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 93	93.06 96	96.10 108	99.88 1	89.07 152	98.33 160	97.55 107	86.81 185	90.39 144	98.65 75	75.09 193	99.98 9	93.32 98	97.53 95	99.26 83

DP-MVS Recon		95.85 52	95.15 58	97.95 20	99.87 2	94.38 43	99.60 17	97.48 118	86.58 188	94.42 89	99.13 31	87.36 78	99.98 9	93.64 91	98.33 84	99.48 67

MCST-MVS		98.18 2	97.95 5	98.86 1	99.85 3	96.60 5	99.70 10	97.98 53	97.18 2	95.96 64	99.33 9	92.62 11	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 52	97.05 3	99.41 1	99.59 2	92.89 10	100.00 1	98.99 5	99.90 4	99.96 4

MG-MVS		97.24 13	96.83 21	98.47 9	99.79 5	95.71 12	99.07 72	99.06 15	94.45 19	96.42 59	98.70 73	88.81 51	99.74 60	95.35 65	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 58	96.54 4	98.84 7	99.46 6	92.55 12	99.98 9	98.25 22	99.93 1	99.94 7

region2R		96.30 42	96.17 38	96.70 78	99.70 7	90.31 127	99.46 30	97.66 85	90.55 84	97.07 43	99.07 36	86.85 87	99.97 14	95.43 63	99.74 20	99.81 21

HFP-MVS		96.42 38	96.26 35	96.90 63	99.69 8	90.96 113	99.47 27	97.81 67	90.54 85	96.88 46	99.05 39	87.57 69	99.96 17	95.65 57	99.72 22	99.78 29

#test#		96.48 35	96.34 33	96.90 63	99.69 8	90.96 113	99.53 24	97.81 67	90.94 78	96.88 46	99.05 39	87.57 69	99.96 17	95.87 56	99.72 22	99.78 29

ACMMPR		96.28 43	96.14 41	96.73 75	99.68 10	90.47 125	99.47 27	97.80 69	90.54 85	96.83 53	99.03 41	86.51 93	99.95 20	95.65 57	99.72 22	99.75 35

CP-MVS		96.22 44	96.15 40	96.42 94	99.67 11	89.62 145	99.70 10	97.61 97	90.07 100	96.00 61	99.16 25	87.43 73	99.92 26	96.03 54	99.72 22	99.70 44

CPTT-MVS		94.60 80	94.43 68	95.09 139	99.66 12	86.85 199	99.44 31	97.47 119	83.22 248	94.34 92	98.96 51	82.50 146	99.55 78	94.81 74	99.50 42	98.88 111

MSLP-MVS++		97.50 9	97.45 10	97.63 28	99.65 13	93.21 59	99.70 10	98.13 46	94.61 16	97.78 32	99.46 6	89.85 40	99.81 52	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 35	97.55 107	92.43 50	93.82 102	99.12 32	87.30 80	99.91 28	94.02 83	99.06 60	99.74 38

XVS		96.47 36	96.37 31	96.77 71	99.62 15	90.66 123	99.43 33	97.58 101	92.41 54	96.86 49	98.96 51	87.37 75	99.87 37	95.65 57	99.43 47	99.78 29

X-MVStestdata		90.69 167	88.66 180	96.77 71	99.62 15	90.66 123	99.43 33	97.58 101	92.41 54	96.86 49	29.59 364	87.37 75	99.87 37	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 99	97.64 91	96.51 6	95.88 65	99.39 8	87.35 79	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 69	97.44 124	89.02 121	97.90 30	99.22 16	88.90 50	99.49 88	94.63 78	99.79 17	99.68 47

test_prior3		97.07 19	97.09 13	97.01 52	99.58 19	91.77 83	99.57 19	97.57 104	91.43 70	98.12 21	98.97 48	90.43 35	99.49 88	98.33 19	99.81 15	99.79 25

test_prior				97.01 52	99.58 19	91.77 83		97.57 104					99.49 88			99.79 25

APDe-MVS		97.53 7	97.47 8	97.70 26	99.58 19	93.63 52	99.56 21	97.52 111	93.59 33	98.01 26	99.12 32	90.80 31	99.55 78	99.26 3	99.79 17	99.93 8

mPP-MVS		95.90 51	95.75 49	96.38 96	99.58 19	89.41 150	99.26 50	97.41 128	90.66 80	94.82 84	98.95 53	86.15 99	99.98 9	95.24 68	99.64 30	99.74 38

TEST9					99.57 23	93.17 60	99.38 38	97.66 85	89.57 106	98.39 13	99.18 21	90.88 28	99.66 65

train_agg		97.20 14	97.08 14	97.57 32	99.57 23	93.17 60	99.38 38	97.66 85	90.18 93	98.39 13	99.18 21	90.94 26	99.66 65	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 72	99.36 42	97.67 84	89.59 104	98.36 15	99.16 25	90.57 33	99.68 62	98.58 13	99.85 10	99.88 14

test_8					99.55 26	93.07 64	99.37 41	97.64 91	90.18 93	98.36 15	99.19 19	90.94 26	99.64 71

test_part2					99.54 27	95.42 14				98.13 18

v1.0		40.64 336	54.18 328	0.00 354	99.54 27	0.00 369	0.00 360	97.69 81	92.81 45	98.13 18	99.48 5	0.00 371	0.00 366	0.00 363	0.00 364	0.00 364

HSP-MVS		97.73 5	98.15 2	96.44 93	99.54 27	90.14 130	99.41 36	97.47 119	95.46 14	98.60 10	99.19 19	95.71 4	99.49 88	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 73	99.35 44	97.64 91	90.38 88	97.98 27	99.17 23	90.84 30	99.61 74	98.57 15	99.78 19	99.87 18

agg_prior					99.54 27	92.66 73		97.64 91		97.98 27			99.61 74

CSCG		94.87 69	94.71 63	95.36 130	99.54 27	86.49 209	99.34 46	98.15 44	82.71 257	90.15 147	99.25 12	89.48 44	99.86 42	94.97 72	98.82 72	99.72 41

HPM-MVS++		97.72 6	97.59 7	98.14 14	99.53 33	94.76 30	99.19 53	97.75 74	95.66 11	98.21 17	99.29 10	91.10 18	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 58	97.44 124	90.08 99	98.59 11	99.07 36	89.06 47	99.42 97	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 49	97.72 79	94.50 17	98.64 9	99.54 3	93.32 9	99.97 14	99.58 1	99.90 4	99.95 6

PGM-MVS		95.85 52	95.65 51	96.45 92	99.50 35	89.77 142	98.22 175	98.90 17	89.19 115	96.74 55	98.95 53	85.91 101	99.92 26	93.94 84	99.46 44	99.66 50

MP-MVS		96.00 48	95.82 46	96.54 89	99.47 37	90.13 132	99.36 42	97.41 128	90.64 83	95.49 74	98.95 53	85.51 105	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 38	93.11 62	98.89 96	97.94 54	92.89 42	96.90 45	99.02 42	89.78 41	99.53 81	97.06 32	99.26 56	99.75 35

Regformer-2		96.94 24	96.78 23	97.42 37	99.46 38	92.97 68	98.89 96	97.93 55	92.86 44	96.88 46	99.02 42	89.74 42	99.53 81	97.03 33	99.26 56	99.75 35

PAPM_NR		95.43 59	95.05 60	96.57 88	99.42 40	90.14 130	98.58 132	97.51 113	90.65 82	92.44 113	98.90 58	87.77 68	99.90 30	90.88 120	99.32 53	99.68 47

Regformer-3		96.50 34	96.36 32	96.91 62	99.34 41	91.72 86	98.71 110	97.90 57	92.48 49	96.00 61	98.95 53	88.60 53	99.52 84	96.44 45	98.83 70	99.49 65

Regformer-4		96.45 37	96.33 34	96.81 70	99.34 41	91.44 94	98.71 110	97.88 58	92.43 50	95.97 63	98.95 53	88.42 57	99.51 85	96.40 46	98.83 70	99.49 65

PHI-MVS		96.65 30	96.46 29	97.21 46	99.34 41	91.77 83	99.70 10	98.05 48	86.48 190	98.05 23	99.20 18	89.33 45	99.96 17	98.38 18	99.62 34	99.90 10

test12				97.83 23	99.33 44	94.45 40		97.55 107		97.56 33		88.60 53	99.50 87		99.71 26	99.55 60

SMA-MVS		97.25 11	97.00 16	98.00 19	99.31 45	94.20 46	99.16 58	97.65 90	89.55 108	99.22 2	99.53 4	90.34 38	99.99 4	98.43 17	99.83 13	99.81 21

zzz-MVS		96.21 45	95.96 42	96.96 60	99.29 46	91.19 103	98.69 114	97.45 121	92.58 46	94.39 90	99.24 14	86.43 95	99.99 4	96.22 48	99.40 50	99.71 42

MTAPA		96.09 47	95.80 48	96.96 60	99.29 46	91.19 103	97.23 221	97.45 121	92.58 46	94.39 90	99.24 14	86.43 95	99.99 4	96.22 48	99.40 50	99.71 42

HPM-MVS		95.41 61	95.22 57	95.99 110	99.29 46	89.14 151	99.17 57	97.09 155	87.28 176	95.40 75	98.48 87	84.93 113	99.38 100	95.64 61	99.65 29	99.47 68
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
ACMMP		94.67 77	94.30 70	95.79 116	99.25 49	88.13 169	98.41 154	98.67 24	90.38 88	91.43 125	98.72 71	82.22 153	99.95 20	93.83 88	95.76 124	99.29 79
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 58	95.65 51	95.62 120	99.24 50	87.80 175	98.42 152	97.22 141	88.93 126	96.64 58	98.98 47	85.49 106	99.36 102	96.68 40	99.27 55	99.70 44

API-MVS		94.78 71	94.18 73	96.59 87	99.21 51	90.06 136	98.80 104	97.78 72	83.59 238	93.85 100	99.21 17	83.79 123	99.97 14	92.37 108	99.00 63	99.74 38

PLC	91.07 3	94.23 86	94.01 77	94.87 146	99.17 52	87.49 181	99.25 51	96.55 182	88.43 142	91.26 128	98.21 96	85.92 100	99.86 42	89.77 131	97.57 93	97.24 175
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
EI-MVSNet-Vis-set		95.76 57	95.63 53	96.17 104	99.14 53	90.33 126	98.49 143	97.82 64	91.92 61	94.75 85	98.88 60	87.06 83	99.48 93	95.40 64	97.17 101	98.70 126

TSAR-MVS + MP.		97.44 10	97.46 9	97.39 40	99.12 54	93.49 57	98.52 137	97.50 116	94.46 18	98.99 3	98.64 76	91.58 15	99.08 117	98.49 16	99.83 13	99.60 58

HPM-MVS_fast		94.89 68	94.62 64	95.70 119	99.11 55	88.44 166	99.14 66	97.11 151	85.82 196	95.69 70	98.47 88	83.46 127	99.32 106	93.16 100	99.63 33	99.35 72

