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.

DeepPCF-MVS	89.82 1	94.61 14	96.17 3	89.91 173	97.09 84	70.21 281	98.99 12	96.69 57	95.57 1	95.08 23	99.23 1	86.40 21	99.87 8	97.84 3	98.66 26	99.65 2

test0726					99.05 8	85.18 45	99.11 6	96.78 42	88.75 45	97.65 2	98.91 2	87.69 14

DPE-MVS		95.32 7	95.55 7	94.64 22	98.79 15	84.87 55	97.77 51	96.74 50	86.11 86	96.54 10	98.89 3	88.39 13	99.74 24	97.67 4	99.05 9	99.31 10

9.14			94.26 23		98.10 51		98.14 30	96.52 81	84.74 118	94.83 27	98.80 4	82.80 42	99.37 60	95.95 17	98.42 34

DPM-MVS		96.21 2	95.53 8	98.26 1	96.26 91	95.09 1	99.15 4	96.98 29	93.39 9	96.45 11	98.79 5	90.17 6	99.99 1	89.33 93	99.25 3	99.70 1

CNVR-MVS		96.30 1	96.54 1	95.55 10	99.31 5	87.69 17	99.06 7	97.12 22	94.66 3	96.79 6	98.78 6	86.42 20	99.95 3	97.59 5	99.18 4	99.00 18

save filter2										96.89 5	98.76 7	85.05 24	99.82 15	96.69 12	98.95 14	98.24 57

DVP-MVS		95.58 6	95.91 6	94.57 23	99.05 8	85.18 45	99.06 7	96.46 87	88.75 45	96.69 7	98.76 7	87.69 14	99.76 16	97.90 1	98.85 16	98.77 25

test_0728_THIRD									88.38 52	96.69 7	98.76 7	89.64 9	99.76 16	97.47 6	98.84 18	99.38 6

SMA-MVS		94.70 13	94.68 13	94.76 19	98.02 55	85.94 32	97.47 73	96.77 45	85.32 104	97.92 1	98.70 10	83.09 41	99.84 12	95.79 19	99.08 7	98.49 40

MSLP-MVS++		94.28 18	94.39 19	93.97 38	98.30 44	84.06 65	98.64 18	96.93 35	90.71 23	93.08 46	98.70 10	79.98 62	99.21 71	94.12 36	99.07 8	98.63 34

NCCC		95.63 4	95.94 5	94.69 21	99.21 7	85.15 50	99.16 3	96.96 32	94.11 6	95.59 17	98.64 12	85.07 23	99.91 4	95.61 22	99.10 6	99.00 18

DeepC-MVS_fast	89.06 2	94.48 16	94.30 22	95.02 15	98.86 13	85.68 37	98.06 36	96.64 66	93.64 8	91.74 61	98.54 13	80.17 61	99.90 5	92.28 58	98.75 22	99.49 4
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
HPM-MVS++		95.32 7	95.48 9	94.85 17	98.62 28	86.04 30	97.81 49	96.93 35	92.45 11	95.69 16	98.50 14	85.38 22	99.85 10	94.75 29	99.18 4	98.65 33

PHI-MVS		93.59 32	93.63 29	93.48 60	98.05 54	81.76 113	98.64 18	97.13 21	82.60 168	94.09 37	98.49 15	80.35 56	99.85 10	94.74 30	98.62 27	98.83 23

testtj		94.09 24	94.08 25	94.09 36	99.28 6	83.32 80	97.59 64	96.61 69	83.60 151	94.77 29	98.46 16	82.72 43	99.64 38	95.29 25	98.42 34	99.32 9

MCST-MVS		96.17 3	96.12 4	96.32 4	99.42 2	89.36 6	98.94 13	97.10 23	95.17 2	92.11 55	98.46 16	87.33 16	99.97 2	97.21 8	99.31 1	99.63 3

MP-MVS-pluss		92.58 53	92.35 51	93.29 65	97.30 80	82.53 93	96.44 147	96.04 123	84.68 121	89.12 96	98.37 18	77.48 93	99.74 24	93.31 47	98.38 39	97.59 105
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
SteuartSystems-ACMMP		94.13 22	94.44 18	93.20 68	95.41 112	81.35 122	99.02 11	96.59 72	89.50 35	94.18 36	98.36 19	83.68 37	99.45 56	94.77 28	98.45 32	98.81 24
Skip Steuart: Steuart Systems R&D Blog.
MSP-MVS		95.62 5	96.54 1	92.86 84	98.31 43	80.10 153	97.42 81	96.78 42	92.20 13	97.11 4	98.29 20	93.46 1	99.10 85	96.01 15	99.30 2	99.38 6

APDe-MVS		94.56 15	94.75 12	93.96 39	98.84 14	83.40 78	98.04 38	96.41 92	85.79 94	95.00 24	98.28 21	84.32 32	99.18 78	97.35 7	98.77 21	99.28 11

CDPH-MVS		93.12 37	92.91 41	93.74 45	98.65 24	83.88 66	97.67 60	96.26 108	83.00 161	93.22 44	98.24 22	81.31 49	99.21 71	89.12 94	98.74 23	98.14 64

APD-MVS		93.61 31	93.59 30	93.69 48	98.76 16	83.26 81	97.21 89	96.09 119	82.41 170	94.65 30	98.21 23	81.96 48	98.81 103	94.65 31	98.36 41	99.01 17
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
zzz-MVS		92.74 44	92.71 44	92.86 84	97.90 57	80.85 132	96.47 143	96.33 103	87.92 60	90.20 81	98.18 24	76.71 106	99.76 16	92.57 55	98.09 46	97.96 80

MTAPA		92.45 55	92.31 52	92.86 84	97.90 57	80.85 132	92.88 250	96.33 103	87.92 60	90.20 81	98.18 24	76.71 106	99.76 16	92.57 55	98.09 46	97.96 80

PS-MVSNAJ		94.17 21	93.52 32	96.10 5	95.65 106	92.35 2	98.21 28	95.79 134	92.42 12	96.24 12	98.18 24	71.04 169	99.17 79	96.77 10	97.39 65	96.79 138

MAR-MVS		90.63 87	90.22 82	91.86 122	98.47 36	78.20 199	97.18 93	96.61 69	83.87 143	88.18 109	98.18 24	68.71 181	99.75 22	83.66 139	97.15 69	97.63 102
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
SD-MVS		94.84 11	95.02 11	94.29 28	97.87 61	84.61 58	97.76 55	96.19 114	89.59 34	96.66 9	98.17 28	84.33 29	99.60 42	96.09 14	98.50 30	98.66 32
Zhenlong Yuan, Jiakai Cao, Zhaoxin Li, Hao Jiang and Zhaoqi Wang: SD-MVS: Segmentation-driven Deformation Multi-View Stereo with Spherical Refinement and EM optimization. AAAI2024
xiu_mvs_v2_base		93.92 28	93.26 34	95.91 6	95.07 124	92.02 3	98.19 29	95.68 139	92.06 14	96.01 15	98.14 29	70.83 172	98.96 93	96.74 11	96.57 81	96.76 141

agg_prior1		94.10 23	94.31 21	93.48 60	98.59 29	83.13 83	97.77 51	96.56 76	84.38 130	94.19 34	98.13 30	84.66 26	99.16 80	95.74 20	98.74 23	98.15 63

Regformer-1		94.00 27	94.04 26	93.87 41	98.41 37	84.29 61	97.43 79	97.04 25	89.50 35	92.75 50	98.13 30	82.60 45	99.26 66	93.55 41	96.99 71	98.06 69

Regformer-2		93.92 28	94.01 27	93.67 49	98.41 37	83.75 70	97.43 79	97.00 27	89.43 37	92.69 51	98.13 30	82.48 46	99.22 69	93.51 42	96.99 71	98.04 70

PAPR		92.74 44	92.17 57	94.45 24	98.89 12	84.87 55	97.20 91	96.20 112	87.73 66	88.40 105	98.12 33	78.71 76	99.76 16	87.99 106	96.28 83	98.74 26

test_8					98.63 27	83.64 74	97.81 49	96.63 68	84.50 126	95.10 22	98.11 34	84.33 29	99.23 67

TEST9					98.64 25	83.71 71	97.82 47	96.65 63	84.29 134	95.16 20	98.09 35	84.39 28	99.36 61

train_agg		94.28 18	94.45 17	93.74 45	98.64 25	83.71 71	97.82 47	96.65 63	84.50 126	95.16 20	98.09 35	84.33 29	99.36 61	95.91 18	98.96 13	98.16 61

CP-MVS		92.54 54	92.60 49	92.34 105	98.50 34	79.90 156	98.40 22	96.40 94	84.75 117	90.48 78	98.09 35	77.40 94	99.21 71	91.15 66	98.23 45	97.92 83

旧先验1					97.39 75	79.58 165		96.54 79			98.08 38	84.00 33			97.42 64	97.62 103

SR-MVS		92.16 58	92.27 53	91.83 125	98.37 40	78.41 190	96.67 136	95.76 135	82.19 174	91.97 56	98.07 39	76.44 108	98.64 107	93.71 38	97.27 67	98.45 43

test_prior3		94.03 26	94.34 20	93.09 73	98.68 19	81.91 106	98.37 23	96.40 94	86.08 88	94.57 31	98.02 40	83.14 39	99.06 87	95.05 26	98.79 19	98.29 52

test_prior2							98.37 23		86.08 88	94.57 31	98.02 40	83.14 39		95.05 26	98.79 19

ACMMP_NAP		93.46 33	93.23 35	94.17 33	97.16 82	84.28 62	96.82 125	96.65 63	86.24 84	94.27 33	97.99 42	77.94 85	99.83 14	93.39 43	98.57 28	98.39 46

testdata				90.13 163	95.92 100	74.17 250		96.49 86	73.49 270	94.82 28	97.99 42	78.80 75	97.93 128	83.53 142	97.52 59	98.29 52

region2R		92.72 47	92.70 46	92.79 88	98.68 19	80.53 143	97.53 69	96.51 82	85.22 107	91.94 57	97.98 44	77.26 95	99.67 36	90.83 71	98.37 40	98.18 59

CSCG		92.02 60	91.65 64	93.12 71	98.53 31	80.59 139	97.47 73	97.18 20	77.06 247	84.64 137	97.98 44	83.98 34	99.52 49	90.72 73	97.33 66	99.23 13

HFP-MVS		92.89 41	92.86 43	92.98 78	98.71 17	81.12 125	97.58 65	96.70 55	85.20 109	91.75 59	97.97 46	78.47 78	99.71 28	90.95 67	98.41 36	98.12 66

