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.

DVP-MVS++		81.67 1	82.40 1	79.47 10	87.24 14	59.15 69	88.18 1	87.15 3	65.04 17	84.26 5	91.86 6	67.01 1	90.84 3	79.48 7	91.38 2	88.42 32

OPU-MVS				79.83 7	87.54 11	60.93 35	87.82 7				89.89 51	67.01 1	90.33 12	73.16 72	91.15 4	88.23 40

SED-MVS		81.56 2	82.30 2	79.32 13	87.77 4	58.90 79	87.82 7	86.78 10	64.18 35	85.97 1	91.84 8	66.87 3	90.83 5	78.63 20	90.87 5	88.23 40

test_241102_ONE					87.77 4	58.90 79		86.78 10	64.20 34	85.97 1	91.34 18	66.87 3	90.78 7

PC_three_1452									55.09 261	84.46 4	89.84 52	66.68 5	89.41 24	74.24 62	91.38 2	88.42 32

test_241102_TWO								86.73 12	64.18 35	84.26 5	91.84 8	65.19 6	90.83 5	78.63 20	90.70 7	87.65 64

DPE-MVS		80.56 5	80.98 5	79.29 15	87.27 13	60.56 41	85.71 31	86.42 16	63.28 52	83.27 15	91.83 10	64.96 7	90.47 11	76.41 41	89.67 20	86.84 99
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
DVP-MVS		80.84 4	81.64 3	78.42 38	87.75 7	59.07 74	87.85 5	85.03 43	64.26 32	83.82 8	92.00 3	64.82 8	90.75 8	78.66 18	90.61 11	85.45 170
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
test0726					87.75 7	59.07 74	87.86 4	86.83 8	64.26 32	84.19 7	91.92 5	64.82 8

MED-MVS		80.42 6	80.87 6	79.07 25	85.30 51	59.25 64	86.84 11	85.86 24	63.31 49	83.65 12	91.48 12	64.70 10	89.91 16	77.02 35	89.69 18	88.06 50

test_0728_THIRD									65.04 17	83.82 8	92.00 3	64.69 11	90.75 8	79.48 7	90.63 10	88.09 47

test_one_0601					87.58 9	59.30 62		86.84 7	65.01 21	83.80 11	91.86 6	64.03 12

MSP-MVS		81.06 3	81.40 4	80.02 1	86.21 33	62.73 9	86.09 22	86.83 8	65.51 13	83.81 10	90.51 30	63.71 13	89.23 26	81.51 2	88.44 31	88.09 47
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
test-260524					86.59 25	59.16 67		86.47 15		82.32 18		62.54 14	89.91 16	77.25 30	89.69 18

dcpmvs_2		74.55 72	75.23 59	72.48 200	82.34 89	53.34 178	77.87 166	81.46 139	57.80 193	75.49 55	86.81 120	62.22 15	77.75 324	71.09 93	82.02 109	86.34 123

TestfortrainingZip				78.05 44	84.66 63	58.22 88	86.84 11	85.98 23	63.31 49	79.39 25	88.94 65	62.01 16	89.61 22		86.45 64	86.34 123

aaEdge-Enhanced		80.04 10	80.36 9	79.08 24	86.63 23	59.25 64	85.62 32	86.73 12	63.10 58	82.27 19	90.57 27	61.90 17	89.88 19	77.02 35	89.43 24	88.10 45

DeepPCF-MVS	69.58 1	79.03 15	79.00 16	79.13 19	84.92 61	60.32 46	83.03 68	85.33 35	62.86 64	80.17 22	90.03 47	61.76 18	88.95 30	74.21 63	88.67 30	88.12 44

TestfortrainingZip a		79.61 13	79.84 13	78.92 30	85.30 51	59.08 73	86.84 11	86.01 21	63.31 49	82.37 17	91.48 12	60.88 19	89.61 22	76.25 44	86.13 66	88.06 50

CSCG		76.92 37	76.75 35	77.41 56	83.96 70	59.60 56	82.95 69	86.50 14	60.78 112	75.27 58	84.83 185	60.76 20	86.56 85	67.86 120	87.87 45	86.06 137

TSAR-MVS + MP.		78.44 22	78.28 22	78.90 31	84.96 57	61.41 26	84.03 56	83.82 76	59.34 156	79.37 26	89.76 54	59.84 21	87.62 59	76.69 38	86.74 59	87.68 63
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
sasdasda		74.67 68	74.98 63	73.71 158	78.94 159	50.56 246	80.23 108	83.87 69	60.30 129	77.15 43	86.56 135	59.65 22	82.00 214	66.01 147	82.12 106	88.58 29

canonicalmvs		74.67 68	74.98 63	73.71 158	78.94 159	50.56 246	80.23 108	83.87 69	60.30 129	77.15 43	86.56 135	59.65 22	82.00 214	66.01 147	82.12 106	88.58 29

APDe-MVS		80.16 9	80.59 7	78.86 33	86.64 21	60.02 48	88.12 3	86.42 16	62.94 61	82.40 16	92.12 2	59.64 24	89.76 20	78.70 15	88.32 35	86.79 101
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
DELS-MVS		74.76 66	74.46 69	75.65 90	77.84 202	52.25 209	75.59 239	84.17 57	63.76 41	73.15 111	82.79 237	59.58 25	86.80 77	67.24 131	86.04 67	87.89 52
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
CNVR-MVS		79.84 12	79.97 12	79.45 11	87.90 2	62.17 17	84.37 45	85.03 43	66.96 5	77.58 40	90.06 45	59.47 26	89.13 28	78.67 17	89.73 16	87.03 92

casdiffmvs_mvg		76.14 51	76.30 44	75.66 89	76.46 261	51.83 220	79.67 122	85.08 40	65.02 20	75.84 51	88.58 75	59.42 27	85.08 129	72.75 75	83.93 84	90.08 2
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
Casviewmamba		76.62 42	76.52 42	76.90 62	77.91 199	53.66 166	80.76 103	84.47 50	66.73 8	75.75 54	88.63 74	59.17 28	86.66 81	72.28 80	83.01 92	90.39 1

MCST-MVS		77.48 32	77.45 31	77.54 53	86.67 20	58.36 86	83.22 66	86.93 5	56.91 211	74.91 68	88.19 77	59.15 29	87.68 58	73.67 69	87.45 49	86.57 112

hybridcas		74.86 64	75.07 61	74.24 129	76.30 262	50.58 243	79.30 128	83.88 68	63.15 57	74.69 75	88.13 79	58.91 30	82.98 179	68.30 107	82.93 97	89.15 11

nrg030		72.96 106	73.01 100	72.84 189	75.41 281	50.24 255	80.02 113	82.89 117	58.36 177	74.44 79	86.73 125	58.90 31	80.83 247	65.84 151	74.46 249	87.44 73

HPM-MVS++		79.88 11	80.14 11	79.10 21	88.17 1	64.80 1	86.59 16	83.70 81	65.37 14	78.78 30	90.64 24	58.63 32	87.24 62	79.00 14	90.37 14	85.26 182

SF-MVS		78.82 16	79.22 15	77.60 52	82.88 84	57.83 92	84.99 37	88.13 2	61.86 90	79.16 27	90.75 23	57.96 33	87.09 71	77.08 34	90.18 15	87.87 54

casdiffmvs		74.80 65	74.89 65	74.53 119	75.59 276	50.37 252	78.17 157	85.06 42	62.80 68	74.40 80	87.86 88	57.88 34	83.61 162	69.46 102	82.79 102	89.59 5
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
MGCFI-Net		72.45 118	73.34 95	69.81 280	77.77 204	43.21 367	75.84 236	81.18 154	59.59 151	75.45 56	86.64 128	57.74 35	77.94 316	63.92 168	81.90 112	88.30 36

DeepC-MVS	69.38 2	78.56 20	78.14 25	79.83 7	83.60 72	61.62 23	84.17 53	86.85 6	63.23 54	73.84 95	90.25 40	57.68 36	89.96 15	74.62 61	89.03 26	87.89 52
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
baseline		74.61 70	74.70 66	74.34 124	75.70 271	49.99 263	77.54 178	84.63 49	62.73 69	73.98 87	87.79 91	57.67 37	83.82 158	69.49 100	82.74 103	89.20 10

patch_mono-2		69.85 179	71.09 136	66.16 343	79.11 156	54.80 149	71.97 324	74.31 312	53.50 301	70.90 154	84.17 207	57.63 38	63.31 441	66.17 144	82.02 109	80.38 327

9.14			78.75 18		83.10 79		84.15 54	88.26 1	59.90 140	78.57 32	90.36 35	57.51 39	86.86 76	77.39 29	89.52 23

MM		80.20 8	80.28 10	79.99 2	82.19 91	60.01 49	86.19 21	83.93 62	73.19 1	77.08 46	91.21 20	57.23 40	90.73 10	83.35 1	88.12 38	89.22 9

fmvsm_s_conf0.5_n_9		75.16 61	75.22 60	75.01 102	78.34 182	55.37 140	77.30 188	73.95 321	61.40 97	79.46 24	90.14 41	57.07 41	81.15 234	80.00 5	79.31 156	88.51 31

MVSMamba_PlusPlus		75.75 57	75.44 55	76.67 69	80.84 114	53.06 186	78.62 140	85.13 39	59.65 146	71.53 145	87.47 95	56.92 42	88.17 41	72.18 82	86.63 62	88.80 16

DPM-MVS		75.47 59	75.00 62	76.88 63	81.38 105	59.16 67	79.94 115	85.71 29	56.59 222	72.46 130	86.76 121	56.89 43	87.86 52	66.36 143	88.91 29	83.64 246

UniMVSNet_NR-MVSNet		71.11 145	71.00 138	71.44 233	79.20 151	44.13 353	76.02 231	82.60 121	66.48 12	68.20 201	84.60 198	56.82 44	82.82 195	54.62 262	70.43 317	87.36 81

SMA-MVS		80.28 7	80.39 8	79.95 4	86.60 24	61.95 19	86.33 17	85.75 28	62.49 72	82.20 20	92.28 1	56.53 45	89.70 21	79.85 6	91.48 1	88.19 42
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
BridgeMVS		76.58 43	76.55 41	76.68 68	81.73 97	52.90 189	80.94 99	85.70 30	61.12 105	74.90 69	87.17 112	56.46 46	88.14 42	72.87 74	88.03 42	89.00 12

Effi-MVS+		73.31 97	72.54 109	75.62 91	77.87 200	53.64 167	79.62 124	79.61 183	61.63 95	72.02 137	82.61 242	56.44 47	85.97 108	63.99 167	79.07 167	87.25 85

alignmvs		73.86 85	73.99 79	73.45 172	78.20 186	50.50 248	78.57 142	82.43 123	59.40 154	76.57 48	86.71 127	56.42 48	81.23 233	65.84 151	81.79 113	88.62 26

viewdifsd2359ckpt07		71.90 131	71.97 117	71.69 223	74.81 296	48.08 307	75.30 244	80.49 169	60.00 137	71.63 142	86.33 144	56.34 49	79.25 280	65.40 155	77.41 202	87.76 60

test_prior2							81.75 89		60.37 125	75.01 64	89.06 61	56.22 50		72.19 81	88.96 28

fmvsm_l_conf0.5_n_9		73.27 98	73.66 87	72.09 209	73.82 322	52.72 197	77.45 182	74.28 314	56.61 221	77.10 45	88.16 78	56.17 51	77.09 340	78.27 24	81.13 121	86.48 116

E2		73.72 88	73.60 88	74.06 140	77.16 230	50.40 250	76.97 200	83.74 78	61.64 93	73.36 102	86.75 124	56.14 52	82.99 176	67.50 128	79.18 164	88.80 16

E3		73.72 88	73.60 88	74.06 140	77.16 230	50.40 250	76.97 200	83.74 78	61.64 93	73.36 102	86.76 121	56.13 53	82.99 176	67.50 128	79.18 164	88.80 16

ZD-MVS					86.64 21	60.38 45		82.70 120	57.95 187	78.10 35	90.06 45	56.12 54	88.84 32	74.05 65	87.00 55

viewcassd2359sk11		73.56 90	73.41 93	74.00 144	77.13 236	50.35 253	76.86 208	83.69 82	61.23 102	73.14 112	86.38 142	56.09 55	82.96 180	67.15 132	79.01 169	88.70 25

E4		73.91 84	73.83 84	74.15 135	77.13 236	50.47 249	77.15 195	83.79 77	62.21 81	73.61 97	87.19 111	56.08 56	83.03 174	67.91 119	79.35 154	88.94 14

E3new		73.41 94	73.22 96	73.95 147	77.06 241	50.31 254	76.78 211	83.66 83	60.90 108	72.93 120	86.02 155	55.99 57	82.95 182	66.89 140	78.77 174	88.61 27

E6new		74.10 78	74.09 75	74.15 135	77.14 232	50.74 236	78.24 149	83.85 74	62.34 76	73.95 89	87.27 104	55.98 58	82.95 182	68.17 111	79.85 142	88.77 19

E6		74.10 78	74.09 75	74.15 135	77.14 232	50.74 236	78.24 149	83.85 74	62.34 76	73.95 89	87.27 104	55.98 58	82.95 182	68.17 111	79.85 142	88.77 19

E5new		74.10 78	74.09 75	74.15 135	77.14 232	50.74 236	78.24 149	83.86 72	62.34 76	73.95 89	87.27 104	55.97 60	82.95 182	68.16 113	79.86 140	88.77 19

E5		74.10 78	74.09 75	74.15 135	77.14 232	50.74 236	78.24 149	83.86 72	62.34 76	73.95 89	87.27 104	55.97 60	82.95 182	68.16 113	79.86 140	88.77 19

TSAR-MVS + GP.		74.90 63	74.15 74	77.17 60	82.00 93	58.77 82	81.80 88	78.57 211	58.58 172	74.32 82	84.51 201	55.94 62	87.22 65	67.11 133	84.48 79	85.52 163

fmvsm_s_conf0.5_n_3		73.55 91	74.39 70	71.03 253	74.09 321	51.86 219	77.77 172	75.60 284	61.18 103	78.67 31	88.98 63	55.88 63	77.73 325	78.69 16	78.68 177	83.50 249

MVS_Test		72.45 118	72.46 110	72.42 204	74.88 292	48.50 298	76.28 221	83.14 109	59.40 154	72.46 130	84.68 190	55.66 64	81.12 235	65.98 150	79.66 147	87.63 65

APD-MVS		78.02 26	78.04 26	77.98 46	86.44 29	60.81 38	85.52 33	84.36 54	60.61 116	79.05 28	90.30 38	55.54 65	88.32 38	73.48 71	87.03 52	84.83 197
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
NCCC		78.58 19	78.31 21	79.39 12	87.51 12	62.61 13	85.20 36	84.42 53	66.73 8	74.67 77	89.38 58	55.30 66	89.18 27	74.19 64	87.34 50	86.38 119

FIs		70.82 155	71.43 126	68.98 295	78.33 183	38.14 424	76.96 202	83.59 86	61.02 106	67.33 230	86.73 125	55.07 67	81.64 220	54.61 264	79.22 160	87.14 90

CS-MVS		76.25 50	75.98 48	77.06 61	80.15 130	55.63 132	84.51 44	83.90 65	63.24 53	73.30 105	87.27 104	55.06 68	86.30 97	71.78 87	84.58 74	89.25 8

fmvsm_l_conf0.5_n_3		73.23 99	73.13 99	73.55 168	74.40 310	55.13 143	78.97 132	74.96 303	56.64 215	74.76 74	88.75 72	55.02 69	78.77 304	76.33 42	78.31 188	86.74 104

SD-MVS		77.70 30	77.62 30	77.93 47	84.47 65	61.88 21	84.55 43	83.87 69	60.37 125	79.89 23	89.38 58	54.97 70	85.58 117	76.12 46	84.94 72	86.33 126
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		76.46 45	75.93 49	78.06 43	81.29 106	57.53 97	82.35 80	83.31 98	67.78 3	70.09 165	86.34 143	54.92 71	88.90 31	72.68 76	84.55 75	87.76 60

MP-MVS		78.35 23	78.26 24	78.64 35	86.54 27	63.47 4	86.02 24	83.55 87	63.89 40	73.60 98	90.60 25	54.85 72	86.72 79	77.20 32	88.06 40	85.74 155
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
test_fmvsmconf_n		73.01 104	72.59 107	74.27 127	71.28 376	55.88 126	78.21 156	75.56 286	54.31 285	74.86 70	87.80 90	54.72 73	80.23 263	78.07 26	78.48 183	86.70 105

mvs_anonymous		68.03 235	67.51 222	69.59 283	72.08 357	44.57 350	71.99 323	75.23 295	51.67 328	67.06 237	82.57 247	54.68 74	77.94 316	56.56 244	75.71 235	86.26 133

test12				77.76 51	84.52 64	58.41 85		83.36 94		72.93 120		54.61 75	88.05 45		88.12 38	86.81 100

FC-MVSNet-test		69.80 182	70.58 148	67.46 318	77.61 217	34.73 458	76.05 229	83.19 107	60.84 110	65.88 264	86.46 139	54.52 76	80.76 250	52.52 279	78.12 190	86.91 95

