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.

MSP-MVS		82.30 6	83.47 1	78.80 66	82.99 133	52.71 166	85.04 179	88.63 50	66.08 116	86.77 4	92.75 47	72.05 1	91.46 80	83.35 29	93.53 1	92.23 40
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
DVP-MVS++		82.44 3	82.38 6	82.62 5	91.77 4	57.49 19	84.98 183	88.88 39	58.00 284	83.60 7	93.39 27	67.21 2	96.39 4	81.64 43	91.98 4	93.98 6

OPU-MVS				81.71 14	92.05 3	55.97 52	92.48 3				94.01 10	67.21 2	95.10 16	89.82 3	92.55 3	94.06 4

PC_three_1452									66.58 101	87.27 3	93.70 18	66.82 4	94.95 18	89.74 4	91.98 4	93.98 6

TestfortrainingZip				83.28 1	90.91 7	58.80 10	87.61 72	91.34 10	56.28 330	88.36 1	95.55 1	65.41 5	96.39 4		88.20 15	94.63 3

DPM-MVS		82.39 4	82.36 7	82.49 6	80.12 232	59.50 5	92.24 8	90.72 18	69.37 57	83.22 9	94.47 4	63.81 6	93.18 39	74.02 115	93.25 2	94.80 1

WBMVS		73.93 136	73.39 128	75.55 193	87.82 42	55.21 70	89.37 39	87.29 82	67.27 86	63.70 233	80.30 320	60.32 7	86.47 301	61.58 233	62.85 325	84.97 287

DELS-MVS		82.32 5	82.50 5	81.79 13	86.80 51	56.89 31	92.77 2	86.30 109	77.83 1	77.88 48	92.13 58	60.24 8	94.78 20	78.97 63	89.61 8	93.69 9
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
dcpmvs_2		79.33 23	78.94 23	80.49 27	89.75 13	56.54 39	84.83 191	83.68 206	67.85 78	69.36 152	90.24 110	60.20 9	92.10 67	84.14 23	80.40 91	92.82 26

baseline2		75.15 111	74.54 110	76.98 142	81.67 175	51.74 197	83.84 227	91.94 3	69.97 47	58.98 298	86.02 221	59.73 10	91.73 74	68.37 170	70.40 246	87.48 226

CSCG		80.41 15	79.72 16	82.49 6	89.12 26	57.67 17	89.29 45	91.54 5	59.19 260	71.82 107	90.05 118	59.72 11	96.04 11	78.37 69	88.40 14	93.75 8

GG-mvs-BLEND				77.77 113	86.68 52	50.61 224	68.67 435	88.45 59		68.73 160	87.45 196	59.15 12	90.67 112	54.83 307	87.67 18	92.03 49

testing3-2		72.30 175	72.35 148	72.15 310	83.07 128	47.64 329	85.46 159	89.81 26	66.17 112	61.96 260	84.88 243	58.93 13	82.27 373	55.87 297	64.97 295	86.54 255

SED-MVS		81.92 8	81.75 9	82.44 8	89.48 18	56.89 31	92.48 3	88.94 37	57.50 298	84.61 5	94.09 8	58.81 14	96.37 7	82.28 37	87.60 19	94.06 4

test_241102_ONE					89.48 18	56.89 31		88.94 37	57.53 296	84.61 5	93.29 31	58.81 14	96.45 1

gg-mvs-nofinetune		67.43 288	64.53 316	76.13 172	85.95 60	47.79 327	64.38 451	88.28 62	39.34 453	66.62 177	41.27 493	58.69 16	89.00 182	49.64 349	86.62 32	91.59 68

BridgeMVS		80.28 16	79.73 15	81.90 12	86.47 55	59.34 7	80.45 331	89.51 28	69.76 52	71.05 125	86.66 210	58.68 17	93.24 37	84.64 20	90.40 6	93.14 19

UBG		78.86 26	78.86 24	78.86 64	87.80 43	55.43 59	87.67 70	91.21 12	72.83 11	72.10 101	88.40 153	58.53 18	89.08 177	73.21 130	77.98 124	92.08 45

myMVS_eth3d28		77.77 42	77.94 33	77.27 130	87.58 45	52.89 160	86.06 125	91.33 11	74.15 7	68.16 165	88.24 163	58.17 19	88.31 221	69.88 156	77.87 125	90.61 119

MED-MVS		79.56 21	79.39 19	80.06 43	84.34 94	54.93 86	87.61 72	87.22 84	56.22 331	81.85 18	92.98 41	58.11 20	93.75 32	80.19 52	85.96 38	91.52 73

testing11		79.18 24	78.85 25	80.16 37	88.33 32	56.99 28	88.31 58	92.06 1	72.82 12	70.62 140	88.37 155	57.69 21	92.30 59	75.25 100	76.24 154	91.20 92

MVSMamba_PlusPlus		75.28 105	73.39 128	80.96 23	80.85 207	58.25 12	74.47 392	87.61 79	50.53 389	65.24 198	83.41 268	57.38 22	92.83 43	73.92 117	87.13 22	91.80 61

testing99		78.45 28	77.78 37	80.45 30	88.28 35	56.81 34	87.95 65	91.49 6	71.72 19	70.84 133	88.09 172	57.29 23	92.63 52	69.24 162	75.13 178	91.91 54

CostFormer		73.89 139	72.30 151	78.66 73	82.36 153	56.58 36	75.56 380	85.30 140	66.06 117	70.50 142	76.88 366	57.02 24	89.06 178	68.27 172	68.74 262	90.33 129

test_0728_THIRD									58.00 284	81.91 16	93.64 20	56.54 25	96.44 2	81.64 43	86.86 27	92.23 40

DPE-MVS		79.82 19	79.66 17	80.29 33	89.27 25	55.08 78	88.70 52	87.92 70	55.55 340	81.21 24	93.69 19	56.51 26	94.27 26	78.36 70	85.70 43	91.51 75
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
ETVMVS		75.80 95	75.44 85	76.89 145	86.23 59	50.38 235	85.55 154	91.42 7	71.30 28	68.80 159	87.94 180	56.42 27	89.24 171	56.54 290	74.75 187	91.07 98

DeepPCF-MVS	69.37 1	80.65 13	81.56 11	77.94 109	85.46 72	49.56 257	90.99 21	86.66 100	70.58 37	80.07 33	95.30 2	56.18 28	90.97 103	82.57 36	86.22 37	93.28 14

test_241102_TWO								88.76 46	57.50 298	83.60 7	94.09 8	56.14 29	96.37 7	82.28 37	87.43 21	92.55 32

testing91		78.30 35	77.54 40	80.61 25	88.16 38	57.12 27	87.94 66	91.07 16	71.43 24	70.75 135	88.04 177	55.82 30	92.65 49	69.61 157	75.00 183	92.05 48

patch_mono-2		80.84 12	81.59 10	78.62 78	90.34 10	53.77 129	88.08 60	88.36 61	76.17 2	79.40 40	91.09 82	55.43 31	90.09 135	85.01 16	80.40 91	91.99 53

testing222		77.70 44	77.22 47	79.14 55	86.95 49	54.89 95	87.18 90	91.96 2	72.29 14	71.17 123	88.70 143	55.19 32	91.24 87	65.18 200	76.32 152	91.29 86

DVP-MVS		81.30 10	81.00 13	82.20 9	89.40 21	57.45 21	92.34 5	89.99 23	57.71 292	81.91 16	93.64 20	55.17 33	96.44 2	81.68 41	87.13 22	92.72 29
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
test0726					89.40 21	57.45 21	92.32 7	88.63 50	57.71 292	83.14 10	93.96 11	55.17 33

TSAR-MVS + MP.		78.31 34	78.26 28	78.48 90	81.33 192	56.31 45	81.59 306	86.41 106	69.61 54	81.72 20	88.16 168	55.09 35	88.04 231	74.12 114	86.31 35	91.09 96
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
BP-MVS1		76.09 82	75.55 81	77.71 115	79.49 247	52.27 179	84.70 195	90.49 20	64.44 143	69.86 149	90.31 109	55.05 36	91.35 82	70.07 154	75.58 171	89.53 161

baseline1		72.51 168	72.12 158	73.69 263	85.05 79	44.46 385	83.51 237	86.13 114	71.61 22	64.64 211	87.97 179	55.00 37	89.48 162	59.07 256	56.05 389	87.13 238

test_one_0601					89.39 23	57.29 24		88.09 67	57.21 306	82.06 15	93.39 27	54.94 38

MM		82.69 2	83.29 3	80.89 24	84.38 93	55.40 63	92.16 10	89.85 25	75.28 4	82.41 12	93.86 14	54.30 39	93.98 27	90.29 1	87.13 22	93.30 13

TSAR-MVS + GP.		77.82 41	77.59 39	78.49 89	85.25 77	50.27 242	90.02 26	90.57 19	56.58 323	74.26 71	91.60 77	54.26 40	92.16 64	75.87 92	79.91 99	93.05 21

EPP-MVSNet		71.14 200	70.07 203	74.33 240	79.18 258	46.52 353	83.81 228	86.49 104	56.32 329	57.95 321	84.90 242	54.23 41	89.14 176	58.14 270	69.65 252	87.33 230

MCST-MVS		83.01 1	83.30 2	82.15 11	92.84 2	57.58 18	93.77 1	91.10 13	75.95 3	77.10 52	93.09 36	54.15 42	95.57 13	85.80 13	85.87 41	93.31 12

alignmvs		78.08 38	77.98 32	78.39 96	83.53 113	53.22 147	89.77 32	85.45 132	66.11 114	76.59 56	91.99 65	54.07 43	89.05 179	77.34 80	77.00 137	92.89 24

test-260524					88.20 37	55.35 65		88.22 64		80.74 28		53.67 44	94.67 21	80.11 55	85.96 38

GDP-MVS		75.27 106	74.38 111	77.95 108	79.04 262	52.86 162	85.22 167	86.19 112	62.43 198	70.66 138	90.40 107	53.51 45	91.60 76	69.25 161	72.68 213	89.39 168

WTY-MVS		77.47 48	77.52 41	77.30 128	88.33 32	46.25 362	88.46 56	90.32 21	71.40 25	72.32 98	91.72 72	53.44 46	92.37 58	66.28 185	75.42 172	93.28 14

FBQ-MVS		78.34 33	77.25 45	81.62 16	86.35 57	59.48 6	86.95 97	90.95 17	72.89 10	71.91 106	87.60 194	53.35 47	92.65 49	70.19 152	75.03 182	92.72 29

IB-MVS	68.87 2	74.01 134	72.03 162	79.94 44	83.04 130	55.50 57	90.24 25	88.65 48	67.14 90	61.38 265	81.74 305	53.21 48	94.28 24	60.45 247	62.41 328	90.03 146
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
HPM-MVS++		80.50 14	80.71 14	79.88 45	87.34 47	55.20 73	89.93 29	87.55 80	66.04 119	79.46 38	93.00 40	53.10 49	91.76 72	80.40 51	89.56 9	92.68 31

miper_enhance_ethall		69.77 234	68.90 224	72.38 304	78.93 266	49.91 248	83.29 248	78.85 319	64.90 139	59.37 290	79.46 331	52.77 50	85.16 341	63.78 212	58.72 357	82.08 347

MVSTER		73.25 152	72.33 149	76.01 176	85.54 70	53.76 130	83.52 233	87.16 87	67.06 94	63.88 228	81.66 306	52.77 50	90.44 122	64.66 205	64.69 301	83.84 314

CNVR-MVS		81.76 9	81.90 8	81.33 20	90.04 11	57.70 16	91.71 11	88.87 41	70.31 39	77.64 51	93.87 13	52.58 52	93.91 30	84.17 22	87.92 17	92.39 35

FIs		70.00 229	70.24 200	69.30 363	77.93 291	38.55 440	83.99 221	87.72 76	66.86 99	57.66 328	84.17 252	52.28 53	85.31 336	52.72 329	68.80 261	84.02 304

tpm2		70.82 209	68.44 230	77.98 105	80.78 209	56.11 48	74.21 395	81.28 259	60.24 238	68.04 167	75.27 384	52.26 54	88.50 210	55.82 300	68.03 267	89.33 170

thisisatest0515		73.64 146	72.20 154	77.97 106	81.63 178	53.01 156	86.69 110	88.81 44	62.53 194	64.06 223	85.65 225	52.15 55	92.50 54	58.43 263	69.84 249	88.39 205

aaEdge-Enhanced		79.48 22	79.20 22	80.35 32	88.96 27	54.93 86	88.65 53	88.50 58	56.62 320	79.87 35	92.88 44	51.96 56	94.36 23	80.19 52	85.13 50	91.76 62

casdiffmvs_mvg		77.75 43	77.28 44	79.16 54	80.42 226	54.44 115	87.76 67	85.46 131	71.67 21	71.38 118	88.35 158	51.58 57	91.22 88	79.02 62	79.89 101	91.83 59
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
UniMVSNet_NR-MVSNet		68.82 256	68.29 233	70.40 349	75.71 342	42.59 411	84.23 212	86.78 96	66.31 108	58.51 312	82.45 288	51.57 58	84.64 350	53.11 320	55.96 390	83.96 310

PAPM		76.76 64	76.07 71	78.81 65	80.20 230	59.11 8	86.86 103	86.23 110	68.60 65	70.18 147	88.84 141	51.57 58	87.16 275	65.48 193	86.68 31	90.15 138

tttt0517		68.33 268	66.29 280	74.46 233	78.08 286	49.06 271	80.88 324	89.08 35	54.40 358	54.75 366	80.77 315	51.31 60	90.33 126	49.35 351	58.01 369	83.99 306

mvs_anonymous		72.29 176	70.74 182	76.94 144	82.85 140	54.72 104	78.43 362	81.54 253	63.77 162	61.69 262	79.32 333	51.11 61	85.31 336	62.15 229	75.79 162	90.79 113

HY-MVS	67.03 5	73.90 138	73.14 134	76.18 171	84.70 85	47.36 337	75.56 380	86.36 108	66.27 109	70.66 138	83.91 257	51.05 62	89.31 168	67.10 179	72.61 214	91.88 56

thisisatest0530		70.47 220	68.56 226	76.20 169	79.78 241	51.52 203	83.49 239	88.58 56	57.62 295	58.60 311	82.79 276	51.03 63	91.48 79	52.84 324	62.36 330	85.59 278

sasdasda		78.17 36	77.86 35	79.12 57	84.30 97	54.22 119	87.71 68	84.57 183	67.70 82	77.70 49	92.11 61	50.90 64	89.95 139	78.18 73	77.54 129	93.20 16

miper_ehance_all_eth		68.70 262	67.58 251	72.08 312	76.91 318	49.48 263	82.47 278	78.45 334	62.68 192	58.28 320	77.88 347	50.90 64	85.01 344	61.91 230	58.72 357	81.75 352

canonicalmvs		78.17 36	77.86 35	79.12 57	84.30 97	54.22 119	87.71 68	84.57 183	67.70 82	77.70 49	92.11 61	50.90 64	89.95 139	78.18 73	77.54 129	93.20 16

casdiffmvs		77.36 50	76.85 54	78.88 63	80.40 227	54.66 109	87.06 93	85.88 118	72.11 17	71.57 111	88.63 148	50.89 67	90.35 125	76.00 90	79.11 110	91.63 67
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
MGCNet		82.10 7	82.64 4	80.47 29	86.63 53	54.69 106	92.20 9	86.66 100	74.48 5	82.63 11	93.80 16	50.83 68	93.70 34	90.11 2	86.44 34	93.01 22

fmvsm_s_conf0.5_n_9		76.66 67	76.94 53	75.85 181	79.54 246	48.30 303	82.63 270	71.84 418	70.25 41	80.63 30	94.53 3	50.78 69	87.42 265	88.32 5	73.92 195	91.82 60

baseline		76.86 60	76.24 67	78.71 69	80.47 221	54.20 123	83.90 225	84.88 167	71.38 26	71.51 114	89.15 136	50.51 70	90.55 118	75.71 93	78.65 115	91.39 78

TestfortrainingZip a		77.64 45	76.79 58	80.20 35	84.34 94	54.79 99	87.61 72	87.03 89	56.22 331	78.78 41	92.98 41	50.45 71	94.28 24	74.37 109	79.31 108	91.52 73

MVS_Test		75.85 91	74.93 99	78.62 78	84.08 102	55.20 73	83.99 221	85.17 147	68.07 74	73.38 80	82.76 277	50.44 72	89.00 182	65.90 189	80.61 87	91.64 66

FC-MVSNet-test		67.49 286	67.91 239	66.21 397	76.06 333	33.06 462	80.82 325	87.18 86	64.44 143	54.81 364	82.87 274	50.40 73	82.60 371	48.05 362	66.55 280	82.98 338

nrg030		72.27 178	71.56 166	74.42 235	75.93 339	50.60 225	86.97 95	83.21 218	62.75 189	67.15 173	84.38 248	50.07 74	86.66 295	71.19 145	62.37 329	85.99 267

fmvsm_l_conf0.5_n		75.95 87	76.16 69	75.31 206	76.01 337	48.44 296	84.98 183	71.08 428	63.50 171	81.70 21	93.52 23	50.00 75	87.18 274	87.80 6	76.87 141	90.32 130

cl22		68.85 254	67.69 249	72.35 305	78.07 287	49.98 247	82.45 279	78.48 333	62.50 196	58.46 316	77.95 345	49.99 76	85.17 340	62.55 223	58.72 357	81.90 350

fmvsm_l_conf0.5_n_a		75.88 90	76.07 71	75.31 206	76.08 332	48.34 299	85.24 166	70.62 431	63.13 179	81.45 22	93.62 22	49.98 77	87.40 267	87.76 7	76.77 143	90.20 135

tpmrst		71.04 205	69.77 207	74.86 224	83.19 124	55.86 54	75.64 377	78.73 326	67.88 77	64.99 204	73.73 396	49.96 78	79.56 407	65.92 188	67.85 270	89.14 177

