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	96.53 100	98.41 80	91.73 125	98.01 61	99.02 1	96.37 11	99.30 5	98.92 21	92.39 41	99.79 40	99.16 12	99.46 42	98.08 208

PGM-MVS		96.81 53	96.53 64	97.65 43	99.35 22	93.53 61	97.65 122	98.98 2	92.22 161	97.14 70	98.44 58	91.17 68	99.85 18	94.35 145	99.46 42	99.57 32

MVS_111021_HR		96.68 64	96.58 63	96.99 80	98.46 75	92.31 106	96.20 283	98.90 3	94.30 84	95.86 128	97.74 126	92.33 42	99.38 130	96.04 90	99.42 52	99.28 73

test_fmvsmconf_n		97.49 18	97.56 13	97.29 60	97.44 159	92.37 103	97.91 80	98.88 4	95.83 17	98.92 21	99.05 12	91.45 58	99.80 35	99.12 14	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 32	98.65 31	98.90 23	91.97 49	99.80 35	97.63 36	99.21 77	99.57 32

ACMMP		96.27 81	95.93 84	97.28 62	99.24 30	92.62 94	98.25 36	98.81 6	92.99 134	94.56 165	98.39 62	88.96 98	99.85 18	94.57 142	97.63 157	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 82	96.19 80	96.39 118	98.23 99	91.35 147	96.24 281	98.79 7	93.99 91	95.80 130	97.65 136	89.92 88	99.24 143	95.87 94	99.20 82	98.58 154

patch_mono-2		96.83 52	97.44 21	95.01 207	99.05 41	85.39 344	96.98 206	98.77 8	94.70 64	97.99 45	98.66 41	93.61 19	99.91 1	97.67 35	99.50 36	99.72 12

fmvsm_s_conf0.5_n		96.85 49	97.13 26	96.04 142	98.07 114	90.28 195	97.97 72	98.76 9	94.93 46	98.84 26	99.06 11	88.80 102	99.65 73	99.06 16	98.63 117	98.18 194

fmvsm_l_conf0.5_n		97.65 7	97.75 7	97.34 57	98.21 100	92.75 88	97.83 92	98.73 10	95.04 43	99.30 5	98.84 34	93.34 22	99.78 43	99.32 6	99.13 92	99.50 48

fmvsm_s_conf0.5_n_a		96.75 57	96.93 41	96.20 134	97.64 145	90.72 177	98.00 62	98.73 10	94.55 71	98.91 22	99.08 7	88.22 114	99.63 82	98.91 19	98.37 130	98.25 189

fmvsm_l_conf0.5_n_9		97.59 11	97.79 5	96.97 82	98.28 89	91.49 139	97.61 131	98.71 12	97.10 4	99.70 1	98.93 20	90.95 73	99.77 46	99.35 5	99.53 29	99.65 19

FC-MVSNet-test		93.94 165	93.57 157	95.04 205	95.48 296	91.45 144	98.12 51	98.71 12	93.37 116	90.23 276	96.70 202	87.66 124	97.85 327	91.49 207	90.39 317	95.83 302

UniMVSNet (Re)		93.31 191	92.55 204	95.61 175	95.39 302	93.34 67	97.39 165	98.71 12	93.14 129	90.10 285	94.83 304	87.71 123	98.03 300	91.67 205	83.99 390	95.46 321

fmvsm_l_conf0.5_n_a		97.63 9	97.76 6	97.26 64	98.25 94	92.59 96	97.81 97	98.68 15	94.93 46	99.24 8	98.87 29	93.52 20	99.79 40	99.32 6	99.21 77	99.40 62

FIs		94.09 156	93.70 153	95.27 194	95.70 285	92.03 118	98.10 52	98.68 15	93.36 118	90.39 273	96.70 202	87.63 127	97.94 318	92.25 185	90.50 316	95.84 301

WR-MVS_H		92.00 248	91.35 245	93.95 273	95.09 329	89.47 224	98.04 59	98.68 15	91.46 190	88.34 336	94.68 311	85.86 160	97.56 356	85.77 331	84.24 388	94.82 366

fmvsm_s_conf0.5_n_4		96.75 57	97.07 29	95.79 162	97.76 136	89.57 218	97.66 121	98.66 18	95.36 28	99.03 14	98.90 23	88.39 110	99.73 55	99.17 11	98.66 115	98.08 208

VPA-MVSNet		93.24 193	92.48 209	95.51 181	95.70 285	92.39 102	97.86 85	98.66 18	92.30 158	92.09 234	95.37 279	80.49 271	98.40 254	93.95 151	85.86 361	95.75 310

fmvsm_l_conf0.5_n_3		97.64 8	97.60 11	97.79 30	98.14 107	93.94 52	97.93 78	98.65 20	96.70 6	99.38 3	99.07 10	89.92 88	99.81 30	99.16 12	99.43 49	99.61 26

fmvsm_s_conf0.5_n_3		97.15 31	97.36 23	96.52 102	97.98 120	91.19 155	97.84 89	98.65 20	97.08 5	99.25 7	99.10 5	87.88 121	99.79 40	99.32 6	99.18 84	98.59 153

fmvsm_s_conf0.5_n_8		97.32 25	97.48 20	96.85 83	98.28 89	91.07 163	97.76 102	98.62 22	97.53 2	99.20 10	99.12 4	88.24 113	99.81 30	99.41 3	99.17 85	99.67 14

fmvsm_s_conf0.5_n_2		96.62 65	96.82 50	96.02 144	97.98 120	90.43 187	97.50 146	98.59 23	96.59 8	99.31 4	99.08 7	84.47 186	99.75 52	99.37 4	98.45 127	97.88 221

UniMVSNet_NR-MVSNet		93.37 189	92.67 198	95.47 187	95.34 308	92.83 85	97.17 189	98.58 24	92.98 139	90.13 281	95.80 255	88.37 112	97.85 327	91.71 202	83.93 391	95.73 312

CSCG		96.05 85	95.91 85	96.46 112	99.24 30	90.47 184	98.30 29	98.57 25	89.01 282	93.97 183	97.57 146	92.62 37	99.76 48	94.66 136	99.27 70	99.15 83

fmvsm_s_conf0.5_n_9		97.33 24	97.57 12	96.62 96	98.43 78	90.32 194	97.80 98	98.53 26	97.24 3	99.62 2	99.14 1	88.65 105	99.80 35	99.54 1	99.15 89	99.74 8

fmvsm_s_conf0.5_n_6		97.08 34	97.17 25	96.81 84	97.28 164	91.73 125	97.75 104	98.50 27	94.86 50	99.22 9	98.78 38	89.75 91	99.76 48	99.10 15	99.29 68	98.94 112

MSLP-MVS++		96.94 43	97.06 30	96.59 97	98.72 60	91.86 123	97.67 118	98.49 28	94.66 67	97.24 66	98.41 61	92.31 44	98.94 189	96.61 65	99.46 42	98.96 108

HyFIR lowres test		93.66 177	92.92 186	95.87 154	98.24 95	89.88 209	94.58 358	98.49 28	85.06 379	93.78 186	95.78 259	82.86 221	98.67 229	91.77 200	95.71 215	99.07 94

CHOSEN 1792x2688		94.15 151	93.51 163	96.06 140	98.27 91	89.38 229	95.18 344	98.48 30	85.60 369	93.76 187	97.11 177	83.15 211	99.61 84	91.33 210	98.72 113	99.19 79

fmvsm_s_conf0.5_n_7		96.45 72	96.80 52	95.37 190	97.29 163	88.38 262	97.23 183	98.47 31	95.14 37	98.43 36	99.09 6	87.58 128	99.72 59	98.80 23	99.21 77	98.02 212

fmvsm_s_conf0.5_n_5		97.00 40	96.97 38	97.09 75	97.58 155	92.56 97	97.68 117	98.47 31	94.02 89	98.90 23	98.89 26	88.94 99	99.78 43	99.18 10	99.03 101	98.93 116

PHI-MVS		96.77 55	96.46 71	97.71 41	98.40 81	94.07 48	98.21 43	98.45 33	89.86 254	97.11 72	98.01 98	92.52 39	99.69 67	96.03 91	99.53 29	99.36 68

fmvsm_s_conf0.1_n		96.58 68	96.77 55	96.01 147	96.67 215	90.25 196	97.91 80	98.38 34	94.48 75	98.84 26	99.14 1	88.06 116	99.62 83	98.82 21	98.60 119	98.15 198

PVSNet_BlendedMVS		94.06 157	93.92 147	94.47 241	98.27 91	89.46 226	96.73 231	98.36 35	90.17 246	94.36 170	95.24 287	88.02 117	99.58 92	93.44 163	90.72 312	94.36 386

PVSNet_Blended		94.87 129	94.56 127	95.81 160	98.27 91	89.46 226	95.47 327	98.36 35	88.84 291	94.36 170	96.09 244	88.02 117	99.58 92	93.44 163	98.18 139	98.40 175

3Dnovator	91.36 5	95.19 117	94.44 135	97.44 53	96.56 225	93.36 66	98.65 12	98.36 35	94.12 86	89.25 315	98.06 92	82.20 238	99.77 46	93.41 165	99.32 66	99.18 80

FOURS1					99.55 1	93.34 67	99.29 1	98.35 38	94.98 44	98.49 34

DPE-MVS		97.86 4	97.65 9	98.47 5	99.17 34	95.78 7	97.21 186	98.35 38	95.16 36	98.71 30	98.80 36	95.05 10	99.89 3	96.70 63	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 74	96.47 68	96.16 136	95.48 296	90.69 178	97.91 80	98.33 40	94.07 87	98.93 18	99.14 1	87.44 135	99.61 84	98.63 24	98.32 132	98.18 194

HFP-MVS		97.14 32	96.92 42	97.83 26	99.42 7	94.12 46	98.52 16	98.32 41	93.21 121	97.18 67	98.29 78	92.08 46	99.83 26	95.63 107	99.59 19	99.54 41

ACMMPR		97.07 36	96.84 46	97.79 30	99.44 6	93.88 53	98.52 16	98.31 42	93.21 121	97.15 69	98.33 72	91.35 62	99.86 9	95.63 107	99.59 19	99.62 23

test_fmvsmvis_n_1920		96.70 60	96.84 46	96.31 123	96.62 217	91.73 125	97.98 66	98.30 43	96.19 12	96.10 118	98.95 18	89.42 92	99.76 48	98.90 20	99.08 96	97.43 248

APDe-MVS		97.82 5	97.73 8	98.08 18	99.15 35	94.82 28	98.81 8	98.30 43	94.76 62	98.30 38	98.90 23	93.77 17	99.68 69	97.93 27	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 45	94.92 48	98.99 16	98.92 21	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 45	95.55 25	98.56 33	97.81 121	93.90 15	99.65 73	96.62 64	99.21 77	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 47	94.78 59	98.93 18	98.87 29	96.04 2	99.86 9	97.45 44	99.58 23	99.59 28

test_0728_SECOND				98.51 4	99.45 3	95.93 5	98.21 43	98.28 47					99.86 9	97.52 40	99.67 6	99.75 6

CP-MVS		97.02 38	96.81 51	97.64 45	99.33 23	93.54 60	98.80 9	98.28 47	92.99 134	96.45 105	98.30 77	91.90 50	99.85 18	95.61 109	99.68 4	99.54 41

test_fmvsmconf0.1_n		97.09 33	97.06 30	97.19 69	95.67 287	92.21 110	97.95 75	98.27 50	95.78 21	98.40 37	99.00 14	89.99 86	99.78 43	99.06 16	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 50	95.13 38	99.19 11	98.89 26	95.54 5	99.85 18	97.52 40	99.66 10	99.56 36

test_241102_TWO								98.27 50	95.13 38	98.93 18	98.89 26	94.99 11	99.85 18	97.52 40	99.65 13	99.74 8

test_241102_ONE					99.42 7	95.30 17		98.27 50	95.09 41	99.19 11	98.81 35	95.54 5	99.65 73

SF-MVS		97.39 21	97.13 26	98.17 15	99.02 44	95.28 19	98.23 40	98.27 50	92.37 157	98.27 39	98.65 43	93.33 23	99.72 59	96.49 69	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 50	95.34 30	98.11 41	98.56 45	94.53 12	99.71 61	96.57 67	99.62 17	99.65 19
Skip Steuart: Steuart Systems R&D Blog.
test_one_0601					99.32 24	95.20 20		98.25 56	95.13 38	98.48 35	98.87 29	95.16 7

PVSNet_Blended_VisFu		95.27 110	94.91 116	96.38 119	98.20 101	90.86 171	97.27 177	98.25 56	90.21 245	94.18 176	97.27 166	87.48 134	99.73 55	93.53 160	97.77 155	98.55 156

region2R		97.07 36	96.84 46	97.77 34	99.46 2	93.79 55	98.52 16	98.24 58	93.19 124	97.14 70	98.34 69	91.59 57	99.87 7	95.46 113	99.59 19	99.64 21

PS-CasMVS		91.55 268	90.84 269	93.69 290	94.96 333	88.28 265	97.84 89	98.24 58	91.46 190	88.04 347	95.80 255	79.67 287	97.48 364	87.02 311	84.54 385	95.31 335

DU-MVS		92.90 211	92.04 220	95.49 184	94.95 334	92.83 85	97.16 190	98.24 58	93.02 133	90.13 281	95.71 262	83.47 203	97.85 327	91.71 202	83.93 391	95.78 306

9.14			96.75 56		98.93 52		97.73 108	98.23 61	91.28 199	97.88 49	98.44 58	93.00 26	99.65 73	95.76 100	99.47 41

reproduce_model		97.51 17	97.51 17	97.50 50	98.99 48	93.01 78	97.79 100	98.21 62	95.73 22	97.99 45	99.03 13	92.63 36	99.82 28	97.80 29	99.42 52	99.67 14

D2MVS		91.30 285	90.95 263	92.35 338	94.71 349	85.52 339	96.18 285	98.21 62	88.89 289	86.60 376	93.82 360	79.92 283	97.95 316	89.29 260	90.95 309	93.56 401

reproduce-ours		97.53 15	97.51 17	97.60 47	98.97 49	93.31 69	97.71 113	98.20 64	95.80 19	97.88 49	98.98 16	92.91 27	99.81 30	97.68 31	99.43 49	99.67 14

our_new_method		97.53 15	97.51 17	97.60 47	98.97 49	93.31 69	97.71 113	98.20 64	95.80 19	97.88 49	98.98 16	92.91 27	99.81 30	97.68 31	99.43 49	99.67 14

