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.

DeepPCF-MVS	89.82 1	94.61 25	96.17 5	89.91 252	97.09 99	70.21 393	98.99 28	96.69 83	95.57 2	95.08 56	99.23 1	86.40 32	99.87 10	97.84 32	98.66 32	99.65 6

DeepC-MVS_fast	89.06 2	94.48 31	94.30 39	95.02 23	98.86 24	85.68 52	98.06 72	96.64 92	93.64 20	91.74 111	98.54 27	80.17 84	99.90 7	92.28 111	98.75 29	99.49 8
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
DeepC-MVS	86.58 3	91.53 112	91.06 114	92.94 104	94.52 175	81.89 153	95.95 241	95.98 166	90.76 55	83.76 236	96.76 140	73.24 214	99.71 58	91.67 123	96.96 98	97.22 171
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
IB-MVS	85.34 4	88.67 186	87.14 208	93.26 87	93.12 232	84.32 87	98.76 36	97.27 22	87.19 131	79.36 291	90.45 303	83.92 54	98.53 150	84.41 213	69.79 376	96.93 191
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
PCF-MVS	84.09 5	86.77 233	85.00 248	92.08 158	92.06 290	83.07 115	92.14 364	94.47 268	79.63 322	76.90 316	94.78 210	71.15 243	99.20 110	72.87 337	91.05 199	93.98 285
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
HY-MVS	84.06 6	91.63 109	90.37 131	95.39 20	96.12 115	88.25 18	90.22 385	97.58 15	88.33 94	90.50 130	91.96 280	79.26 95	99.06 122	90.29 150	89.07 218	98.88 39

PLC	83.97 7	88.00 207	87.38 202	89.83 255	98.02 62	76.46 321	97.16 145	94.43 274	79.26 331	81.98 262	96.28 151	69.36 260	99.27 99	77.71 289	92.25 184	93.77 289
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
3Dnovator+	82.88 8	89.63 162	87.85 186	94.99 24	94.49 181	86.76 35	97.84 86	95.74 187	86.10 156	75.47 341	96.02 156	65.00 294	99.51 85	82.91 236	97.07 95	98.72 50

PVSNet	82.34 9	89.02 174	87.79 188	92.71 117	95.49 139	81.50 168	97.70 98	97.29 20	87.76 110	85.47 206	95.12 196	56.90 362	98.90 133	80.33 258	94.02 150	97.71 126

3Dnovator	82.32 10	89.33 167	87.64 191	94.42 38	93.73 207	85.70 50	97.73 96	96.75 74	86.73 146	76.21 330	95.93 157	62.17 313	99.68 64	81.67 246	97.81 66	97.88 107

ACMP	81.66 11	84.00 287	83.22 281	86.33 330	91.53 304	72.95 366	95.91 245	93.79 322	83.70 237	73.79 352	92.22 271	54.31 382	96.89 266	83.98 217	79.74 310	89.16 344
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
TAPA-MVS	81.61 12	85.02 269	83.67 269	89.06 269	96.79 101	73.27 361	95.92 243	94.79 243	74.81 378	80.47 277	96.83 136	71.07 244	98.19 170	49.82 442	92.57 174	95.71 240
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
ACMM	80.70 13	83.72 292	82.85 289	86.31 333	91.19 309	72.12 372	95.88 248	94.29 285	80.44 299	77.02 314	91.96 280	55.24 374	97.14 251	79.30 273	80.38 307	89.67 331
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
OpenMVS	79.58 14	86.09 244	83.62 274	93.50 80	90.95 315	86.71 36	97.44 121	95.83 182	75.35 372	72.64 367	95.72 163	57.42 359	99.64 68	71.41 346	95.85 129	94.13 282

PVSNet_0	77.72 15	81.70 324	78.95 343	89.94 251	90.77 323	76.72 318	95.96 240	96.95 50	85.01 190	70.24 387	88.53 328	52.32 385	98.20 169	86.68 200	44.08 459	94.89 264

ACMH+	76.62 16	77.47 368	74.94 370	85.05 355	91.07 314	71.58 382	93.26 346	90.01 409	71.80 402	64.76 413	88.55 326	41.62 427	96.48 285	62.35 396	71.00 364	87.09 394

ACMH	75.40 17	77.99 361	74.96 369	87.10 321	90.67 324	76.41 323	93.19 349	91.64 387	72.47 399	63.44 418	87.61 345	43.34 420	97.16 246	58.34 413	73.94 346	87.72 380
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
LTVRE_ROB	73.68 18	77.99 361	75.74 366	84.74 358	90.45 328	72.02 373	86.41 421	91.12 396	72.57 398	66.63 404	87.27 349	54.95 377	96.98 258	56.29 423	75.98 333	85.21 416
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
COLMAP_ROB	73.24 19	75.74 379	73.00 386	83.94 371	92.38 261	69.08 401	91.85 368	86.93 430	61.48 439	65.32 411	90.27 306	42.27 425	96.93 263	50.91 438	75.63 337	85.80 413
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
OpenMVS_ROB	68.52 20	73.02 393	69.57 400	83.37 380	80.54 435	71.82 378	93.60 335	88.22 424	62.37 434	61.98 427	83.15 408	35.31 445	95.47 336	45.08 452	75.88 335	82.82 430

CMPMVS	54.94 21	75.71 380	74.56 375	79.17 412	79.69 437	55.98 450	89.59 391	93.30 350	60.28 444	53.85 451	89.07 319	47.68 409	96.33 291	76.55 303	81.02 303	85.22 415
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
MVE	35.65 22	33.85 437	29.49 442	46.92 453	41.86 477	36.28 473	50.45 469	56.52 476	18.75 472	18.28 471	37.84 468	2.41 478	58.41 472	18.71 469	20.62 469	46.06 467
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
PMVS	34.80 23	39.19 436	35.53 439	50.18 452	29.72 479	30.30 477	59.60 468	66.20 472	26.06 468	17.91 472	49.53 465	3.12 477	74.09 467	18.19 470	49.40 448	46.14 466
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MED-MVS test				94.20 47	99.06 10	83.70 100	98.35 54	97.14 30	87.45 118	97.03 25	98.90 5		99.96 3	97.78 34	98.60 34	98.94 34

TestfortrainingZip a		95.44 11	95.38 17	95.64 13	99.06 10	88.36 15	98.35 54	97.14 30	87.45 118	97.03 25	98.90 5	89.87 12	99.96 3	91.98 119	98.60 34	98.61 56

TestfortrainingZip							98.35 54

fmvsm_s_conf0.5_n_10		94.36 32	94.73 27	93.23 89	95.19 151	82.87 120	99.18 8	96.39 126	93.97 17	97.91 7	98.53 29	75.88 166	99.82 22	98.58 10	96.95 99	97.00 187

viewdifsd2359ckpt07		89.04 173	88.30 177	91.27 202	92.32 263	78.90 247	95.89 246	93.77 326	84.48 206	85.18 208	95.16 192	69.83 256	97.70 196	88.75 174	89.29 215	97.22 171

viewdifsd2359ckpt09		90.00 152	89.28 157	92.15 156	93.31 222	81.38 169	96.37 212	93.64 333	86.34 151	86.62 192	95.64 168	71.58 239	97.52 215	88.93 168	91.06 198	97.54 140

viewdifsd2359ckpt13		90.08 149	89.36 154	92.26 146	93.03 234	81.90 152	96.37 212	94.34 280	86.16 153	87.44 179	95.30 182	70.93 249	97.55 209	89.05 167	91.59 191	97.35 164

viewcassd2359sk11		90.66 137	90.06 141	92.47 130	93.22 225	82.21 141	96.70 191	94.47 268	86.94 137	88.22 170	95.50 176	73.15 215	97.59 203	90.86 134	91.48 192	97.60 137

viewdifsd2359ckpt11		86.38 237	85.29 239	89.66 261	90.42 329	75.65 339	95.27 277	92.45 369	85.54 171	84.27 224	94.73 211	62.16 314	97.39 228	87.78 186	74.97 341	95.96 226

viewmacassd2359aftdt		89.89 155	89.01 162	92.52 129	91.56 300	82.46 133	96.32 219	94.06 301	86.41 149	88.11 173	95.01 202	69.68 258	97.47 220	88.73 175	91.19 195	97.63 133

viewmsd2359difaftdt		86.38 237	85.29 239	89.67 260	90.42 329	75.65 339	95.27 277	92.45 369	85.54 171	84.28 223	94.73 211	62.16 314	97.39 228	87.78 186	74.97 341	95.96 226

diffmvs_AUTHOR		90.86 133	90.41 128	92.24 147	92.01 292	82.22 140	96.18 229	93.64 333	87.28 125	90.46 132	95.64 168	72.82 218	97.39 228	93.17 98	92.46 178	97.11 181

FE-MVSNET		69.26 412	66.03 414	78.93 413	73.82 456	68.33 405	89.65 389	84.06 446	70.21 410	57.79 444	76.94 439	41.48 429	86.98 447	45.85 450	54.51 436	81.48 445

fmvsm_l_conf0.5_n_9		94.91 16	95.60 11	92.84 110	95.20 150	80.55 196	99.45 1	96.36 133	95.17 4	98.48 3	98.55 25	80.53 78	99.78 37	98.87 7	97.79 68	98.19 82

mamba_0408		85.26 266	83.10 283	91.74 178	92.94 241	82.53 127	72.52 461	91.77 383	80.36 303	83.50 240	94.01 237	64.97 295	96.90 264	79.37 270	88.51 234	95.79 235

icg_test_0407_2		87.55 220	86.59 222	90.43 232	92.30 267	78.81 252	92.17 363	93.84 314	85.14 183	83.68 237	94.49 220	67.75 266	95.02 360	81.33 247	88.61 225	97.46 151

SSM_04072		84.64 275	83.10 283	89.25 266	92.94 241	82.53 127	72.52 461	91.77 383	80.36 303	83.50 240	94.01 237	64.97 295	89.41 432	79.37 270	88.51 234	95.79 235

SSM_0407		87.33 224	85.87 231	91.71 182	92.94 241	82.53 127	94.30 314	92.33 374	80.11 311	83.50 240	94.18 232	64.68 299	96.80 275	82.34 240	88.51 234	95.79 235

viewmambaseed2359dif		89.52 163	89.02 160	91.03 212	92.24 277	78.83 249	95.89 246	93.77 326	83.04 250	88.28 169	95.80 161	72.08 231	97.40 226	89.76 157	90.32 205	96.87 197

IMVS_0407		87.82 211	86.72 219	91.14 209	92.30 267	78.81 252	93.34 341	93.84 314	85.14 183	83.68 237	94.49 220	67.75 266	97.14 251	81.33 247	88.61 225	97.46 151

viewmanbaseed2359cas		90.74 135	90.07 140	92.76 113	92.98 239	82.93 119	96.53 199	94.28 286	87.08 134	88.96 154	95.64 168	72.03 233	97.58 205	90.85 135	92.26 183	97.76 120

IMVS_0404		85.34 263	83.69 267	90.29 237	92.30 267	78.81 252	90.62 382	93.84 314	85.14 183	72.51 370	94.49 220	54.36 380	94.61 373	81.33 247	88.61 225	97.46 151

SSM_0404		87.69 217	86.26 225	91.95 166	92.94 241	83.02 117	94.69 303	92.33 374	80.11 311	84.65 219	94.18 232	64.68 299	96.90 264	82.34 240	90.44 204	95.94 229

IMVS_0403		88.07 203	87.02 211	91.24 204	92.30 267	78.81 252	93.62 333	93.84 314	85.14 183	84.36 222	94.49 220	69.49 259	97.46 222	81.33 247	88.61 225	97.46 151

SD_0403		81.29 330	81.13 315	81.78 396	90.20 334	60.43 440	89.97 387	91.31 395	83.87 228	71.78 374	93.08 260	63.86 303	89.61 431	60.00 406	86.07 265	95.30 253

fmvsm_s_conf0.5_n_9		94.52 29	95.22 20	92.41 137	95.79 130	78.61 262	98.73 37	96.00 163	94.91 7	97.73 12	98.73 18	79.09 99	99.79 34	99.14 4	96.86 104	98.83 40

ME-MVS		94.82 21	95.04 22	94.17 48	99.17 8	83.70 100	97.66 101	97.22 24	85.79 164	95.34 49	98.90 5	84.89 38	99.86 12	97.78 34	98.60 34	98.94 34

NormalMVS		92.88 65	92.97 67	92.59 126	97.80 68	82.02 144	97.94 79	94.70 246	92.34 31	92.15 102	96.53 147	77.03 138	98.57 145	91.13 128	97.12 92	97.19 177

lecture		93.17 55	93.57 53	91.96 165	97.80 68	78.79 257	98.50 49	96.98 45	86.61 147	94.75 64	98.16 59	78.36 113	99.35 97	93.89 84	97.12 92	97.75 121

SymmetryMVS		92.45 87	92.33 84	92.82 111	95.19 151	82.02 144	97.94 79	97.43 17	92.34 31	92.15 102	96.53 147	77.03 138	98.57 145	91.13 128	91.19 195	97.87 109

Elysia		85.62 254	83.66 270	91.51 190	88.76 358	82.21 141	95.15 286	94.70 246	76.96 362	84.13 226	92.20 272	50.81 391	97.26 240	77.81 283	92.42 179	95.06 259

StellarMVS		85.62 254	83.66 270	91.51 190	88.76 358	82.21 141	95.15 286	94.70 246	76.96 362	84.13 226	92.20 272	50.81 391	97.26 240	77.81 283	92.42 179	95.06 259

KinetiMVS		89.13 171	87.95 184	92.65 120	92.16 282	82.39 136	97.04 159	96.05 159	86.59 148	88.08 174	94.85 208	61.54 325	98.38 161	81.28 252	93.99 154	97.19 177

LuminaMVS		88.02 206	86.89 215	91.43 195	88.65 365	83.16 113	94.84 298	94.41 276	83.67 238	86.56 193	91.95 282	62.04 319	96.88 268	89.78 156	90.06 207	94.24 278

VortexMVS		85.45 261	84.40 257	88.63 279	93.25 223	81.66 164	95.39 273	94.34 280	87.15 133	75.10 345	87.65 343	66.58 283	95.19 349	86.89 198	73.21 353	89.03 350

