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.

fmvsm_s_conf0.5_n_9		97.33 27	97.57 15	96.62 102	98.43 84	90.32 207	97.80 105	98.53 29	97.24 4	99.62 2	99.14 2	88.65 110	99.80 41	99.54 1	99.15 94	99.74 10

MM		97.29 31	96.98 42	98.23 13	98.01 125	95.03 29	98.07 61	95.76 367	97.78 1	97.52 64	98.80 40	88.09 120	99.86 11	99.44 2	99.37 67	99.80 3

fmvsm_s_conf0.5_n_8		97.32 28	97.48 23	96.85 89	98.28 96	91.07 171	97.76 109	98.62 25	97.53 2	99.20 12	99.12 5	88.24 118	99.81 36	99.41 3	99.17 91	99.67 16

fmvsm_s_conf0.5_n_10		97.29 31	97.40 26	96.97 87	98.24 102	91.96 128	97.89 89	98.72 12	96.77 7	99.46 3	99.06 12	87.78 128	99.84 27	99.40 4	99.27 75	99.12 94

fmvsm_s_conf0.5_n_2		96.62 70	96.82 55	96.02 155	97.98 127	90.43 199	97.50 157	98.59 26	96.59 10	99.31 6	99.08 8	84.47 212	99.75 59	99.37 5	98.45 134	97.88 251

fmvsm_l_conf0.5_n_9		97.59 13	97.79 6	96.97 87	98.28 96	91.49 146	97.61 141	98.71 13	97.10 5	99.70 1	98.93 24	90.95 77	99.77 53	99.35 6	99.53 33	99.65 21

fmvsm_s_conf0.5_n_3		97.15 36	97.36 28	96.52 108	97.98 127	91.19 163	97.84 96	98.65 23	97.08 6	99.25 9	99.10 6	87.88 126	99.79 47	99.32 7	99.18 90	98.59 180

fmvsm_l_conf0.5_n_a		97.63 11	97.76 7	97.26 69	98.25 101	92.59 102	97.81 104	98.68 18	94.93 50	99.24 10	98.87 33	93.52 23	99.79 47	99.32 7	99.21 83	99.40 66

fmvsm_l_conf0.5_n		97.65 9	97.75 8	97.34 62	98.21 108	92.75 94	97.83 99	98.73 10	95.04 47	99.30 7	98.84 38	93.34 26	99.78 50	99.32 7	99.13 97	99.50 52

fmvsm_s_conf0.5_n_11		97.30 29	97.59 14	96.43 120	98.42 85	91.37 153	98.04 64	98.00 118	97.30 3	99.45 4	99.21 1	89.28 98	99.80 41	99.27 10	99.35 69	98.12 230

fmvsm_s_conf0.1_n_2		96.33 84	96.44 79	96.00 159	97.30 169	90.37 205	97.53 153	97.92 128	96.52 11	99.14 15	99.08 8	83.21 236	99.74 60	99.22 11	98.06 152	97.88 251

fmvsm_s_conf0.5_n_5		97.00 45	96.97 43	97.09 80	97.58 162	92.56 103	97.68 126	98.47 34	94.02 96	98.90 26	98.89 30	88.94 104	99.78 50	99.18 12	99.03 106	98.93 129

fmvsm_s_conf0.5_n_4		96.75 62	97.07 34	95.79 178	97.76 143	89.57 239	97.66 130	98.66 21	95.36 32	99.03 16	98.90 27	88.39 115	99.73 62	99.17 13	98.66 122	98.08 238

fmvsm_l_conf0.5_n_3		97.64 10	97.60 13	97.79 35	98.14 115	93.94 58	97.93 84	98.65 23	96.70 8	99.38 5	99.07 11	89.92 92	99.81 36	99.16 14	99.43 53	99.61 30

test_fmvsm_n_1920		97.55 16	97.89 4	96.53 106	98.41 87	91.73 132	98.01 67	99.02 1	96.37 13	99.30 7	98.92 25	92.39 45	99.79 47	99.16 14	99.46 46	98.08 238

test_fmvsmconf_n		97.49 21	97.56 16	97.29 65	97.44 166	92.37 109	97.91 86	98.88 4	95.83 19	98.92 24	99.05 14	91.45 62	99.80 41	99.12 16	99.46 46	99.69 15

fmvsm_s_conf0.5_n_6		97.08 39	97.17 30	96.81 90	97.28 171	91.73 132	97.75 111	98.50 30	94.86 54	99.22 11	98.78 42	89.75 95	99.76 55	99.10 17	99.29 73	98.94 125

fmvsm_s_conf0.5_n		96.85 54	97.13 31	96.04 152	98.07 122	90.28 208	97.97 78	98.76 9	94.93 50	98.84 29	99.06 12	88.80 107	99.65 80	99.06 18	98.63 124	98.18 223

test_fmvsmconf0.1_n		97.09 38	97.06 35	97.19 74	95.67 318	92.21 116	97.95 81	98.27 55	95.78 23	98.40 42	99.00 16	89.99 90	99.78 50	99.06 18	99.41 59	99.59 32

MGCNet		96.74 64	96.31 81	98.02 22	96.87 207	94.65 33	97.58 143	94.39 436	96.47 12	97.16 76	98.39 68	87.53 137	99.87 8	98.97 20	99.41 59	99.55 43

fmvsm_s_conf0.5_n_a		96.75 62	96.93 46	96.20 141	97.64 152	90.72 189	98.00 68	98.73 10	94.55 75	98.91 25	99.08 8	88.22 119	99.63 89	98.91 21	98.37 138	98.25 218

test_fmvsmvis_n_1920		96.70 65	96.84 51	96.31 130	96.62 235	91.73 132	97.98 72	98.30 48	96.19 14	96.10 127	98.95 20	89.42 96	99.76 55	98.90 22	99.08 101	97.43 278

fmvsm_s_conf0.1_n		96.58 73	96.77 60	96.01 158	96.67 233	90.25 209	97.91 86	98.38 37	94.48 79	98.84 29	99.14 2	88.06 121	99.62 91	98.82 23	98.60 126	98.15 227

test_fmvsmconf0.01_n		96.15 88	95.85 91	97.03 84	92.66 444	91.83 131	97.97 78	97.84 143	95.57 28	97.53 63	99.00 16	84.20 219	99.76 55	98.82 23	99.08 101	99.48 56

fmvsm_s_conf0.5_n_7		96.45 77	96.80 57	95.37 214	97.29 170	88.38 295	97.23 199	98.47 34	95.14 41	98.43 41	99.09 7	87.58 134	99.72 66	98.80 25	99.21 83	98.02 242

fmvsm_s_conf0.1_n_a		96.40 79	96.47 73	96.16 143	95.48 327	90.69 190	97.91 86	98.33 45	94.07 94	98.93 21	99.14 2	87.44 142	99.61 92	98.63 26	98.32 140	98.18 223

MVSMamba_PlusPlus		96.51 74	96.48 72	96.59 103	98.07 122	91.97 126	98.14 55	97.79 147	90.43 271	97.34 72	97.52 177	91.29 68	99.19 157	98.12 27	99.64 14	98.60 179

test-260524					99.31 29	95.74 9		98.19 74		97.99 52		93.53 22	99.87 8	98.08 28	99.63 16

APDe-MVS		97.82 6	97.73 9	98.08 20	99.15 40	94.82 31	98.81 8	98.30 48	94.76 66	98.30 43	98.90 27	93.77 19	99.68 76	97.93 29	99.69 3	99.75 8
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
test_vis1_n_1920		94.17 175	94.58 149	92.91 364	97.42 167	82.02 436	97.83 99	97.85 138	94.68 69	98.10 49	98.49 58	70.15 423	99.32 143	97.91 30	98.82 114	97.40 280

reproduce_model		97.51 20	97.51 20	97.50 55	98.99 53	93.01 84	97.79 107	98.21 67	95.73 24	97.99 52	99.03 15	92.63 40	99.82 34	97.80 31	99.42 56	99.67 16

BridgeMVS		96.84 56	96.89 48	96.68 94	97.63 154	92.22 115	98.17 54	97.82 145	94.44 81	98.23 45	97.36 187	90.97 76	99.22 154	97.74 32	99.66 10	98.61 178

reproduce-ours		97.53 18	97.51 20	97.60 52	98.97 54	93.31 75	97.71 122	98.20 69	95.80 21	97.88 57	98.98 18	92.91 32	99.81 36	97.68 33	99.43 53	99.67 16

our_new_method		97.53 18	97.51 20	97.60 52	98.97 54	93.31 75	97.71 122	98.20 69	95.80 21	97.88 57	98.98 18	92.91 32	99.81 36	97.68 33	99.43 53	99.67 16

MSC_two_6792asdad				98.86 1	98.67 68	96.94 1		97.93 126					99.86 11	97.68 33	99.67 6	99.77 4

No_MVS				98.86 1	98.67 68	96.94 1		97.93 126					99.86 11	97.68 33	99.67 6	99.77 4

patch_mono-2		96.83 57	97.44 24	95.01 234	99.05 46	85.39 388	96.98 222	98.77 8	94.70 68	97.99 52	98.66 45	93.61 21	99.91 1	97.67 37	99.50 40	99.72 14

lecture		97.58 15	97.63 11	97.43 59	99.37 19	92.93 88	98.86 7	98.85 5	95.27 36	98.65 36	98.90 27	91.97 53	99.80 41	97.63 38	99.21 83	99.57 36

test_vis1_n		92.37 259	92.26 243	92.72 372	94.75 377	82.64 426	98.02 66	96.80 306	91.18 234	97.77 61	97.93 114	58.02 481	98.29 300	97.63 38	98.21 145	97.23 289

test_fmvs1_n		92.73 248	92.88 216	92.29 385	96.08 302	81.05 444	97.98 72	97.08 268	90.72 253	96.79 89	98.18 91	63.07 470	98.45 282	97.62 40	98.42 136	97.36 281

test_fmvs1		93.21 222	93.53 187	92.25 388	96.55 251	81.20 443	97.40 176	96.96 288	90.68 255	96.80 87	98.04 101	69.25 431	98.40 285	97.58 41	98.50 129	97.16 292

SED-MVS		98.05 3	97.99 2	98.24 12	99.42 10	95.30 19	98.25 40	98.27 55	95.13 42	99.19 13	98.89 30	95.54 5	99.85 22	97.52 42	99.66 10	99.56 40

test_241102_TWO								98.27 55	95.13 42	98.93 21	98.89 30	94.99 12	99.85 22	97.52 42	99.65 13	99.74 10

DVP-MVS		97.91 4	97.81 5	98.22 15	99.45 6	95.36 15	98.21 48	97.85 138	94.92 52	98.73 31	98.87 33	95.08 9	99.84 27	97.52 42	99.67 6	99.48 56
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
test_0728_SECOND				98.51 4	99.45 6	95.93 6	98.21 48	98.28 52					99.86 11	97.52 42	99.67 6	99.75 8

DVP-MVS++		98.06 2	97.99 2	98.28 10	98.67 68	95.39 13	99.29 1	98.28 52	94.78 63	98.93 21	98.87 33	96.04 2	99.86 11	97.45 46	99.58 25	99.59 32

test_0728_THIRD									94.78 63	98.73 31	98.87 33	95.87 4	99.84 27	97.45 46	99.72 2	99.77 4

EC-MVSNet		96.42 78	96.47 73	96.26 136	97.01 195	91.52 145	98.89 5	97.75 150	94.42 82	96.64 98	97.68 156	89.32 97	98.60 268	97.45 46	99.11 100	98.67 175

IU-MVS					99.42 10	95.39 13		97.94 125	90.40 273	98.94 20				97.41 49	99.66 10	99.74 10

aaatest				98.00 25	99.56 1	94.50 37	98.69 11	98.70 16	93.45 124	98.73 31	98.53 53		99.86 11	97.40 50	99.58 25	99.65 21

MED-MVS		98.08 1	98.08 1	98.06 21	99.56 1	94.50 37	98.69 11	98.70 16	95.63 25	98.73 31	98.95 20	95.46 7	99.86 11	97.40 50	99.63 16	99.82 1

aaEdge-Enhanced		97.54 17	97.39 27	98.00 25	99.21 37	94.50 37	97.75 111	98.34 44	94.23 89	98.15 47	98.53 53	93.32 29	99.84 27	97.40 50	99.58 25	99.65 21

balanced_ft_v1		95.56 110	95.40 105	96.07 149	97.16 178	90.36 206	98.23 44	97.31 238	92.89 157	96.36 116	97.11 206	83.28 234	99.26 150	97.40 50	98.80 116	98.58 181

mmtdpeth		89.70 373	88.96 371	91.90 397	95.84 313	84.42 404	97.46 168	95.53 386	90.27 274	94.46 196	90.50 455	69.74 429	98.95 196	97.39 54	69.48 492	92.34 466

dcpmvs_2		96.37 81	97.05 38	94.31 285	98.96 56	84.11 409	97.56 147	97.51 195	93.92 100	97.43 69	98.52 55	92.75 36	99.32 143	97.32 55	99.50 40	99.51 49

CS-MVS		96.86 52	97.06 35	96.26 136	98.16 114	91.16 168	99.09 3	97.87 133	95.30 35	97.06 82	98.03 103	91.72 55	98.71 246	97.10 56	99.17 91	98.90 134

TSAR-MVS + MP.		97.42 22	97.33 29	97.69 47	99.25 33	94.24 47	98.07 61	97.85 138	93.72 107	98.57 37	98.35 72	93.69 20	99.40 135	97.06 57	99.46 46	99.44 61
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
CNVR-MVS		97.68 8	97.44 24	98.37 7	98.90 60	95.86 7	97.27 193	98.08 94	95.81 20	97.87 60	98.31 81	94.26 14	99.68 76	97.02 58	99.49 43	99.57 36

SD-MVS		97.41 23	97.53 18	97.06 83	98.57 79	94.46 40	97.92 85	98.14 84	94.82 59	99.01 17	98.55 51	94.18 15	97.41 414	96.94 59	99.64 14	99.32 74
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
SPE-MVS-test		96.89 50	97.04 39	96.45 119	98.29 95	91.66 139	99.03 4	97.85 138	95.84 18	96.90 85	97.97 111	91.24 69	98.75 235	96.92 60	99.33 70	98.94 125

CANet		96.39 80	96.02 87	97.50 55	97.62 155	93.38 70	97.02 215	97.96 123	95.42 31	94.86 181	97.81 140	87.38 144	99.82 34	96.88 61	99.20 88	99.29 75

AstraMVS		94.82 154	94.64 145	95.34 217	96.36 273	88.09 312	97.58 143	94.56 428	94.98 48	95.70 146	97.92 117	81.93 274	98.93 199	96.87 62	95.88 240	98.99 114

TSAR-MVS + GP.		96.69 67	96.49 71	97.27 68	98.31 94	93.39 69	96.79 246	96.72 309	94.17 90	97.44 67	97.66 159	92.76 35	99.33 141	96.86 63	97.76 164	99.08 100

DeepPCF-MVS	93.97 1	96.61 71	97.09 33	95.15 225	98.09 118	86.63 354	96.00 326	98.15 82	95.43 30	97.95 55	98.56 49	93.40 25	99.36 139	96.77 64	99.48 44	99.45 59

BP-MVS1		95.89 98	95.49 98	97.08 82	96.67 233	93.20 79	98.08 59	96.32 335	94.56 74	96.32 117	97.84 134	84.07 222	99.15 166	96.75 65	98.78 117	98.90 134

test_cas_vis1_n_1920		94.48 167	94.55 153	94.28 287	96.78 224	86.45 360	97.63 137	97.64 165	93.32 130	97.68 62	98.36 71	73.75 392	99.08 180	96.73 66	99.05 103	97.31 285

SMA-MVS		97.35 25	97.03 40	98.30 9	99.06 45	95.42 12	97.94 82	98.18 77	90.57 266	98.85 28	98.94 23	93.33 27	99.83 32	96.72 67	99.68 4	99.63 26
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
guyue		95.17 131	94.96 127	95.82 173	96.97 199	89.65 234	97.56 147	95.58 379	94.82 59	95.72 143	97.42 183	82.90 248	98.84 210	96.71 68	96.93 197	98.96 118

DPE-MVS		97.86 5	97.65 10	98.47 5	99.17 39	95.78 8	97.21 202	98.35 41	95.16 40	98.71 35	98.80 40	95.05 11	99.89 3	96.70 69	99.73 1	99.73 13
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
PRO-TEST		94.38 168	94.94 128	92.69 374	97.21 175	80.23 458	97.52 155	97.02 284	93.62 111	94.32 199	97.21 198	81.92 275	99.15 166	96.65 70	99.00 108	98.70 172

TestfortrainingZip a		97.79 7	97.62 12	98.28 10	99.56 1	95.15 25	98.69 11	98.35 41	95.63 25	98.95 19	98.95 20	93.45 24	99.88 4	96.63 71	98.41 137	99.82 1

