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.

PGM-MVS		96.81 29	96.53 32	97.65 31	99.35 13	93.53 46	97.65 70	98.98 1	92.22 88	97.14 24	98.44 17	91.17 44	99.85 11	94.35 69	99.46 26	99.57 14

MVS_111021_HR		96.68 36	96.58 31	96.99 59	98.46 54	92.31 74	96.20 217	98.90 2	94.30 35	95.86 63	97.74 67	92.33 24	99.38 91	96.04 31	99.42 31	99.28 49

ACMMP		96.27 46	95.93 46	97.28 47	99.24 21	92.62 68	98.25 25	98.81 3	92.99 69	94.56 87	98.39 23	88.96 66	99.85 11	94.57 68	97.63 97	99.36 42
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
MVS_111021_LR		96.24 47	96.19 44	96.39 81	98.23 75	91.35 103	96.24 215	98.79 4	93.99 39	95.80 66	97.65 73	89.92 61	99.24 98	95.87 34	99.20 52	98.58 95

FC-MVSNet-test		93.94 100	93.57 92	95.04 142	95.48 193	91.45 101	98.12 30	98.71 5	93.37 55	90.23 181	96.70 113	87.66 83	97.85 245	91.49 122	90.39 225	95.83 209

UniMVSNet (Re)		93.31 119	92.55 125	95.61 114	95.39 196	93.34 53	97.39 103	98.71 5	93.14 65	90.10 190	94.83 206	87.71 82	98.03 218	91.67 120	83.99 288	95.46 225

FIs		94.09 94	93.70 88	95.27 129	95.70 187	92.03 84	98.10 31	98.68 7	93.36 57	90.39 178	96.70 113	87.63 85	97.94 235	92.25 100	90.50 224	95.84 208

WR-MVS_H		92.00 169	91.35 164	93.95 195	95.09 217	89.47 166	98.04 35	98.68 7	91.46 115	88.34 237	94.68 212	85.86 106	97.56 267	85.77 218	84.24 286	94.82 269

VPA-MVSNet		93.24 121	92.48 130	95.51 119	95.70 187	92.39 73	97.86 47	98.66 9	92.30 87	92.09 140	95.37 184	80.49 204	98.40 175	93.95 74	85.86 260	95.75 216

UniMVSNet_NR-MVSNet		93.37 117	92.67 120	95.47 124	95.34 199	92.83 62	97.17 125	98.58 10	92.98 74	90.13 186	95.80 158	88.37 76	97.85 245	91.71 116	83.93 289	95.73 218

CSCG		96.05 51	95.91 47	96.46 79	99.24 21	90.47 131	98.30 21	98.57 11	89.01 179	93.97 98	97.57 82	92.62 19	99.76 24	94.66 67	99.27 46	99.15 56

MSLP-MVS++		96.94 25	97.06 9	96.59 69	98.72 38	91.86 89	97.67 67	98.49 12	94.66 27	97.24 19	98.41 22	92.31 27	98.94 128	96.61 15	99.46 26	98.96 72

HyFIR lowres test		93.66 108	92.92 111	95.87 103	98.24 72	89.88 145	94.58 271	98.49 12	85.06 273	93.78 99	95.78 162	82.86 156	98.67 149	91.77 114	95.71 140	99.07 64

CHOSEN 1792x2688		94.15 90	93.51 96	96.06 96	98.27 69	89.38 174	95.18 264	98.48 14	85.60 266	93.76 100	97.11 100	83.15 135	99.61 48	91.33 125	98.72 73	99.19 52

PHI-MVS		96.77 31	96.46 35	97.71 28	98.40 58	94.07 30	98.21 28	98.45 15	89.86 154	97.11 27	98.01 49	92.52 22	99.69 36	96.03 32	99.53 17	99.36 42

PVSNet_BlendedMVS		94.06 95	93.92 83	94.47 173	98.27 69	89.46 168	96.73 164	98.36 16	90.17 149	94.36 90	95.24 190	88.02 77	99.58 56	93.44 87	90.72 220	94.36 286

PVSNet_Blended		94.87 79	94.56 73	95.81 105	98.27 69	89.46 168	95.47 252	98.36 16	88.84 188	94.36 90	96.09 147	88.02 77	99.58 56	93.44 87	98.18 84	98.40 115

3Dnovator	91.36 5	95.19 69	94.44 80	97.44 40	96.56 150	93.36 52	98.65 6	98.36 16	94.12 37	89.25 225	98.06 46	82.20 174	99.77 23	93.41 89	99.32 42	99.18 53

HFP-MVS		97.14 15	96.92 16	97.83 16	99.42 3	94.12 28	98.52 10	98.32 19	93.21 60	97.18 21	98.29 37	92.08 29	99.83 15	95.63 40	99.59 10	99.54 20

