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 13	85.34 47		96.86 42	92.05 15	98.74 1				98.15 2	98.97 15	99.42 9

SMA-MVS		94.70 15	94.68 15	94.76 22	98.02 64	85.94 35	97.47 81	96.77 51	85.32 112	97.92 2	98.70 16	83.09 47	99.84 12	95.79 24	99.08 8	98.49 46
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 4	96.22 3	94.87 19	99.03 13	85.03 58	99.12 6	96.78 45	88.72 50	97.79 3	98.91 3	88.48 14	99.82 16	98.15 2	98.97 15	99.74 1

test_241102_ONE					99.03 13	85.03 58		96.78 45	88.72 50	97.79 3	98.90 6	88.48 14	99.82 16

test_241102_TWO								96.78 45	88.72 50	97.70 5	98.91 3	87.86 17	99.82 16	98.15 2	99.00 13	99.47 7

test0726					99.05 10	85.18 51	99.11 8	96.78 45	88.75 48	97.65 6	98.91 3	87.69 18

TSAR-MVS + MP.		94.79 14	95.17 12	93.64 57	97.66 75	84.10 74	95.85 194	96.42 100	91.26 20	97.49 7	96.80 111	86.50 23	98.49 127	95.54 28	99.03 11	98.33 53
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 6	96.54 1	92.86 92	98.31 49	80.10 166	97.42 89	96.78 45	92.20 13	97.11 8	98.29 28	93.46 1	99.10 95	96.01 20	99.30 3	99.38 10
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 12	99.31 5	87.69 19	99.06 9	97.12 22	94.66 3	96.79 9	98.78 11	86.42 24	99.95 3	97.59 9	99.18 5	99.00 23

DVP-MVS		95.58 7	95.91 7	94.57 26	99.05 10	85.18 51	99.06 9	96.46 95	88.75 48	96.69 10	98.76 12	87.69 18	99.76 20	97.90 5	98.85 20	98.77 30
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 57	96.69 10	98.76 12	89.64 10	99.76 20	97.47 10	98.84 22	99.38 10

SD-MVS		94.84 13	95.02 13	94.29 34	97.87 70	84.61 67	97.76 60	96.19 123	89.59 38	96.66 12	98.17 36	84.33 32	99.60 46	96.09 18	98.50 37	98.66 37
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
DPE-MVS		95.32 9	95.55 8	94.64 25	98.79 21	84.87 63	97.77 56	96.74 55	86.11 93	96.54 13	98.89 7	88.39 16	99.74 28	97.67 8	99.05 10	99.31 14
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 9	98.26 1	96.26 105	95.09 1	99.15 4	96.98 30	93.39 9	96.45 14	98.79 10	90.17 7	99.99 1	89.33 108	99.25 4	99.70 3

PS-MVSNAJ		94.17 25	93.52 38	96.10 6	95.65 121	92.35 2	98.21 32	95.79 145	92.42 12	96.24 15	98.18 32	71.04 194	99.17 88	96.77 15	97.39 75	96.79 149

旧先验2							96.97 127		74.06 293	96.10 16			97.76 151	88.38 117

test_part2					98.90 17	85.14 57				96.07 17

xiu_mvs_v2_base		93.92 33	93.26 40	95.91 8	95.07 137	92.02 4	98.19 33	95.68 150	92.06 14	96.01 18	98.14 37	70.83 197	98.96 103	96.74 16	96.57 94	96.76 152

ETH3 D test6400		95.56 8	95.41 11	96.00 7	99.02 16	89.42 7	98.75 17	96.80 44	87.28 79	95.88 19	98.95 2	85.92 26	99.41 62	97.15 13	98.95 18	99.18 20

HPM-MVS++		95.32 9	95.48 10	94.85 20	98.62 34	86.04 32	97.81 54	96.93 36	92.45 11	95.69 20	98.50 22	85.38 27	99.85 10	94.75 37	99.18 5	98.65 38

NCCC		95.63 5	95.94 6	94.69 24	99.21 7	85.15 56	99.16 3	96.96 33	94.11 6	95.59 21	98.64 19	85.07 28	99.91 4	95.61 27	99.10 7	99.00 23

ETH3D-3000-0.1		94.43 19	94.42 22	94.45 28	97.78 71	85.78 38	97.98 44	96.53 87	85.29 115	95.45 22	98.81 8	83.36 42	99.38 64	96.07 19	98.53 33	98.19 65

EPNet		94.06 30	94.15 28	93.76 50	97.27 94	84.35 68	98.29 29	97.64 13	94.57 4	95.36 23	96.88 106	79.96 72	99.12 94	91.30 78	96.11 98	97.82 98
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CANet		94.89 12	94.64 16	95.63 10	97.55 81	88.12 13	99.06 9	96.39 107	94.07 7	95.34 24	97.80 65	76.83 113	99.87 8	97.08 14	97.64 68	98.89 26

TEST9					98.64 31	83.71 81	97.82 52	96.65 68	84.29 144	95.16 25	98.09 43	84.39 31	99.36 70

train_agg		94.28 22	94.45 20	93.74 51	98.64 31	83.71 81	97.82 52	96.65 68	84.50 136	95.16 25	98.09 43	84.33 32	99.36 70	95.91 23	98.96 17	98.16 68

test_8					98.63 33	83.64 84	97.81 54	96.63 73	84.50 136	95.10 27	98.11 42	84.33 32	99.23 76

DeepPCF-MVS	89.82 1	94.61 16	96.17 4	89.91 183	97.09 97	70.21 309	98.99 14	96.69 62	95.57 1	95.08 28	99.23 1	86.40 25	99.87 8	97.84 7	98.66 30	99.65 4

xxxxxxxxxxxxxcwj		94.38 20	94.62 17	93.68 55	98.24 52	83.34 89	98.61 22	92.69 289	91.32 18	95.07 29	98.74 14	82.93 48	99.38 64	95.42 30	98.51 34	98.32 54

SF-MVS		94.17 25	94.05 30	94.55 27	97.56 80	85.95 33	97.73 62	96.43 99	84.02 150	95.07 29	98.74 14	82.93 48	99.38 64	95.42 30	98.51 34	98.32 54

APDe-MVS		94.56 17	94.75 14	93.96 45	98.84 20	83.40 88	98.04 42	96.41 101	85.79 101	95.00 31	98.28 29	84.32 35	99.18 87	97.35 11	98.77 25	99.28 15

MVSFormer		91.36 82	90.57 86	93.73 53	93.00 192	88.08 14	94.80 230	94.48 216	80.74 208	94.90 32	97.13 98	78.84 83	95.10 278	83.77 150	97.46 70	98.02 80

lupinMVS		93.87 36	93.58 37	94.75 23	93.00 192	88.08 14	99.15 4	95.50 160	91.03 22	94.90 32	97.66 69	78.84 83	97.56 158	94.64 40	97.46 70	98.62 40

9.14			94.26 27		98.10 60		98.14 34	96.52 88	84.74 127	94.83 34	98.80 9	82.80 51	99.37 68	95.95 22	98.42 42

testdata				90.13 174	95.92 114	74.17 276		96.49 94	73.49 298	94.82 35	97.99 52	78.80 85	97.93 142	83.53 159	97.52 69	98.29 59

testtj		94.09 29	94.08 29	94.09 42	99.28 6	83.32 91	97.59 71	96.61 74	83.60 165	94.77 36	98.46 24	82.72 52	99.64 42	95.29 32	98.42 42	99.32 13

APD-MVS		93.61 37	93.59 36	93.69 54	98.76 22	83.26 92	97.21 98	96.09 128	82.41 186	94.65 37	98.21 31	81.96 57	98.81 114	94.65 39	98.36 50	99.01 22
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
test_prior3		94.03 31	94.34 24	93.09 81	98.68 25	81.91 117	98.37 26	96.40 104	86.08 95	94.57 38	98.02 49	83.14 44	99.06 97	95.05 34	98.79 23	98.29 59

test_prior2							98.37 26		86.08 95	94.57 38	98.02 49	83.14 44		95.05 34	98.79 23

ACMMP_NAP		93.46 39	93.23 41	94.17 39	97.16 95	84.28 71	96.82 136	96.65 68	86.24 91	94.27 40	97.99 52	77.94 96	99.83 15	93.39 52	98.57 32	98.39 51

agg_prior1		94.10 28	94.31 25	93.48 67	98.59 35	83.13 94	97.77 56	96.56 82	84.38 140	94.19 41	98.13 38	84.66 30	99.16 89	95.74 25	98.74 27	98.15 70

agg_prior					98.59 35	83.13 94		96.56 82		94.19 41			99.16 89

SteuartSystems-ACMMP		94.13 27	94.44 21	93.20 76	95.41 126	81.35 134	99.02 13	96.59 78	89.50 39	94.18 43	98.36 27	83.68 40	99.45 60	94.77 36	98.45 40	98.81 29
Skip Steuart: Steuart Systems R&D Blog.
ETH3D cwj APD-0.16		93.91 35	93.76 33	94.36 31	96.70 101	85.74 39	97.22 96	96.41 101	83.94 153	94.13 44	98.69 18	83.13 46	99.37 68	95.25 33	98.39 47	97.97 88

PHI-MVS		93.59 38	93.63 35	93.48 67	98.05 63	81.76 125	98.64 20	97.13 21	82.60 184	94.09 45	98.49 23	80.35 65	99.85 10	94.74 38	98.62 31	98.83 28

TSAR-MVS + GP.		94.35 21	94.50 18	93.89 46	97.38 91	83.04 97	98.10 37	95.29 175	91.57 16	93.81 46	97.45 81	86.64 21	99.43 61	96.28 17	94.01 119	99.20 18

CANet_DTU		90.98 89	90.04 97	93.83 48	94.76 147	86.23 30	96.32 169	93.12 282	93.11 10	93.71 47	96.82 110	63.08 240	99.48 58	84.29 145	95.12 112	95.77 176

VNet		92.11 66	91.22 77	94.79 21	96.91 98	86.98 24	97.91 47	97.96 9	86.38 90	93.65 48	95.74 128	70.16 202	98.95 106	93.39 52	88.87 161	98.43 49

ZD-MVS					99.09 9	83.22 93		96.60 77	82.88 178	93.61 49	98.06 48	82.93 48	99.14 91	95.51 29	98.49 38

CS-MVS		92.29 63	92.54 56	91.51 138	93.78 171	80.14 165	98.36 28	94.53 214	87.91 68	93.39 50	97.23 93	76.09 127	96.96 190	94.36 41	97.26 78	97.43 124

xiu_mvs_v1_base_debu		90.54 98	89.54 108	93.55 62	92.31 206	87.58 20	96.99 122	94.87 192	87.23 81	93.27 51	97.56 76	57.43 279	98.32 132	92.72 64	93.46 127	94.74 195

xiu_mvs_v1_base		90.54 98	89.54 108	93.55 62	92.31 206	87.58 20	96.99 122	94.87 192	87.23 81	93.27 51	97.56 76	57.43 279	98.32 132	92.72 64	93.46 127	94.74 195

xiu_mvs_v1_base_debi		90.54 98	89.54 108	93.55 62	92.31 206	87.58 20	96.99 122	94.87 192	87.23 81	93.27 51	97.56 76	57.43 279	98.32 132	92.72 64	93.46 127	94.74 195

