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 16	99.48 12	99.54 96	99.76 57	99.42 100	99.90 1	99.55 67	98.56 71	99.78 35	99.70 143	98.65 72	99.79 175	99.65 4	99.78 97	99.41 184

CS-MVS-test		99.49 17	99.48 12	99.54 96	99.78 48	99.30 112	99.89 2	99.58 50	98.56 71	99.73 49	99.69 153	98.55 78	99.82 162	99.69 1	99.85 60	99.48 169

RRT_MVS		98.70 148	98.66 137	98.83 219	98.90 298	98.45 216	99.89 2	99.28 276	97.76 165	98.94 238	99.92 8	96.98 141	99.25 290	99.28 42	97.00 273	98.80 235

mvsmamba		98.92 119	98.87 109	99.08 168	99.07 276	99.16 128	99.88 4	99.51 106	98.15 117	99.40 141	99.89 15	97.12 134	99.33 276	99.38 27	97.40 261	98.73 250

MVSFormer		99.17 78	99.12 72	99.29 148	99.51 163	98.94 166	99.88 4	99.46 175	97.55 186	99.80 28	99.65 174	97.39 124	99.28 285	99.03 66	99.85 60	99.65 122

test_djsdf		98.67 154	98.57 154	98.98 183	98.70 327	98.91 170	99.88 4	99.46 175	97.55 186	99.22 186	99.88 21	95.73 184	99.28 285	99.03 66	97.62 238	98.75 245

OurMVSNet-221017-0		97.88 228	97.77 221	98.19 281	98.71 326	96.53 301	99.88 4	99.00 311	97.79 162	98.78 262	99.94 3	91.68 306	99.35 273	97.21 258	96.99 274	98.69 262

DROMVSNet		99.44 33	99.39 22	99.58 90	99.56 153	99.49 91	99.88 4	99.58 50	98.38 87	99.73 49	99.69 153	98.20 103	99.70 211	99.64 5	99.82 83	99.54 152

DVP-MVS++		99.59 3	99.50 10	99.88 6	99.51 163	99.88 8	99.87 9	99.51 106	98.99 31	99.88 6	99.81 69	99.27 5	99.96 20	98.85 94	99.80 90	99.81 46

FOURS1					99.91 1	99.93 1	99.87 9	99.56 58	99.10 12	99.81 25

K. test v3		97.10 293	96.79 294	98.01 293	98.72 324	96.33 308	99.87 9	97.05 371	97.59 181	96.16 351	99.80 84	88.71 341	99.04 320	96.69 291	96.55 280	98.65 284

FC-MVSNet-test		98.75 144	98.62 145	99.15 165	99.08 275	99.45 97	99.86 12	99.60 41	98.23 106	98.70 274	99.82 56	96.80 146	99.22 296	99.07 64	96.38 283	98.79 236

v7n		97.87 230	97.52 245	98.92 193	98.76 320	98.58 200	99.84 13	99.46 175	96.20 301	98.91 242	99.70 143	94.89 212	99.44 252	96.03 305	93.89 334	98.75 245

DTE-MVSNet		97.51 278	97.19 285	98.46 256	98.63 333	98.13 231	99.84 13	99.48 147	96.68 262	97.97 322	99.67 167	92.92 271	98.56 348	96.88 283	92.60 349	98.70 258

3Dnovator	97.25 9	99.24 70	99.05 79	99.81 41	99.12 266	99.66 59	99.84 13	99.74 10	99.09 15	98.92 241	99.90 11	95.94 175	99.98 7	98.95 74	99.92 13	99.79 62

FIs		98.78 141	98.63 140	99.23 158	99.18 253	99.54 82	99.83 16	99.59 44	98.28 99	98.79 261	99.81 69	96.75 150	99.37 266	99.08 63	96.38 283	98.78 237

FMVS2		92.10 334	91.77 337	93.08 350	96.19 368	86.25 372	99.82 17	98.62 350	96.65 265	95.19 357	96.90 367	55.05 382	95.93 377	96.63 295	90.92 357	97.06 366

jajsoiax		98.43 166	98.28 172	98.88 205	98.60 337	98.43 218	99.82 17	99.53 86	98.19 111	98.63 285	99.80 84	93.22 267	99.44 252	99.22 48	97.50 250	98.77 241

OpenMVS	96.50 16	98.47 163	98.12 181	99.52 110	99.04 283	99.53 85	99.82 17	99.72 11	94.56 339	98.08 316	99.88 21	94.73 224	99.98 7	97.47 243	99.76 104	99.06 215

nrg030		98.64 157	98.42 162	99.28 151	99.05 282	99.69 52	99.81 20	99.46 175	98.04 138	99.01 225	99.82 56	96.69 152	99.38 261	99.34 34	94.59 323	98.78 237

HPM-MVS		99.42 42	99.28 54	99.83 36	99.90 4	99.72 47	99.81 20	99.54 75	97.59 181	99.68 62	99.63 187	98.91 40	99.94 58	98.58 136	99.91 18	99.84 22
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
EPP-MVSNet		99.13 84	98.99 92	99.53 104	99.65 124	99.06 143	99.81 20	99.33 252	97.43 201	99.60 95	99.88 21	97.14 133	99.84 144	99.13 57	98.94 183	99.69 108

3Dnovator+	97.12 13	99.18 76	98.97 96	99.82 38	99.17 259	99.68 54	99.81 20	99.51 106	99.20 5	98.72 267	99.89 15	95.68 187	99.97 12	98.86 92	99.86 53	99.81 46

FA-MVS(test-final)		98.75 144	98.53 157	99.41 128	99.55 157	99.05 145	99.80 24	99.01 310	96.59 274	99.58 100	99.59 202	95.39 194	99.90 112	97.78 208	99.49 140	99.28 196

bld_raw_dy_0_64		98.69 151	98.58 153	98.99 181	98.88 301	98.96 158	99.80 24	99.41 209	97.91 148	99.32 162	99.87 27	95.70 186	99.31 282	99.09 61	97.27 266	98.71 253

GeoE		98.85 133	98.62 145	99.53 104	99.61 139	99.08 140	99.80 24	99.51 106	97.10 233	99.31 164	99.78 103	95.23 203	99.77 182	98.21 172	99.03 177	99.75 78

canonicalmvs		99.02 109	98.86 114	99.51 112	99.42 192	99.32 108	99.80 24	99.48 147	98.63 66	99.31 164	98.81 340	97.09 136	99.75 188	99.27 45	97.90 231	99.47 175

v8		97.95 221	97.63 237	98.93 191	98.95 295	98.81 183	99.80 24	99.41 209	96.03 316	99.10 210	99.42 258	94.92 210	99.30 283	96.94 278	94.08 332	98.66 282

Vis-MVSNet (Re-imp)		98.87 122	98.72 128	99.31 141	99.71 95	98.88 172	99.80 24	99.44 196	97.91 148	99.36 154	99.78 103	95.49 192	99.43 256	97.91 197	99.11 168	99.62 135

Anonymous20240521		96.20 309	95.89 310	97.13 328	97.72 356	94.96 339	99.79 30	99.29 274	93.01 353	97.20 339	99.03 325	89.69 334	98.36 352	91.16 358	96.13 288	98.07 344

PS-MVSNAJss		98.92 119	98.92 102	98.90 199	98.78 316	98.53 204	99.78 31	99.54 75	98.07 132	99.00 230	99.76 116	99.01 19	99.37 266	99.13 57	97.23 267	98.81 234

PEN-MVS		97.76 248	97.44 259	98.72 230	98.77 319	98.54 203	99.78 31	99.51 106	97.06 237	98.29 309	99.64 181	92.63 284	98.89 342	98.09 182	93.16 342	98.72 251

anonymousdsp		98.44 165	98.28 172	98.94 189	98.50 342	98.96 158	99.77 33	99.50 127	97.07 235	98.87 249	99.77 110	94.76 222	99.28 285	98.66 123	97.60 239	98.57 312

SixPastTwentyTwo		97.50 279	97.33 275	98.03 290	98.65 331	96.23 311	99.77 33	98.68 348	97.14 226	97.90 323	99.93 4	90.45 323	99.18 304	97.00 272	96.43 282	98.67 274

QAPM		98.67 154	98.30 171	99.80 43	99.20 248	99.67 57	99.77 33	99.72 11	94.74 336	98.73 266	99.90 11	95.78 182	99.98 7	96.96 276	99.88 38	99.76 77

dcpmvs_2		99.23 71	99.58 2	98.16 283	99.83 37	94.68 343	99.76 36	99.52 92	99.07 18	99.98 1	99.88 21	98.56 77	99.93 73	99.67 3	99.98 2	99.87 13

HPM-MVS_fast		99.51 15	99.40 21	99.85 28	99.91 1	99.79 33	99.76 36	99.56 58	97.72 170	99.76 43	99.75 121	99.13 12	99.92 86	99.07 64	99.92 13	99.85 18

v10		97.85 233	97.52 245	98.86 213	98.99 288	98.67 191	99.75 38	99.41 209	95.70 319	98.98 232	99.41 262	94.75 223	99.23 293	96.01 306	94.63 322	98.67 274

APDe-MVS		99.66 1	99.57 3	99.92 1	99.77 54	99.89 4	99.75 38	99.56 58	99.02 21	99.88 6	99.85 36	99.18 10	99.96 20	99.22 48	99.92 13	99.90 1

IS-MVSNet		99.05 105	98.87 109	99.57 92	99.73 84	99.32 108	99.75 38	99.20 289	98.02 141	99.56 104	99.86 31	96.54 156	99.67 217	98.09 182	99.13 167	99.73 90

tttt0517		98.42 167	98.14 179	99.28 151	99.66 119	98.38 221	99.74 41	96.85 372	97.68 174	99.79 30	99.74 127	91.39 313	99.89 121	98.83 100	99.56 135	99.57 148

baseline		99.15 81	99.02 87	99.53 104	99.66 119	99.14 134	99.72 42	99.48 147	98.35 92	99.42 132	99.84 45	96.07 169	99.79 175	99.51 12	99.14 166	99.67 115

RPSCF		98.22 182	98.62 145	96.99 330	99.82 39	91.58 367	99.72 42	99.44 196	96.61 270	99.66 73	99.89 15	95.92 176	99.82 162	97.46 244	99.10 171	99.57 148

CSCG		99.32 57	99.32 36	99.32 140	99.85 26	98.29 223	99.71 44	99.66 27	98.11 124	99.41 136	99.80 84	98.37 95	99.96 20	98.99 70	99.96 7	99.72 96

WR-MVS_H		98.13 193	97.87 212	98.90 199	99.02 285	98.84 177	99.70 45	99.59 44	97.27 215	98.40 302	99.19 309	95.53 190	99.23 293	98.34 164	93.78 335	98.61 306

LTVRE_ROB	97.16 12	98.02 210	97.90 207	98.40 264	99.23 241	96.80 293	99.70 45	99.60 41	97.12 229	98.18 313	99.70 143	91.73 305	99.72 199	98.39 157	97.45 256	98.68 267
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
FMVS		91.90 335	91.26 339	93.84 347	95.52 375	85.92 373	99.69 47	98.53 354	95.31 325	93.87 363	96.37 370	55.33 381	98.27 353	95.70 312	90.98 356	97.32 365

XVS		99.53 12	99.42 18	99.87 12	99.85 26	99.83 17	99.69 47	99.68 19	98.98 34	99.37 150	99.74 127	98.81 49	99.94 58	98.79 105	99.86 53	99.84 22

X-MVStestdata		96.55 301	95.45 316	99.87 12	99.85 26	99.83 17	99.69 47	99.68 19	98.98 34	99.37 150	64.01 385	98.81 49	99.94 58	98.79 105	99.86 53	99.84 22

V42		98.06 200	97.79 216	98.86 213	98.98 291	98.84 177	99.69 47	99.34 245	96.53 276	99.30 166	99.37 273	94.67 227	99.32 279	97.57 232	94.66 321	98.42 327

mPP-MVS		99.44 33	99.30 46	99.86 21	99.88 12	99.79 33	99.69 47	99.48 147	98.12 122	99.50 116	99.75 121	98.78 52	99.97 12	98.57 138	99.89 35	99.83 33

