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		86.90 1	88.67 1	81.57 21	91.50 1	63.30 120	84.80 32	87.77 9	86.18 1	96.26 1	96.06 1	90.32 1	84.49 69	68.08 89	97.05 1	96.93 1

PEN-MVS		80.46 46	82.91 34	73.11 133	89.83 8	39.02 320	77.06 113	82.61 86	80.04 4	90.60 6	92.85 9	74.93 44	85.21 57	63.15 141	95.15 17	95.09 2

PS-CasMVS		80.41 47	82.86 36	73.07 135	89.93 6	39.21 317	77.15 111	81.28 107	79.74 5	90.87 4	92.73 11	75.03 43	84.93 62	63.83 133	95.19 15	95.07 3

CP-MVSNet		79.48 54	81.65 45	72.98 138	89.66 12	39.06 319	76.76 114	80.46 127	78.91 7	90.32 7	91.70 25	68.49 91	84.89 63	63.40 138	95.12 18	95.01 4

WR-MVS_H		80.22 50	82.17 41	74.39 111	89.46 14	42.69 294	78.24 97	82.24 89	78.21 9	89.57 9	92.10 18	68.05 96	85.59 48	66.04 113	95.62 9	94.88 5

DTE-MVSNet		80.35 48	82.89 35	72.74 148	89.84 7	37.34 337	77.16 110	81.81 97	80.45 3	90.92 3	92.95 7	74.57 47	86.12 29	63.65 134	94.68 31	94.76 6

TDRefinement		86.32 2	86.33 2	86.29 1	88.64 31	81.19 4	88.84 4	90.72 1	78.27 8	87.95 14	92.53 13	79.37 13	84.79 66	74.51 48	96.15 2	92.88 7

DU-MVS		74.91 101	75.57 96	72.93 142	83.50 91	45.79 268	69.47 211	80.14 135	65.22 82	81.74 96	87.08 128	61.82 155	81.07 124	56.21 196	94.98 20	91.93 8

NR-MVSNet		73.62 113	74.05 111	72.33 159	83.50 91	43.71 283	65.65 267	77.32 183	64.32 93	75.59 176	87.08 128	62.45 148	81.34 116	54.90 207	95.63 8	91.93 8

v7n		79.37 56	80.41 52	76.28 90	78.67 162	55.81 183	79.22 86	82.51 88	70.72 44	87.54 21	92.44 14	68.00 98	81.34 116	72.84 61	91.72 84	91.69 10

RRT_MVS		78.18 68	77.69 73	79.66 46	83.14 96	61.34 136	83.29 48	80.34 132	57.43 154	86.65 31	91.79 23	50.52 245	86.01 31	71.36 70	94.65 32	91.62 11

mvsmamba		77.20 75	76.37 85	79.69 45	80.34 135	61.52 133	80.58 66	82.12 91	53.54 205	83.93 70	91.03 37	49.49 251	85.97 33	73.26 57	93.08 67	91.59 12

TranMVSNet+NR-MVSNet		76.13 83	77.66 75	71.56 166	84.61 78	42.57 296	70.98 192	78.29 170	68.67 56	83.04 78	89.26 88	72.99 58	80.75 133	55.58 204	95.47 10	91.35 13

FC-MVSNet-test		73.32 119	74.78 102	68.93 209	79.21 150	36.57 339	71.82 179	79.54 146	57.63 153	82.57 87	90.38 64	59.38 184	78.99 160	57.91 184	94.56 34	91.23 14

v10		75.69 87	76.20 88	74.16 114	74.44 223	48.69 234	75.84 132	82.93 81	59.02 138	85.92 41	89.17 93	58.56 191	82.74 96	70.73 73	89.14 150	91.05 15

UniMVSNet_NR-MVSNet		74.90 102	75.65 94	72.64 151	83.04 102	45.79 268	69.26 214	78.81 156	66.66 67	81.74 96	86.88 134	63.26 139	81.07 124	56.21 196	94.98 20	91.05 15

UniMVSNet (Re)		75.00 99	75.48 97	73.56 125	83.14 96	47.92 245	70.41 201	81.04 115	63.67 100	79.54 120	86.37 154	62.83 143	81.82 110	57.10 188	95.25 14	90.94 17

anonymousdsp		78.60 61	77.80 72	81.00 31	78.01 169	74.34 33	80.09 77	76.12 193	50.51 240	89.19 10	90.88 42	71.45 67	77.78 189	73.38 56	90.60 119	90.90 18

v8		75.07 97	75.64 95	73.35 127	73.42 237	47.46 253	75.20 135	81.45 103	60.05 128	85.64 45	89.26 88	58.08 199	81.80 111	69.71 81	87.97 167	90.79 19

IS-MVSNet		75.10 96	75.42 98	74.15 115	79.23 149	48.05 243	79.43 82	78.04 174	70.09 49	79.17 124	88.02 122	53.04 231	83.60 81	58.05 183	93.76 59	90.79 19

FIs		72.56 140	73.80 115	68.84 212	78.74 161	37.74 333	71.02 191	79.83 139	56.12 166	80.88 110	89.45 85	58.18 193	78.28 178	56.63 190	93.36 64	90.51 21

test_djsdf		78.88 59	78.27 69	80.70 35	81.42 124	71.24 52	83.98 36	75.72 198	52.27 214	87.37 26	92.25 16	68.04 97	80.56 134	72.28 67	91.15 99	90.32 22

WR-MVS		71.20 153	72.48 142	67.36 229	84.98 71	35.70 347	64.43 282	68.66 262	65.05 86	81.49 99	86.43 153	57.57 206	76.48 203	50.36 244	93.32 65	89.90 23

OMC-MVS		79.41 55	78.79 63	81.28 29	80.62 132	70.71 58	80.91 63	84.76 46	62.54 112	81.77 94	86.65 145	71.46 66	83.53 83	67.95 93	92.44 76	89.60 24

tttt0517		69.46 175	67.79 201	74.46 107	75.34 205	52.72 202	75.05 136	63.27 299	54.69 183	78.87 127	84.37 185	26.63 377	81.15 120	63.95 130	87.93 168	89.51 25

v2v482		72.55 142	72.58 140	72.43 156	72.92 250	46.72 260	71.41 184	79.13 151	55.27 174	81.17 104	85.25 176	55.41 220	81.13 121	67.25 106	85.46 202	89.43 26

Anonymous20231211		75.54 90	77.19 79	70.59 175	77.67 175	45.70 271	74.73 144	80.19 133	68.80 53	82.95 81	92.91 8	66.26 116	76.76 201	58.41 181	92.77 72	89.30 27

OurMVSNet-221017-0		78.57 62	78.53 67	78.67 61	80.48 133	64.16 113	80.24 74	82.06 92	61.89 116	88.77 12	93.32 4	57.15 208	82.60 98	70.08 77	92.80 71	89.25 28

EI-MVSNet-UG-set		72.63 139	71.68 153	75.47 100	74.67 217	58.64 170	72.02 170	71.50 233	63.53 102	78.58 130	71.39 334	65.98 118	78.53 167	67.30 105	80.18 269	89.23 29

V42		71.06 154	70.83 165	71.72 164	67.25 314	47.14 257	65.94 261	80.35 131	51.35 229	83.40 76	83.23 207	59.25 185	78.80 163	65.91 114	80.81 263	89.23 29

RPSCF		75.76 86	74.37 106	79.93 40	74.81 214	77.53 16	77.53 105	79.30 149	59.44 133	78.88 126	89.80 80	71.26 69	73.09 238	57.45 185	80.89 260	89.17 31

UniMVSNet_ETH3D		76.74 79	79.02 61	69.92 191	89.27 19	43.81 282	74.47 149	71.70 229	72.33 35	85.50 50	93.65 3	77.98 21	76.88 199	54.60 212	91.64 86	89.08 32

v1192		73.40 117	73.42 121	73.32 129	74.65 220	48.67 235	72.21 166	81.73 98	52.76 211	81.85 92	84.56 182	57.12 209	82.24 105	68.58 84	87.33 177	89.06 33

3Dnovator+	73.19 2	81.08 39	80.48 51	82.87 7	81.41 125	72.03 45	84.38 34	86.23 22	77.28 14	80.65 111	90.18 74	59.80 181	87.58 5	73.06 59	91.34 94	89.01 34

EI-MVSNet-Vis-set		72.78 136	71.87 149	75.54 99	74.77 215	59.02 165	72.24 165	71.56 232	63.92 96	78.59 128	71.59 330	66.22 117	78.60 166	67.58 95	80.32 267	89.00 35

v1144		73.29 120	73.39 122	73.01 136	74.12 228	48.11 241	72.01 171	81.08 114	53.83 202	81.77 94	84.68 180	58.07 200	81.91 109	68.10 88	86.86 186	88.99 36

nrg030		74.87 104	75.99 91	71.52 167	74.90 212	49.88 228	74.10 154	82.58 87	54.55 187	83.50 75	89.21 90	71.51 65	75.74 209	61.24 152	92.34 79	88.94 37

v1240		73.06 126	73.14 128	72.84 145	74.74 216	47.27 256	71.88 178	81.11 111	51.80 221	82.28 89	84.21 187	56.22 218	82.34 102	68.82 83	87.17 184	88.91 38

LTVRE_ROB	75.46 1	84.22 6	84.98 7	81.94 20	84.82 73	75.40 25	91.60 3	87.80 7	73.52 24	88.90 11	93.06 6	71.39 68	81.53 114	81.53 3	92.15 82	88.91 38
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
v1921920		72.96 132	72.98 133	72.89 144	74.67 217	47.58 251	71.92 176	80.69 120	51.70 223	81.69 98	83.89 192	56.58 215	82.25 104	68.34 86	87.36 175	88.82 40

EPP-MVSNet		73.86 111	73.38 123	75.31 101	78.19 165	53.35 200	80.45 68	77.32 183	65.11 85	76.47 168	86.80 135	49.47 252	83.77 77	53.89 221	92.72 74	88.81 41

UA-Net		81.56 33	82.28 40	79.40 50	88.91 28	69.16 72	84.67 33	80.01 137	75.34 15	79.80 118	94.91 2	69.79 83	80.25 141	72.63 63	94.46 36	88.78 42

v144192		72.99 130	73.06 131	72.77 146	74.58 221	47.48 252	71.90 177	80.44 128	51.57 224	81.46 100	84.11 189	58.04 201	82.12 106	67.98 92	87.47 173	88.70 43

EI-MVSNet		69.61 173	69.01 181	71.41 169	73.94 230	49.90 224	71.31 187	71.32 238	58.22 143	75.40 181	70.44 337	58.16 194	75.85 205	62.51 143	79.81 273	88.48 44

IterMVS-LS		73.01 128	73.12 130	72.66 150	73.79 232	49.90 224	71.63 181	78.44 166	58.22 143	80.51 112	86.63 146	58.15 195	79.62 150	62.51 143	88.20 161	88.48 44
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
casdiffmvs_mvg		75.26 93	76.18 89	72.52 153	72.87 251	49.47 229	72.94 161	84.71 50	59.49 132	80.90 109	88.81 104	70.07 79	79.71 149	67.40 99	88.39 159	88.40 46
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
HPM-MVS_fast		84.59 4	85.10 6	83.06 4	88.60 32	75.83 23	86.27 24	86.89 15	73.69 23	86.17 37	91.70 25	78.23 19	85.20 58	79.45 12	94.91 24	88.15 47

COLMAP_ROB	72.78 3	83.75 11	84.11 15	82.68 12	82.97 104	74.39 32	87.18 10	88.18 6	78.98 6	86.11 40	91.47 30	79.70 12	85.76 43	66.91 108	95.46 11	87.89 48
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
PS-MVSNAJss		77.54 72	77.35 78	78.13 70	84.88 72	66.37 92	78.55 93	79.59 144	53.48 206	86.29 36	92.43 15	62.39 149	80.25 141	67.90 94	90.61 118	87.77 49

eth_miper_zixun_eth		69.42 176	68.73 187	71.50 168	67.99 306	46.42 263	67.58 238	78.81 156	50.72 237	78.13 135	80.34 243	50.15 249	80.34 139	60.18 164	84.65 218	87.74 50

casdiffmvs		73.06 126	73.84 114	70.72 173	71.32 262	46.71 261	70.93 193	84.26 61	55.62 172	77.46 145	87.10 127	67.09 104	77.81 187	63.95 130	86.83 188	87.64 51
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
LS3D		80.99 41	80.85 49	81.41 25	78.37 163	71.37 50	87.45 7	85.87 26	77.48 12	81.98 91	89.95 78	69.14 86	85.26 54	66.15 110	91.24 96	87.61 52