SDMVSNet		94.17 149	93.61 156	95.86 156	98.09 110	91.37 146	97.35 169	98.20 64	93.18 126	91.79 242	97.28 164	79.13 295	98.93 190	94.61 139	92.84 275	97.28 256

XVS		97.18 29	96.96 40	97.81 28	99.38 14	94.03 50	98.59 13	98.20 64	94.85 51	96.59 93	98.29 78	91.70 53	99.80 35	95.66 102	99.40 57	99.62 23

X-MVStestdata		91.71 257	89.67 323	97.81 28	99.38 14	94.03 50	98.59 13	98.20 64	94.85 51	96.59 93	32.69 461	91.70 53	99.80 35	95.66 102	99.40 57	99.62 23

ACMMP_NAP		97.20 28	96.86 44	98.23 11	99.09 36	95.16 22	97.60 132	98.19 69	92.82 147	97.93 48	98.74 40	91.60 56	99.86 9	96.26 74	99.52 31	99.67 14

CP-MVSNet		91.89 253	91.24 252	93.82 282	95.05 330	88.57 255	97.82 94	98.19 69	91.70 179	88.21 342	95.76 260	81.96 243	97.52 362	87.86 286	84.65 379	95.37 331

ZNCC-MVS		96.96 41	96.67 59	97.85 25	99.37 16	94.12 46	98.49 20	98.18 71	92.64 153	96.39 107	98.18 85	91.61 55	99.88 4	95.59 112	99.55 26	99.57 32

SMA-MVS		97.35 22	97.03 35	98.30 8	99.06 40	95.42 10	97.94 76	98.18 71	90.57 237	98.85 25	98.94 19	93.33 23	99.83 26	96.72 61	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		91.20 290	90.44 286	93.48 301	94.49 357	87.91 280	97.76 102	98.18 71	91.29 196	87.78 351	95.74 261	80.35 274	97.33 375	85.46 335	82.96 401	95.19 346

DELS-MVS		96.61 66	96.38 75	97.30 59	97.79 134	93.19 74	95.96 297	98.18 71	95.23 33	95.87 127	97.65 136	91.45 58	99.70 66	95.87 94	99.44 48	99.00 103
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		89.70 342	88.40 348	93.60 294	95.15 325	90.10 198	97.56 137	98.16 75	87.28 342	86.16 382	94.63 315	77.57 323	98.05 296	74.48 421	84.59 383	92.65 414

VNet		95.89 93	95.45 96	97.21 67	98.07 114	92.94 81	97.50 146	98.15 76	93.87 95	97.52 56	97.61 142	85.29 171	99.53 106	95.81 99	95.27 228	99.16 81

DeepPCF-MVS	93.97 1	96.61 66	97.09 28	95.15 198	98.09 110	86.63 312	96.00 295	98.15 76	95.43 26	97.95 47	98.56 45	93.40 21	99.36 131	96.77 58	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 78	94.82 55	99.01 15	98.55 47	94.18 14	97.41 371	96.94 53	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 49	96.52 65	97.82 27	99.36 20	94.14 45	98.29 30	98.13 79	92.72 150	96.70 85	98.06 92	91.35 62	99.86 9	94.83 129	99.28 69	99.47 54

UA-Net		95.95 90	95.53 92	97.20 68	97.67 141	92.98 80	97.65 122	98.13 79	94.81 57	96.61 91	98.35 66	88.87 100	99.51 111	90.36 235	97.35 168	99.11 89

QAPM		93.45 187	92.27 214	96.98 81	96.77 210	92.62 94	98.39 25	98.12 81	84.50 387	88.27 340	97.77 124	82.39 235	99.81 30	85.40 336	98.81 109	98.51 161

Vis-MVSNet		95.23 114	94.81 117	96.51 106	97.18 169	91.58 136	98.26 35	98.12 81	94.38 82	94.90 155	98.15 87	82.28 236	98.92 192	91.45 209	98.58 121	99.01 100
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
OpenMVS	89.19 12	92.86 214	91.68 235	96.40 116	95.34 308	92.73 90	98.27 33	98.12 81	84.86 382	85.78 384	97.75 125	78.89 305	99.74 53	87.50 301	98.65 116	96.73 273

TranMVSNet+NR-MVSNet		92.50 223	91.63 236	95.14 199	94.76 345	92.07 115	97.53 143	98.11 84	92.90 144	89.56 303	96.12 239	83.16 210	97.60 354	89.30 259	83.20 400	95.75 310

CPTT-MVS		95.57 103	95.19 107	96.70 87	99.27 28	91.48 141	98.33 27	98.11 84	87.79 327	95.17 150	98.03 95	87.09 141	99.61 84	93.51 161	99.42 52	99.02 97

APD-MVS		96.95 42	96.60 61	98.01 20	99.03 43	94.93 27	97.72 111	98.10 86	91.50 188	98.01 44	98.32 74	92.33 42	99.58 92	94.85 127	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 47	96.60 61	97.64 45	99.40 11	93.44 62	98.50 19	98.09 87	93.27 120	95.95 125	98.33 72	91.04 70	99.88 4	95.20 116	99.57 25	99.60 27

ZD-MVS					99.05 41	94.59 32		98.08 88	89.22 275	97.03 75	98.10 88	92.52 39	99.65 73	94.58 141	99.31 67

MTGPA								98.08 88

MTAPA		97.08 34	96.78 54	97.97 23	99.37 16	94.42 36	97.24 179	98.08 88	95.07 42	96.11 117	98.59 44	90.88 76	99.90 2	96.18 86	99.50 36	99.58 31

CNVR-MVS		97.68 6	97.44 21	98.37 7	98.90 55	95.86 6	97.27 177	98.08 88	95.81 18	97.87 52	98.31 75	94.26 13	99.68 69	97.02 52	99.49 39	99.57 32

DP-MVS Recon		95.68 98	95.12 111	97.37 56	99.19 33	94.19 42	97.03 197	98.08 88	88.35 309	95.09 152	97.65 136	89.97 87	99.48 118	92.08 194	98.59 120	98.44 172

SR-MVS		97.01 39	96.86 44	97.47 52	99.09 36	93.27 71	97.98 66	98.07 93	93.75 98	97.45 58	98.48 55	91.43 60	99.59 89	96.22 77	99.27 70	99.54 41

MCST-MVS		97.18 29	96.84 46	98.20 14	99.30 26	95.35 15	97.12 193	98.07 93	93.54 108	96.08 119	97.69 131	93.86 16	99.71 61	96.50 68	99.39 59	99.55 39

NR-MVSNet		92.34 232	91.27 251	95.53 180	94.95 334	93.05 77	97.39 165	98.07 93	92.65 152	84.46 395	95.71 262	85.00 177	97.77 338	89.71 247	83.52 397	95.78 306

MP-MVS-pluss		96.70 60	96.27 78	97.98 22	99.23 32	94.71 29	96.96 208	98.06 96	90.67 227	95.55 141	98.78 38	91.07 69	99.86 9	96.58 66	99.55 26	99.38 66
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
APD-MVS_3200maxsize		96.81 53	96.71 58	97.12 72	99.01 47	92.31 106	97.98 66	98.06 96	93.11 130	97.44 59	98.55 47	90.93 74	99.55 102	96.06 87	99.25 74	99.51 45

MP-MVS		96.77 55	96.45 72	97.72 39	99.39 13	93.80 54	98.41 24	98.06 96	93.37 116	95.54 143	98.34 69	90.59 80	99.88 4	94.83 129	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 69	96.27 78	97.22 66	99.32 24	92.74 89	98.74 10	98.06 96	90.57 237	96.77 82	98.35 66	90.21 83	99.53 106	94.80 132	99.63 16	99.38 66

HPM-MVS		96.69 62	96.45 72	97.40 55	99.36 20	93.11 76	98.87 6	98.06 96	91.17 206	96.40 106	97.99 100	90.99 71	99.58 92	95.61 109	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		94.51 140	93.80 149	96.64 89	97.07 175	91.97 120	96.32 273	98.06 96	88.94 287	94.50 167	96.78 197	84.60 183	99.27 141	91.90 195	96.02 205	98.68 147

DeepC-MVS	93.07 3	96.06 84	95.66 89	97.29 60	97.96 122	93.17 75	97.30 175	98.06 96	93.92 93	93.38 202	98.66 41	86.83 143	99.73 55	95.60 111	99.22 76	98.96 108
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 35	98.11 17	98.77 58	95.06 25	97.34 170	98.04 103	95.96 13	97.09 73	97.88 111	93.18 25	99.71 61	95.84 98	99.17 85	99.56 36

DeepC-MVS_fast	93.89 2	96.93 44	96.64 60	97.78 32	98.64 69	94.30 37	97.41 160	98.04 103	94.81 57	96.59 93	98.37 64	91.24 65	99.64 81	95.16 118	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 46	96.80 52	97.11 74	99.02 44	92.34 104	97.98 66	98.03 105	93.52 111	97.43 61	98.51 50	91.40 61	99.56 100	96.05 88	99.26 72	99.43 59

RE-MVS-def			96.72 57		99.02 44	92.34 104	97.98 66	98.03 105	93.52 111	97.43 61	98.51 50	90.71 78		96.05 88	99.26 72	99.43 59

RPMNet		88.98 348	87.05 362	94.77 225	94.45 359	87.19 296	90.23 434	98.03 105	77.87 434	92.40 220	87.55 441	80.17 278	99.51 111	68.84 441	93.95 261	97.60 241

save fliter					98.91 54	94.28 38	97.02 199	98.02 108	95.35 29

TEST9					98.70 61	94.19 42	96.41 260	98.02 108	88.17 313	96.03 120	97.56 148	92.74 33	99.59 89

train_agg		96.30 80	95.83 88	97.72 39	98.70 61	94.19 42	96.41 260	98.02 108	88.58 300	96.03 120	97.56 148	92.73 34	99.59 89	95.04 120	99.37 63	99.39 64

test_8					98.67 63	94.06 49	96.37 267	98.01 111	88.58 300	95.98 124	97.55 150	92.73 34	99.58 92

agg_prior					98.67 63	93.79 55		98.00 112		95.68 137			99.57 99

test_prior				97.23 65	98.67 63	92.99 79		98.00 112					99.41 126			99.29 71

WR-MVS		92.34 232	91.53 240	94.77 225	95.13 327	90.83 172	96.40 264	97.98 114	91.88 174	89.29 312	95.54 273	82.50 231	97.80 334	89.79 246	85.27 370	95.69 313

HPM-MVS++		97.34 23	96.97 38	98.47 5	99.08 38	96.16 4	97.55 142	97.97 115	95.59 23	96.61 91	97.89 109	92.57 38	99.84 23	95.95 93	99.51 34	99.40 62

CANet		96.39 75	96.02 83	97.50 50	97.62 148	93.38 64	97.02 199	97.96 116	95.42 27	94.86 156	97.81 121	87.38 137	99.82 28	96.88 55	99.20 82	99.29 71

114514_t		93.95 164	93.06 180	96.63 93	99.07 39	91.61 133	97.46 157	97.96 116	77.99 432	93.00 211	97.57 146	86.14 157	99.33 133	89.22 263	99.15 89	98.94 112

IU-MVS					99.42 7	95.39 11		97.94 118	90.40 243	98.94 17				97.41 47	99.66 10	99.74 8

MSC_two_6792asdad				98.86 1	98.67 63	96.94 1		97.93 119					99.86 9	97.68 31	99.67 6	99.77 2

No_MVS				98.86 1	98.67 63	96.94 1		97.93 119					99.86 9	97.68 31	99.67 6	99.77 2

fmvsm_s_conf0.1_n_2		96.33 79	96.44 74	96.00 148	97.30 162	90.37 193	97.53 143	97.92 121	96.52 9	99.14 13	99.08 7	83.21 208	99.74 53	99.22 9	98.06 144	97.88 221

Anonymous20231211		90.63 314	89.42 330	94.27 255	98.24 95	89.19 241	98.05 58	97.89 122	79.95 424	88.25 341	94.96 296	72.56 364	98.13 279	89.70 248	85.14 372	95.49 317

原ACMM1				96.38 119	98.59 71	91.09 162		97.89 122	87.41 338	95.22 149	97.68 132	90.25 82	99.54 104	87.95 285	99.12 94	98.49 164

CDPH-MVS		95.97 89	95.38 101	97.77 34	98.93 52	94.44 35	96.35 268	97.88 124	86.98 346	96.65 89	97.89 109	91.99 48	99.47 119	92.26 183	99.46 42	99.39 64

test11								97.88 124

EIA-MVS		95.53 104	95.47 95	95.71 170	97.06 178	89.63 214	97.82 94	97.87 126	93.57 104	93.92 184	95.04 293	90.61 79	98.95 187	94.62 138	98.68 114	98.54 157

CS-MVS		96.86 47	97.06 30	96.26 129	98.16 106	91.16 160	99.09 3	97.87 126	95.30 31	97.06 74	98.03 95	91.72 51	98.71 224	97.10 50	99.17 85	98.90 121

无先验							95.79 308	97.87 126	83.87 395				99.65 73	87.68 295		98.89 125

3Dnovator+	91.43 4	95.40 105	94.48 133	98.16 16	96.90 192	95.34 16	98.48 21	97.87 126	94.65 68	88.53 332	98.02 97	83.69 199	99.71 61	93.18 169	98.96 104	99.44 57

VPNet		92.23 240	91.31 248	94.99 208	95.56 292	90.96 166	97.22 185	97.86 130	92.96 140	90.96 264	96.62 214	75.06 344	98.20 273	91.90 195	83.65 396	95.80 304

test_vis1_n_1920		94.17 149	94.58 126	92.91 322	97.42 160	82.02 391	97.83 92	97.85 131	94.68 65	98.10 42	98.49 52	70.15 383	99.32 135	97.91 28	98.82 108	97.40 250