CP-MVS		99.45 29	99.32 36	99.85 28	99.83 37	99.75 43	99.69 47	99.52 92	98.07 132	99.53 111	99.63 187	98.93 39	99.97 12	98.74 109	99.91 18	99.83 33

FE-MVS		98.48 162	98.17 176	99.40 129	99.54 158	98.96 158	99.68 53	98.81 333	95.54 321	99.62 88	99.70 143	93.82 256	99.93 73	97.35 251	99.46 141	99.32 193

PS-CasMVS		97.93 222	97.59 240	98.95 188	98.99 288	99.06 143	99.68 53	99.52 92	97.13 227	98.31 307	99.68 161	92.44 293	99.05 319	98.51 147	94.08 332	98.75 245

Vis-MVSNet		99.12 90	98.97 96	99.56 94	99.78 48	99.10 138	99.68 53	99.66 27	98.49 77	99.86 13	99.87 27	94.77 221	99.84 144	99.19 50	99.41 145	99.74 83
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
EIA-MVS		99.18 76	99.09 76	99.45 122	99.49 175	99.18 125	99.67 56	99.53 86	97.66 177	99.40 141	99.44 253	98.10 108	99.81 167	98.94 75	99.62 132	99.35 189

MSP-MVS		99.42 42	99.27 56	99.88 6	99.89 9	99.80 29	99.67 56	99.50 127	98.70 63	99.77 38	99.49 238	98.21 102	99.95 47	98.46 153	99.77 101	99.88 8
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 98	98.97 96	99.48 116	99.49 175	99.14 134	99.67 56	99.34 245	97.31 211	99.58 100	99.76 116	97.65 120	99.82 162	98.87 87	99.07 174	99.46 177

CP-MVSNet		98.09 197	97.78 219	99.01 177	98.97 293	99.24 120	99.67 56	99.46 175	97.25 217	98.48 297	99.64 181	93.79 257	99.06 318	98.63 126	94.10 331	98.74 248

MTAPA		99.52 14	99.39 22	99.89 4	99.90 4	99.86 13	99.66 60	99.47 165	98.79 57	99.68 62	99.81 69	98.43 88	99.97 12	98.88 83	99.90 25	99.83 33

HFP-MVS		99.49 17	99.37 25	99.86 21	99.87 16	99.80 29	99.66 60	99.67 22	98.15 117	99.68 62	99.69 153	99.06 16	99.96 20	98.69 118	99.87 42	99.84 22

mvs_tets		98.40 171	98.23 174	98.91 197	98.67 330	98.51 210	99.66 60	99.53 86	98.19 111	98.65 283	99.81 69	92.75 275	99.44 252	99.31 37	97.48 254	98.77 241

EU-MVSNet		97.98 217	98.03 193	97.81 308	98.72 324	96.65 298	99.66 60	99.66 27	98.09 127	98.35 305	99.82 56	95.25 202	98.01 359	97.41 249	95.30 310	98.78 237

ACMMPR		99.49 17	99.36 27	99.86 21	99.87 16	99.79 33	99.66 60	99.67 22	98.15 117	99.67 68	99.69 153	98.95 32	99.96 20	98.69 118	99.87 42	99.84 22

MP-MVS		99.33 56	99.15 69	99.87 12	99.88 12	99.82 23	99.66 60	99.46 175	98.09 127	99.48 120	99.74 127	98.29 99	99.96 20	97.93 196	99.87 42	99.82 40
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
abl_6		99.44 33	99.31 43	99.83 36	99.85 26	99.75 43	99.66 60	99.59 44	98.13 120	99.82 23	99.81 69	98.60 74	99.96 20	98.46 153	99.88 38	99.79 62

test_part1		97.75 252	97.24 283	99.29 148	99.59 145	99.63 65	99.65 67	99.49 135	96.17 304	98.44 299	99.69 153	89.80 332	99.47 244	98.68 120	93.66 336	98.78 237

region2R		99.48 22	99.35 30	99.87 12	99.88 12	99.80 29	99.65 67	99.66 27	98.13 120	99.66 73	99.68 161	98.96 29	99.96 20	98.62 127	99.87 42	99.84 22

TranMVSNet+NR-MVSNet		97.93 222	97.66 233	98.76 228	98.78 316	98.62 196	99.65 67	99.49 135	97.76 165	98.49 296	99.60 199	94.23 242	98.97 336	98.00 191	92.90 344	98.70 258

mvsany_test		93.77 331	93.45 333	94.74 346	95.78 371	88.01 371	99.64 70	98.25 357	98.28 99	94.31 361	97.97 359	68.89 374	98.51 350	97.50 239	90.37 358	97.71 358

ZNCC-MVS		99.47 25	99.33 34	99.87 12	99.87 16	99.81 27	99.64 70	99.67 22	98.08 131	99.55 108	99.64 181	98.91 40	99.96 20	98.72 113	99.90 25	99.82 40

tfpnnormal		97.84 236	97.47 251	98.98 183	99.20 248	99.22 122	99.64 70	99.61 36	96.32 291	98.27 310	99.70 143	93.35 264	99.44 252	95.69 313	95.40 308	98.27 336

iter_conf_final		98.71 147	98.61 151	98.99 181	99.49 175	98.96 158	99.63 73	99.41 209	98.19 111	99.39 144	99.77 110	94.82 214	99.38 261	99.30 40	97.52 246	98.64 286

SR-MVS-dyc-post		99.45 29	99.31 43	99.85 28	99.76 57	99.82 23	99.63 73	99.52 92	98.38 87	99.76 43	99.82 56	98.53 79	99.95 47	98.61 130	99.81 86	99.77 72

RE-MVS-def			99.34 32		99.76 57	99.82 23	99.63 73	99.52 92	98.38 87	99.76 43	99.82 56	98.75 60		98.61 130	99.81 86	99.77 72

TSAR-MVS + MP.		99.58 5	99.50 10	99.81 41	99.91 1	99.66 59	99.63 73	99.39 221	98.91 46	99.78 35	99.85 36	99.36 2	99.94 58	98.84 97	99.88 38	99.82 40
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
Anonymous20231206		96.22 307	96.03 306	96.79 337	97.31 362	94.14 350	99.63 73	99.08 303	96.17 304	97.04 343	99.06 322	93.94 252	97.76 365	86.96 372	95.06 315	98.47 320

APD-MVS_3200maxsize		99.48 22	99.35 30	99.85 28	99.76 57	99.83 17	99.63 73	99.54 75	98.36 91	99.79 30	99.82 56	98.86 44	99.95 47	98.62 127	99.81 86	99.78 70

test0726					99.85 26	99.89 4	99.62 79	99.50 127	99.10 12	99.86 13	99.82 56	98.94 35

EPNet		98.86 125	98.71 130	99.30 145	97.20 364	98.18 227	99.62 79	98.91 323	99.28 3	98.63 285	99.81 69	95.96 172	99.99 1	99.24 47	99.72 112	99.73 90
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
114514_t		98.93 118	98.67 134	99.72 64	99.85 26	99.53 85	99.62 79	99.59 44	92.65 355	99.71 55	99.78 103	98.06 110	99.90 112	98.84 97	99.91 18	99.74 83

HY-MVS	97.30 7	98.85 133	98.64 139	99.47 119	99.42 192	99.08 140	99.62 79	99.36 236	97.39 206	99.28 170	99.68 161	96.44 160	99.92 86	98.37 161	98.22 217	99.40 186

ACMMP		99.45 29	99.32 36	99.82 38	99.89 9	99.67 57	99.62 79	99.69 18	98.12 122	99.63 84	99.84 45	98.73 63	99.96 20	98.55 144	99.83 77	99.81 46
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 59	99.19 66	99.64 80	99.82 39	99.23 121	99.62 79	99.55 67	98.94 41	99.63 84	99.95 2	95.82 181	99.94 58	99.37 29	99.97 5	99.73 90
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 5	99.56 5	99.64 80	99.78 48	99.15 133	99.61 85	99.45 187	99.01 24	99.89 5	99.82 56	99.01 19	99.92 86	99.56 9	99.95 8	99.85 18

test2506		96.81 297	96.65 295	97.29 325	99.74 76	92.21 365	99.60 86	85.06 389	99.13 8	99.77 38	99.93 4	87.82 354	99.85 138	99.38 27	99.38 146	99.80 56

test1172		99.43 37	99.29 50	99.85 28	99.75 68	99.82 23	99.60 86	99.56 58	98.28 99	99.74 47	99.79 96	98.53 79	99.95 47	98.55 144	99.78 97	99.79 62

SED-MVS		99.61 2	99.52 8	99.88 6	99.84 33	99.90 2	99.60 86	99.48 147	99.08 16	99.91 2	99.81 69	99.20 7	99.96 20	98.91 80	99.85 60	99.79 62

OPU-MVS				99.64 80	99.56 153	99.72 47	99.60 86				99.70 143	99.27 5	99.42 257	98.24 171	99.80 90	99.79 62

GST-MVS		99.40 49	99.24 61	99.85 28	99.86 22	99.79 33	99.60 86	99.67 22	97.97 143	99.63 84	99.68 161	98.52 81	99.95 47	98.38 159	99.86 53	99.81 46

EI-MVSNet-UG-set		99.58 5	99.57 3	99.64 80	99.78 48	99.14 134	99.60 86	99.45 187	99.01 24	99.90 4	99.83 49	98.98 27	99.93 73	99.59 6	99.95 8	99.86 15

ACMH	97.28 8	98.10 196	97.99 197	98.44 260	99.41 195	96.96 287	99.60 86	99.56 58	98.09 127	98.15 314	99.91 9	90.87 320	99.70 211	98.88 83	97.45 256	98.67 274
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ECVR-MVS		98.04 206	98.05 191	98.00 295	99.74 76	94.37 347	99.59 93	94.98 380	99.13 8	99.66 73	99.93 4	90.67 322	99.84 144	99.40 26	99.38 146	99.80 56

SR-MVS		99.43 37	99.29 50	99.86 21	99.75 68	99.83 17	99.59 93	99.62 34	98.21 109	99.73 49	99.79 96	98.68 67	99.96 20	98.44 155	99.77 101	99.79 62

thres100view900		97.76 248	97.45 254	98.69 232	99.72 89	97.86 246	99.59 93	98.74 340	97.93 146	99.26 178	98.62 346	91.75 303	99.83 155	93.22 346	98.18 222	98.37 333

thres600view7		97.86 232	97.51 247	98.92 193	99.72 89	97.95 241	99.59 93	98.74 340	97.94 145	99.27 173	98.62 346	91.75 303	99.86 132	93.73 341	98.19 221	98.96 226

LCM-MVSNet-Re		97.83 238	98.15 178	96.87 335	99.30 224	92.25 364	99.59 93	98.26 356	97.43 201	96.20 350	99.13 315	96.27 165	98.73 347	98.17 178	98.99 181	99.64 129

baseline1		98.31 176	97.95 202	99.38 133	99.50 173	98.74 186	99.59 93	98.93 318	98.41 85	99.14 202	99.60 199	94.59 230	99.79 175	98.48 149	93.29 340	99.61 137

SteuartSystems-ACMMP		99.54 10	99.42 18	99.87 12	99.82 39	99.81 27	99.59 93	99.51 106	98.62 67	99.79 30	99.83 49	99.28 4	99.97 12	98.48 149	99.90 25	99.84 22
Skip Steuart: Steuart Systems R&D Blog.
CPTT-MVS		99.11 95	98.90 105	99.74 59	99.80 45	99.46 96	99.59 93	99.49 135	97.03 240	99.63 84	99.69 153	97.27 131	99.96 20	97.82 205	99.84 68	99.81 46

test1111		98.04 206	98.11 182	97.83 305	99.74 76	93.82 352	99.58 101	95.40 379	99.12 10	99.65 79	99.93 4	90.73 321	99.84 144	99.43 25	99.38 146	99.82 40

