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.

LTVRE_ROB	98.40 1	99.67 3	99.71 2	99.56 26	99.85 16	99.11 64	99.90 1	99.78 36	99.63 29	99.78 39	99.67 30	99.48 10	99.81 216	99.30 61	99.97 21	99.77 48
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
3Dnovator	98.27 2	98.81 114	98.73 116	99.05 138	98.76 311	97.81 182	99.25 43	99.30 211	98.57 158	98.55 264	99.33 109	97.95 127	99.90 79	97.16 215	99.67 211	99.44 190

3Dnovator+	97.89 3	98.69 136	98.51 154	99.24 102	98.81 306	98.40 113	99.02 69	99.19 247	98.99 118	98.07 305	99.28 119	97.11 195	99.84 172	96.84 247	99.32 300	99.47 179

DeepC-MVS	97.60 4	98.97 87	98.93 92	99.10 124	99.35 176	97.98 158	98.01 198	99.46 135	97.56 245	99.54 77	99.50 67	98.97 28	99.84 172	98.06 150	99.92 67	99.49 160
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
DeepPCF-MVS	96.93 5	98.32 198	98.01 228	99.23 104	98.39 374	98.97 73	95.03 419	99.18 251	96.88 307	99.33 124	98.78 257	98.16 110	99.28 423	96.74 255	99.62 227	99.44 190

DeepC-MVS_fast	96.85 6	98.30 201	98.15 213	98.75 190	98.61 345	97.23 219	97.76 240	99.09 270	97.31 274	98.75 236	98.66 283	97.56 161	99.64 330	96.10 307	99.55 254	99.39 210
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
OpenMVS	96.65 7	97.09 310	96.68 321	98.32 261	98.32 377	97.16 229	98.86 91	99.37 174	89.48 443	96.29 405	99.15 158	96.56 229	99.90 79	92.90 395	99.20 322	97.89 415

ACMH	96.65 7	99.25 41	99.24 52	99.26 97	99.72 43	98.38 115	99.07 64	99.55 98	98.30 177	99.65 62	99.45 83	99.22 17	99.76 260	98.44 126	99.77 153	99.64 81
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ACMH+	96.62 9	99.08 74	99.00 85	99.33 85	99.71 47	98.83 83	98.60 114	99.58 80	99.11 95	99.53 81	99.18 148	98.81 38	99.67 311	96.71 260	99.77 153	99.50 153

COLMAP_ROB	96.50 10	98.99 83	98.85 105	99.41 66	99.58 87	99.10 65	98.74 97	99.56 94	99.09 105	99.33 124	99.19 144	98.40 79	99.72 286	95.98 310	99.76 166	99.42 197
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
TAPA-MVS	96.21 11	96.63 332	95.95 343	98.65 203	98.93 277	98.09 142	96.93 325	99.28 223	83.58 456	98.13 300	97.78 366	96.13 247	99.40 404	93.52 384	99.29 307	98.45 381
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
ACMM	96.08 12	98.91 94	98.73 116	99.48 56	99.55 106	99.14 57	98.07 185	99.37 174	97.62 236	99.04 178	98.96 214	98.84 36	99.79 237	97.43 202	99.65 219	99.49 160
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
HY-MVS	95.94 13	95.90 356	95.35 366	97.55 335	97.95 397	94.79 328	98.81 96	96.94 402	92.28 422	95.17 427	98.57 299	89.90 369	99.75 268	91.20 424	97.33 428	98.10 404

OpenMVS_ROB	95.38 14	95.84 359	95.18 372	97.81 302	98.41 373	97.15 230	97.37 294	98.62 343	83.86 455	98.65 247	98.37 323	94.29 310	99.68 307	88.41 439	98.62 380	96.60 446

ACMP	95.32 15	98.41 182	98.09 218	99.36 70	99.51 119	98.79 86	97.68 250	99.38 170	95.76 354	98.81 227	98.82 250	98.36 82	99.82 200	94.75 346	99.77 153	99.48 171
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
PLC	94.65 16	96.51 335	95.73 348	98.85 169	98.75 313	97.91 167	96.42 356	99.06 273	90.94 436	95.59 416	97.38 390	94.41 305	99.59 350	90.93 428	98.04 407	99.05 300
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
PVSNet	93.40 17	95.67 363	95.70 349	95.57 410	98.83 300	88.57 437	92.50 453	97.72 375	92.69 417	96.49 402	96.44 411	93.72 323	99.43 400	93.61 381	99.28 308	98.71 358

PCF-MVS	92.86 18	94.36 385	93.00 403	98.42 249	98.70 325	97.56 198	93.16 451	99.11 267	79.59 460	97.55 343	97.43 387	92.19 346	99.73 279	79.85 458	99.45 279	97.97 412
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
IB-MVS	91.63 19	92.24 421	90.90 425	96.27 391	97.22 435	91.24 419	94.36 438	93.33 446	92.37 420	92.24 455	94.58 446	66.20 459	99.89 95	93.16 392	94.63 453	97.66 428
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
PMVS	91.26 20	97.86 249	97.94 237	97.65 321	99.71 47	97.94 164	98.52 123	98.68 338	98.99 118	97.52 346	99.35 102	97.41 175	98.18 454	91.59 417	99.67 211	96.82 443
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
PVSNet_0	89.98 21	91.15 426	90.30 429	93.70 434	97.72 407	84.34 458	90.24 457	97.42 384	90.20 440	93.79 446	93.09 455	90.90 362	98.89 443	86.57 447	72.76 464	97.87 417

MVE	83.40 22	92.50 416	91.92 418	94.25 426	98.83 300	91.64 408	92.71 452	83.52 466	95.92 350	86.46 464	95.46 432	95.20 283	95.40 462	80.51 457	98.64 377	95.73 455
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
CMPMVS	75.91 23	96.29 343	95.44 361	98.84 170	96.25 455	98.69 94	97.02 318	99.12 265	88.90 446	97.83 324	98.86 237	89.51 373	98.90 442	91.92 409	99.51 265	98.92 326
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
viewdifsd2359ckpt11		98.84 106	99.04 78	98.24 270	99.56 100	95.51 299	97.38 290	99.70 50	99.16 90	99.57 70	99.40 94	98.26 95	99.71 287	98.55 121	99.82 119	99.50 153

viewmacassd2359aftdt		98.86 103	98.87 100	98.83 171	99.53 114	97.32 214	97.70 248	99.64 66	98.22 185	99.25 145	99.27 121	98.40 79	99.61 343	97.98 159	99.87 95	99.55 130

viewmsd2359difaftdt		98.84 106	99.04 78	98.24 270	99.56 100	95.51 299	97.38 290	99.70 50	99.16 90	99.57 70	99.40 94	98.26 95	99.71 287	98.55 121	99.82 119	99.50 153

diffmvs_AUTHOR		98.50 174	98.59 144	98.23 273	99.35 176	95.48 303	96.61 343	99.60 74	98.37 169	98.90 207	99.00 202	97.37 178	99.76 260	98.22 138	99.85 103	99.46 181

fmvsm_l_conf0.5_n_9		99.32 33	99.43 24	98.98 151	99.59 85	97.18 226	97.44 286	99.83 25	99.56 38	99.91 12	99.34 106	99.36 13	99.93 52	99.83 9	99.98 12	99.85 29

mamba_0408		98.80 116	98.88 98	98.55 228	99.27 194	96.50 263	98.00 199	99.60 74	98.93 126	99.22 150	98.84 245	98.59 62	99.89 95	97.74 180	99.72 181	99.27 253

icg_test_0407_2		98.20 216	98.38 178	97.65 321	99.03 258	94.03 355	95.78 395	99.45 139	98.16 197	99.06 169	98.71 267	98.27 93	99.68 307	97.50 196	99.45 279	99.22 270

SSM_04072		98.80 116	98.88 98	98.56 226	99.27 194	96.50 263	98.00 199	99.60 74	98.93 126	99.22 150	98.84 245	98.59 62	99.90 79	97.74 180	99.72 181	99.27 253

SSM_0407		98.86 103	98.96 91	98.55 228	99.27 194	96.50 263	98.04 190	99.66 61	99.09 105	99.22 150	99.02 188	98.79 42	99.87 132	97.87 168	99.72 181	99.27 253

viewmambaseed2359dif		98.19 217	98.26 196	97.99 293	99.02 263	95.03 323	96.59 345	99.53 106	96.21 336	99.00 183	98.99 204	97.62 155	99.61 343	97.62 186	99.72 181	99.33 238

IMVS_0407		98.39 190	98.64 133	97.66 319	99.03 258	94.03 355	98.10 179	99.45 139	98.16 197	99.06 169	98.71 267	98.27 93	99.71 287	97.50 196	99.45 279	99.22 270

viewmanbaseed2359cas		98.58 158	98.54 150	98.70 198	99.28 191	97.13 232	97.47 283	99.55 98	97.55 247	98.96 194	98.92 222	97.77 142	99.59 350	97.59 190	99.77 153	99.39 210

IMVS_0404		98.07 228	98.20 203	97.69 316	99.03 258	94.03 355	96.67 339	99.45 139	98.16 197	98.03 310	98.71 267	96.80 214	99.82 200	97.50 196	99.45 279	99.22 270

SSM_0404		98.90 96	99.01 83	98.57 221	99.42 158	96.59 257	98.13 172	99.66 61	99.09 105	99.30 133	99.02 188	98.79 42	99.89 95	97.87 168	99.80 136	99.23 265

IMVS_0403		98.34 193	98.56 147	97.66 319	99.03 258	94.03 355	97.98 207	99.45 139	98.16 197	98.89 210	98.71 267	97.90 130	99.74 273	97.50 196	99.45 279	99.22 270

SD_0403		96.28 344	95.83 345	97.64 324	98.72 317	94.30 344	98.87 88	98.77 327	97.80 224	96.53 396	98.02 352	97.34 180	99.47 392	76.93 461	99.48 275	99.16 290

fmvsm_s_conf0.5_n_9		99.17 52	99.38 29	98.53 235	99.51 119	95.82 290	97.62 261	99.78 36	99.72 15	99.90 14	99.48 74	98.66 54	99.89 95	99.85 5	99.93 54	99.89 16

NormalMVS		98.26 207	97.97 234	99.15 117	99.64 74	97.83 174	98.28 154	99.43 153	99.24 74	98.80 228	98.85 240	89.76 370	99.94 41	98.04 152	99.67 211	99.68 68

lecture		99.25 41	99.12 68	99.62 9	99.64 74	99.40 12	98.89 87	99.51 111	99.19 85	99.37 115	99.25 132	98.36 82	99.88 113	98.23 137	99.67 211	99.59 104

SymmetryMVS		98.05 230	97.71 255	99.09 128	99.29 189	97.83 174	98.28 154	97.64 382	99.24 74	98.80 228	98.85 240	89.76 370	99.94 41	98.04 152	99.50 272	99.49 160

Elysia		99.15 57	99.14 66	99.18 109	99.63 80	97.92 165	98.50 130	99.43 153	99.67 21	99.70 50	99.13 163	96.66 224	99.98 4	99.54 42	99.96 28	99.64 81

StellarMVS		99.15 57	99.14 66	99.18 109	99.63 80	97.92 165	98.50 130	99.43 153	99.67 21	99.70 50	99.13 163	96.66 224	99.98 4	99.54 42	99.96 28	99.64 81

KinetiMVS		99.03 78	99.02 81	99.03 141	99.70 55	97.48 203	98.43 141	99.29 219	99.70 16	99.60 69	99.07 175	96.13 247	99.94 41	99.42 54	99.87 95	99.68 68

LuminaMVS		98.39 190	98.20 203	98.98 151	99.50 125	97.49 201	97.78 234	97.69 377	98.75 140	99.49 90	99.25 132	92.30 345	99.94 41	99.14 74	99.88 91	99.50 153

VortexMVS		97.98 239	98.31 189	97.02 363	98.88 291	91.45 411	98.03 192	99.47 131	98.65 145	99.55 75	99.47 77	91.49 355	99.81 216	99.32 59	99.91 76	99.80 40

AstraMVS		98.16 223	98.07 223	98.41 250	99.51 119	95.86 287	98.00 199	95.14 431	98.97 121	99.43 101	99.24 134	93.25 325	99.84 172	99.21 69	99.87 95	99.54 135

guyue		98.01 234	97.93 239	98.26 267	99.45 150	95.48 303	98.08 182	96.24 414	98.89 132	99.34 122	99.14 161	91.32 357	99.82 200	99.07 79	99.83 115	99.48 171

sc_t1		99.62 7	99.66 8	99.53 38	99.82 19	99.09 68	99.50 11	99.63 68	99.88 4	99.86 24	99.80 11	99.03 24	99.89 95	99.48 51	99.93 54	99.60 97

tt0320-xc		99.64 5	99.68 5	99.50 53	99.72 43	98.98 71	99.51 10	99.85 18	99.86 6	99.88 21	99.82 5	99.02 26	99.90 79	99.54 42	99.95 38	99.61 95

tt0320		99.61 8	99.65 9	99.48 56	99.71 47	98.94 78	99.54 8	99.83 25	99.87 5	99.89 18	99.82 5	98.75 46	99.90 79	99.54 42	99.95 38	99.59 104

fmvsm_s_conf0.5_n_8		99.13 64	99.26 49	98.74 194	99.51 119	96.44 267	97.65 256	99.65 64	99.66 24	99.78 39	99.48 74	97.92 129	99.93 52	99.72 28	99.95 38	99.87 21

fmvsm_s_conf0.5_n_7		98.83 109	99.04 78	98.20 275	99.30 186	94.83 327	97.23 305	99.36 178	98.64 146	99.84 30	99.43 86	98.10 115	99.91 72	99.56 39	99.96 28	99.87 21

fmvsm_s_conf0.5_n_6		99.08 74	99.21 55	98.69 199	99.36 171	96.51 262	97.62 261	99.68 57	98.43 167	99.85 27	99.10 170	99.12 23	99.88 113	99.77 21	99.92 67	99.67 73

fmvsm_s_conf0.5_n_5		99.07 76	99.10 71	98.99 147	99.47 143	97.22 221	97.40 288	99.83 25	97.61 239	99.85 27	99.30 115	98.80 40	99.95 26	99.71 30	99.90 83	99.78 45

fmvsm_s_conf0.5_n_4		99.01 80	99.22 53	98.38 254	99.31 182	95.48 303	97.56 271	99.73 43	98.87 133	99.75 44	99.27 121	98.80 40	99.86 141	99.80 16	99.90 83	99.81 38

