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.

AdaColmap		93.82 97	93.06 100	96.10 111	99.88 1	89.07 157	98.33 165	97.55 111	86.81 188	90.39 150	98.65 76	75.09 199	99.98 9	93.32 100	97.53 96	99.26 85

DP-MVS Recon		95.85 53	95.15 59	97.95 20	99.87 2	94.38 43	99.60 17	97.48 122	86.58 192	94.42 92	99.13 31	87.36 82	99.98 9	93.64 93	98.33 85	99.48 68

MCST-MVS		98.18 2	97.95 5	98.86 1	99.85 3	96.60 5	99.70 10	97.98 54	97.18 2	95.96 64	99.33 9	92.62 14	100.00 1	98.99 5	99.93 1	99.98 2

CNVR-MVS		98.46 1	98.38 1	98.72 3	99.80 4	96.19 9	99.80 7	97.99 53	97.05 3	99.41 1	99.59 2	92.89 13	100.00 1	98.99 5	99.90 4	99.96 4

MG-MVS		97.24 12	96.83 21	98.47 9	99.79 5	95.71 12	99.07 74	99.06 15	94.45 19	96.42 59	98.70 74	88.81 54	99.74 61	95.35 66	99.86 9	99.97 3

NCCC		98.12 3	98.11 3	98.13 15	99.76 6	94.46 39	99.81 5	97.88 59	96.54 4	98.84 7	99.46 6	92.55 15	99.98 9	98.25 22	99.93 1	99.94 7

region2R		96.30 42	96.17 38	96.70 80	99.70 7	90.31 130	99.46 31	97.66 86	90.55 84	97.07 43	99.07 36	86.85 91	99.97 14	95.43 64	99.74 20	99.81 22

HFP-MVS		96.42 38	96.26 35	96.90 64	99.69 8	90.96 116	99.47 27	97.81 68	90.54 85	96.88 46	99.05 39	87.57 73	99.96 17	95.65 57	99.72 22	99.78 29

#test#		96.48 35	96.34 33	96.90 64	99.69 8	90.96 116	99.53 24	97.81 68	90.94 78	96.88 46	99.05 39	87.57 73	99.96 17	95.87 56	99.72 22	99.78 29

ACMMPR		96.28 43	96.14 41	96.73 77	99.68 10	90.47 128	99.47 27	97.80 70	90.54 85	96.83 53	99.03 41	86.51 97	99.95 20	95.65 57	99.72 22	99.75 35

CP-MVS		96.22 44	96.15 40	96.42 97	99.67 11	89.62 149	99.70 10	97.61 98	90.07 100	96.00 61	99.16 25	87.43 77	99.92 27	96.03 54	99.72 22	99.70 44

CPTT-MVS		94.60 83	94.43 70	95.09 144	99.66 12	86.85 204	99.44 33	97.47 124	83.22 254	94.34 95	98.96 51	82.50 151	99.55 79	94.81 75	99.50 42	98.88 114

MSLP-MVS++		97.50 9	97.45 10	97.63 28	99.65 13	93.21 59	99.70 10	98.13 47	94.61 16	97.78 32	99.46 6	89.85 43	99.81 53	97.97 24	99.91 3	99.88 14

PAPR		96.35 39	95.82 46	97.94 21	99.63 14	94.19 47	99.42 37	97.55 111	92.43 50	93.82 105	99.12 32	87.30 84	99.91 29	94.02 85	99.06 61	99.74 38

XVS		96.47 36	96.37 31	96.77 73	99.62 15	90.66 126	99.43 35	97.58 105	92.41 54	96.86 49	98.96 51	87.37 79	99.87 38	95.65 57	99.43 47	99.78 29

X-MVStestdata		90.69 173	88.66 186	96.77 73	99.62 15	90.66 126	99.43 35	97.58 105	92.41 54	96.86 49	29.59 369	87.37 79	99.87 38	95.65 57	99.43 47	99.78 29

DeepC-MVS_fast	93.52 2	97.16 15	96.84 20	98.13 15	99.61 17	94.45 40	98.85 103	97.64 92	96.51 6	95.88 66	99.39 8	87.35 83	99.99 4	96.61 41	99.69 27	99.96 4
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
CDPH-MVS		96.56 32	96.18 36	97.70 26	99.59 18	93.92 49	99.13 71	97.44 129	89.02 123	97.90 30	99.22 16	88.90 53	99.49 89	94.63 79	99.79 17	99.68 47

test_prior3		97.07 19	97.09 13	97.01 53	99.58 19	91.77 85	99.57 19	97.57 108	91.43 70	98.12 21	98.97 48	90.43 38	99.49 89	98.33 18	99.81 15	99.79 25

test_prior				97.01 53	99.58 19	91.77 85		97.57 108					99.49 89			99.79 25

APDe-MVS		97.53 7	97.47 8	97.70 26	99.58 19	93.63 52	99.56 21	97.52 115	93.59 33	98.01 26	99.12 32	90.80 34	99.55 79	99.26 3	99.79 17	99.93 8

mPP-MVS		95.90 52	95.75 49	96.38 99	99.58 19	89.41 154	99.26 52	97.41 133	90.66 80	94.82 85	98.95 53	86.15 104	99.98 9	95.24 69	99.64 30	99.74 38

TEST9					99.57 23	93.17 60	99.38 40	97.66 86	89.57 107	98.39 13	99.18 21	90.88 31	99.66 66

train_agg		97.20 14	97.08 14	97.57 32	99.57 23	93.17 60	99.38 40	97.66 86	90.18 93	98.39 13	99.18 21	90.94 29	99.66 66	98.58 13	99.85 10	99.88 14

agg_prior3		97.09 18	96.97 17	97.45 35	99.56 25	92.79 73	99.36 44	97.67 85	89.59 105	98.36 15	99.16 25	90.57 36	99.68 63	98.58 13	99.85 10	99.88 14

test_8					99.55 26	93.07 64	99.37 43	97.64 92	90.18 93	98.36 15	99.19 19	90.94 29	99.64 72

test_part2					99.54 27	95.42 14				98.13 18

v1.0		40.64 341	54.18 333	0.00 359	99.54 27	0.00 374	0.00 365	97.69 82	92.81 45	98.13 18	99.48 5	0.00 376	0.00 371	0.00 368	0.00 369	0.00 369

HSP-MVS		97.73 5	98.15 2	96.44 95	99.54 27	90.14 133	99.41 38	97.47 124	95.46 14	98.60 10	99.19 19	95.71 4	99.49 89	98.15 23	99.85 10	99.69 46

agg_prior1		97.12 16	97.03 15	97.38 41	99.54 27	92.66 74	99.35 46	97.64 92	90.38 88	97.98 27	99.17 23	90.84 33	99.61 75	98.57 15	99.78 19	99.87 18

agg_prior					99.54 27	92.66 74		97.64 92		97.98 27			99.61 75

CSCG		94.87 71	94.71 64	95.36 135	99.54 27	86.49 214	99.34 48	98.15 45	82.71 263	90.15 153	99.25 12	89.48 47	99.86 43	94.97 73	98.82 73	99.72 41

HPM-MVS++		97.72 6	97.59 7	98.14 14	99.53 33	94.76 30	99.19 55	97.75 75	95.66 11	98.21 17	99.29 10	91.10 21	99.99 4	97.68 28	99.87 6	99.68 47

APD-MVS		96.95 22	96.72 24	97.63 28	99.51 34	93.58 53	99.16 60	97.44 129	90.08 99	98.59 11	99.07 36	89.06 50	99.42 99	97.92 25	99.66 28	99.88 14
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
ESAPD		98.11 4	98.00 4	98.44 10	99.50 35	95.39 15	99.29 51	97.72 80	94.50 17	98.64 9	99.54 3	93.32 12	99.97 14	99.58 1	99.90 4	99.95 6

PGM-MVS		95.85 53	95.65 52	96.45 94	99.50 35	89.77 146	98.22 180	98.90 17	89.19 116	96.74 55	98.95 53	85.91 106	99.92 27	93.94 86	99.46 44	99.66 50

GST-MVS		95.97 49	95.66 50	96.90 64	99.49 37	91.22 104	99.45 32	97.48 122	89.69 103	95.89 65	98.72 71	86.37 101	99.95 20	94.62 80	99.22 59	99.52 62

MP-MVS		96.00 48	95.82 46	96.54 91	99.47 38	90.13 135	99.36 44	97.41 133	90.64 83	95.49 75	98.95 53	85.51 110	99.98 9	96.00 55	99.59 39	99.52 62
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
Regformer-1		96.97 21	96.80 22	97.47 34	99.46 39	93.11 62	98.89 100	97.94 55	92.89 42	96.90 45	99.02 42	89.78 44	99.53 82	97.06 32	99.26 56	99.75 35

Regformer-2		96.94 24	96.78 23	97.42 37	99.46 39	92.97 69	98.89 100	97.93 56	92.86 44	96.88 46	99.02 42	89.74 45	99.53 82	97.03 33	99.26 56	99.75 35

PAPM_NR		95.43 60	95.05 61	96.57 90	99.42 41	90.14 133	98.58 136	97.51 117	90.65 82	92.44 117	98.90 58	87.77 72	99.90 31	90.88 122	99.32 53	99.68 47

Regformer-3		96.50 34	96.36 32	96.91 63	99.34 42	91.72 88	98.71 114	97.90 58	92.48 49	96.00 61	98.95 53	88.60 56	99.52 85	96.44 45	98.83 71	99.49 66

Regformer-4		96.45 37	96.33 34	96.81 72	99.34 42	91.44 96	98.71 114	97.88 59	92.43 50	95.97 63	98.95 53	88.42 60	99.51 86	96.40 46	98.83 71	99.49 66

PHI-MVS		96.65 30	96.46 29	97.21 47	99.34 42	91.77 85	99.70 10	98.05 49	86.48 195	98.05 23	99.20 18	89.33 48	99.96 17	98.38 17	99.62 34	99.90 10

test12				97.83 23	99.33 45	94.45 40		97.55 111		97.56 33		88.60 56	99.50 88		99.71 26	99.55 60

SMA-MVS		97.24 12	96.99 16	98.00 19	99.30 46	94.20 46	99.16 60	97.65 91	89.55 109	99.22 2	99.52 4	90.34 41	99.99 4	98.32 20	99.83 13	99.82 21

zzz-MVS		96.21 45	95.96 42	96.96 61	99.29 47	91.19 106	98.69 118	97.45 126	92.58 46	94.39 93	99.24 14	86.43 99	99.99 4	96.22 48	99.40 50	99.71 42

MTAPA		96.09 47	95.80 48	96.96 61	99.29 47	91.19 106	97.23 226	97.45 126	92.58 46	94.39 93	99.24 14	86.43 99	99.99 4	96.22 48	99.40 50	99.71 42

HPM-MVS		95.41 62	95.22 58	95.99 113	99.29 47	89.14 155	99.17 59	97.09 160	87.28 179	95.40 76	98.48 88	84.93 118	99.38 102	95.64 61	99.65 29	99.47 69
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
ACMMP		94.67 80	94.30 72	95.79 119	99.25 50	88.13 174	98.41 159	98.67 25	90.38 88	91.43 130	98.72 71	82.22 158	99.95 20	93.83 90	95.76 126	99.29 81
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
APD-MVS_3200maxsize		95.64 59	95.65 52	95.62 123	99.24 51	87.80 180	98.42 157	97.22 146	88.93 128	96.64 58	98.98 47	85.49 111	99.36 104	96.68 40	99.27 55	99.70 44

