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 67	99.02 1	96.37 13	99.30 7	98.92 23	92.39 43	99.79 46	99.16 14	99.46 46	98.08 228

PGM-MVS		96.81 58	96.53 69	97.65 47	99.35 25	93.53 65	97.65 130	98.98 2	92.22 178	97.14 76	98.44 64	91.17 70	99.85 21	94.35 163	99.46 46	99.57 36

MVS_111021_HR		96.68 69	96.58 68	96.99 84	98.46 79	92.31 110	96.20 305	98.90 3	94.30 86	95.86 135	97.74 144	92.33 44	99.38 136	96.04 96	99.42 56	99.28 77

test_fmvsmconf_n		97.49 21	97.56 16	97.29 64	97.44 164	92.37 107	97.91 86	98.88 4	95.83 19	98.92 23	99.05 14	91.45 60	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 51	99.80 40	97.63 37	99.21 83	99.57 36

ACMMP		96.27 86	95.93 88	97.28 66	99.24 33	92.62 98	98.25 40	98.81 6	92.99 140	94.56 183	98.39 68	88.96 101	99.85 21	94.57 157	97.63 163	99.36 72
Qingshan Xu, Weihang Kong, Wenbing Tao, Marc Pollefeys: Multi-Scale Geometric Consistency Guided and Planar Prior Assisted Multi-View Stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence
MVS_111021_LR		96.24 87	96.19 85	96.39 124	98.23 105	91.35 153	96.24 302	98.79 7	93.99 95	95.80 137	97.65 154	89.92 90	99.24 150	95.87 100	99.20 88	98.58 171

patch_mono-2		96.83 57	97.44 24	95.01 226	99.05 45	85.39 376	96.98 218	98.77 8	94.70 66	97.99 51	98.66 43	93.61 21	99.91 1	97.67 36	99.50 40	99.72 13

fmvsm_s_conf0.5_n		96.85 54	97.13 31	96.04 150	98.07 120	90.28 205	97.97 78	98.76 9	94.93 48	98.84 28	99.06 12	88.80 105	99.65 79	99.06 18	98.63 123	98.18 213

fmvsm_l_conf0.5_n		97.65 9	97.75 8	97.34 61	98.21 106	92.75 92	97.83 99	98.73 10	95.04 45	99.30 7	98.84 36	93.34 24	99.78 49	99.32 7	99.13 97	99.50 52

fmvsm_s_conf0.5_n_a		96.75 62	96.93 46	96.20 140	97.64 150	90.72 186	98.00 68	98.73 10	94.55 73	98.91 24	99.08 8	88.22 117	99.63 88	98.91 21	98.37 136	98.25 208

fmvsm_s_conf0.5_n_10		97.29 31	97.40 26	96.97 86	98.24 100	91.96 126	97.89 89	98.72 12	96.77 7	99.46 3	99.06 12	87.78 126	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	97.61 139	98.71 13	97.10 5	99.70 1	98.93 22	90.95 75	99.77 52	99.35 6	99.53 33	99.65 20

FC-MVSNet-test		93.94 183	93.57 175	95.04 224	95.48 316	91.45 149	98.12 56	98.71 13	93.37 122	90.23 296	96.70 222	87.66 128	97.85 349	91.49 227	90.39 336	95.83 324

UniMVSNet (Re)		93.31 210	92.55 223	95.61 190	95.39 322	93.34 71	97.39 173	98.71 13	93.14 135	90.10 305	94.83 324	87.71 127	98.03 322	91.67 225	83.99 412	95.46 343

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 49	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 49	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	97.81 104	98.68 19	94.93 48	99.24 10	98.87 31	93.52 22	99.79 46	99.32 7	99.21 83	99.40 66

FIs		94.09 174	93.70 171	95.27 213	95.70 305	92.03 122	98.10 57	98.68 19	93.36 124	90.39 293	96.70 222	87.63 131	97.94 340	92.25 205	90.50 335	95.84 323

WR-MVS_H		92.00 267	91.35 264	93.95 298	95.09 349	89.47 241	98.04 64	98.68 19	91.46 209	88.34 358	94.68 331	85.86 169	97.56 381	85.77 361	84.24 410	94.82 395

fmvsm_s_conf0.5_n_4		96.75 62	97.07 34	95.79 174	97.76 141	89.57 234	97.66 129	98.66 22	95.36 30	99.03 16	98.90 25	88.39 113	99.73 61	99.17 13	98.66 121	98.08 228

VPA-MVSNet		93.24 212	92.48 228	95.51 200	95.70 305	92.39 106	97.86 92	98.66 22	92.30 175	92.09 254	95.37 299	80.49 292	98.40 274	93.95 169	85.86 383	95.75 332

fmvsm_l_conf0.5_n_3		97.64 10	97.60 13	97.79 34	98.14 113	93.94 56	97.93 84	98.65 24	96.70 8	99.38 5	99.07 11	89.92 90	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	97.98 125	91.19 161	97.84 96	98.65 24	97.08 6	99.25 9	99.10 6	87.88 124	99.79 46	99.32 7	99.18 90	98.59 170

fmvsm_s_conf0.5_n_8		97.32 28	97.48 23	96.85 88	98.28 94	91.07 169	97.76 109	98.62 26	97.53 2	99.20 12	99.12 5	88.24 116	99.81 35	99.41 3	99.17 91	99.67 15

fmvsm_s_conf0.5_n_2		96.62 70	96.82 55	96.02 152	97.98 125	90.43 196	97.50 154	98.59 27	96.59 10	99.31 6	99.08 8	84.47 203	99.75 58	99.37 5	98.45 133	97.88 241

UniMVSNet_NR-MVSNet		93.37 208	92.67 217	95.47 206	95.34 328	92.83 89	97.17 201	98.58 28	92.98 145	90.13 301	95.80 275	88.37 115	97.85 349	91.71 222	83.93 413	95.73 334

CSCG		96.05 90	95.91 89	96.46 117	99.24 33	90.47 193	98.30 33	98.57 29	89.01 304	93.97 203	97.57 164	92.62 39	99.76 54	94.66 151	99.27 75	99.15 87

fmvsm_s_conf0.5_n_9		97.33 27	97.57 15	96.62 101	98.43 82	90.32 204	97.80 105	98.53 30	97.24 4	99.62 2	99.14 2	88.65 108	99.80 40	99.54 1	99.15 94	99.74 9

fmvsm_s_conf0.5_n_6		97.08 39	97.17 30	96.81 89	97.28 169	91.73 130	97.75 111	98.50 31	94.86 52	99.22 11	98.78 40	89.75 93	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	97.67 126	98.49 32	94.66 69	97.24 72	98.41 67	92.31 46	98.94 195	96.61 71	99.46 46	98.96 114

HyFIR lowres test		93.66 195	92.92 205	95.87 163	98.24 100	89.88 220	94.58 388	98.49 32	85.06 402	93.78 206	95.78 279	82.86 239	98.67 248	91.77 220	95.71 234	99.07 100

CHOSEN 1792x2688		94.15 169	93.51 181	96.06 148	98.27 96	89.38 246	95.18 371	98.48 34	85.60 392	93.76 207	97.11 196	83.15 229	99.61 90	91.33 230	98.72 119	99.19 83

fmvsm_s_conf0.5_n_7		96.45 77	96.80 57	95.37 209	97.29 168	88.38 287	97.23 195	98.47 35	95.14 39	98.43 41	99.09 7	87.58 132	99.72 65	98.80 25	99.21 83	98.02 232

fmvsm_s_conf0.5_n_5		97.00 45	96.97 43	97.09 79	97.58 160	92.56 101	97.68 125	98.47 35	94.02 93	98.90 25	98.89 28	88.94 102	99.78 49	99.18 12	99.03 106	98.93 125

PHI-MVS		96.77 60	96.46 76	97.71 45	98.40 86	94.07 52	98.21 48	98.45 37	89.86 274	97.11 78	98.01 104	92.52 41	99.69 73	96.03 97	99.53 33	99.36 72

fmvsm_s_conf0.1_n		96.58 73	96.77 60	96.01 155	96.67 228	90.25 206	97.91 86	98.38 38	94.48 77	98.84 28	99.14 2	88.06 119	99.62 89	98.82 23	98.60 125	98.15 217

PVSNet_BlendedMVS		94.06 175	93.92 165	94.47 264	98.27 96	89.46 243	96.73 250	98.36 39	90.17 267	94.36 188	95.24 307	88.02 120	99.58 98	93.44 183	90.72 331	94.36 416

PVSNet_Blended		94.87 145	94.56 143	95.81 170	98.27 96	89.46 243	95.47 350	98.36 39	88.84 313	94.36 188	96.09 264	88.02 120	99.58 98	93.44 183	98.18 145	98.40 193

3Dnovator	91.36 5	95.19 127	94.44 152	97.44 57	96.56 243	93.36 70	98.65 16	98.36 39	94.12 90	89.25 335	98.06 98	82.20 256	99.77 52	93.41 185	99.32 71	99.18 84

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

DPE-MVS		97.86 6	97.65 11	98.47 6	99.17 38	95.78 8	97.21 198	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	97.75 111	98.34 44	94.23 87	98.15 46	98.53 51	93.32 27	99.84 26	97.40 49	99.58 23	99.65 20

fmvsm_s_conf0.1_n_a		96.40 79	96.47 73	96.16 142	95.48 316	90.69 187	97.91 86	98.33 45	94.07 91	98.93 20	99.14 2	87.44 140	99.61 90	98.63 26	98.32 138	98.18 213

HFP-MVS		97.14 37	96.92 47	97.83 30	99.42 10	94.12 50	98.52 20	98.32 46	93.21 127	97.18 73	98.29 84	92.08 48	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 64	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 72	98.30 48	96.19 14	96.10 125	98.95 20	89.42 94	99.76 54	98.90 22	99.08 101	97.43 268

APDe-MVS		97.82 7	97.73 9	98.08 19	99.15 39	94.82 29	98.81 8	98.30 48	94.76 64	98.30 43	98.90 25	93.77 19	99.68 75	97.93 28	99.69 3	99.75 7
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
test0726					99.45 6	95.36 14	98.31 32	98.29 50	94.92 50	98.99 18	98.92 23	95.08 10

MSP-MVS		97.59 13	97.54 17	97.73 42	99.40 14	93.77 61	98.53 19	98.29 50	95.55 27	98.56 38	97.81 136	93.90 17	99.65 79	96.62 70	99.21 83	99.77 3
Zhenlong Yuan, Cong Liu, Fei Shen, Zhaoxin Li, Jingguo luo, Tianlu Mao and Zhaoqi Wang: MSP-MVS: Multi-granularity Segmentation Prior Guided Multi-View Stereo. AAAI2025
DVP-MVS++		98.06 1	97.99 1	98.28 10	98.67 67	95.39 12	99.29 1	98.28 52	94.78 61	98.93 20	98.87 31	96.04 2	99.86 9	97.45 45	99.58 23	99.59 32

test_0728_SECOND				98.51 5	99.45 6	95.93 6	98.21 48	98.28 52					99.86 9	97.52 41	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 52	99.85 21	95.61 115	99.68 4	99.54 45

test_fmvsmconf0.1_n		97.09 38	97.06 35	97.19 73	95.67 307	92.21 114	97.95 81	98.27 55	95.78 23	98.40 42	99.00 16	89.99 88	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 41	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 41	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	92.37 172	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		95.27 117	94.91 125	96.38 125	98.20 107	90.86 179	97.27 189	98.25 61	90.21 266	94.18 196	97.27 185	87.48 139	99.73 61	93.53 180	97.77 161	98.55 174

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

PS-CasMVS		91.55 289	90.84 288	93.69 315	94.96 353	88.28 290	97.84 96	98.24 63	91.46 209	88.04 369	95.80 275	79.67 308	97.48 394	87.02 341	84.54 407	95.31 357

DU-MVS		92.90 230	92.04 239	95.49 203	94.95 354	92.83 89	97.16 202	98.24 63	93.02 139	90.13 301	95.71 282	83.47 220	97.85 349	91.71 222	83.93 413	95.78 328

9.14			96.75 61		98.93 56		97.73 116	98.23 66	91.28 218	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	97.79 107	98.21 67	95.73 24	97.99 51	99.03 15	92.63 38	99.82 33	97.80 30	99.42 56	99.67 15

D2MVS		91.30 306	90.95 282	92.35 369	94.71 369	85.52 370	96.18 307	98.21 67	88.89 311	86.60 398	93.82 380	79.92 304	97.95 338	89.29 280	90.95 328	93.56 432

reproduce-ours		97.53 18	97.51 20	97.60 51	98.97 53	93.31 73	97.71 121	98.20 69	95.80 21	97.88 55	98.98 18	92.91 30	99.81 35	97.68 32	99.43 53	99.67 15

our_new_method		97.53 18	97.51 20	97.60 51	98.97 53	93.31 73	97.71 121	98.20 69	95.80 21	97.88 55	98.98 18	92.91 30	99.81 35	97.68 32	99.43 53	99.67 15

SDMVSNet		94.17 167	93.61 174	95.86 166	98.09 116	91.37 151	97.35 177	98.20 69	93.18 132	91.79 262	97.28 183	79.13 316	98.93 196	94.61 154	92.84 294	97.28 276

XVS		97.18 34	96.96 45	97.81 32	99.38 17	94.03 54	98.59 17	98.20 69	94.85 53	96.59 99	98.29 84	91.70 55	99.80 40	95.66 108	99.40 61	99.62 27

X-MVStestdata		91.71 276	89.67 344	97.81 32	99.38 17	94.03 54	98.59 17	98.20 69	94.85 53	96.59 99	32.69 494	91.70 55	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	97.60 140	98.19 74	92.82 155	97.93 54	98.74 42	91.60 58	99.86 9	96.26 80	99.52 35	99.67 15

CP-MVSNet		91.89 272	91.24 271	93.82 307	95.05 350	88.57 278	97.82 101	98.19 74	91.70 198	88.21 364	95.76 280	81.96 261	97.52 392	87.86 308	84.65 401	95.37 353

ZNCC-MVS		96.96 46	96.67 64	97.85 29	99.37 19	94.12 50	98.49 24	98.18 76	92.64 162	96.39 113	98.18 91	91.61 57	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 82	98.18 76	90.57 257	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		91.20 311	90.44 307	93.48 332	94.49 377	87.91 308	97.76 109	98.18 76	91.29 215	87.78 373	95.74 281	80.35 295	97.33 405	85.46 365	82.96 423	95.19 368

DELS-MVS		96.61 71	96.38 80	97.30 63	97.79 139	93.19 78	95.96 319	98.18 76	95.23 35	95.87 134	97.65 154	91.45 60	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		89.70 363	88.40 369	93.60 323	95.15 345	90.10 209	97.56 145	98.16 80	87.28 365	86.16 405	94.63 335	77.57 344	98.05 318	74.48 452	84.59 405	92.65 445

