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.

DeepC-MVS_fast	96.59 1	98.81 23	98.54 28	99.62 18	99.90 47	98.85 33	99.24 223	98.47 106	98.14 4	99.08 84	99.91 15	93.09 120	100.00 1	99.04 60	99.99 22	100.00 1
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
DeepPCF-MVS	95.94 2	97.71 87	98.98 12	93.92 277	99.63 94	81.76 355	99.96 25	98.56 80	99.47 1	99.19 81	99.99 1	94.16 91	100.00 1	99.92 13	99.93 67	100.00 1

PLC	95.54 3	97.93 74	97.89 74	98.05 139	99.82 70	94.77 195	99.92 71	98.46 108	93.93 139	97.20 147	99.27 134	95.44 48	99.97 56	97.41 131	99.51 114	99.41 169
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
DeepC-MVS	94.51 4	96.92 114	96.40 120	98.45 120	99.16 118	95.90 158	99.66 160	98.06 196	96.37 51	94.37 200	99.49 117	83.29 242	99.90 80	97.63 127	99.61 107	99.55 148
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
PCF-MVS	94.20 5	95.18 167	94.10 181	98.43 122	98.55 154	95.99 156	97.91 316	97.31 266	90.35 247	89.48 260	99.22 140	85.19 227	99.89 84	90.40 257	98.47 139	99.41 169
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
IB-MVS	92.85 6	94.99 172	93.94 185	98.16 132	97.72 205	95.69 168	99.99 3	98.81 50	94.28 120	92.70 221	96.90 241	95.08 54	99.17 165	96.07 156	73.88 350	99.60 138
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
HY-MVS	92.50 7	97.79 83	97.17 97	99.63 15	98.98 127	99.32 8	97.49 321	99.52 14	95.69 70	98.32 121	97.41 224	93.32 111	99.77 120	98.08 109	95.75 200	99.81 104

TAPA-MVS	92.12 8	94.42 188	93.60 193	96.90 181	99.33 113	91.78 262	99.78 129	98.00 199	89.89 255	94.52 197	99.47 118	91.97 149	99.18 164	69.90 361	99.52 112	99.73 114
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
ACMP	92.05 9	92.74 225	92.42 223	93.73 282	95.91 266	88.72 311	99.81 121	97.53 243	94.13 125	87.00 302	98.23 202	74.07 316	98.47 196	96.22 155	88.86 244	93.99 295
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
ACMM	91.95 10	92.88 222	92.52 221	93.98 276	95.75 272	89.08 309	99.77 132	97.52 245	93.00 166	89.95 246	97.99 210	76.17 300	98.46 199	93.63 209	88.87 243	94.39 256
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
3Dnovator+	91.53 11	96.31 138	95.24 156	99.52 29	96.88 243	98.64 53	99.72 151	98.24 174	95.27 82	88.42 285	98.98 156	82.76 244	99.94 73	97.10 140	99.83 85	99.96 74

3Dnovator	91.47 12	96.28 141	95.34 154	99.08 74	96.82 246	97.47 102	99.45 197	98.81 50	95.52 76	89.39 261	99.00 153	81.97 248	99.95 65	97.27 134	99.83 85	99.84 101

PVSNet	91.05 13	97.13 106	96.69 111	98.45 120	99.52 103	95.81 160	99.95 43	99.65 11	94.73 98	99.04 86	99.21 141	84.48 232	99.95 65	94.92 173	98.74 134	99.58 145

COLMAP_ROB	90.47 14	92.18 239	91.49 241	94.25 264	99.00 126	88.04 322	98.42 298	96.70 323	82.30 344	88.43 283	99.01 151	76.97 290	99.85 99	86.11 303	96.50 184	94.86 240
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
OpenMVS	90.15 15	94.77 177	93.59 194	98.33 127	96.07 260	97.48 101	99.56 179	98.57 78	90.46 244	86.51 308	98.95 164	78.57 282	99.94 73	93.86 198	99.74 96	97.57 229

ACMH+	89.98 16	90.35 276	89.54 273	92.78 304	95.99 263	86.12 332	98.81 272	97.18 274	89.38 258	83.14 331	97.76 217	68.42 337	98.43 201	89.11 269	86.05 274	93.78 311

ACMH	89.72 17	90.64 269	89.63 270	93.66 288	95.64 279	88.64 314	98.55 288	97.45 251	89.03 263	81.62 338	97.61 219	69.75 331	98.41 203	89.37 266	87.62 265	93.92 301
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
LTVRE_ROB	88.28 18	90.29 279	89.05 284	94.02 272	95.08 287	90.15 294	97.19 326	97.43 253	84.91 328	83.99 327	97.06 236	74.00 317	98.28 221	84.08 314	87.71 263	93.62 318
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
PVSNet_0	88.03 19	91.80 248	90.27 260	96.38 200	98.27 169	90.46 288	99.94 61	99.61 12	93.99 135	86.26 315	97.39 226	71.13 328	99.89 84	98.77 78	67.05 365	98.79 210

OpenMVS_ROB	79.82 20	83.77 325	81.68 328	90.03 328	88.30 362	82.82 345	98.46 293	95.22 356	73.92 366	76.00 359	91.29 351	55.00 365	96.94 292	68.40 364	88.51 252	90.34 356

CMPMVS	61.59 21	84.75 319	85.14 313	83.57 348	90.32 354	62.54 375	96.98 332	97.59 237	74.33 365	69.95 368	96.66 249	64.17 351	98.32 217	87.88 284	88.41 253	89.84 360
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
MVE	53.74 22	51.54 348	47.86 352	62.60 362	59.56 386	50.93 381	79.41 376	77.69 386	35.69 381	36.27 383	61.76 382	5.79 391	69.63 381	37.97 382	36.61 378	67.24 376
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
PMVS	49.05 23	53.75 346	51.34 350	60.97 363	40.80 389	34.68 389	74.82 377	89.62 380	37.55 379	28.67 385	72.12 375	7.09 389	81.63 379	43.17 381	68.21 363	66.59 377
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
FMVS2		79.99 334	80.17 333	79.45 352	84.02 370	62.83 373	99.05 245	93.49 370	88.29 283	80.06 347	86.65 366	28.09 378	88.00 372	88.63 272	73.27 352	87.54 368

mvsany_test		82.12 328	81.14 330	85.06 346	81.87 373	70.41 369	97.09 329	92.14 373	91.27 229	77.84 353	88.73 358	39.31 373	95.49 335	90.75 250	71.24 354	89.29 364

FMVS1		68.38 339	66.92 341	72.78 357	78.80 376	50.36 382	90.95 369	87.35 383	55.47 372	58.95 372	88.14 360	20.64 383	87.60 373	57.28 375	64.69 366	80.39 372

APD_test		68.38 339	66.92 341	72.78 357	78.80 376	50.36 382	90.95 369	87.35 383	55.47 372	58.95 372	88.14 360	20.64 383	87.60 373	57.28 375	64.69 366	80.39 372

FMVS		78.40 336	77.59 338	80.81 351	80.82 374	62.48 376	96.96 333	93.08 371	83.44 337	74.57 363	84.57 370	27.95 379	92.63 362	84.15 313	72.79 353	87.32 369

FE-MVS		95.70 158	95.01 165	97.79 147	98.21 172	94.57 196	95.03 352	98.69 57	88.90 270	97.50 142	96.19 263	92.60 133	99.49 154	89.99 262	97.94 157	99.31 181

FA-MVS(test-final)		95.86 150	95.09 162	98.15 135	97.74 200	95.62 170	96.31 341	98.17 183	91.42 226	96.26 173	96.13 266	90.56 171	99.47 156	92.18 226	97.07 173	99.35 176

iter_conf_final		96.01 147	95.93 138	96.28 202	98.38 162	97.03 118	99.87 93	97.03 292	94.05 133	92.61 222	97.98 211	98.01 5	97.34 261	97.02 142	88.39 254	94.47 246

bld_raw_dy_0_64		92.74 225	92.03 229	94.87 237	93.09 323	93.46 222	99.12 231	95.41 351	92.84 171	90.44 240	97.54 220	78.08 286	97.04 285	93.94 197	87.77 262	94.11 284

patch_mono-2		98.24 64	99.12 5	95.59 215	99.67 92	86.91 329	99.95 43	98.89 43	97.60 12	99.90 2	99.76 76	96.54 28	99.98 46	99.94 12	99.82 91	99.88 96

EGC-MVSNET		69.38 337	63.76 346	86.26 344	90.32 354	81.66 356	96.24 343	93.85 368	0.99 386	3.22 387	92.33 348	52.44 367	92.92 361	59.53 374	84.90 283	84.21 370

test2506		97.53 91	97.19 95	98.58 108	98.66 150	96.90 124	98.81 272	99.77 5	94.93 89	97.95 131	98.96 160	92.51 136	99.20 162	94.93 172	98.15 147	99.64 128

test1111		95.57 161	94.98 166	97.37 168	98.56 152	93.37 227	98.86 267	98.45 110	94.95 88	96.63 161	98.95 164	75.21 309	99.11 167	95.02 170	98.14 149	99.64 128

ECVR-MVS		95.66 159	95.05 163	97.51 160	98.66 150	93.71 216	98.85 269	98.45 110	94.93 89	96.86 155	98.96 160	75.22 308	99.20 162	95.34 164	98.15 147	99.64 128

test_blank		0.00 356	0.00 359	0.00 369	0.00 392	0.00 393	0.00 380	0.00 393	0.00 387	0.00 388	0.02 387	0.00 392	0.00 388	0.00 386	0.00 386	0.00 384

DVP-MVS++		99.26 6	99.09 9	99.77 8	99.91 44	99.31 9	99.95 43	98.43 121	96.48 43	99.80 17	99.93 11	97.44 14	100.00 1	99.92 13	99.98 35	100.00 1

FOURS1					99.92 36	97.66 90	99.95 43	98.36 153	95.58 73	99.52 54

MSC_two_6792asdad				99.93 2	99.91 44	99.80 2		98.41 137					100.00 1	99.96 9	100.00 1	100.00 1

PC_three_1452									96.96 30	99.80 17	99.79 64	97.49 10	100.00 1	99.99 5	99.98 35	100.00 1

No_MVS				99.93 2	99.91 44	99.80 2		98.41 137					100.00 1	99.96 9	100.00 1	100.00 1

test_one_0601					99.94 14	99.30 11		98.41 137	96.63 40	99.75 28	99.93 11	97.49 10

eth-test2					0.00 392

eth-test					0.00 392

GeoE		94.36 192	93.48 198	96.99 178	97.29 226	93.54 220	99.96 25	96.72 322	88.35 282	93.43 210	98.94 166	82.05 247	98.05 235	88.12 282	96.48 185	99.37 173

test_method		80.79 330	79.70 334	84.08 347	92.83 329	67.06 372	99.51 187	95.42 350	54.34 374	81.07 342	93.53 335	44.48 372	92.22 364	78.90 342	77.23 340	92.94 332

Anonymous20240521		85.15 317	83.81 318	89.16 333	88.32 361	82.69 346	98.80 274	95.74 343	79.72 351	81.53 339	90.99 352	65.38 348	94.16 351	72.69 357	81.11 310	90.63 355

h-mvs33		94.92 173	94.36 176	96.59 191	98.85 141	91.29 274	98.93 258	98.94 37	95.90 60	98.77 99	98.42 199	90.89 167	99.77 120	97.80 119	70.76 355	98.72 212

hse-mvs2		94.38 189	94.08 182	95.31 224	98.27 169	90.02 297	99.29 219	98.56 80	95.90 60	98.77 99	98.00 208	90.89 167	98.26 225	97.80 119	69.20 361	97.64 227

CL-MVSNet_self_test		84.50 321	83.15 323	88.53 338	86.00 366	81.79 354	98.82 271	97.35 261	85.12 324	83.62 330	90.91 354	76.66 294	91.40 366	69.53 362	60.36 372	92.40 340