CDPH-MVS		93.12 43	92.91 46	93.74 51	98.65 30	83.88 76	97.67 66	96.26 117	83.00 175	93.22 54	98.24 30	81.31 58	99.21 80	89.12 109	98.74 27	98.14 71

ETV-MVS		92.72 53	92.87 47	92.28 114	94.54 152	81.89 119	97.98 44	95.21 178	89.77 37	93.11 55	96.83 108	77.23 109	97.50 165	95.74 25	95.38 107	97.44 123

MSLP-MVS++		94.28 22	94.39 23	93.97 44	98.30 50	84.06 75	98.64 20	96.93 36	90.71 25	93.08 56	98.70 16	79.98 71	99.21 80	94.12 45	99.07 9	98.63 39

alignmvs		92.97 46	92.26 61	95.12 16	95.54 123	87.77 17	98.67 18	96.38 108	88.04 63	93.01 57	97.45 81	79.20 80	98.60 121	93.25 57	88.76 162	98.99 25

canonicalmvs		92.27 64	91.22 77	95.41 13	95.80 116	88.31 11	97.09 115	94.64 208	88.49 55	92.99 58	97.31 88	72.68 177	98.57 123	93.38 54	88.58 165	99.36 12

jason		92.73 52	92.23 62	94.21 38	90.50 252	87.30 23	98.65 19	95.09 181	90.61 26	92.76 59	97.13 98	75.28 148	97.30 174	93.32 55	96.75 93	98.02 80
jason: jason.
Regformer-1		94.00 32	94.04 31	93.87 47	98.41 43	84.29 70	97.43 87	97.04 26	89.50 39	92.75 60	98.13 38	82.60 54	99.26 75	93.55 50	96.99 83	98.06 77

Regformer-2		93.92 33	94.01 32	93.67 56	98.41 43	83.75 80	97.43 87	97.00 28	89.43 41	92.69 61	98.13 38	82.48 55	99.22 78	93.51 51	96.99 83	98.04 78

test12				94.25 35	98.34 47	85.55 44		96.35 111		92.36 62		80.84 60	99.22 78		98.31 52	97.98 87

MG-MVS		94.25 24	93.72 34	95.85 9	99.38 3	89.35 9	97.98 44	98.09 8	89.99 34	92.34 63	96.97 103	81.30 59	98.99 101	88.54 113	98.88 19	99.20 18

hse-mvs3		89.30 119	88.95 117	90.36 167	95.07 137	76.04 255	96.96 128	97.11 23	90.39 30	92.22 64	95.10 150	74.70 155	98.86 111	93.14 59	65.89 318	96.16 168

hse-mvs2		88.22 147	88.21 125	88.25 217	93.54 179	73.41 279	95.41 209	95.89 139	90.39 30	92.22 64	94.22 167	74.70 155	96.66 207	93.14 59	64.37 323	94.69 199

MCST-MVS		96.17 3	96.12 5	96.32 5	99.42 2	89.36 8	98.94 15	97.10 24	95.17 2	92.11 66	98.46 24	87.33 20	99.97 2	97.21 12	99.31 2	99.63 5

SR-MVS		92.16 65	92.27 60	91.83 130	98.37 46	78.41 206	96.67 148	95.76 146	82.19 190	91.97 67	98.07 47	76.44 119	98.64 118	93.71 47	97.27 77	98.45 48

region2R		92.72 53	92.70 51	92.79 95	98.68 25	80.53 155	97.53 76	96.51 89	85.22 116	91.94 68	97.98 54	77.26 105	99.67 40	90.83 85	98.37 49	98.18 66

Effi-MVS+		90.70 94	89.90 102	93.09 81	93.61 176	83.48 86	95.20 216	92.79 287	83.22 169	91.82 69	95.70 130	71.82 185	97.48 166	91.25 79	93.67 124	98.32 54

HFP-MVS		92.89 47	92.86 48	92.98 86	98.71 23	81.12 137	97.58 72	96.70 60	85.20 118	91.75 70	97.97 56	78.47 88	99.71 32	90.95 81	98.41 44	98.12 73

#test#		92.99 45	92.99 44	92.98 86	98.71 23	81.12 137	97.77 56	96.70 60	85.75 102	91.75 70	97.97 56	78.47 88	99.71 32	91.36 77	98.41 44	98.12 73

DeepC-MVS_fast	89.06 2	94.48 18	94.30 26	95.02 17	98.86 19	85.68 42	98.06 40	96.64 71	93.64 8	91.74 72	98.54 20	80.17 70	99.90 5	92.28 70	98.75 26	99.49 6
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 74	92.00 66	90.54 163	98.20 56	74.48 273	96.45 158	95.65 151	81.97 194	91.63 73	98.02 49	75.76 133	98.61 119	93.16 58	97.17 80	98.52 45

ACMMPR		92.69 55	92.67 52	92.75 96	98.66 28	80.57 152	97.58 72	96.69 62	85.20 118	91.57 74	97.92 58	77.01 110	99.67 40	90.95 81	98.41 44	98.00 85

DELS-MVS		94.98 11	94.49 19	96.44 4	96.42 103	90.59 5	99.21 2	97.02 27	94.40 5	91.46 75	97.08 100	83.32 43	99.69 36	92.83 63	98.70 29	99.04 21
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 55	92.71 49	92.63 102	98.52 38	80.29 158	97.37 92	96.44 97	87.04 86	91.38 76	97.83 64	77.24 107	99.59 47	90.46 91	98.07 58	98.02 80

X-MVStestdata		86.26 175	84.14 191	92.63 102	98.52 38	80.29 158	97.37 92	96.44 97	87.04 86	91.38 76	20.73 364	77.24 107	99.59 47	90.46 91	98.07 58	98.02 80

1121		90.66 95	89.82 104	93.16 78	97.39 88	81.71 128	93.33 262	96.66 67	74.45 290	91.38 76	97.55 79	79.27 77	99.52 53	79.95 184	98.43 41	98.26 62

PMMVS		89.46 116	89.92 101	88.06 221	94.64 148	69.57 316	96.22 174	94.95 188	87.27 80	91.37 79	96.54 117	65.88 223	97.39 170	88.54 113	93.89 121	97.23 136

Regformer-3		93.19 41	93.19 42	93.19 77	98.10 60	83.01 98	97.08 117	96.98 30	88.98 45	91.35 80	97.89 59	80.80 61	99.23 76	92.30 69	95.20 109	97.32 130

原ACMM1				91.22 146	97.77 72	78.10 218		96.61 74	81.05 203	91.28 81	97.42 85	77.92 97	98.98 102	79.85 187	98.51 34	96.59 156

Regformer-4		93.06 44	93.12 43	92.89 91	98.10 60	82.20 111	97.08 117	96.92 38	88.87 47	91.23 82	97.89 59	80.57 64	99.19 85	92.21 71	95.20 109	97.29 134

新几何1				93.12 79	97.44 84	81.60 131		96.71 59	74.54 289	91.22 83	97.57 75	79.13 81	99.51 56	77.40 210	98.46 39	98.26 62

UA-Net		88.92 126	88.48 123	90.24 171	94.06 166	77.18 240	93.04 271	94.66 205	87.39 77	91.09 84	93.89 176	74.92 153	98.18 139	75.83 227	91.43 145	95.35 186

ZNCC-MVS		92.75 49	92.60 54	93.23 75	98.24 52	81.82 123	97.63 67	96.50 91	85.00 123	91.05 85	97.74 67	78.38 90	99.80 19	90.48 90	98.34 51	98.07 76

APD-MVS_3200maxsize		91.23 86	91.35 76	90.89 154	97.89 68	76.35 251	96.30 170	95.52 159	79.82 231	91.03 86	97.88 61	74.70 155	98.54 124	92.11 73	96.89 87	97.77 101

GST-MVS		92.43 62	92.22 63	93.04 84	98.17 57	81.64 130	97.40 91	96.38 108	84.71 129	90.90 87	97.40 86	77.55 102	99.76 20	89.75 102	97.74 66	97.72 104

PGM-MVS		91.93 68	91.80 69	92.32 113	98.27 51	79.74 174	95.28 211	97.27 17	83.83 158	90.89 88	97.78 66	76.12 126	99.56 51	88.82 111	97.93 64	97.66 109

SR-MVS-dyc-post		91.29 84	91.45 75	90.80 156	97.76 73	76.03 256	96.20 176	95.44 164	80.56 213	90.72 89	97.84 62	75.76 133	98.61 119	91.99 74	96.79 91	97.75 102

RE-MVS-def			91.18 80		97.76 73	76.03 256	96.20 176	95.44 164	80.56 213	90.72 89	97.84 62	73.36 172		91.99 74	96.79 91	97.75 102

MP-MVS		92.61 58	92.67 52	92.42 109	98.13 59	79.73 175	97.33 94	96.20 121	85.63 104	90.53 91	97.66 69	78.14 94	99.70 35	92.12 72	98.30 53	97.85 95
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 75	90.37 90	95.39 14	96.12 109	88.25 12	90.22 299	97.58 14	88.33 59	90.50 92	91.96 196	79.26 78	99.06 97	90.29 96	89.07 158	98.88 27

CP-MVS		92.54 60	92.60 54	92.34 111	98.50 40	79.90 169	98.40 25	96.40 104	84.75 126	90.48 93	98.09 43	77.40 104	99.21 80	91.15 80	98.23 55	97.92 91

diffmvs		91.17 87	90.74 85	92.44 108	93.11 191	82.50 106	96.25 173	93.62 259	87.79 70	90.40 94	95.93 125	73.44 171	97.42 168	93.62 49	92.55 134	97.41 126

MVS_Test		90.29 105	89.18 112	93.62 59	95.23 130	84.93 61	94.41 236	94.66 205	84.31 142	90.37 95	91.02 209	75.13 150	97.82 149	83.11 165	94.42 115	98.12 73

zzz-MVS		92.74 50	92.71 49	92.86 92	97.90 66	80.85 145	96.47 155	96.33 112	87.92 65	90.20 96	98.18 32	76.71 116	99.76 20	92.57 67	98.09 56	97.96 89

MTAPA		92.45 61	92.31 59	92.86 92	97.90 66	80.85 145	92.88 275	96.33 112	87.92 65	90.20 96	98.18 32	76.71 116	99.76 20	92.57 67	98.09 56	97.96 89

test_yl		91.46 79	90.53 87	94.24 36	97.41 86	85.18 51	98.08 38	97.72 10	80.94 204	89.85 98	96.14 121	75.61 135	98.81 114	90.42 94	88.56 166	98.74 31

DCV-MVSNet		91.46 79	90.53 87	94.24 36	97.41 86	85.18 51	98.08 38	97.72 10	80.94 204	89.85 98	96.14 121	75.61 135	98.81 114	90.42 94	88.56 166	98.74 31

WTY-MVS		92.65 57	91.68 71	95.56 11	96.00 112	88.90 10	98.23 31	97.65 12	88.57 53	89.82 100	97.22 95	79.29 76	99.06 97	89.57 104	88.73 163	98.73 35

MVS_111021_HR		93.41 40	93.39 39	93.47 70	97.34 92	82.83 100	97.56 74	98.27 6	89.16 44	89.71 101	97.14 97	79.77 73	99.56 51	93.65 48	97.94 62	98.02 80

sss		90.87 92	89.96 99	93.60 60	94.15 163	83.84 79	97.14 108	98.13 7	85.93 99	89.68 102	96.09 123	71.67 186	99.30 72	87.69 121	89.16 157	97.66 109

test222					96.15 108	78.41 206	95.87 192	96.46 95	71.97 309	89.66 103	97.45 81	76.33 123			98.24 54	98.30 58

