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	98.08 225

PGM-MVS		96.81 58	96.53 69	97.65 47	99.35 25	93.53 65	97.65 129	98.98 2	92.22 174	97.14 76	98.44 64	91.17 71	99.85 21	94.35 160	99.46 46	99.57 36

MVS_111021_HR		96.68 69	96.58 68	96.99 84	98.46 79	92.31 110	96.20 302	98.90 3	94.30 86	95.86 134	97.74 141	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	94.56 179	98.39 68	88.96 102	99.85 21	94.57 154	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	96.24 299	98.79 7	93.99 95	95.80 136	97.65 151	89.92 91	99.24 149	95.87 100	99.20 88	98.58 169

patch_mono-2		96.83 57	97.44 24	95.01 222	99.05 45	85.39 363	96.98 214	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	98.18 210

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	98.25 205

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		93.94 180	93.57 172	95.04 220	95.48 313	91.45 149	98.12 55	98.71 13	93.37 122	90.23 293	96.70 219	87.66 129	97.85 344	91.49 224	90.39 333	95.83 319

UniMVSNet (Re)		93.31 207	92.55 220	95.61 189	95.39 319	93.34 71	97.39 172	98.71 13	93.14 135	90.10 302	94.83 321	87.71 128	98.03 317	91.67 222	83.99 408	95.46 338

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		94.09 171	93.70 168	95.27 209	95.70 302	92.03 122	98.10 56	98.68 19	93.36 124	90.39 290	96.70 219	87.63 132	97.94 335	92.25 202	90.50 332	95.84 318

WR-MVS_H		92.00 264	91.35 261	93.95 291	95.09 346	89.47 239	98.04 63	98.68 19	91.46 205	88.34 354	94.68 328	85.86 170	97.56 374	85.77 349	84.24 406	94.82 384

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	98.08 225

VPA-MVSNet		93.24 209	92.48 225	95.51 196	95.70 302	92.39 106	97.86 91	98.66 22	92.30 171	92.09 251	95.37 296	80.49 288	98.40 271	93.95 166	85.86 379	95.75 327

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	98.59 168

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	84.47 201	99.75 58	99.37 5	98.45 133	97.88 238

UniMVSNet_NR-MVSNet		93.37 205	92.67 214	95.47 202	95.34 325	92.83 89	97.17 197	98.58 28	92.98 145	90.13 298	95.80 272	88.37 116	97.85 344	91.71 219	83.93 409	95.73 329

CSCG		96.05 90	95.91 90	96.46 117	99.24 33	90.47 192	98.30 33	98.57 29	89.01 300	93.97 200	97.57 161	92.62 40	99.76 54	94.66 148	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		93.66 192	92.92 202	95.87 162	98.24 100	89.88 218	94.58 379	98.49 32	85.06 398	93.78 203	95.78 276	82.86 236	98.67 245	91.77 217	95.71 231	99.07 100

CHOSEN 1792x2688		94.15 166	93.51 178	96.06 147	98.27 96	89.38 244	95.18 364	98.48 34	85.60 388	93.76 204	97.11 194	83.15 226	99.61 90	91.33 227	98.72 119	99.19 83

fmvsm_s_conf0.5_n_7		96.45 77	96.80 57	95.37 205	97.29 169	88.38 278	97.23 191	98.47 35	95.14 39	98.43 41	99.09 7	87.58 133	99.72 65	98.80 25	99.21 83	98.02 229

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	89.86 271	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	98.15 214

PVSNet_BlendedMVS		94.06 172	93.92 162	94.47 258	98.27 96	89.46 241	96.73 247	98.36 39	90.17 263	94.36 185	95.24 304	88.02 121	99.58 98	93.44 180	90.72 328	94.36 404

PVSNet_Blended		94.87 141	94.56 140	95.81 169	98.27 96	89.46 241	95.47 346	98.36 39	88.84 309	94.36 185	96.09 261	88.02 121	99.58 98	93.44 180	98.18 145	98.40 190

3Dnovator	91.36 5	95.19 126	94.44 149	97.44 57	96.56 240	93.36 70	98.65 16	98.36 39	94.12 90	89.25 332	98.06 98	82.20 253	99.77 52	93.41 182	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	97.21 194	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	95.48 313	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	98.18 210

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	97.43 265

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	97.81 133	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	95.67 304	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	92.37 168	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	97.27 185	98.25 61	90.21 262	94.18 193	97.27 183	87.48 140	99.73 61	93.53 177	97.77 161	98.55 171

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		91.55 285	90.84 285	93.69 308	94.96 350	88.28 281	97.84 95	98.24 63	91.46 205	88.04 365	95.80 272	79.67 304	97.48 382	87.02 329	84.54 403	95.31 352

DU-MVS		92.90 227	92.04 236	95.49 199	94.95 351	92.83 89	97.16 198	98.24 63	93.02 139	90.13 298	95.71 279	83.47 218	97.85 344	91.71 219	83.93 409	95.78 323

9.14			96.75 61		98.93 56		97.73 115	98.23 66	91.28 214	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		91.30 302	90.95 279	92.35 356	94.71 366	85.52 357	96.18 304	98.21 67	88.89 307	86.60 394	93.82 377	79.92 300	97.95 333	89.29 277	90.95 325	93.56 419

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		94.17 164	93.61 171	95.86 165	98.09 116	91.37 151	97.35 176	98.20 69	93.18 132	91.79 259	97.28 181	79.13 312	98.93 196	94.61 151	92.84 291	97.28 273

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		91.71 273	89.67 340	97.81 32	99.38 17	94.03 54	98.59 17	98.20 69	94.85 53	96.59 99	32.69 481	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		91.89 269	91.24 268	93.82 300	95.05 347	88.57 271	97.82 100	98.19 74	91.70 194	88.21 360	95.76 277	81.96 258	97.52 380	87.86 303	84.65 397	95.37 348

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	90.57 253	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		91.20 307	90.44 303	93.48 319	94.49 374	87.91 296	97.76 108	98.18 76	91.29 211	87.78 369	95.74 278	80.35 291	97.33 393	85.46 353	82.96 419	95.19 363

DELS-MVS		96.61 71	96.38 80	97.30 63	97.79 140	93.19 78	95.96 316	98.18 76	95.23 35	95.87 133	97.65 151	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		89.70 359	88.40 365	93.60 312	95.15 342	90.10 207	97.56 144	98.16 80	87.28 361	86.16 400	94.63 332	77.57 340	98.05 313	74.48 440	84.59 401	92.65 432

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	97.61 157	85.29 185	99.53 112	95.81 105	95.27 244	99.16 85

DeepPCF-MVS	93.97 1	96.61 71	97.09 33	95.15 213	98.09 116	86.63 329	96.00 314	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	97.41 389	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	94.83 137	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	90.36 252	97.35 174	99.11 94

QAPM		93.45 203	92.27 230	96.98 85	96.77 221	92.62 98	98.39 29	98.12 86	84.50 406	88.27 358	97.77 137	82.39 250	99.81 35	85.40 354	98.81 115	98.51 176

Vis-MVSNet		95.23 121	94.81 128	96.51 111	97.18 175	91.58 141	98.26 39	98.12 86	94.38 84	94.90 168	98.15 93	82.28 251	98.92 198	91.45 226	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	92.86 230	91.68 251	96.40 122	95.34 325	92.73 94	98.27 37	98.12 86	84.86 401	85.78 402	97.75 138	78.89 322	99.74 59	87.50 319	98.65 122	96.73 290

TranMVSNet+NR-MVSNet		92.50 239	91.63 252	95.14 214	94.76 362	92.07 119	97.53 150	98.11 89	92.90 151	89.56 320	96.12 256	83.16 225	97.60 372	89.30 276	83.20 418	95.75 327

CPTT-MVS		95.57 108	95.19 112	96.70 92	99.27 31	91.48 146	98.33 31	98.11 89	87.79 346	95.17 161	98.03 101	87.09 148	99.61 90	93.51 178	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	91.50 203	98.01 50	98.32 80	92.33 45	99.58 98	94.85 134	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	89.22 293	97.03 81	98.10 94	92.52 42	99.65 79	94.58 153	99.31 72

MTGPA								98.08 93

MTAPA		97.08 39	96.78 59	97.97 27	99.37 19	94.42 40	97.24 187	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	97.27 185	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	97.03 205	98.08 93	88.35 327	95.09 163	97.65 151	89.97 90	99.48 124	92.08 211	98.59 126	98.44 187

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	97.12 201	98.07 98	93.54 112	96.08 125	97.69 146	93.86 18	99.71 67	96.50 74	99.39 63	99.55 43

NR-MVSNet		92.34 248	91.27 267	95.53 194	94.95 351	93.05 81	97.39 172	98.07 98	92.65 159	84.46 413	95.71 279	85.00 192	97.77 355	89.71 264	83.52 415	95.78 323

MP-MVS-pluss		96.70 65	96.27 83	97.98 26	99.23 35	94.71 30	96.96 216	98.06 101	90.67 243	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	94.83 137	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	90.57 253	96.77 88	98.35 72	90.21 86	99.53 112	94.80 141	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	91.17 222	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		94.51 155	93.80 164	96.64 94	97.07 181	91.97 124	96.32 291	98.06 101	88.94 305	94.50 182	96.78 214	84.60 198	99.27 147	91.90 212	96.02 221	98.68 162

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	93.38 219	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	97.88 120	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		88.98 365	87.05 379	94.77 241	94.45 376	87.19 313	90.23 455	98.03 110	77.87 454	92.40 237	87.55 461	80.17 295	99.51 117	68.84 461	93.95 277	97.60 258

save fliter					98.91 58	94.28 42	97.02 207	98.02 113	95.35 31

TEST9					98.70 65	94.19 46	96.41 277	98.02 113	88.17 331	96.03 126	97.56 163	92.74 36	99.59 95

train_agg		96.30 85	95.83 93	97.72 43	98.70 65	94.19 46	96.41 277	98.02 113	88.58 318	96.03 126	97.56 163	92.73 37	99.59 95	95.04 126	99.37 67	99.39 68

test_8					98.67 67	94.06 53	96.37 285	98.01 116	88.58 318	95.98 130	97.55 165	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	98.12 217

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		92.34 248	91.53 256	94.77 241	95.13 344	90.83 179	96.40 281	97.98 120	91.88 189	89.29 329	95.54 290	82.50 246	97.80 351	89.79 263	85.27 388	95.69 330

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	97.89 117	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	97.02 207	97.96 122	95.42 29	94.86 169	97.81 133	87.38 143	99.82 33	96.88 60	99.20 88	99.29 75

114514_t		93.95 179	93.06 196	96.63 98	99.07 43	91.61 138	97.46 164	97.96 122	77.99 452	93.00 228	97.57 161	86.14 166	99.33 139	89.22 280	99.15 95	98.94 121

IU-MVS					99.42 10	95.39 12		97.94 124	90.40 260	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	83.21 223	99.74 59	99.22 11	98.06 150	97.88 238

Anonymous20231211		90.63 331	89.42 347	94.27 272	98.24 100	89.19 256	98.05 62	97.89 128	79.95 444	88.25 359	94.96 313	72.56 381	98.13 296	89.70 265	85.14 390	95.49 334

原ACMM1				96.38 125	98.59 75	91.09 168		97.89 128	87.41 357	95.22 160	97.68 147	90.25 85	99.54 110	87.95 302	99.12 100	98.49 179

CDPH-MVS		95.97 94	95.38 106	97.77 38	98.93 56	94.44 39	96.35 286	97.88 130	86.98 365	96.65 95	97.89 117	91.99 51	99.47 125	92.26 200	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	93.92 201	95.04 310	90.61 82	98.95 193	94.62 150	98.68 120	98.54 172

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	98.71 238	97.10 55	99.17 91	98.90 130

无先验							95.79 327	97.87 132	83.87 414				99.65 79	87.68 312		98.89 136

3Dnovator+	91.43 4	95.40 110	94.48 147	98.16 17	96.90 201	95.34 17	98.48 25	97.87 132	94.65 70	88.53 350	98.02 103	83.69 214	99.71 67	93.18 186	98.96 110	99.44 61