VNet		95.89 98	95.45 100	97.21 71	98.07 120	92.94 85	97.50 154	98.15 81	93.87 99	97.52 62	97.61 160	85.29 187	99.53 112	95.81 105	95.27 247	99.16 85

DeepPCF-MVS	93.97 1	96.61 71	97.09 33	95.15 217	98.09 116	86.63 342	96.00 317	98.15 81	95.43 28	97.95 53	98.56 47	93.40 23	99.36 137	96.77 63	99.48 44	99.45 59

SD-MVS		97.41 23	97.53 18	97.06 82	98.57 78	94.46 38	97.92 85	98.14 83	94.82 57	99.01 17	98.55 49	94.18 16	97.41 401	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	92.72 158	96.70 91	98.06 98	91.35 64	99.86 9	94.83 140	99.28 74	99.47 58

UA-Net		95.95 95	95.53 96	97.20 72	97.67 146	92.98 84	97.65 130	98.13 84	94.81 59	96.61 97	98.35 72	88.87 103	99.51 117	90.36 255	97.35 174	99.11 94

QAPM		93.45 206	92.27 233	96.98 85	96.77 221	92.62 98	98.39 29	98.12 86	84.50 410	88.27 362	97.77 140	82.39 253	99.81 35	85.40 366	98.81 114	98.51 179

Vis-MVSNet		95.23 122	94.81 131	96.51 111	97.18 174	91.58 141	98.26 39	98.12 86	94.38 84	94.90 172	98.15 93	82.28 254	98.92 198	91.45 229	98.58 127	99.01 106
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
OpenMVS	89.19 12	92.86 233	91.68 254	96.40 122	95.34 328	92.73 94	98.27 37	98.12 86	84.86 405	85.78 413	97.75 141	78.89 326	99.74 59	87.50 331	98.65 122	96.73 294

TranMVSNet+NR-MVSNet		92.50 242	91.63 255	95.14 218	94.76 365	92.07 119	97.53 151	98.11 89	92.90 151	89.56 323	96.12 259	83.16 228	97.60 378	89.30 279	83.20 422	95.75 332

CPTT-MVS		95.57 108	95.19 112	96.70 92	99.27 31	91.48 146	98.33 31	98.11 89	87.79 350	95.17 162	98.03 101	87.09 147	99.61 90	93.51 181	99.42 56	99.02 103

APD-MVS		96.95 47	96.60 66	98.01 21	99.03 47	94.93 28	97.72 119	98.10 91	91.50 207	98.01 50	98.32 80	92.33 44	99.58 98	94.85 137	99.51 38	99.53 48
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
mPP-MVS		96.86 52	96.60 66	97.64 49	99.40 14	93.44 66	98.50 23	98.09 92	93.27 126	95.95 132	98.33 78	91.04 72	99.88 4	95.20 122	99.57 29	99.60 31

ZD-MVS					99.05 45	94.59 33		98.08 93	89.22 297	97.03 81	98.10 94	92.52 41	99.65 79	94.58 156	99.31 72

MTGPA								98.08 93

MTAPA		97.08 39	96.78 59	97.97 27	99.37 19	94.42 40	97.24 191	98.08 93	95.07 44	96.11 124	98.59 46	90.88 78	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	97.27 189	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	97.03 209	98.08 93	88.35 331	95.09 164	97.65 154	89.97 89	99.48 124	92.08 214	98.59 126	98.44 190

SR-MVS		97.01 44	96.86 49	97.47 56	99.09 40	93.27 75	97.98 72	98.07 98	93.75 102	97.45 64	98.48 61	91.43 62	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	97.12 205	98.07 98	93.54 112	96.08 126	97.69 149	93.86 18	99.71 67	96.50 74	99.39 63	99.55 43

NR-MVSNet		92.34 251	91.27 270	95.53 195	94.95 354	93.05 81	97.39 173	98.07 98	92.65 160	84.46 425	95.71 282	85.00 194	97.77 360	89.71 267	83.52 419	95.78 328

MP-MVS-pluss		96.70 65	96.27 83	97.98 26	99.23 35	94.71 30	96.96 220	98.06 101	90.67 247	95.55 148	98.78 40	91.07 71	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 72	98.06 101	93.11 136	97.44 65	98.55 49	90.93 76	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	95.54 150	98.34 75	90.59 82	99.88 4	94.83 140	99.54 32	99.49 54
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
HPM-MVS_fast		96.51 74	96.27 83	97.22 70	99.32 27	92.74 93	98.74 10	98.06 101	90.57 257	96.77 88	98.35 72	90.21 85	99.53 112	94.80 144	99.63 16	99.38 70

HPM-MVS		96.69 67	96.45 77	97.40 59	99.36 23	93.11 80	98.87 6	98.06 101	91.17 226	96.40 112	97.99 107	90.99 73	99.58 98	95.61 115	99.61 18	99.49 54
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
sss		94.51 158	93.80 167	96.64 94	97.07 181	91.97 124	96.32 294	98.06 101	88.94 309	94.50 185	96.78 217	84.60 200	99.27 147	91.90 215	96.02 224	98.68 165

DeepC-MVS	93.07 3	96.06 89	95.66 93	97.29 64	97.96 127	93.17 79	97.30 183	98.06 101	93.92 97	93.38 222	98.66 43	86.83 149	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	97.34 178	98.04 108	95.96 15	97.09 79	97.88 123	93.18 28	99.71 67	95.84 104	99.17 91	99.56 40

DeepC-MVS_fast	93.89 2	96.93 49	96.64 65	97.78 36	98.64 73	94.30 41	97.41 168	98.04 108	94.81 59	96.59 99	98.37 70	91.24 67	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 72	98.03 110	93.52 115	97.43 67	98.51 56	91.40 63	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 72	98.03 110	93.52 115	97.43 67	98.51 56	90.71 80		96.05 94	99.26 78	99.43 63

RPMNet		88.98 369	87.05 383	94.77 245	94.45 379	87.19 326	90.23 468	98.03 110	77.87 466	92.40 240	87.55 468	80.17 299	99.51 117	68.84 474	93.95 280	97.60 261

save fliter					98.91 58	94.28 42	97.02 211	98.02 113	95.35 31

TEST9					98.70 65	94.19 46	96.41 280	98.02 113	88.17 335	96.03 127	97.56 166	92.74 35	99.59 95

train_agg		96.30 85	95.83 92	97.72 43	98.70 65	94.19 46	96.41 280	98.02 113	88.58 322	96.03 127	97.56 166	92.73 36	99.59 95	95.04 126	99.37 67	99.39 68

test_8					98.67 67	94.06 53	96.37 288	98.01 116	88.58 322	95.98 131	97.55 168	92.73 36	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 64	98.00 117	97.30 3	99.45 4	99.21 1	89.28 96	99.80 40	99.27 10	99.35 69	98.12 220

agg_prior					98.67 67	93.79 59		98.00 117		95.68 144			99.57 105

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

WR-MVS		92.34 251	91.53 259	94.77 245	95.13 347	90.83 180	96.40 284	97.98 120	91.88 193	89.29 332	95.54 293	82.50 249	97.80 356	89.79 266	85.27 392	95.69 335

HPM-MVS++		97.34 26	96.97 43	98.47 6	99.08 42	96.16 4	97.55 150	97.97 121	95.59 25	96.61 97	97.89 118	92.57 40	99.84 26	95.95 99	99.51 38	99.40 66

CANet		96.39 80	96.02 87	97.50 54	97.62 153	93.38 68	97.02 211	97.96 122	95.42 29	94.86 173	97.81 136	87.38 142	99.82 33	96.88 60	99.20 88	99.29 75

114514_t		93.95 182	93.06 199	96.63 98	99.07 43	91.61 138	97.46 165	97.96 122	77.99 464	93.00 231	97.57 164	86.14 165	99.33 139	89.22 283	99.15 94	98.94 121

IU-MVS					99.42 10	95.39 12		97.94 124	90.40 264	98.94 19				97.41 48	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 32	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 32	99.67 6	99.77 3

fmvsm_s_conf0.1_n_2		96.33 84	96.44 79	96.00 156	97.30 167	90.37 202	97.53 151	97.92 127	96.52 11	99.14 15	99.08 8	83.21 226	99.74 59	99.22 11	98.06 150	97.88 241

Anonymous20231211		90.63 335	89.42 351	94.27 278	98.24 100	89.19 258	98.05 63	97.89 128	79.95 455	88.25 363	94.96 316	72.56 390	98.13 301	89.70 268	85.14 394	95.49 339

原ACMM1				96.38 125	98.59 75	91.09 168		97.89 128	87.41 361	95.22 161	97.68 150	90.25 84	99.54 110	87.95 307	99.12 99	98.49 182

CDPH-MVS		95.97 94	95.38 106	97.77 38	98.93 56	94.44 39	96.35 289	97.88 130	86.98 369	96.65 95	97.89 118	91.99 50	99.47 125	92.26 203	99.46 46	99.39 68

test11								97.88 130

EIA-MVS		95.53 110	95.47 99	95.71 185	97.06 184	89.63 230	97.82 101	97.87 132	93.57 108	93.92 204	95.04 313	90.61 81	98.95 193	94.62 153	98.68 120	98.54 175

CS-MVS		96.86 52	97.06 35	96.26 135	98.16 112	91.16 166	99.09 3	97.87 132	95.30 33	97.06 80	98.03 101	91.72 53	98.71 241	97.10 55	99.17 91	98.90 130

无先验							95.79 331	97.87 132	83.87 419				99.65 79	87.68 321		98.89 136

3Dnovator+	91.43 4	95.40 111	94.48 150	98.16 17	96.90 201	95.34 17	98.48 25	97.87 132	94.65 70	88.53 354	98.02 103	83.69 216	99.71 67	93.18 189	98.96 109	99.44 61

VPNet		92.23 259	91.31 267	94.99 228	95.56 312	90.96 172	97.22 197	97.86 136	92.96 146	90.96 284	96.62 234	75.06 365	98.20 295	91.90 215	83.65 418	95.80 326

test_vis1_n_1920		94.17 167	94.58 142	92.91 353	97.42 165	82.02 424	97.83 99	97.85 137	94.68 67	98.10 48	98.49 58	70.15 409	99.32 141	97.91 29	98.82 113	97.40 270

DVP-MVS		97.91 4	97.81 5	98.22 14	99.45 6	95.36 14	98.21 48	97.85 137	94.92 50	98.73 30	98.87 31	95.08 10	99.84 26	97.52 41	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 61	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 67	98.75 231	96.92 59	99.33 70	98.94 121

test_fmvsmconf0.01_n		96.15 88	95.85 91	97.03 83	92.66 432	91.83 129	97.97 78	97.84 141	95.57 26	97.53 61	99.00 16	84.20 209	99.76 54	98.82 23	99.08 101	99.48 56

GDP-MVS		95.62 105	95.13 114	97.09 79	96.79 214	93.26 76	97.89 89	97.83 142	93.58 107	96.80 85	97.82 134	83.06 233	99.16 162	94.40 160	97.95 156	98.87 140

balanced_conf03		96.84 56	96.89 48	96.68 93	97.63 152	92.22 113	98.17 54	97.82 143	94.44 79	98.23 45	97.36 178	90.97 74	99.22 152	97.74 31	99.66 10	98.61 168

AdaColmap		94.34 162	93.68 172	96.31 129	98.59 75	91.68 136	96.59 269	97.81 144	89.87 273	92.15 250	97.06 200	83.62 219	99.54 110	89.34 278	98.07 149	97.70 254

MVSMamba_PlusPlus		96.51 74	96.48 72	96.59 102	98.07 120	91.97 124	98.14 55	97.79 145	90.43 262	97.34 70	97.52 169	91.29 66	99.19 155	98.12 27	99.64 14	98.60 169

KinetiMVS		95.26 118	94.75 136	96.79 90	96.99 194	92.05 120	97.82 101	97.78 146	94.77 63	96.46 109	97.70 147	80.62 289	99.34 138	92.37 202	98.28 140	98.97 111

ETV-MVS		96.02 91	95.89 90	96.40 122	97.16 175	92.44 105	97.47 163	97.77 147	94.55 73	96.48 107	94.51 341	91.23 69	98.92 198	95.65 111	98.19 144	97.82 249

新几何1				97.32 62	98.60 74	93.59 63		97.75 148	81.58 446	95.75 139	97.85 128	90.04 87	99.67 77	86.50 347	99.13 97	98.69 164

旧先验1					98.38 89	93.38 68		97.75 148			98.09 96	92.30 47			99.01 107	99.16 85

EC-MVSNet		96.42 78	96.47 73	96.26 135	97.01 192	91.52 143	98.89 5	97.75 148	94.42 80	96.64 96	97.68 150	89.32 95	98.60 257	97.45 45	99.11 100	98.67 166

EI-MVSNet-Vis-set		96.51 74	96.47 73	96.63 98	98.24 100	91.20 160	96.89 228	97.73 151	94.74 65	96.49 106	98.49 58	90.88 78	99.58 98	96.44 76	98.32 138	99.13 89

PAPM_NR		95.01 136	94.59 141	96.26 135	98.89 60	90.68 188	97.24 191	97.73 151	91.80 194	92.93 236	96.62 234	89.13 99	99.14 167	89.21 284	97.78 160	98.97 111

Anonymous20240529		91.98 268	90.73 295	95.73 183	98.14 113	89.40 245	97.99 69	97.72 153	79.63 457	93.54 215	97.41 175	69.94 411	99.56 106	91.04 237	91.11 324	98.22 210

CHOSEN 280x420		93.12 218	92.72 216	94.34 272	96.71 227	87.27 322	90.29 467	97.72 153	86.61 376	91.34 273	95.29 301	84.29 208	98.41 273	93.25 187	98.94 110	97.35 273

EI-MVSNet-UG-set		96.34 83	96.30 82	96.47 115	98.20 107	90.93 176	96.86 231	97.72 153	94.67 68	96.16 123	98.46 62	90.43 83	99.58 98	96.23 82	97.96 155	98.90 130

LS3D		93.57 199	92.61 221	96.47 115	97.59 156	91.61 138	97.67 126	97.72 153	85.17 400	90.29 295	98.34 75	84.60 200	99.73 61	83.85 389	98.27 141	98.06 230

PAPR		94.18 166	93.42 188	96.48 114	97.64 150	91.42 150	95.55 345	97.71 157	88.99 306	92.34 246	95.82 274	89.19 97	99.11 170	86.14 353	97.38 172	98.90 130

