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.

CS-MVS		99.50 27	99.48 20	99.54 118	99.76 75	99.42 111	99.90 1	99.55 91	98.56 111	99.78 73	99.70 187	98.65 71	99.79 221	99.65 38	99.78 127	99.41 234

mmtdpeth		96.95 351	96.71 350	97.67 370	99.33 261	94.90 396	99.89 2	99.28 315	98.15 162	99.72 94	98.57 406	86.56 409	99.90 141	99.82 26	89.02 431	98.20 401

SPE-MVS-test		99.49 29	99.48 20	99.54 118	99.78 63	99.30 131	99.89 2	99.58 73	98.56 111	99.73 89	99.69 198	98.55 78	99.82 205	99.69 32	99.85 87	99.48 213

MVSFormer		99.17 99	99.12 91	99.29 181	99.51 199	98.94 186	99.88 4	99.46 209	97.55 247	99.80 66	99.65 218	97.39 122	99.28 337	99.03 112	99.85 87	99.65 150

test_djsdf		98.67 185	98.57 186	98.98 219	98.70 391	98.91 191	99.88 4	99.46 209	97.55 247	99.22 228	99.88 46	95.73 198	99.28 337	99.03 112	97.62 295	98.75 305

OurMVSNet-221017-0		97.88 267	97.77 258	98.19 329	98.71 390	96.53 350	99.88 4	99.00 358	97.79 218	98.78 310	99.94 6	91.68 346	99.35 327	97.21 313	96.99 331	98.69 322

EC-MVSNet		99.44 46	99.39 36	99.58 109	99.56 180	99.49 102	99.88 4	99.58 73	98.38 129	99.73 89	99.69 198	98.20 100	99.70 259	99.64 40	99.82 110	99.54 189

DVP-MVS++		99.59 13	99.50 17	99.88 12	99.51 199	99.88 9	99.87 8	99.51 136	98.99 62	99.88 37	99.81 112	99.27 5	99.96 38	98.85 141	99.80 118	99.81 73

FOURS1					99.91 1	99.93 1	99.87 8	99.56 83	99.10 41	99.81 62

K. test v3		97.10 348	96.79 348	98.01 342	98.72 388	96.33 357	99.87 8	97.05 435	97.59 241	96.16 414	99.80 126	88.71 386	99.04 379	96.69 345	96.55 337	98.65 346

FC-MVSNet-test		98.75 178	98.62 179	99.15 203	99.08 330	99.45 108	99.86 11	99.60 62	98.23 152	98.70 322	99.82 98	96.80 149	99.22 351	99.07 108	96.38 340	98.79 295

v7n		97.87 269	97.52 287	98.92 230	98.76 384	98.58 227	99.84 12	99.46 209	96.20 363	98.91 288	99.70 187	94.89 235	99.44 307	96.03 362	93.89 398	98.75 305

DTE-MVSNet		97.51 324	97.19 333	98.46 300	98.63 397	98.13 259	99.84 12	99.48 179	96.68 325	97.97 376	99.67 211	92.92 309	98.56 413	96.88 338	92.60 416	98.70 318

3Dnovator	97.25 9	99.24 91	99.05 102	99.81 54	99.12 319	99.66 64	99.84 12	99.74 10	99.09 48	98.92 287	99.90 31	95.94 186	99.98 17	98.95 121	99.92 36	99.79 86

FIs		98.78 175	98.63 174	99.23 193	99.18 303	99.54 91	99.83 15	99.59 68	98.28 141	98.79 309	99.81 112	96.75 152	99.37 320	99.08 107	96.38 340	98.78 297

MGCFI-Net		99.01 143	98.85 147	99.50 142	99.42 233	99.26 137	99.82 16	99.48 179	98.60 108	99.28 211	98.81 395	97.04 140	99.76 232	99.29 83	97.87 284	99.47 219

test_fmvs3		92.10 402	91.77 405	93.08 416	96.19 435	86.25 436	99.82 16	98.62 410	96.65 328	95.19 422	96.90 436	55.05 451	95.93 443	96.63 350	90.92 425	97.06 432

jajsoiax		98.43 199	98.28 206	98.88 241	98.60 401	98.43 245	99.82 16	99.53 113	98.19 157	98.63 334	99.80 126	93.22 304	99.44 307	99.22 91	97.50 307	98.77 301

OpenMVS	96.50 16	98.47 196	98.12 217	99.52 132	99.04 338	99.53 94	99.82 16	99.72 11	94.56 402	98.08 369	99.88 46	94.73 247	99.98 17	97.47 298	99.76 133	99.06 276

SDMVSNet		99.11 123	98.90 136	99.75 70	99.81 51	99.59 81	99.81 20	99.65 35	98.78 91	99.64 124	99.88 46	94.56 259	99.93 104	99.67 34	98.26 262	99.72 122

nrg030		98.64 189	98.42 196	99.28 185	99.05 336	99.69 56	99.81 20	99.46 209	98.04 189	99.01 270	99.82 98	96.69 154	99.38 317	99.34 74	94.59 385	98.78 297

HPM-MVS		99.42 51	99.28 65	99.83 50	99.90 4	99.72 50	99.81 20	99.54 100	97.59 241	99.68 102	99.63 230	98.91 37	99.94 86	98.58 182	99.91 43	99.84 50
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
EPP-MVSNet		99.13 111	98.99 119	99.53 126	99.65 145	99.06 164	99.81 20	99.33 290	97.43 264	99.60 138	99.88 46	97.14 134	99.84 184	99.13 100	98.94 215	99.69 135

3Dnovator+	97.12 13	99.18 97	98.97 123	99.82 51	99.17 311	99.68 57	99.81 20	99.51 136	99.20 28	98.72 315	99.89 37	95.68 200	99.97 26	98.86 139	99.86 80	99.81 73

sasdasda		99.02 139	98.86 145	99.51 137	99.42 233	99.32 124	99.80 25	99.48 179	98.63 103	99.31 203	98.81 395	97.09 136	99.75 235	99.27 87	97.90 281	99.47 219

FA-MVS(test-final)		98.75 178	98.53 190	99.41 157	99.55 184	99.05 166	99.80 25	99.01 357	96.59 338	99.58 142	99.59 244	95.39 210	99.90 141	97.78 264	99.49 170	99.28 251

GeoE		98.85 167	98.62 179	99.53 126	99.61 162	99.08 161	99.80 25	99.51 136	97.10 296	99.31 203	99.78 147	95.23 221	99.77 228	98.21 222	99.03 209	99.75 100

canonicalmvs		99.02 139	98.86 145	99.51 137	99.42 233	99.32 124	99.80 25	99.48 179	98.63 103	99.31 203	98.81 395	97.09 136	99.75 235	99.27 87	97.90 281	99.47 219

v8		97.95 258	97.63 277	98.93 228	98.95 353	98.81 207	99.80 25	99.41 243	96.03 377	99.10 253	99.42 302	94.92 233	99.30 335	96.94 333	94.08 395	98.66 344

Vis-MVSNet (Re-imp)		98.87 156	98.72 160	99.31 173	99.71 110	98.88 193	99.80 25	99.44 229	97.91 202	99.36 194	99.78 147	95.49 207	99.43 311	97.91 249	99.11 200	99.62 165

Anonymous20240521		96.20 367	95.89 370	97.13 388	97.72 422	94.96 395	99.79 31	99.29 313	93.01 416	97.20 399	99.03 374	89.69 376	98.36 417	91.16 423	96.13 346	98.07 408

PS-MVSNAJss		98.92 150	98.92 132	98.90 236	98.78 377	98.53 231	99.78 32	99.54 100	98.07 180	99.00 274	99.76 160	99.01 18	99.37 320	99.13 100	97.23 324	98.81 294

PEN-MVS		97.76 290	97.44 303	98.72 267	98.77 382	98.54 230	99.78 32	99.51 136	97.06 300	98.29 359	99.64 224	92.63 322	98.89 404	98.09 233	93.16 408	98.72 311

anonymousdsp		98.44 198	98.28 206	98.94 226	98.50 407	98.96 179	99.77 34	99.50 156	97.07 298	98.87 296	99.77 156	94.76 245	99.28 337	98.66 168	97.60 296	98.57 372

SixPastTwentyTwo		97.50 325	97.33 321	98.03 339	98.65 395	96.23 362	99.77 34	98.68 406	97.14 289	97.90 379	99.93 10	90.45 365	99.18 359	97.00 327	96.43 339	98.67 335

QAPM		98.67 185	98.30 205	99.80 58	99.20 297	99.67 61	99.77 34	99.72 11	94.74 399	98.73 314	99.90 31	95.78 196	99.98 17	96.96 331	99.88 69	99.76 99

SSC-MVS		92.73 401	93.73 396	89.72 426	95.02 445	81.38 446	99.76 37	99.23 325	94.87 396	92.80 433	98.93 387	94.71 249	91.37 450	74.49 449	93.80 399	96.42 436

test_vis3_rt		87.04 409	85.81 412	90.73 423	93.99 447	81.96 444	99.76 37	90.23 458	92.81 419	81.35 446	91.56 446	40.06 455	99.07 376	94.27 396	88.23 433	91.15 446

dcpmvs_2		99.23 92	99.58 7	98.16 331	99.83 43	94.68 400	99.76 37	99.52 118	99.07 51	99.98 11	99.88 46	98.56 77	99.93 104	99.67 34	99.98 4	99.87 37

RRT-MVS		98.91 151	98.75 158	99.39 162	99.46 223	98.61 225	99.76 37	99.50 156	98.06 184	99.81 62	99.88 46	93.91 288	99.94 86	99.11 102	99.27 187	99.61 167

HPM-MVS_fast		99.51 25	99.40 34	99.85 37	99.91 1	99.79 35	99.76 37	99.56 83	97.72 226	99.76 83	99.75 165	99.13 12	99.92 116	99.07 108	99.92 36	99.85 43

lecture		99.60 12	99.50 17	99.89 8	99.89 8	99.90 2	99.75 42	99.59 68	99.06 54	99.88 37	99.85 71	98.41 90	99.96 38	99.28 84	99.84 95	99.83 60

MVSMamba_PlusPlus		99.46 38	99.41 33	99.64 94	99.68 125	99.50 101	99.75 42	99.50 156	98.27 143	99.87 43	99.92 17	98.09 105	99.94 86	99.65 38	99.95 20	99.47 219

v10		97.85 272	97.52 287	98.86 248	98.99 346	98.67 216	99.75 42	99.41 243	95.70 381	98.98 277	99.41 306	94.75 246	99.23 347	96.01 364	94.63 384	98.67 335

APDe-MVS		99.66 5	99.57 8	99.92 1	99.77 71	99.89 5	99.75 42	99.56 83	99.02 55	99.88 37	99.85 71	99.18 10	99.96 38	99.22 91	99.92 36	99.90 23
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
IS-MVSNet		99.05 135	98.87 143	99.57 113	99.73 100	99.32 124	99.75 42	99.20 331	98.02 194	99.56 146	99.86 64	96.54 161	99.67 267	98.09 233	99.13 199	99.73 113

test_vis1_n		97.92 262	97.44 303	99.34 166	99.53 190	98.08 262	99.74 47	99.49 167	99.15 31	100.00 1	99.94 6	79.51 439	99.98 17	99.88 23	99.76 133	99.97 4

test_fmvs1_n		98.41 202	98.14 214	99.21 194	99.82 47	97.71 288	99.74 47	99.49 167	99.32 24	99.99 2	99.95 3	85.32 417	99.97 26	99.82 26	99.84 95	99.96 7

balanced_conf03		99.46 38	99.39 36	99.67 83	99.55 184	99.58 86	99.74 47	99.51 136	98.42 126	99.87 43	99.84 84	98.05 108	99.91 128	99.58 44	99.94 28	99.52 196

tttt0517		98.42 200	98.14 214	99.28 185	99.66 138	98.38 248	99.74 47	96.85 437	97.68 232	99.79 68	99.74 170	91.39 354	99.89 156	98.83 147	99.56 163	99.57 183

WB-MVS		93.10 399	94.10 392	90.12 425	95.51 443	81.88 445	99.73 51	99.27 318	95.05 392	93.09 432	98.91 391	94.70 250	91.89 449	76.62 447	94.02 397	96.58 435

test_fmvs2		97.25 342	97.30 324	97.09 390	99.43 231	93.31 421	99.73 51	98.87 380	98.83 81	99.28 211	99.80 126	84.45 422	99.66 270	97.88 251	97.45 312	98.30 394

SD_0403		97.55 319	97.53 286	97.62 372	99.61 162	93.64 418	99.72 53	99.44 229	98.03 191	98.62 337	99.39 314	96.06 179	99.57 289	87.88 436	99.01 212	99.66 145

MonoMVSNet		98.38 206	98.47 194	98.12 336	98.59 403	96.19 364	99.72 53	98.79 391	97.89 204	99.44 171	99.52 272	96.13 176	98.90 403	98.64 170	97.54 302	99.28 251

baseline		99.15 105	99.02 112	99.53 126	99.66 138	99.14 153	99.72 53	99.48 179	98.35 134	99.42 176	99.84 84	96.07 178	99.79 221	99.51 53	99.14 198	99.67 142

RPSCF		98.22 217	98.62 179	96.99 391	99.82 47	91.58 430	99.72 53	99.44 229	96.61 333	99.66 112	99.89 37	95.92 187	99.82 205	97.46 299	99.10 203	99.57 183

CSCG		99.32 74	99.32 50	99.32 172	99.85 28	98.29 250	99.71 57	99.66 28	98.11 172	99.41 180	99.80 126	98.37 93	99.96 38	98.99 116	99.96 15	99.72 122

dmvs_re		98.08 234	98.16 211	97.85 357	99.55 184	94.67 401	99.70 58	98.92 368	98.15 162	99.06 264	99.35 326	93.67 296	99.25 344	97.77 267	97.25 323	99.64 157

WR-MVS_H		98.13 228	97.87 248	98.90 236	99.02 340	98.84 199	99.70 58	99.59 68	97.27 278	98.40 351	99.19 358	95.53 205	99.23 347	98.34 212	93.78 400	98.61 366

mvsmamba		99.06 133	98.96 127	99.36 164	99.47 221	98.64 220	99.70 58	99.05 352	97.61 240	99.65 119	99.83 89	96.54 161	99.92 116	99.19 93	99.62 158	99.51 205

