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.

LCM-MVSNet		99.43 1	99.49 1	99.24 1	99.95 1	98.13 1	99.37 1	99.57 1	99.82 1	99.86 1	99.85 1	99.52 1	99.73 1	97.58 1	99.94 1	99.85 2

LTVRE_ROB	93.87 1	97.93 2	98.16 2	97.26 29	98.81 32	93.86 40	99.07 2	98.98 8	97.01 17	98.92 5	98.78 19	95.22 47	98.61 196	96.85 11	99.77 9	99.31 33
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
TDRefinement		97.68 3	97.60 8	97.93 2	99.02 13	95.95 8	98.61 3	98.81 10	97.41 13	97.28 71	98.46 35	94.62 77	98.84 149	94.64 53	99.53 39	98.99 66

DVP-MVS++		95.93 63	96.34 45	94.70 135	96.54 225	86.66 204	98.45 4	98.22 58	93.26 87	97.54 50	97.36 120	93.12 121	99.38 63	93.88 72	98.68 203	98.04 208

FOURS1					99.21 3	94.68 16	98.45 4	98.81 10	97.73 9	98.27 23

UA-Net		97.35 4	97.24 15	97.69 5	98.22 83	93.87 39	98.42 6	98.19 61	96.95 18	95.46 194	99.23 9	93.45 107	99.57 14	95.34 45	99.89 2	99.63 12

OurMVSNet-221017-0		96.80 19	96.75 25	96.96 38	99.03 12	91.85 82	97.98 7	98.01 102	94.15 64	98.93 4	99.07 10	88.07 251	99.57 14	95.86 27	99.69 17	99.46 22

UniMVSNet_ETH3D		97.13 10	97.72 3	95.35 97	99.51 2	87.38 181	97.70 8	97.54 165	98.16 5	98.94 3	99.33 6	97.84 4	99.08 111	90.73 189	99.73 14	99.59 15

tt0805		95.42 90	95.93 71	93.86 181	98.75 36	88.47 155	97.68 9	94.29 357	96.48 26	95.38 197	93.63 380	94.89 66	97.94 304	95.38 43	96.92 370	95.17 415

HPM-MVS		96.81 18	96.62 31	97.36 26	98.89 23	93.53 51	97.51 10	98.44 26	92.35 104	95.95 157	96.41 214	96.71 11	99.42 37	93.99 70	99.36 66	99.13 50
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
EPP-MVSNet		93.91 177	93.68 194	94.59 146	98.08 91	85.55 239	97.44 11	94.03 365	94.22 63	94.94 235	96.19 239	82.07 339	99.57 14	87.28 310	98.89 157	98.65 132

mvs5depth		95.28 98	95.82 81	93.66 191	96.42 239	83.08 287	97.35 12	99.28 2	96.44 28	96.20 144	99.65 2	84.10 314	98.01 296	94.06 67	98.93 148	99.87 1

LS3D		96.11 56	95.83 79	96.95 39	94.75 359	94.20 23	97.34 13	97.98 105	97.31 14	95.32 202	96.77 182	93.08 123	99.20 95	91.79 152	98.16 273	97.44 289

HPM-MVS_fast		97.01 11	96.89 21	97.39 24	99.12 8	93.92 36	97.16 14	98.17 67	93.11 89	96.48 118	97.36 120	96.92 6	99.34 70	94.31 61	99.38 63	98.92 87

MVSFormer		92.18 260	92.23 251	92.04 286	94.74 362	80.06 349	97.15 15	97.37 180	88.98 219	88.83 445	92.79 408	77.02 409	99.60 9	96.41 18	96.75 378	96.46 354

test_djsdf		96.62 30	96.49 35	97.01 35	98.55 53	91.77 85	97.15 15	97.37 180	88.98 219	98.26 26	98.86 15	93.35 112	99.60 9	96.41 18	99.45 48	99.66 9

IS-MVSNet		94.49 143	94.35 163	94.92 121	98.25 82	86.46 209	97.13 17	94.31 356	96.24 34	96.28 138	96.36 223	82.88 327	99.35 67	88.19 287	99.52 41	98.96 77

Anonymous20231211		96.60 32	97.13 19	95.00 116	97.46 145	86.35 214	97.11 18	98.24 54	97.58 11	98.72 11	98.97 12	93.15 120	99.15 99	93.18 106	99.74 13	99.50 19

anonymousdsp		96.74 24	96.42 38	97.68 7	98.00 102	94.03 29	96.97 19	97.61 156	87.68 266	98.45 21	98.77 20	94.20 90	99.50 23	96.70 13	99.40 61	99.53 17

ACMMP		96.61 31	96.34 45	97.43 21	98.61 45	93.88 37	96.95 20	98.18 63	92.26 107	96.33 130	96.84 179	95.10 54	99.40 51	93.47 90	99.33 73	99.02 63
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
lecture		97.32 6	97.64 6	96.33 54	99.01 15	90.77 107	96.90 21	98.60 16	96.30 33	97.74 41	98.00 55	96.87 8	99.39 54	95.95 24	99.42 54	98.84 98

APDe-MVS		96.46 39	96.64 29	95.93 66	97.68 129	89.38 131	96.90 21	98.41 29	92.52 98	97.43 58	97.92 67	95.11 52	99.50 23	94.45 57	99.30 80	98.92 87
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
tt0320		96.97 13	97.64 6	94.96 120	98.89 23	86.86 197	96.85 23	98.45 25	98.29 3	98.88 6	99.45 3	96.48 13	98.54 214	91.73 155	99.72 15	99.47 21

EGC-MVSNET		80.97 487	75.73 507	96.67 45	98.85 28	94.55 19	96.83 24	96.60 258	2.44 555	5.32 558	98.25 42	92.24 149	98.02 295	91.85 150	99.21 99	97.45 287

v7n		96.82 16	97.31 14	95.33 99	98.54 55	86.81 198	96.83 24	98.07 86	96.59 25	98.46 20	98.43 37	92.91 131	99.52 19	96.25 21	99.76 10	99.65 11

CP-MVS		96.44 42	96.08 60	97.54 14	98.29 77	94.62 18	96.80 26	98.08 83	92.67 96	95.08 229	96.39 220	94.77 73	99.42 37	93.17 107	99.44 51	98.58 146

COLMAP_ROB	91.06 5	96.75 23	96.62 31	97.13 31	98.38 70	94.31 21	96.79 27	98.32 38	96.69 21	96.86 95	97.56 95	95.48 31	98.77 167	90.11 220	99.44 51	98.31 178
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
WR-MVS_H		96.60 32	97.05 20	95.24 106	99.02 13	86.44 210	96.78 28	98.08 83	97.42 12	98.48 19	97.86 73	91.76 162	99.63 7	94.23 63	99.84 3	99.66 9

FE-MVS		89.06 357	88.29 367	91.36 324	94.78 357	79.57 372	96.77 29	90.99 434	84.87 352	92.96 325	96.29 227	60.69 508	98.80 159	80.18 416	97.11 356	95.71 397

CS-MVS		95.77 71	95.58 91	96.37 53	96.84 191	91.72 87	96.73 30	99.06 7	94.23 62	92.48 342	94.79 328	93.56 102	99.49 29	93.47 90	99.05 122	97.89 238

sc_t1		97.21 9	97.71 4	95.71 78	99.06 10	88.89 142	96.72 31	97.79 139	98.34 2	98.97 2	99.40 5	96.81 9	98.79 160	92.58 129	99.72 15	99.45 23

tt0320-xc		97.00 12	97.67 5	94.98 117	98.89 23	86.94 195	96.72 31	98.46 24	98.28 4	98.86 7	99.43 4	96.80 10	98.51 222	91.79 152	99.76 10	99.50 19

pmmvs6		96.80 19	97.36 13	95.15 112	99.12 8	87.82 175	96.68 33	97.86 126	96.10 36	98.14 30	99.28 8	97.94 3	98.21 263	91.38 168	99.69 17	99.42 24

balanced_ft_v1		92.65 238	93.17 214	91.10 340	94.47 372	77.32 427	96.67 34	96.70 249	88.23 247	93.70 283	97.16 144	83.33 320	99.41 43	90.51 196	97.76 313	96.57 342

mvsmamba		90.24 322	89.43 339	92.64 249	95.52 326	82.36 304	96.64 35	92.29 413	81.77 408	92.14 362	96.28 229	70.59 457	99.10 110	84.44 362	95.22 443	96.47 353

3Dnovator	92.54 3	94.80 121	94.90 128	94.47 154	95.47 330	87.06 189	96.63 36	97.28 195	91.82 131	94.34 258	97.41 112	90.60 203	98.65 191	92.47 132	98.11 279	97.70 265

PS-CasMVS		96.69 27	97.43 9	94.49 153	99.13 6	84.09 264	96.61 37	97.97 107	97.91 8	98.64 16	98.13 45	95.24 45	99.65 4	93.39 97	99.84 3	99.72 4

mvs_tets		96.83 15	96.71 26	97.17 30	98.83 29	92.51 70	96.58 38	97.61 156	87.57 269	98.80 10	98.90 14	96.50 12	99.59 13	96.15 22	99.47 44	99.40 27

PEN-MVS		96.69 27	97.39 12	94.61 142	99.16 4	84.50 253	96.54 39	98.05 92	98.06 7	98.64 16	98.25 42	95.01 59	99.65 4	92.95 115	99.83 5	99.68 7

MVSMamba_PlusPlus		94.82 119	95.89 74	91.62 307	97.82 115	78.88 393	96.52 40	97.60 158	97.14 16	94.23 259	98.48 34	87.01 277	99.71 2	95.43 40	98.80 176	96.28 366

DTE-MVSNet		96.74 24	97.43 9	94.67 139	99.13 6	84.68 251	96.51 41	97.94 115	98.14 6	98.67 15	98.32 39	95.04 56	99.69 3	93.27 103	99.82 7	99.62 13

XVS		96.49 37	96.18 53	97.44 19	98.56 49	93.99 32	96.50 42	97.95 112	94.58 55	94.38 256	96.49 207	94.56 80	99.39 54	93.57 82	99.05 122	98.93 83

X-MVStestdata		90.70 300	88.45 361	97.44 19	98.56 49	93.99 32	96.50 42	97.95 112	94.58 55	94.38 256	26.89 553	94.56 80	99.39 54	93.57 82	99.05 122	98.93 83

mmtdpeth		95.82 69	96.02 65	95.23 107	96.91 185	88.62 148	96.49 44	99.26 3	95.07 49	93.41 293	99.29 7	90.25 210	97.27 367	94.49 55	99.01 131	99.80 3

EC-MVSNet		95.44 86	95.62 89	94.89 124	96.93 184	87.69 177	96.48 45	99.14 6	93.93 72	92.77 332	94.52 341	93.95 97	99.49 29	93.62 81	99.22 98	97.51 282

mPP-MVS		96.46 39	96.05 62	97.69 5	98.62 43	94.65 17	96.45 46	97.74 143	92.59 97	95.47 192	96.68 193	94.50 83	99.42 37	93.10 109	99.26 90	98.99 66

QAPM		92.88 223	92.77 226	93.22 219	95.82 302	83.31 276	96.45 46	97.35 186	83.91 371	93.75 279	96.77 182	89.25 229	98.88 142	84.56 360	97.02 363	97.49 284

jajsoiax		96.59 34	96.42 38	97.12 32	98.76 35	92.49 71	96.44 48	97.42 177	86.96 288	98.71 13	98.72 22	95.36 38	99.56 17	95.92 25	99.45 48	99.32 32

Gipuma		95.31 97	95.80 82	93.81 184	97.99 105	90.91 101	96.42 49	97.95 112	96.69 21	91.78 371	98.85 17	91.77 160	95.49 440	91.72 156	99.08 118	95.02 424
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
MSP-MVS		95.34 93	94.63 148	97.48 17	98.67 40	94.05 27	96.41 50	98.18 63	91.26 156	95.12 224	95.15 306	86.60 288	99.50 23	93.43 96	96.81 375	98.89 91
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
SR-MVS-dyc-post		96.84 14	96.60 33	97.56 13	98.07 92	95.27 9	96.37 51	98.12 76	95.66 42	97.00 88	97.03 160	94.85 69	99.42 37	93.49 87	98.84 164	98.00 213

RE-MVS-def			96.66 27		98.07 92	95.27 9	96.37 51	98.12 76	95.66 42	97.00 88	97.03 160	95.40 35		93.49 87	98.84 164	98.00 213

TSAR-MVS + MP.		94.96 112	94.75 137	95.57 87	98.86 27	88.69 145	96.37 51	96.81 239	85.23 338	94.75 243	97.12 151	91.85 158	99.40 51	93.45 92	98.33 250	98.62 142
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		95.32 94	95.10 123	95.96 62	96.86 189	90.75 108	96.33 54	99.20 4	93.99 68	91.03 393	93.73 377	93.52 104	99.55 18	91.81 151	99.45 48	97.58 276

ACMH	88.36 12	96.59 34	97.43 9	94.07 169	98.56 49	85.33 243	96.33 54	98.30 41	94.66 54	98.72 11	98.30 40	97.51 5	98.00 298	94.87 50	99.59 29	98.86 94
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
aaatest				95.52 89	98.69 37	88.21 161	96.32 56	98.58 18	88.79 225	97.38 65	96.22 235		99.39 54	92.89 117	99.10 115	98.96 77

MED-MVS		96.38 47	96.63 30	95.63 83	98.69 37	88.21 161	96.32 56	98.58 18	94.10 65	97.38 65	97.37 115	95.11 52	99.39 54	92.89 117	99.19 102	99.30 34

TestfortrainingZip a		96.50 36	96.80 23	95.62 84	98.69 37	88.28 158	96.32 56	98.06 90	94.10 65	97.65 43	97.37 115	94.54 82	99.28 85	95.41 42	99.04 127	99.30 34

TestfortrainingZip				93.68 190	95.25 338	86.20 219	96.32 56	96.38 273	92.81 92	92.13 363	93.87 373	87.28 269	98.61 196		95.07 447	96.23 370

region2R		96.41 44	96.09 58	97.38 25	98.62 43	93.81 44	96.32 56	97.96 109	92.26 107	95.28 207	96.57 202	95.02 58	99.41 43	93.63 80	99.11 114	98.94 81

testf1		96.77 21	96.49 35	97.60 9	99.01 15	96.70 3	96.31 61	98.33 36	94.96 50	97.30 68	97.93 62	96.05 20	97.90 305	89.32 242	99.23 95	98.19 193

APD_test2		96.77 21	96.49 35	97.60 9	99.01 15	96.70 3	96.31 61	98.33 36	94.96 50	97.30 68	97.93 62	96.05 20	97.90 305	89.32 242	99.23 95	98.19 193

APD-MVS_3200maxsize		96.82 16	96.65 28	97.32 28	97.95 106	93.82 42	96.31 61	98.25 46	95.51 44	96.99 90	97.05 159	95.63 27	99.39 54	93.31 99	98.88 159	98.75 115

CP-MVSNet		96.19 54	96.80 23	94.38 158	98.99 19	83.82 267	96.31 61	97.53 168	97.60 10	98.34 22	97.52 100	91.98 156	99.63 7	93.08 111	99.81 8	99.70 5

HFP-MVS		96.39 46	96.17 55	97.04 34	98.51 58	93.37 52	96.30 65	97.98 105	92.35 104	95.63 184	96.47 208	95.37 36	99.27 88	93.78 76	99.14 112	98.48 156

ACMMPR		96.46 39	96.14 56	97.41 23	98.60 46	93.82 42	96.30 65	97.96 109	92.35 104	95.57 187	96.61 199	94.93 64	99.41 43	93.78 76	99.15 111	99.00 64

3Dnovator+	92.74 2	95.86 68	95.77 83	96.13 57	96.81 194	90.79 106	96.30 65	97.82 134	96.13 35	94.74 244	97.23 137	91.33 176	99.16 98	93.25 104	98.30 256	98.46 157

MIMVSNet1		95.52 82	95.45 95	95.72 77	99.14 5	89.02 139	96.23 68	96.87 233	93.73 76	97.87 35	98.49 33	90.73 200	99.05 118	86.43 328	99.60 27	99.10 57

BridgeMVS		93.45 194	94.17 172	91.28 330	95.81 304	78.40 401	96.20 69	97.48 174	88.56 237	95.29 205	97.20 142	85.56 303	99.21 92	92.52 131	98.91 153	96.24 369

test2506		85.42 441	84.57 444	87.96 448	97.81 116	66.53 519	96.14 70	56.35 554	89.04 217	93.55 288	98.10 47	42.88 545	98.68 186	88.09 293	99.18 106	98.67 130

SR-MVS		96.70 26	96.42 38	97.54 14	98.05 94	94.69 15	96.13 71	98.07 86	95.17 48	96.82 99	96.73 189	95.09 55	99.43 36	92.99 114	98.71 198	98.50 153

MP-MVS		96.14 55	95.68 86	97.51 16	98.81 32	94.06 25	96.10 72	97.78 141	92.73 93	93.48 291	96.72 190	94.23 89	99.42 37	91.99 145	99.29 83	99.05 61
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
ZNCC-MVS		96.42 43	96.20 52	97.07 33	98.80 34	92.79 64	96.08 73	98.16 70	91.74 136	95.34 201	96.36 223	95.68 25	99.44 33	94.41 59	99.28 88	98.97 73