LFMVS		89.27 120	87.64 136	94.16 41	97.16 95	85.52 45	97.18 102	94.66 205	79.17 245	89.63 104	96.57 116	55.35 294	98.22 136	89.52 106	89.54 154	98.74 31

CostFormer		89.08 122	88.39 124	91.15 147	93.13 189	79.15 188	88.61 310	96.11 127	83.14 171	89.58 105	86.93 267	83.83 39	96.87 197	88.22 119	85.92 188	97.42 125

PVSNet_BlendedMVS		90.05 107	89.96 99	90.33 169	97.47 82	83.86 77	98.02 43	96.73 56	87.98 64	89.53 106	89.61 230	76.42 120	99.57 49	94.29 43	79.59 228	87.57 297

PVSNet_Blended		93.13 42	92.98 45	93.57 61	97.47 82	83.86 77	99.32 1	96.73 56	91.02 23	89.53 106	96.21 120	76.42 120	99.57 49	94.29 43	95.81 105	97.29 134

HPM-MVS		91.62 76	91.53 74	91.89 126	97.88 69	79.22 185	96.99 122	95.73 148	82.07 191	89.50 108	97.19 96	75.59 137	98.93 109	90.91 83	97.94 62	97.54 116
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
abl_6		89.80 110	89.71 107	90.07 175	96.53 102	75.52 264	94.48 233	95.04 184	81.12 202	89.22 109	97.00 102	68.83 206	98.96 103	89.86 99	95.27 108	95.73 177

EI-MVSNet-Vis-set		91.84 70	91.77 70	92.04 122	97.60 77	81.17 136	96.61 149	96.87 40	88.20 61	89.19 110	97.55 79	78.69 87	99.14 91	90.29 96	90.94 148	95.80 175

MP-MVS-pluss		92.58 59	92.35 58	93.29 72	97.30 93	82.53 104	96.44 160	96.04 132	84.68 130	89.12 111	98.37 26	77.48 103	99.74 28	93.31 56	98.38 48	97.59 115
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
VDD-MVS		88.28 145	87.02 154	92.06 121	95.09 135	80.18 164	97.55 75	94.45 220	83.09 172	89.10 112	95.92 127	47.97 316	98.49 127	93.08 62	86.91 178	97.52 120

baseline		90.76 93	90.10 96	92.74 97	92.90 196	82.56 103	94.60 232	94.56 213	87.69 73	89.06 113	95.67 132	73.76 166	97.51 164	90.43 93	92.23 140	98.16 68

EIA-MVS		91.73 71	92.05 65	90.78 158	94.52 153	76.40 250	98.06 40	95.34 172	89.19 43	88.90 114	97.28 92	77.56 101	97.73 152	90.77 86	96.86 90	98.20 64

HPM-MVS_fast		90.38 104	90.17 95	91.03 150	97.61 76	77.35 236	97.15 107	95.48 161	79.51 237	88.79 115	96.90 104	71.64 188	98.81 114	87.01 129	97.44 72	96.94 142

PAPM		92.87 48	92.40 57	94.30 33	92.25 213	87.85 16	96.40 164	96.38 108	91.07 21	88.72 116	96.90 104	82.11 56	97.37 171	90.05 98	97.70 67	97.67 108

MVS_111021_LR		91.60 77	91.64 73	91.47 140	95.74 117	78.79 198	96.15 178	96.77 51	88.49 55	88.64 117	97.07 101	72.33 180	99.19 85	93.13 61	96.48 95	96.43 160

casdiffmvs		90.95 90	90.39 89	92.63 102	92.82 197	82.53 104	96.83 135	94.47 218	87.69 73	88.47 118	95.56 136	74.04 163	97.54 162	90.90 84	92.74 132	97.83 97

mPP-MVS		91.88 69	91.82 68	92.07 120	98.38 45	78.63 200	97.29 95	96.09 128	85.12 120	88.45 119	97.66 69	75.53 138	99.68 38	89.83 100	98.02 61	97.88 92

PAPR		92.74 50	92.17 64	94.45 28	98.89 18	84.87 63	97.20 100	96.20 121	87.73 72	88.40 120	98.12 41	78.71 86	99.76 20	87.99 120	96.28 96	98.74 31

tpmrst		88.36 143	87.38 146	91.31 141	94.36 160	79.92 168	87.32 320	95.26 177	85.32 112	88.34 121	86.13 283	80.60 63	96.70 204	83.78 149	85.34 196	97.30 133

GG-mvs-BLEND				93.49 66	94.94 142	86.26 29	81.62 337	97.00 28		88.32 122	94.30 165	91.23 3	96.21 221	88.49 115	97.43 73	98.00 85

EI-MVSNet-UG-set		91.35 83	91.22 77	91.73 131	97.39 88	80.68 149	96.47 155	96.83 43	87.92 65	88.30 123	97.36 87	77.84 98	99.13 93	89.43 107	89.45 155	95.37 185

MAR-MVS		90.63 96	90.22 92	91.86 127	98.47 42	78.20 216	97.18 102	96.61 74	83.87 157	88.18 124	98.18 32	68.71 207	99.75 26	83.66 155	97.15 81	97.63 112
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 72	90.85 82	94.34 32	99.50 1	85.00 60	98.51 24	95.96 135	80.57 212	88.08 125	97.63 74	76.84 112	99.89 7	85.67 135	94.88 113	98.13 72

VDDNet		86.44 172	84.51 183	92.22 116	91.56 233	81.83 122	97.10 114	94.64 208	69.50 320	87.84 126	95.19 143	48.01 315	97.92 147	89.82 101	86.92 177	96.89 146

UGNet		87.73 154	86.55 159	91.27 144	95.16 134	79.11 189	96.35 166	96.23 119	88.14 62	87.83 127	90.48 217	50.65 306	99.09 96	80.13 183	94.03 117	95.60 180
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
tpm2		87.35 158	86.26 161	90.62 161	92.93 195	78.67 199	88.06 315	95.99 133	79.33 240	87.40 128	86.43 278	80.28 67	96.40 212	80.23 181	85.73 192	96.79 149

CPTT-MVS		89.72 112	89.87 103	89.29 195	98.33 48	73.30 282	97.70 64	95.35 171	75.68 280	87.40 128	97.44 84	70.43 199	98.25 135	89.56 105	96.90 86	96.33 165

gg-mvs-nofinetune		85.48 187	82.90 207	93.24 74	94.51 156	85.82 37	79.22 341	96.97 32	61.19 341	87.33 130	53.01 354	90.58 4	96.07 223	86.07 133	97.23 79	97.81 99

CHOSEN 280x420		91.71 73	91.85 67	91.29 143	94.94 142	82.69 101	87.89 316	96.17 124	85.94 98	87.27 131	94.31 164	90.27 6	95.65 249	94.04 46	95.86 103	95.53 182

EPNet_dtu		87.65 155	87.89 130	86.93 246	94.57 150	71.37 303	96.72 143	96.50 91	88.56 54	87.12 132	95.02 152	75.91 131	94.01 300	66.62 283	90.00 152	95.42 184
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
Vis-MVSNet		88.67 134	87.82 132	91.24 145	92.68 198	78.82 195	96.95 129	93.85 245	87.55 75	87.07 133	95.13 148	63.43 238	97.21 179	77.58 207	96.15 97	97.70 107
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
DWT-MVSNet_test		90.52 101	89.80 105	92.70 100	95.73 119	82.20 111	93.69 253	96.55 84	88.34 58	87.04 134	95.34 140	86.53 22	97.55 159	76.32 222	88.66 164	98.34 52

thisisatest0515		90.95 90	90.26 91	93.01 85	94.03 169	84.27 72	97.91 47	96.67 64	83.18 170	86.87 135	95.51 137	88.66 13	97.85 148	80.46 178	89.01 159	96.92 145

TESTMET0.1,1		89.83 109	89.34 111	91.31 141	92.54 204	80.19 163	97.11 111	96.57 80	86.15 92	86.85 136	91.83 200	79.32 75	96.95 191	81.30 173	92.35 138	96.77 151

PVSNet_Blended_VisFu		91.24 85	90.77 84	92.66 101	95.09 135	82.40 107	97.77 56	95.87 142	88.26 60	86.39 137	93.94 175	76.77 114	99.27 73	88.80 112	94.00 120	96.31 166

API-MVS		90.18 106	88.97 115	93.80 49	98.66 28	82.95 99	97.50 80	95.63 154	75.16 284	86.31 138	97.69 68	72.49 178	99.90 5	81.26 174	96.07 99	98.56 42

test-LLR		88.48 139	87.98 129	89.98 179	92.26 211	77.23 238	97.11 111	95.96 135	83.76 160	86.30 139	91.38 203	72.30 181	96.78 202	80.82 175	91.92 142	95.94 172

test-mter		88.95 124	88.60 121	89.98 179	92.26 211	77.23 238	97.11 111	95.96 135	85.32 112	86.30 139	91.38 203	76.37 122	96.78 202	80.82 175	91.92 142	95.94 172

PAPM_NR		91.46 79	90.82 83	93.37 71	98.50 40	81.81 124	95.03 225	96.13 125	84.65 132	86.10 141	97.65 73	79.24 79	99.75 26	83.20 163	96.88 88	98.56 42

MDTV_nov1_ep13_2view						81.74 126	86.80 323		80.65 210	85.65 142		74.26 160		76.52 218		96.98 141

AUN-MVS		86.25 176	85.57 166	88.26 216	93.57 178	73.38 280	95.45 207	95.88 140	83.94 153	85.47 143	94.21 168	73.70 169	96.67 206	83.54 158	64.41 322	94.73 198

PVSNet	82.34 9	89.02 123	87.79 133	92.71 99	95.49 124	81.50 132	97.70 64	97.29 16	87.76 71	85.47 143	95.12 149	56.90 283	98.90 110	80.33 179	94.02 118	97.71 106

EPP-MVSNet		89.76 111	89.72 106	89.87 184	93.78 171	76.02 258	97.22 96	96.51 89	79.35 239	85.11 145	95.01 153	84.82 29	97.10 186	87.46 124	88.21 170	96.50 158

OMC-MVS		88.80 131	88.16 127	90.72 159	95.30 129	77.92 224	94.81 229	94.51 215	86.80 88	84.97 146	96.85 107	67.53 212	98.60 121	85.08 140	87.62 173	95.63 179

CHOSEN 1792x2688		91.07 88	90.21 93	93.64 57	95.18 133	83.53 85	96.26 172	96.13 125	88.92 46	84.90 147	93.10 186	72.86 175	99.62 45	88.86 110	95.67 106	97.79 100

thres200		88.92 126	87.65 135	92.73 98	96.30 104	85.62 43	97.85 50	98.86 1	84.38 140	84.82 148	93.99 174	75.12 151	98.01 140	70.86 266	86.67 179	94.56 200

MDTV_nov1_ep13			83.69 194		94.09 165	81.01 140	86.78 324	96.09 128	83.81 159	84.75 149	84.32 306	74.44 159	96.54 208	63.88 297	85.07 197

