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 25	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 66	68.08 89	97.05 1	96.93 1

PEN-MVS		80.46 50	82.91 37	73.11 134	89.83 9	39.02 295	77.06 112	82.61 90	80.04 4	90.60 5	92.85 9	74.93 46	85.21 54	63.15 134	95.15 18	95.09 2

PS-CasMVS		80.41 51	82.86 39	73.07 135	89.93 7	39.21 292	77.15 110	81.28 109	79.74 5	90.87 3	92.73 11	75.03 44	84.93 58	63.83 126	95.19 16	95.07 3

CP-MVSNet		79.48 59	81.65 49	72.98 138	89.66 13	39.06 294	76.76 113	80.46 126	78.91 7	90.32 6	91.70 24	68.49 95	84.89 59	63.40 132	95.12 19	95.01 4

WR-MVS_H		80.22 55	82.17 44	74.39 113	89.46 15	42.69 269	78.24 97	82.24 93	78.21 9	89.57 8	92.10 18	68.05 100	85.59 44	66.04 107	95.62 9	94.88 5

DTE-MVSNet		80.35 52	82.89 38	72.74 146	89.84 8	37.34 310	77.16 109	81.81 99	80.45 3	90.92 2	92.95 7	74.57 50	86.12 28	63.65 127	94.68 31	94.76 6

TDRefinement		86.32 2	86.33 2	86.29 1	88.64 32	81.19 5	88.84 2	90.72 1	78.27 8	87.95 14	92.53 13	79.37 13	84.79 62	74.51 48	96.15 2	92.88 7

DU-MVS		74.91 102	75.57 97	72.93 140	83.50 95	45.79 243	69.47 198	80.14 133	65.22 83	81.74 92	87.08 119	61.82 151	81.07 124	56.21 184	94.98 21	91.93 8

NR-MVSNet		73.62 116	74.05 111	72.33 155	83.50 95	43.71 258	65.65 254	77.32 175	64.32 93	75.59 176	87.08 119	62.45 144	81.34 116	54.90 195	95.63 8	91.93 8

v7n		79.37 61	80.41 57	76.28 93	78.67 159	55.81 171	79.22 84	82.51 92	70.72 44	87.54 21	92.44 14	68.00 102	81.34 116	72.84 58	91.72 89	91.69 10

TranMVSNet+NR-MVSNet		76.13 86	77.66 80	71.56 161	84.61 80	42.57 271	70.98 182	78.29 162	68.67 56	83.04 76	89.26 85	72.99 62	80.75 134	55.58 192	95.47 10	91.35 11

FC-MVSNet-test		73.32 122	74.78 102	68.93 199	79.21 150	36.57 312	71.82 170	79.54 142	57.63 151	82.57 84	90.38 63	59.38 175	78.99 160	57.91 171	94.56 33	91.23 12

v10		75.69 91	76.20 91	74.16 117	74.44 217	48.69 212	75.84 126	82.93 86	59.02 135	85.92 39	89.17 90	58.56 182	82.74 97	70.73 73	89.14 147	91.05 13

UniMVSNet_NR-MVSNet		74.90 103	75.65 95	72.64 149	83.04 102	45.79 243	69.26 201	78.81 150	66.66 69	81.74 92	86.88 125	63.26 137	81.07 124	56.21 184	94.98 21	91.05 13

UniMVSNet (Re)		75.00 100	75.48 98	73.56 126	83.14 100	47.92 223	70.41 190	81.04 117	63.67 99	79.54 117	86.37 147	62.83 139	81.82 110	57.10 175	95.25 15	90.94 15

anonymousdsp		78.60 67	77.80 79	81.00 37	78.01 166	74.34 36	80.09 74	76.12 184	50.51 233	89.19 9	90.88 39	71.45 73	77.78 191	73.38 55	90.60 121	90.90 16

v8		75.07 99	75.64 96	73.35 128	73.42 229	47.46 230	75.20 131	81.45 105	60.05 126	85.64 43	89.26 85	58.08 189	81.80 112	69.71 81	87.97 159	90.79 17

IS-MVSNet		75.10 98	75.42 99	74.15 118	79.23 149	48.05 221	79.43 79	78.04 165	70.09 49	79.17 122	88.02 114	53.04 218	83.60 80	58.05 170	93.76 62	90.79 17

FIs		72.56 142	73.80 115	68.84 202	78.74 158	37.74 306	71.02 181	79.83 136	56.12 166	80.88 105	89.45 82	58.18 184	78.28 180	56.63 177	93.36 68	90.51 19

test_djsdf		78.88 65	78.27 75	80.70 41	81.42 126	71.24 56	83.98 36	75.72 188	52.27 211	87.37 26	92.25 16	68.04 101	80.56 135	72.28 65	91.15 105	90.32 20

WR-MVS		71.20 153	72.48 140	67.36 219	84.98 73	35.70 320	64.43 268	68.66 251	65.05 86	81.49 95	86.43 146	57.57 195	76.48 207	50.36 229	93.32 69	89.90 21

test_part1		76.97 81	78.21 76	73.25 131	77.87 168	45.76 245	78.27 96	87.26 13	66.69 68	85.31 50	91.43 31	55.95 208	84.24 73	65.71 109	95.43 12	89.75 22

OMC-MVS		79.41 60	78.79 69	81.28 34	80.62 134	70.71 62	80.91 63	84.76 49	62.54 110	81.77 90	86.65 138	71.46 72	83.53 82	67.95 94	92.44 82	89.60 23

tttt0517		69.46 173	67.79 199	74.46 110	75.34 197	52.72 190	75.05 132	63.27 279	54.69 182	78.87 125	84.37 176	26.63 347	81.15 120	63.95 123	87.93 160	89.51 24

v2v482		72.55 144	72.58 139	72.43 152	72.92 241	46.72 236	71.41 174	79.13 145	55.27 173	81.17 99	85.25 169	55.41 209	81.13 121	67.25 103	85.46 192	89.43 25

Anonymous20231211		75.54 92	77.19 83	70.59 170	77.67 173	45.70 247	74.73 140	80.19 131	68.80 53	82.95 78	92.91 8	66.26 116	76.76 205	58.41 168	92.77 77	89.30 26

OurMVSNet-221017-0		78.57 68	78.53 74	78.67 63	80.48 135	64.16 113	80.24 72	82.06 95	61.89 114	88.77 11	93.32 4	57.15 197	82.60 100	70.08 77	92.80 76	89.25 27

EI-MVSNet-UG-set		72.63 141	71.68 151	75.47 102	74.67 211	58.64 160	72.02 162	71.50 225	63.53 101	78.58 128	71.39 314	65.98 118	78.53 169	67.30 102	80.18 258	89.23 28

V42		71.06 154	70.83 163	71.72 159	67.25 286	47.14 234	65.94 248	80.35 130	51.35 223	83.40 75	83.23 194	59.25 176	78.80 163	65.91 108	80.81 252	89.23 28

RPSCF		75.76 89	74.37 107	79.93 46	74.81 206	77.53 19	77.53 104	79.30 143	59.44 131	78.88 124	89.80 77	71.26 75	73.09 236	57.45 172	80.89 250	89.17 30

UniMVSNet_ETH3D		76.74 83	79.02 67	69.92 185	89.27 20	43.81 257	74.47 144	71.70 221	72.33 34	85.50 47	93.65 3	77.98 22	76.88 202	54.60 199	91.64 92	89.08 31

v1192		73.40 120	73.42 122	73.32 130	74.65 214	48.67 213	72.21 160	81.73 100	52.76 208	81.85 89	84.56 174	57.12 198	82.24 107	68.58 84	87.33 170	89.06 32

3Dnovator+	73.19 2	81.08 43	80.48 56	82.87 9	81.41 127	72.03 48	84.38 35	86.23 24	77.28 15	80.65 106	90.18 71	59.80 172	87.58 4	73.06 57	91.34 100	89.01 33

EI-MVSNet-Vis-set		72.78 138	71.87 147	75.54 101	74.77 207	59.02 155	72.24 159	71.56 224	63.92 96	78.59 126	71.59 311	66.22 117	78.60 167	67.58 96	80.32 256	89.00 34

v1144		73.29 123	73.39 123	73.01 136	74.12 223	48.11 220	72.01 163	81.08 116	53.83 199	81.77 90	84.68 172	58.07 190	81.91 109	68.10 88	86.86 178	88.99 35

nrg030		74.87 104	75.99 93	71.52 162	74.90 204	49.88 208	74.10 148	82.58 91	54.55 186	83.50 74	89.21 87	71.51 71	75.74 214	61.24 144	92.34 84	88.94 36

v1240		73.06 128	73.14 129	72.84 143	74.74 208	47.27 233	71.88 169	81.11 113	51.80 217	82.28 87	84.21 178	56.22 206	82.34 104	68.82 82	87.17 176	88.91 37

LTVRE_ROB	75.46 1	84.22 8	84.98 9	81.94 23	84.82 75	75.40 29	91.60 1	87.80 7	73.52 23	88.90 10	93.06 6	71.39 74	81.53 115	81.53 3	92.15 87	88.91 37
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		72.96 134	72.98 135	72.89 142	74.67 211	47.58 228	71.92 167	80.69 120	51.70 219	81.69 94	83.89 182	56.58 204	82.25 106	68.34 86	87.36 168	88.82 39

EPP-MVSNet		73.86 112	73.38 124	75.31 103	78.19 162	53.35 188	80.45 66	77.32 175	65.11 85	76.47 168	86.80 127	49.47 237	83.77 77	53.89 207	92.72 80	88.81 40

UA-Net		81.56 37	82.28 43	79.40 54	88.91 29	69.16 77	84.67 33	80.01 135	75.34 16	79.80 115	94.91 2	69.79 85	80.25 143	72.63 60	94.46 36	88.78 41

v144192		72.99 132	73.06 132	72.77 144	74.58 215	47.48 229	71.90 168	80.44 127	51.57 220	81.46 96	84.11 180	58.04 191	82.12 108	67.98 92	87.47 166	88.70 42

EI-MVSNet		69.61 171	69.01 180	71.41 164	73.94 224	49.90 205	71.31 177	71.32 229	58.22 140	75.40 180	70.44 317	58.16 185	75.85 210	62.51 136	79.81 262	88.48 43

IterMVS-LS		73.01 130	73.12 131	72.66 148	73.79 226	49.90 205	71.63 171	78.44 159	58.22 140	80.51 107	86.63 139	58.15 186	79.62 152	62.51 136	88.20 153	88.48 43
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
HPM-MVS_fast		84.59 6	85.10 8	83.06 6	88.60 33	75.83 26	86.27 23	86.89 16	73.69 22	86.17 35	91.70 24	78.23 20	85.20 55	79.45 13	94.91 25	88.15 45

Regformer-4		74.64 106	73.67 118	77.55 76	74.74 208	64.49 111	72.91 153	75.42 192	67.45 60	80.24 112	72.07 305	68.98 90	80.19 147	70.29 75	80.91 248	87.98 46