ITE_SJBPF				80.35 38	76.94 184	73.60 38		80.48 126	66.87 64	83.64 74	86.18 159	70.25 78	79.90 147	61.12 156	88.95 155	87.56 53

thisisatest0530		67.05 213	65.16 232	72.73 149	73.10 245	50.55 214	71.26 189	63.91 295	50.22 244	74.46 195	80.75 236	26.81 376	80.25 141	59.43 174	86.50 193	87.37 54

CS-MVS		76.51 80	76.00 90	78.06 71	77.02 181	64.77 109	80.78 64	82.66 85	60.39 126	74.15 199	83.30 204	69.65 84	82.07 107	69.27 82	86.75 190	87.36 55

pmmvs6		71.82 148	73.66 118	66.31 241	75.94 201	42.01 298	66.99 249	72.53 224	63.45 104	76.43 169	92.78 10	72.95 59	69.69 274	51.41 235	90.46 120	87.22 56

ACMH+	66.64 10	81.20 36	82.48 39	77.35 78	81.16 129	62.39 125	80.51 67	87.80 7	73.02 26	87.57 20	91.08 36	80.28 9	82.44 99	64.82 122	96.10 4	87.21 57

c3_l		69.82 170	69.89 171	69.61 194	66.24 324	43.48 286	68.12 233	79.61 143	51.43 226	77.72 141	80.18 247	54.61 224	78.15 183	63.62 135	87.50 172	87.20 58

Anonymous20240529		72.56 140	73.79 116	68.86 211	76.89 188	45.21 273	68.80 223	77.25 185	67.16 61	76.89 153	90.44 56	65.95 119	74.19 229	50.75 240	90.00 128	87.18 59

tt0805		76.12 84	78.43 68	69.20 201	81.32 126	41.37 302	76.72 115	77.64 179	63.78 99	82.06 90	87.88 123	79.78 11	79.05 158	64.33 126	92.40 77	87.17 60

baseline		73.10 123	73.96 113	70.51 177	71.46 261	46.39 265	72.08 169	84.40 58	55.95 169	76.62 161	86.46 152	67.20 102	78.03 184	64.22 127	87.27 180	87.11 61

Effi-MVS+-dtu		75.43 91	72.28 146	84.91 2	77.05 179	83.58 1	78.47 94	77.70 178	57.68 149	74.89 185	78.13 279	64.80 131	84.26 74	56.46 194	85.32 207	86.88 62

v148		69.38 178	69.39 174	69.36 197	69.14 293	44.56 277	68.83 220	72.70 222	54.79 181	78.59 128	84.12 188	54.69 222	76.74 202	59.40 175	82.20 243	86.79 63

HPM-MVS		84.12 8	84.63 9	82.60 13	88.21 35	74.40 31	85.24 28	87.21 13	70.69 45	85.14 54	90.42 58	78.99 15	86.62 13	80.83 5	94.93 23	86.79 63
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
iter_conf05		67.34 209	65.62 225	72.50 154	69.82 284	47.06 258	72.19 167	76.86 187	45.32 287	72.86 217	82.85 210	20.53 396	83.73 78	61.13 155	89.02 154	86.70 65

mvs_tets		78.93 58	78.67 65	79.72 43	84.81 74	73.93 35	80.65 65	76.50 191	51.98 219	87.40 23	91.86 21	76.09 33	78.53 167	68.58 84	90.20 123	86.69 66

EC-MVSNet		77.08 77	77.39 77	76.14 92	76.86 189	56.87 177	80.32 73	87.52 11	63.45 104	74.66 191	84.52 183	69.87 82	84.94 61	69.76 79	89.59 139	86.60 67

jajsoiax		78.51 63	78.16 70	79.59 47	84.65 77	73.83 37	80.42 69	76.12 193	51.33 230	87.19 27	91.51 29	73.79 54	78.44 171	68.27 87	90.13 127	86.49 68

cl22		67.14 210	66.51 216	69.03 205	63.20 345	43.46 287	66.88 253	76.25 192	49.22 254	74.48 194	77.88 281	45.49 273	77.40 193	60.64 160	84.59 220	86.24 69

MP-MVS-pluss		82.54 26	83.46 25	79.76 41	88.88 30	68.44 76	81.57 59	86.33 18	63.17 108	85.38 52	91.26 33	76.33 30	84.67 68	83.30 1	94.96 22	86.17 70
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
LPG-MVS_test		83.47 16	84.33 12	80.90 32	87.00 39	70.41 60	82.04 56	86.35 16	69.77 50	87.75 15	91.13 34	81.83 3	86.20 24	77.13 35	95.96 5	86.08 71

LGP-MVS_train				80.90 32	87.00 39	70.41 60		86.35 16	69.77 50	87.75 15	91.13 34	81.83 3	86.20 24	77.13 35	95.96 5	86.08 71

SixPastTwentyTwo		75.77 85	76.34 86	74.06 116	81.69 122	54.84 188	76.47 116	75.49 200	64.10 95	87.73 17	92.24 17	50.45 247	81.30 118	67.41 98	91.46 92	86.04 73

APD-MVS_3200maxsize		83.57 13	84.33 12	81.31 28	82.83 107	73.53 40	85.50 27	87.45 12	74.11 19	86.45 35	90.52 55	80.02 10	84.48 70	77.73 27	94.34 47	85.93 74

DeepC-MVS	72.44 4	81.00 40	80.83 50	81.50 22	86.70 44	70.03 64	82.06 55	87.00 14	59.89 130	80.91 108	90.53 53	72.19 60	88.56 1	73.67 55	94.52 35	85.92 75
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
DIV-MVS_self_test		68.27 195	68.26 192	68.29 219	64.98 336	43.67 284	65.89 262	74.67 206	50.04 247	76.86 155	82.43 216	48.74 260	75.38 211	60.94 157	89.81 133	85.81 76

AllTest		77.66 71	77.43 76	78.35 66	79.19 151	70.81 55	78.60 92	88.64 3	65.37 79	80.09 116	88.17 118	70.33 76	78.43 172	55.60 201	90.90 110	85.81 76

TestCases				78.35 66	79.19 151	70.81 55		88.64 3	65.37 79	80.09 116	88.17 118	70.33 76	78.43 172	55.60 201	90.90 110	85.81 76

ACMP	69.50 8	82.64 25	83.38 26	80.40 37	86.50 45	69.44 67	82.30 53	86.08 23	66.80 65	86.70 30	89.99 76	81.64 6	85.95 34	74.35 50	96.11 3	85.81 76
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
cl____		68.26 196	68.26 192	68.29 219	64.98 336	43.67 284	65.89 262	74.67 206	50.04 247	76.86 155	82.42 217	48.74 260	75.38 211	60.92 158	89.81 133	85.80 80

CS-MVS-test		74.89 103	74.23 109	76.86 81	77.01 182	62.94 123	78.98 88	84.61 55	58.62 141	70.17 254	80.80 235	66.74 112	81.96 108	61.74 148	89.40 145	85.69 81

miper_ehance_all_eth		68.36 191	68.16 196	68.98 206	65.14 335	43.34 288	67.07 248	78.92 155	49.11 256	76.21 172	77.72 282	53.48 229	77.92 186	61.16 154	84.59 220	85.68 82

test_fmvsm_n_1920		69.63 171	68.45 189	73.16 131	70.56 272	65.86 98	70.26 202	78.35 167	37.69 345	74.29 197	78.89 269	61.10 167	68.10 287	65.87 115	79.07 280	85.53 83

MM		78.15 70	77.68 74	79.55 48	80.10 137	65.47 100	80.94 62	78.74 160	71.22 40	72.40 225	88.70 105	60.51 172	87.70 3	77.40 32	89.13 151	85.48 84

SteuartSystems-ACMMP		83.07 21	83.64 22	81.35 26	85.14 68	71.00 54	85.53 26	84.78 45	70.91 43	85.64 45	90.41 59	75.55 38	87.69 4	79.75 7	95.08 19	85.36 85
Skip Steuart: Steuart Systems R&D Blog.
MVS_0304		76.32 82	75.96 92	77.42 76	79.33 146	60.86 147	80.18 76	74.88 205	66.93 62	69.11 265	88.95 101	57.84 204	86.12 29	76.63 37	89.77 136	85.28 86

iter_conf_final		68.69 187	67.00 212	73.76 121	73.68 233	52.33 205	75.96 129	73.54 213	50.56 239	69.90 257	82.85 210	24.76 386	83.73 78	65.40 118	86.33 195	85.22 87

diffmvs		67.42 207	67.50 204	67.20 231	62.26 349	45.21 273	64.87 276	77.04 186	48.21 261	71.74 231	79.70 254	58.40 192	71.17 263	64.99 120	80.27 268	85.22 87
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
Baseline_NR-MVSNet		70.62 160	73.19 127	62.92 272	76.97 183	34.44 355	68.84 219	70.88 248	60.25 127	79.50 121	90.53 53	61.82 155	69.11 278	54.67 211	95.27 13	85.22 87

TAPA-MVS	65.27 12	75.16 95	74.29 108	77.77 72	74.86 213	68.08 77	77.89 101	84.04 68	55.15 176	76.19 173	83.39 198	66.91 106	80.11 145	60.04 168	90.14 126	85.13 90
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
CLD-MVS		72.88 134	72.36 145	74.43 110	77.03 180	54.30 192	68.77 224	83.43 75	52.12 216	76.79 158	74.44 308	69.54 85	83.91 75	55.88 199	93.25 66	85.09 91
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
KD-MVS_self_test		66.38 219	67.51 203	62.97 270	61.76 351	34.39 356	58.11 328	75.30 201	50.84 236	77.12 148	85.42 173	56.84 213	69.44 275	51.07 238	91.16 98	85.08 92

CDPH-MVS		77.33 74	77.06 81	78.14 69	84.21 84	63.98 115	76.07 127	83.45 74	54.20 193	77.68 143	87.18 126	69.98 80	85.37 51	68.01 91	92.72 74	85.08 92

K. test v3		73.67 112	73.61 120	73.87 119	79.78 139	55.62 186	74.69 146	62.04 306	66.16 71	84.76 60	93.23 5	49.47 252	80.97 128	65.66 116	86.67 191	85.02 94

SR-MVS-dyc-post		84.75 3	85.26 5	83.21 3	86.19 49	79.18 6	87.23 8	86.27 19	77.51 10	87.65 18	90.73 47	79.20 14	85.58 49	78.11 23	94.46 36	84.89 95

RE-MVS-def			85.50 3		86.19 49	79.18 6	87.23 8	86.27 19	77.51 10	87.65 18	90.73 47	81.38 7		78.11 23	94.46 36	84.89 95

test2506		61.23 270	60.85 271	62.38 276	78.80 159	27.88 386	67.33 245	37.42 400	54.23 191	67.55 288	88.68 107	17.87 404	74.39 226	46.33 281	89.41 143	84.86 97

ECVR-MVS		64.82 232	65.22 230	63.60 261	78.80 159	31.14 372	66.97 250	56.47 332	54.23 191	69.94 256	88.68 107	37.23 325	74.81 221	45.28 291	89.41 143	84.86 97

HQP_MVS		78.77 60	78.78 64	78.72 60	85.18 66	65.18 104	82.74 51	85.49 29	65.45 76	78.23 133	89.11 95	60.83 170	86.15 27	71.09 71	90.94 106	84.82 99

plane_prior5								85.49 29					86.15 27	71.09 71	90.94 106	84.82 99

SF-MVS		80.72 43	81.80 42	77.48 74	82.03 117	64.40 112	83.41 46	88.46 5	65.28 81	84.29 65	89.18 92	73.73 55	83.22 88	76.01 38	93.77 58	84.81 101

alignmvs		70.54 161	71.00 163	69.15 203	73.50 235	48.04 244	69.85 208	79.62 141	53.94 201	76.54 165	82.00 220	59.00 187	74.68 222	57.32 186	87.21 182	84.72 102

IU-MVS					86.12 53	60.90 145		80.38 129	45.49 284	81.31 101				75.64 41	94.39 41	84.65 103

XVS		83.51 15	83.73 20	82.85 8	89.43 15	77.61 14	86.80 17	84.66 52	72.71 27	82.87 82	90.39 62	73.86 52	86.31 19	78.84 19	94.03 53	84.64 104

X-MVStestdata		76.81 78	74.79 101	82.85 8	89.43 15	77.61 14	86.80 17	84.66 52	72.71 27	82.87 82	9.95 403	73.86 52	86.31 19	78.84 19	94.03 53	84.64 104