KD-MVS_2432*1600		88.00 304	86.10 308	93.70 286	96.91 239	94.04 207	97.17 327	97.12 281	84.93 326	81.96 335	92.41 345	92.48 137	94.51 349	79.23 338	52.68 375	92.56 336

KD-MVS_self_test		83.59 326	82.06 326	88.20 340	86.93 364	80.70 361	97.21 325	96.38 332	82.87 340	82.49 333	88.97 357	67.63 340	92.32 363	73.75 356	62.30 371	91.58 348

AUN-MVS		93.28 213	92.60 216	95.34 222	98.29 166	90.09 295	99.31 214	98.56 80	91.80 214	96.35 172	98.00 208	89.38 184	98.28 221	92.46 222	69.22 360	97.64 227

ZD-MVS					99.92 36	98.57 55		98.52 93	92.34 197	99.31 71	99.83 51	95.06 56	99.80 111	99.70 36	99.97 48

test1172		98.38 54	98.25 49	98.77 93	99.88 54	96.56 134	99.80 125	98.36 153	94.68 100	99.20 78	99.80 60	93.28 114	99.78 116	99.34 48	99.92 71	99.98 55

SR-MVS-dyc-post		98.31 57	98.17 54	98.71 96	99.79 75	96.37 141	99.76 137	98.31 163	94.43 110	99.40 66	99.75 82	93.28 114	99.78 116	98.90 69	99.92 71	99.97 67

RE-MVS-def			98.13 57		99.79 75	96.37 141	99.76 137	98.31 163	94.43 110	99.40 66	99.75 82	92.95 123		98.90 69	99.92 71	99.97 67

SED-MVS		99.28 5	99.11 7	99.77 8	99.93 27	99.30 11	99.96 25	98.43 121	97.27 21	99.80 17	99.94 4	96.71 24	100.00 1	100.00 1	100.00 1	100.00 1

IU-MVS					99.93 27	99.31 9		98.41 137	97.71 8	99.84 9				100.00 1	100.00 1	100.00 1

OPU-MVS				99.93 2	99.89 50	99.80 2	99.96 25				99.80 60	97.44 14	100.00 1	100.00 1	99.98 35	100.00 1

test_241102_TWO								98.43 121	97.27 21	99.80 17	99.94 4	97.18 21	100.00 1	100.00 1	100.00 1	100.00 1

test_241102_ONE					99.93 27	99.30 11		98.43 121	97.26 23	99.80 17	99.88 24	96.71 24	100.00 1

xxxxxxxxxxxxxcwj		98.98 15	98.79 17	99.54 26	99.82 70	98.79 37	99.96 25	97.52 245	97.66 10	99.81 13	99.89 21	94.70 68	99.86 95	99.84 19	99.93 67	99.96 74

SF-MVS		98.67 31	98.40 37	99.50 32	99.77 78	98.67 48	99.90 80	98.21 178	93.53 153	99.81 13	99.89 21	94.70 68	99.86 95	99.84 19	99.93 67	99.96 74

ETH3D cwj APD-0.16		98.40 52	98.07 62	99.40 44	99.59 96	98.41 64	99.86 105	98.24 174	92.18 201	99.73 30	99.87 28	93.47 107	99.85 99	99.74 30	99.95 55	99.93 85

cl22		93.77 203	93.25 207	95.33 223	99.49 106	94.43 199	99.61 172	98.09 193	90.38 245	89.16 270	95.61 279	90.56 171	97.34 261	91.93 228	84.45 287	94.21 271

miper_ehance_all_eth		93.16 215	92.60 216	94.82 241	97.57 211	93.56 219	99.50 189	97.07 287	88.75 273	88.85 275	95.52 285	90.97 164	96.74 302	90.77 249	84.45 287	94.17 273

miper_enhance_ethall		94.36 192	93.98 184	95.49 216	98.68 149	95.24 181	99.73 148	97.29 267	93.28 160	89.86 249	95.97 270	94.37 79	97.05 283	92.20 225	84.45 287	94.19 272

ZNCC-MVS		98.31 57	98.03 63	99.17 60	99.88 54	97.59 91	99.94 61	98.44 113	94.31 118	98.50 113	99.82 55	93.06 121	99.99 40	98.30 100	99.99 22	99.93 85

ETH3 D test6400		98.81 23	98.54 28	99.59 21	99.93 27	98.93 26	99.93 67	98.46 108	94.56 105	99.84 9	99.92 13	94.32 84	99.86 95	99.96 9	99.98 35	100.00 1

dcpmvs_2		97.42 97	98.09 60	95.42 220	99.58 100	87.24 326	99.23 224	96.95 301	94.28 120	98.93 93	99.73 89	94.39 78	99.16 166	99.89 17	99.82 91	99.86 100

cl____		92.31 236	91.58 237	94.52 252	97.33 223	92.77 235	99.57 177	96.78 319	86.97 301	87.56 294	95.51 286	89.43 183	96.62 307	88.60 273	82.44 298	94.16 278

DIV-MVS_self_test		92.32 235	91.60 236	94.47 256	97.31 224	92.74 237	99.58 175	96.75 320	86.99 300	87.64 292	95.54 283	89.55 182	96.50 311	88.58 274	82.44 298	94.17 273

eth_miper_zixun_eth		92.41 234	91.93 231	93.84 280	97.28 227	90.68 282	98.83 270	96.97 300	88.57 278	89.19 269	95.73 276	89.24 189	96.69 305	89.97 263	81.55 304	94.15 279

9.14			98.38 40		99.87 57		99.91 75	98.33 159	93.22 161	99.78 25	99.89 21	94.57 71	99.85 99	99.84 19	99.97 48

testtj		98.89 19	98.69 20	99.52 29	99.94 14	98.56 57	99.90 80	98.55 86	95.14 84	99.72 33	99.84 48	95.46 47	100.00 1	99.65 38	99.99 22	99.99 24

uanet_test		0.00 356	0.00 359	0.00 369	0.00 392	0.00 393	0.00 380	0.00 393	0.00 387	0.00 388	0.00 388	0.00 392	0.00 388	0.00 386	0.00 386	0.00 384

ETH3D-3000-0.1		98.68 30	98.42 33	99.47 37	99.83 68	98.57 55	99.90 80	98.37 151	93.81 144	99.81 13	99.90 19	94.34 80	99.86 95	99.84 19	99.98 35	99.97 67

DCPMVS		0.00 356	0.00 359	0.00 369	0.00 392	0.00 393	0.00 380	0.00 393	0.00 387	0.00 388	0.00 388	0.00 392	0.00 388	0.00 386	0.00 386	0.00 384

save fliter					99.82 70	98.79 37	99.96 25	98.40 141	97.66 10

ET-MVSNet_ETH3D		94.37 190	93.28 206	97.64 155	98.30 165	97.99 77	99.99 3	97.61 233	94.35 115	71.57 366	99.45 121	96.23 31	95.34 339	96.91 148	85.14 282	99.59 139

UniMVSNet_ETH3D		90.06 285	88.58 291	94.49 255	94.67 294	88.09 321	97.81 318	97.57 238	83.91 334	88.44 281	97.41 224	57.44 363	97.62 252	91.41 234	88.59 250	97.77 225

EIA-MVS		97.53 91	97.46 85	97.76 151	98.04 182	94.84 191	99.98 9	97.61 233	94.41 113	97.90 133	99.59 109	92.40 139	98.87 174	98.04 110	99.13 128	99.59 139

miper_refine_blended		88.00 304	86.10 308	93.70 286	96.91 239	94.04 207	97.17 327	97.12 281	84.93 326	81.96 335	92.41 345	92.48 137	94.51 349	79.23 338	52.68 375	92.56 336

miper_lstm_enhance		91.81 245	91.39 243	93.06 300	97.34 221	89.18 308	99.38 205	96.79 318	86.70 304	87.47 296	95.22 303	90.00 177	95.86 333	88.26 278	81.37 306	94.15 279

ETV-MVS		97.92 75	97.80 76	98.25 130	98.14 178	96.48 135	99.98 9	97.63 228	95.61 72	99.29 75	99.46 120	92.55 135	98.82 176	99.02 62	98.54 137	99.46 162

CS-MVS		97.79 83	97.91 73	97.43 164	99.10 120	94.42 200	99.99 3	97.10 283	95.07 85	99.68 37	99.75 82	92.95 123	98.34 215	98.38 96	99.14 127	99.54 151

D2MVS		92.76 224	92.59 219	93.27 294	95.13 285	89.54 305	99.69 154	99.38 22	92.26 199	87.59 293	94.61 322	85.05 229	97.79 246	91.59 233	88.01 259	92.47 339

DVP-MVS		99.30 4	99.16 3	99.73 11	99.93 27	99.29 14	99.95 43	98.32 161	97.28 19	99.83 11	99.91 15	97.22 19	100.00 1	99.99 5	100.00 1	99.89 94
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_THIRD									96.48 43	99.83 11	99.91 15	97.87 6	100.00 1	99.92 13	100.00 1	100.00 1

test_0728_SECOND				99.82 7	99.94 14	99.47 7	99.95 43	98.43 121					100.00 1	99.99 5	100.00 1	100.00 1

test0726					99.93 27	99.29 14	99.96 25	98.42 133	97.28 19	99.86 5	99.94 4	97.22 19

SR-MVS		98.46 46	98.30 48	98.93 87	99.88 54	97.04 117	99.84 112	98.35 156	94.92 91	99.32 70	99.80 60	93.35 109	99.78 116	99.30 50	99.95 55	99.96 74

DPM-MVS		98.83 22	98.46 32	99.97 1	99.33 113	99.92 1	99.96 25	98.44 113	97.96 7	99.55 49	99.94 4	97.18 21	100.00 1	93.81 202	99.94 61	99.98 55

GST-MVS		98.27 60	97.97 67	99.17 60	99.92 36	97.57 92	99.93 67	98.39 144	94.04 134	98.80 97	99.74 87	92.98 122	100.00 1	98.16 103	99.76 95	99.93 85

test_yl		97.83 79	97.37 88	99.21 54	99.18 116	97.98 78	99.64 168	99.27 26	91.43 224	97.88 134	98.99 154	95.84 39	99.84 108	98.82 73	95.32 207	99.79 106

thisisatest0530		97.10 107	96.72 110	98.22 131	97.60 210	96.70 128	99.92 71	98.54 90	91.11 233	97.07 151	98.97 158	97.47 12	99.03 169	93.73 207	96.09 190	98.92 202

Anonymous20240529		92.10 241	90.65 252	96.47 192	98.82 142	90.61 284	98.72 279	98.67 62	75.54 362	93.90 207	98.58 189	66.23 344	99.90 80	94.70 183	90.67 231	98.90 205

Anonymous202405211		93.10 218	91.99 230	96.40 198	99.10 120	89.65 303	98.88 263	97.93 207	83.71 335	94.00 205	98.75 179	68.79 333	99.88 90	95.08 169	91.71 230	99.68 120

DCV-MVSNet		97.83 79	97.37 88	99.21 54	99.18 116	97.98 78	99.64 168	99.27 26	91.43 224	97.88 134	98.99 154	95.84 39	99.84 108	98.82 73	95.32 207	99.79 106

tttt0517		96.85 115	96.49 117	97.92 143	97.48 216	95.89 159	99.85 108	98.54 90	90.72 242	96.63 161	98.93 168	97.47 12	99.02 170	93.03 219	95.76 199	98.85 206

our_test_3		90.39 274	89.48 277	93.12 297	92.40 334	89.57 304	99.33 211	96.35 333	87.84 288	85.30 321	94.99 311	84.14 236	96.09 327	80.38 335	84.56 286	93.71 317

thisisatest0515		97.41 98	97.02 103	98.59 107	97.71 207	97.52 95	99.97 18	98.54 90	91.83 211	97.45 143	99.04 148	97.50 9	99.10 168	94.75 181	96.37 187	99.16 192

ppachtmachnet_test		89.58 292	88.35 294	93.25 295	92.40 334	90.44 289	99.33 211	96.73 321	85.49 321	85.90 319	95.77 273	81.09 259	96.00 331	76.00 353	82.49 297	93.30 325

