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	80.84 1	88.10 13	88.56 13	86.73 53	92.24 71	69.03 103	89.57 90	93.39 30	77.53 45	89.79 18	94.12 42	78.98 12	96.58 35	85.66 40	95.72 24	94.58 31

DeepC-MVS	79.81 2	87.08 35	86.88 38	87.69 33	91.16 84	72.32 43	90.31 71	93.94 14	77.12 57	82.82 103	94.23 38	72.13 47	97.09 16	84.83 49	95.37 31	93.65 74
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
DeepC-MVS_fast	79.65 3	86.91 36	86.62 40	87.76 27	93.52 46	72.37 41	91.26 51	93.04 41	76.62 73	84.22 81	93.36 66	71.44 57	96.76 25	80.82 93	95.33 33	94.16 48
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
3Dnovator+	77.84 4	85.48 60	84.47 76	88.51 7	91.08 86	73.49 16	93.18 11	93.78 18	80.79 8	76.66 200	93.37 65	60.40 196	96.75 26	77.20 123	93.73 64	95.29 5

3Dnovator	76.31 5	83.38 92	82.31 102	86.59 55	87.94 193	72.94 28	90.64 60	92.14 91	77.21 54	75.47 225	92.83 79	58.56 203	94.72 103	73.24 164	92.71 74	92.13 139

ACMP	74.13 6	81.51 125	80.57 127	84.36 110	89.42 130	68.69 119	89.97 77	91.50 117	74.46 121	75.04 247	90.41 137	53.82 241	94.54 107	77.56 119	82.91 212	89.86 221
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
PCF-MVS	73.52 7	80.38 150	78.84 165	85.01 88	87.71 204	68.99 106	83.65 260	91.46 118	63.00 313	77.77 176	90.28 138	66.10 117	95.09 89	61.40 272	88.22 137	90.94 172
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
ACMM	73.20 8	80.78 141	79.84 142	83.58 148	89.31 138	68.37 125	89.99 76	91.60 111	70.28 202	77.25 185	89.66 151	53.37 246	93.53 152	74.24 153	82.85 213	88.85 254
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
TAPA-MVS	73.13 9	79.15 178	77.94 184	82.79 184	89.59 122	62.99 245	88.16 142	91.51 114	65.77 279	77.14 192	91.09 122	60.91 185	93.21 167	50.26 350	87.05 149	92.17 137
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
OpenMVS	72.83 10	79.77 161	78.33 176	84.09 127	85.17 254	69.91 87	90.57 61	90.97 129	66.70 264	72.17 284	91.91 94	54.70 233	93.96 126	61.81 269	90.95 96	88.41 269

PLC	70.83 11	78.05 206	76.37 226	83.08 168	91.88 77	67.80 138	88.19 140	89.46 174	64.33 298	69.87 310	88.38 188	53.66 242	93.58 147	58.86 294	82.73 215	87.86 277
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
HY-MVS	69.67 12	77.95 209	77.15 206	80.36 237	87.57 212	60.21 280	83.37 267	87.78 226	66.11 274	75.37 232	87.06 226	63.27 142	90.48 262	61.38 273	82.43 219	90.40 193

LTVRE_ROB	69.57 13	76.25 244	74.54 251	81.41 211	88.60 166	64.38 213	79.24 322	89.12 190	70.76 191	69.79 312	87.86 202	49.09 299	93.20 170	56.21 320	80.16 245	86.65 307
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
ACMH+	68.96 14	76.01 248	74.01 256	82.03 198	88.60 166	65.31 193	88.86 114	87.55 229	70.25 204	67.75 327	87.47 213	41.27 353	93.19 172	58.37 300	75.94 298	87.60 282

IB-MVS	68.01 15	75.85 250	73.36 265	83.31 156	84.76 263	66.03 173	83.38 266	85.06 267	70.21 205	69.40 314	81.05 339	45.76 326	94.66 106	65.10 239	75.49 304	89.25 238
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
ACMH	67.68 16	75.89 249	73.93 258	81.77 203	88.71 163	66.61 166	88.62 125	89.01 193	69.81 213	66.78 339	86.70 235	41.95 352	91.51 236	55.64 321	78.14 268	87.17 293
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
COLMAP_ROB	66.92 17	73.01 283	70.41 298	80.81 229	87.13 223	65.63 185	88.30 137	84.19 280	62.96 314	63.80 364	87.69 205	38.04 369	92.56 193	46.66 368	74.91 318	84.24 343
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
PVSNet	64.34 18	72.08 293	70.87 293	75.69 306	86.21 237	56.44 324	74.37 366	80.73 326	62.06 327	70.17 303	82.23 331	42.86 344	83.31 343	54.77 325	84.45 186	87.32 290

OpenMVS_ROB	64.09 19	70.56 306	68.19 312	77.65 288	80.26 345	59.41 288	85.01 230	82.96 303	58.76 352	65.43 352	82.33 328	37.63 371	91.23 246	45.34 378	76.03 297	82.32 365

PVSNet_0	57.27 20	61.67 354	59.27 357	68.85 362	79.61 357	57.44 310	68.01 389	73.44 377	55.93 370	58.54 382	70.41 393	44.58 334	77.55 370	47.01 367	35.91 405	71.55 393

CMPMVS	51.72 21	70.19 310	68.16 313	76.28 301	73.15 391	57.55 308	79.47 319	83.92 282	48.02 389	56.48 389	84.81 282	43.13 342	86.42 316	62.67 258	81.81 227	84.89 336
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
PMVS	37.38 22	44.16 377	40.28 381	55.82 386	40.82 421	42.54 404	65.12 400	63.99 401	34.43 406	24.48 412	57.12 405	3.92 422	76.17 381	17.10 413	55.52 389	48.75 407
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MVE	26.22 23	30.37 383	25.89 387	43.81 394	44.55 420	35.46 411	28.87 413	39.07 418	18.20 414	18.58 416	40.18 411	2.68 423	47.37 416	17.07 414	23.78 413	48.60 408
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
reproduce_model		87.28 30	87.39 27	86.95 48	93.10 56	71.24 63	91.60 42	93.19 35	74.69 115	88.80 23	95.61 11	70.29 72	96.44 39	86.20 39	93.08 69	93.16 97

reproduce-ours		87.47 23	87.61 22	87.07 45	93.27 50	71.60 53	91.56 46	93.19 35	74.98 107	88.96 20	95.54 12	71.20 61	96.54 36	86.28 37	93.49 65	93.06 102

our_new_method		87.47 23	87.61 22	87.07 45	93.27 50	71.60 53	91.56 46	93.19 35	74.98 107	88.96 20	95.54 12	71.20 61	96.54 36	86.28 37	93.49 65	93.06 102

mmdepth		0.00 391	0.00 394	0.00 404	0.00 427	0.00 429	0.00 415	0.00 428	0.00 422	0.00 423	0.00 422	0.00 427	0.00 423	0.00 422	0.00 421	0.00 419

monomultidepth		0.00 391	0.00 394	0.00 404	0.00 427	0.00 429	0.00 415	0.00 428	0.00 422	0.00 423	0.00 422	0.00 427	0.00 423	0.00 422	0.00 421	0.00 419

mvs5depth		69.45 316	67.45 328	75.46 312	73.93 382	55.83 334	79.19 324	83.23 294	66.89 260	71.63 290	83.32 311	33.69 380	85.09 329	59.81 284	55.34 391	85.46 326

MVStest1		56.63 360	52.76 366	68.25 367	61.67 408	53.25 362	71.67 374	68.90 391	38.59 401	50.59 397	83.05 316	25.08 394	70.66 398	36.76 395	38.56 404	80.83 375

ttmdpeth		59.91 356	57.10 360	68.34 366	67.13 402	46.65 391	74.64 365	67.41 393	48.30 388	62.52 370	85.04 279	20.40 402	75.93 382	42.55 384	45.90 403	82.44 364

WBMVS		73.43 275	72.81 270	75.28 314	87.91 194	50.99 377	78.59 335	81.31 322	65.51 285	74.47 257	84.83 281	46.39 315	86.68 312	58.41 299	77.86 270	88.17 272

dongtai		45.42 375	45.38 376	45.55 393	73.36 389	26.85 417	67.72 390	34.19 419	54.15 375	49.65 399	56.41 406	25.43 393	62.94 409	19.45 410	28.09 410	46.86 409

kuosan		39.70 379	40.40 380	37.58 396	64.52 405	26.98 415	65.62 398	33.02 420	46.12 391	42.79 403	48.99 409	24.10 398	46.56 417	12.16 418	26.30 411	39.20 410

MVSMamba_PlusPlus		85.99 48	85.96 52	86.05 64	91.09 85	67.64 142	89.63 88	92.65 69	72.89 159	84.64 73	91.71 99	71.85 49	96.03 50	84.77 51	94.45 54	94.49 35

MGCFI-Net		85.06 69	85.51 59	83.70 145	89.42 130	63.01 241	89.43 93	92.62 72	76.43 75	87.53 38	91.34 113	72.82 42	93.42 159	81.28 88	88.74 129	94.66 29

testing91		76.54 235	75.66 234	79.18 262	88.43 173	55.89 333	81.08 295	83.00 301	73.76 136	75.34 233	84.29 291	46.20 321	90.07 267	64.33 244	84.50 182	91.58 150

testing11		75.14 260	74.01 256	78.53 274	88.16 181	56.38 326	80.74 302	80.42 332	70.67 192	72.69 278	83.72 305	43.61 340	89.86 270	62.29 262	83.76 196	89.36 235

testing99		76.09 247	75.12 245	79.00 263	88.16 181	55.50 339	80.79 299	81.40 320	73.30 149	75.17 241	84.27 293	44.48 335	90.02 268	64.28 245	84.22 191	91.48 155

UBG		73.08 282	72.27 277	75.51 310	88.02 189	51.29 375	78.35 339	77.38 357	65.52 283	73.87 263	82.36 327	45.55 328	86.48 315	55.02 323	84.39 188	88.75 259

UWE-MVS		72.13 292	71.49 283	74.03 327	86.66 232	47.70 386	81.40 293	76.89 362	63.60 308	75.59 222	84.22 294	39.94 360	85.62 323	48.98 356	86.13 165	88.77 258

ETVMVS		72.25 291	71.05 290	75.84 304	87.77 203	51.91 367	79.39 320	74.98 369	69.26 226	73.71 264	82.95 318	40.82 357	86.14 318	46.17 372	84.43 187	89.47 232

sasdasda		85.91 52	85.87 54	86.04 65	89.84 117	69.44 98	90.45 68	93.00 46	76.70 71	88.01 31	91.23 115	73.28 36	93.91 133	81.50 85	88.80 126	94.77 23

testing222		74.04 268	72.66 272	78.19 279	87.89 195	55.36 340	81.06 296	79.20 345	71.30 179	74.65 254	83.57 308	39.11 364	88.67 294	51.43 342	85.75 172	90.53 187

WB-MVSnew		71.96 294	71.65 282	72.89 336	84.67 268	51.88 368	82.29 281	77.57 353	62.31 323	73.67 265	83.00 317	53.49 245	81.10 355	45.75 375	82.13 222	85.70 323

fmvsm_l_conf0.5_n_a		84.13 76	84.16 78	84.06 131	85.38 251	68.40 124	88.34 135	86.85 245	67.48 258	87.48 39	93.40 64	70.89 64	91.61 227	88.38 25	89.22 120	92.16 138

fmvsm_l_conf0.5_n		84.47 74	84.54 73	84.27 117	85.42 250	68.81 109	88.49 128	87.26 236	68.08 251	88.03 30	93.49 60	72.04 48	91.77 223	88.90 17	89.14 122	92.24 134

fmvsm_s_conf0.1_n_a		83.32 93	82.99 92	84.28 115	83.79 283	68.07 133	89.34 99	82.85 305	69.80 214	87.36 42	94.06 45	68.34 94	91.56 231	87.95 27	83.46 206	93.21 95

fmvsm_s_conf0.1_n		83.56 87	83.38 85	84.10 123	84.86 262	67.28 153	89.40 97	83.01 300	70.67 192	87.08 44	93.96 53	68.38 93	91.45 239	88.56 22	84.50 182	93.56 80

fmvsm_s_conf0.5_n_a		83.63 85	83.41 84	84.28 115	86.14 238	68.12 131	89.43 93	82.87 304	70.27 203	87.27 43	93.80 57	69.09 84	91.58 229	88.21 26	83.65 201	93.14 99

fmvsm_s_conf0.5_n		83.80 80	83.71 81	84.07 129	86.69 231	67.31 152	89.46 92	83.07 299	71.09 184	86.96 47	93.70 58	69.02 89	91.47 238	88.79 18	84.62 181	93.44 85

MM		89.16 6	89.23 7	88.97 4	90.79 95	73.65 10	92.66 23	91.17 124	86.57 1	87.39 41	94.97 18	71.70 53	97.68 1	92.19 1	95.63 28	95.57 1

