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	98.08 224

PGM-MVS		96.81 58	96.53 69	97.65 47	99.35 25	93.53 65	97.65 129	98.98 2	92.22 173	97.14 76	98.44 64	91.17 71	99.85 21	94.35 159	99.46 46	99.57 36

MVS_111021_HR		96.68 69	96.58 68	96.99 84	98.46 79	92.31 110	96.20 301	98.90 3	94.30 86	95.86 134	97.74 140	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	94.56 178	98.39 68	88.96 102	99.85 21	94.57 153	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	96.24 298	98.79 7	93.99 95	95.80 136	97.65 150	89.92 91	99.24 149	95.87 100	99.20 88	98.58 168

patch_mono-2		96.83 57	97.44 24	95.01 221	99.05 45	85.39 361	96.98 213	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	90.28 202	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	98.18 209

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	90.72 184	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	98.25 204

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		93.94 179	93.57 171	95.04 219	95.48 312	91.45 149	98.12 55	98.71 13	93.37 122	90.23 292	96.70 218	87.66 129	97.85 343	91.49 223	90.39 332	95.83 318

UniMVSNet (Re)		93.31 206	92.55 219	95.61 189	95.39 318	93.34 71	97.39 172	98.71 13	93.14 135	90.10 301	94.83 320	87.71 128	98.03 316	91.67 221	83.99 406	95.46 337

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		94.09 170	93.70 167	95.27 208	95.70 301	92.03 122	98.10 56	98.68 19	93.36 124	90.39 289	96.70 218	87.63 132	97.94 334	92.25 201	90.50 331	95.84 317

WR-MVS_H		92.00 263	91.35 260	93.95 289	95.09 345	89.47 238	98.04 63	98.68 19	91.46 204	88.34 352	94.68 327	85.86 170	97.56 372	85.77 347	84.24 404	94.82 382

fmvsm_s_conf0.5_n_4		96.75 62	97.07 34	95.79 173	97.76 142	89.57 231	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	98.08 224

VPA-MVSNet		93.24 208	92.48 224	95.51 195	95.70 301	92.39 106	97.86 91	98.66 22	92.30 170	92.09 250	95.37 295	80.49 286	98.40 270	93.95 165	85.86 377	95.75 326

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	98.59 167

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	90.43 194	97.50 153	98.59 27	96.59 10	99.31 6	99.08 8	84.47 200	99.75 58	99.37 5	98.45 133	97.88 237

UniMVSNet_NR-MVSNet		93.37 204	92.67 213	95.47 201	95.34 324	92.83 89	97.17 196	98.58 28	92.98 145	90.13 297	95.80 271	88.37 116	97.85 343	91.71 218	83.93 407	95.73 328

CSCG		96.05 90	95.91 90	96.46 117	99.24 33	90.47 191	98.30 33	98.57 29	89.01 298	93.97 199	97.57 160	92.62 40	99.76 54	94.66 147	99.27 75	99.15 87

fmvsm_s_conf0.5_n_9		97.33 27	97.57 15	96.62 101	98.43 82	90.32 201	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		93.66 191	92.92 201	95.87 162	98.24 100	89.88 217	94.58 377	98.49 32	85.06 396	93.78 202	95.78 275	82.86 235	98.67 244	91.77 216	95.71 230	99.07 100

CHOSEN 1792x2688		94.15 165	93.51 177	96.06 147	98.27 96	89.38 243	95.18 363	98.48 34	85.60 386	93.76 203	97.11 193	83.15 225	99.61 90	91.33 226	98.72 119	99.19 83

fmvsm_s_conf0.5_n_7		96.45 77	96.80 57	95.37 204	97.29 169	88.38 276	97.23 190	98.47 35	95.14 39	98.43 41	99.09 7	87.58 133	99.72 65	98.80 25	99.21 83	98.02 228

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	89.86 270	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	90.25 203	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	98.15 213

PVSNet_BlendedMVS		94.06 171	93.92 161	94.47 256	98.27 96	89.46 240	96.73 246	98.36 39	90.17 262	94.36 184	95.24 303	88.02 121	99.58 98	93.44 179	90.72 327	94.36 402

PVSNet_Blended		94.87 140	94.56 139	95.81 169	98.27 96	89.46 240	95.47 345	98.36 39	88.84 307	94.36 184	96.09 260	88.02 121	99.58 98	93.44 179	98.18 145	98.40 189

3Dnovator	91.36 5	95.19 126	94.44 148	97.44 57	96.56 239	93.36 70	98.65 16	98.36 39	94.12 90	89.25 331	98.06 98	82.20 252	99.77 52	93.41 181	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	97.21 193	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	95.48 312	90.69 185	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	98.18 209

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	97.43 264

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	97.81 132	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	95.67 303	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	92.37 167	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	90.86 177	97.27 184	98.25 61	90.21 261	94.18 192	97.27 182	87.48 140	99.73 61	93.53 176	97.77 161	98.55 170

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		91.55 283	90.84 284	93.69 306	94.96 349	88.28 279	97.84 95	98.24 63	91.46 204	88.04 363	95.80 271	79.67 302	97.48 380	87.02 327	84.54 401	95.31 351

DU-MVS		92.90 226	92.04 235	95.49 198	94.95 350	92.83 89	97.16 197	98.24 63	93.02 139	90.13 297	95.71 278	83.47 217	97.85 343	91.71 218	83.93 407	95.78 322

9.14			96.75 61		98.93 56		97.73 115	98.23 66	91.28 213	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		91.30 300	90.95 278	92.35 354	94.71 365	85.52 355	96.18 303	98.21 67	88.89 305	86.60 392	93.82 376	79.92 298	97.95 332	89.29 276	90.95 324	93.56 417

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		94.17 163	93.61 170	95.86 165	98.09 116	91.37 151	97.35 176	98.20 69	93.18 132	91.79 258	97.28 180	79.13 310	98.93 196	94.61 150	92.84 290	97.28 272

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		91.71 272	89.67 338	97.81 32	99.38 17	94.03 54	98.59 17	98.20 69	94.85 53	96.59 99	32.69 479	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		91.89 268	91.24 267	93.82 298	95.05 346	88.57 269	97.82 100	98.19 74	91.70 193	88.21 358	95.76 276	81.96 257	97.52 378	87.86 302	84.65 395	95.37 347

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	90.57 252	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		91.20 305	90.44 301	93.48 317	94.49 373	87.91 294	97.76 108	98.18 76	91.29 210	87.78 367	95.74 277	80.35 289	97.33 391	85.46 351	82.96 417	95.19 362

DELS-MVS		96.61 71	96.38 80	97.30 63	97.79 140	93.19 78	95.96 315	98.18 76	95.23 35	95.87 133	97.65 150	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		89.70 357	88.40 363	93.60 310	95.15 341	90.10 206	97.56 144	98.16 80	87.28 359	86.16 398	94.63 331	77.57 338	98.05 312	74.48 438	84.59 399	92.65 430

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	97.61 156	85.29 184	99.53 112	95.81 105	95.27 243	99.16 85

DeepPCF-MVS	93.97 1	96.61 71	97.09 33	95.15 212	98.09 116	86.63 327	96.00 313	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	97.41 387	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	94.83 136	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	90.36 251	97.35 174	99.11 94

QAPM		93.45 202	92.27 229	96.98 85	96.77 221	92.62 98	98.39 29	98.12 86	84.50 404	88.27 356	97.77 136	82.39 249	99.81 35	85.40 352	98.81 115	98.51 175

Vis-MVSNet		95.23 121	94.81 127	96.51 111	97.18 175	91.58 141	98.26 39	98.12 86	94.38 84	94.90 167	98.15 93	82.28 250	98.92 198	91.45 225	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	92.86 229	91.68 250	96.40 122	95.34 324	92.73 94	98.27 37	98.12 86	84.86 399	85.78 400	97.75 137	78.89 320	99.74 59	87.50 317	98.65 122	96.73 289

TranMVSNet+NR-MVSNet		92.50 238	91.63 251	95.14 213	94.76 361	92.07 119	97.53 150	98.11 89	92.90 151	89.56 319	96.12 255	83.16 224	97.60 370	89.30 275	83.20 416	95.75 326

CPTT-MVS		95.57 108	95.19 112	96.70 92	99.27 31	91.48 146	98.33 31	98.11 89	87.79 344	95.17 161	98.03 101	87.09 148	99.61 90	93.51 177	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	91.50 202	98.01 50	98.32 80	92.33 45	99.58 98	94.85 133	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	89.22 291	97.03 81	98.10 94	92.52 42	99.65 79	94.58 152	99.31 72

MTGPA								98.08 93

MTAPA		97.08 39	96.78 59	97.97 27	99.37 19	94.42 40	97.24 186	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	97.27 184	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	97.03 204	98.08 93	88.35 325	95.09 163	97.65 150	89.97 90	99.48 124	92.08 210	98.59 126	98.44 186

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	97.12 200	98.07 98	93.54 112	96.08 125	97.69 145	93.86 18	99.71 67	96.50 74	99.39 63	99.55 43

NR-MVSNet		92.34 247	91.27 266	95.53 194	94.95 350	93.05 81	97.39 172	98.07 98	92.65 159	84.46 411	95.71 278	85.00 191	97.77 354	89.71 263	83.52 413	95.78 322

MP-MVS-pluss		96.70 65	96.27 83	97.98 26	99.23 35	94.71 30	96.96 215	98.06 101	90.67 242	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	94.83 136	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	90.57 252	96.77 88	98.35 72	90.21 86	99.53 112	94.80 140	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	91.17 221	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		94.51 154	93.80 163	96.64 94	97.07 181	91.97 124	96.32 290	98.06 101	88.94 303	94.50 181	96.78 213	84.60 197	99.27 147	91.90 211	96.02 220	98.68 161

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	93.38 218	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	97.88 119	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		88.98 363	87.05 377	94.77 240	94.45 375	87.19 311	90.23 453	98.03 110	77.87 452	92.40 236	87.55 459	80.17 293	99.51 117	68.84 459	93.95 276	97.60 257

save fliter					98.91 58	94.28 42	97.02 206	98.02 113	95.35 31

TEST9					98.70 65	94.19 46	96.41 276	98.02 113	88.17 329	96.03 126	97.56 162	92.74 36	99.59 95

train_agg		96.30 85	95.83 93	97.72 43	98.70 65	94.19 46	96.41 276	98.02 113	88.58 316	96.03 126	97.56 162	92.73 37	99.59 95	95.04 126	99.37 67	99.39 68

test_8					98.67 67	94.06 53	96.37 284	98.01 116	88.58 316	95.98 130	97.55 164	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	98.12 216

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		92.34 247	91.53 255	94.77 240	95.13 343	90.83 178	96.40 280	97.98 120	91.88 188	89.29 328	95.54 289	82.50 245	97.80 350	89.79 262	85.27 386	95.69 329

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	97.89 116	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	97.02 206	97.96 122	95.42 29	94.86 168	97.81 132	87.38 143	99.82 33	96.88 60	99.20 88	99.29 75

114514_t		93.95 178	93.06 195	96.63 98	99.07 43	91.61 138	97.46 164	97.96 122	77.99 450	93.00 227	97.57 160	86.14 166	99.33 139	89.22 279	99.15 95	98.94 121

IU-MVS					99.42 10	95.39 12		97.94 124	90.40 259	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	90.37 200	97.53 150	97.92 127	96.52 11	99.14 15	99.08 8	83.21 222	99.74 59	99.22 11	98.06 150	97.88 237

Anonymous20231211		90.63 329	89.42 345	94.27 270	98.24 100	89.19 255	98.05 62	97.89 128	79.95 442	88.25 357	94.96 312	72.56 379	98.13 295	89.70 264	85.14 388	95.49 333

原ACMM1				96.38 125	98.59 75	91.09 168		97.89 128	87.41 355	95.22 160	97.68 146	90.25 85	99.54 110	87.95 301	99.12 100	98.49 178

CDPH-MVS		95.97 94	95.38 106	97.77 38	98.93 56	94.44 39	96.35 285	97.88 130	86.98 363	96.65 95	97.89 116	91.99 51	99.47 125	92.26 199	99.46 46	99.39 68

test11								97.88 130

EIA-MVS		95.53 109	95.47 100	95.71 184	97.06 184	89.63 227	97.82 100	97.87 132	93.57 108	93.92 200	95.04 309	90.61 82	98.95 193	94.62 149	98.68 120	98.54 171