MAR-MVS		94.43 82	94.09 75	95.45 129	99.10 56	87.47 182	98.39 158	97.79 71	88.37 144	94.02 97	99.17 23	78.64 177	99.91 28	92.48 107	98.85 69	98.96 103
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 89	93.05 97	97.06 50	99.08 57	92.26 81	98.97 85	97.01 163	82.58 259	92.57 111	98.22 94	80.68 163	99.30 107	89.34 137	99.02 62	99.63 54

EI-MVSNet-UG-set		95.43 59	95.29 55	95.86 115	99.07 58	89.87 139	98.43 151	97.80 69	91.78 64	94.11 96	98.77 65	86.25 98	99.48 93	94.95 73	96.45 108	98.22 150

原ACMM1				96.18 102	99.03 59	90.08 133		97.63 95	88.98 122	97.00 44	98.97 48	88.14 63	99.71 61	88.23 148	99.62 34	98.76 123

SD-MVS		97.51 8	97.40 11	97.81 24	99.01 60	93.79 51	99.33 47	97.38 131	93.73 30	98.83 8	99.02 42	90.87 29	99.88 34	98.69 9	99.74 20	99.77 34

旧先验1					98.97 61	92.90 70		97.74 76			99.15 27	91.05 19			99.33 52	99.60 58

LS3D		90.19 172	88.72 178	94.59 153	98.97 61	86.33 216	96.90 234	96.60 176	74.96 315	84.06 205	98.74 68	75.78 190	99.83 47	74.93 277	97.57 93	97.62 168

CNLPA		93.64 100	92.74 102	96.36 97	98.96 63	90.01 138	99.19 53	95.89 229	86.22 193	89.40 160	98.85 61	80.66 164	99.84 45	88.57 146	96.92 102	99.24 84

MP-MVS-pluss		95.80 54	95.30 54	97.29 43	98.95 64	92.66 73	98.59 131	97.14 147	88.95 124	93.12 106	99.25 12	85.62 102	99.94 22	96.56 43	99.48 43	99.28 81
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
新几何1				97.40 39	98.92 65	92.51 79		97.77 73	85.52 199	96.69 57	99.06 38	88.08 64	99.89 33	84.88 179	99.62 34	99.79 25

DP-MVS		88.75 199	86.56 207	95.34 131	98.92 65	87.45 183	97.64 208	93.52 303	70.55 326	81.49 243	97.25 125	74.43 207	99.88 34	71.14 307	94.09 137	98.67 127

1121		95.19 65	94.45 67	97.42 37	98.88 67	92.58 77	96.22 260	97.75 74	85.50 201	96.86 49	99.01 46	88.59 55	99.90 30	87.64 154	99.60 37	99.79 25

TSAR-MVS + GP.		96.95 22	96.91 18	97.07 49	98.88 67	91.62 88	99.58 18	96.54 183	95.09 15	96.84 52	98.63 77	91.16 16	99.77 57	99.04 4	96.42 109	99.81 21

CANet		97.00 20	96.49 28	98.55 6	98.86 69	96.10 10	99.83 4	97.52 111	95.90 8	97.21 40	98.90 58	82.66 145	99.93 24	98.71 8	98.80 73	99.63 54

ACMMP_Plus		96.59 31	96.18 36	97.81 24	98.82 70	93.55 54	98.88 98	97.59 99	90.66 80	97.98 27	99.14 29	86.59 90	100.00 1	96.47 44	99.46 44	99.89 13

PVSNet_BlendedMVS		93.36 107	93.20 94	93.84 177	98.77 71	91.61 89	99.47 27	98.04 49	91.44 69	94.21 93	92.63 226	83.50 125	99.87 37	97.41 29	83.37 231	90.05 291

PVSNet_Blended		95.94 50	95.66 50	96.75 73	98.77 71	91.61 89	99.88 1	98.04 49	93.64 32	94.21 93	97.76 104	83.50 125	99.87 37	97.41 29	97.75 92	98.79 118

DeepPCF-MVS	93.56 1	96.55 33	97.84 6	92.68 198	98.71 73	78.11 311	99.70 10	97.71 80	98.18 1	97.36 38	99.76 1	90.37 37	99.94 22	99.27 2	99.54 41	99.99 1

EPNet		96.82 26	96.68 26	97.25 45	98.65 74	93.10 63	99.48 26	98.76 18	96.54 4	97.84 31	98.22 94	87.49 72	99.66 65	95.35 65	97.78 91	99.00 98
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
OMC-MVS		93.90 91	93.62 88	94.73 150	98.63 75	87.00 195	98.04 192	96.56 181	92.19 58	92.46 112	98.73 69	79.49 169	99.14 114	92.16 111	94.34 136	98.03 156

abl_6		94.63 79	94.48 66	95.09 139	98.61 76	86.96 196	98.06 191	96.97 165	89.31 111	95.86 67	98.56 80	79.82 166	99.64 71	94.53 80	98.65 79	98.66 128

MVS_111021_HR		96.69 28	96.69 25	96.72 77	98.58 77	91.00 112	99.14 66	99.45 1	93.86 27	95.15 80	98.73 69	88.48 56	99.76 58	97.23 31	99.56 40	99.40 70

0601test		95.27 64	94.60 65	97.28 44	98.53 78	92.98 67	99.05 77	98.70 22	86.76 186	94.65 88	97.74 106	87.78 67	99.44 96	95.57 62	92.61 149	99.44 69

TAPA-MVS	87.50 9	90.35 168	89.05 172	94.25 164	98.48 79	85.17 245	98.42 152	96.58 180	82.44 263	87.24 183	98.53 81	82.77 144	98.84 122	59.09 335	97.88 87	98.72 124
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
MVS_0304		96.12 46	95.26 56	98.69 4	98.44 80	96.54 7	99.70 10	96.89 168	95.76 10	97.53 34	99.12 32	72.42 233	99.93 24	98.75 7	98.69 76	99.61 57

test222					98.32 81	91.21 102	98.08 189	97.58 101	83.74 234	95.87 66	99.02 42	86.74 88			99.64 30	99.81 21

LFMVS		92.23 136	90.84 150	96.42 94	98.24 82	91.08 110	98.24 173	96.22 203	83.39 246	94.74 86	98.31 92	61.12 305	98.85 121	94.45 81	92.82 145	99.32 75

testdata				95.26 134	98.20 83	87.28 191		97.60 98	85.21 205	98.48 12	99.15 27	88.15 62	98.72 130	90.29 125	99.45 46	99.78 29

PatchMatch-RL		91.47 151	90.54 157	94.26 163	98.20 83	86.36 215	96.94 232	97.14 147	87.75 162	88.98 163	95.75 172	71.80 241	99.40 99	80.92 220	97.39 98	97.02 183

MVS_111021_LR		95.78 55	95.94 43	95.28 133	98.19 85	87.69 176	98.80 104	99.26 13	93.39 35	95.04 82	98.69 74	84.09 121	99.76 58	96.96 38	99.06 60	98.38 142

F-COLMAP		92.07 142	91.75 128	93.02 190	98.16 86	82.89 273	98.79 107	95.97 216	86.54 189	87.92 176	97.80 102	78.69 176	99.65 69	85.97 169	95.93 121	96.53 199

Anonymous202405211		88.84 193	87.03 204	94.27 162	98.14 87	84.18 257	98.44 150	95.58 250	76.79 310	89.34 161	96.88 146	53.42 328	99.54 80	87.53 156	87.12 205	99.09 92

VNet		95.08 66	94.26 71	97.55 33	98.07 88	93.88 50	98.68 117	98.73 21	90.33 90	97.16 42	97.43 116	79.19 171	99.53 81	96.91 39	91.85 161	99.24 84

DELS-MVS		97.12 16	96.60 27	98.68 5	98.03 89	96.57 6	99.84 3	97.84 62	96.36 7	95.20 79	98.24 93	88.17 61	99.83 47	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 96	92.83 101	96.28 99	97.99 90	90.22 129	99.38 38	98.93 16	91.42 72	93.66 103	97.68 108	71.29 247	99.64 71	87.94 151	97.20 100	98.98 101

CHOSEN 280x420		96.80 27	96.85 19	96.66 81	97.85 91	94.42 42	94.76 290	98.36 27	92.50 48	95.62 73	97.52 112	97.92 1	97.38 199	98.31 21	98.80 73	98.20 152

thres200		93.69 96	92.59 106	96.97 59	97.76 92	94.74 31	99.35 44	99.36 2	89.23 114	91.21 130	96.97 142	83.42 128	98.77 124	85.08 177	90.96 172	97.39 172

tfpn_ndepth		93.28 111	92.32 109	96.16 105	97.74 93	92.86 71	99.01 81	98.19 40	85.50 201	89.84 152	97.12 134	93.57 7	97.58 184	79.39 234	90.50 180	98.04 155

HY-MVS	88.56 7	95.29 63	94.23 72	98.48 8	97.72 94	96.41 8	94.03 298	98.74 19	92.42 53	95.65 72	94.76 185	86.52 92	99.49 88	95.29 67	92.97 144	99.53 61

Anonymous20231211		84.72 255	82.65 265	90.91 231	97.71 95	84.55 253	97.28 217	96.67 173	66.88 338	79.18 265	90.87 251	58.47 310	96.60 221	82.61 203	74.20 283	91.59 245

tfpn200view9		93.43 104	92.27 112	96.90 63	97.68 96	94.84 24	99.18 55	99.36 2	88.45 139	90.79 133	96.90 144	83.31 129	98.75 126	84.11 187	90.69 174	97.12 177

thres400		93.39 106	92.27 112	96.73 75	97.68 96	94.84 24	99.18 55	99.36 2	88.45 139	90.79 133	96.90 144	83.31 129	98.75 126	84.11 187	90.69 174	96.61 190

tfpn111		93.20 114	92.00 121	96.83 69	97.62 98	94.84 24	99.06 74	99.36 2	87.96 154	90.47 140	96.78 147	83.29 131	98.71 131	82.93 199	90.47 181	96.94 184

conf200view11		93.32 109	92.15 117	96.84 68	97.62 98	94.84 24	99.06 74	99.36 2	87.96 154	90.47 140	96.78 147	83.29 131	98.75 126	84.11 187	90.69 174	96.94 184

thres100view900		93.34 108	92.15 117	96.90 63	97.62 98	94.84 24	99.06 74	99.36 2	87.96 154	90.47 140	96.78 147	83.29 131	98.75 126	84.11 187	90.69 174	97.12 177

thres600view7		93.18 115	92.00 121	96.75 73	97.62 98	94.92 21	99.07 72	99.36 2	87.96 154	90.47 140	96.78 147	83.29 131	98.71 131	82.93 199	90.47 181	96.61 190

WTY-MVS		95.97 49	95.11 59	98.54 7	97.62 98	96.65 4	99.44 31	98.74 19	92.25 57	95.21 78	98.46 90	86.56 91	99.46 95	95.00 71	92.69 148	99.50 64

tfpn1000		92.67 128	91.64 130	95.78 117	97.61 103	92.34 80	98.69 114	98.18 41	84.15 223	88.80 165	96.99 141	93.56 8	97.21 203	76.56 259	90.19 183	97.77 164