#test#		97.02 21	96.75 26	97.83 16	99.42 3	94.12 28	98.15 29	98.32 19	92.57 83	97.18 21	98.29 37	92.08 29	99.83 15	95.12 52	99.59 10	99.54 20

ACMMPR		97.07 18	96.84 19	97.79 20	99.44 2	93.88 34	98.52 10	98.31 21	93.21 60	97.15 23	98.33 31	91.35 42	99.86 8	95.63 40	99.59 10	99.62 8

APDe-MVS		97.82 1	97.73 1	98.08 9	99.15 25	94.82 13	98.81 2	98.30 22	94.76 24	98.30 5	98.90 2	93.77 8	99.68 38	97.93 1	99.69 1	99.75 1

CP-MVS		97.02 21	96.81 22	97.64 33	99.33 14	93.54 45	98.80 3	98.28 23	92.99 69	96.45 45	98.30 36	91.90 34	99.85 11	95.61 42	99.68 2	99.54 20

SteuartSystems-ACMMP		97.62 3	97.53 2	97.87 14	98.39 60	94.25 23	98.43 16	98.27 24	95.34 9	98.11 6	98.56 8	94.53 3	99.71 30	96.57 17	99.62 8	99.65 3
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test_part1								98.26 25				95.31 1			99.63 5	99.63 5

ESAPD		97.57 4	97.29 7	98.41 2	99.28 17	95.74 3	97.50 91	98.26 25	93.81 45	98.10 7	98.53 12	95.31 1	99.87 5	95.19 48	99.63 5	99.63 5

PVSNet_Blended_VisFu		95.27 65	94.91 65	96.38 82	98.20 76	90.86 121	97.27 113	98.25 27	90.21 148	94.18 94	97.27 92	87.48 88	99.73 26	93.53 83	97.77 95	98.55 96

region2R		97.07 18	96.84 19	97.77 23	99.46 1	93.79 38	98.52 10	98.24 28	93.19 63	97.14 24	98.34 28	91.59 40	99.87 5	95.46 45	99.59 10	99.64 4

PS-CasMVS		91.55 194	90.84 187	93.69 214	94.96 222	88.28 201	97.84 50	98.24 28	91.46 115	88.04 244	95.80 158	79.67 217	97.48 272	87.02 199	84.54 284	95.31 238

DU-MVS		92.90 133	92.04 136	95.49 121	94.95 223	92.83 62	97.16 126	98.24 28	93.02 68	90.13 186	95.71 166	83.47 130	97.85 245	91.71 116	83.93 289	95.78 212

XVS		97.18 12	96.96 14	97.81 18	99.38 8	94.03 32	98.59 7	98.20 31	94.85 17	96.59 38	98.29 37	91.70 37	99.80 21	95.66 38	99.40 33	99.62 8

X-MVStestdata		91.71 177	89.67 234	97.81 18	99.38 8	94.03 32	98.59 7	98.20 31	94.85 17	96.59 38	32.69 353	91.70 37	99.80 21	95.66 38	99.40 33	99.62 8

SMA-MVS		97.36 8	97.06 9	98.25 4	99.06 29	95.30 7	97.94 41	98.19 33	90.66 137	99.06 1	98.94 1	93.33 11	99.83 15	96.72 13	99.68 2	99.63 5

ACMMP_Plus		97.20 11	96.86 18	98.23 5	99.09 26	95.16 9	97.60 83	98.19 33	92.82 78	97.93 11	98.74 4	91.60 39	99.86 8	96.26 21	99.52 18	99.67 2

CP-MVSNet		91.89 172	91.24 170	93.82 201	95.05 218	88.57 195	97.82 51	98.19 33	91.70 109	88.21 242	95.76 163	81.96 178	97.52 270	87.86 176	84.65 282	95.37 235

PEN-MVS		91.20 209	90.44 204	93.48 225	94.49 241	87.91 229	97.76 54	98.18 36	91.29 120	87.78 247	95.74 165	80.35 207	97.33 283	85.46 223	82.96 300	95.19 247

DELS-MVS		96.61 37	96.38 38	97.30 45	97.79 100	93.19 54	95.96 229	98.18 36	95.23 11	95.87 62	97.65 73	91.45 41	99.70 35	95.87 34	99.44 30	99.00 70
Christian Sormann, Emanuele Santellani, Mattia Rossi, Andreas Kuhn, Friedrich Fraundorfer: DELS-MVS: Deep Epipolar Line Search for Multi-View Stereo. Winter Conference on Applications of Computer Vision (WACV), 2023
tfpnnormal		89.70 249	88.40 253	93.60 218	95.15 213	90.10 134	97.56 87	98.16 38	87.28 238	86.16 272	94.63 215	77.57 262	98.05 213	74.48 314	84.59 283	92.65 309

