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	63.30 126	84.80 34	87.77 10	86.18 1	96.26 1	96.06 1	90.32 1	84.49 69	68.08 95	97.05 1	96.93 1

PEN-MVS		80.46 51	82.91 37	73.11 141	89.83 9	39.02 304	77.06 117	82.61 95	80.04 5	90.60 6	92.85 9	74.93 47	85.21 57	63.15 141	95.15 18	95.09 2

PS-CasMVS		80.41 52	82.86 39	73.07 142	89.93 7	39.21 301	77.15 115	81.28 114	79.74 6	90.87 4	92.73 11	75.03 45	84.93 62	63.83 132	95.19 16	95.07 3

CP-MVSNet		79.48 60	81.65 50	72.98 145	89.66 13	39.06 303	76.76 119	80.46 133	78.91 8	90.32 7	91.70 24	68.49 98	84.89 63	63.40 138	95.12 19	95.01 4

WR-MVS_H		80.22 56	82.17 45	74.39 120	89.46 15	42.69 278	78.24 102	82.24 98	78.21 10	89.57 9	92.10 18	68.05 103	85.59 47	66.04 113	95.62 9	94.88 5

DTE-MVSNet		80.35 53	82.89 38	72.74 153	89.84 8	37.34 319	77.16 114	81.81 104	80.45 4	90.92 3	92.95 7	74.57 51	86.12 30	63.65 133	94.68 33	94.76 6

TDRefinement		86.32 2	86.33 2	86.29 1	88.64 33	81.19 5	88.84 4	90.72 1	78.27 9	87.95 15	92.53 13	79.37 13	84.79 66	74.51 51	96.15 2	92.88 7

DU-MVS		74.91 106	75.57 100	72.93 147	83.50 98	45.79 252	69.47 203	80.14 140	65.22 86	81.74 96	87.08 125	61.82 157	81.07 130	56.21 191	94.98 21	91.93 8

NR-MVSNet		73.62 120	74.05 115	72.33 162	83.50 98	43.71 267	65.65 261	77.32 182	64.32 96	75.59 180	87.08 125	62.45 150	81.34 122	54.90 202	95.63 8	91.93 8

v7n		79.37 62	80.41 58	76.28 98	78.67 168	55.81 181	79.22 89	82.51 97	70.72 47	87.54 22	92.44 14	68.00 105	81.34 122	72.84 61	91.72 91	91.69 10

TranMVSNet+NR-MVSNet		76.13 89	77.66 81	71.56 167	84.61 83	42.57 280	70.98 187	78.29 169	68.67 59	83.04 79	89.26 88	72.99 63	80.75 140	55.58 199	95.47 10	91.35 11

FC-MVSNet-test		73.32 126	74.78 106	68.93 205	79.21 156	36.57 321	71.82 175	79.54 149	57.63 156	82.57 87	90.38 64	59.38 182	78.99 166	57.91 178	94.56 35	91.23 12

v10		75.69 95	76.20 93	74.16 124	74.44 229	48.69 221	75.84 131	82.93 90	59.02 141	85.92 40	89.17 93	58.56 189	82.74 101	70.73 76	89.14 155	91.05 13

UniMVSNet_NR-MVSNet		74.90 107	75.65 98	72.64 156	83.04 108	45.79 252	69.26 206	78.81 157	66.66 72	81.74 96	86.88 131	63.26 143	81.07 130	56.21 191	94.98 21	91.05 13

UniMVSNet (Re)		75.00 104	75.48 101	73.56 133	83.14 103	47.92 232	70.41 195	81.04 122	63.67 102	79.54 121	86.37 153	62.83 145	81.82 116	57.10 182	95.25 15	90.94 15

anonymousdsp		78.60 68	77.80 80	81.00 37	78.01 175	74.34 38	80.09 79	76.12 191	50.51 241	89.19 10	90.88 40	71.45 74	77.78 197	73.38 58	90.60 125	90.90 16

v8		75.07 103	75.64 99	73.35 135	73.42 241	47.46 239	75.20 136	81.45 110	60.05 132	85.64 44	89.26 88	58.08 196	81.80 118	69.71 85	87.97 168	90.79 17

IS-MVSNet		75.10 102	75.42 102	74.15 125	79.23 155	48.05 230	79.43 84	78.04 172	70.09 52	79.17 126	88.02 120	53.04 225	83.60 84	58.05 177	93.76 64	90.79 17

FIs		72.56 145	73.80 119	68.84 208	78.74 167	37.74 315	71.02 186	79.83 143	56.12 171	80.88 109	89.45 85	58.18 191	78.28 186	56.63 184	93.36 70	90.51 19

test_djsdf		78.88 66	78.27 76	80.70 41	81.42 132	71.24 58	83.98 39	75.72 195	52.27 219	87.37 27	92.25 16	68.04 104	80.56 141	72.28 68	91.15 107	90.32 20

WR-MVS		71.20 156	72.48 143	67.36 225	84.98 76	35.70 329	64.43 276	68.66 258	65.05 89	81.49 99	86.43 152	57.57 202	76.48 213	50.36 237	93.32 71	89.90 21

test_part1		76.97 83	78.21 77	73.25 138	77.87 177	45.76 254	78.27 101	87.26 15	66.69 71	85.31 52	91.43 32	55.95 215	84.24 77	65.71 115	95.43 12	89.75 22

OMC-MVS		79.41 61	78.79 70	81.28 34	80.62 140	70.71 64	80.91 66	84.76 53	62.54 114	81.77 94	86.65 144	71.46 73	83.53 86	67.95 100	92.44 84	89.60 23

tttt0517		69.46 176	67.79 202	74.46 117	75.34 209	52.72 200	75.05 137	63.27 288	54.69 187	78.87 129	84.37 184	26.63 356	81.15 126	63.95 129	87.93 169	89.51 24

v2v482		72.55 147	72.58 142	72.43 159	72.92 253	46.72 245	71.41 179	79.13 152	55.27 178	81.17 103	85.25 176	55.41 216	81.13 127	67.25 109	85.46 202	89.43 25

Anonymous20231211		75.54 96	77.19 85	70.59 176	77.67 182	45.70 256	74.73 145	80.19 138	68.80 56	82.95 81	92.91 8	66.26 121	76.76 211	58.41 175	92.77 79	89.30 26

OurMVSNet-221017-0		78.57 69	78.53 75	78.67 65	80.48 141	64.16 117	80.24 77	82.06 100	61.89 118	88.77 12	93.32 4	57.15 204	82.60 104	70.08 80	92.80 78	89.25 27

EI-MVSNet-UG-set		72.63 144	71.68 154	75.47 109	74.67 223	58.64 168	72.02 167	71.50 232	63.53 104	78.58 132	71.39 324	65.98 123	78.53 175	67.30 108	80.18 269	89.23 28

V42		71.06 157	70.83 166	71.72 165	67.25 298	47.14 243	65.94 255	80.35 137	51.35 231	83.40 78	83.23 203	59.25 183	78.80 169	65.91 114	80.81 263	89.23 28

RPSCF		75.76 93	74.37 111	79.93 46	74.81 218	77.53 20	77.53 109	79.30 150	59.44 137	78.88 128	89.80 80	71.26 76	73.09 246	57.45 179	80.89 261	89.17 30

UniMVSNet_ETH3D		76.74 85	79.02 68	69.92 191	89.27 20	43.81 266	74.47 149	71.70 228	72.33 37	85.50 49	93.65 3	77.98 22	76.88 208	54.60 206	91.64 94	89.08 31

v1192		73.40 124	73.42 126	73.32 137	74.65 226	48.67 222	72.21 165	81.73 105	52.76 216	81.85 92	84.56 181	57.12 205	82.24 112	68.58 90	87.33 179	89.06 32

3Dnovator+	73.19 2	81.08 44	80.48 57	82.87 9	81.41 133	72.03 50	84.38 37	86.23 26	77.28 16	80.65 110	90.18 74	59.80 179	87.58 4	73.06 60	91.34 102	89.01 33

EI-MVSNet-Vis-set		72.78 141	71.87 150	75.54 108	74.77 219	59.02 163	72.24 164	71.56 231	63.92 99	78.59 130	71.59 321	66.22 122	78.60 173	67.58 102	80.32 267	89.00 34

v1144		73.29 127	73.39 127	73.01 143	74.12 235	48.11 229	72.01 168	81.08 121	53.83 207	81.77 94	84.68 179	58.07 197	81.91 115	68.10 94	86.86 189	88.99 35

nrg030		74.87 108	75.99 96	71.52 168	74.90 216	49.88 217	74.10 153	82.58 96	54.55 191	83.50 77	89.21 90	71.51 72	75.74 220	61.24 151	92.34 86	88.94 36

v1240		73.06 132	73.14 133	72.84 150	74.74 220	47.27 242	71.88 174	81.11 118	51.80 225	82.28 90	84.21 186	56.22 213	82.34 108	68.82 88	87.17 185	88.91 37

LTVRE_ROB	75.46 1	84.22 8	84.98 9	81.94 23	84.82 78	75.40 30	91.60 3	87.80 8	73.52 26	88.90 11	93.06 6	71.39 75	81.53 121	81.53 3	92.15 89	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		72.96 138	72.98 138	72.89 149	74.67 223	47.58 237	71.92 172	80.69 127	51.70 227	81.69 98	83.89 190	56.58 211	82.25 111	68.34 92	87.36 177	88.82 39

EPP-MVSNet		73.86 116	73.38 128	75.31 110	78.19 171	53.35 198	80.45 70	77.32 182	65.11 88	76.47 172	86.80 133	49.47 244	83.77 81	53.89 215	92.72 82	88.81 40

UA-Net		81.56 37	82.28 44	79.40 54	88.91 30	69.16 79	84.67 35	80.01 142	75.34 17	79.80 119	94.91 2	69.79 87	80.25 149	72.63 63	94.46 38	88.78 41

v144192		72.99 136	73.06 136	72.77 151	74.58 227	47.48 238	71.90 173	80.44 134	51.57 228	81.46 100	84.11 188	58.04 198	82.12 113	67.98 98	87.47 175	88.70 42

EI-MVSNet		69.61 174	69.01 183	71.41 170	73.94 236	49.90 214	71.31 182	71.32 236	58.22 146	75.40 184	70.44 327	58.16 192	75.85 216	62.51 143	79.81 273	88.48 43

IterMVS-LS		73.01 134	73.12 135	72.66 155	73.79 238	49.90 214	71.63 176	78.44 166	58.22 146	80.51 111	86.63 145	58.15 193	79.62 158	62.51 143	88.20 162	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 34	75.83 27	86.27 25	86.89 18	73.69 25	86.17 36	91.70 24	78.23 20	85.20 58	79.45 13	94.91 25	88.15 45

Regformer-4		74.64 110	73.67 122	77.55 80	74.74 220	64.49 115	72.91 158	75.42 199	67.45 63	80.24 116	72.07 315	68.98 93	80.19 153	70.29 78	80.91 259	87.98 46

COLMAP_ROB	72.78 3	83.75 13	84.11 17	82.68 14	82.97 110	74.39 37	87.18 11	88.18 7	78.98 7	86.11 39	91.47 30	79.70 12	85.76 42	66.91 110	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 78	77.35 84	78.13 75	84.88 77	66.37 99	78.55 96	79.59 147	53.48 210	86.29 35	92.43 15	62.39 151	80.25 149	67.90 101	90.61 124	87.77 48

eth_miper_zixun_eth		69.42 177	68.73 189	71.50 169	67.99 291	46.42 248	67.58 231	78.81 157	50.72 239	78.13 138	80.34 232	50.15 242	80.34 146	60.18 160	84.65 216	87.74 49

casdiffmvs		73.06 132	73.84 118	70.72 174	71.32 263	46.71 246	70.93 188	84.26 70	55.62 176	77.46 149	87.10 124	67.09 110	77.81 195	63.95 129	86.83 190	87.64 50

