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.

LCM-MVSNet		99.93 1	99.92 1	99.94 1	99.99 1	99.97 1	99.90 1	99.89 13	99.98 1	99.99 1	99.96 1	99.77 2	100.00 1	99.81 16	100.00 1	99.85 30

UniMVSNet_ETH3D		99.69 2	99.69 4	99.69 3	99.84 17	99.34 19	99.69 5	99.58 93	99.90 3	99.86 24	99.78 13	99.58 6	99.95 25	99.00 87	99.95 38	99.78 47

pmmvs6		99.67 3	99.70 3	99.60 16	99.90 4	99.27 27	99.53 9	99.76 38	99.64 26	99.84 30	99.83 4	99.50 9	99.87 135	99.36 57	99.92 69	99.64 84

LTVRE_ROB	98.40 1	99.67 3	99.71 2	99.56 26	99.85 16	99.11 64	99.90 1	99.78 35	99.63 28	99.78 39	99.67 30	99.48 10	99.81 223	99.30 62	99.97 21	99.77 50
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
tt0320-xc		99.64 5	99.68 5	99.50 53	99.72 44	98.98 71	99.51 10	99.85 18	99.86 6	99.88 21	99.82 5	99.02 26	99.90 81	99.54 44	99.95 38	99.61 98

mvs_tets		99.63 6	99.67 6	99.49 54	99.88 9	98.61 102	99.34 23	99.71 46	99.27 73	99.90 14	99.74 18	99.68 4	99.97 6	99.55 43	99.99 5	99.88 20

sc_t1		99.62 7	99.66 8	99.53 38	99.82 19	99.09 68	99.50 11	99.63 72	99.88 4	99.86 24	99.80 11	99.03 24	99.89 97	99.48 52	99.93 56	99.60 100

tt0320		99.61 8	99.65 9	99.48 56	99.71 48	98.94 78	99.54 8	99.83 25	99.87 5	99.89 18	99.82 5	98.75 46	99.90 81	99.54 44	99.95 38	99.59 107

jajsoiax		99.58 9	99.61 11	99.48 56	99.87 12	98.61 102	99.28 40	99.66 64	99.09 108	99.89 18	99.68 25	99.53 7	99.97 6	99.50 50	99.99 5	99.87 22

test_fmvsmconf0.01_n		99.57 10	99.63 10	99.36 74	99.87 12	98.13 143	98.08 193	99.95 1	99.45 50	99.98 2	99.75 16	99.80 1	99.97 6	99.82 12	99.99 5	99.99 2

ANet_high		99.57 10	99.67 6	99.28 96	99.89 6	98.09 147	99.14 58	99.93 5	99.82 8	99.93 6	99.81 8	99.17 20	99.94 41	99.31 61	100.00 1	99.82 36

v7n		99.53 12	99.57 13	99.41 69	99.88 9	98.54 110	99.45 14	99.61 81	99.66 23	99.68 57	99.66 32	98.44 82	99.95 25	99.73 28	99.96 28	99.75 60

test_djsdf		99.52 13	99.51 15	99.53 38	99.86 14	98.74 91	99.39 20	99.56 109	99.11 98	99.70 51	99.73 20	99.00 27	99.97 6	99.26 65	99.98 12	99.89 16

anonymousdsp		99.51 14	99.47 21	99.62 10	99.88 9	99.08 69	99.34 23	99.69 53	98.93 129	99.65 63	99.72 21	98.93 32	99.95 25	99.11 77	100.00 1	99.82 36

test_fmvsmconf0.1_n		99.49 15	99.54 14	99.34 83	99.78 24	98.11 144	97.77 250	99.90 11	99.33 65	99.97 3	99.66 32	99.71 3	99.96 13	99.79 19	99.99 5	99.96 8

UA-Net		99.47 16	99.40 27	99.70 2	99.49 144	99.29 24	99.80 4	99.72 44	99.82 8	99.04 191	99.81 8	98.05 127	99.96 13	98.85 98	99.99 5	99.86 28

PS-MVSNAJss		99.46 17	99.49 16	99.35 80	99.90 4	98.15 140	99.20 49	99.65 68	99.48 44	99.92 8	99.71 22	98.07 124	99.96 13	99.53 48	100.00 1	99.93 11

fmvsm_l_conf0.5_n_3		99.45 18	99.48 18	99.34 83	99.59 91	98.21 137	97.82 241	99.84 22	99.41 57	99.92 8	99.41 94	99.51 8	99.95 25	99.84 9	99.97 21	99.87 22

test_fmvsmconf_n		99.44 19	99.48 18	99.31 94	99.64 76	98.10 146	97.68 264	99.84 22	99.29 71	99.92 8	99.57 49	99.60 5	99.96 13	99.74 27	99.98 12	99.89 16

pm-mvs1		99.44 19	99.48 18	99.33 89	99.80 21	98.63 99	99.29 36	99.63 72	99.30 70	99.65 63	99.60 45	99.16 22	99.82 206	99.07 80	99.83 122	99.56 129

TransMVSNet (Re)		99.44 19	99.47 21	99.36 74	99.80 21	98.58 105	99.27 42	99.57 100	99.39 58	99.75 44	99.62 40	99.17 20	99.83 193	99.06 82	99.62 243	99.66 78

DTE-MVSNet		99.43 22	99.35 33	99.66 7	99.71 48	99.30 22	99.31 30	99.51 128	99.64 26	99.56 73	99.46 80	98.23 106	99.97 6	98.78 102	99.93 56	99.72 62

TDRefinement		99.42 23	99.38 28	99.55 28	99.76 30	99.33 20	99.68 6	99.71 46	99.38 59	99.53 82	99.61 43	98.64 60	99.80 232	98.24 143	99.84 111	99.52 159

PEN-MVS		99.41 24	99.34 35	99.62 10	99.73 37	99.14 57	99.29 36	99.54 118	99.62 32	99.56 73	99.42 89	98.16 118	99.96 13	98.78 102	99.93 56	99.77 50

nrg030		99.40 25	99.35 33	99.54 31	99.58 93	99.13 60	98.98 76	99.48 141	99.68 19	99.46 101	99.26 135	98.62 63	99.73 295	99.17 74	99.92 69	99.76 56

PS-CasMVS		99.40 25	99.33 37	99.62 10	99.71 48	99.10 65	99.29 36	99.53 122	99.53 41	99.46 101	99.41 94	98.23 106	99.95 25	98.89 96	99.95 38	99.81 40

MIMVSNet1		99.38 27	99.32 39	99.55 28	99.86 14	99.19 42	99.41 17	99.59 90	99.59 36	99.71 49	99.57 49	97.12 208	99.90 81	99.21 70	99.87 97	99.54 142

OurMVSNet-221017-0		99.37 28	99.31 41	99.53 38	99.91 3	98.98 71	99.63 7	99.58 93	99.44 52	99.78 39	99.76 15	96.39 253	99.92 65	99.44 54	99.92 69	99.68 71

Vis-MVSNet		99.34 29	99.36 32	99.27 99	99.73 37	98.26 129	99.17 54	99.78 35	99.11 98	99.27 144	99.48 75	98.82 37	99.95 25	98.94 91	99.93 56	99.59 107
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
test_fmvsm_n_1920		99.33 30	99.45 23	98.99 151	99.57 102	97.73 194	97.93 225	99.83 25	99.22 78	99.93 6	99.30 123	99.42 11	99.96 13	99.85 6	99.99 5	99.29 270

WR-MVS_H		99.33 30	99.22 54	99.65 8	99.71 48	99.24 30	99.32 26	99.55 113	99.46 49	99.50 93	99.34 113	97.30 196	99.93 53	98.90 94	99.93 56	99.77 50

fmvsm_l_conf0.5_n_9		99.32 32	99.43 24	98.98 155	99.59 91	97.18 240	97.44 305	99.83 25	99.56 39	99.91 12	99.34 113	99.36 13	99.93 53	99.83 10	99.98 12	99.85 30

mmtdpeth		99.30 33	99.42 25	98.92 168	99.58 93	96.89 262	99.48 13	99.92 7	99.92 2	98.26 310	99.80 11	98.33 93	99.91 74	99.56 41	99.95 38	99.97 4

mvs5depth		99.30 33	99.59 12	98.44 267	99.65 70	95.35 334	99.82 3	99.94 2	99.83 7	99.42 110	99.94 2	98.13 121	99.96 13	99.63 36	99.96 28	100.00 1

VPA-MVSNet		99.30 33	99.30 44	99.28 96	99.49 144	98.36 125	99.00 73	99.45 159	99.63 28	99.52 87	99.44 85	98.25 104	99.88 115	99.09 79	99.84 111	99.62 90

sd_testset		99.28 36	99.31 41	99.19 112	99.68 63	98.06 156	99.41 17	99.30 231	99.69 17	99.63 66	99.68 25	99.25 16	99.96 13	97.25 234	99.92 69	99.57 123

Anonymous20231211		99.27 37	99.27 47	99.26 101	99.29 204	98.18 138	99.49 12	99.51 128	99.70 15	99.80 37	99.68 25	96.84 225	99.83 193	99.21 70	99.91 78	99.77 50

FC-MVSNet-test		99.27 37	99.25 52	99.34 83	99.77 27	98.37 122	99.30 35	99.57 100	99.61 34	99.40 115	99.50 68	97.12 208	99.85 157	99.02 86	99.94 50	99.80 42

test_fmvsmvis_n_1920		99.26 39	99.49 16	98.54 253	99.66 69	96.97 254	98.00 211	99.85 18	99.24 75	99.92 8	99.50 68	99.39 12	99.95 25	99.89 3	99.98 12	98.71 388

lecture		99.25 40	99.12 70	99.62 10	99.64 76	99.40 11	98.89 88	99.51 128	99.19 87	99.37 120	99.25 140	98.36 87	99.88 115	98.23 145	99.67 222	99.59 107

testf1		99.25 40	99.16 62	99.51 48	99.89 6	99.63 3	98.71 106	99.69 53	98.90 133	99.43 106	99.35 109	98.86 34	99.67 335	97.81 182	99.81 133	99.24 285

APD_test2		99.25 40	99.16 62	99.51 48	99.89 6	99.63 3	98.71 106	99.69 53	98.90 133	99.43 106	99.35 109	98.86 34	99.67 335	97.81 182	99.81 133	99.24 285

KD-MVS_self_test		99.25 40	99.18 59	99.44 65	99.63 82	99.06 70	98.69 108	99.54 118	99.31 68	99.62 69	99.53 64	97.36 193	99.86 144	99.24 69	99.71 201	99.39 226

ACMH	96.65 7	99.25 40	99.24 53	99.26 101	99.72 44	98.38 120	99.07 65	99.55 113	98.30 188	99.65 63	99.45 84	99.22 17	99.76 269	98.44 129	99.77 161	99.64 84
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
SDMVSNet		99.23 45	99.32 39	98.96 159	99.68 63	97.35 217	98.84 95	99.48 141	99.69 17	99.63 66	99.68 25	99.03 24	99.96 13	97.97 170	99.92 69	99.57 123

fmvsm_s_conf0.5_n_3		99.22 46	99.37 31	98.78 196	99.46 158	96.58 278	97.65 270	99.72 44	99.47 47	99.86 24	99.50 68	98.94 30	99.89 97	99.75 26	99.97 21	99.86 28

fmvsm_s_conf0.5_n_11		99.21 47	99.34 35	98.80 189	99.48 152	96.56 280	97.97 223	99.69 53	99.63 28	99.84 30	99.54 62	98.21 111	99.94 41	99.76 23	99.95 38	99.88 20

fmvsm_l_conf0.5_n		99.21 47	99.28 46	99.02 148	99.64 76	97.28 228	97.82 241	99.76 38	98.73 146	99.82 34	99.09 186	98.81 38	99.95 25	99.86 4	99.96 28	99.83 33

CP-MVSNet		99.21 47	99.09 79	99.56 26	99.65 70	98.96 77	99.13 59	99.34 210	99.42 55	99.33 130	99.26 135	97.01 216	99.94 41	98.74 107	99.93 56	99.79 44

fmvsm_s_conf0.1_n_2		99.20 50	99.38 28	98.65 222	99.69 60	96.08 301	97.49 296	99.90 11	99.53 41	99.88 21	99.64 37	98.51 75	99.90 81	99.83 10	99.98 12	99.97 4

fmvsm_l_conf0.5_n_a		99.19 51	99.27 47	98.94 162	99.65 70	97.05 249	97.80 245	99.76 38	98.70 153	99.78 39	99.11 178	98.79 42	99.95 25	99.85 6	99.96 28	99.83 33

fmvsm_s_conf0.5_n_9		99.17 52	99.38 28	98.53 255	99.51 130	95.82 311	97.62 275	99.78 35	99.72 14	99.90 14	99.48 75	98.66 58	99.89 97	99.85 6	99.93 56	99.89 16

fmvsm_s_conf0.1_n_a		99.17 52	99.30 44	98.80 189	99.75 34	96.59 275	97.97 223	99.86 16	98.22 196	99.88 21	99.71 22	98.59 66	99.84 175	99.73 28	99.98 12	99.98 3

TranMVSNet+NR-MVSNet		99.17 52	99.07 82	99.46 62	99.37 186	98.87 84	98.39 157	99.42 179	99.42 55	99.36 123	99.06 189	98.38 86	99.95 25	98.34 139	99.90 86	99.57 123

FMVSNet1		99.17 52	99.17 60	99.17 115	99.55 116	98.24 131	99.20 49	99.44 167	99.21 80	99.43 106	99.55 56	97.82 151	99.86 144	98.42 135	99.89 92	99.41 216

fmvsm_s_conf0.1_n		99.16 56	99.33 37	98.64 224	99.71 48	96.10 296	97.87 236	99.85 18	98.56 171	99.90 14	99.68 25	98.69 56	99.85 157	99.72 30	99.98 12	99.97 4

FE-MVSNET2		99.15 57	99.22 54	98.94 162	99.70 56	97.49 206	98.62 118	99.67 63	98.85 142	99.34 127	99.54 62	98.47 76	99.81 223	98.93 92	99.91 78	99.51 163

fmvsm_s_conf0.5_n_10		99.15 57	99.27 47	98.78 196	99.47 155	96.56 280	97.75 256	99.71 46	99.60 35	99.74 46	99.44 85	97.96 135	99.95 25	99.86 4	99.94 50	99.82 36

Elysia		99.15 57	99.14 68	99.18 113	99.63 82	97.92 170	98.50 137	99.43 173	99.67 20	99.70 51	99.13 173	96.66 241	99.98 4	99.54 44	99.96 28	99.64 84

StellarMVS		99.15 57	99.14 68	99.18 113	99.63 82	97.92 170	98.50 137	99.43 173	99.67 20	99.70 51	99.13 173	96.66 241	99.98 4	99.54 44	99.96 28	99.64 84

reproduce_model		99.15 57	98.97 95	99.67 4	99.33 196	99.44 9	98.15 181	99.47 150	99.12 97	99.52 87	99.32 121	98.31 94	99.90 81	97.78 185	99.73 184	99.66 78

fmvsm_s_conf0.5_n_2		99.14 62	99.31 41	98.63 228	99.49 144	96.08 301	97.38 310	99.81 31	99.48 44	99.84 30	99.57 49	98.46 80	99.89 97	99.82 12	99.97 21	99.91 13

test_vis3_rt		99.14 62	99.17 60	99.07 135	99.78 24	98.38 120	98.92 83	99.94 2	97.80 239	99.91 12	99.67 30	97.15 207	98.91 475	99.76 23	99.56 266	99.92 12