ACMMP		84.22 6	84.84 8	82.35 17	89.23 21	76.66 22	87.65 6	85.89 25	71.03 42	85.85 42	90.58 51	78.77 16	85.78 42	79.37 15	95.17 16	84.62 106
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
SMA-MVS		82.12 28	82.68 38	80.43 36	88.90 29	69.52 65	85.12 29	84.76 46	63.53 102	84.23 66	91.47 30	72.02 62	87.16 7	79.74 9	94.36 45	84.61 107
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
VDD-MVS		70.81 158	71.44 159	68.91 210	79.07 156	46.51 262	67.82 236	70.83 249	61.23 119	74.07 202	88.69 106	59.86 179	75.62 210	51.11 237	90.28 122	84.61 107

ZNCC-MVS		83.12 20	83.68 21	81.45 24	89.14 24	73.28 42	86.32 23	85.97 24	67.39 60	84.02 68	90.39 62	74.73 45	86.46 15	80.73 6	94.43 40	84.60 109

test1111		64.62 235	65.19 231	62.93 271	79.01 157	29.91 378	65.45 270	54.41 342	54.09 196	71.47 241	88.48 111	37.02 326	74.29 228	46.83 278	89.94 131	84.58 110

bld_raw_dy_0_64		72.85 135	72.76 137	73.09 134	85.08 70	64.80 108	78.72 90	64.22 293	51.92 220	83.13 77	90.26 70	39.21 313	69.91 272	70.73 73	91.60 89	84.56 111

miper_enhance_ethall		65.86 223	65.05 239	68.28 221	61.62 353	42.62 295	64.74 277	77.97 175	42.52 309	73.42 211	72.79 323	49.66 250	77.68 190	58.12 182	84.59 220	84.54 112

GBi-Net		68.30 192	68.79 183	66.81 235	73.14 242	40.68 308	71.96 173	73.03 216	54.81 178	74.72 188	90.36 67	48.63 262	75.20 215	47.12 273	85.37 203	84.54 112

test1		68.30 192	68.79 183	66.81 235	73.14 242	40.68 308	71.96 173	73.03 216	54.81 178	74.72 188	90.36 67	48.63 262	75.20 215	47.12 273	85.37 203	84.54 112

FMVSNet1		71.06 154	72.48 142	66.81 235	77.65 176	40.68 308	71.96 173	73.03 216	61.14 120	79.45 122	90.36 67	60.44 173	75.20 215	50.20 245	88.05 164	84.54 112

TSAR-MVS + MP.		79.05 57	78.81 62	79.74 42	88.94 27	67.52 83	86.61 19	81.38 105	51.71 222	77.15 147	91.42 32	65.49 124	87.20 6	79.44 13	87.17 184	84.51 116
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
PCF-MVS	63.80 13	72.70 138	71.69 152	75.72 96	78.10 166	60.01 155	73.04 160	81.50 101	45.34 286	79.66 119	84.35 186	65.15 128	82.65 97	48.70 258	89.38 146	84.50 117
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
canonicalmvs		72.29 145	73.38 123	69.04 204	74.23 224	47.37 254	73.93 156	83.18 76	54.36 188	76.61 162	81.64 227	72.03 61	75.34 213	57.12 187	87.28 179	84.40 118

TransMVSNet (Re)		69.62 172	71.63 154	63.57 262	76.51 191	35.93 345	65.75 266	71.29 240	61.05 121	75.02 183	89.90 79	65.88 121	70.41 271	49.79 247	89.48 141	84.38 119

OPM-MVS		80.99 41	81.63 46	79.07 54	86.86 43	69.39 68	79.41 84	84.00 69	65.64 73	85.54 49	89.28 87	76.32 31	83.47 84	74.03 52	93.57 62	84.35 120
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
test_0728_THIRD									74.03 21	85.83 43	90.41 59	75.58 37	85.69 45	77.43 30	94.74 29	84.31 121

MSP-MVS		80.49 45	79.67 58	82.96 5	89.70 11	77.46 19	87.16 11	85.10 39	64.94 89	81.05 105	88.38 114	57.10 210	87.10 8	79.75 7	83.87 228	84.31 121
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
GST-MVS		82.79 24	83.27 29	81.34 27	88.99 26	73.29 41	85.94 25	85.13 37	68.58 57	84.14 67	90.21 73	73.37 56	86.41 16	79.09 18	93.98 56	84.30 123

ACMMPR		83.62 12	83.93 17	82.69 11	89.78 10	77.51 18	87.01 14	84.19 64	70.23 46	84.49 63	90.67 50	75.15 41	86.37 18	79.58 10	94.26 49	84.18 124

VDDNet		71.60 150	73.13 129	67.02 234	86.29 47	41.11 304	69.97 205	66.50 272	68.72 55	74.74 187	91.70 25	59.90 178	75.81 207	48.58 260	91.72 84	84.15 125

FA-MVS(test-final)		71.27 152	71.06 162	71.92 163	73.96 229	52.32 206	76.45 118	76.12 193	59.07 137	74.04 204	86.18 159	52.18 235	79.43 154	59.75 172	81.76 250	84.03 126

MVS_Test		69.84 169	70.71 166	67.24 230	67.49 312	43.25 290	69.87 207	81.22 110	52.69 212	71.57 237	86.68 142	62.09 153	74.51 224	66.05 112	78.74 283	83.96 127

region2R		83.54 14	83.86 19	82.58 14	89.82 9	77.53 16	87.06 13	84.23 63	70.19 48	83.86 71	90.72 49	75.20 40	86.27 21	79.41 14	94.25 50	83.95 128

test_fmvsmconf0.01_n		73.91 109	73.64 119	74.71 104	69.79 288	66.25 93	75.90 130	79.90 138	46.03 279	76.48 167	85.02 178	67.96 99	73.97 231	74.47 49	87.22 181	83.90 129

PGM-MVS		83.07 21	83.25 30	82.54 15	89.57 13	77.21 20	82.04 56	85.40 33	67.96 59	84.91 59	90.88 42	75.59 36	86.57 14	78.16 22	94.71 30	83.82 130

pm-mvs1		68.40 190	69.85 172	64.04 258	73.10 245	39.94 314	64.61 280	70.50 250	55.52 173	73.97 205	89.33 86	63.91 137	68.38 284	49.68 249	88.02 165	83.81 131

MSC_two_6792asdad				79.02 55	83.14 96	67.03 87		80.75 118					86.24 22	77.27 33	94.85 25	83.78 132

No_MVS				79.02 55	83.14 96	67.03 87		80.75 118					86.24 22	77.27 33	94.85 25	83.78 132

HQP4-MVS										71.59 233			85.31 52			83.74 134

HQP-MVS		75.24 94	75.01 100	75.94 93	82.37 111	58.80 167	77.32 107	84.12 65	59.08 134	71.58 234	85.96 168	58.09 197	85.30 53	67.38 102	89.16 147	83.73 135

PHI-MVS		74.92 100	74.36 107	76.61 84	76.40 192	62.32 126	80.38 70	83.15 77	54.16 195	73.23 214	80.75 236	62.19 152	83.86 76	68.02 90	90.92 109	83.65 136

test_fmvsmconf0.1_n		73.26 121	72.82 136	74.56 106	69.10 294	66.18 95	74.65 148	79.34 148	45.58 281	75.54 178	83.91 191	67.19 103	73.88 234	73.26 57	86.86 186	83.63 137

DeepPCF-MVS	71.07 5	78.48 65	77.14 80	82.52 16	84.39 83	77.04 21	76.35 121	84.05 67	56.66 162	80.27 115	85.31 175	68.56 90	87.03 10	67.39 100	91.26 95	83.50 138

DVP-MVS++		81.24 35	82.74 37	76.76 82	83.14 96	60.90 145	91.64 1	85.49 29	74.03 21	84.93 56	90.38 64	66.82 108	85.90 38	77.43 30	90.78 114	83.49 139

PC_three_1452									46.98 273	81.83 93	86.28 155	66.55 115	84.47 71	63.31 140	90.78 114	83.49 139

XVG-ACMP-BASELINE		80.54 44	81.06 48	78.98 57	87.01 38	72.91 43	80.23 75	85.56 28	66.56 68	85.64 45	89.57 83	69.12 87	80.55 136	72.51 65	93.37 63	83.48 141

APDe-MVS		82.88 23	84.14 14	79.08 53	84.80 75	66.72 90	86.54 20	85.11 38	72.00 37	86.65 31	91.75 24	78.20 20	87.04 9	77.93 25	94.32 48	83.47 142
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
ANet_high		67.08 211	69.94 170	58.51 306	57.55 375	27.09 388	58.43 325	76.80 189	63.56 101	82.40 88	91.93 20	59.82 180	64.98 316	50.10 246	88.86 156	83.46 143

Effi-MVS+		72.10 146	72.28 146	71.58 165	74.21 226	50.33 217	74.72 145	82.73 83	62.62 111	70.77 246	76.83 289	69.96 81	80.97 128	60.20 163	78.43 287	83.45 144

test_fmvsmconf_n		72.91 133	72.40 144	74.46 107	68.62 298	66.12 96	74.21 153	78.80 158	45.64 280	74.62 192	83.25 206	66.80 111	73.86 235	72.97 60	86.66 192	83.39 145

test12				76.51 86	82.28 114	60.94 144		81.64 100		73.60 207		64.88 130	85.19 59		90.42 121	83.38 146

VPA-MVSNet		68.71 186	70.37 168	63.72 260	76.13 196	38.06 331	64.10 284	71.48 234	56.60 164	74.10 201	88.31 115	64.78 132	69.72 273	47.69 271	90.15 125	83.37 147

ACMMP_NAP		82.33 27	83.28 28	79.46 49	89.28 18	69.09 74	83.62 42	84.98 41	64.77 90	83.97 69	91.02 38	75.53 39	85.93 37	82.00 2	94.36 45	83.35 148

DeepC-MVS_fast	69.89 7	77.17 76	76.33 87	79.70 44	83.90 88	67.94 78	80.06 79	83.75 70	56.73 161	74.88 186	85.32 174	65.54 123	87.79 2	65.61 117	91.14 100	83.35 148
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
test_241102_TWO								84.80 44	72.61 30	84.93 56	89.70 81	77.73 22	85.89 40	75.29 42	94.22 52	83.25 150

test_0728_SECOND				76.57 85	86.20 48	60.57 151	83.77 40	85.49 29					85.90 38	75.86 39	94.39 41	83.25 150

fmvsm_s_conf0.1_n		66.60 216	65.54 226	69.77 192	68.99 295	59.15 162	72.12 168	56.74 330	40.72 326	68.25 282	80.14 248	61.18 166	66.92 299	67.34 104	74.40 321	83.23 152

GeoE		73.14 122	73.77 117	71.26 170	78.09 167	52.64 203	74.32 150	79.56 145	56.32 165	76.35 171	83.36 202	70.76 74	77.96 185	63.32 139	81.84 249	83.18 153

test_fmvsmvis_n_1920		72.36 143	72.49 141	71.96 162	71.29 263	64.06 114	72.79 162	81.82 96	40.23 329	81.25 103	81.04 232	70.62 75	68.69 281	69.74 80	83.60 234	83.14 154

SR-MVS		84.51 5	85.27 4	82.25 18	88.52 33	77.71 13	86.81 16	85.25 36	77.42 13	86.15 38	90.24 71	81.69 5	85.94 35	77.77 26	93.58 61	83.09 155

SED-MVS		81.78 31	83.48 24	76.67 83	86.12 53	61.06 141	83.62 42	84.72 48	72.61 30	87.38 24	89.70 81	77.48 23	85.89 40	75.29 42	94.39 41	83.08 156

OPU-MVS				78.65 62	83.44 94	66.85 89	83.62 42				86.12 163	66.82 108	86.01 31	61.72 149	89.79 135	83.08 156

MVSTER		63.29 251	61.60 264	68.36 217	59.77 366	46.21 266	60.62 310	71.32 238	41.83 312	75.40 181	79.12 265	30.25 365	75.85 205	56.30 195	79.81 273	83.03 158

CANet		73.00 129	71.84 150	76.48 87	75.82 202	61.28 137	74.81 140	80.37 130	63.17 108	62.43 324	80.50 240	61.10 167	85.16 60	64.00 129	84.34 224	83.01 159

Vis-MVSNet		74.85 105	74.56 103	75.72 96	81.63 123	64.64 110	76.35 121	79.06 152	62.85 110	73.33 212	88.41 112	62.54 147	79.59 152	63.94 132	82.92 238	82.94 160
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
miper_lstm_enhance		61.97 263	61.63 263	62.98 269	60.04 360	45.74 270	47.53 374	70.95 246	44.04 295	73.06 215	78.84 270	39.72 309	60.33 333	55.82 200	84.64 219	82.88 161

PAPM_NR		73.91 109	74.16 110	73.16 131	81.90 119	53.50 198	81.28 60	81.40 104	66.17 70	73.30 213	83.31 203	59.96 177	83.10 91	58.45 180	81.66 255	82.87 162