SteuartSystems-ACMMP		79.48 14	79.31 14	79.98 3	83.01 82	62.18 16	87.60 9	85.83 26	66.69 10	78.03 37	90.98 21	54.26 77	90.06 14	78.42 23	89.02 27	87.69 62
Skip Steuart: Steuart Systems R&D Blog.
segment_acmp												54.23 78

MVS_111021_HR		74.02 82	73.46 91	75.69 88	83.01 82	60.63 40	77.29 189	78.40 222	61.18 103	70.58 158	85.97 157	54.18 79	84.00 155	67.52 127	82.98 96	82.45 279

viewmanbaseed2359cas		72.92 107	72.89 102	73.00 184	75.16 287	49.25 283	77.25 192	83.11 111	59.52 153	72.93 120	86.63 130	54.11 80	80.98 240	66.63 141	80.67 126	88.76 24

fmvsm_s_conf0.5_n_11		73.16 100	73.35 94	72.58 194	75.48 278	52.41 208	78.84 134	76.85 256	58.64 170	73.58 99	87.25 109	54.09 81	79.47 275	76.19 45	79.27 157	85.86 145

MGCNet		78.45 21	78.28 22	78.98 29	80.73 116	57.91 91	84.68 41	81.64 135	68.35 2	75.77 52	90.38 34	53.98 82	90.26 13	81.30 3	87.68 46	88.77 19

Fast-Effi-MVS+		70.28 168	69.12 179	73.73 157	78.50 173	51.50 223	75.01 253	79.46 187	56.16 234	68.59 193	79.55 318	53.97 83	84.05 151	53.34 274	77.53 199	85.65 160

ZNCC-MVS		78.82 16	78.67 19	79.30 14	86.43 30	62.05 18	86.62 15	86.01 21	63.32 48	75.08 63	90.47 33	53.96 84	88.68 33	76.48 40	89.63 22	87.16 89

UniMVSNet (Re)		70.63 159	70.20 155	71.89 213	78.55 172	45.29 341	75.94 232	82.92 114	63.68 43	68.16 204	83.59 223	53.89 85	83.49 166	53.97 268	71.12 308	86.89 96

SPE-MVS-test		75.62 58	75.31 58	76.56 73	80.63 120	55.13 143	83.88 59	85.22 36	62.05 86	71.49 146	86.03 154	53.83 86	86.36 95	67.74 122	86.91 56	88.19 42

viewmacassd2359aftdt		73.15 101	73.16 98	73.11 182	75.15 289	49.31 280	77.53 180	83.21 103	60.42 121	73.20 109	87.34 101	53.82 87	81.05 239	67.02 137	80.79 122	88.96 13

test_fmvsmconf0.1_n		72.81 108	72.33 112	74.24 129	69.89 402	55.81 127	78.22 155	75.40 291	54.17 287	75.00 65	88.03 86	53.82 87	80.23 263	78.08 25	78.34 187	86.69 106

fmvsm_l_conf0.5_n		70.99 150	70.82 141	71.48 229	71.45 369	54.40 152	77.18 194	70.46 358	48.67 376	75.17 60	86.86 118	53.77 89	76.86 348	76.33 42	77.51 200	83.17 261

DeepC-MVS_fast	68.24 3	77.25 34	76.63 37	79.12 20	86.15 36	60.86 36	84.71 40	84.85 47	61.98 89	73.06 117	88.88 67	53.72 90	89.06 29	68.27 108	88.04 41	87.42 74
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
TEST9					85.58 45	61.59 24	81.62 91	81.26 150	55.65 245	74.93 66	88.81 68	53.70 91	84.68 141

train_agg		76.27 48	76.15 45	76.64 71	85.58 45	61.59 24	81.62 91	81.26 150	55.86 237	74.93 66	88.81 68	53.70 91	84.68 141	75.24 56	88.33 34	83.65 245

test_8					85.40 48	60.96 34	81.54 94	81.18 154	55.86 237	74.81 71	88.80 70	53.70 91	84.45 145

ETV-MVS		74.46 73	73.84 83	76.33 76	79.27 148	55.24 142	79.22 129	85.00 45	64.97 22	72.65 127	79.46 320	53.65 94	87.87 51	67.45 130	82.91 98	85.89 143

fmvsm_s_conf0.5_n_5		72.69 112	72.80 104	72.37 205	74.11 320	53.21 182	78.12 158	73.31 328	53.98 290	76.81 47	88.05 83	53.38 95	77.37 335	76.64 39	80.78 123	86.53 114

CDPH-MVS		76.31 47	75.67 54	78.22 41	85.35 50	59.14 71	81.31 96	84.02 59	56.32 228	74.05 86	88.98 63	53.34 96	87.92 49	69.23 103	88.42 32	87.59 68

casdiffseed414692147		73.73 87	73.22 96	75.28 99	76.76 252	52.16 211	80.05 112	83.01 112	63.38 47	73.35 104	87.11 113	53.22 97	84.14 149	61.71 198	80.38 133	89.55 6

viewdifsd2359ckpt09		73.42 93	72.45 111	76.30 77	77.25 228	53.27 180	80.36 107	82.48 122	57.96 186	72.24 133	85.73 167	53.22 97	86.27 98	63.79 174	79.06 168	89.36 7

HFP-MVS		78.01 27	77.65 29	79.10 21	86.71 19	62.81 8	86.29 18	84.32 55	62.82 65	73.96 88	90.50 31	53.20 99	88.35 37	74.02 66	87.05 51	86.13 135

EC-MVSNet		75.84 55	75.87 51	75.74 87	78.86 161	52.65 198	83.73 61	86.08 20	63.47 46	72.77 125	87.25 109	53.13 100	87.93 48	71.97 85	85.57 70	86.66 109

viewdifsd2359ckpt13		72.40 121	71.79 120	74.22 131	75.63 273	51.77 221	78.67 138	83.13 110	57.08 204	71.59 143	85.36 178	53.10 101	82.64 200	63.07 184	78.51 182	88.24 39

test_fmvsm_n_1920		71.73 135	71.14 135	73.50 169	72.52 347	56.53 113	75.60 238	76.16 271	48.11 388	77.22 42	85.56 170	53.10 101	77.43 332	74.86 58	77.14 209	86.55 113

fmvsm_l_conf0.5_n_a		70.50 162	70.27 154	71.18 245	71.30 375	54.09 157	76.89 205	69.87 362	47.90 392	74.37 81	86.49 138	53.07 103	76.69 354	75.41 53	77.11 210	82.76 268

fmvsm_s_conf0.5_n_10		74.11 77	73.98 80	74.48 121	74.61 303	52.86 193	78.10 161	77.06 251	57.14 203	78.24 33	88.79 71	52.83 104	82.26 209	77.79 28	81.30 119	88.32 35

fmvsm_s_conf0.5_n_8		74.30 75	74.39 70	74.01 143	75.33 283	52.89 191	78.24 149	77.32 246	61.65 92	78.13 34	88.90 66	52.82 105	81.54 224	78.46 22	78.67 178	87.60 67

EI-MVSNet-Vis-set		72.42 120	71.59 122	74.91 103	78.47 175	54.02 158	77.05 198	79.33 189	65.03 19	71.68 141	79.35 323	52.75 106	84.89 136	66.46 142	74.23 253	85.83 148

fmvsm_s_conf0.5_n_a		69.54 192	68.74 189	71.93 212	72.47 349	53.82 162	78.25 148	62.26 436	49.78 361	73.12 115	86.21 147	52.66 107	76.79 350	75.02 57	68.88 352	85.18 183

GST-MVS		78.14 25	77.85 27	78.99 28	86.05 40	61.82 22	85.84 26	85.21 37	63.56 44	74.29 83	90.03 47	52.56 108	88.53 35	74.79 60	88.34 33	86.63 111

ACMMP_NAP		78.77 18	78.78 17	78.74 34	85.44 47	61.04 31	83.84 60	85.16 38	62.88 63	78.10 35	91.26 19	52.51 109	88.39 36	79.34 9	90.52 13	86.78 102

PCF-MVS	61.88 8	70.95 151	69.49 169	75.35 95	77.63 212	55.71 129	76.04 230	81.81 132	50.30 354	69.66 176	85.40 177	52.51 109	84.89 136	51.82 287	80.24 136	85.45 170
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
MP-MVS-pluss		78.35 23	78.46 20	78.03 45	84.96 57	59.52 58	82.93 70	85.39 34	62.15 82	76.41 50	91.51 11	52.47 111	86.78 78	80.66 4	89.64 21	87.80 58
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
CLD-MVS		73.33 96	72.68 106	75.29 98	78.82 163	53.33 179	78.23 154	84.79 48	61.30 100	70.41 162	81.04 286	52.41 112	87.12 69	64.61 163	82.49 105	85.41 174
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
GeoE		71.01 148	70.15 158	73.60 166	79.57 140	52.17 210	78.93 133	78.12 227	58.02 183	67.76 225	83.87 215	52.36 113	82.72 197	56.90 240	75.79 233	85.92 141

NR-MVSNet		69.54 192	68.85 185	71.59 227	78.05 194	43.81 358	74.20 273	80.86 163	65.18 15	62.76 319	84.52 199	52.35 114	83.59 163	50.96 295	70.78 312	87.37 79

viewmamba		71.13 144	70.66 145	72.56 196	70.23 393	50.07 260	74.25 272	77.85 231	59.92 139	70.94 152	85.55 172	52.30 115	80.25 261	68.42 106	76.47 221	87.35 82

fmvsm_s_conf0.5_n		69.58 190	68.84 186	71.79 218	72.31 355	52.90 189	77.90 164	62.43 434	49.97 359	72.85 123	85.90 160	52.21 116	76.49 357	75.75 48	70.26 324	85.97 139

EI-MVSNet-UG-set		71.92 130	71.06 137	74.52 120	77.98 197	53.56 170	76.62 213	79.16 190	64.40 30	71.18 149	78.95 328	52.19 117	84.66 143	65.47 154	73.57 266	85.32 178

miper_ehance_all_eth		68.03 235	67.24 235	70.40 268	70.54 385	46.21 329	73.98 277	78.68 205	55.07 264	66.05 258	77.80 352	52.16 118	81.31 230	61.53 203	69.32 344	83.67 242

fmvsm_s_conf0.5_n_4		72.04 129	71.85 118	72.58 194	73.74 325	52.49 204	76.69 212	72.42 339	56.42 226	75.32 57	87.04 114	52.13 119	78.01 315	79.29 12	73.65 263	87.26 84

EIA-MVS		71.78 133	70.60 146	75.30 97	79.85 134	53.54 171	77.27 191	83.26 102	57.92 188	66.49 248	79.39 321	52.07 120	86.69 80	60.05 211	79.14 166	85.66 159

fmvsm_s_conf0.1_n_a		69.32 200	68.44 198	71.96 210	70.91 380	53.78 163	78.12 158	62.30 435	49.35 367	73.20 109	86.55 137	51.99 121	76.79 350	74.83 59	68.68 357	85.32 178

c3_l		68.33 227	67.56 218	70.62 264	70.87 381	46.21 329	74.47 267	78.80 201	56.22 233	66.19 254	78.53 336	51.88 122	81.40 227	62.08 192	69.04 350	84.25 215

PAPM_NR		72.63 114	71.80 119	75.13 100	81.72 98	53.42 177	79.91 117	83.28 101	59.14 158	66.31 253	85.90 160	51.86 123	86.06 104	57.45 237	80.62 127	85.91 142

diffmvs_AUTHOR		71.02 147	70.87 140	71.45 232	69.89 402	48.97 289	73.16 300	78.33 224	57.79 194	72.11 136	85.26 179	51.84 124	77.89 320	71.00 94	78.47 185	87.49 71

test_fmvsmvis_n_1920		70.84 152	70.38 151	72.22 208	71.16 377	55.39 139	75.86 234	72.21 342	49.03 371	73.28 107	86.17 149	51.83 125	77.29 337	75.80 47	78.05 191	83.98 225

MG-MVS		73.96 83	73.89 82	74.16 133	85.65 44	49.69 273	81.59 93	81.29 149	61.45 96	71.05 151	88.11 80	51.77 126	87.73 55	61.05 204	83.09 91	85.05 189

EPP-MVSNet		72.16 127	71.31 131	74.71 108	78.68 167	49.70 271	82.10 86	81.65 134	60.40 122	65.94 260	85.84 162	51.74 127	86.37 94	55.93 248	79.55 150	88.07 49

fmvsm_s_conf0.1_n		69.41 198	68.60 192	71.83 215	71.07 378	52.88 192	77.85 168	62.44 433	49.58 364	72.97 118	86.22 146	51.68 128	76.48 358	75.53 52	70.10 328	86.14 134

TranMVSNet+NR-MVSNet		70.36 166	70.10 160	71.17 247	78.64 171	42.97 374	76.53 216	81.16 156	66.95 6	68.53 196	85.42 176	51.61 129	83.07 173	52.32 280	69.70 339	87.46 72

fmvsm_s_conf0.5_n_6		72.59 115	72.87 103	71.73 220	75.14 290	51.96 217	76.28 221	77.12 249	57.63 197	73.85 94	86.91 117	51.54 130	77.87 321	77.18 33	80.18 138	85.37 176

lecture		77.75 28	77.84 28	77.50 54	82.75 86	57.62 95	85.92 25	86.20 19	60.53 118	78.99 29	91.45 14	51.51 131	87.78 54	75.65 50	87.55 47	87.10 91

diffmvs		70.69 158	70.43 149	71.46 230	69.45 409	48.95 290	72.93 303	78.46 217	57.27 201	71.69 140	83.97 214	51.48 132	77.92 319	70.70 96	77.95 193	87.53 70
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
viewmambaseed2359dif		68.91 210	68.18 206	71.11 250	70.21 394	48.05 310	72.28 319	75.90 277	51.96 325	70.93 153	84.47 202	51.37 133	78.59 306	61.55 202	74.97 244	86.68 107

EI-MVSNet		69.27 202	68.44 198	71.73 220	74.47 307	49.39 278	75.20 248	78.45 218	59.60 148	69.16 188	76.51 378	51.29 134	82.50 204	59.86 216	71.45 305	83.30 252

IterMVS-LS		69.22 204	68.48 194	71.43 235	74.44 309	49.40 277	76.23 223	77.55 237	59.60 148	65.85 265	81.59 278	51.28 135	81.58 223	59.87 215	69.90 333	83.30 252
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
TransMVSNet (Re)		64.72 295	64.33 285	65.87 352	75.22 284	38.56 419	74.66 263	75.08 302	58.90 163	61.79 336	82.63 241	51.18 136	78.07 314	43.63 377	55.87 453	80.99 314

miper_enhance_ethall		67.11 258	66.09 262	70.17 272	69.21 413	45.98 332	72.85 306	78.41 221	51.38 338	65.65 268	75.98 388	51.17 137	81.25 231	60.82 206	69.32 344	83.29 254

VNet		69.68 186	70.19 156	68.16 308	79.73 136	41.63 389	70.53 350	77.38 243	60.37 125	70.69 155	86.63 130	51.08 138	77.09 340	53.61 272	81.69 118	85.75 154

VPA-MVSNet		69.02 208	69.47 170	67.69 314	77.42 222	41.00 396	74.04 276	79.68 181	60.06 135	69.26 186	84.81 186	51.06 139	77.58 330	54.44 265	74.43 251	84.48 209

onestephybrid01		71.00 149	70.34 153	72.99 185	70.38 390	50.88 233	74.14 275	77.41 241	58.80 164	71.36 148	84.93 182	50.96 140	80.87 246	67.73 123	77.35 203	87.23 86

PAPR		71.72 136	70.82 141	74.41 123	81.20 110	51.17 225	79.55 126	83.33 97	55.81 240	66.93 240	84.61 195	50.95 141	86.06 104	55.79 251	79.20 161	86.00 138

PHI-MVS		75.87 54	75.36 56	77.41 56	80.62 121	55.91 125	84.28 50	85.78 27	56.08 235	73.41 101	86.58 134	50.94 142	88.54 34	70.79 95	89.71 17	87.79 59

HPM-MVS		77.28 33	76.85 34	78.54 36	85.00 56	60.81 38	82.91 71	85.08 40	62.57 70	73.09 116	89.97 50	50.90 143	87.48 60	75.30 54	86.85 57	87.33 83
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
reproduce-ours		76.90 38	76.58 38	77.87 48	83.99 68	60.46 43	84.75 38	83.34 95	60.22 132	77.85 38	91.42 16	50.67 144	87.69 56	72.46 77	84.53 76	85.46 168

our_new_method		76.90 38	76.58 38	77.87 48	83.99 68	60.46 43	84.75 38	83.34 95	60.22 132	77.85 38	91.42 16	50.67 144	87.69 56	72.46 77	84.53 76	85.46 168

WR-MVS_H		67.02 260	66.92 241	67.33 322	77.95 198	37.75 428	77.57 176	82.11 128	62.03 88	62.65 322	82.48 252	50.57 146	79.46 276	42.91 384	64.01 393	84.79 199

hybridnocas07		69.86 178	69.44 172	71.14 249	68.10 432	48.28 301	72.52 313	77.08 250	56.94 209	70.50 159	84.91 184	50.48 147	78.37 308	67.84 121	76.55 220	86.76 103