Regformer-3		99.57 8	99.53 7	99.68 68	99.76 57	99.29 114	99.58 101	99.44 196	99.01 24	99.87 12	99.80 84	98.97 28	99.91 97	99.44 24	99.92 13	99.83 33

Regformer-4		99.59 3	99.54 6	99.73 61	99.76 57	99.41 102	99.58 101	99.49 135	99.02 21	99.88 6	99.80 84	99.00 25	99.94 58	99.45 22	99.92 13	99.84 22

PGM-MVS		99.45 29	99.31 43	99.86 21	99.87 16	99.78 40	99.58 101	99.65 32	97.84 155	99.71 55	99.80 84	99.12 13	99.97 12	98.33 165	99.87 42	99.83 33

LPG-MVS_test		98.22 182	98.13 180	98.49 249	99.33 215	97.05 276	99.58 101	99.55 67	97.46 194	99.24 181	99.83 49	92.58 285	99.72 199	98.09 182	97.51 248	98.68 267

PHI-MVS		99.30 59	99.17 68	99.70 67	99.56 153	99.52 88	99.58 101	99.80 8	97.12 229	99.62 88	99.73 134	98.58 75	99.90 112	98.61 130	99.91 18	99.68 112

SF-MVS		99.38 51	99.24 61	99.79 46	99.79 46	99.68 54	99.57 107	99.54 75	97.82 161	99.71 55	99.80 84	98.95 32	99.93 73	98.19 174	99.84 68	99.74 83

DVP-MVS		99.57 8	99.47 14	99.88 6	99.85 26	99.89 4	99.57 107	99.37 235	99.10 12	99.81 25	99.80 84	98.94 35	99.96 20	98.93 77	99.86 53	99.81 46
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 2	99.84 33	99.89 4	99.57 107	99.51 106					99.96 20	98.93 77	99.86 53	99.88 8

Effi-MVS+-dtu		98.78 141	98.89 107	98.47 255	99.33 215	96.91 289	99.57 107	99.30 269	98.47 78	99.41 136	98.99 329	96.78 147	99.74 189	98.73 111	99.38 146	98.74 248

v2v482		98.06 200	97.77 221	98.92 193	98.90 298	98.82 181	99.57 107	99.36 236	96.65 265	99.19 195	99.35 279	94.20 243	99.25 290	97.72 217	94.97 317	98.69 262

DSMNet-mixed		97.25 289	97.35 270	96.95 333	97.84 352	93.61 358	99.57 107	96.63 375	96.13 310	98.87 249	98.61 348	94.59 230	97.70 366	95.08 326	98.86 190	99.55 150

KD-MVS_self_test		95.00 321	94.34 326	96.96 332	97.07 367	95.39 330	99.56 113	99.44 196	95.11 328	97.13 341	97.32 365	91.86 301	97.27 369	90.35 361	81.23 373	98.23 340

ETV-MVS		99.26 66	99.21 64	99.40 129	99.46 185	99.30 112	99.56 113	99.52 92	98.52 75	99.44 128	99.27 299	98.41 92	99.86 132	99.10 60	99.59 134	99.04 216

SMA-MVS		99.44 33	99.30 46	99.85 28	99.73 84	99.83 17	99.56 113	99.47 165	97.45 197	99.78 35	99.82 56	99.18 10	99.91 97	98.79 105	99.89 35	99.81 46
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		98.87 122	98.72 128	99.31 141	99.86 22	98.48 214	99.56 113	99.61 36	97.85 153	99.36 154	99.85 36	95.95 173	99.85 138	96.66 293	99.83 77	99.59 143

casdiffmvs		99.13 84	98.98 95	99.56 94	99.65 124	99.16 128	99.56 113	99.50 127	98.33 96	99.41 136	99.86 31	95.92 176	99.83 155	99.45 22	99.16 163	99.70 105

XXY-MVS		98.38 172	98.09 186	99.24 156	99.26 235	99.32 108	99.56 113	99.55 67	97.45 197	98.71 268	99.83 49	93.23 265	99.63 232	98.88 83	96.32 285	98.76 243

ACMH+	97.24 10	97.92 225	97.78 219	98.32 271	99.46 185	96.68 297	99.56 113	99.54 75	98.41 85	97.79 328	99.87 27	90.18 329	99.66 220	98.05 190	97.18 270	98.62 297

ACMM	97.58 5	98.37 173	98.34 167	98.48 251	99.41 195	97.10 270	99.56 113	99.45 187	98.53 74	99.04 222	99.85 36	93.00 269	99.71 205	98.74 109	97.45 256	98.64 286
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
LS3D		99.27 64	99.12 72	99.74 59	99.18 253	99.75 43	99.56 113	99.57 53	98.45 81	99.49 119	99.85 36	97.77 117	99.94 58	98.33 165	99.84 68	99.52 158

v144192		97.92 225	97.60 239	98.87 209	98.83 311	98.65 193	99.55 122	99.34 245	96.20 301	99.32 162	99.40 265	94.36 238	99.26 289	96.37 301	95.03 316	98.70 258

iter_conf05		98.55 160	98.44 160	98.87 209	99.34 213	98.60 199	99.55 122	99.42 206	98.21 109	99.37 150	99.77 110	93.55 261	99.38 261	99.30 40	97.48 254	98.63 294

#test#		99.43 37	99.29 50	99.86 21	99.87 16	99.80 29	99.55 122	99.67 22	97.83 156	99.68 62	99.69 153	99.06 16	99.96 20	98.39 157	99.87 42	99.84 22

API-MVS		99.04 106	99.03 84	99.06 171	99.40 200	99.31 111	99.55 122	99.56 58	98.54 73	99.33 161	99.39 269	98.76 57	99.78 180	96.98 274	99.78 97	98.07 344

thisisatest0530		98.35 174	98.03 193	99.31 141	99.63 129	98.56 201	99.54 126	96.75 374	97.53 190	99.73 49	99.65 174	91.25 316	99.89 121	98.62 127	99.56 135	99.48 169

MTMP							99.54 126	98.88 327

v1144		97.98 217	97.69 230	98.85 216	98.87 305	98.66 192	99.54 126	99.35 241	96.27 295	99.23 185	99.35 279	94.67 227	99.23 293	96.73 288	95.16 313	98.68 267

v148		97.79 246	97.55 241	98.50 248	98.74 321	97.72 252	99.54 126	99.33 252	96.26 296	98.90 244	99.51 232	94.68 226	99.14 306	97.83 204	93.15 343	98.63 294

CostFormer		97.72 258	97.73 227	97.71 312	99.15 264	94.02 351	99.54 126	99.02 309	94.67 337	99.04 222	99.35 279	92.35 295	99.77 182	98.50 148	97.94 230	99.34 191

MVSTER		98.49 161	98.32 169	99.00 179	99.35 209	99.02 147	99.54 126	99.38 227	97.41 204	99.20 192	99.73 134	93.86 255	99.36 270	98.87 87	97.56 243	98.62 297

patch_mono-2		99.26 66	99.62 1	98.16 283	99.81 42	94.59 344	99.52 132	99.64 33	99.33 2	99.73 49	99.90 11	99.00 25	99.99 1	99.69 1	99.98 2	99.89 2

Fast-Effi-MVS+-dtu		98.77 143	98.83 120	98.60 236	99.41 195	96.99 283	99.52 132	99.49 135	98.11 124	99.24 181	99.34 282	96.96 143	99.79 175	97.95 195	99.45 142	99.02 219

Fast-Effi-MVS+		98.70 148	98.43 161	99.51 112	99.51 163	99.28 115	99.52 132	99.47 165	96.11 311	99.01 225	99.34 282	96.20 167	99.84 144	97.88 199	98.82 192	99.39 187

v1921920		97.80 245	97.45 254	98.84 217	98.80 312	98.53 204	99.52 132	99.34 245	96.15 308	99.24 181	99.47 247	93.98 251	99.29 284	95.40 320	95.13 314	98.69 262

MIMVSNet1		95.51 316	95.04 320	96.92 334	97.38 359	95.60 321	99.52 132	99.50 127	93.65 347	96.97 345	99.17 310	85.28 361	96.56 374	88.36 368	95.55 305	98.60 309

UniMVSNet_ETH3D		97.32 287	96.81 293	98.87 209	99.40 200	97.46 258	99.51 137	99.53 86	95.86 318	98.54 293	99.77 110	82.44 368	99.66 220	98.68 120	97.52 246	99.50 167

alignmvs		98.81 137	98.56 155	99.58 90	99.43 191	99.42 100	99.51 137	98.96 316	98.61 68	99.35 157	98.92 337	94.78 218	99.77 182	99.35 30	98.11 227	99.54 152

v1192		97.81 243	97.44 259	98.91 197	98.88 301	98.68 190	99.51 137	99.34 245	96.18 303	99.20 192	99.34 282	94.03 250	99.36 270	95.32 323	95.18 312	98.69 262

test20.03		96.12 311	95.96 308	96.63 338	97.44 358	95.45 328	99.51 137	99.38 227	96.55 275	96.16 351	99.25 302	93.76 259	96.17 375	87.35 371	94.22 329	98.27 336

mvs_anonymous		99.03 108	98.99 92	99.16 163	99.38 204	98.52 208	99.51 137	99.38 227	97.79 162	99.38 148	99.81 69	97.30 129	99.45 247	99.35 30	98.99 181	99.51 164

TAMVS		99.12 90	99.08 77	99.24 156	99.46 185	98.55 202	99.51 137	99.46 175	98.09 127	99.45 124	99.82 56	98.34 96	99.51 242	98.70 115	98.93 184	99.67 115

test_yl		98.86 125	98.63 140	99.54 96	99.49 175	99.18 125	99.50 143	99.07 305	98.22 107	99.61 91	99.51 232	95.37 195	99.84 144	98.60 133	98.33 211	99.59 143

DCV-MVSNet		98.86 125	98.63 140	99.54 96	99.49 175	99.18 125	99.50 143	99.07 305	98.22 107	99.61 91	99.51 232	95.37 195	99.84 144	98.60 133	98.33 211	99.59 143

tfpn200view9		97.72 258	97.38 266	98.72 230	99.69 105	97.96 239	99.50 143	98.73 345	97.83 156	99.17 199	98.45 351	91.67 307	99.83 155	93.22 346	98.18 222	98.37 333

UA-Net		99.42 42	99.29 50	99.80 43	99.62 135	99.55 80	99.50 143	99.70 15	98.79 57	99.77 38	99.96 1	97.45 123	99.96 20	98.92 79	99.90 25	99.89 2

pm-mvs1		97.68 265	97.28 279	98.88 205	99.06 279	98.62 196	99.50 143	99.45 187	96.32 291	97.87 324	99.79 96	92.47 289	99.35 273	97.54 235	93.54 338	98.67 274

EI-MVSNet		98.67 154	98.67 134	98.68 233	99.35 209	97.97 237	99.50 143	99.38 227	96.93 249	99.20 192	99.83 49	97.87 113	99.36 270	98.38 159	97.56 243	98.71 253

CVMVSNet		98.57 159	98.67 134	98.30 273	99.35 209	95.59 322	99.50 143	99.55 67	98.60 69	99.39 144	99.83 49	94.48 235	99.45 247	98.75 108	98.56 204	99.85 18

VPA-MVSNet		98.29 179	97.95 202	99.30 145	99.16 261	99.54 82	99.50 143	99.58 50	98.27 102	99.35 157	99.37 273	92.53 287	99.65 224	99.35 30	94.46 324	98.72 251

thres400		97.77 247	97.38 266	98.92 193	99.69 105	97.96 239	99.50 143	98.73 345	97.83 156	99.17 199	98.45 351	91.67 307	99.83 155	93.22 346	98.18 222	98.96 226