DVP-MVS		97.91 3	97.81 4	98.22 13	99.45 3	95.36 13	98.21 43	97.85 131	94.92 48	98.73 28	98.87 29	95.08 8	99.84 23	97.52 40	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 24	97.69 42	99.25 29	94.24 41	98.07 56	97.85 131	93.72 99	98.57 32	98.35 66	93.69 18	99.40 127	97.06 51	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 45	97.04 34	96.45 113	98.29 88	91.66 132	99.03 4	97.85 131	95.84 16	96.90 77	97.97 102	91.24 65	98.75 215	96.92 54	99.33 65	98.94 112

test_fmvsmconf0.01_n		96.15 83	95.85 87	97.03 79	92.66 410	91.83 124	97.97 72	97.84 135	95.57 24	97.53 55	99.00 14	84.20 192	99.76 48	98.82 21	99.08 96	99.48 52

GDP-MVS		95.62 100	95.13 109	97.09 75	96.79 204	93.26 72	97.89 83	97.83 136	93.58 103	96.80 79	97.82 119	83.06 215	99.16 155	94.40 144	97.95 150	98.87 127

balanced_conf03		96.84 51	96.89 43	96.68 88	97.63 147	92.22 109	98.17 49	97.82 137	94.44 77	98.23 40	97.36 159	90.97 72	99.22 145	97.74 30	99.66 10	98.61 150

AdaColmap		94.34 144	93.68 154	96.31 123	98.59 71	91.68 131	96.59 250	97.81 138	89.87 253	92.15 230	97.06 180	83.62 202	99.54 104	89.34 258	98.07 143	97.70 234

MVSMamba_PlusPlus		96.51 69	96.48 67	96.59 97	98.07 114	91.97 120	98.14 50	97.79 139	90.43 241	97.34 64	97.52 151	91.29 64	99.19 148	98.12 26	99.64 14	98.60 151

KinetiMVS		95.26 111	94.75 121	96.79 85	96.99 187	92.05 116	97.82 94	97.78 140	94.77 61	96.46 103	97.70 129	80.62 268	99.34 132	92.37 182	98.28 134	98.97 105

mamv4		94.66 137	96.10 82	90.37 391	98.01 117	73.41 441	96.82 221	97.78 140	89.95 252	94.52 166	97.43 155	92.91 27	99.09 168	98.28 25	99.16 88	98.60 151

ETV-MVS		96.02 86	95.89 86	96.40 116	97.16 170	92.44 101	97.47 155	97.77 142	94.55 71	96.48 101	94.51 321	91.23 67	98.92 192	95.65 105	98.19 138	97.82 229

新几何1				97.32 58	98.60 70	93.59 59		97.75 143	81.58 415	95.75 132	97.85 115	90.04 85	99.67 71	86.50 317	99.13 92	98.69 146

旧先验1					98.38 84	93.38 64		97.75 143			98.09 90	92.30 45			99.01 102	99.16 81

EC-MVSNet		96.42 73	96.47 68	96.26 129	97.01 185	91.52 138	98.89 5	97.75 143	94.42 78	96.64 90	97.68 132	89.32 93	98.60 237	97.45 44	99.11 95	98.67 148

EI-MVSNet-Vis-set		96.51 69	96.47 68	96.63 93	98.24 95	91.20 154	96.89 213	97.73 146	94.74 63	96.49 100	98.49 52	90.88 76	99.58 92	96.44 70	98.32 132	99.13 85

PAPM_NR		95.01 120	94.59 125	96.26 129	98.89 56	90.68 179	97.24 179	97.73 146	91.80 175	92.93 216	96.62 214	89.13 96	99.14 160	89.21 264	97.78 154	98.97 105

Anonymous20240529		91.98 249	90.73 276	95.73 168	98.14 107	89.40 228	97.99 63	97.72 148	79.63 426	93.54 195	97.41 157	69.94 385	99.56 100	91.04 217	91.11 305	98.22 191

CHOSEN 280x420		93.12 199	92.72 197	94.34 249	96.71 214	87.27 292	90.29 433	97.72 148	86.61 353	91.34 253	95.29 281	84.29 191	98.41 253	93.25 167	98.94 105	97.35 253

EI-MVSNet-UG-set		96.34 78	96.30 77	96.47 110	98.20 101	90.93 168	96.86 216	97.72 148	94.67 66	96.16 116	98.46 56	90.43 81	99.58 92	96.23 76	97.96 149	98.90 121

LS3D		93.57 181	92.61 202	96.47 110	97.59 151	91.61 133	97.67 118	97.72 148	85.17 377	90.29 275	98.34 69	84.60 183	99.73 55	83.85 359	98.27 135	98.06 210

PAPR		94.18 148	93.42 170	96.48 109	97.64 145	91.42 145	95.55 322	97.71 152	88.99 284	92.34 226	95.82 254	89.19 94	99.11 163	86.14 323	97.38 166	98.90 121

UGNet		94.04 159	93.28 173	96.31 123	96.85 196	91.19 155	97.88 84	97.68 153	94.40 80	93.00 211	96.18 234	73.39 361	99.61 84	91.72 201	98.46 126	98.13 199
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				95.46 188	98.18 105	88.90 248		97.66 154	82.73 406	97.03 75	98.07 91	90.06 84	98.85 199	89.67 249	98.98 103	98.64 149

test12				97.65 43	98.46 75	94.26 39		97.66 154		95.52 144		90.89 75	99.46 120		99.25 74	99.22 78

DTE-MVSNet		90.56 315	89.75 321	93.01 318	93.95 372	87.25 293	97.64 126	97.65 156	90.74 222	87.12 364	95.68 265	79.97 282	97.00 388	83.33 360	81.66 407	94.78 373

TAPA-MVS	90.10 7	92.30 235	91.22 254	95.56 177	98.33 86	89.60 216	96.79 224	97.65 156	81.83 412	91.52 248	97.23 169	87.94 119	98.91 194	71.31 436	98.37 130	98.17 197
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
sd_testset		93.10 200	92.45 210	95.05 203	98.09 110	89.21 238	96.89 213	97.64 158	93.18 126	91.79 242	97.28 164	75.35 343	98.65 232	88.99 269	92.84 275	97.28 256

test_cas_vis1_n_1920		94.48 142	94.55 130	94.28 254	96.78 208	86.45 317	97.63 128	97.64 158	93.32 119	97.68 54	98.36 65	73.75 359	99.08 171	96.73 60	99.05 98	97.31 255

NormalMVS		96.36 77	96.11 81	97.12 72	99.37 16	92.90 83	97.99 63	97.63 160	95.92 14	96.57 96	97.93 104	85.34 169	99.50 114	94.99 123	99.21 77	98.97 105

Elysia		94.00 161	93.12 177	96.64 89	96.08 271	92.72 91	97.50 146	97.63 160	91.15 208	94.82 157	97.12 175	74.98 346	99.06 177	90.78 222	98.02 145	98.12 201

StellarMVS		94.00 161	93.12 177	96.64 89	96.08 271	92.72 91	97.50 146	97.63 160	91.15 208	94.82 157	97.12 175	74.98 346	99.06 177	90.78 222	98.02 145	98.12 201

cdsmvs_eth3d_5k		23.24 431	30.99 433	0.00 449	0.00 472	0.00 474	0.00 460	97.63 160	0.00 467	0.00 468	96.88 193	84.38 188	0.00 468	0.00 467	0.00 466	0.00 464

DPM-MVS		95.69 97	94.92 115	98.01 20	98.08 113	95.71 9	95.27 338	97.62 164	90.43 241	95.55 141	97.07 179	91.72 51	99.50 114	89.62 251	98.94 105	98.82 133

sasdasda		96.02 86	95.45 96	97.75 36	97.59 151	95.15 23	98.28 31	97.60 165	94.52 73	96.27 111	96.12 239	87.65 125	99.18 151	96.20 82	94.82 237	98.91 118

canonicalmvs		96.02 86	95.45 96	97.75 36	97.59 151	95.15 23	98.28 31	97.60 165	94.52 73	96.27 111	96.12 239	87.65 125	99.18 151	96.20 82	94.82 237	98.91 118

test222					98.24 95	92.21 110	95.33 333	97.60 165	79.22 428	95.25 147	97.84 117	88.80 102			99.15 89	98.72 143

cascas		91.20 290	90.08 303	94.58 235	94.97 332	89.16 242	93.65 398	97.59 168	79.90 425	89.40 307	92.92 386	75.36 342	98.36 261	92.14 188	94.75 240	96.23 283

h-mvs33		94.15 151	93.52 162	96.04 142	97.81 133	90.22 197	97.62 130	97.58 169	95.19 34	96.74 83	97.45 152	83.67 200	99.61 84	95.85 96	79.73 414	98.29 187

MGCFI-Net		95.94 91	95.40 100	97.56 49	97.59 151	94.62 31	98.21 43	97.57 170	94.41 79	96.17 115	96.16 237	87.54 130	99.17 153	96.19 84	94.73 242	98.91 118

MVSFormer		95.37 106	95.16 108	95.99 149	96.34 248	91.21 152	98.22 41	97.57 170	91.42 192	96.22 113	97.32 160	86.20 155	97.92 321	94.07 148	99.05 98	98.85 129

test_djsdf		93.07 202	92.76 192	94.00 267	93.49 389	88.70 252	98.22 41	97.57 170	91.42 192	90.08 287	95.55 272	82.85 222	97.92 321	94.07 148	91.58 296	95.40 328

OMC-MVS		95.09 119	94.70 122	96.25 132	98.46 75	91.28 148	96.43 257	97.57 170	92.04 170	94.77 161	97.96 103	87.01 142	99.09 168	91.31 211	96.77 187	98.36 179

PS-MVSNAJss		93.74 174	93.51 163	94.44 243	93.91 374	89.28 236	97.75 104	97.56 174	92.50 154	89.94 289	96.54 217	88.65 105	98.18 276	93.83 157	90.90 310	95.86 298

casdiffmvs_mvg		95.81 96	95.57 90	96.51 106	96.87 193	91.49 139	97.50 146	97.56 174	93.99 91	95.13 151	97.92 107	87.89 120	98.78 208	95.97 92	97.33 169	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		92.42 228	91.89 228	94.03 266	93.33 397	88.50 259	97.73 108	97.53 176	92.00 172	88.85 324	96.50 219	75.62 341	98.11 283	93.88 155	91.56 297	95.48 318

mvs_tets		92.31 234	91.76 231	93.94 275	93.41 394	88.29 264	97.63 128	97.53 176	92.04 170	88.76 327	96.45 221	74.62 351	98.09 288	93.91 153	91.48 298	95.45 323

dcpmvs_2		96.37 76	97.05 33	94.31 252	98.96 51	84.11 365	97.56 137	97.51 178	93.92 93	97.43 61	98.52 49	92.75 32	99.32 135	97.32 49	99.50 36	99.51 45

HQP_MVS		93.78 173	93.43 168	94.82 218	96.21 252	89.99 202	97.74 106	97.51 178	94.85 51	91.34 253	96.64 207	81.32 255	98.60 237	93.02 175	92.23 284	95.86 298

plane_prior5								97.51 178					98.60 237	93.02 175	92.23 284	95.86 298

viewmanbaseed2359cas		95.24 113	95.02 113	95.91 152	96.87 193	89.98 204	96.82 221	97.49 181	92.26 159	95.47 145	97.82 119	86.47 148	98.69 225	94.80 132	97.20 177	99.06 95

reproduce_monomvs		91.30 285	91.10 258	91.92 352	96.82 201	82.48 385	97.01 202	97.49 181	94.64 69	88.35 335	95.27 284	70.53 378	98.10 284	95.20 116	84.60 382	95.19 346

PS-MVSNAJ		95.37 106	95.33 103	95.49 184	97.35 161	90.66 180	95.31 335	97.48 183	93.85 96	96.51 99	95.70 264	88.65 105	99.65 73	94.80 132	98.27 135	96.17 287

API-MVS		94.84 130	94.49 132	95.90 153	97.90 128	92.00 119	97.80 98	97.48 183	89.19 276	94.81 159	96.71 200	88.84 101	99.17 153	88.91 271	98.76 112	96.53 276

MG-MVS		95.61 101	95.38 101	96.31 123	98.42 79	90.53 182	96.04 292	97.48 183	93.47 113	95.67 138	98.10 88	89.17 95	99.25 142	91.27 212	98.77 111	99.13 85

MAR-MVS		94.22 147	93.46 165	96.51 106	98.00 119	92.19 113	97.67 118	97.47 186	88.13 317	93.00 211	95.84 252	84.86 181	99.51 111	87.99 284	98.17 140	97.83 228
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		92.98 206	92.53 206	94.32 250	96.12 267	89.20 239	95.28 336	97.47 186	92.66 151	89.90 290	95.62 268	80.58 269	98.40 254	92.73 180	92.40 282	95.38 330
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
UniMVSNet_ETH3D		91.34 283	90.22 299	94.68 229	94.86 341	87.86 281	97.23 183	97.46 188	87.99 318	89.90 290	96.92 191	66.35 413	98.23 270	90.30 236	90.99 308	97.96 215

nrg030		94.05 158	93.31 172	96.27 128	95.22 319	94.59 32	98.34 26	97.46 188	92.93 141	91.21 262	96.64 207	87.23 140	98.22 271	94.99 123	85.80 362	95.98 297

XVG-OURS		93.72 175	93.35 171	94.80 223	97.07 175	88.61 253	94.79 353	97.46 188	91.97 173	93.99 181	97.86 114	81.74 249	98.88 196	92.64 181	92.67 280	96.92 268

LPG-MVS_test		92.94 209	92.56 203	94.10 261	96.16 262	88.26 266	97.65 122	97.46 188	91.29 196	90.12 283	97.16 172	79.05 298	98.73 219	92.25 185	91.89 292	95.31 335

LGP-MVS_train				94.10 261	96.16 262	88.26 266		97.46 188	91.29 196	90.12 283	97.16 172	79.05 298	98.73 219	92.25 185	91.89 292	95.31 335

MVS		91.71 257	90.44 286	95.51 181	95.20 321	91.59 135	96.04 292	97.45 193	73.44 442	87.36 360	95.60 269	85.42 168	99.10 165	85.97 328	97.46 161	95.83 302

