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.

MVSFormer		99.17 73	99.12 67	99.29 140	99.51 151	98.94 154	99.88 1	99.46 167	97.55 170	99.80 24	99.65 158	97.39 117	99.28 269	99.03 53	99.85 58	99.65 112

test_djsdf		98.67 143	98.57 144	98.98 173	98.70 309	98.91 158	99.88 1	99.46 167	97.55 170	99.22 167	99.88 15	95.73 175	99.28 269	99.03 53	97.62 224	98.75 231

OurMVSNet-221017-0		97.88 215	97.77 208	98.19 269	98.71 308	96.53 287	99.88 1	99.00 294	97.79 145	98.78 245	99.94 3	91.68 292	99.35 259	97.21 241	96.99 256	98.69 247

K. test v3		97.10 281	96.79 282	98.01 280	98.72 306	96.33 294	99.87 4	97.05 350	97.59 165	96.16 335	99.80 76	88.71 326	99.04 304	96.69 274	96.55 262	98.65 269

FC-MVSNet-test		98.75 137	98.62 138	99.15 157	99.08 258	99.45 91	99.86 5	99.60 40	98.23 94	98.70 257	99.82 49	96.80 137	99.22 279	99.07 51	96.38 267	98.79 222

v7n		97.87 217	97.52 233	98.92 183	98.76 302	98.58 187	99.84 6	99.46 167	96.20 284	98.91 225	99.70 132	94.89 201	99.44 239	96.03 287	93.89 317	98.75 231

DTE-MVSNet		97.51 267	97.19 274	98.46 244	98.63 315	98.13 218	99.84 6	99.48 139	96.68 246	97.97 306	99.67 151	92.92 257	98.56 329	96.88 266	92.60 332	98.70 243

3Dnovator	97.25 9	99.24 65	99.05 74	99.81 38	99.12 249	99.66 54	99.84 6	99.74 10	99.09 10	98.92 224	99.90 7	95.94 166	99.98 6	98.95 62	99.92 11	99.79 53

FIs		98.78 134	98.63 133	99.23 150	99.18 236	99.54 77	99.83 9	99.59 43	98.28 87	98.79 244	99.81 62	96.75 141	99.37 251	99.08 50	96.38 267	98.78 223

jajsoiax		98.43 154	98.28 161	98.88 195	98.60 319	98.43 204	99.82 10	99.53 82	98.19 98	98.63 268	99.80 76	93.22 253	99.44 239	99.22 35	97.50 235	98.77 227

OpenMVS	96.50 16	98.47 151	98.12 169	99.52 103	99.04 265	99.53 80	99.82 10	99.72 11	94.56 320	98.08 300	99.88 15	94.73 212	99.98 6	97.47 227	99.76 95	99.06 201

nrg030		98.64 146	98.42 151	99.28 143	99.05 264	99.69 47	99.81 12	99.46 167	98.04 122	99.01 207	99.82 49	96.69 143	99.38 248	99.34 24	94.59 306	98.78 223

HPM-MVS		99.42 38	99.28 48	99.83 33	99.90 3	99.72 42	99.81 12	99.54 70	97.59 165	99.68 53	99.63 171	98.91 36	99.94 54	98.58 123	99.91 16	99.84 18
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
EPP-MVSNet		99.13 79	98.99 87	99.53 98	99.65 115	99.06 135	99.81 12	99.33 242	97.43 186	99.60 81	99.88 15	97.14 126	99.84 137	99.13 45	98.94 169	99.69 98

3Dnovator+	97.12 13	99.18 71	98.97 91	99.82 35	99.17 242	99.68 49	99.81 12	99.51 101	99.20 4	98.72 250	99.89 10	95.68 177	99.97 11	98.86 78	99.86 51	99.81 41

canonicalmvs		99.02 104	98.86 108	99.51 105	99.42 175	99.32 102	99.80 16	99.48 139	98.63 57	99.31 145	98.81 320	97.09 128	99.75 176	99.27 32	97.90 217	99.47 163

v8		97.95 208	97.63 224	98.93 181	98.95 278	98.81 171	99.80 16	99.41 200	96.03 299	99.10 191	99.42 242	94.92 199	99.30 267	96.94 261	94.08 315	98.66 267

Vis-MVSNet (Re-imp)		98.87 116	98.72 122	99.31 133	99.71 86	98.88 160	99.80 16	99.44 188	97.91 132	99.36 136	99.78 95	95.49 183	99.43 243	97.91 183	99.11 155	99.62 125

Anonymous20240521		96.20 296	95.89 297	97.13 312	97.72 336	94.96 325	99.79 19	99.29 262	93.01 334	97.20 323	99.03 308	89.69 318	98.36 331	91.16 338	96.13 272	98.07 327

PS-MVSNAJss		98.92 114	98.92 97	98.90 189	98.78 298	98.53 191	99.78 20	99.54 70	98.07 116	99.00 212	99.76 105	99.01 16	99.37 251	99.13 45	97.23 249	98.81 220

PEN-MVS		97.76 235	97.44 247	98.72 219	98.77 301	98.54 190	99.78 20	99.51 101	97.06 221	98.29 293	99.64 165	92.63 270	98.89 325	98.09 168	93.16 325	98.72 237

anonymousdsp		98.44 153	98.28 161	98.94 179	98.50 324	98.96 149	99.77 22	99.50 119	97.07 219	98.87 232	99.77 101	94.76 210	99.28 269	98.66 110	97.60 225	98.57 296

SixPastTwentyTwo		97.50 268	97.33 264	98.03 277	98.65 313	96.23 297	99.77 22	98.68 328	97.14 211	97.90 307	99.93 4	90.45 307	99.18 287	97.00 255	96.43 266	98.67 259

QAPM		98.67 143	98.30 160	99.80 40	99.20 231	99.67 52	99.77 22	99.72 11	94.74 317	98.73 249	99.90 7	95.78 173	99.98 6	96.96 259	99.88 36	99.76 68

HPM-MVS_fast		99.51 14	99.40 16	99.85 25	99.91 1	99.79 30	99.76 25	99.56 55	97.72 153	99.76 37	99.75 110	99.13 10	99.92 80	99.07 51	99.92 11	99.85 14

v10		97.85 220	97.52 233	98.86 202	98.99 271	98.67 179	99.75 26	99.41 200	95.70 302	98.98 215	99.41 245	94.75 211	99.23 276	96.01 288	94.63 305	98.67 259

APDe-MVS		99.66 1	99.57 1	99.92 1	99.77 49	99.89 3	99.75 26	99.56 55	99.02 15	99.88 5	99.85 29	99.18 8	99.96 19	99.22 35	99.92 11	99.90 1

IS-MVSNet		99.05 100	98.87 104	99.57 88	99.73 75	99.32 102	99.75 26	99.20 273	98.02 125	99.56 89	99.86 23	96.54 147	99.67 205	98.09 168	99.13 154	99.73 80

tttt0517		98.42 155	98.14 167	99.28 143	99.66 110	98.38 207	99.74 29	96.85 351	97.68 157	99.79 26	99.74 116	91.39 299	99.89 114	98.83 85	99.56 127	99.57 138

baseline		99.15 76	99.02 82	99.53 98	99.66 110	99.14 126	99.72 30	99.48 139	98.35 80	99.42 117	99.84 38	96.07 160	99.79 165	99.51 7	99.14 153	99.67 105

RPSCF		98.22 170	98.62 138	96.99 314	99.82 37	91.58 349	99.72 30	99.44 188	96.61 253	99.66 64	99.89 10	95.92 167	99.82 153	97.46 228	99.10 158	99.57 138

CS-MVS		99.21 66	99.13 65	99.45 115	99.54 146	99.34 100	99.71 32	99.54 70	98.26 90	98.99 214	99.24 285	98.25 94	99.88 119	98.98 58	99.63 122	99.12 190

CSCG		99.32 53	99.32 30	99.32 132	99.85 25	98.29 209	99.71 32	99.66 27	98.11 108	99.41 121	99.80 76	98.37 88	99.96 19	98.99 57	99.96 5	99.72 86

WR-MVS_H		98.13 182	97.87 199	98.90 189	99.02 268	98.84 165	99.70 34	99.59 43	97.27 200	98.40 285	99.19 292	95.53 181	99.23 276	98.34 151	93.78 318	98.61 290

LTVRE_ROB	97.16 12	98.02 197	97.90 194	98.40 252	99.23 223	96.80 279	99.70 34	99.60 40	97.12 214	98.18 297	99.70 132	91.73 291	99.72 188	98.39 144	97.45 240	98.68 252
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
XVS		99.53 11	99.42 13	99.87 11	99.85 25	99.83 14	99.69 36	99.68 19	98.98 27	99.37 133	99.74 116	98.81 45	99.94 54	98.79 91	99.86 51	99.84 18

X-MVStestdata		96.55 288	95.45 303	99.87 11	99.85 25	99.83 14	99.69 36	99.68 19	98.98 27	99.37 133	64.01 363	98.81 45	99.94 54	98.79 91	99.86 51	99.84 18

V42		98.06 189	97.79 203	98.86 202	98.98 274	98.84 165	99.69 36	99.34 235	96.53 259	99.30 146	99.37 256	94.67 215	99.32 264	97.57 217	94.66 304	98.42 311

mPP-MVS		99.44 30	99.30 40	99.86 18	99.88 11	99.79 30	99.69 36	99.48 139	98.12 106	99.50 101	99.75 110	98.78 48	99.97 11	98.57 125	99.89 33	99.83 29

CP-MVS		99.45 26	99.32 30	99.85 25	99.83 36	99.75 38	99.69 36	99.52 88	98.07 116	99.53 96	99.63 171	98.93 35	99.97 11	98.74 96	99.91 16	99.83 29

PS-CasMVS		97.93 209	97.59 228	98.95 178	98.99 271	99.06 135	99.68 41	99.52 88	97.13 212	98.31 291	99.68 145	92.44 279	99.05 303	98.51 134	94.08 315	98.75 231

Vis-MVSNet		99.12 85	98.97 91	99.56 90	99.78 44	99.10 131	99.68 41	99.66 27	98.49 66	99.86 11	99.87 20	94.77 209	99.84 137	99.19 38	99.41 135	99.74 73
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
EIA-MVS		99.18 71	99.09 71	99.45 115	99.49 160	99.18 118	99.67 43	99.53 82	97.66 161	99.40 126	99.44 237	98.10 101	99.81 157	98.94 63	99.62 124	99.35 176

MSP-MVS		99.42 38	99.27 50	99.88 6	99.89 8	99.80 26	99.67 43	99.50 119	98.70 54	99.77 33	99.49 222	98.21 96	99.95 43	98.46 140	99.77 92	99.88 5
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 93	98.97 91	99.48 109	99.49 160	99.14 126	99.67 43	99.34 235	97.31 196	99.58 86	99.76 105	97.65 113	99.82 153	98.87 75	99.07 161	99.46 165

CP-MVSNet		98.09 186	97.78 206	99.01 169	98.97 276	99.24 113	99.67 43	99.46 167	97.25 202	98.48 280	99.64 165	93.79 244	99.06 302	98.63 113	94.10 314	98.74 235