LS3D		80.99 46	80.85 55	81.41 30	78.37 169	71.37 56	87.45 8	85.87 32	77.48 13	81.98 91	89.95 78	69.14 91	85.26 54	66.15 111	91.24 104	87.61 51

ITE_SJBPF				80.35 44	76.94 191	73.60 43		80.48 132	66.87 67	83.64 76	86.18 158	70.25 83	79.90 156	61.12 154	88.95 158	87.56 52

CS-MVS-test		75.88 91	75.24 103	77.78 77	77.30 185	63.59 122	77.02 118	86.83 19	60.82 126	74.17 201	79.23 248	65.42 130	82.29 110	69.48 86	86.87 187	87.47 53

Regformer-3		72.86 140	72.28 146	74.62 116	74.74 220	60.18 152	72.91 158	71.76 227	64.74 92	78.42 134	72.07 315	67.00 111	76.28 215	67.97 99	80.91 259	87.39 54

CS-MVS		76.42 87	76.03 94	77.57 79	77.07 186	63.35 124	80.80 67	82.77 92	60.39 130	74.11 203	83.29 201	69.71 88	81.97 114	69.28 87	86.87 187	87.38 55

thisisatest0530		67.05 210	65.16 223	72.73 154	73.10 249	50.55 209	71.26 184	63.91 284	50.22 243	74.46 198	80.75 226	26.81 355	80.25 149	59.43 168	86.50 194	87.37 56

pmmvs6		71.82 152	73.66 123	66.31 237	75.94 205	42.01 282	66.99 243	72.53 222	63.45 106	76.43 173	92.78 10	72.95 64	69.69 278	51.41 228	90.46 126	87.22 57

ACMH+	66.64 10	81.20 40	82.48 43	77.35 86	81.16 136	62.39 130	80.51 69	87.80 8	73.02 28	87.57 21	91.08 37	80.28 10	82.44 105	64.82 122	96.10 4	87.21 58

c3_l		69.82 172	69.89 173	69.61 193	66.24 306	43.48 270	68.12 226	79.61 146	51.43 230	77.72 145	80.18 236	54.61 220	78.15 191	63.62 134	87.50 174	87.20 59

Anonymous20240529		72.56 145	73.79 120	68.86 207	76.89 194	45.21 258	68.80 215	77.25 184	67.16 65	76.89 159	90.44 55	65.95 124	74.19 240	50.75 233	90.00 135	87.18 60

baseline		73.10 129	73.96 117	70.51 178	71.46 262	46.39 250	72.08 166	84.40 65	55.95 173	76.62 166	86.46 151	67.20 109	78.03 192	64.22 126	87.27 182	87.11 61

RRT_MVS		73.80 118	71.19 163	81.60 24	71.04 264	70.33 68	78.78 93	74.91 205	56.96 161	77.83 143	85.56 172	32.82 327	87.39 5	71.16 73	91.68 93	87.07 62

Effi-MVS+-dtu		75.43 97	72.28 146	84.91 2	77.05 187	83.58 1	78.47 97	77.70 176	57.68 152	74.89 188	78.13 265	64.80 136	84.26 76	56.46 188	85.32 207	86.88 63

v148		69.38 179	69.39 176	69.36 195	69.14 284	44.56 261	68.83 212	72.70 220	54.79 185	78.59 130	84.12 187	54.69 218	76.74 212	59.40 169	82.20 243	86.79 64

HPM-MVS		84.12 10	84.63 11	82.60 15	88.21 39	74.40 36	85.24 29	87.21 16	70.69 48	85.14 54	90.42 58	78.99 16	86.62 14	80.83 6	94.93 24	86.79 64
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
mvs_tets		78.93 65	78.67 73	79.72 49	84.81 79	73.93 40	80.65 68	76.50 189	51.98 224	87.40 24	91.86 21	76.09 35	78.53 175	68.58 90	90.20 129	86.69 66

DROMVSNet		77.08 82	77.39 83	76.14 101	76.86 195	56.87 175	80.32 75	87.52 13	63.45 106	74.66 195	84.52 182	69.87 86	84.94 61	69.76 84	89.59 145	86.60 67

jajsoiax		78.51 70	78.16 78	79.59 52	84.65 82	73.83 42	80.42 71	76.12 191	51.33 232	87.19 29	91.51 29	73.79 58	78.44 179	68.27 93	90.13 134	86.49 68

cl22		67.14 207	66.51 214	69.03 201	63.20 327	43.46 271	66.88 247	76.25 190	49.22 253	74.48 197	77.88 267	45.49 264	77.40 201	60.64 158	84.59 218	86.24 69

MP-MVS-pluss		82.54 29	83.46 28	79.76 47	88.88 32	68.44 83	81.57 63	86.33 22	63.17 110	85.38 51	91.26 34	76.33 32	84.67 68	83.30 1	94.96 23	86.17 70
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
LPG-MVS_test		83.47 18	84.33 14	80.90 38	87.00 43	70.41 66	82.04 60	86.35 20	69.77 53	87.75 16	91.13 35	81.83 3	86.20 25	77.13 37	95.96 5	86.08 71

LGP-MVS_train				80.90 38	87.00 43	70.41 66		86.35 20	69.77 53	87.75 16	91.13 35	81.83 3	86.20 25	77.13 37	95.96 5	86.08 71

SixPastTwentyTwo		75.77 92	76.34 91	74.06 126	81.69 130	54.84 186	76.47 120	75.49 197	64.10 98	87.73 18	92.24 17	50.45 240	81.30 124	67.41 104	91.46 99	86.04 73

Regformer-2		75.32 98	74.47 109	77.88 76	74.22 231	66.65 97	72.77 160	77.54 178	68.47 61	80.44 112	72.08 313	70.60 80	80.97 134	70.08 80	84.02 226	86.01 74

APD-MVS_3200maxsize		83.57 15	84.33 14	81.31 33	82.83 113	73.53 45	85.50 28	87.45 14	74.11 21	86.45 34	90.52 54	80.02 11	84.48 70	77.73 30	94.34 49	85.93 75

DeepC-MVS	72.44 4	81.00 45	80.83 56	81.50 26	86.70 50	70.03 70	82.06 59	87.00 17	59.89 134	80.91 108	90.53 52	72.19 65	88.56 1	73.67 57	94.52 36	85.92 76
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
DIV-MVS_self_test		68.27 195	68.26 193	68.29 214	64.98 318	43.67 268	65.89 256	74.67 206	50.04 246	76.86 161	82.43 208	48.74 251	75.38 222	60.94 155	89.81 140	85.81 77

AllTest		77.66 77	77.43 82	78.35 71	79.19 157	70.81 61	78.60 95	88.64 3	65.37 83	80.09 117	88.17 115	70.33 81	78.43 180	55.60 196	90.90 116	85.81 77

TestCases				78.35 71	79.19 157	70.81 61		88.64 3	65.37 83	80.09 117	88.17 115	70.33 81	78.43 180	55.60 196	90.90 116	85.81 77

ACMP	69.50 8	82.64 28	83.38 29	80.40 43	86.50 51	69.44 74	82.30 57	86.08 28	66.80 69	86.70 32	89.99 76	81.64 6	85.95 32	74.35 52	96.11 3	85.81 77
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
cl____		68.26 196	68.26 193	68.29 214	64.98 318	43.67 268	65.89 256	74.67 206	50.04 246	76.86 161	82.42 209	48.74 251	75.38 222	60.92 156	89.81 140	85.80 81

Regformer-1		74.28 112	73.63 124	76.21 100	74.22 231	64.12 118	72.77 160	75.46 198	66.86 68	79.27 124	72.08 313	69.29 90	78.74 171	68.73 89	84.02 226	85.77 82

miper_ehance_all_eth		68.36 192	68.16 198	68.98 202	65.14 317	43.34 272	67.07 242	78.92 156	49.11 255	76.21 176	77.72 268	53.48 224	77.92 194	61.16 153	84.59 218	85.68 83

SteuartSystems-ACMMP		83.07 23	83.64 24	81.35 31	85.14 74	71.00 60	85.53 27	84.78 52	70.91 46	85.64 44	90.41 59	75.55 40	87.69 3	79.75 8	95.08 20	85.36 84
Skip Steuart: Steuart Systems R&D Blog.
diffmvs		67.42 206	67.50 205	67.20 227	62.26 331	45.21 258	64.87 270	77.04 185	48.21 261	71.74 231	79.70 241	58.40 190	71.17 268	64.99 120	80.27 268	85.22 85

Baseline_NR-MVSNet		70.62 162	73.19 132	62.92 268	76.97 190	34.44 337	68.84 211	70.88 246	60.25 131	79.50 122	90.53 52	61.82 157	69.11 282	54.67 205	95.27 14	85.22 85

RRT_test8_iter05		65.80 217	65.13 226	67.80 222	67.02 301	40.85 291	67.13 241	75.33 200	49.73 248	72.69 220	81.32 220	24.45 366	77.37 202	61.69 149	86.82 191	85.18 87

TAPA-MVS	65.27 12	75.16 101	74.29 113	77.77 78	74.86 217	68.08 84	77.89 106	84.04 77	55.15 180	76.19 177	83.39 195	66.91 112	80.11 154	60.04 164	90.14 133	85.13 88
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
ETH3D-3000-0.1		79.14 63	79.80 63	77.16 89	80.67 139	64.57 113	80.26 76	87.60 12	60.74 127	82.47 88	88.03 119	71.73 70	81.81 117	73.12 59	93.61 65	85.09 89

CLD-MVS		72.88 139	72.36 145	74.43 119	77.03 189	54.30 190	68.77 216	83.43 84	52.12 221	76.79 164	74.44 294	69.54 89	83.91 79	55.88 194	93.25 72	85.09 89
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		66.38 214	67.51 204	62.97 266	61.76 333	34.39 338	58.11 320	75.30 201	50.84 238	77.12 153	85.42 173	56.84 209	69.44 279	51.07 231	91.16 106	85.08 91

CDPH-MVS		77.33 80	77.06 88	78.14 74	84.21 90	63.98 119	76.07 128	83.45 83	54.20 198	77.68 147	87.18 123	69.98 84	85.37 51	68.01 97	92.72 82	85.08 91

K. test v3		73.67 119	73.61 125	73.87 128	79.78 146	55.62 184	74.69 147	62.04 297	66.16 76	84.76 61	93.23 5	49.47 244	80.97 134	65.66 116	86.67 193	85.02 93

SR-MVS-dyc-post		84.75 5	85.26 7	83.21 3	86.19 55	79.18 9	87.23 9	86.27 23	77.51 11	87.65 19	90.73 46	79.20 14	85.58 48	78.11 25	94.46 38	84.89 94

RE-MVS-def			85.50 4		86.19 55	79.18 9	87.23 9	86.27 23	77.51 11	87.65 19	90.73 46	81.38 7		78.11 25	94.46 38	84.89 94

test2506		61.23 263	60.85 263	62.38 272	78.80 165	27.88 363	67.33 238	37.42 371	54.23 196	67.55 275	88.68 104	17.87 375	74.39 237	46.33 269	89.41 149	84.86 96

ECVR-MVS		64.82 226	65.22 221	63.60 256	78.80 165	31.14 353	66.97 244	56.47 319	54.23 196	69.94 255	88.68 104	37.23 313	74.81 232	45.28 275	89.41 149	84.86 96