Anonymous20240529		87.66 210	85.58 226	93.92 174	97.59 104	85.01 248	98.13 184	97.13 149	66.69 339	88.47 167	96.01 170	55.09 322	99.51 85	87.00 161	84.12 225	97.23 176

HyFIR lowres test		93.68 98	93.29 92	94.87 146	97.57 105	88.04 171	98.18 180	98.47 25	87.57 168	91.24 129	95.05 181	85.49 106	97.46 190	93.22 99	92.82 145	99.10 91

canonicalmvs		95.02 67	93.96 81	98.20 12	97.53 106	95.92 11	98.71 110	96.19 206	91.78 64	95.86 67	98.49 86	79.53 168	99.03 118	96.12 51	91.42 169	99.66 50

view600		92.78 121	91.50 133	96.63 82	97.51 107	94.66 34	98.91 90	99.36 2	87.31 172	89.64 156	96.59 154	83.26 136	98.63 135	80.76 223	90.15 184	96.61 190

view800		92.78 121	91.50 133	96.63 82	97.51 107	94.66 34	98.91 90	99.36 2	87.31 172	89.64 156	96.59 154	83.26 136	98.63 135	80.76 223	90.15 184	96.61 190

conf0.05thres1000		92.78 121	91.50 133	96.63 82	97.51 107	94.66 34	98.91 90	99.36 2	87.31 172	89.64 156	96.59 154	83.26 136	98.63 135	80.76 223	90.15 184	96.61 190

tfpn		92.78 121	91.50 133	96.63 82	97.51 107	94.66 34	98.91 90	99.36 2	87.31 172	89.64 156	96.59 154	83.26 136	98.63 135	80.76 223	90.15 184	96.61 190

CHOSEN 1792x2688		94.35 84	93.82 86	95.95 112	97.40 111	88.74 159	98.41 154	98.27 29	92.18 59	91.43 125	96.40 162	78.88 172	99.81 52	93.59 92	97.81 88	99.30 77

SteuartSystems-ACMMP		97.25 11	97.34 12	97.01 52	97.38 112	91.46 92	99.75 8	97.66 85	94.14 22	98.13 18	99.26 11	92.16 13	99.66 65	97.91 26	99.64 30	99.90 10
Skip Steuart: Steuart Systems R&D Blog.
alignmvs		95.77 56	95.00 61	98.06 18	97.35 113	95.68 13	99.71 9	97.50 116	91.50 68	96.16 60	98.61 78	86.28 97	99.00 119	96.19 50	91.74 163	99.51 63

PS-MVSNAJ		96.87 25	96.40 30	98.29 11	97.35 113	97.29 1	99.03 78	97.11 151	95.83 9	98.97 4	99.14 29	82.48 148	99.60 76	98.60 10	99.08 59	98.00 157

EPNet_dtu		92.28 134	92.15 117	92.70 197	97.29 115	84.84 249	98.64 123	97.82 64	92.91 41	93.02 109	97.02 139	85.48 108	95.70 277	72.25 303	94.89 132	97.55 170
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
MVSTER		92.71 126	92.32 109	93.86 176	97.29 115	92.95 69	99.01 81	96.59 177	90.09 98	85.51 194	94.00 196	94.61 5	96.56 224	90.77 123	83.03 234	92.08 232

EPMVS		92.59 131	91.59 131	95.59 122	97.22 117	90.03 137	91.78 317	98.04 49	90.42 87	91.66 119	90.65 264	86.49 94	97.46 190	81.78 214	96.31 112	99.28 81

tpmvs		89.16 187	87.76 191	93.35 183	97.19 118	84.75 251	90.58 327	97.36 133	81.99 267	84.56 199	89.31 294	83.98 122	98.17 146	74.85 279	90.00 189	97.12 177

DeepC-MVS	91.02 4	94.56 81	93.92 84	96.46 91	97.16 119	90.76 119	98.39 158	97.11 151	93.92 23	88.66 166	98.33 91	78.14 179	99.85 44	95.02 70	98.57 80	98.78 121
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 77	94.11 74	96.34 98	97.14 120	91.10 108	99.32 48	97.43 126	92.10 60	91.53 123	96.38 165	83.29 131	99.68 62	93.42 96	96.37 110	98.25 149

xiu_mvs_v2_base		96.66 29	96.17 38	98.11 17	97.11 121	96.96 2	99.01 81	97.04 159	95.51 13	98.86 6	99.11 35	82.19 154	99.36 102	98.59 12	98.14 85	98.00 157

VDD-MVS		91.24 157	90.18 160	94.45 157	97.08 122	85.84 235	98.40 157	96.10 212	86.99 178	93.36 104	98.16 97	54.27 325	99.20 108	96.59 42	90.63 178	98.31 148

UGNet		91.91 146	90.85 149	95.10 138	97.06 123	88.69 160	98.01 193	98.24 31	92.41 54	92.39 114	93.61 208	60.52 306	99.68 62	88.14 149	97.25 99	96.92 188
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 85	93.35 91	97.20 47	97.03 124	94.71 32	98.62 125	95.54 251	95.61 12	97.21 40	98.47 88	71.88 239	99.84 45	88.38 147	97.46 97	97.04 182

DWT-MVSNet_test		94.36 83	93.95 82	95.62 120	96.99 125	89.47 148	96.62 245	97.38 131	90.96 77	93.07 108	97.27 124	93.73 6	98.09 151	85.86 173	93.65 140	99.29 79

PatchFormer-LS_test		94.08 88	93.60 89	95.53 127	96.92 126	89.57 146	96.51 249	97.34 135	91.29 74	92.22 116	97.18 130	91.66 14	98.02 156	87.05 159	92.21 156	99.00 98

MSDG		88.29 205	86.37 209	94.04 170	96.90 127	86.15 223	96.52 248	94.36 291	77.89 307	79.22 264	96.95 143	69.72 255	99.59 77	73.20 296	92.58 150	96.37 200

BH-w/o		92.32 133	91.79 126	93.91 175	96.85 128	86.18 221	99.11 70	95.74 234	88.13 151	84.81 197	97.00 140	77.26 184	97.91 159	89.16 143	98.03 86	97.64 165

conf0.01		92.06 143	90.99 140	95.24 136	96.84 129	91.39 95	98.31 163	98.20 33	83.57 239	88.08 170	97.34 118	91.05 19	97.40 193	75.80 265	89.74 191	96.94 184

conf0.002		92.06 143	90.99 140	95.24 136	96.84 129	91.39 95	98.31 163	98.20 33	83.57 239	88.08 170	97.34 118	91.05 19	97.40 193	75.80 265	89.74 191	96.94 184

thresconf0.02		92.14 137	90.99 140	95.58 123	96.84 129	91.39 95	98.31 163	98.20 33	83.57 239	88.08 170	97.34 118	91.05 19	97.40 193	75.80 265	89.74 191	97.94 159

tfpn_n400		92.14 137	90.99 140	95.58 123	96.84 129	91.39 95	98.31 163	98.20 33	83.57 239	88.08 170	97.34 118	91.05 19	97.40 193	75.80 265	89.74 191	97.94 159

tfpnconf		92.14 137	90.99 140	95.58 123	96.84 129	91.39 95	98.31 163	98.20 33	83.57 239	88.08 170	97.34 118	91.05 19	97.40 193	75.80 265	89.74 191	97.94 159

tfpnview11		92.14 137	90.99 140	95.58 123	96.84 129	91.39 95	98.31 163	98.20 33	83.57 239	88.08 170	97.34 118	91.05 19	97.40 193	75.80 265	89.74 191	97.94 159

AllTest		84.97 253	83.12 255	90.52 239	96.82 135	78.84 304	95.89 272	92.17 325	77.96 304	75.94 287	95.50 175	55.48 319	99.18 109	71.15 305	87.14 203	93.55 210

TestCases				90.52 239	96.82 135	78.84 304		92.17 325	77.96 304	75.94 287	95.50 175	55.48 319	99.18 109	71.15 305	87.14 203	93.55 210

PMMVS		93.62 101	93.90 85	92.79 194	96.79 137	81.40 284	98.85 99	96.81 169	91.25 75	96.82 54	98.15 98	77.02 185	98.13 148	93.15 101	96.30 113	98.83 115

BH-RMVSNet		91.25 156	89.99 162	95.03 144	96.75 138	88.55 163	98.65 121	94.95 275	87.74 163	87.74 177	97.80 102	68.27 266	98.14 147	80.53 228	97.49 96	98.41 139

MVS_Test		93.67 99	92.67 104	96.69 79	96.72 139	92.66 73	97.22 222	96.03 214	87.69 166	95.12 81	94.03 194	81.55 157	98.28 144	89.17 142	96.46 107	99.14 89

COLMAP_ROB	82.69 18	84.54 260	82.82 259	89.70 258	96.72 139	78.85 303	95.89 272	92.83 318	71.55 323	77.54 280	95.89 171	59.40 309	99.14 114	67.26 315	88.26 199	91.11 257
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
mvs_anonymous		92.50 132	91.65 129	95.06 142	96.60 141	89.64 144	97.06 229	96.44 189	86.64 187	84.14 203	93.93 198	82.49 147	96.17 261	91.47 114	96.08 118	99.35 72

casdiffmvs1		94.78 71	94.34 69	96.11 107	96.60 141	90.85 116	97.95 195	96.52 184	90.16 96	97.22 39	94.64 187	84.99 111	98.18 145	94.40 82	96.60 106	99.30 77

GG-mvs-BLEND				96.98 58	96.53 143	94.81 29	87.20 331	97.74 76		93.91 99	96.40 162	96.56 2	96.94 213	95.08 69	98.95 67	99.20 87

FMVSNet3		88.81 197	87.08 203	93.99 172	96.52 144	94.59 38	98.08 189	96.20 205	85.85 195	82.12 234	91.60 238	74.05 215	95.40 285	79.04 236	80.24 245	91.99 235

BH-untuned		91.46 152	90.84 150	93.33 184	96.51 145	84.83 250	98.84 101	95.50 254	86.44 192	83.50 207	96.70 151	75.49 192	97.77 170	86.78 166	97.81 88	97.40 171

sss		94.85 70	93.94 83	97.58 30	96.43 146	94.09 48	98.93 87	99.16 14	89.50 109	95.27 77	97.85 100	81.50 158	99.65 69	92.79 106	94.02 138	98.99 100

dp		90.16 173	88.83 177	94.14 166	96.38 147	86.42 211	91.57 318	97.06 158	84.76 215	88.81 164	90.19 284	84.29 120	97.43 192	75.05 276	91.35 171	98.56 133

casdiffmvs		94.10 87	93.40 90	96.20 100	96.31 148	91.46 92	97.65 207	96.22 203	88.49 135	95.69 70	94.11 190	83.01 141	98.10 150	93.33 97	95.82 123	99.04 95

TR-MVS		90.77 164	89.44 166	94.76 148	96.31 148	88.02 172	97.92 196	95.96 218	85.52 199	88.22 169	97.23 127	66.80 278	98.09 151	84.58 182	92.38 151	98.17 153