SSC-MVS3.2		98.53 168	98.79 110	97.74 311	99.46 145	93.62 378	96.45 352	99.34 190	99.33 64	98.93 203	98.70 274	97.90 130	99.90 79	99.12 75	99.92 67	99.69 67

testing3-2		93.78 397	93.91 389	93.39 438	98.82 303	81.72 465	97.76 240	95.28 429	98.60 153	96.54 395	96.66 405	65.85 461	99.62 336	96.65 264	98.99 350	98.82 339

myMVS_eth3d28		92.92 412	92.31 408	94.77 421	97.84 402	87.59 444	96.19 370	96.11 417	97.08 296	94.27 437	93.49 453	66.07 460	98.78 445	91.78 412	97.93 410	97.92 414

UWE-MVS-28		90.22 427	89.28 430	93.02 442	94.50 463	82.87 461	96.52 349	87.51 461	95.21 371	92.36 454	96.04 416	71.57 447	98.25 453	72.04 463	97.77 412	97.94 413

fmvsm_l_conf0.5_n_3		99.45 18	99.48 18	99.34 79	99.59 85	98.21 132	97.82 228	99.84 22	99.41 56	99.92 8	99.41 91	99.51 8	99.95 26	99.84 8	99.97 21	99.87 21

fmvsm_s_conf0.5_n_3		99.22 47	99.37 32	98.78 183	99.46 145	96.58 260	97.65 256	99.72 44	99.47 46	99.86 24	99.50 67	98.94 30	99.89 95	99.75 24	99.97 21	99.86 27

fmvsm_s_conf0.5_n_2		99.14 60	99.31 41	98.63 209	99.49 133	96.08 280	97.38 290	99.81 31	99.48 43	99.84 30	99.57 49	98.46 75	99.89 95	99.82 11	99.97 21	99.91 13

fmvsm_s_conf0.1_n_2		99.20 50	99.38 29	98.65 203	99.69 58	96.08 280	97.49 280	99.90 11	99.53 40	99.88 21	99.64 37	98.51 71	99.90 79	99.83 9	99.98 12	99.97 4

GDP-MVS		97.50 275	97.11 294	98.67 202	99.02 263	96.85 245	98.16 169	99.71 46	98.32 175	98.52 269	98.54 301	83.39 417	99.95 26	98.79 99	99.56 250	99.19 280

BP-MVS1		97.40 287	96.97 300	98.71 197	99.07 246	96.81 247	98.34 152	97.18 392	98.58 157	98.17 293	98.61 294	84.01 413	99.94 41	98.97 88	99.78 147	99.37 220

reproduce_monomvs		95.00 379	95.25 368	94.22 427	97.51 427	83.34 459	97.86 224	98.44 351	98.51 163	99.29 134	99.30 115	67.68 454	99.56 362	98.89 94	99.81 125	99.77 48

mmtdpeth		99.30 34	99.42 25	98.92 162	99.58 87	96.89 244	99.48 13	99.92 7	99.92 2	98.26 290	99.80 11	98.33 88	99.91 72	99.56 39	99.95 38	99.97 4

reproduce_model		99.15 57	98.97 89	99.67 4	99.33 180	99.44 10	98.15 170	99.47 131	99.12 94	99.52 83	99.32 113	98.31 89	99.90 79	97.78 174	99.73 173	99.66 75

reproduce-ours		99.09 70	98.90 95	99.67 4	99.27 194	99.49 6	98.00 199	99.42 159	99.05 112	99.48 91	99.27 121	98.29 91	99.89 95	97.61 187	99.71 190	99.62 87

our_new_method		99.09 70	98.90 95	99.67 4	99.27 194	99.49 6	98.00 199	99.42 159	99.05 112	99.48 91	99.27 121	98.29 91	99.89 95	97.61 187	99.71 190	99.62 87

mmdepth		0.00 438	0.00 441	0.00 451	0.00 474	0.00 476	0.00 462	0.00 475	0.00 469	0.00 470	0.00 469	0.00 474	0.00 470	0.00 469	0.00 468	0.00 466

monomultidepth		0.00 438	0.00 441	0.00 451	0.00 474	0.00 476	0.00 462	0.00 475	0.00 469	0.00 470	0.00 469	0.00 474	0.00 470	0.00 469	0.00 468	0.00 466

mvs5depth		99.30 34	99.59 12	98.44 247	99.65 68	95.35 310	99.82 3	99.94 2	99.83 7	99.42 105	99.94 2	98.13 113	99.96 14	99.63 34	99.96 28	100.00 1

MVStest1		95.86 357	95.60 353	96.63 381	95.87 459	91.70 407	97.93 212	98.94 292	98.03 205	99.56 72	99.66 32	71.83 446	98.26 452	99.35 57	99.24 314	99.91 13

ttmdpeth		97.91 241	98.02 227	97.58 330	98.69 330	94.10 351	98.13 172	98.90 301	97.95 211	97.32 361	99.58 47	95.95 262	98.75 446	96.41 287	99.22 318	99.87 21

WBMVS		95.18 374	94.78 380	96.37 387	97.68 415	89.74 434	95.80 394	98.73 335	97.54 249	98.30 284	98.44 316	70.06 448	99.82 200	96.62 266	99.87 95	99.54 135

dongtai		76.24 431	75.95 434	77.12 447	92.39 465	67.91 471	90.16 458	59.44 472	82.04 458	89.42 460	94.67 445	49.68 470	81.74 465	48.06 465	77.66 463	81.72 461

kuosan		69.30 432	68.95 435	70.34 448	87.68 469	65.00 472	91.11 456	59.90 471	69.02 461	74.46 466	88.89 463	48.58 471	68.03 467	28.61 466	72.33 465	77.99 462

MVSMamba_PlusPlus		98.83 109	98.98 88	98.36 258	99.32 181	96.58 260	98.90 83	99.41 163	99.75 11	98.72 239	99.50 67	96.17 245	99.94 41	99.27 63	99.78 147	98.57 374

MGCFI-Net		98.34 193	98.28 192	98.51 237	98.47 363	97.59 197	98.96 77	99.48 123	99.18 88	97.40 356	95.50 429	98.66 54	99.50 383	98.18 141	98.71 370	98.44 384

testing91		93.32 404	92.27 409	96.47 385	97.54 420	91.25 418	96.17 374	96.76 406	97.18 290	93.65 448	93.50 452	65.11 463	99.63 333	93.04 393	97.45 419	98.53 375

testing11		93.08 409	92.02 414	96.26 392	97.56 418	90.83 426	96.32 362	95.70 425	96.47 327	92.66 452	93.73 449	64.36 464	99.59 350	93.77 379	97.57 415	98.37 393

testing99		93.04 410	91.98 417	96.23 394	97.53 422	90.70 428	96.35 360	95.94 421	96.87 308	93.41 449	93.43 454	63.84 465	99.59 350	93.24 391	97.19 429	98.40 389

UBG		93.25 406	92.32 407	96.04 401	97.72 407	90.16 431	95.92 388	95.91 422	96.03 345	93.95 445	93.04 456	69.60 450	99.52 377	90.72 432	97.98 408	98.45 381

UWE-MVS		92.38 418	91.76 421	94.21 428	97.16 436	84.65 454	95.42 409	88.45 460	95.96 348	96.17 406	95.84 424	66.36 457	99.71 287	91.87 411	98.64 377	98.28 396

ETVMVS		92.60 415	91.08 424	97.18 355	97.70 412	93.65 377	96.54 346	95.70 425	96.51 323	94.68 433	92.39 459	61.80 466	99.50 383	86.97 444	97.41 422	98.40 389

sasdasda		98.34 193	98.26 196	98.58 218	98.46 365	97.82 179	98.96 77	99.46 135	99.19 85	97.46 351	95.46 432	98.59 62	99.46 395	98.08 148	98.71 370	98.46 378

testing222		91.96 423	90.37 427	96.72 380	97.47 429	92.59 393	96.11 376	94.76 433	96.83 310	92.90 451	92.87 457	57.92 467	99.55 366	86.93 445	97.52 416	98.00 411

WB-MVSnew		95.73 362	95.57 356	96.23 394	96.70 446	90.70 428	96.07 378	93.86 443	95.60 358	97.04 370	95.45 435	96.00 254	99.55 366	91.04 426	98.31 389	98.43 386

fmvsm_l_conf0.5_n_a		99.19 51	99.27 47	98.94 157	99.65 68	97.05 233	97.80 232	99.76 39	98.70 144	99.78 39	99.11 167	98.79 42	99.95 26	99.85 5	99.96 28	99.83 32

fmvsm_l_conf0.5_n		99.21 48	99.28 46	99.02 144	99.64 74	97.28 216	97.82 228	99.76 39	98.73 141	99.82 33	99.09 174	98.81 38	99.95 26	99.86 4	99.96 28	99.83 32

fmvsm_s_conf0.1_n_a		99.17 52	99.30 44	98.80 177	99.75 34	96.59 257	97.97 211	99.86 16	98.22 185	99.88 21	99.71 22	98.59 62	99.84 172	99.73 26	99.98 12	99.98 3

fmvsm_s_conf0.1_n		99.16 56	99.33 37	98.64 205	99.71 47	96.10 275	97.87 223	99.85 18	98.56 161	99.90 14	99.68 25	98.69 52	99.85 154	99.72 28	99.98 12	99.97 4

fmvsm_s_conf0.5_n_a		99.10 69	99.20 56	98.78 183	99.55 106	96.59 257	97.79 233	99.82 30	98.21 187	99.81 36	99.53 63	98.46 75	99.84 172	99.70 31	99.97 21	99.90 15

fmvsm_s_conf0.5_n		99.09 70	99.26 49	98.61 214	99.55 106	96.09 278	97.74 243	99.81 31	98.55 162	99.85 27	99.55 57	98.60 61	99.84 172	99.69 33	99.98 12	99.89 16

MM		98.22 212	97.99 230	98.91 163	98.66 340	96.97 237	97.89 219	94.44 436	99.54 39	98.95 195	99.14 161	93.50 324	99.92 63	99.80 16	99.96 28	99.85 29

WAC-MVS						90.90 424								91.37 421

Syy-MVS		96.04 351	95.56 357	97.49 341	97.10 438	94.48 339	96.18 372	96.58 409	95.65 356	94.77 431	92.29 460	91.27 358	99.36 409	98.17 143	98.05 405	98.63 368

test_fmvsmconf0.1_n		99.49 15	99.54 14	99.34 79	99.78 24	98.11 139	97.77 237	99.90 11	99.33 64	99.97 3	99.66 32	99.71 3	99.96 14	99.79 18	99.99 5	99.96 8

test_fmvsmconf0.01_n		99.57 10	99.63 10	99.36 70	99.87 12	98.13 138	98.08 182	99.95 1	99.45 49	99.98 2	99.75 16	99.80 1	99.97 7	99.82 11	99.99 5	99.99 2

myMVS_eth3d		91.92 424	90.45 426	96.30 389	97.10 438	90.90 424	96.18 372	96.58 409	95.65 356	94.77 431	92.29 460	53.88 468	99.36 409	89.59 437	98.05 405	98.63 368

testing3		93.51 401	92.09 412	97.75 309	98.60 347	94.40 341	97.32 298	95.26 430	97.56 245	96.79 387	95.50 429	53.57 469	99.77 254	95.26 336	98.97 354	99.08 296

SSC-MVS		98.71 129	98.74 114	98.62 211	99.72 43	96.08 280	98.74 97	98.64 342	99.74 13	99.67 58	99.24 134	94.57 302	99.95 26	99.11 76	99.24 314	99.82 35

test_fmvsmconf_n		99.44 19	99.48 18	99.31 90	99.64 74	98.10 141	97.68 250	99.84 22	99.29 70	99.92 8	99.57 49	99.60 5	99.96 14	99.74 25	99.98 12	99.89 16

WB-MVS		98.52 172	98.55 148	98.43 248	99.65 68	95.59 294	98.52 123	98.77 327	99.65 26	99.52 83	99.00 202	94.34 308	99.93 52	98.65 112	98.83 362	99.76 53

test_fmvsmvis_n_1920		99.26 40	99.49 16	98.54 233	99.66 67	96.97 237	98.00 199	99.85 18	99.24 74	99.92 8	99.50 67	99.39 12	99.95 26	99.89 3	99.98 12	98.71 358

dmvs_re		95.98 354	95.39 364	97.74 311	98.86 294	97.45 206	98.37 148	95.69 427	97.95 211	96.56 394	95.95 419	90.70 363	97.68 457	88.32 440	96.13 444	98.11 403

SDMVSNet		99.23 46	99.32 39	98.96 154	99.68 61	97.35 211	98.84 94	99.48 123	99.69 18	99.63 65	99.68 25	99.03 24	99.96 14	97.97 160	99.92 67	99.57 117

dmvs_testset		92.94 411	92.21 411	95.13 418	98.59 350	90.99 423	97.65 256	92.09 451	96.95 303	94.00 443	93.55 451	92.34 344	96.97 460	72.20 462	92.52 458	97.43 435

sd_testset		99.28 37	99.31 41	99.19 108	99.68 61	98.06 151	99.41 17	99.30 211	99.69 18	99.63 65	99.68 25	99.25 16	99.96 14	97.25 211	99.92 67	99.57 117

test_fmvsm_n_1920		99.33 31	99.45 23	98.99 147	99.57 92	97.73 189	97.93 212	99.83 25	99.22 77	99.93 6	99.30 115	99.42 11	99.96 14	99.85 5	99.99 5	99.29 250

test_cas_vis1_n_1920		98.33 197	98.68 127	97.27 352	99.69 58	92.29 401	98.03 192	99.85 18	97.62 236	99.96 4	99.62 40	93.98 317	99.74 273	99.52 48	99.86 102	99.79 42

test_vis1_n_1920		98.40 184	98.92 93	96.81 376	99.74 36	90.76 427	98.15 170	99.91 9	98.33 173	99.89 18	99.55 57	95.07 287	99.88 113	99.76 22	99.93 54	99.79 42

test_vis1_n		98.31 200	98.50 156	97.73 314	99.76 30	94.17 349	98.68 107	99.91 9	96.31 333	99.79 38	99.57 49	92.85 337	99.42 402	99.79 18	99.84 108	99.60 97

test_fmvs1_n		98.09 226	98.28 192	97.52 338	99.68 61	93.47 380	98.63 110	99.93 5	95.41 367	99.68 56	99.64 37	91.88 351	99.48 389	99.82 11	99.87 95	99.62 87

mvsany_test1		97.60 269	97.54 267	97.77 305	97.72 407	95.35 310	95.36 411	97.13 395	94.13 396	99.71 48	99.33 109	97.93 128	99.30 419	97.60 189	98.94 357	98.67 366