HQP_MVS		78.77 67	78.78 71	78.72 64	85.18 72	65.18 108	82.74 54	85.49 35	65.45 80	78.23 136	89.11 95	60.83 169	86.15 28	71.09 74	90.94 112	84.82 98

plane_prior5								85.49 35					86.15 28	71.09 74	90.94 112	84.82 98

xxxxxxxxxxxxxcwj		80.31 54	80.94 54	78.42 70	87.00 43	67.23 91	79.24 87	88.61 5	56.65 167	84.29 67	89.18 91	73.73 59	83.22 92	76.01 39	93.77 62	84.81 100

SF-MVS		80.72 48	81.80 46	77.48 82	82.03 124	64.40 116	83.41 50	88.46 6	65.28 85	84.29 67	89.18 91	73.73 59	83.22 92	76.01 39	93.77 62	84.81 100

alignmvs		70.54 163	71.00 164	69.15 199	73.50 239	48.04 231	69.85 200	79.62 144	53.94 206	76.54 169	82.00 212	59.00 185	74.68 233	57.32 180	87.21 183	84.72 102

IU-MVS					86.12 59	60.90 144		80.38 135	45.49 280	81.31 101				75.64 44	94.39 43	84.65 103

XVS		83.51 17	83.73 22	82.85 10	89.43 16	77.61 18	86.80 18	84.66 58	72.71 29	82.87 82	90.39 62	73.86 56	86.31 20	78.84 20	94.03 57	84.64 104

X-MVStestdata		76.81 84	74.79 105	82.85 10	89.43 16	77.61 18	86.80 18	84.66 58	72.71 29	82.87 82	9.95 372	73.86 56	86.31 20	78.84 20	94.03 57	84.64 104

ACMMP		84.22 8	84.84 10	82.35 20	89.23 23	76.66 26	87.65 6	85.89 31	71.03 45	85.85 41	90.58 50	78.77 17	85.78 41	79.37 16	95.17 17	84.62 106
Qingshan Xu, Weihang Kong, Wenbing Tao, Marc Pollefeys: Multi-Scale Geometric Consistency Guided and Planar Prior Assisted Multi-View Stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence
SMA-MVS		82.12 32	82.68 42	80.43 42	88.90 31	69.52 72	85.12 30	84.76 53	63.53 104	84.23 69	91.47 30	72.02 67	87.16 7	79.74 10	94.36 47	84.61 107
Yufeng Yin; Xiaoyan Liu; Zichao Zhang: SMA-MVS: Segmentation-Guided Multi-Scale Anchor Deformation Patch Multi-View Stereo. IEEE Transactions on Circuits and Systems for Video Technology
VDD-MVS		70.81 160	71.44 160	68.91 206	79.07 162	46.51 247	67.82 229	70.83 247	61.23 121	74.07 205	88.69 103	59.86 177	75.62 221	51.11 230	90.28 128	84.61 107

ZNCC-MVS		83.12 22	83.68 23	81.45 29	89.14 26	73.28 47	86.32 24	85.97 30	67.39 64	84.02 71	90.39 62	74.73 48	86.46 16	80.73 7	94.43 42	84.60 109

test1111		64.62 229	65.19 222	62.93 267	79.01 163	29.91 357	65.45 264	54.41 327	54.09 201	71.47 241	88.48 107	37.02 314	74.29 239	46.83 266	89.94 138	84.58 110

miper_enhance_ethall		65.86 216	65.05 231	68.28 216	61.62 335	42.62 279	64.74 271	77.97 173	42.52 301	73.42 212	72.79 310	49.66 243	77.68 198	58.12 176	84.59 218	84.54 111

GBi-Net		68.30 193	68.79 185	66.81 231	73.14 246	40.68 292	71.96 169	73.03 214	54.81 182	74.72 192	90.36 67	48.63 253	75.20 226	47.12 261	85.37 203	84.54 111

test1		68.30 193	68.79 185	66.81 231	73.14 246	40.68 292	71.96 169	73.03 214	54.81 182	74.72 192	90.36 67	48.63 253	75.20 226	47.12 261	85.37 203	84.54 111

FMVSNet1		71.06 157	72.48 143	66.81 231	77.65 183	40.68 292	71.96 169	73.03 214	61.14 122	79.45 123	90.36 67	60.44 171	75.20 226	50.20 238	88.05 165	84.54 111

TSAR-MVS + MP.		79.05 64	78.81 69	79.74 48	88.94 29	67.52 88	86.61 20	81.38 112	51.71 226	77.15 151	91.42 33	65.49 128	87.20 6	79.44 14	87.17 185	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
mvs-test1		73.81 117	70.69 168	83.18 5	77.05 187	81.39 3	75.39 134	77.70 176	57.68 152	71.19 244	74.72 290	64.80 136	83.66 83	56.46 188	81.19 257	84.50 116

PCF-MVS	63.80 13	72.70 143	71.69 153	75.72 105	78.10 172	60.01 154	73.04 157	81.50 108	45.34 282	79.66 120	84.35 185	65.15 133	82.65 103	48.70 249	89.38 151	84.50 116
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
canonicalmvs		72.29 149	73.38 128	69.04 200	74.23 230	47.37 240	73.93 154	83.18 85	54.36 193	76.61 167	81.64 219	72.03 66	75.34 224	57.12 181	87.28 181	84.40 118

TransMVSNet (Re)		69.62 173	71.63 155	63.57 257	76.51 197	35.93 327	65.75 260	71.29 238	61.05 123	75.02 186	89.90 79	65.88 126	70.41 276	49.79 240	89.48 147	84.38 119

OPM-MVS		80.99 46	81.63 51	79.07 58	86.86 48	69.39 75	79.41 86	84.00 78	65.64 78	85.54 48	89.28 87	76.32 33	83.47 87	74.03 54	93.57 68	84.35 120
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
test_0728_THIRD									74.03 23	85.83 42	90.41 59	75.58 39	85.69 44	77.43 33	94.74 31	84.31 121

MSP-MVS		80.49 50	79.67 65	82.96 7	89.70 12	77.46 23	87.16 12	85.10 46	64.94 90	81.05 104	88.38 110	57.10 206	87.10 8	79.75 8	83.87 228	84.31 121
Zhenlong Yuan, Cong Liu, Fei Shen, Zhaoxin Li, Jingguo luo, Tianlu Mao and Zhaoqi Wang: MSP-MVS: Multi-granularity Segmentation Prior Guided Multi-View Stereo. AAAI2025
GST-MVS		82.79 27	83.27 32	81.34 32	88.99 28	73.29 46	85.94 26	85.13 44	68.58 60	84.14 70	90.21 73	73.37 61	86.41 17	79.09 19	93.98 60	84.30 123

ACMMPR		83.62 14	83.93 19	82.69 13	89.78 11	77.51 22	87.01 15	84.19 73	70.23 49	84.49 64	90.67 49	75.15 43	86.37 19	79.58 11	94.26 51	84.18 124

VDDNet		71.60 154	73.13 134	67.02 230	86.29 53	41.11 287	69.97 197	66.50 268	68.72 58	74.74 191	91.70 24	59.90 176	75.81 218	48.58 251	91.72 91	84.15 125

abl_6		84.92 3	85.70 3	82.57 17	86.72 49	79.27 8	87.56 7	86.08 28	77.48 13	88.12 14	91.53 28	81.18 8	84.31 75	78.12 24	94.47 37	84.15 125

MVS_Test		69.84 171	70.71 167	67.24 226	67.49 297	43.25 274	69.87 199	81.22 117	52.69 217	71.57 237	86.68 141	62.09 155	74.51 235	66.05 112	78.74 282	83.96 127

region2R		83.54 16	83.86 21	82.58 16	89.82 10	77.53 20	87.06 14	84.23 72	70.19 51	83.86 73	90.72 48	75.20 42	86.27 22	79.41 15	94.25 52	83.95 128

ETH3 D test6400		75.73 94	76.00 95	74.92 114	81.75 128	56.93 173	78.31 99	84.60 62	52.83 215	77.15 151	85.14 177	68.59 96	84.03 78	65.44 118	90.20 129	83.82 129

PGM-MVS		83.07 23	83.25 33	82.54 18	89.57 14	77.21 24	82.04 60	85.40 39	67.96 62	84.91 60	90.88 40	75.59 38	86.57 15	78.16 23	94.71 32	83.82 129

pm-mvs1		68.40 191	69.85 174	64.04 253	73.10 249	39.94 298	64.61 274	70.50 248	55.52 177	73.97 206	89.33 86	63.91 142	68.38 286	49.68 242	88.02 166	83.81 131

MSC_two_6792asdad				79.02 59	83.14 103	67.03 93		80.75 125					86.24 23	77.27 35	94.85 26	83.78 132

No_MVS				79.02 59	83.14 103	67.03 93		80.75 125					86.24 23	77.27 35	94.85 26	83.78 132

HQP4-MVS										71.59 233			85.31 52			83.74 134

HQP-MVS		75.24 100	75.01 104	75.94 102	82.37 117	58.80 165	77.32 111	84.12 74	59.08 138	71.58 234	85.96 167	58.09 194	85.30 53	67.38 106	89.16 152	83.73 135

PHI-MVS		74.92 105	74.36 112	76.61 92	76.40 198	62.32 131	80.38 72	83.15 86	54.16 200	73.23 215	80.75 226	62.19 154	83.86 80	68.02 96	90.92 115	83.65 136

test1172		84.85 4	85.39 5	83.21 3	88.34 38	80.50 6	85.12 30	85.22 43	81.06 3	87.20 28	90.28 70	79.20 14	85.58 48	78.04 27	94.08 56	83.55 137

DeepPCF-MVS	71.07 5	78.48 72	77.14 87	82.52 19	84.39 89	77.04 25	76.35 123	84.05 76	56.66 166	80.27 115	85.31 175	68.56 97	87.03 10	67.39 105	91.26 103	83.50 138

DVP-MVS++		81.24 39	82.74 40	76.76 90	83.14 103	60.90 144	91.64 1	85.49 35	74.03 23	84.93 56	90.38 64	66.82 114	85.90 37	77.43 33	90.78 120	83.49 139

PC_three_1452									46.98 273	81.83 93	86.28 154	66.55 119	84.47 71	63.31 140	90.78 120	83.49 139

XVG-ACMP-BASELINE		80.54 49	81.06 53	78.98 61	87.01 42	72.91 48	80.23 78	85.56 34	66.56 73	85.64 44	89.57 83	69.12 92	80.55 143	72.51 65	93.37 69	83.48 141

APDe-MVS		82.88 26	84.14 16	79.08 57	84.80 80	66.72 96	86.54 21	85.11 45	72.00 41	86.65 33	91.75 23	78.20 21	87.04 9	77.93 28	94.32 50	83.47 142

ANet_high		67.08 208	69.94 172	58.51 301	57.55 355	27.09 364	58.43 318	76.80 187	63.56 103	82.40 89	91.93 20	59.82 178	64.98 308	50.10 239	88.86 159	83.46 143

Effi-MVS+		72.10 150	72.28 146	71.58 166	74.21 234	50.33 210	74.72 146	82.73 93	62.62 113	70.77 247	76.83 275	69.96 85	80.97 134	60.20 159	78.43 286	83.45 144

test12				76.51 94	82.28 120	60.94 143		81.64 107		73.60 208		64.88 135	85.19 59		90.42 127	83.38 145

VPA-MVSNet		68.71 188	70.37 170	63.72 255	76.13 202	38.06 313	64.10 278	71.48 233	56.60 169	74.10 204	88.31 112	64.78 138	69.72 277	47.69 259	90.15 132	83.37 146

ACMMP_NAP		82.33 31	83.28 31	79.46 53	89.28 19	69.09 81	83.62 45	84.98 48	64.77 91	83.97 72	91.02 38	75.53 41	85.93 36	82.00 2	94.36 47	83.35 147

