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	99.70 186	98.65 71	99.79 221	99.65 38	99.78 127	99.41 233

mmtdpeth		96.95 350	96.71 349	97.67 369	99.33 260	94.90 395	99.89 2	99.28 314	98.15 162	99.72 94	98.57 405	86.56 408	99.90 141	99.82 26	89.02 430	98.20 400

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	99.69 197	98.55 78	99.82 205	99.69 32	99.85 87	99.48 212

MVSFormer		99.17 99	99.12 91	99.29 181	99.51 198	98.94 186	99.88 4	99.46 209	97.55 246	99.80 66	99.65 217	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	98.70 390	98.91 191	99.88 4	99.46 209	97.55 246	99.22 228	99.88 46	95.73 198	99.28 337	99.03 112	97.62 294	98.75 304

OurMVSNet-221017-0		97.88 266	97.77 257	98.19 328	98.71 389	96.53 349	99.88 4	99.00 357	97.79 217	98.78 309	99.94 6	91.68 345	99.35 327	97.21 312	96.99 330	98.69 321

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	99.69 197	98.20 100	99.70 259	99.64 40	99.82 110	99.54 188

DVP-MVS++		99.59 13	99.50 17	99.88 12	99.51 198	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		97.10 347	96.79 347	98.01 341	98.72 387	96.33 356	99.87 8	97.05 434	97.59 240	96.16 413	99.80 126	88.71 385	99.04 378	96.69 344	96.55 336	98.65 345

FC-MVSNet-test		98.75 178	98.62 179	99.15 203	99.08 329	99.45 108	99.86 11	99.60 62	98.23 152	98.70 321	99.82 98	96.80 149	99.22 351	99.07 108	96.38 339	98.79 294

v7n		97.87 268	97.52 286	98.92 230	98.76 383	98.58 227	99.84 12	99.46 209	96.20 362	98.91 287	99.70 186	94.89 235	99.44 307	96.03 361	93.89 397	98.75 304

DTE-MVSNet		97.51 323	97.19 332	98.46 299	98.63 396	98.13 259	99.84 12	99.48 179	96.68 324	97.97 375	99.67 210	92.92 308	98.56 412	96.88 337	92.60 415	98.70 317

3Dnovator	97.25 9	99.24 91	99.05 102	99.81 54	99.12 318	99.66 64	99.84 12	99.74 10	99.09 48	98.92 286	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	99.18 302	99.54 91	99.83 15	99.59 68	98.28 141	98.79 308	99.81 112	96.75 152	99.37 320	99.08 107	96.38 339	98.78 296

MGCFI-Net		99.01 143	98.85 147	99.50 142	99.42 232	99.26 137	99.82 16	99.48 179	98.60 108	99.28 211	98.81 394	97.04 140	99.76 232	99.29 83	97.87 283	99.47 218

test_fmvs3		92.10 401	91.77 404	93.08 415	96.19 434	86.25 435	99.82 16	98.62 409	96.65 327	95.19 421	96.90 435	55.05 450	95.93 442	96.63 349	90.92 424	97.06 431

jajsoiax		98.43 198	98.28 205	98.88 241	98.60 400	98.43 245	99.82 16	99.53 113	98.19 157	98.63 333	99.80 126	93.22 303	99.44 307	99.22 91	97.50 306	98.77 300

OpenMVS	96.50 16	98.47 195	98.12 216	99.52 132	99.04 337	99.53 94	99.82 16	99.72 11	94.56 401	98.08 368	99.88 46	94.73 247	99.98 17	97.47 297	99.76 133	99.06 275

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	94.56 258	99.93 104	99.67 34	98.26 261	99.72 122

nrg030		98.64 189	98.42 195	99.28 185	99.05 335	99.69 56	99.81 20	99.46 209	98.04 188	99.01 270	99.82 98	96.69 154	99.38 317	99.34 74	94.59 384	98.78 296

HPM-MVS		99.42 51	99.28 65	99.83 50	99.90 4	99.72 50	99.81 20	99.54 100	97.59 240	99.68 102	99.63 229	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	99.33 289	97.43 263	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	99.17 310	99.68 57	99.81 20	99.51 136	99.20 28	98.72 314	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	99.42 232	99.32 124	99.80 25	99.48 179	98.63 103	99.31 203	98.81 394	97.09 136	99.75 235	99.27 87	97.90 280	99.47 218

FA-MVS(test-final)		98.75 178	98.53 190	99.41 157	99.55 184	99.05 166	99.80 25	99.01 356	96.59 337	99.58 142	99.59 243	95.39 210	99.90 141	97.78 263	99.49 170	99.28 250

GeoE		98.85 167	98.62 179	99.53 126	99.61 162	99.08 161	99.80 25	99.51 136	97.10 295	99.31 203	99.78 146	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	99.42 232	99.32 124	99.80 25	99.48 179	98.63 103	99.31 203	98.81 394	97.09 136	99.75 235	99.27 87	97.90 280	99.47 218

v8		97.95 257	97.63 276	98.93 228	98.95 352	98.81 207	99.80 25	99.41 242	96.03 376	99.10 253	99.42 301	94.92 233	99.30 335	96.94 332	94.08 394	98.66 343

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	97.91 201	99.36 194	99.78 146	95.49 207	99.43 311	97.91 248	99.11 200	99.62 165

Anonymous20240521		96.20 366	95.89 369	97.13 387	97.72 421	94.96 394	99.79 31	99.29 312	93.01 415	97.20 398	99.03 373	89.69 375	98.36 416	91.16 422	96.13 345	98.07 407

PS-MVSNAJss		98.92 150	98.92 132	98.90 236	98.78 376	98.53 231	99.78 32	99.54 100	98.07 180	99.00 274	99.76 159	99.01 18	99.37 320	99.13 100	97.23 323	98.81 293

PEN-MVS		97.76 289	97.44 302	98.72 267	98.77 381	98.54 230	99.78 32	99.51 136	97.06 299	98.29 358	99.64 223	92.63 321	98.89 403	98.09 232	93.16 407	98.72 310

anonymousdsp		98.44 197	98.28 205	98.94 226	98.50 406	98.96 179	99.77 34	99.50 156	97.07 297	98.87 295	99.77 155	94.76 245	99.28 337	98.66 168	97.60 295	98.57 371

SixPastTwentyTwo		97.50 324	97.33 320	98.03 338	98.65 394	96.23 361	99.77 34	98.68 405	97.14 288	97.90 378	99.93 10	90.45 364	99.18 359	97.00 326	96.43 338	98.67 334

QAPM		98.67 185	98.30 204	99.80 58	99.20 296	99.67 61	99.77 34	99.72 11	94.74 398	98.73 313	99.90 31	95.78 196	99.98 17	96.96 330	99.88 69	99.76 99

SSC-MVS		92.73 400	93.73 395	89.72 425	95.02 444	81.38 445	99.76 37	99.23 324	94.87 395	92.80 432	98.93 386	94.71 249	91.37 449	74.49 448	93.80 398	96.42 435

test_vis3_rt		87.04 408	85.81 411	90.73 422	93.99 446	81.96 443	99.76 37	90.23 457	92.81 418	81.35 445	91.56 445	40.06 454	99.07 375	94.27 395	88.23 432	91.15 445

dcpmvs_2		99.23 92	99.58 7	98.16 330	99.83 43	94.68 399	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	99.46 222	98.61 225	99.76 37	99.50 156	98.06 184	99.81 62	99.88 46	93.91 287	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	97.72 225	99.76 83	99.75 164	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	99.47 218

v10		97.85 271	97.52 286	98.86 248	98.99 345	98.67 216	99.75 42	99.41 242	95.70 380	98.98 277	99.41 305	94.75 246	99.23 347	96.01 363	94.63 383	98.67 334

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	99.20 330	98.02 193	99.56 146	99.86 64	96.54 161	99.67 267	98.09 232	99.13 199	99.73 113

test_vis1_n		97.92 261	97.44 302	99.34 166	99.53 189	98.08 262	99.74 47	99.49 167	99.15 31	100.00 1	99.94 6	79.51 438	99.98 17	99.88 23	99.76 133	99.97 4

test_fmvs1_n		98.41 201	98.14 213	99.21 194	99.82 47	97.71 288	99.74 47	99.49 167	99.32 24	99.99 2	99.95 3	85.32 416	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	99.52 195

tttt0517		98.42 199	98.14 213	99.28 185	99.66 138	98.38 248	99.74 47	96.85 436	97.68 231	99.79 68	99.74 169	91.39 353	99.89 156	98.83 147	99.56 163	99.57 182

WB-MVS		93.10 398	94.10 391	90.12 424	95.51 442	81.88 444	99.73 51	99.27 317	95.05 391	93.09 431	98.91 390	94.70 250	91.89 448	76.62 446	94.02 396	96.58 434

test_fmvs2		97.25 341	97.30 323	97.09 389	99.43 230	93.31 420	99.73 51	98.87 379	98.83 81	99.28 211	99.80 126	84.45 421	99.66 270	97.88 250	97.45 311	98.30 393

SD_0403		97.55 318	97.53 285	97.62 371	99.61 162	93.64 417	99.72 53	99.44 229	98.03 190	98.62 336	99.39 313	96.06 179	99.57 289	87.88 435	99.01 212	99.66 145

MonoMVSNet		98.38 205	98.47 193	98.12 335	98.59 402	96.19 363	99.72 53	98.79 390	97.89 203	99.44 171	99.52 271	96.13 176	98.90 402	98.64 170	97.54 301	99.28 250

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		98.22 216	98.62 179	96.99 390	99.82 47	91.58 429	99.72 53	99.44 229	96.61 332	99.66 112	99.89 37	95.92 187	99.82 205	97.46 298	99.10 203	99.57 182

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		98.08 233	98.16 210	97.85 356	99.55 184	94.67 400	99.70 58	98.92 367	98.15 162	99.06 264	99.35 325	93.67 295	99.25 344	97.77 266	97.25 322	99.64 157

WR-MVS_H		98.13 227	97.87 247	98.90 236	99.02 339	98.84 199	99.70 58	99.59 68	97.27 277	98.40 350	99.19 357	95.53 205	99.23 347	98.34 212	93.78 399	98.61 365

mvsmamba		99.06 133	98.96 127	99.36 164	99.47 220	98.64 220	99.70 58	99.05 351	97.61 239	99.65 119	99.83 89	96.54 161	99.92 116	99.19 93	99.62 158	99.51 204

LTVRE_ROB	97.16 12	98.02 245	97.90 242	98.40 309	99.23 289	96.80 338	99.70 58	99.60 62	97.12 291	98.18 365	99.70 186	91.73 344	99.72 247	98.39 205	97.45 311	98.68 326
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		91.90 402	91.26 406	93.84 411	95.52 441	85.92 436	99.69 62	98.53 413	95.31 385	93.87 427	96.37 438	55.33 449	98.27 417	95.70 369	90.98 423	97.32 430