API-MVS		94.78 73	94.18 76	96.59 89	99.21 52	90.06 139	98.80 108	97.78 73	83.59 244	93.85 103	99.21 17	83.79 128	99.97 14	92.37 110	99.00 64	99.74 38

PLC	91.07 3	94.23 89	94.01 80	94.87 151	99.17 53	87.49 186	99.25 53	96.55 187	88.43 144	91.26 133	98.21 97	85.92 105	99.86 43	89.77 134	97.57 94	97.24 181
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
EI-MVSNet-Vis-set		95.76 58	95.63 54	96.17 107	99.14 54	90.33 129	98.49 148	97.82 65	91.92 61	94.75 87	98.88 60	87.06 87	99.48 94	95.40 65	97.17 102	98.70 129

TSAR-MVS + MP.		97.44 10	97.46 9	97.39 40	99.12 55	93.49 57	98.52 141	97.50 120	94.46 18	98.99 3	98.64 77	91.58 18	99.08 119	98.49 16	99.83 13	99.60 58

HPM-MVS_fast		94.89 70	94.62 65	95.70 122	99.11 56	88.44 171	99.14 68	97.11 156	85.82 202	95.69 71	98.47 89	83.46 132	99.32 108	93.16 102	99.63 33	99.35 74

MAR-MVS		94.43 85	94.09 78	95.45 133	99.10 57	87.47 187	98.39 163	97.79 72	88.37 146	94.02 100	99.17 23	78.64 183	99.91 29	92.48 109	98.85 70	98.96 106
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
114514_t		94.06 92	93.05 101	97.06 51	99.08 58	92.26 83	98.97 89	97.01 168	82.58 265	92.57 115	98.22 95	80.68 169	99.30 109	89.34 140	99.02 63	99.63 54

EI-MVSNet-UG-set		95.43 60	95.29 56	95.86 118	99.07 59	89.87 143	98.43 156	97.80 70	91.78 64	94.11 99	98.77 65	86.25 103	99.48 94	94.95 74	96.45 109	98.22 154

原ACMM1				96.18 105	99.03 60	90.08 136		97.63 96	88.98 124	97.00 44	98.97 48	88.14 66	99.71 62	88.23 152	99.62 34	98.76 126

SD-MVS		97.51 8	97.40 11	97.81 24	99.01 61	93.79 51	99.33 49	97.38 136	93.73 30	98.83 8	99.02 42	90.87 32	99.88 35	98.69 9	99.74 20	99.77 34

旧先验1					98.97 62	92.90 71		97.74 77			99.15 27	91.05 22			99.33 52	99.60 58

LS3D		90.19 178	88.72 184	94.59 158	98.97 62	86.33 222	96.90 239	96.60 181	74.96 320	84.06 211	98.74 68	75.78 196	99.83 48	74.93 282	97.57 94	97.62 174

CNLPA		93.64 104	92.74 108	96.36 100	98.96 64	90.01 142	99.19 55	95.89 234	86.22 198	89.40 166	98.85 61	80.66 170	99.84 46	88.57 149	96.92 103	99.24 86

MP-MVS-pluss		95.80 55	95.30 55	97.29 43	98.95 65	92.66 74	98.59 135	97.14 152	88.95 126	93.12 110	99.25 12	85.62 107	99.94 23	96.56 43	99.48 43	99.28 83
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
新几何1				97.40 39	98.92 66	92.51 81		97.77 74	85.52 205	96.69 57	99.06 38	88.08 67	99.89 34	84.88 185	99.62 34	99.79 25

DP-MVS		88.75 205	86.56 213	95.34 136	98.92 66	87.45 188	97.64 213	93.52 308	70.55 331	81.49 249	97.25 129	74.43 213	99.88 35	71.14 312	94.09 139	98.67 130

1121		95.19 67	94.45 69	97.42 37	98.88 68	92.58 79	96.22 265	97.75 75	85.50 207	96.86 49	99.01 46	88.59 58	99.90 31	87.64 158	99.60 37	99.79 25

TSAR-MVS + GP.		96.95 22	96.91 18	97.07 50	98.88 68	91.62 90	99.58 18	96.54 188	95.09 15	96.84 52	98.63 78	91.16 19	99.77 58	99.04 4	96.42 110	99.81 22

CANet		97.00 20	96.49 28	98.55 6	98.86 70	96.10 10	99.83 4	97.52 115	95.90 8	97.21 40	98.90 58	82.66 150	99.93 25	98.71 8	98.80 74	99.63 54

ACMMP_Plus		96.59 31	96.18 36	97.81 24	98.82 71	93.55 54	98.88 102	97.59 103	90.66 80	97.98 27	99.14 29	86.59 94	100.00 1	96.47 44	99.46 44	99.89 13

PVSNet_BlendedMVS		93.36 112	93.20 98	93.84 183	98.77 72	91.61 91	99.47 27	98.04 50	91.44 69	94.21 96	92.63 232	83.50 130	99.87 38	97.41 29	83.37 237	90.05 297

PVSNet_Blended		95.94 51	95.66 50	96.75 75	98.77 72	91.61 91	99.88 1	98.04 50	93.64 32	94.21 96	97.76 105	83.50 130	99.87 38	97.41 29	97.75 93	98.79 121

DeepPCF-MVS	93.56 1	96.55 33	97.84 6	92.68 204	98.71 74	78.11 316	99.70 10	97.71 81	98.18 1	97.36 38	99.76 1	90.37 40	99.94 23	99.27 2	99.54 41	99.99 1

EPNet		96.82 26	96.68 26	97.25 46	98.65 75	93.10 63	99.48 26	98.76 18	96.54 4	97.84 31	98.22 95	87.49 76	99.66 66	95.35 66	97.78 92	99.00 101
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
OMC-MVS		93.90 95	93.62 91	94.73 155	98.63 76	87.00 200	98.04 197	96.56 186	92.19 58	92.46 116	98.73 69	79.49 175	99.14 116	92.16 113	94.34 138	98.03 160

abl_6		94.63 82	94.48 68	95.09 144	98.61 77	86.96 201	98.06 196	96.97 170	89.31 112	95.86 68	98.56 81	79.82 172	99.64 72	94.53 82	98.65 80	98.66 131

MVS_111021_HR		96.69 28	96.69 25	96.72 79	98.58 78	91.00 115	99.14 68	99.45 1	93.86 27	95.15 81	98.73 69	88.48 59	99.76 59	97.23 31	99.56 40	99.40 72

0601test		95.27 65	94.60 66	97.28 44	98.53 79	92.98 67	99.05 79	98.70 22	86.76 189	94.65 90	97.74 107	87.78 70	99.44 97	95.57 62	92.61 152	99.44 70

Anonymous20240521		95.27 65	94.60 66	97.28 44	98.53 79	92.98 67	99.05 79	98.70 22	86.76 189	94.65 90	97.74 107	87.78 70	99.44 97	95.57 62	92.61 152	99.44 70

TAPA-MVS	87.50 9	90.35 174	89.05 178	94.25 169	98.48 81	85.17 251	98.42 157	96.58 185	82.44 269	87.24 189	98.53 82	82.77 149	98.84 124	59.09 340	97.88 88	98.72 127
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
MVS_0304		96.12 46	95.26 57	98.69 4	98.44 82	96.54 7	99.70 10	96.89 173	95.76 10	97.53 34	99.12 32	72.42 239	99.93 25	98.75 7	98.69 77	99.61 57

test222					98.32 83	91.21 105	98.08 194	97.58 105	83.74 240	95.87 67	99.02 42	86.74 92			99.64 30	99.81 22

LFMVS		92.23 142	90.84 156	96.42 97	98.24 84	91.08 113	98.24 178	96.22 208	83.39 252	94.74 88	98.31 93	61.12 310	98.85 123	94.45 83	92.82 148	99.32 77

testdata				95.26 139	98.20 85	87.28 196		97.60 99	85.21 211	98.48 12	99.15 27	88.15 65	98.72 132	90.29 128	99.45 46	99.78 29

PatchMatch-RL		91.47 157	90.54 163	94.26 168	98.20 85	86.36 221	96.94 237	97.14 152	87.75 164	88.98 169	95.75 177	71.80 247	99.40 101	80.92 226	97.39 99	97.02 189

MVS_111021_LR		95.78 56	95.94 43	95.28 138	98.19 87	87.69 181	98.80 108	99.26 13	93.39 35	95.04 83	98.69 75	84.09 126	99.76 59	96.96 38	99.06 61	98.38 145

F-COLMAP		92.07 148	91.75 134	93.02 196	98.16 88	82.89 278	98.79 111	95.97 221	86.54 194	87.92 182	97.80 103	78.69 182	99.65 70	85.97 174	95.93 123	96.53 205

Anonymous202405211		88.84 199	87.03 210	94.27 167	98.14 89	84.18 263	98.44 155	95.58 255	76.79 315	89.34 167	96.88 151	53.42 333	99.54 81	87.53 160	87.12 211	99.09 95

VNet		95.08 68	94.26 73	97.55 33	98.07 90	93.88 50	98.68 121	98.73 21	90.33 90	97.16 42	97.43 119	79.19 177	99.53 82	96.91 39	91.85 167	99.24 86

DELS-MVS		97.12 16	96.60 27	98.68 5	98.03 91	96.57 6	99.84 3	97.84 63	96.36 7	95.20 80	98.24 94	88.17 64	99.83 48	96.11 52	99.60 37	99.64 52
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
PVSNet	87.13 12	93.69 100	92.83 107	96.28 102	97.99 92	90.22 132	99.38 40	98.93 16	91.42 72	93.66 106	97.68 110	71.29 252	99.64 72	87.94 155	97.20 101	98.98 104

CHOSEN 280x420		96.80 27	96.85 19	96.66 83	97.85 93	94.42 42	94.76 295	98.36 28	92.50 48	95.62 74	97.52 115	97.92 1	97.38 205	98.31 21	98.80 74	98.20 156

thres200		93.69 100	92.59 112	96.97 60	97.76 94	94.74 31	99.35 46	99.36 2	89.23 115	91.21 135	96.97 147	83.42 133	98.77 126	85.08 182	90.96 178	97.39 178

tfpn_ndepth		93.28 117	92.32 115	96.16 108	97.74 95	92.86 72	99.01 84	98.19 41	85.50 207	89.84 158	97.12 139	93.57 10	97.58 190	79.39 240	90.50 186	98.04 159

HY-MVS	88.56 7	95.29 64	94.23 74	98.48 8	97.72 96	96.41 8	94.03 303	98.74 19	92.42 53	95.65 73	94.76 190	86.52 96	99.49 89	95.29 68	92.97 147	99.53 61

