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 14	97.89 3	96.53 101	98.41 80	91.73 126	98.01 61	99.02 1	96.37 12	99.30 6	98.92 22	92.39 41	99.79 41	99.16 13	99.46 42	98.08 215

PGM-MVS		96.81 54	96.53 65	97.65 43	99.35 22	93.53 61	97.65 123	98.98 2	92.22 165	97.14 71	98.44 59	91.17 68	99.85 18	94.35 150	99.46 42	99.57 32

MVS_111021_HR		96.68 65	96.58 64	96.99 80	98.46 75	92.31 106	96.20 290	98.90 3	94.30 85	95.86 129	97.74 131	92.33 42	99.38 131	96.04 91	99.42 52	99.28 73

test_fmvsmconf_n		97.49 18	97.56 13	97.29 60	97.44 160	92.37 103	97.91 80	98.88 4	95.83 18	98.92 22	99.05 13	91.45 58	99.80 36	99.12 15	99.46 42	99.69 13

lecture		97.58 13	97.63 10	97.43 54	99.37 16	92.93 82	98.86 7	98.85 5	95.27 33	98.65 32	98.90 24	91.97 49	99.80 36	97.63 37	99.21 78	99.57 32

ACMMP		96.27 82	95.93 85	97.28 62	99.24 30	92.62 94	98.25 36	98.81 6	92.99 135	94.56 169	98.39 63	88.96 98	99.85 18	94.57 144	97.63 158	99.36 68
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 83	96.19 81	96.39 119	98.23 100	91.35 148	96.24 288	98.79 7	93.99 92	95.80 131	97.65 141	89.92 88	99.24 144	95.87 95	99.20 83	98.58 160

patch_mono-2		96.83 53	97.44 21	95.01 212	99.05 41	85.39 351	96.98 207	98.77 8	94.70 65	97.99 46	98.66 42	93.61 19	99.91 1	97.67 36	99.50 36	99.72 12

fmvsm_s_conf0.5_n		96.85 50	97.13 27	96.04 144	98.07 115	90.28 197	97.97 72	98.76 9	94.93 47	98.84 27	99.06 11	88.80 102	99.65 74	99.06 17	98.63 118	98.18 201

fmvsm_l_conf0.5_n		97.65 7	97.75 7	97.34 57	98.21 101	92.75 88	97.83 93	98.73 10	95.04 44	99.30 6	98.84 35	93.34 22	99.78 44	99.32 7	99.13 93	99.50 48

fmvsm_s_conf0.5_n_a		96.75 58	96.93 42	96.20 135	97.64 146	90.72 179	98.00 62	98.73 10	94.55 72	98.91 23	99.08 7	88.22 114	99.63 83	98.91 20	98.37 131	98.25 196

fmvsm_s_conf0.5_n_10		97.29 27	97.40 23	96.97 82	98.24 95	91.96 122	97.89 83	98.72 12	96.77 6	99.46 3	99.06 11	87.78 123	99.84 23	99.40 4	99.27 70	99.12 88

fmvsm_l_conf0.5_n_9		97.59 11	97.79 5	96.97 82	98.28 89	91.49 140	97.61 132	98.71 13	97.10 4	99.70 1	98.93 21	90.95 73	99.77 47	99.35 6	99.53 29	99.65 19

FC-MVSNet-test		93.94 171	93.57 163	95.04 210	95.48 303	91.45 145	98.12 51	98.71 13	93.37 117	90.23 283	96.70 209	87.66 125	97.85 334	91.49 214	90.39 323	95.83 309

UniMVSNet (Re)		93.31 198	92.55 211	95.61 180	95.39 309	93.34 67	97.39 166	98.71 13	93.14 130	90.10 292	94.83 311	87.71 124	98.03 307	91.67 212	83.99 397	95.46 328

fmvsm_l_conf0.5_n_a		97.63 9	97.76 6	97.26 64	98.25 94	92.59 96	97.81 98	98.68 16	94.93 47	99.24 9	98.87 30	93.52 20	99.79 41	99.32 7	99.21 78	99.40 62

FIs		94.09 162	93.70 159	95.27 199	95.70 292	92.03 118	98.10 52	98.68 16	93.36 119	90.39 280	96.70 209	87.63 128	97.94 325	92.25 192	90.50 322	95.84 308

WR-MVS_H		92.00 255	91.35 252	93.95 280	95.09 336	89.47 229	98.04 59	98.68 16	91.46 195	88.34 343	94.68 318	85.86 162	97.56 363	85.77 338	84.24 395	94.82 373

fmvsm_s_conf0.5_n_4		96.75 58	97.07 30	95.79 167	97.76 137	89.57 223	97.66 122	98.66 19	95.36 29	99.03 15	98.90 24	88.39 110	99.73 56	99.17 12	98.66 116	98.08 215

VPA-MVSNet		93.24 200	92.48 216	95.51 186	95.70 292	92.39 102	97.86 86	98.66 19	92.30 162	92.09 241	95.37 286	80.49 278	98.40 261	93.95 156	85.86 368	95.75 317

fmvsm_l_conf0.5_n_3		97.64 8	97.60 11	97.79 30	98.14 108	93.94 52	97.93 78	98.65 21	96.70 7	99.38 4	99.07 10	89.92 88	99.81 31	99.16 13	99.43 49	99.61 26

fmvsm_s_conf0.5_n_3		97.15 32	97.36 24	96.52 103	97.98 121	91.19 156	97.84 90	98.65 21	97.08 5	99.25 8	99.10 5	87.88 121	99.79 41	99.32 7	99.18 85	98.59 159

fmvsm_s_conf0.5_n_8		97.32 25	97.48 20	96.85 84	98.28 89	91.07 164	97.76 103	98.62 23	97.53 2	99.20 11	99.12 4	88.24 113	99.81 31	99.41 3	99.17 86	99.67 14

fmvsm_s_conf0.5_n_2		96.62 66	96.82 51	96.02 146	97.98 121	90.43 189	97.50 147	98.59 24	96.59 9	99.31 5	99.08 7	84.47 192	99.75 53	99.37 5	98.45 128	97.88 228

UniMVSNet_NR-MVSNet		93.37 196	92.67 205	95.47 192	95.34 315	92.83 85	97.17 190	98.58 25	92.98 140	90.13 288	95.80 262	88.37 112	97.85 334	91.71 209	83.93 398	95.73 319

CSCG		96.05 86	95.91 86	96.46 113	99.24 30	90.47 186	98.30 29	98.57 26	89.01 289	93.97 190	97.57 151	92.62 37	99.76 49	94.66 138	99.27 70	99.15 83

fmvsm_s_conf0.5_n_9		97.33 24	97.57 12	96.62 97	98.43 78	90.32 196	97.80 99	98.53 27	97.24 3	99.62 2	99.14 1	88.65 105	99.80 36	99.54 1	99.15 90	99.74 8

fmvsm_s_conf0.5_n_6		97.08 35	97.17 26	96.81 85	97.28 165	91.73 126	97.75 105	98.50 28	94.86 51	99.22 10	98.78 39	89.75 91	99.76 49	99.10 16	99.29 68	98.94 114

MSLP-MVS++		96.94 44	97.06 31	96.59 98	98.72 60	91.86 124	97.67 119	98.49 29	94.66 68	97.24 67	98.41 62	92.31 44	98.94 190	96.61 66	99.46 42	98.96 110

HyFIR lowres test		93.66 183	92.92 193	95.87 157	98.24 95	89.88 212	94.58 366	98.49 29	85.06 386	93.78 193	95.78 266	82.86 227	98.67 235	91.77 207	95.71 221	99.07 96

CHOSEN 1792x2688		94.15 157	93.51 169	96.06 142	98.27 91	89.38 234	95.18 352	98.48 31	85.60 376	93.76 194	97.11 184	83.15 217	99.61 85	91.33 217	98.72 114	99.19 79

fmvsm_s_conf0.5_n_7		96.45 73	96.80 53	95.37 195	97.29 164	88.38 267	97.23 184	98.47 32	95.14 38	98.43 37	99.09 6	87.58 129	99.72 60	98.80 24	99.21 78	98.02 219

fmvsm_s_conf0.5_n_5		97.00 41	96.97 39	97.09 75	97.58 156	92.56 97	97.68 118	98.47 32	94.02 90	98.90 24	98.89 27	88.94 99	99.78 44	99.18 11	99.03 102	98.93 118

PHI-MVS		96.77 56	96.46 72	97.71 41	98.40 81	94.07 48	98.21 43	98.45 34	89.86 261	97.11 73	98.01 99	92.52 39	99.69 68	96.03 92	99.53 29	99.36 68

fmvsm_s_conf0.1_n		96.58 69	96.77 56	96.01 149	96.67 220	90.25 198	97.91 80	98.38 35	94.48 76	98.84 27	99.14 1	88.06 116	99.62 84	98.82 22	98.60 120	98.15 205

PVSNet_BlendedMVS		94.06 163	93.92 153	94.47 247	98.27 91	89.46 231	96.73 236	98.36 36	90.17 253	94.36 175	95.24 294	88.02 117	99.58 93	93.44 170	90.72 318	94.36 393

PVSNet_Blended		94.87 132	94.56 131	95.81 164	98.27 91	89.46 231	95.47 334	98.36 36	88.84 298	94.36 175	96.09 251	88.02 117	99.58 93	93.44 170	98.18 140	98.40 181

3Dnovator	91.36 5	95.19 119	94.44 140	97.44 53	96.56 230	93.36 66	98.65 12	98.36 36	94.12 87	89.25 322	98.06 93	82.20 244	99.77 47	93.41 172	99.32 66	99.18 80

FOURS1					99.55 1	93.34 67	99.29 1	98.35 39	94.98 45	98.49 35

DPE-MVS		97.86 4	97.65 9	98.47 5	99.17 34	95.78 7	97.21 187	98.35 39	95.16 37	98.71 31	98.80 37	95.05 10	99.89 3	96.70 64	99.73 1	99.73 11
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
fmvsm_s_conf0.1_n_a		96.40 75	96.47 69	96.16 137	95.48 303	90.69 180	97.91 80	98.33 41	94.07 88	98.93 19	99.14 1	87.44 136	99.61 85	98.63 25	98.32 133	98.18 201

HFP-MVS		97.14 33	96.92 43	97.83 26	99.42 7	94.12 46	98.52 16	98.32 42	93.21 122	97.18 68	98.29 79	92.08 46	99.83 27	95.63 108	99.59 19	99.54 41

ACMMPR		97.07 37	96.84 47	97.79 30	99.44 6	93.88 53	98.52 16	98.31 43	93.21 122	97.15 70	98.33 73	91.35 62	99.86 9	95.63 108	99.59 19	99.62 23

test_fmvsmvis_n_1920		96.70 61	96.84 47	96.31 124	96.62 222	91.73 126	97.98 66	98.30 44	96.19 13	96.10 119	98.95 19	89.42 92	99.76 49	98.90 21	99.08 97	97.43 255

APDe-MVS		97.82 5	97.73 8	98.08 18	99.15 35	94.82 28	98.81 8	98.30 44	94.76 63	98.30 39	98.90 24	93.77 17	99.68 70	97.93 28	99.69 3	99.75 6
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
test0726					99.45 3	95.36 13	98.31 28	98.29 46	94.92 49	98.99 17	98.92 22	95.08 8

MSP-MVS		97.59 11	97.54 14	97.73 38	99.40 11	93.77 57	98.53 15	98.29 46	95.55 26	98.56 34	97.81 124	93.90 15	99.65 74	96.62 65	99.21 78	99.77 2
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 9	98.67 63	95.39 11	99.29 1	98.28 48	94.78 60	98.93 19	98.87 30	96.04 2	99.86 9	97.45 45	99.58 23	99.59 28

test_0728_SECOND				98.51 4	99.45 3	95.93 5	98.21 43	98.28 48					99.86 9	97.52 41	99.67 6	99.75 6

CP-MVS		97.02 39	96.81 52	97.64 45	99.33 23	93.54 60	98.80 9	98.28 48	92.99 135	96.45 106	98.30 78	91.90 50	99.85 18	95.61 110	99.68 4	99.54 41

test_fmvsmconf0.1_n		97.09 34	97.06 31	97.19 69	95.67 294	92.21 110	97.95 75	98.27 51	95.78 22	98.40 38	99.00 15	89.99 86	99.78 44	99.06 17	99.41 55	99.59 28

SED-MVS		98.05 2	97.99 1	98.24 10	99.42 7	95.30 17	98.25 36	98.27 51	95.13 39	99.19 12	98.89 27	95.54 5	99.85 18	97.52 41	99.66 10	99.56 36

test_241102_TWO								98.27 51	95.13 39	98.93 19	98.89 27	94.99 11	99.85 18	97.52 41	99.65 13	99.74 8

test_241102_ONE					99.42 7	95.30 17		98.27 51	95.09 42	99.19 12	98.81 36	95.54 5	99.65 74

SF-MVS		97.39 21	97.13 27	98.17 15	99.02 44	95.28 19	98.23 40	98.27 51	92.37 161	98.27 40	98.65 44	93.33 23	99.72 60	96.49 70	99.52 31	99.51 45

SteuartSystems-ACMMP		97.62 10	97.53 15	97.87 24	98.39 83	94.25 40	98.43 23	98.27 51	95.34 31	98.11 42	98.56 46	94.53 12	99.71 62	96.57 68	99.62 17	99.65 19
Skip Steuart: Steuart Systems R&D Blog.
test_one_0601					99.32 24	95.20 20		98.25 57	95.13 39	98.48 36	98.87 30	95.16 7

PVSNet_Blended_VisFu		95.27 111	94.91 118	96.38 120	98.20 102	90.86 172	97.27 178	98.25 57	90.21 252	94.18 183	97.27 173	87.48 135	99.73 56	93.53 167	97.77 156	98.55 162

region2R		97.07 37	96.84 47	97.77 34	99.46 2	93.79 55	98.52 16	98.24 59	93.19 125	97.14 71	98.34 70	91.59 57	99.87 7	95.46 114	99.59 19	99.64 21

PS-CasMVS		91.55 275	90.84 276	93.69 297	94.96 340	88.28 270	97.84 90	98.24 59	91.46 195	88.04 354	95.80 262	79.67 294	97.48 371	87.02 318	84.54 392	95.31 342

DU-MVS		92.90 218	92.04 227	95.49 189	94.95 341	92.83 85	97.16 191	98.24 59	93.02 134	90.13 288	95.71 269	83.47 209	97.85 334	91.71 209	83.93 398	95.78 313