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	99.74 169	98.81 47	99.94 86	98.79 152	99.86 80	99.84 50

X-MVStestdata		96.55 358	95.45 377	99.87 18	99.85 28	99.83 20	99.69 62	99.68 20	98.98 65	99.37 191	64.01 454	98.81 47	99.94 86	98.79 152	99.86 80	99.84 50

V42		98.06 235	97.79 252	98.86 248	98.98 348	98.84 199	99.69 62	99.34 281	96.53 339	99.30 207	99.37 319	94.67 252	99.32 332	97.57 287	94.66 382	98.42 385

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	99.75 164	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	99.63 229	98.93 36	99.97 26	98.74 156	99.91 43	99.83 60

FE-MVS		98.48 194	98.17 209	99.40 158	99.54 188	98.96 179	99.68 68	98.81 386	95.54 382	99.62 131	99.70 186	93.82 290	99.93 104	97.35 306	99.46 171	99.32 247

PS-CasMVS		97.93 258	97.59 280	98.95 224	98.99 345	99.06 164	99.68 68	99.52 118	97.13 289	98.31 355	99.68 204	92.44 330	99.05 377	98.51 193	94.08 394	98.75 304

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	94.54 261	99.96 38	98.40 204	99.93 30	99.74 104

BP-MVS1		99.12 117	98.94 131	99.65 88	99.51 198	99.30 131	99.67 71	98.92 367	98.48 118	99.84 50	99.69 197	94.96 228	99.92 116	99.62 41	99.79 125	99.71 131

test_vis1_n_1920		98.63 190	98.40 197	99.31 173	99.86 22	97.94 275	99.67 71	99.62 46	99.43 14	99.99 2	99.91 24	87.29 403	100.00 1	99.92 21	99.92 36	99.98 2

EIA-MVS		99.18 97	99.09 97	99.45 150	99.49 212	99.18 145	99.67 71	99.53 113	97.66 234	99.40 185	99.44 297	98.10 104	99.81 210	98.94 122	99.62 158	99.35 242

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	99.49 281	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	99.49 212	99.14 153	99.67 71	99.34 281	97.31 274	99.58 142	99.76 159	97.65 118	99.82 205	98.87 134	99.07 206	99.46 223

CP-MVSNet		98.09 231	97.78 255	99.01 215	98.97 350	99.24 140	99.67 71	99.46 209	97.25 279	98.48 347	99.64 223	93.79 291	99.06 376	98.63 172	94.10 393	98.74 308

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	99.69 197	99.06 16	99.96 38	98.69 164	99.87 72	99.84 50

mvs_tets		98.40 204	98.23 207	98.91 234	98.67 393	98.51 237	99.66 78	99.53 113	98.19 157	98.65 330	99.81 112	92.75 312	99.44 307	99.31 78	97.48 310	98.77 300

EU-MVSNet		97.98 252	98.03 228	97.81 362	98.72 387	96.65 345	99.66 78	99.66 28	98.09 175	98.35 353	99.82 98	95.25 219	98.01 423	97.41 302	95.30 370	98.78 296

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	99.69 197	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	99.74 169	98.29 96	99.96 38	97.93 247	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	99.76 159	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	99.34 281	99.10 41	99.84 50	99.76 159	95.80 194	99.99 4	99.30 81	98.72 233	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	93.37 298	99.90 141	97.81 260	99.91 43	99.49 209

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	93.37 298	99.90 141	97.81 260	99.91 43	99.49 209

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	93.20 304	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	99.68 204	98.96 25	99.96 38	98.62 173	99.87 72	99.84 50

TranMVSNet+NR-MVSNet		97.93 258	97.66 271	98.76 264	98.78 376	98.62 223	99.65 84	99.49 167	97.76 221	98.49 346	99.60 241	94.23 272	98.97 394	98.00 243	92.90 409	98.70 317

GDP-MVS		99.08 130	98.89 139	99.64 94	99.53 189	99.34 120	99.64 91	99.48 179	98.32 138	99.77 77	99.66 215	95.14 224	99.93 104	98.97 120	99.50 169	99.64 157

ttmdpeth		97.80 285	97.63 276	98.29 319	98.77 381	97.38 299	99.64 91	99.36 269	98.78 91	96.30 411	99.58 247	92.34 333	99.39 315	98.36 210	95.58 363	98.10 405

mvsany_test3		93.77 395	93.45 399	94.74 408	95.78 437	88.01 434	99.64 91	98.25 417	98.28 141	94.31 425	97.97 427	68.89 442	98.51 414	97.50 293	90.37 425	97.71 422

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	99.64 223	98.91 37	99.96 38	98.72 159	99.90 54	99.82 66

tfpnnormal		97.84 275	97.47 294	98.98 219	99.20 296	99.22 142	99.64 91	99.61 55	96.32 353	98.27 359	99.70 186	93.35 300	99.44 307	95.69 370	95.40 368	98.27 395

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	99.39 252	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		96.22 364	96.03 365	96.79 398	97.31 427	94.14 409	99.63 97	99.08 345	96.17 365	97.04 402	99.06 370	93.94 284	97.76 429	86.96 438	95.06 375	98.47 379

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	97.20 429	98.18 255	99.62 102	98.91 372	99.28 26	98.63 333	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	92.65 420	99.71 96	99.78 146	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	99.42 232	99.08 161	99.62 102	99.36 269	97.39 268	99.28 211	99.68 204	96.44 167	99.92 116	98.37 208	98.22 264	99.40 235

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		97.89 265	97.87 247	97.96 347	99.51 198	95.45 380	99.60 109	99.25 320	99.17 29	98.85 300	99.49 281	89.29 379	99.64 278	99.35 69	96.31 342	98.78 296

test2506		96.81 354	96.65 350	97.29 384	99.74 93	92.21 427	99.60 109	85.06 458	99.13 34	99.77 77	99.93 10	87.82 401	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				99.70 186	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	97.97 196	99.63 127	99.68 204	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	98.10 230	97.99 232	98.44 304	99.41 237	96.96 329	99.60 109	99.56 83	98.09 175	98.15 366	99.91 24	90.87 361	99.70 259	98.88 131	97.45 311	98.67 334
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	99.41 242	98.13 168	99.31 203	99.70 186	95.48 208	99.27 340	99.40 65	97.32 320	98.79 294

guyue		99.16 101	99.04 104	99.52 132	99.69 120	98.92 190	99.59 116	98.81 386	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		98.04 241	98.05 226	98.00 343	99.74 93	94.37 406	99.59 116	94.98 446	99.13 34	99.66 112	99.93 10	90.67 363	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	99.79 139	98.68 67	99.96 38	98.44 201	99.77 130	99.79 86

thres100view900		97.76 289	97.45 297	98.69 271	99.72 104	97.86 279	99.59 116	98.74 396	97.93 199	99.26 221	98.62 402	91.75 342	99.83 197	93.22 407	98.18 269	98.37 391

thres600view7		97.86 270	97.51 288	98.92 230	99.72 104	97.95 273	99.59 116	98.74 396	97.94 198	99.27 216	98.62 402	91.75 342	99.86 170	93.73 402	98.19 268	98.96 286

LCM-MVSNet-Re		97.83 278	98.15 212	96.87 396	99.30 269	92.25 426	99.59 116	98.26 416	97.43 263	96.20 412	99.13 363	96.27 173	98.73 409	98.17 227	98.99 213	99.64 157

baseline1		98.31 210	97.95 237	99.38 163	99.50 210	98.74 211	99.59 116	98.93 364	98.41 127	99.14 245	99.60 241	94.59 256	99.79 221	98.48 195	93.29 404	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
Skip Steuart: Steuart Systems R&D Blog.
CPTT-MVS		99.11 123	98.90 136	99.74 73	99.80 57	99.46 107	99.59 116	99.49 167	97.03 303	99.63 127	99.69 197	97.27 130	99.96 38	97.82 258	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	99.38 247	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		95.02 384	96.12 362	91.72 419	99.10 323	80.43 447	99.58 126	97.87 426	97.47 255	95.22 419	98.82 393	93.99 282	95.18 444	88.09 433	94.91 380	99.56 185

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		98.04 241	98.11 217	97.83 359	99.74 93	93.82 411	99.58 126	95.40 445	99.12 39	99.65 119	99.93 10	90.73 362	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	97.84 211	99.71 96	99.80 126	99.12 13	99.97 26	98.33 213	99.87 72	99.83 60

LPG-MVS_test		98.22 216	98.13 215	98.49 291	99.33 260	97.05 318	99.58 126	99.55 91	97.46 256	99.24 223	99.83 89	92.58 322	99.72 247	98.09 232	97.51 304	98.68 326

PHI-MVS		99.30 77	99.17 86	99.70 80	99.56 180	99.52 98	99.58 126	99.80 8	97.12 291	99.62 131	99.73 175	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	98.24 418	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	97.82 216	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	99.37 268	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	98.47 298	99.33 260	96.91 332	99.57 134	99.30 308	98.47 119	99.41 180	98.99 379	96.78 150	99.74 237	98.73 158	99.38 176	98.74 308

v2v482		98.06 235	97.77 257	98.92 230	98.90 358	98.82 205	99.57 134	99.36 269	96.65 327	99.19 237	99.35 325	94.20 273	99.25 344	97.72 273	94.97 377	98.69 321

DSMNet-mixed		97.25 341	97.35 314	96.95 393	97.84 417	93.61 418	99.57 134	96.63 440	96.13 370	98.87 295	98.61 404	94.59 256	97.70 430	95.08 384	98.86 223	99.55 186

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		96.08 370	95.48 375	97.89 353	98.93 353	96.70 340	99.56 141	99.35 276	92.69 419	91.81 436	99.46 294	89.90 372	98.96 396	95.00 386	92.61 414	98.00 414

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	92.02 336	99.88 161	99.54 48	98.26 261	99.72 122

KD-MVS_self_test		95.00 385	94.34 390	96.96 392	97.07 432	95.39 383	99.56 141	99.44 229	95.11 388	97.13 400	97.32 433	91.86 340	97.27 434	90.35 425	81.23 442	98.23 399

ETV-MVS		99.26 86	99.21 79	99.40 158	99.46 222	99.30 131	99.56 141	99.52 118	98.52 115	99.44 171	99.27 347	98.41 90	99.86 170	99.10 105	99.59 161	99.04 276

SMA-MVS		99.44 46	99.30 58	99.85 37	99.73 100	99.83 20	99.56 141	99.47 200	97.45 259	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	97.85 209	99.36 194	99.85 71	95.95 184	99.85 176	96.66 346	99.83 106	99.59 175

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		98.38 205	98.09 221	99.24 191	99.26 281	99.32 124	99.56 141	99.55 91	97.45 259	98.71 315	99.83 89	93.23 301	99.63 284	98.88 131	96.32 341	98.76 302