CDS-MVSNet		89.50 115	88.96 116	91.14 148	91.94 229	80.93 143	97.09 115	95.81 144	84.26 145	84.72 150	94.20 169	80.31 66	95.64 250	83.37 161	88.96 160	96.85 148
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
ACMMP		90.39 102	89.97 98	91.64 133	97.58 79	78.21 215	96.78 139	96.72 58	84.73 128	84.72 150	97.23 93	71.22 191	99.63 44	88.37 118	92.41 137	97.08 140
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 67	91.65 72	93.12 79	98.53 37	80.59 151	97.47 81	97.18 20	77.06 274	84.64 152	97.98 54	83.98 37	99.52 53	90.72 87	97.33 76	99.23 17

ab-mvs		87.08 160	84.94 179	93.48 67	93.34 184	83.67 83	88.82 307	95.70 149	81.18 201	84.55 153	90.14 226	62.72 241	98.94 108	85.49 137	82.54 217	97.85 95

mvs-test1		86.83 166	87.17 149	85.81 262	91.96 226	65.24 329	97.90 49	93.34 273	85.57 105	84.51 154	95.14 147	61.99 249	97.19 181	83.55 156	90.55 150	95.00 190

EPMVS		87.47 157	85.90 165	92.18 117	95.41 126	82.26 110	87.00 322	96.28 116	85.88 100	84.23 155	85.57 289	75.07 152	96.26 218	71.14 264	92.50 135	98.03 79

Anonymous202405211		84.41 203	81.93 222	91.85 129	96.78 100	78.41 206	97.44 83	91.34 306	70.29 316	84.06 156	94.26 166	41.09 338	98.96 103	79.46 189	82.65 216	98.17 67

HyFIR lowres test		89.36 117	88.60 121	91.63 135	94.91 144	80.76 148	95.60 202	95.53 157	82.56 185	84.03 157	91.24 206	78.03 95	96.81 200	87.07 128	88.41 168	97.32 130

tfpn200view9		88.48 139	87.15 150	92.47 106	96.21 106	85.30 49	97.44 83	98.85 2	83.37 167	83.99 158	93.82 177	75.36 145	97.93 142	69.04 272	86.24 185	94.17 202

thres400		88.42 142	87.15 150	92.23 115	96.21 106	85.30 49	97.44 83	98.85 2	83.37 167	83.99 158	93.82 177	75.36 145	97.93 142	69.04 272	86.24 185	93.45 215

tpm		85.55 185	84.47 186	88.80 205	90.19 257	75.39 266	88.79 308	94.69 201	84.83 125	83.96 160	85.21 295	78.22 93	94.68 289	76.32 222	78.02 244	96.34 163

Fast-Effi-MVS+		87.93 152	86.94 156	90.92 153	94.04 167	79.16 187	98.26 30	93.72 255	81.29 200	83.94 161	92.90 187	69.83 203	96.68 205	76.70 216	91.74 144	96.93 143

XVG-OURS-SEG-HR		85.74 183	85.16 175	87.49 235	90.22 256	71.45 302	91.29 293	94.09 235	81.37 199	83.90 162	95.22 141	60.30 258	97.53 163	85.58 136	84.42 200	93.50 213

thisisatest0530		89.65 113	89.02 114	91.53 137	93.46 182	80.78 147	96.52 152	96.67 64	81.69 197	83.79 163	94.90 155	88.85 12	97.68 153	77.80 201	87.49 176	96.14 169

DeepC-MVS	86.58 3	91.53 78	91.06 81	92.94 89	94.52 153	81.89 119	95.95 186	95.98 134	90.76 24	83.76 164	96.76 112	73.24 173	99.71 32	91.67 76	96.96 85	97.22 137
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
IS-MVSNet		88.67 134	88.16 127	90.20 173	93.61 176	76.86 243	96.77 141	93.07 283	84.02 150	83.62 165	95.60 135	74.69 158	96.24 220	78.43 200	93.66 125	97.49 122

thres100view900		88.30 144	86.95 155	92.33 112	96.10 110	84.90 62	97.14 108	98.85 2	82.69 182	83.41 166	93.66 180	75.43 142	97.93 142	69.04 272	86.24 185	94.17 202

thres600view7		88.06 148	86.70 158	92.15 118	96.10 110	85.17 55	97.14 108	98.85 2	82.70 181	83.41 166	93.66 180	75.43 142	97.82 149	67.13 281	85.88 189	93.45 215

XVG-OURS		85.18 190	84.38 187	87.59 230	90.42 254	71.73 299	91.06 296	94.07 236	82.00 193	83.29 168	95.08 151	56.42 288	97.55 159	83.70 154	83.42 205	93.49 214

Vis-MVSNet (Re-imp)		88.88 128	88.87 119	88.91 201	93.89 170	74.43 274	96.93 131	94.19 228	84.39 139	83.22 169	95.67 132	78.24 92	94.70 288	78.88 197	94.40 116	97.61 114

TAMVS		88.48 139	87.79 133	90.56 162	91.09 241	79.18 186	96.45 158	95.88 140	83.64 163	83.12 170	93.33 182	75.94 130	95.74 245	82.40 168	88.27 169	96.75 153

baseline1		88.85 129	87.49 142	92.93 90	95.21 132	86.85 25	95.47 206	94.61 210	87.29 78	83.11 171	94.99 154	80.70 62	96.89 195	82.28 169	73.72 258	95.05 189

AdaColmap		88.81 130	87.61 139	92.39 110	99.33 4	79.95 167	96.70 147	95.58 155	77.51 266	83.05 172	96.69 115	61.90 252	99.72 31	84.29 145	93.47 126	97.50 121

PatchmatchNet		86.83 166	85.12 176	91.95 124	94.12 164	82.27 109	86.55 326	95.64 153	84.59 134	82.98 173	84.99 301	77.26 105	95.96 230	68.61 276	91.34 146	97.64 111
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
SCA		85.63 184	83.64 197	91.60 136	92.30 209	81.86 121	92.88 275	95.56 156	84.85 124	82.52 174	85.12 299	58.04 274	95.39 260	73.89 244	87.58 175	97.54 116

114514_t		88.79 132	87.57 140	92.45 107	98.21 55	81.74 126	96.99 122	95.45 163	75.16 284	82.48 175	95.69 131	68.59 208	98.50 126	80.33 179	95.18 111	97.10 139

PatchT		79.75 262	76.85 272	88.42 210	89.55 268	75.49 265	77.37 347	94.61 210	63.07 333	82.46 176	73.32 345	75.52 139	93.41 310	51.36 339	84.43 199	96.36 161

TR-MVS		86.30 174	84.93 180	90.42 165	94.63 149	77.58 231	96.57 151	93.82 246	80.30 221	82.42 177	95.16 145	58.74 269	97.55 159	74.88 234	87.82 172	96.13 170

HQP-NCC					92.08 220		97.63 67		90.52 27	82.30 178

ACMP_Plane					92.08 220		97.63 67		90.52 27	82.30 178

HQP4-MVS										82.30 178			97.32 172			91.13 222

HQP-MVS		87.91 153	87.55 141	88.98 200	92.08 220	78.48 202	97.63 67	94.80 197	90.52 27	82.30 178	94.56 160	65.40 227	97.32 172	87.67 122	83.01 209	91.13 222

CR-MVSNet		83.53 215	81.36 231	90.06 176	90.16 258	79.75 172	79.02 343	91.12 308	84.24 146	82.27 182	80.35 328	75.45 140	93.67 306	63.37 301	86.25 183	96.75 153

RPMNet		79.85 261	75.92 279	91.64 133	90.16 258	79.75 172	79.02 343	95.44 164	58.43 350	82.27 182	72.55 346	73.03 174	98.41 131	46.10 350	86.25 183	96.75 153

CVMVSNet		84.83 195	85.57 166	82.63 304	91.55 234	60.38 343	95.13 219	95.03 185	80.60 211	82.10 184	94.71 157	66.40 222	90.19 339	74.30 241	90.32 151	97.31 132

PLC	83.97 7	88.00 150	87.38 146	89.83 186	98.02 64	76.46 248	97.16 106	94.43 221	79.26 244	81.98 185	96.28 119	69.36 204	99.27 73	77.71 205	92.25 139	93.77 210
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
JIA-IIPM		79.00 270	77.20 268	84.40 285	89.74 265	64.06 333	75.30 350	95.44 164	62.15 336	81.90 186	59.08 352	78.92 82	95.59 254	66.51 286	85.78 191	93.54 212

Anonymous20240529		83.15 222	80.60 240	90.80 156	95.74 117	78.27 210	96.81 137	94.92 189	60.10 346	81.89 187	92.54 190	45.82 323	98.82 113	79.25 193	78.32 242	95.31 187

tttt0517		88.57 138	88.19 126	89.71 190	93.00 192	75.99 259	95.67 199	96.67 64	80.78 207	81.82 188	94.40 163	88.97 11	97.58 157	76.05 225	86.31 182	95.57 181

BH-RMVSNet		86.84 165	85.28 171	91.49 139	95.35 128	80.26 161	96.95 129	92.21 293	82.86 179	81.77 189	95.46 138	59.34 265	97.64 154	69.79 270	93.81 123	96.57 157

HQP_MVS		87.50 156	87.09 153	88.74 206	91.86 230	77.96 221	97.18 102	94.69 201	89.89 35	81.33 190	94.15 170	64.77 232	97.30 174	87.08 126	82.82 213	90.96 224

plane_prior3						77.75 228			90.17 33	81.33 190

VPA-MVSNet		85.32 188	83.83 193	89.77 189	90.25 255	82.63 102	96.36 165	97.07 25	83.03 174	81.21 192	89.02 236	61.58 253	96.31 217	85.02 142	70.95 273	90.36 231

GeoE		86.36 173	85.20 172	89.83 186	93.17 187	76.13 253	97.53 76	92.11 294	79.58 236	80.99 193	94.01 173	66.60 221	96.17 222	73.48 248	89.30 156	97.20 138

GA-MVS		85.79 182	84.04 192	91.02 151	89.47 270	80.27 160	96.90 132	94.84 195	85.57 105	80.88 194	89.08 234	56.56 287	96.47 211	77.72 204	85.35 195	96.34 163

1112_ss		88.60 137	87.47 144	92.00 123	93.21 185	80.97 142	96.47 155	92.46 291	83.64 163	80.86 195	97.30 90	80.24 68	97.62 155	77.60 206	85.49 193	97.40 127

dp		84.30 206	82.31 217	90.28 170	94.24 162	77.97 220	86.57 325	95.53 157	79.94 230	80.75 196	85.16 297	71.49 190	96.39 213	63.73 298	83.36 206	96.48 159

Test_1112_low_res		88.03 149	86.73 157	91.94 125	93.15 188	80.88 144	96.44 160	92.41 292	83.59 166	80.74 197	91.16 207	80.18 69	97.59 156	77.48 209	85.40 194	97.36 129

cascas		86.50 171	84.48 185	92.55 105	92.64 202	85.95 33	97.04 121	95.07 183	75.32 282	80.50 198	91.02 209	54.33 301	97.98 141	86.79 130	87.62 173	93.71 211