9.14			96.75 57		98.93 52		97.73 109	98.23 62	91.28 204	97.88 50	98.44 59	93.00 26	99.65 74	95.76 101	99.47 41

reproduce_model		97.51 17	97.51 17	97.50 50	98.99 48	93.01 78	97.79 101	98.21 63	95.73 23	97.99 46	99.03 14	92.63 36	99.82 29	97.80 30	99.42 52	99.67 14

D2MVS		91.30 292	90.95 270	92.35 345	94.71 356	85.52 345	96.18 292	98.21 63	88.89 296	86.60 383	93.82 367	79.92 290	97.95 323	89.29 267	90.95 315	93.56 408

reproduce-ours		97.53 15	97.51 17	97.60 47	98.97 49	93.31 69	97.71 114	98.20 65	95.80 20	97.88 50	98.98 17	92.91 27	99.81 31	97.68 32	99.43 49	99.67 14

our_new_method		97.53 15	97.51 17	97.60 47	98.97 49	93.31 69	97.71 114	98.20 65	95.80 20	97.88 50	98.98 17	92.91 27	99.81 31	97.68 32	99.43 49	99.67 14

SDMVSNet		94.17 155	93.61 162	95.86 160	98.09 111	91.37 147	97.35 170	98.20 65	93.18 127	91.79 249	97.28 171	79.13 302	98.93 191	94.61 141	92.84 281	97.28 263

XVS		97.18 30	96.96 41	97.81 28	99.38 14	94.03 50	98.59 13	98.20 65	94.85 52	96.59 94	98.29 79	91.70 53	99.80 36	95.66 103	99.40 57	99.62 23

X-MVStestdata		91.71 264	89.67 330	97.81 28	99.38 14	94.03 50	98.59 13	98.20 65	94.85 52	96.59 94	32.69 469	91.70 53	99.80 36	95.66 103	99.40 57	99.62 23

ACMMP_NAP		97.20 29	96.86 45	98.23 11	99.09 36	95.16 22	97.60 133	98.19 70	92.82 149	97.93 49	98.74 41	91.60 56	99.86 9	96.26 75	99.52 31	99.67 14

CP-MVSNet		91.89 260	91.24 259	93.82 289	95.05 337	88.57 260	97.82 95	98.19 70	91.70 184	88.21 349	95.76 267	81.96 249	97.52 369	87.86 293	84.65 386	95.37 338

ZNCC-MVS		96.96 42	96.67 60	97.85 25	99.37 16	94.12 46	98.49 20	98.18 72	92.64 156	96.39 108	98.18 86	91.61 55	99.88 4	95.59 113	99.55 26	99.57 32

SMA-MVS		97.35 22	97.03 36	98.30 8	99.06 40	95.42 10	97.94 76	98.18 72	90.57 243	98.85 26	98.94 20	93.33 23	99.83 27	96.72 62	99.68 4	99.63 22
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		91.20 297	90.44 293	93.48 308	94.49 364	87.91 285	97.76 103	98.18 72	91.29 201	87.78 358	95.74 268	80.35 281	97.33 382	85.46 342	82.96 408	95.19 353

DELS-MVS		96.61 67	96.38 76	97.30 59	97.79 135	93.19 74	95.96 304	98.18 72	95.23 34	95.87 128	97.65 141	91.45 58	99.70 67	95.87 95	99.44 48	99.00 105
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		89.70 349	88.40 355	93.60 301	95.15 332	90.10 201	97.56 138	98.16 76	87.28 349	86.16 389	94.63 322	77.57 330	98.05 303	74.48 428	84.59 390	92.65 421

VNet		95.89 94	95.45 97	97.21 67	98.07 115	92.94 81	97.50 147	98.15 77	93.87 96	97.52 57	97.61 147	85.29 176	99.53 107	95.81 100	95.27 234	99.16 81

DeepPCF-MVS	93.97 1	96.61 67	97.09 29	95.15 203	98.09 111	86.63 318	96.00 302	98.15 77	95.43 27	97.95 48	98.56 46	93.40 21	99.36 132	96.77 59	99.48 40	99.45 55

SD-MVS		97.41 20	97.53 15	97.06 78	98.57 74	94.46 34	97.92 79	98.14 79	94.82 56	99.01 16	98.55 48	94.18 14	97.41 378	96.94 54	99.64 14	99.32 70
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 50	96.52 66	97.82 27	99.36 20	94.14 45	98.29 30	98.13 80	92.72 152	96.70 86	98.06 93	91.35 62	99.86 9	94.83 130	99.28 69	99.47 54

UA-Net		95.95 91	95.53 93	97.20 68	97.67 142	92.98 80	97.65 123	98.13 80	94.81 58	96.61 92	98.35 67	88.87 100	99.51 112	90.36 242	97.35 169	99.11 90

QAPM		93.45 194	92.27 221	96.98 81	96.77 214	92.62 94	98.39 25	98.12 82	84.50 394	88.27 347	97.77 128	82.39 241	99.81 31	85.40 343	98.81 110	98.51 167

Vis-MVSNet		95.23 116	94.81 119	96.51 107	97.18 170	91.58 137	98.26 35	98.12 82	94.38 83	94.90 158	98.15 88	82.28 242	98.92 193	91.45 216	98.58 122	99.01 102
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
OpenMVS	89.19 12	92.86 221	91.68 242	96.40 117	95.34 315	92.73 90	98.27 33	98.12 82	84.86 389	85.78 391	97.75 129	78.89 312	99.74 54	87.50 308	98.65 117	96.73 280

TranMVSNet+NR-MVSNet		92.50 230	91.63 243	95.14 204	94.76 352	92.07 115	97.53 144	98.11 85	92.90 146	89.56 310	96.12 246	83.16 216	97.60 361	89.30 266	83.20 407	95.75 317

CPTT-MVS		95.57 104	95.19 108	96.70 88	99.27 28	91.48 142	98.33 27	98.11 85	87.79 334	95.17 153	98.03 96	87.09 143	99.61 85	93.51 168	99.42 52	99.02 99

APD-MVS		96.95 43	96.60 62	98.01 20	99.03 43	94.93 27	97.72 112	98.10 87	91.50 193	98.01 45	98.32 75	92.33 42	99.58 93	94.85 128	99.51 34	99.53 44
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
mPP-MVS		96.86 48	96.60 62	97.64 45	99.40 11	93.44 62	98.50 19	98.09 88	93.27 121	95.95 126	98.33 73	91.04 70	99.88 4	95.20 117	99.57 25	99.60 27

ZD-MVS					99.05 41	94.59 32		98.08 89	89.22 282	97.03 76	98.10 89	92.52 39	99.65 74	94.58 143	99.31 67

MTGPA								98.08 89

MTAPA		97.08 35	96.78 55	97.97 23	99.37 16	94.42 36	97.24 180	98.08 89	95.07 43	96.11 118	98.59 45	90.88 76	99.90 2	96.18 87	99.50 36	99.58 31

CNVR-MVS		97.68 6	97.44 21	98.37 7	98.90 55	95.86 6	97.27 178	98.08 89	95.81 19	97.87 53	98.31 76	94.26 13	99.68 70	97.02 53	99.49 39	99.57 32

DP-MVS Recon		95.68 99	95.12 112	97.37 56	99.19 33	94.19 42	97.03 198	98.08 89	88.35 316	95.09 155	97.65 141	89.97 87	99.48 119	92.08 201	98.59 121	98.44 178

SR-MVS		97.01 40	96.86 45	97.47 52	99.09 36	93.27 71	97.98 66	98.07 94	93.75 99	97.45 59	98.48 56	91.43 60	99.59 90	96.22 78	99.27 70	99.54 41

MCST-MVS		97.18 30	96.84 47	98.20 14	99.30 26	95.35 15	97.12 194	98.07 94	93.54 109	96.08 120	97.69 136	93.86 16	99.71 62	96.50 69	99.39 59	99.55 39

NR-MVSNet		92.34 239	91.27 258	95.53 185	94.95 341	93.05 77	97.39 166	98.07 94	92.65 154	84.46 402	95.71 269	85.00 183	97.77 345	89.71 254	83.52 404	95.78 313

MP-MVS-pluss		96.70 61	96.27 79	97.98 22	99.23 32	94.71 29	96.96 209	98.06 97	90.67 233	95.55 142	98.78 39	91.07 69	99.86 9	96.58 67	99.55 26	99.38 66
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
APD-MVS_3200maxsize		96.81 54	96.71 59	97.12 72	99.01 47	92.31 106	97.98 66	98.06 97	93.11 131	97.44 60	98.55 48	90.93 74	99.55 103	96.06 88	99.25 75	99.51 45

MP-MVS		96.77 56	96.45 73	97.72 39	99.39 13	93.80 54	98.41 24	98.06 97	93.37 117	95.54 144	98.34 70	90.59 80	99.88 4	94.83 130	99.54 28	99.49 50
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
HPM-MVS_fast		96.51 70	96.27 79	97.22 66	99.32 24	92.74 89	98.74 10	98.06 97	90.57 243	96.77 83	98.35 67	90.21 83	99.53 107	94.80 134	99.63 16	99.38 66

HPM-MVS		96.69 63	96.45 73	97.40 55	99.36 20	93.11 76	98.87 6	98.06 97	91.17 212	96.40 107	97.99 102	90.99 71	99.58 93	95.61 110	99.61 18	99.49 50
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
sss		94.51 146	93.80 155	96.64 90	97.07 176	91.97 120	96.32 280	98.06 97	88.94 294	94.50 172	96.78 204	84.60 189	99.27 142	91.90 202	96.02 211	98.68 153

DeepC-MVS	93.07 3	96.06 85	95.66 90	97.29 60	97.96 123	93.17 75	97.30 176	98.06 97	93.92 94	93.38 209	98.66 42	86.83 145	99.73 56	95.60 112	99.22 77	98.96 110
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 26	97.03 36	98.11 17	98.77 58	95.06 25	97.34 171	98.04 104	95.96 14	97.09 74	97.88 114	93.18 25	99.71 62	95.84 99	99.17 86	99.56 36

DeepC-MVS_fast	93.89 2	96.93 45	96.64 61	97.78 32	98.64 69	94.30 37	97.41 161	98.04 104	94.81 58	96.59 94	98.37 65	91.24 65	99.64 82	95.16 119	99.52 31	99.42 61
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 47	96.80 53	97.11 74	99.02 44	92.34 104	97.98 66	98.03 106	93.52 112	97.43 62	98.51 51	91.40 61	99.56 101	96.05 89	99.26 73	99.43 59

RE-MVS-def			96.72 58		99.02 44	92.34 104	97.98 66	98.03 106	93.52 112	97.43 62	98.51 51	90.71 78		96.05 89	99.26 73	99.43 59

RPMNet		88.98 355	87.05 369	94.77 231	94.45 366	87.19 302	90.23 442	98.03 106	77.87 442	92.40 227	87.55 449	80.17 285	99.51 112	68.84 449	93.95 267	97.60 248

save fliter					98.91 54	94.28 38	97.02 200	98.02 109	95.35 30

TEST9					98.70 61	94.19 42	96.41 266	98.02 109	88.17 320	96.03 121	97.56 153	92.74 33	99.59 90

train_agg		96.30 81	95.83 89	97.72 39	98.70 61	94.19 42	96.41 266	98.02 109	88.58 307	96.03 121	97.56 153	92.73 34	99.59 90	95.04 121	99.37 63	99.39 64

test_8					98.67 63	94.06 49	96.37 274	98.01 112	88.58 307	95.98 125	97.55 155	92.73 34	99.58 93

agg_prior					98.67 63	93.79 55		98.00 113		95.68 138			99.57 100

test_prior				97.23 65	98.67 63	92.99 79		98.00 113					99.41 127			99.29 71

WR-MVS		92.34 239	91.53 247	94.77 231	95.13 334	90.83 173	96.40 270	97.98 115	91.88 179	89.29 319	95.54 280	82.50 237	97.80 341	89.79 253	85.27 377	95.69 320

HPM-MVS++		97.34 23	96.97 39	98.47 5	99.08 38	96.16 4	97.55 143	97.97 116	95.59 24	96.61 92	97.89 111	92.57 38	99.84 23	95.95 94	99.51 34	99.40 62

CANet		96.39 76	96.02 84	97.50 50	97.62 149	93.38 64	97.02 200	97.96 117	95.42 28	94.86 159	97.81 124	87.38 138	99.82 29	96.88 56	99.20 83	99.29 71

114514_t		93.95 170	93.06 187	96.63 94	99.07 39	91.61 134	97.46 158	97.96 117	77.99 440	93.00 218	97.57 151	86.14 159	99.33 134	89.22 270	99.15 90	98.94 114

IU-MVS					99.42 7	95.39 11		97.94 119	90.40 250	98.94 18				97.41 48	99.66 10	99.74 8

MSC_two_6792asdad				98.86 1	98.67 63	96.94 1		97.93 120					99.86 9	97.68 32	99.67 6	99.77 2

No_MVS				98.86 1	98.67 63	96.94 1		97.93 120					99.86 9	97.68 32	99.67 6	99.77 2

fmvsm_s_conf0.1_n_2		96.33 80	96.44 75	96.00 150	97.30 163	90.37 195	97.53 144	97.92 122	96.52 10	99.14 14	99.08 7	83.21 214	99.74 54	99.22 10	98.06 145	97.88 228

Anonymous20231211		90.63 321	89.42 337	94.27 261	98.24 95	89.19 246	98.05 58	97.89 123	79.95 432	88.25 348	94.96 303	72.56 371	98.13 286	89.70 255	85.14 379	95.49 324

原ACMM1				96.38 120	98.59 71	91.09 163		97.89 123	87.41 345	95.22 152	97.68 137	90.25 82	99.54 105	87.95 292	99.12 95	98.49 170

CDPH-MVS		95.97 90	95.38 102	97.77 34	98.93 52	94.44 35	96.35 275	97.88 125	86.98 353	96.65 90	97.89 111	91.99 48	99.47 120	92.26 190	99.46 42	99.39 64

test11								97.88 125

EIA-MVS		95.53 105	95.47 96	95.71 175	97.06 179	89.63 219	97.82 95	97.87 127	93.57 105	93.92 191	95.04 300	90.61 79	98.95 188	94.62 140	98.68 115	98.54 163

CS-MVS		96.86 48	97.06 31	96.26 130	98.16 107	91.16 161	99.09 3	97.87 127	95.30 32	97.06 75	98.03 96	91.72 51	98.71 228	97.10 51	99.17 86	98.90 123

无先验							95.79 315	97.87 127	83.87 402				99.65 74	87.68 302		98.89 129

3Dnovator+	91.43 4	95.40 106	94.48 138	98.16 16	96.90 195	95.34 16	98.48 21	97.87 127	94.65 69	88.53 339	98.02 98	83.69 205	99.71 62	93.18 176	98.96 105	99.44 57