APD_test1		98.83 109	98.66 130	99.34 79	99.78 24	99.47 9	98.42 144	99.45 139	98.28 182	98.98 186	99.19 144	97.76 143	99.58 357	96.57 271	99.55 254	98.97 317

test_vis1_rt		97.75 259	97.72 254	97.83 300	98.81 306	96.35 270	97.30 300	99.69 52	94.61 383	97.87 320	98.05 350	96.26 243	98.32 451	98.74 105	98.18 394	98.82 339

test_vis3_rt		99.14 60	99.17 58	99.07 131	99.78 24	98.38 115	98.92 82	99.94 2	97.80 224	99.91 12	99.67 30	97.15 192	98.91 441	99.76 22	99.56 250	99.92 12

test_fmvs2		98.70 133	98.97 89	97.89 297	99.54 111	94.05 352	98.55 119	99.92 7	96.78 313	99.72 46	99.78 13	96.60 228	99.67 311	99.91 2	99.90 83	99.94 10

test_fmvs1		97.72 261	97.94 237	97.07 362	98.66 340	92.39 398	97.68 250	99.81 31	95.20 372	99.54 77	99.44 84	91.56 354	99.41 403	99.78 20	99.77 153	99.40 209

test_fmvs3		99.12 67	99.41 26	98.25 268	99.76 30	95.07 322	99.05 67	99.94 2	97.78 227	99.82 33	99.84 3	98.56 68	99.71 287	99.96 1	99.96 28	99.97 4

mvsany_test3		98.87 100	98.92 93	98.74 194	99.38 164	96.94 241	98.58 116	99.10 268	96.49 325	99.96 4	99.81 8	98.18 106	99.45 397	98.97 88	99.79 142	99.83 32

testf1		99.25 41	99.16 60	99.51 48	99.89 6	99.63 4	98.71 104	99.69 52	98.90 130	99.43 101	99.35 102	98.86 34	99.67 311	97.81 171	99.81 125	99.24 263

APD_test2		99.25 41	99.16 60	99.51 48	99.89 6	99.63 4	98.71 104	99.69 52	98.90 130	99.43 101	99.35 102	98.86 34	99.67 311	97.81 171	99.81 125	99.24 263

test_f		98.67 144	98.87 100	98.05 289	99.72 43	95.59 294	98.51 128	99.81 31	96.30 335	99.78 39	99.82 5	96.14 246	98.63 448	99.82 11	99.93 54	99.95 9

FE-MVS		95.66 364	94.95 377	97.77 305	98.53 359	95.28 313	99.40 19	96.09 418	93.11 411	97.96 314	99.26 127	79.10 435	99.77 254	92.40 407	98.71 370	98.27 397

FA-MVS(test-final)		96.99 319	96.82 312	97.50 340	98.70 325	94.78 329	99.34 23	96.99 398	95.07 373	98.48 272	99.33 109	88.41 384	99.65 327	96.13 306	98.92 359	98.07 406

balanced_conf03		98.63 150	98.72 118	98.38 254	98.66 340	96.68 256	98.90 83	99.42 159	98.99 118	98.97 190	99.19 144	95.81 267	99.85 154	98.77 103	99.77 153	98.60 370

MonoMVSNet		96.25 346	96.53 332	95.39 415	96.57 448	91.01 422	98.82 95	97.68 379	98.57 158	98.03 310	99.37 97	90.92 361	97.78 456	94.99 340	93.88 456	97.38 436

patch_mono-2		98.51 173	98.63 135	98.17 278	99.38 164	94.78 329	97.36 295	99.69 52	98.16 197	98.49 271	99.29 118	97.06 196	99.97 7	98.29 134	99.91 76	99.76 53

EGC-MVSNET		85.24 428	80.54 431	99.34 79	99.77 27	99.20 39	99.08 61	99.29 219	12.08 466	20.84 467	99.42 87	97.55 162	99.85 154	97.08 223	99.72 181	98.96 319

test2506		92.39 417	91.89 419	93.89 432	99.38 164	82.28 463	99.32 26	66.03 470	99.08 109	98.77 233	99.57 49	66.26 458	99.84 172	98.71 108	99.95 38	99.54 135

test1111		96.49 338	96.82 312	95.52 411	99.42 158	87.08 446	99.22 45	87.14 462	99.11 95	99.46 96	99.58 47	88.69 378	99.86 141	98.80 98	99.95 38	99.62 87

ECVR-MVS		96.42 340	96.61 326	95.85 403	99.38 164	88.18 441	99.22 45	86.00 464	99.08 109	99.36 118	99.57 49	88.47 383	99.82 200	98.52 123	99.95 38	99.54 135

test_blank		0.00 438	0.00 441	0.00 451	0.00 474	0.00 476	0.00 462	0.00 475	0.00 469	0.00 470	0.00 469	0.00 474	0.00 470	0.00 469	0.00 468	0.00 466

tt0805		98.69 136	98.62 137	98.90 166	99.75 34	99.30 22	99.15 56	96.97 399	98.86 135	98.87 218	97.62 377	98.63 58	98.96 438	99.41 55	98.29 390	98.45 381

DVP-MVS++		98.90 96	98.70 124	99.51 48	98.43 369	99.15 52	99.43 15	99.32 198	98.17 194	99.26 141	99.02 188	98.18 106	99.88 113	97.07 224	99.45 279	99.49 160

FOURS1					99.73 37	99.67 3	99.43 15	99.54 103	99.43 53	99.26 141

MSC_two_6792asdad				99.32 87	98.43 369	98.37 117		98.86 312					99.89 95	97.14 218	99.60 234	99.71 60

PC_three_1452									93.27 408	99.40 110	98.54 301	98.22 102	97.00 459	95.17 337	99.45 279	99.49 160

No_MVS				99.32 87	98.43 369	98.37 117		98.86 312					99.89 95	97.14 218	99.60 234	99.71 60

test_one_0601					99.39 163	99.20 39		99.31 203	98.49 164	98.66 246	99.02 188	97.64 153

eth-test2					0.00 474

eth-test					0.00 474

GeoE		99.05 77	98.99 87	99.25 100	99.44 152	98.35 121	98.73 101	99.56 94	98.42 168	98.91 206	98.81 252	98.94 30	99.91 72	98.35 130	99.73 173	99.49 160

test_method		79.78 429	79.50 432	80.62 445	80.21 470	45.76 473	70.82 461	98.41 355	31.08 465	80.89 465	97.71 370	84.85 404	97.37 458	91.51 419	80.03 462	98.75 355

Anonymous20240521		98.69 136	98.87 100	98.16 280	99.77 27	95.11 321	99.08 61	99.44 147	99.34 63	99.33 124	99.55 57	94.10 316	99.94 41	99.25 66	99.96 28	99.42 197

h-mvs33		97.77 258	97.33 282	99.10 124	99.21 211	97.84 173	98.35 150	98.57 345	99.11 95	98.58 259	99.02 188	88.65 381	99.96 14	98.11 145	96.34 440	99.49 160

hse-mvs2		97.46 280	97.07 295	98.64 205	98.73 315	97.33 212	97.45 285	97.64 382	99.11 95	98.58 259	97.98 355	88.65 381	99.79 237	98.11 145	97.39 423	98.81 344

CL-MVSNet_self_test		97.44 283	97.22 287	98.08 285	98.57 354	95.78 292	94.30 439	98.79 324	96.58 322	98.60 255	98.19 339	94.74 300	99.64 330	96.41 287	98.84 361	98.82 339

KD-MVS_2432*1600		92.87 413	91.99 415	95.51 412	91.37 466	89.27 435	94.07 441	98.14 365	95.42 364	97.25 363	96.44 411	67.86 452	99.24 425	91.28 422	96.08 445	98.02 408

KD-MVS_self_test		99.25 41	99.18 57	99.44 63	99.63 80	99.06 70	98.69 106	99.54 103	99.31 67	99.62 68	99.53 63	97.36 179	99.86 141	99.24 68	99.71 190	99.39 210

AUN-MVS		96.24 348	95.45 360	98.60 216	98.70 325	97.22 221	97.38 290	97.65 380	95.95 349	95.53 423	97.96 359	82.11 425	99.79 237	96.31 293	97.44 420	98.80 349

ZD-MVS					99.01 265	98.84 82		99.07 272	94.10 397	98.05 308	98.12 343	96.36 240	99.86 141	92.70 403	99.19 325

SR-MVS-dyc-post		98.81 114	98.55 148	99.57 21	99.20 215	99.38 13	98.48 136	99.30 211	98.64 146	98.95 195	98.96 214	97.49 172	99.86 141	96.56 275	99.39 290	99.45 186

RE-MVS-def			98.58 145		99.20 215	99.38 13	98.48 136	99.30 211	98.64 146	98.95 195	98.96 214	97.75 144		96.56 275	99.39 290	99.45 186

SED-MVS		98.91 94	98.72 118	99.49 54	99.49 133	99.17 44	98.10 179	99.31 203	98.03 205	99.66 59	99.02 188	98.36 82	99.88 113	96.91 236	99.62 227	99.41 200

IU-MVS					99.49 133	99.15 52		98.87 307	92.97 412	99.41 107				96.76 253	99.62 227	99.66 75

OPU-MVS				98.82 173	98.59 350	98.30 122	98.10 179				98.52 305	98.18 106	98.75 446	94.62 350	99.48 275	99.41 200

test_241102_TWO								99.30 211	98.03 205	99.26 141	99.02 188	97.51 168	99.88 113	96.91 236	99.60 234	99.66 75

test_241102_ONE					99.49 133	99.17 44		99.31 203	97.98 208	99.66 59	98.90 227	98.36 82	99.48 389

SF-MVS		98.53 168	98.27 195	99.32 87	99.31 182	98.75 87	98.19 164	99.41 163	96.77 314	98.83 222	98.90 227	97.80 140	99.82 200	95.68 326	99.52 263	99.38 218

cl22		95.79 360	95.39 364	96.98 366	96.77 445	92.79 390	94.40 437	98.53 347	94.59 384	97.89 318	98.17 340	82.82 422	99.24 425	96.37 289	99.03 343	98.92 326

miper_ehance_all_eth		97.06 312	97.03 297	97.16 359	97.83 403	93.06 384	94.66 429	99.09 270	95.99 347	98.69 241	98.45 315	92.73 340	99.61 343	96.79 249	99.03 343	98.82 339

miper_enhance_ethall		96.01 352	95.74 347	96.81 376	96.41 453	92.27 402	93.69 448	98.89 304	91.14 434	98.30 284	97.35 393	90.58 364	99.58 357	96.31 293	99.03 343	98.60 370

ZNCC-MVS		98.68 141	98.40 173	99.54 31	99.57 92	99.21 33	98.46 138	99.29 219	97.28 277	98.11 302	98.39 320	98.00 122	99.87 132	96.86 246	99.64 221	99.55 130

dcpmvs_2		98.78 120	99.11 69	97.78 304	99.56 100	93.67 375	99.06 65	99.86 16	99.50 42	99.66 59	99.26 127	97.21 190	99.99 2	98.00 157	99.91 76	99.68 68

cl____		97.02 315	96.83 311	97.58 330	97.82 404	94.04 354	94.66 429	99.16 258	97.04 298	98.63 249	98.71 267	88.68 380	99.69 298	97.00 228	99.81 125	99.00 312

DIV-MVS_self_test		97.02 315	96.84 310	97.58 330	97.82 404	94.03 355	94.66 429	99.16 258	97.04 298	98.63 249	98.71 267	88.69 378	99.69 298	97.00 228	99.81 125	99.01 308

eth_miper_zixun_eth		97.23 301	97.25 285	97.17 357	98.00 396	92.77 391	94.71 426	99.18 251	97.27 278	98.56 262	98.74 263	91.89 350	99.69 298	97.06 226	99.81 125	99.05 300

9.14			97.78 248		99.07 246		97.53 275	99.32 198	95.53 361	98.54 266	98.70 274	97.58 159	99.76 260	94.32 363	99.46 277

uanet_test		0.00 438	0.00 441	0.00 451	0.00 474	0.00 476	0.00 462	0.00 475	0.00 469	0.00 470	0.00 469	0.00 474	0.00 470	0.00 469	0.00 468	0.00 466

DCPMVS		0.00 438	0.00 441	0.00 451	0.00 474	0.00 476	0.00 462	0.00 475	0.00 469	0.00 470	0.00 469	0.00 474	0.00 470	0.00 469	0.00 468	0.00 466

save fliter					99.11 237	97.97 159	96.53 348	99.02 284	98.24 183

ET-MVSNet_ETH3D		94.30 388	93.21 399	97.58 330	98.14 389	94.47 340	94.78 425	93.24 447	94.72 381	89.56 459	95.87 422	78.57 438	99.81 216	96.91 236	97.11 432	98.46 378

UniMVSNet_ETH3D		99.69 2	99.69 4	99.69 3	99.84 17	99.34 20	99.69 5	99.58 80	99.90 3	99.86 24	99.78 13	99.58 6	99.95 26	99.00 86	99.95 38	99.78 45

EIA-MVS		98.00 235	97.74 251	98.80 177	98.72 317	98.09 142	98.05 188	99.60 74	97.39 266	96.63 391	95.55 427	97.68 147	99.80 224	96.73 257	99.27 309	98.52 376

miper_refine_blended		92.87 413	91.99 415	95.51 412	91.37 466	89.27 435	94.07 441	98.14 365	95.42 364	97.25 363	96.44 411	67.86 452	99.24 425	91.28 422	96.08 445	98.02 408

miper_lstm_enhance		97.18 305	97.16 290	97.25 354	98.16 387	92.85 389	95.15 417	99.31 203	97.25 280	98.74 238	98.78 257	90.07 367	99.78 248	97.19 213	99.80 136	99.11 295

ETV-MVS		98.03 231	97.86 245	98.56 226	98.69 330	98.07 148	97.51 278	99.50 114	98.10 203	97.50 348	95.51 428	98.41 78	99.88 113	96.27 296	99.24 314	97.71 427

CS-MVS		99.13 64	99.10 71	99.24 102	99.06 251	99.15 52	99.36 22	99.88 14	99.36 62	98.21 292	98.46 314	98.68 53	99.93 52	99.03 84	99.85 103	98.64 367

D2MVS		97.84 255	97.84 246	97.83 300	99.14 233	94.74 331	96.94 323	98.88 305	95.84 352	98.89 210	98.96 214	94.40 306	99.69 298	97.55 191	99.95 38	99.05 300