UGNet		94.04 177	93.28 191	96.31 129	96.85 206	91.19 161	97.88 91	97.68 158	94.40 82	93.00 231	96.18 254	73.39 384	99.61 90	91.72 221	98.46 132	98.13 218
Wanjuan Su, Qingshan Xu, Wenbing Tao: Uncertainty-guided Multi-view Stereo Network for Depth Estimation. IEEE Transactions on Circuits and Systems for Video Technology, 2022
testdata				95.46 207	98.18 111	88.90 269		97.66 159	82.73 435	97.03 81	98.07 97	90.06 86	98.85 205	89.67 269	98.98 108	98.64 167

test12				97.65 47	98.46 79	94.26 43		97.66 159		95.52 151		90.89 77	99.46 126		99.25 80	99.22 82

DTE-MVSNet		90.56 336	89.75 342	93.01 349	93.95 392	87.25 323	97.64 134	97.65 161	90.74 242	87.12 386	95.68 285	79.97 303	97.00 418	83.33 390	81.66 429	94.78 402

TAPA-MVS	90.10 7	92.30 254	91.22 273	95.56 192	98.33 91	89.60 232	96.79 242	97.65 161	81.83 443	91.52 268	97.23 188	87.94 122	98.91 200	71.31 467	98.37 136	98.17 216
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
sd_testset		93.10 219	92.45 229	95.05 222	98.09 116	89.21 255	96.89 228	97.64 163	93.18 132	91.79 262	97.28 183	75.35 364	98.65 251	88.99 290	92.84 294	97.28 276

test_cas_vis1_n_1920		94.48 160	94.55 146	94.28 277	96.78 219	86.45 348	97.63 136	97.64 163	93.32 125	97.68 60	98.36 71	73.75 380	99.08 177	96.73 65	99.05 103	97.31 275

NormalMVS		96.36 82	96.11 86	97.12 76	99.37 19	92.90 87	97.99 69	97.63 165	95.92 16	96.57 102	97.93 111	85.34 185	99.50 120	94.99 129	99.21 83	98.97 111

Elysia		94.00 179	93.12 196	96.64 94	96.08 291	92.72 95	97.50 154	97.63 165	91.15 228	94.82 174	97.12 194	74.98 367	99.06 183	90.78 242	98.02 151	98.12 220

StellarMVS		94.00 179	93.12 196	96.64 94	96.08 291	92.72 95	97.50 154	97.63 165	91.15 228	94.82 174	97.12 194	74.98 367	99.06 183	90.78 242	98.02 151	98.12 220

cdsmvs_eth3d_5k		23.24 463	30.99 465	0.00 482	0.00 505	0.00 507	0.00 494	97.63 165	0.00 500	0.00 501	96.88 213	84.38 205	0.00 501	0.00 500	0.00 499	0.00 497

DPM-MVS		95.69 102	94.92 124	98.01 21	98.08 119	95.71 10	95.27 362	97.62 169	90.43 262	95.55 148	97.07 199	91.72 53	99.50 120	89.62 271	98.94 110	98.82 146

sasdasda		96.02 91	95.45 100	97.75 40	97.59 156	95.15 24	98.28 35	97.60 170	94.52 75	96.27 118	96.12 259	87.65 129	99.18 158	96.20 88	94.82 256	98.91 127

canonicalmvs		96.02 91	95.45 100	97.75 40	97.59 156	95.15 24	98.28 35	97.60 170	94.52 75	96.27 118	96.12 259	87.65 129	99.18 158	96.20 88	94.82 256	98.91 127

test222					98.24 100	92.21 114	95.33 357	97.60 170	79.22 459	95.25 159	97.84 130	88.80 105			99.15 94	98.72 161

cascas		91.20 311	90.08 324	94.58 257	94.97 352	89.16 259	93.65 430	97.59 173	79.90 456	89.40 327	92.92 408	75.36 363	98.36 281	92.14 208	94.75 259	96.23 305

E2		95.20 124	95.00 121	95.79 174	96.79 214	89.66 227	96.82 237	97.58 174	92.35 173	95.28 157	97.83 132	86.68 151	98.76 225	94.79 147	96.92 193	98.95 118

E3		95.20 124	95.00 121	95.79 174	96.77 221	89.66 227	96.82 237	97.58 174	92.35 173	95.28 157	97.83 132	86.69 150	98.76 225	94.79 147	96.92 193	98.95 118

h-mvs33		94.15 169	93.52 180	96.04 150	97.81 138	90.22 207	97.62 138	97.58 174	95.19 36	96.74 89	97.45 171	83.67 217	99.61 90	95.85 102	79.73 436	98.29 206

E5new		95.04 132	94.88 126	95.52 196	96.62 230	89.02 263	97.29 184	97.57 177	92.54 163	95.04 165	97.89 118	85.65 177	98.77 219	94.92 132	96.44 217	98.78 149

E6new		95.04 132	94.88 126	95.52 196	96.60 234	89.02 263	97.29 184	97.57 177	92.54 163	95.04 165	97.90 116	85.66 175	98.77 219	94.92 132	96.44 217	98.78 149

E6		95.04 132	94.88 126	95.52 196	96.60 234	89.02 263	97.29 184	97.57 177	92.54 163	95.04 165	97.90 116	85.66 175	98.77 219	94.92 132	96.44 217	98.78 149

E5		95.04 132	94.88 126	95.52 196	96.62 230	89.02 263	97.29 184	97.57 177	92.54 163	95.04 165	97.89 118	85.65 177	98.77 219	94.92 132	96.44 217	98.78 149

MGCFI-Net		95.94 96	95.40 104	97.56 53	97.59 156	94.62 32	98.21 48	97.57 177	94.41 81	96.17 122	96.16 257	87.54 134	99.17 160	96.19 90	94.73 261	98.91 127

MVSFormer		95.37 112	95.16 113	95.99 157	96.34 267	91.21 158	98.22 46	97.57 177	91.42 211	96.22 120	97.32 179	86.20 163	97.92 343	94.07 166	99.05 103	98.85 142

test_djsdf		93.07 221	92.76 211	94.00 292	93.49 411	88.70 273	98.22 46	97.57 177	91.42 211	90.08 307	95.55 292	82.85 240	97.92 343	94.07 166	91.58 315	95.40 350

OMC-MVS		95.09 129	94.70 137	96.25 138	98.46 79	91.28 154	96.43 276	97.57 177	92.04 189	94.77 178	97.96 110	87.01 148	99.09 175	91.31 231	96.77 198	98.36 197

E4		95.09 129	94.86 130	95.77 177	96.58 238	89.56 235	96.85 232	97.56 185	92.50 167	95.03 169	97.86 126	86.03 166	98.78 215	94.71 150	96.65 207	98.96 114

viewcassd2359sk11		95.26 118	95.09 118	95.80 171	96.95 198	89.72 226	96.80 241	97.56 185	92.21 180	95.37 155	97.80 138	87.17 146	98.77 219	94.82 142	97.10 187	98.90 130

PS-MVSNAJss		93.74 192	93.51 181	94.44 266	93.91 394	89.28 253	97.75 111	97.56 185	92.50 167	89.94 309	96.54 237	88.65 108	98.18 298	93.83 175	90.90 329	95.86 320

casdiffmvs_mvg		95.81 101	95.57 94	96.51 111	96.87 203	91.49 144	97.50 154	97.56 185	93.99 95	95.13 163	97.92 114	87.89 123	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		95.28 116	95.11 117	95.80 171	97.03 189	89.76 224	96.78 246	97.54 189	92.06 188	95.40 154	97.75 141	87.49 138	98.76 225	94.85 137	97.10 187	98.88 138

jajsoiax		92.42 247	91.89 247	94.03 291	93.33 419	88.50 283	97.73 116	97.53 190	92.00 191	88.85 346	96.50 239	75.62 362	98.11 305	93.88 173	91.56 316	95.48 340

mvs_tets		92.31 253	91.76 250	93.94 300	93.41 416	88.29 289	97.63 136	97.53 190	92.04 189	88.76 349	96.45 241	74.62 372	98.09 310	93.91 171	91.48 317	95.45 345

dcpmvs_2		96.37 81	97.05 38	94.31 275	98.96 55	84.11 397	97.56 145	97.51 192	93.92 97	97.43 67	98.52 55	92.75 34	99.32 141	97.32 54	99.50 40	99.51 49

HQP_MVS		93.78 191	93.43 186	94.82 238	96.21 271	89.99 213	97.74 114	97.51 192	94.85 53	91.34 273	96.64 227	81.32 273	98.60 257	93.02 195	92.23 303	95.86 320

plane_prior5								97.51 192					98.60 257	93.02 195	92.23 303	95.86 320

viewmanbaseed2359cas		95.24 121	95.02 120	95.91 160	96.87 203	89.98 215	96.82 237	97.49 195	92.26 176	95.47 152	97.82 134	86.47 156	98.69 243	94.80 144	97.20 183	99.06 101

reproduce_monomvs		91.30 306	91.10 277	91.92 383	96.82 211	82.48 418	97.01 214	97.49 195	94.64 71	88.35 357	95.27 304	70.53 404	98.10 306	95.20 122	84.60 404	95.19 368

viewmacassd2359aftdt		95.07 131	94.80 132	95.87 163	96.53 248	89.84 221	96.90 227	97.48 197	92.44 169	95.36 156	97.89 118	85.23 188	98.68 245	94.40 160	97.00 191	99.09 96

PS-MVSNAJ		95.37 112	95.33 108	95.49 203	97.35 166	90.66 189	95.31 359	97.48 197	93.85 100	96.51 105	95.70 284	88.65 108	99.65 79	94.80 144	98.27 141	96.17 309

API-MVS		94.84 147	94.49 149	95.90 161	97.90 133	92.00 123	97.80 105	97.48 197	89.19 298	94.81 176	96.71 220	88.84 104	99.17 160	88.91 292	98.76 118	96.53 298

MG-MVS		95.61 106	95.38 106	96.31 129	98.42 83	90.53 191	96.04 314	97.48 197	93.47 117	95.67 145	98.10 94	89.17 98	99.25 149	91.27 232	98.77 117	99.13 89

MAR-MVS		94.22 165	93.46 183	96.51 111	98.00 124	92.19 117	97.67 126	97.47 201	88.13 339	93.00 231	95.84 272	84.86 198	99.51 117	87.99 306	98.17 146	97.83 248
Zhenyu Xu, Yiguang Liu, Xuelei Shi, Ying Wang, Yunan Zheng: MARMVS: Matching Ambiguity Reduced Multiple View Stereo for Efficient Large Scale Scene Reconstruction. CVPR 2020
CLD-MVS		92.98 225	92.53 225	94.32 273	96.12 286	89.20 256	95.28 360	97.47 201	92.66 159	89.90 310	95.62 288	80.58 290	98.40 274	92.73 200	92.40 301	95.38 352
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
UniMVSNet_ETH3D		91.34 304	90.22 320	94.68 249	94.86 361	87.86 309	97.23 195	97.46 203	87.99 340	89.90 310	96.92 211	66.35 439	98.23 292	90.30 256	90.99 327	97.96 235

nrg030		94.05 176	93.31 190	96.27 134	95.22 339	94.59 33	98.34 30	97.46 203	92.93 147	91.21 282	96.64 227	87.23 145	98.22 293	94.99 129	85.80 384	95.98 319

XVG-OURS		93.72 193	93.35 189	94.80 243	97.07 181	88.61 276	94.79 383	97.46 203	91.97 192	93.99 201	97.86 126	81.74 267	98.88 202	92.64 201	92.67 299	96.92 289

LPG-MVS_test		92.94 228	92.56 222	94.10 286	96.16 281	88.26 291	97.65 130	97.46 203	91.29 215	90.12 303	97.16 191	79.05 319	98.73 235	92.25 205	91.89 311	95.31 357

LGP-MVS_train				94.10 286	96.16 281	88.26 291		97.46 203	91.29 215	90.12 303	97.16 191	79.05 319	98.73 235	92.25 205	91.89 311	95.31 357

MVS		91.71 276	90.44 307	95.51 200	95.20 341	91.59 140	96.04 314	97.45 208	73.44 474	87.36 382	95.60 289	85.42 184	99.10 172	85.97 358	97.46 167	95.83 324

XVG-OURS-SEG-HR		93.86 188	93.55 176	94.81 240	97.06 184	88.53 282	95.28 360	97.45 208	91.68 199	94.08 200	97.68 150	82.41 252	98.90 201	93.84 174	92.47 300	96.98 284

baseline		95.58 107	95.42 103	96.08 145	96.78 219	90.41 197	97.16 202	97.45 208	93.69 106	95.65 146	97.85 128	87.29 143	98.68 245	95.66 108	97.25 181	99.13 89

ab-mvs		93.57 199	92.55 223	96.64 94	97.28 169	91.96 126	95.40 353	97.45 208	89.81 278	93.22 228	96.28 250	79.62 310	99.46 126	90.74 245	93.11 291	98.50 180

xiu_mvs_v2_base		95.32 115	95.29 109	95.40 208	97.22 171	90.50 192	95.44 352	97.44 212	93.70 105	96.46 109	96.18 254	88.59 112	99.53 112	94.79 147	97.81 159	96.17 309

1314		92.81 237	92.03 240	95.14 218	95.33 331	89.52 240	96.04 314	97.44 212	87.72 354	86.25 403	95.33 300	83.84 214	98.79 214	89.26 281	97.05 190	97.11 282

casdiffmvs		95.64 104	95.49 97	96.08 145	96.76 225	90.45 194	97.29 184	97.44 212	94.00 94	95.46 153	97.98 108	87.52 137	98.73 235	95.64 112	97.33 175	99.08 98
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
viewdifsd2359ckpt07		94.76 153	94.68 138	95.01 226	96.76 225	87.41 318	96.38 286	97.43 215	92.65 160	94.52 184	97.75 141	85.55 182	98.81 211	94.36 162	96.69 204	98.82 146

XXY-MVS		92.16 261	91.23 272	94.95 234	94.75 366	90.94 175	97.47 163	97.43 215	89.14 299	88.90 342	96.43 242	79.71 307	98.24 291	89.56 272	87.68 365	95.67 336

anonymousdsp		92.16 261	91.55 258	93.97 296	92.58 434	89.55 237	97.51 153	97.42 217	89.42 292	88.40 356	94.84 323	80.66 288	97.88 348	91.87 217	91.28 321	94.48 411

Effi-MVS+		94.93 141	94.45 151	96.36 127	96.61 233	91.47 147	96.41 280	97.41 218	91.02 234	94.50 185	95.92 268	87.53 135	98.78 215	93.89 172	96.81 197	98.84 145

RRT-MVS		94.51 158	94.35 155	94.98 230	96.40 261	86.55 345	97.56 145	97.41 218	93.19 130	94.93 171	97.04 201	79.12 317	99.30 145	96.19 90	97.32 177	99.09 96