SMA-MVS		98.76 27	98.48 31	99.62 18	99.87 57	98.87 31	99.86 105	98.38 148	93.19 162	99.77 26	99.94 4	95.54 44	100.00 1	99.74 30	99.99 22	100.00 1
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
GSMVS																99.59 139

DPE-MVS		99.26 6	99.10 8	99.74 10	99.89 50	99.24 18	99.87 93	98.44 113	97.48 16	99.64 39	99.94 4	96.68 26	99.99 40	99.99 5	100.00 1	99.99 24
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
test_part2					99.89 50	99.25 17				99.49 56

test_part1		92.15 240	90.72 250	96.44 196	98.87 140	97.46 103	98.99 251	98.26 172	85.89 312	86.34 313	96.34 259	81.71 250	97.48 256	91.06 240	78.99 325	94.37 257

thres100view900		96.74 122	95.92 140	99.18 57	98.90 137	98.77 40	99.74 143	99.71 6	92.59 187	95.84 180	98.86 174	89.25 187	99.50 150	93.84 199	94.57 211	99.27 185

tfpnnormal		89.29 295	87.61 302	94.34 262	94.35 298	94.13 205	98.95 256	98.94 37	83.94 332	84.47 325	95.51 286	74.84 311	97.39 258	77.05 350	80.41 317	91.48 349

tfpn200view9		96.79 118	95.99 128	99.19 56	98.94 130	98.82 35	99.78 129	99.71 6	92.86 168	96.02 177	98.87 172	89.33 185	99.50 150	93.84 199	94.57 211	99.27 185

c3_l		92.53 231	91.87 233	94.52 252	97.40 218	92.99 233	99.40 200	96.93 306	87.86 287	88.69 278	95.44 289	89.95 178	96.44 313	90.45 254	80.69 316	94.14 282

CHOSEN 280x420		99.01 13	99.03 10	98.95 86	99.38 111	98.87 31	98.46 293	99.42 21	97.03 28	99.02 87	99.09 145	99.35 1	98.21 227	99.73 33	99.78 94	99.77 110

CANet		98.27 60	97.82 75	99.63 15	99.72 88	99.10 21	99.98 9	98.51 100	97.00 29	98.52 111	99.71 93	87.80 201	99.95 65	99.75 28	99.38 119	99.83 102

Fast-Effi-MVS+-dtu		93.72 206	93.86 188	93.29 293	97.06 232	86.16 331	99.80 125	96.83 314	92.66 182	92.58 223	97.83 215	81.39 255	97.67 250	89.75 265	96.87 179	96.05 239

Effi-MVS+-dtu		94.53 186	95.30 155	92.22 308	97.77 197	82.54 348	99.59 174	97.06 288	94.92 91	95.29 190	95.37 295	85.81 220	97.89 244	94.80 178	97.07 173	96.23 237

CANet_DTU		96.76 120	96.15 124	98.60 105	98.78 145	97.53 94	99.84 112	97.63 228	97.25 24	99.20 78	99.64 106	81.36 256	99.98 46	92.77 221	98.89 130	98.28 216

MVS_0304		89.28 296	88.31 295	92.21 309	97.05 233	86.53 330	97.76 319	99.57 13	85.58 320	93.86 208	92.71 342	51.04 370	96.30 319	84.49 312	92.72 229	93.79 310

MP-MVS-pluss		98.07 70	97.64 79	99.38 47	99.74 82	98.41 64	99.74 143	98.18 182	93.35 157	96.45 167	99.85 35	92.64 132	99.97 56	98.91 68	99.89 78	99.77 110
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
MSP-MVS		99.09 9	99.12 5	98.98 83	99.93 27	97.24 109	99.95 43	98.42 133	97.50 15	99.52 54	99.88 24	97.43 16	99.71 134	99.50 41	99.98 35	100.00 1
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
sam_mvs1												94.72 67				99.59 139

sam_mvs												94.25 86

IterMVS-SCA-FT		90.85 265	90.16 264	92.93 301	96.72 252	89.96 298	98.89 261	96.99 296	88.95 268	86.63 306	95.67 277	76.48 296	95.00 343	87.04 294	84.04 293	93.84 307

TSAR-MVS + MP.		98.93 16	98.77 18	99.41 42	99.74 82	98.67 48	99.77 132	98.38 148	96.73 37	99.88 4	99.74 87	94.89 65	99.59 145	99.80 24	99.98 35	99.97 67
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
xiu_mvs_v1_base_debu		97.43 93	97.06 98	98.55 110	97.74 200	98.14 70	99.31 214	97.86 215	96.43 45	99.62 43	99.69 98	85.56 223	99.68 138	99.05 56	98.31 143	97.83 222

OPM-MVS		93.21 214	92.80 212	94.44 258	93.12 321	90.85 280	99.77 132	97.61 233	96.19 55	91.56 229	98.65 183	75.16 310	98.47 196	93.78 205	89.39 238	93.99 295
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
ACMMP_NAP		98.49 44	98.14 56	99.54 26	99.66 93	98.62 54	99.85 108	98.37 151	94.68 100	99.53 51	99.83 51	92.87 125	100.00 1	98.66 87	99.84 84	99.99 24

ambc				83.23 349	77.17 378	62.61 374	87.38 373	94.55 364		76.72 357	86.65 366	30.16 375	96.36 316	84.85 311	69.86 356	90.73 354

zzz-MVS		98.33 56	98.00 65	99.30 50	99.85 60	97.93 81	99.80 125	98.28 168	95.76 66	97.18 148	99.88 24	92.74 129	100.00 1	98.67 84	99.88 80	99.99 24

MTGPA								98.28 168

mvs-test1		95.53 162	95.97 133	94.20 265	97.77 197	85.44 337	99.95 43	97.06 288	94.92 91	96.58 163	98.72 180	85.81 220	98.98 171	94.80 178	98.11 150	98.18 217

CS-MVS-test		97.88 76	97.94 71	97.70 154	99.28 115	95.20 184	99.98 9	97.15 278	95.53 75	99.62 43	99.79 64	92.08 147	98.38 211	98.75 80	99.28 122	99.52 155

Effi-MVS+		96.30 139	95.69 146	98.16 132	97.85 192	96.26 144	97.41 322	97.21 271	90.37 246	98.65 107	98.58 189	86.61 214	98.70 186	97.11 139	97.37 168	99.52 155

xiu_mvs_v2_base		98.23 65	97.97 67	99.02 80	98.69 148	98.66 50	99.52 185	98.08 195	97.05 27	99.86 5	99.86 31	90.65 169	99.71 134	99.39 47	98.63 136	98.69 213

xiu_mvs_v1_base		97.43 93	97.06 98	98.55 110	97.74 200	98.14 70	99.31 214	97.86 215	96.43 45	99.62 43	99.69 98	85.56 223	99.68 138	99.05 56	98.31 143	97.83 222

new-patchmatchnet		81.19 329	79.34 335	86.76 343	82.86 372	80.36 364	97.92 315	95.27 355	82.09 345	72.02 365	86.87 365	62.81 355	90.74 369	71.10 359	63.08 369	89.19 365

pmmvs6		85.69 311	83.84 317	91.26 318	90.00 357	84.41 342	97.82 317	96.15 337	75.86 360	81.29 340	95.39 293	61.21 358	96.87 297	83.52 321	73.29 351	92.50 338

pmmvs5		90.17 283	89.09 282	93.40 291	92.10 338	89.77 302	99.74 143	95.58 348	85.88 314	87.24 301	95.74 274	73.41 319	96.48 312	88.54 275	83.56 294	93.95 298

test_post1							95.78 350				59.23 384	93.20 118	97.74 248	91.06 240

test_post											63.35 381	94.43 72	98.13 230

Fast-Effi-MVS+		95.02 171	94.19 179	97.52 159	97.88 189	94.55 197	99.97 18	97.08 286	88.85 272	94.47 199	97.96 213	84.59 231	98.41 203	89.84 264	97.10 172	99.59 139

patchmatchnet-post											91.70 350	95.12 52	97.95 241

Anonymous20231211		89.86 287	88.44 293	94.13 268	98.93 132	90.68 282	98.54 290	98.26 172	76.28 358	86.73 304	95.54 283	70.60 329	97.56 253	90.82 248	80.27 320	94.15 279

pmmvs-eth3d		84.03 324	81.97 327	90.20 326	84.15 369	87.09 327	98.10 311	94.73 362	83.05 338	74.10 364	87.77 363	65.56 347	94.01 352	81.08 333	69.24 359	89.49 362

GG-mvs-BLEND				98.54 113	98.21 172	98.01 76	93.87 357	98.52 93		97.92 132	97.92 214	99.02 2	97.94 243	98.17 102	99.58 109	99.67 122

xiu_mvs_v1_base_debi		97.43 93	97.06 98	98.55 110	97.74 200	98.14 70	99.31 214	97.86 215	96.43 45	99.62 43	99.69 98	85.56 223	99.68 138	99.05 56	98.31 143	97.83 222

Anonymous20231206		86.32 309	85.42 311	89.02 334	89.11 360	80.53 363	99.05 245	95.28 354	85.43 322	82.82 332	93.92 331	74.40 314	93.44 359	66.99 366	81.83 303	93.08 330

MTAPA		98.29 59	97.96 70	99.30 50	99.85 60	97.93 81	99.39 204	98.28 168	95.76 66	97.18 148	99.88 24	92.74 129	100.00 1	98.67 84	99.88 80	99.99 24

MTMP							99.87 93	96.49 329

gm-plane-assit					96.97 237	93.76 215			91.47 222		98.96 160		98.79 178	94.92 173

test9_res														99.71 35	99.99 22	100.00 1

MVP-Stereo		90.93 261	90.45 256	92.37 307	91.25 348	88.76 310	98.05 313	96.17 336	87.27 295	84.04 326	95.30 298	78.46 284	97.27 271	83.78 318	99.70 100	91.09 350
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
TEST9					99.92 36	98.92 27	99.96 25	98.43 121	93.90 141	99.71 34	99.86 31	95.88 38	99.85 99

train_agg		98.88 20	98.65 22	99.59 21	99.92 36	98.92 27	99.96 25	98.43 121	94.35 115	99.71 34	99.86 31	95.94 35	99.85 99	99.69 37	99.98 35	99.99 24

gg-mvs-nofinetune		93.51 209	91.86 234	98.47 118	97.72 205	97.96 80	92.62 361	98.51 100	74.70 364	97.33 145	69.59 377	98.91 3	97.79 246	97.77 124	99.56 110	99.67 122

SCA		94.69 179	93.81 189	97.33 172	97.10 230	94.44 198	98.86 267	98.32 161	93.30 159	96.17 176	95.59 281	76.48 296	97.95 241	91.06 240	97.43 164	99.59 139

Patchmatch-test		92.65 230	91.50 240	96.10 207	96.85 244	90.49 287	91.50 366	97.19 272	82.76 342	90.23 241	95.59 281	95.02 58	98.00 237	77.41 347	96.98 177	99.82 103

test_8					99.92 36	98.88 30	99.96 25	98.43 121	94.35 115	99.69 36	99.85 35	95.94 35	99.85 99

MS-PatchMatch		90.65 268	90.30 259	91.71 315	94.22 300	85.50 336	98.24 304	97.70 223	88.67 275	86.42 311	96.37 258	67.82 339	98.03 236	83.62 319	99.62 104	91.60 347