#test#		92.99 39	92.99 39	92.98 78	98.71 17	81.12 125	97.77 51	96.70 55	85.75 95	91.75 59	97.97 46	78.47 78	99.71 28	91.36 63	98.41 36	98.12 66

ACMMPR		92.69 49	92.67 47	92.75 89	98.66 22	80.57 140	97.58 65	96.69 57	85.20 109	91.57 62	97.92 48	77.01 100	99.67 36	90.95 67	98.41 36	98.00 77

Regformer-3		93.19 35	93.19 36	93.19 69	98.10 51	83.01 87	97.08 108	96.98 29	88.98 41	91.35 68	97.89 49	80.80 52	99.23 67	92.30 57	95.20 96	97.32 120

Regformer-4		93.06 38	93.12 37	92.89 83	98.10 51	82.20 101	97.08 108	96.92 37	88.87 43	91.23 70	97.89 49	80.57 55	99.19 76	92.21 59	95.20 96	97.29 124

APD-MVS_3200maxsize		91.23 77	91.35 67	90.89 147	97.89 59	76.35 233	96.30 156	95.52 147	79.82 213	91.03 73	97.88 51	74.70 139	98.54 111	92.11 61	96.89 75	97.77 93

XVS		92.69 49	92.71 44	92.63 96	98.52 32	80.29 146	97.37 84	96.44 89	87.04 79	91.38 64	97.83 52	77.24 97	99.59 43	90.46 76	98.07 48	98.02 72

CANet		94.89 10	94.64 14	95.63 8	97.55 68	88.12 11	99.06 7	96.39 97	94.07 7	95.34 19	97.80 53	76.83 103	99.87 8	97.08 9	97.64 58	98.89 21

PGM-MVS		91.93 61	91.80 61	92.32 107	98.27 45	79.74 160	95.28 191	97.27 17	83.83 144	90.89 75	97.78 54	76.12 115	99.56 47	88.82 96	97.93 54	97.66 99

API-MVS		90.18 97	88.97 106	93.80 43	98.66 22	82.95 88	97.50 72	95.63 142	75.16 256	86.31 123	97.69 55	72.49 155	99.90 5	81.26 157	96.07 86	98.56 37

cdsmvs_eth3d_5k		21.43 307	28.57 309	0.00 323	0.00 340	0.00 341	0.00 334	95.93 129	0.00 336	0.00 338	97.66 56	63.57 209	0.00 339	0.00 336	0.00 336	0.00 335

MP-MVS		92.61 52	92.67 47	92.42 103	98.13 50	79.73 161	97.33 86	96.20 112	85.63 97	90.53 76	97.66 56	78.14 83	99.70 31	92.12 60	98.30 43	97.85 87
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
mPP-MVS		91.88 62	91.82 60	92.07 115	98.38 39	78.63 184	97.29 87	96.09 119	85.12 111	88.45 104	97.66 56	75.53 123	99.68 34	89.83 85	98.02 51	97.88 84

lupinMVS		93.87 30	93.58 31	94.75 20	93.00 170	88.08 12	99.15 4	95.50 148	91.03 20	94.90 25	97.66 56	78.84 73	97.56 146	94.64 32	97.46 60	98.62 35

PAPM_NR		91.46 71	90.82 73	93.37 64	98.50 34	81.81 112	95.03 202	96.13 116	84.65 122	86.10 126	97.65 60	79.24 69	99.75 22	83.20 145	96.88 76	98.56 37

DP-MVS Recon		91.72 65	90.85 72	94.34 26	99.50 1	85.00 52	98.51 21	95.96 126	80.57 195	88.08 110	97.63 61	76.84 102	99.89 7	85.67 120	94.88 100	98.13 65

新几何1				93.12 71	97.44 71	81.60 119		96.71 54	74.54 261	91.22 71	97.57 62	79.13 71	99.51 52	77.40 189	98.46 31	98.26 55

xiu_mvs_v1_base_debu		90.54 89	89.54 98	93.55 55	92.31 183	87.58 18	96.99 112	94.87 177	87.23 74	93.27 41	97.56 63	57.43 249	98.32 118	92.72 52	93.46 114	94.74 183

xiu_mvs_v1_base		90.54 89	89.54 98	93.55 55	92.31 183	87.58 18	96.99 112	94.87 177	87.23 74	93.27 41	97.56 63	57.43 249	98.32 118	92.72 52	93.46 114	94.74 183

xiu_mvs_v1_base_debi		90.54 89	89.54 98	93.55 55	92.31 183	87.58 18	96.99 112	94.87 177	87.23 74	93.27 41	97.56 63	57.43 249	98.32 118	92.72 52	93.46 114	94.74 183

EI-MVSNet-Vis-set		91.84 63	91.77 62	92.04 117	97.60 65	81.17 124	96.61 137	96.87 39	88.20 56	89.19 95	97.55 66	78.69 77	99.14 82	90.29 81	90.94 136	95.80 163

1121		90.66 86	89.82 94	93.16 70	97.39 75	81.71 116	93.33 237	96.66 62	74.45 262	91.38 64	97.55 66	79.27 67	99.52 49	79.95 166	98.43 33	98.26 55

CS-MVS		92.88 42	93.09 38	92.26 109	95.21 118	80.70 136	98.84 14	95.26 162	88.83 44	92.50 52	97.48 68	77.49 92	97.63 142	95.34 24	96.88 76	98.46 41

alignmvs		92.97 40	92.26 54	95.12 14	95.54 108	87.77 15	98.67 16	96.38 98	88.04 58	93.01 47	97.45 69	79.20 70	98.60 108	93.25 48	88.76 149	98.99 20

test222					96.15 94	78.41 190	95.87 175	96.46 87	71.97 281	89.66 88	97.45 69	76.33 112			98.24 44	98.30 51

TSAR-MVS + GP.		94.35 17	94.50 15	93.89 40	97.38 78	83.04 86	98.10 33	95.29 160	91.57 15	93.81 38	97.45 69	86.64 17	99.43 57	96.28 13	94.01 106	99.20 14

CPTT-MVS		89.72 104	89.87 93	89.29 182	98.33 42	73.30 254	97.70 58	95.35 157	75.68 252	87.40 113	97.44 72	70.43 173	98.25 121	89.56 90	96.90 74	96.33 154

原ACMM1				91.22 139	97.77 62	78.10 201		96.61 69	81.05 186	91.28 69	97.42 73	77.92 86	98.98 92	79.85 168	98.51 29	96.59 145

GST-MVS		92.43 56	92.22 56	93.04 76	98.17 48	81.64 118	97.40 83	96.38 98	84.71 120	90.90 74	97.40 74	77.55 91	99.76 16	89.75 87	97.74 56	97.72 94

EI-MVSNet-UG-set		91.35 75	91.22 68	91.73 126	97.39 75	80.68 137	96.47 143	96.83 41	87.92 60	88.30 108	97.36 75	77.84 87	99.13 83	89.43 92	89.45 143	95.37 173

canonicalmvs		92.27 57	91.22 68	95.41 11	95.80 102	88.31 9	97.09 106	94.64 193	88.49 50	92.99 48	97.31 76	72.68 154	98.57 110	93.38 45	88.58 152	99.36 8

MVS		90.60 88	88.64 110	96.50 2	94.25 143	90.53 5	93.33 237	97.21 19	77.59 239	78.88 197	97.31 76	71.52 164	99.69 32	89.60 88	98.03 50	99.27 12

1112_ss		88.60 128	87.47 133	92.00 118	93.21 164	80.97 129	96.47 143	92.46 265	83.64 149	80.86 179	97.30 78	80.24 59	97.62 143	77.60 186	85.49 181	97.40 117

ab-mvs-re		8.11 311	10.81 313	0.00 323	0.00 340	0.00 341	0.00 334	0.00 342	0.00 336	0.00 338	97.30 78	0.00 343	0.00 339	0.00 336	0.00 336	0.00 335

ETV-MVS		91.73 64	92.05 58	90.78 150	94.52 136	76.40 232	98.06 36	95.34 158	89.19 39	88.90 99	97.28 80	77.56 90	97.73 139	90.77 72	96.86 79	98.20 58

ACMMP		90.39 93	89.97 88	91.64 128	97.58 67	78.21 198	96.78 128	96.72 53	84.73 119	84.72 135	97.23 81	71.22 166	99.63 40	88.37 103	92.41 124	97.08 129
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
WTY-MVS		92.65 51	91.68 63	95.56 9	96.00 98	88.90 8	98.23 27	97.65 12	88.57 47	89.82 85	97.22 82	79.29 66	99.06 87	89.57 89	88.73 150	98.73 30

HPM-MVS		91.62 68	91.53 66	91.89 121	97.88 60	79.22 170	96.99 112	95.73 137	82.07 175	89.50 93	97.19 83	75.59 122	98.93 99	90.91 69	97.94 52	97.54 106
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
MVS_111021_HR		93.41 34	93.39 33	93.47 63	97.34 79	82.83 89	97.56 67	98.27 6	89.16 40	89.71 86	97.14 84	79.77 64	99.56 47	93.65 39	97.94 52	98.02 72

MVSFormer		91.36 74	90.57 76	93.73 47	93.00 170	88.08 12	94.80 207	94.48 200	80.74 191	94.90 25	97.13 85	78.84 73	95.10 254	83.77 134	97.46 60	98.02 72

jason		92.73 46	92.23 55	94.21 32	90.50 223	87.30 21	98.65 17	95.09 167	90.61 24	92.76 49	97.13 85	75.28 133	97.30 163	93.32 46	96.75 80	98.02 72
jason: jason.
DELS-MVS		94.98 9	94.49 16	96.44 3	96.42 89	90.59 4	99.21 2	97.02 26	94.40 5	91.46 63	97.08 87	83.32 38	99.69 32	92.83 51	98.70 25	99.04 16
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
MVS_111021_LR		91.60 69	91.64 65	91.47 133	95.74 103	78.79 182	96.15 162	96.77 45	88.49 50	88.64 102	97.07 88	72.33 157	99.19 76	93.13 49	96.48 82	96.43 149

abl_6		89.80 102	89.71 97	90.07 164	96.53 88	75.52 240	94.48 210	95.04 170	81.12 185	89.22 94	97.00 89	68.83 180	98.96 93	89.86 84	95.27 95	95.73 165

MG-MVS		94.25 20	93.72 28	95.85 7	99.38 3	89.35 7	97.98 40	98.09 8	89.99 30	92.34 54	96.97 90	81.30 50	98.99 91	88.54 98	98.88 15	99.20 14