VPNet		92.23 256	91.31 264	94.99 224	95.56 309	90.96 172	97.22 193	97.86 136	92.96 146	90.96 281	96.62 231	75.06 361	98.20 290	91.90 212	83.65 414	95.80 321

test_vis1_n_1920		94.17 164	94.58 139	92.91 340	97.42 166	82.02 411	97.83 98	97.85 137	94.68 67	98.10 48	98.49 58	70.15 400	99.32 141	97.91 30	98.82 114	97.40 267

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	98.75 228	96.92 59	99.33 70	98.94 121

test_fmvsmconf0.01_n		96.15 88	95.85 92	97.03 83	92.66 428	91.83 129	97.97 77	97.84 141	95.57 26	97.53 61	99.00 16	84.20 207	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	97.82 131	83.06 230	99.16 161	94.40 157	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	97.36 176	90.97 75	99.22 151	97.74 32	99.66 10	98.61 165

AdaColmap		94.34 159	93.68 169	96.31 129	98.59 75	91.68 136	96.59 266	97.81 144	89.87 270	92.15 247	97.06 197	83.62 217	99.54 110	89.34 275	98.07 149	97.70 251

MVSMamba_PlusPlus		96.51 74	96.48 72	96.59 102	98.07 120	91.97 124	98.14 54	97.79 145	90.43 258	97.34 70	97.52 166	91.29 67	99.19 154	98.12 28	99.64 14	98.60 166

KinetiMVS		95.26 117	94.75 133	96.79 90	96.99 194	92.05 120	97.82 100	97.78 146	94.77 63	96.46 109	97.70 144	80.62 285	99.34 138	92.37 199	98.28 140	98.97 111

mamv4		94.66 152	96.10 87	90.37 410	98.01 123	73.41 461	96.82 233	97.78 146	89.95 269	94.52 180	97.43 171	92.91 30	99.09 174	98.28 27	99.16 94	98.60 166

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	94.51 338	91.23 70	98.92 198	95.65 111	98.19 144	97.82 246

新几何1				97.32 62	98.60 74	93.59 63		97.75 149	81.58 435	95.75 138	97.85 125	90.04 88	99.67 77	86.50 335	99.13 98	98.69 161

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	97.68 147	89.32 96	98.60 254	97.45 46	99.11 101	98.67 163

EI-MVSNet-Vis-set		96.51 74	96.47 73	96.63 98	98.24 100	91.20 160	96.89 224	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		95.01 132	94.59 138	96.26 135	98.89 60	90.68 187	97.24 187	97.73 152	91.80 190	92.93 233	96.62 231	89.13 100	99.14 166	89.21 281	97.78 160	98.97 111

Anonymous20240529		91.98 265	90.73 292	95.73 182	98.14 113	89.40 243	97.99 68	97.72 154	79.63 446	93.54 212	97.41 173	69.94 402	99.56 106	91.04 234	91.11 321	98.22 207

CHOSEN 280x420		93.12 215	92.72 213	94.34 266	96.71 227	87.27 309	90.29 454	97.72 154	86.61 372	91.34 270	95.29 298	84.29 206	98.41 270	93.25 184	98.94 111	97.35 270

EI-MVSNet-UG-set		96.34 83	96.30 82	96.47 115	98.20 107	90.93 175	96.86 227	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		93.57 196	92.61 218	96.47 115	97.59 157	91.61 138	97.67 125	97.72 154	85.17 396	90.29 292	98.34 75	84.60 198	99.73 61	83.85 377	98.27 141	98.06 227

PAPR		94.18 163	93.42 185	96.48 114	97.64 151	91.42 150	95.55 341	97.71 158	88.99 302	92.34 243	95.82 271	89.19 98	99.11 169	86.14 341	97.38 172	98.90 130

UGNet		94.04 174	93.28 188	96.31 129	96.85 206	91.19 161	97.88 90	97.68 159	94.40 82	93.00 228	96.18 251	73.39 378	99.61 90	91.72 218	98.46 132	98.13 215
Wanjuan Su, Qingshan Xu, Wenbing Tao: Uncertainty-guided Multi-view Stereo Network for Depth Estimation. IEEE Transactions on Circuits and Systems for Video Technology, 2022
testdata				95.46 203	98.18 111	88.90 264		97.66 160	82.73 426	97.03 81	98.07 97	90.06 87	98.85 205	89.67 266	98.98 109	98.64 164

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		90.56 332	89.75 338	93.01 336	93.95 389	87.25 310	97.64 133	97.65 162	90.74 238	87.12 382	95.68 282	79.97 299	97.00 406	83.33 378	81.66 425	94.78 391

TAPA-MVS	90.10 7	92.30 251	91.22 270	95.56 191	98.33 91	89.60 230	96.79 239	97.65 162	81.83 432	91.52 265	97.23 186	87.94 123	98.91 200	71.31 455	98.37 136	98.17 213
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
sd_testset		93.10 216	92.45 226	95.05 218	98.09 116	89.21 253	96.89 224	97.64 164	93.18 132	91.79 259	97.28 181	75.35 360	98.65 248	88.99 286	92.84 291	97.28 273

test_cas_vis1_n_1920		94.48 157	94.55 143	94.28 271	96.78 219	86.45 335	97.63 135	97.64 164	93.32 125	97.68 60	98.36 71	73.75 376	99.08 177	96.73 65	99.05 104	97.31 272

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	85.34 183	99.50 120	94.99 129	99.21 83	98.97 111

Elysia		94.00 176	93.12 193	96.64 94	96.08 288	92.72 95	97.50 153	97.63 166	91.15 224	94.82 170	97.12 192	74.98 363	99.06 183	90.78 239	98.02 151	98.12 217

StellarMVS		94.00 176	93.12 193	96.64 94	96.08 288	92.72 95	97.50 153	97.63 166	91.15 224	94.82 170	97.12 192	74.98 363	99.06 183	90.78 239	98.02 151	98.12 217

cdsmvs_eth3d_5k		23.24 450	30.99 452	0.00 469	0.00 492	0.00 494	0.00 481	97.63 166	0.00 487	0.00 488	96.88 210	84.38 203	0.00 488	0.00 487	0.00 486	0.00 484

DPM-MVS		95.69 102	94.92 124	98.01 21	98.08 119	95.71 10	95.27 357	97.62 170	90.43 258	95.55 147	97.07 196	91.72 54	99.50 120	89.62 268	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	96.12 256	87.65 130	99.18 157	96.20 88	94.82 253	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	96.12 256	87.65 130	99.18 157	96.20 88	94.82 253	98.91 127

test222					98.24 100	92.21 114	95.33 352	97.60 171	79.22 448	95.25 158	97.84 127	88.80 106			99.15 95	98.72 158

cascas		91.20 307	90.08 320	94.58 252	94.97 349	89.16 257	93.65 419	97.59 174	79.90 445	89.40 324	92.92 404	75.36 359	98.36 278	92.14 205	94.75 256	96.23 300

E2		95.20 123	95.00 121	95.79 173	96.79 214	89.66 225	96.82 233	97.58 175	92.35 169	95.28 156	97.83 129	86.68 152	98.76 222	94.79 144	96.92 193	98.95 118

E3		95.20 123	95.00 121	95.79 173	96.77 221	89.66 225	96.82 233	97.58 175	92.35 169	95.28 156	97.83 129	86.69 151	98.76 222	94.79 144	96.92 193	98.95 118

h-mvs33		94.15 166	93.52 177	96.04 149	97.81 139	90.22 205	97.62 137	97.58 175	95.19 36	96.74 89	97.45 168	83.67 215	99.61 90	95.85 102	79.73 432	98.29 203

E6		95.04 131	94.88 126	95.52 195	96.60 232	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

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	96.16 254	87.54 135	99.17 159	96.19 90	94.73 258	98.91 127

MVSFormer		95.37 111	95.16 113	95.99 156	96.34 264	91.21 158	98.22 45	97.57 178	91.42 207	96.22 119	97.32 177	86.20 164	97.92 338	94.07 163	99.05 104	98.85 142

test_djsdf		93.07 218	92.76 208	94.00 285	93.49 407	88.70 268	98.22 45	97.57 178	91.42 207	90.08 304	95.55 289	82.85 237	97.92 338	94.07 163	91.58 312	95.40 345

OMC-MVS		95.09 128	94.70 134	96.25 138	98.46 79	91.28 154	96.43 273	97.57 178	92.04 185	94.77 174	97.96 110	87.01 149	99.09 174	91.31 228	96.77 198	98.36 194

E4		95.09 128	94.86 127	95.77 176	96.58 235	89.56 233	96.85 228	97.56 183	92.50 163	95.03 165	97.86 123	86.03 167	98.78 215	94.71 147	96.65 207	98.96 114

viewcassd2359sk11		95.26 117	95.09 118	95.80 170	96.95 198	89.72 224	96.80 238	97.56 183	92.21 176	95.37 154	97.80 135	87.17 147	98.77 219	94.82 139	97.10 187	98.90 130

PS-MVSNAJss		93.74 189	93.51 178	94.44 260	93.91 391	89.28 251	97.75 110	97.56 183	92.50 163	89.94 306	96.54 234	88.65 109	98.18 293	93.83 172	90.90 326	95.86 315

casdiffmvs_mvg		95.81 101	95.57 95	96.51 111	96.87 203	91.49 144	97.50 153	97.56 183	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	96.78 243	97.54 187	92.06 184	95.40 153	97.75 138	87.49 139	98.76 222	94.85 134	97.10 187	98.88 138

jajsoiax		92.42 244	91.89 244	94.03 284	93.33 415	88.50 275	97.73 115	97.53 188	92.00 187	88.85 342	96.50 236	75.62 358	98.11 300	93.88 170	91.56 313	95.48 335

mvs_tets		92.31 250	91.76 247	93.94 293	93.41 412	88.29 280	97.63 135	97.53 188	92.04 185	88.76 345	96.45 238	74.62 368	98.09 305	93.91 168	91.48 314	95.45 340

dcpmvs_2		96.37 81	97.05 38	94.31 269	98.96 55	84.11 384	97.56 144	97.51 190	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		93.78 188	93.43 183	94.82 234	96.21 268	89.99 211	97.74 113	97.51 190	94.85 53	91.34 270	96.64 224	81.32 270	98.60 254	93.02 192	92.23 300	95.86 315

plane_prior5								97.51 190					98.60 254	93.02 192	92.23 300	95.86 315

viewmanbaseed2359cas		95.24 120	95.02 120	95.91 159	96.87 203	89.98 213	96.82 233	97.49 193	92.26 172	95.47 151	97.82 131	86.47 157	98.69 240	94.80 141	97.20 183	99.06 101

reproduce_monomvs		91.30 302	91.10 274	91.92 370	96.82 211	82.48 405	97.01 210	97.49 193	94.64 71	88.35 353	95.27 301	70.53 395	98.10 301	95.20 122	84.60 400	95.19 363

viewmacassd2359aftdt		95.07 130	94.80 129	95.87 162	96.53 245	89.84 219	96.90 223	97.48 195	92.44 165	95.36 155	97.89 117	85.23 186	98.68 242	94.40 157	97.00 191	99.09 96

PS-MVSNAJ		95.37 111	95.33 108	95.49 199	97.35 167	90.66 188	95.31 354	97.48 195	93.85 100	96.51 105	95.70 281	88.65 109	99.65 79	94.80 141	98.27 141	96.17 304

API-MVS		94.84 143	94.49 146	95.90 160	97.90 134	92.00 123	97.80 104	97.48 195	89.19 294	94.81 172	96.71 217	88.84 105	99.17 159	88.91 288	98.76 118	96.53 293

MG-MVS		95.61 106	95.38 106	96.31 129	98.42 83	90.53 190	96.04 311	97.48 195	93.47 117	95.67 144	98.10 94	89.17 99	99.25 148	91.27 229	98.77 117	99.13 89

MAR-MVS		94.22 162	93.46 180	96.51 111	98.00 125	92.19 117	97.67 125	97.47 199	88.13 335	93.00 228	95.84 269	84.86 196	99.51 117	87.99 301	98.17 146	97.83 245
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		92.98 222	92.53 222	94.32 267	96.12 283	89.20 254	95.28 355	97.47 199	92.66 158	89.90 307	95.62 285	80.58 286	98.40 271	92.73 197	92.40 298	95.38 347
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
UniMVSNet_ETH3D		91.34 300	90.22 316	94.68 245	94.86 358	87.86 297	97.23 191	97.46 201	87.99 336	89.90 307	96.92 208	66.35 430	98.23 287	90.30 253	90.99 324	97.96 232

nrg030		94.05 173	93.31 187	96.27 134	95.22 336	94.59 33	98.34 30	97.46 201	92.93 147	91.21 279	96.64 224	87.23 146	98.22 288	94.99 129	85.80 380	95.98 314