VPNet		92.23 247	91.31 255	94.99 214	95.56 299	90.96 167	97.22 186	97.86 131	92.96 141	90.96 271	96.62 221	75.06 351	98.20 280	91.90 202	83.65 403	95.80 311

test_vis1_n_1920		94.17 155	94.58 130	92.91 329	97.42 161	82.02 399	97.83 93	97.85 132	94.68 66	98.10 43	98.49 53	70.15 390	99.32 136	97.91 29	98.82 109	97.40 257

DVP-MVS		97.91 3	97.81 4	98.22 13	99.45 3	95.36 13	98.21 43	97.85 132	94.92 49	98.73 29	98.87 30	95.08 8	99.84 23	97.52 41	99.67 6	99.48 52
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 19	97.33 25	97.69 42	99.25 29	94.24 41	98.07 56	97.85 132	93.72 100	98.57 33	98.35 67	93.69 18	99.40 128	97.06 52	99.46 42	99.44 57
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 46	97.04 35	96.45 114	98.29 88	91.66 133	99.03 4	97.85 132	95.84 17	96.90 78	97.97 104	91.24 65	98.75 218	96.92 55	99.33 65	98.94 114

test_fmvsmconf0.01_n		96.15 84	95.85 88	97.03 79	92.66 417	91.83 125	97.97 72	97.84 136	95.57 25	97.53 56	99.00 15	84.20 198	99.76 49	98.82 22	99.08 97	99.48 52

GDP-MVS		95.62 101	95.13 110	97.09 75	96.79 208	93.26 72	97.89 83	97.83 137	93.58 104	96.80 80	97.82 122	83.06 221	99.16 156	94.40 147	97.95 151	98.87 132

balanced_conf03		96.84 52	96.89 44	96.68 89	97.63 148	92.22 109	98.17 49	97.82 138	94.44 78	98.23 41	97.36 166	90.97 72	99.22 146	97.74 31	99.66 10	98.61 156

AdaColmap		94.34 150	93.68 160	96.31 124	98.59 71	91.68 132	96.59 255	97.81 139	89.87 260	92.15 237	97.06 187	83.62 208	99.54 105	89.34 265	98.07 144	97.70 241

MVSMamba_PlusPlus		96.51 70	96.48 68	96.59 98	98.07 115	91.97 120	98.14 50	97.79 140	90.43 248	97.34 65	97.52 156	91.29 64	99.19 149	98.12 27	99.64 14	98.60 157

KinetiMVS		95.26 112	94.75 124	96.79 86	96.99 188	92.05 116	97.82 95	97.78 141	94.77 62	96.46 104	97.70 134	80.62 275	99.34 133	92.37 189	98.28 135	98.97 107

mamv4		94.66 143	96.10 83	90.37 398	98.01 118	73.41 449	96.82 225	97.78 141	89.95 259	94.52 170	97.43 161	92.91 27	99.09 169	98.28 26	99.16 89	98.60 157

ETV-MVS		96.02 87	95.89 87	96.40 117	97.16 171	92.44 101	97.47 156	97.77 143	94.55 72	96.48 102	94.51 328	91.23 67	98.92 193	95.65 106	98.19 139	97.82 236

新几何1				97.32 58	98.60 70	93.59 59		97.75 144	81.58 423	95.75 133	97.85 118	90.04 85	99.67 72	86.50 324	99.13 93	98.69 152

旧先验1					98.38 84	93.38 64		97.75 144			98.09 91	92.30 45			99.01 103	99.16 81

EC-MVSNet		96.42 74	96.47 69	96.26 130	97.01 186	91.52 139	98.89 5	97.75 144	94.42 79	96.64 91	97.68 137	89.32 93	98.60 244	97.45 45	99.11 96	98.67 154

EI-MVSNet-Vis-set		96.51 70	96.47 69	96.63 94	98.24 95	91.20 155	96.89 217	97.73 147	94.74 64	96.49 101	98.49 53	90.88 76	99.58 93	96.44 71	98.32 133	99.13 85

PAPM_NR		95.01 123	94.59 129	96.26 130	98.89 56	90.68 181	97.24 180	97.73 147	91.80 180	92.93 223	96.62 221	89.13 96	99.14 161	89.21 271	97.78 155	98.97 107

Anonymous20240529		91.98 256	90.73 283	95.73 173	98.14 108	89.40 233	97.99 63	97.72 149	79.63 434	93.54 202	97.41 163	69.94 392	99.56 101	91.04 224	91.11 311	98.22 198

CHOSEN 280x420		93.12 206	92.72 204	94.34 255	96.71 219	87.27 298	90.29 441	97.72 149	86.61 360	91.34 260	95.29 288	84.29 197	98.41 260	93.25 174	98.94 106	97.35 260

EI-MVSNet-UG-set		96.34 79	96.30 78	96.47 111	98.20 102	90.93 169	96.86 220	97.72 149	94.67 67	96.16 117	98.46 57	90.43 81	99.58 93	96.23 77	97.96 150	98.90 123

LS3D		93.57 187	92.61 209	96.47 111	97.59 152	91.61 134	97.67 119	97.72 149	85.17 384	90.29 282	98.34 70	84.60 189	99.73 56	83.85 366	98.27 136	98.06 217

PAPR		94.18 154	93.42 176	96.48 110	97.64 146	91.42 146	95.55 329	97.71 153	88.99 291	92.34 233	95.82 261	89.19 94	99.11 164	86.14 330	97.38 167	98.90 123

UGNet		94.04 165	93.28 179	96.31 124	96.85 200	91.19 156	97.88 85	97.68 154	94.40 81	93.00 218	96.18 241	73.39 368	99.61 85	91.72 208	98.46 127	98.13 206
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				95.46 193	98.18 106	88.90 253		97.66 155	82.73 414	97.03 76	98.07 92	90.06 84	98.85 200	89.67 256	98.98 104	98.64 155

test12				97.65 43	98.46 75	94.26 39		97.66 155		95.52 145		90.89 75	99.46 121		99.25 75	99.22 78

DTE-MVSNet		90.56 322	89.75 328	93.01 325	93.95 379	87.25 299	97.64 127	97.65 157	90.74 228	87.12 371	95.68 272	79.97 289	97.00 395	83.33 367	81.66 414	94.78 380

TAPA-MVS	90.10 7	92.30 242	91.22 261	95.56 182	98.33 86	89.60 221	96.79 229	97.65 157	81.83 420	91.52 255	97.23 176	87.94 119	98.91 195	71.31 443	98.37 131	98.17 204
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
sd_testset		93.10 207	92.45 217	95.05 208	98.09 111	89.21 243	96.89 217	97.64 159	93.18 127	91.79 249	97.28 171	75.35 350	98.65 238	88.99 276	92.84 281	97.28 263

test_cas_vis1_n_1920		94.48 148	94.55 134	94.28 260	96.78 212	86.45 323	97.63 129	97.64 159	93.32 120	97.68 55	98.36 66	73.75 366	99.08 172	96.73 61	99.05 99	97.31 262

NormalMVS		96.36 78	96.11 82	97.12 72	99.37 16	92.90 83	97.99 63	97.63 161	95.92 15	96.57 97	97.93 106	85.34 174	99.50 115	94.99 124	99.21 78	98.97 107

Elysia		94.00 167	93.12 184	96.64 90	96.08 278	92.72 91	97.50 147	97.63 161	91.15 214	94.82 160	97.12 182	74.98 353	99.06 178	90.78 229	98.02 146	98.12 208

StellarMVS		94.00 167	93.12 184	96.64 90	96.08 278	92.72 91	97.50 147	97.63 161	91.15 214	94.82 160	97.12 182	74.98 353	99.06 178	90.78 229	98.02 146	98.12 208

cdsmvs_eth3d_5k		23.24 439	30.99 441	0.00 457	0.00 480	0.00 482	0.00 468	97.63 161	0.00 475	0.00 476	96.88 200	84.38 194	0.00 476	0.00 475	0.00 474	0.00 472

DPM-MVS		95.69 98	94.92 117	98.01 20	98.08 114	95.71 9	95.27 345	97.62 165	90.43 248	95.55 142	97.07 186	91.72 51	99.50 115	89.62 258	98.94 106	98.82 138

sasdasda		96.02 87	95.45 97	97.75 36	97.59 152	95.15 23	98.28 31	97.60 166	94.52 74	96.27 112	96.12 246	87.65 126	99.18 152	96.20 83	94.82 243	98.91 120

canonicalmvs		96.02 87	95.45 97	97.75 36	97.59 152	95.15 23	98.28 31	97.60 166	94.52 74	96.27 112	96.12 246	87.65 126	99.18 152	96.20 83	94.82 243	98.91 120

test222					98.24 95	92.21 110	95.33 340	97.60 166	79.22 436	95.25 150	97.84 120	88.80 102			99.15 90	98.72 149

cascas		91.20 297	90.08 310	94.58 241	94.97 339	89.16 247	93.65 406	97.59 169	79.90 433	89.40 314	92.92 393	75.36 349	98.36 268	92.14 195	94.75 246	96.23 290

h-mvs33		94.15 157	93.52 168	96.04 144	97.81 134	90.22 199	97.62 131	97.58 170	95.19 35	96.74 84	97.45 158	83.67 206	99.61 85	95.85 97	79.73 421	98.29 194

MGCFI-Net		95.94 92	95.40 101	97.56 49	97.59 152	94.62 31	98.21 43	97.57 171	94.41 80	96.17 116	96.16 244	87.54 131	99.17 154	96.19 85	94.73 248	98.91 120

MVSFormer		95.37 107	95.16 109	95.99 151	96.34 254	91.21 153	98.22 41	97.57 171	91.42 197	96.22 114	97.32 167	86.20 157	97.92 328	94.07 153	99.05 99	98.85 134

test_djsdf		93.07 209	92.76 199	94.00 274	93.49 396	88.70 257	98.22 41	97.57 171	91.42 197	90.08 294	95.55 279	82.85 228	97.92 328	94.07 153	91.58 302	95.40 335

OMC-MVS		95.09 121	94.70 125	96.25 133	98.46 75	91.28 149	96.43 262	97.57 171	92.04 175	94.77 164	97.96 105	87.01 144	99.09 169	91.31 218	96.77 190	98.36 185

viewcassd2359sk11		95.26 112	95.09 113	95.80 165	96.95 192	89.72 217	96.80 228	97.56 175	92.21 167	95.37 148	97.80 126	87.17 142	98.77 213	94.82 132	97.10 182	98.90 123

PS-MVSNAJss		93.74 180	93.51 169	94.44 249	93.91 381	89.28 241	97.75 105	97.56 175	92.50 157	89.94 296	96.54 224	88.65 105	98.18 283	93.83 162	90.90 316	95.86 305

casdiffmvs_mvg		95.81 97	95.57 91	96.51 107	96.87 197	91.49 140	97.50 147	97.56 175	93.99 92	95.13 154	97.92 109	87.89 120	98.78 210	95.97 93	97.33 170	99.26 75
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
jajsoiax		92.42 235	91.89 235	94.03 273	93.33 404	88.50 264	97.73 109	97.53 178	92.00 177	88.85 331	96.50 226	75.62 348	98.11 290	93.88 160	91.56 303	95.48 325

mvs_tets		92.31 241	91.76 238	93.94 282	93.41 401	88.29 269	97.63 129	97.53 178	92.04 175	88.76 334	96.45 228	74.62 358	98.09 295	93.91 158	91.48 304	95.45 330

dcpmvs_2		96.37 77	97.05 34	94.31 258	98.96 51	84.11 372	97.56 138	97.51 180	93.92 94	97.43 62	98.52 50	92.75 32	99.32 136	97.32 50	99.50 36	99.51 45

HQP_MVS		93.78 179	93.43 174	94.82 224	96.21 258	89.99 205	97.74 107	97.51 180	94.85 52	91.34 260	96.64 214	81.32 261	98.60 244	93.02 182	92.23 290	95.86 305

plane_prior5								97.51 180					98.60 244	93.02 182	92.23 290	95.86 305

viewmanbaseed2359cas		95.24 115	95.02 115	95.91 154	96.87 197	89.98 207	96.82 225	97.49 183	92.26 163	95.47 146	97.82 122	86.47 150	98.69 230	94.80 134	97.20 178	99.06 97

reproduce_monomvs		91.30 292	91.10 265	91.92 359	96.82 205	82.48 393	97.01 203	97.49 183	94.64 70	88.35 342	95.27 291	70.53 385	98.10 291	95.20 117	84.60 389	95.19 353

viewmacassd2359aftdt		95.07 122	94.80 120	95.87 157	96.53 235	89.84 213	96.90 216	97.48 185	92.44 158	95.36 149	97.89 111	85.23 177	98.68 232	94.40 147	97.00 185	99.09 92

PS-MVSNAJ		95.37 107	95.33 104	95.49 189	97.35 162	90.66 182	95.31 342	97.48 185	93.85 97	96.51 100	95.70 271	88.65 105	99.65 74	94.80 134	98.27 136	96.17 294

API-MVS		94.84 134	94.49 137	95.90 155	97.90 129	92.00 119	97.80 99	97.48 185	89.19 283	94.81 162	96.71 207	88.84 101	99.17 154	88.91 278	98.76 113	96.53 283

MG-MVS		95.61 102	95.38 102	96.31 124	98.42 79	90.53 184	96.04 299	97.48 185	93.47 114	95.67 139	98.10 89	89.17 95	99.25 143	91.27 219	98.77 112	99.13 85

MAR-MVS		94.22 153	93.46 171	96.51 107	98.00 120	92.19 113	97.67 119	97.47 189	88.13 324	93.00 218	95.84 259	84.86 187	99.51 112	87.99 291	98.17 141	97.83 235
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		92.98 213	92.53 213	94.32 256	96.12 273	89.20 244	95.28 343	97.47 189	92.66 153	89.90 297	95.62 275	80.58 276	98.40 261	92.73 187	92.40 288	95.38 337
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
UniMVSNet_ETH3D		91.34 290	90.22 306	94.68 235	94.86 348	87.86 286	97.23 184	97.46 191	87.99 325	89.90 297	96.92 198	66.35 420	98.23 277	90.30 243	90.99 314	97.96 222

nrg030		94.05 164	93.31 178	96.27 129	95.22 326	94.59 32	98.34 26	97.46 191	92.93 142	91.21 269	96.64 214	87.23 141	98.22 278	94.99 124	85.80 369	95.98 304

XVG-OURS		93.72 181	93.35 177	94.80 229	97.07 176	88.61 258	94.79 361	97.46 191	91.97 178	93.99 188	97.86 117	81.74 255	98.88 197	92.64 188	92.67 286	96.92 275