Anonymous20231211		84.72 260	82.65 270	90.91 237	97.71 97	84.55 259	97.28 222	96.67 178	66.88 343	79.18 271	90.87 257	58.47 315	96.60 227	82.61 209	74.20 288	91.59 251

tfpn200view9		93.43 109	92.27 118	96.90 64	97.68 98	94.84 24	99.18 57	99.36 2	88.45 141	90.79 138	96.90 149	83.31 134	98.75 128	84.11 193	90.69 180	97.12 183

thres400		93.39 111	92.27 118	96.73 77	97.68 98	94.84 24	99.18 57	99.36 2	88.45 141	90.79 138	96.90 149	83.31 134	98.75 128	84.11 193	90.69 180	96.61 196

tfpn111		93.20 120	92.00 127	96.83 71	97.62 100	94.84 24	99.06 76	99.36 2	87.96 156	90.47 146	96.78 152	83.29 136	98.71 133	82.93 205	90.47 187	96.94 190

conf200view11		93.32 114	92.15 123	96.84 70	97.62 100	94.84 24	99.06 76	99.36 2	87.96 156	90.47 146	96.78 152	83.29 136	98.75 128	84.11 193	90.69 180	96.94 190

thres100view900		93.34 113	92.15 123	96.90 64	97.62 100	94.84 24	99.06 76	99.36 2	87.96 156	90.47 146	96.78 152	83.29 136	98.75 128	84.11 193	90.69 180	97.12 183

thres600view7		93.18 121	92.00 127	96.75 75	97.62 100	94.92 21	99.07 74	99.36 2	87.96 156	90.47 146	96.78 152	83.29 136	98.71 133	82.93 205	90.47 187	96.61 196

WTY-MVS		95.97 49	95.11 60	98.54 7	97.62 100	96.65 4	99.44 33	98.74 19	92.25 57	95.21 79	98.46 91	86.56 95	99.46 96	95.00 72	92.69 151	99.50 65

tfpn1000		92.67 134	91.64 136	95.78 120	97.61 105	92.34 82	98.69 118	98.18 42	84.15 229	88.80 171	96.99 146	93.56 11	97.21 209	76.56 265	90.19 189	97.77 169

Anonymous20240529		87.66 216	85.58 232	93.92 180	97.59 106	85.01 254	98.13 189	97.13 154	66.69 344	88.47 173	96.01 175	55.09 327	99.51 86	87.00 165	84.12 231	97.23 182

HyFIR lowres test		93.68 102	93.29 96	94.87 151	97.57 107	88.04 176	98.18 185	98.47 26	87.57 170	91.24 134	95.05 186	85.49 111	97.46 196	93.22 101	92.82 148	99.10 94

canonicalmvs		95.02 69	93.96 84	98.20 12	97.53 108	95.92 11	98.71 114	96.19 211	91.78 64	95.86 68	98.49 87	79.53 174	99.03 120	96.12 51	91.42 175	99.66 50

view600		92.78 127	91.50 139	96.63 84	97.51 109	94.66 34	98.91 94	99.36 2	87.31 175	89.64 162	96.59 159	83.26 141	98.63 137	80.76 229	90.15 190	96.61 196

view800		92.78 127	91.50 139	96.63 84	97.51 109	94.66 34	98.91 94	99.36 2	87.31 175	89.64 162	96.59 159	83.26 141	98.63 137	80.76 229	90.15 190	96.61 196

conf0.05thres1000		92.78 127	91.50 139	96.63 84	97.51 109	94.66 34	98.91 94	99.36 2	87.31 175	89.64 162	96.59 159	83.26 141	98.63 137	80.76 229	90.15 190	96.61 196

tfpn		92.78 127	91.50 139	96.63 84	97.51 109	94.66 34	98.91 94	99.36 2	87.31 175	89.64 162	96.59 159	83.26 141	98.63 137	80.76 229	90.15 190	96.61 196

CHOSEN 1792x2688		94.35 87	93.82 89	95.95 115	97.40 113	88.74 164	98.41 159	98.27 30	92.18 59	91.43 130	96.40 167	78.88 178	99.81 53	93.59 94	97.81 89	99.30 79

SteuartSystems-ACMMP		97.25 11	97.34 12	97.01 53	97.38 114	91.46 94	99.75 8	97.66 86	94.14 22	98.13 18	99.26 11	92.16 16	99.66 66	97.91 26	99.64 30	99.90 10
Skip Steuart: Steuart Systems R&D Blog.
alignmvs		95.77 57	95.00 62	98.06 18	97.35 115	95.68 13	99.71 9	97.50 120	91.50 68	96.16 60	98.61 79	86.28 102	99.00 121	96.19 50	91.74 169	99.51 64

PS-MVSNAJ		96.87 25	96.40 30	98.29 11	97.35 115	97.29 1	99.03 81	97.11 156	95.83 9	98.97 4	99.14 29	82.48 153	99.60 77	98.60 10	99.08 60	98.00 161

EPNet_dtu		92.28 140	92.15 123	92.70 203	97.29 117	84.84 255	98.64 127	97.82 65	92.91 41	93.02 113	97.02 144	85.48 113	95.70 282	72.25 308	94.89 134	97.55 176
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
MVSTER		92.71 132	92.32 115	93.86 182	97.29 117	92.95 70	99.01 84	96.59 182	90.09 98	85.51 200	94.00 201	94.61 8	96.56 230	90.77 125	83.03 240	92.08 238

EPMVS		92.59 137	91.59 137	95.59 125	97.22 119	90.03 140	91.78 322	98.04 50	90.42 87	91.66 124	90.65 269	86.49 98	97.46 196	81.78 220	96.31 113	99.28 83

tpmvs		89.16 193	87.76 197	93.35 189	97.19 120	84.75 257	90.58 332	97.36 138	81.99 272	84.56 205	89.31 299	83.98 127	98.17 152	74.85 284	90.00 195	97.12 183

DeepC-MVS	91.02 4	94.56 84	93.92 87	96.46 93	97.16 121	90.76 122	98.39 163	97.11 156	93.92 23	88.66 172	98.33 92	78.14 185	99.85 45	95.02 71	98.57 81	98.78 124
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
PVSNet_Blended_VisFu		94.67 80	94.11 77	96.34 101	97.14 122	91.10 111	99.32 50	97.43 131	92.10 60	91.53 128	96.38 170	83.29 136	99.68 63	93.42 98	96.37 111	98.25 152

xiu_mvs_v2_base		96.66 29	96.17 38	98.11 17	97.11 123	96.96 2	99.01 84	97.04 164	95.51 13	98.86 6	99.11 35	82.19 159	99.36 104	98.59 12	98.14 86	98.00 161

VDD-MVS		91.24 163	90.18 166	94.45 162	97.08 124	85.84 241	98.40 162	96.10 217	86.99 181	93.36 107	98.16 98	54.27 330	99.20 110	96.59 42	90.63 184	98.31 151

UGNet		91.91 152	90.85 155	95.10 143	97.06 125	88.69 165	98.01 198	98.24 32	92.41 54	92.39 118	93.61 214	60.52 311	99.68 63	88.14 153	97.25 100	96.92 194
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
CANet_DTU		94.31 88	93.35 95	97.20 48	97.03 126	94.71 32	98.62 129	95.54 256	95.61 12	97.21 40	98.47 89	71.88 245	99.84 46	88.38 150	97.46 98	97.04 188

DWT-MVSNet_test		94.36 86	93.95 85	95.62 123	96.99 127	89.47 152	96.62 250	97.38 136	90.96 77	93.07 112	97.27 128	93.73 9	98.09 157	85.86 178	93.65 142	99.29 81

PatchFormer-LS_test		94.08 91	93.60 92	95.53 130	96.92 128	89.57 150	96.51 254	97.34 140	91.29 74	92.22 120	97.18 135	91.66 17	98.02 162	87.05 163	92.21 161	99.00 101

MSDG		88.29 211	86.37 215	94.04 176	96.90 129	86.15 229	96.52 253	94.36 296	77.89 312	79.22 270	96.95 148	69.72 260	99.59 78	73.20 301	92.58 154	96.37 206

BH-w/o		92.32 139	91.79 132	93.91 181	96.85 130	86.18 227	99.11 72	95.74 239	88.13 153	84.81 203	97.00 145	77.26 190	97.91 165	89.16 146	98.03 87	97.64 171

conf0.01		92.06 149	90.99 146	95.24 141	96.84 131	91.39 97	98.31 168	98.20 34	83.57 245	88.08 176	97.34 122	91.05 22	97.40 199	75.80 271	89.74 197	96.94 190

conf0.002		92.06 149	90.99 146	95.24 141	96.84 131	91.39 97	98.31 168	98.20 34	83.57 245	88.08 176	97.34 122	91.05 22	97.40 199	75.80 271	89.74 197	96.94 190

thresconf0.02		92.14 143	90.99 146	95.58 126	96.84 131	91.39 97	98.31 168	98.20 34	83.57 245	88.08 176	97.34 122	91.05 22	97.40 199	75.80 271	89.74 197	97.94 163

tfpn_n400		92.14 143	90.99 146	95.58 126	96.84 131	91.39 97	98.31 168	98.20 34	83.57 245	88.08 176	97.34 122	91.05 22	97.40 199	75.80 271	89.74 197	97.94 163

tfpnconf		92.14 143	90.99 146	95.58 126	96.84 131	91.39 97	98.31 168	98.20 34	83.57 245	88.08 176	97.34 122	91.05 22	97.40 199	75.80 271	89.74 197	97.94 163

tfpnview11		92.14 143	90.99 146	95.58 126	96.84 131	91.39 97	98.31 168	98.20 34	83.57 245	88.08 176	97.34 122	91.05 22	97.40 199	75.80 271	89.74 197	97.94 163

AllTest		84.97 258	83.12 260	90.52 245	96.82 137	78.84 309	95.89 277	92.17 330	77.96 309	75.94 292	95.50 180	55.48 324	99.18 111	71.15 310	87.14 209	93.55 216

TestCases				90.52 245	96.82 137	78.84 309		92.17 330	77.96 309	75.94 292	95.50 180	55.48 324	99.18 111	71.15 310	87.14 209	93.55 216

PMMVS		93.62 105	93.90 88	92.79 200	96.79 139	81.40 289	98.85 103	96.81 174	91.25 75	96.82 54	98.15 99	77.02 191	98.13 154	93.15 103	96.30 114	98.83 118

BH-RMVSNet		91.25 162	89.99 168	95.03 149	96.75 140	88.55 168	98.65 125	94.95 280	87.74 165	87.74 183	97.80 103	68.27 271	98.14 153	80.53 234	97.49 97	98.41 142

MVS_Test		93.67 103	92.67 110	96.69 81	96.72 141	92.66 74	97.22 227	96.03 219	87.69 168	95.12 82	94.03 199	81.55 163	98.28 149	89.17 145	96.46 108	99.14 92

COLMAP_ROB	82.69 18	84.54 265	82.82 264	89.70 263	96.72 141	78.85 308	95.89 277	92.83 323	71.55 328	77.54 286	95.89 176	59.40 314	99.14 116	67.26 320	88.26 205	91.11 263
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
mvs_anonymous		92.50 138	91.65 135	95.06 147	96.60 143	89.64 148	97.06 233	96.44 194	86.64 191	84.14 209	93.93 204	82.49 152	96.17 266	91.47 116	96.08 120	99.35 74

casdiffmvs1		94.78 73	94.34 71	96.11 110	96.60 143	90.85 119	97.95 200	96.52 189	90.16 96	97.22 39	94.64 192	84.99 116	98.18 151	94.40 84	96.60 107	99.30 79

GG-mvs-BLEND				96.98 59	96.53 145	94.81 29	87.20 336	97.74 77		93.91 102	96.40 167	96.56 2	96.94 219	95.08 70	98.95 68	99.20 89

FMVSNet3		88.81 203	87.08 209	93.99 178	96.52 146	94.59 38	98.08 194	96.20 210	85.85 201	82.12 240	91.60 244	74.05 221	95.40 290	79.04 242	80.24 251	91.99 241