FIs		99.14 62	99.09 79	99.29 95	99.70 56	98.28 128	99.13 59	99.52 127	99.48 44	99.24 158	99.41 94	96.79 232	99.82 206	98.69 112	99.88 93	99.76 56

XXY-MVS		99.14 62	99.15 67	99.10 128	99.76 30	97.74 192	98.85 93	99.62 78	98.48 175	99.37 120	99.49 74	98.75 46	99.86 144	98.20 148	99.80 144	99.71 63

fmvsm_s_conf0.5_n_8		99.13 66	99.26 50	98.74 209	99.51 130	96.44 287	97.65 270	99.65 68	99.66 23	99.78 39	99.48 75	97.92 138	99.93 53	99.72 30	99.95 38	99.87 22

CS-MVS		99.13 66	99.10 77	99.24 106	99.06 271	99.15 52	99.36 22	99.88 14	99.36 63	98.21 312	98.46 335	98.68 57	99.93 53	99.03 85	99.85 106	98.64 397

SPE-MVS-test		99.13 66	99.09 79	99.26 101	99.13 255	98.97 73	99.31 30	99.88 14	99.44 52	98.16 316	98.51 326	98.64 60	99.93 53	98.91 93	99.85 106	98.88 364

test_fmvs3		99.12 69	99.41 26	98.25 289	99.76 30	95.07 347	99.05 68	99.94 2	97.78 242	99.82 34	99.84 3	98.56 72	99.71 306	99.96 1	99.96 28	99.97 4

casdiffmvs_mvg		99.12 69	99.16 62	98.99 151	99.43 170	97.73 194	98.00 211	99.62 78	99.22 78	99.55 76	99.22 148	98.93 32	99.75 281	98.66 113	99.81 133	99.50 167
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
fmvsm_s_conf0.5_n_a		99.10 71	99.20 58	98.78 196	99.55 116	96.59 275	97.79 246	99.82 30	98.21 198	99.81 36	99.53 64	98.46 80	99.84 175	99.70 33	99.97 21	99.90 15

reproduce-ours		99.09 72	98.90 101	99.67 4	99.27 210	99.49 5	98.00 211	99.42 179	99.05 115	99.48 96	99.27 129	98.29 96	99.89 97	97.61 202	99.71 201	99.62 90

our_new_method		99.09 72	98.90 101	99.67 4	99.27 210	99.49 5	98.00 211	99.42 179	99.05 115	99.48 96	99.27 129	98.29 96	99.89 97	97.61 202	99.71 201	99.62 90

fmvsm_s_conf0.5_n		99.09 72	99.26 50	98.61 234	99.55 116	96.09 299	97.74 257	99.81 31	98.55 172	99.85 27	99.55 56	98.60 65	99.84 175	99.69 35	99.98 12	99.89 16

EC-MVSNet		99.09 72	99.05 83	99.20 110	99.28 207	98.93 79	99.24 44	99.84 22	99.08 112	98.12 321	98.37 344	98.72 49	99.90 81	99.05 83	99.77 161	98.77 382

fmvsm_s_conf0.5_n_6		99.08 76	99.21 57	98.69 217	99.36 187	96.51 282	97.62 275	99.68 59	98.43 177	99.85 27	99.10 181	99.12 23	99.88 115	99.77 22	99.92 69	99.67 76

ACMH+	96.62 9	99.08 76	99.00 91	99.33 89	99.71 48	98.83 86	98.60 121	99.58 93	99.11 98	99.53 82	99.18 158	98.81 38	99.67 335	96.71 285	99.77 161	99.50 167

fmvsm_s_conf0.5_n_5		99.07 78	99.10 77	98.99 151	99.47 155	97.22 234	97.40 307	99.83 25	97.61 256	99.85 27	99.30 123	98.80 40	99.95 25	99.71 32	99.90 86	99.78 47

E5new		99.05 79	99.11 71	98.85 176	99.60 87	97.30 222	98.42 151	99.63 72	98.73 146	99.26 148	99.39 100	98.71 50	99.70 313	98.43 131	99.84 111	99.54 142

E6new		99.05 79	99.11 71	98.85 176	99.60 87	97.30 222	98.42 151	99.63 72	98.73 146	99.26 148	99.39 100	98.71 50	99.70 313	98.43 131	99.84 111	99.54 142

E6		99.05 79	99.11 71	98.85 176	99.60 87	97.30 222	98.42 151	99.63 72	98.73 146	99.26 148	99.39 100	98.71 50	99.70 313	98.43 131	99.84 111	99.54 142

E5		99.05 79	99.11 71	98.85 176	99.60 87	97.30 222	98.42 151	99.63 72	98.73 146	99.26 148	99.39 100	98.71 50	99.70 313	98.43 131	99.84 111	99.54 142

GeoE		99.05 79	98.99 93	99.25 104	99.44 165	98.35 126	98.73 103	99.56 109	98.42 178	98.91 222	98.81 268	98.94 30	99.91 74	98.35 138	99.73 184	99.49 174

KinetiMVS		99.03 84	99.02 87	99.03 145	99.70 56	97.48 209	98.43 148	99.29 239	99.70 15	99.60 70	99.07 188	96.13 266	99.94 41	99.42 55	99.87 97	99.68 71

Gipuma		99.03 84	99.16 62	98.64 224	99.94 2	98.51 112	99.32 26	99.75 41	99.58 38	98.60 274	99.62 40	98.22 109	99.51 413	97.70 195	99.73 184	97.89 446
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
fmvsm_s_conf0.5_n_4		99.01 86	99.22 54	98.38 274	99.31 198	95.48 325	97.56 286	99.73 43	98.87 137	99.75 44	99.27 129	98.80 40	99.86 144	99.80 17	99.90 86	99.81 40

v8		99.01 86	99.16 62	98.57 241	99.47 155	96.31 292	98.90 84	99.47 150	99.03 118	99.52 87	99.57 49	96.93 221	99.81 223	99.60 37	99.98 12	99.60 100

HPM-MVS_fast		99.01 86	98.82 116	99.57 21	99.71 48	99.35 16	99.00 73	99.50 131	97.33 289	98.94 218	98.86 252	98.75 46	99.82 206	97.53 211	99.71 201	99.56 129

usedtu_dtu_shiyan2		98.99 89	98.86 111	99.39 72	99.73 37	98.71 97	99.05 68	99.47 150	99.16 92	99.49 94	99.12 176	96.34 258	99.93 53	98.05 160	99.36 314	99.54 142

APDe-MVS		98.99 89	98.79 119	99.60 16	99.21 229	99.15 52	98.87 89	99.48 141	97.57 260	99.35 125	99.24 142	97.83 148	99.89 97	97.88 177	99.70 208	99.75 60
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
EG-PatchMatch MVS		98.99 89	99.01 89	98.94 162	99.50 136	97.47 210	98.04 202	99.59 90	98.15 213	99.40 115	99.36 108	98.58 71	99.76 269	98.78 102	99.68 216	99.59 107

COLMAP_ROB	96.50 10	98.99 89	98.85 114	99.41 69	99.58 93	99.10 65	98.74 99	99.56 109	99.09 108	99.33 130	99.19 154	98.40 84	99.72 305	95.98 340	99.76 176	99.42 213
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
Baseline_NR-MVSNet		98.98 93	98.86 111	99.36 74	99.82 19	98.55 107	97.47 301	99.57 100	99.37 60	99.21 164	99.61 43	96.76 235	99.83 193	98.06 158	99.83 122	99.71 63

v10		98.97 94	99.11 71	98.55 248	99.44 165	96.21 295	98.90 84	99.55 113	98.73 146	99.48 96	99.60 45	96.63 244	99.83 193	99.70 33	99.99 5	99.61 98

DeepC-MVS	97.60 4	98.97 94	98.93 98	99.10 128	99.35 192	97.98 163	98.01 210	99.46 155	97.56 262	99.54 78	99.50 68	98.97 28	99.84 175	98.06 158	99.92 69	99.49 174
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
baseline		98.96 96	99.02 87	98.76 203	99.38 180	97.26 230	98.49 140	99.50 131	98.86 139	99.19 166	99.06 189	98.23 106	99.69 321	98.71 110	99.76 176	99.33 257

TestfortrainingZip a		98.95 97	98.72 127	99.64 9	99.58 93	99.32 21	98.68 109	99.60 83	96.46 350	99.53 82	98.77 275	97.87 145	99.83 193	98.39 136	99.64 233	99.77 50

casdiffmvs		98.95 97	99.00 91	98.81 186	99.38 180	97.33 219	97.82 241	99.57 100	99.17 91	99.35 125	99.17 162	98.35 91	99.69 321	98.46 128	99.73 184	99.41 216
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
NR-MVSNet		98.95 97	98.82 116	99.36 74	99.16 248	98.72 96	99.22 46	99.20 264	99.10 105	99.72 47	98.76 281	96.38 255	99.86 144	98.00 166	99.82 127	99.50 167

Anonymous20240529		98.93 100	98.87 107	99.12 124	99.19 236	98.22 136	99.01 71	98.99 312	99.25 74	99.54 78	99.37 104	97.04 212	99.80 232	97.89 174	99.52 279	99.35 249

DP-MVS		98.93 100	98.81 118	99.28 96	99.21 229	98.45 116	98.46 145	99.33 216	99.63 28	99.48 96	99.15 168	97.23 202	99.75 281	97.17 238	99.66 230	99.63 89

SED-MVS		98.91 102	98.72 127	99.49 54	99.49 144	99.17 44	98.10 190	99.31 223	98.03 220	99.66 60	99.02 201	98.36 87	99.88 115	96.91 261	99.62 243	99.41 216

ACMM	96.08 12	98.91 102	98.73 125	99.48 56	99.55 116	99.14 57	98.07 197	99.37 194	97.62 253	99.04 191	98.96 228	98.84 36	99.79 245	97.43 222	99.65 231	99.49 174
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
MED-MVS		98.90 104	98.72 127	99.45 63	99.58 93	98.93 79	98.68 109	99.60 83	98.14 214	99.53 82	98.77 275	97.87 145	99.83 193	96.67 290	99.64 233	99.58 115

SSM_0404		98.90 104	99.01 89	98.57 241	99.42 172	96.59 275	98.13 183	99.66 64	99.09 108	99.30 139	99.02 201	98.79 42	99.89 97	97.87 179	99.80 144	99.23 287

DVP-MVS++		98.90 104	98.70 135	99.51 48	98.43 391	99.15 52	99.43 15	99.32 218	98.17 205	99.26 148	99.02 201	98.18 114	99.88 115	97.07 248	99.45 297	99.49 174

tfpnnormal		98.90 104	98.90 101	98.91 169	99.67 67	97.82 184	99.00 73	99.44 167	99.45 50	99.51 92	99.24 142	98.20 113	99.86 144	95.92 342	99.69 211	99.04 333

MTAPA		98.88 108	98.64 146	99.61 14	99.67 67	99.36 15	98.43 148	99.20 264	98.83 144	98.89 226	98.90 242	96.98 218	99.92 65	97.16 239	99.70 208	99.56 129

E4		98.87 109	98.88 104	98.81 186	99.52 127	97.23 231	97.62 275	99.61 81	98.58 166	99.18 170	99.33 116	98.29 96	99.69 321	97.99 168	99.83 122	99.52 159

mvsany_test3		98.87 109	98.92 99	98.74 209	99.38 180	96.94 258	98.58 123	99.10 289	96.49 347	99.96 4	99.81 8	98.18 114	99.45 431	98.97 89	99.79 150	99.83 33

VPNet		98.87 109	98.83 115	99.01 149	99.70 56	97.62 201	98.43 148	99.35 204	99.47 47	99.28 142	99.05 196	96.72 238	99.82 206	98.09 155	99.36 314	99.59 107

UniMVSNet (Re)		98.87 109	98.71 132	99.35 80	99.24 221	98.73 94	97.73 259	99.38 190	98.93 129	99.12 173	98.73 284	96.77 233	99.86 144	98.63 116	99.80 144	99.46 195

viewmacassd2359aftdt		98.86 113	98.87 107	98.83 182	99.53 124	97.32 221	97.70 262	99.64 70	98.22 196	99.25 156	99.27 129	98.40 84	99.61 372	97.98 169	99.87 97	99.55 136

SSM_0407		98.86 113	98.96 97	98.55 248	99.27 210	96.50 283	98.04 202	99.66 64	99.09 108	99.22 161	99.02 201	98.79 42	99.87 135	97.87 179	99.72 192	99.27 275

UniMVSNet_NR-MVSNet		98.86 113	98.68 138	99.40 71	99.17 246	98.74 91	97.68 264	99.40 186	99.14 96	99.06 181	98.59 317	96.71 239	99.93 53	98.57 120	99.77 161	99.53 156

viewdifsd2359ckpt11		98.84 116	99.04 84	98.24 291	99.56 110	95.51 321	97.38 310	99.70 51	99.16 92	99.57 71	99.40 97	98.26 102	99.71 306	98.55 124	99.82 127	99.50 167

viewmsd2359difaftdt		98.84 116	99.04 84	98.24 291	99.56 110	95.51 321	97.38 310	99.70 51	99.16 92	99.57 71	99.40 97	98.26 102	99.71 306	98.55 124	99.82 127	99.50 167

APD-MVS_3200maxsize		98.84 116	98.61 154	99.53 38	99.19 236	99.27 27	98.49 140	99.33 216	98.64 155	99.03 194	98.98 223	97.89 143	99.85 157	96.54 309	99.42 307	99.46 195

fmvsm_s_conf0.5_n_7		98.83 119	99.04 84	98.20 296	99.30 202	94.83 356	97.23 327	99.36 198	98.64 155	99.84 30	99.43 88	98.10 123	99.91 74	99.56 41	99.96 28	99.87 22

MVSMamba_PlusPlus		98.83 119	98.98 94	98.36 278	99.32 197	96.58 278	98.90 84	99.41 183	99.75 10	98.72 257	99.50 68	96.17 264	99.94 41	99.27 64	99.78 155	98.57 404

APD_test1		98.83 119	98.66 143	99.34 83	99.78 24	99.47 8	98.42 151	99.45 159	98.28 193	98.98 201	99.19 154	97.76 155	99.58 386	96.57 301	99.55 270	98.97 347

PM-MVS		98.82 122	98.72 127	99.12 124	99.64 76	98.54 110	97.98 219	99.68 59	97.62 253	99.34 127	99.18 158	97.54 176	99.77 263	97.79 184	99.74 181	99.04 333

DU-MVS		98.82 122	98.63 148	99.39 72	99.16 248	98.74 91	97.54 289	99.25 253	98.84 143	99.06 181	98.76 281	96.76 235	99.93 53	98.57 120	99.77 161	99.50 167

SR-MVS-dyc-post		98.81 124	98.55 161	99.57 21	99.20 233	99.38 12	98.48 143	99.30 231	98.64 155	98.95 211	98.96 228	97.49 185	99.86 144	96.56 305	99.39 310	99.45 200

3Dnovator	98.27 2	98.81 124	98.73 125	99.05 142	98.76 333	97.81 187	99.25 43	99.30 231	98.57 168	98.55 284	99.33 116	97.95 136	99.90 81	97.16 239	99.67 222	99.44 204

mamba_0408		98.80 126	98.88 104	98.55 248	99.27 210	96.50 283	98.00 211	99.60 83	98.93 129	99.22 161	98.84 260	98.59 66	99.89 97	97.74 191	99.72 192	99.27 275

SSM_04072		98.80 126	98.88 104	98.56 246	99.27 210	96.50 283	98.00 211	99.60 83	98.93 129	99.22 161	98.84 260	98.59 66	99.90 81	97.74 191	99.72 192	99.27 275