MSP-MVS		97.59 13	97.54 17	97.73 43	99.40 14	93.77 63	98.53 19	98.29 50	95.55 29	98.56 38	97.81 140	93.90 17	99.65 80	96.62 72	99.21 83	99.77 4
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
MSLP-MVS++		96.94 48	97.06 35	96.59 103	98.72 65	91.86 130	97.67 127	98.49 31	94.66 71	97.24 74	98.41 67	92.31 48	98.94 198	96.61 73	99.46 46	98.96 118

MP-MVS-pluss		96.70 65	96.27 83	97.98 27	99.23 36	94.71 32	96.96 224	98.06 102	90.67 256	95.55 153	98.78 42	91.07 73	99.86 11	96.58 74	99.55 30	99.38 70
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
SteuartSystems-ACMMP		97.62 12	97.53 18	97.87 29	98.39 90	94.25 46	98.43 27	98.27 55	95.34 34	98.11 48	98.56 49	94.53 13	99.71 68	96.57 75	99.62 19	99.65 21
Skip Steuart: Steuart Systems R&D Blog.
MCST-MVS		97.18 34	96.84 51	98.20 16	99.30 30	95.35 17	97.12 209	98.07 99	93.54 118	96.08 128	97.69 155	93.86 18	99.71 68	96.50 76	99.39 63	99.55 43

SF-MVS		97.39 24	97.13 31	98.17 17	99.02 49	95.28 21	98.23 44	98.27 55	92.37 178	98.27 44	98.65 47	93.33 27	99.72 66	96.49 77	99.52 35	99.51 49

EI-MVSNet-Vis-set		96.51 74	96.47 73	96.63 99	98.24 102	91.20 162	96.89 232	97.73 153	94.74 67	96.49 108	98.49 58	90.88 80	99.58 100	96.44 78	98.32 140	99.13 91

LuminaMVS		94.89 148	94.35 162	96.53 106	95.48 327	92.80 93	96.88 234	96.18 352	92.85 158	95.92 136	96.87 225	81.44 282	98.83 211	96.43 79	97.10 192	97.94 247

diffmvs_AUTHOR		95.33 116	95.27 112	95.50 206	96.37 272	89.08 266	96.08 319	97.38 228	93.09 143	96.53 106	97.74 149	86.45 162	98.68 250	96.32 80	97.48 170	98.75 165

VDD-MVS		93.82 198	93.08 207	96.02 155	97.88 136	89.96 223	97.72 119	95.85 363	92.43 176	95.86 138	98.44 64	68.42 440	99.39 136	96.31 81	94.85 266	98.71 171

ACMMP_NAP		97.20 33	96.86 49	98.23 13	99.09 41	95.16 24	97.60 142	98.19 74	92.82 160	97.93 56	98.74 44	91.60 60	99.86 11	96.26 82	99.52 35	99.67 16

diffmvs		95.25 122	95.13 117	95.63 192	96.43 266	89.34 253	95.99 327	97.35 233	92.83 159	96.31 118	97.37 186	86.44 163	98.67 253	96.26 82	97.19 189	98.87 145
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
EI-MVSNet-UG-set		96.34 83	96.30 82	96.47 116	98.20 109	90.93 178	96.86 235	97.72 155	94.67 70	96.16 125	98.46 62	90.43 85	99.58 100	96.23 84	97.96 157	98.90 134

SR-MVS		97.01 44	96.86 49	97.47 57	99.09 41	93.27 77	97.98 72	98.07 99	93.75 106	97.45 66	98.48 61	91.43 64	99.59 97	96.22 85	99.27 75	99.54 45

xiu_mvs_v1_base_debu		95.01 140	94.76 139	95.75 184	96.58 244	91.71 135	96.25 305	97.35 233	92.99 145	96.70 93	96.63 241	82.67 254	99.44 131	96.22 85	97.46 171	96.11 327

xiu_mvs_v1_base		95.01 140	94.76 139	95.75 184	96.58 244	91.71 135	96.25 305	97.35 233	92.99 145	96.70 93	96.63 241	82.67 254	99.44 131	96.22 85	97.46 171	96.11 327

xiu_mvs_v1_base_debi		95.01 140	94.76 139	95.75 184	96.58 244	91.71 135	96.25 305	97.35 233	92.99 145	96.70 93	96.63 241	82.67 254	99.44 131	96.22 85	97.46 171	96.11 327

alignmvs		95.87 100	95.23 113	97.78 37	97.56 164	95.19 23	97.86 92	97.17 257	94.39 85	96.47 110	96.40 255	85.89 174	99.20 156	96.21 89	95.11 264	98.95 122

sasdasda		96.02 91	95.45 101	97.75 41	97.59 158	95.15 25	98.28 35	97.60 172	94.52 77	96.27 120	96.12 270	87.65 131	99.18 160	96.20 90	94.82 268	98.91 131

canonicalmvs		96.02 91	95.45 101	97.75 41	97.59 158	95.15 25	98.28 35	97.60 172	94.52 77	96.27 120	96.12 270	87.65 131	99.18 160	96.20 90	94.82 268	98.91 131

MGCFI-Net		95.94 96	95.40 105	97.56 54	97.59 158	94.62 34	98.21 48	97.57 179	94.41 83	96.17 124	96.16 268	87.54 136	99.17 162	96.19 92	94.73 273	98.91 131

RRT-MVS		94.51 165	94.35 162	94.98 238	96.40 267	86.55 357	97.56 147	97.41 222	93.19 135	94.93 179	97.04 211	79.12 329	99.30 147	96.19 92	97.32 182	99.09 98

MTAPA		97.08 39	96.78 59	97.97 28	99.37 19	94.42 42	97.24 195	98.08 94	95.07 46	96.11 126	98.59 48	90.88 80	99.90 2	96.18 94	99.50 40	99.58 35

APD-MVS_3200maxsize		96.81 58	96.71 63	97.12 77	99.01 52	92.31 112	97.98 72	98.06 102	93.11 141	97.44 67	98.55 51	90.93 78	99.55 110	96.06 95	99.25 80	99.51 49

SR-MVS-dyc-post		96.88 51	96.80 57	97.11 79	99.02 49	92.34 110	97.98 72	98.03 111	93.52 121	97.43 69	98.51 56	91.40 65	99.56 108	96.05 96	99.26 78	99.43 63

RE-MVS-def			96.72 62		99.02 49	92.34 110	97.98 72	98.03 111	93.52 121	97.43 69	98.51 56	90.71 82		96.05 96	99.26 78	99.43 63

MVS_111021_HR		96.68 69	96.58 68	96.99 85	98.46 81	92.31 112	96.20 311	98.90 3	94.30 88	95.86 138	97.74 149	92.33 46	99.38 138	96.04 98	99.42 56	99.28 77

PHI-MVS		96.77 60	96.46 76	97.71 46	98.40 88	94.07 54	98.21 48	98.45 36	89.86 283	97.11 80	98.01 106	92.52 43	99.69 74	96.03 99	99.53 33	99.36 72

casdiffmvs_mvg		95.81 101	95.57 94	96.51 112	96.87 207	91.49 146	97.50 157	97.56 187	93.99 98	95.13 170	97.92 117	87.89 125	98.78 219	95.97 100	97.33 180	99.26 79
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
HPM-MVS++		97.34 26	96.97 43	98.47 5	99.08 43	96.16 5	97.55 152	97.97 122	95.59 27	96.61 99	97.89 122	92.57 42	99.84 27	95.95 101	99.51 38	99.40 66

DELS-MVS		96.61 71	96.38 80	97.30 64	97.79 141	93.19 80	95.96 328	98.18 77	95.23 37	95.87 137	97.65 160	91.45 62	99.70 73	95.87 102	99.44 52	99.00 112
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
MVS_111021_LR		96.24 87	96.19 85	96.39 125	98.23 107	91.35 155	96.24 308	98.79 7	93.99 98	95.80 140	97.65 160	89.92 92	99.24 152	95.87 102	99.20 88	98.58 181

h-mvs33		94.15 178	93.52 189	96.04 152	97.81 140	90.22 210	97.62 140	97.58 176	95.19 38	96.74 91	97.45 180	83.67 227	99.61 92	95.85 104	79.73 449	98.29 216

hse-mvs2		93.45 215	92.99 209	94.81 248	97.02 194	88.59 285	96.69 260	96.47 327	95.19 38	96.74 91	96.16 268	83.67 227	98.48 281	95.85 104	79.13 453	97.35 283

NCCC		97.30 29	97.03 40	98.11 19	98.77 63	95.06 28	97.34 182	98.04 109	95.96 15	97.09 81	97.88 127	93.18 30	99.71 68	95.84 106	99.17 91	99.56 40

VNet		95.89 98	95.45 101	97.21 72	98.07 122	92.94 87	97.50 157	98.15 82	93.87 102	97.52 64	97.61 167	85.29 195	99.53 114	95.81 107	95.27 259	99.16 86

PC_three_1452									90.77 250	98.89 27	98.28 86	96.24 1	98.35 293	95.76 108	99.58 25	99.59 32

9.14			96.75 61		98.93 57		97.73 116	98.23 66	91.28 227	97.88 57	98.44 64	93.00 31	99.65 80	95.76 108	99.47 45

viewmamba		95.18 130	95.15 116	95.26 221	96.31 276	88.25 302	96.29 301	97.27 244	93.61 112	95.65 149	97.91 119	86.79 154	98.64 260	95.69 110	96.82 204	98.88 142

XVS		97.18 34	96.96 45	97.81 33	99.38 17	94.03 56	98.59 17	98.20 69	94.85 55	96.59 101	98.29 84	91.70 57	99.80 41	95.66 111	99.40 61	99.62 27

X-MVStestdata		91.71 285	89.67 354	97.81 33	99.38 17	94.03 56	98.59 17	98.20 69	94.85 55	96.59 101	32.69 546	91.70 57	99.80 41	95.66 111	99.40 61	99.62 27

baseline		95.58 108	95.42 104	96.08 147	96.78 224	90.41 200	97.16 206	97.45 212	93.69 110	95.65 149	97.85 132	87.29 146	98.68 250	95.66 111	97.25 186	99.13 91

ETV-MVS		96.02 91	95.89 90	96.40 123	97.16 178	92.44 107	97.47 166	97.77 149	94.55 75	96.48 109	94.51 352	91.23 71	98.92 201	95.65 114	98.19 146	97.82 259

casdiffmvs		95.64 105	95.49 98	96.08 147	96.76 230	90.45 197	97.29 188	97.44 216	94.00 97	95.46 158	97.98 110	87.52 139	98.73 239	95.64 115	97.33 180	99.08 100
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
HFP-MVS		97.14 37	96.92 47	97.83 31	99.42 10	94.12 52	98.52 20	98.32 46	93.21 132	97.18 75	98.29 84	92.08 50	99.83 32	95.63 116	99.59 21	99.54 45

ACMMPR		97.07 41	96.84 51	97.79 35	99.44 9	93.88 59	98.52 20	98.31 47	93.21 132	97.15 77	98.33 78	91.35 66	99.86 11	95.63 116	99.59 21	99.62 27

HPM-MVS		96.69 67	96.45 77	97.40 60	99.36 23	93.11 82	98.87 6	98.06 102	91.17 235	96.40 114	97.99 109	90.99 75	99.58 100	95.61 118	99.61 20	99.49 54
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
CP-MVS		97.02 43	96.81 56	97.64 50	99.33 26	93.54 66	98.80 9	98.28 52	92.99 145	96.45 113	98.30 83	91.90 54	99.85 22	95.61 118	99.68 4	99.54 45

DeepC-MVS	93.07 3	96.06 89	95.66 93	97.29 65	97.96 129	93.17 81	97.30 187	98.06 102	93.92 100	93.38 233	98.66 45	86.83 153	99.73 62	95.60 120	99.22 82	98.96 118
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
ZNCC-MVS		96.96 46	96.67 64	97.85 30	99.37 19	94.12 52	98.49 24	98.18 77	92.64 168	96.39 115	98.18 91	91.61 59	99.88 4	95.59 121	99.55 30	99.57 36

onestephybrid01		95.12 132	95.01 124	95.46 211	96.39 271	88.92 273	96.28 303	97.27 244	92.67 164	96.00 133	97.73 152	86.28 165	98.66 256	95.58 122	96.85 202	98.79 156

hybridnocas07		94.93 145	94.78 138	95.37 214	96.27 278	88.62 283	96.10 317	97.26 246	92.35 179	95.58 152	97.48 178	85.60 188	98.65 258	95.47 123	96.90 200	98.85 147

region2R		97.07 41	96.84 51	97.77 39	99.46 5	93.79 61	98.52 20	98.24 63	93.19 135	97.14 78	98.34 75	91.59 61	99.87 8	95.46 124	99.59 21	99.64 25

Casviewmamba		95.67 104	95.55 95	96.03 154	96.95 201	90.12 212	97.72 119	97.55 191	94.10 93	95.23 166	98.18 91	87.32 145	98.80 217	95.40 125	97.52 169	99.19 83

OPU-MVS				98.55 3	98.82 62	96.86 3	98.25 40				98.26 87	96.04 2	99.24 152	95.36 126	99.59 21	99.56 40

lupinMVS		94.99 144	94.56 150	96.29 134	96.34 274	91.21 160	95.83 336	96.27 342	88.93 320	96.22 122	96.88 223	86.20 169	98.85 208	95.27 127	99.05 103	98.82 153

hybrid		94.76 158	94.60 147	95.27 219	96.24 280	88.36 296	96.05 321	97.25 249	91.40 221	95.40 159	97.59 170	85.48 191	98.63 263	95.23 128	96.71 212	98.83 152

reproduce_monomvs		91.30 315	91.10 287	91.92 395	96.82 216	82.48 430	97.01 218	97.49 198	94.64 73	88.35 369	95.27 315	70.53 418	98.10 319	95.20 129	84.60 416	95.19 380

mPP-MVS		96.86 52	96.60 66	97.64 50	99.40 14	93.44 68	98.50 23	98.09 93	93.27 131	95.95 135	98.33 78	91.04 74	99.88 4	95.20 129	99.57 29	99.60 31

DeepC-MVS_fast	93.89 2	96.93 49	96.64 65	97.78 37	98.64 74	94.30 43	97.41 172	98.04 109	94.81 61	96.59 101	98.37 70	91.24 69	99.64 88	95.16 131	99.52 35	99.42 65
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
jason		94.84 152	94.39 160	96.18 142	95.52 325	90.93 178	96.09 318	96.52 324	89.28 305	96.01 132	97.32 188	84.70 208	98.77 223	95.15 132	98.91 113	98.85 147
jason: jason.
train_agg		96.30 85	95.83 92	97.72 44	98.70 66	94.19 48	96.41 284	98.02 114	88.58 332	96.03 129	97.56 174	92.73 38	99.59 97	95.04 133	99.37 67	99.39 68

mvsany_test1		93.93 194	93.98 172	93.78 320	94.94 367	86.80 347	94.62 396	92.55 470	88.77 329	96.85 86	98.49 58	88.98 102	98.08 324	95.03 134	95.62 249	96.46 315

test_prior2							96.35 293		92.80 161	96.03 129	97.59 170	92.01 51		95.01 135	99.38 64

NormalMVS		96.36 82	96.11 86	97.12 77	99.37 19	92.90 89	97.99 69	97.63 167	95.92 16	96.57 104	97.93 114	85.34 193	99.50 122	94.99 136	99.21 83	98.97 115

SymmetryMVS		95.94 96	95.54 96	97.15 75	97.85 137	92.90 89	97.99 69	96.91 296	95.92 16	96.57 104	97.93 114	85.34 193	99.50 122	94.99 136	96.39 231	99.05 105

nrg030		94.05 185	93.31 199	96.27 135	95.22 350	94.59 35	98.34 30	97.46 207	92.93 152	91.21 294	96.64 237	87.23 148	98.22 305	94.99 136	85.80 396	95.98 331

E5new		95.04 136	94.88 131	95.52 200	96.62 235	89.02 268	97.29 188	97.57 179	92.54 169	95.04 172	97.89 122	85.65 183	98.77 223	94.92 139	96.44 226	98.78 157

E6new		95.04 136	94.88 131	95.52 200	96.60 240	89.02 268	97.29 188	97.57 179	92.54 169	95.04 172	97.90 120	85.66 181	98.77 223	94.92 139	96.44 226	98.78 157

E6		95.04 136	94.88 131	95.52 200	96.60 240	89.02 268	97.29 188	97.57 179	92.54 169	95.04 172	97.90 120	85.66 181	98.77 223	94.92 139	96.44 226	98.78 157

E5		95.04 136	94.88 131	95.52 200	96.62 235	89.02 268	97.29 188	97.57 179	92.54 169	95.04 172	97.89 122	85.65 183	98.77 223	94.92 139	96.44 226	98.78 157

VDDNet		93.05 231	92.07 246	96.02 155	96.84 211	90.39 201	98.08 59	95.85 363	86.22 396	95.79 141	98.46 62	67.59 443	99.19 157	94.92 139	94.85 266	98.47 195