BH-untuned		91.46 158	90.84 156	93.33 190	96.51 147	84.83 256	98.84 105	95.50 259	86.44 197	83.50 213	96.70 156	75.49 198	97.77 176	86.78 170	97.81 89	97.40 177

sss		94.85 72	93.94 86	97.58 30	96.43 148	94.09 48	98.93 91	99.16 14	89.50 110	95.27 78	97.85 101	81.50 164	99.65 70	92.79 108	94.02 140	98.99 103

dp		90.16 179	88.83 183	94.14 172	96.38 149	86.42 217	91.57 323	97.06 163	84.76 221	88.81 170	90.19 289	84.29 125	97.43 198	75.05 281	91.35 177	98.56 136

casdiffmvs		94.10 90	93.40 94	96.20 103	96.31 150	91.46 94	97.65 212	96.22 208	88.49 137	95.69 71	94.11 195	83.01 146	98.10 156	93.33 99	95.82 125	99.04 98

TR-MVS		90.77 170	89.44 172	94.76 153	96.31 150	88.02 177	97.92 201	95.96 223	85.52 205	88.22 175	97.23 131	66.80 283	98.09 157	84.58 188	92.38 156	98.17 157

diffmvs1		93.54 106	92.90 105	95.48 132	96.29 152	89.10 156	96.97 236	96.17 213	89.13 119	94.77 86	93.94 203	82.05 160	98.20 150	90.64 126	96.12 118	99.15 91

UA-Net		93.30 115	92.62 111	95.34 136	96.27 153	88.53 170	95.88 279	96.97 170	90.90 79	95.37 77	97.07 142	82.38 156	99.10 118	83.91 197	94.86 135	98.38 145

tpmrst		92.78 127	92.16 122	94.65 157	96.27 153	87.45 188	91.83 321	97.10 159	89.10 122	94.68 89	90.69 263	88.22 63	97.73 183	89.78 133	91.80 168	98.77 125

ADS-MVSNet2		87.62 217	86.88 211	89.86 258	96.21 155	79.14 305	87.15 337	92.99 314	83.01 258	89.91 156	87.27 314	78.87 179	92.80 322	74.20 289	92.27 159	97.64 171

ADS-MVSNet		88.99 195	87.30 204	94.07 174	96.21 155	87.56 185	87.15 337	96.78 176	83.01 258	89.91 156	87.27 314	78.87 179	97.01 216	74.20 289	92.27 159	97.64 171

PatchmatchNet		92.05 151	91.04 145	95.06 147	96.17 157	89.04 158	91.26 326	97.26 141	89.56 108	90.64 142	90.56 275	88.35 62	97.11 212	79.53 237	96.07 121	99.03 99
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
gg-mvs-nofinetune		90.00 183	87.71 199	96.89 69	96.15 158	94.69 33	85.15 342	97.74 77	68.32 340	92.97 114	60.16 355	96.10 3	96.84 221	93.89 87	98.87 69	99.14 92

MDTV_nov1_ep13			90.47 165		96.14 159	88.55 168	91.34 325	97.51 117	89.58 106	92.24 119	90.50 278	86.99 90	97.61 189	77.64 254	92.34 157

IS-MVSNet		93.00 124	92.51 113	94.49 160	96.14 159	87.36 194	98.31 168	95.70 243	88.58 136	90.17 152	97.50 116	83.02 145	97.22 208	87.06 162	96.07 121	98.90 113

Vis-MVSNet (Re-imp)		93.26 119	93.00 104	94.06 175	96.14 159	86.71 210	98.68 121	96.70 177	88.30 148	89.71 161	97.64 111	85.43 114	96.39 247	88.06 154	96.32 112	99.08 96

thisisatest0515		94.75 75	94.19 75	96.43 96	96.13 162	92.64 78	99.47 27	97.60 99	87.55 171	93.17 109	97.59 113	94.71 7	98.42 142	88.28 151	93.20 144	98.24 153

diffmvs		93.00 124	92.26 120	95.25 140	96.12 163	88.59 166	96.60 251	96.19 211	88.88 130	94.19 98	93.73 210	80.40 171	98.12 155	89.18 144	95.02 132	99.02 100

ab-mvs		91.05 165	89.17 176	96.69 81	95.96 164	91.72 88	92.62 315	97.23 145	85.61 204	89.74 159	93.89 206	68.55 269	99.42 99	91.09 118	87.84 207	98.92 112

Fast-Effi-MVS+		91.72 154	90.79 159	94.49 160	95.89 165	87.40 191	99.54 23	95.70 243	85.01 217	89.28 168	95.68 178	77.75 187	97.57 194	83.22 201	95.06 131	98.51 138

EPP-MVSNet		93.75 99	93.67 90	94.01 177	95.86 166	85.70 243	98.67 123	97.66 86	84.46 224	91.36 132	97.18 135	91.16 19	97.79 174	92.93 105	93.75 141	98.53 137

Effi-MVS+		93.87 96	93.15 99	96.02 112	95.79 167	90.76 122	96.70 247	95.78 237	86.98 183	95.71 70	97.17 137	79.58 173	98.01 163	94.57 81	96.09 119	99.31 78

tpm cat1		88.89 197	87.27 205	93.76 185	95.79 167	85.32 247	90.76 330	97.09 160	76.14 317	85.72 198	88.59 304	82.92 147	98.04 161	76.96 259	91.43 174	97.90 168

tpmp4_e23		91.05 165	90.07 167	93.97 179	95.77 169	85.30 248	92.64 314	97.09 160	84.42 226	91.53 128	90.31 281	87.38 78	97.82 172	80.86 228	90.62 185	98.79 121

thisisatest0530		94.00 93	93.52 93	95.43 134	95.76 170	90.02 141	98.99 87	97.60 99	86.58 192	91.74 123	97.36 121	94.78 6	98.34 145	86.37 171	92.48 155	97.94 163

3Dnovator+	87.72 8	93.43 109	91.84 131	98.17 13	95.73 171	95.08 20	98.92 93	97.04 164	91.42 72	81.48 250	97.60 112	74.60 206	99.79 56	90.84 123	98.97 65	99.64 52

MVS		93.92 94	92.28 117	98.83 2	95.69 172	96.82 3	96.22 265	98.17 43	84.89 219	84.34 208	98.61 79	79.32 176	99.83 48	93.88 88	99.43 47	99.86 19

cascas		90.93 168	89.33 175	95.76 121	95.69 172	93.03 66	98.99 87	96.59 182	80.49 286	86.79 195	94.45 194	65.23 293	98.60 141	93.52 95	92.18 162	95.66 209

QAPM		91.41 159	89.49 171	97.17 49	95.66 174	93.42 58	98.60 133	97.51 117	80.92 284	81.39 251	97.41 120	72.89 236	99.87 38	82.33 211	98.68 78	98.21 155

tttt0517		93.30 115	93.01 103	94.17 171	95.57 175	86.47 215	98.51 145	97.60 99	85.99 200	90.55 143	97.19 134	94.80 5	98.31 146	85.06 183	91.86 166	97.74 170

1112_ss		92.71 132	91.55 138	96.20 103	95.56 176	91.12 109	98.48 149	94.69 286	88.29 149	86.89 193	98.50 85	87.02 88	98.66 135	84.75 186	89.77 196	98.81 119

LCM-MVSNet-Re		88.59 207	88.61 187	88.51 286	95.53 177	72.68 332	96.85 240	88.43 355	88.45 141	73.14 306	90.63 270	75.82 195	94.38 308	92.95 104	95.71 127	98.48 140

Test_1112_low_res		92.27 141	90.97 152	96.18 105	95.53 177	91.10 111	98.47 151	94.66 287	88.28 150	86.83 194	93.50 218	87.00 89	98.65 136	84.69 187	89.74 197	98.80 120

PCF-MVS	89.78 5	91.26 160	89.63 170	96.16 108	95.44 179	91.58 93	95.29 291	96.10 217	85.07 215	82.75 228	97.45 118	78.28 184	99.78 57	80.60 233	95.65 128	97.12 183
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
3Dnovator	87.35 11	93.17 122	91.77 133	97.37 42	95.41 180	93.07 64	98.82 106	97.85 62	91.53 67	82.56 232	97.58 114	71.97 244	99.82 51	91.01 120	99.23 58	99.22 88

IB-MVS	89.43 6	92.12 147	90.83 158	95.98 114	95.40 181	90.78 121	99.81 5	98.06 48	91.23 76	85.63 199	93.66 213	90.63 35	98.78 125	91.22 117	71.85 311	98.36 148
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
1314		93.44 108	91.98 129	97.84 22	95.24 182	94.38 43	96.22 265	97.92 57	90.18 93	82.28 237	97.71 109	77.63 188	99.80 55	91.94 115	98.67 79	99.34 76

XVG-OURS		90.83 169	90.49 164	91.86 214	95.23 183	81.25 293	95.79 284	95.92 228	88.96 125	90.02 155	98.03 100	71.60 249	99.35 106	91.06 119	87.78 208	94.98 210

TESTMET0.1,1		93.82 97	93.26 97	95.49 131	95.21 184	90.25 131	99.15 65	97.54 114	89.18 118	91.79 122	94.87 188	89.13 49	97.63 187	86.21 172	96.29 115	98.60 132

xiu_mvs_v1_base_debu		94.73 76	93.98 81	96.99 56	95.19 185	95.24 17	98.62 129	96.50 190	92.99 38	97.52 35	98.83 62	72.37 240	99.15 113	97.03 33	96.74 104	96.58 202

xiu_mvs_v1_base		94.73 76	93.98 81	96.99 56	95.19 185	95.24 17	98.62 129	96.50 190	92.99 38	97.52 35	98.83 62	72.37 240	99.15 113	97.03 33	96.74 104	96.58 202

xiu_mvs_v1_base_debi		94.73 76	93.98 81	96.99 56	95.19 185	95.24 17	98.62 129	96.50 190	92.99 38	97.52 35	98.83 62	72.37 240	99.15 113	97.03 33	96.74 104	96.58 202

XVG-OURS-SEG-HR		90.95 167	90.66 162	91.83 215	95.18 188	81.14 295	95.92 276	95.92 228	88.40 145	90.33 151	97.85 101	70.66 255	99.38 102	92.83 107	88.83 204	94.98 210

Effi-MVS+-dtu		89.97 184	90.68 161	87.81 300	95.15 189	71.98 334	97.87 205	95.40 267	91.92 61	87.57 184	91.44 245	74.27 216	96.84 221	89.45 136	93.10 146	94.60 212

mvs-test1		91.57 155	92.20 121	89.70 263	95.15 189	74.34 325	99.51 25	95.40 267	91.92 61	91.02 136	97.25 129	74.27 216	98.08 160	89.45 136	95.83 124	96.67 195

Vis-MVSNet		92.64 135	91.85 130	95.03 149	95.12 191	88.23 172	98.48 149	96.81 174	91.61 66	92.16 121	97.22 132	71.58 250	98.00 164	85.85 179	97.81 89	98.88 114
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
GBi-Net		86.67 235	84.96 240	91.80 217	95.11 192	88.81 161	96.77 242	95.25 274	82.94 260	82.12 240	90.25 283	62.89 302	94.97 297	79.04 242	80.24 251	91.62 248

test1		86.67 235	84.96 240	91.80 217	95.11 192	88.81 161	96.77 242	95.25 274	82.94 260	82.12 240	90.25 283	62.89 302	94.97 297	79.04 242	80.24 251	91.62 248