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	98.71 237	97.10 55	99.17 91	98.90 130

无先验							95.79 326	97.87 132	83.87 412				99.65 79	87.68 311		98.89 136

3Dnovator+	91.43 4	95.40 110	94.48 146	98.16 17	96.90 201	95.34 17	98.48 25	97.87 132	94.65 70	88.53 348	98.02 103	83.69 213	99.71 67	93.18 185	98.96 110	99.44 61

VPNet		92.23 255	91.31 263	94.99 223	95.56 308	90.96 172	97.22 192	97.86 136	92.96 146	90.96 280	96.62 230	75.06 359	98.20 289	91.90 211	83.65 412	95.80 320

test_vis1_n_1920		94.17 163	94.58 138	92.91 338	97.42 166	82.02 409	97.83 98	97.85 137	94.68 67	98.10 48	98.49 58	70.15 398	99.32 141	97.91 30	98.82 114	97.40 266

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	98.75 227	96.92 59	99.33 70	98.94 121

test_fmvsmconf0.01_n		96.15 88	95.85 92	97.03 83	92.66 426	91.83 129	97.97 77	97.84 141	95.57 26	97.53 61	99.00 16	84.20 206	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	97.82 130	83.06 229	99.16 161	94.40 156	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	97.36 175	90.97 75	99.22 151	97.74 32	99.66 10	98.61 164

AdaColmap		94.34 158	93.68 168	96.31 129	98.59 75	91.68 136	96.59 265	97.81 144	89.87 269	92.15 246	97.06 196	83.62 216	99.54 110	89.34 274	98.07 149	97.70 250

MVSMamba_PlusPlus		96.51 74	96.48 72	96.59 102	98.07 120	91.97 124	98.14 54	97.79 145	90.43 257	97.34 70	97.52 165	91.29 67	99.19 154	98.12 28	99.64 14	98.60 165

KinetiMVS		95.26 117	94.75 132	96.79 90	96.99 194	92.05 120	97.82 100	97.78 146	94.77 63	96.46 109	97.70 143	80.62 283	99.34 138	92.37 198	98.28 140	98.97 111

mamv4		94.66 151	96.10 87	90.37 408	98.01 123	73.41 459	96.82 232	97.78 146	89.95 268	94.52 179	97.43 170	92.91 30	99.09 174	98.28 27	99.16 94	98.60 165

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	94.51 337	91.23 70	98.92 198	95.65 111	98.19 144	97.82 245

新几何1				97.32 62	98.60 74	93.59 63		97.75 149	81.58 433	95.75 138	97.85 124	90.04 88	99.67 77	86.50 333	99.13 98	98.69 160

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	97.68 146	89.32 96	98.60 253	97.45 46	99.11 101	98.67 162

EI-MVSNet-Vis-set		96.51 74	96.47 73	96.63 98	98.24 100	91.20 160	96.89 223	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		95.01 131	94.59 137	96.26 135	98.89 60	90.68 186	97.24 186	97.73 152	91.80 189	92.93 232	96.62 230	89.13 100	99.14 166	89.21 280	97.78 160	98.97 111

Anonymous20240529		91.98 264	90.73 291	95.73 182	98.14 113	89.40 242	97.99 68	97.72 154	79.63 444	93.54 211	97.41 172	69.94 400	99.56 106	91.04 233	91.11 320	98.22 206

CHOSEN 280x420		93.12 214	92.72 212	94.34 264	96.71 227	87.27 307	90.29 452	97.72 154	86.61 370	91.34 269	95.29 297	84.29 205	98.41 269	93.25 183	98.94 111	97.35 269

EI-MVSNet-UG-set		96.34 83	96.30 82	96.47 115	98.20 107	90.93 174	96.86 226	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		93.57 195	92.61 217	96.47 115	97.59 157	91.61 138	97.67 125	97.72 154	85.17 394	90.29 291	98.34 75	84.60 197	99.73 61	83.85 375	98.27 141	98.06 226

PAPR		94.18 162	93.42 184	96.48 114	97.64 151	91.42 150	95.55 340	97.71 158	88.99 300	92.34 242	95.82 270	89.19 98	99.11 169	86.14 339	97.38 172	98.90 130

UGNet		94.04 173	93.28 187	96.31 129	96.85 206	91.19 161	97.88 90	97.68 159	94.40 82	93.00 227	96.18 250	73.39 376	99.61 90	91.72 217	98.46 132	98.13 214
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				95.46 202	98.18 111	88.90 262		97.66 160	82.73 424	97.03 81	98.07 97	90.06 87	98.85 205	89.67 265	98.98 109	98.64 163

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		90.56 330	89.75 336	93.01 334	93.95 388	87.25 308	97.64 133	97.65 162	90.74 237	87.12 380	95.68 281	79.97 297	97.00 404	83.33 376	81.66 423	94.78 389

TAPA-MVS	90.10 7	92.30 250	91.22 269	95.56 191	98.33 91	89.60 229	96.79 238	97.65 162	81.83 430	91.52 264	97.23 185	87.94 123	98.91 200	71.31 453	98.37 136	98.17 212
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
sd_testset		93.10 215	92.45 225	95.05 217	98.09 116	89.21 252	96.89 223	97.64 164	93.18 132	91.79 258	97.28 180	75.35 358	98.65 247	88.99 285	92.84 290	97.28 272

test_cas_vis1_n_1920		94.48 156	94.55 142	94.28 269	96.78 219	86.45 333	97.63 135	97.64 164	93.32 125	97.68 60	98.36 71	73.75 374	99.08 177	96.73 65	99.05 104	97.31 271

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	85.34 182	99.50 120	94.99 129	99.21 83	98.97 111

Elysia		94.00 175	93.12 192	96.64 94	96.08 287	92.72 95	97.50 153	97.63 166	91.15 223	94.82 169	97.12 191	74.98 361	99.06 183	90.78 238	98.02 151	98.12 216

StellarMVS		94.00 175	93.12 192	96.64 94	96.08 287	92.72 95	97.50 153	97.63 166	91.15 223	94.82 169	97.12 191	74.98 361	99.06 183	90.78 238	98.02 151	98.12 216

cdsmvs_eth3d_5k		23.24 448	30.99 450	0.00 467	0.00 490	0.00 492	0.00 479	97.63 166	0.00 485	0.00 486	96.88 209	84.38 202	0.00 486	0.00 485	0.00 484	0.00 482

DPM-MVS		95.69 102	94.92 124	98.01 21	98.08 119	95.71 10	95.27 356	97.62 170	90.43 257	95.55 147	97.07 195	91.72 54	99.50 120	89.62 267	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	96.12 255	87.65 130	99.18 157	96.20 88	94.82 252	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	96.12 255	87.65 130	99.18 157	96.20 88	94.82 252	98.91 127

test222					98.24 100	92.21 114	95.33 351	97.60 171	79.22 446	95.25 158	97.84 126	88.80 106			99.15 95	98.72 157

cascas		91.20 305	90.08 318	94.58 250	94.97 348	89.16 256	93.65 417	97.59 174	79.90 443	89.40 323	92.92 402	75.36 357	98.36 277	92.14 204	94.75 255	96.23 299

E2		95.20 123	95.00 121	95.79 173	96.79 214	89.66 224	96.82 232	97.58 175	92.35 168	95.28 156	97.83 128	86.68 152	98.76 221	94.79 143	96.92 193	98.95 118

E3		95.20 123	95.00 121	95.79 173	96.77 221	89.66 224	96.82 232	97.58 175	92.35 168	95.28 156	97.83 128	86.69 151	98.76 221	94.79 143	96.92 193	98.95 118

h-mvs33		94.15 165	93.52 176	96.04 149	97.81 139	90.22 204	97.62 137	97.58 175	95.19 36	96.74 89	97.45 167	83.67 214	99.61 90	95.85 102	79.73 430	98.29 202

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	96.16 253	87.54 135	99.17 159	96.19 90	94.73 257	98.91 127

MVSFormer		95.37 111	95.16 113	95.99 156	96.34 263	91.21 158	98.22 45	97.57 178	91.42 206	96.22 119	97.32 176	86.20 164	97.92 337	94.07 162	99.05 104	98.85 142

test_djsdf		93.07 217	92.76 207	94.00 283	93.49 405	88.70 266	98.22 45	97.57 178	91.42 206	90.08 303	95.55 288	82.85 236	97.92 337	94.07 162	91.58 311	95.40 344

OMC-MVS		95.09 128	94.70 133	96.25 138	98.46 79	91.28 154	96.43 272	97.57 178	92.04 184	94.77 173	97.96 110	87.01 149	99.09 174	91.31 227	96.77 198	98.36 193

E4		95.09 128	94.86 126	95.77 176	96.58 234	89.56 232	96.85 227	97.56 182	92.50 162	95.03 164	97.86 122	86.03 167	98.78 215	94.71 146	96.65 207	98.96 114

viewcassd2359sk11		95.26 117	95.09 118	95.80 170	96.95 198	89.72 223	96.80 237	97.56 182	92.21 175	95.37 154	97.80 134	87.17 147	98.77 219	94.82 138	97.10 187	98.90 130

PS-MVSNAJss		93.74 188	93.51 177	94.44 258	93.91 390	89.28 250	97.75 110	97.56 182	92.50 162	89.94 305	96.54 233	88.65 109	98.18 292	93.83 171	90.90 325	95.86 314

casdiffmvs_mvg		95.81 101	95.57 95	96.51 111	96.87 203	91.49 144	97.50 153	97.56 182	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	89.76 221	96.78 242	97.54 186	92.06 183	95.40 153	97.75 137	87.49 139	98.76 221	94.85 133	97.10 187	98.88 138

jajsoiax		92.42 243	91.89 243	94.03 282	93.33 413	88.50 273	97.73 115	97.53 187	92.00 186	88.85 340	96.50 235	75.62 356	98.11 299	93.88 169	91.56 312	95.48 334

mvs_tets		92.31 249	91.76 246	93.94 291	93.41 410	88.29 278	97.63 135	97.53 187	92.04 184	88.76 343	96.45 237	74.62 366	98.09 304	93.91 167	91.48 313	95.45 339

dcpmvs_2		96.37 81	97.05 38	94.31 267	98.96 55	84.11 382	97.56 144	97.51 189	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		93.78 187	93.43 182	94.82 233	96.21 267	89.99 210	97.74 113	97.51 189	94.85 53	91.34 269	96.64 223	81.32 269	98.60 253	93.02 191	92.23 299	95.86 314

plane_prior5								97.51 189					98.60 253	93.02 191	92.23 299	95.86 314

viewmanbaseed2359cas		95.24 120	95.02 120	95.91 159	96.87 203	89.98 212	96.82 232	97.49 192	92.26 171	95.47 151	97.82 130	86.47 157	98.69 239	94.80 140	97.20 183	99.06 101

reproduce_monomvs		91.30 300	91.10 273	91.92 368	96.82 211	82.48 403	97.01 209	97.49 192	94.64 71	88.35 351	95.27 300	70.53 393	98.10 300	95.20 122	84.60 398	95.19 362

viewmacassd2359aftdt		95.07 130	94.80 128	95.87 162	96.53 244	89.84 218	96.90 222	97.48 194	92.44 164	95.36 155	97.89 116	85.23 185	98.68 241	94.40 156	97.00 191	99.09 96

PS-MVSNAJ		95.37 111	95.33 108	95.49 198	97.35 167	90.66 187	95.31 353	97.48 194	93.85 100	96.51 105	95.70 280	88.65 109	99.65 79	94.80 140	98.27 141	96.17 303

API-MVS		94.84 142	94.49 145	95.90 160	97.90 134	92.00 123	97.80 104	97.48 194	89.19 292	94.81 171	96.71 216	88.84 105	99.17 159	88.91 287	98.76 118	96.53 292

MG-MVS		95.61 106	95.38 106	96.31 129	98.42 83	90.53 189	96.04 310	97.48 194	93.47 117	95.67 144	98.10 94	89.17 99	99.25 148	91.27 228	98.77 117	99.13 89