Fast-Effi-MVS+		68.81 184	68.30 191	70.35 180	74.66 219	48.61 236	66.06 260	78.32 168	50.62 238	71.48 240	75.54 297	68.75 89	79.59 152	50.55 243	78.73 284	82.86 163

HFP-MVS		83.39 17	84.03 16	81.48 23	89.25 20	75.69 24	87.01 14	84.27 60	70.23 46	84.47 64	90.43 57	76.79 26	85.94 35	79.58 10	94.23 51	82.82 164

DELS-MVS		68.83 183	68.31 190	70.38 178	70.55 274	48.31 237	63.78 288	82.13 90	54.00 198	68.96 269	75.17 301	58.95 188	80.06 146	58.55 179	82.74 240	82.76 165
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
CL-MVSNet_self_test		62.44 261	63.40 250	59.55 299	72.34 254	32.38 364	56.39 336	64.84 286	51.21 232	67.46 289	81.01 233	50.75 244	63.51 323	38.47 329	88.12 163	82.75 166

MP-MVS		83.19 18	83.54 23	82.14 19	90.54 4	79.00 8	86.42 22	83.59 73	71.31 39	81.26 102	90.96 39	74.57 47	84.69 67	78.41 21	94.78 27	82.74 167
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
lessismore_v0				72.75 147	79.60 142	56.83 178		57.37 321		83.80 72	89.01 98	47.45 267	78.74 165	64.39 125	86.49 194	82.69 168

fmvsm_s_conf0.5_n		66.34 221	65.27 229	69.57 195	68.20 303	59.14 164	71.66 180	56.48 331	40.92 322	67.78 284	79.46 257	61.23 163	66.90 300	67.39 100	74.32 324	82.66 169

DPE-MVS		82.00 30	83.02 33	78.95 58	85.36 65	67.25 85	82.91 50	84.98 41	73.52 24	85.43 51	90.03 75	76.37 29	86.97 11	74.56 47	94.02 55	82.62 170
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
test_prior				75.27 102	82.15 116	59.85 156		84.33 59					83.39 86			82.58 171

F-COLMAP		75.29 92	73.99 112	79.18 52	81.73 121	71.90 46	81.86 58	82.98 79	59.86 131	72.27 226	84.00 190	64.56 133	83.07 92	51.48 234	87.19 183	82.56 172

CP-MVS		84.12 8	84.55 10	82.80 10	89.42 17	79.74 5	88.19 5	84.43 57	71.96 38	84.70 61	90.56 52	77.12 25	86.18 26	79.24 17	95.36 12	82.49 173

XVG-OURS		79.51 53	79.82 56	78.58 63	86.11 56	74.96 28	76.33 123	84.95 43	66.89 63	82.75 85	88.99 99	66.82 108	78.37 175	74.80 44	90.76 117	82.40 174

mPP-MVS		84.01 10	84.39 11	82.88 6	90.65 3	81.38 3	87.08 12	82.79 82	72.41 34	85.11 55	90.85 44	76.65 28	84.89 63	79.30 16	94.63 33	82.35 175

XVG-OURS-SEG-HR		79.62 52	79.99 55	78.49 64	86.46 46	74.79 29	77.15 111	85.39 34	66.73 66	80.39 114	88.85 103	74.43 50	78.33 177	74.73 46	85.79 200	82.35 175

FMVSNet2		67.48 204	68.21 194	65.29 247	73.14 242	38.94 321	68.81 221	71.21 244	54.81 178	76.73 159	86.48 151	48.63 262	74.60 223	47.98 268	86.11 198	82.35 175

fmvsm_s_conf0.1_n_a		67.37 208	66.36 217	70.37 179	70.86 265	61.17 139	74.00 155	57.18 325	40.77 324	68.83 276	80.88 234	63.11 141	67.61 292	66.94 107	74.72 316	82.33 178

CNVR-MVS		78.49 64	78.59 66	78.16 68	85.86 60	67.40 84	78.12 100	81.50 101	63.92 96	77.51 144	86.56 149	68.43 93	84.82 65	73.83 53	91.61 88	82.26 179

mvs_anonymous		65.08 230	65.49 227	63.83 259	63.79 342	37.60 335	66.52 257	69.82 255	43.44 304	73.46 210	86.08 165	58.79 190	71.75 258	51.90 232	75.63 308	82.15 180

thres600view7		61.82 265	61.38 266	63.12 267	71.81 258	34.93 352	64.64 278	56.99 326	54.78 182	70.33 251	79.74 253	32.07 348	72.42 248	38.61 327	83.46 235	82.02 181

thres400		60.77 275	59.97 277	63.15 266	70.78 266	35.35 349	63.27 293	57.47 319	53.00 209	68.31 280	77.09 287	32.45 345	72.09 251	35.61 352	81.73 251	82.02 181

ETV-MVS		72.72 137	72.16 148	74.38 112	76.90 187	55.95 180	73.34 158	84.67 51	62.04 115	72.19 229	70.81 335	65.90 120	85.24 56	58.64 178	84.96 214	81.95 183

CNLPA		73.44 115	73.03 132	74.66 105	78.27 164	75.29 26	75.99 128	78.49 165	65.39 78	75.67 175	83.22 209	61.23 163	66.77 305	53.70 223	85.33 206	81.92 184

NCCC		78.25 67	78.04 71	78.89 59	85.61 62	69.45 66	79.80 81	80.99 116	65.77 72	75.55 177	86.25 158	67.42 101	85.42 50	70.10 76	90.88 112	81.81 185

fmvsm_s_conf0.5_n_a		67.00 214	65.95 224	70.17 184	69.72 289	61.16 140	73.34 158	56.83 328	40.96 321	68.36 279	80.08 249	62.84 142	67.57 293	66.90 109	74.50 320	81.78 186

PAPR		69.20 179	68.66 188	70.82 172	75.15 209	47.77 248	75.31 134	81.11 111	49.62 251	66.33 295	79.27 261	61.53 158	82.96 93	48.12 266	81.50 257	81.74 187

Anonymous202405211		66.02 222	66.89 214	63.43 265	74.22 225	38.14 329	59.00 319	66.13 274	63.33 107	69.76 260	85.95 169	51.88 236	70.50 268	44.23 294	87.52 171	81.64 188

FMVSNet3		65.00 231	65.16 232	64.52 253	69.47 290	37.56 336	66.63 255	70.38 251	51.55 225	74.72 188	83.27 205	37.89 322	74.44 225	47.12 273	85.37 203	81.57 189

Vis-MVSNet (Re-imp)		62.74 258	63.21 253	61.34 286	72.19 255	31.56 369	67.31 246	53.87 344	53.60 204	69.88 258	83.37 200	40.52 304	70.98 264	41.40 310	86.78 189	81.48 190

test_0402		78.17 69	79.48 59	74.24 113	83.50 91	59.15 162	72.52 163	74.60 208	75.34 15	88.69 13	91.81 22	75.06 42	82.37 101	65.10 119	88.68 157	81.20 191

VPNet		65.58 225	67.56 202	59.65 298	79.72 140	30.17 377	60.27 313	62.14 302	54.19 194	71.24 242	86.63 146	58.80 189	67.62 291	44.17 295	90.87 113	81.18 192

APD-MVS		81.13 38	81.73 44	79.36 51	84.47 80	70.53 59	83.85 38	83.70 71	69.43 52	83.67 73	88.96 100	75.89 34	86.41 16	72.62 64	92.95 69	81.14 193
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
CPTT-MVS		81.51 34	81.76 43	80.76 34	89.20 22	78.75 9	86.48 21	82.03 93	68.80 53	80.92 107	88.52 110	72.00 63	82.39 100	74.80 44	93.04 68	81.14 193

FE-MVS		68.29 194	66.96 213	72.26 160	74.16 227	54.24 193	77.55 104	73.42 215	57.65 152	72.66 220	84.91 179	32.02 350	81.49 115	48.43 262	81.85 248	81.04 195

Fast-Effi-MVS+-dtu		70.00 166	68.74 186	73.77 120	73.47 236	64.53 111	71.36 185	78.14 173	55.81 171	68.84 275	74.71 305	65.36 126	75.75 208	52.00 231	79.00 281	81.03 196

MDA-MVSNet-bldmvs		62.34 262	61.73 260	64.16 254	61.64 352	49.90 224	48.11 372	57.24 324	53.31 207	80.95 106	79.39 259	49.00 258	61.55 330	45.92 284	80.05 270	81.03 196

D2MVS		62.58 260	61.05 269	67.20 231	63.85 341	47.92 245	56.29 337	69.58 256	39.32 333	70.07 255	78.19 277	34.93 333	72.68 241	53.44 226	83.74 230	81.00 198

ACMM	69.25 9	82.11 29	83.31 27	78.49 64	88.17 36	73.96 34	83.11 49	84.52 56	66.40 69	87.45 22	89.16 94	81.02 8	80.52 137	74.27 51	95.73 7	80.98 199
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
hse-mvs2		72.32 144	70.66 167	77.31 79	83.10 101	71.77 47	69.19 216	71.45 235	54.28 189	77.89 137	78.26 275	49.04 256	79.23 155	63.62 135	89.13 151	80.92 200

DP-MVS Recon		73.57 114	72.69 138	76.23 91	82.85 106	63.39 118	74.32 150	82.96 80	57.75 148	70.35 250	81.98 221	64.34 135	84.41 73	49.69 248	89.95 130	80.89 201

EPNet		69.10 181	67.32 206	74.46 107	68.33 302	61.27 138	77.56 103	63.57 297	60.95 122	56.62 358	82.75 212	51.53 240	81.24 119	54.36 217	90.20 123	80.88 202
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
AUN-MVS		70.22 163	67.88 199	77.22 80	82.96 105	71.61 48	69.08 217	71.39 236	49.17 255	71.70 232	78.07 280	37.62 324	79.21 156	61.81 146	89.15 149	80.82 203

MTAPA		83.19 18	83.87 18	81.13 30	91.16 2	78.16 11	84.87 30	80.63 123	72.08 36	84.93 56	90.79 45	74.65 46	84.42 72	80.98 4	94.75 28	80.82 203

HyFIR lowres test		63.01 254	60.47 274	70.61 174	83.04 102	54.10 194	59.93 315	72.24 228	33.67 366	69.00 267	75.63 296	38.69 316	76.93 197	36.60 344	75.45 311	80.81 205

EIA-MVS		68.59 189	67.16 208	72.90 143	75.18 208	55.64 185	69.39 212	81.29 106	52.44 213	64.53 305	70.69 336	60.33 174	82.30 103	54.27 218	76.31 303	80.75 206

MCST-MVS		73.42 116	73.34 125	73.63 124	81.28 127	59.17 161	74.80 142	83.13 78	45.50 282	72.84 218	83.78 194	65.15 128	80.99 126	64.54 123	89.09 153	80.73 207

tfpnnormal		66.48 218	67.93 197	62.16 278	73.40 238	36.65 338	63.45 290	64.99 284	55.97 168	72.82 219	87.80 124	57.06 211	69.10 279	48.31 264	87.54 170	80.72 208

dcpmvs_2		71.02 156	72.65 139	66.16 242	76.06 200	50.49 215	71.97 172	79.36 147	50.34 241	82.81 84	83.63 195	64.38 134	67.27 296	61.54 150	83.71 232	80.71 209

testing3		58.28 292	58.38 290	58.00 309	77.45 178	26.12 393	60.78 309	43.00 386	56.02 167	70.18 253	75.76 294	13.27 411	67.24 297	48.02 267	80.89 260	80.65 210

SD-MVS		80.28 49	81.55 47	76.47 88	83.57 90	67.83 80	83.39 47	85.35 35	64.42 92	86.14 39	87.07 130	74.02 51	80.97 128	77.70 28	92.32 80	80.62 211
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
CANet_DTU		64.04 245	63.83 245	64.66 251	68.39 299	42.97 292	73.45 157	74.50 209	52.05 218	54.78 367	75.44 300	43.99 282	70.42 270	53.49 225	78.41 288	80.59 212

GA-MVS		62.91 255	61.66 261	66.66 239	67.09 316	44.49 278	61.18 306	69.36 258	51.33 230	69.33 264	74.47 307	36.83 327	74.94 218	50.60 242	74.72 316	80.57 213

114514_t		73.40 117	73.33 126	73.64 123	84.15 86	57.11 175	78.20 98	80.02 136	43.76 299	72.55 222	86.07 166	64.00 136	83.35 87	60.14 166	91.03 105	80.45 214