Patchmatch-RL test		86.90 308	85.98 310	89.67 330	84.45 368	75.59 366	89.71 371	92.43 372	86.89 302	77.83 354	90.94 353	94.22 87	93.63 357	87.75 285	69.61 357	99.79 106

cdsmvs_eth3d_5k		23.43 352	31.24 355	0.00 369	0.00 392	0.00 393	0.00 380	98.09 193	0.00 387	0.00 388	99.67 102	83.37 241	0.00 388	0.00 386	0.00 386	0.00 384

pcd_1.5k_mvsjas		7.60 355	10.13 358	0.00 369	0.00 392	0.00 393	0.00 380	0.00 393	0.00 387	0.00 388	0.00 388	91.20 158	0.00 388	0.00 386	0.00 386	0.00 384

agg_prior1		98.88 20	98.66 21	99.54 26	99.93 27	98.77 40	99.96 25	98.43 121	94.63 103	99.63 40	99.85 35	95.79 41	99.85 99	99.72 34	99.99 22	99.99 24

agg_prior2														99.48 42	100.00 1	100.00 1

agg_prior					99.93 27	98.77 40		98.43 121		99.63 40			99.85 99

tmp_tt		65.23 344	62.94 347	72.13 359	44.90 388	50.03 384	81.05 375	89.42 381	38.45 378	48.51 380	99.90 19	54.09 366	78.70 380	91.84 231	18.26 382	87.64 367

canonicalmvs		97.09 109	96.32 121	99.39 46	98.93 132	98.95 25	99.72 151	97.35 261	94.45 108	97.88 134	99.42 122	86.71 212	99.52 147	98.48 93	93.97 220	99.72 116

anonymousdsp		91.79 250	90.92 248	94.41 261	90.76 351	92.93 234	98.93 258	97.17 275	89.08 261	87.46 297	95.30 298	78.43 285	96.92 294	92.38 223	88.73 246	93.39 323

alignmvs		97.81 81	97.33 91	99.25 52	98.77 146	98.66 50	99.99 3	98.44 113	94.40 114	98.41 116	99.47 118	93.65 104	99.42 158	98.57 90	94.26 216	99.67 122

nrg030		93.51 209	92.53 220	96.45 194	94.36 297	97.20 111	99.81 121	97.16 277	91.60 217	89.86 249	97.46 222	86.37 216	97.68 249	95.88 160	80.31 319	94.46 247

v144192		90.79 266	89.52 274	94.59 248	93.11 322	92.77 235	99.56 179	96.99 296	86.38 307	89.82 252	94.95 313	80.50 268	97.10 280	83.98 316	80.41 317	93.90 302

FIs		94.10 195	93.43 199	96.11 206	94.70 293	96.82 126	99.58 175	98.93 41	92.54 190	89.34 263	97.31 227	87.62 203	97.10 280	94.22 195	86.58 271	94.40 254

v1921920		90.46 273	89.12 281	94.50 254	92.96 327	92.46 246	99.49 191	96.98 298	86.10 310	89.61 258	95.30 298	78.55 283	97.03 288	82.17 327	80.89 315	94.01 292

UA-Net		96.54 129	95.96 135	98.27 129	98.23 171	95.71 166	98.00 314	98.45 110	93.72 149	98.41 116	99.27 134	88.71 196	99.66 142	91.19 237	97.69 159	99.44 166

v1192		90.62 271	89.25 279	94.72 244	93.13 319	93.07 230	99.50 189	97.02 293	86.33 308	89.56 259	95.01 308	79.22 276	97.09 282	82.34 326	81.16 308	94.01 292

FC-MVSNet-test		93.81 201	93.15 208	95.80 213	94.30 299	96.20 149	99.42 199	98.89 43	92.33 198	89.03 272	97.27 229	87.39 206	96.83 299	93.20 213	86.48 272	94.36 259

v1144		91.09 259	89.83 267	94.87 237	93.25 318	93.69 217	99.62 171	96.98 298	86.83 303	89.64 257	94.99 311	80.94 260	97.05 283	85.08 309	81.16 308	93.87 305

sosnet-low-res		0.00 356	0.00 359	0.00 369	0.00 392	0.00 393	0.00 380	0.00 393	0.00 387	0.00 388	0.00 388	0.00 392	0.00 388	0.00 386	0.00 386	0.00 384

HFP-MVS		98.56 38	98.37 42	99.14 66	99.96 8	97.43 104	99.95 43	98.61 72	94.77 96	99.31 71	99.85 35	94.22 87	100.00 1	98.70 82	99.98 35	99.98 55

v148		90.70 267	89.63 270	93.92 277	92.97 326	90.97 277	99.75 140	96.89 309	87.51 290	88.27 286	95.01 308	81.67 251	97.04 285	87.40 289	77.17 341	93.75 312

sosnet		0.00 356	0.00 359	0.00 369	0.00 392	0.00 393	0.00 380	0.00 393	0.00 387	0.00 388	0.00 388	0.00 392	0.00 388	0.00 386	0.00 386	0.00 384

uncertanet		0.00 356	0.00 359	0.00 369	0.00 392	0.00 393	0.00 380	0.00 393	0.00 387	0.00 388	0.00 388	0.00 392	0.00 388	0.00 386	0.00 386	0.00 384

AllTest		92.48 232	91.64 235	95.00 233	99.01 124	88.43 316	98.94 257	96.82 316	86.50 305	88.71 276	98.47 197	74.73 312	99.88 90	85.39 306	96.18 188	96.71 233

TestCases				95.00 233	99.01 124	88.43 316		96.82 316	86.50 305	88.71 276	98.47 197	74.73 312	99.88 90	85.39 306	96.18 188	96.71 233

v7n		89.65 291	88.29 296	93.72 283	92.22 336	90.56 286	99.07 240	97.10 283	85.42 323	86.73 304	94.72 316	80.06 271	97.13 277	81.14 332	78.12 332	93.49 320

region2R		98.54 40	98.37 42	99.05 76	99.96 8	97.18 112	99.96 25	98.55 86	94.87 94	99.45 58	99.85 35	94.07 93	100.00 1	98.67 84	100.00 1	99.98 55

iter_conf05		96.07 144	95.95 137	96.44 196	98.43 160	97.52 95	99.91 75	96.85 312	94.16 124	92.49 224	97.98 211	98.20 4	97.34 261	97.26 135	88.29 255	94.45 252

RRT_MVS		93.14 216	92.92 210	93.78 281	93.31 317	90.04 296	99.66 160	97.69 224	92.53 191	88.91 274	97.76 217	84.36 233	96.93 293	95.10 168	86.99 269	94.37 257

PS-MVSNAJss		93.64 208	93.31 205	94.61 247	92.11 337	92.19 251	99.12 231	97.38 259	92.51 193	88.45 280	96.99 240	91.20 158	97.29 269	94.36 189	87.71 263	94.36 259

PS-MVSNAJ		98.44 48	98.20 52	99.16 62	98.80 144	98.92 27	99.54 183	98.17 183	97.34 17	99.85 7	99.85 35	91.20 158	99.89 84	99.41 46	99.67 101	98.69 213

jajsoiax		91.92 243	91.18 245	94.15 266	91.35 346	90.95 278	99.00 250	97.42 255	92.61 185	87.38 298	97.08 234	72.46 321	97.36 259	94.53 187	88.77 245	94.13 283

mvs_tets		91.81 245	91.08 246	94.00 274	91.63 344	90.58 285	98.67 284	97.43 253	92.43 195	87.37 299	97.05 237	71.76 323	97.32 265	94.75 181	88.68 247	94.11 284

#test#		98.59 36	98.41 35	99.14 66	99.96 8	97.43 104	99.95 43	98.61 72	95.00 87	99.31 71	99.85 35	94.22 87	100.00 1	98.78 77	99.98 35	99.98 55

EI-MVSNet-UG-set		98.14 67	97.99 66	98.60 105	99.80 74	96.27 143	99.36 209	98.50 104	95.21 83	98.30 122	99.75 82	93.29 113	99.73 133	98.37 97	99.30 121	99.81 104

EI-MVSNet-Vis-set		98.27 60	98.11 59	98.75 95	99.83 68	96.59 133	99.40 200	98.51 100	95.29 81	98.51 112	99.76 76	93.60 106	99.71 134	98.53 92	99.52 112	99.95 82

Regformer-3		98.58 37	98.41 35	99.10 72	99.84 65	97.57 92	99.66 160	98.52 93	95.79 63	99.01 88	99.77 72	94.40 74	99.75 126	98.82 73	99.83 85	99.98 55

Regformer-4		98.56 38	98.39 39	99.08 74	99.84 65	97.52 95	99.66 160	98.52 93	95.76 66	99.01 88	99.77 72	94.33 83	99.75 126	98.80 76	99.83 85	99.98 55

Regformer-1		98.79 25	98.60 25	99.36 48	99.85 60	98.34 66	99.87 93	98.52 93	96.05 57	99.41 62	99.79 64	94.93 63	99.76 123	99.07 55	99.90 76	99.99 24

Regformer-2		98.78 26	98.59 26	99.36 48	99.85 60	98.32 67	99.87 93	98.52 93	96.04 58	99.41 62	99.79 64	94.92 64	99.76 123	99.05 56	99.90 76	99.98 55

HPM-MVS++		99.07 10	98.88 15	99.63 15	99.90 47	99.02 23	99.95 43	98.56 80	97.56 14	99.44 59	99.85 35	95.38 49	100.00 1	99.31 49	99.99 22	99.87 98

test_prior4						98.05 74	99.94 61

XVS		98.70 29	98.55 27	99.15 64	99.94 14	97.50 99	99.94 61	98.42 133	96.22 53	99.41 62	99.78 70	94.34 80	99.96 58	98.92 66	99.95 55	99.99 24

v1240		90.20 281	88.79 288	94.44 258	93.05 325	92.27 250	99.38 205	96.92 307	85.89 312	89.36 262	94.87 315	77.89 287	97.03 288	80.66 334	81.08 311	94.01 292

test_prior3		98.99 14	98.84 16	99.43 38	99.94 14	98.49 61	99.95 43	98.65 63	95.78 64	99.73 30	99.76 76	96.00 33	99.80 111	99.78 26	100.00 1	99.99 24

pm-mvs1		89.36 294	87.81 301	94.01 273	93.40 316	91.93 257	98.62 287	96.48 330	86.25 309	83.86 328	96.14 265	73.68 318	97.04 285	86.16 302	75.73 348	93.04 331

test_prior2							99.95 43		95.78 64	99.73 30	99.76 76	96.00 33		99.78 26	100.00 1

X-MVStestdata		93.83 199	92.06 228	99.15 64	99.94 14	97.50 99	99.94 61	98.42 133	96.22 53	99.41 62	41.37 385	94.34 80	99.96 58	98.92 66	99.95 55	99.99 24

test_prior				99.43 38	99.94 14	98.49 61		98.65 63					99.80 111			99.99 24

旧先验2							99.46 196		94.21 123	99.85 7			99.95 65	96.96 145

新几何2							99.40 200

新几何1				99.42 41	99.75 81	98.27 68		98.63 69	92.69 180	99.55 49	99.82 55	94.40 74	100.00 1	91.21 236	99.94 61	99.99 24

旧先验1					99.76 79	97.52 95		98.64 66			99.85 35	95.63 43			99.94 61	99.99 24

无先验							99.49 191	98.71 55	93.46 155				100.00 1	94.36 189		99.99 24

原ACMM2							99.90 80

原ACMM1				98.96 85	99.73 86	96.99 120		98.51 100	94.06 131	99.62 43	99.85 35	94.97 62	99.96 58	95.11 167	99.95 55	99.92 91

test222					99.55 101	97.41 107	99.34 210	98.55 86	91.86 210	99.27 76	99.83 51	93.84 100			99.95 55	99.99 24

testdata2													99.99 40	90.54 253

segment_acmp												96.68 26

testdata				98.42 123	99.47 107	95.33 177		98.56 80	93.78 146	99.79 24	99.85 35	93.64 105	99.94 73	94.97 171	99.94 61	100.00 1

testdata1							99.28 220		96.35 52

v8		90.54 272	89.17 280	94.66 245	93.43 314	93.40 226	99.20 226	96.94 305	85.76 315	87.56 294	94.51 323	81.96 249	97.19 273	84.94 310	78.25 330	93.38 324

1314		96.84 116	95.96 135	99.48 36	96.74 251	98.52 59	98.31 300	98.86 47	95.82 62	89.91 247	98.98 156	87.49 204	99.96 58	97.80 119	99.73 97	99.96 74