E3new		95.28 118	95.11 120	95.80 175	97.03 192	89.76 229	96.78 250	97.54 192	92.06 196	95.40 159	97.75 146	87.49 140	98.76 229	94.85 144	97.10 192	98.88 142

mvsmamba		94.57 162	94.14 167	95.87 167	97.03 192	89.93 224	97.84 96	95.85 363	91.34 223	94.79 185	96.80 226	80.67 298	98.81 214	94.85 144	98.12 150	98.85 147

APD-MVS		96.95 47	96.60 66	98.01 23	99.03 48	94.93 30	97.72 119	98.10 92	91.50 215	98.01 51	98.32 80	92.33 46	99.58 100	94.85 144	99.51 38	99.53 48
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
GST-MVS		96.85 54	96.52 70	97.82 32	99.36 23	94.14 51	98.29 34	98.13 85	92.72 163	96.70 93	98.06 99	91.35 66	99.86 11	94.83 147	99.28 74	99.47 58

MP-MVS		96.77 60	96.45 77	97.72 44	99.39 16	93.80 60	98.41 28	98.06 102	93.37 127	95.54 155	98.34 75	90.59 84	99.88 4	94.83 147	99.54 32	99.49 54
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
viewcassd2359sk11		95.26 120	95.09 121	95.80 175	96.95 201	89.72 231	96.80 245	97.56 187	92.21 187	95.37 161	97.80 142	87.17 149	98.77 223	94.82 149	97.10 192	98.90 134

test9_res														94.81 150	99.38 64	99.45 59

viewmanbaseed2359cas		95.24 123	95.02 123	95.91 164	96.87 207	89.98 220	96.82 241	97.49 198	92.26 183	95.47 157	97.82 138	86.47 161	98.69 248	94.80 151	97.20 188	99.06 104

PS-MVSNAJ		95.37 114	95.33 109	95.49 207	97.35 168	90.66 192	95.31 368	97.48 201	93.85 103	96.51 107	95.70 295	88.65 110	99.65 80	94.80 151	98.27 143	96.17 321

HPM-MVS_fast		96.51 74	96.27 83	97.22 71	99.32 27	92.74 95	98.74 10	98.06 102	90.57 266	96.77 90	98.35 72	90.21 87	99.53 114	94.80 151	99.63 16	99.38 70

E2		95.20 126	95.00 125	95.79 178	96.79 219	89.66 232	96.82 241	97.58 176	92.35 179	95.28 163	97.83 136	86.68 156	98.76 229	94.79 154	96.92 198	98.95 122

E3		95.20 126	95.00 125	95.79 178	96.77 226	89.66 232	96.82 241	97.58 176	92.35 179	95.28 163	97.83 136	86.69 155	98.76 229	94.79 154	96.92 198	98.95 122

xiu_mvs_v2_base		95.32 117	95.29 110	95.40 213	97.22 173	90.50 195	95.44 361	97.44 216	93.70 109	96.46 111	96.18 265	88.59 114	99.53 114	94.79 154	97.81 161	96.17 321

hybridcas		95.46 112	95.29 110	95.96 162	96.83 213	90.08 214	97.63 137	97.49 198	93.76 105	94.79 185	98.04 101	86.87 152	98.72 244	94.71 157	97.53 168	99.08 100

E4		95.09 133	94.86 135	95.77 181	96.58 244	89.56 240	96.85 236	97.56 187	92.50 173	95.03 176	97.86 130	86.03 172	98.78 219	94.71 157	96.65 216	98.96 118

CSCG		96.05 90	95.91 89	96.46 118	99.24 34	90.47 196	98.30 33	98.57 28	89.01 314	93.97 213	97.57 172	92.62 41	99.76 55	94.66 159	99.27 75	99.15 88

test_fmvs2		89.77 371	89.93 343	89.31 451	93.68 413	76.37 482	97.64 135	95.90 360	89.84 286	91.49 280	96.26 263	58.77 479	97.10 425	94.65 160	91.13 335	94.46 425

EIA-MVS		95.53 111	95.47 100	95.71 189	97.06 187	89.63 235	97.82 101	97.87 133	93.57 114	93.92 215	95.04 324	90.61 83	98.95 196	94.62 161	98.68 121	98.54 185

SDMVSNet		94.17 175	93.61 183	95.86 170	98.09 118	91.37 153	97.35 181	98.20 69	93.18 137	91.79 273	97.28 192	79.13 328	98.93 199	94.61 162	92.84 306	97.28 286

VortexMVS		92.88 241	92.64 227	93.58 336	96.58 244	87.53 328	96.93 227	97.28 243	92.78 162	89.75 327	94.99 325	82.73 253	97.76 374	94.60 163	88.16 372	95.46 355

ZD-MVS					99.05 46	94.59 35		98.08 94	89.22 307	97.03 83	98.10 95	92.52 43	99.65 80	94.58 164	99.31 72

ACMMP		96.27 86	95.93 88	97.28 67	99.24 34	92.62 100	98.25 40	98.81 6	92.99 145	94.56 192	98.39 68	88.96 103	99.85 22	94.57 165	97.63 165	99.36 72
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
viewdifsd2359ckpt11		93.46 212	93.22 203	94.17 291	96.11 299	85.42 384	96.43 280	97.07 271	92.91 153	94.20 204	98.00 107	80.82 296	98.73 239	94.42 166	89.04 363	98.34 213

viewmsd2359difaftdt		93.46 212	93.23 202	94.17 291	96.12 297	85.42 384	96.43 280	97.08 268	92.91 153	94.21 203	98.00 107	80.82 296	98.74 237	94.41 167	89.05 361	98.34 213

viewmacassd2359aftdt		95.07 135	94.80 137	95.87 167	96.53 254	89.84 226	96.90 231	97.48 201	92.44 175	95.36 162	97.89 122	85.23 196	98.68 250	94.40 168	97.00 196	99.09 98

GDP-MVS		95.62 106	95.13 117	97.09 80	96.79 219	93.26 78	97.89 89	97.83 144	93.58 113	96.80 87	97.82 138	83.06 243	99.16 164	94.40 168	97.95 158	98.87 145

viewdifsd2359ckpt07		94.76 158	94.68 144	95.01 234	96.76 230	87.41 329	96.38 290	97.43 219	92.65 166	94.52 193	97.75 146	85.55 189	98.81 214	94.36 170	96.69 213	98.82 153

PGM-MVS		96.81 58	96.53 69	97.65 48	99.35 25	93.53 67	97.65 131	98.98 2	92.22 185	97.14 78	98.44 64	91.17 72	99.85 22	94.35 171	99.46 46	99.57 36

ET-MVSNet_ETH3D		91.49 303	90.11 333	95.63 192	96.40 267	91.57 144	95.34 365	93.48 457	90.60 264	75.58 487	95.49 306	80.08 311	96.79 440	94.25 172	89.76 353	98.52 187

LFMVS		93.60 205	92.63 228	96.52 108	98.13 117	91.27 157	97.94 82	93.39 458	90.57 266	96.29 119	98.31 81	69.00 433	99.16 164	94.18 173	95.87 241	99.12 94

MVSFormer		95.37 114	95.16 115	95.99 160	96.34 274	91.21 160	98.22 46	97.57 179	91.42 219	96.22 122	97.32 188	86.20 169	97.92 356	94.07 174	99.05 103	98.85 147

test_djsdf		93.07 230	92.76 220	94.00 302	93.49 422	88.70 280	98.22 46	97.57 179	91.42 219	90.08 319	95.55 303	82.85 250	97.92 356	94.07 174	91.58 327	95.40 362

mvs_anonymous		93.82 198	93.74 179	94.06 298	96.44 265	85.41 386	95.81 338	97.05 278	89.85 285	90.09 318	96.36 257	87.44 142	97.75 376	93.97 176	96.69 213	99.02 106

VPA-MVSNet		93.24 221	92.48 237	95.51 204	95.70 316	92.39 108	97.86 92	98.66 21	92.30 182	92.09 265	95.37 310	80.49 303	98.40 285	93.95 177	85.86 395	95.75 344

agg_prior2														93.94 178	99.38 64	99.50 52

mvs_tets		92.31 262	91.76 259	93.94 310	93.41 427	88.29 298	97.63 137	97.53 193	92.04 197	88.76 361	96.45 252	74.62 384	98.09 323	93.91 179	91.48 329	95.45 357

Effi-MVS+		94.93 145	94.45 158	96.36 128	96.61 238	91.47 149	96.41 284	97.41 222	91.02 243	94.50 194	95.92 279	87.53 137	98.78 219	93.89 180	96.81 205	98.84 151

jajsoiax		92.42 256	91.89 256	94.03 301	93.33 430	88.50 291	97.73 116	97.53 193	92.00 199	88.85 358	96.50 250	75.62 374	98.11 318	93.88 181	91.56 328	95.48 352

XVG-OURS-SEG-HR		93.86 197	93.55 185	94.81 248	97.06 187	88.53 290	95.28 369	97.45 212	91.68 207	94.08 210	97.68 156	82.41 262	98.90 204	93.84 182	92.47 312	96.98 295

PS-MVSNAJss		93.74 201	93.51 190	94.44 275	93.91 405	89.28 258	97.75 111	97.56 187	92.50 173	89.94 321	96.54 248	88.65 110	98.18 310	93.83 183	90.90 341	95.86 332

EPNet		95.20 126	94.56 150	97.14 76	92.80 441	92.68 99	97.85 95	94.87 419	96.64 9	92.46 250	97.80 142	86.23 166	99.65 80	93.72 184	98.62 125	99.10 97
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
viewmambaseed2359dif		94.28 171	94.14 167	94.71 256	96.21 281	86.97 343	95.93 330	97.11 264	89.00 315	95.00 178	97.70 153	86.02 173	98.59 272	93.71 185	96.59 218	98.57 183

viewdifsd2359ckpt13		94.87 150	94.52 154	95.90 165	96.88 206	90.19 211	96.92 228	97.36 231	91.26 228	94.65 189	97.46 179	85.79 178	98.64 260	93.64 186	96.76 207	98.88 142

viewdifsd2359ckpt09		94.81 155	94.37 161	96.12 146	96.91 203	90.75 188	96.94 225	97.31 238	90.51 269	94.31 200	97.38 185	85.70 180	98.71 246	93.54 187	96.75 208	98.90 134

PVSNet_Blended_VisFu		95.27 119	94.91 130	96.38 126	98.20 109	90.86 181	97.27 193	98.25 61	90.21 275	94.18 206	97.27 194	87.48 141	99.73 62	93.53 188	97.77 163	98.55 184

CPTT-MVS		95.57 109	95.19 114	96.70 93	99.27 32	91.48 148	98.33 31	98.11 90	87.79 360	95.17 169	98.03 103	87.09 150	99.61 92	93.51 189	99.42 56	99.02 106

MVSTER		93.20 223	92.81 219	94.37 278	96.56 249	89.59 238	97.06 212	97.12 260	91.24 229	91.30 288	95.96 277	82.02 270	98.05 331	93.48 190	90.55 345	95.47 354

PVSNet_BlendedMVS		94.06 184	93.92 174	94.47 273	98.27 98	89.46 248	96.73 254	98.36 38	90.17 276	94.36 197	95.24 318	88.02 122	99.58 100	93.44 191	90.72 343	94.36 429

PVSNet_Blended		94.87 150	94.56 150	95.81 174	98.27 98	89.46 248	95.47 359	98.36 38	88.84 323	94.36 197	96.09 275	88.02 122	99.58 100	93.44 191	98.18 147	98.40 203

3Dnovator	91.36 5	95.19 129	94.44 159	97.44 58	96.56 249	93.36 72	98.65 16	98.36 38	94.12 92	89.25 347	98.06 99	82.20 266	99.77 53	93.41 193	99.32 71	99.18 85

EPP-MVSNet		95.22 125	95.04 122	95.76 182	97.49 165	89.56 240	98.67 15	97.00 286	90.69 254	94.24 202	97.62 166	89.79 94	98.81 214	93.39 194	96.49 223	98.92 130

testing3-2		92.10 273	92.05 247	92.27 386	97.71 146	79.56 465	97.42 170	94.41 435	93.53 119	93.22 239	95.49 306	69.16 432	99.11 173	93.25 195	94.22 281	98.13 228

CHOSEN 280x420		93.12 227	92.72 225	94.34 281	96.71 232	87.27 333	90.29 482	97.72 155	86.61 388	91.34 285	95.29 312	84.29 218	98.41 284	93.25 195	98.94 111	97.35 283

3Dnovator+	91.43 4	95.40 113	94.48 157	98.16 18	96.90 205	95.34 18	98.48 25	97.87 133	94.65 72	88.53 366	98.02 105	83.69 226	99.71 68	93.18 197	98.96 110	99.44 61

test_yl		94.78 156	94.23 165	96.43 120	97.74 144	91.22 158	96.85 236	97.10 265	91.23 232	95.71 144	96.93 218	84.30 216	99.31 145	93.10 198	95.12 262	98.75 165

DCV-MVSNet		94.78 156	94.23 165	96.43 120	97.74 144	91.22 158	96.85 236	97.10 265	91.23 232	95.71 144	96.93 218	84.30 216	99.31 145	93.10 198	95.12 262	98.75 165

test_vis1_rt		86.16 423	85.06 419	89.46 447	93.47 424	80.46 451	96.41 284	86.61 504	85.22 410	79.15 478	88.64 472	52.41 491	97.06 427	93.08 200	90.57 344	90.87 484

test1111		93.19 224	92.82 218	94.30 286	97.58 162	84.56 403	98.21 48	89.02 494	93.53 119	94.58 191	98.21 88	72.69 400	99.05 189	93.06 201	98.48 132	99.28 77

ECVR-MVS		93.19 224	92.73 224	94.57 267	97.66 150	85.41 386	98.21 48	88.23 496	93.43 125	94.70 188	98.21 88	72.57 401	99.07 184	93.05 202	98.49 130	99.25 80

HQP_MVS		93.78 200	93.43 195	94.82 246	96.21 281	89.99 218	97.74 114	97.51 195	94.85 55	91.34 285	96.64 237	81.32 284	98.60 268	93.02 203	92.23 315	95.86 332

plane_prior5								97.51 195					98.60 268	93.02 203	92.23 315	95.86 332

MonoMVSNet		91.92 278	91.77 258	92.37 380	92.94 437	83.11 422	97.09 211	95.55 381	92.91 153	90.85 298	94.55 349	81.27 286	96.52 444	93.01 205	87.76 376	97.47 277

test2506		91.60 293	90.78 300	94.04 300	97.66 150	83.81 412	98.27 37	75.53 516	93.43 125	95.23 166	98.21 88	67.21 446	99.07 184	93.01 205	98.49 130	99.25 80

MVS_Test		94.89 148	94.62 146	95.68 190	96.83 213	89.55 242	96.70 258	97.17 257	91.17 235	95.60 151	96.11 274	87.87 127	98.76 229	93.01 205	97.17 190	98.72 169

CLD-MVS		92.98 234	92.53 234	94.32 283	96.12 297	89.20 261	95.28 369	97.47 205	92.66 165	89.90 322	95.62 299	80.58 301	98.40 285	92.73 208	92.40 313	95.38 364
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
XVG-OURS		93.72 202	93.35 198	94.80 251	97.07 184	88.61 284	94.79 393	97.46 207	91.97 200	93.99 211	97.86 130	81.74 278	98.88 205	92.64 209	92.67 311	96.92 300

dtuplus		94.16 177	93.98 172	94.70 257	96.18 289	86.85 346	96.04 322	97.07 271	89.75 289	95.02 177	97.79 144	84.94 205	98.62 266	92.62 210	96.43 230	98.62 177

KinetiMVS		95.26 120	94.75 142	96.79 91	96.99 197	92.05 122	97.82 101	97.78 148	94.77 65	96.46 111	97.70 153	80.62 300	99.34 140	92.37 211	98.28 142	98.97 115

旧先验2							95.94 329		81.66 461	97.34 72			98.82 212	92.26 212

CDPH-MVS		95.97 94	95.38 107	97.77 39	98.93 57	94.44 41	96.35 293	97.88 131	86.98 380	96.65 97	97.89 122	91.99 52	99.47 127	92.26 212	99.46 46	99.39 68

FIs		94.09 183	93.70 180	95.27 219	95.70 316	92.03 124	98.10 57	98.68 18	93.36 129	90.39 305	96.70 232	87.63 133	97.94 353	92.25 214	90.50 347	95.84 335

LPG-MVS_test		92.94 237	92.56 231	94.10 296	96.16 292	88.26 300	97.65 131	97.46 207	91.29 224	90.12 315	97.16 201	79.05 331	98.73 239	92.25 214	91.89 323	95.31 369

LGP-MVS_train				94.10 296	96.16 292	88.26 300		97.46 207	91.29 224	90.12 315	97.16 201	79.05 331	98.73 239	92.25 214	91.89 323	95.31 369

SSM_0407		94.54 164	94.12 169	95.80 175	96.79 219	90.38 202	96.79 246	97.29 240	91.24 229	93.68 219	97.60 168	85.03 200	98.67 253	92.14 217	96.51 219	98.35 209