VNet		95.89 56	95.45 53	97.21 53	98.07 82	92.94 61	97.50 91	98.15 39	93.87 41	97.52 13	97.61 79	85.29 111	99.53 71	95.81 37	95.27 144	99.16 54

DeepPCF-MVS	93.97 1	96.61 37	97.09 8	95.15 136	98.09 81	86.63 255	96.00 228	98.15 39	95.43 7	97.95 10	98.56 8	93.40 10	99.36 92	96.77 12	99.48 25	99.45 31

SD-MVS		97.41 7	97.53 2	97.06 57	98.57 52	94.46 17	97.92 43	98.14 41	94.82 21	99.01 2	98.55 10	94.18 5	97.41 278	96.94 5	99.64 4	99.32 44

UA-Net		95.95 55	95.53 52	97.20 54	97.67 106	92.98 60	97.65 70	98.13 42	94.81 22	96.61 36	98.35 25	88.87 67	99.51 75	90.36 134	97.35 107	99.11 61

QAPM		93.45 115	92.27 133	96.98 60	96.77 142	92.62 68	98.39 18	98.12 43	84.50 281	88.27 241	97.77 65	82.39 170	99.81 20	85.40 224	98.81 70	98.51 101

Vis-MVSNet		95.23 66	94.81 66	96.51 74	97.18 126	91.58 97	98.26 24	98.12 43	94.38 33	94.90 82	98.15 42	82.28 171	98.92 129	91.45 124	98.58 77	99.01 69
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
OpenMVS	89.19 12	92.86 135	91.68 148	96.40 80	95.34 199	92.73 65	98.27 23	98.12 43	84.86 276	85.78 274	97.75 66	78.89 240	99.74 25	87.50 189	98.65 74	96.73 173

TranMVSNet+NR-MVSNet		92.50 146	91.63 153	95.14 137	94.76 232	92.07 82	97.53 89	98.11 46	92.90 77	89.56 213	96.12 144	83.16 134	97.60 266	89.30 148	83.20 299	95.75 216

CPTT-MVS		95.57 60	95.19 61	96.70 63	99.27 19	91.48 98	98.33 20	98.11 46	87.79 225	95.17 80	98.03 47	87.09 93	99.61 48	93.51 84	99.42 31	99.02 65

Regformer-2		97.16 14	96.99 12	97.67 30	98.32 66	93.84 36	96.83 152	98.10 48	95.24 10	97.49 14	98.25 40	92.57 20	99.61 48	96.80 9	99.29 44	99.56 16

APD-MVS		96.95 24	96.60 29	98.01 10	99.03 30	94.93 12	97.72 61	98.10 48	91.50 113	98.01 9	98.32 33	92.33 24	99.58 56	94.85 61	99.51 20	99.53 23
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
mPP-MVS		96.86 27	96.60 29	97.64 33	99.40 5	93.44 48	98.50 13	98.09 50	93.27 59	95.95 61	98.33 31	91.04 46	99.88 3	95.20 47	99.57 14	99.60 11

zzz-MVS		97.07 18	96.77 25	97.97 12	99.37 10	94.42 19	97.15 127	98.08 51	95.07 14	96.11 52	98.59 6	90.88 50	99.90 1	96.18 28	99.50 22	99.58 12

MTGPA								98.08 51

MTAPA		97.08 17	96.78 24	97.97 12	99.37 10	94.42 19	97.24 115	98.08 51	95.07 14	96.11 52	98.59 6	90.88 50	99.90 1	96.18 28	99.50 22	99.58 12

CNVR-MVS		97.68 2	97.44 5	98.37 3	98.90 33	95.86 2	97.27 113	98.08 51	95.81 3	97.87 12	98.31 34	94.26 4	99.68 38	97.02 4	99.49 24	99.57 14

DP-MVS Recon		95.68 58	95.12 63	97.37 42	99.19 24	94.19 25	97.03 132	98.08 51	88.35 211	95.09 81	97.65 73	89.97 60	99.48 78	92.08 107	98.59 76	98.44 112

MCST-MVS		97.18 12	96.84 19	98.20 6	99.30 16	95.35 5	97.12 129	98.07 56	93.54 53	96.08 54	97.69 69	93.86 7	99.71 30	96.50 18	99.39 35	99.55 18

NR-MVSNet		92.34 155	91.27 169	95.53 118	94.95 223	93.05 57	97.39 103	98.07 56	92.65 82	84.46 283	95.71 166	85.00 115	97.77 254	89.71 140	83.52 296	95.78 212

