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.

test_fmvsm_n_1920		97.55 16	97.89 3	96.53 105	98.41 85	91.73 130	98.01 66	99.02 1	96.37 13	99.30 7	98.92 23	92.39 44	99.79 46	99.16 14	99.46 46	98.08 228

PGM-MVS		96.81 58	96.53 69	97.65 47	99.35 25	93.53 65	97.65 129	98.98 2	92.22 177	97.14 76	98.44 64	91.17 71	99.85 21	94.35 163	99.46 46	99.57 36

MVS_111021_HR		96.68 69	96.58 68	96.99 84	98.46 79	92.31 110	96.20 305	98.90 3	94.30 86	95.86 134	97.74 144	92.33 45	99.38 136	96.04 96	99.42 56	99.28 77

test_fmvsmconf_n		97.49 21	97.56 16	97.29 64	97.44 165	92.37 107	97.91 85	98.88 4	95.83 19	98.92 23	99.05 14	91.45 61	99.80 40	99.12 16	99.46 46	99.69 14

lecture		97.58 15	97.63 12	97.43 58	99.37 19	92.93 86	98.86 7	98.85 5	95.27 34	98.65 36	98.90 25	91.97 52	99.80 40	97.63 38	99.21 83	99.57 36

ACMMP		96.27 86	95.93 89	97.28 66	99.24 33	92.62 98	98.25 40	98.81 6	92.99 140	94.56 182	98.39 68	88.96 102	99.85 21	94.57 157	97.63 163	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
MVS_111021_LR		96.24 87	96.19 85	96.39 124	98.23 105	91.35 153	96.24 302	98.79 7	93.99 95	95.80 136	97.65 154	89.92 91	99.24 149	95.87 100	99.20 88	98.58 172

patch_mono-2		96.83 57	97.44 24	95.01 225	99.05 45	85.39 371	96.98 217	98.77 8	94.70 66	97.99 51	98.66 43	93.61 21	99.91 1	97.67 37	99.50 40	99.72 13

fmvsm_s_conf0.5_n		96.85 54	97.13 31	96.04 149	98.07 120	90.28 203	97.97 77	98.76 9	94.93 48	98.84 28	99.06 12	88.80 106	99.65 79	99.06 18	98.63 123	98.18 213

fmvsm_l_conf0.5_n		97.65 9	97.75 8	97.34 61	98.21 106	92.75 92	97.83 98	98.73 10	95.04 45	99.30 7	98.84 36	93.34 24	99.78 49	99.32 7	99.13 98	99.50 52

fmvsm_s_conf0.5_n_a		96.75 62	96.93 46	96.20 140	97.64 151	90.72 185	98.00 67	98.73 10	94.55 73	98.91 24	99.08 8	88.22 118	99.63 88	98.91 21	98.37 136	98.25 208

fmvsm_s_conf0.5_n_10		97.29 31	97.40 26	96.97 86	98.24 100	91.96 126	97.89 88	98.72 12	96.77 7	99.46 3	99.06 12	87.78 127	99.84 26	99.40 4	99.27 75	99.12 92

fmvsm_l_conf0.5_n_9		97.59 13	97.79 6	96.97 86	98.28 94	91.49 144	97.61 138	98.71 13	97.10 5	99.70 1	98.93 22	90.95 76	99.77 52	99.35 6	99.53 33	99.65 20

FC-MVSNet-test		93.94 183	93.57 175	95.04 223	95.48 316	91.45 149	98.12 55	98.71 13	93.37 122	90.23 296	96.70 222	87.66 129	97.85 347	91.49 227	90.39 336	95.83 322

UniMVSNet (Re)		93.31 210	92.55 223	95.61 189	95.39 322	93.34 71	97.39 172	98.71 13	93.14 135	90.10 305	94.83 324	87.71 128	98.03 320	91.67 225	83.99 411	95.46 341

MED-MVS test				98.00 23	99.56 1	94.50 35	98.69 11	98.70 16	93.45 118	98.73 30	98.53 51		99.86 9	97.40 50	99.58 23	99.65 20

MED-MVS		97.91 4	97.88 4	98.00 23	99.56 1	94.50 35	98.69 11	98.70 16	94.23 87	98.73 30	98.53 51	95.46 7	99.86 9	97.40 50	99.58 23	99.65 20

TestfortrainingZip a		97.92 3	97.70 10	98.58 3	99.56 1	96.08 5	98.69 11	98.70 16	93.45 118	98.73 30	98.53 51	95.46 7	99.86 9	96.63 69	99.58 23	99.80 1

fmvsm_l_conf0.5_n_a		97.63 11	97.76 7	97.26 68	98.25 99	92.59 100	97.81 103	98.68 19	94.93 48	99.24 10	98.87 31	93.52 22	99.79 46	99.32 7	99.21 83	99.40 66

FIs		94.09 174	93.70 171	95.27 212	95.70 305	92.03 122	98.10 56	98.68 19	93.36 124	90.39 293	96.70 222	87.63 132	97.94 338	92.25 205	90.50 335	95.84 321

WR-MVS_H		92.00 267	91.35 264	93.95 294	95.09 349	89.47 239	98.04 63	98.68 19	91.46 208	88.34 357	94.68 331	85.86 170	97.56 378	85.77 357	84.24 409	94.82 391

fmvsm_s_conf0.5_n_4		96.75 62	97.07 34	95.79 173	97.76 142	89.57 232	97.66 128	98.66 22	95.36 30	99.03 16	98.90 25	88.39 114	99.73 61	99.17 13	98.66 121	98.08 228

VPA-MVSNet		93.24 212	92.48 228	95.51 199	95.70 305	92.39 106	97.86 91	98.66 22	92.30 174	92.09 254	95.37 299	80.49 291	98.40 274	93.95 169	85.86 382	95.75 330

fmvsm_l_conf0.5_n_3		97.64 10	97.60 13	97.79 34	98.14 113	93.94 56	97.93 83	98.65 24	96.70 8	99.38 5	99.07 11	89.92 91	99.81 35	99.16 14	99.43 53	99.61 30

fmvsm_s_conf0.5_n_3		97.15 36	97.36 28	96.52 107	97.98 126	91.19 161	97.84 95	98.65 24	97.08 6	99.25 9	99.10 6	87.88 125	99.79 46	99.32 7	99.18 90	98.59 171

fmvsm_s_conf0.5_n_8		97.32 28	97.48 23	96.85 88	98.28 94	91.07 169	97.76 108	98.62 26	97.53 2	99.20 12	99.12 5	88.24 117	99.81 35	99.41 3	99.17 91	99.67 15

fmvsm_s_conf0.5_n_2		96.62 70	96.82 55	96.02 151	97.98 126	90.43 195	97.50 153	98.59 27	96.59 10	99.31 6	99.08 8	84.47 204	99.75 58	99.37 5	98.45 133	97.88 241

UniMVSNet_NR-MVSNet		93.37 208	92.67 217	95.47 205	95.34 328	92.83 89	97.17 200	98.58 28	92.98 145	90.13 301	95.80 275	88.37 116	97.85 347	91.71 222	83.93 412	95.73 332

CSCG		96.05 90	95.91 90	96.46 117	99.24 33	90.47 192	98.30 33	98.57 29	89.01 303	93.97 203	97.57 164	92.62 40	99.76 54	94.66 151	99.27 75	99.15 87

fmvsm_s_conf0.5_n_9		97.33 27	97.57 15	96.62 101	98.43 82	90.32 202	97.80 104	98.53 30	97.24 4	99.62 2	99.14 2	88.65 109	99.80 40	99.54 1	99.15 95	99.74 9

fmvsm_s_conf0.5_n_6		97.08 39	97.17 30	96.81 89	97.28 170	91.73 130	97.75 110	98.50 31	94.86 52	99.22 11	98.78 40	89.75 94	99.76 54	99.10 17	99.29 73	98.94 121

MSLP-MVS++		96.94 48	97.06 35	96.59 102	98.72 64	91.86 128	97.67 125	98.49 32	94.66 69	97.24 72	98.41 67	92.31 47	98.94 195	96.61 71	99.46 46	98.96 114

HyFIR lowres test		93.66 195	92.92 205	95.87 162	98.24 100	89.88 218	94.58 386	98.49 32	85.06 401	93.78 206	95.78 279	82.86 239	98.67 248	91.77 220	95.71 234	99.07 100

CHOSEN 1792x2688		94.15 169	93.51 181	96.06 147	98.27 96	89.38 244	95.18 370	98.48 34	85.60 391	93.76 207	97.11 197	83.15 229	99.61 90	91.33 230	98.72 119	99.19 83

fmvsm_s_conf0.5_n_7		96.45 77	96.80 57	95.37 208	97.29 169	88.38 285	97.23 194	98.47 35	95.14 39	98.43 41	99.09 7	87.58 133	99.72 65	98.80 25	99.21 83	98.02 232

fmvsm_s_conf0.5_n_5		97.00 45	96.97 43	97.09 79	97.58 161	92.56 101	97.68 124	98.47 35	94.02 93	98.90 25	98.89 28	88.94 103	99.78 49	99.18 12	99.03 107	98.93 125

PHI-MVS		96.77 60	96.46 76	97.71 45	98.40 86	94.07 52	98.21 47	98.45 37	89.86 274	97.11 78	98.01 104	92.52 42	99.69 73	96.03 97	99.53 33	99.36 72

fmvsm_s_conf0.1_n		96.58 73	96.77 60	96.01 154	96.67 228	90.25 204	97.91 85	98.38 38	94.48 77	98.84 28	99.14 2	88.06 120	99.62 89	98.82 23	98.60 125	98.15 217

PVSNet_BlendedMVS		94.06 175	93.92 165	94.47 261	98.27 96	89.46 241	96.73 250	98.36 39	90.17 266	94.36 188	95.24 307	88.02 121	99.58 98	93.44 183	90.72 331	94.36 412

PVSNet_Blended		94.87 144	94.56 143	95.81 169	98.27 96	89.46 241	95.47 350	98.36 39	88.84 312	94.36 188	96.09 264	88.02 121	99.58 98	93.44 183	98.18 145	98.40 193

3Dnovator	91.36 5	95.19 126	94.44 152	97.44 57	96.56 243	93.36 70	98.65 16	98.36 39	94.12 90	89.25 335	98.06 98	82.20 256	99.77 52	93.41 185	99.32 71	99.18 84

FOURS1					99.55 4	93.34 71	99.29 1	98.35 42	94.98 46	98.49 39

DPE-MVS		97.86 6	97.65 11	98.47 6	99.17 38	95.78 8	97.21 197	98.35 42	95.16 38	98.71 35	98.80 38	95.05 12	99.89 3	96.70 68	99.73 1	99.73 12
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
ME-MVS		97.54 17	97.39 27	98.00 23	99.21 36	94.50 35	97.75 110	98.34 44	94.23 87	98.15 46	98.53 51	93.32 27	99.84 26	97.40 50	99.58 23	99.65 20

fmvsm_s_conf0.1_n_a		96.40 79	96.47 73	96.16 142	95.48 316	90.69 186	97.91 85	98.33 45	94.07 91	98.93 20	99.14 2	87.44 141	99.61 90	98.63 26	98.32 138	98.18 213

HFP-MVS		97.14 37	96.92 47	97.83 30	99.42 10	94.12 50	98.52 20	98.32 46	93.21 127	97.18 73	98.29 84	92.08 49	99.83 31	95.63 113	99.59 19	99.54 45

ACMMPR		97.07 41	96.84 51	97.79 34	99.44 9	93.88 57	98.52 20	98.31 47	93.21 127	97.15 75	98.33 78	91.35 65	99.86 9	95.63 113	99.59 19	99.62 27

test_fmvsmvis_n_1920		96.70 65	96.84 51	96.31 129	96.62 230	91.73 130	97.98 71	98.30 48	96.19 14	96.10 124	98.95 20	89.42 95	99.76 54	98.90 22	99.08 102	97.43 268

APDe-MVS		97.82 7	97.73 9	98.08 19	99.15 39	94.82 29	98.81 8	98.30 48	94.76 64	98.30 43	98.90 25	93.77 19	99.68 75	97.93 29	99.69 3	99.75 7
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
test0726					99.45 6	95.36 14	98.31 32	98.29 50	94.92 50	98.99 18	98.92 23	95.08 10

MSP-MVS		97.59 13	97.54 17	97.73 42	99.40 14	93.77 61	98.53 19	98.29 50	95.55 27	98.56 38	97.81 136	93.90 17	99.65 79	96.62 70	99.21 83	99.77 3
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
DVP-MVS++		98.06 1	97.99 1	98.28 10	98.67 67	95.39 12	99.29 1	98.28 52	94.78 61	98.93 20	98.87 31	96.04 2	99.86 9	97.45 46	99.58 23	99.59 32

test_0728_SECOND				98.51 5	99.45 6	95.93 6	98.21 47	98.28 52					99.86 9	97.52 42	99.67 6	99.75 7

CP-MVS		97.02 43	96.81 56	97.64 49	99.33 26	93.54 64	98.80 9	98.28 52	92.99 140	96.45 111	98.30 83	91.90 53	99.85 21	95.61 115	99.68 4	99.54 45

test_fmvsmconf0.1_n		97.09 38	97.06 35	97.19 73	95.67 307	92.21 114	97.95 80	98.27 55	95.78 23	98.40 42	99.00 16	89.99 89	99.78 49	99.06 18	99.41 59	99.59 32

SED-MVS		98.05 2	97.99 1	98.24 11	99.42 10	95.30 18	98.25 40	98.27 55	95.13 40	99.19 13	98.89 28	95.54 5	99.85 21	97.52 42	99.66 10	99.56 40

test_241102_TWO								98.27 55	95.13 40	98.93 20	98.89 28	94.99 13	99.85 21	97.52 42	99.65 13	99.74 9

test_241102_ONE					99.42 10	95.30 18		98.27 55	95.09 43	99.19 13	98.81 37	95.54 5	99.65 79

SF-MVS		97.39 24	97.13 31	98.17 16	99.02 48	95.28 20	98.23 44	98.27 55	92.37 171	98.27 44	98.65 45	93.33 25	99.72 65	96.49 75	99.52 35	99.51 49

SteuartSystems-ACMMP		97.62 12	97.53 18	97.87 28	98.39 88	94.25 44	98.43 27	98.27 55	95.34 32	98.11 47	98.56 47	94.53 14	99.71 67	96.57 73	99.62 17	99.65 20
Skip Steuart: Steuart Systems R&D Blog.
test_one_0601					99.32 27	95.20 21		98.25 61	95.13 40	98.48 40	98.87 31	95.16 9

PVSNet_Blended_VisFu		95.27 116	94.91 125	96.38 125	98.20 107	90.86 178	97.27 188	98.25 61	90.21 265	94.18 196	97.27 186	87.48 140	99.73 61	93.53 180	97.77 161	98.55 174

region2R		97.07 41	96.84 51	97.77 38	99.46 5	93.79 59	98.52 20	98.24 63	93.19 130	97.14 76	98.34 75	91.59 60	99.87 7	95.46 119	99.59 19	99.64 25

PS-CasMVS		91.55 288	90.84 288	93.69 311	94.96 353	88.28 288	97.84 95	98.24 63	91.46 208	88.04 368	95.80 275	79.67 307	97.48 390	87.02 337	84.54 406	95.31 355

