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		95.70 1	96.40 1	93.61 2	98.67 1	85.39 29	95.54 3	97.36 1	96.97 1	99.04 1	99.05 1	96.61 1	95.92 8	85.07 36	99.27 3	99.54 1

Anonymous20231211		90.14 31	91.88 12	84.92 117	94.75 35	64.47 178	90.13 38	92.97 57	91.68 3	95.35 12	98.79 2	93.19 3	91.76 157	71.67 170	98.40 21	98.52 7

UA-Net		91.49 14	91.53 18	91.39 22	94.98 31	82.95 50	93.52 5	92.79 64	88.22 16	88.53 122	97.64 3	83.45 64	94.55 57	86.02 33	98.60 14	96.67 36

pmmvs6		86.52 87	88.06 64	81.90 185	92.22 86	62.28 217	84.66 126	89.15 166	83.54 34	89.85 92	97.32 4	88.08 28	86.80 247	70.43 179	97.30 61	96.62 37

wuykxyi23d		88.46 61	88.80 59	87.44 79	90.96 119	93.03 1	85.85 111	81.96 237	74.58 141	98.58 2	97.29 5	87.73 31	87.31 237	82.84 67	99.41 1	81.99 293

OurMVSNet-221017-0		90.01 36	89.74 46	90.83 33	93.16 60	80.37 59	91.91 27	93.11 49	81.10 60	95.32 13	97.24 6	72.94 189	94.85 48	85.07 36	97.78 46	97.26 23

gg-mvs-nofinetune		68.96 285	69.11 278	68.52 300	76.12 316	45.32 326	83.59 151	55.88 344	86.68 20	64.62 328	97.01 7	30.36 345	83.97 278	44.78 321	82.94 299	76.26 320

K. test v3		85.14 109	84.73 119	86.37 90	91.13 116	69.63 146	85.45 116	76.68 264	84.06 32	92.44 51	96.99 8	62.03 239	94.65 52	80.58 91	93.24 182	94.83 80

LTVRE_ROB	86.10 1	93.04 3	93.44 2	91.82 19	93.73 48	85.72 28	96.79 1	95.51 4	88.86 13	95.63 10	96.99 8	84.81 53	93.16 120	91.10 1	97.53 57	96.58 39
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
ANet_high		83.17 160	85.68 104	75.65 261	81.24 278	45.26 327	79.94 231	92.91 59	83.83 33	91.33 69	96.88 10	80.25 102	85.92 259	68.89 191	95.89 108	95.76 53

PS-MVSNAJss		88.31 62	87.90 66	89.56 51	93.31 56	77.96 78	87.94 75	91.97 85	70.73 193	94.19 24	96.67 11	76.94 131	94.57 55	83.07 62	96.28 90	96.15 42

mvs_tets		89.78 44	89.27 51	91.30 24	93.51 51	84.79 36	89.89 44	90.63 127	70.00 200	94.55 18	96.67 11	87.94 29	93.59 91	84.27 48	95.97 104	95.52 65

test_djsdf		89.62 46	89.01 53	91.45 21	92.36 80	82.98 49	91.98 25	90.08 149	71.54 187	94.28 23	96.54 13	81.57 90	94.27 59	86.26 27	96.49 84	97.09 30

SixPastTwentyTwo		87.20 74	87.45 74	86.45 89	92.52 75	69.19 152	87.84 78	88.05 180	81.66 56	94.64 17	96.53 14	65.94 227	94.75 50	83.02 64	96.83 73	95.41 67

jajsoiax		89.41 49	88.81 58	91.19 28	93.38 55	84.72 37	89.70 46	90.29 143	69.27 204	94.39 20	96.38 15	86.02 49	93.52 101	83.96 51	95.92 107	95.34 68

v52		89.97 39	90.60 37	88.07 68	88.69 154	72.01 126	91.35 30	92.64 69	82.22 48	95.97 8	96.31 16	84.82 52	93.98 73	88.59 4	94.83 141	98.23 8

V4		89.97 39	90.60 37	88.07 68	88.69 154	72.01 126	91.35 30	92.64 69	82.22 48	95.98 7	96.31 16	84.80 54	93.98 73	88.59 4	94.83 141	98.23 8

TDRefinement		93.52 2	93.39 3	93.88 1	95.94 13	90.26 4	95.70 2	96.46 2	90.58 8	92.86 41	96.29 18	88.16 26	94.17 66	86.07 32	98.48 19	97.22 26

v7n		90.13 32	90.96 32	87.65 75	91.95 92	71.06 138	89.99 41	93.05 52	86.53 21	94.29 22	96.27 19	82.69 71	94.08 69	86.25 29	97.63 51	97.82 11

DTE-MVSNet		89.98 37	91.91 11	84.21 139	96.51 7	57.84 252	88.93 63	92.84 63	91.92 2	96.16 3	96.23 20	86.95 38	95.99 4	79.05 110	98.57 16	98.80 6

VDDNet		84.35 132	85.39 109	81.25 197	95.13 28	59.32 245	85.42 117	81.11 243	86.41 22	87.41 138	96.21 21	73.61 174	90.61 188	66.33 209	96.85 71	93.81 108

PEN-MVS		90.03 35	91.88 12	84.48 130	96.57 5	58.88 248	88.95 61	93.19 47	91.62 4	96.01 6	96.16 22	87.02 37	95.60 20	78.69 113	98.72 10	98.97 3

anonymousdsp		89.73 45	88.88 56	92.27 7	89.82 139	86.67 12	90.51 35	90.20 148	69.87 201	95.06 14	96.14 23	84.28 57	93.07 127	87.68 13	96.34 88	97.09 30

PS-CasMVS		90.06 34	91.92 9	84.47 131	96.56 6	58.83 249	89.04 60	92.74 66	91.40 5	96.12 4	96.06 24	87.23 35	95.57 21	79.42 109	98.74 7	99.00 2

v748		88.91 57	89.82 45	86.19 99	90.06 135	68.53 154	88.81 65	91.48 99	84.36 28	94.19 24	95.98 25	82.52 74	92.67 137	84.30 47	96.67 77	97.37 20

MIMVSNet1		83.63 152	84.59 129	80.74 202	94.06 42	62.77 202	82.72 179	84.53 226	77.57 103	90.34 79	95.92 26	76.88 137	85.83 261	61.88 233	97.42 58	93.62 113

v13		87.31 72	88.10 63	84.94 116	88.84 151	63.75 184	87.85 77	91.47 102	79.12 81	93.72 29	95.82 27	75.20 146	93.58 94	84.76 42	96.16 95	97.48 17

test_0402		88.65 58	89.58 49	85.88 105	92.55 74	72.22 124	84.01 137	89.44 163	88.63 14	94.38 21	95.77 28	86.38 46	93.59 91	79.84 102	95.21 127	91.82 163

APDe-MVS		91.22 20	91.92 9	89.14 56	92.97 65	78.04 77	92.84 12	94.14 21	83.33 35	93.90 26	95.73 29	88.77 18	96.41 1	87.60 16	97.98 42	92.98 128

Baseline_NR-MVSNet		84.00 145	85.90 100	78.29 230	91.47 107	53.44 281	82.29 190	87.00 201	79.06 83	89.55 106	95.72 30	77.20 125	86.14 257	72.30 165	98.51 18	95.28 70

v12		87.15 75	87.91 65	84.84 119	88.69 154	63.52 187	87.58 80	91.46 103	78.74 89	93.57 32	95.66 31	74.94 150	93.57 95	84.50 45	96.08 100	97.43 18

WR-MVS_H		89.91 42	91.31 25	85.71 108	96.32 9	62.39 211	89.54 53	93.31 41	90.21 10	95.57 11	95.66 31	81.42 92	95.90 9	80.94 85	98.80 4	98.84 5

GBi-Net		82.02 173	82.07 169	81.85 188	86.38 216	61.05 232	86.83 94	88.27 177	72.43 169	86.00 160	95.64 33	63.78 234	90.68 185	65.95 211	93.34 176	93.82 105

test1		82.02 173	82.07 169	81.85 188	86.38 216	61.05 232	86.83 94	88.27 177	72.43 169	86.00 160	95.64 33	63.78 234	90.68 185	65.95 211	93.34 176	93.82 105

FMVSNet1		84.55 123	85.45 108	81.85 188	90.27 131	61.05 232	86.83 94	88.27 177	78.57 91	89.66 98	95.64 33	75.43 143	90.68 185	69.09 189	95.33 122	93.82 105

v11		86.96 76	87.78 69	84.51 128	88.50 160	62.60 207	87.21 86	91.63 94	78.08 98	93.40 34	95.56 36	75.07 147	93.57 95	84.46 46	96.08 100	97.36 21

TransMVSNet (Re)		84.02 144	85.74 102	78.85 221	91.00 118	55.20 272	82.29 190	87.26 191	79.65 74	88.38 127	95.52 37	83.00 68	86.88 242	67.97 199	96.60 79	94.45 88

V9		86.96 76	87.70 70	84.74 123	88.52 159	63.27 193	87.31 85	91.45 105	78.28 94	93.43 33	95.45 38	74.59 158	93.57 95	84.23 49	96.01 103	97.38 19

ACMH	76.49 14	89.34 51	91.14 28	83.96 145	92.50 76	70.36 142	89.55 51	93.84 28	81.89 54	94.70 16	95.44 39	90.69 10	88.31 229	83.33 58	98.30 28	93.20 123
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
wuyk23d		75.13 235	79.30 197	62.63 314	75.56 319	75.18 103	80.89 221	73.10 288	75.06 138	94.76 15	95.32 40	87.73 31	52.85 344	34.16 338	97.11 65	59.85 338

abl_6		93.02 4	93.16 4	92.60 4	94.73 38	88.99 7	93.26 10	94.19 19	89.11 11	94.43 19	95.27 41	91.86 4	95.09 41	87.54 18	98.02 39	93.71 110

pm-mvs1		83.69 150	84.95 116	79.91 209	90.04 137	59.66 242	82.43 185	87.44 188	75.52 133	87.85 132	95.26 42	81.25 94	85.65 263	68.74 193	96.04 102	94.42 89

V14		86.75 82	87.46 73	84.62 126	88.35 163	63.00 198	87.02 91	91.42 108	77.78 100	93.27 35	95.23 43	74.22 161	93.56 98	83.95 52	95.93 106	97.31 22

CP-MVSNet		89.27 52	90.91 34	84.37 134	96.34 8	58.61 251	88.66 68	92.06 82	90.78 6	95.67 9	95.17 44	81.80 88	95.54 24	79.00 111	98.69 11	98.95 4

HPM-MVS_fast		92.50 5	92.54 5	92.37 5	95.93 14	85.81 27	92.99 11	94.23 16	85.21 24	92.51 49	95.13 45	90.65 11	95.34 32	88.06 10	98.15 34	95.95 51