HPM-MVS		98.79 128	98.53 165	99.59 20	99.65 70	99.29 24	99.16 55	99.43 173	96.74 337	98.61 272	98.38 343	98.62 63	99.87 135	96.47 313	99.67 222	99.59 107
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
SteuartSystems-ACMMP		98.79 128	98.54 163	99.54 31	99.73 37	99.16 48	98.23 171	99.31 223	97.92 230	98.90 223	98.90 242	98.00 130	99.88 115	96.15 333	99.72 192	99.58 115
Skip Steuart: Steuart Systems R&D Blog.
dcpmvs_2		98.78 130	99.11 71	97.78 333	99.56 110	93.67 406	99.06 66	99.86 16	99.50 43	99.66 60	99.26 135	97.21 204	99.99 2	98.00 166	99.91 78	99.68 71

V42		98.78 130	98.78 121	98.76 203	99.44 165	97.04 250	98.27 168	99.19 268	97.87 234	99.25 156	99.16 164	96.84 225	99.78 257	99.21 70	99.84 111	99.46 195

test20.03		98.78 130	98.77 122	98.78 196	99.46 158	97.20 237	97.78 247	99.24 258	99.04 117	99.41 112	98.90 242	97.65 162	99.76 269	97.70 195	99.79 150	99.39 226

DVP-MVS		98.77 133	98.52 166	99.52 44	99.50 136	99.21 33	98.02 207	98.84 340	97.97 224	99.08 179	99.02 201	97.61 169	99.88 115	96.99 255	99.63 240	99.48 185
Zhenlong Yuan, Jinguo Luo, Fei Shen, Zhaoxin Li, Cong Liu, Tianlu Mao, Zhaoqi Wang: DVP-MVS: Synergize Depth-Edge and Visibility Prior for Multi-View Stereo. AAAI2025
test_0402		98.76 134	98.71 132	98.93 165	99.56 110	98.14 142	98.45 147	99.34 210	99.28 72	98.95 211	98.91 239	98.34 92	99.79 245	95.63 357	99.91 78	98.86 366

ACMMP_NAP		98.75 135	98.48 176	99.57 21	99.58 93	99.29 24	97.82 241	99.25 253	96.94 322	98.78 248	99.12 176	98.02 128	99.84 175	97.13 244	99.67 222	99.59 107

SixPastTwentyTwo		98.75 135	98.62 150	99.16 118	99.83 18	97.96 167	99.28 40	98.20 393	99.37 60	99.70 51	99.65 36	92.65 362	99.93 53	99.04 84	99.84 111	99.60 100

ACMMP		98.75 135	98.50 170	99.52 44	99.56 110	99.16 48	98.87 89	99.37 194	97.16 310	98.82 242	99.01 212	97.71 158	99.87 135	96.29 325	99.69 211	99.54 142
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
XVS		98.72 138	98.45 181	99.53 38	99.46 158	99.21 33	98.65 114	99.34 210	98.62 160	97.54 367	98.63 310	97.50 182	99.83 193	96.79 274	99.53 276	99.56 129

viewdifsd2359ckpt07		98.71 139	98.86 111	98.26 287	99.43 170	95.65 315	97.20 332	99.66 64	99.20 82	99.29 140	99.01 212	98.29 96	99.73 295	97.92 173	99.75 180	99.39 226

SSC-MVS		98.71 139	98.74 123	98.62 230	99.72 44	96.08 301	98.74 99	98.64 366	99.74 12	99.67 59	99.24 142	94.57 323	99.95 25	99.11 77	99.24 337	99.82 36

SR-MVS		98.71 139	98.43 184	99.57 21	99.18 244	99.35 16	98.36 160	99.29 239	98.29 191	98.88 230	98.85 255	97.53 178	99.87 135	96.14 334	99.31 325	99.48 185

HFP-MVS		98.71 139	98.44 183	99.51 48	99.49 144	99.16 48	98.52 130	99.31 223	97.47 272	98.58 278	98.50 330	97.97 134	99.85 157	96.57 301	99.59 254	99.53 156

LPG-MVS_test		98.71 139	98.46 180	99.47 60	99.57 102	98.97 73	98.23 171	99.48 141	96.60 342	99.10 177	99.06 189	98.71 50	99.83 193	95.58 360	99.78 155	99.62 90

E2		98.70 144	98.68 138	98.73 211	99.40 177	97.10 247	97.48 297	99.57 100	98.09 217	99.00 196	99.20 151	97.90 139	99.67 335	97.73 193	99.77 161	99.43 208

test_fmvs2		98.70 144	98.97 95	97.89 325	99.54 121	94.05 383	98.55 126	99.92 7	96.78 335	99.72 47	99.78 13	96.60 245	99.67 335	99.91 2	99.90 86	99.94 10

ACMMPR		98.70 144	98.42 186	99.54 31	99.52 127	99.14 57	98.52 130	99.31 223	97.47 272	98.56 282	98.54 321	97.75 156	99.88 115	96.57 301	99.59 254	99.58 115

CP-MVS		98.70 144	98.42 186	99.52 44	99.36 187	99.12 62	98.72 104	99.36 198	97.54 266	98.30 304	98.40 340	97.86 147	99.89 97	96.53 310	99.72 192	99.56 129

E3		98.69 148	98.68 138	98.73 211	99.40 177	97.10 247	97.48 297	99.57 100	98.09 217	99.00 196	99.20 151	97.90 139	99.67 335	97.73 193	99.77 161	99.43 208

tt0805		98.69 148	98.62 150	98.90 172	99.75 34	99.30 22	99.15 57	96.97 430	98.86 139	98.87 234	97.62 401	98.63 62	98.96 472	99.41 56	98.29 413	98.45 411

Anonymous20240521		98.69 148	98.87 107	98.16 301	99.77 27	95.11 346	99.08 62	99.44 167	99.34 64	99.33 130	99.55 56	94.10 337	99.94 41	99.25 67	99.96 28	99.42 213

region2R		98.69 148	98.40 188	99.54 31	99.53 124	99.17 44	98.52 130	99.31 223	97.46 277	98.44 295	98.51 326	97.83 148	99.88 115	96.46 314	99.58 259	99.58 115

EI-MVSNet-UG-set		98.69 148	98.71 132	98.62 230	99.10 259	96.37 289	97.23 327	98.87 331	99.20 82	99.19 166	98.99 218	97.30 196	99.85 157	98.77 105	99.79 150	99.65 83

3Dnovator+	97.89 3	98.69 148	98.51 167	99.24 106	98.81 328	98.40 118	99.02 70	99.19 268	98.99 121	98.07 326	99.28 127	97.11 210	99.84 175	96.84 272	99.32 323	99.47 193

ZNCC-MVS		98.68 154	98.40 188	99.54 31	99.57 102	99.21 33	98.46 145	99.29 239	97.28 295	98.11 322	98.39 341	98.00 130	99.87 135	96.86 271	99.64 233	99.55 136

EI-MVSNet-Vis-set		98.68 154	98.70 135	98.63 228	99.09 262	96.40 288	97.23 327	98.86 336	99.20 82	99.18 170	98.97 225	97.29 198	99.85 157	98.72 109	99.78 155	99.64 84

CSCG		98.68 154	98.50 170	99.20 110	99.45 163	98.63 99	98.56 125	99.57 100	97.87 234	98.85 236	98.04 373	97.66 161	99.84 175	96.72 283	99.81 133	99.13 322

test_f		98.67 157	98.87 107	98.05 314	99.72 44	95.59 316	98.51 135	99.81 31	96.30 359	99.78 39	99.82 5	96.14 265	98.63 482	99.82 12	99.93 56	99.95 9

PGM-MVS		98.66 158	98.37 195	99.55 28	99.53 124	99.18 43	98.23 171	99.49 139	97.01 319	98.69 259	98.88 249	98.00 130	99.89 97	95.87 346	99.59 254	99.58 115

GBi-Net		98.65 159	98.47 178	99.17 115	98.90 307	98.24 131	99.20 49	99.44 167	98.59 163	98.95 211	99.55 56	94.14 333	99.86 144	97.77 186	99.69 211	99.41 216

test1		98.65 159	98.47 178	99.17 115	98.90 307	98.24 131	99.20 49	99.44 167	98.59 163	98.95 211	99.55 56	94.14 333	99.86 144	97.77 186	99.69 211	99.41 216

LCM-MVSNet-Re		98.64 161	98.48 176	99.11 126	98.85 319	98.51 112	98.49 140	99.83 25	98.37 179	99.69 55	99.46 80	98.21 111	99.92 65	94.13 398	99.30 328	98.91 359

mPP-MVS		98.64 161	98.34 200	99.54 31	99.54 121	99.17 44	98.63 116	99.24 258	97.47 272	98.09 324	98.68 298	97.62 167	99.89 97	96.22 328	99.62 243	99.57 123

balanced_conf03		98.63 163	98.72 127	98.38 274	98.66 362	96.68 274	98.90 84	99.42 179	98.99 121	98.97 205	99.19 154	95.81 286	99.85 157	98.77 105	99.77 161	98.60 400

TSAR-MVS + MP.		98.63 163	98.49 175	99.06 141	99.64 76	97.90 173	98.51 135	98.94 316	96.96 320	99.24 158	98.89 248	97.83 148	99.81 223	96.88 268	99.49 292	99.48 185
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
LS3D		98.63 163	98.38 193	99.36 74	97.25 458	99.38 12	99.12 61	99.32 218	99.21 80	98.44 295	98.88 249	97.31 195	99.80 232	96.58 299	99.34 319	98.92 356

RPSCF		98.62 166	98.36 196	99.42 67	99.65 70	99.42 10	98.55 126	99.57 100	97.72 247	98.90 223	99.26 135	96.12 268	99.52 407	95.72 353	99.71 201	99.32 260

ME-MVS		98.61 167	98.33 205	99.44 65	99.24 221	98.93 79	97.45 303	99.06 294	98.14 214	99.06 181	98.77 275	96.97 219	99.82 206	96.67 290	99.64 233	99.58 115

GST-MVS		98.61 167	98.30 208	99.52 44	99.51 130	99.20 39	98.26 169	99.25 253	97.44 280	98.67 262	98.39 341	97.68 159	99.85 157	96.00 338	99.51 282	99.52 159

v1192		98.60 169	98.66 143	98.41 270	99.27 210	95.88 307	97.52 291	99.36 198	97.41 281	99.33 130	99.20 151	96.37 256	99.82 206	99.57 39	99.92 69	99.55 136

v1144		98.60 169	98.66 143	98.41 270	99.36 187	95.90 306	97.58 284	99.34 210	97.51 268	99.27 144	99.15 168	96.34 258	99.80 232	99.47 53	99.93 56	99.51 163

FE-MVSNET		98.59 171	98.50 170	98.87 173	99.58 93	97.30 222	98.08 193	99.74 42	96.94 322	98.97 205	99.10 181	96.94 220	99.74 288	97.33 228	99.86 104	99.55 136

DPE-MVS		98.59 171	98.26 215	99.57 21	99.27 210	99.15 52	97.01 342	99.39 188	97.67 249	99.44 105	98.99 218	97.53 178	99.89 97	95.40 364	99.68 216	99.66 78
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
viewmanbaseed2359cas		98.58 173	98.54 163	98.70 215	99.28 207	97.13 246	97.47 301	99.55 113	97.55 264	98.96 210	98.92 236	97.77 154	99.59 379	97.59 205	99.77 161	99.39 226

MP-MVS-pluss		98.57 174	98.23 220	99.60 16	99.69 60	99.35 16	97.16 337	99.38 190	94.87 410	98.97 205	98.99 218	98.01 129	99.88 115	97.29 231	99.70 208	99.58 115
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
OPM-MVS		98.56 175	98.32 206	99.25 104	99.41 175	98.73 94	97.13 339	99.18 272	97.10 313	98.75 254	98.92 236	98.18 114	99.65 355	96.68 289	99.56 266	99.37 237
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
VDD-MVS		98.56 175	98.39 191	99.07 135	99.13 255	98.07 153	98.59 122	97.01 428	99.59 36	99.11 174	99.27 129	94.82 315	99.79 245	98.34 139	99.63 240	99.34 251

v2v482		98.56 175	98.62 150	98.37 277	99.42 172	95.81 312	97.58 284	99.16 279	97.90 232	99.28 142	99.01 212	95.98 278	99.79 245	99.33 59	99.90 86	99.51 163

XVG-ACMP-BASELINE		98.56 175	98.34 200	99.22 109	99.54 121	98.59 104	97.71 260	99.46 155	97.25 298	98.98 201	98.99 218	97.54 176	99.84 175	95.88 343	99.74 181	99.23 287

viewcassd2359sk11		98.55 179	98.51 167	98.67 220	99.29 204	96.99 253	97.39 308	99.54 118	97.73 245	98.81 244	99.08 187	97.55 174	99.66 348	97.52 213	99.67 222	99.36 244

v1240		98.55 179	98.62 150	98.32 281	99.22 227	95.58 318	97.51 293	99.45 159	97.16 310	99.45 104	99.24 142	96.12 268	99.85 157	99.60 37	99.88 93	99.55 136

IterMVS-LS		98.55 179	98.70 135	98.09 307	99.48 152	94.73 361	97.22 331	99.39 188	98.97 124	99.38 118	99.31 122	96.00 273	99.93 53	98.58 118	99.97 21	99.60 100
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v144192		98.54 182	98.57 159	98.45 265	99.21 229	95.98 304	97.63 274	99.36 198	97.15 312	99.32 136	99.18 158	95.84 285	99.84 175	99.50 50	99.91 78	99.54 142

v1921920		98.54 182	98.60 155	98.38 274	99.20 233	95.76 314	97.56 286	99.36 198	97.23 304	99.38 118	99.17 162	96.02 271	99.84 175	99.57 39	99.90 86	99.54 142

SSC-MVS3.2		98.53 184	98.79 119	97.74 340	99.46 158	93.62 409	96.45 375	99.34 210	99.33 65	98.93 219	98.70 294	97.90 139	99.90 81	99.12 76	99.92 69	99.69 70

SF-MVS		98.53 184	98.27 214	99.32 91	99.31 198	98.75 90	98.19 175	99.41 183	96.77 336	98.83 239	98.90 242	97.80 152	99.82 206	95.68 356	99.52 279	99.38 235

XVG-OURS		98.53 184	98.34 200	99.11 126	99.50 136	98.82 88	95.97 404	99.50 131	97.30 293	99.05 189	98.98 223	99.35 14	99.32 450	95.72 353	99.68 216	99.18 307

UGNet		98.53 184	98.45 181	98.79 193	97.94 422	96.96 256	99.08 62	98.54 375	99.10 105	96.82 411	99.47 78	96.55 247	99.84 175	98.56 123	99.94 50	99.55 136
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
WB-MVS		98.52 188	98.55 161	98.43 268	99.65 70	95.59 316	98.52 130	98.77 351	99.65 25	99.52 87	99.00 216	94.34 329	99.93 53	98.65 114	98.83 385	99.76 56

patch_mono-2		98.51 189	98.63 148	98.17 299	99.38 180	94.78 358	97.36 315	99.69 53	98.16 208	98.49 291	99.29 126	97.06 211	99.97 6	98.29 142	99.91 78	99.76 56

diffmvs_AUTHOR		98.50 190	98.59 157	98.23 294	99.35 192	95.48 325	96.61 366	99.60 83	98.37 179	98.90 223	99.00 216	97.37 192	99.76 269	98.22 146	99.85 106	99.46 195

XVG-OURS-SEG-HR		98.49 191	98.28 211	99.14 122	99.49 144	98.83 86	96.54 369	99.48 141	97.32 291	99.11 174	98.61 314	99.33 15	99.30 453	96.23 327	98.38 409	99.28 273