WAC-MVS						42.58 402								39.46 390

Syy-MVS		68.05 328	67.85 318	68.67 364	84.68 265	40.97 407	78.62 333	73.08 378	66.65 268	66.74 340	79.46 355	52.11 258	82.30 348	32.89 399	76.38 293	82.75 362

test_fmvsmconf0.1_n		85.61 59	85.65 57	85.50 75	82.99 305	69.39 100	89.65 86	90.29 152	73.31 148	87.77 34	94.15 41	71.72 52	93.23 165	90.31 4	90.67 100	93.89 61

test_fmvsmconf0.01_n		84.73 73	84.52 75	85.34 78	80.25 346	69.03 103	89.47 91	89.65 169	73.24 152	86.98 46	94.27 35	66.62 109	93.23 165	90.26 5	89.95 112	93.78 67

myMVS_eth3d		67.02 334	66.29 335	69.21 359	84.68 265	42.58 402	78.62 333	73.08 378	66.65 268	66.74 340	79.46 355	31.53 385	82.30 348	39.43 391	76.38 293	82.75 362

testing3		68.56 324	67.67 324	71.22 351	87.33 218	42.87 401	83.06 275	71.54 381	70.36 199	69.08 318	84.38 288	30.33 388	85.69 322	37.50 394	75.45 308	85.09 335

SSC-MVS		53.88 364	53.59 364	54.75 389	72.87 392	19.59 422	73.84 369	60.53 406	57.58 362	49.18 400	73.45 387	46.34 319	75.47 387	16.20 415	32.28 408	69.20 395

test_fmvsmconf_n		85.92 51	86.04 51	85.57 74	85.03 260	69.51 93	89.62 89	90.58 139	73.42 145	87.75 35	94.02 47	72.85 41	93.24 164	90.37 3	90.75 98	93.96 56

WB-MVS		54.94 361	54.72 362	55.60 387	73.50 386	20.90 421	74.27 367	61.19 404	59.16 348	50.61 396	74.15 384	47.19 310	75.78 384	17.31 412	35.07 406	70.12 394

test_fmvsmvis_n_1920		84.02 77	83.87 79	84.49 106	84.12 276	69.37 101	88.15 143	87.96 219	70.01 208	83.95 87	93.23 68	68.80 91	91.51 236	88.61 20	89.96 111	92.57 119

dmvs_re		71.14 298	70.58 294	72.80 337	81.96 322	59.68 284	75.60 357	79.34 343	68.55 244	69.27 317	80.72 345	49.42 293	76.54 375	52.56 336	77.79 271	82.19 367

SDMVSNet		80.38 150	80.18 136	80.99 224	89.03 151	64.94 200	80.45 308	89.40 175	75.19 102	76.61 203	89.98 144	60.61 191	87.69 306	76.83 128	83.55 203	90.33 195

dmvs_testset		62.63 351	64.11 342	58.19 381	78.55 364	24.76 419	75.28 358	65.94 397	67.91 253	60.34 375	76.01 378	53.56 243	73.94 394	31.79 400	67.65 362	75.88 388

sd_testset		77.70 217	77.40 201	78.60 270	89.03 151	60.02 281	79.00 327	85.83 260	75.19 102	76.61 203	89.98 144	54.81 228	85.46 326	62.63 259	83.55 203	90.33 195

test_fmvsm_n_1920		85.29 65	85.34 62	85.13 85	86.12 239	69.93 86	88.65 124	90.78 135	69.97 210	88.27 26	93.98 52	71.39 58	91.54 233	88.49 23	90.45 102	93.91 58

test_cas_vis1_n_1920		73.76 272	73.74 262	73.81 329	75.90 373	59.77 283	80.51 306	82.40 309	58.30 355	81.62 117	85.69 260	44.35 336	76.41 378	76.29 131	78.61 260	85.23 330

test_vis1_n_1920		75.52 254	75.78 230	74.75 321	79.84 352	57.44 310	83.26 268	85.52 263	62.83 317	79.34 144	86.17 252	45.10 332	79.71 360	78.75 107	81.21 232	87.10 299

test_vis1_n		69.85 314	69.21 305	71.77 344	72.66 394	55.27 343	81.48 290	76.21 365	52.03 381	75.30 238	83.20 314	28.97 389	76.22 380	74.60 148	78.41 266	83.81 349

test_fmvs1_n		70.86 302	70.24 300	72.73 338	72.51 395	55.28 342	81.27 294	79.71 340	51.49 384	78.73 151	84.87 280	27.54 391	77.02 372	76.06 134	79.97 249	85.88 321

mvsany_test1		62.30 352	61.26 356	65.41 373	69.52 397	54.86 346	66.86 393	49.78 413	46.65 390	68.50 324	83.21 313	49.15 298	66.28 405	56.93 314	60.77 380	75.11 389

APD_test1		53.31 366	49.93 371	63.42 376	65.68 403	50.13 381	71.59 375	66.90 395	34.43 406	40.58 405	71.56 391	8.65 417	76.27 379	34.64 398	55.36 390	63.86 400

test_vis1_rt		60.28 355	58.42 358	65.84 372	67.25 401	55.60 338	70.44 381	60.94 405	44.33 394	59.00 380	66.64 395	24.91 395	68.67 402	62.80 254	69.48 354	73.25 391

test_vis3_rt		49.26 372	47.02 374	56.00 384	54.30 413	45.27 396	66.76 395	48.08 414	36.83 403	44.38 402	53.20 407	7.17 419	64.07 407	56.77 316	55.66 388	58.65 403

test_fmvs2		68.35 327	67.48 327	70.98 353	69.50 398	51.95 366	80.05 313	76.38 364	49.33 387	74.65 254	84.38 288	23.30 400	75.40 388	74.51 149	75.17 316	85.60 324

test_fmvs1		70.93 301	70.52 295	72.16 342	73.71 384	55.05 344	80.82 297	78.77 347	51.21 385	78.58 156	84.41 287	31.20 386	76.94 373	75.88 137	80.12 248	84.47 341

test_fmvs3		63.36 350	61.82 353	67.98 368	62.51 407	46.96 390	77.37 347	74.03 375	45.24 392	67.50 330	78.79 363	12.16 412	72.98 396	72.77 169	66.02 368	83.99 347

mvsany_test3		53.99 363	51.45 368	61.61 378	55.51 412	44.74 398	63.52 402	45.41 417	43.69 395	58.11 384	76.45 376	17.99 405	63.76 408	54.77 325	47.59 399	76.34 387

testf1		45.72 373	41.96 377	57.00 382	56.90 410	45.32 393	66.14 396	59.26 407	26.19 410	30.89 409	60.96 401	4.14 420	70.64 399	26.39 406	46.73 401	55.04 405

APD_test2		45.72 373	41.96 377	57.00 382	56.90 410	45.32 393	66.14 396	59.26 407	26.19 410	30.89 409	60.96 401	4.14 420	70.64 399	26.39 406	46.73 401	55.04 405

test_f		52.09 368	50.82 369	55.90 385	53.82 415	42.31 405	59.42 405	58.31 409	36.45 404	56.12 391	70.96 392	12.18 411	57.79 411	53.51 331	56.57 387	67.60 396

FE-MVS		77.78 213	75.68 232	84.08 128	88.09 186	66.00 175	83.13 271	87.79 225	68.42 248	78.01 171	85.23 272	45.50 330	95.12 83	59.11 291	85.83 171	91.11 164

FA-MVS(test-final)		80.96 132	79.91 140	84.10 123	88.30 178	65.01 198	84.55 242	90.01 159	73.25 151	79.61 139	87.57 208	58.35 205	94.72 103	71.29 180	86.25 162	92.56 120

balanced_conf03		86.78 37	86.99 33	86.15 61	91.24 83	67.61 143	90.51 62	92.90 56	77.26 51	87.44 40	91.63 103	71.27 60	96.06 49	85.62 42	95.01 37	94.78 22

MonoMVSNet		76.49 240	75.80 229	78.58 271	81.55 329	58.45 292	86.36 198	86.22 254	74.87 112	74.73 252	83.73 304	51.79 267	88.73 292	70.78 183	72.15 342	88.55 266

patch_mono-2		83.65 83	84.54 73	80.99 224	90.06 112	65.83 180	84.21 252	88.74 205	71.60 174	85.01 62	92.44 87	74.51 25	83.50 341	82.15 81	92.15 80	93.64 76

EGC-MVSNET		52.07 369	47.05 373	67.14 370	83.51 289	60.71 271	80.50 307	67.75 392	0.07 419	0.43 420	75.85 381	24.26 397	81.54 352	28.82 402	62.25 376	59.16 402

test2506		77.30 225	76.49 222	79.74 250	90.08 108	52.02 364	87.86 154	63.10 402	74.88 110	80.16 134	92.79 82	38.29 368	92.35 203	68.74 208	92.50 77	94.86 17

test1111		79.43 170	79.18 159	80.15 242	89.99 113	53.31 360	87.33 168	77.05 360	75.04 105	80.23 133	92.77 84	48.97 301	92.33 205	68.87 206	92.40 79	94.81 20

ECVR-MVS		79.61 163	79.26 156	80.67 232	90.08 108	54.69 347	87.89 152	77.44 356	74.88 110	80.27 131	92.79 82	48.96 302	92.45 197	68.55 209	92.50 77	94.86 17

test_blank		0.00 391	0.00 394	0.00 404	0.00 427	0.00 429	0.00 415	0.00 428	0.00 422	0.00 423	0.00 422	0.00 427	0.00 423	0.00 422	0.00 421	0.00 419

tt0805		78.73 188	77.83 188	81.43 210	85.17 254	60.30 278	89.41 96	90.90 131	71.21 181	77.17 191	88.73 176	46.38 316	93.21 167	72.57 171	78.96 259	90.79 175

DVP-MVS++		90.23 1	91.01 1	87.89 24	94.34 27	71.25 59	95.06 1	94.23 3	78.38 33	92.78 4	95.74 6	82.45 3	97.49 4	89.42 9	96.68 2	94.95 10

FOURS1					95.00 10	72.39 39	95.06 1	93.84 15	74.49 120	91.30 15

MSC_two_6792asdad				89.16 1	94.34 27	75.53 2		92.99 49					97.53 2	89.67 6	96.44 9	94.41 37

PC_three_1452									68.21 250	92.02 12	94.00 49	82.09 5	95.98 56	84.58 53	96.68 2	94.95 10

No_MVS				89.16 1	94.34 27	75.53 2		92.99 49					97.53 2	89.67 6	96.44 9	94.41 37

test_one_0601					95.07 7	71.46 57		94.14 5	78.27 35	92.05 11	95.74 6	80.83 11

eth-test2					0.00 427

eth-test					0.00 427

GeoE		81.71 118	81.01 122	83.80 144	89.51 126	64.45 211	88.97 110	88.73 206	71.27 180	78.63 155	89.76 149	66.32 115	93.20 170	69.89 195	86.02 167	93.74 68

test_method		31.52 381	29.28 385	38.23 395	27.03 423	6.50 426	20.94 414	62.21 403	4.05 417	22.35 415	52.50 408	13.33 409	47.58 415	27.04 405	34.04 407	60.62 401

Anonymous20240521		68.80 321	67.22 330	73.55 330	74.33 380	54.11 352	83.18 269	85.61 262	58.15 356	61.68 371	80.94 342	30.71 387	81.27 354	57.00 313	73.34 335	85.28 329

h-mvs33		83.15 95	82.19 103	86.02 67	90.56 98	70.85 73	88.15 143	89.16 186	76.02 87	84.67 70	91.39 112	61.54 170	95.50 66	82.71 76	75.48 305	91.72 147

hse-mvs2		81.72 117	80.94 123	84.07 129	88.72 162	67.68 141	85.87 211	87.26 236	76.02 87	84.67 70	88.22 194	61.54 170	93.48 154	82.71 76	73.44 333	91.06 166

CL-MVSNet_self_test		72.37 289	71.46 284	75.09 316	79.49 359	53.53 356	80.76 301	85.01 269	69.12 232	70.51 297	82.05 333	57.92 208	84.13 336	52.27 337	66.00 369	87.60 282

KD-MVS_2432*1600		66.22 341	63.89 343	73.21 332	75.47 378	53.42 358	70.76 379	84.35 275	64.10 301	66.52 344	78.52 364	34.55 378	84.98 330	50.40 346	50.33 397	81.23 372

KD-MVS_self_test		68.81 320	67.59 326	72.46 341	74.29 381	45.45 392	77.93 343	87.00 241	63.12 310	63.99 362	78.99 362	42.32 347	84.77 333	56.55 318	64.09 374	87.16 295

AUN-MVS		79.21 177	77.60 198	84.05 134	88.71 163	67.61 143	85.84 213	87.26 236	69.08 233	77.23 187	88.14 199	53.20 248	93.47 155	75.50 143	73.45 332	91.06 166

ZD-MVS					94.38 25	72.22 44		92.67 66	70.98 187	87.75 35	94.07 44	74.01 32	96.70 27	84.66 52	94.84 44

SR-MVS-dyc-post		85.77 55	85.61 58	86.23 59	93.06 58	70.63 76	91.88 38	92.27 83	73.53 143	85.69 56	94.45 28	65.00 131	95.56 63	82.75 74	91.87 84	92.50 123