TAPA-MVS	81.61 12	85.02 192	83.67 195	89.06 197	96.79 99	73.27 284	95.92 188	94.79 199	74.81 287	80.47 199	96.83 108	71.07 193	98.19 138	49.82 344	92.57 133	95.71 178
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
OPM-MVS		85.84 180	85.10 177	88.06 221	88.34 281	77.83 227	95.72 197	94.20 227	87.89 69	80.45 200	94.05 172	58.57 270	97.26 178	83.88 148	82.76 215	89.09 261

nrg030		86.79 168	85.43 168	90.87 155	88.76 275	85.34 47	97.06 119	94.33 223	84.31 142	80.45 200	91.98 195	72.36 179	96.36 214	88.48 116	71.13 271	90.93 226

EI-MVSNet		85.80 181	85.20 172	87.59 230	91.55 234	77.41 234	95.13 219	95.36 169	80.43 218	80.33 202	94.71 157	73.72 167	95.97 227	76.96 214	78.64 237	89.39 250

MVSTER		89.25 121	88.92 118	90.24 171	95.98 113	84.66 66	96.79 138	95.36 169	87.19 84	80.33 202	90.61 216	90.02 9	95.97 227	85.38 138	78.64 237	90.09 240

ADS-MVSNet2		79.57 264	77.53 266	85.71 265	93.78 171	72.13 291	79.48 339	86.11 341	73.09 301	80.14 204	79.99 331	62.15 246	90.14 340	59.49 313	83.52 203	94.85 192

ADS-MVSNet		81.26 250	78.36 260	89.96 181	93.78 171	79.78 170	79.48 339	93.60 260	73.09 301	80.14 204	79.99 331	62.15 246	95.24 269	59.49 313	83.52 203	94.85 192

RRT_MVS		86.89 163	85.96 163	89.68 191	95.01 141	84.13 73	96.33 168	94.98 187	84.20 147	80.10 206	92.07 194	70.52 198	95.01 282	83.30 162	77.14 246	89.91 244

baseline2		90.39 102	90.21 93	90.93 152	90.86 246	80.99 141	95.20 216	97.41 15	86.03 97	80.07 207	94.61 159	90.58 4	97.47 167	87.29 125	89.86 153	94.35 201

Effi-MVS+-dtu		84.61 199	84.90 181	83.72 293	91.96 226	63.14 336	94.95 226	93.34 273	85.57 105	79.79 208	87.12 264	61.99 249	95.61 253	83.55 156	85.83 190	92.41 218

VPNet		84.69 198	82.92 206	90.01 177	89.01 274	83.45 87	96.71 145	95.46 162	85.71 103	79.65 209	92.18 193	56.66 286	96.01 226	83.05 166	67.84 305	90.56 228

CLD-MVS		87.97 151	87.48 143	89.44 192	92.16 218	80.54 154	98.14 34	94.92 189	91.41 17	79.43 210	95.40 139	62.34 243	97.27 177	90.60 89	82.90 212	90.50 230
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	88.67 134	87.14 152	93.26 73	93.12 190	84.32 69	98.76 16	97.27 17	87.19 84	79.36 211	90.45 219	83.92 38	98.53 125	84.41 144	69.79 285	96.93 143
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		85.00 193	83.66 196	89.02 199	95.86 115	74.55 272	92.49 279	93.60 260	79.30 242	79.29 212	91.47 201	58.53 271	98.45 129	70.22 269	92.17 141	94.07 206

CNLPA		86.96 161	85.37 170	91.72 132	97.59 78	79.34 183	97.21 98	91.05 311	74.22 291	78.90 213	96.75 113	67.21 216	98.95 106	74.68 236	90.77 149	96.88 147

MVS		90.60 97	88.64 120	96.50 3	94.25 161	90.53 6	93.33 262	97.21 19	77.59 265	78.88 214	97.31 88	71.52 189	99.69 36	89.60 103	98.03 60	99.27 16

mvs_anonymous		88.68 133	87.62 138	91.86 127	94.80 146	81.69 129	93.53 258	94.92 189	82.03 192	78.87 215	90.43 220	75.77 132	95.34 263	85.04 141	93.16 130	98.55 44

MVS_0304		78.43 272	76.70 273	83.60 295	88.22 283	69.81 312	92.91 274	95.10 180	72.32 308	78.71 216	80.29 330	33.78 349	93.37 311	68.77 275	80.23 224	87.63 294

bset_n11_16_dypcd		84.35 204	82.83 210	88.91 201	82.54 331	82.07 113	94.12 247	93.47 264	85.39 111	78.55 217	88.98 237	62.23 244	95.11 276	86.75 131	73.42 260	89.55 249

tpm cat1		83.63 214	81.38 230	90.39 166	93.53 181	78.19 217	85.56 332	95.09 181	70.78 314	78.51 218	83.28 314	74.80 154	97.03 187	66.77 282	84.05 201	95.95 171

UniMVSNet (Re)		85.31 189	84.23 189	88.55 209	89.75 263	80.55 153	96.72 143	96.89 39	85.42 109	78.40 219	88.93 238	75.38 144	95.52 257	78.58 198	68.02 302	89.57 248

FIs		86.73 170	86.10 162	88.61 208	90.05 260	80.21 162	96.14 179	96.95 34	85.56 108	78.37 220	92.30 191	76.73 115	95.28 267	79.51 188	79.27 231	90.35 232

BH-w/o		88.24 146	87.47 144	90.54 163	95.03 140	78.54 201	97.41 90	93.82 246	84.08 148	78.23 221	94.51 162	69.34 205	97.21 179	80.21 182	94.58 114	95.87 174

UniMVSNet_NR-MVSNet		85.49 186	84.59 182	88.21 219	89.44 271	79.36 181	96.71 145	96.41 101	85.22 116	78.11 222	90.98 211	76.97 111	95.14 274	79.14 194	68.30 299	90.12 238

DU-MVS		84.57 200	83.33 203	88.28 215	88.76 275	79.36 181	96.43 162	95.41 168	85.42 109	78.11 222	90.82 212	67.61 210	95.14 274	79.14 194	68.30 299	90.33 233

miper_enhance_ethall		85.95 179	85.20 172	88.19 220	94.85 145	79.76 171	96.00 183	94.06 237	82.98 176	77.74 224	88.76 240	79.42 74	95.46 259	80.58 177	72.42 266	89.36 255

v1144		82.90 228	81.27 232	87.78 226	86.29 299	79.07 192	96.14 179	93.93 240	80.05 227	77.38 225	86.80 269	65.50 225	95.93 233	75.21 232	70.13 280	88.33 282

FC-MVSNet-test		85.96 178	85.39 169	87.66 228	89.38 272	78.02 219	95.65 201	96.87 40	85.12 120	77.34 226	91.94 198	76.28 124	94.74 287	77.09 211	78.82 235	90.21 236

v2v482		83.46 216	81.86 223	88.25 217	86.19 301	79.65 176	96.34 167	94.02 238	81.56 198	77.32 227	88.23 248	65.62 224	96.03 224	77.77 202	69.72 287	89.09 261

Baseline_NR-MVSNet		81.22 251	80.07 248	84.68 277	85.32 315	75.12 268	96.48 154	88.80 329	76.24 278	77.28 228	86.40 279	67.61 210	94.39 294	75.73 229	66.73 316	84.54 328

V42		83.04 225	81.53 228	87.57 232	86.27 300	79.09 191	95.87 192	94.11 233	80.35 220	77.22 229	86.79 270	65.32 229	96.02 225	77.74 203	70.14 279	87.61 296

v144192		82.43 234	80.73 237	87.54 233	85.81 308	78.22 212	95.98 184	93.78 251	79.09 247	77.11 230	86.49 274	64.66 234	95.91 234	74.20 242	69.42 288	88.49 276

ACMM	80.70 13	83.72 213	82.85 208	86.31 256	91.19 239	72.12 292	95.88 191	94.29 224	80.44 216	77.02 231	91.96 196	55.24 295	97.14 185	79.30 192	80.38 223	89.67 247
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
v1192		82.31 238	80.55 241	87.60 229	85.94 305	78.47 205	95.85 194	93.80 249	79.33 240	76.97 232	86.51 273	63.33 239	95.87 235	73.11 249	70.13 280	88.46 278

PCF-MVS	84.09 5	86.77 169	85.00 178	92.08 119	92.06 223	83.07 96	92.14 283	94.47 218	79.63 235	76.90 233	94.78 156	71.15 192	99.20 84	72.87 250	91.05 147	93.98 207
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
cl-mvsnet2		85.11 191	84.17 190	87.92 223	95.06 139	78.82 195	95.51 204	94.22 226	79.74 233	76.77 234	87.92 253	75.96 129	95.68 246	79.93 186	72.42 266	89.27 256

v1921920		82.02 241	80.23 245	87.41 236	85.62 309	77.92 224	95.79 196	93.69 256	78.86 252	76.67 235	86.44 276	62.50 242	95.83 237	72.69 251	69.77 286	88.47 277

WR-MVS		84.32 205	82.96 205	88.41 211	89.38 272	80.32 157	96.59 150	96.25 118	83.97 152	76.63 236	90.36 221	67.53 212	94.86 285	75.82 228	70.09 283	90.06 242

BH-untuned		86.95 162	85.94 164	89.99 178	94.52 153	77.46 233	96.78 139	93.37 272	81.80 195	76.62 237	93.81 179	66.64 220	97.02 188	76.06 224	93.88 122	95.48 183

v1240		81.70 244	79.83 252	87.30 240	85.50 310	77.70 230	95.48 205	93.44 266	78.46 257	76.53 238	86.44 276	60.85 256	95.84 236	71.59 258	70.17 278	88.35 281

PS-MVSNAJss		84.91 194	84.30 188	86.74 247	85.89 307	74.40 275	94.95 226	94.16 230	83.93 155	76.45 239	90.11 227	71.04 194	95.77 240	83.16 164	79.02 234	90.06 242

miper_ehance_all_eth		84.57 200	83.60 199	87.50 234	92.64 202	78.25 211	95.40 210	93.47 264	79.28 243	76.41 240	87.64 256	76.53 118	95.24 269	78.58 198	72.42 266	89.01 266

LPG-MVS_test		84.20 207	83.49 201	86.33 253	90.88 244	73.06 285	95.28 211	94.13 231	82.20 188	76.31 241	93.20 183	54.83 299	96.95 191	83.72 152	80.83 221	88.98 267

LGP-MVS_train				86.33 253	90.88 244	73.06 285		94.13 231	82.20 188	76.31 241	93.20 183	54.83 299	96.95 191	83.72 152	80.83 221	88.98 267

F-COLMAP		84.50 202	83.44 202	87.67 227	95.22 131	72.22 289	95.95 186	93.78 251	75.74 279	76.30 243	95.18 144	59.50 263	98.45 129	72.67 252	86.59 181	92.35 219

tpmvs		83.04 225	80.77 236	89.84 185	95.43 125	77.96 221	85.59 331	95.32 174	75.31 283	76.27 244	83.70 311	73.89 164	97.41 169	59.53 312	81.93 219	94.14 204

RRT_test8_iter05		87.14 159	86.41 160	89.32 194	94.41 158	81.10 139	97.06 119	95.33 173	84.67 131	76.27 244	90.48 217	83.60 41	96.33 215	85.10 139	70.78 274	90.53 229

3Dnovator	82.32 10	89.33 118	87.64 136	94.42 30	93.73 175	85.70 41	97.73 62	96.75 54	86.73 89	76.21 246	95.93 125	62.17 245	99.68 38	81.67 172	97.81 65	97.88 92