FA-MVS(test-final)		91.81 269	91.85 266	91.68 305	94.95 350	79.99 353	96.00 74	93.44 389	87.80 261	94.02 270	97.29 129	77.60 394	98.45 234	88.04 296	97.49 335	96.61 341

GBi-Net		93.21 209	92.96 219	93.97 173	95.40 332	84.29 257	95.99 75	96.56 262	88.63 229	95.10 226	98.53 30	81.31 347	98.98 127	86.74 316	98.38 243	98.65 132

test1		93.21 209	92.96 219	93.97 173	95.40 332	84.29 257	95.99 75	96.56 262	88.63 229	95.10 226	98.53 30	81.31 347	98.98 127	86.74 316	98.38 243	98.65 132

FMVSNet1		94.84 117	95.13 119	93.97 173	97.60 134	84.29 257	95.99 75	96.56 262	92.38 101	97.03 87	98.53 30	90.12 215	98.98 127	88.78 268	99.16 110	98.65 132

RPSCF		95.58 80	94.89 130	97.62 8	97.58 136	96.30 7	95.97 78	97.53 168	92.42 100	93.41 293	97.78 75	91.21 181	97.77 324	91.06 179	97.06 361	98.80 104

SixPastTwentyTwo		94.91 113	95.21 112	93.98 172	98.52 57	83.19 282	95.93 79	94.84 339	94.86 53	98.49 18	98.74 21	81.45 345	99.60 9	94.69 52	99.39 62	99.15 48

ambc				92.98 226	96.88 187	83.01 289	95.92 80	96.38 273		96.41 124	97.48 106	88.26 247	97.80 319	89.96 226	98.93 148	98.12 202

FC-MVSNet-test		95.32 94	95.88 75	93.62 193	98.49 65	81.77 312	95.90 81	98.32 38	93.93 72	97.53 52	97.56 95	88.48 242	99.40 51	92.91 116	99.83 5	99.68 7

reproduce_model		97.35 4	97.24 15	97.70 4	98.44 67	95.08 12	95.88 82	98.50 21	96.62 24	98.27 23	97.93 62	94.57 79	99.50 23	95.57 35	99.35 67	98.52 151

MTAPA		96.65 29	96.38 42	97.47 18	98.95 21	94.05 27	95.88 82	97.62 154	94.46 59	96.29 136	96.94 167	93.56 102	99.37 65	94.29 62	99.42 54	98.99 66

CPTT-MVS		94.74 122	94.12 174	96.60 46	98.15 87	93.01 59	95.84 84	97.66 151	89.21 215	93.28 302	95.46 288	88.89 233	98.98 127	89.80 228	98.82 170	97.80 252

ab-mvs		92.40 248	92.62 236	91.74 300	97.02 176	81.65 316	95.84 84	95.50 314	86.95 289	92.95 326	97.56 95	90.70 201	97.50 347	79.63 425	97.43 340	96.06 379

nrg030		96.32 49	96.55 34	95.62 84	97.83 114	88.55 153	95.77 86	98.29 44	92.68 94	98.03 34	97.91 70	95.13 50	98.95 135	93.85 74	99.49 43	99.36 30

ECVR-MVS		90.12 326	90.16 320	90.00 391	97.81 116	72.68 488	95.76 87	78.54 543	89.04 217	95.36 200	98.10 47	70.51 458	98.64 192	87.10 312	99.18 106	98.67 130

SteuartSystems-ACMMP		96.40 45	96.30 47	96.71 43	98.63 42	91.96 80	95.70 88	98.01 102	93.34 86	96.64 112	96.57 202	94.99 60	99.36 66	93.48 89	99.34 71	98.82 99
Skip Steuart: Steuart Systems R&D Blog.
OpenMVS	89.45 8	92.27 257	92.13 256	92.68 248	94.53 371	84.10 263	95.70 88	97.03 213	82.44 400	91.14 391	96.42 213	88.47 243	98.38 241	85.95 335	97.47 337	95.55 406

GST-MVS		96.24 52	95.99 66	97.00 36	98.65 41	92.71 66	95.69 90	98.01 102	92.08 116	95.74 175	96.28 229	95.22 47	99.42 37	93.17 107	99.06 119	98.88 93

ACMH+	88.43 11	96.48 38	96.82 22	95.47 92	98.54 55	89.06 138	95.65 91	98.61 15	96.10 36	98.16 29	97.52 100	96.90 7	98.62 195	90.30 209	99.60 27	98.72 121

APD_test1		95.91 64	95.42 100	97.36 26	98.82 30	96.62 6	95.64 92	97.64 152	93.38 85	95.89 162	97.23 137	93.35 112	97.66 335	88.20 286	98.66 207	97.79 253

sasdasda		94.59 131	94.69 141	94.30 159	95.60 321	87.03 190	95.59 93	98.24 54	91.56 143	95.21 216	92.04 438	94.95 61	98.66 188	91.45 165	97.57 330	97.20 306

test1111		90.39 314	90.61 307	89.74 399	98.04 97	71.50 496	95.59 93	79.72 539	89.41 208	95.94 158	98.14 44	70.79 456	98.81 156	88.52 279	99.32 77	98.90 90

canonicalmvs		94.59 131	94.69 141	94.30 159	95.60 321	87.03 190	95.59 93	98.24 54	91.56 143	95.21 216	92.04 438	94.95 61	98.66 188	91.45 165	97.57 330	97.20 306

SF-MVS		95.88 67	95.88 75	95.87 72	98.12 88	89.65 123	95.58 96	98.56 20	91.84 128	96.36 129	96.68 193	94.37 87	99.32 77	92.41 134	99.05 122	98.64 138

reproduce-ours		97.28 7	97.19 17	97.57 11	98.37 72	94.84 13	95.57 97	98.40 30	96.36 31	98.18 27	97.78 75	95.47 32	99.50 23	95.26 46	99.33 73	98.36 171

our_new_method		97.28 7	97.19 17	97.57 11	98.37 72	94.84 13	95.57 97	98.40 30	96.36 31	98.18 27	97.78 75	95.47 32	99.50 23	95.26 46	99.33 73	98.36 171

PS-MVSNAJss		96.01 59	96.04 63	95.89 71	98.82 30	88.51 154	95.57 97	97.88 123	88.72 227	98.81 9	98.86 15	90.77 196	99.60 9	95.43 40	99.53 39	99.57 16

PMVS	87.21 14	94.97 111	95.33 107	93.91 178	98.97 20	97.16 2	95.54 100	95.85 298	96.47 27	93.40 296	97.46 107	95.31 41	95.47 441	86.18 332	98.78 181	89.11 511
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
VDDNet		94.03 171	94.27 169	93.31 213	98.87 26	82.36 304	95.51 101	91.78 427	97.19 15	96.32 132	98.60 27	84.24 312	98.75 168	87.09 313	98.83 169	98.81 102

pm-mvs1		95.43 87	95.94 69	93.93 177	98.38 70	85.08 247	95.46 102	97.12 208	91.84 128	97.28 71	98.46 35	95.30 42	97.71 332	90.17 218	99.42 54	98.99 66

RRT-MVS		92.28 254	93.01 218	90.07 386	94.06 385	73.01 484	95.36 103	97.88 123	92.24 109	95.16 221	97.52 100	78.51 379	99.29 81	90.55 194	95.83 415	97.92 233

Vis-MVSNet		95.50 83	95.48 94	95.56 88	98.11 89	89.40 130	95.35 104	98.22 58	92.36 103	94.11 263	98.07 49	92.02 154	99.44 33	93.38 98	97.67 323	97.85 245
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
test0726					98.51 58	86.69 202	95.34 105	98.18 63	91.85 125	97.63 45	97.37 115	95.58 28

FIs		94.90 115	95.35 104	93.55 197	98.28 78	81.76 313	95.33 106	98.14 72	93.05 91	97.07 83	97.18 143	87.65 262	99.29 81	91.72 156	99.69 17	99.61 14

PGM-MVS		96.32 49	95.94 69	97.43 21	98.59 48	93.84 41	95.33 106	98.30 41	91.40 153	95.76 170	96.87 175	95.26 44	99.45 32	92.77 120	99.21 99	99.00 64

LPG-MVS_test		96.38 47	96.23 50	96.84 41	98.36 75	92.13 77	95.33 106	98.25 46	91.78 132	97.07 83	97.22 139	96.38 16	99.28 85	92.07 142	99.59 29	99.11 54

MonoMVSNet		88.46 375	89.28 340	85.98 482	90.52 492	70.07 505	95.31 109	94.81 342	88.38 241	93.47 292	96.13 245	73.21 438	95.07 452	82.61 385	89.12 524	92.81 482

Elysia		96.00 60	96.36 43	94.91 122	98.01 100	85.96 227	95.29 110	97.90 118	95.31 45	98.14 30	97.28 131	88.82 234	99.51 20	97.08 7	99.38 63	99.26 37

StellarMVS		96.00 60	96.36 43	94.91 122	98.01 100	85.96 227	95.29 110	97.90 118	95.31 45	98.14 30	97.28 131	88.82 234	99.51 20	97.08 7	99.38 63	99.26 37

AllTest		94.88 116	94.51 155	96.00 59	98.02 98	92.17 74	95.26 112	98.43 27	90.48 181	95.04 231	96.74 187	92.54 140	97.86 313	85.11 351	98.98 135	97.98 217

MGCFI-Net		94.44 145	94.67 146	93.75 186	95.56 324	85.47 240	95.25 113	98.24 54	91.53 145	95.04 231	92.21 432	94.94 63	98.54 214	91.56 163	97.66 324	97.24 304

SED-MVS		96.00 60	96.41 41	94.76 132	98.51 58	86.97 192	95.21 114	98.10 80	91.95 118	97.63 45	97.25 134	96.48 13	99.35 67	93.29 101	99.29 83	97.95 223

OPU-MVS				95.15 112	96.84 191	89.43 128	95.21 114				95.66 278	93.12 121	98.06 288	86.28 331	98.61 211	97.95 223

DVP-MVS		95.82 69	96.18 53	94.72 134	98.51 58	86.69 202	95.20 116	97.00 215	91.85 125	97.40 63	97.35 123	95.58 28	99.34 70	93.44 93	99.31 78	98.13 201
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
test_0728_SECOND				94.88 125	98.55 53	86.72 201	95.20 116	98.22 58					99.38 63	93.44 93	99.31 78	98.53 150

Anonymous20240529		95.50 83	95.83 79	94.50 151	97.33 153	85.93 229	95.19 118	96.77 244	96.64 23	97.61 48	98.05 50	93.23 117	98.79 160	88.60 275	99.04 127	98.78 111

SMA-MVS		95.77 71	95.54 92	96.47 52	98.27 79	91.19 95	95.09 119	97.79 139	86.48 296	97.42 61	97.51 104	94.47 86	99.29 81	93.55 84	99.29 83	98.93 83
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
NR-MVSNet		95.28 98	95.28 110	95.26 104	97.75 120	87.21 185	95.08 120	97.37 180	93.92 74	97.65 43	95.90 259	90.10 217	99.33 76	90.11 220	99.66 23	99.26 37

TransMVSNet (Re)		95.27 101	96.04 63	92.97 227	98.37 72	81.92 311	95.07 121	96.76 245	93.97 70	97.77 38	98.57 28	95.72 24	97.90 305	88.89 263	99.23 95	99.08 58

UGNet		93.08 214	92.50 241	94.79 131	93.87 392	87.99 168	95.07 121	94.26 360	90.64 174	87.33 478	97.67 86	86.89 282	98.49 224	88.10 292	98.71 198	97.91 235
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
tttt0517		89.81 339	88.90 350	92.55 258	97.00 178	79.73 365	95.03 123	83.65 509	89.88 197	95.30 203	94.79 328	53.64 520	99.39 54	91.99 145	98.79 179	98.54 149

LFMVS		91.33 284	91.16 287	91.82 296	96.27 261	79.36 380	95.01 124	85.61 490	96.04 39	94.82 240	97.06 158	72.03 451	98.46 233	84.96 355	98.70 201	97.65 269

CSCG		94.69 126	94.75 137	94.52 150	97.55 138	87.87 173	95.01 124	97.57 162	92.68 94	96.20 144	93.44 386	91.92 157	98.78 164	89.11 256	99.24 93	96.92 326

GG-mvs-BLEND				83.24 509	85.06 540	71.03 498	94.99 126	65.55 552		74.09 543	75.51 541	44.57 537	94.46 464	59.57 541	87.54 529	84.24 535

EU-MVSNet		87.39 410	86.71 414	89.44 406	93.40 406	76.11 452	94.93 127	90.00 444	57.17 545	95.71 178	97.37 115	64.77 489	97.68 334	92.67 125	94.37 467	94.52 442

KD-MVS_self_test		94.10 167	94.73 140	92.19 277	97.66 131	79.49 375	94.86 128	97.12 208	89.59 205	96.87 94	97.65 88	90.40 208	98.34 248	89.08 257	99.35 67	98.75 115

MTMP							94.82 129	54.62 556

PHI-MVS		94.34 153	93.80 186	95.95 63	95.65 316	91.67 88	94.82 129	97.86 126	87.86 259	93.04 321	94.16 359	91.58 165	98.78 164	90.27 211	98.96 143	97.41 291

gg-mvs-nofinetune		82.10 479	81.02 480	85.34 488	87.46 527	71.04 497	94.74 131	67.56 550	96.44 28	79.43 537	98.99 11	45.24 535	96.15 423	67.18 526	92.17 509	88.85 513

ACMM	88.83 9	96.30 51	96.07 61	96.97 37	98.39 69	92.95 61	94.74 131	98.03 99	90.82 168	97.15 78	96.85 176	96.25 18	99.00 125	93.10 109	99.33 73	98.95 80
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
usedtu_dtu_shiyan2		93.15 213	92.40 245	95.41 95	98.56 49	90.53 111	94.71 133	94.14 363	92.10 115	93.73 282	96.94 167	89.66 225	97.77 324	72.97 499	98.81 172	97.92 233

NormalMVS		94.10 167	93.36 207	96.31 55	99.01 15	90.84 104	94.70 134	97.90 118	90.98 162	93.22 309	95.73 273	78.94 369	99.12 105	90.38 201	99.42 54	98.97 73

SymmetryMVS		93.26 204	92.36 247	95.97 61	97.13 167	90.84 104	94.70 134	91.61 430	90.98 162	93.22 309	95.73 273	78.94 369	99.12 105	90.38 201	98.53 222	97.97 221

SD-MVS		95.19 103	95.73 84	93.55 197	96.62 218	88.88 144	94.67 136	98.05 92	91.26 156	97.25 74	96.40 215	95.42 34	94.36 467	92.72 124	99.19 102	97.40 295
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
API-MVS		91.52 279	91.61 271	91.26 331	94.16 380	86.26 216	94.66 137	94.82 340	91.17 159	92.13 363	91.08 458	90.03 220	97.06 387	79.09 434	97.35 344	90.45 506

v10		94.68 127	95.27 111	92.90 234	96.57 222	80.15 345	94.65 138	97.57 162	90.68 173	97.43 58	98.00 55	88.18 248	99.15 99	94.84 51	99.55 37	99.41 26

v8		94.65 128	95.29 109	92.74 244	96.65 209	79.77 362	94.59 139	97.17 202	91.86 124	97.47 57	97.93 62	88.16 249	99.08 111	94.32 60	99.47 44	99.38 28

APD-MVS		95.00 110	94.69 141	95.93 66	97.38 149	90.88 102	94.59 139	97.81 135	89.22 214	95.46 194	96.17 243	93.42 110	99.34 70	89.30 244	98.87 162	97.56 279
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
VPA-MVSNet		95.14 105	95.67 87	93.58 196	97.76 119	83.15 283	94.58 141	97.58 161	93.39 84	97.05 86	98.04 52	93.25 115	98.51 222	89.75 232	99.59 29	99.08 58

ACMMP_NAP		96.21 53	96.12 57	96.49 51	98.90 22	91.42 92	94.57 142	98.03 99	90.42 184	96.37 127	97.35 123	95.68 25	99.25 89	94.44 58	99.34 71	98.80 104

HQP_MVS		94.26 156	93.93 182	95.23 107	97.71 125	88.12 164	94.56 143	97.81 135	91.74 136	93.31 299	95.59 280	86.93 280	98.95 135	89.26 248	98.51 227	98.60 144

plane_prior2							94.56 143		91.74 136

tfpnnormal		94.27 155	94.87 131	92.48 265	97.71 125	80.88 334	94.55 145	95.41 318	93.70 77	96.67 109	97.72 81	91.40 175	98.18 267	87.45 306	99.18 106	98.36 171

XVG-ACMP-BASELINE		95.68 75	95.34 105	96.69 44	98.40 68	93.04 58	94.54 146	98.05 92	90.45 183	96.31 133	96.76 184	92.91 131	98.72 174	91.19 172	99.42 54	98.32 176

DP-MVS		95.62 76	95.84 78	94.97 118	97.16 163	88.62 148	94.54 146	97.64 152	96.94 19	96.58 116	97.32 127	93.07 125	98.72 174	90.45 198	98.84 164	97.57 277

MIMVSNet		87.13 419	86.54 419	88.89 425	96.05 284	76.11 452	94.39 148	88.51 454	81.37 418	88.27 461	96.75 186	72.38 445	95.52 437	65.71 531	95.47 426	95.03 423

