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	63.30 116	84.80 32	87.77 9	86.18 1	96.26 1	96.06 1	90.32 1	84.49 68	68.08 85	97.05 1	96.93 1

PEN-MVS		80.46 46	82.91 34	73.11 129	89.83 8	39.02 310	77.06 112	82.61 86	80.04 4	90.60 6	92.85 9	74.93 44	85.21 56	63.15 131	95.15 17	95.09 2

PS-CasMVS		80.41 47	82.86 36	73.07 131	89.93 6	39.21 307	77.15 110	81.28 107	79.74 5	90.87 4	92.73 11	75.03 43	84.93 61	63.83 123	95.19 15	95.07 3

CP-MVSNet		79.48 54	81.65 45	72.98 134	89.66 12	39.06 309	76.76 113	80.46 127	78.91 7	90.32 7	91.70 25	68.49 91	84.89 62	63.40 128	95.12 18	95.01 4

WR-MVS_H		80.22 50	82.17 41	74.39 107	89.46 14	42.69 284	78.24 96	82.24 89	78.21 9	89.57 9	92.10 18	68.05 96	85.59 47	66.04 103	95.62 9	94.88 5

DTE-MVSNet		80.35 48	82.89 35	72.74 144	89.84 7	37.34 327	77.16 109	81.81 97	80.45 3	90.92 3	92.95 7	74.57 47	86.12 28	63.65 124	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 65	74.51 47	96.15 2	92.88 7

DU-MVS		74.91 100	75.57 95	72.93 138	83.50 91	45.79 258	69.47 201	80.14 135	65.22 80	81.74 96	87.08 125	61.82 150	81.07 123	56.21 186	94.98 20	91.93 8

NR-MVSNet		73.62 111	74.05 110	72.33 155	83.50 91	43.71 273	65.65 257	77.32 179	64.32 90	75.59 174	87.08 125	62.45 143	81.34 115	54.90 197	95.63 8	91.93 8

v7n		79.37 56	80.41 52	76.28 89	78.67 161	55.81 173	79.22 85	82.51 88	70.72 43	87.54 21	92.44 14	68.00 98	81.34 115	72.84 57	91.72 84	91.69 10

RRT_MVS		78.18 68	77.69 73	79.66 46	83.14 96	61.34 130	83.29 48	80.34 132	57.43 151	86.65 31	91.79 23	50.52 233	86.01 30	71.36 66	94.65 32	91.62 11

mvsmamba		77.20 74	76.37 84	79.69 45	80.34 135	61.52 128	80.58 65	82.12 91	53.54 201	83.93 70	91.03 37	49.49 239	85.97 32	73.26 55	93.08 67	91.59 12

TranMVSNet+NR-MVSNet		76.13 82	77.66 74	71.56 162	84.61 78	42.57 286	70.98 184	78.29 166	68.67 55	83.04 78	89.26 88	72.99 58	80.75 132	55.58 194	95.47 10	91.35 13

FC-MVSNet-test		73.32 117	74.78 101	68.93 200	79.21 149	36.57 329	71.82 172	79.54 145	57.63 150	82.57 87	90.38 64	59.38 176	78.99 159	57.91 174	94.56 34	91.23 14

v10		75.69 86	76.20 87	74.16 110	74.44 219	48.69 224	75.84 130	82.93 81	59.02 135	85.92 41	89.17 92	58.56 183	82.74 95	70.73 69	89.14 150	91.05 15

UniMVSNet_NR-MVSNet		74.90 101	75.65 93	72.64 147	83.04 102	45.79 258	69.26 204	78.81 154	66.66 65	81.74 96	86.88 131	63.26 136	81.07 123	56.21 186	94.98 20	91.05 15

UniMVSNet (Re)		75.00 98	75.48 96	73.56 121	83.14 96	47.92 235	70.41 192	81.04 115	63.67 97	79.54 120	86.37 151	62.83 138	81.82 109	57.10 178	95.25 14	90.94 17

anonymousdsp		78.60 61	77.80 72	81.00 31	78.01 168	74.34 33	80.09 76	76.12 189	50.51 236	89.19 10	90.88 42	71.45 67	77.78 188	73.38 54	90.60 119	90.90 18

v8		75.07 96	75.64 94	73.35 123	73.42 232	47.46 243	75.20 133	81.45 103	60.05 125	85.64 45	89.26 88	58.08 190	81.80 110	69.71 77	87.97 166	90.79 19

IS-MVSNet		75.10 95	75.42 97	74.15 111	79.23 148	48.05 233	79.43 81	78.04 170	70.09 48	79.17 124	88.02 120	53.04 219	83.60 80	58.05 173	93.76 59	90.79 19

FIs		72.56 136	73.80 114	68.84 203	78.74 160	37.74 323	71.02 183	79.83 138	56.12 163	80.88 110	89.45 85	58.18 185	78.28 177	56.63 180	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	75.72 194	52.27 210	87.37 26	92.25 16	68.04 97	80.56 133	72.28 63	91.15 99	90.32 22

WR-MVS		71.20 149	72.48 139	67.36 219	84.98 71	35.70 337	64.43 272	68.66 256	65.05 83	81.49 99	86.43 150	57.57 197	76.48 202	50.36 233	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 62	84.76 46	62.54 109	81.77 94	86.65 142	71.46 66	83.53 82	67.95 89	92.44 76	89.60 24

tttt0517		69.46 171	67.79 197	74.46 104	75.34 201	52.72 192	75.05 134	63.27 293	54.69 179	78.87 127	84.37 181	26.63 363	81.15 119	63.95 120	87.93 167	89.51 25

v2v482		72.55 138	72.58 137	72.43 152	72.92 245	46.72 250	71.41 176	79.13 149	55.27 170	81.17 104	85.25 173	55.41 210	81.13 120	67.25 99	85.46 198	89.43 26

Anonymous20231211		75.54 89	77.19 78	70.59 171	77.67 174	45.70 261	74.73 142	80.19 133	68.80 52	82.95 81	92.91 8	66.26 113	76.76 200	58.41 171	92.77 72	89.30 27

OurMVSNet-221017-0		78.57 62	78.53 67	78.67 60	80.48 133	64.16 109	80.24 73	82.06 92	61.89 113	88.77 12	93.32 4	57.15 199	82.60 97	70.08 73	92.80 71	89.25 28

EI-MVSNet-UG-set		72.63 135	71.68 149	75.47 99	74.67 213	58.64 160	72.02 163	71.50 229	63.53 99	78.58 130	71.39 320	65.98 115	78.53 166	67.30 98	80.18 264	89.23 29

V42		71.06 150	70.83 161	71.72 160	67.25 293	47.14 247	65.94 251	80.35 131	51.35 225	83.40 76	83.23 201	59.25 177	78.80 162	65.91 104	80.81 258	89.23 29

RPSCF		75.76 85	74.37 105	79.93 40	74.81 210	77.53 16	77.53 104	79.30 147	59.44 130	78.88 126	89.80 80	71.26 69	73.09 234	57.45 175	80.89 256	89.17 31

UniMVSNet_ETH3D		76.74 78	79.02 61	69.92 185	89.27 19	43.81 272	74.47 146	71.70 225	72.33 35	85.50 50	93.65 3	77.98 21	76.88 198	54.60 201	91.64 86	89.08 32

v1192		73.40 115	73.42 119	73.32 125	74.65 216	48.67 225	72.21 160	81.73 98	52.76 207	81.85 92	84.56 178	57.12 200	82.24 104	68.58 80	87.33 176	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	59.80 173	87.58 4	73.06 56	91.34 94	89.01 34

EI-MVSNet-Vis-set		72.78 132	71.87 145	75.54 98	74.77 211	59.02 155	72.24 159	71.56 228	63.92 93	78.59 128	71.59 317	66.22 114	78.60 165	67.58 91	80.32 262	89.00 35

v1144		73.29 118	73.39 120	73.01 132	74.12 224	48.11 231	72.01 164	81.08 114	53.83 198	81.77 94	84.68 176	58.07 191	81.91 108	68.10 84	86.86 184	88.99 36

nrg030		74.87 103	75.99 90	71.52 163	74.90 208	49.88 218	74.10 150	82.58 87	54.55 183	83.50 75	89.21 90	71.51 65	75.74 208	61.24 142	92.34 79	88.94 37

v1240		73.06 123	73.14 126	72.84 141	74.74 212	47.27 246	71.88 171	81.11 111	51.80 217	82.28 89	84.21 183	56.22 208	82.34 101	68.82 79	87.17 182	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	81.53 113	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		72.96 129	72.98 131	72.89 140	74.67 213	47.58 241	71.92 169	80.69 120	51.70 219	81.69 98	83.89 187	56.58 206	82.25 103	68.34 82	87.36 174	88.82 40

EPP-MVSNet		73.86 109	73.38 121	75.31 100	78.19 164	53.35 190	80.45 67	77.32 179	65.11 82	76.47 166	86.80 132	49.47 240	83.77 76	53.89 210	92.72 74	88.81 41

UA-Net		81.56 33	82.28 40	79.40 49	88.91 28	69.16 72	84.67 33	80.01 137	75.34 15	79.80 118	94.91 2	69.79 83	80.25 140	72.63 59	94.46 36	88.78 42

v144192		72.99 127	73.06 129	72.77 142	74.58 217	47.48 242	71.90 170	80.44 128	51.57 220	81.46 100	84.11 185	58.04 192	82.12 105	67.98 88	87.47 172	88.70 43

EI-MVSNet		69.61 169	69.01 177	71.41 165	73.94 226	49.90 214	71.31 179	71.32 234	58.22 140	75.40 178	70.44 323	58.16 186	75.85 204	62.51 133	79.81 268	88.48 44

IterMVS-LS		73.01 125	73.12 128	72.66 146	73.79 228	49.90 214	71.63 173	78.44 162	58.22 140	80.51 112	86.63 143	58.15 187	79.62 149	62.51 133	88.20 160	88.48 44
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
casdiffmvs_mvg		75.26 92	76.18 88	72.52 149	72.87 246	49.47 219	72.94 155	84.71 50	59.49 129	80.90 109	88.81 103	70.07 79	79.71 148	67.40 94	88.39 158	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 57	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 42	66.91 100	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 71	77.35 77	78.13 69	84.88 72	66.37 92	78.55 92	79.59 143	53.48 202	86.29 36	92.43 15	62.39 144	80.25 140	67.90 90	90.61 118	87.77 49

eth_miper_zixun_eth		69.42 172	68.73 183	71.50 164	67.99 286	46.42 253	67.58 228	78.81 154	50.72 233	78.13 135	80.34 234	50.15 237	80.34 138	60.18 154	84.65 214	87.74 50

casdiffmvs		73.06 123	73.84 113	70.72 169	71.32 256	46.71 251	70.93 185	84.26 61	55.62 168	77.46 145	87.10 124	67.09 102	77.81 186	63.95 120	86.83 185	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	78.37 162	71.37 50	87.45 7	85.87 26	77.48 12	81.98 91	89.95 78	69.14 86	85.26 53	66.15 101	91.24 96	87.61 52