FMVSNet2		86.90 231	84.79 246	93.24 191	95.11 192	92.54 80	97.67 211	95.86 236	82.94 260	80.55 254	91.17 248	62.89 302	95.29 292	77.23 256	79.71 257	91.90 242

MVSFormer		94.71 79	94.08 79	96.61 88	95.05 195	94.87 22	97.77 208	96.17 213	86.84 186	98.04 24	98.52 83	85.52 108	95.99 272	89.83 131	98.97 65	98.96 106

lupinMVS		96.32 41	95.94 43	97.44 36	95.05 195	94.87 22	99.86 2	96.50 190	93.82 28	98.04 24	98.77 65	85.52 108	98.09 157	96.98 37	98.97 65	99.37 73

CostFormer		92.89 126	92.48 114	94.12 173	94.99 197	85.89 237	92.89 313	97.00 169	86.98 183	95.00 84	90.78 258	90.05 42	97.51 195	92.92 106	91.73 170	98.96 106

Patchmatch-test1		90.10 180	88.61 187	94.57 159	94.95 198	88.83 160	96.26 261	97.21 147	90.06 101	90.03 154	90.68 265	66.61 285	95.83 279	77.31 255	94.36 137	99.05 97

test-LLR		93.11 123	92.68 109	94.40 163	94.94 199	87.27 197	99.15 65	97.25 142	90.21 91	91.57 125	94.04 197	84.89 119	97.58 190	85.94 175	96.13 116	98.36 148

test-mter		93.27 118	92.89 106	94.40 163	94.94 199	87.27 197	99.15 65	97.25 142	88.95 126	91.57 125	94.04 197	88.03 68	97.58 190	85.94 175	96.13 116	98.36 148

tpm2		91.77 153	91.09 144	93.82 184	94.83 201	85.56 246	92.51 316	97.16 151	84.00 231	93.83 104	90.66 268	87.54 75	97.17 210	87.73 157	91.55 173	98.72 127

PVSNet_0	83.28 16	87.31 220	85.16 238	93.74 186	94.78 202	84.59 258	98.91 94	98.69 24	89.81 102	78.59 277	93.23 222	61.95 306	99.34 107	94.75 76	55.72 350	97.30 180

CDS-MVSNet		93.47 107	93.04 102	94.76 153	94.75 203	89.45 153	98.82 106	97.03 166	87.91 160	90.97 137	96.48 165	89.06 50	96.36 249	89.50 135	92.81 150	98.49 139
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
gm-plane-assit					94.69 204	88.14 173			88.22 151		97.20 133		98.29 148	90.79 124

RPSCF		85.33 256	85.55 233	84.67 320	94.63 205	62.28 346	93.73 306	93.76 303	74.38 323	85.23 202	97.06 143	64.09 296	98.31 146	80.98 224	86.08 218	93.41 218

Patchmatch-test		86.25 243	84.06 255	92.82 199	94.42 206	82.88 279	82.88 352	94.23 298	71.58 327	79.39 268	90.62 271	89.00 52	96.42 244	63.03 330	91.37 176	99.16 90

VDDNet		90.08 182	88.54 192	94.69 156	94.41 207	87.68 182	98.21 183	96.40 195	76.21 316	93.33 108	97.75 106	54.93 328	98.77 126	94.71 78	90.96 178	97.61 175

EI-MVSNet		89.87 185	89.38 174	91.36 230	94.32 208	85.87 238	97.61 214	96.59 182	85.10 213	85.51 200	97.10 140	81.30 167	96.56 230	83.85 199	83.03 240	91.64 246

CVMVSNet		90.30 175	90.91 154	88.46 287	94.32 208	73.58 329	97.61 214	97.59 103	90.16 96	88.43 174	97.10 140	76.83 192	92.86 318	82.64 208	93.54 143	98.93 111

testpf		80.59 300	80.13 287	81.97 328	94.25 210	71.65 335	60.37 362	95.46 263	70.99 329	76.97 287	87.74 308	73.58 226	91.67 339	76.86 261	84.97 224	82.60 349

IterMVS-LS		88.34 209	87.44 202	91.04 234	94.10 211	85.85 240	98.10 192	95.48 261	85.12 212	82.03 244	91.21 247	81.35 166	95.63 284	83.86 198	75.73 271	91.63 247
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
TAMVS		92.62 136	92.09 126	94.20 170	94.10 211	87.68 182	98.41 159	96.97 170	87.53 172	89.74 159	96.04 174	84.77 122	96.49 238	88.97 147	92.31 158	98.42 141

PAPM		96.35 39	95.94 43	97.58 30	94.10 211	95.25 16	98.93 91	98.17 43	94.26 20	93.94 101	98.72 71	89.68 46	97.88 168	96.36 47	99.29 54	99.62 56

CLD-MVS		91.06 164	90.71 160	92.10 211	94.05 214	86.10 230	99.55 22	96.29 204	94.16 21	84.70 204	97.17 137	69.62 261	97.82 172	94.74 77	86.08 218	92.39 224
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
HQP-NCC					93.95 215		99.16 60		93.92 23	87.57 184

ACMP_Plane					93.95 215		99.16 60		93.92 23	87.57 184

HQP-MVS		91.50 156	91.23 143	92.29 208	93.95 215	86.39 219	99.16 60	96.37 196	93.92 23	87.57 184	96.67 157	73.34 229	97.77 176	93.82 91	86.29 213	92.72 219

NP-MVS					93.94 218	86.22 226					96.67 157

plane_prior6					93.92 219	86.02 235						72.92 234

ACMP	87.39 10	88.71 206	88.24 195	90.12 253	93.91 220	81.06 296	98.50 146	95.67 245	89.43 111	80.37 256	95.55 179	65.67 290	97.83 171	90.55 127	84.51 227	91.47 253
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
plane_prior1					93.90 221

HQP_MVS		91.26 160	90.95 153	92.16 210	93.84 222	86.07 232	99.02 82	96.30 201	93.38 36	86.99 190	96.52 163	72.92 234	97.75 181	93.46 96	86.17 216	92.67 221

plane_prior7					93.84 222	85.73 242

MVS-HIRNet		79.01 307	75.13 314	90.66 242	93.82 224	81.69 287	85.16 341	93.75 304	54.54 354	74.17 302	59.15 357	57.46 318	96.58 228	63.74 328	94.38 136	93.72 215

FMVSNet5		82.29 281	80.54 286	87.52 302	93.79 225	84.01 265	93.73 306	92.47 327	76.92 314	74.27 301	86.15 322	63.69 299	89.24 343	69.07 315	74.79 278	89.29 308

ACMH+	83.78 15	84.21 269	82.56 273	89.15 275	93.73 226	79.16 304	96.43 255	94.28 297	81.09 281	74.00 303	94.03 199	54.58 329	97.67 184	76.10 268	78.81 259	90.63 285

ACMM	86.95 13	88.77 204	88.22 196	90.43 247	93.61 227	81.34 291	98.50 146	95.92 228	87.88 161	83.85 212	95.20 185	67.20 280	97.89 167	86.90 168	84.90 225	92.06 239
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
OpenMVS	85.28 14	90.75 171	88.84 182	96.48 92	93.58 228	93.51 56	98.80 108	97.41 133	82.59 264	78.62 275	97.49 117	68.00 274	99.82 51	84.52 189	98.55 82	96.11 207

IterMVS		85.81 250	84.67 248	89.22 273	93.51 229	83.67 269	96.32 259	94.80 282	85.09 214	78.69 273	90.17 290	66.57 286	93.17 314	79.48 239	77.42 266	90.81 275
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
CR-MVSNet		88.83 201	87.38 203	93.16 193	93.47 230	86.24 224	84.97 344	94.20 299	88.92 129	90.76 140	86.88 318	84.43 123	94.82 302	70.64 313	92.17 163	98.41 142

RPMNet		84.62 262	81.78 276	93.16 193	93.47 230	86.24 224	84.97 344	96.28 205	64.85 348	90.76 140	78.80 347	80.95 168	94.82 302	53.76 345	92.17 163	98.41 142

semantic-postprocess				89.00 278	93.46 232	82.90 277		94.70 285	85.02 216	78.62 275	90.35 279	66.63 284	93.33 313	79.38 241	77.36 267	90.76 279

Fast-Effi-MVS+-dtu		88.84 199	88.59 190	89.58 266	93.44 233	78.18 314	98.65 125	94.62 288	88.46 140	84.12 210	95.37 184	68.91 266	96.52 236	82.06 214	91.70 171	94.06 213

Patchmtry		83.61 279	81.64 278	89.50 268	93.36 234	82.84 280	84.10 347	94.20 299	69.47 337	79.57 266	86.88 318	84.43 123	94.78 304	68.48 318	74.30 286	90.88 274

LPG-MVS_test		88.86 198	88.47 193	90.06 254	93.35 235	80.95 297	98.22 180	95.94 225	87.73 166	83.17 218	96.11 172	66.28 287	97.77 176	90.19 129	85.19 222	91.46 254

LGP-MVS_train				90.06 254	93.35 235	80.95 297		95.94 225	87.73 166	83.17 218	96.11 172	66.28 287	97.77 176	90.19 129	85.19 222	91.46 254

JIA-IIPM		85.97 246	84.85 244	89.33 272	93.23 237	73.68 328	85.05 343	97.13 154	69.62 336	91.56 127	68.03 353	88.03 68	96.96 217	77.89 253	93.12 145	97.34 179

ACMH	83.09 17	84.60 263	82.61 271	90.57 243	93.18 238	82.94 275	96.27 260	94.92 281	81.01 282	72.61 312	93.61 214	56.54 320	97.79 174	74.31 287	81.07 250	90.99 271
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
PatchT		85.44 255	83.19 259	92.22 209	93.13 239	83.00 274	83.80 350	96.37 196	70.62 330	90.55 143	79.63 345	84.81 121	94.87 300	58.18 342	91.59 172	98.79 121

jason		95.40 63	94.86 63	97.03 52	92.91 240	94.23 45	99.70 10	96.30 201	93.56 34	96.73 56	98.52 83	81.46 165	97.91 165	96.08 53	98.47 83	98.96 106
jason: jason.
LTVRE_ROB	81.71 19	84.59 264	82.72 269	90.18 251	92.89 241	83.18 273	93.15 311	94.74 283	78.99 295	75.14 298	92.69 230	65.64 291	97.63 187	69.46 314	81.82 248	89.74 302
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
VPA-MVSNet		89.10 194	87.66 200	93.45 188	92.56 242	91.02 114	97.97 199	98.32 29	86.92 185	86.03 197	92.01 237	68.84 268	97.10 214	90.92 121	75.34 273	92.23 231

tpm		89.67 187	88.95 180	91.82 216	92.54 243	81.43 288	92.95 312	95.92 228	87.81 162	90.50 145	89.44 296	84.99 116	95.65 283	83.67 200	82.71 243	98.38 145

GA-MVS		90.10 180	88.69 185	94.33 165	92.44 244	87.97 178	99.08 73	96.26 206	89.65 104	86.92 192	93.11 225	68.09 272	96.96 217	82.54 210	90.15 190	98.05 158

FIs		90.70 172	89.87 169	93.18 192	92.29 245	91.12 109	98.17 188	98.25 31	89.11 121	83.44 214	94.82 189	82.26 157	96.17 266	87.76 156	82.76 242	92.25 229

ITE_SJBPF				87.93 298	92.26 246	76.44 320		93.47 309	87.67 169	79.95 261	95.49 182	56.50 321	97.38 205	75.24 280	82.33 246	89.98 299