LTVRE_ROB	97.16 12	98.02 246	97.90 243	98.40 310	99.23 290	96.80 339	99.70 58	99.60 62	97.12 292	98.18 366	99.70 187	91.73 345	99.72 247	98.39 205	97.45 312	98.68 327
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
test_f		91.90 403	91.26 407	93.84 412	95.52 442	85.92 437	99.69 62	98.53 414	95.31 386	93.87 428	96.37 439	55.33 450	98.27 418	95.70 370	90.98 424	97.32 431

XVS		99.53 23	99.42 28	99.87 18	99.85 28	99.83 20	99.69 62	99.68 20	98.98 65	99.37 191	99.74 170	98.81 47	99.94 86	98.79 152	99.86 80	99.84 50

X-MVStestdata		96.55 359	95.45 378	99.87 18	99.85 28	99.83 20	99.69 62	99.68 20	98.98 65	99.37 191	64.01 455	98.81 47	99.94 86	98.79 152	99.86 80	99.84 50

V42		98.06 236	97.79 253	98.86 248	98.98 349	98.84 199	99.69 62	99.34 282	96.53 340	99.30 207	99.37 320	94.67 252	99.32 332	97.57 288	94.66 383	98.42 386

mPP-MVS		99.44 46	99.30 58	99.86 29	99.88 13	99.79 35	99.69 62	99.48 179	98.12 170	99.50 158	99.75 165	98.78 51	99.97 26	98.57 185	99.89 65	99.83 60

CP-MVS		99.45 42	99.32 50	99.85 37	99.83 43	99.75 45	99.69 62	99.52 118	98.07 180	99.53 153	99.63 230	98.93 36	99.97 26	98.74 156	99.91 43	99.83 60

FE-MVS		98.48 195	98.17 210	99.40 158	99.54 189	98.96 179	99.68 68	98.81 387	95.54 383	99.62 131	99.70 187	93.82 291	99.93 104	97.35 307	99.46 171	99.32 248

PS-CasMVS		97.93 259	97.59 281	98.95 224	98.99 346	99.06 164	99.68 68	99.52 118	97.13 290	98.31 356	99.68 205	92.44 331	99.05 378	98.51 193	94.08 395	98.75 305

Vis-MVSNet		99.12 117	98.97 123	99.56 115	99.78 63	99.10 157	99.68 68	99.66 28	98.49 117	99.86 47	99.87 57	94.77 244	99.84 184	99.19 93	99.41 175	99.74 104
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
KinetiMVS		99.12 117	98.92 132	99.70 80	99.67 127	99.40 114	99.67 71	99.63 42	98.73 95	99.94 25	99.81 112	94.54 262	99.96 38	98.40 204	99.93 30	99.74 104

BP-MVS1		99.12 117	98.94 131	99.65 88	99.51 199	99.30 131	99.67 71	98.92 368	98.48 118	99.84 50	99.69 198	94.96 228	99.92 116	99.62 41	99.79 125	99.71 131

test_vis1_n_1920		98.63 190	98.40 198	99.31 173	99.86 22	97.94 275	99.67 71	99.62 46	99.43 14	99.99 2	99.91 24	87.29 404	100.00 1	99.92 21	99.92 36	99.98 2

EIA-MVS		99.18 97	99.09 97	99.45 150	99.49 213	99.18 145	99.67 71	99.53 113	97.66 235	99.40 185	99.44 298	98.10 104	99.81 210	98.94 122	99.62 158	99.35 243

MSP-MVS		99.42 51	99.27 69	99.88 12	99.89 8	99.80 32	99.67 71	99.50 156	98.70 99	99.77 77	99.49 282	98.21 99	99.95 73	98.46 199	99.77 130	99.88 32
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
MVS_Test		99.10 127	98.97 123	99.48 143	99.49 213	99.14 153	99.67 71	99.34 282	97.31 275	99.58 142	99.76 160	97.65 118	99.82 205	98.87 134	99.07 206	99.46 224

CP-MVSNet		98.09 232	97.78 256	99.01 215	98.97 351	99.24 140	99.67 71	99.46 209	97.25 280	98.48 348	99.64 224	93.79 292	99.06 377	98.63 172	94.10 394	98.74 309

MTAPA		99.52 24	99.39 36	99.89 8	99.90 4	99.86 17	99.66 78	99.47 200	98.79 88	99.68 102	99.81 112	98.43 86	99.97 26	98.88 131	99.90 54	99.83 60

HFP-MVS		99.49 29	99.37 40	99.86 29	99.87 17	99.80 32	99.66 78	99.67 23	98.15 162	99.68 102	99.69 198	99.06 16	99.96 38	98.69 164	99.87 72	99.84 50

mvs_tets		98.40 205	98.23 208	98.91 234	98.67 394	98.51 237	99.66 78	99.53 113	98.19 157	98.65 331	99.81 112	92.75 313	99.44 307	99.31 78	97.48 311	98.77 301

EU-MVSNet		97.98 253	98.03 229	97.81 363	98.72 388	96.65 346	99.66 78	99.66 28	98.09 175	98.35 354	99.82 98	95.25 219	98.01 424	97.41 303	95.30 371	98.78 297

ACMMPR		99.49 29	99.36 42	99.86 29	99.87 17	99.79 35	99.66 78	99.67 23	98.15 162	99.67 107	99.69 198	98.95 30	99.96 38	98.69 164	99.87 72	99.84 50

MP-MVS		99.33 72	99.15 87	99.87 18	99.88 13	99.82 26	99.66 78	99.46 209	98.09 175	99.48 162	99.74 170	98.29 96	99.96 38	97.93 248	99.87 72	99.82 66
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
NormalMVS		99.27 83	99.19 83	99.52 132	99.89 8	98.83 202	99.65 84	99.52 118	99.10 41	99.84 50	99.76 160	95.80 194	99.99 4	99.30 81	99.84 95	99.74 104

SymmetryMVS		99.15 105	99.02 112	99.52 132	99.72 104	98.83 202	99.65 84	99.34 282	99.10 41	99.84 50	99.76 160	95.80 194	99.99 4	99.30 81	98.72 234	99.73 113

Elysia		98.88 153	98.65 171	99.58 109	99.58 172	99.34 120	99.65 84	99.52 118	98.26 145	99.83 58	99.87 57	93.37 299	99.90 141	97.81 261	99.91 43	99.49 210

StellarMVS		98.88 153	98.65 171	99.58 109	99.58 172	99.34 120	99.65 84	99.52 118	98.26 145	99.83 58	99.87 57	93.37 299	99.90 141	97.81 261	99.91 43	99.49 210

test_cas_vis1_n_1920		99.16 101	99.01 117	99.61 102	99.81 51	98.86 197	99.65 84	99.64 38	99.39 19	99.97 22	99.94 6	93.20 305	99.98 17	99.55 47	99.91 43	99.99 1

region2R		99.48 33	99.35 44	99.87 18	99.88 13	99.80 32	99.65 84	99.66 28	98.13 168	99.66 112	99.68 205	98.96 25	99.96 38	98.62 173	99.87 72	99.84 50

TranMVSNet+NR-MVSNet		97.93 259	97.66 272	98.76 264	98.78 377	98.62 223	99.65 84	99.49 167	97.76 222	98.49 347	99.60 242	94.23 273	98.97 395	98.00 244	92.90 410	98.70 318

GDP-MVS		99.08 130	98.89 139	99.64 94	99.53 190	99.34 120	99.64 91	99.48 179	98.32 138	99.77 77	99.66 216	95.14 224	99.93 104	98.97 120	99.50 169	99.64 157

ttmdpeth		97.80 286	97.63 277	98.29 320	98.77 382	97.38 299	99.64 91	99.36 270	98.78 91	96.30 412	99.58 248	92.34 334	99.39 315	98.36 210	95.58 364	98.10 406

mvsany_test3		93.77 396	93.45 400	94.74 409	95.78 438	88.01 435	99.64 91	98.25 418	98.28 141	94.31 426	97.97 428	68.89 443	98.51 415	97.50 294	90.37 426	97.71 423

ZNCC-MVS		99.47 36	99.33 48	99.87 18	99.87 17	99.81 30	99.64 91	99.67 23	98.08 179	99.55 150	99.64 224	98.91 37	99.96 38	98.72 159	99.90 54	99.82 66

tfpnnormal		97.84 276	97.47 295	98.98 219	99.20 297	99.22 142	99.64 91	99.61 55	96.32 354	98.27 360	99.70 187	93.35 301	99.44 307	95.69 371	95.40 369	98.27 396

casdiffmvs_mvg		99.15 105	99.02 112	99.55 117	99.66 138	99.09 158	99.64 91	99.56 83	98.26 145	99.45 166	99.87 57	96.03 181	99.81 210	99.54 48	99.15 197	99.73 113
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
SR-MVS-dyc-post		99.45 42	99.31 56	99.85 37	99.76 75	99.82 26	99.63 97	99.52 118	98.38 129	99.76 83	99.82 98	98.53 79	99.95 73	98.61 176	99.81 113	99.77 94

RE-MVS-def			99.34 46		99.76 75	99.82 26	99.63 97	99.52 118	98.38 129	99.76 83	99.82 98	98.75 58		98.61 176	99.81 113	99.77 94

TSAR-MVS + MP.		99.58 14	99.50 17	99.81 54	99.91 1	99.66 64	99.63 97	99.39 253	98.91 75	99.78 73	99.85 71	99.36 2	99.94 86	98.84 144	99.88 69	99.82 66
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
Anonymous20231206		96.22 365	96.03 366	96.79 399	97.31 428	94.14 410	99.63 97	99.08 346	96.17 366	97.04 403	99.06 371	93.94 285	97.76 430	86.96 439	95.06 376	98.47 380

APD-MVS_3200maxsize		99.48 33	99.35 44	99.85 37	99.76 75	99.83 20	99.63 97	99.54 100	98.36 133	99.79 68	99.82 98	98.86 41	99.95 73	98.62 173	99.81 113	99.78 92

test0726					99.85 28	99.89 5	99.62 102	99.50 156	99.10 41	99.86 47	99.82 98	98.94 32

EPNet		98.86 159	98.71 162	99.30 178	97.20 430	98.18 255	99.62 102	98.91 373	99.28 26	98.63 334	99.81 112	95.96 183	99.99 4	99.24 90	99.72 141	99.73 113
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
114514_t		98.93 149	98.67 166	99.72 79	99.85 28	99.53 94	99.62 102	99.59 68	92.65 421	99.71 96	99.78 147	98.06 107	99.90 141	98.84 144	99.91 43	99.74 104

HY-MVS	97.30 7	98.85 167	98.64 173	99.47 147	99.42 233	99.08 161	99.62 102	99.36 270	97.39 269	99.28 211	99.68 205	96.44 167	99.92 116	98.37 208	98.22 265	99.40 236

ACMMP		99.45 42	99.32 50	99.82 51	99.89 8	99.67 61	99.62 102	99.69 18	98.12 170	99.63 127	99.84 84	98.73 63	99.96 38	98.55 191	99.83 106	99.81 73
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
DeepC-MVS	98.35 2	99.30 77	99.19 83	99.64 94	99.82 47	99.23 141	99.62 102	99.55 91	98.94 71	99.63 127	99.95 3	95.82 192	99.94 86	99.37 68	99.97 8	99.73 113
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
EI-MVSNet-Vis-set		99.58 14	99.56 10	99.64 94	99.78 63	99.15 152	99.61 108	99.45 220	99.01 57	99.89 34	99.82 98	99.01 18	99.92 116	99.56 46	99.95 20	99.85 43

reproduce_monomvs		97.89 266	97.87 248	97.96 348	99.51 199	95.45 381	99.60 109	99.25 321	99.17 29	98.85 301	99.49 282	89.29 380	99.64 278	99.35 69	96.31 343	98.78 297

test2506		96.81 355	96.65 351	97.29 385	99.74 93	92.21 428	99.60 109	85.06 459	99.13 34	99.77 77	99.93 10	87.82 402	99.85 176	99.38 67	99.38 176	99.80 82

SED-MVS		99.61 8	99.52 12	99.88 12	99.84 34	99.90 2	99.60 109	99.48 179	99.08 49	99.91 28	99.81 112	99.20 7	99.96 38	98.91 128	99.85 87	99.79 86

OPU-MVS				99.64 94	99.56 180	99.72 50	99.60 109				99.70 187	99.27 5	99.42 313	98.24 221	99.80 118	99.79 86

GST-MVS		99.40 59	99.24 74	99.85 37	99.86 22	99.79 35	99.60 109	99.67 23	97.97 197	99.63 127	99.68 205	98.52 80	99.95 73	98.38 206	99.86 80	99.81 73

EI-MVSNet-UG-set		99.58 14	99.57 8	99.64 94	99.78 63	99.14 153	99.60 109	99.45 220	99.01 57	99.90 31	99.83 89	98.98 24	99.93 104	99.59 42	99.95 20	99.86 39

ACMH	97.28 8	98.10 231	97.99 233	98.44 305	99.41 238	96.96 329	99.60 109	99.56 83	98.09 175	98.15 367	99.91 24	90.87 362	99.70 259	98.88 131	97.45 312	98.67 335
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
VortexMVS		98.67 185	98.66 169	98.68 272	99.62 157	97.96 270	99.59 116	99.41 243	98.13 168	99.31 203	99.70 187	95.48 208	99.27 340	99.40 65	97.32 321	98.79 295

guyue		99.16 101	99.04 104	99.52 132	99.69 120	98.92 190	99.59 116	98.81 387	98.73 95	99.90 31	99.87 57	95.34 213	99.88 161	99.66 37	99.81 113	99.74 104

ECVR-MVS		98.04 242	98.05 227	98.00 344	99.74 93	94.37 407	99.59 116	94.98 447	99.13 34	99.66 112	99.93 10	90.67 364	99.84 184	99.40 65	99.38 176	99.80 82