HPM-MVS_fast		90.38 95	90.17 85	91.03 143	97.61 64	77.35 218	97.15 98	95.48 149	79.51 217	88.79 100	96.90 91	71.64 163	98.81 103	87.01 115	97.44 62	96.94 131

PAPM		92.87 43	92.40 50	94.30 27	92.25 190	87.85 14	96.40 151	96.38 98	91.07 19	88.72 101	96.90 91	82.11 47	97.37 160	90.05 83	97.70 57	97.67 98

EPNet		94.06 25	94.15 24	93.76 44	97.27 81	84.35 59	98.29 25	97.64 13	94.57 4	95.36 18	96.88 93	79.96 63	99.12 84	91.30 64	96.11 85	97.82 90
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
OMC-MVS		88.80 122	88.16 116	90.72 151	95.30 115	77.92 207	94.81 206	94.51 199	86.80 81	84.97 130	96.85 94	67.53 185	98.60 108	85.08 124	87.62 160	95.63 167

EIA-MVS		92.72 47	92.87 42	92.28 108	94.54 135	81.89 108	97.98 40	95.21 164	89.77 33	93.11 45	96.83 95	77.23 99	97.50 154	95.74 20	95.38 94	97.44 114

TAPA-MVS	81.61 12	85.02 177	83.67 178	89.06 184	96.79 86	73.27 256	95.92 171	94.79 184	74.81 259	80.47 183	96.83 95	71.07 168	98.19 124	49.82 314	92.57 120	95.71 166
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
CANet_DTU		90.98 80	90.04 87	93.83 42	94.76 130	86.23 28	96.32 155	93.12 257	93.11 10	93.71 39	96.82 97	63.08 212	99.48 54	84.29 129	95.12 99	95.77 164

TSAR-MVS + MP.		94.79 12	95.17 10	93.64 50	97.66 63	84.10 64	95.85 177	96.42 91	91.26 18	97.49 3	96.80 98	86.50 19	98.49 114	95.54 23	99.03 10	98.33 48
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
DeepC-MVS	86.58 3	91.53 70	91.06 71	92.94 81	94.52 136	81.89 108	95.95 169	95.98 125	90.76 22	83.76 149	96.76 99	73.24 151	99.71 28	91.67 62	96.96 73	97.22 127
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
CNLPA		86.96 150	85.37 156	91.72 127	97.59 66	79.34 168	97.21 89	91.05 284	74.22 263	78.90 196	96.75 100	67.21 189	98.95 96	74.68 214	90.77 137	96.88 136

ET-MVSNet_ETH3D		90.01 100	89.03 104	92.95 80	94.38 141	86.77 24	98.14 30	96.31 106	89.30 38	63.33 296	96.72 101	90.09 7	93.63 284	90.70 74	82.29 206	98.46 41

AdaColmap		88.81 121	87.61 128	92.39 104	99.33 4	79.95 154	96.70 135	95.58 143	77.51 240	83.05 157	96.69 102	61.90 223	99.72 27	84.29 129	93.47 113	97.50 111

LFMVS		89.27 111	87.64 125	94.16 35	97.16 82	85.52 40	97.18 93	94.66 190	79.17 224	89.63 89	96.57 103	55.35 265	98.22 122	89.52 91	89.54 142	98.74 26

PMMVS		89.46 108	89.92 91	88.06 205	94.64 131	69.57 288	96.22 160	94.95 173	87.27 73	91.37 67	96.54 104	65.88 195	97.39 159	88.54 98	93.89 108	97.23 126

1314		88.94 116	87.20 137	94.17 33	93.21 164	85.73 35	93.33 237	96.64 66	82.89 162	75.98 226	96.36 105	66.83 192	99.39 58	83.52 143	96.02 88	97.39 118

PLC	83.97 7	88.00 140	87.38 135	89.83 176	98.02 55	76.46 230	97.16 97	94.43 205	79.26 223	81.98 170	96.28 106	69.36 178	99.27 64	77.71 185	92.25 127	93.77 196
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
PVSNet_Blended		93.13 36	92.98 40	93.57 54	97.47 69	83.86 67	99.32 1	96.73 51	91.02 21	89.53 91	96.21 107	76.42 109	99.57 45	94.29 34	95.81 92	97.29 124

test_yl		91.46 71	90.53 77	94.24 30	97.41 73	85.18 45	98.08 34	97.72 10	80.94 187	89.85 83	96.14 108	75.61 120	98.81 103	90.42 79	88.56 153	98.74 26

DCV-MVSNet		91.46 71	90.53 77	94.24 30	97.41 73	85.18 45	98.08 34	97.72 10	80.94 187	89.85 83	96.14 108	75.61 120	98.81 103	90.42 79	88.56 153	98.74 26

sss		90.87 83	89.96 89	93.60 53	94.15 145	83.84 69	97.14 99	98.13 7	85.93 92	89.68 87	96.09 110	71.67 161	99.30 63	87.69 107	89.16 144	97.66 99

3Dnovator+	82.88 8	89.63 106	87.85 120	94.99 16	94.49 140	86.76 25	97.84 46	95.74 136	86.10 87	75.47 233	96.02 111	65.00 203	99.51 52	82.91 149	97.07 70	98.72 31

diffmvs		91.17 78	90.74 75	92.44 102	93.11 169	82.50 95	96.25 159	93.62 240	87.79 64	90.40 79	95.93 112	73.44 150	97.42 157	93.62 40	92.55 121	97.41 116

3Dnovator	82.32 10	89.33 110	87.64 125	94.42 25	93.73 156	85.70 36	97.73 57	96.75 49	86.73 82	76.21 224	95.93 112	62.17 216	99.68 34	81.67 155	97.81 55	97.88 84

VDD-MVS		88.28 136	87.02 143	92.06 116	95.09 122	80.18 152	97.55 68	94.45 204	83.09 158	89.10 97	95.92 114	47.97 287	98.49 114	93.08 50	86.91 165	97.52 110

VNet		92.11 59	91.22 68	94.79 18	96.91 85	86.98 22	97.91 42	97.96 9	86.38 83	93.65 40	95.74 115	70.16 176	98.95 96	93.39 43	88.87 148	98.43 44

OpenMVS	79.58 14	86.09 163	83.62 181	93.50 58	90.95 216	86.71 26	97.44 75	95.83 132	75.35 253	72.64 253	95.72 116	57.42 252	99.64 38	71.41 235	95.85 91	94.13 191

Effi-MVS+		90.70 85	89.90 92	93.09 73	93.61 157	83.48 76	95.20 195	92.79 261	83.22 155	91.82 58	95.70 117	71.82 160	97.48 155	91.25 65	93.67 111	98.32 49

114514_t		88.79 123	87.57 129	92.45 101	98.21 47	81.74 114	96.99 112	95.45 152	75.16 256	82.48 160	95.69 118	68.59 182	98.50 113	80.33 161	95.18 98	97.10 128

baseline		90.76 84	90.10 86	92.74 90	92.90 174	82.56 92	94.60 209	94.56 198	87.69 67	89.06 98	95.67 119	73.76 146	97.51 153	90.43 78	92.23 128	98.16 61

Vis-MVSNet (Re-imp)		88.88 119	88.87 109	88.91 188	93.89 152	74.43 248	96.93 120	94.19 212	84.39 129	83.22 154	95.67 119	78.24 81	94.70 263	78.88 177	94.40 103	97.61 104

QAPM		86.88 152	84.51 167	93.98 37	94.04 149	85.89 33	97.19 92	96.05 122	73.62 267	75.12 236	95.62 121	62.02 219	99.74 24	70.88 241	96.06 87	96.30 156

IS-MVSNet		88.67 125	88.16 116	90.20 162	93.61 157	76.86 225	96.77 130	93.07 258	84.02 139	83.62 150	95.60 122	74.69 140	96.24 205	78.43 179	93.66 112	97.49 112

casdiffmvs		90.95 81	90.39 79	92.63 96	92.82 175	82.53 93	96.83 124	94.47 202	87.69 67	88.47 103	95.56 123	74.04 143	97.54 151	90.90 70	92.74 119	97.83 89

thisisatest0515		90.95 81	90.26 81	93.01 77	94.03 151	84.27 63	97.91 42	96.67 59	83.18 156	86.87 120	95.51 124	88.66 12	97.85 135	80.46 160	89.01 146	96.92 134

BH-RMVSNet		86.84 153	85.28 157	91.49 132	95.35 114	80.26 149	96.95 118	92.21 267	82.86 163	81.77 174	95.46 125	59.34 235	97.64 141	69.79 246	93.81 110	96.57 146

CLD-MVS		87.97 141	87.48 132	89.44 180	92.16 193	80.54 142	98.14 30	94.92 174	91.41 16	79.43 193	95.40 126	62.34 215	97.27 166	90.60 75	82.90 200	90.50 215
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
DWT-MVSNet_test		90.52 92	89.80 95	92.70 93	95.73 105	82.20 101	93.69 227	96.55 78	88.34 53	87.04 119	95.34 127	86.53 18	97.55 147	76.32 200	88.66 151	98.34 47

XVG-OURS-SEG-HR		85.74 169	85.16 159	87.49 217	90.22 227	71.45 274	91.29 267	94.09 218	81.37 182	83.90 147	95.22 128	60.30 228	97.53 152	85.58 121	84.42 188	93.50 199

PatchFormer-LS_test		90.14 98	89.30 102	92.65 95	95.43 110	82.46 96	93.46 233	96.35 101	88.56 48	84.82 132	95.22 128	84.63 27	97.55 147	78.40 180	86.81 166	97.94 82

LS3D		82.22 217	79.94 228	89.06 184	97.43 72	74.06 252	93.20 244	92.05 268	61.90 307	73.33 246	95.21 130	59.35 234	99.21 71	54.54 302	92.48 123	93.90 195

VDDNet		86.44 160	84.51 167	92.22 111	91.56 206	81.83 111	97.10 105	94.64 193	69.50 291	87.84 111	95.19 131	48.01 286	97.92 133	89.82 86	86.92 164	96.89 135

F-COLMAP		84.50 185	83.44 185	87.67 210	95.22 117	72.22 261	95.95 169	93.78 232	75.74 251	76.30 222	95.18 132	59.50 233	98.45 116	72.67 227	86.59 169	92.35 205