K. test v3		93.37 198	93.27 211	93.66 191	98.05 94	82.62 300	94.35 149	86.62 475	96.05 38	97.51 54	98.85 17	76.59 419	99.65 4	93.21 105	98.20 271	98.73 120

Vis-MVSNet (Re-imp)		90.42 311	90.16 320	91.20 336	97.66 131	77.32 427	94.33 150	87.66 467	91.20 158	92.99 322	95.13 308	75.40 425	98.28 251	77.86 441	99.19 102	97.99 216

ANet_high		94.83 118	96.28 48	90.47 373	96.65 209	73.16 482	94.33 150	98.74 13	96.39 30	98.09 33	98.93 13	93.37 111	98.70 182	90.38 201	99.68 20	99.53 17

MM		94.41 147	94.14 173	95.22 109	95.84 300	87.21 185	94.31 152	90.92 437	94.48 58	92.80 330	97.52 100	85.27 304	99.49 29	96.58 17	99.57 35	98.97 73

ALIKED-LG		89.78 341	88.57 358	93.39 209	93.97 387	95.11 11	94.30 153	95.57 311	79.81 432	93.27 303	94.93 318	72.44 443	92.52 485	75.11 475	97.77 312	92.53 487

SD_0403		88.79 368	88.88 351	88.51 436	95.89 298	72.58 490	94.27 154	95.24 325	83.77 375	87.92 468	94.38 351	87.70 259	96.47 414	66.36 529	94.40 464	96.49 351

test_fmvsmconf0.01_n		95.90 65	96.09 58	95.31 102	97.30 155	89.21 133	94.24 155	98.76 12	86.25 304	97.56 49	98.66 23	95.73 23	98.44 236	97.35 3	98.99 133	98.27 183

ACMP	88.15 13	95.71 74	95.43 99	96.54 48	98.17 86	91.73 86	94.24 155	98.08 83	89.46 207	96.61 114	96.47 208	95.85 22	99.12 105	90.45 198	99.56 36	98.77 114
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
MAR-MVS		90.32 320	88.87 352	94.66 141	94.82 354	91.85 82	94.22 157	94.75 345	80.91 423	87.52 476	88.07 495	86.63 287	97.87 312	76.67 456	96.21 403	94.25 450
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
aaEdge-Enhanced		95.61 77	95.65 88	95.49 91	97.62 133	88.21 161	94.21 158	97.87 125	92.48 99	96.38 125	96.22 235	94.06 94	99.32 77	92.89 117	99.10 115	98.96 77

FMVSNet2		92.78 230	92.73 230	92.95 229	95.40 332	81.98 310	94.18 159	95.53 313	88.63 229	96.05 152	97.37 115	81.31 347	98.81 156	87.38 309	98.67 205	98.06 204

fmvsm_s_conf0.1_n_a		94.26 156	94.37 160	93.95 176	97.36 151	85.72 235	94.15 160	95.44 315	83.25 381	95.51 189	98.05 50	92.54 140	97.19 377	95.55 36	97.46 338	98.94 81

Anonymous20240521		92.86 227	93.57 199	90.74 363	96.57 222	75.50 460	94.15 160	95.60 304	89.38 209	95.90 161	97.90 72	80.39 356	97.96 302	92.60 128	99.68 20	98.75 115

GeoE		94.55 135	94.68 145	94.15 164	97.23 158	85.11 246	94.14 162	97.34 187	88.71 228	95.26 210	95.50 286	94.65 76	99.12 105	90.94 183	98.40 238	98.23 186

9.14			94.81 132		97.49 141		94.11 163	98.37 34	87.56 270	95.38 197	96.03 252	94.66 75	99.08 111	90.70 190	98.97 141

BP-MVS1		91.77 270	91.10 289	93.75 186	96.42 239	83.40 273	94.10 164	91.89 424	91.27 155	93.36 297	94.85 323	64.43 490	99.29 81	94.88 49	98.74 190	98.56 148

HPM-MVS++		95.02 109	94.39 158	96.91 40	97.88 111	93.58 50	94.09 165	96.99 217	91.05 161	92.40 347	95.22 304	91.03 190	99.25 89	92.11 139	98.69 202	97.90 236

usedtu_blend_shiyan5		89.08 356	88.33 364	91.34 325	91.29 471	79.59 368	94.02 166	97.13 206	90.07 193	90.09 415	83.30 528	72.25 446	98.10 280	81.45 402	95.32 434	96.33 360

HY-MVS	82.50 18	86.81 427	85.93 429	89.47 404	93.63 398	77.93 412	94.02 166	91.58 431	75.68 471	83.64 512	93.64 379	77.40 399	97.42 356	71.70 507	92.07 510	93.05 478

Effi-MVS+-dtu		93.90 178	92.60 238	97.77 3	94.74 362	96.67 5	94.00 168	95.41 318	89.94 195	91.93 368	92.13 435	90.12 215	98.97 132	87.68 303	97.48 336	97.67 268

Effi-MVS+		92.79 229	92.74 228	92.94 231	95.10 347	83.30 277	94.00 168	97.53 168	91.36 154	89.35 436	90.65 468	94.01 96	98.66 188	87.40 308	95.30 438	96.88 331

VDD-MVS		94.37 150	94.37 160	94.40 157	97.49 141	86.07 223	93.97 170	93.28 391	94.49 57	96.24 140	97.78 75	87.99 255	98.79 160	88.92 261	99.14 112	98.34 175

Casviewmamba		95.48 85	95.97 67	94.04 170	96.94 181	84.57 252	93.96 171	98.29 44	93.94 71	96.76 104	97.14 148	95.27 43	98.72 174	92.37 136	99.02 130	98.82 99

SSM_0404		94.38 148	94.69 141	93.43 207	97.16 163	83.23 279	93.95 172	97.84 130	91.46 149	95.70 179	96.56 204	92.50 144	99.08 111	88.83 264	98.23 264	97.98 217

h-mvs33		92.89 222	91.99 260	95.58 86	96.97 179	90.55 110	93.94 173	94.01 369	89.23 212	93.95 272	96.19 239	76.88 414	99.14 101	91.02 180	95.71 418	97.04 318

KinetiMVS		95.09 107	95.40 101	94.15 164	97.42 148	84.35 256	93.91 174	96.69 250	94.41 60	96.67 109	97.25 134	87.67 260	99.14 101	95.78 29	98.81 172	98.97 73

EPNet		89.80 340	88.25 370	94.45 155	83.91 542	86.18 220	93.87 175	87.07 473	91.16 160	80.64 534	94.72 330	78.83 371	98.89 141	85.17 345	98.89 157	98.28 181
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
test_fmvs3		92.42 247	92.40 245	92.46 267	93.80 396	87.28 183	93.86 176	97.05 212	76.86 465	96.25 139	98.66 23	82.87 328	91.26 496	95.44 39	96.83 374	98.82 99

DeepC-MVS	91.39 4	95.43 87	95.33 107	95.71 78	97.67 130	90.17 117	93.86 176	98.02 101	87.35 273	96.22 142	97.99 58	94.48 85	99.05 118	92.73 123	99.68 20	97.93 228
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
RoMa-HiRes		94.64 129	94.29 165	95.68 81	97.47 144	93.88 37	93.83 178	96.23 281	88.05 253	97.75 39	96.20 238	88.58 240	94.93 458	91.33 169	99.17 109	98.22 188

LCM-MVSNet-Re		94.20 163	94.58 150	93.04 224	95.91 295	83.13 285	93.79 179	99.19 5	92.00 117	98.84 8	98.04 52	93.64 101	99.02 123	81.28 405	98.54 221	96.96 324

TranMVSNet+NR-MVSNet		96.07 58	96.26 49	95.50 90	98.26 80	87.69 177	93.75 180	97.86 126	95.96 41	97.48 56	97.14 148	95.33 40	99.44 33	90.79 187	99.76 10	99.38 28

fmvsm_s_conf0.1_n		94.19 165	94.41 157	93.52 203	97.22 160	84.37 254	93.73 181	95.26 324	84.45 360	95.76 170	98.00 55	91.85 158	97.21 374	95.62 31	97.82 310	98.98 70

PAPM_NR		91.03 292	90.81 299	91.68 305	96.73 202	81.10 329	93.72 182	96.35 275	88.19 249	88.77 451	92.12 436	85.09 307	97.25 370	82.40 390	93.90 480	96.68 340

baseline		94.26 156	94.80 133	92.64 249	96.08 281	80.99 331	93.69 183	98.04 98	90.80 169	94.89 238	96.32 225	93.19 118	98.48 229	91.68 158	98.51 227	98.43 160

dcpmvs_2		93.96 175	95.01 126	90.82 359	97.60 134	74.04 476	93.68 184	98.85 9	89.80 199	97.82 36	97.01 163	91.14 186	99.21 92	90.56 193	98.59 214	99.19 45

SSM_0407		94.23 161	94.56 152	93.24 218	96.65 209	82.79 294	93.66 185	97.84 130	91.46 149	95.19 218	96.56 204	92.50 144	98.99 126	88.83 264	98.32 252	97.93 228

GDP-MVS		91.56 277	90.83 298	93.77 185	96.34 251	83.65 269	93.66 185	98.12 76	87.32 275	92.98 324	94.71 331	63.58 496	99.30 80	92.61 127	98.14 276	98.35 174

fmvsm_s_conf0.5_n_a		94.02 172	94.08 176	93.84 182	96.72 204	85.73 234	93.65 187	95.23 326	83.30 379	95.13 223	97.56 95	92.22 150	97.17 378	95.51 37	97.41 341	98.64 138

FE-MVSNET2		94.07 170	94.47 156	92.90 234	97.45 147	81.26 325	93.58 188	97.54 165	88.28 245	96.46 120	97.92 67	91.41 174	98.74 171	88.12 291	99.44 51	98.69 128

casdiffseed414692147		94.56 134	94.90 128	93.54 199	96.60 219	83.33 275	93.57 189	98.06 90	91.57 142	95.26 210	97.31 128	94.06 94	98.39 237	88.67 271	98.95 145	98.91 89

fmvsm_s_conf0.5_n_11		94.91 113	95.44 98	93.33 212	96.45 235	83.11 286	93.56 190	98.64 14	89.76 200	95.70 179	97.97 59	92.32 146	98.08 282	95.62 31	98.95 145	98.79 106

F-COLMAP		92.28 254	91.06 290	95.95 63	97.52 139	91.90 81	93.53 191	97.18 201	83.98 370	88.70 453	94.04 362	88.41 245	98.55 213	80.17 417	95.99 409	97.39 296

test_fmvsmconf0.1_n		95.61 77	95.72 85	95.26 104	96.85 190	89.20 134	93.51 192	98.60 16	85.68 325	97.42 61	98.30 40	95.34 39	98.39 237	96.85 11	98.98 135	98.19 193

FMVSNet5		87.82 395	86.56 418	91.62 307	92.31 434	79.81 360	93.49 193	94.81 342	83.26 380	91.36 380	96.93 169	52.77 523	97.49 350	76.07 464	98.03 290	97.55 280

DPE-MVS		95.89 66	95.88 75	95.92 68	97.93 108	89.83 121	93.46 194	98.30 41	92.37 102	97.75 39	96.95 166	95.14 49	99.51 20	91.74 154	99.28 88	98.41 164
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
alignmvs		93.26 204	92.85 224	94.50 151	95.70 311	87.45 180	93.45 195	95.76 299	91.58 141	95.25 213	92.42 425	81.96 342	98.72 174	91.61 159	97.87 308	97.33 300

test_fmvsmvis_n_1920		95.08 108	95.40 101	94.13 167	96.66 208	87.75 176	93.44 196	98.49 23	85.57 329	98.27 23	97.11 152	94.11 93	97.75 328	96.26 20	98.72 196	96.89 329

114514_t		90.51 308	89.80 330	92.63 252	98.00 102	82.24 307	93.40 197	97.29 193	65.84 534	89.40 435	94.80 327	86.99 278	98.75 168	83.88 372	98.61 211	96.89 329

fmvsm_s_conf0.5_n		94.00 173	94.20 171	93.42 208	96.69 206	84.37 254	93.38 198	95.13 330	84.50 359	95.40 196	97.55 99	91.77 160	97.20 375	95.59 33	97.79 311	98.69 128

LuminaMVS		93.43 196	93.18 213	94.16 163	97.32 154	85.29 244	93.36 199	93.94 371	88.09 252	97.12 81	96.43 211	80.11 357	98.98 127	93.53 85	98.76 184	98.21 189

DeepC-MVS_fast	89.96 7	93.73 182	93.44 204	94.60 145	96.14 275	87.90 172	93.36 199	97.14 204	85.53 330	93.90 275	95.45 289	91.30 178	98.59 201	89.51 238	98.62 210	97.31 301
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
hybridcas		94.81 120	95.45 95	92.88 236	96.74 201	81.36 323	93.32 201	98.13 73	92.16 113	96.79 102	96.98 165	94.91 65	98.53 218	91.16 173	98.90 154	98.75 115

MP-MVS-pluss		96.08 57	95.92 72	96.57 47	99.06 10	91.21 94	93.25 202	98.32 38	87.89 258	96.86 95	97.38 114	95.55 30	99.39 54	95.47 38	99.47 44	99.11 54
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
test_0402		95.73 73	96.22 51	94.26 161	98.19 85	85.77 233	93.24 203	97.24 198	96.88 20	97.69 42	97.77 79	94.12 92	99.13 104	91.54 164	99.29 83	97.88 239

test_fmvsmconf_n		95.43 87	95.50 93	95.22 109	96.48 234	89.19 135	93.23 204	98.36 35	85.61 328	96.92 93	98.02 54	95.23 46	98.38 241	96.69 14	98.95 145	98.09 203

test_fmvsm_n_1920		94.72 123	94.74 139	94.67 139	96.30 257	88.62 148	93.19 205	98.07 86	85.63 327	97.08 82	97.35 123	90.86 193	97.66 335	95.70 30	98.48 230	97.74 263

sd_testset		93.94 176	94.39 158	92.61 255	97.93 108	83.24 278	93.17 206	95.04 332	93.65 81	95.51 189	98.63 25	94.49 84	95.89 432	81.72 398	99.35 67	98.70 125

fmvsm_s_conf0.5_n_3		95.20 102	95.95 68	92.94 231	96.60 219	82.18 308	93.13 207	98.39 32	91.44 151	97.16 77	97.68 84	93.03 128	97.82 316	97.54 2	98.63 208	98.81 102

MSLP-MVS++		93.25 207	93.88 183	91.37 323	96.34 251	82.81 293	93.11 208	97.74 143	89.37 210	94.08 265	95.29 302	90.40 208	96.35 420	90.35 205	98.25 261	94.96 426

baseline1		87.62 401	87.31 392	88.54 434	94.71 365	74.27 472	93.10 209	88.20 459	86.20 306	92.18 360	93.04 395	73.21 438	95.52 437	79.32 430	85.82 533	95.83 392

fmvsm_l_conf0.5_n_3		95.19 103	95.36 103	94.68 137	96.79 197	87.49 179	93.05 210	98.38 33	87.21 278	96.59 115	97.76 80	94.20 90	98.11 277	95.90 26	98.40 238	98.42 161

RoMa-SfM		93.45 194	92.92 223	95.03 115	96.77 199	94.01 31	93.01 211	95.19 328	83.99 369	97.28 71	95.33 300	87.17 272	93.66 474	88.55 278	99.00 132	97.42 290

plane_prior						88.12 164	93.01 211		88.98 219						98.06 287

fmvsm_s_conf0.5_n_10		94.63 130	95.11 121	93.18 221	96.28 258	83.51 271	93.00 213	98.25 46	88.37 243	97.43 58	97.70 82	88.90 232	98.63 194	97.15 5	98.90 154	97.41 291

guyue		92.60 239	92.62 236	92.52 264	96.73 202	81.00 330	93.00 213	91.83 426	88.28 245	96.38 125	96.23 234	80.71 353	98.37 245	92.06 144	98.37 248	98.20 191

thres100view900		87.35 411	86.89 408	88.72 429	96.14 275	73.09 483	93.00 213	85.31 494	92.13 114	93.26 305	90.96 461	63.42 497	98.28 251	71.27 510	96.54 388	94.79 435

E5new		94.50 138	95.15 114	92.55 258	97.04 172	80.27 341	92.96 216	98.25 46	90.18 188	95.77 167	97.45 108	94.85 69	98.59 201	91.16 173	98.73 192	98.79 106

E6new		94.50 138	95.15 114	92.55 258	97.04 172	80.28 339	92.96 216	98.25 46	90.18 188	95.76 170	97.45 108	94.86 67	98.59 201	91.16 173	98.73 192	98.79 106

E6		94.50 138	95.15 114	92.55 258	97.04 172	80.28 339	92.96 216	98.25 46	90.18 188	95.76 170	97.45 108	94.86 67	98.59 201	91.16 173	98.73 192	98.79 106

E5		94.50 138	95.15 114	92.55 258	97.04 172	80.27 341	92.96 216	98.25 46	90.18 188	95.77 167	97.45 108	94.85 69	98.59 201	91.16 173	98.73 192	98.79 106

fmvsm_s_conf0.5_n_9		95.58 80	95.91 73	94.59 146	97.25 156	86.26 216	92.96 216	97.86 126	91.88 123	97.52 53	98.13 45	91.45 173	98.54 214	97.17 4	98.99 133	98.98 70