RE-MVS-def			85.48 60		93.06 58	70.63 76	91.88 38	92.27 83	73.53 143	85.69 56	94.45 28	63.87 137		82.75 74	91.87 84	92.50 123

SED-MVS		90.08 2	90.85 2	87.77 26	95.30 2	70.98 66	93.57 7	94.06 10	77.24 52	93.10 1	95.72 8	82.99 1	97.44 7	89.07 14	96.63 4	94.88 14

IU-MVS					95.30 2	71.25 59		92.95 55	66.81 261	92.39 6				88.94 16	96.63 4	94.85 19

OPU-MVS				89.06 3	94.62 15	75.42 4	93.57 7				94.02 47	82.45 3	96.87 20	83.77 63	96.48 8	94.88 14

test_241102_TWO								94.06 10	77.24 52	92.78 4	95.72 8	81.26 8	97.44 7	89.07 14	96.58 6	94.26 46

test_241102_ONE					95.30 2	70.98 66		94.06 10	77.17 55	93.10 1	95.39 14	82.99 1	97.27 12

SF-MVS		88.46 12	88.74 12	87.64 35	92.78 64	71.95 49	92.40 24	94.74 2	75.71 91	89.16 19	95.10 16	75.65 21	96.19 46	87.07 34	96.01 17	94.79 21

cl22		78.07 205	77.01 208	81.23 217	82.37 319	61.83 259	83.55 264	87.98 218	68.96 238	75.06 246	83.87 298	61.40 175	91.88 220	73.53 158	76.39 290	89.98 216

miper_ehance_all_eth		78.59 193	77.76 193	81.08 222	82.66 312	61.56 262	83.65 260	89.15 187	68.87 239	75.55 224	83.79 302	66.49 112	92.03 213	73.25 163	76.39 290	89.64 228

miper_enhance_ethall		77.87 212	76.86 212	80.92 227	81.65 326	61.38 264	82.68 277	88.98 194	65.52 283	75.47 225	82.30 329	65.76 124	92.00 215	72.95 166	76.39 290	89.39 234

ZNCC-MVS		87.94 18	87.85 19	88.20 12	94.39 24	73.33 19	93.03 14	93.81 17	76.81 65	85.24 60	94.32 33	71.76 51	96.93 19	85.53 43	95.79 22	94.32 43

dcpmvs_2		85.63 58	86.15 48	84.06 131	91.71 78	64.94 200	86.47 194	91.87 102	73.63 138	86.60 50	93.02 75	76.57 15	91.87 221	83.36 65	92.15 80	95.35 3

cl____		77.72 215	76.76 216	80.58 233	82.49 316	60.48 275	83.09 272	87.87 222	69.22 228	74.38 259	85.22 273	62.10 163	91.53 234	71.09 181	75.41 309	89.73 227

DIV-MVS_self_test		77.72 215	76.76 216	80.58 233	82.48 317	60.48 275	83.09 272	87.86 223	69.22 228	74.38 259	85.24 271	62.10 163	91.53 234	71.09 181	75.40 310	89.74 226

eth_miper_zixun_eth		77.92 210	76.69 219	81.61 207	83.00 303	61.98 256	83.15 270	89.20 185	69.52 221	74.86 250	84.35 290	61.76 166	92.56 193	71.50 178	72.89 337	90.28 198

9.14			88.26 15		92.84 63		91.52 48	94.75 1	73.93 132	88.57 25	94.67 21	75.57 22	95.79 58	86.77 35	95.76 23

uanet_test		0.00 391	0.00 394	0.00 404	0.00 427	0.00 429	0.00 415	0.00 428	0.00 422	0.00 423	0.00 422	0.00 427	0.00 423	0.00 422	0.00 421	0.00 419

DCPMVS		0.00 391	0.00 394	0.00 404	0.00 427	0.00 429	0.00 415	0.00 428	0.00 422	0.00 423	0.00 422	0.00 427	0.00 423	0.00 422	0.00 421	0.00 419

save fliter					93.80 40	72.35 42	90.47 66	91.17 124	74.31 123

ET-MVSNet_ETH3D		78.63 191	76.63 221	84.64 101	86.73 230	69.47 95	85.01 230	84.61 272	69.54 220	66.51 346	86.59 239	50.16 284	91.75 224	76.26 132	84.24 190	92.69 116

UniMVSNet_ETH3D		79.10 180	78.24 178	81.70 204	86.85 226	60.24 279	87.28 170	88.79 200	74.25 125	76.84 194	90.53 136	49.48 292	91.56 231	67.98 213	82.15 221	93.29 90

EIA-MVS		83.31 94	82.80 96	84.82 96	89.59 122	65.59 186	88.21 139	92.68 65	74.66 117	78.96 147	86.42 246	69.06 86	95.26 78	75.54 142	90.09 108	93.62 77

miper_refine_blended		66.22 341	63.89 343	73.21 332	75.47 378	53.42 358	70.76 379	84.35 275	64.10 301	66.52 344	78.52 364	34.55 378	84.98 330	50.40 346	50.33 397	81.23 372

miper_lstm_enhance		74.11 267	73.11 268	77.13 296	80.11 348	59.62 285	72.23 372	86.92 244	66.76 263	70.40 299	82.92 319	56.93 219	82.92 345	69.06 204	72.63 338	88.87 253

ETV-MVS		84.90 72	84.67 72	85.59 73	89.39 133	68.66 120	88.74 120	92.64 71	79.97 15	84.10 84	85.71 259	69.32 82	95.38 74	80.82 93	91.37 91	92.72 113

CS-MVS		86.69 39	86.95 35	85.90 69	90.76 96	67.57 145	92.83 17	93.30 32	79.67 17	84.57 76	92.27 89	71.47 56	95.02 91	84.24 59	93.46 67	95.13 7

D2MVS		74.82 261	73.21 266	79.64 254	79.81 353	62.56 249	80.34 310	87.35 234	64.37 297	68.86 319	82.66 324	46.37 317	90.10 266	67.91 214	81.24 231	86.25 311

DVP-MVS		89.60 3	90.35 3	87.33 40	95.27 5	71.25 59	93.49 9	92.73 64	77.33 49	92.12 9	95.78 4	80.98 9	97.40 9	89.08 12	96.41 12	93.33 89
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									78.38 33	92.12 9	95.78 4	81.46 7	97.40 9	89.42 9	96.57 7	94.67 26

test_0728_SECOND				87.71 32	95.34 1	71.43 58	93.49 9	94.23 3					97.49 4	89.08 12	96.41 12	94.21 47

test0726					95.27 5	71.25 59	93.60 6	94.11 6	77.33 49	92.81 3	95.79 3	80.98 9

SR-MVS		86.73 38	86.67 39	86.91 49	94.11 37	72.11 47	92.37 28	92.56 74	74.50 119	86.84 48	94.65 22	67.31 105	95.77 59	84.80 50	92.85 72	92.84 112

DPM-MVS		84.93 70	84.29 77	86.84 50	90.20 105	73.04 23	87.12 173	93.04 41	69.80 214	82.85 102	91.22 117	73.06 39	96.02 52	76.72 130	94.63 48	91.46 157

GST-MVS		87.42 27	87.26 28	87.89 24	94.12 36	72.97 24	92.39 26	93.43 28	76.89 63	84.68 69	93.99 51	70.67 68	96.82 22	84.18 61	95.01 37	93.90 60

test_yl		81.17 128	80.47 130	83.24 160	89.13 146	63.62 224	86.21 202	89.95 161	72.43 163	81.78 115	89.61 153	57.50 213	93.58 147	70.75 184	86.90 151	92.52 121

thisisatest0530		79.40 172	77.76 193	84.31 113	87.69 206	65.10 197	87.36 166	84.26 279	70.04 206	77.42 181	88.26 193	49.94 287	94.79 101	70.20 190	84.70 180	93.03 105

Anonymous20240529		80.19 156	78.89 164	84.10 123	90.60 97	64.75 204	88.95 111	90.90 131	65.97 278	80.59 129	91.17 120	49.97 286	93.73 145	69.16 203	82.70 217	93.81 65

Anonymous202405211		78.25 198	77.01 208	81.99 199	91.03 87	60.67 272	84.77 235	83.90 283	70.65 196	80.00 135	91.20 118	41.08 355	91.43 240	65.21 237	85.26 174	93.85 62

DCV-MVSNet		81.17 128	80.47 130	83.24 160	89.13 146	63.62 224	86.21 202	89.95 161	72.43 163	81.78 115	89.61 153	57.50 213	93.58 147	70.75 184	86.90 151	92.52 121

tttt0517		79.40 172	77.91 185	83.90 143	88.10 185	63.84 221	88.37 134	84.05 281	71.45 177	76.78 197	89.12 167	49.93 289	94.89 96	70.18 191	83.18 210	92.96 110

our_test_3		69.14 318	67.00 331	75.57 308	79.80 354	58.80 289	77.96 342	77.81 351	59.55 344	62.90 368	78.25 367	47.43 307	83.97 337	51.71 339	67.58 363	83.93 348

thisisatest0515		77.33 224	75.38 240	83.18 163	85.27 253	63.80 222	82.11 283	83.27 293	65.06 288	75.91 217	83.84 300	49.54 291	94.27 116	67.24 221	86.19 163	91.48 155

ppachtmachnet_test		70.04 311	67.34 329	78.14 280	79.80 354	61.13 265	79.19 324	80.59 328	59.16 348	65.27 353	79.29 357	46.75 314	87.29 308	49.33 354	66.72 364	86.00 320

SMA-MVS		89.08 8	89.23 7	88.61 6	94.25 31	73.73 9	92.40 24	93.63 21	74.77 114	92.29 7	95.97 2	74.28 29	97.24 13	88.58 21	96.91 1	94.87 16
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																88.96 250

DPE-MVS		89.48 5	89.98 4	88.01 16	94.80 11	72.69 31	91.59 43	94.10 8	75.90 89	92.29 7	95.66 10	81.67 6	97.38 11	87.44 33	96.34 15	93.95 57
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
test_part2					95.06 8	72.65 32				91.80 13

thres100view900		76.50 237	75.55 236	79.33 258	89.52 125	56.99 315	85.83 214	83.23 294	73.94 131	76.32 209	87.12 223	51.89 264	91.95 216	48.33 359	83.75 197	89.07 239

tfpnnormal		74.39 263	73.16 267	78.08 281	86.10 241	58.05 297	84.65 239	87.53 230	70.32 201	71.22 294	85.63 263	54.97 227	89.86 270	43.03 382	75.02 317	86.32 310

tfpn200view9		76.42 241	75.37 241	79.55 257	89.13 146	57.65 306	85.17 225	83.60 286	73.41 146	76.45 205	86.39 247	52.12 256	91.95 216	48.33 359	83.75 197	89.07 239

c3_l		78.75 187	77.91 185	81.26 216	82.89 307	61.56 262	84.09 255	89.13 189	69.97 210	75.56 223	84.29 291	66.36 114	92.09 212	73.47 160	75.48 305	90.12 204

CHOSEN 280x420		66.51 338	64.71 339	71.90 343	81.45 331	63.52 229	57.98 406	68.95 390	53.57 376	62.59 369	76.70 374	46.22 320	75.29 389	55.25 322	79.68 250	76.88 386

CANet		86.45 42	86.10 49	87.51 37	90.09 107	70.94 70	89.70 85	92.59 73	81.78 4	81.32 119	91.43 111	70.34 70	97.23 14	84.26 57	93.36 68	94.37 40

Fast-Effi-MVS+-dtu		78.02 207	76.49 222	82.62 189	83.16 299	66.96 163	86.94 178	87.45 233	72.45 160	71.49 292	84.17 295	54.79 232	91.58 229	67.61 216	80.31 244	89.30 237

Effi-MVS+-dtu		80.03 158	78.57 169	84.42 108	85.13 258	68.74 114	88.77 117	88.10 215	74.99 106	74.97 248	83.49 309	57.27 216	93.36 160	73.53 158	80.88 235	91.18 162

CANet_DTU		80.61 143	79.87 141	82.83 179	85.60 247	63.17 240	87.36 166	88.65 207	76.37 80	75.88 218	88.44 187	53.51 244	93.07 179	73.30 162	89.74 115	92.25 132

MVS_0304		87.69 20	87.55 24	88.12 13	89.45 129	71.76 51	91.47 49	89.54 171	82.14 3	86.65 49	94.28 34	68.28 95	97.46 6	90.81 2	95.31 34	95.15 6

MP-MVS-pluss		87.67 21	87.72 20	87.54 36	93.64 44	72.04 48	89.80 81	93.50 25	75.17 104	86.34 51	95.29 15	70.86 65	96.00 54	88.78 19	96.04 16	94.58 31
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
MSP-MVS		89.51 4	89.91 5	88.30 10	94.28 30	73.46 17	92.90 16	94.11 6	80.27 10	91.35 14	94.16 40	78.35 13	96.77 24	89.59 8	94.22 60	94.67 26
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												51.32 271				88.96 250

sam_mvs												50.01 285

IterMVS-SCA-FT		75.43 256	73.87 260	80.11 243	82.69 311	64.85 202	81.57 289	83.47 290	69.16 231	70.49 298	84.15 296	51.95 262	88.15 300	69.23 201	72.14 343	87.34 289