ITE_SJBPF				80.35 38	76.94 182	73.60 38		80.48 126	66.87 62	83.64 74	86.18 156	70.25 78	79.90 146	61.12 146	88.95 154	87.56 53

thisisatest0530		67.05 207	65.16 221	72.73 145	73.10 240	50.55 204	71.26 181	63.91 289	50.22 239	74.46 191	80.75 228	26.81 362	80.25 140	59.43 164	86.50 189	87.37 54

CS-MVS		76.51 79	76.00 89	78.06 70	77.02 179	64.77 105	80.78 63	82.66 85	60.39 123	74.15 195	83.30 199	69.65 84	82.07 106	69.27 78	86.75 187	87.36 55

pmmvs6		71.82 144	73.66 117	66.31 231	75.94 197	42.01 288	66.99 239	72.53 220	63.45 101	76.43 167	92.78 10	72.95 59	69.69 270	51.41 224	90.46 120	87.22 56

ACMH+	66.64 10	81.20 36	82.48 39	77.35 77	81.16 129	62.39 121	80.51 66	87.80 7	73.02 26	87.57 20	91.08 36	80.28 9	82.44 98	64.82 112	96.10 4	87.21 57

c3_l		69.82 166	69.89 167	69.61 187	66.24 302	43.48 276	68.12 223	79.61 142	51.43 222	77.72 141	80.18 238	54.61 214	78.15 182	63.62 125	87.50 171	87.20 58

Anonymous20240529		72.56 136	73.79 115	68.86 202	76.89 185	45.21 263	68.80 213	77.25 181	67.16 59	76.89 153	90.44 56	65.95 116	74.19 228	50.75 229	90.00 128	87.18 59

tt0805		76.12 83	78.43 68	69.20 192	81.32 126	41.37 292	76.72 114	77.64 175	63.78 96	82.06 90	87.88 121	79.78 11	79.05 157	64.33 116	92.40 77	87.17 60

baseline		73.10 120	73.96 112	70.51 173	71.46 255	46.39 255	72.08 162	84.40 58	55.95 165	76.62 161	86.46 149	67.20 101	78.03 183	64.22 117	87.27 179	87.11 61

Effi-MVS+-dtu		75.43 90	72.28 142	84.91 2	77.05 177	83.58 1	78.47 93	77.70 174	57.68 146	74.89 182	78.13 267	64.80 128	84.26 73	56.46 184	85.32 203	86.88 62

v148		69.38 174	69.39 170	69.36 189	69.14 279	44.56 267	68.83 210	72.70 218	54.79 177	78.59 128	84.12 184	54.69 212	76.74 201	59.40 165	82.20 239	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 44	85.14 54	90.42 58	78.99 15	86.62 12	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		67.34 203	65.62 216	72.50 150	69.82 272	47.06 248	72.19 161	76.86 183	45.32 277	72.86 212	82.85 204	20.53 379	83.73 77	61.13 145	89.02 153	86.70 65

mvs_tets		78.93 58	78.67 65	79.72 43	84.81 74	73.93 35	80.65 64	76.50 187	51.98 215	87.40 23	91.86 21	76.09 33	78.53 166	68.58 80	90.20 123	86.69 66

EC-MVSNet		77.08 76	77.39 76	76.14 91	76.86 186	56.87 167	80.32 72	87.52 11	63.45 101	74.66 188	84.52 179	69.87 82	84.94 60	69.76 75	89.59 139	86.60 67

jajsoiax		78.51 63	78.16 70	79.59 47	84.65 77	73.83 37	80.42 68	76.12 189	51.33 226	87.19 27	91.51 29	73.79 54	78.44 170	68.27 83	90.13 127	86.49 68

cl22		67.14 204	66.51 211	69.03 196	63.20 323	43.46 277	66.88 243	76.25 188	49.22 248	74.48 190	77.88 269	45.49 259	77.40 192	60.64 150	84.59 216	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	63.17 105	85.38 52	91.26 33	76.33 30	84.67 67	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 49	87.75 15	91.13 34	81.83 3	86.20 23	77.13 34	95.96 5	86.08 71

LGP-MVS_train				80.90 32	87.00 39	70.41 60		86.35 16	69.77 49	87.75 15	91.13 34	81.83 3	86.20 23	77.13 34	95.96 5	86.08 71

SixPastTwentyTwo		75.77 84	76.34 85	74.06 112	81.69 122	54.84 178	76.47 115	75.49 196	64.10 92	87.73 17	92.24 17	50.45 235	81.30 117	67.41 93	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 69	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	59.89 127	80.91 108	90.53 53	72.19 60	88.56 1	73.67 53	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		68.27 191	68.26 188	68.29 209	64.98 314	43.67 274	65.89 252	74.67 202	50.04 242	76.86 155	82.43 210	48.74 247	75.38 210	60.94 147	89.81 133	85.81 76

AllTest		77.66 70	77.43 75	78.35 65	79.19 150	70.81 55	78.60 91	88.64 3	65.37 77	80.09 116	88.17 116	70.33 76	78.43 171	55.60 191	90.90 110	85.81 76

TestCases				78.35 65	79.19 150	70.81 55		88.64 3	65.37 77	80.09 116	88.17 116	70.33 76	78.43 171	55.60 191	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 63	86.70 30	89.99 76	81.64 6	85.95 33	74.35 48	96.11 3	85.81 76
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
cl____		68.26 192	68.26 188	68.29 209	64.98 314	43.67 274	65.89 252	74.67 202	50.04 242	76.86 155	82.42 211	48.74 247	75.38 210	60.92 148	89.81 133	85.80 80

CS-MVS-test		74.89 102	74.23 108	76.86 80	77.01 180	62.94 119	78.98 87	84.61 55	58.62 138	70.17 247	80.80 227	66.74 109	81.96 107	61.74 138	89.40 145	85.69 81

miper_ehance_all_eth		68.36 187	68.16 192	68.98 197	65.14 313	43.34 278	67.07 238	78.92 153	49.11 250	76.21 170	77.72 270	53.48 218	77.92 185	61.16 144	84.59 216	85.68 82

test_fmvsm_n_1920		69.63 167	68.45 185	73.16 127	70.56 264	65.86 95	70.26 193	78.35 163	37.69 323	74.29 193	78.89 257	61.10 160	68.10 281	65.87 105	79.07 275	85.53 83

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 42	85.64 45	90.41 59	75.55 38	87.69 3	79.75 7	95.08 19	85.36 84
Skip Steuart: Steuart Systems R&D Blog.
MVS_0304		76.32 81	75.96 91	77.42 75	79.33 145	60.86 139	80.18 75	74.88 201	66.93 60	69.11 257	88.95 100	57.84 195	86.12 28	76.63 36	89.77 136	85.28 85

iter_conf_final		68.69 183	67.00 208	73.76 117	73.68 229	52.33 195	75.96 128	73.54 209	50.56 235	69.90 250	82.85 204	24.76 372	83.73 77	65.40 108	86.33 191	85.22 86

diffmvs		67.42 202	67.50 200	67.20 221	62.26 327	45.21 263	64.87 266	77.04 182	48.21 255	71.74 225	79.70 243	58.40 184	71.17 259	64.99 110	80.27 263	85.22 86
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		70.62 156	73.19 125	62.92 262	76.97 181	34.44 345	68.84 209	70.88 244	60.25 124	79.50 121	90.53 53	61.82 150	69.11 274	54.67 200	95.27 13	85.22 86

TAPA-MVS	65.27 12	75.16 94	74.29 107	77.77 71	74.86 209	68.08 77	77.89 100	84.04 68	55.15 172	76.19 171	83.39 193	66.91 104	80.11 144	60.04 158	90.14 126	85.13 89
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
CLD-MVS		72.88 130	72.36 141	74.43 106	77.03 178	54.30 182	68.77 214	83.43 75	52.12 212	76.79 158	74.44 295	69.54 85	83.91 74	55.88 189	93.25 66	85.09 90
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		66.38 210	67.51 199	62.97 260	61.76 329	34.39 346	58.11 316	75.30 197	50.84 232	77.12 148	85.42 170	56.84 204	69.44 271	51.07 227	91.16 98	85.08 91

CDPH-MVS		77.33 73	77.06 80	78.14 68	84.21 84	63.98 111	76.07 126	83.45 74	54.20 189	77.68 143	87.18 123	69.98 80	85.37 50	68.01 87	92.72 74	85.08 91

K. test v3		73.67 110	73.61 118	73.87 115	79.78 138	55.62 176	74.69 144	62.04 300	66.16 69	84.76 60	93.23 5	49.47 240	80.97 127	65.66 106	86.67 188	85.02 93

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 48	78.11 23	94.46 36	84.89 94

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 94

test2506		61.23 259	60.85 259	62.38 266	78.80 158	27.88 371	67.33 235	37.42 378	54.23 187	67.55 275	88.68 105	17.87 384	74.39 225	46.33 268	89.41 143	84.86 96

ECVR-MVS		64.82 222	65.22 219	63.60 251	78.80 158	31.14 361	66.97 240	56.47 322	54.23 187	69.94 249	88.68 105	37.23 311	74.81 220	45.28 277	89.41 143	84.86 96

HQP_MVS		78.77 60	78.78 64	78.72 59	85.18 66	65.18 100	82.74 51	85.49 29	65.45 74	78.23 133	89.11 94	60.83 163	86.15 26	71.09 67	90.94 106	84.82 98

plane_prior5								85.49 29					86.15 26	71.09 67	90.94 106	84.82 98

SF-MVS		80.72 43	81.80 42	77.48 73	82.03 117	64.40 108	83.41 46	88.46 5	65.28 79	84.29 65	89.18 91	73.73 55	83.22 87	76.01 37	93.77 58	84.81 100

alignmvs		70.54 157	71.00 159	69.15 194	73.50 230	48.04 234	69.85 198	79.62 140	53.94 197	76.54 164	82.00 214	59.00 179	74.68 221	57.32 176	87.21 180	84.72 101

IU-MVS					86.12 53	60.90 137		80.38 129	45.49 274	81.31 101				75.64 40	94.39 41	84.65 102

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 18	78.84 19	94.03 53	84.64 103

X-MVStestdata		76.81 77	74.79 100	82.85 8	89.43 15	77.61 14	86.80 17	84.66 52	72.71 27	82.87 82	9.95 381	73.86 52	86.31 18	78.84 19	94.03 53	84.64 103

ACMMP		84.22 6	84.84 8	82.35 17	89.23 21	76.66 22	87.65 6	85.89 25	71.03 41	85.85 42	90.58 51	78.77 16	85.78 41	79.37 15	95.17 16	84.62 105
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 99	84.23 66	91.47 30	72.02 62	87.16 6	79.74 9	94.36 45	84.61 106
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		70.81 154	71.44 155	68.91 201	79.07 155	46.51 252	67.82 226	70.83 245	61.23 116	74.07 198	88.69 104	59.86 171	75.62 209	51.11 226	90.28 122	84.61 106

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 58	84.02 68	90.39 62	74.73 45	86.46 14	80.73 6	94.43 40	84.60 108

test1111		64.62 225	65.19 220	62.93 261	79.01 156	29.91 365	65.45 260	54.41 331	54.09 192	71.47 235	88.48 109	37.02 312	74.29 227	46.83 265	89.94 131	84.58 109

bld_raw_dy_0_64		72.85 131	72.76 134	73.09 130	85.08 70	64.80 104	78.72 89	64.22 287	51.92 216	83.13 77	90.26 70	39.21 299	69.91 268	70.73 69	91.60 89	84.56 110

miper_enhance_ethall		65.86 213	65.05 228	68.28 211	61.62 331	42.62 285	64.74 267	77.97 171	42.52 295	73.42 206	72.79 310	49.66 238	77.68 189	58.12 172	84.59 216	84.54 111