EPNet		73.09 103	72.16 114	75.90 81	75.95 268	56.28 116	83.05 67	72.39 340	66.53 11	65.27 275	87.00 115	50.40 148	85.47 122	62.48 190	86.32 65	85.94 140
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
WR-MVS		68.47 224	68.47 196	68.44 303	80.20 127	39.84 406	73.75 286	76.07 274	64.68 25	68.11 209	83.63 222	50.39 149	79.14 287	49.78 300	69.66 340	86.34 123

test_fmvsmconf0.01_n		72.17 125	71.50 124	74.16 133	67.96 434	55.58 135	78.06 162	74.67 307	54.19 286	74.54 78	88.23 76	50.35 150	80.24 262	78.07 26	77.46 201	86.65 110

dtuplus		68.48 223	67.76 213	70.63 263	70.33 392	48.09 306	72.62 309	75.88 279	52.33 319	71.09 150	84.66 192	50.09 151	77.93 318	58.02 233	74.82 247	85.87 144

viewdifsd2359ckpt11		69.13 205	68.38 201	71.38 237	71.57 366	48.61 295	73.22 298	73.18 331	57.65 195	70.67 156	84.73 188	50.03 152	79.80 267	63.25 180	71.10 309	85.74 155

viewmsd2359difaftdt		69.13 205	68.38 201	71.38 237	71.57 366	48.61 295	73.22 298	73.18 331	57.65 195	70.67 156	84.73 188	50.03 152	79.80 267	63.25 180	71.10 309	85.74 155

region2R		77.67 31	77.18 33	79.15 18	86.76 17	62.95 6	86.29 18	84.16 58	62.81 67	73.30 105	90.58 26	49.90 154	88.21 40	73.78 68	87.03 52	86.29 132

fmvsm_s_conf0.5_n_7		69.54 192	69.67 166	69.15 294	73.47 330	51.41 224	70.35 354	73.34 327	57.05 206	68.41 197	85.83 163	49.86 155	72.84 378	71.86 86	76.83 215	83.19 257

UA-Net		73.13 102	72.93 101	73.76 153	83.58 73	51.66 222	78.75 135	77.66 235	67.75 4	72.61 128	89.42 56	49.82 156	83.29 169	53.61 272	83.14 90	86.32 128

ACMMPR		77.71 29	77.23 32	79.16 17	86.75 18	62.93 7	86.29 18	84.24 56	62.82 65	73.55 100	90.56 29	49.80 157	88.24 39	74.02 66	87.03 52	86.32 128

hybrid		69.38 199	68.93 184	70.75 259	67.86 436	48.20 303	72.49 315	76.90 254	55.23 257	70.42 161	84.34 205	49.76 158	77.62 329	67.11 133	76.20 224	86.42 118

API-MVS		72.17 125	71.41 127	74.45 122	81.95 95	57.22 101	84.03 56	80.38 172	59.89 144	68.40 198	82.33 255	49.64 159	87.83 53	51.87 286	84.16 83	78.30 363

reproduce_model		76.43 46	76.08 46	77.49 55	83.47 76	60.09 47	84.60 42	82.90 115	59.65 146	77.31 41	91.43 15	49.62 160	87.24 62	71.99 84	83.75 88	85.14 184

NormalMVS		76.26 49	75.74 52	77.83 50	82.75 86	59.89 52	84.36 46	83.21 103	64.69 23	74.21 84	87.40 97	49.48 161	86.17 100	68.04 117	87.55 47	87.42 74

SymmetryMVS		75.28 60	74.60 67	77.30 59	83.85 71	59.89 52	84.36 46	75.51 288	64.69 23	74.21 84	87.40 97	49.48 161	86.17 100	68.04 117	83.88 85	85.85 146

ab-mvs		66.65 269	66.42 252	67.37 320	76.17 265	41.73 386	70.41 353	76.14 273	53.99 289	65.98 259	83.51 227	49.48 161	76.24 362	48.60 313	73.46 270	84.14 220

balanced_ft_v1		72.98 105	72.55 108	74.27 127	79.52 142	50.64 241	77.78 171	83.29 99	56.76 212	67.88 215	85.95 158	49.42 164	85.29 127	68.64 105	83.76 87	86.87 97

v8		70.33 167	69.28 175	73.49 170	73.15 334	50.22 256	78.62 140	80.78 164	60.79 111	66.45 250	82.11 266	49.35 165	84.98 132	63.58 177	68.71 355	85.28 180

IS-MVSNet		71.57 137	71.00 138	73.27 178	78.86 161	45.63 338	80.22 110	78.69 204	64.14 38	66.46 249	87.36 100	49.30 166	85.60 115	50.26 299	83.71 89	88.59 28

XXY-MVS		60.68 352	61.67 324	57.70 428	70.43 388	38.45 421	64.19 419	66.47 392	48.05 390	63.22 308	80.86 292	49.28 167	60.47 450	45.25 359	67.28 369	74.19 421

cdsmvs_eth3d_5k		17.50 478	23.34 475	0.00 541	0.00 565	0.00 567	0.00 553	78.63 206	0.00 560	0.00 561	82.18 260	49.25 168	0.00 559	0.00 560	0.00 558	0.00 557

PVSNet_Blended_VisFu		71.45 141	70.39 150	74.65 112	82.01 92	58.82 81	79.93 116	80.35 173	55.09 261	65.82 267	82.16 263	49.17 169	82.64 200	60.34 209	78.62 180	82.50 278

PVSNet_BlendedMVS		68.56 222	67.72 215	71.07 252	77.03 247	50.57 244	74.50 266	81.52 136	53.66 300	64.22 300	79.72 314	49.13 170	82.87 191	55.82 249	73.92 257	79.77 346

PVSNet_Blended		68.59 218	67.72 215	71.19 244	77.03 247	50.57 244	72.51 314	81.52 136	51.91 326	64.22 300	77.77 355	49.13 170	82.87 191	55.82 249	79.58 148	80.14 336

DU-MVS		70.01 174	69.53 168	71.44 233	78.05 194	44.13 353	75.01 253	81.51 138	64.37 31	68.20 201	84.52 199	49.12 172	82.82 195	54.62 262	70.43 317	87.37 79

Baseline_NR-MVSNet		67.05 259	67.56 218	65.50 357	75.65 272	37.70 430	75.42 242	74.65 308	59.90 140	68.14 205	83.15 235	49.12 172	77.20 338	52.23 281	69.78 335	81.60 292

VPNet		67.52 248	68.11 209	65.74 353	79.18 153	36.80 439	72.17 321	72.83 336	62.04 87	67.79 223	85.83 163	48.88 174	76.60 356	51.30 291	72.97 280	83.81 234

PRO-TEST		70.71 157	69.90 161	73.16 181	77.69 208	46.08 331	70.69 347	82.79 119	57.81 191	58.42 381	85.08 180	48.68 175	87.92 49	65.99 149	81.92 111	85.48 165

MTAPA		76.90 38	76.42 43	78.35 39	86.08 39	63.57 2	74.92 257	80.97 161	65.13 16	75.77 52	90.88 22	48.63 176	86.66 81	77.23 31	88.17 37	84.81 198

原ACMM1				74.69 109	85.39 49	59.40 59		83.42 91	51.47 337	70.27 164	86.61 132	48.61 177	86.51 90	53.85 270	87.96 43	78.16 365

v148		68.24 230	67.19 238	71.40 236	70.43 388	47.77 313	75.76 237	77.03 252	58.91 162	67.36 229	80.10 306	48.60 178	81.89 216	60.01 212	66.52 375	84.53 207

PGM-MVS		76.77 41	76.06 47	78.88 32	86.14 37	62.73 9	82.55 78	83.74 78	61.71 91	72.45 132	90.34 37	48.48 179	88.13 43	72.32 79	86.85 57	85.78 149

Test By Simon												48.33 180

CP-MVS		77.12 36	76.68 36	78.43 37	86.05 40	63.18 5	87.55 10	83.45 90	62.44 74	72.68 126	90.50 31	48.18 181	87.34 61	73.59 70	85.71 68	84.76 201

MVS		67.37 250	66.33 256	70.51 267	75.46 279	50.94 229	73.95 279	81.85 131	41.57 453	62.54 325	78.57 335	47.98 182	85.47 122	52.97 277	82.05 108	75.14 405

XVS		77.17 35	76.56 40	79.00 26	86.32 31	62.62 11	85.83 27	83.92 63	64.55 26	72.17 134	90.01 49	47.95 183	88.01 46	71.55 90	86.74 59	86.37 121

X-MVStestdata		70.21 169	67.28 231	79.00 26	86.32 31	62.62 11	85.83 27	83.92 63	64.55 26	72.17 134	6.49 529	47.95 183	88.01 46	71.55 90	86.74 59	86.37 121

SDMVSNet		68.03 235	68.10 210	67.84 310	77.13 236	48.72 294	65.32 407	79.10 191	58.02 183	65.08 282	82.55 248	47.83 185	73.40 375	63.92 168	73.92 257	81.41 297

MAR-MVS		71.51 138	70.15 158	75.60 92	81.84 96	59.39 60	81.38 95	82.90 115	54.90 273	68.08 211	78.70 329	47.73 186	85.51 119	51.68 290	84.17 82	81.88 290
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
PAPM		67.92 239	66.69 245	71.63 226	78.09 192	49.02 286	77.09 197	81.24 152	51.04 346	60.91 348	83.98 213	47.71 187	84.99 130	40.81 398	79.32 155	80.90 315

SR-MVS		76.13 52	75.70 53	77.40 58	85.87 42	61.20 29	85.52 33	82.19 126	59.99 138	75.10 62	90.35 36	47.66 188	86.52 89	71.64 89	82.99 94	84.47 210

cl22		67.47 249	66.45 249	70.54 266	69.85 404	46.49 325	73.85 284	77.35 244	55.07 264	65.51 270	77.92 345	47.64 189	81.10 236	61.58 201	69.32 344	84.01 224

v10		70.21 169	69.02 180	73.81 150	73.51 328	50.92 231	78.74 136	81.39 141	60.05 136	66.39 251	81.83 271	47.58 190	85.41 125	62.80 187	68.86 354	85.09 188

v1144		70.42 164	69.31 174	73.76 153	73.22 332	50.64 241	77.83 169	81.43 140	58.58 172	69.40 181	81.16 283	47.53 191	85.29 127	64.01 166	70.64 313	85.34 177

v2v482		70.50 162	69.45 171	73.66 161	72.62 344	50.03 262	77.58 175	80.51 168	59.90 140	69.52 177	82.14 264	47.53 191	84.88 138	65.07 158	70.17 326	86.09 136

pm-mvs1		65.24 290	64.97 281	66.04 347	72.38 352	39.40 413	72.62 309	75.63 283	55.53 248	62.35 332	83.18 234	47.45 193	76.47 359	49.06 310	66.54 374	82.24 283

HY-MVS	56.14 13	64.55 300	63.89 289	66.55 335	74.73 299	41.02 393	69.96 359	74.43 309	49.29 368	61.66 340	80.92 290	47.43 194	76.68 355	44.91 363	71.69 301	81.94 288

cl____		67.18 255	66.26 260	69.94 275	70.20 395	45.74 334	73.30 293	76.83 258	55.10 259	65.27 275	79.57 317	47.39 195	80.53 253	59.41 220	69.22 348	83.53 248

DIV-MVS_self_test		67.18 255	66.26 260	69.94 275	70.20 395	45.74 334	73.29 295	76.83 258	55.10 259	65.27 275	79.58 316	47.38 196	80.53 253	59.43 219	69.22 348	83.54 247

eth_miper_zixun_eth		67.63 246	66.28 259	71.67 224	71.60 365	48.33 300	73.68 287	77.88 229	55.80 241	65.91 261	78.62 334	47.35 197	82.88 190	59.45 218	66.25 376	83.81 234

OPM-MVS		74.73 67	74.25 73	76.19 78	80.81 115	59.01 77	82.60 77	83.64 84	63.74 42	72.52 129	87.49 94	47.18 198	85.88 110	69.47 101	80.78 123	83.66 244
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
baseline1		63.81 310	63.87 291	63.62 377	76.29 263	36.36 442	71.78 328	67.29 385	56.05 236	64.23 299	82.95 236	47.11 199	74.41 371	47.30 328	61.85 422	80.10 337

pcd_1.5k_mvsjas		3.92 497	5.23 496	0.00 541	0.00 565	0.00 567	0.00 553	0.00 565	0.00 560	0.00 561	0.00 559	47.05 200	0.00 559	0.00 560	0.00 558	0.00 557

PS-MVSNAJss		72.24 123	71.21 133	75.31 96	78.50 173	55.93 124	81.63 90	82.12 127	56.24 232	70.02 169	85.68 169	47.05 200	84.34 147	65.27 156	74.41 252	85.67 158

PS-MVSNAJ		70.51 161	69.70 165	72.93 187	81.52 100	55.79 128	74.92 257	79.00 195	55.04 267	69.88 173	78.66 331	47.05 200	82.19 210	61.61 199	79.58 148	80.83 316

WTY-MVS		59.75 364	60.39 347	57.85 426	72.32 354	37.83 427	61.05 443	64.18 413	45.95 420	61.91 334	79.11 326	47.01 203	60.88 449	42.50 387	69.49 343	74.83 411

xiu_mvs_v2_base		70.52 160	69.75 163	72.84 189	81.21 109	55.63 132	75.11 250	78.92 197	54.92 272	69.96 172	79.68 315	47.00 204	82.09 212	61.60 200	79.37 151	80.81 317

v144192		69.71 183	68.51 193	73.33 177	73.10 335	50.13 258	77.54 178	80.64 165	56.65 214	68.57 195	80.55 296	46.87 205	84.96 134	62.98 185	69.66 340	84.89 196

PEN-MVS		66.60 270	66.45 249	67.04 324	77.11 240	36.56 441	77.03 199	80.42 171	62.95 60	62.51 327	84.03 211	46.69 206	79.07 290	44.22 366	63.08 406	85.51 164

mPP-MVS		76.54 44	75.93 49	78.34 40	86.47 28	63.50 3	85.74 30	82.28 125	62.90 62	71.77 139	90.26 39	46.61 207	86.55 88	71.71 88	85.66 69	84.97 193

IMVS_0403		69.09 207	68.14 208	71.95 211	77.06 241	49.73 267	74.51 265	78.60 207	52.70 311	66.69 244	82.58 243	46.43 208	83.38 167	59.20 222	75.46 239	82.74 269

CP-MVSNet		66.49 273	66.41 253	66.72 327	77.67 210	36.33 444	76.83 210	79.52 185	62.45 73	62.54 325	83.47 229	46.32 209	78.37 308	45.47 356	63.43 402	85.45 170

V42		68.65 217	67.35 229	72.56 196	68.93 419	50.18 257	72.90 305	79.47 186	56.92 210	69.45 180	80.26 302	46.29 210	82.99 176	64.07 164	67.82 363	84.53 207

1112_ss		64.00 309	63.36 302	65.93 349	79.28 147	42.58 378	71.35 332	72.36 341	46.41 413	60.55 351	77.89 349	46.27 211	73.28 376	46.18 342	69.97 330	81.92 289

MSLP-MVS++		73.77 86	73.47 90	74.66 111	83.02 81	59.29 63	82.30 85	81.88 130	59.34 156	71.59 143	86.83 119	45.94 212	83.65 161	65.09 157	85.22 71	81.06 312

PS-CasMVS		66.42 274	66.32 257	66.70 329	77.60 218	36.30 446	76.94 203	79.61 183	62.36 75	62.43 330	83.66 221	45.69 213	78.37 308	45.35 358	63.26 404	85.42 173

APD-MVS_3200maxsize		74.96 62	74.39 70	76.67 69	82.20 90	58.24 87	83.67 62	83.29 99	58.41 175	73.71 96	90.14 41	45.62 214	85.99 107	69.64 99	82.85 101	85.78 149

DTE-MVSNet		65.58 284	65.34 276	66.31 339	76.06 267	34.79 455	76.43 218	79.38 188	62.55 71	61.66 340	83.83 216	45.60 215	79.15 286	41.64 396	60.88 428	85.00 190

BH-w/o		66.85 263	65.83 265	69.90 278	79.29 145	52.46 205	74.66 263	76.65 263	54.51 282	64.85 289	78.12 339	45.59 216	82.95 182	43.26 380	75.54 237	74.27 420

h-mvs33		72.71 111	71.49 125	76.40 74	81.99 94	59.58 57	76.92 204	76.74 262	60.40 122	74.81 71	85.95 158	45.54 217	85.76 113	70.41 97	70.61 315	83.86 233

hse-mvs2		71.04 146	69.86 162	74.60 115	79.58 139	57.12 108	73.96 278	75.25 294	60.40 122	74.81 71	81.95 268	45.54 217	82.90 188	70.41 97	66.83 372	83.77 238

HQP2-MVS												45.46 219

HQP-MVS		73.45 92	72.80 104	75.40 94	80.66 117	54.94 145	82.31 82	83.90 65	62.10 83	67.85 216	85.54 174	45.46 219	86.93 74	67.04 135	80.35 134	84.32 212

ACMMP		76.02 53	75.33 57	78.07 42	85.20 54	61.91 20	85.49 35	84.44 52	63.04 59	69.80 175	89.74 55	45.43 221	87.16 68	72.01 83	82.87 100	85.14 184
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
OMC-MVS		71.40 142	70.60 146	73.78 151	76.60 257	53.15 183	79.74 121	79.78 179	58.37 176	68.75 192	86.45 140	45.43 221	80.60 251	62.58 188	77.73 195	87.58 69