Patchmtry		90.11 327	89.92 327	90.66 367	90.35 498	77.00 433	92.96 216	92.81 399	90.25 187	94.74 244	96.93 169	67.11 472	97.52 346	85.17 345	98.98 135	97.46 286

LF4IMVS		92.72 233	92.02 259	94.84 128	95.65 316	91.99 79	92.92 222	96.60 258	85.08 346	92.44 345	93.62 381	86.80 283	96.35 420	86.81 315	98.25 261	96.18 373

UniMVSNet (Re)		95.32 94	95.15 114	95.80 74	97.79 118	88.91 141	92.91 223	98.07 86	93.46 83	96.31 133	95.97 258	90.14 214	99.34 70	92.11 139	99.64 25	99.16 47

TAPA-MVS	88.58 10	92.49 245	91.75 269	94.73 133	96.50 231	89.69 122	92.91 223	97.68 148	78.02 456	92.79 331	94.10 360	90.85 194	97.96 302	84.76 358	98.16 273	96.54 343
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
AstraMVS		92.75 232	92.73 230	92.79 242	97.02 176	81.48 321	92.88 225	90.62 441	87.99 255	96.48 118	96.71 191	82.02 340	98.48 229	92.44 133	98.46 232	98.40 168

MGCNet		92.88 223	92.27 250	94.69 136	92.35 433	86.03 224	92.88 225	89.68 445	90.53 180	91.52 377	96.43 211	82.52 335	99.32 77	95.01 48	99.54 38	98.71 124

ALIKED-MNN		88.42 377	87.16 399	92.21 275	93.47 402	93.93 35	92.87 227	95.20 327	71.10 509	87.62 473	93.76 376	77.41 398	91.34 495	74.50 483	98.53 222	91.36 496

fmvsm_s_conf0.5_n_5		94.50 138	94.80 133	93.60 194	96.80 195	84.93 248	92.81 228	97.59 160	85.27 337	96.85 98	97.29 129	91.48 172	98.05 289	96.67 15	98.47 231	97.83 247

thisisatest0530		88.69 372	87.52 385	92.20 276	96.33 253	79.36 380	92.81 228	84.01 506	86.44 298	93.67 284	92.68 413	53.62 521	99.25 89	89.65 237	98.45 233	98.00 213

EIA-MVS		92.35 251	92.03 258	93.30 215	95.81 304	83.97 265	92.80 230	98.17 67	87.71 264	89.79 427	87.56 497	91.17 185	99.18 97	87.97 298	97.27 347	96.77 337

IMVS_0407		92.28 254	92.83 225	90.63 369	95.19 342	76.72 441	92.79 231	96.89 227	85.92 315	93.55 288	94.50 342	91.06 187	98.07 286	88.49 280	97.07 357	97.10 310

thres600view7		87.66 399	87.10 404	89.36 410	96.05 284	73.17 481	92.72 232	85.31 494	91.89 122	93.29 301	90.97 460	63.42 497	98.39 237	73.23 496	96.99 368	96.51 347

wuyk23d		87.83 394	90.79 301	78.96 524	90.46 496	88.63 147	92.72 232	90.67 440	91.65 140	98.68 14	97.64 89	96.06 19	77.53 547	59.84 540	99.41 60	70.73 545

fmvsm_s_conf0.5_n_6		94.14 166	94.54 153	92.95 229	96.51 230	82.74 298	92.71 234	98.13 73	86.56 294	96.44 121	96.85 176	88.51 241	98.05 289	96.03 23	99.09 117	98.06 204

test_fmvs2		90.62 307	90.40 315	91.29 329	91.93 451	85.46 241	92.70 235	96.48 268	74.44 482	94.91 237	97.59 92	75.52 424	90.57 499	93.44 93	96.56 387	97.84 246

V42		93.43 196	93.58 198	92.97 227	95.34 336	81.22 327	92.67 236	96.49 267	87.25 276	96.20 144	96.37 222	87.32 268	98.85 148	92.39 135	98.21 269	98.85 97

casdiffmvs_mvg		95.10 106	95.62 89	93.53 201	96.25 264	83.23 279	92.66 237	98.19 61	93.06 90	97.49 55	97.15 147	94.78 72	98.71 181	92.27 137	98.72 196	98.65 132
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
OPM-MVS		95.61 77	95.45 95	96.08 58	98.49 65	91.00 98	92.65 238	97.33 188	90.05 194	96.77 103	96.85 176	95.04 56	98.56 211	92.77 120	99.06 119	98.70 125
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
fmvsm_s_conf0.5_n_8		94.70 125	95.34 105	92.78 243	96.77 199	81.50 320	92.64 239	98.50 21	91.51 148	97.22 75	97.93 62	88.07 251	98.45 234	96.62 16	98.80 176	98.39 169

test_vis1_n		89.01 361	89.01 346	89.03 418	92.57 426	82.46 303	92.62 240	96.06 290	73.02 495	90.40 408	95.77 271	74.86 428	89.68 506	90.78 188	94.98 449	94.95 427

fmvsm_s_conf0.5_n_4		94.26 156	94.58 150	93.31 213	96.40 241	82.73 299	92.59 241	97.41 178	86.60 292	96.33 130	97.07 156	89.91 221	98.07 286	96.88 10	98.01 293	99.13 50

DU-MVS		95.28 98	95.12 120	95.75 76	97.75 120	88.59 151	92.58 242	97.81 135	93.99 68	96.80 100	95.90 259	90.10 217	99.41 43	91.60 160	99.58 33	99.26 37

FMVSNet3		90.78 297	90.32 318	92.16 281	93.03 417	79.92 356	92.54 243	94.95 335	86.17 309	95.10 226	96.01 254	69.97 461	98.75 168	86.74 316	98.38 243	97.82 250

hse-mvs2		92.24 258	91.20 284	95.38 96	96.16 272	90.65 109	92.52 244	92.01 423	89.23 212	93.95 272	92.99 397	76.88 414	98.69 184	91.02 180	96.03 406	96.81 334

MVS_Test		92.57 243	93.29 208	90.40 376	93.53 401	75.85 455	92.52 244	96.96 219	88.73 226	92.35 351	96.70 192	90.77 196	98.37 245	92.53 130	95.49 425	96.99 320

CR-MVSNet		87.89 392	87.12 402	90.22 381	91.01 480	78.93 389	92.52 244	92.81 399	73.08 494	89.10 440	96.93 169	67.11 472	97.64 338	88.80 267	92.70 503	94.08 453

RPMNet		90.31 321	90.14 323	90.81 360	91.01 480	78.93 389	92.52 244	98.12 76	91.91 121	89.10 440	96.89 172	68.84 465	99.41 43	90.17 218	92.70 503	94.08 453

fmvsm_l_conf0.5_n		93.79 180	93.81 184	93.73 188	96.16 272	86.26 216	92.46 248	96.72 247	81.69 411	95.77 167	97.11 152	90.83 195	97.82 316	95.58 34	97.99 297	97.11 309

XVG-OURS-SEG-HR		95.38 91	95.00 127	96.51 49	98.10 90	94.07 24	92.46 248	98.13 73	90.69 172	93.75 279	96.25 233	98.03 2	97.02 388	92.08 141	95.55 423	98.45 158

EI-MVSNet-Vis-set		94.36 151	94.28 167	94.61 142	92.55 427	85.98 226	92.44 250	94.69 347	93.70 77	96.12 149	95.81 265	91.24 179	98.86 146	93.76 79	98.22 268	98.98 70

Anonymous202405211		92.58 241	92.50 241	92.83 239	96.55 224	83.22 281	92.43 251	91.64 429	94.10 65	95.59 186	96.64 195	81.88 344	97.50 347	85.12 350	98.52 225	97.77 257

AUN-MVS		90.05 332	88.30 366	95.32 101	96.09 280	90.52 112	92.42 252	92.05 422	82.08 404	88.45 458	92.86 404	65.76 482	98.69 184	88.91 262	96.07 405	96.75 339

EI-MVSNet-UG-set		94.35 152	94.27 169	94.59 146	92.46 430	85.87 231	92.42 252	94.69 347	93.67 80	96.13 148	95.84 263	91.20 182	98.86 146	93.78 76	98.23 264	99.03 62

IMVS_0403		92.20 259	92.70 233	90.69 365	95.19 342	76.72 441	92.39 254	96.89 227	85.92 315	93.66 285	94.50 342	90.18 212	98.24 259	88.49 280	97.07 357	97.10 310

NCCC		94.08 169	93.54 201	95.70 80	96.49 232	89.90 120	92.39 254	96.91 226	90.64 174	92.33 354	94.60 337	90.58 204	98.96 133	90.21 215	97.70 321	98.23 186

viewdifsd2359ckpt09		92.60 239	92.34 249	93.36 210	95.94 294	83.36 274	92.35 256	97.93 117	83.17 385	92.92 327	94.66 334	89.87 222	98.57 207	86.51 326	97.71 320	98.15 198

casdiffmvs		94.32 154	94.80 133	92.85 238	96.05 284	81.44 322	92.35 256	98.05 92	91.53 145	95.75 174	96.80 180	93.35 112	98.49 224	91.01 182	98.32 252	98.64 138
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
ETV-MVS		92.99 218	92.74 228	93.72 189	95.86 299	86.30 215	92.33 258	97.84 130	91.70 139	92.81 329	86.17 509	92.22 150	99.19 96	88.03 297	97.73 316	95.66 401

fmvsm_l_conf0.5_n_a		93.59 188	93.63 195	93.49 205	96.10 279	85.66 237	92.32 259	96.57 261	81.32 419	95.63 184	97.14 148	90.19 211	97.73 331	95.37 44	98.03 290	97.07 314

EI-MVSNet		92.99 218	93.26 212	92.19 277	92.12 444	79.21 386	92.32 259	94.67 349	91.77 134	95.24 214	95.85 261	87.14 274	98.49 224	91.99 145	98.26 259	98.86 94

CVMVSNet		85.16 443	84.72 441	86.48 474	92.12 444	70.19 501	92.32 259	88.17 460	56.15 546	90.64 403	95.85 261	67.97 470	96.69 405	88.78 268	90.52 520	92.56 485

VortexMVS		92.13 261	92.56 239	90.85 356	94.54 370	76.17 451	92.30 262	96.63 257	86.20 306	96.66 111	96.79 181	79.87 360	98.16 271	91.27 171	98.76 184	98.24 185

test_fmvs1_n		88.73 371	88.38 363	89.76 397	92.06 446	82.53 301	92.30 262	96.59 260	71.14 508	92.58 339	95.41 295	68.55 466	89.57 508	91.12 178	95.66 420	97.18 308

OMC-MVS		94.22 162	93.69 193	95.81 73	97.25 156	91.27 93	92.27 264	97.40 179	87.10 286	94.56 250	95.42 292	93.74 99	98.11 277	86.62 321	98.85 163	98.06 204

PM-MVS		93.33 201	92.67 235	95.33 99	96.58 221	94.06 25	92.26 265	92.18 415	85.92 315	96.22 142	96.61 199	85.64 301	95.99 430	90.35 205	98.23 264	95.93 386

UniMVSNet_NR-MVSNet		95.35 92	95.21 112	95.76 75	97.69 128	88.59 151	92.26 265	97.84 130	94.91 52	96.80 100	95.78 270	90.42 206	99.41 43	91.60 160	99.58 33	99.29 36

AdaColmap		91.63 275	91.36 279	92.47 266	95.56 324	86.36 213	92.24 267	96.27 278	88.88 223	89.90 424	92.69 412	91.65 163	98.32 249	77.38 448	97.64 325	92.72 484

PRO-TEST		90.68 301	90.65 306	90.79 361	93.47 402	76.93 437	92.17 268	96.97 218	84.00 368	89.28 437	92.10 437	86.75 285	98.48 229	85.17 345	95.93 410	96.95 325

PVSNet_Blended_VisFu		91.63 275	91.20 284	92.94 231	97.73 123	83.95 266	92.14 269	97.46 175	78.85 451	92.35 351	94.98 315	84.16 313	99.08 111	86.36 329	96.77 377	95.79 394

Baseline_NR-MVSNet		94.47 144	95.09 124	92.60 256	98.50 64	80.82 335	92.08 270	96.68 253	93.82 75	96.29 136	98.56 29	90.10 217	97.75 328	90.10 222	99.66 23	99.24 41

Fast-Effi-MVS+-dtu		92.77 231	92.16 253	94.58 149	94.66 367	88.25 159	92.05 271	96.65 255	89.62 204	90.08 419	91.23 454	92.56 139	98.60 199	86.30 330	96.27 398	96.90 327

save fliter					97.46 145	88.05 167	92.04 272	97.08 210	87.63 267

PatchT		87.51 406	88.17 375	85.55 486	90.64 488	66.91 516	92.02 273	86.09 481	92.20 110	89.05 444	97.16 144	64.15 492	96.37 419	89.21 251	92.98 501	93.37 473

EG-PatchMatch MVS		94.54 136	94.67 146	94.14 166	97.87 113	86.50 206	92.00 274	96.74 246	88.16 251	96.93 92	97.61 91	93.04 127	97.90 305	91.60 160	98.12 278	98.03 211

fmvsm_s_conf0.1_n_2		94.38 148	94.78 136	93.19 220	97.07 171	81.72 315	91.97 275	97.51 171	87.05 287	97.31 67	97.92 67	88.29 246	98.15 273	97.10 6	98.81 172	99.70 5

v144192		93.20 211	93.54 201	92.16 281	96.05 284	78.26 408	91.95 276	97.14 204	84.98 350	95.96 156	96.11 248	87.08 276	99.04 121	93.79 75	98.84 164	99.17 46

VNet		92.67 236	92.96 219	91.79 297	96.27 261	80.15 345	91.95 276	94.98 334	92.19 111	94.52 252	96.07 250	87.43 266	97.39 359	84.83 356	98.38 243	97.83 247

1314		86.46 432	86.33 426	86.87 469	91.65 462	74.54 467	91.94 278	94.10 364	74.28 485	84.78 499	87.33 502	83.03 326	95.00 453	78.72 436	91.16 517	91.06 500

MVS		84.98 445	84.30 446	87.01 464	91.03 479	77.69 421	91.94 278	94.16 362	59.36 544	84.23 505	87.50 500	85.66 299	96.80 402	71.79 505	93.05 500	86.54 532

SDMVSNet		94.43 146	95.02 125	92.69 247	97.93 108	82.88 291	91.92 280	95.99 295	93.65 81	95.51 189	98.63 25	94.60 78	96.48 412	87.57 304	99.35 67	98.70 125

fmvsm_l_conf0.5_n_9		94.51 137	95.11 121	92.72 245	96.70 205	83.14 284	91.91 281	97.89 122	88.44 239	97.30 68	97.57 93	91.60 164	97.54 344	95.82 28	98.74 190	97.47 285

tfpn200view9		87.05 421	86.52 420	88.67 430	95.77 307	72.94 485	91.89 282	86.00 482	90.84 166	92.61 337	89.80 472	63.93 493	98.28 251	71.27 510	96.54 388	94.79 435

thres400		87.20 416	86.52 420	89.24 416	95.77 307	72.94 485	91.89 282	86.00 482	90.84 166	92.61 337	89.80 472	63.93 493	98.28 251	71.27 510	96.54 388	96.51 347

v1921920		93.26 204	93.61 197	92.19 277	96.04 288	78.31 407	91.88 284	97.24 198	85.17 341	96.19 147	96.19 239	86.76 284	99.05 118	94.18 64	98.84 164	99.22 42

XXY-MVS		92.58 241	93.16 215	90.84 357	97.75 120	79.84 357	91.87 285	96.22 284	85.94 314	95.53 188	97.68 84	92.69 137	94.48 463	83.21 377	97.51 333	98.21 189

IterMVS-LS		93.78 181	94.28 167	92.27 270	96.27 261	79.21 386	91.87 285	96.78 241	91.77 134	96.57 117	97.07 156	87.15 273	98.74 171	91.99 145	99.03 129	98.86 94
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v1144		93.50 191	93.81 184	92.57 257	96.28 258	79.61 367	91.86 287	96.96 219	86.95 289	95.91 160	96.32 225	87.65 262	98.96 133	93.51 86	98.88 159	99.13 50

v1192		93.49 192	93.78 187	92.62 254	96.16 272	79.62 366	91.83 288	97.22 200	86.07 310	96.10 151	96.38 221	87.22 270	99.02 123	94.14 65	98.88 159	99.22 42

v1240		93.29 202	93.71 192	92.06 285	96.01 289	77.89 414	91.81 289	97.37 180	85.12 344	96.69 108	96.40 215	86.67 286	99.07 117	94.51 54	98.76 184	99.22 42