AstraMVS		88.99 175	88.35 176	90.92 216	90.81 322	78.29 271	96.73 186	94.24 288	89.96 68	86.13 199	95.04 199	62.12 318	97.41 224	92.54 109	87.57 250	97.06 186

guyue		89.85 156	89.33 156	91.40 197	92.53 260	80.15 212	96.82 179	95.68 190	89.66 72	86.43 194	94.23 228	67.00 276	97.16 246	91.96 120	89.65 211	96.89 194

sc_t1		72.37 396	68.03 407	85.39 350	83.78 423	70.51 389	91.27 376	83.70 449	52.46 456	68.29 394	82.02 412	30.58 454	94.81 366	64.50 385	55.69 431	90.85 312

tt0320-xc		69.70 406	65.27 418	82.99 383	84.33 414	71.92 376	89.56 394	82.08 453	50.11 457	61.87 429	77.50 433	30.48 455	92.34 406	60.30 404	51.20 445	84.71 419

tt0320		70.21 405	66.07 413	82.64 387	83.42 426	70.82 387	89.63 390	84.10 445	49.75 459	62.71 424	77.28 435	33.35 447	92.45 405	58.78 412	55.62 432	84.64 420

fmvsm_s_conf0.5_n_8		94.52 29	95.04 22	92.96 102	95.15 155	81.14 175	99.09 19	96.66 88	95.53 3	97.84 9	98.71 19	76.33 156	99.81 26	99.24 1	96.85 106	97.92 105

fmvsm_s_conf0.5_n_7		92.88 65	93.82 45	90.08 243	92.79 251	76.45 322	98.54 47	96.74 75	92.28 33	95.22 51	98.49 33	74.91 190	98.15 173	98.28 15	97.13 91	95.63 241

fmvsm_s_conf0.5_n_6		94.17 37	94.70 28	92.58 127	93.50 217	81.20 173	99.08 20	96.48 115	92.24 34	98.62 2	98.39 43	78.58 109	99.72 55	98.08 25	97.36 82	96.81 199

fmvsm_s_conf0.5_n_5		93.57 50	93.75 46	93.01 99	92.87 247	82.73 123	98.93 31	95.90 176	90.96 54	95.61 46	98.39 43	76.57 149	99.63 70	98.32 14	96.24 117	96.68 208

fmvsm_s_conf0.5_n_4		93.59 48	94.32 38	91.41 196	93.89 202	79.24 236	98.89 33	96.53 107	92.82 26	97.37 20	98.47 36	77.21 137	99.78 37	98.11 24	95.59 133	95.21 257

SSC-MVS3.2		81.06 334	79.49 338	85.75 342	89.78 342	73.00 364	94.40 310	95.23 221	83.76 234	76.61 321	87.82 341	49.48 400	94.88 362	66.80 371	71.56 361	89.38 335

testing3-2		91.37 116	91.01 116	92.44 134	95.93 123	83.77 97	98.83 35	97.45 16	86.88 139	86.63 191	94.69 215	84.57 42	97.75 194	89.65 159	84.44 277	95.80 233

myMVS_eth3d28		92.72 73	92.23 88	94.21 45	96.16 113	87.46 30	97.37 129	96.99 44	88.13 100	88.18 171	95.47 177	84.12 50	98.04 176	92.46 110	91.17 197	97.14 180

UWE-MVS-28		85.41 262	86.36 224	82.59 389	91.12 312	66.81 415	93.88 327	97.03 41	83.86 230	78.55 297	93.84 244	77.76 125	88.55 436	73.47 335	87.69 246	92.41 302

fmvsm_l_conf0.5_n_3		94.61 25	94.92 25	93.68 69	94.52 175	82.80 122	99.33 2	96.37 131	95.08 6	97.59 18	98.48 35	77.40 130	99.79 34	98.28 15	97.21 87	98.44 65

fmvsm_s_conf0.5_n_3		93.95 43	94.53 31	92.20 152	94.41 184	80.04 215	98.90 32	95.96 168	94.53 11	97.63 17	98.58 24	75.95 163	99.79 34	98.25 17	96.60 112	96.77 202

fmvsm_s_conf0.5_n_2		92.97 61	93.38 59	91.73 179	94.10 196	80.64 193	98.96 29	95.89 177	94.09 15	97.05 24	98.40 42	68.92 262	99.80 30	98.53 12	94.50 145	94.74 269

fmvsm_s_conf0.1_n_2		92.26 94	92.48 80	91.60 187	92.29 272	80.55 196	98.73 37	94.33 283	93.80 19	96.18 39	98.11 62	66.93 278	99.75 47	98.19 20	93.74 159	94.50 276

GDP-MVS		92.85 68	92.55 78	93.75 61	92.82 248	85.76 48	97.63 102	95.05 228	88.34 93	93.15 84	97.10 125	86.92 27	98.01 179	87.95 184	94.00 152	97.47 150

BP-MVS1		93.55 51	93.50 55	93.71 66	92.64 256	85.39 61	97.78 91	96.84 60	89.52 74	92.00 105	97.06 128	88.21 21	98.03 177	91.45 124	96.00 126	97.70 127

reproduce_monomvs		87.80 212	87.60 195	88.40 284	96.56 103	80.26 207	95.80 254	96.32 137	91.56 44	73.60 353	88.36 331	88.53 17	96.25 295	90.47 144	67.23 402	88.67 360

mmtdpeth		78.04 360	76.76 359	81.86 395	89.60 350	66.12 418	92.34 362	87.18 428	76.83 364	85.55 205	76.49 440	46.77 411	97.02 255	90.85 135	45.24 456	82.43 436

reproduce_model		92.53 85	92.87 69	91.50 192	97.41 88	77.14 312	96.02 237	95.91 175	83.65 239	92.45 93	98.39 43	79.75 91	99.21 105	95.27 68	96.98 97	98.14 87

reproduce-ours		92.70 76	93.02 64	91.75 176	97.45 84	77.77 295	96.16 230	95.94 172	84.12 217	92.45 93	98.43 39	80.06 86	99.24 101	95.35 65	97.18 88	98.24 79

our_new_method		92.70 76	93.02 64	91.75 176	97.45 84	77.77 295	96.16 230	95.94 172	84.12 217	92.45 93	98.43 39	80.06 86	99.24 101	95.35 65	97.18 88	98.24 79

mmdepth		0.00 446	0.00 449	0.00 460	0.00 483	0.00 485	0.00 472	0.00 484	0.00 478	0.00 479	0.00 478	0.00 482	0.00 479	0.00 478	0.00 477	0.00 475

monomultidepth		0.00 446	0.00 449	0.00 460	0.00 483	0.00 485	0.00 472	0.00 484	0.00 478	0.00 479	0.00 478	0.00 482	0.00 479	0.00 478	0.00 477	0.00 475

mvs5depth		71.40 402	68.36 406	80.54 405	75.31 454	65.56 420	79.94 443	85.14 439	69.11 417	71.75 375	81.59 415	41.02 432	93.94 387	60.90 403	50.46 446	82.10 438

MVStest1		66.93 417	63.01 421	78.69 414	78.56 440	71.43 384	85.51 428	86.81 431	49.79 458	48.57 454	84.15 399	53.46 383	83.31 454	43.14 455	37.15 465	81.34 446

ttmdpeth		69.58 407	66.92 411	77.54 421	75.95 453	62.40 433	88.09 405	84.32 444	62.87 433	65.70 410	86.25 371	36.53 439	88.53 437	55.65 427	46.96 455	81.70 443

WBMVS		87.73 214	86.79 216	90.56 228	95.61 135	85.68 52	97.63 102	95.52 200	83.77 233	78.30 301	88.44 330	86.14 33	95.78 317	82.54 238	73.15 354	90.21 320

dongtai		69.47 409	68.98 405	70.93 433	86.87 383	58.45 446	88.19 404	93.18 355	63.98 430	56.04 447	80.17 425	70.97 248	79.24 460	33.46 461	47.94 452	75.09 454

kuosan		73.55 388	72.39 389	77.01 422	89.68 347	66.72 416	85.24 430	93.44 341	67.76 419	60.04 437	83.40 406	71.90 234	84.25 453	45.34 451	54.75 433	80.06 448

MVSMamba_PlusPlus		92.37 91	91.55 103	94.83 28	95.37 143	87.69 25	95.60 263	95.42 211	74.65 380	93.95 74	92.81 262	83.11 60	97.70 196	94.49 77	98.53 38	99.11 28

MGCFI-Net		91.95 99	91.03 115	94.72 32	95.68 133	86.38 37	96.93 171	94.48 265	88.25 96	92.78 91	97.24 117	72.34 225	98.46 155	93.13 101	88.43 237	99.32 19

testing91		91.90 102	91.31 108	93.66 70	95.99 119	85.68 52	97.39 128	96.89 55	86.75 145	88.85 157	95.23 186	83.93 53	97.90 188	88.91 169	87.89 244	97.41 158

testing11		92.48 86	92.04 95	93.78 59	95.94 122	86.00 42	97.56 110	97.08 37	87.52 116	89.32 147	95.40 179	84.60 41	98.02 178	91.93 121	89.04 219	97.32 165

testing99		91.91 101	91.35 106	93.60 74	95.98 120	85.70 50	97.31 133	96.92 54	86.82 141	88.91 155	95.25 183	84.26 49	97.89 189	88.80 172	87.94 243	97.21 174

UBG		92.68 80	92.35 82	93.70 67	95.61 135	85.65 55	97.25 135	97.06 39	87.92 105	89.28 148	95.03 200	86.06 34	98.07 174	92.24 112	90.69 203	97.37 162

UWE-MVS		88.56 191	88.91 167	87.50 310	94.17 191	72.19 370	95.82 253	97.05 40	84.96 192	84.78 215	93.51 253	81.33 70	94.75 368	79.43 269	89.17 216	95.57 244

ETVMVS		90.99 127	90.26 132	93.19 92	95.81 127	85.64 56	96.97 166	97.18 28	85.43 173	88.77 160	94.86 207	82.00 68	96.37 289	82.70 237	88.60 229	97.57 139

sasdasda		92.27 92	91.22 109	95.41 18	95.80 128	88.31 16	97.09 155	94.64 256	88.49 88	92.99 88	97.31 111	72.68 220	98.57 145	93.38 92	88.58 230	99.36 16

testing222		91.09 124	90.49 126	92.87 106	95.82 126	85.04 74	96.51 202	97.28 21	86.05 158	89.13 150	95.34 181	80.16 85	96.62 282	85.82 202	88.31 239	96.96 189

WB-MVSnew		84.08 286	83.51 277	85.80 339	91.34 307	76.69 319	95.62 262	96.27 140	81.77 277	81.81 266	92.81 262	58.23 346	94.70 370	66.66 373	87.06 252	85.99 409

fmvsm_l_conf0.5_n_a		94.91 16	95.30 18	93.72 65	94.50 180	84.30 88	99.14 13	96.00 163	91.94 41	97.91 7	98.60 23	84.78 40	99.77 40	98.84 8	96.03 124	97.08 184

fmvsm_l_conf0.5_n		94.89 18	95.24 19	93.86 56	94.42 183	84.61 83	99.13 14	96.15 151	92.06 38	97.92 5	98.52 31	84.52 43	99.74 50	98.76 9	95.67 131	97.22 171

fmvsm_s_conf0.1_n_a		92.38 90	92.49 79	92.06 160	88.08 372	81.62 166	97.97 78	96.01 162	90.62 57	96.58 33	98.33 49	74.09 203	99.71 58	97.23 42	93.46 165	94.86 265

fmvsm_s_conf0.1_n		92.93 63	93.16 63	92.24 147	90.52 326	81.92 150	98.42 51	96.24 143	91.17 48	96.02 42	98.35 48	75.34 183	99.74 50	97.84 32	94.58 143	95.05 261

fmvsm_s_conf0.5_n_a		93.34 54	93.71 48	92.22 150	93.38 220	81.71 162	98.86 34	96.98 45	91.64 42	96.85 27	98.55 25	75.58 172	99.77 40	97.88 31	93.68 160	95.18 258

fmvsm_s_conf0.5_n		93.69 46	94.13 43	92.34 139	94.56 172	82.01 146	99.07 21	97.13 32	92.09 36	96.25 37	98.53 29	76.47 151	99.80 30	98.39 13	94.71 141	95.22 256

MM		95.85 6	95.74 10	96.15 8	96.34 107	89.50 9	99.18 8	98.10 8	95.68 1	96.64 32	97.92 77	80.72 74	99.80 30	99.16 2	97.96 61	99.15 27

WAC-MVS						67.18 410								49.00 444

Syy-MVS		77.97 363	78.05 348	77.74 419	92.13 284	56.85 448	93.97 323	94.23 289	82.43 265	73.39 356	93.57 251	57.95 352	87.86 440	32.40 462	82.34 297	88.51 363

test_fmvsmconf0.1_n		93.08 59	93.22 62	92.65 120	88.45 367	80.81 188	99.00 27	95.11 224	93.21 23	94.00 73	97.91 79	76.84 143	99.59 74	97.91 28	96.55 114	97.54 140

test_fmvsmconf0.01_n		91.08 125	90.68 121	92.29 144	82.43 429	80.12 213	97.94 79	93.93 305	92.07 37	91.97 106	97.60 98	67.56 270	99.53 82	97.09 44	95.56 134	97.21 174

myMVS_eth3d		81.93 321	82.18 297	81.18 400	92.13 284	67.18 410	93.97 323	94.23 289	82.43 265	73.39 356	93.57 251	76.98 141	87.86 440	50.53 440	82.34 297	88.51 363

testing3		80.74 339	81.17 313	79.44 410	91.15 311	63.48 429	97.16 145	95.76 185	80.83 288	71.36 377	93.15 258	78.22 115	87.30 445	43.19 454	79.67 311	87.55 388

SSC-MVS		56.01 425	54.96 426	59.17 448	68.42 461	34.13 475	84.98 432	69.23 468	58.08 452	45.36 458	71.67 456	50.30 397	77.46 462	14.28 472	32.33 467	65.91 461

test_fmvsmconf_n		93.99 42	94.36 37	92.86 107	92.82 248	81.12 176	99.26 6	96.37 131	93.47 21	95.16 52	98.21 53	79.00 100	99.64 68	98.21 19	96.73 110	97.83 114

WB-MVS		57.26 422	56.22 425	60.39 447	69.29 459	35.91 474	86.39 422	70.06 467	59.84 448	46.46 457	72.71 450	51.18 389	78.11 461	15.19 471	34.89 466	67.14 460

test_fmvsmvis_n_1920		92.12 96	92.10 93	92.17 154	90.87 318	81.04 179	98.34 57	93.90 309	92.71 27	87.24 184	97.90 80	74.83 191	99.72 55	96.96 46	96.20 118	95.76 239

dmvs_re		84.10 285	82.90 287	87.70 301	91.41 306	73.28 359	90.59 383	93.19 353	85.02 189	77.96 306	93.68 248	57.92 354	96.18 298	75.50 315	80.87 304	93.63 291