DeepC-MVS_fast	69.89 7	77.17 81	76.33 92	79.70 50	83.90 95	67.94 85	80.06 81	83.75 79	56.73 165	74.88 189	85.32 174	65.54 127	87.79 2	65.61 117	91.14 108	83.35 147
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 51	72.61 32	84.93 56	89.70 81	77.73 23	85.89 39	75.29 45	94.22 55	83.25 149

ETH3D cwj APD-0.16		78.38 74	78.72 72	77.38 84	80.09 144	66.16 101	79.08 90	86.13 27	57.55 157	80.93 106	87.76 122	71.98 69	82.73 102	72.11 71	92.83 77	83.25 149

test_0728_SECOND				76.57 93	86.20 54	60.57 149	83.77 43	85.49 35					85.90 37	75.86 42	94.39 43	83.25 149

GeoE		73.14 128	73.77 121	71.26 171	78.09 173	52.64 201	74.32 150	79.56 148	56.32 170	76.35 175	83.36 199	70.76 79	77.96 193	63.32 139	81.84 248	83.18 152

SR-MVS		84.51 7	85.27 6	82.25 21	88.52 35	77.71 17	86.81 17	85.25 42	77.42 15	86.15 37	90.24 71	81.69 5	85.94 33	77.77 29	93.58 67	83.09 153

SED-MVS		81.78 35	83.48 27	76.67 91	86.12 59	61.06 140	83.62 45	84.72 55	72.61 32	87.38 25	89.70 81	77.48 24	85.89 39	75.29 45	94.39 43	83.08 154

OPU-MVS				78.65 66	83.44 101	66.85 95	83.62 45				86.12 162	66.82 114	86.01 31	61.72 148	89.79 142	83.08 154

MVSTER		63.29 244	61.60 256	68.36 212	59.77 347	46.21 251	60.62 306	71.32 236	41.83 304	75.40 184	79.12 252	30.25 348	75.85 216	56.30 190	79.81 273	83.03 156

CANet		73.00 135	71.84 151	76.48 95	75.82 206	61.28 138	74.81 141	80.37 136	63.17 110	62.43 305	80.50 230	61.10 167	85.16 60	64.00 128	84.34 222	83.01 157

Vis-MVSNet		74.85 109	74.56 107	75.72 105	81.63 131	64.64 112	76.35 123	79.06 153	62.85 112	73.33 213	88.41 108	62.54 149	79.59 160	63.94 131	82.92 237	82.94 158
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
miper_lstm_enhance		61.97 257	61.63 255	62.98 265	60.04 343	45.74 255	47.53 348	70.95 244	44.04 289	73.06 216	78.84 256	39.72 300	60.33 323	55.82 195	84.64 217	82.88 159

PAPM_NR		73.91 115	74.16 114	73.16 140	81.90 126	53.50 196	81.28 64	81.40 111	66.17 75	73.30 214	83.31 200	59.96 175	83.10 96	58.45 174	81.66 253	82.87 160

Fast-Effi-MVS+		68.81 186	68.30 192	70.35 180	74.66 225	48.61 223	66.06 254	78.32 167	50.62 240	71.48 240	75.54 282	68.75 95	79.59 160	50.55 236	78.73 283	82.86 161

HFP-MVS		83.39 19	84.03 18	81.48 27	89.25 21	75.69 28	87.01 15	84.27 68	70.23 49	84.47 65	90.43 56	76.79 27	85.94 33	79.58 11	94.23 53	82.82 162

#test#		82.40 30	82.71 41	81.48 27	89.25 21	75.69 28	84.47 36	84.27 68	64.45 94	84.47 65	90.43 56	76.79 27	85.94 33	76.01 39	94.23 53	82.82 162

DELS-MVS		68.83 185	68.31 191	70.38 179	70.55 272	48.31 225	63.78 283	82.13 99	54.00 203	68.96 264	75.17 286	58.95 186	80.06 155	58.55 173	82.74 239	82.76 164
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		62.44 255	63.40 241	59.55 294	72.34 256	32.38 346	56.39 324	64.84 277	51.21 234	67.46 276	81.01 225	50.75 238	63.51 315	38.47 311	88.12 164	82.75 165

MP-MVS		83.19 20	83.54 26	82.14 22	90.54 5	79.00 11	86.42 23	83.59 82	71.31 43	81.26 102	90.96 39	74.57 51	84.69 67	78.41 22	94.78 28	82.74 166
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
lessismore_v0				72.75 152	79.60 149	56.83 176		57.37 312		83.80 74	89.01 98	47.45 258	78.74 171	64.39 125	86.49 195	82.69 167

DPE-MVS		82.00 34	83.02 36	78.95 62	85.36 71	67.25 90	82.91 53	84.98 48	73.52 26	85.43 50	90.03 75	76.37 31	86.97 11	74.56 50	94.02 59	82.62 168
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
test_prior3		76.71 86	77.19 85	75.27 111	82.15 122	59.85 155	75.57 132	84.33 66	58.92 142	76.53 170	86.78 135	67.83 106	83.39 89	69.81 82	92.76 80	82.58 169

test_prior				75.27 111	82.15 122	59.85 155		84.33 66					83.39 89			82.58 169

F-COLMAP		75.29 99	73.99 116	79.18 56	81.73 129	71.90 51	81.86 62	82.98 88	59.86 135	72.27 226	84.00 189	64.56 139	83.07 97	51.48 227	87.19 184	82.56 171

CP-MVS		84.12 10	84.55 12	82.80 12	89.42 18	79.74 7	88.19 5	84.43 64	71.96 42	84.70 62	90.56 51	77.12 26	86.18 27	79.24 18	95.36 13	82.49 172

testtj		81.19 41	81.70 49	79.67 51	83.95 93	69.77 71	83.58 48	84.63 60	72.13 38	82.85 84	88.36 111	75.00 46	86.79 12	71.99 72	92.84 76	82.44 173

XVG-OURS		79.51 59	79.82 62	78.58 67	86.11 62	74.96 33	76.33 125	84.95 50	66.89 66	82.75 85	88.99 99	66.82 114	78.37 183	74.80 47	90.76 123	82.40 174

mPP-MVS		84.01 12	84.39 13	82.88 8	90.65 4	81.38 4	87.08 13	82.79 91	72.41 36	85.11 55	90.85 42	76.65 30	84.89 63	79.30 17	94.63 34	82.35 175

XVG-OURS-SEG-HR		79.62 58	79.99 61	78.49 68	86.46 52	74.79 34	77.15 115	85.39 40	66.73 70	80.39 114	88.85 102	74.43 54	78.33 185	74.73 49	85.79 200	82.35 175

FMVSNet2		67.48 204	68.21 196	65.29 242	73.14 246	38.94 305	68.81 213	71.21 242	54.81 182	76.73 165	86.48 150	48.63 253	74.60 234	47.98 256	86.11 198	82.35 175

CNVR-MVS		78.49 71	78.59 74	78.16 73	85.86 66	67.40 89	78.12 105	81.50 108	63.92 99	77.51 148	86.56 148	68.43 100	84.82 65	73.83 55	91.61 96	82.26 178

mvs_anonymous		65.08 224	65.49 220	63.83 254	63.79 324	37.60 317	66.52 251	69.82 252	43.44 296	73.46 211	86.08 164	58.79 188	71.75 265	51.90 225	75.63 301	82.15 179

thres600view7		61.82 259	61.38 258	63.12 263	71.81 260	34.93 334	64.64 272	56.99 316	54.78 186	70.33 252	79.74 240	32.07 334	72.42 256	38.61 309	83.46 233	82.02 180

thres400		60.77 268	59.97 269	63.15 262	70.78 266	35.35 331	63.27 289	57.47 310	53.00 213	68.31 269	77.09 273	32.45 331	72.09 258	35.61 331	81.73 249	82.02 180

ETV-MVS		72.72 142	72.16 149	74.38 121	76.90 193	55.95 178	73.34 156	84.67 57	62.04 117	72.19 229	70.81 325	65.90 125	85.24 56	58.64 172	84.96 214	81.95 182

CNLPA		73.44 122	73.03 137	74.66 115	78.27 170	75.29 31	75.99 129	78.49 165	65.39 82	75.67 179	83.22 205	61.23 165	66.77 300	53.70 217	85.33 206	81.92 183

NCCC		78.25 75	78.04 79	78.89 63	85.61 68	69.45 73	79.80 83	80.99 123	65.77 77	75.55 181	86.25 157	67.42 108	85.42 50	70.10 79	90.88 118	81.81 184

PAPR		69.20 181	68.66 190	70.82 173	75.15 213	47.77 234	75.31 135	81.11 118	49.62 251	66.33 281	79.27 247	61.53 160	82.96 98	48.12 255	81.50 255	81.74 185

Anonymous202405211		66.02 215	66.89 213	63.43 260	74.22 231	38.14 311	59.00 315	66.13 269	63.33 109	69.76 258	85.95 168	51.88 230	70.50 273	44.23 279	87.52 173	81.64 186

FMVSNet3		65.00 225	65.16 223	64.52 248	69.47 280	37.56 318	66.63 249	70.38 249	51.55 229	74.72 192	83.27 202	37.89 311	74.44 236	47.12 261	85.37 203	81.57 187

Vis-MVSNet (Re-imp)		62.74 251	63.21 244	61.34 281	72.19 257	31.56 350	67.31 239	53.87 328	53.60 209	69.88 256	83.37 197	40.52 296	70.98 269	41.40 293	86.78 192	81.48 188

test_0402		78.17 76	79.48 66	74.24 123	83.50 98	59.15 162	72.52 162	74.60 208	75.34 17	88.69 13	91.81 22	75.06 44	82.37 107	65.10 119	88.68 160	81.20 189

VPNet		65.58 219	67.56 203	59.65 293	79.72 147	30.17 356	60.27 309	62.14 292	54.19 199	71.24 242	86.63 145	58.80 187	67.62 291	44.17 280	90.87 119	81.18 190

APD-MVS		81.13 43	81.73 48	79.36 55	84.47 85	70.53 65	83.85 41	83.70 80	69.43 55	83.67 75	88.96 100	75.89 36	86.41 17	72.62 64	92.95 74	81.14 191
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
CPTT-MVS		81.51 38	81.76 47	80.76 40	89.20 24	78.75 12	86.48 22	82.03 101	68.80 56	80.92 107	88.52 106	72.00 68	82.39 106	74.80 47	93.04 73	81.14 191

Fast-Effi-MVS+-dtu		70.00 168	68.74 188	73.77 129	73.47 240	64.53 114	71.36 180	78.14 171	55.81 175	68.84 267	74.71 291	65.36 131	75.75 219	52.00 224	79.00 280	81.03 193

MDA-MVSNet-bldmvs		62.34 256	61.73 252	64.16 249	61.64 334	49.90 214	48.11 346	57.24 315	53.31 211	80.95 105	79.39 245	49.00 249	61.55 321	45.92 271	80.05 270	81.03 193

D2MVS		62.58 253	61.05 261	67.20 227	63.85 323	47.92 232	56.29 325	69.58 253	39.32 317	70.07 254	78.19 263	34.93 320	72.68 249	53.44 220	83.74 230	81.00 195

ACMM	69.25 9	82.11 33	83.31 30	78.49 68	88.17 40	73.96 39	83.11 52	84.52 63	66.40 74	87.45 23	89.16 94	81.02 9	80.52 144	74.27 53	95.73 7	80.98 196
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
hse-mvs2		72.32 148	70.66 169	77.31 87	83.10 107	71.77 52	69.19 208	71.45 234	54.28 194	77.89 140	78.26 261	49.04 247	79.23 162	63.62 134	89.13 156	80.92 197

DP-MVS Recon		73.57 121	72.69 141	76.23 99	82.85 112	63.39 123	74.32 150	82.96 89	57.75 151	70.35 251	81.98 213	64.34 140	84.41 74	49.69 241	89.95 137	80.89 198