XVG-OURS-SEG-HR		93.86 170	93.55 158	94.81 220	97.06 178	88.53 258	95.28 336	97.45 193	91.68 180	94.08 180	97.68 132	82.41 234	98.90 195	93.84 156	92.47 281	96.98 264

baseline		95.58 102	95.42 99	96.08 138	96.78 208	90.41 188	97.16 190	97.45 193	93.69 102	95.65 139	97.85 115	87.29 138	98.68 227	95.66 102	97.25 175	99.13 85

ab-mvs		93.57 181	92.55 204	96.64 89	97.28 164	91.96 122	95.40 329	97.45 193	89.81 258	93.22 208	96.28 230	79.62 289	99.46 120	90.74 225	93.11 272	98.50 162

xiu_mvs_v2_base		95.32 109	95.29 104	95.40 189	97.22 166	90.50 183	95.44 328	97.44 197	93.70 101	96.46 103	96.18 234	88.59 109	99.53 106	94.79 135	97.81 153	96.17 287

1314		92.81 218	92.03 221	95.14 199	95.33 311	89.52 223	96.04 292	97.44 197	87.72 331	86.25 381	95.33 280	83.84 197	98.79 207	89.26 261	97.05 182	97.11 262

casdiffmvs		95.64 99	95.49 93	96.08 138	96.76 213	90.45 185	97.29 176	97.44 197	94.00 90	95.46 146	97.98 101	87.52 133	98.73 219	95.64 106	97.33 169	99.08 92
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
XXY-MVS		92.16 242	91.23 253	94.95 214	94.75 346	90.94 167	97.47 155	97.43 200	89.14 277	88.90 320	96.43 222	79.71 286	98.24 269	89.56 252	87.68 343	95.67 314

anonymousdsp		92.16 242	91.55 239	93.97 271	92.58 412	89.55 220	97.51 145	97.42 201	89.42 270	88.40 334	94.84 303	80.66 267	97.88 326	91.87 197	91.28 302	94.48 381

Effi-MVS+		94.93 125	94.45 134	96.36 121	96.61 218	91.47 142	96.41 260	97.41 202	91.02 214	94.50 167	95.92 248	87.53 131	98.78 208	93.89 154	96.81 186	98.84 132

RRT-MVS		94.51 140	94.35 137	94.98 210	96.40 242	86.55 315	97.56 137	97.41 202	93.19 124	94.93 154	97.04 181	79.12 296	99.30 139	96.19 84	97.32 171	99.09 91

HQP3-MVS								97.39 204							92.10 289

HQP-MVS		93.19 196	92.74 195	94.54 238	95.86 277	89.33 232	96.65 241	97.39 204	93.55 105	90.14 277	95.87 250	80.95 259	98.50 247	92.13 191	92.10 289	95.78 306

PLC	91.00 6	94.11 155	93.43 168	96.13 137	98.58 73	91.15 161	96.69 237	97.39 204	87.29 341	91.37 252	96.71 200	88.39 110	99.52 110	87.33 304	97.13 180	97.73 232
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
diffmvs_AUTHOR		95.33 108	95.27 105	95.50 183	96.37 246	89.08 244	96.08 290	97.38 207	93.09 132	96.53 98	97.74 126	86.45 149	98.68 227	96.32 72	97.48 160	98.75 139

v7n		90.76 307	89.86 314	93.45 303	93.54 386	87.60 287	97.70 116	97.37 208	88.85 290	87.65 353	94.08 351	81.08 258	98.10 284	84.68 345	83.79 395	94.66 378

UnsupCasMVSNet_eth		85.99 384	84.45 388	90.62 387	89.97 430	82.40 388	93.62 399	97.37 208	89.86 254	78.59 432	92.37 396	65.25 421	95.35 422	82.27 373	70.75 440	94.10 392

ACMM	89.79 8	92.96 207	92.50 208	94.35 247	96.30 250	88.71 251	97.58 133	97.36 210	91.40 194	90.53 270	96.65 206	79.77 285	98.75 215	91.24 213	91.64 294	95.59 316
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
xiu_mvs_v1_base_debu		95.01 120	94.76 118	95.75 165	96.58 221	91.71 128	96.25 278	97.35 211	92.99 134	96.70 85	96.63 211	82.67 226	99.44 123	96.22 77	97.46 161	96.11 293

xiu_mvs_v1_base		95.01 120	94.76 118	95.75 165	96.58 221	91.71 128	96.25 278	97.35 211	92.99 134	96.70 85	96.63 211	82.67 226	99.44 123	96.22 77	97.46 161	96.11 293

xiu_mvs_v1_base_debi		95.01 120	94.76 118	95.75 165	96.58 221	91.71 128	96.25 278	97.35 211	92.99 134	96.70 85	96.63 211	82.67 226	99.44 123	96.22 77	97.46 161	96.11 293

diffmvs		95.25 112	95.13 109	95.63 173	96.43 241	89.34 231	95.99 296	97.35 211	92.83 146	96.31 109	97.37 158	86.44 150	98.67 229	96.26 74	97.19 178	98.87 127
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		94.71 136	94.02 145	96.79 85	97.71 139	92.05 116	96.59 250	97.35 211	90.61 233	94.64 163	96.93 188	86.41 151	99.39 128	91.20 214	94.71 243	98.94 112

SSM_0407		94.54 139	94.12 144	95.80 161	96.79 204	90.38 190	96.79 224	97.29 216	91.24 200	93.68 188	97.60 143	85.03 175	98.67 229	92.14 188	96.51 195	98.35 181

SSM_0404		94.73 135	94.31 139	95.98 150	97.05 180	90.90 170	97.01 202	97.29 216	91.24 200	94.17 177	97.60 143	85.03 175	98.76 212	92.14 188	97.30 172	98.29 187

F-COLMAP		93.58 179	92.98 184	95.37 190	98.40 81	88.98 246	97.18 188	97.29 216	87.75 330	90.49 271	97.10 178	85.21 172	99.50 114	86.70 314	96.72 190	97.63 236

VortexMVS		92.88 213	92.64 199	93.58 296	96.58 221	87.53 288	96.93 210	97.28 219	92.78 149	89.75 295	94.99 294	82.73 225	97.76 339	94.60 140	88.16 338	95.46 321

XVG-ACMP-BASELINE		90.93 303	90.21 300	93.09 316	94.31 365	85.89 332	95.33 333	97.26 220	91.06 213	89.38 308	95.44 278	68.61 396	98.60 237	89.46 254	91.05 306	94.79 371

PCF-MVS	89.48 11	91.56 267	89.95 311	96.36 121	96.60 219	92.52 99	92.51 418	97.26 220	79.41 427	88.90 320	96.56 216	84.04 196	99.55 102	77.01 412	97.30 172	97.01 263
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
ACMP	89.59 10	92.62 222	92.14 217	94.05 264	96.40 242	88.20 269	97.36 168	97.25 222	91.52 187	88.30 338	96.64 207	78.46 310	98.72 223	91.86 198	91.48 298	95.23 342
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
icg_test_0407_2		93.58 179	93.46 165	93.94 275	96.19 256	86.16 326	93.73 393	97.24 223	91.54 183	93.50 197	97.04 181	85.64 165	96.91 391	90.68 227	95.59 219	98.76 135

IMVS_0407		93.94 165	93.75 151	94.49 240	96.19 256	86.16 326	96.35 268	97.24 223	91.54 183	93.50 197	97.04 181	85.64 165	98.54 244	90.68 227	95.59 219	98.76 135

IMVS_0404		92.44 226	91.92 226	94.00 267	96.19 256	86.16 326	93.84 390	97.24 223	91.54 183	88.17 344	97.04 181	76.96 328	97.09 382	90.68 227	95.59 219	98.76 135

IMVS_0403		93.98 163	93.79 150	94.55 237	96.19 256	86.16 326	96.35 268	97.24 223	91.54 183	93.59 192	97.04 181	85.86 160	98.73 219	90.68 227	95.59 219	98.76 135

OPM-MVS		93.28 192	92.76 192	94.82 218	94.63 352	90.77 175	96.65 241	97.18 227	93.72 99	91.68 246	97.26 167	79.33 293	98.63 234	92.13 191	92.28 283	95.07 349
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
PatchMatch-RL		92.90 211	92.02 222	95.56 177	98.19 103	90.80 173	95.27 338	97.18 227	87.96 319	91.86 241	95.68 265	80.44 272	98.99 185	84.01 354	97.54 159	96.89 269

alignmvs		95.87 95	95.23 106	97.78 32	97.56 157	95.19 21	97.86 85	97.17 229	94.39 81	96.47 102	96.40 224	85.89 159	99.20 147	96.21 81	95.11 233	98.95 111

MVS_Test		94.89 127	94.62 124	95.68 171	96.83 199	89.55 220	96.70 235	97.17 229	91.17 206	95.60 140	96.11 243	87.87 122	98.76 212	93.01 177	97.17 179	98.72 143

Fast-Effi-MVS+		93.46 185	92.75 194	95.59 176	96.77 210	90.03 199	96.81 223	97.13 231	88.19 312	91.30 256	94.27 339	86.21 154	98.63 234	87.66 296	96.46 201	98.12 201

EI-MVSNet		93.03 204	92.88 188	93.48 301	95.77 283	86.98 301	96.44 255	97.12 232	90.66 229	91.30 256	97.64 139	86.56 145	98.05 296	89.91 242	90.55 314	95.41 325

MVSTER		93.20 195	92.81 191	94.37 246	96.56 225	89.59 217	97.06 196	97.12 232	91.24 200	91.30 256	95.96 246	82.02 242	98.05 296	93.48 162	90.55 314	95.47 320

viewmambaseed2359dif		94.28 145	94.14 142	94.71 228	96.21 252	86.97 302	95.93 299	97.11 234	89.00 283	95.00 153	97.70 129	86.02 158	98.59 241	93.71 159	96.59 194	98.57 155

test_yl		94.78 133	94.23 140	96.43 114	97.74 137	91.22 150	96.85 217	97.10 235	91.23 203	95.71 134	96.93 188	84.30 189	99.31 137	93.10 170	95.12 231	98.75 139

DCV-MVSNet		94.78 133	94.23 140	96.43 114	97.74 137	91.22 150	96.85 217	97.10 235	91.23 203	95.71 134	96.93 188	84.30 189	99.31 137	93.10 170	95.12 231	98.75 139

LTVRE_ROB	88.41 13	90.99 299	89.92 313	94.19 256	96.18 260	89.55 220	96.31 274	97.09 237	87.88 322	85.67 385	95.91 249	78.79 306	98.57 242	81.50 376	89.98 319	94.44 384
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		93.46 185	93.23 175	94.17 257	96.12 267	85.42 341	96.43 257	97.08 238	92.91 142	94.21 174	98.00 99	80.82 265	98.74 217	94.41 143	89.05 328	98.34 185

test_fmvs1_n		92.73 220	92.88 188	92.29 342	96.08 271	81.05 399	97.98 66	97.08 238	90.72 224	96.79 81	98.18 85	63.07 425	98.45 251	97.62 38	98.42 129	97.36 251

v10		91.04 297	90.23 297	93.49 300	94.12 368	88.16 272	97.32 173	97.08 238	88.26 311	88.29 339	94.22 344	82.17 239	97.97 308	86.45 318	84.12 389	94.33 387

mamba_0408		93.70 176	92.99 181	95.83 158	96.79 204	90.38 190	88.69 443	97.07 241	90.96 216	93.68 188	97.31 162	84.97 178	98.76 212	90.95 218	96.51 195	98.35 181

SSM_04072		93.51 184	92.99 181	95.05 203	96.79 204	90.38 190	88.69 443	97.07 241	90.96 216	93.68 188	97.31 162	84.97 178	96.42 402	90.95 218	96.51 195	98.35 181

v144192		91.06 296	90.28 293	93.39 304	93.66 383	87.23 295	96.83 220	97.07 241	87.43 337	89.69 298	94.28 338	81.48 252	98.00 303	87.18 308	84.92 378	94.93 357

v1192		91.07 295	90.23 297	93.58 296	93.70 380	87.82 283	96.73 231	97.07 241	87.77 328	89.58 301	94.32 336	80.90 263	97.97 308	86.52 316	85.48 365	94.95 353

v8		91.29 287	90.53 285	93.57 298	94.15 367	88.12 273	97.34 170	97.06 245	88.99 284	88.32 337	94.26 341	83.08 213	98.01 302	87.62 298	83.92 393	94.57 380

mvs_anonymous		93.82 171	93.74 152	94.06 263	96.44 240	85.41 342	95.81 306	97.05 246	89.85 256	90.09 286	96.36 226	87.44 135	97.75 341	93.97 150	96.69 191	99.02 97

IterMVS-LS		92.29 236	91.94 225	93.34 306	96.25 251	86.97 302	96.57 253	97.05 246	90.67 227	89.50 306	94.80 306	86.59 144	97.64 349	89.91 242	86.11 360	95.40 328
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v1921920		90.85 305	90.03 308	93.29 308	93.55 385	86.96 304	96.74 230	97.04 248	87.36 339	89.52 305	94.34 333	80.23 277	97.97 308	86.27 319	85.21 371	94.94 355

CDS-MVSNet		94.14 154	93.54 159	95.93 151	96.18 260	91.46 143	96.33 272	97.04 248	88.97 286	93.56 193	96.51 218	87.55 129	97.89 325	89.80 245	95.95 207	98.44 172
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
SSC-MVS3.2		89.74 341	89.26 334	91.19 376	95.16 322	80.29 410	94.53 360	97.03 250	91.79 176	88.86 323	94.10 348	69.94 385	97.82 331	85.29 337	86.66 356	95.45 323

v1144		91.37 280	90.60 281	93.68 291	93.89 375	88.23 268	96.84 219	97.03 250	88.37 308	89.69 298	94.39 328	82.04 241	97.98 305	87.80 288	85.37 367	94.84 363

v1240		90.70 311	89.85 315	93.23 310	93.51 388	86.80 305	96.61 247	97.02 252	87.16 344	89.58 301	94.31 337	79.55 290	97.98 305	85.52 334	85.44 366	94.90 360

EPP-MVSNet		95.22 115	95.04 112	95.76 163	97.49 158	89.56 219	98.67 11	97.00 253	90.69 225	94.24 173	97.62 141	89.79 90	98.81 205	93.39 166	96.49 199	98.92 117