PMVS	80.48 6	90.08 33	90.66 36	88.34 64	96.71 2	92.97 2	90.31 36	89.57 161	88.51 15	90.11 81	95.12 46	90.98 8	88.92 218	77.55 123	97.07 66	83.13 280
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
COLMAP_ROB	83.01 3	91.97 8	91.95 8	92.04 10	93.68 49	86.15 18	93.37 8	95.10 7	90.28 9	92.11 54	95.03 47	89.75 15	94.93 46	79.95 101	98.27 29	95.04 76
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
v15		86.56 85	87.25 77	84.51 128	88.15 170	62.72 203	86.72 101	91.40 110	77.38 105	93.11 37	95.00 48	73.93 166	93.55 99	83.67 56	95.86 110	97.26 23

MP-MVS-pluss		90.81 24	91.08 29	89.99 46	95.97 12	79.88 63	88.13 73	94.51 11	75.79 129	92.94 38	94.96 49	88.36 20	95.01 44	90.70 2	98.40 21	95.09 75
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
ACMH+	77.89 11	90.73 25	91.50 19	88.44 62	93.00 64	76.26 98	89.65 49	95.55 3	87.72 18	93.89 27	94.94 50	91.62 5	93.44 107	78.35 115	98.76 5	95.61 64

ACMMP_Plus		90.65 26	91.07 30	89.42 52	95.93 14	79.54 68	89.95 42	93.68 31	77.65 101	91.97 59	94.89 51	88.38 19	95.45 28	89.27 3	97.87 45	93.27 120

Gipuma		84.44 126	86.33 92	78.78 222	84.20 254	73.57 111	89.55 51	90.44 132	84.24 29	84.38 187	94.89 51	76.35 140	80.40 292	76.14 134	96.80 75	82.36 288
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
TSAR-MVS + MP.		88.14 64	87.82 68	89.09 57	95.72 19	76.74 92	92.49 20	91.19 118	67.85 219	86.63 150	94.84 53	79.58 107	95.96 7	87.62 14	94.50 151	94.56 81

LPG-MVS_test		91.47 16	91.68 15	90.82 34	94.75 35	81.69 51	90.00 39	94.27 13	82.35 46	93.67 30	94.82 54	91.18 6	95.52 25	85.36 34	98.73 8	95.23 72

LGP-MVS_train				90.82 34	94.75 35	81.69 51		94.27 13	82.35 46	93.67 30	94.82 54	91.18 6	95.52 25	85.36 34	98.73 8	95.23 72

DeepC-MVS	82.31 4	89.15 54	89.08 52	89.37 53	93.64 50	79.07 70	88.54 69	94.20 17	73.53 151	89.71 96	94.82 54	85.09 51	95.77 15	84.17 50	98.03 38	93.26 121
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
RPSCF		88.00 65	86.93 84	91.22 27	90.08 133	89.30 6	89.68 48	91.11 119	79.26 79	89.68 97	94.81 57	82.44 75	87.74 234	76.54 133	88.74 249	96.61 38

nrg030		87.85 68	88.49 61	85.91 103	90.07 134	69.73 144	87.86 76	94.20 17	74.04 146	92.70 46	94.66 58	85.88 50	91.50 161	79.72 103	97.32 60	96.50 40

FC-MVSNet-test		85.93 101	87.05 80	82.58 176	92.25 84	56.44 262	85.75 112	93.09 50	77.33 110	91.94 60	94.65 59	74.78 154	93.41 109	75.11 140	98.58 15	97.88 10

v17		86.32 90	86.95 83	84.44 132	88.00 172	62.62 206	86.74 99	91.48 99	77.17 113	92.74 44	94.56 60	73.74 170	93.53 100	83.27 59	94.87 140	97.18 27

testing_2		84.36 130	84.64 128	83.50 163	86.74 213	63.97 183	84.56 128	90.31 138	66.22 228	91.62 64	94.55 61	75.88 141	91.95 149	77.02 131	94.89 137	94.56 81

v16		86.24 93	86.85 86	84.43 133	87.96 174	62.59 208	86.73 100	91.48 99	77.17 113	92.67 47	94.55 61	73.63 171	93.52 101	83.26 60	94.16 155	97.17 28

LCM-MVSNet-Re		83.48 155	85.06 112	78.75 223	85.94 231	55.75 267	80.05 228	94.27 13	76.47 120	96.09 5	94.54 63	83.31 66	89.75 205	59.95 247	94.89 137	90.75 185

v10		86.54 86	87.10 78	84.84 119	88.16 169	63.28 192	86.64 103	92.20 79	75.42 136	92.81 43	94.50 64	74.05 164	94.06 70	83.88 53	96.28 90	97.17 28

v8		86.22 95	86.83 87	84.36 135	87.82 180	62.35 212	86.42 106	91.33 113	76.78 118	92.73 45	94.48 65	73.41 178	93.72 82	83.10 61	95.41 120	97.01 33

VPA-MVSNet		83.47 156	84.73 119	79.69 213	90.29 130	57.52 255	81.30 215	88.69 170	76.29 121	87.58 135	94.44 66	80.60 99	87.20 238	66.60 208	96.82 74	94.34 91

lessismore_v0				85.95 102	91.10 117	70.99 139		70.91 306		91.79 61	94.42 67	61.76 240	92.93 130	79.52 108	93.03 186	93.93 102

PGM-MVS		91.20 21	90.95 33	91.93 13	95.67 20	85.85 25	90.00 39	93.90 27	80.32 67	91.74 63	94.41 68	88.17 25	95.98 5	86.37 25	97.99 41	93.96 101

MPTG		91.27 19	91.26 27	91.29 25	96.59 3	86.29 14	88.94 62	91.81 90	84.07 30	92.00 57	94.40 69	86.63 40	95.28 35	88.59 4	98.31 26	92.30 151

MTAPA		91.52 13	91.60 16	91.29 25	96.59 3	86.29 14	92.02 24	91.81 90	84.07 30	92.00 57	94.40 69	86.63 40	95.28 35	88.59 4	98.31 26	92.30 151

APD-MVS_3200maxsize		92.05 7	92.24 6	91.48 20	93.02 63	85.17 31	92.47 21	95.05 8	87.65 19	93.21 36	94.39 71	90.09 14	95.08 42	86.67 24	97.60 55	94.18 94

MP-MVS		91.14 23	90.91 34	91.83 18	96.18 10	86.88 11	92.20 22	93.03 55	82.59 44	88.52 123	94.37 72	86.74 39	95.41 30	86.32 26	98.21 32	93.19 124

VDD-MVS		84.23 136	84.58 130	83.20 166	91.17 115	65.16 174	83.25 165	84.97 224	79.79 71	87.18 140	94.27 73	74.77 155	90.89 179	69.24 186	96.54 81	93.55 118

3Dnovator+	83.92 2	89.97 39	89.66 47	90.92 32	91.27 111	81.66 54	91.25 32	94.13 22	88.89 12	88.83 117	94.26 74	77.55 123	95.86 12	84.88 39	95.87 109	95.24 71

mPP-MVS		91.69 10	91.47 20	92.37 5	96.04 11	88.48 8	92.72 14	92.60 71	83.09 38	91.54 65	94.25 75	87.67 33	95.51 27	87.21 23	98.11 35	93.12 125

region2R		91.44 17	91.30 26	91.87 16	95.75 17	85.90 23	92.63 17	93.30 42	81.91 53	90.88 76	94.21 76	87.75 30	95.87 10	87.60 16	97.71 50	93.83 103

v18		85.99 99	86.55 90	84.30 137	87.73 186	62.29 216	86.40 107	91.49 98	76.64 119	92.40 52	94.20 77	73.28 182	93.52 101	82.87 65	93.99 159	97.09 30

ACMMPR		91.49 14	91.35 24	91.92 14	95.74 18	85.88 24	92.58 18	93.25 46	81.99 51	91.40 68	94.17 78	87.51 34	95.87 10	87.74 11	97.76 47	93.99 99

tfpnnormal		81.79 176	82.95 157	78.31 229	88.93 150	55.40 268	80.83 223	82.85 232	76.81 117	85.90 163	94.14 79	74.58 159	86.51 252	66.82 207	95.68 117	93.01 127

ACMMP		91.91 9	91.87 14	92.03 11	95.53 24	85.91 22	93.35 9	94.16 20	82.52 45	92.39 53	94.14 79	89.15 17	95.62 19	87.35 19	98.24 30	94.56 81
Qingshan Xu, Weihang Kong, Wenbing Tao, Marc Pollefeys: Multi-Scale Geometric Consistency Guided and Planar Prior Assisted Multi-View Stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence
Vis-MVSNet		86.86 79	86.58 89	87.72 73	92.09 88	77.43 84	87.35 84	92.09 81	78.87 86	84.27 192	94.05 81	78.35 115	93.65 84	80.54 92	91.58 207	92.08 158
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
XVS		91.54 12	91.36 22	92.08 8	95.64 21	86.25 16	92.64 15	93.33 39	85.07 25	89.99 85	94.03 82	86.57 42	95.80 13	87.35 19	97.62 52	94.20 92

CP-MVS		91.67 11	91.58 17	91.96 12	95.29 27	87.62 9	93.38 7	93.36 37	83.16 37	91.06 71	94.00 83	88.26 23	95.71 18	87.28 22	98.39 23	92.55 143

FIs		85.35 108	86.27 94	82.60 175	91.86 95	57.31 256	85.10 120	93.05 52	75.83 128	91.02 72	93.97 84	73.57 175	92.91 132	73.97 148	98.02 39	97.58 15

SteuartSystems-ACMMP		91.16 22	91.36 22	90.55 37	93.91 46	80.97 58	91.49 29	93.48 36	82.82 42	92.60 48	93.97 84	88.19 24	96.29 3	87.61 15	98.20 33	94.39 90
Skip Steuart: Steuart Systems R&D Blog.
ambc				82.98 169	90.55 128	64.86 175	88.20 71	89.15 166		89.40 109	93.96 86	71.67 207	91.38 168	78.83 112	96.55 80	92.71 134

HFP-MVS		91.30 18	91.39 21	91.02 29	95.43 25	84.66 39	92.58 18	93.29 44	81.99 51	91.47 66	93.96 86	88.35 21	95.56 22	87.74 11	97.74 48	92.85 129

#test#		90.49 30	90.31 41	91.02 29	95.43 25	84.66 39	90.65 34	93.29 44	77.00 116	91.47 66	93.96 86	88.35 21	95.56 22	84.88 39	97.74 48	92.85 129

LS3D		90.60 28	90.34 40	91.38 23	89.03 148	84.23 42	93.58 4	94.68 10	90.65 7	90.33 80	93.95 89	84.50 56	95.37 31	80.87 86	95.50 119	94.53 85

HPM-MVS		92.13 6	92.20 7	91.91 15	95.58 23	84.67 38	93.51 6	94.85 9	82.88 41	91.77 62	93.94 90	90.55 13	95.73 17	88.50 8	98.23 31	95.33 69

