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 2	88.67 2	81.57 25	91.50 2	63.30 126	84.80 35	87.77 11	86.18 2	96.26 2	96.06 1	90.32 1	84.49 72	68.08 104	97.05 2	96.93 1

PEN-MVS		80.46 51	82.91 39	73.11 141	89.83 9	39.02 354	77.06 119	82.61 96	80.04 5	90.60 7	92.85 12	74.93 48	85.21 60	63.15 157	95.15 22	95.09 2

PS-CasMVS		80.41 52	82.86 41	73.07 142	89.93 7	39.21 351	77.15 117	81.28 118	79.74 6	90.87 5	92.73 14	75.03 47	84.93 65	63.83 149	95.19 20	95.07 3

CP-MVSNet		79.48 59	81.65 50	72.98 145	89.66 13	39.06 353	76.76 120	80.46 139	78.91 9	90.32 8	91.70 33	68.49 100	84.89 66	63.40 154	95.12 23	95.01 4

WR-MVS_H		80.22 55	82.17 46	74.39 119	89.46 15	42.69 322	78.24 103	82.24 100	78.21 13	89.57 10	92.10 21	68.05 105	85.59 50	66.04 128	95.62 10	94.88 5

DTE-MVSNet		80.35 53	82.89 40	72.74 159	89.84 8	37.34 371	77.16 116	81.81 108	80.45 4	90.92 4	92.95 10	74.57 51	86.12 31	63.65 150	94.68 36	94.76 6

mamv4		90.28 1	88.75 1	94.85 1	93.34 1	96.17 1	82.69 58	91.63 1	86.34 1	97.97 1	94.77 3	66.57 126	95.38 1	87.74 1	97.72 1	93.00 7

TDRefinement		86.32 3	86.33 3	86.29 2	88.64 32	81.19 5	88.84 4	90.72 2	78.27 12	87.95 18	92.53 16	79.37 15	84.79 69	74.51 56	96.15 3	92.88 8

DU-MVS		74.91 107	75.57 101	72.93 149	83.50 96	45.79 294	69.47 228	80.14 146	65.22 92	81.74 98	87.08 140	61.82 169	81.07 133	56.21 220	94.98 25	91.93 9

NR-MVSNet		73.62 120	74.05 120	72.33 169	83.50 96	43.71 310	65.65 290	77.32 195	64.32 103	75.59 193	87.08 140	62.45 162	81.34 125	54.90 234	95.63 9	91.93 9

v7n		79.37 61	80.41 57	76.28 96	78.67 170	55.81 199	79.22 92	82.51 98	70.72 50	87.54 25	92.44 17	68.00 107	81.34 125	72.84 69	91.72 88	91.69 11

TranMVSNet+NR-MVSNet		76.13 88	77.66 80	71.56 178	84.61 82	42.57 324	70.98 209	78.29 182	68.67 62	83.04 80	89.26 94	72.99 62	80.75 142	55.58 229	95.47 12	91.35 12

FC-MVSNet-test		73.32 127	74.78 107	68.93 229	79.21 156	36.57 373	71.82 196	79.54 158	57.63 168	82.57 89	90.38 71	59.38 200	78.99 169	57.91 205	94.56 38	91.23 13

v10		75.69 92	76.20 94	74.16 122	74.44 237	48.69 257	75.84 139	82.93 89	59.02 152	85.92 45	89.17 99	58.56 210	82.74 104	70.73 84	89.14 156	91.05 14

UniMVSNet_NR-MVSNet		74.90 108	75.65 99	72.64 162	83.04 106	45.79 294	69.26 233	78.81 168	66.66 76	81.74 98	86.88 147	63.26 153	81.07 133	56.21 220	94.98 25	91.05 14

UniMVSNet (Re)		75.00 105	75.48 102	73.56 133	83.14 101	47.92 268	70.41 218	81.04 126	63.67 110	79.54 122	86.37 167	62.83 157	81.82 118	57.10 212	95.25 16	90.94 16

anonymousdsp		78.60 66	77.80 78	81.00 35	78.01 177	74.34 37	80.09 82	76.12 209	50.51 262	89.19 11	90.88 49	71.45 73	77.78 199	73.38 65	90.60 124	90.90 17

v8		75.07 103	75.64 100	73.35 135	73.42 253	47.46 278	75.20 142	81.45 114	60.05 142	85.64 49	89.26 94	58.08 219	81.80 120	69.71 95	87.97 176	90.79 18

IS-MVSNet		75.10 102	75.42 103	74.15 123	79.23 155	48.05 266	79.43 88	78.04 186	70.09 55	79.17 127	88.02 129	53.04 257	83.60 85	58.05 204	93.76 63	90.79 18

FIs		72.56 150	73.80 124	68.84 232	78.74 169	37.74 367	71.02 208	79.83 151	56.12 184	80.88 112	89.45 91	58.18 213	78.28 188	56.63 214	93.36 68	90.51 20

test_djsdf		78.88 64	78.27 74	80.70 39	81.42 128	71.24 56	83.98 41	75.72 214	52.27 237	87.37 30	92.25 19	68.04 106	80.56 143	72.28 76	91.15 103	90.32 21

WR-MVS		71.20 171	72.48 157	67.36 253	84.98 75	35.70 381	64.43 312	68.66 292	65.05 96	81.49 101	86.43 166	57.57 225	76.48 214	50.36 273	93.32 69	89.90 22

BP-MVS1		71.60 164	70.06 190	76.20 98	74.07 244	55.22 205	74.29 161	73.44 231	57.29 170	73.87 233	84.65 196	32.57 376	83.49 89	72.43 75	87.94 177	89.89 23

OMC-MVS		79.41 60	78.79 68	81.28 33	80.62 137	70.71 62	80.91 70	84.76 51	62.54 123	81.77 96	86.65 158	71.46 72	83.53 88	67.95 108	92.44 79	89.60 24

tttt0517		69.46 198	67.79 228	74.46 115	75.34 219	52.72 222	75.05 144	63.27 331	54.69 203	78.87 131	84.37 203	26.63 411	81.15 129	63.95 146	87.93 178	89.51 25

v2v482		72.55 152	72.58 154	72.43 166	72.92 268	46.72 286	71.41 201	79.13 163	55.27 193	81.17 106	85.25 190	55.41 244	81.13 130	67.25 121	85.46 217	89.43 26

Anonymous20231211		75.54 95	77.19 86	70.59 191	77.67 183	45.70 297	74.73 152	80.19 144	68.80 59	82.95 83	92.91 11	66.26 128	76.76 212	58.41 202	92.77 75	89.30 27

Elysia		77.52 77	77.43 81	77.78 76	79.01 163	60.26 158	76.55 122	84.34 64	67.82 66	78.73 132	87.94 130	58.68 208	83.79 81	74.70 52	89.10 159	89.28 28

StellarMVS		77.52 77	77.43 81	77.78 76	79.01 163	60.26 158	76.55 122	84.34 64	67.82 66	78.73 132	87.94 130	58.68 208	83.79 81	74.70 52	89.10 159	89.28 28

OurMVSNet-221017-0		78.57 67	78.53 72	78.67 63	80.48 138	64.16 118	80.24 80	82.06 103	61.89 127	88.77 16	93.32 6	57.15 229	82.60 106	70.08 90	92.80 74	89.25 30

EI-MVSNet-UG-set		72.63 148	71.68 168	75.47 108	74.67 231	58.64 179	72.02 187	71.50 254	63.53 112	78.58 137	71.39 371	65.98 130	78.53 177	67.30 120	80.18 300	89.23 31

V42		71.06 173	70.83 182	71.72 176	67.25 347	47.14 283	65.94 284	80.35 143	51.35 252	83.40 79	83.23 230	59.25 201	78.80 172	65.91 129	80.81 288	89.23 31

RPSCF		75.76 91	74.37 112	79.93 44	74.81 228	77.53 18	77.53 111	79.30 161	59.44 147	78.88 130	89.80 86	71.26 75	73.09 253	57.45 208	80.89 285	89.17 33

UniMVSNet_ETH3D		76.74 85	79.02 66	69.92 208	89.27 20	43.81 309	74.47 158	71.70 249	72.33 41	85.50 54	93.65 4	77.98 24	76.88 210	54.60 239	91.64 90	89.08 34

v1192		73.40 125	73.42 132	73.32 137	74.65 234	48.67 258	72.21 182	81.73 109	52.76 233	81.85 94	84.56 199	57.12 230	82.24 113	68.58 99	87.33 188	89.06 35

3Dnovator+	73.19 2	81.08 44	80.48 56	82.87 8	81.41 129	72.03 49	84.38 39	86.23 24	77.28 18	80.65 113	90.18 80	59.80 197	87.58 6	73.06 67	91.34 98	89.01 36

EI-MVSNet-Vis-set		72.78 145	71.87 164	75.54 107	74.77 229	59.02 172	72.24 181	71.56 253	63.92 106	78.59 135	71.59 367	66.22 129	78.60 176	67.58 110	80.32 297	89.00 37

v1144		73.29 128	73.39 133	73.01 143	74.12 243	48.11 264	72.01 188	81.08 125	53.83 225	81.77 96	84.68 194	58.07 220	81.91 117	68.10 103	86.86 199	88.99 38

nrg030		74.87 110	75.99 97	71.52 179	74.90 226	49.88 250	74.10 164	82.58 97	54.55 208	83.50 78	89.21 96	71.51 71	75.74 221	61.24 171	92.34 82	88.94 39

v1240		73.06 134	73.14 140	72.84 155	74.74 230	47.27 282	71.88 195	81.11 122	51.80 243	82.28 91	84.21 205	56.22 240	82.34 110	68.82 98	87.17 196	88.91 40

LTVRE_ROB	75.46 1	84.22 10	84.98 12	81.94 24	84.82 77	75.40 29	91.60 3	87.80 9	73.52 29	88.90 15	93.06 9	71.39 74	81.53 123	81.53 5	92.15 85	88.91 40
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		72.96 141	72.98 146	72.89 152	74.67 231	47.58 275	71.92 193	80.69 131	51.70 245	81.69 100	83.89 214	56.58 236	82.25 112	68.34 101	87.36 185	88.82 42

EPP-MVSNet		73.86 118	73.38 134	75.31 109	78.19 173	53.35 220	80.45 74	77.32 195	65.11 95	76.47 182	86.80 148	49.47 280	83.77 83	53.89 248	92.72 77	88.81 43

UA-Net		81.56 38	82.28 45	79.40 52	88.91 29	69.16 77	84.67 36	80.01 149	75.34 19	79.80 120	94.91 2	69.79 92	80.25 150	72.63 71	94.46 40	88.78 44

v144192		72.99 138	73.06 144	72.77 157	74.58 235	47.48 277	71.90 194	80.44 140	51.57 246	81.46 102	84.11 209	58.04 221	82.12 114	67.98 107	87.47 183	88.70 45

EI-MVSNet		69.61 196	69.01 205	71.41 181	73.94 246	49.90 246	71.31 204	71.32 259	58.22 158	75.40 199	70.44 374	58.16 214	75.85 217	62.51 160	79.81 306	88.48 46

IterMVS-LS		73.01 136	73.12 142	72.66 161	73.79 249	49.90 246	71.63 198	78.44 178	58.22 158	80.51 114	86.63 159	58.15 215	79.62 159	62.51 160	88.20 170	88.48 46
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
casdiffmvs_mvg		75.26 99	76.18 95	72.52 164	72.87 269	49.47 251	72.94 175	84.71 55	59.49 146	80.90 111	88.81 110	70.07 88	79.71 158	67.40 114	88.39 168	88.40 48
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
GDP-MVS		70.84 177	69.24 200	75.62 105	76.44 203	55.65 201	74.62 157	82.78 92	49.63 273	72.10 257	83.79 216	31.86 384	82.84 102	64.93 136	87.01 198	88.39 49

HPM-MVS_fast		84.59 8	85.10 10	83.06 5	88.60 33	75.83 27	86.27 27	86.89 17	73.69 27	86.17 41	91.70 33	78.23 22	85.20 61	79.45 17	94.91 29	88.15 50

tt0320-xc		71.50 166	73.63 129	65.08 276	79.77 146	40.46 344	64.80 304	68.86 288	67.08 70	76.84 167	93.24 7	70.33 84	66.77 333	49.76 277	92.02 86	88.02 51

COLMAP_ROB	72.78 3	83.75 15	84.11 20	82.68 13	82.97 108	74.39 36	87.18 11	88.18 8	78.98 8	86.11 44	91.47 38	79.70 14	85.76 45	66.91 123	95.46 13	87.89 52
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 76	77.35 85	78.13 73	84.88 76	66.37 98	78.55 98	79.59 156	53.48 228	86.29 40	92.43 18	62.39 163	80.25 150	67.90 109	90.61 123	87.77 53

eth_miper_zixun_eth		69.42 199	68.73 211	71.50 180	67.99 336	46.42 289	67.58 260	78.81 168	50.72 260	78.13 142	80.34 274	50.15 277	80.34 148	60.18 182	84.65 233	87.74 54

casdiffmvs		73.06 134	73.84 123	70.72 189	71.32 287	46.71 287	70.93 210	84.26 68	55.62 190	77.46 154	87.10 139	67.09 115	77.81 197	63.95 146	86.83 201	87.64 55
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
LS3D		80.99 46	80.85 54	81.41 29	78.37 171	71.37 54	87.45 8	85.87 28	77.48 16	81.98 93	89.95 84	69.14 95	85.26 57	66.15 125	91.24 100	87.61 56

ITE_SJBPF				80.35 42	76.94 194	73.60 42		80.48 138	66.87 72	83.64 77	86.18 172	70.25 87	79.90 156	61.12 174	88.95 163	87.56 57

thisisatest0530		67.05 240	65.16 262	72.73 160	73.10 262	50.55 236	71.26 206	63.91 326	50.22 266	74.46 220	80.75 266	26.81 410	80.25 150	59.43 193	86.50 206	87.37 58