GBi-Net		68.30 188	68.79 179	66.81 225	73.14 237	40.68 298	71.96 166	73.03 212	54.81 174	74.72 185	90.36 67	48.63 249	75.20 214	47.12 260	85.37 199	84.54 111

test1		68.30 188	68.79 179	66.81 225	73.14 237	40.68 298	71.96 166	73.03 212	54.81 174	74.72 185	90.36 67	48.63 249	75.20 214	47.12 260	85.37 199	84.54 111

FMVSNet1		71.06 150	72.48 139	66.81 225	77.65 175	40.68 298	71.96 166	73.03 212	61.14 117	79.45 122	90.36 67	60.44 165	75.20 214	50.20 234	88.05 163	84.54 111

TSAR-MVS + MP.		79.05 57	78.81 62	79.74 42	88.94 27	67.52 83	86.61 19	81.38 105	51.71 218	77.15 147	91.42 32	65.49 121	87.20 5	79.44 13	87.17 182	84.51 115
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	72.70 134	71.69 148	75.72 95	78.10 165	60.01 147	73.04 154	81.50 101	45.34 276	79.66 119	84.35 182	65.15 125	82.65 96	48.70 246	89.38 146	84.50 116
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
canonicalmvs		72.29 141	73.38 121	69.04 195	74.23 220	47.37 244	73.93 151	83.18 76	54.36 184	76.61 162	81.64 221	72.03 61	75.34 212	57.12 177	87.28 178	84.40 117

TransMVSNet (Re)		69.62 168	71.63 150	63.57 252	76.51 188	35.93 335	65.75 256	71.29 236	61.05 118	75.02 180	89.90 79	65.88 118	70.41 267	49.79 236	89.48 141	84.38 118

OPM-MVS		80.99 41	81.63 46	79.07 53	86.86 43	69.39 68	79.41 83	84.00 69	65.64 71	85.54 49	89.28 87	76.32 31	83.47 83	74.03 50	93.57 62	84.35 119
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 44	77.43 30	94.74 29	84.31 120

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 86	81.05 105	88.38 112	57.10 201	87.10 7	79.75 7	83.87 224	84.31 120
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 56	84.14 67	90.21 73	73.37 56	86.41 15	79.09 18	93.98 56	84.30 122

ACMMPR		83.62 12	83.93 17	82.69 11	89.78 10	77.51 18	87.01 14	84.19 64	70.23 45	84.49 63	90.67 50	75.15 41	86.37 17	79.58 10	94.26 49	84.18 123

VDDNet		71.60 146	73.13 127	67.02 224	86.29 47	41.11 294	69.97 195	66.50 266	68.72 54	74.74 184	91.70 25	59.90 170	75.81 206	48.58 248	91.72 84	84.15 124

FA-MVS(test-final)		71.27 148	71.06 158	71.92 159	73.96 225	52.32 196	76.45 117	76.12 189	59.07 134	74.04 199	86.18 156	52.18 223	79.43 153	59.75 162	81.76 246	84.03 125

MVS_Test		69.84 165	70.71 162	67.24 220	67.49 292	43.25 280	69.87 197	81.22 110	52.69 208	71.57 231	86.68 139	62.09 148	74.51 223	66.05 102	78.74 278	83.96 126

region2R		83.54 14	83.86 19	82.58 14	89.82 9	77.53 16	87.06 13	84.23 63	70.19 47	83.86 71	90.72 49	75.20 40	86.27 20	79.41 14	94.25 50	83.95 127

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 57	84.91 59	90.88 42	75.59 36	86.57 13	78.16 22	94.71 30	83.82 128

pm-mvs1		68.40 186	69.85 168	64.04 248	73.10 240	39.94 304	64.61 270	70.50 246	55.52 169	73.97 200	89.33 86	63.91 134	68.38 279	49.68 238	88.02 164	83.81 129

MSC_two_6792asdad				79.02 54	83.14 96	67.03 87		80.75 118					86.24 21	77.27 32	94.85 25	83.78 130

No_MVS				79.02 54	83.14 96	67.03 87		80.75 118					86.24 21	77.27 32	94.85 25	83.78 130

HQP4-MVS										71.59 227			85.31 51			83.74 132

HQP-MVS		75.24 93	75.01 99	75.94 92	82.37 111	58.80 157	77.32 106	84.12 65	59.08 131	71.58 228	85.96 165	58.09 188	85.30 52	67.38 96	89.16 147	83.73 133

PHI-MVS		74.92 99	74.36 106	76.61 83	76.40 189	62.32 122	80.38 69	83.15 77	54.16 191	73.23 209	80.75 228	62.19 147	83.86 75	68.02 86	90.92 109	83.65 134

DeepPCF-MVS	71.07 5	78.48 65	77.14 79	82.52 16	84.39 83	77.04 21	76.35 120	84.05 67	56.66 159	80.27 115	85.31 172	68.56 90	87.03 9	67.39 95	91.26 95	83.50 135

DVP-MVS++		81.24 35	82.74 37	76.76 81	83.14 96	60.90 137	91.64 1	85.49 29	74.03 21	84.93 56	90.38 64	66.82 106	85.90 37	77.43 30	90.78 114	83.49 136

PC_three_1452									46.98 267	81.83 93	86.28 152	66.55 112	84.47 70	63.31 130	90.78 114	83.49 136

XVG-ACMP-BASELINE		80.54 44	81.06 48	78.98 56	87.01 38	72.91 43	80.23 74	85.56 28	66.56 66	85.64 45	89.57 83	69.12 87	80.55 135	72.51 61	93.37 63	83.48 138

APDe-MVS		82.88 23	84.14 14	79.08 52	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 8	77.93 25	94.32 48	83.47 139

ANet_high		67.08 205	69.94 166	58.51 296	57.55 353	27.09 372	58.43 314	76.80 185	63.56 98	82.40 88	91.93 20	59.82 172	64.98 301	50.10 235	88.86 155	83.46 140

Effi-MVS+		72.10 142	72.28 142	71.58 161	74.21 222	50.33 207	74.72 143	82.73 83	62.62 108	70.77 240	76.83 277	69.96 81	80.97 127	60.20 153	78.43 282	83.45 141

test12				76.51 85	82.28 114	60.94 136		81.64 100		73.60 202		64.88 127	85.19 58		90.42 121	83.38 142

VPA-MVSNet		68.71 182	70.37 164	63.72 250	76.13 193	38.06 321	64.10 274	71.48 230	56.60 161	74.10 197	88.31 113	64.78 129	69.72 269	47.69 258	90.15 125	83.37 143

ACMMP_NAP		82.33 27	83.28 28	79.46 48	89.28 18	69.09 74	83.62 42	84.98 41	64.77 87	83.97 69	91.02 38	75.53 39	85.93 36	82.00 2	94.36 45	83.35 144

DeepC-MVS_fast	69.89 7	77.17 75	76.33 86	79.70 44	83.90 88	67.94 78	80.06 78	83.75 70	56.73 158	74.88 183	85.32 171	65.54 120	87.79 2	65.61 107	91.14 100	83.35 144
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 39	75.29 41	94.22 52	83.25 146

test_0728_SECOND				76.57 84	86.20 48	60.57 143	83.77 40	85.49 29					85.90 37	75.86 38	94.39 41	83.25 146

GeoE		73.14 119	73.77 116	71.26 166	78.09 166	52.64 193	74.32 147	79.56 144	56.32 162	76.35 169	83.36 197	70.76 74	77.96 184	63.32 129	81.84 245	83.18 148

test_fmvsmvis_n_1920		72.36 139	72.49 138	71.96 158	71.29 257	64.06 110	72.79 156	81.82 96	40.23 311	81.25 103	81.04 225	70.62 75	68.69 276	69.74 76	83.60 230	83.14 149

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 34	77.77 26	93.58 61	83.09 150

SED-MVS		81.78 31	83.48 24	76.67 82	86.12 53	61.06 133	83.62 42	84.72 48	72.61 30	87.38 24	89.70 81	77.48 23	85.89 39	75.29 41	94.39 41	83.08 151

OPU-MVS				78.65 61	83.44 94	66.85 89	83.62 42				86.12 160	66.82 106	86.01 30	61.72 139	89.79 135	83.08 151

MVSTER		63.29 241	61.60 252	68.36 207	59.77 344	46.21 256	60.62 299	71.32 234	41.83 298	75.40 178	79.12 253	30.25 351	75.85 204	56.30 185	79.81 268	83.03 153

CANet		73.00 126	71.84 146	76.48 86	75.82 198	61.28 131	74.81 138	80.37 130	63.17 105	62.43 310	80.50 232	61.10 160	85.16 59	64.00 119	84.34 220	83.01 154

Vis-MVSNet		74.85 104	74.56 102	75.72 95	81.63 123	64.64 106	76.35 120	79.06 150	62.85 107	73.33 207	88.41 110	62.54 142	79.59 151	63.94 122	82.92 234	82.94 155
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
miper_lstm_enhance		61.97 253	61.63 251	62.98 259	60.04 338	45.74 260	47.53 352	70.95 242	44.04 284	73.06 210	78.84 258	39.72 295	60.33 317	55.82 190	84.64 215	82.88 156

PAPM_NR		73.91 108	74.16 109	73.16 127	81.90 119	53.50 188	81.28 60	81.40 104	66.17 68	73.30 208	83.31 198	59.96 169	83.10 90	58.45 170	81.66 251	82.87 157

Fast-Effi-MVS+		68.81 180	68.30 187	70.35 175	74.66 215	48.61 226	66.06 250	78.32 164	50.62 234	71.48 234	75.54 284	68.75 89	79.59 151	50.55 232	78.73 279	82.86 158

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 45	84.47 64	90.43 57	76.79 26	85.94 34	79.58 10	94.23 51	82.82 159

DELS-MVS		68.83 179	68.31 186	70.38 174	70.55 266	48.31 227	63.78 278	82.13 90	54.00 194	68.96 261	75.17 288	58.95 180	80.06 145	58.55 169	82.74 236	82.76 160
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		62.44 251	63.40 238	59.55 289	72.34 249	32.38 354	56.39 323	64.84 280	51.21 228	67.46 276	81.01 226	50.75 232	63.51 308	38.47 315	88.12 162	82.75 161

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 66	78.41 21	94.78 27	82.74 162
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
lessismore_v0				72.75 143	79.60 141	56.83 168		57.37 315		83.80 72	89.01 97	47.45 254	78.74 164	64.39 115	86.49 190	82.69 163

DPE-MVS		82.00 30	83.02 33	78.95 57	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 10	74.56 46	94.02 55	82.62 164
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
test_prior				75.27 101	82.15 116	59.85 148		84.33 59					83.39 85			82.58 165

F-COLMAP		75.29 91	73.99 111	79.18 51	81.73 121	71.90 46	81.86 58	82.98 79	59.86 128	72.27 220	84.00 186	64.56 130	83.07 91	51.48 223	87.19 181	82.56 166