ACMH+	97.24 10	97.92 261	97.78 255	98.32 316	99.46 222	96.68 344	99.56 141	99.54 100	98.41 127	97.79 384	99.87 57	90.18 370	99.66 270	98.05 240	97.18 326	98.62 356

ACMM	97.58 5	98.37 207	98.34 200	98.48 293	99.41 237	97.10 312	99.56 141	99.45 220	98.53 114	99.04 267	99.85 71	93.00 306	99.71 253	98.74 156	97.45 311	98.64 347
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	99.18 302	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	99.52 195

testing3-2		97.84 275	97.70 267	98.24 325	99.53 189	95.37 384	99.55 155	98.67 406	98.46 120	99.27 216	99.34 329	86.58 407	99.83 197	99.32 77	98.63 236	99.52 195

test_fmvsmconf0.01_n		99.22 94	99.03 107	99.79 61	98.42 409	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	91.41 352	99.96 38	99.82 26	99.92 36	99.90 23

v144192		97.92 261	97.60 279	98.87 245	98.83 370	98.65 218	99.55 155	99.34 281	96.20 362	99.32 202	99.40 309	94.36 268	99.26 343	96.37 357	95.03 376	98.70 317

API-MVS		99.04 136	99.03 107	99.06 209	99.40 242	99.31 128	99.55 155	99.56 83	98.54 113	99.33 201	99.39 313	98.76 55	99.78 226	96.98 328	99.78 127	98.07 407

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	99.52 195	99.62 76	99.54 160	99.62 46	98.69 100	99.99 2	99.96 1	94.47 265	99.94 86	99.88 23	99.92 36	99.98 2

APD_test1		95.87 372	96.49 354	94.00 410	99.53 189	84.01 439	99.54 160	99.32 299	95.91 378	97.99 373	99.85 71	85.49 414	99.88 161	91.96 418	98.84 225	98.12 404

thisisatest0530		98.35 208	98.03 228	99.31 173	99.63 151	98.56 228	99.54 160	96.75 438	97.53 250	99.73 89	99.65 217	91.25 357	99.89 156	98.62 173	99.56 163	99.48 212

MTMP							99.54 160	98.88 377

v1144		97.98 252	97.69 268	98.85 251	98.87 363	98.66 217	99.54 160	99.35 276	96.27 357	99.23 227	99.35 325	94.67 252	99.23 347	96.73 341	95.16 373	98.68 326

v148		97.79 287	97.55 281	98.50 290	98.74 384	97.72 285	99.54 160	99.33 289	96.26 358	98.90 289	99.51 275	94.68 251	99.14 363	97.83 257	93.15 408	98.63 354

CostFormer		97.72 299	97.73 264	97.71 367	99.15 316	94.02 410	99.54 160	99.02 355	94.67 399	99.04 267	99.35 325	92.35 332	99.77 228	98.50 194	97.94 279	99.34 245

MVSTER		98.49 193	98.32 202	99.00 217	99.35 254	99.02 168	99.54 160	99.38 260	97.41 266	99.20 234	99.73 175	93.86 289	99.36 324	98.87 134	97.56 299	98.62 356

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	94.53 263	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	98.87 379	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	98.16 330	99.81 51	94.59 402	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	99.41 237	96.99 325	99.52 170	99.49 167	98.11 172	99.24 223	99.34 329	96.96 144	99.79 221	97.95 246	99.45 172	99.02 279

Fast-Effi-MVS+		98.70 182	98.43 194	99.51 137	99.51 198	99.28 134	99.52 170	99.47 200	96.11 371	99.01 270	99.34 329	96.20 175	99.84 184	97.88 250	98.82 227	99.39 236

v1921920		97.80 285	97.45 297	98.84 252	98.80 372	98.53 231	99.52 170	99.34 281	96.15 368	99.24 223	99.47 290	93.98 283	99.29 336	95.40 378	95.13 374	98.69 321

MIMVSNet1		95.51 378	95.04 383	96.92 395	97.38 424	95.60 373	99.52 170	99.50 156	93.65 409	96.97 404	99.17 358	85.28 417	96.56 439	88.36 432	95.55 365	98.60 368

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		97.32 338	96.81 346	98.87 245	99.40 242	97.46 296	99.51 179	99.53 113	95.86 379	98.54 343	99.77 155	82.44 430	99.66 270	98.68 166	97.52 303	99.50 208

alignmvs		98.81 171	98.56 188	99.58 109	99.43 230	99.42 111	99.51 179	98.96 362	98.61 106	99.35 197	98.92 389	94.78 241	99.77 228	99.35 69	98.11 274	99.54 188

v1192		97.81 283	97.44 302	98.91 234	98.88 360	98.68 215	99.51 179	99.34 281	96.18 364	99.20 234	99.34 329	94.03 281	99.36 324	95.32 380	95.18 372	98.69 321

test20.03		96.12 368	95.96 367	96.63 399	97.44 423	95.45 380	99.51 179	99.38 260	96.55 338	96.16 413	99.25 350	93.76 293	96.17 440	87.35 437	94.22 390	98.27 395

mvs_anonymous		99.03 138	98.99 119	99.16 199	99.38 247	98.52 235	99.51 179	99.38 260	97.79 217	99.38 189	99.81 112	97.30 128	99.45 302	99.35 69	98.99 213	99.51 204

TAMVS		99.12 117	99.08 98	99.24 191	99.46 222	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	99.51 198	99.67 61	99.50 187	99.64 38	99.43 14	99.98 11	99.78 146	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	99.44 229	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	99.49 212	99.18 145	99.50 187	99.07 348	98.22 153	99.61 135	99.51 275	95.37 211	99.84 184	98.60 179	98.33 255	99.59 175

DCV-MVSNet		98.86 159	98.63 174	99.54 118	99.49 212	99.18 145	99.50 187	99.07 348	98.22 153	99.61 135	99.51 275	95.37 211	99.84 184	98.60 179	98.33 255	99.59 175

tfpn200view9		97.72 299	97.38 310	98.72 267	99.69 120	97.96 270	99.50 187	98.73 402	97.83 212	99.17 242	98.45 409	91.67 346	99.83 197	93.22 407	98.18 269	98.37 391

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		97.68 307	97.28 326	98.88 241	99.06 332	98.62 223	99.50 187	99.45 220	96.32 353	97.87 380	99.79 139	92.47 326	99.35 327	97.54 290	93.54 401	98.67 334

EI-MVSNet		98.67 185	98.67 166	98.68 272	99.35 254	97.97 268	99.50 187	99.38 260	96.93 312	99.20 234	99.83 89	97.87 111	99.36 324	98.38 206	97.56 299	98.71 312

CVMVSNet		98.57 192	98.67 166	98.30 318	99.35 254	95.59 374	99.50 187	99.55 91	98.60 108	99.39 187	99.83 89	94.48 264	99.45 302	98.75 155	98.56 243	99.85 43

VPA-MVSNet		98.29 213	97.95 237	99.30 178	99.16 312	99.54 91	99.50 187	99.58 73	98.27 143	99.35 197	99.37 319	92.53 324	99.65 275	99.35 69	94.46 385	98.72 310

thres400		97.77 288	97.38 310	98.92 230	99.69 120	97.96 270	99.50 187	98.73 402	97.83 212	99.17 242	98.45 409	91.67 346	99.83 197	93.22 407	98.18 269	98.96 286

APD-MVS		99.27 83	99.08 98	99.84 49	99.75 85	99.79 35	99.50 187	99.50 156	97.16 287	99.77 77	99.82 98	98.78 51	99.94 86	97.56 288	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	99.52 195

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		95.81 374	95.65 373	96.32 403	99.67 127	91.35 430	99.49 199	96.74 439	98.25 148	95.24 418	98.10 424	74.96 439	99.90 141	99.53 50	98.85 224	97.70 424

TransMVSNet (Re)		97.15 345	96.58 351	98.86 248	99.12 318	98.85 198	99.49 199	98.91 372	95.48 383	97.16 399	99.80 126	93.38 297	99.11 371	94.16 398	91.73 418	98.62 356

UniMVSNet (Re)		98.29 213	98.00 231	99.13 204	99.00 342	99.36 119	99.49 199	99.51 136	97.95 197	98.97 279	99.13 363	96.30 172	99.38 317	98.36 210	93.34 403	98.66 343

EPMVS		97.82 281	97.65 272	98.35 313	98.88 360	95.98 366	99.49 199	94.71 448	97.57 243	99.26 221	99.48 287	92.46 329	99.71 253	97.87 252	99.08 205	99.35 242

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		97.34 336	97.15 333	97.93 349	99.02 339	95.76 371	99.48 205	99.58 73	97.62 238	99.09 256	99.53 267	87.95 397	99.27 340	96.42 353	95.66 361	98.75 304

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	94.64 255	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		97.88 266	97.54 284	98.90 236	99.71 110	98.53 231	99.48 205	99.57 78	94.16 404	98.81 304	99.68 204	93.23 301	99.42 313	98.84 144	94.42 387	98.76 302

v1240		97.69 304	97.32 321	98.79 260	98.85 367	98.43 245	99.48 205	99.36 269	96.11 371	99.27 216	99.36 322	93.76 293	99.24 346	94.46 392	95.23 371	98.70 317

VPNet		97.84 275	97.44 302	99.01 215	99.21 294	98.94 186	99.48 205	99.57 78	98.38 129	99.28 211	99.73 175	88.89 382	99.39 315	99.19 93	93.27 405	98.71 312

UniMVSNet_NR-MVSNet		98.22 216	97.97 234	98.96 222	98.92 355	98.98 172	99.48 205	99.53 113	97.76 221	98.71 315	99.46 294	96.43 168	99.22 351	98.57 185	92.87 411	98.69 321

TDRefinement		95.42 380	94.57 388	97.97 345	89.83 451	96.11 365	99.48 205	98.75 393	96.74 320	96.68 407	99.88 46	88.65 388	99.71 253	98.37 208	82.74 440	98.09 406

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		97.97 255	97.61 278	99.02 214	98.87 363	99.26 137	99.47 214	99.42 239	97.63 236	97.08 401	99.50 278	95.07 226	99.13 366	97.86 253	93.59 400	98.68 326

PVSNet_Blended_VisFu		99.36 67	99.28 65	99.61 102	99.86 22	99.07 163	99.47 214	99.93 2	97.66 234	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	99.35 254	99.31 128	99.46 217	99.13 339	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	97.70 430	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		97.28 339	96.76 348	98.82 254	99.37 250	98.07 263	99.45 220	99.36 269	97.56 245	97.89 379	98.95 384	83.70 424	98.82 404	96.03 361	98.56 243	99.58 179

tt0805		97.97 255	97.77 257	98.57 282	99.59 170	96.61 347	99.45 220	99.08 345	98.21 155	98.88 292	99.80 126	88.66 387	99.70 259	98.58 182	97.72 289	99.39 236

tpm2		97.44 331	97.34 317	97.74 366	99.15 316	94.36 407	99.45 220	98.94 363	93.45 413	98.90 289	99.44 297	91.35 354	99.59 288	97.31 307	98.07 275	99.29 249