CNVR-MVS		94.58 133	94.29 165	95.46 93	96.94 181	89.35 132	91.81 289	96.80 240	89.66 203	93.90 275	95.44 290	92.80 135	98.72 174	92.74 122	98.52 225	98.32 176

viewdifsd2359ckpt13		92.57 243	92.48 243	92.83 239	95.60 321	82.35 306	91.80 291	97.49 173	85.04 348	93.14 315	95.41 295	90.94 192	98.25 257	86.68 319	96.24 401	97.87 242

v2v482		93.29 202	93.63 195	92.29 269	96.35 250	78.82 395	91.77 292	96.28 277	88.45 238	95.70 179	96.26 232	86.02 295	98.90 139	93.02 112	98.81 172	99.14 49

fmvsm_s_conf0.5_n_2		94.25 160	94.63 148	93.10 223	96.65 209	81.75 314	91.72 293	97.25 196	86.93 291	97.20 76	97.67 86	88.44 244	98.14 276	97.06 9	98.77 182	99.42 24

EPNet_dtu		85.63 439	84.37 445	89.40 409	86.30 533	74.33 471	91.64 294	88.26 457	84.84 353	72.96 545	89.85 470	71.27 455	97.69 333	76.60 457	97.62 326	96.18 373
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
viewmacassd2359aftdt		93.83 179	94.36 162	92.24 273	96.45 235	79.58 371	91.60 295	97.96 109	89.14 216	95.05 230	97.09 155	93.69 100	98.48 229	89.79 229	98.43 235	98.65 132

PLC	85.34 15	90.40 312	88.92 348	94.85 127	96.53 228	90.02 118	91.58 296	96.48 268	80.16 430	86.14 485	92.18 433	85.73 298	98.25 257	76.87 454	94.61 461	96.30 364
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
FE-MVSNET		92.02 265	92.22 252	91.41 320	96.63 217	79.08 388	91.53 297	96.84 237	85.52 333	95.16 221	96.14 244	83.97 315	97.50 347	85.48 341	98.75 188	97.64 270

VPNet		93.08 214	93.76 188	91.03 343	98.60 46	75.83 458	91.51 298	95.62 303	91.84 128	95.74 175	97.10 154	89.31 228	98.32 249	85.07 353	99.06 119	98.93 83

ttmdpeth		86.91 426	86.57 417	87.91 452	89.68 509	74.24 473	91.49 299	87.09 471	79.84 431	89.46 434	97.86 73	65.42 484	91.04 497	81.57 400	96.74 380	98.44 159

XVG-OURS		94.72 123	94.12 174	96.50 50	98.00 102	94.23 22	91.48 300	98.17 67	90.72 171	95.30 203	96.47 208	87.94 256	96.98 389	91.41 167	97.61 327	98.30 180

HQP-NCC					96.36 247		91.37 301		87.16 281	88.81 447

ACMP_Plane					96.36 247		91.37 301		87.16 281	88.81 447

HQP-MVS		92.09 262	91.49 276	93.88 179	96.36 247	84.89 249	91.37 301	97.31 190	87.16 281	88.81 447	93.40 387	84.76 309	98.60 199	86.55 324	97.73 316	98.14 200

MCST-MVS		92.91 221	92.51 240	94.10 168	97.52 139	85.72 235	91.36 304	97.13 206	80.33 429	92.91 328	94.24 354	91.23 180	98.72 174	89.99 224	97.93 304	97.86 243

E4		94.00 173	94.53 154	92.42 268	96.78 198	79.99 353	91.33 305	98.16 70	89.69 201	95.27 208	97.16 144	93.94 98	98.64 192	89.99 224	98.42 237	98.61 143

v148		92.87 225	93.29 208	91.62 307	96.25 264	77.72 420	91.28 306	95.05 331	89.69 201	95.93 159	96.04 251	87.34 267	98.38 241	90.05 223	97.99 297	98.78 111

tpmvs		84.22 453	83.97 452	84.94 492	87.09 530	65.18 526	91.21 307	88.35 455	82.87 391	85.21 491	90.96 461	65.24 487	96.75 403	79.60 429	85.25 534	92.90 481

FBQ-MVS		83.72 460	81.80 471	89.47 404	93.62 399	76.73 440	91.20 308	87.89 465	81.52 415	84.88 498	83.74 524	49.19 526	96.66 407	70.51 517	93.70 484	95.00 425

reproduce_monomvs		87.13 419	86.90 407	87.84 454	90.92 483	68.15 511	91.19 309	93.75 376	85.84 320	94.21 261	95.83 264	42.99 542	97.10 382	89.46 240	97.88 307	98.26 184

viewmanbaseed2359cas		93.08 214	93.43 205	92.01 289	95.69 312	79.29 382	91.15 310	97.70 147	87.45 272	94.18 262	96.12 246	92.31 147	98.37 245	88.58 276	97.73 316	98.38 170

CANet		92.38 249	91.99 260	93.52 203	93.82 395	83.46 272	91.14 311	97.00 215	89.81 198	86.47 482	94.04 362	87.90 257	99.21 92	89.50 239	98.27 258	97.90 236

CNLPA		91.72 273	91.20 284	93.26 217	96.17 271	91.02 96	91.14 311	95.55 312	90.16 192	90.87 397	93.56 384	86.31 291	94.40 466	79.92 423	97.12 355	94.37 447

DP-MVS Recon		92.31 253	91.88 265	93.60 194	97.18 162	86.87 196	91.10 313	97.37 180	84.92 351	92.08 365	94.08 361	88.59 238	98.20 264	83.50 374	98.14 276	95.73 396

OpenMVS_ROB	85.12 16	89.52 345	89.05 344	90.92 352	94.58 369	81.21 328	91.10 313	93.41 390	77.03 464	93.41 293	93.99 366	83.23 322	97.80 319	79.93 421	94.80 456	93.74 464

MVStest1		84.79 447	84.06 451	86.98 465	77.73 553	74.76 463	91.08 315	85.63 487	77.70 457	96.86 95	97.97 59	41.05 549	88.24 519	92.22 138	96.28 397	97.94 225

E2		93.53 189	93.96 179	92.25 271	96.39 242	79.76 363	91.06 316	98.05 92	88.58 234	94.71 247	96.64 195	93.08 123	98.57 207	89.16 252	97.97 299	98.42 161

E3		93.53 189	93.96 179	92.25 271	96.39 242	79.76 363	91.06 316	98.05 92	88.58 234	94.71 247	96.64 195	93.07 125	98.57 207	89.16 252	97.97 299	98.42 161

TSAR-MVS + GP.		93.07 217	92.41 244	95.06 114	95.82 302	90.87 103	90.97 318	92.61 408	88.04 254	94.61 249	93.79 375	88.08 250	97.81 318	89.41 241	98.39 242	96.50 350

DKM		92.97 220	92.35 248	94.81 129	96.53 228	93.72 46	90.94 319	94.88 337	85.21 339	96.42 123	95.18 305	83.11 323	93.06 481	89.66 236	99.24 93	97.64 270

MVP-Stereo		90.07 331	88.92 348	93.54 199	96.31 255	86.49 207	90.93 320	95.59 308	79.80 433	91.48 378	95.59 280	80.79 351	97.39 359	78.57 439	91.19 516	96.76 338
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
DKM-HiRes		92.87 225	91.94 262	95.65 82	97.16 163	93.66 47	90.90 321	94.27 359	87.11 285	95.29 205	95.39 297	77.59 395	95.36 444	90.86 185	98.92 152	97.94 225

viewcassd2359sk11		93.16 212	93.51 203	92.13 283	96.07 282	79.59 368	90.88 322	97.97 107	87.82 260	94.23 259	96.19 239	92.31 147	98.53 218	88.58 276	97.51 333	98.28 181

MVSTER		89.32 349	88.75 353	91.03 343	90.10 503	76.62 446	90.85 323	94.67 349	82.27 401	95.24 214	95.79 266	61.09 506	98.49 224	90.49 197	98.26 259	97.97 221

pmmvs-eth3d		91.54 278	90.73 303	93.99 171	95.76 309	87.86 174	90.83 324	93.98 370	78.23 455	94.02 270	96.22 235	82.62 334	96.83 399	86.57 322	98.33 250	97.29 302

fmvsm_s_conf0.5_n_7		93.61 186	93.94 181	92.63 252	96.11 278	82.76 297	90.81 325	97.55 164	86.57 293	93.14 315	97.69 83	90.17 213	96.83 399	94.46 56	98.93 148	98.31 178

CANet_DTU		89.85 338	89.17 342	91.87 293	92.20 439	80.02 352	90.79 326	95.87 297	86.02 311	82.53 522	91.77 445	80.01 358	98.57 207	85.66 339	97.70 321	97.01 319

E3new		92.83 228	93.10 216	92.04 286	95.78 306	79.45 376	90.76 327	97.90 118	87.23 277	93.79 278	95.70 276	91.55 166	98.49 224	88.17 289	96.99 368	98.16 196

SSC-MVS		90.16 324	92.96 219	81.78 516	97.88 111	48.48 553	90.75 328	87.69 466	96.02 40	96.70 107	97.63 90	85.60 302	97.80 319	85.73 338	98.60 213	99.06 60

test_prior4						89.91 119	90.74 329

LoFTR		90.05 332	89.57 337	91.50 314	93.73 397	91.47 90	90.72 330	89.37 449	81.71 410	97.13 79	96.40 215	74.09 433	92.38 486	84.18 367	98.79 179	90.63 505

TinyColmap		92.00 266	92.76 227	89.71 400	95.62 320	77.02 432	90.72 330	96.17 287	87.70 265	95.26 210	96.29 227	92.54 140	96.45 415	81.77 396	98.77 182	95.66 401

CDPH-MVS		92.67 236	91.83 267	95.18 111	96.94 181	88.46 156	90.70 332	97.07 211	77.38 459	92.34 353	95.08 312	92.67 138	98.88 142	85.74 337	98.57 216	98.20 191

SP-LightGlue		90.98 293	90.67 304	91.92 292	91.04 478	91.02 96	90.68 333	94.22 361	89.56 206	90.35 412	92.90 403	77.08 405	89.38 511	93.92 71	96.27 398	95.35 412

test_vis1_n_1920		89.45 346	89.85 329	88.28 441	93.59 400	76.71 445	90.67 334	97.78 141	79.67 437	90.30 413	96.11 248	76.62 418	92.17 488	90.31 208	93.57 486	95.96 384

DSMNet-mixed		82.21 476	81.56 474	84.16 501	89.57 512	70.00 506	90.65 335	77.66 545	54.99 547	83.30 516	97.57 93	77.89 392	90.50 501	66.86 528	95.54 424	91.97 490

TEST9					96.45 235	89.46 126	90.60 336	96.92 223	79.09 447	90.49 404	94.39 348	91.31 177	98.88 142

train_agg		92.71 234	91.83 267	95.35 97	96.45 235	89.46 126	90.60 336	96.92 223	79.37 441	90.49 404	94.39 348	91.20 182	98.88 142	88.66 272	98.43 235	97.72 264

PatchmatchNet		85.22 442	84.64 442	86.98 465	89.51 513	69.83 507	90.52 338	87.34 470	78.87 450	87.22 479	92.74 410	66.91 474	96.53 409	81.77 396	86.88 531	94.58 441
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
test_8					96.37 244	89.14 136	90.51 339	96.89 227	79.37 441	90.42 406	94.36 352	91.20 182	98.82 151

viewdifsd2359ckpt11		93.36 199	93.99 177	91.48 315	95.50 328	78.39 403	90.47 340	96.69 250	88.59 232	96.03 154	96.88 173	93.48 105	97.63 339	90.20 216	98.07 285	98.41 164

viewmsd2359difaftdt		93.36 199	93.99 177	91.48 315	95.50 328	78.39 403	90.47 340	96.69 250	88.59 232	96.03 154	96.88 173	93.48 105	97.63 339	90.20 216	98.07 285	98.41 164

test_yl		90.11 327	89.73 334	91.26 331	94.09 383	79.82 358	90.44 342	92.65 405	90.90 164	93.19 312	93.30 389	73.90 434	98.03 292	82.23 391	96.87 371	95.93 386

DCV-MVSNet		90.11 327	89.73 334	91.26 331	94.09 383	79.82 358	90.44 342	92.65 405	90.90 164	93.19 312	93.30 389	73.90 434	98.03 292	82.23 391	96.87 371	95.93 386

tpm2		81.46 482	80.35 490	84.80 493	89.90 506	65.14 527	90.44 342	85.36 492	65.82 535	82.05 526	92.44 423	57.94 511	96.69 405	70.71 514	88.49 527	92.56 485

test_fmvs1		87.59 402	87.27 394	88.54 434	88.32 522	81.26 325	90.43 345	95.72 301	70.55 515	91.70 372	94.63 335	68.13 467	89.42 510	90.59 192	95.34 433	94.94 429

test_vis3_rt		90.40 312	90.03 325	91.52 313	92.58 425	88.95 140	90.38 346	97.72 146	73.30 492	97.79 37	97.51 104	77.05 406	87.10 529	89.03 258	94.89 452	98.50 153

SP-DiffGlue		90.34 318	90.20 319	90.76 362	90.52 492	90.29 114	90.37 347	94.02 367	87.19 279	93.85 277	92.55 417	78.24 383	87.50 522	89.68 234	95.41 428	94.49 443

DenseAffine		91.92 267	90.90 293	94.97 118	96.37 244	93.07 56	90.35 348	93.65 379	84.62 357	95.66 183	94.39 348	78.19 385	94.97 457	86.02 334	98.90 154	96.87 332

CostFormer		83.09 468	82.21 468	85.73 483	89.27 516	67.01 515	90.35 348	86.47 476	70.42 516	83.52 514	93.23 392	61.18 505	96.85 398	77.21 450	88.26 528	93.34 474

TAMVS		90.16 324	89.05 344	93.49 205	96.49 232	86.37 212	90.34 350	92.55 409	80.84 426	92.99 322	94.57 340	81.94 343	98.20 264	73.51 494	98.21 269	95.90 389

ALIKED-NN		85.96 436	84.14 449	91.44 319	91.73 458	93.37 52	90.32 351	93.65 379	67.84 527	82.08 524	92.92 401	72.88 440	90.01 504	69.17 520	96.64 383	90.93 501

SP-SuperGlue		91.30 286	91.15 288	91.75 299	91.06 477	90.99 99	90.32 351	93.55 385	90.63 176	91.17 388	93.82 374	79.84 361	88.92 515	93.30 100	96.63 384	95.34 413

EPMVS		81.17 486	80.37 489	83.58 507	85.58 536	65.08 528	90.31 353	71.34 549	77.31 462	85.80 488	91.30 453	59.38 509	92.70 484	79.99 418	82.34 540	92.96 480

CMPMVS	68.83 22	87.28 413	85.67 433	92.09 284	88.77 520	85.42 242	90.31 353	94.38 355	70.02 518	88.00 465	93.30 389	73.78 436	94.03 472	75.96 466	96.54 388	96.83 333
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
test_post1							90.21 355				5.85 558	65.36 485	96.00 429	79.61 427

test_prior2							90.21 355		89.33 211	90.77 399	94.81 325	90.41 207		88.21 285	98.55 218

MVS_111021_LR		93.66 183	93.28 210	94.80 130	96.25 264	90.95 100	90.21 355	95.43 317	87.91 256	93.74 281	94.40 347	92.88 133	96.38 418	90.39 200	98.28 257	97.07 314

ELoFTR		89.04 358	88.72 354	89.99 392	94.38 376	89.08 137	90.15 358	89.10 450	75.60 473	95.85 164	96.52 206	75.00 427	89.26 512	83.82 373	98.08 283	91.61 495

WR-MVS		93.49 192	93.72 189	92.80 241	97.57 137	80.03 351	90.14 359	95.68 302	93.70 77	96.62 113	95.39 297	87.21 271	99.04 121	87.50 305	99.64 25	99.33 31

tpmrst		82.85 472	82.93 463	82.64 511	87.65 524	58.99 544	90.14 359	87.90 464	75.54 474	83.93 508	91.63 449	66.79 477	95.36 444	81.21 407	81.54 541	93.57 472

PVSNet_BlendedMVS		90.35 317	89.96 326	91.54 312	94.81 355	78.80 397	90.14 359	96.93 221	79.43 440	88.68 455	95.06 313	86.27 292	98.15 273	80.27 413	98.04 289	97.68 267

viewdifsd2359ckpt07		93.63 184	94.33 164	91.55 310	96.19 270	77.86 415	90.11 362	97.74 143	90.76 170	96.11 150	96.61 199	94.37 87	98.27 255	88.82 266	98.23 264	98.51 152

BH-untuned		90.68 301	90.90 293	90.05 390	95.98 290	79.57 372	90.04 363	94.94 336	87.91 256	94.07 266	93.00 396	87.76 258	97.78 323	79.19 432	95.17 444	92.80 483

新几何2							90.02 364

旧先验2							90.00 365		68.65 524	92.71 335			96.52 410	85.15 348

无先验							89.94 366	95.75 300	70.81 513				98.59 201	81.17 408		94.81 433

viewmamba		92.69 235	93.03 217	91.69 304	93.92 390	79.50 374	89.92 367	97.33 188	88.86 224	93.13 317	95.79 266	90.97 191	97.65 337	90.86 185	96.45 392	97.94 225

xiu_mvs_v1_base_debu		91.47 281	91.52 273	91.33 326	95.69 312	81.56 317	89.92 367	96.05 292	83.22 382	91.26 383	90.74 463	91.55 166	98.82 151	89.29 245	95.91 411	93.62 468

xiu_mvs_v1_base		91.47 281	91.52 273	91.33 326	95.69 312	81.56 317	89.92 367	96.05 292	83.22 382	91.26 383	90.74 463	91.55 166	98.82 151	89.29 245	95.91 411	93.62 468

xiu_mvs_v1_base_debi		91.47 281	91.52 273	91.33 326	95.69 312	81.56 317	89.92 367	96.05 292	83.22 382	91.26 383	90.74 463	91.55 166	98.82 151	89.29 245	95.91 411	93.62 468