IterMVS-SCA-FT		67.68 202	66.07 221	72.49 155	73.34 239	58.20 172	63.80 287	65.55 280	48.10 262	76.91 152	82.64 215	45.20 274	78.84 162	61.20 153	77.89 294	80.44 215

ambc				70.10 187	77.74 173	50.21 219	74.28 152	77.93 177		79.26 123	88.29 116	54.11 227	79.77 148	64.43 124	91.10 103	80.30 216

thisisatest0515		60.48 277	57.86 293	68.34 218	67.25 314	46.42 263	60.58 311	62.14 302	40.82 323	63.58 318	69.12 351	26.28 379	78.34 176	48.83 256	82.13 244	80.26 217

LFMVS		67.06 212	67.89 198	64.56 252	78.02 168	38.25 328	70.81 196	59.60 313	65.18 83	71.06 244	86.56 149	43.85 283	75.22 214	46.35 280	89.63 137	80.21 218

UGNet		70.20 164	69.05 179	73.65 122	76.24 194	63.64 116	75.87 131	72.53 224	61.48 118	60.93 334	86.14 162	52.37 234	77.12 195	50.67 241	85.21 208	80.17 219
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
MIMVSNet1		66.57 217	69.23 177	58.59 305	81.26 128	37.73 334	64.06 285	57.62 318	57.02 157	78.40 132	90.75 46	62.65 144	58.10 344	41.77 308	89.58 140	79.95 220

test_yl		65.11 228	65.09 236	65.18 248	70.59 270	40.86 306	63.22 295	72.79 219	57.91 146	68.88 273	79.07 267	42.85 290	74.89 219	45.50 288	84.97 211	79.81 221

DCV-MVSNet		65.11 228	65.09 236	65.18 248	70.59 270	40.86 306	63.22 295	72.79 219	57.91 146	68.88 273	79.07 267	42.85 290	74.89 219	45.50 288	84.97 211	79.81 221

cascas		64.59 236	62.77 257	70.05 188	75.27 206	50.02 221	61.79 301	71.61 230	42.46 310	63.68 316	68.89 356	49.33 254	80.35 138	47.82 270	84.05 227	79.78 223

ET-MVSNet_ETH3D		63.32 250	60.69 273	71.20 171	70.15 281	55.66 184	65.02 275	64.32 291	43.28 308	68.99 268	72.05 328	25.46 383	78.19 182	54.16 220	82.80 239	79.74 224

APD_test1		75.04 98	75.38 99	74.02 117	69.89 283	70.15 62	76.46 117	79.71 140	65.50 75	82.99 80	88.60 109	66.94 105	72.35 249	59.77 171	88.54 158	79.56 225

testf1		75.66 88	76.57 82	72.95 139	67.07 318	67.62 81	76.10 125	80.68 121	64.95 87	86.58 33	90.94 40	71.20 70	71.68 259	60.46 161	91.13 101	79.56 225

APD_test2		75.66 88	76.57 82	72.95 139	67.07 318	67.62 81	76.10 125	80.68 121	64.95 87	86.58 33	90.94 40	71.20 70	71.68 259	60.46 161	91.13 101	79.56 225

CSCG		74.12 108	74.39 105	73.33 128	79.35 145	61.66 132	77.45 106	81.98 94	62.47 114	79.06 125	80.19 246	61.83 154	78.79 164	59.83 170	87.35 176	79.54 228

ACMH	63.62 14	77.50 73	80.11 54	69.68 193	79.61 141	56.28 179	78.81 89	83.62 72	63.41 106	87.14 29	90.23 72	76.11 32	73.32 236	67.58 95	94.44 39	79.44 229
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
MG-MVS		70.47 162	71.34 160	67.85 224	79.26 148	40.42 312	74.67 147	75.15 204	58.41 142	68.74 277	88.14 121	56.08 219	83.69 80	59.90 169	81.71 254	79.43 230

DVP-MVS		81.15 37	83.12 32	75.24 103	86.16 51	60.78 148	83.77 40	80.58 125	72.48 32	85.83 43	90.41 59	78.57 17	85.69 45	75.86 39	94.39 41	79.24 231
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
VNet		64.01 246	65.15 234	60.57 292	73.28 240	35.61 348	57.60 330	67.08 269	54.61 185	66.76 294	83.37 200	56.28 217	66.87 301	42.19 304	85.20 209	79.23 232

TSAR-MVS + GP.		73.08 124	71.60 156	77.54 73	78.99 158	70.73 57	74.96 137	69.38 257	60.73 124	74.39 196	78.44 273	57.72 205	82.78 95	60.16 165	89.60 138	79.11 233

SSC-MVS		61.79 266	66.08 220	48.89 355	76.91 185	10.00 409	53.56 356	47.37 374	68.20 58	76.56 163	89.21 90	54.13 226	57.59 345	54.75 209	74.07 325	79.08 234

HPM-MVS++		79.89 51	79.80 57	80.18 39	89.02 25	78.44 10	83.49 45	80.18 134	64.71 91	78.11 136	88.39 113	65.46 125	83.14 89	77.64 29	91.20 97	78.94 235

DP-MVS		78.44 66	79.29 60	75.90 94	81.86 120	65.33 102	79.05 87	84.63 54	74.83 18	80.41 113	86.27 156	71.68 64	83.45 85	62.45 145	92.40 77	78.92 236

PLC	62.01 16	71.79 149	70.28 169	76.33 89	80.31 136	68.63 75	78.18 99	81.24 108	54.57 186	67.09 293	80.63 238	59.44 182	81.74 113	46.91 276	84.17 225	78.63 237
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
PVSNet_Blended_VisFu		70.04 165	68.88 182	73.53 126	82.71 108	63.62 117	74.81 140	81.95 95	48.53 260	67.16 292	79.18 264	51.42 241	78.38 174	54.39 216	79.72 276	78.60 238

h-mvs33		73.08 124	71.61 155	77.48 74	83.89 89	72.89 44	70.47 199	71.12 245	54.28 189	77.89 137	83.41 197	49.04 256	80.98 127	63.62 135	90.77 116	78.58 239

agg_prior2														70.70 75	90.93 108	78.55 240

ppachtmachnet_test		60.26 279	59.61 280	62.20 277	67.70 310	44.33 279	58.18 327	60.96 309	40.75 325	65.80 298	72.57 324	41.23 297	63.92 320	46.87 277	82.42 242	78.33 241

BH-RMVSNet		68.69 187	68.20 195	70.14 186	76.40 192	53.90 197	64.62 279	73.48 214	58.01 145	73.91 206	81.78 223	59.09 186	78.22 179	48.59 259	77.96 293	78.31 242

PVSNet_BlendedMVS		65.38 226	64.30 240	68.61 215	69.81 285	49.36 230	65.60 269	78.96 153	45.50 282	59.98 337	78.61 271	51.82 237	78.20 180	44.30 292	84.11 226	78.27 243

ab-mvs		64.11 244	65.13 235	61.05 288	71.99 257	38.03 332	67.59 237	68.79 261	49.08 257	65.32 301	86.26 157	58.02 202	66.85 303	39.33 320	79.79 275	78.27 243

EGC-MVSNET		64.77 234	61.17 267	75.60 98	86.90 42	74.47 30	84.04 35	68.62 263	0.60 405	1.13 407	91.61 28	65.32 127	74.15 230	64.01 128	88.28 160	78.17 245

MVSFormer		69.93 168	69.03 180	72.63 152	74.93 210	59.19 159	83.98 36	75.72 198	52.27 214	63.53 319	76.74 290	43.19 287	80.56 134	72.28 67	78.67 285	78.14 246

jason		64.47 239	62.84 256	69.34 199	76.91 185	59.20 158	67.15 247	65.67 277	35.29 356	65.16 302	76.74 290	44.67 278	70.68 265	54.74 210	79.28 279	78.14 246
jason: jason.
new-patchmatchnet		52.89 324	55.76 309	44.26 372	59.94 364	6.31 410	37.36 394	50.76 361	41.10 318	64.28 308	79.82 252	44.77 277	48.43 365	36.24 348	87.61 169	78.03 248

CDS-MVSNet		64.33 242	62.66 258	69.35 198	80.44 134	58.28 171	65.26 272	65.66 278	44.36 294	67.30 291	75.54 297	43.27 286	71.77 256	37.68 334	84.44 223	78.01 249
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
TAMVS		65.31 227	63.75 246	69.97 190	82.23 115	59.76 157	66.78 254	63.37 298	45.20 288	69.79 259	79.37 260	47.42 268	72.17 250	34.48 357	85.15 210	77.99 250

test_fmvs3		56.78 298	55.99 307	59.12 301	53.96 393	48.09 242	58.76 322	66.22 273	27.54 384	76.66 160	68.69 359	25.32 385	51.31 354	53.42 227	73.38 330	77.97 251

LCM-MVSNet-Re		69.10 181	71.57 157	61.70 281	70.37 277	34.30 357	61.45 302	79.62 141	56.81 159	89.59 8	88.16 120	68.44 92	72.94 239	42.30 303	87.33 177	77.85 252

Patchmtry		60.91 272	63.01 255	54.62 325	66.10 327	26.27 392	67.47 240	56.40 333	54.05 197	72.04 230	86.66 143	33.19 339	60.17 334	43.69 296	87.45 174	77.42 253

test9_res														72.12 69	91.37 93	77.40 254

WB-MVS		60.04 280	64.19 242	47.59 357	76.09 197	10.22 408	52.44 361	46.74 375	65.17 84	74.07 202	87.48 125	53.48 229	55.28 348	49.36 252	72.84 333	77.28 255

SDMVSNet		66.36 220	67.85 200	61.88 280	73.04 248	46.14 267	58.54 323	71.36 237	51.42 227	68.93 271	82.72 213	65.62 122	62.22 328	54.41 215	84.67 216	77.28 255

sd_testset		63.55 247	65.38 228	58.07 308	73.04 248	38.83 323	57.41 331	65.44 281	51.42 227	68.93 271	82.72 213	63.76 138	58.11 343	41.05 312	84.67 216	77.28 255

train_agg		76.38 81	76.55 84	75.86 95	85.47 63	69.32 70	76.42 119	78.69 161	54.00 198	76.97 149	86.74 139	66.60 113	81.10 122	72.50 66	91.56 90	77.15 258

lupinMVS		63.36 249	61.49 265	68.97 207	74.93 210	59.19 159	65.80 265	64.52 290	34.68 361	63.53 319	74.25 311	43.19 287	70.62 266	53.88 222	78.67 285	77.10 259

thres100view900		61.17 271	61.09 268	61.39 285	72.14 256	35.01 351	65.42 271	56.99 326	55.23 175	70.71 247	79.90 251	32.07 348	72.09 251	35.61 352	81.73 251	77.08 260

tfpn200view9		60.35 278	59.97 277	61.51 283	70.78 266	35.35 349	63.27 293	57.47 319	53.00 209	68.31 280	77.09 287	32.45 345	72.09 251	35.61 352	81.73 251	77.08 260

fmvsm_l_conf0.5_n		67.48 204	66.88 215	69.28 200	67.41 313	62.04 127	70.69 197	69.85 254	39.46 332	69.59 261	81.09 231	58.15 195	68.73 280	67.51 97	78.16 292	77.07 262

fmvsm_l_conf0.5_n_a		66.66 215	65.97 223	68.72 214	67.09 316	61.38 135	70.03 204	69.15 259	38.59 339	68.41 278	80.36 242	56.56 216	68.32 285	66.10 111	77.45 296	76.46 263

MVS_111021_HR		72.98 131	72.97 134	72.99 137	80.82 130	65.47 100	68.81 221	72.77 221	57.67 150	75.76 174	82.38 218	71.01 72	77.17 194	61.38 151	86.15 196	76.32 264

xiu_mvs_v1_base_debu		67.87 198	67.07 209	70.26 181	79.13 153	61.90 129	67.34 242	71.25 241	47.98 263	67.70 285	74.19 313	61.31 160	72.62 243	56.51 191	78.26 289	76.27 265

xiu_mvs_v1_base		67.87 198	67.07 209	70.26 181	79.13 153	61.90 129	67.34 242	71.25 241	47.98 263	67.70 285	74.19 313	61.31 160	72.62 243	56.51 191	78.26 289	76.27 265

xiu_mvs_v1_base_debi		67.87 198	67.07 209	70.26 181	79.13 153	61.90 129	67.34 242	71.25 241	47.98 263	67.70 285	74.19 313	61.31 160	72.62 243	56.51 191	78.26 289	76.27 265

baseline2		55.57 306	52.74 325	64.05 257	65.26 331	44.11 280	62.38 298	54.43 341	39.03 336	51.21 379	67.35 367	33.66 337	72.45 247	37.14 339	64.22 374	75.60 268