SSM_0404		94.73 160	94.31 164	95.98 161	97.05 189	90.90 180	97.01 218	97.29 240	91.24 229	94.17 207	97.60 168	85.03 200	98.76 229	92.14 217	97.30 183	98.29 216

cascas		91.20 320	90.08 334	94.58 266	94.97 363	89.16 264	93.65 441	97.59 175	79.90 472	89.40 339	92.92 421	75.36 375	98.36 292	92.14 217	94.75 271	96.23 317

OPM-MVS		93.28 220	92.76 220	94.82 246	94.63 383	90.77 186	96.65 264	97.18 255	93.72 107	91.68 277	97.26 195	79.33 326	98.63 263	92.13 220	92.28 314	95.07 385
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
BP-MVS														92.13 220

HQP-MVS		93.19 224	92.74 223	94.54 269	95.86 308	89.33 254	96.65 264	97.39 224	93.55 115	90.14 309	95.87 281	80.95 290	98.50 278	92.13 220	92.10 320	95.78 340

DP-MVS Recon		95.68 103	95.12 119	97.37 61	99.19 38	94.19 48	97.03 213	98.08 94	88.35 341	95.09 171	97.65 160	89.97 91	99.48 126	92.08 223	98.59 127	98.44 200

VPNet		92.23 268	91.31 276	94.99 236	95.56 323	90.96 174	97.22 201	97.86 137	92.96 151	90.96 296	96.62 244	75.06 377	98.20 307	91.90 224	83.65 430	95.80 338

sss		94.51 165	93.80 176	96.64 95	97.07 184	91.97 126	96.32 298	98.06 102	88.94 319	94.50 194	96.78 227	84.60 209	99.27 149	91.90 224	96.02 235	98.68 174

anonymousdsp		92.16 270	91.55 267	93.97 306	92.58 446	89.55 242	97.51 156	97.42 221	89.42 302	88.40 368	94.84 334	80.66 299	97.88 361	91.87 226	91.28 333	94.48 424

test_fmvs3		83.21 444	83.02 439	83.78 472	86.77 497	68.34 500	96.76 252	94.91 414	86.49 389	84.14 445	89.48 466	36.04 504	91.73 497	91.86 227	80.77 445	91.26 483

ACMP	89.59 10	92.62 250	92.14 245	94.05 299	96.40 267	88.20 307	97.36 180	97.25 249	91.52 214	88.30 372	96.64 237	78.46 343	98.72 244	91.86 227	91.48 329	95.23 376
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
HyFIR lowres test		93.66 204	92.92 214	95.87 167	98.24 102	89.88 225	94.58 398	98.49 31	85.06 414	93.78 217	95.78 290	82.86 249	98.67 253	91.77 229	95.71 246	99.07 103

UGNet		94.04 186	93.28 200	96.31 130	96.85 210	91.19 163	97.88 91	97.68 160	94.40 84	93.00 242	96.18 265	73.39 396	99.61 92	91.72 230	98.46 133	98.13 228
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
UniMVSNet_NR-MVSNet		93.37 217	92.67 226	95.47 210	95.34 339	92.83 91	97.17 205	98.58 27	92.98 150	90.13 313	95.80 286	88.37 117	97.85 362	91.71 231	83.93 425	95.73 346

DU-MVS		92.90 239	92.04 248	95.49 207	94.95 365	92.83 91	97.16 206	98.24 63	93.02 144	90.13 313	95.71 293	83.47 230	97.85 362	91.71 231	83.93 425	95.78 340

Effi-MVS+-dtu		93.08 229	93.21 204	92.68 376	96.02 305	83.25 419	97.14 208	96.72 309	93.85 103	91.20 295	93.44 411	83.08 241	98.30 299	91.69 233	95.73 245	96.50 312

UniMVSNet (Re)		93.31 219	92.55 232	95.61 194	95.39 333	93.34 73	97.39 177	98.71 13	93.14 140	90.10 317	94.83 335	87.71 129	98.03 335	91.67 234	83.99 424	95.46 355

LCM-MVSNet-Re		92.50 251	92.52 235	92.44 378	96.82 216	81.89 437	96.92 228	93.71 455	92.41 177	84.30 441	94.60 347	85.08 199	97.03 429	91.51 235	97.36 178	98.40 203

FC-MVSNet-test		93.94 192	93.57 184	95.04 232	95.48 327	91.45 151	98.12 56	98.71 13	93.37 127	90.23 308	96.70 232	87.66 130	97.85 362	91.49 236	90.39 348	95.83 336

PMMVS		92.86 242	92.34 240	94.42 277	94.92 368	86.73 350	94.53 400	96.38 333	84.78 419	94.27 201	95.12 323	83.13 240	98.40 285	91.47 237	96.49 223	98.12 230

Vis-MVSNet		95.23 124	94.81 136	96.51 112	97.18 177	91.58 143	98.26 39	98.12 87	94.38 86	94.90 180	98.15 94	82.28 264	98.92 201	91.45 238	98.58 128	99.01 109
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
CHOSEN 1792x2688		94.15 178	93.51 190	96.06 150	98.27 98	89.38 251	95.18 380	98.48 33	85.60 404	93.76 218	97.11 206	83.15 239	99.61 92	91.33 239	98.72 120	99.19 83

OMC-MVS		95.09 133	94.70 143	96.25 139	98.46 81	91.28 156	96.43 280	97.57 179	92.04 197	94.77 187	97.96 112	87.01 151	99.09 178	91.31 240	96.77 206	98.36 207

MG-MVS		95.61 107	95.38 107	96.31 130	98.42 85	90.53 194	96.04 322	97.48 201	93.47 123	95.67 148	98.10 95	89.17 100	99.25 151	91.27 241	98.77 118	99.13 91

ACMM	89.79 8	92.96 235	92.50 236	94.35 279	96.30 277	88.71 279	97.58 143	97.36 231	91.40 221	90.53 302	96.65 236	79.77 317	98.75 235	91.24 242	91.64 325	95.59 350
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
WTY-MVS		94.71 161	94.02 170	96.79 91	97.71 146	92.05 122	96.59 273	97.35 233	90.61 262	94.64 190	96.93 218	86.41 164	99.39 136	91.20 243	94.71 274	98.94 125

testing11		91.68 288	90.75 303	94.47 273	96.53 254	86.56 356	95.76 342	94.51 431	91.10 241	91.24 293	93.59 404	68.59 437	98.86 206	91.10 244	94.29 279	98.00 244

tt0805		91.09 324	90.07 337	94.16 294	95.61 320	88.31 297	97.56 147	96.51 325	89.56 295	89.17 350	95.64 298	67.08 450	98.38 291	91.07 245	88.44 370	95.80 338

Anonymous20240529		91.98 277	90.73 305	95.73 187	98.14 115	89.40 250	97.99 69	97.72 155	79.63 473	93.54 226	97.41 184	69.94 425	99.56 108	91.04 246	91.11 336	98.22 220

mamba_0408		93.70 203	92.99 209	95.83 172	96.79 219	90.38 202	88.69 492	97.07 271	90.96 245	93.68 219	97.31 190	84.97 203	98.76 229	90.95 247	96.51 219	98.35 209

SSM_04072		93.51 211	92.99 209	95.05 230	96.79 219	90.38 202	88.69 492	97.07 271	90.96 245	93.68 219	97.31 190	84.97 203	96.42 446	90.95 247	96.51 219	98.35 209

AUN-MVS		91.76 284	90.75 303	94.81 248	97.00 196	88.57 286	96.65 264	96.49 326	89.63 293	92.15 261	96.12 270	78.66 340	98.50 278	90.83 249	79.18 452	97.36 281

mvsany_test3		83.59 442	82.44 444	87.03 465	83.80 502	73.82 490	93.70 436	90.92 488	86.42 390	82.51 458	90.26 458	46.76 496	95.71 457	90.82 250	76.76 462	91.57 477

Elysia		94.00 188	93.12 205	96.64 95	96.08 302	92.72 97	97.50 157	97.63 167	91.15 237	94.82 182	97.12 204	74.98 379	99.06 186	90.78 251	98.02 153	98.12 230

StellarMVS		94.00 188	93.12 205	96.64 95	96.08 302	92.72 97	97.50 157	97.63 167	91.15 237	94.82 182	97.12 204	74.98 379	99.06 186	90.78 251	98.02 153	98.12 230

CANet_DTU		94.37 169	93.65 182	96.55 105	96.46 264	92.13 120	96.21 309	96.67 316	94.38 86	93.53 227	97.03 216	79.34 325	99.71 68	90.76 253	98.45 134	97.82 259

ab-mvs		93.57 208	92.55 232	96.64 95	97.28 171	91.96 128	95.40 362	97.45 212	89.81 287	93.22 239	96.28 261	79.62 322	99.46 128	90.74 254	93.11 303	98.50 190

CostFormer		91.18 323	90.70 307	92.62 377	94.84 373	81.76 438	94.09 422	94.43 433	84.15 426	92.72 249	93.77 393	79.43 324	98.20 307	90.70 255	92.18 318	97.90 249

casdiffseed414692147		94.55 163	94.02 170	96.15 144	96.61 238	90.79 184	97.42 170	97.39 224	92.18 192	93.95 214	97.64 163	84.37 215	98.66 256	90.68 256	95.91 239	99.00 112

icg_test_0407_2		93.58 206	93.46 192	93.94 310	96.19 285	86.16 369	93.73 435	97.24 251	91.54 210	93.50 228	97.04 211	85.64 186	96.91 435	90.68 256	95.59 250	98.76 161

IMVS_0407		93.94 192	93.75 178	94.49 272	96.19 285	86.16 369	96.35 293	97.24 251	91.54 210	93.50 228	97.04 211	85.64 186	98.54 275	90.68 256	95.59 250	98.76 161

IMVS_0404		92.44 254	91.92 254	94.00 302	96.19 285	86.16 369	93.84 432	97.24 251	91.54 210	88.17 378	97.04 211	76.96 361	97.09 426	90.68 256	95.59 250	98.76 161

IMVS_0403		93.98 190	93.79 177	94.55 268	96.19 285	86.16 369	96.35 293	97.24 251	91.54 210	93.59 223	97.04 211	85.86 175	98.73 239	90.68 256	95.59 250	98.76 161

Anonymous202405211		92.07 274	90.83 299	95.76 182	98.19 111	88.75 278	97.58 143	95.00 408	86.00 399	93.64 222	97.45 180	66.24 455	99.53 114	90.68 256	92.71 309	99.01 109

testing99		91.62 292	90.72 306	94.32 283	96.48 261	86.11 374	95.81 338	94.76 421	91.55 209	91.75 275	93.44 411	68.55 438	98.82 212	90.43 262	93.69 296	98.04 241

tpmrst		91.44 305	91.32 275	91.79 403	95.15 356	79.20 471	93.42 446	95.37 390	88.55 335	93.49 230	93.67 399	82.49 260	98.27 302	90.41 263	89.34 357	97.90 249

thisisatest0530		93.03 232	92.21 244	95.49 207	97.07 184	89.11 265	97.49 165	92.19 475	90.16 277	94.09 209	96.41 254	76.43 367	99.05 189	90.38 264	95.68 247	98.31 215

UA-Net		95.95 95	95.53 97	97.20 73	97.67 148	92.98 86	97.65 131	98.13 85	94.81 61	96.61 99	98.35 72	88.87 105	99.51 119	90.36 265	97.35 179	99.11 96

UniMVSNet_ETH3D		91.34 313	90.22 330	94.68 258	94.86 372	87.86 320	97.23 199	97.46 207	87.99 350	89.90 322	96.92 221	66.35 453	98.23 304	90.30 266	90.99 339	97.96 245

tttt0517		92.96 235	92.33 241	94.87 245	97.11 182	87.16 339	97.97 78	92.09 476	90.63 260	93.88 216	97.01 217	76.50 364	99.06 186	90.29 267	95.45 256	98.38 205

testing91		91.90 280	91.02 289	94.53 270	96.54 252	86.55 357	95.86 334	95.64 376	91.77 204	91.89 270	93.47 409	69.94 425	98.86 206	90.23 268	93.86 294	98.18 223

FA-MVS(test-final)		93.52 210	92.92 214	95.31 218	96.77 226	88.54 288	94.82 392	96.21 349	89.61 294	94.20 204	95.25 317	83.24 235	99.14 170	90.01 269	96.16 234	98.25 218

IS-MVSNet		94.90 147	94.52 154	96.05 151	97.67 148	90.56 193	98.44 26	96.22 347	93.21 132	93.99 211	97.74 149	85.55 189	98.45 282	89.98 270	97.86 159	99.14 90

miper_enhance_ethall		91.54 300	91.01 290	93.15 356	95.35 338	87.07 341	93.97 424	96.90 297	86.79 384	89.17 350	93.43 414	86.55 159	97.64 386	89.97 271	86.93 385	94.74 418

EI-MVSNet		93.03 232	92.88 216	93.48 343	95.77 314	86.98 342	96.44 278	97.12 260	90.66 258	91.30 288	97.64 163	86.56 158	98.05 331	89.91 272	90.55 345	95.41 359

IterMVS-LS		92.29 264	91.94 253	93.34 348	96.25 279	86.97 343	96.57 276	97.05 278	90.67 256	89.50 338	94.80 337	86.59 157	97.64 386	89.91 272	86.11 394	95.40 362
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
cl22		91.21 319	90.56 315	93.14 357	96.09 301	86.80 347	94.41 409	96.58 323	87.80 359	88.58 365	93.99 386	80.85 295	97.62 389	89.87 274	86.93 385	94.99 388

CDS-MVSNet		94.14 181	93.54 186	95.93 163	96.18 289	91.46 150	96.33 297	97.04 280	88.97 318	93.56 224	96.51 249	87.55 135	97.89 360	89.80 275	95.95 237	98.44 200
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
WR-MVS		92.34 260	91.53 268	94.77 253	95.13 358	90.83 182	96.40 288	97.98 121	91.88 201	89.29 344	95.54 304	82.50 259	97.80 369	89.79 276	85.27 404	95.69 347

NR-MVSNet		92.34 260	91.27 279	95.53 199	94.95 365	93.05 83	97.39 177	98.07 99	92.65 166	84.46 438	95.71 293	85.00 202	97.77 373	89.71 277	83.52 431	95.78 340

Anonymous20231211		90.63 345	89.42 361	94.27 288	98.24 102	89.19 263	98.05 63	97.89 129	79.95 471	88.25 375	94.96 327	72.56 402	98.13 314	89.70 278	85.14 406	95.49 351

testdata				95.46 211	98.18 113	88.90 275		97.66 161	82.73 451	97.03 83	98.07 98	90.06 88	98.85 208	89.67 279	98.98 109	98.64 176

Baseline_NR-MVSNet		91.20 320	90.62 311	92.95 363	93.83 408	88.03 313	97.01 218	95.12 404	88.42 339	89.70 329	95.13 322	83.47 230	97.44 411	89.66 280	83.24 433	93.37 449

DPM-MVS		95.69 102	94.92 129	98.01 23	98.08 121	95.71 11	95.27 371	97.62 171	90.43 271	95.55 153	97.07 209	91.72 55	99.50 122	89.62 281	98.94 111	98.82 153

XXY-MVS		92.16 270	91.23 281	94.95 242	94.75 377	90.94 177	97.47 166	97.43 219	89.14 309	88.90 354	96.43 253	79.71 318	98.24 303	89.56 282	87.68 377	95.67 348

miper_ehance_all_eth		91.59 294	91.13 285	92.97 362	95.55 324	86.57 355	94.47 405	96.88 300	87.77 361	88.88 356	94.01 384	86.22 167	97.54 401	89.49 283	86.93 385	94.79 413

WBMVS		90.69 344	89.99 341	92.81 369	96.48 261	85.00 396	95.21 377	96.30 337	89.46 300	89.04 353	94.05 383	72.45 403	97.82 366	89.46 284	87.41 382	95.61 349

XVG-ACMP-BASELINE		90.93 333	90.21 331	93.09 358	94.31 396	85.89 375	95.33 366	97.26 246	91.06 242	89.38 340	95.44 309	68.61 436	98.60 268	89.46 284	91.05 337	94.79 413

thisisatest0515		92.29 264	91.30 277	95.25 222	96.60 240	88.90 275	94.36 411	92.32 473	87.92 352	93.43 232	94.57 348	77.28 358	99.00 193	89.42 286	95.86 242	97.86 255

c3_l		91.38 308	90.89 293	92.88 366	95.58 322	86.30 363	94.68 395	96.84 304	88.17 345	88.83 360	94.23 373	85.65 183	97.47 408	89.36 287	84.63 414	94.89 397

AdaColmap		94.34 170	93.68 181	96.31 130	98.59 76	91.68 138	96.59 273	97.81 146	89.87 282	92.15 261	97.06 210	83.62 229	99.54 112	89.34 288	98.07 151	97.70 264

TranMVSNet+NR-MVSNet		92.50 251	91.63 264	95.14 226	94.76 376	92.07 121	97.53 153	98.11 90	92.90 156	89.56 335	96.12 270	83.16 238	97.60 391	89.30 289	83.20 434	95.75 344

D2MVS		91.30 315	90.95 292	92.35 381	94.71 380	85.52 382	96.18 313	98.21 67	88.89 321	86.60 410	93.82 391	79.92 315	97.95 351	89.29 290	90.95 340	93.56 445