UA-Net		93.30 110	92.62 105	95.34 131	96.27 150	88.53 165	95.88 274	96.97 165	90.90 79	95.37 76	97.07 137	82.38 151	99.10 116	83.91 191	94.86 133	98.38 142

tpmrst		92.78 121	92.16 116	94.65 152	96.27 150	87.45 183	91.83 316	97.10 154	89.10 120	94.68 87	90.69 258	88.22 60	97.73 177	89.78 130	91.80 162	98.77 122

ADS-MVSNet2		87.62 211	86.88 205	89.86 253	96.21 152	79.14 300	87.15 332	92.99 309	83.01 252	89.91 150	87.27 309	78.87 173	92.80 317	74.20 284	92.27 154	97.64 165

ADS-MVSNet		88.99 189	87.30 198	94.07 168	96.21 152	87.56 180	87.15 332	96.78 171	83.01 252	89.91 150	87.27 309	78.87 173	97.01 210	74.20 284	92.27 154	97.64 165

PatchmatchNet		92.05 145	91.04 139	95.06 142	96.17 154	89.04 153	91.26 321	97.26 136	89.56 107	90.64 137	90.56 270	88.35 59	97.11 206	79.53 231	96.07 119	99.03 96
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
gg-mvs-nofinetune		90.00 177	87.71 193	96.89 67	96.15 155	94.69 33	85.15 337	97.74 76	68.32 335	92.97 110	60.16 350	96.10 3	96.84 215	93.89 85	98.87 68	99.14 89

MDTV_nov1_ep13			90.47 159		96.14 156	88.55 163	91.34 320	97.51 113	89.58 105	92.24 115	90.50 273	86.99 86	97.61 183	77.64 248	92.34 152

IS-MVSNet		93.00 118	92.51 107	94.49 155	96.14 156	87.36 189	98.31 163	95.70 238	88.58 134	90.17 146	97.50 113	83.02 140	97.22 202	87.06 158	96.07 119	98.90 110

Vis-MVSNet (Re-imp)		93.26 113	93.00 99	94.06 169	96.14 156	86.71 205	98.68 117	96.70 172	88.30 146	89.71 155	97.64 109	85.43 109	96.39 241	88.06 150	96.32 111	99.08 93

diffmvs		93.00 118	92.26 114	95.25 135	96.12 159	88.59 161	96.60 246	96.19 206	88.88 128	94.19 95	93.73 204	80.40 165	98.12 149	89.18 141	95.02 130	99.02 97

ab-mvs		91.05 159	89.17 170	96.69 79	95.96 160	91.72 86	92.62 310	97.23 140	85.61 198	89.74 153	93.89 200	68.55 264	99.42 97	91.09 116	87.84 201	98.92 109

Fast-Effi-MVS+		91.72 148	90.79 153	94.49 155	95.89 161	87.40 186	99.54 23	95.70 238	85.01 211	89.28 162	95.68 173	77.75 181	97.57 188	83.22 195	95.06 129	98.51 135

EPP-MVSNet		93.75 95	93.67 87	94.01 171	95.86 162	85.70 237	98.67 119	97.66 85	84.46 218	91.36 127	97.18 130	91.16 16	97.79 168	92.93 103	93.75 139	98.53 134

Effi-MVS+		93.87 92	93.15 95	96.02 109	95.79 163	90.76 119	96.70 242	95.78 232	86.98 180	95.71 69	97.17 132	79.58 167	98.01 157	94.57 79	96.09 117	99.31 76

tpm cat1		88.89 191	87.27 199	93.76 179	95.79 163	85.32 241	90.76 325	97.09 155	76.14 312	85.72 192	88.59 299	82.92 142	98.04 155	76.96 253	91.43 168	97.90 163

tpmp4_e23		91.05 159	90.07 161	93.97 173	95.77 165	85.30 242	92.64 309	97.09 155	84.42 220	91.53 123	90.31 276	87.38 74	97.82 166	80.86 222	90.62 179	98.79 118

3Dnovator+	87.72 8	93.43 104	91.84 125	98.17 13	95.73 166	95.08 20	98.92 89	97.04 159	91.42 72	81.48 244	97.60 110	74.60 200	99.79 55	90.84 121	98.97 64	99.64 52

MVS		93.92 90	92.28 111	98.83 2	95.69 167	96.82 3	96.22 260	98.17 42	84.89 213	84.34 202	98.61 78	79.32 170	99.83 47	93.88 86	99.43 47	99.86 19

cascas		90.93 162	89.33 169	95.76 118	95.69 167	93.03 66	98.99 84	96.59 177	80.49 281	86.79 189	94.45 189	65.23 288	98.60 139	93.52 93	92.18 157	95.66 203

QAPM		91.41 153	89.49 165	97.17 48	95.66 169	93.42 58	98.60 129	97.51 113	80.92 279	81.39 245	97.41 117	72.89 230	99.87 37	82.33 205	98.68 77	98.21 151

1112_ss		92.71 126	91.55 132	96.20 100	95.56 170	91.12 106	98.48 144	94.69 281	88.29 147	86.89 187	98.50 84	87.02 84	98.66 133	84.75 180	89.77 190	98.81 116

LCM-MVSNet-Re		88.59 201	88.61 181	88.51 281	95.53 171	72.68 327	96.85 235	88.43 350	88.45 139	73.14 301	90.63 265	75.82 189	94.38 303	92.95 102	95.71 125	98.48 137

Test_1112_low_res		92.27 135	90.97 146	96.18 102	95.53 171	91.10 108	98.47 146	94.66 282	88.28 148	86.83 188	93.50 212	87.00 85	98.65 134	84.69 181	89.74 191	98.80 117

PCF-MVS	89.78 5	91.26 154	89.63 164	96.16 105	95.44 173	91.58 91	95.29 286	96.10 212	85.07 209	82.75 222	97.45 115	78.28 178	99.78 56	80.60 227	95.65 126	97.12 177
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
3Dnovator	87.35 11	93.17 116	91.77 127	97.37 42	95.41 174	93.07 64	98.82 102	97.85 61	91.53 67	82.56 226	97.58 111	71.97 238	99.82 50	91.01 118	99.23 58	99.22 86

IB-MVS	89.43 6	92.12 141	90.83 152	95.98 111	95.40 175	90.78 118	99.81 5	98.06 47	91.23 76	85.63 193	93.66 207	90.63 32	98.78 123	91.22 115	71.85 306	98.36 145
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		93.44 103	91.98 123	97.84 22	95.24 176	94.38 43	96.22 260	97.92 56	90.18 93	82.28 231	97.71 107	77.63 182	99.80 54	91.94 113	98.67 78	99.34 74

XVG-OURS		90.83 163	90.49 158	91.86 208	95.23 177	81.25 288	95.79 279	95.92 223	88.96 123	90.02 149	98.03 99	71.60 243	99.35 104	91.06 117	87.78 202	94.98 204

TESTMET0.1,1		93.82 93	93.26 93	95.49 128	95.21 178	90.25 128	99.15 63	97.54 110	89.18 117	91.79 118	94.87 183	89.13 46	97.63 181	86.21 167	96.29 114	98.60 129

xiu_mvs_v1_base_debu		94.73 73	93.98 78	96.99 55	95.19 179	95.24 17	98.62 125	96.50 185	92.99 38	97.52 35	98.83 62	72.37 234	99.15 111	97.03 33	96.74 103	96.58 196

xiu_mvs_v1_base		94.73 73	93.98 78	96.99 55	95.19 179	95.24 17	98.62 125	96.50 185	92.99 38	97.52 35	98.83 62	72.37 234	99.15 111	97.03 33	96.74 103	96.58 196

xiu_mvs_v1_base_debi		94.73 73	93.98 78	96.99 55	95.19 179	95.24 17	98.62 125	96.50 185	92.99 38	97.52 35	98.83 62	72.37 234	99.15 111	97.03 33	96.74 103	96.58 196

XVG-OURS-SEG-HR		90.95 161	90.66 156	91.83 209	95.18 182	81.14 290	95.92 271	95.92 223	88.40 143	90.33 145	97.85 100	70.66 250	99.38 100	92.83 105	88.83 198	94.98 204

Effi-MVS+-dtu		89.97 178	90.68 155	87.81 295	95.15 183	71.98 329	97.87 200	95.40 262	91.92 61	87.57 178	91.44 239	74.27 210	96.84 215	89.45 133	93.10 143	94.60 206

mvs-test1		91.57 149	92.20 115	89.70 258	95.15 183	74.34 320	99.51 25	95.40 262	91.92 61	91.02 131	97.25 125	74.27 210	98.08 154	89.45 133	95.83 122	96.67 189

Vis-MVSNet		92.64 129	91.85 124	95.03 144	95.12 185	88.23 167	98.48 144	96.81 169	91.61 66	92.16 117	97.22 128	71.58 244	98.00 158	85.85 174	97.81 88	98.88 111
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
GBi-Net		86.67 229	84.96 234	91.80 211	95.11 186	88.81 156	96.77 237	95.25 269	82.94 254	82.12 234	90.25 278	62.89 297	94.97 292	79.04 236	80.24 245	91.62 242

test1		86.67 229	84.96 234	91.80 211	95.11 186	88.81 156	96.77 237	95.25 269	82.94 254	82.12 234	90.25 278	62.89 297	94.97 292	79.04 236	80.24 245	91.62 242

FMVSNet2		86.90 225	84.79 240	93.24 185	95.11 186	92.54 78	97.67 206	95.86 231	82.94 254	80.55 248	91.17 242	62.89 297	95.29 287	77.23 250	79.71 251	91.90 236

MVSFormer		94.71 76	94.08 76	96.61 86	95.05 189	94.87 22	97.77 203	96.17 208	86.84 183	98.04 24	98.52 82	85.52 103	95.99 267	89.83 128	98.97 64	98.96 103

lupinMVS		96.32 41	95.94 43	97.44 36	95.05 189	94.87 22	99.86 2	96.50 185	93.82 28	98.04 24	98.77 65	85.52 103	98.09 151	96.98 37	98.97 64	99.37 71

CostFormer		92.89 120	92.48 108	94.12 167	94.99 191	85.89 231	92.89 308	97.00 164	86.98 180	95.00 83	90.78 253	90.05 39	97.51 189	92.92 104	91.73 164	98.96 103

Patchmatch-test1		90.10 174	88.61 181	94.57 154	94.95 192	88.83 155	96.26 256	97.21 142	90.06 101	90.03 148	90.68 260	66.61 280	95.83 274	77.31 249	94.36 135	99.05 94

test-LLR		93.11 117	92.68 103	94.40 158	94.94 193	87.27 192	99.15 63	97.25 137	90.21 91	91.57 120	94.04 192	84.89 114	97.58 184	85.94 170	96.13 115	98.36 145

test-mter		93.27 112	92.89 100	94.40 158	94.94 193	87.27 192	99.15 63	97.25 137	88.95 124	91.57 120	94.04 192	88.03 65	97.58 184	85.94 170	96.13 115	98.36 145

tpm2		91.77 147	91.09 138	93.82 178	94.83 195	85.56 240	92.51 311	97.16 146	84.00 225	93.83 101	90.66 263	87.54 71	97.17 204	87.73 153	91.55 167	98.72 124