MatchFormer		85.84 438	85.60 435	86.56 473	90.63 489	87.98 170	89.85 371	83.79 508	72.98 496	95.69 182	94.88 322	69.40 463	87.92 520	74.60 479	98.55 218	83.77 537

mvs_anonymous		90.37 316	91.30 282	87.58 456	92.17 442	68.00 512	89.84 372	94.73 346	83.82 373	93.22 309	97.40 113	87.54 264	97.40 358	87.94 299	95.05 448	97.34 299

onestephybrid01		92.06 263	92.07 257	92.04 286	93.45 405	80.93 333	89.82 373	96.78 241	87.60 268	91.68 373	95.43 291	88.73 236	97.43 354	88.32 284	96.85 373	97.76 258

ArgMatch-SfM		91.28 287	90.08 324	94.88 125	95.22 340	92.66 68	89.81 374	94.51 353	79.15 446	95.27 208	93.71 378	78.33 380	95.52 437	86.11 333	98.63 208	96.46 354

test20.03		90.80 296	90.85 297	90.63 369	95.63 319	79.24 384	89.81 374	92.87 398	89.90 196	94.39 255	96.40 215	85.77 296	95.27 449	73.86 493	99.05 122	97.39 296

testing3		83.66 461	82.52 465	87.08 462	95.84 300	65.84 524	89.80 376	77.17 547	88.17 250	90.84 398	88.63 488	30.95 554	98.11 277	84.05 368	97.19 353	97.28 303

WB-MVS		89.44 347	92.15 255	81.32 517	97.73 123	48.22 554	89.73 377	87.98 463	95.24 47	96.05 152	96.99 164	85.18 305	96.95 391	82.45 389	97.97 299	98.78 111

1112_ss		88.42 377	87.41 390	91.45 317	96.69 206	80.99 331	89.72 378	96.72 247	73.37 491	87.00 480	90.69 466	77.38 400	98.20 264	81.38 404	93.72 483	95.15 417

UnsupCasMVSNet_eth		90.33 319	90.34 317	90.28 378	94.64 368	80.24 343	89.69 379	95.88 296	85.77 322	93.94 274	95.69 277	81.99 341	92.98 483	84.21 366	91.30 515	97.62 272

PMatch-Up-SfM		92.38 249	91.36 279	95.46 93	96.22 267	92.32 73	89.61 380	95.31 322	85.08 346	96.71 106	96.12 246	75.90 422	97.27 367	89.73 233	97.54 332	96.78 336

SP-MNN		89.68 342	89.55 338	90.06 389	90.43 497	88.06 166	89.60 381	92.13 419	86.42 300	89.57 432	92.55 417	78.14 387	87.91 521	90.35 205	96.74 380	94.22 451

MG-MVS		89.54 344	89.80 330	88.76 427	94.88 351	72.47 492	89.60 381	92.44 411	85.82 321	89.48 433	95.98 257	82.85 329	97.74 330	81.87 395	95.27 440	96.08 378

Patchmatch-test		86.10 435	86.01 428	86.38 478	90.63 489	74.22 474	89.57 383	86.69 474	85.73 324	89.81 426	92.83 405	65.24 487	91.04 497	77.82 444	95.78 416	93.88 461

Anonymous20231206		88.77 369	88.29 367	90.20 383	96.31 255	78.81 396	89.56 384	93.49 387	74.26 486	92.38 348	95.58 283	82.21 336	95.43 443	72.07 503	98.75 188	96.34 359

dmvs_re		84.69 449	83.94 453	86.95 467	92.24 436	82.93 290	89.51 385	87.37 469	84.38 363	85.37 490	85.08 519	72.44 443	86.59 534	68.05 523	91.03 519	91.33 497

PMatch-SfM		91.76 271	90.58 310	95.30 103	95.64 318	91.67 88	89.49 386	94.79 344	84.45 360	96.31 133	96.02 253	71.68 452	97.26 369	89.13 255	97.75 314	96.98 321

DeepPCF-MVS	90.46 6	94.20 163	93.56 200	96.14 56	95.96 291	92.96 60	89.48 387	97.46 175	85.14 343	96.23 141	95.42 292	93.19 118	98.08 282	90.37 204	98.76 184	97.38 298

test_cas_vis1_n_1920		88.25 382	88.27 369	88.20 444	92.19 440	78.92 391	89.45 388	95.44 315	75.29 479	93.23 308	95.65 279	71.58 453	90.23 503	88.05 294	93.55 488	95.44 409

SCA		87.43 409	87.21 396	88.10 446	92.01 448	71.98 494	89.43 389	88.11 461	82.26 402	88.71 452	92.83 405	78.65 375	97.59 341	79.61 427	93.30 492	94.75 437

testgi		90.38 315	91.34 281	87.50 457	97.49 141	71.54 495	89.43 389	95.16 329	88.38 241	94.54 251	94.68 333	92.88 133	93.09 480	71.60 508	97.85 309	97.88 239

JIA-IIPM		85.08 444	83.04 460	91.19 337	87.56 525	86.14 221	89.40 391	84.44 503	88.98 219	82.20 523	97.95 61	56.82 515	96.15 423	76.55 460	83.45 537	91.30 498

原ACMM2							89.34 392

tpm		84.38 451	84.08 450	85.30 489	90.47 495	63.43 534	89.34 392	85.63 487	77.24 463	87.62 473	95.03 314	61.00 507	97.30 363	79.26 431	91.09 518	95.16 416

MVS_111021_HR		93.63 184	93.42 206	94.26 161	96.65 209	86.96 194	89.30 394	96.23 281	88.36 244	93.57 287	94.60 337	93.45 107	97.77 324	90.23 214	98.38 243	98.03 211

tpm cat1		80.61 492	79.46 495	84.07 502	88.78 519	65.06 529	89.26 395	88.23 458	62.27 542	81.90 528	89.66 479	62.70 502	95.29 448	71.72 506	80.60 542	91.86 493

CDS-MVSNet		89.55 343	88.22 373	93.53 201	95.37 335	86.49 207	89.26 395	93.59 382	79.76 435	91.15 390	92.31 428	77.12 404	98.38 241	77.51 446	97.92 305	95.71 397
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
Fast-Effi-MVS+		91.28 287	90.86 296	92.53 263	95.45 331	82.53 301	89.25 397	96.52 266	85.00 349	89.91 423	88.55 490	92.94 129	98.84 149	84.72 359	95.44 427	96.22 371

BH-RMVSNet		90.47 310	90.44 313	90.56 372	95.21 341	78.65 399	89.15 398	93.94 371	88.21 248	92.74 334	94.22 355	86.38 289	97.88 309	78.67 437	95.39 430	95.14 418

diffmvs_AUTHOR		92.34 252	92.70 233	91.26 331	94.20 379	78.42 400	89.12 399	97.60 158	87.16 281	93.17 314	95.50 286	88.66 237	97.57 343	91.30 170	97.61 327	97.79 253

ArgMatch-Sym		90.98 293	89.75 333	94.68 137	95.17 346	92.64 69	89.09 400	93.46 388	78.60 452	95.11 225	92.37 426	80.44 354	95.24 450	85.04 354	98.44 234	96.18 373

thres200		85.85 437	85.18 439	87.88 453	94.44 373	72.52 491	89.08 401	86.21 478	88.57 236	91.44 379	88.40 491	64.22 491	98.00 298	68.35 522	95.88 414	93.12 475

dtuplus		90.63 306	90.59 309	90.74 363	93.85 394	77.43 425	89.01 402	96.16 288	81.42 416	92.77 332	95.54 285	88.59 238	97.28 364	81.99 394	96.00 407	97.50 283

USDC		89.02 359	89.08 343	88.84 426	95.07 348	74.50 469	88.97 403	96.39 272	73.21 493	93.27 303	96.28 229	82.16 338	96.39 417	77.55 445	98.80 176	95.62 404

testdata1							88.96 404		88.44 239

pmmvs5		87.87 393	87.14 400	90.07 386	93.26 410	76.97 436	88.89 405	92.18 415	73.71 489	88.36 459	93.89 370	76.86 416	96.73 404	80.32 412	96.81 375	96.51 347

dmvs_testset		78.23 504	78.99 497	75.94 526	91.99 449	55.34 550	88.86 406	78.70 542	82.69 392	81.64 530	79.46 538	75.93 421	85.74 537	48.78 547	82.85 539	86.76 531

patch_mono-2		92.46 246	92.72 232	91.71 302	96.65 209	78.91 392	88.85 407	97.17 202	83.89 372	92.45 344	96.76 184	89.86 223	97.09 383	90.24 213	98.59 214	99.12 53

viewmambaseed2359dif		90.77 298	90.81 299	90.64 368	93.46 404	77.04 431	88.83 408	96.29 276	80.79 427	92.21 359	95.11 309	88.99 231	97.28 364	85.39 344	96.20 404	97.59 275

test222					96.95 180	85.27 245	88.83 408	93.61 381	65.09 536	90.74 400	94.85 323	84.62 311			97.36 343	93.91 459

mamba_0408		93.60 187	93.72 189	93.27 216	96.65 209	82.79 294	88.81 410	97.68 148	90.62 177	95.19 218	96.01 254	91.54 170	99.08 111	88.63 273	98.32 252	97.93 228

SSM_04072		93.25 207	93.72 189	91.84 294	96.65 209	82.79 294	88.81 410	97.68 148	90.62 177	95.19 218	96.01 254	91.54 170	94.81 459	88.63 273	98.32 252	97.93 228

baseline2		83.38 465	81.54 476	88.90 424	91.38 468	72.84 487	88.78 412	81.22 530	78.97 448	79.82 536	87.56 497	61.73 504	97.80 319	74.30 488	90.05 522	96.05 380

diffmvs		91.74 272	91.93 263	91.15 339	93.06 415	78.17 409	88.77 413	97.51 171	86.28 303	92.42 346	93.96 367	88.04 253	97.46 351	90.69 191	96.67 382	97.82 250
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
hybrid		91.14 290	91.24 283	90.83 358	93.15 411	77.49 423	88.76 414	96.87 233	84.51 358	91.25 386	95.23 303	87.14 274	97.25 370	88.05 294	96.24 401	97.76 258

hybridnocas07		91.51 280	91.66 270	91.04 342	93.14 413	78.03 410	88.75 415	96.92 223	85.97 313	91.63 376	95.31 301	87.67 260	97.31 362	88.97 259	96.61 386	97.79 253

MDTV_nov1_ep13			83.88 455		89.42 514	61.52 537	88.74 416	87.41 468	73.99 487	84.96 497	94.01 365	65.25 486	95.53 436	78.02 440	93.16 495

D2MVS		89.93 335	89.60 336	90.92 352	94.03 386	78.40 401	88.69 417	94.85 338	78.96 449	93.08 318	95.09 311	74.57 429	96.94 392	88.19 287	98.96 143	97.41 291

TR-MVS		87.70 397	87.17 398	89.27 414	94.11 382	79.26 383	88.69 417	91.86 425	81.94 405	90.69 402	89.79 474	82.82 330	97.42 356	72.65 501	91.98 511	91.14 499

PatchMatch-RL		89.18 350	88.02 378	92.64 249	95.90 296	92.87 62	88.67 419	91.06 433	80.34 428	90.03 421	91.67 448	83.34 319	94.42 465	76.35 461	94.84 455	90.64 504

PAPR		87.65 400	86.77 412	90.27 379	92.85 422	77.38 426	88.56 420	96.23 281	76.82 467	84.98 496	89.75 476	86.08 294	97.16 380	72.33 502	93.35 491	96.26 368

testing3-2		83.95 458	84.22 448	83.13 510	96.28 258	54.34 552	88.51 421	83.01 517	92.19 111	89.09 443	90.98 459	45.51 534	97.44 353	74.38 486	98.01 293	97.60 274

MDTV_nov1_ep13_2view						42.48 557	88.45 422		67.22 529	83.56 513		66.80 475		72.86 500		94.06 455

WB-MVSnew		84.20 454	83.89 454	85.16 491	91.62 463	66.15 523	88.44 423	81.00 531	76.23 470	87.98 466	87.77 496	84.98 308	93.35 478	62.85 538	94.10 477	95.98 383

jason		89.17 353	88.32 365	91.70 303	95.73 310	80.07 348	88.10 424	93.22 392	71.98 502	90.09 415	92.79 408	78.53 378	98.56 211	87.43 307	97.06 361	96.46 354
jason: jason.
mvsany_test3		89.11 355	88.21 374	91.83 295	91.30 470	90.25 115	88.09 425	78.76 541	76.37 469	96.43 122	98.39 38	83.79 317	90.43 502	86.57 322	94.20 472	94.80 434

SP-NN		88.21 383	87.96 379	88.97 421	89.33 515	87.99 168	88.06 426	90.93 436	85.48 335	84.50 500	91.11 457	77.25 403	84.79 539	90.55 194	94.42 463	94.14 452

BH-w/o		87.21 415	87.02 406	87.79 455	94.77 358	77.27 429	87.90 427	93.21 394	81.74 409	89.99 422	88.39 492	83.47 318	96.93 394	71.29 509	92.43 507	89.15 510

MS-PatchMatch		88.05 387	87.75 381	88.95 422	93.28 408	77.93 412	87.88 428	92.49 410	75.42 475	92.57 340	93.59 383	80.44 354	94.24 470	81.28 405	92.75 502	94.69 440

UWE-MVS-28		74.73 509	73.18 510	79.35 522	85.42 538	55.55 549	87.63 429	65.92 551	74.39 484	77.33 540	88.19 493	47.63 530	89.48 509	39.01 549	93.14 497	93.03 479

DELS-MVS		92.05 264	92.16 253	91.72 301	94.44 373	80.13 347	87.62 430	97.25 196	87.34 274	92.22 357	93.18 394	89.54 227	98.73 173	89.67 235	98.20 271	96.30 364
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
ADS-MVSNet2		84.01 456	82.20 469	89.41 408	89.04 517	76.37 450	87.57 431	90.98 435	72.71 499	84.46 501	92.45 421	68.08 468	96.48 412	70.58 515	83.97 535	95.38 410

ADS-MVSNet		82.25 475	81.55 475	84.34 499	89.04 517	65.30 525	87.57 431	85.13 498	72.71 499	84.46 501	92.45 421	68.08 468	92.33 487	70.58 515	83.97 535	95.38 410

IterMVS-SCA-FT		91.65 274	91.55 272	91.94 291	93.89 391	79.22 385	87.56 433	93.51 386	91.53 145	95.37 199	96.62 198	78.65 375	98.90 139	91.89 149	94.95 451	97.70 265

IterMVS		90.18 323	90.16 320	90.21 382	93.15 411	75.98 454	87.56 433	92.97 397	86.43 299	94.09 264	96.40 215	78.32 381	97.43 354	87.87 300	94.69 459	97.23 305
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Test_1112_low_res		87.50 408	86.58 416	90.25 380	96.80 195	77.75 419	87.53 435	96.25 279	69.73 521	86.47 482	93.61 382	75.67 423	97.88 309	79.95 419	93.20 494	95.11 421

c3_l		91.32 285	91.42 277	91.00 346	92.29 435	76.79 439	87.52 436	96.42 271	85.76 323	94.72 246	93.89 370	82.73 331	98.16 271	90.93 184	98.55 218	98.04 208

blended_shiyan8		88.43 376	87.44 387	91.40 321	92.37 431	79.45 376	87.43 437	93.92 373	82.51 397	91.24 387	85.42 515	74.35 430	98.23 261	84.43 363	95.28 439	96.52 346

blended_shiyan6		88.42 377	87.43 388	91.40 321	92.37 431	79.43 378	87.41 438	93.91 374	82.51 397	91.17 388	85.44 514	74.34 431	98.24 259	84.38 364	95.32 434	96.53 345

UnsupCasMVSNet_bld		88.50 374	88.03 377	89.90 393	95.52 326	78.88 393	87.39 439	94.02 367	79.32 444	93.06 319	94.02 364	80.72 352	94.27 468	75.16 474	93.08 499	96.54 343

gbinet_0.2-2-1-0.02		88.14 386	86.86 409	91.99 290	90.70 487	80.51 337	87.36 440	93.01 395	83.45 377	90.38 409	82.42 534	72.73 441	98.54 214	85.40 342	96.27 398	96.90 327

lupinMVS		88.34 381	87.31 392	91.45 317	94.74 362	80.06 349	87.23 441	92.27 414	71.10 509	88.83 445	91.15 455	77.02 409	98.53 218	86.67 320	96.75 378	95.76 395

pmmvs4		88.95 364	87.70 383	92.70 246	94.30 377	85.60 238	87.22 442	92.16 417	74.62 481	89.75 429	94.19 357	77.97 391	96.41 416	82.71 381	96.36 394	96.09 377

WTY-MVS		86.93 425	86.50 422	88.24 442	94.96 349	74.64 465	87.19 443	92.07 421	78.29 454	88.32 460	91.59 450	78.06 389	94.27 468	74.88 477	93.15 496	95.80 393