CANet		80.90 11	81.17 12	80.09 42	87.62 44	54.21 121	91.60 14	86.47 105	73.13 9	79.89 34	93.10 34	49.88 79	92.98 40	84.09 24	84.75 55	93.08 20

nomal-1		72.45 169	71.14 176	76.37 161	84.65 86	56.28 46	68.39 437	88.28 62	67.21 88	62.98 244	80.23 321	49.71 80	86.05 318	69.36 160	69.48 255	86.78 252

ET-MVSNet_ETH3D		75.23 109	74.08 116	78.67 72	84.52 90	55.59 55	88.92 49	89.21 33	68.06 75	53.13 381	90.22 112	49.71 80	87.62 257	72.12 141	70.82 237	92.82 26

hybridcas		76.66 67	75.99 74	78.65 75	79.25 255	54.46 114	86.82 105	85.53 128	70.88 34	70.40 145	88.21 165	49.55 82	90.12 134	74.42 108	78.88 114	91.37 80

c3_l		67.97 274	66.66 273	71.91 323	76.20 331	49.31 268	82.13 286	78.00 342	61.99 204	57.64 329	76.94 363	49.41 83	84.93 345	60.62 242	57.01 380	81.49 357

Vis-MVSNet (Re-imp)		65.52 325	65.63 297	65.17 407	77.49 302	30.54 472	75.49 383	77.73 349	59.34 255	52.26 388	86.69 209	49.38 84	80.53 394	37.07 413	75.28 174	84.42 295

EPNet		78.36 32	78.49 27	77.97 106	85.49 71	52.04 182	89.36 41	84.07 198	73.22 8	77.03 53	91.72 72	49.32 85	90.17 133	73.46 125	82.77 67	91.69 64
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
fmvsm_l_conf0.5_n_9		77.10 53	77.48 42	75.98 178	77.54 301	47.77 328	86.35 116	73.46 409	68.69 64	81.07 25	94.40 5	49.06 86	88.89 191	87.39 8	79.32 107	91.27 89

testing3		59.97 371	60.19 361	59.32 442	77.60 295	30.01 478	81.75 298	81.79 247	53.54 364	50.34 407	79.94 323	48.99 87	76.91 431	17.19 493	50.59 421	71.03 465

Casviewmamba		76.27 77	75.48 83	78.63 77	79.14 259	54.27 118	85.81 135	83.09 221	70.96 31	70.41 144	88.36 157	48.71 88	90.81 107	75.92 91	76.95 138	90.80 112

E3new		76.85 61	76.24 67	78.66 73	81.62 179	55.01 81	86.94 98	85.10 156	71.55 23	71.93 105	88.61 149	48.40 89	89.60 156	74.50 106	77.53 131	91.36 81

tpm		68.36 266	67.48 256	70.97 340	79.93 235	51.34 207	76.58 374	78.75 325	67.73 80	63.54 240	74.86 386	48.33 90	72.36 459	53.93 314	63.71 309	89.21 174

APDe-MVS		78.44 29	78.20 29	79.19 52	88.56 28	54.55 112	89.76 33	87.77 74	55.91 335	78.56 44	92.49 53	48.20 91	92.65 49	79.49 57	83.04 66	90.39 126
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
fmvsm_s_conf0.5_n_11		76.28 76	76.81 56	74.71 228	79.21 256	46.90 343	85.03 180	73.96 398	69.00 62	79.70 37	93.88 12	48.07 92	87.71 251	84.26 21	78.15 122	89.50 164

MG-MVS		78.42 30	76.99 52	82.73 3	93.17 1	64.46 1	89.93 29	88.51 57	64.83 140	73.52 78	88.09 172	48.07 92	92.19 63	62.24 227	84.53 57	91.53 72

DeepC-MVS	67.15 4	76.90 59	76.27 66	78.80 66	80.70 211	55.02 80	86.39 114	86.71 98	66.96 98	67.91 168	89.97 120	48.03 94	91.41 81	75.60 95	84.14 59	89.96 148
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
viewcassd2359sk11		76.66 67	76.01 73	78.62 78	81.14 195	54.95 84	86.88 102	85.04 158	71.37 27	71.76 108	88.44 152	48.02 95	89.57 159	74.17 113	77.23 133	91.33 85

MGCFI-Net		74.07 133	74.64 109	72.34 306	82.90 137	43.33 403	80.04 340	79.96 288	65.61 122	74.93 63	91.85 68	48.01 96	80.86 386	71.41 144	77.10 134	92.84 25

test_prior2							89.04 48		61.88 207	73.55 77	91.46 81	48.01 96		74.73 103	85.46 45

myMVS_eth3d		63.52 344	63.56 325	63.40 420	81.73 170	34.28 454	80.97 321	81.02 262	60.93 227	55.06 360	82.64 283	48.00 98	80.81 387	23.42 478	58.32 361	75.10 436

balanced_ft_v1		75.25 107	73.90 121	79.29 50	85.59 68	56.72 35	74.35 394	87.27 83	60.24 238	59.07 297	85.17 233	47.76 99	90.51 119	82.62 35	83.06 65	90.64 117

SF-MVS		77.64 45	77.42 43	78.32 99	83.75 110	52.47 171	86.63 112	87.80 71	58.78 272	74.63 66	92.38 55	47.75 100	91.35 82	78.18 73	86.85 28	91.15 95

test2506		72.91 158	72.43 147	74.32 241	80.12 232	44.18 392	83.19 252	84.77 172	64.02 153	65.97 186	87.43 197	47.67 101	88.72 197	59.08 255	79.66 103	90.08 144

E2		76.39 73	75.67 77	78.56 85	80.49 219	54.87 96	86.80 106	84.95 162	71.09 29	71.51 114	88.21 165	47.55 102	89.53 160	73.65 121	76.77 143	91.29 86

E3		76.39 73	75.67 77	78.56 85	80.49 219	54.87 96	86.80 106	84.95 162	71.09 29	71.51 114	88.21 165	47.55 102	89.53 160	73.65 121	76.77 143	91.29 86

fmvsm_s_conf0.5_n_3		74.97 115	75.42 86	73.62 266	76.99 315	46.67 348	83.13 255	71.14 427	66.20 111	82.13 14	93.76 17	47.49 104	84.00 356	81.95 40	76.02 157	90.19 137

1112_ss		70.05 227	69.37 213	72.10 311	80.77 210	42.78 409	85.12 176	76.75 366	59.69 247	61.19 267	92.12 59	47.48 105	83.84 358	53.04 322	68.21 265	89.66 155

fmvsm_s_conf0.5_n_10		76.80 62	76.81 56	76.78 152	78.91 267	47.85 323	83.44 240	74.66 389	68.93 63	81.31 23	94.12 7	47.44 106	90.82 106	83.43 28	79.06 112	91.66 65

Effi-MVS+		75.24 108	73.61 127	80.16 37	81.92 165	57.42 23	85.21 168	76.71 369	60.68 233	73.32 81	89.34 131	47.30 107	91.63 75	68.28 171	79.72 102	91.42 77

UniMVSNet (Re)		67.71 280	66.80 269	70.45 347	74.44 363	42.93 407	82.42 280	84.90 166	63.69 166	59.63 284	80.99 312	47.18 108	85.23 339	51.17 341	56.75 381	83.19 332

test12				79.24 51	86.89 50	56.08 49		85.16 149		72.27 99		47.15 109	91.10 93		85.93 40	90.54 123

PVSNet_Blended_VisFu		73.40 150	72.44 146	76.30 162	81.32 193	54.70 105	85.81 135	78.82 321	63.70 165	64.53 215	85.38 231	47.11 110	87.38 268	67.75 175	77.55 128	86.81 251

fmvsm_s_conf0.5_n_8		76.50 71	76.68 61	75.94 179	78.67 272	47.92 321	85.18 170	74.71 388	68.09 71	80.67 29	94.26 6	47.09 111	89.26 170	86.62 10	74.85 185	90.65 116

test_fmvsm_n_1920		75.56 102	75.54 82	75.61 189	74.60 362	49.51 262	81.82 295	74.08 395	66.52 104	80.40 31	93.46 25	46.95 112	89.72 148	86.69 9	75.30 173	87.61 224

NCCC		79.57 20	79.23 21	80.59 26	89.50 16	56.99 28	91.38 16	88.17 65	67.71 81	73.81 75	92.75 47	46.88 113	93.28 36	78.79 66	84.07 60	91.50 76

viewmanbaseed2359cas		76.71 66	76.16 69	78.37 98	81.16 194	55.05 79	86.96 96	85.32 138	71.71 20	72.25 100	88.50 151	46.86 114	88.96 186	74.55 105	78.08 123	91.08 97

E4		75.99 85	75.16 92	78.48 90	79.56 245	54.74 101	86.66 111	84.80 170	70.62 35	71.16 124	87.90 181	46.84 115	89.47 164	72.70 132	76.20 156	91.23 90

PRO-TEST		70.63 215	70.25 199	71.76 326	78.23 285	38.48 441	66.45 444	84.09 196	65.04 138	46.57 432	82.73 280	46.83 116	89.59 158	79.18 60	83.17 64	87.21 236

fmvsm_s_conf0.5_n_6		76.17 80	76.84 55	74.15 246	77.42 304	46.46 354	85.53 156	77.86 346	69.78 51	79.78 36	92.90 43	46.80 117	84.81 347	84.67 19	76.86 142	91.17 94

9.14			78.19 30		85.67 66		88.32 57	88.84 43	59.89 242	74.58 68	92.62 50	46.80 117	92.66 48	81.40 48	85.62 44

VNet		77.99 40	77.92 34	78.19 102	87.43 46	50.12 243	90.93 22	91.41 8	67.48 85	75.12 61	90.15 116	46.77 119	91.00 98	73.52 123	78.46 118	93.44 10

PVSNet_BlendedMVS		73.42 149	73.30 130	73.76 260	85.91 61	51.83 191	86.18 121	84.24 192	65.40 128	69.09 156	80.86 314	46.70 120	88.13 227	75.43 96	65.92 291	81.33 365

PVSNet_Blended		76.53 70	76.54 62	76.50 158	85.91 61	51.83 191	88.89 50	84.24 192	67.82 79	69.09 156	89.33 133	46.70 120	88.13 227	75.43 96	81.48 80	89.55 159

0.3-1-1-0.015		72.75 162	71.06 178	77.81 111	80.58 216	50.62 223	89.45 37	88.60 54	63.74 164	65.56 194	81.82 303	46.61 122	90.64 115	62.86 221	60.35 341	92.17 43

SMA-MVS		79.10 25	78.76 26	80.12 40	84.42 91	55.87 53	87.58 79	86.76 97	61.48 215	80.26 32	93.10 34	46.53 123	92.41 56	79.97 56	88.77 11	92.08 45
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
NormalMVS		77.09 54	77.02 50	77.32 127	81.66 176	52.32 175	89.31 42	82.11 237	72.20 15	73.23 83	91.05 83	46.52 124	91.00 98	76.23 87	80.83 84	88.64 191

SymmetryMVS		77.43 49	77.09 49	78.44 94	82.56 149	52.32 175	89.31 42	84.15 195	72.20 15	73.23 83	91.05 83	46.52 124	91.00 98	76.23 87	78.55 117	92.00 52

viewdifsd2359ckpt07		74.81 119	74.01 119	77.21 134	79.62 243	53.13 152	85.70 149	83.75 204	68.12 70	68.14 166	87.33 200	46.51 126	87.92 234	73.32 126	73.63 199	90.57 120

test_fmvsmconf_n		74.41 125	74.05 117	75.49 198	74.16 370	48.38 297	82.66 268	72.57 413	67.05 95	75.11 62	92.88 44	46.35 127	87.81 241	83.93 25	71.71 225	90.28 131

fmvsm_l_conf0.5_n_3		75.73 100	75.78 75	75.61 189	76.03 335	48.33 301	85.34 160	72.92 412	67.16 89	78.55 45	93.85 15	46.22 128	87.53 261	85.61 14	76.30 153	90.98 105

tpm cat1		66.28 316	62.78 328	76.77 153	81.40 190	57.14 26	70.03 428	77.19 358	53.00 369	58.76 306	70.73 433	46.17 129	86.73 292	43.27 389	64.46 303	86.44 259

fmvsm_s_conf0.5_n_4		74.92 116	74.88 100	75.03 218	75.96 338	47.53 331	85.84 134	73.19 411	67.07 93	79.43 39	92.60 51	46.12 130	88.03 232	84.70 18	69.01 256	89.53 161

cl____		67.43 288	65.93 290	71.95 320	76.33 325	48.02 314	82.58 271	79.12 314	61.30 218	56.72 345	76.92 364	46.12 130	86.44 303	57.98 272	56.31 384	81.38 364

viewdifsd2359ckpt13		75.96 86	75.07 94	78.65 75	81.14 195	55.21 70	86.15 122	84.95 162	69.98 46	70.49 143	88.16 168	46.10 132	89.86 141	72.39 135	76.23 155	90.89 109

DIV-MVS_self_test		67.43 288	65.93 290	71.94 321	76.33 325	48.01 315	82.57 272	79.11 315	61.31 217	56.73 344	76.92 364	46.09 133	86.43 304	57.98 272	56.31 384	81.39 363

0.4-1-1-0.2		72.79 161	71.07 177	77.94 109	80.58 216	50.83 219	89.59 35	88.63 50	63.94 159	65.74 192	81.80 304	46.05 134	90.68 111	62.98 220	60.35 341	92.31 39

IS-MVSNet		68.80 258	67.55 253	72.54 297	78.50 279	43.43 400	81.03 319	79.35 310	59.12 265	57.27 338	86.71 208	46.05 134	87.70 252	44.32 385	75.60 170	86.49 258

diffmvs		75.11 112	74.65 108	76.46 159	78.52 278	53.35 142	83.28 249	79.94 289	70.51 38	71.64 110	88.72 142	46.02 136	86.08 317	77.52 78	75.75 168	89.96 148
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
viewdifsd2359ckpt09		74.92 116	73.70 125	78.60 82	80.28 228	54.94 85	84.77 193	80.56 275	69.96 48	69.38 151	88.38 154	46.01 137	90.50 120	72.44 134	71.49 229	90.38 127

0.4-1-1-0.1		72.39 170	70.70 183	77.46 123	80.45 222	50.04 245	89.09 47	88.45 59	63.06 180	64.91 207	81.60 308	45.98 138	90.46 121	62.40 224	60.34 343	91.88 56

E6new		75.74 96	74.80 104	78.56 85	79.85 236	54.92 91	85.87 130	84.72 175	70.19 42	70.90 129	87.73 188	45.98 138	89.71 149	72.16 137	75.78 165	91.06 99

E6		75.74 96	74.80 104	78.56 85	79.85 236	54.92 91	85.87 130	84.72 175	70.19 42	70.90 129	87.73 188	45.98 138	89.71 149	72.16 137	75.78 165	91.06 99

E5new		75.74 96	74.80 104	78.57 83	79.85 236	54.93 86	85.87 130	84.72 175	70.19 42	70.90 129	87.74 186	45.97 141	89.71 149	72.15 139	75.79 162	91.06 99

E5		75.74 96	74.80 104	78.57 83	79.85 236	54.93 86	85.87 130	84.72 175	70.19 42	70.90 129	87.74 186	45.97 141	89.71 149	72.15 139	75.79 162	91.06 99

blend_shiyan4		67.33 293	65.28 306	73.45 271	70.71 411	47.96 318	86.21 120	85.65 126	56.45 327	52.18 389	72.99 406	45.89 143	88.50 210	56.81 287	60.68 339	83.90 312

EI-MVSNet		69.70 239	68.70 225	72.68 293	75.00 356	48.90 279	79.54 349	87.16 87	61.05 223	63.88 228	83.74 260	45.87 144	90.44 122	57.42 283	64.68 302	78.70 393

IterMVS-LS		66.63 309	65.36 305	70.42 348	75.10 354	48.90 279	81.45 314	76.69 370	61.05 223	55.71 355	77.10 360	45.86 145	83.65 362	57.44 282	57.88 373	78.70 393
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
diffmvs_AUTHOR		74.80 120	74.30 113	76.29 163	77.34 305	53.19 148	83.17 254	79.50 303	69.93 49	71.55 112	88.57 150	45.85 146	86.03 320	77.17 82	75.64 169	89.67 154

EIA-MVS		75.92 88	75.18 91	78.13 103	85.14 78	51.60 200	87.17 91	85.32 138	64.69 141	68.56 161	90.53 101	45.79 147	91.58 77	67.21 178	82.18 73	91.20 92