XVG-OURS		93.72 190	93.35 186	94.80 239	97.07 181	88.61 269	94.79 374	97.46 201	91.97 188	93.99 198	97.86 123	81.74 264	98.88 202	92.64 198	92.67 296	96.92 285

LPG-MVS_test		92.94 225	92.56 219	94.10 279	96.16 278	88.26 282	97.65 129	97.46 201	91.29 211	90.12 300	97.16 189	79.05 315	98.73 232	92.25 202	91.89 308	95.31 352

LGP-MVS_train				94.10 279	96.16 278	88.26 282		97.46 201	91.29 211	90.12 300	97.16 189	79.05 315	98.73 232	92.25 202	91.89 308	95.31 352

MVS		91.71 273	90.44 303	95.51 196	95.20 338	91.59 140	96.04 311	97.45 206	73.44 462	87.36 378	95.60 286	85.42 182	99.10 171	85.97 346	97.46 167	95.83 319

XVG-OURS-SEG-HR		93.86 185	93.55 173	94.81 236	97.06 184	88.53 274	95.28 355	97.45 206	91.68 195	94.08 197	97.68 147	82.41 249	98.90 201	93.84 171	92.47 297	96.98 281

baseline		95.58 107	95.42 104	96.08 145	96.78 219	90.41 196	97.16 198	97.45 206	93.69 106	95.65 145	97.85 125	87.29 144	98.68 242	95.66 108	97.25 181	99.13 89

ab-mvs		93.57 196	92.55 220	96.64 94	97.28 170	91.96 126	95.40 348	97.45 206	89.81 275	93.22 225	96.28 247	79.62 306	99.46 126	90.74 242	93.11 288	98.50 177

xiu_mvs_v2_base		95.32 114	95.29 109	95.40 204	97.22 172	90.50 191	95.44 347	97.44 210	93.70 105	96.46 109	96.18 251	88.59 113	99.53 112	94.79 144	97.81 159	96.17 304

1314		92.81 234	92.03 237	95.14 214	95.33 328	89.52 238	96.04 311	97.44 210	87.72 350	86.25 399	95.33 297	83.84 212	98.79 214	89.26 278	97.05 190	97.11 279

casdiffmvs		95.64 104	95.49 98	96.08 145	96.76 225	90.45 193	97.29 183	97.44 210	94.00 94	95.46 152	97.98 108	87.52 138	98.73 232	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		94.76 149	94.68 135	95.01 222	96.76 225	87.41 305	96.38 283	97.43 213	92.65 159	94.52 180	97.75 138	85.55 180	98.81 211	94.36 159	96.69 204	98.82 146

XXY-MVS		92.16 258	91.23 269	94.95 230	94.75 363	90.94 174	97.47 162	97.43 213	89.14 295	88.90 338	96.43 239	79.71 303	98.24 286	89.56 269	87.68 361	95.67 331

anonymousdsp		92.16 258	91.55 255	93.97 289	92.58 430	89.55 235	97.51 152	97.42 215	89.42 288	88.40 352	94.84 320	80.66 284	97.88 343	91.87 214	91.28 318	94.48 399

Effi-MVS+		94.93 137	94.45 148	96.36 127	96.61 231	91.47 147	96.41 277	97.41 216	91.02 230	94.50 182	95.92 265	87.53 136	98.78 215	93.89 169	96.81 197	98.84 145

RRT-MVS		94.51 155	94.35 152	94.98 226	96.40 258	86.55 332	97.56 144	97.41 216	93.19 130	94.93 167	97.04 198	79.12 313	99.30 145	96.19 90	97.32 177	99.09 96

HQP3-MVS								97.39 218							92.10 305

HQP-MVS		93.19 212	92.74 211	94.54 255	95.86 294	89.33 247	96.65 257	97.39 218	93.55 109	90.14 294	95.87 267	80.95 275	98.50 264	92.13 208	92.10 305	95.78 323

PLC	91.00 6	94.11 170	93.43 183	96.13 143	98.58 77	91.15 167	96.69 253	97.39 218	87.29 360	91.37 269	96.71 217	88.39 114	99.52 116	87.33 322	97.13 186	97.73 249
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	95.50 198	96.37 262	89.08 259	96.08 309	97.38 221	93.09 138	96.53 104	97.74 141	86.45 158	98.68 242	96.32 78	97.48 166	98.75 154

v7n		90.76 324	89.86 331	93.45 321	93.54 404	87.60 303	97.70 123	97.37 222	88.85 308	87.65 371	94.08 368	81.08 274	98.10 301	84.68 363	83.79 413	94.66 396

UnsupCasMVSNet_eth		85.99 402	84.45 406	90.62 406	89.97 448	82.40 408	93.62 420	97.37 222	89.86 271	78.59 452	92.37 414	65.25 440	95.35 441	82.27 392	70.75 460	94.10 410

viewdifsd2359ckpt13		94.87 141	94.52 144	95.90 160	96.88 202	90.19 206	96.92 220	97.36 224	91.26 215	94.65 176	97.46 167	85.79 173	98.64 249	93.64 175	96.76 199	98.88 138

ACMM	89.79 8	92.96 223	92.50 224	94.35 264	96.30 266	88.71 267	97.58 140	97.36 224	91.40 209	90.53 287	96.65 223	79.77 302	98.75 228	91.24 230	91.64 310	95.59 333
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
xiu_mvs_v1_base_debu		95.01 132	94.76 130	95.75 179	96.58 235	91.71 133	96.25 296	97.35 226	92.99 140	96.70 91	96.63 228	82.67 241	99.44 129	96.22 83	97.46 167	96.11 310

xiu_mvs_v1_base		95.01 132	94.76 130	95.75 179	96.58 235	91.71 133	96.25 296	97.35 226	92.99 140	96.70 91	96.63 228	82.67 241	99.44 129	96.22 83	97.46 167	96.11 310

xiu_mvs_v1_base_debi		95.01 132	94.76 130	95.75 179	96.58 235	91.71 133	96.25 296	97.35 226	92.99 140	96.70 91	96.63 228	82.67 241	99.44 129	96.22 83	97.46 167	96.11 310

diffmvs		95.25 119	95.13 114	95.63 187	96.43 257	89.34 246	95.99 315	97.35 226	92.83 153	96.31 115	97.37 175	86.44 159	98.67 245	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		94.71 151	94.02 160	96.79 90	97.71 145	92.05 120	96.59 266	97.35 226	90.61 249	94.64 177	96.93 205	86.41 160	99.39 134	91.20 231	94.71 259	98.94 121

viewdifsd2359ckpt09		94.81 146	94.37 151	96.12 144	96.91 199	90.75 184	96.94 217	97.31 231	90.51 256	94.31 187	97.38 174	85.70 175	98.71 238	93.54 176	96.75 200	98.90 130

SSM_0407		94.54 154	94.12 159	95.80 170	96.79 214	90.38 198	96.79 239	97.29 232	91.24 216	93.68 205	97.60 158	85.03 190	98.67 245	92.14 205	96.51 210	98.35 196

SSM_0404		94.73 150	94.31 154	95.98 157	97.05 186	90.90 177	97.01 210	97.29 232	91.24 216	94.17 194	97.60 158	85.03 190	98.76 222	92.14 205	97.30 178	98.29 203

F-COLMAP		93.58 194	92.98 200	95.37 205	98.40 86	88.98 262	97.18 196	97.29 232	87.75 349	90.49 288	97.10 195	85.21 187	99.50 120	86.70 332	96.72 203	97.63 253

VortexMVS		92.88 229	92.64 215	93.58 314	96.58 235	87.53 304	96.93 219	97.28 235	92.78 156	89.75 312	94.99 311	82.73 240	97.76 356	94.60 152	88.16 356	95.46 338

XVG-ACMP-BASELINE		90.93 320	90.21 317	93.09 334	94.31 382	85.89 350	95.33 352	97.26 236	91.06 229	89.38 325	95.44 295	68.61 413	98.60 254	89.46 271	91.05 322	94.79 389

PCF-MVS	89.48 11	91.56 284	89.95 328	96.36 127	96.60 232	92.52 103	92.51 439	97.26 236	79.41 447	88.90 338	96.56 233	84.04 211	99.55 108	77.01 431	97.30 178	97.01 280
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
ACMP	89.59 10	92.62 238	92.14 233	94.05 282	96.40 258	88.20 285	97.36 175	97.25 238	91.52 202	88.30 356	96.64 224	78.46 327	98.72 237	91.86 215	91.48 314	95.23 359
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
icg_test_0407_2		93.58 194	93.46 180	93.94 293	96.19 272	86.16 344	93.73 414	97.24 239	91.54 198	93.50 214	97.04 198	85.64 178	96.91 409	90.68 244	95.59 235	98.76 150

IMVS_0407		93.94 180	93.75 166	94.49 257	96.19 272	86.16 344	96.35 286	97.24 239	91.54 198	93.50 214	97.04 198	85.64 178	98.54 261	90.68 244	95.59 235	98.76 150

IMVS_0404		92.44 242	91.92 242	94.00 285	96.19 272	86.16 344	93.84 411	97.24 239	91.54 198	88.17 362	97.04 198	76.96 345	97.09 400	90.68 244	95.59 235	98.76 150

IMVS_0403		93.98 178	93.79 165	94.55 254	96.19 272	86.16 344	96.35 286	97.24 239	91.54 198	93.59 209	97.04 198	85.86 170	98.73 232	90.68 244	95.59 235	98.76 150

OPM-MVS		93.28 208	92.76 208	94.82 234	94.63 369	90.77 182	96.65 257	97.18 243	93.72 103	91.68 263	97.26 184	79.33 310	98.63 251	92.13 208	92.28 299	95.07 367
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
PatchMatch-RL		92.90 227	92.02 238	95.56 191	98.19 109	90.80 180	95.27 357	97.18 243	87.96 337	91.86 258	95.68 282	80.44 289	98.99 191	84.01 372	97.54 165	96.89 286

alignmvs		95.87 100	95.23 111	97.78 36	97.56 163	95.19 22	97.86 91	97.17 245	94.39 83	96.47 108	96.40 241	85.89 169	99.20 153	96.21 87	95.11 249	98.95 118

MVS_Test		94.89 139	94.62 137	95.68 185	96.83 209	89.55 235	96.70 251	97.17 245	91.17 222	95.60 146	96.11 260	87.87 126	98.76 222	93.01 194	97.17 185	98.72 158

Fast-Effi-MVS+		93.46 200	92.75 210	95.59 190	96.77 221	90.03 208	96.81 237	97.13 247	88.19 330	91.30 273	94.27 356	86.21 163	98.63 251	87.66 313	96.46 216	98.12 217

FE-MVSNET3		91.65 277	90.67 296	94.60 247	93.65 402	90.95 173	94.86 372	97.12 248	89.69 278	89.21 333	93.62 387	81.17 273	97.67 363	87.54 317	89.14 344	95.17 365

EI-MVSNet		93.03 220	92.88 204	93.48 319	95.77 300	86.98 318	96.44 271	97.12 248	90.66 245	91.30 273	97.64 154	86.56 154	98.05 313	89.91 259	90.55 330	95.41 342

MVSTER		93.20 211	92.81 207	94.37 263	96.56 240	89.59 231	97.06 204	97.12 248	91.24 216	91.30 273	95.96 263	82.02 257	98.05 313	93.48 179	90.55 330	95.47 337

viewmambaseed2359dif		94.28 160	94.14 157	94.71 244	96.21 268	86.97 319	95.93 318	97.11 251	89.00 301	95.00 166	97.70 144	86.02 168	98.59 258	93.71 174	96.59 209	98.57 170

test_yl		94.78 147	94.23 155	96.43 119	97.74 143	91.22 156	96.85 228	97.10 252	91.23 219	95.71 140	96.93 205	84.30 204	99.31 143	93.10 187	95.12 247	98.75 154

DCV-MVSNet		94.78 147	94.23 155	96.43 119	97.74 143	91.22 156	96.85 228	97.10 252	91.23 219	95.71 140	96.93 205	84.30 204	99.31 143	93.10 187	95.12 247	98.75 154