MTAPA		99.52 13	99.39 17	99.89 4	99.90 3	99.86 10	99.66 47	99.47 157	98.79 48	99.68 53	99.81 62	98.43 81	99.97 11	98.88 71	99.90 23	99.83 29

HFP-MVS		99.49 15	99.37 19	99.86 18	99.87 15	99.80 26	99.66 47	99.67 22	98.15 102	99.68 53	99.69 139	99.06 13	99.96 19	98.69 105	99.87 40	99.84 18

mvs_tets		98.40 159	98.23 163	98.91 187	98.67 312	98.51 197	99.66 47	99.53 82	98.19 98	98.65 266	99.81 62	92.75 261	99.44 239	99.31 27	97.48 239	98.77 227

EU-MVSNet		97.98 204	98.03 179	97.81 294	98.72 306	96.65 284	99.66 47	99.66 27	98.09 111	98.35 289	99.82 49	95.25 193	98.01 337	97.41 233	95.30 294	98.78 223

ACMMPR		99.49 15	99.36 21	99.86 18	99.87 15	99.79 30	99.66 47	99.67 22	98.15 102	99.67 59	99.69 139	98.95 28	99.96 19	98.69 105	99.87 40	99.84 18

MP-MVS		99.33 52	99.15 63	99.87 11	99.88 11	99.82 20	99.66 47	99.46 167	98.09 111	99.48 105	99.74 116	98.29 92	99.96 19	97.93 182	99.87 40	99.82 36
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
abl_6		99.44 30	99.31 37	99.83 33	99.85 25	99.75 38	99.66 47	99.59 43	98.13 104	99.82 20	99.81 62	98.60 69	99.96 19	98.46 140	99.88 36	99.79 53

test_part1		97.75 239	97.24 272	99.29 140	99.59 135	99.63 60	99.65 54	99.49 127	96.17 287	98.44 282	99.69 139	89.80 316	99.47 231	98.68 107	93.66 319	98.78 223

region2R		99.48 19	99.35 24	99.87 11	99.88 11	99.80 26	99.65 54	99.66 27	98.13 104	99.66 64	99.68 145	98.96 25	99.96 19	98.62 114	99.87 40	99.84 18

TranMVSNet+NR-MVSNet		97.93 209	97.66 220	98.76 216	98.78 298	98.62 184	99.65 54	99.49 127	97.76 148	98.49 279	99.60 184	94.23 230	98.97 320	98.00 177	92.90 327	98.70 243

ZNCC-MVS		99.47 22	99.33 28	99.87 11	99.87 15	99.81 24	99.64 57	99.67 22	98.08 115	99.55 93	99.64 165	98.91 36	99.96 19	98.72 100	99.90 23	99.82 36

tfpnnormal		97.84 223	97.47 239	98.98 173	99.20 231	99.22 115	99.64 57	99.61 35	96.32 274	98.27 294	99.70 132	93.35 250	99.44 239	95.69 294	95.40 292	98.27 320

SR-MVS-dyc-post		99.45 26	99.31 37	99.85 25	99.76 52	99.82 20	99.63 59	99.52 88	98.38 76	99.76 37	99.82 49	98.53 72	99.95 43	98.61 117	99.81 80	99.77 63

RE-MVS-def			99.34 26		99.76 52	99.82 20	99.63 59	99.52 88	98.38 76	99.76 37	99.82 49	98.75 56		98.61 117	99.81 80	99.77 63

TSAR-MVS + MP.		99.58 4	99.50 8	99.81 38	99.91 1	99.66 54	99.63 59	99.39 210	98.91 37	99.78 31	99.85 29	99.36 2	99.94 54	98.84 82	99.88 36	99.82 36
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
Anonymous20231206		96.22 294	96.03 293	96.79 321	97.31 342	94.14 334	99.63 59	99.08 287	96.17 287	97.04 327	99.06 305	93.94 240	97.76 343	86.96 351	95.06 299	98.47 304

APD-MVS_3200maxsize		99.48 19	99.35 24	99.85 25	99.76 52	99.83 14	99.63 59	99.54 70	98.36 79	99.79 26	99.82 49	98.86 40	99.95 43	98.62 114	99.81 80	99.78 61

RRT_test8_iter05		97.72 245	97.60 226	98.08 274	99.23 223	96.08 300	99.63 59	99.49 127	97.54 173	98.94 221	99.81 62	87.99 336	99.35 259	99.21 37	96.51 264	98.81 220

test0726					99.85 25	99.89 3	99.62 65	99.50 119	99.10 8	99.86 11	99.82 49	98.94 31

EPNet		98.86 119	98.71 124	99.30 137	97.20 344	98.18 214	99.62 65	98.91 306	99.28 2	98.63 268	99.81 62	95.96 163	99.99 1	99.24 34	99.72 103	99.73 80
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
114514_t		98.93 113	98.67 128	99.72 61	99.85 25	99.53 80	99.62 65	99.59 43	92.65 336	99.71 46	99.78 95	98.06 103	99.90 106	98.84 82	99.91 16	99.74 73

HY-MVS	97.30 7	98.85 127	98.64 132	99.47 112	99.42 175	99.08 133	99.62 65	99.36 226	97.39 191	99.28 151	99.68 145	96.44 151	99.92 80	98.37 148	98.22 203	99.40 173

ACMMP		99.45 26	99.32 30	99.82 35	99.89 8	99.67 52	99.62 65	99.69 18	98.12 106	99.63 71	99.84 38	98.73 59	99.96 19	98.55 131	99.83 72	99.81 41
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 55	99.19 60	99.64 77	99.82 37	99.23 114	99.62 65	99.55 63	98.94 33	99.63 71	99.95 2	95.82 172	99.94 54	99.37 19	99.97 3	99.73 80
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 4	99.56 3	99.64 77	99.78 44	99.15 125	99.61 71	99.45 179	99.01 18	99.89 4	99.82 49	99.01 16	99.92 80	99.56 4	99.95 6	99.85 14

test1172		99.43 33	99.29 44	99.85 25	99.75 62	99.82 20	99.60 72	99.56 55	98.28 87	99.74 41	99.79 88	98.53 72	99.95 43	98.55 131	99.78 89	99.79 53

SED-MVS		99.61 2	99.52 6	99.88 6	99.84 32	99.90 1	99.60 72	99.48 139	99.08 11	99.91 1	99.81 62	99.20 5	99.96 19	98.91 68	99.85 58	99.79 53

OPU-MVS				99.64 77	99.56 143	99.72 42	99.60 72				99.70 132	99.27 4	99.42 244	98.24 157	99.80 84	99.79 53

GST-MVS		99.40 45	99.24 55	99.85 25	99.86 21	99.79 30	99.60 72	99.67 22	97.97 127	99.63 71	99.68 145	98.52 74	99.95 43	98.38 146	99.86 51	99.81 41

EI-MVSNet-UG-set		99.58 4	99.57 1	99.64 77	99.78 44	99.14 126	99.60 72	99.45 179	99.01 18	99.90 3	99.83 42	98.98 23	99.93 69	99.59 1	99.95 6	99.86 11

ACMH	97.28 8	98.10 185	97.99 183	98.44 248	99.41 178	96.96 273	99.60 72	99.56 55	98.09 111	98.15 298	99.91 5	90.87 306	99.70 200	98.88 71	97.45 240	98.67 259
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
SR-MVS		99.43 33	99.29 44	99.86 18	99.75 62	99.83 14	99.59 78	99.62 33	98.21 97	99.73 43	99.79 88	98.68 63	99.96 19	98.44 142	99.77 92	99.79 53

thres100view900		97.76 235	97.45 242	98.69 221	99.72 80	97.86 233	99.59 78	98.74 320	97.93 130	99.26 159	98.62 327	91.75 289	99.83 146	93.22 326	98.18 207	98.37 317

thres600view7		97.86 219	97.51 235	98.92 183	99.72 80	97.95 228	99.59 78	98.74 320	97.94 129	99.27 154	98.62 327	91.75 289	99.86 126	93.73 322	98.19 206	98.96 212

LCM-MVSNet-Re		97.83 225	98.15 166	96.87 319	99.30 206	92.25 347	99.59 78	98.26 335	97.43 186	96.20 334	99.13 298	96.27 156	98.73 328	98.17 163	98.99 167	99.64 119

baseline1		98.31 164	97.95 189	99.38 125	99.50 158	98.74 174	99.59 78	98.93 301	98.41 74	99.14 183	99.60 184	94.59 218	99.79 165	98.48 136	93.29 323	99.61 127

SteuartSystems-ACMMP		99.54 9	99.42 13	99.87 11	99.82 37	99.81 24	99.59 78	99.51 101	98.62 58	99.79 26	99.83 42	99.28 3	99.97 11	98.48 136	99.90 23	99.84 18
Skip Steuart: Steuart Systems R&D Blog.
CPTT-MVS		99.11 90	98.90 100	99.74 56	99.80 41	99.46 90	99.59 78	99.49 127	97.03 224	99.63 71	99.69 139	97.27 124	99.96 19	97.82 191	99.84 65	99.81 41

Regformer-3		99.57 7	99.53 5	99.68 65	99.76 52	99.29 107	99.58 85	99.44 188	99.01 18	99.87 10	99.80 76	98.97 24	99.91 91	99.44 18	99.92 11	99.83 29

Regformer-4		99.59 3	99.54 4	99.73 58	99.76 52	99.41 95	99.58 85	99.49 127	99.02 15	99.88 5	99.80 76	99.00 22	99.94 54	99.45 16	99.92 11	99.84 18

PGM-MVS		99.45 26	99.31 37	99.86 18	99.87 15	99.78 37	99.58 85	99.65 32	97.84 138	99.71 46	99.80 76	99.12 11	99.97 11	98.33 152	99.87 40	99.83 29

LPG-MVS_test		98.22 170	98.13 168	98.49 237	99.33 197	97.05 263	99.58 85	99.55 63	97.46 179	99.24 162	99.83 42	92.58 271	99.72 188	98.09 168	97.51 233	98.68 252

PHI-MVS		99.30 55	99.17 62	99.70 64	99.56 143	99.52 83	99.58 85	99.80 8	97.12 214	99.62 75	99.73 123	98.58 70	99.90 106	98.61 117	99.91 16	99.68 102

SF-MVS		99.38 47	99.24 55	99.79 43	99.79 42	99.68 49	99.57 90	99.54 70	97.82 144	99.71 46	99.80 76	98.95 28	99.93 69	98.19 159	99.84 65	99.74 73

DVP-MVS		99.57 7	99.47 9	99.88 6	99.85 25	99.89 3	99.57 90	99.37 225	99.10 8	99.81 22	99.80 76	98.94 31	99.96 19	98.93 65	99.86 51	99.81 41
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 32	99.89 3	99.57 90	99.51 101					99.96 19	98.93 65	99.86 51	99.88 5

Effi-MVS+-dtu		98.78 134	98.89 102	98.47 243	99.33 197	96.91 275	99.57 90	99.30 257	98.47 67	99.41 121	98.99 311	96.78 138	99.74 177	98.73 98	99.38 136	98.74 235

v2v482		98.06 189	97.77 208	98.92 183	98.90 281	98.82 169	99.57 90	99.36 226	96.65 249	99.19 176	99.35 262	94.20 231	99.25 274	97.72 202	94.97 301	98.69 247