HQP3-MVS								97.39 220							92.10 308

HQP-MVS		93.19 215	92.74 214	94.54 260	95.86 297	89.33 249	96.65 260	97.39 220	93.55 109	90.14 297	95.87 270	80.95 279	98.50 267	92.13 211	92.10 308	95.78 328

PLC	91.00 6	94.11 173	93.43 186	96.13 143	98.58 77	91.15 167	96.69 256	97.39 220	87.29 364	91.37 272	96.71 220	88.39 113	99.52 116	87.33 334	97.13 186	97.73 252
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
diffmvs_AUTHOR		95.33 114	95.27 110	95.50 202	96.37 265	89.08 261	96.08 312	97.38 223	93.09 138	96.53 104	97.74 144	86.45 157	98.68 245	96.32 78	97.48 166	98.75 157

v7n		90.76 328	89.86 335	93.45 334	93.54 408	87.60 316	97.70 124	97.37 224	88.85 312	87.65 375	94.08 371	81.08 278	98.10 306	84.68 375	83.79 417	94.66 408

UnsupCasMVSNet_eth		85.99 414	84.45 416	90.62 419	89.97 454	82.40 421	93.62 431	97.37 224	89.86 274	78.59 464	92.37 418	65.25 451	95.35 453	82.27 404	70.75 472	94.10 422

viewdifsd2359ckpt13		94.87 145	94.52 147	95.90 161	96.88 202	90.19 208	96.92 224	97.36 226	91.26 219	94.65 180	97.46 170	85.79 172	98.64 252	93.64 178	96.76 199	98.88 138

ACMM	89.79 8	92.96 226	92.50 227	94.35 270	96.30 269	88.71 272	97.58 141	97.36 226	91.40 213	90.53 290	96.65 226	79.77 306	98.75 231	91.24 233	91.64 313	95.59 338
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
xiu_mvs_v1_base_debu		95.01 136	94.76 133	95.75 180	96.58 238	91.71 133	96.25 299	97.35 228	92.99 140	96.70 91	96.63 231	82.67 244	99.44 129	96.22 83	97.46 167	96.11 315

xiu_mvs_v1_base		95.01 136	94.76 133	95.75 180	96.58 238	91.71 133	96.25 299	97.35 228	92.99 140	96.70 91	96.63 231	82.67 244	99.44 129	96.22 83	97.46 167	96.11 315

xiu_mvs_v1_base_debi		95.01 136	94.76 133	95.75 180	96.58 238	91.71 133	96.25 299	97.35 228	92.99 140	96.70 91	96.63 231	82.67 244	99.44 129	96.22 83	97.46 167	96.11 315

diffmvs		95.25 120	95.13 114	95.63 188	96.43 260	89.34 248	95.99 318	97.35 228	92.83 154	96.31 116	97.37 177	86.44 158	98.67 248	96.26 80	97.19 184	98.87 140
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
WTY-MVS		94.71 155	94.02 163	96.79 90	97.71 144	92.05 120	96.59 269	97.35 228	90.61 253	94.64 181	96.93 208	86.41 159	99.39 134	91.20 234	94.71 262	98.94 121

viewdifsd2359ckpt09		94.81 150	94.37 154	96.12 144	96.91 199	90.75 185	96.94 221	97.31 233	90.51 260	94.31 190	97.38 176	85.70 174	98.71 241	93.54 179	96.75 200	98.90 130

balanced_ft_v1		95.56 109	95.40 104	96.07 147	97.16 175	90.36 203	98.23 44	97.31 233	92.89 152	96.36 114	97.11 196	83.28 224	99.26 148	97.40 49	98.80 115	98.58 171

SSM_0407		94.54 157	94.12 162	95.80 171	96.79 214	90.38 199	96.79 242	97.29 235	91.24 220	93.68 208	97.60 161	85.03 192	98.67 248	92.14 208	96.51 210	98.35 199

SSM_0404		94.73 154	94.31 157	95.98 158	97.05 186	90.90 178	97.01 214	97.29 235	91.24 220	94.17 197	97.60 161	85.03 192	98.76 225	92.14 208	97.30 178	98.29 206

F-COLMAP		93.58 197	92.98 203	95.37 209	98.40 86	88.98 267	97.18 200	97.29 235	87.75 353	90.49 291	97.10 198	85.21 189	99.50 120	86.70 344	96.72 203	97.63 256

VortexMVS		92.88 232	92.64 218	93.58 325	96.58 238	87.53 317	96.93 223	97.28 238	92.78 157	89.75 315	94.99 314	82.73 243	97.76 361	94.60 155	88.16 360	95.46 343

XVG-ACMP-BASELINE		90.93 324	90.21 321	93.09 347	94.31 385	85.89 363	95.33 357	97.26 239	91.06 233	89.38 328	95.44 298	68.61 422	98.60 257	89.46 274	91.05 325	94.79 400

PCF-MVS	89.48 11	91.56 288	89.95 332	96.36 127	96.60 234	92.52 103	92.51 451	97.26 239	79.41 458	88.90 342	96.56 236	84.04 213	99.55 108	77.01 443	97.30 178	97.01 283
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
ACMP	89.59 10	92.62 241	92.14 236	94.05 289	96.40 261	88.20 297	97.36 176	97.25 241	91.52 206	88.30 360	96.64 227	78.46 331	98.72 240	91.86 218	91.48 317	95.23 364
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
icg_test_0407_2		93.58 197	93.46 183	93.94 300	96.19 275	86.16 357	93.73 425	97.24 242	91.54 202	93.50 217	97.04 201	85.64 180	96.91 421	90.68 247	95.59 238	98.76 153

IMVS_0407		93.94 183	93.75 169	94.49 263	96.19 275	86.16 357	96.35 289	97.24 242	91.54 202	93.50 217	97.04 201	85.64 180	98.54 264	90.68 247	95.59 238	98.76 153

IMVS_0404		92.44 245	91.92 245	94.00 292	96.19 275	86.16 357	93.84 422	97.24 242	91.54 202	88.17 366	97.04 201	76.96 349	97.09 412	90.68 247	95.59 238	98.76 153

IMVS_0403		93.98 181	93.79 168	94.55 259	96.19 275	86.16 357	96.35 289	97.24 242	91.54 202	93.59 212	97.04 201	85.86 169	98.73 235	90.68 247	95.59 238	98.76 153

OPM-MVS		93.28 211	92.76 211	94.82 238	94.63 372	90.77 183	96.65 260	97.18 246	93.72 103	91.68 266	97.26 186	79.33 314	98.63 254	92.13 211	92.28 302	95.07 373
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
PatchMatch-RL		92.90 230	92.02 241	95.56 192	98.19 109	90.80 181	95.27 362	97.18 246	87.96 341	91.86 261	95.68 285	80.44 293	98.99 191	84.01 384	97.54 165	96.89 290

alignmvs		95.87 100	95.23 111	97.78 36	97.56 162	95.19 22	97.86 92	97.17 248	94.39 83	96.47 108	96.40 244	85.89 168	99.20 154	96.21 87	95.11 252	98.95 118

MVS_Test		94.89 143	94.62 140	95.68 186	96.83 209	89.55 237	96.70 254	97.17 248	91.17 226	95.60 147	96.11 263	87.87 125	98.76 225	93.01 197	97.17 185	98.72 161

Fast-Effi-MVS+		93.46 203	92.75 213	95.59 191	96.77 221	90.03 210	96.81 240	97.13 250	88.19 334	91.30 276	94.27 359	86.21 162	98.63 254	87.66 324	96.46 216	98.12 220

usedtu_dtu_shiyan1		91.65 280	90.67 299	94.60 251	93.65 405	90.95 173	94.86 380	97.12 251	89.69 281	89.21 336	93.62 390	81.17 276	97.67 368	87.54 328	89.14 347	95.17 370

FE-MVSNET3		91.65 280	90.67 299	94.60 251	93.65 405	90.95 173	94.86 380	97.12 251	89.69 281	89.21 336	93.62 390	81.17 276	97.67 368	87.54 328	89.14 347	95.17 370

EI-MVSNet		93.03 223	92.88 207	93.48 332	95.77 303	86.98 331	96.44 274	97.12 251	90.66 249	91.30 276	97.64 157	86.56 153	98.05 318	89.91 262	90.55 333	95.41 347

MVSTER		93.20 214	92.81 210	94.37 269	96.56 243	89.59 233	97.06 208	97.12 251	91.24 220	91.30 276	95.96 266	82.02 260	98.05 318	93.48 182	90.55 333	95.47 342

viewmambaseed2359dif		94.28 163	94.14 160	94.71 248	96.21 271	86.97 332	95.93 321	97.11 255	89.00 305	95.00 170	97.70 147	86.02 167	98.59 261	93.71 177	96.59 209	98.57 173

test_yl		94.78 151	94.23 158	96.43 119	97.74 142	91.22 156	96.85 232	97.10 256	91.23 223	95.71 141	96.93 208	84.30 206	99.31 143	93.10 190	95.12 250	98.75 157

DCV-MVSNet		94.78 151	94.23 158	96.43 119	97.74 142	91.22 156	96.85 232	97.10 256	91.23 223	95.71 141	96.93 208	84.30 206	99.31 143	93.10 190	95.12 250	98.75 157

LTVRE_ROB	88.41 13	90.99 320	89.92 334	94.19 280	96.18 279	89.55 237	96.31 295	97.09 258	87.88 344	85.67 414	95.91 269	78.79 327	98.57 262	81.50 407	89.98 338	94.44 414
Andreas Kuhn, Heiko HirschmÃ¼ller, Daniel Scharstein, Helmut Mayer: A TV Prior for High-Quality Scalable Multi-View Stereo Reconstruction. International Journal of Computer Vision 2016
viewmsd2359difaftdt		93.46 203	93.23 193	94.17 281	96.12 286	85.42 372	96.43 276	97.08 259	92.91 148	94.21 193	98.00 105	80.82 285	98.74 233	94.41 159	89.05 349	98.34 203

test_fmvs1_n		92.73 239	92.88 207	92.29 373	96.08 291	81.05 432	97.98 72	97.08 259	90.72 244	96.79 87	98.18 91	63.07 456	98.45 271	97.62 39	98.42 135	97.36 271

v10		91.04 318	90.23 318	93.49 331	94.12 388	88.16 300	97.32 181	97.08 259	88.26 333	88.29 361	94.22 364	82.17 257	97.97 330	86.45 348	84.12 411	94.33 417

viewdifsd2359ckpt11		93.46 203	93.22 194	94.17 281	96.11 288	85.42 372	96.43 276	97.07 262	92.91 148	94.20 194	98.00 105	80.82 285	98.73 235	94.42 158	89.04 351	98.34 203

mamba_0408		93.70 194	92.99 200	95.83 168	96.79 214	90.38 199	88.69 477	97.07 262	90.96 236	93.68 208	97.31 181	84.97 195	98.76 225	90.95 238	96.51 210	98.35 199

SSM_04072		93.51 202	92.99 200	95.05 222	96.79 214	90.38 199	88.69 477	97.07 262	90.96 236	93.68 208	97.31 181	84.97 195	96.42 432	90.95 238	96.51 210	98.35 199

v144192		91.06 317	90.28 314	93.39 335	93.66 403	87.23 325	96.83 236	97.07 262	87.43 360	89.69 318	94.28 358	81.48 270	98.00 325	87.18 338	84.92 400	94.93 381

v1192		91.07 316	90.23 318	93.58 325	93.70 400	87.82 311	96.73 250	97.07 262	87.77 351	89.58 321	94.32 356	80.90 283	97.97 330	86.52 346	85.48 387	94.95 377

v8		91.29 308	90.53 306	93.57 327	94.15 387	88.12 301	97.34 178	97.06 267	88.99 306	88.32 359	94.26 361	83.08 231	98.01 324	87.62 326	83.92 415	94.57 410

mvs_anonymous		93.82 189	93.74 170	94.06 288	96.44 259	85.41 374	95.81 329	97.05 268	89.85 276	90.09 306	96.36 246	87.44 140	97.75 363	93.97 168	96.69 204	99.02 103

IterMVS-LS		92.29 255	91.94 244	93.34 337	96.25 270	86.97 332	96.57 272	97.05 268	90.67 247	89.50 326	94.80 326	86.59 152	97.64 373	89.91 262	86.11 382	95.40 350
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v1921920		90.85 326	90.03 329	93.29 339	93.55 407	86.96 334	96.74 249	97.04 270	87.36 362	89.52 325	94.34 353	80.23 298	97.97 330	86.27 349	85.21 393	94.94 379

CDS-MVSNet		94.14 172	93.54 177	95.93 159	96.18 279	91.46 148	96.33 293	97.04 270	88.97 308	93.56 213	96.51 238	87.55 133	97.89 347	89.80 265	95.95 226	98.44 190
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
SSC-MVS3.2		89.74 362	89.26 355	91.19 408	95.16 342	80.29 443	94.53 390	97.03 272	91.79 195	88.86 345	94.10 368	69.94 411	97.82 353	85.29 367	86.66 378	95.45 345

v1144		91.37 301	90.60 302	93.68 317	93.89 395	88.23 293	96.84 235	97.03 272	88.37 330	89.69 318	94.39 348	82.04 259	97.98 327	87.80 311	85.37 389	94.84 389

v1240		90.70 332	89.85 336	93.23 341	93.51 410	86.80 335	96.61 266	97.02 274	87.16 367	89.58 321	94.31 357	79.55 311	97.98 327	85.52 364	85.44 388	94.90 384

EPP-MVSNet		95.22 123	95.04 119	95.76 178	97.49 163	89.56 235	98.67 15	97.00 275	90.69 245	94.24 192	97.62 159	89.79 92	98.81 211	93.39 186	96.49 214	98.92 126

V42		91.58 287	90.87 284	93.73 311	94.05 391	88.50 283	97.32 181	96.97 276	88.80 318	89.71 316	94.33 354	82.54 248	98.05 318	89.01 289	85.07 396	94.64 409

test_fmvs1		93.21 213	93.53 178	92.25 376	96.55 245	81.20 431	97.40 172	96.96 277	90.68 246	96.80 85	98.04 100	69.25 417	98.40 274	97.58 40	98.50 128	97.16 281

FMVSNet2		91.31 305	90.08 324	94.99 228	96.51 252	92.21 114	97.41 168	96.95 278	88.82 315	88.62 351	94.75 328	73.87 376	97.42 400	85.20 370	88.55 357	95.35 354