LTVRE_ROB	88.41 13	90.99 316	89.92 330	94.19 273	96.18 276	89.55 235	96.31 292	97.09 254	87.88 340	85.67 403	95.91 266	78.79 323	98.57 259	81.50 395	89.98 335	94.44 402
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		93.46 200	93.23 190	94.17 274	96.12 283	85.42 359	96.43 273	97.08 255	92.91 148	94.21 190	98.00 105	80.82 281	98.74 230	94.41 156	89.05 345	98.34 200

test_fmvs1_n		92.73 236	92.88 204	92.29 360	96.08 288	81.05 419	97.98 71	97.08 255	90.72 240	96.79 87	98.18 91	63.07 444	98.45 268	97.62 40	98.42 135	97.36 268

v10		91.04 314	90.23 314	93.49 318	94.12 385	88.16 288	97.32 180	97.08 255	88.26 329	88.29 357	94.22 361	82.17 254	97.97 325	86.45 336	84.12 407	94.33 405

viewdifsd2359ckpt11		93.46 200	93.22 191	94.17 274	96.11 285	85.42 359	96.43 273	97.07 258	92.91 148	94.20 191	98.00 105	80.82 281	98.73 232	94.42 155	89.04 347	98.34 200

mamba_0408		93.70 191	92.99 197	95.83 167	96.79 214	90.38 198	88.69 464	97.07 258	90.96 232	93.68 205	97.31 179	84.97 193	98.76 222	90.95 235	96.51 210	98.35 196

SSM_04072		93.51 199	92.99 197	95.05 218	96.79 214	90.38 198	88.69 464	97.07 258	90.96 232	93.68 205	97.31 179	84.97 193	96.42 420	90.95 235	96.51 210	98.35 196

v144192		91.06 313	90.28 310	93.39 322	93.66 400	87.23 312	96.83 232	97.07 258	87.43 356	89.69 315	94.28 355	81.48 267	98.00 320	87.18 326	84.92 396	94.93 375

v1192		91.07 312	90.23 314	93.58 314	93.70 397	87.82 299	96.73 247	97.07 258	87.77 347	89.58 318	94.32 353	80.90 279	97.97 325	86.52 334	85.48 383	94.95 371

v8		91.29 304	90.53 302	93.57 316	94.15 384	88.12 289	97.34 177	97.06 263	88.99 302	88.32 355	94.26 358	83.08 228	98.01 319	87.62 315	83.92 411	94.57 398

mvs_anonymous		93.82 186	93.74 167	94.06 281	96.44 256	85.41 361	95.81 325	97.05 264	89.85 273	90.09 303	96.36 243	87.44 141	97.75 358	93.97 165	96.69 204	99.02 103

IterMVS-LS		92.29 252	91.94 241	93.34 324	96.25 267	86.97 319	96.57 269	97.05 264	90.67 243	89.50 323	94.80 323	86.59 153	97.64 367	89.91 259	86.11 378	95.40 345
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v1921920		90.85 322	90.03 325	93.29 326	93.55 403	86.96 321	96.74 246	97.04 266	87.36 358	89.52 322	94.34 350	80.23 294	97.97 325	86.27 337	85.21 389	94.94 373

CDS-MVSNet		94.14 169	93.54 174	95.93 158	96.18 276	91.46 148	96.33 290	97.04 266	88.97 304	93.56 210	96.51 235	87.55 134	97.89 342	89.80 262	95.95 223	98.44 187
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
SSC-MVS3.2		89.74 358	89.26 351	91.19 395	95.16 339	80.29 430	94.53 381	97.03 268	91.79 191	88.86 341	94.10 365	69.94 402	97.82 348	85.29 355	86.66 374	95.45 340

v1144		91.37 297	90.60 298	93.68 309	93.89 392	88.23 284	96.84 231	97.03 268	88.37 326	89.69 315	94.39 345	82.04 256	97.98 322	87.80 305	85.37 385	94.84 381

v1240		90.70 328	89.85 332	93.23 328	93.51 406	86.80 322	96.61 263	97.02 270	87.16 363	89.58 318	94.31 354	79.55 307	97.98 322	85.52 352	85.44 384	94.90 378

EPP-MVSNet		95.22 122	95.04 119	95.76 177	97.49 164	89.56 233	98.67 15	97.00 271	90.69 241	94.24 189	97.62 156	89.79 93	98.81 211	93.39 183	96.49 214	98.92 126

V42		91.58 283	90.87 281	93.73 304	94.05 388	88.50 275	97.32 180	96.97 272	88.80 314	89.71 313	94.33 351	82.54 245	98.05 313	89.01 285	85.07 392	94.64 397

test_fmvs1		93.21 210	93.53 175	92.25 363	96.55 242	81.20 418	97.40 171	96.96 273	90.68 242	96.80 85	98.04 100	69.25 408	98.40 271	97.58 41	98.50 128	97.16 278

FMVSNet2		91.31 301	90.08 320	94.99 224	96.51 249	92.21 114	97.41 167	96.95 274	88.82 311	88.62 347	94.75 325	73.87 372	97.42 388	85.20 358	88.55 353	95.35 349

ACMH	87.59 16	90.53 333	89.42 347	93.87 298	96.21 268	87.92 294	97.24 187	96.94 275	88.45 324	83.91 423	96.27 248	71.92 384	98.62 253	84.43 366	89.43 341	95.05 369
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
GBi-Net		91.35 298	90.27 311	94.59 248	96.51 249	91.18 163	97.50 153	96.93 276	88.82 311	89.35 326	94.51 338	73.87 372	97.29 395	86.12 342	88.82 348	95.31 352

test1		91.35 298	90.27 311	94.59 248	96.51 249	91.18 163	97.50 153	96.93 276	88.82 311	89.35 326	94.51 338	73.87 372	97.29 395	86.12 342	88.82 348	95.31 352

FMVSNet3		91.78 271	90.69 295	95.03 221	96.53 245	92.27 112	97.02 207	96.93 276	89.79 276	89.35 326	94.65 331	77.01 343	97.47 383	86.12 342	88.82 348	95.35 349

FMVSNet1		89.88 353	88.31 366	94.59 248	95.41 318	91.18 163	97.50 153	96.93 276	86.62 371	87.41 376	94.51 338	65.94 435	97.29 395	83.04 381	87.43 364	95.31 352

GeoE		93.89 183	93.28 188	95.72 183	96.96 197	89.75 223	98.24 43	96.92 280	89.47 285	92.12 249	97.21 187	84.42 202	98.39 276	87.71 308	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	96.91 281	95.92 16	96.57 102	97.93 111	85.34 183	99.50 120	94.99 129	96.39 218	99.05 102

miper_enhance_ethall		91.54 287	91.01 277	93.15 332	95.35 324	87.07 317	93.97 403	96.90 282	86.79 369	89.17 334	93.43 398	86.55 155	97.64 367	89.97 258	86.93 369	94.74 393

eth_miper_zixun_eth		91.02 315	90.59 299	92.34 358	95.33 328	84.35 380	94.10 400	96.90 282	88.56 320	88.84 343	94.33 351	84.08 209	97.60 372	88.77 291	84.37 405	95.06 368

TAMVS		94.01 175	93.46 180	95.64 186	96.16 278	90.45 193	96.71 250	96.89 284	89.27 292	93.46 217	96.92 208	87.29 144	97.94 335	88.70 293	95.74 229	98.53 173

miper_ehance_all_eth		91.59 281	91.13 273	92.97 338	95.55 310	86.57 330	94.47 384	96.88 285	87.77 347	88.88 340	94.01 370	86.22 162	97.54 376	89.49 270	86.93 369	94.79 389

v2v482		91.59 281	90.85 284	93.80 301	93.87 393	88.17 287	96.94 217	96.88 285	89.54 282	89.53 321	94.90 317	81.70 265	98.02 318	89.25 279	85.04 394	95.20 360

CNLPA		94.28 160	93.53 175	96.52 107	98.38 89	92.55 102	96.59 266	96.88 285	90.13 266	91.91 255	97.24 185	85.21 187	99.09 174	87.64 314	97.83 158	97.92 235

PAPM		91.52 288	90.30 309	95.20 211	95.30 331	89.83 220	93.38 425	96.85 288	86.26 379	88.59 348	95.80 272	84.88 195	98.15 295	75.67 436	95.93 224	97.63 253

c3_l		91.38 295	90.89 280	92.88 342	95.58 308	86.30 338	94.68 376	96.84 289	88.17 331	88.83 344	94.23 359	85.65 177	97.47 383	89.36 274	84.63 398	94.89 379

pm-mvs1		90.72 327	89.65 342	93.96 290	94.29 383	89.63 228	97.79 106	96.82 290	89.07 297	86.12 401	95.48 294	78.61 325	97.78 353	86.97 330	81.67 424	94.46 400

test_vis1_n		92.37 247	92.26 231	92.72 348	94.75 363	82.64 401	98.02 65	96.80 291	91.18 221	97.77 59	97.93 111	58.02 454	98.29 284	97.63 38	98.21 143	97.23 276

CMPMVS	62.92 21	85.62 407	84.92 401	87.74 433	89.14 453	73.12 463	94.17 398	96.80 291	73.98 459	73.65 461	94.93 315	66.36 429	97.61 371	83.95 374	91.28 318	92.48 437
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
MS-PatchMatch		90.27 340	89.77 336	91.78 379	94.33 380	84.72 377	95.55 341	96.73 293	86.17 381	86.36 398	95.28 300	71.28 389	97.80 351	84.09 371	98.14 147	92.81 429

Effi-MVS+-dtu		93.08 217	93.21 192	92.68 351	96.02 291	83.25 394	97.14 200	96.72 294	93.85 100	91.20 280	93.44 395	83.08 228	98.30 283	91.69 221	95.73 230	96.50 295

TSAR-MVS + GP.		96.69 67	96.49 71	97.27 67	98.31 92	93.39 67	96.79 239	96.72 294	94.17 89	97.44 65	97.66 150	92.76 34	99.33 139	96.86 62	97.76 162	99.08 98

1112_ss		93.37 205	92.42 227	96.21 139	97.05 186	90.99 170	96.31 292	96.72 294	86.87 368	89.83 310	96.69 221	86.51 156	99.14 166	88.12 298	93.67 282	98.50 177

PVSNet	86.66 18	92.24 255	91.74 250	93.73 304	97.77 141	83.69 391	92.88 434	96.72 294	87.91 339	93.00 228	94.86 319	78.51 326	99.05 186	86.53 333	97.45 171	98.47 182

miper_lstm_enhance		90.50 336	90.06 324	91.83 375	95.33 328	83.74 388	93.86 409	96.70 298	87.56 354	87.79 368	93.81 378	83.45 220	96.92 408	87.39 320	84.62 399	94.82 384

v148		90.99 316	90.38 305	92.81 345	93.83 394	85.80 351	96.78 243	96.68 299	89.45 287	88.75 346	93.93 374	82.96 234	97.82 348	87.83 304	83.25 416	94.80 387

ACMH+	87.92 14	90.20 344	89.18 353	93.25 327	96.48 252	86.45 335	96.99 213	96.68 299	88.83 310	84.79 412	96.22 250	70.16 399	98.53 262	84.42 367	88.04 357	94.77 392

CANet_DTU		94.37 158	93.65 170	96.55 104	96.46 255	92.13 118	96.21 300	96.67 301	94.38 84	93.53 213	97.03 203	79.34 309	99.71 67	90.76 241	98.45 133	97.82 246

cl____		90.96 319	90.32 307	92.89 341	95.37 322	86.21 341	94.46 386	96.64 302	87.82 343	88.15 363	94.18 362	82.98 232	97.54 376	87.70 309	85.59 381	94.92 377

HY-MVS	89.66 9	93.87 184	92.95 201	96.63 98	97.10 180	92.49 104	95.64 338	96.64 302	89.05 299	93.00 228	95.79 275	85.77 174	99.45 128	89.16 284	94.35 261	97.96 232

Test_1112_low_res		92.84 232	91.84 245	95.85 166	97.04 188	89.97 215	95.53 343	96.64 302	85.38 391	89.65 317	95.18 305	85.86 170	99.10 171	87.70 309	93.58 287	98.49 179