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 25	79.24 17	95.36 12	82.49 167

XVG-OURS		79.51 53	79.82 56	78.58 62	86.11 56	74.96 28	76.33 122	84.95 43	66.89 61	82.75 85	88.99 98	66.82 106	78.37 174	74.80 43	90.76 117	82.40 168

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 62	79.30 16	94.63 33	82.35 169

XVG-OURS-SEG-HR		79.62 52	79.99 55	78.49 63	86.46 46	74.79 29	77.15 110	85.39 34	66.73 64	80.39 114	88.85 102	74.43 50	78.33 176	74.73 45	85.79 196	82.35 169

FMVSNet2		67.48 200	68.21 190	65.29 237	73.14 237	38.94 311	68.81 211	71.21 240	54.81 174	76.73 159	86.48 148	48.63 249	74.60 222	47.98 255	86.11 194	82.35 169

CNVR-MVS		78.49 64	78.59 66	78.16 67	85.86 60	67.40 84	78.12 99	81.50 101	63.92 93	77.51 144	86.56 146	68.43 93	84.82 64	73.83 51	91.61 88	82.26 172

mvs_anonymous		65.08 220	65.49 217	63.83 249	63.79 320	37.60 325	66.52 247	69.82 250	43.44 290	73.46 205	86.08 162	58.79 182	71.75 254	51.90 221	75.63 297	82.15 173

thres600view7		61.82 255	61.38 254	63.12 257	71.81 253	34.93 342	64.64 268	56.99 319	54.78 178	70.33 245	79.74 242	32.07 334	72.42 244	38.61 313	83.46 231	82.02 174

thres400		60.77 264	59.97 265	63.15 256	70.78 259	35.35 339	63.27 283	57.47 313	53.00 205	68.31 269	77.09 275	32.45 331	72.09 247	35.61 335	81.73 247	82.02 174

ETV-MVS		72.72 133	72.16 144	74.38 108	76.90 184	55.95 170	73.34 153	84.67 51	62.04 112	72.19 223	70.81 321	65.90 117	85.24 55	58.64 168	84.96 210	81.95 176

CNLPA		73.44 113	73.03 130	74.66 103	78.27 163	75.29 26	75.99 127	78.49 161	65.39 76	75.67 173	83.22 203	61.23 158	66.77 294	53.70 212	85.33 202	81.92 177

NCCC		78.25 67	78.04 71	78.89 58	85.61 62	69.45 66	79.80 80	80.99 116	65.77 70	75.55 175	86.25 155	67.42 100	85.42 49	70.10 72	90.88 112	81.81 178

PAPR		69.20 175	68.66 184	70.82 168	75.15 205	47.77 238	75.31 132	81.11 111	49.62 246	66.33 282	79.27 249	61.53 153	82.96 92	48.12 254	81.50 253	81.74 179

Anonymous202405211		66.02 212	66.89 210	63.43 255	74.22 221	38.14 319	59.00 308	66.13 268	63.33 104	69.76 253	85.95 166	51.88 224	70.50 264	44.23 280	87.52 170	81.64 180

FMVSNet3		65.00 221	65.16 221	64.52 243	69.47 276	37.56 326	66.63 245	70.38 247	51.55 221	74.72 185	83.27 200	37.89 308	74.44 224	47.12 260	85.37 199	81.57 181

Vis-MVSNet (Re-imp)		62.74 248	63.21 241	61.34 276	72.19 250	31.56 358	67.31 236	53.87 332	53.60 200	69.88 251	83.37 195	40.52 290	70.98 260	41.40 296	86.78 186	81.48 182

test_0402		78.17 69	79.48 59	74.24 109	83.50 91	59.15 154	72.52 157	74.60 204	75.34 15	88.69 13	91.81 22	75.06 42	82.37 100	65.10 109	88.68 156	81.20 183

VPNet		65.58 215	67.56 198	59.65 288	79.72 139	30.17 364	60.27 302	62.14 296	54.19 190	71.24 236	86.63 143	58.80 181	67.62 285	44.17 281	90.87 113	81.18 184

APD-MVS		81.13 38	81.73 44	79.36 50	84.47 80	70.53 59	83.85 38	83.70 71	69.43 51	83.67 73	88.96 99	75.89 34	86.41 15	72.62 60	92.95 69	81.14 185
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 52	80.92 107	88.52 108	72.00 63	82.39 99	74.80 43	93.04 68	81.14 185

FE-MVS		68.29 190	66.96 209	72.26 156	74.16 223	54.24 183	77.55 103	73.42 211	57.65 149	72.66 215	84.91 175	32.02 336	81.49 114	48.43 250	81.85 244	81.04 187

Fast-Effi-MVS+-dtu		70.00 162	68.74 182	73.77 116	73.47 231	64.53 107	71.36 177	78.14 169	55.81 167	68.84 267	74.71 292	65.36 123	75.75 207	52.00 220	79.00 276	81.03 188

MDA-MVSNet-bldmvs		62.34 252	61.73 248	64.16 244	61.64 330	49.90 214	48.11 350	57.24 318	53.31 203	80.95 106	79.39 247	49.00 245	61.55 315	45.92 270	80.05 265	81.03 188

D2MVS		62.58 250	61.05 257	67.20 221	63.85 319	47.92 235	56.29 324	69.58 251	39.32 314	70.07 248	78.19 265	34.93 319	72.68 237	53.44 215	83.74 226	81.00 190

ACMM	69.25 9	82.11 29	83.31 27	78.49 63	88.17 36	73.96 34	83.11 49	84.52 56	66.40 67	87.45 22	89.16 93	81.02 8	80.52 136	74.27 49	95.73 7	80.98 191
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
hse-mvs2		72.32 140	70.66 163	77.31 78	83.10 101	71.77 47	69.19 206	71.45 231	54.28 185	77.89 137	78.26 263	49.04 243	79.23 154	63.62 125	89.13 151	80.92 192

DP-MVS Recon		73.57 112	72.69 135	76.23 90	82.85 106	63.39 114	74.32 147	82.96 80	57.75 145	70.35 244	81.98 215	64.34 132	84.41 72	49.69 237	89.95 130	80.89 193

EPNet		69.10 177	67.32 202	74.46 104	68.33 283	61.27 132	77.56 102	63.57 291	60.95 119	56.62 338	82.75 206	51.53 228	81.24 118	54.36 206	90.20 123	80.88 194
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
AUN-MVS		70.22 159	67.88 195	77.22 79	82.96 105	71.61 48	69.08 207	71.39 232	49.17 249	71.70 226	78.07 268	37.62 310	79.21 155	61.81 136	89.15 149	80.82 195

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 71	80.98 4	94.75 28	80.82 195

HyFIR lowres test		63.01 244	60.47 262	70.61 170	83.04 102	54.10 184	59.93 304	72.24 224	33.67 344	69.00 259	75.63 283	38.69 302	76.93 196	36.60 328	75.45 300	80.81 197

EIA-MVS		68.59 185	67.16 204	72.90 139	75.18 204	55.64 175	69.39 202	81.29 106	52.44 209	64.53 292	70.69 322	60.33 166	82.30 102	54.27 207	76.31 293	80.75 198

MCST-MVS		73.42 114	73.34 123	73.63 120	81.28 127	59.17 153	74.80 140	83.13 78	45.50 272	72.84 213	83.78 189	65.15 125	80.99 125	64.54 113	89.09 152	80.73 199

tfpnnormal		66.48 209	67.93 193	62.16 268	73.40 233	36.65 328	63.45 280	64.99 278	55.97 164	72.82 214	87.80 122	57.06 202	69.10 275	48.31 252	87.54 169	80.72 200

dcpmvs_2		71.02 152	72.65 136	66.16 232	76.06 196	50.49 205	71.97 165	79.36 146	50.34 237	82.81 84	83.63 190	64.38 131	67.27 288	61.54 140	83.71 228	80.71 201

SD-MVS		80.28 49	81.55 47	76.47 87	83.57 90	67.83 80	83.39 47	85.35 35	64.42 89	86.14 39	87.07 127	74.02 51	80.97 127	77.70 28	92.32 80	80.62 202
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		64.04 235	63.83 233	64.66 241	68.39 280	42.97 282	73.45 152	74.50 205	52.05 214	54.78 346	75.44 287	43.99 268	70.42 266	53.49 214	78.41 283	80.59 203

GA-MVS		62.91 245	61.66 249	66.66 229	67.09 295	44.49 268	61.18 296	69.36 253	51.33 226	69.33 256	74.47 294	36.83 313	74.94 217	50.60 231	74.72 305	80.57 204

114514_t		73.40 115	73.33 124	73.64 119	84.15 86	57.11 165	78.20 97	80.02 136	43.76 286	72.55 217	86.07 163	64.00 133	83.35 86	60.14 156	91.03 105	80.45 205

IterMVS-SCA-FT		67.68 198	66.07 214	72.49 151	73.34 234	58.20 162	63.80 277	65.55 274	48.10 256	76.91 152	82.64 209	45.20 260	78.84 161	61.20 143	77.89 288	80.44 206

ambc				70.10 181	77.74 172	50.21 209	74.28 149	77.93 173		79.26 123	88.29 114	54.11 216	79.77 147	64.43 114	91.10 103	80.30 207

thisisatest0515		60.48 266	57.86 280	68.34 208	67.25 293	46.42 253	60.58 300	62.14 296	40.82 307	63.58 305	69.12 334	26.28 365	78.34 175	48.83 244	82.13 240	80.26 208

LFMVS		67.06 206	67.89 194	64.56 242	78.02 167	38.25 318	70.81 188	59.60 307	65.18 81	71.06 238	86.56 146	43.85 269	75.22 213	46.35 267	89.63 137	80.21 209

UGNet		70.20 160	69.05 175	73.65 118	76.24 191	63.64 112	75.87 129	72.53 220	61.48 115	60.93 320	86.14 159	52.37 222	77.12 194	50.67 230	85.21 204	80.17 210
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		66.57 208	69.23 173	58.59 295	81.26 128	37.73 324	64.06 275	57.62 312	57.02 154	78.40 132	90.75 46	62.65 139	58.10 325	41.77 294	89.58 140	79.95 211

test_yl		65.11 218	65.09 225	65.18 238	70.59 262	40.86 296	63.22 285	72.79 215	57.91 143	68.88 265	79.07 255	42.85 276	74.89 218	45.50 274	84.97 207	79.81 212

DCV-MVSNet		65.11 218	65.09 225	65.18 238	70.59 262	40.86 296	63.22 285	72.79 215	57.91 143	68.88 265	79.07 255	42.85 276	74.89 218	45.50 274	84.97 207	79.81 212

cascas		64.59 226	62.77 245	70.05 182	75.27 202	50.02 211	61.79 291	71.61 226	42.46 296	63.68 303	68.89 338	49.33 242	80.35 137	47.82 257	84.05 223	79.78 214

ET-MVSNet_ETH3D		63.32 240	60.69 261	71.20 167	70.15 270	55.66 174	65.02 265	64.32 285	43.28 294	68.99 260	72.05 315	25.46 369	78.19 181	54.16 209	82.80 235	79.74 215