DWT-MVSNet_test		97.53 264	97.40 253	97.93 285	99.03 267	94.86 327	99.57 90	98.63 329	96.59 257	98.36 288	98.79 321	89.32 321	99.74 177	98.14 166	98.16 211	99.20 186

DSMNet-mixed		97.25 277	97.35 259	96.95 317	97.84 334	93.61 341	99.57 90	96.63 354	96.13 293	98.87 232	98.61 329	94.59 218	97.70 344	95.08 307	98.86 176	99.55 140

DIV-MVS_2432*1600		95.00 308	94.34 313	96.96 316	97.07 347	95.39 316	99.56 97	99.44 188	95.11 309	97.13 325	97.32 345	91.86 287	97.27 347	90.35 341	81.23 350	98.23 323

ETV-MVS		99.26 62	99.21 58	99.40 122	99.46 168	99.30 106	99.56 97	99.52 88	98.52 64	99.44 113	99.27 282	98.41 85	99.86 126	99.10 48	99.59 126	99.04 202

SMA-MVS		99.44 30	99.30 40	99.85 25	99.73 75	99.83 14	99.56 97	99.47 157	97.45 182	99.78 31	99.82 49	99.18 8	99.91 91	98.79 91	99.89 33	99.81 41
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		98.87 116	98.72 122	99.31 133	99.86 21	98.48 201	99.56 97	99.61 35	97.85 136	99.36 136	99.85 29	95.95 164	99.85 132	96.66 276	99.83 72	99.59 133

casdiffmvs		99.13 79	98.98 90	99.56 90	99.65 115	99.16 121	99.56 97	99.50 119	98.33 84	99.41 121	99.86 23	95.92 167	99.83 146	99.45 16	99.16 150	99.70 95

XXY-MVS		98.38 160	98.09 173	99.24 148	99.26 217	99.32 102	99.56 97	99.55 63	97.45 182	98.71 251	99.83 42	93.23 251	99.63 219	98.88 71	96.32 269	98.76 229

ACMH+	97.24 10	97.92 212	97.78 206	98.32 259	99.46 168	96.68 283	99.56 97	99.54 70	98.41 74	97.79 312	99.87 20	90.18 313	99.66 208	98.05 176	97.18 252	98.62 281

ACMM	97.58 5	98.37 161	98.34 156	98.48 239	99.41 178	97.10 257	99.56 97	99.45 179	98.53 63	99.04 204	99.85 29	93.00 255	99.71 194	98.74 96	97.45 240	98.64 271
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
LS3D		99.27 60	99.12 67	99.74 56	99.18 236	99.75 38	99.56 97	99.57 50	98.45 70	99.49 104	99.85 29	97.77 110	99.94 54	98.33 152	99.84 65	99.52 147

v144192		97.92 212	97.60 226	98.87 199	98.83 293	98.65 181	99.55 106	99.34 235	96.20 284	99.32 144	99.40 248	94.36 226	99.26 273	96.37 283	95.03 300	98.70 243

#test#		99.43 33	99.29 44	99.86 18	99.87 15	99.80 26	99.55 106	99.67 22	97.83 139	99.68 53	99.69 139	99.06 13	99.96 19	98.39 144	99.87 40	99.84 18

API-MVS		99.04 101	99.03 79	99.06 162	99.40 183	99.31 105	99.55 106	99.56 55	98.54 62	99.33 143	99.39 252	98.76 53	99.78 169	96.98 257	99.78 89	98.07 327

thisisatest0530		98.35 162	98.03 179	99.31 133	99.63 120	98.56 188	99.54 109	96.75 353	97.53 175	99.73 43	99.65 158	91.25 302	99.89 114	98.62 114	99.56 127	99.48 158

MTMP							99.54 109	98.88 310

v1144		97.98 204	97.69 217	98.85 205	98.87 287	98.66 180	99.54 109	99.35 231	96.27 278	99.23 166	99.35 262	94.67 215	99.23 276	96.73 271	95.16 297	98.68 252

v148		97.79 233	97.55 229	98.50 236	98.74 303	97.72 239	99.54 109	99.33 242	96.26 279	98.90 227	99.51 216	94.68 214	99.14 290	97.83 190	93.15 326	98.63 279

CostFormer		97.72 245	97.73 214	97.71 298	99.15 247	94.02 335	99.54 109	99.02 293	94.67 318	99.04 204	99.35 262	92.35 281	99.77 171	98.50 135	97.94 216	99.34 178

MVSTER		98.49 150	98.32 158	99.00 171	99.35 192	99.02 138	99.54 109	99.38 216	97.41 189	99.20 173	99.73 123	93.86 243	99.36 255	98.87 75	97.56 229	98.62 281

Fast-Effi-MVS+-dtu		98.77 136	98.83 114	98.60 225	99.41 178	96.99 269	99.52 115	99.49 127	98.11 108	99.24 162	99.34 265	96.96 134	99.79 165	97.95 181	99.45 132	99.02 205

Fast-Effi-MVS+		98.70 139	98.43 150	99.51 105	99.51 151	99.28 108	99.52 115	99.47 157	96.11 294	99.01 207	99.34 265	96.20 158	99.84 137	97.88 185	98.82 178	99.39 174

v1921920		97.80 232	97.45 242	98.84 206	98.80 294	98.53 191	99.52 115	99.34 235	96.15 291	99.24 162	99.47 231	93.98 239	99.29 268	95.40 301	95.13 298	98.69 247

MIMVSNet1		95.51 303	95.04 307	96.92 318	97.38 339	95.60 307	99.52 115	99.50 119	93.65 328	96.97 329	99.17 293	85.28 346	96.56 352	88.36 347	95.55 289	98.60 293

UniMVSNet_ETH3D		97.32 275	96.81 281	98.87 199	99.40 183	97.46 245	99.51 119	99.53 82	95.86 301	98.54 276	99.77 101	82.44 352	99.66 208	98.68 107	97.52 232	99.50 156

alignmvs		98.81 130	98.56 145	99.58 87	99.43 174	99.42 94	99.51 119	98.96 299	98.61 59	99.35 139	98.92 317	94.78 206	99.77 171	99.35 20	98.11 213	99.54 142

v1192		97.81 230	97.44 247	98.91 187	98.88 283	98.68 178	99.51 119	99.34 235	96.18 286	99.20 173	99.34 265	94.03 238	99.36 255	95.32 304	95.18 296	98.69 247

test20.03		96.12 298	95.96 295	96.63 322	97.44 338	95.45 314	99.51 119	99.38 216	96.55 258	96.16 335	99.25 284	93.76 246	96.17 353	87.35 350	94.22 312	98.27 320

mvs_anonymous		99.03 103	98.99 87	99.16 155	99.38 187	98.52 195	99.51 119	99.38 216	97.79 145	99.38 131	99.81 62	97.30 122	99.45 234	99.35 20	98.99 167	99.51 153

TAMVS		99.12 85	99.08 72	99.24 148	99.46 168	98.55 189	99.51 119	99.46 167	98.09 111	99.45 109	99.82 49	98.34 89	99.51 229	98.70 102	98.93 170	99.67 105

test_yl		98.86 119	98.63 133	99.54 92	99.49 160	99.18 118	99.50 125	99.07 289	98.22 95	99.61 77	99.51 216	95.37 186	99.84 137	98.60 120	98.33 197	99.59 133

DCV-MVSNet		98.86 119	98.63 133	99.54 92	99.49 160	99.18 118	99.50 125	99.07 289	98.22 95	99.61 77	99.51 216	95.37 186	99.84 137	98.60 120	98.33 197	99.59 133

tfpn200view9		97.72 245	97.38 255	98.72 219	99.69 96	97.96 226	99.50 125	98.73 325	97.83 139	99.17 180	98.45 332	91.67 293	99.83 146	93.22 326	98.18 207	98.37 317

UA-Net		99.42 38	99.29 44	99.80 40	99.62 126	99.55 75	99.50 125	99.70 15	98.79 48	99.77 33	99.96 1	97.45 116	99.96 19	98.92 67	99.90 23	99.89 2

pm-mvs1		97.68 253	97.28 268	98.88 195	99.06 261	98.62 184	99.50 125	99.45 179	96.32 274	97.87 308	99.79 88	92.47 275	99.35 259	97.54 220	93.54 321	98.67 259

EI-MVSNet		98.67 143	98.67 128	98.68 222	99.35 192	97.97 224	99.50 125	99.38 216	96.93 233	99.20 173	99.83 42	97.87 106	99.36 255	98.38 146	97.56 229	98.71 239

CVMVSNet		98.57 149	98.67 128	98.30 261	99.35 192	95.59 308	99.50 125	99.55 63	98.60 60	99.39 128	99.83 42	94.48 223	99.45 234	98.75 95	98.56 190	99.85 14

VPA-MVSNet		98.29 167	97.95 189	99.30 137	99.16 244	99.54 77	99.50 125	99.58 49	98.27 89	99.35 139	99.37 256	92.53 273	99.65 212	99.35 20	94.46 307	98.72 237

thres400		97.77 234	97.38 255	98.92 183	99.69 96	97.96 226	99.50 125	98.73 325	97.83 139	99.17 180	98.45 332	91.67 293	99.83 146	93.22 326	98.18 207	98.96 212

APD-MVS		99.27 60	99.08 72	99.84 32	99.75 62	99.79 30	99.50 125	99.50 119	97.16 210	99.77 33	99.82 49	98.78 48	99.94 54	97.56 218	99.86 51	99.80 49
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
RRT_MVS		98.60 148	98.44 149	99.05 164	98.88 283	99.14 126	99.49 135	99.38 216	97.76 148	99.29 149	99.86 23	95.38 185	99.36 255	98.81 90	97.16 253	98.64 271

Regformer-1		99.53 11	99.47 9	99.72 61	99.71 86	99.44 92	99.49 135	99.46 167	98.95 32	99.83 17	99.76 105	99.01 16	99.93 69	99.17 41	99.87 40	99.80 49

Regformer-2		99.54 9	99.47 9	99.75 51	99.71 86	99.52 83	99.49 135	99.49 127	98.94 33	99.83 17	99.76 105	99.01 16	99.94 54	99.15 44	99.87 40	99.80 49

TransMVSNet (Re)		97.15 279	96.58 283	98.86 202	99.12 249	98.85 164	99.49 135	98.91 306	95.48 304	97.16 324	99.80 76	93.38 249	99.11 298	94.16 319	91.73 334	98.62 281

UniMVSNet (Re)		98.29 167	98.00 182	99.13 158	99.00 270	99.36 99	99.49 135	99.51 101	97.95 128	98.97 217	99.13 298	96.30 155	99.38 248	98.36 150	93.34 322	98.66 267

EPMVS		97.82 228	97.65 221	98.35 256	98.88 283	95.98 301	99.49 135	94.71 359	97.57 168	99.26 159	99.48 228	92.46 278	99.71 194	97.87 186	99.08 160	99.35 176

Anonymous20231211		97.88 215	97.54 232	98.90 189	99.71 86	98.53 191	99.48 141	99.57 50	94.16 323	98.81 240	99.68 145	93.23 251	99.42 244	98.84 82	94.42 309	98.76 229

v1240		97.69 251	97.32 265	98.79 213	98.85 291	98.43 204	99.48 141	99.36 226	96.11 294	99.27 154	99.36 259	93.76 246	99.24 275	94.46 314	95.23 295	98.70 243