TSAR-MVS + MP.		88.02 17	88.11 16	87.72 30	93.68 43	72.13 46	91.41 50	92.35 81	74.62 118	88.90 22	93.85 55	75.75 20	96.00 54	87.80 28	94.63 48	95.04 8
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		80.80 138	79.72 144	84.03 136	87.35 213	70.19 82	85.56 217	88.77 201	69.06 234	81.83 111	88.16 195	50.91 275	92.85 186	78.29 114	87.56 141	89.06 241

OPM-MVS		83.50 88	82.95 93	85.14 83	88.79 159	70.95 69	89.13 107	91.52 113	77.55 44	80.96 126	91.75 98	60.71 187	94.50 110	79.67 103	86.51 158	89.97 217
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
ACMMP_NAP		88.05 16	88.08 17	87.94 19	93.70 41	73.05 22	90.86 57	93.59 23	76.27 83	88.14 27	95.09 17	71.06 63	96.67 29	87.67 29	96.37 14	94.09 51

ambc				75.24 315	73.16 390	50.51 380	63.05 404	87.47 232		64.28 359	77.81 370	17.80 406	89.73 274	57.88 305	60.64 381	85.49 325

MTGPA								92.02 92

CS-MVS-test		86.29 46	86.48 41	85.71 71	91.02 88	67.21 158	92.36 29	93.78 18	78.97 28	83.51 95	91.20 118	70.65 69	95.15 82	81.96 82	94.89 42	94.77 23

Effi-MVS+		83.62 86	83.08 89	85.24 81	88.38 175	67.45 147	88.89 113	89.15 187	75.50 96	82.27 107	88.28 191	69.61 79	94.45 112	77.81 117	87.84 139	93.84 64

xiu_mvs_v2_base		81.69 119	81.05 120	83.60 147	89.15 145	68.03 135	84.46 245	90.02 158	70.67 192	81.30 122	86.53 244	63.17 145	94.19 121	75.60 141	88.54 132	88.57 265

xiu_mvs_v1_base		80.80 138	79.72 144	84.03 136	87.35 213	70.19 82	85.56 217	88.77 201	69.06 234	81.83 111	88.16 195	50.91 275	92.85 186	78.29 114	87.56 141	89.06 241

new-patchmatchnet		61.73 353	61.73 354	61.70 377	72.74 393	24.50 420	69.16 386	78.03 350	61.40 330	56.72 388	75.53 382	38.42 366	76.48 377	45.95 374	57.67 384	84.13 345

pmmvs6		74.69 262	73.39 264	78.61 269	81.38 333	57.48 309	86.64 189	87.95 220	64.99 291	70.18 302	86.61 238	50.43 282	89.52 277	62.12 265	70.18 353	88.83 255

pmmvs5		71.55 295	70.20 301	75.61 307	77.83 366	56.39 325	81.74 286	80.89 323	57.76 359	67.46 331	84.49 285	49.26 297	85.32 328	57.08 312	75.29 313	85.11 334

test_post1							78.90 330				5.43 418	48.81 304	85.44 327	59.25 289

test_post											5.46 417	50.36 283	84.24 335

Fast-Effi-MVS+		80.81 136	79.92 139	83.47 150	88.85 153	64.51 207	85.53 222	89.39 176	70.79 189	78.49 159	85.06 277	67.54 102	93.58 147	67.03 225	86.58 156	92.32 129

patchmatchnet-post											74.00 385	51.12 274	88.60 295

Anonymous20231211		78.97 184	77.69 196	82.81 181	90.54 99	64.29 214	90.11 75	91.51 114	65.01 290	76.16 216	88.13 200	50.56 280	93.03 183	69.68 198	77.56 275	91.11 164

pmmvs-eth3d		70.50 307	67.83 320	78.52 275	77.37 369	66.18 172	81.82 284	81.51 318	58.90 351	63.90 363	80.42 347	42.69 345	86.28 317	58.56 297	65.30 371	83.11 357

GG-mvs-BLEND				75.38 313	81.59 328	55.80 335	79.32 321	69.63 386		67.19 334	73.67 386	43.24 341	88.90 291	50.41 345	84.50 182	81.45 371

xiu_mvs_v1_base_debi		80.80 138	79.72 144	84.03 136	87.35 213	70.19 82	85.56 217	88.77 201	69.06 234	81.83 111	88.16 195	50.91 275	92.85 186	78.29 114	87.56 141	89.06 241

Anonymous20231206		68.60 322	67.80 321	71.02 352	80.23 347	50.75 379	78.30 340	80.47 330	56.79 366	66.11 349	82.63 325	46.35 318	78.95 363	43.62 381	75.70 300	83.36 354

MTAPA		87.23 31	87.00 32	87.90 22	94.18 35	74.25 5	86.58 191	92.02 92	79.45 19	85.88 53	94.80 19	68.07 96	96.21 45	86.69 36	95.34 32	93.23 92

MTMP							92.18 34	32.83 421

gm-plane-assit					81.40 332	53.83 355			62.72 320		80.94 342		92.39 200	63.40 251

test9_res														84.90 46	95.70 26	92.87 111

MVP-Stereo		76.12 245	74.46 253	81.13 221	85.37 252	69.79 89	84.42 248	87.95 220	65.03 289	67.46 331	85.33 269	53.28 247	91.73 226	58.01 304	83.27 208	81.85 369
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
TEST9					93.26 52	72.96 25	88.75 118	91.89 100	68.44 247	85.00 63	93.10 70	74.36 28	95.41 72

train_agg		86.43 43	86.20 45	87.13 44	93.26 52	72.96 25	88.75 118	91.89 100	68.69 242	85.00 63	93.10 70	74.43 26	95.41 72	84.97 45	95.71 25	93.02 106

gg-mvs-nofinetune		69.95 312	67.96 316	75.94 303	83.07 300	54.51 350	77.23 348	70.29 384	63.11 311	70.32 300	62.33 397	43.62 339	88.69 293	53.88 329	87.76 140	84.62 340

SCA		74.22 266	72.33 276	79.91 246	84.05 279	62.17 254	79.96 315	79.29 344	66.30 273	72.38 282	80.13 349	51.95 262	88.60 295	59.25 289	77.67 274	88.96 250

Patchmatch-test		64.82 346	63.24 347	69.57 357	79.42 360	49.82 383	63.49 403	69.05 389	51.98 382	59.95 378	80.13 349	50.91 275	70.98 397	40.66 388	73.57 330	87.90 276

test_8					93.13 54	72.57 35	88.68 123	91.84 104	68.69 242	84.87 67	93.10 70	74.43 26	95.16 81

MS-PatchMatch		73.83 271	72.67 271	77.30 294	83.87 282	66.02 174	81.82 284	84.66 271	61.37 332	68.61 322	82.82 322	47.29 308	88.21 299	59.27 288	84.32 189	77.68 384

Patchmatch-RL test		70.24 309	67.78 322	77.61 289	77.43 368	59.57 287	71.16 376	70.33 383	62.94 315	68.65 321	72.77 388	50.62 279	85.49 325	69.58 199	66.58 366	87.77 279

cdsmvs_eth3d_5k		19.96 384	26.61 386	0.00 404	0.00 427	0.00 429	0.00 415	89.26 182	0.00 422	0.00 423	88.61 181	61.62 169	0.00 423	0.00 422	0.00 421	0.00 419

pcd_1.5k_mvsjas		5.26 390	7.02 393	0.00 404	0.00 427	0.00 429	0.00 415	0.00 428	0.00 422	0.00 423	0.00 422	63.15 146	0.00 423	0.00 422	0.00 421	0.00 419

agg_prior2														82.91 72	95.45 29	92.70 114

agg_prior					92.85 62	71.94 50		91.78 107		84.41 78			94.93 92

tmp_tt		18.61 385	21.40 388	10.23 401	4.82 424	10.11 424	34.70 411	30.74 422	1.48 418	23.91 414	26.07 415	28.42 390	13.41 420	27.12 404	15.35 417	7.17 415

canonicalmvs		85.91 52	85.87 54	86.04 65	89.84 117	69.44 98	90.45 68	93.00 46	76.70 71	88.01 31	91.23 115	73.28 36	93.91 133	81.50 85	88.80 126	94.77 23

anonymousdsp		78.60 192	77.15 206	82.98 174	80.51 344	67.08 159	87.24 171	89.53 172	65.66 281	75.16 242	87.19 221	52.52 249	92.25 207	77.17 124	79.34 256	89.61 229

alignmvs		85.48 60	85.32 64	85.96 68	89.51 126	69.47 95	89.74 83	92.47 75	76.17 84	87.73 37	91.46 110	70.32 71	93.78 139	81.51 84	88.95 123	94.63 30

nrg030		83.88 78	83.53 82	84.96 90	86.77 229	69.28 102	90.46 67	92.67 66	74.79 113	82.95 99	91.33 114	72.70 43	93.09 178	80.79 95	79.28 257	92.50 123

v144192		79.47 168	78.37 174	82.78 185	83.35 291	63.96 219	86.96 177	90.36 148	69.99 209	77.50 179	85.67 262	60.66 189	93.77 141	74.27 152	76.58 286	90.62 182

FIs		82.07 111	82.42 98	81.04 223	88.80 158	58.34 294	88.26 138	93.49 26	76.93 62	78.47 160	91.04 124	69.92 76	92.34 204	69.87 196	84.97 176	92.44 127

v1921920		79.22 176	78.03 182	82.80 182	83.30 293	63.94 220	86.80 183	90.33 149	69.91 212	77.48 180	85.53 265	58.44 204	93.75 143	73.60 157	76.85 283	90.71 180

UA-Net		85.08 68	84.96 69	85.45 76	92.07 73	68.07 133	89.78 82	90.86 134	82.48 2	84.60 75	93.20 69	69.35 81	95.22 79	71.39 179	90.88 97	93.07 101

v1192		79.59 165	78.43 173	83.07 169	83.55 288	64.52 206	86.93 179	90.58 139	70.83 188	77.78 175	85.90 255	59.15 200	93.94 129	73.96 155	77.19 278	90.76 177

FC-MVSNet-test		81.52 123	82.02 108	80.03 244	88.42 174	55.97 332	87.95 148	93.42 29	77.10 58	77.38 182	90.98 130	69.96 75	91.79 222	68.46 211	84.50 182	92.33 128

v1144		80.03 158	79.03 161	83.01 172	83.78 284	64.51 207	87.11 174	90.57 141	71.96 168	78.08 170	86.20 251	61.41 174	93.94 129	74.93 146	77.23 276	90.60 184

sosnet-low-res		0.00 391	0.00 394	0.00 404	0.00 427	0.00 429	0.00 415	0.00 428	0.00 422	0.00 423	0.00 422	0.00 427	0.00 423	0.00 422	0.00 421	0.00 419

HFP-MVS		87.58 22	87.47 26	87.94 19	94.58 16	73.54 15	93.04 12	93.24 33	76.78 67	84.91 65	94.44 30	70.78 66	96.61 32	84.53 54	94.89 42	93.66 70

v148		78.72 189	77.80 190	81.47 209	82.73 310	61.96 257	86.30 200	88.08 216	73.26 150	76.18 213	85.47 267	62.46 156	92.36 202	71.92 175	73.82 329	90.09 207

sosnet		0.00 391	0.00 394	0.00 404	0.00 427	0.00 429	0.00 415	0.00 428	0.00 422	0.00 423	0.00 422	0.00 427	0.00 423	0.00 422	0.00 421	0.00 419

uncertanet		0.00 391	0.00 394	0.00 404	0.00 427	0.00 429	0.00 415	0.00 428	0.00 422	0.00 423	0.00 422	0.00 427	0.00 423	0.00 422	0.00 421	0.00 419

AllTest		70.96 300	68.09 315	79.58 255	85.15 256	63.62 224	84.58 241	79.83 338	62.31 323	60.32 376	86.73 229	32.02 382	88.96 289	50.28 348	71.57 347	86.15 314

TestCases				79.58 255	85.15 256	63.62 224		79.83 338	62.31 323	60.32 376	86.73 229	32.02 382	88.96 289	50.28 348	71.57 347	86.15 314

v7n		78.97 184	77.58 199	83.14 165	83.45 290	65.51 187	88.32 136	91.21 122	73.69 137	72.41 281	86.32 249	57.93 207	93.81 138	69.18 202	75.65 301	90.11 205

region2R		87.42 27	87.20 31	88.09 14	94.63 14	73.55 13	93.03 14	93.12 40	76.73 70	84.45 77	94.52 23	69.09 84	96.70 27	84.37 56	94.83 45	94.03 54

RRT-MVS		82.60 106	82.10 105	84.10 123	87.98 192	62.94 246	87.45 164	91.27 120	77.42 48	79.85 136	90.28 138	56.62 221	94.70 105	79.87 102	88.15 138	94.67 26

mamv4		76.81 232	78.23 180	72.54 340	86.12 239	65.75 184	78.76 331	82.07 313	64.12 300	72.97 273	91.02 127	67.97 97	68.08 404	83.04 70	78.02 269	83.80 350