DIV-MVS_self_test		90.97 318	90.33 306	92.88 342	95.36 323	86.19 343	94.46 386	96.63 305	87.82 343	88.18 361	94.23 359	82.99 231	97.53 378	87.72 306	85.57 382	94.93 375

Fast-Effi-MVS+-dtu		92.29 252	91.99 239	93.21 330	95.27 332	85.52 357	97.03 205	96.63 305	92.09 182	89.11 336	95.14 307	80.33 292	98.08 306	87.54 317	94.74 257	96.03 313

UnsupCasMVSNet_bld		82.13 424	79.46 429	90.14 413	88.00 461	82.47 406	90.89 452	96.62 307	78.94 449	75.61 456	84.40 467	56.63 457	96.31 422	77.30 428	66.77 468	91.63 448

cl22		91.21 306	90.56 301	93.14 333	96.09 287	86.80 322	94.41 388	96.58 308	87.80 345	88.58 349	93.99 372	80.85 280	97.62 370	89.87 261	86.93 369	94.99 370

jason		94.84 143	94.39 150	96.18 141	95.52 311	90.93 175	96.09 308	96.52 309	89.28 291	96.01 129	97.32 177	84.70 197	98.77 219	95.15 125	98.91 113	98.85 142
jason: jason.
tt0805		91.09 311	90.07 323	94.16 277	95.61 306	88.31 279	97.56 144	96.51 310	89.56 281	89.17 334	95.64 284	67.08 427	98.38 277	91.07 233	88.44 354	95.80 321

AUN-MVS		91.76 272	90.75 290	94.81 236	97.00 193	88.57 271	96.65 257	96.49 311	89.63 279	92.15 247	96.12 256	78.66 324	98.50 264	90.83 237	79.18 435	97.36 268

hse-mvs2		93.45 203	92.99 197	94.81 236	97.02 191	88.59 270	96.69 253	96.47 312	95.19 36	96.74 89	96.16 254	83.67 215	98.48 267	95.85 102	79.13 436	97.35 270

SD_0403		90.01 348	90.02 326	89.96 416	95.65 305	76.76 451	95.76 329	96.46 313	90.58 252	86.59 395	96.29 246	82.12 255	94.78 445	73.00 450	93.76 280	98.35 196

EG-PatchMatch MVS		87.02 388	85.44 393	91.76 381	92.67 427	85.00 371	96.08 309	96.45 314	83.41 422	79.52 446	93.49 392	57.10 456	97.72 360	79.34 419	90.87 327	92.56 434

KD-MVS_self_test		85.95 403	84.95 400	88.96 427	89.55 452	79.11 445	95.13 365	96.42 315	85.91 384	84.07 421	90.48 437	70.03 401	94.82 444	80.04 411	72.94 457	92.94 427

FE-MVSNET2		86.36 396	84.68 405	91.39 389	87.67 463	86.47 334	96.21 300	96.41 316	87.87 341	79.31 448	89.64 445	65.29 439	95.58 436	82.42 390	77.28 442	92.14 445

pmmvs6		87.81 380	86.19 388	92.69 350	91.32 440	86.30 338	97.34 177	96.41 316	80.59 443	84.05 422	94.37 347	67.37 422	97.67 363	84.75 362	79.51 434	94.09 412

PMMVS		92.86 230	92.34 228	94.42 262	94.92 354	86.73 325	94.53 381	96.38 318	84.78 403	94.27 188	95.12 309	83.13 227	98.40 271	91.47 225	96.49 214	98.12 217

RPSCF		90.75 325	90.86 282	90.42 409	96.84 207	76.29 454	95.61 339	96.34 319	83.89 412	91.38 268	97.87 121	76.45 349	98.78 215	87.16 327	92.23 300	96.20 302

BP-MVS1		95.89 98	95.49 98	97.08 81	96.67 228	93.20 77	98.08 58	96.32 320	94.56 72	96.32 114	97.84 127	84.07 210	99.15 163	96.75 64	98.78 116	98.90 130

MSDG		91.42 293	90.24 313	94.96 229	97.15 178	88.91 263	93.69 417	96.32 320	85.72 387	86.93 391	96.47 237	80.24 293	98.98 192	80.57 408	95.05 250	96.98 281

WBMVS		90.69 330	89.99 327	92.81 345	96.48 252	85.00 371	95.21 362	96.30 322	89.46 286	89.04 337	94.05 369	72.45 382	97.82 348	89.46 271	87.41 366	95.61 332

OurMVSNet-221017-0		90.51 335	90.19 318	91.44 387	93.41 412	81.25 416	96.98 214	96.28 323	91.68 195	86.55 396	96.30 245	74.20 371	97.98 322	88.96 287	87.40 367	95.09 366

MVP-Stereo		90.74 326	90.08 320	92.71 349	93.19 417	88.20 285	95.86 322	96.27 324	86.07 382	84.86 411	94.76 324	77.84 338	97.75 358	83.88 376	98.01 153	92.17 444
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
lupinMVS		94.99 136	94.56 140	96.29 133	96.34 264	91.21 158	95.83 324	96.27 324	88.93 306	96.22 119	96.88 210	86.20 164	98.85 205	95.27 121	99.05 104	98.82 146

BH-untuned		92.94 225	92.62 217	93.92 297	97.22 172	86.16 344	96.40 281	96.25 326	90.06 267	89.79 311	96.17 253	83.19 224	98.35 279	87.19 325	97.27 180	97.24 275

CL-MVSNet_self_test		86.31 398	85.15 397	89.80 418	88.83 456	81.74 414	93.93 406	96.22 327	86.67 370	85.03 409	90.80 435	78.09 334	94.50 446	74.92 439	71.86 459	93.15 425

IS-MVSNet		94.90 138	94.52 144	96.05 148	97.67 147	90.56 189	98.44 26	96.22 327	93.21 127	93.99 198	97.74 141	85.55 180	98.45 268	89.98 257	97.86 157	99.14 88

FA-MVS(test-final)		93.52 198	92.92 202	95.31 208	96.77 221	88.54 273	94.82 373	96.21 329	89.61 280	94.20 191	95.25 303	83.24 222	99.14 166	90.01 256	96.16 220	98.25 205

GA-MVS		91.38 295	90.31 308	94.59 248	94.65 368	87.62 302	94.34 391	96.19 330	90.73 239	90.35 291	93.83 375	71.84 385	97.96 329	87.22 324	93.61 285	98.21 208

LuminaMVS		94.89 139	94.35 152	96.53 105	95.48 313	92.80 91	96.88 226	96.18 331	92.85 152	95.92 132	96.87 212	81.44 268	98.83 208	96.43 77	97.10 187	97.94 234

IterMVS-SCA-FT		90.31 338	89.81 334	91.82 376	95.52 311	84.20 383	94.30 394	96.15 332	90.61 249	87.39 377	94.27 356	75.80 355	96.44 419	87.34 321	86.88 373	94.82 384

IterMVS		90.15 346	89.67 340	91.61 383	95.48 313	83.72 389	94.33 392	96.12 333	89.99 268	87.31 380	94.15 364	75.78 357	96.27 423	86.97 330	86.89 372	94.83 382
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
DP-MVS		92.76 235	91.51 259	96.52 107	98.77 62	90.99 170	97.38 174	96.08 334	82.38 428	89.29 329	97.87 121	83.77 213	99.69 73	81.37 401	96.69 204	98.89 136

pmmvs4		90.93 320	89.85 332	94.17 274	93.34 414	90.79 181	94.60 378	96.02 335	84.62 404	87.45 374	95.15 306	81.88 262	97.45 385	87.70 309	87.87 359	94.27 409

ppachtmachnet_test		88.35 375	87.29 374	91.53 384	92.45 433	83.57 392	93.75 413	95.97 336	84.28 407	85.32 408	94.18 362	79.00 321	96.93 407	75.71 435	84.99 395	94.10 410

Anonymous20240521		86.42 395	85.44 393	89.34 425	90.33 445	79.79 436	96.73 247	95.92 337	83.71 417	83.25 427	91.36 432	63.92 442	96.01 424	78.39 423	85.36 386	92.22 442

ITE_SJBPF				92.43 354	95.34 325	85.37 364		95.92 337	91.47 204	87.75 370	96.39 242	71.00 391	97.96 329	82.36 391	89.86 337	93.97 415

test_fmvs2		89.77 357	89.93 329	89.31 426	93.68 399	76.37 453	97.64 133	95.90 339	89.84 274	91.49 266	96.26 249	58.77 452	97.10 399	94.65 149	91.13 320	94.46 400

USDC		88.94 366	87.83 371	92.27 361	94.66 367	84.96 373	93.86 409	95.90 339	87.34 359	83.40 425	95.56 288	67.43 421	98.19 292	82.64 389	89.67 339	93.66 418

COLMAP_ROB	87.81 15	90.40 337	89.28 350	93.79 302	97.95 129	87.13 316	96.92 220	95.89 341	82.83 425	86.88 393	97.18 188	73.77 375	99.29 146	78.44 422	93.62 284	94.95 371
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
VDD-MVS		93.82 186	93.08 195	96.02 151	97.88 135	89.96 216	97.72 118	95.85 342	92.43 166	95.86 134	98.44 64	68.42 417	99.39 134	96.31 79	94.85 251	98.71 160

VDDNet		93.05 219	92.07 234	96.02 151	96.84 207	90.39 197	98.08 58	95.85 342	86.22 380	95.79 137	98.46 62	67.59 420	99.19 154	94.92 132	94.85 251	98.47 182

mvsmamba		94.57 153	94.14 157	95.87 162	97.03 189	89.93 217	97.84 95	95.85 342	91.34 210	94.79 173	96.80 213	80.67 283	98.81 211	94.85 134	98.12 148	98.85 142

Vis-MVSNet (Re-imp)		94.15 166	93.88 163	94.95 230	97.61 155	87.92 294	98.10 56	95.80 345	92.22 174	93.02 227	97.45 168	84.53 200	97.91 341	88.24 297	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	95.76 346	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		84.81 413	82.64 416	91.31 390	91.07 442	85.34 365	91.22 447	95.75 347	85.56 389	83.09 428	90.21 440	67.21 423	95.89 426	77.18 429	62.48 472	92.69 430

miper_refine_blended		84.81 413	82.64 416	91.31 390	91.07 442	85.34 365	91.22 447	95.75 347	85.56 389	83.09 428	90.21 440	67.21 423	95.89 426	77.18 429	62.48 472	92.69 430

FE-MVS		92.05 263	91.05 275	95.08 217	96.83 209	87.93 293	93.91 408	95.70 349	86.30 377	94.15 195	94.97 312	76.59 347	99.21 152	84.10 370	96.86 195	98.09 224

tpm cat1		88.36 374	87.21 377	91.81 377	95.13 344	80.55 425	92.58 438	95.70 349	74.97 458	87.45 374	91.96 425	78.01 337	98.17 294	80.39 410	88.74 351	96.72 291

our_test_3		88.78 370	87.98 370	91.20 394	92.45 433	82.53 403	93.61 421	95.69 351	85.77 386	84.88 410	93.71 380	79.99 298	96.78 415	79.47 416	86.24 375	94.28 408

BH-w/o		92.14 260	91.75 248	93.31 325	96.99 194	85.73 354	95.67 333	95.69 351	88.73 316	89.26 331	94.82 322	82.97 233	98.07 310	85.26 357	96.32 219	96.13 309

CR-MVSNet		90.82 323	89.77 336	93.95 291	94.45 376	87.19 313	90.23 455	95.68 353	86.89 367	92.40 237	92.36 417	80.91 277	97.05 402	81.09 405	93.95 277	97.60 258

Patchmtry		88.64 372	87.25 375	92.78 347	94.09 386	86.64 326	89.82 459	95.68 353	80.81 440	87.63 372	92.36 417	80.91 277	97.03 403	78.86 420	85.12 391	94.67 395

testing91		91.90 268	91.02 276	94.53 256	96.54 243	86.55 332	95.86 322	95.64 355	91.77 192	91.89 256	93.47 394	69.94 402	98.86 203	90.23 255	93.86 279	98.18 210

BH-RMVSNet		92.72 237	91.97 240	94.97 228	97.16 176	87.99 292	96.15 306	95.60 356	90.62 248	91.87 257	97.15 191	78.41 328	98.57 259	83.16 379	97.60 164	98.36 194