FMVSNet2		97.72 299	97.36 312	98.80 259	99.51 198	98.84 199	99.45 220	99.42 239	96.49 341	98.86 299	99.29 342	90.26 366	98.98 387	96.44 352	96.56 335	98.58 370

CDS-MVSNet		99.09 128	99.03 107	99.25 188	99.42 232	98.73 212	99.45 220	99.46 209	98.11 172	99.46 165	99.77 155	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	99.37 250	99.66 64	99.45 220	99.54 100	96.61 332	99.01 270	99.40 309	97.09 136	99.86 170	97.68 278	99.53 166	99.10 264
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	94.04 280	99.96 38	99.96 11	99.93 30	99.93 20

UGNet		98.87 156	98.69 164	99.40 158	99.22 293	98.72 213	99.44 226	99.68 20	99.24 27	99.18 241	99.42 301	92.74 314	99.96 38	99.34 74	99.94 28	99.53 194
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	97.77 220	99.30 207	99.81 112	94.20 273	99.93 104	99.17 98	98.82 227	99.49 209

test_0402		96.64 357	96.24 359	97.85 356	98.85 367	96.43 353	99.44 226	99.26 318	93.52 410	96.98 403	99.52 271	88.52 391	99.20 358	92.58 417	97.50 306	97.93 419

ACMP	97.20 11	98.06 235	97.94 239	98.45 301	99.37 250	97.01 323	99.44 226	99.49 167	97.54 249	98.45 348	99.79 139	91.95 338	99.72 247	97.91 248	97.49 309	98.62 356
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
GG-mvs-BLEND				98.45 301	98.55 404	98.16 256	99.43 231	93.68 450		97.23 395	98.46 408	89.30 378	99.22 351	95.43 377	98.22 264	97.98 416

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	99.53 267	98.64 72	99.96 38	98.44 201	99.80 118	99.79 86

tpm cat1		97.39 333	97.36 312	97.50 378	99.17 310	93.73 413	99.43 231	99.31 303	91.27 424	98.71 315	99.08 367	94.31 271	99.77 228	96.41 355	98.50 247	99.00 280

tpm		97.67 310	97.55 281	98.03 338	99.02 339	95.01 392	99.43 231	98.54 412	96.44 347	99.12 248	99.34 329	91.83 341	99.60 287	97.75 269	96.46 337	99.48 212

GBi-Net		97.68 307	97.48 291	98.29 319	99.51 198	97.26 305	99.43 231	99.48 179	96.49 341	99.07 259	99.32 337	90.26 366	98.98 387	97.10 320	96.65 332	98.62 356

test1		97.68 307	97.48 291	98.29 319	99.51 198	97.26 305	99.43 231	99.48 179	96.49 341	99.07 259	99.32 337	90.26 366	98.98 387	97.10 320	96.65 332	98.62 356

FMVSNet1		96.84 353	96.36 357	98.29 319	99.32 267	97.26 305	99.43 231	99.48 179	95.11 388	98.55 342	99.32 337	83.95 423	98.98 387	95.81 366	96.26 343	98.62 356

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	99.54 188

testgi		97.65 312	97.50 289	98.13 334	99.36 253	96.45 352	99.42 238	99.48 179	97.76 221	97.87 380	99.45 296	91.09 358	98.81 405	94.53 391	98.52 246	99.13 263

F-COLMAP		99.19 95	99.04 104	99.64 94	99.78 63	99.27 136	99.42 238	99.54 100	97.29 276	99.41 180	99.59 243	98.42 88	99.93 104	98.19 224	99.69 146	99.73 113

Anonymous202405211		98.30 212	97.98 233	99.26 187	99.57 176	98.16 256	99.41 243	98.55 411	96.03 376	99.19 237	99.74 169	91.87 339	99.92 116	99.16 99	98.29 260	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	99.78 146	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	99.52 195	99.56 87	99.41 243	99.39 252	99.01 57	99.74 87	99.78 146	95.56 204	99.92 116	99.52 52	98.18 269	99.72 122

baseline2		97.87 268	97.55 281	98.82 254	99.18 302	98.02 265	99.41 243	96.58 442	96.97 306	96.51 408	99.17 358	93.43 296	99.57 289	97.71 274	99.03 209	98.86 290

DU-MVS		98.08 233	97.79 252	98.96 222	98.87 363	98.98 172	99.41 243	99.45 220	97.87 205	98.71 315	99.50 278	94.82 237	99.22 351	98.57 185	92.87 411	98.68 326

Baseline_NR-MVSNet		97.76 289	97.45 297	98.68 272	99.09 326	98.29 250	99.41 243	98.85 381	95.65 381	98.63 333	99.67 210	94.82 237	99.10 373	98.07 239	92.89 410	98.64 347

XVG-ACMP-BASELINE		97.83 278	97.71 266	98.20 327	99.11 320	96.33 356	99.41 243	99.52 118	98.06 184	99.05 266	99.50 278	89.64 376	99.73 243	97.73 271	97.38 318	98.53 373

DP-MVS		99.16 101	98.95 129	99.78 64	99.77 71	99.53 94	99.41 243	99.50 156	97.03 303	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	97.53 250	99.64 124	99.78 146	98.84 44	99.91 128	97.63 279	99.82 110

D2MVS		98.41 201	98.50 191	98.15 333	99.26 281	96.62 346	99.40 251	99.61 55	97.71 226	98.98 277	99.36 322	96.04 180	99.67 267	98.70 161	97.41 316	98.15 403

Anonymous20240529		98.09 231	97.68 269	99.34 166	99.66 138	98.44 244	99.40 251	99.43 237	93.67 408	99.22 228	99.89 37	90.23 369	99.93 104	99.26 89	98.33 255	99.66 145

FMVSNet3		98.03 243	97.76 261	98.84 252	99.39 245	98.98 172	99.40 251	99.38 260	96.67 325	99.07 259	99.28 344	92.93 307	98.98 387	97.10 320	96.65 332	98.56 372

LFMVS		97.90 264	97.35 314	99.54 118	99.52 195	99.01 170	99.39 255	98.24 418	97.10 295	99.65 119	99.79 139	84.79 419	99.91 128	99.28 84	98.38 252	99.69 135

HQP_MVS		98.27 215	98.22 208	98.44 304	99.29 273	96.97 327	99.39 255	99.47 200	98.97 68	99.11 250	99.61 238	92.71 317	99.69 264	97.78 263	97.63 292	98.67 334

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	99.38 260	97.70 229	99.28 211	99.28 344	98.34 94	99.85 176	96.96 330	99.45 172	99.69 135

gg-mvs-nofinetune		96.17 367	95.32 379	98.73 265	98.79 373	98.14 258	99.38 260	94.09 449	91.07 427	98.07 371	91.04 447	89.62 377	99.35 327	96.75 340	99.09 204	98.68 326

VDDNet		97.55 318	97.02 340	99.16 199	99.49 212	98.12 261	99.38 260	99.30 308	95.35 384	99.68 102	99.90 31	82.62 429	99.93 104	99.31 78	98.13 273	99.42 230

MVS_0304		99.15 105	98.96 127	99.73 76	98.92 355	99.37 116	99.37 262	96.92 435	99.51 2	99.66 112	99.78 146	96.69 154	99.97 26	99.84 25	99.97 8	99.84 50

pmmvs6		96.53 359	96.09 364	97.82 361	98.69 391	95.47 379	99.37 262	99.47 200	93.46 412	97.41 389	99.78 146	87.06 405	99.33 330	96.92 335	92.70 413	98.65 345

PM-MVS		92.96 399	92.23 403	95.14 407	95.61 438	89.98 433	99.37 262	98.21 420	94.80 397	95.04 423	97.69 428	65.06 443	97.90 426	94.30 393	89.98 428	97.54 428

WTY-MVS		99.06 133	98.88 142	99.61 102	99.62 157	99.16 148	99.37 262	99.56 83	98.04 188	99.53 153	99.62 234	96.84 147	99.94 86	98.85 141	98.49 248	99.72 122

IterMVS-LS		98.46 196	98.42 195	98.58 281	99.59 170	98.00 266	99.37 262	99.43 237	96.94 311	99.07 259	99.59 243	97.87 111	99.03 380	98.32 215	95.62 362	98.71 312
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
h-mvs33		97.70 303	97.28 326	98.97 221	99.70 115	97.27 303	99.36 267	99.45 220	98.94 71	99.66 112	99.64 223	94.93 231	99.99 4	99.48 59	84.36 437	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		96.44 361	96.12 362	97.40 381	98.65 394	95.65 372	99.36 267	99.51 136	97.13 289	96.04 415	98.99 379	88.40 392	98.17 419	96.71 342	90.27 426	98.40 388

sss		99.17 99	99.05 102	99.53 126	99.62 157	98.97 175	99.36 267	99.62 46	97.83 212	99.67 107	99.65 217	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	99.41 237	99.16 148	99.35 272	99.57 78	98.82 82	99.51 157	99.61 238	96.46 165	99.95 73	99.59 42	99.98 4	99.65 150

pmmvs-eth3d		95.34 382	94.73 385	97.15 385	95.53 440	95.94 367	99.35 272	99.10 342	95.13 386	93.55 428	97.54 429	88.15 396	97.91 425	94.58 390	89.69 429	97.61 425

MDTV_nov1_ep13_2view						95.18 389	99.35 272		96.84 316	99.58 142		95.19 222		97.82 258		99.46 223

VDD-MVS		97.73 297	97.35 314	98.88 241	99.47 220	97.12 311	99.34 275	98.85 381	98.19 157	99.67 107	99.85 71	82.98 427	99.92 116	99.49 58	98.32 259	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	97.55 246	98.70 321	99.89 37	95.83 191	99.90 141	98.10 231	99.90 54	99.08 269
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
myMVS_eth3d28		97.69 304	97.34 317	98.73 265	99.27 278	97.52 294	99.33 277	98.78 391	98.03 190	98.82 303	98.49 407	86.64 406	99.46 300	98.44 201	98.24 263	99.23 257

EGC-MVSNET		82.80 412	77.86 418	97.62 371	97.91 415	96.12 364	99.33 277	99.28 314	8.40 455	25.05 456	99.27 347	84.11 422	99.33 330	89.20 428	98.22 264	97.42 429

ETVMVS		97.50 324	96.90 344	99.29 181	99.23 289	98.78 210	99.32 279	98.90 374	97.52 252	98.56 341	98.09 425	84.72 420	99.69 264	97.86 253	97.88 282	99.39 236