TranMVSNet+NR-MVSNet		83.24 221	81.71 225	87.83 224	87.71 288	78.81 197	96.13 181	94.82 196	84.52 135	76.18 247	90.78 214	64.07 235	94.60 290	74.60 239	66.59 317	90.09 240

cl_fuxian		83.80 211	82.65 213	87.25 241	92.10 219	77.74 229	95.25 214	93.04 284	78.58 255	76.01 248	87.21 263	75.25 149	95.11 276	77.54 208	68.89 293	88.91 272

1314		88.94 125	87.20 148	94.17 39	93.21 185	85.73 40	93.33 262	96.64 71	82.89 177	75.98 249	96.36 118	66.83 219	99.39 63	83.52 160	96.02 101	97.39 128

test_part1		84.72 196	82.85 208	90.34 168	95.73 119	84.79 65	96.75 142	94.10 234	79.05 251	75.97 250	89.51 231	67.69 209	95.94 231	79.34 190	67.50 308	90.30 235

Fast-Effi-MVS+-dtu		83.33 218	82.60 214	85.50 268	89.55 268	69.38 317	96.09 182	91.38 303	82.30 187	75.96 251	91.41 202	56.71 284	95.58 255	75.13 233	84.90 198	91.54 220

XXY-MVS		83.84 210	82.00 221	89.35 193	87.13 292	81.38 133	95.72 197	94.26 225	80.15 225	75.92 252	90.63 215	61.96 251	96.52 209	78.98 196	73.28 264	90.14 237

GBi-Net		82.42 235	80.43 243	88.39 212	92.66 199	81.95 114	94.30 241	93.38 269	79.06 248	75.82 253	85.66 285	56.38 289	93.84 302	71.23 261	75.38 252	89.38 252

test1		82.42 235	80.43 243	88.39 212	92.66 199	81.95 114	94.30 241	93.38 269	79.06 248	75.82 253	85.66 285	56.38 289	93.84 302	71.23 261	75.38 252	89.38 252

FMVSNet3		84.71 197	82.71 212	90.70 160	94.55 151	87.71 18	95.92 188	94.67 204	81.73 196	75.82 253	88.08 251	66.99 217	94.47 292	71.23 261	75.38 252	89.91 244

eth_miper_zixun_eth		83.12 223	82.01 220	86.47 252	91.85 232	74.80 269	94.33 239	93.18 279	79.11 246	75.74 256	87.25 262	72.71 176	95.32 265	76.78 215	67.13 312	89.27 256

IterMVS-LS		83.93 209	82.80 211	87.31 239	91.46 237	77.39 235	95.66 200	93.43 267	80.44 216	75.51 257	87.26 261	73.72 167	95.16 273	76.99 212	70.72 276	89.39 250
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
3Dnovator+	82.88 8	89.63 114	87.85 131	94.99 18	94.49 157	86.76 27	97.84 51	95.74 147	86.10 94	75.47 258	96.02 124	65.00 231	99.51 56	82.91 167	97.07 82	98.72 36

test_djsdf		83.00 227	82.45 216	84.64 279	84.07 326	69.78 313	94.80 230	94.48 216	80.74 208	75.41 259	87.70 255	61.32 255	95.10 278	83.77 150	79.76 225	89.04 264

v148		82.41 237	80.89 234	86.99 245	86.18 302	76.81 244	96.27 171	93.82 246	80.49 215	75.28 260	86.11 284	67.32 215	95.75 242	75.48 230	67.03 314	88.42 280

QAPM		86.88 164	84.51 183	93.98 43	94.04 167	85.89 36	97.19 101	96.05 131	73.62 295	75.12 261	95.62 134	62.02 248	99.74 28	70.88 265	96.06 100	96.30 167

UniMVSNet_ETH3D		80.86 255	78.75 259	87.22 242	86.31 298	72.02 293	91.95 284	93.76 254	73.51 296	75.06 262	90.16 225	43.04 332	95.66 247	76.37 221	78.55 240	93.98 207

cl-mvsnet____		83.27 219	82.12 218	86.74 247	92.20 214	75.95 260	95.11 221	93.27 276	78.44 258	74.82 263	87.02 266	74.19 161	95.19 271	74.67 237	69.32 289	89.09 261

cl-mvsnet1		83.27 219	82.12 218	86.74 247	92.19 215	75.92 261	95.11 221	93.26 277	78.44 258	74.81 264	87.08 265	74.19 161	95.19 271	74.66 238	69.30 290	89.11 260

FMVSNet2		82.79 229	80.44 242	89.83 186	92.66 199	85.43 46	95.42 208	94.35 222	79.06 248	74.46 265	87.28 259	56.38 289	94.31 295	69.72 271	74.68 255	89.76 246

MIMVSNet		79.18 269	75.99 278	88.72 207	87.37 291	80.66 150	79.96 338	91.82 298	77.38 268	74.33 266	81.87 320	41.78 335	90.74 335	66.36 288	83.10 208	94.76 194

RPSCF		77.73 279	76.63 274	81.06 312	88.66 279	55.76 351	87.77 317	87.88 334	64.82 332	74.14 267	92.79 188	49.22 312	96.81 200	67.47 280	76.88 247	90.62 227

ACMP	81.66 11	84.00 208	83.22 204	86.33 253	91.53 236	72.95 287	95.91 190	93.79 250	83.70 162	73.79 268	92.22 192	54.31 302	96.89 195	83.98 147	79.74 227	89.16 259
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
pmmvs5		81.34 249	79.54 253	86.73 250	85.02 317	76.91 242	96.22 174	91.65 301	77.65 264	73.55 269	88.61 242	55.70 292	94.43 293	74.12 243	73.35 263	88.86 273

jajsoiax		82.12 240	81.15 233	85.03 273	84.19 324	70.70 305	94.22 245	93.95 239	83.07 173	73.48 270	89.75 228	49.66 311	95.37 262	82.24 170	79.76 225	89.02 265

mvs_tets		81.74 243	80.71 238	84.84 274	84.22 323	70.29 308	93.91 250	93.78 251	82.77 180	73.37 271	89.46 232	47.36 320	95.31 266	81.99 171	79.55 230	88.92 271

pmmvs4		82.54 233	80.79 235	87.79 225	86.11 303	80.49 156	93.55 257	93.18 279	77.29 269	73.35 272	89.40 233	65.26 230	95.05 281	75.32 231	73.61 259	87.83 290

LS3D		82.22 239	79.94 251	89.06 197	97.43 85	74.06 278	93.20 269	92.05 295	61.90 337	73.33 273	95.21 142	59.35 264	99.21 80	54.54 332	92.48 136	93.90 209

v10		81.43 248	79.53 254	87.11 243	86.38 296	78.87 194	94.31 240	93.43 267	77.88 261	73.24 274	85.26 293	65.44 226	95.75 242	72.14 255	67.71 306	86.72 308

v8		81.88 242	80.06 249	87.32 238	86.63 295	79.04 193	94.41 236	93.65 258	78.77 253	73.19 275	85.57 289	66.87 218	95.81 238	73.84 246	67.61 307	87.11 304

test0.0.03 1		82.79 229	82.48 215	83.74 292	86.81 294	72.22 289	96.52 152	95.03 185	83.76 160	73.00 276	93.20 183	72.30 181	88.88 342	64.15 296	77.52 245	90.12 238

anonymousdsp		80.98 254	79.97 250	84.01 287	81.73 332	70.44 307	92.49 279	93.58 262	77.10 273	72.98 277	86.31 280	57.58 278	94.90 283	79.32 191	78.63 239	86.69 309

XVG-ACMP-BASELINE		79.38 267	77.90 264	83.81 289	84.98 318	67.14 326	89.03 306	93.18 279	80.26 224	72.87 278	88.15 250	38.55 341	96.26 218	76.05 225	78.05 243	88.02 287

WR-MVS_H		81.02 252	80.09 246	83.79 290	88.08 285	71.26 304	94.46 234	96.54 85	80.08 226	72.81 279	86.82 268	70.36 200	92.65 315	64.18 295	67.50 308	87.46 301

OpenMVS	79.58 14	86.09 177	83.62 198	93.50 65	90.95 243	86.71 28	97.44 83	95.83 143	75.35 281	72.64 280	95.72 129	57.42 282	99.64 42	71.41 259	95.85 104	94.13 205

Anonymous20231211		79.72 263	77.19 269	87.33 237	95.59 122	77.16 241	95.18 218	94.18 229	59.31 348	72.57 281	86.20 282	47.89 317	95.66 247	74.53 240	69.24 291	89.18 258

CP-MVSNet		81.01 253	80.08 247	83.79 290	87.91 286	70.51 306	94.29 244	95.65 151	80.83 206	72.54 282	88.84 239	63.71 236	92.32 318	68.58 277	68.36 298	88.55 275

miper_lstm_enhance		81.66 246	80.66 239	84.67 278	91.19 239	71.97 295	91.94 285	93.19 278	77.86 262	72.27 283	85.26 293	73.46 170	93.42 309	73.71 247	67.05 313	88.61 274

PS-CasMVS		80.27 259	79.18 255	83.52 297	87.56 290	69.88 311	94.08 248	95.29 175	80.27 223	72.08 284	88.51 246	59.22 267	92.23 320	67.49 279	68.15 301	88.45 279

FMVSNet1		79.50 265	76.54 275	88.39 212	88.47 280	81.95 114	94.30 241	93.38 269	73.14 300	72.04 285	85.66 285	43.86 326	93.84 302	65.48 290	72.53 265	89.38 252

PEN-MVS		79.47 266	78.26 262	83.08 300	86.36 297	68.58 319	93.85 251	94.77 200	79.76 232	71.37 286	88.55 243	59.79 259	92.46 316	64.50 294	65.40 319	88.19 284

Patchmtry		77.36 283	74.59 288	85.67 266	89.75 263	75.75 263	77.85 346	91.12 308	60.28 344	71.23 287	80.35 328	75.45 140	93.56 308	57.94 318	67.34 311	87.68 293

IterMVS		80.67 256	79.16 256	85.20 271	89.79 262	76.08 254	92.97 273	91.86 297	80.28 222	71.20 288	85.14 298	57.93 277	91.34 329	72.52 253	70.74 275	88.18 285
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
DP-MVS		81.47 247	78.28 261	91.04 149	98.14 58	78.48 202	95.09 224	86.97 336	61.14 342	71.12 289	92.78 189	59.59 261	99.38 64	53.11 336	86.61 180	95.27 188

IterMVS-SCA-FT		80.51 258	79.10 257	84.73 276	89.63 267	74.66 270	92.98 272	91.81 299	80.05 227	71.06 290	85.18 296	58.04 274	91.40 328	72.48 254	70.70 277	88.12 286

v7n		79.32 268	77.34 267	85.28 270	84.05 327	72.89 288	93.38 260	93.87 244	75.02 286	70.68 291	84.37 305	59.58 262	95.62 252	67.60 278	67.50 308	87.32 303

MS-PatchMatch		83.05 224	81.82 224	86.72 251	89.64 266	79.10 190	94.88 228	94.59 212	79.70 234	70.67 292	89.65 229	50.43 308	96.82 199	70.82 268	95.99 102	84.25 331