XVG-ACMP-BASELINE		89.98 37	89.84 44	90.41 39	94.91 33	84.50 41	89.49 55	93.98 24	79.68 73	92.09 55	93.89 91	83.80 60	93.10 123	82.67 69	98.04 36	93.64 112

TranMVSNet+NR-MVSNet		87.86 67	88.76 60	85.18 114	94.02 43	64.13 180	84.38 131	91.29 114	84.88 27	92.06 56	93.84 92	86.45 44	93.73 81	73.22 156	98.66 12	97.69 12

ACMP	79.16 10	90.54 29	90.60 37	90.35 41	94.36 40	80.98 57	89.16 59	94.05 23	79.03 84	92.87 40	93.74 93	90.60 12	95.21 39	82.87 65	98.76 5	94.87 77
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
OPM-MVS		89.80 43	89.97 42	89.27 54	94.76 34	79.86 64	86.76 97	92.78 65	78.78 87	92.51 49	93.64 94	88.13 27	93.84 80	84.83 41	97.55 56	94.10 97

ACMM	79.39 9	90.65 26	90.99 31	89.63 49	95.03 30	83.53 44	89.62 50	93.35 38	79.20 80	93.83 28	93.60 95	90.81 9	92.96 128	85.02 38	98.45 20	92.41 146
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
XVG-OURS		89.18 53	88.83 57	90.23 43	94.28 41	86.11 20	85.91 109	93.60 34	80.16 69	89.13 113	93.44 96	83.82 59	90.98 174	83.86 54	95.30 126	93.60 114

XVG-OURS-SEG-HR		89.59 47	89.37 50	90.28 42	94.47 39	85.95 21	86.84 93	93.91 26	80.07 70	86.75 148	93.26 97	93.64 2	90.93 176	84.60 44	90.75 227	93.97 100

APD-MVS		89.54 48	89.63 48	89.26 55	92.57 73	81.34 56	90.19 37	93.08 51	80.87 62	91.13 70	93.19 98	86.22 47	95.97 6	82.23 73	97.18 64	90.45 194
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
3Dnovator	80.37 7	84.80 118	84.71 122	85.06 115	86.36 219	74.71 105	88.77 66	90.00 152	75.65 132	84.96 172	93.17 99	74.06 163	91.19 169	78.28 117	91.09 213	89.29 210

ab-mvs		79.67 198	80.56 186	76.99 247	88.48 161	56.93 258	84.70 125	86.06 208	68.95 210	80.78 231	93.08 100	75.30 145	84.62 272	56.78 270	90.90 223	89.43 207

AllTest		87.97 66	87.40 76	89.68 47	91.59 99	83.40 45	89.50 54	95.44 5	79.47 75	88.00 130	93.03 101	82.66 72	91.47 162	70.81 172	96.14 97	94.16 95

TestCases				89.68 47	91.59 99	83.40 45		95.44 5	79.47 75	88.00 130	93.03 101	82.66 72	91.47 162	70.81 172	96.14 97	94.16 95

FMVSNet2		81.31 179	81.61 175	80.41 205	86.38 216	58.75 250	83.93 141	86.58 204	72.43 169	87.65 134	92.98 103	63.78 234	90.22 196	66.86 204	93.92 161	92.27 153

JIA-IIPM		69.41 283	66.64 293	77.70 239	73.19 334	71.24 137	75.67 278	65.56 328	70.42 195	65.18 324	92.97 104	33.64 342	83.06 281	53.52 289	69.61 339	78.79 316

HQP_MVS		87.75 70	87.43 75	88.70 60	93.45 52	76.42 96	89.45 56	93.61 32	79.44 77	86.55 151	92.95 105	74.84 152	95.22 37	80.78 88	95.83 111	94.46 86

plane_prior4											92.95 105

DP-MVS		88.60 60	89.01 53	87.36 80	91.30 109	77.50 83	87.55 81	92.97 57	87.95 17	89.62 102	92.87 107	84.56 55	93.89 77	77.65 122	96.62 78	90.70 186

VPNet		80.25 193	81.68 173	75.94 259	92.46 77	47.98 322	76.70 269	81.67 241	73.45 152	84.87 175	92.82 108	74.66 157	86.51 252	61.66 236	96.85 71	93.33 119

mvs_anonymous		78.13 205	78.76 200	76.23 258	79.24 293	50.31 312	78.69 253	84.82 225	61.60 266	83.09 206	92.82 108	73.89 168	87.01 239	68.33 197	86.41 273	91.37 173

UGNet		82.78 162	81.64 174	86.21 97	86.20 225	76.24 99	86.86 92	85.68 212	77.07 115	73.76 287	92.82 108	69.64 212	91.82 155	69.04 190	93.69 168	90.56 191
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
PatchT		70.52 272	72.76 253	63.79 313	79.38 291	33.53 343	77.63 262	65.37 329	73.61 150	71.77 297	92.79 111	44.38 320	75.65 305	64.53 222	85.37 281	82.18 291

LFMVS		80.15 196	80.56 186	78.89 220	89.19 146	55.93 264	85.22 119	73.78 280	82.96 40	84.28 191	92.72 112	57.38 264	90.07 203	63.80 224	95.75 114	90.68 187

RPMNet		76.06 228	75.79 223	76.85 250	79.58 288	62.64 204	82.58 181	71.75 299	74.80 139	75.72 273	92.59 113	48.69 289	84.07 275	73.48 154	82.91 300	83.85 267

IS-MVSNet		86.66 84	86.82 88	86.17 100	92.05 90	66.87 165	91.21 33	88.64 171	86.30 23	89.60 105	92.59 113	69.22 214	94.91 47	73.89 149	97.89 44	96.72 35

QAPM		82.59 165	82.59 163	82.58 176	86.44 214	66.69 166	89.94 43	90.36 135	67.97 216	84.94 174	92.58 115	72.71 193	92.18 145	70.63 177	87.73 261	88.85 216

MG-MVS		80.32 192	80.94 183	78.47 228	88.18 167	52.62 288	82.29 190	85.01 223	72.01 179	79.24 250	92.54 116	69.36 213	93.36 111	70.65 176	89.19 244	89.45 205

MVS_Test		82.47 167	83.22 153	80.22 207	82.62 270	57.75 254	82.54 184	91.96 86	71.16 190	82.89 207	92.52 117	77.41 124	90.50 190	80.04 100	87.84 260	92.40 147

CR-MVSNet		74.00 245	73.04 251	76.85 250	79.58 288	62.64 204	82.58 181	76.90 261	50.50 321	75.72 273	92.38 118	48.07 291	84.07 275	68.72 194	82.91 300	83.85 267

Patchmtry		76.56 224	77.46 207	73.83 272	79.37 292	46.60 324	82.41 186	76.90 261	73.81 149	85.56 168	92.38 118	48.07 291	83.98 277	63.36 227	95.31 125	90.92 179

CPTT-MVS		89.39 50	88.98 55	90.63 36	95.09 29	86.95 10	92.09 23	92.30 77	79.74 72	87.50 137	92.38 118	81.42 92	93.28 115	83.07 62	97.24 62	91.67 166

IterMVS-LS		84.73 119	84.98 115	83.96 145	87.35 195	63.66 185	83.25 165	89.88 154	76.06 123	89.62 102	92.37 121	73.40 180	92.52 140	78.16 118	94.77 145	95.69 57
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
SD-MVS		88.96 55	89.88 43	86.22 95	91.63 98	77.07 88	89.82 45	93.77 30	78.90 85	92.88 39	92.29 122	86.11 48	90.22 196	86.24 30	97.24 62	91.36 174

HPM-MVS++		88.93 56	88.45 62	90.38 40	94.92 32	85.85 25	89.70 46	91.27 115	78.20 95	86.69 149	92.28 123	80.36 101	95.06 43	86.17 31	96.49 84	90.22 198

HSP-MVS		88.63 59	87.84 67	91.02 29	95.76 16	86.14 19	92.75 13	91.01 122	78.43 92	89.16 112	92.25 124	72.03 204	96.36 2	88.21 9	90.93 222	90.55 192

TinyColmap		81.25 182	82.34 167	77.99 235	85.33 237	60.68 236	82.32 189	88.33 175	71.26 189	86.97 146	92.22 125	77.10 128	86.98 241	62.37 230	95.17 129	86.31 240

DU-MVS		86.80 81	86.99 81	86.21 97	93.24 58	67.02 163	83.16 168	92.21 78	81.73 55	90.92 73	91.97 126	77.20 125	93.99 71	74.16 145	98.35 24	97.61 13

NR-MVSNet		86.00 97	86.22 95	85.34 112	93.24 58	64.56 177	82.21 194	90.46 131	80.99 61	88.42 125	91.97 126	77.56 122	93.85 78	72.46 164	98.65 13	97.61 13

OpenMVS	76.72 13	81.98 175	82.00 171	81.93 184	84.42 249	68.22 157	88.50 70	89.48 162	66.92 223	81.80 220	91.86 128	72.59 197	90.16 198	71.19 171	91.25 211	87.40 230

FMVSNet5		72.10 263	71.69 263	73.32 274	81.57 276	53.02 284	76.77 268	78.37 254	63.31 250	76.37 265	91.85 129	36.68 338	78.98 296	47.87 312	92.45 195	87.95 224

旧先验1					91.97 91	71.77 130		81.78 240			91.84 130	73.92 167			93.65 170	83.61 270

EPP-MVSNet		85.47 107	85.04 113	86.77 84	91.52 105	69.37 147	91.63 28	87.98 183	81.51 58	87.05 145	91.83 131	66.18 226	95.29 33	70.75 174	96.89 70	95.64 59

UniMVSNet_NR-MVSNet		86.84 80	87.06 79	86.17 100	92.86 69	67.02 163	82.55 183	91.56 95	83.08 39	90.92 73	91.82 132	78.25 116	93.99 71	74.16 145	98.35 24	97.49 16

UniMVSNet (Re)		86.87 78	86.98 82	86.55 87	93.11 62	68.48 155	83.80 146	92.87 60	80.37 65	89.61 104	91.81 133	77.72 120	94.18 64	75.00 142	98.53 17	96.99 34

MIMVSNet		71.09 269	71.59 264	69.57 293	87.23 201	50.07 314	78.91 250	71.83 297	60.20 273	71.26 300	91.76 134	55.08 275	76.09 302	41.06 327	87.02 268	82.54 285

testdata				79.54 217	92.87 67	72.34 121		80.14 249	59.91 274	85.47 170	91.75 135	67.96 219	85.24 265	68.57 196	92.18 202	81.06 312

CDPH-MVS		86.17 96	85.54 106	88.05 71	92.25 84	75.45 101	83.85 143	92.01 83	65.91 231	86.19 158	91.75 135	83.77 61	94.98 45	77.43 126	96.71 76	93.73 109

test_prior3		86.31 91	86.31 93	86.32 91	90.59 126	71.99 128	83.37 161	92.85 61	75.43 134	84.58 183	91.57 137	81.92 86	94.17 66	79.54 106	96.97 68	92.80 131

test_prior2							83.37 161		75.43 134	84.58 183	91.57 137	81.92 86		79.54 106	96.97 68