DU-MVS		92.90 230	92.04 239	95.49 202	94.95 354	92.83 89	97.16 201	98.24 63	93.02 139	90.13 301	95.71 282	83.47 221	97.85 347	91.71 222	83.93 412	95.78 326

9.14			96.75 61		98.93 56		97.73 115	98.23 66	91.28 217	97.88 55	98.44 64	93.00 29	99.65 79	95.76 106	99.47 45

reproduce_model		97.51 20	97.51 20	97.50 54	98.99 52	93.01 82	97.79 106	98.21 67	95.73 24	97.99 51	99.03 15	92.63 39	99.82 33	97.80 31	99.42 56	99.67 15

D2MVS		91.30 305	90.95 282	92.35 364	94.71 369	85.52 365	96.18 307	98.21 67	88.89 310	86.60 397	93.82 380	79.92 303	97.95 336	89.29 280	90.95 328	93.56 427

reproduce-ours		97.53 18	97.51 20	97.60 51	98.97 53	93.31 73	97.71 120	98.20 69	95.80 21	97.88 55	98.98 18	92.91 30	99.81 35	97.68 33	99.43 53	99.67 15

our_new_method		97.53 18	97.51 20	97.60 51	98.97 53	93.31 73	97.71 120	98.20 69	95.80 21	97.88 55	98.98 18	92.91 30	99.81 35	97.68 33	99.43 53	99.67 15

SDMVSNet		94.17 167	93.61 174	95.86 165	98.09 116	91.37 151	97.35 176	98.20 69	93.18 132	91.79 262	97.28 184	79.13 315	98.93 196	94.61 154	92.84 294	97.28 276

XVS		97.18 34	96.96 45	97.81 32	99.38 17	94.03 54	98.59 17	98.20 69	94.85 53	96.59 99	98.29 84	91.70 56	99.80 40	95.66 108	99.40 61	99.62 27

X-MVStestdata		91.71 276	89.67 343	97.81 32	99.38 17	94.03 54	98.59 17	98.20 69	94.85 53	96.59 99	32.69 489	91.70 56	99.80 40	95.66 108	99.40 61	99.62 27

ACMMP_NAP		97.20 33	96.86 49	98.23 12	99.09 40	95.16 23	97.60 139	98.19 74	92.82 154	97.93 54	98.74 42	91.60 59	99.86 9	96.26 80	99.52 35	99.67 15

CP-MVSNet		91.89 272	91.24 271	93.82 303	95.05 350	88.57 276	97.82 100	98.19 74	91.70 197	88.21 363	95.76 280	81.96 261	97.52 388	87.86 306	84.65 400	95.37 351

ZNCC-MVS		96.96 46	96.67 64	97.85 29	99.37 19	94.12 50	98.49 24	98.18 76	92.64 161	96.39 113	98.18 91	91.61 58	99.88 4	95.59 118	99.55 30	99.57 36

SMA-MVS		97.35 25	97.03 40	98.30 9	99.06 44	95.42 11	97.94 81	98.18 76	90.57 256	98.85 27	98.94 21	93.33 25	99.83 31	96.72 66	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
PEN-MVS		91.20 310	90.44 306	93.48 327	94.49 377	87.91 304	97.76 108	98.18 76	91.29 214	87.78 372	95.74 281	80.35 294	97.33 401	85.46 361	82.96 422	95.19 366

DELS-MVS		96.61 71	96.38 80	97.30 63	97.79 140	93.19 78	95.96 319	98.18 76	95.23 35	95.87 133	97.65 154	91.45 61	99.70 72	95.87 100	99.44 52	99.00 109
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
tfpnnormal		89.70 362	88.40 368	93.60 319	95.15 345	90.10 207	97.56 144	98.16 80	87.28 364	86.16 404	94.63 335	77.57 343	98.05 316	74.48 448	84.59 404	92.65 440

VNet		95.89 98	95.45 101	97.21 71	98.07 120	92.94 85	97.50 153	98.15 81	93.87 99	97.52 62	97.61 160	85.29 188	99.53 112	95.81 105	95.27 247	99.16 85

DeepPCF-MVS	93.97 1	96.61 71	97.09 33	95.15 216	98.09 116	86.63 337	96.00 317	98.15 81	95.43 28	97.95 53	98.56 47	93.40 23	99.36 137	96.77 63	99.48 44	99.45 59

SD-MVS		97.41 23	97.53 18	97.06 82	98.57 78	94.46 38	97.92 84	98.14 83	94.82 57	99.01 17	98.55 49	94.18 16	97.41 397	96.94 58	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
GST-MVS		96.85 54	96.52 70	97.82 31	99.36 23	94.14 49	98.29 34	98.13 84	92.72 157	96.70 91	98.06 98	91.35 65	99.86 9	94.83 140	99.28 74	99.47 58

UA-Net		95.95 95	95.53 97	97.20 72	97.67 147	92.98 84	97.65 129	98.13 84	94.81 59	96.61 97	98.35 72	88.87 104	99.51 117	90.36 255	97.35 174	99.11 94

QAPM		93.45 206	92.27 233	96.98 85	96.77 221	92.62 98	98.39 29	98.12 86	84.50 409	88.27 361	97.77 140	82.39 253	99.81 35	85.40 362	98.81 115	98.51 179

Vis-MVSNet		95.23 121	94.81 131	96.51 111	97.18 175	91.58 141	98.26 39	98.12 86	94.38 84	94.90 171	98.15 93	82.28 254	98.92 198	91.45 229	98.58 127	99.01 106
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
OpenMVS	89.19 12	92.86 233	91.68 254	96.40 122	95.34 328	92.73 94	98.27 37	98.12 86	84.86 404	85.78 410	97.75 141	78.89 325	99.74 59	87.50 327	98.65 122	96.73 293

TranMVSNet+NR-MVSNet		92.50 242	91.63 255	95.14 217	94.76 365	92.07 119	97.53 150	98.11 89	92.90 151	89.56 323	96.12 259	83.16 228	97.60 375	89.30 279	83.20 421	95.75 330

CPTT-MVS		95.57 108	95.19 112	96.70 92	99.27 31	91.48 146	98.33 31	98.11 89	87.79 349	95.17 161	98.03 101	87.09 148	99.61 90	93.51 181	99.42 56	99.02 103

APD-MVS		96.95 47	96.60 66	98.01 21	99.03 47	94.93 28	97.72 118	98.10 91	91.50 206	98.01 50	98.32 80	92.33 45	99.58 98	94.85 137	99.51 38	99.53 48
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
mPP-MVS		96.86 52	96.60 66	97.64 49	99.40 14	93.44 66	98.50 23	98.09 92	93.27 126	95.95 131	98.33 78	91.04 73	99.88 4	95.20 122	99.57 29	99.60 31

ZD-MVS					99.05 45	94.59 33		98.08 93	89.22 296	97.03 81	98.10 94	92.52 42	99.65 79	94.58 156	99.31 72

MTGPA								98.08 93

MTAPA		97.08 39	96.78 59	97.97 27	99.37 19	94.42 40	97.24 190	98.08 93	95.07 44	96.11 123	98.59 46	90.88 79	99.90 2	96.18 92	99.50 40	99.58 35

CNVR-MVS		97.68 8	97.44 24	98.37 8	98.90 59	95.86 7	97.27 188	98.08 93	95.81 20	97.87 58	98.31 81	94.26 15	99.68 75	97.02 57	99.49 43	99.57 36

DP-MVS Recon		95.68 103	95.12 116	97.37 60	99.19 37	94.19 46	97.03 208	98.08 93	88.35 330	95.09 163	97.65 154	89.97 90	99.48 124	92.08 214	98.59 126	98.44 190

SR-MVS		97.01 44	96.86 49	97.47 56	99.09 40	93.27 75	97.98 71	98.07 98	93.75 102	97.45 64	98.48 61	91.43 63	99.59 95	96.22 83	99.27 75	99.54 45

MCST-MVS		97.18 34	96.84 51	98.20 15	99.30 29	95.35 16	97.12 204	98.07 98	93.54 112	96.08 125	97.69 149	93.86 18	99.71 67	96.50 74	99.39 63	99.55 43

NR-MVSNet		92.34 251	91.27 270	95.53 194	94.95 354	93.05 81	97.39 172	98.07 98	92.65 159	84.46 421	95.71 282	85.00 195	97.77 358	89.71 267	83.52 418	95.78 326

MP-MVS-pluss		96.70 65	96.27 83	97.98 26	99.23 35	94.71 30	96.96 219	98.06 101	90.67 246	95.55 147	98.78 40	91.07 72	99.86 9	96.58 72	99.55 30	99.38 70
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
APD-MVS_3200maxsize		96.81 58	96.71 63	97.12 76	99.01 51	92.31 110	97.98 71	98.06 101	93.11 136	97.44 65	98.55 49	90.93 77	99.55 108	96.06 93	99.25 80	99.51 49

MP-MVS		96.77 60	96.45 77	97.72 43	99.39 16	93.80 58	98.41 28	98.06 101	93.37 122	95.54 149	98.34 75	90.59 83	99.88 4	94.83 140	99.54 32	99.49 54
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
HPM-MVS_fast		96.51 74	96.27 83	97.22 70	99.32 27	92.74 93	98.74 10	98.06 101	90.57 256	96.77 88	98.35 72	90.21 86	99.53 112	94.80 144	99.63 16	99.38 70

HPM-MVS		96.69 67	96.45 77	97.40 59	99.36 23	93.11 80	98.87 6	98.06 101	91.17 225	96.40 112	97.99 107	90.99 74	99.58 98	95.61 115	99.61 18	99.49 54
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
sss		94.51 158	93.80 167	96.64 94	97.07 181	91.97 124	96.32 294	98.06 101	88.94 308	94.50 185	96.78 217	84.60 201	99.27 147	91.90 215	96.02 224	98.68 165

DeepC-MVS	93.07 3	96.06 89	95.66 94	97.29 64	97.96 128	93.17 79	97.30 182	98.06 101	93.92 97	93.38 222	98.66 43	86.83 150	99.73 61	95.60 117	99.22 82	98.96 114
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
NCCC		97.30 29	97.03 40	98.11 18	98.77 62	95.06 26	97.34 177	98.04 108	95.96 15	97.09 79	97.88 123	93.18 28	99.71 67	95.84 104	99.17 91	99.56 40

DeepC-MVS_fast	93.89 2	96.93 49	96.64 65	97.78 36	98.64 73	94.30 41	97.41 167	98.04 108	94.81 59	96.59 99	98.37 70	91.24 68	99.64 87	95.16 124	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
SR-MVS-dyc-post		96.88 51	96.80 57	97.11 78	99.02 48	92.34 108	97.98 71	98.03 110	93.52 115	97.43 67	98.51 56	91.40 64	99.56 106	96.05 94	99.26 78	99.43 63

RE-MVS-def			96.72 62		99.02 48	92.34 108	97.98 71	98.03 110	93.52 115	97.43 67	98.51 56	90.71 81		96.05 94	99.26 78	99.43 63

RPMNet		88.98 368	87.05 382	94.77 244	94.45 379	87.19 321	90.23 463	98.03 110	77.87 462	92.40 240	87.55 466	80.17 298	99.51 117	68.84 469	93.95 280	97.60 261

save fliter					98.91 58	94.28 42	97.02 210	98.02 113	95.35 31

TEST9					98.70 65	94.19 46	96.41 280	98.02 113	88.17 334	96.03 126	97.56 166	92.74 36	99.59 95

train_agg		96.30 85	95.83 93	97.72 43	98.70 65	94.19 46	96.41 280	98.02 113	88.58 321	96.03 126	97.56 166	92.73 37	99.59 95	95.04 126	99.37 67	99.39 68

test_8					98.67 67	94.06 53	96.37 288	98.01 116	88.58 321	95.98 130	97.55 168	92.73 37	99.58 98

fmvsm_s_conf0.5_n_11		97.30 29	97.59 14	96.43 119	98.42 83	91.37 151	98.04 63	98.00 117	97.30 3	99.45 4	99.21 1	89.28 97	99.80 40	99.27 10	99.35 69	98.12 220

agg_prior					98.67 67	93.79 59		98.00 117		95.68 143			99.57 105

test_prior				97.23 69	98.67 67	92.99 83		98.00 117					99.41 132			99.29 75

WR-MVS		92.34 251	91.53 259	94.77 244	95.13 347	90.83 179	96.40 284	97.98 120	91.88 192	89.29 332	95.54 293	82.50 249	97.80 354	89.79 266	85.27 391	95.69 333

HPM-MVS++		97.34 26	96.97 43	98.47 6	99.08 42	96.16 4	97.55 149	97.97 121	95.59 25	96.61 97	97.89 118	92.57 41	99.84 26	95.95 99	99.51 38	99.40 66

CANet		96.39 80	96.02 88	97.50 54	97.62 154	93.38 68	97.02 210	97.96 122	95.42 29	94.86 172	97.81 136	87.38 143	99.82 33	96.88 60	99.20 88	99.29 75

114514_t		93.95 182	93.06 199	96.63 98	99.07 43	91.61 138	97.46 164	97.96 122	77.99 460	93.00 231	97.57 164	86.14 166	99.33 139	89.22 283	99.15 95	98.94 121

IU-MVS					99.42 10	95.39 12		97.94 124	90.40 263	98.94 19				97.41 49	99.66 10	99.74 9

MSC_two_6792asdad				98.86 1	98.67 67	96.94 1		97.93 125					99.86 9	97.68 33	99.67 6	99.77 3

No_MVS				98.86 1	98.67 67	96.94 1		97.93 125					99.86 9	97.68 33	99.67 6	99.77 3

fmvsm_s_conf0.1_n_2		96.33 84	96.44 79	96.00 155	97.30 168	90.37 201	97.53 150	97.92 127	96.52 11	99.14 15	99.08 8	83.21 226	99.74 59	99.22 11	98.06 150	97.88 241

Anonymous20231211		90.63 334	89.42 350	94.27 275	98.24 100	89.19 256	98.05 62	97.89 128	79.95 451	88.25 362	94.96 316	72.56 388	98.13 299	89.70 268	85.14 393	95.49 337

原ACMM1				96.38 125	98.59 75	91.09 168		97.89 128	87.41 360	95.22 160	97.68 150	90.25 85	99.54 110	87.95 305	99.12 100	98.49 182

CDPH-MVS		95.97 94	95.38 106	97.77 38	98.93 56	94.44 39	96.35 289	97.88 130	86.98 368	96.65 95	97.89 118	91.99 51	99.47 125	92.26 203	99.46 46	99.39 68

test11								97.88 130

EIA-MVS		95.53 109	95.47 100	95.71 184	97.06 184	89.63 228	97.82 100	97.87 132	93.57 108	93.92 204	95.04 313	90.61 82	98.95 193	94.62 153	98.68 120	98.54 175

CS-MVS		96.86 52	97.06 35	96.26 135	98.16 112	91.16 166	99.09 3	97.87 132	95.30 33	97.06 80	98.03 101	91.72 54	98.71 241	97.10 55	99.17 91	98.90 130

无先验							95.79 331	97.87 132	83.87 417				99.65 79	87.68 319		98.89 136

3Dnovator+	91.43 4	95.40 110	94.48 150	98.16 17	96.90 201	95.34 17	98.48 25	97.87 132	94.65 70	88.53 353	98.02 103	83.69 217	99.71 67	93.18 189	98.96 110	99.44 61