SR-MVS		99.43 49	99.29 62	99.86 29	99.75 85	99.83 20	99.59 116	99.62 46	98.21 155	99.73 89	99.79 140	98.68 67	99.96 38	98.44 201	99.77 130	99.79 86

thres100view900		97.76 290	97.45 298	98.69 271	99.72 104	97.86 279	99.59 116	98.74 397	97.93 200	99.26 221	98.62 403	91.75 343	99.83 197	93.22 408	98.18 270	98.37 392

thres600view7		97.86 271	97.51 289	98.92 230	99.72 104	97.95 273	99.59 116	98.74 397	97.94 199	99.27 216	98.62 403	91.75 343	99.86 170	93.73 403	98.19 269	98.96 287

LCM-MVSNet-Re		97.83 279	98.15 213	96.87 397	99.30 270	92.25 427	99.59 116	98.26 417	97.43 264	96.20 413	99.13 364	96.27 173	98.73 410	98.17 227	98.99 213	99.64 157

baseline1		98.31 211	97.95 238	99.38 163	99.50 211	98.74 211	99.59 116	98.93 365	98.41 127	99.14 245	99.60 242	94.59 257	99.79 221	98.48 195	93.29 405	99.61 167

SteuartSystems-ACMMP		99.54 20	99.42 28	99.87 18	99.82 47	99.81 30	99.59 116	99.51 136	98.62 105	99.79 68	99.83 89	99.28 4	99.97 26	98.48 195	99.90 54	99.84 50
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CPTT-MVS		99.11 123	98.90 136	99.74 73	99.80 57	99.46 107	99.59 116	99.49 167	97.03 304	99.63 127	99.69 198	97.27 130	99.96 38	97.82 259	99.84 95	99.81 73

icg_test_0403		98.86 159	98.89 139	98.78 262	99.55 184	96.93 330	99.58 126	99.44 229	98.05 186	99.68 102	99.80 126	96.81 148	99.80 217	98.15 230	98.92 218	99.60 170

test_fmvsmvis_n_1920		99.65 6	99.61 6	99.77 67	99.38 248	99.37 116	99.58 126	99.62 46	99.41 18	99.87 43	99.92 17	98.81 47	100.00 1	99.97 1	99.93 30	99.94 15

dmvs_testset		95.02 385	96.12 363	91.72 420	99.10 324	80.43 448	99.58 126	97.87 427	97.47 256	95.22 420	98.82 394	93.99 283	95.18 445	88.09 434	94.91 381	99.56 186

test_fmvsm_n_1920		99.69 4	99.66 3	99.78 64	99.84 34	99.44 109	99.58 126	99.69 18	99.43 14	99.98 11	99.91 24	98.62 73	100.00 1	99.97 1	99.95 20	99.90 23

test1111		98.04 242	98.11 218	97.83 360	99.74 93	93.82 412	99.58 126	95.40 446	99.12 39	99.65 119	99.93 10	90.73 363	99.84 184	99.43 64	99.38 176	99.82 66

PGM-MVS		99.45 42	99.31 56	99.86 29	99.87 17	99.78 41	99.58 126	99.65 35	97.84 212	99.71 96	99.80 126	99.12 13	99.97 26	98.33 213	99.87 72	99.83 60

LPG-MVS_test		98.22 217	98.13 216	98.49 292	99.33 261	97.05 318	99.58 126	99.55 91	97.46 257	99.24 223	99.83 89	92.58 323	99.72 247	98.09 233	97.51 305	98.68 327

PHI-MVS		99.30 77	99.17 86	99.70 80	99.56 180	99.52 98	99.58 126	99.80 8	97.12 292	99.62 131	99.73 176	98.58 75	99.90 141	98.61 176	99.91 43	99.68 139

AstraMVS		99.09 128	99.03 107	99.25 188	99.66 138	98.13 259	99.57 134	98.24 419	98.82 82	99.91 28	99.88 46	95.81 193	99.90 141	99.72 29	99.67 151	99.74 104

SF-MVS		99.38 62	99.24 74	99.79 61	99.79 61	99.68 57	99.57 134	99.54 100	97.82 217	99.71 96	99.80 126	98.95 30	99.93 104	98.19 224	99.84 95	99.74 104

DVP-MVS		99.57 17	99.47 22	99.88 12	99.85 28	99.89 5	99.57 134	99.37 269	99.10 41	99.81 62	99.80 126	98.94 32	99.96 38	98.93 125	99.86 80	99.81 73
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				99.91 3	99.84 34	99.89 5	99.57 134	99.51 136					99.96 38	98.93 125	99.86 80	99.88 32

Effi-MVS+-dtu		98.78 175	98.89 139	98.47 299	99.33 261	96.91 333	99.57 134	99.30 309	98.47 119	99.41 180	98.99 380	96.78 150	99.74 237	98.73 158	99.38 176	98.74 309

v2v482		98.06 236	97.77 258	98.92 230	98.90 359	98.82 205	99.57 134	99.36 270	96.65 328	99.19 237	99.35 326	94.20 274	99.25 344	97.72 274	94.97 378	98.69 322

DSMNet-mixed		97.25 342	97.35 315	96.95 394	97.84 418	93.61 419	99.57 134	96.63 441	96.13 371	98.87 296	98.61 405	94.59 257	97.70 431	95.08 385	98.86 224	99.55 187

reproduce_model		99.63 7	99.54 11	99.90 5	99.78 63	99.88 9	99.56 141	99.55 91	99.15 31	99.90 31	99.90 31	99.00 22	99.97 26	99.11 102	99.91 43	99.86 39

MVStest1		96.08 371	95.48 376	97.89 354	98.93 354	96.70 341	99.56 141	99.35 277	92.69 420	91.81 437	99.46 295	89.90 373	98.96 397	95.00 387	92.61 415	98.00 415

fmvsm_l_conf0.5_n_a		99.71 1	99.67 1	99.85 37	99.86 22	99.61 78	99.56 141	99.63 42	99.48 3	99.98 11	99.83 89	98.75 58	99.99 4	99.97 1	99.96 15	99.94 15

fmvsm_l_conf0.5_n		99.71 1	99.67 1	99.85 37	99.84 34	99.63 75	99.56 141	99.63 42	99.47 4	99.98 11	99.82 98	98.75 58	99.99 4	99.97 1	99.97 8	99.94 15

sd_testset		98.75 178	98.57 186	99.29 181	99.81 51	98.26 252	99.56 141	99.62 46	98.78 91	99.64 124	99.88 46	92.02 337	99.88 161	99.54 48	98.26 262	99.72 122

KD-MVS_self_test		95.00 386	94.34 391	96.96 393	97.07 433	95.39 384	99.56 141	99.44 229	95.11 389	97.13 401	97.32 434	91.86 341	97.27 435	90.35 426	81.23 443	98.23 400

ETV-MVS		99.26 86	99.21 79	99.40 158	99.46 223	99.30 131	99.56 141	99.52 118	98.52 115	99.44 171	99.27 348	98.41 90	99.86 170	99.10 105	99.59 161	99.04 277

SMA-MVS		99.44 46	99.30 58	99.85 37	99.73 100	99.83 20	99.56 141	99.47 200	97.45 260	99.78 73	99.82 98	99.18 10	99.91 128	98.79 152	99.89 65	99.81 73
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
AllTest		98.87 156	98.72 160	99.31 173	99.86 22	98.48 241	99.56 141	99.61 55	97.85 210	99.36 194	99.85 71	95.95 184	99.85 176	96.66 347	99.83 106	99.59 176

casdiffmvs		99.13 111	98.98 122	99.56 115	99.65 145	99.16 148	99.56 141	99.50 156	98.33 137	99.41 180	99.86 64	95.92 187	99.83 197	99.45 63	99.16 194	99.70 133
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
XXY-MVS		98.38 206	98.09 222	99.24 191	99.26 282	99.32 124	99.56 141	99.55 91	97.45 260	98.71 316	99.83 89	93.23 302	99.63 284	98.88 131	96.32 342	98.76 303

ACMH+	97.24 10	97.92 262	97.78 256	98.32 317	99.46 223	96.68 345	99.56 141	99.54 100	98.41 127	97.79 385	99.87 57	90.18 371	99.66 270	98.05 241	97.18 327	98.62 357

ACMM	97.58 5	98.37 208	98.34 201	98.48 294	99.41 238	97.10 312	99.56 141	99.45 220	98.53 114	99.04 267	99.85 71	93.00 307	99.71 253	98.74 156	97.45 312	98.64 348
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
LS3D		99.27 83	99.12 91	99.74 73	99.18 303	99.75 45	99.56 141	99.57 78	98.45 122	99.49 161	99.85 71	97.77 115	99.94 86	98.33 213	99.84 95	99.52 196

testing3-2		97.84 276	97.70 268	98.24 326	99.53 190	95.37 385	99.55 155	98.67 407	98.46 120	99.27 216	99.34 330	86.58 408	99.83 197	99.32 77	98.63 237	99.52 196

test_fmvsmconf0.01_n		99.22 94	99.03 107	99.79 61	98.42 410	99.48 104	99.55 155	99.51 136	99.39 19	99.78 73	99.93 10	94.80 239	99.95 73	99.93 20	99.95 20	99.94 15

test_fmvs1		98.88 153	98.79 155	99.16 199	99.69 120	97.61 292	99.55 155	99.49 167	99.32 24	99.98 11	99.91 24	91.41 353	99.96 38	99.82 26	99.92 36	99.90 23

v144192		97.92 262	97.60 280	98.87 245	98.83 371	98.65 218	99.55 155	99.34 282	96.20 363	99.32 202	99.40 310	94.36 269	99.26 343	96.37 358	95.03 377	98.70 318

API-MVS		99.04 136	99.03 107	99.06 209	99.40 243	99.31 128	99.55 155	99.56 83	98.54 113	99.33 201	99.39 314	98.76 55	99.78 226	96.98 329	99.78 127	98.07 408

fmvsm_l_conf0.5_n_3		99.61 8	99.51 16	99.92 1	99.84 34	99.82 26	99.54 160	99.66 28	99.46 7	99.98 11	99.89 37	97.27 130	99.99 4	99.97 1	99.95 20	99.95 11

fmvsm_s_conf0.1_n_a		99.26 86	99.06 100	99.85 37	99.52 196	99.62 76	99.54 160	99.62 46	98.69 100	99.99 2	99.96 1	94.47 266	99.94 86	99.88 23	99.92 36	99.98 2

APD_test1		95.87 373	96.49 355	94.00 411	99.53 190	84.01 440	99.54 160	99.32 300	95.91 379	97.99 374	99.85 71	85.49 415	99.88 161	91.96 419	98.84 226	98.12 405

thisisatest0530		98.35 209	98.03 229	99.31 173	99.63 151	98.56 228	99.54 160	96.75 439	97.53 251	99.73 89	99.65 218	91.25 358	99.89 156	98.62 173	99.56 163	99.48 213

MTMP							99.54 160	98.88 378

v1144		97.98 253	97.69 269	98.85 251	98.87 364	98.66 217	99.54 160	99.35 277	96.27 358	99.23 227	99.35 326	94.67 252	99.23 347	96.73 342	95.16 374	98.68 327

v148		97.79 288	97.55 282	98.50 291	98.74 385	97.72 285	99.54 160	99.33 290	96.26 359	98.90 290	99.51 276	94.68 251	99.14 363	97.83 258	93.15 409	98.63 355

CostFormer		97.72 300	97.73 265	97.71 368	99.15 317	94.02 411	99.54 160	99.02 356	94.67 400	99.04 267	99.35 326	92.35 333	99.77 228	98.50 194	97.94 280	99.34 246

MVSTER		98.49 194	98.32 203	99.00 217	99.35 255	99.02 168	99.54 160	99.38 261	97.41 267	99.20 234	99.73 176	93.86 290	99.36 324	98.87 134	97.56 300	98.62 357

fmvsm_s_conf0.1_n		99.29 79	99.10 93	99.86 29	99.70 115	99.65 68	99.53 169	99.62 46	98.74 94	99.99 2	99.95 3	94.53 264	99.94 86	99.89 22	99.96 15	99.97 4

reproduce-ours		99.61 8	99.52 12	99.90 5	99.76 75	99.88 9	99.52 170	99.54 100	99.13 34	99.89 34	99.89 37	98.96 25	99.96 38	99.04 110	99.90 54	99.85 43

our_new_method		99.61 8	99.52 12	99.90 5	99.76 75	99.88 9	99.52 170	99.54 100	99.13 34	99.89 34	99.89 37	98.96 25	99.96 38	99.04 110	99.90 54	99.85 43

fmvsm_s_conf0.5_n_a		99.56 18	99.47 22	99.85 37	99.83 43	99.64 74	99.52 170	99.65 35	99.10 41	99.98 11	99.92 17	97.35 126	99.96 38	99.94 18	99.92 36	99.95 11

MM		99.40 59	99.28 65	99.74 73	99.67 127	99.31 128	99.52 170	98.87 380	99.55 1	99.74 87	99.80 126	96.47 164	99.98 17	99.97 1	99.97 8	99.94 15

patch_mono-2		99.26 86	99.62 5	98.16 331	99.81 51	94.59 403	99.52 170	99.64 38	99.33 23	99.73 89	99.90 31	99.00 22	99.99 4	99.69 32	99.98 4	99.89 26

Fast-Effi-MVS+-dtu		98.77 177	98.83 151	98.60 277	99.41 238	96.99 325	99.52 170	99.49 167	98.11 172	99.24 223	99.34 330	96.96 144	99.79 221	97.95 247	99.45 172	99.02 280

Fast-Effi-MVS+		98.70 182	98.43 195	99.51 137	99.51 199	99.28 134	99.52 170	99.47 200	96.11 372	99.01 270	99.34 330	96.20 175	99.84 184	97.88 251	98.82 228	99.39 237

v1921920		97.80 286	97.45 298	98.84 252	98.80 373	98.53 231	99.52 170	99.34 282	96.15 369	99.24 223	99.47 291	93.98 284	99.29 336	95.40 379	95.13 375	98.69 322