BH-untuned		68.27 228	67.29 230	71.21 243	79.74 135	53.22 181	76.06 228	77.46 240	57.19 202	66.10 257	81.61 276	45.37 223	83.50 165	45.42 357	76.68 218	76.91 388

v1192		69.97 176	68.68 190	73.85 148	73.19 333	50.94 229	77.68 174	81.36 143	57.51 199	68.95 191	80.85 293	45.28 224	85.33 126	62.97 186	70.37 319	85.27 181

HQP_MVS		74.31 74	73.73 85	76.06 79	81.41 103	56.31 114	84.22 51	84.01 60	64.52 28	69.27 184	86.10 151	45.26 225	87.21 66	68.16 113	80.58 129	84.65 202

plane_prior6					81.20 110	56.24 118						45.26 225

icg_test_0407_2		66.41 275	66.75 244	65.37 361	77.06 241	49.73 267	63.79 423	78.60 207	52.70 311	66.19 254	82.58 243	45.17 227	63.65 440	59.20 222	75.46 239	82.74 269

IMVS_0407		68.90 211	67.93 211	71.82 216	77.06 241	49.73 267	74.40 270	78.60 207	52.70 311	66.19 254	82.58 243	45.17 227	83.00 175	59.20 222	75.46 239	82.74 269

SD_0403		63.07 320	63.49 300	61.82 391	75.16 287	31.14 480	71.89 327	73.47 325	53.34 303	58.22 384	81.81 272	45.17 227	73.86 374	37.43 420	74.87 246	80.45 324

CL-MVSNet_self_test		61.53 346	60.94 338	63.30 380	68.95 417	36.93 438	67.60 385	72.80 337	55.67 244	59.95 358	76.63 373	45.01 230	72.22 385	39.74 408	62.09 421	80.74 319

SR-MVS-dyc-post		74.57 71	73.90 81	76.58 72	83.49 74	59.87 54	84.29 48	81.36 143	58.07 181	73.14 112	90.07 43	44.74 231	85.84 111	68.20 109	81.76 114	84.03 222

v1921920		69.47 196	68.17 207	73.36 176	73.06 336	50.10 259	77.39 183	80.56 166	56.58 223	68.59 193	80.37 298	44.72 232	84.98 132	62.47 191	69.82 334	85.00 190

RRT-MVS		71.46 140	70.70 144	73.74 156	77.76 205	49.30 281	76.60 214	80.45 170	61.25 101	68.17 203	84.78 187	44.64 233	84.90 135	64.79 159	77.88 194	87.03 92

Vis-MVSNet (Re-imp)		63.69 311	63.88 290	63.14 382	74.75 298	31.04 481	71.16 337	63.64 420	56.32 228	59.80 361	84.99 181	44.51 234	75.46 366	39.12 411	80.62 127	82.92 264

DP-MVS Recon		72.15 128	70.73 143	76.40 74	86.57 26	57.99 90	81.15 98	82.96 113	57.03 207	66.78 241	85.56 170	44.50 235	88.11 44	51.77 288	80.23 137	83.10 262

TAMVS		66.78 267	65.27 278	71.33 242	79.16 155	53.67 165	73.84 285	69.59 366	52.32 321	65.28 274	81.72 274	44.49 236	77.40 334	42.32 388	78.66 179	82.92 264

Vis-MVSNet		72.18 124	71.37 129	74.61 114	81.29 106	55.41 138	80.90 100	78.28 225	60.73 113	69.23 187	88.09 81	44.36 237	82.65 199	57.68 235	81.75 116	85.77 152
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
旧先验1					83.04 80	53.15 183		67.52 382			87.85 89	44.08 238			80.76 125	78.03 370

Test_1112_low_res		62.32 332	61.77 323	64.00 374	79.08 157	39.53 412	68.17 380	70.17 359	43.25 441	59.03 372	79.90 308	44.08 238	71.24 391	43.79 374	68.42 358	81.25 304

fmvsm_s_conf0.5_n_2		69.82 180	69.27 176	71.46 230	72.00 359	51.08 226	73.30 293	67.79 381	55.06 266	75.24 59	87.51 93	44.02 240	77.00 344	75.67 49	72.86 281	86.31 131

MVSFormer		71.50 139	70.38 151	74.88 104	78.76 164	57.15 106	82.79 72	78.48 215	51.26 341	69.49 178	83.22 232	43.99 241	83.24 170	66.06 145	79.37 151	84.23 216

lupinMVS		69.57 191	68.28 205	73.44 173	78.76 164	57.15 106	76.57 215	73.29 330	46.19 415	69.49 178	82.18 260	43.99 241	79.23 281	64.66 161	79.37 151	83.93 227

v7n		69.01 209	67.36 228	73.98 145	72.51 348	52.65 198	78.54 144	81.30 148	60.26 131	62.67 321	81.62 275	43.61 243	84.49 144	57.01 239	68.70 356	84.79 199

CDS-MVSNet		66.80 266	65.37 275	71.10 251	78.98 158	53.13 185	73.27 297	71.07 350	52.15 322	64.72 290	80.23 303	43.56 244	77.10 339	45.48 355	78.88 170	83.05 263
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
jason		69.65 187	68.39 200	73.43 174	78.27 185	56.88 110	77.12 196	73.71 324	46.53 412	69.34 183	83.22 232	43.37 245	79.18 282	64.77 160	79.20 161	84.23 216
jason: jason.
v1240		69.24 203	67.91 212	73.25 180	73.02 338	49.82 265	77.21 193	80.54 167	56.43 225	68.34 200	80.51 297	43.33 246	84.99 130	62.03 195	69.77 337	84.95 194

SSM_0407		70.41 165	68.96 183	74.75 107	78.65 168	53.46 173	77.28 190	80.00 177	53.88 292	68.14 205	84.61 195	43.21 247	86.26 99	58.80 227	76.11 226	84.54 204

SSM_0404		70.84 152	69.41 173	75.12 101	79.20 151	53.86 160	77.89 165	80.00 177	53.88 292	69.40 181	84.61 195	43.21 247	86.56 85	58.80 227	77.68 197	84.95 194

LCM-MVSNet-Re		61.88 343	61.35 329	63.46 378	74.58 305	31.48 479	61.42 438	58.14 452	58.71 168	53.02 446	79.55 318	43.07 249	76.80 349	45.69 347	77.96 192	82.11 287

RE-MVS-def			73.71 86		83.49 74	59.87 54	84.29 48	81.36 143	58.07 181	73.14 112	90.07 43	43.06 250		68.20 109	81.76 114	84.03 222

baseline2		63.42 313	61.26 332	69.89 279	72.55 346	47.62 315	71.54 330	68.38 377	50.11 356	54.82 423	75.55 393	43.06 250	80.96 241	48.13 318	67.16 370	81.11 309

fmvsm_s_conf0.1_n_2		69.64 188	69.01 182	71.52 228	71.66 364	51.04 227	73.39 292	67.14 387	55.02 270	75.11 61	87.64 92	42.94 252	77.01 343	75.55 51	72.63 287	86.52 115

FA-MVS(test-final)		69.82 180	68.48 194	73.84 149	78.44 176	50.04 261	75.58 241	78.99 196	58.16 179	67.59 226	82.14 264	42.66 253	85.63 114	56.60 241	76.19 225	85.84 147

BH-RMVSNet		68.81 213	67.42 225	72.97 186	80.11 131	52.53 202	74.26 271	76.29 270	58.48 174	68.38 199	84.20 206	42.59 254	83.83 157	46.53 337	75.91 231	82.56 273

LFMVS		71.78 133	71.59 122	72.32 206	83.40 77	46.38 326	79.75 120	71.08 349	64.18 35	72.80 124	88.64 73	42.58 255	83.72 159	57.41 238	84.49 78	86.86 98

test_yl		69.69 184	69.13 177	71.36 239	78.37 180	45.74 334	74.71 261	80.20 174	57.91 189	70.01 170	83.83 216	42.44 256	82.87 191	54.97 258	79.72 145	85.48 165

DCV-MVSNet		69.69 184	69.13 177	71.36 239	78.37 180	45.74 334	74.71 261	80.20 174	57.91 189	70.01 170	83.83 216	42.44 256	82.87 191	54.97 258	79.72 145	85.48 165

3Dnovator	64.47 5	72.49 117	71.39 128	75.79 84	77.70 207	58.99 78	80.66 105	83.15 108	62.24 80	65.46 271	86.59 133	42.38 258	85.52 118	59.59 217	84.72 73	82.85 267

VDD-MVS		72.50 116	72.09 115	73.75 155	81.58 99	49.69 273	77.76 173	77.63 236	63.21 55	73.21 108	89.02 62	42.14 259	83.32 168	61.72 197	82.50 104	88.25 38

3Dnovator+	66.72 4	75.84 55	74.57 68	79.66 9	82.40 88	59.92 51	85.83 27	86.32 18	66.92 7	67.80 222	89.24 60	42.03 260	89.38 25	64.07 164	86.50 63	89.69 4

SSC-MVS3.2		60.57 354	61.39 328	58.12 424	74.29 314	32.63 473	59.52 448	65.53 401	59.90 140	62.45 328	79.75 313	41.96 261	63.90 439	39.47 409	69.65 342	77.84 372

MVS_111021_LR		69.50 195	68.78 188	71.65 225	78.38 178	59.33 61	74.82 259	70.11 360	58.08 180	67.83 221	84.68 190	41.96 261	76.34 361	65.62 153	77.54 198	79.30 351

CPTT-MVS		72.78 109	72.08 116	74.87 105	84.88 62	61.41 26	84.15 54	77.86 230	55.27 255	67.51 228	88.08 82	41.93 263	81.85 217	69.04 104	80.01 139	81.35 302

usedtu_dtu_shiyan1		64.34 304	63.57 296	66.66 331	72.44 350	40.74 399	69.60 365	76.80 260	53.21 304	61.73 338	77.92 345	41.92 264	77.68 327	46.23 340	72.25 294	81.57 293

FE-MVSNET3		64.34 304	63.57 296	66.66 331	72.44 350	40.74 399	69.60 365	76.80 260	53.21 304	61.73 338	77.92 345	41.92 264	77.68 327	46.23 340	72.25 294	81.57 293

GBi-Net		67.21 252	66.55 247	69.19 289	77.63 212	43.33 364	77.31 185	77.83 232	56.62 218	65.04 284	82.70 238	41.85 266	80.33 258	47.18 329	72.76 283	83.92 228

test1		67.21 252	66.55 247	69.19 289	77.63 212	43.33 364	77.31 185	77.83 232	56.62 218	65.04 284	82.70 238	41.85 266	80.33 258	47.18 329	72.76 283	83.92 228

FMVSNet2		66.93 262	66.31 258	68.79 298	77.63 212	42.98 373	76.11 226	77.47 238	56.62 218	65.22 281	82.17 262	41.85 266	80.18 265	47.05 335	72.72 286	83.20 256

mamba_0408		67.78 243	65.42 272	74.85 106	78.65 168	53.46 173	50.83 482	79.09 192	53.75 295	68.14 205	83.83 216	41.79 269	86.56 85	56.58 242	76.11 226	84.54 204

SSM_04072		64.98 294	65.42 272	63.68 376	78.65 168	53.46 173	50.83 482	79.09 192	53.75 295	68.14 205	83.83 216	41.79 269	53.03 485	56.58 242	76.11 226	84.54 204

KinetiMVS		71.26 143	70.16 157	74.57 117	74.59 304	52.77 196	75.91 233	81.20 153	60.72 114	69.10 190	85.71 168	41.67 271	83.53 164	63.91 170	78.62 180	87.42 74

CostFormer		64.04 308	62.51 313	68.61 300	71.88 361	45.77 333	71.30 334	70.60 357	47.55 399	64.31 296	76.61 376	41.63 272	79.62 272	49.74 302	69.00 351	80.42 325

AdaColmap		69.99 175	68.66 191	73.97 146	84.94 59	57.83 92	82.63 76	78.71 203	56.28 231	64.34 294	84.14 208	41.57 273	87.06 72	46.45 338	78.88 170	77.02 384

Effi-MVS+-dtu		69.64 188	67.53 221	75.95 80	76.10 266	62.29 15	80.20 111	76.06 275	59.83 145	65.26 278	77.09 365	41.56 274	84.02 154	60.60 208	71.09 311	81.53 295

QAPM		70.05 173	68.81 187	73.78 151	76.54 259	53.43 176	83.23 65	83.48 88	52.89 309	65.90 262	86.29 145	41.55 275	86.49 91	51.01 293	78.40 186	81.42 296

VDDNet		71.81 132	71.33 130	73.26 179	82.80 85	47.60 317	78.74 136	75.27 293	59.59 151	72.94 119	89.40 57	41.51 276	83.91 156	58.75 229	82.99 94	88.26 37

CHOSEN 1792x2688		65.08 293	62.84 310	71.82 216	81.49 102	56.26 117	66.32 395	74.20 317	40.53 459	63.16 311	78.65 332	41.30 277	77.80 323	45.80 346	74.09 254	81.40 299

新几何1				70.76 258	85.66 43	61.13 30		66.43 393	44.68 427	70.29 163	86.64 128	41.29 278	75.23 367	49.72 303	81.75 116	75.93 396

tpmrst		58.24 380	58.70 364	56.84 430	66.97 442	34.32 461	69.57 368	61.14 442	47.17 406	58.58 379	71.60 428	41.28 279	60.41 451	49.20 308	62.84 408	75.78 398

tfpnnormal		62.47 327	61.63 325	64.99 366	74.81 296	39.01 415	71.22 335	73.72 323	55.22 258	60.21 352	80.09 307	41.26 280	76.98 346	30.02 470	68.09 361	78.97 357

guyue		68.10 234	67.23 237	70.71 262	73.67 327	49.27 282	73.65 288	76.04 276	55.62 247	67.84 220	82.26 258	41.24 281	78.91 302	61.01 205	73.72 261	83.94 226

sd_testset		64.46 301	64.45 284	64.51 369	77.13 236	42.25 381	62.67 430	72.11 343	58.02 183	65.08 282	82.55 248	41.22 282	69.88 401	47.32 327	73.92 257	81.41 297

HPM-MVS_fast		74.30 75	73.46 91	76.80 65	84.45 66	59.04 76	83.65 63	81.05 158	60.15 134	70.43 160	89.84 52	41.09 283	85.59 116	67.61 126	82.90 99	85.77 152

BP-MVS1		73.41 94	72.25 113	76.88 63	76.68 254	53.70 164	79.15 130	81.07 157	60.66 115	71.81 138	87.39 99	40.93 284	87.24 62	71.23 92	81.29 120	89.71 3

114514_t		70.83 154	69.56 167	74.64 113	86.21 33	54.63 150	82.34 81	81.81 132	48.22 385	63.01 315	85.83 163	40.92 285	87.10 70	57.91 234	79.79 144	82.18 284

VortexMVS		66.41 275	65.50 271	69.16 293	73.75 323	48.14 304	73.41 291	78.28 225	53.73 297	64.98 288	78.33 337	40.62 286	79.07 290	58.88 226	67.50 366	80.26 333

WB-MVSnew		59.66 365	59.69 353	59.56 406	75.19 286	35.78 452	69.34 370	64.28 412	46.88 409	61.76 337	75.79 389	40.61 287	65.20 433	32.16 452	71.21 306	77.70 373

HyFIR lowres test		65.67 283	63.01 308	73.67 160	79.97 133	55.65 131	69.07 373	75.52 287	42.68 447	63.53 305	77.95 343	40.43 288	81.64 220	46.01 344	71.91 298	83.73 240

miper_lstm_enhance		62.03 338	60.88 339	65.49 358	66.71 445	46.25 327	56.29 465	75.70 282	50.68 349	61.27 344	75.48 395	40.21 289	68.03 411	56.31 246	65.25 383	82.18 284

GDP-MVS		72.64 113	71.28 132	76.70 66	77.72 206	54.22 156	79.57 125	84.45 51	55.30 254	71.38 147	86.97 116	39.94 290	87.00 73	67.02 137	79.20 161	88.89 15

FMVSNet3		66.32 277	65.61 269	68.46 302	76.48 260	42.34 379	74.98 255	77.15 248	55.83 239	65.04 284	81.16 283	39.91 291	80.14 266	47.18 329	72.76 283	82.90 266

Syy-MVS		56.00 399	56.23 390	55.32 437	74.69 300	26.44 497	65.52 402	57.49 456	50.97 347	56.52 403	72.18 421	39.89 292	68.09 409	24.20 489	64.59 390	71.44 450

MVP-Stereo		65.41 287	63.80 292	70.22 269	77.62 216	55.53 136	76.30 220	78.53 213	50.59 352	56.47 405	78.65 332	39.84 293	82.68 198	44.10 370	72.12 297	72.44 436
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
TR-MVS		66.59 272	65.07 280	71.17 247	79.18 153	49.63 275	73.48 289	75.20 297	52.95 307	67.90 213	80.33 301	39.81 294	83.68 160	43.20 381	73.56 267	80.20 334

pmmvs6		63.69 311	62.82 311	66.27 341	70.63 383	39.27 414	73.13 301	75.47 290	52.69 316	59.75 363	82.30 256	39.71 295	77.03 342	47.40 324	64.35 392	82.53 275