MVS		76.91 57	75.48 83	81.23 21	84.56 89	55.21 70	80.23 337	91.64 4	58.65 274	65.37 196	91.48 80	45.72 148	95.05 17	72.11 142	89.52 10	93.44 10

PAPM_NR		71.80 188	69.98 205	77.26 132	81.54 185	53.34 143	78.60 361	85.25 144	53.46 365	60.53 275	88.66 144	45.69 149	89.24 171	56.49 291	79.62 105	89.19 175

UWE-MVS-28		67.43 288	67.98 238	65.75 400	75.66 343	34.74 452	80.00 343	88.17 65	64.21 149	57.27 338	84.14 253	45.68 150	78.82 410	44.33 383	72.40 217	83.70 320

hybridnocas07		74.65 121	74.00 120	76.61 156	77.58 297	52.72 165	83.64 231	79.72 295	69.43 56	70.80 134	88.33 160	45.56 151	87.34 269	76.88 84	74.07 191	89.78 152

viewmambaseed2359dif		73.51 148	72.78 140	75.71 186	76.93 317	51.89 189	82.81 265	79.66 298	65.46 124	70.29 146	88.05 175	45.55 152	85.85 328	73.49 124	72.76 212	89.39 168

CS-MVS		76.77 63	76.70 60	76.99 141	83.55 112	48.75 284	88.60 54	85.18 146	66.38 107	72.47 96	91.62 76	45.53 153	90.99 102	74.48 107	82.51 69	91.23 90

DeepC-MVS_fast	67.50 3	78.00 39	77.63 38	79.13 56	88.52 29	55.12 75	89.95 28	85.98 116	68.31 66	71.33 119	92.75 47	45.52 154	90.37 124	71.15 146	85.14 49	91.91 54
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
fmvsm_s_conf0.5_n		74.48 122	74.12 115	75.56 192	76.96 316	47.85 323	85.32 164	69.80 438	64.16 151	78.74 42	93.48 24	45.51 155	89.29 169	86.48 11	66.62 278	89.55 159

viewmacassd2359aftdt		75.91 89	75.14 93	78.21 101	79.40 249	54.82 98	86.71 109	84.98 160	70.89 33	71.52 113	87.89 182	45.43 156	88.85 195	72.35 136	77.08 135	90.97 106

fmvsm_s_conf0.5_n_5		75.02 113	75.07 94	74.88 223	74.33 367	47.83 325	83.99 221	73.54 404	67.10 91	76.32 57	92.43 54	45.42 157	86.35 307	82.98 31	79.50 106	90.47 125

fmvsm_s_conf0.5_n_a		73.68 145	73.15 132	75.29 209	75.45 346	48.05 313	83.88 226	68.84 443	63.43 173	78.60 43	93.37 29	45.32 158	88.92 190	85.39 15	64.04 305	88.89 183

Test_1112_low_res		67.18 297	66.23 282	70.02 357	78.75 270	41.02 428	83.43 241	73.69 401	57.29 302	58.45 317	82.39 290	45.30 159	80.88 385	50.50 343	66.26 288	88.16 208

ETV-MVS		77.17 52	76.74 59	78.48 90	81.80 168	54.55 112	86.13 123	85.33 137	68.20 69	73.10 85	90.52 102	45.23 160	90.66 113	79.37 58	80.95 81	90.22 133

SPE-MVS-test		77.20 51	77.25 45	77.05 136	84.60 88	49.04 274	89.42 38	85.83 120	65.90 120	72.85 89	91.98 67	45.10 161	91.27 85	75.02 102	84.56 56	90.84 110

NR-MVSNet		67.25 295	65.99 288	71.04 339	73.27 379	43.91 394	85.32 164	84.75 173	66.05 118	53.65 379	82.11 298	45.05 162	85.97 325	47.55 364	56.18 387	83.24 330

UWE-MVS		72.17 179	72.15 156	72.21 308	82.26 154	44.29 389	86.83 104	89.58 27	65.58 123	65.82 189	85.06 236	45.02 163	84.35 352	54.07 312	75.18 175	87.99 215

train_agg		76.91 57	76.40 64	78.45 93	85.68 64	55.42 60	87.59 77	84.00 199	57.84 289	72.99 86	90.98 87	44.99 164	88.58 203	78.19 71	85.32 47	91.34 84

test_8					85.72 63	55.31 66	87.60 76	83.88 202	57.84 289	72.84 90	90.99 86	44.99 164	88.34 218

segment_acmp												44.97 166

hybrid		74.44 124	73.79 124	76.39 160	77.31 307	52.89 160	83.37 247	79.79 293	68.21 68	71.01 126	88.14 170	44.93 167	86.68 293	77.29 81	74.11 190	89.59 157

test_fmvsmconf0.1_n		73.69 144	73.15 132	75.34 204	70.71 411	48.26 304	82.15 284	71.83 419	66.75 100	74.47 70	92.59 52	44.89 168	87.78 248	83.59 27	71.35 232	89.97 147

TEST9					85.68 64	55.42 60	87.59 77	84.00 199	57.72 291	72.99 86	90.98 87	44.87 169	88.58 203

eth_miper_zixun_eth		66.98 304	65.28 306	72.06 313	75.61 344	50.40 232	81.00 320	76.97 365	62.00 203	56.99 342	76.97 362	44.84 170	85.58 331	58.75 260	54.42 403	80.21 381

MVSFormer		73.53 147	72.19 155	77.57 118	83.02 131	55.24 68	81.63 303	81.44 255	50.28 390	76.67 54	90.91 93	44.82 171	86.11 312	60.83 239	80.09 95	91.36 81

lupinMVS		78.38 31	78.11 31	79.19 52	83.02 131	55.24 68	91.57 15	84.82 168	69.12 60	76.67 54	92.02 63	44.82 171	90.23 131	80.83 50	80.09 95	92.08 45

WR-MVS		67.58 283	66.76 270	70.04 356	75.92 340	45.06 382	86.23 119	85.28 142	64.31 146	58.50 314	81.00 311	44.80 173	82.00 378	49.21 353	55.57 395	83.06 335

fmvsm_s_conf0.1_n		73.80 140	73.26 131	75.43 199	73.28 378	47.80 326	84.57 203	69.43 440	63.34 174	78.40 46	93.29 31	44.73 174	89.22 173	85.99 12	66.28 287	89.26 171

viewmamba		73.92 137	73.03 138	76.58 157	77.56 299	52.73 164	82.91 263	78.77 323	69.23 59	68.85 158	88.01 178	44.71 175	87.57 259	73.86 118	73.40 202	89.44 167

ZD-MVS					89.55 15	53.46 135		84.38 186	57.02 308	73.97 73	91.03 85	44.57 176	91.17 90	75.41 99	81.78 78

onestephybrid01		74.31 128	73.65 126	76.27 164	77.58 297	51.99 184	82.22 283	78.44 335	69.26 58	70.95 128	88.11 171	44.46 177	87.30 270	78.01 76	73.86 197	89.51 163

Fast-Effi-MVS+		72.73 163	71.15 175	77.48 121	82.75 143	54.76 100	86.77 108	80.64 271	63.05 181	65.93 187	84.01 254	44.42 178	89.03 180	56.45 294	76.36 151	88.64 191

dtuplus		73.09 155	72.29 152	75.52 197	76.27 329	51.82 193	82.99 261	79.98 286	65.08 137	70.11 148	87.66 192	44.38 179	85.64 330	71.56 143	72.55 215	89.11 178

fmvsm_s_conf0.1_n_a		72.82 160	72.05 160	75.12 215	70.95 409	47.97 316	82.72 267	68.43 445	62.52 195	78.17 47	93.08 37	44.21 180	88.86 192	84.82 17	63.54 312	88.54 199

PCF-MVS	61.03 10	70.10 225	68.40 231	75.22 214	77.15 313	51.99 184	79.30 354	82.12 236	56.47 326	61.88 261	86.48 214	43.98 181	87.24 273	55.37 305	72.79 211	86.43 260
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
CDS-MVSNet		70.48 219	69.43 211	73.64 264	77.56 299	48.83 281	83.51 237	77.45 354	63.27 176	62.33 252	85.54 228	43.85 182	83.29 368	57.38 284	74.00 192	88.79 187
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
EI-MVSNet-Vis-set		73.19 153	72.60 143	74.99 221	82.56 149	49.80 252	82.55 274	89.00 36	66.17 112	65.89 188	88.98 137	43.83 183	92.29 60	65.38 199	69.01 256	82.87 340

APD-MVS		76.15 81	75.68 76	77.54 120	88.52 29	53.44 138	87.26 89	85.03 159	53.79 362	74.91 64	91.68 74	43.80 184	90.31 127	74.36 110	81.82 76	88.87 184
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
MVS_111021_HR		76.39 73	75.38 88	79.42 48	85.33 75	56.47 41	88.15 59	84.97 161	65.15 136	66.06 185	89.88 121	43.79 185	92.16 64	75.03 101	80.03 98	89.64 156

thres100view900		66.87 306	65.42 304	71.24 334	83.29 121	43.15 405	81.67 302	87.78 72	59.04 266	55.92 354	82.18 297	43.73 186	87.80 244	28.80 454	66.36 284	82.78 342

thres600view7		66.46 313	65.12 310	70.47 346	83.41 115	43.80 396	82.15 284	87.78 72	59.37 254	56.02 353	82.21 296	43.73 186	86.90 284	26.51 466	64.94 296	80.71 375

v148		68.24 271	66.35 278	73.88 255	71.76 397	51.47 204	84.23 212	81.90 246	63.69 166	58.94 299	76.44 371	43.72 188	87.78 248	60.63 241	55.86 392	82.39 345

SD-MVS		76.18 79	74.85 101	80.18 36	85.39 73	56.90 30	85.75 140	82.45 233	56.79 316	74.48 69	91.81 69	43.72 188	90.75 109	74.61 104	78.65 115	92.91 23
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
XXY-MVS		70.18 221	69.28 217	72.89 285	77.64 293	42.88 408	85.06 177	87.50 81	62.58 193	62.66 250	82.34 295	43.64 190	89.83 144	58.42 265	63.70 310	85.96 269

tfpn200view9		67.57 284	66.13 284	71.89 324	84.05 103	45.07 379	83.40 243	87.71 77	60.79 230	57.79 325	82.76 277	43.53 191	87.80 244	28.80 454	66.36 284	82.78 342

thres400		67.40 292	66.13 284	71.19 336	84.05 103	45.07 379	83.40 243	87.71 77	60.79 230	57.79 325	82.76 277	43.53 191	87.80 244	28.80 454	66.36 284	80.71 375

PAPR		75.20 110	74.13 114	78.41 95	88.31 34	55.10 77	84.31 210	85.66 124	63.76 163	67.55 170	90.73 98	43.48 193	89.40 165	66.36 184	77.03 136	90.73 114

kuosan		50.20 436	50.09 421	50.52 462	73.09 381	29.09 485	65.25 446	74.89 386	48.27 405	41.34 455	60.85 470	43.45 194	67.48 470	18.59 491	25.07 494	55.01 488

fmvsm_s_conf0.5_n_7		73.10 154	73.89 123	70.72 343	74.17 369	46.03 367	83.28 249	74.19 393	67.10 91	73.94 74	91.73 71	43.42 195	77.61 426	83.92 26	73.26 204	88.53 200

MP-MVS		74.99 114	74.33 112	76.95 143	82.89 138	53.05 155	85.63 150	83.50 212	57.86 288	67.25 172	90.24 110	43.38 196	88.85 195	76.03 89	82.23 72	88.96 181
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
casdiffseed414692147		74.22 129	72.73 141	78.69 70	79.85 236	54.64 110	85.13 172	83.67 210	69.07 61	69.41 150	86.47 215	43.27 197	90.69 110	63.77 213	73.91 196	90.73 114

EI-MVSNet-UG-set		72.37 172	71.73 163	74.29 242	81.60 181	49.29 269	81.85 293	88.64 49	65.29 133	65.05 201	88.29 162	43.18 198	91.83 71	63.74 214	67.97 268	81.75 352

thres200		68.71 260	67.27 261	73.02 279	84.73 84	46.76 347	85.03 180	87.73 75	62.34 199	59.87 279	83.45 267	43.15 199	88.32 220	31.25 446	67.91 269	83.98 308

PHI-MVS		77.49 47	77.00 51	78.95 60	85.33 75	50.69 222	88.57 55	88.59 55	58.14 281	73.60 76	93.31 30	43.14 200	93.79 31	73.81 119	88.53 13	92.37 36

ab-mvs		70.65 214	69.11 220	75.29 209	80.87 206	46.23 365	73.48 401	85.24 145	59.99 241	66.65 176	80.94 313	43.13 201	88.69 198	63.58 215	68.07 266	90.95 107

CDPH-MVS		76.05 84	75.19 90	78.62 78	86.51 54	54.98 83	87.32 84	84.59 182	58.62 275	70.75 135	90.85 95	43.10 202	90.63 116	70.50 150	84.51 58	90.24 132

reproduce_monomvs		69.71 235	68.52 228	73.29 276	86.43 56	48.21 306	83.91 224	86.17 113	68.02 76	54.91 362	77.46 353	42.96 203	88.86 192	68.44 169	48.38 432	82.80 341

v8		67.25 295	64.99 312	74.04 249	72.89 385	53.31 145	82.37 281	80.11 284	61.54 213	54.29 372	76.02 380	42.89 204	88.41 214	58.43 263	56.36 382	80.39 379

EC-MVSNet		75.30 104	75.20 89	75.62 188	80.98 200	49.00 275	87.43 80	84.68 180	63.49 172	70.97 127	90.15 116	42.86 205	91.14 92	74.33 111	81.90 75	86.71 253

h-mvs33		73.95 135	72.89 139	77.15 135	80.17 231	50.37 236	84.68 197	83.33 213	68.08 72	71.97 103	88.65 147	42.50 206	91.15 91	78.82 64	57.78 375	89.91 150

hse-mvs2		71.44 196	70.68 184	73.73 262	76.34 324	47.44 336	79.45 352	79.47 305	68.08 72	71.97 103	86.01 223	42.50 206	86.93 283	78.82 64	53.46 413	86.83 249

SteuartSystems-ACMMP		77.08 55	76.33 65	79.34 49	80.98 200	55.31 66	89.76 33	86.91 92	62.94 183	71.65 109	91.56 78	42.33 208	92.56 53	77.14 83	83.69 62	90.15 138
Skip Steuart: Steuart Systems R&D Blog.
HyFIR lowres test		69.94 232	67.58 251	77.04 137	77.11 314	57.29 24	81.49 313	79.11 315	58.27 279	58.86 303	80.41 317	42.33 208	86.96 281	61.91 230	68.68 263	86.87 243

ZNCC-MVS		75.82 94	75.02 97	78.23 100	83.88 108	53.80 128	86.91 101	86.05 115	59.71 246	67.85 169	90.55 100	42.23 210	91.02 96	72.66 133	85.29 48	89.87 151

FMVSNet3		68.84 255	67.40 257	73.19 278	85.05 79	48.53 291	85.71 146	85.36 135	60.90 229	57.58 330	79.15 336	42.16 211	86.77 290	47.25 367	63.40 313	84.27 299

VPA-MVSNet		71.12 201	70.66 185	72.49 299	78.75 270	44.43 387	87.64 71	90.02 22	63.97 157	65.02 202	81.58 309	42.14 212	87.42 265	63.42 216	63.38 316	85.63 277

jason		77.01 56	76.45 63	78.69 70	79.69 242	54.74 101	90.56 24	83.99 201	68.26 67	74.10 72	90.91 93	42.14 212	89.99 137	79.30 59	79.12 109	91.36 81
jason: jason.
CLD-MVS		75.60 101	75.39 87	76.24 166	80.69 212	52.40 172	90.69 23	86.20 111	74.40 6	65.01 203	88.93 138	42.05 214	90.58 117	76.57 86	73.96 193	85.73 273
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
test_yl		75.85 91	74.83 102	78.91 61	88.08 40	51.94 186	91.30 17	89.28 31	57.91 286	71.19 121	89.20 134	42.03 215	92.77 45	69.41 158	75.07 180	92.01 50

DCV-MVSNet		75.85 91	74.83 102	78.91 61	88.08 40	51.94 186	91.30 17	89.28 31	57.91 286	71.19 121	89.20 134	42.03 215	92.77 45	69.41 158	75.07 180	92.01 50

TAMVS		69.51 243	68.16 236	73.56 268	76.30 327	48.71 287	82.57 272	77.17 359	62.10 201	61.32 266	84.23 251	41.90 217	83.46 365	54.80 309	73.09 208	88.50 202

TransMVSNet (Re)		62.82 352	60.76 354	69.02 365	73.98 372	41.61 422	86.36 115	79.30 313	56.90 309	52.53 384	76.44 371	41.85 218	87.60 258	38.83 406	40.61 463	77.86 407

VPNet		72.07 180	71.42 170	74.04 249	78.64 276	47.17 341	89.91 31	87.97 69	72.56 13	64.66 210	85.04 239	41.83 219	88.33 219	61.17 237	60.97 337	86.62 254

v2v482		69.55 242	67.64 250	75.26 213	72.32 392	53.83 127	84.93 187	81.94 242	65.37 130	60.80 271	79.25 334	41.62 220	88.98 185	63.03 219	59.51 350	82.98 338