UniMVSNet (Re)		89.50 190	88.32 194	93.03 195	92.21 247	90.96 116	98.90 99	98.39 27	89.13 119	83.22 215	92.03 235	81.69 162	96.34 255	86.79 169	72.53 302	91.81 243

UniMVSNet_NR-MVSNet		89.60 188	88.55 191	92.75 202	92.17 248	90.07 137	98.74 113	98.15 45	88.37 146	83.21 216	93.98 202	82.86 148	95.93 276	86.95 166	72.47 303	92.25 229

TinyColmap		80.42 302	77.94 302	87.85 299	92.09 249	78.58 311	93.74 305	89.94 350	74.99 319	69.77 318	91.78 241	46.09 344	97.58 190	65.17 327	77.89 263	87.38 321

MS-PatchMatch		86.75 233	85.92 221	89.22 273	91.97 250	82.47 282	96.91 238	96.14 216	83.74 240	77.73 283	93.53 217	58.19 316	97.37 207	76.75 263	98.35 84	87.84 316

VPNet		88.30 210	86.57 212	93.49 187	91.95 251	91.35 103	98.18 185	97.20 148	88.61 135	84.52 207	94.89 187	62.21 305	96.76 225	89.34 140	72.26 307	92.36 225

FMVSNet1		83.94 275	81.32 283	91.80 217	91.94 252	88.81 161	96.77 242	95.25 274	77.98 307	78.25 282	90.25 283	50.37 340	94.97 297	73.27 300	77.81 264	91.62 248

WR-MVS		88.54 208	87.22 207	92.52 206	91.93 253	89.50 151	98.56 137	97.84 63	86.99 181	81.87 247	93.81 207	74.25 218	95.92 278	85.29 180	74.43 281	92.12 236

LP		77.80 315	74.39 317	88.01 296	91.93 253	79.02 307	80.88 354	92.90 320	65.43 346	72.00 313	81.29 337	65.78 289	92.73 327	43.76 354	75.58 272	92.27 228

FC-MVSNet-test		90.22 177	89.40 173	92.67 205	91.78 255	89.86 144	97.89 202	98.22 33	88.81 132	82.96 223	94.66 191	81.90 161	95.96 274	85.89 177	82.52 245	92.20 234

MIMVSNet		84.48 266	81.83 275	92.42 207	91.73 256	87.36 194	85.52 340	94.42 294	81.40 278	81.91 245	87.58 310	51.92 336	92.81 321	73.84 294	88.15 206	97.08 187

USDC		84.74 259	82.93 261	90.16 252	91.73 256	83.54 270	95.00 293	93.30 310	88.77 133	73.19 305	93.30 220	53.62 332	97.65 186	75.88 270	81.54 249	89.30 307

nrg030		90.23 176	88.87 181	94.32 166	91.53 258	93.54 55	98.79 111	95.89 234	88.12 154	84.55 206	94.61 193	78.80 181	96.88 220	92.35 111	75.21 274	92.53 223

DU-MVS		88.83 201	87.51 201	92.79 200	91.46 259	90.07 137	98.71 114	97.62 97	88.87 131	83.21 216	93.68 211	74.63 204	95.93 276	86.95 166	72.47 303	92.36 225

NR-MVSNet		87.74 215	86.00 220	92.96 197	91.46 259	90.68 125	96.65 249	97.42 132	88.02 155	73.42 304	93.68 211	77.31 189	95.83 279	84.26 190	71.82 312	92.36 225

tfpnnormal		83.65 277	81.35 282	90.56 244	91.37 261	88.06 175	97.29 221	97.87 61	78.51 301	76.20 289	90.91 255	64.78 294	96.47 241	61.71 333	73.50 295	87.13 326

test_0402		78.81 309	76.33 311	86.26 310	91.18 262	78.44 313	95.88 279	91.34 341	68.55 338	70.51 316	89.91 291	52.65 335	94.99 296	47.14 350	79.78 256	85.34 343

test0.0.03 1		88.96 196	88.61 187	90.03 256	91.09 263	84.43 260	98.97 89	97.02 167	90.21 91	80.29 257	96.31 171	84.89 119	91.93 338	72.98 304	85.70 221	93.73 214

WR-MVS_H		86.53 239	85.49 234	89.66 265	91.04 264	83.31 272	97.53 216	98.20 34	84.95 218	79.64 264	90.90 256	78.01 186	95.33 291	76.29 267	72.81 299	90.35 289

CP-MVSNet		86.54 238	85.45 235	89.79 261	91.02 265	82.78 281	97.38 219	97.56 110	85.37 209	79.53 267	93.03 226	71.86 246	95.25 293	79.92 235	73.43 297	91.34 257

TranMVSNet+NR-MVSNet		87.75 213	86.31 216	92.07 212	90.81 266	88.56 167	98.33 165	97.18 149	87.76 163	81.87 247	93.90 205	72.45 238	95.43 288	83.13 203	71.30 315	92.23 231

PS-CasMVS		85.81 250	84.58 249	89.49 270	90.77 267	82.11 284	97.20 228	97.36 138	84.83 220	79.12 272	92.84 229	67.42 279	95.16 295	78.39 249	73.25 298	91.21 261

DeepMVS_CX				76.08 335	90.74 268	51.65 358		90.84 343	86.47 196	57.89 347	87.98 306	35.88 356	92.60 329	65.77 326	65.06 328	83.97 345

OPM-MVS		89.76 186	89.15 177	91.57 225	90.53 269	85.58 245	98.11 191	95.93 227	92.88 43	86.05 196	96.47 166	67.06 282	97.87 169	89.29 143	86.08 218	91.26 260

XXY-MVS		87.75 213	86.02 219	92.95 198	90.46 270	89.70 147	97.71 210	95.90 232	84.02 230	80.95 252	94.05 196	67.51 278	97.10 214	85.16 181	78.41 260	92.04 240

v1neww		87.29 221	85.88 222	91.50 226	90.07 271	86.87 202	98.45 152	95.66 248	83.84 237	83.07 221	90.99 251	74.58 208	96.56 230	81.96 217	74.33 284	91.07 267

v7new		87.29 221	85.88 222	91.50 226	90.07 271	86.87 202	98.45 152	95.66 248	83.84 237	83.07 221	90.99 251	74.58 208	96.56 230	81.96 217	74.33 284	91.07 267

v7		86.91 230	85.45 235	91.29 231	90.06 273	86.73 208	98.26 176	95.49 260	83.08 257	82.95 224	90.96 254	73.37 227	96.42 244	79.90 236	74.97 275	90.71 282

v18		82.00 283	79.76 291	88.72 281	90.03 274	86.81 207	96.17 270	93.12 311	78.70 298	68.39 321	82.10 327	74.64 202	93.00 315	74.21 288	60.45 338	86.35 330

v10		85.73 253	84.01 256	90.87 239	90.03 274	86.73 208	97.20 228	95.22 279	81.25 280	79.85 263	89.75 293	73.30 232	96.28 263	76.87 260	72.64 301	89.61 305

v16		81.90 286	79.65 292	88.65 282	90.02 276	86.66 211	96.01 274	93.07 313	78.53 300	68.27 323	82.05 328	74.39 214	92.96 316	74.02 292	60.48 337	86.33 332

v8		86.11 244	84.45 250	91.10 233	89.99 277	86.85 204	97.24 225	95.36 269	81.99 272	79.89 262	89.86 292	74.53 210	96.39 247	78.83 246	72.32 305	90.05 297

v6		87.27 223	85.86 224	91.50 226	89.97 278	86.84 206	98.45 152	95.67 245	83.85 236	83.11 220	90.97 253	74.46 211	96.58 228	81.97 216	74.34 283	91.09 264

v17		81.87 288	79.61 293	88.64 283	89.91 279	86.64 212	96.01 274	93.08 312	78.54 299	68.27 323	81.96 329	74.44 212	92.95 317	74.03 291	60.22 340	86.34 331

V42		87.00 229	85.68 231	90.98 236	89.91 279	86.08 231	98.32 167	95.61 253	83.67 243	82.72 229	90.67 266	74.00 222	96.53 234	81.94 219	74.28 287	90.32 290

XVG-ACMP-BASELINE		85.86 248	84.95 242	88.57 284	89.90 281	77.12 319	94.30 299	95.60 254	87.40 174	82.12 240	92.99 228	53.42 333	97.66 185	85.02 184	83.83 233	90.92 273

PEN-MVS		85.21 257	83.93 257	89.07 277	89.89 282	81.31 292	97.09 232	97.24 144	84.45 225	78.66 274	92.68 231	68.44 270	94.87 300	75.98 269	70.92 316	91.04 270

v1141		87.23 225	85.75 228	91.67 222	89.88 283	87.43 190	98.52 141	95.62 251	83.91 233	82.83 227	90.69 263	74.70 201	96.49 238	81.53 223	74.08 291	91.07 267

divwei89l23v2f112		87.23 225	85.75 228	91.66 223	89.88 283	87.40 191	98.53 140	95.62 251	83.91 233	82.84 226	90.67 266	74.75 200	96.49 238	81.55 221	74.05 293	91.08 265

v1		87.23 225	85.76 226	91.66 223	89.88 283	87.37 193	98.54 139	95.64 250	83.91 233	82.88 225	90.70 261	74.64 202	96.53 234	81.54 222	74.08 291	91.08 265

v15		81.62 289	79.32 296	88.52 285	89.80 286	86.56 213	95.83 283	92.96 316	78.50 302	67.88 327	81.68 331	74.22 219	92.82 320	73.46 298	59.55 341	86.18 335

V14		81.55 291	79.26 297	88.42 288	89.80 286	86.33 222	95.72 286	92.96 316	78.35 303	67.82 328	81.70 330	74.13 220	92.78 324	73.32 299	59.50 343	86.16 337

v1144		86.83 232	85.31 237	91.40 229	89.75 288	87.21 199	98.31 168	95.45 264	83.22 254	82.70 230	90.78 258	73.36 228	96.36 249	79.49 238	74.69 279	90.63 285

V9		81.46 292	79.15 298	88.39 291	89.75 288	86.17 228	95.62 287	92.92 318	78.22 304	67.65 332	81.64 332	73.95 223	92.80 322	73.15 302	59.43 346	86.21 334