LPG-MVS_test		92.94 216	92.56 210	94.10 268	96.16 268	88.26 271	97.65 123	97.46 191	91.29 201	90.12 290	97.16 179	79.05 305	98.73 222	92.25 192	91.89 298	95.31 342

LGP-MVS_train				94.10 268	96.16 268	88.26 271		97.46 191	91.29 201	90.12 290	97.16 179	79.05 305	98.73 222	92.25 192	91.89 298	95.31 342

MVS		91.71 264	90.44 293	95.51 186	95.20 328	91.59 136	96.04 299	97.45 196	73.44 450	87.36 367	95.60 276	85.42 173	99.10 166	85.97 335	97.46 162	95.83 309

XVG-OURS-SEG-HR		93.86 176	93.55 164	94.81 226	97.06 179	88.53 263	95.28 343	97.45 196	91.68 185	94.08 187	97.68 137	82.41 240	98.90 196	93.84 161	92.47 287	96.98 271

baseline		95.58 103	95.42 100	96.08 140	96.78 212	90.41 190	97.16 191	97.45 196	93.69 103	95.65 140	97.85 118	87.29 139	98.68 232	95.66 103	97.25 176	99.13 85

ab-mvs		93.57 187	92.55 211	96.64 90	97.28 165	91.96 122	95.40 336	97.45 196	89.81 265	93.22 215	96.28 237	79.62 296	99.46 121	90.74 232	93.11 278	98.50 168

xiu_mvs_v2_base		95.32 110	95.29 105	95.40 194	97.22 167	90.50 185	95.44 335	97.44 200	93.70 102	96.46 104	96.18 241	88.59 109	99.53 107	94.79 137	97.81 154	96.17 294

1314		92.81 225	92.03 228	95.14 204	95.33 318	89.52 228	96.04 299	97.44 200	87.72 338	86.25 388	95.33 287	83.84 203	98.79 209	89.26 268	97.05 184	97.11 269

casdiffmvs		95.64 100	95.49 94	96.08 140	96.76 217	90.45 187	97.29 177	97.44 200	94.00 91	95.46 147	97.98 103	87.52 134	98.73 222	95.64 107	97.33 170	99.08 94
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		94.76 140	94.68 126	95.01 212	96.76 217	87.41 294	96.38 272	97.43 203	92.65 154	94.52 170	97.75 129	85.55 171	98.81 206	94.36 149	96.69 196	98.82 138

XXY-MVS		92.16 249	91.23 260	94.95 220	94.75 353	90.94 168	97.47 156	97.43 203	89.14 284	88.90 327	96.43 229	79.71 293	98.24 276	89.56 259	87.68 350	95.67 321

anonymousdsp		92.16 249	91.55 246	93.97 278	92.58 419	89.55 225	97.51 146	97.42 205	89.42 277	88.40 341	94.84 310	80.66 274	97.88 333	91.87 204	91.28 308	94.48 388

Effi-MVS+		94.93 128	94.45 139	96.36 122	96.61 223	91.47 143	96.41 266	97.41 206	91.02 220	94.50 172	95.92 255	87.53 132	98.78 210	93.89 159	96.81 189	98.84 137

RRT-MVS		94.51 146	94.35 143	94.98 216	96.40 248	86.55 321	97.56 138	97.41 206	93.19 125	94.93 157	97.04 188	79.12 303	99.30 140	96.19 85	97.32 172	99.09 92

HQP3-MVS								97.39 208							92.10 295

HQP-MVS		93.19 203	92.74 202	94.54 244	95.86 284	89.33 237	96.65 246	97.39 208	93.55 106	90.14 284	95.87 257	80.95 265	98.50 254	92.13 198	92.10 295	95.78 313

PLC	91.00 6	94.11 161	93.43 174	96.13 138	98.58 73	91.15 162	96.69 242	97.39 208	87.29 348	91.37 259	96.71 207	88.39 110	99.52 111	87.33 311	97.13 181	97.73 239
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
diffmvs_AUTHOR		95.33 109	95.27 106	95.50 188	96.37 252	89.08 249	96.08 297	97.38 211	93.09 133	96.53 99	97.74 131	86.45 151	98.68 232	96.32 73	97.48 161	98.75 145

v7n		90.76 314	89.86 321	93.45 310	93.54 393	87.60 292	97.70 117	97.37 212	88.85 297	87.65 360	94.08 358	81.08 264	98.10 291	84.68 352	83.79 402	94.66 385

UnsupCasMVSNet_eth		85.99 391	84.45 395	90.62 394	89.97 437	82.40 396	93.62 407	97.37 212	89.86 261	78.59 440	92.37 403	65.25 429	95.35 429	82.27 380	70.75 448	94.10 399

viewdifsd2359ckpt13		94.87 132	94.52 135	95.90 155	96.88 196	90.19 200	96.92 213	97.36 214	91.26 205	94.65 166	97.46 157	85.79 165	98.64 239	93.64 165	96.76 191	98.88 131

ACMM	89.79 8	92.96 214	92.50 215	94.35 253	96.30 256	88.71 256	97.58 134	97.36 214	91.40 199	90.53 277	96.65 213	79.77 292	98.75 218	91.24 220	91.64 300	95.59 323
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
xiu_mvs_v1_base_debu		95.01 123	94.76 121	95.75 170	96.58 226	91.71 129	96.25 285	97.35 216	92.99 135	96.70 86	96.63 218	82.67 232	99.44 124	96.22 78	97.46 162	96.11 300

xiu_mvs_v1_base		95.01 123	94.76 121	95.75 170	96.58 226	91.71 129	96.25 285	97.35 216	92.99 135	96.70 86	96.63 218	82.67 232	99.44 124	96.22 78	97.46 162	96.11 300

xiu_mvs_v1_base_debi		95.01 123	94.76 121	95.75 170	96.58 226	91.71 129	96.25 285	97.35 216	92.99 135	96.70 86	96.63 218	82.67 232	99.44 124	96.22 78	97.46 162	96.11 300

diffmvs		95.25 114	95.13 110	95.63 178	96.43 247	89.34 236	95.99 303	97.35 216	92.83 148	96.31 110	97.37 165	86.44 152	98.67 235	96.26 75	97.19 179	98.87 132
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		94.71 142	94.02 151	96.79 86	97.71 140	92.05 116	96.59 255	97.35 216	90.61 239	94.64 167	96.93 195	86.41 153	99.39 129	91.20 221	94.71 249	98.94 114

viewdifsd2359ckpt09		94.81 137	94.37 142	96.12 139	96.91 193	90.75 178	96.94 210	97.31 221	90.51 246	94.31 177	97.38 164	85.70 167	98.71 228	93.54 166	96.75 192	98.90 123

SSM_0407		94.54 145	94.12 150	95.80 165	96.79 208	90.38 192	96.79 229	97.29 222	91.24 206	93.68 195	97.60 148	85.03 181	98.67 235	92.14 195	96.51 201	98.35 187

SSM_0404		94.73 141	94.31 145	95.98 152	97.05 181	90.90 171	97.01 203	97.29 222	91.24 206	94.17 184	97.60 148	85.03 181	98.76 215	92.14 195	97.30 173	98.29 194

F-COLMAP		93.58 185	92.98 191	95.37 195	98.40 81	88.98 251	97.18 189	97.29 222	87.75 337	90.49 278	97.10 185	85.21 178	99.50 115	86.70 321	96.72 195	97.63 243

VortexMVS		92.88 220	92.64 206	93.58 303	96.58 226	87.53 293	96.93 212	97.28 225	92.78 151	89.75 302	94.99 301	82.73 231	97.76 346	94.60 142	88.16 345	95.46 328

XVG-ACMP-BASELINE		90.93 310	90.21 307	93.09 323	94.31 372	85.89 338	95.33 340	97.26 226	91.06 219	89.38 315	95.44 285	68.61 403	98.60 244	89.46 261	91.05 312	94.79 378

PCF-MVS	89.48 11	91.56 274	89.95 318	96.36 122	96.60 224	92.52 99	92.51 426	97.26 226	79.41 435	88.90 327	96.56 223	84.04 202	99.55 103	77.01 419	97.30 173	97.01 270
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
ACMP	89.59 10	92.62 229	92.14 224	94.05 271	96.40 248	88.20 274	97.36 169	97.25 228	91.52 192	88.30 345	96.64 214	78.46 317	98.72 227	91.86 205	91.48 304	95.23 349
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
icg_test_0407_2		93.58 185	93.46 171	93.94 282	96.19 262	86.16 332	93.73 401	97.24 229	91.54 188	93.50 204	97.04 188	85.64 169	96.91 398	90.68 234	95.59 225	98.76 141

IMVS_0407		93.94 171	93.75 157	94.49 246	96.19 262	86.16 332	96.35 275	97.24 229	91.54 188	93.50 204	97.04 188	85.64 169	98.54 251	90.68 234	95.59 225	98.76 141

IMVS_0404		92.44 233	91.92 233	94.00 274	96.19 262	86.16 332	93.84 398	97.24 229	91.54 188	88.17 351	97.04 188	76.96 335	97.09 389	90.68 234	95.59 225	98.76 141

IMVS_0403		93.98 169	93.79 156	94.55 243	96.19 262	86.16 332	96.35 275	97.24 229	91.54 188	93.59 199	97.04 188	85.86 162	98.73 222	90.68 234	95.59 225	98.76 141

OPM-MVS		93.28 199	92.76 199	94.82 224	94.63 359	90.77 176	96.65 246	97.18 233	93.72 100	91.68 253	97.26 174	79.33 300	98.63 241	92.13 198	92.28 289	95.07 356
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
PatchMatch-RL		92.90 218	92.02 229	95.56 182	98.19 104	90.80 174	95.27 345	97.18 233	87.96 326	91.86 248	95.68 272	80.44 279	98.99 186	84.01 361	97.54 160	96.89 276

alignmvs		95.87 96	95.23 107	97.78 32	97.56 158	95.19 21	97.86 86	97.17 235	94.39 82	96.47 103	96.40 231	85.89 161	99.20 148	96.21 82	95.11 239	98.95 113

MVS_Test		94.89 130	94.62 128	95.68 176	96.83 203	89.55 225	96.70 240	97.17 235	91.17 212	95.60 141	96.11 250	87.87 122	98.76 215	93.01 184	97.17 180	98.72 149

Fast-Effi-MVS+		93.46 191	92.75 201	95.59 181	96.77 214	90.03 202	96.81 227	97.13 237	88.19 319	91.30 263	94.27 346	86.21 156	98.63 241	87.66 303	96.46 207	98.12 208

EI-MVSNet		93.03 211	92.88 195	93.48 308	95.77 290	86.98 307	96.44 260	97.12 238	90.66 235	91.30 263	97.64 144	86.56 147	98.05 303	89.91 249	90.55 320	95.41 332

MVSTER		93.20 202	92.81 198	94.37 252	96.56 230	89.59 222	97.06 197	97.12 238	91.24 206	91.30 263	95.96 253	82.02 248	98.05 303	93.48 169	90.55 320	95.47 327

viewmambaseed2359dif		94.28 151	94.14 148	94.71 234	96.21 258	86.97 308	95.93 306	97.11 240	89.00 290	95.00 156	97.70 134	86.02 160	98.59 248	93.71 164	96.59 200	98.57 161

test_yl		94.78 138	94.23 146	96.43 115	97.74 138	91.22 151	96.85 221	97.10 241	91.23 209	95.71 135	96.93 195	84.30 195	99.31 138	93.10 177	95.12 237	98.75 145

DCV-MVSNet		94.78 138	94.23 146	96.43 115	97.74 138	91.22 151	96.85 221	97.10 241	91.23 209	95.71 135	96.93 195	84.30 195	99.31 138	93.10 177	95.12 237	98.75 145

LTVRE_ROB	88.41 13	90.99 306	89.92 320	94.19 262	96.18 266	89.55 225	96.31 281	97.09 243	87.88 329	85.67 392	95.91 256	78.79 313	98.57 249	81.50 383	89.98 325	94.44 391
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		93.46 191	93.23 181	94.17 263	96.12 273	85.42 347	96.43 262	97.08 244	92.91 143	94.21 180	98.00 100	80.82 271	98.74 220	94.41 146	89.05 334	98.34 191

test_fmvs1_n		92.73 227	92.88 195	92.29 349	96.08 278	81.05 407	97.98 66	97.08 244	90.72 230	96.79 82	98.18 86	63.07 433	98.45 258	97.62 39	98.42 130	97.36 258

v10		91.04 304	90.23 304	93.49 307	94.12 375	88.16 277	97.32 174	97.08 244	88.26 318	88.29 346	94.22 351	82.17 245	97.97 315	86.45 325	84.12 396	94.33 394

viewdifsd2359ckpt11		93.46 191	93.22 182	94.17 263	96.11 275	85.42 347	96.43 262	97.07 247	92.91 143	94.20 181	98.00 100	80.82 271	98.73 222	94.42 145	89.04 336	98.34 191

mamba_0408		93.70 182	92.99 188	95.83 162	96.79 208	90.38 192	88.69 451	97.07 247	90.96 222	93.68 195	97.31 169	84.97 184	98.76 215	90.95 225	96.51 201	98.35 187

SSM_04072		93.51 190	92.99 188	95.05 208	96.79 208	90.38 192	88.69 451	97.07 247	90.96 222	93.68 195	97.31 169	84.97 184	96.42 409	90.95 225	96.51 201	98.35 187

v144192		91.06 303	90.28 300	93.39 311	93.66 390	87.23 301	96.83 224	97.07 247	87.43 344	89.69 305	94.28 345	81.48 258	98.00 310	87.18 315	84.92 385	94.93 364

v1192		91.07 302	90.23 304	93.58 303	93.70 387	87.82 288	96.73 236	97.07 247	87.77 335	89.58 308	94.32 343	80.90 269	97.97 315	86.52 323	85.48 372	94.95 360

v8		91.29 294	90.53 292	93.57 305	94.15 374	88.12 278	97.34 171	97.06 252	88.99 291	88.32 344	94.26 348	83.08 219	98.01 309	87.62 305	83.92 400	94.57 387

mvs_anonymous		93.82 177	93.74 158	94.06 270	96.44 246	85.41 349	95.81 313	97.05 253	89.85 263	90.09 293	96.36 233	87.44 136	97.75 348	93.97 155	96.69 196	99.02 99

IterMVS-LS		92.29 243	91.94 232	93.34 313	96.25 257	86.97 308	96.57 258	97.05 253	90.67 233	89.50 313	94.80 313	86.59 146	97.64 356	89.91 249	86.11 367	95.40 335
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v1921920		90.85 312	90.03 315	93.29 315	93.55 392	86.96 310	96.74 235	97.04 255	87.36 346	89.52 312	94.34 340	80.23 284	97.97 315	86.27 326	85.21 378	94.94 362