MP-MVS-pluss		96.70 33	96.27 40	97.98 11	99.23 23	94.71 14	96.96 139	98.06 58	90.67 135	95.55 75	98.78 3	91.07 45	99.86 8	96.58 16	99.55 15	99.38 40
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
APD-MVS_3200maxsize		96.81 29	96.71 27	97.12 56	99.01 31	92.31 74	97.98 40	98.06 58	93.11 66	97.44 16	98.55 10	90.93 48	99.55 66	96.06 30	99.25 47	99.51 24

MP-MVS		96.77 31	96.45 36	97.72 26	99.39 7	93.80 37	98.41 17	98.06 58	93.37 55	95.54 76	98.34 28	90.59 53	99.88 3	94.83 62	99.54 16	99.49 27

HPM-MVS_fast		96.51 39	96.27 40	97.22 52	99.32 15	92.74 64	98.74 4	98.06 58	90.57 145	96.77 31	98.35 25	90.21 57	99.53 71	94.80 64	99.63 5	99.38 40

HPM-MVS		96.69 34	96.45 36	97.40 41	99.36 12	93.11 56	98.87 1	98.06 58	91.17 124	96.40 46	97.99 51	90.99 47	99.58 56	95.61 42	99.61 9	99.49 27
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
sss		94.51 84	93.80 86	96.64 64	97.07 130	91.97 87	96.32 206	98.06 58	88.94 184	94.50 88	96.78 108	84.60 120	99.27 96	91.90 110	96.02 132	98.68 93

DeepC-MVS	93.07 3	96.06 50	95.66 51	97.29 46	97.96 88	93.17 55	97.30 112	98.06 58	93.92 40	93.38 106	98.66 5	86.83 95	99.73 26	95.60 44	99.22 50	98.96 72
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
NCCC		97.30 10	97.03 11	98.11 8	98.77 36	95.06 11	97.34 107	98.04 65	95.96 2	97.09 28	97.88 55	93.18 12	99.71 30	95.84 36	99.17 54	99.56 16

DeepC-MVS_fast	93.89 2	96.93 26	96.64 28	97.78 21	98.64 47	94.30 21	97.41 99	98.04 65	94.81 22	96.59 38	98.37 24	91.24 43	99.64 47	95.16 50	99.52 18	99.42 35
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
abl_6		96.40 42	96.21 42	96.98 60	98.89 34	92.20 79	97.89 45	98.03 67	93.34 58	97.22 20	98.42 19	87.93 80	99.72 29	95.10 53	99.07 62	99.02 65

TEST9					98.70 39	94.19 25	96.41 194	98.02 68	88.17 218	96.03 55	97.56 84	92.74 15	99.59 53

train_agg		96.30 45	95.83 48	97.72 26	98.70 39	94.19 25	96.41 194	98.02 68	88.58 197	96.03 55	97.56 84	92.73 16	99.59 53	95.04 54	99.37 40	99.39 37

test_8					98.67 41	94.06 31	96.37 201	98.01 70	88.58 197	95.98 60	97.55 86	92.73 16	99.58 56

Regformer-4		96.97 23	96.87 17	97.25 49	98.34 63	92.66 67	96.96 139	98.01 70	95.12 13	97.14 24	98.42 19	91.82 35	99.61 48	96.90 6	99.13 57	99.50 25

agg_prior3		96.16 49	95.67 50	97.62 36	98.67 41	93.88 34	96.41 194	98.00 72	87.93 222	95.81 65	97.47 88	92.33 24	99.59 53	95.04 54	99.37 40	99.39 37

agg_prior1		96.22 48	95.77 49	97.56 37	98.67 41	93.79 38	96.28 210	98.00 72	88.76 194	95.68 69	97.55 86	92.70 18	99.57 64	95.01 56	99.32 42	99.32 44

agg_prior					98.67 41	93.79 38		98.00 72		95.68 69			99.57 64

test_prior3		96.46 41	96.20 43	97.23 50	98.67 41	92.99 58	96.35 202	98.00 72	92.80 79	96.03 55	97.59 80	92.01 31	99.41 86	95.01 56	99.38 36	99.29 46

test_prior				97.23 50	98.67 41	92.99 58		98.00 72					99.41 86			99.29 46

Regformer-1		97.10 16	96.96 14	97.54 38	98.32 66	93.48 47	96.83 152	97.99 77	95.20 12	97.46 15	98.25 40	92.48 23	99.58 56	96.79 11	99.29 44	99.55 18