FMVSNet2		98.49 191	98.40 188	98.75 205	98.90 307	97.14 245	98.61 120	99.13 285	98.59 163	99.19 166	99.28 127	94.14 333	99.82 206	97.97 170	99.80 144	99.29 270

pmmvs-eth3d		98.47 193	98.34 200	98.86 175	99.30 202	97.76 190	97.16 337	99.28 243	95.54 391	99.42 110	99.19 154	97.27 199	99.63 362	97.89 174	99.97 21	99.20 297

MP-MVS		98.46 194	98.09 237	99.54 31	99.57 102	99.22 32	98.50 137	99.19 268	97.61 256	97.58 363	98.66 303	97.40 190	99.88 115	94.72 379	99.60 250	99.54 142
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
v148		98.45 195	98.60 155	98.00 317	99.44 165	94.98 349	97.44 305	99.06 294	98.30 188	99.32 136	98.97 225	96.65 243	99.62 365	98.37 137	99.85 106	99.39 226

AllTest		98.44 196	98.20 222	99.16 118	99.50 136	98.55 107	98.25 170	99.58 93	96.80 333	98.88 230	99.06 189	97.65 162	99.57 388	94.45 386	99.61 248	99.37 237

VNet		98.42 197	98.30 208	98.79 193	98.79 332	97.29 227	98.23 171	98.66 363	99.31 68	98.85 236	98.80 269	94.80 318	99.78 257	98.13 152	99.13 356	99.31 264

E3new		98.41 198	98.34 200	98.62 230	99.19 236	96.90 261	97.32 318	99.50 131	97.40 283	98.63 267	98.92 236	97.21 204	99.65 355	97.34 226	99.52 279	99.31 264

ab-mvs		98.41 198	98.36 196	98.59 237	99.19 236	97.23 231	99.32 26	98.81 345	97.66 250	98.62 270	99.40 97	96.82 228	99.80 232	95.88 343	99.51 282	98.75 385

ACMP	95.32 15	98.41 198	98.09 237	99.36 74	99.51 130	98.79 89	97.68 264	99.38 190	95.76 385	98.81 244	98.82 265	98.36 87	99.82 206	94.75 376	99.77 161	99.48 185
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
test_vis1_n_1920		98.40 201	98.92 99	96.81 407	99.74 36	90.76 460	98.15 181	99.91 9	98.33 184	99.89 18	99.55 56	95.07 308	99.88 115	99.76 23	99.93 56	99.79 44

SMA-MVS		98.40 201	98.03 245	99.51 48	99.16 248	99.21 33	98.05 200	99.22 261	94.16 426	98.98 201	99.10 181	97.52 180	99.79 245	96.45 315	99.64 233	99.53 156
Yufeng Yin; Xiaoyan Liu; Zichao Zhang: SMA-MVS: Segmentation-Guided Multi-Scale Anchor Deformation Patch Multi-View Stereo. IEEE Transactions on Circuits and Systems for Video Technology
MSP-MVS		98.40 201	98.00 248	99.61 14	99.57 102	99.25 29	98.57 124	99.35 204	97.55 264	99.31 138	97.71 394	94.61 322	99.88 115	96.14 334	99.19 348	99.70 68
Zhenlong Yuan, Cong Liu, Fei Shen, Zhaoxin Li, Jingguo luo, Tianlu Mao and Zhaoqi Wang: MSP-MVS: Multi-granularity Segmentation Prior Guided Multi-View Stereo. AAAI2025
SD-MVS		98.40 201	98.68 138	97.54 367	98.96 295	97.99 160	97.88 233	99.36 198	98.20 202	99.63 66	99.04 198	98.76 45	95.33 497	96.56 305	99.74 181	99.31 264
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
EI-MVSNet		98.40 201	98.51 167	98.04 315	99.10 259	94.73 361	97.20 332	98.87 331	98.97 124	99.06 181	99.02 201	96.00 273	99.80 232	98.58 118	99.82 127	99.60 100

WR-MVS		98.40 201	98.19 226	99.03 145	99.00 288	97.65 198	96.85 352	98.94 316	98.57 168	98.89 226	98.50 330	95.60 291	99.85 157	97.54 210	99.85 106	99.59 107

viewdifsd2359ckpt13		98.39 207	98.29 210	98.70 215	99.26 219	97.19 238	97.51 293	99.48 141	96.94 322	98.58 278	98.82 265	97.47 187	99.55 395	97.21 236	99.33 321	99.34 251

IMVS_0407		98.39 207	98.64 146	97.66 350	99.03 278	94.03 386	98.10 190	99.45 159	98.16 208	99.06 181	98.71 287	98.27 100	99.71 306	97.50 214	99.45 297	99.22 292

LuminaMVS		98.39 207	98.20 222	98.98 155	99.50 136	97.49 206	97.78 247	97.69 408	98.75 145	99.49 94	99.25 140	92.30 366	99.94 41	99.14 75	99.88 93	99.50 167

new-patchmatchnet		98.35 210	98.74 123	97.18 386	99.24 221	92.23 434	96.42 379	99.48 141	98.30 188	99.69 55	99.53 64	97.44 188	99.82 206	98.84 99	99.77 161	99.49 174

IMVS_0403		98.34 211	98.56 160	97.66 350	99.03 278	94.03 386	97.98 219	99.45 159	98.16 208	98.89 226	98.71 287	97.90 139	99.74 288	97.50 214	99.45 297	99.22 292

MGCFI-Net		98.34 211	98.28 211	98.51 257	98.47 385	97.59 202	98.96 78	99.48 141	99.18 90	97.40 380	95.50 453	98.66 58	99.50 414	98.18 149	98.71 393	98.44 414

sasdasda		98.34 211	98.26 215	98.58 238	98.46 387	97.82 184	98.96 78	99.46 155	99.19 87	97.46 374	95.46 456	98.59 66	99.46 429	98.08 156	98.71 393	98.46 408

canonicalmvs		98.34 211	98.26 215	98.58 238	98.46 387	97.82 184	98.96 78	99.46 155	99.19 87	97.46 374	95.46 456	98.59 66	99.46 429	98.08 156	98.71 393	98.46 408

test_cas_vis1_n_1920		98.33 215	98.68 138	97.27 383	99.69 60	92.29 432	98.03 204	99.85 18	97.62 253	99.96 4	99.62 40	93.98 338	99.74 288	99.52 49	99.86 104	99.79 44

testgi		98.32 216	98.39 191	98.13 303	99.57 102	95.54 319	97.78 247	99.49 139	97.37 286	99.19 166	97.65 398	98.96 29	99.49 418	96.50 312	98.99 373	99.34 251

DeepPCF-MVS	96.93 5	98.32 216	98.01 247	99.23 108	98.39 396	98.97 73	95.03 445	99.18 272	96.88 327	99.33 130	98.78 273	98.16 118	99.28 457	96.74 280	99.62 243	99.44 204

test_vis1_n		98.31 218	98.50 170	97.73 343	99.76 30	94.17 378	98.68 109	99.91 9	96.31 357	99.79 38	99.57 49	92.85 358	99.42 436	99.79 19	99.84 111	99.60 100

MVS_111021_LR		98.30 219	98.12 235	98.83 182	99.16 248	98.03 158	96.09 400	99.30 231	97.58 259	98.10 323	98.24 355	98.25 104	99.34 447	96.69 288	99.65 231	99.12 323

EPP-MVSNet		98.30 219	98.04 244	99.07 135	99.56 110	97.83 179	99.29 36	98.07 399	99.03 118	98.59 276	99.13 173	92.16 368	99.90 81	96.87 269	99.68 216	99.49 174

DeepC-MVS_fast	96.85 6	98.30 219	98.15 232	98.75 205	98.61 367	97.23 231	97.76 253	99.09 291	97.31 292	98.75 254	98.66 303	97.56 173	99.64 359	96.10 337	99.55 270	99.39 226
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
PHI-MVS		98.29 222	97.95 254	99.34 83	98.44 390	99.16 48	98.12 187	99.38 190	96.01 373	98.06 327	98.43 338	97.80 152	99.67 335	95.69 355	99.58 259	99.20 297

balanced_ft_v1		98.28 223	98.35 199	98.10 306	98.08 416	96.23 294	99.23 45	99.26 251	98.34 182	97.46 374	99.42 89	95.38 300	99.88 115	98.60 117	99.34 319	98.17 431

Fast-Effi-MVS+-dtu		98.27 224	98.09 237	98.81 186	98.43 391	98.11 144	97.61 280	99.50 131	98.64 155	97.39 382	97.52 406	98.12 122	99.95 25	96.90 266	98.71 393	98.38 421

DELS-MVS		98.27 224	98.20 222	98.48 262	98.86 316	96.70 272	95.60 424	99.20 264	97.73 245	98.45 294	98.71 287	97.50 182	99.82 206	98.21 147	99.59 254	98.93 355
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
NormalMVS		98.26 226	97.97 253	99.15 121	99.64 76	97.83 179	98.28 165	99.43 173	99.24 75	98.80 246	98.85 255	89.76 393	99.94 41	98.04 161	99.67 222	99.68 71

Effi-MVS+-dtu		98.26 226	97.90 262	99.35 80	98.02 419	99.49 5	98.02 207	99.16 279	98.29 191	97.64 358	97.99 376	96.44 252	99.95 25	96.66 293	98.93 381	98.60 400

MVSFormer		98.26 226	98.43 184	97.77 334	98.88 313	93.89 399	99.39 20	99.56 109	99.11 98	98.16 316	98.13 363	93.81 341	99.97 6	99.26 65	99.57 263	99.43 208

MVS_111021_HR		98.25 229	98.08 240	98.75 205	99.09 262	97.46 211	95.97 404	99.27 246	97.60 258	97.99 334	98.25 354	98.15 120	99.38 442	96.87 269	99.57 263	99.42 213

TAMVS		98.24 230	98.05 243	98.80 189	99.07 266	97.18 240	97.88 233	98.81 345	96.66 341	99.17 172	99.21 149	94.81 317	99.77 263	96.96 259	99.88 93	99.44 204

MM		98.22 231	97.99 249	98.91 169	98.66 362	96.97 254	97.89 232	94.44 467	99.54 40	98.95 211	99.14 171	93.50 345	99.92 65	99.80 17	99.96 28	99.85 30

diffmvs		98.22 231	98.24 219	98.17 299	99.00 288	95.44 329	96.38 381	99.58 93	97.79 241	98.53 287	98.50 330	96.76 235	99.74 288	97.95 172	99.64 233	99.34 251
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
Anonymous20231206		98.21 233	98.21 221	98.20 296	99.51 130	95.43 330	98.13 183	99.32 218	96.16 366	98.93 219	98.82 265	96.00 273	99.83 193	97.32 230	99.73 184	99.36 244

VDDNet		98.21 233	97.95 254	99.01 149	99.58 93	97.74 192	99.01 71	97.29 421	99.67 20	98.97 205	99.50 68	90.45 388	99.80 232	97.88 177	99.20 345	99.48 185

icg_test_0407_2		98.20 235	98.38 193	97.65 352	99.03 278	94.03 386	95.78 418	99.45 159	98.16 208	99.06 181	98.71 287	98.27 100	99.68 331	97.50 214	99.45 297	99.22 292

viewmambaseed2359dif		98.19 236	98.26 215	97.99 318	99.02 285	95.03 348	96.59 368	99.53 122	96.21 361	99.00 196	98.99 218	97.62 167	99.61 372	97.62 201	99.72 192	99.33 257

IS-MVSNet		98.19 236	97.90 262	99.08 133	99.57 102	97.97 164	99.31 30	98.32 386	99.01 120	98.98 201	99.03 200	91.59 376	99.79 245	95.49 362	99.80 144	99.48 185

MVS_Test		98.18 238	98.36 196	97.67 348	98.48 384	94.73 361	98.18 176	99.02 306	97.69 248	98.04 330	99.11 178	97.22 203	99.56 391	98.57 120	98.90 383	98.71 388

TSAR-MVS + GP.		98.18 238	97.98 250	98.77 201	98.71 343	97.88 174	96.32 385	98.66 363	96.33 355	99.23 160	98.51 326	97.48 186	99.40 438	97.16 239	99.46 295	99.02 336

CNVR-MVS		98.17 240	97.87 264	99.07 135	98.67 357	98.24 131	97.01 342	98.93 319	97.25 298	97.62 359	98.34 348	97.27 199	99.57 388	96.42 316	99.33 321	99.39 226

PVSNet_Blended_VisFu		98.17 240	98.15 232	98.22 295	99.73 37	95.15 343	97.36 315	99.68 59	94.45 420	98.99 200	99.27 129	96.87 224	99.94 41	97.13 244	99.91 78	99.57 123

AstraMVS		98.16 242	98.07 242	98.41 270	99.51 130	95.86 308	98.00 211	95.14 462	98.97 124	99.43 106	99.24 142	93.25 346	99.84 175	99.21 70	99.87 97	99.54 142

viewdifsd2359ckpt09		98.13 243	97.92 259	98.77 201	99.18 244	97.35 217	97.29 322	99.53 122	95.81 383	98.09 324	98.47 334	96.34 258	99.66 348	97.02 251	99.51 282	99.29 270

HPM-MVS++		98.10 244	97.64 282	99.48 56	99.09 262	99.13 60	97.52 291	98.75 356	97.46 277	96.90 406	97.83 387	96.01 272	99.84 175	95.82 350	99.35 317	99.46 195

APD-MVS		98.10 244	97.67 277	99.42 67	99.11 257	98.93 79	97.76 253	99.28 243	94.97 407	98.72 257	98.77 275	97.04 212	99.85 157	93.79 408	99.54 272	99.49 174
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
test_fmvs1_n		98.09 246	98.28 211	97.52 369	99.68 63	93.47 411	98.63 116	99.93 5	95.41 398	99.68 57	99.64 37	91.88 374	99.48 422	99.82 12	99.87 97	99.62 90

MVP-Stereo		98.08 247	97.92 259	98.57 241	98.96 295	96.79 266	97.90 231	99.18 272	96.41 353	98.46 293	98.95 232	95.93 282	99.60 375	96.51 311	98.98 376	99.31 264
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
IMVS_0404		98.07 248	98.20 222	97.69 345	99.03 278	94.03 386	96.67 362	99.45 159	98.16 208	98.03 331	98.71 287	96.80 231	99.82 206	97.50 214	99.45 297	99.22 292

PMMVS2		98.07 248	98.08 240	98.04 315	99.41 175	94.59 367	94.59 460	99.40 186	97.50 269	98.82 242	98.83 262	96.83 227	99.84 175	97.50 214	99.81 133	99.71 63

SymmetryMVS		98.05 250	97.71 275	99.09 132	99.29 204	97.83 179	98.28 165	97.64 413	99.24 75	98.80 246	98.85 255	89.76 393	99.94 41	98.04 161	99.50 290	99.49 174

ETV-MVS		98.03 251	97.86 265	98.56 246	98.69 352	98.07 153	97.51 293	99.50 131	98.10 216	97.50 371	95.51 452	98.41 83	99.88 115	96.27 326	99.24 337	97.71 458

Effi-MVS+		98.02 252	97.82 267	98.62 230	98.53 381	97.19 238	97.33 317	99.68 59	97.30 293	96.68 418	97.46 410	98.56 72	99.80 232	96.63 295	98.20 416	98.86 366

MSLP-MVS++		98.02 252	98.14 234	97.64 355	98.58 374	95.19 342	97.48 297	99.23 260	97.47 272	97.90 340	98.62 312	97.04 212	98.81 478	97.55 208	99.41 308	98.94 354

guyue		98.01 254	97.93 258	98.26 287	99.45 163	95.48 325	98.08 193	96.24 445	98.89 135	99.34 127	99.14 171	91.32 380	99.82 206	99.07 80	99.83 122	99.48 185