OpenMVS	62.51 15	68.76 185	68.75 185	68.78 213	70.56 272	53.91 196	78.29 96	77.35 182	48.85 258	70.22 252	83.52 196	52.65 233	76.93 197	55.31 205	81.99 245	75.49 269

3Dnovator	65.95 11	71.50 151	71.22 161	72.34 158	73.16 241	63.09 121	78.37 95	78.32 168	57.67 150	72.22 228	84.61 181	54.77 221	78.47 169	60.82 159	81.07 259	75.45 270

1112_ss		59.48 284	58.99 284	60.96 290	77.84 171	42.39 297	61.42 303	68.45 264	37.96 343	59.93 340	67.46 365	45.11 276	65.07 315	40.89 314	71.81 342	75.41 271

IterMVS		63.12 253	62.48 259	65.02 250	66.34 323	52.86 201	63.81 286	62.25 301	46.57 275	71.51 239	80.40 241	44.60 279	66.82 304	51.38 236	75.47 310	75.38 272
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Test_1112_low_res		58.78 289	58.69 286	59.04 303	79.41 144	38.13 330	57.62 329	66.98 270	34.74 359	59.62 343	77.56 284	42.92 289	63.65 322	38.66 326	70.73 349	75.35 273

test_vis3_rt		51.94 333	51.04 339	54.65 324	46.32 404	50.13 220	44.34 383	78.17 171	23.62 396	68.95 270	62.81 378	21.41 394	38.52 396	41.49 309	72.22 339	75.30 274

QAPM		69.18 180	69.26 176	68.94 208	71.61 259	52.58 204	80.37 71	78.79 159	49.63 250	73.51 208	85.14 177	53.66 228	79.12 157	55.11 206	75.54 309	75.11 275

DPM-MVS		69.98 167	69.22 178	72.26 160	82.69 109	58.82 166	70.53 198	81.23 109	47.79 267	64.16 309	80.21 244	51.32 242	83.12 90	60.14 166	84.95 215	74.83 276

pmmvs-eth3d		64.41 241	63.27 252	67.82 226	75.81 203	60.18 154	69.49 210	62.05 305	38.81 338	74.13 200	82.23 219	43.76 284	68.65 282	42.53 302	80.63 266	74.63 277

testing99		55.16 308	54.56 317	56.98 314	70.13 282	30.58 376	54.55 352	54.11 343	49.53 252	56.76 356	70.14 343	22.76 392	65.79 310	36.99 341	76.04 305	74.57 278

testing91		55.74 303	55.29 313	57.08 312	70.63 269	30.85 374	54.94 349	56.31 335	50.34 241	57.08 352	70.10 344	24.50 388	65.86 309	36.98 342	76.75 300	74.53 279

MSDG		67.47 206	67.48 205	67.46 228	70.70 268	54.69 190	66.90 252	78.17 171	60.88 123	70.41 249	74.76 303	61.22 165	73.18 237	47.38 272	76.87 299	74.49 280

WB-MVSnew		53.94 318	54.76 315	51.49 340	71.53 260	28.05 384	58.22 326	50.36 362	37.94 344	59.16 344	70.17 342	49.21 255	51.94 353	24.49 394	71.80 343	74.47 281

MAR-MVS		67.72 201	66.16 219	72.40 157	74.45 222	64.99 107	74.87 138	77.50 181	48.67 259	65.78 299	68.58 360	57.01 212	77.79 188	46.68 279	81.92 246	74.42 282
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
baseline1		57.82 295	58.36 291	56.19 317	69.17 292	30.76 375	62.94 297	55.21 337	46.04 278	63.83 314	78.47 272	41.20 298	63.68 321	39.44 319	68.99 359	74.13 283

EU-MVSNet		60.82 273	60.80 272	60.86 291	68.37 300	41.16 303	72.27 164	68.27 265	26.96 386	69.08 266	75.71 295	32.09 347	67.44 294	55.59 203	78.90 282	73.97 284

HY-MVS	49.31 19	57.96 294	57.59 295	59.10 302	66.85 320	36.17 342	65.13 274	65.39 282	39.24 335	54.69 369	78.14 278	44.28 281	67.18 298	33.75 362	70.79 348	73.95 285

TR-MVS		64.59 236	63.54 249	67.73 227	75.75 204	50.83 213	63.39 291	70.29 252	49.33 253	71.55 238	74.55 306	50.94 243	78.46 170	40.43 316	75.69 307	73.89 286

IB-MVS	49.67 18	59.69 283	56.96 299	67.90 223	68.19 304	50.30 218	61.42 303	65.18 283	47.57 269	55.83 362	67.15 369	23.77 390	79.60 151	43.56 298	79.97 271	73.79 287
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
Anonymous20240521		63.55 247	66.07 221	55.99 318	66.18 326	44.04 281	68.77 224	68.80 260	46.99 272	72.57 221	85.84 170	39.87 308	50.22 357	53.40 228	92.23 81	73.71 288

AdaColmap		74.22 107	74.56 103	73.20 130	81.95 118	60.97 143	79.43 82	80.90 117	65.57 74	72.54 223	81.76 225	70.98 73	85.26 54	47.88 269	90.00 128	73.37 289

PAPM		61.79 266	60.37 275	66.05 243	76.09 197	41.87 299	69.30 213	76.79 190	40.64 327	53.80 372	79.62 256	44.38 280	82.92 94	29.64 377	73.11 332	73.36 290

MVS_111021_LR		72.10 146	71.82 151	72.95 139	79.53 143	73.90 36	70.45 200	66.64 271	56.87 158	76.81 157	81.76 225	68.78 88	71.76 257	61.81 146	83.74 230	73.18 291

UWE-MVS		52.94 323	52.70 326	53.65 328	73.56 234	27.49 387	57.30 332	49.57 365	38.56 340	62.79 322	71.42 333	19.49 400	60.41 332	24.33 396	77.33 297	73.06 292

原ACMM1				73.90 118	85.90 57	65.15 106		81.67 99	50.97 234	74.25 198	86.16 161	61.60 157	83.54 82	56.75 189	91.08 104	73.00 293

CHOSEN 1792x2688		58.09 293	56.30 304	63.45 264	79.95 138	50.93 212	54.07 354	65.59 279	28.56 382	61.53 327	74.33 309	41.09 300	66.52 307	33.91 360	67.69 367	72.92 294

testing222		53.37 319	52.50 329	55.98 319	70.51 275	29.68 379	56.20 339	51.85 357	46.19 277	56.76 356	68.94 354	19.18 401	65.39 312	25.87 390	76.98 298	72.87 295

TinyColmap		67.98 197	69.28 175	64.08 256	67.98 307	46.82 259	70.04 203	75.26 202	53.05 208	77.36 146	86.79 136	59.39 183	72.59 246	45.64 286	88.01 166	72.83 296

FMVSNet5		55.08 309	55.54 310	53.71 327	65.80 328	33.50 361	56.22 338	52.50 354	43.72 301	61.06 331	83.38 199	25.46 383	54.87 349	30.11 374	81.64 256	72.75 297

EG-PatchMatch MVS		70.70 159	70.88 164	70.16 185	82.64 110	58.80 167	71.48 182	73.64 212	54.98 177	76.55 164	81.77 224	61.10 167	78.94 161	54.87 208	80.84 262	72.74 298

PVSNet_Blended		62.90 256	61.64 262	66.69 238	69.81 285	49.36 230	61.23 305	78.96 153	42.04 311	59.98 337	68.86 357	51.82 237	78.20 180	44.30 292	77.77 295	72.52 299

CostFormer		57.35 297	56.14 305	60.97 289	63.76 343	38.43 325	67.50 239	60.22 311	37.14 349	59.12 345	76.34 292	32.78 342	71.99 254	39.12 323	69.27 358	72.47 300

PS-MVSNAJ		64.27 243	63.73 247	65.90 245	77.82 172	51.42 209	63.33 292	72.33 226	45.09 290	61.60 326	68.04 362	62.39 149	73.95 232	49.07 254	73.87 327	72.34 301

xiu_mvs_v2_base		64.43 240	63.96 244	65.85 246	77.72 174	51.32 210	63.63 289	72.31 227	45.06 291	61.70 325	69.66 348	62.56 145	73.93 233	49.06 255	73.91 326	72.31 302

PMVS	70.70 6	81.70 32	83.15 31	77.36 77	90.35 5	82.82 2	82.15 54	79.22 150	74.08 20	87.16 28	91.97 19	84.80 2	76.97 196	64.98 121	93.61 60	72.28 303
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
1314		59.83 282	58.86 285	62.74 273	65.71 329	44.78 276	68.59 226	72.63 223	33.54 368	61.05 332	67.29 368	43.62 285	71.26 262	49.49 251	67.84 366	72.19 304

无先验							74.82 139	70.94 247	47.75 268				76.85 200	54.47 213		72.09 305

LF4IMVS		67.50 203	67.31 207	68.08 222	58.86 370	61.93 128	71.43 183	75.90 197	44.67 293	72.42 224	80.20 245	57.16 207	70.44 269	58.99 177	86.12 197	71.88 306

pmmvs4		60.78 274	59.04 283	66.00 244	73.06 247	57.67 174	64.53 281	60.22 311	36.91 350	65.96 296	77.27 286	39.66 310	68.54 283	38.87 324	74.89 315	71.80 307

MSLP-MVS++		74.48 106	75.78 93	70.59 175	84.66 76	62.40 124	78.65 91	84.24 62	60.55 125	77.71 142	81.98 221	63.12 140	77.64 191	62.95 142	88.14 162	71.73 308

MDTV_nov1_ep13_2view						18.41 403	53.74 355		31.57 376	44.89 394		29.90 369		32.93 364		71.48 309

patch_mono-2		62.73 259	64.08 243	58.68 304	70.36 278	55.87 182	60.84 308	64.11 294	41.23 317	64.04 310	78.22 276	60.00 176	48.80 361	54.17 219	83.71 232	71.37 310

tpm2		56.12 300	54.64 316	60.55 293	66.24 324	36.01 343	68.14 232	56.77 329	33.60 367	58.25 348	75.52 299	30.25 365	74.33 227	33.27 363	69.76 357	71.32 311

CMPMVS	48.73 20	61.54 269	60.89 270	63.52 263	61.08 355	51.55 208	68.07 234	68.00 266	33.88 363	65.87 297	81.25 229	37.91 321	67.71 289	49.32 253	82.60 241	71.31 312
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
API-MVS		70.97 157	71.51 158	69.37 196	75.20 207	55.94 181	80.99 61	76.84 188	62.48 113	71.24 242	77.51 285	61.51 159	80.96 131	52.04 230	85.76 201	71.22 313

OpenMVS_ROB	54.93 17	63.23 252	63.28 251	63.07 268	69.81 285	45.34 272	68.52 228	67.14 268	43.74 300	70.61 248	79.22 262	47.90 266	72.66 242	48.75 257	73.84 328	71.21 314

thres200		57.55 296	57.02 298	59.17 300	67.89 309	34.93 352	58.91 321	57.25 323	50.24 243	64.01 311	71.46 332	32.49 344	71.39 261	31.31 369	79.57 277	71.19 315

test20.03		55.74 303	57.51 296	50.42 344	59.89 365	32.09 366	50.63 366	49.01 367	50.11 245	65.07 303	83.23 207	45.61 272	48.11 366	30.22 373	83.82 229	71.07 316

our_test_3		56.46 299	56.51 302	56.30 316	67.70 310	39.66 316	55.36 345	52.34 356	40.57 328	63.85 313	69.91 347	40.04 307	58.22 342	43.49 299	75.29 314	71.03 317

test_fmvs2		54.80 310	54.11 319	56.88 315	51.76 397	49.95 223	56.70 335	65.80 276	26.22 389	69.42 262	65.25 372	31.82 351	49.98 358	49.63 250	70.36 351	70.71 318

BH-untuned		69.39 177	69.46 173	69.18 202	77.96 170	56.88 176	68.47 230	77.53 180	56.77 160	77.79 140	79.63 255	60.30 175	80.20 144	46.04 283	80.65 264	70.47 319

EPNet_dtu		58.93 288	58.52 287	60.16 296	67.91 308	47.70 250	69.97 205	58.02 317	49.73 249	47.28 389	73.02 322	38.14 318	62.34 326	36.57 345	85.99 199	70.43 320
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
USDC		62.80 257	63.10 254	61.89 279	65.19 332	43.30 289	67.42 241	74.20 210	35.80 355	72.25 227	84.48 184	45.67 271	71.95 255	37.95 333	84.97 211	70.42 321