VPNet		97.84 223	97.44 247	99.01 169	99.21 229	98.94 154	99.48 141	99.57 50	98.38 76	99.28 151	99.73 123	88.89 325	99.39 246	99.19 38	93.27 324	98.71 239

UniMVSNet_NR-MVSNet		98.22 170	97.97 185	98.96 176	98.92 280	98.98 142	99.48 141	99.53 82	97.76 148	98.71 251	99.46 235	96.43 152	99.22 279	98.57 125	92.87 329	98.69 247

TDRefinement		95.42 305	94.57 311	97.97 283	89.83 358	96.11 299	99.48 141	98.75 317	96.74 242	96.68 330	99.88 15	88.65 328	99.71 194	98.37 148	82.74 348	98.09 326

ACMMP_NAP		99.47 22	99.34 26	99.88 6	99.87 15	99.86 10	99.47 146	99.48 139	98.05 121	99.76 37	99.86 23	98.82 44	99.93 69	98.82 89	99.91 16	99.84 18

NR-MVSNet		97.97 207	97.61 225	99.02 168	98.87 287	99.26 111	99.47 146	99.42 198	97.63 163	97.08 326	99.50 219	95.07 196	99.13 293	97.86 187	93.59 320	98.68 252

PVSNet_Blended_VisFu		99.36 49	99.28 48	99.61 82	99.86 21	99.07 134	99.47 146	99.93 2	97.66 161	99.71 46	99.86 23	97.73 111	99.96 19	99.47 14	99.82 78	99.79 53

SD-MVS		99.41 42	99.52 6	99.05 164	99.74 70	99.68 49	99.46 149	99.52 88	99.11 7	99.88 5	99.91 5	99.43 1	97.70 344	98.72 100	99.93 10	99.77 63
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		97.44 272	97.34 262	97.74 297	99.15 247	94.36 332	99.45 150	98.94 300	93.45 332	98.90 227	99.44 237	91.35 300	99.59 223	97.31 235	98.07 214	99.29 181

FMVSNet2		97.72 245	97.36 257	98.80 212	99.51 151	98.84 165	99.45 150	99.42 198	96.49 261	98.86 237	99.29 277	90.26 309	98.98 313	96.44 280	96.56 261	98.58 295

CDS-MVSNet		99.09 94	99.03 79	99.25 146	99.42 175	98.73 175	99.45 150	99.46 167	98.11 108	99.46 108	99.77 101	98.01 104	99.37 251	98.70 102	98.92 172	99.66 108
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
MAR-MVS		98.86 119	98.63 133	99.54 92	99.37 189	99.66 54	99.45 150	99.54 70	96.61 253	99.01 207	99.40 248	97.09 128	99.86 126	97.68 208	99.53 130	99.10 191
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 85	98.87 104	99.86 18	99.72 80	99.79 30	99.44 154	99.51 101	97.29 198	99.59 84	99.74 116	98.15 100	99.96 19	96.74 270	99.69 109	99.81 41

mvs-test1		98.86 119	98.84 110	98.89 192	99.33 197	97.77 236	99.44 154	99.30 257	98.47 67	99.10 191	99.43 239	96.78 138	99.95 43	98.73 98	99.02 165	98.96 212

UGNet		98.87 116	98.69 126	99.40 122	99.22 227	98.72 176	99.44 154	99.68 19	99.24 3	99.18 179	99.42 242	92.74 263	99.96 19	99.34 24	99.94 9	99.53 146
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		98.86 119	98.63 133	99.54 92	99.64 117	99.19 116	99.44 154	99.54 70	97.77 147	99.30 146	99.81 62	94.20 231	99.93 69	99.17 41	98.82 178	99.49 157

test_0402		96.64 287	96.24 289	97.85 290	98.85 291	96.43 291	99.44 154	99.26 265	93.52 329	96.98 328	99.52 212	88.52 330	99.20 286	92.58 335	97.50 235	97.93 338

ACMP	97.20 11	98.06 189	97.94 191	98.45 245	99.37 189	97.01 267	99.44 154	99.49 127	97.54 173	98.45 281	99.79 88	91.95 285	99.72 188	97.91 183	97.49 238	98.62 281
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
GG-mvs-BLEND				98.45 245	98.55 322	98.16 215	99.43 160	93.68 361		97.23 321	98.46 331	89.30 322	99.22 279	95.43 300	98.22 203	97.98 335

HPM-MVS++		99.39 46	99.23 57	99.87 11	99.75 62	99.84 13	99.43 160	99.51 101	98.68 56	99.27 154	99.53 209	98.64 68	99.96 19	98.44 142	99.80 84	99.79 53

tpm cat1		97.39 273	97.36 257	97.50 305	99.17 242	93.73 337	99.43 160	99.31 253	91.27 340	98.71 251	99.08 302	94.31 229	99.77 171	96.41 282	98.50 193	99.00 206

tpm		97.67 256	97.55 229	98.03 277	99.02 268	95.01 323	99.43 160	98.54 333	96.44 268	99.12 186	99.34 265	91.83 288	99.60 222	97.75 198	96.46 265	99.48 158

GBi-Net		97.68 253	97.48 237	98.29 262	99.51 151	97.26 252	99.43 160	99.48 139	96.49 261	99.07 198	99.32 272	90.26 309	98.98 313	97.10 250	96.65 258	98.62 281

test1		97.68 253	97.48 237	98.29 262	99.51 151	97.26 252	99.43 160	99.48 139	96.49 261	99.07 198	99.32 272	90.26 309	98.98 313	97.10 250	96.65 258	98.62 281

FMVSNet1		96.84 284	96.36 287	98.29 262	99.32 204	97.26 252	99.43 160	99.48 139	95.11 309	98.55 275	99.32 272	83.95 348	98.98 313	95.81 291	96.26 270	98.62 281

testgi		97.65 258	97.50 236	98.13 273	99.36 191	96.45 290	99.42 167	99.48 139	97.76 148	97.87 308	99.45 236	91.09 303	98.81 326	94.53 313	98.52 192	99.13 189

F-COLMAP		99.19 69	99.04 77	99.64 77	99.78 44	99.27 110	99.42 167	99.54 70	97.29 198	99.41 121	99.59 187	98.42 84	99.93 69	98.19 159	99.69 109	99.73 80

Anonymous202405211		98.30 166	97.98 184	99.26 145	99.57 139	98.16 215	99.41 169	98.55 332	96.03 299	99.19 176	99.74 116	91.87 286	99.92 80	99.16 43	98.29 202	99.70 95

MSLP-MVS++		99.46 24	99.47 9	99.44 120	99.60 133	99.16 121	99.41 169	99.71 13	98.98 27	99.45 109	99.78 95	99.19 7	99.54 228	99.28 30	99.84 65	99.63 123

VNet		99.11 90	98.90 100	99.73 58	99.52 149	99.56 73	99.41 169	99.39 210	99.01 18	99.74 41	99.78 95	95.56 180	99.92 80	99.52 6	98.18 207	99.72 86

baseline2		97.87 217	97.55 229	98.82 208	99.18 236	98.02 221	99.41 169	96.58 355	96.97 227	96.51 331	99.17 293	93.43 248	99.57 224	97.71 203	99.03 164	98.86 217

DU-MVS		98.08 188	97.79 203	98.96 176	98.87 287	98.98 142	99.41 169	99.45 179	97.87 133	98.71 251	99.50 219	94.82 203	99.22 279	98.57 125	92.87 329	98.68 252

Baseline_NR-MVSNet		97.76 235	97.45 242	98.68 222	99.09 256	98.29 209	99.41 169	98.85 312	95.65 303	98.63 268	99.67 151	94.82 203	99.10 300	98.07 175	92.89 328	98.64 271

XVG-ACMP-BASELINE		97.83 225	97.71 216	98.20 268	99.11 251	96.33 294	99.41 169	99.52 88	98.06 120	99.05 203	99.50 219	89.64 319	99.73 184	97.73 200	97.38 246	98.53 298

DP-MVS		99.16 75	98.95 95	99.78 45	99.77 49	99.53 80	99.41 169	99.50 119	97.03 224	99.04 204	99.88 15	97.39 117	99.92 80	98.66 110	99.90 23	99.87 10

9.14			99.10 69		99.72 80		99.40 177	99.51 101	97.53 175	99.64 70	99.78 95	98.84 42	99.91 91	97.63 209	99.82 78

D2MVS		98.41 157	98.50 147	98.15 272	99.26 217	96.62 285	99.40 177	99.61 35	97.71 154	98.98 215	99.36 259	96.04 161	99.67 205	98.70 102	97.41 244	98.15 325

Anonymous20240529		98.09 186	97.68 218	99.34 127	99.66 110	98.44 203	99.40 177	99.43 196	93.67 327	99.22 167	99.89 10	90.23 312	99.93 69	99.26 33	98.33 197	99.66 108

FMVSNet3		98.03 195	97.76 211	98.84 206	99.39 186	98.98 142	99.40 177	99.38 216	96.67 247	99.07 198	99.28 279	92.93 256	98.98 313	97.10 250	96.65 258	98.56 297

LFMVS		97.90 214	97.35 259	99.54 92	99.52 149	99.01 140	99.39 181	98.24 336	97.10 218	99.65 68	99.79 88	84.79 347	99.91 91	99.28 30	98.38 196	99.69 98

HQP_MVS		98.27 169	98.22 164	98.44 248	99.29 210	96.97 271	99.39 181	99.47 157	98.97 30	99.11 188	99.61 181	92.71 266	99.69 203	97.78 194	97.63 222	98.67 259

plane_prior2							99.39 181		98.97 30

CHOSEN 1792x2688		99.19 69	99.10 69	99.45 115	99.89 8	98.52 195	99.39 181	99.94 1	98.73 52	99.11 188	99.89 10	95.50 182	99.94 54	99.50 8	99.97 3	99.89 2

PAPM_NR		99.04 101	98.84 110	99.66 68	99.74 70	99.44 92	99.39 181	99.38 216	97.70 155	99.28 151	99.28 279	98.34 89	99.85 132	96.96 259	99.45 132	99.69 98

ETH3D-3000-0.1		99.21 66	99.02 82	99.77 47	99.73 75	99.69 47	99.38 186	99.51 101	97.45 182	99.61 77	99.75 110	98.51 75	99.91 91	97.45 230	99.83 72	99.71 93

gg-mvs-nofinetune		96.17 297	95.32 305	98.73 217	98.79 295	98.14 217	99.38 186	94.09 360	91.07 343	98.07 303	91.04 356	89.62 320	99.35 259	96.75 269	99.09 159	98.68 252

VDDNet		97.55 262	97.02 278	99.16 155	99.49 160	98.12 219	99.38 186	99.30 257	95.35 306	99.68 53	99.90 7	82.62 351	99.93 69	99.31 27	98.13 212	99.42 170

pmmvs6		96.53 289	96.09 292	97.82 293	98.69 310	95.47 313	99.37 189	99.47 157	93.46 331	97.41 317	99.78 95	87.06 341	99.33 263	96.92 264	92.70 331	98.65 269