APD_test1		75.04 97	75.38 98	74.02 113	69.89 271	70.15 62	76.46 116	79.71 139	65.50 73	82.99 80	88.60 107	66.94 103	72.35 245	59.77 161	88.54 157	79.56 216

testf1		75.66 87	76.57 81	72.95 135	67.07 296	67.62 81	76.10 124	80.68 121	64.95 84	86.58 33	90.94 40	71.20 70	71.68 255	60.46 151	91.13 101	79.56 216

APD_test2		75.66 87	76.57 81	72.95 135	67.07 296	67.62 81	76.10 124	80.68 121	64.95 84	86.58 33	90.94 40	71.20 70	71.68 255	60.46 151	91.13 101	79.56 216

CSCG		74.12 107	74.39 104	73.33 124	79.35 144	61.66 127	77.45 105	81.98 94	62.47 111	79.06 125	80.19 237	61.83 149	78.79 163	59.83 160	87.35 175	79.54 219

ACMH	63.62 14	77.50 72	80.11 54	69.68 186	79.61 140	56.28 169	78.81 88	83.62 72	63.41 103	87.14 29	90.23 72	76.11 32	73.32 232	67.58 91	94.44 39	79.44 220
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
MG-MVS		70.47 158	71.34 156	67.85 214	79.26 147	40.42 302	74.67 145	75.15 200	58.41 139	68.74 268	88.14 119	56.08 209	83.69 79	59.90 159	81.71 250	79.43 221

DVP-MVS		81.15 37	83.12 32	75.24 102	86.16 51	60.78 140	83.77 40	80.58 125	72.48 32	85.83 43	90.41 59	78.57 17	85.69 44	75.86 38	94.39 41	79.24 222
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		64.01 236	65.15 223	60.57 282	73.28 235	35.61 338	57.60 318	67.08 263	54.61 181	66.76 281	83.37 195	56.28 207	66.87 290	42.19 290	85.20 205	79.23 223

TSAR-MVS + GP.		73.08 121	71.60 152	77.54 72	78.99 157	70.73 57	74.96 135	69.38 252	60.73 121	74.39 192	78.44 261	57.72 196	82.78 94	60.16 155	89.60 138	79.11 224

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 88	78.11 136	88.39 111	65.46 122	83.14 88	77.64 29	91.20 97	78.94 225

DP-MVS		78.44 66	79.29 60	75.90 93	81.86 120	65.33 98	79.05 86	84.63 54	74.83 18	80.41 113	86.27 153	71.68 64	83.45 84	62.45 135	92.40 77	78.92 226

PLC	62.01 16	71.79 145	70.28 165	76.33 88	80.31 136	68.63 75	78.18 98	81.24 108	54.57 182	67.09 280	80.63 230	59.44 174	81.74 112	46.91 263	84.17 221	78.63 227
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
PVSNet_Blended_VisFu		70.04 161	68.88 178	73.53 122	82.71 108	63.62 113	74.81 138	81.95 95	48.53 254	67.16 279	79.18 252	51.42 229	78.38 173	54.39 205	79.72 271	78.60 228

h-mvs33		73.08 121	71.61 151	77.48 73	83.89 89	72.89 44	70.47 190	71.12 241	54.28 185	77.89 137	83.41 192	49.04 243	80.98 126	63.62 125	90.77 116	78.58 229

agg_prior2														70.70 71	90.93 108	78.55 230

ppachtmachnet_test		60.26 268	59.61 268	62.20 267	67.70 290	44.33 269	58.18 315	60.96 303	40.75 308	65.80 285	72.57 311	41.23 283	63.92 305	46.87 264	82.42 238	78.33 231

BH-RMVSNet		68.69 183	68.20 191	70.14 180	76.40 189	53.90 187	64.62 269	73.48 210	58.01 142	73.91 201	81.78 217	59.09 178	78.22 178	48.59 247	77.96 287	78.31 232

PVSNet_BlendedMVS		65.38 216	64.30 229	68.61 205	69.81 273	49.36 220	65.60 259	78.96 151	45.50 272	59.98 323	78.61 259	51.82 225	78.20 179	44.30 278	84.11 222	78.27 233

ab-mvs		64.11 234	65.13 224	61.05 278	71.99 252	38.03 322	67.59 227	68.79 255	49.08 251	65.32 288	86.26 154	58.02 193	66.85 292	39.33 306	79.79 270	78.27 233

EGC-MVSNET		64.77 224	61.17 255	75.60 97	86.90 42	74.47 30	84.04 35	68.62 257	0.60 383	1.13 385	91.61 28	65.32 124	74.15 229	64.01 118	88.28 159	78.17 235

MVSFormer		69.93 164	69.03 176	72.63 148	74.93 206	59.19 151	83.98 36	75.72 194	52.27 210	63.53 306	76.74 278	43.19 273	80.56 133	72.28 63	78.67 280	78.14 236

jason		64.47 229	62.84 244	69.34 191	76.91 183	59.20 150	67.15 237	65.67 271	35.29 334	65.16 289	76.74 278	44.67 264	70.68 261	54.74 199	79.28 274	78.14 236
jason: jason.
new-patchmatchnet		52.89 304	55.76 296	44.26 350	59.94 342	6.31 387	37.36 372	50.76 347	41.10 304	64.28 295	79.82 241	44.77 263	48.43 343	36.24 332	87.61 168	78.03 238

CDS-MVSNet		64.33 232	62.66 246	69.35 190	80.44 134	58.28 161	65.26 262	65.66 272	44.36 283	67.30 278	75.54 284	43.27 272	71.77 252	37.68 320	84.44 219	78.01 239
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
TAMVS		65.31 217	63.75 234	69.97 184	82.23 115	59.76 149	66.78 244	63.37 292	45.20 278	69.79 252	79.37 248	47.42 255	72.17 246	34.48 340	85.15 206	77.99 240

test_fmvs3		56.78 285	55.99 294	59.12 291	53.96 371	48.09 232	58.76 311	66.22 267	27.54 362	76.66 160	68.69 341	25.32 371	51.31 332	53.42 216	73.38 313	77.97 241

LCM-MVSNet-Re		69.10 177	71.57 153	61.70 271	70.37 267	34.30 347	61.45 292	79.62 140	56.81 156	89.59 8	88.16 118	68.44 92	72.94 235	42.30 289	87.33 176	77.85 242

Patchmtry		60.91 261	63.01 243	54.62 310	66.10 305	26.27 375	67.47 230	56.40 323	54.05 193	72.04 224	86.66 140	33.19 325	60.17 318	43.69 282	87.45 173	77.42 243

test9_res														72.12 65	91.37 93	77.40 244

SDMVSNet		66.36 211	67.85 196	61.88 270	73.04 243	46.14 257	58.54 312	71.36 233	51.42 223	68.93 263	82.72 207	65.62 119	62.22 313	54.41 204	84.67 212	77.28 245

sd_testset		63.55 237	65.38 218	58.07 298	73.04 243	38.83 313	57.41 319	65.44 275	51.42 223	68.93 263	82.72 207	63.76 135	58.11 324	41.05 298	84.67 212	77.28 245

train_agg		76.38 80	76.55 83	75.86 94	85.47 63	69.32 70	76.42 118	78.69 157	54.00 194	76.97 149	86.74 136	66.60 110	81.10 121	72.50 62	91.56 90	77.15 247

lupinMVS		63.36 239	61.49 253	68.97 198	74.93 206	59.19 151	65.80 255	64.52 284	34.68 339	63.53 306	74.25 298	43.19 273	70.62 262	53.88 211	78.67 280	77.10 248

thres100view900		61.17 260	61.09 256	61.39 275	72.14 251	35.01 341	65.42 261	56.99 319	55.23 171	70.71 241	79.90 240	32.07 334	72.09 247	35.61 335	81.73 247	77.08 249

tfpn200view9		60.35 267	59.97 265	61.51 273	70.78 259	35.35 339	63.27 283	57.47 313	53.00 205	68.31 269	77.09 275	32.45 331	72.09 247	35.61 335	81.73 247	77.08 249

MVS_111021_HR		72.98 128	72.97 132	72.99 133	80.82 130	65.47 97	68.81 211	72.77 217	57.67 147	75.76 172	82.38 212	71.01 72	77.17 193	61.38 141	86.15 192	76.32 251

xiu_mvs_v1_base_debu		67.87 194	67.07 205	70.26 176	79.13 152	61.90 124	67.34 232	71.25 237	47.98 257	67.70 272	74.19 300	61.31 155	72.62 239	56.51 181	78.26 284	76.27 252

xiu_mvs_v1_base		67.87 194	67.07 205	70.26 176	79.13 152	61.90 124	67.34 232	71.25 237	47.98 257	67.70 272	74.19 300	61.31 155	72.62 239	56.51 181	78.26 284	76.27 252

xiu_mvs_v1_base_debi		67.87 194	67.07 205	70.26 176	79.13 152	61.90 124	67.34 232	71.25 237	47.98 257	67.70 272	74.19 300	61.31 155	72.62 239	56.51 181	78.26 284	76.27 252

baseline2		55.57 292	52.74 308	64.05 247	65.26 309	44.11 270	62.38 288	54.43 330	39.03 317	51.21 357	67.35 347	33.66 323	72.45 243	37.14 325	64.22 352	75.60 255

OpenMVS	62.51 15	68.76 181	68.75 181	68.78 204	70.56 264	53.91 186	78.29 95	77.35 178	48.85 252	70.22 246	83.52 191	52.65 221	76.93 196	55.31 195	81.99 241	75.49 256

3Dnovator	65.95 11	71.50 147	71.22 157	72.34 154	73.16 236	63.09 117	78.37 94	78.32 164	57.67 147	72.22 222	84.61 177	54.77 211	78.47 168	60.82 149	81.07 255	75.45 257

1112_ss		59.48 272	58.99 272	60.96 280	77.84 170	42.39 287	61.42 293	68.45 258	37.96 322	59.93 326	67.46 345	45.11 262	65.07 300	40.89 300	71.81 321	75.41 258

IterMVS		63.12 243	62.48 247	65.02 240	66.34 301	52.86 191	63.81 276	62.25 295	46.57 269	71.51 233	80.40 233	44.60 265	66.82 293	51.38 225	75.47 299	75.38 259
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Test_1112_low_res		58.78 277	58.69 274	59.04 293	79.41 143	38.13 320	57.62 317	66.98 264	34.74 337	59.62 329	77.56 272	42.92 275	63.65 307	38.66 312	70.73 327	75.35 260

test_vis3_rt		51.94 313	51.04 319	54.65 309	46.32 382	50.13 210	44.34 361	78.17 167	23.62 374	68.95 262	62.81 356	21.41 377	38.52 374	41.49 295	72.22 318	75.30 261

QAPM		69.18 176	69.26 172	68.94 199	71.61 254	52.58 194	80.37 70	78.79 156	49.63 245	73.51 203	85.14 174	53.66 217	79.12 156	55.11 196	75.54 298	75.11 262

DPM-MVS		69.98 163	69.22 174	72.26 156	82.69 109	58.82 156	70.53 189	81.23 109	47.79 261	64.16 296	80.21 235	51.32 230	83.12 89	60.14 156	84.95 211	74.83 263

pmmvs-eth3d		64.41 231	63.27 240	67.82 216	75.81 199	60.18 146	69.49 200	62.05 299	38.81 319	74.13 196	82.23 213	43.76 270	68.65 277	42.53 288	80.63 261	74.63 264