MAR-MVS		94.22 161	93.46 179	96.51 111	98.00 125	92.19 117	97.67 125	97.47 198	88.13 333	93.00 227	95.84 268	84.86 195	99.51 117	87.99 300	98.17 146	97.83 244
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		92.98 221	92.53 221	94.32 265	96.12 282	89.20 253	95.28 354	97.47 198	92.66 158	89.90 306	95.62 284	80.58 284	98.40 270	92.73 196	92.40 297	95.38 346
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
UniMVSNet_ETH3D		91.34 298	90.22 314	94.68 244	94.86 357	87.86 295	97.23 190	97.46 200	87.99 334	89.90 306	96.92 207	66.35 428	98.23 286	90.30 252	90.99 323	97.96 231

nrg030		94.05 172	93.31 186	96.27 134	95.22 335	94.59 33	98.34 30	97.46 200	92.93 147	91.21 278	96.64 223	87.23 146	98.22 287	94.99 129	85.80 378	95.98 313

XVG-OURS		93.72 189	93.35 185	94.80 238	97.07 181	88.61 267	94.79 372	97.46 200	91.97 187	93.99 197	97.86 122	81.74 263	98.88 202	92.64 197	92.67 295	96.92 284

LPG-MVS_test		92.94 224	92.56 218	94.10 277	96.16 277	88.26 280	97.65 129	97.46 200	91.29 210	90.12 299	97.16 188	79.05 313	98.73 231	92.25 201	91.89 307	95.31 351

LGP-MVS_train				94.10 277	96.16 277	88.26 280		97.46 200	91.29 210	90.12 299	97.16 188	79.05 313	98.73 231	92.25 201	91.89 307	95.31 351

MVS		91.71 272	90.44 301	95.51 195	95.20 337	91.59 140	96.04 310	97.45 205	73.44 460	87.36 376	95.60 285	85.42 181	99.10 171	85.97 344	97.46 167	95.83 318

XVG-OURS-SEG-HR		93.86 184	93.55 172	94.81 235	97.06 184	88.53 272	95.28 354	97.45 205	91.68 194	94.08 196	97.68 146	82.41 248	98.90 201	93.84 170	92.47 296	96.98 280

baseline		95.58 107	95.42 104	96.08 145	96.78 219	90.41 195	97.16 197	97.45 205	93.69 106	95.65 145	97.85 124	87.29 144	98.68 241	95.66 108	97.25 181	99.13 89

ab-mvs		93.57 195	92.55 219	96.64 94	97.28 170	91.96 126	95.40 347	97.45 205	89.81 274	93.22 224	96.28 246	79.62 304	99.46 126	90.74 241	93.11 287	98.50 176

xiu_mvs_v2_base		95.32 114	95.29 109	95.40 203	97.22 172	90.50 190	95.44 346	97.44 209	93.70 105	96.46 109	96.18 250	88.59 113	99.53 112	94.79 143	97.81 159	96.17 303

1314		92.81 233	92.03 236	95.14 213	95.33 327	89.52 237	96.04 310	97.44 209	87.72 348	86.25 397	95.33 296	83.84 211	98.79 214	89.26 277	97.05 190	97.11 278

casdiffmvs		95.64 104	95.49 98	96.08 145	96.76 225	90.45 192	97.29 183	97.44 209	94.00 94	95.46 152	97.98 108	87.52 138	98.73 231	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		94.76 148	94.68 134	95.01 221	96.76 225	87.41 303	96.38 282	97.43 212	92.65 159	94.52 179	97.75 137	85.55 179	98.81 211	94.36 158	96.69 204	98.82 146

XXY-MVS		92.16 257	91.23 268	94.95 229	94.75 362	90.94 173	97.47 162	97.43 212	89.14 293	88.90 336	96.43 238	79.71 301	98.24 285	89.56 268	87.68 359	95.67 330

anonymousdsp		92.16 257	91.55 254	93.97 287	92.58 428	89.55 234	97.51 152	97.42 214	89.42 286	88.40 350	94.84 319	80.66 282	97.88 342	91.87 213	91.28 317	94.48 397

Effi-MVS+		94.93 136	94.45 147	96.36 127	96.61 231	91.47 147	96.41 276	97.41 215	91.02 229	94.50 181	95.92 264	87.53 136	98.78 215	93.89 168	96.81 197	98.84 145

RRT-MVS		94.51 154	94.35 151	94.98 225	96.40 257	86.55 330	97.56 144	97.41 215	93.19 130	94.93 166	97.04 197	79.12 311	99.30 145	96.19 90	97.32 177	99.09 96

HQP3-MVS								97.39 217							92.10 304

HQP-MVS		93.19 211	92.74 210	94.54 253	95.86 293	89.33 246	96.65 256	97.39 217	93.55 109	90.14 293	95.87 266	80.95 273	98.50 263	92.13 207	92.10 304	95.78 322

PLC	91.00 6	94.11 169	93.43 182	96.13 143	98.58 77	91.15 167	96.69 252	97.39 217	87.29 358	91.37 268	96.71 216	88.39 114	99.52 116	87.33 320	97.13 186	97.73 248
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	95.50 197	96.37 261	89.08 258	96.08 308	97.38 220	93.09 138	96.53 104	97.74 140	86.45 158	98.68 241	96.32 78	97.48 166	98.75 153

v7n		90.76 322	89.86 329	93.45 319	93.54 402	87.60 301	97.70 123	97.37 221	88.85 306	87.65 369	94.08 367	81.08 272	98.10 300	84.68 361	83.79 411	94.66 394

UnsupCasMVSNet_eth		85.99 400	84.45 404	90.62 404	89.97 446	82.40 406	93.62 418	97.37 221	89.86 270	78.59 450	92.37 412	65.25 438	95.35 439	82.27 390	70.75 458	94.10 408

viewdifsd2359ckpt13		94.87 140	94.52 143	95.90 160	96.88 202	90.19 205	96.92 219	97.36 223	91.26 214	94.65 175	97.46 166	85.79 173	98.64 248	93.64 174	96.76 199	98.88 138

ACMM	89.79 8	92.96 222	92.50 223	94.35 262	96.30 265	88.71 265	97.58 140	97.36 223	91.40 208	90.53 286	96.65 222	79.77 300	98.75 227	91.24 229	91.64 309	95.59 332
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
xiu_mvs_v1_base_debu		95.01 131	94.76 129	95.75 179	96.58 234	91.71 133	96.25 295	97.35 225	92.99 140	96.70 91	96.63 227	82.67 240	99.44 129	96.22 83	97.46 167	96.11 309

xiu_mvs_v1_base		95.01 131	94.76 129	95.75 179	96.58 234	91.71 133	96.25 295	97.35 225	92.99 140	96.70 91	96.63 227	82.67 240	99.44 129	96.22 83	97.46 167	96.11 309

xiu_mvs_v1_base_debi		95.01 131	94.76 129	95.75 179	96.58 234	91.71 133	96.25 295	97.35 225	92.99 140	96.70 91	96.63 227	82.67 240	99.44 129	96.22 83	97.46 167	96.11 309

diffmvs		95.25 119	95.13 114	95.63 187	96.43 256	89.34 245	95.99 314	97.35 225	92.83 153	96.31 115	97.37 174	86.44 159	98.67 244	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		94.71 150	94.02 159	96.79 90	97.71 145	92.05 120	96.59 265	97.35 225	90.61 248	94.64 176	96.93 204	86.41 160	99.39 134	91.20 230	94.71 258	98.94 121

viewdifsd2359ckpt09		94.81 145	94.37 150	96.12 144	96.91 199	90.75 183	96.94 216	97.31 230	90.51 255	94.31 186	97.38 173	85.70 175	98.71 237	93.54 175	96.75 200	98.90 130

SSM_0407		94.54 153	94.12 158	95.80 170	96.79 214	90.38 197	96.79 238	97.29 231	91.24 215	93.68 204	97.60 157	85.03 189	98.67 244	92.14 204	96.51 210	98.35 195

SSM_0404		94.73 149	94.31 153	95.98 157	97.05 186	90.90 176	97.01 209	97.29 231	91.24 215	94.17 193	97.60 157	85.03 189	98.76 221	92.14 204	97.30 178	98.29 202

F-COLMAP		93.58 193	92.98 199	95.37 204	98.40 86	88.98 260	97.18 195	97.29 231	87.75 347	90.49 287	97.10 194	85.21 186	99.50 120	86.70 330	96.72 203	97.63 252

VortexMVS		92.88 228	92.64 214	93.58 312	96.58 234	87.53 302	96.93 218	97.28 234	92.78 156	89.75 311	94.99 310	82.73 239	97.76 355	94.60 151	88.16 354	95.46 337

XVG-ACMP-BASELINE		90.93 318	90.21 315	93.09 332	94.31 381	85.89 348	95.33 351	97.26 235	91.06 228	89.38 324	95.44 294	68.61 411	98.60 253	89.46 270	91.05 321	94.79 387

PCF-MVS	89.48 11	91.56 282	89.95 326	96.36 127	96.60 232	92.52 103	92.51 437	97.26 235	79.41 445	88.90 336	96.56 232	84.04 210	99.55 108	77.01 429	97.30 178	97.01 279
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
ACMP	89.59 10	92.62 237	92.14 232	94.05 280	96.40 257	88.20 283	97.36 175	97.25 237	91.52 201	88.30 354	96.64 223	78.46 325	98.72 236	91.86 214	91.48 313	95.23 358
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
icg_test_0407_2		93.58 193	93.46 179	93.94 291	96.19 271	86.16 342	93.73 412	97.24 238	91.54 197	93.50 213	97.04 197	85.64 177	96.91 407	90.68 243	95.59 234	98.76 149

IMVS_0407		93.94 179	93.75 165	94.49 255	96.19 271	86.16 342	96.35 285	97.24 238	91.54 197	93.50 213	97.04 197	85.64 177	98.54 260	90.68 243	95.59 234	98.76 149

IMVS_0404		92.44 241	91.92 241	94.00 283	96.19 271	86.16 342	93.84 409	97.24 238	91.54 197	88.17 360	97.04 197	76.96 343	97.09 398	90.68 243	95.59 234	98.76 149

IMVS_0403		93.98 177	93.79 164	94.55 252	96.19 271	86.16 342	96.35 285	97.24 238	91.54 197	93.59 208	97.04 197	85.86 170	98.73 231	90.68 243	95.59 234	98.76 149

OPM-MVS		93.28 207	92.76 207	94.82 233	94.63 368	90.77 181	96.65 256	97.18 242	93.72 103	91.68 262	97.26 183	79.33 308	98.63 250	92.13 207	92.28 298	95.07 365
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
PatchMatch-RL		92.90 226	92.02 237	95.56 191	98.19 109	90.80 179	95.27 356	97.18 242	87.96 335	91.86 257	95.68 281	80.44 287	98.99 191	84.01 370	97.54 165	96.89 285

alignmvs		95.87 100	95.23 111	97.78 36	97.56 163	95.19 22	97.86 91	97.17 244	94.39 83	96.47 108	96.40 240	85.89 169	99.20 153	96.21 87	95.11 248	98.95 118

MVS_Test		94.89 138	94.62 136	95.68 185	96.83 209	89.55 234	96.70 250	97.17 244	91.17 221	95.60 146	96.11 259	87.87 126	98.76 221	93.01 193	97.17 185	98.72 157

Fast-Effi-MVS+		93.46 199	92.75 209	95.59 190	96.77 221	90.03 207	96.81 236	97.13 246	88.19 328	91.30 272	94.27 355	86.21 163	98.63 250	87.66 312	96.46 216	98.12 216

EI-MVSNet		93.03 219	92.88 203	93.48 317	95.77 299	86.98 316	96.44 270	97.12 247	90.66 244	91.30 272	97.64 153	86.56 154	98.05 312	89.91 258	90.55 329	95.41 341

MVSTER		93.20 210	92.81 206	94.37 261	96.56 239	89.59 230	97.06 203	97.12 247	91.24 215	91.30 272	95.96 262	82.02 256	98.05 312	93.48 178	90.55 329	95.47 336

viewmambaseed2359dif		94.28 159	94.14 156	94.71 243	96.21 267	86.97 317	95.93 317	97.11 249	89.00 299	95.00 165	97.70 143	86.02 168	98.59 257	93.71 173	96.59 209	98.57 169

test_yl		94.78 146	94.23 154	96.43 119	97.74 143	91.22 156	96.85 227	97.10 250	91.23 218	95.71 140	96.93 204	84.30 203	99.31 143	93.10 186	95.12 246	98.75 153