DTE-MVSNet		78.37 273	77.06 270	82.32 307	85.22 316	67.17 325	93.40 259	93.66 257	78.71 254	70.53 293	88.29 247	59.06 268	92.23 320	61.38 308	63.28 328	87.56 298

pm-mvs1		80.05 260	78.02 263	86.15 258	85.42 311	75.81 262	95.11 221	92.69 289	77.13 271	70.36 294	87.43 258	58.44 272	95.27 268	71.36 260	64.25 324	87.36 302

D2MVS		82.67 231	81.55 227	86.04 260	87.77 287	76.47 247	95.21 215	96.58 79	82.66 183	70.26 295	85.46 292	60.39 257	95.80 239	76.40 220	79.18 232	85.83 321

PVSNet_0	77.72 15	81.70 244	78.95 258	89.94 182	90.77 249	76.72 246	95.96 185	96.95 34	85.01 122	70.24 296	88.53 245	52.32 303	98.20 137	86.68 132	44.08 353	94.89 191

CL-MVSNet_2432*1600		75.81 292	74.14 294	80.83 314	78.33 342	67.79 322	94.22 245	93.52 263	77.28 270	69.82 297	81.54 322	61.47 254	89.22 341	57.59 321	53.51 340	85.48 323

tfpnnormal		78.14 275	75.42 281	86.31 256	88.33 282	79.24 184	94.41 236	96.22 120	73.51 296	69.81 298	85.52 291	55.43 293	95.75 242	47.65 348	67.86 304	83.95 334

EU-MVSNet		76.92 287	76.95 271	76.83 326	84.10 325	54.73 353	91.77 288	92.71 288	72.74 304	69.57 299	88.69 241	58.03 276	87.43 347	64.91 293	70.00 284	88.33 282

ITE_SJBPF				82.38 305	87.00 293	65.59 328		89.55 322	79.99 229	69.37 300	91.30 205	41.60 337	95.33 264	62.86 303	74.63 256	86.24 314

DSMNet-mixed		73.13 304	72.45 300	75.19 332	77.51 345	46.82 356	85.09 333	82.01 353	67.61 327	69.27 301	81.33 323	50.89 305	86.28 349	54.54 332	83.80 202	92.46 217

MVP-Stereo		82.65 232	81.67 226	85.59 267	86.10 304	78.29 209	93.33 262	92.82 286	77.75 263	69.17 302	87.98 252	59.28 266	95.76 241	71.77 256	96.88 88	82.73 339
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
MSDG		80.62 257	77.77 265	89.14 196	93.43 183	77.24 237	91.89 286	90.18 318	69.86 319	68.02 303	91.94 198	52.21 304	98.84 112	59.32 315	83.12 207	91.35 221

NR-MVSNet		83.35 217	81.52 229	88.84 203	88.76 275	81.31 135	94.45 235	95.16 179	84.65 132	67.81 304	90.82 212	70.36 200	94.87 284	74.75 235	66.89 315	90.33 233

TransMVSNet (Re)		76.94 286	74.38 290	84.62 280	85.92 306	75.25 267	95.28 211	89.18 326	73.88 294	67.22 305	86.46 275	59.64 260	94.10 298	59.24 316	52.57 344	84.50 329

Anonymous20231206		75.29 295	73.64 296	80.22 316	80.75 333	63.38 335	93.36 261	90.71 316	73.09 301	67.12 306	83.70 311	50.33 309	90.85 334	53.63 335	70.10 282	86.44 311

ppachtmachnet_test		77.19 284	74.22 292	86.13 259	85.39 312	78.22 212	93.98 249	91.36 305	71.74 311	67.11 307	84.87 302	56.67 285	93.37 311	52.21 337	64.59 321	86.80 307

KD-MVS_2432*1600		77.63 280	74.92 285	85.77 263	90.86 246	79.44 179	88.08 313	93.92 241	76.26 276	67.05 308	82.78 316	72.15 183	91.92 323	61.53 305	41.62 354	85.94 319

miper_refine_blended		77.63 280	74.92 285	85.77 263	90.86 246	79.44 179	88.08 313	93.92 241	76.26 276	67.05 308	82.78 316	72.15 183	91.92 323	61.53 305	41.62 354	85.94 319

Patchmatch-test		78.25 274	74.72 287	88.83 204	91.20 238	74.10 277	73.91 353	88.70 332	59.89 347	66.82 310	85.12 299	78.38 90	94.54 291	48.84 346	79.58 229	97.86 94

LTVRE_ROB	73.68 18	77.99 276	75.74 280	84.74 275	90.45 253	72.02 293	86.41 327	91.12 308	72.57 306	66.63 311	87.27 260	54.95 298	96.98 189	56.29 327	75.98 248	85.21 325
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		77.18 285	76.06 277	80.55 315	83.78 328	60.00 344	90.35 298	91.05 311	77.01 275	66.62 312	87.92 253	47.73 318	94.03 299	71.63 257	68.44 297	87.62 295

testgi		74.88 297	73.40 297	79.32 320	80.13 337	61.75 339	93.21 268	86.64 339	79.49 238	66.56 313	91.06 208	35.51 347	88.67 343	56.79 326	71.25 270	87.56 298

LCM-MVSNet-Re		83.75 212	83.54 200	84.39 286	93.54 179	64.14 332	92.51 278	84.03 348	83.90 156	66.14 314	86.59 272	67.36 214	92.68 314	84.89 143	92.87 131	96.35 162

pmmvs6		74.65 298	71.67 302	83.60 295	79.13 340	69.94 310	93.31 266	90.88 315	61.05 343	65.83 315	84.15 308	43.43 328	94.83 286	66.62 283	60.63 331	86.02 318

our_test_3		77.90 278	75.37 282	85.48 269	85.39 312	76.74 245	93.63 254	91.67 300	73.39 299	65.72 316	84.65 304	58.20 273	93.13 313	57.82 319	67.87 303	86.57 310

COLMAP_ROB	73.24 19	75.74 293	73.00 299	83.94 288	92.38 205	69.08 318	91.85 287	86.93 337	61.48 340	65.32 317	90.27 222	42.27 334	96.93 194	50.91 341	75.63 251	85.80 322
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
FMVSNet5		76.46 289	74.16 293	83.35 299	90.05 260	76.17 252	89.58 302	89.85 320	71.39 313	65.29 318	80.42 327	50.61 307	87.70 346	61.05 310	69.24 291	86.18 315

ACMH+	76.62 16	77.47 282	74.94 284	85.05 272	91.07 242	71.58 301	93.26 267	90.01 319	71.80 310	64.76 319	88.55 243	41.62 336	96.48 210	62.35 304	71.00 272	87.09 305

Patchmatch-RL test		76.65 288	74.01 295	84.55 281	77.37 346	64.23 331	78.49 345	82.84 352	78.48 256	64.63 320	73.40 344	76.05 128	91.70 327	76.99 212	57.84 334	97.72 104

SixPastTwentyTwo		76.04 290	74.32 291	81.22 311	84.54 320	61.43 342	91.16 294	89.30 325	77.89 260	64.04 321	86.31 280	48.23 313	94.29 296	63.54 300	63.84 326	87.93 289

AllTest		75.92 291	73.06 298	84.47 282	92.18 216	67.29 323	91.07 295	84.43 346	67.63 323	63.48 322	90.18 223	38.20 342	97.16 182	57.04 323	73.37 261	88.97 269

TestCases				84.47 282	92.18 216	67.29 323		84.43 346	67.63 323	63.48 322	90.18 223	38.20 342	97.16 182	57.04 323	73.37 261	88.97 269

ACMH	75.40 17	77.99 276	74.96 283	87.10 244	90.67 250	76.41 249	93.19 270	91.64 302	72.47 307	63.44 324	87.61 257	43.34 329	97.16 182	58.34 317	73.94 257	87.72 291
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ET-MVSNet_ETH3D		90.01 108	89.03 113	92.95 88	94.38 159	86.77 26	98.14 34	96.31 115	89.30 42	63.33 325	96.72 114	90.09 8	93.63 307	90.70 88	82.29 218	98.46 47

USDC		78.65 271	76.25 276	85.85 261	87.58 289	74.60 271	89.58 302	90.58 317	84.05 149	63.13 326	88.23 248	40.69 340	96.86 198	66.57 285	75.81 250	86.09 317

LF4IMVS		72.36 308	70.82 305	76.95 325	79.18 339	56.33 348	86.12 328	86.11 341	69.30 321	63.06 327	86.66 271	33.03 351	92.25 319	65.33 291	68.64 295	82.28 343

DIV-MVS_2432*1600		70.97 313	69.31 313	75.95 331	76.24 352	55.39 352	87.45 318	90.94 314	70.20 317	62.96 328	77.48 338	44.01 325	88.09 344	61.25 309	53.26 341	84.37 330

Anonymous20240521		72.06 310	69.91 310	78.50 322	77.11 347	61.67 341	91.62 292	90.97 313	65.52 330	62.37 329	79.05 334	36.32 344	90.96 333	57.75 320	68.52 296	82.87 336

test_0402		72.68 306	69.54 312	82.09 308	88.67 278	71.81 298	92.72 277	86.77 338	61.52 339	62.21 330	83.91 309	43.22 330	93.76 305	34.60 355	72.23 269	80.72 347

OpenMVS_ROB	68.52 20	73.02 305	69.57 311	83.37 298	80.54 336	71.82 297	93.60 256	88.22 333	62.37 335	61.98 331	83.15 315	35.31 348	95.47 258	45.08 351	75.88 249	82.82 337

MVS-HIRNet		71.36 312	67.00 316	84.46 284	90.58 251	69.74 314	79.15 342	87.74 335	46.09 353	61.96 332	50.50 355	45.14 324	95.64 250	53.74 334	88.11 171	88.00 288

test20.03		72.36 308	71.15 304	75.98 330	77.79 343	59.16 346	92.40 281	89.35 324	74.09 292	61.50 333	84.32 306	48.09 314	85.54 352	50.63 342	62.15 330	83.24 335

PM-MVS		69.32 315	66.93 317	76.49 327	73.60 354	55.84 350	85.91 329	79.32 357	74.72 288	61.09 334	78.18 336	21.76 357	91.10 332	70.86 266	56.90 336	82.51 340

TDRefinement		69.20 316	65.78 320	79.48 319	66.04 358	62.21 338	88.21 312	86.12 340	62.92 334	61.03 335	85.61 288	33.23 350	94.16 297	55.82 330	53.02 342	82.08 344

ambc				76.02 329	68.11 356	51.43 354	64.97 356	89.59 321		60.49 336	74.49 341	17.17 360	92.46 316	61.50 307	52.85 343	84.17 332

pmmvs-eth3d		73.59 300	70.66 306	82.38 305	76.40 350	73.38 280	89.39 305	89.43 323	72.69 305	60.34 337	77.79 337	46.43 322	91.26 331	66.42 287	57.06 335	82.51 340

K. test v3		73.62 299	71.59 303	79.69 318	82.98 330	59.85 345	90.85 297	88.83 328	77.13 271	58.90 338	82.11 318	43.62 327	91.72 326	65.83 289	54.10 339	87.50 300

EG-PatchMatch MVS		74.92 296	72.02 301	83.62 294	83.76 329	73.28 283	93.62 255	92.04 296	68.57 322	58.88 339	83.80 310	31.87 353	95.57 256	56.97 325	78.67 236	82.00 345

lessismore_v0				79.98 317	80.59 335	58.34 347		80.87 354		58.49 340	83.46 313	43.10 331	93.89 301	63.11 302	48.68 346	87.72 291