ET-MVSNet_ETH3D		86.15 434	84.27 447	91.79 297	93.04 416	81.28 324	87.17 444	86.14 479	79.57 438	83.65 511	88.66 487	57.10 513	98.18 267	87.74 302	95.40 429	95.90 389

MVS-HIRNet		78.83 503	80.60 486	73.51 528	93.07 414	47.37 555	87.10 445	78.00 544	68.94 523	77.53 539	97.26 133	71.45 454	94.62 461	63.28 536	88.74 526	78.55 544

blend_shiyan4		83.29 466	80.66 485	91.19 337	91.86 452	79.59 368	87.05 446	93.91 374	82.66 393	89.60 431	83.36 527	42.82 547	98.10 280	81.45 402	73.26 547	95.87 391

xiu_mvs_v2_base		89.00 362	89.19 341	88.46 439	94.86 353	74.63 466	86.97 447	95.60 304	80.88 424	87.83 469	88.62 489	91.04 189	98.81 156	82.51 388	94.38 466	91.93 491

DPM-MVS		89.35 348	88.40 362	92.18 280	96.13 277	84.20 261	86.96 448	96.15 289	75.40 476	87.36 477	91.55 452	83.30 321	98.01 296	82.17 393	96.62 385	94.32 449

eth_miper_zixun_eth		90.72 299	90.61 307	91.05 341	92.04 447	76.84 438	86.91 449	96.67 254	85.21 339	94.41 254	93.92 368	79.53 364	98.26 256	89.76 231	97.02 363	98.06 204

dp		79.28 501	78.62 500	81.24 518	85.97 535	56.45 547	86.91 449	85.26 496	72.97 497	81.45 531	89.17 486	56.01 517	95.45 442	73.19 497	76.68 546	91.82 494

sss		87.23 414	86.82 410	88.46 439	93.96 388	77.94 411	86.84 451	92.78 402	77.59 458	87.61 475	91.83 444	78.75 373	91.92 490	77.84 442	94.20 472	95.52 408

miper_ehance_all_eth		90.48 309	90.42 314	90.69 365	91.62 463	76.57 447	86.83 452	96.18 286	83.38 378	94.06 267	92.66 414	82.20 337	98.04 291	89.79 229	97.02 363	97.45 287

CLD-MVS		91.82 268	91.41 278	93.04 224	96.37 244	83.65 269	86.82 453	97.29 193	84.65 356	92.27 355	89.67 478	92.20 152	97.85 315	83.95 371	99.47 44	97.62 272
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
cl____		90.65 304	90.56 311	90.91 354	91.85 453	76.98 435	86.75 454	95.36 320	85.53 330	94.06 267	94.89 319	77.36 402	97.98 301	90.27 211	98.98 135	97.76 258

DIV-MVS_self_test		90.65 304	90.56 311	90.91 354	91.85 453	76.99 434	86.75 454	95.36 320	85.52 333	94.06 267	94.89 319	77.37 401	97.99 300	90.28 210	98.97 141	97.76 258

SIFT-MNN		87.81 396	87.11 403	89.90 393	92.19 440	93.62 48	86.73 456	84.68 500	87.19 279	90.95 394	92.80 407	73.54 437	87.09 531	78.62 438	97.32 345	88.98 512

PS-MVSNAJ		88.86 366	88.99 347	88.48 438	94.88 351	74.71 464	86.69 457	95.60 304	80.88 424	87.83 469	87.37 501	90.77 196	98.82 151	82.52 387	94.37 467	91.93 491

PVSNet_Blended		88.74 370	88.16 376	90.46 375	94.81 355	78.80 397	86.64 458	96.93 221	74.67 480	88.68 455	89.18 485	86.27 292	98.15 273	80.27 413	96.00 407	94.44 446

MSDG		90.82 295	90.67 304	91.26 331	94.16 380	83.08 287	86.63 459	96.19 285	90.60 179	91.94 367	91.89 442	89.16 230	95.75 434	80.96 410	94.51 462	94.95 427

cl22		89.02 359	88.50 360	90.59 371	89.76 507	76.45 448	86.62 460	94.03 365	82.98 390	92.65 336	92.49 420	72.05 450	97.53 345	88.93 260	97.02 363	97.78 256

CL-MVSNet_self_test		90.04 334	89.90 328	90.47 373	95.24 339	77.81 416	86.60 461	92.62 407	85.64 326	93.25 307	93.92 368	83.84 316	96.06 427	79.93 421	98.03 290	97.53 281

IMVS_0404		90.67 303	91.06 290	89.50 403	95.19 342	76.72 441	86.58 462	96.89 227	85.92 315	89.17 439	94.50 342	85.77 296	94.67 460	88.49 280	97.07 357	97.10 310

PCF-MVS	84.52 17	89.12 354	87.71 382	93.34 211	96.06 283	85.84 232	86.58 462	97.31 190	68.46 525	93.61 286	93.89 370	87.51 265	98.52 221	67.85 524	98.11 279	95.66 401
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
test_f		86.65 428	87.13 401	85.19 490	90.28 499	86.11 222	86.52 464	91.66 428	69.76 520	95.73 177	97.21 141	69.51 462	81.28 545	89.15 254	94.40 464	88.17 517

SIFT-NCM-Cal		87.99 388	87.39 391	89.77 396	92.16 443	93.98 34	86.51 465	82.96 518	85.99 312	91.10 392	92.99 397	80.00 359	87.11 528	77.21 450	97.60 329	88.22 516

UWE-MVS		80.29 495	79.10 496	83.87 504	91.97 450	59.56 542	86.50 466	77.43 546	75.40 476	87.79 471	88.10 494	44.08 539	96.90 396	64.23 533	96.36 394	95.14 418

testing91		83.56 463	82.45 466	86.91 468	92.92 420	67.29 513	86.33 467	88.07 462	86.22 305	84.26 504	85.76 511	48.15 529	97.17 378	76.27 463	94.08 478	96.27 367

usedtu_dtu_shiyan1		89.18 350	88.59 356	90.95 350	94.75 359	77.79 417	86.25 468	94.63 351	81.61 412	90.88 395	92.24 430	77.03 407	98.08 282	82.62 383	97.27 347	96.97 322

FE-MVSNET3		89.18 350	88.59 356	90.95 350	94.75 359	77.79 417	86.25 468	94.63 351	81.61 412	90.88 395	92.25 429	77.03 407	98.08 282	82.62 383	97.27 347	96.97 322

testing222		80.54 493	78.53 501	86.58 472	92.54 429	68.60 510	86.24 470	82.72 521	83.78 374	82.68 521	84.24 522	39.25 551	95.94 431	60.25 539	95.09 446	95.20 414

Patchmatch-RL test		88.81 367	88.52 359	89.69 401	95.33 337	79.94 355	86.22 471	92.71 403	78.46 453	95.80 166	94.18 358	66.25 480	95.33 447	89.22 250	98.53 222	93.78 462

ETVMVS		79.85 498	77.94 506	85.59 484	92.97 418	66.20 522	86.13 472	80.99 532	81.41 417	83.52 514	83.89 523	41.81 548	94.98 456	56.47 543	94.25 471	95.61 405

testing99		82.94 470	81.72 472	86.59 471	92.55 427	66.53 519	86.08 473	85.70 485	85.47 336	83.95 507	85.70 512	45.87 533	97.07 386	76.58 459	93.56 487	96.17 376

Syy-MVS		84.81 446	84.93 440	84.42 498	91.71 459	63.36 535	85.89 474	81.49 525	81.03 420	85.13 493	81.64 536	77.44 397	95.00 453	85.94 336	94.12 475	94.91 430

myMVS_eth3d		79.62 500	78.26 502	83.72 506	91.71 459	61.25 539	85.89 474	81.49 525	81.03 420	85.13 493	81.64 536	32.12 553	95.00 453	71.17 513	94.12 475	94.91 430

testing11		81.98 480	80.52 487	86.38 478	92.69 424	67.13 514	85.79 476	84.80 499	82.16 403	81.19 533	85.41 516	45.24 535	96.88 397	74.14 490	93.24 493	95.14 418

FPMVS		84.50 450	83.28 458	88.16 445	96.32 254	94.49 20	85.76 477	85.47 491	83.09 387	85.20 492	94.26 353	63.79 495	86.58 535	63.72 535	91.88 513	83.40 538

dtuonlycased		90.11 327	90.39 316	89.28 413	97.09 170	72.61 489	85.75 478	95.27 323	81.57 414	94.42 253	94.89 319	90.47 205	96.81 401	78.74 435	95.27 440	98.41 164

SIFT-NN-NCMNet		86.55 431	85.56 436	89.51 402	91.84 455	94.02 30	85.72 479	81.31 528	84.33 364	86.13 486	91.77 445	79.22 367	87.46 523	74.06 491	95.70 419	87.07 529

nomal-1		83.48 464	81.65 473	88.98 420	91.07 476	80.73 336	85.66 480	86.34 477	80.98 422	83.93 508	86.95 503	51.44 524	91.71 491	74.53 482	93.93 479	94.49 443

IB-MVS	77.21 19	83.11 467	81.05 479	89.29 411	91.15 475	75.85 455	85.66 480	86.00 482	79.70 436	82.02 527	86.61 505	48.26 527	98.39 237	77.84 442	92.22 508	93.63 467
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
wanda-best-256-512		87.53 404	86.39 424	90.97 348	91.29 471	78.39 403	85.63 482	93.75 376	81.91 406	90.09 415	83.30 528	72.25 446	98.18 267	83.96 369	95.32 434	96.33 360

FE-blended-shiyan7		87.53 404	86.39 424	90.97 348	91.29 471	78.39 403	85.63 482	93.75 376	81.91 406	90.09 415	83.30 528	72.25 446	98.18 267	83.96 369	95.32 434	96.33 360

SIFT-ConvMatch		87.94 390	87.21 396	90.11 385	91.67 461	93.60 49	85.55 484	83.12 516	86.48 296	92.15 361	92.98 399	78.11 388	88.58 517	76.60 457	98.25 261	88.14 518

MDA-MVSNet-bldmvs		91.04 291	90.88 295	91.55 310	94.68 366	80.16 344	85.49 485	92.14 418	90.41 185	94.93 236	95.79 266	85.10 306	96.93 394	85.15 348	94.19 474	97.57 277

SIFT-UMatch		87.96 389	87.52 385	89.29 411	91.48 466	92.84 63	85.46 486	83.94 507	87.47 271	91.86 369	92.92 401	76.78 417	87.35 525	79.73 424	98.00 296	87.69 520

test_vis1_rt		85.58 440	84.58 443	88.60 433	87.97 523	86.76 199	85.45 487	93.59 382	66.43 531	87.64 472	89.20 484	79.33 365	85.38 538	81.59 399	89.98 523	93.66 466

new-patchmatchnet		88.97 363	90.79 301	83.50 508	94.28 378	55.83 548	85.34 488	93.56 384	86.18 308	95.47 192	95.73 273	83.10 324	96.51 411	85.40 342	98.06 287	98.16 196

miper_enhance_ethall		88.42 377	87.87 380	90.07 386	88.67 521	75.52 459	85.10 489	95.59 308	75.68 471	92.49 341	89.45 481	78.96 368	97.88 309	87.86 301	97.02 363	96.81 334

HyFIR lowres test		87.19 417	85.51 437	92.24 273	97.12 169	80.51 337	85.03 490	96.06 290	66.11 533	91.66 374	92.98 399	70.12 459	99.14 101	75.29 470	95.23 442	97.07 314

SIFT-NN-UMatch		86.43 433	85.66 434	88.76 427	90.73 486	92.76 65	84.99 491	81.25 529	84.13 366	88.17 463	92.04 438	76.90 413	86.62 533	76.34 462	96.36 394	86.91 530

SIFT-NN-PointCN		86.59 430	85.79 431	88.99 419	90.15 501	92.46 72	84.96 492	82.76 520	83.11 386	88.70 453	92.34 427	77.62 393	87.10 529	75.03 476	97.44 339	87.42 523

pmmvs3		80.83 489	78.96 498	86.45 475	87.23 528	77.48 424	84.87 493	82.31 522	63.83 539	85.03 495	89.50 480	49.66 525	93.10 479	73.12 498	95.10 445	88.78 515

SIFT-CM-Cal		87.51 406	86.76 413	89.76 397	91.48 466	93.30 55	84.73 494	84.04 505	85.53 330	91.66 374	92.58 416	77.01 411	88.75 516	75.29 470	98.56 217	87.24 525

test0.0.03 1		82.48 474	81.47 477	85.48 487	89.70 508	73.57 480	84.73 494	81.64 524	83.07 388	88.13 464	86.61 505	62.86 500	89.10 514	66.24 530	90.29 521	93.77 463

N_pmnet		88.90 365	87.25 395	93.83 183	94.40 375	93.81 44	84.73 494	87.09 471	79.36 443	93.26 305	92.43 424	79.29 366	91.68 492	77.50 447	97.22 352	96.00 381

GA-MVS		87.70 397	86.82 410	90.31 377	93.27 409	77.22 430	84.72 497	92.79 401	85.11 345	89.82 425	90.07 469	66.80 475	97.76 327	84.56 360	94.27 470	95.96 384

myMVS_eth3d28		80.97 487	80.42 488	82.62 512	93.35 407	58.25 546	84.70 498	85.62 489	86.31 302	84.04 506	85.20 518	46.00 532	94.07 471	62.93 537	95.65 421	95.53 407

icg_test_0407_2		91.18 289	91.92 264	88.94 423	95.19 342	76.72 441	84.66 499	96.89 227	85.92 315	93.55 288	94.50 342	91.06 187	92.99 482	88.49 280	97.07 357	97.10 310

SIFT-NN-CMatch		86.64 429	85.79 431	89.18 417	91.21 474	93.07 56	84.60 500	80.33 536	84.07 367	89.10 440	91.58 451	78.69 374	87.33 526	75.28 472	97.28 346	87.13 528

ppachtmachnet_test		88.61 373	88.64 355	88.50 437	91.76 456	70.99 499	84.59 501	92.98 396	79.30 445	92.38 348	93.53 385	79.57 363	97.45 352	86.50 327	97.17 354	97.07 314

CHOSEN 1792x2688		87.19 417	85.92 430	91.00 346	97.13 167	79.41 379	84.51 502	95.60 304	64.14 538	90.07 420	94.81 325	78.26 382	97.14 381	73.34 495	95.38 431	96.46 354

SIFT-NN		84.10 455	83.04 460	87.28 461	90.76 485	92.16 76	84.45 503	81.34 527	83.54 376	83.80 510	89.75 476	70.08 460	82.09 544	68.68 521	94.96 450	87.60 521

thisisatest0515		84.72 448	82.99 462	89.90 393	92.96 419	75.33 461	84.36 504	83.42 511	77.37 460	88.27 461	86.65 504	53.94 519	98.72 174	82.56 386	97.40 342	95.67 400

SIFT-UM-Cal		87.93 391	87.42 389	89.44 406	90.95 482	92.71 66	84.33 505	88.32 456	86.32 301	90.41 407	92.73 411	78.78 372	88.31 518	76.83 455	98.16 273	87.31 524

cascas		87.02 423	86.28 427	89.25 415	91.56 465	76.45 448	84.33 505	96.78 241	71.01 511	86.89 481	85.91 510	81.35 346	96.94 392	83.09 378	95.60 422	94.35 448

new_pmnet		81.22 484	81.01 481	81.86 514	90.92 483	70.15 502	84.03 507	80.25 538	70.83 512	85.97 487	89.78 475	67.93 471	84.65 540	67.44 525	91.90 512	90.78 503

PAPM		81.91 481	80.11 492	87.31 460	93.87 392	72.32 493	84.02 508	93.22 392	69.47 522	76.13 542	89.84 471	72.15 449	97.23 372	53.27 545	89.02 525	92.37 488

SIFT-PointCN		87.02 423	86.47 423	88.65 432	90.27 500	91.47 90	83.91 509	84.08 504	84.84 353	91.35 381	92.24 430	75.25 426	87.29 527	77.11 453	99.20 101	87.20 527

UBG		80.28 496	78.94 499	84.31 500	92.86 421	61.77 536	83.87 510	83.31 515	77.33 461	82.78 520	83.72 525	47.60 531	96.06 427	65.47 532	93.48 489	95.11 421

SIFT-PCN-Cal		87.04 422	86.65 415	88.22 443	90.09 504	90.20 116	83.84 511	85.36 492	85.16 342	91.83 370	91.84 443	78.22 384	87.02 532	74.79 478	98.71 198	87.44 522

our_test_3		87.55 403	87.59 384	87.44 458	91.76 456	70.48 500	83.83 512	90.55 442	79.79 434	92.06 366	92.17 434	78.63 377	95.63 435	84.77 357	94.73 457	96.22 371

WBMVS		84.00 457	83.48 456	85.56 485	92.71 423	61.52 537	83.82 513	89.38 448	79.56 439	90.74 400	93.20 393	48.21 528	97.28 364	75.63 468	98.10 281	97.88 239

miper_lstm_enhance		89.90 336	89.80 330	90.19 384	91.37 469	77.50 422	83.82 513	95.00 333	84.84 353	93.05 320	94.96 316	76.53 420	95.20 451	89.96 226	98.67 205	97.86 243

test-LLR		83.58 462	83.17 459	84.79 494	89.68 509	66.86 517	83.08 515	84.52 501	83.07 388	82.85 518	84.78 520	62.86 500	93.49 476	82.85 379	94.86 453	94.03 456