VPNet		92.23 259	91.31 267	94.99 227	95.56 312	90.96 172	97.22 196	97.86 136	92.96 146	90.96 284	96.62 234	75.06 364	98.20 293	91.90 215	83.65 417	95.80 324

test_vis1_n_1920		94.17 167	94.58 142	92.91 348	97.42 166	82.02 419	97.83 98	97.85 137	94.68 67	98.10 48	98.49 58	70.15 407	99.32 141	97.91 30	98.82 114	97.40 270

DVP-MVS		97.91 4	97.81 5	98.22 14	99.45 6	95.36 14	98.21 47	97.85 137	94.92 50	98.73 30	98.87 31	95.08 10	99.84 26	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
TSAR-MVS + MP.		97.42 22	97.33 29	97.69 46	99.25 32	94.24 45	98.07 60	97.85 137	93.72 103	98.57 37	98.35 72	93.69 20	99.40 133	97.06 56	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
SPE-MVS-test		96.89 50	97.04 39	96.45 118	98.29 93	91.66 137	99.03 4	97.85 137	95.84 18	96.90 83	97.97 109	91.24 68	98.75 231	96.92 59	99.33 70	98.94 121

test_fmvsmconf0.01_n		96.15 88	95.85 92	97.03 83	92.66 431	91.83 129	97.97 77	97.84 141	95.57 26	97.53 61	99.00 16	84.20 210	99.76 54	98.82 23	99.08 102	99.48 56

GDP-MVS		95.62 105	95.13 114	97.09 79	96.79 214	93.26 76	97.89 88	97.83 142	93.58 107	96.80 85	97.82 134	83.06 233	99.16 161	94.40 160	97.95 156	98.87 140

balanced_conf03		96.84 56	96.89 48	96.68 93	97.63 153	92.22 113	98.17 53	97.82 143	94.44 79	98.23 45	97.36 179	90.97 75	99.22 151	97.74 32	99.66 10	98.61 168

AdaColmap		94.34 162	93.68 172	96.31 129	98.59 75	91.68 136	96.59 269	97.81 144	89.87 273	92.15 250	97.06 200	83.62 220	99.54 110	89.34 278	98.07 149	97.70 254

MVSMamba_PlusPlus		96.51 74	96.48 72	96.59 102	98.07 120	91.97 124	98.14 54	97.79 145	90.43 261	97.34 70	97.52 169	91.29 67	99.19 154	98.12 28	99.64 14	98.60 169

KinetiMVS		95.26 117	94.75 136	96.79 90	96.99 194	92.05 120	97.82 100	97.78 146	94.77 63	96.46 109	97.70 147	80.62 288	99.34 138	92.37 202	98.28 140	98.97 111

mamv4		94.66 155	96.10 87	90.37 418	98.01 123	73.41 469	96.82 236	97.78 146	89.95 272	94.52 183	97.43 174	92.91 30	99.09 174	98.28 27	99.16 94	98.60 169

ETV-MVS		96.02 91	95.89 91	96.40 122	97.16 176	92.44 105	97.47 162	97.77 148	94.55 73	96.48 107	94.51 341	91.23 70	98.92 198	95.65 111	98.19 144	97.82 249

新几何1				97.32 62	98.60 74	93.59 63		97.75 149	81.58 442	95.75 138	97.85 128	90.04 88	99.67 77	86.50 343	99.13 98	98.69 164

旧先验1					98.38 89	93.38 68		97.75 149			98.09 96	92.30 48			99.01 108	99.16 85

EC-MVSNet		96.42 78	96.47 73	96.26 135	97.01 192	91.52 143	98.89 5	97.75 149	94.42 80	96.64 96	97.68 150	89.32 96	98.60 257	97.45 46	99.11 101	98.67 166

EI-MVSNet-Vis-set		96.51 74	96.47 73	96.63 98	98.24 100	91.20 160	96.89 227	97.73 152	94.74 65	96.49 106	98.49 58	90.88 79	99.58 98	96.44 76	98.32 138	99.13 89

PAPM_NR		95.01 135	94.59 141	96.26 135	98.89 60	90.68 187	97.24 190	97.73 152	91.80 193	92.93 236	96.62 234	89.13 100	99.14 166	89.21 284	97.78 160	98.97 111

Anonymous20240529		91.98 268	90.73 295	95.73 182	98.14 113	89.40 243	97.99 68	97.72 154	79.63 453	93.54 215	97.41 176	69.94 409	99.56 106	91.04 237	91.11 324	98.22 210

CHOSEN 280x420		93.12 218	92.72 216	94.34 269	96.71 227	87.27 317	90.29 462	97.72 154	86.61 375	91.34 273	95.29 301	84.29 209	98.41 273	93.25 187	98.94 111	97.35 273

EI-MVSNet-UG-set		96.34 83	96.30 82	96.47 115	98.20 107	90.93 175	96.86 230	97.72 154	94.67 68	96.16 122	98.46 62	90.43 84	99.58 98	96.23 82	97.96 155	98.90 130

LS3D		93.57 199	92.61 221	96.47 115	97.59 157	91.61 138	97.67 125	97.72 154	85.17 399	90.29 295	98.34 75	84.60 201	99.73 61	83.85 385	98.27 141	98.06 230

PAPR		94.18 166	93.42 188	96.48 114	97.64 151	91.42 150	95.55 345	97.71 158	88.99 305	92.34 246	95.82 274	89.19 98	99.11 169	86.14 349	97.38 172	98.90 130

UGNet		94.04 177	93.28 191	96.31 129	96.85 206	91.19 161	97.88 90	97.68 159	94.40 82	93.00 231	96.18 254	73.39 383	99.61 90	91.72 221	98.46 132	98.13 218
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
testdata				95.46 206	98.18 111	88.90 267		97.66 160	82.73 432	97.03 81	98.07 97	90.06 87	98.85 205	89.67 269	98.98 109	98.64 167

test12				97.65 47	98.46 79	94.26 43		97.66 160		95.52 150		90.89 78	99.46 126		99.25 80	99.22 82

DTE-MVSNet		90.56 335	89.75 341	93.01 344	93.95 392	87.25 318	97.64 133	97.65 162	90.74 241	87.12 385	95.68 285	79.97 302	97.00 414	83.33 386	81.66 428	94.78 398

TAPA-MVS	90.10 7	92.30 254	91.22 273	95.56 191	98.33 91	89.60 230	96.79 242	97.65 162	81.83 439	91.52 268	97.23 189	87.94 123	98.91 200	71.31 463	98.37 136	98.17 216
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
sd_testset		93.10 219	92.45 229	95.05 221	98.09 116	89.21 253	96.89 227	97.64 164	93.18 132	91.79 262	97.28 184	75.35 363	98.65 251	88.99 289	92.84 294	97.28 276

test_cas_vis1_n_1920		94.48 160	94.55 146	94.28 274	96.78 219	86.45 343	97.63 135	97.64 164	93.32 125	97.68 60	98.36 71	73.75 379	99.08 177	96.73 65	99.05 104	97.31 275

NormalMVS		96.36 82	96.11 86	97.12 76	99.37 19	92.90 87	97.99 68	97.63 166	95.92 16	96.57 102	97.93 111	85.34 186	99.50 120	94.99 129	99.21 83	98.97 111

Elysia		94.00 179	93.12 196	96.64 94	96.08 291	92.72 95	97.50 153	97.63 166	91.15 227	94.82 173	97.12 195	74.98 366	99.06 183	90.78 242	98.02 151	98.12 220

StellarMVS		94.00 179	93.12 196	96.64 94	96.08 291	92.72 95	97.50 153	97.63 166	91.15 227	94.82 173	97.12 195	74.98 366	99.06 183	90.78 242	98.02 151	98.12 220

cdsmvs_eth3d_5k		23.24 458	30.99 460	0.00 477	0.00 500	0.00 502	0.00 489	97.63 166	0.00 495	0.00 496	96.88 213	84.38 206	0.00 496	0.00 495	0.00 494	0.00 492

DPM-MVS		95.69 102	94.92 124	98.01 21	98.08 119	95.71 10	95.27 361	97.62 170	90.43 261	95.55 147	97.07 199	91.72 54	99.50 120	89.62 271	98.94 111	98.82 146

sasdasda		96.02 91	95.45 101	97.75 40	97.59 157	95.15 24	98.28 35	97.60 171	94.52 75	96.27 117	96.12 259	87.65 130	99.18 157	96.20 88	94.82 256	98.91 127

canonicalmvs		96.02 91	95.45 101	97.75 40	97.59 157	95.15 24	98.28 35	97.60 171	94.52 75	96.27 117	96.12 259	87.65 130	99.18 157	96.20 88	94.82 256	98.91 127

test222					98.24 100	92.21 114	95.33 356	97.60 171	79.22 455	95.25 158	97.84 130	88.80 106			99.15 95	98.72 161

cascas		91.20 310	90.08 323	94.58 255	94.97 352	89.16 257	93.65 427	97.59 174	79.90 452	89.40 327	92.92 407	75.36 362	98.36 281	92.14 208	94.75 259	96.23 303

E2		95.20 123	95.00 121	95.79 173	96.79 214	89.66 225	96.82 236	97.58 175	92.35 172	95.28 156	97.83 132	86.68 152	98.76 225	94.79 147	96.92 193	98.95 118

E3		95.20 123	95.00 121	95.79 173	96.77 221	89.66 225	96.82 236	97.58 175	92.35 172	95.28 156	97.83 132	86.69 151	98.76 225	94.79 147	96.92 193	98.95 118

h-mvs33		94.15 169	93.52 180	96.04 149	97.81 139	90.22 205	97.62 137	97.58 175	95.19 36	96.74 89	97.45 171	83.67 218	99.61 90	95.85 102	79.73 435	98.29 206

E5new		95.04 131	94.88 126	95.52 195	96.62 230	89.02 261	97.29 183	97.57 178	92.54 162	95.04 164	97.89 118	85.65 178	98.77 219	94.92 132	96.44 217	98.78 149

E6new		95.04 131	94.88 126	95.52 195	96.60 234	89.02 261	97.29 183	97.57 178	92.54 162	95.04 164	97.90 116	85.66 176	98.77 219	94.92 132	96.44 217	98.78 149

E6		95.04 131	94.88 126	95.52 195	96.60 234	89.02 261	97.29 183	97.57 178	92.54 162	95.04 164	97.90 116	85.66 176	98.77 219	94.92 132	96.44 217	98.78 149

E5		95.04 131	94.88 126	95.52 195	96.62 230	89.02 261	97.29 183	97.57 178	92.54 162	95.04 164	97.89 118	85.65 178	98.77 219	94.92 132	96.44 217	98.78 149

MGCFI-Net		95.94 96	95.40 105	97.56 53	97.59 157	94.62 32	98.21 47	97.57 178	94.41 81	96.17 121	96.16 257	87.54 135	99.17 159	96.19 90	94.73 261	98.91 127

MVSFormer		95.37 111	95.16 113	95.99 156	96.34 267	91.21 158	98.22 45	97.57 178	91.42 210	96.22 119	97.32 180	86.20 164	97.92 341	94.07 166	99.05 104	98.85 142

test_djsdf		93.07 221	92.76 211	94.00 288	93.49 410	88.70 271	98.22 45	97.57 178	91.42 210	90.08 307	95.55 292	82.85 240	97.92 341	94.07 166	91.58 315	95.40 348

OMC-MVS		95.09 128	94.70 137	96.25 138	98.46 79	91.28 154	96.43 276	97.57 178	92.04 188	94.77 177	97.96 110	87.01 149	99.09 174	91.31 231	96.77 198	98.36 197

E4		95.09 128	94.86 130	95.77 176	96.58 238	89.56 233	96.85 231	97.56 186	92.50 166	95.03 168	97.86 126	86.03 167	98.78 215	94.71 150	96.65 207	98.96 114

viewcassd2359sk11		95.26 117	95.09 118	95.80 170	96.95 198	89.72 224	96.80 241	97.56 186	92.21 179	95.37 154	97.80 138	87.17 147	98.77 219	94.82 142	97.10 187	98.90 130

PS-MVSNAJss		93.74 192	93.51 181	94.44 263	93.91 394	89.28 251	97.75 110	97.56 186	92.50 166	89.94 309	96.54 237	88.65 109	98.18 296	93.83 175	90.90 329	95.86 318

casdiffmvs_mvg		95.81 101	95.57 95	96.51 111	96.87 203	91.49 144	97.50 153	97.56 186	93.99 95	95.13 162	97.92 114	87.89 124	98.78 215	95.97 98	97.33 175	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
E3new		95.28 115	95.11 117	95.80 170	97.03 189	89.76 222	96.78 246	97.54 190	92.06 187	95.40 153	97.75 141	87.49 139	98.76 225	94.85 137	97.10 187	98.88 138

jajsoiax		92.42 247	91.89 247	94.03 287	93.33 418	88.50 281	97.73 115	97.53 191	92.00 190	88.85 345	96.50 239	75.62 361	98.11 303	93.88 173	91.56 316	95.48 338

mvs_tets		92.31 253	91.76 250	93.94 296	93.41 415	88.29 287	97.63 135	97.53 191	92.04 188	88.76 348	96.45 241	74.62 371	98.09 308	93.91 171	91.48 317	95.45 343

dcpmvs_2		96.37 81	97.05 38	94.31 272	98.96 55	84.11 392	97.56 144	97.51 193	93.92 97	97.43 67	98.52 55	92.75 35	99.32 141	97.32 54	99.50 40	99.51 49

HQP_MVS		93.78 191	93.43 186	94.82 237	96.21 271	89.99 211	97.74 113	97.51 193	94.85 53	91.34 273	96.64 227	81.32 273	98.60 257	93.02 195	92.23 303	95.86 318

plane_prior5								97.51 193					98.60 257	93.02 195	92.23 303	95.86 318

viewmanbaseed2359cas		95.24 120	95.02 120	95.91 159	96.87 203	89.98 213	96.82 236	97.49 196	92.26 175	95.47 151	97.82 134	86.47 157	98.69 243	94.80 144	97.20 183	99.06 101

reproduce_monomvs		91.30 305	91.10 277	91.92 378	96.82 211	82.48 413	97.01 213	97.49 196	94.64 71	88.35 356	95.27 304	70.53 402	98.10 304	95.20 122	84.60 403	95.19 366

viewmacassd2359aftdt		95.07 130	94.80 132	95.87 162	96.53 248	89.84 219	96.90 226	97.48 198	92.44 168	95.36 155	97.89 118	85.23 189	98.68 245	94.40 160	97.00 191	99.09 96

PS-MVSNAJ		95.37 111	95.33 108	95.49 202	97.35 167	90.66 188	95.31 358	97.48 198	93.85 100	96.51 105	95.70 284	88.65 109	99.65 79	94.80 144	98.27 141	96.17 307

API-MVS		94.84 146	94.49 149	95.90 160	97.90 134	92.00 123	97.80 104	97.48 198	89.19 297	94.81 175	96.71 220	88.84 105	99.17 159	88.91 291	98.76 118	96.53 296

MG-MVS		95.61 106	95.38 106	96.31 129	98.42 83	90.53 190	96.04 314	97.48 198	93.47 117	95.67 144	98.10 94	89.17 99	99.25 148	91.27 232	98.77 117	99.13 89