1121		98.03 71	97.57 83	99.40 44	99.74 82	98.21 69	98.31 300	98.62 70	92.78 175	99.53 51	99.83 51	95.08 54	100.00 1	94.36 189	99.92 71	99.99 24

LFMVS		94.75 178	93.56 196	98.30 128	99.03 123	95.70 167	98.74 277	97.98 202	87.81 289	98.47 114	99.39 126	67.43 341	99.53 146	98.01 111	95.20 209	99.67 122

VDD-MVS		93.77 203	92.94 209	96.27 203	98.55 154	90.22 292	98.77 276	97.79 220	90.85 239	96.82 157	99.42 122	61.18 359	99.77 120	98.95 63	94.13 217	98.82 208

VDDNet		93.12 217	91.91 232	96.76 185	96.67 254	92.65 243	98.69 282	98.21 178	82.81 341	97.75 137	99.28 131	61.57 357	99.48 155	98.09 108	94.09 218	98.15 218

v10		90.25 280	88.82 287	94.57 250	93.53 312	93.43 224	99.08 236	96.87 311	85.00 325	87.34 300	94.51 323	80.93 261	97.02 290	82.85 323	79.23 324	93.26 326

VPNet		91.81 245	90.46 254	95.85 212	94.74 292	95.54 172	98.98 252	98.59 75	92.14 202	90.77 237	97.44 223	68.73 335	97.54 254	94.89 176	77.89 333	94.46 247

MVS		96.60 128	95.56 149	99.72 12	96.85 244	99.22 19	98.31 300	98.94 37	91.57 218	90.90 235	99.61 108	86.66 213	99.96 58	97.36 132	99.88 80	99.99 24

v2v482		91.30 254	90.07 266	95.01 232	93.13 319	93.79 213	99.77 132	97.02 293	88.05 285	89.25 265	95.37 295	80.73 263	97.15 275	87.28 291	80.04 322	94.09 286

V42		91.28 256	90.12 265	94.74 242	93.42 315	93.46 222	99.68 156	97.02 293	87.36 293	89.85 251	95.05 306	81.31 257	97.34 261	87.34 290	80.07 321	93.40 322

SD-MVS		98.92 17	98.70 19	99.56 24	99.70 90	98.73 45	99.94 61	98.34 158	96.38 48	99.81 13	99.76 76	94.59 70	99.98 46	99.84 19	99.96 52	99.97 67
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
GA-MVS		93.83 199	92.84 211	96.80 183	95.73 273	93.57 218	99.88 90	97.24 270	92.57 189	92.92 217	96.66 249	78.73 281	97.67 250	87.75 285	94.06 219	99.17 191

MSLP-MVS++		99.13 8	99.01 11	99.49 34	99.94 14	98.46 63	99.98 9	98.86 47	97.10 26	99.80 17	99.94 4	95.92 37	100.00 1	99.51 40	100.00 1	100.00 1

APDe-MVS		99.06 11	98.91 14	99.51 31	99.94 14	98.76 44	99.91 75	98.39 144	97.20 25	99.46 57	99.85 35	95.53 46	99.79 114	99.86 18	100.00 1	99.99 24

APD-MVS_3200maxsize		98.25 63	98.08 61	98.78 92	99.81 73	96.60 132	99.82 119	98.30 166	93.95 138	99.37 68	99.77 72	92.84 126	99.76 123	98.95 63	99.92 71	99.97 67

ADS-MVSNet2		93.80 202	93.88 187	93.55 290	97.87 190	85.94 333	94.24 353	96.84 313	90.07 251	96.43 168	94.48 325	90.29 175	95.37 338	87.44 287	97.23 169	99.36 174

EI-MVSNet		93.73 205	93.40 203	94.74 242	96.80 247	92.69 240	99.06 241	97.67 226	88.96 267	91.39 230	99.02 149	88.75 195	97.30 266	91.07 239	87.85 260	94.22 269

Regformer		0.00 356	0.00 359	0.00 369	0.00 392	0.00 393	0.00 380	0.00 393	0.00 387	0.00 388	0.00 388	0.00 392	0.00 388	0.00 386	0.00 386	0.00 384

CVMVSNet		94.68 181	94.94 167	93.89 279	96.80 247	86.92 328	99.06 241	98.98 35	94.45 108	94.23 203	99.02 149	85.60 222	95.31 340	90.91 246	95.39 206	99.43 167

pmmvs4		92.10 241	91.07 247	95.18 228	92.82 330	94.96 188	99.48 193	96.83 314	87.45 292	88.66 279	96.56 254	83.78 238	96.83 299	89.29 267	84.77 285	93.75 312

EU-MVSNet		90.14 284	90.34 258	89.54 331	92.55 333	81.06 359	98.69 282	98.04 198	91.41 227	86.59 307	96.84 247	80.83 262	93.31 360	86.20 301	81.91 302	94.26 266

VNet		97.21 105	96.57 115	99.13 71	98.97 128	97.82 84	99.03 248	99.21 28	94.31 118	99.18 82	98.88 170	86.26 218	99.89 84	98.93 65	94.32 215	99.69 119

test-LLR		96.47 131	96.04 126	97.78 148	97.02 235	95.44 173	99.96 25	98.21 178	94.07 129	95.55 185	96.38 256	93.90 98	98.27 223	90.42 255	98.83 132	99.64 128

TESTMET0.1,1		96.74 122	96.26 122	98.16 132	97.36 220	96.48 135	99.96 25	98.29 167	91.93 208	95.77 183	98.07 206	95.54 44	98.29 219	90.55 252	98.89 130	99.70 117

test-mter		96.39 135	95.93 138	97.78 148	97.02 235	95.44 173	99.96 25	98.21 178	91.81 213	95.55 185	96.38 256	95.17 51	98.27 223	90.42 255	98.83 132	99.64 128

VPA-MVSNet		92.70 227	91.55 239	96.16 205	95.09 286	96.20 149	98.88 263	99.00 34	91.02 236	91.82 227	95.29 301	76.05 302	97.96 240	95.62 163	81.19 307	94.30 264

ACMMPR		98.50 43	98.32 46	99.05 76	99.96 8	97.18 112	99.95 43	98.60 74	94.77 96	99.31 71	99.84 48	93.73 102	100.00 1	98.70 82	99.98 35	99.98 55

testgi		89.01 298	88.04 299	91.90 313	93.49 313	84.89 340	99.73 148	95.66 346	93.89 143	85.14 322	98.17 203	59.68 360	94.66 348	77.73 346	88.88 242	96.16 238

test20.03		84.72 320	83.99 314	86.91 342	88.19 363	80.62 362	98.88 263	95.94 340	88.36 281	78.87 349	94.62 321	68.75 334	89.11 371	66.52 367	75.82 346	91.00 351

thres600view7		96.69 125	95.87 143	99.14 66	98.90 137	98.78 39	99.74 143	99.71 6	92.59 187	95.84 180	98.86 174	89.25 187	99.50 150	93.44 211	94.50 214	99.16 192

ADS-MVSNet		94.79 175	94.02 183	97.11 177	97.87 190	93.79 213	94.24 353	98.16 187	90.07 251	96.43 168	94.48 325	90.29 175	98.19 228	87.44 287	97.23 169	99.36 174

MP-MVS		98.23 65	97.97 67	99.03 78	99.94 14	97.17 115	99.95 43	98.39 144	94.70 99	98.26 125	99.81 59	91.84 152	100.00 1	98.85 72	99.97 48	99.93 85
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
testmvs		40.60 350	44.45 353	29.05 367	19.49 391	14.11 392	99.68 156	18.47 390	20.74 383	64.59 369	98.48 196	10.95 388	17.09 387	56.66 377	11.01 383	55.94 380

thres400		96.78 119	95.99 128	99.16 62	98.94 130	98.82 35	99.78 129	99.71 6	92.86 168	96.02 177	98.87 172	89.33 185	99.50 150	93.84 199	94.57 211	99.16 192

test123		37.68 351	39.14 354	33.31 366	19.94 390	24.83 391	98.36 299	9.75 391	15.53 384	51.31 379	87.14 364	19.62 386	17.74 386	47.10 379	3.47 385	57.36 379

thres200		96.96 111	96.21 123	99.22 53	98.97 128	98.84 34	99.85 108	99.71 6	93.17 163	96.26 173	98.88 170	89.87 179	99.51 148	94.26 193	94.91 210	99.31 181

test0.0.03 1		93.86 198	93.61 191	94.64 246	95.02 289	92.18 252	99.93 67	98.58 76	94.07 129	87.96 289	98.50 192	93.90 98	94.96 344	81.33 331	93.17 226	96.78 232

pmmvs3		80.27 332	77.77 337	87.76 341	80.32 375	82.43 349	98.23 305	91.97 374	72.74 367	78.75 350	87.97 362	57.30 364	90.99 368	70.31 360	62.37 370	89.87 359

EMVS		51.44 349	51.22 351	52.11 365	70.71 381	44.97 387	94.04 355	75.66 388	35.34 382	42.40 382	61.56 383	28.93 377	65.87 384	27.64 384	24.73 380	45.49 381

E-PMN		52.30 347	52.18 349	52.67 364	71.51 380	45.40 385	93.62 359	76.60 387	36.01 380	43.50 381	64.13 380	27.11 380	67.31 383	31.06 383	26.06 379	45.30 382

PGM-MVS		98.34 55	98.13 57	98.99 82	99.92 36	97.00 119	99.75 140	99.50 17	93.90 141	99.37 68	99.76 76	93.24 117	100.00 1	97.75 126	99.96 52	99.98 55

LCM-MVSNet-Re		92.31 236	92.60 216	91.43 316	97.53 212	79.27 365	99.02 249	91.83 375	92.07 204	80.31 344	94.38 328	83.50 240	95.48 336	97.22 137	97.58 162	99.54 151

LCM-MVSNet		67.77 341	64.73 344	76.87 354	62.95 385	56.25 380	89.37 372	93.74 369	44.53 377	61.99 370	80.74 372	20.42 385	86.53 376	69.37 363	59.50 374	87.84 366

MCST-MVS		99.32 3	99.14 4	99.86 5	99.97 3	99.59 5	99.97 18	98.64 66	98.47 2	99.13 83	99.92 13	96.38 30	100.00 1	99.74 30	100.00 1	100.00 1

mvs_anonymous		95.65 160	95.03 164	97.53 158	98.19 174	95.74 164	99.33 211	97.49 249	90.87 238	90.47 239	97.10 233	88.23 199	97.16 274	95.92 159	97.66 161	99.68 120

MVS_Test		96.46 132	95.74 145	98.61 104	98.18 175	97.23 110	99.31 214	97.15 278	91.07 234	98.84 95	97.05 237	88.17 200	98.97 172	94.39 188	97.50 163	99.61 136

MDA-MVSNet-bldmvs		84.09 323	81.52 329	91.81 314	91.32 347	88.00 323	98.67 284	95.92 341	80.22 350	55.60 377	93.32 337	68.29 338	93.60 358	73.76 355	76.61 345	93.82 309

CDPH-MVS		98.65 32	98.36 44	99.49 34	99.94 14	98.73 45	99.87 93	98.33 159	93.97 136	99.76 27	99.87 28	94.99 61	99.75 126	98.55 91	100.00 1	99.98 55

test12				99.43 38	99.74 82	98.56 57		98.40 141		99.65 38		94.76 66	99.75 126		99.98 35	99.99 24

casdiffmvs		96.42 134	95.97 133	97.77 150	97.30 225	94.98 187	99.84 112	97.09 285	93.75 148	96.58 163	99.26 137	85.07 228	98.78 179	97.77 124	97.04 175	99.54 151

diffmvs		97.00 110	96.64 112	98.09 137	97.64 208	96.17 151	99.81 121	97.19 272	94.67 102	98.95 91	99.28 131	86.43 215	98.76 181	98.37 97	97.42 166	99.33 179

baseline2		96.71 124	96.49 117	97.37 168	95.63 280	95.96 157	99.74 143	98.88 45	92.94 167	91.61 228	98.97 158	97.72 7	98.62 190	94.83 177	98.08 154	97.53 230

baseline1		95.78 153	94.86 168	98.54 113	98.47 159	98.07 73	99.06 241	97.99 200	92.68 181	94.13 204	98.62 186	93.28 114	98.69 187	93.79 204	85.76 275	98.84 207