CDS-MVSNet		94.14 160	93.54 165	95.93 153	96.18 266	91.46 144	96.33 279	97.04 255	88.97 293	93.56 200	96.51 225	87.55 130	97.89 332	89.80 252	95.95 213	98.44 178
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
SSC-MVS3.2		89.74 348	89.26 341	91.19 383	95.16 329	80.29 418	94.53 368	97.03 257	91.79 181	88.86 330	94.10 355	69.94 392	97.82 338	85.29 344	86.66 363	95.45 330

v1144		91.37 287	90.60 288	93.68 298	93.89 382	88.23 273	96.84 223	97.03 257	88.37 315	89.69 305	94.39 335	82.04 247	97.98 312	87.80 295	85.37 374	94.84 370

v1240		90.70 318	89.85 322	93.23 317	93.51 395	86.80 311	96.61 252	97.02 259	87.16 351	89.58 308	94.31 344	79.55 297	97.98 312	85.52 341	85.44 373	94.90 367

EPP-MVSNet		95.22 117	95.04 114	95.76 168	97.49 159	89.56 224	98.67 11	97.00 260	90.69 231	94.24 179	97.62 146	89.79 90	98.81 206	93.39 173	96.49 205	98.92 119

V42		91.58 273	90.87 272	93.73 293	94.05 378	88.50 264	97.32 174	96.97 261	88.80 303	89.71 303	94.33 341	82.54 236	98.05 303	89.01 275	85.07 381	94.64 386

test_fmvs1		93.21 201	93.53 166	92.25 352	96.55 232	81.20 406	97.40 165	96.96 262	90.68 232	96.80 80	98.04 95	69.25 398	98.40 261	97.58 40	98.50 123	97.16 268

FMVSNet2		91.31 291	90.08 310	94.99 214	96.51 239	92.21 110	97.41 161	96.95 263	88.82 300	88.62 336	94.75 315	73.87 362	97.42 377	85.20 347	88.55 342	95.35 339

ACMH	87.59 16	90.53 323	89.42 337	93.87 287	96.21 258	87.92 283	97.24 180	96.94 264	88.45 313	83.91 412	96.27 238	71.92 374	98.62 243	84.43 355	89.43 331	95.05 358
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
GBi-Net		91.35 288	90.27 301	94.59 237	96.51 239	91.18 158	97.50 147	96.93 265	88.82 300	89.35 316	94.51 328	73.87 362	97.29 384	86.12 331	88.82 337	95.31 342

test1		91.35 288	90.27 301	94.59 237	96.51 239	91.18 158	97.50 147	96.93 265	88.82 300	89.35 316	94.51 328	73.87 362	97.29 384	86.12 331	88.82 337	95.31 342

FMVSNet3		91.78 262	90.69 286	95.03 211	96.53 235	92.27 108	97.02 200	96.93 265	89.79 266	89.35 316	94.65 321	77.01 333	97.47 372	86.12 331	88.82 337	95.35 339

FMVSNet1		89.88 343	88.31 356	94.59 237	95.41 308	91.18 158	97.50 147	96.93 265	86.62 359	87.41 365	94.51 328	65.94 425	97.29 384	83.04 370	87.43 353	95.31 342

GeoE		93.89 174	93.28 179	95.72 174	96.96 191	89.75 216	98.24 39	96.92 269	89.47 274	92.12 239	97.21 177	84.42 193	98.39 266	87.71 298	96.50 204	99.01 102

SymmetryMVS		95.94 92	95.54 92	97.15 70	97.85 131	92.90 83	97.99 63	96.91 270	95.92 15	96.57 97	97.93 106	85.34 174	99.50 115	94.99 124	96.39 208	99.05 98

miper_enhance_ethall		91.54 277	91.01 268	93.15 321	95.35 314	87.07 306	93.97 390	96.90 271	86.79 357	89.17 323	93.43 387	86.55 148	97.64 356	89.97 248	86.93 358	94.74 382

eth_miper_zixun_eth		91.02 305	90.59 289	92.34 347	95.33 318	84.35 368	94.10 387	96.90 271	88.56 309	88.84 332	94.33 341	84.08 200	97.60 361	88.77 281	84.37 394	95.06 357

TAMVS		94.01 166	93.46 171	95.64 177	96.16 268	90.45 187	96.71 239	96.89 273	89.27 281	93.46 207	96.92 198	87.29 139	97.94 325	88.70 283	95.74 219	98.53 164

miper_ehance_all_eth		91.59 271	91.13 264	92.97 327	95.55 300	86.57 319	94.47 371	96.88 274	87.77 335	88.88 329	94.01 360	86.22 155	97.54 365	89.49 260	86.93 358	94.79 378

v2v482		91.59 271	90.85 275	93.80 290	93.87 383	88.17 276	96.94 210	96.88 274	89.54 271	89.53 311	94.90 307	81.70 256	98.02 308	89.25 269	85.04 383	95.20 350

CNLPA		94.28 151	93.53 166	96.52 103	98.38 84	92.55 98	96.59 255	96.88 274	90.13 256	91.91 245	97.24 175	85.21 178	99.09 169	87.64 304	97.83 153	97.92 225

PAPM		91.52 278	90.30 299	95.20 201	95.30 321	89.83 214	93.38 412	96.85 277	86.26 367	88.59 337	95.80 262	84.88 186	98.15 285	75.67 424	95.93 214	97.63 243

c3_l		91.38 285	90.89 271	92.88 331	95.58 298	86.30 326	94.68 363	96.84 278	88.17 320	88.83 333	94.23 349	85.65 168	97.47 372	89.36 264	84.63 387	94.89 368

pm-mvs1		90.72 317	89.65 332	93.96 279	94.29 373	89.63 219	97.79 101	96.82 279	89.07 286	86.12 390	95.48 284	78.61 315	97.78 343	86.97 319	81.67 413	94.46 389

test_vis1_n		92.37 238	92.26 222	92.72 337	94.75 353	82.64 389	98.02 60	96.80 280	91.18 211	97.77 54	97.93 106	58.02 443	98.29 274	97.63 37	98.21 138	97.23 266

CMPMVS	62.92 21	85.62 396	84.92 391	87.74 421	89.14 442	73.12 451	94.17 385	96.80 280	73.98 447	73.65 449	94.93 305	66.36 419	97.61 360	83.95 363	91.28 308	92.48 426
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
MS-PatchMatch		90.27 330	89.77 326	91.78 368	94.33 370	84.72 365	95.55 329	96.73 282	86.17 369	86.36 387	95.28 290	71.28 379	97.80 341	84.09 360	98.14 142	92.81 418

Effi-MVS+-dtu		93.08 208	93.21 183	92.68 340	96.02 281	83.25 382	97.14 193	96.72 283	93.85 97	91.20 270	93.44 384	83.08 219	98.30 273	91.69 211	95.73 220	96.50 285

TSAR-MVS + GP.		96.69 63	96.49 67	97.27 63	98.31 87	93.39 63	96.79 229	96.72 283	94.17 86	97.44 60	97.66 140	92.76 31	99.33 134	96.86 58	97.76 157	99.08 94

1112_ss		93.37 196	92.42 218	96.21 134	97.05 181	90.99 165	96.31 281	96.72 283	86.87 356	89.83 300	96.69 211	86.51 149	99.14 161	88.12 288	93.67 272	98.50 168

PVSNet	86.66 18	92.24 246	91.74 241	93.73 293	97.77 136	83.69 379	92.88 421	96.72 283	87.91 328	93.00 218	94.86 309	78.51 316	99.05 181	86.53 322	97.45 166	98.47 173

miper_lstm_enhance		90.50 326	90.06 314	91.83 364	95.33 318	83.74 376	93.86 396	96.70 287	87.56 342	87.79 357	93.81 368	83.45 211	96.92 397	87.39 309	84.62 388	94.82 373

v148		90.99 306	90.38 295	92.81 334	93.83 384	85.80 339	96.78 233	96.68 288	89.45 276	88.75 335	93.93 364	82.96 225	97.82 338	87.83 294	83.25 405	94.80 376

ACMH+	87.92 14	90.20 334	89.18 343	93.25 316	96.48 242	86.45 323	96.99 206	96.68 288	88.83 299	84.79 401	96.22 240	70.16 389	98.53 252	84.42 356	88.04 346	94.77 381

CANet_DTU		94.37 149	93.65 161	96.55 100	96.46 245	92.13 114	96.21 289	96.67 290	94.38 83	93.53 203	97.03 193	79.34 299	99.71 62	90.76 231	98.45 128	97.82 236

cl____		90.96 309	90.32 297	92.89 330	95.37 312	86.21 329	94.46 373	96.64 291	87.82 331	88.15 352	94.18 352	82.98 223	97.54 365	87.70 299	85.59 370	94.92 366

HY-MVS	89.66 9	93.87 175	92.95 192	96.63 94	97.10 175	92.49 100	95.64 326	96.64 291	89.05 288	93.00 218	95.79 265	85.77 166	99.45 123	89.16 274	94.35 251	97.96 222

Test_1112_low_res		92.84 223	91.84 236	95.85 161	97.04 183	89.97 209	95.53 331	96.64 291	85.38 379	89.65 307	95.18 295	85.86 162	99.10 166	87.70 299	93.58 277	98.49 170

DIV-MVS_self_test		90.97 308	90.33 296	92.88 331	95.36 313	86.19 331	94.46 373	96.63 294	87.82 331	88.18 350	94.23 349	82.99 222	97.53 367	87.72 296	85.57 371	94.93 364

Fast-Effi-MVS+-dtu		92.29 243	91.99 230	93.21 319	95.27 322	85.52 345	97.03 198	96.63 294	92.09 173	89.11 325	95.14 297	80.33 282	98.08 296	87.54 307	94.74 247	96.03 303

UnsupCasMVSNet_bld		82.13 413	79.46 418	90.14 401	88.00 450	82.47 394	90.89 439	96.62 296	78.94 437	75.61 444	84.40 455	56.63 446	96.31 411	77.30 416	66.77 456	91.63 436

cl22		91.21 296	90.56 291	93.14 322	96.09 277	86.80 311	94.41 375	96.58 297	87.80 333	88.58 338	93.99 362	80.85 270	97.62 359	89.87 251	86.93 358	94.99 359

jason		94.84 134	94.39 141	96.18 136	95.52 301	90.93 169	96.09 296	96.52 298	89.28 280	96.01 124	97.32 167	84.70 188	98.77 213	95.15 120	98.91 108	98.85 134
jason: jason.
tt0805		91.09 301	90.07 313	94.16 266	95.61 296	88.31 268	97.56 138	96.51 299	89.56 270	89.17 323	95.64 274	67.08 417	98.38 267	91.07 223	88.44 343	95.80 311

AUN-MVS		91.76 263	90.75 281	94.81 226	97.00 187	88.57 260	96.65 246	96.49 300	89.63 268	92.15 237	96.12 246	78.66 314	98.50 254	90.83 227	79.18 424	97.36 258

hse-mvs2		93.45 194	92.99 188	94.81 226	97.02 185	88.59 259	96.69 242	96.47 301	95.19 35	96.74 84	96.16 244	83.67 206	98.48 257	95.85 97	79.13 425	97.35 260

SD_0403		90.01 338	90.02 316	89.96 404	95.65 295	76.76 439	95.76 317	96.46 302	90.58 242	86.59 384	96.29 236	82.12 246	94.78 433	73.00 438	93.76 270	98.35 187

EG-PatchMatch MVS		87.02 378	85.44 383	91.76 370	92.67 416	85.00 359	96.08 297	96.45 303	83.41 410	79.52 435	93.49 381	57.10 445	97.72 350	79.34 407	90.87 317	92.56 423

KD-MVS_self_test		85.95 392	84.95 390	88.96 415	89.55 441	79.11 433	95.13 353	96.42 304	85.91 372	84.07 410	90.48 426	70.03 391	94.82 432	80.04 399	72.94 445	92.94 416

pmmvs6		87.81 370	86.19 378	92.69 339	91.32 429	86.30 326	97.34 171	96.41 305	80.59 431	84.05 411	94.37 337	67.37 412	97.67 353	84.75 351	79.51 423	94.09 401

PMMVS		92.86 221	92.34 219	94.42 251	94.92 344	86.73 314	94.53 368	96.38 306	84.78 391	94.27 178	95.12 299	83.13 218	98.40 261	91.47 215	96.49 205	98.12 208

RPSCF		90.75 315	90.86 273	90.42 397	96.84 201	76.29 442	95.61 327	96.34 307	83.89 400	91.38 258	97.87 115	76.45 339	98.78 210	87.16 316	92.23 290	96.20 292

BP-MVS1		95.89 94	95.49 94	97.08 77	96.67 220	93.20 73	98.08 54	96.32 308	94.56 71	96.32 109	97.84 120	84.07 201	99.15 158	96.75 60	98.78 111	98.90 123

MSDG		91.42 283	90.24 303	94.96 219	97.15 173	88.91 252	93.69 404	96.32 308	85.72 375	86.93 380	96.47 227	80.24 283	98.98 187	80.57 396	95.05 240	96.98 271

WBMVS		90.69 320	89.99 317	92.81 334	96.48 242	85.00 359	95.21 350	96.30 310	89.46 275	89.04 326	94.05 359	72.45 372	97.82 338	89.46 261	87.41 355	95.61 322

OurMVSNet-221017-0		90.51 325	90.19 308	91.44 376	93.41 401	81.25 404	96.98 207	96.28 311	91.68 185	86.55 385	96.30 235	74.20 361	97.98 312	88.96 277	87.40 356	95.09 355

MVP-Stereo		90.74 316	90.08 310	92.71 338	93.19 406	88.20 274	95.86 310	96.27 312	86.07 370	84.86 400	94.76 314	77.84 328	97.75 348	83.88 365	98.01 148	92.17 433
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
lupinMVS		94.99 127	94.56 131	96.29 128	96.34 254	91.21 153	95.83 312	96.27 312	88.93 295	96.22 114	96.88 200	86.20 157	98.85 200	95.27 116	99.05 99	98.82 138

BH-untuned		92.94 216	92.62 208	93.92 286	97.22 167	86.16 332	96.40 270	96.25 314	90.06 257	89.79 301	96.17 243	83.19 215	98.35 269	87.19 314	97.27 175	97.24 265

CL-MVSNet_self_test		86.31 387	85.15 387	89.80 406	88.83 445	81.74 402	93.93 393	96.22 315	86.67 358	85.03 398	90.80 424	78.09 324	94.50 434	74.92 427	71.86 447	93.15 414