API-MVS		74.17 131	72.07 159	80.49 27	90.02 12	58.55 11	87.30 86	84.27 189	57.51 297	65.77 191	87.77 185	41.61 221	95.97 12	51.71 336	82.63 68	86.94 241

GeoE		69.96 231	67.88 243	76.22 167	81.11 198	51.71 198	84.15 215	76.74 368	59.83 243	60.91 269	84.38 248	41.56 222	88.10 229	51.67 337	70.57 240	88.84 185

CHOSEN 1792x2688		76.24 78	74.03 118	82.88 2	83.09 127	62.84 2	85.73 144	85.39 134	69.79 50	64.87 208	83.49 266	41.52 223	93.69 35	70.55 148	81.82 76	92.12 44

LFMVS		78.52 27	77.14 48	82.67 4	89.58 14	58.90 9	91.27 19	88.05 68	63.22 177	74.63 66	90.83 96	41.38 224	94.40 22	75.42 98	79.90 100	94.72 2

MAR-MVS		76.76 64	75.60 80	80.21 34	90.87 8	54.68 107	89.14 46	89.11 34	62.95 182	70.54 141	92.33 56	41.05 225	94.95 18	57.90 276	86.55 33	91.00 104
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
dongtai		43.51 446	44.07 447	41.82 473	63.75 464	21.90 498	63.80 452	72.05 417	39.59 452	33.35 484	54.54 483	41.04 226	57.30 487	10.75 505	17.77 503	46.26 496

test_fmvsmvis_n_1920		71.29 197	70.38 193	74.00 251	71.04 408	48.79 283	79.19 355	64.62 457	62.75 189	66.73 174	91.99 65	40.94 227	88.35 217	83.00 30	73.18 205	84.85 291

GST-MVS		74.87 118	73.90 121	77.77 113	83.30 120	53.45 137	85.75 140	85.29 141	59.22 259	66.50 181	89.85 122	40.94 227	90.76 108	70.94 147	83.35 63	89.10 179

usedtu_dtu_shiyan1		69.05 249	67.91 239	72.46 301	75.40 347	46.24 363	85.74 142	86.80 94	65.23 134	58.75 307	80.31 318	40.90 229	86.83 286	53.29 317	64.77 297	84.31 297

FE-MVSNET3		69.05 249	67.91 239	72.46 301	75.39 348	46.24 363	85.74 142	86.80 94	65.23 134	58.75 307	80.31 318	40.90 229	86.83 286	53.29 317	64.77 297	84.31 297

DU-MVS		66.84 307	65.74 295	70.16 352	73.27 379	42.59 411	81.50 311	82.92 226	63.53 170	58.51 312	82.11 298	40.75 231	84.64 350	53.11 320	55.96 390	83.24 330

Baseline_NR-MVSNet		65.49 327	64.27 320	69.13 364	74.37 366	41.65 421	83.39 245	78.85 319	59.56 249	59.62 285	76.88 366	40.75 231	87.44 264	49.99 345	55.05 397	78.28 402

miper_lstm_enhance		63.91 339	62.30 333	68.75 371	75.06 355	46.78 346	69.02 432	81.14 260	59.68 248	52.76 383	72.39 414	40.71 233	77.99 420	56.81 287	53.09 414	81.48 359

HFP-MVS		74.37 126	73.13 136	78.10 104	84.30 97	53.68 131	85.58 151	84.36 187	56.82 314	65.78 190	90.56 99	40.70 234	90.90 104	69.18 163	80.88 82	89.71 153

RRT-MVS		73.29 151	71.37 171	79.07 59	84.63 87	54.16 124	78.16 363	86.64 102	61.67 210	60.17 277	82.35 294	40.63 235	92.26 62	70.19 152	77.87 125	90.81 111

CL-MVSNet_self_test		62.98 350	61.14 351	68.50 377	65.86 450	42.96 406	84.37 206	82.98 224	60.98 225	53.95 375	72.70 410	40.43 236	83.71 361	41.10 399	47.93 436	78.83 392

ACMMP_NAP		76.43 72	75.66 79	78.73 68	81.92 165	54.67 108	84.06 219	85.35 136	61.10 222	72.99 86	91.50 79	40.25 237	91.00 98	76.84 85	86.98 26	90.51 124

v1144		68.81 257	66.82 268	74.80 226	72.34 391	53.46 135	84.68 197	81.77 249	64.25 148	60.28 276	77.91 346	40.23 238	88.95 187	60.37 248	59.52 349	81.97 348

WR-MVS_H		58.91 383	58.04 375	61.54 433	69.07 433	33.83 459	76.91 370	81.99 241	51.40 382	48.17 416	74.67 387	40.23 238	74.15 446	31.78 443	48.10 434	76.64 422

原ACMM1				76.13 172	84.89 83	54.59 111		85.26 143	51.98 376	66.70 175	87.07 204	40.15 240	89.70 153	51.23 340	85.06 53	84.10 302

MVP-Stereo		70.97 206	70.44 189	72.59 296	76.03 335	51.36 206	85.02 182	86.99 91	60.31 237	56.53 349	78.92 338	40.11 241	90.00 136	60.00 251	90.01 7	76.41 425
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
v10		66.61 310	64.20 321	73.83 258	72.59 388	53.37 141	81.88 292	79.91 291	61.11 221	54.09 374	75.60 382	40.06 242	88.26 225	56.47 292	56.10 388	79.86 385

test_fmvsmconf0.01_n		71.97 183	70.95 181	75.04 217	66.21 447	47.87 322	80.35 334	70.08 435	65.85 121	72.69 91	91.68 74	39.99 243	87.67 253	82.03 39	69.66 251	89.58 158

MP-MVS-pluss		75.54 103	75.03 96	77.04 137	81.37 191	52.65 168	84.34 209	84.46 185	61.16 219	69.14 155	91.76 70	39.98 244	88.99 184	78.19 71	84.89 54	89.48 166
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
TranMVSNet+NR-MVSNet		66.94 305	65.61 298	70.93 341	73.45 375	43.38 401	83.02 260	84.25 190	65.31 132	58.33 319	81.90 302	39.92 245	85.52 332	49.43 350	54.89 399	83.89 313

Patchmatch-test		53.33 420	48.17 433	68.81 369	73.31 376	42.38 415	42.98 491	58.23 472	32.53 476	38.79 466	70.77 431	39.66 246	73.51 452	25.18 469	52.06 418	90.55 121

viewdifsd2359ckpt11		70.68 212	69.10 221	75.40 200	75.33 350	50.85 217	81.57 307	78.00 342	66.99 96	64.96 205	85.52 229	39.52 247	86.81 288	68.86 166	61.15 336	88.56 197

viewmsd2359difaftdt		70.68 212	69.10 221	75.40 200	75.33 350	50.85 217	81.57 307	78.00 342	66.99 96	64.96 205	85.52 229	39.52 247	86.81 288	68.86 166	61.16 335	88.56 197

Test By Simon												39.38 249

v144192		67.86 276	65.76 294	74.16 245	71.68 398	53.09 153	84.14 216	80.83 268	62.85 188	59.21 295	77.28 357	39.30 250	88.00 233	58.67 261	57.88 373	81.40 362

BH-w/o		70.02 228	68.51 229	74.56 231	82.77 142	50.39 233	86.60 113	78.14 340	59.77 245	59.65 283	85.57 227	39.27 251	87.30 270	49.86 347	74.94 184	85.99 267

dmvs_testset		57.65 394	58.21 374	55.97 454	74.62 361	9.82 516	63.75 453	63.34 463	67.23 87	48.89 414	83.68 265	39.12 252	76.14 438	23.43 476	59.80 348	81.96 349

CR-MVSNet		62.47 358	59.04 370	72.77 289	73.97 373	56.57 37	60.52 466	71.72 421	60.04 240	57.49 333	65.86 451	38.94 253	80.31 396	42.86 393	59.93 345	81.42 360

Patchmtry		56.56 400	52.95 407	67.42 384	72.53 389	50.59 226	59.05 470	71.72 421	37.86 460	46.92 428	65.86 451	38.94 253	80.06 400	36.94 415	46.72 446	71.60 461

sam_mvs1												38.86 255				88.13 211

UA-Net		67.32 294	66.23 282	70.59 345	78.85 268	41.23 427	73.60 399	75.45 382	61.54 213	66.61 178	84.53 247	38.73 256	86.57 300	42.48 396	74.24 189	83.98 308

cdsmvs_eth3d_5k		18.33 475	24.44 467	0.00 541	0.00 564	0.00 567	0.00 553	89.40 29	0.00 557	0.00 561	92.02 63	38.55 257	0.00 559	0.00 560	0.00 558	0.00 557

patchmatchnet-post											59.74 474	38.41 258	79.91 403

CHOSEN 280x420		57.53 396	56.38 388	60.97 438	74.01 371	48.10 311	46.30 486	54.31 479	48.18 407	50.88 404	77.43 355	38.37 259	59.16 485	54.83 307	63.14 321	75.66 429

lecture		74.14 132	73.05 137	77.44 124	81.66 176	50.39 233	87.43 80	84.22 194	51.38 383	72.10 101	90.95 92	38.31 260	93.23 38	70.51 149	80.83 84	88.69 189

SD_0403		65.51 326	65.18 309	66.48 396	78.37 282	29.94 479	74.64 391	78.55 331	66.47 105	54.87 363	84.35 250	38.20 261	82.47 372	38.90 405	72.30 220	87.05 239

V42		67.66 281	65.60 299	73.86 256	70.69 414	53.63 132	81.50 311	78.61 329	63.85 160	59.49 289	77.49 352	37.98 262	87.65 254	62.33 225	58.43 360	80.29 380

tpmvs		62.45 359	59.42 366	71.53 331	83.93 105	54.32 116	70.03 428	77.61 351	51.91 377	53.48 380	68.29 442	37.91 263	86.66 295	33.36 436	58.27 363	73.62 447

PatchmatchNet		67.07 302	63.63 324	77.40 125	83.10 125	58.03 13	72.11 419	77.77 348	58.85 270	59.37 290	70.83 430	37.84 264	84.93 345	42.96 392	69.83 250	89.26 171
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
pcd_1.5k_mvsjas		3.15 494	4.20 496	0.00 541	0.00 564	0.00 567	0.00 553	0.00 565	0.00 557	0.00 561	0.00 559	37.77 265	0.00 559	0.00 560	0.00 558	0.00 557

PS-MVSNAJss		68.78 259	67.17 263	73.62 266	73.01 382	48.33 301	84.95 186	84.81 169	59.30 258	58.91 302	79.84 326	37.77 265	88.86 192	62.83 222	63.12 322	83.67 322

PS-MVSNAJ		80.06 17	79.52 18	81.68 15	85.58 69	60.97 3	91.69 12	87.02 90	70.62 35	80.75 27	93.22 33	37.77 265	92.50 54	82.75 33	86.25 36	91.57 70

pm-mvs1		64.12 337	62.56 331	68.78 370	71.68 398	38.87 438	82.89 264	81.57 252	55.54 341	53.89 376	77.82 348	37.73 268	86.74 291	48.46 360	53.49 411	80.72 374

RPMNet		59.29 375	54.25 400	74.42 235	73.97 373	56.57 37	60.52 466	76.98 362	35.72 470	57.49 333	58.87 477	37.73 268	85.26 338	27.01 465	59.93 345	81.42 360

IMVS_0403		72.39 170	70.59 187	77.79 112	82.26 154	50.87 213	81.76 296	85.16 149	62.91 184	64.87 208	86.07 217	37.71 270	92.40 57	64.03 208	70.55 241	90.09 140

SDMVSNet		71.89 185	70.62 186	75.70 187	81.70 172	51.61 199	73.89 396	88.72 47	66.58 101	61.64 263	82.38 291	37.63 271	89.48 162	77.44 79	65.60 292	86.01 265

xiu_mvs_v2_base		79.86 18	79.31 20	81.53 17	85.03 81	60.73 4	91.65 13	86.86 93	70.30 40	80.77 26	93.07 38	37.63 271	92.28 61	82.73 34	85.71 42	91.57 70

Patchmatch-RL test		58.72 386	54.32 399	71.92 322	63.91 463	44.25 390	61.73 462	55.19 477	57.38 301	49.31 412	54.24 484	37.60 273	80.89 384	62.19 228	47.28 441	90.63 118

HPM-MVS		72.60 165	71.50 167	75.89 180	82.02 161	51.42 205	80.70 328	83.05 222	56.12 334	64.03 224	89.53 127	37.55 274	88.37 215	70.48 151	80.04 97	87.88 216
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
test_post											16.22 519	37.52 275	84.72 348

PatchT		56.60 399	52.97 406	67.48 383	72.94 384	46.16 366	57.30 474	73.78 400	38.77 455	54.37 370	57.26 480	37.52 275	78.06 417	32.02 441	52.79 415	78.23 404

v1192		67.96 275	65.74 295	74.63 230	71.79 396	53.43 140	84.06 219	80.99 266	63.19 178	59.56 286	77.46 353	37.50 277	88.65 199	58.20 269	58.93 356	81.79 351

HQP2-MVS												37.35 278

HQP-MVS		72.34 173	71.44 169	75.03 218	79.02 263	51.56 201	88.00 61	83.68 206	65.45 125	64.48 216	85.13 234	37.35 278	88.62 200	66.70 180	73.12 206	84.91 289

region2R		73.75 142	72.55 144	77.33 126	83.90 107	52.98 157	85.54 155	84.09 196	56.83 313	65.10 200	90.45 103	37.34 280	90.24 130	68.89 165	80.83 84	88.77 188

TESTMET0.1,1		72.86 159	72.33 149	74.46 233	81.98 162	50.77 220	85.13 172	85.47 130	66.09 115	67.30 171	83.69 263	37.27 281	83.57 363	65.06 202	78.97 113	89.05 180

mvsmamba		69.38 244	67.52 255	74.95 222	82.86 139	52.22 180	67.36 441	76.75 366	61.14 220	49.43 410	82.04 300	37.26 282	84.14 354	73.93 116	76.91 139	88.50 202

ACMMPR		73.76 141	72.61 142	77.24 133	83.92 106	52.96 158	85.58 151	84.29 188	56.82 314	65.12 199	90.45 103	37.24 283	90.18 132	69.18 163	80.84 83	88.58 195

MonoMVSNet		66.80 308	64.41 317	73.96 252	76.21 330	48.07 312	76.56 375	78.26 338	64.34 145	54.32 371	74.02 393	37.21 284	86.36 306	64.85 203	53.96 406	87.45 228

sss		70.49 218	70.13 201	71.58 330	81.59 182	39.02 436	80.78 326	84.71 179	59.34 255	66.61 178	88.09 172	37.17 285	85.52 332	61.82 232	71.02 235	90.20 135

reproduce-ours		71.77 190	70.43 190	75.78 183	81.96 163	49.54 260	82.54 275	81.01 264	48.77 402	69.21 153	90.96 89	37.13 286	89.40 165	66.28 185	76.01 158	88.39 205

our_new_method		71.77 190	70.43 190	75.78 183	81.96 163	49.54 260	82.54 275	81.01 264	48.77 402	69.21 153	90.96 89	37.13 286	89.40 165	66.28 185	76.01 158	88.39 205

EPNet_dtu		66.25 317	66.71 271	64.87 409	78.66 275	34.12 457	82.80 266	75.51 380	61.75 208	64.47 219	86.90 205	37.06 288	72.46 458	43.65 388	69.63 253	88.02 214
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
v1921920		67.45 287	65.23 308	74.10 248	71.51 401	52.90 159	83.75 230	80.44 276	62.48 197	59.12 296	77.13 358	36.98 289	87.90 236	57.53 281	58.14 367	81.49 357

旧先验1					81.57 184	47.48 333		71.83 419			88.66 144	36.94 290			78.34 120	88.67 190

test-LLR		69.65 240	69.01 223	71.60 328	78.67 272	48.17 307	85.13 172	79.72 295	59.18 262	63.13 242	82.58 285	36.91 291	80.24 397	60.56 243	75.17 176	86.39 261

test0.0.03 1		62.54 355	62.44 332	62.86 425	72.28 394	29.51 482	82.93 262	78.78 322	59.18 262	53.07 382	82.41 289	36.91 291	77.39 427	37.45 409	58.96 355	81.66 355

MDTV_nov1_ep13_2view						43.62 397	71.13 424		54.95 351	59.29 294		36.76 293		46.33 374		87.32 231

KD-MVS_2432*1600		59.04 381	56.44 385	66.86 390	79.07 260	45.87 370	72.13 417	80.42 277	55.03 349	48.15 417	71.01 428	36.73 294	78.05 418	35.21 426	30.18 488	76.67 419

miper_refine_blended		59.04 381	56.44 385	66.86 390	79.07 260	45.87 370	72.13 417	80.42 277	55.03 349	48.15 417	71.01 428	36.73 294	78.05 418	35.21 426	30.18 488	76.67 419

GBi-Net		67.09 300	65.47 301	71.96 317	82.71 144	46.36 356	83.52 233	83.31 214	58.55 276	57.58 330	76.23 375	36.72 296	86.20 308	47.25 367	63.40 313	83.32 327

test1		67.09 300	65.47 301	71.96 317	82.71 144	46.36 356	83.52 233	83.31 214	58.55 276	57.58 330	76.23 375	36.72 296	86.20 308	47.25 367	63.40 313	83.32 327