V42		91.58 266	90.87 265	93.73 286	94.05 371	88.50 259	97.32 173	96.97 254	88.80 296	89.71 296	94.33 334	82.54 230	98.05 296	89.01 268	85.07 374	94.64 379

test_fmvs1		93.21 194	93.53 160	92.25 345	96.55 227	81.20 398	97.40 164	96.96 255	90.68 226	96.80 79	98.04 94	69.25 391	98.40 254	97.58 39	98.50 122	97.16 261

FMVSNet2		91.31 284	90.08 303	94.99 208	96.51 233	92.21 110	97.41 160	96.95 256	88.82 293	88.62 329	94.75 308	73.87 355	97.42 370	85.20 340	88.55 335	95.35 332

ACMH	87.59 16	90.53 316	89.42 330	93.87 280	96.21 252	87.92 278	97.24 179	96.94 257	88.45 306	83.91 405	96.27 231	71.92 367	98.62 236	84.43 348	89.43 325	95.05 351
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
GBi-Net		91.35 281	90.27 294	94.59 231	96.51 233	91.18 157	97.50 146	96.93 258	88.82 293	89.35 309	94.51 321	73.87 355	97.29 377	86.12 324	88.82 330	95.31 335

test1		91.35 281	90.27 294	94.59 231	96.51 233	91.18 157	97.50 146	96.93 258	88.82 293	89.35 309	94.51 321	73.87 355	97.29 377	86.12 324	88.82 330	95.31 335

FMVSNet3		91.78 255	90.69 279	95.03 206	96.53 230	92.27 108	97.02 199	96.93 258	89.79 259	89.35 309	94.65 314	77.01 326	97.47 365	86.12 324	88.82 330	95.35 332

FMVSNet1		89.88 336	88.31 349	94.59 231	95.41 301	91.18 157	97.50 146	96.93 258	86.62 352	87.41 358	94.51 321	65.94 418	97.29 377	83.04 363	87.43 346	95.31 335

GeoE		93.89 168	93.28 173	95.72 169	96.96 190	89.75 212	98.24 39	96.92 262	89.47 267	92.12 232	97.21 170	84.42 187	98.39 259	87.71 291	96.50 198	99.01 100

SymmetryMVS		95.94 91	95.54 91	97.15 70	97.85 130	92.90 83	97.99 63	96.91 263	95.92 14	96.57 96	97.93 104	85.34 169	99.50 114	94.99 123	96.39 202	99.05 96

miper_enhance_ethall		91.54 270	91.01 261	93.15 314	95.35 307	87.07 300	93.97 382	96.90 264	86.79 350	89.17 316	93.43 380	86.55 146	97.64 349	89.97 241	86.93 351	94.74 375

eth_miper_zixun_eth		91.02 298	90.59 282	92.34 340	95.33 311	84.35 361	94.10 379	96.90 264	88.56 302	88.84 325	94.33 334	84.08 194	97.60 354	88.77 274	84.37 387	95.06 350

TAMVS		94.01 160	93.46 165	95.64 172	96.16 262	90.45 185	96.71 234	96.89 266	89.27 274	93.46 200	96.92 191	87.29 138	97.94 318	88.70 276	95.74 213	98.53 158

miper_ehance_all_eth		91.59 264	91.13 257	92.97 320	95.55 293	86.57 313	94.47 363	96.88 267	87.77 328	88.88 322	94.01 353	86.22 153	97.54 358	89.49 253	86.93 351	94.79 371

v2v482		91.59 264	90.85 268	93.80 283	93.87 376	88.17 271	96.94 209	96.88 267	89.54 264	89.53 304	94.90 300	81.70 250	98.02 301	89.25 262	85.04 376	95.20 343

CNLPA		94.28 145	93.53 160	96.52 102	98.38 84	92.55 98	96.59 250	96.88 267	90.13 249	91.91 238	97.24 168	85.21 172	99.09 168	87.64 297	97.83 152	97.92 218

PAPM		91.52 271	90.30 292	95.20 196	95.30 314	89.83 210	93.38 404	96.85 270	86.26 360	88.59 330	95.80 255	84.88 180	98.15 278	75.67 417	95.93 208	97.63 236

c3_l		91.38 278	90.89 264	92.88 324	95.58 291	86.30 320	94.68 355	96.84 271	88.17 313	88.83 326	94.23 342	85.65 164	97.47 365	89.36 257	84.63 380	94.89 361

pm-mvs1		90.72 310	89.65 325	93.96 272	94.29 366	89.63 214	97.79 100	96.82 272	89.07 279	86.12 383	95.48 277	78.61 308	97.78 336	86.97 312	81.67 406	94.46 382

test_vis1_n		92.37 231	92.26 215	92.72 330	94.75 346	82.64 381	98.02 60	96.80 273	91.18 205	97.77 53	97.93 104	58.02 435	98.29 267	97.63 36	98.21 137	97.23 259

CMPMVS	62.92 21	85.62 389	84.92 384	87.74 413	89.14 435	73.12 443	94.17 377	96.80 273	73.98 439	73.65 441	94.93 298	66.36 412	97.61 353	83.95 356	91.28 302	92.48 419
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
MS-PatchMatch		90.27 323	89.77 319	91.78 361	94.33 363	84.72 358	95.55 322	96.73 275	86.17 362	86.36 380	95.28 283	71.28 372	97.80 334	84.09 353	98.14 141	92.81 411

Effi-MVS+-dtu		93.08 201	93.21 176	92.68 333	96.02 274	83.25 375	97.14 192	96.72 276	93.85 96	91.20 263	93.44 377	83.08 213	98.30 266	91.69 204	95.73 214	96.50 278

TSAR-MVS + GP.		96.69 62	96.49 66	97.27 63	98.31 87	93.39 63	96.79 224	96.72 276	94.17 85	97.44 59	97.66 135	92.76 31	99.33 133	96.86 57	97.76 156	99.08 92

1112_ss		93.37 189	92.42 211	96.21 133	97.05 180	90.99 164	96.31 274	96.72 276	86.87 349	89.83 293	96.69 204	86.51 147	99.14 160	88.12 281	93.67 266	98.50 162

PVSNet	86.66 18	92.24 239	91.74 234	93.73 286	97.77 135	83.69 372	92.88 413	96.72 276	87.91 321	93.00 211	94.86 302	78.51 309	99.05 180	86.53 315	97.45 165	98.47 167

miper_lstm_enhance		90.50 319	90.06 307	91.83 357	95.33 311	83.74 369	93.86 388	96.70 280	87.56 335	87.79 350	93.81 361	83.45 205	96.92 390	87.39 302	84.62 381	94.82 366

v148		90.99 299	90.38 288	92.81 327	93.83 377	85.80 333	96.78 228	96.68 281	89.45 269	88.75 328	93.93 357	82.96 219	97.82 331	87.83 287	83.25 398	94.80 369

ACMH+	87.92 14	90.20 327	89.18 336	93.25 309	96.48 236	86.45 317	96.99 205	96.68 281	88.83 292	84.79 394	96.22 233	70.16 382	98.53 245	84.42 349	88.04 339	94.77 374

CANet_DTU		94.37 143	93.65 155	96.55 99	96.46 239	92.13 114	96.21 282	96.67 283	94.38 82	93.53 196	97.03 186	79.34 292	99.71 61	90.76 224	98.45 127	97.82 229

cl____		90.96 302	90.32 290	92.89 323	95.37 305	86.21 323	94.46 365	96.64 284	87.82 324	88.15 345	94.18 345	82.98 217	97.54 358	87.70 292	85.59 363	94.92 359

HY-MVS	89.66 9	93.87 169	92.95 185	96.63 93	97.10 174	92.49 100	95.64 319	96.64 284	89.05 281	93.00 211	95.79 258	85.77 163	99.45 122	89.16 267	94.35 245	97.96 215

Test_1112_low_res		92.84 216	91.84 229	95.85 157	97.04 182	89.97 206	95.53 324	96.64 284	85.38 372	89.65 300	95.18 288	85.86 160	99.10 165	87.70 292	93.58 271	98.49 164

DIV-MVS_self_test		90.97 301	90.33 289	92.88 324	95.36 306	86.19 325	94.46 365	96.63 287	87.82 324	88.18 343	94.23 342	82.99 216	97.53 360	87.72 289	85.57 364	94.93 357

Fast-Effi-MVS+-dtu		92.29 236	91.99 223	93.21 312	95.27 315	85.52 339	97.03 197	96.63 287	92.09 168	89.11 318	95.14 290	80.33 275	98.08 289	87.54 300	94.74 241	96.03 296

UnsupCasMVSNet_bld		82.13 405	79.46 410	90.14 394	88.00 443	82.47 386	90.89 431	96.62 289	78.94 429	75.61 436	84.40 447	56.63 438	96.31 404	77.30 409	66.77 448	91.63 429

cl22		91.21 289	90.56 284	93.14 315	96.09 270	86.80 305	94.41 367	96.58 290	87.80 326	88.58 331	93.99 355	80.85 264	97.62 352	89.87 244	86.93 351	94.99 352

jason		94.84 130	94.39 136	96.18 135	95.52 294	90.93 168	96.09 289	96.52 291	89.28 273	96.01 123	97.32 160	84.70 182	98.77 211	95.15 119	98.91 107	98.85 129
jason: jason.
tt0805		91.09 294	90.07 306	94.16 259	95.61 289	88.31 263	97.56 137	96.51 292	89.56 263	89.17 316	95.64 267	67.08 410	98.38 260	91.07 216	88.44 336	95.80 304

AUN-MVS		91.76 256	90.75 274	94.81 220	97.00 186	88.57 255	96.65 241	96.49 293	89.63 261	92.15 230	96.12 239	78.66 307	98.50 247	90.83 220	79.18 417	97.36 251

hse-mvs2		93.45 187	92.99 181	94.81 220	97.02 184	88.59 254	96.69 237	96.47 294	95.19 34	96.74 83	96.16 237	83.67 200	98.48 250	95.85 96	79.13 418	97.35 253

SD_0403		90.01 331	90.02 309	89.96 397	95.65 288	76.76 431	95.76 310	96.46 295	90.58 236	86.59 377	96.29 229	82.12 240	94.78 426	73.00 431	93.76 264	98.35 181

EG-PatchMatch MVS		87.02 371	85.44 376	91.76 363	92.67 409	85.00 352	96.08 290	96.45 296	83.41 402	79.52 428	93.49 374	57.10 437	97.72 343	79.34 400	90.87 311	92.56 416

KD-MVS_self_test		85.95 385	84.95 383	88.96 407	89.55 434	79.11 425	95.13 345	96.42 297	85.91 365	84.07 403	90.48 419	70.03 384	94.82 425	80.04 392	72.94 437	92.94 409

pmmvs6		87.81 363	86.19 371	92.69 332	91.32 422	86.30 320	97.34 170	96.41 298	80.59 423	84.05 404	94.37 330	67.37 405	97.67 346	84.75 344	79.51 416	94.09 394

PMMVS		92.86 214	92.34 212	94.42 245	94.92 337	86.73 308	94.53 360	96.38 299	84.78 384	94.27 172	95.12 292	83.13 212	98.40 254	91.47 208	96.49 199	98.12 201

RPSCF		90.75 308	90.86 266	90.42 390	96.84 197	76.29 434	95.61 320	96.34 300	83.89 393	91.38 251	97.87 112	76.45 332	98.78 208	87.16 309	92.23 284	96.20 285

BP-MVS1		95.89 93	95.49 93	97.08 77	96.67 215	93.20 73	98.08 54	96.32 301	94.56 70	96.32 108	97.84 117	84.07 195	99.15 157	96.75 59	98.78 110	98.90 121

MSDG		91.42 276	90.24 296	94.96 213	97.15 172	88.91 247	93.69 396	96.32 301	85.72 368	86.93 373	96.47 220	80.24 276	98.98 186	80.57 389	95.05 234	96.98 264

WBMVS		90.69 313	89.99 310	92.81 327	96.48 236	85.00 352	95.21 343	96.30 303	89.46 268	89.04 319	94.05 352	72.45 365	97.82 331	89.46 254	87.41 348	95.61 315

OurMVSNet-221017-0		90.51 318	90.19 301	91.44 369	93.41 394	81.25 396	96.98 206	96.28 304	91.68 180	86.55 378	96.30 228	74.20 354	97.98 305	88.96 270	87.40 349	95.09 348

MVP-Stereo		90.74 309	90.08 303	92.71 331	93.19 399	88.20 269	95.86 303	96.27 305	86.07 363	84.86 393	94.76 307	77.84 321	97.75 341	83.88 358	98.01 147	92.17 426
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
lupinMVS		94.99 124	94.56 127	96.29 127	96.34 248	91.21 152	95.83 305	96.27 305	88.93 288	96.22 113	96.88 193	86.20 155	98.85 199	95.27 115	99.05 98	98.82 133

BH-untuned		92.94 209	92.62 201	93.92 279	97.22 166	86.16 326	96.40 264	96.25 307	90.06 250	89.79 294	96.17 236	83.19 209	98.35 262	87.19 307	97.27 174	97.24 258

CL-MVSNet_self_test		86.31 380	85.15 380	89.80 399	88.83 438	81.74 394	93.93 385	96.22 308	86.67 351	85.03 391	90.80 417	78.09 317	94.50 427	74.92 420	71.86 439	93.15 407

IS-MVSNet		94.90 126	94.52 131	96.05 141	97.67 141	90.56 181	98.44 22	96.22 308	93.21 121	93.99 181	97.74 126	85.55 167	98.45 251	89.98 240	97.86 151	99.14 84

FA-MVS(test-final)		93.52 183	92.92 186	95.31 193	96.77 210	88.54 257	94.82 352	96.21 310	89.61 262	94.20 175	95.25 286	83.24 207	99.14 160	90.01 239	96.16 204	98.25 189

GA-MVS		91.38 278	90.31 291	94.59 231	94.65 351	87.62 286	94.34 370	96.19 311	90.73 223	90.35 274	93.83 358	71.84 368	97.96 312	87.22 306	93.61 269	98.21 192

LuminaMVS		94.89 127	94.35 137	96.53 100	95.48 296	92.80 87	96.88 215	96.18 312	92.85 145	95.92 126	96.87 195	81.44 253	98.83 202	96.43 71	97.10 181	97.94 217

IterMVS-SCA-FT		90.31 321	89.81 317	91.82 358	95.52 294	84.20 364	94.30 373	96.15 313	90.61 233	87.39 359	94.27 339	75.80 338	96.44 401	87.34 303	86.88 355	94.82 366