DVP-MVS		98.77 123	98.52 153	99.52 44	99.50 125	99.21 33	98.02 195	98.84 316	97.97 209	99.08 167	99.02 188	97.61 157	99.88 113	96.99 230	99.63 224	99.48 171
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									98.17 194	99.08 167	99.02 188	97.89 132	99.88 113	97.07 224	99.71 190	99.70 65

test_0728_SECOND				99.60 15	99.50 125	99.23 31	98.02 195	99.32 198					99.88 113	96.99 230	99.63 224	99.68 68

test0726					99.50 125	99.21 33	98.17 168	99.35 184	97.97 209	99.26 141	99.06 176	97.61 157

SR-MVS		98.71 129	98.43 169	99.57 21	99.18 225	99.35 17	98.36 149	99.29 219	98.29 180	98.88 214	98.85 240	97.53 165	99.87 132	96.14 304	99.31 302	99.48 171

DPM-MVS		96.32 342	95.59 355	98.51 237	98.76 311	97.21 223	94.54 435	98.26 359	91.94 424	96.37 403	97.25 394	93.06 332	99.43 400	91.42 420	98.74 366	98.89 331

GST-MVS		98.61 154	98.30 190	99.52 44	99.51 119	99.20 39	98.26 158	99.25 232	97.44 263	98.67 244	98.39 320	97.68 147	99.85 154	96.00 308	99.51 265	99.52 147

test_yl		96.69 328	96.29 338	97.90 295	98.28 379	95.24 314	97.29 301	97.36 386	98.21 187	98.17 293	97.86 362	86.27 392	99.55 366	94.87 344	98.32 387	98.89 331

thisisatest0530		95.27 372	94.45 383	97.74 311	99.19 218	94.37 342	97.86 224	90.20 457	97.17 291	98.22 291	97.65 374	73.53 445	99.90 79	96.90 241	99.35 296	98.95 320

Anonymous20240529		98.93 92	98.87 100	99.12 120	99.19 218	98.22 131	99.01 70	98.99 290	99.25 73	99.54 77	99.37 97	97.04 197	99.80 224	97.89 163	99.52 263	99.35 231

Anonymous202405211		97.90 242	97.50 270	99.08 129	98.90 285	98.25 125	98.53 122	96.16 415	98.87 133	99.11 162	98.86 237	90.40 366	99.78 248	97.36 205	99.31 302	99.19 280

DCV-MVSNet		96.69 328	96.29 338	97.90 295	98.28 379	95.24 314	97.29 301	97.36 386	98.21 187	98.17 293	97.86 362	86.27 392	99.55 366	94.87 344	98.32 387	98.89 331

tttt0517		95.64 365	94.98 375	97.64 324	99.36 171	93.81 370	98.72 102	90.47 456	98.08 204	98.67 244	98.34 327	73.88 444	99.92 63	97.77 175	99.51 265	99.20 275

our_test_3		97.39 288	97.73 253	96.34 388	98.70 325	89.78 433	94.61 432	98.97 291	96.50 324	99.04 178	98.85 240	95.98 259	99.84 172	97.26 210	99.67 211	99.41 200

thisisatest0515		94.12 392	93.16 400	96.97 367	98.60 347	92.90 388	93.77 447	90.61 455	94.10 397	96.91 377	95.87 422	74.99 443	99.80 224	94.52 353	99.12 336	98.20 399

ppachtmachnet_test		97.50 275	97.74 251	96.78 378	98.70 325	91.23 420	94.55 434	99.05 276	96.36 330	99.21 153	98.79 255	96.39 236	99.78 248	96.74 255	99.82 119	99.34 233

SMA-MVS		98.40 184	98.03 226	99.51 48	99.16 228	99.21 33	98.05 188	99.22 240	94.16 395	98.98 186	99.10 170	97.52 167	99.79 237	96.45 285	99.64 221	99.53 144
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																98.81 344

DPE-MVS		98.59 157	98.26 196	99.57 21	99.27 194	99.15 52	97.01 319	99.39 168	97.67 232	99.44 100	98.99 204	97.53 165	99.89 95	95.40 334	99.68 205	99.66 75
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
test_part2					99.36 171	99.10 65				99.05 176

thres100view900		94.19 389	93.67 394	95.75 406	99.06 251	91.35 414	98.03 192	94.24 440	98.33 173	97.40 356	94.98 440	79.84 429	99.62 336	83.05 452	98.08 402	96.29 447

tfpnnormal		98.90 96	98.90 95	98.91 163	99.67 65	97.82 179	99.00 72	99.44 147	99.45 49	99.51 88	99.24 134	98.20 105	99.86 141	95.92 312	99.69 200	99.04 304

tfpn200view9		94.03 393	93.44 396	95.78 405	98.93 277	91.44 412	97.60 266	94.29 438	97.94 213	97.10 366	94.31 447	79.67 431	99.62 336	83.05 452	98.08 402	96.29 447

c3_l		97.36 289	97.37 278	97.31 349	98.09 392	93.25 382	95.01 420	99.16 258	97.05 297	98.77 233	98.72 266	92.88 335	99.64 330	96.93 235	99.76 166	99.05 300

CHOSEN 280x420		95.51 369	95.47 358	95.65 409	98.25 381	88.27 440	93.25 450	98.88 305	93.53 405	94.65 434	97.15 397	86.17 394	99.93 52	97.41 203	99.93 54	98.73 357

CANet		97.87 248	97.76 249	98.19 277	97.75 406	95.51 299	96.76 334	99.05 276	97.74 228	96.93 374	98.21 337	95.59 273	99.89 95	97.86 170	99.93 54	99.19 280

Fast-Effi-MVS+-dtu		98.27 205	98.09 218	98.81 175	98.43 369	98.11 139	97.61 265	99.50 114	98.64 146	97.39 358	97.52 382	98.12 114	99.95 26	96.90 241	98.71 370	98.38 391

Effi-MVS+-dtu		98.26 207	97.90 242	99.35 76	98.02 395	99.49 6	98.02 195	99.16 258	98.29 180	97.64 335	97.99 354	96.44 235	99.95 26	96.66 263	98.93 358	98.60 370

CANet_DTU		97.26 297	97.06 296	97.84 299	97.57 417	94.65 336	96.19 370	98.79 324	97.23 286	95.14 428	98.24 334	93.22 327	99.84 172	97.34 206	99.84 108	99.04 304

MVS_0304		97.44 283	97.01 299	98.72 196	96.42 452	96.74 252	97.20 310	91.97 452	98.46 166	98.30 284	98.79 255	92.74 339	99.91 72	99.30 61	99.94 49	99.52 147

MP-MVS-pluss		98.57 159	98.23 201	99.60 15	99.69 58	99.35 17	97.16 314	99.38 170	94.87 379	98.97 190	98.99 204	98.01 121	99.88 113	97.29 208	99.70 197	99.58 112
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
MSP-MVS		98.40 184	98.00 229	99.61 13	99.57 92	99.25 29	98.57 117	99.35 184	97.55 247	99.31 132	97.71 370	94.61 301	99.88 113	96.14 304	99.19 325	99.70 65
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												84.74 406				98.81 344

sam_mvs												84.29 412

IterMVS-SCA-FT		97.85 254	98.18 208	96.87 372	99.27 194	91.16 421	95.53 403	99.25 232	99.10 102	99.41 107	99.35 102	93.10 330	99.96 14	98.65 112	99.94 49	99.49 160

TSAR-MVS + MP.		98.63 150	98.49 160	99.06 137	99.64 74	97.90 168	98.51 128	98.94 292	96.96 302	99.24 147	98.89 233	97.83 135	99.81 216	96.88 243	99.49 274	99.48 171
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		97.86 249	98.17 209	96.92 369	98.98 270	93.91 365	96.45 352	99.17 255	97.85 221	98.41 278	97.14 398	98.47 72	99.92 63	98.02 154	99.05 339	96.92 440

OPM-MVS		98.56 160	98.32 188	99.25 100	99.41 161	98.73 91	97.13 316	99.18 251	97.10 295	98.75 236	98.92 222	98.18 106	99.65 327	96.68 262	99.56 250	99.37 220
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
ACMMP_NAP		98.75 125	98.48 161	99.57 21	99.58 87	99.29 24	97.82 228	99.25 232	96.94 304	98.78 230	99.12 166	98.02 120	99.84 172	97.13 220	99.67 211	99.59 104

ambc				98.24 270	98.82 303	95.97 284	98.62 112	99.00 289		99.27 137	99.21 141	96.99 202	99.50 383	96.55 278	99.50 272	99.26 259

MTGPA								99.20 243

SPE-MVS-test		99.13 64	99.09 73	99.26 97	99.13 235	98.97 73	99.31 30	99.88 14	99.44 51	98.16 296	98.51 306	98.64 56	99.93 52	98.91 91	99.85 103	98.88 334

Effi-MVS+		98.02 232	97.82 247	98.62 211	98.53 359	97.19 225	97.33 297	99.68 57	97.30 275	96.68 389	97.46 386	98.56 68	99.80 224	96.63 265	98.20 393	98.86 336

xiu_mvs_v2_base		97.16 307	97.49 271	96.17 397	98.54 357	92.46 396	95.45 407	98.84 316	97.25 280	97.48 350	96.49 408	98.31 89	99.90 79	96.34 292	98.68 375	96.15 451

xiu_mvs_v1_base		97.86 249	98.17 209	96.92 369	98.98 270	93.91 365	96.45 352	99.17 255	97.85 221	98.41 278	97.14 398	98.47 72	99.92 63	98.02 154	99.05 339	96.92 440

new-patchmatchnet		98.35 192	98.74 114	97.18 355	99.24 204	92.23 403	96.42 356	99.48 123	98.30 177	99.69 54	99.53 63	97.44 174	99.82 200	98.84 97	99.77 153	99.49 160

pmmvs6		99.67 3	99.70 3	99.60 15	99.90 4	99.27 27	99.53 9	99.76 39	99.64 27	99.84 30	99.83 4	99.50 9	99.87 132	99.36 56	99.92 67	99.64 81

pmmvs5		97.64 267	97.49 271	98.08 285	99.14 233	95.12 320	96.70 338	99.05 276	93.77 402	98.62 251	98.83 247	93.23 326	99.75 268	98.33 133	99.76 166	99.36 227

test_post1							97.59 268				20.48 468	83.07 420	99.66 322	94.16 364

test_post											21.25 467	83.86 415	99.70 294

Fast-Effi-MVS+		97.67 265	97.38 277	98.57 221	98.71 321	97.43 208	97.23 305	99.45 139	94.82 380	96.13 407	96.51 407	98.52 70	99.91 72	96.19 300	98.83 362	98.37 393

patchmatchnet-post											98.77 259	84.37 409	99.85 154

Anonymous20231211		99.27 38	99.27 47	99.26 97	99.29 189	98.18 133	99.49 12	99.51 111	99.70 16	99.80 37	99.68 25	96.84 208	99.83 190	99.21 69	99.91 76	99.77 48

pmmvs-eth3d		98.47 177	98.34 184	98.86 168	99.30 186	97.76 185	97.16 314	99.28 223	95.54 360	99.42 105	99.19 144	97.27 185	99.63 333	97.89 163	99.97 21	99.20 275

GG-mvs-BLEND				94.76 422	94.54 462	92.13 404	99.31 30	80.47 468		88.73 462	91.01 462	67.59 455	98.16 455	82.30 456	94.53 454	93.98 458

xiu_mvs_v1_base_debi		97.86 249	98.17 209	96.92 369	98.98 270	93.91 365	96.45 352	99.17 255	97.85 221	98.41 278	97.14 398	98.47 72	99.92 63	98.02 154	99.05 339	96.92 440

Anonymous20231206		98.21 214	98.21 202	98.20 275	99.51 119	95.43 308	98.13 172	99.32 198	96.16 339	98.93 203	98.82 250	96.00 254	99.83 190	97.32 207	99.73 173	99.36 227

MTAPA		98.88 99	98.64 133	99.61 13	99.67 65	99.36 16	98.43 141	99.20 243	98.83 139	98.89 210	98.90 227	96.98 203	99.92 63	97.16 215	99.70 197	99.56 123

MTMP							97.93 212	91.91 453

gm-plane-assit					94.83 461	81.97 464			88.07 449		94.99 439		99.60 346	91.76 413

test9_res														93.28 390	99.15 330	99.38 218

MVP-Stereo		98.08 227	97.92 240	98.57 221	98.96 273	96.79 248	97.90 218	99.18 251	96.41 329	98.46 273	98.95 218	95.93 263	99.60 346	96.51 281	98.98 353	99.31 245
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
TEST9					98.71 321	98.08 146	95.96 383	99.03 281	91.40 430	95.85 413	97.53 380	96.52 231	99.76 260

train_agg		97.10 309	96.45 334	99.07 131	98.71 321	98.08 146	95.96 383	99.03 281	91.64 425	95.85 413	97.53 380	96.47 233	99.76 260	93.67 380	99.16 328	99.36 227

gg-mvs-nofinetune		92.37 419	91.20 423	95.85 403	95.80 460	92.38 399	99.31 30	81.84 467	99.75 11	91.83 456	99.74 18	68.29 451	99.02 435	87.15 443	97.12 431	96.16 450

SCA		96.41 341	96.66 324	95.67 407	98.24 382	88.35 439	95.85 392	96.88 404	96.11 340	97.67 334	98.67 280	93.10 330	99.85 154	94.16 364	99.22 318	98.81 344

Patchmatch-test		96.55 334	96.34 336	97.17 357	98.35 375	93.06 384	98.40 145	97.79 373	97.33 271	98.41 278	98.67 280	83.68 416	99.69 298	95.16 338	99.31 302	98.77 352

test_8					98.67 335	98.01 154	95.91 389	99.02 284	91.64 425	95.79 415	97.50 383	96.47 233	99.76 260

MS-PatchMatch		97.68 264	97.75 250	97.45 344	98.23 384	93.78 371	97.29 301	98.84 316	96.10 341	98.64 248	98.65 285	96.04 251	99.36 409	96.84 247	99.14 331	99.20 275