SDMVSNet		87.02 226	85.61 233	91.24 204	94.14 193	83.30 110	93.88 327	95.98 166	84.30 212	79.63 288	92.01 276	58.23 346	97.68 198	90.28 152	82.02 300	92.75 298

dmvs_testset		72.00 400	73.36 384	67.91 436	83.83 422	31.90 476	85.30 429	77.12 461	82.80 258	63.05 422	92.46 267	61.54 325	82.55 458	42.22 457	71.89 360	89.29 340

sd_testset		84.62 276	83.11 282	89.17 267	94.14 193	77.78 294	91.54 374	94.38 278	84.30 212	79.63 288	92.01 276	52.28 386	96.98 258	77.67 290	82.02 300	92.75 298

test_fmvsm_n_1920		94.81 22	95.60 11	92.45 132	95.29 146	80.96 183	99.29 4	97.21 25	94.50 12	97.29 21	98.44 38	82.15 66	99.78 37	98.56 11	97.68 71	96.61 209

test_cas_vis1_n_1920		89.90 154	90.02 143	89.54 262	90.14 338	74.63 347	98.71 39	94.43 274	93.04 25	92.40 96	96.35 150	53.41 384	99.08 121	95.59 61	96.16 119	94.90 263

test_vis1_n_1920		89.95 153	90.59 122	88.03 296	92.36 262	68.98 402	99.12 15	94.34 280	93.86 18	93.64 78	97.01 130	51.54 388	99.59 74	96.76 49	96.71 111	95.53 246

test_vis1_n		85.60 256	85.70 232	85.33 351	84.79 410	64.98 421	96.83 177	91.61 388	87.36 123	91.00 124	94.84 209	36.14 441	97.18 245	95.66 59	93.03 170	93.82 288

test_fmvs1_n		86.34 240	86.72 219	85.17 354	87.54 379	63.64 428	96.91 173	92.37 373	87.49 117	91.33 117	95.58 173	40.81 434	98.46 155	95.00 70	93.49 163	93.41 297

mvsany_test1		87.58 219	88.22 178	85.67 344	89.78 342	67.18 410	95.25 279	87.93 425	83.96 224	88.79 158	97.06 128	72.52 222	94.53 376	92.21 113	86.45 258	95.30 253

APD_test1		56.56 424	53.58 428	65.50 438	67.93 463	46.51 463	77.24 454	72.95 464	38.09 462	42.75 460	75.17 442	13.38 467	82.78 457	40.19 458	54.53 435	67.23 459

test_vis1_rt		73.96 385	72.40 388	78.64 416	83.91 421	61.16 439	95.63 261	68.18 469	76.32 366	60.09 436	74.77 443	29.01 457	97.54 212	87.74 188	75.94 334	77.22 452

test_vis3_rt		54.10 427	51.04 430	63.27 444	58.16 468	46.08 465	84.17 434	49.32 479	56.48 454	36.56 463	49.48 466	8.03 474	91.91 414	67.29 369	49.87 447	51.82 465

test_fmvs2		79.59 348	79.90 334	78.67 415	82.86 428	55.82 452	95.20 282	89.55 412	81.09 284	80.12 284	89.80 312	34.31 446	93.51 396	87.82 185	78.36 326	86.69 398

test_fmvs1		87.79 213	88.52 173	85.62 346	92.98 239	64.31 423	97.88 84	92.42 371	87.95 104	92.24 99	95.82 160	47.94 406	98.44 159	95.31 67	94.09 148	94.09 283

test_fmvs3		69.56 408	69.19 403	70.67 434	69.01 460	47.05 460	90.87 380	86.81 431	71.31 406	66.79 403	77.15 436	16.40 464	83.17 456	81.84 245	62.51 421	81.79 442

mvsany_test3		67.19 416	65.34 417	72.72 432	63.08 466	48.57 459	83.12 438	78.09 460	72.07 400	61.21 431	77.11 437	22.94 459	87.78 442	78.59 279	51.88 444	81.80 441

testf1		45.70 432	42.41 434	55.58 449	53.29 473	40.02 471	68.96 464	62.67 473	27.45 466	29.85 466	61.58 458	5.98 475	73.83 468	28.49 466	43.46 460	52.90 463

APD_test2		45.70 432	42.41 434	55.58 449	53.29 473	40.02 471	68.96 464	62.67 473	27.45 466	29.85 466	61.58 458	5.98 475	73.83 468	28.49 466	43.46 460	52.90 463

test_f		64.01 420	62.13 423	69.65 435	63.00 467	45.30 466	83.66 437	80.68 456	61.30 440	55.70 448	72.62 451	14.23 466	84.64 452	69.84 358	58.11 427	79.00 449

FE-MVS		86.06 245	84.15 263	91.78 175	94.33 187	79.81 219	84.58 433	96.61 95	76.69 365	85.00 211	87.38 347	70.71 251	98.37 162	70.39 356	91.70 190	97.17 179

FA-MVS(test-final)		87.71 216	86.23 227	92.17 154	94.19 190	80.55 196	87.16 415	96.07 158	82.12 272	85.98 201	88.35 332	72.04 232	98.49 152	80.26 260	89.87 209	97.48 149

balanced_conf03		94.60 27	94.30 39	95.48 17	96.45 105	88.82 14	96.33 218	95.58 195	91.12 49	95.84 44	93.87 243	83.47 57	98.37 162	97.26 41	98.81 24	99.24 23

MonoMVSNet		85.68 252	84.22 261	90.03 245	88.43 368	77.83 292	92.95 353	91.46 389	87.28 125	78.11 303	85.96 375	66.31 285	94.81 366	90.71 140	76.81 332	97.46 151

patch_mono-2		95.14 14	96.08 7	92.33 141	98.44 46	77.84 291	98.43 50	97.21 25	92.58 28	97.68 15	97.65 95	86.88 28	99.83 20	98.25 17	97.60 73	99.33 18

EGC-MVSNET		52.46 429	47.56 432	67.15 437	81.98 430	60.11 442	82.54 440	72.44 465	0.11 477	0.70 478	74.59 444	25.11 458	83.26 455	29.04 464	61.51 423	58.09 462

test2506		90.96 129	90.39 129	92.65 120	93.54 211	82.46 133	96.37 212	97.35 19	86.78 143	87.55 178	95.25 183	77.83 123	97.50 217	84.07 216	94.80 139	97.98 101

test1111		88.11 202	87.04 210	91.35 198	93.15 229	78.79 257	96.57 196	90.78 404	86.88 139	85.04 210	95.20 189	57.23 361	97.39 228	83.88 218	94.59 142	97.87 109

ECVR-MVS		88.35 197	87.25 204	91.65 183	93.54 211	79.40 232	96.56 198	90.78 404	86.78 143	85.57 204	95.25 183	57.25 360	97.56 207	84.73 212	94.80 139	97.98 101

test_blank		0.00 446	0.00 449	0.00 460	0.00 483	0.00 485	0.00 472	0.00 484	0.00 478	0.00 479	0.00 478	0.00 482	0.00 479	0.00 478	0.00 477	0.00 475

tt0805		81.20 333	79.06 342	87.61 304	86.50 386	72.97 365	93.66 331	95.48 203	74.11 383	76.23 329	91.99 278	41.36 430	97.40 226	77.44 295	74.78 343	92.45 301

DVP-MVS++		96.05 4	96.41 3	94.96 25	99.05 12	85.34 62	98.13 66	96.77 70	88.38 91	97.70 13	98.77 13	92.06 3	99.84 16	97.47 38	99.37 1	99.70 3

FOURS1					98.51 42	78.01 283	98.13 66	96.21 146	83.04 250	94.39 68

MSC_two_6792asdad				97.14 3	99.05 12	92.19 4		96.83 61					99.81 26	98.08 25	98.81 24	99.43 11

PC_three_1452									91.12 49	98.33 4	98.42 41	92.51 2	99.81 26	98.96 6	99.37 1	99.70 3

No_MVS				97.14 3	99.05 12	92.19 4		96.83 61					99.81 26	98.08 25	98.81 24	99.43 11

test_one_0601					98.91 21	84.56 85		96.70 81	88.06 101	96.57 34	98.77 13	88.04 22

eth-test2					0.00 483

eth-test					0.00 483

GeoE		86.36 239	85.20 242	89.83 255	93.17 228	76.13 327	97.53 113	92.11 377	79.58 323	80.99 271	94.01 237	66.60 282	96.17 299	73.48 334	89.30 214	97.20 176

test_method		56.77 423	54.53 427	63.49 443	76.49 448	40.70 469	75.68 455	74.24 463	19.47 471	48.73 453	71.89 454	19.31 461	65.80 471	57.46 418	47.51 454	83.97 426

Anonymous20240521		72.06 399	69.91 399	78.50 417	77.11 447	61.67 437	91.62 373	90.97 401	65.52 427	62.37 425	79.05 429	36.32 440	90.96 423	57.75 416	68.52 387	82.87 429

h-mvs33		89.30 168	88.95 165	90.36 235	95.07 158	76.04 329	96.96 168	97.11 35	90.39 62	92.22 100	95.10 197	74.70 193	98.86 134	93.14 99	65.89 409	96.16 222

hse-mvs2		88.22 201	88.21 179	88.25 290	93.54 211	73.41 355	95.41 271	95.89 177	90.39 62	92.22 100	94.22 229	74.70 193	96.66 281	93.14 99	64.37 414	94.69 274

CL-MVSNet_self_test		75.81 378	74.14 380	80.83 403	78.33 442	67.79 407	94.22 319	93.52 339	77.28 356	69.82 388	81.54 417	61.47 327	89.22 433	57.59 417	53.51 439	85.48 414

KD-MVS_2432*1600		77.63 366	74.92 371	85.77 340	90.86 319	79.44 230	88.08 406	93.92 307	76.26 367	67.05 400	82.78 409	72.15 229	91.92 412	61.53 397	41.62 462	85.94 410

KD-MVS_self_test		70.97 404	69.31 402	75.95 429	76.24 452	55.39 454	87.45 411	90.94 402	70.20 411	62.96 423	77.48 434	44.01 416	88.09 438	61.25 401	53.26 440	84.37 423

AUN-MVS		86.25 243	85.57 234	88.26 289	93.57 210	73.38 356	95.45 269	95.88 179	83.94 225	85.47 206	94.21 230	73.70 210	96.67 280	83.54 228	64.41 413	94.73 273

ZD-MVS					99.09 9	83.22 112		96.60 98	82.88 256	93.61 79	98.06 69	82.93 62	99.14 115	95.51 63	98.49 42

SR-MVS-dyc-post		91.29 119	91.45 105	90.80 221	97.76 72	76.03 330	96.20 227	95.44 207	80.56 296	90.72 127	97.84 83	75.76 168	98.61 142	91.99 117	96.79 107	97.75 121

RE-MVS-def			91.18 113		97.76 72	76.03 330	96.20 227	95.44 207	80.56 296	90.72 127	97.84 83	73.36 213		91.99 117	96.79 107	97.75 121

SED-MVS		95.88 5	96.22 4	94.87 26	99.03 18	85.03 75	99.12 15	96.78 64	88.72 83	97.79 10	98.91 2	88.48 18	99.82 22	98.15 21	98.97 17	99.74 1

IU-MVS					99.03 18	85.34 62		96.86 59	92.05 40	98.74 1				98.15 21	98.97 17	99.42 13

OPU-MVS				97.30 2	99.19 7	92.31 3	99.12 15				98.54 27	92.06 3	99.84 16	99.11 5	99.37 1	99.74 1

test_241102_TWO								96.78 64	88.72 83	97.70 13	98.91 2	87.86 23	99.82 22	98.15 21	99.00 15	99.47 9

test_241102_ONE					99.03 18	85.03 75		96.78 64	88.72 83	97.79 10	98.90 5	88.48 18	99.82 22

SF-MVS		94.17 37	94.05 44	94.55 36	97.56 80	85.95 43	97.73 96	96.43 120	84.02 221	95.07 57	98.74 17	82.93 62	99.38 92	95.42 64	98.51 39	98.32 71

cl22		85.11 268	84.17 262	87.92 297	95.06 160	78.82 250	95.51 266	94.22 291	79.74 320	76.77 317	87.92 339	75.96 162	95.68 324	79.93 265	72.42 356	89.27 341

miper_ehance_all_eth		84.57 278	83.60 275	87.50 310	92.64 256	78.25 274	95.40 272	93.47 340	79.28 330	76.41 324	87.64 344	76.53 150	95.24 347	78.58 280	72.42 356	89.01 352

miper_enhance_ethall		85.95 247	85.20 242	88.19 293	94.85 165	79.76 221	96.00 238	94.06 301	82.98 254	77.74 307	88.76 323	79.42 92	95.46 337	80.58 256	72.42 356	89.36 339

ZNCC-MVS		92.75 69	92.60 76	93.23 89	98.24 54	81.82 157	97.63 102	96.50 111	85.00 191	91.05 122	97.74 88	78.38 111	99.80 30	90.48 143	98.34 51	98.07 92

dcpmvs_2		93.10 58	93.46 57	92.02 163	97.77 70	79.73 225	94.82 299	93.86 312	86.91 138	91.33 117	96.76 140	85.20 36	98.06 175	96.90 47	97.60 73	98.27 77

cl____		83.27 298	82.12 298	86.74 324	92.20 278	75.95 334	95.11 290	93.27 351	78.44 344	74.82 347	87.02 355	74.19 201	95.19 349	74.67 323	69.32 380	89.09 346

DIV-MVS_self_test		83.27 298	82.12 298	86.74 324	92.19 279	75.92 336	95.11 290	93.26 352	78.44 344	74.81 348	87.08 354	74.19 201	95.19 349	74.66 324	69.30 381	89.11 345

eth_miper_zixun_eth		83.12 302	82.01 300	86.47 329	91.85 299	74.80 345	94.33 312	93.18 355	79.11 333	75.74 339	87.25 351	72.71 219	95.32 343	76.78 301	67.13 403	89.27 341

9.14			94.26 41		98.10 60		98.14 63	96.52 108	84.74 196	94.83 62	98.80 10	82.80 64	99.37 94	95.95 55	98.42 45

uanet_test		0.00 446	0.00 449	0.00 460	0.00 483	0.00 485	0.00 472	0.00 484	0.00 478	0.00 479	0.00 478	0.00 482	0.00 479	0.00 478	0.00 477	0.00 475

DCPMVS		0.00 446	0.00 449	0.00 460	0.00 483	0.00 485	0.00 472	0.00 484	0.00 478	0.00 479	0.00 478	0.00 482	0.00 479	0.00 478	0.00 477	0.00 475

save fliter					98.24 54	83.34 109	98.61 45	96.57 102	91.32 46

ET-MVSNet_ETH3D		90.01 151	89.03 159	92.95 103	94.38 185	86.77 34	98.14 63	96.31 138	89.30 77	63.33 419	96.72 143	90.09 10	93.63 394	90.70 141	82.29 299	98.46 63

UniMVSNet_ETH3D		80.86 338	78.75 344	87.22 319	86.31 389	72.02 373	91.95 365	93.76 328	73.51 388	75.06 346	90.16 309	43.04 423	95.66 325	76.37 307	78.55 324	93.98 285