EPNet		69.10 183	67.32 207	74.46 117	68.33 288	61.27 139	77.56 108	63.57 286	60.95 124	56.62 331	82.75 206	51.53 234	81.24 125	54.36 211	90.20 129	80.88 199
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
AUN-MVS		70.22 165	67.88 201	77.22 88	82.96 111	71.61 53	69.08 209	71.39 235	49.17 254	71.70 232	78.07 266	37.62 312	79.21 163	61.81 146	89.15 154	80.82 200

zzz-MVS		83.01 25	83.63 25	81.13 35	91.16 2	78.16 14	82.72 56	80.63 128	72.08 39	84.93 56	90.79 43	74.65 49	84.42 72	80.98 4	94.75 29	80.82 200

MTAPA		83.19 20	83.87 20	81.13 35	91.16 2	78.16 14	84.87 32	80.63 128	72.08 39	84.93 56	90.79 43	74.65 49	84.42 72	80.98 4	94.75 29	80.82 200

HyFIR lowres test		63.01 247	60.47 266	70.61 175	83.04 108	54.10 191	59.93 311	72.24 226	33.67 347	69.00 262	75.63 281	38.69 305	76.93 206	36.60 324	75.45 304	80.81 203

EIA-MVS		68.59 190	67.16 209	72.90 148	75.18 212	55.64 183	69.39 204	81.29 113	52.44 218	64.53 289	70.69 326	60.33 172	82.30 109	54.27 212	76.31 297	80.75 204

MCST-MVS		73.42 123	73.34 130	73.63 132	81.28 134	59.17 161	74.80 143	83.13 87	45.50 278	72.84 218	83.78 192	65.15 133	80.99 132	64.54 123	89.09 157	80.73 205

tfpnnormal		66.48 213	67.93 199	62.16 274	73.40 242	36.65 320	63.45 286	64.99 276	55.97 172	72.82 219	87.80 121	57.06 207	69.10 283	48.31 254	87.54 172	80.72 206

SD-MVS		80.28 55	81.55 52	76.47 96	83.57 97	67.83 87	83.39 51	85.35 41	64.42 95	86.14 38	87.07 127	74.02 55	80.97 134	77.70 31	92.32 87	80.62 207
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		64.04 239	63.83 236	64.66 246	68.39 285	42.97 276	73.45 155	74.50 209	52.05 223	54.78 338	75.44 285	43.99 273	70.42 275	53.49 219	78.41 287	80.59 208

GA-MVS		62.91 248	61.66 253	66.66 235	67.09 300	44.49 262	61.18 303	69.36 255	51.33 232	69.33 260	74.47 293	36.83 315	74.94 229	50.60 235	74.72 309	80.57 209

114514_t		73.40 124	73.33 131	73.64 131	84.15 92	57.11 172	78.20 103	80.02 141	43.76 292	72.55 222	86.07 165	64.00 141	83.35 91	60.14 162	91.03 111	80.45 210

IterMVS-SCA-FT		67.68 202	66.07 217	72.49 158	73.34 243	58.20 170	63.80 282	65.55 273	48.10 262	76.91 158	82.64 207	45.20 265	78.84 168	61.20 152	77.89 292	80.44 211

ambc				70.10 187	77.74 180	50.21 212	74.28 152	77.93 175		79.26 125	88.29 113	54.11 222	79.77 157	64.43 124	91.10 109	80.30 212

thisisatest0515		60.48 270	57.86 284	68.34 213	67.25 298	46.42 248	60.58 307	62.14 292	40.82 312	63.58 300	69.12 338	26.28 358	78.34 184	48.83 247	82.13 244	80.26 213

LFMVS		67.06 209	67.89 200	64.56 247	78.02 174	38.25 310	70.81 191	59.60 303	65.18 87	71.06 245	86.56 148	43.85 274	75.22 225	46.35 268	89.63 143	80.21 214

UGNet		70.20 166	69.05 181	73.65 130	76.24 200	63.64 120	75.87 130	72.53 222	61.48 120	60.93 315	86.14 161	52.37 229	77.12 204	50.67 234	85.21 208	80.17 215
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		66.57 212	69.23 179	58.59 300	81.26 135	37.73 316	64.06 279	57.62 309	57.02 160	78.40 135	90.75 45	62.65 146	58.10 330	41.77 292	89.58 146	79.95 216

test_yl		65.11 222	65.09 228	65.18 243	70.59 269	40.86 289	63.22 291	72.79 217	57.91 149	68.88 265	79.07 254	42.85 281	74.89 230	45.50 273	84.97 211	79.81 217

DCV-MVSNet		65.11 222	65.09 228	65.18 243	70.59 269	40.86 289	63.22 291	72.79 217	57.91 149	68.88 265	79.07 254	42.85 281	74.89 230	45.50 273	84.97 211	79.81 217

cascas		64.59 230	62.77 248	70.05 188	75.27 210	50.02 213	61.79 298	71.61 229	42.46 302	63.68 299	68.89 342	49.33 246	80.35 145	47.82 258	84.05 225	79.78 219

ET-MVSNet_ETH3D		63.32 243	60.69 265	71.20 172	70.15 276	55.66 182	65.02 269	64.32 282	43.28 300	68.99 263	72.05 319	25.46 362	78.19 190	54.16 214	82.80 238	79.74 220

CSCG		74.12 114	74.39 110	73.33 136	79.35 152	61.66 136	77.45 110	81.98 102	62.47 116	79.06 127	80.19 235	61.83 156	78.79 170	59.83 166	87.35 178	79.54 221

ACMH	63.62 14	77.50 79	80.11 60	69.68 192	79.61 148	56.28 177	78.81 92	83.62 81	63.41 108	87.14 31	90.23 72	76.11 34	73.32 244	67.58 102	94.44 41	79.44 222
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
MG-MVS		70.47 164	71.34 161	67.85 219	79.26 154	40.42 296	74.67 148	75.15 204	58.41 145	68.74 268	88.14 118	56.08 214	83.69 82	59.90 165	81.71 252	79.43 223

DVP-MVS		81.15 42	83.12 35	75.24 113	86.16 57	60.78 146	83.77 43	80.58 131	72.48 34	85.83 42	90.41 59	78.57 18	85.69 44	75.86 42	94.39 43	79.24 224
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		64.01 240	65.15 225	60.57 287	73.28 244	35.61 330	57.60 322	67.08 265	54.61 189	66.76 280	83.37 197	56.28 212	66.87 296	42.19 288	85.20 209	79.23 225

TSAR-MVS + GP.		73.08 130	71.60 157	77.54 81	78.99 164	70.73 63	74.96 138	69.38 254	60.73 128	74.39 199	78.44 259	57.72 201	82.78 100	60.16 161	89.60 144	79.11 226

HPM-MVS++		79.89 57	79.80 63	80.18 45	89.02 27	78.44 13	83.49 49	80.18 139	64.71 93	78.11 139	88.39 109	65.46 129	83.14 94	77.64 32	91.20 105	78.94 227

DP-MVS		78.44 73	79.29 67	75.90 103	81.86 127	65.33 106	79.05 91	84.63 60	74.83 20	80.41 113	86.27 155	71.68 71	83.45 88	62.45 145	92.40 85	78.92 228

PLC	62.01 16	71.79 153	70.28 171	76.33 97	80.31 143	68.63 82	78.18 104	81.24 115	54.57 190	67.09 279	80.63 228	59.44 180	81.74 120	46.91 264	84.17 223	78.63 229
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
PVSNet_Blended_VisFu		70.04 167	68.88 184	73.53 134	82.71 114	63.62 121	74.81 141	81.95 103	48.53 260	67.16 278	79.18 251	51.42 235	78.38 182	54.39 210	79.72 276	78.60 230

h-mvs33		73.08 130	71.61 156	77.48 82	83.89 96	72.89 49	70.47 193	71.12 243	54.28 194	77.89 140	83.41 194	49.04 247	80.98 133	63.62 134	90.77 122	78.58 231

agg_prior2														70.70 77	90.93 114	78.55 232

ppachtmachnet_test		60.26 272	59.61 272	62.20 273	67.70 295	44.33 263	58.18 319	60.96 300	40.75 313	65.80 284	72.57 311	41.23 288	63.92 312	46.87 265	82.42 242	78.33 233

BH-RMVSNet		68.69 189	68.20 197	70.14 185	76.40 198	53.90 194	64.62 273	73.48 213	58.01 148	73.91 207	81.78 215	59.09 184	78.22 187	48.59 250	77.96 291	78.31 234

bset_n11_16_dypcd		66.91 211	65.84 219	70.12 186	72.95 252	53.54 195	63.64 284	68.65 259	48.54 259	72.54 223	74.28 297	40.58 295	78.54 174	63.52 137	87.82 170	78.29 235

PVSNet_BlendedMVS		65.38 220	64.30 232	68.61 210	69.81 277	49.36 218	65.60 263	78.96 154	45.50 278	59.98 318	78.61 257	51.82 231	78.20 188	44.30 277	84.11 224	78.27 236

ab-mvs		64.11 238	65.13 226	61.05 283	71.99 259	38.03 314	67.59 230	68.79 257	49.08 256	65.32 286	86.26 156	58.02 199	66.85 298	39.33 303	79.79 275	78.27 236

EGC-MVSNET		64.77 228	61.17 259	75.60 107	86.90 47	74.47 35	84.04 38	68.62 260	0.60 374	1.13 376	91.61 27	65.32 132	74.15 241	64.01 127	88.28 161	78.17 238

MVSFormer		69.93 170	69.03 182	72.63 157	74.93 214	59.19 159	83.98 39	75.72 195	52.27 219	63.53 301	76.74 276	43.19 278	80.56 141	72.28 68	78.67 284	78.14 239

jason		64.47 233	62.84 247	69.34 197	76.91 192	59.20 158	67.15 240	65.67 270	35.29 337	65.16 287	76.74 276	44.67 269	70.68 270	54.74 204	79.28 279	78.14 239
jason: jason.
new-patchmatchnet		52.89 306	55.76 299	44.26 343	59.94 345	6.31 378	37.36 365	50.76 342	41.10 309	64.28 292	79.82 239	44.77 268	48.43 343	36.24 328	87.61 171	78.03 241

CDS-MVSNet		64.33 236	62.66 249	69.35 196	80.44 142	58.28 169	65.26 266	65.66 271	44.36 288	67.30 277	75.54 282	43.27 277	71.77 263	37.68 316	84.44 221	78.01 242
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
TAMVS		65.31 221	63.75 237	69.97 190	82.23 121	59.76 157	66.78 248	63.37 287	45.20 283	69.79 257	79.37 246	47.42 259	72.17 257	34.48 335	85.15 210	77.99 243

LCM-MVSNet-Re		69.10 183	71.57 158	61.70 276	70.37 273	34.30 339	61.45 299	79.62 144	56.81 163	89.59 8	88.16 117	68.44 99	72.94 247	42.30 287	87.33 179	77.85 244

Patchmtry		60.91 265	63.01 246	54.62 311	66.10 309	26.27 367	67.47 233	56.40 320	54.05 202	72.04 230	86.66 142	33.19 325	60.17 324	43.69 281	87.45 176	77.42 245

test9_res														72.12 70	91.37 101	77.40 246

train_agg		76.38 88	76.55 89	75.86 104	85.47 69	69.32 77	76.42 121	78.69 160	54.00 203	76.97 154	86.74 138	66.60 117	81.10 128	72.50 66	91.56 97	77.15 247

lupinMVS		63.36 242	61.49 257	68.97 203	74.93 214	59.19 159	65.80 259	64.52 281	34.68 342	63.53 301	74.25 298	43.19 278	70.62 271	53.88 216	78.67 284	77.10 248

thres100view900		61.17 264	61.09 260	61.39 280	72.14 258	35.01 333	65.42 265	56.99 316	55.23 179	70.71 248	79.90 238	32.07 334	72.09 258	35.61 331	81.73 249	77.08 249

tfpn200view9		60.35 271	59.97 269	61.51 278	70.78 266	35.35 331	63.27 289	57.47 310	53.00 213	68.31 269	77.09 273	32.45 331	72.09 258	35.61 331	81.73 249	77.08 249

agg_prior1		75.89 90	76.41 90	74.31 122	84.44 87	66.02 102	76.12 127	78.62 163	54.40 192	76.95 156	86.85 132	66.44 120	80.34 146	72.45 67	91.42 100	76.57 251