Patchmatch-RL test		97.26 297	97.02 298	97.99 293	99.52 117	95.53 298	96.13 375	99.71 46	97.47 255	99.27 137	99.16 154	84.30 411	99.62 336	97.89 163	99.77 153	98.81 344

cdsmvs_eth3d_5k		24.66 433	32.88 436	0.00 451	0.00 474	0.00 476	0.00 462	99.10 268	0.00 469	0.00 470	97.58 378	99.21 18	0.00 470	0.00 469	0.00 468	0.00 466

pcd_1.5k_mvsjas		8.17 436	10.90 439	0.00 451	0.00 474	0.00 476	0.00 462	0.00 475	0.00 469	0.00 470	0.00 469	98.07 116	0.00 470	0.00 469	0.00 468	0.00 466

agg_prior2														92.50 406	99.16 328	99.37 220

agg_prior					98.68 334	97.99 155		99.01 287		95.59 416			99.77 254

tmp_tt		78.77 430	78.73 433	78.90 446	58.45 471	74.76 470	94.20 440	78.26 469	39.16 464	86.71 463	92.82 458	80.50 427	75.19 466	86.16 448	92.29 459	86.74 460

canonicalmvs		98.34 193	98.26 196	98.58 218	98.46 365	97.82 179	98.96 77	99.46 135	99.19 85	97.46 351	95.46 432	98.59 62	99.46 395	98.08 148	98.71 370	98.46 378

anonymousdsp		99.51 14	99.47 21	99.62 9	99.88 9	99.08 69	99.34 23	99.69 52	98.93 126	99.65 62	99.72 21	98.93 32	99.95 26	99.11 76	100.00 1	99.82 35

alignmvs		97.35 290	96.88 307	98.78 183	98.54 357	98.09 142	97.71 246	97.69 377	99.20 81	97.59 339	95.90 421	88.12 386	99.55 366	98.18 141	98.96 355	98.70 361

nrg030		99.40 26	99.35 34	99.54 31	99.58 87	99.13 60	98.98 75	99.48 123	99.68 20	99.46 96	99.26 127	98.62 59	99.73 279	99.17 73	99.92 67	99.76 53

v144192		98.54 166	98.57 146	98.45 245	99.21 211	95.98 283	97.63 260	99.36 178	97.15 294	99.32 130	99.18 148	95.84 266	99.84 172	99.50 49	99.91 76	99.54 135

FIs		99.14 60	99.09 73	99.29 91	99.70 55	98.28 123	99.13 58	99.52 110	99.48 43	99.24 147	99.41 91	96.79 215	99.82 200	98.69 110	99.88 91	99.76 53

v1921920		98.54 166	98.60 142	98.38 254	99.20 215	95.76 293	97.56 271	99.36 178	97.23 286	99.38 113	99.17 152	96.02 252	99.84 172	99.57 37	99.90 83	99.54 135

UA-Net		99.47 16	99.40 27	99.70 2	99.49 133	99.29 24	99.80 4	99.72 44	99.82 8	99.04 178	99.81 8	98.05 119	99.96 14	98.85 96	99.99 5	99.86 27

v1192		98.60 155	98.66 130	98.41 250	99.27 194	95.88 286	97.52 276	99.36 178	97.41 264	99.33 124	99.20 143	96.37 239	99.82 200	99.57 37	99.92 67	99.55 130

FC-MVSNet-test		99.27 38	99.25 51	99.34 79	99.77 27	98.37 117	99.30 35	99.57 87	99.61 34	99.40 110	99.50 67	97.12 193	99.85 154	99.02 85	99.94 49	99.80 40

v1144		98.60 155	98.66 130	98.41 250	99.36 171	95.90 285	97.58 269	99.34 190	97.51 251	99.27 137	99.15 158	96.34 241	99.80 224	99.47 52	99.93 54	99.51 150

sosnet-low-res		0.00 438	0.00 441	0.00 451	0.00 474	0.00 476	0.00 462	0.00 475	0.00 469	0.00 470	0.00 469	0.00 474	0.00 470	0.00 469	0.00 468	0.00 466

HFP-MVS		98.71 129	98.44 168	99.51 48	99.49 133	99.16 48	98.52 123	99.31 203	97.47 255	98.58 259	98.50 310	97.97 126	99.85 154	96.57 271	99.59 238	99.53 144

v148		98.45 179	98.60 142	98.00 292	99.44 152	94.98 324	97.44 286	99.06 273	98.30 177	99.32 130	98.97 211	96.65 226	99.62 336	98.37 129	99.85 103	99.39 210

sosnet		0.00 438	0.00 441	0.00 451	0.00 474	0.00 476	0.00 462	0.00 475	0.00 469	0.00 470	0.00 469	0.00 474	0.00 470	0.00 469	0.00 468	0.00 466

uncertanet		0.00 438	0.00 441	0.00 451	0.00 474	0.00 476	0.00 462	0.00 475	0.00 469	0.00 470	0.00 469	0.00 474	0.00 470	0.00 469	0.00 468	0.00 466

AllTest		98.44 180	98.20 203	99.16 114	99.50 125	98.55 103	98.25 159	99.58 80	96.80 311	98.88 214	99.06 176	97.65 150	99.57 359	94.45 356	99.61 232	99.37 220

TestCases				99.16 114	99.50 125	98.55 103		99.58 80	96.80 311	98.88 214	99.06 176	97.65 150	99.57 359	94.45 356	99.61 232	99.37 220

v7n		99.53 12	99.57 13	99.41 66	99.88 9	98.54 106	99.45 14	99.61 73	99.66 24	99.68 56	99.66 32	98.44 77	99.95 26	99.73 26	99.96 28	99.75 57

region2R		98.69 136	98.40 173	99.54 31	99.53 114	99.17 44	98.52 123	99.31 203	97.46 260	98.44 275	98.51 306	97.83 135	99.88 113	96.46 284	99.58 243	99.58 112

RRT-MVS		97.88 246	97.98 231	97.61 327	98.15 388	93.77 372	98.97 76	99.64 66	99.16 90	98.69 241	99.42 87	91.60 352	99.89 95	97.63 185	98.52 384	99.16 290

mamv4		99.44 19	99.39 28	99.58 20	99.30 186	99.74 2	99.04 68	99.81 31	99.77 10	99.82 33	99.57 49	97.82 138	99.98 4	99.53 46	99.89 89	99.01 308

PS-MVSNAJss		99.46 17	99.49 16	99.35 76	99.90 4	98.15 135	99.20 48	99.65 64	99.48 43	99.92 8	99.71 22	98.07 116	99.96 14	99.53 46	100.00 1	99.93 11

PS-MVSNAJ		97.08 311	97.39 276	96.16 399	98.56 355	92.46 396	95.24 414	98.85 315	97.25 280	97.49 349	95.99 418	98.07 116	99.90 79	96.37 289	98.67 376	96.12 452

jajsoiax		99.58 9	99.61 11	99.48 56	99.87 12	98.61 98	99.28 40	99.66 61	99.09 105	99.89 18	99.68 25	99.53 7	99.97 7	99.50 49	99.99 5	99.87 21

mvs_tets		99.63 6	99.67 6	99.49 54	99.88 9	98.61 98	99.34 23	99.71 46	99.27 72	99.90 14	99.74 18	99.68 4	99.97 7	99.55 41	99.99 5	99.88 20

EI-MVSNet-UG-set		98.69 136	98.71 121	98.62 211	99.10 239	96.37 269	97.23 305	98.87 307	99.20 81	99.19 155	98.99 204	97.30 182	99.85 154	98.77 103	99.79 142	99.65 80

EI-MVSNet-Vis-set		98.68 141	98.70 124	98.63 209	99.09 242	96.40 268	97.23 305	98.86 312	99.20 81	99.18 159	98.97 211	97.29 184	99.85 154	98.72 107	99.78 147	99.64 81

HPM-MVS++		98.10 224	97.64 262	99.48 56	99.09 242	99.13 60	97.52 276	98.75 332	97.46 260	96.90 380	97.83 365	96.01 253	99.84 172	95.82 320	99.35 296	99.46 181

test_prior4						97.97 159	95.86 390

XVS		98.72 128	98.45 166	99.53 38	99.46 145	99.21 33	98.65 108	99.34 190	98.62 151	97.54 344	98.63 290	97.50 169	99.83 190	96.79 249	99.53 260	99.56 123

v1240		98.55 164	98.62 137	98.32 261	99.22 209	95.58 296	97.51 278	99.45 139	97.16 292	99.45 99	99.24 134	96.12 249	99.85 154	99.60 35	99.88 91	99.55 130

pm-mvs1		99.44 19	99.48 18	99.33 85	99.80 21	98.63 95	99.29 36	99.63 68	99.30 69	99.65 62	99.60 45	99.16 22	99.82 200	99.07 79	99.83 115	99.56 123

test_prior2							95.74 397		96.48 326	96.11 408	97.63 376	95.92 264		94.16 364	99.20 322

X-MVStestdata		94.32 386	92.59 405	99.53 38	99.46 145	99.21 33	98.65 108	99.34 190	98.62 151	97.54 344	45.85 464	97.50 169	99.83 190	96.79 249	99.53 260	99.56 123

test_prior				98.95 156	98.69 330	97.95 163		99.03 281					99.59 350			99.30 248

旧先验2							95.76 396		88.56 448	97.52 346			99.66 322	94.48 354

新几何2							95.93 386

新几何1				98.91 163	98.94 275	97.76 185		98.76 329	87.58 450	96.75 388	98.10 345	94.80 297	99.78 248	92.73 402	99.00 348	99.20 275

旧先验1					98.82 303	97.45 206		98.76 329			98.34 327	95.50 277			99.01 347	99.23 265

无先验							95.74 397	98.74 334	89.38 444				99.73 279	92.38 408		99.22 270

原ACMM2							95.53 403

原ACMM1				98.35 259	98.90 285	96.25 273		98.83 320	92.48 419	96.07 410	98.10 345	95.39 280	99.71 287	92.61 405	98.99 350	99.08 296

test222					98.92 281	96.93 242	95.54 402	98.78 326	85.72 453	96.86 383	98.11 344	94.43 304			99.10 338	99.23 265

testdata2													99.79 237	92.80 400

segment_acmp												97.02 200

testdata				98.09 282	98.93 277	95.40 309		98.80 323	90.08 441	97.45 353	98.37 323	95.26 282	99.70 294	93.58 383	98.95 356	99.17 287

testdata1							95.44 408		96.32 332

v8		99.01 80	99.16 60	98.57 221	99.47 143	96.31 272	98.90 83	99.47 131	99.03 115	99.52 83	99.57 49	96.93 204	99.81 216	99.60 35	99.98 12	99.60 97

1314		95.74 361	95.60 353	96.17 397	97.53 422	92.75 392	98.07 185	98.31 358	91.22 432	94.25 438	96.68 404	95.53 274	99.03 434	91.64 416	97.18 430	96.74 444

LFMVS		97.20 303	96.72 318	98.64 205	98.72 317	96.95 240	98.93 81	94.14 442	99.74 13	98.78 230	99.01 199	84.45 408	99.73 279	97.44 201	99.27 309	99.25 260

VDD-MVS		98.56 160	98.39 176	99.07 131	99.13 235	98.07 148	98.59 115	97.01 397	99.59 35	99.11 162	99.27 121	94.82 294	99.79 237	98.34 131	99.63 224	99.34 233

VDDNet		98.21 214	97.95 235	99.01 145	99.58 87	97.74 187	99.01 70	97.29 390	99.67 21	98.97 190	99.50 67	90.45 365	99.80 224	97.88 166	99.20 322	99.48 171

v10		98.97 87	99.11 69	98.55 228	99.44 152	96.21 274	98.90 83	99.55 98	98.73 141	99.48 91	99.60 45	96.63 227	99.83 190	99.70 31	99.99 5	99.61 95

VPNet		98.87 100	98.83 106	99.01 145	99.70 55	97.62 196	98.43 141	99.35 184	99.47 46	99.28 135	99.05 183	96.72 221	99.82 200	98.09 147	99.36 294	99.59 104

MVS		93.19 407	92.09 412	96.50 384	96.91 441	94.03 355	98.07 185	98.06 369	68.01 462	94.56 436	96.48 409	95.96 261	99.30 419	83.84 451	96.89 435	96.17 449

v2v482		98.56 160	98.62 137	98.37 257	99.42 158	95.81 291	97.58 269	99.16 258	97.90 217	99.28 135	99.01 199	95.98 259	99.79 237	99.33 58	99.90 83	99.51 150

V42		98.78 120	98.78 112	98.76 188	99.44 152	97.04 234	98.27 157	99.19 247	97.87 219	99.25 145	99.16 154	96.84 208	99.78 248	99.21 69	99.84 108	99.46 181

SD-MVS		98.40 184	98.68 127	97.54 336	98.96 273	97.99 155	97.88 220	99.36 178	98.20 191	99.63 65	99.04 185	98.76 45	95.33 463	96.56 275	99.74 170	99.31 245
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		95.86 357	95.32 367	97.49 341	98.60 347	94.15 350	93.83 446	97.93 371	95.49 362	96.68 389	97.42 388	83.21 418	99.30 419	96.22 298	98.55 383	99.01 308

MSLP-MVS++		98.02 232	98.14 215	97.64 324	98.58 352	95.19 317	97.48 281	99.23 239	97.47 255	97.90 317	98.62 292	97.04 197	98.81 444	97.55 191	99.41 288	98.94 324

APDe-MVS		98.99 83	98.79 110	99.60 15	99.21 211	99.15 52	98.87 88	99.48 123	97.57 243	99.35 120	99.24 134	97.83 135	99.89 95	97.88 166	99.70 197	99.75 57
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
APD-MVS_3200maxsize		98.84 106	98.61 141	99.53 38	99.19 218	99.27 27	98.49 133	99.33 196	98.64 146	99.03 181	98.98 209	97.89 132	99.85 154	96.54 279	99.42 287	99.46 181

ADS-MVSNet2		95.43 370	94.98 375	96.76 379	98.14 389	91.74 406	97.92 215	97.76 374	90.23 437	96.51 399	98.91 224	85.61 399	99.85 154	92.88 396	96.90 433	98.69 362

EI-MVSNet		98.40 184	98.51 154	98.04 290	99.10 239	94.73 332	97.20 310	98.87 307	98.97 121	99.06 169	99.02 188	96.00 254	99.80 224	98.58 115	99.82 119	99.60 97

Regformer		0.00 438	0.00 441	0.00 451	0.00 474	0.00 476	0.00 462	0.00 475	0.00 469	0.00 470	0.00 469	0.00 474	0.00 470	0.00 469	0.00 468	0.00 466