1314		92.81 246	92.03 249	95.14 226	95.33 342	89.52 245	96.04 322	97.44 216	87.72 364	86.25 415	95.33 311	83.84 224	98.79 218	89.26 291	97.05 195	97.11 293

v2v482		91.59 294	90.85 297	93.80 318	93.87 407	88.17 309	96.94 225	96.88 300	89.54 296	89.53 336	94.90 331	81.70 279	98.02 336	89.25 292	85.04 410	95.20 377

114514_t		93.95 191	93.06 208	96.63 99	99.07 44	91.61 140	97.46 168	97.96 123	77.99 480	93.00 242	97.57 172	86.14 171	99.33 141	89.22 293	99.15 94	98.94 125

PAPM_NR		95.01 140	94.59 148	96.26 136	98.89 61	90.68 191	97.24 195	97.73 153	91.80 202	92.93 247	96.62 244	89.13 101	99.14 170	89.21 294	97.78 162	98.97 115

baseline1		92.82 245	91.90 255	95.55 198	97.20 176	90.77 186	97.19 203	94.58 427	92.20 188	92.36 254	96.34 258	84.16 220	98.21 306	89.20 295	83.90 428	97.68 265

IB-MVS	87.33 17	89.91 364	88.28 381	94.79 252	95.26 349	87.70 324	95.12 384	93.95 450	89.35 304	87.03 402	92.49 428	70.74 417	99.19 157	89.18 296	81.37 442	97.49 275
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
HY-MVS	89.66 9	93.87 196	92.95 213	96.63 99	97.10 183	92.49 106	95.64 351	96.64 317	89.05 313	93.00 242	95.79 289	85.77 179	99.45 130	89.16 297	94.35 276	97.96 245

0.4-1-1-0.1		86.83 410	84.27 430	94.50 271	91.39 459	88.23 303	92.62 463	92.27 474	84.04 428	86.01 422	83.30 501	65.29 463	98.31 297	89.08 298	74.45 471	96.96 299

V42		91.58 296	90.87 294	93.73 321	94.05 402	88.50 291	97.32 185	96.97 287	88.80 328	89.71 328	94.33 365	82.54 258	98.05 331	89.01 299	85.07 408	94.64 422

sd_testset		93.10 228	92.45 238	95.05 230	98.09 118	89.21 260	96.89 232	97.64 165	93.18 137	91.79 273	97.28 192	75.35 376	98.65 258	88.99 300	92.84 306	97.28 286

0.3-1-1-0.015		86.11 425	83.37 436	94.34 281	90.58 465	88.02 314	91.64 471	92.45 472	83.56 439	84.46 438	81.84 504	62.73 473	98.31 297	88.98 301	74.09 474	96.70 307

OurMVSNet-221017-0		90.51 349	90.19 332	91.44 412	93.41 427	81.25 441	96.98 222	96.28 341	91.68 207	86.55 412	96.30 259	74.20 387	97.98 340	88.96 302	87.40 383	95.09 384

API-MVS		94.84 152	94.49 156	95.90 165	97.90 135	92.00 125	97.80 105	97.48 201	89.19 308	94.81 184	96.71 230	88.84 106	99.17 162	88.91 303	98.76 119	96.53 310

0.4-1-1-0.2		86.27 421	83.62 435	94.20 289	90.38 466	87.69 325	91.04 477	92.52 471	83.43 442	85.22 433	81.49 506	65.31 462	98.29 300	88.90 304	74.30 473	96.64 308

test-LLR		91.42 306	91.19 283	92.12 391	94.59 384	80.66 447	94.29 416	92.98 463	91.11 239	90.76 300	92.37 431	79.02 333	98.07 328	88.81 305	96.74 209	97.63 266

test-mter		90.19 359	89.54 358	92.12 391	94.59 384	80.66 447	94.29 416	92.98 463	87.68 366	90.76 300	92.37 431	67.67 442	98.07 328	88.81 305	96.74 209	97.63 266

eth_miper_zixun_eth		91.02 328	90.59 313	92.34 383	95.33 342	84.35 405	94.10 421	96.90 297	88.56 334	88.84 359	94.33 365	84.08 221	97.60 391	88.77 307	84.37 421	95.06 386

myMVS_eth3d28		91.52 301	90.97 291	93.17 355	96.91 203	83.24 420	95.61 352	94.96 412	92.24 184	91.98 267	93.28 416	69.31 430	98.40 285	88.71 308	95.68 247	97.88 251

TAMVS		94.01 187	93.46 192	95.64 191	96.16 292	90.45 197	96.71 257	96.89 299	89.27 306	93.46 231	96.92 221	87.29 146	97.94 353	88.70 309	95.74 244	98.53 186

Patchmatch-RL test		87.38 399	86.24 401	90.81 427	88.74 483	78.40 475	88.12 499	93.17 460	87.11 379	82.17 461	89.29 467	81.95 272	95.60 461	88.64 310	77.02 460	98.41 202

baseline2		91.63 291	90.86 295	93.94 310	94.33 394	86.32 362	95.92 331	91.64 480	89.37 303	86.94 406	94.69 341	81.62 280	98.69 248	88.64 310	94.57 275	96.81 303

TESTMET0.1,1		90.06 361	89.42 361	91.97 394	94.41 392	80.62 449	94.29 416	91.97 478	87.28 376	90.44 304	92.47 430	68.79 434	97.67 381	88.50 312	96.60 217	97.61 270

Vis-MVSNet (Re-imp)		94.15 178	93.88 175	94.95 242	97.61 156	87.92 317	98.10 57	95.80 366	92.22 185	93.02 241	97.45 180	84.53 211	97.91 359	88.24 313	97.97 156	99.02 106

1112_ss		93.37 217	92.42 239	96.21 140	97.05 189	90.99 172	96.31 299	96.72 309	86.87 383	89.83 325	96.69 234	86.51 160	99.14 170	88.12 314	93.67 297	98.50 190

UBG		91.55 298	90.76 301	93.94 310	96.52 257	85.06 395	95.22 375	94.54 429	90.47 270	91.98 267	92.71 423	72.02 404	98.74 237	88.10 315	95.26 260	98.01 243

CVMVSNet		91.23 318	91.75 260	89.67 444	95.77 314	74.69 487	96.44 278	94.88 416	85.81 401	92.18 260	97.64 163	79.07 330	95.58 462	88.06 316	95.86 242	98.74 168

MAR-MVS		94.22 173	93.46 192	96.51 112	98.00 126	92.19 119	97.67 127	97.47 205	88.13 349	93.00 242	95.84 283	84.86 207	99.51 119	87.99 317	98.17 148	97.83 258
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
原ACMM1				96.38 126	98.59 76	91.09 170		97.89 129	87.41 372	95.22 168	97.68 156	90.25 86	99.54 112	87.95 318	99.12 99	98.49 192

CP-MVSNet		91.89 281	91.24 280	93.82 317	95.05 361	88.57 286	97.82 101	98.19 74	91.70 206	88.21 376	95.76 291	81.96 271	97.52 405	87.86 319	84.65 413	95.37 365

v148		90.99 329	90.38 319	92.81 369	93.83 408	85.80 376	96.78 250	96.68 314	89.45 301	88.75 362	93.93 388	82.96 247	97.82 366	87.83 320	83.25 432	94.80 411

blended_shiyan8		87.58 397	85.55 408	93.66 330	88.76 482	88.54 288	95.21 377	96.29 340	82.81 448	86.25 415	87.73 481	73.70 393	97.58 393	87.81 321	71.42 484	94.85 401

v1144		91.37 310	90.60 312	93.68 328	93.89 406	88.23 303	96.84 239	97.03 282	88.37 340	89.69 330	94.39 359	82.04 269	97.98 340	87.80 322	85.37 401	94.84 402

usedtu_blend_shiyan5		87.06 407	84.84 422	93.69 325	88.54 486	88.70 280	95.83 336	95.54 382	78.74 477	85.92 423	86.89 490	73.03 398	97.55 396	87.73 323	71.36 485	94.83 403

blend_shiyan4		86.87 409	84.61 427	93.67 329	88.87 478	88.70 280	95.17 381	96.30 337	82.80 449	86.16 417	87.11 487	65.12 466	97.55 396	87.73 323	72.21 481	94.75 417

DIV-MVS_self_test		90.97 331	90.33 320	92.88 366	95.36 337	86.19 368	94.46 407	96.63 320	87.82 357	88.18 377	94.23 373	82.99 244	97.53 403	87.72 325	85.57 398	94.93 393

gm-plane-assit					93.22 431	78.89 474			84.82 418		93.52 406		98.64 260	87.72 325

GeoE		93.89 195	93.28 200	95.72 188	96.96 200	89.75 230	98.24 43	96.92 295	89.47 299	92.12 263	97.21 198	84.42 213	98.39 290	87.71 327	96.50 222	99.01 109

blended_shiyan6		87.55 398	85.52 409	93.64 331	88.78 480	88.50 291	95.23 374	96.30 337	82.80 449	86.09 421	87.70 482	73.69 394	97.56 394	87.70 328	71.36 485	94.86 398

cl____		90.96 332	90.32 321	92.89 365	95.37 336	86.21 366	94.46 407	96.64 317	87.82 357	88.15 379	94.18 376	82.98 245	97.54 401	87.70 328	85.59 397	94.92 395

pmmvs4		90.93 333	89.85 346	94.17 291	93.34 429	90.79 184	94.60 397	96.02 356	84.62 420	87.45 390	95.15 320	81.88 276	97.45 410	87.70 328	87.87 375	94.27 434

Test_1112_low_res		92.84 244	91.84 257	95.85 171	97.04 191	89.97 222	95.53 356	96.64 317	85.38 407	89.65 332	95.18 319	85.86 175	99.10 175	87.70 328	93.58 302	98.49 192

wanda-best-256-512		87.29 401	85.21 414	93.53 339	88.54 486	88.21 305	94.51 403	96.27 342	82.69 452	85.92 423	86.89 490	73.04 397	97.55 396	87.68 332	71.36 485	94.83 403

FE-blended-shiyan7		87.29 401	85.21 414	93.53 339	88.54 486	88.21 305	94.51 403	96.27 342	82.69 452	85.92 423	86.89 490	73.03 398	97.55 396	87.68 332	71.36 485	94.83 403

无先验							95.79 340	97.87 133	83.87 432				99.65 80	87.68 332		98.89 140

Fast-Effi-MVS+		93.46 212	92.75 222	95.59 195	96.77 226	90.03 215	96.81 244	97.13 259	88.19 344	91.30 288	94.27 370	86.21 168	98.63 263	87.66 335	96.46 225	98.12 230

CNLPA		94.28 171	93.53 187	96.52 108	98.38 91	92.55 104	96.59 273	96.88 300	90.13 279	91.91 269	97.24 196	85.21 197	99.09 178	87.64 336	97.83 160	97.92 248

v8		91.29 317	90.53 316	93.57 338	94.15 398	88.12 311	97.34 182	97.06 277	88.99 316	88.32 371	94.26 372	83.08 241	98.01 337	87.62 337	83.92 427	94.57 423

pmmvs5		89.86 369	88.87 374	92.82 368	92.86 439	86.23 365	96.26 304	95.39 388	84.24 425	87.12 398	94.51 352	74.27 386	97.36 418	87.61 338	87.57 378	94.86 398

usedtu_dtu_shiyan1		91.65 289	90.67 309	94.60 260	93.65 416	90.95 175	94.86 390	97.12 260	89.69 291	89.21 348	93.62 401	81.17 287	97.67 381	87.54 339	89.14 359	95.17 382

FE-MVSNET3		91.65 289	90.67 309	94.60 260	93.65 416	90.95 175	94.86 390	97.12 260	89.69 291	89.21 348	93.62 401	81.17 287	97.67 381	87.54 339	89.14 359	95.17 382

Fast-Effi-MVS+-dtu		92.29 264	91.99 251	93.21 354	95.27 346	85.52 382	97.03 213	96.63 320	92.09 194	89.11 352	95.14 321	80.33 307	98.08 324	87.54 339	94.74 272	96.03 330

OpenMVS	89.19 12	92.86 242	91.68 263	96.40 123	95.34 339	92.73 96	98.27 37	98.12 87	84.86 417	85.78 426	97.75 146	78.89 338	99.74 60	87.50 342	98.65 123	96.73 305

gbinet_0.2-2-1-0.02		87.30 400	85.16 416	93.69 325	88.70 485	88.81 277	95.14 382	96.20 350	83.03 446	86.14 419	87.06 488	71.26 412	97.40 415	87.46 343	71.49 483	94.86 398

dtuonly		90.88 335	91.13 285	90.13 438	92.98 436	75.01 486	92.74 461	95.54 382	87.69 365	91.37 283	96.61 246	79.65 321	98.15 312	87.44 344	96.21 233	97.23 289

miper_lstm_enhance		90.50 350	90.06 338	91.83 400	95.33 342	83.74 413	93.86 430	96.70 313	87.56 369	87.79 384	93.81 392	83.45 232	96.92 434	87.39 345	84.62 415	94.82 408

IterMVS-SCA-FT		90.31 352	89.81 348	91.82 401	95.52 325	84.20 408	94.30 415	96.15 353	90.61 262	87.39 393	94.27 370	75.80 371	96.44 445	87.34 346	86.88 389	94.82 408

PLC	91.00 6	94.11 182	93.43 195	96.13 145	98.58 78	91.15 169	96.69 260	97.39 224	87.29 375	91.37 283	96.71 230	88.39 115	99.52 118	87.33 347	97.13 191	97.73 262
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
tpm		90.25 355	89.74 353	91.76 406	93.92 404	79.73 463	93.98 423	93.54 456	88.28 342	91.99 266	93.25 417	77.51 357	97.44 411	87.30 348	87.94 374	98.12 230

GA-MVS		91.38 308	90.31 322	94.59 262	94.65 382	87.62 326	94.34 412	96.19 351	90.73 252	90.35 306	93.83 389	71.84 406	97.96 347	87.22 349	93.61 300	98.21 221

BH-untuned		92.94 237	92.62 229	93.92 314	97.22 173	86.16 369	96.40 288	96.25 346	90.06 280	89.79 326	96.17 267	83.19 237	98.35 293	87.19 350	97.27 185	97.24 288

v144192		91.06 326	90.28 324	93.39 346	93.66 414	87.23 336	96.83 240	97.07 271	87.43 371	89.69 330	94.28 369	81.48 281	98.00 338	87.18 351	84.92 412	94.93 393

RPSCF		90.75 339	90.86 295	90.42 434	96.84 211	76.29 483	95.61 352	96.34 334	83.89 430	91.38 282	97.87 128	76.45 365	98.78 219	87.16 352	92.23 315	96.20 319

test_f		80.57 453	79.62 455	83.41 474	83.38 506	67.80 502	93.57 444	93.72 454	80.80 468	77.91 483	87.63 483	33.40 505	92.08 496	87.14 353	79.04 454	90.34 487

PS-CasMVS		91.55 298	90.84 298	93.69 325	94.96 364	88.28 299	97.84 96	98.24 63	91.46 217	88.04 381	95.80 286	79.67 319	97.48 407	87.02 354	84.54 419	95.31 369

pm-mvs1		90.72 341	89.65 356	93.96 307	94.29 397	89.63 235	97.79 107	96.82 305	89.07 311	86.12 420	95.48 308	78.61 341	97.78 371	86.97 355	81.67 440	94.46 425

IterMVS		90.15 360	89.67 354	91.61 408	95.48 327	83.72 414	94.33 413	96.12 354	89.99 281	87.31 396	94.15 378	75.78 373	96.27 449	86.97 355	86.89 388	94.83 403
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
F-COLMAP		93.58 206	92.98 212	95.37 214	98.40 88	88.98 272	97.18 204	97.29 240	87.75 363	90.49 303	97.10 208	85.21 197	99.50 122	86.70 357	96.72 211	97.63 266

PVSNet	86.66 18	92.24 267	91.74 262	93.73 321	97.77 142	83.69 416	92.88 456	96.72 309	87.91 353	93.00 242	94.86 333	78.51 342	99.05 189	86.53 358	97.45 175	98.47 195

v1192		91.07 325	90.23 328	93.58 336	93.70 411	87.82 322	96.73 254	97.07 271	87.77 361	89.58 333	94.32 367	80.90 294	97.97 343	86.52 359	85.48 399	94.95 389

新几何1				97.32 63	98.60 75	93.59 65		97.75 150	81.58 462	95.75 142	97.85 132	90.04 89	99.67 78	86.50 360	99.13 97	98.69 173

v10		91.04 327	90.23 328	93.49 342	94.12 399	88.16 310	97.32 185	97.08 268	88.26 343	88.29 373	94.22 375	82.17 267	97.97 343	86.45 361	84.12 423	94.33 430

v1921920		90.85 336	90.03 339	93.29 350	93.55 418	86.96 345	96.74 253	97.04 280	87.36 373	89.52 337	94.34 364	80.23 309	97.97 343	86.27 362	85.21 405	94.94 391