DCV-MVSNet		94.78 146	94.23 154	96.43 119	97.74 143	91.22 156	96.85 227	97.10 250	91.23 218	95.71 140	96.93 204	84.30 203	99.31 143	93.10 186	95.12 246	98.75 153

LTVRE_ROB	88.41 13	90.99 314	89.92 328	94.19 271	96.18 275	89.55 234	96.31 291	97.09 252	87.88 338	85.67 401	95.91 265	78.79 321	98.57 258	81.50 393	89.98 334	94.44 400
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		93.46 199	93.23 189	94.17 272	96.12 282	85.42 357	96.43 272	97.08 253	92.91 148	94.21 189	98.00 105	80.82 279	98.74 229	94.41 155	89.05 343	98.34 199

test_fmvs1_n		92.73 235	92.88 203	92.29 358	96.08 287	81.05 417	97.98 71	97.08 253	90.72 239	96.79 87	98.18 91	63.07 442	98.45 267	97.62 40	98.42 135	97.36 267

v10		91.04 312	90.23 312	93.49 316	94.12 384	88.16 286	97.32 180	97.08 253	88.26 327	88.29 355	94.22 360	82.17 253	97.97 324	86.45 334	84.12 405	94.33 403

viewdifsd2359ckpt11		93.46 199	93.22 190	94.17 272	96.11 284	85.42 357	96.43 272	97.07 256	92.91 148	94.20 190	98.00 105	80.82 279	98.73 231	94.42 154	89.04 345	98.34 199

mamba_0408		93.70 190	92.99 196	95.83 167	96.79 214	90.38 197	88.69 462	97.07 256	90.96 231	93.68 204	97.31 178	84.97 192	98.76 221	90.95 234	96.51 210	98.35 195

SSM_04072		93.51 198	92.99 196	95.05 217	96.79 214	90.38 197	88.69 462	97.07 256	90.96 231	93.68 204	97.31 178	84.97 192	96.42 418	90.95 234	96.51 210	98.35 195

v144192		91.06 311	90.28 308	93.39 320	93.66 399	87.23 310	96.83 231	97.07 256	87.43 354	89.69 314	94.28 354	81.48 266	98.00 319	87.18 324	84.92 394	94.93 373

v1192		91.07 310	90.23 312	93.58 312	93.70 396	87.82 297	96.73 246	97.07 256	87.77 345	89.58 317	94.32 352	80.90 277	97.97 324	86.52 332	85.48 381	94.95 369

v8		91.29 302	90.53 300	93.57 314	94.15 383	88.12 287	97.34 177	97.06 261	88.99 300	88.32 353	94.26 357	83.08 227	98.01 318	87.62 314	83.92 409	94.57 396

mvs_anonymous		93.82 185	93.74 166	94.06 279	96.44 255	85.41 359	95.81 324	97.05 262	89.85 272	90.09 302	96.36 242	87.44 141	97.75 357	93.97 164	96.69 204	99.02 103

IterMVS-LS		92.29 251	91.94 240	93.34 322	96.25 266	86.97 317	96.57 268	97.05 262	90.67 242	89.50 322	94.80 322	86.59 153	97.64 365	89.91 258	86.11 376	95.40 344
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v1921920		90.85 320	90.03 323	93.29 324	93.55 401	86.96 319	96.74 245	97.04 264	87.36 356	89.52 321	94.34 349	80.23 292	97.97 324	86.27 335	85.21 387	94.94 371

CDS-MVSNet		94.14 168	93.54 173	95.93 158	96.18 275	91.46 148	96.33 289	97.04 264	88.97 302	93.56 209	96.51 234	87.55 134	97.89 341	89.80 261	95.95 222	98.44 186
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
SSC-MVS3.2		89.74 356	89.26 349	91.19 393	95.16 338	80.29 428	94.53 379	97.03 266	91.79 190	88.86 339	94.10 364	69.94 400	97.82 347	85.29 353	86.66 372	95.45 339

v1144		91.37 295	90.60 296	93.68 307	93.89 391	88.23 282	96.84 230	97.03 266	88.37 324	89.69 314	94.39 344	82.04 255	97.98 321	87.80 304	85.37 383	94.84 379

v1240		90.70 326	89.85 330	93.23 326	93.51 404	86.80 320	96.61 262	97.02 268	87.16 361	89.58 317	94.31 353	79.55 305	97.98 321	85.52 350	85.44 382	94.90 376

EPP-MVSNet		95.22 122	95.04 119	95.76 177	97.49 164	89.56 232	98.67 15	97.00 269	90.69 240	94.24 188	97.62 155	89.79 93	98.81 211	93.39 182	96.49 214	98.92 126

V42		91.58 281	90.87 280	93.73 302	94.05 387	88.50 273	97.32 180	96.97 270	88.80 312	89.71 312	94.33 350	82.54 244	98.05 312	89.01 284	85.07 390	94.64 395

test_fmvs1		93.21 209	93.53 174	92.25 361	96.55 241	81.20 416	97.40 171	96.96 271	90.68 241	96.80 85	98.04 100	69.25 406	98.40 270	97.58 41	98.50 128	97.16 277

FMVSNet2		91.31 299	90.08 318	94.99 223	96.51 248	92.21 114	97.41 167	96.95 272	88.82 309	88.62 345	94.75 324	73.87 370	97.42 386	85.20 356	88.55 351	95.35 348

ACMH	87.59 16	90.53 331	89.42 345	93.87 296	96.21 267	87.92 292	97.24 186	96.94 273	88.45 322	83.91 421	96.27 247	71.92 382	98.62 252	84.43 364	89.43 340	95.05 367
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
GBi-Net		91.35 296	90.27 309	94.59 246	96.51 248	91.18 163	97.50 153	96.93 274	88.82 309	89.35 325	94.51 337	73.87 370	97.29 393	86.12 340	88.82 346	95.31 351

test1		91.35 296	90.27 309	94.59 246	96.51 248	91.18 163	97.50 153	96.93 274	88.82 309	89.35 325	94.51 337	73.87 370	97.29 393	86.12 340	88.82 346	95.31 351

FMVSNet3		91.78 270	90.69 294	95.03 220	96.53 244	92.27 112	97.02 206	96.93 274	89.79 275	89.35 325	94.65 330	77.01 341	97.47 381	86.12 340	88.82 346	95.35 348

FMVSNet1		89.88 351	88.31 364	94.59 246	95.41 317	91.18 163	97.50 153	96.93 274	86.62 369	87.41 374	94.51 337	65.94 433	97.29 393	83.04 379	87.43 362	95.31 351

GeoE		93.89 182	93.28 187	95.72 183	96.96 197	89.75 222	98.24 43	96.92 278	89.47 283	92.12 248	97.21 186	84.42 201	98.39 275	87.71 307	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	96.91 279	95.92 16	96.57 102	97.93 111	85.34 182	99.50 120	94.99 129	96.39 217	99.05 102

miper_enhance_ethall		91.54 285	91.01 276	93.15 330	95.35 323	87.07 315	93.97 401	96.90 280	86.79 367	89.17 332	93.43 396	86.55 155	97.64 365	89.97 257	86.93 367	94.74 391

eth_miper_zixun_eth		91.02 313	90.59 297	92.34 356	95.33 327	84.35 378	94.10 398	96.90 280	88.56 318	88.84 341	94.33 350	84.08 208	97.60 370	88.77 290	84.37 403	95.06 366

TAMVS		94.01 174	93.46 179	95.64 186	96.16 277	90.45 192	96.71 249	96.89 282	89.27 290	93.46 216	96.92 207	87.29 144	97.94 334	88.70 292	95.74 228	98.53 172

miper_ehance_all_eth		91.59 279	91.13 272	92.97 336	95.55 309	86.57 328	94.47 382	96.88 283	87.77 345	88.88 338	94.01 369	86.22 162	97.54 374	89.49 269	86.93 367	94.79 387

v2v482		91.59 279	90.85 283	93.80 299	93.87 392	88.17 285	96.94 216	96.88 283	89.54 280	89.53 320	94.90 316	81.70 264	98.02 317	89.25 278	85.04 392	95.20 359

CNLPA		94.28 159	93.53 174	96.52 107	98.38 89	92.55 102	96.59 265	96.88 283	90.13 265	91.91 254	97.24 184	85.21 186	99.09 174	87.64 313	97.83 158	97.92 234

PAPM		91.52 286	90.30 307	95.20 210	95.30 330	89.83 219	93.38 423	96.85 286	86.26 377	88.59 346	95.80 271	84.88 194	98.15 294	75.67 434	95.93 223	97.63 252

c3_l		91.38 293	90.89 279	92.88 340	95.58 307	86.30 336	94.68 374	96.84 287	88.17 329	88.83 342	94.23 358	85.65 176	97.47 381	89.36 273	84.63 396	94.89 377

pm-mvs1		90.72 325	89.65 340	93.96 288	94.29 382	89.63 227	97.79 106	96.82 288	89.07 295	86.12 399	95.48 293	78.61 323	97.78 352	86.97 328	81.67 422	94.46 398

test_vis1_n		92.37 246	92.26 230	92.72 346	94.75 362	82.64 399	98.02 65	96.80 289	91.18 220	97.77 59	97.93 111	58.02 452	98.29 283	97.63 38	98.21 143	97.23 275

CMPMVS	62.92 21	85.62 405	84.92 399	87.74 431	89.14 451	73.12 461	94.17 396	96.80 289	73.98 457	73.65 459	94.93 314	66.36 427	97.61 369	83.95 372	91.28 317	92.48 435
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
MS-PatchMatch		90.27 338	89.77 334	91.78 377	94.33 379	84.72 375	95.55 340	96.73 291	86.17 379	86.36 396	95.28 299	71.28 387	97.80 350	84.09 369	98.14 147	92.81 427

Effi-MVS+-dtu		93.08 216	93.21 191	92.68 349	96.02 290	83.25 392	97.14 199	96.72 292	93.85 100	91.20 279	93.44 393	83.08 227	98.30 282	91.69 220	95.73 229	96.50 294

TSAR-MVS + GP.		96.69 67	96.49 71	97.27 67	98.31 92	93.39 67	96.79 238	96.72 292	94.17 89	97.44 65	97.66 149	92.76 34	99.33 139	96.86 62	97.76 162	99.08 98

1112_ss		93.37 204	92.42 226	96.21 139	97.05 186	90.99 170	96.31 291	96.72 292	86.87 366	89.83 309	96.69 220	86.51 156	99.14 166	88.12 297	93.67 281	98.50 176

PVSNet	86.66 18	92.24 254	91.74 249	93.73 302	97.77 141	83.69 389	92.88 432	96.72 292	87.91 337	93.00 227	94.86 318	78.51 324	99.05 186	86.53 331	97.45 171	98.47 181

miper_lstm_enhance		90.50 334	90.06 322	91.83 373	95.33 327	83.74 386	93.86 407	96.70 296	87.56 352	87.79 366	93.81 377	83.45 219	96.92 406	87.39 318	84.62 397	94.82 382

v148		90.99 314	90.38 303	92.81 343	93.83 393	85.80 349	96.78 242	96.68 297	89.45 285	88.75 344	93.93 373	82.96 233	97.82 347	87.83 303	83.25 414	94.80 385

ACMH+	87.92 14	90.20 342	89.18 351	93.25 325	96.48 251	86.45 333	96.99 212	96.68 297	88.83 308	84.79 410	96.22 249	70.16 397	98.53 261	84.42 365	88.04 355	94.77 390

CANet_DTU		94.37 157	93.65 169	96.55 104	96.46 254	92.13 118	96.21 299	96.67 299	94.38 84	93.53 212	97.03 202	79.34 307	99.71 67	90.76 240	98.45 133	97.82 245

cl____		90.96 317	90.32 305	92.89 339	95.37 321	86.21 339	94.46 384	96.64 300	87.82 341	88.15 361	94.18 361	82.98 231	97.54 374	87.70 308	85.59 379	94.92 375

HY-MVS	89.66 9	93.87 183	92.95 200	96.63 98	97.10 180	92.49 104	95.64 337	96.64 300	89.05 297	93.00 227	95.79 274	85.77 174	99.45 128	89.16 283	94.35 260	97.96 231

Test_1112_low_res		92.84 231	91.84 244	95.85 166	97.04 188	89.97 214	95.53 342	96.64 300	85.38 389	89.65 316	95.18 304	85.86 170	99.10 171	87.70 308	93.58 286	98.49 178