COLMAP_ROB	72.78 3	83.75 13	84.11 17	82.68 14	82.97 104	74.39 35	87.18 9	88.18 6	78.98 6	86.11 38	91.47 29	79.70 12	85.76 39	66.91 104	95.46 11	87.89 47
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 77	77.35 82	78.13 73	84.88 74	66.37 95	78.55 91	79.59 140	53.48 202	86.29 34	92.43 15	62.39 145	80.25 143	67.90 95	90.61 120	87.77 48

eth_miper_zixun_eth		69.42 174	68.73 186	71.50 163	67.99 279	46.42 239	67.58 226	78.81 150	50.72 231	78.13 134	80.34 223	50.15 235	80.34 140	60.18 153	84.65 206	87.74 49

casdiffmvs		73.06 128	73.84 114	70.72 168	71.32 252	46.71 237	70.93 183	84.26 66	55.62 171	77.46 145	87.10 118	67.09 107	77.81 189	63.95 123	86.83 179	87.64 50

LS3D		80.99 45	80.85 54	81.41 30	78.37 160	71.37 54	87.45 6	85.87 30	77.48 12	81.98 88	89.95 75	69.14 88	85.26 51	66.15 105	91.24 102	87.61 51

ITE_SJBPF				80.35 44	76.94 180	73.60 41		80.48 125	66.87 64	83.64 73	86.18 151	70.25 82	79.90 150	61.12 147	88.95 150	87.56 52

Regformer-3		72.86 136	72.28 143	74.62 109	74.74 208	60.18 144	72.91 153	71.76 220	64.74 89	78.42 130	72.07 305	67.00 108	76.28 209	67.97 93	80.91 248	87.39 53

thisisatest0530		67.05 207	65.16 218	72.73 147	73.10 237	50.55 199	71.26 179	63.91 275	50.22 235	74.46 193	80.75 217	26.81 346	80.25 143	59.43 161	86.50 183	87.37 54

pmmvs6		71.82 149	73.66 119	66.31 231	75.94 193	42.01 273	66.99 237	72.53 215	63.45 103	76.43 169	92.78 10	72.95 63	69.69 268	51.41 220	90.46 122	87.22 55

ACMH+	66.64 10	81.20 39	82.48 42	77.35 82	81.16 130	62.39 124	80.51 65	87.80 7	73.02 25	87.57 20	91.08 36	80.28 10	82.44 101	64.82 116	96.10 4	87.21 56

cl_fuxian		69.82 169	69.89 170	69.61 187	66.24 294	43.48 261	68.12 221	79.61 139	51.43 222	77.72 141	80.18 227	54.61 213	78.15 185	63.62 128	87.50 165	87.20 57

Anonymous20240529		72.56 142	73.79 116	68.86 201	76.89 183	45.21 249	68.80 210	77.25 177	67.16 62	76.89 155	90.44 54	65.95 119	74.19 231	50.75 225	90.00 131	87.18 58

baseline		73.10 125	73.96 113	70.51 172	71.46 251	46.39 241	72.08 161	84.40 61	55.95 168	76.62 162	86.46 145	67.20 106	78.03 186	64.22 121	87.27 173	87.11 59

RRT_MVS		73.80 114	71.19 160	81.60 24	71.04 253	70.33 66	78.78 88	74.91 198	56.96 156	77.83 139	85.56 165	32.82 318	87.39 5	71.16 70	91.68 91	87.07 60

Effi-MVS+-dtu		75.43 93	72.28 143	84.91 2	77.05 176	83.58 1	78.47 92	77.70 169	57.68 147	74.89 184	78.13 255	64.80 130	84.26 72	56.46 181	85.32 197	86.88 61

v148		69.38 176	69.39 173	69.36 189	69.14 272	44.56 252	68.83 207	72.70 213	54.79 180	78.59 126	84.12 179	54.69 211	76.74 206	59.40 162	82.20 232	86.79 62

HPM-MVS		84.12 10	84.63 11	82.60 15	88.21 38	74.40 34	85.24 27	87.21 14	70.69 45	85.14 52	90.42 57	78.99 16	86.62 14	80.83 6	94.93 24	86.79 62
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
mvs_tets		78.93 64	78.67 72	79.72 49	84.81 76	73.93 38	80.65 64	76.50 182	51.98 216	87.40 23	91.86 21	76.09 34	78.53 169	68.58 84	90.20 125	86.69 64

jajsoiax		78.51 69	78.16 77	79.59 52	84.65 79	73.83 40	80.42 67	76.12 184	51.33 224	87.19 28	91.51 28	73.79 57	78.44 173	68.27 87	90.13 130	86.49 65

cl-mvsnet2		67.14 204	66.51 211	69.03 195	63.20 315	43.46 262	66.88 240	76.25 183	49.22 245	74.48 192	77.88 257	45.49 257	77.40 195	60.64 151	84.59 208	86.24 66

MP-MVS-pluss		82.54 29	83.46 28	79.76 47	88.88 31	68.44 81	81.57 60	86.33 19	63.17 106	85.38 49	91.26 33	76.33 31	84.67 64	83.30 1	94.96 23	86.17 67
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
LPG-MVS_test		83.47 18	84.33 14	80.90 38	87.00 42	70.41 64	82.04 57	86.35 17	69.77 50	87.75 15	91.13 34	81.83 3	86.20 23	77.13 34	95.96 5	86.08 68

LGP-MVS_train				80.90 38	87.00 42	70.41 64		86.35 17	69.77 50	87.75 15	91.13 34	81.83 3	86.20 23	77.13 34	95.96 5	86.08 68

SixPastTwentyTwo		75.77 88	76.34 89	74.06 119	81.69 124	54.84 176	76.47 114	75.49 190	64.10 95	87.73 17	92.24 17	50.45 233	81.30 118	67.41 98	91.46 97	86.04 70

Regformer-2		75.32 94	74.47 105	77.88 74	74.22 219	66.65 93	72.77 155	77.54 171	68.47 58	80.44 108	72.08 303	70.60 79	80.97 128	70.08 77	84.02 216	86.01 71

APD-MVS_3200maxsize		83.57 15	84.33 14	81.31 33	82.83 107	73.53 43	85.50 26	87.45 12	74.11 19	86.45 33	90.52 53	80.02 11	84.48 67	77.73 30	94.34 47	85.93 72

DeepC-MVS	72.44 4	81.00 44	80.83 55	81.50 26	86.70 48	70.03 68	82.06 56	87.00 15	59.89 128	80.91 104	90.53 51	72.19 64	88.56 1	73.67 54	94.52 34	85.92 73
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
cl-mvsnet1		68.27 192	68.26 190	68.29 208	64.98 306	43.67 259	65.89 249	74.67 199	50.04 238	76.86 157	82.43 199	48.74 244	75.38 216	60.94 148	89.81 135	85.81 74

AllTest		77.66 76	77.43 81	78.35 69	79.19 151	70.81 59	78.60 90	88.64 2	65.37 80	80.09 113	88.17 109	70.33 80	78.43 174	55.60 189	90.90 114	85.81 74

TestCases				78.35 69	79.19 151	70.81 59		88.64 2	65.37 80	80.09 113	88.17 109	70.33 80	78.43 174	55.60 189	90.90 114	85.81 74

ACMP	69.50 8	82.64 28	83.38 29	80.40 43	86.50 49	69.44 72	82.30 54	86.08 26	66.80 66	86.70 31	89.99 73	81.64 6	85.95 30	74.35 49	96.11 3	85.81 74
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
cl-mvsnet____		68.26 193	68.26 190	68.29 208	64.98 306	43.67 259	65.89 249	74.67 199	50.04 238	76.86 157	82.42 200	48.74 244	75.38 216	60.92 149	89.81 135	85.80 78

Regformer-1		74.28 108	73.63 120	76.21 95	74.22 219	64.12 114	72.77 155	75.46 191	66.86 65	79.27 120	72.08 303	69.29 87	78.74 165	68.73 83	84.02 216	85.77 79

miper_ehance_all_eth		68.36 189	68.16 195	68.98 196	65.14 305	43.34 263	67.07 236	78.92 149	49.11 247	76.21 172	77.72 258	53.48 217	77.92 188	61.16 146	84.59 208	85.68 80

SteuartSystems-ACMMP		83.07 23	83.64 24	81.35 31	85.14 71	71.00 58	85.53 25	84.78 48	70.91 43	85.64 43	90.41 58	75.55 39	87.69 3	79.75 8	95.08 20	85.36 81
Skip Steuart: Steuart Systems R&D Blog.
diffmvs		67.42 203	67.50 202	67.20 221	62.26 319	45.21 249	64.87 262	77.04 178	48.21 253	71.74 224	79.70 232	58.40 183	71.17 258	64.99 114	80.27 257	85.22 82

Baseline_NR-MVSNet		70.62 159	73.19 128	62.92 260	76.97 179	34.44 328	68.84 206	70.88 239	60.25 125	79.50 118	90.53 51	61.82 151	69.11 272	54.67 198	95.27 14	85.22 82

RRT_test8_iter05		65.80 214	65.13 221	67.80 216	67.02 289	40.85 282	67.13 235	75.33 193	49.73 240	72.69 213	81.32 211	24.45 357	77.37 196	61.69 142	86.82 180	85.18 84

TAPA-MVS	65.27 12	75.16 97	74.29 109	77.77 75	74.86 205	68.08 82	77.89 101	84.04 73	55.15 175	76.19 173	83.39 187	66.91 109	80.11 148	60.04 157	90.14 129	85.13 85
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
ETH3D-3000-0.1		79.14 62	79.80 62	77.16 85	80.67 133	64.57 109	80.26 71	87.60 11	60.74 122	82.47 85	88.03 113	71.73 69	81.81 111	73.12 56	93.61 63	85.09 86

CLD-MVS		72.88 135	72.36 142	74.43 112	77.03 178	54.30 180	68.77 211	83.43 80	52.12 213	76.79 160	74.44 284	69.54 86	83.91 75	55.88 187	93.25 70	85.09 86
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
DIV-MVS_2432*1600		66.38 211	67.51 201	62.97 259	61.76 321	34.39 329	58.11 311	75.30 194	50.84 230	77.12 149	85.42 166	56.84 202	69.44 269	51.07 223	91.16 104	85.08 88

CDPH-MVS		77.33 79	77.06 86	78.14 72	84.21 87	63.98 115	76.07 122	83.45 79	54.20 191	77.68 143	87.18 117	69.98 83	85.37 48	68.01 91	92.72 80	85.08 88

K. test v3		73.67 115	73.61 121	73.87 121	79.78 140	55.62 174	74.69 142	62.04 288	66.16 73	84.76 58	93.23 5	49.47 237	80.97 128	65.66 110	86.67 182	85.02 90

SR-MVS-dyc-post		84.75 5	85.26 7	83.21 3	86.19 53	79.18 9	87.23 7	86.27 21	77.51 10	87.65 18	90.73 45	79.20 14	85.58 45	78.11 25	94.46 36	84.89 91

RE-MVS-def			85.50 4		86.19 53	79.18 9	87.23 7	86.27 21	77.51 10	87.65 18	90.73 45	81.38 7		78.11 25	94.46 36	84.89 91

HQP_MVS		78.77 66	78.78 70	78.72 62	85.18 69	65.18 104	82.74 51	85.49 32	65.45 77	78.23 132	89.11 92	60.83 163	86.15 26	71.09 71	90.94 110	84.82 93

plane_prior5								85.49 32					86.15 26	71.09 71	90.94 110	84.82 93

xxxxxxxxxxxxxcwj		80.31 53	80.94 53	78.42 68	87.00 42	67.23 89	79.24 82	88.61 4	56.65 162	84.29 64	89.18 88	73.73 58	83.22 88	76.01 36	93.77 60	84.81 95