APD-MVS		99.27 64	99.08 77	99.84 35	99.75 68	99.79 33	99.50 143	99.50 127	97.16 225	99.77 38	99.82 56	98.78 52	99.94 58	97.56 233	99.86 53	99.80 56
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
Regformer-1		99.53 12	99.47 14	99.72 64	99.71 95	99.44 98	99.49 153	99.46 175	98.95 40	99.83 20	99.76 116	99.01 19	99.93 73	99.17 53	99.87 42	99.80 56

Regformer-2		99.54 10	99.47 14	99.75 54	99.71 95	99.52 88	99.49 153	99.49 135	98.94 41	99.83 20	99.76 116	99.01 19	99.94 58	99.15 56	99.87 42	99.80 56

TransMVSNet (Re)		97.15 291	96.58 296	98.86 213	99.12 266	98.85 176	99.49 153	98.91 323	95.48 322	97.16 340	99.80 84	93.38 263	99.11 314	94.16 338	91.73 351	98.62 297

UniMVSNet (Re)		98.29 179	98.00 196	99.13 166	99.00 287	99.36 106	99.49 153	99.51 106	97.95 144	98.97 234	99.13 315	96.30 164	99.38 261	98.36 163	93.34 339	98.66 282

EPMVS		97.82 241	97.65 234	98.35 268	98.88 301	95.98 315	99.49 153	94.71 382	97.57 184	99.26 178	99.48 244	92.46 292	99.71 205	97.87 200	99.08 173	99.35 189

Anonymous20231211		97.88 228	97.54 244	98.90 199	99.71 95	98.53 204	99.48 158	99.57 53	94.16 342	98.81 257	99.68 161	93.23 265	99.42 257	98.84 97	94.42 326	98.76 243

v1240		97.69 263	97.32 276	98.79 225	98.85 309	98.43 218	99.48 158	99.36 236	96.11 311	99.27 173	99.36 276	93.76 259	99.24 292	94.46 333	95.23 311	98.70 258

VPNet		97.84 236	97.44 259	99.01 177	99.21 246	98.94 166	99.48 158	99.57 53	98.38 87	99.28 170	99.73 134	88.89 340	99.39 259	99.19 50	93.27 341	98.71 253

UniMVSNet_NR-MVSNet		98.22 182	97.97 199	98.96 186	98.92 297	98.98 151	99.48 158	99.53 86	97.76 165	98.71 268	99.46 251	96.43 161	99.22 296	98.57 138	92.87 346	98.69 262

TDRefinement		95.42 318	94.57 324	97.97 297	89.83 382	96.11 314	99.48 158	98.75 337	96.74 258	96.68 346	99.88 21	88.65 343	99.71 205	98.37 161	82.74 371	98.09 343

ACMMP_NAP		99.47 25	99.34 32	99.88 6	99.87 16	99.86 13	99.47 163	99.48 147	98.05 137	99.76 43	99.86 31	98.82 48	99.93 73	98.82 104	99.91 18	99.84 22

NR-MVSNet		97.97 220	97.61 238	99.02 176	98.87 305	99.26 118	99.47 163	99.42 206	97.63 179	97.08 342	99.50 235	95.07 206	99.13 309	97.86 201	93.59 337	98.68 267

PVSNet_Blended_VisFu		99.36 53	99.28 54	99.61 85	99.86 22	99.07 142	99.47 163	99.93 2	97.66 177	99.71 55	99.86 31	97.73 118	99.96 20	99.47 20	99.82 83	99.79 62

SD-MVS		99.41 46	99.52 8	99.05 173	99.74 76	99.68 54	99.46 166	99.52 92	99.11 11	99.88 6	99.91 9	99.43 1	97.70 366	98.72 113	99.93 12	99.77 72
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
tpm2		97.44 284	97.34 273	97.74 311	99.15 264	94.36 348	99.45 167	98.94 317	93.45 351	98.90 244	99.44 253	91.35 314	99.59 236	97.31 252	98.07 228	99.29 195

FMVSNet2		97.72 258	97.36 268	98.80 224	99.51 163	98.84 177	99.45 167	99.42 206	96.49 278	98.86 254	99.29 294	90.26 325	98.98 329	96.44 298	96.56 279	98.58 311

CDS-MVSNet		99.09 99	99.03 84	99.25 154	99.42 192	98.73 187	99.45 167	99.46 175	98.11 124	99.46 123	99.77 110	98.01 111	99.37 266	98.70 115	98.92 186	99.66 118
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
MAR-MVS		98.86 125	98.63 140	99.54 96	99.37 206	99.66 59	99.45 167	99.54 75	96.61 270	99.01 225	99.40 265	97.09 136	99.86 132	97.68 223	99.53 138	99.10 204
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
testtj		99.12 90	98.87 109	99.86 21	99.72 89	99.79 33	99.44 171	99.51 106	97.29 213	99.59 98	99.74 127	98.15 107	99.96 20	96.74 287	99.69 118	99.81 46

mvs-test1		98.86 125	98.84 116	98.89 202	99.33 215	97.77 249	99.44 171	99.30 269	98.47 78	99.10 210	99.43 255	96.78 147	99.95 47	98.73 111	99.02 179	98.96 226

UGNet		98.87 122	98.69 132	99.40 129	99.22 244	98.72 188	99.44 171	99.68 19	99.24 4	99.18 198	99.42 258	92.74 277	99.96 20	99.34 34	99.94 11	99.53 157
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		98.86 125	98.63 140	99.54 96	99.64 126	99.19 123	99.44 171	99.54 75	97.77 164	99.30 166	99.81 69	94.20 243	99.93 73	99.17 53	98.82 192	99.49 168

test_0402		96.64 300	96.24 302	97.85 303	98.85 309	96.43 305	99.44 171	99.26 279	93.52 348	96.98 344	99.52 228	88.52 345	99.20 303	92.58 355	97.50 250	97.93 355

ACMP	97.20 11	98.06 200	97.94 204	98.45 257	99.37 206	97.01 281	99.44 171	99.49 135	97.54 189	98.45 298	99.79 96	91.95 299	99.72 199	97.91 197	97.49 253	98.62 297
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
GG-mvs-BLEND				98.45 257	98.55 340	98.16 228	99.43 177	93.68 384		97.23 337	98.46 350	89.30 337	99.22 296	95.43 319	98.22 217	97.98 352

HPM-MVS++		99.39 50	99.23 63	99.87 12	99.75 68	99.84 16	99.43 177	99.51 106	98.68 65	99.27 173	99.53 225	98.64 73	99.96 20	98.44 155	99.80 90	99.79 62

tpm cat1		97.39 285	97.36 268	97.50 320	99.17 259	93.73 354	99.43 177	99.31 265	91.27 359	98.71 268	99.08 319	94.31 241	99.77 182	96.41 300	98.50 207	99.00 220

tpm		97.67 268	97.55 241	98.03 290	99.02 285	95.01 337	99.43 177	98.54 353	96.44 285	99.12 205	99.34 282	91.83 302	99.60 235	97.75 213	96.46 281	99.48 169

GBi-Net		97.68 265	97.48 249	98.29 274	99.51 163	97.26 265	99.43 177	99.48 147	96.49 278	99.07 216	99.32 289	90.26 325	98.98 329	97.10 267	96.65 276	98.62 297

test1		97.68 265	97.48 249	98.29 274	99.51 163	97.26 265	99.43 177	99.48 147	96.49 278	99.07 216	99.32 289	90.26 325	98.98 329	97.10 267	96.65 276	98.62 297

FMVSNet1		96.84 296	96.36 300	98.29 274	99.32 222	97.26 265	99.43 177	99.48 147	95.11 328	98.55 292	99.32 289	83.95 364	98.98 329	95.81 309	96.26 286	98.62 297

testgi		97.65 270	97.50 248	98.13 287	99.36 208	96.45 304	99.42 184	99.48 147	97.76 165	97.87 324	99.45 252	91.09 317	98.81 343	94.53 332	98.52 206	99.13 203

F-COLMAP		99.19 74	99.04 82	99.64 80	99.78 48	99.27 117	99.42 184	99.54 75	97.29 213	99.41 136	99.59 202	98.42 91	99.93 73	98.19 174	99.69 118	99.73 90

Anonymous202405211		98.30 178	97.98 198	99.26 153	99.57 149	98.16 228	99.41 186	98.55 352	96.03 316	99.19 195	99.74 127	91.87 300	99.92 86	99.16 55	98.29 216	99.70 105

MSLP-MVS++		99.46 27	99.47 14	99.44 126	99.60 143	99.16 128	99.41 186	99.71 13	98.98 34	99.45 124	99.78 103	99.19 9	99.54 241	99.28 42	99.84 68	99.63 133

VNet		99.11 95	98.90 105	99.73 61	99.52 161	99.56 78	99.41 186	99.39 221	99.01 24	99.74 47	99.78 103	95.56 189	99.92 86	99.52 11	98.18 222	99.72 96

baseline2		97.87 230	97.55 241	98.82 220	99.18 253	98.02 234	99.41 186	96.58 376	96.97 243	96.51 347	99.17 310	93.43 262	99.57 237	97.71 218	99.03 177	98.86 231

DU-MVS		98.08 199	97.79 216	98.96 186	98.87 305	98.98 151	99.41 186	99.45 187	97.87 150	98.71 268	99.50 235	94.82 214	99.22 296	98.57 138	92.87 346	98.68 267

Baseline_NR-MVSNet		97.76 248	97.45 254	98.68 233	99.09 273	98.29 223	99.41 186	98.85 329	95.65 320	98.63 285	99.67 167	94.82 214	99.10 316	98.07 189	92.89 345	98.64 286

XVG-ACMP-BASELINE		97.83 238	97.71 229	98.20 280	99.11 268	96.33 308	99.41 186	99.52 92	98.06 136	99.05 221	99.50 235	89.64 335	99.73 195	97.73 215	97.38 263	98.53 314

DP-MVS		99.16 80	98.95 100	99.78 48	99.77 54	99.53 85	99.41 186	99.50 127	97.03 240	99.04 222	99.88 21	97.39 124	99.92 86	98.66 123	99.90 25	99.87 13

9.14			99.10 74		99.72 89		99.40 194	99.51 106	97.53 190	99.64 83	99.78 103	98.84 46	99.91 97	97.63 224	99.82 83

D2MVS		98.41 169	98.50 158	98.15 286	99.26 235	96.62 299	99.40 194	99.61 36	97.71 171	98.98 232	99.36 276	96.04 170	99.67 217	98.70 115	97.41 260	98.15 342

Anonymous20240529		98.09 197	97.68 231	99.34 135	99.66 119	98.44 217	99.40 194	99.43 204	93.67 346	99.22 186	99.89 15	90.23 328	99.93 73	99.26 46	98.33 211	99.66 118

FMVSNet3		98.03 208	97.76 224	98.84 217	99.39 203	98.98 151	99.40 194	99.38 227	96.67 263	99.07 216	99.28 296	92.93 270	98.98 329	97.10 267	96.65 276	98.56 313

LFMVS		97.90 227	97.35 270	99.54 96	99.52 161	99.01 149	99.39 198	98.24 358	97.10 233	99.65 79	99.79 96	84.79 362	99.91 97	99.28 42	98.38 210	99.69 108

HQP_MVS		98.27 181	98.22 175	98.44 260	99.29 228	96.97 285	99.39 198	99.47 165	98.97 37	99.11 207	99.61 196	92.71 280	99.69 215	97.78 208	97.63 236	98.67 274

plane_prior2							99.39 198		98.97 37

CHOSEN 1792x2688		99.19 74	99.10 74	99.45 122	99.89 9	98.52 208	99.39 198	99.94 1	98.73 61	99.11 207	99.89 15	95.50 191	99.94 58	99.50 13	99.97 5	99.89 2

PAPM_NR		99.04 106	98.84 116	99.66 71	99.74 76	99.44 98	99.39 198	99.38 227	97.70 172	99.28 170	99.28 296	98.34 96	99.85 138	96.96 276	99.45 142	99.69 108