PVSNet_0	82.17 19	85.46 408	83.64 411	90.92 398	95.27 332	79.49 441	90.55 453	95.60 356	83.76 416	83.00 430	89.95 442	71.09 390	97.97 325	82.75 387	60.79 474	95.31 352

guyue		95.17 127	94.96 123	95.82 168	96.97 196	89.65 227	97.56 144	95.58 358	94.82 57	95.72 139	97.42 172	82.90 235	98.84 207	96.71 67	96.93 192	98.96 114

SCA		91.84 270	91.18 272	93.83 299	95.59 307	84.95 374	94.72 375	95.58 358	90.82 235	92.25 245	93.69 382	75.80 355	98.10 301	86.20 339	95.98 222	98.45 184

MonoMVSNet		91.92 266	91.77 246	92.37 355	92.94 421	83.11 397	97.09 203	95.55 360	92.91 148	90.85 283	94.55 335	81.27 272	96.52 418	93.01 194	87.76 360	97.47 264

AllTest		90.23 342	88.98 356	93.98 287	97.94 130	86.64 326	96.51 270	95.54 361	85.38 391	85.49 405	96.77 215	70.28 397	99.15 163	80.02 412	92.87 289	96.15 307

TestCases				93.98 287	97.94 130	86.64 326		95.54 361	85.38 391	85.49 405	96.77 215	70.28 397	99.15 163	80.02 412	92.87 289	96.15 307

mmtdpeth		89.70 359	88.96 357	91.90 372	95.84 299	84.42 379	97.46 164	95.53 363	90.27 261	94.46 184	90.50 436	69.74 406	98.95 193	97.39 53	69.48 463	92.34 438

tpmvs		89.83 356	89.15 354	91.89 373	94.92 354	80.30 429	93.11 430	95.46 364	86.28 378	88.08 364	92.65 407	80.44 289	98.52 263	81.47 397	89.92 336	96.84 287

pmmvs5		89.86 355	88.87 360	92.82 344	92.86 423	86.23 340	96.26 295	95.39 365	84.24 408	87.12 382	94.51 338	74.27 370	97.36 392	87.61 316	87.57 362	94.86 380

PatchmatchNet		91.91 267	91.35 261	93.59 313	95.38 320	84.11 384	93.15 429	95.39 365	89.54 282	92.10 250	93.68 384	82.82 238	98.13 296	84.81 361	95.32 243	98.52 174
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
tpmrst		91.44 292	91.32 263	91.79 378	95.15 342	79.20 444	93.42 424	95.37 367	88.55 321	93.49 216	93.67 385	82.49 247	98.27 285	90.41 250	89.34 342	97.90 236

Anonymous20231206		87.09 387	86.14 389	89.93 417	91.22 441	80.35 427	96.11 307	95.35 368	83.57 419	84.16 417	93.02 402	73.54 377	95.61 434	72.16 452	86.14 377	93.84 417

MIMVSNet1		84.93 411	83.05 413	90.56 407	89.56 451	84.84 376	95.40 348	95.35 368	83.91 411	80.38 442	92.21 422	57.23 455	93.34 459	70.69 458	82.75 422	93.50 420

TDRefinement		86.53 391	84.76 403	91.85 374	82.23 475	84.25 381	96.38 283	95.35 368	84.97 400	84.09 420	94.94 314	65.76 436	98.34 282	84.60 365	74.52 453	92.97 426

TR-MVS		91.48 291	90.59 299	94.16 277	96.40 258	87.33 306	95.67 333	95.34 371	87.68 351	91.46 267	95.52 291	76.77 346	98.35 279	82.85 384	93.61 285	96.79 289

EPNet_dtu		91.71 273	91.28 266	92.99 337	93.76 396	83.71 390	96.69 253	95.28 372	93.15 134	87.02 387	95.95 264	83.37 221	97.38 391	79.46 417	96.84 196	97.88 238
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
FMVSNet5		87.29 384	85.79 391	91.78 379	94.80 361	87.28 308	95.49 345	95.28 372	84.09 410	83.85 424	91.82 426	62.95 445	94.17 450	78.48 421	85.34 387	93.91 416

MDTV_nov1_ep13			90.76 288		95.22 336	80.33 428	93.03 432	95.28 372	88.14 334	92.84 234	93.83 375	81.34 269	98.08 306	82.86 382	94.34 262

LF4IMVS		87.94 378	87.25 375	89.98 415	92.38 435	80.05 435	94.38 389	95.25 375	87.59 353	84.34 414	94.74 326	64.31 441	97.66 366	84.83 360	87.45 363	92.23 441

TransMVSNet (Re)		88.94 366	87.56 372	93.08 335	94.35 379	88.45 277	97.73 115	95.23 376	87.47 355	84.26 416	95.29 298	79.86 301	97.33 393	79.44 418	74.44 454	93.45 422

test20.03		86.14 401	85.40 395	88.35 428	90.12 446	80.06 434	95.90 321	95.20 377	88.59 317	81.29 437	93.62 387	71.43 388	92.65 463	71.26 456	81.17 427	92.34 438

new-patchmatchnet		83.18 420	81.87 423	87.11 436	86.88 466	75.99 455	93.70 415	95.18 378	85.02 399	77.30 455	88.40 454	65.99 434	93.88 455	74.19 444	70.18 461	91.47 453

MDA-MVSNet_test_wron		85.87 405	84.23 408	90.80 404	92.38 435	82.57 402	93.17 427	95.15 379	82.15 429	67.65 467	92.33 420	78.20 330	95.51 438	77.33 426	79.74 431	94.31 407

YYNet1		85.87 405	84.23 408	90.78 405	92.38 435	82.46 407	93.17 427	95.14 380	82.12 430	67.69 465	92.36 417	78.16 333	95.50 439	77.31 427	79.73 432	94.39 403

Baseline_NR-MVSNet		91.20 307	90.62 297	92.95 339	93.83 394	88.03 291	97.01 210	95.12 381	88.42 325	89.70 314	95.13 308	83.47 218	97.44 386	89.66 267	83.24 417	93.37 423

thres200		92.23 256	91.39 260	94.75 243	97.61 155	89.03 260	96.60 265	95.09 382	92.08 183	93.28 222	94.00 371	78.39 329	99.04 189	81.26 404	94.18 268	96.19 303

ADS-MVSNet		89.89 352	88.68 362	93.53 317	95.86 294	84.89 375	90.93 450	95.07 383	83.23 423	91.28 276	91.81 427	79.01 319	97.85 344	79.52 414	91.39 316	97.84 243

pmmvs-eth3d		86.22 399	84.45 406	91.53 384	88.34 460	87.25 310	94.47 384	95.01 384	83.47 420	79.51 447	89.61 446	69.75 405	95.71 431	83.13 380	76.73 446	91.64 447

Anonymous202405211		92.07 262	90.83 286	95.76 177	98.19 109	88.75 266	97.58 140	95.00 385	86.00 383	93.64 208	97.45 168	66.24 432	99.53 112	90.68 244	92.71 294	99.01 106

MDA-MVSNet-bldmvs		85.00 410	82.95 415	91.17 396	93.13 419	83.33 393	94.56 380	95.00 385	84.57 405	65.13 471	92.65 407	70.45 396	95.85 428	73.57 447	77.49 441	94.33 405

ambc				86.56 439	83.60 472	70.00 466	85.69 471	94.97 387		80.60 441	88.45 453	37.42 473	96.84 412	82.69 388	75.44 451	92.86 428

testgi		87.97 377	87.21 377	90.24 412	92.86 423	80.76 420	96.67 256	94.97 387	91.74 193	85.52 404	95.83 270	62.66 447	94.47 448	76.25 433	88.36 355	95.48 335

myMVS_eth3d28		91.52 288	90.97 278	93.17 331	96.91 199	83.24 395	95.61 339	94.96 389	92.24 173	91.98 253	93.28 399	69.31 407	98.40 271	88.71 292	95.68 232	97.88 238

dp		88.90 368	88.26 368	90.81 402	94.58 372	76.62 452	92.85 435	94.93 390	85.12 397	90.07 305	93.07 401	75.81 354	98.12 299	80.53 409	87.42 365	97.71 250

test_fmvs3		83.21 419	83.02 414	83.78 443	86.77 467	68.34 469	96.76 245	94.91 391	86.49 373	84.14 419	89.48 447	36.04 474	91.73 465	91.86 215	80.77 429	91.26 455

test_0402		86.46 394	84.79 402	91.45 386	95.02 348	85.55 356	96.29 294	94.89 392	80.90 437	82.21 433	93.97 373	68.21 418	97.29 395	62.98 466	88.68 352	91.51 450

tfpn200view9		92.38 246	91.52 257	94.95 230	97.85 136	89.29 249	97.41 167	94.88 393	92.19 179	93.27 223	94.46 343	78.17 331	99.08 177	81.40 398	94.08 272	96.48 296

CVMVSNet		91.23 305	91.75 248	89.67 419	95.77 300	74.69 456	96.44 271	94.88 393	85.81 385	92.18 246	97.64 154	79.07 314	95.58 436	88.06 300	95.86 227	98.74 157

thres400		92.42 244	91.52 257	95.12 216	97.85 136	89.29 249	97.41 167	94.88 393	92.19 179	93.27 223	94.46 343	78.17 331	99.08 177	81.40 398	94.08 272	96.98 281

tt0320		85.39 409	83.12 412	92.19 365	93.44 411	85.79 352	96.19 303	94.87 396	71.19 465	82.92 431	91.76 429	58.43 453	96.81 413	81.03 406	78.26 440	93.98 414

EPNet		95.20 123	94.56 140	97.14 75	92.80 425	92.68 97	97.85 94	94.87 396	96.64 9	92.46 236	97.80 135	86.23 161	99.65 79	93.72 173	98.62 124	99.10 95
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
testing99		91.62 279	90.72 293	94.32 267	96.48 252	86.11 349	95.81 325	94.76 398	91.55 197	91.75 261	93.44 395	68.55 415	98.82 209	90.43 249	93.69 281	98.04 228

sc_t1		86.48 393	84.10 410	93.63 310	93.45 410	85.76 353	96.79 239	94.71 399	73.06 463	86.45 397	94.35 348	55.13 460	97.95 333	84.38 368	78.55 439	97.18 277

SixPastTwentyTwo		89.15 364	88.54 364	90.98 397	93.49 407	80.28 431	96.70 251	94.70 400	90.78 236	84.15 418	95.57 287	71.78 386	97.71 361	84.63 364	85.07 392	94.94 373

thres100view900		92.43 243	91.58 254	94.98 226	97.92 132	89.37 245	97.71 120	94.66 401	92.20 177	93.31 221	94.90 317	78.06 335	99.08 177	81.40 398	94.08 272	96.48 296

thres600view7		92.49 241	91.60 253	95.18 212	97.91 133	89.47 239	97.65 129	94.66 401	92.18 181	93.33 220	94.91 316	78.06 335	99.10 171	81.61 394	94.06 276	96.98 281

PatchT		88.87 369	87.42 373	93.22 329	94.08 387	85.10 369	89.51 460	94.64 403	81.92 431	92.36 240	88.15 457	80.05 297	97.01 405	72.43 451	93.65 283	97.54 261

baseline1		92.82 233	91.90 243	95.55 193	97.20 174	90.77 182	97.19 195	94.58 404	92.20 177	92.36 240	96.34 244	84.16 208	98.21 289	89.20 282	83.90 412	97.68 252

AstraMVS		94.82 145	94.64 136	95.34 207	96.36 263	88.09 290	97.58 140	94.56 405	94.98 46	95.70 142	97.92 114	81.93 261	98.93 196	96.87 61	95.88 225	98.99 110

UBG		91.55 285	90.76 288	93.94 293	96.52 248	85.06 370	95.22 360	94.54 406	90.47 257	91.98 253	92.71 406	72.02 383	98.74 230	88.10 299	95.26 245	98.01 230

Gipuma		67.86 440	65.41 442	75.18 456	92.66 428	73.45 460	66.50 478	94.52 407	53.33 476	57.80 477	66.07 477	30.81 476	89.20 469	48.15 475	78.88 438	62.90 477
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
testing11		91.68 276	90.75 290	94.47 258	96.53 245	86.56 331	95.76 329	94.51 408	91.10 228	91.24 278	93.59 389	68.59 414	98.86 203	91.10 232	94.29 264	98.00 231