EIA-MVS		91.73 105	92.05 94	90.78 223	94.52 175	76.40 324	98.06 72	95.34 216	89.19 78	88.90 156	97.28 116	77.56 127	97.73 195	90.77 138	96.86 104	98.20 81

miper_refine_blended		77.63 366	74.92 371	85.77 340	90.86 319	79.44 230	88.08 406	93.92 307	76.26 367	67.05 400	82.78 409	72.15 229	91.92 412	61.53 397	41.62 462	85.94 410

miper_lstm_enhance		81.66 326	80.66 321	84.67 361	91.19 309	71.97 375	91.94 366	93.19 353	77.86 348	72.27 371	85.26 384	73.46 211	93.42 397	73.71 333	67.05 404	88.61 361

ETV-MVS		92.72 73	92.87 69	92.28 145	94.54 174	81.89 153	97.98 76	95.21 222	89.77 71	93.11 85	96.83 136	77.23 136	97.50 217	95.74 58	95.38 135	97.44 156

CS-MVS		92.73 71	93.48 56	90.48 231	96.27 109	75.93 335	98.55 46	94.93 232	89.32 76	94.54 67	97.67 90	78.91 102	97.02 255	93.80 85	97.32 84	98.49 61

D2MVS		82.67 310	81.55 307	86.04 337	87.77 375	76.47 320	95.21 281	96.58 101	82.66 262	70.26 386	85.46 383	60.39 330	95.80 315	76.40 306	79.18 316	85.83 412

DVP-MVS		95.58 9	95.91 9	94.57 35	99.05 12	85.18 67	99.06 22	96.46 116	88.75 81	96.69 29	98.76 15	87.69 24	99.76 42	97.90 29	98.85 21	98.77 43
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									88.38 91	96.69 29	98.76 15	89.64 13	99.76 42	97.47 38	98.84 23	99.38 14

test_0728_SECOND				95.14 21	99.04 17	86.14 40	99.06 22	96.77 70					99.84 16	97.90 29	98.85 21	99.45 10

test0726					99.05 12	85.18 67	99.11 18	96.78 64	88.75 81	97.65 16	98.91 2	87.69 24

SR-MVS		92.16 95	92.27 86	91.83 174	98.37 48	78.41 268	96.67 193	95.76 185	82.19 271	91.97 106	98.07 68	76.44 152	98.64 141	93.71 87	97.27 85	98.45 64

DPM-MVS		96.21 2	95.53 14	98.26 1	96.26 110	95.09 1	99.15 11	96.98 45	93.39 22	96.45 36	98.79 11	90.17 9	99.99 1	89.33 165	99.25 6	99.70 3

GST-MVS		92.43 89	92.22 90	93.04 98	98.17 57	81.64 165	97.40 127	96.38 128	84.71 198	90.90 125	97.40 109	77.55 128	99.76 42	89.75 158	97.74 69	97.72 124

test_yl		91.46 113	90.53 124	94.24 43	97.41 88	85.18 67	98.08 69	97.72 11	80.94 286	89.85 136	96.14 153	75.61 169	98.81 137	90.42 148	88.56 232	98.74 45

thisisatest0530		89.65 161	89.02 160	91.53 189	93.46 218	80.78 189	96.52 200	96.67 85	81.69 279	83.79 235	94.90 206	88.85 15	97.68 198	77.80 285	87.49 251	96.14 223

Anonymous20240529		83.15 301	80.60 322	90.80 221	95.74 131	78.27 273	96.81 181	94.92 233	60.10 446	81.89 264	92.54 266	45.82 414	98.82 136	79.25 274	78.32 327	95.31 252

Anonymous202405211		84.41 281	81.93 302	91.85 173	96.78 102	78.41 268	97.44 121	91.34 393	70.29 409	84.06 228	94.26 227	41.09 431	98.96 127	79.46 268	82.65 295	98.17 84

DCV-MVSNet		91.46 113	90.53 124	94.24 43	97.41 88	85.18 67	98.08 69	97.72 11	80.94 286	89.85 136	96.14 153	75.61 169	98.81 137	90.42 148	88.56 232	98.74 45

tttt0517		88.57 190	88.19 180	89.71 259	93.00 235	75.99 333	95.67 258	96.67 85	80.78 290	81.82 265	94.40 224	88.97 14	97.58 205	76.05 310	86.31 259	95.57 244

our_test_3		77.90 364	75.37 368	85.48 349	85.39 403	76.74 317	93.63 332	91.67 385	73.39 391	65.72 409	84.65 395	58.20 348	93.13 400	57.82 415	67.87 394	86.57 400

thisisatest0515		90.95 130	90.26 132	93.01 99	94.03 201	84.27 90	97.91 82	96.67 85	83.18 246	86.87 189	95.51 175	88.66 16	97.85 190	80.46 257	89.01 220	96.92 193

ppachtmachnet_test		77.19 370	74.22 378	86.13 336	85.39 403	78.22 275	93.98 322	91.36 392	71.74 403	67.11 399	84.87 393	56.67 364	93.37 399	52.21 434	64.59 412	86.80 396

SMA-MVS		94.70 24	94.68 29	94.76 30	98.02 62	85.94 45	97.47 118	96.77 70	85.32 176	97.92 5	98.70 20	83.09 61	99.84 16	95.79 57	99.08 10	98.49 61
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																97.54 140

DPE-MVS		95.32 12	95.55 13	94.64 34	98.79 26	84.87 80	97.77 92	96.74 75	86.11 155	96.54 35	98.89 9	88.39 20	99.74 50	97.67 36	99.05 12	99.31 20
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
test_part2					98.90 22	85.14 73				96.07 41

thres100view900		88.30 198	86.95 213	92.33 141	96.10 116	84.90 79	97.14 148	98.85 2	82.69 261	83.41 243	93.66 249	75.43 177	97.93 182	69.04 361	86.24 262	94.17 279

tfpnnormal		78.14 359	75.42 367	86.31 333	88.33 370	79.24 236	94.41 307	96.22 145	73.51 388	69.81 389	85.52 382	55.43 372	95.75 320	47.65 447	67.86 395	83.95 427

tfpn200view9		88.48 192	87.15 206	92.47 130	96.21 111	85.30 65	97.44 121	98.85 2	83.37 243	83.99 230	93.82 245	75.36 180	97.93 182	69.04 361	86.24 262	94.17 279

c3_l		83.80 290	82.65 292	87.25 318	92.10 286	77.74 299	95.25 279	93.04 361	78.58 341	76.01 332	87.21 352	75.25 185	95.11 355	77.54 293	68.89 384	88.91 358

CHOSEN 280x420		91.71 108	91.85 96	91.29 201	94.94 162	82.69 124	87.89 409	96.17 150	85.94 161	87.27 183	94.31 225	90.27 8	95.65 327	94.04 83	95.86 128	95.53 246

CANet		94.89 18	94.64 30	95.63 14	97.55 81	88.12 19	99.06 22	96.39 126	94.07 16	95.34 49	97.80 86	76.83 145	99.87 10	97.08 45	97.64 72	98.89 38

Fast-Effi-MVS+-dtu		83.33 297	82.60 293	85.50 348	89.55 351	69.38 400	96.09 236	91.38 390	82.30 268	75.96 334	91.41 287	56.71 363	95.58 333	75.13 319	84.90 276	91.54 305

Effi-MVS+-dtu		84.61 277	84.90 251	83.72 376	91.96 294	63.14 431	94.95 295	93.34 349	85.57 168	79.79 286	87.12 353	61.99 321	95.61 331	83.55 227	85.83 268	92.41 302

CANet_DTU		90.98 128	90.04 142	93.83 57	94.76 168	86.23 39	96.32 219	93.12 359	93.11 24	93.71 76	96.82 138	63.08 309	99.48 87	84.29 214	95.12 137	95.77 238

MGCNet		95.58 9	95.44 16	96.01 10	97.63 75	89.26 12	99.27 5	96.59 99	94.71 8	97.08 23	97.99 71	78.69 107	99.86 12	99.15 3	97.85 65	98.91 37

MP-MVS-pluss		92.58 83	92.35 82	93.29 86	97.30 95	82.53 127	96.44 207	96.04 161	84.68 199	89.12 151	98.37 46	77.48 129	99.74 50	93.31 95	98.38 48	97.59 138
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
MSP-MVS		95.62 8	96.54 1	92.86 107	98.31 51	80.10 214	97.42 125	96.78 64	92.20 35	97.11 22	98.29 50	93.46 1	99.10 119	96.01 53	99.30 5	99.38 14
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												77.59 126				97.54 140

sam_mvs												75.35 182

IterMVS-SCA-FT		80.51 342	79.10 341	84.73 359	89.63 349	74.66 346	92.98 351	91.81 382	80.05 314	71.06 381	85.18 387	58.04 349	91.40 418	72.48 341	70.70 368	88.12 375

TSAR-MVS + MP.		94.79 23	95.17 21	93.64 71	97.66 74	84.10 91	95.85 251	96.42 121	91.26 47	97.49 19	96.80 139	86.50 30	98.49 152	95.54 62	99.03 13	98.33 70
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		90.54 140	89.54 151	93.55 77	92.31 264	87.58 27	96.99 161	94.87 236	87.23 128	93.27 80	97.56 100	57.43 356	98.32 164	92.72 105	93.46 165	94.74 269

OPM-MVS		85.84 248	85.10 247	88.06 294	88.34 369	77.83 292	95.72 256	94.20 292	87.89 108	80.45 278	94.05 236	58.57 343	97.26 240	83.88 218	82.76 294	89.09 346
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
ACMMP_NAP		93.46 52	93.23 61	94.17 48	97.16 97	84.28 89	96.82 179	96.65 89	86.24 152	94.27 69	97.99 71	77.94 119	99.83 20	93.39 90	98.57 37	98.39 68

ambc				76.02 427	68.11 462	51.43 457	64.97 466	89.59 411		60.49 434	74.49 445	17.17 463	92.46 403	61.50 399	52.85 442	84.17 425

MTGPA								96.33 135

SPE-MVS-test		92.98 60	93.67 49	90.90 218	96.52 104	76.87 314	98.68 40	94.73 245	90.36 64	94.84 61	97.89 81	77.94 119	97.15 250	94.28 81	97.80 67	98.70 51

Effi-MVS+		90.70 136	89.90 148	93.09 96	93.61 208	83.48 106	95.20 282	92.79 365	83.22 245	91.82 109	95.70 164	71.82 235	97.48 219	91.25 126	93.67 161	98.32 71

xiu_mvs_v2_base		93.92 44	93.26 60	95.91 11	95.07 158	92.02 6	98.19 62	95.68 190	92.06 38	96.01 43	98.14 60	70.83 250	98.96 127	96.74 50	96.57 113	96.76 204

xiu_mvs_v1_base		90.54 140	89.54 151	93.55 77	92.31 264	87.58 27	96.99 161	94.87 236	87.23 128	93.27 80	97.56 100	57.43 356	98.32 164	92.72 105	93.46 165	94.74 269

new-patchmatchnet		68.85 414	65.93 415	77.61 420	73.57 458	63.94 427	90.11 386	88.73 422	71.62 404	55.08 449	73.60 447	40.84 433	87.22 446	51.35 437	48.49 451	81.67 444

pmmvs6		74.65 384	71.67 391	83.60 378	79.13 439	69.94 394	93.31 345	90.88 403	61.05 443	65.83 408	84.15 399	43.43 419	94.83 365	66.62 374	60.63 424	86.02 408

pmmvs5		81.34 329	79.54 336	86.73 327	85.02 408	76.91 313	96.22 225	91.65 386	77.65 350	73.55 354	88.61 325	55.70 371	94.43 378	74.12 329	73.35 351	88.86 359

test_post1							85.88 425				30.24 473	73.77 206	95.07 358	73.89 330

test_post											33.80 470	76.17 159	95.97 304

Fast-Effi-MVS+		87.93 209	86.94 214	90.92 216	94.04 199	79.16 240	98.26 59	93.72 329	81.29 282	83.94 233	92.90 261	69.83 256	96.68 279	76.70 302	91.74 189	96.93 191

patchmatchnet-post											77.09 438	77.78 124	95.39 338

Anonymous20231211		79.72 347	77.19 355	87.33 314	95.59 137	77.16 311	95.18 285	94.18 294	59.31 449	72.57 368	86.20 372	47.89 407	95.66 325	74.53 326	69.24 382	89.18 343

pmmvs-eth3d		73.59 387	70.66 395	82.38 390	76.40 450	73.38 356	89.39 396	89.43 414	72.69 397	60.34 435	77.79 432	46.43 413	91.26 421	66.42 378	57.06 429	82.51 433

GG-mvs-BLEND				93.49 81	94.94 162	86.26 38	81.62 441	97.00 43		88.32 167	94.30 226	91.23 5	96.21 297	88.49 178	97.43 79	98.00 99

xiu_mvs_v1_base_debi		90.54 140	89.54 151	93.55 77	92.31 264	87.58 27	96.99 161	94.87 236	87.23 128	93.27 80	97.56 100	57.43 356	98.32 164	92.72 105	93.46 165	94.74 269

Anonymous20231206		75.29 381	73.64 382	80.22 406	80.75 432	63.38 430	93.36 340	90.71 406	73.09 393	67.12 398	83.70 403	50.33 396	90.85 424	53.63 432	70.10 373	86.44 401

MTAPA		92.45 87	92.31 85	92.86 107	97.90 64	80.85 187	92.88 354	96.33 135	87.92 105	90.20 135	98.18 55	76.71 148	99.76 42	92.57 108	98.09 56	97.96 104

MTMP							97.53 113	68.16 470

gm-plane-assit					92.27 273	79.64 228			84.47 207		95.15 194		97.93 182	85.81 203

test9_res														96.00 54	99.03 13	98.31 73

MVP-Stereo		82.65 311	81.67 306	85.59 347	86.10 395	78.29 271	93.33 342	92.82 364	77.75 349	69.17 393	87.98 338	59.28 339	95.76 319	71.77 343	96.88 102	82.73 432
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
TEST9					98.64 34	83.71 98	97.82 87	96.65 89	84.29 214	95.16 52	98.09 64	84.39 44	99.36 95

train_agg		94.28 34	94.45 34	93.74 62	98.64 34	83.71 98	97.82 87	96.65 89	84.50 204	95.16 52	98.09 64	84.33 45	99.36 95	95.91 56	98.96 19	98.16 85

gg-mvs-nofinetune		85.48 260	82.90 287	93.24 88	94.51 179	85.82 47	79.22 446	96.97 48	61.19 441	87.33 182	53.01 463	90.58 6	96.07 300	86.07 201	97.23 86	97.81 117

SCA		85.63 253	83.64 273	91.60 187	92.30 267	81.86 155	92.88 354	95.56 197	84.85 193	82.52 251	85.12 390	58.04 349	95.39 338	73.89 330	87.58 249	97.54 140