XVG-OURS-SEG-HR		68.81 213	67.47 224	72.82 191	74.40 310	56.87 111	70.59 349	79.04 194	54.77 275	66.99 238	86.01 156	39.57 296	78.21 312	62.54 189	73.33 273	83.37 251

Anonymous20231211		69.28 201	68.47 196	71.73 220	80.28 123	47.18 321	79.98 114	82.37 124	54.61 278	67.24 233	84.01 212	39.43 297	82.41 207	55.45 256	72.83 282	85.62 161

blended_shiyan8		62.46 328	60.71 343	67.71 312	69.15 415	43.43 362	70.83 343	76.52 264	51.49 335	57.67 389	71.36 432	39.38 298	79.07 290	47.37 325	62.67 409	80.62 321

blended_shiyan6		62.46 328	60.71 343	67.71 312	69.14 416	43.42 363	70.82 344	76.52 264	51.50 334	57.64 390	71.37 431	39.38 298	79.08 289	47.36 326	62.67 409	80.65 320

Fast-Effi-MVS+-dtu		67.37 250	65.33 277	73.48 171	72.94 339	57.78 94	77.47 181	76.88 255	57.60 198	61.97 333	76.85 369	39.31 300	80.49 256	54.72 261	70.28 323	82.17 286

dmvs_testset		50.16 435	51.90 424	44.94 472	66.49 447	11.78 516	61.01 444	51.50 475	51.17 345	50.30 461	67.44 463	39.28 301	60.29 452	22.38 492	57.49 446	62.76 478

ACMP	63.53 6	72.30 122	71.20 134	75.59 93	80.28 123	57.54 96	82.74 74	82.84 118	60.58 117	65.24 279	86.18 148	39.25 302	86.03 106	66.95 139	76.79 216	83.22 255
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
Anonymous20240529		69.91 177	69.02 180	72.56 196	80.19 128	47.65 314	77.56 177	80.99 160	55.45 251	69.88 173	86.76 121	39.24 303	82.18 211	54.04 267	77.10 211	87.85 55

LPG-MVS_test		72.74 110	71.74 121	75.76 85	80.22 125	57.51 98	82.55 78	83.40 92	61.32 98	66.67 246	87.33 102	39.15 304	86.59 83	67.70 124	77.30 207	83.19 257

LGP-MVS_train				75.76 85	80.22 125	57.51 98		83.40 92	61.32 98	66.67 246	87.33 102	39.15 304	86.59 83	67.70 124	77.30 207	83.19 257

TAPA-MVS	59.36 10	66.60 270	65.20 279	70.81 257	76.63 256	48.75 292	76.52 217	80.04 176	50.64 351	65.24 279	84.93 182	39.15 304	78.54 307	36.77 426	76.88 214	85.14 184
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
AstraMVS		67.86 241	66.83 242	70.93 255	73.50 329	49.34 279	73.28 296	74.01 319	55.45 251	68.10 210	83.28 230	38.93 307	79.14 287	63.22 182	71.74 300	84.30 214

OpenMVS	61.03 9	68.85 212	67.56 218	72.70 193	74.26 315	53.99 159	81.21 97	81.34 147	52.70 311	62.75 320	85.55 172	38.86 308	84.14 149	48.41 315	83.01 92	79.97 338

wanda-best-256-512		62.00 340	60.17 349	67.49 316	68.53 425	43.07 371	69.65 362	76.38 268	51.26 341	57.10 396	69.95 444	38.83 309	79.04 293	47.14 333	62.67 409	80.37 328

FE-blended-shiyan7		62.00 340	60.17 349	67.49 316	68.53 425	43.07 371	69.65 362	76.38 268	51.26 341	57.10 396	69.95 444	38.83 309	79.04 293	47.14 333	62.67 409	80.37 328

usedtu_blend_shiyan5		62.63 324	60.77 342	68.20 306	68.53 425	44.64 347	73.47 290	77.00 253	51.91 326	57.10 396	69.95 444	38.83 309	79.61 273	47.44 321	62.67 409	80.37 328

sss		56.17 398	56.57 385	54.96 439	66.93 443	36.32 445	57.94 456	61.69 439	41.67 451	58.64 377	75.32 398	38.72 312	56.25 474	42.04 391	66.19 377	72.31 440

ACMM	61.98 7	70.80 156	69.73 164	74.02 142	80.59 122	58.59 84	82.68 75	82.02 129	55.46 250	67.18 235	84.39 204	38.51 313	83.17 172	60.65 207	76.10 229	80.30 332
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
MVSTER		67.16 257	65.58 270	71.88 214	70.37 391	49.70 271	70.25 356	78.45 218	51.52 333	69.16 188	80.37 298	38.45 314	82.50 204	60.19 210	71.46 304	83.44 250

test_djsdf		69.45 197	67.74 214	74.58 116	74.57 306	54.92 147	82.79 72	78.48 215	51.26 341	65.41 272	83.49 228	38.37 315	83.24 170	66.06 145	69.25 347	85.56 162

MonoMVSNet		64.15 306	63.31 304	66.69 330	70.51 386	44.12 355	74.47 267	74.21 316	57.81 191	63.03 313	76.62 374	38.33 316	77.31 336	54.22 266	60.59 434	78.64 360

tpm2		62.07 336	60.10 351	67.99 309	72.79 341	43.86 357	71.05 341	66.85 390	43.14 443	62.77 318	75.39 397	38.32 317	80.80 248	41.69 393	68.88 352	79.32 350

tpm cat1		59.25 370	56.95 379	66.15 344	72.19 356	46.96 322	68.09 381	65.76 398	40.03 463	57.81 388	70.56 437	38.32 317	74.51 370	38.26 416	61.50 425	77.00 385

CNLPA		65.43 286	64.02 288	69.68 281	78.73 166	58.07 89	77.82 170	70.71 356	51.49 335	61.57 342	83.58 226	38.23 319	70.82 393	43.90 372	70.10 328	80.16 335

1314		64.61 299	63.21 306	68.80 297	71.87 362	47.46 318	73.95 279	78.39 223	42.88 446	59.97 357	76.60 377	38.11 320	79.39 278	54.84 260	72.32 291	79.55 347

testdata				64.66 367	81.52 100	52.93 188		65.29 403	46.09 416	73.88 93	87.46 96	38.08 321	66.26 426	53.31 275	78.48 183	74.78 413

FMVSNet1		66.70 268	65.87 264	69.19 289	77.49 220	43.33 364	77.31 185	77.83 232	56.45 224	64.60 293	82.70 238	38.08 321	80.33 258	46.08 343	72.31 292	83.92 228

UniMVSNet_ETH3D		67.60 247	67.07 240	69.18 292	77.39 223	42.29 380	74.18 274	75.59 285	60.37 125	66.77 242	86.06 153	37.64 323	78.93 300	52.16 282	73.49 268	86.32 128

EPNet_dtu		61.90 342	61.97 321	61.68 392	72.89 340	39.78 407	75.85 235	65.62 400	55.09 261	54.56 428	79.36 322	37.59 324	67.02 420	39.80 407	76.95 212	78.25 364
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
IterMVS-SCA-FT		62.49 326	61.52 326	65.40 360	71.99 360	50.80 235	71.15 338	69.63 365	45.71 421	60.61 350	77.93 344	37.45 325	65.99 429	55.67 253	63.50 401	79.42 349

SCA		60.49 356	58.38 367	66.80 326	74.14 319	48.06 308	63.35 426	63.23 425	49.13 370	59.33 370	72.10 423	37.45 325	74.27 372	44.17 367	62.57 415	78.05 367

tt0805		67.77 244	67.24 235	69.34 288	74.87 293	40.08 403	77.36 184	81.37 142	55.31 253	66.33 252	84.65 193	37.35 327	82.55 203	55.65 254	72.28 293	85.39 175

IterMVS		62.79 323	61.27 331	67.35 321	69.37 410	52.04 215	71.17 336	68.24 379	52.63 317	59.82 360	76.91 368	37.32 328	72.36 381	52.80 278	63.19 405	77.66 374
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
tfpn200view9		63.18 318	62.18 319	66.21 342	76.85 250	39.62 410	71.96 325	69.44 369	56.63 216	62.61 323	79.83 309	37.18 329	79.17 283	31.84 456	73.25 275	79.83 343

thres400		63.31 314	62.18 319	66.72 327	76.85 250	39.62 410	71.96 325	69.44 369	56.63 216	62.61 323	79.83 309	37.18 329	79.17 283	31.84 456	73.25 275	81.36 300

tpm		57.34 387	58.16 369	54.86 440	71.80 363	34.77 456	67.47 388	56.04 466	48.20 387	60.10 354	76.92 367	37.17 331	53.41 484	40.76 399	65.01 384	76.40 392

test222					83.14 78	58.68 83	72.57 312	63.45 423	41.78 449	67.56 227	86.12 150	37.13 332			78.73 176	74.98 409

AUN-MVS		68.45 226	66.41 253	74.57 117	79.53 141	57.08 109	73.93 281	75.23 295	54.44 283	66.69 244	81.85 270	37.10 333	82.89 189	62.07 193	66.84 371	83.75 239

thres200		62.20 335	61.16 335	65.34 362	75.38 282	39.99 405	69.60 365	69.29 371	55.64 246	61.87 335	76.99 366	37.07 334	78.96 299	31.28 464	73.28 274	77.06 383

thres100view900		63.28 316	62.41 315	65.89 350	77.31 226	38.66 418	72.65 307	69.11 373	57.07 205	62.45 328	81.03 287	37.01 335	79.17 283	31.84 456	73.25 275	79.83 343

thres600view7		63.30 315	62.27 317	66.41 337	77.18 229	38.87 416	72.35 317	69.11 373	56.98 208	62.37 331	80.96 289	37.01 335	79.00 298	31.43 463	73.05 279	81.36 300

gbinet_0.2-2-1-0.02		62.43 330	60.41 346	68.49 301	68.91 420	43.71 359	71.73 329	75.89 278	52.10 323	58.33 382	69.67 451	36.86 337	80.59 252	47.18 329	63.05 407	81.16 308

DP-MVS		65.68 282	63.66 295	71.75 219	84.93 60	56.87 111	80.74 104	73.16 333	53.06 306	59.09 371	82.35 254	36.79 338	85.94 109	32.82 450	69.96 331	72.45 435

mvsmamba		68.47 224	66.56 246	74.21 132	79.60 138	52.95 187	74.94 256	75.48 289	52.09 324	60.10 354	83.27 231	36.54 339	84.70 140	59.32 221	77.69 196	84.99 192

XVG-OURS		68.76 216	67.37 227	72.90 188	74.32 313	57.22 101	70.09 358	78.81 200	55.24 256	67.79 223	85.81 166	36.54 339	78.28 311	62.04 194	75.74 234	83.19 257

dtuonlycased		55.96 400	54.88 403	59.22 412	68.38 430	40.38 401	69.17 372	63.12 428	40.00 464	53.62 438	68.84 456	36.27 341	66.23 427	40.57 400	53.92 462	71.06 456

FBQ-MVS		66.84 264	65.39 274	71.18 245	79.22 150	47.61 316	76.89 205	74.70 306	56.31 230	65.84 266	77.22 361	36.21 342	82.07 213	45.20 360	76.94 213	83.87 231

ECVR-MVS		67.72 245	67.51 222	68.35 304	79.46 143	36.29 447	74.79 260	66.93 389	58.72 166	67.19 234	88.05 83	36.10 343	81.38 228	52.07 283	84.25 80	87.39 77

test1111		67.21 252	67.14 239	67.42 319	79.24 149	34.76 457	73.89 283	65.65 399	58.71 168	66.96 239	87.95 87	36.09 344	80.53 253	52.03 284	83.79 86	86.97 94

pmmvs4		61.48 348	59.39 355	67.76 311	71.57 366	53.86 160	71.42 331	65.34 402	44.20 432	59.46 366	77.92 345	35.90 345	74.71 369	43.87 373	64.87 386	74.71 415

CR-MVSNet		59.91 361	57.90 372	65.96 348	69.96 400	52.07 213	65.31 408	63.15 426	42.48 448	59.36 367	74.84 400	35.83 346	70.75 394	45.50 353	64.65 388	75.06 406

Patchmtry		57.16 388	56.47 386	59.23 411	69.17 414	34.58 459	62.98 428	63.15 426	44.53 428	56.83 400	74.84 400	35.83 346	68.71 406	40.03 403	60.91 427	74.39 419

dmvs_re		56.77 391	56.83 381	56.61 431	69.23 412	41.02 393	58.37 453	64.18 413	50.59 352	57.45 394	71.42 429	35.54 348	58.94 460	37.23 422	67.45 367	69.87 465

FE-MVSNET2		62.01 339	60.88 339	65.42 359	68.74 421	38.43 422	72.92 304	77.39 242	54.74 277	55.40 415	76.71 371	35.46 349	76.72 353	44.25 365	62.31 418	81.10 310

RPMNet		61.53 346	58.42 366	70.86 256	69.96 400	52.07 213	65.31 408	81.36 143	43.20 442	59.36 367	70.15 442	35.37 350	85.47 122	36.42 433	64.65 388	75.06 406

CANet_DTU		68.18 232	67.71 217	69.59 283	74.83 295	46.24 328	78.66 139	76.85 256	59.60 148	63.45 306	82.09 267	35.25 351	77.41 333	59.88 214	78.76 175	85.14 184

thisisatest0530		67.92 239	65.78 266	74.33 125	76.29 263	51.03 228	76.89 205	74.25 315	53.67 299	65.59 269	81.76 273	35.15 352	85.50 120	55.94 247	72.47 288	86.47 117

tttt0517		67.83 242	65.66 268	74.33 125	76.69 253	50.82 234	77.86 167	73.99 320	54.54 281	64.64 292	82.53 251	35.06 353	85.50 120	55.71 252	69.91 332	86.67 108

test_0402		63.25 317	61.01 337	69.96 274	80.00 132	54.37 153	76.86 208	72.02 344	54.58 280	58.71 374	80.79 295	35.00 354	84.36 146	26.41 485	64.71 387	71.15 454

thisisatest0515		65.83 281	63.50 299	72.82 191	73.75 323	49.50 276	71.32 333	73.12 335	49.39 366	63.82 302	76.50 380	34.95 355	84.84 139	53.20 276	75.49 238	84.13 221

IMVS_0404		64.63 298	64.22 286	65.88 351	77.06 241	49.73 267	64.40 416	78.60 207	52.70 311	53.16 444	82.58 243	34.82 356	65.16 434	59.20 222	75.46 239	82.74 269

sam_mvs1												34.74 357				78.05 367

pmmvs5		56.47 394	55.68 394	58.86 416	61.41 474	36.71 440	66.37 394	62.75 429	40.38 460	53.70 435	76.62 374	34.56 358	67.05 419	40.02 404	65.27 382	72.83 429

patchmatchnet-post											64.03 475	34.50 359	74.27 372

PatchmatchNet		59.84 362	58.24 368	64.65 368	73.05 337	46.70 324	69.42 369	62.18 437	47.55 399	58.88 373	71.96 425	34.49 360	69.16 403	42.99 383	63.60 399	78.07 366
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
Patchmatch-test		49.08 438	48.28 440	51.50 462	64.40 459	30.85 482	45.68 493	48.46 485	35.60 473	46.10 475	72.10 423	34.47 361	46.37 497	27.08 483	60.65 432	77.27 380

MS-PatchMatch		62.42 331	61.46 327	65.31 363	75.21 285	52.10 212	72.05 322	74.05 318	46.41 413	57.42 395	74.36 404	34.35 362	77.57 331	45.62 349	73.67 262	66.26 474

dtuonly		54.95 411	55.26 400	54.01 445	59.03 485	35.99 448	61.92 435	56.33 462	38.48 468	54.61 427	77.85 351	34.27 363	51.60 491	45.10 361	69.74 338	74.43 417

tpmvs		58.47 374	56.95 379	63.03 384	70.20 395	41.21 392	67.90 383	67.23 386	49.62 363	54.73 425	70.84 435	34.14 364	76.24 362	36.64 430	61.29 426	71.64 446

testing91		64.46 301	63.80 292	66.47 336	78.43 177	40.06 404	67.63 384	69.59 366	59.06 159	63.18 310	78.05 341	34.05 365	76.99 345	48.30 316	75.87 232	82.37 281

PMMVS		53.96 414	53.26 420	56.04 433	62.60 468	50.92 231	61.17 441	56.09 465	32.81 478	53.51 441	66.84 468	34.04 366	59.93 454	44.14 369	68.18 360	57.27 487

Patchmatch-RL test		58.16 381	55.49 397	66.15 344	67.92 435	48.89 291	60.66 445	51.07 478	47.86 395	59.36 367	62.71 479	34.02 367	72.27 384	56.41 245	59.40 438	77.30 379

WB-MVS		43.26 448	43.41 448	42.83 476	63.32 464	10.32 518	58.17 455	45.20 493	45.42 422	40.44 488	67.26 466	34.01 368	58.98 459	11.96 508	24.88 501	59.20 481

test_post											3.55 536	33.90 369	66.52 423

WBMVS		60.54 355	60.61 345	60.34 404	78.00 196	35.95 450	64.55 415	64.89 405	49.63 362	63.39 307	78.70 329	33.85 370	67.65 414	42.10 390	70.35 321	77.43 377