CS-MVS		76.51 86	76.00 96	78.06 74	77.02 191	64.77 114	80.78 71	82.66 95	60.39 140	74.15 225	83.30 227	69.65 93	82.07 115	69.27 96	86.75 203	87.36 59

pmmvs6		71.82 161	73.66 127	66.31 266	75.94 213	42.01 326	66.99 271	72.53 243	63.45 114	76.43 183	92.78 13	72.95 63	69.69 298	51.41 264	90.46 125	87.22 60

tt0320		71.34 169	73.47 131	64.97 278	79.92 144	40.81 337	65.22 296	69.07 287	66.72 75	76.15 188	93.36 5	70.35 83	66.90 327	49.31 285	91.09 108	87.21 61

ACMH+	66.64 10	81.20 41	82.48 44	77.35 84	81.16 133	62.39 131	80.51 73	87.80 9	73.02 31	87.57 24	91.08 44	80.28 9	82.44 107	64.82 137	96.10 5	87.21 61

c3_l		69.82 193	69.89 193	69.61 211	66.24 358	43.48 313	68.12 255	79.61 155	51.43 248	77.72 149	80.18 278	54.61 248	78.15 193	63.62 151	87.50 182	87.20 63

Anonymous20240529		72.56 150	73.79 125	68.86 231	76.89 198	45.21 300	68.80 243	77.25 197	67.16 69	76.89 163	90.44 63	65.95 131	74.19 244	50.75 269	90.00 133	87.18 64

tt0805		76.12 89	78.43 73	69.20 219	81.32 130	41.37 330	76.72 121	77.64 191	63.78 109	82.06 92	87.88 132	79.78 11	79.05 167	64.33 141	92.40 80	87.17 65

baseline		73.10 131	73.96 122	70.51 193	71.46 285	46.39 291	72.08 185	84.40 63	55.95 187	76.62 173	86.46 165	67.20 113	78.03 194	64.22 142	87.27 192	87.11 66

Effi-MVS+-dtu		75.43 97	72.28 161	84.91 3	77.05 189	83.58 2	78.47 99	77.70 190	57.68 164	74.89 208	78.13 314	64.80 144	84.26 77	56.46 218	85.32 222	86.88 67

v148		69.38 201	69.39 197	69.36 215	69.14 322	44.56 304	68.83 240	72.70 241	54.79 201	78.59 135	84.12 207	54.69 246	76.74 213	59.40 194	82.20 266	86.79 68

HPM-MVS		84.12 12	84.63 14	82.60 14	88.21 36	74.40 35	85.24 31	87.21 15	70.69 51	85.14 58	90.42 65	78.99 17	86.62 15	80.83 7	94.93 28	86.79 68
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
mvs_tets		78.93 63	78.67 70	79.72 47	84.81 78	73.93 39	80.65 72	76.50 204	51.98 242	87.40 27	91.86 29	76.09 37	78.53 177	68.58 99	90.20 128	86.69 70

EC-MVSNet		77.08 82	77.39 84	76.14 99	76.86 199	56.87 192	80.32 79	87.52 13	63.45 114	74.66 214	84.52 201	69.87 91	84.94 64	69.76 93	89.59 144	86.60 71

fmvsm_s_conf0.1_n_2		69.14 204	68.42 214	71.28 182	68.30 332	57.60 188	65.06 299	69.91 277	48.24 291	74.56 218	82.84 233	55.55 243	69.73 296	70.66 86	80.69 291	86.52 72

jajsoiax		78.51 68	78.16 76	79.59 49	84.65 81	73.83 41	80.42 75	76.12 209	51.33 253	87.19 32	91.51 37	73.79 58	78.44 181	68.27 102	90.13 132	86.49 73

sc_t1		72.50 153	74.23 115	67.33 254	80.05 142	46.99 284	66.58 279	69.48 282	66.28 79	77.62 152	91.83 30	70.98 79	68.62 309	53.86 250	91.40 96	86.37 74

KinetiMVS		72.61 149	72.54 155	72.82 156	71.47 284	55.27 204	68.54 249	76.50 204	61.70 129	74.95 207	86.08 178	59.17 202	76.95 207	69.96 91	84.45 238	86.24 75

cl22		67.14 236	66.51 246	69.03 225	63.20 380	43.46 314	66.88 275	76.25 207	49.22 279	74.48 219	77.88 316	45.49 304	77.40 203	60.64 178	84.59 235	86.24 75

MP-MVS-pluss		82.54 31	83.46 30	79.76 45	88.88 31	68.44 81	81.57 65	86.33 20	63.17 118	85.38 56	91.26 41	76.33 34	84.67 71	83.30 2	94.96 27	86.17 77
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
LPG-MVS_test		83.47 20	84.33 17	80.90 36	87.00 40	70.41 64	82.04 62	86.35 18	69.77 56	87.75 19	91.13 42	81.83 3	86.20 26	77.13 40	95.96 6	86.08 78

LGP-MVS_train				80.90 36	87.00 40	70.41 64		86.35 18	69.77 56	87.75 19	91.13 42	81.83 3	86.20 26	77.13 40	95.96 6	86.08 78

SixPastTwentyTwo		75.77 90	76.34 92	74.06 124	81.69 126	54.84 207	76.47 124	75.49 216	64.10 105	87.73 21	92.24 20	50.45 275	81.30 127	67.41 113	91.46 95	86.04 80

MVSMamba_PlusPlus		76.88 83	78.21 75	72.88 153	80.83 134	48.71 256	83.28 53	82.79 90	72.78 32	79.17 127	91.94 25	56.47 238	83.95 78	70.51 88	86.15 208	85.99 81

APD-MVS_3200maxsize		83.57 17	84.33 17	81.31 32	82.83 111	73.53 44	85.50 30	87.45 14	74.11 23	86.45 39	90.52 62	80.02 10	84.48 73	77.73 32	94.34 51	85.93 82

DeepC-MVS	72.44 4	81.00 45	80.83 55	81.50 26	86.70 45	70.03 68	82.06 61	87.00 16	59.89 144	80.91 110	90.53 60	72.19 65	88.56 2	73.67 64	94.52 39	85.92 83
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		68.27 221	68.26 217	68.29 240	64.98 371	43.67 311	65.89 285	74.67 222	50.04 269	76.86 165	82.43 238	48.74 290	75.38 223	60.94 175	89.81 139	85.81 84

AllTest		77.66 75	77.43 81	78.35 69	79.19 157	70.81 59	78.60 97	88.64 4	65.37 89	80.09 118	88.17 125	70.33 84	78.43 182	55.60 226	90.90 115	85.81 84

TestCases				78.35 69	79.19 157	70.81 59		88.64 4	65.37 89	80.09 118	88.17 125	70.33 84	78.43 182	55.60 226	90.90 115	85.81 84

ACMP	69.50 8	82.64 30	83.38 31	80.40 41	86.50 46	69.44 72	82.30 59	86.08 25	66.80 73	86.70 35	89.99 82	81.64 6	85.95 35	74.35 58	96.11 4	85.81 84
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
fmvsm_s_conf0.5_n_2		68.93 207	68.23 219	71.02 186	67.78 340	57.58 189	64.74 306	69.56 281	48.16 293	74.38 222	82.32 241	56.00 242	69.68 299	70.65 87	80.52 295	85.80 88

cl____		68.26 222	68.26 217	68.29 240	64.98 371	43.67 311	65.89 285	74.67 222	50.04 269	76.86 165	82.42 239	48.74 290	75.38 223	60.92 176	89.81 139	85.80 88

SPE-MVS-test		74.89 109	74.23 115	76.86 87	77.01 192	62.94 129	78.98 94	84.61 60	58.62 155	70.17 284	80.80 265	66.74 123	81.96 116	61.74 166	89.40 151	85.69 90

miper_ehance_all_eth		68.36 217	68.16 222	68.98 226	65.14 370	43.34 315	67.07 270	78.92 167	49.11 281	76.21 186	77.72 317	53.48 254	77.92 196	61.16 173	84.59 235	85.68 91

lecture		83.41 21	85.02 11	78.58 65	83.87 94	67.26 90	84.47 37	88.27 7	73.64 28	87.35 31	91.96 24	78.55 21	82.92 100	81.59 4	95.50 11	85.56 92

test_fmvsm_n_1920		69.63 194	68.45 213	73.16 139	70.56 298	65.86 104	70.26 219	78.35 179	37.69 382	74.29 223	78.89 304	61.10 181	68.10 314	65.87 130	79.07 313	85.53 93

MM		78.15 74	77.68 79	79.55 50	80.10 141	65.47 106	80.94 69	78.74 172	71.22 46	72.40 252	88.70 111	60.51 186	87.70 4	77.40 37	89.13 157	85.48 94

SteuartSystems-ACMMP		83.07 26	83.64 27	81.35 30	85.14 73	71.00 58	85.53 29	84.78 50	70.91 49	85.64 49	90.41 66	75.55 42	87.69 5	79.75 12	95.08 24	85.36 95
Skip Steuart: Steuart Systems R&D Blog.
balanced_conf03		73.59 121	74.06 119	72.17 173	77.48 186	47.72 273	81.43 66	82.20 101	54.38 209	79.19 126	87.68 134	54.41 249	83.57 86	63.98 145	85.78 214	85.22 96

diffmvs		67.42 233	67.50 231	67.20 256	62.26 385	45.21 300	64.87 302	77.04 200	48.21 292	71.74 259	79.70 286	58.40 212	71.17 281	64.99 134	80.27 298	85.22 96
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
Baseline_NR-MVSNet		70.62 180	73.19 139	62.92 301	76.97 193	34.44 389	68.84 238	70.88 270	60.25 141	79.50 123	90.53 60	61.82 169	69.11 303	54.67 238	95.27 15	85.22 96

TAPA-MVS	65.27 12	75.16 101	74.29 114	77.77 78	74.86 227	68.08 82	77.89 107	84.04 75	55.15 195	76.19 187	83.39 221	66.91 117	80.11 154	60.04 187	90.14 131	85.13 99
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
fmvsm_s_conf0.5_n_4		70.18 186	69.83 195	71.24 184	71.65 281	58.59 180	69.29 232	71.66 250	48.69 288	71.62 261	82.11 243	59.94 193	70.03 294	74.52 55	78.96 315	85.10 100

CLD-MVS		72.88 143	72.36 160	74.43 118	77.03 190	54.30 211	68.77 244	83.43 82	52.12 239	76.79 169	74.44 345	69.54 94	83.91 79	55.88 223	93.25 70	85.09 101
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		66.38 247	67.51 230	62.97 299	61.76 387	34.39 390	58.11 362	75.30 217	50.84 259	77.12 158	85.42 187	56.84 234	69.44 300	51.07 267	91.16 102	85.08 102

CDPH-MVS		77.33 80	77.06 88	78.14 72	84.21 88	63.98 121	76.07 135	83.45 81	54.20 216	77.68 151	87.18 138	69.98 89	85.37 53	68.01 106	92.72 77	85.08 102

K. test v3		73.67 119	73.61 130	73.87 127	79.78 145	55.62 203	74.69 154	62.04 338	66.16 81	84.76 64	93.23 8	49.47 280	80.97 137	65.66 131	86.67 204	85.02 104

SR-MVS-dyc-post		84.75 7	85.26 9	83.21 4	86.19 50	79.18 7	87.23 9	86.27 21	77.51 14	87.65 22	90.73 54	79.20 16	85.58 51	78.11 28	94.46 40	84.89 105

RE-MVS-def			85.50 7		86.19 50	79.18 7	87.23 9	86.27 21	77.51 14	87.65 22	90.73 54	81.38 7		78.11 28	94.46 40	84.89 105

MVS_0304		75.45 96	74.66 108	77.83 75	75.58 218	61.53 139	78.29 101	77.18 198	63.15 120	69.97 287	87.20 137	57.54 226	87.05 10	74.05 60	88.96 162	84.89 105

test2506		61.23 301	60.85 302	62.38 305	78.80 167	27.88 420	67.33 267	37.42 439	54.23 214	67.55 320	88.68 113	17.87 443	74.39 241	46.33 314	89.41 149	84.86 108

ECVR-MVS		64.82 262	65.22 260	63.60 289	78.80 167	31.14 406	66.97 272	56.47 364	54.23 214	69.94 288	88.68 113	37.23 356	74.81 236	45.28 324	89.41 149	84.86 108

HQP_MVS		78.77 65	78.78 69	78.72 62	85.18 70	65.18 110	82.74 56	85.49 33	65.45 86	78.23 140	89.11 101	60.83 184	86.15 29	71.09 81	90.94 111	84.82 110

plane_prior5								85.49 33					86.15 29	71.09 81	90.94 111	84.82 110

SF-MVS		80.72 48	81.80 47	77.48 80	82.03 121	64.40 117	83.41 51	88.46 6	65.28 91	84.29 69	89.18 98	73.73 59	83.22 94	76.01 42	93.77 62	84.81 112

fmvsm_s_conf0.5_n_8		72.87 144	72.85 148	72.93 149	72.25 275	59.01 173	72.35 179	80.13 147	56.32 182	75.74 191	84.12 207	60.14 190	75.05 232	71.71 79	82.90 258	84.75 113

alignmvs		70.54 181	71.00 180	69.15 221	73.50 251	48.04 267	69.85 225	79.62 153	53.94 224	76.54 178	82.00 245	59.00 204	74.68 237	57.32 209	87.21 194	84.72 114

IU-MVS					86.12 54	60.90 150		80.38 141	45.49 318	81.31 103				75.64 46	94.39 45	84.65 115

XVS		83.51 19	83.73 25	82.85 9	89.43 16	77.61 16	86.80 20	84.66 57	72.71 33	82.87 84	90.39 69	73.86 56	86.31 21	78.84 24	94.03 57	84.64 116

X-MVStestdata		76.81 84	74.79 106	82.85 9	89.43 16	77.61 16	86.80 20	84.66 57	72.71 33	82.87 84	9.95 445	73.86 56	86.31 21	78.84 24	94.03 57	84.64 116