MAR-MVS		94.22 165	93.46 183	96.51 111	98.00 125	92.19 117	97.67 125	97.47 202	88.13 338	93.00 231	95.84 272	84.86 199	99.51 117	87.99 304	98.17 146	97.83 248
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
CLD-MVS		92.98 225	92.53 225	94.32 270	96.12 286	89.20 254	95.28 359	97.47 202	92.66 158	89.90 310	95.62 288	80.58 289	98.40 274	92.73 200	92.40 301	95.38 350
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
UniMVSNet_ETH3D		91.34 303	90.22 319	94.68 248	94.86 361	87.86 305	97.23 194	97.46 204	87.99 339	89.90 310	96.92 211	66.35 437	98.23 290	90.30 256	90.99 327	97.96 235

nrg030		94.05 176	93.31 190	96.27 134	95.22 339	94.59 33	98.34 30	97.46 204	92.93 147	91.21 282	96.64 227	87.23 146	98.22 291	94.99 129	85.80 383	95.98 317

XVG-OURS		93.72 193	93.35 189	94.80 242	97.07 181	88.61 274	94.79 381	97.46 204	91.97 191	93.99 201	97.86 126	81.74 267	98.88 202	92.64 201	92.67 299	96.92 288

LPG-MVS_test		92.94 228	92.56 222	94.10 282	96.16 281	88.26 289	97.65 129	97.46 204	91.29 214	90.12 303	97.16 192	79.05 318	98.73 235	92.25 205	91.89 311	95.31 355

LGP-MVS_train				94.10 282	96.16 281	88.26 289		97.46 204	91.29 214	90.12 303	97.16 192	79.05 318	98.73 235	92.25 205	91.89 311	95.31 355

MVS		91.71 276	90.44 306	95.51 199	95.20 341	91.59 140	96.04 314	97.45 209	73.44 470	87.36 381	95.60 289	85.42 185	99.10 171	85.97 354	97.46 167	95.83 322

XVG-OURS-SEG-HR		93.86 188	93.55 176	94.81 239	97.06 184	88.53 280	95.28 359	97.45 209	91.68 198	94.08 200	97.68 150	82.41 252	98.90 201	93.84 174	92.47 300	96.98 284

baseline		95.58 107	95.42 104	96.08 145	96.78 219	90.41 196	97.16 201	97.45 209	93.69 106	95.65 145	97.85 128	87.29 144	98.68 245	95.66 108	97.25 181	99.13 89

ab-mvs		93.57 199	92.55 223	96.64 94	97.28 170	91.96 126	95.40 352	97.45 209	89.81 278	93.22 228	96.28 250	79.62 309	99.46 126	90.74 245	93.11 291	98.50 180

xiu_mvs_v2_base		95.32 114	95.29 109	95.40 207	97.22 172	90.50 191	95.44 351	97.44 213	93.70 105	96.46 109	96.18 254	88.59 113	99.53 112	94.79 147	97.81 159	96.17 307

1314		92.81 237	92.03 240	95.14 217	95.33 331	89.52 238	96.04 314	97.44 213	87.72 353	86.25 402	95.33 300	83.84 215	98.79 214	89.26 281	97.05 190	97.11 282

casdiffmvs		95.64 104	95.49 98	96.08 145	96.76 225	90.45 193	97.29 183	97.44 213	94.00 94	95.46 152	97.98 108	87.52 138	98.73 235	95.64 112	97.33 175	99.08 98
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
viewdifsd2359ckpt07		94.76 152	94.68 138	95.01 225	96.76 225	87.41 313	96.38 286	97.43 216	92.65 159	94.52 183	97.75 141	85.55 183	98.81 211	94.36 162	96.69 204	98.82 146

XXY-MVS		92.16 261	91.23 272	94.95 233	94.75 366	90.94 174	97.47 162	97.43 216	89.14 298	88.90 341	96.43 242	79.71 306	98.24 289	89.56 272	87.68 364	95.67 334

anonymousdsp		92.16 261	91.55 258	93.97 292	92.58 433	89.55 235	97.51 152	97.42 218	89.42 291	88.40 355	94.84 323	80.66 287	97.88 346	91.87 217	91.28 321	94.48 407

Effi-MVS+		94.93 140	94.45 151	96.36 127	96.61 233	91.47 147	96.41 280	97.41 219	91.02 233	94.50 185	95.92 268	87.53 136	98.78 215	93.89 172	96.81 197	98.84 145

RRT-MVS		94.51 158	94.35 155	94.98 229	96.40 261	86.55 340	97.56 144	97.41 219	93.19 130	94.93 170	97.04 201	79.12 316	99.30 145	96.19 90	97.32 177	99.09 96

HQP3-MVS								97.39 221							92.10 308

HQP-MVS		93.19 215	92.74 214	94.54 258	95.86 297	89.33 247	96.65 260	97.39 221	93.55 109	90.14 297	95.87 270	80.95 278	98.50 267	92.13 211	92.10 308	95.78 326

PLC	91.00 6	94.11 173	93.43 186	96.13 143	98.58 77	91.15 167	96.69 256	97.39 221	87.29 363	91.37 272	96.71 220	88.39 114	99.52 116	87.33 330	97.13 186	97.73 252
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
diffmvs_AUTHOR		95.33 113	95.27 110	95.50 201	96.37 265	89.08 259	96.08 312	97.38 224	93.09 138	96.53 104	97.74 144	86.45 158	98.68 245	96.32 78	97.48 166	98.75 157

v7n		90.76 327	89.86 334	93.45 329	93.54 407	87.60 311	97.70 123	97.37 225	88.85 311	87.65 374	94.08 371	81.08 277	98.10 304	84.68 371	83.79 416	94.66 404

UnsupCasMVSNet_eth		85.99 410	84.45 414	90.62 414	89.97 451	82.40 416	93.62 428	97.37 225	89.86 274	78.59 460	92.37 417	65.25 447	95.35 449	82.27 400	70.75 468	94.10 418

viewdifsd2359ckpt13		94.87 144	94.52 147	95.90 160	96.88 202	90.19 206	96.92 223	97.36 227	91.26 218	94.65 179	97.46 170	85.79 173	98.64 252	93.64 178	96.76 199	98.88 138

ACMM	89.79 8	92.96 226	92.50 227	94.35 267	96.30 269	88.71 270	97.58 140	97.36 227	91.40 212	90.53 290	96.65 226	79.77 305	98.75 231	91.24 233	91.64 313	95.59 336
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
xiu_mvs_v1_base_debu		95.01 135	94.76 133	95.75 179	96.58 238	91.71 133	96.25 299	97.35 229	92.99 140	96.70 91	96.63 231	82.67 244	99.44 129	96.22 83	97.46 167	96.11 313

xiu_mvs_v1_base		95.01 135	94.76 133	95.75 179	96.58 238	91.71 133	96.25 299	97.35 229	92.99 140	96.70 91	96.63 231	82.67 244	99.44 129	96.22 83	97.46 167	96.11 313

xiu_mvs_v1_base_debi		95.01 135	94.76 133	95.75 179	96.58 238	91.71 133	96.25 299	97.35 229	92.99 140	96.70 91	96.63 231	82.67 244	99.44 129	96.22 83	97.46 167	96.11 313

diffmvs		95.25 119	95.13 114	95.63 187	96.43 260	89.34 246	95.99 318	97.35 229	92.83 153	96.31 115	97.37 178	86.44 159	98.67 248	96.26 80	97.19 184	98.87 140
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
WTY-MVS		94.71 154	94.02 163	96.79 90	97.71 145	92.05 120	96.59 269	97.35 229	90.61 252	94.64 180	96.93 208	86.41 160	99.39 134	91.20 234	94.71 262	98.94 121

viewdifsd2359ckpt09		94.81 149	94.37 154	96.12 144	96.91 199	90.75 184	96.94 220	97.31 234	90.51 259	94.31 190	97.38 177	85.70 175	98.71 241	93.54 179	96.75 200	98.90 130

SSM_0407		94.54 157	94.12 162	95.80 170	96.79 214	90.38 198	96.79 242	97.29 235	91.24 219	93.68 208	97.60 161	85.03 193	98.67 248	92.14 208	96.51 210	98.35 199

SSM_0404		94.73 153	94.31 157	95.98 157	97.05 186	90.90 177	97.01 213	97.29 235	91.24 219	94.17 197	97.60 161	85.03 193	98.76 225	92.14 208	97.30 178	98.29 206

F-COLMAP		93.58 197	92.98 203	95.37 208	98.40 86	88.98 265	97.18 199	97.29 235	87.75 352	90.49 291	97.10 198	85.21 190	99.50 120	86.70 340	96.72 203	97.63 256

VortexMVS		92.88 232	92.64 218	93.58 321	96.58 238	87.53 312	96.93 222	97.28 238	92.78 156	89.75 315	94.99 314	82.73 243	97.76 359	94.60 155	88.16 359	95.46 341

XVG-ACMP-BASELINE		90.93 323	90.21 320	93.09 342	94.31 385	85.89 358	95.33 356	97.26 239	91.06 232	89.38 328	95.44 298	68.61 420	98.60 257	89.46 274	91.05 325	94.79 396

PCF-MVS	89.48 11	91.56 287	89.95 331	96.36 127	96.60 234	92.52 103	92.51 447	97.26 239	79.41 454	88.90 341	96.56 236	84.04 214	99.55 108	77.01 439	97.30 178	97.01 283
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
ACMP	89.59 10	92.62 241	92.14 236	94.05 285	96.40 261	88.20 293	97.36 175	97.25 241	91.52 205	88.30 359	96.64 227	78.46 330	98.72 240	91.86 218	91.48 317	95.23 362
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
icg_test_0407_2		93.58 197	93.46 183	93.94 296	96.19 275	86.16 352	93.73 422	97.24 242	91.54 201	93.50 217	97.04 201	85.64 181	96.91 417	90.68 247	95.59 238	98.76 153

IMVS_0407		93.94 183	93.75 169	94.49 260	96.19 275	86.16 352	96.35 289	97.24 242	91.54 201	93.50 217	97.04 201	85.64 181	98.54 264	90.68 247	95.59 238	98.76 153

IMVS_0404		92.44 245	91.92 245	94.00 288	96.19 275	86.16 352	93.84 419	97.24 242	91.54 201	88.17 365	97.04 201	76.96 348	97.09 408	90.68 247	95.59 238	98.76 153

IMVS_0403		93.98 181	93.79 168	94.55 257	96.19 275	86.16 352	96.35 289	97.24 242	91.54 201	93.59 212	97.04 201	85.86 170	98.73 235	90.68 247	95.59 238	98.76 153

OPM-MVS		93.28 211	92.76 211	94.82 237	94.63 372	90.77 182	96.65 260	97.18 246	93.72 103	91.68 266	97.26 187	79.33 313	98.63 254	92.13 211	92.28 302	95.07 370
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
PatchMatch-RL		92.90 230	92.02 241	95.56 191	98.19 109	90.80 180	95.27 361	97.18 246	87.96 340	91.86 261	95.68 285	80.44 292	98.99 191	84.01 380	97.54 165	96.89 289

alignmvs		95.87 100	95.23 111	97.78 36	97.56 163	95.19 22	97.86 91	97.17 248	94.39 83	96.47 108	96.40 244	85.89 169	99.20 153	96.21 87	95.11 252	98.95 118

MVS_Test		94.89 142	94.62 140	95.68 185	96.83 209	89.55 235	96.70 254	97.17 248	91.17 225	95.60 146	96.11 263	87.87 126	98.76 225	93.01 197	97.17 185	98.72 161

Fast-Effi-MVS+		93.46 203	92.75 213	95.59 190	96.77 221	90.03 208	96.81 240	97.13 250	88.19 333	91.30 276	94.27 359	86.21 163	98.63 254	87.66 321	96.46 216	98.12 220

FE-MVSNET3		91.65 280	90.67 299	94.60 250	93.65 405	90.95 173	94.86 379	97.12 251	89.69 281	89.21 336	93.62 390	81.17 276	97.67 366	87.54 325	89.14 347	95.17 368

EI-MVSNet		93.03 223	92.88 207	93.48 327	95.77 303	86.98 326	96.44 274	97.12 251	90.66 248	91.30 276	97.64 157	86.56 154	98.05 316	89.91 262	90.55 333	95.41 345

MVSTER		93.20 214	92.81 210	94.37 266	96.56 243	89.59 231	97.06 207	97.12 251	91.24 219	91.30 276	95.96 266	82.02 260	98.05 316	93.48 182	90.55 333	95.47 340

viewmambaseed2359dif		94.28 163	94.14 160	94.71 247	96.21 271	86.97 327	95.93 321	97.11 254	89.00 304	95.00 169	97.70 147	86.02 168	98.59 261	93.71 177	96.59 209	98.57 173

test_yl		94.78 150	94.23 158	96.43 119	97.74 143	91.22 156	96.85 231	97.10 255	91.23 222	95.71 140	96.93 208	84.30 207	99.31 143	93.10 190	95.12 250	98.75 157

DCV-MVSNet		94.78 150	94.23 158	96.43 119	97.74 143	91.22 156	96.85 231	97.10 255	91.23 222	95.71 140	96.93 208	84.30 207	99.31 143	93.10 190	95.12 250	98.75 157

LTVRE_ROB	88.41 13	90.99 319	89.92 333	94.19 276	96.18 279	89.55 235	96.31 295	97.09 257	87.88 343	85.67 411	95.91 269	78.79 326	98.57 262	81.50 403	89.98 338	94.44 410
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
viewmsd2359difaftdt		93.46 203	93.23 193	94.17 277	96.12 286	85.42 367	96.43 276	97.08 258	92.91 148	94.21 193	98.00 105	80.82 284	98.74 233	94.41 159	89.05 348	98.34 203

test_fmvs1_n		92.73 239	92.88 207	92.29 368	96.08 291	81.05 427	97.98 71	97.08 258	90.72 243	96.79 87	98.18 91	63.07 452	98.45 271	97.62 40	98.42 135	97.36 271

v10		91.04 317	90.23 317	93.49 326	94.12 388	88.16 296	97.32 180	97.08 258	88.26 332	88.29 360	94.22 364	82.17 257	97.97 328	86.45 344	84.12 410	94.33 413

viewdifsd2359ckpt11		93.46 203	93.22 194	94.17 277	96.11 288	85.42 367	96.43 276	97.07 261	92.91 148	94.20 194	98.00 105	80.82 284	98.73 235	94.42 158	89.04 350	98.34 203

mamba_0408		93.70 194	92.99 200	95.83 167	96.79 214	90.38 198	88.69 472	97.07 261	90.96 235	93.68 208	97.31 182	84.97 196	98.76 225	90.95 238	96.51 210	98.35 199