TR-MVS		86.30 161	84.93 164	90.42 156	94.63 132	77.58 213	96.57 139	93.82 227	80.30 202	82.42 162	95.16 133	58.74 239	97.55 147	74.88 212	87.82 159	96.13 158

gm-plane-assit					92.27 187	79.64 163			84.47 128		95.15 134		97.93 128	85.81 119

mvs-test1		86.83 154	87.17 138	85.81 240	91.96 200	65.24 300	97.90 44	93.34 251	85.57 98	84.51 139	95.14 135	61.99 220	97.19 170	83.55 140	90.55 138	95.00 178

Vis-MVSNet		88.67 125	87.82 121	91.24 138	92.68 176	78.82 180	96.95 118	93.85 226	87.55 69	87.07 118	95.13 136	63.43 210	97.21 168	77.58 187	96.15 84	97.70 97
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
PVSNet	82.34 9	89.02 114	87.79 122	92.71 92	95.49 109	81.50 120	97.70 58	97.29 16	87.76 65	85.47 128	95.12 137	56.90 253	98.90 100	80.33 161	94.02 105	97.71 96

XVG-OURS		85.18 176	84.38 171	87.59 213	90.42 225	71.73 271	91.06 270	94.07 219	82.00 177	83.29 153	95.08 138	56.42 259	97.55 147	83.70 138	83.42 193	93.49 200

EPNet_dtu		87.65 145	87.89 119	86.93 227	94.57 133	71.37 275	96.72 131	96.50 84	88.56 48	87.12 117	95.02 139	75.91 118	94.01 276	66.62 259	90.00 140	95.42 172
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
EPP-MVSNet		89.76 103	89.72 96	89.87 174	93.78 153	76.02 236	97.22 88	96.51 82	79.35 219	85.11 129	95.01 140	84.82 25	97.10 175	87.46 110	88.21 157	96.50 147

baseline1		88.85 120	87.49 131	92.93 82	95.21 118	86.85 23	95.47 189	94.61 195	87.29 72	83.11 156	94.99 141	80.70 53	96.89 183	82.28 151	73.72 246	95.05 177

thisisatest0530		89.65 105	89.02 105	91.53 131	93.46 161	80.78 134	96.52 140	96.67 59	81.69 180	83.79 148	94.90 142	88.85 11	97.68 140	77.80 181	87.49 163	96.14 157

PCF-MVS	84.09 5	86.77 157	85.00 162	92.08 114	92.06 197	83.07 85	92.14 258	94.47 202	79.63 216	76.90 214	94.78 143	71.15 167	99.20 75	72.87 225	91.05 135	93.98 193
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
EI-MVSNet		85.80 167	85.20 158	87.59 213	91.55 207	77.41 216	95.13 198	95.36 155	80.43 199	80.33 186	94.71 144	73.72 147	95.97 211	76.96 193	78.64 225	89.39 233

CVMVSNet		84.83 180	85.57 153	82.63 280	91.55 207	60.38 313	95.13 198	95.03 171	80.60 194	82.10 169	94.71 144	66.40 194	90.19 313	74.30 217	90.32 139	97.31 122

baseline2		90.39 93	90.21 83	90.93 145	90.86 219	80.99 128	95.20 195	97.41 15	86.03 90	80.07 190	94.61 146	90.58 3	97.47 156	87.29 111	89.86 141	94.35 187

NP-MVS					92.04 198	78.22 195					94.56 147

HQP-MVS		87.91 143	87.55 130	88.98 187	92.08 194	78.48 186	97.63 61	94.80 182	90.52 25	82.30 163	94.56 147	65.40 199	97.32 161	87.67 108	83.01 197	91.13 208

BH-w/o		88.24 137	87.47 133	90.54 155	95.03 125	78.54 185	97.41 82	93.82 227	84.08 137	78.23 203	94.51 149	69.34 179	97.21 168	80.21 164	94.58 101	95.87 162

tttt0517		88.57 129	88.19 115	89.71 179	93.00 170	75.99 237	95.67 182	96.67 59	80.78 190	81.82 173	94.40 150	88.97 10	97.58 145	76.05 203	86.31 170	95.57 169

CHOSEN 280x420		91.71 66	91.85 59	91.29 136	94.94 126	82.69 90	87.89 289	96.17 115	85.94 91	87.27 116	94.31 151	90.27 5	95.65 232	94.04 37	95.86 90	95.53 170

GG-mvs-BLEND				93.49 59	94.94 126	86.26 27	81.62 309	97.00 27		88.32 107	94.30 152	91.23 2	96.21 206	88.49 100	97.43 63	98.00 77

Anonymous202405211		84.41 186	81.93 199	91.85 124	96.78 87	78.41 190	97.44 75	91.34 279	70.29 288	84.06 141	94.26 153	41.09 308	98.96 93	79.46 170	82.65 204	98.17 60

CDS-MVSNet		89.50 107	88.96 107	91.14 141	91.94 203	80.93 130	97.09 106	95.81 133	84.26 135	84.72 135	94.20 154	80.31 57	95.64 233	83.37 144	88.96 147	96.85 137
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
HQP_MVS		87.50 146	87.09 142	88.74 192	91.86 204	77.96 204	97.18 93	94.69 186	89.89 31	81.33 175	94.15 155	64.77 204	97.30 163	87.08 112	82.82 201	90.96 210

plane_prior4											94.15 155

OPM-MVS		85.84 166	85.10 161	88.06 205	88.34 252	77.83 210	95.72 180	94.20 211	87.89 63	80.45 184	94.05 157	58.57 240	97.26 167	83.88 132	82.76 203	89.09 240

thres200		88.92 117	87.65 124	92.73 91	96.30 90	85.62 38	97.85 45	98.86 1	84.38 130	84.82 132	93.99 158	75.12 135	98.01 126	70.86 242	86.67 167	94.56 186

PVSNet_Blended_VisFu		91.24 76	90.77 74	92.66 94	95.09 122	82.40 97	97.77 51	95.87 131	88.26 55	86.39 122	93.94 159	76.77 104	99.27 64	88.80 97	94.00 107	96.31 155

UA-Net		88.92 117	88.48 113	90.24 160	94.06 148	77.18 222	93.04 246	94.66 190	87.39 71	91.09 72	93.89 160	74.92 137	98.18 125	75.83 205	91.43 133	95.35 174

tfpn200view9		88.48 130	87.15 139	92.47 100	96.21 92	85.30 43	97.44 75	98.85 2	83.37 153	83.99 143	93.82 161	75.36 130	97.93 128	69.04 248	86.24 173	94.17 188

thres400		88.42 133	87.15 139	92.23 110	96.21 92	85.30 43	97.44 75	98.85 2	83.37 153	83.99 143	93.82 161	75.36 130	97.93 128	69.04 248	86.24 173	93.45 201

BH-untuned		86.95 151	85.94 151	89.99 168	94.52 136	77.46 215	96.78 128	93.37 250	81.80 178	76.62 217	93.81 163	66.64 193	97.02 177	76.06 202	93.88 109	95.48 171

thres100view900		88.30 135	86.95 144	92.33 106	96.10 96	84.90 54	97.14 99	98.85 2	82.69 166	83.41 151	93.66 164	75.43 127	97.93 128	69.04 248	86.24 173	94.17 188

thres600view7		88.06 138	86.70 147	92.15 113	96.10 96	85.17 49	97.14 99	98.85 2	82.70 165	83.41 151	93.66 164	75.43 127	97.82 136	67.13 257	85.88 177	93.45 201

TAMVS		88.48 130	87.79 122	90.56 154	91.09 214	79.18 171	96.45 146	95.88 130	83.64 149	83.12 155	93.33 166	75.94 117	95.74 229	82.40 150	88.27 156	96.75 142

test0.0.03 1		82.79 207	82.48 195	83.74 268	86.81 265	72.22 261	96.52 140	95.03 171	83.76 146	73.00 249	93.20 167	72.30 158	88.88 315	64.15 272	77.52 233	90.12 222

LPG-MVS_test		84.20 189	83.49 184	86.33 231	90.88 217	73.06 257	95.28 191	94.13 215	82.20 172	76.31 220	93.20 167	54.83 270	96.95 179	83.72 136	80.83 209	88.98 244

LGP-MVS_train				86.33 231	90.88 217	73.06 257		94.13 215	82.20 172	76.31 220	93.20 167	54.83 270	96.95 179	83.72 136	80.83 209	88.98 244

CHOSEN 1792x2688		91.07 79	90.21 83	93.64 50	95.18 120	83.53 75	96.26 158	96.13 116	88.92 42	84.90 131	93.10 170	72.86 153	99.62 41	88.86 95	95.67 93	97.79 92

Fast-Effi-MVS+		87.93 142	86.94 145	90.92 146	94.04 149	79.16 172	98.26 26	93.72 236	81.29 183	83.94 146	92.90 171	69.83 177	96.68 193	76.70 194	91.74 132	96.93 132

RPSCF		77.73 257	76.63 251	81.06 288	88.66 250	55.76 322	87.77 290	87.88 305	64.82 302	74.14 240	92.79 172	49.22 283	96.81 188	67.47 256	76.88 234	90.62 213

DP-MVS		81.47 225	78.28 238	91.04 142	98.14 49	78.48 186	95.09 201	86.97 307	61.14 312	71.12 262	92.78 173	59.59 231	99.38 59	53.11 306	86.61 168	95.27 176

Anonymous20240529		83.15 201	80.60 217	90.80 149	95.74 103	78.27 194	96.81 126	94.92 174	60.10 316	81.89 172	92.54 174	45.82 294	98.82 102	79.25 173	78.32 230	95.31 175

FIs		86.73 158	86.10 150	88.61 194	90.05 231	80.21 150	96.14 163	96.95 33	85.56 101	78.37 202	92.30 175	76.73 105	95.28 248	79.51 169	79.27 219	90.35 217

ACMP	81.66 11	84.00 190	83.22 187	86.33 231	91.53 209	72.95 259	95.91 173	93.79 231	83.70 148	73.79 241	92.22 176	54.31 273	96.89 183	83.98 131	79.74 215	89.16 239
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
VPNet		84.69 182	82.92 189	90.01 167	89.01 245	83.45 77	96.71 133	95.46 150	85.71 96	79.65 192	92.18 177	56.66 257	96.01 210	83.05 148	67.84 285	90.56 214

nrg030		86.79 156	85.43 154	90.87 148	88.76 246	85.34 42	97.06 110	94.33 207	84.31 132	80.45 184	91.98 178	72.36 156	96.36 200	88.48 101	71.13 255	90.93 212