Patchmatch-test		78.25 358	74.72 373	88.83 275	91.20 308	74.10 353	73.91 459	88.70 423	59.89 447	66.82 402	85.12 390	78.38 111	94.54 375	48.84 445	79.58 313	97.86 111

test_8					98.63 36	83.64 103	97.81 89	96.63 94	84.50 204	95.10 55	98.11 62	84.33 45	99.23 103

MS-PatchMatch		83.05 303	81.82 304	86.72 328	89.64 348	79.10 243	94.88 297	94.59 261	79.70 321	70.67 383	89.65 314	50.43 395	96.82 272	70.82 355	95.99 127	84.25 424

Patchmatch-RL test		76.65 374	74.01 381	84.55 364	77.37 446	64.23 424	78.49 450	82.84 452	78.48 342	64.63 414	73.40 448	76.05 161	91.70 417	76.99 298	57.84 428	97.72 124

cdsmvs_eth3d_5k		21.43 440	28.57 443	0.00 460	0.00 483	0.00 485	0.00 472	95.93 174	0.00 478	0.00 479	97.66 91	63.57 305	0.00 479	0.00 478	0.00 477	0.00 475

pcd_1.5k_mvsjas		5.92 445	7.89 448	0.00 460	0.00 483	0.00 485	0.00 472	0.00 484	0.00 478	0.00 479	0.00 478	71.04 245	0.00 479	0.00 478	0.00 477	0.00 475

agg_prior2														94.30 78	99.00 15	98.57 57

agg_prior					98.59 38	83.13 114		96.56 104		94.19 70			99.16 114

tmp_tt		41.54 435	41.93 437	40.38 454	20.10 480	26.84 478	61.93 467	59.09 475	14.81 473	28.51 468	80.58 421	35.53 443	48.33 475	63.70 391	13.11 472	45.96 468

canonicalmvs		92.27 92	91.22 109	95.41 18	95.80 128	88.31 16	97.09 155	94.64 256	88.49 88	92.99 88	97.31 111	72.68 220	98.57 145	93.38 92	88.58 230	99.36 16

anonymousdsp		80.98 337	79.97 332	84.01 370	81.73 431	70.44 391	92.49 358	93.58 338	77.10 359	72.98 364	86.31 369	57.58 355	94.90 361	79.32 272	78.63 323	86.69 398

alignmvs		92.97 61	92.26 87	95.12 22	95.54 138	87.77 23	98.67 41	96.38 128	88.04 102	93.01 87	97.45 104	79.20 97	98.60 143	93.25 96	88.76 223	98.99 33

nrg030		86.79 232	85.43 236	90.87 220	88.76 358	85.34 62	97.06 158	94.33 283	84.31 210	80.45 278	91.98 279	72.36 224	96.36 290	88.48 179	71.13 363	90.93 311

v144192		82.43 313	80.73 319	87.54 309	85.81 399	78.22 275	95.98 239	93.78 323	79.09 334	77.11 313	86.49 363	64.66 301	95.91 310	74.20 328	69.42 379	88.49 365

FIs		86.73 234	86.10 228	88.61 280	90.05 339	80.21 209	96.14 233	96.95 50	85.56 170	78.37 300	92.30 270	76.73 147	95.28 345	79.51 267	79.27 315	90.35 317

v1921920		82.02 320	80.23 327	87.41 313	85.62 400	77.92 288	95.79 255	93.69 330	78.86 338	76.67 318	86.44 365	62.50 311	95.83 313	72.69 338	69.77 377	88.47 366

UA-Net		88.92 178	88.48 174	90.24 239	94.06 198	77.18 310	93.04 350	94.66 253	87.39 122	91.09 121	93.89 242	74.92 189	98.18 171	75.83 312	91.43 193	95.35 251

v1192		82.31 317	80.55 323	87.60 305	85.94 396	78.47 267	95.85 251	93.80 321	79.33 327	76.97 315	86.51 362	63.33 308	95.87 311	73.11 336	70.13 371	88.46 367

FC-MVSNet-test		85.96 246	85.39 237	87.66 303	89.38 355	78.02 282	95.65 260	96.87 57	85.12 187	77.34 309	91.94 283	76.28 158	94.74 369	77.09 297	78.82 319	90.21 320

v1144		82.90 307	81.27 312	87.78 300	86.29 390	79.07 245	96.14 233	93.93 305	80.05 314	77.38 308	86.80 358	65.50 288	95.93 309	75.21 318	70.13 371	88.33 371

sosnet-low-res		0.00 446	0.00 449	0.00 460	0.00 483	0.00 485	0.00 472	0.00 484	0.00 478	0.00 479	0.00 478	0.00 482	0.00 479	0.00 478	0.00 477	0.00 475

HFP-MVS		92.89 64	92.86 71	92.98 101	98.71 28	81.12 176	97.58 108	96.70 81	85.20 181	91.75 110	97.97 76	78.47 110	99.71 58	90.95 130	98.41 46	98.12 90

v148		82.41 316	80.89 316	86.99 322	86.18 393	76.81 316	96.27 222	93.82 318	80.49 298	75.28 343	86.11 374	67.32 274	95.75 320	75.48 316	67.03 405	88.42 369

sosnet		0.00 446	0.00 449	0.00 460	0.00 483	0.00 485	0.00 472	0.00 484	0.00 478	0.00 479	0.00 478	0.00 482	0.00 479	0.00 478	0.00 477	0.00 475

uncertanet		0.00 446	0.00 449	0.00 460	0.00 483	0.00 485	0.00 472	0.00 484	0.00 478	0.00 479	0.00 478	0.00 482	0.00 479	0.00 478	0.00 477	0.00 475

AllTest		75.92 377	73.06 385	84.47 365	92.18 280	67.29 408	91.07 378	84.43 442	67.63 420	63.48 416	90.18 307	38.20 437	97.16 246	57.04 419	73.37 349	88.97 355

TestCases				84.47 365	92.18 280	67.29 408		84.43 442	67.63 420	63.48 416	90.18 307	38.20 437	97.16 246	57.04 419	73.37 349	88.97 355

v7n		79.32 353	77.34 353	85.28 352	84.05 420	72.89 367	93.38 339	93.87 311	75.02 377	70.68 382	84.37 396	59.58 335	95.62 330	67.60 366	67.50 399	87.32 392

region2R		92.72 73	92.70 73	92.79 112	98.68 29	80.53 201	97.53 113	96.51 109	85.22 179	91.94 108	97.98 74	77.26 132	99.67 66	90.83 137	98.37 49	98.18 83

RRT-MVS		89.67 160	88.67 169	92.67 118	94.44 182	81.08 178	94.34 311	94.45 271	86.05 158	85.79 202	92.39 268	63.39 307	98.16 172	93.22 97	93.95 155	98.76 44

mamv4		85.50 258	86.76 217	81.72 397	93.23 224	54.93 455	89.95 388	92.94 362	69.96 412	79.00 293	92.20 272	80.69 76	94.22 382	92.06 116	90.77 201	96.01 225

PS-MVSNAJss		84.91 271	84.30 259	86.74 324	85.89 398	74.40 351	94.95 295	94.16 295	83.93 226	76.45 323	90.11 311	71.04 245	95.77 318	83.16 233	79.02 318	90.06 327

PS-MVSNAJ		94.17 37	93.52 54	96.10 9	95.65 134	92.35 2	98.21 61	95.79 184	92.42 30	96.24 38	98.18 55	71.04 245	99.17 113	96.77 48	97.39 81	96.79 200

jajsoiax		82.12 319	81.15 314	85.03 356	84.19 417	70.70 388	94.22 319	93.95 304	83.07 249	73.48 355	89.75 313	49.66 399	95.37 340	82.24 243	79.76 308	89.02 351

mvs_tets		81.74 323	80.71 320	84.84 357	84.22 416	70.29 392	93.91 326	93.78 323	82.77 259	73.37 358	89.46 316	47.36 410	95.31 344	81.99 244	79.55 314	88.92 357

EI-MVSNet-UG-set		91.35 118	91.22 109	91.73 179	97.39 91	80.68 191	96.47 204	96.83 61	87.92 105	88.30 168	97.36 110	77.84 122	99.13 117	89.43 164	89.45 213	95.37 250

EI-MVSNet-Vis-set		91.84 104	91.77 99	92.04 162	97.60 77	81.17 174	96.61 194	96.87 57	88.20 98	89.19 149	97.55 103	78.69 107	99.14 115	90.29 150	90.94 200	95.80 233

HPM-MVS++		95.32 12	95.48 15	94.85 27	98.62 37	86.04 41	97.81 89	96.93 52	92.45 29	95.69 45	98.50 32	85.38 35	99.85 14	94.75 73	99.18 7	98.65 53

test_prior4						82.34 137	97.75 95

XVS		92.69 78	92.71 72	92.63 123	98.52 40	80.29 204	97.37 129	96.44 118	87.04 135	91.38 114	97.83 85	77.24 134	99.59 74	90.46 145	98.07 57	98.02 94

v1240		81.70 324	79.83 335	87.30 317	85.50 401	77.70 300	95.48 267	93.44 341	78.46 343	76.53 322	86.44 365	60.85 329	95.84 312	71.59 345	70.17 369	88.35 370

pm-mvs1		80.05 344	78.02 349	86.15 335	85.42 402	75.81 337	95.11 290	92.69 367	77.13 357	70.36 385	87.43 346	58.44 345	95.27 346	71.36 347	64.25 415	87.36 391

test_prior2							98.37 53		86.08 157	94.57 66	98.02 70	83.14 59		95.05 69	98.79 27

X-MVStestdata		86.26 242	84.14 264	92.63 123	98.52 40	80.29 204	97.37 129	96.44 118	87.04 135	91.38 114	20.73 474	77.24 134	99.59 74	90.46 145	98.07 57	98.02 94

test_prior				93.09 96	98.68 29	81.91 151		96.40 124					99.06 122			98.29 75

旧先验2							96.97 166		74.06 385	96.10 40			97.76 193	88.38 180

新几何2							96.42 210

新几何1				93.12 94	97.44 86	81.60 167		96.71 80	74.54 381	91.22 120	97.57 99	79.13 98	99.51 85	77.40 296	98.46 43	98.26 78

旧先验1					97.39 91	79.58 229		96.54 105			98.08 67	84.00 51			97.42 80	97.62 135

无先验							96.87 175	96.78 64	77.39 353				99.52 83	79.95 264		98.43 66

原ACMM2							96.84 176

原ACMM1				91.22 207	97.77 70	78.10 281		96.61 95	81.05 285	91.28 119	97.42 108	77.92 121	98.98 126	79.85 266	98.51 39	96.59 210

test222					96.15 114	78.41 268	95.87 249	96.46 116	71.97 401	89.66 141	97.45 104	76.33 156			98.24 54	98.30 74

testdata2													99.48 87	76.45 305

segment_acmp												82.69 65

testdata				90.13 242	95.92 124	74.17 352		96.49 114	73.49 390	94.82 63	97.99 71	78.80 105	97.93 182	83.53 229	97.52 75	98.29 75

testdata1							95.57 265		87.44 120

v8		81.88 322	80.06 331	87.32 315	86.63 385	79.04 246	94.41 307	93.65 332	78.77 339	73.19 362	85.57 380	66.87 279	95.81 314	73.84 332	67.61 398	87.11 393

1314		88.94 177	87.20 205	94.17 48	93.21 226	85.73 49	93.33 342	96.64 92	82.89 255	75.98 333	96.36 149	66.83 280	99.39 91	83.52 230	96.02 125	97.39 161

LFMVS		89.27 169	87.64 191	94.16 51	97.16 97	85.52 59	97.18 141	94.66 253	79.17 332	89.63 142	96.57 145	55.35 373	98.22 168	89.52 163	89.54 212	98.74 45

VDD-MVS		88.28 199	87.02 211	92.06 160	95.09 156	80.18 211	97.55 112	94.45 271	83.09 248	89.10 152	95.92 159	47.97 405	98.49 152	93.08 103	86.91 254	97.52 146

VDDNet		86.44 236	84.51 253	92.22 150	91.56 300	81.83 156	97.10 154	94.64 256	69.50 415	87.84 176	95.19 190	48.01 404	97.92 187	89.82 155	86.92 253	96.89 194

v10		81.43 328	79.53 337	87.11 320	86.38 387	78.87 248	94.31 313	93.43 343	77.88 347	73.24 361	85.26 384	65.44 289	95.75 320	72.14 342	67.71 397	86.72 397

VPNet		84.69 274	82.92 286	90.01 246	89.01 357	83.45 107	96.71 189	95.46 205	85.71 166	79.65 287	92.18 275	56.66 365	96.01 303	83.05 235	67.84 396	90.56 314

MVS		90.60 139	88.64 170	96.50 5	94.25 188	90.53 8	93.33 342	97.21 25	77.59 351	78.88 295	97.31 111	71.52 240	99.69 62	89.60 160	98.03 59	99.27 22

v2v482		83.46 295	81.86 303	88.25 290	86.19 392	79.65 227	96.34 217	94.02 303	81.56 280	77.32 310	88.23 334	65.62 287	96.03 301	77.77 286	69.72 378	89.09 346

V42		83.04 304	81.53 308	87.57 308	86.27 391	79.09 244	95.87 249	94.11 298	80.35 305	77.22 312	86.79 359	65.32 292	96.02 302	77.74 287	70.14 370	87.61 384

SD-MVS		94.84 20	95.02 24	94.29 41	97.87 67	84.61 83	97.76 94	96.19 149	89.59 73	96.66 31	98.17 58	84.33 45	99.60 73	96.09 52	98.50 41	98.66 52
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		85.79 250	84.04 265	91.02 214	89.47 353	80.27 206	96.90 174	94.84 239	85.57 168	80.88 272	89.08 318	56.56 366	96.47 286	77.72 288	85.35 273	96.34 217

MSLP-MVS++		94.28 34	94.39 36	93.97 53	98.30 52	84.06 92	98.64 43	96.93 52	90.71 56	93.08 86	98.70 20	79.98 88	99.21 105	94.12 82	99.07 11	98.63 54

APDe-MVS		94.56 28	94.75 26	93.96 54	98.84 25	83.40 108	98.04 74	96.41 122	85.79 164	95.00 58	98.28 51	84.32 48	99.18 112	97.35 40	98.77 28	99.28 21
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
APD-MVS_3200maxsize		91.23 121	91.35 106	90.89 219	97.89 65	76.35 325	96.30 221	95.52 200	79.82 318	91.03 123	97.88 82	74.70 193	98.54 149	92.11 115	96.89 101	97.77 119

ADS-MVSNet2		79.57 349	77.53 352	85.71 343	93.78 204	72.13 371	79.48 444	86.11 436	73.09 393	80.14 282	79.99 426	62.15 316	90.14 430	59.49 408	83.52 282	94.85 266