CVMVSNet		96.25 346	97.21 288	93.38 439	99.10 239	80.56 467	97.20 310	98.19 364	96.94 304	99.00 183	99.02 188	89.50 374	99.80 224	96.36 291	99.59 238	99.78 45

pmmvs4		97.58 272	97.28 283	98.51 237	98.84 298	96.93 242	95.40 410	98.52 348	93.60 404	98.61 253	98.65 285	95.10 286	99.60 346	96.97 233	99.79 142	98.99 313

EU-MVSNet		97.66 266	98.50 156	95.13 418	99.63 80	85.84 449	98.35 150	98.21 361	98.23 184	99.54 77	99.46 79	95.02 288	99.68 307	98.24 135	99.87 95	99.87 21

VNet		98.42 181	98.30 190	98.79 180	98.79 310	97.29 215	98.23 160	98.66 339	99.31 67	98.85 219	98.80 253	94.80 297	99.78 248	98.13 144	99.13 333	99.31 245

test-LLR		93.90 395	93.85 390	94.04 429	96.53 449	84.62 455	94.05 443	92.39 449	96.17 337	94.12 440	95.07 436	82.30 423	99.67 311	95.87 316	98.18 394	97.82 418

TESTMET0.1,1		92.19 422	91.77 420	93.46 436	96.48 451	82.80 462	94.05 443	91.52 454	94.45 389	94.00 443	94.88 442	66.65 456	99.56 362	95.78 321	98.11 400	98.02 408

test-mter		92.33 420	91.76 421	94.04 429	96.53 449	84.62 455	94.05 443	92.39 449	94.00 400	94.12 440	95.07 436	65.63 462	99.67 311	95.87 316	98.18 394	97.82 418

VPA-MVSNet		99.30 34	99.30 44	99.28 92	99.49 133	98.36 120	99.00 72	99.45 139	99.63 29	99.52 83	99.44 84	98.25 97	99.88 113	99.09 78	99.84 108	99.62 87

ACMMPR		98.70 133	98.42 171	99.54 31	99.52 117	99.14 57	98.52 123	99.31 203	97.47 255	98.56 262	98.54 301	97.75 144	99.88 113	96.57 271	99.59 238	99.58 112

testgi		98.32 198	98.39 176	98.13 281	99.57 92	95.54 297	97.78 234	99.49 121	97.37 268	99.19 155	97.65 374	98.96 29	99.49 386	96.50 282	98.99 350	99.34 233

test20.03		98.78 120	98.77 113	98.78 183	99.46 145	97.20 224	97.78 234	99.24 237	99.04 114	99.41 107	98.90 227	97.65 150	99.76 260	97.70 182	99.79 142	99.39 210

thres600view7		94.45 384	93.83 391	96.29 390	99.06 251	91.53 409	97.99 206	94.24 440	98.34 172	97.44 354	95.01 438	79.84 429	99.67 311	84.33 450	98.23 391	97.66 428

ADS-MVSNet		95.24 373	94.93 378	96.18 396	98.14 389	90.10 432	97.92 215	97.32 389	90.23 437	96.51 399	98.91 224	85.61 399	99.74 273	92.88 396	96.90 433	98.69 362

MP-MVS		98.46 178	98.09 218	99.54 31	99.57 92	99.22 32	98.50 130	99.19 247	97.61 239	97.58 340	98.66 283	97.40 176	99.88 113	94.72 349	99.60 234	99.54 135
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
testmvs		17.12 434	20.53 437	6.87 450	12.05 472	4.20 475	93.62 449	6.73 473	4.62 468	10.41 468	24.33 465	8.28 473	3.56 469	9.69 468	15.07 466	12.86 465

thres400		94.14 391	93.44 396	96.24 393	98.93 277	91.44 412	97.60 266	94.29 438	97.94 213	97.10 366	94.31 447	79.67 431	99.62 336	83.05 452	98.08 402	97.66 428

test123		17.04 435	20.11 438	7.82 449	10.25 473	4.91 474	94.80 424	4.47 474	4.93 467	10.00 469	24.28 466	9.69 472	3.64 468	10.14 467	12.43 467	14.92 464

thres200		93.72 399	93.14 401	95.46 414	98.66 340	91.29 416	96.61 343	94.63 435	97.39 266	96.83 384	93.71 450	79.88 428	99.56 362	82.40 455	98.13 399	95.54 456

test0.0.03 1		94.51 383	93.69 393	96.99 365	96.05 456	93.61 379	94.97 421	93.49 444	96.17 337	97.57 342	94.88 442	82.30 423	99.01 437	93.60 382	94.17 455	98.37 393

pmmvs3		95.03 377	94.40 384	96.93 368	97.70 412	92.53 395	95.08 418	97.71 376	88.57 447	97.71 331	98.08 348	79.39 433	99.82 200	96.19 300	99.11 337	98.43 386

EMVS		93.83 396	94.02 388	93.23 440	96.83 444	84.96 452	89.77 460	96.32 413	97.92 215	97.43 355	96.36 414	86.17 394	98.93 440	87.68 442	97.73 413	95.81 454

E-PMN		94.17 390	94.37 385	93.58 435	96.86 442	85.71 451	90.11 459	97.07 396	98.17 194	97.82 326	97.19 395	84.62 407	98.94 439	89.77 435	97.68 414	96.09 453

PGM-MVS		98.66 145	98.37 180	99.55 28	99.53 114	99.18 43	98.23 160	99.49 121	97.01 301	98.69 241	98.88 234	98.00 122	99.89 95	95.87 316	99.59 238	99.58 112

LCM-MVSNet-Re		98.64 148	98.48 161	99.11 122	98.85 297	98.51 108	98.49 133	99.83 25	98.37 169	99.69 54	99.46 79	98.21 104	99.92 63	94.13 368	99.30 305	98.91 329

LCM-MVSNet		99.93 1	99.92 1	99.94 1	99.99 1	99.97 1	99.90 1	99.89 13	99.98 1	99.99 1	99.96 1	99.77 2	100.00 1	99.81 15	100.00 1	99.85 29

MCST-MVS		98.00 235	97.63 263	99.10 124	99.24 204	98.17 134	96.89 328	98.73 335	95.66 355	97.92 315	97.70 372	97.17 191	99.66 322	96.18 302	99.23 317	99.47 179

mvs_anonymous		97.83 257	98.16 212	96.87 372	98.18 386	91.89 405	97.31 299	98.90 301	97.37 268	98.83 222	99.46 79	96.28 242	99.79 237	98.90 92	98.16 397	98.95 320

MVS_Test		98.18 219	98.36 181	97.67 317	98.48 362	94.73 332	98.18 165	99.02 284	97.69 231	98.04 309	99.11 167	97.22 189	99.56 362	98.57 117	98.90 360	98.71 358

MDA-MVSNet-bldmvs		97.94 240	97.91 241	98.06 287	99.44 152	94.96 325	96.63 342	99.15 263	98.35 171	98.83 222	99.11 167	94.31 309	99.85 154	96.60 268	98.72 368	99.37 220

CDPH-MVS		97.26 297	96.66 324	99.07 131	99.00 266	98.15 135	96.03 379	99.01 287	91.21 433	97.79 327	97.85 364	96.89 206	99.69 298	92.75 401	99.38 293	99.39 210

test12				98.93 159	98.58 352	97.83 174		98.66 339		96.53 396		95.51 276	99.69 298		99.13 333	99.27 253

casdiffmvs		98.95 90	99.00 85	98.81 175	99.38 164	97.33 212	97.82 228	99.57 87	99.17 89	99.35 120	99.17 152	98.35 86	99.69 298	98.46 125	99.73 173	99.41 200
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		98.22 212	98.24 200	98.17 278	99.00 266	95.44 307	96.38 358	99.58 80	97.79 226	98.53 267	98.50 310	96.76 218	99.74 273	97.95 162	99.64 221	99.34 233
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		93.73 398	92.83 404	96.42 386	97.70 412	91.28 417	96.84 330	89.77 458	93.96 401	92.44 453	95.93 420	79.14 434	99.77 254	92.94 394	96.76 437	98.21 398

baseline1		95.96 355	95.44 361	97.52 338	98.51 361	93.99 362	98.39 146	96.09 418	98.21 187	98.40 282	97.76 368	86.88 388	99.63 333	95.42 333	89.27 461	98.95 320

YYNet1		97.60 269	97.67 257	97.39 348	99.04 255	93.04 387	95.27 412	98.38 356	97.25 280	98.92 205	98.95 218	95.48 278	99.73 279	96.99 230	98.74 366	99.41 200

PMMVS2		98.07 228	98.08 221	98.04 290	99.41 161	94.59 338	94.59 433	99.40 166	97.50 252	98.82 225	98.83 247	96.83 210	99.84 172	97.50 196	99.81 125	99.71 60

MDA-MVSNet_test_wron		97.60 269	97.66 260	97.41 347	99.04 255	93.09 383	95.27 412	98.42 353	97.26 279	98.88 214	98.95 218	95.43 279	99.73 279	97.02 227	98.72 368	99.41 200

tpmvs		95.02 378	95.25 368	94.33 425	96.39 454	85.87 448	98.08 182	96.83 405	95.46 363	95.51 424	98.69 276	85.91 397	99.53 373	94.16 364	96.23 442	97.58 431

PM-MVS		98.82 112	98.72 118	99.12 120	99.64 74	98.54 106	97.98 207	99.68 57	97.62 236	99.34 122	99.18 148	97.54 163	99.77 254	97.79 173	99.74 170	99.04 304

HQP_MVS		97.99 238	97.67 257	98.93 159	99.19 218	97.65 193	97.77 237	99.27 226	98.20 191	97.79 327	97.98 355	94.90 290	99.70 294	94.42 358	99.51 265	99.45 186

plane_prior7					99.19 218	97.87 170

plane_prior6					98.99 269	97.70 191						94.90 290

plane_prior5								99.27 226					99.70 294	94.42 358	99.51 265	99.45 186

plane_prior4											97.98 355

plane_prior3						97.78 184			97.41 264	97.79 327

plane_prior2							97.77 237		98.20 191

plane_prior1					99.05 254

plane_prior						97.65 193	97.07 317		96.72 316						99.36 294

PS-CasMVS		99.40 26	99.33 37	99.62 9	99.71 47	99.10 65	99.29 36	99.53 106	99.53 40	99.46 96	99.41 91	98.23 99	99.95 26	98.89 94	99.95 38	99.81 38

UniMVSNet_NR-MVSNet		98.86 103	98.68 127	99.40 68	99.17 226	98.74 88	97.68 250	99.40 166	99.14 93	99.06 169	98.59 297	96.71 222	99.93 52	98.57 117	99.77 153	99.53 144

PEN-MVS		99.41 25	99.34 36	99.62 9	99.73 37	99.14 57	99.29 36	99.54 103	99.62 32	99.56 72	99.42 87	98.16 110	99.96 14	98.78 100	99.93 54	99.77 48

TransMVSNet (Re)		99.44 19	99.47 21	99.36 70	99.80 21	98.58 101	99.27 42	99.57 87	99.39 57	99.75 44	99.62 40	99.17 20	99.83 190	99.06 81	99.62 227	99.66 75

DTE-MVSNet		99.43 23	99.35 34	99.66 7	99.71 47	99.30 22	99.31 30	99.51 111	99.64 27	99.56 72	99.46 79	98.23 99	99.97 7	98.78 100	99.93 54	99.72 59

DU-MVS		98.82 112	98.63 135	99.39 69	99.16 228	98.74 88	97.54 274	99.25 232	98.84 138	99.06 169	98.76 261	96.76 218	99.93 52	98.57 117	99.77 153	99.50 153

UniMVSNet (Re)		98.87 100	98.71 121	99.35 76	99.24 204	98.73 91	97.73 245	99.38 170	98.93 126	99.12 161	98.73 264	96.77 216	99.86 141	98.63 114	99.80 136	99.46 181

CP-MVSNet		99.21 48	99.09 73	99.56 26	99.65 68	98.96 77	99.13 58	99.34 190	99.42 54	99.33 124	99.26 127	97.01 201	99.94 41	98.74 105	99.93 54	99.79 42

WR-MVS_H		99.33 31	99.22 53	99.65 8	99.71 47	99.24 30	99.32 26	99.55 98	99.46 48	99.50 89	99.34 106	97.30 182	99.93 52	98.90 92	99.93 54	99.77 48

WR-MVS		98.40 184	98.19 207	99.03 141	99.00 266	97.65 193	96.85 329	98.94 292	98.57 158	98.89 210	98.50 310	95.60 272	99.85 154	97.54 193	99.85 103	99.59 104

NR-MVSNet		98.95 90	98.82 107	99.36 70	99.16 228	98.72 93	99.22 45	99.20 243	99.10 102	99.72 46	98.76 261	96.38 238	99.86 141	98.00 157	99.82 119	99.50 153

Baseline_NR-MVSNet		98.98 86	98.86 104	99.36 70	99.82 19	98.55 103	97.47 283	99.57 87	99.37 59	99.21 153	99.61 43	96.76 218	99.83 190	98.06 150	99.83 115	99.71 60

TranMVSNet+NR-MVSNet		99.17 52	99.07 76	99.46 62	99.37 170	98.87 81	98.39 146	99.42 159	99.42 54	99.36 118	99.06 176	98.38 81	99.95 26	98.34 131	99.90 83	99.57 117

TSAR-MVS + GP.		98.18 219	97.98 231	98.77 187	98.71 321	97.88 169	96.32 362	98.66 339	96.33 331	99.23 149	98.51 306	97.48 173	99.40 404	97.16 215	99.46 277	99.02 307

n2								0.00 475

nn								0.00 475

mPP-MVS		98.64 148	98.34 184	99.54 31	99.54 111	99.17 44	98.63 110	99.24 237	97.47 255	98.09 304	98.68 278	97.62 155	99.89 95	96.22 298	99.62 227	99.57 117

door-mid								99.57 87

XVG-OURS-SEG-HR		98.49 175	98.28 192	99.14 118	99.49 133	98.83 83	96.54 346	99.48 123	97.32 273	99.11 162	98.61 294	99.33 15	99.30 419	96.23 297	98.38 386	99.28 252

mvsmamba		97.57 273	97.26 284	98.51 237	98.69 330	96.73 253	98.74 97	97.25 391	97.03 300	97.88 319	99.23 139	90.95 360	99.87 132	96.61 267	99.00 348	98.91 329