PLC	56.13 14	65.09 292	63.21 306	70.72 261	81.04 112	54.87 148	78.57 142	77.47 238	48.51 380	55.71 410	81.89 269	33.71 371	79.71 269	41.66 394	70.37 319	77.58 375
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
ET-MVSNet_ETH3D		67.96 238	65.72 267	74.68 110	76.67 255	55.62 134	75.11 250	74.74 304	52.91 308	60.03 356	80.12 305	33.68 372	82.64 200	61.86 196	76.34 222	85.78 149

GA-MVS		65.53 285	63.70 294	71.02 254	70.87 381	48.10 305	70.48 351	74.40 310	56.69 213	64.70 291	76.77 370	33.66 373	81.10 236	55.42 257	70.32 322	83.87 231

LS3D		64.71 296	62.50 314	71.34 241	79.72 137	55.71 129	79.82 118	74.72 305	48.50 381	56.62 401	84.62 194	33.59 374	82.34 208	29.65 472	75.23 243	75.97 395

sam_mvs												33.43 375

PatchT		53.17 423	53.44 419	52.33 458	68.29 431	25.34 501	58.21 454	54.41 469	44.46 430	54.56 428	69.05 455	33.32 376	60.94 448	36.93 425	61.76 424	70.73 458

test20.03		53.87 416	54.02 413	53.41 451	61.47 473	28.11 490	61.30 439	59.21 448	51.34 340	52.09 449	77.43 359	33.29 377	58.55 462	29.76 471	60.27 436	73.58 425

UBG		59.62 367	59.53 354	59.89 405	78.12 191	35.92 451	64.11 421	60.81 444	49.45 365	61.34 343	75.55 393	33.05 378	67.39 418	38.68 413	74.62 248	76.35 393

our_test_3		56.49 393	54.42 407	62.68 386	69.51 407	45.48 339	66.08 396	61.49 440	44.11 435	50.73 457	69.60 452	33.05 378	68.15 408	38.38 415	56.86 448	74.40 418

anonymousdsp		67.00 261	64.82 282	73.57 167	70.09 398	56.13 119	76.35 219	77.35 244	48.43 382	64.99 287	80.84 294	33.01 380	80.34 257	64.66 161	67.64 365	84.23 216

MDTV_nov1_ep13_2view						25.89 499	61.22 440		40.10 462	51.10 452		32.97 381		38.49 414		78.61 361

IB-MVS	56.42 12	65.40 288	62.73 312	73.40 175	74.89 291	52.78 195	73.09 302	75.13 298	55.69 243	58.48 380	73.73 411	32.86 382	86.32 96	50.63 296	70.11 327	81.10 310
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
xiu_mvs_v1_base_debu		68.58 219	67.28 231	72.48 200	78.19 187	57.19 103	75.28 245	75.09 299	51.61 330	70.04 166	81.41 280	32.79 383	79.02 295	63.81 171	77.31 204	81.22 305

xiu_mvs_v1_base		68.58 219	67.28 231	72.48 200	78.19 187	57.19 103	75.28 245	75.09 299	51.61 330	70.04 166	81.41 280	32.79 383	79.02 295	63.81 171	77.31 204	81.22 305

xiu_mvs_v1_base_debi		68.58 219	67.28 231	72.48 200	78.19 187	57.19 103	75.28 245	75.09 299	51.61 330	70.04 166	81.41 280	32.79 383	79.02 295	63.81 171	77.31 204	81.22 305

Anonymous20231206		55.10 410	55.30 399	54.48 442	69.81 405	33.94 465	62.91 429	62.13 438	41.08 455	55.18 418	75.65 391	32.75 386	56.59 473	30.32 469	67.86 362	72.91 427

UGNet		68.81 213	67.39 226	73.06 183	78.33 183	54.47 151	79.77 119	75.40 291	60.45 120	63.22 308	84.40 203	32.71 387	80.91 245	51.71 289	80.56 131	83.81 234
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
LuminaMVS		68.24 230	66.82 243	72.51 199	73.46 331	53.60 169	76.23 223	78.88 198	52.78 310	68.08 211	80.13 304	32.70 388	81.41 226	63.16 183	75.97 230	82.53 275

myMVS_eth3d28		60.66 353	61.04 336	59.51 407	77.32 225	31.58 478	63.11 427	63.87 417	59.00 160	60.90 349	78.26 338	32.69 389	66.15 428	36.10 435	78.13 189	80.81 317

SSC-MVS		41.96 453	41.99 452	41.90 477	62.46 469	9.28 520	57.41 461	44.32 497	43.38 439	38.30 494	66.45 469	32.67 390	58.42 463	10.98 510	21.91 504	57.99 485

test-LLR		58.15 382	58.13 371	58.22 421	68.57 423	44.80 344	65.46 404	57.92 453	50.08 357	55.44 413	69.82 448	32.62 391	57.44 467	49.66 304	73.62 264	72.41 437

test0.0.03 1		53.32 422	53.59 418	52.50 457	62.81 467	29.45 485	59.51 449	54.11 470	50.08 357	54.40 430	74.31 405	32.62 391	55.92 476	30.50 467	63.95 395	72.15 442

MDTV_nov1_ep13			57.00 378		72.73 342	38.26 423	65.02 412	64.73 408	44.74 426	55.46 412	72.48 418	32.61 393	70.47 395	37.47 419	67.75 364

testing99		64.05 307	63.29 305	66.34 338	78.17 190	39.76 408	67.33 389	68.00 380	58.60 171	63.03 313	78.10 340	32.57 394	76.94 347	48.22 317	75.58 236	82.34 282

cascas		65.98 279	63.42 301	73.64 163	77.26 227	52.58 201	72.26 320	77.21 247	48.56 378	61.21 345	74.60 403	32.57 394	85.82 112	50.38 298	76.75 217	82.52 277

nomal-1		58.46 375	57.31 375	61.90 390	68.64 422	49.90 264	55.10 468	63.49 421	48.22 385	59.51 365	72.40 419	32.56 396	65.29 432	45.60 350	70.25 325	70.51 459

test_post1							68.67 375				3.64 535	32.39 397	69.49 402	44.17 367

CVMVSNet		59.63 366	59.14 357	61.08 401	74.47 307	38.84 417	75.20 248	68.74 375	31.15 481	58.24 383	76.51 378	32.39 397	68.58 407	49.77 301	65.84 379	75.81 397

testing3-2		62.06 337	62.36 316	61.17 399	79.29 145	30.31 483	64.09 422	63.49 421	63.50 45	62.84 316	82.22 259	32.35 399	69.02 405	40.01 405	73.43 271	84.17 219

ppachtmachnet_test		58.06 383	55.38 398	66.10 346	69.51 407	48.99 287	68.01 382	66.13 397	44.50 429	54.05 433	70.74 436	32.09 400	72.34 383	36.68 429	56.71 451	76.99 387

MIMVSNet		57.35 386	57.07 377	58.22 421	74.21 316	37.18 433	62.46 431	60.88 443	48.88 374	55.29 417	75.99 387	31.68 401	62.04 446	31.87 455	72.35 290	75.43 403

testing11		62.81 322	61.90 322	65.54 355	78.38 178	40.76 398	67.59 386	66.78 391	55.48 249	60.13 353	77.11 364	31.67 402	76.79 350	45.53 352	74.45 250	79.06 354

test_vis1_n_1920		58.86 371	59.06 360	58.25 420	63.76 461	43.14 369	67.49 387	66.36 394	40.22 461	65.89 263	71.95 426	31.04 403	59.75 455	59.94 213	64.90 385	71.85 444

PVSNet_0	43.31 20	47.46 443	45.64 446	52.92 454	67.60 438	44.65 346	54.06 472	54.64 467	41.59 452	46.15 474	58.75 485	30.99 404	58.66 461	32.18 451	24.81 502	55.46 489

gg-mvs-nofinetune		57.86 384	56.43 387	62.18 388	72.62 344	35.35 453	66.57 392	56.33 462	50.65 350	57.64 390	57.10 488	30.65 405	76.36 360	37.38 421	78.88 170	74.82 412

D2MVS		62.30 333	60.29 348	68.34 305	66.46 448	48.42 299	65.70 399	73.42 326	47.71 396	58.16 385	75.02 399	30.51 406	77.71 326	53.96 269	71.68 302	78.90 358

GG-mvs-BLEND				62.34 387	71.36 374	37.04 437	69.20 371	57.33 458		54.73 425	65.48 473	30.37 407	77.82 322	34.82 440	74.93 245	72.17 441

tt0320		58.59 373	56.81 382	63.92 375	75.46 279	41.32 391	68.63 376	64.06 416	47.05 407	56.19 407	74.19 406	30.34 408	71.36 389	39.92 406	55.45 454	79.09 353

MDA-MVSNet-bldmvs		53.87 416	50.81 429	63.05 383	66.25 449	48.58 297	56.93 463	63.82 418	48.09 389	41.22 485	70.48 440	30.34 408	68.00 412	34.24 442	45.92 482	72.57 432

EPMVS		53.96 414	53.69 417	54.79 441	66.12 451	31.96 477	62.34 433	49.05 482	44.42 431	55.54 411	71.33 433	30.22 410	56.70 470	41.65 395	62.54 416	75.71 399

Elysia		70.19 171	68.29 203	75.88 82	74.15 317	54.33 154	78.26 146	83.21 103	55.04 267	67.28 231	83.59 223	30.16 411	86.11 102	63.67 175	79.26 158	87.20 87

StellarMVS		70.19 171	68.29 203	75.88 82	74.15 317	54.33 154	78.26 146	83.21 103	55.04 267	67.28 231	83.59 223	30.16 411	86.11 102	63.67 175	79.26 158	87.20 87

YYNet1		50.73 433	48.96 435	56.03 434	61.10 476	41.78 385	51.94 477	56.44 460	40.94 457	44.84 477	67.80 460	30.08 413	55.08 480	36.77 426	50.71 472	71.22 452

MDA-MVSNet_test_wron		50.71 434	48.95 436	56.00 435	61.17 475	41.84 384	51.90 478	56.45 459	40.96 456	44.79 478	67.84 459	30.04 414	55.07 481	36.71 428	50.69 473	71.11 455

test_cas_vis1_n_1920		56.91 390	56.71 383	57.51 429	59.13 484	45.40 340	63.58 424	61.29 441	36.24 472	67.14 236	71.85 427	29.89 415	56.69 471	57.65 236	63.58 400	70.46 460

Anonymous202405211		66.84 264	65.99 263	69.40 287	80.19 128	42.21 382	71.11 339	71.31 348	58.80 164	67.90 213	86.39 141	29.83 416	79.65 270	49.60 306	78.78 173	86.33 126

ETVMVS		59.51 368	58.81 361	61.58 394	77.46 221	34.87 454	64.94 413	59.35 447	54.06 288	61.08 347	76.67 372	29.54 417	71.87 387	32.16 452	74.07 255	78.01 371

MSDG		61.81 344	59.23 356	69.55 286	72.64 343	52.63 200	70.45 352	75.81 280	51.38 338	53.70 435	76.11 383	29.52 418	81.08 238	37.70 418	65.79 380	74.93 410

CMPMVS	42.80 21	57.81 385	55.97 391	63.32 379	60.98 478	47.38 319	64.66 414	69.50 368	32.06 479	46.83 471	77.80 352	29.50 419	71.36 389	48.68 312	73.75 260	71.21 453
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
LTVRE_ROB	55.42 16	63.15 319	61.23 333	68.92 296	76.57 258	47.80 311	59.92 447	76.39 267	54.35 284	58.67 376	82.46 253	29.44 420	81.49 225	42.12 389	71.14 307	77.46 376
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
UnsupCasMVSNet_eth		53.16 424	52.47 421	55.23 438	59.45 482	33.39 469	59.43 450	69.13 372	45.98 417	50.35 460	72.32 420	29.30 421	58.26 464	42.02 392	44.30 484	74.05 422

FE-MVSNET		55.16 409	53.75 416	59.41 408	65.29 455	33.20 470	67.21 390	66.21 396	48.39 384	49.56 463	73.53 413	29.03 422	72.51 380	30.38 468	54.10 461	72.52 433

CHOSEN 280x420		47.83 441	46.36 445	52.24 460	67.37 440	49.78 266	38.91 501	43.11 499	35.00 474	43.27 483	63.30 478	28.95 423	49.19 493	36.53 431	60.80 429	57.76 486

pmmvs-eth3d		58.81 372	56.31 389	66.30 340	67.61 437	52.42 207	72.30 318	64.76 407	43.55 438	54.94 422	74.19 406	28.95 423	72.60 379	43.31 378	57.21 447	73.88 424

dp		51.89 428	51.60 426	52.77 455	68.44 429	32.45 475	62.36 432	54.57 468	44.16 433	49.31 464	67.91 458	28.87 425	56.61 472	33.89 443	54.89 457	69.24 470

FE-MVS		65.91 280	63.33 303	73.63 164	77.36 224	51.95 218	72.62 309	75.81 280	53.70 298	65.31 273	78.96 327	28.81 426	86.39 93	43.93 371	73.48 269	82.55 274

tt0320-xc		58.33 378	56.41 388	64.08 373	75.79 270	41.34 390	68.30 379	62.72 430	47.90 392	56.29 406	74.16 408	28.53 427	71.04 392	41.50 397	52.50 467	79.88 341

testing222		62.29 334	61.31 330	65.25 364	77.87 200	38.53 420	68.34 378	66.31 395	56.37 227	63.15 312	77.58 358	28.47 428	76.18 364	37.04 424	76.65 219	81.05 313

KD-MVS_self_test		55.22 407	53.89 414	59.21 413	57.80 488	27.47 493	57.75 459	74.32 311	47.38 401	50.90 454	70.00 443	28.45 429	70.30 399	40.44 401	57.92 444	79.87 342

0.4-1-1-0.2		58.31 379	55.53 396	66.64 333	67.46 439	42.78 377	64.38 417	70.97 352	47.65 397	53.38 443	59.02 484	28.39 430	78.72 305	44.86 364	63.63 398	78.42 362

blend_shiyan4		61.38 349	59.10 359	68.20 306	68.94 418	44.64 347	70.81 345	76.52 264	51.63 329	57.56 392	69.94 447	28.30 431	79.61 273	47.44 321	60.78 430	80.36 331

jajsoiax		68.25 229	66.45 249	73.66 161	75.62 274	55.49 137	80.82 101	78.51 214	52.33 319	64.33 295	84.11 209	28.28 432	81.81 219	63.48 178	70.62 314	83.67 242

0.4-1-1-0.1		59.29 369	56.70 384	67.07 323	69.35 411	43.16 368	66.59 391	70.87 354	48.59 377	55.11 419	62.25 480	28.22 433	78.92 301	45.49 354	63.79 396	79.14 352

sc_t1		59.76 363	57.84 373	65.54 355	74.87 293	42.95 375	69.61 364	64.16 415	48.90 373	58.68 375	77.12 363	28.19 434	72.35 382	43.75 376	55.28 455	81.31 303

RPSCF		55.80 402	54.22 412	60.53 403	65.13 456	42.91 376	64.30 418	57.62 455	36.84 471	58.05 387	82.28 257	28.01 435	56.24 475	37.14 423	58.61 442	82.44 280

F-COLMAP		63.05 321	60.87 341	69.58 285	76.99 249	53.63 168	78.12 158	76.16 271	47.97 391	52.41 448	81.61 276	27.87 436	78.11 313	40.07 402	66.66 373	77.00 385

K. test v3		60.47 357	57.11 376	70.56 265	73.74 325	48.22 302	75.10 252	62.55 431	58.27 178	53.62 438	76.31 382	27.81 437	81.59 222	47.42 323	39.18 491	81.88 290

UWE-MVS-28		52.25 426	52.35 423	51.93 461	66.99 441	22.79 505	63.48 425	48.31 486	46.78 410	52.73 447	76.11 383	27.78 438	57.82 466	20.58 497	68.41 359	75.17 404

ACMH+	57.40 11	66.12 278	64.06 287	72.30 207	77.79 203	52.83 194	80.39 106	78.03 228	57.30 200	57.47 393	82.55 248	27.68 439	84.17 148	45.54 351	69.78 335	79.90 340

UnsupCasMVSNet_bld		50.07 436	48.87 437	53.66 448	60.97 479	33.67 467	57.62 460	64.56 410	39.47 466	47.38 468	64.02 477	27.47 440	59.32 456	34.69 441	43.68 485	67.98 473

mvs_tets		68.18 232	66.36 255	73.63 164	75.61 275	55.35 141	80.77 102	78.56 212	52.48 318	64.27 297	84.10 210	27.45 441	81.84 218	63.45 179	70.56 316	83.69 241

lessismore_v0				69.91 277	71.42 372	47.80 311		50.90 479		50.39 459	75.56 392	27.43 442	81.33 229	45.91 345	34.10 497	80.59 322

UWE-MVS		60.18 359	59.78 352	61.39 397	77.67 210	33.92 466	69.04 374	63.82 418	48.56 378	64.27 297	77.64 357	27.20 443	70.40 398	33.56 447	76.24 223	79.83 343