PVSNet_0	83.28 16	87.31 214	85.16 232	93.74 180	94.78 196	84.59 252	98.91 90	98.69 23	89.81 102	78.59 271	93.23 216	61.95 301	99.34 105	94.75 75	55.72 345	97.30 174

CDS-MVSNet		93.47 102	93.04 98	94.76 148	94.75 197	89.45 149	98.82 102	97.03 161	87.91 158	90.97 132	96.48 160	89.06 47	96.36 243	89.50 132	92.81 147	98.49 136
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
gm-plane-assit					94.69 198	88.14 168			88.22 149		97.20 129		98.29 143	90.79 122

RPSCF		85.33 251	85.55 227	84.67 315	94.63 199	62.28 341	93.73 301	93.76 298	74.38 318	85.23 196	97.06 138	64.09 291	98.31 142	80.98 218	86.08 212	93.41 212

Patchmatch-test		86.25 237	84.06 249	92.82 193	94.42 200	82.88 274	82.88 347	94.23 293	71.58 322	79.39 262	90.62 266	89.00 49	96.42 238	63.03 325	91.37 170	99.16 88

VDDNet		90.08 176	88.54 186	94.69 151	94.41 201	87.68 177	98.21 178	96.40 190	76.21 311	93.33 105	97.75 105	54.93 323	98.77 124	94.71 77	90.96 172	97.61 169

EI-MVSNet		89.87 179	89.38 168	91.36 224	94.32 202	85.87 232	97.61 209	96.59 177	85.10 207	85.51 194	97.10 135	81.30 161	96.56 224	83.85 193	83.03 234	91.64 240

CVMVSNet		90.30 169	90.91 148	88.46 282	94.32 202	73.58 324	97.61 209	97.59 99	90.16 96	88.43 168	97.10 135	76.83 186	92.86 313	82.64 202	93.54 141	98.93 108

testpf		80.59 295	80.13 282	81.97 323	94.25 204	71.65 330	60.37 357	95.46 258	70.99 324	76.97 281	87.74 303	73.58 220	91.67 334	76.86 255	84.97 218	82.60 344

IterMVS-LS		88.34 203	87.44 196	91.04 228	94.10 205	85.85 234	98.10 187	95.48 256	85.12 206	82.03 238	91.21 241	81.35 160	95.63 279	83.86 192	75.73 265	91.63 241
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
TAMVS		92.62 130	92.09 120	94.20 165	94.10 205	87.68 177	98.41 154	96.97 165	87.53 169	89.74 153	96.04 169	84.77 117	96.49 232	88.97 144	92.31 153	98.42 138

PAPM		96.35 39	95.94 43	97.58 30	94.10 205	95.25 16	98.93 87	98.17 42	94.26 20	93.94 98	98.72 71	89.68 43	97.88 162	96.36 47	99.29 54	99.62 56

CLD-MVS		91.06 158	90.71 154	92.10 205	94.05 208	86.10 224	99.55 22	96.29 199	94.16 21	84.70 198	97.17 132	69.62 256	97.82 166	94.74 76	86.08 212	92.39 218
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
HQP-NCC					93.95 209		99.16 58		93.92 23	87.57 178

ACMP_Plane					93.95 209		99.16 58		93.92 23	87.57 178

HQP-MVS		91.50 150	91.23 137	92.29 202	93.95 209	86.39 213	99.16 58	96.37 191	93.92 23	87.57 178	96.67 152	73.34 223	97.77 170	93.82 89	86.29 207	92.72 213

NP-MVS					93.94 212	86.22 220					96.67 152

plane_prior6					93.92 213	86.02 229						72.92 228

ACMP	87.39 10	88.71 200	88.24 189	90.12 247	93.91 214	81.06 291	98.50 141	95.67 240	89.43 110	80.37 250	95.55 174	65.67 285	97.83 165	90.55 124	84.51 221	91.47 247
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
plane_prior1					93.90 215

HQP_MVS		91.26 154	90.95 147	92.16 204	93.84 216	86.07 226	99.02 79	96.30 196	93.38 36	86.99 184	96.52 158	72.92 228	97.75 175	93.46 94	86.17 210	92.67 215

plane_prior7					93.84 216	85.73 236

MVS-HIRNet		79.01 302	75.13 309	90.66 236	93.82 218	81.69 282	85.16 336	93.75 299	54.54 349	74.17 297	59.15 352	57.46 313	96.58 222	63.74 323	94.38 134	93.72 209

FMVSNet5		82.29 276	80.54 281	87.52 297	93.79 219	84.01 259	93.73 301	92.47 322	76.92 309	74.27 296	86.15 317	63.69 294	89.24 338	69.07 310	74.79 272	89.29 303

ACMH+	83.78 15	84.21 264	82.56 268	89.15 270	93.73 220	79.16 299	96.43 250	94.28 292	81.09 276	74.00 298	94.03 194	54.58 324	97.67 178	76.10 262	78.81 253	90.63 279

ACMM	86.95 13	88.77 198	88.22 190	90.43 241	93.61 221	81.34 286	98.50 141	95.92 223	87.88 159	83.85 206	95.20 180	67.20 275	97.89 161	86.90 164	84.90 219	92.06 233
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
OpenMVS	85.28 14	90.75 165	88.84 176	96.48 90	93.58 222	93.51 56	98.80 104	97.41 128	82.59 258	78.62 269	97.49 114	68.00 269	99.82 50	84.52 183	98.55 81	96.11 201

IterMVS		85.81 244	84.67 242	89.22 268	93.51 223	83.67 264	96.32 254	94.80 277	85.09 208	78.69 267	90.17 285	66.57 281	93.17 309	79.48 233	77.42 260	90.81 269
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
CR-MVSNet		88.83 195	87.38 197	93.16 187	93.47 224	86.24 218	84.97 339	94.20 294	88.92 127	90.76 135	86.88 313	84.43 118	94.82 297	70.64 308	92.17 158	98.41 139

RPMNet		84.62 257	81.78 271	93.16 187	93.47 224	86.24 218	84.97 339	96.28 200	64.85 343	90.76 135	78.80 342	80.95 162	94.82 297	53.76 340	92.17 158	98.41 139

semantic-postprocess				89.00 273	93.46 226	82.90 272		94.70 280	85.02 210	78.62 269	90.35 274	66.63 279	93.33 308	79.38 235	77.36 261	90.76 273

Fast-Effi-MVS+-dtu		88.84 193	88.59 184	89.58 261	93.44 227	78.18 309	98.65 121	94.62 283	88.46 138	84.12 204	95.37 179	68.91 261	96.52 230	82.06 208	91.70 165	94.06 207

Patchmtry		83.61 274	81.64 273	89.50 263	93.36 228	82.84 275	84.10 342	94.20 294	69.47 332	79.57 260	86.88 313	84.43 118	94.78 299	68.48 313	74.30 281	90.88 268

LPG-MVS_test		88.86 192	88.47 187	90.06 248	93.35 229	80.95 292	98.22 175	95.94 220	87.73 164	83.17 212	96.11 167	66.28 282	97.77 170	90.19 126	85.19 216	91.46 248

LGP-MVS_train				90.06 248	93.35 229	80.95 292		95.94 220	87.73 164	83.17 212	96.11 167	66.28 282	97.77 170	90.19 126	85.19 216	91.46 248

JIA-IIPM		85.97 240	84.85 238	89.33 267	93.23 231	73.68 323	85.05 338	97.13 149	69.62 331	91.56 122	68.03 348	88.03 65	96.96 211	77.89 247	93.12 142	97.34 173

ACMH	83.09 17	84.60 258	82.61 266	90.57 237	93.18 232	82.94 270	96.27 255	94.92 276	81.01 277	72.61 307	93.61 208	56.54 315	97.79 168	74.31 282	81.07 244	90.99 265
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
PatchT		85.44 250	83.19 254	92.22 203	93.13 233	83.00 269	83.80 345	96.37 191	70.62 325	90.55 138	79.63 340	84.81 116	94.87 295	58.18 337	91.59 166	98.79 118

jason		95.40 62	94.86 62	97.03 51	92.91 234	94.23 45	99.70 10	96.30 196	93.56 34	96.73 56	98.52 82	81.46 159	97.91 159	96.08 53	98.47 82	98.96 103
jason: jason.
LTVRE_ROB	81.71 19	84.59 259	82.72 264	90.18 245	92.89 235	83.18 268	93.15 306	94.74 278	78.99 290	75.14 293	92.69 224	65.64 286	97.63 181	69.46 309	81.82 242	89.74 297
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		89.10 188	87.66 194	93.45 182	92.56 236	91.02 111	97.97 194	98.32 28	86.92 182	86.03 191	92.01 231	68.84 263	97.10 208	90.92 119	75.34 267	92.23 225

tpm		89.67 181	88.95 174	91.82 210	92.54 237	81.43 283	92.95 307	95.92 223	87.81 160	90.50 139	89.44 291	84.99 111	95.65 278	83.67 194	82.71 237	98.38 142

GA-MVS		90.10 174	88.69 179	94.33 160	92.44 238	87.97 173	99.08 71	96.26 201	89.65 103	86.92 186	93.11 219	68.09 267	96.96 211	82.54 204	90.15 184	98.05 154

FIs		90.70 166	89.87 163	93.18 186	92.29 239	91.12 106	98.17 183	98.25 30	89.11 119	83.44 208	94.82 184	82.26 152	96.17 261	87.76 152	82.76 236	92.25 223

ITE_SJBPF				87.93 293	92.26 240	76.44 315		93.47 304	87.67 167	79.95 255	95.49 177	56.50 316	97.38 199	75.24 275	82.33 240	89.98 294

UniMVSNet (Re)		89.50 184	88.32 188	93.03 189	92.21 241	90.96 113	98.90 95	98.39 26	89.13 118	83.22 209	92.03 229	81.69 156	96.34 250	86.79 165	72.53 297	91.81 237

UniMVSNet_NR-MVSNet		89.60 182	88.55 185	92.75 196	92.17 242	90.07 134	98.74 109	98.15 44	88.37 144	83.21 210	93.98 197	82.86 143	95.93 271	86.95 162	72.47 298	92.25 223

TinyColmap		80.42 297	77.94 297	87.85 294	92.09 243	78.58 306	93.74 300	89.94 345	74.99 314	69.77 313	91.78 235	46.09 339	97.58 184	65.17 322	77.89 257	87.38 316

MS-PatchMatch		86.75 227	85.92 215	89.22 268	91.97 244	82.47 277	96.91 233	96.14 211	83.74 234	77.73 277	93.53 211	58.19 311	97.37 201	76.75 257	98.35 83	87.84 311

VPNet		88.30 204	86.57 206	93.49 181	91.95 245	91.35 101	98.18 180	97.20 143	88.61 133	84.52 201	94.89 182	62.21 300	96.76 219	89.34 137	72.26 302	92.36 219