EIA-MVS		98.00 255	97.74 271	98.80 189	98.72 339	98.09 147	98.05 200	99.60 83	97.39 284	96.63 420	95.55 451	97.68 159	99.80 232	96.73 282	99.27 332	98.52 406

MCST-MVS		98.00 255	97.63 283	99.10 128	99.24 221	98.17 139	96.89 351	98.73 359	95.66 386	97.92 338	97.70 396	97.17 206	99.66 348	96.18 332	99.23 340	99.47 193

K. test v3		98.00 255	97.66 280	99.03 145	99.79 23	97.56 203	99.19 53	92.47 479	99.62 32	99.52 87	99.66 32	89.61 395	99.96 13	99.25 67	99.81 133	99.56 129

HQP_MVS		97.99 258	97.67 277	98.93 165	99.19 236	97.65 198	97.77 250	99.27 246	98.20 202	97.79 350	97.98 377	94.90 311	99.70 313	94.42 388	99.51 282	99.45 200

VortexMVS		97.98 259	98.31 207	97.02 394	98.88 313	91.45 443	98.03 204	99.47 150	98.65 154	99.55 76	99.47 78	91.49 378	99.81 223	99.32 60	99.91 78	99.80 42

MDA-MVSNet-bldmvs		97.94 260	97.91 261	98.06 312	99.44 165	94.96 350	96.63 365	99.15 284	98.35 181	98.83 239	99.11 178	94.31 330	99.85 157	96.60 298	98.72 391	99.37 237

ttmdpeth		97.91 261	98.02 246	97.58 361	98.69 352	94.10 382	98.13 183	98.90 325	97.95 226	97.32 385	99.58 47	95.95 281	98.75 480	96.41 317	99.22 341	99.87 22

Anonymous202405211		97.90 262	97.50 290	99.08 133	98.90 307	98.25 130	98.53 129	96.16 446	98.87 137	99.11 174	98.86 252	90.40 389	99.78 257	97.36 225	99.31 325	99.19 303

LF4IMVS		97.90 262	97.69 276	98.52 256	99.17 246	97.66 197	97.19 336	99.47 150	96.31 357	97.85 346	98.20 359	96.71 239	99.52 407	94.62 380	99.72 192	98.38 421

UnsupCasMVSNet_eth		97.89 264	97.60 285	98.75 205	99.31 198	97.17 242	97.62 275	99.35 204	98.72 152	98.76 253	98.68 298	92.57 363	99.74 288	97.76 190	95.60 478	99.34 251

TinyColmap		97.89 264	97.98 250	97.60 359	98.86 316	94.35 372	96.21 391	99.44 167	97.45 279	99.06 181	98.88 249	97.99 133	99.28 457	94.38 392	99.58 259	99.18 307

RRT-MVS		97.88 266	97.98 250	97.61 358	98.15 411	93.77 403	98.97 77	99.64 70	99.16 92	98.69 259	99.42 89	91.60 375	99.89 97	97.63 200	98.52 407	99.16 317

OMC-MVS		97.88 266	97.49 291	99.04 144	98.89 312	98.63 99	96.94 346	99.25 253	95.02 405	98.53 287	98.51 326	97.27 199	99.47 425	93.50 416	99.51 282	99.01 338

CANet		97.87 268	97.76 269	98.19 298	97.75 430	95.51 321	96.76 357	99.05 298	97.74 244	96.93 400	98.21 358	95.59 292	99.89 97	97.86 181	99.93 56	99.19 303

xiu_mvs_v1_base_debu		97.86 269	98.17 228	96.92 400	98.98 292	93.91 396	96.45 375	99.17 276	97.85 236	98.41 298	97.14 422	98.47 76	99.92 65	98.02 163	99.05 362	96.92 471

xiu_mvs_v1_base		97.86 269	98.17 228	96.92 400	98.98 292	93.91 396	96.45 375	99.17 276	97.85 236	98.41 298	97.14 422	98.47 76	99.92 65	98.02 163	99.05 362	96.92 471

xiu_mvs_v1_base_debi		97.86 269	98.17 228	96.92 400	98.98 292	93.91 396	96.45 375	99.17 276	97.85 236	98.41 298	97.14 422	98.47 76	99.92 65	98.02 163	99.05 362	96.92 471

NCCC		97.86 269	97.47 294	99.05 142	98.61 367	98.07 153	96.98 344	98.90 325	97.63 252	97.04 396	97.93 382	95.99 277	99.66 348	95.31 365	98.82 387	99.43 208

PMVS	91.26 20	97.86 269	97.94 256	97.65 352	99.71 48	97.94 169	98.52 130	98.68 362	98.99 121	97.52 369	99.35 109	97.41 189	98.18 488	91.59 450	99.67 222	96.82 474
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
IterMVS-SCA-FT		97.85 274	98.18 227	96.87 403	99.27 210	91.16 453	95.53 426	99.25 253	99.10 105	99.41 112	99.35 109	93.10 351	99.96 13	98.65 114	99.94 50	99.49 174

D2MVS		97.84 275	97.84 266	97.83 329	99.14 253	94.74 360	96.94 346	98.88 329	95.84 380	98.89 226	98.96 228	94.40 327	99.69 321	97.55 208	99.95 38	99.05 329

CPTT-MVS		97.84 275	97.36 299	99.27 99	99.31 198	98.46 115	98.29 164	99.27 246	94.90 409	97.83 347	98.37 344	94.90 311	99.84 175	93.85 407	99.54 272	99.51 163

mvs_anonymous		97.83 277	98.16 231	96.87 403	98.18 409	91.89 436	97.31 320	98.90 325	97.37 286	98.83 239	99.46 80	96.28 261	99.79 245	98.90 94	98.16 420	98.95 350

h-mvs33		97.77 278	97.33 302	99.10 128	99.21 229	97.84 178	98.35 161	98.57 372	99.11 98	98.58 278	99.02 201	88.65 404	99.96 13	98.11 153	96.34 465	99.49 174

test_vis1_rt		97.75 279	97.72 274	97.83 329	98.81 328	96.35 290	97.30 321	99.69 53	94.61 414	97.87 343	98.05 372	96.26 262	98.32 485	98.74 107	98.18 417	98.82 369

IterMVS		97.73 280	98.11 236	96.57 413	99.24 221	90.28 463	95.52 428	99.21 262	98.86 139	99.33 130	99.33 116	93.11 350	99.94 41	98.49 127	99.94 50	99.48 185
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
test_fmvs1		97.72 281	97.94 256	97.07 393	98.66 362	92.39 429	97.68 264	99.81 31	95.20 403	99.54 78	99.44 85	91.56 377	99.41 437	99.78 21	99.77 161	99.40 225

MSDG		97.71 282	97.52 289	98.28 286	98.91 306	96.82 264	94.42 465	99.37 194	97.65 251	98.37 303	98.29 353	97.40 190	99.33 449	94.09 399	99.22 341	98.68 395

CDS-MVSNet		97.69 283	97.35 300	98.69 217	98.73 337	97.02 252	96.92 350	98.75 356	95.89 379	98.59 276	98.67 300	92.08 372	99.74 288	96.72 283	99.81 133	99.32 260
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
MS-PatchMatch		97.68 284	97.75 270	97.45 375	98.23 407	93.78 402	97.29 322	98.84 340	96.10 368	98.64 266	98.65 305	96.04 270	99.36 443	96.84 272	99.14 354	99.20 297

Fast-Effi-MVS+		97.67 285	97.38 297	98.57 241	98.71 343	97.43 214	97.23 327	99.45 159	94.82 411	96.13 436	96.51 431	98.52 74	99.91 74	96.19 330	98.83 385	98.37 423

EU-MVSNet		97.66 286	98.50 170	95.13 452	99.63 82	85.84 483	98.35 161	98.21 392	98.23 195	99.54 78	99.46 80	95.02 309	99.68 331	98.24 143	99.87 97	99.87 22

pmmvs5		97.64 287	97.49 291	98.08 310	99.14 253	95.12 345	96.70 361	99.05 298	93.77 433	98.62 270	98.83 262	93.23 347	99.75 281	98.33 141	99.76 176	99.36 244

N_pmnet		97.63 288	97.17 309	98.99 151	99.27 210	97.86 176	95.98 403	93.41 476	95.25 400	99.47 100	98.90 242	95.63 290	99.85 157	96.91 261	99.73 184	99.27 275

mvsany_test1		97.60 289	97.54 287	97.77 334	97.72 431	95.35 334	95.36 434	97.13 426	94.13 427	99.71 49	99.33 116	97.93 137	99.30 453	97.60 204	98.94 380	98.67 396

YYNet1		97.60 289	97.67 277	97.39 379	99.04 275	93.04 418	95.27 437	98.38 385	97.25 298	98.92 221	98.95 232	95.48 297	99.73 295	96.99 255	98.74 389	99.41 216

MDA-MVSNet_test_wron		97.60 289	97.66 280	97.41 378	99.04 275	93.09 414	95.27 437	98.42 382	97.26 297	98.88 230	98.95 232	95.43 298	99.73 295	97.02 251	98.72 391	99.41 216

pmmvs4		97.58 292	97.28 303	98.51 257	98.84 320	96.93 259	95.40 433	98.52 377	93.60 435	98.61 272	98.65 305	95.10 307	99.60 375	96.97 258	99.79 150	98.99 342

mvsmamba		97.57 293	97.26 304	98.51 257	98.69 352	96.73 271	98.74 99	97.25 422	97.03 318	97.88 342	99.23 147	90.95 383	99.87 135	96.61 297	99.00 371	98.91 359

PVSNet_BlendedMVS		97.55 294	97.53 288	97.60 359	98.92 303	93.77 403	96.64 364	99.43 173	94.49 416	97.62 359	99.18 158	96.82 228	99.67 335	94.73 377	99.93 56	99.36 244

GDP-MVS		97.50 295	97.11 316	98.67 220	99.02 285	96.85 263	98.16 180	99.71 46	98.32 186	98.52 289	98.54 321	83.39 446	99.95 25	98.79 101	99.56 266	99.19 303

ppachtmachnet_test		97.50 295	97.74 271	96.78 409	98.70 347	91.23 452	94.55 461	99.05 298	96.36 354	99.21 164	98.79 271	96.39 253	99.78 257	96.74 280	99.82 127	99.34 251

FMVSNet3		97.50 295	97.24 306	98.29 285	98.08 416	95.83 310	97.86 237	98.91 324	97.89 233	98.95 211	98.95 232	87.06 412	99.81 223	97.77 186	99.69 211	99.23 287

CHOSEN 1792x2688		97.49 298	97.14 313	98.54 253	99.68 63	96.09 299	96.50 373	99.62 78	91.58 458	98.84 238	98.97 225	92.36 364	99.88 115	96.76 278	99.95 38	99.67 76

CLD-MVS		97.49 298	97.16 310	98.48 262	99.07 266	97.03 251	94.71 452	99.21 262	94.46 418	98.06 327	97.16 420	97.57 172	99.48 422	94.46 385	99.78 155	98.95 350
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
hse-mvs2		97.46 300	97.07 317	98.64 224	98.73 337	97.33 219	97.45 303	97.64 413	99.11 98	98.58 278	97.98 377	88.65 404	99.79 245	98.11 153	97.39 447	98.81 374

Vis-MVSNet (Re-imp)		97.46 300	97.16 310	98.34 280	99.55 116	96.10 296	98.94 81	98.44 380	98.32 186	98.16 316	98.62 312	88.76 400	99.73 295	93.88 405	99.79 150	99.18 307

jason		97.45 302	97.35 300	97.76 337	99.24 221	93.93 395	95.86 413	98.42 382	94.24 424	98.50 290	98.13 363	94.82 315	99.91 74	97.22 235	99.73 184	99.43 208
jason: jason.
CL-MVSNet_self_test		97.44 303	97.22 307	98.08 310	98.57 376	95.78 313	94.30 468	98.79 348	96.58 344	98.60 274	98.19 360	94.74 321	99.64 359	96.41 317	98.84 384	98.82 369

MGCNet		97.44 303	97.01 321	98.72 213	96.42 482	96.74 270	97.20 332	91.97 486	98.46 176	98.30 304	98.79 271	92.74 360	99.91 74	99.30 62	99.94 50	99.52 159

DSMNet-mixed		97.42 305	97.60 285	96.87 403	99.15 252	91.46 442	98.54 128	99.12 286	92.87 446	97.58 363	99.63 39	96.21 263	99.90 81	95.74 352	99.54 272	99.27 275

USDC		97.41 306	97.40 295	97.44 376	98.94 297	93.67 406	95.17 441	99.53 122	94.03 430	98.97 205	99.10 181	95.29 301	99.34 447	95.84 349	99.73 184	99.30 268

BP-MVS1		97.40 307	96.97 322	98.71 214	99.07 266	96.81 265	98.34 163	97.18 423	98.58 166	98.17 313	98.61 314	84.01 442	99.94 41	98.97 89	99.78 155	99.37 237

our_test_3		97.39 308	97.73 273	96.34 419	98.70 347	89.78 467	94.61 459	98.97 315	96.50 346	99.04 191	98.85 255	95.98 278	99.84 175	97.26 233	99.67 222	99.41 216

usedtu_dtu_shiyan1		97.37 309	97.13 314	98.11 304	99.03 278	95.40 331	94.47 463	98.99 312	96.87 328	97.97 335	97.81 388	92.12 369	99.75 281	97.49 219	99.43 305	99.16 317

FE-MVSNET3		97.37 309	97.13 314	98.11 304	99.03 278	95.40 331	94.47 463	98.99 312	96.87 328	97.97 335	97.81 388	92.12 369	99.75 281	97.49 219	99.43 305	99.16 317

c3_l		97.36 311	97.37 298	97.31 380	98.09 415	93.25 413	95.01 446	99.16 279	97.05 315	98.77 251	98.72 286	92.88 356	99.64 359	96.93 260	99.76 176	99.05 329

alignmvs		97.35 312	96.88 329	98.78 196	98.54 379	98.09 147	97.71 260	97.69 408	99.20 82	97.59 362	95.90 445	88.12 409	99.55 395	98.18 149	98.96 378	98.70 391

Patchmtry		97.35 312	96.97 322	98.50 261	97.31 457	96.47 286	98.18 176	98.92 322	98.95 128	98.78 248	99.37 104	85.44 430	99.85 157	95.96 341	99.83 122	99.17 311

DP-MVS Recon		97.33 314	96.92 326	98.57 241	99.09 262	97.99 160	96.79 354	99.35 204	93.18 440	97.71 354	98.07 371	95.00 310	99.31 451	93.97 401	99.13 356	98.42 418

QAPM		97.31 315	96.81 336	98.82 184	98.80 331	97.49 206	99.06 66	99.19 268	90.22 470	97.69 356	99.16 164	96.91 222	99.90 81	90.89 463	99.41 308	99.07 327

UnsupCasMVSNet_bld		97.30 316	96.92 326	98.45 265	99.28 207	96.78 269	96.20 392	99.27 246	95.42 395	98.28 308	98.30 352	93.16 349	99.71 306	94.99 370	97.37 448	98.87 365

F-COLMAP		97.30 316	96.68 343	99.14 122	99.19 236	98.39 119	97.27 326	99.30 231	92.93 444	96.62 421	98.00 375	95.73 288	99.68 331	92.62 437	98.46 408	99.35 249

1112_ss		97.29 318	96.86 330	98.58 238	99.34 195	96.32 291	96.75 358	99.58 93	93.14 441	96.89 407	97.48 408	92.11 371	99.86 144	96.91 261	99.54 272	99.57 123