WR-MVS		92.34 155	91.53 159	94.77 160	95.13 215	90.83 122	96.40 198	97.98 78	91.88 106	89.29 222	95.54 176	82.50 165	97.80 250	89.79 139	85.27 267	95.69 219

HPM-MVS++		97.34 9	96.97 13	98.47 1	99.08 27	96.16 1	97.55 88	97.97 79	95.59 4	96.61 36	97.89 53	92.57 20	99.84 14	95.95 33	99.51 20	99.40 36

CANet		96.39 43	96.02 45	97.50 39	97.62 109	93.38 50	97.02 134	97.96 80	95.42 8	94.86 83	97.81 62	87.38 90	99.82 19	96.88 7	99.20 52	99.29 46

114514_t		93.95 99	93.06 108	96.63 66	99.07 28	91.61 94	97.46 98	97.96 80	77.99 325	93.00 121	97.57 82	86.14 104	99.33 93	89.22 151	99.15 55	98.94 75

MVS_0304		96.05 51	95.45 53	97.85 15	97.75 103	94.50 16	96.87 149	97.95 82	95.46 6	95.60 73	98.01 49	80.96 192	99.83 15	97.23 2	99.25 47	99.23 50

原ACMM1				96.38 82	98.59 49	91.09 114		97.89 83	87.41 234	95.22 79	97.68 70	90.25 55	99.54 68	87.95 175	99.12 60	98.49 105

CDPH-MVS		95.97 54	95.38 56	97.77 23	98.93 32	94.44 18	96.35 202	97.88 84	86.98 246	96.65 35	97.89 53	91.99 33	99.47 79	92.26 98	99.46 26	99.39 37

test11								97.88 84

无先验							95.79 237	97.87 86	83.87 289				99.65 42	87.68 182		98.89 81

3Dnovator+	91.43 4	95.40 61	94.48 78	98.16 7	96.90 136	95.34 6	98.48 14	97.87 86	94.65 28	88.53 235	98.02 48	83.69 128	99.71 30	93.18 92	98.96 67	99.44 33

VPNet		92.23 162	91.31 167	94.99 144	95.56 190	90.96 117	97.22 120	97.86 88	92.96 75	90.96 170	96.62 125	75.06 276	98.20 188	91.90 110	83.65 295	95.80 211

HSP-MVS		97.53 5	97.49 4	97.63 35	99.40 5	93.77 41	98.53 9	97.85 89	95.55 5	98.56 4	97.81 62	93.90 6	99.65 42	96.62 14	99.21 51	99.48 29

TSAR-MVS + MP.		97.42 6	97.33 6	97.69 29	99.25 20	94.24 24	98.07 34	97.85 89	93.72 47	98.57 3	98.35 25	93.69 9	99.40 88	97.06 3	99.46 26	99.44 33

AdaColmap		94.34 86	93.68 90	96.31 86	98.59 49	91.68 93	96.59 185	97.81 91	89.87 153	92.15 138	97.06 102	83.62 129	99.54 68	89.34 147	98.07 87	97.70 143

Regformer-3		96.85 28	96.80 23	97.01 58	98.34 63	92.02 85	96.96 139	97.76 92	95.01 16	97.08 29	98.42 19	91.71 36	99.54 68	96.80 9	99.13 57	99.48 29

新几何1				97.32 44	98.60 48	93.59 44		97.75 93	81.58 307	95.75 68	97.85 59	90.04 59	99.67 40	86.50 205	99.13 57	98.69 92

旧先验1					98.38 61	93.38 50		97.75 93			98.09 44	92.30 28			99.01 65	99.16 54

EI-MVSNet-Vis-set		96.51 39	96.47 34	96.63 66	98.24 72	91.20 108	96.89 148	97.73 95	94.74 25	96.49 42	98.49 14	90.88 50	99.58 56	96.44 19	98.32 81	99.13 58

1121		94.71 82	93.83 85	97.34 43	98.57 52	93.64 43	96.04 224	97.73 95	81.56 309	95.68 69	97.85 59	90.23 56	99.65 42	87.68 182	99.12 60	98.73 88

PAPM_NR		95.01 71	94.59 72	96.26 91	98.89 34	90.68 126	97.24 115	97.73 95	91.80 107	92.93 126	96.62 125	89.13 65	99.14 107	89.21 152	97.78 94	98.97 71

CHOSEN 280x420		93.12 124	92.72 119	94.34 179	96.71 144	87.27 238	90.29 327	97.72 98	86.61 256	91.34 154	95.29 187	84.29 124	98.41 174	93.25 91	98.94 68	97.35 156

EI-MVSNet-UG-set		96.34 44	96.30 39	96.47 77	98.20 76	90.93 119	96.86 150	97.72 98	94.67 26	96.16 51	98.46 15	90.43 54	99.58 56	96.23 22	97.96 90	98.90 79