TransMVSNet (Re)		81.97 284	79.61 293	89.08 276	89.70 290	84.01 265	97.26 223	91.85 336	78.84 296	73.07 308	91.62 243	67.17 281	95.21 294	67.50 319	59.46 345	88.02 315

v12		81.37 294	79.05 299	88.33 292	89.68 291	86.05 234	95.48 289	92.92 318	78.08 305	67.55 333	81.58 333	73.75 224	92.75 325	73.05 303	59.37 347	86.18 335

v11		81.38 293	79.03 300	88.41 289	89.68 291	86.43 216	95.74 285	92.82 325	78.03 306	67.74 329	81.45 335	73.33 231	92.69 328	72.23 309	60.27 339	86.11 339

v13		81.30 295	78.99 301	88.25 293	89.61 293	85.87 238	95.39 290	92.90 320	77.93 311	67.45 336	81.52 334	73.66 225	92.75 325	72.91 305	59.53 342	86.14 338

v2v482		87.27 223	85.76 226	91.78 221	89.59 294	87.58 184	98.56 137	95.54 256	84.53 223	82.51 233	91.78 241	73.11 233	96.47 241	82.07 213	74.14 290	91.30 259

pm-mvs1		84.68 261	82.78 267	90.40 248	89.58 295	85.18 250	97.31 220	94.73 284	81.93 274	76.05 291	92.01 237	65.48 292	96.11 269	78.75 247	69.14 319	89.91 300

pmmvs4		87.58 218	86.17 218	91.80 217	89.58 295	88.92 159	97.25 224	95.28 273	82.54 266	80.49 255	93.17 224	75.62 197	96.05 271	82.75 207	78.90 258	90.42 288

v1192		86.32 242	84.71 247	91.17 232	89.53 297	86.40 218	98.13 189	95.44 265	82.52 267	82.42 235	90.62 271	71.58 250	96.33 256	77.23 256	74.88 276	90.79 277

pcd1.5k->3k		35.91 343	37.64 343	30.74 355	89.49 298	0.00 374	0.00 365	96.36 199	0.00 369	0.00 371	0.00 371	69.17 265	0.00 371	0.00 368	83.71 235	92.21 233

v144192		86.40 240	84.89 243	90.91 237	89.48 299	85.59 244	98.21 183	95.43 266	82.45 268	82.62 231	90.58 274	72.79 237	96.36 249	78.45 248	74.04 294	90.79 277

v148		86.38 241	85.06 239	90.37 249	89.47 300	84.10 264	98.52 141	95.48 261	83.80 239	80.93 253	90.22 286	74.60 206	96.31 259	80.92 226	71.55 313	90.69 283

v1921920		86.02 245	84.44 251	90.77 240	89.32 301	85.20 249	98.10 192	95.35 271	82.19 270	82.25 238	90.71 260	70.73 253	96.30 262	76.85 262	74.49 280	90.80 276

v1240		85.77 252	84.11 254	90.73 241	89.26 302	85.15 252	97.88 204	95.23 278	81.89 275	82.16 239	90.55 276	69.60 262	96.31 259	75.59 279	74.87 277	90.72 281

DI_MVS_plusplus_test		89.41 191	87.24 206	95.92 117	89.06 303	90.75 124	98.18 185	96.63 179	89.29 114	70.54 315	90.31 281	63.50 300	98.40 143	92.25 112	95.44 129	98.60 132

our_test_3		84.47 267	82.80 265	89.50 268	89.01 304	83.90 267	97.03 234	94.56 289	81.33 279	75.36 297	90.52 277	71.69 248	94.54 307	68.81 316	76.84 268	90.07 295

ppachtmachnet_test		83.63 278	81.57 280	89.80 260	89.01 304	85.09 253	97.13 231	94.50 290	78.84 296	76.14 290	91.00 250	69.78 259	94.61 306	63.40 329	74.36 282	89.71 304

DTE-MVSNet		84.14 273	82.80 265	88.14 294	88.95 306	79.87 303	96.81 241	96.24 207	83.50 251	77.60 285	92.52 233	67.89 276	94.24 309	72.64 307	69.05 320	90.32 290

test_normal		89.37 192	87.18 208	95.93 116	88.94 307	90.83 120	98.24 178	96.62 180	89.31 112	70.38 317	90.20 288	63.50 300	98.37 144	92.06 114	95.41 130	98.59 135

PS-MVSNAJss		89.54 189	89.05 178	91.00 235	88.77 308	84.36 261	97.39 217	95.97 221	88.47 138	81.88 246	93.80 208	82.48 153	96.50 237	89.34 140	83.34 238	92.15 235

Baseline_NR-MVSNet		85.83 249	84.82 245	88.87 280	88.73 309	83.34 271	98.63 128	91.66 337	80.41 287	82.44 234	91.35 246	74.63 204	95.42 289	84.13 192	71.39 314	87.84 316

MVP-Stereo		86.61 237	85.83 225	88.93 279	88.70 310	83.85 268	96.07 272	94.41 295	82.15 271	75.64 295	91.96 239	67.65 277	96.45 243	77.20 258	98.72 76	86.51 329
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
EU-MVSNet		84.19 271	84.42 252	83.52 323	88.64 311	67.37 343	96.04 273	95.76 238	85.29 210	78.44 280	93.18 223	70.67 254	91.48 341	75.79 277	75.98 269	91.70 245

pmmvs5		85.87 247	84.40 253	90.30 250	88.53 312	84.23 262	98.60 133	93.71 305	81.53 277	80.29 257	92.02 236	64.51 295	95.52 286	82.04 215	78.34 261	91.15 262

MDA-MVSNet-bldmvs		77.82 314	74.75 316	87.03 306	88.33 313	78.52 312	96.34 258	92.85 322	75.57 318	48.87 354	87.89 307	57.32 319	92.49 332	60.79 335	64.80 329	90.08 294

N_pmnet		70.19 325	69.87 324	71.12 339	88.24 314	30.63 370	95.85 282	28.70 372	70.18 334	68.73 320	86.55 320	64.04 297	93.81 310	53.12 346	73.46 296	88.94 311

v7n		84.42 268	82.75 268	89.43 271	88.15 315	81.86 285	96.75 245	95.67 245	80.53 285	78.38 281	89.43 297	69.89 257	96.35 254	73.83 295	72.13 309	90.07 295

SixPastTwentyTwo		82.63 280	81.58 279	85.79 313	88.12 316	71.01 337	95.17 292	92.54 326	84.33 227	72.93 309	92.08 234	60.41 312	95.61 285	74.47 286	74.15 289	90.75 280

test_djsdf		88.26 212	87.73 198	89.84 259	88.05 317	82.21 283	97.77 208	96.17 213	86.84 186	82.41 236	91.95 240	72.07 243	95.99 272	89.83 131	84.50 228	91.32 258

mvs_tets		87.09 228	86.22 217	89.71 262	87.87 318	81.39 290	96.73 246	95.90 232	88.19 152	79.99 260	93.61 214	59.96 313	96.31 259	89.40 139	84.34 230	91.43 256

OurMVSNet-221017-0		84.13 274	83.59 258	85.77 314	87.81 319	70.24 338	94.89 294	93.65 307	86.08 199	76.53 288	93.28 221	61.41 308	96.14 268	80.95 225	77.69 265	90.93 272

YYNet1		79.64 306	77.04 308	87.43 304	87.80 320	79.98 300	96.23 263	94.44 292	73.83 325	51.83 351	87.53 312	67.96 275	92.07 337	66.00 325	67.75 325	90.23 292

MDA-MVSNet_test_wron		79.65 305	77.05 307	87.45 303	87.79 321	80.13 299	96.25 262	94.44 292	73.87 324	51.80 352	87.47 313	68.04 273	92.12 336	66.02 324	67.79 324	90.09 293

jajsoiax		87.35 219	86.51 214	89.87 257	87.75 322	81.74 286	97.03 234	95.98 220	88.47 138	80.15 259	93.80 208	61.47 307	96.36 249	89.44 138	84.47 229	91.50 252

v748		83.84 276	82.31 274	88.41 289	87.65 323	79.10 306	96.66 248	95.51 258	80.09 288	77.65 284	88.53 305	69.81 258	96.23 264	75.67 278	69.25 318	89.91 300

v52		84.19 271	82.92 262	88.01 296	87.64 324	79.92 301	96.23 263	95.32 272	79.87 290	78.51 278	89.05 300	69.50 264	96.32 257	77.95 252	72.24 308	87.79 319

V4		84.20 270	82.92 262	88.02 295	87.59 325	79.91 302	96.21 268	95.36 269	79.88 289	78.51 278	89.00 301	69.52 263	96.32 257	77.96 251	72.29 306	87.83 318

K. test v3		81.04 296	79.77 290	84.83 318	87.41 326	70.23 339	95.60 288	93.93 302	83.70 242	67.51 334	89.35 298	55.76 322	93.58 312	76.67 264	68.03 323	90.67 284

testgi		82.29 281	81.00 285	86.17 311	87.24 327	74.84 324	97.39 217	91.62 338	88.63 134	75.85 294	95.42 183	46.07 345	91.55 340	66.87 323	79.94 254	92.12 236

LF4IMVS		81.94 285	81.17 284	84.25 321	87.23 328	68.87 342	93.35 310	91.93 335	83.35 253	75.40 296	93.00 227	49.25 342	96.65 226	78.88 245	78.11 262	87.22 325

EG-PatchMatch MVS		79.92 303	77.59 303	86.90 307	87.06 329	77.90 318	96.20 269	94.06 301	74.61 321	66.53 338	88.76 303	40.40 353	96.20 265	67.02 321	83.66 236	86.61 327

Gipuma		54.77 333	52.22 335	62.40 346	86.50 330	59.37 349	50.20 363	90.35 347	36.52 359	41.20 358	49.49 361	18.33 363	81.29 356	32.10 360	65.34 327	46.54 362
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
anonymousdsp		86.69 234	85.75 228	89.53 267	86.46 331	82.94 275	96.39 256	95.71 242	83.97 232	79.63 265	90.70 261	68.85 267	95.94 275	86.01 173	84.02 232	89.72 303

lessismore_v0				85.08 316	85.59 332	69.28 341		90.56 345		67.68 331	90.21 287	54.21 331	95.46 287	73.88 293	62.64 332	90.50 287

CMPMVS	58.40 21	80.48 301	80.11 289	81.59 330	85.10 333	59.56 348	94.14 302	95.95 224	68.54 339	60.71 344	93.31 219	55.35 326	97.87 169	83.06 204	84.85 226	87.33 322
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
Anonymous20231206		80.76 299	79.42 295	84.79 319	84.78 334	72.98 330	96.53 252	92.97 315	79.56 292	74.33 300	88.83 302	61.27 309	92.15 335	60.59 336	75.92 270	89.24 309

Test4		85.71 254	82.59 272	95.07 146	84.45 335	89.84 145	97.20 228	95.73 240	89.19 116	64.59 340	87.58 310	40.59 352	96.77 224	88.95 148	95.01 133	98.60 132

DSMNet-mixed		81.60 290	81.43 281	82.10 326	84.36 336	60.79 347	93.63 308	86.74 357	79.00 294	79.32 269	87.15 316	63.87 298	89.78 342	66.89 322	91.92 165	95.73 208

pmmvs6		79.90 304	77.31 305	87.67 301	84.17 337	78.13 315	95.86 281	93.68 306	67.94 341	72.67 311	89.62 295	50.98 339	95.75 281	74.80 285	66.04 326	89.14 310

new_pmnet		76.02 317	73.71 318	82.95 324	83.88 338	72.85 331	91.26 326	92.26 329	70.44 332	62.60 342	81.37 336	47.64 343	92.32 333	61.85 332	72.10 310	83.68 346

OpenMVS_ROB	73.86 20	77.99 313	75.06 315	86.77 308	83.81 339	77.94 317	96.38 257	91.53 340	67.54 342	68.38 322	87.13 317	43.94 346	96.08 270	55.03 344	81.83 247	86.29 333

test20.03		78.51 311	77.48 304	81.62 329	83.07 340	71.03 336	96.11 271	92.83 323	81.66 276	69.31 319	89.68 294	57.53 317	87.29 347	58.65 341	68.47 321	86.53 328

UnsupCasMVSNet_eth		78.90 308	76.67 310	85.58 315	82.81 341	74.94 323	91.98 320	96.31 200	84.64 222	65.84 339	87.71 309	51.33 337	92.23 334	72.89 306	56.50 349	89.56 306