PS-MVSNAJss		82.07 111	81.31 115	84.34 112	86.51 234	67.27 154	89.27 100	91.51 114	71.75 169	79.37 142	90.22 142	63.15 146	94.27 116	77.69 118	82.36 220	91.49 154

PS-MVSNAJ		81.69 119	81.02 121	83.70 145	89.51 126	68.21 130	84.28 251	90.09 157	70.79 189	81.26 123	85.62 264	63.15 146	94.29 114	75.62 140	88.87 125	88.59 264

jajsoiax		79.29 175	77.96 183	83.27 158	84.68 265	66.57 167	89.25 101	90.16 155	69.20 230	75.46 227	89.49 157	45.75 327	93.13 176	76.84 127	80.80 237	90.11 205

mvs_tets		79.13 179	77.77 192	83.22 162	84.70 264	66.37 169	89.17 102	90.19 154	69.38 223	75.40 230	89.46 160	44.17 337	93.15 174	76.78 129	80.70 239	90.14 202

EI-MVSNet-UG-set		83.81 79	83.38 85	85.09 86	87.87 196	67.53 146	87.44 165	89.66 168	79.74 16	82.23 108	89.41 164	70.24 73	94.74 102	79.95 100	83.92 193	92.99 109

EI-MVSNet-Vis-set		84.19 75	83.81 80	85.31 79	88.18 180	67.85 137	87.66 157	89.73 167	80.05 14	82.95 99	89.59 155	70.74 67	94.82 99	80.66 96	84.72 179	93.28 91

HPM-MVS++		89.02 9	89.15 9	88.63 5	95.01 9	76.03 1	92.38 27	92.85 59	80.26 11	87.78 33	94.27 35	75.89 19	96.81 23	87.45 32	96.44 9	93.05 104

test_prior4						72.60 34	89.01 109

XVS		87.18 32	86.91 37	88.00 17	94.42 20	73.33 19	92.78 18	92.99 49	79.14 21	83.67 92	94.17 39	67.45 103	96.60 33	83.06 68	94.50 51	94.07 52

v1240		78.99 183	77.78 191	82.64 188	83.21 295	63.54 228	86.62 190	90.30 151	69.74 219	77.33 183	85.68 261	57.04 218	93.76 142	73.13 165	76.92 280	90.62 182

pm-mvs1		77.25 226	76.68 220	78.93 265	84.22 274	58.62 291	86.41 195	88.36 212	71.37 178	73.31 268	88.01 201	61.22 180	89.15 284	64.24 246	73.01 336	89.03 245

test_prior2							88.85 115		75.41 97	84.91 65	93.54 59	74.28 29		83.31 66	95.86 20

X-MVStestdata		80.37 152	77.83 188	88.00 17	94.42 20	73.33 19	92.78 18	92.99 49	79.14 21	83.67 92	12.47 416	67.45 103	96.60 33	83.06 68	94.50 51	94.07 52

test_prior				86.33 57	92.61 68	69.59 91		92.97 54					95.48 67			93.91 58

旧先验2							86.56 192		58.10 357	87.04 45			88.98 287	74.07 154

新几何2							86.29 201

新几何1				83.42 152	93.13 54	70.71 74		85.48 264	57.43 363	81.80 114	91.98 93	63.28 141	92.27 206	64.60 243	92.99 70	87.27 291

旧先验1					91.96 74	65.79 182		86.37 252			93.08 74	69.31 83			92.74 73	88.74 261

无先验							87.48 161	88.98 194	60.00 340				94.12 123	67.28 220		88.97 249

原ACMM2							86.86 181

原ACMM1				84.35 111	93.01 60	68.79 110		92.44 76	63.96 306	81.09 124	91.57 106	66.06 119	95.45 68	67.19 222	94.82 46	88.81 256

test222					91.50 80	68.26 128	84.16 253	83.20 297	54.63 374	79.74 137	91.63 103	58.97 201			91.42 90	86.77 304

testdata2													91.01 254	62.37 261

segment_acmp												73.08 38

testdata				79.97 245	90.90 91	64.21 215		84.71 270	59.27 347	85.40 58	92.91 76	62.02 165	89.08 285	68.95 205	91.37 91	86.63 308

testdata1							84.14 254		75.71 91

v8		79.97 160	79.02 162	82.80 182	84.09 277	64.50 209	87.96 147	90.29 152	74.13 129	75.24 240	86.81 228	62.88 151	93.89 136	74.39 151	75.40 310	90.00 213

1314		76.53 236	75.30 243	80.21 241	83.93 281	62.32 252	84.66 237	88.81 199	60.23 338	70.16 304	84.07 297	55.30 226	90.73 259	67.37 219	83.21 209	87.59 284

LFMVS		81.82 116	81.23 117	83.57 149	91.89 76	63.43 233	89.84 78	81.85 316	77.04 60	83.21 96	93.10 70	52.26 254	93.43 158	71.98 174	89.95 112	93.85 62

VDD-MVS		83.01 100	82.36 101	84.96 90	91.02 88	66.40 168	88.91 112	88.11 214	77.57 41	84.39 79	93.29 67	52.19 255	93.91 133	77.05 125	88.70 130	94.57 33

VDDNet		81.52 123	80.67 126	84.05 134	90.44 101	64.13 217	89.73 84	85.91 259	71.11 183	83.18 97	93.48 61	50.54 281	93.49 153	73.40 161	88.25 136	94.54 34

v10		79.74 162	78.67 166	82.97 175	84.06 278	64.95 199	87.88 153	90.62 138	73.11 153	75.11 244	86.56 242	61.46 173	94.05 125	73.68 156	75.55 303	89.90 219

VPNet		78.69 190	78.66 167	78.76 267	88.31 177	55.72 336	84.45 246	86.63 248	76.79 66	78.26 164	90.55 135	59.30 199	89.70 275	66.63 226	77.05 279	90.88 173

MVS		78.19 202	76.99 210	81.78 202	85.66 245	66.99 160	84.66 237	90.47 143	55.08 373	72.02 286	85.27 270	63.83 138	94.11 124	66.10 230	89.80 114	84.24 343

v2v482		80.23 154	79.29 155	83.05 170	83.62 286	64.14 216	87.04 175	89.97 160	73.61 139	78.18 167	87.22 219	61.10 182	93.82 137	76.11 133	76.78 285	91.18 162

V42		79.38 174	78.24 178	82.83 179	81.10 338	65.50 188	85.55 220	89.82 163	71.57 175	78.21 165	86.12 253	60.66 189	93.18 173	75.64 139	75.46 307	89.81 224

SD-MVS		88.06 14	88.50 14	86.71 54	92.60 69	72.71 29	91.81 41	93.19 35	77.87 36	90.32 17	94.00 49	74.83 23	93.78 139	87.63 30	94.27 59	93.65 74
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		76.87 231	75.17 244	81.97 200	82.75 309	62.58 248	81.44 292	86.35 253	72.16 167	74.74 251	82.89 320	46.20 321	92.02 214	68.85 207	81.09 233	91.30 160

MSLP-MVS++		85.43 62	85.76 56	84.45 107	91.93 75	70.24 79	90.71 59	92.86 58	77.46 47	84.22 81	92.81 81	67.16 107	92.94 184	80.36 97	94.35 57	90.16 201

APDe-MVS		89.15 7	89.63 6	87.73 28	94.49 18	71.69 52	93.83 4	93.96 13	75.70 93	91.06 16	96.03 1	76.84 14	97.03 17	89.09 11	95.65 27	94.47 36
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
APD-MVS_3200maxsize		85.97 50	85.88 53	86.22 60	92.69 66	69.53 92	91.93 37	92.99 49	73.54 142	85.94 52	94.51 26	65.80 123	95.61 62	83.04 70	92.51 76	93.53 83

ADS-MVSNet2		66.20 343	63.33 346	74.82 319	79.92 350	58.75 290	67.55 391	75.19 368	53.37 377	65.25 354	75.86 379	42.32 347	80.53 358	41.57 386	68.91 358	85.18 331

EI-MVSNet		80.52 148	79.98 138	82.12 195	84.28 272	63.19 239	86.41 195	88.95 197	74.18 127	78.69 152	87.54 211	66.62 109	92.43 198	72.57 171	80.57 241	90.74 179

Regformer		0.00 391	0.00 394	0.00 404	0.00 427	0.00 429	0.00 415	0.00 428	0.00 422	0.00 423	0.00 422	0.00 427	0.00 423	0.00 422	0.00 421	0.00 419

CVMVSNet		72.99 284	72.58 273	74.25 325	84.28 272	50.85 378	86.41 195	83.45 291	44.56 393	73.23 270	87.54 211	49.38 294	85.70 321	65.90 232	78.44 264	86.19 313

pmmvs4		74.03 270	71.91 279	80.39 236	81.96 322	68.32 126	81.45 291	82.14 311	59.32 346	69.87 310	85.13 275	52.40 252	88.13 301	60.21 281	74.74 320	84.73 339

EU-MVSNet		68.53 325	67.61 325	71.31 350	78.51 365	47.01 389	84.47 243	84.27 278	42.27 396	66.44 347	84.79 283	40.44 358	83.76 338	58.76 296	68.54 361	83.17 355

VNet		82.21 108	82.41 99	81.62 205	90.82 93	60.93 267	84.47 243	89.78 164	76.36 81	84.07 85	91.88 96	64.71 132	90.26 263	70.68 186	88.89 124	93.66 70

test-LLR		72.94 285	72.43 274	74.48 322	81.35 334	58.04 298	78.38 336	77.46 354	66.66 265	69.95 308	79.00 360	48.06 305	79.24 361	66.13 228	84.83 177	86.15 314

TESTMET0.1,1		69.89 313	69.00 307	72.55 339	79.27 362	56.85 316	78.38 336	74.71 373	57.64 360	68.09 325	77.19 373	37.75 370	76.70 374	63.92 247	84.09 192	84.10 346

test-mter		71.41 296	70.39 299	74.48 322	81.35 334	58.04 298	78.38 336	77.46 354	60.32 337	69.95 308	79.00 360	36.08 375	79.24 361	66.13 228	84.83 177	86.15 314

VPA-MVSNet		80.60 144	80.55 128	80.76 230	88.07 187	60.80 270	86.86 181	91.58 112	75.67 94	80.24 132	89.45 162	63.34 140	90.25 264	70.51 188	79.22 258	91.23 161

ACMMPR		87.44 25	87.23 30	88.08 15	94.64 13	73.59 12	93.04 12	93.20 34	76.78 67	84.66 72	94.52 23	68.81 90	96.65 30	84.53 54	94.90 41	94.00 55

testgi		66.67 337	66.53 334	67.08 371	75.62 376	41.69 406	75.93 352	76.50 363	66.11 274	65.20 356	86.59 239	35.72 376	74.71 390	43.71 380	73.38 334	84.84 337

test20.03		67.45 331	66.95 332	68.94 360	75.48 377	44.84 397	77.50 345	77.67 352	66.66 265	63.01 366	83.80 301	47.02 311	78.40 365	42.53 385	68.86 360	83.58 352

thres600view7		76.50 237	75.44 237	79.68 252	89.40 132	57.16 312	85.53 222	83.23 294	73.79 135	76.26 210	87.09 224	51.89 264	91.89 219	48.05 364	83.72 200	90.00 213

ADS-MVSNet		64.36 347	62.88 350	68.78 363	79.92 350	47.17 388	67.55 391	71.18 382	53.37 377	65.25 354	75.86 379	42.32 347	73.99 393	41.57 386	68.91 358	85.18 331

MP-MVS		87.71 19	87.64 21	87.93 21	94.36 26	73.88 6	92.71 22	92.65 69	77.57 41	83.84 89	94.40 32	72.24 45	96.28 43	85.65 41	95.30 35	93.62 77
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
testmvs		6.04 389	8.02 392	0.10 403	0.08 425	0.03 428	69.74 382	0.04 426	0.05 420	0.31 421	1.68 420	0.02 426	0.04 421	0.24 420	0.02 419	0.25 418

thres400		76.50 237	75.37 241	79.86 247	89.13 146	57.65 306	85.17 225	83.60 286	73.41 146	76.45 205	86.39 247	52.12 256	91.95 216	48.33 359	83.75 197	90.00 213

test123		6.12 388	8.11 391	0.14 402	0.06 426	0.09 427	71.05 377	0.03 427	0.04 421	0.25 422	1.30 421	0.05 425	0.03 422	0.21 421	0.01 420	0.29 417

thres200		75.55 253	74.47 252	78.82 266	87.78 202	57.85 303	83.07 274	83.51 289	72.44 162	75.84 219	84.42 286	52.08 259	91.75 224	47.41 366	83.64 202	86.86 302

test0.0.03 1		68.00 329	67.69 323	68.90 361	77.55 367	47.43 387	75.70 356	72.95 380	66.66 265	66.56 342	82.29 330	48.06 305	75.87 383	44.97 379	74.51 322	83.41 353

pmmvs3		57.79 358	54.26 363	68.37 365	64.02 406	56.72 319	75.12 362	65.17 398	40.20 398	52.93 394	69.86 394	20.36 403	75.48 386	45.45 377	55.25 392	72.90 392