HY-MVS	84.06 6	91.63 67	90.37 80	95.39 12	96.12 95	88.25 10	90.22 273	97.58 14	88.33 54	90.50 77	91.96 179	79.26 68	99.06 87	90.29 81	89.07 145	98.88 22

ACMM	80.70 13	83.72 194	82.85 191	86.31 234	91.19 212	72.12 264	95.88 174	94.29 208	80.44 197	77.02 212	91.96 179	55.24 266	97.14 174	79.30 172	80.38 211	89.67 230
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
FC-MVSNet-test		85.96 164	85.39 155	87.66 211	89.38 243	78.02 202	95.65 184	96.87 39	85.12 111	77.34 207	91.94 181	76.28 113	94.74 262	77.09 190	78.82 223	90.21 220

MSDG		80.62 235	77.77 242	89.14 183	93.43 162	77.24 219	91.89 261	90.18 289	69.86 290	68.02 275	91.94 181	52.21 275	98.84 101	59.32 288	83.12 195	91.35 207

TESTMET0.1,1		89.83 101	89.34 101	91.31 134	92.54 181	80.19 151	97.11 102	96.57 74	86.15 85	86.85 121	91.83 183	79.32 65	96.95 179	81.30 156	92.35 125	96.77 140

PatchMatch-RL		85.00 178	83.66 179	89.02 186	95.86 101	74.55 247	92.49 254	93.60 241	79.30 222	79.29 195	91.47 184	58.53 241	98.45 116	70.22 245	92.17 129	94.07 192

Fast-Effi-MVS+-dtu		83.33 199	82.60 194	85.50 244	89.55 239	69.38 289	96.09 166	91.38 276	82.30 171	75.96 227	91.41 185	56.71 254	95.58 238	75.13 211	84.90 186	91.54 206

test-LLR		88.48 130	87.98 118	89.98 169	92.26 188	77.23 220	97.11 102	95.96 126	83.76 146	86.30 124	91.38 186	72.30 158	96.78 190	80.82 158	91.92 130	95.94 160

test-mter		88.95 115	88.60 111	89.98 169	92.26 188	77.23 220	97.11 102	95.96 126	85.32 104	86.30 124	91.38 186	76.37 111	96.78 190	80.82 158	91.92 130	95.94 160

ITE_SJBPF				82.38 281	87.00 264	65.59 299		89.55 293	79.99 211	69.37 272	91.30 188	41.60 307	95.33 246	62.86 279	74.63 244	86.24 290

HyFIR lowres test		89.36 109	88.60 111	91.63 129	94.91 128	80.76 135	95.60 185	95.53 145	82.56 169	84.03 142	91.24 189	78.03 84	96.81 188	87.07 114	88.41 155	97.32 120

Test_1112_low_res		88.03 139	86.73 146	91.94 120	93.15 166	80.88 131	96.44 147	92.41 266	83.59 152	80.74 181	91.16 190	80.18 60	97.59 144	77.48 188	85.40 182	97.36 119

testgi		74.88 273	73.40 271	79.32 295	80.13 308	61.75 310	93.21 243	86.64 310	79.49 218	66.56 283	91.06 191	35.51 316	88.67 316	56.79 296	71.25 254	87.56 274

MVS_Test		90.29 96	89.18 103	93.62 52	95.23 116	84.93 53	94.41 213	94.66 190	84.31 132	90.37 80	91.02 192	75.13 134	97.82 136	83.11 147	94.42 102	98.12 66

cascas		86.50 159	84.48 169	92.55 99	92.64 180	85.95 31	97.04 111	95.07 169	75.32 254	80.50 182	91.02 192	54.33 272	97.98 127	86.79 116	87.62 160	93.71 197

UniMVSNet_NR-MVSNet		85.49 172	84.59 166	88.21 204	89.44 242	79.36 166	96.71 133	96.41 92	85.22 107	78.11 204	90.98 194	76.97 101	95.14 252	79.14 174	68.30 279	90.12 222

DU-MVS		84.57 184	83.33 186	88.28 202	88.76 246	79.36 166	96.43 149	95.41 154	85.42 102	78.11 204	90.82 195	67.61 183	95.14 252	79.14 174	68.30 279	90.33 218

NR-MVSNet		83.35 198	81.52 206	88.84 189	88.76 246	81.31 123	94.45 212	95.16 165	84.65 122	67.81 276	90.82 195	70.36 174	94.87 259	74.75 213	66.89 293	90.33 218

TranMVSNet+NR-MVSNet		83.24 200	81.71 202	87.83 207	87.71 259	78.81 181	96.13 165	94.82 181	84.52 125	76.18 225	90.78 197	64.07 207	94.60 265	74.60 215	66.59 295	90.09 224

XXY-MVS		83.84 192	82.00 198	89.35 181	87.13 263	81.38 121	95.72 180	94.26 209	80.15 207	75.92 228	90.63 198	61.96 222	96.52 195	78.98 176	73.28 251	90.14 221

MVSTER		89.25 112	88.92 108	90.24 160	95.98 99	84.66 57	96.79 127	95.36 155	87.19 77	80.33 186	90.61 199	90.02 8	95.97 211	85.38 123	78.64 225	90.09 224

UGNet		87.73 144	86.55 148	91.27 137	95.16 121	79.11 174	96.35 153	96.23 110	88.14 57	87.83 112	90.48 200	50.65 277	99.09 86	80.13 165	94.03 104	95.60 168
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
IB-MVS	85.34 4	88.67 125	87.14 141	93.26 66	93.12 168	84.32 60	98.76 15	97.27 17	87.19 77	79.36 194	90.45 201	83.92 35	98.53 112	84.41 128	69.79 268	96.93 132
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
mvs_anonymous		88.68 124	87.62 127	91.86 122	94.80 129	81.69 117	93.53 232	94.92 174	82.03 176	78.87 198	90.43 202	75.77 119	95.34 245	85.04 125	93.16 117	98.55 39

WR-MVS		84.32 187	82.96 188	88.41 197	89.38 243	80.32 145	96.59 138	96.25 109	83.97 140	76.63 216	90.36 203	67.53 185	94.86 260	75.82 206	70.09 266	90.06 226

COLMAP_ROB	73.24 19	75.74 269	73.00 274	83.94 264	92.38 182	69.08 290	91.85 262	86.93 308	61.48 310	65.32 288	90.27 204	42.27 304	96.93 182	50.91 311	75.63 238	85.80 297
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
AllTest		75.92 268	73.06 273	84.47 258	92.18 191	67.29 294	91.07 269	84.43 317	67.63 294	63.48 293	90.18 205	38.20 312	97.16 171	57.04 293	73.37 248	88.97 246

TestCases				84.47 258	92.18 191	67.29 294		84.43 317	67.63 294	63.48 293	90.18 205	38.20 312	97.16 171	57.04 293	73.37 248	88.97 246

UniMVSNet_ETH3D		80.86 233	78.75 236	87.22 223	86.31 270	72.02 265	91.95 259	93.76 235	73.51 268	75.06 237	90.16 207	43.04 302	95.66 230	76.37 199	78.55 228	93.98 193

ab-mvs		87.08 149	84.94 163	93.48 60	93.34 163	83.67 73	88.82 282	95.70 138	81.18 184	84.55 138	90.14 208	62.72 213	98.94 98	85.49 122	82.54 205	97.85 87

PS-MVSNAJss		84.91 179	84.30 172	86.74 228	85.89 279	74.40 249	94.95 203	94.16 214	83.93 141	76.45 219	90.11 209	71.04 169	95.77 224	83.16 146	79.02 222	90.06 226

jajsoiax		82.12 218	81.15 210	85.03 249	84.19 296	70.70 277	94.22 221	93.95 221	83.07 159	73.48 243	89.75 210	49.66 282	95.37 244	82.24 153	79.76 213	89.02 243

MS-PatchMatch		83.05 202	81.82 201	86.72 230	89.64 237	79.10 175	94.88 205	94.59 197	79.70 215	70.67 265	89.65 211	50.43 279	96.82 187	70.82 244	95.99 89	84.25 304

PVSNet_BlendedMVS		90.05 99	89.96 89	90.33 158	97.47 69	83.86 67	98.02 39	96.73 51	87.98 59	89.53 91	89.61 212	76.42 109	99.57 45	94.29 34	79.59 216	87.57 273

mvs_tets		81.74 221	80.71 215	84.84 250	84.22 295	70.29 280	93.91 224	93.78 232	82.77 164	73.37 244	89.46 213	47.36 291	95.31 247	81.99 154	79.55 218	88.92 248

pmmvs4		82.54 211	80.79 212	87.79 208	86.11 275	80.49 144	93.55 231	93.18 255	77.29 243	73.35 245	89.40 214	65.26 202	95.05 257	75.32 209	73.61 247	87.83 266

GA-MVS		85.79 168	84.04 175	91.02 144	89.47 241	80.27 148	96.90 121	94.84 180	85.57 98	80.88 178	89.08 215	56.56 258	96.47 197	77.72 184	85.35 183	96.34 152

CMPMVS	54.94 21	75.71 270	74.56 264	79.17 297	79.69 309	55.98 320	89.59 276	93.30 253	60.28 314	53.85 317	89.07 216	47.68 290	96.33 201	76.55 195	81.02 208	85.22 298
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
VPA-MVSNet		85.32 174	83.83 176	89.77 178	90.25 226	82.63 91	96.36 152	97.07 24	83.03 160	81.21 177	89.02 217	61.58 224	96.31 202	85.02 126	70.95 257	90.36 216

UniMVSNet (Re)		85.31 175	84.23 173	88.55 195	89.75 234	80.55 141	96.72 131	96.89 38	85.42 102	78.40 201	88.93 218	75.38 129	95.52 240	78.58 178	68.02 282	89.57 231

CP-MVSNet		81.01 231	80.08 224	83.79 266	87.91 257	70.51 278	94.29 220	95.65 140	80.83 189	72.54 255	88.84 219	63.71 208	92.32 295	68.58 253	68.36 278	88.55 251

EU-MVSNet		76.92 264	76.95 248	76.83 301	84.10 297	54.73 323	91.77 263	92.71 262	72.74 276	69.57 271	88.69 220	58.03 246	87.43 319	64.91 269	70.00 267	88.33 258

pmmvs5		81.34 227	79.54 230	86.73 229	85.02 289	76.91 224	96.22 160	91.65 274	77.65 238	73.55 242	88.61 221	55.70 263	94.43 268	74.12 219	73.35 250	88.86 249