EI-MVSNet		85.80 249	85.20 242	87.59 306	91.55 302	77.41 304	95.13 288	95.36 213	80.43 301	80.33 280	94.71 213	73.72 208	95.97 304	76.96 300	78.64 321	89.39 333

Regformer		0.00 446	0.00 449	0.00 460	0.00 483	0.00 485	0.00 472	0.00 484	0.00 478	0.00 479	0.00 478	0.00 482	0.00 479	0.00 478	0.00 477	0.00 475

CVMVSNet		84.83 272	85.57 234	82.63 388	91.55 302	60.38 441	95.13 288	95.03 229	80.60 294	82.10 261	94.71 213	66.40 284	90.19 429	74.30 327	90.32 205	97.31 167

pmmvs4		82.54 312	80.79 317	87.79 299	86.11 394	80.49 202	93.55 336	93.18 355	77.29 355	73.35 359	89.40 317	65.26 293	95.05 359	75.32 317	73.61 348	87.83 379

EU-MVSNet		76.92 373	76.95 357	76.83 424	84.10 418	54.73 456	91.77 369	92.71 366	72.74 396	69.57 390	88.69 324	58.03 351	87.43 444	64.91 384	70.00 375	88.33 371

VNet		92.11 97	91.22 109	94.79 29	96.91 100	86.98 32	97.91 82	97.96 10	86.38 150	93.65 77	95.74 162	70.16 255	98.95 129	93.39 90	88.87 222	98.43 66

test-LLR		88.48 192	87.98 183	89.98 248	92.26 274	77.23 308	97.11 151	95.96 168	83.76 234	86.30 197	91.38 288	72.30 227	96.78 276	80.82 254	91.92 187	95.94 229

TESTMET0.1,1		89.83 157	89.34 155	91.31 199	92.54 259	80.19 210	97.11 151	96.57 102	86.15 154	86.85 190	91.83 285	79.32 93	96.95 260	81.30 251	92.35 182	96.77 202

test-mter		88.95 176	88.60 171	89.98 248	92.26 274	77.23 308	97.11 151	95.96 168	85.32 176	86.30 197	91.38 288	76.37 155	96.78 276	80.82 254	91.92 187	95.94 229

VPA-MVSNet		85.32 264	83.83 266	89.77 258	90.25 332	82.63 125	96.36 215	97.07 38	83.03 252	81.21 270	89.02 320	61.58 324	96.31 292	85.02 210	70.95 365	90.36 316

ACMMPR		92.69 78	92.67 74	92.75 114	98.66 31	80.57 195	97.58 108	96.69 83	85.20 181	91.57 112	97.92 77	77.01 140	99.67 66	90.95 130	98.41 46	98.00 99

testgi		74.88 383	73.40 383	79.32 411	80.13 436	61.75 435	93.21 347	86.64 434	79.49 325	66.56 406	91.06 293	35.51 444	88.67 435	56.79 422	71.25 362	87.56 386

test20.03		72.36 397	71.15 393	75.98 428	77.79 443	59.16 445	92.40 360	89.35 415	74.09 384	61.50 430	84.32 397	48.09 403	85.54 451	50.63 439	62.15 422	83.24 428

thres600view7		88.06 204	86.70 221	92.15 156	96.10 116	85.17 71	97.14 148	98.85 2	82.70 260	83.41 243	93.66 249	75.43 177	97.82 191	67.13 370	85.88 267	93.45 295

ADS-MVSNet		81.26 331	78.36 345	89.96 250	93.78 204	79.78 220	79.48 444	93.60 336	73.09 393	80.14 282	79.99 426	62.15 316	95.24 347	59.49 408	83.52 282	94.85 266

MP-MVS		92.61 82	92.67 74	92.42 136	98.13 59	79.73 225	97.33 132	96.20 147	85.63 167	90.53 129	97.66 91	78.14 117	99.70 61	92.12 114	98.30 53	97.85 112
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
testmvs		9.92 442	12.94 445	0.84 459	0.65 481	0.29 484	93.78 330	0.39 482	0.42 475	2.85 476	15.84 475	0.17 481	0.30 478	2.18 476	0.21 475	1.91 473

thres400		88.42 195	87.15 206	92.23 149	96.21 111	85.30 65	97.44 121	98.85 2	83.37 243	83.99 230	93.82 245	75.36 180	97.93 182	69.04 361	86.24 262	93.45 295

test123		9.07 443	11.73 446	1.11 458	0.50 482	0.77 483	89.44 395	0.20 483	0.34 476	2.15 477	10.72 476	0.34 480	0.32 477	1.79 477	0.08 476	2.23 472

thres200		88.92 178	87.65 190	92.73 116	96.30 108	85.62 57	97.85 85	98.86 1	84.38 209	84.82 214	93.99 240	75.12 187	98.01 179	70.86 353	86.67 255	94.56 275

test0.0.03 1		82.79 308	82.48 294	83.74 375	86.81 384	72.22 368	96.52 200	95.03 229	83.76 234	73.00 363	93.20 255	72.30 227	88.88 434	64.15 388	77.52 330	90.12 323

pmmvs3		65.75 419	62.18 422	76.45 426	67.12 464	64.54 422	88.68 400	85.05 440	54.77 455	57.54 446	73.79 446	29.40 456	86.21 449	55.49 428	47.77 453	78.62 450

EMVS		31.70 439	31.45 441	32.48 456	50.72 475	23.95 480	74.78 457	52.30 478	20.36 470	16.08 474	31.48 472	12.80 468	53.60 474	11.39 474	13.10 473	19.88 471

E-PMN		32.70 438	32.39 440	33.65 455	53.35 472	25.70 479	74.07 458	53.33 477	21.08 469	17.17 473	33.63 471	11.85 470	54.84 473	12.98 473	14.04 470	20.42 470

PGM-MVS		91.93 100	91.80 98	92.32 143	98.27 53	79.74 224	95.28 274	97.27 22	83.83 231	90.89 126	97.78 87	76.12 160	99.56 80	88.82 171	97.93 64	97.66 130

LCM-MVSNet-Re		83.75 291	83.54 276	84.39 369	93.54 211	64.14 425	92.51 357	84.03 447	83.90 227	66.14 407	86.59 361	67.36 273	92.68 401	84.89 211	92.87 171	96.35 216

LCM-MVSNet		52.52 428	48.24 431	65.35 439	47.63 476	41.45 468	72.55 460	83.62 450	31.75 464	37.66 462	57.92 462	9.19 473	76.76 464	49.26 443	44.60 458	77.84 451

MCST-MVS		96.17 3	96.12 6	96.32 7	99.42 2	89.36 10	98.94 30	97.10 36	95.17 4	92.11 104	98.46 37	87.33 26	99.97 2	97.21 43	99.31 4	99.63 7

mvs_anonymous		88.68 185	87.62 193	91.86 171	94.80 167	81.69 163	93.53 337	94.92 233	82.03 274	78.87 296	90.43 304	75.77 167	95.34 341	85.04 209	93.16 169	98.55 60

MVS_Test		90.29 147	89.18 158	93.62 73	95.23 147	84.93 78	94.41 307	94.66 253	84.31 210	90.37 134	91.02 294	75.13 186	97.82 191	83.11 234	94.42 146	98.12 90

MDA-MVSNet-bldmvs		71.45 401	67.94 408	81.98 394	85.33 405	68.50 404	92.35 361	88.76 421	70.40 408	42.99 459	81.96 413	46.57 412	91.31 420	48.75 446	54.39 437	86.11 406

CDPH-MVS		93.12 57	92.91 68	93.74 62	98.65 33	83.88 93	97.67 100	96.26 141	83.00 253	93.22 83	98.24 52	81.31 71	99.21 105	89.12 166	98.74 30	98.14 87

test12				94.25 42	98.34 49	85.55 58		96.35 134		92.36 97		80.84 73	99.22 104		98.31 52	97.98 101

casdiffmvs		90.95 130	90.39 129	92.63 123	92.82 248	82.53 127	96.83 177	94.47 268	87.69 112	88.47 163	95.56 174	74.04 204	97.54 212	90.90 133	92.74 173	97.83 114
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
diffmvs		91.17 122	90.74 120	92.44 134	93.11 233	82.50 132	96.25 224	93.62 335	87.79 109	90.40 133	95.93 157	73.44 212	97.42 223	93.62 89	92.55 175	97.41 158
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
baseline2		90.39 144	90.21 135	90.93 215	90.86 319	80.99 181	95.20 282	97.41 18	86.03 160	80.07 285	94.61 216	90.58 6	97.47 220	87.29 193	89.86 210	94.35 277

baseline1		88.85 181	87.49 198	92.93 105	95.21 149	86.85 33	95.47 268	94.61 259	87.29 124	83.11 248	94.99 204	80.70 75	96.89 266	82.28 242	73.72 347	95.05 261

YYNet1		73.53 390	70.43 397	82.85 385	84.52 413	71.73 380	91.69 371	91.37 391	67.63 420	46.79 455	81.21 419	55.04 376	90.43 427	55.93 424	59.70 426	86.38 402

PMMVS2		50.90 430	46.31 433	64.67 440	55.53 470	46.67 462	77.30 453	71.02 466	40.89 461	34.16 465	59.32 460	9.83 472	76.14 466	40.09 459	28.63 468	71.21 455

MDA-MVSNet_test_wron		73.54 389	70.43 397	82.86 384	84.55 411	71.85 377	91.74 370	91.32 394	67.63 420	46.73 456	81.09 420	55.11 375	90.42 428	55.91 425	59.76 425	86.31 403

tpmvs		83.04 304	80.77 318	89.84 254	95.43 140	77.96 285	85.59 426	95.32 217	75.31 374	76.27 328	83.70 403	73.89 205	97.41 224	59.53 407	81.93 302	94.14 281

PM-MVS		69.32 411	66.93 410	76.49 425	73.60 457	55.84 451	85.91 424	79.32 459	74.72 379	61.09 432	78.18 431	21.76 460	91.10 422	70.86 353	56.90 430	82.51 433

HQP_MVS		87.50 221	87.09 209	88.74 277	91.86 297	77.96 285	97.18 141	94.69 249	89.89 69	81.33 268	94.15 234	64.77 297	97.30 236	87.08 194	82.82 292	90.96 309

plane_prior7					91.86 297	77.55 302

plane_prior6					91.98 293	77.92 288						64.77 297

plane_prior5								94.69 249					97.30 236	87.08 194	82.82 292	90.96 309

plane_prior4											94.15 234

plane_prior3						77.75 298			90.17 66	81.33 268

plane_prior2							97.18 141		89.89 69

plane_prior1					91.95 295

plane_prior						77.96 285	97.52 116		90.36 64						82.96 290

PS-CasMVS		80.27 343	79.18 339	83.52 379	87.56 378	69.88 395	94.08 321	95.29 218	80.27 308	72.08 372	88.51 329	59.22 340	92.23 409	67.49 367	68.15 392	88.45 368

UniMVSNet_NR-MVSNet		85.49 259	84.59 252	88.21 292	89.44 354	79.36 233	96.71 189	96.41 122	85.22 179	78.11 303	90.98 296	76.97 142	95.14 353	79.14 275	68.30 390	90.12 323

PEN-MVS		79.47 351	78.26 347	83.08 382	86.36 388	68.58 403	93.85 329	94.77 244	79.76 319	71.37 376	88.55 326	59.79 332	92.46 403	64.50 385	65.40 410	88.19 373

TransMVSNet (Re)		76.94 372	74.38 376	84.62 363	85.92 397	75.25 343	95.28 274	89.18 417	73.88 386	67.22 397	86.46 364	59.64 333	94.10 384	59.24 411	52.57 443	84.50 422

DTE-MVSNet		78.37 357	77.06 356	82.32 392	85.22 407	67.17 413	93.40 338	93.66 331	78.71 340	70.53 384	88.29 333	59.06 341	92.23 409	61.38 400	63.28 419	87.56 386

DU-MVS		84.57 278	83.33 280	88.28 288	88.76 358	79.36 233	96.43 209	95.41 212	85.42 174	78.11 303	90.82 297	67.61 268	95.14 353	79.14 275	68.30 390	90.33 318

UniMVSNet (Re)		85.31 265	84.23 260	88.55 281	89.75 344	80.55 196	96.72 187	96.89 55	85.42 174	78.40 299	88.93 321	75.38 179	95.52 335	78.58 280	68.02 393	89.57 332

CP-MVSNet		81.01 336	80.08 329	83.79 373	87.91 374	70.51 389	94.29 318	95.65 192	80.83 288	72.54 369	88.84 322	63.71 304	92.32 407	68.58 365	68.36 389	88.55 362

WR-MVS_H		81.02 335	80.09 328	83.79 373	88.08 372	71.26 386	94.46 305	96.54 105	80.08 313	72.81 366	86.82 357	70.36 253	92.65 402	64.18 387	67.50 399	87.46 390

WR-MVS		84.32 282	82.96 285	88.41 283	89.38 355	80.32 203	96.59 195	96.25 142	83.97 223	76.63 319	90.36 305	67.53 271	94.86 364	75.82 313	70.09 374	90.06 327

NR-MVSNet		83.35 296	81.52 309	88.84 274	88.76 358	81.31 172	94.45 306	95.16 223	84.65 200	67.81 396	90.82 297	70.36 253	94.87 363	74.75 321	66.89 406	90.33 318

Baseline_NR-MVSNet		81.22 332	80.07 330	84.68 360	85.32 406	75.12 344	96.48 203	88.80 420	76.24 369	77.28 311	86.40 368	67.61 268	94.39 379	75.73 314	66.73 407	84.54 421

TranMVSNet+NR-MVSNet		83.24 300	81.71 305	87.83 298	87.71 376	78.81 252	96.13 235	94.82 240	84.52 203	76.18 331	90.78 299	64.07 302	94.60 374	74.60 325	66.59 408	90.09 325

TSAR-MVS + GP.		94.35 33	94.50 32	93.89 55	97.38 93	83.04 116	98.10 68	95.29 218	91.57 43	93.81 75	97.45 104	86.64 29	99.43 90	96.28 51	94.01 151	99.20 25

n2								0.00 484

nn								0.00 484

mPP-MVS		91.88 103	91.82 97	92.07 159	98.38 47	78.63 261	97.29 134	96.09 155	85.12 187	88.45 164	97.66 91	75.53 173	99.68 64	89.83 154	98.02 60	97.88 107

door-mid								79.75 458

XVG-OURS-SEG-HR		85.74 251	85.16 245	87.49 312	90.22 333	71.45 383	91.29 375	94.09 299	81.37 281	83.90 234	95.22 187	60.30 331	97.53 214	85.58 205	84.42 279	93.50 293

mvsmamba		90.53 143	90.08 139	91.88 170	94.81 166	80.93 184	93.94 325	94.45 271	88.24 97	87.02 188	92.35 269	68.04 265	95.80 315	94.86 71	97.03 96	98.92 36