MVSFormer		98.26 207	98.43 169	97.77 305	98.88 291	93.89 368	99.39 20	99.56 94	99.11 95	98.16 296	98.13 341	93.81 320	99.97 7	99.26 64	99.57 247	99.43 194

jason		97.45 282	97.35 280	97.76 308	99.24 204	93.93 364	95.86 390	98.42 353	94.24 393	98.50 270	98.13 341	94.82 294	99.91 72	97.22 212	99.73 173	99.43 194
jason: jason.
lupinMVS		97.06 312	96.86 308	97.65 321	98.88 291	93.89 368	95.48 406	97.97 370	93.53 405	98.16 296	97.58 378	93.81 320	99.91 72	96.77 252	99.57 247	99.17 287

test_djsdf		99.52 13	99.51 15	99.53 38	99.86 14	98.74 88	99.39 20	99.56 94	99.11 95	99.70 50	99.73 20	99.00 27	99.97 7	99.26 64	99.98 12	99.89 16

HPM-MVS_fast		99.01 80	98.82 107	99.57 21	99.71 47	99.35 17	99.00 72	99.50 114	97.33 271	98.94 202	98.86 237	98.75 46	99.82 200	97.53 194	99.71 190	99.56 123

K. test v3		98.00 235	97.66 260	99.03 141	99.79 23	97.56 198	99.19 52	92.47 448	99.62 32	99.52 83	99.66 32	89.61 372	99.96 14	99.25 66	99.81 125	99.56 123

lessismore_v0				98.97 153	99.73 37	97.53 200		86.71 463		99.37 115	99.52 66	89.93 368	99.92 63	98.99 87	99.72 181	99.44 190

SixPastTwentyTwo		98.75 125	98.62 137	99.16 114	99.83 18	97.96 162	99.28 40	98.20 362	99.37 59	99.70 50	99.65 36	92.65 341	99.93 52	99.04 83	99.84 108	99.60 97

OurMVSNet-221017-0		99.37 29	99.31 41	99.53 38	99.91 3	98.98 71	99.63 7	99.58 80	99.44 51	99.78 39	99.76 15	96.39 236	99.92 63	99.44 53	99.92 67	99.68 68

HPM-MVS		98.79 118	98.53 152	99.59 19	99.65 68	99.29 24	99.16 54	99.43 153	96.74 315	98.61 253	98.38 322	98.62 59	99.87 132	96.47 283	99.67 211	99.59 104
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
XVG-OURS		98.53 168	98.34 184	99.11 122	99.50 125	98.82 85	95.97 381	99.50 114	97.30 275	99.05 176	98.98 209	99.35 14	99.32 416	95.72 323	99.68 205	99.18 283

XVG-ACMP-BASELINE		98.56 160	98.34 184	99.22 105	99.54 111	98.59 100	97.71 246	99.46 135	97.25 280	98.98 186	98.99 204	97.54 163	99.84 172	95.88 313	99.74 170	99.23 265

casdiffmvs_mvg		99.12 67	99.16 60	98.99 147	99.43 157	97.73 189	98.00 199	99.62 70	99.22 77	99.55 75	99.22 140	98.93 32	99.75 268	98.66 111	99.81 125	99.50 153
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		98.71 129	98.46 165	99.47 60	99.57 92	98.97 73	98.23 160	99.48 123	96.60 320	99.10 165	99.06 176	98.71 50	99.83 190	95.58 330	99.78 147	99.62 87

LGP-MVS_train				99.47 60	99.57 92	98.97 73		99.48 123	96.60 320	99.10 165	99.06 176	98.71 50	99.83 190	95.58 330	99.78 147	99.62 87

baseline		98.96 89	99.02 81	98.76 188	99.38 164	97.26 218	98.49 133	99.50 114	98.86 135	99.19 155	99.06 176	98.23 99	99.69 298	98.71 108	99.76 166	99.33 238

test11								98.87 307

door								99.41 163

EPNet_dtu		94.93 380	94.78 380	95.38 416	93.58 464	87.68 443	96.78 332	95.69 427	97.35 270	89.14 461	98.09 347	88.15 385	99.49 386	94.95 343	99.30 305	98.98 314
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CHOSEN 1792x2688		97.49 278	97.14 293	98.54 233	99.68 61	96.09 278	96.50 350	99.62 70	91.58 427	98.84 221	98.97 211	92.36 343	99.88 113	96.76 253	99.95 38	99.67 73

EPNet		96.14 349	95.44 361	98.25 268	90.76 468	95.50 302	97.92 215	94.65 434	98.97 121	92.98 450	98.85 240	89.12 376	99.87 132	95.99 309	99.68 205	99.39 210
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
HQP5-MVS						96.79 248

HQP-NCC					98.67 335		96.29 364		96.05 342	95.55 419

ACMP_Plane					98.67 335		96.29 364		96.05 342	95.55 419

APD-MVS		98.10 224	97.67 257	99.42 64	99.11 237	98.93 79	97.76 240	99.28 223	94.97 376	98.72 239	98.77 259	97.04 197	99.85 154	93.79 378	99.54 256	99.49 160
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
BP-MVS														92.82 398

HQP4-MVS										95.56 418			99.54 371			99.32 241

HQP3-MVS								99.04 279							99.26 312

HQP2-MVS												93.84 318

CNVR-MVS		98.17 221	97.87 244	99.07 131	98.67 335	98.24 126	97.01 319	98.93 295	97.25 280	97.62 336	98.34 327	97.27 185	99.57 359	96.42 286	99.33 299	99.39 210

NCCC		97.86 249	97.47 274	99.05 138	98.61 345	98.07 148	96.98 321	98.90 301	97.63 235	97.04 370	97.93 360	95.99 258	99.66 322	95.31 335	98.82 364	99.43 194

114514_t		96.50 337	95.77 346	98.69 199	99.48 141	97.43 208	97.84 227	99.55 98	81.42 459	96.51 399	98.58 298	95.53 274	99.67 311	93.41 388	99.58 243	98.98 314

CP-MVS		98.70 133	98.42 171	99.52 44	99.36 171	99.12 62	98.72 102	99.36 178	97.54 249	98.30 284	98.40 319	97.86 134	99.89 95	96.53 280	99.72 181	99.56 123

DSMNet-mixed		97.42 285	97.60 265	96.87 372	99.15 232	91.46 410	98.54 121	99.12 265	92.87 415	97.58 340	99.63 39	96.21 244	99.90 79	95.74 322	99.54 256	99.27 253

tpm2		93.09 408	92.58 406	94.62 423	97.56 418	86.53 447	97.66 254	95.79 424	86.15 452	94.07 442	98.23 336	75.95 441	99.53 373	90.91 429	96.86 436	97.81 420

NP-MVS					98.84 298	97.39 210					96.84 401

EG-PatchMatch MVS		98.99 83	99.01 83	98.94 157	99.50 125	97.47 204	98.04 190	99.59 78	98.15 202	99.40 110	99.36 101	98.58 67	99.76 260	98.78 100	99.68 205	99.59 104

tpm cat1		93.29 405	93.13 402	93.75 433	97.39 431	84.74 453	97.39 289	97.65 380	83.39 457	94.16 439	98.41 318	82.86 421	99.39 406	91.56 418	95.35 450	97.14 439

SteuartSystems-ACMMP		98.79 118	98.54 150	99.54 31	99.73 37	99.16 48	98.23 160	99.31 203	97.92 215	98.90 207	98.90 227	98.00 122	99.88 113	96.15 303	99.72 181	99.58 112
Skip Steuart: Steuart Systems R&D Blog.
CostFormer		93.97 394	93.78 392	94.51 424	97.53 422	85.83 450	97.98 207	95.96 420	89.29 445	94.99 430	98.63 290	78.63 437	99.62 336	94.54 352	96.50 438	98.09 405

CR-MVSNet		96.28 344	95.95 343	97.28 351	97.71 410	94.22 345	98.11 177	98.92 298	92.31 421	96.91 377	99.37 97	85.44 402	99.81 216	97.39 204	97.36 426	97.81 420

JIA-IIPM		95.52 368	95.03 374	97.00 364	96.85 443	94.03 355	96.93 325	95.82 423	99.20 81	94.63 435	99.71 22	83.09 419	99.60 346	94.42 358	94.64 452	97.36 437

Patchmtry		97.35 290	96.97 300	98.50 241	97.31 433	96.47 266	98.18 165	98.92 298	98.95 125	98.78 230	99.37 97	85.44 402	99.85 154	95.96 311	99.83 115	99.17 287

PatchT		96.65 331	96.35 335	97.54 336	97.40 430	95.32 312	97.98 207	96.64 408	99.33 64	96.89 381	99.42 87	84.32 410	99.81 216	97.69 184	97.49 417	97.48 433

tpmrst		95.07 376	95.46 359	93.91 431	97.11 437	84.36 457	97.62 261	96.96 400	94.98 375	96.35 404	98.80 253	85.46 401	99.59 350	95.60 328	96.23 442	97.79 423

BH-w/o		95.13 375	94.89 379	95.86 402	98.20 385	91.31 415	95.65 399	97.37 385	93.64 403	96.52 398	95.70 425	93.04 333	99.02 435	88.10 441	95.82 447	97.24 438

tpm		94.67 382	94.34 386	95.66 408	97.68 415	88.42 438	97.88 220	94.90 432	94.46 387	96.03 412	98.56 300	78.66 436	99.79 237	95.88 313	95.01 451	98.78 351

DELS-MVS		98.27 205	98.20 203	98.48 242	98.86 294	96.70 254	95.60 401	99.20 243	97.73 229	98.45 274	98.71 267	97.50 169	99.82 200	98.21 139	99.59 238	98.93 325
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		96.83 324	96.75 317	97.08 360	98.74 314	93.33 381	96.71 337	98.26 359	96.72 316	98.44 275	97.37 391	95.20 283	99.47 392	91.89 410	97.43 421	98.44 384

RPMNet		97.02 315	96.93 302	97.30 350	97.71 410	94.22 345	98.11 177	99.30 211	99.37 59	96.91 377	99.34 106	86.72 389	99.87 132	97.53 194	97.36 426	97.81 420

MVSTER		96.86 323	96.55 330	97.79 303	97.91 400	94.21 347	97.56 271	98.87 307	97.49 254	99.06 169	99.05 183	80.72 426	99.80 224	98.44 126	99.82 119	99.37 220

CPTT-MVS		97.84 255	97.36 279	99.27 95	99.31 182	98.46 111	98.29 153	99.27 226	94.90 378	97.83 324	98.37 323	94.90 290	99.84 172	93.85 377	99.54 256	99.51 150

GBi-Net		98.65 146	98.47 163	99.17 111	98.90 285	98.24 126	99.20 48	99.44 147	98.59 154	98.95 195	99.55 57	94.14 312	99.86 141	97.77 175	99.69 200	99.41 200

PVSNet_Blended_VisFu		98.17 221	98.15 213	98.22 274	99.73 37	95.15 318	97.36 295	99.68 57	94.45 389	98.99 185	99.27 121	96.87 207	99.94 41	97.13 220	99.91 76	99.57 117

PVSNet_BlendedMVS		97.55 274	97.53 268	97.60 328	98.92 281	93.77 372	96.64 341	99.43 153	94.49 385	97.62 336	99.18 148	96.82 211	99.67 311	94.73 347	99.93 54	99.36 227

UnsupCasMVSNet_eth		97.89 244	97.60 265	98.75 190	99.31 182	97.17 228	97.62 261	99.35 184	98.72 143	98.76 235	98.68 278	92.57 342	99.74 273	97.76 179	95.60 448	99.34 233

UnsupCasMVSNet_bld		97.30 294	96.92 304	98.45 245	99.28 191	96.78 251	96.20 369	99.27 226	95.42 364	98.28 288	98.30 331	93.16 328	99.71 287	94.99 340	97.37 424	98.87 335

PVSNet_Blended		96.88 322	96.68 321	97.47 343	98.92 281	93.77 372	94.71 426	99.43 153	90.98 435	97.62 336	97.36 392	96.82 211	99.67 311	94.73 347	99.56 250	98.98 314

FMVSNet5		96.01 352	95.20 371	98.41 250	97.53 422	96.10 275	98.74 97	99.50 114	97.22 289	98.03 310	99.04 185	69.80 449	99.88 113	97.27 209	99.71 190	99.25 260

test1		98.65 146	98.47 163	99.17 111	98.90 285	98.24 126	99.20 48	99.44 147	98.59 154	98.95 195	99.55 57	94.14 312	99.86 141	97.77 175	99.69 200	99.41 200

new_pmnet		96.99 319	96.76 316	97.67 317	98.72 317	94.89 326	95.95 385	98.20 362	92.62 418	98.55 264	98.54 301	94.88 293	99.52 377	93.96 372	99.44 286	98.59 373

FMVSNet3		97.50 275	97.24 286	98.29 265	98.08 393	95.83 289	97.86 224	98.91 300	97.89 218	98.95 195	98.95 218	87.06 387	99.81 216	97.77 175	99.69 200	99.23 265

dp		93.47 402	93.59 395	93.13 441	96.64 447	81.62 466	97.66 254	96.42 412	92.80 416	96.11 408	98.64 288	78.55 439	99.59 350	93.31 389	92.18 460	98.16 401

FMVSNet2		98.49 175	98.40 173	98.75 190	98.90 285	97.14 231	98.61 113	99.13 264	98.59 154	99.19 155	99.28 119	94.14 312	99.82 200	97.97 160	99.80 136	99.29 250

FMVSNet1		99.17 52	99.17 58	99.17 111	99.55 106	98.24 126	99.20 48	99.44 147	99.21 79	99.43 101	99.55 57	97.82 138	99.86 141	98.42 128	99.89 89	99.41 200

N_pmnet		97.63 268	97.17 289	98.99 147	99.27 194	97.86 171	95.98 380	93.41 445	95.25 369	99.47 95	98.90 227	95.63 271	99.85 154	96.91 236	99.73 173	99.27 253

cascas		94.79 381	94.33 387	96.15 400	96.02 458	92.36 400	92.34 455	99.26 231	85.34 454	95.08 429	94.96 441	92.96 334	98.53 449	94.41 361	98.59 381	97.56 432

BH-RMVSNet		96.83 324	96.58 329	97.58 330	98.47 363	94.05 352	96.67 339	97.36 386	96.70 318	97.87 320	97.98 355	95.14 285	99.44 399	90.47 433	98.58 382	99.25 260

UGNet		98.53 168	98.45 166	98.79 180	97.94 398	96.96 239	99.08 61	98.54 346	99.10 102	96.82 385	99.47 77	96.55 230	99.84 172	98.56 120	99.94 49	99.55 130
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		96.67 330	96.27 340	97.87 298	98.81 306	94.61 337	96.77 333	97.92 372	94.94 377	97.12 365	97.74 369	91.11 359	99.82 200	93.89 374	98.15 398	99.18 283