FMVSNet5		96.43 362	96.19 361	97.15 385	99.11 320	95.89 368	99.32 279	99.52 118	94.47 403	98.34 354	99.07 368	87.54 402	97.07 435	92.61 416	95.72 359	98.47 379

dp		97.75 293	97.80 251	97.59 375	99.10 323	93.71 414	99.32 279	98.88 377	96.48 344	99.08 258	99.55 258	92.67 320	99.82 205	96.52 350	98.58 240	99.24 256

tpmvs		97.98 252	98.02 230	97.84 358	99.04 337	94.73 397	99.31 282	99.20 330	96.10 375	98.76 311	99.42 301	94.94 230	99.81 210	96.97 329	98.45 249	98.97 284

tpmrst		98.33 209	98.48 192	97.90 352	99.16 312	94.78 396	99.31 282	99.11 341	97.27 277	99.45 166	99.59 243	95.33 214	99.84 184	98.48 195	98.61 237	99.09 268

testing99		97.36 334	96.94 343	98.63 275	99.18 302	96.70 340	99.30 284	98.93 364	97.71 226	98.23 360	98.26 417	84.92 418	99.84 184	98.04 241	97.85 285	99.35 242

MP-MVS-pluss		99.37 63	99.20 81	99.88 12	99.90 4	99.87 16	99.30 284	99.52 118	97.18 285	99.60 138	99.79 139	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	99.63 229	98.72 64	99.90 141	98.25 220	99.63 157	99.80 82

JIA-IIPM		97.50 324	97.02 340	98.93 228	98.73 385	97.80 281	99.30 284	98.97 360	91.73 423	98.91 287	94.86 441	95.10 225	99.71 253	97.58 283	97.98 277	99.28 250

BH-RMVSNet		98.41 201	98.08 222	99.40 158	99.41 237	98.83 202	99.30 284	98.77 392	97.70 229	98.94 284	99.65 217	92.91 310	99.74 237	96.52 350	99.55 165	99.64 157

testing11		97.50 324	97.10 337	98.71 269	99.20 296	96.91 332	99.29 289	98.82 384	97.89 203	98.21 363	98.40 411	85.63 413	99.83 197	98.45 200	98.04 276	99.37 240

Syy-MVS		97.09 348	97.14 334	96.95 393	99.00 342	92.73 424	99.29 289	99.39 252	97.06 299	97.41 389	98.15 420	93.92 286	98.68 410	91.71 419	98.34 253	99.45 226

myMVS_eth3d		96.89 351	96.37 356	98.43 306	99.00 342	97.16 309	99.29 289	99.39 252	97.06 299	97.41 389	98.15 420	83.46 426	98.68 410	95.27 381	98.34 253	99.45 226

MCST-MVS		99.43 49	99.30 58	99.82 51	99.79 61	99.74 48	99.29 289	99.40 249	98.79 88	99.52 155	99.62 234	98.91 37	99.90 141	98.64 170	99.75 135	99.82 66

LF4IMVS		97.52 321	97.46 296	97.70 368	98.98 348	95.55 375	99.29 289	98.82 384	98.07 180	98.66 324	99.64 223	89.97 371	99.61 286	97.01 325	96.68 331	97.94 418

hse-mvs2		97.50 324	97.14 334	98.59 278	99.49 212	97.05 318	99.28 294	99.22 326	98.94 71	99.66 112	99.42 301	94.93 231	99.65 275	99.48 59	83.80 439	99.08 269

OPM-MVS		98.19 220	98.10 218	98.45 301	98.88 360	97.07 316	99.28 294	99.38 260	98.57 110	99.22 228	99.81 112	92.12 334	99.66 270	98.08 236	97.54 301	98.61 365
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	99.71 182	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	97.42 265	99.67 107	99.37 319	97.53 119	99.88 161	98.98 117	97.29 321	98.42 385

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	97.79 262	99.81 113	99.60 170

testing222		97.16 344	96.50 353	99.16 199	99.16 312	98.47 243	99.27 299	98.66 407	97.71 226	98.23 360	98.15 420	82.28 432	99.84 184	97.36 305	97.66 291	99.18 260

AUN-MVS		96.88 352	96.31 358	98.59 278	99.48 219	97.04 321	99.27 299	99.22 326	97.44 262	98.51 344	99.41 305	91.97 337	99.66 270	97.71 274	83.83 438	99.07 274

pmmvs5		97.52 321	97.30 323	98.16 330	98.57 403	96.73 339	99.27 299	98.90 374	96.14 369	98.37 352	99.53 267	91.54 351	99.14 363	97.51 292	95.87 354	98.63 354

1314		98.68 184	98.54 189	99.11 205	98.89 359	98.65 218	99.27 299	99.49 167	96.89 313	97.99 373	99.56 255	97.72 117	99.83 197	97.74 270	99.27 187	98.84 292

MVS		97.28 339	96.55 352	99.48 143	98.78 376	98.95 183	99.27 299	99.39 252	83.53 441	98.08 368	99.54 263	96.97 143	99.87 167	94.23 396	99.16 194	99.63 162

BH-untuned		98.42 199	98.36 198	98.59 278	99.49 212	96.70 340	99.27 299	99.13 339	97.24 281	98.80 306	99.38 316	95.75 197	99.74 237	97.07 324	99.16 194	99.33 246

MDTV_nov1_ep13			98.32 202		99.11 320	94.44 404	99.27 299	98.74 396	97.51 253	99.40 185	99.62 234	94.78 241	99.76 232	97.59 282	98.81 229

DP-MVS Recon		99.12 117	98.95 129	99.65 88	99.74 93	99.70 54	99.27 299	99.57 78	96.40 351	99.42 176	99.68 204	98.75 58	99.80 217	97.98 244	99.72 141	99.44 228

PatchmatchNet		98.31 210	98.36 198	98.19 328	99.16 312	95.32 385	99.27 299	98.92 367	97.37 269	99.37 191	99.58 247	94.90 234	99.70 259	97.43 301	99.21 191	99.54 188
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
thres200		97.61 315	97.28 326	98.62 276	99.64 148	98.03 264	99.26 308	98.74 396	97.68 231	99.09 256	98.32 415	91.66 348	99.81 210	92.88 412	98.22 264	98.03 410

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	99.71 182	98.96 25	99.85 176	98.59 181	99.80 118	99.77 94

tt0320		95.71 377	95.07 381	97.62 371	99.05 335	95.02 391	99.25 310	99.52 118	86.81 436	97.97 375	99.72 179	83.58 425	99.15 361	96.38 356	93.35 402	98.68 326

1112_ss		98.98 145	98.77 156	99.59 106	99.68 125	99.02 168	99.25 310	99.48 179	97.23 282	99.13 246	99.58 247	96.93 145	99.90 141	98.87 134	98.78 230	99.84 50

TAPA-MVS	97.07 15	97.74 295	97.34 317	98.94 226	99.70 115	97.53 293	99.25 310	99.51 136	91.90 422	99.30 207	99.63 229	98.78 51	99.64 278	88.09 433	99.87 72	99.65 150
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
UWE-MVS-28		97.36 334	97.24 330	97.75 364	98.84 369	94.44 404	99.24 313	97.58 431	97.98 195	99.00 274	99.00 377	91.35 354	99.53 295	93.75 401	98.39 251	99.27 254

UBG		97.85 271	97.48 291	98.95 224	99.25 285	97.64 290	99.24 313	98.74 396	97.90 202	98.64 331	98.20 419	88.65 388	99.81 210	98.27 218	98.40 250	99.42 230

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	96.85 315	99.27 216	99.48 287	98.25 98	99.91 128	97.76 267	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				65.14 453	94.18 276	99.71 253	97.58 283

ADS-MVSNet2		98.02 245	98.07 225	97.87 354	99.33 260	95.19 388	99.23 316	99.08 345	96.24 359	99.10 253	99.67 210	94.11 277	98.93 399	96.81 338	99.05 207	99.48 212

ADS-MVSNet		98.20 219	98.08 222	98.56 285	99.33 260	96.48 351	99.23 316	99.15 336	96.24 359	99.10 253	99.67 210	94.11 277	99.71 253	96.81 338	99.05 207	99.48 212

EPNet_dtu		98.03 243	97.96 235	98.23 326	98.27 411	95.54 377	99.23 316	98.75 393	99.02 55	97.82 382	99.71 182	96.11 177	99.48 297	93.04 410	99.65 154	99.69 135
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CR-MVSNet		98.17 223	97.93 240	98.87 245	99.18 302	98.49 239	99.22 320	99.33 289	96.96 307	99.56 146	99.38 316	94.33 269	99.00 385	94.83 389	98.58 240	99.14 261

RPMNet		96.72 355	95.90 368	99.19 196	99.18 302	98.49 239	99.22 320	99.52 118	88.72 434	99.56 146	97.38 431	94.08 279	99.95 73	86.87 439	98.58 240	99.14 261

sc_t1		95.75 375	95.05 382	97.87 354	98.83 370	94.61 401	99.21 322	99.45 220	87.45 435	97.97 375	99.85 71	81.19 435	99.43 311	98.27 218	93.20 406	99.57 182

WBMVS		97.74 295	97.50 289	98.46 299	99.24 287	97.43 297	99.21 322	99.42 239	97.45 259	98.96 281	99.41 305	88.83 383	99.23 347	98.94 122	96.02 347	98.71 312

plane_prior						96.97 327	99.21 322		98.45 122						97.60 295

tt0320-xc		95.31 383	94.59 387	97.45 379	98.92 355	94.73 397	99.20 325	99.31 303	86.74 437	97.23 395	99.72 179	81.14 436	98.95 397	97.08 323	91.98 417	98.67 334

testing91		97.44 331	97.02 340	98.71 269	99.18 302	96.89 334	99.19 326	99.04 352	97.78 219	98.31 355	98.29 416	85.41 415	99.85 176	98.01 242	97.95 278	99.39 236

WR-MVS		98.06 235	97.73 264	99.06 209	98.86 366	99.25 139	99.19 326	99.35 276	97.30 275	98.66 324	99.43 299	93.94 284	99.21 356	98.58 182	94.28 389	98.71 312

new-patchmatchnet		94.48 391	94.08 392	95.67 406	95.08 443	92.41 425	99.18 328	99.28 314	94.55 402	93.49 429	97.37 432	87.86 400	97.01 436	91.57 420	88.36 431	97.61 425

AdaColmap		99.01 143	98.80 152	99.66 84	99.56 180	99.54 91	99.18 328	99.70 15	98.18 160	99.35 197	99.63 229	96.32 171	99.90 141	97.48 295	99.77 130	99.55 186

EG-PatchMatch MVS		95.97 371	95.69 372	96.81 397	97.78 418	92.79 423	99.16 330	98.93 364	96.16 366	94.08 426	99.22 353	82.72 428	99.47 298	95.67 372	97.50 306	98.17 401