FMVSNet1		83.94 270	81.32 278	91.80 211	91.94 246	88.81 156	96.77 237	95.25 269	77.98 302	78.25 276	90.25 278	50.37 335	94.97 292	73.27 295	77.81 258	91.62 242

WR-MVS		88.54 202	87.22 201	92.52 200	91.93 247	89.50 147	98.56 133	97.84 62	86.99 178	81.87 241	93.81 201	74.25 212	95.92 273	85.29 175	74.43 276	92.12 230

LP		77.80 310	74.39 312	88.01 291	91.93 247	79.02 302	80.88 349	92.90 315	65.43 341	72.00 308	81.29 332	65.78 284	92.73 322	43.76 349	75.58 266	92.27 222

FC-MVSNet-test		90.22 171	89.40 167	92.67 199	91.78 249	89.86 140	97.89 197	98.22 32	88.81 130	82.96 217	94.66 186	81.90 155	95.96 269	85.89 172	82.52 239	92.20 228

MIMVSNet		84.48 261	81.83 270	92.42 201	91.73 250	87.36 189	85.52 335	94.42 289	81.40 273	81.91 239	87.58 305	51.92 331	92.81 316	73.84 289	88.15 200	97.08 181

USDC		84.74 254	82.93 256	90.16 246	91.73 250	83.54 265	95.00 288	93.30 305	88.77 131	73.19 300	93.30 214	53.62 327	97.65 180	75.88 264	81.54 243	89.30 302

nrg030		90.23 170	88.87 175	94.32 161	91.53 252	93.54 55	98.79 107	95.89 229	88.12 152	84.55 200	94.61 188	78.80 175	96.88 214	92.35 109	75.21 268	92.53 217

DU-MVS		88.83 195	87.51 195	92.79 194	91.46 253	90.07 134	98.71 110	97.62 96	88.87 129	83.21 210	93.68 205	74.63 198	95.93 271	86.95 162	72.47 298	92.36 219

NR-MVSNet		87.74 209	86.00 214	92.96 191	91.46 253	90.68 122	96.65 244	97.42 127	88.02 153	73.42 299	93.68 205	77.31 183	95.83 274	84.26 184	71.82 307	92.36 219

tfpnnormal		83.65 272	81.35 277	90.56 238	91.37 255	88.06 170	97.29 216	97.87 60	78.51 296	76.20 284	90.91 249	64.78 289	96.47 235	61.71 328	73.50 290	87.13 321

test_0402		78.81 304	76.33 306	86.26 305	91.18 256	78.44 308	95.88 274	91.34 336	68.55 333	70.51 311	89.91 286	52.65 330	94.99 291	47.14 345	79.78 250	85.34 338

test0.0.03 1		88.96 190	88.61 181	90.03 251	91.09 257	84.43 254	98.97 85	97.02 162	90.21 91	80.29 251	96.31 166	84.89 114	91.93 333	72.98 299	85.70 215	93.73 208

WR-MVS_H		86.53 233	85.49 228	89.66 260	91.04 258	83.31 267	97.53 211	98.20 33	84.95 212	79.64 258	90.90 250	78.01 180	95.33 286	76.29 261	72.81 294	90.35 283

CP-MVSNet		86.54 232	85.45 229	89.79 256	91.02 259	82.78 276	97.38 214	97.56 106	85.37 203	79.53 261	93.03 220	71.86 240	95.25 288	79.92 229	73.43 292	91.34 251

TranMVSNet+NR-MVSNet		87.75 207	86.31 210	92.07 206	90.81 260	88.56 162	98.33 160	97.18 144	87.76 161	81.87 241	93.90 199	72.45 232	95.43 283	83.13 197	71.30 310	92.23 225

PS-CasMVS		85.81 244	84.58 243	89.49 265	90.77 261	82.11 279	97.20 223	97.36 133	84.83 214	79.12 266	92.84 223	67.42 274	95.16 290	78.39 243	73.25 293	91.21 255

DeepMVS_CX				76.08 330	90.74 262	51.65 353		90.84 338	86.47 191	57.89 342	87.98 301	35.88 351	92.60 324	65.77 321	65.06 323	83.97 340

Anonymous20240521		85.45 249	83.91 252	90.05 250	90.73 263	83.74 263	97.13 226	96.15 210	82.08 266	76.93 282	90.84 252	71.53 246	96.36 243	75.26 274	74.57 274	90.04 293

OPM-MVS		89.76 180	89.15 171	91.57 219	90.53 264	85.58 239	98.11 186	95.93 222	92.88 43	86.05 190	96.47 161	67.06 277	97.87 163	89.29 140	86.08 212	91.26 254

XXY-MVS		87.75 207	86.02 213	92.95 192	90.46 265	89.70 143	97.71 205	95.90 227	84.02 224	80.95 246	94.05 191	67.51 273	97.10 208	85.16 176	78.41 254	92.04 234

v1neww		87.29 215	85.88 216	91.50 220	90.07 266	86.87 197	98.45 147	95.66 243	83.84 231	83.07 215	90.99 245	74.58 202	96.56 224	81.96 211	74.33 279	91.07 261

v7new		87.29 215	85.88 216	91.50 220	90.07 266	86.87 197	98.45 147	95.66 243	83.84 231	83.07 215	90.99 245	74.58 202	96.56 224	81.96 211	74.33 279	91.07 261

v7		86.91 224	85.45 229	91.29 225	90.06 268	86.73 203	98.26 171	95.49 255	83.08 251	82.95 218	90.96 248	73.37 221	96.42 238	79.90 230	74.97 269	90.71 276

v18		82.00 278	79.76 286	88.72 276	90.03 269	86.81 202	96.17 265	93.12 306	78.70 293	68.39 316	82.10 322	74.64 196	93.00 310	74.21 283	60.45 333	86.35 325

v10		85.73 247	84.01 250	90.87 233	90.03 269	86.73 203	97.20 223	95.22 274	81.25 275	79.85 257	89.75 288	73.30 226	96.28 258	76.87 254	72.64 296	89.61 300

v16		81.90 281	79.65 287	88.65 277	90.02 271	86.66 206	96.01 269	93.07 308	78.53 295	68.27 318	82.05 323	74.39 208	92.96 311	74.02 287	60.48 332	86.33 327

v8		86.11 238	84.45 244	91.10 227	89.99 272	86.85 199	97.24 220	95.36 264	81.99 267	79.89 256	89.86 287	74.53 204	96.39 241	78.83 240	72.32 300	90.05 291

v6		87.27 217	85.86 218	91.50 220	89.97 273	86.84 201	98.45 147	95.67 240	83.85 230	83.11 214	90.97 247	74.46 205	96.58 222	81.97 210	74.34 278	91.09 258

v17		81.87 283	79.61 288	88.64 278	89.91 274	86.64 207	96.01 269	93.08 307	78.54 294	68.27 318	81.96 324	74.44 206	92.95 312	74.03 286	60.22 335	86.34 326

V42		87.00 223	85.68 225	90.98 230	89.91 274	86.08 225	98.32 162	95.61 248	83.67 237	82.72 223	90.67 261	74.00 216	96.53 228	81.94 213	74.28 282	90.32 284

XVG-ACMP-BASELINE		85.86 242	84.95 236	88.57 279	89.90 276	77.12 314	94.30 294	95.60 249	87.40 171	82.12 234	92.99 222	53.42 328	97.66 179	85.02 178	83.83 227	90.92 267

PEN-MVS		85.21 252	83.93 251	89.07 272	89.89 277	81.31 287	97.09 228	97.24 139	84.45 219	78.66 268	92.68 225	68.44 265	94.87 295	75.98 263	70.92 311	91.04 264

v1141		87.23 219	85.75 222	91.67 216	89.88 278	87.43 185	98.52 137	95.62 246	83.91 227	82.83 221	90.69 258	74.70 195	96.49 232	81.53 217	74.08 286	91.07 261

divwei89l23v2f112		87.23 219	85.75 222	91.66 217	89.88 278	87.40 186	98.53 136	95.62 246	83.91 227	82.84 220	90.67 261	74.75 194	96.49 232	81.55 215	74.05 288	91.08 259

v1		87.23 219	85.76 220	91.66 217	89.88 278	87.37 188	98.54 135	95.64 245	83.91 227	82.88 219	90.70 256	74.64 196	96.53 228	81.54 216	74.08 286	91.08 259

v15		81.62 284	79.32 291	88.52 280	89.80 281	86.56 208	95.83 278	92.96 311	78.50 297	67.88 322	81.68 326	74.22 213	92.82 315	73.46 293	59.55 336	86.18 330

V14		81.55 286	79.26 292	88.42 283	89.80 281	86.33 216	95.72 281	92.96 311	78.35 298	67.82 323	81.70 325	74.13 214	92.78 319	73.32 294	59.50 338	86.16 332

v1144		86.83 226	85.31 231	91.40 223	89.75 283	87.21 194	98.31 163	95.45 259	83.22 248	82.70 224	90.78 253	73.36 222	96.36 243	79.49 232	74.69 273	90.63 279

V9		81.46 287	79.15 293	88.39 286	89.75 283	86.17 222	95.62 282	92.92 313	78.22 299	67.65 327	81.64 327	73.95 217	92.80 317	73.15 297	59.43 341	86.21 329

TransMVSNet (Re)		81.97 279	79.61 288	89.08 271	89.70 285	84.01 259	97.26 218	91.85 331	78.84 291	73.07 303	91.62 237	67.17 276	95.21 289	67.50 314	59.46 340	88.02 310

v12		81.37 289	79.05 294	88.33 287	89.68 286	86.05 228	95.48 284	92.92 313	78.08 300	67.55 328	81.58 328	73.75 218	92.75 320	73.05 298	59.37 342	86.18 330

v11		81.38 288	79.03 295	88.41 284	89.68 286	86.43 210	95.74 280	92.82 320	78.03 301	67.74 324	81.45 330	73.33 225	92.69 323	72.23 304	60.27 334	86.11 334

v13		81.30 290	78.99 296	88.25 288	89.61 288	85.87 232	95.39 285	92.90 315	77.93 306	67.45 331	81.52 329	73.66 219	92.75 320	72.91 300	59.53 337	86.14 333

v2v482		87.27 217	85.76 220	91.78 215	89.59 289	87.58 179	98.56 133	95.54 251	84.53 217	82.51 227	91.78 235	73.11 227	96.47 235	82.07 207	74.14 285	91.30 253

pm-mvs1		84.68 256	82.78 262	90.40 242	89.58 290	85.18 244	97.31 215	94.73 279	81.93 269	76.05 286	92.01 231	65.48 287	96.11 264	78.75 241	69.14 314	89.91 295

pmmvs4		87.58 212	86.17 212	91.80 211	89.58 290	88.92 154	97.25 219	95.28 268	82.54 260	80.49 249	93.17 218	75.62 191	96.05 266	82.75 201	78.90 252	90.42 282

v1192		86.32 236	84.71 241	91.17 226	89.53 292	86.40 212	98.13 184	95.44 260	82.52 261	82.42 229	90.62 266	71.58 244	96.33 251	77.23 250	74.88 270	90.79 271

pcd1.5k->3k		35.91 338	37.64 338	30.74 350	89.49 293	0.00 369	0.00 360	96.36 194	0.00 364	0.00 366	0.00 366	69.17 260	0.00 366	0.00 363	83.71 229	92.21 227

v144192		86.40 234	84.89 237	90.91 231	89.48 294	85.59 238	98.21 178	95.43 261	82.45 262	82.62 225	90.58 269	72.79 231	96.36 243	78.45 242	74.04 289	90.79 271

v148		86.38 235	85.06 233	90.37 243	89.47 295	84.10 258	98.52 137	95.48 256	83.80 233	80.93 247	90.22 281	74.60 200	96.31 254	80.92 220	71.55 308	90.69 277

v1921920		86.02 239	84.44 245	90.77 234	89.32 296	85.20 243	98.10 187	95.35 266	82.19 264	82.25 232	90.71 255	70.73 248	96.30 257	76.85 256	74.49 275	90.80 270

v1240		85.77 246	84.11 248	90.73 235	89.26 297	85.15 246	97.88 199	95.23 273	81.89 270	82.16 233	90.55 271	69.60 257	96.31 254	75.59 273	74.87 271	90.72 275