SSM_04072		93.51 202	92.99 200	95.05 221	96.79 214	90.38 198	88.69 472	97.07 261	90.96 235	93.68 208	97.31 182	84.97 196	96.42 428	90.95 238	96.51 210	98.35 199

v144192		91.06 316	90.28 313	93.39 330	93.66 403	87.23 320	96.83 235	97.07 261	87.43 359	89.69 318	94.28 358	81.48 270	98.00 323	87.18 334	84.92 399	94.93 378

v1192		91.07 315	90.23 317	93.58 321	93.70 400	87.82 307	96.73 250	97.07 261	87.77 350	89.58 321	94.32 356	80.90 282	97.97 328	86.52 342	85.48 386	94.95 374

v8		91.29 307	90.53 305	93.57 323	94.15 387	88.12 297	97.34 177	97.06 266	88.99 305	88.32 358	94.26 361	83.08 231	98.01 322	87.62 323	83.92 414	94.57 406

mvs_anonymous		93.82 189	93.74 170	94.06 284	96.44 259	85.41 369	95.81 329	97.05 267	89.85 276	90.09 306	96.36 246	87.44 141	97.75 361	93.97 168	96.69 204	99.02 103

IterMVS-LS		92.29 255	91.94 244	93.34 332	96.25 270	86.97 327	96.57 272	97.05 267	90.67 246	89.50 326	94.80 326	86.59 153	97.64 370	89.91 262	86.11 381	95.40 348
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v1921920		90.85 325	90.03 328	93.29 334	93.55 406	86.96 329	96.74 249	97.04 269	87.36 361	89.52 325	94.34 353	80.23 297	97.97 328	86.27 345	85.21 392	94.94 376

CDS-MVSNet		94.14 172	93.54 177	95.93 158	96.18 279	91.46 148	96.33 293	97.04 269	88.97 307	93.56 213	96.51 238	87.55 134	97.89 345	89.80 265	95.95 226	98.44 190
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
SSC-MVS3.2		89.74 361	89.26 354	91.19 403	95.16 342	80.29 438	94.53 388	97.03 271	91.79 194	88.86 344	94.10 368	69.94 409	97.82 351	85.29 363	86.66 377	95.45 343

v1144		91.37 300	90.60 301	93.68 313	93.89 395	88.23 291	96.84 234	97.03 271	88.37 329	89.69 318	94.39 348	82.04 259	97.98 325	87.80 309	85.37 388	94.84 386

v1240		90.70 331	89.85 335	93.23 336	93.51 409	86.80 330	96.61 266	97.02 273	87.16 366	89.58 321	94.31 357	79.55 310	97.98 325	85.52 360	85.44 387	94.90 381

EPP-MVSNet		95.22 122	95.04 119	95.76 177	97.49 164	89.56 233	98.67 15	97.00 274	90.69 244	94.24 192	97.62 159	89.79 93	98.81 211	93.39 186	96.49 214	98.92 126

V42		91.58 286	90.87 284	93.73 307	94.05 391	88.50 281	97.32 180	96.97 275	88.80 317	89.71 316	94.33 354	82.54 248	98.05 316	89.01 288	85.07 395	94.64 405

test_fmvs1		93.21 213	93.53 178	92.25 371	96.55 245	81.20 426	97.40 171	96.96 276	90.68 245	96.80 85	98.04 100	69.25 415	98.40 274	97.58 41	98.50 128	97.16 281

FMVSNet2		91.31 304	90.08 323	94.99 227	96.51 252	92.21 114	97.41 167	96.95 277	88.82 314	88.62 350	94.75 328	73.87 375	97.42 396	85.20 366	88.55 356	95.35 352

ACMH	87.59 16	90.53 336	89.42 350	93.87 301	96.21 271	87.92 302	97.24 190	96.94 278	88.45 327	83.91 431	96.27 251	71.92 391	98.62 256	84.43 374	89.43 344	95.05 372
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
GBi-Net		91.35 301	90.27 314	94.59 251	96.51 252	91.18 163	97.50 153	96.93 279	88.82 314	89.35 329	94.51 341	73.87 375	97.29 403	86.12 350	88.82 351	95.31 355

test1		91.35 301	90.27 314	94.59 251	96.51 252	91.18 163	97.50 153	96.93 279	88.82 314	89.35 329	94.51 341	73.87 375	97.29 403	86.12 350	88.82 351	95.31 355

FMVSNet3		91.78 274	90.69 298	95.03 224	96.53 248	92.27 112	97.02 210	96.93 279	89.79 279	89.35 329	94.65 334	77.01 346	97.47 391	86.12 350	88.82 351	95.35 352

FMVSNet1		89.88 356	88.31 369	94.59 251	95.41 321	91.18 163	97.50 153	96.93 279	86.62 374	87.41 379	94.51 341	65.94 442	97.29 403	83.04 389	87.43 367	95.31 355

GeoE		93.89 186	93.28 191	95.72 183	96.96 197	89.75 223	98.24 43	96.92 283	89.47 288	92.12 252	97.21 190	84.42 205	98.39 279	87.71 314	96.50 213	99.01 106

SymmetryMVS		95.94 96	95.54 96	97.15 74	97.85 136	92.90 87	97.99 68	96.91 284	95.92 16	96.57 102	97.93 111	85.34 186	99.50 120	94.99 129	96.39 221	99.05 102

miper_enhance_ethall		91.54 290	91.01 280	93.15 340	95.35 327	87.07 325	93.97 411	96.90 285	86.79 372	89.17 337	93.43 401	86.55 155	97.64 370	89.97 261	86.93 372	94.74 401

eth_miper_zixun_eth		91.02 318	90.59 302	92.34 366	95.33 331	84.35 388	94.10 408	96.90 285	88.56 323	88.84 346	94.33 354	84.08 212	97.60 375	88.77 294	84.37 408	95.06 371

TAMVS		94.01 178	93.46 183	95.64 186	96.16 281	90.45 193	96.71 253	96.89 287	89.27 295	93.46 220	96.92 211	87.29 144	97.94 338	88.70 296	95.74 232	98.53 176

miper_ehance_all_eth		91.59 284	91.13 276	92.97 346	95.55 313	86.57 338	94.47 392	96.88 288	87.77 350	88.88 343	94.01 373	86.22 162	97.54 384	89.49 273	86.93 372	94.79 396

v2v482		91.59 284	90.85 287	93.80 304	93.87 396	88.17 295	96.94 220	96.88 288	89.54 285	89.53 324	94.90 320	81.70 268	98.02 321	89.25 282	85.04 397	95.20 363

CNLPA		94.28 163	93.53 178	96.52 107	98.38 89	92.55 102	96.59 269	96.88 288	90.13 269	91.91 258	97.24 188	85.21 190	99.09 174	87.64 322	97.83 158	97.92 238

PAPM		91.52 291	90.30 312	95.20 214	95.30 334	89.83 220	93.38 433	96.85 291	86.26 382	88.59 351	95.80 275	84.88 198	98.15 298	75.67 444	95.93 227	97.63 256

c3_l		91.38 298	90.89 283	92.88 350	95.58 311	86.30 346	94.68 383	96.84 292	88.17 334	88.83 347	94.23 362	85.65 178	97.47 391	89.36 277	84.63 401	94.89 382

pm-mvs1		90.72 330	89.65 345	93.96 293	94.29 386	89.63 228	97.79 106	96.82 293	89.07 300	86.12 406	95.48 297	78.61 328	97.78 356	86.97 338	81.67 427	94.46 408

test_vis1_n		92.37 250	92.26 234	92.72 356	94.75 366	82.64 409	98.02 65	96.80 294	91.18 224	97.77 59	97.93 111	58.02 462	98.29 287	97.63 38	98.21 143	97.23 279

CMPMVS	62.92 21	85.62 415	84.92 407	87.74 441	89.14 456	73.12 471	94.17 406	96.80 294	73.98 467	73.65 469	94.93 318	66.36 436	97.61 374	83.95 382	91.28 321	92.48 445
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
MS-PatchMatch		90.27 343	89.77 339	91.78 387	94.33 383	84.72 385	95.55 345	96.73 296	86.17 384	86.36 401	95.28 303	71.28 396	97.80 354	84.09 379	98.14 147	92.81 437

Effi-MVS+-dtu		93.08 220	93.21 195	92.68 359	96.02 294	83.25 402	97.14 203	96.72 297	93.85 100	91.20 283	93.44 398	83.08 231	98.30 286	91.69 224	95.73 233	96.50 298

TSAR-MVS + GP.		96.69 67	96.49 71	97.27 67	98.31 92	93.39 67	96.79 242	96.72 297	94.17 89	97.44 65	97.66 153	92.76 34	99.33 139	96.86 62	97.76 162	99.08 98

1112_ss		93.37 208	92.42 230	96.21 139	97.05 186	90.99 170	96.31 295	96.72 297	86.87 371	89.83 313	96.69 224	86.51 156	99.14 166	88.12 301	93.67 285	98.50 180

PVSNet	86.66 18	92.24 258	91.74 253	93.73 307	97.77 141	83.69 399	92.88 442	96.72 297	87.91 342	93.00 231	94.86 322	78.51 329	99.05 186	86.53 341	97.45 171	98.47 185

miper_lstm_enhance		90.50 339	90.06 327	91.83 383	95.33 331	83.74 396	93.86 417	96.70 301	87.56 357	87.79 371	93.81 381	83.45 223	96.92 416	87.39 328	84.62 402	94.82 391

v148		90.99 319	90.38 308	92.81 353	93.83 397	85.80 359	96.78 246	96.68 302	89.45 290	88.75 349	93.93 377	82.96 237	97.82 351	87.83 307	83.25 419	94.80 394

ACMH+	87.92 14	90.20 347	89.18 356	93.25 335	96.48 255	86.45 343	96.99 216	96.68 302	88.83 313	84.79 420	96.22 253	70.16 406	98.53 265	84.42 375	88.04 360	94.77 399

CANet_DTU		94.37 161	93.65 173	96.55 104	96.46 258	92.13 118	96.21 303	96.67 304	94.38 84	93.53 216	97.03 206	79.34 312	99.71 67	90.76 244	98.45 133	97.82 249

cl____		90.96 322	90.32 310	92.89 349	95.37 325	86.21 349	94.46 394	96.64 305	87.82 346	88.15 366	94.18 365	82.98 235	97.54 384	87.70 315	85.59 384	94.92 380

HY-MVS	89.66 9	93.87 187	92.95 204	96.63 98	97.10 180	92.49 104	95.64 342	96.64 305	89.05 302	93.00 231	95.79 278	85.77 174	99.45 128	89.16 287	94.35 264	97.96 235

Test_1112_low_res		92.84 235	91.84 248	95.85 166	97.04 188	89.97 215	95.53 347	96.64 305	85.38 394	89.65 320	95.18 308	85.86 170	99.10 171	87.70 315	93.58 290	98.49 182

DIV-MVS_self_test		90.97 321	90.33 309	92.88 350	95.36 326	86.19 351	94.46 394	96.63 308	87.82 346	88.18 364	94.23 362	82.99 234	97.53 386	87.72 312	85.57 385	94.93 378

Fast-Effi-MVS+-dtu		92.29 255	91.99 242	93.21 338	95.27 335	85.52 365	97.03 208	96.63 308	92.09 185	89.11 339	95.14 310	80.33 295	98.08 309	87.54 325	94.74 260	96.03 316

UnsupCasMVSNet_bld		82.13 432	79.46 437	90.14 421	88.00 469	82.47 414	90.89 460	96.62 310	78.94 456	75.61 464	84.40 475	56.63 465	96.31 430	77.30 436	66.77 476	91.63 456

cl22		91.21 309	90.56 304	93.14 341	96.09 290	86.80 330	94.41 396	96.58 311	87.80 348	88.58 352	93.99 375	80.85 283	97.62 373	89.87 264	86.93 372	94.99 373

jason		94.84 146	94.39 153	96.18 141	95.52 314	90.93 175	96.09 311	96.52 312	89.28 294	96.01 129	97.32 180	84.70 200	98.77 219	95.15 125	98.91 113	98.85 142
jason: jason.
tt0805		91.09 314	90.07 326	94.16 280	95.61 309	88.31 286	97.56 144	96.51 313	89.56 284	89.17 337	95.64 287	67.08 434	98.38 280	91.07 236	88.44 357	95.80 324

AUN-MVS		91.76 275	90.75 293	94.81 239	97.00 193	88.57 276	96.65 260	96.49 314	89.63 282	92.15 250	96.12 259	78.66 327	98.50 267	90.83 240	79.18 438	97.36 271

hse-mvs2		93.45 206	92.99 200	94.81 239	97.02 191	88.59 275	96.69 256	96.47 315	95.19 36	96.74 89	96.16 257	83.67 218	98.48 270	95.85 102	79.13 439	97.35 273

SD_0403		90.01 351	90.02 329	89.96 424	95.65 308	76.76 459	95.76 333	96.46 316	90.58 255	86.59 398	96.29 249	82.12 258	94.78 453	73.00 458	93.76 283	98.35 199

EG-PatchMatch MVS		87.02 395	85.44 398	91.76 389	92.67 430	85.00 379	96.08 312	96.45 317	83.41 425	79.52 454	93.49 395	57.10 464	97.72 363	79.34 427	90.87 330	92.56 442

KD-MVS_self_test		85.95 411	84.95 406	88.96 435	89.55 455	79.11 453	95.13 372	96.42 318	85.91 387	84.07 429	90.48 440	70.03 408	94.82 452	80.04 419	72.94 460	92.94 435

FE-MVSNET2		86.36 404	84.68 412	91.39 397	87.67 471	86.47 342	96.21 303	96.41 319	87.87 344	79.31 456	89.64 448	65.29 446	95.58 444	82.42 398	77.28 445	92.14 453

pmmvs6		87.81 383	86.19 391	92.69 358	91.32 443	86.30 346	97.34 177	96.41 319	80.59 450	84.05 430	94.37 350	67.37 429	97.67 366	84.75 370	79.51 437	94.09 420

PMMVS		92.86 233	92.34 231	94.42 265	94.92 357	86.73 333	94.53 388	96.38 321	84.78 406	94.27 191	95.12 312	83.13 230	98.40 274	91.47 228	96.49 214	98.12 220

RPSCF		90.75 328	90.86 285	90.42 417	96.84 207	76.29 462	95.61 343	96.34 322	83.89 415	91.38 271	97.87 124	76.45 352	98.78 215	87.16 335	92.23 303	96.20 305

BP-MVS1		95.89 98	95.49 98	97.08 81	96.67 228	93.20 77	98.08 58	96.32 323	94.56 72	96.32 114	97.84 130	84.07 213	99.15 163	96.75 64	98.78 116	98.90 130

MSDG		91.42 296	90.24 316	94.96 232	97.15 178	88.91 266	93.69 425	96.32 323	85.72 390	86.93 394	96.47 240	80.24 296	98.98 192	80.57 416	95.05 253	96.98 284

blended_shiyan6		87.55 387	85.52 397	93.64 316	88.78 461	88.50 281	95.23 364	96.30 325	82.80 430	86.09 407	87.70 464	73.69 381	97.56 378	87.70 315	71.36 465	94.86 383

blend_shiyan4		86.87 396	84.61 413	93.67 314	88.87 459	88.70 271	95.17 371	96.30 325	82.80 430	86.16 404	87.11 468	65.12 448	97.55 380	87.73 310	72.21 462	94.75 400

WBMVS		90.69 333	89.99 330	92.81 353	96.48 255	85.00 379	95.21 367	96.30 325	89.46 289	89.04 340	94.05 372	72.45 389	97.82 351	89.46 274	87.41 369	95.61 335

blended_shiyan8		87.58 386	85.55 396	93.66 315	88.76 463	88.54 278	95.21 367	96.29 328	82.81 429	86.25 402	87.73 463	73.70 380	97.58 377	87.81 308	71.42 464	94.85 385