CostFormer		91.18 310	90.70 294	92.62 352	94.84 359	81.76 413	94.09 401	94.43 409	84.15 409	92.72 235	93.77 379	79.43 308	98.20 290	90.70 243	92.18 303	97.90 236

tpm2		89.96 349	89.21 352	92.23 364	94.91 356	81.25 416	93.78 412	94.42 410	80.62 442	91.56 264	93.44 395	76.44 350	97.94 335	85.60 351	92.08 307	97.49 262

testing3-2		92.10 261	92.05 235	92.27 361	97.71 145	79.56 438	97.42 166	94.41 411	93.53 113	93.22 225	95.49 292	69.16 409	99.11 169	93.25 184	94.22 266	98.13 215

MGCNet		96.74 64	96.31 81	98.02 20	96.87 203	94.65 31	97.58 140	94.39 412	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		88.26 376	87.04 380	91.91 371	93.52 405	81.42 415	89.38 461	94.38 413	80.84 439	90.93 282	80.74 469	79.22 311	97.92 338	82.76 386	91.62 311	96.38 299

dmvs_re		90.21 343	89.50 345	92.35 356	95.47 317	85.15 367	95.70 332	94.37 414	90.94 234	88.42 351	93.57 390	74.63 367	95.67 433	82.80 385	89.57 340	96.22 301

Patchmatch-test		89.42 362	87.99 369	93.70 307	95.27 332	85.11 368	88.98 462	94.37 414	81.11 436	87.10 385	93.69 382	82.28 251	97.50 381	74.37 442	94.76 255	98.48 181

LCM-MVSNet		72.55 433	69.39 437	82.03 445	70.81 485	65.42 474	90.12 457	94.36 416	55.02 475	65.88 469	81.72 468	24.16 482	89.96 466	74.32 443	68.10 466	90.71 458

ADS-MVSNet2		89.45 361	88.59 363	92.03 368	95.86 294	82.26 409	90.93 450	94.32 417	83.23 423	91.28 276	91.81 427	79.01 319	95.99 425	79.52 414	91.39 316	97.84 243

mvs5depth		86.53 391	85.08 398	90.87 399	88.74 458	82.52 404	91.91 443	94.23 418	86.35 376	87.11 384	93.70 381	66.52 428	97.76 356	81.37 401	75.80 448	92.31 440

EU-MVSNet		88.72 371	88.90 359	88.20 430	93.15 418	74.21 458	96.63 262	94.22 419	85.18 395	87.32 379	95.97 262	76.16 352	94.98 443	85.27 356	86.17 376	95.41 342

tt0320-xc		84.83 412	82.33 420	92.31 359	93.66 400	86.20 342	96.17 305	94.06 420	71.26 464	82.04 435	92.22 421	55.07 461	96.72 416	81.49 396	75.04 452	94.02 413

MIMVSNet		88.50 373	86.76 383	93.72 306	94.84 359	87.77 300	91.39 445	94.05 421	86.41 375	87.99 366	92.59 410	63.27 443	95.82 430	77.44 425	92.84 291	97.57 260

OpenMVS_ROB	81.14 20	84.42 415	82.28 421	90.83 400	90.06 447	84.05 386	95.73 331	94.04 422	73.89 461	80.17 445	91.53 431	59.15 451	97.64 367	66.92 464	89.05 345	90.80 457

TinyColmap		86.82 389	85.35 396	91.21 392	94.91 356	82.99 399	93.94 405	94.02 423	83.58 418	81.56 436	94.68 328	62.34 448	98.13 296	75.78 434	87.35 368	92.52 436

ETVMVS		90.52 334	89.14 355	94.67 246	96.81 213	87.85 298	95.91 320	93.97 424	89.71 277	92.34 243	92.48 412	65.41 438	97.96 329	81.37 401	94.27 265	98.21 208

IB-MVS	87.33 17	89.91 350	88.28 367	94.79 240	95.26 335	87.70 301	95.12 366	93.95 425	89.35 290	87.03 386	92.49 411	70.74 394	99.19 154	89.18 283	81.37 426	97.49 262
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		87.13 386	87.02 381	87.47 434	95.16 339	73.21 462	95.00 368	93.93 426	88.55 321	86.96 388	91.99 423	75.90 353	94.00 452	61.59 468	94.11 269	95.20 360

myMVS_eth3d		87.18 385	86.38 386	89.58 420	95.16 339	79.53 439	95.00 368	93.93 426	88.55 321	86.96 388	91.99 423	56.23 458	94.00 452	75.47 438	94.11 269	95.20 360

testing222		90.31 338	88.96 357	94.35 264	96.54 243	87.29 307	95.50 344	93.84 428	90.97 231	91.75 261	92.96 403	62.18 449	98.00 320	82.86 382	94.08 272	97.76 248

test_f		80.57 426	79.62 428	83.41 444	83.38 473	67.80 471	93.57 422	93.72 429	80.80 441	77.91 454	87.63 460	33.40 475	92.08 464	87.14 328	79.04 437	90.34 459

LCM-MVSNet-Re		92.50 239	92.52 223	92.44 353	96.82 211	81.89 412	96.92 220	93.71 430	92.41 167	84.30 415	94.60 333	85.08 189	97.03 403	91.51 223	97.36 173	98.40 190

tpm		90.25 341	89.74 339	91.76 381	93.92 390	79.73 437	93.98 402	93.54 431	88.28 328	91.99 252	93.25 400	77.51 341	97.44 386	87.30 323	87.94 358	98.12 217

ET-MVSNet_ETH3D		91.49 290	90.11 319	95.63 187	96.40 258	91.57 142	95.34 351	93.48 432	90.60 251	75.58 457	95.49 292	80.08 296	96.79 414	94.25 161	89.76 338	98.52 174

LFMVS		93.60 193	92.63 216	96.52 107	98.13 115	91.27 155	97.94 81	93.39 433	90.57 253	96.29 116	98.31 81	69.00 410	99.16 161	94.18 162	95.87 226	99.12 92

MVStest1		82.38 423	80.04 427	89.37 423	87.63 464	82.83 400	95.03 367	93.37 434	73.90 460	73.50 462	94.35 348	62.89 446	93.25 461	73.80 445	65.92 469	92.04 446

FE-MVSNET		83.85 416	81.97 422	89.51 421	87.19 465	83.19 396	95.21 362	93.17 435	83.45 421	78.90 450	89.05 450	65.46 437	93.84 456	69.71 460	75.56 450	91.51 450

Patchmatch-RL test		87.38 383	86.24 387	90.81 402	88.74 458	78.40 448	88.12 469	93.17 435	87.11 364	82.17 434	89.29 448	81.95 259	95.60 435	88.64 294	77.02 443	98.41 189

ttmdpeth		85.91 404	84.76 403	89.36 424	89.14 453	80.25 432	95.66 336	93.16 437	83.77 415	83.39 426	95.26 302	66.24 432	95.26 442	80.65 407	75.57 449	92.57 433

test-LLR		91.42 293	91.19 271	92.12 366	94.59 370	80.66 422	94.29 395	92.98 438	91.11 226	90.76 285	92.37 414	79.02 317	98.07 310	88.81 289	96.74 201	97.63 253

test-mter		90.19 345	89.54 344	92.12 366	94.59 370	80.66 422	94.29 395	92.98 438	87.68 351	90.76 285	92.37 414	67.67 419	98.07 310	88.81 289	96.74 201	97.63 253

WB-MVSnew		89.88 353	89.56 343	90.82 401	94.57 373	83.06 398	95.65 337	92.85 440	87.86 342	90.83 284	94.10 365	79.66 305	96.88 410	76.34 432	94.19 267	92.54 435

testing3		87.67 381	86.88 382	90.05 414	96.14 281	80.71 421	97.10 202	92.85 440	90.15 265	87.54 373	94.55 335	55.70 459	94.10 451	73.77 446	94.10 271	95.35 349

test_method		66.11 441	64.89 443	69.79 459	72.62 483	35.23 491	65.19 479	92.83 442	20.35 481	65.20 470	88.08 458	43.14 471	82.70 476	73.12 449	63.46 471	91.45 454

test0.0.03 1		89.37 363	88.70 361	91.41 388	92.47 432	85.63 355	95.22 360	92.70 443	91.11 226	86.91 392	93.65 386	79.02 317	93.19 462	78.00 424	89.18 343	95.41 342

new_pmnet		82.89 421	81.12 426	88.18 431	89.63 450	80.18 433	91.77 444	92.57 444	76.79 456	75.56 458	88.23 456	61.22 450	94.48 447	71.43 454	82.92 420	89.87 460

mvsany_test1		93.93 182	93.98 161	93.78 303	94.94 353	86.80 322	94.62 377	92.55 445	88.77 315	96.85 84	98.49 58	88.98 101	98.08 306	95.03 127	95.62 234	96.46 298

thisisatest0515		92.29 252	91.30 265	95.25 210	96.60 232	88.90 264	94.36 390	92.32 446	87.92 338	93.43 218	94.57 334	77.28 342	99.00 190	89.42 273	95.86 227	97.86 242

thisisatest0530		93.03 220	92.21 232	95.49 199	97.07 181	89.11 258	97.49 161	92.19 447	90.16 264	94.09 196	96.41 240	76.43 351	99.05 186	90.38 251	95.68 232	98.31 202

tttt0517		92.96 223	92.33 229	94.87 233	97.11 179	87.16 315	97.97 77	92.09 448	90.63 247	93.88 202	97.01 204	76.50 348	99.06 183	90.29 254	95.45 241	98.38 192

K. test v3		87.64 382	86.75 384	90.32 411	93.02 420	79.48 442	96.61 263	92.08 449	90.66 245	80.25 444	94.09 367	67.21 423	96.65 417	85.96 347	80.83 428	94.83 382

TESTMET0.1,1		90.06 347	89.42 347	91.97 369	94.41 378	80.62 424	94.29 395	91.97 450	87.28 361	90.44 289	92.47 413	68.79 411	97.67 363	88.50 296	96.60 208	97.61 257

PM-MVS		83.48 418	81.86 424	88.31 429	87.83 462	77.59 450	93.43 423	91.75 451	86.91 366	80.63 440	89.91 443	44.42 470	95.84 429	85.17 359	76.73 446	91.50 452

baseline2		91.63 278	90.86 282	93.94 293	94.33 380	86.32 337	95.92 319	91.64 452	89.37 289	86.94 390	94.69 327	81.62 266	98.69 240	88.64 294	94.57 260	96.81 288

APD_test1		79.31 428	77.70 431	84.14 442	89.11 455	69.07 468	92.36 442	91.50 453	69.07 467	73.87 460	92.63 409	39.93 472	94.32 449	70.54 459	80.25 430	89.02 462

FPMVS		71.27 434	69.85 436	75.50 455	74.64 480	59.03 480	91.30 446	91.50 453	58.80 472	57.92 476	88.28 455	29.98 478	85.53 475	53.43 473	82.84 421	81.95 468

door								91.13 455

door-mid								91.06 456

EGC-MVSNET		68.77 439	63.01 445	86.07 441	92.49 431	82.24 410	93.96 404	90.96 457	0.71 486	2.62 487	90.89 434	53.66 462	93.46 457	57.25 471	84.55 402	82.51 467

mvsany_test3		83.59 417	82.44 419	87.03 437	83.80 470	73.82 459	93.70 415	90.92 458	86.42 374	82.51 432	90.26 439	46.76 469	95.71 431	90.82 238	76.76 445	91.57 449

pmmvs3		79.97 427	77.50 432	87.39 435	82.80 474	79.38 443	92.70 437	90.75 459	70.69 466	78.66 451	87.47 462	51.34 465	93.40 458	73.39 448	69.65 462	89.38 461

UWE-MVS		89.91 350	89.48 346	91.21 392	95.88 293	78.23 449	94.91 371	90.26 460	89.11 296	92.35 242	94.52 337	68.76 412	97.96 329	83.95 374	95.59 235	97.42 266

DSMNet-mixed		86.34 397	86.12 390	87.00 438	89.88 449	70.43 464	94.93 370	90.08 461	77.97 453	85.42 407	92.78 405	74.44 369	93.96 454	74.43 441	95.14 246	96.62 292