XXY-MVS		99.14 60	99.15 65	99.10 124	99.76 30	97.74 187	98.85 92	99.62 70	98.48 165	99.37 115	99.49 73	98.75 46	99.86 141	98.20 140	99.80 136	99.71 60

EC-MVSNet		99.09 70	99.05 77	99.20 106	99.28 191	98.93 79	99.24 44	99.84 22	99.08 109	98.12 301	98.37 323	98.72 49	99.90 79	99.05 82	99.77 153	98.77 352

sss		97.21 302	96.93 302	98.06 287	98.83 300	95.22 316	96.75 335	98.48 350	94.49 385	97.27 362	97.90 361	92.77 338	99.80 224	96.57 271	99.32 300	99.16 290

Test_1112_low_res		96.99 319	96.55 330	98.31 263	99.35 176	95.47 306	95.84 393	99.53 106	91.51 429	96.80 386	98.48 313	91.36 356	99.83 190	96.58 269	99.53 260	99.62 87

1112_ss		97.29 296	96.86 308	98.58 218	99.34 179	96.32 271	96.75 335	99.58 80	93.14 410	96.89 381	97.48 384	92.11 348	99.86 141	96.91 236	99.54 256	99.57 117

ab-mvs-re		8.12 437	10.83 440	0.00 451	0.00 474	0.00 476	0.00 462	0.00 475	0.00 469	0.00 470	97.48 384	0.00 474	0.00 470	0.00 469	0.00 468	0.00 466

ab-mvs		98.41 182	98.36 181	98.59 217	99.19 218	97.23 219	99.32 26	98.81 321	97.66 233	98.62 251	99.40 94	96.82 211	99.80 224	95.88 313	99.51 265	98.75 355

TR-MVS		95.55 367	95.12 373	96.86 375	97.54 420	93.94 363	96.49 351	96.53 411	94.36 392	97.03 372	96.61 406	94.26 311	99.16 431	86.91 446	96.31 441	97.47 434

MDTV_nov1_ep13_2view						74.92 469	97.69 249		90.06 442	97.75 330		85.78 398		93.52 384		98.69 362

MDTV_nov1_ep13			95.22 370		97.06 440	83.20 460	97.74 243	96.16 415	94.37 391	96.99 373	98.83 247	83.95 414	99.53 373	93.90 373	97.95 409

MIMVSNet1		99.38 28	99.32 39	99.55 28	99.86 14	99.19 42	99.41 17	99.59 78	99.59 35	99.71 48	99.57 49	97.12 193	99.90 79	99.21 69	99.87 95	99.54 135

MIMVSNet		96.62 333	96.25 341	97.71 315	99.04 255	94.66 335	99.16 54	96.92 403	97.23 286	97.87 320	99.10 170	86.11 396	99.65 327	91.65 415	99.21 321	98.82 339

IterMVS-LS		98.55 164	98.70 124	98.09 282	99.48 141	94.73 332	97.22 309	99.39 168	98.97 121	99.38 113	99.31 114	96.00 254	99.93 52	98.58 115	99.97 21	99.60 97
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
CDS-MVSNet		97.69 263	97.35 280	98.69 199	98.73 315	97.02 236	96.92 327	98.75 332	95.89 351	98.59 257	98.67 280	92.08 349	99.74 273	96.72 258	99.81 125	99.32 241
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
ACMMP++_ref															99.77 153

IterMVS		97.73 260	98.11 217	96.57 382	99.24 204	90.28 430	95.52 405	99.21 241	98.86 135	99.33 124	99.33 109	93.11 329	99.94 41	98.49 124	99.94 49	99.48 171
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
DP-MVS Recon		97.33 292	96.92 304	98.57 221	99.09 242	97.99 155	96.79 331	99.35 184	93.18 409	97.71 331	98.07 349	95.00 289	99.31 417	93.97 371	99.13 333	98.42 388

MVS_111021_LR		98.30 201	98.12 216	98.83 171	99.16 228	98.03 153	96.09 377	99.30 211	97.58 242	98.10 303	98.24 334	98.25 97	99.34 413	96.69 261	99.65 219	99.12 294

DP-MVS		98.93 92	98.81 109	99.28 92	99.21 211	98.45 112	98.46 138	99.33 196	99.63 29	99.48 91	99.15 158	97.23 188	99.75 268	97.17 214	99.66 218	99.63 86

ACMMP++															99.68 205

HQP-MVS		97.00 318	96.49 333	98.55 228	98.67 335	96.79 248	96.29 364	99.04 279	96.05 342	95.55 419	96.84 401	93.84 318	99.54 371	92.82 398	99.26 312	99.32 241

QAPM		97.31 293	96.81 314	98.82 173	98.80 309	97.49 201	99.06 65	99.19 247	90.22 439	97.69 333	99.16 154	96.91 205	99.90 79	90.89 430	99.41 288	99.07 298

Vis-MVSNet		99.34 30	99.36 33	99.27 95	99.73 37	98.26 124	99.17 53	99.78 36	99.11 95	99.27 137	99.48 74	98.82 37	99.95 26	98.94 90	99.93 54	99.59 104
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
MVS-HIRNet		94.32 386	95.62 352	90.42 444	98.46 365	75.36 468	96.29 364	89.13 459	95.25 369	95.38 425	99.75 16	92.88 335	99.19 429	94.07 370	99.39 290	96.72 445

IS-MVSNet		98.19 217	97.90 242	99.08 129	99.57 92	97.97 159	99.31 30	98.32 357	99.01 117	98.98 186	99.03 187	91.59 353	99.79 237	95.49 332	99.80 136	99.48 171

HyFIR lowres test		97.19 304	96.60 328	98.96 154	99.62 84	97.28 216	95.17 415	99.50 114	94.21 394	99.01 182	98.32 330	86.61 390	99.99 2	97.10 222	99.84 108	99.60 97

EPMVS		93.72 399	93.27 398	95.09 420	96.04 457	87.76 442	98.13 172	85.01 465	94.69 382	96.92 375	98.64 288	78.47 440	99.31 417	95.04 339	96.46 439	98.20 399

PAPM_NR		96.82 326	96.32 337	98.30 264	99.07 246	96.69 255	97.48 281	98.76 329	95.81 353	96.61 393	96.47 410	94.12 315	99.17 430	90.82 431	97.78 411	99.06 299

TAMVS		98.24 211	98.05 224	98.80 177	99.07 246	97.18 226	97.88 220	98.81 321	96.66 319	99.17 160	99.21 141	94.81 296	99.77 254	96.96 234	99.88 91	99.44 190

PAPR		95.29 371	94.47 382	97.75 309	97.50 428	95.14 319	94.89 423	98.71 337	91.39 431	95.35 426	95.48 431	94.57 302	99.14 433	84.95 449	97.37 424	98.97 317

RPSCF		98.62 153	98.36 181	99.42 64	99.65 68	99.42 11	98.55 119	99.57 87	97.72 230	98.90 207	99.26 127	96.12 249	99.52 377	95.72 323	99.71 190	99.32 241

Vis-MVSNet (Re-imp)		97.46 280	97.16 290	98.34 260	99.55 106	96.10 275	98.94 80	98.44 351	98.32 175	98.16 296	98.62 292	88.76 377	99.73 279	93.88 375	99.79 142	99.18 283

test_0402		98.76 124	98.71 121	98.93 159	99.56 100	98.14 137	98.45 140	99.34 190	99.28 71	98.95 195	98.91 224	98.34 87	99.79 237	95.63 327	99.91 76	98.86 336

MVS_111021_HR		98.25 210	98.08 221	98.75 190	99.09 242	97.46 205	95.97 381	99.27 226	97.60 241	97.99 313	98.25 333	98.15 112	99.38 408	96.87 244	99.57 247	99.42 197

CSCG		98.68 141	98.50 156	99.20 106	99.45 150	98.63 95	98.56 118	99.57 87	97.87 219	98.85 219	98.04 351	97.66 149	99.84 172	96.72 258	99.81 125	99.13 293

PatchMatch-RL		97.24 300	96.78 315	98.61 214	99.03 258	97.83 174	96.36 359	99.06 273	93.49 407	97.36 360	97.78 366	95.75 268	99.49 386	93.44 387	98.77 365	98.52 376

API-MVS		97.04 314	96.91 306	97.42 346	97.88 401	98.23 130	98.18 165	98.50 349	97.57 243	97.39 358	96.75 403	96.77 216	99.15 432	90.16 434	99.02 346	94.88 457

Test By Simon												96.52 231

TDRefinement		99.42 24	99.38 29	99.55 28	99.76 30	99.33 21	99.68 6	99.71 46	99.38 58	99.53 81	99.61 43	98.64 56	99.80 224	98.24 135	99.84 108	99.52 147

USDC		97.41 286	97.40 275	97.44 345	98.94 275	93.67 375	95.17 415	99.53 106	94.03 399	98.97 190	99.10 170	95.29 281	99.34 413	95.84 319	99.73 173	99.30 248

EPP-MVSNet		98.30 201	98.04 225	99.07 131	99.56 100	97.83 174	99.29 36	98.07 368	99.03 115	98.59 257	99.13 163	92.16 347	99.90 79	96.87 244	99.68 205	99.49 160

PMMVS		96.51 335	95.98 342	98.09 282	97.53 422	95.84 288	94.92 422	98.84 316	91.58 427	96.05 411	95.58 426	95.68 270	99.66 322	95.59 329	98.09 401	98.76 354

PAPM		91.88 425	90.34 428	96.51 383	98.06 394	92.56 394	92.44 454	97.17 393	86.35 451	90.38 458	96.01 417	86.61 390	99.21 428	70.65 464	95.43 449	97.75 424

ACMMP		98.75 125	98.50 156	99.52 44	99.56 100	99.16 48	98.87 88	99.37 174	97.16 292	98.82 225	99.01 199	97.71 146	99.87 132	96.29 295	99.69 200	99.54 135
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		97.17 306	96.71 319	98.55 228	98.56 355	98.05 152	96.33 361	98.93 295	96.91 306	97.06 369	97.39 389	94.38 307	99.45 397	91.66 414	99.18 327	98.14 402

PatchmatchNet		95.58 366	95.67 351	95.30 417	97.34 432	87.32 445	97.65 256	96.65 407	95.30 368	97.07 368	98.69 276	84.77 405	99.75 268	94.97 342	98.64 377	98.83 338
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
PHI-MVS		98.29 204	97.95 235	99.34 79	98.44 368	99.16 48	98.12 176	99.38 170	96.01 346	98.06 306	98.43 317	97.80 140	99.67 311	95.69 325	99.58 243	99.20 275

F-COLMAP		97.30 294	96.68 321	99.14 118	99.19 218	98.39 114	97.27 304	99.30 211	92.93 413	96.62 392	98.00 353	95.73 269	99.68 307	92.62 404	98.46 385	99.35 231

ANet_high		99.57 10	99.67 6	99.28 92	99.89 6	98.09 142	99.14 57	99.93 5	99.82 8	99.93 6	99.81 8	99.17 20	99.94 41	99.31 60	100.00 1	99.82 35

wuyk23d		96.06 350	97.62 264	91.38 443	98.65 344	98.57 102	98.85 92	96.95 401	96.86 309	99.90 14	99.16 154	99.18 19	98.40 450	89.23 438	99.77 153	77.18 463

OMC-MVS		97.88 246	97.49 271	99.04 140	98.89 290	98.63 95	96.94 323	99.25 232	95.02 374	98.53 267	98.51 306	97.27 185	99.47 392	93.50 386	99.51 265	99.01 308

MG-MVS		96.77 327	96.61 326	97.26 353	98.31 378	93.06 384	95.93 386	98.12 367	96.45 328	97.92 315	98.73 264	93.77 322	99.39 406	91.19 425	99.04 342	99.33 238

AdaColmap		97.14 308	96.71 319	98.46 244	98.34 376	97.80 183	96.95 322	98.93 295	95.58 359	96.92 375	97.66 373	95.87 265	99.53 373	90.97 427	99.14 331	98.04 407

uanet		0.00 438	0.00 441	0.00 451	0.00 474	0.00 476	0.00 462	0.00 475	0.00 469	0.00 470	0.00 469	0.00 474	0.00 470	0.00 469	0.00 468	0.00 466

ITE_SJBPF				98.87 167	99.22 209	98.48 110		99.35 184	97.50 252	98.28 288	98.60 296	97.64 153	99.35 412	93.86 376	99.27 309	98.79 350

DeepMVS_CX				93.44 437	98.24 382	94.21 347		94.34 437	64.28 463	91.34 457	94.87 444	89.45 375	92.77 464	77.54 460	93.14 457	93.35 459

TinyColmap		97.89 244	97.98 231	97.60 328	98.86 294	94.35 343	96.21 368	99.44 147	97.45 262	99.06 169	98.88 234	97.99 125	99.28 423	94.38 362	99.58 243	99.18 283

MAR-MVS		96.47 339	95.70 349	98.79 180	97.92 399	99.12 62	98.28 154	98.60 344	92.16 423	95.54 422	96.17 415	94.77 299	99.52 377	89.62 436	98.23 391	97.72 426
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		97.90 242	97.69 256	98.52 236	99.17 226	97.66 192	97.19 313	99.47 131	96.31 333	97.85 323	98.20 338	96.71 222	99.52 377	94.62 350	99.72 181	98.38 391

MSDG		97.71 262	97.52 269	98.28 266	98.91 284	96.82 246	94.42 436	99.37 174	97.65 234	98.37 283	98.29 332	97.40 176	99.33 415	94.09 369	99.22 318	98.68 365

LS3D		98.63 150	98.38 178	99.36 70	97.25 434	99.38 13	99.12 60	99.32 198	99.21 79	98.44 275	98.88 234	97.31 181	99.80 224	96.58 269	99.34 298	98.92 326

CLD-MVS		97.49 278	97.16 290	98.48 242	99.07 246	97.03 235	94.71 426	99.21 241	94.46 387	98.06 306	97.16 396	97.57 160	99.48 389	94.46 355	99.78 147	98.95 320
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
FPMVS		93.44 403	92.23 410	97.08 360	99.25 203	97.86 171	95.61 400	97.16 394	92.90 414	93.76 447	98.65 285	75.94 442	95.66 461	79.30 459	97.49 417	97.73 425

Gipuma		99.03 78	99.16 60	98.64 205	99.94 2	98.51 108	99.32 26	99.75 42	99.58 37	98.60 255	99.62 40	98.22 102	99.51 382	97.70 182	99.73 173	97.89 415
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015

High-resolution multi-view benchmark