PEN-MVS		79.47 243	78.26 239	83.08 276	86.36 269	68.58 291	93.85 225	94.77 185	79.76 214	71.37 259	88.55 222	59.79 229	92.46 293	64.50 270	65.40 296	88.19 260

ACMH+	76.62 16	77.47 258	74.94 261	85.05 248	91.07 215	71.58 273	93.26 242	90.01 290	71.80 282	64.76 290	88.55 222	41.62 306	96.48 196	62.35 280	71.00 256	87.09 281

PVSNet_0	77.72 15	81.70 222	78.95 235	89.94 172	90.77 220	76.72 228	95.96 168	96.95 33	85.01 114	70.24 269	88.53 224	52.32 274	98.20 123	86.68 117	44.08 325	94.89 179

PS-CasMVS		80.27 237	79.18 232	83.52 273	87.56 261	69.88 283	94.08 222	95.29 160	80.27 204	72.08 257	88.51 225	59.22 237	92.23 297	67.49 255	68.15 281	88.45 255

DTE-MVSNet		78.37 251	77.06 247	82.32 283	85.22 288	67.17 296	93.40 234	93.66 238	78.71 231	70.53 266	88.29 226	59.06 238	92.23 297	61.38 282	63.28 303	87.56 274

v2v482		83.46 197	81.86 200	88.25 203	86.19 273	79.65 162	96.34 154	94.02 220	81.56 181	77.32 208	88.23 227	65.62 196	96.03 208	77.77 182	69.72 270	89.09 240

USDC		78.65 249	76.25 253	85.85 239	87.58 260	74.60 246	89.58 277	90.58 288	84.05 138	63.13 297	88.23 227	40.69 310	96.86 186	66.57 261	75.81 237	86.09 293

XVG-ACMP-BASELINE		79.38 244	77.90 241	83.81 265	84.98 290	67.14 297	89.03 281	93.18 255	80.26 205	72.87 251	88.15 229	38.55 311	96.26 203	76.05 203	78.05 231	88.02 263

FMVSNet3		84.71 181	82.71 193	90.70 152	94.55 134	87.71 16	95.92 171	94.67 189	81.73 179	75.82 229	88.08 230	66.99 190	94.47 267	71.23 237	75.38 239	89.91 228

MVP-Stereo		82.65 210	81.67 203	85.59 243	86.10 276	78.29 193	93.33 237	92.82 260	77.75 237	69.17 274	87.98 231	59.28 236	95.76 225	71.77 232	96.88 76	82.73 310
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
OurMVSNet-221017-0		77.18 261	76.06 254	80.55 290	83.78 300	60.00 314	90.35 272	91.05 284	77.01 248	66.62 282	87.92 232	47.73 289	94.03 275	71.63 233	68.44 277	87.62 271

test_djsdf		83.00 205	82.45 196	84.64 255	84.07 298	69.78 285	94.80 207	94.48 200	80.74 191	75.41 234	87.70 233	61.32 225	95.10 254	83.77 134	79.76 213	89.04 242

ACMH	75.40 17	77.99 254	74.96 260	87.10 225	90.67 221	76.41 231	93.19 245	91.64 275	72.47 279	63.44 295	87.61 234	43.34 299	97.16 171	58.34 290	73.94 245	87.72 267
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
pm-mvs1		80.05 238	78.02 240	86.15 236	85.42 283	75.81 238	95.11 200	92.69 263	77.13 244	70.36 267	87.43 235	58.44 242	95.27 249	71.36 236	64.25 299	87.36 278

FMVSNet2		82.79 207	80.44 219	89.83 176	92.66 177	85.43 41	95.42 190	94.35 206	79.06 226	74.46 238	87.28 236	56.38 260	94.31 270	69.72 247	74.68 243	89.76 229

LTVRE_ROB	73.68 18	77.99 254	75.74 257	84.74 251	90.45 224	72.02 265	86.41 299	91.12 281	72.57 278	66.63 281	87.27 237	54.95 269	96.98 178	56.29 297	75.98 235	85.21 299
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
IterMVS-LS		83.93 191	82.80 192	87.31 221	91.46 210	77.39 217	95.66 183	93.43 245	80.44 197	75.51 232	87.26 238	73.72 147	95.16 251	76.99 191	70.72 259	89.39 233
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Effi-MVS+-dtu		84.61 183	84.90 165	83.72 269	91.96 200	63.14 307	94.95 203	93.34 251	85.57 98	79.79 191	87.12 239	61.99 220	95.61 236	83.55 140	85.83 178	92.41 204

CostFormer		89.08 113	88.39 114	91.15 140	93.13 167	79.15 173	88.61 285	96.11 118	83.14 157	89.58 90	86.93 240	83.83 36	96.87 185	88.22 104	85.92 176	97.42 115

DI_MVS_plusplus_test		85.92 165	83.61 182	92.86 84	86.43 267	83.20 82	95.57 186	95.46 150	85.10 113	65.99 285	86.84 241	56.70 255	97.89 134	88.10 105	92.33 126	97.48 113

WR-MVS_H		81.02 230	80.09 223	83.79 266	88.08 256	71.26 276	94.46 211	96.54 79	80.08 208	72.81 252	86.82 242	70.36 174	92.65 292	64.18 271	67.50 288	87.46 277

v1144		82.90 206	81.27 209	87.78 209	86.29 271	79.07 177	96.14 163	93.93 222	80.05 209	77.38 206	86.80 243	65.50 197	95.93 216	75.21 210	70.13 263	88.33 258

V42		83.04 203	81.53 205	87.57 215	86.27 272	79.09 176	95.87 175	94.11 217	80.35 201	77.22 210	86.79 244	65.32 201	96.02 209	77.74 183	70.14 262	87.61 272

LF4IMVS		72.36 284	70.82 280	76.95 300	79.18 310	56.33 319	86.12 300	86.11 312	69.30 292	63.06 298	86.66 245	33.03 321	92.25 296	65.33 267	68.64 276	82.28 314

LCM-MVSNet-Re		83.75 193	83.54 183	84.39 262	93.54 159	64.14 303	92.51 253	84.03 319	83.90 142	66.14 284	86.59 246	67.36 187	92.68 291	84.89 127	92.87 118	96.35 151

v1192		82.31 216	80.55 218	87.60 212	85.94 277	78.47 189	95.85 177	93.80 230	79.33 220	76.97 213	86.51 247	63.33 211	95.87 218	73.11 224	70.13 263	88.46 254

v144192		82.43 212	80.73 214	87.54 216	85.81 280	78.22 195	95.98 167	93.78 232	79.09 225	77.11 211	86.49 248	64.66 206	95.91 217	74.20 218	69.42 271	88.49 252

TransMVSNet (Re)		76.94 263	74.38 265	84.62 256	85.92 278	75.25 243	95.28 191	89.18 297	73.88 266	67.22 277	86.46 249	59.64 230	94.10 274	59.24 289	52.57 317	84.50 303

v1921920		82.02 219	80.23 222	87.41 218	85.62 281	77.92 207	95.79 179	93.69 237	78.86 229	76.67 215	86.44 250	62.50 214	95.83 220	72.69 226	69.77 269	88.47 253

v1240		81.70 222	79.83 229	87.30 222	85.50 282	77.70 212	95.48 188	93.44 244	78.46 233	76.53 218	86.44 250	60.85 226	95.84 219	71.59 234	70.17 261	88.35 257

tpm2		87.35 148	86.26 149	90.62 153	92.93 173	78.67 183	88.06 288	95.99 124	79.33 220	87.40 113	86.43 252	80.28 58	96.40 198	80.23 163	85.73 180	96.79 138

Baseline_NR-MVSNet		81.22 229	80.07 225	84.68 253	85.32 287	75.12 244	96.48 142	88.80 300	76.24 250	77.28 209	86.40 253	67.61 183	94.39 269	75.73 207	66.73 294	84.54 302

anonymousdsp		80.98 232	79.97 227	84.01 263	81.73 303	70.44 279	92.49 254	93.58 243	77.10 246	72.98 250	86.31 254	57.58 248	94.90 258	79.32 171	78.63 227	86.69 285

SixPastTwentyTwo		76.04 267	74.32 266	81.22 287	84.54 292	61.43 312	91.16 268	89.30 296	77.89 234	64.04 292	86.31 254	48.23 284	94.29 271	63.54 276	63.84 301	87.93 265

Anonymous20231211		79.72 240	77.19 246	87.33 219	95.59 107	77.16 223	95.18 197	94.18 213	59.31 318	72.57 254	86.20 256	47.89 288	95.66 230	74.53 216	69.24 272	89.18 238

tpmrst		88.36 134	87.38 135	91.31 134	94.36 142	79.92 155	87.32 292	95.26 162	85.32 104	88.34 106	86.13 257	80.60 54	96.70 192	83.78 133	85.34 184	97.30 123

v148		82.41 215	80.89 211	86.99 226	86.18 274	76.81 226	96.27 157	93.82 227	80.49 196	75.28 235	86.11 258	67.32 188	95.75 226	75.48 208	67.03 292	88.42 256

GBi-Net		82.42 213	80.43 220	88.39 198	92.66 177	81.95 103	94.30 217	93.38 247	79.06 226	75.82 229	85.66 259	56.38 260	93.84 278	71.23 237	75.38 239	89.38 235

test1		82.42 213	80.43 220	88.39 198	92.66 177	81.95 103	94.30 217	93.38 247	79.06 226	75.82 229	85.66 259	56.38 260	93.84 278	71.23 237	75.38 239	89.38 235

FMVSNet1		79.50 242	76.54 252	88.39 198	88.47 251	81.95 103	94.30 217	93.38 247	73.14 272	72.04 258	85.66 259	43.86 296	93.84 278	65.48 266	72.53 252	89.38 235

TDRefinement		69.20 290	65.78 293	79.48 294	66.04 327	62.21 309	88.21 287	86.12 311	62.92 304	61.03 304	85.61 262	33.23 320	94.16 272	55.82 300	53.02 315	82.08 315

v8		81.88 220	80.06 226	87.32 220	86.63 266	79.04 178	94.41 213	93.65 239	78.77 230	73.19 248	85.57 263	66.87 191	95.81 221	73.84 222	67.61 287	87.11 280

EPMVS		87.47 147	85.90 152	92.18 112	95.41 112	82.26 100	87.00 294	96.28 107	85.88 93	84.23 140	85.57 263	75.07 136	96.26 203	71.14 240	92.50 122	98.03 71

tfpnnormal		78.14 253	75.42 258	86.31 234	88.33 253	79.24 169	94.41 213	96.22 111	73.51 268	69.81 270	85.52 265	55.43 264	95.75 226	47.65 318	67.86 284	83.95 306