ACMH	87.59 16	90.53 337	89.42 351	93.87 305	96.21 271	87.92 306	97.24 191	96.94 279	88.45 328	83.91 435	96.27 251	71.92 393	98.62 256	84.43 378	89.43 344	95.05 375
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
GBi-Net		91.35 302	90.27 315	94.59 253	96.51 252	91.18 163	97.50 154	96.93 280	88.82 315	89.35 329	94.51 341	73.87 376	97.29 407	86.12 354	88.82 352	95.31 357

test1		91.35 302	90.27 315	94.59 253	96.51 252	91.18 163	97.50 154	96.93 280	88.82 315	89.35 329	94.51 341	73.87 376	97.29 407	86.12 354	88.82 352	95.31 357

FMVSNet3		91.78 274	90.69 298	95.03 225	96.53 248	92.27 112	97.02 211	96.93 280	89.79 279	89.35 329	94.65 334	77.01 347	97.47 395	86.12 354	88.82 352	95.35 354

FMVSNet1		89.88 357	88.31 370	94.59 253	95.41 321	91.18 163	97.50 154	96.93 280	86.62 375	87.41 380	94.51 341	65.94 444	97.29 407	83.04 393	87.43 368	95.31 357

GeoE		93.89 186	93.28 191	95.72 184	96.96 197	89.75 225	98.24 43	96.92 284	89.47 289	92.12 252	97.21 189	84.42 204	98.39 279	87.71 316	96.50 213	99.01 106

SymmetryMVS		95.94 96	95.54 95	97.15 74	97.85 135	92.90 87	97.99 69	96.91 285	95.92 16	96.57 102	97.93 111	85.34 185	99.50 120	94.99 129	96.39 221	99.05 102

miper_enhance_ethall		91.54 291	91.01 280	93.15 345	95.35 327	87.07 330	93.97 414	96.90 286	86.79 373	89.17 338	93.43 402	86.55 154	97.64 373	89.97 261	86.93 373	94.74 405

eth_miper_zixun_eth		91.02 319	90.59 303	92.34 371	95.33 331	84.35 393	94.10 411	96.90 286	88.56 324	88.84 347	94.33 354	84.08 211	97.60 378	88.77 296	84.37 409	95.06 374

TAMVS		94.01 178	93.46 183	95.64 187	96.16 281	90.45 194	96.71 253	96.89 288	89.27 296	93.46 220	96.92 211	87.29 143	97.94 340	88.70 298	95.74 232	98.53 176

miper_ehance_all_eth		91.59 285	91.13 276	92.97 351	95.55 313	86.57 343	94.47 395	96.88 289	87.77 351	88.88 344	94.01 373	86.22 161	97.54 388	89.49 273	86.93 373	94.79 400

v2v482		91.59 285	90.85 287	93.80 308	93.87 396	88.17 299	96.94 221	96.88 289	89.54 286	89.53 324	94.90 320	81.70 268	98.02 323	89.25 282	85.04 398	95.20 365

CNLPA		94.28 163	93.53 178	96.52 107	98.38 89	92.55 102	96.59 269	96.88 289	90.13 270	91.91 258	97.24 187	85.21 189	99.09 175	87.64 325	97.83 158	97.92 238

PAPM		91.52 292	90.30 313	95.20 215	95.30 334	89.83 222	93.38 436	96.85 292	86.26 383	88.59 352	95.80 275	84.88 197	98.15 300	75.67 448	95.93 227	97.63 256

c3_l		91.38 299	90.89 283	92.88 355	95.58 311	86.30 351	94.68 385	96.84 293	88.17 335	88.83 348	94.23 362	85.65 177	97.47 395	89.36 277	84.63 402	94.89 385

pm-mvs1		90.72 331	89.65 346	93.96 297	94.29 386	89.63 230	97.79 107	96.82 294	89.07 301	86.12 407	95.48 297	78.61 329	97.78 358	86.97 342	81.67 428	94.46 412

test_vis1_n		92.37 250	92.26 234	92.72 361	94.75 366	82.64 414	98.02 66	96.80 295	91.18 225	97.77 59	97.93 111	58.02 466	98.29 288	97.63 37	98.21 143	97.23 279

CMPMVS	62.92 21	85.62 419	84.92 409	87.74 446	89.14 459	73.12 476	94.17 409	96.80 295	73.98 471	73.65 474	94.93 318	66.36 438	97.61 377	83.95 386	91.28 321	92.48 450
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
MS-PatchMatch		90.27 344	89.77 340	91.78 392	94.33 383	84.72 390	95.55 345	96.73 297	86.17 385	86.36 402	95.28 303	71.28 398	97.80 356	84.09 383	98.14 147	92.81 442

Effi-MVS+-dtu		93.08 220	93.21 195	92.68 364	96.02 294	83.25 407	97.14 204	96.72 298	93.85 100	91.20 283	93.44 399	83.08 231	98.30 287	91.69 224	95.73 233	96.50 300

TSAR-MVS + GP.		96.69 67	96.49 71	97.27 67	98.31 92	93.39 67	96.79 242	96.72 298	94.17 89	97.44 65	97.66 153	92.76 33	99.33 139	96.86 62	97.76 162	99.08 98

1112_ss		93.37 208	92.42 230	96.21 139	97.05 186	90.99 170	96.31 295	96.72 298	86.87 372	89.83 313	96.69 224	86.51 155	99.14 167	88.12 303	93.67 285	98.50 180

PVSNet	86.66 18	92.24 258	91.74 253	93.73 311	97.77 140	83.69 404	92.88 445	96.72 298	87.91 343	93.00 231	94.86 322	78.51 330	99.05 186	86.53 345	97.45 171	98.47 185

miper_lstm_enhance		90.50 340	90.06 328	91.83 388	95.33 331	83.74 401	93.86 420	96.70 302	87.56 358	87.79 372	93.81 381	83.45 222	96.92 420	87.39 332	84.62 403	94.82 395

v148		90.99 320	90.38 309	92.81 358	93.83 397	85.80 364	96.78 246	96.68 303	89.45 291	88.75 350	93.93 377	82.96 237	97.82 353	87.83 309	83.25 420	94.80 398

ACMH+	87.92 14	90.20 348	89.18 357	93.25 340	96.48 255	86.45 348	96.99 217	96.68 303	88.83 314	84.79 424	96.22 253	70.16 408	98.53 265	84.42 379	88.04 361	94.77 403

CANet_DTU		94.37 161	93.65 173	96.55 104	96.46 258	92.13 118	96.21 303	96.67 305	94.38 84	93.53 216	97.03 206	79.34 313	99.71 67	90.76 244	98.45 133	97.82 249

cl____		90.96 323	90.32 311	92.89 354	95.37 325	86.21 354	94.46 397	96.64 306	87.82 347	88.15 367	94.18 365	82.98 235	97.54 388	87.70 317	85.59 385	94.92 383

HY-MVS	89.66 9	93.87 187	92.95 204	96.63 98	97.10 180	92.49 104	95.64 342	96.64 306	89.05 303	93.00 231	95.79 278	85.77 173	99.45 128	89.16 287	94.35 264	97.96 235

Test_1112_low_res		92.84 235	91.84 248	95.85 167	97.04 188	89.97 217	95.53 347	96.64 306	85.38 395	89.65 320	95.18 308	85.86 169	99.10 172	87.70 317	93.58 290	98.49 182

DIV-MVS_self_test		90.97 322	90.33 310	92.88 355	95.36 326	86.19 356	94.46 397	96.63 309	87.82 347	88.18 365	94.23 362	82.99 234	97.53 390	87.72 314	85.57 386	94.93 381

Fast-Effi-MVS+-dtu		92.29 255	91.99 242	93.21 343	95.27 335	85.52 370	97.03 209	96.63 309	92.09 186	89.11 340	95.14 310	80.33 296	98.08 311	87.54 328	94.74 260	96.03 318

UnsupCasMVSNet_bld		82.13 436	79.46 441	90.14 425	88.00 473	82.47 419	90.89 465	96.62 311	78.94 460	75.61 469	84.40 478	56.63 469	96.31 434	77.30 440	66.77 481	91.63 461

cl22		91.21 310	90.56 305	93.14 346	96.09 290	86.80 335	94.41 399	96.58 312	87.80 349	88.58 353	93.99 375	80.85 284	97.62 376	89.87 264	86.93 373	94.99 376

jason		94.84 147	94.39 153	96.18 141	95.52 314	90.93 176	96.09 311	96.52 313	89.28 295	96.01 130	97.32 179	84.70 199	98.77 219	95.15 125	98.91 112	98.85 142
jason: jason.
tt0805		91.09 315	90.07 327	94.16 284	95.61 309	88.31 288	97.56 145	96.51 314	89.56 285	89.17 338	95.64 287	67.08 436	98.38 280	91.07 236	88.44 358	95.80 326

AUN-MVS		91.76 275	90.75 293	94.81 240	97.00 193	88.57 278	96.65 260	96.49 315	89.63 283	92.15 250	96.12 259	78.66 328	98.50 267	90.83 240	79.18 439	97.36 271

hse-mvs2		93.45 206	92.99 200	94.81 240	97.02 191	88.59 277	96.69 256	96.47 316	95.19 36	96.74 89	96.16 257	83.67 217	98.48 270	95.85 102	79.13 440	97.35 273

SD_0403		90.01 352	90.02 330	89.96 428	95.65 308	76.76 465	95.76 333	96.46 317	90.58 256	86.59 399	96.29 249	82.12 258	94.78 457	73.00 462	93.76 283	98.35 199

EG-PatchMatch MVS		87.02 397	85.44 399	91.76 394	92.67 431	85.00 384	96.08 312	96.45 318	83.41 428	79.52 458	93.49 396	57.10 468	97.72 365	79.34 431	90.87 330	92.56 447

KD-MVS_self_test		85.95 415	84.95 408	88.96 440	89.55 458	79.11 459	95.13 373	96.42 319	85.91 388	84.07 433	90.48 441	70.03 410	94.82 456	80.04 423	72.94 463	92.94 440

FE-MVSNET2		86.36 407	84.68 414	91.39 402	87.67 475	86.47 347	96.21 303	96.41 320	87.87 345	79.31 460	89.64 449	65.29 449	95.58 448	82.42 402	77.28 446	92.14 458

pmmvs6		87.81 384	86.19 392	92.69 363	91.32 445	86.30 351	97.34 178	96.41 320	80.59 454	84.05 434	94.37 350	67.37 431	97.67 368	84.75 374	79.51 438	94.09 424

PMMVS		92.86 233	92.34 231	94.42 268	94.92 357	86.73 338	94.53 390	96.38 322	84.78 407	94.27 191	95.12 312	83.13 230	98.40 274	91.47 228	96.49 214	98.12 220

RPSCF		90.75 329	90.86 285	90.42 422	96.84 207	76.29 468	95.61 343	96.34 323	83.89 417	91.38 271	97.87 124	76.45 353	98.78 215	87.16 339	92.23 303	96.20 307

BP-MVS1		95.89 98	95.49 97	97.08 81	96.67 228	93.20 77	98.08 59	96.32 324	94.56 72	96.32 115	97.84 130	84.07 212	99.15 164	96.75 64	98.78 116	98.90 130

MSDG		91.42 297	90.24 317	94.96 233	97.15 178	88.91 268	93.69 428	96.32 324	85.72 391	86.93 395	96.47 240	80.24 297	98.98 192	80.57 420	95.05 253	96.98 284

blended_shiyan6		87.55 388	85.52 398	93.64 320	88.78 464	88.50 283	95.23 365	96.30 326	82.80 433	86.09 408	87.70 466	73.69 382	97.56 381	87.70 317	71.36 468	94.86 386

blend_shiyan4		86.87 398	84.61 415	93.67 318	88.87 462	88.70 273	95.17 372	96.30 326	82.80 433	86.16 405	87.11 470	65.12 452	97.55 383	87.73 312	72.21 465	94.75 404

WBMVS		90.69 334	89.99 331	92.81 358	96.48 255	85.00 384	95.21 368	96.30 326	89.46 290	89.04 341	94.05 372	72.45 391	97.82 353	89.46 274	87.41 370	95.61 337

blended_shiyan8		87.58 387	85.55 397	93.66 319	88.76 466	88.54 280	95.21 368	96.29 329	82.81 432	86.25 403	87.73 465	73.70 381	97.58 380	87.81 310	71.42 467	94.85 388

OurMVSNet-221017-0		90.51 339	90.19 322	91.44 400	93.41 416	81.25 429	96.98 218	96.28 330	91.68 199	86.55 400	96.30 248	74.20 375	97.98 327	88.96 291	87.40 371	95.09 372

wanda-best-256-512		87.29 390	85.21 403	93.53 328	88.54 469	88.21 295	94.51 393	96.27 331	82.69 436	85.92 410	86.89 472	73.04 385	97.55 383	87.68 321	71.36 468	94.83 390

FE-blended-shiyan7		87.29 390	85.21 403	93.53 328	88.54 469	88.21 295	94.51 393	96.27 331	82.69 436	85.92 410	86.89 472	73.03 386	97.55 383	87.68 321	71.36 468	94.83 390

MVP-Stereo		90.74 330	90.08 324	92.71 362	93.19 421	88.20 297	95.86 325	96.27 331	86.07 386	84.86 423	94.76 327	77.84 342	97.75 363	83.88 388	98.01 153	92.17 457
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
lupinMVS		94.99 140	94.56 143	96.29 133	96.34 267	91.21 158	95.83 327	96.27 331	88.93 310	96.22 120	96.88 213	86.20 163	98.85 205	95.27 121	99.05 103	98.82 146

BH-untuned		92.94 228	92.62 220	93.92 304	97.22 171	86.16 357	96.40 284	96.25 335	90.06 271	89.79 314	96.17 256	83.19 227	98.35 282	87.19 337	97.27 180	97.24 278

CL-MVSNet_self_test		86.31 409	85.15 405	89.80 430	88.83 463	81.74 427	93.93 417	96.22 336	86.67 374	85.03 421	90.80 439	78.09 338	94.50 458	74.92 451	71.86 466	93.15 438

IS-MVSNet		94.90 142	94.52 147	96.05 149	97.67 146	90.56 190	98.44 26	96.22 336	93.21 127	93.99 201	97.74 144	85.55 182	98.45 271	89.98 260	97.86 157	99.14 88

FA-MVS(test-final)		93.52 201	92.92 205	95.31 212	96.77 221	88.54 280	94.82 382	96.21 338	89.61 284	94.20 194	95.25 306	83.24 225	99.14 167	90.01 259	96.16 223	98.25 208

GA-MVS		91.38 299	90.31 312	94.59 253	94.65 371	87.62 315	94.34 402	96.19 339	90.73 243	90.35 294	93.83 378	71.84 394	97.96 334	87.22 336	93.61 288	98.21 211

LuminaMVS		94.89 143	94.35 155	96.53 105	95.48 316	92.80 91	96.88 230	96.18 340	92.85 153	95.92 133	96.87 215	81.44 271	98.83 208	96.43 77	97.10 187	97.94 237