IterMVS		90.15 329	89.67 323	91.61 365	95.48 296	83.72 370	94.33 371	96.12 314	89.99 251	87.31 362	94.15 347	75.78 340	96.27 405	86.97 312	86.89 354	94.83 364
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
DP-MVS		92.76 219	91.51 243	96.52 102	98.77 58	90.99 164	97.38 167	96.08 315	82.38 408	89.29 312	97.87 112	83.77 198	99.69 67	81.37 382	96.69 191	98.89 125

pmmvs4		90.93 303	89.85 315	94.17 257	93.34 396	90.79 174	94.60 357	96.02 316	84.62 385	87.45 356	95.15 289	81.88 247	97.45 367	87.70 292	87.87 341	94.27 391

ppachtmachnet_test		88.35 358	87.29 357	91.53 366	92.45 415	83.57 373	93.75 392	95.97 317	84.28 388	85.32 390	94.18 345	79.00 304	96.93 389	75.71 416	84.99 377	94.10 392

Anonymous20240521		86.42 378	85.44 376	89.34 405	90.33 427	79.79 416	96.73 231	95.92 318	83.71 398	83.25 409	91.36 414	63.92 423	96.01 406	78.39 404	85.36 368	92.22 424

ITE_SJBPF				92.43 336	95.34 308	85.37 345		95.92 318	91.47 189	87.75 352	96.39 225	71.00 374	97.96 312	82.36 372	89.86 321	93.97 397

test_fmvs2		89.77 340	89.93 312	89.31 406	93.68 382	76.37 433	97.64 126	95.90 320	89.84 257	91.49 249	96.26 232	58.77 433	97.10 381	94.65 137	91.13 304	94.46 382

USDC		88.94 349	87.83 354	92.27 343	94.66 350	84.96 354	93.86 388	95.90 320	87.34 340	83.40 407	95.56 271	67.43 404	98.19 275	82.64 371	89.67 323	93.66 400

COLMAP_ROB	87.81 15	90.40 320	89.28 333	93.79 284	97.95 123	87.13 299	96.92 211	95.89 322	82.83 405	86.88 375	97.18 171	73.77 358	99.29 140	78.44 403	93.62 268	94.95 353
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
VDD-MVS		93.82 171	93.08 179	96.02 144	97.88 129	89.96 207	97.72 111	95.85 323	92.43 155	95.86 128	98.44 58	68.42 400	99.39 128	96.31 73	94.85 235	98.71 145

VDDNet		93.05 203	92.07 218	96.02 144	96.84 197	90.39 189	98.08 54	95.85 323	86.22 361	95.79 131	98.46 56	67.59 403	99.19 148	94.92 126	94.85 235	98.47 167

mvsmamba		94.57 138	94.14 142	95.87 154	97.03 183	89.93 208	97.84 89	95.85 323	91.34 195	94.79 160	96.80 196	80.67 266	98.81 205	94.85 127	98.12 142	98.85 129

Vis-MVSNet (Re-imp)		94.15 151	93.88 148	94.95 214	97.61 149	87.92 278	98.10 52	95.80 326	92.22 161	93.02 210	97.45 152	84.53 185	97.91 324	88.24 280	97.97 148	99.02 97

MM		97.29 27	96.98 37	98.23 11	98.01 117	95.03 26	98.07 56	95.76 327	97.78 1	97.52 56	98.80 36	88.09 115	99.86 9	99.44 2	99.37 63	99.80 1

KD-MVS_2432*1600		84.81 395	82.64 398	91.31 371	91.07 424	85.34 346	91.22 426	95.75 328	85.56 370	83.09 410	90.21 422	67.21 406	95.89 408	77.18 410	62.48 452	92.69 412

miper_refine_blended		84.81 395	82.64 398	91.31 371	91.07 424	85.34 346	91.22 426	95.75 328	85.56 370	83.09 410	90.21 422	67.21 406	95.89 408	77.18 410	62.48 452	92.69 412

FE-MVS		92.05 247	91.05 259	95.08 202	96.83 199	87.93 277	93.91 387	95.70 330	86.30 358	94.15 178	94.97 295	76.59 330	99.21 146	84.10 352	96.86 184	98.09 207

tpm cat1		88.36 357	87.21 360	91.81 359	95.13 327	80.55 405	92.58 417	95.70 330	74.97 438	87.45 356	91.96 407	78.01 320	98.17 277	80.39 391	88.74 333	96.72 274

our_test_3		88.78 353	87.98 353	91.20 375	92.45 415	82.53 383	93.61 400	95.69 332	85.77 367	84.88 392	93.71 363	79.99 281	96.78 397	79.47 397	86.24 357	94.28 390

BH-w/o		92.14 244	91.75 232	93.31 307	96.99 187	85.73 336	95.67 314	95.69 332	88.73 298	89.26 314	94.82 305	82.97 218	98.07 293	85.26 339	96.32 203	96.13 292

CR-MVSNet		90.82 306	89.77 319	93.95 273	94.45 359	87.19 296	90.23 434	95.68 334	86.89 348	92.40 220	92.36 399	80.91 261	97.05 384	81.09 386	93.95 261	97.60 241

Patchmtry		88.64 355	87.25 358	92.78 329	94.09 369	86.64 309	89.82 438	95.68 334	80.81 420	87.63 354	92.36 399	80.91 261	97.03 385	78.86 401	85.12 373	94.67 377

testing91		91.90 252	91.02 260	94.53 239	96.54 228	86.55 315	95.86 303	95.64 336	91.77 177	91.89 239	93.47 376	69.94 385	98.86 197	90.23 238	93.86 263	98.18 194

BH-RMVSNet		92.72 221	91.97 224	94.97 212	97.16 170	87.99 276	96.15 287	95.60 337	90.62 232	91.87 240	97.15 174	78.41 311	98.57 242	83.16 361	97.60 158	98.36 179

PVSNet_0	82.17 19	85.46 390	83.64 393	90.92 379	95.27 315	79.49 421	90.55 432	95.60 337	83.76 397	83.00 412	89.95 424	71.09 373	97.97 308	82.75 369	60.79 454	95.31 335

guyue		95.17 118	94.96 114	95.82 159	96.97 189	89.65 213	97.56 137	95.58 339	94.82 55	95.72 133	97.42 156	82.90 220	98.84 201	96.71 62	96.93 183	98.96 108

SCA		91.84 254	91.18 256	93.83 281	95.59 290	84.95 355	94.72 354	95.58 339	90.82 219	92.25 228	93.69 365	75.80 338	98.10 284	86.20 321	95.98 206	98.45 169

MonoMVSNet		91.92 250	91.77 230	92.37 337	92.94 403	83.11 377	97.09 195	95.55 341	92.91 142	90.85 266	94.55 318	81.27 257	96.52 400	93.01 177	87.76 342	97.47 247

AllTest		90.23 325	88.98 339	93.98 269	97.94 124	86.64 309	96.51 254	95.54 342	85.38 372	85.49 387	96.77 198	70.28 380	99.15 157	80.02 393	92.87 273	96.15 290

TestCases				93.98 269	97.94 124	86.64 309		95.54 342	85.38 372	85.49 387	96.77 198	70.28 380	99.15 157	80.02 393	92.87 273	96.15 290

mmtdpeth		89.70 342	88.96 340	91.90 354	95.84 282	84.42 360	97.46 157	95.53 344	90.27 244	94.46 169	90.50 418	69.74 389	98.95 187	97.39 48	69.48 443	92.34 420

tpmvs		89.83 339	89.15 337	91.89 355	94.92 337	80.30 409	93.11 409	95.46 345	86.28 359	88.08 346	92.65 389	80.44 272	98.52 246	81.47 378	89.92 320	96.84 270

pmmvs5		89.86 338	88.87 343	92.82 326	92.86 405	86.23 322	96.26 277	95.39 346	84.24 389	87.12 364	94.51 321	74.27 353	97.36 374	87.61 299	87.57 344	94.86 362

PatchmatchNet		91.91 251	91.35 245	93.59 295	95.38 303	84.11 365	93.15 408	95.39 346	89.54 264	92.10 233	93.68 367	82.82 223	98.13 279	84.81 343	95.32 227	98.52 159
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
tpmrst		91.44 275	91.32 247	91.79 360	95.15 325	79.20 424	93.42 403	95.37 348	88.55 303	93.49 199	93.67 368	82.49 232	98.27 268	90.41 233	89.34 326	97.90 219

Anonymous20231206		87.09 370	86.14 372	89.93 398	91.22 423	80.35 407	96.11 288	95.35 349	83.57 400	84.16 399	93.02 384	73.54 360	95.61 416	72.16 433	86.14 359	93.84 399

MIMVSNet1		84.93 393	83.05 395	90.56 388	89.56 433	84.84 357	95.40 329	95.35 349	83.91 392	80.38 424	92.21 404	57.23 436	93.34 439	70.69 439	82.75 404	93.50 402

TDRefinement		86.53 374	84.76 386	91.85 356	82.23 455	84.25 362	96.38 266	95.35 349	84.97 381	84.09 402	94.94 297	65.76 419	98.34 265	84.60 347	74.52 433	92.97 408

TR-MVS		91.48 274	90.59 282	94.16 259	96.40 242	87.33 289	95.67 314	95.34 352	87.68 332	91.46 250	95.52 274	76.77 329	98.35 262	82.85 366	93.61 269	96.79 272

EPNet_dtu		91.71 257	91.28 250	92.99 319	93.76 379	83.71 371	96.69 237	95.28 353	93.15 128	87.02 369	95.95 247	83.37 206	97.38 373	79.46 398	96.84 185	97.88 221
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
FMVSNet5		87.29 367	85.79 374	91.78 361	94.80 344	87.28 291	95.49 326	95.28 353	84.09 391	83.85 406	91.82 408	62.95 426	94.17 431	78.48 402	85.34 369	93.91 398

MDTV_nov1_ep13			90.76 272		95.22 319	80.33 408	93.03 411	95.28 353	88.14 316	92.84 217	93.83 358	81.34 254	98.08 289	82.86 364	94.34 246

LF4IMVS		87.94 361	87.25 358	89.98 396	92.38 417	80.05 415	94.38 368	95.25 356	87.59 334	84.34 396	94.74 309	64.31 422	97.66 348	84.83 342	87.45 345	92.23 423

TransMVSNet (Re)		88.94 349	87.56 355	93.08 317	94.35 362	88.45 261	97.73 108	95.23 357	87.47 336	84.26 398	95.29 281	79.86 284	97.33 375	79.44 399	74.44 434	93.45 404

test20.03		86.14 383	85.40 378	88.35 408	90.12 428	80.06 414	95.90 302	95.20 358	88.59 299	81.29 419	93.62 370	71.43 371	92.65 443	71.26 437	81.17 409	92.34 420

new-patchmatchnet		83.18 401	81.87 404	87.11 416	86.88 446	75.99 435	93.70 394	95.18 359	85.02 380	77.30 435	88.40 434	65.99 417	93.88 436	74.19 425	70.18 441	91.47 433

MDA-MVSNet_test_wron		85.87 387	84.23 390	90.80 385	92.38 417	82.57 382	93.17 406	95.15 360	82.15 409	67.65 447	92.33 402	78.20 313	95.51 419	77.33 407	79.74 413	94.31 389

YYNet1		85.87 387	84.23 390	90.78 386	92.38 417	82.46 387	93.17 406	95.14 361	82.12 410	67.69 445	92.36 399	78.16 316	95.50 420	77.31 408	79.73 414	94.39 385

Baseline_NR-MVSNet		91.20 290	90.62 280	92.95 321	93.83 377	88.03 275	97.01 202	95.12 362	88.42 307	89.70 297	95.13 291	83.47 203	97.44 368	89.66 250	83.24 399	93.37 405

thres200		92.23 240	91.39 244	94.75 227	97.61 149	89.03 245	96.60 249	95.09 363	92.08 169	93.28 205	94.00 354	78.39 312	99.04 183	81.26 385	94.18 252	96.19 286

ADS-MVSNet		89.89 335	88.68 345	93.53 299	95.86 277	84.89 356	90.93 429	95.07 364	83.23 403	91.28 259	91.81 409	79.01 302	97.85 327	79.52 395	91.39 300	97.84 226

pmmvs-eth3d		86.22 381	84.45 388	91.53 366	88.34 442	87.25 293	94.47 363	95.01 365	83.47 401	79.51 429	89.61 427	69.75 388	95.71 413	83.13 362	76.73 427	91.64 428

Anonymous202405211		92.07 246	90.83 270	95.76 163	98.19 103	88.75 250	97.58 133	95.00 366	86.00 364	93.64 191	97.45 152	66.24 415	99.53 106	90.68 227	92.71 278	99.01 100

MDA-MVSNet-bldmvs		85.00 392	82.95 397	91.17 377	93.13 401	83.33 374	94.56 359	95.00 366	84.57 386	65.13 451	92.65 389	70.45 379	95.85 410	73.57 428	77.49 423	94.33 387

ambc				86.56 419	83.60 452	70.00 446	85.69 450	94.97 368		80.60 423	88.45 433	37.42 454	96.84 394	82.69 370	75.44 431	92.86 410

testgi		87.97 360	87.21 360	90.24 393	92.86 405	80.76 400	96.67 240	94.97 368	91.74 178	85.52 386	95.83 253	62.66 428	94.47 429	76.25 414	88.36 337	95.48 318

myMVS_eth3d28		91.52 271	90.97 262	93.17 313	96.91 191	83.24 376	95.61 320	94.96 370	92.24 160	91.98 236	93.28 381	69.31 390	98.40 254	88.71 275	95.68 216	97.88 221

dp		88.90 351	88.26 351	90.81 383	94.58 355	76.62 432	92.85 414	94.93 371	85.12 378	90.07 288	93.07 383	75.81 337	98.12 282	80.53 390	87.42 347	97.71 233

test_fmvs3		83.21 400	83.02 396	83.78 423	86.77 447	68.34 449	96.76 229	94.91 372	86.49 354	84.14 401	89.48 428	36.04 455	91.73 445	91.86 198	80.77 411	91.26 435

test_0402		86.46 377	84.79 385	91.45 368	95.02 331	85.55 338	96.29 276	94.89 373	80.90 417	82.21 415	93.97 356	68.21 401	97.29 377	62.98 446	88.68 334	91.51 431

tfpn200view9		92.38 230	91.52 241	94.95 214	97.85 130	89.29 234	97.41 160	94.88 374	92.19 165	93.27 206	94.46 326	78.17 314	99.08 171	81.40 379	94.08 256	96.48 279