MSDG		67.47 201	67.48 201	67.46 218	70.70 261	54.69 180	66.90 242	78.17 167	60.88 120	70.41 243	74.76 290	61.22 159	73.18 233	47.38 259	76.87 290	74.49 265

MAR-MVS		67.72 197	66.16 213	72.40 153	74.45 218	64.99 103	74.87 136	77.50 177	48.67 253	65.78 286	68.58 342	57.01 203	77.79 187	46.68 266	81.92 242	74.42 266
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		57.82 282	58.36 278	56.19 304	69.17 278	30.76 363	62.94 287	55.21 326	46.04 271	63.83 301	78.47 260	41.20 284	63.68 306	39.44 305	68.99 337	74.13 267

EU-MVSNet		60.82 262	60.80 260	60.86 281	68.37 281	41.16 293	72.27 158	68.27 259	26.96 364	69.08 258	75.71 282	32.09 333	67.44 286	55.59 193	78.90 277	73.97 268

HY-MVS	49.31 19	57.96 281	57.59 282	59.10 292	66.85 298	36.17 332	65.13 264	65.39 276	39.24 316	54.69 348	78.14 266	44.28 267	67.18 289	33.75 344	70.79 326	73.95 269

TR-MVS		64.59 226	63.54 237	67.73 217	75.75 200	50.83 203	63.39 281	70.29 248	49.33 247	71.55 232	74.55 293	50.94 231	78.46 169	40.43 302	75.69 296	73.89 270

IB-MVS	49.67 18	59.69 271	56.96 286	67.90 213	68.19 284	50.30 208	61.42 293	65.18 277	47.57 263	55.83 342	67.15 349	23.77 375	79.60 150	43.56 284	79.97 266	73.79 271
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		63.55 237	66.07 214	55.99 305	66.18 304	44.04 271	68.77 214	68.80 254	46.99 266	72.57 216	85.84 167	39.87 294	50.22 335	53.40 217	92.23 81	73.71 272

AdaColmap		74.22 106	74.56 102	73.20 126	81.95 118	60.97 135	79.43 81	80.90 117	65.57 72	72.54 218	81.76 219	70.98 73	85.26 53	47.88 256	90.00 128	73.37 273

PAPM		61.79 256	60.37 263	66.05 233	76.09 194	41.87 289	69.30 203	76.79 186	40.64 309	53.80 351	79.62 245	44.38 266	82.92 93	29.64 358	73.11 315	73.36 274

MVS_111021_LR		72.10 142	71.82 147	72.95 135	79.53 142	73.90 36	70.45 191	66.64 265	56.87 155	76.81 157	81.76 219	68.78 88	71.76 253	61.81 136	83.74 226	73.18 275

原ACMM1				73.90 114	85.90 57	65.15 102		81.67 99	50.97 230	74.25 194	86.16 158	61.60 152	83.54 81	56.75 179	91.08 104	73.00 276

CHOSEN 1792x2688		58.09 280	56.30 291	63.45 254	79.95 137	50.93 202	54.07 335	65.59 273	28.56 360	61.53 313	74.33 296	41.09 286	66.52 296	33.91 343	67.69 345	72.92 277

TinyColmap		67.98 193	69.28 171	64.08 246	67.98 287	46.82 249	70.04 194	75.26 198	53.05 204	77.36 146	86.79 133	59.39 175	72.59 242	45.64 272	88.01 165	72.83 278

FMVSNet5		55.08 294	55.54 297	53.71 312	65.80 306	33.50 351	56.22 325	52.50 342	43.72 288	61.06 317	83.38 194	25.46 369	54.87 328	30.11 355	81.64 252	72.75 279

EG-PatchMatch MVS		70.70 155	70.88 160	70.16 179	82.64 110	58.80 157	71.48 174	73.64 208	54.98 173	76.55 163	81.77 218	61.10 160	78.94 160	54.87 198	80.84 257	72.74 280

PVSNet_Blended		62.90 246	61.64 250	66.69 228	69.81 273	49.36 220	61.23 295	78.96 151	42.04 297	59.98 323	68.86 339	51.82 225	78.20 179	44.30 278	77.77 289	72.52 281

CostFormer		57.35 284	56.14 292	60.97 279	63.76 321	38.43 315	67.50 229	60.22 305	37.14 327	59.12 330	76.34 280	32.78 328	71.99 250	39.12 309	69.27 336	72.47 282

PS-MVSNAJ		64.27 233	63.73 235	65.90 235	77.82 171	51.42 199	63.33 282	72.33 222	45.09 280	61.60 312	68.04 343	62.39 144	73.95 230	49.07 242	73.87 311	72.34 283

xiu_mvs_v2_base		64.43 230	63.96 232	65.85 236	77.72 173	51.32 200	63.63 279	72.31 223	45.06 281	61.70 311	69.66 331	62.56 140	73.93 231	49.06 243	73.91 310	72.31 284

PMVS	70.70 6	81.70 32	83.15 31	77.36 76	90.35 5	82.82 2	82.15 54	79.22 148	74.08 20	87.16 28	91.97 19	84.80 2	76.97 195	64.98 111	93.61 60	72.28 285
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
1314		59.83 270	58.86 273	62.74 263	65.71 307	44.78 266	68.59 216	72.63 219	33.54 346	61.05 318	67.29 348	43.62 271	71.26 258	49.49 240	67.84 344	72.19 286

无先验							74.82 137	70.94 243	47.75 262				76.85 199	54.47 202		72.09 287

LF4IMVS		67.50 199	67.31 203	68.08 212	58.86 348	61.93 123	71.43 175	75.90 193	44.67 282	72.42 219	80.20 236	57.16 198	70.44 265	58.99 167	86.12 193	71.88 288

pmmvs4		60.78 263	59.04 271	66.00 234	73.06 242	57.67 164	64.53 271	60.22 305	36.91 328	65.96 283	77.27 274	39.66 296	68.54 278	38.87 310	74.89 304	71.80 289

MSLP-MVS++		74.48 105	75.78 92	70.59 171	84.66 76	62.40 120	78.65 90	84.24 62	60.55 122	77.71 142	81.98 215	63.12 137	77.64 190	62.95 132	88.14 161	71.73 290

MDTV_nov1_ep13_2view						18.41 382	53.74 336		31.57 354	44.89 372		29.90 355		32.93 346		71.48 291

patch_mono-2		62.73 249	64.08 231	58.68 294	70.36 268	55.87 172	60.84 298	64.11 288	41.23 303	64.04 297	78.22 264	60.00 168	48.80 339	54.17 208	83.71 228	71.37 292

tpm2		56.12 287	54.64 300	60.55 283	66.24 302	36.01 333	68.14 222	56.77 321	33.60 345	58.25 333	75.52 286	30.25 351	74.33 226	33.27 345	69.76 335	71.32 293

CMPMVS	48.73 20	61.54 258	60.89 258	63.52 253	61.08 333	51.55 198	68.07 224	68.00 260	33.88 341	65.87 284	81.25 223	37.91 307	67.71 283	49.32 241	82.60 237	71.31 294
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
API-MVS		70.97 153	71.51 154	69.37 188	75.20 203	55.94 171	80.99 61	76.84 184	62.48 110	71.24 236	77.51 273	61.51 154	80.96 130	52.04 219	85.76 197	71.22 295

OpenMVS_ROB	54.93 17	63.23 242	63.28 239	63.07 258	69.81 273	45.34 262	68.52 218	67.14 262	43.74 287	70.61 242	79.22 250	47.90 253	72.66 238	48.75 245	73.84 312	71.21 296

thres200		57.55 283	57.02 285	59.17 290	67.89 289	34.93 342	58.91 310	57.25 317	50.24 238	64.01 298	71.46 319	32.49 330	71.39 257	31.31 350	79.57 272	71.19 297

test20.03		55.74 290	57.51 283	50.42 326	59.89 343	32.09 356	50.63 344	49.01 351	50.11 240	65.07 290	83.23 201	45.61 258	48.11 344	30.22 354	83.82 225	71.07 298

our_test_3		56.46 286	56.51 289	56.30 303	67.70 290	39.66 306	55.36 331	52.34 343	40.57 310	63.85 300	69.91 330	40.04 293	58.22 323	43.49 285	75.29 303	71.03 299

test_fmvs2		54.80 295	54.11 302	56.88 302	51.76 375	49.95 213	56.70 322	65.80 270	26.22 367	69.42 254	65.25 352	31.82 337	49.98 336	49.63 239	70.36 329	70.71 300

BH-untuned		69.39 173	69.46 169	69.18 193	77.96 169	56.88 166	68.47 220	77.53 176	56.77 157	77.79 140	79.63 244	60.30 167	80.20 143	46.04 269	80.65 259	70.47 301

EPNet_dtu		58.93 276	58.52 275	60.16 286	67.91 288	47.70 240	69.97 195	58.02 311	49.73 244	47.28 367	73.02 309	38.14 304	62.34 311	36.57 329	85.99 195	70.43 302
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
USDC		62.80 247	63.10 242	61.89 269	65.19 310	43.30 279	67.42 231	74.20 206	35.80 333	72.25 221	84.48 180	45.67 257	71.95 251	37.95 319	84.97 207	70.42 303

GSMVS																70.05 304

sam_mvs1												31.41 340				70.05 304

SCA		58.57 279	58.04 279	60.17 285	70.17 269	41.07 295	65.19 263	53.38 338	43.34 293	61.00 319	73.48 304	45.20 260	69.38 272	40.34 303	70.31 330	70.05 304

tpmvs		55.84 288	55.45 298	57.01 301	60.33 337	33.20 352	65.89 252	59.29 309	47.52 264	56.04 340	73.60 303	31.05 346	68.06 282	40.64 301	64.64 350	69.77 307

旧先验1					84.55 79	60.36 145		63.69 290			87.05 128	54.65 213			83.34 232	69.66 308

CR-MVSNet		58.96 275	58.49 276	60.36 284	66.37 299	48.24 229	70.93 185	56.40 323	32.87 347	61.35 314	86.66 140	33.19 325	63.22 309	48.50 249	70.17 331	69.62 309

RPMNet		65.77 214	65.08 227	67.84 215	66.37 299	48.24 229	70.93 185	86.27 19	54.66 180	61.35 314	86.77 135	33.29 324	85.67 46	55.93 188	70.17 331	69.62 309

tpm cat1		54.02 300	52.63 309	58.19 297	64.85 316	39.86 305	66.26 249	57.28 316	32.16 349	56.90 336	70.39 325	32.75 329	65.30 299	34.29 341	58.79 365	69.41 311

PatchmatchNet		54.60 296	54.27 301	55.59 306	65.17 312	39.08 308	66.92 241	51.80 344	39.89 312	58.39 331	73.12 308	31.69 339	58.33 322	43.01 287	58.38 368	69.38 312
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
YYNet1		52.58 306	53.50 304	49.85 327	54.15 368	36.45 331	40.53 365	46.55 359	38.09 321	75.52 176	73.31 307	41.08 287	43.88 361	41.10 297	71.14 325	69.21 313