ACMMP		84.22 10	84.84 13	82.35 18	89.23 22	76.66 26	87.65 7	85.89 27	71.03 48	85.85 46	90.58 58	78.77 18	85.78 44	79.37 20	95.17 21	84.62 118
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 33	82.68 43	80.43 40	88.90 30	69.52 70	85.12 32	84.76 51	63.53 112	84.23 70	91.47 38	72.02 68	87.16 8	79.74 14	94.36 49	84.61 119
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		70.81 178	71.44 176	68.91 230	79.07 162	46.51 288	67.82 258	70.83 271	61.23 131	74.07 228	88.69 112	59.86 195	75.62 222	51.11 266	90.28 127	84.61 119

ZNCC-MVS		83.12 25	83.68 26	81.45 28	89.14 25	73.28 46	86.32 26	85.97 26	67.39 68	84.02 72	90.39 69	74.73 49	86.46 17	80.73 8	94.43 44	84.60 121

test1111		64.62 265	65.19 261	62.93 300	79.01 163	29.91 412	65.45 293	54.41 374	54.09 219	71.47 270	88.48 118	37.02 357	74.29 243	46.83 310	89.94 137	84.58 122

miper_enhance_ethall		65.86 253	65.05 269	68.28 242	61.62 389	42.62 323	64.74 306	77.97 187	42.52 344	73.42 239	72.79 360	49.66 278	77.68 200	58.12 203	84.59 235	84.54 123

GBi-Net		68.30 218	68.79 207	66.81 260	73.14 259	40.68 340	71.96 190	73.03 234	54.81 198	74.72 211	90.36 74	48.63 292	75.20 229	47.12 305	85.37 218	84.54 123

test1		68.30 218	68.79 207	66.81 260	73.14 259	40.68 340	71.96 190	73.03 234	54.81 198	74.72 211	90.36 74	48.63 292	75.20 229	47.12 305	85.37 218	84.54 123

FMVSNet1		71.06 173	72.48 157	66.81 260	77.65 184	40.68 340	71.96 190	73.03 234	61.14 132	79.45 124	90.36 74	60.44 187	75.20 229	50.20 274	88.05 173	84.54 123

TSAR-MVS + MP.		79.05 62	78.81 67	79.74 46	88.94 28	67.52 88	86.61 22	81.38 116	51.71 244	77.15 157	91.42 40	65.49 136	87.20 7	79.44 18	87.17 196	84.51 127
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
PCF-MVS	63.80 13	72.70 147	71.69 167	75.72 103	78.10 174	60.01 161	73.04 173	81.50 112	45.34 321	79.66 121	84.35 204	65.15 141	82.65 105	48.70 290	89.38 152	84.50 128
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
sasdasda		72.29 156	73.38 134	69.04 223	74.23 238	47.37 279	73.93 166	83.18 83	54.36 210	76.61 174	81.64 255	72.03 66	75.34 225	57.12 210	87.28 190	84.40 129

canonicalmvs		72.29 156	73.38 134	69.04 223	74.23 238	47.37 279	73.93 166	83.18 83	54.36 210	76.61 174	81.64 255	72.03 66	75.34 225	57.12 210	87.28 190	84.40 129

TransMVSNet (Re)		69.62 195	71.63 170	63.57 290	76.51 202	35.93 379	65.75 289	71.29 261	61.05 133	75.02 205	89.90 85	65.88 133	70.41 291	49.79 276	89.48 147	84.38 131

OPM-MVS		80.99 46	81.63 51	79.07 56	86.86 44	69.39 73	79.41 90	84.00 76	65.64 83	85.54 53	89.28 93	76.32 35	83.47 90	74.03 61	93.57 66	84.35 132
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
test_0728_THIRD									74.03 25	85.83 47	90.41 66	75.58 41	85.69 47	77.43 35	94.74 34	84.31 133

MSP-MVS		80.49 50	79.67 63	82.96 6	89.70 12	77.46 23	87.16 12	85.10 44	64.94 99	81.05 107	88.38 121	57.10 231	87.10 9	79.75 12	83.87 245	84.31 133
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 29	83.27 34	81.34 31	88.99 27	73.29 45	85.94 28	85.13 42	68.58 63	84.14 71	90.21 79	73.37 60	86.41 18	79.09 23	93.98 60	84.30 135

MGCFI-Net		71.70 163	73.10 143	67.49 251	73.23 257	43.08 318	72.06 186	82.43 99	54.58 206	75.97 189	82.00 245	72.42 64	75.22 227	57.84 206	87.34 187	84.18 136

ACMMPR		83.62 16	83.93 22	82.69 12	89.78 11	77.51 22	87.01 17	84.19 71	70.23 52	84.49 67	90.67 57	75.15 45	86.37 20	79.58 15	94.26 53	84.18 136

VDDNet		71.60 164	73.13 141	67.02 259	86.29 48	41.11 332	69.97 222	66.50 304	68.72 61	74.74 210	91.70 33	59.90 194	75.81 219	48.58 292	91.72 88	84.15 138

FA-MVS(test-final)		71.27 170	71.06 179	71.92 175	73.96 245	52.32 225	76.45 126	76.12 209	59.07 151	74.04 230	86.18 172	52.18 262	79.43 163	59.75 191	81.76 274	84.03 139

MVS_Test		69.84 192	70.71 185	67.24 255	67.49 345	43.25 317	69.87 224	81.22 121	52.69 234	71.57 266	86.68 155	62.09 167	74.51 239	66.05 127	78.74 317	83.96 140

region2R		83.54 18	83.86 24	82.58 15	89.82 10	77.53 18	87.06 16	84.23 70	70.19 54	83.86 74	90.72 56	75.20 44	86.27 23	79.41 19	94.25 54	83.95 141

test_fmvsmconf0.01_n		73.91 116	73.64 128	74.71 112	69.79 316	66.25 99	75.90 137	79.90 150	46.03 312	76.48 181	85.02 192	67.96 109	73.97 246	74.47 57	87.22 193	83.90 142

PGM-MVS		83.07 26	83.25 35	82.54 16	89.57 14	77.21 24	82.04 62	85.40 37	67.96 65	84.91 63	90.88 49	75.59 40	86.57 16	78.16 27	94.71 35	83.82 143

pm-mvs1		68.40 216	69.85 194	64.04 286	73.10 262	39.94 348	64.61 310	70.50 273	55.52 191	73.97 231	89.33 92	63.91 151	68.38 311	49.68 279	88.02 174	83.81 144

MSC_two_6792asdad				79.02 57	83.14 101	67.03 93		80.75 129					86.24 24	77.27 38	94.85 30	83.78 145

No_MVS				79.02 57	83.14 101	67.03 93		80.75 129					86.24 24	77.27 38	94.85 30	83.78 145

HQP4-MVS										71.59 262			85.31 54			83.74 147

HQP-MVS		75.24 100	75.01 105	75.94 100	82.37 115	58.80 176	77.32 113	84.12 72	59.08 148	71.58 263	85.96 182	58.09 217	85.30 55	67.38 117	89.16 153	83.73 148

PHI-MVS		74.92 106	74.36 113	76.61 90	76.40 204	62.32 132	80.38 76	83.15 85	54.16 218	73.23 242	80.75 266	62.19 166	83.86 80	68.02 105	90.92 114	83.65 149

test_fmvsmconf0.1_n		73.26 129	72.82 151	74.56 114	69.10 323	66.18 101	74.65 156	79.34 160	45.58 315	75.54 195	83.91 213	67.19 114	73.88 249	73.26 66	86.86 199	83.63 150

RRT-MVS		70.33 183	70.73 184	69.14 222	71.93 279	45.24 299	75.10 143	75.08 221	60.85 137	78.62 134	87.36 136	49.54 279	78.64 175	60.16 183	77.90 330	83.55 151

DeepPCF-MVS	71.07 5	78.48 70	77.14 87	82.52 17	84.39 87	77.04 25	76.35 129	84.05 74	56.66 179	80.27 117	85.31 189	68.56 99	87.03 12	67.39 115	91.26 99	83.50 152

DVP-MVS++		81.24 40	82.74 42	76.76 88	83.14 101	60.90 150	91.64 1	85.49 33	74.03 25	84.93 60	90.38 71	66.82 119	85.90 40	77.43 35	90.78 119	83.49 153

PC_three_1452									46.98 306	81.83 95	86.28 168	66.55 127	84.47 74	63.31 156	90.78 119	83.49 153

XVG-ACMP-BASELINE		80.54 49	81.06 53	78.98 59	87.01 39	72.91 47	80.23 81	85.56 32	66.56 77	85.64 49	89.57 89	69.12 96	80.55 145	72.51 73	93.37 67	83.48 155

APDe-MVS		82.88 28	84.14 19	79.08 55	84.80 79	66.72 96	86.54 23	85.11 43	72.00 43	86.65 36	91.75 32	78.20 23	87.04 11	77.93 30	94.32 52	83.47 156
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
ANet_high		67.08 238	69.94 192	58.51 339	57.55 414	27.09 422	58.43 359	76.80 202	63.56 111	82.40 90	91.93 26	59.82 196	64.98 346	50.10 275	88.86 164	83.46 157

Effi-MVS+		72.10 158	72.28 161	71.58 177	74.21 241	50.33 239	74.72 153	82.73 93	62.62 122	70.77 276	76.83 325	69.96 90	80.97 137	60.20 181	78.43 322	83.45 158

test_fmvsmconf_n		72.91 142	72.40 159	74.46 115	68.62 327	66.12 102	74.21 163	78.80 170	45.64 314	74.62 216	83.25 229	66.80 122	73.86 250	72.97 68	86.66 205	83.39 159

test12				76.51 92	82.28 118	60.94 149		81.64 111		73.60 235		64.88 143	85.19 62		90.42 126	83.38 160

VPA-MVSNet		68.71 213	70.37 188	63.72 288	76.13 208	38.06 365	64.10 314	71.48 255	56.60 181	74.10 227	88.31 122	64.78 145	69.72 297	47.69 303	90.15 130	83.37 161

ACMMP_NAP		82.33 32	83.28 33	79.46 51	89.28 19	69.09 79	83.62 47	84.98 46	64.77 100	83.97 73	91.02 45	75.53 43	85.93 38	82.00 3	94.36 49	83.35 162

DeepC-MVS_fast	69.89 7	77.17 81	76.33 93	79.70 48	83.90 92	67.94 83	80.06 84	83.75 77	56.73 178	74.88 209	85.32 188	65.54 135	87.79 3	65.61 132	91.14 104	83.35 162
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 49	72.61 36	84.93 60	89.70 87	77.73 25	85.89 42	75.29 47	94.22 56	83.25 164

test_0728_SECOND				76.57 91	86.20 49	60.57 155	83.77 45	85.49 33					85.90 40	75.86 43	94.39 45	83.25 164

fmvsm_s_conf0.1_n		66.60 244	65.54 256	69.77 209	68.99 324	59.15 169	72.12 184	56.74 362	40.72 362	68.25 314	80.14 279	61.18 180	66.92 326	67.34 119	74.40 358	83.23 166

GeoE		73.14 130	73.77 126	71.26 183	78.09 175	52.64 223	74.32 159	79.56 157	56.32 182	76.35 185	83.36 225	70.76 81	77.96 195	63.32 155	81.84 273	83.18 167

test_fmvsmvis_n_1920		72.36 154	72.49 156	71.96 174	71.29 288	64.06 120	72.79 176	81.82 107	40.23 365	81.25 105	81.04 261	70.62 82	68.69 306	69.74 94	83.60 252	83.14 168

SR-MVS		84.51 9	85.27 8	82.25 19	88.52 34	77.71 15	86.81 19	85.25 41	77.42 17	86.15 42	90.24 77	81.69 5	85.94 36	77.77 31	93.58 65	83.09 169

SED-MVS		81.78 36	83.48 29	76.67 89	86.12 54	61.06 146	83.62 47	84.72 53	72.61 36	87.38 28	89.70 87	77.48 27	85.89 42	75.29 47	94.39 45	83.08 170

OPU-MVS				78.65 64	83.44 99	66.85 95	83.62 47				86.12 176	66.82 119	86.01 34	61.72 167	89.79 141	83.08 170

MVSTER		63.29 281	61.60 295	68.36 238	59.77 403	46.21 292	60.62 341	71.32 259	41.83 348	75.40 199	79.12 300	30.25 399	75.85 217	56.30 219	79.81 306	83.03 172

CANet		73.00 137	71.84 165	76.48 93	75.82 215	61.28 142	74.81 148	80.37 142	63.17 118	62.43 361	80.50 271	61.10 181	85.16 63	64.00 144	84.34 241	83.01 173

Vis-MVSNet		74.85 111	74.56 109	75.72 103	81.63 127	64.64 115	76.35 129	79.06 164	62.85 121	73.33 240	88.41 119	62.54 161	79.59 161	63.94 148	82.92 257	82.94 174
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
fmvsm_s_conf0.5_n_5		71.46 168	71.62 171	70.99 187	73.89 248	59.95 162	73.02 174	73.08 233	45.15 323	77.30 156	84.06 210	64.73 146	70.08 293	71.20 80	82.10 268	82.92 175

fmvsm_l_conf0.5_n_3		71.98 160	71.68 168	72.88 153	72.84 270	64.15 119	73.48 168	77.11 199	48.97 286	71.31 271	84.18 206	67.98 108	71.60 278	68.86 97	80.43 296	82.89 176

miper_lstm_enhance		61.97 294	61.63 294	62.98 298	60.04 397	45.74 296	47.53 413	70.95 268	44.04 330	73.06 243	78.84 305	39.72 340	60.33 363	55.82 225	84.64 234	82.88 177

PAPM_NR		73.91 116	74.16 117	73.16 139	81.90 123	53.50 218	81.28 67	81.40 115	66.17 80	73.30 241	83.31 226	59.96 192	83.10 97	58.45 201	81.66 279	82.87 178