ETH3D-3000-0.1		99.21 72	99.02 87	99.77 50	99.73 84	99.69 52	99.38 203	99.51 106	97.45 197	99.61 91	99.75 121	98.51 82	99.91 97	97.45 246	99.83 77	99.71 103

gg-mvs-nofinetune		96.17 310	95.32 318	98.73 229	98.79 313	98.14 230	99.38 203	94.09 383	91.07 362	98.07 319	91.04 378	89.62 336	99.35 273	96.75 286	99.09 172	98.68 267

VDDNet		97.55 274	97.02 290	99.16 163	99.49 175	98.12 232	99.38 203	99.30 269	95.35 324	99.68 62	99.90 11	82.62 367	99.93 73	99.31 37	98.13 226	99.42 182

pmmvs6		96.53 302	96.09 305	97.82 307	98.69 328	95.47 327	99.37 206	99.47 165	93.46 350	97.41 333	99.78 103	87.06 356	99.33 276	96.92 281	92.70 348	98.65 284

PM-MVS		92.96 333	92.23 336	95.14 345	95.61 372	89.98 370	99.37 206	98.21 359	94.80 335	95.04 359	97.69 360	65.06 375	97.90 362	94.30 334	89.98 361	97.54 363

WTY-MVS		99.06 103	98.88 108	99.61 85	99.62 135	99.16 128	99.37 206	99.56 58	98.04 138	99.53 111	99.62 192	96.84 145	99.94 58	98.85 94	98.49 208	99.72 96

IterMVS-LS		98.46 164	98.42 162	98.58 240	99.59 145	98.00 235	99.37 206	99.43 204	96.94 248	99.07 216	99.59 202	97.87 113	99.03 322	98.32 167	95.62 303	98.71 253
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
h-mvs33		97.70 262	97.28 279	98.97 185	99.70 102	97.27 263	99.36 210	99.45 187	98.94 41	99.66 73	99.64 181	94.93 208	99.99 1	99.48 18	84.36 368	99.65 122

DPE-MVS		99.46 27	99.32 36	99.91 2	99.78 48	99.88 8	99.36 210	99.51 106	98.73 61	99.88 6	99.84 45	98.72 64	99.96 20	98.16 179	99.87 42	99.88 8
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
zzz-MVS		99.49 17	99.36 27	99.89 4	99.90 4	99.86 13	99.36 210	99.47 165	98.79 57	99.68 62	99.81 69	98.43 88	99.97 12	98.88 83	99.90 25	99.83 33

UnsupCasMVSNet_eth		96.44 304	96.12 304	97.40 322	98.65 331	95.65 320	99.36 210	99.51 106	97.13 227	96.04 353	98.99 329	88.40 346	98.17 355	96.71 289	90.27 359	98.40 330

sss		99.17 78	99.05 79	99.53 104	99.62 135	98.97 154	99.36 210	99.62 34	97.83 156	99.67 68	99.65 174	97.37 128	99.95 47	99.19 50	99.19 162	99.68 112

DeepC-MVS_fast	98.69 1	99.49 17	99.39 22	99.77 50	99.63 129	99.59 73	99.36 210	99.46 175	99.07 18	99.79 30	99.82 56	98.85 45	99.92 86	98.68 120	99.87 42	99.82 40
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 69	99.14 70	99.59 87	99.41 195	99.16 128	99.35 216	99.57 53	98.82 52	99.51 115	99.61 196	96.46 158	99.95 47	99.59 6	99.98 2	99.65 122

pmmvs-eth3d		95.34 320	94.73 322	97.15 326	95.53 374	95.94 316	99.35 216	99.10 300	95.13 326	93.55 364	97.54 361	88.15 350	97.91 361	94.58 331	89.69 362	97.61 360

MDTV_nov1_ep13_2view						95.18 335	99.35 216		96.84 253	99.58 100		95.19 204		97.82 205		99.46 177

VDD-MVS		97.73 256	97.35 270	98.88 205	99.47 184	97.12 269	99.34 219	98.85 329	98.19 111	99.67 68	99.85 36	82.98 365	99.92 86	99.49 17	98.32 215	99.60 139

COLMAP_ROB	97.56 6	98.86 125	98.75 127	99.17 162	99.88 12	98.53 204	99.34 219	99.59 44	97.55 186	98.70 274	99.89 15	95.83 180	99.90 112	98.10 181	99.90 25	99.08 209
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
EGC-MVSNET		82.80 343	77.86 349	97.62 314	97.91 350	96.12 313	99.33 221	99.28 276	8.40 386	25.05 387	99.27 299	84.11 363	99.33 276	89.20 364	98.22 217	97.42 364

FMVSNet5		96.43 305	96.19 303	97.15 326	99.11 268	95.89 317	99.32 222	99.52 92	94.47 341	98.34 306	99.07 320	87.54 355	97.07 370	92.61 354	95.72 301	98.47 320

dp		97.75 252	97.80 215	97.59 316	99.10 271	93.71 355	99.32 222	98.88 327	96.48 282	99.08 215	99.55 216	92.67 283	99.82 162	96.52 296	98.58 201	99.24 198

tpmvs		97.98 217	98.02 195	97.84 304	99.04 283	94.73 342	99.31 224	99.20 289	96.10 315	98.76 264	99.42 258	94.94 207	99.81 167	96.97 275	98.45 209	98.97 224

tpmrst		98.33 175	98.48 159	97.90 301	99.16 261	94.78 341	99.31 224	99.11 299	97.27 215	99.45 124	99.59 202	95.33 197	99.84 144	98.48 149	98.61 198	99.09 208

MP-MVS-pluss		99.37 52	99.20 65	99.88 6	99.90 4	99.87 12	99.30 226	99.52 92	97.18 223	99.60 95	99.79 96	98.79 51	99.95 47	98.83 100	99.91 18	99.83 33
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
NCCC		99.34 55	99.19 66	99.79 46	99.61 139	99.65 62	99.30 226	99.48 147	98.86 48	99.21 189	99.63 187	98.72 64	99.90 112	98.25 170	99.63 131	99.80 56

JIA-IIPM		97.50 279	97.02 290	98.93 191	98.73 322	97.80 248	99.30 226	98.97 314	91.73 358	98.91 242	94.86 373	95.10 205	99.71 205	97.58 228	97.98 229	99.28 196

BH-RMVSNet		98.41 169	98.08 187	99.40 129	99.41 195	98.83 180	99.30 226	98.77 336	97.70 172	98.94 238	99.65 174	92.91 273	99.74 189	96.52 296	99.55 137	99.64 129

MCST-MVS		99.43 37	99.30 46	99.82 38	99.79 46	99.74 46	99.29 230	99.40 217	98.79 57	99.52 113	99.62 192	98.91 40	99.90 112	98.64 125	99.75 105	99.82 40

LF4IMVS		97.52 276	97.46 253	97.70 313	98.98 291	95.55 323	99.29 230	98.82 332	98.07 132	98.66 277	99.64 181	89.97 330	99.61 234	97.01 271	96.68 275	97.94 354

hse-mvs2		97.50 279	97.14 286	98.59 237	99.49 175	97.05 276	99.28 232	99.22 285	98.94 41	99.66 73	99.42 258	94.93 208	99.65 224	99.48 18	83.80 370	99.08 209

OPM-MVS		98.19 186	98.10 183	98.45 257	98.88 301	97.07 274	99.28 232	99.38 227	98.57 70	99.22 186	99.81 69	92.12 296	99.66 220	98.08 186	97.54 245	98.61 306
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
diffmvs		99.14 82	99.02 87	99.51 112	99.61 139	98.96 158	99.28 232	99.49 135	98.46 80	99.72 54	99.71 139	96.50 157	99.88 126	99.31 37	99.11 168	99.67 115

PVSNet_BlendedMVS		98.86 125	98.80 121	99.03 175	99.76 57	98.79 184	99.28 232	99.91 3	97.42 203	99.67 68	99.37 273	97.53 121	99.88 126	98.98 71	97.29 265	98.42 327

OMC-MVS		99.08 101	99.04 82	99.20 159	99.67 110	98.22 226	99.28 232	99.52 92	98.07 132	99.66 73	99.81 69	97.79 116	99.78 180	97.79 207	99.81 86	99.60 139

AUN-MVS		96.88 295	96.31 301	98.59 237	99.48 183	97.04 279	99.27 237	99.22 285	97.44 200	98.51 294	99.41 262	91.97 298	99.66 220	97.71 218	83.83 369	99.07 214

pmmvs5		97.52 276	97.30 278	98.16 283	98.57 339	96.73 294	99.27 237	98.90 325	96.14 309	98.37 304	99.53 225	91.54 312	99.14 306	97.51 238	95.87 296	98.63 294

1314		98.68 153	98.54 156	99.11 167	98.89 300	98.65 193	99.27 237	99.49 135	96.89 250	97.99 321	99.56 213	97.72 119	99.83 155	97.74 214	99.27 157	98.84 233

1121		99.09 99	98.87 109	99.75 54	99.74 76	99.60 70	99.27 237	99.48 147	96.82 256	99.25 180	99.65 174	98.38 93	99.93 73	97.53 236	99.67 125	99.73 90

MVS		97.28 288	96.55 297	99.48 116	98.78 316	98.95 163	99.27 237	99.39 221	83.53 372	98.08 316	99.54 221	96.97 142	99.87 129	94.23 336	99.16 163	99.63 133

BH-untuned		98.42 167	98.36 164	98.59 237	99.49 175	96.70 295	99.27 237	99.13 298	97.24 219	98.80 259	99.38 270	95.75 183	99.74 189	97.07 270	99.16 163	99.33 192

MDTV_nov1_ep13			98.32 169		99.11 268	94.44 346	99.27 237	98.74 340	97.51 192	99.40 141	99.62 192	94.78 218	99.76 186	97.59 227	98.81 194

DP-MVS Recon		99.12 90	98.95 100	99.65 75	99.74 76	99.70 51	99.27 237	99.57 53	96.40 289	99.42 132	99.68 161	98.75 60	99.80 172	97.98 192	99.72 112	99.44 180

PatchmatchNet		98.31 176	98.36 164	98.19 281	99.16 261	95.32 331	99.27 237	98.92 320	97.37 207	99.37 150	99.58 206	94.90 211	99.70 211	97.43 248	99.21 160	99.54 152
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
thres200		97.61 272	97.28 279	98.62 235	99.64 126	98.03 233	99.26 246	98.74 340	97.68 174	99.09 214	98.32 355	91.66 309	99.81 167	92.88 350	98.22 217	98.03 347

CNVR-MVS		99.42 42	99.30 46	99.78 48	99.62 135	99.71 49	99.26 246	99.52 92	98.82 52	99.39 144	99.71 139	98.96 29	99.85 138	98.59 135	99.80 90	99.77 72

1112_ss		98.98 114	98.77 124	99.59 87	99.68 109	99.02 147	99.25 248	99.48 147	97.23 220	99.13 203	99.58 206	96.93 144	99.90 112	98.87 87	98.78 195	99.84 22

TAPA-MVS	97.07 15	97.74 255	97.34 273	98.94 189	99.70 102	97.53 256	99.25 248	99.51 106	91.90 357	99.30 166	99.63 187	98.78 52	99.64 227	88.09 369	99.87 42	99.65 122
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
PLC	97.94 4	99.02 109	98.85 115	99.53 104	99.66 119	99.01 149	99.24 250	99.52 92	96.85 252	99.27 173	99.48 244	98.25 101	99.91 97	97.76 211	99.62 132	99.65 122
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
ETH3D cwj APD-0.16		99.06 103	98.84 116	99.72 64	99.51 163	99.60 70	99.23 251	99.44 196	97.04 238	99.39 144	99.67 167	98.30 98	99.92 86	97.27 254	99.69 118	99.64 129

test_post1							99.23 251				65.14 384	94.18 246	99.71 205	97.58 228

ADS-MVSNet2		98.02 210	98.07 190	97.87 302	99.33 215	95.19 334	99.23 251	99.08 303	96.24 298	99.10 210	99.67 167	94.11 247	98.93 339	96.81 284	99.05 175	99.48 169