SF-MVS		80.72 47	81.80 45	77.48 78	82.03 118	64.40 112	83.41 47	88.46 5	65.28 82	84.29 64	89.18 88	73.73 58	83.22 88	76.01 36	93.77 60	84.81 95

alignmvs		70.54 160	71.00 161	69.15 193	73.50 227	48.04 222	69.85 195	79.62 137	53.94 198	76.54 165	82.00 203	59.00 178	74.68 226	57.32 173	87.21 174	84.72 97

IU-MVS					86.12 57	60.90 137		80.38 128	45.49 271	81.31 97				75.64 41	94.39 41	84.65 98

XVS		83.51 17	83.73 22	82.85 10	89.43 16	77.61 17	86.80 16	84.66 54	72.71 26	82.87 79	90.39 61	73.86 55	86.31 20	78.84 20	94.03 55	84.64 99

X-MVStestdata		76.81 82	74.79 101	82.85 10	89.43 16	77.61 17	86.80 16	84.66 54	72.71 26	82.87 79	9.95 362	73.86 55	86.31 20	78.84 20	94.03 55	84.64 99

ACMMP		84.22 8	84.84 10	82.35 20	89.23 23	76.66 25	87.65 4	85.89 29	71.03 42	85.85 40	90.58 49	78.77 17	85.78 38	79.37 16	95.17 17	84.62 101
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 32	82.68 41	80.43 42	88.90 30	69.52 70	85.12 28	84.76 49	63.53 101	84.23 66	91.47 29	72.02 66	87.16 7	79.74 10	94.36 45	84.61 102
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		70.81 157	71.44 157	68.91 200	79.07 156	46.51 238	67.82 224	70.83 240	61.23 117	74.07 198	88.69 100	59.86 170	75.62 215	51.11 222	90.28 124	84.61 102

ZNCC-MVS		83.12 22	83.68 23	81.45 29	89.14 25	73.28 45	86.32 22	85.97 28	67.39 61	84.02 68	90.39 61	74.73 47	86.46 16	80.73 7	94.43 40	84.60 104

miper_enhance_ethall		65.86 213	65.05 226	68.28 210	61.62 323	42.62 270	64.74 263	77.97 166	42.52 292	73.42 205	72.79 300	49.66 236	77.68 192	58.12 169	84.59 208	84.54 105

GBi-Net		68.30 190	68.79 182	66.81 225	73.14 234	40.68 283	71.96 164	73.03 207	54.81 177	74.72 188	90.36 64	48.63 246	75.20 220	47.12 253	85.37 193	84.54 105

test1		68.30 190	68.79 182	66.81 225	73.14 234	40.68 283	71.96 164	73.03 207	54.81 177	74.72 188	90.36 64	48.63 246	75.20 220	47.12 253	85.37 193	84.54 105

FMVSNet1		71.06 154	72.48 140	66.81 225	77.65 174	40.68 283	71.96 164	73.03 207	61.14 118	79.45 119	90.36 64	60.44 165	75.20 220	50.20 230	88.05 156	84.54 105

TSAR-MVS + MP.		79.05 63	78.81 68	79.74 48	88.94 28	67.52 86	86.61 18	81.38 107	51.71 218	77.15 147	91.42 32	65.49 123	87.20 6	79.44 14	87.17 176	84.51 109
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
mvs-test1		73.81 113	70.69 165	83.18 5	77.05 176	81.39 3	75.39 129	77.70 169	57.68 147	71.19 236	74.72 280	64.80 130	83.66 79	56.46 181	81.19 246	84.50 110

PCF-MVS	63.80 13	72.70 140	71.69 150	75.72 99	78.10 163	60.01 146	73.04 152	81.50 103	45.34 273	79.66 116	84.35 177	65.15 127	82.65 99	48.70 241	89.38 143	84.50 110
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
canonicalmvs		72.29 146	73.38 124	69.04 194	74.23 218	47.37 231	73.93 149	83.18 81	54.36 188	76.61 163	81.64 210	72.03 65	75.34 218	57.12 174	87.28 172	84.40 112

TransMVSNet (Re)		69.62 170	71.63 152	63.57 250	76.51 185	35.93 318	65.75 253	71.29 231	61.05 119	75.02 182	89.90 76	65.88 121	70.41 266	49.79 232	89.48 141	84.38 113

OPM-MVS		80.99 45	81.63 50	79.07 58	86.86 46	69.39 73	79.41 81	84.00 74	65.64 75	85.54 46	89.28 84	76.32 32	83.47 83	74.03 51	93.57 66	84.35 114

test_0728_THIRD									74.03 21	85.83 41	90.41 58	75.58 38	85.69 41	77.43 33	94.74 29	84.31 115

MSP-MVS		80.49 49	79.67 64	82.96 7	89.70 12	77.46 22	87.16 10	85.10 42	64.94 87	81.05 100	88.38 104	57.10 199	87.10 8	79.75 8	83.87 218	84.31 115
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 27	83.27 32	81.34 32	88.99 27	73.29 44	85.94 24	85.13 40	68.58 57	84.14 67	90.21 70	73.37 60	86.41 17	79.09 19	93.98 58	84.30 117

ACMMPR		83.62 14	83.93 19	82.69 13	89.78 11	77.51 21	87.01 13	84.19 69	70.23 46	84.49 61	90.67 48	75.15 42	86.37 19	79.58 11	94.26 49	84.18 118

VDDNet		71.60 151	73.13 130	67.02 224	86.29 51	41.11 278	69.97 192	66.50 260	68.72 55	74.74 187	91.70 24	59.90 169	75.81 212	48.58 243	91.72 89	84.15 119

abl_6		84.92 3	85.70 3	82.57 17	86.72 47	79.27 8	87.56 5	86.08 26	77.48 12	88.12 13	91.53 27	81.18 8	84.31 71	78.12 24	94.47 35	84.15 119

MVS_Test		69.84 168	70.71 164	67.24 220	67.49 285	43.25 265	69.87 194	81.22 112	52.69 209	71.57 230	86.68 135	62.09 149	74.51 228	66.05 106	78.74 271	83.96 121

region2R		83.54 16	83.86 21	82.58 16	89.82 10	77.53 19	87.06 12	84.23 68	70.19 48	83.86 70	90.72 47	75.20 41	86.27 22	79.41 15	94.25 50	83.95 122

ETH3 D test6400		75.73 90	76.00 92	74.92 107	81.75 122	56.93 165	78.31 94	84.60 58	52.83 207	77.15 147	85.14 170	68.59 93	84.03 74	65.44 112	90.20 125	83.82 123

PGM-MVS		83.07 23	83.25 33	82.54 18	89.57 14	77.21 23	82.04 57	85.40 35	67.96 59	84.91 57	90.88 39	75.59 37	86.57 15	78.16 23	94.71 30	83.82 123

pm-mvs1		68.40 188	69.85 171	64.04 247	73.10 237	39.94 289	64.61 266	70.50 241	55.52 172	73.97 199	89.33 83	63.91 136	68.38 276	49.68 234	88.02 157	83.81 125

HQP4-MVS										71.59 226			85.31 49			83.74 126

HQP-MVS		75.24 96	75.01 100	75.94 96	82.37 111	58.80 157	77.32 106	84.12 70	59.08 132	71.58 227	85.96 160	58.09 187	85.30 50	67.38 100	89.16 144	83.73 127

PHI-MVS		74.92 101	74.36 108	76.61 87	76.40 186	62.32 125	80.38 68	83.15 82	54.16 193	73.23 208	80.75 217	62.19 148	83.86 76	68.02 90	90.92 113	83.65 128

test1172		84.85 4	85.39 5	83.21 3	88.34 37	80.50 6	85.12 28	85.22 39	81.06 2	87.20 27	90.28 67	79.20 14	85.58 45	78.04 27	94.08 54	83.55 129

DeepPCF-MVS	71.07 5	78.48 71	77.14 85	82.52 19	84.39 86	77.04 24	76.35 117	84.05 72	56.66 161	80.27 111	85.31 168	68.56 94	87.03 10	67.39 99	91.26 101	83.50 130

XVG-ACMP-BASELINE		80.54 48	81.06 52	78.98 59	87.01 41	72.91 46	80.23 73	85.56 31	66.56 70	85.64 43	89.57 80	69.12 89	80.55 137	72.51 62	93.37 67	83.48 131

APDe-MVS		82.88 26	84.14 16	79.08 57	84.80 77	66.72 92	86.54 19	85.11 41	72.00 38	86.65 32	91.75 23	78.20 21	87.04 9	77.93 28	94.32 48	83.47 132

ANet_high		67.08 205	69.94 169	58.51 291	57.55 343	27.09 352	58.43 309	76.80 180	63.56 100	82.40 86	91.93 20	59.82 171	64.98 298	50.10 231	88.86 151	83.46 133

Effi-MVS+		72.10 147	72.28 143	71.58 160	74.21 222	50.33 200	74.72 141	82.73 88	62.62 109	70.77 239	76.83 265	69.96 84	80.97 128	60.20 152	78.43 275	83.45 134

test12				76.51 89	82.28 114	60.94 136		81.64 102		73.60 201		64.88 129	85.19 56		90.42 123	83.38 135

VPA-MVSNet		68.71 185	70.37 167	63.72 249	76.13 190	38.06 304	64.10 270	71.48 226	56.60 164	74.10 197	88.31 106	64.78 132	69.72 267	47.69 251	90.15 128	83.37 136

ACMMP_NAP		82.33 31	83.28 31	79.46 53	89.28 19	69.09 79	83.62 42	84.98 44	64.77 88	83.97 69	91.02 37	75.53 40	85.93 34	82.00 2	94.36 45	83.35 137

DeepC-MVS_fast	69.89 7	77.17 80	76.33 90	79.70 50	83.90 92	67.94 83	80.06 76	83.75 75	56.73 160	74.88 185	85.32 167	65.54 122	87.79 2	65.61 111	91.14 106	83.35 137
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 47	72.61 29	84.93 54	89.70 78	77.73 23	85.89 36	75.29 42	94.22 53	83.25 139

ETH3D cwj APD-0.16		78.38 73	78.72 71	77.38 80	80.09 138	66.16 97	79.08 85	86.13 25	57.55 152	80.93 102	87.76 116	71.98 68	82.73 98	72.11 68	92.83 75	83.25 139

test_0728_SECOND				76.57 88	86.20 52	60.57 141	83.77 40	85.49 32					85.90 35	75.86 39	94.39 41	83.25 139

GeoE		73.14 124	73.77 117	71.26 165	78.09 164	52.64 191	74.32 145	79.56 141	56.32 165	76.35 171	83.36 191	70.76 78	77.96 187	63.32 133	81.84 237	83.18 142

SR-MVS		84.51 7	85.27 6	82.25 21	88.52 34	77.71 16	86.81 15	85.25 38	77.42 14	86.15 36	90.24 68	81.69 5	85.94 31	77.77 29	93.58 65	83.09 143

SED-MVS		81.78 35	83.48 27	76.67 86	86.12 57	61.06 133	83.62 42	84.72 51	72.61 29	87.38 24	89.70 78	77.48 24	85.89 36	75.29 42	94.39 41	83.08 144