ACMH	55.70 15	65.20 291	63.57 296	70.07 273	78.07 193	52.01 216	79.48 127	79.69 180	55.75 242	56.59 402	80.98 288	27.12 444	80.94 242	42.90 385	71.58 303	77.25 382
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
SixPastTwentyTwo		61.65 345	58.80 363	70.20 271	75.80 269	47.22 320	75.59 239	69.68 364	54.61 278	54.11 432	79.26 324	27.07 445	82.96 180	43.27 379	49.79 476	80.41 326

mmtdpeth		60.40 358	59.12 358	64.27 372	69.59 406	48.99 287	70.67 348	70.06 361	54.96 271	62.78 317	73.26 416	27.00 446	67.66 413	58.44 232	45.29 483	76.16 394

PVSNet	50.76 19	58.40 376	57.39 374	61.42 395	75.53 277	44.04 356	61.43 437	63.45 423	47.04 408	56.91 399	73.61 412	27.00 446	64.76 435	39.12 411	72.40 289	75.47 402

OpenMVS_ROB	52.78 18	60.03 360	58.14 370	65.69 354	70.47 387	44.82 343	75.33 243	70.86 355	45.04 424	56.06 408	76.00 385	26.89 448	79.65 270	35.36 439	67.29 368	72.60 431

0.3-1-1-0.015		58.40 376	55.56 395	66.91 325	68.08 433	43.09 370	65.25 410	70.96 353	47.89 394	53.10 445	59.82 483	26.48 449	78.79 303	45.07 362	63.43 402	78.84 359

ADS-MVSNet2		51.33 431	48.76 438	59.07 415	66.02 452	44.60 349	50.90 480	59.76 446	36.90 469	50.74 455	66.18 471	26.38 450	63.11 442	27.17 481	54.76 458	69.50 467

ADS-MVSNet		48.48 440	47.77 441	50.63 463	66.02 452	29.92 484	50.90 480	50.87 480	36.90 469	50.74 455	66.18 471	26.38 450	52.47 487	27.17 481	54.76 458	69.50 467

N_pmnet		39.35 458	40.28 455	36.54 483	63.76 461	1.62 538	49.37 485	0.76 536	34.62 475	43.61 482	66.38 470	26.25 452	42.57 501	26.02 486	51.77 469	65.44 475

MVS-HIRNet		45.52 445	44.48 447	48.65 466	68.49 428	34.05 464	59.41 451	44.50 496	27.03 488	37.96 495	50.47 498	26.16 453	64.10 436	26.74 484	59.52 437	47.82 496

test2506		65.33 289	64.61 283	67.50 315	79.46 143	34.19 463	74.43 269	51.92 474	58.72 166	66.75 243	88.05 83	25.99 454	80.92 244	51.94 285	84.25 80	87.39 77

FMVSNet5		55.86 401	54.93 401	58.66 418	71.05 379	36.35 443	64.18 420	62.48 432	46.76 411	50.66 458	74.73 402	25.80 455	64.04 437	33.11 448	65.57 381	75.59 400

new-patchmatchnet		47.56 442	47.73 442	47.06 467	58.81 486	9.37 519	48.78 486	59.21 448	43.28 440	44.22 480	68.66 457	25.67 456	57.20 469	31.57 462	49.35 477	74.62 416

reproduce_monomvs		62.56 325	61.20 334	66.62 334	70.62 384	44.30 352	70.13 357	73.13 334	54.78 274	61.13 346	76.37 381	25.63 457	75.63 365	58.75 229	60.29 435	79.93 339

MIMVSNet1		55.17 408	54.31 410	57.77 427	70.03 399	32.01 476	65.68 400	64.81 406	49.19 369	46.75 472	76.00 385	25.53 458	64.04 437	28.65 475	62.13 420	77.26 381

PatchMatch-RL		56.25 397	54.55 406	61.32 398	77.06 241	56.07 121	65.57 401	54.10 471	44.13 434	53.49 442	71.27 434	25.20 459	66.78 421	36.52 432	63.66 397	61.12 479

JIA-IIPM		51.56 429	47.68 443	63.21 381	64.61 458	50.73 240	47.71 489	58.77 450	42.90 445	48.46 466	51.72 492	24.97 460	70.24 400	36.06 436	53.89 463	68.64 471

EU-MVSNet		55.61 404	54.41 408	59.19 414	65.41 454	33.42 468	72.44 316	71.91 345	28.81 483	51.27 451	73.87 410	24.76 461	69.08 404	43.04 382	58.20 443	75.06 406

EG-PatchMatch MVS		64.71 296	62.87 309	70.22 269	77.68 209	53.48 172	77.99 163	78.82 199	53.37 302	56.03 409	77.41 360	24.75 462	84.04 152	46.37 339	73.42 272	73.14 426

TESTMET0.1,1		55.28 406	54.90 402	56.42 432	66.56 446	43.67 360	65.46 404	56.27 464	39.18 467	53.83 434	67.44 463	24.21 463	55.46 478	48.04 319	73.11 278	70.13 463

mvsany_test1		39.38 457	38.16 460	43.02 475	49.05 497	34.28 462	44.16 497	25.94 512	22.74 497	46.57 473	62.21 481	23.85 464	41.16 505	33.01 449	35.91 494	53.63 490

COLMAP_ROB	52.97 17	61.27 351	58.81 361	68.64 299	74.63 302	52.51 203	78.42 145	73.30 329	49.92 360	50.96 453	81.51 279	23.06 465	79.40 277	31.63 460	65.85 378	74.01 423
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
testgi		51.90 427	52.37 422	50.51 464	60.39 481	23.55 504	58.42 452	58.15 451	49.03 371	51.83 450	79.21 325	22.39 466	55.59 477	29.24 474	62.64 414	72.40 439

DSMNet-mixed		39.30 459	38.72 458	41.03 478	51.22 496	19.66 508	45.53 494	31.35 508	15.83 506	39.80 490	67.42 465	22.19 467	45.13 498	22.43 491	52.69 466	58.31 484

test-mter		56.42 395	55.82 393	58.22 421	68.57 423	44.80 344	65.46 404	57.92 453	39.94 465	55.44 413	69.82 448	21.92 468	57.44 467	49.66 304	73.62 264	72.41 437

KD-MVS_2432*1600		53.45 418	51.50 427	59.30 409	62.82 465	37.14 434	55.33 466	71.79 346	47.34 403	55.09 420	70.52 438	21.91 469	70.45 396	35.72 437	42.97 486	70.31 461

miper_refine_blended		53.45 418	51.50 427	59.30 409	62.82 465	37.14 434	55.33 466	71.79 346	47.34 403	55.09 420	70.52 438	21.91 469	70.45 396	35.72 437	42.97 486	70.31 461

myMVS_eth3d		54.86 412	54.61 405	55.61 436	74.69 300	27.31 494	65.52 402	57.49 456	50.97 347	56.52 403	72.18 421	21.87 471	68.09 409	27.70 479	64.59 390	71.44 450

OurMVSNet-221017-0		61.37 350	58.63 365	69.61 282	72.05 358	48.06 308	73.93 281	72.51 338	47.23 405	54.74 424	80.92 290	21.49 472	81.24 232	48.57 314	56.22 452	79.53 348

testing3		56.54 392	55.92 392	58.41 419	77.52 219	27.93 491	69.72 361	56.36 461	54.75 276	58.63 378	77.80 352	20.88 473	71.75 388	25.31 488	62.25 419	75.53 401

ITE_SJBPF				62.09 389	66.16 450	44.55 351		64.32 411	47.36 402	55.31 416	80.34 300	19.27 474	62.68 444	36.29 434	62.39 417	79.04 355

usedtu_dtu_shiyan2		53.34 421	50.78 430	61.00 402	61.86 472	39.63 409	68.47 377	64.58 409	42.94 444	45.22 476	67.61 462	19.25 475	66.71 422	28.08 477	59.05 441	76.66 389

AllTest		57.08 389	54.65 404	64.39 370	71.44 370	49.03 284	69.92 360	67.30 383	45.97 418	47.16 469	79.77 311	17.47 476	67.56 416	33.65 444	59.16 439	76.57 390

TestCases				64.39 370	71.44 370	49.03 284		67.30 383	45.97 418	47.16 469	79.77 311	17.47 476	67.56 416	33.65 444	59.16 439	76.57 390

mvs5depth		55.64 403	53.81 415	61.11 400	59.39 483	40.98 397	65.89 397	68.28 378	50.21 355	58.11 386	75.42 396	17.03 478	67.63 415	43.79 374	46.21 480	74.73 414

Anonymous20240521		55.30 405	54.41 408	57.96 425	60.92 480	41.73 386	71.09 340	71.06 351	41.18 454	48.65 465	73.31 414	16.93 479	59.25 457	42.54 386	64.01 393	72.90 428

dongtai		34.52 463	34.94 463	33.26 486	61.06 477	16.00 512	52.79 476	23.78 514	40.71 458	39.33 492	48.65 502	16.91 480	48.34 494	12.18 507	19.05 506	35.44 506

test_fmvs1		51.32 432	50.48 432	53.81 447	53.57 490	37.51 431	60.63 446	51.16 476	28.02 487	63.62 304	69.23 454	16.41 481	53.93 483	51.01 293	60.70 431	69.99 464

XVG-ACMP-BASELINE		64.36 303	62.23 318	70.74 260	72.35 353	52.45 206	70.80 346	78.45 218	53.84 294	59.87 359	81.10 285	16.24 482	79.32 279	55.64 255	71.76 299	80.47 323

kuosan		29.62 470	30.82 469	26.02 491	52.99 491	16.22 511	51.09 479	22.71 515	33.91 477	33.99 496	40.85 504	15.89 483	33.11 510	7.59 521	18.37 507	28.72 508

tmp_tt		9.43 484	11.14 485	4.30 509	2.38 539	4.40 525	13.62 513	16.08 518	0.39 529	15.89 510	13.06 526	15.80 484	5.54 527	12.63 506	10.46 514	2.95 527

USDC		56.35 396	54.24 411	62.69 385	64.74 457	40.31 402	65.05 411	73.83 322	43.93 436	47.58 467	77.71 356	15.36 485	75.05 368	38.19 417	61.81 423	72.70 430

test_fmvs1_n		51.37 430	50.35 433	54.42 444	52.85 492	37.71 429	61.16 442	51.93 473	28.15 485	63.81 303	69.73 450	13.72 486	53.95 482	51.16 292	60.65 432	71.59 447

test_vis1_n		49.89 437	48.69 439	53.50 450	53.97 489	37.38 432	61.53 436	47.33 490	28.54 484	59.62 364	67.10 467	13.52 487	52.27 488	49.07 309	57.52 445	70.84 457

EGC-MVSNET		42.47 451	38.48 459	54.46 443	74.33 312	48.73 293	70.33 355	51.10 477	0.03 556	0.18 555	67.78 461	13.28 488	66.49 424	18.91 499	50.36 474	48.15 494

MVStest1		42.65 450	39.29 457	52.71 456	47.26 502	34.58 459	54.41 471	50.84 481	23.35 493	39.31 493	74.08 409	12.57 489	55.09 479	23.32 490	28.47 500	68.47 472

ANet_high		41.38 454	37.47 461	53.11 453	39.73 510	24.45 502	56.94 462	69.69 363	47.65 397	26.04 502	52.32 491	12.44 490	62.38 445	21.80 493	10.61 513	72.49 434

FPMVS		42.18 452	41.11 454	45.39 469	58.03 487	41.01 395	49.50 484	53.81 472	30.07 482	33.71 497	64.03 475	11.69 491	52.08 490	14.01 503	55.11 456	43.09 498

TinyColmap		54.14 413	51.72 425	61.40 396	66.84 444	41.97 383	66.52 393	68.51 376	44.81 425	42.69 484	75.77 390	11.66 492	72.94 377	31.96 454	56.77 450	69.27 469

test_fmvs2		48.69 439	47.49 444	52.29 459	48.63 499	33.06 472	57.76 458	48.05 488	25.71 491	59.76 362	69.60 452	11.57 493	52.23 489	49.45 307	56.86 448	71.58 448

TDRefinement		53.44 420	50.72 431	61.60 393	64.31 460	46.96 322	70.89 342	65.27 404	41.78 449	44.61 479	77.98 342	11.52 494	66.36 425	28.57 476	51.59 470	71.49 449

ambc				65.13 365	63.72 463	37.07 436	47.66 490	78.78 202		54.37 431	71.42 429	11.24 495	80.94 242	45.64 348	53.85 464	77.38 378

test_vis1_rt		41.35 455	39.45 456	47.03 468	46.65 503	37.86 426	47.76 488	38.65 502	23.10 495	44.21 481	51.22 496	11.20 496	44.08 499	39.27 410	53.02 465	59.14 482

pmmvs3		44.92 446	41.95 453	53.86 446	52.58 494	43.55 361	62.11 434	46.90 492	26.05 490	40.63 486	60.19 482	11.08 497	57.91 465	31.83 459	46.15 481	60.11 480

new_pmnet		34.13 464	34.29 465	33.64 485	52.63 493	18.23 510	44.43 496	33.90 507	22.81 496	30.89 499	53.18 490	10.48 498	35.72 509	20.77 496	39.51 490	46.98 497

LF4IMVS		42.95 449	42.26 451	45.04 470	48.30 500	32.50 474	54.80 469	48.49 484	28.03 486	40.51 487	70.16 441	9.24 499	43.89 500	31.63 460	49.18 478	58.72 483

PM-MVS		52.33 425	50.19 434	58.75 417	62.10 470	45.14 342	65.75 398	40.38 501	43.60 437	53.52 440	72.65 417	9.16 500	65.87 430	50.41 297	54.18 460	65.24 477

ttmdpeth		45.56 444	42.95 449	53.39 452	52.33 495	29.15 486	57.77 457	48.20 487	31.81 480	49.86 462	77.21 362	8.69 501	59.16 458	27.31 480	33.40 498	71.84 445

EMVS		22.97 473	21.84 477	26.36 490	40.20 509	19.53 509	41.95 499	34.64 506	17.09 503	9.73 520	22.83 518	7.29 502	42.22 504	9.18 516	13.66 511	17.32 514

E-PMN		23.77 472	22.73 476	26.90 489	42.02 506	20.67 507	42.66 498	35.70 505	17.43 502	10.28 519	25.05 516	6.42 503	42.39 503	10.28 513	14.71 509	17.63 513

test_method		19.68 477	18.10 480	24.41 493	13.68 522	3.11 532	12.06 515	42.37 500	2.00 521	11.97 515	36.38 507	5.77 504	29.35 513	15.06 501	23.65 503	40.76 502

mvsany_test3		32.62 465	30.57 470	38.77 481	36.16 513	24.20 503	38.10 502	20.63 516	19.14 501	40.36 489	57.43 487	5.06 505	36.63 508	29.59 473	28.66 499	55.49 488

test_f		31.86 467	31.05 468	34.28 484	32.33 516	21.86 506	32.34 504	30.46 509	16.02 505	39.78 491	55.45 489	4.80 506	32.36 511	30.61 466	37.66 493	48.64 492

Gipuma		34.77 462	31.91 467	43.33 474	62.05 471	37.87 425	20.39 508	67.03 388	23.23 494	18.41 509	25.84 515	4.24 507	62.73 443	14.71 502	51.32 471	29.38 507
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
test_fmvs3		44.30 447	42.55 450	49.55 465	42.83 504	27.15 496	53.03 474	44.93 494	22.03 499	53.69 437	64.94 474	4.21 508	49.63 492	47.47 320	49.82 475	71.88 443

PMMVS2		27.40 471	25.91 474	31.87 488	39.46 511	6.57 523	31.17 505	28.52 510	23.96 492	20.45 508	48.94 501	4.20 509	37.94 506	16.51 500	19.97 505	51.09 491

LCM-MVSNet		40.30 456	35.88 462	53.57 449	42.24 505	29.15 486	45.21 495	60.53 445	22.23 498	28.02 500	50.98 497	3.72 510	61.78 447	31.22 465	38.76 492	69.78 466

DeepMVS_CX				12.03 498	17.97 519	10.91 517		10.60 519	7.46 513	11.07 517	28.36 514	3.28 511	11.29 519	8.01 518	9.74 515	13.89 518

APD_test1		37.39 460	34.94 463	44.72 473	48.88 498	33.19 471	52.95 475	44.00 498	19.49 500	27.28 501	58.59 486	3.18 512	52.84 486	18.92 498	41.17 489	48.14 495

PMVS	28.69 22	36.22 461	33.29 466	45.02 471	36.82 512	35.98 449	54.68 470	48.74 483	26.31 489	21.02 507	51.61 494	2.88 513	60.10 453	9.99 514	47.58 479	38.99 504
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
VLMVS_CLIP		8.61 486	9.36 487	6.34 506	7.07 528	4.23 527	8.66 520	10.16 520	1.75 522	13.91 512	20.41 520	2.33 514	10.32 521	6.21 523	13.74 510	4.49 524

VLMVS		2.25 501	2.47 504	1.62 515	2.41 538	1.01 542	1.61 534	0.72 538	0.07 555	4.27 530	6.17 530	2.11 515	1.03 536	1.17 531	3.66 523	2.83 528

test_vis3_rt		32.09 466	30.20 471	37.76 482	35.36 514	27.48 492	40.60 500	28.29 511	16.69 504	32.52 498	40.53 506	1.96 516	37.40 507	33.64 446	42.21 488	48.39 493