IS-MVSNet		94.90 129	94.52 135	96.05 143	97.67 142	90.56 183	98.44 22	96.22 315	93.21 122	93.99 188	97.74 131	85.55 171	98.45 258	89.98 247	97.86 152	99.14 84

FA-MVS(test-final)		93.52 189	92.92 193	95.31 198	96.77 214	88.54 262	94.82 360	96.21 317	89.61 269	94.20 181	95.25 293	83.24 213	99.14 161	90.01 246	96.16 210	98.25 196

GA-MVS		91.38 285	90.31 298	94.59 237	94.65 358	87.62 291	94.34 378	96.19 318	90.73 229	90.35 281	93.83 365	71.84 375	97.96 319	87.22 313	93.61 275	98.21 199

LuminaMVS		94.89 130	94.35 143	96.53 101	95.48 303	92.80 87	96.88 219	96.18 319	92.85 147	95.92 127	96.87 202	81.44 259	98.83 203	96.43 72	97.10 182	97.94 224

IterMVS-SCA-FT		90.31 328	89.81 324	91.82 365	95.52 301	84.20 371	94.30 381	96.15 320	90.61 239	87.39 366	94.27 346	75.80 345	96.44 408	87.34 310	86.88 362	94.82 373

IterMVS		90.15 336	89.67 330	91.61 372	95.48 303	83.72 377	94.33 379	96.12 321	89.99 258	87.31 369	94.15 354	75.78 347	96.27 412	86.97 319	86.89 361	94.83 371
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
DP-MVS		92.76 226	91.51 250	96.52 103	98.77 58	90.99 165	97.38 168	96.08 322	82.38 416	89.29 319	97.87 115	83.77 204	99.69 68	81.37 389	96.69 196	98.89 129

pmmvs4		90.93 310	89.85 322	94.17 263	93.34 403	90.79 175	94.60 365	96.02 323	84.62 392	87.45 363	95.15 296	81.88 253	97.45 374	87.70 299	87.87 348	94.27 398

ppachtmachnet_test		88.35 365	87.29 364	91.53 373	92.45 422	83.57 380	93.75 400	95.97 324	84.28 395	85.32 397	94.18 352	79.00 311	96.93 396	75.71 423	84.99 384	94.10 399

Anonymous20240521		86.42 385	85.44 383	89.34 413	90.33 434	79.79 424	96.73 236	95.92 325	83.71 405	83.25 416	91.36 421	63.92 431	96.01 413	78.39 411	85.36 375	92.22 431

ITE_SJBPF				92.43 343	95.34 315	85.37 352		95.92 325	91.47 194	87.75 359	96.39 232	71.00 381	97.96 319	82.36 379	89.86 327	93.97 404

test_fmvs2		89.77 347	89.93 319	89.31 414	93.68 389	76.37 441	97.64 127	95.90 327	89.84 264	91.49 256	96.26 239	58.77 441	97.10 388	94.65 139	91.13 310	94.46 389

USDC		88.94 356	87.83 361	92.27 350	94.66 357	84.96 361	93.86 396	95.90 327	87.34 347	83.40 414	95.56 278	67.43 411	98.19 282	82.64 378	89.67 329	93.66 407

COLMAP_ROB	87.81 15	90.40 327	89.28 340	93.79 291	97.95 124	87.13 305	96.92 213	95.89 329	82.83 413	86.88 382	97.18 178	73.77 365	99.29 141	78.44 410	93.62 274	94.95 360
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
VDD-MVS		93.82 177	93.08 186	96.02 146	97.88 130	89.96 210	97.72 112	95.85 330	92.43 159	95.86 129	98.44 59	68.42 407	99.39 129	96.31 74	94.85 241	98.71 151

VDDNet		93.05 210	92.07 225	96.02 146	96.84 201	90.39 191	98.08 54	95.85 330	86.22 368	95.79 132	98.46 57	67.59 410	99.19 149	94.92 127	94.85 241	98.47 173

mvsmamba		94.57 144	94.14 148	95.87 157	97.03 184	89.93 211	97.84 90	95.85 330	91.34 200	94.79 163	96.80 203	80.67 273	98.81 206	94.85 128	98.12 143	98.85 134

Vis-MVSNet (Re-imp)		94.15 157	93.88 154	94.95 220	97.61 150	87.92 283	98.10 52	95.80 333	92.22 165	93.02 217	97.45 158	84.53 191	97.91 331	88.24 287	97.97 149	99.02 99

MM		97.29 27	96.98 38	98.23 11	98.01 118	95.03 26	98.07 56	95.76 334	97.78 1	97.52 57	98.80 37	88.09 115	99.86 9	99.44 2	99.37 63	99.80 1

KD-MVS_2432*1600		84.81 402	82.64 405	91.31 378	91.07 431	85.34 353	91.22 434	95.75 335	85.56 377	83.09 417	90.21 429	67.21 413	95.89 415	77.18 417	62.48 460	92.69 419

miper_refine_blended		84.81 402	82.64 405	91.31 378	91.07 431	85.34 353	91.22 434	95.75 335	85.56 377	83.09 417	90.21 429	67.21 413	95.89 415	77.18 417	62.48 460	92.69 419

FE-MVS		92.05 254	91.05 266	95.08 207	96.83 203	87.93 282	93.91 395	95.70 337	86.30 365	94.15 185	94.97 302	76.59 337	99.21 147	84.10 359	96.86 187	98.09 214

tpm cat1		88.36 364	87.21 367	91.81 366	95.13 334	80.55 413	92.58 425	95.70 337	74.97 446	87.45 363	91.96 414	78.01 327	98.17 284	80.39 398	88.74 340	96.72 281

our_test_3		88.78 360	87.98 360	91.20 382	92.45 422	82.53 391	93.61 408	95.69 339	85.77 374	84.88 399	93.71 370	79.99 288	96.78 404	79.47 404	86.24 364	94.28 397

BH-w/o		92.14 251	91.75 239	93.31 314	96.99 188	85.73 342	95.67 321	95.69 339	88.73 305	89.26 321	94.82 312	82.97 224	98.07 300	85.26 346	96.32 209	96.13 299

CR-MVSNet		90.82 313	89.77 326	93.95 280	94.45 366	87.19 302	90.23 442	95.68 341	86.89 355	92.40 227	92.36 406	80.91 267	97.05 391	81.09 393	93.95 267	97.60 248

Patchmtry		88.64 362	87.25 365	92.78 336	94.09 376	86.64 315	89.82 446	95.68 341	80.81 428	87.63 361	92.36 406	80.91 267	97.03 392	78.86 408	85.12 380	94.67 384

testing91		91.90 259	91.02 267	94.53 245	96.54 233	86.55 321	95.86 310	95.64 343	91.77 182	91.89 246	93.47 383	69.94 392	98.86 198	90.23 245	93.86 269	98.18 201

BH-RMVSNet		92.72 228	91.97 231	94.97 218	97.16 171	87.99 281	96.15 294	95.60 344	90.62 238	91.87 247	97.15 181	78.41 318	98.57 249	83.16 368	97.60 159	98.36 185

PVSNet_0	82.17 19	85.46 397	83.64 400	90.92 386	95.27 322	79.49 429	90.55 440	95.60 344	83.76 404	83.00 419	89.95 431	71.09 380	97.97 315	82.75 376	60.79 462	95.31 342

guyue		95.17 120	94.96 116	95.82 163	96.97 190	89.65 218	97.56 138	95.58 346	94.82 56	95.72 134	97.42 162	82.90 226	98.84 202	96.71 63	96.93 186	98.96 110

SCA		91.84 261	91.18 263	93.83 288	95.59 297	84.95 362	94.72 362	95.58 346	90.82 225	92.25 235	93.69 372	75.80 345	98.10 291	86.20 328	95.98 212	98.45 175

MonoMVSNet		91.92 257	91.77 237	92.37 344	92.94 410	83.11 385	97.09 196	95.55 348	92.91 143	90.85 273	94.55 325	81.27 263	96.52 407	93.01 184	87.76 349	97.47 254

AllTest		90.23 332	88.98 346	93.98 276	97.94 125	86.64 315	96.51 259	95.54 349	85.38 379	85.49 394	96.77 205	70.28 387	99.15 158	80.02 400	92.87 279	96.15 297

TestCases				93.98 276	97.94 125	86.64 315		95.54 349	85.38 379	85.49 394	96.77 205	70.28 387	99.15 158	80.02 400	92.87 279	96.15 297

mmtdpeth		89.70 349	88.96 347	91.90 361	95.84 289	84.42 367	97.46 158	95.53 351	90.27 251	94.46 174	90.50 425	69.74 396	98.95 188	97.39 49	69.48 451	92.34 427

tpmvs		89.83 346	89.15 344	91.89 362	94.92 344	80.30 417	93.11 417	95.46 352	86.28 366	88.08 353	92.65 396	80.44 279	98.52 253	81.47 385	89.92 326	96.84 277

pmmvs5		89.86 345	88.87 350	92.82 333	92.86 412	86.23 328	96.26 284	95.39 353	84.24 396	87.12 371	94.51 328	74.27 360	97.36 381	87.61 306	87.57 351	94.86 369

PatchmatchNet		91.91 258	91.35 252	93.59 302	95.38 310	84.11 372	93.15 416	95.39 353	89.54 271	92.10 240	93.68 374	82.82 229	98.13 286	84.81 350	95.32 233	98.52 165
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
tpmrst		91.44 282	91.32 254	91.79 367	95.15 332	79.20 432	93.42 411	95.37 355	88.55 310	93.49 206	93.67 375	82.49 238	98.27 275	90.41 240	89.34 332	97.90 226

Anonymous20231206		87.09 377	86.14 379	89.93 405	91.22 430	80.35 415	96.11 295	95.35 356	83.57 407	84.16 406	93.02 391	73.54 367	95.61 423	72.16 440	86.14 366	93.84 406

MIMVSNet1		84.93 400	83.05 402	90.56 395	89.56 440	84.84 364	95.40 336	95.35 356	83.91 399	80.38 431	92.21 411	57.23 444	93.34 447	70.69 446	82.75 411	93.50 409

TDRefinement		86.53 381	84.76 393	91.85 363	82.23 463	84.25 369	96.38 272	95.35 356	84.97 388	84.09 409	94.94 304	65.76 426	98.34 272	84.60 354	74.52 441	92.97 415

TR-MVS		91.48 281	90.59 289	94.16 266	96.40 248	87.33 295	95.67 321	95.34 359	87.68 339	91.46 257	95.52 281	76.77 336	98.35 269	82.85 373	93.61 275	96.79 279

EPNet_dtu		91.71 264	91.28 257	92.99 326	93.76 386	83.71 378	96.69 242	95.28 360	93.15 129	87.02 376	95.95 254	83.37 212	97.38 380	79.46 405	96.84 188	97.88 228
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
FMVSNet5		87.29 374	85.79 381	91.78 368	94.80 351	87.28 297	95.49 333	95.28 360	84.09 398	83.85 413	91.82 415	62.95 434	94.17 438	78.48 409	85.34 376	93.91 405

MDTV_nov1_ep13			90.76 279		95.22 326	80.33 416	93.03 419	95.28 360	88.14 323	92.84 224	93.83 365	81.34 260	98.08 296	82.86 371	94.34 252

LF4IMVS		87.94 368	87.25 365	89.98 403	92.38 424	80.05 423	94.38 376	95.25 363	87.59 341	84.34 403	94.74 316	64.31 430	97.66 355	84.83 349	87.45 352	92.23 430

TransMVSNet (Re)		88.94 356	87.56 362	93.08 324	94.35 369	88.45 266	97.73 109	95.23 364	87.47 343	84.26 405	95.29 288	79.86 291	97.33 382	79.44 406	74.44 442	93.45 411

test20.03		86.14 390	85.40 385	88.35 416	90.12 435	80.06 422	95.90 309	95.20 365	88.59 306	81.29 426	93.62 377	71.43 378	92.65 451	71.26 444	81.17 416	92.34 427

new-patchmatchnet		83.18 409	81.87 412	87.11 424	86.88 454	75.99 443	93.70 402	95.18 366	85.02 387	77.30 443	88.40 442	65.99 424	93.88 443	74.19 432	70.18 449	91.47 441

MDA-MVSNet_test_wron		85.87 394	84.23 397	90.80 392	92.38 424	82.57 390	93.17 414	95.15 367	82.15 417	67.65 455	92.33 409	78.20 320	95.51 426	77.33 414	79.74 420	94.31 396

YYNet1		85.87 394	84.23 397	90.78 393	92.38 424	82.46 395	93.17 414	95.14 368	82.12 418	67.69 453	92.36 406	78.16 323	95.50 427	77.31 415	79.73 421	94.39 392

Baseline_NR-MVSNet		91.20 297	90.62 287	92.95 328	93.83 384	88.03 280	97.01 203	95.12 369	88.42 314	89.70 304	95.13 298	83.47 209	97.44 375	89.66 257	83.24 406	93.37 412

thres200		92.23 247	91.39 251	94.75 233	97.61 150	89.03 250	96.60 254	95.09 370	92.08 174	93.28 212	94.00 361	78.39 319	99.04 184	81.26 392	94.18 258	96.19 293

ADS-MVSNet		89.89 342	88.68 352	93.53 306	95.86 284	84.89 363	90.93 437	95.07 371	83.23 411	91.28 266	91.81 416	79.01 309	97.85 334	79.52 402	91.39 306	97.84 233

pmmvs-eth3d		86.22 388	84.45 395	91.53 373	88.34 449	87.25 299	94.47 371	95.01 372	83.47 408	79.51 436	89.61 434	69.75 395	95.71 420	83.13 369	76.73 434	91.64 435

Anonymous202405211		92.07 253	90.83 277	95.76 168	98.19 104	88.75 255	97.58 134	95.00 373	86.00 371	93.64 198	97.45 158	66.24 422	99.53 107	90.68 234	92.71 284	99.01 102