CVMVSNet		59.21 274	58.44 277	61.51 273	73.94 226	47.76 239	71.31 179	64.56 283	26.91 366	60.34 322	70.44 323	36.24 316	67.65 284	53.57 213	68.66 339	69.12 314

MDA-MVSNet_test_wron		52.57 307	53.49 306	49.81 328	54.24 367	36.47 330	40.48 366	46.58 358	38.13 320	75.47 177	73.32 306	41.05 288	43.85 362	40.98 299	71.20 324	69.10 315

MVP-Stereo		61.56 257	59.22 269	68.58 206	79.28 146	60.44 144	69.20 205	71.57 227	43.58 289	56.42 339	78.37 262	39.57 297	76.46 203	34.86 339	60.16 362	68.86 316
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
新几何1				69.99 183	88.37 34	71.34 51		62.08 298	43.85 285	74.99 181	86.11 161	52.85 220	70.57 263	50.99 228	83.23 233	68.05 317

UnsupCasMVSNet_eth		52.26 309	53.29 307	49.16 332	55.08 364	33.67 350	50.03 345	58.79 310	37.67 324	63.43 308	74.75 291	41.82 281	45.83 349	38.59 314	59.42 364	67.98 318

Patchmatch-test		47.93 326	49.96 327	41.84 354	57.42 354	24.26 378	48.75 347	41.49 372	39.30 315	56.79 337	73.48 304	30.48 350	33.87 377	29.29 360	72.61 316	67.39 319

Patchmatch-RL test		59.95 269	59.12 270	62.44 265	72.46 248	54.61 181	59.63 305	47.51 357	41.05 306	74.58 189	74.30 297	31.06 345	65.31 298	51.61 222	79.85 267	67.39 319

testgi		54.00 301	56.86 287	45.45 344	58.20 351	25.81 376	49.05 346	49.50 350	45.43 275	67.84 271	81.17 224	51.81 227	43.20 364	29.30 359	79.41 273	67.34 321

test222					87.30 37	69.15 73	67.85 225	59.59 308	41.06 305	73.05 211	85.72 169	48.03 252			80.65 259	66.92 322

pmmvs5		52.49 308	52.58 310	52.21 320	54.99 365	32.38 354	55.45 330	53.84 333	32.15 350	55.49 344	74.81 289	38.08 305	57.37 326	34.02 342	74.40 308	66.88 323

Anonymous20231206		54.13 298	55.82 295	49.04 334	70.89 258	35.96 334	51.73 341	50.87 346	34.86 335	62.49 309	79.22 250	42.52 279	44.29 360	27.95 365	81.88 243	66.88 323

tpm		50.60 318	52.42 311	45.14 346	65.18 311	26.29 374	60.30 301	43.50 362	37.41 325	57.01 335	79.09 254	30.20 353	42.32 365	32.77 347	66.36 347	66.81 325

testdata				64.13 245	85.87 59	63.34 115		61.80 301	47.83 260	76.42 168	86.60 145	48.83 246	62.31 312	54.46 203	81.26 254	66.74 326

MIMVSNet		54.39 297	56.12 293	49.20 331	72.57 247	30.91 362	59.98 303	48.43 354	41.66 299	55.94 341	83.86 188	41.19 285	50.42 334	26.05 368	75.38 301	66.27 327

tpmrst		50.15 320	51.38 316	46.45 341	56.05 359	24.77 377	64.40 273	49.98 348	36.14 330	53.32 352	69.59 332	35.16 318	48.69 340	39.24 307	58.51 367	65.89 328

EPMVS		45.74 331	46.53 334	43.39 352	54.14 369	22.33 380	55.02 332	35.00 381	34.69 338	51.09 358	70.20 327	25.92 367	42.04 367	37.19 324	55.50 372	65.78 329

PVSNet	43.83 21	51.56 314	51.17 317	52.73 317	68.34 282	38.27 317	48.22 349	53.56 336	36.41 329	54.29 349	64.94 353	34.60 320	54.20 331	30.34 353	69.87 333	65.71 330

test_fmvs1_n		52.70 305	52.01 312	54.76 308	53.83 372	50.36 206	55.80 328	65.90 269	24.96 370	65.39 287	60.64 364	27.69 360	48.46 341	45.88 271	67.99 342	65.46 331

BH-w/o		64.81 223	64.29 230	66.36 230	76.08 195	54.71 179	65.61 258	75.23 199	50.10 241	71.05 239	71.86 316	54.33 215	79.02 158	38.20 317	76.14 294	65.36 332

XXY-MVS		55.19 293	57.40 284	48.56 335	64.45 317	34.84 344	51.54 342	53.59 334	38.99 318	63.79 302	79.43 246	56.59 205	45.57 350	36.92 327	71.29 323	65.25 333

ADS-MVSNet2		48.76 324	47.25 333	53.29 316	55.90 361	40.54 301	47.34 353	54.99 328	31.41 355	50.48 360	72.06 313	31.23 342	54.26 330	25.93 369	55.93 370	65.07 334

ADS-MVSNet		44.62 337	45.58 336	41.73 355	55.90 361	20.83 381	47.34 353	39.94 376	31.41 355	50.48 360	72.06 313	31.23 342	39.31 372	25.93 369	55.93 370	65.07 334

KD-MVS_2432*1600		52.05 311	51.58 314	53.44 314	52.11 373	31.20 359	44.88 359	64.83 281	41.53 300	64.37 293	70.03 328	15.61 388	64.20 302	36.25 330	74.61 306	64.93 336

miper_refine_blended		52.05 311	51.58 314	53.44 314	52.11 373	31.20 359	44.88 359	64.83 281	41.53 300	64.37 293	70.03 328	15.61 388	64.20 302	36.25 330	74.61 306	64.93 336

test0.0.03 1		47.72 327	48.31 329	45.93 342	55.53 363	29.39 366	46.40 356	41.21 374	43.41 291	55.81 343	67.65 344	29.22 357	43.77 363	25.73 371	69.87 333	64.62 338

JIA-IIPM		54.03 299	51.62 313	61.25 277	59.14 347	55.21 177	59.10 307	47.72 355	50.85 231	50.31 363	85.81 168	20.10 381	63.97 304	36.16 333	55.41 373	64.55 339

PatchT		53.35 302	56.47 290	43.99 351	64.19 318	17.46 383	59.15 306	43.10 364	52.11 213	54.74 347	86.95 129	29.97 354	49.98 336	43.62 283	74.40 308	64.53 340

test_vis1_n		51.27 316	50.41 325	53.83 311	56.99 355	50.01 212	56.75 321	60.53 304	25.68 368	59.74 328	57.86 368	29.40 356	47.41 346	43.10 286	63.66 353	64.08 341

gg-mvs-nofinetune		55.75 289	56.75 288	52.72 318	62.87 324	28.04 370	68.92 208	41.36 373	71.09 40	50.80 359	92.63 12	20.74 378	66.86 291	29.97 356	72.41 317	63.25 342

MVS		60.62 265	59.97 265	62.58 264	68.13 285	47.28 245	68.59 216	73.96 207	32.19 348	59.94 325	68.86 339	50.48 234	77.64 190	41.85 293	75.74 295	62.83 343

N_pmnet		52.06 310	51.11 318	54.92 307	59.64 345	71.03 53	37.42 371	61.62 302	33.68 343	57.12 334	72.10 312	37.94 306	31.03 378	29.13 364	71.35 322	62.70 344

Gipuma		69.55 170	72.83 133	59.70 287	63.63 322	53.97 185	80.08 77	75.93 192	64.24 91	73.49 204	88.93 101	57.89 194	62.46 310	59.75 162	91.55 91	62.67 345
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
test_fmvs1		51.51 315	50.86 322	53.48 313	49.72 378	49.35 222	54.11 334	64.96 279	24.64 372	63.66 304	59.61 367	28.33 359	48.45 342	45.38 276	67.30 346	62.66 346

WTY-MVS		49.39 323	50.31 326	46.62 340	61.22 332	32.00 357	46.61 355	49.77 349	33.87 342	54.12 350	69.55 333	41.96 280	45.40 352	31.28 351	64.42 351	62.47 347

test_vis1_rt		46.70 330	45.24 338	51.06 324	44.58 383	51.04 201	39.91 367	67.56 261	21.84 378	51.94 355	50.79 376	33.83 322	39.77 371	35.25 338	61.50 359	62.38 348

test-LLR		50.43 319	50.69 324	49.64 329	60.76 334	41.87 289	53.18 337	45.48 360	43.41 291	49.41 364	60.47 365	29.22 357	44.73 357	42.09 291	72.14 319	62.33 349

test-mter		48.56 325	48.20 330	49.64 329	60.76 334	41.87 289	53.18 337	45.48 360	31.91 353	49.41 364	60.47 365	18.34 382	44.73 357	42.09 291	72.14 319	62.33 349

test_vis1_n_1920		52.96 303	53.50 304	51.32 323	59.15 346	44.90 265	56.13 326	64.29 286	30.56 358	59.87 327	60.68 363	40.16 292	47.47 345	48.25 253	62.46 356	61.58 351

UnsupCasMVSNet_bld		50.01 321	51.03 320	46.95 337	58.61 349	32.64 353	48.31 348	53.27 339	34.27 340	60.47 321	71.53 318	41.40 282	47.07 347	30.68 352	60.78 361	61.13 352

sss		47.59 328	48.32 328	45.40 345	56.73 358	33.96 348	45.17 358	48.51 353	32.11 352	52.37 354	65.79 350	40.39 291	41.91 368	31.85 348	61.97 358	60.35 353

PM-MVS		64.49 228	63.61 236	67.14 223	76.68 187	75.15 27	68.49 219	42.85 365	51.17 229	77.85 139	80.51 231	45.76 256	66.31 297	52.83 218	76.35 292	59.96 354

test_cas_vis1_n_1920		50.90 317	50.92 321	50.83 325	54.12 370	47.80 237	51.44 343	54.61 329	26.95 365	63.95 299	60.85 362	37.86 309	44.97 355	45.53 273	62.97 355	59.72 355

GG-mvs-BLEND				52.24 319	60.64 336	29.21 368	69.73 199	42.41 366		45.47 370	52.33 374	20.43 380	68.16 280	25.52 372	65.42 349	59.36 356

dmvs_re		49.91 322	50.77 323	47.34 336	59.98 339	38.86 312	53.18 337	53.58 335	39.75 313	55.06 345	61.58 361	36.42 315	44.40 359	29.15 363	68.23 340	58.75 357

TESTMET0.1,1		45.17 334	44.93 340	45.89 343	56.02 360	38.31 316	53.18 337	41.94 371	27.85 361	44.86 373	56.47 370	17.93 383	41.50 369	38.08 318	68.06 341	57.85 358

mvsany_test3		43.76 341	41.01 345	52.01 321	48.09 380	57.74 163	42.47 363	23.85 387	23.30 375	64.80 291	62.17 359	27.12 361	40.59 370	29.17 362	48.11 377	57.69 359

MS-PatchMatch		55.59 291	54.89 299	57.68 299	69.18 277	49.05 223	61.00 297	62.93 294	35.98 331	58.36 332	68.93 337	36.71 314	66.59 295	37.62 322	63.30 354	57.39 360

dp		44.09 339	44.88 341	41.72 356	58.53 350	23.18 379	54.70 333	42.38 368	34.80 336	44.25 375	65.61 351	24.48 374	44.80 356	29.77 357	49.42 376	57.18 361