Fast-Effi-MVS+		68.81 210	68.30 216	70.35 197	74.66 233	48.61 259	66.06 283	78.32 180	50.62 261	71.48 269	75.54 333	68.75 98	79.59 161	50.55 272	78.73 318	82.86 179

HFP-MVS		83.39 22	84.03 21	81.48 27	89.25 21	75.69 28	87.01 17	84.27 67	70.23 52	84.47 68	90.43 64	76.79 30	85.94 36	79.58 15	94.23 55	82.82 180

DELS-MVS		68.83 209	68.31 215	70.38 195	70.55 300	48.31 260	63.78 318	82.13 102	54.00 221	68.96 299	75.17 338	58.95 205	80.06 155	58.55 200	82.74 261	82.76 181
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		62.44 292	63.40 281	59.55 331	72.34 274	32.38 398	56.39 370	64.84 318	51.21 255	67.46 321	81.01 262	50.75 273	63.51 353	38.47 363	88.12 172	82.75 182

MP-MVS		83.19 23	83.54 28	82.14 20	90.54 5	79.00 9	86.42 25	83.59 80	71.31 45	81.26 104	90.96 46	74.57 51	84.69 70	78.41 26	94.78 32	82.74 183
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
reproduce-ours		84.97 4	85.93 4	82.10 21	86.11 57	77.53 18	87.08 13	85.81 29	78.70 10	88.94 13	91.88 27	79.74 12	86.05 32	79.90 10	95.21 17	82.72 184

our_new_method		84.97 4	85.93 4	82.10 21	86.11 57	77.53 18	87.08 13	85.81 29	78.70 10	88.94 13	91.88 27	79.74 12	86.05 32	79.90 10	95.21 17	82.72 184

lessismore_v0				72.75 158	79.60 149	56.83 193		57.37 353		83.80 75	89.01 105	47.45 297	78.74 174	64.39 140	86.49 207	82.69 186

fmvsm_s_conf0.5_n		66.34 250	65.27 259	69.57 212	68.20 333	59.14 171	71.66 197	56.48 363	40.92 358	67.78 316	79.46 291	61.23 177	66.90 327	67.39 115	74.32 361	82.66 187

DPE-MVS		82.00 35	83.02 38	78.95 60	85.36 69	67.25 91	82.91 55	84.98 46	73.52 29	85.43 55	90.03 81	76.37 33	86.97 13	74.56 54	94.02 59	82.62 188
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
test_prior				75.27 110	82.15 120	59.85 163		84.33 66					83.39 92			82.58 189

F-COLMAP		75.29 98	73.99 121	79.18 54	81.73 125	71.90 50	81.86 64	82.98 87	59.86 145	72.27 253	84.00 212	64.56 147	83.07 98	51.48 262	87.19 195	82.56 190

CP-MVS		84.12 12	84.55 15	82.80 11	89.42 18	79.74 6	88.19 5	84.43 62	71.96 44	84.70 65	90.56 59	77.12 29	86.18 28	79.24 22	95.36 14	82.49 191

XVG-OURS		79.51 58	79.82 61	78.58 65	86.11 57	74.96 32	76.33 131	84.95 48	66.89 71	82.75 87	88.99 106	66.82 119	78.37 185	74.80 49	90.76 122	82.40 192

mPP-MVS		84.01 14	84.39 16	82.88 7	90.65 4	81.38 4	87.08 13	82.79 90	72.41 40	85.11 59	90.85 51	76.65 32	84.89 66	79.30 21	94.63 37	82.35 193

XVG-OURS-SEG-HR		79.62 57	79.99 60	78.49 67	86.46 47	74.79 33	77.15 117	85.39 38	66.73 74	80.39 116	88.85 109	74.43 54	78.33 187	74.73 51	85.79 213	82.35 193

FMVSNet2		67.48 230	68.21 220	65.29 273	73.14 259	38.94 355	68.81 241	71.21 266	54.81 198	76.73 170	86.48 164	48.63 292	74.60 238	47.98 300	86.11 211	82.35 193

fmvsm_s_conf0.1_n_a		67.37 234	66.36 247	70.37 196	70.86 290	61.17 144	74.00 165	57.18 357	40.77 360	68.83 307	80.88 263	63.11 155	67.61 319	66.94 122	74.72 353	82.33 196

CNVR-MVS		78.49 69	78.59 71	78.16 71	85.86 63	67.40 89	78.12 106	81.50 112	63.92 106	77.51 153	86.56 162	68.43 102	84.82 68	73.83 62	91.61 92	82.26 197

mvs_anonymous		65.08 260	65.49 257	63.83 287	63.79 377	37.60 369	66.52 280	69.82 279	43.44 339	73.46 238	86.08 178	58.79 207	71.75 275	51.90 260	75.63 345	82.15 198

reproduce_model		84.87 6	85.80 6	82.05 23	85.52 66	78.14 13	87.69 6	85.36 39	79.26 7	89.12 12	92.10 21	77.52 26	85.92 39	80.47 9	95.20 19	82.10 199

LuminaMVS		71.15 172	70.79 183	72.24 172	77.20 188	58.34 182	72.18 183	76.20 208	54.91 197	77.74 148	81.93 249	49.17 285	76.31 216	62.12 163	85.66 216	82.07 200

thres600view7		61.82 296	61.38 297	63.12 296	71.81 280	34.93 386	64.64 308	56.99 358	54.78 202	70.33 281	79.74 284	32.07 381	72.42 263	38.61 361	83.46 253	82.02 201

thres400		60.77 306	59.97 308	63.15 295	70.78 291	35.35 383	63.27 323	57.47 351	53.00 231	68.31 312	77.09 323	32.45 378	72.09 267	35.61 387	81.73 275	82.02 201

ETV-MVS		72.72 146	72.16 163	74.38 120	76.90 197	55.95 196	73.34 170	84.67 56	62.04 126	72.19 256	70.81 372	65.90 132	85.24 59	58.64 199	84.96 229	81.95 203

testing3-2		56.85 331	57.62 328	54.53 360	75.84 214	22.23 440	51.26 401	49.10 403	61.04 134	63.74 350	79.73 285	22.29 430	59.44 367	31.16 407	84.43 240	81.92 204

CNLPA		73.44 123	73.03 145	74.66 113	78.27 172	75.29 30	75.99 136	78.49 177	65.39 88	75.67 192	83.22 232	61.23 177	66.77 333	53.70 251	85.33 221	81.92 204

NCCC		78.25 72	78.04 77	78.89 61	85.61 65	69.45 71	79.80 87	80.99 127	65.77 82	75.55 194	86.25 171	67.42 112	85.42 52	70.10 89	90.88 117	81.81 206

fmvsm_s_conf0.5_n_a		67.00 241	65.95 255	70.17 201	69.72 317	61.16 145	73.34 170	56.83 360	40.96 357	68.36 311	80.08 280	62.84 156	67.57 320	66.90 124	74.50 357	81.78 207

AstraMVS		67.11 237	66.84 244	67.92 244	70.75 293	51.36 229	64.77 305	67.06 301	49.03 284	75.40 199	82.05 244	51.26 270	70.65 285	58.89 198	82.32 265	81.77 208

mvsmamba		68.87 208	67.30 235	73.57 132	76.58 201	53.70 217	84.43 38	74.25 226	45.38 320	76.63 172	84.55 200	35.85 362	85.27 56	49.54 281	78.49 321	81.75 209

PAPR		69.20 202	68.66 212	70.82 188	75.15 223	47.77 271	75.31 141	81.11 122	49.62 275	66.33 327	79.27 296	61.53 172	82.96 99	48.12 298	81.50 282	81.74 210

Anonymous202405211		66.02 251	66.89 242	63.43 293	74.22 240	38.14 363	59.00 352	66.13 306	63.33 117	69.76 291	85.95 183	51.88 263	70.50 288	44.23 327	87.52 181	81.64 211

FMVSNet3		65.00 261	65.16 262	64.52 281	69.47 318	37.56 370	66.63 277	70.38 274	51.55 247	74.72 211	83.27 228	37.89 353	74.44 240	47.12 305	85.37 218	81.57 212

Vis-MVSNet (Re-imp)		62.74 289	63.21 284	61.34 317	72.19 276	31.56 403	67.31 268	53.87 376	53.60 227	69.88 289	83.37 223	40.52 335	70.98 283	41.40 344	86.78 202	81.48 213

guyue		66.95 242	66.74 245	67.56 250	70.12 311	51.14 231	65.05 300	68.68 291	49.98 271	74.64 215	80.83 264	50.77 272	70.34 292	57.72 207	82.89 259	81.21 214

test_0402		78.17 73	79.48 64	74.24 121	83.50 96	59.15 169	72.52 177	74.60 224	75.34 19	88.69 17	91.81 31	75.06 46	82.37 109	65.10 133	88.68 165	81.20 215

VPNet		65.58 255	67.56 229	59.65 330	79.72 147	30.17 411	60.27 344	62.14 334	54.19 217	71.24 272	86.63 159	58.80 206	67.62 318	44.17 328	90.87 118	81.18 216

APD-MVS		81.13 43	81.73 49	79.36 53	84.47 84	70.53 63	83.85 43	83.70 78	69.43 58	83.67 76	88.96 107	75.89 38	86.41 18	72.62 72	92.95 72	81.14 217
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
CPTT-MVS		81.51 39	81.76 48	80.76 38	89.20 23	78.75 10	86.48 24	82.03 104	68.80 59	80.92 109	88.52 117	72.00 69	82.39 108	74.80 49	93.04 71	81.14 217

FE-MVS		68.29 220	66.96 240	72.26 170	74.16 242	54.24 212	77.55 110	73.42 232	57.65 167	72.66 247	84.91 193	32.02 383	81.49 124	48.43 294	81.85 272	81.04 219

Fast-Effi-MVS+-dtu		70.00 189	68.74 210	73.77 128	73.47 252	64.53 116	71.36 202	78.14 185	55.81 189	68.84 306	74.71 342	65.36 138	75.75 220	52.00 259	79.00 314	81.03 220

MDA-MVSNet-bldmvs		62.34 293	61.73 291	64.16 282	61.64 388	49.90 246	48.11 411	57.24 356	53.31 229	80.95 108	79.39 294	49.00 288	61.55 360	45.92 317	80.05 301	81.03 220

D2MVS		62.58 291	61.05 300	67.20 256	63.85 376	47.92 268	56.29 371	69.58 280	39.32 369	70.07 286	78.19 312	34.93 365	72.68 256	53.44 254	83.74 248	81.00 222

ACMM	69.25 9	82.11 34	83.31 32	78.49 67	88.17 37	73.96 38	83.11 54	84.52 61	66.40 78	87.45 26	89.16 100	81.02 8	80.52 146	74.27 59	95.73 8	80.98 223
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
hse-mvs2		72.32 155	70.66 186	77.31 85	83.10 105	71.77 51	69.19 235	71.45 256	54.28 212	77.89 144	78.26 310	49.04 286	79.23 164	63.62 151	89.13 157	80.92 224

DP-MVS Recon		73.57 122	72.69 152	76.23 97	82.85 110	63.39 124	74.32 159	82.96 88	57.75 163	70.35 280	81.98 247	64.34 149	84.41 76	49.69 278	89.95 136	80.89 225

EPNet		69.10 205	67.32 233	74.46 115	68.33 331	61.27 143	77.56 109	63.57 328	60.95 135	56.62 395	82.75 234	51.53 267	81.24 128	54.36 244	90.20 128	80.88 226
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
AUN-MVS		70.22 184	67.88 226	77.22 86	82.96 109	71.61 52	69.08 236	71.39 257	49.17 280	71.70 260	78.07 315	37.62 355	79.21 165	61.81 164	89.15 155	80.82 227

MTAPA		83.19 23	83.87 23	81.13 34	91.16 3	78.16 12	84.87 33	80.63 135	72.08 42	84.93 60	90.79 52	74.65 50	84.42 75	80.98 6	94.75 33	80.82 227

HyFIR lowres test		63.01 284	60.47 305	70.61 190	83.04 106	54.10 213	59.93 347	72.24 248	33.67 407	69.00 297	75.63 332	38.69 347	76.93 208	36.60 379	75.45 348	80.81 229

EIA-MVS		68.59 215	67.16 236	72.90 151	75.18 222	55.64 202	69.39 229	81.29 117	52.44 236	64.53 338	70.69 373	60.33 188	82.30 111	54.27 245	76.31 340	80.75 230

MCST-MVS		73.42 124	73.34 137	73.63 131	81.28 131	59.17 168	74.80 150	83.13 86	45.50 316	72.84 245	83.78 217	65.15 141	80.99 135	64.54 138	89.09 161	80.73 231

tfpnnormal		66.48 246	67.93 224	62.16 307	73.40 254	36.65 372	63.45 320	64.99 316	55.97 186	72.82 246	87.80 133	57.06 232	69.10 304	48.31 296	87.54 180	80.72 232

dcpmvs_2		71.02 175	72.65 153	66.16 267	76.06 212	50.49 237	71.97 189	79.36 159	50.34 263	82.81 86	83.63 218	64.38 148	67.27 323	61.54 168	83.71 250	80.71 233

testing3		58.28 324	58.38 322	58.00 342	77.45 187	26.12 429	60.78 340	43.00 425	56.02 185	70.18 283	75.76 330	13.27 451	67.24 324	48.02 299	80.89 285	80.65 234

SD-MVS		80.28 54	81.55 52	76.47 94	83.57 95	67.83 85	83.39 52	85.35 40	64.42 102	86.14 43	87.07 142	74.02 55	80.97 137	77.70 33	92.32 83	80.62 235
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		64.04 275	63.83 275	64.66 279	68.39 328	42.97 320	73.45 169	74.50 225	52.05 241	54.78 405	75.44 336	43.99 313	70.42 290	53.49 253	78.41 323	80.59 236

GA-MVS		62.91 285	61.66 292	66.66 264	67.09 349	44.49 305	61.18 336	69.36 284	51.33 253	69.33 295	74.47 344	36.83 358	74.94 233	50.60 271	74.72 353	80.57 237