MVSFormer		91.36 117	90.57 123	93.73 64	93.00 235	88.08 20	94.80 301	94.48 265	80.74 291	94.90 59	97.13 122	78.84 103	95.10 356	83.77 221	97.46 76	98.02 94

jason		92.73 71	92.23 88	94.21 45	90.50 327	87.30 31	98.65 42	95.09 225	90.61 58	92.76 92	97.13 122	75.28 184	97.30 236	93.32 94	96.75 109	98.02 94
jason: jason.
lupinMVS		93.87 45	93.58 52	94.75 31	93.00 235	88.08 20	99.15 11	95.50 202	91.03 52	94.90 59	97.66 91	78.84 103	97.56 207	94.64 76	97.46 76	98.62 55

test_djsdf		83.00 306	82.45 295	84.64 362	84.07 419	69.78 396	94.80 301	94.48 265	80.74 291	75.41 342	87.70 342	61.32 328	95.10 356	83.77 221	79.76 308	89.04 349

HPM-MVS_fast		90.38 146	90.17 137	91.03 212	97.61 76	77.35 306	97.15 147	95.48 203	79.51 324	88.79 158	96.90 132	71.64 238	98.81 137	87.01 197	97.44 78	96.94 190

K. test v3		73.62 386	71.59 392	79.69 408	82.98 427	59.85 444	90.85 381	88.83 419	77.13 357	58.90 438	82.11 411	43.62 418	91.72 416	65.83 380	54.10 438	87.50 389

lessismore_v0				79.98 407	80.59 434	58.34 447		80.87 455		58.49 440	83.46 405	43.10 422	93.89 388	63.11 394	48.68 449	87.72 380

SixPastTwentyTwo		76.04 376	74.32 377	81.22 399	84.54 412	61.43 438	91.16 377	89.30 416	77.89 346	64.04 415	86.31 369	48.23 402	94.29 381	63.54 392	63.84 417	87.93 378

OurMVSNet-221017-0		77.18 371	76.06 363	80.55 404	83.78 423	60.00 443	90.35 384	91.05 399	77.01 361	66.62 405	87.92 339	47.73 408	94.03 385	71.63 344	68.44 388	87.62 383

HPM-MVS		91.62 110	91.53 104	91.89 169	97.88 66	79.22 238	96.99 161	95.73 188	82.07 273	89.50 146	97.19 120	75.59 171	98.93 132	90.91 132	97.94 62	97.54 140
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
XVG-OURS		85.18 267	84.38 258	87.59 306	90.42 329	71.73 380	91.06 379	94.07 300	82.00 275	83.29 245	95.08 198	56.42 367	97.55 209	83.70 225	83.42 284	93.49 294

XVG-ACMP-BASELINE		79.38 352	77.90 350	83.81 372	84.98 409	67.14 414	89.03 397	93.18 355	80.26 309	72.87 365	88.15 336	38.55 436	96.26 293	76.05 310	78.05 328	88.02 376

casdiffmvs_mvg		91.13 123	90.45 127	93.17 93	92.99 238	83.58 104	97.46 120	94.56 262	87.69 112	87.19 185	94.98 205	74.50 198	97.60 202	91.88 122	92.79 172	98.34 69
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
LPG-MVS_test		84.20 284	83.49 278	86.33 330	90.88 316	73.06 362	95.28 274	94.13 296	82.20 269	76.31 325	93.20 255	54.83 378	96.95 260	83.72 223	80.83 305	88.98 353

LGP-MVS_train				86.33 330	90.88 316	73.06 362		94.13 296	82.20 269	76.31 325	93.20 255	54.83 378	96.95 260	83.72 223	80.83 305	88.98 353

baseline		90.76 134	90.10 138	92.74 115	92.90 246	82.56 126	94.60 304	94.56 262	87.69 112	89.06 153	95.67 166	73.76 207	97.51 216	90.43 147	92.23 185	98.16 85

test11								96.50 111

door								80.13 457

EPNet_dtu		87.65 218	87.89 185	86.93 323	94.57 171	71.37 385	96.72 187	96.50 111	88.56 87	87.12 186	95.02 201	75.91 165	94.01 386	66.62 374	90.00 208	95.42 249
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CHOSEN 1792x2688		91.07 126	90.21 135	93.64 71	95.18 153	83.53 105	96.26 223	96.13 152	88.92 80	84.90 213	93.10 259	72.86 217	99.62 72	88.86 170	95.67 131	97.79 118

EPNet		94.06 41	94.15 42	93.76 60	97.27 96	84.35 86	98.29 58	97.64 14	94.57 10	95.36 48	96.88 134	79.96 89	99.12 118	91.30 125	96.11 121	97.82 116
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
HQP5-MVS						78.48 264

HQP-NCC					92.08 287		97.63 102		90.52 59	82.30 255

ACMP_Plane					92.08 287		97.63 102		90.52 59	82.30 255

APD-MVS		93.61 47	93.59 51	93.69 68	98.76 27	83.26 111	97.21 137	96.09 155	82.41 267	94.65 65	98.21 53	81.96 69	98.81 137	94.65 75	98.36 50	99.01 30
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
BP-MVS														87.67 190

HQP4-MVS										82.30 255			97.32 234			91.13 307

HQP3-MVS								94.80 241							83.01 288

HQP2-MVS												65.40 290

CNVR-MVS		96.30 1	96.54 1	95.55 16	99.31 5	87.69 25	99.06 22	97.12 34	94.66 9	96.79 28	98.78 12	86.42 31	99.95 5	97.59 37	99.18 7	99.00 31

NCCC		95.63 7	95.94 8	94.69 33	99.21 6	85.15 72	99.16 10	96.96 49	94.11 14	95.59 47	98.64 22	85.07 37	99.91 6	95.61 60	99.10 9	99.00 31

114514_t		88.79 184	87.57 196	92.45 132	98.21 56	81.74 160	96.99 161	95.45 206	75.16 375	82.48 252	95.69 165	68.59 264	98.50 151	80.33 258	95.18 136	97.10 183

CP-MVS		92.54 84	92.60 76	92.34 139	98.50 43	79.90 218	98.40 52	96.40 124	84.75 195	90.48 131	98.09 64	77.40 130	99.21 105	91.15 127	98.23 55	97.92 105

DSMNet-mixed		73.13 392	72.45 387	75.19 430	77.51 445	46.82 461	85.09 431	82.01 454	67.61 424	69.27 392	81.33 418	50.89 390	86.28 448	54.54 429	83.80 281	92.46 300

tpm2		87.35 223	86.26 225	90.62 226	92.93 245	78.67 260	88.06 408	95.99 165	79.33 327	87.40 180	86.43 367	80.28 81	96.40 287	80.23 261	85.73 270	96.79 200

NP-MVS					92.04 291	78.22 275					94.56 217

EG-PatchMatch MVS		74.92 382	72.02 390	83.62 377	83.76 425	73.28 359	93.62 333	92.04 379	68.57 418	58.88 439	83.80 402	31.87 451	95.57 334	56.97 421	78.67 320	82.00 440

tpm cat1		83.63 293	81.38 310	90.39 234	93.53 216	78.19 280	85.56 427	95.09 225	70.78 407	78.51 298	83.28 407	74.80 192	97.03 254	66.77 372	84.05 280	95.95 228

SteuartSystems-ACMMP		94.13 40	94.44 35	93.20 91	95.41 141	81.35 171	99.02 26	96.59 99	89.50 75	94.18 71	98.36 47	83.68 56	99.45 89	94.77 72	98.45 44	98.81 42
Skip Steuart: Steuart Systems R&D Blog.
CostFormer		89.08 172	88.39 175	91.15 208	93.13 231	79.15 241	88.61 401	96.11 154	83.14 247	89.58 143	86.93 356	83.83 55	96.87 269	88.22 182	85.92 266	97.42 157

CR-MVSNet		83.53 294	81.36 311	90.06 244	90.16 336	79.75 222	79.02 448	91.12 396	84.24 216	82.27 259	80.35 423	75.45 175	93.67 393	63.37 393	86.25 260	96.75 205

JIA-IIPM		79.00 355	77.20 354	84.40 368	89.74 346	64.06 426	75.30 456	95.44 207	62.15 435	81.90 263	59.08 461	78.92 101	95.59 332	66.51 377	85.78 269	93.54 292

Patchmtry		77.36 369	74.59 374	85.67 344	89.75 344	75.75 338	77.85 451	91.12 396	60.28 444	71.23 378	80.35 423	75.45 175	93.56 395	57.94 414	67.34 401	87.68 382

PatchT		79.75 346	76.85 358	88.42 282	89.55 351	75.49 341	77.37 452	94.61 259	63.07 431	82.46 253	73.32 449	75.52 174	93.41 398	51.36 436	84.43 278	96.36 215

tpmrst		88.36 196	87.38 202	91.31 199	94.36 186	79.92 217	87.32 413	95.26 220	85.32 176	88.34 166	86.13 373	80.60 77	96.70 278	83.78 220	85.34 274	97.30 168

BH-w/o		88.24 200	87.47 200	90.54 230	95.03 161	78.54 263	97.41 126	93.82 318	84.08 219	78.23 302	94.51 219	69.34 261	97.21 243	80.21 262	94.58 143	95.87 232

tpm		85.55 257	84.47 256	88.80 276	90.19 335	75.39 342	88.79 399	94.69 249	84.83 194	83.96 232	85.21 386	78.22 115	94.68 372	76.32 308	78.02 329	96.34 217

DELS-MVS		94.98 15	94.49 33	96.44 6	96.42 106	90.59 7	99.21 7	97.02 42	94.40 13	91.46 113	97.08 126	83.32 58	99.69 62	92.83 104	98.70 31	99.04 29
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		86.95 228	85.94 229	89.99 247	94.52 175	77.46 303	96.78 183	93.37 348	81.80 276	76.62 320	93.81 247	66.64 281	97.02 255	76.06 309	93.88 157	95.48 248

RPMNet		79.85 345	75.92 365	91.64 184	90.16 336	79.75 222	79.02 448	95.44 207	58.43 451	82.27 259	72.55 452	73.03 216	98.41 160	46.10 449	86.25 260	96.75 205

MVSTER		89.25 170	88.92 166	90.24 239	95.98 120	84.66 82	96.79 182	95.36 213	87.19 131	80.33 280	90.61 301	90.02 11	95.97 304	85.38 207	78.64 321	90.09 325

CPTT-MVS		89.72 159	89.87 149	89.29 265	98.33 50	73.30 358	97.70 98	95.35 215	75.68 371	87.40 180	97.44 107	70.43 252	98.25 167	89.56 162	96.90 100	96.33 219

GBi-Net		82.42 314	80.43 325	88.39 285	92.66 253	81.95 147	94.30 314	93.38 345	79.06 335	75.82 336	85.66 376	56.38 368	93.84 389	71.23 348	75.38 338	89.38 335

PVSNet_Blended_VisFu		91.24 120	90.77 119	92.66 119	95.09 156	82.40 135	97.77 92	95.87 181	88.26 95	86.39 195	93.94 241	76.77 146	99.27 99	88.80 172	94.00 152	96.31 220

PVSNet_BlendedMVS		90.05 150	89.96 145	90.33 236	97.47 82	83.86 94	98.02 75	96.73 77	87.98 103	89.53 144	89.61 315	76.42 153	99.57 78	94.29 79	79.59 312	87.57 385

UnsupCasMVSNet_eth		73.25 391	70.57 396	81.30 398	77.53 444	66.33 417	87.24 414	93.89 310	80.38 302	57.90 443	81.59 415	42.91 424	90.56 426	65.18 383	48.51 450	87.01 395

UnsupCasMVSNet_bld		68.60 415	64.50 419	80.92 402	74.63 455	67.80 406	83.97 435	92.94 362	65.12 428	54.63 450	68.23 457	35.97 442	92.17 411	60.13 405	44.83 457	82.78 431

PVSNet_Blended		93.13 56	92.98 66	93.57 76	97.47 82	83.86 94	99.32 3	96.73 77	91.02 53	89.53 144	96.21 152	76.42 153	99.57 78	94.29 79	95.81 130	97.29 169

FMVSNet5		76.46 375	74.16 379	83.35 381	90.05 339	76.17 326	89.58 392	89.85 410	71.39 405	65.29 412	80.42 422	50.61 394	87.70 443	61.05 402	69.24 382	86.18 405

test1		82.42 314	80.43 325	88.39 285	92.66 253	81.95 147	94.30 314	93.38 345	79.06 335	75.82 336	85.66 376	56.38 368	93.84 389	71.23 348	75.38 338	89.38 335

new_pmnet		66.18 418	63.18 420	75.18 431	76.27 451	61.74 436	83.79 436	84.66 441	56.64 453	51.57 452	71.85 455	31.29 452	87.93 439	49.98 441	62.55 420	75.86 453

FMVSNet3		84.71 273	82.71 291	90.70 225	94.55 173	87.71 24	95.92 243	94.67 252	81.73 278	75.82 336	88.08 337	66.99 277	94.47 377	71.23 348	75.38 338	89.91 329

dp		84.30 283	82.31 296	90.28 238	94.24 189	77.97 284	86.57 419	95.53 198	79.94 317	80.75 274	85.16 388	71.49 241	96.39 288	63.73 390	83.36 285	96.48 213

FMVSNet2		82.79 308	80.44 324	89.83 255	92.66 253	85.43 60	95.42 270	94.35 279	79.06 335	74.46 349	87.28 348	56.38 368	94.31 380	69.72 360	74.68 344	89.76 330

FMVSNet1		79.50 350	76.54 361	88.39 285	88.47 366	81.95 147	94.30 314	93.38 345	73.14 392	72.04 373	85.66 376	43.86 417	93.84 389	65.48 381	72.53 355	89.38 335

N_pmnet		61.30 421	60.20 424	64.60 441	84.32 415	17.00 482	91.67 372	10.98 480	61.77 437	58.45 441	78.55 430	49.89 398	91.83 415	42.27 456	63.94 416	84.97 417

cascas		86.50 235	84.48 255	92.55 128	92.64 256	85.95 43	97.04 159	95.07 227	75.32 373	80.50 276	91.02 294	54.33 381	97.98 181	86.79 199	87.62 247	93.71 290

BH-RMVSNet		86.84 230	85.28 241	91.49 193	95.35 144	80.26 207	96.95 169	92.21 376	82.86 257	81.77 267	95.46 178	59.34 338	97.64 200	69.79 359	93.81 158	96.57 211