MIMVSNet1		95.51 379	95.04 384	96.92 396	97.38 425	95.60 374	99.52 170	99.50 156	93.65 410	96.97 405	99.17 359	85.28 418	96.56 440	88.36 433	95.55 366	98.60 369

fmvsm_s_conf0.5_n_8		99.54 20	99.42 28	99.89 8	99.83 43	99.74 48	99.51 179	99.62 46	99.46 7	99.99 2	99.90 31	96.60 157	99.98 17	99.95 13	99.95 20	99.96 7

fmvsm_s_conf0.5_n		99.51 25	99.40 34	99.85 37	99.84 34	99.65 68	99.51 179	99.67 23	99.13 34	99.98 11	99.92 17	96.60 157	99.96 38	99.95 13	99.96 15	99.95 11

UniMVSNet_ETH3D		97.32 339	96.81 347	98.87 245	99.40 243	97.46 296	99.51 179	99.53 113	95.86 380	98.54 344	99.77 156	82.44 431	99.66 270	98.68 166	97.52 304	99.50 209

alignmvs		98.81 171	98.56 188	99.58 109	99.43 231	99.42 111	99.51 179	98.96 363	98.61 106	99.35 197	98.92 390	94.78 241	99.77 228	99.35 69	98.11 275	99.54 189

v1192		97.81 284	97.44 303	98.91 234	98.88 361	98.68 215	99.51 179	99.34 282	96.18 365	99.20 234	99.34 330	94.03 282	99.36 324	95.32 381	95.18 373	98.69 322

test20.03		96.12 369	95.96 368	96.63 400	97.44 424	95.45 381	99.51 179	99.38 261	96.55 339	96.16 414	99.25 351	93.76 294	96.17 441	87.35 438	94.22 391	98.27 396

mvs_anonymous		99.03 138	98.99 119	99.16 199	99.38 248	98.52 235	99.51 179	99.38 261	97.79 218	99.38 189	99.81 112	97.30 128	99.45 302	99.35 69	98.99 213	99.51 205

TAMVS		99.12 117	99.08 98	99.24 191	99.46 223	98.55 229	99.51 179	99.46 209	98.09 175	99.45 166	99.82 98	98.34 94	99.51 296	98.70 161	98.93 216	99.67 142

fmvsm_s_conf0.5_n_6		99.54 20	99.44 27	99.85 37	99.51 199	99.67 61	99.50 187	99.64 38	99.43 14	99.98 11	99.78 147	97.26 132	99.95 73	99.95 13	99.93 30	99.92 21

test_fmvsmconf0.1_n		99.55 19	99.45 26	99.86 29	99.44 230	99.65 68	99.50 187	99.61 55	99.45 11	99.87 43	99.92 17	97.31 127	99.97 26	99.95 13	99.99 1	99.97 4

test_yl		98.86 159	98.63 174	99.54 118	99.49 213	99.18 145	99.50 187	99.07 349	98.22 153	99.61 135	99.51 276	95.37 211	99.84 184	98.60 179	98.33 256	99.59 176

DCV-MVSNet		98.86 159	98.63 174	99.54 118	99.49 213	99.18 145	99.50 187	99.07 349	98.22 153	99.61 135	99.51 276	95.37 211	99.84 184	98.60 179	98.33 256	99.59 176

tfpn200view9		97.72 300	97.38 311	98.72 267	99.69 120	97.96 270	99.50 187	98.73 403	97.83 213	99.17 242	98.45 410	91.67 347	99.83 197	93.22 408	98.18 270	98.37 392

UA-Net		99.42 51	99.29 62	99.80 58	99.62 157	99.55 89	99.50 187	99.70 15	98.79 88	99.77 77	99.96 1	97.45 121	99.96 38	98.92 127	99.90 54	99.89 26

pm-mvs1		97.68 308	97.28 327	98.88 241	99.06 333	98.62 223	99.50 187	99.45 220	96.32 354	97.87 381	99.79 140	92.47 327	99.35 327	97.54 291	93.54 402	98.67 335

EI-MVSNet		98.67 185	98.67 166	98.68 272	99.35 255	97.97 268	99.50 187	99.38 261	96.93 313	99.20 234	99.83 89	97.87 111	99.36 324	98.38 206	97.56 300	98.71 313

CVMVSNet		98.57 192	98.67 166	98.30 319	99.35 255	95.59 375	99.50 187	99.55 91	98.60 108	99.39 187	99.83 89	94.48 265	99.45 302	98.75 155	98.56 244	99.85 43

VPA-MVSNet		98.29 214	97.95 238	99.30 178	99.16 313	99.54 91	99.50 187	99.58 73	98.27 143	99.35 197	99.37 320	92.53 325	99.65 275	99.35 69	94.46 386	98.72 311

thres400		97.77 289	97.38 311	98.92 230	99.69 120	97.96 270	99.50 187	98.73 403	97.83 213	99.17 242	98.45 410	91.67 347	99.83 197	93.22 408	98.18 270	98.96 287

APD-MVS		99.27 83	99.08 98	99.84 49	99.75 85	99.79 35	99.50 187	99.50 156	97.16 288	99.77 77	99.82 98	98.78 51	99.94 86	97.56 289	99.86 80	99.80 82
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
mamba_0404		99.16 101	99.06 100	99.44 154	99.65 145	98.96 179	99.49 199	99.50 156	98.14 167	99.62 131	99.85 71	96.85 146	99.85 176	99.19 93	99.26 189	99.52 196

fmvsm_s_conf0.5_n_4		99.36 67	99.24 74	99.73 76	99.78 63	99.53 94	99.49 199	99.60 62	99.42 17	99.99 2	99.86 64	95.15 223	99.95 73	99.95 13	99.89 65	99.73 113

test_vis1_rt		95.81 375	95.65 374	96.32 404	99.67 127	91.35 431	99.49 199	96.74 440	98.25 148	95.24 419	98.10 425	74.96 440	99.90 141	99.53 50	98.85 225	97.70 425

TransMVSNet (Re)		97.15 346	96.58 352	98.86 248	99.12 319	98.85 198	99.49 199	98.91 373	95.48 384	97.16 400	99.80 126	93.38 298	99.11 372	94.16 399	91.73 419	98.62 357

UniMVSNet (Re)		98.29 214	98.00 232	99.13 204	99.00 343	99.36 119	99.49 199	99.51 136	97.95 198	98.97 279	99.13 364	96.30 172	99.38 317	98.36 210	93.34 404	98.66 344

EPMVS		97.82 282	97.65 273	98.35 314	98.88 361	95.98 367	99.49 199	94.71 449	97.57 244	99.26 221	99.48 288	92.46 330	99.71 253	97.87 253	99.08 205	99.35 243

fmvsm_s_conf0.5_n_9		99.41 55	99.28 65	99.81 54	99.84 34	99.52 98	99.48 205	99.62 46	99.46 7	99.99 2	99.92 17	95.24 220	99.96 38	99.97 1	99.97 8	99.96 7

SSC-MVS3.2		97.34 337	97.15 334	97.93 350	99.02 340	95.76 372	99.48 205	99.58 73	97.62 239	99.09 256	99.53 268	87.95 398	99.27 340	96.42 354	95.66 362	98.75 305

fmvsm_s_conf0.5_n_3		99.37 63	99.20 81	99.87 18	99.75 85	99.70 54	99.48 205	99.66 28	99.45 11	99.99 2	99.93 10	94.64 256	99.97 26	99.94 18	99.97 8	99.95 11

test_fmvsmconf_n		99.70 3	99.64 4	99.87 18	99.80 57	99.66 64	99.48 205	99.64 38	99.45 11	99.92 27	99.92 17	98.62 73	99.99 4	99.96 11	99.99 1	99.96 7

Anonymous20231211		97.88 267	97.54 285	98.90 236	99.71 110	98.53 231	99.48 205	99.57 78	94.16 405	98.81 305	99.68 205	93.23 302	99.42 313	98.84 144	94.42 388	98.76 303

v1240		97.69 305	97.32 322	98.79 260	98.85 368	98.43 245	99.48 205	99.36 270	96.11 372	99.27 216	99.36 323	93.76 294	99.24 346	94.46 393	95.23 372	98.70 318

VPNet		97.84 276	97.44 303	99.01 215	99.21 295	98.94 186	99.48 205	99.57 78	98.38 129	99.28 211	99.73 176	88.89 383	99.39 315	99.19 93	93.27 406	98.71 313

UniMVSNet_NR-MVSNet		98.22 217	97.97 235	98.96 222	98.92 356	98.98 172	99.48 205	99.53 113	97.76 222	98.71 316	99.46 295	96.43 168	99.22 351	98.57 185	92.87 412	98.69 322

TDRefinement		95.42 381	94.57 389	97.97 346	89.83 452	96.11 366	99.48 205	98.75 394	96.74 321	96.68 408	99.88 46	88.65 389	99.71 253	98.37 208	82.74 441	98.09 407

ACMMP_NAP		99.47 36	99.34 46	99.88 12	99.87 17	99.86 17	99.47 214	99.48 179	98.05 186	99.76 83	99.86 64	98.82 46	99.93 104	98.82 151	99.91 43	99.84 50

NR-MVSNet		97.97 256	97.61 279	99.02 214	98.87 364	99.26 137	99.47 214	99.42 240	97.63 237	97.08 402	99.50 279	95.07 226	99.13 366	97.86 254	93.59 401	98.68 327

PVSNet_Blended_VisFu		99.36 67	99.28 65	99.61 102	99.86 22	99.07 163	99.47 214	99.93 2	97.66 235	99.71 96	99.86 64	97.73 116	99.96 38	99.47 61	99.82 110	99.79 86

LuminaMVS		99.23 92	99.10 93	99.61 102	99.35 255	99.31 128	99.46 217	99.13 340	98.61 106	99.86 47	99.89 37	96.41 169	99.91 128	99.67 34	99.51 167	99.63 162

fmvsm_s_conf0.1_n_2		99.37 63	99.22 78	99.81 54	99.77 71	99.75 45	99.46 217	99.60 62	99.47 4	99.98 11	99.94 6	94.98 227	99.95 73	99.97 1	99.79 125	99.73 113

SD-MVS		99.41 55	99.52 12	99.05 211	99.74 93	99.68 57	99.46 217	99.52 118	99.11 40	99.88 37	99.91 24	99.43 1	97.70 431	98.72 159	99.93 30	99.77 94
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
testing3		97.28 340	96.76 349	98.82 254	99.37 251	98.07 263	99.45 220	99.36 270	97.56 246	97.89 380	98.95 385	83.70 425	98.82 405	96.03 362	98.56 244	99.58 180

tt0805		97.97 256	97.77 258	98.57 282	99.59 170	96.61 348	99.45 220	99.08 346	98.21 155	98.88 293	99.80 126	88.66 388	99.70 259	98.58 182	97.72 290	99.39 237

tpm2		97.44 332	97.34 318	97.74 367	99.15 317	94.36 408	99.45 220	98.94 364	93.45 414	98.90 290	99.44 298	91.35 355	99.59 288	97.31 308	98.07 276	99.29 250

FMVSNet2		97.72 300	97.36 313	98.80 259	99.51 199	98.84 199	99.45 220	99.42 240	96.49 342	98.86 300	99.29 343	90.26 367	98.98 388	96.44 353	96.56 336	98.58 371

CDS-MVSNet		99.09 128	99.03 107	99.25 188	99.42 233	98.73 212	99.45 220	99.46 209	98.11 172	99.46 165	99.77 156	98.01 109	99.37 320	98.70 161	98.92 218	99.66 145
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
MAR-MVS		98.86 159	98.63 174	99.54 118	99.37 251	99.66 64	99.45 220	99.54 100	96.61 333	99.01 270	99.40 310	97.09 136	99.86 170	97.68 279	99.53 166	99.10 265
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
fmvsm_s_conf0.5_n_2		99.32 74	99.13 89	99.89 8	99.80 57	99.77 42	99.44 226	99.58 73	99.47 4	99.99 2	99.93 10	94.04 281	99.96 38	99.96 11	99.93 30	99.93 20

UGNet		98.87 156	98.69 164	99.40 158	99.22 294	98.72 213	99.44 226	99.68 20	99.24 27	99.18 241	99.42 302	92.74 315	99.96 38	99.34 74	99.94 28	99.53 195
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
ab-mvs		98.86 159	98.63 174	99.54 118	99.64 148	99.19 143	99.44 226	99.54 100	97.77 221	99.30 207	99.81 112	94.20 274	99.93 104	99.17 98	98.82 228	99.49 210

test_0402		96.64 358	96.24 360	97.85 357	98.85 368	96.43 354	99.44 226	99.26 319	93.52 411	96.98 404	99.52 272	88.52 392	99.20 358	92.58 418	97.50 307	97.93 420

ACMP	97.20 11	98.06 236	97.94 240	98.45 302	99.37 251	97.01 323	99.44 226	99.49 167	97.54 250	98.45 349	99.79 140	91.95 339	99.72 247	97.91 249	97.49 310	98.62 357
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
GG-mvs-BLEND				98.45 302	98.55 405	98.16 256	99.43 231	93.68 451		97.23 396	98.46 409	89.30 379	99.22 351	95.43 378	98.22 265	97.98 417

HPM-MVS++		99.39 61	99.23 77	99.87 18	99.75 85	99.84 19	99.43 231	99.51 136	98.68 102	99.27 216	99.53 268	98.64 72	99.96 38	98.44 201	99.80 118	99.79 86

tpm cat1		97.39 334	97.36 313	97.50 379	99.17 311	93.73 414	99.43 231	99.31 304	91.27 425	98.71 316	99.08 368	94.31 272	99.77 228	96.41 356	98.50 248	99.00 281

tpm		97.67 311	97.55 282	98.03 339	99.02 340	95.01 393	99.43 231	98.54 413	96.44 348	99.12 248	99.34 330	91.83 342	99.60 287	97.75 270	96.46 338	99.48 213

GBi-Net		97.68 308	97.48 292	98.29 320	99.51 199	97.26 305	99.43 231	99.48 179	96.49 342	99.07 259	99.32 338	90.26 367	98.98 388	97.10 321	96.65 333	98.62 357

test1		97.68 308	97.48 292	98.29 320	99.51 199	97.26 305	99.43 231	99.48 179	96.49 342	99.07 259	99.32 338	90.26 367	98.98 388	97.10 321	96.65 333	98.62 357