PM-MVS		92.96 319	92.23 322	95.14 329	95.61 349	89.98 352	99.37 189	98.21 337	94.80 316	95.04 341	97.69 340	65.06 357	97.90 340	94.30 315	89.98 339	97.54 345

WTY-MVS		99.06 98	98.88 103	99.61 82	99.62 126	99.16 121	99.37 189	99.56 55	98.04 122	99.53 96	99.62 177	96.84 136	99.94 54	98.85 80	98.49 194	99.72 86

IterMVS-LS		98.46 152	98.42 151	98.58 228	99.59 135	98.00 222	99.37 189	99.43 196	96.94 232	99.07 198	99.59 187	97.87 106	99.03 306	98.32 154	95.62 287	98.71 239
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
hse-mvs3		97.70 250	97.28 268	98.97 175	99.70 93	97.27 250	99.36 193	99.45 179	98.94 33	99.66 64	99.64 165	94.93 198	99.99 1	99.48 13	84.36 346	99.65 112

DPE-MVS		99.46 24	99.32 30	99.91 2	99.78 44	99.88 7	99.36 193	99.51 101	98.73 52	99.88 5	99.84 38	98.72 60	99.96 19	98.16 164	99.87 40	99.88 5

zzz-MVS		99.49 15	99.36 21	99.89 4	99.90 3	99.86 10	99.36 193	99.47 157	98.79 48	99.68 53	99.81 62	98.43 81	99.97 11	98.88 71	99.90 23	99.83 29

UnsupCasMVSNet_eth		96.44 291	96.12 291	97.40 307	98.65 313	95.65 306	99.36 193	99.51 101	97.13 212	96.04 337	98.99 311	88.40 331	98.17 333	96.71 272	90.27 337	98.40 314

sss		99.17 73	99.05 74	99.53 98	99.62 126	98.97 145	99.36 193	99.62 33	97.83 139	99.67 59	99.65 158	97.37 121	99.95 43	99.19 38	99.19 149	99.68 102

DeepC-MVS_fast	98.69 1	99.49 15	99.39 17	99.77 47	99.63 120	99.59 68	99.36 193	99.46 167	99.07 13	99.79 26	99.82 49	98.85 41	99.92 80	98.68 107	99.87 40	99.82 36
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 64	99.14 64	99.59 84	99.41 178	99.16 121	99.35 199	99.57 50	98.82 43	99.51 100	99.61 181	96.46 149	99.95 43	99.59 1	99.98 2	99.65 112

pmmvs-eth3d		95.34 307	94.73 309	97.15 310	95.53 351	95.94 302	99.35 199	99.10 284	95.13 307	93.55 344	97.54 341	88.15 335	97.91 339	94.58 312	89.69 340	97.61 342

MDTV_nov1_ep13_2view						95.18 321	99.35 199		96.84 237	99.58 86		95.19 194		97.82 191		99.46 165

VDD-MVS		97.73 243	97.35 259	98.88 195	99.47 167	97.12 256	99.34 202	98.85 312	98.19 98	99.67 59	99.85 29	82.98 349	99.92 80	99.49 12	98.32 201	99.60 129

COLMAP_ROB	97.56 6	98.86 119	98.75 121	99.17 154	99.88 11	98.53 191	99.34 202	99.59 43	97.55 170	98.70 257	99.89 10	95.83 171	99.90 106	98.10 167	99.90 23	99.08 196
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
FMVSNet5		96.43 292	96.19 290	97.15 310	99.11 251	95.89 303	99.32 204	99.52 88	94.47 322	98.34 290	99.07 303	87.54 340	97.07 348	92.61 334	95.72 285	98.47 304

dp		97.75 239	97.80 202	97.59 301	99.10 254	93.71 338	99.32 204	98.88 310	96.48 265	99.08 197	99.55 200	92.67 269	99.82 153	96.52 278	98.58 187	99.24 183

tpmvs		97.98 204	98.02 181	97.84 291	99.04 265	94.73 329	99.31 206	99.20 273	96.10 298	98.76 247	99.42 242	94.94 197	99.81 157	96.97 258	98.45 195	98.97 210

tpmrst		98.33 163	98.48 148	97.90 288	99.16 244	94.78 328	99.31 206	99.11 283	97.27 200	99.45 109	99.59 187	95.33 188	99.84 137	98.48 136	98.61 184	99.09 195

MP-MVS-pluss		99.37 48	99.20 59	99.88 6	99.90 3	99.87 9	99.30 208	99.52 88	97.18 208	99.60 81	99.79 88	98.79 47	99.95 43	98.83 85	99.91 16	99.83 29
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
NCCC		99.34 51	99.19 60	99.79 43	99.61 130	99.65 57	99.30 208	99.48 139	98.86 39	99.21 170	99.63 171	98.72 60	99.90 106	98.25 156	99.63 122	99.80 49

JIA-IIPM		97.50 268	97.02 278	98.93 181	98.73 304	97.80 235	99.30 208	98.97 297	91.73 339	98.91 225	94.86 351	95.10 195	99.71 194	97.58 213	97.98 215	99.28 182

BH-RMVSNet		98.41 157	98.08 174	99.40 122	99.41 178	98.83 168	99.30 208	98.77 316	97.70 155	98.94 221	99.65 158	92.91 259	99.74 177	96.52 278	99.55 129	99.64 119

MCST-MVS		99.43 33	99.30 40	99.82 35	99.79 42	99.74 41	99.29 212	99.40 206	98.79 48	99.52 98	99.62 177	98.91 36	99.90 106	98.64 112	99.75 96	99.82 36

LF4IMVS		97.52 265	97.46 241	97.70 299	98.98 274	95.55 309	99.29 212	98.82 315	98.07 116	98.66 260	99.64 165	89.97 314	99.61 221	97.01 254	96.68 257	97.94 337

OPM-MVS		98.19 174	98.10 170	98.45 245	98.88 283	97.07 261	99.28 214	99.38 216	98.57 61	99.22 167	99.81 62	92.12 282	99.66 208	98.08 172	97.54 231	98.61 290

diffmvs		99.14 77	99.02 82	99.51 105	99.61 130	98.96 149	99.28 214	99.49 127	98.46 69	99.72 45	99.71 128	96.50 148	99.88 119	99.31 27	99.11 155	99.67 105

PVSNet_BlendedMVS		98.86 119	98.80 115	99.03 167	99.76 52	98.79 172	99.28 214	99.91 3	97.42 188	99.67 59	99.37 256	97.53 114	99.88 119	98.98 58	97.29 248	98.42 311

OMC-MVS		99.08 96	99.04 77	99.20 151	99.67 101	98.22 213	99.28 214	99.52 88	98.07 116	99.66 64	99.81 62	97.79 109	99.78 169	97.79 193	99.81 80	99.60 129

AUN-MVS		96.88 283	96.31 288	98.59 226	99.48 166	97.04 265	99.27 218	99.22 270	97.44 185	98.51 277	99.41 245	91.97 284	99.66 208	97.71 203	83.83 347	99.07 200

pmmvs5		97.52 265	97.30 267	98.16 271	98.57 321	96.73 280	99.27 218	98.90 308	96.14 292	98.37 287	99.53 209	91.54 298	99.14 290	97.51 223	95.87 280	98.63 279

1314		98.68 142	98.54 146	99.11 159	98.89 282	98.65 181	99.27 218	99.49 127	96.89 234	97.99 305	99.56 197	97.72 112	99.83 146	97.74 199	99.27 144	98.84 219

1121		99.09 94	98.87 104	99.75 51	99.74 70	99.60 65	99.27 218	99.48 139	96.82 240	99.25 161	99.65 158	98.38 86	99.93 69	97.53 221	99.67 116	99.73 80

MVS		97.28 276	96.55 284	99.48 109	98.78 298	98.95 151	99.27 218	99.39 210	83.53 351	98.08 300	99.54 205	96.97 133	99.87 123	94.23 317	99.16 150	99.63 123

BH-untuned		98.42 155	98.36 153	98.59 226	99.49 160	96.70 281	99.27 218	99.13 282	97.24 204	98.80 242	99.38 253	95.75 174	99.74 177	97.07 253	99.16 150	99.33 179

MDTV_nov1_ep13			98.32 158		99.11 251	94.44 331	99.27 218	98.74 320	97.51 177	99.40 126	99.62 177	94.78 206	99.76 174	97.59 212	98.81 180

DP-MVS Recon		99.12 85	98.95 95	99.65 72	99.74 70	99.70 46	99.27 218	99.57 50	96.40 272	99.42 117	99.68 145	98.75 56	99.80 162	97.98 178	99.72 103	99.44 168

PatchmatchNet		98.31 164	98.36 153	98.19 269	99.16 244	95.32 317	99.27 218	98.92 303	97.37 192	99.37 133	99.58 190	94.90 200	99.70 200	97.43 232	99.21 147	99.54 142
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
thres200		97.61 260	97.28 268	98.62 224	99.64 117	98.03 220	99.26 227	98.74 320	97.68 157	99.09 196	98.32 336	91.66 295	99.81 157	92.88 330	98.22 203	98.03 330

CNVR-MVS		99.42 38	99.30 40	99.78 45	99.62 126	99.71 44	99.26 227	99.52 88	98.82 43	99.39 128	99.71 128	98.96 25	99.85 132	98.59 122	99.80 84	99.77 63

1112_ss		98.98 109	98.77 118	99.59 84	99.68 100	99.02 138	99.25 229	99.48 139	97.23 205	99.13 184	99.58 190	96.93 135	99.90 106	98.87 75	98.78 181	99.84 18

TAPA-MVS	97.07 15	97.74 242	97.34 262	98.94 179	99.70 93	97.53 243	99.25 229	99.51 101	91.90 338	99.30 146	99.63 171	98.78 48	99.64 214	88.09 348	99.87 40	99.65 112
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
PLC	97.94 4	99.02 104	98.85 109	99.53 98	99.66 110	99.01 140	99.24 231	99.52 88	96.85 236	99.27 154	99.48 228	98.25 94	99.91 91	97.76 196	99.62 124	99.65 112
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 98	98.84 110	99.72 61	99.51 151	99.60 65	99.23 232	99.44 188	97.04 222	99.39 128	99.67 151	98.30 91	99.92 80	97.27 237	99.69 109	99.64 119

test_post1							99.23 232				65.14 362	94.18 234	99.71 194	97.58 213

ADS-MVSNet2		98.02 197	98.07 177	97.87 289	99.33 197	95.19 320	99.23 232	99.08 287	96.24 281	99.10 191	99.67 151	94.11 235	98.93 322	96.81 267	99.05 162	99.48 158

ADS-MVSNet		98.20 173	98.08 174	98.56 231	99.33 197	96.48 289	99.23 232	99.15 279	96.24 281	99.10 191	99.67 151	94.11 235	99.71 194	96.81 267	99.05 162	99.48 158

EPNet_dtu		98.03 195	97.96 187	98.23 267	98.27 328	95.54 311	99.23 232	98.75 317	99.02 15	97.82 310	99.71 128	96.11 159	99.48 230	93.04 329	99.65 119	99.69 98
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CR-MVSNet		98.17 177	97.93 192	98.87 199	99.18 236	98.49 199	99.22 237	99.33 242	96.96 228	99.56 89	99.38 253	94.33 227	99.00 311	94.83 311	98.58 187	99.14 187