D2MVS		82.67 209	81.55 204	86.04 238	87.77 258	76.47 229	95.21 194	96.58 73	82.66 167	70.26 268	85.46 266	60.39 227	95.80 223	76.40 198	79.18 220	85.83 296

miper_lstm_enhance		81.66 224	80.66 216	84.67 254	91.19 212	71.97 267	91.94 260	93.19 254	77.86 236	72.27 256	85.26 267	73.46 149	93.42 286	73.71 223	67.05 291	88.61 250

v10		81.43 226	79.53 231	87.11 224	86.38 268	78.87 179	94.31 216	93.43 245	77.88 235	73.24 247	85.26 267	65.44 198	95.75 226	72.14 230	67.71 286	86.72 284

tpm		85.55 171	84.47 170	88.80 191	90.19 228	75.39 242	88.79 283	94.69 186	84.83 116	83.96 145	85.21 269	78.22 82	94.68 264	76.32 200	78.02 232	96.34 152

IterMVS-SCA-FT		80.51 236	79.10 234	84.73 252	89.63 238	74.66 245	92.98 247	91.81 272	80.05 209	71.06 263	85.18 270	58.04 244	91.40 303	72.48 229	70.70 260	88.12 262

dp		84.30 188	82.31 197	90.28 159	94.24 144	77.97 203	86.57 297	95.53 145	79.94 212	80.75 180	85.16 271	71.49 165	96.39 199	63.73 274	83.36 194	96.48 148

IterMVS		80.67 234	79.16 233	85.20 247	89.79 233	76.08 235	92.97 248	91.86 270	80.28 203	71.20 261	85.14 272	57.93 247	91.34 304	72.52 228	70.74 258	88.18 261
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
SCA		85.63 170	83.64 180	91.60 130	92.30 186	81.86 110	92.88 250	95.56 144	84.85 115	82.52 159	85.12 273	58.04 244	95.39 242	73.89 220	87.58 162	97.54 106

Patchmatch-test		78.25 252	74.72 262	88.83 190	91.20 211	74.10 251	73.91 324	88.70 303	59.89 317	66.82 280	85.12 273	78.38 80	94.54 266	48.84 316	79.58 217	97.86 86

PatchmatchNet		86.83 154	85.12 160	91.95 119	94.12 146	82.27 99	86.55 298	95.64 141	84.59 124	82.98 158	84.99 275	77.26 95	95.96 214	68.61 252	91.34 134	97.64 101
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
ppachtmachnet_test		77.19 260	74.22 267	86.13 237	85.39 284	78.22 195	93.98 223	91.36 278	71.74 283	67.11 279	84.87 276	56.67 256	93.37 288	52.21 307	64.59 298	86.80 283

TinyColmap		72.41 283	68.99 287	82.68 279	88.11 255	69.59 287	88.41 286	85.20 314	65.55 300	57.91 311	84.82 277	30.80 325	95.94 215	51.38 308	68.70 275	82.49 313

our_test_3		77.90 256	75.37 259	85.48 245	85.39 284	76.74 227	93.63 228	91.67 273	73.39 271	65.72 287	84.65 278	58.20 243	93.13 290	57.82 292	67.87 283	86.57 286

v7n		79.32 245	77.34 244	85.28 246	84.05 299	72.89 260	93.38 235	93.87 225	75.02 258	70.68 264	84.37 279	59.58 232	95.62 235	67.60 254	67.50 288	87.32 279

test20.03		72.36 284	71.15 279	75.98 305	77.79 313	59.16 316	92.40 256	89.35 295	74.09 264	61.50 302	84.32 280	48.09 285	85.54 324	50.63 312	62.15 305	83.24 307

MDTV_nov1_ep13			83.69 177		94.09 147	81.01 127	86.78 296	96.09 119	83.81 145	84.75 134	84.32 280	74.44 141	96.54 194	63.88 273	85.07 185

pmmvs6		74.65 274	71.67 277	83.60 271	79.13 311	69.94 282	93.31 241	90.88 286	61.05 313	65.83 286	84.15 282	43.43 298	94.83 261	66.62 259	60.63 306	86.02 294

test_0402		72.68 282	69.54 286	82.09 284	88.67 249	71.81 270	92.72 252	86.77 309	61.52 309	62.21 299	83.91 283	43.22 300	93.76 281	34.60 325	72.23 253	80.72 318

EG-PatchMatch MVS		74.92 272	72.02 276	83.62 270	83.76 301	73.28 255	93.62 229	92.04 269	68.57 293	58.88 308	83.80 284	31.87 323	95.57 239	56.97 295	78.67 224	82.00 316

Anonymous20231206		75.29 271	73.64 270	80.22 291	80.75 304	63.38 306	93.36 236	90.71 287	73.09 273	67.12 278	83.70 285	50.33 280	90.85 308	53.63 305	70.10 265	86.44 287

tpmvs		83.04 203	80.77 213	89.84 175	95.43 110	77.96 204	85.59 303	95.32 159	75.31 255	76.27 223	83.70 285	73.89 144	97.41 158	59.53 285	81.93 207	94.14 190

lessismore_v0				79.98 292	80.59 306	58.34 317		80.87 325		58.49 309	83.46 287	43.10 301	93.89 277	63.11 278	48.68 319	87.72 267

tpm cat1		83.63 195	81.38 207	90.39 157	93.53 160	78.19 200	85.56 304	95.09 167	70.78 286	78.51 200	83.28 288	74.80 138	97.03 176	66.77 258	84.05 189	95.95 159

OpenMVS_ROB	68.52 20	73.02 281	69.57 285	83.37 274	80.54 307	71.82 269	93.60 230	88.22 304	62.37 305	61.98 300	83.15 289	35.31 317	95.47 241	45.08 321	75.88 236	82.82 308

K. test v3		73.62 275	71.59 278	79.69 293	82.98 302	59.85 315	90.85 271	88.83 299	77.13 244	58.90 307	82.11 290	43.62 297	91.72 301	65.83 265	54.10 314	87.50 276

MDA-MVSNet-bldmvs		71.45 286	67.94 288	81.98 285	85.33 286	68.50 292	92.35 257	88.76 301	70.40 287	42.99 322	81.96 291	46.57 292	91.31 305	48.75 317	54.39 313	86.11 292

MIMVSNet		79.18 247	75.99 255	88.72 193	87.37 262	80.66 138	79.96 310	91.82 271	77.38 242	74.33 239	81.87 292	41.78 305	90.74 309	66.36 264	83.10 196	94.76 182

UnsupCasMVSNet_eth		73.25 279	70.57 282	81.30 286	77.53 314	66.33 298	87.24 293	93.89 224	80.38 200	57.90 312	81.59 293	42.91 303	90.56 310	65.18 268	48.51 320	87.01 282

DSMNet-mixed		73.13 280	72.45 275	75.19 306	77.51 315	46.82 326	85.09 305	82.01 324	67.61 298	69.27 273	81.33 294	50.89 276	86.28 321	54.54 302	83.80 190	92.46 203

YYNet1		73.53 278	70.43 283	82.85 278	84.52 293	71.73 271	91.69 265	91.37 277	67.63 294	46.79 320	81.21 295	55.04 268	90.43 311	55.93 298	59.70 308	86.38 288

MDA-MVSNet_test_wron		73.54 277	70.43 283	82.86 277	84.55 291	71.85 268	91.74 264	91.32 280	67.63 294	46.73 321	81.09 296	55.11 267	90.42 312	55.91 299	59.76 307	86.31 289

tmp_tt		41.54 302	41.93 303	40.38 317	20.10 337	26.84 334	61.93 328	59.09 334	14.81 332	28.51 328	80.58 297	35.53 315	48.33 335	63.70 275	13.11 331	45.96 328

FMVSNet5		76.46 266	74.16 268	83.35 275	90.05 231	76.17 234	89.58 277	89.85 291	71.39 285	65.29 289	80.42 298	50.61 278	87.70 318	61.05 283	69.24 272	86.18 291

CR-MVSNet		83.53 196	81.36 208	90.06 165	90.16 229	79.75 158	79.02 315	91.12 281	84.24 136	82.27 167	80.35 299	75.45 125	93.67 282	63.37 277	86.25 171	96.75 142

Patchmtry		77.36 259	74.59 263	85.67 242	89.75 234	75.75 239	77.85 318	91.12 281	60.28 314	71.23 260	80.35 299	75.45 125	93.56 285	57.94 291	67.34 290	87.68 269

MVS_0304		78.43 250	76.70 250	83.60 271	88.22 254	69.81 284	92.91 249	95.10 166	72.32 280	78.71 199	80.29 301	33.78 319	93.37 288	68.77 251	80.23 212	87.63 270

ADS-MVSNet2		79.57 241	77.53 243	85.71 241	93.78 153	72.13 263	79.48 311	86.11 312	73.09 273	80.14 188	79.99 302	62.15 217	90.14 314	59.49 286	83.52 191	94.85 180

ADS-MVSNet		81.26 228	78.36 237	89.96 171	93.78 153	79.78 157	79.48 311	93.60 241	73.09 273	80.14 188	79.99 302	62.15 217	95.24 250	59.49 286	83.52 191	94.85 180

MIMVSNet1		69.44 288	66.65 291	77.84 298	76.48 317	62.84 308	87.42 291	88.97 298	66.96 299	57.75 313	79.72 304	32.77 322	85.83 323	46.32 319	63.42 302	84.85 301

N_pmnet		61.30 296	60.20 297	64.60 310	84.32 294	17.00 338	91.67 266	10.98 338	61.77 308	58.45 310	78.55 305	49.89 281	91.83 300	42.27 323	63.94 300	84.97 300