DIV-MVS_self_test		90.97 316	90.33 304	92.88 340	95.36 322	86.19 341	94.46 384	96.63 303	87.82 341	88.18 359	94.23 358	82.99 230	97.53 376	87.72 305	85.57 380	94.93 373

Fast-Effi-MVS+-dtu		92.29 251	91.99 238	93.21 328	95.27 331	85.52 355	97.03 204	96.63 303	92.09 181	89.11 334	95.14 306	80.33 290	98.08 305	87.54 316	94.74 256	96.03 312

UnsupCasMVSNet_bld		82.13 422	79.46 427	90.14 411	88.00 459	82.47 404	90.89 450	96.62 305	78.94 447	75.61 454	84.40 465	56.63 455	96.31 420	77.30 426	66.77 466	91.63 446

cl22		91.21 304	90.56 299	93.14 331	96.09 286	86.80 320	94.41 386	96.58 306	87.80 343	88.58 347	93.99 371	80.85 278	97.62 368	89.87 260	86.93 367	94.99 368

jason		94.84 142	94.39 149	96.18 141	95.52 310	90.93 174	96.09 307	96.52 307	89.28 289	96.01 129	97.32 176	84.70 196	98.77 219	95.15 125	98.91 113	98.85 142
jason: jason.
tt0805		91.09 309	90.07 321	94.16 275	95.61 305	88.31 277	97.56 144	96.51 308	89.56 279	89.17 332	95.64 283	67.08 425	98.38 276	91.07 232	88.44 352	95.80 320

AUN-MVS		91.76 271	90.75 289	94.81 235	97.00 193	88.57 269	96.65 256	96.49 309	89.63 277	92.15 246	96.12 255	78.66 322	98.50 263	90.83 236	79.18 433	97.36 267

hse-mvs2		93.45 202	92.99 196	94.81 235	97.02 191	88.59 268	96.69 252	96.47 310	95.19 36	96.74 89	96.16 253	83.67 214	98.48 266	95.85 102	79.13 434	97.35 269

SD_0403		90.01 346	90.02 324	89.96 414	95.65 304	76.76 449	95.76 328	96.46 311	90.58 251	86.59 393	96.29 245	82.12 254	94.78 443	73.00 448	93.76 279	98.35 195

EG-PatchMatch MVS		87.02 386	85.44 391	91.76 379	92.67 425	85.00 369	96.08 308	96.45 312	83.41 420	79.52 444	93.49 390	57.10 454	97.72 359	79.34 417	90.87 326	92.56 432

KD-MVS_self_test		85.95 401	84.95 398	88.96 425	89.55 450	79.11 443	95.13 364	96.42 313	85.91 382	84.07 419	90.48 435	70.03 399	94.82 442	80.04 409	72.94 455	92.94 425

FE-MVSNET2		86.36 394	84.68 403	91.39 387	87.67 461	86.47 332	96.21 299	96.41 314	87.87 339	79.31 446	89.64 443	65.29 437	95.58 434	82.42 388	77.28 440	92.14 443

pmmvs6		87.81 378	86.19 386	92.69 348	91.32 438	86.30 336	97.34 177	96.41 314	80.59 441	84.05 420	94.37 346	67.37 420	97.67 362	84.75 360	79.51 432	94.09 410

PMMVS		92.86 229	92.34 227	94.42 260	94.92 353	86.73 323	94.53 379	96.38 316	84.78 401	94.27 187	95.12 308	83.13 226	98.40 270	91.47 224	96.49 214	98.12 216

RPSCF		90.75 323	90.86 281	90.42 407	96.84 207	76.29 452	95.61 338	96.34 317	83.89 410	91.38 267	97.87 120	76.45 347	98.78 215	87.16 325	92.23 299	96.20 301

BP-MVS1		95.89 98	95.49 98	97.08 81	96.67 228	93.20 77	98.08 58	96.32 318	94.56 72	96.32 114	97.84 126	84.07 209	99.15 163	96.75 64	98.78 116	98.90 130

MSDG		91.42 291	90.24 311	94.96 228	97.15 178	88.91 261	93.69 415	96.32 318	85.72 385	86.93 389	96.47 236	80.24 291	98.98 192	80.57 406	95.05 249	96.98 280

WBMVS		90.69 328	89.99 325	92.81 343	96.48 251	85.00 369	95.21 361	96.30 320	89.46 284	89.04 335	94.05 368	72.45 380	97.82 347	89.46 270	87.41 364	95.61 331

OurMVSNet-221017-0		90.51 333	90.19 316	91.44 385	93.41 410	81.25 414	96.98 213	96.28 321	91.68 194	86.55 394	96.30 244	74.20 369	97.98 321	88.96 286	87.40 365	95.09 364

MVP-Stereo		90.74 324	90.08 318	92.71 347	93.19 415	88.20 283	95.86 321	96.27 322	86.07 380	84.86 409	94.76 323	77.84 336	97.75 357	83.88 374	98.01 153	92.17 442
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
lupinMVS		94.99 135	94.56 139	96.29 133	96.34 263	91.21 158	95.83 323	96.27 322	88.93 304	96.22 119	96.88 209	86.20 164	98.85 205	95.27 121	99.05 104	98.82 146

BH-untuned		92.94 224	92.62 216	93.92 295	97.22 172	86.16 342	96.40 280	96.25 324	90.06 266	89.79 310	96.17 252	83.19 223	98.35 278	87.19 323	97.27 180	97.24 274

CL-MVSNet_self_test		86.31 396	85.15 395	89.80 416	88.83 454	81.74 412	93.93 404	96.22 325	86.67 368	85.03 407	90.80 433	78.09 332	94.50 444	74.92 437	71.86 457	93.15 423

IS-MVSNet		94.90 137	94.52 143	96.05 148	97.67 147	90.56 188	98.44 26	96.22 325	93.21 127	93.99 197	97.74 140	85.55 179	98.45 267	89.98 256	97.86 157	99.14 88

FA-MVS(test-final)		93.52 197	92.92 201	95.31 207	96.77 221	88.54 271	94.82 371	96.21 327	89.61 278	94.20 190	95.25 302	83.24 221	99.14 166	90.01 255	96.16 219	98.25 204

GA-MVS		91.38 293	90.31 306	94.59 246	94.65 367	87.62 300	94.34 389	96.19 328	90.73 238	90.35 290	93.83 374	71.84 383	97.96 328	87.22 322	93.61 284	98.21 207

LuminaMVS		94.89 138	94.35 151	96.53 105	95.48 312	92.80 91	96.88 225	96.18 329	92.85 152	95.92 132	96.87 211	81.44 267	98.83 208	96.43 77	97.10 187	97.94 233

IterMVS-SCA-FT		90.31 336	89.81 332	91.82 374	95.52 310	84.20 381	94.30 392	96.15 330	90.61 248	87.39 375	94.27 355	75.80 353	96.44 417	87.34 319	86.88 371	94.82 382

IterMVS		90.15 344	89.67 338	91.61 381	95.48 312	83.72 387	94.33 390	96.12 331	89.99 267	87.31 378	94.15 363	75.78 355	96.27 421	86.97 328	86.89 370	94.83 380
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
DP-MVS		92.76 234	91.51 258	96.52 107	98.77 62	90.99 170	97.38 174	96.08 332	82.38 426	89.29 328	97.87 120	83.77 212	99.69 73	81.37 399	96.69 204	98.89 136

pmmvs4		90.93 318	89.85 330	94.17 272	93.34 412	90.79 180	94.60 376	96.02 333	84.62 402	87.45 372	95.15 305	81.88 261	97.45 383	87.70 308	87.87 357	94.27 407

ppachtmachnet_test		88.35 373	87.29 372	91.53 382	92.45 431	83.57 390	93.75 411	95.97 334	84.28 405	85.32 406	94.18 361	79.00 319	96.93 405	75.71 433	84.99 393	94.10 408

Anonymous20240521		86.42 393	85.44 391	89.34 423	90.33 443	79.79 434	96.73 246	95.92 335	83.71 415	83.25 425	91.36 430	63.92 440	96.01 422	78.39 421	85.36 384	92.22 440

ITE_SJBPF				92.43 352	95.34 324	85.37 362		95.92 335	91.47 203	87.75 368	96.39 241	71.00 389	97.96 328	82.36 389	89.86 336	93.97 413

test_fmvs2		89.77 355	89.93 327	89.31 424	93.68 398	76.37 451	97.64 133	95.90 337	89.84 273	91.49 265	96.26 248	58.77 450	97.10 397	94.65 148	91.13 319	94.46 398

USDC		88.94 364	87.83 369	92.27 359	94.66 366	84.96 371	93.86 407	95.90 337	87.34 357	83.40 423	95.56 287	67.43 419	98.19 291	82.64 387	89.67 338	93.66 416

COLMAP_ROB	87.81 15	90.40 335	89.28 348	93.79 300	97.95 129	87.13 314	96.92 219	95.89 339	82.83 423	86.88 391	97.18 187	73.77 373	99.29 146	78.44 420	93.62 283	94.95 369
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
VDD-MVS		93.82 185	93.08 194	96.02 151	97.88 135	89.96 215	97.72 118	95.85 340	92.43 165	95.86 134	98.44 64	68.42 415	99.39 134	96.31 79	94.85 250	98.71 159

VDDNet		93.05 218	92.07 233	96.02 151	96.84 207	90.39 196	98.08 58	95.85 340	86.22 378	95.79 137	98.46 62	67.59 418	99.19 154	94.92 132	94.85 250	98.47 181

mvsmamba		94.57 152	94.14 156	95.87 162	97.03 189	89.93 216	97.84 95	95.85 340	91.34 209	94.79 172	96.80 212	80.67 281	98.81 211	94.85 133	98.12 148	98.85 142

Vis-MVSNet (Re-imp)		94.15 165	93.88 162	94.95 229	97.61 155	87.92 292	98.10 56	95.80 343	92.22 173	93.02 226	97.45 167	84.53 199	97.91 340	88.24 296	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	95.76 344	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		84.81 411	82.64 414	91.31 388	91.07 440	85.34 363	91.22 445	95.75 345	85.56 387	83.09 426	90.21 438	67.21 421	95.89 424	77.18 427	62.48 470	92.69 428

miper_refine_blended		84.81 411	82.64 414	91.31 388	91.07 440	85.34 363	91.22 445	95.75 345	85.56 387	83.09 426	90.21 438	67.21 421	95.89 424	77.18 427	62.48 470	92.69 428

FE-MVS		92.05 262	91.05 274	95.08 216	96.83 209	87.93 291	93.91 406	95.70 347	86.30 375	94.15 194	94.97 311	76.59 345	99.21 152	84.10 368	96.86 195	98.09 223

tpm cat1		88.36 372	87.21 375	91.81 375	95.13 343	80.55 423	92.58 436	95.70 347	74.97 456	87.45 372	91.96 423	78.01 335	98.17 293	80.39 408	88.74 349	96.72 290

our_test_3		88.78 368	87.98 368	91.20 392	92.45 431	82.53 401	93.61 419	95.69 349	85.77 384	84.88 408	93.71 379	79.99 296	96.78 413	79.47 414	86.24 373	94.28 406

BH-w/o		92.14 259	91.75 247	93.31 323	96.99 194	85.73 352	95.67 332	95.69 349	88.73 314	89.26 330	94.82 321	82.97 232	98.07 309	85.26 355	96.32 218	96.13 308

CR-MVSNet		90.82 321	89.77 334	93.95 289	94.45 375	87.19 311	90.23 453	95.68 351	86.89 365	92.40 236	92.36 415	80.91 275	97.05 400	81.09 403	93.95 276	97.60 257

Patchmtry		88.64 370	87.25 373	92.78 345	94.09 385	86.64 324	89.82 457	95.68 351	80.81 438	87.63 370	92.36 415	80.91 275	97.03 401	78.86 418	85.12 389	94.67 393

testing91		91.90 267	91.02 275	94.53 254	96.54 242	86.55 330	95.86 321	95.64 353	91.77 191	91.89 255	93.47 392	69.94 400	98.86 203	90.23 254	93.86 278	98.18 209

BH-RMVSNet		92.72 236	91.97 239	94.97 227	97.16 176	87.99 290	96.15 305	95.60 354	90.62 247	91.87 256	97.15 190	78.41 326	98.57 258	83.16 377	97.60 164	98.36 193