ADS-MVSNet		98.20 185	98.08 187	98.56 243	99.33 215	96.48 303	99.23 251	99.15 295	96.24 298	99.10 210	99.67 167	94.11 247	99.71 205	96.81 284	99.05 175	99.48 169

EPNet_dtu		98.03 208	97.96 200	98.23 279	98.27 346	95.54 325	99.23 251	98.75 337	99.02 21	97.82 326	99.71 139	96.11 168	99.48 243	93.04 349	99.65 128	99.69 108
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CR-MVSNet		98.17 189	97.93 205	98.87 209	99.18 253	98.49 212	99.22 256	99.33 252	96.96 244	99.56 104	99.38 270	94.33 239	99.00 327	94.83 330	98.58 201	99.14 201

RPMNet		96.72 299	95.90 309	99.19 160	99.18 253	98.49 212	99.22 256	99.52 92	88.72 368	99.56 104	97.38 363	94.08 249	99.95 47	86.87 373	98.58 201	99.14 201

plane_prior						96.97 285	99.21 258		98.45 81						97.60 239

WR-MVS		98.06 200	97.73 227	99.06 171	98.86 308	99.25 119	99.19 259	99.35 241	97.30 212	98.66 277	99.43 255	93.94 252	99.21 301	98.58 136	94.28 328	98.71 253

new-patchmatchnet		94.48 327	94.08 327	95.67 344	95.08 376	92.41 363	99.18 260	99.28 276	94.55 340	93.49 365	97.37 364	87.86 353	97.01 371	91.57 356	88.36 363	97.61 360

AdaColmap		99.01 112	98.80 121	99.66 71	99.56 153	99.54 82	99.18 260	99.70 15	98.18 115	99.35 157	99.63 187	96.32 163	99.90 112	97.48 241	99.77 101	99.55 150

ETH3 D test6400		98.70 148	98.35 166	99.73 61	99.69 105	99.60 70	99.16 262	99.45 187	95.42 323	99.27 173	99.60 199	97.39 124	99.91 97	95.36 322	99.83 77	99.70 105

EG-PatchMatch MVS		95.97 313	95.69 313	96.81 336	97.78 353	92.79 362	99.16 262	98.93 318	96.16 306	94.08 362	99.22 305	82.72 366	99.47 244	95.67 315	97.50 250	98.17 341

PatchT		97.03 294	96.44 299	98.79 225	98.99 288	98.34 222	99.16 262	99.07 305	92.13 356	99.52 113	97.31 366	94.54 234	98.98 329	88.54 367	98.73 197	99.03 217

CNLPA		99.14 82	98.99 92	99.59 87	99.58 147	99.41 102	99.16 262	99.44 196	98.45 81	99.19 195	99.49 238	98.08 109	99.89 121	97.73 215	99.75 105	99.48 169

MDA-MVSNet-bldmvs		94.96 322	93.98 328	97.92 299	98.24 347	97.27 263	99.15 266	99.33 252	93.80 345	80.09 379	99.03 325	88.31 347	97.86 363	93.49 344	94.36 327	98.62 297

CDPH-MVS		99.13 84	98.91 104	99.80 43	99.75 68	99.71 49	99.15 266	99.41 209	96.60 272	99.60 95	99.55 216	98.83 47	99.90 112	97.48 241	99.83 77	99.78 70

xxxxxxxxxxxxxcwj		99.43 37	99.32 36	99.75 54	99.76 57	99.59 73	99.14 268	99.53 86	99.00 28	99.71 55	99.80 84	98.95 32	99.93 73	98.19 174	99.84 68	99.74 83

save fliter					99.76 57	99.59 73	99.14 268	99.40 217	99.00 28

FMVS1		90.42 338	90.68 340	89.65 357	97.78 353	73.97 384	99.13 270	98.81 333	89.62 364	91.80 369	98.93 335	62.23 378	98.80 344	86.61 374	91.17 353	96.19 370

APD_test		90.42 338	90.68 340	89.65 357	97.78 353	73.97 384	99.13 270	98.81 333	89.62 364	91.80 369	98.93 335	62.23 378	98.80 344	86.61 374	91.17 353	96.19 370

xiu_mvs_v1_base_debu		99.29 61	99.27 56	99.34 135	99.63 129	98.97 154	99.12 272	99.51 106	98.86 48	99.84 15	99.47 247	98.18 104	99.99 1	99.50 13	99.31 154	99.08 209

xiu_mvs_v1_base		99.29 61	99.27 56	99.34 135	99.63 129	98.97 154	99.12 272	99.51 106	98.86 48	99.84 15	99.47 247	98.18 104	99.99 1	99.50 13	99.31 154	99.08 209

xiu_mvs_v1_base_debi		99.29 61	99.27 56	99.34 135	99.63 129	98.97 154	99.12 272	99.51 106	98.86 48	99.84 15	99.47 247	98.18 104	99.99 1	99.50 13	99.31 154	99.08 209

XVG-OURS-SEG-HR		98.69 151	98.62 145	98.89 202	99.71 95	97.74 250	99.12 272	99.54 75	98.44 84	99.42 132	99.71 139	94.20 243	99.92 86	98.54 146	98.90 188	99.00 220

jason		99.13 84	99.03 84	99.45 122	99.46 185	98.87 173	99.12 272	99.26 279	98.03 140	99.79 30	99.65 174	97.02 139	99.85 138	99.02 68	99.90 25	99.65 122
jason: jason.
N_pmnet		94.95 323	95.83 311	92.31 352	98.47 343	79.33 379	99.12 272	92.81 387	93.87 344	97.68 329	99.13 315	93.87 254	99.01 326	91.38 357	96.19 287	98.59 310

MDA-MVSNet_test_wron		95.45 317	94.60 323	98.01 293	98.16 348	97.21 268	99.11 278	99.24 283	93.49 349	80.73 378	98.98 332	93.02 268	98.18 354	94.22 337	94.45 325	98.64 286

Patchmtry		97.75 252	97.40 265	98.81 222	99.10 271	98.87 173	99.11 278	99.33 252	94.83 334	98.81 257	99.38 270	94.33 239	99.02 324	96.10 303	95.57 304	98.53 314

YYNet1		95.36 319	94.51 325	97.92 299	97.89 351	97.10 270	99.10 280	99.23 284	93.26 352	80.77 377	99.04 324	92.81 274	98.02 358	94.30 334	94.18 330	98.64 286

CANet_DTU		98.97 116	98.87 109	99.25 154	99.33 215	98.42 220	99.08 281	99.30 269	99.16 6	99.43 129	99.75 121	95.27 199	99.97 12	98.56 141	99.95 8	99.36 188

SCA		98.19 186	98.16 177	98.27 278	99.30 224	95.55 323	99.07 282	98.97 314	97.57 184	99.43 129	99.57 210	92.72 278	99.74 189	97.58 228	99.20 161	99.52 158

TSAR-MVS + GP.		99.36 53	99.36 27	99.36 134	99.67 110	98.61 198	99.07 282	99.33 252	99.00 28	99.82 23	99.81 69	99.06 16	99.84 144	99.09 61	99.42 144	99.65 122

MG-MVS		99.13 84	99.02 87	99.45 122	99.57 149	98.63 195	99.07 282	99.34 245	98.99 31	99.61 91	99.82 56	97.98 112	99.87 129	97.00 272	99.80 90	99.85 18

PatchMatch-RL		98.84 136	98.62 145	99.52 110	99.71 95	99.28 115	99.06 285	99.77 9	97.74 169	99.50 116	99.53 225	95.41 193	99.84 144	97.17 265	99.64 129	99.44 180

OpenMVS_ROB	92.34 20	94.38 328	93.70 332	96.41 341	97.38 359	93.17 360	99.06 285	98.75 337	86.58 369	94.84 360	98.26 356	81.53 369	99.32 279	89.01 365	97.87 232	96.76 367

TEST9					99.67 110	99.65 62	99.05 287	99.41 209	96.22 300	98.95 236	99.49 238	98.77 55	99.91 97

train_agg		99.02 109	98.77 124	99.77 50	99.67 110	99.65 62	99.05 287	99.41 209	96.28 293	98.95 236	99.49 238	98.76 57	99.91 97	97.63 224	99.72 112	99.75 78

lupinMVS		99.13 84	99.01 91	99.46 121	99.51 163	98.94 166	99.05 287	99.16 294	97.86 151	99.80 28	99.56 213	97.39 124	99.86 132	98.94 75	99.85 60	99.58 147

DELS-MVS		99.48 22	99.42 18	99.65 75	99.72 89	99.40 104	99.05 287	99.66 27	99.14 7	99.57 103	99.80 84	98.46 86	99.94 58	99.57 8	99.84 68	99.60 139
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		96.38 306	96.03 306	97.41 321	98.13 349	95.16 336	99.05 287	99.20 289	93.94 343	97.39 334	98.79 341	91.61 311	99.04 320	90.43 360	95.77 298	98.05 346

MVS_0304		96.79 298	96.52 298	97.59 316	99.22 244	94.92 340	99.04 292	99.59 44	96.49 278	98.43 300	98.99 329	80.48 371	99.39 259	97.15 266	99.27 157	98.47 320

Patchmatch-test		97.93 222	97.65 234	98.77 227	99.18 253	97.07 274	99.03 293	99.14 297	96.16 306	98.74 265	99.57 210	94.56 232	99.72 199	93.36 345	99.11 168	99.52 158

test_8					99.67 110	99.61 68	99.03 293	99.41 209	96.28 293	98.93 240	99.48 244	98.76 57	99.91 97

Test_1112_low_res		98.89 121	98.66 137	99.57 92	99.69 105	98.95 163	99.03 293	99.47 165	96.98 242	99.15 201	99.23 304	96.77 149	99.89 121	98.83 100	98.78 195	99.86 15

IterMVS-SCA-FT		97.82 241	97.75 225	98.06 289	99.57 149	96.36 307	99.02 296	99.49 135	97.18 223	98.71 268	99.72 138	92.72 278	99.14 306	97.44 247	95.86 297	98.67 274

xiu_mvs_v2_base		99.26 66	99.25 60	99.29 148	99.53 159	98.91 170	99.02 296	99.45 187	98.80 56	99.71 55	99.26 301	98.94 35	99.98 7	99.34 34	99.23 159	98.98 223

MIMVSNet		97.73 256	97.45 254	98.57 241	99.45 190	97.50 257	99.02 296	98.98 313	96.11 311	99.41 136	99.14 314	90.28 324	98.74 346	95.74 311	98.93 184	99.47 175

IterMVS		97.83 238	97.77 221	98.02 292	99.58 147	96.27 310	99.02 296	99.48 147	97.22 221	98.71 268	99.70 143	92.75 275	99.13 309	97.46 244	96.00 291	98.67 274
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
HyFIR lowres test		99.11 95	98.92 102	99.65 75	99.90 4	99.37 105	99.02 296	99.91 3	97.67 176	99.59 98	99.75 121	95.90 178	99.73 195	99.53 10	99.02 179	99.86 15

新几何2							99.01 301

BH-w/o		98.00 215	97.89 211	98.32 271	99.35 209	96.20 312	99.01 301	98.90 325	96.42 287	98.38 303	99.00 328	95.26 201	99.72 199	96.06 304	98.61 198	99.03 217

agg_prior1		99.01 112	98.76 126	99.76 53	99.67 110	99.62 66	98.99 303	99.40 217	96.26 296	98.87 249	99.49 238	98.77 55	99.91 97	97.69 221	99.72 112	99.75 78

test_prior4						99.56 78	98.99 303

无先验							98.99 303	99.51 106	96.89 250				99.93 73	97.53 236		99.72 96

pmmvs4		98.13 193	97.90 207	98.81 222	98.61 336	98.87 173	98.99 303	99.21 288	96.44 285	99.06 220	99.58 206	95.90 178	99.11 314	97.18 264	96.11 289	98.46 324