MVE	17.77 23	21.41 474	17.77 481	32.34 487	34.34 515	25.44 500	16.11 510	24.11 513	11.19 510	13.22 513	31.92 510	1.58 517	30.95 512	10.47 512	17.03 508	40.62 503
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
testf1		31.46 468	28.89 472	39.16 479	41.99 507	28.78 488	46.45 491	37.56 503	14.28 507	21.10 505	48.96 499	1.48 518	47.11 495	13.63 504	34.56 495	41.60 500

APD_test2		31.46 468	28.89 472	39.16 479	41.99 507	28.78 488	46.45 491	37.56 503	14.28 507	21.10 505	48.96 499	1.48 518	47.11 495	13.63 504	34.56 495	41.60 500

MVS_clip		4.22 495	4.98 497	1.95 512	5.46 531	1.99 533	3.96 522	0.34 543	0.36 530	7.04 524	17.25 521	0.66 520	0.80 537	4.04 524	5.70 520	3.07 526

ArgMatch-Sym		21.00 475	19.89 478	24.35 494	23.32 517	15.10 513	32.50 503	4.90 522	11.83 509	24.09 503	51.35 495	0.56 521	19.55 515	21.24 494	9.18 516	38.40 505

ArgMatch-SfM		20.82 476	19.10 479	25.97 492	21.54 518	13.77 514	29.84 507	6.08 521	9.69 511	22.36 504	51.71 493	0.53 522	21.69 514	20.98 495	9.18 516	42.43 499

wuyk23d		13.32 480	12.52 483	15.71 496	47.54 501	26.27 498	31.06 506	1.98 526	4.93 516	5.18 527	1.94 542	0.45 523	18.54 516	6.81 522	12.83 512	2.33 529

PDCNetPlus		9.23 485	8.89 489	10.23 501	13.70 521	3.70 528	12.27 514	1.51 528	3.98 517	6.73 525	29.50 513	0.24 524	8.07 524	7.83 519	4.30 522	18.93 511

DenseAffine		14.16 479	13.16 482	17.15 495	17.01 520	8.89 521	19.68 509	2.17 525	7.89 512	15.00 511	40.64 505	0.19 525	15.28 517	11.16 509	4.69 521	27.27 509

RoMa-SfM		11.96 481	11.39 484	13.68 497	10.24 524	6.80 522	15.83 511	1.33 529	6.34 514	13.06 514	41.41 503	0.16 526	12.72 518	10.58 511	3.56 524	21.52 510

LoFTR		9.45 483	9.00 488	10.79 500	10.22 525	4.31 526	11.11 516	4.11 523	2.40 520	10.53 518	30.89 511	0.13 527	10.75 520	3.12 526	8.52 518	17.31 515

ALIKED-LG		2.35 500	2.54 503	1.78 513	5.54 530	1.79 535	3.81 523	0.96 533	0.33 531	1.86 534	7.18 528	0.13 527	1.60 532	0.20 541	2.81 526	1.94 530

RoMa-HiRes		8.28 487	8.27 491	8.28 502	6.12 529	3.67 529	10.07 518	0.74 537	3.93 518	9.17 521	34.46 509	0.12 529	7.12 525	7.80 520	2.05 531	14.04 517

DKM		10.33 482	10.10 486	11.02 499	10.54 523	5.43 524	14.18 512	1.03 532	4.97 515	11.74 516	36.09 508	0.11 530	9.09 522	9.38 515	2.85 525	18.53 512

SP-DiffGlue		0.98 506	1.05 509	0.75 521	0.81 560	0.40 550	1.24 535	0.37 541	0.19 534	1.26 539	3.80 534	0.11 530	0.34 544	0.51 533	1.18 537	1.52 535

MASt3R-SfM		3.33 498	3.70 499	2.21 511	2.02 543	1.04 541	3.52 526	1.05 531	0.67 527	4.93 528	16.68 522	0.10 532	1.50 534	2.06 528	2.29 530	4.09 525

ALIKED-NN		1.96 503	2.12 506	1.48 516	4.72 533	1.65 536	3.19 529	0.77 534	0.23 533	1.43 537	5.87 532	0.10 532	1.37 535	0.16 543	2.61 529	1.42 537

SP-SuperGlue		0.93 508	0.98 511	0.77 518	2.54 537	0.38 551	1.70 531	0.34 543	0.17 535	0.52 540	2.13 539	0.10 532	0.36 543	0.26 537	1.10 539	1.57 534

SP-LightGlue		0.94 507	0.99 510	0.78 517	2.60 536	0.38 551	1.71 530	0.34 543	0.17 535	0.50 541	2.14 538	0.09 535	0.38 541	0.26 537	1.13 538	1.59 532

ALIKED-MNN		2.09 502	2.23 505	1.67 514	5.15 532	1.82 534	3.53 525	0.77 534	0.25 532	1.45 536	6.03 531	0.09 535	1.52 533	0.17 542	2.64 528	1.66 531

DKM-HiRes		7.91 488	7.93 492	7.83 503	7.35 527	3.58 530	10.03 519	0.66 539	3.58 519	9.05 522	30.62 512	0.08 537	5.66 526	8.09 517	1.91 532	14.26 516

SP-NN		0.85 511	0.90 514	0.73 522	2.22 542	0.33 557	1.63 533	0.31 547	0.14 538	0.47 543	1.97 541	0.08 537	0.38 541	0.25 539	1.01 542	1.47 536

GLUNet-SfM		4.33 494	3.64 500	6.41 505	3.38 534	1.65 536	3.23 528	1.54 527	0.66 528	6.36 526	15.13 525	0.08 537	5.54 527	0.94 532	1.44 535	12.05 519

MatchFormer		7.03 489	6.96 493	7.26 504	7.64 526	3.36 531	10.21 517	3.04 524	1.31 523	9.02 523	22.94 517	0.08 537	8.15 523	1.46 530	6.91 519	10.26 520

SP-MNN		0.89 509	0.93 513	0.77 518	2.32 541	0.34 555	1.68 532	0.33 546	0.13 539	0.49 542	2.07 540	0.08 537	0.39 540	0.25 539	1.07 541	1.58 533

MVS_baseline		1.38 504	1.71 507	0.39 527	1.08 558	0.02 565	0.39 551	0.06 563	0.01 557	2.77 533	7.83 527	0.07 542	0.00 559	0.47 534	2.72 527	1.14 539

XFeat-MNN		1.07 505	1.17 508	0.77 518	0.52 561	0.31 558	1.15 536	0.41 540	0.15 537	1.62 535	4.35 533	0.07 542	0.77 538	0.38 535	1.88 533	1.22 538

XFeat-NN		0.87 510	0.97 512	0.59 523	0.48 562	0.24 561	0.94 537	0.29 548	0.12 540	1.41 538	3.45 537	0.06 544	0.56 539	0.29 536	1.65 534	0.95 540

ELoFTR		4.04 496	3.55 501	5.50 507	2.33 540	1.25 540	3.58 524	1.18 530	0.90 525	4.23 531	16.28 523	0.03 545	5.46 529	1.95 529	1.42 536	9.81 521

SIFT-UM-Cal		0.41 522	0.46 524	0.28 535	1.35 552	0.29 559	0.57 547	0.08 560	0.09 544	0.20 553	1.10 552	0.02 546	0.23 554	0.03 552	0.68 553	0.30 553

SIFT-NCM-Cal		0.51 515	0.55 518	0.38 528	1.66 547	0.45 547	0.75 541	0.12 553	0.09 544	0.21 552	1.18 550	0.02 546	0.27 547	0.03 552	0.89 546	0.43 547

SIFT-CM-Cal		0.42 521	0.46 524	0.31 534	1.40 551	0.35 554	0.56 548	0.09 559	0.09 544	0.20 553	1.09 553	0.02 546	0.23 554	0.03 552	0.66 554	0.34 551

SIFT-NN-UMatch		0.48 517	0.52 520	0.36 530	1.27 554	0.36 553	0.75 541	0.12 553	0.10 541	0.25 549	1.29 545	0.02 546	0.26 549	0.04 544	0.85 547	0.44 545

SIFT-NN-NCMNet		0.53 514	0.58 517	0.40 526	1.60 548	0.49 546	0.80 540	0.15 552	0.09 544	0.28 547	1.29 545	0.02 546	0.27 547	0.04 544	0.94 544	0.44 545

SIFT-NN-CMatch		0.49 516	0.53 519	0.38 528	1.35 552	0.41 549	0.70 543	0.12 553	0.09 544	0.30 545	1.28 547	0.02 546	0.26 549	0.04 544	0.83 548	0.47 543

SIFT-NN-PointCN		0.44 520	0.47 523	0.33 532	1.17 555	0.29 559	0.64 545	0.11 556	0.09 544	0.25 549	1.14 551	0.02 546	0.25 551	0.03 552	0.78 549	0.46 544

SIFT-NN		0.60 512	0.65 515	0.45 524	1.90 545	0.55 544	0.90 538	0.16 550	0.10 541	0.34 544	1.43 543	0.02 546	0.28 545	0.04 544	0.95 543	0.50 541

SIFT-UMatch		0.45 519	0.50 522	0.32 533	1.46 550	0.34 555	0.66 544	0.10 558	0.09 544	0.22 551	1.19 549	0.02 546	0.25 551	0.04 544	0.73 551	0.36 550

SIFT-ConvMatch		0.48 517	0.52 520	0.35 531	1.51 549	0.42 548	0.64 545	0.11 556	0.09 544	0.26 548	1.24 548	0.02 546	0.25 551	0.04 544	0.76 550	0.38 548

SIFT-MNN		0.56 513	0.61 516	0.43 525	1.75 546	0.50 545	0.82 539	0.16 550	0.10 541	0.30 545	1.38 544	0.02 546	0.28 545	0.04 544	0.92 545	0.50 541

PMatch-Up-SfM		3.14 499	3.26 502	2.81 510	1.97 544	1.00 543	3.35 527	0.23 549	0.79 526	3.44 532	16.19 524	0.01 557	2.11 531	2.62 527	0.70 552	5.32 523

PMatch-SfM		4.42 493	4.43 498	4.39 508	2.90 535	1.50 539	4.85 521	0.36 542	1.17 524	4.73 529	20.99 519	0.01 557	3.26 530	3.74 525	1.10 539	8.40 522

SIFT-PCN-Cal		0.36 523	0.39 526	0.26 536	1.16 556	0.21 562	0.46 550	0.07 562	0.08 552	0.17 556	0.92 554	0.01 557	0.20 557	0.03 552	0.59 556	0.37 549

SIFT-NCMNet		0.30 525	0.33 528	0.19 538	1.04 559	0.18 564	0.39 551	0.05 564	0.08 552	0.14 558	0.77 556	0.01 557	0.16 558	0.02 559	0.49 557	0.22 554

SIFT-PointCN		0.36 523	0.39 526	0.25 537	1.14 557	0.21 562	0.50 549	0.08 560	0.08 552	0.17 556	0.89 555	0.01 557	0.21 556	0.03 552	0.60 555	0.34 551

test123		4.73 491	6.30 494	0.02 539	0.01 563	0.01 566	56.36 464	0.00 565	0.01 557	0.04 559	0.21 558	0.01 557	0.00 559	0.03 552	0.00 558	0.04 555

mmdepth		0.00 526	0.00 529	0.00 541	0.00 565	0.00 567	0.00 553	0.00 565	0.00 560	0.00 561	0.00 559	0.00 563	0.00 559	0.00 560	0.00 558	0.00 557

monomultidepth		0.00 526	0.00 529	0.00 541	0.00 565	0.00 567	0.00 553	0.00 565	0.00 560	0.00 561	0.00 559	0.00 563	0.00 559	0.00 560	0.00 558	0.00 557

test_blank		0.00 526	0.00 529	0.00 541	0.00 565	0.00 567	0.00 553	0.00 565	0.00 560	0.00 561	0.00 559	0.00 563	0.00 559	0.00 560	0.00 558	0.00 557

uanet_test		0.00 526	0.00 529	0.00 541	0.00 565	0.00 567	0.00 553	0.00 565	0.00 560	0.00 561	0.00 559	0.00 563	0.00 559	0.00 560	0.00 558	0.00 557

DCPMVS		0.00 526	0.00 529	0.00 541	0.00 565	0.00 567	0.00 553	0.00 565	0.00 560	0.00 561	0.00 559	0.00 563	0.00 559	0.00 560	0.00 558	0.00 557

sosnet-low-res		0.00 526	0.00 529	0.00 541	0.00 565	0.00 567	0.00 553	0.00 565	0.00 560	0.00 561	0.00 559	0.00 563	0.00 559	0.00 560	0.00 558	0.00 557

sosnet		0.00 526	0.00 529	0.00 541	0.00 565	0.00 567	0.00 553	0.00 565	0.00 560	0.00 561	0.00 559	0.00 563	0.00 559	0.00 560	0.00 558	0.00 557

uncertanet		0.00 526	0.00 529	0.00 541	0.00 565	0.00 567	0.00 553	0.00 565	0.00 560	0.00 561	0.00 559	0.00 563	0.00 559	0.00 560	0.00 558	0.00 557

Regformer		0.00 526	0.00 529	0.00 541	0.00 565	0.00 567	0.00 553	0.00 565	0.00 560	0.00 561	0.00 559	0.00 563	0.00 559	0.00 560	0.00 558	0.00 557

testmvs		4.52 492	6.03 495	0.01 540	0.01 563	0.00 567	53.86 473	0.00 565	0.01 557	0.04 559	0.27 557	0.00 563	0.00 559	0.04 544	0.00 558	0.03 556

ab-mvs-re		6.49 490	8.65 490	0.00 541	0.00 565	0.00 567	0.00 553	0.00 565	0.00 560	0.00 561	77.89 349	0.00 563	0.00 559	0.00 560	0.00 558	0.00 557

uanet		0.00 526	0.00 529	0.00 541	0.00 565	0.00 567	0.00 553	0.00 565	0.00 560	0.00 561	0.00 559	0.00 563	0.00 559	0.00 560	0.00 558	0.00 557

PatchmatchNet2					0.00 565	13.27 515	48.02 487	44.92 495	34.52 476
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. CVPR 2021
PatchmatchNet1														25.92 487	51.90 468	65.44 475
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. CVPR 2021
PatchmatchNet3													42.51 502
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. CVPR 2021
aaatest				79.09 23	85.30 51	59.25 64	86.84 11	85.86 24	60.95 107	83.65 12	90.57 27		89.91 16	77.02 35	89.43 24	88.10 45

WAC-MVS						27.31 494								27.77 478

FOURS1					86.12 38	60.82 37	88.18 1	83.61 85	60.87 109	81.50 21

MSC_two_6792asdad				79.95 4	87.24 14	61.04 31		85.62 31					90.96 1	79.31 10	90.65 8	87.85 55

No_MVS				79.95 4	87.24 14	61.04 31		85.62 31					90.96 1	79.31 10	90.65 8	87.85 55

eth-test2					0.00 565

eth-test					0.00 565

IU-MVS					87.77 4	59.15 69		85.53 33	53.93 291	84.64 3				79.07 13	90.87 5	88.37 34

save fliter					86.17 35	61.30 28	83.98 58	79.66 182	59.00 160

test_0728_SECOND				79.19 16	87.82 3	59.11 72	87.85 5	87.15 3					90.84 3	78.66 18	90.61 11	87.62 66

GSMVS																78.05 367

test_part2					87.58 9	60.47 42				83.42 14

MTGPA								80.97 161

MTMP							86.03 23	17.08 517

gm-plane-assit					71.40 373	41.72 388			48.85 375		73.31 414		82.48 206	48.90 311

test9_res														75.28 55	88.31 36	83.81 234

agg_prior2														73.09 73	87.93 44	84.33 211

agg_prior					85.04 55	59.96 50		81.04 159		74.68 76			84.04 152

test_prior4						62.51 14	82.08 87

test_prior				76.69 67	84.20 67	57.27 100		84.88 46					86.43 92			86.38 119

旧先验2							76.08 227		45.32 423	76.55 49			65.56 431	58.75 229

新几何2							76.12 225

无先验							79.66 123	74.30 313	48.40 383				80.78 249	53.62 271		79.03 356

原ACMM2							79.02 131

testdata2													72.18 386	46.95 336

testdata1							72.65 307		60.50 119

plane_prior7					81.41 103	55.96 123

plane_prior5								84.01 60					87.21 66	68.16 113	80.58 129	84.65 202

plane_prior4											86.10 151

plane_prior3						56.09 120			63.92 39	69.27 184

plane_prior2							84.22 51		64.52 28

plane_prior1					81.27 108

plane_prior						56.31 114	83.58 64		63.19 56						80.48 132

n2								0.00 565

nn								0.00 565

door-mid								47.19 491

test11								83.47 89

door								47.60 489

HQP5-MVS						54.94 145

HQP-NCC					80.66 117		82.31 82		62.10 83	67.85 216

ACMP_Plane					80.66 117		82.31 82		62.10 83	67.85 216

BP-MVS														67.04 135

HQP4-MVS										67.85 216			86.93 74			84.32 212

HQP3-MVS								83.90 65							80.35 134

NP-MVS					80.98 113	56.05 122					85.54 174

ACMMP++_ref															74.07 255

ACMMP++															72.16 296

High-resolution multi-view benchmark