MVE	27.91 23	36.69 348	35.64 351	39.84 358	43.37 384	35.85 336	19.49 377	24.61 385	24.68 371	39.05 379	62.63 358	38.67 303	27.10 382	21.04 378	47.25 378	56.56 362
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
pmmvs3		46.71 329	45.09 339	51.55 322	56.76 357	48.25 228	55.78 329	39.53 377	24.13 373	50.35 362	63.40 354	15.90 387	51.08 333	29.29 360	70.69 328	55.33 363

PatchMatch-RL		58.68 278	57.72 281	61.57 272	76.21 192	73.59 39	61.83 290	49.00 352	47.30 265	61.08 316	68.97 336	50.16 236	59.01 321	36.06 334	68.84 338	52.10 364

dmvs_testset		45.26 333	47.51 331	38.49 360	59.96 341	14.71 385	58.50 313	43.39 363	41.30 302	51.79 356	56.48 369	39.44 298	49.91 338	21.42 377	55.35 374	50.85 365

wuyk23d		61.97 253	66.25 212	49.12 333	58.19 352	60.77 142	66.32 248	52.97 340	55.93 166	90.62 5	86.91 130	73.07 57	35.98 376	20.63 379	91.63 87	50.62 366

PMMVS2		37.74 346	40.87 346	28.36 363	42.41 385	5.35 388	24.61 376	27.75 383	32.15 350	47.85 366	70.27 326	35.85 317	29.51 380	19.08 380	67.85 343	50.22 367

DSMNet-mixed		43.18 342	44.66 342	38.75 359	54.75 366	28.88 369	57.06 320	27.42 384	13.47 380	47.27 368	77.67 271	38.83 301	39.29 373	25.32 373	60.12 363	48.08 368

new_pmnet		37.55 347	39.80 349	30.79 362	56.83 356	16.46 384	39.35 368	30.65 382	25.59 369	45.26 371	61.60 360	24.54 373	28.02 381	21.60 376	52.80 375	47.90 369

CHOSEN 280x420		41.62 343	39.89 348	46.80 339	61.81 328	51.59 197	33.56 375	35.74 380	27.48 363	37.64 381	53.53 371	23.24 376	42.09 366	27.39 366	58.64 366	46.72 370

EMVS		44.61 338	44.45 343	45.10 347	48.91 379	43.00 281	37.92 370	41.10 375	46.75 268	38.00 380	48.43 378	26.42 364	46.27 348	37.11 326	75.38 301	46.03 371

E-PMN		45.17 334	45.36 337	44.60 348	50.07 376	42.75 283	38.66 369	42.29 369	46.39 270	39.55 378	51.15 375	26.00 366	45.37 353	37.68 320	76.41 291	45.69 372

test_f		43.79 340	45.63 335	38.24 361	42.29 386	38.58 314	34.76 374	47.68 356	22.22 377	67.34 277	63.15 355	31.82 337	30.60 379	39.19 308	62.28 357	45.53 373

mvsany_test1		37.88 345	35.74 350	44.28 349	47.28 381	49.90 214	36.54 373	24.37 386	19.56 379	45.76 369	53.46 372	32.99 327	37.97 375	26.17 367	35.52 379	44.99 374

PMMVS		44.69 336	43.95 344	46.92 338	50.05 377	53.47 189	48.08 351	42.40 367	22.36 376	44.01 376	53.05 373	42.60 278	45.49 351	31.69 349	61.36 360	41.79 375

PVSNet_0	36.71 22	41.12 344	40.78 347	42.14 353	59.97 340	40.13 303	40.97 364	42.24 370	30.81 357	44.86 373	49.41 377	40.70 289	45.12 354	23.15 375	34.96 380	41.16 376

FPMVS		59.43 273	60.07 264	57.51 300	77.62 176	71.52 49	62.33 289	50.92 345	57.40 152	69.40 255	80.00 239	39.14 300	61.92 314	37.47 323	66.36 347	39.09 377

MVS-HIRNet		45.53 332	47.29 332	40.24 357	62.29 326	26.82 373	56.02 327	37.41 379	29.74 359	43.69 377	81.27 222	33.96 321	55.48 327	24.46 374	56.79 369	38.43 378

test_method		19.26 349	19.12 353	19.71 364	9.09 388	1.91 390	7.79 379	53.44 337	1.42 382	10.27 384	35.80 379	17.42 385	25.11 383	12.44 381	24.38 382	32.10 379

DeepMVS_CX				11.83 365	15.51 387	13.86 386		11.25 390	5.76 381	20.85 383	26.46 380	17.06 386	9.22 384	9.69 383	13.82 383	12.42 380

tmp_tt		11.98 351	14.73 354	3.72 366	2.28 389	4.62 389	19.44 378	14.50 389	0.47 384	21.55 382	9.58 382	25.78 368	4.57 385	11.61 382	27.37 381	1.96 381

testmvs		4.06 355	5.28 358	0.41 367	0.64 391	0.16 392	42.54 362	0.31 392	0.26 386	0.50 387	1.40 386	0.77 390	0.17 386	0.56 384	0.55 385	0.90 382

test123		4.43 354	5.78 357	0.39 368	0.97 390	0.28 391	46.33 357	0.45 391	0.31 385	0.62 386	1.50 385	0.61 391	0.11 387	0.56 384	0.63 384	0.77 383

test_blank		0.00 356	0.00 359	0.00 369	0.00 392	0.00 393	0.00 380	0.00 393	0.00 387	0.00 388	0.00 387	0.00 392	0.00 388	0.00 386	0.00 386	0.00 384

uanet_test		0.00 356	0.00 359	0.00 369	0.00 392	0.00 393	0.00 380	0.00 393	0.00 387	0.00 388	0.00 387	0.00 392	0.00 388	0.00 386	0.00 386	0.00 384

DCPMVS		0.00 356	0.00 359	0.00 369	0.00 392	0.00 393	0.00 380	0.00 393	0.00 387	0.00 388	0.00 387	0.00 392	0.00 388	0.00 386	0.00 386	0.00 384

cdsmvs_eth3d_5k		17.71 350	23.62 352	0.00 369	0.00 392	0.00 393	0.00 380	70.17 249	0.00 387	0.00 388	74.25 298	68.16 95	0.00 388	0.00 386	0.00 386	0.00 384

pcd_1.5k_mvsjas		5.20 353	6.93 356	0.00 369	0.00 392	0.00 393	0.00 380	0.00 393	0.00 387	0.00 388	0.00 387	62.39 144	0.00 388	0.00 386	0.00 386	0.00 384

sosnet-low-res		0.00 356	0.00 359	0.00 369	0.00 392	0.00 393	0.00 380	0.00 393	0.00 387	0.00 388	0.00 387	0.00 392	0.00 388	0.00 386	0.00 386	0.00 384

sosnet		0.00 356	0.00 359	0.00 369	0.00 392	0.00 393	0.00 380	0.00 393	0.00 387	0.00 388	0.00 387	0.00 392	0.00 388	0.00 386	0.00 386	0.00 384

uncertanet		0.00 356	0.00 359	0.00 369	0.00 392	0.00 393	0.00 380	0.00 393	0.00 387	0.00 388	0.00 387	0.00 392	0.00 388	0.00 386	0.00 386	0.00 384

Regformer		0.00 356	0.00 359	0.00 369	0.00 392	0.00 393	0.00 380	0.00 393	0.00 387	0.00 388	0.00 387	0.00 392	0.00 388	0.00 386	0.00 386	0.00 384

ab-mvs-re		5.62 352	7.50 355	0.00 369	0.00 392	0.00 393	0.00 380	0.00 393	0.00 387	0.00 388	67.46 345	0.00 392	0.00 388	0.00 386	0.00 386	0.00 384

uanet		0.00 356	0.00 359	0.00 369	0.00 392	0.00 393	0.00 380	0.00 393	0.00 387	0.00 388	0.00 387	0.00 392	0.00 388	0.00 386	0.00 386	0.00 384

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	61.45 129		85.63 27	75.27 17	85.62 48	90.38 64	76.72 27

eth-test2					0.00 392

eth-test					0.00 392

ZD-MVS					83.91 87	69.36 69		81.09 113	58.91 137	82.73 86	89.11 94	75.77 35	86.63 11	72.73 58	92.93 70

test_241102_ONE					86.12 53	61.06 133		84.72 48	72.64 29	87.38 24	89.47 84	77.48 23	85.74 43

9.14			80.22 53		80.68 131		80.35 71	87.69 10	59.90 126	83.00 79	88.20 115	74.57 47	81.75 111	73.75 52	93.78 57

save fliter					87.00 39	67.23 86	79.24 84	77.94 172	56.65 160

test0726					86.16 51	60.78 140	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												31.21 344

MTGPA								80.63 123

test_post1							66.63 245				2.08 383	30.66 349	59.33 320	40.34 303

test_post											1.99 384	30.91 347	54.76 329

patchmatchnet-post											68.99 335	31.32 341	69.38 272

MTMP							84.83 31	19.26 388

gm-plane-assit					62.51 325	33.91 349			37.25 326		62.71 357		72.74 236	38.70 311

TEST9					85.47 63	69.32 70	76.42 118	78.69 157	53.73 199	76.97 149	86.74 136	66.84 105	81.10 121

test_8					85.09 69	67.89 79	76.26 123	78.66 159	54.00 194	76.89 153	86.72 138	66.60 110	80.89 131

agg_prior					84.44 82	66.02 94		78.62 160		76.95 151			80.34 138

test_prior4						70.14 63	77.57 101

test_prior2							75.57 131		58.92 136	76.53 165	86.78 134	67.83 99		69.81 74	92.76 73

旧先验2							71.17 182		45.11 279	78.54 131			61.28 316	59.19 166

新几何2							71.33 178

原ACMM2							74.78 141

testdata2													67.30 287	48.34 251

segment_acmp												68.30 94

testdata1							68.34 221		57.24 153

plane_prior7					85.18 66	66.21 93

plane_prior6					84.18 85	65.31 99						60.83 163

plane_prior4											89.11 94

plane_prior3						65.67 96			63.82 95	78.23 133

plane_prior2							82.74 51		65.45 74

plane_prior1					84.46 81

plane_prior						65.18 100	80.06 78		61.88 114						89.91 132

n2								0.00 393

nn								0.00 393

door-mid								55.02 327

test11								82.71 84

door								52.91 341

HQP5-MVS						58.80 157

HQP-NCC					82.37 111		77.32 106		59.08 131	71.58 228

ACMP_Plane					82.37 111		77.32 106		59.08 131	71.58 228

BP-MVS														67.38 96

HQP3-MVS								84.12 65							89.16 147

HQP2-MVS												58.09 188

NP-MVS					83.34 95	63.07 118					85.97 164

MDTV_nov1_ep13			54.05 303		65.54 308	29.30 367	59.00 308	55.22 325	35.96 332	52.44 353	75.98 281	30.77 348	59.62 319	38.21 316	73.33 314

ACMMP++_ref															89.47 142

ACMMP++															91.96 83

Test By Simon												62.56 140

High-resolution multi-view benchmark