OurMVSNet-221017-0		90.51 338	90.19 321	91.44 395	93.41 415	81.25 424	96.98 217	96.28 329	91.68 198	86.55 399	96.30 248	74.20 374	97.98 325	88.96 290	87.40 370	95.09 369

FE-blended-shiyan7		87.29 389	85.21 402	93.53 324	88.54 466	88.21 292	94.51 391	96.27 330	82.69 433	85.92 408	86.89 470	73.03 384	97.55 380	87.68 319	71.36 465	94.83 387

MVP-Stereo		90.74 329	90.08 323	92.71 357	93.19 420	88.20 293	95.86 325	96.27 330	86.07 385	84.86 419	94.76 327	77.84 341	97.75 361	83.88 384	98.01 153	92.17 452
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
lupinMVS		94.99 139	94.56 143	96.29 133	96.34 267	91.21 158	95.83 327	96.27 330	88.93 309	96.22 119	96.88 213	86.20 164	98.85 205	95.27 121	99.05 104	98.82 146

BH-untuned		92.94 228	92.62 220	93.92 300	97.22 172	86.16 352	96.40 284	96.25 333	90.06 270	89.79 314	96.17 256	83.19 227	98.35 282	87.19 333	97.27 180	97.24 278

CL-MVSNet_self_test		86.31 406	85.15 403	89.80 426	88.83 460	81.74 422	93.93 414	96.22 334	86.67 373	85.03 417	90.80 438	78.09 337	94.50 454	74.92 447	71.86 463	93.15 433

IS-MVSNet		94.90 141	94.52 147	96.05 148	97.67 147	90.56 189	98.44 26	96.22 334	93.21 127	93.99 201	97.74 144	85.55 183	98.45 271	89.98 260	97.86 157	99.14 88

FA-MVS(test-final)		93.52 201	92.92 205	95.31 211	96.77 221	88.54 278	94.82 380	96.21 336	89.61 283	94.20 194	95.25 306	83.24 225	99.14 166	90.01 259	96.16 223	98.25 208

GA-MVS		91.38 298	90.31 311	94.59 251	94.65 371	87.62 310	94.34 399	96.19 337	90.73 242	90.35 294	93.83 378	71.84 392	97.96 332	87.22 332	93.61 288	98.21 211

LuminaMVS		94.89 142	94.35 155	96.53 105	95.48 316	92.80 91	96.88 229	96.18 338	92.85 152	95.92 132	96.87 215	81.44 271	98.83 208	96.43 77	97.10 187	97.94 237

IterMVS-SCA-FT		90.31 341	89.81 337	91.82 384	95.52 314	84.20 391	94.30 402	96.15 339	90.61 252	87.39 380	94.27 359	75.80 358	96.44 427	87.34 329	86.88 376	94.82 391

IterMVS		90.15 349	89.67 343	91.61 391	95.48 316	83.72 397	94.33 400	96.12 340	89.99 271	87.31 383	94.15 367	75.78 360	96.27 431	86.97 338	86.89 375	94.83 387
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
DP-MVS		92.76 238	91.51 262	96.52 107	98.77 62	90.99 170	97.38 174	96.08 341	82.38 435	89.29 332	97.87 124	83.77 216	99.69 73	81.37 409	96.69 204	98.89 136

pmmvs4		90.93 323	89.85 335	94.17 277	93.34 417	90.79 181	94.60 385	96.02 342	84.62 407	87.45 377	95.15 309	81.88 265	97.45 393	87.70 315	87.87 362	94.27 417

ppachtmachnet_test		88.35 378	87.29 377	91.53 392	92.45 436	83.57 400	93.75 421	95.97 343	84.28 410	85.32 416	94.18 365	79.00 324	96.93 415	75.71 443	84.99 398	94.10 418

Anonymous20240521		86.42 403	85.44 398	89.34 433	90.33 448	79.79 444	96.73 250	95.92 344	83.71 420	83.25 435	91.36 435	63.92 450	96.01 432	78.39 431	85.36 389	92.22 450

ITE_SJBPF				92.43 362	95.34 328	85.37 372		95.92 344	91.47 207	87.75 373	96.39 245	71.00 398	97.96 332	82.36 399	89.86 340	93.97 423

test_fmvs2		89.77 360	89.93 332	89.31 434	93.68 402	76.37 461	97.64 133	95.90 346	89.84 277	91.49 269	96.26 252	58.77 460	97.10 407	94.65 152	91.13 323	94.46 408

USDC		88.94 369	87.83 374	92.27 369	94.66 370	84.96 381	93.86 417	95.90 346	87.34 362	83.40 433	95.56 291	67.43 428	98.19 295	82.64 397	89.67 342	93.66 426

COLMAP_ROB	87.81 15	90.40 340	89.28 353	93.79 305	97.95 129	87.13 324	96.92 223	95.89 348	82.83 428	86.88 396	97.18 191	73.77 378	99.29 146	78.44 430	93.62 287	94.95 374
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
VDD-MVS		93.82 189	93.08 198	96.02 151	97.88 135	89.96 216	97.72 118	95.85 349	92.43 169	95.86 134	98.44 64	68.42 424	99.39 134	96.31 79	94.85 254	98.71 163

VDDNet		93.05 222	92.07 237	96.02 151	96.84 207	90.39 197	98.08 58	95.85 349	86.22 383	95.79 137	98.46 62	67.59 427	99.19 154	94.92 132	94.85 254	98.47 185

mvsmamba		94.57 156	94.14 160	95.87 162	97.03 189	89.93 217	97.84 95	95.85 349	91.34 213	94.79 176	96.80 216	80.67 286	98.81 211	94.85 137	98.12 148	98.85 142

Vis-MVSNet (Re-imp)		94.15 169	93.88 166	94.95 233	97.61 155	87.92 302	98.10 56	95.80 352	92.22 177	93.02 230	97.45 171	84.53 203	97.91 344	88.24 300	97.97 154	99.02 103

MM		97.29 31	96.98 42	98.23 12	98.01 123	95.03 27	98.07 60	95.76 353	97.78 1	97.52 62	98.80 38	88.09 119	99.86 9	99.44 2	99.37 67	99.80 1

KD-MVS_2432*1600		84.81 421	82.64 424	91.31 398	91.07 445	85.34 373	91.22 455	95.75 354	85.56 392	83.09 436	90.21 443	67.21 430	95.89 434	77.18 437	62.48 480	92.69 438

miper_refine_blended		84.81 421	82.64 424	91.31 398	91.07 445	85.34 373	91.22 455	95.75 354	85.56 392	83.09 436	90.21 443	67.21 430	95.89 434	77.18 437	62.48 480	92.69 438

FE-MVS		92.05 266	91.05 278	95.08 220	96.83 209	87.93 301	93.91 416	95.70 356	86.30 380	94.15 198	94.97 315	76.59 350	99.21 152	84.10 378	96.86 195	98.09 227

tpm cat1		88.36 377	87.21 380	91.81 385	95.13 347	80.55 433	92.58 446	95.70 356	74.97 466	87.45 377	91.96 428	78.01 340	98.17 297	80.39 418	88.74 354	96.72 294

our_test_3		88.78 373	87.98 373	91.20 402	92.45 436	82.53 411	93.61 429	95.69 358	85.77 389	84.88 418	93.71 383	79.99 301	96.78 423	79.47 424	86.24 378	94.28 416

BH-w/o		92.14 263	91.75 251	93.31 333	96.99 194	85.73 362	95.67 337	95.69 358	88.73 319	89.26 334	94.82 325	82.97 236	98.07 313	85.26 365	96.32 222	96.13 312

CR-MVSNet		90.82 326	89.77 339	93.95 294	94.45 379	87.19 321	90.23 463	95.68 360	86.89 370	92.40 240	92.36 420	80.91 280	97.05 410	81.09 413	93.95 280	97.60 261

Patchmtry		88.64 375	87.25 378	92.78 355	94.09 389	86.64 334	89.82 467	95.68 360	80.81 447	87.63 375	92.36 420	80.91 280	97.03 411	78.86 428	85.12 394	94.67 403

testing91		91.90 271	91.02 279	94.53 259	96.54 246	86.55 340	95.86 325	95.64 362	91.77 195	91.89 259	93.47 397	69.94 409	98.86 203	90.23 258	93.86 282	98.18 213

BH-RMVSNet		92.72 240	91.97 243	94.97 231	97.16 176	87.99 300	96.15 309	95.60 363	90.62 251	91.87 260	97.15 194	78.41 331	98.57 262	83.16 387	97.60 164	98.36 197

PVSNet_0	82.17 19	85.46 416	83.64 419	90.92 406	95.27 335	79.49 449	90.55 461	95.60 363	83.76 419	83.00 438	89.95 445	71.09 397	97.97 328	82.75 395	60.79 482	95.31 355

guyue		95.17 127	94.96 123	95.82 168	96.97 196	89.65 227	97.56 144	95.58 365	94.82 57	95.72 139	97.42 175	82.90 238	98.84 207	96.71 67	96.93 192	98.96 114

SCA		91.84 273	91.18 275	93.83 302	95.59 310	84.95 382	94.72 382	95.58 365	90.82 238	92.25 248	93.69 385	75.80 358	98.10 304	86.20 347	95.98 225	98.45 187

MonoMVSNet		91.92 269	91.77 249	92.37 363	92.94 424	83.11 405	97.09 206	95.55 367	92.91 148	90.85 286	94.55 338	81.27 275	96.52 426	93.01 197	87.76 363	97.47 267

usedtu_blend_shiyan5		87.06 394	84.84 408	93.69 311	88.54 466	88.70 271	95.83 327	95.54 368	78.74 457	85.92 408	86.89 470	73.03 384	97.55 380	87.73 310	71.36 465	94.83 387

AllTest		90.23 345	88.98 359	93.98 290	97.94 130	86.64 334	96.51 273	95.54 368	85.38 394	85.49 413	96.77 218	70.28 404	99.15 163	80.02 420	92.87 292	96.15 310

TestCases				93.98 290	97.94 130	86.64 334		95.54 368	85.38 394	85.49 413	96.77 218	70.28 404	99.15 163	80.02 420	92.87 292	96.15 310

mmtdpeth		89.70 362	88.96 360	91.90 380	95.84 302	84.42 387	97.46 164	95.53 371	90.27 264	94.46 187	90.50 439	69.74 413	98.95 193	97.39 53	69.48 471	92.34 446

tpmvs		89.83 359	89.15 357	91.89 381	94.92 357	80.30 437	93.11 438	95.46 372	86.28 381	88.08 367	92.65 410	80.44 292	98.52 266	81.47 405	89.92 339	96.84 290

pmmvs5		89.86 358	88.87 363	92.82 352	92.86 426	86.23 348	96.26 298	95.39 373	84.24 411	87.12 385	94.51 341	74.27 373	97.36 400	87.61 324	87.57 365	94.86 383

PatchmatchNet		91.91 270	91.35 264	93.59 320	95.38 323	84.11 392	93.15 437	95.39 373	89.54 285	92.10 253	93.68 387	82.82 241	98.13 299	84.81 369	95.32 246	98.52 177
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
tpmrst		91.44 295	91.32 266	91.79 386	95.15 345	79.20 452	93.42 432	95.37 375	88.55 324	93.49 219	93.67 388	82.49 250	98.27 288	90.41 253	89.34 345	97.90 239

Anonymous20231206		87.09 393	86.14 392	89.93 425	91.22 444	80.35 435	96.11 310	95.35 376	83.57 422	84.16 425	93.02 405	73.54 382	95.61 442	72.16 460	86.14 380	93.84 425

MIMVSNet1		84.93 419	83.05 421	90.56 415	89.56 454	84.84 384	95.40 352	95.35 376	83.91 414	80.38 450	92.21 425	57.23 463	93.34 467	70.69 466	82.75 425	93.50 428

TDRefinement		86.53 399	84.76 410	91.85 382	82.23 483	84.25 389	96.38 286	95.35 376	84.97 403	84.09 428	94.94 317	65.76 443	98.34 285	84.60 373	74.52 456	92.97 434

TR-MVS		91.48 294	90.59 302	94.16 280	96.40 261	87.33 314	95.67 337	95.34 379	87.68 354	91.46 270	95.52 294	76.77 349	98.35 282	82.85 392	93.61 288	96.79 292

EPNet_dtu		91.71 276	91.28 269	92.99 345	93.76 399	83.71 398	96.69 256	95.28 380	93.15 134	87.02 390	95.95 267	83.37 224	97.38 399	79.46 425	96.84 196	97.88 241
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
FMVSNet5		87.29 389	85.79 394	91.78 387	94.80 364	87.28 316	95.49 349	95.28 380	84.09 413	83.85 432	91.82 429	62.95 453	94.17 458	78.48 429	85.34 390	93.91 424

MDTV_nov1_ep13			90.76 291		95.22 339	80.33 436	93.03 440	95.28 380	88.14 337	92.84 237	93.83 378	81.34 272	98.08 309	82.86 390	94.34 265

LF4IMVS		87.94 381	87.25 378	89.98 423	92.38 438	80.05 443	94.38 397	95.25 383	87.59 356	84.34 422	94.74 329	64.31 449	97.66 369	84.83 368	87.45 366	92.23 449

TransMVSNet (Re)		88.94 369	87.56 375	93.08 343	94.35 382	88.45 284	97.73 115	95.23 384	87.47 358	84.26 424	95.29 301	79.86 304	97.33 401	79.44 426	74.44 457	93.45 430

test20.03		86.14 409	85.40 400	88.35 436	90.12 449	80.06 442	95.90 324	95.20 385	88.59 320	81.29 445	93.62 390	71.43 395	92.65 471	71.26 464	81.17 430	92.34 446

new-patchmatchnet		83.18 428	81.87 431	87.11 444	86.88 474	75.99 463	93.70 423	95.18 386	85.02 402	77.30 463	88.40 457	65.99 441	93.88 463	74.19 452	70.18 469	91.47 461

MDA-MVSNet_test_wron		85.87 413	84.23 416	90.80 412	92.38 438	82.57 410	93.17 435	95.15 387	82.15 436	67.65 475	92.33 423	78.20 333	95.51 446	77.33 434	79.74 434	94.31 415

YYNet1		85.87 413	84.23 416	90.78 413	92.38 438	82.46 415	93.17 435	95.14 388	82.12 437	67.69 473	92.36 420	78.16 336	95.50 447	77.31 435	79.73 435	94.39 411

Baseline_NR-MVSNet		91.20 310	90.62 300	92.95 347	93.83 397	88.03 299	97.01 213	95.12 389	88.42 328	89.70 317	95.13 311	83.47 221	97.44 394	89.66 270	83.24 420	93.37 431

thres200		92.23 259	91.39 263	94.75 246	97.61 155	89.03 260	96.60 268	95.09 390	92.08 186	93.28 225	94.00 374	78.39 332	99.04 189	81.26 412	94.18 271	96.19 306

ADS-MVSNet		89.89 355	88.68 365	93.53 324	95.86 297	84.89 383	90.93 458	95.07 391	83.23 426	91.28 279	91.81 430	79.01 322	97.85 347	79.52 422	91.39 319	97.84 246

pmmvs-eth3d		86.22 407	84.45 414	91.53 392	88.34 468	87.25 318	94.47 392	95.01 392	83.47 423	79.51 455	89.61 449	69.75 412	95.71 439	83.13 388	76.73 449	91.64 455