EMVS		30.81 382	29.65 384	34.27 398	50.96 418	25.95 418	56.58 408	46.80 416	24.01 413	15.53 418	30.68 414	12.47 410	54.43 414	12.81 417	17.05 415	22.43 414

E-PMN		31.77 380	30.64 383	35.15 397	52.87 417	27.67 414	57.09 407	47.86 415	24.64 412	16.40 417	33.05 413	11.23 413	54.90 413	14.46 416	18.15 414	22.87 413

PGM-MVS		86.68 40	86.27 44	87.90 22	94.22 33	73.38 18	90.22 73	93.04 41	75.53 95	83.86 88	94.42 31	67.87 100	96.64 31	82.70 78	94.57 50	93.66 70

LCM-MVSNet-Re		77.05 227	76.94 211	77.36 292	87.20 221	51.60 371	80.06 312	80.46 331	75.20 101	67.69 328	86.72 231	62.48 155	88.98 287	63.44 250	89.25 119	91.51 152

LCM-MVSNet		54.25 362	49.68 372	67.97 369	53.73 416	45.28 395	66.85 394	80.78 325	35.96 405	39.45 406	62.23 399	8.70 416	78.06 368	48.24 362	51.20 396	80.57 377

MCST-MVS		87.37 29	87.25 29	87.73 28	94.53 17	72.46 38	89.82 79	93.82 16	73.07 154	84.86 68	92.89 77	76.22 17	96.33 41	84.89 48	95.13 36	94.40 39

mvs_anonymous		79.42 171	79.11 160	80.34 238	84.45 271	57.97 300	82.59 278	87.62 228	67.40 259	76.17 215	88.56 184	68.47 92	89.59 276	70.65 187	86.05 166	93.47 84

MVS_Test		83.15 95	83.06 90	83.41 154	86.86 225	63.21 237	86.11 205	92.00 94	74.31 123	82.87 101	89.44 163	70.03 74	93.21 167	77.39 122	88.50 134	93.81 65

MDA-MVSNet-bldmvs		66.68 336	63.66 345	75.75 305	79.28 361	60.56 274	73.92 368	78.35 349	64.43 295	50.13 398	79.87 353	44.02 338	83.67 339	46.10 373	56.86 385	83.03 359

CDPH-MVS		85.76 56	85.29 66	87.17 43	93.49 47	71.08 64	88.58 126	92.42 79	68.32 249	84.61 74	93.48 61	72.32 44	96.15 48	79.00 104	95.43 30	94.28 45

test12				86.80 52	92.63 67	70.70 75		91.79 106		82.71 105		71.67 54	96.16 47		94.50 51	93.54 82

casdiffmvs		85.11 67	85.14 67	85.01 88	87.20 221	65.77 183	87.75 155	92.83 60	77.84 37	84.36 80	92.38 88	72.15 46	93.93 132	81.27 89	90.48 101	95.33 4
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		82.10 109	81.88 111	82.76 187	83.00 303	63.78 223	83.68 259	89.76 165	72.94 157	82.02 110	89.85 147	65.96 122	90.79 257	82.38 80	87.30 146	93.71 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
baseline2		75.70 251	73.83 261	81.30 215	83.26 294	61.79 260	82.57 279	80.65 327	66.81 261	66.88 337	83.42 310	57.86 209	92.19 209	63.47 249	79.57 251	89.91 218

baseline1		76.98 229	76.75 218	77.66 287	88.13 183	55.66 337	85.12 228	81.89 314	73.04 155	76.79 196	88.90 172	62.43 157	87.78 305	63.30 252	71.18 349	89.55 231

YYNet1		65.03 344	62.91 349	71.38 346	75.85 374	56.60 322	69.12 387	74.66 374	57.28 364	54.12 392	77.87 369	45.85 324	74.48 391	49.95 351	61.52 379	83.05 358

PMMVS2		40.82 378	38.86 382	46.69 392	53.84 414	16.45 423	48.61 409	49.92 412	37.49 402	31.67 407	60.97 400	8.14 418	56.42 412	28.42 403	30.72 409	67.19 397

MDA-MVSNet_test_wron		65.03 344	62.92 348	71.37 347	75.93 372	56.73 318	69.09 388	74.73 372	57.28 364	54.03 393	77.89 368	45.88 323	74.39 392	49.89 352	61.55 378	82.99 360

tpmvs		71.09 299	69.29 304	76.49 300	82.04 321	56.04 331	78.92 329	81.37 321	64.05 303	67.18 335	78.28 366	49.74 290	89.77 272	49.67 353	72.37 339	83.67 351

PM-MVS		66.41 339	64.14 341	73.20 334	73.92 383	56.45 323	78.97 328	64.96 400	63.88 307	64.72 357	80.24 348	19.84 404	83.44 342	66.24 227	64.52 373	79.71 380

HQP_MVS		83.64 84	83.14 88	85.14 83	90.08 108	68.71 116	91.25 52	92.44 76	79.12 23	78.92 149	91.00 128	60.42 194	95.38 74	78.71 108	86.32 160	91.33 158

plane_prior7					90.08 108	68.51 123

plane_prior6					89.84 117	68.70 118						60.42 194

plane_prior5								92.44 76					95.38 74	78.71 108	86.32 160	91.33 158

plane_prior4											91.00 128

plane_prior3						68.60 121			78.44 31	78.92 149

plane_prior2							91.25 52		79.12 23

plane_prior1					89.90 116

plane_prior						68.71 116	90.38 70		77.62 39						86.16 164

PS-CasMVS		78.01 208	78.09 181	77.77 286	87.71 204	54.39 351	88.02 145	91.22 121	77.50 46	73.26 269	88.64 180	60.73 186	88.41 298	61.88 267	73.88 328	90.53 187

UniMVSNet_NR-MVSNet		81.88 114	81.54 114	82.92 176	88.46 171	63.46 231	87.13 172	92.37 80	80.19 12	78.38 161	89.14 166	71.66 55	93.05 180	70.05 192	76.46 288	92.25 132

PEN-MVS		77.73 214	77.69 196	77.84 284	87.07 224	53.91 354	87.91 151	91.18 123	77.56 43	73.14 271	88.82 175	61.23 179	89.17 283	59.95 282	72.37 339	90.43 191

TransMVSNet (Re)		75.39 258	74.56 250	77.86 283	85.50 249	57.10 314	86.78 185	86.09 258	72.17 166	71.53 291	87.34 214	63.01 150	89.31 281	56.84 315	61.83 377	87.17 293

DTE-MVSNet		76.99 228	76.80 214	77.54 291	86.24 236	53.06 363	87.52 160	90.66 137	77.08 59	72.50 279	88.67 179	60.48 193	89.52 277	57.33 310	70.74 351	90.05 212

DU-MVS		81.12 130	80.52 129	82.90 177	87.80 199	63.46 231	87.02 176	91.87 102	79.01 26	78.38 161	89.07 168	65.02 129	93.05 180	70.05 192	76.46 288	92.20 135

UniMVSNet (Re)		81.60 122	81.11 119	83.09 167	88.38 175	64.41 212	87.60 158	93.02 45	78.42 32	78.56 157	88.16 195	69.78 77	93.26 163	69.58 199	76.49 287	91.60 148

CP-MVSNet		78.22 199	78.34 175	77.84 284	87.83 198	54.54 349	87.94 149	91.17 124	77.65 38	73.48 267	88.49 185	62.24 161	88.43 297	62.19 263	74.07 324	90.55 186

WR-MVS_H		78.51 194	78.49 170	78.56 272	88.02 189	56.38 326	88.43 129	92.67 66	77.14 56	73.89 262	87.55 210	66.25 116	89.24 282	58.92 293	73.55 331	90.06 211

WR-MVS		79.49 167	79.22 158	80.27 240	88.79 159	58.35 293	85.06 229	88.61 209	78.56 30	77.65 177	88.34 189	63.81 139	90.66 260	64.98 240	77.22 277	91.80 146

NR-MVSNet		80.23 154	79.38 151	82.78 185	87.80 199	63.34 234	86.31 199	91.09 128	79.01 26	72.17 284	89.07 168	67.20 106	92.81 189	66.08 231	75.65 301	92.20 135

Baseline_NR-MVSNet		78.15 203	78.33 176	77.61 289	85.79 243	56.21 330	86.78 185	85.76 261	73.60 140	77.93 173	87.57 208	65.02 129	88.99 286	67.14 223	75.33 312	87.63 281

TranMVSNet+NR-MVSNet		80.84 134	80.31 133	82.42 192	87.85 197	62.33 251	87.74 156	91.33 119	80.55 9	77.99 172	89.86 146	65.23 127	92.62 190	67.05 224	75.24 315	92.30 130

TSAR-MVS + GP.		85.71 57	85.33 63	86.84 50	91.34 81	72.50 36	89.07 108	87.28 235	76.41 76	85.80 54	90.22 142	74.15 31	95.37 77	81.82 83	91.88 83	92.65 118

n2								0.00 428

nn								0.00 428

mPP-MVS		86.67 41	86.32 43	87.72 30	94.41 22	73.55 13	92.74 20	92.22 87	76.87 64	82.81 104	94.25 37	66.44 113	96.24 44	82.88 73	94.28 58	93.38 86

door-mid								69.98 385

XVG-OURS-SEG-HR		80.81 136	79.76 143	83.96 141	85.60 247	68.78 111	83.54 265	90.50 142	70.66 195	76.71 199	91.66 100	60.69 188	91.26 244	76.94 126	81.58 228	91.83 144

mvsmamba		80.60 144	79.38 151	84.27 117	89.74 120	67.24 156	87.47 162	86.95 242	70.02 207	75.38 231	88.93 171	51.24 272	92.56 193	75.47 144	89.22 120	93.00 108

MVSFormer		82.85 101	82.05 107	85.24 81	87.35 213	70.21 80	90.50 64	90.38 145	68.55 244	81.32 119	89.47 158	61.68 167	93.46 156	78.98 105	90.26 105	92.05 141

jason		81.39 126	80.29 134	84.70 100	86.63 233	69.90 88	85.95 208	86.77 246	63.24 309	81.07 125	89.47 158	61.08 183	92.15 210	78.33 113	90.07 110	92.05 141
jason: jason.
lupinMVS		81.39 126	80.27 135	84.76 99	87.35 213	70.21 80	85.55 220	86.41 250	62.85 316	81.32 119	88.61 181	61.68 167	92.24 208	78.41 112	90.26 105	91.83 144

test_djsdf		80.30 153	79.32 154	83.27 158	83.98 280	65.37 192	90.50 64	90.38 145	68.55 244	76.19 212	88.70 177	56.44 222	93.46 156	78.98 105	80.14 247	90.97 171

HPM-MVS_fast		85.35 64	84.95 70	86.57 56	93.69 42	70.58 78	92.15 35	91.62 110	73.89 133	82.67 106	94.09 43	62.60 152	95.54 65	80.93 91	92.93 71	93.57 79

K. test v3		71.19 297	68.51 309	79.21 261	83.04 302	57.78 305	84.35 250	76.91 361	72.90 158	62.99 367	82.86 321	39.27 362	91.09 252	61.65 270	52.66 394	88.75 259

lessismore_v0				78.97 264	81.01 339	57.15 313		65.99 396		61.16 373	82.82 322	39.12 363	91.34 243	59.67 285	46.92 400	88.43 268

SixPastTwentyTwo		73.37 276	71.26 289	79.70 251	85.08 259	57.89 302	85.57 216	83.56 288	71.03 186	65.66 350	85.88 256	42.10 350	92.57 192	59.11 291	63.34 375	88.65 263

OurMVSNet-221017-0		74.26 265	72.42 275	79.80 249	83.76 285	59.59 286	85.92 210	86.64 247	66.39 272	66.96 336	87.58 207	39.46 361	91.60 228	65.76 234	69.27 356	88.22 270

HPM-MVS		87.11 33	86.98 34	87.50 38	93.88 39	72.16 45	92.19 33	93.33 31	76.07 86	83.81 90	93.95 54	69.77 78	96.01 53	85.15 44	94.66 47	94.32 43
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
XVG-OURS		80.41 149	79.23 157	83.97 140	85.64 246	69.02 105	83.03 276	90.39 144	71.09 184	77.63 178	91.49 109	54.62 235	91.35 242	75.71 138	83.47 205	91.54 151

XVG-ACMP-BASELINE		76.11 246	74.27 255	81.62 205	83.20 296	64.67 205	83.60 263	89.75 166	69.75 217	71.85 287	87.09 224	32.78 381	92.11 211	69.99 194	80.43 243	88.09 273

casdiffmvs_mvg		85.99 48	86.09 50	85.70 72	87.65 207	67.22 157	88.69 122	93.04 41	79.64 18	85.33 59	92.54 86	73.30 35	94.50 110	83.49 64	91.14 94	95.37 2
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		82.08 110	81.27 116	84.50 104	89.23 142	68.76 112	90.22 73	91.94 98	75.37 98	76.64 201	91.51 107	54.29 236	94.91 93	78.44 110	83.78 194	89.83 222