WR-MVS		83.56 153	84.40 135	81.06 200	93.43 54	54.88 273	78.67 254	85.02 222	81.24 59	90.74 77	91.56 139	72.85 190	91.08 172	68.00 198	98.04 36	97.23 25

test20.03		73.75 247	74.59 235	71.22 288	81.11 280	51.12 302	70.15 305	72.10 294	70.42 195	80.28 243	91.50 140	64.21 231	74.72 308	46.96 317	94.58 150	87.82 227

CNVR-MVS		87.81 69	87.68 71	88.21 65	92.87 67	77.30 87	85.25 118	91.23 116	77.31 111	87.07 144	91.47 141	82.94 69	94.71 51	84.67 43	96.27 92	92.62 142

v2v482		84.09 141	84.24 141	83.62 156	87.13 204	61.40 226	82.71 180	89.71 158	72.19 177	89.55 106	91.41 142	70.70 211	93.20 117	81.02 83	93.76 166	96.25 41

v7		84.81 117	85.00 114	84.23 138	88.15 170	63.27 193	83.79 147	91.39 111	71.10 191	90.07 82	91.28 143	74.04 165	93.63 86	81.48 79	93.67 169	95.79 52

USDC		76.63 222	76.73 213	76.34 256	83.46 262	57.20 257	80.02 229	88.04 181	52.14 308	83.65 198	91.25 144	63.24 237	86.65 251	54.66 284	94.11 156	85.17 250

v1neww		84.43 127	84.66 125	83.75 150	87.81 181	62.34 213	83.59 151	90.27 144	72.33 173	89.93 89	91.22 145	73.28 182	93.29 112	80.25 97	93.25 180	95.62 60

v7new		84.43 127	84.66 125	83.75 150	87.81 181	62.34 213	83.59 151	90.27 144	72.33 173	89.93 89	91.22 145	73.28 182	93.29 112	80.25 97	93.25 180	95.62 60

OMC-MVS		88.19 63	87.52 72	90.19 44	91.94 94	81.68 53	87.49 83	93.17 48	76.02 125	88.64 120	91.22 145	84.24 58	93.37 110	77.97 121	97.03 67	95.52 65

ITE_SJBPF				90.11 45	90.72 124	84.97 33		90.30 140	81.56 57	90.02 84	91.20 148	82.40 76	90.81 181	73.58 152	94.66 148	94.56 81

v6		84.43 127	84.66 125	83.75 150	87.81 181	62.34 213	83.59 151	90.26 146	72.33 173	89.94 88	91.19 149	73.30 181	93.29 112	80.26 96	93.26 179	95.62 60

1121		80.86 186	79.81 196	84.02 142	93.93 45	78.70 74	81.64 208	80.18 248	55.43 290	83.67 196	91.15 150	71.29 208	91.41 166	67.95 200	93.06 185	81.96 294

MVS-HIRNet		61.16 309	62.92 304	55.87 326	79.09 294	35.34 342	71.83 299	57.98 343	46.56 330	59.05 339	91.14 151	49.95 286	76.43 301	38.74 332	71.92 333	55.84 341

v1141		84.16 138	84.38 136	83.52 160	87.32 197	61.70 222	82.79 176	89.74 155	71.90 184	89.64 99	91.12 152	72.68 195	93.10 123	80.39 95	93.80 164	95.75 54

divwei89l23v2f112		84.16 138	84.38 136	83.52 160	87.32 197	61.70 222	82.79 176	89.74 155	71.90 184	89.64 99	91.12 152	72.68 195	93.10 123	80.40 93	93.81 163	95.75 54

v1		84.16 138	84.38 136	83.52 160	87.33 196	61.71 220	82.79 176	89.73 157	71.89 186	89.64 99	91.11 154	72.72 192	93.10 123	80.40 93	93.79 165	95.75 54

新几何1				82.95 170	93.96 44	78.56 75		80.24 247	55.45 289	83.93 194	91.08 155	71.19 209	88.33 228	65.84 214	93.07 184	81.95 295

agg_prior1		85.72 104	85.20 111	87.28 81	91.58 102	77.69 80	83.69 150	90.30 140	66.29 227	84.32 189	91.07 156	82.13 79	93.18 118	81.02 83	96.36 87	90.98 176

EG-PatchMatch MVS		84.08 142	84.11 142	83.98 144	92.22 86	72.61 118	82.20 196	87.02 200	72.63 168	88.86 115	91.02 157	78.52 112	91.11 171	73.41 155	91.09 213	88.21 219

v1144		84.54 125	84.72 121	84.00 143	87.67 188	62.55 209	82.97 171	90.93 123	70.32 197	89.80 94	90.99 158	73.50 176	93.48 105	81.69 78	94.65 149	95.97 49

TEST9					92.34 81	79.70 66	83.94 139	90.32 136	65.41 239	84.49 185	90.97 159	82.03 82	93.63 86

train_agg		85.98 100	85.28 110	88.07 68	92.34 81	79.70 66	83.94 139	90.32 136	65.79 232	84.49 185	90.97 159	81.93 84	93.63 86	81.21 80	96.54 81	90.88 181

test_8					92.09 88	78.87 72	83.82 144	90.31 138	65.79 232	84.36 188	90.96 161	81.93 84	93.44 107

XXY-MVS		74.44 244	76.19 221	69.21 294	84.61 245	52.43 289	71.70 300	77.18 259	60.73 271	80.60 236	90.96 161	75.44 142	69.35 318	56.13 273	88.33 251	85.86 245

v1192		84.57 122	84.69 123	84.21 139	87.75 185	62.88 200	83.02 170	91.43 106	69.08 208	89.98 87	90.89 163	72.70 194	93.62 90	82.41 70	94.97 135	96.13 43

NCCC		87.36 71	86.87 85	88.83 58	92.32 83	78.84 73	86.58 104	91.09 120	78.77 88	84.85 176	90.89 163	80.85 96	95.29 33	81.14 82	95.32 123	92.34 150

test222					93.31 56	76.54 93	79.38 242	77.79 256	52.59 303	82.36 210	90.84 165	66.83 223			91.69 205	81.25 307

V42		83.47 156	83.37 152	83.75 150	83.16 266	63.33 191	81.31 213	90.23 147	69.51 203	90.91 75	90.81 166	74.16 162	92.29 144	80.06 99	90.22 235	95.62 60

114514_t		83.10 161	82.54 164	84.77 122	92.90 66	69.10 153	86.65 102	90.62 128	54.66 293	81.46 223	90.81 166	76.98 130	94.38 58	72.62 163	96.18 94	90.82 184

VNet		79.31 199	80.27 190	76.44 254	87.92 175	53.95 277	75.58 280	84.35 227	74.39 144	82.23 211	90.72 168	72.84 191	84.39 274	60.38 246	93.98 160	90.97 177

DeepC-MVS_fast	80.27 8	86.23 94	85.65 105	87.96 72	91.30 109	76.92 89	87.19 87	91.99 84	70.56 194	84.96 172	90.69 169	80.01 104	95.14 40	78.37 114	95.78 113	91.82 163
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
DeepPCF-MVS	81.24 5	87.28 73	86.21 96	90.49 38	91.48 106	84.90 34	83.41 160	92.38 76	70.25 198	89.35 110	90.68 170	82.85 70	94.57 55	79.55 105	95.95 105	92.00 159

原ACMM1				84.60 127	92.81 71	74.01 109		91.50 97	62.59 256	82.73 209	90.67 171	76.53 138	94.25 61	69.24 186	95.69 116	85.55 247

v148		82.31 168	82.48 165	81.81 191	85.59 233	59.66 242	81.47 211	86.02 209	72.85 165	88.05 129	90.65 172	70.73 210	90.91 178	75.15 139	91.79 203	94.87 77

v1240		84.30 133	84.51 132	83.65 155	87.65 189	61.26 229	82.85 174	91.54 96	67.94 217	90.68 78	90.65 172	71.71 206	93.64 85	82.84 67	94.78 143	96.07 45

agg_prior3		85.76 103	84.95 116	88.16 66	92.43 78	79.92 62	83.98 138	90.03 151	65.11 241	83.66 197	90.64 174	81.00 95	93.67 83	81.21 80	96.54 81	90.88 181

v144192		84.24 135	84.41 134	83.71 154	87.59 191	61.57 225	82.95 172	91.03 121	67.82 220	89.80 94	90.49 175	73.28 182	93.51 104	81.88 77	94.89 137	96.04 47

FMVSNet3		78.80 202	78.55 202	79.57 216	82.89 268	56.89 260	81.76 205	85.77 211	69.04 209	86.00 160	90.44 176	51.75 282	90.09 202	65.95 211	93.34 176	91.72 165

v1921920		84.23 136	84.37 139	83.79 148	87.64 190	61.71 220	82.91 173	91.20 117	67.94 217	90.06 83	90.34 177	72.04 203	93.59 91	82.32 72	94.91 136	96.07 45

no-one		71.52 267	70.43 272	74.81 264	78.45 301	63.41 188	57.73 334	77.03 260	51.46 314	77.17 262	90.33 178	54.96 276	80.35 293	47.41 313	99.29 2	80.68 314

DSMNet-mixed		60.98 311	61.61 308	59.09 325	72.88 337	45.05 329	74.70 286	46.61 350	26.20 344	65.34 323	90.32 179	55.46 272	63.12 338	41.72 326	81.30 310	69.09 332

pmmvs-eth3d		78.42 204	77.04 210	82.57 178	87.44 193	74.41 107	80.86 222	79.67 251	55.68 288	84.69 178	90.31 180	60.91 243	85.42 264	62.20 231	91.59 206	87.88 226

semantic-postprocess				84.34 136	83.93 258	69.66 145		81.09 244	72.43 169	86.47 157	90.19 181	57.56 263	93.15 122	77.45 125	86.39 274	90.22 198

PM-MVS		80.20 195	79.00 199	83.78 149	88.17 168	86.66 13	81.31 213	66.81 327	69.64 202	88.33 128	90.19 181	64.58 230	83.63 280	71.99 169	90.03 236	81.06 312

NP-MVS					91.95 92	74.55 106					90.17 183

HQP-MVS		84.61 121	84.06 143	86.27 93	91.19 112	70.66 140	84.77 122	92.68 67	73.30 157	80.55 239	90.17 183	72.10 200	94.61 53	77.30 127	94.47 152	93.56 116

testgi		72.36 260	74.61 233	65.59 307	80.56 286	42.82 335	68.29 309	73.35 284	66.87 224	81.84 217	89.93 185	72.08 202	66.92 326	46.05 319	92.54 194	87.01 234