FMVSNet1		96.84 354	96.36 358	98.29 320	99.32 268	97.26 305	99.43 231	99.48 179	95.11 389	98.55 343	99.32 338	83.95 424	98.98 388	95.81 367	96.26 344	98.62 357

fmvsm_s_conf0.5_n_7		99.34 70	99.29 62	99.48 143	99.70 115	98.63 221	99.42 238	99.63 42	99.46 7	99.98 11	99.88 46	95.59 203	99.96 38	99.97 1	99.98 4	99.85 43

fmvsm_s_conf0.5_n_5		99.37 63	99.21 79	99.86 29	99.80 57	99.68 57	99.42 238	99.61 55	99.37 21	99.97 22	99.86 64	94.96 228	99.99 4	99.97 1	99.93 30	99.92 21

mamv4		99.33 72	99.42 28	99.07 207	99.67 127	97.73 283	99.42 238	99.60 62	98.15 162	99.94 25	99.91 24	98.42 88	99.94 86	99.72 29	99.96 15	99.54 189

testgi		97.65 313	97.50 290	98.13 335	99.36 254	96.45 353	99.42 238	99.48 179	97.76 222	97.87 381	99.45 297	91.09 359	98.81 406	94.53 392	98.52 247	99.13 264

F-COLMAP		99.19 95	99.04 104	99.64 94	99.78 63	99.27 136	99.42 238	99.54 100	97.29 277	99.41 180	99.59 244	98.42 88	99.93 104	98.19 224	99.69 146	99.73 113

Anonymous202405211		98.30 213	97.98 234	99.26 187	99.57 176	98.16 256	99.41 243	98.55 412	96.03 377	99.19 237	99.74 170	91.87 340	99.92 116	99.16 99	98.29 261	99.70 133

MSLP-MVS++		99.46 38	99.47 22	99.44 154	99.60 168	99.16 148	99.41 243	99.71 13	98.98 65	99.45 166	99.78 147	99.19 9	99.54 294	99.28 84	99.84 95	99.63 162

VNet		99.11 123	98.90 136	99.73 76	99.52 196	99.56 87	99.41 243	99.39 253	99.01 57	99.74 87	99.78 147	95.56 204	99.92 116	99.52 52	98.18 270	99.72 122

baseline2		97.87 269	97.55 282	98.82 254	99.18 303	98.02 265	99.41 243	96.58 443	96.97 307	96.51 409	99.17 359	93.43 297	99.57 289	97.71 275	99.03 209	98.86 291

DU-MVS		98.08 234	97.79 253	98.96 222	98.87 364	98.98 172	99.41 243	99.45 220	97.87 206	98.71 316	99.50 279	94.82 237	99.22 351	98.57 185	92.87 412	98.68 327

Baseline_NR-MVSNet		97.76 290	97.45 298	98.68 272	99.09 327	98.29 250	99.41 243	98.85 382	95.65 382	98.63 334	99.67 211	94.82 237	99.10 374	98.07 240	92.89 411	98.64 348

XVG-ACMP-BASELINE		97.83 279	97.71 267	98.20 328	99.11 321	96.33 357	99.41 243	99.52 118	98.06 184	99.05 266	99.50 279	89.64 377	99.73 243	97.73 272	97.38 319	98.53 374

DP-MVS		99.16 101	98.95 129	99.78 64	99.77 71	99.53 94	99.41 243	99.50 156	97.03 304	99.04 267	99.88 46	97.39 122	99.92 116	98.66 168	99.90 54	99.87 37

9.14			99.10 93		99.72 104		99.40 251	99.51 136	97.53 251	99.64 124	99.78 147	98.84 44	99.91 128	97.63 280	99.82 110

D2MVS		98.41 202	98.50 192	98.15 334	99.26 282	96.62 347	99.40 251	99.61 55	97.71 227	98.98 277	99.36 323	96.04 180	99.67 267	98.70 161	97.41 317	98.15 404

Anonymous20240529		98.09 232	97.68 270	99.34 166	99.66 138	98.44 244	99.40 251	99.43 238	93.67 409	99.22 228	99.89 37	90.23 370	99.93 104	99.26 89	98.33 256	99.66 145

FMVSNet3		98.03 244	97.76 262	98.84 252	99.39 246	98.98 172	99.40 251	99.38 261	96.67 326	99.07 259	99.28 345	92.93 308	98.98 388	97.10 321	96.65 333	98.56 373

LFMVS		97.90 265	97.35 315	99.54 118	99.52 196	99.01 170	99.39 255	98.24 419	97.10 296	99.65 119	99.79 140	84.79 420	99.91 128	99.28 84	98.38 253	99.69 135

HQP_MVS		98.27 216	98.22 209	98.44 305	99.29 274	96.97 327	99.39 255	99.47 200	98.97 68	99.11 250	99.61 239	92.71 318	99.69 264	97.78 264	97.63 293	98.67 335

plane_prior2							99.39 255		98.97 68

CHOSEN 1792x2688		99.19 95	99.10 93	99.45 150	99.89 8	98.52 235	99.39 255	99.94 1	98.73 95	99.11 250	99.89 37	95.50 206	99.94 86	99.50 54	99.97 8	99.89 26

PAPM_NR		99.04 136	98.84 149	99.66 84	99.74 93	99.44 109	99.39 255	99.38 261	97.70 230	99.28 211	99.28 345	98.34 94	99.85 176	96.96 331	99.45 172	99.69 135

gg-mvs-nofinetune		96.17 368	95.32 380	98.73 265	98.79 374	98.14 258	99.38 260	94.09 450	91.07 428	98.07 372	91.04 448	89.62 378	99.35 327	96.75 341	99.09 204	98.68 327

VDDNet		97.55 319	97.02 341	99.16 199	99.49 213	98.12 261	99.38 260	99.30 309	95.35 385	99.68 102	99.90 31	82.62 430	99.93 104	99.31 78	98.13 274	99.42 231

MVS_0304		99.15 105	98.96 127	99.73 76	98.92 356	99.37 116	99.37 262	96.92 436	99.51 2	99.66 112	99.78 147	96.69 154	99.97 26	99.84 25	99.97 8	99.84 50

pmmvs6		96.53 360	96.09 365	97.82 362	98.69 392	95.47 380	99.37 262	99.47 200	93.46 413	97.41 390	99.78 147	87.06 406	99.33 330	96.92 336	92.70 414	98.65 346

PM-MVS		92.96 400	92.23 404	95.14 408	95.61 439	89.98 434	99.37 262	98.21 421	94.80 398	95.04 424	97.69 429	65.06 444	97.90 427	94.30 394	89.98 429	97.54 429

WTY-MVS		99.06 133	98.88 142	99.61 102	99.62 157	99.16 148	99.37 262	99.56 83	98.04 189	99.53 153	99.62 235	96.84 147	99.94 86	98.85 141	98.49 249	99.72 122

IterMVS-LS		98.46 197	98.42 196	98.58 281	99.59 170	98.00 266	99.37 262	99.43 238	96.94 312	99.07 259	99.59 244	97.87 111	99.03 381	98.32 215	95.62 363	98.71 313
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
h-mvs33		97.70 304	97.28 327	98.97 221	99.70 115	97.27 303	99.36 267	99.45 220	98.94 71	99.66 112	99.64 224	94.93 231	99.99 4	99.48 59	84.36 438	99.65 150

DPE-MVS		99.46 38	99.32 50	99.91 3	99.78 63	99.88 9	99.36 267	99.51 136	98.73 95	99.88 37	99.84 84	98.72 64	99.96 38	98.16 228	99.87 72	99.88 32
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
UnsupCasMVSNet_eth		96.44 362	96.12 363	97.40 382	98.65 395	95.65 373	99.36 267	99.51 136	97.13 290	96.04 416	98.99 380	88.40 393	98.17 420	96.71 343	90.27 427	98.40 389

sss		99.17 99	99.05 102	99.53 126	99.62 157	98.97 175	99.36 267	99.62 46	97.83 213	99.67 107	99.65 218	97.37 125	99.95 73	99.19 93	99.19 193	99.68 139

DeepC-MVS_fast	98.69 1	99.49 29	99.39 36	99.77 67	99.63 151	99.59 81	99.36 267	99.46 209	99.07 51	99.79 68	99.82 98	98.85 42	99.92 116	98.68 166	99.87 72	99.82 66
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
CANet		99.25 90	99.14 88	99.59 106	99.41 238	99.16 148	99.35 272	99.57 78	98.82 82	99.51 157	99.61 239	96.46 165	99.95 73	99.59 42	99.98 4	99.65 150

pmmvs-eth3d		95.34 383	94.73 386	97.15 386	95.53 441	95.94 368	99.35 272	99.10 343	95.13 387	93.55 429	97.54 430	88.15 397	97.91 426	94.58 391	89.69 430	97.61 426

MDTV_nov1_ep13_2view						95.18 390	99.35 272		96.84 317	99.58 142		95.19 222		97.82 259		99.46 224

VDD-MVS		97.73 298	97.35 315	98.88 241	99.47 221	97.12 311	99.34 275	98.85 382	98.19 157	99.67 107	99.85 71	82.98 428	99.92 116	99.49 58	98.32 260	99.60 170

COLMAP_ROB	97.56 6	98.86 159	98.75 158	99.17 198	99.88 13	98.53 231	99.34 275	99.59 68	97.55 247	98.70 322	99.89 37	95.83 191	99.90 141	98.10 232	99.90 54	99.08 270
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
myMVS_eth3d28		97.69 305	97.34 318	98.73 265	99.27 279	97.52 294	99.33 277	98.78 392	98.03 191	98.82 304	98.49 408	86.64 407	99.46 300	98.44 201	98.24 264	99.23 258

EGC-MVSNET		82.80 413	77.86 419	97.62 372	97.91 416	96.12 365	99.33 277	99.28 315	8.40 456	25.05 457	99.27 348	84.11 423	99.33 330	89.20 429	98.22 265	97.42 430

ETVMVS		97.50 325	96.90 345	99.29 181	99.23 290	98.78 210	99.32 279	98.90 375	97.52 253	98.56 342	98.09 426	84.72 421	99.69 264	97.86 254	97.88 283	99.39 237

FMVSNet5		96.43 363	96.19 362	97.15 386	99.11 321	95.89 369	99.32 279	99.52 118	94.47 404	98.34 355	99.07 369	87.54 403	97.07 436	92.61 417	95.72 360	98.47 380

dp		97.75 294	97.80 252	97.59 376	99.10 324	93.71 415	99.32 279	98.88 378	96.48 345	99.08 258	99.55 259	92.67 321	99.82 205	96.52 351	98.58 241	99.24 257

tpmvs		97.98 253	98.02 231	97.84 359	99.04 338	94.73 398	99.31 282	99.20 331	96.10 376	98.76 312	99.42 302	94.94 230	99.81 210	96.97 330	98.45 250	98.97 285

tpmrst		98.33 210	98.48 193	97.90 353	99.16 313	94.78 397	99.31 282	99.11 342	97.27 278	99.45 166	99.59 244	95.33 214	99.84 184	98.48 195	98.61 238	99.09 269

testing99		97.36 335	96.94 344	98.63 275	99.18 303	96.70 341	99.30 284	98.93 365	97.71 227	98.23 361	98.26 418	84.92 419	99.84 184	98.04 242	97.85 286	99.35 243

MP-MVS-pluss		99.37 63	99.20 81	99.88 12	99.90 4	99.87 16	99.30 284	99.52 118	97.18 286	99.60 138	99.79 140	98.79 50	99.95 73	98.83 147	99.91 43	99.83 60
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
NCCC		99.34 70	99.19 83	99.79 61	99.61 162	99.65 68	99.30 284	99.48 179	98.86 77	99.21 231	99.63 230	98.72 64	99.90 141	98.25 220	99.63 157	99.80 82

JIA-IIPM		97.50 325	97.02 341	98.93 228	98.73 386	97.80 281	99.30 284	98.97 361	91.73 424	98.91 288	94.86 442	95.10 225	99.71 253	97.58 284	97.98 278	99.28 251

BH-RMVSNet		98.41 202	98.08 223	99.40 158	99.41 238	98.83 202	99.30 284	98.77 393	97.70 230	98.94 285	99.65 218	92.91 311	99.74 237	96.52 351	99.55 165	99.64 157

testing11		97.50 325	97.10 338	98.71 269	99.20 297	96.91 333	99.29 289	98.82 385	97.89 204	98.21 364	98.40 412	85.63 414	99.83 197	98.45 200	98.04 277	99.37 241

Syy-MVS		97.09 349	97.14 335	96.95 394	99.00 343	92.73 425	99.29 289	99.39 253	97.06 300	97.41 390	98.15 421	93.92 287	98.68 411	91.71 420	98.34 254	99.45 227

myMVS_eth3d		96.89 352	96.37 357	98.43 307	99.00 343	97.16 309	99.29 289	99.39 253	97.06 300	97.41 390	98.15 421	83.46 427	98.68 411	95.27 382	98.34 254	99.45 227

MCST-MVS		99.43 49	99.30 58	99.82 51	99.79 61	99.74 48	99.29 289	99.40 250	98.79 88	99.52 155	99.62 235	98.91 37	99.90 141	98.64 170	99.75 135	99.82 66

LF4IMVS		97.52 322	97.46 297	97.70 369	98.98 349	95.55 376	99.29 289	98.82 385	98.07 180	98.66 325	99.64 224	89.97 372	99.61 286	97.01 326	96.68 332	97.94 419

hse-mvs2		97.50 325	97.14 335	98.59 278	99.49 213	97.05 318	99.28 294	99.22 327	98.94 71	99.66 112	99.42 302	94.93 231	99.65 275	99.48 59	83.80 440	99.08 270

OPM-MVS		98.19 221	98.10 219	98.45 302	98.88 361	97.07 316	99.28 294	99.38 261	98.57 110	99.22 228	99.81 112	92.12 335	99.66 270	98.08 237	97.54 302	98.61 366
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
diffmvs		99.14 109	99.02 112	99.51 137	99.61 162	98.96 179	99.28 294	99.49 167	98.46 120	99.72 94	99.71 183	96.50 163	99.88 161	99.31 78	99.11 200	99.67 142
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
PVSNet_BlendedMVS		98.86 159	98.80 152	99.03 213	99.76 75	98.79 208	99.28 294	99.91 3	97.42 266	99.67 107	99.37 320	97.53 119	99.88 161	98.98 117	97.29 322	98.42 386