CVMVSNet		91.23 288	91.75 232	89.67 400	95.77 283	74.69 436	96.44 255	94.88 374	85.81 366	92.18 229	97.64 139	79.07 297	95.58 418	88.06 283	95.86 211	98.74 142

thres400		92.42 228	91.52 241	95.12 201	97.85 130	89.29 234	97.41 160	94.88 374	92.19 165	93.27 206	94.46 326	78.17 314	99.08 171	81.40 379	94.08 256	96.98 264

tt0320		85.39 391	83.12 394	92.19 347	93.44 393	85.79 334	96.19 284	94.87 377	71.19 445	82.92 413	91.76 411	58.43 434	96.81 395	81.03 387	78.26 422	93.98 396

EPNet		95.20 116	94.56 127	97.14 71	92.80 407	92.68 93	97.85 88	94.87 377	96.64 7	92.46 219	97.80 123	86.23 152	99.65 73	93.72 158	98.62 118	99.10 90
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
testing99		91.62 262	90.72 277	94.32 250	96.48 236	86.11 331	95.81 306	94.76 379	91.55 182	91.75 244	93.44 377	68.55 398	98.82 203	90.43 232	93.69 265	98.04 211

sc_t1		86.48 376	84.10 392	93.63 292	93.45 392	85.76 335	96.79 224	94.71 380	73.06 443	86.45 379	94.35 331	55.13 441	97.95 316	84.38 350	78.55 421	97.18 260

SixPastTwentyTwo		89.15 347	88.54 347	90.98 378	93.49 389	80.28 411	96.70 235	94.70 381	90.78 220	84.15 400	95.57 270	71.78 369	97.71 344	84.63 346	85.07 374	94.94 355

thres100view900		92.43 227	91.58 238	94.98 210	97.92 126	89.37 230	97.71 113	94.66 382	92.20 163	93.31 204	94.90 300	78.06 318	99.08 171	81.40 379	94.08 256	96.48 279

thres600view7		92.49 225	91.60 237	95.18 197	97.91 127	89.47 224	97.65 122	94.66 382	92.18 167	93.33 203	94.91 299	78.06 318	99.10 165	81.61 375	94.06 260	96.98 264

PatchT		88.87 352	87.42 356	93.22 311	94.08 370	85.10 350	89.51 439	94.64 384	81.92 411	92.36 223	88.15 437	80.05 280	97.01 387	72.43 432	93.65 267	97.54 244

baseline1		92.82 217	91.90 227	95.55 179	97.20 168	90.77 175	97.19 187	94.58 385	92.20 163	92.36 223	96.34 227	84.16 193	98.21 272	89.20 265	83.90 394	97.68 235

AstraMVS		94.82 132	94.64 123	95.34 192	96.36 247	88.09 274	97.58 133	94.56 386	94.98 44	95.70 136	97.92 107	81.93 246	98.93 190	96.87 56	95.88 209	98.99 104

UBG		91.55 268	90.76 272	93.94 275	96.52 232	85.06 351	95.22 341	94.54 387	90.47 240	91.98 236	92.71 388	72.02 366	98.74 217	88.10 282	95.26 229	98.01 213

Gipuma		67.86 421	65.41 423	75.18 436	92.66 410	73.45 440	66.50 457	94.52 388	53.33 456	57.80 457	66.07 457	30.81 457	89.20 449	48.15 455	78.88 420	62.90 457
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
testing11		91.68 260	90.75 274	94.47 241	96.53 230	86.56 314	95.76 310	94.51 389	91.10 212	91.24 261	93.59 371	68.59 397	98.86 197	91.10 215	94.29 248	98.00 214

CostFormer		91.18 293	90.70 278	92.62 334	94.84 342	81.76 393	94.09 380	94.43 390	84.15 390	92.72 218	93.77 362	79.43 291	98.20 273	90.70 226	92.18 287	97.90 219

tpm2		89.96 332	89.21 335	92.23 346	94.91 339	81.25 396	93.78 391	94.42 391	80.62 422	91.56 247	93.44 377	76.44 333	97.94 318	85.60 333	92.08 291	97.49 245

testing3-2		92.10 245	92.05 219	92.27 343	97.71 139	79.56 418	97.42 159	94.41 392	93.53 109	93.22 208	95.49 275	69.16 392	99.11 163	93.25 167	94.22 250	98.13 199

MVS_0304		96.74 59	96.31 76	98.02 19	96.87 193	94.65 30	97.58 133	94.39 393	96.47 10	97.16 68	98.39 62	87.53 131	99.87 7	98.97 18	99.41 55	99.55 39

JIA-IIPM		88.26 359	87.04 363	91.91 353	93.52 387	81.42 395	89.38 440	94.38 394	80.84 419	90.93 265	80.74 449	79.22 294	97.92 321	82.76 368	91.62 295	96.38 282

dmvs_re		90.21 326	89.50 328	92.35 338	95.47 300	85.15 348	95.70 313	94.37 395	90.94 218	88.42 333	93.57 372	74.63 350	95.67 415	82.80 367	89.57 324	96.22 284

Patchmatch-test		89.42 345	87.99 352	93.70 289	95.27 315	85.11 349	88.98 441	94.37 395	81.11 416	87.10 367	93.69 365	82.28 236	97.50 363	74.37 423	94.76 239	98.48 166

LCM-MVSNet		72.55 414	69.39 418	82.03 425	70.81 465	65.42 454	90.12 436	94.36 397	55.02 455	65.88 449	81.72 448	24.16 463	89.96 446	74.32 424	68.10 446	90.71 438

ADS-MVSNet2		89.45 344	88.59 346	92.03 350	95.86 277	82.26 389	90.93 429	94.32 398	83.23 403	91.28 259	91.81 409	79.01 302	95.99 407	79.52 395	91.39 300	97.84 226

mvs5depth		86.53 374	85.08 381	90.87 380	88.74 440	82.52 384	91.91 422	94.23 399	86.35 357	87.11 366	93.70 364	66.52 411	97.76 339	81.37 382	75.80 429	92.31 422

EU-MVSNet		88.72 354	88.90 342	88.20 410	93.15 400	74.21 438	96.63 246	94.22 400	85.18 376	87.32 361	95.97 245	76.16 335	94.98 424	85.27 338	86.17 358	95.41 325

tt0320-xc		84.83 394	82.33 402	92.31 341	93.66 383	86.20 324	96.17 286	94.06 401	71.26 444	82.04 417	92.22 403	55.07 442	96.72 398	81.49 377	75.04 432	94.02 395

MIMVSNet		88.50 356	86.76 366	93.72 288	94.84 342	87.77 284	91.39 424	94.05 402	86.41 356	87.99 348	92.59 392	63.27 424	95.82 412	77.44 406	92.84 275	97.57 243

OpenMVS_ROB	81.14 20	84.42 397	82.28 403	90.83 381	90.06 429	84.05 367	95.73 312	94.04 403	73.89 441	80.17 427	91.53 413	59.15 432	97.64 349	66.92 444	89.05 328	90.80 437

TinyColmap		86.82 372	85.35 379	91.21 373	94.91 339	82.99 379	93.94 384	94.02 404	83.58 399	81.56 418	94.68 311	62.34 429	98.13 279	75.78 415	87.35 350	92.52 418

ETVMVS		90.52 317	89.14 338	94.67 230	96.81 203	87.85 282	95.91 301	93.97 405	89.71 260	92.34 226	92.48 394	65.41 420	97.96 312	81.37 382	94.27 249	98.21 192

IB-MVS	87.33 17	89.91 333	88.28 350	94.79 224	95.26 318	87.70 285	95.12 346	93.95 406	89.35 272	87.03 368	92.49 393	70.74 377	99.19 148	89.18 266	81.37 408	97.49 245
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		87.13 369	87.02 364	87.47 414	95.16 322	73.21 442	95.00 348	93.93 407	88.55 303	86.96 370	91.99 405	75.90 336	94.00 433	61.59 448	94.11 253	95.20 343

myMVS_eth3d		87.18 368	86.38 369	89.58 401	95.16 322	79.53 419	95.00 348	93.93 407	88.55 303	86.96 370	91.99 405	56.23 439	94.00 433	75.47 419	94.11 253	95.20 343

testing222		90.31 321	88.96 340	94.35 247	96.54 228	87.29 290	95.50 325	93.84 409	90.97 215	91.75 244	92.96 385	62.18 430	98.00 303	82.86 364	94.08 256	97.76 231

test_f		80.57 407	79.62 409	83.41 424	83.38 453	67.80 451	93.57 401	93.72 410	80.80 421	77.91 434	87.63 440	33.40 456	92.08 444	87.14 310	79.04 419	90.34 439

LCM-MVSNet-Re		92.50 223	92.52 207	92.44 335	96.82 201	81.89 392	96.92 211	93.71 411	92.41 156	84.30 397	94.60 316	85.08 174	97.03 385	91.51 206	97.36 167	98.40 175

tpm		90.25 324	89.74 322	91.76 363	93.92 373	79.73 417	93.98 381	93.54 412	88.28 310	91.99 235	93.25 382	77.51 324	97.44 368	87.30 305	87.94 340	98.12 201

ET-MVSNet_ETH3D		91.49 273	90.11 302	95.63 173	96.40 242	91.57 137	95.34 332	93.48 413	90.60 235	75.58 437	95.49 275	80.08 279	96.79 396	94.25 146	89.76 322	98.52 159

LFMVS		93.60 178	92.63 200	96.52 102	98.13 109	91.27 149	97.94 76	93.39 414	90.57 237	96.29 110	98.31 75	69.00 393	99.16 155	94.18 147	95.87 210	99.12 88

MVStest1		82.38 404	80.04 408	89.37 403	87.63 445	82.83 380	95.03 347	93.37 415	73.90 440	73.50 442	94.35 331	62.89 427	93.25 441	73.80 426	65.92 449	92.04 427

Patchmatch-RL test		87.38 366	86.24 370	90.81 383	88.74 440	78.40 428	88.12 448	93.17 416	87.11 345	82.17 416	89.29 429	81.95 244	95.60 417	88.64 277	77.02 424	98.41 174

ttmdpeth		85.91 386	84.76 386	89.36 404	89.14 435	80.25 412	95.66 317	93.16 417	83.77 396	83.39 408	95.26 285	66.24 415	95.26 423	80.65 388	75.57 430	92.57 415

test-LLR		91.42 276	91.19 255	92.12 348	94.59 353	80.66 402	94.29 374	92.98 418	91.11 210	90.76 268	92.37 396	79.02 300	98.07 293	88.81 272	96.74 188	97.63 236

test-mter		90.19 328	89.54 327	92.12 348	94.59 353	80.66 402	94.29 374	92.98 418	87.68 332	90.76 268	92.37 396	67.67 402	98.07 293	88.81 272	96.74 188	97.63 236

WB-MVSnew		89.88 336	89.56 326	90.82 382	94.57 356	83.06 378	95.65 318	92.85 420	87.86 323	90.83 267	94.10 348	79.66 288	96.88 392	76.34 413	94.19 251	92.54 417

testing3		87.67 364	86.88 365	90.05 395	96.14 265	80.71 401	97.10 194	92.85 420	90.15 248	87.54 355	94.55 318	55.70 440	94.10 432	73.77 427	94.10 255	95.35 332

test_method		66.11 422	64.89 424	69.79 439	72.62 463	35.23 471	65.19 458	92.83 422	20.35 461	65.20 450	88.08 438	43.14 452	82.70 456	73.12 430	63.46 451	91.45 434

test0.0.03 1		89.37 346	88.70 344	91.41 370	92.47 414	85.63 337	95.22 341	92.70 423	91.11 210	86.91 374	93.65 369	79.02 300	93.19 442	78.00 405	89.18 327	95.41 325

new_pmnet		82.89 402	81.12 407	88.18 411	89.63 432	80.18 413	91.77 423	92.57 424	76.79 436	75.56 438	88.23 436	61.22 431	94.48 428	71.43 435	82.92 402	89.87 440

mvsany_test1		93.93 167	93.98 146	93.78 285	94.94 336	86.80 305	94.62 356	92.55 425	88.77 297	96.85 78	98.49 52	88.98 97	98.08 289	95.03 121	95.62 218	96.46 281

thisisatest0515		92.29 236	91.30 249	95.25 195	96.60 219	88.90 248	94.36 369	92.32 426	87.92 320	93.43 201	94.57 317	77.28 325	99.00 184	89.42 256	95.86 211	97.86 225

thisisatest0530		93.03 204	92.21 216	95.49 184	97.07 175	89.11 243	97.49 154	92.19 427	90.16 247	94.09 179	96.41 223	76.43 334	99.05 180	90.38 234	95.68 216	98.31 186

tttt0517		92.96 207	92.33 213	94.87 217	97.11 173	87.16 298	97.97 72	92.09 428	90.63 231	93.88 185	97.01 187	76.50 331	99.06 177	90.29 237	95.45 225	98.38 177

K. test v3		87.64 365	86.75 367	90.32 392	93.02 402	79.48 422	96.61 247	92.08 429	90.66 229	80.25 426	94.09 350	67.21 406	96.65 399	85.96 329	80.83 410	94.83 364

TESTMET0.1,1		90.06 330	89.42 330	91.97 351	94.41 361	80.62 404	94.29 374	91.97 430	87.28 342	90.44 272	92.47 395	68.79 394	97.67 346	88.50 279	96.60 193	97.61 240

PM-MVS		83.48 399	81.86 405	88.31 409	87.83 444	77.59 430	93.43 402	91.75 431	86.91 347	80.63 422	89.91 425	44.42 451	95.84 411	85.17 341	76.73 427	91.50 432

baseline2		91.63 261	90.86 266	93.94 275	94.33 363	86.32 319	95.92 300	91.64 432	89.37 271	86.94 372	94.69 310	81.62 251	98.69 225	88.64 277	94.57 244	96.81 271