LGP-MVS_train				84.50 104	89.23 142	68.76 112		91.94 98	75.37 98	76.64 201	91.51 107	54.29 236	94.91 93	78.44 110	83.78 194	89.83 222

baseline		84.93 70	84.98 68	84.80 98	87.30 219	65.39 191	87.30 169	92.88 57	77.62 39	84.04 86	92.26 90	71.81 50	93.96 126	81.31 87	90.30 104	95.03 9

test11								92.23 86

door								69.44 388

EPNet_dtu		75.46 255	74.86 246	77.23 295	82.57 314	54.60 348	86.89 180	83.09 298	71.64 170	66.25 348	85.86 257	55.99 223	88.04 302	54.92 324	86.55 157	89.05 244
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CHOSEN 1792x2688		77.63 219	75.69 231	83.44 151	89.98 114	68.58 122	78.70 332	87.50 231	56.38 368	75.80 220	86.84 227	58.67 202	91.40 241	61.58 271	85.75 172	90.34 194

EPNet		83.72 82	82.92 94	86.14 63	84.22 274	69.48 94	91.05 56	85.27 265	81.30 6	76.83 195	91.65 101	66.09 118	95.56 63	76.00 136	93.85 62	93.38 86
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
HQP5-MVS						66.98 161

HQP-NCC					89.33 135		89.17 102		76.41 76	77.23 187

ACMP_Plane					89.33 135		89.17 102		76.41 76	77.23 187

APD-MVS		87.44 25	87.52 25	87.19 42	94.24 32	72.39 39	91.86 40	92.83 60	73.01 156	88.58 24	94.52 23	73.36 34	96.49 38	84.26 57	95.01 37	92.70 114
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
BP-MVS														77.47 120

HQP4-MVS										77.24 186			95.11 85			91.03 168

HQP3-MVS								92.19 89							85.99 168

HQP2-MVS												60.17 197

CNVR-MVS		88.93 10	89.13 10	88.33 8	94.77 12	73.82 8	90.51 62	93.00 46	80.90 7	88.06 29	94.06 45	76.43 16	96.84 21	88.48 24	95.99 18	94.34 42

NCCC		88.06 14	88.01 18	88.24 11	94.41 22	73.62 11	91.22 54	92.83 60	81.50 5	85.79 55	93.47 63	73.02 40	97.00 18	84.90 46	94.94 40	94.10 50

114514_t		80.68 142	79.51 148	84.20 120	94.09 38	67.27 154	89.64 87	91.11 127	58.75 353	74.08 261	90.72 132	58.10 206	95.04 90	69.70 197	89.42 118	90.30 197

CP-MVS		87.11 33	86.92 36	87.68 34	94.20 34	73.86 7	93.98 3	92.82 63	76.62 73	83.68 91	94.46 27	67.93 98	95.95 57	84.20 60	94.39 55	93.23 92

DSMNet-mixed		57.77 359	56.90 361	60.38 379	67.70 400	35.61 410	69.18 385	53.97 411	32.30 409	57.49 386	79.88 352	40.39 359	68.57 403	38.78 392	72.37 339	76.97 385

tpm2		73.26 279	71.46 284	78.63 268	83.34 292	56.71 320	80.65 304	80.40 333	56.63 367	73.55 266	82.02 334	51.80 266	91.24 245	56.35 319	78.42 265	87.95 274

NP-MVS					89.62 121	68.32 126					90.24 140

EG-PatchMatch MVS		74.04 268	71.82 280	80.71 231	84.92 261	67.42 148	85.86 212	88.08 216	66.04 276	64.22 360	83.85 299	35.10 377	92.56 193	57.44 308	80.83 236	82.16 368

tpm cat1		70.57 305	68.31 311	77.35 293	82.41 318	57.95 301	78.08 341	80.22 336	52.04 380	68.54 323	77.66 371	52.00 261	87.84 304	51.77 338	72.07 344	86.25 311

SteuartSystems-ACMMP		88.72 11	88.86 11	88.32 9	92.14 72	72.96 25	93.73 5	93.67 20	80.19 12	88.10 28	94.80 19	73.76 33	97.11 15	87.51 31	95.82 21	94.90 13
Skip Steuart: Steuart Systems R&D Blog.
CostFormer		75.24 259	73.90 259	79.27 259	82.65 313	58.27 295	80.80 298	82.73 307	61.57 329	75.33 237	83.13 315	55.52 224	91.07 253	64.98 240	78.34 267	88.45 267

CR-MVSNet		73.37 276	71.27 288	79.67 253	81.32 336	65.19 194	75.92 353	80.30 334	59.92 341	72.73 276	81.19 337	52.50 250	86.69 311	59.84 283	77.71 272	87.11 297

JIA-IIPM		66.32 340	62.82 351	76.82 298	77.09 370	61.72 261	65.34 399	75.38 367	58.04 358	64.51 358	62.32 398	42.05 351	86.51 314	51.45 341	69.22 357	82.21 366

Patchmtry		70.74 303	69.16 306	75.49 311	80.72 340	54.07 353	74.94 364	80.30 334	58.34 354	70.01 305	81.19 337	52.50 250	86.54 313	53.37 332	71.09 350	85.87 322

PatchT		68.46 326	67.85 318	70.29 355	80.70 341	43.93 399	72.47 371	74.88 370	60.15 339	70.55 296	76.57 375	49.94 287	81.59 351	50.58 344	74.83 319	85.34 328

tpmrst		72.39 287	72.13 278	73.18 335	80.54 343	49.91 382	79.91 316	79.08 346	63.11 311	71.69 289	79.95 351	55.32 225	82.77 346	65.66 235	73.89 327	86.87 301

BH-w/o		78.21 200	77.33 204	80.84 228	88.81 157	65.13 196	84.87 233	87.85 224	69.75 217	74.52 256	84.74 284	61.34 176	93.11 177	58.24 302	85.84 170	84.27 342

tpm		72.37 289	71.71 281	74.35 324	82.19 320	52.00 365	79.22 323	77.29 358	64.56 294	72.95 274	83.68 307	51.35 270	83.26 344	58.33 301	75.80 299	87.81 278

DELS-MVS		85.41 63	85.30 65	85.77 70	88.49 169	67.93 136	85.52 224	93.44 27	78.70 29	83.63 94	89.03 170	74.57 24	95.71 61	80.26 99	94.04 61	93.66 70
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		79.47 168	78.60 168	82.05 197	89.19 144	65.91 178	86.07 206	88.52 210	72.18 165	75.42 229	87.69 205	61.15 181	93.54 151	60.38 279	86.83 153	86.70 306

RPMNet		73.51 274	70.49 296	82.58 190	81.32 336	65.19 194	75.92 353	92.27 83	57.60 361	72.73 276	76.45 376	52.30 253	95.43 70	48.14 363	77.71 272	87.11 297

MVSTER		79.01 182	77.88 187	82.38 193	83.07 300	64.80 203	84.08 256	88.95 197	69.01 237	78.69 152	87.17 222	54.70 233	92.43 198	74.69 147	80.57 241	89.89 220

CPTT-MVS		83.73 81	83.33 87	84.92 93	93.28 49	70.86 72	92.09 36	90.38 145	68.75 241	79.57 140	92.83 79	60.60 192	93.04 182	80.92 92	91.56 89	90.86 174

GBi-Net		78.40 195	77.40 201	81.40 212	87.60 208	63.01 241	88.39 131	89.28 179	71.63 171	75.34 233	87.28 215	54.80 229	91.11 247	62.72 255	79.57 251	90.09 207

PVSNet_Blended_VisFu		82.62 103	81.83 112	84.96 90	90.80 94	69.76 90	88.74 120	91.70 109	69.39 222	78.96 147	88.46 186	65.47 125	94.87 98	74.42 150	88.57 131	90.24 199

PVSNet_BlendedMVS		80.60 144	80.02 137	82.36 194	88.85 153	65.40 189	86.16 204	92.00 94	69.34 224	78.11 168	86.09 254	66.02 120	94.27 116	71.52 176	82.06 223	87.39 287

UnsupCasMVSNet_eth		67.33 332	65.99 336	71.37 347	73.48 387	51.47 373	75.16 360	85.19 266	65.20 286	60.78 374	80.93 344	42.35 346	77.20 371	57.12 311	53.69 393	85.44 327

UnsupCasMVSNet_bld		63.70 349	61.53 355	70.21 356	73.69 385	51.39 374	72.82 370	81.89 314	55.63 371	57.81 385	71.80 390	38.67 365	78.61 364	49.26 355	52.21 395	80.63 376

PVSNet_Blended		80.98 131	80.34 132	82.90 177	88.85 153	65.40 189	84.43 247	92.00 94	67.62 255	78.11 168	85.05 278	66.02 120	94.27 116	71.52 176	89.50 116	89.01 246

FMVSNet5		69.50 315	67.96 316	74.15 326	82.97 306	55.35 341	80.01 314	82.12 312	62.56 321	63.02 365	81.53 336	36.92 372	81.92 350	48.42 358	74.06 325	85.17 333

test1		78.40 195	77.40 201	81.40 212	87.60 208	63.01 241	88.39 131	89.28 179	71.63 171	75.34 233	87.28 215	54.80 229	91.11 247	62.72 255	79.57 251	90.09 207

new_pmnet		50.91 370	50.29 370	52.78 390	68.58 399	34.94 412	63.71 401	56.63 410	39.73 399	44.95 401	65.47 396	21.93 401	58.48 410	34.98 397	56.62 386	64.92 398

FMVSNet3		77.88 211	76.85 213	80.97 226	86.84 227	62.36 250	86.52 193	88.77 201	71.13 182	75.34 233	86.66 237	54.07 239	91.10 250	62.72 255	79.57 251	89.45 233

dp		66.80 335	65.43 337	70.90 354	79.74 356	48.82 385	75.12 362	74.77 371	59.61 343	64.08 361	77.23 372	42.89 343	80.72 357	48.86 357	66.58 366	83.16 356

FMVSNet2		78.20 201	77.21 205	81.20 218	87.60 208	62.89 247	87.47 162	89.02 192	71.63 171	75.29 239	87.28 215	54.80 229	91.10 250	62.38 260	79.38 255	89.61 229

FMVSNet1		77.44 221	76.12 228	81.40 212	86.81 228	63.01 241	88.39 131	89.28 179	70.49 198	74.39 258	87.28 215	49.06 300	91.11 247	60.91 276	78.52 262	90.09 207

N_pmnet		52.79 367	53.26 365	51.40 391	78.99 363	7.68 425	69.52 383	3.89 424	51.63 383	57.01 387	74.98 383	40.83 356	65.96 406	37.78 393	64.67 372	80.56 378

cascas		76.72 234	74.64 248	82.99 173	85.78 244	65.88 179	82.33 280	89.21 184	60.85 334	72.74 275	81.02 340	47.28 309	93.75 143	67.48 218	85.02 175	89.34 236

BH-RMVSNet		79.61 163	78.44 172	83.14 165	89.38 134	65.93 177	84.95 232	87.15 239	73.56 141	78.19 166	89.79 148	56.67 220	93.36 160	59.53 287	86.74 154	90.13 203

UGNet		80.83 135	79.59 147	84.54 103	88.04 188	68.09 132	89.42 95	88.16 213	76.95 61	76.22 211	89.46 160	49.30 296	93.94 129	68.48 210	90.31 103	91.60 148
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		75.65 252	75.68 232	75.57 308	86.40 235	56.82 317	77.92 344	82.40 309	65.10 287	76.18 213	87.72 203	63.13 149	80.90 356	60.31 280	81.96 224	89.00 248

XXY-MVS		75.41 257	75.56 235	74.96 317	83.59 287	57.82 304	80.59 305	83.87 284	66.54 271	74.93 249	88.31 190	63.24 143	80.09 359	62.16 264	76.85 283	86.97 300

EC-MVSNet		86.01 47	86.38 42	84.91 94	89.31 138	66.27 171	92.32 30	93.63 21	79.37 20	84.17 83	91.88 96	69.04 88	95.43 70	83.93 62	93.77 63	93.01 107

sss		73.60 273	73.64 263	73.51 331	82.80 308	55.01 345	76.12 351	81.69 317	62.47 322	74.68 253	85.85 258	57.32 215	78.11 367	60.86 277	80.93 234	87.39 287

Test_1112_low_res		76.40 242	75.44 237	79.27 259	89.28 140	58.09 296	81.69 287	87.07 240	59.53 345	72.48 280	86.67 236	61.30 177	89.33 280	60.81 278	80.15 246	90.41 192

1112_ss		77.40 223	76.43 224	80.32 239	89.11 150	60.41 277	83.65 260	87.72 227	62.13 326	73.05 272	86.72 231	62.58 154	89.97 269	62.11 266	80.80 237	90.59 185

ab-mvs-re		7.23 387	9.64 390	0.00 404	0.00 427	0.00 429	0.00 415	0.00 428	0.00 422	0.00 423	86.72 231	0.00 427	0.00 423	0.00 422	0.00 421	0.00 419

ab-mvs		79.51 166	78.97 163	81.14 220	88.46 171	60.91 268	83.84 257	89.24 183	70.36 199	79.03 146	88.87 174	63.23 144	90.21 265	65.12 238	82.57 218	92.28 131