FMVSNet2		67.57 284	65.79 293	72.90 283	82.71 144	47.97 316	85.15 171	84.93 165	58.55 276	56.71 346	78.26 344	36.72 296	86.67 294	46.15 375	62.94 324	84.07 303

AUN-MVS		68.20 272	66.35 278	73.76 260	76.37 323	47.45 335	79.52 351	79.52 302	60.98 225	62.34 251	86.02 221	36.59 299	86.94 282	62.32 226	53.47 412	86.89 242

reproduce_model		71.07 203	69.67 209	75.28 211	81.51 188	48.82 282	81.73 299	80.57 274	47.81 408	68.26 163	90.78 97	36.49 300	88.60 202	65.12 201	74.76 186	88.42 204

BH-untuned		68.28 269	66.40 277	73.91 254	81.62 179	50.01 246	85.56 153	77.39 355	57.63 294	57.47 335	83.69 263	36.36 301	87.08 277	44.81 380	73.08 209	84.65 292

fmvsm_s_conf0.5_n_2		72.02 181	71.72 164	72.92 282	76.79 319	45.90 368	84.48 204	66.11 451	64.26 147	76.12 58	93.40 26	36.26 302	86.04 319	81.47 45	66.54 281	86.82 250

EPMVS		68.45 265	65.44 303	77.47 122	84.91 82	56.17 47	71.89 421	81.91 245	61.72 209	60.85 270	72.49 411	36.21 303	87.06 278	47.32 366	71.62 226	89.17 176

wanda-best-256-512		64.87 328	62.23 334	72.81 286	70.49 416	46.85 344	85.71 146	85.71 122	56.85 310	51.25 395	72.31 417	36.16 304	87.84 238	52.67 330	48.90 426	83.73 315

FE-blended-shiyan7		64.87 328	62.23 334	72.81 286	70.49 416	46.85 344	85.71 146	85.71 122	56.85 310	51.25 395	72.31 417	36.16 304	87.84 238	52.67 330	48.90 426	83.73 315

usedtu_blend_shiyan5		63.62 343	60.36 359	73.40 272	70.49 416	47.96 318	79.13 356	80.68 270	47.51 412	51.25 395	72.31 417	36.16 304	88.50 210	56.81 287	48.90 426	83.73 315

MSLP-MVS++		74.21 130	72.25 153	80.11 41	81.45 189	56.47 41	86.32 117	79.65 300	58.19 280	66.36 182	92.29 57	36.11 307	90.66 113	67.39 176	82.49 70	93.18 18

FA-MVS(test-final)		69.00 253	66.60 275	76.19 170	83.48 114	47.96 318	74.73 388	82.07 240	57.27 303	62.18 254	78.47 342	36.09 308	92.89 41	53.76 316	71.32 233	87.73 220

MTAPA		72.73 163	71.22 173	77.27 130	81.54 185	53.57 133	67.06 443	81.31 257	59.41 253	68.39 162	90.96 89	36.07 309	89.01 181	73.80 120	82.45 71	89.23 173

HQP_MVS		70.96 207	69.91 206	74.12 247	77.95 289	49.57 254	85.76 138	82.59 229	63.60 168	62.15 256	83.28 271	36.04 310	88.30 222	65.46 194	72.34 218	84.49 293

plane_prior6					78.42 281	49.39 267						36.04 310

sam_mvs												35.99 312

blended_shiyan6		64.70 330	62.04 338	72.69 291	70.34 419	46.60 352	85.48 157	85.65 126	56.59 322	50.91 403	72.18 421	35.82 313	87.81 241	52.46 334	48.90 426	83.66 323

blended_shiyan8		64.70 330	62.04 338	72.69 291	70.33 420	46.62 350	85.48 157	85.66 124	56.58 323	50.94 402	72.18 421	35.81 314	87.80 244	52.47 333	48.91 425	83.65 324

PGM-MVS		72.60 165	71.20 174	76.80 150	82.95 134	52.82 163	83.07 258	82.14 235	56.51 325	63.18 241	89.81 123	35.68 315	89.76 147	67.30 177	80.19 94	87.83 217

icg_test_0407_2		71.26 198	69.99 204	75.09 216	82.26 154	50.87 213	79.65 347	85.16 149	62.91 184	63.68 234	86.07 217	35.56 316	84.32 353	64.03 208	70.55 241	90.09 140

IMVS_0407		71.97 183	70.10 202	77.57 118	82.26 154	50.87 213	80.69 329	85.16 149	62.91 184	63.68 234	86.07 217	35.56 316	91.75 73	64.03 208	70.55 241	90.09 140

XVS		72.92 157	71.62 165	76.81 148	83.41 115	52.48 169	84.88 188	83.20 219	58.03 282	63.91 226	89.63 126	35.50 318	89.78 145	65.50 191	80.50 89	88.16 208

X-MVStestdata		65.85 322	62.20 336	76.81 148	83.41 115	52.48 169	84.88 188	83.20 219	58.03 282	63.91 226	4.82 531	35.50 318	89.78 145	65.50 191	80.50 89	88.16 208

v1240		66.99 303	64.68 314	73.93 253	71.38 405	52.66 167	83.39 245	79.98 286	61.97 205	58.44 318	77.11 359	35.25 320	87.81 241	56.46 293	58.15 365	81.33 365

test1111		71.06 204	70.42 192	72.97 281	79.48 248	41.49 424	84.82 192	82.74 228	64.20 150	62.98 244	87.43 197	35.20 321	87.92 234	58.54 262	78.42 119	89.49 165

dp		64.41 333	61.58 343	72.90 283	82.40 151	54.09 125	72.53 409	76.59 372	60.39 236	55.68 356	70.39 434	35.18 322	76.90 433	39.34 404	61.71 332	87.73 220

Syy-MVS		61.51 364	61.35 348	62.00 429	81.73 170	30.09 476	80.97 321	81.02 262	60.93 227	55.06 360	82.64 283	35.09 323	80.81 387	16.40 495	58.32 361	75.10 436

ECVR-MVS		71.81 187	71.00 180	74.26 243	80.12 232	43.49 398	84.69 196	82.16 234	64.02 153	64.64 211	87.43 197	35.04 324	89.21 174	61.24 236	79.66 103	90.08 144

CP-MVS		72.59 167	71.46 168	76.00 177	82.93 136	52.32 175	86.93 100	82.48 232	55.15 347	63.65 236	90.44 106	35.03 325	88.53 209	68.69 168	77.83 127	87.15 237

fmvsm_s_conf0.1_n_2		71.45 195	71.01 179	72.78 288	75.37 349	45.82 372	84.18 214	64.59 459	64.02 153	75.67 59	93.02 39	34.99 326	85.99 322	81.18 49	66.04 290	86.52 257

CP-MVSNet		58.54 390	57.57 378	61.46 434	68.50 437	33.96 458	76.90 371	78.60 330	51.67 381	47.83 420	76.60 370	34.99 326	72.79 456	35.45 423	47.58 438	77.64 412

guyue		70.53 217	69.12 219	74.76 227	77.61 294	47.53 331	84.86 190	85.17 147	62.70 191	62.18 254	83.74 260	34.72 328	89.86 141	64.69 204	66.38 283	86.87 243

dmvs_re		67.61 282	66.00 287	72.42 303	81.86 167	43.45 399	64.67 450	80.00 285	69.56 55	60.07 278	85.00 240	34.71 329	87.63 255	51.48 338	66.68 276	86.17 264

MDTV_nov1_ep13			61.56 344		81.68 174	55.12 75	72.41 412	78.18 339	59.19 260	58.85 304	69.29 439	34.69 330	86.16 311	36.76 418	62.96 323

SSC-MVS3.2		68.13 273	66.89 265	71.85 325	82.26 154	43.97 393	82.09 287	89.29 30	71.74 18	61.12 268	79.83 327	34.60 331	87.45 263	41.23 398	59.85 347	84.14 300

WB-MVSnew		69.36 245	68.24 234	72.72 290	79.26 254	49.40 266	85.72 145	88.85 42	61.33 216	64.59 214	82.38 291	34.57 332	87.53 261	46.82 371	70.63 238	81.22 369

3Dnovator	64.70 6	74.46 123	72.48 145	80.41 31	82.84 141	55.40 63	83.08 257	88.61 53	67.61 84	59.85 280	88.66 144	34.57 332	93.97 28	58.42 265	88.70 12	91.85 58

VortexMVS		68.49 264	66.84 267	73.46 270	81.10 199	48.75 284	84.63 200	84.73 174	62.05 202	57.22 340	77.08 361	34.54 334	89.20 175	63.08 217	57.12 379	82.43 344

Vis-MVSNet		70.61 216	69.34 214	74.42 235	80.95 205	48.49 293	86.03 127	77.51 353	58.74 273	65.55 195	87.78 184	34.37 335	85.95 326	52.53 332	80.61 87	88.80 186
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
test_post1							70.84 425				14.72 522	34.33 336	83.86 357	48.80 355

OPM-MVS		70.75 211	69.58 210	74.26 243	75.55 345	51.34 207	86.05 126	83.29 217	61.94 206	62.95 246	85.77 224	34.15 337	88.44 213	65.44 197	71.07 234	82.99 336
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
DP-MVS Recon		71.99 182	70.31 195	77.01 139	90.65 9	53.44 138	89.37 39	82.97 225	56.33 328	63.56 239	89.47 128	34.02 338	92.15 66	54.05 313	72.41 216	85.43 280

PEN-MVS		58.35 391	57.15 380	61.94 430	67.55 444	34.39 453	77.01 369	78.35 337	51.87 378	47.72 421	76.73 368	33.91 339	73.75 450	34.03 433	47.17 442	77.68 410

QAPM		71.88 186	69.33 215	79.52 46	82.20 160	54.30 117	86.30 118	88.77 45	56.61 321	59.72 282	87.48 195	33.90 340	95.36 14	47.48 365	81.49 79	88.90 182

新几何1				73.30 275	83.10 125	53.48 134		71.43 425	45.55 428	66.14 183	87.17 202	33.88 341	80.54 393	48.50 358	80.33 93	85.88 272

1314		71.11 202	69.41 212	76.22 167	79.32 252	50.49 228	80.23 337	85.14 155	59.44 252	58.93 300	88.89 140	33.83 342	89.60 156	61.49 234	77.42 132	88.57 196

SR-MVS		70.92 208	69.73 208	74.50 232	83.38 119	50.48 230	84.27 211	79.35 310	48.96 400	66.57 180	90.45 103	33.65 343	87.11 276	66.42 182	74.56 188	85.91 270

mPP-MVS		71.79 189	70.38 193	76.04 175	82.65 147	52.06 181	84.45 205	81.78 248	55.59 339	62.05 259	89.68 125	33.48 344	88.28 224	65.45 196	78.24 121	87.77 219

OMC-MVS		65.97 321	65.06 311	68.71 372	72.97 383	42.58 413	78.61 360	75.35 383	54.72 353	59.31 292	86.25 216	33.30 345	77.88 422	57.99 271	67.05 274	85.66 275

BH-RMVSNet		70.08 226	68.01 237	76.27 164	84.21 101	51.22 211	87.29 87	79.33 312	58.96 269	63.63 237	86.77 207	33.29 346	90.30 129	44.63 382	73.96 193	87.30 232

gbinet_0.2-2-1-0.02		64.20 335	61.39 346	72.63 294	70.85 410	46.32 360	85.92 129	85.98 116	55.27 346	51.88 392	72.29 420	33.14 347	87.82 240	48.50 358	48.72 430	83.73 315

SSM_0407		69.71 235	67.38 258	76.69 155	80.45 222	51.81 194	81.36 315	80.18 281	54.07 360	63.82 230	85.05 237	33.09 348	91.01 97	59.40 252	68.97 258	87.25 233

SSM_0404		70.13 222	67.87 246	76.88 146	80.22 229	52.00 183	81.71 301	80.18 281	54.07 360	65.36 197	85.05 237	33.09 348	91.03 94	59.40 252	71.80 224	87.63 223

JIA-IIPM		52.33 426	47.77 436	66.03 398	71.20 406	46.92 342	40.00 496	76.48 373	37.10 463	46.73 429	37.02 497	32.96 350	77.88 422	35.97 420	52.45 417	73.29 451

PS-CasMVS		58.12 392	57.03 382	61.37 435	68.24 441	33.80 460	76.73 373	78.01 341	51.20 384	47.54 424	76.20 378	32.85 351	72.76 457	35.17 428	47.37 440	77.55 413

DTE-MVSNet		57.03 397	55.73 392	60.95 439	65.94 449	32.57 465	75.71 376	77.09 361	51.16 385	46.65 431	76.34 373	32.84 352	73.22 455	30.94 447	44.87 451	77.06 415

pmmvs4		63.34 347	61.07 352	70.16 352	70.14 422	50.53 227	79.97 344	71.41 426	55.08 348	54.12 373	78.58 340	32.79 353	82.09 377	50.33 344	57.22 378	77.86 407

TR-MVS		69.71 235	67.85 247	75.27 212	82.94 135	48.48 294	87.40 83	80.86 267	57.15 307	64.61 213	87.08 203	32.67 354	89.64 155	46.38 373	71.55 228	87.68 222

VDD-MVS		76.08 83	74.97 98	79.44 47	84.27 100	53.33 144	91.13 20	85.88 118	65.33 131	72.37 97	89.34 131	32.52 355	92.76 47	77.90 77	75.96 160	92.22 42

3Dnovator+	62.71 7	72.29 176	70.50 188	77.65 117	83.40 118	51.29 209	87.32 84	86.40 107	59.01 267	58.49 315	88.32 161	32.40 356	91.27 85	57.04 285	82.15 74	90.38 127

tfpnnormal		61.47 365	59.09 369	68.62 374	76.29 328	41.69 420	81.14 318	85.16 149	54.48 356	51.32 394	73.63 400	32.32 357	86.89 285	21.78 482	55.71 394	77.29 414

MS-PatchMatch		72.34 173	71.26 172	75.61 189	82.38 152	55.55 56	88.00 61	89.95 24	65.38 129	56.51 350	80.74 316	32.28 358	92.89 41	57.95 274	88.10 16	78.39 400

KinetiMVS		71.15 199	69.25 218	76.82 147	77.99 288	50.49 228	85.05 178	86.51 103	59.78 244	64.10 222	85.34 232	32.16 359	91.33 84	58.82 259	73.54 201	88.64 191

v7n		62.50 357	59.27 368	72.20 309	67.25 445	49.83 251	77.87 366	80.12 283	52.50 373	48.80 415	73.07 404	32.10 360	87.90 236	46.83 370	54.92 398	78.86 391

IterMVS		63.77 342	61.67 342	70.08 354	72.68 387	51.24 210	80.44 332	75.51 380	60.51 235	51.41 393	73.70 399	32.08 361	78.91 408	54.30 311	54.35 404	80.08 383
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
IterMVS-SCA-FT		59.12 378	58.81 372	60.08 440	70.68 415	45.07 379	80.42 333	74.25 392	43.54 443	50.02 408	73.73 396	31.97 362	56.74 489	51.06 342	53.60 410	78.42 399

SCA		63.84 340	60.01 363	75.32 205	78.58 277	57.92 14	61.61 463	77.53 352	56.71 317	57.75 327	70.77 431	31.97 362	79.91 403	48.80 355	56.36 382	88.13 211

ACMMP		70.81 210	69.29 216	75.39 203	81.52 187	51.92 188	83.43 241	83.03 223	56.67 319	58.80 305	88.91 139	31.92 364	88.58 203	65.89 190	73.39 203	85.67 274
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
mamba_0408		66.33 315	62.87 326	76.70 154	80.45 222	51.81 194	46.11 487	78.90 317	55.46 342	63.82 230	84.54 244	31.91 365	91.03 94	55.68 301	68.97 258	87.25 233

SSM_04072		64.04 338	62.87 326	67.56 382	80.45 222	51.81 194	46.11 487	78.90 317	55.46 342	63.82 230	84.54 244	31.91 365	63.62 474	55.68 301	68.97 258	87.25 233

APD-MVS_3200maxsize		69.62 241	68.23 235	73.80 259	81.58 183	48.22 305	81.91 291	79.50 303	48.21 406	64.24 221	89.75 124	31.91 365	87.55 260	63.08 217	73.85 198	85.64 276

VDDNet		74.37 126	72.13 157	81.09 22	79.58 244	56.52 40	90.02 26	86.70 99	52.61 372	71.23 120	87.20 201	31.75 368	93.96 29	74.30 112	75.77 167	92.79 28

pmmvs5		62.80 353	61.18 350	67.66 381	69.53 429	42.37 416	82.65 269	75.19 384	54.30 359	52.03 390	78.51 341	31.64 369	80.67 389	48.60 357	58.15 365	79.95 384

LCM-MVSNet-Re		58.82 384	56.54 383	65.68 401	79.31 253	29.09 485	61.39 465	45.79 486	60.73 232	37.65 469	72.47 412	31.42 370	81.08 383	49.66 348	70.41 245	86.87 243

AstraMVS		70.12 223	68.56 226	74.81 225	76.48 322	47.48 333	84.35 208	82.58 231	63.80 161	62.09 258	84.54 244	31.39 371	89.96 138	68.24 173	63.58 311	87.00 240

testdata				67.08 388	77.59 296	45.46 376		69.20 441	44.47 436	71.50 117	88.34 159	31.21 372	70.76 464	52.20 335	75.88 161	85.03 285