IterMVS-SCA-FT		90.31 342	89.81 338	91.82 389	95.52 314	84.20 396	94.30 405	96.15 341	90.61 253	87.39 381	94.27 359	75.80 359	96.44 431	87.34 333	86.88 377	94.82 395

IterMVS		90.15 350	89.67 344	91.61 396	95.48 316	83.72 402	94.33 403	96.12 342	89.99 272	87.31 384	94.15 367	75.78 361	96.27 435	86.97 342	86.89 376	94.83 390
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
DP-MVS		92.76 238	91.51 262	96.52 107	98.77 62	90.99 170	97.38 175	96.08 343	82.38 439	89.29 332	97.87 124	83.77 215	99.69 73	81.37 413	96.69 204	98.89 136

pmmvs4		90.93 324	89.85 336	94.17 281	93.34 418	90.79 182	94.60 387	96.02 344	84.62 408	87.45 378	95.15 309	81.88 265	97.45 397	87.70 317	87.87 363	94.27 421

ppachtmachnet_test		88.35 379	87.29 378	91.53 397	92.45 437	83.57 405	93.75 424	95.97 345	84.28 411	85.32 419	94.18 365	79.00 325	96.93 419	75.71 447	84.99 399	94.10 422

Anonymous20240521		86.42 406	85.44 399	89.34 437	90.33 451	79.79 449	96.73 250	95.92 346	83.71 422	83.25 439	91.36 436	63.92 454	96.01 436	78.39 435	85.36 390	92.22 455

ITE_SJBPF				92.43 367	95.34 328	85.37 377		95.92 346	91.47 208	87.75 374	96.39 245	71.00 400	97.96 334	82.36 403	89.86 340	93.97 427

test_fmvs2		89.77 361	89.93 333	89.31 438	93.68 402	76.37 467	97.64 134	95.90 348	89.84 277	91.49 269	96.26 252	58.77 464	97.10 411	94.65 152	91.13 323	94.46 412

USDC		88.94 370	87.83 375	92.27 374	94.66 370	84.96 386	93.86 420	95.90 348	87.34 363	83.40 437	95.56 291	67.43 430	98.19 297	82.64 401	89.67 342	93.66 431

COLMAP_ROB	87.81 15	90.40 341	89.28 354	93.79 309	97.95 128	87.13 329	96.92 224	95.89 350	82.83 431	86.88 397	97.18 190	73.77 379	99.29 146	78.44 434	93.62 287	94.95 377
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
VDD-MVS		93.82 189	93.08 198	96.02 152	97.88 134	89.96 218	97.72 119	95.85 351	92.43 170	95.86 135	98.44 64	68.42 426	99.39 134	96.31 79	94.85 254	98.71 163

VDDNet		93.05 222	92.07 237	96.02 152	96.84 207	90.39 198	98.08 59	95.85 351	86.22 384	95.79 138	98.46 62	67.59 429	99.19 155	94.92 132	94.85 254	98.47 185

mvsmamba		94.57 156	94.14 160	95.87 163	97.03 189	89.93 219	97.84 96	95.85 351	91.34 214	94.79 177	96.80 216	80.67 287	98.81 211	94.85 137	98.12 148	98.85 142

Vis-MVSNet (Re-imp)		94.15 169	93.88 166	94.95 234	97.61 154	87.92 306	98.10 57	95.80 354	92.22 178	93.02 230	97.45 171	84.53 202	97.91 346	88.24 302	97.97 154	99.02 103

MM		97.29 31	96.98 42	98.23 12	98.01 123	95.03 27	98.07 61	95.76 355	97.78 1	97.52 62	98.80 38	88.09 118	99.86 9	99.44 2	99.37 67	99.80 1

KD-MVS_2432*1600		84.81 425	82.64 428	91.31 403	91.07 447	85.34 378	91.22 459	95.75 356	85.56 393	83.09 440	90.21 444	67.21 432	95.89 438	77.18 441	62.48 485	92.69 443

miper_refine_blended		84.81 425	82.64 428	91.31 403	91.07 447	85.34 378	91.22 459	95.75 356	85.56 393	83.09 440	90.21 444	67.21 432	95.89 438	77.18 441	62.48 485	92.69 443

FE-MVS		92.05 266	91.05 278	95.08 221	96.83 209	87.93 305	93.91 419	95.70 358	86.30 381	94.15 198	94.97 315	76.59 351	99.21 153	84.10 382	96.86 195	98.09 227

tpm cat1		88.36 378	87.21 381	91.81 390	95.13 347	80.55 438	92.58 450	95.70 358	74.97 470	87.45 378	91.96 429	78.01 341	98.17 299	80.39 422	88.74 355	96.72 295

our_test_3		88.78 374	87.98 374	91.20 407	92.45 437	82.53 416	93.61 432	95.69 360	85.77 390	84.88 422	93.71 383	79.99 302	96.78 427	79.47 428	86.24 379	94.28 420

BH-w/o		92.14 263	91.75 251	93.31 338	96.99 194	85.73 367	95.67 337	95.69 360	88.73 320	89.26 334	94.82 325	82.97 236	98.07 315	85.26 369	96.32 222	96.13 314

CR-MVSNet		90.82 327	89.77 340	93.95 298	94.45 379	87.19 326	90.23 468	95.68 362	86.89 371	92.40 240	92.36 421	80.91 281	97.05 414	81.09 417	93.95 280	97.60 261

Patchmtry		88.64 376	87.25 379	92.78 360	94.09 389	86.64 339	89.82 472	95.68 362	80.81 451	87.63 376	92.36 421	80.91 281	97.03 415	78.86 432	85.12 395	94.67 407

testing91		91.90 271	91.02 279	94.53 261	96.54 246	86.55 345	95.86 325	95.64 364	91.77 196	91.89 259	93.47 398	69.94 411	98.86 203	90.23 258	93.86 282	98.18 213

BH-RMVSNet		92.72 240	91.97 243	94.97 232	97.16 175	87.99 304	96.15 309	95.60 365	90.62 252	91.87 260	97.15 193	78.41 332	98.57 262	83.16 391	97.60 164	98.36 197

PVSNet_0	82.17 19	85.46 420	83.64 422	90.92 411	95.27 335	79.49 455	90.55 466	95.60 365	83.76 421	83.00 442	89.95 446	71.09 399	97.97 330	82.75 399	60.79 487	95.31 357

guyue		95.17 128	94.96 123	95.82 169	96.97 196	89.65 229	97.56 145	95.58 367	94.82 57	95.72 140	97.42 174	82.90 238	98.84 207	96.71 67	96.93 192	98.96 114

SCA		91.84 273	91.18 275	93.83 306	95.59 310	84.95 387	94.72 384	95.58 367	90.82 239	92.25 248	93.69 385	75.80 359	98.10 306	86.20 351	95.98 225	98.45 187

MonoMVSNet		91.92 269	91.77 249	92.37 368	92.94 425	83.11 410	97.09 207	95.55 369	92.91 148	90.85 286	94.55 338	81.27 275	96.52 430	93.01 197	87.76 364	97.47 267

usedtu_blend_shiyan5		87.06 396	84.84 410	93.69 315	88.54 469	88.70 273	95.83 327	95.54 370	78.74 461	85.92 410	86.89 472	73.03 386	97.55 383	87.73 312	71.36 468	94.83 390

AllTest		90.23 346	88.98 360	93.98 294	97.94 129	86.64 339	96.51 273	95.54 370	85.38 395	85.49 416	96.77 218	70.28 406	99.15 164	80.02 424	92.87 292	96.15 312

TestCases				93.98 294	97.94 129	86.64 339		95.54 370	85.38 395	85.49 416	96.77 218	70.28 406	99.15 164	80.02 424	92.87 292	96.15 312

mmtdpeth		89.70 363	88.96 361	91.90 385	95.84 302	84.42 392	97.46 165	95.53 373	90.27 265	94.46 187	90.50 440	69.74 415	98.95 193	97.39 53	69.48 475	92.34 451

tpmvs		89.83 360	89.15 358	91.89 386	94.92 357	80.30 442	93.11 441	95.46 374	86.28 382	88.08 368	92.65 411	80.44 293	98.52 266	81.47 409	89.92 339	96.84 291

pmmvs5		89.86 359	88.87 364	92.82 357	92.86 427	86.23 353	96.26 298	95.39 375	84.24 412	87.12 386	94.51 341	74.27 374	97.36 404	87.61 327	87.57 366	94.86 386

PatchmatchNet		91.91 270	91.35 264	93.59 324	95.38 323	84.11 397	93.15 440	95.39 375	89.54 286	92.10 253	93.68 387	82.82 241	98.13 301	84.81 373	95.32 246	98.52 177
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
tpmrst		91.44 296	91.32 266	91.79 391	95.15 345	79.20 458	93.42 435	95.37 377	88.55 325	93.49 219	93.67 388	82.49 250	98.27 290	90.41 253	89.34 345	97.90 239

Anonymous20231206		87.09 395	86.14 393	89.93 429	91.22 446	80.35 440	96.11 310	95.35 378	83.57 424	84.16 429	93.02 406	73.54 383	95.61 446	72.16 464	86.14 381	93.84 429

MIMVSNet1		84.93 423	83.05 425	90.56 420	89.56 457	84.84 389	95.40 353	95.35 378	83.91 416	80.38 454	92.21 426	57.23 467	93.34 472	70.69 470	82.75 426	93.50 433

TDRefinement		86.53 402	84.76 412	91.85 387	82.23 487	84.25 394	96.38 286	95.35 378	84.97 404	84.09 432	94.94 317	65.76 445	98.34 285	84.60 377	74.52 457	92.97 439

TR-MVS		91.48 295	90.59 303	94.16 284	96.40 261	87.33 319	95.67 337	95.34 381	87.68 355	91.46 270	95.52 294	76.77 350	98.35 282	82.85 396	93.61 288	96.79 293

EPNet_dtu		91.71 276	91.28 269	92.99 350	93.76 399	83.71 403	96.69 256	95.28 382	93.15 134	87.02 391	95.95 267	83.37 223	97.38 403	79.46 429	96.84 196	97.88 241
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
FMVSNet5		87.29 390	85.79 395	91.78 392	94.80 364	87.28 321	95.49 349	95.28 382	84.09 414	83.85 436	91.82 430	62.95 457	94.17 463	78.48 433	85.34 391	93.91 428

MDTV_nov1_ep13			90.76 291		95.22 339	80.33 441	93.03 443	95.28 382	88.14 338	92.84 237	93.83 378	81.34 272	98.08 311	82.86 394	94.34 265

LF4IMVS		87.94 382	87.25 379	89.98 427	92.38 439	80.05 448	94.38 400	95.25 385	87.59 357	84.34 426	94.74 329	64.31 453	97.66 372	84.83 372	87.45 367	92.23 454

TransMVSNet (Re)		88.94 370	87.56 376	93.08 348	94.35 382	88.45 286	97.73 116	95.23 386	87.47 359	84.26 428	95.29 301	79.86 305	97.33 405	79.44 430	74.44 459	93.45 435

test20.03		86.14 413	85.40 401	88.35 441	90.12 452	80.06 447	95.90 324	95.20 387	88.59 321	81.29 449	93.62 390	71.43 397	92.65 476	71.26 468	81.17 431	92.34 451

new-patchmatchnet		83.18 432	81.87 435	87.11 449	86.88 478	75.99 469	93.70 426	95.18 388	85.02 403	77.30 467	88.40 458	65.99 443	93.88 468	74.19 456	70.18 473	91.47 466

MDA-MVSNet_test_wron		85.87 417	84.23 419	90.80 417	92.38 439	82.57 415	93.17 438	95.15 389	82.15 440	67.65 480	92.33 424	78.20 334	95.51 450	77.33 438	79.74 435	94.31 419

YYNet1		85.87 417	84.23 419	90.78 418	92.38 439	82.46 420	93.17 438	95.14 390	82.12 441	67.69 478	92.36 421	78.16 337	95.50 451	77.31 439	79.73 436	94.39 415

Baseline_NR-MVSNet		91.20 311	90.62 301	92.95 352	93.83 397	88.03 303	97.01 214	95.12 391	88.42 329	89.70 317	95.13 311	83.47 220	97.44 398	89.66 270	83.24 421	93.37 436

thres200		92.23 259	91.39 263	94.75 247	97.61 154	89.03 262	96.60 268	95.09 392	92.08 187	93.28 225	94.00 374	78.39 333	99.04 189	81.26 416	94.18 271	96.19 308

ADS-MVSNet		89.89 356	88.68 366	93.53 328	95.86 297	84.89 388	90.93 463	95.07 393	83.23 429	91.28 279	91.81 431	79.01 323	97.85 349	79.52 426	91.39 319	97.84 246

pmmvs-eth3d		86.22 411	84.45 416	91.53 397	88.34 472	87.25 323	94.47 395	95.01 394	83.47 425	79.51 459	89.61 450	69.75 414	95.71 443	83.13 392	76.73 450	91.64 460

Anonymous202405211		92.07 265	90.83 289	95.76 178	98.19 109	88.75 271	97.58 141	95.00 395	86.00 387	93.64 211	97.45 171	66.24 441	99.53 112	90.68 247	92.71 297	99.01 106

MDA-MVSNet-bldmvs		85.00 422	82.95 427	91.17 409	93.13 423	83.33 406	94.56 389	95.00 395	84.57 409	65.13 484	92.65 411	70.45 405	95.85 440	73.57 459	77.49 445	94.33 417

ambc				86.56 452	83.60 484	70.00 479	85.69 484	94.97 397		80.60 453	88.45 457	37.42 486	96.84 424	82.69 400	75.44 455	92.86 441

testgi		87.97 381	87.21 381	90.24 424	92.86 427	80.76 433	96.67 259	94.97 397	91.74 197	85.52 415	95.83 273	62.66 459	94.47 460	76.25 445	88.36 359	95.48 340

myMVS_eth3d28		91.52 292	90.97 281	93.17 344	96.91 199	83.24 408	95.61 343	94.96 399	92.24 177	91.98 256	93.28 403	69.31 416	98.40 274	88.71 297	95.68 235	97.88 241

dp		88.90 372	88.26 372	90.81 415	94.58 375	76.62 466	92.85 446	94.93 400	85.12 401	90.07 308	93.07 405	75.81 358	98.12 304	80.53 421	87.42 369	97.71 253

test_fmvs3		83.21 431	83.02 426	83.78 456	86.77 479	68.34 482	96.76 248	94.91 401	86.49 377	84.14 431	89.48 451	36.04 487	91.73 478	91.86 218	80.77 433	91.26 468

test_0402		86.46 405	84.79 411	91.45 399	95.02 351	85.55 369	96.29 297	94.89 402	80.90 448	82.21 445	93.97 376	68.21 427	97.29 407	62.98 479	88.68 356	91.51 463

tfpn200view9		92.38 249	91.52 260	94.95 234	97.85 135	89.29 251	97.41 168	94.88 403	92.19 183	93.27 226	94.46 346	78.17 335	99.08 177	81.40 410	94.08 275	96.48 301