PCF-MVS	74.62 15	82.15 171	80.92 184	85.84 106	89.43 141	72.30 122	80.53 224	91.82 89	57.36 281	87.81 133	89.92 186	77.67 121	93.63 86	58.69 256	95.08 131	91.58 169
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
diffmvs		79.20 200	79.04 198	79.69 213	78.64 299	58.90 247	81.79 204	87.61 187	65.07 242	73.65 289	89.80 187	73.10 188	87.79 233	75.02 141	86.63 270	92.38 148

Vis-MVSNet (Re-imp)		77.82 207	77.79 206	77.92 236	88.82 152	51.29 300	83.28 163	71.97 295	74.04 146	82.23 211	89.78 188	57.38 264	89.41 209	57.22 268	95.41 120	93.05 126

MCST-MVS		84.36 130	83.93 147	85.63 109	91.59 99	71.58 135	83.52 155	92.13 80	61.82 262	83.96 193	89.75 189	79.93 106	93.46 106	78.33 116	94.34 154	91.87 162

test_normal		81.23 183	81.16 180	81.43 194	84.77 244	61.99 219	81.46 212	86.95 202	63.16 252	87.22 139	89.63 190	73.62 172	91.65 159	72.92 161	90.70 228	90.65 189

DI_MVS_plusplus_test		81.27 181	81.26 178	81.29 196	84.98 239	61.65 224	81.98 199	87.25 192	63.56 247	87.56 136	89.60 191	73.62 172	91.83 154	72.20 166	90.59 233	90.38 196

TAPA-MVS	77.73 12	85.71 105	84.83 118	88.37 63	88.78 153	79.72 65	87.15 89	93.50 35	69.17 206	85.80 164	89.56 192	80.76 97	92.13 146	73.21 160	95.51 118	93.25 122
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
testmv		70.47 273	70.70 268	69.77 291	86.22 224	53.89 278	67.32 314	71.91 296	63.32 249	78.16 255	89.47 193	56.12 269	73.10 309	36.43 335	87.33 264	82.33 289

MSLP-MVS++		85.00 114	86.03 99	81.90 185	91.84 96	71.56 136	86.75 98	93.02 56	75.95 126	87.12 141	89.39 194	77.98 117	89.40 210	77.46 124	94.78 143	84.75 258

MVS_111021_HR		84.63 120	84.34 140	85.49 111	90.18 132	75.86 100	79.23 248	87.13 196	73.35 154	85.56 168	89.34 195	83.60 63	90.50 190	76.64 132	94.05 158	90.09 203

IterMVS		76.91 216	76.34 220	78.64 225	80.91 282	64.03 181	76.30 274	79.03 252	64.88 244	83.11 204	89.16 196	59.90 250	84.46 273	68.61 195	85.15 285	87.42 229
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
F-COLMAP		84.97 115	83.42 150	89.63 49	92.39 79	83.40 45	88.83 64	91.92 87	73.19 161	80.18 244	89.15 197	77.04 129	93.28 115	65.82 215	92.28 198	92.21 156

MVS_111021_LR		84.28 134	83.76 148	85.83 107	89.23 145	83.07 48	80.99 220	83.56 228	72.71 167	86.07 159	89.07 198	81.75 89	86.19 256	77.11 129	93.36 175	88.24 218

MDA-MVSNet-bldmvs		77.47 210	76.90 211	79.16 219	79.03 295	64.59 176	66.58 317	75.67 268	73.15 162	88.86 115	88.99 199	66.94 222	81.23 289	64.71 218	88.22 256	91.64 167

EPNet		80.37 191	78.41 203	86.23 94	76.75 311	73.28 113	87.18 88	77.45 258	76.24 122	68.14 312	88.93 200	65.41 229	93.85 78	69.47 184	96.12 99	91.55 170
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
Anonymous20231206		71.38 268	71.88 262	69.88 289	86.31 220	54.37 274	70.39 304	74.62 274	52.57 304	76.73 263	88.76 201	59.94 249	72.06 311	44.35 322	93.23 183	83.23 278

EU-MVSNet		75.12 236	74.43 237	77.18 246	83.11 267	59.48 244	85.71 114	82.43 234	39.76 341	85.64 166	88.76 201	44.71 319	87.88 232	73.86 150	85.88 277	84.16 266

MVSTER		77.09 214	75.70 226	81.25 197	75.27 325	61.08 231	77.49 265	85.07 220	60.78 270	86.55 151	88.68 203	43.14 323	90.25 193	73.69 151	90.67 229	92.42 145

CNLPA		83.55 154	83.10 156	84.90 118	89.34 143	83.87 43	84.54 129	88.77 168	79.09 82	83.54 200	88.66 204	74.87 151	81.73 288	66.84 206	92.29 197	89.11 211

BH-RMVSNet		80.53 190	80.22 191	81.49 193	87.19 203	66.21 168	77.79 261	86.23 206	74.21 145	83.69 195	88.50 205	73.25 186	90.75 182	63.18 229	87.90 258	87.52 228

MVS_0304		84.88 116	83.96 146	87.64 76	87.43 194	74.83 104	84.18 133	93.30 42	77.48 104	77.39 261	88.46 206	74.53 160	95.74 16	78.09 120	94.75 147	92.36 149

view600		76.79 217	76.54 214	77.56 240	87.91 176	50.77 306	81.92 200	71.35 302	77.38 105	84.62 179	88.40 207	45.18 313	89.26 211	58.58 257	93.49 171	92.66 136

view800		76.79 217	76.54 214	77.56 240	87.91 176	50.77 306	81.92 200	71.35 302	77.38 105	84.62 179	88.40 207	45.18 313	89.26 211	58.58 257	93.49 171	92.66 136

conf0.05thres1000		76.79 217	76.54 214	77.56 240	87.91 176	50.77 306	81.92 200	71.35 302	77.38 105	84.62 179	88.40 207	45.18 313	89.26 211	58.58 257	93.49 171	92.66 136

tfpn		76.79 217	76.54 214	77.56 240	87.91 176	50.77 306	81.92 200	71.35 302	77.38 105	84.62 179	88.40 207	45.18 313	89.26 211	58.58 257	93.49 171	92.66 136

DP-MVS Recon		84.05 143	83.22 153	86.52 88	91.73 97	75.27 102	83.23 167	92.40 74	72.04 178	82.04 214	88.33 211	77.91 119	93.95 76	66.17 210	95.12 130	90.34 197

Test4		81.31 179	81.13 181	81.88 187	84.89 241	63.05 197	82.37 187	90.50 130	62.75 255	89.00 114	88.29 212	67.55 220	91.68 158	73.55 153	91.24 212	90.89 180

UnsupCasMVSNet_eth		71.63 266	72.30 259	69.62 292	76.47 313	52.70 287	70.03 306	80.97 245	59.18 275	79.36 249	88.21 213	60.50 244	69.12 319	58.33 263	77.62 322	87.04 233

tpm		67.95 287	68.08 286	67.55 302	78.74 298	43.53 333	75.60 279	67.10 326	54.92 292	72.23 295	88.10 214	42.87 324	75.97 303	52.21 292	80.95 312	83.15 279

CSCG		86.26 92	86.47 91	85.60 110	90.87 121	74.26 108	87.98 74	91.85 88	80.35 66	89.54 108	88.01 215	79.09 109	92.13 146	75.51 137	95.06 132	90.41 195

alignmvs		83.94 147	83.98 145	83.80 147	87.80 184	67.88 159	84.54 129	91.42 108	73.27 160	88.41 126	87.96 216	72.33 199	90.83 180	76.02 135	94.11 156	92.69 135

MVP-Stereo		75.81 231	73.51 247	82.71 174	89.35 142	73.62 110	80.06 227	85.20 217	60.30 272	73.96 286	87.94 217	57.89 261	89.45 208	52.02 293	74.87 327	85.06 252
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
new-patchmatchnet		70.10 276	73.37 248	60.29 322	81.23 279	16.95 350	59.54 328	74.62 274	62.93 253	80.97 227	87.93 218	62.83 238	71.90 312	55.24 280	95.01 134	92.00 159

PAPM_NR		83.23 158	83.19 155	83.33 164	90.90 120	65.98 169	88.19 72	90.78 124	78.13 97	80.87 230	87.92 219	73.49 177	92.42 141	70.07 180	88.40 250	91.60 168

LF4IMVS		82.75 163	81.93 172	85.19 113	82.08 271	80.15 61	85.53 115	88.76 169	68.01 214	85.58 167	87.75 220	71.80 205	86.85 243	74.02 147	93.87 162	88.58 217

PHI-MVS		86.38 89	85.81 101	88.08 67	88.44 162	77.34 85	89.35 58	93.05 52	73.15 162	84.76 177	87.70 221	78.87 111	94.18 64	80.67 90	96.29 89	92.73 133

FPMVS		72.29 262	72.00 261	73.14 278	88.63 157	85.00 32	74.65 287	67.39 321	71.94 183	77.80 258	87.66 222	50.48 285	75.83 304	49.95 301	79.51 314	58.58 340

CMPMVS	59.41 20	75.12 236	73.57 243	79.77 210	75.84 317	67.22 161	81.21 216	82.18 235	50.78 318	76.50 264	87.66 222	55.20 274	82.99 282	62.17 232	90.64 232	89.09 212
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
canonicalmvs		85.50 106	86.14 97	83.58 157	87.97 173	67.13 162	87.55 81	94.32 12	73.44 153	88.47 124	87.54 224	86.45 44	91.06 173	75.76 136	93.76 166	92.54 144

CANet		83.79 149	82.85 158	86.63 85	86.17 226	72.21 125	83.76 148	91.43 106	77.24 112	74.39 284	87.45 225	75.36 144	95.42 29	77.03 130	92.83 190	92.25 155

OpenMVS_ROB	70.19 17	77.77 209	77.46 207	78.71 224	84.39 250	61.15 230	81.18 217	82.52 233	62.45 259	83.34 201	87.37 226	66.20 225	88.66 226	64.69 219	85.02 286	86.32 239

PVSNet_Blended_VisFu		81.55 177	80.49 188	84.70 125	91.58 102	73.24 114	84.21 132	91.67 93	62.86 254	80.94 228	87.16 227	67.27 221	92.87 133	69.82 182	88.94 246	87.99 223

AdaColmap		83.66 151	83.69 149	83.57 158	90.05 136	72.26 123	86.29 108	90.00 152	78.19 96	81.65 221	87.16 227	83.40 65	94.24 62	61.69 235	94.76 146	84.21 265

PVSNet_BlendedMVS		78.80 202	77.84 205	81.65 192	84.43 247	63.41 188	79.49 238	90.44 132	61.70 265	75.43 275	87.07 229	69.11 215	91.44 164	60.68 244	92.24 199	90.11 202

TAMVS		78.08 206	76.36 219	83.23 165	90.62 125	72.87 115	79.08 249	80.01 250	61.72 264	81.35 225	86.92 230	63.96 233	88.78 223	50.61 299	93.01 187	88.04 222