SR-MVS-dyc-post		68.27 270	66.87 266	72.48 300	80.96 202	48.14 309	81.54 309	76.98 362	46.42 420	62.75 248	89.42 129	31.17 373	86.09 316	60.52 245	72.06 222	83.19 332

GA-MVS		69.04 251	66.70 272	76.06 174	75.11 353	52.36 173	83.12 256	80.23 280	63.32 175	60.65 273	79.22 335	30.98 374	88.37 215	61.25 235	66.41 282	87.46 227

OpenMVS	61.00 11	69.99 230	67.55 253	77.30 128	78.37 282	54.07 126	84.36 207	85.76 121	57.22 305	56.71 346	87.67 191	30.79 375	92.83 43	43.04 391	84.06 61	85.01 286

Effi-MVS+-dtu		66.24 318	64.96 313	70.08 354	75.17 352	49.64 253	82.01 288	74.48 391	62.15 200	57.83 323	76.08 379	30.59 376	83.79 359	65.40 198	60.93 338	76.81 418

sd_testset		67.79 279	65.95 289	73.32 273	81.70 172	46.33 359	68.99 433	80.30 279	66.58 101	61.64 263	82.38 291	30.45 377	87.63 255	55.86 298	65.60 292	86.01 265

test222					79.36 250	50.97 212	77.99 365	67.84 446	42.54 447	62.84 247	86.53 212	30.26 378			76.91 139	85.23 281

MVS_111021_LR		69.07 248	67.91 239	72.54 297	77.27 308	49.56 257	79.77 345	73.96 398	59.33 257	60.73 272	87.82 183	30.19 379	81.53 379	69.94 155	72.19 221	86.53 256

dtuonly		62.58 354	61.91 341	64.58 411	66.49 446	44.72 383	75.64 377	65.78 453	57.26 304	55.48 359	83.93 256	30.08 380	67.36 471	56.40 296	66.10 289	81.67 354

114514_t		69.87 233	67.88 243	75.85 181	88.38 31	52.35 174	86.94 98	83.68 206	53.70 363	55.68 356	85.60 226	30.07 381	91.20 89	55.84 299	71.02 235	83.99 306

CPTT-MVS		67.15 298	65.84 292	71.07 338	80.96 202	50.32 239	81.94 290	74.10 394	46.18 426	57.91 322	87.64 193	29.57 382	81.31 381	64.10 207	70.18 248	81.56 356

CANet_DTU		73.71 143	73.14 134	75.40 200	82.61 148	50.05 244	84.67 199	79.36 309	69.72 53	75.39 60	90.03 119	29.41 383	85.93 327	67.99 174	79.11 110	90.22 133

AdaColmap		67.86 276	65.48 300	75.00 220	88.15 39	54.99 82	86.10 124	76.63 371	49.30 397	57.80 324	86.65 211	29.39 384	88.94 189	45.10 379	70.21 247	81.06 370

RE-MVS-def			66.66 273		80.96 202	48.14 309	81.54 309	76.98 362	46.42 420	62.75 248	89.42 129	29.28 385		60.52 245	72.06 222	83.19 332

CVMVSNet		60.85 368	60.44 357	62.07 427	75.00 356	32.73 464	79.54 349	73.49 405	36.98 464	56.28 352	83.74 260	29.28 385	69.53 467	46.48 372	63.23 318	83.94 311

PMMVS		72.98 156	72.05 160	75.78 183	83.57 111	48.60 288	84.08 217	82.85 227	61.62 211	68.24 164	90.33 108	28.35 387	87.78 248	72.71 131	76.69 146	90.95 107

our_test_3		59.11 379	55.08 396	71.18 337	71.42 403	53.29 146	81.96 289	74.52 390	48.32 404	42.08 449	69.28 440	28.14 388	82.15 375	34.35 432	45.68 450	78.11 405

Fast-Effi-MVS+-dtu		66.53 312	64.10 322	73.84 257	72.41 390	52.30 178	84.73 194	75.66 378	59.51 250	56.34 351	79.11 337	28.11 389	85.85 328	57.74 280	63.29 317	83.35 326

Anonymous20231211		66.08 320	63.67 323	73.31 274	83.07 128	48.75 284	86.01 128	84.67 181	45.27 430	56.54 348	76.67 369	28.06 390	88.95 187	52.78 326	59.95 344	82.23 346

Anonymous20240529		69.71 235	67.28 260	77.00 140	83.78 109	50.36 237	88.87 51	85.10 156	47.22 413	64.03 224	83.37 269	27.93 391	92.10 67	57.78 279	67.44 272	88.53 200

HPM-MVS_fast		67.86 276	66.28 281	72.61 295	80.67 213	48.34 299	81.18 317	75.95 377	50.81 386	59.55 287	88.05 175	27.86 392	85.98 323	58.83 258	73.58 200	83.51 325

FMVSNet1		64.57 332	62.11 337	71.96 317	77.32 306	46.36 356	83.52 233	83.31 214	52.43 374	54.42 369	76.23 375	27.80 393	86.20 308	42.59 395	61.34 334	83.32 327

CNLPA		60.59 369	58.44 373	67.05 389	79.21 256	47.26 339	79.75 346	64.34 461	42.46 448	51.90 391	83.94 255	27.79 394	75.41 443	37.12 411	59.49 351	78.47 397

TAPA-MVS	56.12 14	61.82 363	60.18 362	66.71 392	78.48 280	37.97 444	75.19 385	76.41 374	46.82 416	57.04 341	86.52 213	27.67 395	77.03 430	26.50 467	67.02 275	85.14 284
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
pmmvs6		59.64 373	57.15 380	67.09 387	66.01 448	36.86 448	80.50 330	78.64 327	45.05 432	49.05 413	73.94 394	27.28 396	86.10 314	43.96 387	49.94 423	78.31 401

test-mter		68.36 266	67.29 259	71.60 328	78.67 272	48.17 307	85.13 172	79.72 295	53.38 366	63.13 242	82.58 285	27.23 397	80.24 397	60.56 243	75.17 176	86.39 261

D2MVS		63.49 345	61.39 346	69.77 358	69.29 431	48.93 278	78.89 358	77.71 350	60.64 234	49.70 409	72.10 425	27.08 398	83.48 364	54.48 310	62.65 326	76.90 416

XVG-OURS-SEG-HR		62.02 361	59.54 365	69.46 361	65.30 453	45.88 369	65.06 448	73.57 403	46.45 419	57.42 336	83.35 270	26.95 399	78.09 416	53.77 315	64.03 306	84.42 295

IMVS_0404		69.11 247	67.25 262	74.68 229	82.26 154	50.87 213	76.74 372	85.16 149	62.91 184	50.76 406	86.07 217	26.76 400	83.06 370	64.03 208	70.55 241	90.09 140

test_djsdf		63.84 340	61.56 344	70.70 344	68.78 434	44.69 384	81.63 303	81.44 255	50.28 390	52.27 387	76.26 374	26.72 401	86.11 312	60.83 239	55.84 393	81.29 368

Anonymous20231206		59.08 380	57.59 377	63.55 417	68.77 435	32.14 468	80.26 336	79.78 294	50.00 394	49.39 411	72.39 414	26.64 402	78.36 413	33.12 439	57.94 370	80.14 382

ppachtmachnet_test		58.56 388	54.34 398	71.24 334	71.42 403	54.74 101	81.84 294	72.27 415	49.02 399	45.86 436	68.99 441	26.27 403	83.30 367	30.12 449	43.23 456	75.69 428

test20.03		55.22 408	54.07 401	58.68 446	63.14 468	25.00 491	77.69 367	74.78 387	52.64 371	43.43 443	72.39 414	26.21 404	74.76 445	29.31 452	47.05 444	76.28 426

FE-MVS		64.15 336	60.43 358	75.30 208	80.85 207	49.86 250	68.28 438	78.37 336	50.26 393	59.31 292	73.79 395	26.19 405	91.92 70	40.19 401	66.67 277	84.12 301

FMVSNet5		58.61 387	56.45 384	65.10 408	77.20 312	39.74 432	74.77 387	77.12 360	50.27 392	43.28 445	67.71 444	26.15 406	76.90 433	36.78 417	54.78 400	78.65 395

dtuonlycased		54.12 413	52.39 413	59.30 443	64.31 461	41.80 419	78.63 359	65.85 452	50.56 388	42.00 450	60.21 472	26.14 407	73.31 453	43.06 390	40.73 461	62.79 483

ACMP	61.11 9	66.24 318	64.33 319	72.00 316	74.89 358	49.12 270	83.18 253	79.83 292	55.41 344	52.29 386	82.68 282	25.83 408	86.10 314	60.89 238	63.94 308	80.78 373
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
MIMVSNet		63.12 349	60.29 360	71.61 327	75.92 340	46.65 349	65.15 447	81.94 242	59.14 264	54.65 367	69.47 437	25.74 409	80.63 391	41.03 400	69.56 254	87.55 225

LPG-MVS_test		66.44 314	64.58 315	72.02 314	74.42 364	48.60 288	83.07 258	80.64 271	54.69 354	53.75 377	83.83 258	25.73 410	86.98 279	60.33 249	64.71 299	80.48 377

LGP-MVS_train				72.02 314	74.42 364	48.60 288		80.64 271	54.69 354	53.75 377	83.83 258	25.73 410	86.98 279	60.33 249	64.71 299	80.48 377

test_vis1_n_1920		68.59 263	68.31 232	69.44 362	69.16 432	41.51 423	84.63 200	68.58 444	58.80 271	73.26 82	88.37 155	25.30 412	80.60 392	79.10 61	67.55 271	86.23 263

ACMM	58.35 12	64.35 334	62.01 340	71.38 332	74.21 368	48.51 292	82.25 282	79.66 298	47.61 410	54.54 368	80.11 322	25.26 413	86.00 321	51.26 339	63.16 320	79.64 386
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
FE-MVSNET2		58.78 385	56.44 385	65.82 399	63.57 466	38.92 437	79.59 348	81.75 251	56.14 333	43.06 447	68.15 443	25.22 414	80.64 390	42.29 397	48.16 433	77.91 406

XVG-OURS		61.88 362	59.34 367	69.49 360	65.37 452	46.27 361	64.80 449	73.49 405	47.04 415	57.41 337	82.85 275	25.15 415	78.18 414	53.00 323	64.98 294	84.01 305

PVSNet_0	57.04 13	61.19 366	57.24 379	73.02 279	77.45 303	50.31 240	79.43 353	77.36 357	63.96 158	47.51 425	72.45 413	25.03 416	83.78 360	52.76 328	19.22 502	84.96 288

WB-MVS		37.41 455	36.37 455	40.54 476	54.23 484	10.43 515	65.29 445	43.75 489	34.86 475	27.81 493	54.63 482	24.94 417	63.21 475	6.81 512	15.00 505	47.98 495

UniMVSNet_ETH3D		62.51 356	60.49 356	68.57 376	68.30 440	40.88 430	73.89 396	79.93 290	51.81 380	54.77 365	79.61 330	24.80 418	81.10 382	49.93 346	61.35 333	83.73 315

DP-MVS		59.24 376	56.12 389	68.63 373	88.24 36	50.35 238	82.51 277	64.43 460	41.10 450	46.70 430	78.77 339	24.75 419	88.57 206	22.26 480	56.29 386	66.96 472

test_cas_vis1_n_1920		67.10 299	66.60 275	68.59 375	65.17 455	43.23 404	83.23 251	69.84 437	55.34 345	70.67 137	87.71 190	24.70 420	76.66 435	78.57 68	64.20 304	85.89 271

LuminaMVS		66.60 311	64.37 318	73.27 277	70.06 425	49.57 254	80.77 327	81.76 250	50.81 386	60.56 274	78.41 343	24.50 421	87.26 272	64.24 206	68.25 264	82.99 336

tt0805		63.39 346	61.31 349	69.64 359	69.36 430	38.87 438	78.00 364	85.48 129	48.82 401	55.66 358	81.66 306	24.38 422	86.37 305	49.04 354	59.36 353	83.68 321

cascas		69.01 252	66.13 284	77.66 116	79.36 250	55.41 62	86.99 94	83.75 204	56.69 318	58.92 301	81.35 310	24.31 423	92.10 67	53.23 319	70.61 239	85.46 279

CMPMVS	40.41 21	55.34 407	52.64 410	63.46 419	60.88 474	43.84 395	61.58 464	71.06 429	30.43 482	36.33 472	74.63 388	24.14 424	75.44 442	48.05 362	66.62 278	71.12 464
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
UGNet		68.71 260	67.11 264	73.50 269	80.55 218	47.61 330	84.08 217	78.51 332	59.45 251	65.68 193	82.73 280	23.78 425	85.08 343	52.80 325	76.40 147	87.80 218
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
YYNet1		53.82 416	49.96 422	65.41 405	70.09 424	48.95 276	72.30 413	71.66 423	44.25 439	31.89 486	63.07 461	23.73 426	73.95 448	33.26 437	39.40 468	73.34 449

MDA-MVSNet_test_wron		53.82 416	49.95 423	65.43 404	70.13 423	49.05 272	72.30 413	71.65 424	44.23 440	31.85 487	63.13 460	23.68 427	74.01 447	33.25 438	39.35 469	73.23 452

PLC	52.38 18	60.89 367	58.97 371	66.68 394	81.77 169	45.70 374	78.96 357	74.04 397	43.66 442	47.63 422	83.19 273	23.52 428	77.78 425	37.47 408	60.46 340	76.55 424
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
SSC-MVS		35.20 457	34.30 459	37.90 478	52.58 486	8.65 518	61.86 461	41.64 493	31.81 480	25.54 496	52.94 488	23.39 429	59.28 484	6.10 514	12.86 507	45.78 498

ADS-MVSNet2		55.21 409	51.44 415	66.51 395	80.60 214	49.56 257	55.03 478	65.44 454	44.72 434	51.00 399	61.19 468	22.83 430	75.41 443	28.54 457	53.63 408	74.57 441

ADS-MVSNet		56.17 403	51.95 414	68.84 367	80.60 214	53.07 154	55.03 478	70.02 436	44.72 434	51.00 399	61.19 468	22.83 430	78.88 409	28.54 457	53.63 408	74.57 441

test_0402		56.45 401	53.03 405	66.69 393	76.78 320	50.31 240	81.76 296	69.61 439	42.79 446	43.88 440	72.13 423	22.82 432	86.46 302	16.57 494	50.94 420	63.31 481

UnsupCasMVSNet_eth		57.56 395	55.15 394	64.79 410	64.57 460	33.12 461	73.17 404	83.87 203	58.98 268	41.75 453	70.03 435	22.54 433	79.92 401	46.12 376	35.31 475	81.32 367

xiu_mvs_v1_base_debu		71.60 192	70.29 196	75.55 193	77.26 309	53.15 149	85.34 160	79.37 306	55.83 336	72.54 92	90.19 113	22.38 434	86.66 295	73.28 127	76.39 148	86.85 246

xiu_mvs_v1_base		71.60 192	70.29 196	75.55 193	77.26 309	53.15 149	85.34 160	79.37 306	55.83 336	72.54 92	90.19 113	22.38 434	86.66 295	73.28 127	76.39 148	86.85 246

xiu_mvs_v1_base_debi		71.60 192	70.29 196	75.55 193	77.26 309	53.15 149	85.34 160	79.37 306	55.83 336	72.54 92	90.19 113	22.38 434	86.66 295	73.28 127	76.39 148	86.85 246

LS3D		56.40 402	53.82 402	64.12 413	81.12 197	45.69 375	73.42 402	66.14 450	35.30 474	43.24 446	79.88 324	22.18 437	79.62 406	19.10 489	64.00 307	67.05 471

PVSNet	62.49 8	69.27 246	67.81 248	73.64 264	84.41 92	51.85 190	84.63 200	77.80 347	66.42 106	59.80 281	84.95 241	22.14 438	80.44 395	55.03 306	75.11 179	88.62 194

MDA-MVSNet-bldmvs		51.56 429	47.75 437	63.00 422	71.60 400	47.32 338	69.70 431	72.12 416	43.81 441	27.65 494	63.38 459	21.97 439	75.96 439	27.30 464	32.19 483	65.70 477

pmmvs-eth3d		55.97 405	52.78 409	65.54 403	61.02 473	46.44 355	75.36 384	67.72 447	49.61 396	43.65 442	67.58 445	21.63 440	77.04 429	44.11 386	44.33 452	73.15 453

anonymousdsp		60.46 370	57.65 376	68.88 366	63.63 465	45.09 378	72.93 405	78.63 328	46.52 418	51.12 398	72.80 409	21.46 441	83.07 369	57.79 278	53.97 405	78.47 397

MVS-HIRNet		49.01 438	44.71 442	61.92 431	76.06 333	46.61 351	63.23 456	54.90 478	24.77 489	33.56 481	36.60 499	21.28 442	75.88 441	29.49 451	62.54 327	63.26 482

Anonymous202405211		70.11 224	67.88 243	76.79 151	87.20 48	47.24 340	89.49 36	77.38 356	54.88 352	66.14 183	86.84 206	20.93 443	91.54 78	56.45 294	71.62 226	91.59 68