N_pmnet		61.30 321	60.20 324	64.60 336	84.32 322	17.00 369	91.67 291	10.98 368	61.77 338	58.45 341	78.55 335	49.89 310	91.83 325	42.27 353	63.94 325	84.97 326

TinyColmap		72.41 307	68.99 314	82.68 303	88.11 284	69.59 315	88.41 311	85.20 343	65.55 329	57.91 342	84.82 303	30.80 355	95.94 231	51.38 338	68.70 294	82.49 342

UnsupCasMVSNet_eth		73.25 303	70.57 307	81.30 310	77.53 344	66.33 327	87.24 321	93.89 243	80.38 219	57.90 343	81.59 321	42.91 333	90.56 336	65.18 292	48.51 347	87.01 306

MIMVSNet1		69.44 314	66.65 318	77.84 323	76.48 349	62.84 337	87.42 319	88.97 327	66.96 328	57.75 344	79.72 333	32.77 352	85.83 351	46.32 349	63.42 327	84.85 327

pmmvs3		65.75 320	62.18 323	76.45 328	67.12 357	64.54 330	88.68 309	85.05 344	54.77 352	57.54 345	73.79 342	29.40 356	86.21 350	55.49 331	47.77 349	78.62 348

new-patchmatchnet		68.85 317	65.93 319	77.61 324	73.57 355	63.94 334	90.11 300	88.73 331	71.62 312	55.08 346	73.60 343	40.84 339	87.22 348	51.35 340	48.49 348	81.67 346

UnsupCasMVSNet_bld		68.60 318	64.50 321	80.92 313	74.63 353	67.80 321	83.97 334	92.94 285	65.12 331	54.63 347	68.23 350	35.97 345	92.17 322	60.13 311	44.83 351	82.78 338

CMPMVS	54.94 21	75.71 294	74.56 289	79.17 321	79.69 338	55.98 349	89.59 301	93.30 275	60.28 344	53.85 348	89.07 235	47.68 319	96.33 215	76.55 217	81.02 220	85.22 324
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
new_pmnet		66.18 319	63.18 322	75.18 333	76.27 351	61.74 340	83.79 335	84.66 345	56.64 351	51.57 349	71.85 349	31.29 354	87.93 345	49.98 343	62.55 329	75.86 350

test_method		56.77 322	54.53 325	63.49 338	76.49 348	40.70 361	75.68 349	74.24 359	19.47 361	48.73 350	71.89 348	19.31 358	65.80 360	57.46 322	47.51 350	83.97 333

YYNet1		73.53 302	70.43 308	82.85 302	84.52 321	71.73 299	91.69 290	91.37 304	67.63 323	46.79 351	81.21 324	55.04 297	90.43 337	55.93 328	59.70 333	86.38 312

MDA-MVSNet_test_wron		73.54 301	70.43 308	82.86 301	84.55 319	71.85 296	91.74 289	91.32 307	67.63 323	46.73 352	81.09 325	55.11 296	90.42 338	55.91 329	59.76 332	86.31 313

MDA-MVSNet-bldmvs		71.45 311	67.94 315	81.98 309	85.33 314	68.50 320	92.35 282	88.76 330	70.40 315	42.99 353	81.96 319	46.57 321	91.31 330	48.75 347	54.39 338	86.11 316

DeepMVS_CX				64.06 337	78.53 341	43.26 359		68.11 362	69.94 318	38.55 354	76.14 340	18.53 359	79.34 353	43.72 352	41.62 354	69.57 353

LCM-MVSNet		52.52 324	48.24 327	65.35 334	47.63 364	41.45 360	72.55 354	83.62 350	31.75 356	37.66 355	57.92 353	9.19 367	76.76 355	49.26 345	44.60 352	77.84 349

FPMVS		55.09 323	52.93 326	61.57 339	55.98 359	40.51 362	83.11 336	83.41 351	37.61 355	34.95 356	71.95 347	14.40 361	76.95 354	29.81 356	65.16 320	67.25 354

PMMVS2		50.90 325	46.31 328	64.67 335	55.53 360	46.67 357	77.30 348	71.02 360	40.89 354	34.16 357	59.32 351	9.83 366	76.14 357	40.09 354	28.63 357	71.21 351

tmp_tt		41.54 328	41.93 330	40.38 344	20.10 368	26.84 365	61.93 357	59.09 364	14.81 363	28.51 358	80.58 326	35.53 346	48.33 364	63.70 299	13.11 361	45.96 357

Gipuma		45.11 327	42.05 329	54.30 341	80.69 334	51.30 355	35.80 360	83.81 349	28.13 357	27.94 359	34.53 359	11.41 365	76.70 356	21.45 358	54.65 337	34.90 358
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
ANet_high		46.22 326	41.28 331	61.04 340	39.91 366	46.25 358	70.59 355	76.18 358	58.87 349	23.09 360	48.00 357	12.58 363	66.54 359	28.65 357	13.62 360	70.35 352

MVE	35.65 22	33.85 330	29.49 335	46.92 343	41.86 365	36.28 363	50.45 359	56.52 365	18.75 362	18.28 361	37.84 358	2.41 369	58.41 361	18.71 359	20.62 358	46.06 356
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
PMVS	34.80 23	39.19 329	35.53 332	50.18 342	29.72 367	30.30 364	59.60 358	66.20 363	26.06 358	17.91 362	49.53 356	3.12 368	74.09 358	18.19 360	49.40 345	46.14 355
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
E-PMN		32.70 331	32.39 333	33.65 345	53.35 362	25.70 366	74.07 352	53.33 366	21.08 359	17.17 363	33.63 361	11.85 364	54.84 362	12.98 361	14.04 359	20.42 359

EMVS		31.70 332	31.45 334	32.48 346	50.72 363	23.95 367	74.78 351	52.30 367	20.36 360	16.08 364	31.48 362	12.80 362	53.60 363	11.39 362	13.10 362	19.88 360

wuyk23d		14.10 334	13.89 337	14.72 347	55.23 361	22.91 368	33.83 361	3.56 369	4.94 364	4.11 365	2.28 367	2.06 370	19.66 365	10.23 363	8.74 363	1.59 363

testmvs		9.92 335	12.94 338	0.84 349	0.65 369	0.29 371	93.78 252	0.39 370	0.42 365	2.85 366	15.84 365	0.17 372	0.30 367	2.18 364	0.21 364	1.91 362

test123		9.07 336	11.73 339	1.11 348	0.50 370	0.77 370	89.44 304	0.20 371	0.34 366	2.15 367	10.72 366	0.34 371	0.32 366	1.79 365	0.08 365	2.23 361

uanet_test		0.00 339	0.00 342	0.00 350	0.00 371	0.00 372	0.00 362	0.00 372	0.00 367	0.00 368	0.00 368	0.00 373	0.00 368	0.00 366	0.00 366	0.00 364

cdsmvs_eth3d_5k		21.43 333	28.57 336	0.00 350	0.00 371	0.00 372	0.00 362	95.93 138	0.00 367	0.00 368	97.66 69	63.57 237	0.00 368	0.00 366	0.00 366	0.00 364

pcd_1.5k_mvsjas		5.92 338	7.89 341	0.00 350	0.00 371	0.00 372	0.00 362	0.00 372	0.00 367	0.00 368	0.00 368	71.04 194	0.00 368	0.00 366	0.00 366	0.00 364

sosnet-low-res		0.00 339	0.00 342	0.00 350	0.00 371	0.00 372	0.00 362	0.00 372	0.00 367	0.00 368	0.00 368	0.00 373	0.00 368	0.00 366	0.00 366	0.00 364

sosnet		0.00 339	0.00 342	0.00 350	0.00 371	0.00 372	0.00 362	0.00 372	0.00 367	0.00 368	0.00 368	0.00 373	0.00 368	0.00 366	0.00 366	0.00 364

uncertanet		0.00 339	0.00 342	0.00 350	0.00 371	0.00 372	0.00 362	0.00 372	0.00 367	0.00 368	0.00 368	0.00 373	0.00 368	0.00 366	0.00 366	0.00 364

Regformer		0.00 339	0.00 342	0.00 350	0.00 371	0.00 372	0.00 362	0.00 372	0.00 367	0.00 368	0.00 368	0.00 373	0.00 368	0.00 366	0.00 366	0.00 364

ab-mvs-re		8.11 337	10.81 340	0.00 350	0.00 371	0.00 372	0.00 362	0.00 372	0.00 367	0.00 368	97.30 90	0.00 373	0.00 368	0.00 366	0.00 366	0.00 364

uanet		0.00 339	0.00 342	0.00 350	0.00 371	0.00 372	0.00 362	0.00 372	0.00 367	0.00 368	0.00 368	0.00 373	0.00 368	0.00 366	0.00 366	0.00 364

OPU-MVS				97.30 2	99.19 8	92.31 3	99.12 6				98.54 20	92.06 2	99.84 12	99.11 1	99.37 1	99.74 1

save fliter					98.24 52	83.34 89	98.61 22	96.57 80	91.32 18

test_0728_SECOND				95.14 15	99.04 12	86.14 31	99.06 9	96.77 51					99.84 12	97.90 5	98.85 20	99.45 8

GSMVS																97.54 116

sam_mvs1												77.59 100				97.54 116

sam_mvs												75.35 147

MTGPA								96.33 112

test_post1							85.88 330				30.24 363	73.77 165	95.07 280	73.89 244

test_post											33.80 360	76.17 125	95.97 227

patchmatchnet-post											77.09 339	77.78 99	95.39 260

MTMP							97.53 76	68.16 361

gm-plane-assit					92.27 210	79.64 177			84.47 138		95.15 146		97.93 142	85.81 134

test9_res														96.00 21	99.03 11	98.31 57

agg_prior2														94.30 42	99.00 13	98.57 41

test_prior4						82.34 108	97.75 61

test_prior				93.09 81	98.68 25	81.91 117		96.40 104					99.06 97			98.29 59

新几何2							96.42 163

旧先验1					97.39 88	79.58 178		96.54 85			98.08 46	84.00 36			97.42 74	97.62 113

无先验							96.87 133	96.78 45	77.39 267				99.52 53	79.95 184		98.43 49

原ACMM2							96.84 134

testdata2													99.48 58	76.45 219

segment_acmp												82.69 53

testdata1							95.57 203		87.44 76

plane_prior7					91.86 230	77.55 232

plane_prior6					91.98 225	77.92 224						64.77 232

plane_prior5								94.69 201					97.30 174	87.08 126	82.82 213	90.96 224

plane_prior4											94.15 170

plane_prior2							97.18 102		89.89 35

plane_prior1					91.95 228

plane_prior						77.96 221	97.52 79		90.36 32						82.96 211

n2								0.00 372

nn								0.00 372

door-mid								79.75 356

test11								96.50 91

door								80.13 355

HQP5-MVS						78.48 202

BP-MVS														87.67 122

HQP3-MVS								94.80 197							83.01 209

HQP2-MVS												65.40 227

NP-MVS					92.04 224	78.22 212					94.56 160

ACMMP++_ref															78.45 241

ACMMP++															79.05 233

Test By Simon												71.65 187

High-resolution multi-view benchmark