PVSNet_0	82.17 19	85.46 406	83.64 409	90.92 396	95.27 331	79.49 439	90.55 451	95.60 354	83.76 414	83.00 428	89.95 440	71.09 388	97.97 324	82.75 385	60.79 472	95.31 351

guyue		95.17 127	94.96 123	95.82 168	96.97 196	89.65 226	97.56 144	95.58 356	94.82 57	95.72 139	97.42 171	82.90 234	98.84 207	96.71 67	96.93 192	98.96 114

SCA		91.84 269	91.18 271	93.83 297	95.59 306	84.95 372	94.72 373	95.58 356	90.82 234	92.25 244	93.69 381	75.80 353	98.10 300	86.20 337	95.98 221	98.45 183

MonoMVSNet		91.92 265	91.77 245	92.37 353	92.94 419	83.11 395	97.09 202	95.55 358	92.91 148	90.85 282	94.55 334	81.27 271	96.52 416	93.01 193	87.76 358	97.47 263

AllTest		90.23 340	88.98 354	93.98 285	97.94 130	86.64 324	96.51 269	95.54 359	85.38 389	85.49 403	96.77 214	70.28 395	99.15 163	80.02 410	92.87 288	96.15 306

TestCases				93.98 285	97.94 130	86.64 324		95.54 359	85.38 389	85.49 403	96.77 214	70.28 395	99.15 163	80.02 410	92.87 288	96.15 306

mmtdpeth		89.70 357	88.96 355	91.90 370	95.84 298	84.42 377	97.46 164	95.53 361	90.27 260	94.46 183	90.50 434	69.74 404	98.95 193	97.39 53	69.48 461	92.34 436

tpmvs		89.83 354	89.15 352	91.89 371	94.92 353	80.30 427	93.11 428	95.46 362	86.28 376	88.08 362	92.65 405	80.44 287	98.52 262	81.47 395	89.92 335	96.84 286

pmmvs5		89.86 353	88.87 358	92.82 342	92.86 421	86.23 338	96.26 294	95.39 363	84.24 406	87.12 380	94.51 337	74.27 368	97.36 390	87.61 315	87.57 360	94.86 378

PatchmatchNet		91.91 266	91.35 260	93.59 311	95.38 319	84.11 382	93.15 427	95.39 363	89.54 280	92.10 249	93.68 383	82.82 237	98.13 295	84.81 359	95.32 242	98.52 173
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
tpmrst		91.44 290	91.32 262	91.79 376	95.15 341	79.20 442	93.42 422	95.37 365	88.55 319	93.49 215	93.67 384	82.49 246	98.27 284	90.41 249	89.34 341	97.90 235

Anonymous20231206		87.09 385	86.14 387	89.93 415	91.22 439	80.35 425	96.11 306	95.35 366	83.57 417	84.16 415	93.02 400	73.54 375	95.61 432	72.16 450	86.14 375	93.84 415

MIMVSNet1		84.93 409	83.05 411	90.56 405	89.56 449	84.84 374	95.40 347	95.35 366	83.91 409	80.38 440	92.21 420	57.23 453	93.34 457	70.69 456	82.75 420	93.50 418

TDRefinement		86.53 389	84.76 401	91.85 372	82.23 473	84.25 379	96.38 282	95.35 366	84.97 398	84.09 418	94.94 313	65.76 434	98.34 281	84.60 363	74.52 451	92.97 424

TR-MVS		91.48 289	90.59 297	94.16 275	96.40 257	87.33 304	95.67 332	95.34 369	87.68 349	91.46 266	95.52 290	76.77 344	98.35 278	82.85 382	93.61 284	96.79 288

EPNet_dtu		91.71 272	91.28 265	92.99 335	93.76 395	83.71 388	96.69 252	95.28 370	93.15 134	87.02 385	95.95 263	83.37 220	97.38 389	79.46 415	96.84 196	97.88 237
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
FMVSNet5		87.29 382	85.79 389	91.78 377	94.80 360	87.28 306	95.49 344	95.28 370	84.09 408	83.85 422	91.82 424	62.95 443	94.17 448	78.48 419	85.34 385	93.91 414

MDTV_nov1_ep13			90.76 287		95.22 335	80.33 426	93.03 430	95.28 370	88.14 332	92.84 233	93.83 374	81.34 268	98.08 305	82.86 380	94.34 261

LF4IMVS		87.94 376	87.25 373	89.98 413	92.38 433	80.05 433	94.38 387	95.25 373	87.59 351	84.34 412	94.74 325	64.31 439	97.66 364	84.83 358	87.45 361	92.23 439

TransMVSNet (Re)		88.94 364	87.56 370	93.08 333	94.35 378	88.45 275	97.73 115	95.23 374	87.47 353	84.26 414	95.29 297	79.86 299	97.33 391	79.44 416	74.44 452	93.45 420

test20.03		86.14 399	85.40 393	88.35 426	90.12 444	80.06 432	95.90 320	95.20 375	88.59 315	81.29 435	93.62 386	71.43 386	92.65 461	71.26 454	81.17 425	92.34 436

new-patchmatchnet		83.18 418	81.87 421	87.11 434	86.88 464	75.99 453	93.70 413	95.18 376	85.02 397	77.30 453	88.40 452	65.99 432	93.88 453	74.19 442	70.18 459	91.47 451

MDA-MVSNet_test_wron		85.87 403	84.23 406	90.80 402	92.38 433	82.57 400	93.17 425	95.15 377	82.15 427	67.65 465	92.33 418	78.20 328	95.51 436	77.33 424	79.74 429	94.31 405

YYNet1		85.87 403	84.23 406	90.78 403	92.38 433	82.46 405	93.17 425	95.14 378	82.12 428	67.69 463	92.36 415	78.16 331	95.50 437	77.31 425	79.73 430	94.39 401

Baseline_NR-MVSNet		91.20 305	90.62 295	92.95 337	93.83 393	88.03 289	97.01 209	95.12 379	88.42 323	89.70 313	95.13 307	83.47 217	97.44 384	89.66 266	83.24 415	93.37 421

thres200		92.23 255	91.39 259	94.75 242	97.61 155	89.03 259	96.60 264	95.09 380	92.08 182	93.28 221	94.00 370	78.39 327	99.04 189	81.26 402	94.18 267	96.19 302

ADS-MVSNet		89.89 350	88.68 360	93.53 315	95.86 293	84.89 373	90.93 448	95.07 381	83.23 421	91.28 275	91.81 425	79.01 317	97.85 343	79.52 412	91.39 315	97.84 242

pmmvs-eth3d		86.22 397	84.45 404	91.53 382	88.34 458	87.25 308	94.47 382	95.01 382	83.47 418	79.51 445	89.61 444	69.75 403	95.71 429	83.13 378	76.73 444	91.64 445

Anonymous202405211		92.07 261	90.83 285	95.76 177	98.19 109	88.75 264	97.58 140	95.00 383	86.00 381	93.64 207	97.45 167	66.24 430	99.53 112	90.68 243	92.71 293	99.01 106

MDA-MVSNet-bldmvs		85.00 408	82.95 413	91.17 394	93.13 417	83.33 391	94.56 378	95.00 383	84.57 403	65.13 469	92.65 405	70.45 394	95.85 426	73.57 445	77.49 439	94.33 403

ambc				86.56 437	83.60 470	70.00 464	85.69 469	94.97 385		80.60 439	88.45 451	37.42 471	96.84 410	82.69 386	75.44 449	92.86 426

testgi		87.97 375	87.21 375	90.24 410	92.86 421	80.76 418	96.67 255	94.97 385	91.74 192	85.52 402	95.83 269	62.66 445	94.47 446	76.25 431	88.36 353	95.48 334

myMVS_eth3d28		91.52 286	90.97 277	93.17 329	96.91 199	83.24 393	95.61 338	94.96 387	92.24 172	91.98 252	93.28 397	69.31 405	98.40 270	88.71 291	95.68 231	97.88 237

dp		88.90 366	88.26 366	90.81 400	94.58 371	76.62 450	92.85 433	94.93 388	85.12 395	90.07 304	93.07 399	75.81 352	98.12 298	80.53 407	87.42 363	97.71 249

test_fmvs3		83.21 417	83.02 412	83.78 441	86.77 465	68.34 467	96.76 244	94.91 389	86.49 371	84.14 417	89.48 445	36.04 472	91.73 463	91.86 214	80.77 427	91.26 453

test_0402		86.46 392	84.79 400	91.45 384	95.02 347	85.55 354	96.29 293	94.89 390	80.90 435	82.21 431	93.97 372	68.21 416	97.29 393	62.98 464	88.68 350	91.51 448

tfpn200view9		92.38 245	91.52 256	94.95 229	97.85 136	89.29 248	97.41 167	94.88 391	92.19 178	93.27 222	94.46 342	78.17 329	99.08 177	81.40 396	94.08 271	96.48 295

CVMVSNet		91.23 303	91.75 247	89.67 417	95.77 299	74.69 454	96.44 270	94.88 391	85.81 383	92.18 245	97.64 153	79.07 312	95.58 434	88.06 299	95.86 226	98.74 156

thres400		92.42 243	91.52 256	95.12 215	97.85 136	89.29 248	97.41 167	94.88 391	92.19 178	93.27 222	94.46 342	78.17 329	99.08 177	81.40 396	94.08 271	96.98 280

tt0320		85.39 407	83.12 410	92.19 363	93.44 409	85.79 350	96.19 302	94.87 394	71.19 463	82.92 429	91.76 427	58.43 451	96.81 411	81.03 404	78.26 438	93.98 412

EPNet		95.20 123	94.56 139	97.14 75	92.80 423	92.68 97	97.85 94	94.87 394	96.64 9	92.46 235	97.80 134	86.23 161	99.65 79	93.72 172	98.62 124	99.10 95
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
testing99		91.62 277	90.72 292	94.32 265	96.48 251	86.11 347	95.81 324	94.76 396	91.55 196	91.75 260	93.44 393	68.55 413	98.82 209	90.43 248	93.69 280	98.04 227

sc_t1		86.48 391	84.10 408	93.63 308	93.45 408	85.76 351	96.79 238	94.71 397	73.06 461	86.45 395	94.35 347	55.13 458	97.95 332	84.38 366	78.55 437	97.18 276

SixPastTwentyTwo		89.15 362	88.54 362	90.98 395	93.49 405	80.28 429	96.70 250	94.70 398	90.78 235	84.15 416	95.57 286	71.78 384	97.71 360	84.63 362	85.07 390	94.94 371

thres100view900		92.43 242	91.58 253	94.98 225	97.92 132	89.37 244	97.71 120	94.66 399	92.20 176	93.31 220	94.90 316	78.06 333	99.08 177	81.40 396	94.08 271	96.48 295

thres600view7		92.49 240	91.60 252	95.18 211	97.91 133	89.47 238	97.65 129	94.66 399	92.18 180	93.33 219	94.91 315	78.06 333	99.10 171	81.61 392	94.06 275	96.98 280

PatchT		88.87 367	87.42 371	93.22 327	94.08 386	85.10 367	89.51 458	94.64 401	81.92 429	92.36 239	88.15 455	80.05 295	97.01 403	72.43 449	93.65 282	97.54 260

baseline1		92.82 232	91.90 242	95.55 193	97.20 174	90.77 181	97.19 194	94.58 402	92.20 176	92.36 239	96.34 243	84.16 207	98.21 288	89.20 281	83.90 410	97.68 251

AstraMVS		94.82 144	94.64 135	95.34 206	96.36 262	88.09 288	97.58 140	94.56 403	94.98 46	95.70 142	97.92 114	81.93 260	98.93 196	96.87 61	95.88 224	98.99 110

UBG		91.55 283	90.76 287	93.94 291	96.52 247	85.06 368	95.22 359	94.54 404	90.47 256	91.98 252	92.71 404	72.02 381	98.74 229	88.10 298	95.26 244	98.01 229

Gipuma		67.86 438	65.41 440	75.18 454	92.66 426	73.45 458	66.50 476	94.52 405	53.33 474	57.80 475	66.07 475	30.81 474	89.20 467	48.15 473	78.88 436	62.90 475
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
testing11		91.68 275	90.75 289	94.47 256	96.53 244	86.56 329	95.76 328	94.51 406	91.10 227	91.24 277	93.59 387	68.59 412	98.86 203	91.10 231	94.29 263	98.00 230