LS3D		93.57 112	92.61 123	96.47 77	97.59 112	91.61 94	97.67 67	97.72 98	85.17 271	90.29 180	98.34 28	84.60 120	99.73 26	83.85 250	98.27 82	98.06 129

PAPR		94.18 89	93.42 102	96.48 76	97.64 108	91.42 102	95.55 247	97.71 101	88.99 180	92.34 134	95.82 157	89.19 63	99.11 109	86.14 210	97.38 105	98.90 79

pcd1.5k->3k		38.37 330	40.51 331	31.96 343	94.29 249	0.00 362	0.00 353	97.69 102	0.00 357	0.00 358	0.00 359	81.45 186	0.00 360	0.00 357	91.11 214	95.89 204

UGNet		94.04 97	93.28 105	96.31 86	96.85 137	91.19 109	97.88 46	97.68 103	94.40 31	93.00 121	96.18 141	73.39 290	99.61 48	91.72 115	98.46 78	98.13 124
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
testdata				95.46 125	98.18 79	88.90 191		97.66 104	82.73 298	97.03 30	98.07 45	90.06 58	98.85 136	89.67 141	98.98 66	98.64 94

test12				97.65 31	98.46 54	94.26 22		97.66 104		95.52 77		90.89 49	99.46 80		99.25 47	99.22 51

DTE-MVSNet		90.56 231	89.75 232	93.01 242	93.95 272	87.25 239	97.64 74	97.65 106	90.74 132	87.12 261	95.68 169	79.97 213	97.00 294	83.33 254	81.66 307	94.78 274

TAPA-MVS	90.10 7	92.30 158	91.22 172	95.56 116	98.33 65	89.60 159	96.79 159	97.65 106	81.83 304	91.52 149	97.23 95	87.94 79	98.91 130	71.31 325	98.37 80	98.17 123
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
cdsmvs_eth3d_5k		23.24 332	30.99 332	0.00 347	0.00 361	0.00 362	0.00 353	97.63 108	0.00 357	0.00 358	96.88 106	84.38 123	0.00 360	0.00 357	0.00 358	0.00 358

canonicalmvs		96.02 53	95.45 53	97.75 25	97.59 112	95.15 10	98.28 22	97.60 109	94.52 29	96.27 48	96.12 144	87.65 84	99.18 102	96.20 27	94.82 150	98.91 78

test222					98.24 72	92.21 77	95.33 256	97.60 109	79.22 320	95.25 78	97.84 61	88.80 69			99.15 55	98.72 89

cascas		91.20 209	90.08 217	94.58 171	94.97 221	89.16 187	93.65 293	97.59 111	79.90 317	89.40 217	92.92 279	75.36 274	98.36 178	92.14 103	94.75 152	96.23 187

MVSFormer		95.37 62	95.16 62	95.99 100	96.34 162	91.21 106	98.22 26	97.57 112	91.42 117	96.22 49	97.32 90	86.20 102	97.92 239	94.07 71	99.05 63	98.85 83

test_djsdf		93.07 126	92.76 114	94.00 190	93.49 287	88.70 193	98.22 26	97.57 112	91.42 117	90.08 192	95.55 175	82.85 157	97.92 239	94.07 71	91.58 206	95.40 232

OMC-MVS		95.09 70	94.70 70	96.25 92	98.46 54	91.28 104	96.43 192	97.57 112	92.04 102	94.77 85	97.96 52	87.01 94	99.09 118	91.31 126	96.77 119	98.36 119

PS-MVSNAJss		93.74 106	93.51 96	94.44 174	93.91 274	89.28 183	97.75 55	97.56 115	92.50 84	89.94 194	96.54 128	88.65 71	98.18 191	93.83 80	90.90 217	95.86 205

jajsoiax		92.42 152	91.89 142	94.03 189	93.33 293	88.50 197	97.73 59	97.53 116	92.00 104	88.85 229	96.50 130	75.62 273	98.11 197	93.88 78	91.56 207	95.48 222

mvs_tets		92.31 157	91.76 144	93.94 198	93.41 289	88.29 200	97.63 81	97.53 116	92.04 102	88.76 230	96.45 132	74.62 280	98.09 200	93.91 76	91.48 208	95.45 226

HQP_MVS		93.78 105	93.43 100	94.82 154	96.21 166	89.99 138	97.74 57	97.51 118	94.85 17	91.34 154	96.64 118	81.32 188	98.60 154	93.02 93	92.23 193	95.86 205

plane_prior5								97.51 118					98.60 154	93.02 93	92.23 193	95.86 205

PS-MVSNAJ		95.37 62	95.33 58	95.49 121	97.35 122	90.66 127	95.31 258	97.48 120	93.85 42	96.51 41	95.70 168	88.65 71	99.65 42	94.80 64	98.27 82	96.17 190