CANet_DTU		97.26 319	97.06 318	97.84 328	97.57 441	94.65 365	96.19 393	98.79 348	97.23 304	95.14 459	98.24 355	93.22 348	99.84 175	97.34 226	99.84 111	99.04 333

Patchmatch-RL test		97.26 319	97.02 320	97.99 318	99.52 127	95.53 320	96.13 398	99.71 46	97.47 272	99.27 144	99.16 164	84.30 440	99.62 365	97.89 174	99.77 161	98.81 374

CDPH-MVS		97.26 319	96.66 346	99.07 135	99.00 288	98.15 140	96.03 402	99.01 309	91.21 464	97.79 350	97.85 386	96.89 223	99.69 321	92.75 434	99.38 313	99.39 226

PatchMatch-RL		97.24 322	96.78 337	98.61 234	99.03 278	97.83 179	96.36 382	99.06 294	93.49 438	97.36 384	97.78 390	95.75 287	99.49 418	93.44 417	98.77 388	98.52 406

eth_miper_zixun_eth		97.23 323	97.25 305	97.17 388	98.00 420	92.77 422	94.71 452	99.18 272	97.27 296	98.56 282	98.74 283	91.89 373	99.69 321	97.06 250	99.81 133	99.05 329

sss		97.21 324	96.93 324	98.06 312	98.83 322	95.22 341	96.75 358	98.48 379	94.49 416	97.27 386	97.90 383	92.77 359	99.80 232	96.57 301	99.32 323	99.16 317

LFMVS		97.20 325	96.72 340	98.64 224	98.72 339	96.95 257	98.93 82	94.14 473	99.74 12	98.78 248	99.01 212	84.45 437	99.73 295	97.44 221	99.27 332	99.25 282

HyFIR lowres test		97.19 326	96.60 350	98.96 159	99.62 86	97.28 228	95.17 441	99.50 131	94.21 425	99.01 195	98.32 351	86.61 415	99.99 2	97.10 246	99.84 111	99.60 100

miper_lstm_enhance		97.18 327	97.16 310	97.25 385	98.16 410	92.85 420	95.15 443	99.31 223	97.25 298	98.74 256	98.78 273	90.07 390	99.78 257	97.19 237	99.80 144	99.11 324

CNLPA		97.17 328	96.71 341	98.55 248	98.56 377	98.05 157	96.33 384	98.93 319	96.91 326	97.06 394	97.39 413	94.38 328	99.45 431	91.66 447	99.18 350	98.14 433

xiu_mvs_v2_base		97.16 329	97.49 291	96.17 428	98.54 379	92.46 427	95.45 430	98.84 340	97.25 298	97.48 373	96.49 432	98.31 94	99.90 81	96.34 322	98.68 398	96.15 485

AdaColmap		97.14 330	96.71 341	98.46 264	98.34 398	97.80 188	96.95 345	98.93 319	95.58 390	96.92 401	97.66 397	95.87 284	99.53 403	90.97 460	99.14 354	98.04 438

train_agg		97.10 331	96.45 356	99.07 135	98.71 343	98.08 151	95.96 406	99.03 303	91.64 456	95.85 444	97.53 404	96.47 250	99.76 269	93.67 410	99.16 351	99.36 244

OpenMVS	96.65 7	97.09 332	96.68 343	98.32 281	98.32 399	97.16 243	98.86 92	99.37 194	89.48 475	96.29 434	99.15 168	96.56 246	99.90 81	92.90 428	99.20 345	97.89 446

PS-MVSNAJ		97.08 333	97.39 296	96.16 430	98.56 377	92.46 427	95.24 439	98.85 339	97.25 298	97.49 372	95.99 442	98.07 124	99.90 81	96.37 319	98.67 399	96.12 486

miper_ehance_all_eth		97.06 334	97.03 319	97.16 390	97.83 427	93.06 415	94.66 456	99.09 291	95.99 375	98.69 259	98.45 336	92.73 361	99.61 372	96.79 274	99.03 366	98.82 369

lupinMVS		97.06 334	96.86 330	97.65 352	98.88 313	93.89 399	95.48 429	97.97 401	93.53 436	98.16 316	97.58 402	93.81 341	99.91 74	96.77 277	99.57 263	99.17 311

API-MVS		97.04 336	96.91 328	97.42 377	97.88 425	98.23 135	98.18 176	98.50 378	97.57 260	97.39 382	96.75 427	96.77 233	99.15 466	90.16 467	99.02 369	94.88 491

cl____		97.02 337	96.83 333	97.58 361	97.82 428	94.04 385	94.66 456	99.16 279	97.04 316	98.63 267	98.71 287	88.68 403	99.69 321	97.00 253	99.81 133	99.00 341

DIV-MVS_self_test		97.02 337	96.84 332	97.58 361	97.82 428	94.03 386	94.66 456	99.16 279	97.04 316	98.63 267	98.71 287	88.69 401	99.69 321	97.00 253	99.81 133	99.01 338

RPMNet		97.02 337	96.93 324	97.30 381	97.71 434	94.22 374	98.11 188	99.30 231	99.37 60	96.91 403	99.34 113	86.72 414	99.87 135	97.53 211	97.36 450	97.81 451

HQP-MVS		97.00 340	96.49 355	98.55 248	98.67 357	96.79 266	96.29 387	99.04 301	96.05 369	95.55 450	96.84 425	93.84 339	99.54 401	92.82 431	99.26 335	99.32 260

FA-MVS(test-final)		96.99 341	96.82 334	97.50 371	98.70 347	94.78 358	99.34 23	96.99 429	95.07 404	98.48 292	99.33 116	88.41 407	99.65 355	96.13 336	98.92 382	98.07 437

new_pmnet		96.99 341	96.76 338	97.67 348	98.72 339	94.89 353	95.95 408	98.20 393	92.62 449	98.55 284	98.54 321	94.88 314	99.52 407	93.96 402	99.44 304	98.59 403

Test_1112_low_res		96.99 341	96.55 352	98.31 283	99.35 192	95.47 328	95.84 416	99.53 122	91.51 460	96.80 412	98.48 333	91.36 379	99.83 193	96.58 299	99.53 276	99.62 90

PVSNet_Blended		96.88 344	96.68 343	97.47 374	98.92 303	93.77 403	94.71 452	99.43 173	90.98 466	97.62 359	97.36 416	96.82 228	99.67 335	94.73 377	99.56 266	98.98 343

MVSTER		96.86 345	96.55 352	97.79 332	97.91 424	94.21 376	97.56 286	98.87 331	97.49 271	99.06 181	99.05 196	80.72 455	99.80 232	98.44 129	99.82 127	99.37 237

BH-untuned		96.83 346	96.75 339	97.08 391	98.74 336	93.33 412	96.71 360	98.26 389	96.72 338	98.44 295	97.37 415	95.20 303	99.47 425	91.89 443	97.43 445	98.44 414

BH-RMVSNet		96.83 346	96.58 351	97.58 361	98.47 385	94.05 383	96.67 362	97.36 417	96.70 340	97.87 343	97.98 377	95.14 306	99.44 433	90.47 466	98.58 405	99.25 282

PAPM_NR		96.82 348	96.32 359	98.30 284	99.07 266	96.69 273	97.48 297	98.76 353	95.81 383	96.61 422	96.47 434	94.12 336	99.17 464	90.82 464	97.78 434	99.06 328

MG-MVS		96.77 349	96.61 348	97.26 384	98.31 400	93.06 415	95.93 409	98.12 398	96.45 352	97.92 338	98.73 284	93.77 343	99.39 440	91.19 458	99.04 365	99.33 257

test_yl		96.69 350	96.29 360	97.90 323	98.28 402	95.24 339	97.29 322	97.36 417	98.21 198	98.17 313	97.86 384	86.27 417	99.55 395	94.87 374	98.32 410	98.89 361

DCV-MVSNet		96.69 350	96.29 360	97.90 323	98.28 402	95.24 339	97.29 322	97.36 417	98.21 198	98.17 313	97.86 384	86.27 417	99.55 395	94.87 374	98.32 410	98.89 361

WTY-MVS		96.67 352	96.27 362	97.87 327	98.81 328	94.61 366	96.77 356	97.92 403	94.94 408	97.12 389	97.74 393	91.11 382	99.82 206	93.89 404	98.15 421	99.18 307

PatchT		96.65 353	96.35 357	97.54 367	97.40 454	95.32 337	97.98 219	96.64 439	99.33 65	96.89 407	99.42 89	84.32 439	99.81 223	97.69 197	97.49 441	97.48 464

TAPA-MVS	96.21 11	96.63 354	95.95 365	98.65 222	98.93 299	98.09 147	96.93 348	99.28 243	83.58 490	98.13 320	97.78 390	96.13 266	99.40 438	93.52 414	99.29 330	98.45 411
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
MIMVSNet		96.62 355	96.25 363	97.71 344	99.04 275	94.66 364	99.16 55	96.92 434	97.23 304	97.87 343	99.10 181	86.11 421	99.65 355	91.65 448	99.21 344	98.82 369

Patchmatch-test		96.55 356	96.34 358	97.17 388	98.35 397	93.06 415	98.40 156	97.79 404	97.33 289	98.41 298	98.67 300	83.68 445	99.69 321	95.16 368	99.31 325	98.77 382

PMMVS		96.51 357	95.98 364	98.09 307	97.53 446	95.84 309	94.92 448	98.84 340	91.58 458	96.05 441	95.58 450	95.68 289	99.66 348	95.59 359	98.09 424	98.76 384

PLC	94.65 16	96.51 357	95.73 370	98.85 176	98.75 335	97.91 172	96.42 379	99.06 294	90.94 467	95.59 447	97.38 414	94.41 326	99.59 379	90.93 461	98.04 430	99.05 329
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
114514_t		96.50 359	95.77 368	98.69 217	99.48 152	97.43 214	97.84 240	99.55 113	81.42 493	96.51 428	98.58 318	95.53 293	99.67 335	93.41 418	99.58 259	98.98 343

test1111		96.49 360	96.82 334	95.52 444	99.42 172	87.08 480	99.22 46	87.14 496	99.11 98	99.46 101	99.58 47	88.69 401	99.86 144	98.80 100	99.95 38	99.62 90

MAR-MVS		96.47 361	95.70 371	98.79 193	97.92 423	99.12 62	98.28 165	98.60 368	92.16 454	95.54 453	96.17 439	94.77 320	99.52 407	89.62 469	98.23 414	97.72 457
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
ECVR-MVS		96.42 362	96.61 348	95.85 435	99.38 180	88.18 475	99.22 46	86.00 498	99.08 112	99.36 123	99.57 49	88.47 406	99.82 206	98.52 126	99.95 38	99.54 142

SCA		96.41 363	96.66 346	95.67 439	98.24 405	88.35 473	95.85 415	96.88 435	96.11 367	97.67 357	98.67 300	93.10 351	99.85 157	94.16 394	99.22 341	98.81 374

DPM-MVS		96.32 364	95.59 378	98.51 257	98.76 333	97.21 236	94.54 462	98.26 389	91.94 455	96.37 432	97.25 418	93.06 353	99.43 434	91.42 453	98.74 389	98.89 361

CMPMVS	75.91 23	96.29 365	95.44 384	98.84 181	96.25 485	98.69 98	97.02 341	99.12 286	88.90 479	97.83 347	98.86 252	89.51 396	98.90 476	91.92 442	99.51 282	98.92 356
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
SD_0403		96.28 366	95.83 367	97.64 355	98.72 339	94.30 373	98.87 89	98.77 351	97.80 239	96.53 425	98.02 374	97.34 194	99.47 425	76.93 494	99.48 293	99.16 317

CR-MVSNet		96.28 366	95.95 365	97.28 382	97.71 434	94.22 374	98.11 188	98.92 322	92.31 452	96.91 403	99.37 104	85.44 430	99.81 223	97.39 224	97.36 450	97.81 451

MonoMVSNet		96.25 368	96.53 354	95.39 448	96.57 475	91.01 454	98.82 97	97.68 410	98.57 168	98.03 331	99.37 104	90.92 384	97.78 490	94.99 370	93.88 486	97.38 467

CVMVSNet		96.25 368	97.21 308	93.38 473	99.10 259	80.56 501	97.20 332	98.19 395	96.94 322	99.00 196	99.02 201	89.50 397	99.80 232	96.36 321	99.59 254	99.78 47

AUN-MVS		96.24 370	95.45 383	98.60 236	98.70 347	97.22 234	97.38 310	97.65 411	95.95 377	95.53 454	97.96 381	82.11 454	99.79 245	96.31 323	97.44 444	98.80 379

usedtu_blend_shiyan5		96.20 371	95.62 374	97.94 321	96.53 476	94.93 351	98.83 96	99.59 90	98.89 135	96.71 415	91.16 489	86.05 422	99.73 295	96.70 286	96.09 470	99.17 311

EPNet		96.14 372	95.44 384	98.25 289	90.76 502	95.50 324	97.92 228	94.65 465	98.97 124	92.98 481	98.85 255	89.12 399	99.87 135	95.99 339	99.68 216	99.39 226
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
wuyk23d		96.06 373	97.62 284	91.38 477	98.65 366	98.57 106	98.85 93	96.95 432	96.86 331	99.90 14	99.16 164	99.18 19	98.40 484	89.23 471	99.77 161	77.18 497

Syy-MVS		96.04 374	95.56 380	97.49 372	97.10 462	94.48 368	96.18 395	96.58 440	95.65 387	94.77 462	92.29 486	91.27 381	99.36 443	98.17 151	98.05 428	98.63 398

miper_enhance_ethall		96.01 375	95.74 369	96.81 407	96.41 483	92.27 433	93.69 479	98.89 328	91.14 465	98.30 304	97.35 417	90.58 387	99.58 386	96.31 323	99.03 366	98.60 400

FMVSNet5		96.01 375	95.20 396	98.41 270	97.53 446	96.10 296	98.74 99	99.50 131	97.22 307	98.03 331	99.04 198	69.80 478	99.88 115	97.27 232	99.71 201	99.25 282

blended_shiyan6		95.99 377	95.33 390	97.95 320	97.06 464	94.89 353	95.34 435	98.58 370	96.17 362	97.06 394	92.41 483	87.64 410	99.76 269	97.64 199	96.09 470	99.19 303

blended_shiyan8		95.98 378	95.33 390	97.94 321	97.05 466	94.87 355	95.34 435	98.59 369	96.17 362	97.09 392	92.39 484	87.62 411	99.76 269	97.65 198	96.05 476	99.20 297

dmvs_re		95.98 378	95.39 387	97.74 340	98.86 316	97.45 212	98.37 159	95.69 458	97.95 226	96.56 423	95.95 443	90.70 386	97.68 491	88.32 473	96.13 469	98.11 434

baseline1		95.96 380	95.44 384	97.52 369	98.51 383	93.99 393	98.39 157	96.09 449	98.21 198	98.40 302	97.76 392	86.88 413	99.63 362	95.42 363	89.27 491	98.95 350

HY-MVS	95.94 13	95.90 381	95.35 389	97.55 366	97.95 421	94.79 357	98.81 98	96.94 433	92.28 453	95.17 458	98.57 319	89.90 392	99.75 281	91.20 457	97.33 452	98.10 435

MVStest1		95.86 382	95.60 376	96.63 412	95.87 490	91.70 438	97.93 225	98.94 316	98.03 220	99.56 73	99.66 32	71.83 475	98.26 486	99.35 58	99.24 337	99.91 13

GA-MVS		95.86 382	95.32 392	97.49 372	98.60 369	94.15 379	93.83 477	97.93 402	95.49 393	96.68 418	97.42 412	83.21 447	99.30 453	96.22 328	98.55 406	99.01 338