OPU-MVS				78.65 64	83.44 98	66.85 91	83.62 42				86.12 155	66.82 111	86.01 29	61.72 141	89.79 137	83.08 144

MVSTER		63.29 238	61.60 250	68.36 206	59.77 335	46.21 242	60.62 297	71.32 229	41.83 295	75.40 180	79.12 242	30.25 339	75.85 210	56.30 183	79.81 262	83.03 146

CANet		73.00 131	71.84 148	76.48 90	75.82 194	61.28 131	74.81 136	80.37 129	63.17 106	62.43 295	80.50 221	61.10 161	85.16 57	64.00 122	84.34 212	83.01 147

Vis-MVSNet		74.85 105	74.56 103	75.72 99	81.63 125	64.64 108	76.35 117	79.06 146	62.85 108	73.33 206	88.41 102	62.54 143	79.59 154	63.94 125	82.92 226	82.94 148
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
miper_lstm_enhance		61.97 250	61.63 249	62.98 258	60.04 331	45.74 246	47.53 339	70.95 237	44.04 280	73.06 209	78.84 247	39.72 293	60.33 313	55.82 188	84.64 207	82.88 149

PAPM_NR		73.91 111	74.16 110	73.16 133	81.90 120	53.50 186	81.28 61	81.40 106	66.17 72	73.30 207	83.31 192	59.96 168	83.10 92	58.45 167	81.66 242	82.87 150

Fast-Effi-MVS+		68.81 183	68.30 189	70.35 174	74.66 213	48.61 214	66.06 247	78.32 160	50.62 232	71.48 233	75.54 272	68.75 92	79.59 154	50.55 228	78.73 272	82.86 151

HFP-MVS		83.39 19	84.03 18	81.48 27	89.25 21	75.69 27	87.01 13	84.27 64	70.23 46	84.47 62	90.43 55	76.79 27	85.94 31	79.58 11	94.23 51	82.82 152

#test#		82.40 30	82.71 40	81.48 27	89.25 21	75.69 27	84.47 34	84.27 64	64.45 91	84.47 62	90.43 55	76.79 27	85.94 31	76.01 36	94.23 51	82.82 152

DELS-MVS		68.83 182	68.31 188	70.38 173	70.55 261	48.31 216	63.78 275	82.13 94	54.00 195	68.96 256	75.17 276	58.95 179	80.06 149	58.55 166	82.74 228	82.76 154
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_2432*1600		62.44 248	63.40 235	59.55 285	72.34 245	32.38 337	56.39 315	64.84 269	51.21 226	67.46 267	81.01 216	50.75 231	63.51 305	38.47 300	88.12 155	82.75 155

MP-MVS		83.19 20	83.54 26	82.14 22	90.54 5	79.00 11	86.42 21	83.59 78	71.31 40	81.26 98	90.96 38	74.57 50	84.69 63	78.41 22	94.78 26	82.74 156
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
lessismore_v0				72.75 145	79.60 143	56.83 167		57.37 303		83.80 71	89.01 95	47.45 251	78.74 165	64.39 119	86.49 184	82.69 157

DPE-MVS		82.00 34	83.02 36	78.95 60	85.36 68	67.25 88	82.91 50	84.98 44	73.52 23	85.43 48	90.03 72	76.37 30	86.97 11	74.56 47	94.02 57	82.62 158
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
test_prior3		76.71 84	77.19 83	75.27 104	82.15 116	59.85 147	75.57 127	84.33 62	58.92 136	76.53 166	86.78 129	67.83 103	83.39 85	69.81 79	92.76 78	82.58 159

test_prior				75.27 104	82.15 116	59.85 147		84.33 62					83.39 85			82.58 159

F-COLMAP		75.29 95	73.99 112	79.18 56	81.73 123	71.90 49	81.86 59	82.98 84	59.86 129	72.27 219	84.00 181	64.56 133	83.07 93	51.48 219	87.19 175	82.56 161

CP-MVS		84.12 10	84.55 12	82.80 12	89.42 18	79.74 7	88.19 3	84.43 60	71.96 39	84.70 59	90.56 50	77.12 26	86.18 25	79.24 18	95.36 13	82.49 162

testtj		81.19 40	81.70 48	79.67 51	83.95 90	69.77 69	83.58 45	84.63 56	72.13 35	82.85 81	88.36 105	75.00 45	86.79 12	71.99 69	92.84 74	82.44 163

XVG-OURS		79.51 58	79.82 61	78.58 65	86.11 60	74.96 32	76.33 119	84.95 46	66.89 63	82.75 82	88.99 96	66.82 111	78.37 177	74.80 44	90.76 119	82.40 164

mPP-MVS		84.01 12	84.39 13	82.88 8	90.65 4	81.38 4	87.08 11	82.79 87	72.41 33	85.11 53	90.85 41	76.65 29	84.89 59	79.30 17	94.63 32	82.35 165

XVG-OURS-SEG-HR		79.62 57	79.99 60	78.49 66	86.46 50	74.79 33	77.15 110	85.39 36	66.73 67	80.39 110	88.85 99	74.43 53	78.33 179	74.73 46	85.79 190	82.35 165

FMVSNet2		67.48 201	68.21 193	65.29 236	73.14 234	38.94 296	68.81 208	71.21 235	54.81 177	76.73 161	86.48 144	48.63 246	74.60 227	47.98 248	86.11 187	82.35 165

CNVR-MVS		78.49 70	78.59 73	78.16 71	85.86 64	67.40 87	78.12 100	81.50 103	63.92 96	77.51 144	86.56 142	68.43 97	84.82 61	73.83 52	91.61 94	82.26 168

mvs_anonymous		65.08 221	65.49 217	63.83 248	63.79 312	37.60 308	66.52 244	69.82 245	43.44 287	73.46 204	86.08 157	58.79 181	71.75 255	51.90 217	75.63 290	82.15 169

CS-MVS		72.86 136	73.06 132	72.24 157	72.87 242	50.11 203	75.90 124	86.28 20	58.16 142	69.77 249	79.12 242	65.24 126	84.56 65	64.26 120	85.95 189	82.05 170

thres600view7		61.82 252	61.38 252	63.12 256	71.81 249	34.93 325	64.64 264	56.99 307	54.78 181	70.33 244	79.74 231	32.07 325	72.42 246	38.61 298	83.46 222	82.02 171

thres400		60.77 260	59.97 261	63.15 255	70.78 255	35.35 322	63.27 281	57.47 301	53.00 205	68.31 261	77.09 263	32.45 322	72.09 248	35.61 320	81.73 238	82.02 171

ETV-MVS		72.72 139	72.16 146	74.38 114	76.90 182	55.95 169	73.34 151	84.67 53	62.04 113	72.19 222	70.81 315	65.90 120	85.24 53	58.64 165	84.96 204	81.95 173

CNLPA		73.44 118	73.03 134	74.66 108	78.27 161	75.29 30	75.99 123	78.49 158	65.39 79	75.67 175	83.22 196	61.23 159	66.77 290	53.70 209	85.33 196	81.92 174

NCCC		78.25 74	78.04 78	78.89 61	85.61 65	69.45 71	79.80 78	80.99 118	65.77 74	75.55 177	86.25 150	67.42 105	85.42 47	70.10 76	90.88 116	81.81 175

PAPR		69.20 178	68.66 187	70.82 167	75.15 201	47.77 225	75.31 130	81.11 113	49.62 243	66.33 272	79.27 238	61.53 154	82.96 94	48.12 247	81.50 244	81.74 176

Anonymous202405211		66.02 212	66.89 210	63.43 253	74.22 219	38.14 302	59.00 306	66.13 261	63.33 105	69.76 250	85.95 161	51.88 223	70.50 263	44.23 268	87.52 164	81.64 177

FMVSNet3		65.00 222	65.16 218	64.52 242	69.47 268	37.56 309	66.63 242	70.38 242	51.55 221	74.72 188	83.27 193	37.89 304	74.44 229	47.12 253	85.37 193	81.57 178

Vis-MVSNet (Re-imp)		62.74 245	63.21 238	61.34 272	72.19 246	31.56 341	67.31 233	53.87 317	53.60 201	69.88 247	83.37 189	40.52 289	70.98 259	41.40 282	86.78 181	81.48 179

test_0402		78.17 75	79.48 65	74.24 116	83.50 95	59.15 154	72.52 157	74.60 201	75.34 16	88.69 12	91.81 22	75.06 43	82.37 103	65.10 113	88.68 152	81.20 180

VPNet		65.58 216	67.56 200	59.65 284	79.72 141	30.17 346	60.27 300	62.14 283	54.19 192	71.24 234	86.63 139	58.80 180	67.62 281	44.17 269	90.87 117	81.18 181

APD-MVS		81.13 42	81.73 47	79.36 55	84.47 82	70.53 63	83.85 38	83.70 76	69.43 52	83.67 72	88.96 97	75.89 35	86.41 17	72.62 61	92.95 72	81.14 182
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
CPTT-MVS		81.51 38	81.76 46	80.76 40	89.20 24	78.75 12	86.48 20	82.03 96	68.80 53	80.92 103	88.52 101	72.00 67	82.39 102	74.80 44	93.04 71	81.14 182

Fast-Effi-MVS+-dtu		70.00 165	68.74 185	73.77 122	73.47 228	64.53 110	71.36 175	78.14 164	55.81 170	68.84 259	74.71 281	65.36 125	75.75 213	52.00 216	79.00 269	81.03 184

MDA-MVSNet-bldmvs		62.34 249	61.73 246	64.16 243	61.64 322	49.90 205	48.11 337	57.24 306	53.31 203	80.95 101	79.39 236	49.00 242	61.55 311	45.92 261	80.05 259	81.03 184

D2MVS		62.58 246	61.05 254	67.20 221	63.85 311	47.92 223	56.29 316	69.58 246	39.32 307	70.07 246	78.19 253	34.93 311	72.68 239	53.44 212	83.74 220	81.00 186

ACMM	69.25 9	82.11 33	83.31 30	78.49 66	88.17 39	73.96 37	83.11 49	84.52 59	66.40 71	87.45 22	89.16 91	81.02 9	80.52 138	74.27 50	95.73 7	80.98 187
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
hse-mvs2		72.32 145	70.66 166	77.31 83	83.10 101	71.77 50	69.19 203	71.45 227	54.28 189	77.89 136	78.26 252	49.04 240	79.23 156	63.62 128	89.13 148	80.92 188

DP-MVS Recon		73.57 117	72.69 138	76.23 94	82.85 106	63.39 118	74.32 145	82.96 85	57.75 146	70.35 243	81.98 204	64.34 134	84.41 70	49.69 233	89.95 133	80.89 189

EPNet		69.10 180	67.32 204	74.46 110	68.33 276	61.27 132	77.56 103	63.57 277	60.95 120	56.62 321	82.75 197	51.53 227	81.24 119	54.36 204	90.20 125	80.88 190
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
AUN-MVS		70.22 162	67.88 198	77.22 84	82.96 105	71.61 51	69.08 204	71.39 228	49.17 246	71.70 225	78.07 256	37.62 305	79.21 157	61.81 139	89.15 146	80.82 191

zzz-MVS		83.01 25	83.63 25	81.13 35	91.16 2	78.16 14	82.72 53	80.63 121	72.08 36	84.93 54	90.79 42	74.65 48	84.42 68	80.98 4	94.75 27	80.82 191