HQP-NCC					99.19 250		98.98 307		98.24 103	98.66 277

ACMP_Plane					99.19 250		98.98 307		98.24 103	98.66 277

HQP-MVS		98.02 210	97.90 207	98.37 267	99.19 250	96.83 290	98.98 307	99.39 221	98.24 103	98.66 277	99.40 265	92.47 289	99.64 227	97.19 262	97.58 241	98.64 286

PS-MVSNAJ		99.32 57	99.32 36	99.30 145	99.57 149	98.94 166	98.97 310	99.46 175	98.92 45	99.71 55	99.24 303	99.01 19	99.98 7	99.35 30	99.66 126	98.97 224

MVP-Stereo		97.81 243	97.75 225	97.99 296	97.53 357	96.60 300	98.96 311	98.85 329	97.22 221	97.23 337	99.36 276	95.28 198	99.46 246	95.51 317	99.78 97	97.92 356
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
test_prior3		99.21 72	99.05 79	99.68 68	99.67 110	99.48 93	98.96 311	99.56 58	98.34 93	99.01 225	99.52 228	98.68 67	99.83 155	97.96 193	99.74 108	99.74 83

test_prior2							98.96 311		98.34 93	99.01 225	99.52 228	98.68 67		97.96 193	99.74 108

旧先验2							98.96 311		96.70 261	99.47 121			99.94 58	98.19 174

原ACMM2							98.95 315

MVS_111021_HR		99.41 46	99.32 36	99.66 71	99.72 89	99.47 95	98.95 315	99.85 6	98.82 52	99.54 109	99.73 134	98.51 82	99.74 189	98.91 80	99.88 38	99.77 72

MVS_111021_LR		99.41 46	99.33 34	99.65 75	99.77 54	99.51 90	98.94 317	99.85 6	98.82 52	99.65 79	99.74 127	98.51 82	99.80 172	98.83 100	99.89 35	99.64 129

pmmvs3		94.09 330	93.25 334	96.60 339	94.76 377	94.49 345	98.92 318	98.18 361	89.66 363	96.48 348	98.06 358	86.28 357	97.33 368	89.68 363	87.20 365	97.97 353

XVG-OURS		98.73 146	98.68 133	98.88 205	99.70 102	97.73 251	98.92 318	99.55 67	98.52 75	99.45 124	99.84 45	95.27 199	99.91 97	98.08 186	98.84 191	99.00 220

test222					99.75 68	99.49 91	98.91 320	99.49 135	96.42 287	99.34 160	99.65 174	98.28 100			99.69 118	99.72 96

PMMVS2		86.87 340	85.37 344	91.35 355	90.21 381	83.80 374	98.89 321	97.45 370	83.13 373	91.67 371	95.03 371	48.49 384	94.70 378	85.86 376	77.62 375	95.54 372

miper_lstm_enhance		98.00 215	97.91 206	98.28 277	99.34 213	97.43 259	98.88 322	99.36 236	96.48 282	98.80 259	99.55 216	95.98 171	98.91 340	97.27 254	95.50 307	98.51 316

MVS-HIRNet		95.75 315	95.16 319	97.51 319	99.30 224	93.69 356	98.88 322	95.78 377	85.09 371	98.78 262	92.65 375	91.29 315	99.37 266	94.85 329	99.85 60	99.46 177

TR-MVS		97.76 248	97.41 264	98.82 220	99.06 279	97.87 244	98.87 324	98.56 351	96.63 269	98.68 276	99.22 305	92.49 288	99.65 224	95.40 320	97.79 233	98.95 229

testdata1							98.85 325		98.32 97

ET-MVSNet_ETH3D		96.49 303	95.64 314	99.05 173	99.53 159	98.82 181	98.84 326	97.51 369	97.63 179	84.77 373	99.21 308	92.09 297	98.91 340	98.98 71	92.21 350	99.41 184

our_test_3		97.65 270	97.68 231	97.55 318	98.62 334	94.97 338	98.84 326	99.30 269	96.83 255	98.19 312	99.34 282	97.01 140	99.02 324	95.00 328	96.01 290	98.64 286

MS-PatchMatch		97.24 290	97.32 276	96.99 330	98.45 344	93.51 359	98.82 328	99.32 262	97.41 204	98.13 315	99.30 292	88.99 339	99.56 238	95.68 314	99.80 90	97.90 357

c3_l		98.12 195	98.04 192	98.38 266	99.30 224	97.69 255	98.81 329	99.33 252	96.67 263	98.83 255	99.34 282	97.11 135	98.99 328	97.58 228	95.34 309	98.48 318

ppachtmachnet_test		97.49 282	97.45 254	97.61 315	98.62 334	95.24 332	98.80 330	99.46 175	96.11 311	98.22 311	99.62 192	96.45 159	98.97 336	93.77 340	95.97 295	98.61 306

PAPR		98.63 158	98.34 167	99.51 112	99.40 200	99.03 146	98.80 330	99.36 236	96.33 290	99.00 230	99.12 318	98.46 86	99.84 144	95.23 324	99.37 153	99.66 118

test0.0.03 1		97.71 261	97.42 263	98.56 243	98.41 345	97.82 247	98.78 332	98.63 349	97.34 208	98.05 320	98.98 332	94.45 236	98.98 329	95.04 327	97.15 271	98.89 230

PVSNet_Blended		99.08 101	98.97 96	99.42 127	99.76 57	98.79 184	98.78 332	99.91 3	96.74 258	99.67 68	99.49 238	97.53 121	99.88 126	98.98 71	99.85 60	99.60 139

PMMVS		98.80 140	98.62 145	99.34 135	99.27 233	98.70 189	98.76 334	99.31 265	97.34 208	99.21 189	99.07 320	97.20 132	99.82 162	98.56 141	98.87 189	99.52 158

test123		39.01 352	42.50 354	28.53 367	39.17 390	20.91 391	98.75 335	19.17 392	19.83 385	38.57 384	66.67 382	33.16 387	15.42 386	37.50 385	29.66 384	49.26 381

MSDG		98.98 114	98.80 121	99.53 104	99.76 57	99.19 123	98.75 335	99.55 67	97.25 217	99.47 121	99.77 110	97.82 115	99.87 129	96.93 279	99.90 25	99.54 152

CLD-MVS		98.16 190	98.10 183	98.33 269	99.29 228	96.82 292	98.75 335	99.44 196	97.83 156	99.13 203	99.55 216	92.92 271	99.67 217	98.32 167	97.69 235	98.48 318
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		98.18 188	98.10 183	98.41 262	99.23 241	97.72 252	98.72 338	99.31 265	96.60 272	98.88 247	99.29 294	97.29 130	99.13 309	97.60 226	95.99 292	98.38 332

cl____		98.01 213	97.84 214	98.55 245	99.25 239	97.97 237	98.71 339	99.34 245	96.47 284	98.59 291	99.54 221	95.65 188	99.21 301	97.21 258	95.77 298	98.46 324

DIV-MVS_self_test		98.01 213	97.85 213	98.48 251	99.24 240	97.95 241	98.71 339	99.35 241	96.50 277	98.60 290	99.54 221	95.72 185	99.03 322	97.21 258	95.77 298	98.46 324

test-LLR		98.06 200	97.90 207	98.55 245	98.79 313	97.10 270	98.67 341	97.75 365	97.34 208	98.61 288	98.85 338	94.45 236	99.45 247	97.25 256	99.38 146	99.10 204

TESTMET0.1,1		97.55 274	97.27 282	98.40 264	98.93 296	96.53 301	98.67 341	97.61 368	96.96 244	98.64 284	99.28 296	88.63 344	99.45 247	97.30 253	99.38 146	99.21 200

test-mter		97.49 282	97.13 287	98.55 245	98.79 313	97.10 270	98.67 341	97.75 365	96.65 265	98.61 288	98.85 338	88.23 348	99.45 247	97.25 256	99.38 146	99.10 204

IB-MVS	95.67 18	96.22 307	95.44 317	98.57 241	99.21 246	96.70 295	98.65 344	97.74 367	96.71 260	97.27 336	98.54 349	86.03 358	99.92 86	98.47 152	86.30 366	99.10 204
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		98.95 117	98.71 130	99.66 71	99.63 129	99.55 80	98.64 345	99.10 300	97.93 146	99.42 132	99.55 216	98.67 70	99.80 172	95.80 310	99.68 123	99.61 137

thisisatest0515		98.14 192	97.79 216	99.19 160	99.50 173	98.50 211	98.61 346	96.82 373	96.95 246	99.54 109	99.43 255	91.66 309	99.86 132	98.08 186	99.51 139	99.22 199

DeepPCF-MVS	98.18 3	98.81 137	99.37 25	97.12 329	99.60 143	91.75 366	98.61 346	99.44 196	99.35 1	99.83 20	99.85 36	98.70 66	99.81 167	99.02 68	99.91 18	99.81 46

cl22		97.85 233	97.64 236	98.48 251	99.09 273	97.87 244	98.60 348	99.33 252	97.11 232	98.87 249	99.22 305	92.38 294	99.17 305	98.21 172	95.99 292	98.42 327

GA-MVS		97.85 233	97.47 251	99.00 179	99.38 204	97.99 236	98.57 349	99.15 295	97.04 238	98.90 244	99.30 292	89.83 331	99.38 261	96.70 290	98.33 211	99.62 135

TinyColmap		97.12 292	96.89 292	97.83 305	99.07 276	95.52 326	98.57 349	98.74 340	97.58 183	97.81 327	99.79 96	88.16 349	99.56 238	95.10 325	97.21 268	98.39 331

eth_miper_zixun_eth		98.05 205	97.96 200	98.33 269	99.26 235	97.38 260	98.56 351	99.31 265	96.65 265	98.88 247	99.52 228	96.58 154	99.12 313	97.39 250	95.53 306	98.47 320

CMPMVS	69.68 23	94.13 329	94.90 321	91.84 353	97.24 363	80.01 378	98.52 352	99.48 147	89.01 366	91.99 368	99.67 167	85.67 360	99.13 309	95.44 318	97.03 272	96.39 369
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
USDC		97.34 286	97.20 284	97.75 310	99.07 276	95.20 333	98.51 353	99.04 308	97.99 142	98.31 307	99.86 31	89.02 338	99.55 240	95.67 315	97.36 264	98.49 317

ambc				93.06 351	92.68 378	82.36 375	98.47 354	98.73 345		95.09 358	97.41 362	55.55 380	99.10 316	96.42 299	91.32 352	97.71 358

miper_enhance_ethall		98.16 190	98.08 187	98.41 262	98.96 294	97.72 252	98.45 355	99.32 262	96.95 246	98.97 234	99.17 310	97.06 138	99.22 296	97.86 201	95.99 292	98.29 335

CHOSEN 280x420		99.12 90	99.13 71	99.08 168	99.66 119	97.89 243	98.43 356	99.71 13	98.88 47	99.62 88	99.76 116	96.63 153	99.70 211	99.46 21	99.99 1	99.66 118

testmvs		39.17 351	43.78 353	25.37 368	36.04 391	16.84 392	98.36 357	26.56 390	20.06 384	38.51 385	67.32 381	29.64 388	15.30 387	37.59 384	39.90 383	43.98 382

FPMVS		84.93 342	85.65 343	82.75 363	86.77 384	63.39 388	98.35 358	98.92 320	74.11 375	83.39 375	98.98 332	50.85 383	92.40 380	84.54 377	94.97 317	92.46 374

KD-MVS_2432*1600		94.62 324	93.72 330	97.31 323	97.19 365	95.82 318	98.34 359	99.20 289	95.00 331	97.57 330	98.35 353	87.95 351	98.10 356	92.87 351	77.00 376	98.01 348

miper_refine_blended		94.62 324	93.72 330	97.31 323	97.19 365	95.82 318	98.34 359	99.20 289	95.00 331	97.57 330	98.35 353	87.95 351	98.10 356	92.87 351	77.00 376	98.01 348

CL-MVSNet_self_test		94.49 326	93.97 329	96.08 342	96.16 369	93.67 357	98.33 361	99.38 227	95.13 326	97.33 335	98.15 357	92.69 282	96.57 373	88.67 366	79.87 374	97.99 351