Anonymous202405211		92.07 265	90.83 289	95.76 177	98.19 109	88.75 269	97.58 140	95.00 393	86.00 386	93.64 211	97.45 171	66.24 439	99.53 112	90.68 247	92.71 297	99.01 106

MDA-MVSNet-bldmvs		85.00 418	82.95 423	91.17 404	93.13 422	83.33 401	94.56 387	95.00 393	84.57 408	65.13 479	92.65 410	70.45 403	95.85 436	73.57 455	77.49 444	94.33 413

ambc				86.56 447	83.60 480	70.00 474	85.69 479	94.97 395		80.60 449	88.45 456	37.42 481	96.84 420	82.69 396	75.44 454	92.86 436

testgi		87.97 380	87.21 380	90.24 420	92.86 426	80.76 428	96.67 259	94.97 395	91.74 196	85.52 412	95.83 273	62.66 455	94.47 456	76.25 441	88.36 358	95.48 338

myMVS_eth3d28		91.52 291	90.97 281	93.17 339	96.91 199	83.24 403	95.61 343	94.96 397	92.24 176	91.98 256	93.28 402	69.31 414	98.40 274	88.71 295	95.68 235	97.88 241

dp		88.90 371	88.26 371	90.81 410	94.58 375	76.62 460	92.85 443	94.93 398	85.12 400	90.07 308	93.07 404	75.81 357	98.12 302	80.53 417	87.42 368	97.71 253

test_fmvs3		83.21 427	83.02 422	83.78 451	86.77 475	68.34 477	96.76 248	94.91 399	86.49 376	84.14 427	89.48 450	36.04 482	91.73 473	91.86 218	80.77 432	91.26 463

test_0402		86.46 402	84.79 409	91.45 394	95.02 351	85.55 364	96.29 297	94.89 400	80.90 444	82.21 441	93.97 376	68.21 425	97.29 403	62.98 474	88.68 355	91.51 458

tfpn200view9		92.38 249	91.52 260	94.95 233	97.85 136	89.29 249	97.41 167	94.88 401	92.19 182	93.27 226	94.46 346	78.17 334	99.08 177	81.40 406	94.08 275	96.48 299

CVMVSNet		91.23 308	91.75 251	89.67 427	95.77 303	74.69 464	96.44 274	94.88 401	85.81 388	92.18 249	97.64 157	79.07 317	95.58 444	88.06 303	95.86 230	98.74 160

thres400		92.42 247	91.52 260	95.12 219	97.85 136	89.29 249	97.41 167	94.88 401	92.19 182	93.27 226	94.46 346	78.17 334	99.08 177	81.40 406	94.08 275	96.98 284

tt0320		85.39 417	83.12 420	92.19 373	93.44 414	85.79 360	96.19 306	94.87 404	71.19 473	82.92 439	91.76 432	58.43 461	96.81 421	81.03 414	78.26 443	93.98 422

EPNet		95.20 123	94.56 143	97.14 75	92.80 428	92.68 97	97.85 94	94.87 404	96.64 9	92.46 239	97.80 138	86.23 161	99.65 79	93.72 176	98.62 124	99.10 95
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
testing99		91.62 282	90.72 296	94.32 270	96.48 255	86.11 357	95.81 329	94.76 406	91.55 200	91.75 264	93.44 398	68.55 422	98.82 209	90.43 252	93.69 284	98.04 231

sc_t1		86.48 401	84.10 418	93.63 317	93.45 413	85.76 361	96.79 242	94.71 407	73.06 471	86.45 400	94.35 351	55.13 468	97.95 336	84.38 376	78.55 442	97.18 280

SixPastTwentyTwo		89.15 367	88.54 367	90.98 405	93.49 410	80.28 439	96.70 254	94.70 408	90.78 239	84.15 426	95.57 290	71.78 393	97.71 364	84.63 372	85.07 395	94.94 376

thres100view900		92.43 246	91.58 257	94.98 229	97.92 132	89.37 245	97.71 120	94.66 409	92.20 180	93.31 224	94.90 320	78.06 338	99.08 177	81.40 406	94.08 275	96.48 299

thres600view7		92.49 244	91.60 256	95.18 215	97.91 133	89.47 239	97.65 129	94.66 409	92.18 184	93.33 223	94.91 319	78.06 338	99.10 171	81.61 402	94.06 279	96.98 284

PatchT		88.87 372	87.42 376	93.22 337	94.08 390	85.10 377	89.51 468	94.64 411	81.92 438	92.36 243	88.15 460	80.05 300	97.01 413	72.43 459	93.65 286	97.54 264

baseline1		92.82 236	91.90 246	95.55 193	97.20 174	90.77 182	97.19 198	94.58 412	92.20 180	92.36 243	96.34 247	84.16 211	98.21 292	89.20 285	83.90 415	97.68 255

AstraMVS		94.82 148	94.64 139	95.34 210	96.36 266	88.09 298	97.58 140	94.56 413	94.98 46	95.70 142	97.92 114	81.93 264	98.93 196	96.87 61	95.88 228	98.99 110

UBG		91.55 288	90.76 291	93.94 296	96.52 251	85.06 378	95.22 365	94.54 414	90.47 260	91.98 256	92.71 409	72.02 390	98.74 233	88.10 302	95.26 248	98.01 233

Gipuma		67.86 448	65.41 450	75.18 464	92.66 431	73.45 468	66.50 486	94.52 415	53.33 484	57.80 485	66.07 485	30.81 484	89.20 477	48.15 483	78.88 441	62.90 485
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
testing11		91.68 279	90.75 293	94.47 261	96.53 248	86.56 339	95.76 333	94.51 416	91.10 231	91.24 281	93.59 392	68.59 421	98.86 203	91.10 235	94.29 267	98.00 234

CostFormer		91.18 313	90.70 297	92.62 360	94.84 362	81.76 421	94.09 409	94.43 417	84.15 412	92.72 238	93.77 382	79.43 311	98.20 293	90.70 246	92.18 306	97.90 239

tpm2		89.96 352	89.21 355	92.23 372	94.91 359	81.25 424	93.78 420	94.42 418	80.62 449	91.56 267	93.44 398	76.44 353	97.94 338	85.60 359	92.08 310	97.49 265

testing3-2		92.10 264	92.05 238	92.27 369	97.71 145	79.56 446	97.42 166	94.41 419	93.53 113	93.22 228	95.49 295	69.16 416	99.11 169	93.25 187	94.22 269	98.13 218

MGCNet		96.74 64	96.31 81	98.02 20	96.87 203	94.65 31	97.58 140	94.39 420	96.47 12	97.16 74	98.39 68	87.53 136	99.87 7	98.97 20	99.41 59	99.55 43

JIA-IIPM		88.26 379	87.04 383	91.91 379	93.52 408	81.42 423	89.38 469	94.38 421	80.84 446	90.93 285	80.74 477	79.22 314	97.92 341	82.76 394	91.62 314	96.38 302

dmvs_re		90.21 346	89.50 348	92.35 364	95.47 320	85.15 375	95.70 336	94.37 422	90.94 237	88.42 354	93.57 393	74.63 370	95.67 441	82.80 393	89.57 343	96.22 304

Patchmatch-test		89.42 365	87.99 372	93.70 310	95.27 335	85.11 376	88.98 470	94.37 422	81.11 443	87.10 388	93.69 385	82.28 254	97.50 389	74.37 450	94.76 258	98.48 184

LCM-MVSNet		72.55 441	69.39 445	82.03 453	70.81 493	65.42 482	90.12 465	94.36 424	55.02 483	65.88 477	81.72 476	24.16 490	89.96 474	74.32 451	68.10 474	90.71 466

ADS-MVSNet2		89.45 364	88.59 366	92.03 376	95.86 297	82.26 417	90.93 458	94.32 425	83.23 426	91.28 279	91.81 430	79.01 322	95.99 433	79.52 422	91.39 319	97.84 246

mvs5depth		86.53 399	85.08 404	90.87 407	88.74 464	82.52 412	91.91 451	94.23 426	86.35 379	87.11 387	93.70 384	66.52 435	97.76 359	81.37 409	75.80 451	92.31 448

EU-MVSNet		88.72 374	88.90 362	88.20 438	93.15 421	74.21 466	96.63 265	94.22 427	85.18 398	87.32 382	95.97 265	76.16 355	94.98 451	85.27 364	86.17 379	95.41 345

tt0320-xc		84.83 420	82.33 428	92.31 367	93.66 403	86.20 350	96.17 308	94.06 428	71.26 472	82.04 443	92.22 424	55.07 469	96.72 424	81.49 404	75.04 455	94.02 421

MIMVSNet		88.50 376	86.76 386	93.72 309	94.84 362	87.77 308	91.39 453	94.05 429	86.41 378	87.99 369	92.59 413	63.27 451	95.82 438	77.44 433	92.84 294	97.57 263

OpenMVS_ROB	81.14 20	84.42 423	82.28 429	90.83 408	90.06 450	84.05 394	95.73 335	94.04 430	73.89 469	80.17 453	91.53 434	59.15 459	97.64 370	66.92 472	89.05 348	90.80 465

TinyColmap		86.82 397	85.35 401	91.21 400	94.91 359	82.99 407	93.94 413	94.02 431	83.58 421	81.56 444	94.68 331	62.34 456	98.13 299	75.78 442	87.35 371	92.52 444

ETVMVS		90.52 337	89.14 358	94.67 249	96.81 213	87.85 306	95.91 323	93.97 432	89.71 280	92.34 246	92.48 415	65.41 445	97.96 332	81.37 409	94.27 268	98.21 211

IB-MVS	87.33 17	89.91 353	88.28 370	94.79 243	95.26 338	87.70 309	95.12 373	93.95 433	89.35 293	87.03 389	92.49 414	70.74 401	99.19 154	89.18 286	81.37 429	97.49 265
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
Syy-MVS		87.13 392	87.02 384	87.47 442	95.16 342	73.21 470	95.00 375	93.93 434	88.55 324	86.96 391	91.99 426	75.90 356	94.00 460	61.59 476	94.11 272	95.20 363

myMVS_eth3d		87.18 391	86.38 389	89.58 428	95.16 342	79.53 447	95.00 375	93.93 434	88.55 324	86.96 391	91.99 426	56.23 466	94.00 460	75.47 446	94.11 272	95.20 363

testing222		90.31 341	88.96 360	94.35 267	96.54 246	87.29 315	95.50 348	93.84 436	90.97 234	91.75 264	92.96 406	62.18 457	98.00 323	82.86 390	94.08 275	97.76 251

test_f		80.57 434	79.62 436	83.41 452	83.38 481	67.80 479	93.57 430	93.72 437	80.80 448	77.91 462	87.63 465	33.40 483	92.08 472	87.14 336	79.04 440	90.34 467

LCM-MVSNet-Re		92.50 242	92.52 226	92.44 361	96.82 211	81.89 420	96.92 223	93.71 438	92.41 170	84.30 423	94.60 336	85.08 192	97.03 411	91.51 226	97.36 173	98.40 193

tpm		90.25 344	89.74 342	91.76 389	93.92 393	79.73 445	93.98 410	93.54 439	88.28 331	91.99 255	93.25 403	77.51 344	97.44 394	87.30 331	87.94 361	98.12 220

ET-MVSNet_ETH3D		91.49 293	90.11 322	95.63 187	96.40 261	91.57 142	95.34 355	93.48 440	90.60 254	75.58 465	95.49 295	80.08 299	96.79 422	94.25 164	89.76 341	98.52 177

LFMVS		93.60 196	92.63 219	96.52 107	98.13 115	91.27 155	97.94 81	93.39 441	90.57 256	96.29 116	98.31 81	69.00 417	99.16 161	94.18 165	95.87 229	99.12 92

MVStest1		82.38 431	80.04 435	89.37 431	87.63 472	82.83 408	95.03 374	93.37 442	73.90 468	73.50 470	94.35 351	62.89 454	93.25 469	73.80 453	65.92 477	92.04 454

FE-MVSNET		83.85 424	81.97 430	89.51 429	87.19 473	83.19 404	95.21 367	93.17 443	83.45 424	78.90 458	89.05 453	65.46 444	93.84 464	69.71 468	75.56 453	91.51 458

Patchmatch-RL test		87.38 388	86.24 390	90.81 410	88.74 464	78.40 456	88.12 477	93.17 443	87.11 367	82.17 442	89.29 451	81.95 262	95.60 443	88.64 297	77.02 446	98.41 192

ttmdpeth		85.91 412	84.76 410	89.36 432	89.14 456	80.25 440	95.66 340	93.16 445	83.77 418	83.39 434	95.26 305	66.24 439	95.26 450	80.65 415	75.57 452	92.57 441

test-LLR		91.42 296	91.19 274	92.12 374	94.59 373	80.66 430	94.29 403	92.98 446	91.11 229	90.76 288	92.37 417	79.02 320	98.07 313	88.81 292	96.74 201	97.63 256

test-mter		90.19 348	89.54 347	92.12 374	94.59 373	80.66 430	94.29 403	92.98 446	87.68 354	90.76 288	92.37 417	67.67 426	98.07 313	88.81 292	96.74 201	97.63 256

WB-MVSnew		89.88 356	89.56 346	90.82 409	94.57 376	83.06 406	95.65 341	92.85 448	87.86 345	90.83 287	94.10 368	79.66 308	96.88 418	76.34 440	94.19 270	92.54 443

testing3		87.67 384	86.88 385	90.05 422	96.14 284	80.71 429	97.10 205	92.85 448	90.15 268	87.54 376	94.55 338	55.70 467	94.10 459	73.77 454	94.10 274	95.35 352

test_method		66.11 449	64.89 451	69.79 467	72.62 491	35.23 499	65.19 487	92.83 450	20.35 489	65.20 478	88.08 461	43.14 479	82.70 484	73.12 457	63.46 479	91.45 462

test0.0.03 1		89.37 366	88.70 364	91.41 396	92.47 435	85.63 363	95.22 365	92.70 451	91.11 229	86.91 395	93.65 389	79.02 320	93.19 470	78.00 432	89.18 346	95.41 345

new_pmnet		82.89 429	81.12 434	88.18 439	89.63 453	80.18 441	91.77 452	92.57 452	76.79 464	75.56 466	88.23 459	61.22 458	94.48 455	71.43 462	82.92 423	89.87 468

mvsany_test1		93.93 185	93.98 164	93.78 306	94.94 356	86.80 330	94.62 384	92.55 453	88.77 318	96.85 84	98.49 58	88.98 101	98.08 309	95.03 127	95.62 237	96.46 301

thisisatest0515		92.29 255	91.30 268	95.25 213	96.60 234	88.90 267	94.36 398	92.32 454	87.92 341	93.43 221	94.57 337	77.28 345	99.00 190	89.42 276	95.86 230	97.86 245

thisisatest0530		93.03 223	92.21 235	95.49 202	97.07 181	89.11 258	97.49 161	92.19 455	90.16 267	94.09 199	96.41 243	76.43 354	99.05 186	90.38 254	95.68 235	98.31 205

tttt0517		92.96 226	92.33 232	94.87 236	97.11 179	87.16 323	97.97 77	92.09 456	90.63 250	93.88 205	97.01 207	76.50 351	99.06 183	90.29 257	95.45 244	98.38 195