MTAPA		83.19 20	83.87 20	81.13 35	91.16 2	78.16 14	84.87 30	80.63 121	72.08 36	84.93 54	90.79 42	74.65 48	84.42 68	80.98 4	94.75 27	80.82 191

HyFIR lowres test		63.01 241	60.47 258	70.61 169	83.04 102	54.10 181	59.93 302	72.24 219	33.67 337	69.00 254	75.63 271	38.69 298	76.93 200	36.60 313	75.45 293	80.81 194

EIA-MVS		68.59 187	67.16 206	72.90 141	75.18 200	55.64 173	69.39 199	81.29 108	52.44 210	64.53 280	70.69 316	60.33 166	82.30 105	54.27 205	76.31 286	80.75 195

MCST-MVS		73.42 119	73.34 126	73.63 125	81.28 128	59.17 153	74.80 138	83.13 83	45.50 269	72.84 211	83.78 184	65.15 127	80.99 126	64.54 117	89.09 149	80.73 196

tfpnnormal		66.48 210	67.93 196	62.16 265	73.40 230	36.65 311	63.45 278	64.99 268	55.97 167	72.82 212	87.80 115	57.06 200	69.10 273	48.31 246	87.54 163	80.72 197

SD-MVS		80.28 54	81.55 51	76.47 91	83.57 94	67.83 85	83.39 48	85.35 37	64.42 92	86.14 37	87.07 121	74.02 54	80.97 128	77.70 31	92.32 85	80.62 198
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		64.04 233	63.83 230	64.66 240	68.39 273	42.97 267	73.45 150	74.50 202	52.05 215	54.78 328	75.44 275	43.99 266	70.42 265	53.49 211	78.41 276	80.59 199

GA-MVS		62.91 242	61.66 247	66.66 229	67.09 288	44.49 253	61.18 295	69.36 248	51.33 224	69.33 252	74.47 283	36.83 306	74.94 223	50.60 227	74.72 298	80.57 200

114514_t		73.40 120	73.33 127	73.64 124	84.15 89	57.11 164	78.20 98	80.02 134	43.76 283	72.55 215	86.07 158	64.00 135	83.35 87	60.14 155	91.03 109	80.45 201

IterMVS-SCA-FT		67.68 199	66.07 214	72.49 151	73.34 231	58.20 162	63.80 274	65.55 265	48.10 254	76.91 154	82.64 198	45.20 258	78.84 162	61.20 145	77.89 281	80.44 202

ambc				70.10 181	77.74 171	50.21 202	74.28 147	77.93 168		79.26 121	88.29 107	54.11 215	79.77 151	64.43 118	91.10 107	80.30 203

thisisatest0515		60.48 262	57.86 276	68.34 207	67.25 286	46.42 239	60.58 298	62.14 283	40.82 302	63.58 290	69.12 328	26.28 349	78.34 178	48.83 239	82.13 233	80.26 204

LFMVS		67.06 206	67.89 197	64.56 241	78.02 165	38.25 301	70.81 186	59.60 294	65.18 84	71.06 237	86.56 142	43.85 267	75.22 219	46.35 259	89.63 138	80.21 205

UGNet		70.20 163	69.05 178	73.65 123	76.24 188	63.64 116	75.87 125	72.53 215	61.48 116	60.93 305	86.14 154	52.37 222	77.12 198	50.67 226	85.21 198	80.17 206
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		66.57 209	69.23 176	58.59 290	81.26 129	37.73 307	64.06 271	57.62 300	57.02 155	78.40 131	90.75 44	62.65 140	58.10 320	41.77 281	89.58 140	79.95 207

test_yl		65.11 219	65.09 223	65.18 237	70.59 258	40.86 280	63.22 283	72.79 210	57.91 144	68.88 257	79.07 245	42.85 274	74.89 224	45.50 263	84.97 201	79.81 208

DCV-MVSNet		65.11 219	65.09 223	65.18 237	70.59 258	40.86 280	63.22 283	72.79 210	57.91 144	68.88 257	79.07 245	42.85 274	74.89 224	45.50 263	84.97 201	79.81 208

cascas		64.59 224	62.77 242	70.05 182	75.27 198	50.02 204	61.79 290	71.61 222	42.46 293	63.68 289	68.89 332	49.33 239	80.35 139	47.82 250	84.05 215	79.78 210

ET-MVSNet_ETH3D		63.32 237	60.69 257	71.20 166	70.15 264	55.66 172	65.02 261	64.32 274	43.28 291	68.99 255	72.05 309	25.46 353	78.19 184	54.16 206	82.80 227	79.74 211

CSCG		74.12 110	74.39 106	73.33 129	79.35 146	61.66 130	77.45 105	81.98 97	62.47 112	79.06 123	80.19 226	61.83 150	78.79 164	59.83 159	87.35 169	79.54 212

ACMH	63.62 14	77.50 78	80.11 59	69.68 186	79.61 142	56.28 168	78.81 87	83.62 77	63.41 104	87.14 30	90.23 69	76.11 33	73.32 234	67.58 96	94.44 39	79.44 213
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
MG-MVS		70.47 161	71.34 158	67.85 213	79.26 148	40.42 287	74.67 143	75.15 197	58.41 139	68.74 260	88.14 112	56.08 207	83.69 78	59.90 158	81.71 241	79.43 214

DVP-MVS		81.15 41	83.12 35	75.24 106	86.16 55	60.78 138	83.77 40	80.58 124	72.48 31	85.83 41	90.41 58	78.57 18	85.69 41	75.86 39	94.39 41	79.24 215
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		64.01 234	65.15 220	60.57 278	73.28 232	35.61 321	57.60 313	67.08 257	54.61 184	66.76 271	83.37 189	56.28 205	66.87 286	42.19 277	85.20 199	79.23 216

TSAR-MVS + GP.		73.08 126	71.60 154	77.54 77	78.99 157	70.73 61	74.96 133	69.38 247	60.73 123	74.39 194	78.44 250	57.72 194	82.78 96	60.16 154	89.60 139	79.11 217

HPM-MVS++		79.89 56	79.80 62	80.18 45	89.02 26	78.44 13	83.49 46	80.18 132	64.71 90	78.11 135	88.39 103	65.46 124	83.14 90	77.64 32	91.20 103	78.94 218

DP-MVS		78.44 72	79.29 66	75.90 97	81.86 121	65.33 102	79.05 86	84.63 56	74.83 18	80.41 109	86.27 148	71.68 70	83.45 84	62.45 138	92.40 83	78.92 219

PLC	62.01 16	71.79 150	70.28 168	76.33 92	80.31 137	68.63 80	78.18 99	81.24 110	54.57 185	67.09 270	80.63 219	59.44 173	81.74 114	46.91 256	84.17 213	78.63 220
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
PVSNet_Blended_VisFu		70.04 164	68.88 181	73.53 127	82.71 108	63.62 117	74.81 136	81.95 98	48.53 252	67.16 269	79.18 241	51.42 228	78.38 176	54.39 203	79.72 265	78.60 221

hse-mvs3		73.08 126	71.61 153	77.48 78	83.89 93	72.89 47	70.47 188	71.12 236	54.28 189	77.89 136	83.41 186	49.04 240	80.98 127	63.62 128	90.77 118	78.58 222

agg_prior2														70.70 74	90.93 112	78.55 223

ppachtmachnet_test		60.26 264	59.61 264	62.20 264	67.70 283	44.33 254	58.18 310	60.96 291	40.75 303	65.80 275	72.57 301	41.23 281	63.92 302	46.87 257	82.42 231	78.33 224

BH-RMVSNet		68.69 186	68.20 194	70.14 179	76.40 186	53.90 184	64.62 265	73.48 206	58.01 143	73.91 200	81.78 206	59.09 177	78.22 181	48.59 242	77.96 280	78.31 225

bset_n11_16_dypcd		66.91 208	65.84 216	70.12 180	72.95 240	53.54 185	63.64 276	68.65 252	48.54 251	72.54 216	74.28 287	40.58 288	78.54 168	63.52 131	87.82 161	78.29 226

PVSNet_BlendedMVS		65.38 217	64.30 227	68.61 204	69.81 265	49.36 209	65.60 256	78.96 147	45.50 269	59.98 308	78.61 248	51.82 224	78.20 182	44.30 266	84.11 214	78.27 227

ab-mvs		64.11 232	65.13 221	61.05 274	71.99 248	38.03 305	67.59 225	68.79 250	49.08 248	65.32 277	86.26 149	58.02 192	66.85 288	39.33 292	79.79 264	78.27 227

MVSFormer		69.93 167	69.03 179	72.63 150	74.93 202	59.19 151	83.98 36	75.72 188	52.27 211	63.53 291	76.74 266	43.19 271	80.56 135	72.28 65	78.67 273	78.14 229

jason		64.47 227	62.84 241	69.34 191	76.91 181	59.20 150	67.15 234	65.67 262	35.29 327	65.16 278	76.74 266	44.67 262	70.68 260	54.74 197	79.28 268	78.14 229
jason: jason.
new-patchmatchnet		52.89 298	55.76 291	44.26 333	59.94 333	6.31 366	37.36 356	50.76 331	41.10 299	64.28 283	79.82 230	44.77 261	48.43 332	36.24 317	87.61 162	78.03 231

CDS-MVSNet		64.33 230	62.66 243	69.35 190	80.44 136	58.28 161	65.26 258	65.66 263	44.36 279	67.30 268	75.54 272	43.27 270	71.77 253	37.68 305	84.44 211	78.01 232
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
TAMVS		65.31 218	63.75 231	69.97 184	82.23 115	59.76 149	66.78 241	63.37 278	45.20 274	69.79 248	79.37 237	47.42 252	72.17 247	34.48 324	85.15 200	77.99 233

LCM-MVSNet-Re		69.10 180	71.57 155	61.70 267	70.37 262	34.30 330	61.45 291	79.62 137	56.81 158	89.59 7	88.16 111	68.44 96	72.94 237	42.30 276	87.33 170	77.85 234

Patchmtry		60.91 257	63.01 240	54.62 301	66.10 297	26.27 355	67.47 228	56.40 310	54.05 194	72.04 223	86.66 136	33.19 316	60.17 314	43.69 270	87.45 167	77.42 235

test9_res														72.12 67	91.37 99	77.40 236

train_agg		76.38 85	76.55 87	75.86 98	85.47 66	69.32 75	76.42 115	78.69 153	54.00 195	76.97 150	86.74 132	66.60 113	81.10 122	72.50 63	91.56 95	77.15 237

lupinMVS		63.36 236	61.49 251	68.97 197	74.93 202	59.19 151	65.80 252	64.52 273	34.68 332	63.53 291	74.25 288	43.19 271	70.62 261	53.88 208	78.67 273	77.10 238

thres100view900		61.17 256	61.09 253	61.39 271	72.14 247	35.01 324	65.42 257	56.99 307	55.23 174	70.71 240	79.90 229	32.07 325	72.09 248	35.61 320	81.73 238	77.08 239

tfpn200view9		60.35 263	59.97 261	61.51 269	70.78 255	35.35 322	63.27 281	57.47 301	53.00 205	68.31 261	77.09 263	32.45 322	72.09 248	35.61 320	81.73 238	77.08 239

agg_prior1		75.89 87	76.41 88	74.31 115	84.44 84	66.02 98	76.12 121	78.62 156	54.40 187	76.95 152	86.85 126	66.44 115	80.34 140	72.45 64	91.42 98	76.57 241