GSMVS																70.05 322

sam_mvs1												31.41 354				70.05 322

SCA		58.57 291	58.04 292	60.17 295	70.17 280	41.07 305	65.19 273	53.38 350	43.34 307	61.00 333	73.48 317	45.20 274	69.38 276	40.34 317	70.31 352	70.05 322

testing11		53.13 321	52.26 331	55.75 320	70.44 276	31.73 368	54.75 350	52.40 355	44.81 292	52.36 376	68.40 361	21.83 393	65.74 311	32.64 366	72.73 334	69.78 325

tpmvs		55.84 301	55.45 311	57.01 313	60.33 359	33.20 362	65.89 262	59.29 315	47.52 270	56.04 360	73.60 316	31.05 360	68.06 288	40.64 315	64.64 372	69.77 326

旧先验1					84.55 79	60.36 153		63.69 296			87.05 131	54.65 223			83.34 236	69.66 327

CR-MVSNet		58.96 287	58.49 288	60.36 294	66.37 321	48.24 239	70.93 193	56.40 333	32.87 369	61.35 328	86.66 143	33.19 339	63.22 324	48.50 261	70.17 353	69.62 328

RPMNet		65.77 224	65.08 238	67.84 225	66.37 321	48.24 239	70.93 193	86.27 19	54.66 184	61.35 328	86.77 138	33.29 338	85.67 47	55.93 198	70.17 353	69.62 328

tpm cat1		54.02 316	52.63 327	58.19 307	64.85 338	39.86 315	66.26 259	57.28 322	32.16 371	56.90 354	70.39 339	32.75 343	65.30 314	34.29 358	58.79 387	69.41 330

PatchmatchNet		54.60 311	54.27 318	55.59 321	65.17 334	39.08 318	66.92 251	51.80 358	39.89 330	58.39 346	73.12 321	31.69 353	58.33 341	43.01 301	58.38 390	69.38 331
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
YYNet1		52.58 326	53.50 321	49.85 347	54.15 390	36.45 341	40.53 387	46.55 377	38.09 342	75.52 179	73.31 320	41.08 301	43.88 383	41.10 311	71.14 347	69.21 332

CVMVSNet		59.21 286	58.44 289	61.51 283	73.94 230	47.76 249	71.31 187	64.56 289	26.91 388	60.34 336	70.44 337	36.24 330	67.65 290	53.57 224	68.66 361	69.12 333

MDA-MVSNet_test_wron		52.57 327	53.49 323	49.81 348	54.24 389	36.47 340	40.48 388	46.58 376	38.13 341	75.47 180	73.32 319	41.05 302	43.85 384	40.98 313	71.20 346	69.10 334

MVP-Stereo		61.56 268	59.22 281	68.58 216	79.28 147	60.44 152	69.20 215	71.57 231	43.58 302	56.42 359	78.37 274	39.57 311	76.46 204	34.86 356	60.16 384	68.86 335
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
ETVMVS		50.32 341	49.87 349	51.68 338	70.30 279	26.66 390	52.33 362	43.93 382	43.54 303	54.91 366	67.95 363	20.01 399	60.17 334	22.47 398	73.40 329	68.22 336

Syy-MVS		54.13 313	55.45 311	50.18 345	68.77 296	23.59 397	55.02 346	44.55 380	43.80 297	58.05 349	64.07 374	46.22 269	58.83 339	46.16 282	72.36 337	68.12 337

myMVS_eth3d		50.36 340	50.52 345	49.88 346	68.77 296	22.69 399	55.02 346	44.55 380	43.80 297	58.05 349	64.07 374	14.16 410	58.83 339	33.90 361	72.36 337	68.12 337

新几何1				69.99 189	88.37 34	71.34 51		62.08 304	43.85 296	74.99 184	86.11 164	52.85 232	70.57 267	50.99 239	83.23 237	68.05 339

UnsupCasMVSNet_eth		52.26 329	53.29 324	49.16 352	55.08 386	33.67 360	50.03 367	58.79 316	37.67 346	63.43 321	74.75 304	41.82 295	45.83 371	38.59 328	59.42 386	67.98 340

Patchmatch-test		47.93 348	49.96 348	41.84 376	57.42 376	24.26 396	48.75 369	41.49 394	39.30 334	56.79 355	73.48 317	30.48 364	33.87 399	29.29 379	72.61 335	67.39 341

Patchmatch-RL test		59.95 281	59.12 282	62.44 275	72.46 253	54.61 191	59.63 316	47.51 373	41.05 320	74.58 193	74.30 310	31.06 359	65.31 313	51.61 233	79.85 272	67.39 341

testgi		54.00 317	56.86 300	45.45 366	58.20 373	25.81 394	49.05 368	49.50 366	45.43 285	67.84 283	81.17 230	51.81 239	43.20 386	29.30 378	79.41 278	67.34 343

test222					87.30 37	69.15 73	67.85 235	59.59 314	41.06 319	73.05 216	85.72 172	48.03 265			80.65 264	66.92 344

pmmvs5		52.49 328	52.58 328	52.21 336	54.99 387	32.38 364	55.45 344	53.84 345	32.15 372	55.49 364	74.81 302	38.08 319	57.37 346	34.02 359	74.40 321	66.88 345

Anonymous20231206		54.13 313	55.82 308	49.04 354	70.89 264	35.96 344	51.73 363	50.87 360	34.86 357	62.49 323	79.22 262	42.52 293	44.29 382	27.95 384	81.88 247	66.88 345

tpm		50.60 338	52.42 330	45.14 368	65.18 333	26.29 391	60.30 312	43.50 383	37.41 347	57.01 353	79.09 266	30.20 367	42.32 387	32.77 365	66.36 369	66.81 347

testdata				64.13 255	85.87 59	63.34 119		61.80 307	47.83 266	76.42 170	86.60 148	48.83 259	62.31 327	54.46 214	81.26 258	66.74 348

MIMVSNet		54.39 312	56.12 306	49.20 351	72.57 252	30.91 373	59.98 314	48.43 370	41.66 313	55.94 361	83.86 193	41.19 299	50.42 356	26.05 387	75.38 312	66.27 349

tpmrst		50.15 342	51.38 336	46.45 363	56.05 381	24.77 395	64.40 283	49.98 363	36.14 352	53.32 373	69.59 349	35.16 332	48.69 362	39.24 321	58.51 389	65.89 350

EPMVS		45.74 353	46.53 356	43.39 374	54.14 391	22.33 401	55.02 346	35.00 403	34.69 360	51.09 380	70.20 341	25.92 381	42.04 389	37.19 338	55.50 394	65.78 351

PVSNet	43.83 21	51.56 334	51.17 337	52.73 333	68.34 301	38.27 327	48.22 371	53.56 348	36.41 351	54.29 370	64.94 373	34.60 334	54.20 352	30.34 372	69.87 355	65.71 352

test_fmvs1_n		52.70 325	52.01 332	54.76 323	53.83 394	50.36 216	55.80 342	65.90 275	24.96 392	65.39 300	60.64 386	27.69 374	48.46 363	45.88 285	67.99 364	65.46 353

BH-w/o		64.81 233	64.29 241	66.36 240	76.08 199	54.71 189	65.61 268	75.23 203	50.10 246	71.05 245	71.86 329	54.33 225	79.02 159	38.20 331	76.14 304	65.36 354

XXY-MVS		55.19 307	57.40 297	48.56 356	64.45 339	34.84 354	51.54 364	53.59 346	38.99 337	63.79 315	79.43 258	56.59 214	45.57 372	36.92 343	71.29 345	65.25 355

ADS-MVSNet2		48.76 346	47.25 355	53.29 332	55.90 383	40.54 311	47.34 375	54.99 339	31.41 377	50.48 382	72.06 326	31.23 356	54.26 351	25.93 388	55.93 392	65.07 356

ADS-MVSNet		44.62 359	45.58 358	41.73 377	55.90 383	20.83 402	47.34 375	39.94 398	31.41 377	50.48 382	72.06 326	31.23 356	39.31 394	25.93 388	55.93 392	65.07 356

KD-MVS_2432*1600		52.05 331	51.58 334	53.44 330	52.11 395	31.20 370	44.88 381	64.83 287	41.53 314	64.37 306	70.03 345	15.61 408	64.20 317	36.25 346	74.61 318	64.93 358

miper_refine_blended		52.05 331	51.58 334	53.44 330	52.11 395	31.20 370	44.88 381	64.83 287	41.53 314	64.37 306	70.03 345	15.61 408	64.20 317	36.25 346	74.61 318	64.93 358

test0.0.03 1		47.72 349	48.31 351	45.93 364	55.53 385	29.39 380	46.40 378	41.21 396	43.41 305	55.81 363	67.65 364	29.22 371	43.77 385	25.73 391	69.87 355	64.62 360

JIA-IIPM		54.03 315	51.62 333	61.25 287	59.14 369	55.21 187	59.10 318	47.72 371	50.85 235	50.31 385	85.81 171	20.10 398	63.97 319	36.16 349	55.41 395	64.55 361

PatchT		53.35 320	56.47 303	43.99 373	64.19 340	17.46 404	59.15 317	43.10 385	52.11 217	54.74 368	86.95 132	29.97 368	49.98 358	43.62 297	74.40 321	64.53 362

test_vis1_n		51.27 336	50.41 346	53.83 326	56.99 377	50.01 222	56.75 334	60.53 310	25.68 390	59.74 342	57.86 390	29.40 370	47.41 368	43.10 300	63.66 375	64.08 363

gg-mvs-nofinetune		55.75 302	56.75 301	52.72 334	62.87 346	28.04 385	68.92 218	41.36 395	71.09 41	50.80 381	92.63 12	20.74 395	66.86 302	29.97 375	72.41 336	63.25 364

MVS		60.62 276	59.97 277	62.58 274	68.13 305	47.28 255	68.59 226	73.96 211	32.19 370	59.94 339	68.86 357	50.48 246	77.64 191	41.85 307	75.74 306	62.83 365

N_pmnet		52.06 330	51.11 338	54.92 322	59.64 367	71.03 53	37.42 393	61.62 308	33.68 365	57.12 351	72.10 325	37.94 320	31.03 400	29.13 383	71.35 344	62.70 366

Gipuma		69.55 174	72.83 135	59.70 297	63.63 344	53.97 195	80.08 78	75.93 196	64.24 94	73.49 209	88.93 102	57.89 203	62.46 325	59.75 172	91.55 91	62.67 367
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
test_fmvs1		51.51 335	50.86 342	53.48 329	49.72 400	49.35 232	54.11 353	64.96 285	24.64 394	63.66 317	59.61 389	28.33 373	48.45 364	45.38 290	67.30 368	62.66 368

WTY-MVS		49.39 345	50.31 347	46.62 362	61.22 354	32.00 367	46.61 377	49.77 364	33.87 364	54.12 371	69.55 350	41.96 294	45.40 374	31.28 370	64.42 373	62.47 369

test_vis1_rt		46.70 352	45.24 360	51.06 342	44.58 405	51.04 211	39.91 389	67.56 267	21.84 400	51.94 377	50.79 398	33.83 336	39.77 393	35.25 355	61.50 381	62.38 370

test-LLR		50.43 339	50.69 344	49.64 349	60.76 356	41.87 299	53.18 357	45.48 378	43.41 305	49.41 386	60.47 387	29.22 371	44.73 379	42.09 305	72.14 340	62.33 371

test-mter		48.56 347	48.20 352	49.64 349	60.76 356	41.87 299	53.18 357	45.48 378	31.91 375	49.41 386	60.47 387	18.34 402	44.73 379	42.09 305	72.14 340	62.33 371

test_vis1_n_1920		52.96 322	53.50 321	51.32 341	59.15 368	44.90 275	56.13 340	64.29 292	30.56 380	59.87 341	60.68 385	40.16 306	47.47 367	48.25 265	62.46 378	61.58 373

UnsupCasMVSNet_bld		50.01 343	51.03 340	46.95 359	58.61 371	32.64 363	48.31 370	53.27 351	34.27 362	60.47 335	71.53 331	41.40 296	47.07 369	30.68 371	60.78 383	61.13 374

sss		47.59 350	48.32 350	45.40 367	56.73 380	33.96 358	45.17 380	48.51 369	32.11 374	52.37 375	65.79 370	40.39 305	41.91 390	31.85 367	61.97 380	60.35 375

PM-MVS		64.49 238	63.61 248	67.14 233	76.68 190	75.15 27	68.49 229	42.85 387	51.17 233	77.85 139	80.51 239	45.76 270	66.31 308	52.83 229	76.35 302	59.96 376

test_cas_vis1_n_1920		50.90 337	50.92 341	50.83 343	54.12 392	47.80 247	51.44 365	54.61 340	26.95 387	63.95 312	60.85 384	37.86 323	44.97 377	45.53 287	62.97 377	59.72 377