PVSNet	96.02 17	98.85 133	98.84 116	98.89 202	99.73 84	97.28 262	98.32 362	99.60 41	97.86 151	99.50 116	99.57 210	96.75 150	99.86 132	98.56 141	99.70 117	99.54 152

PAPM		97.59 273	97.09 288	99.07 170	99.06 279	98.26 225	98.30 363	99.10 300	94.88 333	98.08 316	99.34 282	96.27 165	99.64 227	89.87 362	98.92 186	99.31 194

Patchmatch-RL test		95.84 314	95.81 312	95.95 343	95.61 372	90.57 368	98.24 364	98.39 355	95.10 330	95.20 356	98.67 345	94.78 218	97.77 364	96.28 302	90.02 360	99.51 164

UnsupCasMVSNet_bld		93.53 332	92.51 335	96.58 340	97.38 359	93.82 352	98.24 364	99.48 147	91.10 361	93.10 366	96.66 368	74.89 372	98.37 351	94.03 339	87.71 364	97.56 362

LCM-MVSNet		86.80 341	85.22 345	91.53 354	87.81 383	80.96 377	98.23 366	98.99 312	71.05 376	90.13 372	96.51 369	48.45 385	96.88 372	90.51 359	85.30 367	96.76 367

cascas		97.69 263	97.43 262	98.48 251	98.60 337	97.30 261	98.18 367	99.39 221	92.96 354	98.41 301	98.78 342	93.77 258	99.27 288	98.16 179	98.61 198	98.86 231

Effi-MVS+		98.81 137	98.59 152	99.48 116	99.46 185	99.12 137	98.08 368	99.50 127	97.50 193	99.38 148	99.41 262	96.37 162	99.81 167	99.11 59	98.54 205	99.51 164

PCF-MVS	97.08 14	97.66 269	97.06 289	99.47 119	99.61 139	99.09 139	98.04 369	99.25 281	91.24 360	98.51 294	99.70 143	94.55 233	99.91 97	92.76 353	99.85 60	99.42 182
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
PVSNet_0	94.43 19	96.09 312	95.47 315	97.94 298	99.31 223	94.34 349	97.81 370	99.70 15	97.12 229	97.46 332	98.75 343	89.71 333	99.79 175	97.69 221	81.69 372	99.68 112

E-PMN		80.61 345	79.88 347	82.81 362	90.75 380	76.38 382	97.69 371	95.76 378	66.44 380	83.52 374	92.25 376	62.54 377	87.16 382	68.53 381	61.40 379	84.89 380

ANet_high		77.30 347	74.86 351	84.62 361	75.88 387	77.61 380	97.63 372	93.15 386	88.81 367	64.27 382	89.29 379	36.51 386	83.93 384	75.89 379	52.31 381	92.33 376

EMVS		80.02 346	79.22 348	82.43 364	91.19 379	76.40 381	97.55 373	92.49 388	66.36 381	83.01 376	91.27 377	64.63 376	85.79 383	65.82 382	60.65 380	85.08 379

MVE	76.82 21	76.91 348	74.31 352	84.70 360	85.38 386	76.05 383	96.88 374	93.17 385	67.39 379	71.28 381	89.01 380	21.66 391	87.69 381	71.74 380	72.29 378	90.35 377
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
test_method		91.10 336	91.36 338	90.31 356	95.85 370	73.72 386	94.89 375	99.25 281	68.39 378	95.82 354	99.02 327	80.50 370	98.95 338	93.64 342	94.89 320	98.25 338

Gipuma		90.99 337	90.15 342	93.51 348	98.73 322	90.12 369	93.98 376	99.45 187	79.32 374	92.28 367	94.91 372	69.61 373	97.98 360	87.42 370	95.67 302	92.45 375
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PMVS	70.75 22	75.98 349	74.97 350	79.01 365	70.98 388	55.18 389	93.37 377	98.21 359	65.08 382	61.78 383	93.83 374	21.74 390	92.53 379	78.59 378	91.12 355	89.34 378
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
tmp_tt		82.80 343	81.52 346	86.66 359	66.61 389	68.44 387	92.79 378	97.92 363	68.96 377	80.04 380	99.85 36	85.77 359	96.15 376	97.86 201	43.89 382	95.39 373

wuyk23d		40.18 350	41.29 355	36.84 366	86.18 385	49.12 390	79.73 379	22.81 391	27.64 383	25.46 386	28.45 386	21.98 389	48.89 385	55.80 383	23.56 385	12.51 383

test_blank		0.13 356	0.17 359	0.00 369	0.00 392	0.00 393	0.00 380	0.00 393	0.00 387	0.00 388	1.57 387	0.00 392	0.00 388	0.00 386	0.00 386	0.00 384

uanet_test		0.02 357	0.03 360	0.00 369	0.00 392	0.00 393	0.00 380	0.00 393	0.00 387	0.00 388	0.27 388	0.00 392	0.00 388	0.00 386	0.00 386	0.00 384

DCPMVS		0.02 357	0.03 360	0.00 369	0.00 392	0.00 393	0.00 380	0.00 393	0.00 387	0.00 388	0.27 388	0.00 392	0.00 388	0.00 386	0.00 386	0.00 384

cdsmvs_eth3d_5k		24.64 353	32.85 356	0.00 369	0.00 392	0.00 393	0.00 380	99.51 106	0.00 387	0.00 388	99.56 213	96.58 154	0.00 388	0.00 386	0.00 386	0.00 384

pcd_1.5k_mvsjas		8.27 355	11.03 358	0.00 369	0.00 392	0.00 393	0.00 380	0.00 393	0.00 387	0.00 388	0.27 388	99.01 19	0.00 388	0.00 386	0.00 386	0.00 384

sosnet-low-res		0.02 357	0.03 360	0.00 369	0.00 392	0.00 393	0.00 380	0.00 393	0.00 387	0.00 388	0.27 388	0.00 392	0.00 388	0.00 386	0.00 386	0.00 384

sosnet		0.02 357	0.03 360	0.00 369	0.00 392	0.00 393	0.00 380	0.00 393	0.00 387	0.00 388	0.27 388	0.00 392	0.00 388	0.00 386	0.00 386	0.00 384

uncertanet		0.02 357	0.03 360	0.00 369	0.00 392	0.00 393	0.00 380	0.00 393	0.00 387	0.00 388	0.27 388	0.00 392	0.00 388	0.00 386	0.00 386	0.00 384

Regformer		0.02 357	0.03 360	0.00 369	0.00 392	0.00 393	0.00 380	0.00 393	0.00 387	0.00 388	0.27 388	0.00 392	0.00 388	0.00 386	0.00 386	0.00 384

ab-mvs-re		8.30 354	11.06 357	0.00 369	0.00 392	0.00 393	0.00 380	0.00 393	0.00 387	0.00 388	99.58 206	0.00 392	0.00 388	0.00 386	0.00 386	0.00 384

uanet		0.02 357	0.03 360	0.00 369	0.00 392	0.00 393	0.00 380	0.00 393	0.00 387	0.00 388	0.27 388	0.00 392	0.00 388	0.00 386	0.00 386	0.00 384

MSC_two_6792asdad				99.87 12	99.51 163	99.76 41		99.33 252					99.96 20	98.87 87	99.84 68	99.89 2

PC_three_1452									98.18 115	99.84 15	99.70 143	99.31 3	98.52 349	98.30 169	99.80 90	99.81 46

No_MVS				99.87 12	99.51 163	99.76 41		99.33 252					99.96 20	98.87 87	99.84 68	99.89 2

test_one_0601					99.81 42	99.88 8		99.49 135	98.97 37	99.65 79	99.81 69	99.09 14

eth-test2					0.00 392

eth-test					0.00 392

ZD-MVS					99.71 95	99.79 33		99.61 36	96.84 253	99.56 104	99.54 221	98.58 75	99.96 20	96.93 279	99.75 105

IU-MVS					99.84 33	99.88 8		99.32 262	98.30 98	99.84 15				98.86 92	99.85 60	99.89 2

test_241102_TWO								99.48 147	99.08 16	99.88 6	99.81 69	98.94 35	99.96 20	98.91 80	99.84 68	99.88 8

test_241102_ONE					99.84 33	99.90 2		99.48 147	99.07 18	99.91 2	99.74 127	99.20 7	99.76 186

test_0728_THIRD									98.99 31	99.81 25	99.80 84	99.09 14	99.96 20	98.85 94	99.90 25	99.88 8

GSMVS																99.52 158

test_part2					99.81 42	99.83 17				99.77 38

sam_mvs1												94.86 213				99.52 158

sam_mvs												94.72 225

MTGPA								99.47 165

test_post											65.99 383	94.65 229	99.73 195

patchmatchnet-post											98.70 344	94.79 217	99.74 189

gm-plane-assit					98.54 341	92.96 361			94.65 338		99.15 313		99.64 227	97.56 233

test9_res														97.49 240	99.72 112	99.75 78

agg_prior2														97.21 258	99.73 111	99.75 78

agg_prior					99.67 110	99.62 66		99.40 217		98.87 249			99.91 97

TestCases				99.31 141	99.86 22	98.48 214		99.61 36	97.85 153	99.36 154	99.85 36	95.95 173	99.85 138	96.66 293	99.83 77	99.59 143

test_prior				99.68 68	99.67 110	99.48 93		99.56 58					99.83 155			99.74 83

新几何1				99.75 54	99.75 68	99.59 73		99.54 75	96.76 257	99.29 169	99.64 181	98.43 88	99.94 58	96.92 281	99.66 126	99.72 96

旧先验1					99.74 76	99.59 73		99.54 75			99.69 153	98.47 85			99.68 123	99.73 90

原ACMM1				99.65 75	99.73 84	99.33 107		99.47 165	97.46 194	99.12 205	99.66 173	98.67 70	99.91 97	97.70 220	99.69 118	99.71 103

testdata2													99.95 47	96.67 292

segment_acmp												98.96 29

testdata				99.54 96	99.75 68	98.95 163		99.51 106	97.07 235	99.43 129	99.70 143	98.87 43	99.94 58	97.76 211	99.64 129	99.72 96

test12				99.75 54	99.64 126	99.61 68		99.29 274		99.21 189		98.38 93	99.89 121		99.74 108	99.74 83

plane_prior7					99.29 228	97.03 280

plane_prior6					99.27 233	96.98 284						92.71 280

plane_prior5								99.47 165					99.69 215	97.78 208	97.63 236	98.67 274

plane_prior4											99.61 196

plane_prior3						97.00 282			98.69 64	99.11 207

plane_prior1					99.26 235

n2								0.00 393

nn								0.00 393

door-mid								98.05 362

lessismore_v0				97.79 309	98.69 328	95.44 329		94.75 381		95.71 355	99.87 27	88.69 342	99.32 279	95.89 307	94.93 319	98.62 297

LGP-MVS_train				98.49 249	99.33 215	97.05 276		99.55 67	97.46 194	99.24 181	99.83 49	92.58 285	99.72 199	98.09 182	97.51 248	98.68 267

test11								99.35 241

door								97.92 363

HQP5-MVS						96.83 290

BP-MVS														97.19 262

HQP4-MVS										98.66 277			99.64 227			98.64 286

HQP3-MVS								99.39 221							97.58 241

HQP2-MVS												92.47 289

NP-MVS					99.23 241	96.92 288					99.40 265

ACMMP++_ref															97.19 269

ACMMP++															97.43 259

Test By Simon												98.75 60

ITE_SJBPF				98.08 288	99.29 228	96.37 306		98.92 320	98.34 93	98.83 255	99.75 121	91.09 317	99.62 233	95.82 308	97.40 261	98.25 338

DeepMVS_CX				93.34 349	99.29 228	82.27 376		99.22 285	85.15 370	96.33 349	99.05 323	90.97 319	99.73 195	93.57 343	97.77 234	98.01 348

High-resolution multi-view benchmark