PatchT		97.03 349	96.44 355	98.79 260	98.99 345	98.34 249	99.16 330	99.07 348	92.13 421	99.52 155	97.31 434	94.54 261	98.98 387	88.54 431	98.73 232	99.03 277

CNLPA		99.14 109	98.99 119	99.59 106	99.58 172	99.41 113	99.16 330	99.44 229	98.45 122	99.19 237	99.49 281	98.08 106	99.89 156	97.73 271	99.75 135	99.48 212

MDA-MVSNet-bldmvs		94.96 386	93.98 393	97.92 350	98.24 412	97.27 303	99.15 333	99.33 289	93.80 407	80.09 448	99.03 373	88.31 393	97.86 427	93.49 405	94.36 388	98.62 356

CDPH-MVS		99.13 111	98.91 135	99.80 58	99.75 85	99.71 52	99.15 333	99.41 242	96.60 335	99.60 138	99.55 258	98.83 45	99.90 141	97.48 295	99.83 106	99.78 92

save fliter					99.76 75	99.59 81	99.14 335	99.40 249	99.00 60

WB-MVSnew		97.65 312	97.65 272	97.63 370	98.78 376	97.62 291	99.13 336	98.33 415	97.36 270	99.07 259	98.94 385	95.64 202	99.15 361	92.95 411	98.68 235	96.12 439

testf1		90.42 406	90.68 407	89.65 426	97.78 418	73.97 454	99.13 336	98.81 386	89.62 429	91.80 437	98.93 386	62.23 446	98.80 406	86.61 440	91.17 420	96.19 437

APD_test2		90.42 406	90.68 407	89.65 426	97.78 418	73.97 454	99.13 336	98.81 386	89.62 429	91.80 437	98.93 386	62.23 446	98.80 406	86.61 440	91.17 420	96.19 437

xiu_mvs_v1_base_debu		99.29 79	99.27 69	99.34 166	99.63 151	98.97 175	99.12 339	99.51 136	98.86 77	99.84 50	99.47 290	98.18 101	99.99 4	99.50 54	99.31 184	99.08 269

xiu_mvs_v1_base		99.29 79	99.27 69	99.34 166	99.63 151	98.97 175	99.12 339	99.51 136	98.86 77	99.84 50	99.47 290	98.18 101	99.99 4	99.50 54	99.31 184	99.08 269

xiu_mvs_v1_base_debi		99.29 79	99.27 69	99.34 166	99.63 151	98.97 175	99.12 339	99.51 136	98.86 77	99.84 50	99.47 290	98.18 101	99.99 4	99.50 54	99.31 184	99.08 269

XVG-OURS-SEG-HR		98.69 183	98.62 179	98.89 239	99.71 110	97.74 282	99.12 339	99.54 100	98.44 125	99.42 176	99.71 182	94.20 273	99.92 116	98.54 192	98.90 221	99.00 280

jason		99.13 111	99.03 107	99.45 150	99.46 222	98.87 194	99.12 339	99.26 318	98.03 190	99.79 68	99.65 217	97.02 141	99.85 176	99.02 114	99.90 54	99.65 150
jason: jason.
N_pmnet		94.95 387	95.83 370	92.31 417	98.47 407	79.33 449	99.12 339	92.81 455	93.87 406	97.68 385	99.13 363	93.87 288	99.01 384	91.38 421	96.19 344	98.59 369

MDA-MVSNet_test_wron		95.45 379	94.60 386	98.01 341	98.16 413	97.21 308	99.11 345	99.24 323	93.49 411	80.73 447	98.98 381	93.02 305	98.18 418	94.22 397	94.45 386	98.64 347

Patchmtry		97.75 293	97.40 309	98.81 257	99.10 323	98.87 194	99.11 345	99.33 289	94.83 396	98.81 304	99.38 316	94.33 269	99.02 382	96.10 359	95.57 364	98.53 373

YYNet1		95.36 381	94.51 389	97.92 350	97.89 416	97.10 312	99.10 347	99.23 324	93.26 414	80.77 446	99.04 372	92.81 311	98.02 422	94.30 393	94.18 391	98.64 347

CANet_DTU		98.97 147	98.87 143	99.25 188	99.33 260	98.42 247	99.08 348	99.30 308	99.16 30	99.43 173	99.75 164	95.27 216	99.97 26	98.56 188	99.95 20	99.36 241

SCA		98.19 220	98.16 210	98.27 324	99.30 269	95.55 375	99.07 349	98.97 360	97.57 243	99.43 173	99.57 252	92.72 315	99.74 237	97.58 283	99.20 192	99.52 195

TSAR-MVS + GP.		99.36 67	99.36 42	99.36 164	99.67 127	98.61 225	99.07 349	99.33 289	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	99.07 349	99.34 281	98.99 62	99.61 135	99.82 98	97.98 110	99.87 167	97.00 326	99.80 118	99.85 43

PatchMatch-RL		98.84 170	98.62 179	99.52 132	99.71 110	99.28 134	99.06 352	99.77 9	97.74 224	99.50 158	99.53 267	95.41 209	99.84 184	97.17 319	99.64 155	99.44 228

OpenMVS_ROB	92.34 20	94.38 392	93.70 398	96.41 402	97.38 424	93.17 421	99.06 352	98.75 393	86.58 438	94.84 424	98.26 417	81.53 433	99.32 332	89.01 429	97.87 283	96.76 432

TEST9					99.67 127	99.65 68	99.05 354	99.41 242	96.22 361	98.95 282	99.49 281	98.77 54	99.91 128

train_agg		99.02 139	98.77 156	99.77 67	99.67 127	99.65 68	99.05 354	99.41 242	96.28 355	98.95 282	99.49 281	98.76 55	99.91 128	97.63 279	99.72 141	99.75 100

lupinMVS		99.13 111	99.01 117	99.46 149	99.51 198	98.94 186	99.05 354	99.16 335	97.86 206	99.80 66	99.56 255	97.39 122	99.86 170	98.94 122	99.85 87	99.58 179

DELS-MVS		99.48 33	99.42 28	99.65 88	99.72 104	99.40 114	99.05 354	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		96.38 363	96.03 365	97.41 380	98.13 414	95.16 390	99.05 354	99.20 330	93.94 405	97.39 392	98.79 397	91.61 350	99.04 378	90.43 424	95.77 356	98.05 409

Patchmatch-test		97.93 258	97.65 272	98.77 263	99.18 302	97.07 316	99.03 359	99.14 338	96.16 366	98.74 312	99.57 252	94.56 258	99.72 247	93.36 406	99.11 200	99.52 195

test_8					99.67 127	99.61 78	99.03 359	99.41 242	96.28 355	98.93 285	99.48 287	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	99.03 359	99.47 200	96.98 305	99.15 244	99.23 352	96.77 151	99.89 156	98.83 147	98.78 230	99.86 39

IterMVS-SCA-FT		97.82 281	97.75 262	98.06 337	99.57 176	96.36 355	99.02 362	99.49 167	97.18 285	98.71 315	99.72 179	92.72 315	99.14 363	97.44 300	95.86 355	98.67 334

xiu_mvs_v2_base		99.26 86	99.25 73	99.29 181	99.53 189	98.91 191	99.02 362	99.45 220	98.80 87	99.71 96	99.26 349	98.94 32	99.98 17	99.34 74	99.23 190	98.98 283

MIMVSNet		97.73 297	97.45 297	98.57 282	99.45 228	97.50 295	99.02 362	98.98 359	96.11 371	99.41 180	99.14 362	90.28 365	98.74 408	95.74 368	98.93 216	99.47 218

IterMVS		97.83 278	97.77 257	98.02 340	99.58 172	96.27 359	99.02 362	99.48 179	97.22 283	98.71 315	99.70 186	92.75 312	99.13 366	97.46 298	96.00 349	98.67 334
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	99.02 362	99.91 3	97.67 233	99.59 141	99.75 164	95.90 189	99.73 243	99.53 50	99.02 211	99.86 39

UWE-MVS		97.58 317	97.29 325	98.48 293	99.09 326	96.25 360	99.01 367	96.61 441	97.86 206	99.19 237	99.01 376	88.72 384	99.90 141	97.38 304	98.69 234	99.28 250

新几何2							99.01 367

BH-w/o		98.00 250	97.89 246	98.32 316	99.35 254	96.20 362	99.01 367	98.90 374	96.42 349	98.38 351	99.00 377	95.26 218	99.72 247	96.06 360	98.61 237	99.03 277

test_prior4						99.56 87	98.99 370

无先验							98.99 370	99.51 136	96.89 313				99.93 104	97.53 291		99.72 122

pmmvs4		98.13 227	97.90 242	98.81 257	98.61 399	98.87 194	98.99 370	99.21 329	96.44 347	99.06 264	99.58 247	95.90 189	99.11 371	97.18 318	96.11 346	98.46 382

HQP-NCC					99.19 299		98.98 373		98.24 149	98.66 324

ACMP_Plane					99.19 299		98.98 373		98.24 149	98.66 324

HQP-MVS		98.02 245	97.90 242	98.37 312	99.19 299	96.83 335	98.98 373	99.39 252	98.24 149	98.66 324	99.40 309	92.47 326	99.64 278	97.19 316	97.58 297	98.64 347

PS-MVSNAJ		99.32 74	99.32 50	99.30 178	99.57 176	98.94 186	98.97 376	99.46 209	98.92 74	99.71 96	99.24 351	99.01 18	99.98 17	99.35 69	99.66 152	98.97 284

MVP-Stereo		97.81 283	97.75 262	97.99 344	97.53 422	96.60 348	98.96 377	98.85 381	97.22 283	97.23 395	99.36 322	95.28 215	99.46 300	95.51 374	99.78 127	97.92 420
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
test_prior2							98.96 377		98.34 135	99.01 270	99.52 271	98.68 67		97.96 245	99.74 138

旧先验2							98.96 377		96.70 323	99.47 163			99.94 86	98.19 224

原ACMM2							98.95 380

MVS_111021_HR		99.41 55	99.32 50	99.66 84	99.72 104	99.47 106	98.95 380	99.85 6	98.82 82	99.54 151	99.73 175	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	98.94 382	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	98.94 382	99.85 6	98.82 82	99.65 119	99.74 169	98.51 81	99.80 217	98.83 147	99.89 65	99.64 157

pmmvs3		94.09 394	93.25 400	96.60 400	94.76 445	94.49 403	98.92 384	98.18 422	89.66 428	96.48 409	98.06 426	86.28 409	97.33 433	89.68 427	87.20 434	97.97 417

XVG-OURS		98.73 181	98.68 165	98.88 241	99.70 115	97.73 283	98.92 384	99.55 91	98.52 115	99.45 166	99.84 84	95.27 216	99.91 128	98.08 236	98.84 225	99.00 280

test222					99.75 85	99.49 102	98.91 386	99.49 167	96.42 349	99.34 200	99.65 217	98.28 97			99.69 146	99.72 122