MVS_111021_HR		72.98 137	72.97 139	72.99 144	80.82 137	65.47 105	68.81 213	72.77 219	57.67 154	75.76 178	82.38 210	71.01 77	77.17 203	61.38 150	86.15 196	76.32 252

xiu_mvs_v1_base_debu		67.87 198	67.07 210	70.26 181	79.13 159	61.90 133	67.34 235	71.25 239	47.98 263	67.70 272	74.19 300	61.31 162	72.62 251	56.51 185	78.26 288	76.27 253

xiu_mvs_v1_base		67.87 198	67.07 210	70.26 181	79.13 159	61.90 133	67.34 235	71.25 239	47.98 263	67.70 272	74.19 300	61.31 162	72.62 251	56.51 185	78.26 288	76.27 253

xiu_mvs_v1_base_debi		67.87 198	67.07 210	70.26 181	79.13 159	61.90 133	67.34 235	71.25 239	47.98 263	67.70 272	74.19 300	61.31 162	72.62 251	56.51 185	78.26 288	76.27 253

baseline2		55.57 295	52.74 309	64.05 252	65.26 313	44.11 264	62.38 294	54.43 326	39.03 320	51.21 349	67.35 350	33.66 323	72.45 255	37.14 321	64.22 350	75.60 256

OpenMVS	62.51 15	68.76 187	68.75 187	68.78 209	70.56 271	53.91 193	78.29 100	77.35 181	48.85 257	70.22 253	83.52 193	52.65 228	76.93 206	55.31 200	81.99 245	75.49 257

3Dnovator	65.95 11	71.50 155	71.22 162	72.34 161	73.16 245	63.09 127	78.37 98	78.32 167	57.67 154	72.22 228	84.61 180	54.77 217	78.47 177	60.82 157	81.07 258	75.45 258

1112_ss		59.48 276	58.99 276	60.96 285	77.84 178	42.39 281	61.42 300	68.45 261	37.96 326	59.93 321	67.46 348	45.11 267	65.07 307	40.89 297	71.81 323	75.41 259

IterMVS		63.12 246	62.48 250	65.02 245	66.34 305	52.86 199	63.81 281	62.25 291	46.57 275	71.51 239	80.40 231	44.60 270	66.82 299	51.38 229	75.47 303	75.38 260
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Test_1112_low_res		58.78 281	58.69 278	59.04 297	79.41 151	38.13 312	57.62 321	66.98 266	34.74 340	59.62 322	77.56 270	42.92 280	63.65 314	38.66 308	70.73 329	75.35 261

QAPM		69.18 182	69.26 178	68.94 204	71.61 261	52.58 202	80.37 73	78.79 159	49.63 250	73.51 209	85.14 177	53.66 223	79.12 164	55.11 201	75.54 302	75.11 262

MVS_0304		62.51 254	62.27 251	63.25 261	69.39 281	48.47 224	64.05 280	62.48 290	59.69 136	54.10 343	81.04 224	45.71 261	66.31 303	41.38 294	82.58 241	74.96 263

DPM-MVS		69.98 169	69.22 180	72.26 163	82.69 115	58.82 164	70.53 192	81.23 116	47.79 267	64.16 293	80.21 233	51.32 236	83.12 95	60.14 162	84.95 215	74.83 264

pmmvs-eth3d		64.41 235	63.27 243	67.82 221	75.81 207	60.18 152	69.49 202	62.05 296	38.81 322	74.13 202	82.23 211	43.76 275	68.65 284	42.53 286	80.63 266	74.63 265

MSDG		67.47 205	67.48 206	67.46 224	70.70 268	54.69 188	66.90 246	78.17 170	60.88 125	70.41 250	74.76 288	61.22 166	73.18 245	47.38 260	76.87 294	74.49 266

MAR-MVS		67.72 201	66.16 216	72.40 160	74.45 228	64.99 111	74.87 139	77.50 180	48.67 258	65.78 285	68.58 345	57.01 208	77.79 196	46.68 267	81.92 246	74.42 267
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		57.82 286	58.36 282	56.19 307	69.17 283	30.76 355	62.94 293	55.21 323	46.04 277	63.83 297	78.47 258	41.20 289	63.68 313	39.44 302	68.99 338	74.13 268

EU-MVSNet		60.82 266	60.80 264	60.86 286	68.37 286	41.16 286	72.27 163	68.27 262	26.96 365	69.08 261	75.71 280	32.09 333	67.44 292	55.59 198	78.90 281	73.97 269

HY-MVS	49.31 19	57.96 285	57.59 286	59.10 296	66.85 302	36.17 324	65.13 268	65.39 274	39.24 319	54.69 340	78.14 264	44.28 272	67.18 295	33.75 339	70.79 328	73.95 270

TR-MVS		64.59 230	63.54 240	67.73 223	75.75 208	50.83 208	63.39 287	70.29 250	49.33 252	71.55 238	74.55 292	50.94 237	78.46 178	40.43 299	75.69 300	73.89 271

IB-MVS	49.67 18	59.69 275	56.96 290	67.90 218	68.19 289	50.30 211	61.42 300	65.18 275	47.57 269	55.83 335	67.15 352	23.77 367	79.60 159	43.56 283	79.97 271	73.79 272
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		63.55 241	66.07 217	55.99 308	66.18 308	44.04 265	68.77 216	68.80 256	46.99 272	72.57 221	85.84 169	39.87 299	50.22 339	53.40 221	92.23 88	73.71 273

AdaColmap		74.22 113	74.56 107	73.20 139	81.95 125	60.97 142	79.43 84	80.90 124	65.57 79	72.54 223	81.76 217	70.98 78	85.26 54	47.88 257	90.00 135	73.37 274

PAPM		61.79 260	60.37 267	66.05 238	76.09 203	41.87 283	69.30 205	76.79 188	40.64 314	53.80 344	79.62 243	44.38 271	82.92 99	29.64 353	73.11 318	73.36 275

MVS_111021_LR		72.10 150	71.82 152	72.95 146	79.53 150	73.90 41	70.45 194	66.64 267	56.87 162	76.81 163	81.76 217	68.78 94	71.76 264	61.81 146	83.74 230	73.18 276

原ACMM1				73.90 127	85.90 63	65.15 110		81.67 106	50.97 236	74.25 200	86.16 160	61.60 159	83.54 85	56.75 183	91.08 110	73.00 277

CHOSEN 1792x2688		58.09 284	56.30 295	63.45 259	79.95 145	50.93 207	54.07 333	65.59 272	28.56 362	61.53 308	74.33 295	41.09 291	66.52 302	33.91 338	67.69 344	72.92 278

TinyColmap		67.98 197	69.28 177	64.08 251	67.98 292	46.82 244	70.04 196	75.26 202	53.05 212	77.36 150	86.79 134	59.39 181	72.59 254	45.64 272	88.01 167	72.83 279

FMVSNet5		55.08 297	55.54 300	53.71 312	65.80 310	33.50 343	56.22 326	52.50 337	43.72 294	61.06 312	83.38 196	25.46 362	54.87 333	30.11 350	81.64 254	72.75 280

EG-PatchMatch MVS		70.70 161	70.88 165	70.16 184	82.64 116	58.80 165	71.48 177	73.64 212	54.98 181	76.55 168	81.77 216	61.10 167	78.94 167	54.87 203	80.84 262	72.74 281

PVSNet_Blended		62.90 249	61.64 254	66.69 234	69.81 277	49.36 218	61.23 302	78.96 154	42.04 303	59.98 318	68.86 343	51.82 231	78.20 188	44.30 277	77.77 293	72.52 282

CostFormer		57.35 288	56.14 296	60.97 284	63.76 325	38.43 307	67.50 232	60.22 301	37.14 330	59.12 323	76.34 278	32.78 328	71.99 261	39.12 305	69.27 337	72.47 283

PS-MVSNAJ		64.27 237	63.73 238	65.90 240	77.82 179	51.42 205	63.33 288	72.33 224	45.09 285	61.60 307	68.04 346	62.39 151	73.95 242	49.07 245	73.87 315	72.34 284

xiu_mvs_v2_base		64.43 234	63.96 235	65.85 241	77.72 181	51.32 206	63.63 285	72.31 225	45.06 286	61.70 306	69.66 335	62.56 147	73.93 243	49.06 246	73.91 314	72.31 285

PMVS	70.70 6	81.70 36	83.15 34	77.36 85	90.35 6	82.82 2	82.15 58	79.22 151	74.08 22	87.16 30	91.97 19	84.80 2	76.97 205	64.98 121	93.61 65	72.28 286
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
1314		59.83 274	58.86 277	62.74 269	65.71 311	44.78 260	68.59 218	72.63 221	33.54 349	61.05 313	67.29 351	43.62 276	71.26 267	49.49 243	67.84 343	72.19 287

无先验							74.82 140	70.94 245	47.75 268				76.85 209	54.47 207		72.09 288

LF4IMVS		67.50 203	67.31 208	68.08 217	58.86 350	61.93 132	71.43 178	75.90 194	44.67 287	72.42 225	80.20 234	57.16 203	70.44 274	58.99 171	86.12 197	71.88 289

pmmvs4		60.78 267	59.04 275	66.00 239	73.06 251	57.67 171	64.53 275	60.22 301	36.91 331	65.96 282	77.27 272	39.66 301	68.54 285	38.87 306	74.89 308	71.80 290

MSLP-MVS++		74.48 111	75.78 97	70.59 176	84.66 81	62.40 129	78.65 94	84.24 71	60.55 129	77.71 146	81.98 213	63.12 144	77.64 199	62.95 142	88.14 163	71.73 291

MDTV_nov1_ep13_2view						18.41 374	53.74 334		31.57 357	44.89 363		29.90 352		32.93 341		71.48 292

patch_mono-2		62.73 252	64.08 234	58.68 299	70.36 274	55.87 180	60.84 305	64.11 283	41.23 308	64.04 294	78.22 262	60.00 174	48.80 341	54.17 213	83.71 232	71.37 293

tpm2		56.12 290	54.64 303	60.55 288	66.24 306	36.01 325	68.14 225	56.77 318	33.60 348	58.25 326	75.52 284	30.25 348	74.33 238	33.27 340	69.76 336	71.32 294

CMPMVS	48.73 20	61.54 262	60.89 262	63.52 258	61.08 338	51.55 204	68.07 227	68.00 263	33.88 344	65.87 283	81.25 222	37.91 310	67.71 289	49.32 244	82.60 240	71.31 295
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
API-MVS		70.97 159	71.51 159	69.37 194	75.20 211	55.94 179	80.99 65	76.84 186	62.48 115	71.24 242	77.51 271	61.51 161	80.96 138	52.04 223	85.76 201	71.22 296