FE-MVSNET		51.43 430	48.22 432	61.06 437	60.78 475	32.48 466	73.85 398	64.62 457	46.30 425	37.47 470	66.27 449	20.80 444	77.38 428	23.43 476	40.48 464	73.31 450

UnsupCasMVSNet_bld		53.86 415	50.53 419	63.84 414	63.52 467	34.75 451	71.38 422	81.92 244	46.53 417	38.95 465	57.93 478	20.55 445	80.20 399	39.91 403	34.09 482	76.57 423

Elysia		65.59 323	62.65 329	74.42 235	69.85 426	49.46 264	80.04 340	82.11 237	46.32 423	58.74 309	79.64 328	20.30 446	88.57 206	55.48 303	71.37 230	85.22 282

StellarMVS		65.59 323	62.65 329	74.42 235	69.85 426	49.46 264	80.04 340	82.11 237	46.32 423	58.74 309	79.64 328	20.30 446	88.57 206	55.48 303	71.37 230	85.22 282

EU-MVSNet		52.63 422	50.72 418	58.37 447	62.69 470	28.13 488	72.60 408	75.97 376	30.94 481	40.76 460	72.11 424	20.16 448	70.80 463	35.11 429	46.11 448	76.19 427

N_pmnet		41.25 448	39.77 451	45.66 469	68.50 437	0.82 539	72.51 410	0.38 537	35.61 471	35.26 476	61.51 467	20.07 449	67.74 468	23.51 474	40.63 462	68.42 470

MSDG		59.44 374	55.14 395	72.32 307	74.69 359	50.71 221	74.39 393	73.58 402	44.44 437	43.40 444	77.52 351	19.45 450	90.87 105	31.31 445	57.49 377	75.38 431

tt0320		52.45 424	48.75 428	63.55 417	71.47 402	41.85 418	72.42 411	59.73 470	36.33 469	44.52 437	61.55 466	19.34 451	76.45 437	33.53 434	39.85 466	72.36 456

K. test v3		54.04 414	49.42 427	67.92 380	68.55 436	42.57 414	75.51 382	63.07 464	52.07 375	39.21 463	64.59 457	19.34 451	82.21 374	37.11 412	25.31 493	78.97 390

lessismore_v0				67.98 379	64.76 459	41.25 426		45.75 487		36.03 474	65.63 454	19.29 453	84.11 355	35.67 421	21.24 499	78.59 396

KD-MVS_self_test		49.24 437	46.85 439	56.44 452	54.32 483	22.87 494	57.39 473	73.36 410	44.36 438	37.98 468	59.30 476	18.97 454	71.17 462	33.48 435	42.44 457	75.26 433

OpenMVS_ROB	53.19 17	59.20 377	56.00 390	68.83 368	71.13 407	44.30 388	83.64 231	75.02 385	46.42 420	46.48 433	73.03 405	18.69 455	88.14 226	27.74 462	61.80 331	74.05 444

mvsany_test1		43.38 447	42.57 449	45.82 468	50.96 492	26.10 490	55.80 476	27.74 509	27.15 486	47.41 426	74.39 390	18.67 456	44.95 501	44.66 381	36.31 473	66.40 474

LTVRE_ROB	45.45 19	52.73 421	49.74 425	61.69 432	69.78 428	34.99 450	44.52 489	67.60 448	43.11 445	43.79 441	74.03 392	18.54 457	81.45 380	28.39 459	57.94 370	68.62 468
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
SixPastTwentyTwo		54.37 410	50.10 420	67.21 386	70.70 413	41.46 425	74.73 388	64.69 456	47.56 411	39.12 464	69.49 436	18.49 458	84.69 349	31.87 442	34.20 481	75.48 430

new-patchmatchnet		48.21 439	46.55 440	53.18 458	57.73 479	18.19 508	70.24 426	71.02 430	45.70 427	33.70 480	60.23 471	18.00 459	69.86 466	27.97 461	34.35 479	71.49 463

tt0320-xc		52.22 427	48.38 431	63.75 416	72.19 395	42.25 417	72.19 416	57.59 474	37.24 462	44.41 438	61.56 465	17.90 460	75.89 440	35.60 422	36.73 472	73.12 454

F-COLMAP		55.96 406	53.65 404	62.87 424	72.76 386	42.77 410	74.70 390	70.37 433	40.03 451	41.11 458	79.36 332	17.77 461	73.70 451	32.80 440	53.96 406	72.15 457

sc_t1		53.51 419	49.92 424	64.29 412	70.33 420	39.55 435	72.93 405	59.60 471	38.74 456	47.16 427	66.47 448	17.59 462	76.50 436	36.83 416	39.62 467	76.82 417

jajsoiax		63.21 348	60.84 353	70.32 350	68.33 439	44.45 386	81.23 316	81.05 261	53.37 367	50.96 401	77.81 349	17.49 463	85.49 334	59.31 254	58.05 368	81.02 371

RPSCF		45.77 444	44.13 446	50.68 460	57.67 480	29.66 481	54.92 480	45.25 488	26.69 487	45.92 435	75.92 381	17.43 464	45.70 500	27.44 463	45.95 449	76.67 419

mmtdpeth		57.93 393	54.78 397	67.39 385	72.32 392	43.38 401	72.72 407	68.93 442	54.45 357	56.85 343	62.43 462	17.02 465	83.46 365	57.95 274	30.31 487	75.31 432

PatchMatch-RL		56.66 398	53.75 403	65.37 406	77.91 292	45.28 377	69.78 430	60.38 468	41.35 449	47.57 423	73.73 396	16.83 466	76.91 431	36.99 414	59.21 354	73.92 445

mvs_tets		62.96 351	60.55 355	70.19 351	68.22 442	44.24 391	80.90 323	80.74 269	52.99 370	50.82 405	77.56 350	16.74 467	85.44 335	59.04 257	57.94 370	80.89 372

ACMH+	54.58 15	58.55 389	55.24 393	68.50 377	74.68 360	45.80 373	80.27 335	70.21 434	47.15 414	42.77 448	75.48 383	16.73 468	85.98 323	35.10 430	54.78 400	73.72 446

ACMH	53.70 16	59.78 372	55.94 391	71.28 333	76.59 321	48.35 298	80.15 339	76.11 375	49.74 395	41.91 452	73.45 403	16.50 469	90.31 127	31.42 444	57.63 376	75.17 434
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
MIMVSNet1		50.35 435	47.81 435	57.96 448	61.53 472	27.80 489	67.40 440	74.06 396	43.25 444	33.31 485	65.38 456	16.03 470	71.34 461	21.80 481	47.55 439	74.75 438

DSMNet-mixed		38.35 452	35.36 457	47.33 467	48.11 498	14.91 512	37.87 497	36.60 500	19.18 494	34.37 478	59.56 475	15.53 471	53.01 493	20.14 487	46.89 445	74.07 443

EG-PatchMatch MVS		62.40 360	59.59 364	70.81 342	73.29 377	49.05 272	85.81 135	84.78 171	51.85 379	44.19 439	73.48 402	15.52 472	89.85 143	40.16 402	67.24 273	73.54 448

testgi		54.25 412	52.57 411	59.29 444	62.76 469	21.65 500	72.21 415	70.47 432	53.25 368	41.94 451	77.33 356	14.28 473	77.95 421	29.18 453	51.72 419	78.28 402

COLMAP_ROB	43.60 20	50.90 433	48.05 434	59.47 441	67.81 443	40.57 431	71.25 423	62.72 466	36.49 467	36.19 473	73.51 401	13.48 474	73.92 449	20.71 484	50.26 422	63.92 480
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
OurMVSNet-221017-0		52.39 425	48.73 429	63.35 421	65.21 454	38.42 442	68.54 436	64.95 455	38.19 457	39.57 462	71.43 427	13.23 475	79.92 401	37.16 410	40.32 465	71.72 460

usedtu_dtu_shiyan2		50.47 434	46.43 441	62.61 426	51.66 489	31.70 471	75.62 379	75.65 379	36.36 468	34.89 477	56.91 481	12.01 476	78.40 412	30.87 448	43.86 453	77.72 409

MVStest1		38.35 452	34.53 458	49.82 464	51.43 490	30.41 473	50.39 482	55.25 476	17.56 497	26.45 495	65.85 453	11.72 477	57.00 488	14.79 496	17.31 504	62.05 484

test_fmvs1		53.60 418	52.54 412	56.78 450	58.07 477	30.26 474	68.95 434	42.19 492	32.46 477	63.59 238	82.56 287	11.55 478	60.81 479	58.25 268	55.27 396	79.28 387

tmp_tt		9.44 480	10.68 483	5.73 503	2.49 534	4.21 524	10.48 513	18.04 516	0.34 527	12.59 508	20.49 515	11.39 479	7.03 521	13.84 499	6.46 514	5.95 522

ITE_SJBPF				51.84 459	58.03 478	31.94 470		53.57 482	36.67 465	41.32 456	75.23 385	11.17 480	51.57 494	25.81 468	48.04 435	72.02 459

Anonymous20240521		51.65 428	48.42 430	61.34 436	56.43 482	39.65 434	73.57 400	73.47 408	36.64 466	36.59 471	63.98 458	10.75 481	72.25 460	35.35 424	49.01 424	72.11 458

mvs5depth		50.97 432	46.98 438	62.95 423	56.63 481	34.23 456	62.73 460	67.35 449	45.03 433	48.00 419	65.41 455	10.40 482	79.88 405	36.00 419	31.27 486	74.73 439

AllTest		47.32 441	44.66 443	55.32 456	65.08 456	37.50 446	62.96 458	54.25 480	35.45 472	33.42 482	72.82 407	9.98 483	59.33 482	24.13 472	43.84 454	69.13 466

TestCases				55.32 456	65.08 456	37.50 446		54.25 480	35.45 472	33.42 482	72.82 407	9.98 483	59.33 482	24.13 472	43.84 454	69.13 466

USDC		54.36 411	51.23 416	63.76 415	64.29 462	37.71 445	62.84 459	73.48 407	56.85 310	35.47 475	71.94 426	9.23 485	78.43 411	38.43 407	48.57 431	75.13 435

XVG-ACMP-BASELINE		56.03 404	52.85 408	65.58 402	61.91 471	40.95 429	63.36 454	72.43 414	45.20 431	46.02 434	74.09 391	9.20 486	78.12 415	45.13 378	58.27 363	77.66 411

test_fmvs1_n		52.55 423	51.19 417	56.65 451	51.90 488	30.14 475	67.66 439	42.84 491	32.27 478	62.30 253	82.02 301	9.12 487	60.84 478	57.82 277	54.75 402	78.99 389

test_vis1_n		51.19 431	49.66 426	55.76 455	51.26 491	29.85 480	67.20 442	38.86 496	32.12 479	59.50 288	79.86 325	8.78 488	58.23 486	56.95 286	52.46 416	79.19 388

pmmvs3		45.53 445	41.55 450	57.44 449	48.97 496	39.68 433	70.06 427	57.66 473	28.32 485	34.06 479	57.29 479	8.50 489	66.85 472	34.86 431	34.26 480	65.80 476

EGC-MVSNET		33.75 459	30.42 463	43.75 472	64.94 458	36.21 449	60.47 468	40.70 495	0.02 556	0.10 553	53.79 485	7.39 490	60.26 480	11.09 503	35.23 477	34.79 500

test_fmvs2		45.89 443	44.32 445	50.62 461	45.85 500	24.70 492	58.87 472	37.84 499	25.22 488	52.46 385	74.56 389	7.07 491	54.69 490	49.28 352	47.70 437	72.48 455

ANet_high		34.39 458	29.59 464	48.78 465	30.34 510	22.28 496	55.53 477	63.79 462	38.11 458	15.47 503	36.56 500	6.94 492	59.98 481	13.93 498	5.64 515	64.08 479

FPMVS		35.40 456	33.67 460	40.57 475	46.34 499	28.74 487	41.05 493	57.05 475	20.37 493	22.27 498	53.38 486	6.87 493	44.94 502	8.62 506	47.11 443	48.01 494

test_vis1_rt		40.29 451	38.64 452	45.25 470	48.91 497	30.09 476	59.44 469	27.07 510	24.52 490	38.48 467	51.67 489	6.71 494	49.44 495	44.33 383	46.59 447	56.23 486

new_pmnet		33.56 460	31.89 462	38.59 477	49.01 495	20.42 501	51.01 481	37.92 498	20.58 491	23.45 497	46.79 491	6.66 495	49.28 497	20.00 488	31.57 485	46.09 497

TinyColmap		48.15 440	44.49 444	59.13 445	65.73 451	38.04 443	63.34 455	62.86 465	38.78 454	29.48 489	67.23 447	6.46 496	73.30 454	24.59 471	41.90 459	66.04 475

ambc				62.06 428	53.98 485	29.38 483	35.08 499	79.65 300		41.37 454	59.96 473	6.27 497	82.15 375	35.34 425	38.22 470	74.65 440

TDRefinement		40.91 449	38.37 453	48.55 466	50.45 493	33.03 463	58.98 471	50.97 483	28.50 483	29.89 488	67.39 446	6.21 498	54.51 491	17.67 492	35.25 476	58.11 485

ttmdpeth		40.58 450	37.50 454	49.85 463	49.40 494	22.71 495	56.65 475	46.78 484	28.35 484	40.29 461	69.42 438	5.35 499	61.86 477	20.16 486	21.06 500	64.96 478

PM-MVS		46.92 442	43.76 448	56.41 453	52.18 487	32.26 467	63.21 457	38.18 497	37.99 459	40.78 459	66.20 450	5.09 500	65.42 473	48.19 361	41.99 458	71.54 462

LF4IMVS		33.04 461	32.55 461	34.52 481	40.96 501	22.03 497	44.45 490	35.62 501	20.42 492	28.12 492	62.35 463	5.03 501	31.88 513	21.61 483	34.42 478	49.63 493

EMVS		18.42 474	17.66 478	20.71 492	34.13 507	12.64 514	46.94 485	29.94 507	10.46 507	5.58 521	14.93 521	4.23 502	38.83 505	5.24 518	7.51 512	10.67 516

E-PMN		19.16 473	18.40 477	21.44 491	36.19 505	13.63 513	47.59 484	30.89 505	10.73 505	5.91 519	16.59 518	3.66 503	39.77 504	5.95 515	8.14 510	10.92 515

test_method		24.09 470	21.07 474	33.16 484	27.67 514	8.35 521	26.63 505	35.11 503	3.40 515	14.35 504	36.98 498	3.46 504	35.31 508	19.08 490	22.95 496	55.81 487

mvsany_test3		28.00 463	25.98 465	34.05 482	28.97 511	15.31 510	34.54 500	18.17 515	16.24 498	29.30 490	53.37 487	2.79 505	33.38 512	30.01 450	20.41 501	53.45 490

test_f		27.12 465	24.85 466	33.93 483	26.17 516	15.25 511	30.24 504	22.38 514	12.53 503	28.23 491	49.43 490	2.59 506	34.34 511	25.12 470	26.99 491	52.20 491

test_fmvs3		37.95 454	35.75 456	44.55 471	35.50 506	18.92 504	48.32 483	34.00 504	18.36 496	41.31 457	61.58 464	2.29 507	48.06 499	42.72 394	37.71 471	66.66 473

PMMVS2		26.71 466	22.98 471	37.87 479	36.89 504	8.51 519	42.51 492	29.32 508	19.09 495	13.01 506	37.54 496	2.23 508	53.11 492	14.54 497	11.71 508	51.99 492

Gipuma		27.47 464	24.26 469	37.12 480	60.55 476	29.17 484	11.68 511	60.00 469	14.18 500	10.52 512	15.12 520	2.20 509	63.01 476	8.39 507	35.65 474	19.18 508
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
LCM-MVSNet		28.07 462	23.85 470	40.71 474	27.46 515	18.93 503	30.82 503	46.19 485	12.76 502	16.40 500	34.70 502	1.90 510	48.69 498	20.25 485	24.22 495	54.51 489

DeepMVS_CX				13.10 495	21.34 518	8.99 517		10.02 520	10.59 506	7.53 516	30.55 506	1.82 511	14.55 514	6.83 511	7.52 511	15.75 510

VLMVS		5.96 487	6.29 490	4.99 504	5.31 527	1.01 534	4.24 521	0.93 527	0.06 540	8.90 513	26.22 510	1.69 512	1.62 531	3.76 524	5.49 516	12.33 512

APD_test1		26.46 467	24.41 468	32.62 486	37.58 503	21.74 499	40.50 495	30.39 506	11.45 504	16.33 501	43.76 492	1.63 513	41.62 503	11.24 502	26.82 492	34.51 501

VLMVS_CLIP		11.28 479	11.90 482	9.42 498	7.54 523	3.26 526	13.10 510	10.36 519	1.51 521	15.95 502	32.54 505	1.51 514	12.70 516	10.98 504	13.62 506	12.29 513

PMVS	19.57 22	25.07 468	22.43 473	32.99 485	23.12 517	22.98 493	40.98 494	35.19 502	15.99 499	11.95 511	35.87 501	1.47 515	49.29 496	5.41 517	31.90 484	26.70 507
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
test_vis3_rt		24.79 469	22.95 472	30.31 487	28.59 512	18.92 504	37.43 498	17.27 517	12.90 501	21.28 499	29.92 507	1.02 516	36.35 506	28.28 460	29.82 490	35.65 499