PMMVS2		86.87 409	85.37 413	91.35 421	90.21 450	83.80 440	98.89 387	97.45 433	83.13 442	91.67 439	95.03 439	48.49 452	94.70 445	85.86 442	77.62 444	95.54 440

miper_lstm_enhance		98.00 250	97.91 241	98.28 323	99.34 259	97.43 297	98.88 388	99.36 269	96.48 344	98.80 306	99.55 258	95.98 182	98.91 400	97.27 309	95.50 367	98.51 375

MVS-HIRNet		95.75 375	95.16 380	97.51 377	99.30 269	93.69 415	98.88 388	95.78 443	85.09 440	98.78 309	92.65 443	91.29 356	99.37 320	94.85 388	99.85 87	99.46 223

TR-MVS		97.76 289	97.41 308	98.82 254	99.06 332	97.87 277	98.87 390	98.56 410	96.63 331	98.68 323	99.22 353	92.49 325	99.65 275	95.40 378	97.79 287	98.95 288

testdata1							98.85 391		98.32 138

ET-MVSNet_ETH3D		96.49 360	95.64 374	99.05 211	99.53 189	98.82 205	98.84 392	97.51 432	97.63 236	84.77 441	99.21 356	92.09 335	98.91 400	98.98 117	92.21 416	99.41 233

our_test_3		97.65 312	97.68 269	97.55 376	98.62 397	94.97 393	98.84 392	99.30 308	96.83 318	98.19 364	99.34 329	97.01 142	99.02 382	95.00 386	96.01 348	98.64 347

MS-PatchMatch		97.24 343	97.32 321	96.99 390	98.45 408	93.51 419	98.82 394	99.32 299	97.41 266	98.13 367	99.30 340	88.99 381	99.56 291	95.68 371	99.80 118	97.90 421

c3_l		98.12 229	98.04 227	98.38 311	99.30 269	97.69 289	98.81 395	99.33 289	96.67 325	98.83 301	99.34 329	97.11 135	98.99 386	97.58 283	95.34 369	98.48 377

ppachtmachnet_test		97.49 329	97.45 297	97.61 374	98.62 397	95.24 386	98.80 396	99.46 209	96.11 371	98.22 362	99.62 234	96.45 166	98.97 394	93.77 400	95.97 353	98.61 365

PAPR		98.63 190	98.34 200	99.51 137	99.40 242	99.03 167	98.80 396	99.36 269	96.33 352	99.00 274	99.12 366	98.46 84	99.84 184	95.23 382	99.37 183	99.66 145

test0.0.03 1		97.71 302	97.42 307	98.56 285	98.41 410	97.82 280	98.78 398	98.63 408	97.34 271	98.05 372	98.98 381	94.45 266	98.98 387	95.04 385	97.15 327	98.89 289

PVSNet_Blended		99.08 130	98.97 123	99.42 156	99.76 75	98.79 208	98.78 398	99.91 3	96.74 320	99.67 107	99.49 281	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	99.27 278	98.70 214	98.76 400	99.31 303	97.34 271	99.21 231	99.07 368	97.20 133	99.82 205	98.56 188	98.87 222	99.52 195

test123		39.01 421	42.50 423	28.53 436	39.17 459	20.91 461	98.75 401	19.17 461	19.83 454	38.57 453	66.67 451	33.16 456	15.42 455	37.50 455	29.66 453	49.26 450

MSDG		98.98 145	98.80 152	99.53 126	99.76 75	99.19 143	98.75 401	99.55 91	97.25 279	99.47 163	99.77 155	97.82 113	99.87 167	96.93 333	99.90 54	99.54 188

CLD-MVS		98.16 224	98.10 218	98.33 314	99.29 273	96.82 337	98.75 401	99.44 229	97.83 212	99.13 246	99.55 258	92.92 308	99.67 267	98.32 215	97.69 290	98.48 377
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		98.18 222	98.10 218	98.41 307	99.23 289	97.72 285	98.72 404	99.31 303	96.60 335	98.88 292	99.29 342	97.29 129	99.13 366	97.60 281	95.99 350	98.38 390

cl____		98.01 248	97.84 250	98.55 287	99.25 285	97.97 268	98.71 405	99.34 281	96.47 346	98.59 340	99.54 263	95.65 201	99.21 356	97.21 312	95.77 356	98.46 382

DIV-MVS_self_test		98.01 248	97.85 249	98.48 293	99.24 287	97.95 273	98.71 405	99.35 276	96.50 340	98.60 339	99.54 263	95.72 199	99.03 380	97.21 312	95.77 356	98.46 382

test-LLR		98.06 235	97.90 242	98.55 287	98.79 373	97.10 312	98.67 407	97.75 427	97.34 271	98.61 337	98.85 391	94.45 266	99.45 302	97.25 310	99.38 176	99.10 264

TESTMET0.1,1		97.55 318	97.27 329	98.40 309	98.93 353	96.53 349	98.67 407	97.61 430	96.96 307	98.64 331	99.28 344	88.63 390	99.45 302	97.30 308	99.38 176	99.21 259

test-mter		97.49 329	97.13 336	98.55 287	98.79 373	97.10 312	98.67 407	97.75 427	96.65 327	98.61 337	98.85 391	88.23 394	99.45 302	97.25 310	99.38 176	99.10 264

mvs5depth		96.66 356	96.22 360	97.97 345	97.00 433	96.28 358	98.66 410	99.03 354	96.61 332	96.93 405	99.79 139	87.20 404	99.47 298	96.65 348	94.13 392	98.16 402

IB-MVS	95.67 18	96.22 364	95.44 378	98.57 282	99.21 294	96.70 340	98.65 411	97.74 429	96.71 322	97.27 394	98.54 406	86.03 410	99.92 116	98.47 198	86.30 435	99.10 264
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	98.64 412	99.10 342	97.93 199	99.42 176	99.55 258	98.67 69	99.80 217	95.80 367	99.68 149	99.61 167

thisisatest0515		98.14 226	97.79 252	99.19 196	99.50 210	98.50 238	98.61 413	96.82 437	96.95 309	99.54 151	99.43 299	91.66 348	99.86 170	98.08 236	99.51 167	99.22 258

DeepPCF-MVS	98.18 3	98.81 171	99.37 40	97.12 388	99.60 168	91.75 428	98.61 413	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		97.85 271	97.64 275	98.48 293	99.09 326	97.87 277	98.60 415	99.33 289	97.11 294	98.87 295	99.22 353	92.38 331	99.17 360	98.21 222	95.99 350	98.42 385

GA-MVS		97.85 271	97.47 294	99.00 217	99.38 247	97.99 267	98.57 416	99.15 336	97.04 302	98.90 289	99.30 340	89.83 373	99.38 317	96.70 343	98.33 255	99.62 165

TinyColmap		97.12 346	96.89 345	97.83 359	99.07 330	95.52 378	98.57 416	98.74 396	97.58 242	97.81 383	99.79 139	88.16 395	99.56 291	95.10 383	97.21 324	98.39 389

eth_miper_zixun_eth		98.05 240	97.96 235	98.33 314	99.26 281	97.38 299	98.56 418	99.31 303	96.65 327	98.88 292	99.52 271	96.58 159	99.12 370	97.39 303	95.53 366	98.47 379

CMPMVS	69.68 23	94.13 393	94.90 384	91.84 418	97.24 428	80.01 448	98.52 419	99.48 179	89.01 432	91.99 435	99.67 210	85.67 412	99.13 366	95.44 376	97.03 329	96.39 436
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
USDC		97.34 336	97.20 331	97.75 364	99.07 330	95.20 387	98.51 420	99.04 352	97.99 194	98.31 355	99.86 64	89.02 380	99.55 293	95.67 372	97.36 319	98.49 376

ambc				93.06 416	92.68 447	82.36 441	98.47 421	98.73 402		95.09 422	97.41 430	55.55 448	99.10 373	96.42 353	91.32 419	97.71 422

miper_enhance_ethall		98.16 224	98.08 222	98.41 307	98.96 351	97.72 285	98.45 422	99.32 299	96.95 309	98.97 279	99.17 358	97.06 139	99.22 351	97.86 253	95.99 350	98.29 394

CHOSEN 280x420		99.12 117	99.13 89	99.08 206	99.66 138	97.89 276	98.43 423	99.71 13	98.88 76	99.62 131	99.76 159	96.63 156	99.70 259	99.46 62	99.99 1	99.66 145

testmvs		39.17 420	43.78 422	25.37 437	36.04 460	16.84 462	98.36 424	26.56 459	20.06 453	38.51 454	67.32 450	29.64 457	15.30 456	37.59 454	39.90 452	43.98 451

FPMVS		84.93 411	85.65 412	82.75 432	86.77 453	63.39 458	98.35 425	98.92 367	74.11 444	83.39 443	98.98 381	50.85 451	92.40 447	84.54 443	94.97 377	92.46 442

KD-MVS_2432*1600		94.62 388	93.72 396	97.31 382	97.19 430	95.82 369	98.34 426	99.20 330	95.00 392	97.57 386	98.35 413	87.95 397	98.10 420	92.87 413	77.00 445	98.01 411

miper_refine_blended		94.62 388	93.72 396	97.31 382	97.19 430	95.82 369	98.34 426	99.20 330	95.00 392	97.57 386	98.35 413	87.95 397	98.10 420	92.87 413	77.00 445	98.01 411

CL-MVSNet_self_test		94.49 390	93.97 394	96.08 404	96.16 435	93.67 416	98.33 428	99.38 260	95.13 386	97.33 393	98.15 420	92.69 319	96.57 438	88.67 430	79.87 443	97.99 415

PVSNet	96.02 17	98.85 167	98.84 149	98.89 239	99.73 100	97.28 302	98.32 429	99.60 62	97.86 206	99.50 158	99.57 252	96.75 152	99.86 170	98.56 188	99.70 145	99.54 188

PAPM		97.59 316	97.09 338	99.07 207	99.06 332	98.26 252	98.30 430	99.10 342	94.88 394	98.08 368	99.34 329	96.27 173	99.64 278	89.87 426	98.92 218	99.31 248

Patchmatch-RL test		95.84 373	95.81 371	95.95 405	95.61 438	90.57 431	98.24 431	98.39 414	95.10 390	95.20 420	98.67 401	94.78 241	97.77 428	96.28 358	90.02 427	99.51 204

UnsupCasMVSNet_bld		93.53 396	92.51 402	96.58 401	97.38 424	93.82 411	98.24 431	99.48 179	91.10 426	93.10 430	96.66 436	74.89 440	98.37 415	94.03 399	87.71 433	97.56 427