SymmetryMVS		74.00 115	72.85 148	77.43 82	85.17 72	70.01 69	79.92 86	68.48 294	58.60 156	75.21 202	84.02 211	52.85 258	81.82 118	61.45 169	89.99 135	80.47 238

114514_t		73.40 125	73.33 138	73.64 130	84.15 90	57.11 190	78.20 104	80.02 148	43.76 334	72.55 249	86.07 180	64.00 150	83.35 93	60.14 185	91.03 110	80.45 239

IterMVS-SCA-FT		67.68 228	66.07 251	72.49 165	73.34 255	58.20 185	63.80 317	65.55 312	48.10 294	76.91 162	82.64 237	45.20 305	78.84 171	61.20 172	77.89 331	80.44 240

ttmdpeth		56.40 334	55.45 345	59.25 332	55.63 424	40.69 339	58.94 354	49.72 399	36.22 391	65.39 332	86.97 144	23.16 426	56.69 380	42.30 336	80.74 290	80.36 241

ambc				70.10 204	77.74 181	50.21 241	74.28 162	77.93 189		79.26 125	88.29 123	54.11 252	79.77 157	64.43 139	91.10 107	80.30 242

fmvsm_s_conf0.5_n_6		70.08 187	69.97 191	70.39 194	72.99 267	58.93 174	68.84 238	76.40 206	49.08 282	68.75 308	81.65 254	57.34 227	71.97 271	70.91 83	83.81 247	80.26 243

thisisatest0515		60.48 308	57.86 326	68.34 239	67.25 347	46.42 289	60.58 342	62.14 334	40.82 359	63.58 354	69.12 390	26.28 413	78.34 186	48.83 288	82.13 267	80.26 243

LFMVS		67.06 239	67.89 225	64.56 280	78.02 176	38.25 362	70.81 213	59.60 345	65.18 93	71.06 274	86.56 162	43.85 314	75.22 227	46.35 313	89.63 142	80.21 245

UGNet		70.20 185	69.05 203	73.65 129	76.24 206	63.64 122	75.87 138	72.53 243	61.48 130	60.93 371	86.14 175	52.37 261	77.12 205	50.67 270	85.21 223	80.17 246
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		66.57 245	69.23 201	58.59 338	81.26 132	37.73 368	64.06 315	57.62 350	57.02 172	78.40 139	90.75 53	62.65 158	58.10 376	41.77 342	89.58 145	79.95 247

test_yl		65.11 258	65.09 266	65.18 274	70.59 296	40.86 335	63.22 325	72.79 237	57.91 161	68.88 304	79.07 302	42.85 321	74.89 234	45.50 321	84.97 226	79.81 248

DCV-MVSNet		65.11 258	65.09 266	65.18 274	70.59 296	40.86 335	63.22 325	72.79 237	57.91 161	68.88 304	79.07 302	42.85 321	74.89 234	45.50 321	84.97 226	79.81 248

cascas		64.59 266	62.77 288	70.05 205	75.27 220	50.02 243	61.79 331	71.61 251	42.46 345	63.68 351	68.89 395	49.33 282	80.35 147	47.82 302	84.05 244	79.78 250

ET-MVSNet_ETH3D		63.32 280	60.69 304	71.20 185	70.15 309	55.66 200	65.02 301	64.32 323	43.28 343	68.99 298	72.05 365	25.46 417	78.19 192	54.16 247	82.80 260	79.74 251

APD_test1		75.04 104	75.38 104	74.02 125	69.89 312	70.15 66	76.46 125	79.71 152	65.50 85	82.99 82	88.60 116	66.94 116	72.35 264	59.77 190	88.54 166	79.56 252

testf1		75.66 93	76.57 89	72.95 146	67.07 351	67.62 86	76.10 133	80.68 132	64.95 97	86.58 37	90.94 47	71.20 76	71.68 276	60.46 179	91.13 105	79.56 252

APD_test2		75.66 93	76.57 89	72.95 146	67.07 351	67.62 86	76.10 133	80.68 132	64.95 97	86.58 37	90.94 47	71.20 76	71.68 276	60.46 179	91.13 105	79.56 252

CSCG		74.12 114	74.39 111	73.33 136	79.35 152	61.66 138	77.45 112	81.98 105	62.47 125	79.06 129	80.19 277	61.83 168	78.79 173	59.83 189	87.35 186	79.54 255

ACMH	63.62 14	77.50 79	80.11 59	69.68 210	79.61 148	56.28 194	78.81 95	83.62 79	63.41 116	87.14 34	90.23 78	76.11 36	73.32 251	67.58 110	94.44 43	79.44 256
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
MG-MVS		70.47 182	71.34 177	67.85 246	79.26 154	40.42 345	74.67 155	75.15 220	58.41 157	68.74 309	88.14 128	56.08 241	83.69 84	59.90 188	81.71 278	79.43 257

DVP-MVS		81.15 42	83.12 37	75.24 111	86.16 52	60.78 152	83.77 45	80.58 137	72.48 38	85.83 47	90.41 66	78.57 19	85.69 47	75.86 43	94.39 45	79.24 258
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		64.01 276	65.15 264	60.57 324	73.28 256	35.61 382	57.60 364	67.08 300	54.61 205	66.76 326	83.37 223	56.28 239	66.87 329	42.19 338	85.20 224	79.23 259

TSAR-MVS + GP.		73.08 132	71.60 173	77.54 79	78.99 166	70.73 61	74.96 145	69.38 283	60.73 138	74.39 221	78.44 308	57.72 224	82.78 103	60.16 183	89.60 143	79.11 260

SSC-MVS		61.79 297	66.08 250	48.89 393	76.91 195	10.00 451	53.56 390	47.37 411	68.20 64	76.56 176	89.21 96	54.13 251	57.59 377	54.75 236	74.07 362	79.08 261

HPM-MVS++		79.89 56	79.80 62	80.18 43	89.02 26	78.44 11	83.49 50	80.18 145	64.71 101	78.11 143	88.39 120	65.46 137	83.14 95	77.64 34	91.20 101	78.94 262

DP-MVS		78.44 71	79.29 65	75.90 101	81.86 124	65.33 108	79.05 93	84.63 59	74.83 22	80.41 115	86.27 169	71.68 70	83.45 91	62.45 162	92.40 80	78.92 263

PLC	62.01 16	71.79 162	70.28 189	76.33 95	80.31 140	68.63 80	78.18 105	81.24 119	54.57 207	67.09 325	80.63 269	59.44 198	81.74 122	46.91 308	84.17 242	78.63 264
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
PVSNet_Blended_VisFu		70.04 188	68.88 206	73.53 134	82.71 112	63.62 123	74.81 148	81.95 106	48.53 290	67.16 324	79.18 299	51.42 268	78.38 184	54.39 243	79.72 309	78.60 265

h-mvs33		73.08 132	71.61 172	77.48 80	83.89 93	72.89 48	70.47 216	71.12 267	54.28 212	77.89 144	83.41 220	49.04 286	80.98 136	63.62 151	90.77 121	78.58 266

agg_prior2														70.70 85	90.93 113	78.55 267

fmvsm_s_conf0.5_n_7		67.30 235	66.92 241	68.43 237	72.78 271	58.22 184	60.90 338	72.51 245	49.62 275	63.66 352	80.65 268	58.56 210	68.63 308	62.83 159	80.76 289	78.45 268

ppachtmachnet_test		60.26 310	59.61 311	62.20 306	67.70 342	44.33 306	58.18 361	60.96 341	40.75 361	65.80 330	72.57 361	41.23 328	63.92 350	46.87 309	82.42 264	78.33 269

BH-RMVSNet		68.69 214	68.20 221	70.14 203	76.40 204	53.90 216	64.62 309	73.48 230	58.01 160	73.91 232	81.78 250	59.09 203	78.22 189	48.59 291	77.96 329	78.31 270

PVSNet_BlendedMVS		65.38 256	64.30 270	68.61 235	69.81 313	49.36 252	65.60 292	78.96 165	45.50 316	59.98 374	78.61 306	51.82 264	78.20 190	44.30 325	84.11 243	78.27 271

ab-mvs		64.11 274	65.13 265	61.05 319	71.99 278	38.03 366	67.59 259	68.79 290	49.08 282	65.32 334	86.26 170	58.02 222	66.85 331	39.33 354	79.79 308	78.27 271

VortexMVS		65.93 252	66.04 253	65.58 272	67.63 344	47.55 276	64.81 303	72.75 240	47.37 303	75.17 203	79.62 289	49.28 283	71.00 282	55.20 231	82.51 263	78.21 273

EGC-MVSNET		64.77 264	61.17 298	75.60 106	86.90 43	74.47 34	84.04 40	68.62 293	0.60 447	1.13 449	91.61 36	65.32 139	74.15 245	64.01 143	88.28 169	78.17 274

MVSFormer		69.93 191	69.03 204	72.63 163	74.93 224	59.19 166	83.98 41	75.72 214	52.27 237	63.53 355	76.74 326	43.19 318	80.56 143	72.28 76	78.67 319	78.14 275

jason		64.47 269	62.84 287	69.34 217	76.91 195	59.20 165	67.15 269	65.67 309	35.29 396	65.16 335	76.74 326	44.67 309	70.68 284	54.74 237	79.28 312	78.14 275
jason: jason.
new-patchmatchnet		52.89 361	55.76 343	44.26 411	59.94 401	6.31 452	37.36 436	50.76 395	41.10 354	64.28 341	79.82 283	44.77 308	48.43 404	36.24 383	87.61 179	78.03 277

CDS-MVSNet		64.33 272	62.66 289	69.35 216	80.44 139	58.28 183	65.26 295	65.66 310	44.36 329	67.30 323	75.54 333	43.27 317	71.77 273	37.68 369	84.44 239	78.01 278
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
TAMVS		65.31 257	63.75 276	69.97 207	82.23 119	59.76 164	66.78 276	63.37 330	45.20 322	69.79 290	79.37 295	47.42 298	72.17 266	34.48 392	85.15 225	77.99 279

test_fmvs3		56.78 332	55.99 341	59.12 334	53.96 433	48.09 265	58.76 356	66.22 305	27.54 425	76.66 171	68.69 398	25.32 419	51.31 392	53.42 255	73.38 367	77.97 280

LCM-MVSNet-Re		69.10 205	71.57 174	61.70 310	70.37 303	34.30 391	61.45 332	79.62 153	56.81 175	89.59 9	88.16 127	68.44 101	72.94 254	42.30 336	87.33 188	77.85 281

fmvsm_s_conf0.5_n_3		72.97 140	74.13 118	69.47 213	71.40 286	58.36 181	73.07 172	80.64 134	56.86 174	75.49 197	84.67 195	67.86 110	72.33 265	75.68 45	81.54 281	77.73 282

Patchmtry		60.91 303	63.01 286	54.62 359	66.10 361	26.27 428	67.47 262	56.40 365	54.05 220	72.04 258	86.66 156	33.19 371	60.17 364	43.69 329	87.45 184	77.42 283

test9_res														72.12 78	91.37 97	77.40 284

WB-MVS		60.04 311	64.19 272	47.59 396	76.09 209	10.22 450	52.44 396	46.74 413	65.17 94	74.07 228	87.48 135	53.48 254	55.28 383	49.36 283	72.84 370	77.28 285

SDMVSNet		66.36 248	67.85 227	61.88 309	73.04 265	46.14 293	58.54 357	71.36 258	51.42 249	68.93 302	82.72 235	65.62 134	62.22 358	54.41 242	84.67 231	77.28 285

sd_testset		63.55 277	65.38 258	58.07 341	73.04 265	38.83 357	57.41 365	65.44 313	51.42 249	68.93 302	82.72 235	63.76 152	58.11 375	41.05 346	84.67 231	77.28 285

reproduce_monomvs		58.94 319	58.14 324	61.35 316	59.70 404	40.98 334	60.24 345	63.51 329	45.85 313	68.95 300	75.31 337	18.27 441	65.82 339	51.47 263	79.97 302	77.26 288

train_agg		76.38 87	76.55 91	75.86 102	85.47 67	69.32 75	76.42 127	78.69 173	54.00 221	76.97 159	86.74 152	66.60 124	81.10 131	72.50 74	91.56 93	77.15 289

lupinMVS		63.36 279	61.49 296	68.97 227	74.93 224	59.19 166	65.80 288	64.52 322	34.68 402	63.53 355	74.25 348	43.19 318	70.62 286	53.88 249	78.67 319	77.10 290

thres100view900		61.17 302	61.09 299	61.39 315	72.14 277	35.01 385	65.42 294	56.99 358	55.23 194	70.71 277	79.90 282	32.07 381	72.09 267	35.61 387	81.73 275	77.08 291

tfpn200view9		60.35 309	59.97 308	61.51 312	70.78 291	35.35 383	63.27 323	57.47 351	53.00 231	68.31 312	77.09 323	32.45 378	72.09 267	35.61 387	81.73 275	77.08 291

fmvsm_l_conf0.5_n		67.48 230	66.88 243	69.28 218	67.41 346	62.04 133	70.69 214	69.85 278	39.46 368	69.59 292	81.09 260	58.15 215	68.73 305	67.51 112	78.16 328	77.07 293

mmtdpeth		68.76 211	70.55 187	63.40 294	67.06 353	56.26 195	68.73 246	71.22 265	55.47 192	70.09 285	88.64 115	65.29 140	56.89 379	58.94 197	89.50 146	77.04 294

fmvsm_l_conf0.5_n_a		66.66 243	65.97 254	68.72 234	67.09 349	61.38 141	70.03 221	69.15 286	38.59 376	68.41 310	80.36 273	56.56 237	68.32 312	66.10 126	77.45 333	76.46 295

MVStest1		55.38 342	54.97 349	56.58 349	43.72 446	40.07 347	59.13 350	47.09 412	34.83 398	76.53 179	84.65 196	13.55 450	53.30 389	55.04 233	80.23 299	76.38 296