TESTMET0.1,1		79.09 502	78.04 504	82.25 513	87.52 526	64.03 532	83.08 515	80.62 534	70.28 517	80.16 535	83.22 531	44.13 538	90.56 500	79.95 419	93.36 490	92.15 489

test-mter		81.21 485	80.01 493	84.79 494	89.68 509	66.86 517	83.08 515	84.52 501	73.85 488	82.85 518	84.78 520	43.66 540	93.49 476	82.85 379	94.86 453	94.03 456

SSC-MVS3.2		89.88 337	91.06 290	86.31 480	95.90 296	63.76 533	82.68 518	92.43 412	91.42 152	92.37 350	94.58 339	86.34 290	96.60 408	84.35 365	99.50 42	98.57 147

SIFT-NCMNet		87.31 412	87.07 405	88.02 447	90.01 505	91.85 82	82.65 519	89.57 447	86.52 295	93.34 298	92.51 419	78.05 390	86.22 536	71.95 504	98.98 135	86.01 533

test123		9.49 521	12.01 524	1.91 539	2.87 563	1.30 565	82.38 520	1.34 566	1.36 556	2.84 559	6.56 556	2.45 562	0.97 559	2.73 558	5.56 558	3.47 555

PMMVS		83.00 469	81.11 478	88.66 431	83.81 543	86.44 210	82.24 521	85.65 486	61.75 543	82.07 525	85.64 513	79.75 362	91.59 494	75.99 465	93.09 498	87.94 519

MASt3R-SfM		82.76 473	82.17 470	84.53 496	83.29 545	86.01 225	82.08 522	80.49 535	63.10 541	92.22 357	94.20 356	69.18 464	77.62 546	79.63 425	95.37 432	89.94 509

KD-MVS_2432*1600		82.17 477	80.75 483	86.42 476	82.04 547	70.09 503	81.75 523	90.80 438	82.56 394	90.37 410	89.30 482	42.90 543	96.11 425	74.47 484	92.55 505	93.06 476

miper_refine_blended		82.17 477	80.75 483	86.42 476	82.04 547	70.09 503	81.75 523	90.80 438	82.56 394	90.37 410	89.30 482	42.90 543	96.11 425	74.47 484	92.55 505	93.06 476

dtuonly		84.38 451	85.24 438	81.80 515	87.13 529	58.46 545	81.58 525	92.71 403	74.41 483	85.68 489	92.62 415	78.17 386	92.13 489	79.15 433	95.73 417	94.82 432

mvsany_test1		83.91 459	82.93 463	86.84 470	86.18 534	85.93 229	81.11 526	75.03 548	70.80 514	88.57 457	94.63 335	83.08 325	87.38 524	80.39 411	86.57 532	87.21 526

YYNet1		88.17 384	88.24 371	87.93 450	92.21 438	73.62 479	80.75 527	88.77 452	82.51 397	94.99 234	95.11 309	82.70 332	93.70 473	83.33 375	93.83 481	96.48 352

MDA-MVSNet_test_wron		88.16 385	88.23 372	87.93 450	92.22 437	73.71 478	80.71 528	88.84 451	82.52 396	94.88 239	95.14 307	82.70 332	93.61 475	83.28 376	93.80 482	96.46 354

PatchmatchNet2					0.00 565	54.43 551	80.66 529	86.13 480	76.71 468
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. CVPR 2021
0.4-1-1-0.1		77.15 505	73.55 509	87.95 449	85.49 537	75.84 457	80.59 530	82.87 519	73.51 490	73.61 544	68.65 544	42.84 546	97.22 373	75.20 473	79.18 543	90.80 502

testmvs		9.02 522	11.42 525	1.81 540	2.77 564	1.13 566	79.44 531	1.90 564	1.18 557	2.65 560	6.80 555	1.95 563	0.87 560	2.62 559	3.45 559	3.44 556

0.3-1-1-0.015		75.73 508	71.83 514	87.44 458	83.47 544	74.98 462	78.69 532	83.38 513	72.24 501	70.43 547	65.81 545	39.55 550	97.08 384	74.57 480	78.30 545	90.28 507

PVSNet	76.22 20	82.89 471	82.37 467	84.48 497	93.96 388	64.38 531	78.60 533	88.61 453	71.50 506	84.43 503	86.36 508	74.27 432	94.60 462	69.87 518	93.69 485	94.46 445

0.4-1-1-0.2		75.80 507	72.05 513	87.04 463	82.70 546	74.17 475	77.51 534	83.48 510	71.80 503	71.57 546	65.16 546	43.07 541	96.96 390	74.34 487	78.78 544	90.00 508

dongtai		53.72 513	53.79 516	53.51 532	79.69 549	36.70 558	77.18 535	32.53 561	71.69 504	68.63 549	60.79 548	26.65 556	73.11 549	30.67 552	36.29 554	50.73 547

XFeat-MNN		80.76 490	79.73 494	83.85 505	79.29 551	82.86 292	76.90 536	83.32 514	69.86 519	92.27 355	87.53 499	57.82 512	84.65 540	74.17 489	96.44 393	84.03 536

kuosan		43.63 515	44.25 519	41.78 533	66.04 555	34.37 559	75.56 537	32.62 560	53.25 548	50.46 553	51.18 549	25.28 557	49.13 553	13.44 555	30.41 555	41.84 549

PDCNetPlus		79.66 499	78.21 503	84.01 503	79.49 550	73.91 477	75.29 538	96.44 270	66.51 530	89.20 438	91.98 441	30.56 555	84.51 542	75.48 469	98.93 148	93.62 468

PVSNet_0	70.34 21	74.58 510	72.96 511	79.47 521	90.63 489	66.24 521	73.26 539	83.40 512	63.67 540	78.02 538	78.35 540	72.53 442	89.59 507	56.68 542	60.05 550	82.57 541

E-PMN		80.72 491	80.86 482	80.29 520	85.11 539	68.77 509	72.96 540	81.97 523	87.76 263	83.25 517	83.01 532	62.22 503	89.17 513	77.15 452	94.31 469	82.93 539

CHOSEN 280x420		80.04 497	77.97 505	86.23 481	90.13 502	74.53 468	72.87 541	89.59 446	66.38 532	76.29 541	85.32 517	56.96 514	95.36 444	69.49 519	94.72 458	88.79 514

EMVS		80.35 494	80.28 491	80.54 519	84.73 541	69.07 508	72.54 542	80.73 533	87.80 261	81.66 529	81.73 535	62.89 499	89.84 505	75.79 467	94.65 460	82.71 540

XFeat-NN		75.97 506	74.88 508	79.25 523	77.98 552	79.81 360	70.81 543	79.50 540	64.75 537	86.32 484	82.83 533	53.44 522	76.70 548	66.89 527	91.40 514	81.23 543

PMMVS2		81.31 483	83.44 457	74.92 527	90.52 492	46.49 556	69.19 544	85.23 497	84.30 365	87.95 467	94.71 331	76.95 412	84.36 543	64.07 534	98.09 282	93.89 460

tmp_tt		37.97 516	44.33 518	18.88 535	11.80 560	21.54 561	63.51 545	45.66 558	4.23 554	51.34 552	50.48 550	59.08 510	22.11 556	44.50 548	68.35 549	13.00 551

MVE	59.87 23	73.86 511	72.65 512	77.47 525	87.00 532	74.35 470	61.37 546	60.93 553	67.27 528	69.69 548	86.49 507	81.24 350	72.33 550	56.45 544	83.45 537	85.74 534
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
GLUNet-SfM		58.71 512	56.43 515	65.55 529	45.28 556	59.80 541	54.31 547	55.90 555	37.80 549	81.24 532	73.75 543	38.27 552	70.23 552	34.22 551	87.09 530	66.64 546

test_method		50.44 514	48.94 517	54.93 530	39.68 557	12.38 563	28.59 548	90.09 443	6.82 553	41.10 554	78.41 539	54.41 518	70.69 551	50.12 546	51.26 551	81.72 542

MVS_clip		28.84 517	32.57 520	17.67 536	37.77 558	25.94 560	27.92 549	7.17 562	9.16 552	54.91 551	62.94 547	20.70 558	10.56 557	26.96 553	45.58 552	16.52 550

VLMVS_CLIP		26.72 518	28.23 522	22.16 534	23.46 559	19.29 562	25.04 550	38.45 559	10.30 551	37.65 555	43.37 551	16.55 559	34.48 555	19.59 554	39.68 553	12.71 552

VLMVS		7.75 523	8.50 528	5.52 537	7.85 562	5.47 564	5.34 551	3.06 563	0.41 558	11.88 557	15.91 554	11.95 560	3.89 558	3.42 557	16.65 557	7.20 553

MVS_baseline		9.63 520	12.05 523	2.37 538	9.15 561	0.73 567	5.23 552	1.75 565	0.31 559	26.23 556	30.60 552	5.95 561	0.00 561	4.43 556	24.78 556	6.38 554

mmdepth		0.00 526	0.00 529	0.00 541	0.00 565	0.00 568	0.00 553	0.00 567	0.00 560	0.00 561	0.00 559	0.00 564	0.00 561	0.00 560	0.00 560	0.00 557

monomultidepth		0.00 526	0.00 529	0.00 541	0.00 565	0.00 568	0.00 553	0.00 567	0.00 560	0.00 561	0.00 559	0.00 564	0.00 561	0.00 560	0.00 560	0.00 557

test_blank		0.00 526	0.00 529	0.00 541	0.00 565	0.00 568	0.00 553	0.00 567	0.00 560	0.00 561	0.00 559	0.00 564	0.00 561	0.00 560	0.00 560	0.00 557

uanet_test		0.00 526	0.00 529	0.00 541	0.00 565	0.00 568	0.00 553	0.00 567	0.00 560	0.00 561	0.00 559	0.00 564	0.00 561	0.00 560	0.00 560	0.00 557

DCPMVS		0.00 526	0.00 529	0.00 541	0.00 565	0.00 568	0.00 553	0.00 567	0.00 560	0.00 561	0.00 559	0.00 564	0.00 561	0.00 560	0.00 560	0.00 557

cdsmvs_eth3d_5k		23.35 519	31.13 521	0.00 541	0.00 565	0.00 568	0.00 553	95.58 310	0.00 560	0.00 561	91.15 455	93.43 109	0.00 561	0.00 560	0.00 560	0.00 557

pcd_1.5k_mvsjas		7.56 524	10.09 526	0.00 541	0.00 565	0.00 568	0.00 553	0.00 567	0.00 560	0.00 561	0.00 559	90.77 196	0.00 561	0.00 560	0.00 560	0.00 557

sosnet-low-res		0.00 526	0.00 529	0.00 541	0.00 565	0.00 568	0.00 553	0.00 567	0.00 560	0.00 561	0.00 559	0.00 564	0.00 561	0.00 560	0.00 560	0.00 557

sosnet		0.00 526	0.00 529	0.00 541	0.00 565	0.00 568	0.00 553	0.00 567	0.00 560	0.00 561	0.00 559	0.00 564	0.00 561	0.00 560	0.00 560	0.00 557

uncertanet		0.00 526	0.00 529	0.00 541	0.00 565	0.00 568	0.00 553	0.00 567	0.00 560	0.00 561	0.00 559	0.00 564	0.00 561	0.00 560	0.00 560	0.00 557

Regformer		0.00 526	0.00 529	0.00 541	0.00 565	0.00 568	0.00 553	0.00 567	0.00 560	0.00 561	0.00 559	0.00 564	0.00 561	0.00 560	0.00 560	0.00 557

ab-mvs-re		7.56 524	10.08 527	0.00 541	0.00 565	0.00 568	0.00 553	0.00 567	0.00 560	0.00 561	90.69 466	0.00 564	0.00 561	0.00 560	0.00 560	0.00 557

uanet		0.00 526	0.00 529	0.00 541	0.00 565	0.00 568	0.00 553	0.00 567	0.00 560	0.00 561	0.00 559	0.00 564	0.00 561	0.00 560	0.00 560	0.00 557

PatchmatchNet1														77.38 448	97.25 351	96.00 381
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. CVPR 2021
PatchmatchNet3													91.63 493
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. CVPR 2021
test-260524					97.94 107	87.97 171		97.94 115		96.37 127		93.24 116	99.34 70	94.10 66	99.19 102

WAC-MVS						61.25 539								74.55 481

MSC_two_6792asdad				95.90 69	96.54 225	89.57 124		96.87 233					99.41 43	94.06 67	99.30 80	98.72 121

PC_three_1452									75.31 478	95.87 163	95.75 272	92.93 130	96.34 422	87.18 311	98.68 203	98.04 208

No_MVS				95.90 69	96.54 225	89.57 124		96.87 233					99.41 43	94.06 67	99.30 80	98.72 121

test_one_0601					98.26 80	87.14 187		98.18 63	94.25 61	96.99 90	97.36 120	95.13 50

eth-test2					0.00 565

eth-test					0.00 565

ZD-MVS					97.23 158	90.32 113		97.54 165	84.40 362	94.78 242	95.79 266	92.76 136	99.39 54	88.72 270	98.40 238

IU-MVS					98.51 58	86.66 204		96.83 238	72.74 498	95.83 165				93.00 113	99.29 83	98.64 138

test_241102_TWO								98.10 80	91.95 118	97.54 50	97.25 134	95.37 36	99.35 67	93.29 101	99.25 91	98.49 155

test_241102_ONE					98.51 58	86.97 192		98.10 80	91.85 125	97.63 45	97.03 160	96.48 13	98.95 135

test_0728_THIRD									93.26 87	97.40 63	97.35 123	94.69 74	99.34 70	93.88 72	99.42 54	98.89 91

GSMVS																94.75 437

test_part2					98.21 84	89.41 129				96.72 105

sam_mvs1												66.64 478				94.75 437

sam_mvs												66.41 479

MTGPA								97.62 154

test_post											6.07 557	65.74 483	95.84 433

patchmatchnet-post											91.71 447	66.22 481	97.59 341

gm-plane-assit					87.08 531	59.33 543			71.22 507		83.58 526		97.20 375	73.95 492

test9_res														88.16 290	98.40 238	97.83 247

agg_prior2														87.06 314	98.36 249	97.98 217

agg_prior					96.20 268	88.89 142		96.88 232		90.21 414			98.78 164

TestCases				96.00 59	98.02 98	92.17 74		98.43 27	90.48 181	95.04 231	96.74 187	92.54 140	97.86 313	85.11 351	98.98 135	97.98 217

test_prior				94.61 142	95.95 292	87.23 184		97.36 185					98.68 186			97.93 228

新几何1				93.17 222	97.16 163	87.29 182		94.43 354	67.95 526	91.29 382	94.94 317	86.97 279	98.23 261	81.06 409	97.75 314	93.98 458

旧先验1					96.20 268	84.17 262		94.82 340			95.57 284	89.57 226			97.89 306	96.32 363

原ACMM1				92.87 237	96.91 185	84.22 260		97.01 214	76.84 466	89.64 430	94.46 346	88.00 254	98.70 182	81.53 401	98.01 293	95.70 399

testdata2													98.03 292	80.24 415

segment_acmp												92.14 153

testdata				91.03 343	96.87 188	82.01 309		94.28 358	71.55 505	92.46 343	95.42 292	85.65 300	97.38 361	82.64 382	97.27 347	93.70 465

test12				94.43 156	95.95 292	86.75 200		96.24 280		89.76 428		89.79 224	98.79 160		97.95 303	97.75 262

plane_prior7					97.71 125	88.68 146

plane_prior6					97.21 161	88.23 160						86.93 280

plane_prior5								97.81 135					98.95 135	89.26 248	98.51 227	98.60 144

plane_prior4											95.59 280

plane_prior3						88.43 157			90.35 186	93.31 299

plane_prior1					97.38 149

n2								0.00 567

nn								0.00 567

door-mid								92.13 419

lessismore_v0				93.87 180	98.05 94	83.77 268		80.32 537		97.13 79	97.91 70	77.49 396	99.11 109	92.62 126	98.08 283	98.74 119

LGP-MVS_train				96.84 41	98.36 75	92.13 77		98.25 46	91.78 132	97.07 83	97.22 139	96.38 16	99.28 85	92.07 142	99.59 29	99.11 54

test11								96.65 255

door								91.26 432

HQP5-MVS						84.89 249

BP-MVS														86.55 324

HQP4-MVS										88.81 447			98.61 196			98.15 198

HQP3-MVS								97.31 190							97.73 316

HQP2-MVS												84.76 309

NP-MVS					96.82 193	87.10 188					93.40 387

ACMMP++_ref															98.82 170

ACMMP++															99.25 91

Test By Simon												90.61 202

ITE_SJBPF				95.95 63	97.34 152	93.36 54		96.55 265	91.93 120	94.82 240	95.39 297	91.99 155	97.08 384	85.53 340	97.96 302	97.41 291

DeepMVS_CX				53.83 531	70.38 554	64.56 530		48.52 557	33.01 550	65.50 550	74.21 542	56.19 516	46.64 554	38.45 550	70.07 548	50.30 548

High-resolution multi-view benchmark