YYNet1		85.50 315	83.33 320	92.00 311	90.89 350	88.38 319	99.22 225	96.55 327	79.60 353	57.26 375	92.72 341	79.09 279	93.78 356	77.25 348	77.37 339	93.84 307

PMMVS2		67.15 342	64.15 345	76.14 355	70.56 382	62.07 377	93.89 356	87.52 382	58.09 371	60.02 371	78.32 373	22.38 382	84.54 377	59.56 373	47.03 377	81.80 371

MDA-MVSNet_test_wron		85.51 314	83.32 321	92.10 310	90.96 349	88.58 315	99.20 226	96.52 328	79.70 352	57.12 376	92.69 343	79.11 278	93.86 355	77.10 349	77.46 338	93.86 306

tpmvs		94.28 194	93.57 195	96.40 198	98.55 154	91.50 272	95.70 351	98.55 86	87.47 291	92.15 225	94.26 329	91.42 154	98.95 173	88.15 280	95.85 196	98.76 211

PM-MVS		80.47 331	78.88 336	85.26 345	83.79 371	72.22 368	95.89 349	91.08 376	85.71 318	76.56 358	88.30 359	36.64 374	93.90 354	82.39 325	69.57 358	89.66 361

HQP_MVS		94.49 187	94.36 176	94.87 237	95.71 276	91.74 263	99.84 112	97.87 213	96.38 48	93.01 215	98.59 187	80.47 269	98.37 213	97.79 122	89.55 235	94.52 243

plane_prior7					95.71 276	91.59 271

plane_prior6					95.76 271	91.72 266						80.47 269

plane_prior5								97.87 213					98.37 213	97.79 122	89.55 235	94.52 243

plane_prior4											98.59 187

plane_prior3						91.64 269			96.63 40	93.01 215

plane_prior2							99.84 112		96.38 48

plane_prior1					95.73 273

plane_prior						91.74 263	99.86 105		96.76 36						89.59 234

PS-CasMVS		90.63 270	89.51 275	93.99 275	93.83 306	91.70 267	98.98 252	98.52 93	88.48 279	86.15 316	96.53 255	75.46 304	96.31 318	88.83 271	78.86 328	93.95 298

UniMVSNet_NR-MVSNet		92.95 221	92.11 226	95.49 216	94.61 295	95.28 179	99.83 118	99.08 31	91.49 220	89.21 267	96.86 244	87.14 208	96.73 303	93.20 213	77.52 336	94.46 247

PEN-MVS		90.19 282	89.06 283	93.57 289	93.06 324	90.90 279	99.06 241	98.47 106	88.11 284	85.91 318	96.30 260	76.67 293	95.94 332	87.07 293	76.91 343	93.89 303

TransMVSNet (Re)		87.25 307	85.28 312	93.16 296	93.56 311	91.03 276	98.54 290	94.05 366	83.69 336	81.09 341	96.16 264	75.32 305	96.40 314	76.69 351	68.41 362	92.06 343

DTE-MVSNet		89.40 293	88.24 297	92.88 302	92.66 332	89.95 299	99.10 233	98.22 177	87.29 294	85.12 323	96.22 262	76.27 299	95.30 341	83.56 320	75.74 347	93.41 321

DU-MVS		92.46 233	91.45 242	95.49 216	94.05 302	95.28 179	99.81 121	98.74 54	92.25 200	89.21 267	96.64 251	81.66 252	96.73 303	93.20 213	77.52 336	94.46 247

UniMVSNet (Re)		93.07 219	92.13 225	95.88 210	94.84 290	96.24 148	99.88 90	98.98 35	92.49 194	89.25 265	95.40 291	87.09 209	97.14 276	93.13 217	78.16 331	94.26 266

CP-MVSNet		91.23 257	90.22 261	94.26 263	93.96 304	92.39 248	99.09 234	98.57 78	88.95 268	86.42 311	96.57 253	79.19 277	96.37 315	90.29 258	78.95 326	94.02 290

WR-MVS_H		91.30 254	90.35 257	94.15 266	94.17 301	92.62 244	99.17 229	98.94 37	88.87 271	86.48 310	94.46 327	84.36 233	96.61 308	88.19 279	78.51 329	93.21 328

WR-MVS		92.31 236	91.25 244	95.48 219	94.45 296	95.29 178	99.60 173	98.68 59	90.10 250	88.07 288	96.89 242	80.68 264	96.80 301	93.14 216	79.67 323	94.36 259

NR-MVSNet		91.56 253	90.22 261	95.60 214	94.05 302	95.76 163	98.25 303	98.70 56	91.16 232	80.78 343	96.64 251	83.23 243	96.57 309	91.41 234	77.73 335	94.46 247

Baseline_NR-MVSNet		90.33 277	89.51 275	92.81 303	92.84 328	89.95 299	99.77 132	93.94 367	84.69 330	89.04 271	95.66 278	81.66 252	96.52 310	90.99 243	76.98 342	91.97 345

TranMVSNet+NR-MVSNet		91.68 252	90.61 253	94.87 237	93.69 309	93.98 210	99.69 154	98.65 63	91.03 235	88.44 281	96.83 248	80.05 272	96.18 323	90.26 259	76.89 344	94.45 252

TSAR-MVS + GP.		98.60 34	98.51 30	98.86 90	99.73 86	96.63 130	99.97 18	97.92 209	98.07 5	98.76 101	99.55 112	95.00 60	99.94 73	99.91 16	97.68 160	99.99 24

abl_6		97.67 88	97.34 90	98.66 100	99.68 91	96.11 155	99.68 156	98.14 190	93.80 145	99.27 76	99.70 95	88.65 197	99.98 46	97.46 130	99.72 98	99.89 94

n2								0.00 393

nn								0.00 393

mPP-MVS		98.39 53	98.20 52	98.97 84	99.97 3	96.92 123	99.95 43	98.38 148	95.04 86	98.61 109	99.80 60	93.39 108	100.00 1	98.64 88	100.00 1	99.98 55

door-mid								89.69 379

XVG-OURS-SEG-HR		94.79 175	94.70 172	95.08 230	98.05 181	89.19 306	99.08 236	97.54 241	93.66 150	94.87 194	99.58 110	78.78 280	99.79 114	97.31 133	93.40 224	96.25 235

mvsmamba		94.10 195	93.72 190	95.25 226	93.57 310	94.13 205	99.67 159	96.45 331	93.63 152	91.34 232	97.77 216	86.29 217	97.22 272	96.65 151	88.10 258	94.40 254

MVSFormer		96.94 112	96.60 113	97.95 141	97.28 227	97.70 88	99.55 181	97.27 268	91.17 230	99.43 60	99.54 114	90.92 165	96.89 295	94.67 184	99.62 104	99.25 187

jason		97.24 103	96.86 105	98.38 126	95.73 273	97.32 108	99.97 18	97.40 258	95.34 80	98.60 110	99.54 114	87.70 202	98.56 192	97.94 116	99.47 115	99.25 187
jason: jason.
lupinMVS		97.85 78	97.60 81	98.62 103	97.28 227	97.70 88	99.99 3	97.55 239	95.50 77	99.43 60	99.67 102	90.92 165	98.71 185	98.40 95	99.62 104	99.45 164

test_djsdf		92.83 223	92.29 224	94.47 256	91.90 340	92.46 246	99.55 181	97.27 268	91.17 230	89.96 245	96.07 269	81.10 258	96.89 295	94.67 184	88.91 241	94.05 289

HPM-MVS_fast		97.80 82	97.50 84	98.68 98	99.79 75	96.42 137	99.88 90	98.16 187	91.75 215	98.94 92	99.54 114	91.82 153	99.65 143	97.62 128	99.99 22	99.99 24

K. test v3		88.05 303	87.24 305	90.47 324	91.82 342	82.23 351	98.96 255	97.42 255	89.05 262	76.93 356	95.60 280	68.49 336	95.42 337	85.87 305	81.01 313	93.75 312

lessismore_v0				90.53 322	90.58 352	80.90 360		95.80 342		77.01 355	95.84 271	66.15 345	96.95 291	83.03 322	75.05 349	93.74 315

SixPastTwentyTwo		88.73 299	88.01 300	90.88 319	91.85 341	82.24 350	98.22 306	95.18 358	88.97 266	82.26 334	96.89 242	71.75 324	96.67 306	84.00 315	82.98 295	93.72 316

OurMVSNet-221017-0		89.81 288	89.48 277	90.83 321	91.64 343	81.21 357	98.17 308	95.38 353	91.48 221	85.65 320	97.31 227	72.66 320	97.29 269	88.15 280	84.83 284	93.97 297

HPM-MVS		97.96 72	97.72 77	98.68 98	99.84 65	96.39 140	99.90 80	98.17 183	92.61 185	98.62 108	99.57 111	91.87 151	99.67 141	98.87 71	99.99 22	99.99 24
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
XVG-OURS		94.82 174	94.74 171	95.06 231	98.00 183	89.19 306	99.08 236	97.55 239	94.10 127	94.71 195	99.62 107	80.51 267	99.74 130	96.04 157	93.06 228	96.25 235

XVG-ACMP-BASELINE		91.22 258	90.75 249	92.63 305	93.73 308	85.61 334	98.52 292	97.44 252	92.77 176	89.90 248	96.85 245	66.64 343	98.39 207	92.29 224	88.61 248	93.89 303

LPG-MVS_test		92.96 220	92.71 214	93.71 284	95.43 282	88.67 312	99.75 140	97.62 230	92.81 172	90.05 242	98.49 193	75.24 306	98.40 205	95.84 161	89.12 239	94.07 287

LGP-MVS_train				93.71 284	95.43 282	88.67 312		97.62 230	92.81 172	90.05 242	98.49 193	75.24 306	98.40 205	95.84 161	89.12 239	94.07 287

baseline		96.43 133	95.98 130	97.76 151	97.34 221	95.17 185	99.51 187	97.17 275	93.92 140	96.90 154	99.28 131	85.37 226	98.64 189	97.50 129	96.86 180	99.46 162

test11								98.44 113

door								90.31 377

EPNet_dtu		95.71 156	95.39 152	96.66 189	98.92 134	93.41 225	99.57 177	98.90 42	96.19 55	97.52 140	98.56 191	92.65 131	97.36 259	77.89 345	98.33 142	99.20 190
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CHOSEN 1792x2688		96.81 117	96.53 116	97.64 155	98.91 136	93.07 230	99.65 164	99.80 3	95.64 71	95.39 188	98.86 174	84.35 235	99.90 80	96.98 144	99.16 126	99.95 82

EPNet		98.49 44	98.40 37	98.77 93	99.62 95	96.80 127	99.90 80	99.51 16	97.60 12	99.20 78	99.36 129	93.71 103	99.91 79	97.99 113	98.71 135	99.61 136
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
HQP5-MVS						91.85 259

HQP-NCC					95.78 267		99.87 93		96.82 32	93.37 211

ACMP_Plane					95.78 267		99.87 93		96.82 32	93.37 211

APD-MVS		98.62 33	98.35 45	99.41 42	99.90 47	98.51 60	99.87 93	98.36 153	94.08 128	99.74 29	99.73 89	94.08 92	99.74 130	99.42 45	99.99 22	99.99 24
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
BP-MVS														97.92 117

HQP4-MVS										93.37 211			98.39 207			94.53 241

HQP3-MVS								97.89 211							89.60 232

HQP2-MVS												80.65 265

CNVR-MVS		99.40 1	99.26 1	99.84 6	99.98 2	99.51 6	99.98 9	98.69 57	98.20 3	99.93 1	99.98 2	96.82 23	100.00 1	99.75 28	100.00 1	99.99 24

NCCC		99.37 2	99.25 2	99.71 13	99.96 8	99.15 20	99.97 18	98.62 70	98.02 6	99.90 2	99.95 3	97.33 17	100.00 1	99.54 39	100.00 1	100.00 1