LCM-MVSNet		86.80 410	85.22 414	91.53 420	87.81 452	80.96 446	98.23 433	98.99 358	71.05 445	90.13 440	96.51 437	48.45 453	96.88 437	90.51 423	85.30 436	96.76 432

cascas		97.69 304	97.43 306	98.48 293	98.60 400	97.30 301	98.18 434	99.39 252	92.96 416	98.41 349	98.78 398	93.77 292	99.27 340	98.16 228	98.61 237	98.86 290

kuosan		90.92 405	90.11 410	93.34 413	98.78 376	85.59 438	98.15 435	93.16 453	89.37 431	92.07 434	98.38 412	81.48 434	95.19 443	62.54 452	97.04 328	99.25 255

Effi-MVS+		98.81 171	98.59 185	99.48 143	99.46 222	99.12 156	98.08 436	99.50 156	97.50 254	99.38 189	99.41 305	96.37 170	99.81 210	99.11 102	98.54 245	99.51 204

PCF-MVS	97.08 14	97.66 311	97.06 339	99.47 147	99.61 162	99.09 158	98.04 437	99.25 320	91.24 425	98.51 344	99.70 186	94.55 260	99.91 128	92.76 415	99.85 87	99.42 230
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
PVSNet_0	94.43 19	96.09 369	95.47 376	97.94 348	99.31 268	94.34 408	97.81 438	99.70 15	97.12 291	97.46 388	98.75 399	89.71 374	99.79 221	97.69 277	81.69 441	99.68 139

E-PMN		80.61 414	79.88 416	82.81 431	90.75 449	76.38 452	97.69 439	95.76 444	66.44 449	83.52 442	92.25 444	62.54 445	87.16 451	68.53 450	61.40 448	84.89 449

dongtai		93.26 397	92.93 401	94.25 409	99.39 245	85.68 437	97.68 440	93.27 451	92.87 417	96.85 406	99.39 313	82.33 431	97.48 432	76.78 445	97.80 286	99.58 179

ANet_high		77.30 416	74.86 420	84.62 430	75.88 456	77.61 450	97.63 441	93.15 454	88.81 433	64.27 451	89.29 448	36.51 455	83.93 453	75.89 447	52.31 450	92.33 444

EMVS		80.02 415	79.22 417	82.43 433	91.19 448	76.40 451	97.55 442	92.49 456	66.36 450	83.01 444	91.27 446	64.63 444	85.79 452	65.82 451	60.65 449	85.08 448

MVE	76.82 21	76.91 417	74.31 421	84.70 429	85.38 455	76.05 453	96.88 443	93.17 452	67.39 448	71.28 450	89.01 449	21.66 460	87.69 450	71.74 449	72.29 447	90.35 446
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
test_method		91.10 403	91.36 405	90.31 423	95.85 436	73.72 456	94.89 444	99.25 320	68.39 447	95.82 416	99.02 375	80.50 437	98.95 397	93.64 403	94.89 381	98.25 397

Gipuma		90.99 404	90.15 409	93.51 412	98.73 385	90.12 432	93.98 445	99.45 220	79.32 443	92.28 433	94.91 440	69.61 441	97.98 424	87.42 436	95.67 360	92.45 443
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PMVS	70.75 22	75.98 418	74.97 419	79.01 434	70.98 457	55.18 459	93.37 446	98.21 420	65.08 451	61.78 452	93.83 442	21.74 459	92.53 446	78.59 444	91.12 422	89.34 447
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
tmp_tt		82.80 412	81.52 415	86.66 428	66.61 458	68.44 457	92.79 447	97.92 424	68.96 446	80.04 449	99.85 71	85.77 411	96.15 441	97.86 253	43.89 451	95.39 441

wuyk23d		40.18 419	41.29 424	36.84 435	86.18 454	49.12 460	79.73 448	22.81 460	27.64 452	25.46 455	28.45 455	21.98 458	48.89 454	55.80 453	23.56 454	12.51 452

mmdepth		0.02 426	0.03 429	0.00 438	0.00 461	0.00 463	0.00 449	0.00 462	0.00 456	0.00 457	0.27 457	0.00 461	0.00 457	0.00 456	0.00 455	0.00 453

monomultidepth		0.02 426	0.03 429	0.00 438	0.00 461	0.00 463	0.00 449	0.00 462	0.00 456	0.00 457	0.27 457	0.00 461	0.00 457	0.00 456	0.00 455	0.00 453

test_blank		0.13 425	0.17 428	0.00 438	0.00 461	0.00 463	0.00 449	0.00 462	0.00 456	0.00 457	1.57 456	0.00 461	0.00 457	0.00 456	0.00 455	0.00 453

uanet_test		0.02 426	0.03 429	0.00 438	0.00 461	0.00 463	0.00 449	0.00 462	0.00 456	0.00 457	0.27 457	0.00 461	0.00 457	0.00 456	0.00 455	0.00 453

DCPMVS		0.02 426	0.03 429	0.00 438	0.00 461	0.00 463	0.00 449	0.00 462	0.00 456	0.00 457	0.27 457	0.00 461	0.00 457	0.00 456	0.00 455	0.00 453

cdsmvs_eth3d_5k		24.64 422	32.85 425	0.00 438	0.00 461	0.00 463	0.00 449	99.51 136	0.00 456	0.00 457	99.56 255	96.58 159	0.00 457	0.00 456	0.00 455	0.00 453

pcd_1.5k_mvsjas		8.27 424	11.03 427	0.00 438	0.00 461	0.00 463	0.00 449	0.00 462	0.00 456	0.00 457	0.27 457	99.01 18	0.00 457	0.00 456	0.00 455	0.00 453

sosnet-low-res		0.02 426	0.03 429	0.00 438	0.00 461	0.00 463	0.00 449	0.00 462	0.00 456	0.00 457	0.27 457	0.00 461	0.00 457	0.00 456	0.00 455	0.00 453

sosnet		0.02 426	0.03 429	0.00 438	0.00 461	0.00 463	0.00 449	0.00 462	0.00 456	0.00 457	0.27 457	0.00 461	0.00 457	0.00 456	0.00 455	0.00 453

uncertanet		0.02 426	0.03 429	0.00 438	0.00 461	0.00 463	0.00 449	0.00 462	0.00 456	0.00 457	0.27 457	0.00 461	0.00 457	0.00 456	0.00 455	0.00 453

Regformer		0.02 426	0.03 429	0.00 438	0.00 461	0.00 463	0.00 449	0.00 462	0.00 456	0.00 457	0.27 457	0.00 461	0.00 457	0.00 456	0.00 455	0.00 453

ab-mvs-re		8.30 423	11.06 426	0.00 438	0.00 461	0.00 463	0.00 449	0.00 462	0.00 456	0.00 457	99.58 247	0.00 461	0.00 457	0.00 456	0.00 455	0.00 453

uanet		0.02 426	0.03 429	0.00 438	0.00 461	0.00 463	0.00 449	0.00 462	0.00 456	0.00 457	0.27 457	0.00 461	0.00 457	0.00 456	0.00 455	0.00 453

WAC-MVS						97.16 309								95.47 375

MSC_two_6792asdad				99.87 18	99.51 198	99.76 43		99.33 289					99.96 38	98.87 134	99.84 95	99.89 26

PC_three_1452									98.18 160	99.84 50	99.70 186	99.31 3	98.52 413	98.30 217	99.80 118	99.81 73

No_MVS				99.87 18	99.51 198	99.76 43		99.33 289					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					0.00 461

eth-test					0.00 461

ZD-MVS					99.71 110	99.79 35		99.61 55	96.84 316	99.56 146	99.54 263	98.58 75	99.96 38	96.93 333	99.75 135

IU-MVS					99.84 34	99.88 9		99.32 299	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	99.74 169	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																99.52 195

test_part2					99.81 51	99.83 20				99.77 77

sam_mvs1												94.86 236				99.52 195

sam_mvs												94.72 248

MTGPA								99.47 200

test_post											65.99 452	94.65 254	99.73 243

patchmatchnet-post											98.70 400	94.79 240	99.74 237

gm-plane-assit					98.54 405	92.96 422			94.65 400		99.15 361		99.64 278	97.56 288

test9_res														97.49 294	99.72 141	99.75 100

agg_prior2														97.21 312	99.73 140	99.75 100

agg_prior					99.67 127	99.62 76		99.40 249		98.87 295			99.91 128

TestCases				99.31 173	99.86 22	98.48 241		99.61 55	97.85 209	99.36 194	99.85 71	95.95 184	99.85 176	96.66 346	99.83 106	99.59 175

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	96.76 319	99.29 210	99.64 223	98.43 86	99.94 86	96.92 335	99.66 152	99.72 122

旧先验1					99.74 93	99.59 81		99.54 100			99.69 197	98.47 83			99.68 149	99.73 113

原ACMM1				99.65 88	99.73 100	99.33 123		99.47 200	97.46 256	99.12 248	99.66 215	98.67 69	99.91 128	97.70 276	99.69 146	99.71 131

testdata2													99.95 73	96.67 345

segment_acmp												98.96 25

testdata				99.54 118	99.75 85	98.95 183		99.51 136	97.07 297	99.43 173	99.70 186	98.87 40	99.94 86	97.76 267	99.64 155	99.72 122

test12				99.75 70	99.64 148	99.61 78		99.29 312		99.21 231		98.38 92	99.89 156		99.74 138	99.74 104

plane_prior7					99.29 273	97.03 322

plane_prior6					99.27 278	96.98 326						92.71 317

plane_prior5								99.47 200					99.69 264	97.78 263	97.63 292	98.67 334

plane_prior4											99.61 238

plane_prior3						97.00 324			98.69 100	99.11 250

plane_prior1					99.26 281

n2								0.00 462

nn								0.00 462

door-mid								98.05 423

lessismore_v0				97.79 363	98.69 391	95.44 382		94.75 447		95.71 417	99.87 57	88.69 386	99.32 332	95.89 364	94.93 379	98.62 356

LGP-MVS_train				98.49 291	99.33 260	97.05 318		99.55 91	97.46 256	99.24 223	99.83 89	92.58 322	99.72 247	98.09 232	97.51 304	98.68 326

test11								99.35 276

door								97.92 424

HQP5-MVS						96.83 335

BP-MVS														97.19 316

HQP4-MVS										98.66 324			99.64 278			98.64 347

HQP3-MVS								99.39 252							97.58 297

HQP2-MVS												92.47 326

NP-MVS					99.23 289	96.92 331					99.40 309

ACMMP++_ref															97.19 325

ACMMP++															97.43 315

Test By Simon												98.75 58

ITE_SJBPF				98.08 336	99.29 273	96.37 354		98.92 367	98.34 135	98.83 301	99.75 164	91.09 358	99.62 285	95.82 365	97.40 317	98.25 397

DeepMVS_CX				93.34 413	99.29 273	82.27 442		99.22 326	85.15 439	96.33 410	99.05 371	90.97 360	99.73 243	93.57 404	97.77 288	98.01 411

High-resolution multi-view benchmark