MVS_111021_HR		72.98 139	72.97 147	72.99 144	80.82 135	65.47 106	68.81 241	72.77 239	57.67 165	75.76 190	82.38 240	71.01 78	77.17 204	61.38 170	86.15 208	76.32 297

xiu_mvs_v1_base_debu		67.87 224	67.07 237	70.26 198	79.13 159	61.90 135	67.34 264	71.25 262	47.98 295	67.70 317	74.19 350	61.31 174	72.62 258	56.51 215	78.26 325	76.27 298

xiu_mvs_v1_base		67.87 224	67.07 237	70.26 198	79.13 159	61.90 135	67.34 264	71.25 262	47.98 295	67.70 317	74.19 350	61.31 174	72.62 258	56.51 215	78.26 325	76.27 298

xiu_mvs_v1_base_debi		67.87 224	67.07 237	70.26 198	79.13 159	61.90 135	67.34 264	71.25 262	47.98 295	67.70 317	74.19 350	61.31 174	72.62 258	56.51 215	78.26 325	76.27 298

baseline2		55.57 341	52.74 362	64.05 285	65.26 366	44.11 307	62.38 328	54.43 373	39.03 373	51.21 418	67.35 406	33.66 369	72.45 262	37.14 374	64.22 414	75.60 301

OpenMVS	62.51 15	68.76 211	68.75 209	68.78 233	70.56 298	53.91 215	78.29 101	77.35 194	48.85 287	70.22 282	83.52 219	52.65 260	76.93 208	55.31 230	81.99 269	75.49 302

3Dnovator	65.95 11	71.50 166	71.22 178	72.34 168	73.16 258	63.09 127	78.37 100	78.32 180	57.67 165	72.22 255	84.61 198	54.77 245	78.47 179	60.82 177	81.07 284	75.45 303

1112_ss		59.48 315	58.99 316	60.96 321	77.84 179	42.39 325	61.42 333	68.45 295	37.96 380	59.93 377	67.46 404	45.11 307	65.07 345	40.89 348	71.81 379	75.41 304

IterMVS		63.12 283	62.48 290	65.02 277	66.34 357	52.86 221	63.81 316	62.25 333	46.57 308	71.51 268	80.40 272	44.60 310	66.82 332	51.38 265	75.47 347	75.38 305
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Test_1112_low_res		58.78 321	58.69 318	59.04 336	79.41 151	38.13 364	57.62 363	66.98 302	34.74 400	59.62 380	77.56 319	42.92 320	63.65 352	38.66 360	70.73 387	75.35 306

test_vis3_rt		51.94 370	51.04 377	54.65 358	46.32 444	50.13 242	44.34 424	78.17 183	23.62 438	68.95 300	62.81 418	21.41 432	38.52 437	41.49 343	72.22 376	75.30 307

QAPM		69.18 203	69.26 199	68.94 228	71.61 282	52.58 224	80.37 77	78.79 171	49.63 273	73.51 236	85.14 191	53.66 253	79.12 166	55.11 232	75.54 346	75.11 308

DPM-MVS		69.98 190	69.22 202	72.26 170	82.69 113	58.82 175	70.53 215	81.23 120	47.79 299	64.16 342	80.21 275	51.32 269	83.12 96	60.14 185	84.95 230	74.83 309

mvs5depth		66.35 249	67.98 223	61.47 314	62.43 383	51.05 232	69.38 230	69.24 285	56.74 177	73.62 234	89.06 104	46.96 299	58.63 372	55.87 224	88.49 167	74.73 310

pmmvs-eth3d		64.41 271	63.27 283	67.82 248	75.81 216	60.18 160	69.49 227	62.05 337	38.81 375	74.13 226	82.23 242	43.76 315	68.65 307	42.53 335	80.63 294	74.63 311

testing99		55.16 344	54.56 353	56.98 347	70.13 310	30.58 410	54.55 386	54.11 375	49.53 277	56.76 393	70.14 381	22.76 428	65.79 340	36.99 376	76.04 342	74.57 312

testing91		55.74 338	55.29 348	57.08 345	70.63 295	30.85 408	54.94 383	56.31 367	50.34 263	57.08 389	70.10 382	24.50 421	65.86 338	36.98 377	76.75 337	74.53 313

MSDG		67.47 232	67.48 232	67.46 252	70.70 294	54.69 209	66.90 274	78.17 183	60.88 136	70.41 279	74.76 340	61.22 179	73.18 252	47.38 304	76.87 336	74.49 314

WB-MVSnew		53.94 354	54.76 351	51.49 375	71.53 283	28.05 418	58.22 360	50.36 396	37.94 381	59.16 381	70.17 380	49.21 284	51.94 391	24.49 432	71.80 380	74.47 315

MAR-MVS		67.72 227	66.16 249	72.40 167	74.45 236	64.99 113	74.87 146	77.50 193	48.67 289	65.78 331	68.58 399	57.01 233	77.79 198	46.68 311	81.92 270	74.42 316
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		57.82 327	58.36 323	56.19 351	69.17 321	30.76 409	62.94 327	55.21 369	46.04 311	63.83 348	78.47 307	41.20 329	63.68 351	39.44 353	68.99 398	74.13 317

EU-MVSNet		60.82 304	60.80 303	60.86 322	68.37 329	41.16 331	72.27 180	68.27 296	26.96 427	69.08 296	75.71 331	32.09 380	67.44 321	55.59 228	78.90 316	73.97 318

HY-MVS	49.31 19	57.96 326	57.59 329	59.10 335	66.85 354	36.17 376	65.13 298	65.39 314	39.24 372	54.69 407	78.14 313	44.28 312	67.18 325	33.75 397	70.79 386	73.95 319

TR-MVS		64.59 266	63.54 279	67.73 249	75.75 217	50.83 235	63.39 321	70.29 275	49.33 278	71.55 267	74.55 343	50.94 271	78.46 180	40.43 350	75.69 344	73.89 320

IB-MVS	49.67 18	59.69 314	56.96 333	67.90 245	68.19 334	50.30 240	61.42 333	65.18 315	47.57 301	55.83 399	67.15 408	23.77 423	79.60 160	43.56 331	79.97 302	73.79 321
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		63.55 277	66.07 251	55.99 352	66.18 360	44.04 308	68.77 244	68.80 289	46.99 305	72.57 248	85.84 184	39.87 339	50.22 396	53.40 256	92.23 84	73.71 322

AdaColmap		74.22 113	74.56 109	73.20 138	81.95 122	60.97 148	79.43 88	80.90 128	65.57 84	72.54 250	81.76 252	70.98 79	85.26 57	47.88 301	90.00 133	73.37 323

PAPM		61.79 297	60.37 306	66.05 268	76.09 209	41.87 327	69.30 231	76.79 203	40.64 363	53.80 410	79.62 289	44.38 311	82.92 100	29.64 414	73.11 369	73.36 324

MVS_111021_LR		72.10 158	71.82 166	72.95 146	79.53 150	73.90 40	70.45 217	66.64 303	56.87 173	76.81 168	81.76 252	68.78 97	71.76 274	61.81 164	83.74 248	73.18 325

UWE-MVS		52.94 360	52.70 363	53.65 363	73.56 250	27.49 421	57.30 366	49.57 400	38.56 377	62.79 359	71.42 370	19.49 438	60.41 362	24.33 434	77.33 334	73.06 326

原ACMM1				73.90 126	85.90 60	65.15 112		81.67 110	50.97 257	74.25 224	86.16 174	61.60 171	83.54 87	56.75 213	91.08 109	73.00 327

myMVS_eth3d28		51.35 373	51.99 370	49.44 388	69.21 319	22.51 438	49.82 406	49.11 402	49.00 285	55.03 403	70.31 377	22.73 429	52.88 390	24.33 434	78.39 324	72.92 328

CHOSEN 1792x2688		58.09 325	56.30 338	63.45 292	79.95 143	50.93 234	54.07 388	65.59 311	28.56 423	61.53 364	74.33 346	41.09 331	66.52 336	33.91 395	67.69 406	72.92 328

testing222		53.37 356	52.50 366	55.98 353	70.51 301	29.68 413	56.20 373	51.85 389	46.19 310	56.76 393	68.94 393	19.18 439	65.39 342	25.87 428	76.98 335	72.87 330

TinyColmap		67.98 223	69.28 198	64.08 284	67.98 337	46.82 285	70.04 220	75.26 218	53.05 230	77.36 155	86.79 149	59.39 199	72.59 261	45.64 319	88.01 175	72.83 331

FMVSNet5		55.08 345	55.54 344	53.71 362	65.80 362	33.50 395	56.22 372	52.50 386	43.72 336	61.06 368	83.38 222	25.46 417	54.87 384	30.11 411	81.64 280	72.75 332

EG-PatchMatch MVS		70.70 179	70.88 181	70.16 202	82.64 114	58.80 176	71.48 199	73.64 229	54.98 196	76.55 177	81.77 251	61.10 181	78.94 170	54.87 235	80.84 287	72.74 333

PVSNet_Blended		62.90 286	61.64 293	66.69 263	69.81 313	49.36 252	61.23 335	78.96 165	42.04 346	59.98 374	68.86 396	51.82 264	78.20 190	44.30 325	77.77 332	72.52 334

CostFormer		57.35 329	56.14 339	60.97 320	63.76 378	38.43 359	67.50 261	60.22 343	37.14 387	59.12 382	76.34 328	32.78 374	71.99 270	39.12 357	69.27 396	72.47 335

SSC-MVS3.2		57.01 330	59.50 312	49.57 387	67.73 341	25.95 430	46.68 416	51.75 391	51.41 251	63.84 347	79.66 287	53.28 256	50.34 395	37.85 368	83.28 255	72.41 336

PS-MVSNAJ		64.27 273	63.73 277	65.90 270	77.82 180	51.42 228	63.33 322	72.33 246	45.09 325	61.60 363	68.04 401	62.39 163	73.95 247	49.07 286	73.87 364	72.34 337

xiu_mvs_v2_base		64.43 270	63.96 274	65.85 271	77.72 182	51.32 230	63.63 319	72.31 247	45.06 326	61.70 362	69.66 386	62.56 159	73.93 248	49.06 287	73.91 363	72.31 338

PMVS	70.70 6	81.70 37	83.15 36	77.36 83	90.35 6	82.82 3	82.15 60	79.22 162	74.08 24	87.16 33	91.97 23	84.80 2	76.97 206	64.98 135	93.61 64	72.28 339
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
1314		59.83 313	58.86 317	62.74 302	65.71 363	44.78 303	68.59 247	72.63 242	33.54 409	61.05 369	67.29 407	43.62 316	71.26 280	49.49 282	67.84 405	72.19 340

无先验							74.82 147	70.94 269	47.75 300				76.85 211	54.47 240		72.09 341

LF4IMVS		67.50 229	67.31 234	68.08 243	58.86 408	61.93 134	71.43 200	75.90 213	44.67 328	72.42 251	80.20 276	57.16 228	70.44 289	58.99 196	86.12 210	71.88 342

pmmvs4		60.78 305	59.04 315	66.00 269	73.06 264	57.67 187	64.53 311	60.22 343	36.91 388	65.96 328	77.27 321	39.66 341	68.54 310	38.87 358	74.89 352	71.80 343

MSLP-MVS++		74.48 112	75.78 98	70.59 191	84.66 80	62.40 130	78.65 96	84.24 69	60.55 139	77.71 150	81.98 247	63.12 154	77.64 201	62.95 158	88.14 171	71.73 344

MDTV_nov1_ep13_2view						18.41 442	53.74 389		31.57 417	44.89 435		29.90 403		32.93 399		71.48 345

MonoMVSNet		62.75 288	63.42 280	60.73 323	65.60 364	40.77 338	72.49 178	70.56 272	52.49 235	75.07 204	79.42 293	39.52 343	69.97 295	46.59 312	69.06 397	71.44 346

patch_mono-2		62.73 290	64.08 273	58.68 337	70.36 304	55.87 198	60.84 339	64.11 325	41.23 353	64.04 343	78.22 311	60.00 191	48.80 400	54.17 246	83.71 250	71.37 347

tpm2		56.12 335	54.64 352	60.55 325	66.24 358	36.01 377	68.14 254	56.77 361	33.60 408	58.25 385	75.52 335	30.25 399	74.33 242	33.27 398	69.76 395	71.32 348

CMPMVS	48.73 20	61.54 300	60.89 301	63.52 291	61.08 391	51.55 227	68.07 256	68.00 297	33.88 404	65.87 329	81.25 258	37.91 352	67.71 316	49.32 284	82.60 262	71.31 349
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
API-MVS		70.97 176	71.51 175	69.37 214	75.20 221	55.94 197	80.99 68	76.84 201	62.48 124	71.24 272	77.51 320	61.51 173	80.96 140	52.04 258	85.76 215	71.22 350

OpenMVS_ROB	54.93 17	63.23 282	63.28 282	63.07 297	69.81 313	45.34 298	68.52 250	67.14 299	43.74 335	70.61 278	79.22 297	47.90 296	72.66 257	48.75 289	73.84 365	71.21 351

thres200		57.55 328	57.02 332	59.17 333	67.89 339	34.93 386	58.91 355	57.25 355	50.24 265	64.01 344	71.46 369	32.49 377	71.39 279	31.31 405	79.57 310	71.19 352

WBMVS		53.38 355	54.14 355	51.11 377	70.16 308	26.66 424	50.52 404	51.64 392	39.32 369	63.08 358	77.16 322	23.53 424	55.56 381	31.99 402	79.88 304	71.11 353

test20.03		55.74 338	57.51 330	50.42 380	59.89 402	32.09 400	50.63 402	49.01 404	50.11 267	65.07 336	83.23 230	45.61 303	48.11 405	30.22 410	83.82 246	71.07 354

our_test_3		56.46 333	56.51 336	56.30 350	67.70 342	39.66 350	55.36 379	52.34 388	40.57 364	63.85 346	69.91 385	40.04 338	58.22 374	43.49 332	75.29 351	71.03 355

test_fmvs2		54.80 346	54.11 356	56.88 348	51.76 437	49.95 245	56.70 369	65.80 308	26.22 430	69.42 293	65.25 412	31.82 385	49.98 397	49.63 280	70.36 389	70.71 356