BH-untuned		80.96 185	80.99 182	80.84 201	88.55 158	68.23 156	80.33 226	88.46 172	72.79 166	86.55 151	86.76 231	74.72 156	91.77 156	61.79 234	88.99 245	82.52 286

1111		61.71 305	63.77 301	55.55 328	78.05 302	25.74 347	60.62 325	67.52 319	66.09 229	74.68 281	86.50 232	16.00 353	59.22 341	38.79 330	85.65 278	81.70 298

.test1245		48.02 322	54.41 321	28.84 335	78.05 302	25.74 347	60.62 325	67.52 319	66.09 229	74.68 281	86.50 232	16.00 353	59.22 341	38.79 330	1.47 348	1.55 347

pmmvs4		74.92 239	72.98 252	80.73 203	84.95 240	71.71 134	76.23 275	77.59 257	52.83 302	77.73 259	86.38 234	56.35 267	84.97 268	57.72 267	87.05 267	85.51 248

conf200view11		75.62 232	75.05 230	77.34 245	87.27 199	51.88 293	81.07 218	73.26 285	75.68 130	83.25 202	86.37 235	45.54 304	88.80 219	51.98 294	90.99 217	91.34 175

thres100view900		75.45 233	75.05 230	76.66 253	87.27 199	51.88 293	81.07 218	73.26 285	75.68 130	83.25 202	86.37 235	45.54 304	88.80 219	51.98 294	90.99 217	89.31 208

Patchmatch-RL test		74.48 242	73.68 241	76.89 249	84.83 242	66.54 167	72.29 298	69.16 314	57.70 279	86.76 147	86.33 237	45.79 303	82.59 284	69.63 183	90.65 231	81.54 302

PLC	73.85 16	82.09 172	80.31 189	87.45 78	90.86 122	80.29 60	85.88 110	90.65 126	68.17 213	76.32 267	86.33 237	73.12 187	92.61 139	61.40 239	90.02 237	89.44 206
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
thres600view7		75.97 229	75.35 229	77.85 238	87.01 209	51.84 296	80.45 225	73.26 285	75.20 137	83.10 205	86.31 239	45.54 304	89.05 215	55.03 282	92.24 199	92.66 136

HY-MVS	64.64 18	73.03 254	72.47 258	74.71 265	83.36 263	54.19 275	82.14 197	81.96 237	56.76 286	69.57 308	86.21 240	60.03 248	84.83 271	49.58 305	82.65 302	85.11 251

TSAR-MVS + GP.		83.95 146	82.69 160	87.72 73	89.27 144	81.45 55	83.72 149	81.58 242	74.73 140	85.66 165	86.06 241	72.56 198	92.69 136	75.44 138	95.21 127	89.01 215

Test_1112_low_res		73.90 246	73.08 250	76.35 255	90.35 129	55.95 263	73.40 296	86.17 207	50.70 319	73.14 290	85.94 242	58.31 259	85.90 260	56.51 271	83.22 297	87.20 232

Effi-MVS+-dtu		85.82 102	83.38 151	93.14 3	87.13 204	91.15 3	87.70 79	88.42 173	74.57 142	83.56 199	85.65 243	78.49 113	94.21 63	72.04 167	92.88 189	94.05 98

MDTV_nov1_ep13			68.29 285		78.03 304	43.87 332	74.12 290	72.22 293	52.17 306	67.02 317	85.54 244	45.36 309	80.85 290	55.73 274	84.42 292

EI-MVSNet-Vis-set		85.12 110	84.53 131	86.88 82	84.01 257	72.76 116	83.91 142	85.18 218	80.44 64	88.75 118	85.49 245	80.08 103	91.92 150	82.02 74	90.85 225	95.97 49

Regformer-3		85.06 111	84.67 124	86.22 95	84.27 251	73.43 112	84.07 135	85.26 216	80.77 63	88.62 121	85.48 246	80.56 100	90.39 192	81.99 75	91.04 215	94.85 79

Regformer-4		86.41 88	85.71 103	88.52 61	84.27 251	77.57 82	84.07 135	88.00 182	82.82 42	89.84 93	85.48 246	82.06 80	92.77 134	83.83 55	91.04 215	95.22 74

CHOSEN 1792x2688		72.45 259	70.56 269	78.13 232	90.02 138	63.08 196	68.72 308	83.16 229	42.99 338	75.92 271	85.46 248	57.22 266	85.18 267	49.87 303	81.67 306	86.14 241

EI-MVSNet-UG-set		85.04 112	84.44 133	86.85 83	83.87 260	72.52 119	83.82 144	85.15 219	80.27 68	88.75 118	85.45 249	79.95 105	91.90 151	81.92 76	90.80 226	96.13 43

MDA-MVSNet_test_wron		70.05 278	70.44 270	68.88 296	73.84 330	53.47 280	58.93 333	67.28 322	58.43 276	87.09 143	85.40 250	59.80 252	67.25 324	59.66 250	83.54 295	85.92 244

YYNet1		70.06 277	70.44 270	68.90 295	73.76 331	53.42 282	58.99 332	67.20 323	58.42 277	87.10 142	85.39 251	59.82 251	67.32 323	59.79 248	83.50 296	85.96 242

pmmvs5		70.73 271	70.07 274	72.72 281	77.03 310	52.73 286	74.14 289	75.65 269	50.36 322	72.17 296	85.37 252	55.42 273	80.67 291	52.86 291	87.59 263	84.77 257

UnsupCasMVSNet_bld		69.21 284	69.68 276	67.82 301	79.42 290	51.15 301	67.82 313	75.79 266	54.15 295	77.47 260	85.36 253	59.26 255	70.64 314	48.46 309	79.35 316	81.66 300

EI-MVSNet		82.61 164	82.42 166	83.20 166	83.25 264	63.66 185	83.50 158	85.07 220	76.06 123	86.55 151	85.10 254	73.41 178	90.25 193	78.15 119	90.67 229	95.68 58

CVMVSNet		72.62 257	71.41 266	76.28 257	83.25 264	60.34 238	83.50 158	79.02 253	37.77 342	76.33 266	85.10 254	49.60 287	87.41 236	70.54 178	77.54 323	81.08 310

Regformer-1		86.00 97	85.50 107	87.49 77	84.18 255	76.90 90	83.52 155	87.94 184	82.18 50	89.19 111	85.07 256	82.28 78	91.89 152	82.40 71	92.72 192	93.69 111

Regformer-2		86.74 83	86.08 98	88.73 59	84.18 255	79.20 69	83.52 155	89.33 164	83.33 35	89.92 91	85.07 256	83.23 67	93.16 120	83.39 57	92.72 192	93.83 103

MVSFormer		82.23 170	81.57 176	84.19 141	85.54 234	69.26 149	91.98 25	90.08 149	71.54 187	76.23 268	85.07 256	58.69 257	94.27 59	86.26 27	88.77 247	89.03 213

jason		77.42 211	75.75 225	82.43 181	87.10 207	69.27 148	77.99 258	81.94 239	51.47 313	77.84 256	85.07 256	60.32 246	89.00 216	70.74 175	89.27 243	89.03 213
jason: jason.
PMMVS2		55.64 318	59.27 315	44.74 333	64.30 348	12.32 351	40.60 342	49.79 349	53.19 300	65.06 327	84.81 260	53.60 279	49.76 345	32.68 341	89.41 240	72.15 327

CostFormer		69.98 279	68.68 282	73.87 271	77.14 308	50.72 310	79.26 244	74.51 276	51.94 310	70.97 303	84.75 261	45.16 317	87.49 235	55.16 281	79.23 317	83.40 274

PAPM		71.77 264	70.06 275	76.92 248	86.39 215	53.97 276	76.62 270	86.62 203	53.44 299	63.97 329	84.73 262	57.79 262	92.34 142	39.65 329	81.33 309	84.45 260

PAPR		78.84 201	78.10 204	81.07 199	85.17 238	60.22 239	82.21 194	90.57 129	62.51 257	75.32 277	84.61 263	74.99 149	92.30 143	59.48 254	88.04 257	90.68 187

tfpn200view9		74.86 240	74.23 238	76.74 252	86.24 222	52.12 290	79.24 245	73.87 278	73.34 155	81.82 218	84.60 264	46.02 298	88.80 219	51.98 294	90.99 217	89.31 208

thres400		75.14 234	74.23 238	77.86 237	86.24 222	52.12 290	79.24 245	73.87 278	73.34 155	81.82 218	84.60 264	46.02 298	88.80 219	51.98 294	90.99 217	92.66 136

HyFIR lowres test		75.12 236	72.66 254	82.50 179	91.44 108	65.19 173	72.47 297	87.31 190	46.79 329	80.29 242	84.30 266	52.70 280	92.10 148	51.88 298	86.73 269	90.22 198

Effi-MVS+		83.90 148	84.01 144	83.57 158	87.22 202	65.61 172	86.55 105	92.40 74	78.64 90	81.34 226	84.18 267	83.65 62	92.93 130	74.22 144	87.87 259	92.17 157

tpmp4_e23		69.43 281	67.33 288	75.72 260	78.53 300	52.75 285	82.13 198	74.91 271	49.23 326	66.37 318	84.17 268	41.28 330	88.67 225	49.73 304	79.63 313	85.75 246

API-MVS		82.28 169	82.61 162	81.30 195	86.29 221	69.79 143	88.71 67	87.67 186	78.42 93	82.15 213	84.15 269	77.98 117	91.59 160	65.39 216	92.75 191	82.51 287

DELS-MVS		81.44 178	81.25 179	82.03 183	84.27 251	62.87 201	76.47 273	92.49 73	70.97 192	81.64 222	83.83 270	75.03 148	92.70 135	74.29 143	92.22 201	90.51 193
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
CANet_DTU		77.81 208	77.05 209	80.09 208	81.37 277	59.90 241	83.26 164	88.29 176	69.16 207	67.83 315	83.72 271	60.93 242	89.47 206	69.22 188	89.70 239	90.88 181

tpm2		68.45 286	66.83 290	73.30 275	78.93 296	48.50 319	79.76 232	71.76 298	47.50 328	69.92 307	83.60 272	42.07 326	88.40 227	48.44 310	79.51 314	83.01 281

Fast-Effi-MVS+-dtu		82.54 166	81.41 177	85.90 104	85.60 232	76.53 95	83.07 169	89.62 160	73.02 164	79.11 251	83.51 273	80.74 98	90.24 195	68.76 192	89.29 241	90.94 178

CDS-MVSNet		77.32 212	75.40 227	83.06 168	89.00 149	72.48 120	77.90 260	82.17 236	60.81 269	78.94 252	83.49 274	59.30 254	88.76 224	54.64 285	92.37 196	87.93 225
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
MSDG		80.06 197	79.99 195	80.25 206	83.91 259	68.04 158	77.51 264	89.19 165	77.65 101	81.94 215	83.45 275	76.37 139	86.31 255	63.31 228	86.59 271	86.41 238