APD_test1		79.31 409	77.70 412	84.14 422	89.11 437	69.07 448	92.36 421	91.50 433	69.07 447	73.87 440	92.63 391	39.93 453	94.32 430	70.54 440	80.25 412	89.02 442

FPMVS		71.27 415	69.85 417	75.50 435	74.64 460	59.03 460	91.30 425	91.50 433	58.80 452	57.92 456	88.28 435	29.98 459	85.53 455	53.43 453	82.84 403	81.95 448

door								91.13 435

door-mid								91.06 436

EGC-MVSNET		68.77 420	63.01 426	86.07 421	92.49 413	82.24 390	93.96 383	90.96 437	0.71 466	2.62 467	90.89 416	53.66 443	93.46 437	57.25 451	84.55 384	82.51 447

mvsany_test3		83.59 398	82.44 401	87.03 417	83.80 450	73.82 439	93.70 394	90.92 438	86.42 355	82.51 414	90.26 421	46.76 450	95.71 413	90.82 221	76.76 426	91.57 430

pmmvs3		79.97 408	77.50 413	87.39 415	82.80 454	79.38 423	92.70 416	90.75 439	70.69 446	78.66 431	87.47 442	51.34 446	93.40 438	73.39 429	69.65 442	89.38 441

UWE-MVS		89.91 333	89.48 329	91.21 373	95.88 276	78.23 429	94.91 351	90.26 440	89.11 278	92.35 225	94.52 320	68.76 395	97.96 312	83.95 356	95.59 219	97.42 249

DSMNet-mixed		86.34 379	86.12 373	87.00 418	89.88 431	70.43 444	94.93 350	90.08 441	77.97 433	85.42 389	92.78 387	74.44 352	93.96 435	74.43 422	95.14 230	96.62 275

MVS-HIRNet		82.47 403	81.21 406	86.26 420	95.38 303	69.21 447	88.96 442	89.49 442	66.28 449	80.79 421	74.08 454	68.48 399	97.39 372	71.93 434	95.47 224	92.18 425

WB-MVS		76.77 411	76.63 414	77.18 430	85.32 448	56.82 462	94.53 360	89.39 443	82.66 407	71.35 443	89.18 430	75.03 345	88.88 450	35.42 459	66.79 447	85.84 444

test1111		93.19 196	92.82 190	94.30 253	97.58 155	84.56 359	98.21 43	89.02 444	93.53 109	94.58 164	98.21 82	72.69 362	99.05 180	93.06 173	98.48 125	99.28 73

SSC-MVS		76.05 412	75.83 415	76.72 434	84.77 449	56.22 463	94.32 372	88.96 445	81.82 413	70.52 444	88.91 431	74.79 349	88.71 451	33.69 460	64.71 450	85.23 445

ECVR-MVS		93.19 196	92.73 196	94.57 236	97.66 143	85.41 342	98.21 43	88.23 446	93.43 114	94.70 162	98.21 82	72.57 363	99.07 175	93.05 174	98.49 123	99.25 76

EPMVS		90.70 311	89.81 317	93.37 305	94.73 348	84.21 363	93.67 397	88.02 447	89.50 266	92.38 222	93.49 374	77.82 322	97.78 336	86.03 327	92.68 279	98.11 206

ANet_high		63.94 424	59.58 427	77.02 431	61.24 467	66.06 452	85.66 451	87.93 448	78.53 431	42.94 459	71.04 456	25.42 462	80.71 458	52.60 454	30.83 460	84.28 446

PMMVS2		70.19 416	66.92 420	80.01 426	76.35 459	65.67 453	86.22 449	87.58 449	64.83 451	62.38 452	80.29 451	26.78 461	88.49 453	63.79 445	54.07 456	85.88 443

lessismore_v0				90.45 389	91.96 420	79.09 426		87.19 450		80.32 425	94.39 328	66.31 414	97.55 357	84.00 355	76.84 425	94.70 376

PMVS	53.92 22	58.58 425	55.40 428	68.12 440	51.00 468	48.64 465	78.86 454	87.10 451	46.77 457	35.84 463	74.28 453	8.76 467	86.34 454	42.07 457	73.91 435	69.38 454
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
UWE-MVS-28		86.81 373	86.41 368	88.02 412	92.87 404	74.60 437	95.38 331	86.70 452	88.17 313	87.28 363	94.67 313	70.83 376	93.30 440	67.45 442	94.31 247	96.17 287

test_vis1_rt		86.16 382	85.06 382	89.46 402	93.47 391	80.46 406	96.41 260	86.61 453	85.22 375	79.15 430	88.64 432	52.41 445	97.06 383	93.08 172	90.57 313	90.87 436

testf1		69.31 418	66.76 421	76.94 432	78.61 457	61.93 456	88.27 446	86.11 454	55.62 453	59.69 453	85.31 445	20.19 465	89.32 447	57.62 449	69.44 444	79.58 449

APD_test2		69.31 418	66.76 421	76.94 432	78.61 457	61.93 456	88.27 446	86.11 454	55.62 453	59.69 453	85.31 445	20.19 465	89.32 447	57.62 449	69.44 444	79.58 449

gg-mvs-nofinetune		87.82 362	85.61 375	94.44 243	94.46 358	89.27 237	91.21 428	84.61 456	80.88 418	89.89 292	74.98 452	71.50 370	97.53 360	85.75 332	97.21 176	96.51 277

dmvs_testset		81.38 406	82.60 400	77.73 429	91.74 421	51.49 464	93.03 411	84.21 457	89.07 279	78.28 433	91.25 415	76.97 327	88.53 452	56.57 452	82.24 405	93.16 406

GG-mvs-BLEND				93.62 293	93.69 381	89.20 239	92.39 420	83.33 458		87.98 349	89.84 426	71.00 374	96.87 393	82.08 374	95.40 226	94.80 369

MTMP							97.86 85	82.03 459

DeepMVS_CX				74.68 437	90.84 426	64.34 455		81.61 460	65.34 450	67.47 448	88.01 439	48.60 449	80.13 459	62.33 447	73.68 436	79.58 449

E-PMN		53.28 426	52.56 430	55.43 443	74.43 461	47.13 466	83.63 453	76.30 461	42.23 458	42.59 460	62.22 459	28.57 460	74.40 460	31.53 461	31.51 459	44.78 458

test2506		91.60 263	90.78 271	94.04 265	97.66 143	83.81 368	98.27 33	75.53 462	93.43 114	95.23 148	98.21 82	67.21 406	99.07 175	93.01 177	98.49 123	99.25 76

EMVS		52.08 428	51.31 431	54.39 444	72.62 463	45.39 468	83.84 452	75.51 463	41.13 459	40.77 461	59.65 460	30.08 458	73.60 461	28.31 463	29.90 461	44.18 459

test_vis3_rt		72.73 413	70.55 416	79.27 427	80.02 456	68.13 450	93.92 386	74.30 464	76.90 435	58.99 455	73.58 455	20.29 464	95.37 421	84.16 351	72.80 438	74.31 452

MVE	50.73 23	53.25 427	48.81 432	66.58 442	65.34 466	57.50 461	72.49 456	70.94 465	40.15 460	39.28 462	63.51 458	6.89 469	73.48 462	38.29 458	42.38 458	68.76 456
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
tmp_tt		51.94 429	53.82 429	46.29 445	33.73 469	45.30 469	78.32 455	67.24 466	18.02 462	50.93 458	87.05 443	52.99 444	53.11 464	70.76 438	25.29 462	40.46 460

kuosan		65.27 423	64.66 425	67.11 441	83.80 450	61.32 459	88.53 445	60.77 467	68.22 448	67.67 446	80.52 450	49.12 448	70.76 463	29.67 462	53.64 457	69.26 455

dongtai		69.99 417	69.33 419	71.98 438	88.78 439	61.64 458	89.86 437	59.93 468	75.67 437	74.96 439	85.45 444	50.19 447	81.66 457	43.86 456	55.27 455	72.63 453

N_pmnet		78.73 410	78.71 411	78.79 428	92.80 407	46.50 467	94.14 378	43.71 469	78.61 430	80.83 420	91.66 412	74.94 348	96.36 403	67.24 443	84.45 386	93.50 402

wuyk23d		25.11 430	24.57 434	26.74 446	73.98 462	39.89 470	57.88 459	9.80 470	12.27 463	10.39 464	6.97 466	7.03 468	36.44 465	25.43 464	17.39 463	3.89 463

testmvs		13.36 432	16.33 435	4.48 448	5.04 470	2.26 473	93.18 405	3.28 471	2.70 464	8.24 465	21.66 462	2.29 471	2.19 466	7.58 465	2.96 464	9.00 462

test123		13.04 433	15.66 436	5.18 447	4.51 471	3.45 472	92.50 419	1.81 472	2.50 465	7.58 466	20.15 463	3.67 470	2.18 467	7.13 466	1.07 465	9.90 461

mmdepth		0.00 436	0.00 439	0.00 449	0.00 472	0.00 474	0.00 460	0.00 473	0.00 467	0.00 468	0.00 467	0.00 472	0.00 468	0.00 467	0.00 466	0.00 464

monomultidepth		0.00 436	0.00 439	0.00 449	0.00 472	0.00 474	0.00 460	0.00 473	0.00 467	0.00 468	0.00 467	0.00 472	0.00 468	0.00 467	0.00 466	0.00 464

test_blank		0.00 436	0.00 439	0.00 449	0.00 472	0.00 474	0.00 460	0.00 473	0.00 467	0.00 468	0.00 467	0.00 472	0.00 468	0.00 467	0.00 466	0.00 464

uanet_test		0.00 436	0.00 439	0.00 449	0.00 472	0.00 474	0.00 460	0.00 473	0.00 467	0.00 468	0.00 467	0.00 472	0.00 468	0.00 467	0.00 466	0.00 464

DCPMVS		0.00 436	0.00 439	0.00 449	0.00 472	0.00 474	0.00 460	0.00 473	0.00 467	0.00 468	0.00 467	0.00 472	0.00 468	0.00 467	0.00 466	0.00 464

pcd_1.5k_mvsjas		7.39 435	9.85 438	0.00 449	0.00 472	0.00 474	0.00 460	0.00 473	0.00 467	0.00 468	0.00 467	88.65 105	0.00 468	0.00 467	0.00 466	0.00 464

sosnet-low-res		0.00 436	0.00 439	0.00 449	0.00 472	0.00 474	0.00 460	0.00 473	0.00 467	0.00 468	0.00 467	0.00 472	0.00 468	0.00 467	0.00 466	0.00 464

sosnet		0.00 436	0.00 439	0.00 449	0.00 472	0.00 474	0.00 460	0.00 473	0.00 467	0.00 468	0.00 467	0.00 472	0.00 468	0.00 467	0.00 466	0.00 464

uncertanet		0.00 436	0.00 439	0.00 449	0.00 472	0.00 474	0.00 460	0.00 473	0.00 467	0.00 468	0.00 467	0.00 472	0.00 468	0.00 467	0.00 466	0.00 464

Regformer		0.00 436	0.00 439	0.00 449	0.00 472	0.00 474	0.00 460	0.00 473	0.00 467	0.00 468	0.00 467	0.00 472	0.00 468	0.00 467	0.00 466	0.00 464

n2								0.00 473

nn								0.00 473

ab-mvs-re		8.06 434	10.74 437	0.00 449	0.00 472	0.00 474	0.00 460	0.00 473	0.00 467	0.00 468	96.69 204	0.00 472	0.00 468	0.00 467	0.00 466	0.00 464

uanet		0.00 436	0.00 439	0.00 449	0.00 472	0.00 474	0.00 460	0.00 473	0.00 467	0.00 468	0.00 467	0.00 472	0.00 468	0.00 467	0.00 466	0.00 464

WAC-MVS						79.53 419								75.56 418

PC_three_1452									90.77 221	98.89 24	98.28 80	96.24 1	98.35 262	95.76 100	99.58 23	99.59 28

eth-test2					0.00 472

eth-test					0.00 472

OPU-MVS				98.55 3	98.82 57	96.86 3	98.25 36				98.26 81	96.04 2	99.24 143	95.36 114	99.59 19	99.56 36

test_0728_THIRD									94.78 59	98.73 28	98.87 29	95.87 4	99.84 23	97.45 44	99.72 2	99.77 2

GSMVS																98.45 169

test_part2					99.28 27	95.74 8				98.10 42

sam_mvs1												82.76 224				98.45 169

sam_mvs												81.94 245

test_post1							92.81 415				16.58 465	80.53 270	97.68 345	86.20 321

test_post											17.58 464	81.76 248	98.08 289

patchmatchnet-post											90.45 420	82.65 229	98.10 284

gm-plane-assit					93.22 398	78.89 427			84.82 383		93.52 373		98.64 233	87.72 289

test9_res														94.81 131	99.38 60	99.45 55

agg_prior2														93.94 152	99.38 60	99.50 48

test_prior4						93.66 58	96.42 259

test_prior2							96.35 268		92.80 148	96.03 120	97.59 145	92.01 47		95.01 122	99.38 60

旧先验2							95.94 298		81.66 414	97.34 64			98.82 203	92.26 183

新几何2							95.79 308

原ACMM2							95.67 314

testdata2													99.67 71	85.96 329

segment_acmp												92.89 30

testdata1							95.26 340		93.10 131

plane_prior7					96.21 252	89.98 204

plane_prior6					96.10 269	90.00 200						81.32 255

plane_prior4											96.64 207

plane_prior3						90.00 200			94.46 76	91.34 253

plane_prior2							97.74 106		94.85 51

plane_prior1					96.14 265

plane_prior						89.99 202	97.24 179		94.06 88						92.16 288

HQP5-MVS						89.33 232

HQP-NCC					95.86 277		96.65 241		93.55 105	90.14 277

ACMP_Plane					95.86 277		96.65 241		93.55 105	90.14 277

BP-MVS														92.13 191

HQP4-MVS										90.14 277			98.50 247			95.78 306

HQP2-MVS												80.95 259

NP-MVS					95.99 275	89.81 211					95.87 250

MDTV_nov1_ep13_2view						70.35 445	93.10 410		83.88 394	93.55 194		82.47 233		86.25 320		98.38 177

ACMMP++_ref															90.30 318

ACMMP++															91.02 307

Test By Simon												88.73 104

High-resolution multi-view benchmark