane_prior7					91.86 243	77.55 242

plane_prior6					91.98 238	77.92 232						64.77 242

plane_prior5								94.69 209					97.30 184	87.08 136	82.82 225	90.96 236

plane_prior4											94.15 179

plane_prior2							97.18 109		89.89 39

plane_prior1					91.95 241

plane_prior						77.96 229	97.52 85		90.36 34						82.96 223

n2								0.00 393

nn								0.00 393

door-mid								79.75 373

test11								96.50 97

door								80.13 372

HQP5-MVS						78.48 209

BP-MVS														87.67 132

HQP3-MVS								94.80 204							83.01 221

HQP2-MVS												65.40 236

NP-MVS					92.04 237	78.22 219					94.56 169

ACMMP++_ref															78.45 257

ACMMP++															79.05 248

Test By Simon												71.65 196

High-resolution multi-view benchmark