Patchmatch-test		65.91 296	67.38 287	61.48 319	75.51 321	43.21 334	68.84 307	63.79 331	62.48 258	72.80 292	83.42 276	44.89 318	59.52 340	48.27 311	86.45 272	81.70 298

LP		69.42 282	68.30 284	72.77 280	71.48 342	56.84 261	73.66 294	74.84 272	63.52 248	70.95 304	83.35 277	49.55 288	77.15 300	57.13 269	70.21 335	84.33 262

tfpn1000		73.63 251	73.58 242	73.79 273	85.46 236	50.31 312	79.99 230	68.18 318	72.33 173	80.66 235	83.05 278	39.80 335	86.74 250	60.96 242	91.78 204	84.32 263

test1235678		65.57 298	65.73 295	65.06 310	82.84 269	50.90 304	62.90 322	69.26 312	57.17 284	72.36 294	83.04 279	46.02 298	70.10 315	32.79 340	85.24 284	74.19 324

mvs-test1		84.55 123	82.12 168	91.84 17	87.13 204	89.54 5	85.05 121	88.42 173	74.57 142	80.60 236	82.98 280	78.49 113	93.98 73	72.04 167	89.77 238	92.00 159

ADS-MVSNet2		65.87 297	63.64 302	72.55 283	73.16 335	56.92 259	67.10 315	74.81 273	49.74 323	66.04 320	82.97 281	46.71 293	77.26 298	42.29 324	69.96 337	83.46 272

ADS-MVSNet		61.90 304	62.19 306	61.03 321	73.16 335	36.42 341	67.10 315	61.75 335	49.74 323	66.04 320	82.97 281	46.71 293	63.21 337	42.29 324	69.96 337	83.46 272

PatchmatchNet		69.71 280	68.83 280	72.33 284	77.66 306	53.60 279	79.29 243	69.99 310	57.66 280	72.53 293	82.93 283	46.45 295	80.08 295	60.91 243	72.09 332	83.31 277
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
cdsmvs_eth3d_5k		20.81 324	27.75 325	0.00 340	0.00 354	0.00 355	0.00 345	85.44 214	0.00 349	0.00 351	82.82 284	81.46 91	0.00 352	0.00 349	0.00 351	0.00 349

lupinMVS		76.37 226	74.46 236	82.09 182	85.54 234	69.26 149	76.79 267	80.77 246	50.68 320	76.23 268	82.82 284	58.69 257	88.94 217	69.85 181	88.77 247	88.07 220

xiu_mvs_v1_base_debu		80.84 187	80.14 192	82.93 171	88.31 164	71.73 131	79.53 235	87.17 193	65.43 236	79.59 246	82.73 286	76.94 131	90.14 199	73.22 156	88.33 251	86.90 235

xiu_mvs_v1_base		80.84 187	80.14 192	82.93 171	88.31 164	71.73 131	79.53 235	87.17 193	65.43 236	79.59 246	82.73 286	76.94 131	90.14 199	73.22 156	88.33 251	86.90 235

xiu_mvs_v1_base_debi		80.84 187	80.14 192	82.93 171	88.31 164	71.73 131	79.53 235	87.17 193	65.43 236	79.59 246	82.73 286	76.94 131	90.14 199	73.22 156	88.33 251	86.90 235

tfpn_n400		73.65 248	73.53 244	73.98 268	86.13 227	50.06 315	79.45 239	68.54 315	72.01 179	80.76 232	82.50 289	41.39 327	86.83 244	59.66 250	91.36 208	85.06 252

tfpnconf		73.65 248	73.53 244	73.98 268	86.13 227	50.06 315	79.45 239	68.54 315	72.01 179	80.76 232	82.50 289	41.39 327	86.83 244	59.66 250	91.36 208	85.06 252

tfpnview11		73.65 248	73.53 244	73.98 268	86.13 227	50.06 315	79.45 239	68.54 315	72.01 179	80.76 232	82.50 289	41.39 327	86.83 244	59.66 250	91.36 208	85.06 252

N_pmnet		70.20 274	68.80 281	74.38 267	80.91 282	84.81 35	59.12 331	76.45 265	55.06 291	75.31 278	82.36 292	55.74 270	54.82 343	47.02 315	87.24 266	83.52 271

Patchmatch-test1		72.75 256	72.61 255	73.19 277	81.62 275	55.86 265	78.89 251	71.37 301	61.73 263	74.93 280	82.15 293	60.46 245	81.80 286	59.68 249	82.63 304	81.92 296

TR-MVS		76.77 221	75.79 223	79.72 212	86.10 230	65.79 171	77.14 266	83.02 230	65.20 240	81.40 224	82.10 294	66.30 224	90.73 184	55.57 277	85.27 282	82.65 282

Fast-Effi-MVS+		81.04 184	80.57 185	82.46 180	87.50 192	63.22 195	78.37 255	89.63 159	68.01 214	81.87 216	82.08 295	82.31 77	92.65 138	67.10 202	88.30 255	91.51 171

tpmvs		70.16 275	69.56 277	71.96 286	74.71 329	48.13 320	79.63 233	75.45 270	65.02 243	70.26 305	81.88 296	45.34 310	85.68 262	58.34 262	75.39 326	82.08 292

GA-MVS		75.83 230	74.61 233	79.48 218	81.87 273	59.25 246	73.42 295	82.88 231	68.68 211	79.75 245	81.80 297	50.62 284	89.46 207	66.85 205	85.64 279	89.72 204

patchmatchnet-post											81.71 298	45.93 301	87.01 239

WTY-MVS		67.91 288	68.35 283	66.58 305	80.82 284	48.12 321	65.96 318	72.60 290	53.67 298	71.20 301	81.68 299	58.97 256	69.06 320	48.57 308	81.67 306	82.55 284

CLD-MVS		83.18 159	82.64 161	84.79 121	89.05 147	67.82 160	77.93 259	92.52 72	68.33 212	85.07 171	81.54 300	82.06 80	92.96 128	69.35 185	97.91 43	93.57 115
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
MS-PatchMatch		70.93 270	70.22 273	73.06 279	81.85 274	62.50 210	73.82 293	77.90 255	52.44 305	75.92 271	81.27 301	55.67 271	81.75 287	55.37 279	77.70 321	74.94 322

PatchMatch-RL		74.48 242	73.22 249	78.27 231	87.70 187	85.26 30	75.92 276	70.09 309	64.34 246	76.09 270	81.25 302	65.87 228	78.07 297	53.86 287	83.82 294	71.48 328

EPNet_dtu		72.87 255	71.33 267	77.49 244	77.72 305	60.55 237	82.35 188	75.79 266	66.49 226	58.39 342	81.06 303	53.68 278	85.98 258	53.55 288	92.97 188	85.95 243
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
thres200		72.34 261	71.55 265	74.70 266	83.48 261	51.60 297	75.02 284	73.71 281	70.14 199	78.56 253	80.57 304	46.20 296	88.20 230	46.99 316	89.29 241	84.32 263

tpmrst		66.28 295	66.69 292	65.05 311	72.82 338	39.33 338	78.20 256	70.69 307	53.16 301	67.88 314	80.36 305	48.18 290	74.75 307	58.13 264	70.79 334	81.08 310

sss		66.92 291	67.26 289	65.90 306	77.23 307	51.10 303	64.79 319	71.72 300	52.12 309	70.13 306	80.18 306	57.96 260	65.36 333	50.21 300	81.01 311	81.25 307

EPMVS		62.47 301	62.63 305	62.01 315	70.63 343	38.74 339	74.76 285	52.86 346	53.91 297	67.71 316	80.01 307	39.40 336	66.60 328	55.54 278	68.81 341	80.68 314

BH-w/o		76.57 223	76.07 222	78.10 233	86.88 211	65.92 170	77.63 262	86.33 205	65.69 234	80.89 229	79.95 308	68.97 217	90.74 183	53.01 290	85.25 283	77.62 317

tfpn_ndepth		72.54 258	72.30 259	73.24 276	84.81 243	51.42 298	79.24 245	70.49 308	69.26 205	78.48 254	79.80 309	40.16 334	86.77 248	58.08 266	90.43 234	81.53 303

1112_ss		74.82 241	73.74 240	78.04 234	89.57 140	60.04 240	76.49 272	87.09 199	54.31 294	73.66 288	79.80 309	60.25 247	86.76 249	58.37 261	84.15 293	87.32 231

ab-mvs-re		6.65 326	8.87 327	0.00 340	0.00 354	0.00 355	0.00 345	0.00 357	0.00 349	0.00 351	79.80 309	0.00 358	0.00 352	0.00 349	0.00 351	0.00 349

tpm cat1		66.76 292	65.21 297	71.42 287	77.09 309	50.62 311	78.01 257	73.68 282	44.89 334	68.64 309	79.00 312	45.51 307	82.42 285	49.91 302	70.15 336	81.23 309

xiu_mvs_v2_base		77.19 213	76.75 212	78.52 227	87.01 209	61.30 228	75.55 281	87.12 198	61.24 268	74.45 283	78.79 313	77.20 125	90.93 176	64.62 221	84.80 290	83.32 276

MAR-MVS		80.24 194	78.74 201	84.73 124	86.87 212	78.18 76	85.75 112	87.81 185	65.67 235	77.84 256	78.50 314	73.79 169	90.53 189	61.59 238	90.87 224	85.49 249
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
PVSNet_Blended		76.49 225	75.40 227	79.76 211	84.43 247	63.41 188	75.14 283	90.44 132	57.36 281	75.43 275	78.30 315	69.11 215	91.44 164	60.68 244	87.70 262	84.42 261

MVS		73.21 253	72.59 256	75.06 263	80.97 281	60.81 235	81.64 208	85.92 210	46.03 332	71.68 298	77.54 316	68.47 218	89.77 204	55.70 276	85.39 280	74.60 323

test0.0.03 1		64.66 300	64.36 299	65.57 308	75.03 327	46.89 323	64.69 320	61.58 337	62.43 260	71.18 302	77.54 316	43.41 321	68.47 321	40.75 328	82.65 302	81.35 304

dp		60.70 312	60.29 313	61.92 317	72.04 340	38.67 340	70.83 301	64.08 330	51.28 315	60.75 333	77.28 318	36.59 339	71.58 313	47.41 313	62.34 343	75.52 321

PS-MVSNAJ		77.04 215	76.53 218	78.56 226	87.09 208	61.40 226	75.26 282	87.13 196	61.25 267	74.38 285	77.22 319	76.94 131	90.94 175	64.63 220	84.83 289	83.35 275