RPMNet		96.72 286	95.90 296	99.19 152	99.18 236	98.49 199	99.22 237	99.52 88	88.72 347	99.56 89	97.38 343	94.08 237	99.95 43	86.87 352	98.58 187	99.14 187

plane_prior						96.97 271	99.21 239		98.45 70						97.60 225

WR-MVS		98.06 189	97.73 214	99.06 162	98.86 290	99.25 112	99.19 240	99.35 231	97.30 197	98.66 260	99.43 239	93.94 240	99.21 284	98.58 123	94.28 311	98.71 239

new-patchmatchnet		94.48 314	94.08 314	95.67 328	95.08 352	92.41 346	99.18 241	99.28 264	94.55 321	93.49 345	97.37 344	87.86 339	97.01 349	91.57 336	88.36 341	97.61 342

AdaColmap		99.01 107	98.80 115	99.66 68	99.56 143	99.54 77	99.18 241	99.70 15	98.18 101	99.35 139	99.63 171	96.32 154	99.90 106	97.48 225	99.77 92	99.55 140

ETH3 D test6400		98.70 139	98.35 155	99.73 58	99.69 96	99.60 65	99.16 243	99.45 179	95.42 305	99.27 154	99.60 184	97.39 117	99.91 91	95.36 303	99.83 72	99.70 95

EG-PatchMatch MVS		95.97 300	95.69 300	96.81 320	97.78 335	92.79 345	99.16 243	98.93 301	96.16 289	94.08 343	99.22 288	82.72 350	99.47 231	95.67 296	97.50 235	98.17 324

PatchT		97.03 282	96.44 286	98.79 213	98.99 271	98.34 208	99.16 243	99.07 289	92.13 337	99.52 98	97.31 346	94.54 222	98.98 313	88.54 346	98.73 183	99.03 203

CNLPA		99.14 77	98.99 87	99.59 84	99.58 137	99.41 95	99.16 243	99.44 188	98.45 70	99.19 176	99.49 222	98.08 102	99.89 114	97.73 200	99.75 96	99.48 158

MDA-MVSNet-bldmvs		94.96 309	93.98 315	97.92 286	98.24 330	97.27 250	99.15 247	99.33 242	93.80 326	80.09 357	99.03 308	88.31 332	97.86 341	93.49 324	94.36 310	98.62 281

CDPH-MVS		99.13 79	98.91 99	99.80 40	99.75 62	99.71 44	99.15 247	99.41 200	96.60 255	99.60 81	99.55 200	98.83 43	99.90 106	97.48 225	99.83 72	99.78 61

xxxxxxxxxxxxxcwj		99.43 33	99.32 30	99.75 51	99.76 52	99.59 68	99.14 249	99.53 82	99.00 22	99.71 46	99.80 76	98.95 28	99.93 69	98.19 159	99.84 65	99.74 73

save fliter					99.76 52	99.59 68	99.14 249	99.40 206	99.00 22

xiu_mvs_v1_base_debu		99.29 57	99.27 50	99.34 127	99.63 120	98.97 145	99.12 251	99.51 101	98.86 39	99.84 13	99.47 231	98.18 97	99.99 1	99.50 8	99.31 141	99.08 196

xiu_mvs_v1_base		99.29 57	99.27 50	99.34 127	99.63 120	98.97 145	99.12 251	99.51 101	98.86 39	99.84 13	99.47 231	98.18 97	99.99 1	99.50 8	99.31 141	99.08 196

xiu_mvs_v1_base_debi		99.29 57	99.27 50	99.34 127	99.63 120	98.97 145	99.12 251	99.51 101	98.86 39	99.84 13	99.47 231	98.18 97	99.99 1	99.50 8	99.31 141	99.08 196

XVG-OURS-SEG-HR		98.69 141	98.62 138	98.89 192	99.71 86	97.74 237	99.12 251	99.54 70	98.44 73	99.42 117	99.71 128	94.20 231	99.92 80	98.54 133	98.90 174	99.00 206

jason		99.13 79	99.03 79	99.45 115	99.46 168	98.87 161	99.12 251	99.26 265	98.03 124	99.79 26	99.65 158	97.02 131	99.85 132	99.02 55	99.90 23	99.65 112
jason: jason.
N_pmnet		94.95 310	95.83 298	92.31 333	98.47 325	79.33 358	99.12 251	92.81 364	93.87 325	97.68 313	99.13 298	93.87 242	99.01 310	91.38 337	96.19 271	98.59 294

MDA-MVSNet_test_wron		95.45 304	94.60 310	98.01 280	98.16 331	97.21 255	99.11 257	99.24 268	93.49 330	80.73 356	98.98 314	93.02 254	98.18 332	94.22 318	94.45 308	98.64 271

Patchmtry		97.75 239	97.40 253	98.81 210	99.10 254	98.87 161	99.11 257	99.33 242	94.83 315	98.81 240	99.38 253	94.33 227	99.02 308	96.10 285	95.57 288	98.53 298

YYNet1		95.36 306	94.51 312	97.92 286	97.89 333	97.10 257	99.10 259	99.23 269	93.26 333	80.77 355	99.04 307	92.81 260	98.02 336	94.30 315	94.18 313	98.64 271

CANet_DTU		98.97 111	98.87 104	99.25 146	99.33 197	98.42 206	99.08 260	99.30 257	99.16 5	99.43 114	99.75 110	95.27 190	99.97 11	98.56 128	99.95 6	99.36 175

SCA		98.19 174	98.16 165	98.27 266	99.30 206	95.55 309	99.07 261	98.97 297	97.57 168	99.43 114	99.57 194	92.72 264	99.74 177	97.58 213	99.20 148	99.52 147

TSAR-MVS + GP.		99.36 49	99.36 21	99.36 126	99.67 101	98.61 186	99.07 261	99.33 242	99.00 22	99.82 20	99.81 62	99.06 13	99.84 137	99.09 49	99.42 134	99.65 112

MG-MVS		99.13 79	99.02 82	99.45 115	99.57 139	98.63 183	99.07 261	99.34 235	98.99 25	99.61 77	99.82 49	97.98 105	99.87 123	97.00 255	99.80 84	99.85 14

PatchMatch-RL		98.84 129	98.62 138	99.52 103	99.71 86	99.28 108	99.06 264	99.77 9	97.74 152	99.50 101	99.53 209	95.41 184	99.84 137	97.17 248	99.64 120	99.44 168

OpenMVS_ROB	92.34 20	94.38 315	93.70 319	96.41 325	97.38 339	93.17 343	99.06 264	98.75 317	86.58 348	94.84 342	98.26 337	81.53 353	99.32 264	89.01 344	97.87 218	96.76 346

TEST9					99.67 101	99.65 57	99.05 266	99.41 200	96.22 283	98.95 219	99.49 222	98.77 51	99.91 91

train_agg		99.02 104	98.77 118	99.77 47	99.67 101	99.65 57	99.05 266	99.41 200	96.28 276	98.95 219	99.49 222	98.76 53	99.91 91	97.63 209	99.72 103	99.75 69

lupinMVS		99.13 79	99.01 86	99.46 114	99.51 151	98.94 154	99.05 266	99.16 278	97.86 134	99.80 24	99.56 197	97.39 117	99.86 126	98.94 63	99.85 58	99.58 137

DELS-MVS		99.48 19	99.42 13	99.65 72	99.72 80	99.40 97	99.05 266	99.66 27	99.14 6	99.57 88	99.80 76	98.46 79	99.94 54	99.57 3	99.84 65	99.60 129
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		96.38 293	96.03 293	97.41 306	98.13 332	95.16 322	99.05 266	99.20 273	93.94 324	97.39 318	98.79 321	91.61 297	99.04 304	90.43 340	95.77 282	98.05 329

MVS_0304		96.79 285	96.52 285	97.59 301	99.22 227	94.92 326	99.04 271	99.59 43	96.49 261	98.43 283	98.99 311	80.48 354	99.39 246	97.15 249	99.27 144	98.47 304

Patchmatch-test		97.93 209	97.65 221	98.77 215	99.18 236	97.07 261	99.03 272	99.14 281	96.16 289	98.74 248	99.57 194	94.56 220	99.72 188	93.36 325	99.11 155	99.52 147

test_8					99.67 101	99.61 63	99.03 272	99.41 200	96.28 276	98.93 223	99.48 228	98.76 53	99.91 91

Test_1112_low_res		98.89 115	98.66 131	99.57 88	99.69 96	98.95 151	99.03 272	99.47 157	96.98 226	99.15 182	99.23 287	96.77 140	99.89 114	98.83 85	98.78 181	99.86 11

IterMVS-SCA-FT		97.82 228	97.75 212	98.06 276	99.57 139	96.36 293	99.02 275	99.49 127	97.18 208	98.71 251	99.72 127	92.72 264	99.14 290	97.44 231	95.86 281	98.67 259

xiu_mvs_v2_base		99.26 62	99.25 54	99.29 140	99.53 147	98.91 158	99.02 275	99.45 179	98.80 47	99.71 46	99.26 283	98.94 31	99.98 6	99.34 24	99.23 146	98.98 209

MIMVSNet		97.73 243	97.45 242	98.57 229	99.45 173	97.50 244	99.02 275	98.98 296	96.11 294	99.41 121	99.14 297	90.28 308	98.74 327	95.74 293	98.93 170	99.47 163

IterMVS		97.83 225	97.77 208	98.02 279	99.58 137	96.27 296	99.02 275	99.48 139	97.22 206	98.71 251	99.70 132	92.75 261	99.13 293	97.46 228	96.00 275	98.67 259
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
HyFIR lowres test		99.11 90	98.92 97	99.65 72	99.90 3	99.37 98	99.02 275	99.91 3	97.67 160	99.59 84	99.75 110	95.90 169	99.73 184	99.53 5	99.02 165	99.86 11

新几何2							99.01 280

BH-w/o		98.00 202	97.89 198	98.32 259	99.35 192	96.20 298	99.01 280	98.90 308	96.42 270	98.38 286	99.00 310	95.26 192	99.72 188	96.06 286	98.61 184	99.03 203

agg_prior1		99.01 107	98.76 120	99.76 50	99.67 101	99.62 61	98.99 282	99.40 206	96.26 279	98.87 232	99.49 222	98.77 51	99.91 91	97.69 206	99.72 103	99.75 69

test_prior4						99.56 73	98.99 282

无先验							98.99 282	99.51 101	96.89 234				99.93 69	97.53 221		99.72 86

pmmvs4		98.13 182	97.90 194	98.81 210	98.61 318	98.87 161	98.99 282	99.21 272	96.44 268	99.06 202	99.58 190	95.90 169	99.11 298	97.18 247	96.11 273	98.46 308

HQP-NCC					99.19 233		98.98 286		98.24 91	98.66 260

ACMP_Plane					99.19 233		98.98 286		98.24 91	98.66 260

HQP-MVS		98.02 197	97.90 194	98.37 255	99.19 233	96.83 276	98.98 286	99.39 210	98.24 91	98.66 260	99.40 248	92.47 275	99.64 214	97.19 245	97.58 227	98.64 271

PS-MVSNAJ		99.32 53	99.32 30	99.30 137	99.57 139	98.94 154	98.97 289	99.46 167	98.92 36	99.71 46	99.24 285	99.01 16	99.98 6	99.35 20	99.66 117	98.97 210