K. test v3		87.64 385	86.75 387	90.32 419	93.02 423	79.48 450	96.61 266	92.08 457	90.66 248	80.25 452	94.09 370	67.21 430	96.65 425	85.96 355	80.83 431	94.83 387

TESTMET0.1,1		90.06 350	89.42 350	91.97 377	94.41 381	80.62 432	94.29 403	91.97 458	87.28 364	90.44 292	92.47 416	68.79 418	97.67 366	88.50 299	96.60 208	97.61 260

PM-MVS		83.48 426	81.86 432	88.31 437	87.83 470	77.59 458	93.43 431	91.75 459	86.91 369	80.63 448	89.91 446	44.42 478	95.84 437	85.17 367	76.73 449	91.50 460

baseline2		91.63 281	90.86 285	93.94 296	94.33 383	86.32 345	95.92 322	91.64 460	89.37 292	86.94 393	94.69 330	81.62 269	98.69 243	88.64 297	94.57 263	96.81 291

APD_test1		79.31 436	77.70 439	84.14 450	89.11 458	69.07 476	92.36 450	91.50 461	69.07 475	73.87 468	92.63 412	39.93 480	94.32 457	70.54 467	80.25 433	89.02 470

FPMVS		71.27 442	69.85 444	75.50 463	74.64 488	59.03 488	91.30 454	91.50 461	58.80 480	57.92 484	88.28 458	29.98 486	85.53 483	53.43 481	82.84 424	81.95 476

door								91.13 463

door-mid								91.06 464

EGC-MVSNET		68.77 447	63.01 453	86.07 449	92.49 434	82.24 418	93.96 412	90.96 465	0.71 494	2.62 495	90.89 437	53.66 470	93.46 465	57.25 479	84.55 405	82.51 475

mvsany_test3		83.59 425	82.44 427	87.03 445	83.80 478	73.82 467	93.70 423	90.92 466	86.42 377	82.51 440	90.26 442	46.76 477	95.71 439	90.82 241	76.76 448	91.57 457

pmmvs3		79.97 435	77.50 440	87.39 443	82.80 482	79.38 451	92.70 445	90.75 467	70.69 474	78.66 459	87.47 467	51.34 473	93.40 466	73.39 456	69.65 470	89.38 469

UWE-MVS		89.91 353	89.48 349	91.21 400	95.88 296	78.23 457	94.91 378	90.26 468	89.11 299	92.35 245	94.52 340	68.76 419	97.96 332	83.95 382	95.59 238	97.42 269

DSMNet-mixed		86.34 405	86.12 393	87.00 446	89.88 452	70.43 472	94.93 377	90.08 469	77.97 461	85.42 415	92.78 408	74.44 372	93.96 462	74.43 449	95.14 249	96.62 295

MVS-HIRNet		82.47 430	81.21 433	86.26 448	95.38 323	69.21 475	88.96 471	89.49 470	66.28 477	80.79 447	74.08 482	68.48 423	97.39 398	71.93 461	95.47 243	92.18 451

WB-MVS		76.77 438	76.63 441	77.18 458	85.32 476	56.82 490	94.53 388	89.39 471	82.66 434	71.35 471	89.18 452	75.03 365	88.88 478	35.42 487	66.79 475	85.84 472

test1111		93.19 215	92.82 209	94.30 273	97.58 161	84.56 386	98.21 47	89.02 472	93.53 113	94.58 181	98.21 88	72.69 386	99.05 186	93.06 193	98.48 131	99.28 77

SSC-MVS		76.05 439	75.83 442	76.72 462	84.77 477	56.22 491	94.32 401	88.96 473	81.82 440	70.52 472	88.91 454	74.79 369	88.71 479	33.69 488	64.71 478	85.23 473

ECVR-MVS		93.19 215	92.73 215	94.57 256	97.66 149	85.41 369	98.21 47	88.23 474	93.43 120	94.70 178	98.21 88	72.57 387	99.07 181	93.05 194	98.49 129	99.25 80

EPMVS		90.70 331	89.81 337	93.37 331	94.73 368	84.21 390	93.67 426	88.02 475	89.50 287	92.38 242	93.49 395	77.82 342	97.78 356	86.03 353	92.68 298	98.11 226

ANet_high		63.94 451	59.58 454	77.02 459	61.24 495	66.06 480	85.66 480	87.93 476	78.53 459	42.94 487	71.04 484	25.42 489	80.71 486	52.60 482	30.83 488	84.28 474

PMMVS2		70.19 443	66.92 447	80.01 454	76.35 487	65.67 481	86.22 478	87.58 477	64.83 479	62.38 480	80.29 479	26.78 488	88.49 481	63.79 473	54.07 484	85.88 471

lessismore_v0				90.45 416	91.96 441	79.09 454		87.19 478		80.32 451	94.39 348	66.31 438	97.55 380	84.00 381	76.84 447	94.70 402

PMVS	53.92 22	58.58 452	55.40 455	68.12 468	51.00 496	48.64 493	78.86 483	87.10 479	46.77 485	35.84 491	74.28 481	8.76 494	86.34 482	42.07 485	73.91 458	69.38 482
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
UWE-MVS-28		86.81 398	86.41 388	88.02 440	92.87 425	74.60 465	95.38 354	86.70 480	88.17 334	87.28 384	94.67 333	70.83 400	93.30 468	67.45 470	94.31 266	96.17 307

test_vis1_rt		86.16 408	85.06 405	89.46 430	93.47 412	80.46 434	96.41 280	86.61 481	85.22 397	79.15 457	88.64 455	52.41 472	97.06 409	93.08 192	90.57 332	90.87 464

testf1		69.31 445	66.76 448	76.94 460	78.61 485	61.93 484	88.27 475	86.11 482	55.62 481	59.69 481	85.31 473	20.19 492	89.32 475	57.62 477	69.44 472	79.58 477

APD_test2		69.31 445	66.76 448	76.94 460	78.61 485	61.93 484	88.27 475	86.11 482	55.62 481	59.69 481	85.31 473	20.19 492	89.32 475	57.62 477	69.44 472	79.58 477

gg-mvs-nofinetune		87.82 382	85.61 395	94.44 263	94.46 378	89.27 252	91.21 457	84.61 484	80.88 445	89.89 312	74.98 480	71.50 394	97.53 386	85.75 358	97.21 182	96.51 297

dmvs_testset		81.38 433	82.60 426	77.73 457	91.74 442	51.49 492	93.03 440	84.21 485	89.07 300	78.28 461	91.25 436	76.97 347	88.53 480	56.57 480	82.24 426	93.16 432

GG-mvs-BLEND				93.62 318	93.69 401	89.20 254	92.39 449	83.33 486		87.98 370	89.84 447	71.00 398	96.87 419	82.08 401	95.40 245	94.80 394

MTMP							97.86 91	82.03 487

DeepMVS_CX				74.68 465	90.84 447	64.34 483		81.61 488	65.34 478	67.47 476	88.01 462	48.60 476	80.13 487	62.33 475	73.68 459	79.58 477

E-PMN		53.28 453	52.56 457	55.43 471	74.43 489	47.13 494	83.63 482	76.30 489	42.23 486	42.59 488	62.22 487	28.57 487	74.40 488	31.53 489	31.51 487	44.78 486

test2506		91.60 283	90.78 290	94.04 286	97.66 149	83.81 395	98.27 37	75.53 490	93.43 120	95.23 159	98.21 88	67.21 430	99.07 181	93.01 197	98.49 129	99.25 80

EMVS		52.08 455	51.31 458	54.39 472	72.62 491	45.39 496	83.84 481	75.51 491	41.13 487	40.77 489	59.65 488	30.08 485	73.60 489	28.31 491	29.90 489	44.18 487

test_vis3_rt		72.73 440	70.55 443	79.27 455	80.02 484	68.13 478	93.92 415	74.30 492	76.90 463	58.99 483	73.58 483	20.29 491	95.37 448	84.16 377	72.80 461	74.31 480

MVE	50.73 23	53.25 454	48.81 459	66.58 470	65.34 494	57.50 489	72.49 485	70.94 493	40.15 488	39.28 490	63.51 486	6.89 496	73.48 490	38.29 486	42.38 486	68.76 484
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
tmp_tt		51.94 456	53.82 456	46.29 473	33.73 497	45.30 497	78.32 484	67.24 494	18.02 490	50.93 486	87.05 469	52.99 471	53.11 492	70.76 465	25.29 490	40.46 488

kuosan		65.27 450	64.66 452	67.11 469	83.80 478	61.32 487	88.53 474	60.77 495	68.22 476	67.67 474	80.52 478	49.12 475	70.76 491	29.67 490	53.64 485	69.26 483

dongtai		69.99 444	69.33 446	71.98 466	88.78 461	61.64 486	89.86 466	59.93 496	75.67 465	74.96 467	85.45 472	50.19 474	81.66 485	43.86 484	55.27 483	72.63 481

N_pmnet		78.73 437	78.71 438	78.79 456	92.80 428	46.50 495	94.14 407	43.71 497	78.61 458	80.83 446	91.66 433	74.94 368	96.36 429	67.24 471	84.45 407	93.50 428

wuyk23d		25.11 457	24.57 461	26.74 474	73.98 490	39.89 498	57.88 488	9.80 498	12.27 491	10.39 492	6.97 494	7.03 495	36.44 493	25.43 492	17.39 491	3.89 491

testmvs		13.36 459	16.33 462	4.48 476	5.04 498	2.26 501	93.18 434	3.28 499	2.70 492	8.24 493	21.66 490	2.29 498	2.19 494	7.58 493	2.96 492	9.00 490

test123		13.04 460	15.66 463	5.18 475	4.51 499	3.45 500	92.50 448	1.81 500	2.50 493	7.58 494	20.15 491	3.67 497	2.18 495	7.13 494	1.07 493	9.90 489

mmdepth		0.00 463	0.00 466	0.00 477	0.00 500	0.00 502	0.00 489	0.00 501	0.00 495	0.00 496	0.00 495	0.00 499	0.00 496	0.00 495	0.00 494	0.00 492

monomultidepth		0.00 463	0.00 466	0.00 477	0.00 500	0.00 502	0.00 489	0.00 501	0.00 495	0.00 496	0.00 495	0.00 499	0.00 496	0.00 495	0.00 494	0.00 492

test_blank		0.00 463	0.00 466	0.00 477	0.00 500	0.00 502	0.00 489	0.00 501	0.00 495	0.00 496	0.00 495	0.00 499	0.00 496	0.00 495	0.00 494	0.00 492

uanet_test		0.00 463	0.00 466	0.00 477	0.00 500	0.00 502	0.00 489	0.00 501	0.00 495	0.00 496	0.00 495	0.00 499	0.00 496	0.00 495	0.00 494	0.00 492

DCPMVS		0.00 463	0.00 466	0.00 477	0.00 500	0.00 502	0.00 489	0.00 501	0.00 495	0.00 496	0.00 495	0.00 499	0.00 496	0.00 495	0.00 494	0.00 492

pcd_1.5k_mvsjas		7.39 462	9.85 465	0.00 477	0.00 500	0.00 502	0.00 489	0.00 501	0.00 495	0.00 496	0.00 495	88.65 109	0.00 496	0.00 495	0.00 494	0.00 492

sosnet-low-res		0.00 463	0.00 466	0.00 477	0.00 500	0.00 502	0.00 489	0.00 501	0.00 495	0.00 496	0.00 495	0.00 499	0.00 496	0.00 495	0.00 494	0.00 492

sosnet		0.00 463	0.00 466	0.00 477	0.00 500	0.00 502	0.00 489	0.00 501	0.00 495	0.00 496	0.00 495	0.00 499	0.00 496	0.00 495	0.00 494	0.00 492

uncertanet		0.00 463	0.00 466	0.00 477	0.00 500	0.00 502	0.00 489	0.00 501	0.00 495	0.00 496	0.00 495	0.00 499	0.00 496	0.00 495	0.00 494	0.00 492

Regformer		0.00 463	0.00 466	0.00 477	0.00 500	0.00 502	0.00 489	0.00 501	0.00 495	0.00 496	0.00 495	0.00 499	0.00 496	0.00 495	0.00 494	0.00 492

n2								0.00 501

nn								0.00 501

ab-mvs-re		8.06 461	10.74 464	0.00 477	0.00 500	0.00 502	0.00 489	0.00 501	0.00 495	0.00 496	96.69 224	0.00 499	0.00 496	0.00 495	0.00 494	0.00 492

uanet		0.00 463	0.00 466	0.00 477	0.00 500	0.00 502	0.00 489	0.00 501	0.00 495	0.00 496	0.00 495	0.00 499	0.00 496	0.00 495	0.00 494	0.00 492

TestfortrainingZip							98.69 11

WAC-MVS						79.53 447								75.56 445

PC_three_1452									90.77 240	98.89 26	98.28 86	96.24 1	98.35 282	95.76 106	99.58 23	99.59 32

eth-test2					0.00 500

eth-test					0.00 500

OPU-MVS				98.55 4	98.82 61	96.86 3	98.25 40				98.26 87	96.04 2	99.24 149	95.36 120	99.59 19	99.56 40

test_0728_THIRD									94.78 61	98.73 30	98.87 31	95.87 4	99.84 26	97.45 46	99.72 2	99.77 3

GSMVS																98.45 187

test_part2					99.28 30	95.74 9				98.10 48

sam_mvs1												82.76 242				98.45 187

sam_mvs												81.94 263

test_post1							92.81 444				16.58 493	80.53 290	97.68 365	86.20 347

test_post											17.58 492	81.76 266	98.08 309

patchmatchnet-post											90.45 441	82.65 247	98.10 304

gm-plane-assit					93.22 419	78.89 455			84.82 405		93.52 394		98.64 252	87.72 312

test9_res														94.81 143	99.38 64	99.45 59

agg_prior2														93.94 170	99.38 64	99.50 52

test_prior4						93.66 62	96.42 279

test_prior2							96.35 289		92.80 155	96.03 126	97.59 163	92.01 50		95.01 128	99.38 64

旧先验2							95.94 320		81.66 441	97.34 70			98.82 209	92.26 203

新几何2							95.79 331

原ACMM2							95.67 337

testdata2													99.67 77	85.96 355

segment_acmp												92.89 33

testdata1							95.26 363		93.10 137

plane_prior7					96.21 271	89.98 213

plane_prior6					96.10 289	90.00 209						81.32 273

plane_prior4											96.64 227

plane_prior3						90.00 209			94.46 78	91.34 273

plane_prior2							97.74 113		94.85 53

plane_prior1					96.14 284

plane_prior						89.99 211	97.24 190		94.06 92						92.16 307

HQP5-MVS						89.33 247

HQP-NCC					95.86 297		96.65 260		93.55 109	90.14 297

ACMP_Plane					95.86 297		96.65 260		93.55 109	90.14 297

BP-MVS														92.13 211

HQP4-MVS										90.14 297			98.50 267			95.78 326

HQP2-MVS												80.95 278

NP-MVS					95.99 295	89.81 221					95.87 270

MDTV_nov1_ep13_2view						70.35 473	93.10 439		83.88 416	93.55 214		82.47 251		86.25 346		98.38 195

ACMMP++_ref															90.30 337

ACMMP++															91.02 326

Test By Simon												88.73 108

High-resolution multi-view benchmark