CostFormer		91.18 308	90.70 293	92.62 350	94.84 358	81.76 411	94.09 399	94.43 407	84.15 407	92.72 234	93.77 378	79.43 306	98.20 289	90.70 242	92.18 302	97.90 235

tpm2		89.96 347	89.21 350	92.23 362	94.91 355	81.25 414	93.78 410	94.42 408	80.62 440	91.56 263	93.44 393	76.44 348	97.94 334	85.60 349	92.08 306	97.49 261

testing3-2		92.10 260	92.05 234	92.27 359	97.71 145	79.56 436	97.42 166	94.41 409	93.53 113	93.22 224	95.49 291	69.16 407	99.11 169	93.25 183	94.22 265	98.13 214

MGCNet		96.74 64	96.31 81	98.02 20	96.87 203	94.65 31	97.58 140	94.39 410	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		88.26 374	87.04 378	91.91 369	93.52 403	81.42 413	89.38 459	94.38 411	80.84 437	90.93 281	80.74 467	79.22 309	97.92 337	82.76 384	91.62 310	96.38 298

dmvs_re		90.21 341	89.50 343	92.35 354	95.47 316	85.15 365	95.70 331	94.37 412	90.94 233	88.42 349	93.57 388	74.63 365	95.67 431	82.80 383	89.57 339	96.22 300

Patchmatch-test		89.42 360	87.99 367	93.70 305	95.27 331	85.11 366	88.98 460	94.37 412	81.11 434	87.10 383	93.69 381	82.28 250	97.50 379	74.37 440	94.76 254	98.48 180

LCM-MVSNet		72.55 431	69.39 435	82.03 443	70.81 483	65.42 472	90.12 455	94.36 414	55.02 473	65.88 467	81.72 466	24.16 480	89.96 464	74.32 441	68.10 464	90.71 456

ADS-MVSNet2		89.45 359	88.59 361	92.03 366	95.86 293	82.26 407	90.93 448	94.32 415	83.23 421	91.28 275	91.81 425	79.01 317	95.99 423	79.52 412	91.39 315	97.84 242

mvs5depth		86.53 389	85.08 396	90.87 397	88.74 456	82.52 402	91.91 441	94.23 416	86.35 374	87.11 382	93.70 380	66.52 426	97.76 355	81.37 399	75.80 446	92.31 438

EU-MVSNet		88.72 369	88.90 357	88.20 428	93.15 416	74.21 456	96.63 261	94.22 417	85.18 393	87.32 377	95.97 261	76.16 350	94.98 441	85.27 354	86.17 374	95.41 341

tt0320-xc		84.83 410	82.33 418	92.31 357	93.66 399	86.20 340	96.17 304	94.06 418	71.26 462	82.04 433	92.22 419	55.07 459	96.72 414	81.49 394	75.04 450	94.02 411

MIMVSNet		88.50 371	86.76 381	93.72 304	94.84 358	87.77 298	91.39 443	94.05 419	86.41 373	87.99 364	92.59 408	63.27 441	95.82 428	77.44 423	92.84 290	97.57 259

OpenMVS_ROB	81.14 20	84.42 413	82.28 419	90.83 398	90.06 445	84.05 384	95.73 330	94.04 420	73.89 459	80.17 443	91.53 429	59.15 449	97.64 365	66.92 462	89.05 343	90.80 455

TinyColmap		86.82 387	85.35 394	91.21 390	94.91 355	82.99 397	93.94 403	94.02 421	83.58 416	81.56 434	94.68 327	62.34 446	98.13 295	75.78 432	87.35 366	92.52 434

ETVMVS		90.52 332	89.14 353	94.67 245	96.81 213	87.85 296	95.91 319	93.97 422	89.71 276	92.34 242	92.48 410	65.41 436	97.96 328	81.37 399	94.27 264	98.21 207

IB-MVS	87.33 17	89.91 348	88.28 365	94.79 239	95.26 334	87.70 299	95.12 365	93.95 423	89.35 288	87.03 384	92.49 409	70.74 392	99.19 154	89.18 282	81.37 424	97.49 261
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		87.13 384	87.02 379	87.47 432	95.16 338	73.21 460	95.00 367	93.93 424	88.55 319	86.96 386	91.99 421	75.90 351	94.00 450	61.59 466	94.11 268	95.20 359

myMVS_eth3d		87.18 383	86.38 384	89.58 418	95.16 338	79.53 437	95.00 367	93.93 424	88.55 319	86.96 386	91.99 421	56.23 456	94.00 450	75.47 436	94.11 268	95.20 359

testing222		90.31 336	88.96 355	94.35 262	96.54 242	87.29 305	95.50 343	93.84 426	90.97 230	91.75 260	92.96 401	62.18 447	98.00 319	82.86 380	94.08 271	97.76 247

test_f		80.57 424	79.62 426	83.41 442	83.38 471	67.80 469	93.57 420	93.72 427	80.80 439	77.91 452	87.63 458	33.40 473	92.08 462	87.14 326	79.04 435	90.34 457

LCM-MVSNet-Re		92.50 238	92.52 222	92.44 351	96.82 211	81.89 410	96.92 219	93.71 428	92.41 166	84.30 413	94.60 332	85.08 188	97.03 401	91.51 222	97.36 173	98.40 189

tpm		90.25 339	89.74 337	91.76 379	93.92 389	79.73 435	93.98 400	93.54 429	88.28 326	91.99 251	93.25 398	77.51 339	97.44 384	87.30 321	87.94 356	98.12 216

ET-MVSNet_ETH3D		91.49 288	90.11 317	95.63 187	96.40 257	91.57 142	95.34 350	93.48 430	90.60 250	75.58 455	95.49 291	80.08 294	96.79 412	94.25 160	89.76 337	98.52 173

LFMVS		93.60 192	92.63 215	96.52 107	98.13 115	91.27 155	97.94 81	93.39 431	90.57 252	96.29 116	98.31 81	69.00 408	99.16 161	94.18 161	95.87 225	99.12 92

MVStest1		82.38 421	80.04 425	89.37 421	87.63 462	82.83 398	95.03 366	93.37 432	73.90 458	73.50 460	94.35 347	62.89 444	93.25 459	73.80 443	65.92 467	92.04 444

FE-MVSNET		83.85 414	81.97 420	89.51 419	87.19 463	83.19 394	95.21 361	93.17 433	83.45 419	78.90 448	89.05 448	65.46 435	93.84 454	69.71 458	75.56 448	91.51 448

Patchmatch-RL test		87.38 381	86.24 385	90.81 400	88.74 456	78.40 446	88.12 467	93.17 433	87.11 362	82.17 432	89.29 446	81.95 258	95.60 433	88.64 293	77.02 441	98.41 188

ttmdpeth		85.91 402	84.76 401	89.36 422	89.14 451	80.25 430	95.66 335	93.16 435	83.77 413	83.39 424	95.26 301	66.24 430	95.26 440	80.65 405	75.57 447	92.57 431

test-LLR		91.42 291	91.19 270	92.12 364	94.59 369	80.66 420	94.29 393	92.98 436	91.11 225	90.76 284	92.37 412	79.02 315	98.07 309	88.81 288	96.74 201	97.63 252

test-mter		90.19 343	89.54 342	92.12 364	94.59 369	80.66 420	94.29 393	92.98 436	87.68 349	90.76 284	92.37 412	67.67 417	98.07 309	88.81 288	96.74 201	97.63 252

WB-MVSnew		89.88 351	89.56 341	90.82 399	94.57 372	83.06 396	95.65 336	92.85 438	87.86 340	90.83 283	94.10 364	79.66 303	96.88 408	76.34 430	94.19 266	92.54 433

testing3		87.67 379	86.88 380	90.05 412	96.14 280	80.71 419	97.10 201	92.85 438	90.15 264	87.54 371	94.55 334	55.70 457	94.10 449	73.77 444	94.10 270	95.35 348

test_method		66.11 439	64.89 441	69.79 457	72.62 481	35.23 489	65.19 477	92.83 440	20.35 479	65.20 468	88.08 456	43.14 469	82.70 474	73.12 447	63.46 469	91.45 452

test0.0.03 1		89.37 361	88.70 359	91.41 386	92.47 430	85.63 353	95.22 359	92.70 441	91.11 225	86.91 390	93.65 385	79.02 315	93.19 460	78.00 422	89.18 342	95.41 341

new_pmnet		82.89 419	81.12 424	88.18 429	89.63 448	80.18 431	91.77 442	92.57 442	76.79 454	75.56 456	88.23 454	61.22 448	94.48 445	71.43 452	82.92 418	89.87 458

mvsany_test1		93.93 181	93.98 160	93.78 301	94.94 352	86.80 320	94.62 375	92.55 443	88.77 313	96.85 84	98.49 58	88.98 101	98.08 305	95.03 127	95.62 233	96.46 297

thisisatest0515		92.29 251	91.30 264	95.25 209	96.60 232	88.90 262	94.36 388	92.32 444	87.92 336	93.43 217	94.57 333	77.28 340	99.00 190	89.42 272	95.86 226	97.86 241

thisisatest0530		93.03 219	92.21 231	95.49 198	97.07 181	89.11 257	97.49 161	92.19 445	90.16 263	94.09 195	96.41 239	76.43 349	99.05 186	90.38 250	95.68 231	98.31 201

tttt0517		92.96 222	92.33 228	94.87 232	97.11 179	87.16 313	97.97 77	92.09 446	90.63 246	93.88 201	97.01 203	76.50 346	99.06 183	90.29 253	95.45 240	98.38 191

K. test v3		87.64 380	86.75 382	90.32 409	93.02 418	79.48 440	96.61 262	92.08 447	90.66 244	80.25 442	94.09 366	67.21 421	96.65 415	85.96 345	80.83 426	94.83 380

TESTMET0.1,1		90.06 345	89.42 345	91.97 367	94.41 377	80.62 422	94.29 393	91.97 448	87.28 359	90.44 288	92.47 411	68.79 409	97.67 362	88.50 295	96.60 208	97.61 256

PM-MVS		83.48 416	81.86 422	88.31 427	87.83 460	77.59 448	93.43 421	91.75 449	86.91 364	80.63 438	89.91 441	44.42 468	95.84 427	85.17 357	76.73 444	91.50 450

baseline2		91.63 276	90.86 281	93.94 291	94.33 379	86.32 335	95.92 318	91.64 450	89.37 287	86.94 388	94.69 326	81.62 265	98.69 239	88.64 293	94.57 259	96.81 287

APD_test1		79.31 426	77.70 429	84.14 440	89.11 453	69.07 466	92.36 440	91.50 451	69.07 465	73.87 458	92.63 407	39.93 470	94.32 447	70.54 457	80.25 428	89.02 460

FPMVS		71.27 432	69.85 434	75.50 453	74.64 478	59.03 478	91.30 444	91.50 451	58.80 470	57.92 474	88.28 453	29.98 476	85.53 473	53.43 471	82.84 419	81.95 466

door								91.13 453

door-mid								91.06 454

EGC-MVSNET		68.77 437	63.01 443	86.07 439	92.49 429	82.24 408	93.96 402	90.96 455	0.71 484	2.62 485	90.89 432	53.66 460	93.46 455	57.25 469	84.55 400	82.51 465

mvsany_test3		83.59 415	82.44 417	87.03 435	83.80 468	73.82 457	93.70 413	90.92 456	86.42 372	82.51 430	90.26 437	46.76 467	95.71 429	90.82 237	76.76 443	91.57 447

pmmvs3		79.97 425	77.50 430	87.39 433	82.80 472	79.38 441	92.70 435	90.75 457	70.69 464	78.66 449	87.47 460	51.34 463	93.40 456	73.39 446	69.65 460	89.38 459

UWE-MVS		89.91 348	89.48 344	91.21 390	95.88 292	78.23 447	94.91 370	90.26 458	89.11 294	92.35 241	94.52 336	68.76 410	97.96 328	83.95 372	95.59 234	97.42 265

DSMNet-mixed		86.34 395	86.12 388	87.00 436	89.88 447	70.43 462	94.93 369	90.08 459	77.97 451	85.42 405	92.78 403	74.44 367	93.96 452	74.43 439	95.14 245	96.62 291