114514_t		97.41 98	96.83 106	99.14 66	99.51 105	97.83 83	99.89 88	98.27 171	88.48 279	99.06 85	99.66 104	90.30 174	99.64 144	96.32 154	99.97 48	99.96 74

CP-MVS		98.45 47	98.32 46	98.87 89	99.96 8	96.62 131	99.97 18	98.39 144	94.43 110	98.90 94	99.87 28	94.30 85	100.00 1	99.04 60	99.99 22	99.99 24

DSMNet-mixed		88.28 302	88.24 297	88.42 339	89.64 358	75.38 367	98.06 312	89.86 378	85.59 319	88.20 287	92.14 349	76.15 301	91.95 365	78.46 343	96.05 191	97.92 221

tpm2		95.47 164	95.18 159	96.35 201	96.91 239	91.70 267	96.96 333	97.93 207	88.04 286	98.44 115	95.40 291	93.32 111	97.97 238	94.00 196	95.61 202	99.38 171

NP-MVS					95.77 270	91.79 261					98.65 183

EG-PatchMatch MVS		85.35 316	83.81 318	89.99 329	90.39 353	81.89 353	98.21 307	96.09 338	81.78 346	74.73 362	93.72 334	51.56 369	97.12 279	79.16 341	88.61 248	90.96 352

tpm cat1		93.51 209	92.52 221	96.47 192	97.77 197	91.47 273	96.13 344	98.06 196	80.98 348	92.91 218	93.78 333	89.66 180	98.87 174	87.03 295	96.39 186	99.09 198

SteuartSystems-ACMMP		99.02 12	98.97 13	99.18 57	98.72 147	97.71 86	99.98 9	98.44 113	96.85 31	99.80 17	99.91 15	97.57 8	99.85 99	99.44 44	99.99 22	99.99 24
Skip Steuart: Steuart Systems R&D Blog.
CostFormer		96.10 143	95.88 142	96.78 184	97.03 234	92.55 245	97.08 330	97.83 218	90.04 253	98.72 103	94.89 314	95.01 59	98.29 219	96.54 152	95.77 198	99.50 159

CR-MVSNet		93.45 212	92.62 215	95.94 209	96.29 256	92.66 241	92.01 364	96.23 334	92.62 184	96.94 152	93.31 338	91.04 162	96.03 329	79.23 338	95.96 193	99.13 196

JIA-IIPM		91.76 251	90.70 251	94.94 235	96.11 259	87.51 324	93.16 360	98.13 192	75.79 361	97.58 139	77.68 374	92.84 126	97.97 238	88.47 277	96.54 182	99.33 179

Patchmtry		89.70 290	88.49 292	93.33 292	96.24 258	89.94 301	91.37 367	96.23 334	78.22 355	87.69 291	93.31 338	91.04 162	96.03 329	80.18 337	82.10 300	94.02 290

PatchT		90.38 275	88.75 289	95.25 226	95.99 263	90.16 293	91.22 368	97.54 241	76.80 357	97.26 146	86.01 368	91.88 150	96.07 328	66.16 368	95.91 195	99.51 157

tpmrst		96.27 142	95.98 130	97.13 175	97.96 185	93.15 229	96.34 340	98.17 183	92.07 204	98.71 104	95.12 305	93.91 97	98.73 183	94.91 175	96.62 181	99.50 159

BH-w/o		95.71 156	95.38 153	96.68 188	98.49 158	92.28 249	99.84 112	97.50 248	92.12 203	92.06 226	98.79 178	84.69 230	98.67 188	95.29 166	99.66 102	99.09 198

tpm		93.70 207	93.41 202	94.58 249	95.36 284	87.41 325	97.01 331	96.90 308	90.85 239	96.72 160	94.14 330	90.40 173	96.84 298	90.75 250	88.54 251	99.51 157

DELS-MVS		98.54 40	98.22 50	99.50 32	99.15 119	98.65 52	100.00 1	98.58 76	97.70 9	98.21 127	99.24 139	92.58 134	99.94 73	98.63 89	99.94 61	99.92 91
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
BH-untuned		95.18 167	94.83 169	96.22 204	98.36 164	91.22 275	99.80 125	97.32 265	90.91 237	91.08 233	98.67 182	83.51 239	98.54 194	94.23 194	99.61 107	98.92 202

RPMNet		89.76 289	87.28 304	97.19 174	96.29 256	92.66 241	92.01 364	98.31 163	70.19 369	96.94 152	85.87 369	87.25 207	99.78 116	62.69 371	95.96 193	99.13 196

MVSTER		95.53 162	95.22 157	96.45 194	98.56 152	97.72 85	99.91 75	97.67 226	92.38 196	91.39 230	97.14 231	97.24 18	97.30 266	94.80 178	87.85 260	94.34 263

CPTT-MVS		97.64 89	97.32 92	98.58 108	99.97 3	95.77 162	99.96 25	98.35 156	89.90 254	98.36 119	99.79 64	91.18 161	99.99 40	98.37 97	99.99 22	99.99 24

GBi-Net		90.88 263	89.82 268	94.08 269	97.53 212	91.97 254	98.43 295	96.95 301	87.05 297	89.68 253	94.72 316	71.34 325	96.11 324	87.01 296	85.65 276	94.17 273

PVSNet_Blended_VisFu		97.27 102	96.81 107	98.66 100	98.81 143	96.67 129	99.92 71	98.64 66	94.51 107	96.38 171	98.49 193	89.05 191	99.88 90	97.10 140	98.34 141	99.43 167

PVSNet_BlendedMVS		96.05 145	95.82 144	96.72 187	99.59 96	96.99 120	99.95 43	99.10 29	94.06 131	98.27 123	95.80 272	89.00 192	99.95 65	99.12 53	87.53 266	93.24 327

UnsupCasMVSNet_eth		85.52 313	83.99 314	90.10 327	89.36 359	83.51 344	96.65 336	97.99 200	89.14 260	75.89 360	93.83 332	63.25 354	93.92 353	81.92 329	67.90 364	92.88 333

UnsupCasMVSNet_bld		79.97 335	77.03 339	88.78 336	85.62 367	81.98 352	93.66 358	97.35 261	75.51 363	70.79 367	83.05 371	48.70 371	94.91 345	78.31 344	60.29 373	89.46 363

PVSNet_Blended		97.94 73	97.64 79	98.83 91	99.59 96	96.99 120	100.00 1	99.10 29	95.38 78	98.27 123	99.08 146	89.00 192	99.95 65	99.12 53	99.25 123	99.57 146

FMVSNet5		88.32 301	87.47 303	90.88 319	96.90 242	88.39 318	97.28 324	95.68 345	82.60 343	84.67 324	92.40 347	79.83 273	91.16 367	76.39 352	81.51 305	93.09 329

test1		90.88 263	89.82 268	94.08 269	97.53 212	91.97 254	98.43 295	96.95 301	87.05 297	89.68 253	94.72 316	71.34 325	96.11 324	87.01 296	85.65 276	94.17 273

new_pmnet		84.49 322	82.92 324	89.21 332	90.03 356	82.60 347	96.89 335	95.62 347	80.59 349	75.77 361	89.17 356	65.04 350	94.79 347	72.12 358	81.02 312	90.23 357

FMVSNet3		92.69 228	91.58 237	95.99 208	98.29 166	97.42 106	99.26 222	97.62 230	89.80 256	89.68 253	95.32 297	81.62 254	96.27 320	87.01 296	85.65 276	94.29 265

dp		95.05 170	94.43 175	96.91 180	97.99 184	92.73 239	96.29 342	97.98 202	89.70 257	95.93 179	94.67 320	93.83 101	98.45 200	86.91 299	96.53 183	99.54 151

FMVSNet2		91.02 260	89.56 272	95.41 221	97.53 212	95.74 164	98.98 252	97.41 257	87.05 297	88.43 283	95.00 310	71.34 325	96.24 322	85.12 308	85.21 281	94.25 268

FMVSNet1		88.50 300	86.64 306	94.08 269	95.62 281	91.97 254	98.43 295	96.95 301	83.00 339	86.08 317	94.72 316	59.09 361	96.11 324	81.82 330	84.07 291	94.17 273

N_pmnet		80.06 333	80.78 331	77.89 353	91.94 339	45.28 386	98.80 274	56.82 389	78.10 356	80.08 346	93.33 336	77.03 289	95.76 334	68.14 365	82.81 296	92.64 335

cascas		94.64 182	93.61 191	97.74 153	97.82 194	96.26 144	99.96 25	97.78 221	85.76 315	94.00 205	97.54 220	76.95 291	99.21 161	97.23 136	95.43 205	97.76 226

BH-RMVSNet		95.18 167	94.31 178	97.80 145	98.17 176	95.23 182	99.76 137	97.53 243	92.52 192	94.27 202	99.25 138	76.84 292	98.80 177	90.89 247	99.54 111	99.35 176

UGNet		95.33 166	94.57 173	97.62 157	98.55 154	94.85 190	98.67 284	99.32 25	95.75 69	96.80 158	96.27 261	72.18 322	99.96 58	94.58 186	99.05 129	98.04 220
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
WTY-MVS		98.10 69	97.60 81	99.60 20	98.92 134	99.28 16	99.89 88	99.52 14	95.58 73	98.24 126	99.39 126	93.33 110	99.74 130	97.98 115	95.58 203	99.78 109

XXY-MVS		91.82 244	90.46 254	95.88 210	93.91 305	95.40 176	98.87 266	97.69 224	88.63 277	87.87 290	97.08 234	74.38 315	97.89 244	91.66 232	84.07 291	94.35 262

DROMVSNet		97.38 100	97.24 93	97.80 145	97.41 217	95.64 169	99.99 3	97.06 288	94.59 104	99.63 40	99.32 130	89.20 190	98.14 229	98.76 79	99.23 124	99.62 133

sss		97.57 90	97.03 102	99.18 57	98.37 163	98.04 75	99.73 148	99.38 22	93.46 155	98.76 101	99.06 147	91.21 157	99.89 84	96.33 153	97.01 176	99.62 133

Test_1112_low_res		95.72 154	94.83 169	98.42 123	97.79 196	96.41 138	99.65 164	96.65 325	92.70 179	92.86 220	96.13 266	92.15 145	99.30 159	91.88 230	93.64 222	99.55 148

1112_ss		96.01 147	95.20 158	98.42 123	97.80 195	96.41 138	99.65 164	96.66 324	92.71 178	92.88 219	99.40 124	92.16 144	99.30 159	91.92 229	93.66 221	99.55 148

ab-mvs-re		8.28 354	11.04 357	0.00 369	0.00 392	0.00 393	0.00 380	0.00 393	0.00 387	0.00 388	99.40 124	0.00 392	0.00 388	0.00 386	0.00 386	0.00 384

ab-mvs		94.69 179	93.42 200	98.51 116	98.07 180	96.26 144	96.49 338	98.68 59	90.31 248	94.54 196	97.00 239	76.30 298	99.71 134	95.98 158	93.38 225	99.56 147

TR-MVS		94.54 184	93.56 196	97.49 161	97.96 185	94.34 202	98.71 280	97.51 247	90.30 249	94.51 198	98.69 181	75.56 303	98.77 180	92.82 220	95.99 192	99.35 176

MDTV_nov1_ep13_2view						96.26 144	96.11 345		91.89 209	98.06 128		94.40 74		94.30 192		99.67 122

MDTV_nov1_ep13			95.69 146		97.90 188	94.15 204	95.98 347	98.44 113	93.12 164	97.98 130	95.74 274	95.10 53	98.58 191	90.02 261	96.92 178

MIMVSNet1		82.58 327	80.51 332	88.78 336	86.68 365	84.20 343	96.65 336	95.41 351	78.75 354	78.59 351	92.44 344	51.88 368	89.76 370	65.26 370	78.95 326	92.38 341