OpenMVS_ROB	54.93 17	63.23 245	63.28 242	63.07 264	69.81 277	45.34 257	68.52 221	67.14 264	43.74 293	70.61 249	79.22 249	47.90 257	72.66 250	48.75 248	73.84 316	71.21 297

thres200		57.55 287	57.02 289	59.17 295	67.89 294	34.93 334	58.91 317	57.25 314	50.24 242	64.01 295	71.46 323	32.49 330	71.39 266	31.31 345	79.57 277	71.19 298

test20.03		55.74 293	57.51 287	50.42 321	59.89 346	32.09 348	50.63 340	49.01 346	50.11 244	65.07 288	83.23 203	45.61 263	48.11 344	30.22 349	83.82 229	71.07 299

our_test_3		56.46 289	56.51 293	56.30 306	67.70 295	39.66 300	55.36 330	52.34 338	40.57 315	63.85 296	69.91 334	40.04 298	58.22 329	43.49 284	75.29 307	71.03 300

BH-untuned		69.39 178	69.46 175	69.18 198	77.96 176	56.88 174	68.47 223	77.53 179	56.77 164	77.79 144	79.63 242	60.30 173	80.20 152	46.04 270	80.65 264	70.47 301

EPNet_dtu		58.93 280	58.52 279	60.16 291	67.91 293	47.70 236	69.97 197	58.02 307	49.73 248	47.28 359	73.02 309	38.14 307	62.34 318	36.57 325	85.99 199	70.43 302
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
USDC		62.80 250	63.10 245	61.89 275	65.19 314	43.30 273	67.42 234	74.20 210	35.80 336	72.25 227	84.48 183	45.67 262	71.95 262	37.95 315	84.97 211	70.42 303

DWT-MVSNet_test		53.04 305	51.12 318	58.77 298	61.23 336	38.67 306	62.16 296	57.74 308	38.24 323	51.76 348	59.07 362	21.36 369	67.40 293	44.80 276	63.76 351	70.25 304

GSMVS																70.05 305

sam_mvs1												31.41 337				70.05 305

SCA		58.57 283	58.04 283	60.17 290	70.17 275	41.07 288	65.19 267	53.38 333	43.34 299	61.00 314	73.48 304	45.20 265	69.38 280	40.34 300	70.31 331	70.05 305

tpmvs		55.84 291	55.45 301	57.01 305	60.33 342	33.20 344	65.89 256	59.29 305	47.52 270	56.04 333	73.60 303	31.05 343	68.06 288	40.64 298	64.64 348	69.77 308

旧先验1					84.55 84	60.36 151		63.69 285			87.05 128	54.65 219			83.34 234	69.66 309

CR-MVSNet		58.96 279	58.49 280	60.36 289	66.37 303	48.24 227	70.93 188	56.40 320	32.87 350	61.35 309	86.66 142	33.19 325	63.22 316	48.50 252	70.17 332	69.62 310

RPMNet		65.77 218	65.08 230	67.84 220	66.37 303	48.24 227	70.93 188	86.27 23	54.66 188	61.35 309	86.77 137	33.29 324	85.67 46	55.93 193	70.17 332	69.62 310

tpm cat1		54.02 302	52.63 310	58.19 302	64.85 320	39.86 299	66.26 253	57.28 313	32.16 352	56.90 329	70.39 329	32.75 329	65.30 306	34.29 336	58.79 359	69.41 312

PatchmatchNet		54.60 298	54.27 304	55.59 309	65.17 316	39.08 302	66.92 245	51.80 339	39.89 316	58.39 324	73.12 308	31.69 336	58.33 328	43.01 285	58.38 362	69.38 313
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
YYNet1		52.58 307	53.50 306	49.85 322	54.15 369	36.45 323	40.53 359	46.55 353	38.09 325	75.52 182	73.31 307	41.08 292	43.88 357	41.10 295	71.14 327	69.21 314

CVMVSNet		59.21 278	58.44 281	61.51 278	73.94 236	47.76 235	71.31 182	64.56 280	26.91 366	60.34 317	70.44 327	36.24 317	67.65 290	53.57 218	68.66 340	69.12 315

MDA-MVSNet_test_wron		52.57 308	53.49 307	49.81 323	54.24 368	36.47 322	40.48 360	46.58 352	38.13 324	75.47 183	73.32 306	41.05 293	43.85 358	40.98 296	71.20 326	69.10 316

MVP-Stereo		61.56 261	59.22 273	68.58 211	79.28 153	60.44 150	69.20 207	71.57 230	43.58 295	56.42 332	78.37 260	39.57 302	76.46 214	34.86 334	60.16 356	68.86 317
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
1121		69.23 180	68.26 193	72.12 164	88.36 37	71.40 55	68.59 218	62.06 295	43.80 291	74.75 190	86.18 158	52.92 226	76.85 209	54.47 207	83.27 235	68.12 318

新几何1				69.99 189	88.37 36	71.34 57		62.08 294	43.85 290	74.99 187	86.11 163	52.85 227	70.57 272	50.99 232	83.23 236	68.05 319

UnsupCasMVSNet_eth		52.26 310	53.29 308	49.16 327	55.08 365	33.67 342	50.03 341	58.79 306	37.67 327	63.43 303	74.75 289	41.82 286	45.83 347	38.59 310	59.42 358	67.98 320

Patchmatch-test		47.93 322	49.96 323	41.84 347	57.42 356	24.26 370	48.75 343	41.49 365	39.30 318	56.79 330	73.48 304	30.48 347	33.87 369	29.29 355	72.61 319	67.39 321

Patchmatch-RL test		59.95 273	59.12 274	62.44 271	72.46 255	54.61 189	59.63 312	47.51 351	41.05 311	74.58 196	74.30 296	31.06 342	65.31 305	51.61 226	79.85 272	67.39 321

testgi		54.00 303	56.86 291	45.45 338	58.20 353	25.81 368	49.05 342	49.50 345	45.43 281	67.84 271	81.17 223	51.81 233	43.20 360	29.30 354	79.41 278	67.34 323

test222					87.30 41	69.15 80	67.85 228	59.59 304	41.06 310	73.05 217	85.72 171	48.03 256			80.65 264	66.92 324

pmmvs5		52.49 309	52.58 311	52.21 319	54.99 366	32.38 346	55.45 329	53.84 329	32.15 353	55.49 337	74.81 287	38.08 308	57.37 331	34.02 337	74.40 312	66.88 325

Anonymous20231206		54.13 300	55.82 298	49.04 329	70.89 265	35.96 326	51.73 338	50.87 341	34.86 338	62.49 304	79.22 249	42.52 284	44.29 356	27.95 358	81.88 247	66.88 325

tpm		50.60 315	52.42 312	45.14 340	65.18 315	26.29 366	60.30 308	43.50 356	37.41 328	57.01 328	79.09 253	30.20 350	42.32 361	32.77 342	66.36 345	66.81 327

testdata				64.13 250	85.87 65	63.34 125		61.80 298	47.83 266	76.42 174	86.60 147	48.83 250	62.31 319	54.46 209	81.26 256	66.74 328

MIMVSNet		54.39 299	56.12 297	49.20 326	72.57 254	30.91 354	59.98 310	48.43 349	41.66 305	55.94 334	83.86 191	41.19 290	50.42 338	26.05 360	75.38 305	66.27 329

tpmrst		50.15 317	51.38 316	46.45 335	56.05 360	24.77 369	64.40 277	49.98 343	36.14 333	53.32 345	69.59 336	35.16 319	48.69 342	39.24 304	58.51 361	65.89 330

EPMVS		45.74 326	46.53 329	43.39 345	54.14 370	22.33 372	55.02 331	35.00 374	34.69 341	51.09 350	70.20 331	25.92 360	42.04 363	37.19 320	55.50 366	65.78 331

PVSNet	43.83 21	51.56 314	51.17 317	52.73 316	68.34 287	38.27 309	48.22 345	53.56 331	36.41 332	54.29 341	64.94 355	34.60 321	54.20 336	30.34 348	69.87 334	65.71 332

BH-w/o		64.81 227	64.29 233	66.36 236	76.08 204	54.71 187	65.61 262	75.23 203	50.10 245	71.05 246	71.86 320	54.33 221	79.02 165	38.20 313	76.14 298	65.36 333

XXY-MVS		55.19 296	57.40 288	48.56 330	64.45 321	34.84 336	51.54 339	53.59 330	38.99 321	63.79 298	79.43 244	56.59 210	45.57 348	36.92 323	71.29 325	65.25 334

ADS-MVSNet2		48.76 320	47.25 328	53.29 315	55.90 362	40.54 295	47.34 349	54.99 325	31.41 358	50.48 352	72.06 317	31.23 339	54.26 335	25.93 361	55.93 364	65.07 335

ADS-MVSNet		44.62 331	45.58 330	41.73 348	55.90 362	20.83 373	47.34 349	39.94 369	31.41 358	50.48 352	72.06 317	31.23 339	39.31 366	25.93 361	55.93 364	65.07 335

KD-MVS_2432*1600		52.05 312	51.58 314	53.44 313	52.11 371	31.20 351	44.88 355	64.83 278	41.53 306	64.37 290	70.03 332	15.61 379	64.20 309	36.25 326	74.61 310	64.93 337

miper_refine_blended		52.05 312	51.58 314	53.44 313	52.11 371	31.20 351	44.88 355	64.83 278	41.53 306	64.37 290	70.03 332	15.61 379	64.20 309	36.25 326	74.61 310	64.93 337

test0.0.03 1		47.72 323	48.31 325	45.93 336	55.53 364	29.39 358	46.40 352	41.21 367	43.41 297	55.81 336	67.65 347	29.22 353	43.77 359	25.73 363	69.87 334	64.62 339

JIA-IIPM		54.03 301	51.62 313	61.25 282	59.14 349	55.21 185	59.10 314	47.72 350	50.85 237	50.31 355	85.81 170	20.10 372	63.97 311	36.16 329	55.41 367	64.55 340

PatchT		53.35 304	56.47 294	43.99 344	64.19 322	17.46 375	59.15 313	43.10 357	52.11 222	54.74 339	86.95 129	29.97 351	49.98 340	43.62 282	74.40 312	64.53 341

gg-mvs-nofinetune		55.75 292	56.75 292	52.72 317	62.87 328	28.04 362	68.92 210	41.36 366	71.09 44	50.80 351	92.63 12	20.74 370	66.86 297	29.97 351	72.41 320	63.25 342

MVS		60.62 269	59.97 269	62.58 270	68.13 290	47.28 241	68.59 218	73.96 211	32.19 351	59.94 320	68.86 343	50.48 239	77.64 199	41.85 291	75.74 299	62.83 343

N_pmnet		52.06 311	51.11 319	54.92 310	59.64 348	71.03 59	37.42 364	61.62 299	33.68 346	57.12 327	72.10 312	37.94 309	31.03 370	29.13 357	71.35 324	62.70 344

Gipuma		69.55 175	72.83 140	59.70 292	63.63 326	53.97 192	80.08 80	75.93 193	64.24 97	73.49 210	88.93 101	57.89 200	62.46 317	59.75 167	91.55 98	62.67 345
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
WTY-MVS		49.39 319	50.31 322	46.62 334	61.22 337	32.00 349	46.61 351	49.77 344	33.87 345	54.12 342	69.55 337	41.96 285	45.40 350	31.28 346	64.42 349	62.47 346

test-LLR		50.43 316	50.69 321	49.64 324	60.76 339	41.87 283	53.18 335	45.48 354	43.41 297	49.41 356	60.47 360	29.22 353	44.73 354	42.09 289	72.14 321	62.33 347

test-mter		48.56 321	48.20 326	49.64 324	60.76 339	41.87 283	53.18 335	45.48 354	31.91 356	49.41 356	60.47 360	18.34 373	44.73 354	42.09 289	72.14 321	62.33 347

UnsupCasMVSNet_bld		50.01 318	51.03 320	46.95 331	58.61 351	32.64 345	48.31 344	53.27 334	34.27 343	60.47 316	71.53 322	41.40 287	47.07 345	30.68 347	60.78 355	61.13 349

sss		47.59 324	48.32 324	45.40 339	56.73 359	33.96 340	45.17 354	48.51 348	32.11 355	52.37 347	65.79 353	40.39 297	41.91 364	31.85 343	61.97 353	60.35 350