MVS-HIRNet		82.47 422	81.21 425	86.26 440	95.38 320	69.21 467	88.96 463	89.49 462	66.28 469	80.79 439	74.08 474	68.48 416	97.39 390	71.93 453	95.47 240	92.18 443

WB-MVS		76.77 430	76.63 433	77.18 450	85.32 468	56.82 482	94.53 381	89.39 463	82.66 427	71.35 463	89.18 449	75.03 362	88.88 470	35.42 479	66.79 467	85.84 464

test1111		93.19 212	92.82 206	94.30 270	97.58 161	84.56 378	98.21 47	89.02 464	93.53 113	94.58 178	98.21 88	72.69 379	99.05 186	93.06 190	98.48 131	99.28 77

SSC-MVS		76.05 431	75.83 434	76.72 454	84.77 469	56.22 483	94.32 393	88.96 465	81.82 433	70.52 464	88.91 451	74.79 366	88.71 471	33.69 480	64.71 470	85.23 465

ECVR-MVS		93.19 212	92.73 212	94.57 253	97.66 149	85.41 361	98.21 47	88.23 466	93.43 120	94.70 175	98.21 88	72.57 380	99.07 181	93.05 191	98.49 129	99.25 80

EPMVS		90.70 328	89.81 334	93.37 323	94.73 365	84.21 382	93.67 418	88.02 467	89.50 284	92.38 239	93.49 392	77.82 339	97.78 353	86.03 345	92.68 295	98.11 223

ANet_high		63.94 443	59.58 446	77.02 451	61.24 487	66.06 472	85.66 472	87.93 468	78.53 451	42.94 479	71.04 476	25.42 481	80.71 478	52.60 474	30.83 480	84.28 466

PMMVS2		70.19 435	66.92 439	80.01 446	76.35 479	65.67 473	86.22 470	87.58 469	64.83 471	62.38 472	80.29 471	26.78 480	88.49 473	63.79 465	54.07 476	85.88 463

lessismore_v0				90.45 408	91.96 438	79.09 446		87.19 470		80.32 443	94.39 345	66.31 431	97.55 375	84.00 373	76.84 444	94.70 394

PMVS	53.92 22	58.58 444	55.40 447	68.12 460	51.00 488	48.64 485	78.86 475	87.10 471	46.77 477	35.84 483	74.28 473	8.76 486	86.34 474	42.07 477	73.91 455	69.38 474
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
UWE-MVS-28		86.81 390	86.41 385	88.02 432	92.87 422	74.60 457	95.38 350	86.70 472	88.17 331	87.28 381	94.67 330	70.83 393	93.30 460	67.45 462	94.31 263	96.17 304

test_vis1_rt		86.16 400	85.06 399	89.46 422	93.47 409	80.46 426	96.41 277	86.61 473	85.22 394	79.15 449	88.64 452	52.41 464	97.06 401	93.08 189	90.57 329	90.87 456

testf1		69.31 437	66.76 440	76.94 452	78.61 477	61.93 476	88.27 467	86.11 474	55.62 473	59.69 473	85.31 465	20.19 484	89.32 467	57.62 469	69.44 464	79.58 469

APD_test2		69.31 437	66.76 440	76.94 452	78.61 477	61.93 476	88.27 467	86.11 474	55.62 473	59.69 473	85.31 465	20.19 484	89.32 467	57.62 469	69.44 464	79.58 469

gg-mvs-nofinetune		87.82 379	85.61 392	94.44 260	94.46 375	89.27 252	91.21 449	84.61 476	80.88 438	89.89 309	74.98 472	71.50 387	97.53 378	85.75 350	97.21 182	96.51 294

dmvs_testset		81.38 425	82.60 418	77.73 449	91.74 439	51.49 484	93.03 432	84.21 477	89.07 297	78.28 453	91.25 433	76.97 344	88.53 472	56.57 472	82.24 423	93.16 424

GG-mvs-BLEND				93.62 311	93.69 398	89.20 254	92.39 441	83.33 478		87.98 367	89.84 444	71.00 391	96.87 411	82.08 393	95.40 242	94.80 387

MTMP							97.86 91	82.03 479

DeepMVS_CX				74.68 457	90.84 444	64.34 475		81.61 480	65.34 470	67.47 468	88.01 459	48.60 468	80.13 479	62.33 467	73.68 456	79.58 469

E-PMN		53.28 445	52.56 449	55.43 463	74.43 481	47.13 486	83.63 474	76.30 481	42.23 478	42.59 480	62.22 479	28.57 479	74.40 480	31.53 481	31.51 479	44.78 478

test2506		91.60 280	90.78 287	94.04 283	97.66 149	83.81 387	98.27 37	75.53 482	93.43 120	95.23 159	98.21 88	67.21 423	99.07 181	93.01 194	98.49 129	99.25 80

EMVS		52.08 447	51.31 450	54.39 464	72.62 483	45.39 488	83.84 473	75.51 483	41.13 479	40.77 481	59.65 480	30.08 477	73.60 481	28.31 483	29.90 481	44.18 479

test_vis3_rt		72.73 432	70.55 435	79.27 447	80.02 476	68.13 470	93.92 407	74.30 484	76.90 455	58.99 475	73.58 475	20.29 483	95.37 440	84.16 369	72.80 458	74.31 472

MVE	50.73 23	53.25 446	48.81 451	66.58 462	65.34 486	57.50 481	72.49 477	70.94 485	40.15 480	39.28 482	63.51 478	6.89 488	73.48 482	38.29 478	42.38 478	68.76 476
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
tmp_tt		51.94 448	53.82 448	46.29 465	33.73 489	45.30 489	78.32 476	67.24 486	18.02 482	50.93 478	87.05 463	52.99 463	53.11 484	70.76 457	25.29 482	40.46 480

kuosan		65.27 442	64.66 444	67.11 461	83.80 470	61.32 479	88.53 466	60.77 487	68.22 468	67.67 466	80.52 470	49.12 467	70.76 483	29.67 482	53.64 477	69.26 475

dongtai		69.99 436	69.33 438	71.98 458	88.78 457	61.64 478	89.86 458	59.93 488	75.67 457	74.96 459	85.45 464	50.19 466	81.66 477	43.86 476	55.27 475	72.63 473

N_pmnet		78.73 429	78.71 430	78.79 448	92.80 425	46.50 487	94.14 399	43.71 489	78.61 450	80.83 438	91.66 430	74.94 365	96.36 421	67.24 463	84.45 404	93.50 420

wuyk23d		25.11 449	24.57 453	26.74 466	73.98 482	39.89 490	57.88 480	9.80 490	12.27 483	10.39 484	6.97 486	7.03 487	36.44 485	25.43 484	17.39 483	3.89 483

testmvs		13.36 451	16.33 454	4.48 468	5.04 490	2.26 493	93.18 426	3.28 491	2.70 484	8.24 485	21.66 482	2.29 490	2.19 486	7.58 485	2.96 484	9.00 482

test123		13.04 452	15.66 455	5.18 467	4.51 491	3.45 492	92.50 440	1.81 492	2.50 485	7.58 486	20.15 483	3.67 489	2.18 487	7.13 486	1.07 485	9.90 481

mmdepth		0.00 455	0.00 458	0.00 469	0.00 492	0.00 494	0.00 481	0.00 493	0.00 487	0.00 488	0.00 487	0.00 491	0.00 488	0.00 487	0.00 486	0.00 484

monomultidepth		0.00 455	0.00 458	0.00 469	0.00 492	0.00 494	0.00 481	0.00 493	0.00 487	0.00 488	0.00 487	0.00 491	0.00 488	0.00 487	0.00 486	0.00 484

test_blank		0.00 455	0.00 458	0.00 469	0.00 492	0.00 494	0.00 481	0.00 493	0.00 487	0.00 488	0.00 487	0.00 491	0.00 488	0.00 487	0.00 486	0.00 484

uanet_test		0.00 455	0.00 458	0.00 469	0.00 492	0.00 494	0.00 481	0.00 493	0.00 487	0.00 488	0.00 487	0.00 491	0.00 488	0.00 487	0.00 486	0.00 484

DCPMVS		0.00 455	0.00 458	0.00 469	0.00 492	0.00 494	0.00 481	0.00 493	0.00 487	0.00 488	0.00 487	0.00 491	0.00 488	0.00 487	0.00 486	0.00 484

pcd_1.5k_mvsjas		7.39 454	9.85 457	0.00 469	0.00 492	0.00 494	0.00 481	0.00 493	0.00 487	0.00 488	0.00 487	88.65 109	0.00 488	0.00 487	0.00 486	0.00 484

sosnet-low-res		0.00 455	0.00 458	0.00 469	0.00 492	0.00 494	0.00 481	0.00 493	0.00 487	0.00 488	0.00 487	0.00 491	0.00 488	0.00 487	0.00 486	0.00 484

sosnet		0.00 455	0.00 458	0.00 469	0.00 492	0.00 494	0.00 481	0.00 493	0.00 487	0.00 488	0.00 487	0.00 491	0.00 488	0.00 487	0.00 486	0.00 484

uncertanet		0.00 455	0.00 458	0.00 469	0.00 492	0.00 494	0.00 481	0.00 493	0.00 487	0.00 488	0.00 487	0.00 491	0.00 488	0.00 487	0.00 486	0.00 484

Regformer		0.00 455	0.00 458	0.00 469	0.00 492	0.00 494	0.00 481	0.00 493	0.00 487	0.00 488	0.00 487	0.00 491	0.00 488	0.00 487	0.00 486	0.00 484

n2								0.00 493

nn								0.00 493

ab-mvs-re		8.06 453	10.74 456	0.00 469	0.00 492	0.00 494	0.00 481	0.00 493	0.00 487	0.00 488	96.69 221	0.00 491	0.00 488	0.00 487	0.00 486	0.00 484

uanet		0.00 455	0.00 458	0.00 469	0.00 492	0.00 494	0.00 481	0.00 493	0.00 487	0.00 488	0.00 487	0.00 491	0.00 488	0.00 487	0.00 486	0.00 484

TestfortrainingZip							98.69 11

WAC-MVS						79.53 439								75.56 437

PC_three_1452									90.77 237	98.89 26	98.28 86	96.24 1	98.35 279	95.76 106	99.58 23	99.59 32

eth-test2					0.00 492

eth-test					0.00 492

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																98.45 184

test_part2					99.28 30	95.74 9				98.10 48

sam_mvs1												82.76 239				98.45 184

sam_mvs												81.94 260

test_post1							92.81 436				16.58 485	80.53 287	97.68 362	86.20 339

test_post											17.58 484	81.76 263	98.08 306

patchmatchnet-post											90.45 438	82.65 244	98.10 301

gm-plane-assit					93.22 416	78.89 447			84.82 402		93.52 391		98.64 249	87.72 306

test9_res														94.81 140	99.38 64	99.45 59

agg_prior2														93.94 167	99.38 64	99.50 52

test_prior4						93.66 62	96.42 276

test_prior2							96.35 286		92.80 155	96.03 126	97.59 160	92.01 50		95.01 128	99.38 64

旧先验2							95.94 317		81.66 434	97.34 70			98.82 209	92.26 200

新几何2							95.79 327

原ACMM2							95.67 333

testdata2													99.67 77	85.96 347

segment_acmp												92.89 33

testdata1							95.26 359		93.10 137

plane_prior7					96.21 268	89.98 213

plane_prior6					96.10 286	90.00 209						81.32 270

plane_prior4											96.64 224

plane_prior3						90.00 209			94.46 78	91.34 270

plane_prior2							97.74 113		94.85 53

plane_prior1					96.14 281

plane_prior						89.99 211	97.24 187		94.06 92						92.16 304

HQP5-MVS						89.33 247

HQP-NCC					95.86 294		96.65 257		93.55 109	90.14 294

ACMP_Plane					95.86 294		96.65 257		93.55 109	90.14 294

BP-MVS														92.13 208

HQP4-MVS										90.14 294			98.50 264			95.78 323

HQP2-MVS												80.95 275

NP-MVS					95.99 292	89.81 221					95.87 267

MDTV_nov1_ep13_2view						70.35 465	93.10 431		83.88 413	93.55 211		82.47 248		86.25 338		98.38 192

ACMMP++_ref															90.30 334

ACMMP++															91.02 323

Test By Simon												88.73 108

High-resolution multi-view benchmark