GG-mvs-BLEND				52.24 335	60.64 358	29.21 382	69.73 209	42.41 388		45.47 392	52.33 396	20.43 397	68.16 286	25.52 392	65.42 371	59.36 378

dmvs_re		49.91 344	50.77 343	47.34 358	59.98 361	38.86 322	53.18 357	53.58 347	39.75 331	55.06 365	61.58 383	36.42 329	44.40 381	29.15 382	68.23 362	58.75 379

TESTMET0.1,1		45.17 356	44.93 362	45.89 365	56.02 382	38.31 326	53.18 357	41.94 393	27.85 383	44.86 395	56.47 392	17.93 403	41.50 391	38.08 332	68.06 363	57.85 380

mvsany_test3		43.76 363	41.01 367	52.01 337	48.09 402	57.74 173	42.47 385	23.85 409	23.30 397	64.80 304	62.17 381	27.12 375	40.59 392	29.17 381	48.11 399	57.69 381

MS-PatchMatch		55.59 305	54.89 314	57.68 310	69.18 291	49.05 233	61.00 307	62.93 300	35.98 353	58.36 347	68.93 355	36.71 328	66.59 306	37.62 336	63.30 376	57.39 382

dp		44.09 361	44.88 363	41.72 378	58.53 372	23.18 398	54.70 351	42.38 390	34.80 358	44.25 397	65.61 371	24.48 389	44.80 378	29.77 376	49.42 398	57.18 383

MVE	27.91 23	36.69 370	35.64 373	39.84 380	43.37 406	35.85 346	19.49 399	24.61 407	24.68 393	39.05 401	62.63 380	38.67 317	27.10 404	21.04 401	47.25 400	56.56 384
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
pmmvs3		46.71 351	45.09 361	51.55 339	56.76 379	48.25 238	55.78 343	39.53 399	24.13 395	50.35 384	63.40 376	15.90 407	51.08 355	29.29 379	70.69 350	55.33 385

PatchMatch-RL		58.68 290	57.72 294	61.57 282	76.21 195	73.59 39	61.83 300	49.00 368	47.30 271	61.08 330	68.97 353	50.16 248	59.01 338	36.06 351	68.84 360	52.10 386

dmvs_testset		45.26 355	47.51 353	38.49 382	59.96 363	14.71 406	58.50 324	43.39 384	41.30 316	51.79 378	56.48 391	39.44 312	49.91 360	21.42 400	55.35 396	50.85 387

wuyk23d		61.97 263	66.25 218	49.12 353	58.19 374	60.77 150	66.32 258	52.97 352	55.93 170	90.62 5	86.91 133	73.07 57	35.98 398	20.63 402	91.63 87	50.62 388

PMMVS2		37.74 368	40.87 368	28.36 385	42.41 407	5.35 411	24.61 398	27.75 405	32.15 372	47.85 388	70.27 340	35.85 331	29.51 402	19.08 403	67.85 365	50.22 389

DSMNet-mixed		43.18 364	44.66 364	38.75 381	54.75 388	28.88 383	57.06 333	27.42 406	13.47 402	47.27 390	77.67 283	38.83 315	39.29 395	25.32 393	60.12 385	48.08 390

new_pmnet		37.55 369	39.80 371	30.79 384	56.83 378	16.46 405	39.35 390	30.65 404	25.59 391	45.26 393	61.60 382	24.54 387	28.02 403	21.60 399	52.80 397	47.90 391

CHOSEN 280x420		41.62 365	39.89 370	46.80 361	61.81 350	51.59 207	33.56 397	35.74 402	27.48 385	37.64 403	53.53 393	23.24 391	42.09 388	27.39 385	58.64 388	46.72 392

EMVS		44.61 360	44.45 365	45.10 369	48.91 401	43.00 291	37.92 392	41.10 397	46.75 274	38.00 402	48.43 400	26.42 378	46.27 370	37.11 340	75.38 312	46.03 393

E-PMN		45.17 356	45.36 359	44.60 370	50.07 398	42.75 293	38.66 391	42.29 391	46.39 276	39.55 400	51.15 397	26.00 380	45.37 375	37.68 334	76.41 301	45.69 394

test_f		43.79 362	45.63 357	38.24 383	42.29 408	38.58 324	34.76 396	47.68 372	22.22 399	67.34 290	63.15 377	31.82 351	30.60 401	39.19 322	62.28 379	45.53 395

mvsany_test1		37.88 367	35.74 372	44.28 371	47.28 403	49.90 224	36.54 395	24.37 408	19.56 401	45.76 391	53.46 394	32.99 341	37.97 397	26.17 386	35.52 401	44.99 396

PMMVS		44.69 358	43.95 366	46.92 360	50.05 399	53.47 199	48.08 373	42.40 389	22.36 398	44.01 398	53.05 395	42.60 292	45.49 373	31.69 368	61.36 382	41.79 397

PVSNet_0	36.71 22	41.12 366	40.78 369	42.14 375	59.97 362	40.13 313	40.97 386	42.24 392	30.81 379	44.86 395	49.41 399	40.70 303	45.12 376	23.15 397	34.96 402	41.16 398

FPMVS		59.43 285	60.07 276	57.51 311	77.62 177	71.52 49	62.33 299	50.92 359	57.40 155	69.40 263	80.00 250	39.14 314	61.92 329	37.47 337	66.36 369	39.09 399

MVS-HIRNet		45.53 354	47.29 354	40.24 379	62.29 348	26.82 389	56.02 341	37.41 401	29.74 381	43.69 399	81.27 228	33.96 335	55.48 347	24.46 395	56.79 391	38.43 400

test_method		19.26 371	19.12 375	19.71 386	9.09 410	1.91 413	7.79 401	53.44 349	1.42 404	10.27 406	35.80 401	17.42 405	25.11 405	12.44 404	24.38 404	32.10 401

DeepMVS_CX				11.83 387	15.51 409	13.86 407		11.25 412	5.76 403	20.85 405	26.46 402	17.06 406	9.22 406	9.69 406	13.82 405	12.42 402

tmp_tt		11.98 373	14.73 376	3.72 388	2.28 411	4.62 412	19.44 400	14.50 411	0.47 406	21.55 404	9.58 404	25.78 382	4.57 407	11.61 405	27.37 403	1.96 403

testmvs		4.06 377	5.28 380	0.41 389	0.64 413	0.16 415	42.54 384	0.31 414	0.26 408	0.50 409	1.40 408	0.77 412	0.17 408	0.56 407	0.55 407	0.90 404

test123		4.43 376	5.78 379	0.39 390	0.97 412	0.28 414	46.33 379	0.45 413	0.31 407	0.62 408	1.50 407	0.61 413	0.11 409	0.56 407	0.63 406	0.77 405

test_blank		0.00 378	0.00 381	0.00 391	0.00 414	0.00 416	0.00 402	0.00 415	0.00 409	0.00 410	0.00 409	0.00 414	0.00 410	0.00 409	0.00 408	0.00 406

uanet_test		0.00 378	0.00 381	0.00 391	0.00 414	0.00 416	0.00 402	0.00 415	0.00 409	0.00 410	0.00 409	0.00 414	0.00 410	0.00 409	0.00 408	0.00 406

DCPMVS		0.00 378	0.00 381	0.00 391	0.00 414	0.00 416	0.00 402	0.00 415	0.00 409	0.00 410	0.00 409	0.00 414	0.00 410	0.00 409	0.00 408	0.00 406

cdsmvs_eth3d_5k		17.71 372	23.62 374	0.00 391	0.00 414	0.00 416	0.00 402	70.17 253	0.00 409	0.00 410	74.25 311	68.16 95	0.00 410	0.00 409	0.00 408	0.00 406

pcd_1.5k_mvsjas		5.20 375	6.93 378	0.00 391	0.00 414	0.00 416	0.00 402	0.00 415	0.00 409	0.00 410	0.00 409	62.39 149	0.00 410	0.00 409	0.00 408	0.00 406

sosnet-low-res		0.00 378	0.00 381	0.00 391	0.00 414	0.00 416	0.00 402	0.00 415	0.00 409	0.00 410	0.00 409	0.00 414	0.00 410	0.00 409	0.00 408	0.00 406

sosnet		0.00 378	0.00 381	0.00 391	0.00 414	0.00 416	0.00 402	0.00 415	0.00 409	0.00 410	0.00 409	0.00 414	0.00 410	0.00 409	0.00 408	0.00 406

uncertanet		0.00 378	0.00 381	0.00 391	0.00 414	0.00 416	0.00 402	0.00 415	0.00 409	0.00 410	0.00 409	0.00 414	0.00 410	0.00 409	0.00 408	0.00 406

Regformer		0.00 378	0.00 381	0.00 391	0.00 414	0.00 416	0.00 402	0.00 415	0.00 409	0.00 410	0.00 409	0.00 414	0.00 410	0.00 409	0.00 408	0.00 406

ab-mvs-re		5.62 374	7.50 377	0.00 391	0.00 414	0.00 416	0.00 402	0.00 415	0.00 409	0.00 410	67.46 365	0.00 414	0.00 410	0.00 409	0.00 408	0.00 406

uanet		0.00 378	0.00 381	0.00 391	0.00 414	0.00 416	0.00 402	0.00 415	0.00 409	0.00 410	0.00 409	0.00 414	0.00 410	0.00 409	0.00 408	0.00 406

WAC-MVS						22.69 399								36.10 350

FOURS1					89.19 23	77.84 12	91.64 1	89.11 2	84.05 2	91.57 2

test_one_0601					85.84 61	61.45 134		85.63 27	75.27 17	85.62 48	90.38 64	76.72 27

eth-test2					0.00 414

eth-test					0.00 414

ZD-MVS					83.91 87	69.36 69		81.09 113	58.91 140	82.73 86	89.11 95	75.77 35	86.63 12	72.73 62	92.93 70

test_241102_ONE					86.12 53	61.06 141		84.72 48	72.64 29	87.38 24	89.47 84	77.48 23	85.74 44

9.14			80.22 53		80.68 131		80.35 72	87.69 10	59.90 129	83.00 79	88.20 117	74.57 47	81.75 112	73.75 54	93.78 57

save fliter					87.00 39	67.23 86	79.24 85	77.94 176	56.65 163

test0726					86.16 51	60.78 148	83.81 39	85.10 39	72.48 32	85.27 53	89.96 77	78.57 17

test_part2					85.90 57	66.44 91				84.61 62

sam_mvs												31.21 358

MTGPA								80.63 123

test_post1							66.63 255				2.08 405	30.66 363	59.33 337	40.34 317

test_post											1.99 406	30.91 361	54.76 350

patchmatchnet-post											68.99 352	31.32 355	69.38 276

MTMP							84.83 31	19.26 410

gm-plane-assit					62.51 347	33.91 359			37.25 348		62.71 379		72.74 240	38.70 325

TEST9					85.47 63	69.32 70	76.42 119	78.69 161	53.73 203	76.97 149	86.74 139	66.84 107	81.10 122

test_8					85.09 69	67.89 79	76.26 124	78.66 163	54.00 198	76.89 153	86.72 141	66.60 113	80.89 132

agg_prior					84.44 82	66.02 97		78.62 164		76.95 151			80.34 139

test_prior4						70.14 63	77.57 102

test_prior2							75.57 133		58.92 139	76.53 166	86.78 137	67.83 100		69.81 78	92.76 73

旧先验2							71.17 190		45.11 289	78.54 131			61.28 331	59.19 176

新几何2							71.33 186

原ACMM2							74.78 143

testdata2													67.30 295	48.34 263

segment_acmp												68.30 94

testdata1							68.34 231		57.24 156

plane_prior7					85.18 66	66.21 94

plane_prior6					84.18 85	65.31 103						60.83 170

plane_prior4											89.11 95

plane_prior3						65.67 99			63.82 98	78.23 133

plane_prior2							82.74 51		65.45 76

plane_prior1					84.46 81

plane_prior						65.18 104	80.06 79		61.88 117						89.91 132

n2								0.00 415

nn								0.00 415

door-mid								55.02 338

test11								82.71 84

door								52.91 353

HQP5-MVS						58.80 167

HQP-NCC					82.37 111		77.32 107		59.08 134	71.58 234

ACMP_Plane					82.37 111		77.32 107		59.08 134	71.58 234

BP-MVS														67.38 102

HQP3-MVS								84.12 65							89.16 147

HQP2-MVS												58.09 197

NP-MVS					83.34 95	63.07 122					85.97 167

MDTV_nov1_ep13			54.05 320		65.54 330	29.30 381	59.00 319	55.22 336	35.96 354	52.44 374	75.98 293	30.77 362	59.62 336	38.21 330	73.33 331

ACMMP++_ref															89.47 142

ACMMP++															91.96 83

Test By Simon												62.56 145

High-resolution multi-view benchmark