CVMVSNet		91.23 309	91.75 251	89.67 431	95.77 303	74.69 470	96.44 274	94.88 403	85.81 389	92.18 249	97.64 157	79.07 318	95.58 448	88.06 305	95.86 230	98.74 160

thres400		92.42 247	91.52 260	95.12 220	97.85 135	89.29 251	97.41 168	94.88 403	92.19 183	93.27 226	94.46 346	78.17 335	99.08 177	81.40 410	94.08 275	96.98 284

tt0320		85.39 421	83.12 424	92.19 378	93.44 415	85.79 365	96.19 306	94.87 406	71.19 478	82.92 443	91.76 433	58.43 465	96.81 425	81.03 418	78.26 444	93.98 426

EPNet		95.20 124	94.56 143	97.14 75	92.80 429	92.68 97	97.85 95	94.87 406	96.64 9	92.46 239	97.80 138	86.23 160	99.65 79	93.72 176	98.62 124	99.10 95
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
testing99		91.62 283	90.72 296	94.32 273	96.48 255	86.11 362	95.81 329	94.76 408	91.55 201	91.75 264	93.44 399	68.55 424	98.82 209	90.43 252	93.69 284	98.04 231

sc_t1		86.48 404	84.10 421	93.63 321	93.45 414	85.76 366	96.79 242	94.71 409	73.06 475	86.45 401	94.35 351	55.13 472	97.95 338	84.38 380	78.55 443	97.18 280

SixPastTwentyTwo		89.15 368	88.54 368	90.98 410	93.49 411	80.28 444	96.70 254	94.70 410	90.78 240	84.15 430	95.57 290	71.78 395	97.71 366	84.63 376	85.07 396	94.94 379

thres100view900		92.43 246	91.58 257	94.98 230	97.92 131	89.37 247	97.71 121	94.66 411	92.20 181	93.31 224	94.90 320	78.06 339	99.08 177	81.40 410	94.08 275	96.48 301

thres600view7		92.49 244	91.60 256	95.18 216	97.91 132	89.47 241	97.65 130	94.66 411	92.18 185	93.33 223	94.91 319	78.06 339	99.10 172	81.61 406	94.06 279	96.98 284

PatchT		88.87 373	87.42 377	93.22 342	94.08 390	85.10 382	89.51 473	94.64 413	81.92 442	92.36 243	88.15 461	80.05 301	97.01 417	72.43 463	93.65 286	97.54 264

baseline1		92.82 236	91.90 246	95.55 194	97.20 173	90.77 183	97.19 199	94.58 414	92.20 181	92.36 243	96.34 247	84.16 210	98.21 294	89.20 285	83.90 416	97.68 255

AstraMVS		94.82 149	94.64 139	95.34 211	96.36 266	88.09 302	97.58 141	94.56 415	94.98 46	95.70 143	97.92 114	81.93 264	98.93 196	96.87 61	95.88 228	98.99 110

UBG		91.55 289	90.76 291	93.94 300	96.52 251	85.06 383	95.22 366	94.54 416	90.47 261	91.98 256	92.71 410	72.02 392	98.74 233	88.10 304	95.26 248	98.01 233

Gipuma		67.86 453	65.41 455	75.18 469	92.66 432	73.45 474	66.50 491	94.52 417	53.33 489	57.80 490	66.07 490	30.81 489	89.20 482	48.15 488	78.88 442	62.90 490
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
testing11		91.68 279	90.75 293	94.47 264	96.53 248	86.56 344	95.76 333	94.51 418	91.10 232	91.24 281	93.59 393	68.59 423	98.86 203	91.10 235	94.29 267	98.00 234

CostFormer		91.18 314	90.70 297	92.62 365	94.84 362	81.76 426	94.09 412	94.43 419	84.15 413	92.72 238	93.77 382	79.43 312	98.20 295	90.70 246	92.18 306	97.90 239

tpm2		89.96 353	89.21 356	92.23 377	94.91 359	81.25 429	93.78 423	94.42 420	80.62 453	91.56 267	93.44 399	76.44 354	97.94 340	85.60 363	92.08 310	97.49 265

testing3-2		92.10 264	92.05 238	92.27 374	97.71 144	79.56 452	97.42 167	94.41 421	93.53 113	93.22 228	95.49 295	69.16 418	99.11 170	93.25 187	94.22 269	98.13 218

MGCNet		96.74 64	96.31 81	98.02 20	96.87 203	94.65 31	97.58 141	94.39 422	96.47 12	97.16 74	98.39 68	87.53 135	99.87 7	98.97 20	99.41 59	99.55 43

JIA-IIPM		88.26 380	87.04 384	91.91 384	93.52 409	81.42 428	89.38 474	94.38 423	80.84 450	90.93 285	80.74 482	79.22 315	97.92 343	82.76 398	91.62 314	96.38 304

dmvs_re		90.21 347	89.50 349	92.35 369	95.47 320	85.15 380	95.70 336	94.37 424	90.94 238	88.42 355	93.57 394	74.63 371	95.67 445	82.80 397	89.57 343	96.22 306

Patchmatch-test		89.42 366	87.99 373	93.70 314	95.27 335	85.11 381	88.98 475	94.37 424	81.11 447	87.10 389	93.69 385	82.28 254	97.50 393	74.37 454	94.76 258	98.48 184

LCM-MVSNet		72.55 446	69.39 450	82.03 458	70.81 498	65.42 487	90.12 470	94.36 426	55.02 488	65.88 482	81.72 480	24.16 495	89.96 479	74.32 455	68.10 479	90.71 471

ADS-MVSNet2		89.45 365	88.59 367	92.03 381	95.86 297	82.26 422	90.93 463	94.32 427	83.23 429	91.28 279	91.81 431	79.01 323	95.99 437	79.52 426	91.39 319	97.84 246

mvs5depth		86.53 402	85.08 406	90.87 412	88.74 467	82.52 417	91.91 455	94.23 428	86.35 380	87.11 388	93.70 384	66.52 437	97.76 361	81.37 413	75.80 452	92.31 453

EU-MVSNet		88.72 375	88.90 363	88.20 443	93.15 422	74.21 472	96.63 265	94.22 429	85.18 399	87.32 383	95.97 265	76.16 356	94.98 455	85.27 368	86.17 380	95.41 347

usedtu_dtu_shiyan2		80.00 439	76.91 445	89.27 439	82.13 488	79.69 451	95.45 351	94.20 430	72.95 476	75.80 468	87.75 464	44.44 482	94.30 462	70.64 471	68.81 478	93.84 429

tt0320-xc		84.83 424	82.33 432	92.31 372	93.66 403	86.20 355	96.17 308	94.06 431	71.26 477	82.04 447	92.22 425	55.07 473	96.72 428	81.49 408	75.04 456	94.02 425

MIMVSNet		88.50 377	86.76 387	93.72 313	94.84 362	87.77 312	91.39 457	94.05 432	86.41 379	87.99 370	92.59 414	63.27 455	95.82 442	77.44 437	92.84 294	97.57 263

OpenMVS_ROB	81.14 20	84.42 427	82.28 433	90.83 413	90.06 453	84.05 399	95.73 335	94.04 433	73.89 473	80.17 457	91.53 435	59.15 463	97.64 373	66.92 477	89.05 349	90.80 470

TinyColmap		86.82 400	85.35 402	91.21 405	94.91 359	82.99 412	93.94 416	94.02 434	83.58 423	81.56 448	94.68 331	62.34 460	98.13 301	75.78 446	87.35 372	92.52 449

ETVMVS		90.52 338	89.14 359	94.67 250	96.81 213	87.85 310	95.91 323	93.97 435	89.71 280	92.34 246	92.48 416	65.41 447	97.96 334	81.37 413	94.27 268	98.21 211

IB-MVS	87.33 17	89.91 354	88.28 371	94.79 244	95.26 338	87.70 313	95.12 374	93.95 436	89.35 294	87.03 390	92.49 415	70.74 403	99.19 155	89.18 286	81.37 430	97.49 265
Christian Sormann, Mattia Rossi, Andreas Kuhn and Friedrich Fraundorfer: IB-MVS: An Iterative Algorithm for Deep Multi-View Stereo based on Binary Decisions. BMVC 2021
Syy-MVS		87.13 394	87.02 385	87.47 447	95.16 342	73.21 475	95.00 376	93.93 437	88.55 325	86.96 392	91.99 427	75.90 357	94.00 465	61.59 481	94.11 272	95.20 365

myMVS_eth3d		87.18 393	86.38 390	89.58 432	95.16 342	79.53 453	95.00 376	93.93 437	88.55 325	86.96 392	91.99 427	56.23 470	94.00 465	75.47 450	94.11 272	95.20 365

testing222		90.31 342	88.96 361	94.35 270	96.54 246	87.29 320	95.50 348	93.84 439	90.97 235	91.75 264	92.96 407	62.18 461	98.00 325	82.86 394	94.08 275	97.76 251

test_f		80.57 438	79.62 440	83.41 457	83.38 485	67.80 484	93.57 433	93.72 440	80.80 452	77.91 466	87.63 467	33.40 488	92.08 477	87.14 340	79.04 441	90.34 472

LCM-MVSNet-Re		92.50 242	92.52 226	92.44 366	96.82 211	81.89 425	96.92 224	93.71 441	92.41 171	84.30 427	94.60 336	85.08 191	97.03 415	91.51 226	97.36 173	98.40 193

tpm		90.25 345	89.74 343	91.76 394	93.92 393	79.73 450	93.98 413	93.54 442	88.28 332	91.99 255	93.25 404	77.51 345	97.44 398	87.30 335	87.94 362	98.12 220

ET-MVSNet_ETH3D		91.49 294	90.11 323	95.63 188	96.40 261	91.57 142	95.34 356	93.48 443	90.60 255	75.58 470	95.49 295	80.08 300	96.79 426	94.25 164	89.76 341	98.52 177

LFMVS		93.60 196	92.63 219	96.52 107	98.13 115	91.27 155	97.94 82	93.39 444	90.57 257	96.29 117	98.31 81	69.00 419	99.16 162	94.18 165	95.87 229	99.12 92

MVStest1		82.38 435	80.04 439	89.37 435	87.63 476	82.83 413	95.03 375	93.37 445	73.90 472	73.50 475	94.35 351	62.89 458	93.25 474	73.80 457	65.92 482	92.04 459

FE-MVSNET		83.85 428	81.97 434	89.51 433	87.19 477	83.19 409	95.21 368	93.17 446	83.45 426	78.90 462	89.05 454	65.46 446	93.84 469	69.71 473	75.56 454	91.51 463

Patchmatch-RL test		87.38 389	86.24 391	90.81 415	88.74 467	78.40 462	88.12 482	93.17 446	87.11 368	82.17 446	89.29 452	81.95 262	95.60 447	88.64 299	77.02 447	98.41 192

ttmdpeth		85.91 416	84.76 412	89.36 436	89.14 459	80.25 445	95.66 340	93.16 448	83.77 420	83.39 438	95.26 305	66.24 441	95.26 454	80.65 419	75.57 453	92.57 446

test-LLR		91.42 297	91.19 274	92.12 379	94.59 373	80.66 435	94.29 406	92.98 449	91.11 230	90.76 288	92.37 418	79.02 321	98.07 315	88.81 294	96.74 201	97.63 256

test-mter		90.19 349	89.54 348	92.12 379	94.59 373	80.66 435	94.29 406	92.98 449	87.68 355	90.76 288	92.37 418	67.67 428	98.07 315	88.81 294	96.74 201	97.63 256

WB-MVSnew		89.88 357	89.56 347	90.82 414	94.57 376	83.06 411	95.65 341	92.85 451	87.86 346	90.83 287	94.10 368	79.66 309	96.88 422	76.34 444	94.19 270	92.54 448

testing3		87.67 385	86.88 386	90.05 426	96.14 284	80.71 434	97.10 206	92.85 451	90.15 269	87.54 377	94.55 338	55.70 471	94.10 464	73.77 458	94.10 274	95.35 354

test_method		66.11 454	64.89 456	69.79 472	72.62 496	35.23 504	65.19 492	92.83 453	20.35 494	65.20 483	88.08 462	43.14 484	82.70 489	73.12 461	63.46 484	91.45 467

test0.0.03 1		89.37 367	88.70 365	91.41 401	92.47 436	85.63 368	95.22 366	92.70 454	91.11 230	86.91 396	93.65 389	79.02 321	93.19 475	78.00 436	89.18 346	95.41 347

new_pmnet		82.89 433	81.12 438	88.18 444	89.63 456	80.18 446	91.77 456	92.57 455	76.79 468	75.56 471	88.23 460	61.22 462	94.48 459	71.43 466	82.92 424	89.87 473

mvsany_test1		93.93 185	93.98 164	93.78 310	94.94 356	86.80 335	94.62 386	92.55 456	88.77 319	96.85 84	98.49 58	88.98 100	98.08 311	95.03 127	95.62 237	96.46 303

0.4-1-1-0.2		86.27 410	83.62 423	94.20 279	90.38 450	87.69 314	91.04 462	92.52 457	83.43 427	85.22 420	81.49 481	65.31 448	98.29 288	88.90 293	74.30 460	96.64 296

thisisatest0515		92.29 255	91.30 268	95.25 214	96.60 234	88.90 269	94.36 401	92.32 458	87.92 342	93.43 221	94.57 337	77.28 346	99.00 190	89.42 276	95.86 230	97.86 245

0.4-1-1-0.1		86.83 399	84.27 418	94.50 262	91.39 444	88.23 293	92.62 449	92.27 459	84.04 415	86.01 409	83.30 479	65.29 449	98.31 286	89.08 288	74.45 458	96.96 288

thisisatest0530		93.03 223	92.21 235	95.49 203	97.07 181	89.11 260	97.49 162	92.19 460	90.16 268	94.09 199	96.41 243	76.43 355	99.05 186	90.38 254	95.68 235	98.31 205

tttt0517		92.96 226	92.33 232	94.87 237	97.11 179	87.16 328	97.97 78	92.09 461	90.63 251	93.88 205	97.01 207	76.50 352	99.06 183	90.29 257	95.45 244	98.38 195

K. test v3		87.64 386	86.75 388	90.32 423	93.02 424	79.48 456	96.61 266	92.08 462	90.66 249	80.25 456	94.09 370	67.21 432	96.65 429	85.96 359	80.83 432	94.83 390

TESTMET0.1,1		90.06 351	89.42 351	91.97 382	94.41 381	80.62 437	94.29 406	91.97 463	87.28 365	90.44 292	92.47 417	68.79 420	97.67 368	88.50 301	96.60 208	97.61 260