TR-MVS		77.44 221	76.18 227	81.20 218	88.24 179	63.24 236	84.61 240	86.40 251	67.55 256	77.81 174	86.48 245	54.10 238	93.15 174	57.75 306	82.72 216	87.20 292

MDTV_nov1_ep13_2view						37.79 409	75.16 360		55.10 372	66.53 343		49.34 295		53.98 328		87.94 275

MDTV_nov1_ep13			69.97 302		83.18 297	53.48 357	77.10 349	80.18 337	60.45 335	69.33 316	80.44 346	48.89 303	86.90 310	51.60 340	78.51 263

MIMVSNet1		68.58 323	66.78 333	73.98 328	80.07 349	51.82 369	80.77 300	84.37 274	64.40 296	59.75 379	82.16 332	36.47 373	83.63 340	42.73 383	70.33 352	86.48 309

MIMVSNet		70.69 304	69.30 303	74.88 318	84.52 269	56.35 328	75.87 355	79.42 342	64.59 293	67.76 326	82.41 326	41.10 354	81.54 352	46.64 370	81.34 229	86.75 305

IterMVS-LS		80.06 157	79.38 151	82.11 196	85.89 242	63.20 238	86.79 184	89.34 177	74.19 126	75.45 228	86.72 231	66.62 109	92.39 200	72.58 170	76.86 282	90.75 178
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
CDS-MVSNet		79.07 181	77.70 195	83.17 164	87.60 208	68.23 129	84.40 249	86.20 255	67.49 257	76.36 208	86.54 243	61.54 170	90.79 257	61.86 268	87.33 145	90.49 189
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
ACMMP++_ref															81.95 225

IterMVS		74.29 264	72.94 269	78.35 277	81.53 330	63.49 230	81.58 288	82.49 308	68.06 252	69.99 307	83.69 306	51.66 269	85.54 324	65.85 233	71.64 346	86.01 318
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
DP-MVS Recon		83.11 98	82.09 106	86.15 61	94.44 19	70.92 71	88.79 116	92.20 88	70.53 197	79.17 145	91.03 126	64.12 135	96.03 50	68.39 212	90.14 107	91.50 153

MVS_111021_LR		82.61 104	82.11 104	84.11 122	88.82 156	71.58 55	85.15 227	86.16 256	74.69 115	80.47 130	91.04 124	62.29 159	90.55 261	80.33 98	90.08 109	90.20 200

DP-MVS		76.78 233	74.57 249	83.42 152	93.29 48	69.46 97	88.55 127	83.70 285	63.98 305	70.20 301	88.89 173	54.01 240	94.80 100	46.66 368	81.88 226	86.01 318

ACMMP++															81.25 230

HQP-MVS		82.61 104	82.02 108	84.37 109	89.33 135	66.98 161	89.17 102	92.19 89	76.41 76	77.23 187	90.23 141	60.17 197	95.11 85	77.47 120	85.99 168	91.03 168

QAPM		80.88 133	79.50 149	85.03 87	88.01 191	68.97 107	91.59 43	92.00 94	66.63 270	75.15 243	92.16 91	57.70 210	95.45 68	63.52 248	88.76 128	90.66 181

Vis-MVSNet		83.46 89	82.80 96	85.43 77	90.25 104	68.74 114	90.30 72	90.13 156	76.33 82	80.87 127	92.89 77	61.00 184	94.20 120	72.45 173	90.97 95	93.35 88
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
MVS-HIRNet		59.14 357	57.67 359	63.57 375	81.65 326	43.50 400	71.73 373	65.06 399	39.59 400	51.43 395	57.73 403	38.34 367	82.58 347	39.53 389	73.95 326	64.62 399

IS-MVSNet		83.15 95	82.81 95	84.18 121	89.94 115	63.30 235	91.59 43	88.46 211	79.04 25	79.49 141	92.16 91	65.10 128	94.28 115	67.71 215	91.86 86	94.95 10

HyFIR lowres test		77.53 220	75.40 239	83.94 142	89.59 122	66.62 165	80.36 309	88.64 208	56.29 369	76.45 205	85.17 274	57.64 211	93.28 162	61.34 274	83.10 211	91.91 143

EPMVS		69.02 319	68.16 313	71.59 345	79.61 357	49.80 384	77.40 346	66.93 394	62.82 318	70.01 305	79.05 358	45.79 325	77.86 369	56.58 317	75.26 314	87.13 296

PAPM_NR		83.02 99	82.41 99	84.82 96	92.47 70	66.37 169	87.93 150	91.80 105	73.82 134	77.32 184	90.66 133	67.90 99	94.90 95	70.37 189	89.48 117	93.19 96

TAMVS		78.89 186	77.51 200	83.03 171	87.80 199	67.79 139	84.72 236	85.05 268	67.63 254	76.75 198	87.70 204	62.25 160	90.82 256	58.53 298	87.13 148	90.49 189

PAPR		81.66 121	80.89 124	83.99 139	90.27 103	64.00 218	86.76 187	91.77 108	68.84 240	77.13 193	89.50 156	67.63 101	94.88 97	67.55 217	88.52 133	93.09 100

RPSCF		73.23 280	71.46 284	78.54 273	82.50 315	59.85 282	82.18 282	82.84 306	58.96 350	71.15 295	89.41 164	45.48 331	84.77 333	58.82 295	71.83 345	91.02 170

Vis-MVSNet (Re-imp)		78.36 197	78.45 171	78.07 282	88.64 165	51.78 370	86.70 188	79.63 341	74.14 128	75.11 244	90.83 131	61.29 178	89.75 273	58.10 303	91.60 87	92.69 116

test_0402		72.79 286	70.44 297	79.84 248	88.13 183	65.99 176	85.93 209	84.29 277	65.57 282	67.40 333	85.49 266	46.92 312	92.61 191	35.88 396	74.38 323	80.94 374

MVS_111021_HR		85.14 66	84.75 71	86.32 58	91.65 79	72.70 30	85.98 207	90.33 149	76.11 85	82.08 109	91.61 105	71.36 59	94.17 122	81.02 90	92.58 75	92.08 140

CSCG		86.41 45	86.19 46	87.07 45	92.91 61	72.48 37	90.81 58	93.56 24	73.95 130	83.16 98	91.07 123	75.94 18	95.19 80	79.94 101	94.38 56	93.55 81

PatchMatch-RL		72.38 288	70.90 292	76.80 299	88.60 166	67.38 150	79.53 318	76.17 366	62.75 319	69.36 315	82.00 335	45.51 329	84.89 332	53.62 330	80.58 240	78.12 383

API-MVS		81.99 113	81.23 117	84.26 119	90.94 90	70.18 85	91.10 55	89.32 178	71.51 176	78.66 154	88.28 191	65.26 126	95.10 88	64.74 242	91.23 93	87.51 285

Test By Simon												64.33 133

TDRefinement		67.49 330	64.34 340	76.92 297	73.47 388	61.07 266	84.86 234	82.98 302	59.77 342	58.30 383	85.13 275	26.06 392	87.89 303	47.92 365	60.59 382	81.81 370

USDC		70.33 308	68.37 310	76.21 302	80.60 342	56.23 329	79.19 324	86.49 249	60.89 333	61.29 372	85.47 267	31.78 384	89.47 279	53.37 332	76.21 296	82.94 361

EPP-MVSNet		83.40 91	83.02 91	84.57 102	90.13 106	64.47 210	92.32 30	90.73 136	74.45 122	79.35 143	91.10 121	69.05 87	95.12 83	72.78 168	87.22 147	94.13 49

PMMVS		69.34 317	68.67 308	71.35 349	75.67 375	62.03 255	75.17 359	73.46 376	50.00 386	68.68 320	79.05 358	52.07 260	78.13 366	61.16 275	82.77 214	73.90 390

PAPM		77.68 218	76.40 225	81.51 208	87.29 220	61.85 258	83.78 258	89.59 170	64.74 292	71.23 293	88.70 177	62.59 153	93.66 146	52.66 335	87.03 150	89.01 246

ACMMP		85.89 54	85.39 61	87.38 39	93.59 45	72.63 33	92.74 20	93.18 39	76.78 67	80.73 128	93.82 56	64.33 133	96.29 42	82.67 79	90.69 99	93.23 92
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		78.08 204	76.79 215	81.97 200	90.40 102	71.07 65	87.59 159	84.55 273	66.03 277	72.38 282	89.64 152	57.56 212	86.04 319	59.61 286	83.35 207	88.79 257

PatchmatchNet		73.12 281	71.33 287	78.49 276	83.18 297	60.85 269	79.63 317	78.57 348	64.13 299	71.73 288	79.81 354	51.20 273	85.97 320	57.40 309	76.36 295	88.66 262
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
PHI-MVS		86.43 43	86.17 47	87.24 41	90.88 92	70.96 68	92.27 32	94.07 9	72.45 160	85.22 61	91.90 95	69.47 80	96.42 40	83.28 67	95.94 19	94.35 41

F-COLMAP		76.38 243	74.33 254	82.50 191	89.28 140	66.95 164	88.41 130	89.03 191	64.05 303	66.83 338	88.61 181	46.78 313	92.89 185	57.48 307	78.55 261	87.67 280

ANet_high		50.57 371	46.10 375	63.99 374	48.67 419	39.13 408	70.99 378	80.85 324	61.39 331	31.18 408	57.70 404	17.02 407	73.65 395	31.22 401	15.89 416	79.18 381

wuyk23d		16.82 386	15.94 389	19.46 400	58.74 409	31.45 413	39.22 410	3.74 425	6.84 416	6.04 419	2.70 419	1.27 424	24.29 419	10.54 419	14.40 418	2.63 416

OMC-MVS		82.69 102	81.97 110	84.85 95	88.75 161	67.42 148	87.98 146	90.87 133	74.92 109	79.72 138	91.65 101	62.19 162	93.96 126	75.26 145	86.42 159	93.16 97

MG-MVS		83.41 90	83.45 83	83.28 157	92.74 65	62.28 253	88.17 141	89.50 173	75.22 100	81.49 118	92.74 85	66.75 108	95.11 85	72.85 167	91.58 88	92.45 126

AdaColmap		80.58 147	79.42 150	84.06 131	93.09 57	68.91 108	89.36 98	88.97 196	69.27 225	75.70 221	89.69 150	57.20 217	95.77 59	63.06 253	88.41 135	87.50 286

uanet		0.00 391	0.00 394	0.00 404	0.00 427	0.00 429	0.00 415	0.00 428	0.00 422	0.00 423	0.00 422	0.00 427	0.00 423	0.00 422	0.00 421	0.00 419

ITE_SJBPF				78.22 278	81.77 325	60.57 273		83.30 292	69.25 227	67.54 329	87.20 220	36.33 374	87.28 309	54.34 327	74.62 321	86.80 303

DeepMVS_CX				27.40 399	40.17 422	26.90 416		24.59 423	17.44 415	23.95 413	48.61 410	9.77 414	26.48 418	18.06 411	24.47 412	28.83 412

TinyColmap		67.30 333	64.81 338	74.76 320	81.92 324	56.68 321	80.29 311	81.49 319	60.33 336	56.27 390	83.22 312	24.77 396	87.66 307	45.52 376	69.47 355	79.95 379

MAR-MVS		81.84 115	80.70 125	85.27 80	91.32 82	71.53 56	89.82 79	90.92 130	69.77 216	78.50 158	86.21 250	62.36 158	94.52 109	65.36 236	92.05 82	89.77 225
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		64.02 348	62.19 352	69.50 358	70.90 396	53.29 361	76.13 350	77.18 359	52.65 379	58.59 381	80.98 341	23.55 399	76.52 376	53.06 334	66.66 365	78.68 382

MSDG		73.36 278	70.99 291	80.49 235	84.51 270	65.80 181	80.71 303	86.13 257	65.70 280	65.46 351	83.74 303	44.60 333	90.91 255	51.13 343	76.89 281	84.74 338

LS3D		76.95 230	74.82 247	83.37 155	90.45 100	67.36 151	89.15 106	86.94 243	61.87 328	69.52 313	90.61 134	51.71 268	94.53 108	46.38 371	86.71 155	88.21 271

CLD-MVS		82.31 107	81.65 113	84.29 114	88.47 170	67.73 140	85.81 215	92.35 81	75.78 90	78.33 163	86.58 241	64.01 136	94.35 113	76.05 135	87.48 144	90.79 175
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
FPMVS		53.68 365	51.64 367	59.81 380	65.08 404	51.03 376	69.48 384	69.58 387	41.46 397	40.67 404	72.32 389	16.46 408	70.00 401	24.24 408	65.42 370	58.40 404

Gipuma		45.18 376	41.86 379	55.16 388	77.03 371	51.52 372	32.50 412	80.52 329	32.46 408	27.12 411	35.02 412	9.52 415	75.50 385	22.31 409	60.21 383	38.45 411
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015

High-resolution multi-view benchmark