BH-untuned		69.39 200	69.46 196	69.18 220	77.96 178	56.88 191	68.47 252	77.53 192	56.77 176	77.79 147	79.63 288	60.30 189	80.20 153	46.04 316	80.65 292	70.47 357

EPNet_dtu		58.93 320	58.52 319	60.16 328	67.91 338	47.70 274	69.97 222	58.02 349	49.73 272	47.28 430	73.02 359	38.14 349	62.34 356	36.57 380	85.99 212	70.43 358
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
USDC		62.80 287	63.10 285	61.89 308	65.19 367	43.30 316	67.42 263	74.20 227	35.80 395	72.25 254	84.48 202	45.67 302	71.95 272	37.95 367	84.97 226	70.42 359

GSMVS																70.05 360

sam_mvs1												31.41 388				70.05 360

SCA		58.57 323	58.04 325	60.17 327	70.17 307	41.07 333	65.19 297	53.38 382	43.34 342	61.00 370	73.48 354	45.20 305	69.38 301	40.34 351	70.31 390	70.05 360

testing11		53.13 358	52.26 368	55.75 354	70.44 302	31.73 402	54.75 384	52.40 387	44.81 327	52.36 415	68.40 400	21.83 431	65.74 341	32.64 401	72.73 371	69.78 363

tpmvs		55.84 336	55.45 345	57.01 346	60.33 395	33.20 396	65.89 285	59.29 347	47.52 302	56.04 397	73.60 353	31.05 394	68.06 315	40.64 349	64.64 412	69.77 364

旧先验1					84.55 83	60.36 157		63.69 327			87.05 143	54.65 247			83.34 254	69.66 365

CR-MVSNet		58.96 318	58.49 320	60.36 326	66.37 355	48.24 262	70.93 210	56.40 365	32.87 410	61.35 365	86.66 156	33.19 371	63.22 354	48.50 293	70.17 391	69.62 366

RPMNet		65.77 254	65.08 268	67.84 247	66.37 355	48.24 262	70.93 210	86.27 21	54.66 204	61.35 365	86.77 151	33.29 370	85.67 49	55.93 222	70.17 391	69.62 366

tpm cat1		54.02 352	52.63 364	58.19 340	64.85 373	39.86 349	66.26 282	57.28 354	32.16 412	56.90 391	70.39 376	32.75 375	65.30 344	34.29 393	58.79 427	69.41 368

PatchmatchNet		54.60 347	54.27 354	55.59 355	65.17 369	39.08 352	66.92 273	51.80 390	39.89 366	58.39 383	73.12 358	31.69 387	58.33 373	43.01 334	58.38 430	69.38 369
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
YYNet1		52.58 363	53.50 358	49.85 383	54.15 430	36.45 375	40.53 429	46.55 415	38.09 379	75.52 196	73.31 357	41.08 332	43.88 423	41.10 345	71.14 385	69.21 370

CVMVSNet		59.21 317	58.44 321	61.51 312	73.94 246	47.76 272	71.31 204	64.56 321	26.91 429	60.34 373	70.44 374	36.24 361	67.65 317	53.57 252	68.66 400	69.12 371

MDA-MVSNet_test_wron		52.57 364	53.49 360	49.81 384	54.24 429	36.47 374	40.48 430	46.58 414	38.13 378	75.47 198	73.32 356	41.05 333	43.85 424	40.98 347	71.20 384	69.10 372

MVP-Stereo		61.56 299	59.22 313	68.58 236	79.28 153	60.44 156	69.20 234	71.57 252	43.58 337	56.42 396	78.37 309	39.57 342	76.46 215	34.86 391	60.16 424	68.86 373
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
UWE-MVS-28		44.18 400	44.37 405	43.61 413	60.10 396	16.96 444	52.62 395	33.27 443	36.79 389	48.86 427	69.47 389	19.96 437	45.65 411	13.40 444	64.83 411	68.23 374

ETVMVS		50.32 379	49.87 387	51.68 373	70.30 306	26.66 424	52.33 397	43.93 421	43.54 338	54.91 404	67.95 402	20.01 436	60.17 364	22.47 437	73.40 366	68.22 375

Syy-MVS		54.13 349	55.45 345	50.18 381	68.77 325	23.59 434	55.02 380	44.55 419	43.80 332	58.05 386	64.07 414	46.22 300	58.83 370	46.16 315	72.36 374	68.12 376

myMVS_eth3d		50.36 378	50.52 383	49.88 382	68.77 325	22.69 436	55.02 380	44.55 419	43.80 332	58.05 386	64.07 414	14.16 449	58.83 370	33.90 396	72.36 374	68.12 376

新几何1				69.99 206	88.37 35	71.34 55		62.08 336	43.85 331	74.99 206	86.11 177	52.85 258	70.57 287	50.99 268	83.23 256	68.05 378

UnsupCasMVSNet_eth		52.26 366	53.29 361	49.16 390	55.08 426	33.67 394	50.03 405	58.79 348	37.67 383	63.43 357	74.75 341	41.82 326	45.83 410	38.59 362	59.42 426	67.98 379

Patchmatch-test		47.93 387	49.96 386	41.84 416	57.42 415	24.26 433	48.75 408	41.49 433	39.30 371	56.79 392	73.48 354	30.48 398	33.87 440	29.29 416	72.61 372	67.39 380

Patchmatch-RL test		59.95 312	59.12 314	62.44 304	72.46 273	54.61 210	59.63 348	47.51 410	41.05 356	74.58 217	74.30 347	31.06 393	65.31 343	51.61 261	79.85 305	67.39 380

testgi		54.00 353	56.86 334	45.45 405	58.20 412	25.81 431	49.05 407	49.50 401	45.43 319	67.84 315	81.17 259	51.81 266	43.20 426	29.30 415	79.41 311	67.34 382

test222					87.30 38	69.15 78	67.85 257	59.59 346	41.06 355	73.05 244	85.72 186	48.03 295			80.65 292	66.92 383

pmmvs5		52.49 365	52.58 365	52.21 371	54.99 427	32.38 398	55.45 378	53.84 377	32.15 413	55.49 401	74.81 339	38.08 350	57.37 378	34.02 394	74.40 358	66.88 384

Anonymous20231206		54.13 349	55.82 342	49.04 392	70.89 289	35.96 378	51.73 398	50.87 394	34.86 397	62.49 360	79.22 297	42.52 324	44.29 422	27.95 421	81.88 271	66.88 384

tpm		50.60 376	52.42 367	45.14 407	65.18 368	26.29 427	60.30 343	43.50 422	37.41 385	57.01 390	79.09 301	30.20 401	42.32 427	32.77 400	66.36 408	66.81 386

testdata				64.13 283	85.87 62	63.34 125		61.80 339	47.83 298	76.42 184	86.60 161	48.83 289	62.31 357	54.46 241	81.26 283	66.74 387

MIMVSNet		54.39 348	56.12 340	49.20 389	72.57 272	30.91 407	59.98 346	48.43 407	41.66 349	55.94 398	83.86 215	41.19 330	50.42 394	26.05 425	75.38 349	66.27 388

tpmrst		50.15 380	51.38 374	46.45 402	56.05 420	24.77 432	64.40 313	49.98 397	36.14 392	53.32 412	69.59 387	35.16 364	48.69 401	39.24 355	58.51 429	65.89 389

EPMVS		45.74 392	46.53 395	43.39 414	54.14 431	22.33 439	55.02 380	35.00 442	34.69 401	51.09 419	70.20 379	25.92 415	42.04 429	37.19 373	55.50 434	65.78 390

PVSNet	43.83 21	51.56 371	51.17 375	52.73 368	68.34 330	38.27 361	48.22 410	53.56 380	36.41 390	54.29 408	64.94 413	34.60 366	54.20 387	30.34 409	69.87 393	65.71 391

test_fmvs1_n		52.70 362	52.01 369	54.76 357	53.83 434	50.36 238	55.80 376	65.90 307	24.96 434	65.39 332	60.64 426	27.69 408	48.46 402	45.88 318	67.99 403	65.46 392

BH-w/o		64.81 263	64.29 271	66.36 265	76.08 211	54.71 208	65.61 291	75.23 219	50.10 268	71.05 275	71.86 366	54.33 250	79.02 168	38.20 365	76.14 341	65.36 393

XXY-MVS		55.19 343	57.40 331	48.56 395	64.45 374	34.84 388	51.54 399	53.59 378	38.99 374	63.79 349	79.43 292	56.59 235	45.57 412	36.92 378	71.29 383	65.25 394

UBG		49.18 384	49.35 388	48.66 394	70.36 304	26.56 426	50.53 403	45.61 416	37.43 384	53.37 411	65.97 409	23.03 427	54.20 387	26.29 423	71.54 381	65.20 395

ADS-MVSNet2		48.76 385	47.25 394	53.29 367	55.90 422	40.54 343	47.34 414	54.99 371	31.41 418	50.48 421	72.06 363	31.23 390	54.26 386	25.93 426	55.93 432	65.07 396

ADS-MVSNet		44.62 398	45.58 397	41.73 417	55.90 422	20.83 441	47.34 414	39.94 437	31.41 418	50.48 421	72.06 363	31.23 390	39.31 435	25.93 426	55.93 432	65.07 396

KD-MVS_2432*1600		52.05 368	51.58 372	53.44 365	52.11 435	31.20 404	44.88 422	64.83 319	41.53 350	64.37 339	70.03 383	15.61 447	64.20 347	36.25 381	74.61 355	64.93 398

miper_refine_blended		52.05 368	51.58 372	53.44 365	52.11 435	31.20 404	44.88 422	64.83 319	41.53 350	64.37 339	70.03 383	15.61 447	64.20 347	36.25 381	74.61 355	64.93 398

test0.0.03 1		47.72 388	48.31 390	45.93 403	55.53 425	29.39 414	46.40 418	41.21 435	43.41 340	55.81 400	67.65 403	29.22 405	43.77 425	25.73 429	69.87 393	64.62 400

JIA-IIPM		54.03 351	51.62 371	61.25 318	59.14 407	55.21 206	59.10 351	47.72 408	50.85 258	50.31 424	85.81 185	20.10 435	63.97 349	36.16 384	55.41 435	64.55 401

PatchT		53.35 357	56.47 337	43.99 412	64.19 375	17.46 443	59.15 349	43.10 424	52.11 240	54.74 406	86.95 145	29.97 402	49.98 397	43.62 330	74.40 358	64.53 402

test_vis1_n		51.27 374	50.41 384	53.83 361	56.99 416	50.01 244	56.75 368	60.53 342	25.68 432	59.74 379	57.86 430	29.40 404	47.41 407	43.10 333	63.66 415	64.08 403

gg-mvs-nofinetune		55.75 337	56.75 335	52.72 369	62.87 381	28.04 419	68.92 237	41.36 434	71.09 47	50.80 420	92.63 15	20.74 433	66.86 330	29.97 412	72.41 373	63.25 404

MVS		60.62 307	59.97 308	62.58 303	68.13 335	47.28 281	68.59 247	73.96 228	32.19 411	59.94 376	68.86 396	50.48 274	77.64 201	41.85 341	75.74 343	62.83 405

N_pmnet		52.06 367	51.11 376	54.92 356	59.64 405	71.03 57	37.42 435	61.62 340	33.68 406	57.12 388	72.10 362	37.94 351	31.03 441	29.13 420	71.35 382	62.70 406

Gipuma		69.55 197	72.83 150	59.70 329	63.63 379	53.97 214	80.08 83	75.93 212	64.24 104	73.49 237	88.93 108	57.89 223	62.46 355	59.75 191	91.55 94	62.67 407
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
test_fmvs1		51.51 372	50.86 380	53.48 364	49.72 440	49.35 254	54.11 387	64.96 317	24.64 436	63.66 352	59.61 429	28.33 407	48.45 403	45.38 323	67.30 407	62.66 408

WTY-MVS		49.39 383	50.31 385	46.62 401	61.22 390	32.00 401	46.61 417	49.77 398	33.87 405	54.12 409	69.55 388	41.96 325	45.40 414	31.28 406	64.42 413	62.47 409

test_vis1_rt		46.70 391	45.24 399	51.06 378	44.58 445	51.04 233	39.91 431	67.56 298	21.84 442	51.94 416	50.79 438	33.83 368	39.77 434	35.25 390	61.50 421	62.38 410

test-LLR		50.43 377	50.69 382	49.64 385	60.76 392	41.87 327	53.18 391	45.48 417	43.41 340	49.41 425	60.47 427	29.22 405	44.73 419	42.09 339	72.14 377	62.33 411

test-mter		48.56 386	48.20 391	49.64 385	60.76 392	41.87 327	53.18 391	45.48 417	31.91 416	49.41 425	60.47 427	18.34 440	44.73 419	42.09 339	72.14 377	62.33 411

test_vis1_n_1920		52.96 359	53.50 358	51.32 376	59.15 406	44.90 302	56.13 374	64.29 324	30.56 421	59.87 378	60.68 425	40.16 337	47.47 406	48.25 297	62.46 418	61.58 413

UnsupCasMVSNet_bld		50.01 381	51.03 378	46.95 398	58.61 409	32.64 397	48.31 409	53.27 383	34.27 403	60.47 372	71.53 368	41.40 327	47.07 408	30.68 408	60.78 423	61.13 414

sss		47.59 389	48.32 389	45.40 406	56.73 419	33.96 392	45.17 420	48.51 406	32.11 415	52.37 414	65.79 410	40.39 336	41.91 430	31.85 403	61.97 420	60.35 415