OMC-MVS		99.08 130	99.04 104	99.20 195	99.67 127	98.22 254	99.28 294	99.52 118	98.07 180	99.66 112	99.81 112	97.79 114	99.78 226	97.79 263	99.81 113	99.60 170

testing222		97.16 345	96.50 354	99.16 199	99.16 313	98.47 243	99.27 299	98.66 408	97.71 227	98.23 361	98.15 421	82.28 433	99.84 184	97.36 306	97.66 292	99.18 261

AUN-MVS		96.88 353	96.31 359	98.59 278	99.48 220	97.04 321	99.27 299	99.22 327	97.44 263	98.51 345	99.41 306	91.97 338	99.66 270	97.71 275	83.83 439	99.07 275

pmmvs5		97.52 322	97.30 324	98.16 331	98.57 404	96.73 340	99.27 299	98.90 375	96.14 370	98.37 353	99.53 268	91.54 352	99.14 363	97.51 293	95.87 355	98.63 355

1314		98.68 184	98.54 189	99.11 205	98.89 360	98.65 218	99.27 299	99.49 167	96.89 314	97.99 374	99.56 256	97.72 117	99.83 197	97.74 271	99.27 187	98.84 293

MVS		97.28 340	96.55 353	99.48 143	98.78 377	98.95 183	99.27 299	99.39 253	83.53 442	98.08 369	99.54 264	96.97 143	99.87 167	94.23 397	99.16 194	99.63 162

BH-untuned		98.42 200	98.36 199	98.59 278	99.49 213	96.70 341	99.27 299	99.13 340	97.24 282	98.80 307	99.38 317	95.75 197	99.74 237	97.07 325	99.16 194	99.33 247

MDTV_nov1_ep13			98.32 203		99.11 321	94.44 405	99.27 299	98.74 397	97.51 254	99.40 185	99.62 235	94.78 241	99.76 232	97.59 283	98.81 230

DP-MVS Recon		99.12 117	98.95 129	99.65 88	99.74 93	99.70 54	99.27 299	99.57 78	96.40 352	99.42 176	99.68 205	98.75 58	99.80 217	97.98 245	99.72 141	99.44 229

PatchmatchNet		98.31 211	98.36 199	98.19 329	99.16 313	95.32 386	99.27 299	98.92 368	97.37 270	99.37 191	99.58 248	94.90 234	99.70 259	97.43 302	99.21 191	99.54 189
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
thres200		97.61 316	97.28 327	98.62 276	99.64 148	98.03 264	99.26 308	98.74 397	97.68 232	99.09 256	98.32 416	91.66 349	99.81 210	92.88 413	98.22 265	98.03 411

CNVR-MVS		99.42 51	99.30 58	99.78 64	99.62 157	99.71 52	99.26 308	99.52 118	98.82 82	99.39 187	99.71 183	98.96 25	99.85 176	98.59 181	99.80 118	99.77 94

tt0320		95.71 378	95.07 382	97.62 372	99.05 336	95.02 392	99.25 310	99.52 118	86.81 437	97.97 376	99.72 180	83.58 426	99.15 361	96.38 357	93.35 403	98.68 327

1112_ss		98.98 145	98.77 156	99.59 106	99.68 125	99.02 168	99.25 310	99.48 179	97.23 283	99.13 246	99.58 248	96.93 145	99.90 141	98.87 134	98.78 231	99.84 50

TAPA-MVS	97.07 15	97.74 296	97.34 318	98.94 226	99.70 115	97.53 293	99.25 310	99.51 136	91.90 423	99.30 207	99.63 230	98.78 51	99.64 278	88.09 434	99.87 72	99.65 150
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
UWE-MVS-28		97.36 335	97.24 331	97.75 365	98.84 370	94.44 405	99.24 313	97.58 432	97.98 196	99.00 274	99.00 378	91.35 355	99.53 295	93.75 402	98.39 252	99.27 255

UBG		97.85 272	97.48 292	98.95 224	99.25 286	97.64 290	99.24 313	98.74 397	97.90 203	98.64 332	98.20 420	88.65 389	99.81 210	98.27 218	98.40 251	99.42 231

PLC	97.94 4	99.02 139	98.85 147	99.53 126	99.66 138	99.01 170	99.24 313	99.52 118	96.85 316	99.27 216	99.48 288	98.25 98	99.91 128	97.76 268	99.62 158	99.65 150
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
test_post1							99.23 316				65.14 454	94.18 277	99.71 253	97.58 284

ADS-MVSNet2		98.02 246	98.07 226	97.87 355	99.33 261	95.19 389	99.23 316	99.08 346	96.24 360	99.10 253	99.67 211	94.11 278	98.93 400	96.81 339	99.05 207	99.48 213

ADS-MVSNet		98.20 220	98.08 223	98.56 285	99.33 261	96.48 352	99.23 316	99.15 337	96.24 360	99.10 253	99.67 211	94.11 278	99.71 253	96.81 339	99.05 207	99.48 213

EPNet_dtu		98.03 244	97.96 236	98.23 327	98.27 412	95.54 378	99.23 316	98.75 394	99.02 55	97.82 383	99.71 183	96.11 177	99.48 297	93.04 411	99.65 154	99.69 135
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CR-MVSNet		98.17 224	97.93 241	98.87 245	99.18 303	98.49 239	99.22 320	99.33 290	96.96 308	99.56 146	99.38 317	94.33 270	99.00 386	94.83 390	98.58 241	99.14 262

RPMNet		96.72 356	95.90 369	99.19 196	99.18 303	98.49 239	99.22 320	99.52 118	88.72 435	99.56 146	97.38 432	94.08 280	99.95 73	86.87 440	98.58 241	99.14 262

sc_t1		95.75 376	95.05 383	97.87 355	98.83 371	94.61 402	99.21 322	99.45 220	87.45 436	97.97 376	99.85 71	81.19 436	99.43 311	98.27 218	93.20 407	99.57 183

WBMVS		97.74 296	97.50 290	98.46 300	99.24 288	97.43 297	99.21 322	99.42 240	97.45 260	98.96 281	99.41 306	88.83 384	99.23 347	98.94 122	96.02 348	98.71 313

plane_prior						96.97 327	99.21 322		98.45 122						97.60 296

ICG_test_0404		98.53 193	98.52 191	98.55 287	99.55 184	96.93 330	99.20 325	99.44 229	98.05 186	98.96 281	99.80 126	94.66 254	99.13 366	98.15 230	98.92 218	99.60 170

tt0320-xc		95.31 384	94.59 388	97.45 380	98.92 356	94.73 398	99.20 325	99.31 304	86.74 438	97.23 396	99.72 180	81.14 437	98.95 398	97.08 324	91.98 418	98.67 335

testing91		97.44 332	97.02 341	98.71 269	99.18 303	96.89 335	99.19 327	99.04 353	97.78 220	98.31 356	98.29 417	85.41 416	99.85 176	98.01 243	97.95 279	99.39 237

WR-MVS		98.06 236	97.73 265	99.06 209	98.86 367	99.25 139	99.19 327	99.35 277	97.30 276	98.66 325	99.43 300	93.94 285	99.21 356	98.58 182	94.28 390	98.71 313

new-patchmatchnet		94.48 392	94.08 393	95.67 407	95.08 444	92.41 426	99.18 329	99.28 315	94.55 403	93.49 430	97.37 433	87.86 401	97.01 437	91.57 421	88.36 432	97.61 426

AdaColmap		99.01 143	98.80 152	99.66 84	99.56 180	99.54 91	99.18 329	99.70 15	98.18 160	99.35 197	99.63 230	96.32 171	99.90 141	97.48 296	99.77 130	99.55 187

EG-PatchMatch MVS		95.97 372	95.69 373	96.81 398	97.78 419	92.79 424	99.16 331	98.93 365	96.16 367	94.08 427	99.22 354	82.72 429	99.47 298	95.67 373	97.50 307	98.17 402

PatchT		97.03 350	96.44 356	98.79 260	98.99 346	98.34 249	99.16 331	99.07 349	92.13 422	99.52 155	97.31 435	94.54 262	98.98 388	88.54 432	98.73 233	99.03 278

CNLPA		99.14 109	98.99 119	99.59 106	99.58 172	99.41 113	99.16 331	99.44 229	98.45 122	99.19 237	99.49 282	98.08 106	99.89 156	97.73 272	99.75 135	99.48 213

MDA-MVSNet-bldmvs		94.96 387	93.98 394	97.92 351	98.24 413	97.27 303	99.15 334	99.33 290	93.80 408	80.09 449	99.03 374	88.31 394	97.86 428	93.49 406	94.36 389	98.62 357

CDPH-MVS		99.13 111	98.91 135	99.80 58	99.75 85	99.71 52	99.15 334	99.41 243	96.60 336	99.60 138	99.55 259	98.83 45	99.90 141	97.48 296	99.83 106	99.78 92

save fliter					99.76 75	99.59 81	99.14 336	99.40 250	99.00 60

WB-MVSnew		97.65 313	97.65 273	97.63 371	98.78 377	97.62 291	99.13 337	98.33 416	97.36 271	99.07 259	98.94 386	95.64 202	99.15 361	92.95 412	98.68 236	96.12 440

testf1		90.42 407	90.68 408	89.65 427	97.78 419	73.97 455	99.13 337	98.81 387	89.62 430	91.80 438	98.93 387	62.23 447	98.80 407	86.61 441	91.17 421	96.19 438

APD_test2		90.42 407	90.68 408	89.65 427	97.78 419	73.97 455	99.13 337	98.81 387	89.62 430	91.80 438	98.93 387	62.23 447	98.80 407	86.61 441	91.17 421	96.19 438

xiu_mvs_v1_base_debu		99.29 79	99.27 69	99.34 166	99.63 151	98.97 175	99.12 340	99.51 136	98.86 77	99.84 50	99.47 291	98.18 101	99.99 4	99.50 54	99.31 184	99.08 270

xiu_mvs_v1_base		99.29 79	99.27 69	99.34 166	99.63 151	98.97 175	99.12 340	99.51 136	98.86 77	99.84 50	99.47 291	98.18 101	99.99 4	99.50 54	99.31 184	99.08 270

xiu_mvs_v1_base_debi		99.29 79	99.27 69	99.34 166	99.63 151	98.97 175	99.12 340	99.51 136	98.86 77	99.84 50	99.47 291	98.18 101	99.99 4	99.50 54	99.31 184	99.08 270

XVG-OURS-SEG-HR		98.69 183	98.62 179	98.89 239	99.71 110	97.74 282	99.12 340	99.54 100	98.44 125	99.42 176	99.71 183	94.20 274	99.92 116	98.54 192	98.90 222	99.00 281

jason		99.13 111	99.03 107	99.45 150	99.46 223	98.87 194	99.12 340	99.26 319	98.03 191	99.79 68	99.65 218	97.02 141	99.85 176	99.02 114	99.90 54	99.65 150
jason: jason.
N_pmnet		94.95 388	95.83 371	92.31 418	98.47 408	79.33 450	99.12 340	92.81 456	93.87 407	97.68 386	99.13 364	93.87 289	99.01 385	91.38 422	96.19 345	98.59 370

MDA-MVSNet_test_wron		95.45 380	94.60 387	98.01 342	98.16 414	97.21 308	99.11 346	99.24 324	93.49 412	80.73 448	98.98 382	93.02 306	98.18 419	94.22 398	94.45 387	98.64 348

Patchmtry		97.75 294	97.40 310	98.81 257	99.10 324	98.87 194	99.11 346	99.33 290	94.83 397	98.81 305	99.38 317	94.33 270	99.02 383	96.10 360	95.57 365	98.53 374

YYNet1		95.36 382	94.51 390	97.92 351	97.89 417	97.10 312	99.10 348	99.23 325	93.26 415	80.77 447	99.04 373	92.81 312	98.02 423	94.30 394	94.18 392	98.64 348

CANet_DTU		98.97 147	98.87 143	99.25 188	99.33 261	98.42 247	99.08 349	99.30 309	99.16 30	99.43 173	99.75 165	95.27 216	99.97 26	98.56 188	99.95 20	99.36 242

SCA		98.19 221	98.16 211	98.27 325	99.30 270	95.55 376	99.07 350	98.97 361	97.57 244	99.43 173	99.57 253	92.72 316	99.74 237	97.58 284	99.20 192	99.52 196

TSAR-MVS + GP.		99.36 67	99.36 42	99.36 164	99.67 127	98.61 225	99.07 350	99.33 290	99.00 60	99.82 61	99.81 112	99.06 16	99.84 184	99.09 106	99.42 174	99.65 150

MG-MVS		99.13 111	99.02 112	99.45 150	99.57 176	98.63 221	99.07 350	99.34 282	98.99 62	99.61 135	99.82 98	97.98 110	99.87 167	97.00 327	99.80 118	99.85 43

PatchMatch-RL		98.84 170	98.62 179	99.52 132	99.71 110	99.28 134	99.06 353	99.77 9	97.74 225	99.50 158	99.53 268	95.41 209	99.84 184	97.17 320	99.64 155	99.44 229

OpenMVS_ROB	92.34 20	94.38 393	93.70 399	96.41 403	97.38 425	93.17 422	99.06 353	98.75 394	86.58 439	94.84 425	98.26 418	81.53 434	99.32 332	89.01 430	97.87 284	96.76 433

TEST9					99.67 127	99.65 68	99.05 355	99.41 243	96.22 362	98.95 283	99.49 282	98.77 54	99.91 128

train_agg		99.02 139	98.77 156	99.77 67	99.67 127	99.65 68	99.05 355	99.41 243	96.28 356	98.95 283	99.49 282	98.76 55	99.91 128	97.63 280	99.72 141	99.75 100

lupinMVS		99.13 111	99.01 117	99.46 149	99.51 199	98.94 186	99.05 355	99.16 336	97.86 207	99.80 66	99.56 256	97.39 122	99.86 170	98.94 122	99.85 87	99.58 180

DELS-MVS		99.48 33	99.42 28	99.65 88	99.72 104	99.40 114	99.05 355	99.66 28	99.14 33	99.57 145	99.80 126	98.46 84	99.94 86	99.57 45	99.84 95	99.60 170
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
new_pmnet		96.38 364	96.03 366	97.41 381	98.13 415	95.16 391	99.05 355	99.20 331	93.94 406	97.39 393	98.79 398	91.61 351	99.04 379	90.43 425	95.77 357	98.05 410