PM-MVS		69.32 289	66.93 290	76.49 302	73.60 322	55.84 321	85.91 301	79.32 328	74.72 260	61.09 303	78.18 306	21.76 328	91.10 307	70.86 242	56.90 311	82.51 311

pmmvs-eth3d		73.59 276	70.66 281	82.38 281	76.40 318	73.38 253	89.39 280	89.43 294	72.69 277	60.34 306	77.79 307	46.43 293	91.26 306	66.42 263	57.06 310	82.51 311

patchmatchnet-post											77.09 308	77.78 88	95.39 242

DeepMVS_CX				64.06 311	78.53 312	43.26 329		68.11 332	69.94 289	38.55 323	76.14 309	18.53 329	79.34 325	43.72 322	41.62 326	69.57 324

testing_2		76.96 262	73.18 272	88.30 201	75.87 320	79.64 163	89.92 275	94.21 210	80.16 206	51.23 319	75.94 310	33.94 318	95.81 221	82.28 151	75.12 242	89.46 232

ambc				76.02 304	68.11 325	51.43 324	64.97 327	89.59 292		60.49 305	74.49 311	17.17 330	92.46 293	61.50 281	52.85 316	84.17 305

pmmvs3		65.75 294	62.18 296	76.45 303	67.12 326	64.54 301	88.68 284	85.05 315	54.77 322	57.54 314	73.79 312	29.40 326	86.21 322	55.49 301	47.77 322	78.62 319

new-patchmatchnet		68.85 291	65.93 292	77.61 299	73.57 323	63.94 305	90.11 274	88.73 302	71.62 284	55.08 315	73.60 313	40.84 309	87.22 320	51.35 310	48.49 321	81.67 317

Patchmatch-RL test		76.65 265	74.01 269	84.55 257	77.37 316	64.23 302	78.49 317	82.84 323	78.48 232	64.63 291	73.40 314	76.05 116	91.70 302	76.99 191	57.84 309	97.72 94

PatchT		79.75 239	76.85 249	88.42 196	89.55 239	75.49 241	77.37 319	94.61 195	63.07 303	82.46 161	73.32 315	75.52 124	93.41 287	51.36 309	84.43 187	96.36 150

RPMNet		79.32 245	75.75 256	90.06 165	90.16 229	79.75 158	79.02 315	93.92 223	58.43 320	82.27 167	72.55 316	73.03 152	93.67 282	46.10 320	86.25 171	96.75 142

FPMVS		55.09 297	52.93 299	61.57 312	55.98 328	40.51 331	83.11 308	83.41 322	37.61 325	34.95 326	71.95 317	14.40 331	76.95 326	29.81 326	65.16 297	67.25 325

new_pmnet		66.18 293	63.18 295	75.18 307	76.27 319	61.74 311	83.79 307	84.66 316	56.64 321	51.57 318	71.85 318	31.29 324	87.93 317	49.98 313	62.55 304	75.86 321

UnsupCasMVSNet_bld		68.60 292	64.50 294	80.92 289	74.63 321	67.80 293	83.97 306	92.94 259	65.12 301	54.63 316	68.23 319	35.97 314	92.17 299	60.13 284	44.83 323	82.78 309

PMMVS2		50.90 299	46.31 301	64.67 309	55.53 329	46.67 327	77.30 320	71.02 330	40.89 324	34.16 327	59.32 320	9.83 336	76.14 329	40.09 324	28.63 327	71.21 322

JIA-IIPM		79.00 248	77.20 245	84.40 261	89.74 236	64.06 304	75.30 321	95.44 153	62.15 306	81.90 171	59.08 321	78.92 72	95.59 237	66.51 262	85.78 179	93.54 198

LCM-MVSNet		52.52 298	48.24 300	65.35 308	47.63 333	41.45 330	72.55 325	83.62 321	31.75 326	37.66 325	57.92 322	9.19 337	76.76 327	49.26 315	44.60 324	77.84 320

test_normal		64.21 295	59.18 298	79.27 296	69.09 324	57.72 318	33.97 332	92.62 264	76.83 249	38.24 324	55.06 323	26.05 327	94.15 273	71.97 231	68.81 274	85.95 295

gg-mvs-nofinetune		85.48 173	82.90 190	93.24 67	94.51 139	85.82 34	79.22 313	96.97 31	61.19 311	87.33 115	53.01 324	90.58 3	96.07 207	86.07 118	97.23 68	97.81 91

MVS-HIRNet		71.36 287	67.00 289	84.46 260	90.58 222	69.74 286	79.15 314	87.74 306	46.09 323	61.96 301	50.50 325	45.14 295	95.64 233	53.74 304	88.11 158	88.00 264

PMVS	34.80 23	39.19 303	35.53 305	50.18 315	29.72 336	30.30 333	59.60 329	66.20 333	26.06 328	17.91 332	49.53 326	3.12 338	74.09 330	18.19 330	49.40 318	46.14 326
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
ANet_high		46.22 300	41.28 304	61.04 313	39.91 335	46.25 328	70.59 326	76.18 329	58.87 319	23.09 330	48.00 327	12.58 333	66.54 331	28.65 327	13.62 330	70.35 323

MVE	35.65 22	33.85 304	29.49 308	46.92 316	41.86 334	36.28 332	50.45 330	56.52 335	18.75 331	18.28 331	37.84 328	2.41 339	58.41 332	18.71 329	20.62 328	46.06 327
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
Gipuma		45.11 301	42.05 302	54.30 314	80.69 305	51.30 325	35.80 331	83.81 320	28.13 327	27.94 329	34.53 329	11.41 335	76.70 328	21.45 328	54.65 312	34.90 329
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
test_post											33.80 330	76.17 114	95.97 211

E-PMN		32.70 305	32.39 306	33.65 318	53.35 331	25.70 335	74.07 323	53.33 336	21.08 329	17.17 333	33.63 331	11.85 334	54.84 333	12.98 331	14.04 329	20.42 330

EMVS		31.70 306	31.45 307	32.48 319	50.72 332	23.95 336	74.78 322	52.30 337	20.36 330	16.08 334	31.48 332	12.80 332	53.60 334	11.39 332	13.10 332	19.88 331

test_post1							85.88 302				30.24 333	73.77 145	95.07 256	73.89 220

X-MVStestdata		86.26 162	84.14 174	92.63 96	98.52 32	80.29 146	97.37 84	96.44 89	87.04 79	91.38 64	20.73 334	77.24 97	99.59 43	90.46 76	98.07 48	98.02 72

testmvs		9.92 309	12.94 311	0.84 322	0.65 338	0.29 340	93.78 226	0.39 340	0.42 334	2.85 336	15.84 335	0.17 342	0.30 338	2.18 334	0.21 334	1.91 333

test123		9.07 310	11.73 312	1.11 321	0.50 339	0.77 339	89.44 279	0.20 341	0.34 335	2.15 337	10.72 336	0.34 341	0.32 337	1.79 335	0.08 335	2.23 332

wuyk23d		14.10 308	13.89 310	14.72 320	55.23 330	22.91 337	33.83 333	3.56 339	4.94 333	4.11 335	2.28 337	2.06 340	19.66 336	10.23 333	8.74 333	1.59 334

pcd_1.5k_mvsjas		5.92 312	7.89 314	0.00 323	0.00 340	0.00 341	0.00 334	0.00 342	0.00 336	0.00 338	0.00 338	71.04 169	0.00 339	0.00 336	0.00 336	0.00 335

sosnet-low-res		0.00 313	0.00 315	0.00 323	0.00 340	0.00 341	0.00 334	0.00 342	0.00 336	0.00 338	0.00 338	0.00 343	0.00 339	0.00 336	0.00 336	0.00 335

sosnet		0.00 313	0.00 315	0.00 323	0.00 340	0.00 341	0.00 334	0.00 342	0.00 336	0.00 338	0.00 338	0.00 343	0.00 339	0.00 336	0.00 336	0.00 335

uncertanet		0.00 313	0.00 315	0.00 323	0.00 340	0.00 341	0.00 334	0.00 342	0.00 336	0.00 338	0.00 338	0.00 343	0.00 339	0.00 336	0.00 336	0.00 335

Regformer		0.00 313	0.00 315	0.00 323	0.00 340	0.00 341	0.00 334	0.00 342	0.00 336	0.00 338	0.00 338	0.00 343	0.00 339	0.00 336	0.00 336	0.00 335

uanet		0.00 313	0.00 315	0.00 323	0.00 340	0.00 341	0.00 334	0.00 342	0.00 336	0.00 338	0.00 338	0.00 343	0.00 339	0.00 336	0.00 336	0.00 335

save fliter					98.24 46	83.34 79	98.61 20	96.57 74	91.32 17

test_0728_SECOND				95.14 13	99.04 10	86.14 29	99.06 7	96.77 45					99.84 12	97.90 1	98.85 16	99.45 5

GSMVS																97.54 106

test_part2					98.90 11	85.14 51				96.07 14

test_part1				0.00 323		0.00 341	0.00 334	96.77 45				0.00 343	0.00 339	0.00 336	0.00 336	0.00 335

sam_mvs1												77.59 89				97.54 106

sam_mvs												75.35 132

MTGPA								96.33 103

MTMP							97.53 69	68.16 331

test9_res														96.00 16	99.03 10	98.31 50

agg_prior2														94.30 33	99.00 12	98.57 36

agg_prior					98.59 29	83.13 83		96.56 76		94.19 34			99.16 80

test_prior4						82.34 98	97.75 56

test_prior				93.09 73	98.68 19	81.91 106		96.40 94					99.06 87			98.29 52

旧先验2							96.97 117		74.06 265	96.10 13			97.76 138	88.38 102

新几何2							96.42 150

无先验							96.87 122	96.78 42	77.39 241				99.52 49	79.95 166		98.43 44

原ACMM2							96.84 123

testdata2													99.48 54	76.45 197

segment_acmp												82.69 44

testdata1							95.57 186		87.44 70

test12				94.25 29	98.34 41	85.55 39		96.35 101		92.36 53		80.84 51	99.22 69		98.31 42	97.98 79

plane_prior7					91.86 204	77.55 214

plane_prior6					91.98 199	77.92 207						64.77 204

plane_prior5								94.69 186					97.30 163	87.08 112	82.82 201	90.96 210

plane_prior3						77.75 211			90.17 29	81.33 175

plane_prior2							97.18 93		89.89 31

plane_prior1					91.95 202

plane_prior						77.96 204	97.52 71		90.36 28						82.96 199

n2								0.00 342

nn								0.00 342

door-mid								79.75 327

test11								96.50 84

door								80.13 326

HQP5-MVS						78.48 186

HQP-NCC					92.08 194		97.63 61		90.52 25	82.30 163

ACMP_Plane					92.08 194		97.63 61		90.52 25	82.30 163

BP-MVS														87.67 108

HQP4-MVS										82.30 163			97.32 161			91.13 208

HQP3-MVS								94.80 182							83.01 197

HQP2-MVS												65.40 199

MDTV_nov1_ep13_2view						81.74 114	86.80 295		80.65 193	85.65 127		74.26 142		76.52 196		96.98 130

ACMMP++_ref															78.45 229

ACMMP++															79.05 221

Test By Simon												71.65 162

High-resolution multi-view benchmark