MVE	16.60 23	17.34 476	13.39 479	29.16 488	28.43 513	19.72 502	13.73 509	23.63 513	7.23 510	7.96 515	21.41 513	0.80 517	36.08 507	6.97 510	10.39 509	31.69 502
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
testf1		21.11 471	19.08 475	27.18 489	30.56 508	18.28 506	33.43 501	24.48 511	8.02 508	12.02 509	33.50 503	0.75 518	35.09 509	7.68 508	21.32 497	28.17 504

APD_test2		21.11 471	19.08 475	27.18 489	30.56 508	18.28 506	33.43 501	24.48 511	8.02 508	12.02 509	33.50 503	0.75 518	35.09 509	7.68 508	21.32 497	28.17 504

MVS_clip		3.10 495	3.65 498	1.44 511	3.78 530	1.17 533	2.78 522	0.19 541	0.20 530	4.48 525	14.54 523	0.35 520	0.47 537	2.92 525	3.64 520	2.67 527

ArgMatch-Sym		13.78 477	13.16 480	15.65 494	13.75 519	8.38 520	21.56 506	2.56 522	7.09 511	14.16 505	40.67 494	0.28 521	11.85 518	13.55 500	4.84 517	26.71 506

ArgMatch-SfM		13.59 478	12.41 481	17.15 493	12.50 520	7.57 522	19.17 508	3.21 521	5.58 512	12.94 507	39.91 495	0.26 522	13.40 515	13.23 501	4.84 517	30.48 503

wuyk23d		9.11 481	8.77 485	10.15 496	40.18 502	16.76 509	20.28 507	1.01 526	2.58 517	2.66 528	0.98 542	0.23 523	12.49 517	4.08 523	6.90 513	1.19 529

PDCNetPlus		5.70 489	5.56 492	6.14 501	8.32 522	1.98 529	7.37 516	0.76 529	2.18 518	3.69 526	20.81 514	0.12 524	4.60 524	4.55 520	2.21 523	11.83 514

DenseAffine		8.44 482	7.90 488	10.07 497	9.51 521	4.71 523	11.43 512	1.10 525	4.32 513	8.26 514	27.67 509	0.09 525	8.71 519	6.30 513	2.41 522	16.80 509

RoMa-SfM		7.02 484	6.78 489	7.74 499	5.47 526	3.55 525	8.83 514	0.67 530	3.41 514	7.06 517	27.85 508	0.08 526	7.13 520	5.86 516	1.82 524	12.53 511

LoFTR		5.36 490	5.09 493	6.17 500	5.52 525	2.23 528	6.04 517	2.15 523	1.23 522	5.61 520	19.15 516	0.07 527	5.98 522	1.61 527	4.48 519	10.30 518

RoMa-HiRes		4.68 491	4.75 494	4.46 505	3.18 531	1.88 530	5.38 519	0.37 538	2.04 519	4.84 522	21.68 512	0.06 528	3.78 526	4.17 522	1.04 531	7.71 521

ALIKED-LG		1.21 501	1.31 505	0.90 514	2.88 532	0.91 536	1.96 524	0.48 534	0.17 531	0.94 534	3.75 532	0.06 528	0.81 533	0.10 541	1.43 527	0.99 530

SP-DiffGlue		0.50 506	0.53 509	0.38 521	0.41 560	0.20 550	0.62 535	0.19 541	0.09 534	0.64 539	1.95 536	0.06 528	0.17 544	0.26 534	0.60 537	0.77 536

MASt3R-SfM		1.80 499	2.02 501	1.14 513	1.03 543	0.52 542	1.83 526	0.53 532	0.34 527	2.55 529	9.61 527	0.05 531	0.77 534	1.06 529	1.16 530	2.14 528

ALIKED-NN		1.00 504	1.09 507	0.75 516	2.44 535	0.84 538	1.63 530	0.39 535	0.12 532	0.72 537	3.04 534	0.05 531	0.70 536	0.08 543	1.32 529	0.72 538

DKM		5.93 488	5.87 491	6.10 502	5.64 524	2.81 527	7.85 515	0.52 533	2.62 516	6.30 518	23.31 511	0.05 531	4.93 523	5.11 519	1.45 526	10.57 517

SP-LightGlue		0.48 507	0.50 510	0.40 517	1.33 538	0.19 551	0.86 531	0.17 544	0.08 536	0.25 541	1.08 538	0.05 531	0.19 541	0.13 537	0.57 538	0.80 533

SP-SuperGlue		0.47 508	0.50 510	0.39 518	1.30 539	0.19 551	0.86 531	0.17 544	0.09 534	0.26 540	1.08 538	0.05 531	0.18 543	0.13 537	0.55 539	0.79 535

ALIKED-MNN		1.07 503	1.15 506	0.84 515	2.67 533	0.92 535	1.81 527	0.39 535	0.12 532	0.73 536	3.13 533	0.05 531	0.77 534	0.09 542	1.34 528	0.84 532

DKM-HiRes		4.42 492	4.49 495	4.23 506	3.85 529	1.83 531	5.38 519	0.33 539	1.86 520	4.78 523	18.85 517	0.04 537	2.97 528	4.34 521	0.97 532	7.88 520

SP-NN		0.43 511	0.45 514	0.37 522	1.13 542	0.17 555	0.82 534	0.16 546	0.07 538	0.24 542	1.00 541	0.04 537	0.19 541	0.12 539	0.51 542	0.74 537

GLUNet-SfM		2.60 496	2.13 500	4.01 508	1.95 536	0.86 537	1.72 528	0.81 528	0.34 527	3.35 527	9.72 526	0.04 537	3.15 527	0.50 532	0.73 535	8.02 519

MatchFormer		3.89 493	3.84 497	4.03 507	4.08 528	1.73 532	5.52 518	1.59 524	0.67 523	4.77 524	13.56 524	0.04 537	4.50 525	0.74 531	3.60 521	5.85 523

SP-MNN		0.45 509	0.47 513	0.39 518	1.18 541	0.17 555	0.85 533	0.16 546	0.07 538	0.24 542	1.05 540	0.04 537	0.20 540	0.12 539	0.54 541	0.80 533

MVS_baseline		1.13 502	1.40 504	0.34 523	0.74 550	0.01 565	0.24 550	0.03 563	0.00 557	1.75 532	7.74 530	0.03 542	0.00 559	0.31 533	1.74 525	0.99 530

XFeat-NN		0.44 510	0.49 512	0.30 524	0.24 562	0.12 561	0.48 537	0.15 548	0.06 540	0.71 538	1.78 537	0.03 542	0.28 539	0.14 536	0.83 534	0.48 540

XFeat-MNN		0.55 505	0.60 508	0.39 518	0.26 561	0.16 558	0.58 536	0.20 540	0.08 536	0.82 535	2.26 535	0.03 542	0.39 538	0.19 535	0.95 533	0.62 539

ELoFTR		2.17 498	1.90 502	2.99 509	1.19 540	0.63 541	1.84 525	0.60 531	0.46 525	2.17 531	9.10 528	0.02 545	2.92 529	1.00 530	0.72 536	5.42 524

PMatch-SfM		2.38 497	2.41 499	2.29 510	1.48 537	0.76 540	2.51 523	0.18 543	0.59 524	2.43 530	12.04 525	0.01 546	1.67 530	1.93 526	0.55 539	4.44 525

SIFT-UM-Cal		0.21 521	0.23 524	0.14 535	0.68 553	0.15 559	0.29 547	0.04 560	0.05 542	0.10 553	0.56 552	0.01 546	0.12 554	0.02 544	0.34 553	0.15 553

SIFT-NCM-Cal		0.26 515	0.28 518	0.19 528	0.84 547	0.23 547	0.38 541	0.06 553	0.05 542	0.11 551	0.59 550	0.01 546	0.14 545	0.02 544	0.45 546	0.21 547

SIFT-CM-Cal		0.21 521	0.23 524	0.15 534	0.71 552	0.18 553	0.28 548	0.05 556	0.05 542	0.10 553	0.55 553	0.01 546	0.12 554	0.01 556	0.33 554	0.17 551

SIFT-PCN-Cal		0.18 523	0.20 526	0.13 536	0.58 557	0.10 563	0.23 551	0.04 560	0.04 552	0.08 556	0.47 554	0.01 546	0.10 556	0.01 556	0.30 555	0.19 548

SIFT-NN-UMatch		0.24 517	0.26 519	0.18 530	0.64 555	0.18 553	0.38 541	0.06 553	0.05 542	0.12 550	0.65 545	0.01 546	0.13 549	0.02 544	0.43 547	0.22 545

SIFT-NN-NCMNet		0.27 514	0.29 517	0.20 527	0.81 548	0.24 546	0.40 540	0.08 550	0.05 542	0.14 547	0.65 545	0.01 546	0.14 545	0.02 544	0.47 544	0.22 545

SIFT-NN-CMatch		0.25 516	0.26 519	0.19 528	0.68 553	0.21 548	0.35 543	0.06 553	0.05 542	0.15 545	0.65 545	0.01 546	0.13 549	0.02 544	0.41 548	0.23 543

SIFT-NN-PointCN		0.22 520	0.24 523	0.17 532	0.59 556	0.14 560	0.32 545	0.05 556	0.04 552	0.13 548	0.57 551	0.01 546	0.13 549	0.02 544	0.39 549	0.23 543

SIFT-NN		0.30 512	0.33 515	0.22 525	0.96 545	0.28 544	0.45 538	0.08 550	0.05 542	0.17 544	0.72 543	0.01 546	0.14 545	0.02 544	0.48 543	0.25 541

SIFT-UMatch		0.23 519	0.25 522	0.16 533	0.74 550	0.17 555	0.33 544	0.05 556	0.05 542	0.11 551	0.60 549	0.01 546	0.13 549	0.02 544	0.37 551	0.18 550

SIFT-NCMNet		0.15 525	0.17 528	0.10 538	0.52 559	0.09 564	0.19 552	0.02 564	0.04 552	0.07 558	0.39 556	0.01 546	0.08 558	0.01 556	0.24 557	0.11 554

SIFT-ConvMatch		0.24 517	0.26 519	0.18 530	0.76 549	0.21 548	0.32 545	0.05 556	0.05 542	0.13 548	0.63 548	0.01 546	0.13 549	0.02 544	0.38 550	0.19 548

SIFT-PointCN		0.18 523	0.20 526	0.13 536	0.58 557	0.11 562	0.25 549	0.04 560	0.04 552	0.08 556	0.45 555	0.01 546	0.10 556	0.01 556	0.30 555	0.17 551

SIFT-MNN		0.28 513	0.31 516	0.21 526	0.89 546	0.25 545	0.41 539	0.08 550	0.05 542	0.15 545	0.70 544	0.01 546	0.14 545	0.02 544	0.46 545	0.25 541

PMatch-Up-SfM		1.67 500	1.74 503	1.44 511	1.00 544	0.50 543	1.72 528	0.11 549	0.40 526	1.75 532	8.98 529	0.00 561	1.07 532	1.34 528	0.35 552	2.76 526

mmdepth		0.00 526	0.00 529	0.00 541	0.00 564	0.00 567	0.00 553	0.00 565	0.00 557	0.00 561	0.00 559	0.00 561	0.00 559	0.00 560	0.00 558	0.00 557

monomultidepth		0.00 526	0.00 529	0.00 541	0.00 564	0.00 567	0.00 553	0.00 565	0.00 557	0.00 561	0.00 559	0.00 561	0.00 559	0.00 560	0.00 558	0.00 557

test_blank		0.00 526	0.00 529	0.00 541	0.00 564	0.00 567	0.00 553	0.00 565	0.00 557	0.00 561	0.00 559	0.00 561	0.00 559	0.00 560	0.00 558	0.00 557

uanet_test		0.00 526	0.00 529	0.00 541	0.00 564	0.00 567	0.00 553	0.00 565	0.00 557	0.00 561	0.00 559	0.00 561	0.00 559	0.00 560	0.00 558	0.00 557

DCPMVS		0.00 526	0.00 529	0.00 541	0.00 564	0.00 567	0.00 553	0.00 565	0.00 557	0.00 561	0.00 559	0.00 561	0.00 559	0.00 560	0.00 558	0.00 557

sosnet-low-res		0.00 526	0.00 529	0.00 541	0.00 564	0.00 567	0.00 553	0.00 565	0.00 557	0.00 561	0.00 559	0.00 561	0.00 559	0.00 560	0.00 558	0.00 557

sosnet		0.00 526	0.00 529	0.00 541	0.00 564	0.00 567	0.00 553	0.00 565	0.00 557	0.00 561	0.00 559	0.00 561	0.00 559	0.00 560	0.00 558	0.00 557

uncertanet		0.00 526	0.00 529	0.00 541	0.00 564	0.00 567	0.00 553	0.00 565	0.00 557	0.00 561	0.00 559	0.00 561	0.00 559	0.00 560	0.00 558	0.00 557

Regformer		0.00 526	0.00 529	0.00 541	0.00 564	0.00 567	0.00 553	0.00 565	0.00 557	0.00 561	0.00 559	0.00 561	0.00 559	0.00 560	0.00 558	0.00 557

testmvs		6.14 485	8.18 486	0.01 539	0.01 563	0.00 567	73.40 403	0.00 565	0.00 557	0.02 559	0.15 557	0.00 561	0.00 559	0.02 544	0.00 558	0.02 555

test123		6.01 486	8.01 487	0.01 539	0.00 564	0.01 565	71.93 420	0.00 565	0.00 557	0.02 559	0.11 558	0.00 561	0.00 559	0.02 544	0.00 558	0.02 555

ab-mvs-re		7.68 483	10.24 484	0.00 541	0.00 564	0.00 567	0.00 553	0.00 565	0.00 557	0.00 561	92.12 59	0.00 561	0.00 559	0.00 560	0.00 558	0.00 557

uanet		0.00 526	0.00 529	0.00 541	0.00 564	0.00 567	0.00 553	0.00 565	0.00 557	0.00 561	0.00 559	0.00 561	0.00 559	0.00 560	0.00 558	0.00 557

PatchmatchNet2					0.00 564	32.03 469	74.85 386	61.13 467	37.29 461
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. CVPR 2021
PatchmatchNet1														23.45 475	40.77 460	68.54 469
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. CVPR 2021
PatchmatchNet3													67.71 469
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. CVPR 2021
aaatest				80.14 39	84.34 94	54.93 86	87.61 72	87.22 84	57.43 300	81.85 18	92.88 44		93.75 32	80.19 52	85.13 50	91.76 62

WAC-MVS						34.28 454								22.56 479

FOURS1					83.24 122	49.90 249	84.98 183	78.76 324	47.71 409	73.42 79

MSC_two_6792asdad				81.53 17	91.77 4	56.03 50		91.10 13					96.22 9	81.46 46	86.80 29	92.34 37

No_MVS				81.53 17	91.77 4	56.03 50		91.10 13					96.22 9	81.46 46	86.80 29	92.34 37

eth-test2					0.00 564

eth-test					0.00 564

IU-MVS					89.48 18	57.49 19		91.38 9	66.22 110	88.26 2				82.83 32	87.60 19	92.44 34

save fliter					85.35 74	56.34 44	89.31 42	81.46 254	61.55 212

test_0728_SECOND				82.20 9	89.50 16	57.73 15	92.34 5	88.88 39					96.39 4	81.68 41	87.13 22	92.47 33

GSMVS																88.13 211

test_part2					89.33 24	55.48 58				82.27 13

MTGPA								81.31 257

MTMP							87.27 88	15.34 518

gm-plane-assit					83.24 122	54.21 121			70.91 32		88.23 164		95.25 15	66.37 183

test9_res														78.72 67	85.44 46	91.39 78

agg_prior2														75.65 94	85.11 52	91.01 103

agg_prior					85.64 67	54.92 91		83.61 211		72.53 95			88.10 229

test_prior4						56.39 43	87.15 92

test_prior				78.39 96	86.35 57	54.91 94		85.45 132					89.70 153			90.55 121

旧先验2							81.73 299		45.53 429	74.66 65			70.48 465	58.31 267

新几何2							81.61 305

无先验							85.19 169	78.00 342	49.08 398				85.13 342	52.78 326		87.45 228

原ACMM2							83.77 229

testdata2													77.81 424	45.64 377

testdata1							77.55 368		64.14 152

plane_prior7					77.95 289	48.46 295

plane_prior5								82.59 229					88.30 222	65.46 194	72.34 218	84.49 293

plane_prior4											83.28 271

plane_prior3						48.95 276			64.01 156	62.15 256

plane_prior2							85.76 138		63.60 168

plane_prior1					78.31 284

plane_prior						49.57 254	87.43 80		64.57 142						72.84 210

n2								0.00 565

nn								0.00 565

door-mid								41.31 494

test11								84.25 190

door								43.27 490

HQP5-MVS						51.56 201

HQP-NCC					79.02 263		88.00 61		65.45 125	64.48 216

ACMP_Plane					79.02 263		88.00 61		65.45 125	64.48 216

BP-MVS														66.70 180

HQP4-MVS										64.47 219			88.61 201			84.91 289

HQP3-MVS								83.68 206							73.12 206

NP-MVS					78.76 269	50.43 231					85.12 235

ACMMP++_ref															63.20 319

ACMMP++															59.38 352

High-resolution multi-view benchmark