MDA-MVSNet-bldmvs		85.00 399	82.95 404	91.17 384	93.13 408	83.33 381	94.56 367	95.00 373	84.57 393	65.13 459	92.65 396	70.45 386	95.85 417	73.57 435	77.49 430	94.33 394

ambc				86.56 427	83.60 460	70.00 454	85.69 458	94.97 375		80.60 430	88.45 441	37.42 462	96.84 401	82.69 377	75.44 439	92.86 417

testgi		87.97 367	87.21 367	90.24 400	92.86 412	80.76 408	96.67 245	94.97 375	91.74 183	85.52 393	95.83 260	62.66 436	94.47 436	76.25 421	88.36 344	95.48 325

myMVS_eth3d28		91.52 278	90.97 269	93.17 320	96.91 193	83.24 383	95.61 327	94.96 377	92.24 164	91.98 243	93.28 388	69.31 397	98.40 261	88.71 282	95.68 222	97.88 228

dp		88.90 358	88.26 358	90.81 390	94.58 362	76.62 440	92.85 422	94.93 378	85.12 385	90.07 295	93.07 390	75.81 344	98.12 289	80.53 397	87.42 354	97.71 240

test_fmvs3		83.21 408	83.02 403	83.78 431	86.77 455	68.34 457	96.76 234	94.91 379	86.49 361	84.14 408	89.48 435	36.04 463	91.73 453	91.86 205	80.77 418	91.26 443

test_0402		86.46 384	84.79 392	91.45 375	95.02 338	85.55 344	96.29 283	94.89 380	80.90 425	82.21 422	93.97 363	68.21 408	97.29 384	62.98 454	88.68 341	91.51 438

tfpn200view9		92.38 237	91.52 248	94.95 220	97.85 131	89.29 239	97.41 161	94.88 381	92.19 170	93.27 213	94.46 333	78.17 321	99.08 172	81.40 386	94.08 262	96.48 286

CVMVSNet		91.23 295	91.75 239	89.67 407	95.77 290	74.69 444	96.44 260	94.88 381	85.81 373	92.18 236	97.64 144	79.07 304	95.58 425	88.06 290	95.86 217	98.74 148

thres400		92.42 235	91.52 248	95.12 206	97.85 131	89.29 239	97.41 161	94.88 381	92.19 170	93.27 213	94.46 333	78.17 321	99.08 172	81.40 386	94.08 262	96.98 271

tt0320		85.39 398	83.12 401	92.19 354	93.44 400	85.79 340	96.19 291	94.87 384	71.19 453	82.92 420	91.76 418	58.43 442	96.81 402	81.03 394	78.26 429	93.98 403

EPNet		95.20 118	94.56 131	97.14 71	92.80 414	92.68 93	97.85 89	94.87 384	96.64 8	92.46 226	97.80 126	86.23 154	99.65 74	93.72 163	98.62 119	99.10 91
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
testing99		91.62 269	90.72 284	94.32 256	96.48 242	86.11 337	95.81 313	94.76 386	91.55 187	91.75 251	93.44 384	68.55 405	98.82 204	90.43 239	93.69 271	98.04 218

sc_t1		86.48 383	84.10 399	93.63 299	93.45 399	85.76 341	96.79 229	94.71 387	73.06 451	86.45 386	94.35 338	55.13 449	97.95 323	84.38 357	78.55 428	97.18 267

SixPastTwentyTwo		89.15 354	88.54 354	90.98 385	93.49 396	80.28 419	96.70 240	94.70 388	90.78 226	84.15 407	95.57 277	71.78 376	97.71 351	84.63 353	85.07 381	94.94 362

thres100view900		92.43 234	91.58 245	94.98 216	97.92 127	89.37 235	97.71 114	94.66 389	92.20 168	93.31 211	94.90 307	78.06 325	99.08 172	81.40 386	94.08 262	96.48 286

thres600view7		92.49 232	91.60 244	95.18 202	97.91 128	89.47 229	97.65 123	94.66 389	92.18 172	93.33 210	94.91 306	78.06 325	99.10 166	81.61 382	94.06 266	96.98 271

PatchT		88.87 359	87.42 363	93.22 318	94.08 377	85.10 357	89.51 447	94.64 391	81.92 419	92.36 230	88.15 445	80.05 287	97.01 394	72.43 439	93.65 273	97.54 251

baseline1		92.82 224	91.90 234	95.55 184	97.20 169	90.77 176	97.19 188	94.58 392	92.20 168	92.36 230	96.34 234	84.16 199	98.21 279	89.20 272	83.90 401	97.68 242

AstraMVS		94.82 136	94.64 127	95.34 197	96.36 253	88.09 279	97.58 134	94.56 393	94.98 45	95.70 137	97.92 109	81.93 252	98.93 191	96.87 57	95.88 215	98.99 106

UBG		91.55 275	90.76 279	93.94 282	96.52 238	85.06 358	95.22 348	94.54 394	90.47 247	91.98 243	92.71 395	72.02 373	98.74 220	88.10 289	95.26 235	98.01 220

Gipuma		67.86 429	65.41 431	75.18 444	92.66 417	73.45 448	66.50 465	94.52 395	53.33 464	57.80 465	66.07 465	30.81 465	89.20 457	48.15 463	78.88 427	62.90 465
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
testing11		91.68 267	90.75 281	94.47 247	96.53 235	86.56 320	95.76 317	94.51 396	91.10 218	91.24 268	93.59 378	68.59 404	98.86 198	91.10 222	94.29 254	98.00 221

CostFormer		91.18 300	90.70 285	92.62 341	94.84 349	81.76 401	94.09 388	94.43 397	84.15 397	92.72 225	93.77 369	79.43 298	98.20 280	90.70 233	92.18 293	97.90 226

tpm2		89.96 339	89.21 342	92.23 353	94.91 346	81.25 404	93.78 399	94.42 398	80.62 430	91.56 254	93.44 384	76.44 340	97.94 325	85.60 340	92.08 297	97.49 252

testing3-2		92.10 252	92.05 226	92.27 350	97.71 140	79.56 426	97.42 160	94.41 399	93.53 110	93.22 215	95.49 282	69.16 399	99.11 164	93.25 174	94.22 256	98.13 206

MGCNet		96.74 60	96.31 77	98.02 19	96.87 197	94.65 30	97.58 134	94.39 400	96.47 11	97.16 69	98.39 63	87.53 132	99.87 7	98.97 19	99.41 55	99.55 39

JIA-IIPM		88.26 366	87.04 370	91.91 360	93.52 394	81.42 403	89.38 448	94.38 401	80.84 427	90.93 272	80.74 457	79.22 301	97.92 328	82.76 375	91.62 301	96.38 289

dmvs_re		90.21 333	89.50 335	92.35 345	95.47 307	85.15 355	95.70 320	94.37 402	90.94 224	88.42 340	93.57 379	74.63 357	95.67 422	82.80 374	89.57 330	96.22 291

Patchmatch-test		89.42 352	87.99 359	93.70 296	95.27 322	85.11 356	88.98 449	94.37 402	81.11 424	87.10 374	93.69 372	82.28 242	97.50 370	74.37 430	94.76 245	98.48 172

LCM-MVSNet		72.55 422	69.39 426	82.03 433	70.81 473	65.42 462	90.12 444	94.36 404	55.02 463	65.88 457	81.72 456	24.16 471	89.96 454	74.32 431	68.10 454	90.71 446

ADS-MVSNet2		89.45 351	88.59 353	92.03 357	95.86 284	82.26 397	90.93 437	94.32 405	83.23 411	91.28 266	91.81 416	79.01 309	95.99 414	79.52 402	91.39 306	97.84 233

mvs5depth		86.53 381	85.08 388	90.87 387	88.74 447	82.52 392	91.91 430	94.23 406	86.35 364	87.11 373	93.70 371	66.52 418	97.76 346	81.37 389	75.80 436	92.31 429

EU-MVSNet		88.72 361	88.90 349	88.20 418	93.15 407	74.21 446	96.63 251	94.22 407	85.18 383	87.32 368	95.97 252	76.16 342	94.98 431	85.27 345	86.17 365	95.41 332

tt0320-xc		84.83 401	82.33 409	92.31 348	93.66 390	86.20 330	96.17 293	94.06 408	71.26 452	82.04 424	92.22 410	55.07 450	96.72 405	81.49 384	75.04 440	94.02 402

MIMVSNet		88.50 363	86.76 373	93.72 295	94.84 349	87.77 289	91.39 432	94.05 409	86.41 363	87.99 355	92.59 399	63.27 432	95.82 419	77.44 413	92.84 281	97.57 250

OpenMVS_ROB	81.14 20	84.42 404	82.28 410	90.83 388	90.06 436	84.05 374	95.73 319	94.04 410	73.89 449	80.17 434	91.53 420	59.15 440	97.64 356	66.92 452	89.05 334	90.80 445

TinyColmap		86.82 379	85.35 386	91.21 380	94.91 346	82.99 387	93.94 392	94.02 411	83.58 406	81.56 425	94.68 318	62.34 437	98.13 286	75.78 422	87.35 357	92.52 425

ETVMVS		90.52 324	89.14 345	94.67 236	96.81 207	87.85 287	95.91 308	93.97 412	89.71 267	92.34 233	92.48 401	65.41 428	97.96 319	81.37 389	94.27 255	98.21 199

IB-MVS	87.33 17	89.91 340	88.28 357	94.79 230	95.26 325	87.70 290	95.12 354	93.95 413	89.35 279	87.03 375	92.49 400	70.74 384	99.19 149	89.18 273	81.37 415	97.49 252
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		87.13 376	87.02 371	87.47 422	95.16 329	73.21 450	95.00 356	93.93 414	88.55 310	86.96 377	91.99 412	75.90 343	94.00 440	61.59 456	94.11 259	95.20 350

myMVS_eth3d		87.18 375	86.38 376	89.58 408	95.16 329	79.53 427	95.00 356	93.93 414	88.55 310	86.96 377	91.99 412	56.23 447	94.00 440	75.47 426	94.11 259	95.20 350

testing222		90.31 328	88.96 347	94.35 253	96.54 233	87.29 296	95.50 332	93.84 416	90.97 221	91.75 251	92.96 392	62.18 438	98.00 310	82.86 371	94.08 262	97.76 238

test_f		80.57 415	79.62 417	83.41 432	83.38 461	67.80 459	93.57 409	93.72 417	80.80 429	77.91 442	87.63 448	33.40 464	92.08 452	87.14 317	79.04 426	90.34 447

LCM-MVSNet-Re		92.50 230	92.52 214	92.44 342	96.82 205	81.89 400	96.92 213	93.71 418	92.41 160	84.30 404	94.60 323	85.08 180	97.03 392	91.51 213	97.36 168	98.40 181

tpm		90.25 331	89.74 329	91.76 370	93.92 380	79.73 425	93.98 389	93.54 419	88.28 317	91.99 242	93.25 389	77.51 331	97.44 375	87.30 312	87.94 347	98.12 208

ET-MVSNet_ETH3D		91.49 280	90.11 309	95.63 178	96.40 248	91.57 138	95.34 339	93.48 420	90.60 241	75.58 445	95.49 282	80.08 286	96.79 403	94.25 151	89.76 328	98.52 165

LFMVS		93.60 184	92.63 207	96.52 103	98.13 110	91.27 150	97.94 76	93.39 421	90.57 243	96.29 111	98.31 76	69.00 400	99.16 156	94.18 152	95.87 216	99.12 88

MVStest1		82.38 412	80.04 416	89.37 411	87.63 452	82.83 388	95.03 355	93.37 422	73.90 448	73.50 450	94.35 338	62.89 435	93.25 449	73.80 433	65.92 457	92.04 434

FE-MVSNET		83.85 405	81.97 411	89.51 409	87.19 453	83.19 384	95.21 350	93.17 423	83.45 409	78.90 438	89.05 438	65.46 427	93.84 444	69.71 448	75.56 438	91.51 438

Patchmatch-RL test		87.38 373	86.24 377	90.81 390	88.74 447	78.40 436	88.12 456	93.17 423	87.11 352	82.17 423	89.29 436	81.95 250	95.60 424	88.64 284	77.02 431	98.41 180

ttmdpeth		85.91 393	84.76 393	89.36 412	89.14 442	80.25 420	95.66 324	93.16 425	83.77 403	83.39 415	95.26 292	66.24 422	95.26 430	80.65 395	75.57 437	92.57 422

test-LLR		91.42 283	91.19 262	92.12 355	94.59 360	80.66 410	94.29 382	92.98 426	91.11 216	90.76 275	92.37 403	79.02 307	98.07 300	88.81 279	96.74 193	97.63 243

test-mter		90.19 335	89.54 334	92.12 355	94.59 360	80.66 410	94.29 382	92.98 426	87.68 339	90.76 275	92.37 403	67.67 409	98.07 300	88.81 279	96.74 193	97.63 243

WB-MVSnew		89.88 343	89.56 333	90.82 389	94.57 363	83.06 386	95.65 325	92.85 428	87.86 330	90.83 274	94.10 355	79.66 295	96.88 399	76.34 420	94.19 257	92.54 424

testing3		87.67 371	86.88 372	90.05 402	96.14 271	80.71 409	97.10 195	92.85 428	90.15 255	87.54 362	94.55 325	55.70 448	94.10 439	73.77 434	94.10 261	95.35 339

test_method		66.11 430	64.89 432	69.79 447	72.62 471	35.23 479	65.19 466	92.83 430	20.35 469	65.20 458	88.08 446	43.14 460	82.70 464	73.12 437	63.46 459	91.45 442

test0.0.03 1		89.37 353	88.70 351	91.41 377	92.47 421	85.63 343	95.22 348	92.70 431	91.11 216	86.91 381	93.65 376	79.02 307	93.19 450	78.00 412	89.18 333	95.41 332

new_pmnet		82.89 410	81.12 415	88.18 419	89.63 439	80.18 421	91.77 431	92.57 432	76.79 444	75.56 446	88.23 444	61.22 439	94.48 435	71.43 442	82.92 409	89.87 448

mvsany_test1		93.93 173	93.98 152	93.78 292	94.94 343	86.80 311	94.62 364	92.55 433	88.77 304	96.85 79	98.49 53	88.98 97	98.08 296	95.03 122	95.62 224	96.46 288

thisisatest0515		92.29 243	91.30 256	95.25 200	96.60 224	88.90 253	94.36 377	92.32 434	87.92 327	93.43 208	94.57 324	77.28 332	99.00 185	89.42 263	95.86 217	97.86 232

thisisatest0530		93.03 211	92.21 223	95.49 189	97.07 176	89.11 248	97.49 155	92.19 435	90.16 254	94.09 186	96.41 230	76.43 341	99.05 181	90.38 241	95.68 222	98.31 193

tttt0517		92.96 214	92.33 220	94.87 223	97.11 174	87.16 304	97.97 72	92.09 436	90.63 237	93.88 192	97.01 194	76.50 338	99.06 178	90.29 244	95.45 231	98.38 183

K. test v3		87.64 372	86.75 374	90.32 399	93.02 409	79.48 430	96.61 252	92.08 437	90.66 235	80.25 433	94.09 357	67.21 413	96.65 406	85.96 336	80.83 417	94.83 371

TESTMET0.1,1		90.06 337	89.42 337	91.97 358	94.41 368	80.62 412	94.29 382	91.97 438	87.28 349	90.44 279	92.47 402	68.79 401	97.67 353	88.50 286	96.60 199	97.61 247