DI_MVS_plusplus_test		89.41 185	87.24 200	95.92 114	89.06 298	90.75 121	98.18 180	96.63 174	89.29 113	70.54 310	90.31 276	63.50 295	98.40 140	92.25 110	95.44 127	98.60 129

our_test_3		84.47 262	82.80 260	89.50 263	89.01 299	83.90 261	97.03 230	94.56 284	81.33 274	75.36 292	90.52 272	71.69 242	94.54 302	68.81 311	76.84 262	90.07 289

ppachtmachnet_test		83.63 273	81.57 275	89.80 255	89.01 299	85.09 247	97.13 226	94.50 285	78.84 291	76.14 285	91.00 244	69.78 254	94.61 301	63.40 324	74.36 277	89.71 299

DTE-MVSNet		84.14 268	82.80 260	88.14 289	88.95 301	79.87 298	96.81 236	96.24 202	83.50 245	77.60 279	92.52 227	67.89 271	94.24 304	72.64 302	69.05 315	90.32 284

test_normal		89.37 186	87.18 202	95.93 113	88.94 302	90.83 117	98.24 173	96.62 175	89.31 111	70.38 312	90.20 283	63.50 295	98.37 141	92.06 112	95.41 128	98.59 132

PS-MVSNAJss		89.54 183	89.05 172	91.00 229	88.77 303	84.36 255	97.39 212	95.97 216	88.47 136	81.88 240	93.80 202	82.48 148	96.50 231	89.34 137	83.34 232	92.15 229

Baseline_NR-MVSNet		85.83 243	84.82 239	88.87 275	88.73 304	83.34 266	98.63 124	91.66 332	80.41 282	82.44 228	91.35 240	74.63 198	95.42 284	84.13 186	71.39 309	87.84 311

MVP-Stereo		86.61 231	85.83 219	88.93 274	88.70 305	83.85 262	96.07 267	94.41 290	82.15 265	75.64 290	91.96 233	67.65 272	96.45 237	77.20 252	98.72 75	86.51 324
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
EU-MVSNet		84.19 266	84.42 246	83.52 318	88.64 306	67.37 338	96.04 268	95.76 233	85.29 204	78.44 274	93.18 217	70.67 249	91.48 336	75.79 271	75.98 263	91.70 239

pmmvs5		85.87 241	84.40 247	90.30 244	88.53 307	84.23 256	98.60 129	93.71 300	81.53 272	80.29 251	92.02 230	64.51 290	95.52 281	82.04 209	78.34 255	91.15 256

MDA-MVSNet-bldmvs		77.82 309	74.75 311	87.03 301	88.33 308	78.52 307	96.34 253	92.85 317	75.57 313	48.87 349	87.89 302	57.32 314	92.49 327	60.79 330	64.80 324	90.08 288

N_pmnet		70.19 320	69.87 319	71.12 334	88.24 309	30.63 365	95.85 277	28.70 367	70.18 329	68.73 315	86.55 315	64.04 292	93.81 305	53.12 341	73.46 291	88.94 306

v7n		84.42 263	82.75 263	89.43 266	88.15 310	81.86 280	96.75 240	95.67 240	80.53 280	78.38 275	89.43 292	69.89 252	96.35 249	73.83 290	72.13 304	90.07 289

SixPastTwentyTwo		82.63 275	81.58 274	85.79 308	88.12 311	71.01 332	95.17 287	92.54 321	84.33 221	72.93 304	92.08 228	60.41 307	95.61 280	74.47 281	74.15 284	90.75 274

test_djsdf		88.26 206	87.73 192	89.84 254	88.05 312	82.21 278	97.77 203	96.17 208	86.84 183	82.41 230	91.95 234	72.07 237	95.99 267	89.83 128	84.50 222	91.32 252

mvs_tets		87.09 222	86.22 211	89.71 257	87.87 313	81.39 285	96.73 241	95.90 227	88.19 150	79.99 254	93.61 208	59.96 308	96.31 254	89.40 136	84.34 224	91.43 250

OurMVSNet-221017-0		84.13 269	83.59 253	85.77 309	87.81 314	70.24 333	94.89 289	93.65 302	86.08 194	76.53 283	93.28 215	61.41 303	96.14 263	80.95 219	77.69 259	90.93 266

YYNet1		79.64 301	77.04 303	87.43 299	87.80 315	79.98 295	96.23 258	94.44 287	73.83 320	51.83 346	87.53 307	67.96 270	92.07 332	66.00 320	67.75 320	90.23 286

MDA-MVSNet_test_wron		79.65 300	77.05 302	87.45 298	87.79 316	80.13 294	96.25 257	94.44 287	73.87 319	51.80 347	87.47 308	68.04 268	92.12 331	66.02 319	67.79 319	90.09 287

jajsoiax		87.35 213	86.51 208	89.87 252	87.75 317	81.74 281	97.03 230	95.98 215	88.47 136	80.15 253	93.80 202	61.47 302	96.36 243	89.44 135	84.47 223	91.50 246

v748		83.84 271	82.31 269	88.41 284	87.65 318	79.10 301	96.66 243	95.51 253	80.09 283	77.65 278	88.53 300	69.81 253	96.23 259	75.67 272	69.25 313	89.91 295

v52		84.19 266	82.92 257	88.01 291	87.64 319	79.92 296	96.23 258	95.32 267	79.87 285	78.51 272	89.05 295	69.50 259	96.32 252	77.95 246	72.24 303	87.79 314

V4		84.20 265	82.92 257	88.02 290	87.59 320	79.91 297	96.21 263	95.36 264	79.88 284	78.51 272	89.00 296	69.52 258	96.32 252	77.96 245	72.29 301	87.83 313

K. test v3		81.04 291	79.77 285	84.83 313	87.41 321	70.23 334	95.60 283	93.93 297	83.70 236	67.51 329	89.35 293	55.76 317	93.58 307	76.67 258	68.03 318	90.67 278

testgi		82.29 276	81.00 280	86.17 306	87.24 322	74.84 319	97.39 212	91.62 333	88.63 132	75.85 289	95.42 178	46.07 340	91.55 335	66.87 318	79.94 248	92.12 230

LF4IMVS		81.94 280	81.17 279	84.25 316	87.23 323	68.87 337	93.35 305	91.93 330	83.35 247	75.40 291	93.00 221	49.25 337	96.65 220	78.88 239	78.11 256	87.22 320

EG-PatchMatch MVS		79.92 298	77.59 298	86.90 302	87.06 324	77.90 313	96.20 264	94.06 296	74.61 316	66.53 333	88.76 298	40.40 348	96.20 260	67.02 316	83.66 230	86.61 322

Gipuma		54.77 328	52.22 330	62.40 341	86.50 325	59.37 344	50.20 358	90.35 342	36.52 354	41.20 353	49.49 356	18.33 358	81.29 351	32.10 355	65.34 322	46.54 357
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
anonymousdsp		86.69 228	85.75 222	89.53 262	86.46 326	82.94 270	96.39 251	95.71 237	83.97 226	79.63 259	90.70 256	68.85 262	95.94 270	86.01 168	84.02 226	89.72 298

lessismore_v0				85.08 311	85.59 327	69.28 336		90.56 340		67.68 326	90.21 282	54.21 326	95.46 282	73.88 288	62.64 327	90.50 281

CMPMVS	58.40 21	80.48 296	80.11 284	81.59 325	85.10 328	59.56 343	94.14 297	95.95 219	68.54 334	60.71 339	93.31 213	55.35 321	97.87 163	83.06 198	84.85 220	87.33 317
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
Anonymous20231206		80.76 294	79.42 290	84.79 314	84.78 329	72.98 325	96.53 247	92.97 310	79.56 287	74.33 295	88.83 297	61.27 304	92.15 330	60.59 331	75.92 264	89.24 304

Test4		85.71 248	82.59 267	95.07 141	84.45 330	89.84 141	97.20 223	95.73 235	89.19 115	64.59 335	87.58 305	40.59 347	96.77 218	88.95 145	95.01 131	98.60 129

DSMNet-mixed		81.60 285	81.43 276	82.10 321	84.36 331	60.79 342	93.63 303	86.74 352	79.00 289	79.32 263	87.15 311	63.87 293	89.78 337	66.89 317	91.92 160	95.73 202

pmmvs6		79.90 299	77.31 300	87.67 296	84.17 332	78.13 310	95.86 276	93.68 301	67.94 336	72.67 306	89.62 290	50.98 334	95.75 276	74.80 280	66.04 321	89.14 305

new_pmnet		76.02 312	73.71 313	82.95 319	83.88 333	72.85 326	91.26 321	92.26 324	70.44 327	62.60 337	81.37 331	47.64 338	92.32 328	61.85 327	72.10 305	83.68 341

OpenMVS_ROB	73.86 20	77.99 308	75.06 310	86.77 303	83.81 334	77.94 312	96.38 252	91.53 335	67.54 337	68.38 317	87.13 312	43.94 341	96.08 265	55.03 339	81.83 241	86.29 328

test20.03		78.51 306	77.48 299	81.62 324	83.07 335	71.03 331	96.11 266	92.83 318	81.66 271	69.31 314	89.68 289	57.53 312	87.29 342	58.65 336	68.47 316	86.53 323

UnsupCasMVSNet_eth		78.90 303	76.67 305	85.58 310	82.81 336	74.94 318	91.98 315	96.31 195	84.64 216	65.84 334	87.71 304	51.33 332	92.23 329	72.89 301	56.50 344	89.56 301

MIMVSNet1		75.92 313	73.30 314	83.81 317	81.29 337	75.57 317	92.26 314	92.05 328	73.09 321	67.48 330	86.18 316	40.87 346	87.64 341	55.78 338	70.68 312	88.21 307

test2356		80.96 292	81.77 272	78.52 328	81.02 338	62.33 340	98.22 175	94.49 286	79.38 288	74.56 294	90.34 275	70.65 251	85.10 346	60.83 329	86.42 206	88.14 308