Patchmatch-test		97.93 259	97.65 273	98.77 263	99.18 303	97.07 316	99.03 360	99.14 339	96.16 367	98.74 313	99.57 253	94.56 259	99.72 247	93.36 407	99.11 200	99.52 196

test_8					99.67 127	99.61 78	99.03 360	99.41 243	96.28 356	98.93 286	99.48 288	98.76 55	99.91 128

Test_1112_low_res		98.89 152	98.66 169	99.57 113	99.69 120	98.95 183	99.03 360	99.47 200	96.98 306	99.15 244	99.23 353	96.77 151	99.89 156	98.83 147	98.78 231	99.86 39

IterMVS-SCA-FT		97.82 282	97.75 263	98.06 338	99.57 176	96.36 356	99.02 363	99.49 167	97.18 286	98.71 316	99.72 180	92.72 316	99.14 363	97.44 301	95.86 356	98.67 335

xiu_mvs_v2_base		99.26 86	99.25 73	99.29 181	99.53 190	98.91 191	99.02 363	99.45 220	98.80 87	99.71 96	99.26 350	98.94 32	99.98 17	99.34 74	99.23 190	98.98 284

MIMVSNet		97.73 298	97.45 298	98.57 282	99.45 229	97.50 295	99.02 363	98.98 360	96.11 372	99.41 180	99.14 363	90.28 366	98.74 409	95.74 369	98.93 216	99.47 219

IterMVS		97.83 279	97.77 258	98.02 341	99.58 172	96.27 360	99.02 363	99.48 179	97.22 284	98.71 316	99.70 187	92.75 313	99.13 366	97.46 299	96.00 350	98.67 335
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
HyFIR lowres test		99.11 123	98.92 132	99.65 88	99.90 4	99.37 116	99.02 363	99.91 3	97.67 234	99.59 141	99.75 165	95.90 189	99.73 243	99.53 50	99.02 211	99.86 39

UWE-MVS		97.58 318	97.29 326	98.48 294	99.09 327	96.25 361	99.01 368	96.61 442	97.86 207	99.19 237	99.01 377	88.72 385	99.90 141	97.38 305	98.69 235	99.28 251

新几何2							99.01 368

BH-w/o		98.00 251	97.89 247	98.32 317	99.35 255	96.20 363	99.01 368	98.90 375	96.42 350	98.38 352	99.00 378	95.26 218	99.72 247	96.06 361	98.61 238	99.03 278

test_prior4						99.56 87	98.99 371

无先验							98.99 371	99.51 136	96.89 314				99.93 104	97.53 292		99.72 122

pmmvs4		98.13 228	97.90 243	98.81 257	98.61 400	98.87 194	98.99 371	99.21 330	96.44 348	99.06 264	99.58 248	95.90 189	99.11 372	97.18 319	96.11 347	98.46 383

HQP-NCC					99.19 300		98.98 374		98.24 149	98.66 325

ACMP_Plane					99.19 300		98.98 374		98.24 149	98.66 325

HQP-MVS		98.02 246	97.90 243	98.37 313	99.19 300	96.83 336	98.98 374	99.39 253	98.24 149	98.66 325	99.40 310	92.47 327	99.64 278	97.19 317	97.58 298	98.64 348

PS-MVSNAJ		99.32 74	99.32 50	99.30 178	99.57 176	98.94 186	98.97 377	99.46 209	98.92 74	99.71 96	99.24 352	99.01 18	99.98 17	99.35 69	99.66 152	98.97 285

MVP-Stereo		97.81 284	97.75 263	97.99 345	97.53 423	96.60 349	98.96 378	98.85 382	97.22 284	97.23 396	99.36 323	95.28 215	99.46 300	95.51 375	99.78 127	97.92 421
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
test_prior2							98.96 378		98.34 135	99.01 270	99.52 272	98.68 67		97.96 246	99.74 138

旧先验2							98.96 378		96.70 324	99.47 163			99.94 86	98.19 224

原ACMM2							98.95 381

MVS_111021_HR		99.41 55	99.32 50	99.66 84	99.72 104	99.47 106	98.95 381	99.85 6	98.82 82	99.54 151	99.73 176	98.51 81	99.74 237	98.91 128	99.88 69	99.77 94

mvsany_test1		99.50 27	99.46 25	99.62 101	99.61 162	99.09 158	98.94 383	99.48 179	99.10 41	99.96 24	99.91 24	98.85 42	99.96 38	99.72 29	99.58 162	99.82 66

MVS_111021_LR		99.41 55	99.33 48	99.65 88	99.77 71	99.51 100	98.94 383	99.85 6	98.82 82	99.65 119	99.74 170	98.51 81	99.80 217	98.83 147	99.89 65	99.64 157

pmmvs3		94.09 395	93.25 401	96.60 401	94.76 446	94.49 404	98.92 385	98.18 423	89.66 429	96.48 410	98.06 427	86.28 410	97.33 434	89.68 428	87.20 435	97.97 418

XVG-OURS		98.73 181	98.68 165	98.88 241	99.70 115	97.73 283	98.92 385	99.55 91	98.52 115	99.45 166	99.84 84	95.27 216	99.91 128	98.08 237	98.84 226	99.00 281

test222					99.75 85	99.49 102	98.91 387	99.49 167	96.42 350	99.34 200	99.65 218	98.28 97			99.69 146	99.72 122

PMMVS2		86.87 410	85.37 414	91.35 422	90.21 451	83.80 441	98.89 388	97.45 434	83.13 443	91.67 440	95.03 440	48.49 453	94.70 446	85.86 443	77.62 445	95.54 441

miper_lstm_enhance		98.00 251	97.91 242	98.28 324	99.34 260	97.43 297	98.88 389	99.36 270	96.48 345	98.80 307	99.55 259	95.98 182	98.91 401	97.27 310	95.50 368	98.51 376

MVS-HIRNet		95.75 376	95.16 381	97.51 378	99.30 270	93.69 416	98.88 389	95.78 444	85.09 441	98.78 310	92.65 444	91.29 357	99.37 320	94.85 389	99.85 87	99.46 224

TR-MVS		97.76 290	97.41 309	98.82 254	99.06 333	97.87 277	98.87 391	98.56 411	96.63 332	98.68 324	99.22 354	92.49 326	99.65 275	95.40 379	97.79 288	98.95 289

testdata1							98.85 392		98.32 138

ET-MVSNet_ETH3D		96.49 361	95.64 375	99.05 211	99.53 190	98.82 205	98.84 393	97.51 433	97.63 237	84.77 442	99.21 357	92.09 336	98.91 401	98.98 117	92.21 417	99.41 234

our_test_3		97.65 313	97.68 270	97.55 377	98.62 398	94.97 394	98.84 393	99.30 309	96.83 319	98.19 365	99.34 330	97.01 142	99.02 383	95.00 387	96.01 349	98.64 348

MS-PatchMatch		97.24 344	97.32 322	96.99 391	98.45 409	93.51 420	98.82 395	99.32 300	97.41 267	98.13 368	99.30 341	88.99 382	99.56 291	95.68 372	99.80 118	97.90 422

c3_l		98.12 230	98.04 228	98.38 312	99.30 270	97.69 289	98.81 396	99.33 290	96.67 326	98.83 302	99.34 330	97.11 135	98.99 387	97.58 284	95.34 370	98.48 378

ppachtmachnet_test		97.49 330	97.45 298	97.61 375	98.62 398	95.24 387	98.80 397	99.46 209	96.11 372	98.22 363	99.62 235	96.45 166	98.97 395	93.77 401	95.97 354	98.61 366

PAPR		98.63 190	98.34 201	99.51 137	99.40 243	99.03 167	98.80 397	99.36 270	96.33 353	99.00 274	99.12 367	98.46 84	99.84 184	95.23 383	99.37 183	99.66 145

test0.0.03 1		97.71 303	97.42 308	98.56 285	98.41 411	97.82 280	98.78 399	98.63 409	97.34 272	98.05 373	98.98 382	94.45 267	98.98 388	95.04 386	97.15 328	98.89 290

PVSNet_Blended		99.08 130	98.97 123	99.42 156	99.76 75	98.79 208	98.78 399	99.91 3	96.74 321	99.67 107	99.49 282	97.53 119	99.88 161	98.98 117	99.85 87	99.60 170

PMMVS		98.80 174	98.62 179	99.34 166	99.27 279	98.70 214	98.76 401	99.31 304	97.34 272	99.21 231	99.07 369	97.20 133	99.82 205	98.56 188	98.87 223	99.52 196

test123		39.01 422	42.50 424	28.53 437	39.17 460	20.91 462	98.75 402	19.17 462	19.83 455	38.57 454	66.67 452	33.16 457	15.42 456	37.50 456	29.66 454	49.26 451

MSDG		98.98 145	98.80 152	99.53 126	99.76 75	99.19 143	98.75 402	99.55 91	97.25 280	99.47 163	99.77 156	97.82 113	99.87 167	96.93 334	99.90 54	99.54 189

CLD-MVS		98.16 225	98.10 219	98.33 315	99.29 274	96.82 338	98.75 402	99.44 229	97.83 213	99.13 246	99.55 259	92.92 309	99.67 267	98.32 215	97.69 291	98.48 378
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
miper_ehance_all_eth		98.18 223	98.10 219	98.41 308	99.23 290	97.72 285	98.72 405	99.31 304	96.60 336	98.88 293	99.29 343	97.29 129	99.13 366	97.60 282	95.99 351	98.38 391

cl____		98.01 249	97.84 251	98.55 287	99.25 286	97.97 268	98.71 406	99.34 282	96.47 347	98.59 341	99.54 264	95.65 201	99.21 356	97.21 313	95.77 357	98.46 383

DIV-MVS_self_test		98.01 249	97.85 250	98.48 294	99.24 288	97.95 273	98.71 406	99.35 277	96.50 341	98.60 340	99.54 264	95.72 199	99.03 381	97.21 313	95.77 357	98.46 383

test-LLR		98.06 236	97.90 243	98.55 287	98.79 374	97.10 312	98.67 408	97.75 428	97.34 272	98.61 338	98.85 392	94.45 267	99.45 302	97.25 311	99.38 176	99.10 265

TESTMET0.1,1		97.55 319	97.27 330	98.40 310	98.93 354	96.53 350	98.67 408	97.61 431	96.96 308	98.64 332	99.28 345	88.63 391	99.45 302	97.30 309	99.38 176	99.21 260

test-mter		97.49 330	97.13 337	98.55 287	98.79 374	97.10 312	98.67 408	97.75 428	96.65 328	98.61 338	98.85 392	88.23 395	99.45 302	97.25 311	99.38 176	99.10 265

mvs5depth		96.66 357	96.22 361	97.97 346	97.00 434	96.28 359	98.66 411	99.03 355	96.61 333	96.93 406	99.79 140	87.20 405	99.47 298	96.65 349	94.13 393	98.16 403

IB-MVS	95.67 18	96.22 365	95.44 379	98.57 282	99.21 295	96.70 341	98.65 412	97.74 430	96.71 323	97.27 395	98.54 407	86.03 411	99.92 116	98.47 198	86.30 436	99.10 265
Christian Sormann, Mattia Rossi, Andreas Kuhn and Friedrich Fraundorfer: IB-MVS: An Iterative Algorithm for Deep Multi-View Stereo based on Binary Decisions. BMVC 2021
DPM-MVS		98.95 148	98.71 162	99.66 84	99.63 151	99.55 89	98.64 413	99.10 343	97.93 200	99.42 176	99.55 259	98.67 69	99.80 217	95.80 368	99.68 149	99.61 167

thisisatest0515		98.14 227	97.79 253	99.19 196	99.50 211	98.50 238	98.61 414	96.82 438	96.95 310	99.54 151	99.43 300	91.66 349	99.86 170	98.08 237	99.51 167	99.22 259

DeepPCF-MVS	98.18 3	98.81 171	99.37 40	97.12 389	99.60 168	91.75 429	98.61 414	99.44 229	99.35 22	99.83 58	99.85 71	98.70 66	99.81 210	99.02 114	99.91 43	99.81 73

cl22		97.85 272	97.64 276	98.48 294	99.09 327	97.87 277	98.60 416	99.33 290	97.11 295	98.87 296	99.22 354	92.38 332	99.17 360	98.21 222	95.99 351	98.42 386

GA-MVS		97.85 272	97.47 295	99.00 217	99.38 248	97.99 267	98.57 417	99.15 337	97.04 303	98.90 290	99.30 341	89.83 374	99.38 317	96.70 344	98.33 256	99.62 165

TinyColmap		97.12 347	96.89 346	97.83 360	99.07 331	95.52 379	98.57 417	98.74 397	97.58 243	97.81 384	99.79 140	88.16 396	99.56 291	95.10 384	97.21 325	98.39 390

eth_miper_zixun_eth		98.05 241	97.96 236	98.33 315	99.26 282	97.38 299	98.56 419	99.31 304	96.65 328	98.88 293	99.52 272	96.58 159	99.12 371	97.39 304	95.53 367	98.47 380

CMPMVS	69.68 23	94.13 394	94.90 385	91.84 419	97.24 429	80.01 449	98.52 420	99.48 179	89.01 433	91.99 436	99.67 211	85.67 413	99.13 366	95.44 377	97.03 330	96.39 437
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
USDC		97.34 337	97.20 332	97.75 365	99.07 331	95.20 388	98.51 421	99.04 353	97.99 195	98.31 356	99.86 64	89.02 381	99.55 293	95.67 373	97.36 320	98.49 377

ambc				93.06 417	92.68 448	82.36 442	98.47 422	98.73 403		95.09 423	97.41 431	55.55 449	99.10 374	96.42 354	91.32 420	97.71 423

miper_enhance_ethall		98.16 225	98.08 223	98.41 308	98.96 352	97.72 285	98.45 423	99.32 300	96.95 310	98.97 279	99.17 359	97.06 139	99.22 351	97.86 254	95.99 351	98.29 395