MDTV_nov1_ep13_2view						70.35 496	93.10 453		83.88 431	93.55 225		82.47 261		86.25 363		98.38 205

test_post1							92.81 459				16.58 551	80.53 302	97.68 380	86.20 364

SCA		91.84 282	91.18 284	93.83 316	95.59 321	84.95 399	94.72 394	95.58 379	90.82 248	92.25 259	93.69 396	75.80 371	98.10 319	86.20 364	95.98 236	98.45 197

PAPR		94.18 174	93.42 197	96.48 115	97.64 152	91.42 152	95.55 354	97.71 159	88.99 316	92.34 257	95.82 285	89.19 99	99.11 173	86.14 366	97.38 177	98.90 134

GBi-Net		91.35 311	90.27 325	94.59 262	96.51 258	91.18 165	97.50 157	96.93 291	88.82 325	89.35 341	94.51 352	73.87 388	97.29 421	86.12 367	88.82 364	95.31 369

test1		91.35 311	90.27 325	94.59 262	96.51 258	91.18 165	97.50 157	96.93 291	88.82 325	89.35 341	94.51 352	73.87 388	97.29 421	86.12 367	88.82 364	95.31 369

FMVSNet3		91.78 283	90.69 308	95.03 233	96.53 254	92.27 114	97.02 215	96.93 291	89.79 288	89.35 341	94.65 345	77.01 359	97.47 408	86.12 367	88.82 364	95.35 366

EPMVS		90.70 342	89.81 348	93.37 347	94.73 379	84.21 407	93.67 439	88.02 497	89.50 298	92.38 253	93.49 407	77.82 355	97.78 371	86.03 370	92.68 310	98.11 236

MVS		91.71 285	90.44 317	95.51 204	95.20 352	91.59 142	96.04 322	97.45 212	73.44 490	87.36 394	95.60 300	85.42 192	99.10 175	85.97 371	97.46 171	95.83 336

testdata2													99.67 78	85.96 372

K. test v3		87.64 396	86.75 398	90.32 435	93.02 435	79.48 469	96.61 270	92.08 477	90.66 258	80.25 473	94.09 381	67.21 446	96.65 443	85.96 372	80.83 444	94.83 403

WR-MVS_H		92.00 276	91.35 273	93.95 308	95.09 360	89.47 246	98.04 64	98.68 18	91.46 217	88.34 370	94.68 342	85.86 175	97.56 394	85.77 374	84.24 422	94.82 408

gg-mvs-nofinetune		87.82 393	85.61 407	94.44 275	94.46 389	89.27 259	91.21 476	84.61 507	80.88 465	89.89 324	74.98 513	71.50 409	97.53 403	85.75 375	97.21 187	96.51 311

tpm2		89.96 363	89.21 366	92.23 389	94.91 370	81.25 441	93.78 433	94.42 434	80.62 469	91.56 278	93.44 411	76.44 366	97.94 353	85.60 376	92.08 322	97.49 275

v1240		90.70 342	89.85 346	93.23 352	93.51 421	86.80 347	96.61 270	97.02 284	87.16 378	89.58 333	94.31 368	79.55 323	97.98 340	85.52 377	85.44 400	94.90 396

PEN-MVS		91.20 320	90.44 317	93.48 343	94.49 388	87.91 319	97.76 109	98.18 77	91.29 224	87.78 385	95.74 292	80.35 306	97.33 419	85.46 378	82.96 435	95.19 380

QAPM		93.45 215	92.27 242	96.98 86	96.77 226	92.62 100	98.39 29	98.12 87	84.50 422	88.27 374	97.77 145	82.39 263	99.81 36	85.40 379	98.81 115	98.51 189

SSC-MVS3.2		89.74 372	89.26 365	91.19 420	95.16 353	80.29 455	94.53 400	97.03 282	91.79 203	88.86 357	94.10 379	69.94 425	97.82 366	85.29 380	86.66 390	95.45 357

EU-MVSNet		88.72 385	88.90 373	88.20 456	93.15 433	74.21 489	96.63 269	94.22 443	85.18 411	87.32 395	95.97 276	76.16 368	94.98 470	85.27 381	86.17 392	95.41 359

BH-w/o		92.14 272	91.75 260	93.31 349	96.99 197	85.73 379	95.67 346	95.69 372	88.73 330	89.26 346	94.82 336	82.97 246	98.07 328	85.26 382	96.32 232	96.13 326

FMVSNet2		91.31 314	90.08 334	94.99 236	96.51 258	92.21 116	97.41 172	96.95 289	88.82 325	88.62 363	94.75 339	73.87 388	97.42 413	85.20 383	88.55 369	95.35 366

PM-MVS		83.48 443	81.86 449	88.31 455	87.83 491	77.59 477	93.43 445	91.75 479	86.91 381	80.63 469	89.91 462	44.42 500	95.84 455	85.17 384	76.73 463	91.50 480

LF4IMVS		87.94 392	87.25 389	89.98 440	92.38 452	80.05 461	94.38 410	95.25 398	87.59 368	84.34 440	94.74 340	64.31 467	97.66 385	84.83 385	87.45 379	92.23 469

PatchmatchNet		91.91 279	91.35 273	93.59 335	95.38 334	84.11 409	93.15 451	95.39 388	89.54 296	92.10 264	93.68 398	82.82 251	98.13 314	84.81 386	95.32 258	98.52 187
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
pmmvs6		87.81 394	86.19 402	92.69 374	91.32 460	86.30 363	97.34 182	96.41 331	80.59 470	84.05 448	94.37 361	67.37 445	97.67 381	84.75 387	79.51 451	94.09 437

v7n		90.76 338	89.86 345	93.45 345	93.54 419	87.60 327	97.70 125	97.37 229	88.85 322	87.65 387	94.08 382	81.08 289	98.10 319	84.68 388	83.79 429	94.66 421

SixPastTwentyTwo		89.15 378	88.54 378	90.98 422	93.49 422	80.28 456	96.70 258	94.70 423	90.78 249	84.15 444	95.57 301	71.78 407	97.71 379	84.63 389	85.07 408	94.94 391

TDRefinement		86.53 413	84.76 424	91.85 399	82.23 509	84.25 406	96.38 290	95.35 391	84.97 416	84.09 446	94.94 328	65.76 459	98.34 296	84.60 390	74.52 470	92.97 452

ACMH	87.59 16	90.53 347	89.42 361	93.87 315	96.21 281	87.92 317	97.24 195	96.94 290	88.45 338	83.91 449	96.27 262	71.92 405	98.62 266	84.43 391	89.43 356	95.05 387
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ACMH+	87.92 14	90.20 358	89.18 367	93.25 351	96.48 261	86.45 360	96.99 221	96.68 314	88.83 324	84.79 437	96.22 264	70.16 422	98.53 276	84.42 392	88.04 373	94.77 416

sc_t1		86.48 415	84.10 433	93.63 332	93.45 425	85.76 378	96.79 246	94.71 422	73.06 491	86.45 413	94.35 362	55.13 487	97.95 351	84.38 393	78.55 456	97.18 291

test_vis3_rt		72.73 460	70.55 463	79.27 480	80.02 514	68.13 501	93.92 428	74.30 519	76.90 483	58.99 510	73.58 516	20.29 519	95.37 467	84.16 394	72.80 479	74.31 512

FE-MVS		92.05 275	91.05 288	95.08 229	96.83 213	87.93 316	93.91 429	95.70 370	86.30 393	94.15 208	94.97 326	76.59 363	99.21 155	84.10 395	96.86 201	98.09 237

MS-PatchMatch		90.27 354	89.77 350	91.78 404	94.33 394	84.72 402	95.55 354	96.73 308	86.17 397	86.36 414	95.28 314	71.28 411	97.80 369	84.09 396	98.14 149	92.81 455

PatchMatch-RL		92.90 239	92.02 250	95.56 196	98.19 111	90.80 183	95.27 371	97.18 255	87.96 351	91.86 272	95.68 296	80.44 304	98.99 194	84.01 397	97.54 167	96.89 301

lessismore_v0				90.45 433	91.96 455	79.09 473		87.19 501		80.32 472	94.39 359	66.31 454	97.55 396	84.00 398	76.84 461	94.70 419

UWE-MVS		89.91 364	89.48 360	91.21 417	95.88 307	78.23 476	94.91 389	90.26 490	89.11 310	92.35 256	94.52 351	68.76 435	97.96 347	83.95 399	95.59 250	97.42 279

CMPMVS	62.92 21	85.62 432	84.92 421	87.74 459	89.14 475	73.12 493	94.17 419	96.80 306	73.98 487	73.65 491	94.93 329	66.36 452	97.61 390	83.95 399	91.28 333	92.48 464
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
MVP-Stereo		90.74 340	90.08 334	92.71 373	93.19 432	88.20 307	95.86 334	96.27 342	86.07 398	84.86 436	94.76 338	77.84 354	97.75 376	83.88 401	98.01 155	92.17 472
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
LS3D		93.57 208	92.61 230	96.47 116	97.59 158	91.61 140	97.67 127	97.72 155	85.17 412	90.29 307	98.34 75	84.60 209	99.73 62	83.85 402	98.27 143	98.06 240

DTE-MVSNet		90.56 346	89.75 352	93.01 360	93.95 403	87.25 334	97.64 135	97.65 163	90.74 251	87.12 398	95.68 296	79.97 314	97.00 432	83.33 403	81.66 441	94.78 415

BH-RMVSNet		92.72 249	91.97 252	94.97 240	97.16 178	87.99 315	96.15 315	95.60 377	90.62 261	91.87 271	97.15 203	78.41 344	98.57 273	83.16 404	97.60 166	98.36 207

pmmvs-eth3d		86.22 422	84.45 428	91.53 409	88.34 489	87.25 334	94.47 405	95.01 407	83.47 440	79.51 476	89.61 465	69.75 428	95.71 457	83.13 405	76.73 463	91.64 475

FMVSNet1		89.88 367	88.31 380	94.59 262	95.41 332	91.18 165	97.50 157	96.93 291	86.62 387	87.41 392	94.51 352	65.94 458	97.29 421	83.04 406	87.43 380	95.31 369

testing222		90.31 352	88.96 371	94.35 279	96.54 252	87.29 331	95.50 357	93.84 453	90.97 244	91.75 275	92.96 420	62.18 476	98.00 338	82.86 407	94.08 287	97.76 261

MDTV_nov1_ep13			90.76 301		95.22 350	80.33 453	93.03 454	95.28 395	88.14 348	92.84 248	93.83 389	81.34 283	98.08 324	82.86 407	94.34 277

TR-MVS		91.48 304	90.59 313	94.16 294	96.40 267	87.33 330	95.67 346	95.34 394	87.68 366	91.46 281	95.52 305	76.77 362	98.35 293	82.85 409	93.61 300	96.79 304

dmvs_re		90.21 357	89.50 359	92.35 381	95.47 331	85.15 392	95.70 345	94.37 438	90.94 247	88.42 367	93.57 405	74.63 383	95.67 459	82.80 410	89.57 355	96.22 318

JIA-IIPM		88.26 390	87.04 394	91.91 396	93.52 420	81.42 440	89.38 489	94.38 437	80.84 466	90.93 297	80.74 508	79.22 327	97.92 356	82.76 411	91.62 326	96.38 316

PVSNet_0	82.17 19	85.46 433	83.64 434	90.92 423	95.27 346	79.49 468	90.55 481	95.60 377	83.76 434	83.00 456	89.95 461	71.09 413	97.97 343	82.75 412	60.79 506	95.31 369

ambc				86.56 468	83.60 504	70.00 497	85.69 504	94.97 410		80.60 470	88.45 473	37.42 503	96.84 438	82.69 413	75.44 468	92.86 454

USDC		88.94 380	87.83 385	92.27 386	94.66 381	84.96 398	93.86 430	95.90 360	87.34 374	83.40 451	95.56 302	67.43 444	98.19 309	82.64 414	89.67 354	93.66 444

FE-MVSNET2		86.36 418	84.68 426	91.39 414	87.67 492	86.47 359	96.21 309	96.41 331	87.87 355	79.31 477	89.64 464	65.29 463	95.58 462	82.42 415	77.28 459	92.14 473

ITE_SJBPF				92.43 379	95.34 339	85.37 389		95.92 358	91.47 216	87.75 386	96.39 256	71.00 414	97.96 347	82.36 416	89.86 352	93.97 440

UnsupCasMVSNet_eth		85.99 426	84.45 428	90.62 431	89.97 470	82.40 433	93.62 442	97.37 229	89.86 283	78.59 481	92.37 431	65.25 465	95.35 468	82.27 417	70.75 489	94.10 435

GG-mvs-BLEND				93.62 333	93.69 412	89.20 261	92.39 467	83.33 510		87.98 383	89.84 463	71.00 414	96.87 437	82.08 418	95.40 257	94.80 411

ArgMatch-SfM		83.09 446	81.67 451	87.34 462	91.48 458	76.29 483	92.76 460	91.31 484	84.26 424	81.99 463	93.35 415	45.52 497	92.98 493	81.83 419	72.49 480	92.76 456

ArgMatch-Sym		83.08 447	81.73 450	87.11 463	91.53 457	76.72 480	92.86 457	91.54 481	83.66 436	82.34 459	93.45 410	44.99 498	92.15 495	81.78 420	73.46 477	92.47 465

thres600view7		92.49 253	91.60 265	95.18 224	97.91 134	89.47 246	97.65 131	94.66 424	92.18 192	93.33 234	94.91 330	78.06 351	99.10 175	81.61 421	94.06 291	96.98 295

LTVRE_ROB	88.41 13	90.99 329	89.92 344	94.19 290	96.18 289	89.55 242	96.31 299	97.09 267	87.88 354	85.67 427	95.91 280	78.79 339	98.57 273	81.50 422	89.98 350	94.44 427
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
tt0320-xc		84.83 437	82.33 445	92.31 384	93.66 414	86.20 367	96.17 314	94.06 445	71.26 493	82.04 462	92.22 438	55.07 488	96.72 442	81.49 423	75.04 469	94.02 438

tpmvs		89.83 370	89.15 368	91.89 398	94.92 368	80.30 454	93.11 452	95.46 387	86.28 394	88.08 380	92.65 424	80.44 304	98.52 277	81.47 424	89.92 351	96.84 302

thres100view900		92.43 255	91.58 266	94.98 238	97.92 133	89.37 252	97.71 122	94.66 424	92.20 188	93.31 235	94.90 331	78.06 351	99.08 180	81.40 425	94.08 287	96.48 313

tfpn200view9		92.38 258	91.52 269	94.95 242	97.85 137	89.29 256	97.41 172	94.88 416	92.19 190	93.27 237	94.46 357	78.17 347	99.08 180	81.40 425	94.08 287	96.48 313

thres400		92.42 256	91.52 269	95.12 228	97.85 137	89.29 256	97.41 172	94.88 416	92.19 190	93.27 237	94.46 357	78.17 347	99.08 180	81.40 425	94.08 287	96.98 295

mvs5depth		86.53 413	85.08 418	90.87 424	88.74 483	82.52 429	91.91 469	94.23 442	86.35 392	87.11 400	93.70 395	66.52 451	97.76 374	81.37 428	75.80 465	92.31 468

ETVMVS		90.52 348	89.14 369	94.67 259	96.81 218	87.85 321	95.91 332	93.97 449	89.71 290	92.34 257	92.48 429	65.41 461	97.96 347	81.37 428	94.27 280	98.21 221

DP-MVS		92.76 247	91.51 271	96.52 108	98.77 63	90.99 172	97.38 179	96.08 355	82.38 455	89.29 344	97.87 128	83.77 225	99.69 74	81.37 428	96.69 213	98.89 140

thres200		92.23 268	91.39 272	94.75 255	97.61 156	89.03 267	96.60 272	95.09 405	92.08 195	93.28 236	94.00 385	78.39 345	99.04 192	81.26 431	94.18 283	96.19 320

CR-MVSNet		90.82 337	89.77 350	93.95 308	94.45 390	87.19 337	90.23 483	95.68 374	86.89 382	92.40 251	92.36 434	80.91 292	97.05 428	81.09 432	93.95 292	97.60 271

tt0320		85.39 434	83.12 437	92.19 390	93.44 426	85.79 377	96.19 312	94.87 419	71.19 494	82.92 457	91.76 447	58.43 480	96.81 439	81.03 433	78.26 457	93.98 439

ttmdpeth		85.91 428	84.76 424	89.36 449	89.14 475	80.25 457	95.66 349	93.16 462	83.77 433	83.39 452	95.26 316	66.24 455	95.26 469	80.65 434	75.57 466	92.57 460

MSDG		91.42 306	90.24 327	94.96 241	97.15 181	88.91 274	93.69 438	96.32 335	85.72 403	86.93 407	96.47 251	80.24 308	98.98 195	80.57 435	95.05 265	96.98 295

dp		88.90 382	88.26 382	90.81 427	94.58 386	76.62 481	92.85 458	94.93 413	85.12 413	90.07 320	93.07 418	75.81 370	98.12 317	80.53 436	87.42 381	97.71 263

tpm cat1		88.36 388	87.21 391	91.81 402	95.13 358	80.55 450	92.58 464	95.70 370	74.97 486	87.45 390	91.96 443	78.01 353	98.17 311	80.39 437	88.74 367	96.72 306