Patchmatch-RL test		81.90 281	80.13 282	87.23 300	80.71 339	70.12 335	84.07 343	88.19 351	83.16 250	70.57 309	82.18 321	87.18 81	92.59 325	82.28 206	62.78 326	98.98 101

pmmvs-eth3d		78.71 305	76.16 307	86.38 304	80.25 340	81.19 289	94.17 296	92.13 327	77.97 303	66.90 332	82.31 320	55.76 317	92.56 326	73.63 292	62.31 329	85.38 336

testus		77.11 311	76.95 304	77.58 329	80.02 341	58.93 345	97.78 201	90.48 341	79.68 286	72.84 305	90.61 268	37.72 350	86.57 345	60.28 333	83.18 233	87.23 319

UnsupCasMVSNet_bld		73.85 316	70.14 318	84.99 312	79.44 342	75.73 316	88.53 330	95.24 272	70.12 330	61.94 338	74.81 344	41.41 345	93.62 306	68.65 312	51.13 350	85.62 335

PM-MVS		74.88 314	72.85 315	80.98 326	78.98 343	64.75 339	90.81 324	85.77 354	80.95 278	68.23 321	82.81 319	29.08 353	92.84 314	76.54 260	62.46 328	85.36 337

testing_2		80.92 293	77.24 301	91.98 207	78.88 344	87.83 174	93.96 299	95.72 236	84.27 222	56.20 344	80.42 335	38.64 349	96.40 240	87.20 157	79.85 249	91.72 238

new-patchmatchnet		74.80 315	72.40 316	81.99 322	78.36 345	72.20 328	94.44 291	92.36 323	77.06 308	63.47 336	79.98 339	51.04 333	88.85 339	60.53 332	54.35 346	84.92 339

pmmvs3		72.86 317	69.76 320	82.17 320	73.86 346	74.19 321	94.20 295	89.01 348	64.23 344	67.72 325	80.91 334	41.48 344	88.65 340	62.40 326	54.02 347	83.68 341

1111		72.28 318	71.36 317	75.02 332	73.04 347	57.38 347	92.30 312	90.22 343	62.27 345	59.46 340	80.36 336	76.23 187	87.07 343	44.29 347	64.08 325	80.59 345

.test1245		61.50 323	64.44 323	52.65 347	73.04 347	57.38 347	92.30 312	90.22 343	62.27 345	59.46 340	80.36 336	76.23 187	87.07 343	44.29 347	1.80 361	13.50 361

ambc				79.60 327	72.76 349	56.61 349	76.20 351	92.01 329		68.25 320	80.23 338	23.34 354	94.73 300	73.78 291	60.81 331	87.48 315

test1235678		71.07 319	69.53 321	75.71 331	71.87 350	55.27 351	94.32 292	90.76 339	70.23 328	57.61 343	79.06 341	43.13 342	83.72 348	50.48 342	68.30 317	88.14 308

TDRefinement		78.01 307	75.31 308	86.10 307	70.06 351	73.84 322	93.59 304	91.58 334	74.51 317	73.08 302	91.04 243	49.63 336	97.12 205	74.88 278	59.47 339	87.33 317

test12356		66.36 321	65.12 322	70.08 337	66.92 352	50.46 354	89.96 328	88.58 349	66.00 340	53.38 345	78.13 343	32.89 352	82.87 349	48.36 344	61.87 330	76.92 346

PMMVS2		58.97 326	55.07 327	70.69 336	62.72 353	55.37 350	85.97 334	80.52 358	49.48 350	45.94 350	68.31 347	15.73 361	80.78 352	49.79 343	37.12 351	75.91 348

E-PMN		41.02 335	40.93 335	41.29 348	61.97 354	33.83 362	84.00 344	65.17 365	27.17 357	27.56 356	46.72 358	17.63 360	60.41 361	19.32 358	18.82 356	29.61 358

PNet_i23d		48.05 331	44.98 333	57.28 343	60.15 355	42.39 360	80.85 350	73.14 363	36.78 353	27.46 357	56.66 353	6.38 364	68.34 357	36.65 353	26.72 353	61.10 353

wuyk23d		16.71 341	16.73 343	16.65 351	60.15 355	25.22 366	41.24 359	5.17 368	6.56 361	5.48 365	3.61 365	3.64 366	22.72 363	15.20 360	9.52 360	1.99 363

FPMVS		61.57 322	60.32 324	65.34 339	60.14 357	42.44 359	91.02 323	89.72 346	44.15 351	42.63 352	80.93 333	19.02 356	80.59 353	42.50 350	72.76 295	73.00 349

EMVS		39.96 337	39.88 336	40.18 349	59.57 358	32.12 364	84.79 341	64.57 366	26.27 358	26.14 359	44.18 361	18.73 357	59.29 362	17.03 359	17.67 358	29.12 359

no-one		56.69 327	51.89 331	71.08 335	59.35 359	58.65 346	83.78 346	84.81 357	61.73 347	36.46 355	56.52 354	18.15 359	84.78 347	47.03 346	19.19 355	69.81 351

testmv		60.41 324	57.98 325	67.69 338	58.16 360	47.14 356	89.09 329	86.74 352	61.52 348	44.30 351	68.44 346	20.98 355	79.92 354	40.94 351	51.67 348	76.01 347

LCM-MVSNet		60.07 325	56.37 326	71.18 333	54.81 361	48.67 355	82.17 348	89.48 347	37.95 352	49.13 348	69.12 345	13.75 363	81.76 350	59.28 334	51.63 349	83.10 343

MVE	44.00 22	41.70 334	37.64 338	53.90 346	49.46 362	43.37 358	65.09 356	66.66 364	26.19 359	25.77 360	48.53 357	3.58 368	63.35 360	26.15 357	27.28 352	54.97 356
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
wuykxyi23d		43.53 333	37.95 337	60.27 342	45.36 363	44.79 357	68.27 354	74.26 362	33.48 355	18.21 363	40.16 363	3.64 366	71.01 356	38.85 352	19.31 354	65.02 352

ANet_high		50.71 330	46.17 332	64.33 340	44.27 364	52.30 352	76.13 352	78.73 359	64.95 342	27.37 358	55.23 355	14.61 362	67.74 358	36.01 354	18.23 357	72.95 350

PMVS	41.42 23	45.67 332	42.50 334	55.17 345	34.28 365	32.37 363	66.24 355	78.71 360	30.72 356	22.04 361	59.59 351	4.59 365	77.85 355	27.49 356	58.84 343	55.29 355
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
tmp_tt		53.66 329	52.86 329	56.05 344	32.75 366	41.97 361	73.42 353	76.12 361	21.91 360	39.68 354	96.39 164	42.59 343	65.10 359	78.00 244	14.92 359	61.08 354

testmvs		18.81 340	23.05 341	6.10 353	4.48 367	2.29 368	97.78 201	3.00 369	3.27 362	18.60 362	62.71 349	1.53 370	2.49 365	14.26 361	1.80 361	13.50 361

test123		16.58 342	19.47 342	7.91 352	3.59 368	5.37 367	94.32 292	1.39 370	2.49 363	13.98 364	44.60 360	2.91 369	2.65 364	11.35 362	0.57 363	15.70 360

cdsmvs_eth3d_5k		22.52 339	30.03 340	0.00 354	0.00 369	0.00 369	0.00 360	97.17 145	0.00 364	0.00 366	98.77 65	74.35 209	0.00 366	0.00 363	0.00 364	0.00 364

pcd_1.5k_mvsjas		6.87 344	9.16 345	0.00 354	0.00 369	0.00 369	0.00 360	0.00 371	0.00 364	0.00 366	0.00 366	82.48 148	0.00 366	0.00 363	0.00 364	0.00 364

sosnet-low-res		0.00 345	0.00 346	0.00 354	0.00 369	0.00 369	0.00 360	0.00 371	0.00 364	0.00 366	0.00 366	0.00 371	0.00 366	0.00 363	0.00 364	0.00 364

sosnet		0.00 345	0.00 346	0.00 354	0.00 369	0.00 369	0.00 360	0.00 371	0.00 364	0.00 366	0.00 366	0.00 371	0.00 366	0.00 363	0.00 364	0.00 364

uncertanet		0.00 345	0.00 346	0.00 354	0.00 369	0.00 369	0.00 360	0.00 371	0.00 364	0.00 366	0.00 366	0.00 371	0.00 366	0.00 363	0.00 364	0.00 364

Regformer		0.00 345	0.00 346	0.00 354	0.00 369	0.00 369	0.00 360	0.00 371	0.00 364	0.00 366	0.00 366	0.00 371	0.00 366	0.00 363	0.00 364	0.00 364

ab-mvs-re		8.21 343	10.94 344	0.00 354	0.00 369	0.00 369	0.00 360	0.00 371	0.00 364	0.00 366	98.50 84	0.00 371	0.00 366	0.00 363	0.00 364	0.00 364

uanet		0.00 345	0.00 346	0.00 354	0.00 369	0.00 369	0.00 360	0.00 371	0.00 364	0.00 366	0.00 366	0.00 371	0.00 366	0.00 363	0.00 364	0.00 364

GSMVS																98.84 113

test_part1				0.00 354		0.00 369	0.00 360	97.69 81				0.00 371	0.00 366	0.00 363	0.00 364	0.00 364

sam_mvs1												88.39 58				98.84 113

sam_mvs												87.08 82

MTGPA								97.45 121

test_post1							90.74 326				41.37 362	85.38 110	96.36 243	83.16 196

test_post											46.00 359	87.37 75	97.11 206

patchmatchnet-post											84.86 318	88.73 52	96.81 217

MTMP							99.21 52	91.09 337

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 82	99.41 36

test_prior2							99.57 19		91.43 70	98.12 21	98.97 48	90.43 35		98.33 19	99.81 15

旧先验2							98.67 119		85.75 197	98.96 5			98.97 120	93.84 87

新几何2							98.26 171

无先验							98.52 137	97.82 64	87.20 177				99.90 30	87.64 154		99.85 20

原ACMM2							98.69 114

testdata2													99.88 34	84.16 185

segment_acmp												90.56 34

testdata1							97.89 197		92.43 50

plane_prior5								96.30 196					97.75 175	93.46 94	86.17 210	92.67 215

plane_prior4											96.52 158

plane_prior3						85.91 230			93.65 31	86.99 184

plane_prior2							99.02 79		93.38 36

plane_prior						86.07 226	99.14 66		93.81 29						86.26 209

n2								0.00 371

nn								0.00 371

door-mid								84.90 356

test11								97.68 83

door								85.30 355

HQP5-MVS						86.39 213

BP-MVS														93.82 89

HQP4-MVS										87.57 178			97.77 170			92.72 213

HQP3-MVS								96.37 191							86.29 207

HQP2-MVS												73.34 223

MDTV_nov1_ep13_2view						91.17 105	91.38 319		87.45 170	93.08 107		86.67 89		87.02 160		98.95 107

ACMMP++_ref															82.64 238

ACMMP++															83.83 227

Test By Simon												83.62 124

High-resolution multi-view benchmark