CHOSEN 280x420		99.12 117	99.13 89	99.08 206	99.66 138	97.89 276	98.43 424	99.71 13	98.88 76	99.62 131	99.76 160	96.63 156	99.70 259	99.46 62	99.99 1	99.66 145

testmvs		39.17 421	43.78 423	25.37 438	36.04 461	16.84 463	98.36 425	26.56 460	20.06 454	38.51 455	67.32 451	29.64 458	15.30 457	37.59 455	39.90 453	43.98 452

FPMVS		84.93 412	85.65 413	82.75 433	86.77 454	63.39 459	98.35 426	98.92 368	74.11 445	83.39 444	98.98 382	50.85 452	92.40 448	84.54 444	94.97 378	92.46 443

KD-MVS_2432*1600		94.62 389	93.72 397	97.31 383	97.19 431	95.82 370	98.34 427	99.20 331	95.00 393	97.57 387	98.35 414	87.95 398	98.10 421	92.87 414	77.00 446	98.01 412

miper_refine_blended		94.62 389	93.72 397	97.31 383	97.19 431	95.82 370	98.34 427	99.20 331	95.00 393	97.57 387	98.35 414	87.95 398	98.10 421	92.87 414	77.00 446	98.01 412

CL-MVSNet_self_test		94.49 391	93.97 395	96.08 405	96.16 436	93.67 417	98.33 429	99.38 261	95.13 387	97.33 394	98.15 421	92.69 320	96.57 439	88.67 431	79.87 444	97.99 416

PVSNet	96.02 17	98.85 167	98.84 149	98.89 239	99.73 100	97.28 302	98.32 430	99.60 62	97.86 207	99.50 158	99.57 253	96.75 152	99.86 170	98.56 188	99.70 145	99.54 189

PAPM		97.59 317	97.09 339	99.07 207	99.06 333	98.26 252	98.30 431	99.10 343	94.88 395	98.08 369	99.34 330	96.27 173	99.64 278	89.87 427	98.92 218	99.31 249

Patchmatch-RL test		95.84 374	95.81 372	95.95 406	95.61 439	90.57 432	98.24 432	98.39 415	95.10 391	95.20 421	98.67 402	94.78 241	97.77 429	96.28 359	90.02 428	99.51 205

UnsupCasMVSNet_bld		93.53 397	92.51 403	96.58 402	97.38 425	93.82 412	98.24 432	99.48 179	91.10 427	93.10 431	96.66 437	74.89 441	98.37 416	94.03 400	87.71 434	97.56 428

LCM-MVSNet		86.80 411	85.22 415	91.53 421	87.81 453	80.96 447	98.23 434	98.99 359	71.05 446	90.13 441	96.51 438	48.45 454	96.88 438	90.51 424	85.30 437	96.76 433

cascas		97.69 305	97.43 307	98.48 294	98.60 401	97.30 301	98.18 435	99.39 253	92.96 417	98.41 350	98.78 399	93.77 293	99.27 340	98.16 228	98.61 238	98.86 291

kuosan		90.92 406	90.11 411	93.34 414	98.78 377	85.59 439	98.15 436	93.16 454	89.37 432	92.07 435	98.38 413	81.48 435	95.19 444	62.54 453	97.04 329	99.25 256

Effi-MVS+		98.81 171	98.59 185	99.48 143	99.46 223	99.12 156	98.08 437	99.50 156	97.50 255	99.38 189	99.41 306	96.37 170	99.81 210	99.11 102	98.54 246	99.51 205

PCF-MVS	97.08 14	97.66 312	97.06 340	99.47 147	99.61 162	99.09 158	98.04 438	99.25 321	91.24 426	98.51 345	99.70 187	94.55 261	99.91 128	92.76 416	99.85 87	99.42 231
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
PVSNet_0	94.43 19	96.09 370	95.47 377	97.94 349	99.31 269	94.34 409	97.81 439	99.70 15	97.12 292	97.46 389	98.75 400	89.71 375	99.79 221	97.69 278	81.69 442	99.68 139

E-PMN		80.61 415	79.88 417	82.81 432	90.75 450	76.38 453	97.69 440	95.76 445	66.44 450	83.52 443	92.25 445	62.54 446	87.16 452	68.53 451	61.40 449	84.89 450

dongtai		93.26 398	92.93 402	94.25 410	99.39 246	85.68 438	97.68 441	93.27 452	92.87 418	96.85 407	99.39 314	82.33 432	97.48 433	76.78 446	97.80 287	99.58 180

ANet_high		77.30 417	74.86 421	84.62 431	75.88 457	77.61 451	97.63 442	93.15 455	88.81 434	64.27 452	89.29 449	36.51 456	83.93 454	75.89 448	52.31 451	92.33 445

EMVS		80.02 416	79.22 418	82.43 434	91.19 449	76.40 452	97.55 443	92.49 457	66.36 451	83.01 445	91.27 447	64.63 445	85.79 453	65.82 452	60.65 450	85.08 449

MVE	76.82 21	76.91 418	74.31 422	84.70 430	85.38 456	76.05 454	96.88 444	93.17 453	67.39 449	71.28 451	89.01 450	21.66 461	87.69 451	71.74 450	72.29 448	90.35 447
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
test_method		91.10 404	91.36 406	90.31 424	95.85 437	73.72 457	94.89 445	99.25 321	68.39 448	95.82 417	99.02 376	80.50 438	98.95 398	93.64 404	94.89 382	98.25 398

Gipuma		90.99 405	90.15 410	93.51 413	98.73 386	90.12 433	93.98 446	99.45 220	79.32 444	92.28 434	94.91 441	69.61 442	97.98 425	87.42 437	95.67 361	92.45 444
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PMVS	70.75 22	75.98 419	74.97 420	79.01 435	70.98 458	55.18 460	93.37 447	98.21 421	65.08 452	61.78 453	93.83 443	21.74 460	92.53 447	78.59 445	91.12 423	89.34 448
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
tmp_tt		82.80 413	81.52 416	86.66 429	66.61 459	68.44 458	92.79 448	97.92 425	68.96 447	80.04 450	99.85 71	85.77 412	96.15 442	97.86 254	43.89 452	95.39 442

wuyk23d		40.18 420	41.29 425	36.84 436	86.18 455	49.12 461	79.73 449	22.81 461	27.64 453	25.46 456	28.45 456	21.98 459	48.89 455	55.80 454	23.56 455	12.51 453

mmdepth		0.02 427	0.03 430	0.00 439	0.00 462	0.00 464	0.00 450	0.00 463	0.00 457	0.00 458	0.27 458	0.00 462	0.00 458	0.00 457	0.00 456	0.00 454

monomultidepth		0.02 427	0.03 430	0.00 439	0.00 462	0.00 464	0.00 450	0.00 463	0.00 457	0.00 458	0.27 458	0.00 462	0.00 458	0.00 457	0.00 456	0.00 454

test_blank		0.13 426	0.17 429	0.00 439	0.00 462	0.00 464	0.00 450	0.00 463	0.00 457	0.00 458	1.57 457	0.00 462	0.00 458	0.00 457	0.00 456	0.00 454

uanet_test		0.02 427	0.03 430	0.00 439	0.00 462	0.00 464	0.00 450	0.00 463	0.00 457	0.00 458	0.27 458	0.00 462	0.00 458	0.00 457	0.00 456	0.00 454

DCPMVS		0.02 427	0.03 430	0.00 439	0.00 462	0.00 464	0.00 450	0.00 463	0.00 457	0.00 458	0.27 458	0.00 462	0.00 458	0.00 457	0.00 456	0.00 454

cdsmvs_eth3d_5k		24.64 423	32.85 426	0.00 439	0.00 462	0.00 464	0.00 450	99.51 136	0.00 457	0.00 458	99.56 256	96.58 159	0.00 458	0.00 457	0.00 456	0.00 454

pcd_1.5k_mvsjas		8.27 425	11.03 428	0.00 439	0.00 462	0.00 464	0.00 450	0.00 463	0.00 457	0.00 458	0.27 458	99.01 18	0.00 458	0.00 457	0.00 456	0.00 454

sosnet-low-res		0.02 427	0.03 430	0.00 439	0.00 462	0.00 464	0.00 450	0.00 463	0.00 457	0.00 458	0.27 458	0.00 462	0.00 458	0.00 457	0.00 456	0.00 454

sosnet		0.02 427	0.03 430	0.00 439	0.00 462	0.00 464	0.00 450	0.00 463	0.00 457	0.00 458	0.27 458	0.00 462	0.00 458	0.00 457	0.00 456	0.00 454

uncertanet		0.02 427	0.03 430	0.00 439	0.00 462	0.00 464	0.00 450	0.00 463	0.00 457	0.00 458	0.27 458	0.00 462	0.00 458	0.00 457	0.00 456	0.00 454

Regformer		0.02 427	0.03 430	0.00 439	0.00 462	0.00 464	0.00 450	0.00 463	0.00 457	0.00 458	0.27 458	0.00 462	0.00 458	0.00 457	0.00 456	0.00 454

ab-mvs-re		8.30 424	11.06 427	0.00 439	0.00 462	0.00 464	0.00 450	0.00 463	0.00 457	0.00 458	99.58 248	0.00 462	0.00 458	0.00 457	0.00 456	0.00 454

uanet		0.02 427	0.03 430	0.00 439	0.00 462	0.00 464	0.00 450	0.00 463	0.00 457	0.00 458	0.27 458	0.00 462	0.00 458	0.00 457	0.00 456	0.00 454

WAC-MVS						97.16 309								95.47 376

MSC_two_6792asdad				99.87 18	99.51 199	99.76 43		99.33 290					99.96 38	98.87 134	99.84 95	99.89 26

PC_three_1452									98.18 160	99.84 50	99.70 187	99.31 3	98.52 414	98.30 217	99.80 118	99.81 73

No_MVS				99.87 18	99.51 199	99.76 43		99.33 290					99.96 38	98.87 134	99.84 95	99.89 26

test_one_0601					99.81 51	99.88 9		99.49 167	98.97 68	99.65 119	99.81 112	99.09 14

eth-test2					0.00 462

eth-test					0.00 462

ZD-MVS					99.71 110	99.79 35		99.61 55	96.84 317	99.56 146	99.54 264	98.58 75	99.96 38	96.93 334	99.75 135

IU-MVS					99.84 34	99.88 9		99.32 300	98.30 140	99.84 50				98.86 139	99.85 87	99.89 26

test_241102_TWO								99.48 179	99.08 49	99.88 37	99.81 112	98.94 32	99.96 38	98.91 128	99.84 95	99.88 32

test_241102_ONE					99.84 34	99.90 2		99.48 179	99.07 51	99.91 28	99.74 170	99.20 7	99.76 232

test_0728_THIRD									98.99 62	99.81 62	99.80 126	99.09 14	99.96 38	98.85 141	99.90 54	99.88 32

GSMVS																99.52 196

test_part2					99.81 51	99.83 20				99.77 77

sam_mvs1												94.86 236				99.52 196

sam_mvs												94.72 248

MTGPA								99.47 200

test_post											65.99 453	94.65 255	99.73 243

patchmatchnet-post											98.70 401	94.79 240	99.74 237

gm-plane-assit					98.54 406	92.96 423			94.65 401		99.15 362		99.64 278	97.56 289

test9_res														97.49 295	99.72 141	99.75 100

agg_prior2														97.21 313	99.73 140	99.75 100

agg_prior					99.67 127	99.62 76		99.40 250		98.87 296			99.91 128

TestCases				99.31 173	99.86 22	98.48 241		99.61 55	97.85 210	99.36 194	99.85 71	95.95 184	99.85 176	96.66 347	99.83 106	99.59 176

test_prior				99.68 82	99.67 127	99.48 104		99.56 83					99.83 197			99.74 104

新几何1				99.75 70	99.75 85	99.59 81		99.54 100	96.76 320	99.29 210	99.64 224	98.43 86	99.94 86	96.92 336	99.66 152	99.72 122

旧先验1					99.74 93	99.59 81		99.54 100			99.69 198	98.47 83			99.68 149	99.73 113

原ACMM1				99.65 88	99.73 100	99.33 123		99.47 200	97.46 257	99.12 248	99.66 216	98.67 69	99.91 128	97.70 277	99.69 146	99.71 131

testdata2													99.95 73	96.67 346

segment_acmp												98.96 25

testdata				99.54 118	99.75 85	98.95 183		99.51 136	97.07 298	99.43 173	99.70 187	98.87 40	99.94 86	97.76 268	99.64 155	99.72 122

test12				99.75 70	99.64 148	99.61 78		99.29 313		99.21 231		98.38 92	99.89 156		99.74 138	99.74 104

plane_prior7					99.29 274	97.03 322

plane_prior6					99.27 279	96.98 326						92.71 318

plane_prior5								99.47 200					99.69 264	97.78 264	97.63 293	98.67 335

plane_prior4											99.61 239

plane_prior3						97.00 324			98.69 100	99.11 250

plane_prior1					99.26 282

n2								0.00 463

nn								0.00 463

door-mid								98.05 424

lessismore_v0				97.79 364	98.69 392	95.44 383		94.75 448		95.71 418	99.87 57	88.69 387	99.32 332	95.89 365	94.93 380	98.62 357

LGP-MVS_train				98.49 292	99.33 261	97.05 318		99.55 91	97.46 257	99.24 223	99.83 89	92.58 323	99.72 247	98.09 233	97.51 305	98.68 327

test11								99.35 277

door								97.92 425

HQP5-MVS						96.83 336

BP-MVS														97.19 317

HQP4-MVS										98.66 325			99.64 278			98.64 348

HQP3-MVS								99.39 253							97.58 298

HQP2-MVS												92.47 327

NP-MVS					99.23 290	96.92 332					99.40 310

ACMMP++_ref															97.19 326

ACMMP++															97.43 316

Test By Simon												98.75 58

ITE_SJBPF				98.08 337	99.29 274	96.37 355		98.92 368	98.34 135	98.83 302	99.75 165	91.09 359	99.62 285	95.82 366	97.40 318	98.25 398

DeepMVS_CX				93.34 414	99.29 274	82.27 443		99.22 327	85.15 440	96.33 411	99.05 372	90.97 361	99.73 243	93.57 405	97.77 289	98.01 412

High-resolution multi-view benchmark