KD-MVS_self_test		85.95 427	84.95 420	88.96 453	89.55 474	79.11 472	95.13 383	96.42 330	85.91 400	84.07 447	90.48 456	70.03 424	94.82 471	80.04 438	72.94 478	92.94 453

AllTest		90.23 356	88.98 370	93.98 304	97.94 131	86.64 351	96.51 277	95.54 382	85.38 407	85.49 429	96.77 228	70.28 420	99.15 166	80.02 439	92.87 304	96.15 324

TestCases				93.98 304	97.94 131	86.64 351		95.54 382	85.38 407	85.49 429	96.77 228	70.28 420	99.15 166	80.02 439	92.87 304	96.15 324

dtuonlycased		85.91 428	85.69 406	86.60 467	92.42 451	76.96 478	93.66 440	94.49 432	86.68 385	80.87 466	92.00 440	71.52 408	93.23 491	79.58 441	79.97 447	89.60 490

ADS-MVSNet2		89.45 375	88.59 377	92.03 393	95.86 308	82.26 434	90.93 478	94.32 441	83.23 444	91.28 291	91.81 445	79.01 335	95.99 451	79.52 442	91.39 331	97.84 256

ADS-MVSNet		89.89 366	88.68 376	93.53 339	95.86 308	84.89 400	90.93 478	95.07 406	83.23 444	91.28 291	91.81 445	79.01 335	97.85 362	79.52 442	91.39 331	97.84 256

our_test_3		88.78 384	87.98 384	91.20 419	92.45 449	82.53 428	93.61 443	95.69 372	85.77 402	84.88 435	93.71 394	79.99 313	96.78 441	79.47 444	86.24 391	94.28 433

EPNet_dtu		91.71 285	91.28 278	92.99 361	93.76 410	83.71 415	96.69 260	95.28 395	93.15 139	87.02 403	95.95 278	83.37 233	97.38 417	79.46 445	96.84 203	97.88 251
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
TransMVSNet (Re)		88.94 380	87.56 386	93.08 359	94.35 393	88.45 294	97.73 116	95.23 399	87.47 370	84.26 442	95.29 312	79.86 316	97.33 419	79.44 446	74.44 472	93.45 448

EG-PatchMatch MVS		87.02 408	85.44 410	91.76 406	92.67 443	85.00 396	96.08 319	96.45 329	83.41 443	79.52 475	93.49 407	57.10 483	97.72 378	79.34 447	90.87 342	92.56 461

Patchmtry		88.64 386	87.25 389	92.78 371	94.09 400	86.64 351	89.82 487	95.68 374	80.81 467	87.63 388	92.36 434	80.91 292	97.03 429	78.86 448	85.12 407	94.67 420

FMVSNet5		87.29 401	85.79 405	91.78 404	94.80 375	87.28 332	95.49 358	95.28 395	84.09 427	83.85 450	91.82 444	62.95 471	94.17 478	78.48 449	85.34 403	93.91 441

COLMAP_ROB	87.81 15	90.40 351	89.28 364	93.79 319	97.95 130	87.13 340	96.92 228	95.89 362	82.83 447	86.88 409	97.18 200	73.77 391	99.29 148	78.44 450	93.62 299	94.95 389
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
Anonymous20240521		86.42 417	85.44 410	89.34 450	90.33 467	79.79 462	96.73 254	95.92 358	83.71 435	83.25 453	91.36 451	63.92 468	96.01 450	78.39 451	85.36 402	92.22 470

test0.0.03 1		89.37 377	88.70 375	91.41 413	92.47 448	85.63 380	95.22 375	92.70 468	91.11 239	86.91 408	93.65 400	79.02 333	93.19 492	78.00 452	89.18 358	95.41 359

MIMVSNet		88.50 387	86.76 397	93.72 323	94.84 373	87.77 323	91.39 472	94.05 446	86.41 391	87.99 382	92.59 427	63.27 469	95.82 456	77.44 453	92.84 306	97.57 273

MDA-MVSNet_test_wron		85.87 430	84.23 431	90.80 429	92.38 452	82.57 427	93.17 449	95.15 402	82.15 456	67.65 498	92.33 437	78.20 346	95.51 465	77.33 454	79.74 448	94.31 432

YYNet1		85.87 430	84.23 431	90.78 430	92.38 452	82.46 432	93.17 449	95.14 403	82.12 457	67.69 496	92.36 434	78.16 349	95.50 466	77.31 455	79.73 449	94.39 428

UnsupCasMVSNet_bld		82.13 451	79.46 456	90.14 437	88.00 490	82.47 431	90.89 480	96.62 322	78.94 476	75.61 486	84.40 499	56.63 484	96.31 448	77.30 456	66.77 498	91.63 476

KD-MVS_2432*1600		84.81 438	82.64 441	91.31 415	91.07 462	85.34 390	91.22 474	95.75 368	85.56 405	83.09 454	90.21 459	67.21 446	95.89 452	77.18 457	62.48 504	92.69 457

miper_refine_blended		84.81 438	82.64 441	91.31 415	91.07 462	85.34 390	91.22 474	95.75 368	85.56 405	83.09 454	90.21 459	67.21 446	95.89 452	77.18 457	62.48 504	92.69 457

PCF-MVS	89.48 11	91.56 297	89.95 342	96.36 128	96.60 240	92.52 105	92.51 465	97.26 246	79.41 474	88.90 354	96.56 247	84.04 223	99.55 110	77.01 459	97.30 183	97.01 294
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
WB-MVSnew		89.88 367	89.56 357	90.82 426	94.57 387	83.06 423	95.65 350	92.85 465	87.86 356	90.83 299	94.10 379	79.66 320	96.88 436	76.34 460	94.19 282	92.54 462

testgi		87.97 391	87.21 391	90.24 436	92.86 439	80.76 445	96.67 263	94.97 410	91.74 205	85.52 428	95.83 284	62.66 474	94.47 475	76.25 461	88.36 371	95.48 352

MASt3R-SfM		71.17 465	70.37 464	73.55 494	74.50 522	51.20 523	82.17 510	80.88 514	64.49 504	72.54 493	91.37 450	25.17 513	81.85 515	75.86 462	66.37 499	87.59 494

TinyColmap		86.82 411	85.35 413	91.21 417	94.91 370	82.99 424	93.94 426	94.02 448	83.58 437	81.56 464	94.68 342	62.34 475	98.13 314	75.78 463	87.35 384	92.52 463

ppachtmachnet_test		88.35 389	87.29 388	91.53 409	92.45 449	83.57 417	93.75 434	95.97 357	84.28 423	85.32 432	94.18 376	79.00 337	96.93 433	75.71 464	84.99 411	94.10 435

PAPM		91.52 301	90.30 323	95.20 223	95.30 345	89.83 227	93.38 447	96.85 303	86.26 395	88.59 364	95.80 286	84.88 206	98.15 312	75.67 465	95.93 238	97.63 266

WAC-MVS						79.53 466								75.56 466

myMVS_eth3d		87.18 404	86.38 400	89.58 445	95.16 353	79.53 466	95.00 386	93.93 451	88.55 335	86.96 404	91.99 441	56.23 485	94.00 481	75.47 467	94.11 284	95.20 377

CL-MVSNet_self_test		86.31 420	85.15 417	89.80 443	88.83 479	81.74 439	93.93 427	96.22 347	86.67 386	85.03 434	90.80 454	78.09 350	94.50 473	74.92 468	71.86 482	93.15 451

tfpnnormal		89.70 373	88.40 379	93.60 334	95.15 356	90.10 213	97.56 147	98.16 81	87.28 376	86.16 417	94.63 346	77.57 356	98.05 331	74.48 469	84.59 417	92.65 459

DSMNet-mixed		86.34 419	86.12 404	87.00 466	89.88 471	70.43 495	94.93 388	90.08 491	77.97 481	85.42 431	92.78 422	74.44 385	93.96 483	74.43 470	95.14 261	96.62 309

Patchmatch-test		89.42 376	87.99 383	93.70 324	95.27 346	85.11 393	88.98 490	94.37 438	81.11 463	87.10 401	93.69 396	82.28 264	97.50 406	74.37 471	94.76 270	98.48 194

LCM-MVSNet		72.55 461	69.39 466	82.03 476	70.81 531	65.42 507	90.12 485	94.36 440	55.02 512	65.88 500	81.72 505	24.16 514	89.96 498	74.32 472	68.10 496	90.71 486

new-patchmatchnet		83.18 445	81.87 448	87.11 463	86.88 496	75.99 485	93.70 436	95.18 401	85.02 415	77.30 484	88.40 474	65.99 457	93.88 484	74.19 473	70.18 490	91.47 481

MVStest1		82.38 450	80.04 454	89.37 448	87.63 493	82.83 425	95.03 385	93.37 459	73.90 488	73.50 492	94.35 362	62.89 472	93.25 490	73.80 474	65.92 500	92.04 474

testing3		87.67 395	86.88 396	90.05 439	96.14 295	80.71 446	97.10 210	92.85 465	90.15 278	87.54 389	94.55 349	55.70 486	94.10 479	73.77 475	94.10 286	95.35 366

MDA-MVSNet-bldmvs		85.00 435	82.95 440	91.17 421	93.13 434	83.33 418	94.56 399	95.00 408	84.57 421	65.13 502	92.65 424	70.45 419	95.85 454	73.57 476	77.49 458	94.33 430

pmmvs3		79.97 455	77.50 459	87.39 461	82.80 508	79.38 470	92.70 462	90.75 489	70.69 495	78.66 480	87.47 485	51.34 492	93.40 487	73.39 477	69.65 491	89.38 491

test_method		66.11 475	64.89 475	69.79 498	72.62 529	35.23 537	65.19 526	92.83 467	20.35 532	65.20 501	88.08 478	43.14 501	82.70 514	73.12 478	63.46 502	91.45 482

SD_0403		90.01 362	90.02 340	89.96 441	95.65 319	76.76 479	95.76 342	96.46 328	90.58 265	86.59 411	96.29 260	82.12 268	94.78 472	73.00 479	93.76 295	98.35 209

PatchT		88.87 383	87.42 387	93.22 353	94.08 401	85.10 394	89.51 488	94.64 426	81.92 458	92.36 254	88.15 477	80.05 312	97.01 431	72.43 480	93.65 298	97.54 274

Anonymous20231206		87.09 406	86.14 403	89.93 442	91.22 461	80.35 452	96.11 316	95.35 391	83.57 438	84.16 443	93.02 419	73.54 395	95.61 460	72.16 481	86.14 393	93.84 442

MVS-HIRNet		82.47 449	81.21 452	86.26 469	95.38 334	69.21 498	88.96 491	89.49 492	66.28 499	80.79 468	74.08 515	68.48 439	97.39 416	71.93 482	95.47 255	92.18 471

new_pmnet		82.89 448	81.12 453	88.18 457	89.63 472	80.18 459	91.77 470	92.57 469	76.79 484	75.56 488	88.23 476	61.22 477	94.48 474	71.43 483	82.92 436	89.87 488

TAPA-MVS	90.10 7	92.30 263	91.22 282	95.56 196	98.33 93	89.60 237	96.79 246	97.65 163	81.83 459	91.52 279	97.23 197	87.94 124	98.91 203	71.31 484	98.37 138	98.17 226
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
test20.03		86.14 424	85.40 412	88.35 454	90.12 468	80.06 460	95.90 333	95.20 400	88.59 331	81.29 465	93.62 401	71.43 410	92.65 494	71.26 485	81.17 443	92.34 466

tmp_tt		51.94 488	53.82 486	46.29 510	33.73 556	45.30 533	78.32 513	67.24 523	18.02 534	50.93 517	87.05 489	52.99 490	53.11 530	70.76 486	25.29 538	40.46 531

MIMVSNet1		84.93 436	83.05 438	90.56 432	89.56 473	84.84 401	95.40 362	95.35 391	83.91 429	80.38 471	92.21 439	57.23 482	93.34 488	70.69 487	82.75 438	93.50 446

usedtu_dtu_shiyan2		80.00 454	76.91 460	89.27 452	82.13 510	79.69 464	95.45 360	94.20 444	72.95 492	75.80 485	87.75 480	44.44 499	94.30 477	70.64 488	68.81 495	93.84 442

APD_test1		79.31 456	77.70 458	84.14 471	89.11 477	69.07 499	92.36 468	91.50 482	69.07 496	73.87 490	92.63 426	39.93 502	94.32 476	70.54 489	80.25 446	89.02 492

FE-MVSNET		83.85 441	81.97 447	89.51 446	87.19 495	83.19 421	95.21 377	93.17 460	83.45 441	78.90 479	89.05 469	65.46 460	93.84 485	69.71 490	75.56 467	91.51 478

RPMNet		88.98 379	87.05 393	94.77 253	94.45 390	87.19 337	90.23 483	98.03 111	77.87 482	92.40 251	87.55 484	80.17 310	99.51 119	68.84 491	93.95 292	97.60 271

UWE-MVS-28		86.81 412	86.41 399	88.02 458	92.87 438	74.60 488	95.38 364	86.70 503	88.17 345	87.28 397	94.67 344	70.83 416	93.30 489	67.45 492	94.31 278	96.17 321

N_pmnet		78.73 457	78.71 457	78.79 482	92.80 441	46.50 531	94.14 420	43.71 532	78.61 478	80.83 467	91.66 448	74.94 381	96.36 447	67.24 493	84.45 420	93.50 446

OpenMVS_ROB	81.14 20	84.42 440	82.28 446	90.83 425	90.06 469	84.05 411	95.73 344	94.04 447	73.89 489	80.17 474	91.53 449	59.15 478	97.64 386	66.92 494	89.05 361	90.80 485

DenseAffine		72.53 462	69.17 468	82.59 475	87.49 494	70.91 494	88.38 496	81.13 513	67.58 498	64.27 504	87.44 486	23.61 516	88.47 506	66.10 495	56.56 508	88.38 493

PDCNetPlus		61.05 480	58.26 483	69.44 499	75.52 520	55.68 521	81.49 511	51.76 529	62.45 506	51.54 516	82.02 503	23.69 515	78.90 520	65.91 496	29.91 532	73.74 513

DKM		67.96 472	64.19 477	79.27 480	83.41 505	64.35 508	86.88 502	68.11 522	63.15 505	59.36 508	86.08 494	16.45 528	86.15 509	64.54 497	49.73 513	87.32 495

PMMVS2		70.19 467	66.92 471	80.01 478	76.35 519	65.67 505	86.22 503	87.58 499	64.83 503	62.38 505	80.29 510	26.78 510	88.49 505	63.79 498	54.07 511	85.88 497

RoMa-SfM		70.64 466	67.48 470	80.09 477	84.70 501	66.61 503	88.62 494	73.09 520	65.10 502	64.98 503	88.91 470	22.38 517	87.00 507	63.51 499	56.06 509	86.67 496

test_0402		86.46 416	84.79 423	91.45 411	95.02 362	85.55 381	96.29 301	94.89 415	80.90 464	82.21 460	93.97 387	68.21 441	97.29 421	62.98 500	88.68 368	91.51 478

DeepMVS_CX				74.68 493	90.84 464	64.34 509		81.61 512	65.34 501	67.47 499	88.01 479	48.60 495	80.13 519	62.33 501	73.68 476	79.58 509

Syy-MVS		87.13 405	87.02 395	87.47 460	95.16 353	73.21 492	95.00 386	93.93 451	88.55 335	86.96 404	91.99 441	75.90 369	94.00 481	61.59 502	94.11 284	95.20 377

LoFTR		72.43 463	68.71 469	83.60 473	85.67 498	65.61 506	88.04 500	87.40 500	66.11 500	55.94 514	85.54 495	25.43 511	95.55 464	60.87 503	63.38 503	89.63 489

DKM-HiRes		64.02 478	59.97 481	76.17 489	79.46 515	59.20 515	84.48 507	58.37 527	58.52 509	56.03 513	83.71 500	13.19 534	83.72 513	60.49 504	45.50 515	85.59 499

RoMa-HiRes		64.40 477	60.91 480	74.89 492	78.66 516	58.85 517	85.22 506	58.46 526	58.65 508	59.29 509	86.60 493	16.97 525	83.91 512	59.14 505	45.20 516	81.91 508

PMatch-SfM		57.38 483	52.53 488	71.95 497	68.62 532	49.38 524	77.61 514	45.82 530	52.41 516	46.59 518	82.04 502	4.86 551	81.03 517	58.34 506	36.49 526	85.43 500

testf1		69.31 469	66.76 472	76.94 486	78.61 517	61.93 510	88.27 497	86.11 505	55.62 510	59.69 506	85.31 497	20.19 520	89.32 499	57.62 507	69.44 493	79.58 509

APD_test2		69.31 469	66.76 472	76.94 486	78.61 517	61.93 510	88.27 497	86.11 505	55.62 510	59.69 506	85.31 497	20.19 520	89.32 499	57.62 507	69.44 493	79.58 509