PM-MVS		83.48 430	81.86 436	88.31 442	87.83 474	77.59 464	93.43 434	91.75 464	86.91 370	80.63 452	89.91 447	44.42 483	95.84 441	85.17 371	76.73 450	91.50 465

baseline2		91.63 282	90.86 285	93.94 300	94.33 383	86.32 350	95.92 322	91.64 465	89.37 293	86.94 394	94.69 330	81.62 269	98.69 243	88.64 299	94.57 263	96.81 292

APD_test1		79.31 441	77.70 443	84.14 455	89.11 461	69.07 481	92.36 454	91.50 466	69.07 480	73.87 473	92.63 413	39.93 485	94.32 461	70.54 472	80.25 434	89.02 475

FPMVS		71.27 447	69.85 449	75.50 468	74.64 493	59.03 493	91.30 458	91.50 466	58.80 485	57.92 489	88.28 459	29.98 491	85.53 488	53.43 486	82.84 425	81.95 481

door								91.13 468

door-mid								91.06 469

EGC-MVSNET		68.77 452	63.01 458	86.07 454	92.49 435	82.24 423	93.96 415	90.96 470	0.71 499	2.62 500	90.89 438	53.66 474	93.46 470	57.25 484	84.55 406	82.51 480

mvsany_test3		83.59 429	82.44 431	87.03 450	83.80 482	73.82 473	93.70 426	90.92 471	86.42 378	82.51 444	90.26 443	46.76 481	95.71 443	90.82 241	76.76 449	91.57 462

pmmvs3		79.97 440	77.50 444	87.39 448	82.80 486	79.38 457	92.70 448	90.75 472	70.69 479	78.66 463	87.47 469	51.34 477	93.40 471	73.39 460	69.65 474	89.38 474

UWE-MVS		89.91 354	89.48 350	91.21 405	95.88 296	78.23 463	94.91 379	90.26 473	89.11 300	92.35 245	94.52 340	68.76 421	97.96 334	83.95 386	95.59 238	97.42 269

DSMNet-mixed		86.34 408	86.12 394	87.00 451	89.88 455	70.43 477	94.93 378	90.08 474	77.97 465	85.42 418	92.78 409	74.44 373	93.96 467	74.43 453	95.14 249	96.62 297

MVS-HIRNet		82.47 434	81.21 437	86.26 453	95.38 323	69.21 480	88.96 476	89.49 475	66.28 482	80.79 451	74.08 487	68.48 425	97.39 402	71.93 465	95.47 243	92.18 456

WB-MVS		76.77 443	76.63 446	77.18 463	85.32 480	56.82 495	94.53 390	89.39 476	82.66 438	71.35 476	89.18 453	75.03 366	88.88 483	35.42 492	66.79 480	85.84 477

test1111		93.19 215	92.82 209	94.30 276	97.58 160	84.56 391	98.21 48	89.02 477	93.53 113	94.58 182	98.21 88	72.69 388	99.05 186	93.06 193	98.48 131	99.28 77

SSC-MVS		76.05 444	75.83 447	76.72 467	84.77 481	56.22 496	94.32 404	88.96 478	81.82 444	70.52 477	88.91 455	74.79 370	88.71 484	33.69 493	64.71 483	85.23 478

ECVR-MVS		93.19 215	92.73 215	94.57 258	97.66 148	85.41 374	98.21 48	88.23 479	93.43 120	94.70 179	98.21 88	72.57 389	99.07 181	93.05 194	98.49 129	99.25 80

EPMVS		90.70 332	89.81 338	93.37 336	94.73 368	84.21 395	93.67 429	88.02 480	89.50 288	92.38 242	93.49 396	77.82 343	97.78 358	86.03 357	92.68 298	98.11 226

ANet_high		63.94 456	59.58 459	77.02 464	61.24 500	66.06 485	85.66 485	87.93 481	78.53 463	42.94 492	71.04 489	25.42 494	80.71 491	52.60 487	30.83 493	84.28 479

PMMVS2		70.19 448	66.92 452	80.01 459	76.35 492	65.67 486	86.22 483	87.58 482	64.83 484	62.38 485	80.29 484	26.78 493	88.49 486	63.79 478	54.07 489	85.88 476

lessismore_v0				90.45 421	91.96 442	79.09 460		87.19 483		80.32 455	94.39 348	66.31 440	97.55 383	84.00 385	76.84 448	94.70 406

PMVS	53.92 22	58.58 457	55.40 460	68.12 473	51.00 501	48.64 498	78.86 488	87.10 484	46.77 490	35.84 496	74.28 486	8.76 499	86.34 487	42.07 490	73.91 461	69.38 487
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
UWE-MVS-28		86.81 401	86.41 389	88.02 445	92.87 426	74.60 471	95.38 355	86.70 485	88.17 335	87.28 385	94.67 333	70.83 402	93.30 473	67.45 475	94.31 266	96.17 309

test_vis1_rt		86.16 412	85.06 407	89.46 434	93.47 413	80.46 439	96.41 280	86.61 486	85.22 398	79.15 461	88.64 456	52.41 476	97.06 413	93.08 192	90.57 332	90.87 469

testf1		69.31 450	66.76 453	76.94 465	78.61 490	61.93 489	88.27 480	86.11 487	55.62 486	59.69 486	85.31 476	20.19 497	89.32 480	57.62 482	69.44 476	79.58 482

APD_test2		69.31 450	66.76 453	76.94 465	78.61 490	61.93 489	88.27 480	86.11 487	55.62 486	59.69 486	85.31 476	20.19 497	89.32 480	57.62 482	69.44 476	79.58 482

gg-mvs-nofinetune		87.82 383	85.61 396	94.44 266	94.46 378	89.27 254	91.21 461	84.61 489	80.88 449	89.89 312	74.98 485	71.50 396	97.53 390	85.75 362	97.21 182	96.51 299

dmvs_testset		81.38 437	82.60 430	77.73 462	91.74 443	51.49 497	93.03 443	84.21 490	89.07 301	78.28 465	91.25 437	76.97 348	88.53 485	56.57 485	82.24 427	93.16 437

GG-mvs-BLEND				93.62 322	93.69 401	89.20 256	92.39 453	83.33 491		87.98 371	89.84 448	71.00 400	96.87 423	82.08 405	95.40 245	94.80 398

MTMP							97.86 92	82.03 492

DeepMVS_CX				74.68 470	90.84 449	64.34 488		81.61 493	65.34 483	67.47 481	88.01 463	48.60 480	80.13 492	62.33 480	73.68 462	79.58 482

E-PMN		53.28 458	52.56 462	55.43 476	74.43 494	47.13 499	83.63 487	76.30 494	42.23 491	42.59 493	62.22 492	28.57 492	74.40 493	31.53 494	31.51 492	44.78 491

test2506		91.60 284	90.78 290	94.04 290	97.66 148	83.81 400	98.27 37	75.53 495	93.43 120	95.23 160	98.21 88	67.21 432	99.07 181	93.01 197	98.49 129	99.25 80

EMVS		52.08 460	51.31 463	54.39 477	72.62 496	45.39 501	83.84 486	75.51 496	41.13 492	40.77 494	59.65 493	30.08 490	73.60 494	28.31 496	29.90 494	44.18 492

test_vis3_rt		72.73 445	70.55 448	79.27 460	80.02 489	68.13 483	93.92 418	74.30 497	76.90 467	58.99 488	73.58 488	20.29 496	95.37 452	84.16 381	72.80 464	74.31 485

MVE	50.73 23	53.25 459	48.81 464	66.58 475	65.34 499	57.50 494	72.49 490	70.94 498	40.15 493	39.28 495	63.51 491	6.89 501	73.48 495	38.29 491	42.38 491	68.76 489
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
tmp_tt		51.94 461	53.82 461	46.29 478	33.73 502	45.30 502	78.32 489	67.24 499	18.02 495	50.93 491	87.05 471	52.99 475	53.11 497	70.76 469	25.29 495	40.46 493

kuosan		65.27 455	64.66 457	67.11 474	83.80 482	61.32 492	88.53 479	60.77 500	68.22 481	67.67 479	80.52 483	49.12 479	70.76 496	29.67 495	53.64 490	69.26 488

dongtai		69.99 449	69.33 451	71.98 471	88.78 464	61.64 491	89.86 471	59.93 501	75.67 469	74.96 472	85.45 475	50.19 478	81.66 490	43.86 489	55.27 488	72.63 486

N_pmnet		78.73 442	78.71 442	78.79 461	92.80 429	46.50 500	94.14 410	43.71 502	78.61 462	80.83 450	91.66 434	74.94 369	96.36 433	67.24 476	84.45 408	93.50 433

wuyk23d		25.11 462	24.57 466	26.74 479	73.98 495	39.89 503	57.88 493	9.80 503	12.27 496	10.39 497	6.97 499	7.03 500	36.44 498	25.43 497	17.39 496	3.89 496

testmvs		13.36 464	16.33 467	4.48 481	5.04 503	2.26 506	93.18 437	3.28 504	2.70 497	8.24 498	21.66 495	2.29 503	2.19 499	7.58 498	2.96 497	9.00 495

test123		13.04 465	15.66 468	5.18 480	4.51 504	3.45 505	92.50 452	1.81 505	2.50 498	7.58 499	20.15 496	3.67 502	2.18 500	7.13 499	1.07 498	9.90 494

mmdepth		0.00 468	0.00 471	0.00 482	0.00 505	0.00 507	0.00 494	0.00 506	0.00 500	0.00 501	0.00 500	0.00 504	0.00 501	0.00 500	0.00 499	0.00 497

monomultidepth		0.00 468	0.00 471	0.00 482	0.00 505	0.00 507	0.00 494	0.00 506	0.00 500	0.00 501	0.00 500	0.00 504	0.00 501	0.00 500	0.00 499	0.00 497

test_blank		0.00 468	0.00 471	0.00 482	0.00 505	0.00 507	0.00 494	0.00 506	0.00 500	0.00 501	0.00 500	0.00 504	0.00 501	0.00 500	0.00 499	0.00 497

uanet_test		0.00 468	0.00 471	0.00 482	0.00 505	0.00 507	0.00 494	0.00 506	0.00 500	0.00 501	0.00 500	0.00 504	0.00 501	0.00 500	0.00 499	0.00 497

DCPMVS		0.00 468	0.00 471	0.00 482	0.00 505	0.00 507	0.00 494	0.00 506	0.00 500	0.00 501	0.00 500	0.00 504	0.00 501	0.00 500	0.00 499	0.00 497

pcd_1.5k_mvsjas		7.39 467	9.85 470	0.00 482	0.00 505	0.00 507	0.00 494	0.00 506	0.00 500	0.00 501	0.00 500	88.65 108	0.00 501	0.00 500	0.00 499	0.00 497

sosnet-low-res		0.00 468	0.00 471	0.00 482	0.00 505	0.00 507	0.00 494	0.00 506	0.00 500	0.00 501	0.00 500	0.00 504	0.00 501	0.00 500	0.00 499	0.00 497

sosnet		0.00 468	0.00 471	0.00 482	0.00 505	0.00 507	0.00 494	0.00 506	0.00 500	0.00 501	0.00 500	0.00 504	0.00 501	0.00 500	0.00 499	0.00 497

uncertanet		0.00 468	0.00 471	0.00 482	0.00 505	0.00 507	0.00 494	0.00 506	0.00 500	0.00 501	0.00 500	0.00 504	0.00 501	0.00 500	0.00 499	0.00 497

Regformer		0.00 468	0.00 471	0.00 482	0.00 505	0.00 507	0.00 494	0.00 506	0.00 500	0.00 501	0.00 500	0.00 504	0.00 501	0.00 500	0.00 499	0.00 497

n2								0.00 506

nn								0.00 506

ab-mvs-re		8.06 466	10.74 469	0.00 482	0.00 505	0.00 507	0.00 494	0.00 506	0.00 500	0.00 501	96.69 224	0.00 504	0.00 501	0.00 500	0.00 499	0.00 497

uanet		0.00 468	0.00 471	0.00 482	0.00 505	0.00 507	0.00 494	0.00 506	0.00 500	0.00 501	0.00 500	0.00 504	0.00 501	0.00 500	0.00 499	0.00 497

TestfortrainingZip							98.69 11

WAC-MVS						79.53 453								75.56 449

PC_three_1452									90.77 241	98.89 26	98.28 86	96.24 1	98.35 282	95.76 106	99.58 23	99.59 32

eth-test2					0.00 505

eth-test					0.00 505

OPU-MVS				98.55 4	98.82 61	96.86 3	98.25 40				98.26 87	96.04 2	99.24 150	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 45	99.72 2	99.77 3

GSMVS																98.45 187

test_part2					99.28 30	95.74 9				98.10 48

sam_mvs1												82.76 242				98.45 187

sam_mvs												81.94 263

test_post1							92.81 447				16.58 498	80.53 291	97.68 367	86.20 351

test_post											17.58 497	81.76 266	98.08 311

patchmatchnet-post											90.45 442	82.65 247	98.10 306

gm-plane-assit					93.22 420	78.89 461			84.82 406		93.52 395		98.64 252	87.72 314

test9_res														94.81 143	99.38 64	99.45 59

agg_prior2														93.94 170	99.38 64	99.50 52

test_prior4						93.66 62	96.42 279

test_prior2							96.35 289		92.80 156	96.03 127	97.59 163	92.01 49		95.01 128	99.38 64

旧先验2							95.94 320		81.66 445	97.34 70			98.82 209	92.26 203

新几何2							95.79 331

原ACMM2							95.67 337

testdata2													99.67 77	85.96 359

segment_acmp												92.89 32

testdata1							95.26 364		93.10 137

plane_prior7					96.21 271	89.98 215

plane_prior6					96.10 289	90.00 211						81.32 273

plane_prior4											96.64 227

plane_prior3						90.00 211			94.46 78	91.34 273

plane_prior2							97.74 114		94.85 53

plane_prior1					96.14 284

plane_prior						89.99 213	97.24 191		94.06 92						92.16 307

HQP5-MVS						89.33 249

HQP-NCC					95.86 297		96.65 260		93.55 109	90.14 297

ACMP_Plane					95.86 297		96.65 260		93.55 109	90.14 297

BP-MVS														92.13 211

HQP4-MVS										90.14 297			98.50 267			95.78 328

HQP2-MVS												80.95 279

NP-MVS					95.99 295	89.81 223					95.87 270

MDTV_nov1_ep13_2view						70.35 478	93.10 442		83.88 418	93.55 214		82.47 251		86.25 350		98.38 195

ACMMP++_ref															90.30 337

ACMMP++															91.02 326

Test By Simon												88.73 107

High-resolution multi-view benchmark