MVS_111021_HR		72.98 133	72.97 136	72.99 137	80.82 131	65.47 101	68.81 208	72.77 212	57.67 149	75.76 174	82.38 201	71.01 76	77.17 197	61.38 143	86.15 185	76.32 242

xiu_mvs_v1_base_debu		67.87 195	67.07 207	70.26 175	79.13 153	61.90 127	67.34 230	71.25 232	47.98 255	67.70 264	74.19 290	61.31 156	72.62 241	56.51 178	78.26 277	76.27 243

xiu_mvs_v1_base		67.87 195	67.07 207	70.26 175	79.13 153	61.90 127	67.34 230	71.25 232	47.98 255	67.70 264	74.19 290	61.31 156	72.62 241	56.51 178	78.26 277	76.27 243

xiu_mvs_v1_base_debi		67.87 195	67.07 207	70.26 175	79.13 153	61.90 127	67.34 230	71.25 232	47.98 255	67.70 264	74.19 290	61.31 156	72.62 241	56.51 178	78.26 277	76.27 243

baseline2		55.57 287	52.74 301	64.05 246	65.26 301	44.11 255	62.38 286	54.43 316	39.03 310	51.21 339	67.35 340	33.66 314	72.45 245	37.14 310	64.22 339	75.60 246

OpenMVS	62.51 15	68.76 184	68.75 184	68.78 203	70.56 260	53.91 183	78.29 95	77.35 174	48.85 249	70.22 245	83.52 185	52.65 221	76.93 200	55.31 193	81.99 234	75.49 247

3Dnovator	65.95 11	71.50 152	71.22 159	72.34 154	73.16 233	63.09 121	78.37 93	78.32 160	57.67 149	72.22 221	84.61 173	54.77 210	78.47 171	60.82 150	81.07 247	75.45 248

1112_ss		59.48 268	58.99 268	60.96 276	77.84 169	42.39 272	61.42 292	68.45 253	37.96 316	59.93 311	67.46 338	45.11 260	65.07 297	40.89 286	71.81 312	75.41 249

IterMVS		63.12 240	62.48 244	65.02 239	66.34 293	52.86 189	63.81 273	62.25 282	46.57 266	71.51 232	80.40 222	44.60 263	66.82 289	51.38 221	75.47 292	75.38 250
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Test_1112_low_res		58.78 273	58.69 270	59.04 288	79.41 145	38.13 303	57.62 312	66.98 258	34.74 330	59.62 312	77.56 260	42.92 273	63.65 304	38.66 297	70.73 318	75.35 251

QAPM		69.18 179	69.26 175	68.94 198	71.61 250	52.58 192	80.37 69	78.79 152	49.63 242	73.51 202	85.14 170	53.66 216	79.12 158	55.11 194	75.54 291	75.11 252

MVS_0304		62.51 247	62.27 245	63.25 254	69.39 269	48.47 215	64.05 272	62.48 281	59.69 130	54.10 333	81.04 215	45.71 254	66.31 293	41.38 283	82.58 230	74.96 253

DPM-MVS		69.98 166	69.22 177	72.26 156	82.69 109	58.82 156	70.53 187	81.23 111	47.79 259	64.16 284	80.21 224	51.32 229	83.12 91	60.14 155	84.95 205	74.83 254

pmmvs-eth3d		64.41 229	63.27 237	67.82 215	75.81 195	60.18 144	69.49 197	62.05 287	38.81 312	74.13 196	82.23 202	43.76 268	68.65 274	42.53 275	80.63 255	74.63 255

MSDG		67.47 202	67.48 203	67.46 218	70.70 257	54.69 178	66.90 239	78.17 163	60.88 121	70.41 242	74.76 278	61.22 160	73.18 235	47.38 252	76.87 283	74.49 256

MAR-MVS		67.72 198	66.16 213	72.40 153	74.45 216	64.99 107	74.87 134	77.50 173	48.67 250	65.78 276	68.58 335	57.01 201	77.79 190	46.68 258	81.92 235	74.42 257
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		57.82 278	58.36 274	56.19 297	69.17 271	30.76 345	62.94 285	55.21 313	46.04 268	63.83 287	78.47 249	41.20 282	63.68 303	39.44 291	68.99 327	74.13 258

EU-MVSNet		60.82 258	60.80 256	60.86 277	68.37 274	41.16 277	72.27 158	68.27 254	26.96 355	69.08 253	75.71 270	32.09 324	67.44 282	55.59 191	78.90 270	73.97 259

HY-MVS	49.31 19	57.96 277	57.59 278	59.10 287	66.85 290	36.17 315	65.13 260	65.39 266	39.24 309	54.69 330	78.14 254	44.28 265	67.18 285	33.75 328	70.79 317	73.95 260

TR-MVS		64.59 224	63.54 234	67.73 217	75.75 196	50.83 198	63.39 279	70.29 243	49.33 244	71.55 231	74.55 282	50.94 230	78.46 172	40.43 288	75.69 289	73.89 261

IB-MVS	49.67 18	59.69 267	56.96 282	67.90 212	68.19 277	50.30 201	61.42 292	65.18 267	47.57 261	55.83 325	67.15 342	23.77 358	79.60 153	43.56 272	79.97 260	73.79 262
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		63.55 235	66.07 214	55.99 298	66.18 296	44.04 256	68.77 211	68.80 249	46.99 264	72.57 214	85.84 162	39.87 292	50.22 329	53.40 213	92.23 86	73.71 263

AdaColmap		74.22 109	74.56 103	73.20 132	81.95 119	60.97 135	79.43 79	80.90 119	65.57 76	72.54 216	81.76 208	70.98 77	85.26 51	47.88 249	90.00 131	73.37 264

PAPM		61.79 253	60.37 259	66.05 232	76.09 191	41.87 274	69.30 200	76.79 181	40.64 304	53.80 334	79.62 234	44.38 264	82.92 95	29.64 342	73.11 307	73.36 265

MVS_111021_LR		72.10 147	71.82 149	72.95 139	79.53 144	73.90 39	70.45 189	66.64 259	56.87 157	76.81 159	81.76 208	68.78 91	71.76 254	61.81 139	83.74 220	73.18 266

原ACMM1				73.90 120	85.90 61	65.15 106		81.67 101	50.97 228	74.25 195	86.16 153	61.60 153	83.54 81	56.75 176	91.08 108	73.00 267

CHOSEN 1792x2688		58.09 276	56.30 287	63.45 252	79.95 139	50.93 197	54.07 324	65.59 264	28.56 352	61.53 298	74.33 285	41.09 284	66.52 292	33.91 327	67.69 333	72.92 268

TinyColmap		67.98 194	69.28 174	64.08 245	67.98 280	46.82 235	70.04 191	75.26 195	53.05 204	77.36 146	86.79 128	59.39 174	72.59 244	45.64 262	88.01 158	72.83 269

FMVSNet5		55.08 289	55.54 292	53.71 302	65.80 298	33.50 334	56.22 317	52.50 326	43.72 285	61.06 302	83.38 188	25.46 353	54.87 323	30.11 339	81.64 243	72.75 270

EG-PatchMatch MVS		70.70 158	70.88 162	70.16 178	82.64 110	58.80 157	71.48 172	73.64 205	54.98 176	76.55 164	81.77 207	61.10 161	78.94 161	54.87 196	80.84 251	72.74 271

PVSNet_Blended		62.90 243	61.64 248	66.69 228	69.81 265	49.36 209	61.23 294	78.96 147	42.04 294	59.98 308	68.86 333	51.82 224	78.20 182	44.30 266	77.77 282	72.52 272

CostFormer		57.35 280	56.14 288	60.97 275	63.76 313	38.43 298	67.50 227	60.22 292	37.14 320	59.12 313	76.34 268	32.78 319	71.99 251	39.12 294	69.27 326	72.47 273

PS-MVSNAJ		64.27 231	63.73 232	65.90 234	77.82 170	51.42 195	63.33 280	72.33 217	45.09 276	61.60 297	68.04 336	62.39 145	73.95 232	49.07 237	73.87 304	72.34 274

xiu_mvs_v2_base		64.43 228	63.96 229	65.85 235	77.72 172	51.32 196	63.63 277	72.31 218	45.06 277	61.70 296	69.66 325	62.56 141	73.93 233	49.06 238	73.91 303	72.31 275

PMVS	70.70 6	81.70 36	83.15 34	77.36 81	90.35 6	82.82 2	82.15 55	79.22 144	74.08 20	87.16 29	91.97 19	84.80 2	76.97 199	64.98 115	93.61 63	72.28 276
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
1314		59.83 266	58.86 269	62.74 261	65.71 299	44.78 251	68.59 213	72.63 214	33.54 339	61.05 303	67.29 341	43.62 269	71.26 257	49.49 235	67.84 332	72.19 277

无先验							74.82 135	70.94 238	47.75 260				76.85 203	54.47 200		72.09 278

LF4IMVS		67.50 200	67.31 205	68.08 211	58.86 338	61.93 126	71.43 173	75.90 187	44.67 278	72.42 218	80.20 225	57.16 196	70.44 264	58.99 164	86.12 186	71.88 279

pmmvs4		60.78 259	59.04 267	66.00 233	73.06 239	57.67 163	64.53 267	60.22 292	36.91 321	65.96 273	77.27 262	39.66 294	68.54 275	38.87 295	74.89 297	71.80 280

MSLP-MVS++		74.48 107	75.78 94	70.59 170	84.66 78	62.40 123	78.65 89	84.24 67	60.55 124	77.71 142	81.98 204	63.12 138	77.64 193	62.95 135	88.14 154	71.73 281

MDTV_nov1_ep13_2view						18.41 362	53.74 325		31.57 347	44.89 353		29.90 343		32.93 330		71.48 282

tpm2		56.12 282	54.64 295	60.55 279	66.24 294	36.01 316	68.14 220	56.77 309	33.60 338	58.25 316	75.52 274	30.25 339	74.33 230	33.27 329	69.76 325	71.32 283

CMPMVS	48.73 20	61.54 255	60.89 255	63.52 251	61.08 326	51.55 194	68.07 222	68.00 255	33.88 334	65.87 274	81.25 213	37.91 303	67.71 279	49.32 236	82.60 229	71.31 284
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
API-MVS		70.97 156	71.51 156	69.37 188	75.20 199	55.94 170	80.99 62	76.84 179	62.48 111	71.24 234	77.51 261	61.51 155	80.96 132	52.04 215	85.76 191	71.22 285

OpenMVS_ROB	54.93 17	63.23 239	63.28 236	63.07 257	69.81 265	45.34 248	68.52 216	67.14 256	43.74 284	70.61 241	79.22 239	47.90 250	72.66 240	48.75 240	73.84 305	71.21 286

thres200		57.55 279	57.02 281	59.17 286	67.89 282	34.93 325	58.91 308	57.25 305	50.24 234	64.01 285	71.46 313	32.49 321	71.39 256	31.31 334	79.57 266	71.19 287

test20.03		55.74 285	57.51 279	50.42 311	59.89 334	32.09 339	50.63 331	49.01 335	50.11 236	65.07 279	83.23 194	45.61 256	48.11 333	30.22 338	83.82 219	71.07 288

our_test_3		56.46 281	56.51 285	56.30 296	67.70 283	39.66 291	55.36 321	52.34 327	40.57 305	63.85 286	69.91 324	40.04 291	58.22 319	43.49 273	75.29 296	71.03 289