MVP-Stereo		97.81 230	97.75 212	97.99 282	97.53 337	96.60 286	98.96 290	98.85 312	97.22 206	97.23 321	99.36 259	95.28 189	99.46 233	95.51 298	99.78 89	97.92 339
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
test_prior3		99.21 66	99.05 74	99.68 65	99.67 101	99.48 87	98.96 290	99.56 55	98.34 81	99.01 207	99.52 212	98.68 63	99.83 146	97.96 179	99.74 99	99.74 73

test_prior2							98.96 290		98.34 81	99.01 207	99.52 212	98.68 63		97.96 179	99.74 99

旧先验2							98.96 290		96.70 245	99.47 106			99.94 54	98.19 159

原ACMM2							98.95 294

MVS_111021_HR		99.41 42	99.32 30	99.66 68	99.72 80	99.47 89	98.95 294	99.85 6	98.82 43	99.54 94	99.73 123	98.51 75	99.74 177	98.91 68	99.88 36	99.77 63

MVS_111021_LR		99.41 42	99.33 28	99.65 72	99.77 49	99.51 85	98.94 296	99.85 6	98.82 43	99.65 68	99.74 116	98.51 75	99.80 162	98.83 85	99.89 33	99.64 119

pmmvs3		94.09 317	93.25 320	96.60 323	94.76 353	94.49 330	98.92 297	98.18 339	89.66 344	96.48 332	98.06 339	86.28 342	97.33 346	89.68 343	87.20 343	97.97 336

XVG-OURS		98.73 138	98.68 127	98.88 195	99.70 93	97.73 238	98.92 297	99.55 63	98.52 64	99.45 109	99.84 38	95.27 190	99.91 91	98.08 172	98.84 177	99.00 206

test222					99.75 62	99.49 86	98.91 299	99.49 127	96.42 270	99.34 142	99.65 158	98.28 93			99.69 109	99.72 86

PMMVS2		86.87 321	85.37 325	91.35 336	90.21 357	83.80 353	98.89 300	97.45 349	83.13 352	91.67 349	95.03 349	48.49 362	94.70 355	85.86 353	77.62 352	95.54 349

miper_lstm_enhance		98.00 202	97.91 193	98.28 265	99.34 196	97.43 246	98.88 301	99.36 226	96.48 265	98.80 242	99.55 200	95.98 162	98.91 323	97.27 237	95.50 291	98.51 300

MVS-HIRNet		95.75 302	95.16 306	97.51 304	99.30 206	93.69 339	98.88 301	95.78 356	85.09 350	98.78 245	92.65 353	91.29 301	99.37 251	94.85 310	99.85 58	99.46 165

TR-MVS		97.76 235	97.41 252	98.82 208	99.06 261	97.87 231	98.87 303	98.56 331	96.63 252	98.68 259	99.22 288	92.49 274	99.65 212	95.40 301	97.79 219	98.95 215

testdata1							98.85 304		98.32 85

ET-MVSNet_ETH3D		96.49 290	95.64 301	99.05 164	99.53 147	98.82 169	98.84 305	97.51 348	97.63 163	84.77 351	99.21 291	92.09 283	98.91 323	98.98 58	92.21 333	99.41 172

our_test_3		97.65 258	97.68 218	97.55 303	98.62 316	94.97 324	98.84 305	99.30 257	96.83 239	98.19 296	99.34 265	97.01 132	99.02 308	95.00 309	96.01 274	98.64 271

MS-PatchMatch		97.24 278	97.32 265	96.99 314	98.45 326	93.51 342	98.82 307	99.32 250	97.41 189	98.13 299	99.30 275	88.99 324	99.56 225	95.68 295	99.80 84	97.90 340

cl_fuxian		98.12 184	98.04 178	98.38 254	99.30 206	97.69 242	98.81 308	99.33 242	96.67 247	98.83 238	99.34 265	97.11 127	98.99 312	97.58 213	95.34 293	98.48 302

ppachtmachnet_test		97.49 270	97.45 242	97.61 300	98.62 316	95.24 318	98.80 309	99.46 167	96.11 294	98.22 295	99.62 177	96.45 150	98.97 320	93.77 321	95.97 279	98.61 290

PAPR		98.63 147	98.34 156	99.51 105	99.40 183	99.03 137	98.80 309	99.36 226	96.33 273	99.00 212	99.12 301	98.46 79	99.84 137	95.23 305	99.37 140	99.66 108

test0.0.03 1		97.71 249	97.42 251	98.56 231	98.41 327	97.82 234	98.78 311	98.63 329	97.34 193	98.05 304	98.98 314	94.45 224	98.98 313	95.04 308	97.15 254	98.89 216

PVSNet_Blended		99.08 96	98.97 91	99.42 121	99.76 52	98.79 172	98.78 311	99.91 3	96.74 242	99.67 59	99.49 222	97.53 114	99.88 119	98.98 58	99.85 58	99.60 129

PMMVS		98.80 133	98.62 138	99.34 127	99.27 215	98.70 177	98.76 313	99.31 253	97.34 193	99.21 170	99.07 303	97.20 125	99.82 153	98.56 128	98.87 175	99.52 147

test123		39.01 332	42.50 334	28.53 345	39.17 366	20.91 367	98.75 314	19.17 368	19.83 363	38.57 362	66.67 360	33.16 365	15.42 363	37.50 362	29.66 361	49.26 358

MSDG		98.98 109	98.80 115	99.53 98	99.76 52	99.19 116	98.75 314	99.55 63	97.25 202	99.47 106	99.77 101	97.82 108	99.87 123	96.93 262	99.90 23	99.54 142

CLD-MVS		98.16 178	98.10 170	98.33 257	99.29 210	96.82 278	98.75 314	99.44 188	97.83 139	99.13 184	99.55 200	92.92 257	99.67 205	98.32 154	97.69 221	98.48 302
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		98.18 176	98.10 170	98.41 250	99.23 223	97.72 239	98.72 317	99.31 253	96.60 255	98.88 230	99.29 277	97.29 123	99.13 293	97.60 211	95.99 276	98.38 316

cl-mvsnet_		98.01 200	97.84 201	98.55 233	99.25 221	97.97 224	98.71 318	99.34 235	96.47 267	98.59 274	99.54 205	95.65 179	99.21 284	97.21 241	95.77 282	98.46 308

cl-mvsnet1		98.01 200	97.85 200	98.48 239	99.24 222	97.95 228	98.71 318	99.35 231	96.50 260	98.60 273	99.54 205	95.72 176	99.03 306	97.21 241	95.77 282	98.46 308

test-LLR		98.06 189	97.90 194	98.55 233	98.79 295	97.10 257	98.67 320	97.75 344	97.34 193	98.61 271	98.85 318	94.45 224	99.45 234	97.25 239	99.38 136	99.10 191

TESTMET0.1,1		97.55 262	97.27 271	98.40 252	98.93 279	96.53 287	98.67 320	97.61 347	96.96 228	98.64 267	99.28 279	88.63 329	99.45 234	97.30 236	99.38 136	99.21 185

test-mter		97.49 270	97.13 275	98.55 233	98.79 295	97.10 257	98.67 320	97.75 344	96.65 249	98.61 271	98.85 318	88.23 333	99.45 234	97.25 239	99.38 136	99.10 191

IB-MVS	95.67 18	96.22 294	95.44 304	98.57 229	99.21 229	96.70 281	98.65 323	97.74 346	96.71 244	97.27 320	98.54 330	86.03 343	99.92 80	98.47 139	86.30 344	99.10 191
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		98.95 112	98.71 124	99.66 68	99.63 120	99.55 75	98.64 324	99.10 284	97.93 130	99.42 117	99.55 200	98.67 66	99.80 162	95.80 292	99.68 114	99.61 127

thisisatest0515		98.14 181	97.79 203	99.19 152	99.50 158	98.50 198	98.61 325	96.82 352	96.95 230	99.54 94	99.43 239	91.66 295	99.86 126	98.08 172	99.51 131	99.22 184

DeepPCF-MVS	98.18 3	98.81 130	99.37 19	97.12 313	99.60 133	91.75 348	98.61 325	99.44 188	99.35 1	99.83 17	99.85 29	98.70 62	99.81 157	99.02 55	99.91 16	99.81 41

cl-mvsnet2		97.85 220	97.64 223	98.48 239	99.09 256	97.87 231	98.60 327	99.33 242	97.11 217	98.87 232	99.22 288	92.38 280	99.17 288	98.21 158	95.99 276	98.42 311

GA-MVS		97.85 220	97.47 239	99.00 171	99.38 187	97.99 223	98.57 328	99.15 279	97.04 222	98.90 227	99.30 275	89.83 315	99.38 248	96.70 273	98.33 197	99.62 125

TinyColmap		97.12 280	96.89 280	97.83 292	99.07 259	95.52 312	98.57 328	98.74 320	97.58 167	97.81 311	99.79 88	88.16 334	99.56 225	95.10 306	97.21 250	98.39 315

eth_miper_zixun_eth		98.05 194	97.96 187	98.33 257	99.26 217	97.38 247	98.56 330	99.31 253	96.65 249	98.88 230	99.52 212	96.58 145	99.12 297	97.39 234	95.53 290	98.47 304

CMPMVS	69.68 23	94.13 316	94.90 308	91.84 334	97.24 343	80.01 357	98.52 331	99.48 139	89.01 345	91.99 348	99.67 151	85.67 345	99.13 293	95.44 299	97.03 255	96.39 348
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
USDC		97.34 274	97.20 273	97.75 296	99.07 259	95.20 319	98.51 332	99.04 292	97.99 126	98.31 291	99.86 23	89.02 323	99.55 227	95.67 296	97.36 247	98.49 301

ambc				93.06 332	92.68 354	82.36 354	98.47 333	98.73 325		95.09 340	97.41 342	55.55 360	99.10 300	96.42 281	91.32 335	97.71 341

miper_enhance_ethall		98.16 178	98.08 174	98.41 250	98.96 277	97.72 239	98.45 334	99.32 250	96.95 230	98.97 217	99.17 293	97.06 130	99.22 279	97.86 187	95.99 276	98.29 319

CHOSEN 280x420		99.12 85	99.13 65	99.08 160	99.66 110	97.89 230	98.43 335	99.71 13	98.88 38	99.62 75	99.76 105	96.63 144	99.70 200	99.46 15	99.99 1	99.66 108

testmvs		39.17 331	43.78 333	25.37 346	36.04 367	16.84 368	98.36 336	26.56 366	20.06 362	38.51 363	67.32 359	29.64 366	15.30 364	37.59 361	39.90 360	43.98 359

FPMVS		84.93 323	85.65 324	82.75 341	86.77 360	63.39 364	98.35 337	98.92 303	74.11 354	83.39 353	98.98 314	50.85 361	92.40 357	84.54 354	94.97 301	92.46 351

KD-MVS_2432*1600		94.62 311	93.72 317	97.31 308	97.19 345	95.82 304	98.34 338	99.20 273	95.00 312	97.57 314	98.35 334	87.95 337	98.10 334	92.87 331	77.00 353	98.01 331

miper_refine_blended		94.62 311	93.72 317	97.31 308	97.19 345	95.82 304	98.34 338	99.20 273	95.00 312	97.57 314	98.35 334	87.95 337	98.10 334	92.87 331	77.00 353	98.01 331