API-MVS		94.84 80	94.49 77	95.90 102	97.90 96	92.00 86	97.80 52	97.48 120	89.19 169	94.81 84	96.71 111	88.84 68	99.17 103	88.91 160	98.76 72	96.53 180

MG-MVS		95.61 59	95.38 56	96.31 86	98.42 57	90.53 129	96.04 224	97.48 120	93.47 54	95.67 72	98.10 43	89.17 64	99.25 97	91.27 127	98.77 71	99.13 58

MAR-MVS		94.22 88	93.46 98	96.51 74	98.00 83	92.19 80	97.67 67	97.47 123	88.13 220	93.00 121	95.84 155	84.86 118	99.51 75	87.99 174	98.17 85	97.83 138
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
CLD-MVS		92.98 129	92.53 127	94.32 180	96.12 175	89.20 185	95.28 259	97.47 123	92.66 81	89.90 195	95.62 171	80.58 202	98.40 175	92.73 96	92.40 191	95.38 234
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
nrg030		94.05 96	93.31 104	96.27 90	95.22 209	94.59 15	98.34 19	97.46 125	92.93 76	91.21 168	96.64 118	87.23 92	98.22 187	94.99 59	85.80 261	95.98 203

XVG-OURS		93.72 107	93.35 103	94.80 157	97.07 130	88.61 194	94.79 268	97.46 125	91.97 105	93.99 96	97.86 58	81.74 183	98.88 135	92.64 97	92.67 189	96.92 168

LPG-MVS_test		92.94 131	92.56 124	94.10 185	96.16 171	88.26 202	97.65 70	97.46 125	91.29 120	90.12 188	97.16 97	79.05 225	98.73 146	92.25 100	91.89 201	95.31 238

LGP-MVS_train				94.10 185	96.16 171	88.26 202		97.46 125	91.29 120	90.12 188	97.16 97	79.05 225	98.73 146	92.25 100	91.89 201	95.31 238

MVS		91.71 177	90.44 204	95.51 119	95.20 211	91.59 96	96.04 224	97.45 129	73.44 337	87.36 257	95.60 172	85.42 110	99.10 115	85.97 215	97.46 100	95.83 209

XVG-OURS-SEG-HR		93.86 102	93.55 93	94.81 156	97.06 132	88.53 196	95.28 259	97.45 129	91.68 110	94.08 95	97.68 70	82.41 169	98.90 131	93.84 79	92.47 190	96.98 160

ab-mvs		93.57 112	92.55 125	96.64 64	97.28 123	91.96 88	95.40 254	97.45 129	89.81 158	93.22 114	96.28 138	79.62 218	99.46 80	90.74 131	93.11 184	98.50 103

xiu_mvs_v2_base		95.32 64	95.29 59	95.40 127	97.22 124	90.50 130	95.44 253	97.44 132	93.70 49	96.46 44	96.18 141	88.59 74	99.53 71	94.79 66	97.81 93	96.17 190

1314		92.81 138	92.03 137	95.14 137	95.33 202	89.52 165	96.04 224	97.44 132	87.72 228	86.25 271	95.33 186	83.84 126	98.79 140	89.26 149	97.05 113	97.11 158

XXY-MVS		92.16 164	91.23 171	94.95 149	94.75 233	90.94 118	97.47 97	97.43 134	89.14 176	88.90 227	96.43 133	79.71 216	98.24 186	89.56 144	87.68 248	95.67 220

anonymousdsp		92.16 164	91.55 158	93.97 193	92.58 310	89.55 162	97.51 90	97.42 135	89.42 164	88.40 236	94.84 204	80.66 201	97.88 244	91.87 112	91.28 212	94.48 282

Effi-MVS+		94.93 76	94.45 79	96.36 84	96.61 145	91.47 99	96.41 194	97.41 136	91.02 129	94.50 88	95.92 151	87.53 87	98.78 141	93.89 77	96.81 118	98.84 85

HQP3-MVS								97.39 137							92.10 198

HQP-MVS		93.19 123	92.74 118	94.54 172	95.86 180	89.33 178	96.65 177	97.39 137	93.55 50	90.14 182	95.87 153	80.95 193	98.50 163	92.13 104	92.10 198	95.78 212