BH-untuned		69.39 175	69.46 172	69.18 192	77.96 167	56.88 166	68.47 218	77.53 172	56.77 159	77.79 140	79.63 233	60.30 167	80.20 146	46.04 260	80.65 253	70.47 290

EPNet_dtu		58.93 272	58.52 271	60.16 282	67.91 281	47.70 227	69.97 192	58.02 298	49.73 240	47.28 349	73.02 299	38.14 300	62.34 308	36.57 314	85.99 188	70.43 291
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
USDC		62.80 244	63.10 239	61.89 266	65.19 302	43.30 264	67.42 229	74.20 203	35.80 326	72.25 220	84.48 175	45.67 255	71.95 252	37.95 304	84.97 201	70.42 292

DWT-MVSNet_test		53.04 297	51.12 310	58.77 289	61.23 324	38.67 297	62.16 288	57.74 299	38.24 313	51.76 338	59.07 352	21.36 360	67.40 283	44.80 265	63.76 340	70.25 293

GSMVS																70.05 294

sam_mvs1												31.41 328				70.05 294

SCA		58.57 275	58.04 275	60.17 281	70.17 263	41.07 279	65.19 259	53.38 322	43.34 290	61.00 304	73.48 294	45.20 258	69.38 270	40.34 289	70.31 320	70.05 294

tpmvs		55.84 283	55.45 293	57.01 295	60.33 330	33.20 335	65.89 249	59.29 296	47.52 262	56.04 323	73.60 293	31.05 334	68.06 278	40.64 287	64.64 337	69.77 297

旧先验1					84.55 81	60.36 143		63.69 276			87.05 122	54.65 212			83.34 223	69.66 298

CR-MVSNet		58.96 271	58.49 272	60.36 280	66.37 291	48.24 218	70.93 183	56.40 310	32.87 340	61.35 299	86.66 136	33.19 316	63.22 306	48.50 244	70.17 321	69.62 299

RPMNet		65.77 215	65.08 225	67.84 214	66.37 291	48.24 218	70.93 183	86.27 21	54.66 183	61.35 299	86.77 131	33.29 315	85.67 43	55.93 186	70.17 321	69.62 299

tpm cat1		54.02 294	52.63 302	58.19 292	64.85 308	39.86 290	66.26 246	57.28 304	32.16 342	56.90 319	70.39 319	32.75 320	65.30 296	34.29 325	58.79 348	69.41 301

PatchmatchNet		54.60 290	54.27 296	55.59 299	65.17 304	39.08 293	66.92 238	51.80 328	39.89 306	58.39 314	73.12 298	31.69 327	58.33 318	43.01 274	58.38 351	69.38 302
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
YYNet1		52.58 299	53.50 298	49.85 312	54.15 357	36.45 314	40.53 350	46.55 342	38.09 315	75.52 178	73.31 297	41.08 285	43.88 346	41.10 284	71.14 316	69.21 303

CVMVSNet		59.21 270	58.44 273	61.51 269	73.94 224	47.76 226	71.31 177	64.56 272	26.91 356	60.34 307	70.44 317	36.24 308	67.65 280	53.57 210	68.66 329	69.12 304

MDA-MVSNet_test_wron		52.57 300	53.49 299	49.81 313	54.24 356	36.47 313	40.48 351	46.58 341	38.13 314	75.47 179	73.32 296	41.05 286	43.85 347	40.98 285	71.20 315	69.10 305

MVP-Stereo		61.56 254	59.22 265	68.58 205	79.28 147	60.44 142	69.20 202	71.57 223	43.58 286	56.42 322	78.37 251	39.57 295	76.46 208	34.86 323	60.16 345	68.86 306
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
1121		69.23 177	68.26 190	72.12 158	88.36 36	71.40 53	68.59 213	62.06 286	43.80 282	74.75 186	86.18 151	52.92 219	76.85 203	54.47 200	83.27 224	68.12 307

新几何1				69.99 183	88.37 35	71.34 55		62.08 285	43.85 281	74.99 183	86.11 156	52.85 220	70.57 262	50.99 224	83.23 225	68.05 308

UnsupCasMVSNet_eth		52.26 302	53.29 300	49.16 317	55.08 353	33.67 333	50.03 332	58.79 297	37.67 317	63.43 293	74.75 279	41.82 279	45.83 336	38.59 299	59.42 347	67.98 309

Patchmatch-test		47.93 314	49.96 315	41.84 337	57.42 344	24.26 358	48.75 334	41.49 354	39.30 308	56.79 320	73.48 294	30.48 338	33.87 358	29.29 344	72.61 308	67.39 310

Patchmatch-RL test		59.95 265	59.12 266	62.44 263	72.46 244	54.61 179	59.63 303	47.51 340	41.05 301	74.58 191	74.30 286	31.06 333	65.31 295	51.61 218	79.85 261	67.39 310

testgi		54.00 295	56.86 283	45.45 328	58.20 341	25.81 356	49.05 333	49.50 334	45.43 272	67.84 263	81.17 214	51.81 226	43.20 349	29.30 343	79.41 267	67.34 312

test222					87.30 40	69.15 78	67.85 223	59.59 295	41.06 300	73.05 210	85.72 164	48.03 249			80.65 253	66.92 313

pmmvs5		52.49 301	52.58 303	52.21 309	54.99 354	32.38 337	55.45 320	53.84 318	32.15 343	55.49 327	74.81 277	38.08 301	57.37 321	34.02 326	74.40 301	66.88 314

Anonymous20231206		54.13 292	55.82 290	49.04 319	70.89 254	35.96 317	51.73 329	50.87 330	34.86 328	62.49 294	79.22 239	42.52 277	44.29 345	27.95 347	81.88 236	66.88 314

tpm		50.60 307	52.42 304	45.14 330	65.18 303	26.29 354	60.30 299	43.50 345	37.41 318	57.01 318	79.09 244	30.20 341	42.32 350	32.77 331	66.36 334	66.81 316

testdata				64.13 244	85.87 63	63.34 119		61.80 289	47.83 258	76.42 170	86.60 141	48.83 243	62.31 309	54.46 202	81.26 245	66.74 317

MIMVSNet		54.39 291	56.12 289	49.20 316	72.57 243	30.91 344	59.98 301	48.43 338	41.66 296	55.94 324	83.86 183	41.19 283	50.42 328	26.05 349	75.38 294	66.27 318

tpmrst		50.15 309	51.38 308	46.45 325	56.05 348	24.77 357	64.40 269	49.98 332	36.14 323	53.32 335	69.59 326	35.16 310	48.69 331	39.24 293	58.51 350	65.89 319

EPMVS		45.74 318	46.53 321	43.39 335	54.14 358	22.33 360	55.02 322	35.00 362	34.69 331	51.09 340	70.20 321	25.92 351	42.04 352	37.19 309	55.50 355	65.78 320

PVSNet	43.83 21	51.56 306	51.17 309	52.73 306	68.34 275	38.27 300	48.22 336	53.56 320	36.41 322	54.29 331	64.94 345	34.60 312	54.20 326	30.34 337	69.87 323	65.71 321

BH-w/o		64.81 223	64.29 228	66.36 230	76.08 192	54.71 177	65.61 255	75.23 196	50.10 237	71.05 238	71.86 310	54.33 214	79.02 159	38.20 302	76.14 287	65.36 322

XXY-MVS		55.19 288	57.40 280	48.56 320	64.45 309	34.84 327	51.54 330	53.59 319	38.99 311	63.79 288	79.43 235	56.59 203	45.57 337	36.92 312	71.29 314	65.25 323

ADS-MVSNet2		48.76 312	47.25 320	53.29 305	55.90 350	40.54 286	47.34 340	54.99 315	31.41 348	50.48 342	72.06 307	31.23 330	54.26 325	25.93 350	55.93 353	65.07 324

ADS-MVSNet		44.62 323	45.58 322	41.73 338	55.90 350	20.83 361	47.34 340	39.94 358	31.41 348	50.48 342	72.06 307	31.23 330	39.31 355	25.93 350	55.93 353	65.07 324

KD-MVS_2432*1600		52.05 304	51.58 306	53.44 303	52.11 359	31.20 342	44.88 346	64.83 270	41.53 297	64.37 281	70.03 322	15.61 369	64.20 299	36.25 315	74.61 299	64.93 326

miper_refine_blended		52.05 304	51.58 306	53.44 303	52.11 359	31.20 342	44.88 346	64.83 270	41.53 297	64.37 281	70.03 322	15.61 369	64.20 299	36.25 315	74.61 299	64.93 326

test0.0.03 1		47.72 315	48.31 317	45.93 326	55.53 352	29.39 347	46.40 343	41.21 356	43.41 288	55.81 326	67.65 337	29.22 344	43.77 348	25.73 352	69.87 323	64.62 328

JIA-IIPM		54.03 293	51.62 305	61.25 273	59.14 337	55.21 175	59.10 305	47.72 339	50.85 229	50.31 345	85.81 163	20.10 363	63.97 301	36.16 318	55.41 356	64.55 329

PatchT		53.35 296	56.47 286	43.99 334	64.19 310	17.46 363	59.15 304	43.10 346	52.11 214	54.74 329	86.95 123	29.97 342	49.98 330	43.62 271	74.40 301	64.53 330

gg-mvs-nofinetune		55.75 284	56.75 284	52.72 307	62.87 316	28.04 351	68.92 205	41.36 355	71.09 41	50.80 341	92.63 12	20.74 361	66.86 287	29.97 340	72.41 309	63.25 331

MVS		60.62 261	59.97 261	62.58 262	68.13 278	47.28 232	68.59 213	73.96 204	32.19 341	59.94 310	68.86 333	50.48 232	77.64 193	41.85 280	75.74 288	62.83 332

N_pmnet		52.06 303	51.11 311	54.92 300	59.64 336	71.03 57	37.42 355	61.62 290	33.68 336	57.12 317	72.10 302	37.94 302	31.03 359	29.13 346	71.35 313	62.70 333

Gipuma		69.55 172	72.83 137	59.70 283	63.63 314	53.97 182	80.08 75	75.93 186	64.24 94	73.49 203	88.93 98	57.89 193	62.46 307	59.75 160	91.55 96	62.67 334
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
WTY-MVS		49.39 311	50.31 314	46.62 324	61.22 325	32.00 340	46.61 342	49.77 333	33.87 335	54.12 332	69.55 327	41.96 278	45.40 339	31.28 335	64.42 338	62.47 335

test-LLR		50.43 308	50.69 313	49.64 314	60.76 327	41.87 274	53.18 326	45.48 343	43.41 288	49.41 346	60.47 350	29.22 344	44.73 343	42.09 278	72.14 310	62.33 336

test-mter		48.56 313	48.20 318	49.64 314	60.76 327	41.87 274	53.18 326	45.48 343	31.91 346	49.41 346	60.47 350	18.34 364	44.73 343	42.09 278	72.14 310	62.33 336