MIMVSNet		90.30 278	88.67 290	95.17 229	96.45 255	91.64 269	92.39 362	97.15 278	85.99 311	90.50 238	93.19 340	66.95 342	94.86 346	82.01 328	93.43 223	99.01 201

IterMVS-LS		92.69 228	92.11 226	94.43 260	96.80 247	92.74 237	99.45 197	96.89 309	88.98 265	89.65 256	95.38 294	88.77 194	96.34 317	90.98 244	82.04 301	94.22 269
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
CDS-MVSNet		96.34 136	96.07 125	97.13 175	97.37 219	94.96 188	99.53 184	97.91 210	91.55 219	95.37 189	98.32 201	95.05 57	97.13 277	93.80 203	95.75 200	99.30 183
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
ACMMP++_ref															87.04 268

IterMVS		90.91 262	90.17 263	93.12 297	96.78 250	90.42 290	98.89 261	97.05 291	89.03 263	86.49 309	95.42 290	76.59 295	95.02 342	87.22 292	84.09 290	93.93 300
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
DP-MVS Recon		98.41 50	98.02 64	99.56 24	99.97 3	98.70 47	99.92 71	98.44 113	92.06 206	98.40 118	99.84 48	95.68 42	100.00 1	98.19 101	99.71 99	99.97 67

MVS_111021_LR		98.42 49	98.38 40	98.53 115	99.39 110	95.79 161	99.87 93	99.86 2	96.70 38	98.78 98	99.79 64	92.03 148	99.90 80	99.17 52	99.86 83	99.88 96

DP-MVS		94.54 184	93.42 200	97.91 144	99.46 109	94.04 207	98.93 258	97.48 250	81.15 347	90.04 244	99.55 112	87.02 210	99.95 65	88.97 270	98.11 150	99.73 114

ACMMP++															88.23 256

HQP-MVS		94.61 183	94.50 174	94.92 236	95.78 267	91.85 259	99.87 93	97.89 211	96.82 32	93.37 211	98.65 183	80.65 265	98.39 207	97.92 117	89.60 232	94.53 241

QAPM		95.40 165	94.17 180	99.10 72	96.92 238	97.71 86	99.40 200	98.68 59	89.31 259	88.94 273	98.89 169	82.48 245	99.96 58	93.12 218	99.83 85	99.62 133

Vis-MVSNet		95.72 154	95.15 160	97.45 162	97.62 209	94.28 203	99.28 220	98.24 174	94.27 122	96.84 156	98.94 166	79.39 275	98.76 181	93.25 212	98.49 138	99.30 183
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
MVS-HIRNet		86.22 310	83.19 322	95.31 224	96.71 253	90.29 291	92.12 363	97.33 264	62.85 370	86.82 303	70.37 376	69.37 332	97.49 255	75.12 354	97.99 156	98.15 218

IS-MVSNet		96.29 140	95.90 141	97.45 162	98.13 179	94.80 193	99.08 236	97.61 233	92.02 207	95.54 187	98.96 160	90.64 170	98.08 232	93.73 207	97.41 167	99.47 161

HyFIR lowres test		96.66 127	96.43 119	97.36 170	99.05 122	93.91 212	99.70 153	99.80 3	90.54 243	96.26 173	98.08 205	92.15 145	98.23 226	96.84 149	95.46 204	99.93 85

EPMVS		96.53 130	96.01 127	98.09 137	98.43 160	96.12 154	96.36 339	99.43 20	93.53 153	97.64 138	95.04 307	94.41 73	98.38 211	91.13 238	98.11 150	99.75 112

PAPM_NR		98.12 68	97.93 72	98.70 97	99.94 14	96.13 152	99.82 119	98.43 121	94.56 105	97.52 140	99.70 95	94.40 74	99.98 46	97.00 143	99.98 35	99.99 24

TAMVS		95.85 151	95.58 148	96.65 190	97.07 231	93.50 221	99.17 229	97.82 219	91.39 228	95.02 193	98.01 207	92.20 143	97.30 266	93.75 206	95.83 197	99.14 195

PAPR		98.52 42	98.16 55	99.58 23	99.97 3	98.77 40	99.95 43	98.43 121	95.35 79	98.03 129	99.75 82	94.03 94	99.98 46	98.11 106	99.83 85	99.99 24

RPSCF		91.80 248	92.79 213	88.83 335	98.15 177	69.87 370	98.11 310	96.60 326	83.93 333	94.33 201	99.27 134	79.60 274	99.46 157	91.99 227	93.16 227	97.18 231

Vis-MVSNet (Re-imp)		96.32 137	95.98 130	97.35 171	97.93 187	94.82 192	99.47 194	98.15 189	91.83 211	95.09 192	99.11 144	91.37 156	97.47 257	93.47 210	97.43 164	99.74 113

test_0402		85.58 312	83.94 316	90.50 323	93.81 307	85.04 339	98.55 288	95.20 357	76.01 359	79.72 348	95.13 304	64.15 352	96.26 321	66.04 369	86.88 270	90.21 358

MVS_111021_HR		98.72 28	98.62 24	99.01 81	99.36 112	97.18 112	99.93 67	99.90 1	96.81 35	98.67 105	99.77 72	93.92 96	99.89 84	99.27 51	99.94 61	99.96 74

CSCG		97.10 107	97.04 101	97.27 173	99.89 50	91.92 258	99.90 80	99.07 32	88.67 275	95.26 191	99.82 55	93.17 119	99.98 46	98.15 104	99.47 115	99.90 93

PatchMatch-RL		96.04 146	95.40 151	97.95 141	99.59 96	95.22 183	99.52 185	99.07 32	93.96 137	96.49 166	98.35 200	82.28 246	99.82 110	90.15 260	99.22 125	98.81 209

API-MVS		97.86 77	97.66 78	98.47 118	99.52 103	95.41 175	99.47 194	98.87 46	91.68 216	98.84 95	99.85 35	92.34 141	99.99 40	98.44 94	99.96 52	100.00 1

Test By Simon												92.82 128

TDRefinement		84.76 318	82.56 325	91.38 317	74.58 379	84.80 341	97.36 323	94.56 363	84.73 329	80.21 345	96.12 268	63.56 353	98.39 207	87.92 283	63.97 368	90.95 353

USDC		90.00 286	88.96 285	93.10 299	94.81 291	88.16 320	98.71 280	95.54 349	93.66 150	83.75 329	97.20 230	65.58 346	98.31 218	83.96 317	87.49 267	92.85 334

EPP-MVSNet		96.69 125	96.60 113	96.96 179	97.74 200	93.05 232	99.37 207	98.56 80	88.75 273	95.83 182	99.01 151	96.01 32	98.56 192	96.92 147	97.20 171	99.25 187

PMMVS		96.76 120	96.76 109	96.76 185	98.28 168	92.10 253	99.91 75	97.98 202	94.12 126	99.53 51	99.39 126	86.93 211	98.73 183	96.95 146	97.73 158	99.45 164

PAPM		98.60 34	98.42 33	99.14 66	96.05 261	98.96 24	99.90 80	99.35 24	96.68 39	98.35 120	99.66 104	96.45 29	98.51 195	99.45 43	99.89 78	99.96 74

ACMMP		97.74 86	97.44 86	98.66 100	99.92 36	96.13 152	99.18 228	99.45 18	94.84 95	96.41 170	99.71 93	91.40 155	99.99 40	97.99 113	98.03 155	99.87 98
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
CNLPA		97.76 85	97.38 87	98.92 88	99.53 102	96.84 125	99.87 93	98.14 190	93.78 146	96.55 165	99.69 98	92.28 142	99.98 46	97.13 138	99.44 117	99.93 85

PatchmatchNet		95.94 149	95.45 150	97.39 167	97.83 193	94.41 201	96.05 346	98.40 141	92.86 168	97.09 150	95.28 302	94.21 90	98.07 234	89.26 268	98.11 150	99.70 117
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
PHI-MVS		98.41 50	98.21 51	99.03 78	99.86 59	97.10 116	99.98 9	98.80 52	90.78 241	99.62 43	99.78 70	95.30 50	100.00 1	99.80 24	99.93 67	99.99 24

F-COLMAP		96.93 113	96.95 104	96.87 182	99.71 89	91.74 263	99.85 108	97.95 205	93.11 165	95.72 184	99.16 143	92.35 140	99.94 73	95.32 165	99.35 120	98.92 202

ANet_high		56.10 345	52.24 348	67.66 361	49.27 387	56.82 379	83.94 374	82.02 385	70.47 368	33.28 384	64.54 379	17.23 387	69.16 382	45.59 380	23.85 381	77.02 375

wuyk23d		20.37 353	20.84 356	18.99 368	65.34 384	27.73 390	50.43 379	7.67 392	9.50 385	8.01 386	6.34 386	6.13 390	26.24 385	23.40 385	10.69 384	2.99 383

OMC-MVS		97.28 101	97.23 94	97.41 165	99.76 79	93.36 228	99.65 164	97.95 205	96.03 59	97.41 144	99.70 95	89.61 181	99.51 148	96.73 150	98.25 146	99.38 171

MG-MVS		98.91 18	98.65 22	99.68 14	99.94 14	99.07 22	99.64 168	99.44 19	97.33 18	99.00 90	99.72 91	94.03 94	99.98 46	98.73 81	100.00 1	100.00 1

AdaColmap		97.23 104	96.80 108	98.51 116	99.99 1	95.60 171	99.09 234	98.84 49	93.32 158	96.74 159	99.72 91	86.04 219	100.00 1	98.01 111	99.43 118	99.94 84

uanet		0.00 356	0.00 359	0.00 369	0.00 392	0.00 393	0.00 380	0.00 393	0.00 387	0.00 388	0.00 388	0.00 392	0.00 388	0.00 386	0.00 386	0.00 384

ITE_SJBPF				92.38 306	95.69 278	85.14 338		95.71 344	92.81 172	89.33 264	98.11 204	70.23 330	98.42 202	85.91 304	88.16 257	93.59 319

DeepMVS_CX				82.92 350	95.98 265	58.66 378		96.01 339	92.72 177	78.34 352	95.51 286	58.29 362	98.08 232	82.57 324	85.29 279	92.03 344

TinyColmap		87.87 306	86.51 307	91.94 312	95.05 288	85.57 335	97.65 320	94.08 365	84.40 331	81.82 337	96.85 245	62.14 356	98.33 216	80.25 336	86.37 273	91.91 346

MAR-MVS		97.43 93	97.19 95	98.15 135	99.47 107	94.79 194	99.05 245	98.76 53	92.65 183	98.66 106	99.82 55	88.52 198	99.98 46	98.12 105	99.63 103	99.67 122
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
LF4IMVS		89.25 297	88.85 286	90.45 325	92.81 331	81.19 358	98.12 309	94.79 360	91.44 223	86.29 314	97.11 232	65.30 349	98.11 231	88.53 276	85.25 280	92.07 342

MSDG		94.37 190	93.36 204	97.40 166	98.88 139	93.95 211	99.37 207	97.38 259	85.75 317	90.80 236	99.17 142	84.11 237	99.88 90	86.35 300	98.43 140	98.36 215

LS3D		95.84 152	95.11 161	98.02 140	99.85 60	95.10 186	98.74 277	98.50 104	87.22 296	93.66 209	99.86 31	87.45 205	99.95 65	90.94 245	99.81 93	99.02 200

CLD-MVS		94.06 197	93.90 186	94.55 251	96.02 262	90.69 281	99.98 9	97.72 222	96.62 42	91.05 234	98.85 177	77.21 288	98.47 196	98.11 106	89.51 237	94.48 245
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
FPMVS		68.72 338	68.72 340	68.71 360	65.95 383	44.27 388	95.97 348	94.74 361	51.13 375	53.26 378	90.50 355	25.11 381	83.00 378	60.80 372	80.97 314	78.87 374

Gipuma		66.95 343	65.00 343	72.79 356	91.52 345	67.96 371	66.16 378	95.15 359	47.89 376	58.54 374	67.99 378	29.74 376	87.54 375	50.20 378	77.83 334	62.87 378
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015

High-resolution multi-view benchmark