IB-MVS	62.13 19	71.64 265	68.97 279	79.66 215	80.80 285	62.26 218	73.94 291	76.90 261	63.27 251	68.63 311	76.79 320	33.83 341	91.84 153	59.28 255	87.26 265	84.88 256
Christian Sormann, Mattia Rossi, Andreas Kuhn and Friedrich Fraundorfer: IB-MVS: An Iterative Algorithm for Deep Multi-View Stereo based on Binary Decisions. BMVC 2021
1314		73.22 252	72.56 257	75.20 262	80.41 287	57.84 252	81.64 208	85.36 215	51.68 311	73.10 291	76.65 321	61.45 241	85.19 266	63.54 225	79.21 318	82.59 283

cascas		76.29 227	74.81 232	80.72 204	84.47 246	62.94 199	73.89 292	87.34 189	55.94 287	75.16 279	76.53 322	63.97 232	91.16 170	65.00 217	90.97 221	88.06 221

pmmvs3		62.47 301	60.02 314	69.80 290	71.58 341	64.00 182	70.52 303	58.44 341	39.77 340	66.05 319	75.84 323	27.10 350	72.28 310	46.15 318	84.77 291	73.11 326

new_pmnet		55.69 317	57.66 316	49.76 330	75.47 322	30.59 344	59.56 327	51.45 348	43.62 337	62.49 330	75.48 324	40.96 332	49.15 346	37.39 334	72.52 330	69.55 331

test12356		54.91 319	57.14 317	48.22 332	75.83 318	17.47 349	52.31 340	69.20 313	51.66 312	60.11 335	75.40 325	29.77 347	62.62 339	27.64 343	72.37 331	64.59 334

PVSNet	58.17 21	66.41 294	65.63 296	68.75 297	81.96 272	49.88 318	62.19 324	72.51 292	51.03 316	68.04 313	75.34 326	50.84 283	74.77 306	45.82 320	82.96 298	81.60 301

MVE	40.22 23	51.82 321	50.47 323	55.87 326	62.66 349	51.91 292	31.61 344	39.28 351	40.65 339	50.76 346	74.98 327	56.24 268	44.67 347	33.94 339	64.11 342	71.04 330
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
testus		62.33 303	63.03 303	60.20 323	78.78 297	40.74 336	59.14 329	69.80 311	49.26 325	71.41 299	74.72 328	52.33 281	63.52 335	29.84 342	82.01 305	76.36 319

testpf		58.55 314	61.58 309	49.48 331	66.03 345	40.05 337	74.40 288	58.07 342	64.72 245	59.36 337	72.67 329	22.76 351	66.92 326	67.07 203	69.15 340	41.46 343

test-LLR		67.21 290	66.74 291	68.63 298	76.45 314	55.21 270	67.89 310	67.14 324	62.43 260	65.08 325	72.39 330	43.41 321	69.37 316	61.00 240	84.89 287	81.31 305

test-mter		65.00 299	63.79 300	68.63 298	76.45 314	55.21 270	67.89 310	67.14 324	50.98 317	65.08 325	72.39 330	28.27 348	69.37 316	61.00 240	84.89 287	81.31 305

gm-plane-assit					75.42 323	44.97 330			52.17 306		72.36 332		87.90 231	54.10 286

TESTMET0.1,1		61.29 308	60.32 312	64.19 312	72.06 339	51.30 299	67.89 310	62.09 333	45.27 333	60.65 334	69.01 333	27.93 349	64.74 334	56.31 272	81.65 308	76.53 318

PMMVS		61.65 306	60.38 311	65.47 309	65.40 347	69.26 149	63.97 321	61.73 336	36.80 343	60.11 335	68.43 334	59.42 253	66.35 329	48.97 307	78.57 319	60.81 337

test2356		56.69 315	55.15 319	61.32 320	73.20 333	44.11 331	54.95 336	62.52 332	48.75 327	62.45 331	68.42 335	21.10 352	65.67 332	26.86 344	78.08 320	74.19 324

CHOSEN 280x420		59.08 313	56.52 318	66.76 304	76.51 312	64.39 179	49.62 341	59.00 339	43.86 336	55.66 345	68.41 336	35.55 340	68.21 322	43.25 323	76.78 325	67.69 333

DWT-MVSNet_test		66.43 293	64.37 298	72.63 282	74.86 328	50.86 305	76.52 271	72.74 289	54.06 296	65.50 322	68.30 337	32.13 343	84.84 270	61.63 237	73.59 328	82.19 290

PatchFormer-LS_test		67.91 288	66.49 294	72.17 285	75.29 324	51.85 295	75.68 277	73.62 283	57.23 283	68.64 309	68.13 338	42.19 325	82.76 283	64.06 223	73.51 329	81.89 297

PNet_i23d		52.13 320	51.24 322	54.79 329	75.56 319	45.26 327	54.54 337	52.55 347	66.95 222	57.19 343	65.82 339	13.15 355	63.40 336	36.39 336	39.04 346	55.71 342

E-PMN		61.59 307	61.62 307	61.49 318	66.81 344	55.40 268	53.77 338	60.34 338	66.80 225	58.90 340	65.50 340	40.48 333	66.12 330	55.72 275	86.25 275	62.95 336

EMVS		61.10 310	60.81 310	61.99 316	65.96 346	55.86 265	53.10 339	58.97 340	67.06 221	56.89 344	63.33 341	40.98 331	67.03 325	54.79 283	86.18 276	63.08 335

PVSNet_0	51.08 22	56.10 316	54.97 320	59.48 324	75.12 326	53.28 283	55.16 335	61.89 334	44.30 335	59.16 338	62.48 342	54.22 277	65.91 331	35.40 337	47.01 344	59.25 339

GG-mvs-BLEND				67.16 303	73.36 332	46.54 325	84.15 134	55.04 345		58.64 341	61.95 343	29.93 346	83.87 279	38.71 333	76.92 324	71.07 329

DeepMVS_CX				24.13 336	32.95 350	29.49 345		21.63 354	12.07 345	37.95 347	45.07 344	30.84 344	19.21 348	17.94 346	33.06 347	23.69 344

tmp_tt		20.25 325	24.50 326	7.49 337	4.47 351	8.70 352	34.17 343	25.16 353	1.00 346	32.43 348	18.49 345	39.37 337	9.21 349	21.64 345	43.75 345	4.57 345

X-MVStestdata		85.04 112	82.70 159	92.08 8	95.64 21	86.25 16	92.64 15	93.33 39	85.07 25	89.99 85	16.05 346	86.57 42	95.80 13	87.35 19	97.62 52	94.20 92

test_post1							78.85 252				3.13 347	45.19 312	80.13 294	58.11 265

test_post											3.10 348	45.43 308	77.22 299

testmvs		5.91 329	7.65 330	0.72 339	1.20 352	0.37 354	59.14 329	0.67 356	0.49 348	1.11 349	2.76 349	0.94 357	0.24 351	1.02 348	1.47 348	1.55 347

test123		6.27 328	8.08 329	0.84 338	1.11 353	0.57 353	62.90 322	0.82 355	0.54 347	1.07 350	2.75 350	1.26 356	0.30 350	1.04 347	1.26 350	1.66 346

pcd_1.5k_mvsjas		6.41 327	8.55 328	0.00 340	0.00 354	0.00 355	0.00 345	0.00 357	0.00 349	0.00 351	0.00 351	76.94 131	0.00 352	0.00 349	0.00 351	0.00 349

pcd1.5k->3k		38.83 323	41.11 324	32.01 334	93.13 61	0.00 355	0.00 345	91.38 112	0.00 349	0.00 351	0.00 351	89.24 16	0.00 352	0.00 349	96.24 93	96.02 48

sosnet-low-res		0.00 330	0.00 331	0.00 340	0.00 354	0.00 355	0.00 345	0.00 357	0.00 349	0.00 351	0.00 351	0.00 358	0.00 352	0.00 349	0.00 351	0.00 349

sosnet		0.00 330	0.00 331	0.00 340	0.00 354	0.00 355	0.00 345	0.00 357	0.00 349	0.00 351	0.00 351	0.00 358	0.00 352	0.00 349	0.00 351	0.00 349

uncertanet		0.00 330	0.00 331	0.00 340	0.00 354	0.00 355	0.00 345	0.00 357	0.00 349	0.00 351	0.00 351	0.00 358	0.00 352	0.00 349	0.00 351	0.00 349

Regformer		0.00 330	0.00 331	0.00 340	0.00 354	0.00 355	0.00 345	0.00 357	0.00 349	0.00 351	0.00 351	0.00 358	0.00 352	0.00 349	0.00 351	0.00 349

uanet		0.00 330	0.00 331	0.00 340	0.00 354	0.00 355	0.00 345	0.00 357	0.00 349	0.00 351	0.00 351	0.00 358	0.00 352	0.00 349	0.00 351	0.00 349

test_part2					93.86 47	77.77 79				92.84 42

test_part1								93.93 25				87.19 36			97.61 54	91.48 172

test_full								93.79 29

sam_mvs1												46.11 297

sam_mvs												45.92 302

MTGPA								91.81 90

MTMP								33.14 352

test9_res														80.83 87	96.45 86	90.57 190

agg_prior2														79.68 104	96.16 95	90.22 198

agg_prior					91.58 102	77.69 80		90.30 140		84.32 189			93.18 118

test_prior4						78.97 71	84.59 127

test_prior				86.32 91	90.59 126	71.99 128		92.85 61					94.17 66			92.80 131

旧先验2							81.73 206		56.88 285	86.54 156			84.90 269	72.81 162

新几何2							81.72 207

无先验							82.81 175	85.62 213	58.09 278				91.41 166	67.95 200		84.48 259

原ACMM2							82.26 193

testdata2													86.43 254	63.52 226

segment_acmp												81.94 83

testdata1							79.62 234		73.95 148

test12				86.57 86	90.74 123	72.63 117		90.69 125		82.76 208		79.20 108	94.80 49		95.32 123	92.27 153

plane_prior7					93.45 52	77.31 86

plane_prior6					92.61 72	76.54 93						74.84 152

plane_prior5								93.61 32					95.22 37	80.78 88	95.83 111	94.46 86

plane_prior3						76.85 91			77.79 99	86.55 151

plane_prior2							89.45 56		79.44 77

plane_prior1					92.83 70

plane_prior						76.42 96	87.15 89		75.94 127						95.03 133

n2								0.00 357

nn								0.00 357

door-mid								74.45 277

test11								91.46 103

door								72.57 291

HQP5-MVS						70.66 140

HQP-NCC					91.19 112		84.77 122		73.30 157	80.55 239

ACMP_Plane					91.19 112		84.77 122		73.30 157	80.55 239

BP-MVS														77.30 127

HQP4-MVS										80.56 238			94.61 53			93.56 116

HQP3-MVS								92.68 67							94.47 152

HQP2-MVS												72.10 200

MDTV_nov1_ep13_2view						27.60 346	70.76 302		46.47 331	61.27 332		45.20 311		49.18 306		83.75 269

ACMMP++_ref															95.74 115

ACMMP++															97.35 59

Test By Simon												79.09 109

High-resolution multi-view benchmark