OpenMVS_ROB	95.38 14	95.84 384	95.18 397	97.81 331	98.41 395	97.15 244	97.37 314	98.62 367	83.86 489	98.65 265	98.37 344	94.29 331	99.68 331	88.41 472	98.62 403	96.60 478

cl22		95.79 385	95.39 387	96.98 397	96.77 472	92.79 421	94.40 466	98.53 376	94.59 415	97.89 341	98.17 361	82.82 451	99.24 459	96.37 319	99.03 366	98.92 356

1314		95.74 386	95.60 376	96.17 428	97.53 446	92.75 423	98.07 197	98.31 387	91.22 463	94.25 469	96.68 428	95.53 293	99.03 468	91.64 449	97.18 454	96.74 476

WB-MVSnew		95.73 387	95.57 379	96.23 425	96.70 473	90.70 461	96.07 401	93.86 474	95.60 389	97.04 396	95.45 459	96.00 273	99.55 395	91.04 459	98.31 412	98.43 416

PVSNet	93.40 17	95.67 388	95.70 371	95.57 442	98.83 322	88.57 471	92.50 484	97.72 406	92.69 448	96.49 431	96.44 435	93.72 344	99.43 434	93.61 411	99.28 331	98.71 388

FE-MVS		95.66 389	94.95 402	97.77 334	98.53 381	95.28 338	99.40 19	96.09 449	93.11 442	97.96 337	99.26 135	79.10 464	99.77 263	92.40 440	98.71 393	98.27 427

tttt0517		95.64 390	94.98 400	97.64 355	99.36 187	93.81 401	98.72 104	90.47 490	98.08 219	98.67 262	98.34 348	73.88 473	99.92 65	97.77 186	99.51 282	99.20 297

PatchmatchNet		95.58 391	95.67 373	95.30 451	97.34 456	87.32 479	97.65 270	96.65 438	95.30 399	97.07 393	98.69 296	84.77 434	99.75 281	94.97 372	98.64 400	98.83 368
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
TR-MVS		95.55 392	95.12 398	96.86 406	97.54 444	93.94 394	96.49 374	96.53 442	94.36 423	97.03 398	96.61 430	94.26 332	99.16 465	86.91 479	96.31 466	97.47 465

JIA-IIPM		95.52 393	95.03 399	97.00 395	96.85 469	94.03 386	96.93 348	95.82 454	99.20 82	94.63 466	99.71 22	83.09 448	99.60 375	94.42 388	94.64 482	97.36 468

CHOSEN 280x420		95.51 394	95.47 381	95.65 441	98.25 404	88.27 474	93.25 481	98.88 329	93.53 436	94.65 465	97.15 421	86.17 419	99.93 53	97.41 223	99.93 56	98.73 387

wanda-best-256-512		95.48 395	94.74 407	97.68 346	96.53 476	94.12 380	94.17 470	98.57 372	95.84 380	96.71 415	91.16 489	86.05 422	99.76 269	97.57 206	96.09 470	99.17 311

FE-blended-shiyan7		95.48 395	94.74 407	97.68 346	96.53 476	94.12 380	94.17 470	98.57 372	95.84 380	96.71 415	91.16 489	86.05 422	99.76 269	97.57 206	96.09 470	99.17 311

gbinet_0.2-2-1-0.02		95.44 397	94.55 409	98.14 302	95.99 489	95.34 336	94.71 452	98.29 388	96.00 374	96.05 441	90.50 493	84.99 432	99.79 245	97.33 228	97.07 457	99.28 273

ADS-MVSNet2		95.43 398	94.98 400	96.76 410	98.14 412	91.74 437	97.92 228	97.76 405	90.23 468	96.51 428	98.91 239	85.61 427	99.85 157	92.88 429	96.90 458	98.69 392

PAPR		95.29 399	94.47 410	97.75 338	97.50 452	95.14 344	94.89 449	98.71 361	91.39 462	95.35 457	95.48 455	94.57 323	99.14 467	84.95 482	97.37 448	98.97 347

thisisatest0530		95.27 400	94.45 411	97.74 340	99.19 236	94.37 371	97.86 237	90.20 491	97.17 309	98.22 311	97.65 398	73.53 474	99.90 81	96.90 266	99.35 317	98.95 350

ADS-MVSNet		95.24 401	94.93 403	96.18 427	98.14 412	90.10 465	97.92 228	97.32 420	90.23 468	96.51 428	98.91 239	85.61 427	99.74 288	92.88 429	96.90 458	98.69 392

WBMVS		95.18 402	94.78 405	96.37 418	97.68 439	89.74 468	95.80 417	98.73 359	97.54 266	98.30 304	98.44 337	70.06 477	99.82 206	96.62 296	99.87 97	99.54 142

BH-w/o		95.13 403	94.89 404	95.86 434	98.20 408	91.31 447	95.65 422	97.37 416	93.64 434	96.52 427	95.70 449	93.04 354	99.02 469	88.10 474	95.82 477	97.24 469

tpmrst		95.07 404	95.46 382	93.91 465	97.11 461	84.36 491	97.62 275	96.96 431	94.98 406	96.35 433	98.80 269	85.46 429	99.59 379	95.60 358	96.23 467	97.79 454

pmmvs3		95.03 405	94.40 412	96.93 399	97.70 436	92.53 426	95.08 444	97.71 407	88.57 481	97.71 354	98.08 370	79.39 462	99.82 206	96.19 330	99.11 360	98.43 416

tpmvs		95.02 406	95.25 393	94.33 459	96.39 484	85.87 482	98.08 193	96.83 436	95.46 394	95.51 455	98.69 296	85.91 425	99.53 403	94.16 394	96.23 467	97.58 462

reproduce_monomvs		95.00 407	95.25 393	94.22 461	97.51 451	83.34 493	97.86 237	98.44 380	98.51 173	99.29 140	99.30 123	67.68 483	99.56 391	98.89 96	99.81 133	99.77 50

EPNet_dtu		94.93 408	94.78 405	95.38 449	93.58 495	87.68 477	96.78 355	95.69 458	97.35 288	89.14 492	98.09 369	88.15 408	99.49 418	94.95 373	99.30 328	98.98 343
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
cascas		94.79 409	94.33 415	96.15 431	96.02 488	92.36 431	92.34 486	99.26 251	85.34 488	95.08 460	94.96 465	92.96 355	98.53 483	94.41 391	98.59 404	97.56 463

tpm		94.67 410	94.34 414	95.66 440	97.68 439	88.42 472	97.88 233	94.90 463	94.46 418	96.03 443	98.56 320	78.66 465	99.79 245	95.88 343	95.01 481	98.78 381

test0.0.03 1		94.51 411	93.69 421	96.99 396	96.05 486	93.61 410	94.97 447	93.49 475	96.17 362	97.57 365	94.88 466	82.30 452	99.01 471	93.60 412	94.17 485	98.37 423

thres600view7		94.45 412	93.83 419	96.29 421	99.06 271	91.53 441	97.99 218	94.24 471	98.34 182	97.44 378	95.01 462	79.84 458	99.67 335	84.33 483	98.23 414	97.66 459

PCF-MVS	92.86 18	94.36 413	93.00 431	98.42 269	98.70 347	97.56 203	93.16 482	99.11 288	79.59 494	97.55 366	97.43 411	92.19 367	99.73 295	79.85 491	99.45 297	97.97 443
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
X-MVStestdata		94.32 414	92.59 433	99.53 38	99.46 158	99.21 33	98.65 114	99.34 210	98.62 160	97.54 367	45.85 498	97.50 182	99.83 193	96.79 274	99.53 276	99.56 129

MVS-HIRNet		94.32 414	95.62 374	90.42 478	98.46 387	75.36 502	96.29 387	89.13 493	95.25 400	95.38 456	99.75 16	92.88 356	99.19 463	94.07 400	99.39 310	96.72 477

ET-MVSNet_ETH3D		94.30 416	93.21 427	97.58 361	98.14 412	94.47 369	94.78 451	93.24 478	94.72 412	89.56 490	95.87 446	78.57 467	99.81 223	96.91 261	97.11 456	98.46 408

thres100view900		94.19 417	93.67 422	95.75 438	99.06 271	91.35 446	98.03 204	94.24 471	98.33 184	97.40 380	94.98 464	79.84 458	99.62 365	83.05 485	98.08 425	96.29 481

E-PMN		94.17 418	94.37 413	93.58 469	96.86 468	85.71 485	90.11 491	97.07 427	98.17 205	97.82 349	97.19 419	84.62 436	98.94 473	89.77 468	97.68 437	96.09 487

thres400		94.14 419	93.44 424	96.24 424	98.93 299	91.44 444	97.60 281	94.29 469	97.94 228	97.10 390	94.31 471	79.67 460	99.62 365	83.05 485	98.08 425	97.66 459

thisisatest0515		94.12 420	93.16 428	96.97 398	98.60 369	92.90 419	93.77 478	90.61 489	94.10 428	96.91 403	95.87 446	74.99 472	99.80 232	94.52 383	99.12 359	98.20 429

tfpn200view9		94.03 421	93.44 424	95.78 437	98.93 299	91.44 444	97.60 281	94.29 469	97.94 228	97.10 390	94.31 471	79.67 460	99.62 365	83.05 485	98.08 425	96.29 481

CostFormer		93.97 422	93.78 420	94.51 458	97.53 446	85.83 484	97.98 219	95.96 451	89.29 477	94.99 461	98.63 310	78.63 466	99.62 365	94.54 382	96.50 463	98.09 436

test-LLR		93.90 423	93.85 418	94.04 463	96.53 476	84.62 489	94.05 474	92.39 480	96.17 362	94.12 471	95.07 460	82.30 452	99.67 335	95.87 346	98.18 417	97.82 449

EMVS		93.83 424	94.02 416	93.23 474	96.83 470	84.96 486	89.77 492	96.32 444	97.92 230	97.43 379	96.36 438	86.17 419	98.93 474	87.68 475	97.73 436	95.81 488

testing3-2		93.78 425	93.91 417	93.39 472	98.82 325	81.72 499	97.76 253	95.28 460	98.60 162	96.54 424	96.66 429	65.85 490	99.62 365	96.65 294	98.99 373	98.82 369

baseline2		93.73 426	92.83 432	96.42 417	97.70 436	91.28 449	96.84 353	89.77 492	93.96 432	92.44 484	95.93 444	79.14 463	99.77 263	92.94 426	96.76 462	98.21 428

thres200		93.72 427	93.14 429	95.46 447	98.66 362	91.29 448	96.61 366	94.63 466	97.39 284	96.83 410	93.71 474	79.88 457	99.56 391	82.40 488	98.13 422	95.54 490

EPMVS		93.72 427	93.27 426	95.09 454	96.04 487	87.76 476	98.13 183	85.01 499	94.69 413	96.92 401	98.64 308	78.47 469	99.31 451	95.04 369	96.46 464	98.20 429

testing3		93.51 429	92.09 440	97.75 338	98.60 369	94.40 370	97.32 318	95.26 461	97.56 262	96.79 413	95.50 453	53.57 502	99.77 263	95.26 366	98.97 377	99.08 325

dp		93.47 430	93.59 423	93.13 475	96.64 474	81.62 500	97.66 268	96.42 443	92.80 447	96.11 437	98.64 308	78.55 468	99.59 379	93.31 419	92.18 490	98.16 432

FPMVS		93.44 431	92.23 438	97.08 391	99.25 220	97.86 176	95.61 423	97.16 425	92.90 445	93.76 478	98.65 305	75.94 471	95.66 495	79.30 492	97.49 441	97.73 456

testing91		93.32 432	92.27 437	96.47 416	97.54 444	91.25 450	96.17 397	96.76 437	97.18 308	93.65 479	93.50 476	65.11 492	99.63 362	93.04 424	97.45 443	98.53 405

tpm cat1		93.29 433	93.13 430	93.75 467	97.39 455	84.74 487	97.39 308	97.65 411	83.39 491	94.16 470	98.41 339	82.86 450	99.39 440	91.56 451	95.35 480	97.14 470

UBG		93.25 434	92.32 435	96.04 432	97.72 431	90.16 464	95.92 411	95.91 453	96.03 372	93.95 476	93.04 480	69.60 479	99.52 407	90.72 465	97.98 431	98.45 411

MVS		93.19 435	92.09 440	96.50 415	96.91 467	94.03 386	98.07 197	98.06 400	68.01 496	94.56 467	96.48 433	95.96 280	99.30 453	83.84 484	96.89 460	96.17 483

tpm2		93.09 436	92.58 434	94.62 457	97.56 442	86.53 481	97.66 268	95.79 455	86.15 486	94.07 473	98.23 357	75.95 470	99.53 403	90.91 462	96.86 461	97.81 451

testing11		93.08 437	92.02 442	96.26 423	97.56 442	90.83 458	96.32 385	95.70 456	96.47 349	92.66 483	93.73 473	64.36 493	99.59 379	93.77 409	97.57 438	98.37 423

testing99		93.04 438	91.98 445	96.23 425	97.53 446	90.70 461	96.35 383	95.94 452	96.87 328	93.41 480	93.43 478	63.84 494	99.59 379	93.24 422	97.19 453	98.40 419

dmvs_testset		92.94 439	92.21 439	95.13 452	98.59 372	90.99 455	97.65 270	92.09 482	96.95 321	94.00 474	93.55 475	92.34 365	96.97 494	72.20 495	92.52 488	97.43 466

myMVS_eth3d28		92.92 440	92.31 436	94.77 455	97.84 426	87.59 478	96.19 393	96.11 448	97.08 314	94.27 468	93.49 477	66.07 489	98.78 479	91.78 445	97.93 433	97.92 445

KD-MVS_2432*1600		92.87 441	91.99 443	95.51 445	91.37 499	89.27 469	94.07 472	98.14 396	95.42 395	97.25 387	96.44 435	67.86 481	99.24 459	91.28 455	96.08 474	98.02 439

miper_refine_blended		92.87 441	91.99 443	95.51 445	91.37 499	89.27 469	94.07 472	98.14 396	95.42 395	97.25 387	96.44 435	67.86 481	99.24 459	91.28 455	96.08 474	98.02 439

ETVMVS		92.60 443	91.08 452	97.18 386	97.70 436	93.65 408	96.54 369	95.70 456	96.51 345	94.68 464	92.39 484	61.80 498	99.50 414	86.97 477	97.41 446	98.40 419

MVE	83.40 22	92.50 444	91.92 446	94.25 460	98.83 322	91.64 439	92.71 483	83.52 500	95.92 378	86.46 495	95.46 456	95.20 303	95.40 496	80.51 490	98.64 400	95.73 489
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
test2506		92.39 445	91.89 447	93.89 466	99.38 180	82.28 497	99.32 26	66.03 504	99.08 112	98.77 251	99.57 49	66.26 487	99.84 175	98.71 110	99.95 38	99.54 142

UWE-MVS		92.38 446	91.76 449	94.21 462	97.16 460	84.65 488	95.42 432	88.45 494	95.96 376	96.17 435	95.84 448	66.36 486	99.71 306	91.87 444	98.64 400	98.28 426

gg-mvs-nofinetune		92.37 447	91.20 451	95.85 435	95.80 491	92.38 430	99.31 30	81.84 501	99.75 10	91.83 487	99.74 18	68.29 480	99.02 469	87.15 476	97.12 455	96.16 484

test-mter		92.33 448	91.76 449	94.04 463	96.53 476	84.62 489	94.05 474	92.39 480	94.00 431	94.12 471	95.07 460	65.63 491	99.67 335	95.87 346	98.18 417	97.82 449