PM-MVS		64.49 232	63.61 239	67.14 229	76.68 196	75.15 32	68.49 222	42.85 358	51.17 235	77.85 142	80.51 229	45.76 260	66.31 303	52.83 222	76.35 296	59.96 351

GG-mvs-BLEND				52.24 318	60.64 341	29.21 360	69.73 201	42.41 359		45.47 361	52.33 366	20.43 371	68.16 287	25.52 364	65.42 347	59.36 352

TESTMET0.1,1		45.17 328	44.93 333	45.89 337	56.02 361	38.31 308	53.18 335	41.94 364	27.85 363	44.86 364	56.47 363	17.93 374	41.50 365	38.08 314	68.06 341	57.85 353

MS-PatchMatch		55.59 294	54.89 302	57.68 303	69.18 282	49.05 220	61.00 304	62.93 289	35.98 334	58.36 325	68.93 341	36.71 316	66.59 301	37.62 318	63.30 352	57.39 354

dp		44.09 333	44.88 334	41.72 349	58.53 352	23.18 371	54.70 332	42.38 361	34.80 339	44.25 366	65.61 354	24.48 365	44.80 353	29.77 352	49.42 369	57.18 355

MVE	27.91 23	36.69 339	35.64 342	39.84 351	43.37 376	35.85 328	19.49 368	24.61 378	24.68 368	39.05 370	62.63 358	38.67 306	27.10 373	21.04 369	47.25 370	56.56 356
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
pmmvs3		46.71 325	45.09 332	51.55 320	56.76 358	48.25 226	55.78 328	39.53 370	24.13 369	50.35 354	63.40 356	15.90 378	51.08 337	29.29 355	70.69 330	55.33 357

PatchMatch-RL		58.68 282	57.72 285	61.57 277	76.21 201	73.59 44	61.83 297	49.00 347	47.30 271	61.08 311	68.97 340	50.16 241	59.01 327	36.06 330	68.84 339	52.10 358

wuyk23d		61.97 257	66.25 215	49.12 328	58.19 354	60.77 148	66.32 252	52.97 335	55.93 174	90.62 5	86.91 130	73.07 62	35.98 368	20.63 370	91.63 95	50.62 359

PMMVS2		37.74 337	40.87 338	28.36 354	42.41 377	5.35 379	24.61 367	27.75 376	32.15 353	47.85 358	70.27 330	35.85 318	29.51 371	19.08 371	67.85 342	50.22 360

DSMNet-mixed		43.18 334	44.66 335	38.75 352	54.75 367	28.88 361	57.06 323	27.42 377	13.47 371	47.27 360	77.67 269	38.83 304	39.29 367	25.32 365	60.12 357	48.08 361

new_pmnet		37.55 338	39.80 341	30.79 353	56.83 357	16.46 376	39.35 361	30.65 375	25.59 367	45.26 362	61.60 359	24.54 364	28.02 372	21.60 368	52.80 368	47.90 362

CHOSEN 280x420		41.62 335	39.89 340	46.80 333	61.81 332	51.59 203	33.56 366	35.74 373	27.48 364	37.64 372	53.53 364	23.24 368	42.09 362	27.39 359	58.64 360	46.72 363

EMVS		44.61 332	44.45 336	45.10 341	48.91 375	43.00 275	37.92 363	41.10 368	46.75 274	38.00 371	48.43 369	26.42 357	46.27 346	37.11 322	75.38 305	46.03 364

E-PMN		45.17 328	45.36 331	44.60 342	50.07 373	42.75 277	38.66 362	42.29 362	46.39 276	39.55 369	51.15 367	26.00 359	45.37 351	37.68 316	76.41 295	45.69 365

PMMVS		44.69 330	43.95 337	46.92 332	50.05 374	53.47 197	48.08 347	42.40 360	22.36 370	44.01 367	53.05 365	42.60 283	45.49 349	31.69 344	61.36 354	41.79 366

PVSNet_0	36.71 22	41.12 336	40.78 339	42.14 346	59.97 344	40.13 297	40.97 358	42.24 363	30.81 360	44.86 364	49.41 368	40.70 294	45.12 352	23.15 367	34.96 371	41.16 367

FPMVS		59.43 277	60.07 268	57.51 304	77.62 184	71.52 54	62.33 295	50.92 340	57.40 158	69.40 259	80.00 237	39.14 303	61.92 320	37.47 319	66.36 345	39.09 368

MVS-HIRNet		45.53 327	47.29 327	40.24 350	62.29 330	26.82 365	56.02 327	37.41 372	29.74 361	43.69 368	81.27 221	33.96 322	55.48 332	24.46 366	56.79 363	38.43 369

test_method		19.26 340	19.12 344	19.71 355	9.09 379	1.91 381	7.79 370	53.44 332	1.42 373	10.27 375	35.80 370	17.42 376	25.11 374	12.44 372	24.38 373	32.10 370

DeepMVS_CX				11.83 356	15.51 378	13.86 377		11.25 381	5.76 372	20.85 374	26.46 371	17.06 377	9.22 375	9.69 374	13.82 374	12.42 371

tmp_tt		11.98 342	14.73 345	3.72 357	2.28 380	4.62 380	19.44 369	14.50 380	0.47 375	21.55 373	9.58 373	25.78 361	4.57 376	11.61 373	27.37 372	1.96 372

testmvs		4.06 346	5.28 349	0.41 358	0.64 382	0.16 383	42.54 357	0.31 383	0.26 377	0.50 378	1.40 377	0.77 381	0.17 377	0.56 375	0.55 376	0.90 373

test123		4.43 345	5.78 348	0.39 359	0.97 381	0.28 382	46.33 353	0.45 382	0.31 376	0.62 377	1.50 376	0.61 382	0.11 378	0.56 375	0.63 375	0.77 374

test_blank		0.00 347	0.00 350	0.00 360	0.00 383	0.00 384	0.00 371	0.00 384	0.00 378	0.00 379	0.00 378	0.00 383	0.00 379	0.00 377	0.00 377	0.00 375

uanet_test		0.00 347	0.00 350	0.00 360	0.00 383	0.00 384	0.00 371	0.00 384	0.00 378	0.00 379	0.00 378	0.00 383	0.00 379	0.00 377	0.00 377	0.00 375

cdsmvs_eth3d_5k		17.71 341	23.62 343	0.00 360	0.00 383	0.00 384	0.00 371	70.17 251	0.00 378	0.00 379	74.25 298	68.16 102	0.00 379	0.00 377	0.00 377	0.00 375

pcd_1.5k_mvsjas		5.20 344	6.93 347	0.00 360	0.00 383	0.00 384	0.00 371	0.00 384	0.00 378	0.00 379	0.00 378	62.39 151	0.00 379	0.00 377	0.00 377	0.00 375

sosnet-low-res		0.00 347	0.00 350	0.00 360	0.00 383	0.00 384	0.00 371	0.00 384	0.00 378	0.00 379	0.00 378	0.00 383	0.00 379	0.00 377	0.00 377	0.00 375

sosnet		0.00 347	0.00 350	0.00 360	0.00 383	0.00 384	0.00 371	0.00 384	0.00 378	0.00 379	0.00 378	0.00 383	0.00 379	0.00 377	0.00 377	0.00 375

uncertanet		0.00 347	0.00 350	0.00 360	0.00 383	0.00 384	0.00 371	0.00 384	0.00 378	0.00 379	0.00 378	0.00 383	0.00 379	0.00 377	0.00 377	0.00 375

Regformer		0.00 347	0.00 350	0.00 360	0.00 383	0.00 384	0.00 371	0.00 384	0.00 378	0.00 379	0.00 378	0.00 383	0.00 379	0.00 377	0.00 377	0.00 375

ab-mvs-re		5.62 343	7.50 346	0.00 360	0.00 383	0.00 384	0.00 371	0.00 384	0.00 378	0.00 379	67.46 348	0.00 383	0.00 379	0.00 377	0.00 377	0.00 375

uanet		0.00 347	0.00 350	0.00 360	0.00 383	0.00 384	0.00 371	0.00 384	0.00 378	0.00 379	0.00 378	0.00 383	0.00 379	0.00 377	0.00 377	0.00 375

FOURS1					89.19 25	77.84 16	91.64 1	89.11 2	84.05 2	91.57 2

test_one_0601					85.84 67	61.45 137		85.63 33	75.27 19	85.62 47	90.38 64	76.72 29

eth-test2					0.00 383

eth-test					0.00 383

ZD-MVS					83.91 94	69.36 76		81.09 120	58.91 144	82.73 86	89.11 95	75.77 37	86.63 13	72.73 62	92.93 75

test_241102_ONE					86.12 59	61.06 140		84.72 55	72.64 31	87.38 25	89.47 84	77.48 24	85.74 43

9.14			80.22 59		80.68 138		80.35 74	87.69 11	59.90 133	83.00 80	88.20 114	74.57 51	81.75 119	73.75 56	93.78 61

save fliter					87.00 43	67.23 91	79.24 87	77.94 174	56.65 167

test0726					86.16 57	60.78 146	83.81 42	85.10 46	72.48 34	85.27 53	89.96 77	78.57 18

test_part2					85.90 63	66.44 98				84.61 63

sam_mvs												31.21 341

MTGPA								80.63 128

test_post1							66.63 249				2.08 374	30.66 346	59.33 326	40.34 300

test_post											1.99 375	30.91 344	54.76 334

patchmatchnet-post											68.99 339	31.32 338	69.38 280

MTMP							84.83 33	19.26 379

gm-plane-assit					62.51 329	33.91 341			37.25 329		62.71 357		72.74 248	38.70 307

TEST9					85.47 69	69.32 77	76.42 121	78.69 160	53.73 208	76.97 154	86.74 138	66.84 113	81.10 128

test_8					85.09 75	67.89 86	76.26 126	78.66 162	54.00 203	76.89 159	86.72 140	66.60 117	80.89 139

agg_prior					84.44 87	66.02 102		78.62 163		76.95 156			80.34 146

test_prior4						70.14 69	77.57 107

test_prior2							75.57 132		58.92 142	76.53 170	86.78 135	67.83 106		69.81 82	92.76 80

旧先验2							71.17 185		45.11 284	78.54 133			61.28 322	59.19 170

新几何2							71.33 181

原ACMM2							74.78 144

testdata2													67.30 294	48.34 253

segment_acmp												68.30 101

testdata1							68.34 224		57.24 159

plane_prior7					85.18 72	66.21 100

plane_prior6					84.18 91	65.31 107						60.83 169

plane_prior4											89.11 95

plane_prior3						65.67 104			63.82 101	78.23 136

plane_prior2							82.74 54		65.45 80

plane_prior1					84.46 86

plane_prior						65.18 108	80.06 81		61.88 119						89.91 139

n2								0.00 384

nn								0.00 384

door-mid								55.02 324

test11								82.71 94

door								52.91 336

HQP5-MVS						58.80 165

HQP-NCC					82.37 117		77.32 111		59.08 138	71.58 234

ACMP_Plane					82.37 117		77.32 111		59.08 138	71.58 234

BP-MVS														67.38 106

HQP3-MVS								84.12 74							89.16 152

HQP2-MVS												58.09 194

NP-MVS					83.34 102	63.07 128					85.97 166

MDTV_nov1_ep13			54.05 305		65.54 312	29.30 359	59.00 315	55.22 322	35.96 335	52.44 346	75.98 279	30.77 345	59.62 325	38.21 312	73.33 317

ACMMP++_ref															89.47 148

ACMMP++															91.96 90

Test By Simon												62.56 147

High-resolution multi-view benchmark