MVS-HIRNet		82.47 420	81.21 423	86.26 438	95.38 319	69.21 465	88.96 461	89.49 460	66.28 467	80.79 437	74.08 472	68.48 414	97.39 388	71.93 451	95.47 239	92.18 441

WB-MVS		76.77 428	76.63 431	77.18 448	85.32 466	56.82 480	94.53 379	89.39 461	82.66 425	71.35 461	89.18 447	75.03 360	88.88 468	35.42 477	66.79 465	85.84 462

test1111		93.19 211	92.82 205	94.30 268	97.58 161	84.56 376	98.21 47	89.02 462	93.53 113	94.58 177	98.21 88	72.69 377	99.05 186	93.06 189	98.48 131	99.28 77

SSC-MVS		76.05 429	75.83 432	76.72 452	84.77 467	56.22 481	94.32 391	88.96 463	81.82 431	70.52 462	88.91 449	74.79 364	88.71 469	33.69 478	64.71 468	85.23 463

ECVR-MVS		93.19 211	92.73 211	94.57 251	97.66 149	85.41 359	98.21 47	88.23 464	93.43 120	94.70 174	98.21 88	72.57 378	99.07 181	93.05 190	98.49 129	99.25 80

EPMVS		90.70 326	89.81 332	93.37 321	94.73 364	84.21 380	93.67 416	88.02 465	89.50 282	92.38 238	93.49 390	77.82 337	97.78 352	86.03 343	92.68 294	98.11 222

ANet_high		63.94 441	59.58 444	77.02 449	61.24 485	66.06 470	85.66 470	87.93 466	78.53 449	42.94 477	71.04 474	25.42 479	80.71 476	52.60 472	30.83 478	84.28 464

PMMVS2		70.19 433	66.92 437	80.01 444	76.35 477	65.67 471	86.22 468	87.58 467	64.83 469	62.38 470	80.29 469	26.78 478	88.49 471	63.79 463	54.07 474	85.88 461

lessismore_v0				90.45 406	91.96 436	79.09 444		87.19 468		80.32 441	94.39 344	66.31 429	97.55 373	84.00 371	76.84 442	94.70 392

PMVS	53.92 22	58.58 442	55.40 445	68.12 458	51.00 486	48.64 483	78.86 473	87.10 469	46.77 475	35.84 481	74.28 471	8.76 484	86.34 472	42.07 475	73.91 453	69.38 472
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
UWE-MVS-28		86.81 388	86.41 383	88.02 430	92.87 420	74.60 455	95.38 349	86.70 470	88.17 329	87.28 379	94.67 329	70.83 391	93.30 458	67.45 460	94.31 262	96.17 303

test_vis1_rt		86.16 398	85.06 397	89.46 420	93.47 407	80.46 424	96.41 276	86.61 471	85.22 392	79.15 447	88.64 450	52.41 462	97.06 399	93.08 188	90.57 328	90.87 454

testf1		69.31 435	66.76 438	76.94 450	78.61 475	61.93 474	88.27 465	86.11 472	55.62 471	59.69 471	85.31 463	20.19 482	89.32 465	57.62 467	69.44 462	79.58 467

APD_test2		69.31 435	66.76 438	76.94 450	78.61 475	61.93 474	88.27 465	86.11 472	55.62 471	59.69 471	85.31 463	20.19 482	89.32 465	57.62 467	69.44 462	79.58 467

gg-mvs-nofinetune		87.82 377	85.61 390	94.44 258	94.46 374	89.27 251	91.21 447	84.61 474	80.88 436	89.89 308	74.98 470	71.50 385	97.53 376	85.75 348	97.21 182	96.51 293

dmvs_testset		81.38 423	82.60 416	77.73 447	91.74 437	51.49 482	93.03 430	84.21 475	89.07 295	78.28 451	91.25 431	76.97 342	88.53 470	56.57 470	82.24 421	93.16 422

GG-mvs-BLEND				93.62 309	93.69 397	89.20 253	92.39 439	83.33 476		87.98 365	89.84 442	71.00 389	96.87 409	82.08 391	95.40 241	94.80 385

MTMP							97.86 91	82.03 477

DeepMVS_CX				74.68 455	90.84 442	64.34 473		81.61 478	65.34 468	67.47 466	88.01 457	48.60 466	80.13 477	62.33 465	73.68 454	79.58 467

E-PMN		53.28 443	52.56 447	55.43 461	74.43 479	47.13 484	83.63 472	76.30 479	42.23 476	42.59 478	62.22 477	28.57 477	74.40 478	31.53 479	31.51 477	44.78 476

test2506		91.60 278	90.78 286	94.04 281	97.66 149	83.81 385	98.27 37	75.53 480	93.43 120	95.23 159	98.21 88	67.21 421	99.07 181	93.01 193	98.49 129	99.25 80

EMVS		52.08 445	51.31 448	54.39 462	72.62 481	45.39 486	83.84 471	75.51 481	41.13 477	40.77 479	59.65 478	30.08 475	73.60 479	28.31 481	29.90 479	44.18 477

test_vis3_rt		72.73 430	70.55 433	79.27 445	80.02 474	68.13 468	93.92 405	74.30 482	76.90 453	58.99 473	73.58 473	20.29 481	95.37 438	84.16 367	72.80 456	74.31 470

MVE	50.73 23	53.25 444	48.81 449	66.58 460	65.34 484	57.50 479	72.49 475	70.94 483	40.15 478	39.28 480	63.51 476	6.89 486	73.48 480	38.29 476	42.38 476	68.76 474
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
tmp_tt		51.94 446	53.82 446	46.29 463	33.73 487	45.30 487	78.32 474	67.24 484	18.02 480	50.93 476	87.05 461	52.99 461	53.11 482	70.76 455	25.29 480	40.46 478

kuosan		65.27 440	64.66 442	67.11 459	83.80 468	61.32 477	88.53 464	60.77 485	68.22 466	67.67 464	80.52 468	49.12 465	70.76 481	29.67 480	53.64 475	69.26 473

dongtai		69.99 434	69.33 436	71.98 456	88.78 455	61.64 476	89.86 456	59.93 486	75.67 455	74.96 457	85.45 462	50.19 464	81.66 475	43.86 474	55.27 473	72.63 471

N_pmnet		78.73 427	78.71 428	78.79 446	92.80 423	46.50 485	94.14 397	43.71 487	78.61 448	80.83 436	91.66 428	74.94 363	96.36 419	67.24 461	84.45 402	93.50 418

wuyk23d		25.11 447	24.57 451	26.74 464	73.98 480	39.89 488	57.88 478	9.80 488	12.27 481	10.39 482	6.97 484	7.03 485	36.44 483	25.43 482	17.39 481	3.89 481

testmvs		13.36 449	16.33 452	4.48 466	5.04 488	2.26 491	93.18 424	3.28 489	2.70 482	8.24 483	21.66 480	2.29 488	2.19 484	7.58 483	2.96 482	9.00 480

test123		13.04 450	15.66 453	5.18 465	4.51 489	3.45 490	92.50 438	1.81 490	2.50 483	7.58 484	20.15 481	3.67 487	2.18 485	7.13 484	1.07 483	9.90 479

mmdepth		0.00 453	0.00 456	0.00 467	0.00 490	0.00 492	0.00 479	0.00 491	0.00 485	0.00 486	0.00 485	0.00 489	0.00 486	0.00 485	0.00 484	0.00 482

monomultidepth		0.00 453	0.00 456	0.00 467	0.00 490	0.00 492	0.00 479	0.00 491	0.00 485	0.00 486	0.00 485	0.00 489	0.00 486	0.00 485	0.00 484	0.00 482

test_blank		0.00 453	0.00 456	0.00 467	0.00 490	0.00 492	0.00 479	0.00 491	0.00 485	0.00 486	0.00 485	0.00 489	0.00 486	0.00 485	0.00 484	0.00 482

uanet_test		0.00 453	0.00 456	0.00 467	0.00 490	0.00 492	0.00 479	0.00 491	0.00 485	0.00 486	0.00 485	0.00 489	0.00 486	0.00 485	0.00 484	0.00 482

DCPMVS		0.00 453	0.00 456	0.00 467	0.00 490	0.00 492	0.00 479	0.00 491	0.00 485	0.00 486	0.00 485	0.00 489	0.00 486	0.00 485	0.00 484	0.00 482

pcd_1.5k_mvsjas		7.39 452	9.85 455	0.00 467	0.00 490	0.00 492	0.00 479	0.00 491	0.00 485	0.00 486	0.00 485	88.65 109	0.00 486	0.00 485	0.00 484	0.00 482

sosnet-low-res		0.00 453	0.00 456	0.00 467	0.00 490	0.00 492	0.00 479	0.00 491	0.00 485	0.00 486	0.00 485	0.00 489	0.00 486	0.00 485	0.00 484	0.00 482

sosnet		0.00 453	0.00 456	0.00 467	0.00 490	0.00 492	0.00 479	0.00 491	0.00 485	0.00 486	0.00 485	0.00 489	0.00 486	0.00 485	0.00 484	0.00 482

uncertanet		0.00 453	0.00 456	0.00 467	0.00 490	0.00 492	0.00 479	0.00 491	0.00 485	0.00 486	0.00 485	0.00 489	0.00 486	0.00 485	0.00 484	0.00 482

Regformer		0.00 453	0.00 456	0.00 467	0.00 490	0.00 492	0.00 479	0.00 491	0.00 485	0.00 486	0.00 485	0.00 489	0.00 486	0.00 485	0.00 484	0.00 482

n2								0.00 491

nn								0.00 491

ab-mvs-re		8.06 451	10.74 454	0.00 467	0.00 490	0.00 492	0.00 479	0.00 491	0.00 485	0.00 486	96.69 220	0.00 489	0.00 486	0.00 485	0.00 484	0.00 482

uanet		0.00 453	0.00 456	0.00 467	0.00 490	0.00 492	0.00 479	0.00 491	0.00 485	0.00 486	0.00 485	0.00 489	0.00 486	0.00 485	0.00 484	0.00 482

TestfortrainingZip							98.69 11

WAC-MVS						79.53 437								75.56 435

PC_three_1452									90.77 236	98.89 26	98.28 86	96.24 1	98.35 278	95.76 106	99.58 23	99.59 32

eth-test2					0.00 490

eth-test					0.00 490

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																98.45 183

test_part2					99.28 30	95.74 9				98.10 48

sam_mvs1												82.76 238				98.45 183

sam_mvs												81.94 259

test_post1							92.81 434				16.58 483	80.53 285	97.68 361	86.20 337

test_post											17.58 482	81.76 262	98.08 305

patchmatchnet-post											90.45 436	82.65 243	98.10 300

gm-plane-assit					93.22 414	78.89 445			84.82 400		93.52 389		98.64 248	87.72 305

test9_res														94.81 139	99.38 64	99.45 59

agg_prior2														93.94 166	99.38 64	99.50 52

test_prior4						93.66 62	96.42 275

test_prior2							96.35 285		92.80 155	96.03 126	97.59 159	92.01 50		95.01 128	99.38 64

旧先验2							95.94 316		81.66 432	97.34 70			98.82 209	92.26 199

新几何2							95.79 326

原ACMM2							95.67 332

testdata2													99.67 77	85.96 345

segment_acmp												92.89 33

testdata1							95.26 358		93.10 137

plane_prior7					96.21 267	89.98 212

plane_prior6					96.10 285	90.00 208						81.32 269

plane_prior4											96.64 223

plane_prior3						90.00 208			94.46 78	91.34 269

plane_prior2							97.74 113		94.85 53

plane_prior1					96.14 280

plane_prior						89.99 210	97.24 186		94.06 92						92.16 303

HQP5-MVS						89.33 246

HQP-NCC					95.86 293		96.65 256		93.55 109	90.14 293

ACMP_Plane					95.86 293		96.65 256		93.55 109	90.14 293

BP-MVS														92.13 207

HQP4-MVS										90.14 293			98.50 263			95.78 322

HQP2-MVS												80.95 273

NP-MVS					95.99 291	89.81 220					95.87 266

MDTV_nov1_ep13_2view						70.35 463	93.10 429		83.88 411	93.55 210		82.47 247		86.25 336		98.38 191

ACMMP++_ref															90.30 333

ACMMP++															91.02 322

Test By Simon												88.73 108

High-resolution multi-view benchmark