EGC-MVSNET		68.77 471	63.01 479	86.07 470	92.49 447	82.24 435	93.96 425	90.96 487	0.71 552	2.62 554	90.89 453	53.66 489	93.46 486	57.25 509	84.55 418	82.51 506

dmvs_testset		81.38 452	82.60 443	77.73 483	91.74 456	51.49 522	93.03 454	84.21 509	89.07 311	78.28 482	91.25 452	76.97 360	88.53 504	56.57 510	82.24 439	93.16 450

ELoFTR		60.03 481	55.86 484	72.52 495	67.65 533	48.49 526	76.21 515	75.14 518	53.94 514	45.93 519	79.98 511	9.14 536	85.06 511	55.39 511	39.36 524	84.02 504

PMatch-Up-SfM		52.53 486	47.58 491	67.36 501	63.24 536	43.29 534	72.10 517	34.71 542	47.03 517	43.51 520	79.07 512	3.90 554	75.83 521	54.68 512	30.02 531	82.95 505

FPMVS		71.27 464	69.85 465	75.50 490	74.64 521	59.03 516	91.30 473	91.50 482	58.80 507	57.92 511	88.28 475	29.98 508	85.53 510	53.43 513	82.84 437	81.95 507

ANet_high		63.94 479	59.58 482	77.02 485	61.24 538	66.06 504	85.66 505	87.93 498	78.53 479	42.94 521	71.04 517	25.42 512	80.71 518	52.60 514	30.83 529	84.28 503

MatchFormer		67.84 474	63.81 478	79.93 479	83.26 507	60.99 514	87.61 501	84.49 508	54.89 513	51.76 515	81.06 507	22.08 518	94.10 479	50.36 515	58.82 507	84.72 502

Gipuma		67.86 473	65.41 474	75.18 491	92.66 444	73.45 491	66.50 525	94.52 430	53.33 515	57.80 512	66.07 520	30.81 506	89.20 501	48.15 516	78.88 455	62.90 524
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
dongtai		69.99 468	69.33 467	71.98 496	88.78 480	61.64 512	89.86 486	59.93 525	75.67 485	74.96 489	85.45 496	50.19 493	81.66 516	43.86 517	55.27 510	72.63 515

PMVS	53.92 22	58.58 482	55.40 485	68.12 500	51.00 551	48.64 525	78.86 512	87.10 502	46.77 518	35.84 528	74.28 514	8.76 537	86.34 508	42.07 518	73.91 475	69.38 516
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MVE	50.73 23	53.25 485	48.81 490	66.58 503	65.34 534	57.50 518	72.49 516	70.94 521	40.15 521	39.28 525	63.51 521	6.89 540	73.48 525	38.29 519	42.38 521	68.76 518
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
GLUNet-SfM		46.44 490	41.21 499	62.14 504	51.92 548	38.44 536	58.72 528	57.51 528	34.08 522	34.61 529	67.84 519	11.40 535	74.90 522	35.48 520	19.30 543	73.08 514

WB-MVS		76.77 458	76.63 461	77.18 484	85.32 499	56.82 519	94.53 400	89.39 493	82.66 454	71.35 494	89.18 468	75.03 378	88.88 502	35.42 521	66.79 497	85.84 498

SP-DiffGlue		43.94 493	43.32 494	45.79 513	47.79 553	33.03 538	63.37 527	42.65 535	25.71 526	41.26 523	69.27 518	18.83 523	38.88 537	34.96 522	46.05 514	65.47 523

SSC-MVS		76.05 459	75.83 462	76.72 488	84.77 500	56.22 520	94.32 414	88.96 495	81.82 460	70.52 495	88.91 470	74.79 382	88.71 503	33.69 523	64.71 501	85.23 501

E-PMN		53.28 484	52.56 487	55.43 505	74.43 523	47.13 530	83.63 509	76.30 515	42.23 519	42.59 522	62.22 524	28.57 509	74.40 523	31.53 524	31.51 527	44.78 528

kuosan		65.27 476	64.66 476	67.11 502	83.80 502	61.32 513	88.53 495	60.77 524	68.22 497	67.67 497	80.52 509	49.12 494	70.76 526	29.67 525	53.64 512	69.26 517

EMVS		52.08 487	51.31 489	54.39 507	72.62 529	45.39 532	83.84 508	75.51 517	41.13 520	40.77 524	59.65 526	30.08 507	73.60 524	28.31 526	29.90 533	44.18 529

wuyk23d		25.11 506	24.57 510	26.74 522	73.98 525	39.89 535	57.88 529	9.80 557	12.27 546	10.39 548	6.97 552	7.03 539	36.44 538	25.43 527	17.39 545	3.89 549

XFeat-MNN		35.01 498	34.34 501	37.02 516	42.54 554	25.71 552	54.01 530	39.41 539	20.70 531	30.13 536	55.85 531	14.08 532	44.62 531	22.90 528	29.45 536	40.75 530

XFeat-NN		33.93 499	33.70 502	34.60 517	41.69 555	24.48 553	51.85 531	36.02 541	19.55 533	31.20 534	56.38 529	13.46 533	40.91 532	22.51 529	30.65 530	38.42 532

SP-SuperGlue		43.33 495	42.50 496	45.81 512	73.95 526	31.24 541	71.34 519	41.17 536	23.96 527	33.42 531	56.47 528	16.72 527	39.64 535	21.11 530	44.32 518	66.57 520

SP-LightGlue		43.37 494	42.49 497	46.03 511	74.26 524	31.37 540	71.24 520	40.98 537	23.86 528	33.18 532	56.34 530	16.78 526	39.73 534	21.09 531	44.68 517	66.97 519

SP-NN		42.37 496	41.40 498	45.29 515	72.86 528	30.45 543	70.32 523	39.16 540	22.21 529	31.32 533	56.73 527	15.45 529	39.53 536	20.27 532	44.25 519	65.88 522

SP-MNN		42.11 497	40.98 500	45.49 514	72.87 527	30.19 545	70.72 522	39.96 538	20.98 530	30.21 535	55.72 532	15.26 530	40.07 533	19.70 533	43.42 520	66.21 521

ALIKED-NN		46.19 491	43.87 493	53.16 509	80.39 513	47.77 528	69.82 524	43.65 533	27.89 524	36.60 527	63.35 522	17.30 524	61.29 529	15.84 534	39.98 523	50.41 527

ALIKED-LG		47.63 489	45.22 492	54.88 506	81.48 511	48.47 527	71.83 518	45.44 531	32.66 523	37.07 526	63.26 523	19.21 522	63.71 527	15.49 535	40.53 522	52.46 525

ALIKED-MNN		45.42 492	42.62 495	53.80 508	80.52 512	47.58 529	70.83 521	43.05 534	27.21 525	34.32 530	61.10 525	14.85 531	62.94 528	14.90 536	36.82 525	50.89 526

testmvs		13.36 516	16.33 519	4.48 534	5.04 557	2.26 560	93.18 448	3.28 558	2.70 550	8.24 552	21.66 548	2.29 557	2.19 553	7.58 537	2.96 551	9.00 548

test123		13.04 517	15.66 520	5.18 533	4.51 558	3.45 559	92.50 466	1.81 559	2.50 551	7.58 553	20.15 549	3.67 555	2.18 554	7.13 538	1.07 552	9.90 547

SIFT-NN		28.47 500	28.54 504	28.27 518	64.38 535	31.62 539	48.50 532	24.78 543	14.32 535	19.55 537	40.46 533	7.22 538	31.96 539	6.20 539	31.47 528	21.24 533

SIFT-MNN		27.50 501	27.40 505	27.80 519	61.71 537	30.57 542	46.59 533	24.66 544	14.04 536	17.35 538	39.90 534	6.52 541	31.80 540	6.13 540	29.65 534	21.04 534

SIFT-NN-NCMNet		27.16 502	27.05 506	27.51 520	59.97 540	30.42 544	46.49 534	24.52 545	13.94 538	17.23 539	39.47 535	6.39 542	31.40 541	5.94 541	29.49 535	20.72 536

SIFT-NN-UMatch		25.24 505	25.01 509	25.92 525	54.55 546	27.33 549	44.97 535	22.85 546	13.97 537	13.40 543	39.41 536	6.28 543	30.23 544	5.83 542	23.82 539	20.21 537

SIFT-NN-CMatch		25.59 504	25.23 508	26.67 523	56.47 544	28.89 548	42.75 537	22.52 548	13.89 539	16.98 540	39.39 537	6.26 544	30.38 543	5.77 543	22.99 540	20.75 535

SIFT-UMatch		24.03 508	23.67 513	25.10 526	57.10 543	26.49 551	42.43 538	20.05 551	13.49 542	12.40 545	38.51 539	5.45 549	30.07 546	5.56 544	18.08 544	18.74 540

SIFT-NN-PointCN		23.81 509	23.84 512	23.73 528	52.41 547	22.80 555	42.30 539	20.98 550	13.02 545	15.14 541	37.74 542	6.20 545	28.40 548	5.52 545	21.24 541	19.98 538

SIFT-ConvMatch		24.62 507	24.14 511	26.03 524	58.66 541	29.15 547	40.80 540	21.31 549	13.69 540	13.51 542	38.52 538	5.65 547	30.22 545	5.51 546	19.65 542	18.73 541

SIFT-NCM-Cal		25.87 503	25.57 507	26.75 521	60.60 539	29.37 546	44.96 536	22.64 547	13.57 541	11.67 546	37.90 540	5.81 546	31.26 542	5.32 547	27.70 537	19.63 539

SIFT-UM-Cal		22.52 512	22.27 515	23.27 529	56.41 545	23.87 554	39.94 541	16.81 554	13.33 544	10.54 547	37.90 540	5.16 550	28.36 549	5.23 548	15.12 548	17.57 543

SIFT-CM-Cal		23.18 511	22.70 514	24.60 527	57.42 542	26.79 550	37.63 542	18.36 552	13.35 543	12.57 544	37.37 543	5.54 548	28.79 547	5.17 549	16.92 547	18.23 542

SIFT-PCN-Cal		20.26 514	20.34 517	20.01 531	51.70 549	17.74 557	35.64 544	16.15 555	11.90 548	10.28 549	33.69 544	4.55 552	25.68 550	4.57 550	14.59 549	16.60 545

SIFT-PointCN		20.70 513	20.89 516	20.14 530	51.62 550	18.11 556	37.52 543	17.71 553	12.03 547	10.05 550	33.23 545	4.33 553	25.40 551	4.55 551	16.94 546	16.90 544

SIFT-NCMNet		17.70 515	17.74 518	17.60 532	49.47 552	16.50 558	30.22 545	10.39 556	11.77 549	8.79 551	29.74 547	3.61 556	22.42 552	3.97 552	11.69 550	13.89 546

mmdepth		0.00 520	0.00 523	0.00 535	0.00 559	0.00 561	0.00 546	0.00 560	0.00 553	0.00 555	0.00 553	0.00 558	0.00 555	0.00 553	0.00 553	0.00 550

monomultidepth		0.00 520	0.00 523	0.00 535	0.00 559	0.00 561	0.00 546	0.00 560	0.00 553	0.00 555	0.00 553	0.00 558	0.00 555	0.00 553	0.00 553	0.00 550

test_blank		0.00 520	0.00 523	0.00 535	0.00 559	0.00 561	0.00 546	0.00 560	0.00 553	0.00 555	0.00 553	0.00 558	0.00 555	0.00 553	0.00 553	0.00 550

uanet_test		0.00 520	0.00 523	0.00 535	0.00 559	0.00 561	0.00 546	0.00 560	0.00 553	0.00 555	0.00 553	0.00 558	0.00 555	0.00 553	0.00 553	0.00 550

DCPMVS		0.00 520	0.00 523	0.00 535	0.00 559	0.00 561	0.00 546	0.00 560	0.00 553	0.00 555	0.00 553	0.00 558	0.00 555	0.00 553	0.00 553	0.00 550

cdsmvs_eth3d_5k		23.24 510	30.99 503	0.00 535	0.00 559	0.00 561	0.00 546	97.63 167	0.00 553	0.00 555	96.88 223	84.38 214	0.00 555	0.00 553	0.00 553	0.00 550

pcd_1.5k_mvsjas		7.39 519	9.85 522	0.00 535	0.00 559	0.00 561	0.00 546	0.00 560	0.00 553	0.00 555	0.00 553	88.65 110	0.00 555	0.00 553	0.00 553	0.00 550

sosnet-low-res		0.00 520	0.00 523	0.00 535	0.00 559	0.00 561	0.00 546	0.00 560	0.00 553	0.00 555	0.00 553	0.00 558	0.00 555	0.00 553	0.00 553	0.00 550

sosnet		0.00 520	0.00 523	0.00 535	0.00 559	0.00 561	0.00 546	0.00 560	0.00 553	0.00 555	0.00 553	0.00 558	0.00 555	0.00 553	0.00 553	0.00 550

uncertanet		0.00 520	0.00 523	0.00 535	0.00 559	0.00 561	0.00 546	0.00 560	0.00 553	0.00 555	0.00 553	0.00 558	0.00 555	0.00 553	0.00 553	0.00 550

Regformer		0.00 520	0.00 523	0.00 535	0.00 559	0.00 561	0.00 546	0.00 560	0.00 553	0.00 555	0.00 553	0.00 558	0.00 555	0.00 553	0.00 553	0.00 550

ab-mvs-re		8.06 518	10.74 521	0.00 535	0.00 559	0.00 561	0.00 546	0.00 560	0.00 553	0.00 555	96.69 234	0.00 558	0.00 555	0.00 553	0.00 553	0.00 550

uanet		0.00 520	0.00 523	0.00 535	0.00 559	0.00 561	0.00 546	0.00 560	0.00 553	0.00 555	0.00 553	0.00 558	0.00 555	0.00 553	0.00 553	0.00 550

TestfortrainingZip				98.34 8	98.54 80	96.25 4	98.69 11	97.85 138	94.15 91	98.17 46	97.94 113	94.00 16	99.63 89		97.45 175	99.15 88

FOURS1					99.55 4	93.34 73	99.29 1	98.35 41	94.98 48	98.49 39

test_one_0601					99.32 27	95.20 22		98.25 61	95.13 42	98.48 40	98.87 33	95.16 8

eth-test2					0.00 559

eth-test					0.00 559

test_241102_ONE					99.42 10	95.30 19		98.27 55	95.09 45	99.19 13	98.81 39	95.54 5	99.65 80

save fliter					98.91 59	94.28 44	97.02 215	98.02 114	95.35 33

test0726					99.45 6	95.36 15	98.31 32	98.29 50	94.92 52	98.99 18	98.92 25	95.08 9

GSMVS																98.45 197

test_part2					99.28 31	95.74 9				98.10 49

sam_mvs1												82.76 252				98.45 197

sam_mvs												81.94 273

MTGPA								98.08 94

test_post											17.58 550	81.76 277	98.08 324

patchmatchnet-post											90.45 457	82.65 257	98.10 319

MTMP							97.86 92	82.03 511

TEST9					98.70 66	94.19 48	96.41 284	98.02 114	88.17 345	96.03 129	97.56 174	92.74 37	99.59 97

test_8					98.67 68	94.06 55	96.37 292	98.01 117	88.58 332	95.98 134	97.55 176	92.73 38	99.58 100

agg_prior					98.67 68	93.79 61		98.00 118		95.68 147			99.57 107

test_prior4						93.66 64	96.42 283

test_prior				97.23 70	98.67 68	92.99 85		98.00 118					99.41 134			99.29 75

新几何2							95.79 340

旧先验1					98.38 91	93.38 70		97.75 150			98.09 97	92.30 49			99.01 107	99.16 86

原ACMM2							95.67 346

test222					98.24 102	92.21 116	95.33 366	97.60 172	79.22 475	95.25 165	97.84 134	88.80 107			99.15 94	98.72 169

segment_acmp												92.89 34

testdata1							95.26 373		93.10 142

test12				97.65 48	98.46 81	94.26 45		97.66 161		95.52 156		90.89 79	99.46 128		99.25 80	99.22 82

plane_prior7					96.21 281	89.98 220

plane_prior6					96.10 300	90.00 216						81.32 284

plane_prior4											96.64 237

plane_prior3						90.00 216			94.46 80	91.34 285

plane_prior2							97.74 114		94.85 55

plane_prior1					96.14 295

plane_prior						89.99 218	97.24 195		94.06 95						92.16 319

n2								0.00 560

nn								0.00 560

door-mid								91.06 486

test11								97.88 131

door								91.13 485

HQP5-MVS						89.33 254

HQP-NCC					95.86 308		96.65 264		93.55 115	90.14 309

ACMP_Plane					95.86 308		96.65 264		93.55 115	90.14 309

HQP4-MVS										90.14 309			98.50 278			95.78 340

HQP3-MVS								97.39 224							92.10 320

HQP2-MVS												80.95 290

NP-MVS					95.99 306	89.81 228					95.87 281

ACMMP++_ref															90.30 349

ACMMP++															91.02 338

Test By Simon												88.73 109

High-resolution multi-view benchmark