UnsupCasMVSNet_bld		50.01 310	51.03 312	46.95 321	58.61 339	32.64 336	48.31 335	53.27 323	34.27 333	60.47 306	71.53 312	41.40 280	47.07 334	30.68 336	60.78 344	61.13 338

sss		47.59 316	48.32 316	45.40 329	56.73 347	33.96 331	45.17 345	48.51 337	32.11 345	52.37 337	65.79 343	40.39 290	41.91 353	31.85 332	61.97 342	60.35 339

PM-MVS		64.49 226	63.61 233	67.14 223	76.68 184	75.15 31	68.49 217	42.85 347	51.17 227	77.85 138	80.51 220	45.76 253	66.31 293	52.83 214	76.35 285	59.96 340

GG-mvs-BLEND				52.24 308	60.64 329	29.21 349	69.73 196	42.41 348		45.47 351	52.33 356	20.43 362	68.16 277	25.52 353	65.42 336	59.36 341

TESTMET0.1,1		45.17 320	44.93 325	45.89 327	56.02 349	38.31 299	53.18 326	41.94 353	27.85 353	44.86 354	56.47 353	17.93 365	41.50 354	38.08 303	68.06 330	57.85 342

MS-PatchMatch		55.59 286	54.89 294	57.68 293	69.18 270	49.05 211	61.00 296	62.93 280	35.98 324	58.36 315	68.93 331	36.71 307	66.59 291	37.62 307	63.30 341	57.39 343

dp		44.09 325	44.88 326	41.72 339	58.53 340	23.18 359	54.70 323	42.38 350	34.80 329	44.25 356	65.61 344	24.48 356	44.80 342	29.77 341	49.42 358	57.18 344

MVE	27.91 23	36.69 331	35.64 334	39.84 341	43.37 364	35.85 319	19.49 359	24.61 366	24.68 358	39.05 360	62.63 348	38.67 299	27.10 362	21.04 358	47.25 359	56.56 345
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
pmmvs3		46.71 317	45.09 324	51.55 310	56.76 346	48.25 217	55.78 319	39.53 359	24.13 359	50.35 344	63.40 346	15.90 368	51.08 327	29.29 344	70.69 319	55.33 346

PatchMatch-RL		58.68 274	57.72 277	61.57 268	76.21 189	73.59 42	61.83 289	49.00 336	47.30 263	61.08 301	68.97 330	50.16 234	59.01 317	36.06 319	68.84 328	52.10 347

wuyk23d		61.97 250	66.25 212	49.12 318	58.19 342	60.77 140	66.32 245	52.97 324	55.93 169	90.62 4	86.91 124	73.07 61	35.98 357	20.63 359	91.63 93	50.62 348

PMMVS2		37.74 329	40.87 330	28.36 344	42.41 365	5.35 367	24.61 358	27.75 364	32.15 343	47.85 348	70.27 320	35.85 309	29.51 360	19.08 360	67.85 331	50.22 349

DSMNet-mixed		43.18 326	44.66 327	38.75 342	54.75 355	28.88 350	57.06 314	27.42 365	13.47 361	47.27 350	77.67 259	38.83 297	39.29 356	25.32 354	60.12 346	48.08 350

new_pmnet		37.55 330	39.80 333	30.79 343	56.83 345	16.46 364	39.35 352	30.65 363	25.59 357	45.26 352	61.60 349	24.54 355	28.02 361	21.60 357	52.80 357	47.90 351

CHOSEN 280x420		41.62 327	39.89 332	46.80 323	61.81 320	51.59 193	33.56 357	35.74 361	27.48 354	37.64 362	53.53 354	23.24 359	42.09 351	27.39 348	58.64 349	46.72 352

EMVS		44.61 324	44.45 328	45.10 331	48.91 363	43.00 266	37.92 354	41.10 357	46.75 265	38.00 361	48.43 359	26.42 348	46.27 335	37.11 311	75.38 294	46.03 353

E-PMN		45.17 320	45.36 323	44.60 332	50.07 361	42.75 268	38.66 353	42.29 351	46.39 267	39.55 359	51.15 357	26.00 350	45.37 340	37.68 305	76.41 284	45.69 354

PMMVS		44.69 322	43.95 329	46.92 322	50.05 362	53.47 187	48.08 338	42.40 349	22.36 360	44.01 357	53.05 355	42.60 276	45.49 338	31.69 333	61.36 343	41.79 355

PVSNet_0	36.71 22	41.12 328	40.78 331	42.14 336	59.97 332	40.13 288	40.97 349	42.24 352	30.81 350	44.86 354	49.41 358	40.70 287	45.12 341	23.15 356	34.96 360	41.16 356

FPMVS		59.43 269	60.07 260	57.51 294	77.62 175	71.52 52	62.33 287	50.92 329	57.40 153	69.40 251	80.00 228	39.14 296	61.92 310	37.47 308	66.36 334	39.09 357

MVS-HIRNet		45.53 319	47.29 319	40.24 340	62.29 318	26.82 353	56.02 318	37.41 360	29.74 351	43.69 358	81.27 212	33.96 313	55.48 322	24.46 355	56.79 352	38.43 358

test_method		19.26 332	19.12 336	19.71 345	9.09 367	1.91 369	7.79 361	53.44 321	1.42 363	10.27 365	35.80 360	17.42 366	25.11 363	12.44 361	24.38 362	32.10 359

DeepMVS_CX				11.83 346	15.51 366	13.86 365		11.25 369	5.76 362	20.85 364	26.46 361	17.06 367	9.22 364	9.69 363	13.82 363	12.42 360

tmp_tt		11.98 334	14.73 337	3.72 347	2.28 368	4.62 368	19.44 360	14.50 368	0.47 364	21.55 363	9.58 363	25.78 352	4.57 365	11.61 362	27.37 361	1.96 361

testmvs		4.06 338	5.28 341	0.41 348	0.64 370	0.16 371	42.54 348	0.31 371	0.26 366	0.50 367	1.40 367	0.77 371	0.17 366	0.56 364	0.55 365	0.90 362

test123		4.43 337	5.78 340	0.39 349	0.97 369	0.28 370	46.33 344	0.45 370	0.31 365	0.62 366	1.50 366	0.61 372	0.11 367	0.56 364	0.63 364	0.77 363

uanet_test		0.00 339	0.00 342	0.00 350	0.00 371	0.00 372	0.00 362	0.00 372	0.00 367	0.00 368	0.00 368	0.00 373	0.00 368	0.00 366	0.00 366	0.00 364

cdsmvs_eth3d_5k		17.71 333	23.62 335	0.00 350	0.00 371	0.00 372	0.00 362	70.17 244	0.00 367	0.00 368	74.25 288	68.16 99	0.00 368	0.00 366	0.00 366	0.00 364

pcd_1.5k_mvsjas		5.20 336	6.93 339	0.00 350	0.00 371	0.00 372	0.00 362	0.00 372	0.00 367	0.00 368	0.00 368	62.39 145	0.00 368	0.00 366	0.00 366	0.00 364

sosnet-low-res		0.00 339	0.00 342	0.00 350	0.00 371	0.00 372	0.00 362	0.00 372	0.00 367	0.00 368	0.00 368	0.00 373	0.00 368	0.00 366	0.00 366	0.00 364

sosnet		0.00 339	0.00 342	0.00 350	0.00 371	0.00 372	0.00 362	0.00 372	0.00 367	0.00 368	0.00 368	0.00 373	0.00 368	0.00 366	0.00 366	0.00 364

uncertanet		0.00 339	0.00 342	0.00 350	0.00 371	0.00 372	0.00 362	0.00 372	0.00 367	0.00 368	0.00 368	0.00 373	0.00 368	0.00 366	0.00 366	0.00 364

Regformer		0.00 339	0.00 342	0.00 350	0.00 371	0.00 372	0.00 362	0.00 372	0.00 367	0.00 368	0.00 368	0.00 373	0.00 368	0.00 366	0.00 366	0.00 364

ab-mvs-re		5.62 335	7.50 338	0.00 350	0.00 371	0.00 372	0.00 362	0.00 372	0.00 367	0.00 368	67.46 338	0.00 373	0.00 368	0.00 366	0.00 366	0.00 364

uanet		0.00 339	0.00 342	0.00 350	0.00 371	0.00 372	0.00 362	0.00 372	0.00 367	0.00 368	0.00 368	0.00 373	0.00 368	0.00 366	0.00 366	0.00 364

ZD-MVS					83.91 91	69.36 74		81.09 115	58.91 138	82.73 83	89.11 92	75.77 36	86.63 13	72.73 59	92.93 73

test_241102_ONE					86.12 57	61.06 133		84.72 51	72.64 28	87.38 24	89.47 81	77.48 24	85.74 40

9.14			80.22 58		80.68 132		80.35 70	87.69 10	59.90 127	83.00 77	88.20 108	74.57 50	81.75 113	73.75 53	93.78 59

save fliter					87.00 42	67.23 89	79.24 82	77.94 167	56.65 162

test0726					86.16 55	60.78 138	83.81 39	85.10 42	72.48 31	85.27 51	89.96 74	78.57 18

test_part2					85.90 61	66.44 94				84.61 60

sam_mvs												31.21 332

MTGPA								80.63 121

test_post1							66.63 242				2.08 364	30.66 337	59.33 316	40.34 289

test_post											1.99 365	30.91 335	54.76 324

patchmatchnet-post											68.99 329	31.32 329	69.38 270

MTMP							84.83 31	19.26 367

gm-plane-assit					62.51 317	33.91 332			37.25 319		62.71 347		72.74 238	38.70 296

TEST9					85.47 66	69.32 75	76.42 115	78.69 153	53.73 200	76.97 150	86.74 132	66.84 110	81.10 122

test_8					85.09 72	67.89 84	76.26 120	78.66 155	54.00 195	76.89 155	86.72 134	66.60 113	80.89 133

agg_prior					84.44 84	66.02 98		78.62 156		76.95 152			80.34 140

test_prior4						70.14 67	77.57 102

test_prior2							75.57 127		58.92 136	76.53 166	86.78 129	67.83 103		69.81 79	92.76 78

旧先验2							71.17 180		45.11 275	78.54 129			61.28 312	59.19 163

新几何2							71.33 176

原ACMM2							74.78 139

testdata2													67.30 284	48.34 245

segment_acmp												68.30 98

testdata1							68.34 219		57.24 154

plane_prior7					85.18 69	66.21 96

plane_prior6					84.18 88	65.31 103						60.83 163

plane_prior4											89.11 92

plane_prior3						65.67 100			63.82 98	78.23 132

plane_prior2							82.74 51		65.45 77

plane_prior1					84.46 83

plane_prior						65.18 104	80.06 76		61.88 115						89.91 134

n2								0.00 372

nn								0.00 372

door-mid								55.02 314

test11								82.71 89

door								52.91 325

HQP5-MVS						58.80 157

HQP-NCC					82.37 111		77.32 106		59.08 132	71.58 227

ACMP_Plane					82.37 111		77.32 106		59.08 132	71.58 227

BP-MVS														67.38 100

HQP3-MVS								84.12 70							89.16 144

HQP2-MVS												58.09 187

NP-MVS					83.34 99	63.07 122					85.97 159

MDTV_nov1_ep13			54.05 297		65.54 300	29.30 348	59.00 306	55.22 312	35.96 325	52.44 336	75.98 269	30.77 336	59.62 315	38.21 301	73.33 306

ACMMP++_ref															89.47 142

ACMMP++															91.96 88

Test By Simon												62.56 141

High-resolution multi-view benchmark