CL-MVSNet_2432*1600		94.49 313	93.97 316	96.08 326	96.16 348	93.67 340	98.33 340	99.38 216	95.13 307	97.33 319	98.15 338	92.69 268	96.57 351	88.67 345	79.87 351	97.99 334

PVSNet	96.02 17	98.85 127	98.84 110	98.89 192	99.73 75	97.28 249	98.32 341	99.60 40	97.86 134	99.50 101	99.57 194	96.75 141	99.86 126	98.56 128	99.70 108	99.54 142

PAPM		97.59 261	97.09 276	99.07 161	99.06 261	98.26 212	98.30 342	99.10 284	94.88 314	98.08 300	99.34 265	96.27 156	99.64 214	89.87 342	98.92 172	99.31 180

Patchmatch-RL test		95.84 301	95.81 299	95.95 327	95.61 349	90.57 350	98.24 343	98.39 334	95.10 311	95.20 339	98.67 326	94.78 206	97.77 342	96.28 284	90.02 338	99.51 153

UnsupCasMVSNet_bld		93.53 318	92.51 321	96.58 324	97.38 339	93.82 336	98.24 343	99.48 139	91.10 342	93.10 346	96.66 347	74.89 355	98.37 330	94.03 320	87.71 342	97.56 344

LCM-MVSNet		86.80 322	85.22 326	91.53 335	87.81 359	80.96 356	98.23 345	98.99 295	71.05 355	90.13 350	96.51 348	48.45 363	96.88 350	90.51 339	85.30 345	96.76 346

cascas		97.69 251	97.43 250	98.48 239	98.60 319	97.30 248	98.18 346	99.39 210	92.96 335	98.41 284	98.78 323	93.77 245	99.27 272	98.16 164	98.61 184	98.86 217

Effi-MVS+		98.81 130	98.59 143	99.48 109	99.46 168	99.12 130	98.08 347	99.50 119	97.50 178	99.38 131	99.41 245	96.37 153	99.81 157	99.11 47	98.54 191	99.51 153

PCF-MVS	97.08 14	97.66 257	97.06 277	99.47 112	99.61 130	99.09 132	98.04 348	99.25 267	91.24 341	98.51 277	99.70 132	94.55 221	99.91 91	92.76 333	99.85 58	99.42 170
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
bset_n11_16_dypcd		98.16 178	97.97 185	98.73 217	98.26 329	98.28 211	97.99 349	98.01 341	97.68 157	99.10 191	99.63 171	95.68 177	99.15 289	98.78 94	96.55 262	98.75 231

PVSNet_0	94.43 19	96.09 299	95.47 302	97.94 284	99.31 205	94.34 333	97.81 350	99.70 15	97.12 214	97.46 316	98.75 324	89.71 317	99.79 165	97.69 206	81.69 349	99.68 102

E-PMN		80.61 325	79.88 328	82.81 340	90.75 356	76.38 361	97.69 351	95.76 357	66.44 358	83.52 352	92.25 354	62.54 359	87.16 359	68.53 358	61.40 356	84.89 357

ANet_high		77.30 327	74.86 331	84.62 339	75.88 363	77.61 359	97.63 352	93.15 363	88.81 346	64.27 360	89.29 357	36.51 364	83.93 361	75.89 356	52.31 358	92.33 353

EMVS		80.02 326	79.22 329	82.43 342	91.19 355	76.40 360	97.55 353	92.49 365	66.36 359	83.01 354	91.27 355	64.63 358	85.79 360	65.82 359	60.65 357	85.08 356

MVE	76.82 21	76.91 328	74.31 332	84.70 338	85.38 362	76.05 362	96.88 354	93.17 362	67.39 357	71.28 359	89.01 358	21.66 369	87.69 358	71.74 357	72.29 355	90.35 354
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
Gipuma		90.99 320	90.15 323	93.51 330	98.73 304	90.12 351	93.98 355	99.45 179	79.32 353	92.28 347	94.91 350	69.61 356	97.98 338	87.42 349	95.67 286	92.45 352
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PMVS	70.75 22	75.98 329	74.97 330	79.01 343	70.98 364	55.18 365	93.37 356	98.21 337	65.08 360	61.78 361	93.83 352	21.74 368	92.53 356	78.59 355	91.12 336	89.34 355
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
tmp_tt		82.80 324	81.52 327	86.66 337	66.61 365	68.44 363	92.79 357	97.92 342	68.96 356	80.04 358	99.85 29	85.77 344	96.15 354	97.86 187	43.89 359	95.39 350

wuyk23d		40.18 330	41.29 335	36.84 344	86.18 361	49.12 366	79.73 358	22.81 367	27.64 361	25.46 364	28.45 364	21.98 367	48.89 362	55.80 360	23.56 362	12.51 360

uanet_test		0.02 336	0.03 339	0.00 347	0.00 368	0.00 369	0.00 359	0.00 369	0.00 364	0.00 365	0.27 365	0.00 370	0.00 365	0.00 363	0.00 363	0.00 361

cdsmvs_eth3d_5k		24.64 333	32.85 336	0.00 347	0.00 368	0.00 369	0.00 359	99.51 101	0.00 364	0.00 365	99.56 197	96.58 145	0.00 365	0.00 363	0.00 363	0.00 361

pcd_1.5k_mvsjas		8.27 335	11.03 338	0.00 347	0.00 368	0.00 369	0.00 359	0.00 369	0.00 364	0.00 365	0.27 365	99.01 16	0.00 365	0.00 363	0.00 363	0.00 361

sosnet-low-res		0.02 336	0.03 339	0.00 347	0.00 368	0.00 369	0.00 359	0.00 369	0.00 364	0.00 365	0.27 365	0.00 370	0.00 365	0.00 363	0.00 363	0.00 361

sosnet		0.02 336	0.03 339	0.00 347	0.00 368	0.00 369	0.00 359	0.00 369	0.00 364	0.00 365	0.27 365	0.00 370	0.00 365	0.00 363	0.00 363	0.00 361

uncertanet		0.02 336	0.03 339	0.00 347	0.00 368	0.00 369	0.00 359	0.00 369	0.00 364	0.00 365	0.27 365	0.00 370	0.00 365	0.00 363	0.00 363	0.00 361

Regformer		0.02 336	0.03 339	0.00 347	0.00 368	0.00 369	0.00 359	0.00 369	0.00 364	0.00 365	0.27 365	0.00 370	0.00 365	0.00 363	0.00 363	0.00 361

ab-mvs-re		8.30 334	11.06 337	0.00 347	0.00 368	0.00 369	0.00 359	0.00 369	0.00 364	0.00 365	99.58 190	0.00 370	0.00 365	0.00 363	0.00 363	0.00 361

uanet		0.02 336	0.03 339	0.00 347	0.00 368	0.00 369	0.00 359	0.00 369	0.00 364	0.00 365	0.27 365	0.00 370	0.00 365	0.00 363	0.00 363	0.00 361

ZD-MVS					99.71 86	99.79 30		99.61 35	96.84 237	99.56 89	99.54 205	98.58 70	99.96 19	96.93 262	99.75 96

IU-MVS					99.84 32	99.88 7		99.32 250	98.30 86	99.84 13				98.86 78	99.85 58	99.89 2

test_241102_TWO								99.48 139	99.08 11	99.88 5	99.81 62	98.94 31	99.96 19	98.91 68	99.84 65	99.88 5

test_241102_ONE					99.84 32	99.90 1		99.48 139	99.07 13	99.91 1	99.74 116	99.20 5	99.76 174

test_0728_THIRD									98.99 25	99.81 22	99.80 76	99.09 12	99.96 19	98.85 80	99.90 23	99.88 5

GSMVS																99.52 147

test_part2					99.81 40	99.83 14				99.77 33

sam_mvs1												94.86 202				99.52 147

sam_mvs												94.72 213

MTGPA								99.47 157

test_post											65.99 361	94.65 217	99.73 184

patchmatchnet-post											98.70 325	94.79 205	99.74 177

gm-plane-assit					98.54 323	92.96 344			94.65 319		99.15 296		99.64 214	97.56 218

test9_res														97.49 224	99.72 103	99.75 69

agg_prior2														97.21 241	99.73 102	99.75 69

agg_prior					99.67 101	99.62 61		99.40 206		98.87 232			99.91 91

TestCases				99.31 133	99.86 21	98.48 201		99.61 35	97.85 136	99.36 136	99.85 29	95.95 164	99.85 132	96.66 276	99.83 72	99.59 133

test_prior				99.68 65	99.67 101	99.48 87		99.56 55					99.83 146			99.74 73

新几何1				99.75 51	99.75 62	99.59 68		99.54 70	96.76 241	99.29 149	99.64 165	98.43 81	99.94 54	96.92 264	99.66 117	99.72 86

旧先验1					99.74 70	99.59 68		99.54 70			99.69 139	98.47 78			99.68 114	99.73 80

原ACMM1				99.65 72	99.73 75	99.33 101		99.47 157	97.46 179	99.12 186	99.66 157	98.67 66	99.91 91	97.70 205	99.69 109	99.71 93

testdata2													99.95 43	96.67 275

segment_acmp												98.96 25

testdata				99.54 92	99.75 62	98.95 151		99.51 101	97.07 219	99.43 114	99.70 132	98.87 39	99.94 54	97.76 196	99.64 120	99.72 86

test12				99.75 51	99.64 117	99.61 63		99.29 262		99.21 170		98.38 86	99.89 114		99.74 99	99.74 73

plane_prior7					99.29 210	97.03 266

plane_prior6					99.27 215	96.98 270						92.71 266

plane_prior5								99.47 157					99.69 203	97.78 194	97.63 222	98.67 259

plane_prior4											99.61 181

plane_prior3						97.00 268			98.69 55	99.11 188

plane_prior1					99.26 217

n2								0.00 369

nn								0.00 369

door-mid								98.05 340

lessismore_v0				97.79 295	98.69 310	95.44 315		94.75 358		95.71 338	99.87 20	88.69 327	99.32 264	95.89 289	94.93 303	98.62 281

LGP-MVS_train				98.49 237	99.33 197	97.05 263		99.55 63	97.46 179	99.24 162	99.83 42	92.58 271	99.72 188	98.09 168	97.51 233	98.68 252

test11								99.35 231

door								97.92 342

HQP5-MVS						96.83 276

BP-MVS														97.19 245

HQP4-MVS										98.66 260			99.64 214			98.64 271

HQP3-MVS								99.39 210							97.58 227

HQP2-MVS												92.47 275

NP-MVS					99.23 223	96.92 274					99.40 248

ACMMP++_ref															97.19 251

ACMMP++															97.43 243

Test By Simon												98.75 56

ITE_SJBPF				98.08 274	99.29 210	96.37 292		98.92 303	98.34 81	98.83 238	99.75 110	91.09 303	99.62 220	95.82 290	97.40 245	98.25 322

DeepMVS_CX				93.34 331	99.29 210	82.27 355		99.22 270	85.15 349	96.33 333	99.05 306	90.97 305	99.73 184	93.57 323	97.77 220	98.01 331

High-resolution multi-view benchmark