MIMVSNet1		75.92 318	73.30 319	83.81 322	81.29 342	75.57 322	92.26 319	92.05 333	73.09 326	67.48 335	86.18 321	40.87 351	87.64 346	55.78 343	70.68 317	88.21 312

test2356		80.96 297	81.77 277	78.52 333	81.02 343	62.33 345	98.22 180	94.49 291	79.38 293	74.56 299	90.34 280	70.65 256	85.10 351	60.83 334	86.42 212	88.14 313

Patchmatch-RL test		81.90 286	80.13 287	87.23 305	80.71 344	70.12 340	84.07 348	88.19 356	83.16 256	70.57 314	82.18 326	87.18 85	92.59 330	82.28 212	62.78 331	98.98 104

pmmvs-eth3d		78.71 310	76.16 312	86.38 309	80.25 345	81.19 294	94.17 301	92.13 332	77.97 308	66.90 337	82.31 325	55.76 322	92.56 331	73.63 297	62.31 334	85.38 341

testus		77.11 316	76.95 309	77.58 334	80.02 346	58.93 350	97.78 206	90.48 346	79.68 291	72.84 310	90.61 273	37.72 355	86.57 350	60.28 338	83.18 239	87.23 324

UnsupCasMVSNet_bld		73.85 321	70.14 323	84.99 317	79.44 347	75.73 321	88.53 335	95.24 277	70.12 335	61.94 343	74.81 349	41.41 350	93.62 311	68.65 317	51.13 355	85.62 340

PM-MVS		74.88 319	72.85 320	80.98 331	78.98 348	64.75 344	90.81 329	85.77 359	80.95 283	68.23 326	82.81 324	29.08 358	92.84 319	76.54 266	62.46 333	85.36 342

testing_2		80.92 298	77.24 306	91.98 213	78.88 349	87.83 179	93.96 304	95.72 241	84.27 228	56.20 349	80.42 340	38.64 354	96.40 246	87.20 161	79.85 255	91.72 244

new-patchmatchnet		74.80 320	72.40 321	81.99 327	78.36 350	72.20 333	94.44 296	92.36 328	77.06 313	63.47 341	79.98 344	51.04 338	88.85 344	60.53 337	54.35 351	84.92 344

pmmvs3		72.86 322	69.76 325	82.17 325	73.86 351	74.19 326	94.20 300	89.01 353	64.23 349	67.72 330	80.91 339	41.48 349	88.65 345	62.40 331	54.02 352	83.68 346

1111		72.28 323	71.36 322	75.02 337	73.04 352	57.38 352	92.30 317	90.22 348	62.27 350	59.46 345	80.36 341	76.23 193	87.07 348	44.29 352	64.08 330	80.59 350

.test1245		61.50 328	64.44 328	52.65 352	73.04 352	57.38 352	92.30 317	90.22 348	62.27 350	59.46 345	80.36 341	76.23 193	87.07 348	44.29 352	1.80 366	13.50 366

ambc				79.60 332	72.76 354	56.61 354	76.20 356	92.01 334		68.25 325	80.23 343	23.34 359	94.73 305	73.78 296	60.81 336	87.48 320

test1235678		71.07 324	69.53 326	75.71 336	71.87 355	55.27 356	94.32 297	90.76 344	70.23 333	57.61 348	79.06 346	43.13 347	83.72 353	50.48 347	68.30 322	88.14 313

TDRefinement		78.01 312	75.31 313	86.10 312	70.06 356	73.84 327	93.59 309	91.58 339	74.51 322	73.08 307	91.04 249	49.63 341	97.12 211	74.88 283	59.47 344	87.33 322

test12356		66.36 326	65.12 327	70.08 342	66.92 357	50.46 359	89.96 333	88.58 354	66.00 345	53.38 350	78.13 348	32.89 357	82.87 354	48.36 349	61.87 335	76.92 351

PMMVS2		58.97 331	55.07 332	70.69 341	62.72 358	55.37 355	85.97 339	80.52 363	49.48 355	45.94 355	68.31 352	15.73 366	80.78 357	49.79 348	37.12 356	75.91 353

E-PMN		41.02 340	40.93 340	41.29 353	61.97 359	33.83 367	84.00 349	65.17 370	27.17 362	27.56 361	46.72 363	17.63 365	60.41 366	19.32 363	18.82 361	29.61 363

PNet_i23d		48.05 336	44.98 338	57.28 348	60.15 360	42.39 365	80.85 355	73.14 368	36.78 358	27.46 362	56.66 358	6.38 369	68.34 362	36.65 358	26.72 358	61.10 358

wuyk23d		16.71 346	16.73 348	16.65 356	60.15 360	25.22 371	41.24 364	5.17 373	6.56 366	5.48 370	3.61 370	3.64 371	22.72 368	15.20 365	9.52 365	1.99 368

FPMVS		61.57 327	60.32 329	65.34 344	60.14 362	42.44 364	91.02 328	89.72 351	44.15 356	42.63 357	80.93 338	19.02 361	80.59 358	42.50 355	72.76 300	73.00 354

EMVS		39.96 342	39.88 341	40.18 354	59.57 363	32.12 369	84.79 346	64.57 371	26.27 363	26.14 364	44.18 366	18.73 362	59.29 367	17.03 364	17.67 363	29.12 364

no-one		56.69 332	51.89 336	71.08 340	59.35 364	58.65 351	83.78 351	84.81 362	61.73 352	36.46 360	56.52 359	18.15 364	84.78 352	47.03 351	19.19 360	69.81 356

testmv		60.41 329	57.98 330	67.69 343	58.16 365	47.14 361	89.09 334	86.74 357	61.52 353	44.30 356	68.44 351	20.98 360	79.92 359	40.94 356	51.67 353	76.01 352

LCM-MVSNet		60.07 330	56.37 331	71.18 338	54.81 366	48.67 360	82.17 353	89.48 352	37.95 357	49.13 353	69.12 350	13.75 368	81.76 355	59.28 339	51.63 354	83.10 348

MVE	44.00 22	41.70 339	37.64 343	53.90 351	49.46 367	43.37 363	65.09 361	66.66 369	26.19 364	25.77 365	48.53 362	3.58 373	63.35 365	26.15 362	27.28 357	54.97 361
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
wuykxyi23d		43.53 338	37.95 342	60.27 347	45.36 368	44.79 362	68.27 359	74.26 367	33.48 360	18.21 368	40.16 368	3.64 371	71.01 361	38.85 357	19.31 359	65.02 357

ANet_high		50.71 335	46.17 337	64.33 345	44.27 369	52.30 357	76.13 357	78.73 364	64.95 347	27.37 363	55.23 360	14.61 367	67.74 363	36.01 359	18.23 362	72.95 355

PMVS	41.42 23	45.67 337	42.50 339	55.17 350	34.28 370	32.37 368	66.24 360	78.71 365	30.72 361	22.04 366	59.59 356	4.59 370	77.85 360	27.49 361	58.84 348	55.29 360
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
tmp_tt		53.66 334	52.86 334	56.05 349	32.75 371	41.97 366	73.42 358	76.12 366	21.91 365	39.68 359	96.39 169	42.59 348	65.10 364	78.00 250	14.92 364	61.08 359

testmvs		18.81 345	23.05 346	6.10 358	4.48 372	2.29 373	97.78 206	3.00 374	3.27 367	18.60 367	62.71 354	1.53 375	2.49 370	14.26 366	1.80 366	13.50 366

test123		16.58 347	19.47 347	7.91 357	3.59 373	5.37 372	94.32 297	1.39 375	2.49 368	13.98 369	44.60 365	2.91 374	2.65 369	11.35 367	0.57 368	15.70 365

cdsmvs_eth3d_5k		22.52 344	30.03 345	0.00 359	0.00 374	0.00 374	0.00 365	97.17 150	0.00 369	0.00 371	98.77 65	74.35 215	0.00 371	0.00 368	0.00 369	0.00 369

pcd_1.5k_mvsjas		6.87 349	9.16 350	0.00 359	0.00 374	0.00 374	0.00 365	0.00 376	0.00 369	0.00 371	0.00 371	82.48 153	0.00 371	0.00 368	0.00 369	0.00 369

sosnet-low-res		0.00 350	0.00 351	0.00 359	0.00 374	0.00 374	0.00 365	0.00 376	0.00 369	0.00 371	0.00 371	0.00 376	0.00 371	0.00 368	0.00 369	0.00 369

sosnet		0.00 350	0.00 351	0.00 359	0.00 374	0.00 374	0.00 365	0.00 376	0.00 369	0.00 371	0.00 371	0.00 376	0.00 371	0.00 368	0.00 369	0.00 369

uncertanet		0.00 350	0.00 351	0.00 359	0.00 374	0.00 374	0.00 365	0.00 376	0.00 369	0.00 371	0.00 371	0.00 376	0.00 371	0.00 368	0.00 369	0.00 369

Regformer		0.00 350	0.00 351	0.00 359	0.00 374	0.00 374	0.00 365	0.00 376	0.00 369	0.00 371	0.00 371	0.00 376	0.00 371	0.00 368	0.00 369	0.00 369

ab-mvs-re		8.21 348	10.94 349	0.00 359	0.00 374	0.00 374	0.00 365	0.00 376	0.00 369	0.00 371	98.50 85	0.00 376	0.00 371	0.00 368	0.00 369	0.00 369

uanet		0.00 350	0.00 351	0.00 359	0.00 374	0.00 374	0.00 365	0.00 376	0.00 369	0.00 371	0.00 371	0.00 376	0.00 371	0.00 368	0.00 369	0.00 369

GSMVS																98.84 116

test_part1				0.00 359		0.00 374	0.00 365	97.69 82				0.00 376	0.00 371	0.00 368	0.00 369	0.00 369

sam_mvs1												88.39 61				98.84 116

sam_mvs												87.08 86

MTGPA								97.45 126

test_post1							90.74 331				41.37 367	85.38 115	96.36 249	83.16 202

test_post											46.00 364	87.37 79	97.11 212

patchmatchnet-post											84.86 323	88.73 55	96.81 223

MTMP							99.21 54	91.09 342

test9_res														98.60 10	99.87 6	99.90 10

agg_prior2														97.84 27	99.87 6	99.91 9

test_prior4						92.00 84	99.41 38

test_prior2							99.57 19		91.43 70	98.12 21	98.97 48	90.43 38		98.33 18	99.81 15

旧先验2							98.67 123		85.75 203	98.96 5			98.97 122	93.84 89

新几何2							98.26 176

无先验							98.52 141	97.82 65	87.20 180				99.90 31	87.64 158		99.85 20

原ACMM2							98.69 118

testdata2													99.88 35	84.16 191

segment_acmp												90.56 37

testdata1							97.89 202		92.43 50

plane_prior5								96.30 201					97.75 181	93.46 96	86.17 216	92.67 221

plane_prior4											96.52 163

plane_prior3						85.91 236			93.65 31	86.99 190

plane_prior2							99.02 82		93.38 36

plane_prior						86.07 232	99.14 68		93.81 29						86.26 215

n2								0.00 376

nn								0.00 376

door-mid								84.90 361

test11								97.68 84

door								85.30 360

HQP5-MVS						86.39 219

BP-MVS														93.82 91

HQP4-MVS										87.57 184			97.77 176			92.72 219

HQP3-MVS								96.37 196							86.29 213

HQP2-MVS												73.34 229

MDTV_nov1_ep13_2view						91.17 108	91.38 324		87.45 173	93.08 111		86.67 93		87.02 164		98.95 110

ACMMP++_ref															82.64 244

ACMMP++															83.83 233

Test By Simon												83.62 129

High-resolution multi-view benchmark