IB-MVS	91.63 19	92.24 449	90.90 453	96.27 422	97.22 459	91.24 451	94.36 467	93.33 477	92.37 451	92.24 486	94.58 470	66.20 488	99.89 97	93.16 423	94.63 483	97.66 459
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
TESTMET0.1,1		92.19 450	91.77 448	93.46 470	96.48 481	82.80 496	94.05 474	91.52 488	94.45 420	94.00 474	94.88 466	66.65 485	99.56 391	95.78 351	98.11 423	98.02 439

blend_shiyan4		92.09 451	90.16 458	97.88 326	96.78 471	94.93 351	95.24 439	98.58 370	96.22 360	96.07 439	91.42 488	63.46 497	99.73 295	96.70 286	76.98 497	98.98 343

testing222		91.96 452	90.37 455	96.72 411	97.47 453	92.59 424	96.11 399	94.76 464	96.83 332	92.90 482	92.87 481	57.92 500	99.55 395	86.93 478	97.52 440	98.00 442

myMVS_eth3d		91.92 453	90.45 454	96.30 420	97.10 462	90.90 456	96.18 395	96.58 440	95.65 387	94.77 462	92.29 486	53.88 501	99.36 443	89.59 470	98.05 428	98.63 398

PAPM		91.88 454	90.34 456	96.51 414	98.06 418	92.56 425	92.44 485	97.17 424	86.35 485	90.38 489	96.01 441	86.61 415	99.21 462	70.65 497	95.43 479	97.75 455

PVSNet_0	89.98 21	91.15 455	90.30 457	93.70 468	97.72 431	84.34 492	90.24 489	97.42 415	90.20 471	93.79 477	93.09 479	90.90 385	98.89 477	86.57 480	72.76 498	97.87 448

UWE-MVS-28		90.22 456	89.28 459	93.02 476	94.50 494	82.87 495	96.52 372	87.51 495	95.21 402	92.36 485	96.04 440	71.57 476	98.25 487	72.04 496	97.77 435	97.94 444

0.4-1-1-0.1		88.42 457	85.91 460	95.94 433	93.08 496	91.54 440	90.99 488	92.04 484	89.96 474	84.83 496	83.25 495	63.75 495	99.52 407	93.25 421	82.07 492	96.75 475

0.4-1-1-0.2		87.49 458	84.89 461	95.31 450	91.33 501	90.08 466	88.47 494	92.07 483	88.70 480	84.06 497	81.08 497	63.62 496	99.49 418	92.93 427	81.71 493	96.37 480

0.3-1-1-0.015		87.27 459	84.50 462	95.57 442	91.70 498	90.77 459	89.41 493	92.04 484	88.98 478	82.46 498	81.35 496	60.36 499	99.50 414	92.96 425	81.23 494	96.45 479

EGC-MVSNET		85.24 460	80.54 463	99.34 83	99.77 27	99.20 39	99.08 62	99.29 239	12.08 500	20.84 501	99.42 89	97.55 174	99.85 157	97.08 247	99.72 192	98.96 349

test_method		79.78 461	79.50 464	80.62 479	80.21 504	45.76 507	70.82 495	98.41 384	31.08 499	80.89 499	97.71 394	84.85 433	97.37 492	91.51 452	80.03 495	98.75 385

tmp_tt		78.77 462	78.73 465	78.90 480	58.45 505	74.76 504	94.20 469	78.26 503	39.16 498	86.71 494	92.82 482	80.50 456	75.19 500	86.16 481	92.29 489	86.74 494

dongtai		76.24 463	75.95 466	77.12 481	92.39 497	67.91 505	90.16 490	59.44 506	82.04 492	89.42 491	94.67 469	49.68 503	81.74 499	48.06 498	77.66 496	81.72 495

kuosan		69.30 464	68.95 467	70.34 482	87.68 503	65.00 506	91.11 487	59.90 505	69.02 495	74.46 500	88.89 494	48.58 504	68.03 501	28.61 499	72.33 499	77.99 496

cdsmvs_eth3d_5k		24.66 465	32.88 468	0.00 485	0.00 508	0.00 510	0.00 496	99.10 289	0.00 503	0.00 504	97.58 402	99.21 18	0.00 504	0.00 502	0.00 502	0.00 500

testmvs		17.12 466	20.53 469	6.87 484	12.05 506	4.20 509	93.62 480	6.73 507	4.62 502	10.41 502	24.33 499	8.28 506	3.56 503	9.69 501	15.07 500	12.86 499

test123		17.04 467	20.11 470	7.82 483	10.25 507	4.91 508	94.80 450	4.47 508	4.93 501	10.00 503	24.28 500	9.69 505	3.64 502	10.14 500	12.43 501	14.92 498

pcd_1.5k_mvsjas		8.17 468	10.90 471	0.00 485	0.00 508	0.00 510	0.00 496	0.00 509	0.00 503	0.00 504	0.00 503	98.07 124	0.00 504	0.00 502	0.00 502	0.00 500

ab-mvs-re		8.12 469	10.83 472	0.00 485	0.00 508	0.00 510	0.00 496	0.00 509	0.00 503	0.00 504	97.48 408	0.00 507	0.00 504	0.00 502	0.00 502	0.00 500

mmdepth		0.00 470	0.00 473	0.00 485	0.00 508	0.00 510	0.00 496	0.00 509	0.00 503	0.00 504	0.00 503	0.00 507	0.00 504	0.00 502	0.00 502	0.00 500

monomultidepth		0.00 470	0.00 473	0.00 485	0.00 508	0.00 510	0.00 496	0.00 509	0.00 503	0.00 504	0.00 503	0.00 507	0.00 504	0.00 502	0.00 502	0.00 500

test_blank		0.00 470	0.00 473	0.00 485	0.00 508	0.00 510	0.00 496	0.00 509	0.00 503	0.00 504	0.00 503	0.00 507	0.00 504	0.00 502	0.00 502	0.00 500

uanet_test		0.00 470	0.00 473	0.00 485	0.00 508	0.00 510	0.00 496	0.00 509	0.00 503	0.00 504	0.00 503	0.00 507	0.00 504	0.00 502	0.00 502	0.00 500

DCPMVS		0.00 470	0.00 473	0.00 485	0.00 508	0.00 510	0.00 496	0.00 509	0.00 503	0.00 504	0.00 503	0.00 507	0.00 504	0.00 502	0.00 502	0.00 500

sosnet-low-res		0.00 470	0.00 473	0.00 485	0.00 508	0.00 510	0.00 496	0.00 509	0.00 503	0.00 504	0.00 503	0.00 507	0.00 504	0.00 502	0.00 502	0.00 500

sosnet		0.00 470	0.00 473	0.00 485	0.00 508	0.00 510	0.00 496	0.00 509	0.00 503	0.00 504	0.00 503	0.00 507	0.00 504	0.00 502	0.00 502	0.00 500

uncertanet		0.00 470	0.00 473	0.00 485	0.00 508	0.00 510	0.00 496	0.00 509	0.00 503	0.00 504	0.00 503	0.00 507	0.00 504	0.00 502	0.00 502	0.00 500

Regformer		0.00 470	0.00 473	0.00 485	0.00 508	0.00 510	0.00 496	0.00 509	0.00 503	0.00 504	0.00 503	0.00 507	0.00 504	0.00 502	0.00 502	0.00 500

uanet		0.00 470	0.00 473	0.00 485	0.00 508	0.00 510	0.00 496	0.00 509	0.00 503	0.00 504	0.00 503	0.00 507	0.00 504	0.00 502	0.00 502	0.00 500

MED-MVS test				99.45 63	99.58 93	98.93 79	98.68 109	99.60 83	96.46 350	99.53 82	98.77 275		99.83 193	96.67 290	99.64 233	99.58 115

TestfortrainingZip				98.97 157	98.30 401	98.43 117	98.68 109	98.26 389	97.76 243	98.86 235	98.16 362	95.15 305	99.47 425		97.55 439	99.02 336

WAC-MVS						90.90 456								91.37 454

FOURS1					99.73 37	99.67 2	99.43 15	99.54 118	99.43 54	99.26 148

MSC_two_6792asdad				99.32 91	98.43 391	98.37 122		98.86 336					99.89 97	97.14 242	99.60 250	99.71 63

PC_three_1452									93.27 439	99.40 115	98.54 321	98.22 109	97.00 493	95.17 367	99.45 297	99.49 174

No_MVS				99.32 91	98.43 391	98.37 122		98.86 336					99.89 97	97.14 242	99.60 250	99.71 63

test_one_0601					99.39 179	99.20 39		99.31 223	98.49 174	98.66 264	99.02 201	97.64 165

eth-test2					0.00 508

eth-test					0.00 508

ZD-MVS					99.01 287	98.84 85		99.07 293	94.10 428	98.05 329	98.12 365	96.36 257	99.86 144	92.70 436	99.19 348

RE-MVS-def			98.58 158		99.20 233	99.38 12	98.48 143	99.30 231	98.64 155	98.95 211	98.96 228	97.75 156		96.56 305	99.39 310	99.45 200

IU-MVS					99.49 144	99.15 52		98.87 331	92.97 443	99.41 112				96.76 278	99.62 243	99.66 78

OPU-MVS				98.82 184	98.59 372	98.30 127	98.10 190				98.52 325	98.18 114	98.75 480	94.62 380	99.48 293	99.41 216

test_241102_TWO								99.30 231	98.03 220	99.26 148	99.02 201	97.51 181	99.88 115	96.91 261	99.60 250	99.66 78

test_241102_ONE					99.49 144	99.17 44		99.31 223	97.98 223	99.66 60	98.90 242	98.36 87	99.48 422

9.14			97.78 268		99.07 266		97.53 290	99.32 218	95.53 392	98.54 286	98.70 294	97.58 171	99.76 269	94.32 393	99.46 295

save fliter					99.11 257	97.97 164	96.53 371	99.02 306	98.24 194

test_0728_THIRD									98.17 205	99.08 179	99.02 201	97.89 143	99.88 115	97.07 248	99.71 201	99.70 68

test_0728_SECOND				99.60 16	99.50 136	99.23 31	98.02 207	99.32 218					99.88 115	96.99 255	99.63 240	99.68 71

test0726					99.50 136	99.21 33	98.17 179	99.35 204	97.97 224	99.26 148	99.06 189	97.61 169

GSMVS																98.81 374

test_part2					99.36 187	99.10 65				99.05 189

sam_mvs1												84.74 435				98.81 374

sam_mvs												84.29 441

ambc				98.24 291	98.82 325	95.97 305	98.62 118	99.00 311		99.27 144	99.21 149	96.99 217	99.50 414	96.55 308	99.50 290	99.26 281

MTGPA								99.20 264

test_post1							97.59 283				20.48 502	83.07 449	99.66 348	94.16 394

test_post											21.25 501	83.86 444	99.70 313

patchmatchnet-post											98.77 275	84.37 438	99.85 157

GG-mvs-BLEND				94.76 456	94.54 493	92.13 435	99.31 30	80.47 502		88.73 493	91.01 492	67.59 484	98.16 489	82.30 489	94.53 484	93.98 492

MTMP							97.93 225	91.91 487

gm-plane-assit					94.83 492	81.97 498			88.07 483		94.99 463		99.60 375	91.76 446

test9_res														93.28 420	99.15 353	99.38 235

TEST9					98.71 343	98.08 151	95.96 406	99.03 303	91.40 461	95.85 444	97.53 404	96.52 248	99.76 269

test_8					98.67 357	98.01 159	95.91 412	99.02 306	91.64 456	95.79 446	97.50 407	96.47 250	99.76 269

agg_prior2														92.50 439	99.16 351	99.37 237

agg_prior					98.68 356	97.99 160		99.01 309		95.59 447			99.77 263

TestCases				99.16 118	99.50 136	98.55 107		99.58 93	96.80 333	98.88 230	99.06 189	97.65 162	99.57 388	94.45 386	99.61 248	99.37 237

test_prior4						97.97 164	95.86 413

test_prior2							95.74 420		96.48 348	96.11 437	97.63 400	95.92 283		94.16 394	99.20 345

test_prior				98.95 161	98.69 352	97.95 168		99.03 303					99.59 379			99.30 268

旧先验2							95.76 419		88.56 482	97.52 369			99.66 348	94.48 384

新几何2							95.93 409

新几何1				98.91 169	98.94 297	97.76 190		98.76 353	87.58 484	96.75 414	98.10 367	94.80 318	99.78 257	92.73 435	99.00 371	99.20 297

旧先验1					98.82 325	97.45 212		98.76 353			98.34 348	95.50 296			99.01 370	99.23 287

无先验							95.74 420	98.74 358	89.38 476				99.73 295	92.38 441		99.22 292

原ACMM2							95.53 426

原ACMM1				98.35 279	98.90 307	96.25 293		98.83 344	92.48 450	96.07 439	98.10 367	95.39 299	99.71 306	92.61 438	98.99 373	99.08 325

test222					98.92 303	96.93 259	95.54 425	98.78 350	85.72 487	96.86 409	98.11 366	94.43 325			99.10 361	99.23 287

testdata2													99.79 245	92.80 433

segment_acmp												97.02 215

testdata				98.09 307	98.93 299	95.40 331		98.80 347	90.08 472	97.45 377	98.37 344	95.26 302	99.70 313	93.58 413	98.95 379	99.17 311

testdata1							95.44 431		96.32 356

test12				98.93 165	98.58 374	97.83 179		98.66 363		96.53 425		95.51 295	99.69 321		99.13 356	99.27 275

plane_prior7					99.19 236	97.87 175

plane_prior6					98.99 291	97.70 196						94.90 311

plane_prior5								99.27 246					99.70 313	94.42 388	99.51 282	99.45 200

plane_prior4											97.98 377

plane_prior3						97.78 189			97.41 281	97.79 350

plane_prior2							97.77 250		98.20 202

plane_prior1					99.05 274

plane_prior						97.65 198	97.07 340		96.72 338						99.36 314

n2								0.00 509

nn								0.00 509

door-mid								99.57 100

lessismore_v0				98.97 157	99.73 37	97.53 205		86.71 497		99.37 120	99.52 67	89.93 391	99.92 65	98.99 88	99.72 192	99.44 204

LGP-MVS_train				99.47 60	99.57 102	98.97 73		99.48 141	96.60 342	99.10 177	99.06 189	98.71 50	99.83 193	95.58 360	99.78 155	99.62 90

test11								98.87 331

door								99.41 183

HQP5-MVS						96.79 266

HQP-NCC					98.67 357		96.29 387		96.05 369	95.55 450

ACMP_Plane					98.67 357		96.29 387		96.05 369	95.55 450

BP-MVS														92.82 431

HQP4-MVS										95.56 449			99.54 401			99.32 260

HQP3-MVS								99.04 301							99.26 335

HQP2-MVS												93.84 339

NP-MVS					98.84 320	97.39 216					96.84 425

MDTV_nov1_ep13_2view						74.92 503	97.69 263		90.06 473	97.75 353		85.78 426		93.52 414		98.69 392

MDTV_nov1_ep13			95.22 395		97.06 464	83.20 494	97.74 257	96.16 446	94.37 422	96.99 399	98.83 262	83.95 443	99.53 403	93.90 403	97.95 432

ACMMP++_ref															99.77 161

ACMMP++															99.68 216

Test By Simon												96.52 248

ITE_SJBPF				98.87 173	99.22 227	98.48 114		99.35 204	97.50 269	98.28 308	98.60 316	97.64 165	99.35 446	93.86 406	99.27 332	98.79 380

DeepMVS_CX				93.44 471	98.24 405	94.21 376		94.34 468	64.28 497	91.34 488	94.87 468	89.45 398	92.77 498	77.54 493	93.14 487	93.35 493

High-resolution multi-view benchmark