PM-MVS		83.48 407	81.86 413	88.31 417	87.83 451	77.59 438	93.43 410	91.75 439	86.91 354	80.63 429	89.91 432	44.42 459	95.84 418	85.17 348	76.73 434	91.50 440

baseline2		91.63 268	90.86 273	93.94 282	94.33 370	86.32 325	95.92 307	91.64 440	89.37 278	86.94 379	94.69 317	81.62 257	98.69 230	88.64 284	94.57 250	96.81 278

APD_test1		79.31 417	77.70 420	84.14 430	89.11 444	69.07 456	92.36 429	91.50 441	69.07 455	73.87 448	92.63 398	39.93 461	94.32 437	70.54 447	80.25 419	89.02 450

FPMVS		71.27 423	69.85 425	75.50 443	74.64 468	59.03 468	91.30 433	91.50 441	58.80 460	57.92 464	88.28 443	29.98 467	85.53 463	53.43 461	82.84 410	81.95 456

door								91.13 443

door-mid								91.06 444

EGC-MVSNET		68.77 428	63.01 434	86.07 429	92.49 420	82.24 398	93.96 391	90.96 445	0.71 474	2.62 475	90.89 423	53.66 451	93.46 445	57.25 459	84.55 391	82.51 455

mvsany_test3		83.59 406	82.44 408	87.03 425	83.80 458	73.82 447	93.70 402	90.92 446	86.42 362	82.51 421	90.26 428	46.76 458	95.71 420	90.82 228	76.76 433	91.57 437

pmmvs3		79.97 416	77.50 421	87.39 423	82.80 462	79.38 431	92.70 424	90.75 447	70.69 454	78.66 439	87.47 450	51.34 454	93.40 446	73.39 436	69.65 450	89.38 449

UWE-MVS		89.91 340	89.48 336	91.21 380	95.88 283	78.23 437	94.91 359	90.26 448	89.11 285	92.35 232	94.52 327	68.76 402	97.96 319	83.95 363	95.59 225	97.42 256

DSMNet-mixed		86.34 386	86.12 380	87.00 426	89.88 438	70.43 452	94.93 358	90.08 449	77.97 441	85.42 396	92.78 394	74.44 359	93.96 442	74.43 429	95.14 236	96.62 282

MVS-HIRNet		82.47 411	81.21 414	86.26 428	95.38 310	69.21 455	88.96 450	89.49 450	66.28 457	80.79 428	74.08 462	68.48 406	97.39 379	71.93 441	95.47 230	92.18 432

WB-MVS		76.77 419	76.63 422	77.18 438	85.32 456	56.82 470	94.53 368	89.39 451	82.66 415	71.35 451	89.18 437	75.03 352	88.88 458	35.42 467	66.79 455	85.84 452

test1111		93.19 203	92.82 197	94.30 259	97.58 156	84.56 366	98.21 43	89.02 452	93.53 110	94.58 168	98.21 83	72.69 369	99.05 181	93.06 180	98.48 126	99.28 73

SSC-MVS		76.05 420	75.83 423	76.72 442	84.77 457	56.22 471	94.32 380	88.96 453	81.82 421	70.52 452	88.91 439	74.79 356	88.71 459	33.69 468	64.71 458	85.23 453

ECVR-MVS		93.19 203	92.73 203	94.57 242	97.66 144	85.41 349	98.21 43	88.23 454	93.43 115	94.70 165	98.21 83	72.57 370	99.07 176	93.05 181	98.49 124	99.25 76

EPMVS		90.70 318	89.81 324	93.37 312	94.73 355	84.21 370	93.67 405	88.02 455	89.50 273	92.38 229	93.49 381	77.82 329	97.78 343	86.03 334	92.68 285	98.11 213

ANet_high		63.94 432	59.58 435	77.02 439	61.24 475	66.06 460	85.66 459	87.93 456	78.53 439	42.94 467	71.04 464	25.42 470	80.71 466	52.60 462	30.83 468	84.28 454

PMMVS2		70.19 424	66.92 428	80.01 434	76.35 467	65.67 461	86.22 457	87.58 457	64.83 459	62.38 460	80.29 459	26.78 469	88.49 461	63.79 453	54.07 464	85.88 451

lessismore_v0				90.45 396	91.96 427	79.09 434		87.19 458		80.32 432	94.39 335	66.31 421	97.55 364	84.00 362	76.84 432	94.70 383

PMVS	53.92 22	58.58 433	55.40 436	68.12 448	51.00 476	48.64 473	78.86 462	87.10 459	46.77 465	35.84 471	74.28 461	8.76 475	86.34 462	42.07 465	73.91 443	69.38 462
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
UWE-MVS-28		86.81 380	86.41 375	88.02 420	92.87 411	74.60 445	95.38 338	86.70 460	88.17 320	87.28 370	94.67 320	70.83 383	93.30 448	67.45 450	94.31 253	96.17 294

test_vis1_rt		86.16 389	85.06 389	89.46 410	93.47 398	80.46 414	96.41 266	86.61 461	85.22 382	79.15 437	88.64 440	52.41 453	97.06 390	93.08 179	90.57 319	90.87 444

testf1		69.31 426	66.76 429	76.94 440	78.61 465	61.93 464	88.27 454	86.11 462	55.62 461	59.69 461	85.31 453	20.19 473	89.32 455	57.62 457	69.44 452	79.58 457

APD_test2		69.31 426	66.76 429	76.94 440	78.61 465	61.93 464	88.27 454	86.11 462	55.62 461	59.69 461	85.31 453	20.19 473	89.32 455	57.62 457	69.44 452	79.58 457

gg-mvs-nofinetune		87.82 369	85.61 382	94.44 249	94.46 365	89.27 242	91.21 436	84.61 464	80.88 426	89.89 299	74.98 460	71.50 377	97.53 367	85.75 339	97.21 177	96.51 284

dmvs_testset		81.38 414	82.60 407	77.73 437	91.74 428	51.49 472	93.03 419	84.21 465	89.07 286	78.28 441	91.25 422	76.97 334	88.53 460	56.57 460	82.24 412	93.16 413

GG-mvs-BLEND				93.62 300	93.69 388	89.20 244	92.39 428	83.33 466		87.98 356	89.84 433	71.00 381	96.87 400	82.08 381	95.40 232	94.80 376

MTMP							97.86 86	82.03 467

DeepMVS_CX				74.68 445	90.84 433	64.34 463		81.61 468	65.34 458	67.47 456	88.01 447	48.60 457	80.13 467	62.33 455	73.68 444	79.58 457

E-PMN		53.28 434	52.56 438	55.43 451	74.43 469	47.13 474	83.63 461	76.30 469	42.23 466	42.59 468	62.22 467	28.57 468	74.40 468	31.53 469	31.51 467	44.78 466

test2506		91.60 270	90.78 278	94.04 272	97.66 144	83.81 375	98.27 33	75.53 470	93.43 115	95.23 151	98.21 83	67.21 413	99.07 176	93.01 184	98.49 124	99.25 76

EMVS		52.08 436	51.31 439	54.39 452	72.62 471	45.39 476	83.84 460	75.51 471	41.13 467	40.77 469	59.65 468	30.08 466	73.60 469	28.31 471	29.90 469	44.18 467

test_vis3_rt		72.73 421	70.55 424	79.27 435	80.02 464	68.13 458	93.92 394	74.30 472	76.90 443	58.99 463	73.58 463	20.29 472	95.37 428	84.16 358	72.80 446	74.31 460

MVE	50.73 23	53.25 435	48.81 440	66.58 450	65.34 474	57.50 469	72.49 464	70.94 473	40.15 468	39.28 470	63.51 466	6.89 477	73.48 470	38.29 466	42.38 466	68.76 464
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
tmp_tt		51.94 437	53.82 437	46.29 453	33.73 477	45.30 477	78.32 463	67.24 474	18.02 470	50.93 466	87.05 451	52.99 452	53.11 472	70.76 445	25.29 470	40.46 468

kuosan		65.27 431	64.66 433	67.11 449	83.80 458	61.32 467	88.53 453	60.77 475	68.22 456	67.67 454	80.52 458	49.12 456	70.76 471	29.67 470	53.64 465	69.26 463

dongtai		69.99 425	69.33 427	71.98 446	88.78 446	61.64 466	89.86 445	59.93 476	75.67 445	74.96 447	85.45 452	50.19 455	81.66 465	43.86 464	55.27 463	72.63 461

N_pmnet		78.73 418	78.71 419	78.79 436	92.80 414	46.50 475	94.14 386	43.71 477	78.61 438	80.83 427	91.66 419	74.94 355	96.36 410	67.24 451	84.45 393	93.50 409

wuyk23d		25.11 438	24.57 442	26.74 454	73.98 470	39.89 478	57.88 467	9.80 478	12.27 471	10.39 472	6.97 474	7.03 476	36.44 473	25.43 472	17.39 471	3.89 471

testmvs		13.36 440	16.33 443	4.48 456	5.04 478	2.26 481	93.18 413	3.28 479	2.70 472	8.24 473	21.66 470	2.29 479	2.19 474	7.58 473	2.96 472	9.00 470

test123		13.04 441	15.66 444	5.18 455	4.51 479	3.45 480	92.50 427	1.81 480	2.50 473	7.58 474	20.15 471	3.67 478	2.18 475	7.13 474	1.07 473	9.90 469

mmdepth		0.00 444	0.00 447	0.00 457	0.00 480	0.00 482	0.00 468	0.00 481	0.00 475	0.00 476	0.00 475	0.00 480	0.00 476	0.00 475	0.00 474	0.00 472

monomultidepth		0.00 444	0.00 447	0.00 457	0.00 480	0.00 482	0.00 468	0.00 481	0.00 475	0.00 476	0.00 475	0.00 480	0.00 476	0.00 475	0.00 474	0.00 472

test_blank		0.00 444	0.00 447	0.00 457	0.00 480	0.00 482	0.00 468	0.00 481	0.00 475	0.00 476	0.00 475	0.00 480	0.00 476	0.00 475	0.00 474	0.00 472

uanet_test		0.00 444	0.00 447	0.00 457	0.00 480	0.00 482	0.00 468	0.00 481	0.00 475	0.00 476	0.00 475	0.00 480	0.00 476	0.00 475	0.00 474	0.00 472

DCPMVS		0.00 444	0.00 447	0.00 457	0.00 480	0.00 482	0.00 468	0.00 481	0.00 475	0.00 476	0.00 475	0.00 480	0.00 476	0.00 475	0.00 474	0.00 472

pcd_1.5k_mvsjas		7.39 443	9.85 446	0.00 457	0.00 480	0.00 482	0.00 468	0.00 481	0.00 475	0.00 476	0.00 475	88.65 105	0.00 476	0.00 475	0.00 474	0.00 472

sosnet-low-res		0.00 444	0.00 447	0.00 457	0.00 480	0.00 482	0.00 468	0.00 481	0.00 475	0.00 476	0.00 475	0.00 480	0.00 476	0.00 475	0.00 474	0.00 472

sosnet		0.00 444	0.00 447	0.00 457	0.00 480	0.00 482	0.00 468	0.00 481	0.00 475	0.00 476	0.00 475	0.00 480	0.00 476	0.00 475	0.00 474	0.00 472

uncertanet		0.00 444	0.00 447	0.00 457	0.00 480	0.00 482	0.00 468	0.00 481	0.00 475	0.00 476	0.00 475	0.00 480	0.00 476	0.00 475	0.00 474	0.00 472

Regformer		0.00 444	0.00 447	0.00 457	0.00 480	0.00 482	0.00 468	0.00 481	0.00 475	0.00 476	0.00 475	0.00 480	0.00 476	0.00 475	0.00 474	0.00 472

n2								0.00 481

nn								0.00 481

ab-mvs-re		8.06 442	10.74 445	0.00 457	0.00 480	0.00 482	0.00 468	0.00 481	0.00 475	0.00 476	96.69 211	0.00 480	0.00 476	0.00 475	0.00 474	0.00 472

uanet		0.00 444	0.00 447	0.00 457	0.00 480	0.00 482	0.00 468	0.00 481	0.00 475	0.00 476	0.00 475	0.00 480	0.00 476	0.00 475	0.00 474	0.00 472

WAC-MVS						79.53 427								75.56 425

PC_three_1452									90.77 227	98.89 25	98.28 81	96.24 1	98.35 269	95.76 101	99.58 23	99.59 28

eth-test2					0.00 480

eth-test					0.00 480

OPU-MVS				98.55 3	98.82 57	96.86 3	98.25 36				98.26 82	96.04 2	99.24 144	95.36 115	99.59 19	99.56 36

test_0728_THIRD									94.78 60	98.73 29	98.87 30	95.87 4	99.84 23	97.45 45	99.72 2	99.77 2

GSMVS																98.45 175

test_part2					99.28 27	95.74 8				98.10 43

sam_mvs1												82.76 230				98.45 175

sam_mvs												81.94 251

test_post1							92.81 423				16.58 473	80.53 277	97.68 352	86.20 328

test_post											17.58 472	81.76 254	98.08 296

patchmatchnet-post											90.45 427	82.65 235	98.10 291

gm-plane-assit					93.22 405	78.89 435			84.82 390		93.52 380		98.64 239	87.72 296

test9_res														94.81 133	99.38 60	99.45 55

agg_prior2														93.94 157	99.38 60	99.50 48

test_prior4						93.66 58	96.42 265

test_prior2							96.35 275		92.80 150	96.03 121	97.59 150	92.01 47		95.01 123	99.38 60

旧先验2							95.94 305		81.66 422	97.34 65			98.82 204	92.26 190

新几何2							95.79 315

原ACMM2							95.67 321

testdata2													99.67 72	85.96 336

segment_acmp												92.89 30

testdata1							95.26 347		93.10 132

plane_prior7					96.21 258	89.98 207

plane_prior6					96.10 276	90.00 203						81.32 261

plane_prior4											96.64 214

plane_prior3						90.00 203			94.46 77	91.34 260

plane_prior2							97.74 107		94.85 52

plane_prior1					96.14 271

plane_prior						89.99 205	97.24 180		94.06 89						92.16 294

HQP5-MVS						89.33 237

HQP-NCC					95.86 284		96.65 246		93.55 106	90.14 284

ACMP_Plane					95.86 284		96.65 246		93.55 106	90.14 284

BP-MVS														92.13 198

HQP4-MVS										90.14 284			98.50 254			95.78 313

HQP2-MVS												80.95 265

NP-MVS					95.99 282	89.81 215					95.87 257

MDTV_nov1_ep13_2view						70.35 453	93.10 418		83.88 401	93.55 201		82.47 239		86.25 327		98.38 183

ACMMP++_ref															90.30 324

ACMMP++															91.02 313

Test By Simon												88.73 104

High-resolution multi-view benchmark