UGNet		87.73 214	86.55 223	91.27 202	95.16 154	79.11 242	96.35 216	96.23 144	88.14 99	87.83 177	90.48 302	50.65 393	99.09 120	80.13 263	94.03 149	95.60 243
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		92.65 81	91.68 100	95.56 15	96.00 118	88.90 13	98.23 60	97.65 13	88.57 86	89.82 138	97.22 119	79.29 94	99.06 122	89.57 161	88.73 224	98.73 49

XXY-MVS		83.84 289	82.00 301	89.35 264	87.13 381	81.38 169	95.72 256	94.26 287	80.15 310	75.92 335	90.63 300	61.96 322	96.52 284	78.98 277	73.28 352	90.14 322

EC-MVSNet		91.73 105	92.11 92	90.58 227	93.54 211	77.77 295	98.07 71	94.40 277	87.44 120	92.99 88	97.11 124	74.59 197	96.87 269	93.75 86	97.08 94	97.11 181

sss		90.87 132	89.96 145	93.60 74	94.15 192	83.84 96	97.14 148	98.13 7	85.93 162	89.68 140	96.09 155	71.67 236	99.30 98	87.69 189	89.16 217	97.66 130

Test_1112_low_res		88.03 205	86.73 218	91.94 168	93.15 229	80.88 186	96.44 207	92.41 372	83.59 242	80.74 275	91.16 292	80.18 83	97.59 203	77.48 294	85.40 272	97.36 163

1112_ss		88.60 189	87.47 200	92.00 164	93.21 226	80.97 182	96.47 204	92.46 368	83.64 240	80.86 273	97.30 114	80.24 82	97.62 201	77.60 291	85.49 271	97.40 160

ab-mvs-re		8.11 444	10.81 447	0.00 460	0.00 483	0.00 485	0.00 472	0.00 484	0.00 478	0.00 479	97.30 114	0.00 482	0.00 479	0.00 478	0.00 477	0.00 475

ab-mvs		87.08 225	84.94 249	93.48 82	93.34 221	83.67 102	88.82 398	95.70 189	81.18 283	84.55 221	90.14 310	62.72 310	98.94 131	85.49 206	82.54 296	97.85 112

TR-MVS		86.30 241	84.93 250	90.42 233	94.63 170	77.58 301	96.57 196	93.82 318	80.30 306	82.42 254	95.16 192	58.74 342	97.55 209	74.88 320	87.82 245	96.13 224

MDTV_nov1_ep13_2view						81.74 160	86.80 417		80.65 293	85.65 203		74.26 200		76.52 304		96.98 188

MDTV_nov1_ep13			83.69 267		94.09 197	81.01 180	86.78 418	96.09 155	83.81 232	84.75 216	84.32 397	74.44 199	96.54 283	63.88 389	85.07 275

MIMVSNet1		69.44 410	66.65 412	77.84 418	76.48 449	62.84 432	87.42 412	88.97 418	66.96 425	57.75 445	79.72 428	32.77 450	85.83 450	46.32 448	63.42 418	84.85 418

MIMVSNet		79.18 354	75.99 364	88.72 278	87.37 380	80.66 192	79.96 442	91.82 381	77.38 354	74.33 350	81.87 414	41.78 426	90.74 425	66.36 379	83.10 287	94.76 268

IterMVS-LS		83.93 288	82.80 290	87.31 316	91.46 305	77.39 305	95.66 259	93.43 343	80.44 299	75.51 340	87.26 350	73.72 208	95.16 352	76.99 298	70.72 367	89.39 333
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
CDS-MVSNet		89.50 164	88.96 164	91.14 209	91.94 296	80.93 184	97.09 155	95.81 183	84.26 215	84.72 217	94.20 231	80.31 80	95.64 328	83.37 231	88.96 221	96.85 198
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
ACMMP++_ref															78.45 325

IterMVS		80.67 340	79.16 340	85.20 353	89.79 341	76.08 328	92.97 352	91.86 380	80.28 307	71.20 379	85.14 389	57.93 353	91.34 419	72.52 340	70.74 366	88.18 374
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
DP-MVS Recon		91.72 107	90.85 117	94.34 39	99.50 1	85.00 77	98.51 48	95.96 168	80.57 295	88.08 174	97.63 97	76.84 143	99.89 9	85.67 204	94.88 138	98.13 89

MVS_111021_LR		91.60 111	91.64 102	91.47 194	95.74 131	78.79 257	96.15 232	96.77 70	88.49 88	88.64 162	97.07 127	72.33 226	99.19 111	93.13 101	96.48 115	96.43 214

DP-MVS		81.47 327	78.28 346	91.04 211	98.14 58	78.48 264	95.09 293	86.97 429	61.14 442	71.12 380	92.78 265	59.59 334	99.38 92	53.11 433	86.61 256	95.27 255

ACMMP++															79.05 317

HQP-MVS		87.91 210	87.55 197	88.98 272	92.08 287	78.48 264	97.63 102	94.80 241	90.52 59	82.30 255	94.56 217	65.40 290	97.32 234	87.67 190	83.01 288	91.13 307

QAPM		86.88 229	84.51 253	93.98 52	94.04 199	85.89 46	97.19 140	96.05 159	73.62 387	75.12 344	95.62 171	62.02 320	99.74 50	70.88 352	96.06 123	96.30 221

Vis-MVSNet		88.67 186	87.82 187	91.24 204	92.68 252	78.82 250	96.95 169	93.85 313	87.55 115	87.07 187	95.13 195	63.43 306	97.21 243	77.58 292	96.15 120	97.70 127
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
MVS-HIRNet		71.36 403	67.00 409	84.46 367	90.58 325	69.74 397	79.15 447	87.74 427	46.09 460	61.96 428	50.50 464	45.14 415	95.64 328	53.74 431	88.11 242	88.00 377

IS-MVSNet		88.67 186	88.16 181	90.20 241	93.61 208	76.86 315	96.77 185	93.07 360	84.02 221	83.62 239	95.60 172	74.69 196	96.24 296	78.43 282	93.66 162	97.49 148

HyFIR lowres test		89.36 166	88.60 171	91.63 186	94.91 164	80.76 190	95.60 263	95.53 198	82.56 264	84.03 229	91.24 291	78.03 118	96.81 273	87.07 196	88.41 238	97.32 165

EPMVS		87.47 222	85.90 230	92.18 153	95.41 141	82.26 139	87.00 416	96.28 139	85.88 163	84.23 225	85.57 380	75.07 188	96.26 293	71.14 351	92.50 176	98.03 93

PAPM_NR		91.46 113	90.82 118	93.37 85	98.50 43	81.81 158	95.03 294	96.13 152	84.65 200	86.10 200	97.65 95	79.24 96	99.75 47	83.20 232	96.88 102	98.56 58

TAMVS		88.48 192	87.79 188	90.56 228	91.09 313	79.18 239	96.45 206	95.88 179	83.64 240	83.12 247	93.33 254	75.94 164	95.74 323	82.40 239	88.27 240	96.75 205

PAPR		92.74 70	92.17 91	94.45 37	98.89 23	84.87 80	97.20 139	96.20 147	87.73 111	88.40 165	98.12 61	78.71 106	99.76 42	87.99 183	96.28 116	98.74 45

RPSCF		77.73 365	76.63 360	81.06 401	88.66 364	55.76 453	87.77 410	87.88 426	64.82 429	74.14 351	92.79 264	49.22 401	96.81 273	67.47 368	76.88 331	90.62 313

Vis-MVSNet (Re-imp)		88.88 180	88.87 168	88.91 273	93.89 202	74.43 350	96.93 171	94.19 293	84.39 208	83.22 246	95.67 166	78.24 114	94.70 370	78.88 278	94.40 147	97.61 136

test_0402		72.68 394	69.54 401	82.09 393	88.67 363	71.81 379	92.72 356	86.77 433	61.52 438	62.21 426	83.91 401	43.22 421	93.76 392	34.60 460	72.23 359	80.72 447

MVS_111021_HR		93.41 53	93.39 58	93.47 84	97.34 94	82.83 121	97.56 110	98.27 6	89.16 79	89.71 139	97.14 121	79.77 90	99.56 80	93.65 88	97.94 62	98.02 94

CSCG		92.02 98	91.65 101	93.12 94	98.53 39	80.59 194	97.47 118	97.18 28	77.06 360	84.64 220	97.98 74	83.98 52	99.52 83	90.72 139	97.33 83	99.23 24

PatchMatch-RL		85.00 270	83.66 270	89.02 271	95.86 125	74.55 349	92.49 358	93.60 336	79.30 329	79.29 292	91.47 286	58.53 344	98.45 157	70.22 357	92.17 186	94.07 284

API-MVS		90.18 148	88.97 163	93.80 58	98.66 31	82.95 118	97.50 117	95.63 194	75.16 375	86.31 196	97.69 89	72.49 223	99.90 7	81.26 253	96.07 122	98.56 58

Test By Simon												71.65 237

TDRefinement		69.20 413	65.78 416	79.48 409	66.04 465	62.21 434	88.21 403	86.12 435	62.92 432	61.03 433	85.61 379	33.23 448	94.16 383	55.82 426	53.02 441	82.08 439

USDC		78.65 356	76.25 362	85.85 338	87.58 377	74.60 348	89.58 392	90.58 407	84.05 220	63.13 420	88.23 334	40.69 435	96.86 271	66.57 376	75.81 336	86.09 407

EPP-MVSNet		89.76 158	89.72 150	89.87 253	93.78 204	76.02 332	97.22 136	96.51 109	79.35 326	85.11 209	95.01 202	84.82 39	97.10 253	87.46 192	88.21 241	96.50 212

PMMVS		89.46 165	89.92 147	88.06 294	94.64 169	69.57 399	96.22 225	94.95 231	87.27 127	91.37 116	96.54 146	65.88 286	97.39 228	88.54 176	93.89 156	97.23 170

PAPM		92.87 67	92.40 81	94.30 40	92.25 276	87.85 22	96.40 211	96.38 128	91.07 51	88.72 161	96.90 132	82.11 67	97.37 233	90.05 153	97.70 70	97.67 129

ACMMP		90.39 144	89.97 144	91.64 184	97.58 79	78.21 278	96.78 183	96.72 79	84.73 197	84.72 217	97.23 118	71.22 242	99.63 70	88.37 181	92.41 181	97.08 184
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		86.96 227	85.37 238	91.72 181	97.59 78	79.34 235	97.21 137	91.05 399	74.22 382	78.90 294	96.75 142	67.21 275	98.95 129	74.68 322	90.77 201	96.88 196

PatchmatchNet		86.83 231	85.12 246	91.95 166	94.12 195	82.27 138	86.55 420	95.64 193	84.59 202	82.98 250	84.99 392	77.26 132	95.96 307	68.61 364	91.34 194	97.64 132
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
PHI-MVS		93.59 48	93.63 50	93.48 82	98.05 61	81.76 159	98.64 43	97.13 32	82.60 263	94.09 72	98.49 33	80.35 79	99.85 14	94.74 74	98.62 33	98.83 40

F-COLMAP		84.50 280	83.44 279	87.67 302	95.22 148	72.22 368	95.95 241	93.78 323	75.74 370	76.30 327	95.18 191	59.50 336	98.45 157	72.67 339	86.59 257	92.35 304

ANet_high		46.22 431	41.28 438	61.04 446	39.91 478	46.25 464	70.59 463	76.18 462	58.87 450	23.09 470	48.00 467	12.58 469	66.54 470	28.65 465	13.62 471	70.35 456

wuyk23d		14.10 441	13.89 444	14.72 457	55.23 471	22.91 481	33.83 471	3.56 481	4.94 474	4.11 475	2.28 477	2.06 479	19.66 476	10.23 475	8.74 474	1.59 474

OMC-MVS		88.80 183	88.16 181	90.72 224	95.30 145	77.92 288	94.81 300	94.51 264	86.80 142	84.97 212	96.85 135	67.53 271	98.60 143	85.08 208	87.62 247	95.63 241

MG-MVS		94.25 36	93.72 47	95.85 12	99.38 3	89.35 11	97.98 76	98.09 9	89.99 67	92.34 98	96.97 131	81.30 72	98.99 125	88.54 176	98.88 20	99.20 25

AdaColmap		88.81 182	87.61 194	92.39 138	99.33 4	79.95 216	96.70 191	95.58 195	77.51 352	83.05 249	96.69 144	61.90 323	99.72 55	84.29 214	93.47 164	97.50 147

uanet		0.00 446	0.00 449	0.00 460	0.00 483	0.00 485	0.00 472	0.00 484	0.00 478	0.00 479	0.00 478	0.00 482	0.00 479	0.00 478	0.00 477	0.00 475

ITE_SJBPF				82.38 390	87.00 382	65.59 419		89.55 412	79.99 316	69.37 391	91.30 290	41.60 428	95.33 342	62.86 395	74.63 345	86.24 404

DeepMVS_CX				64.06 442	78.53 441	43.26 467		68.11 471	69.94 413	38.55 461	76.14 441	18.53 462	79.34 459	43.72 453	41.62 462	69.57 457

TinyColmap		72.41 395	68.99 404	82.68 386	88.11 371	69.59 398	88.41 402	85.20 438	65.55 426	57.91 442	84.82 394	30.80 453	95.94 308	51.38 435	68.70 385	82.49 435

MAR-MVS		90.63 138	90.22 134	91.86 171	98.47 45	78.20 279	97.18 141	96.61 95	83.87 228	88.18 171	98.18 55	68.71 263	99.75 47	83.66 226	97.15 90	97.63 133
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		72.36 397	70.82 394	76.95 423	79.18 438	56.33 449	86.12 423	86.11 436	69.30 416	63.06 421	86.66 360	33.03 449	92.25 408	65.33 382	68.64 386	82.28 437

MSDG		80.62 341	77.77 351	89.14 268	93.43 219	77.24 307	91.89 367	90.18 408	69.86 414	68.02 395	91.94 283	52.21 387	98.84 135	59.32 410	83.12 286	91.35 306

LS3D		82.22 318	79.94 333	89.06 269	97.43 87	74.06 354	93.20 348	92.05 378	61.90 436	73.33 360	95.21 188	59.35 337	99.21 105	54.54 429	92.48 177	93.90 287

CLD-MVS		87.97 208	87.48 199	89.44 263	92.16 282	80.54 200	98.14 63	94.92 233	91.41 45	79.43 290	95.40 179	62.34 312	97.27 239	90.60 142	82.90 291	90.50 315
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
FPMVS		55.09 426	52.93 429	61.57 445	55.98 469	40.51 470	83.11 439	83.41 451	37.61 463	34.95 464	71.95 453	14.40 465	76.95 463	29.81 463	65.16 411	67.25 458

Gipuma		45.11 434	42.05 436	54.30 451	80.69 433	51.30 458	35.80 470	83.81 448	28.13 465	27.94 469	34.53 469	11.41 471	76.70 465	21.45 468	54.65 434	34.90 469
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015

High-resolution multi-view benchmark