PM-MVS		64.49 268	63.61 278	67.14 258	76.68 200	75.15 31	68.49 251	42.85 426	51.17 256	77.85 146	80.51 270	45.76 301	66.31 337	52.83 257	76.35 339	59.96 416

test_cas_vis1_n_1920		50.90 375	50.92 379	50.83 379	54.12 432	47.80 270	51.44 400	54.61 372	26.95 428	63.95 345	60.85 424	37.86 354	44.97 417	45.53 320	62.97 417	59.72 417

GG-mvs-BLEND				52.24 370	60.64 394	29.21 416	69.73 226	42.41 427		45.47 433	52.33 436	20.43 434	68.16 313	25.52 430	65.42 410	59.36 418

dmvs_re		49.91 382	50.77 381	47.34 397	59.98 398	38.86 356	53.18 391	53.58 379	39.75 367	55.06 402	61.58 423	36.42 360	44.40 421	29.15 419	68.23 401	58.75 419

TESTMET0.1,1		45.17 395	44.93 401	45.89 404	56.02 421	38.31 360	53.18 391	41.94 432	27.85 424	44.86 436	56.47 432	17.93 442	41.50 432	38.08 366	68.06 402	57.85 420

mvsany_test3		43.76 403	41.01 407	52.01 372	48.09 442	57.74 186	42.47 426	23.85 449	23.30 439	64.80 337	62.17 421	27.12 409	40.59 433	29.17 418	48.11 439	57.69 421

MS-PatchMatch		55.59 340	54.89 350	57.68 343	69.18 320	49.05 255	61.00 337	62.93 332	35.98 393	58.36 384	68.93 394	36.71 359	66.59 335	37.62 371	63.30 416	57.39 422

dp		44.09 401	44.88 402	41.72 418	58.53 411	23.18 435	54.70 385	42.38 429	34.80 399	44.25 438	65.61 411	24.48 422	44.80 418	29.77 413	49.42 438	57.18 423

MVE	27.91 23	36.69 410	35.64 413	39.84 420	43.37 447	35.85 380	19.49 441	24.61 447	24.68 435	39.05 442	62.63 420	38.67 348	27.10 445	21.04 440	47.25 440	56.56 424
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
pmmvs3		46.71 390	45.09 400	51.55 374	56.76 418	48.25 261	55.78 377	39.53 438	24.13 437	50.35 423	63.40 416	15.90 446	51.08 393	29.29 416	70.69 388	55.33 425

PatchMatch-RL		58.68 322	57.72 327	61.57 311	76.21 207	73.59 43	61.83 330	49.00 405	47.30 304	61.08 367	68.97 392	50.16 276	59.01 369	36.06 386	68.84 399	52.10 426

dmvs_testset		45.26 394	47.51 392	38.49 422	59.96 400	14.71 446	58.50 358	43.39 423	41.30 352	51.79 417	56.48 431	39.44 344	49.91 399	21.42 439	55.35 436	50.85 427

wuyk23d		61.97 294	66.25 248	49.12 391	58.19 413	60.77 154	66.32 281	52.97 384	55.93 188	90.62 6	86.91 146	73.07 61	35.98 439	20.63 441	91.63 91	50.62 428

PMMVS2		37.74 408	40.87 408	28.36 425	42.41 448	5.35 453	24.61 440	27.75 445	32.15 413	47.85 429	70.27 378	35.85 362	29.51 443	19.08 442	67.85 404	50.22 429

DSMNet-mixed		43.18 404	44.66 403	38.75 421	54.75 428	28.88 417	57.06 367	27.42 446	13.47 444	47.27 431	77.67 318	38.83 346	39.29 436	25.32 431	60.12 425	48.08 430

new_pmnet		37.55 409	39.80 411	30.79 424	56.83 417	16.46 445	39.35 432	30.65 444	25.59 433	45.26 434	61.60 422	24.54 420	28.02 444	21.60 438	52.80 437	47.90 431

CHOSEN 280x420		41.62 405	39.89 410	46.80 400	61.81 386	51.59 226	33.56 439	35.74 441	27.48 426	37.64 444	53.53 433	23.24 425	42.09 428	27.39 422	58.64 428	46.72 432

EMVS		44.61 399	44.45 404	45.10 408	48.91 441	43.00 319	37.92 434	41.10 436	46.75 307	38.00 443	48.43 440	26.42 412	46.27 409	37.11 375	75.38 349	46.03 433

E-PMN		45.17 395	45.36 398	44.60 409	50.07 438	42.75 321	38.66 433	42.29 430	46.39 309	39.55 441	51.15 437	26.00 414	45.37 415	37.68 369	76.41 338	45.69 434

test_f		43.79 402	45.63 396	38.24 423	42.29 449	38.58 358	34.76 438	47.68 409	22.22 441	67.34 322	63.15 417	31.82 385	30.60 442	39.19 356	62.28 419	45.53 435

mvsany_test1		37.88 407	35.74 412	44.28 410	47.28 443	49.90 246	36.54 437	24.37 448	19.56 443	45.76 432	53.46 434	32.99 373	37.97 438	26.17 424	35.52 441	44.99 436

PMMVS		44.69 397	43.95 406	46.92 399	50.05 439	53.47 219	48.08 412	42.40 428	22.36 440	44.01 439	53.05 435	42.60 323	45.49 413	31.69 404	61.36 422	41.79 437

PVSNet_0	36.71 22	41.12 406	40.78 409	42.14 415	59.97 399	40.13 346	40.97 428	42.24 431	30.81 420	44.86 436	49.41 439	40.70 334	45.12 416	23.15 436	34.96 442	41.16 438

FPMVS		59.43 316	60.07 307	57.51 344	77.62 185	71.52 53	62.33 329	50.92 393	57.40 169	69.40 294	80.00 281	39.14 345	61.92 359	37.47 372	66.36 408	39.09 439

MVS-HIRNet		45.53 393	47.29 393	40.24 419	62.29 384	26.82 423	56.02 375	37.41 440	29.74 422	43.69 440	81.27 257	33.96 367	55.48 382	24.46 433	56.79 431	38.43 440

test_method		19.26 413	19.12 417	19.71 427	9.09 452	1.91 455	7.79 443	53.44 381	1.42 446	10.27 448	35.80 442	17.42 444	25.11 446	12.44 445	24.38 444	32.10 441

dongtai		31.66 411	32.98 414	27.71 426	58.58 410	12.61 448	45.02 421	14.24 452	41.90 347	47.93 428	43.91 441	10.65 452	41.81 431	14.06 443	20.53 445	28.72 442

kuosan		22.02 412	23.52 416	17.54 428	41.56 450	11.24 449	41.99 427	13.39 453	26.13 431	28.87 445	30.75 443	9.72 453	21.94 447	4.77 448	14.49 446	19.43 443

DeepMVS_CX				11.83 429	15.51 451	13.86 447		11.25 454	5.76 445	20.85 447	26.46 444	17.06 445	9.22 448	9.69 447	13.82 447	12.42 444

tmp_tt		11.98 415	14.73 418	3.72 430	2.28 453	4.62 454	19.44 442	14.50 451	0.47 448	21.55 446	9.58 446	25.78 416	4.57 449	11.61 446	27.37 443	1.96 445

testmvs		4.06 419	5.28 422	0.41 431	0.64 455	0.16 457	42.54 425	0.31 456	0.26 450	0.50 451	1.40 450	0.77 454	0.17 450	0.56 449	0.55 449	0.90 446

test123		4.43 418	5.78 421	0.39 432	0.97 454	0.28 456	46.33 419	0.45 455	0.31 449	0.62 450	1.50 449	0.61 455	0.11 451	0.56 449	0.63 448	0.77 447

mmdepth		0.00 420	0.00 423	0.00 433	0.00 456	0.00 458	0.00 444	0.00 457	0.00 451	0.00 452	0.00 451	0.00 456	0.00 452	0.00 451	0.00 450	0.00 448

monomultidepth		0.00 420	0.00 423	0.00 433	0.00 456	0.00 458	0.00 444	0.00 457	0.00 451	0.00 452	0.00 451	0.00 456	0.00 452	0.00 451	0.00 450	0.00 448

test_blank		0.00 420	0.00 423	0.00 433	0.00 456	0.00 458	0.00 444	0.00 457	0.00 451	0.00 452	0.00 451	0.00 456	0.00 452	0.00 451	0.00 450	0.00 448

uanet_test		0.00 420	0.00 423	0.00 433	0.00 456	0.00 458	0.00 444	0.00 457	0.00 451	0.00 452	0.00 451	0.00 456	0.00 452	0.00 451	0.00 450	0.00 448

DCPMVS		0.00 420	0.00 423	0.00 433	0.00 456	0.00 458	0.00 444	0.00 457	0.00 451	0.00 452	0.00 451	0.00 456	0.00 452	0.00 451	0.00 450	0.00 448

cdsmvs_eth3d_5k		17.71 414	23.62 415	0.00 433	0.00 456	0.00 458	0.00 444	70.17 276	0.00 451	0.00 452	74.25 348	68.16 104	0.00 452	0.00 451	0.00 450	0.00 448

pcd_1.5k_mvsjas		5.20 417	6.93 420	0.00 433	0.00 456	0.00 458	0.00 444	0.00 457	0.00 451	0.00 452	0.00 451	62.39 163	0.00 452	0.00 451	0.00 450	0.00 448

sosnet-low-res		0.00 420	0.00 423	0.00 433	0.00 456	0.00 458	0.00 444	0.00 457	0.00 451	0.00 452	0.00 451	0.00 456	0.00 452	0.00 451	0.00 450	0.00 448

sosnet		0.00 420	0.00 423	0.00 433	0.00 456	0.00 458	0.00 444	0.00 457	0.00 451	0.00 452	0.00 451	0.00 456	0.00 452	0.00 451	0.00 450	0.00 448

uncertanet		0.00 420	0.00 423	0.00 433	0.00 456	0.00 458	0.00 444	0.00 457	0.00 451	0.00 452	0.00 451	0.00 456	0.00 452	0.00 451	0.00 450	0.00 448

Regformer		0.00 420	0.00 423	0.00 433	0.00 456	0.00 458	0.00 444	0.00 457	0.00 451	0.00 452	0.00 451	0.00 456	0.00 452	0.00 451	0.00 450	0.00 448

ab-mvs-re		5.62 416	7.50 419	0.00 433	0.00 456	0.00 458	0.00 444	0.00 457	0.00 451	0.00 452	67.46 404	0.00 456	0.00 452	0.00 451	0.00 450	0.00 448

uanet		0.00 420	0.00 423	0.00 433	0.00 456	0.00 458	0.00 444	0.00 457	0.00 451	0.00 452	0.00 451	0.00 456	0.00 452	0.00 451	0.00 450	0.00 448

WAC-MVS						22.69 436								36.10 385

FOURS1					89.19 24	77.84 14	91.64 1	89.11 3	84.05 3	91.57 3

test_one_0601					85.84 64	61.45 140		85.63 31	75.27 21	85.62 52	90.38 71	76.72 31

eth-test2					0.00 456

eth-test					0.00 456

ZD-MVS					83.91 91	69.36 74		81.09 124	58.91 154	82.73 88	89.11 101	75.77 39	86.63 14	72.73 70	92.93 73

test_241102_ONE					86.12 54	61.06 146		84.72 53	72.64 35	87.38 28	89.47 90	77.48 27	85.74 46

9.14			80.22 58		80.68 136		80.35 78	87.69 12	59.90 143	83.00 81	88.20 124	74.57 51	81.75 121	73.75 63	93.78 61

save fliter					87.00 40	67.23 92	79.24 91	77.94 188	56.65 180

test0726					86.16 52	60.78 152	83.81 44	85.10 44	72.48 38	85.27 57	89.96 83	78.57 19

test_part2					85.90 60	66.44 97				84.61 66

sam_mvs												31.21 392

MTGPA								80.63 135

test_post1							66.63 277				2.08 447	30.66 397	59.33 368	40.34 351

test_post											1.99 448	30.91 395	54.76 385

patchmatchnet-post											68.99 391	31.32 389	69.38 301

MTMP							84.83 34	19.26 450

gm-plane-assit					62.51 382	33.91 393			37.25 386		62.71 419		72.74 255	38.70 359

TEST9					85.47 67	69.32 75	76.42 127	78.69 173	53.73 226	76.97 159	86.74 152	66.84 118	81.10 131

test_8					85.09 74	67.89 84	76.26 132	78.66 175	54.00 221	76.89 163	86.72 154	66.60 124	80.89 141

agg_prior					84.44 86	66.02 103		78.62 176		76.95 161			80.34 148

test_prior4						70.14 67	77.57 108

test_prior2							75.57 140		58.92 153	76.53 179	86.78 150	67.83 111		69.81 92	92.76 76

旧先验2							71.17 207		45.11 324	78.54 138			61.28 361	59.19 195

新几何2							71.33 203

原ACMM2							74.78 151

testdata2													67.30 322	48.34 295

segment_acmp												68.30 103

testdata1							68.34 253		57.24 171

plane_prior7					85.18 70	66.21 100

plane_prior6					84.18 89	65.31 109						60.83 184

plane_prior4											89.11 101

plane_prior3						65.67 105			63.82 108	78.23 140

plane_prior2							82.74 56		65.45 86

plane_prior1					84.46 85

plane_prior						65.18 110	80.06 84		61.88 128						89.91 138

n2								0.00 457

nn								0.00 457

door-mid								55.02 370

test11								82.71 94

door								52.91 385

HQP5-MVS						58.80 176

HQP-NCC					82.37 115		77.32 113		59.08 148	71.58 263

ACMP_Plane					82.37 115		77.32 113		59.08 148	71.58 263

BP-MVS														67.38 117

HQP3-MVS								84.12 72							89.16 153

HQP2-MVS												58.09 217

NP-MVS					83.34 100	63.07 128					85.97 181

MDTV_nov1_ep13			54.05 357		65.54 365	29.30 415	59.00 352	55.22 368	35.96 394	52.44 413	75.98 329	30.77 396	59.62 366	38.21 364	73.33 368

ACMMP++_ref															89.47 148

ACMMP++															91.96 87

Test By Simon												62.56 159

High-resolution multi-view benchmark