PLC	91.00 6	94.11 93	93.43 100	96.13 95	98.58 51	91.15 113	96.69 174	97.39 137	87.29 237	91.37 152	96.71 111	88.39 75	99.52 74	87.33 193	97.13 112	97.73 141
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
v7n		90.76 222	89.86 226	93.45 228	93.54 284	87.60 235	97.70 66	97.37 140	88.85 187	87.65 251	94.08 251	81.08 190	98.10 198	84.68 233	83.79 294	94.66 278

UnsupCasMVSNet_eth		85.99 292	84.45 293	90.62 299	89.97 324	82.40 295	93.62 294	97.37 140	89.86 154	78.59 325	92.37 288	65.25 323	95.35 323	82.27 268	70.75 337	94.10 291

ACMM	89.79 8	92.96 130	92.50 129	94.35 178	96.30 164	88.71 192	97.58 86	97.36 142	91.40 119	90.53 174	96.65 117	79.77 215	98.75 145	91.24 128	91.64 204	95.59 221
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
xiu_mvs_v1_base_debu		95.01 71	94.76 67	95.75 108	96.58 147	91.71 90	96.25 212	97.35 143	92.99 69	96.70 32	96.63 122	82.67 160	99.44 83	96.22 23	97.46 100	96.11 195

xiu_mvs_v1_base		95.01 71	94.76 67	95.75 108	96.58 147	91.71 90	96.25 212	97.35 143	92.99 69	96.70 32	96.63 122	82.67 160	99.44 83	96.22 23	97.46 100	96.11 195

xiu_mvs_v1_base_debi		95.01 71	94.76 67	95.75 108	96.58 147	91.71 90	96.25 212	97.35 143	92.99 69	96.70 32	96.63 122	82.67 160	99.44 83	96.22 23	97.46 100	96.11 195

WTY-MVS		94.71 82	94.02 82	96.79 62	97.71 105	92.05 83	96.59 185	97.35 143	90.61 142	94.64 86	96.93 104	86.41 99	99.39 89	91.20 129	94.71 154	98.94 75

F-COLMAP		93.58 111	92.98 109	95.37 128	98.40 58	88.98 189	97.18 124	97.29 147	87.75 227	90.49 175	97.10 101	85.21 112	99.50 77	86.70 202	96.72 122	97.63 144

XVG-ACMP-BASELINE		90.93 218	90.21 215	93.09 240	94.31 248	85.89 260	95.33 256	97.26 148	91.06 128	89.38 218	95.44 183	68.61 309	98.60 154	89.46 146	91.05 215	94.79 273

PCF-MVS	89.48 11	91.56 193	89.95 223	96.36 84	96.60 146	92.52 71	92.51 311	97.26 148	79.41 318	88.90 227	96.56 127	84.04 125	99.55 66	77.01 309	97.30 108	97.01 159
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
ACMP	89.59 10	92.62 141	92.14 134	94.05 188	96.40 160	88.20 208	97.36 106	97.25 150	91.52 112	88.30 239	96.64 118	78.46 244	98.72 148	91.86 113	91.48 208	95.23 245
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
OPM-MVS		93.28 120	92.76 114	94.82 154	94.63 237	90.77 125	96.65 177	97.18 151	93.72 47	91.68 147	97.26 93	79.33 222	98.63 151	92.13 104	92.28 192	95.07 251

PatchMatch-RL		92.90 133	92.02 138	95.56 116	98.19 78	90.80 123	95.27 261	97.18 151	87.96 221	91.86 144	95.68 169	80.44 205	98.99 126	84.01 246	97.54 99	96.89 169

alignmvs		95.87 57	95.23 60	97.78 21	97.56 114	95.19 8	97.86 47	97.17 153	94.39 32	96.47 43	96.40 134	85.89 105	99.20 99	96.21 26	95.11 146	98.95 74

v748		90.34 235	89.54 237	92.75 250	93.25 294	85.71 263	97.61 82	97.17 153	88.54 200	87.20 260	93.54 267	81.02 191	98.01 222	85.73 220	81.80 304	94.52 281

MVS_Test		94.89 78	94.62 71	95.68 112	96.83 140	89.55 162	96.70 172	97.17 153	91.17 124	95.60 73	96.11 146	87.87 81	98.76 144	93.01 95	97.17 111	98.72 89

V4		90.71 227	90.00 221	92.82 245	93.21 298	87.03 245	97.59 85	97.16 156	88.21 214	87.69 249	93.92 256	80.93 195	98.06 210	87.39 190	83.90 292	93.39 301

v52		90.70 228	90.00 221	92.82 245	93.24 295	87.03 245	97.60 83	97.14 157	88.21 214	87.69 249	93.94 254	80.91 196	98.07 205	87.39 190	83.87 293	93.36 303

diffmvs		93.43 116	92.75 116	95.48 123	96.47 157	89.61 158	96.09 221	97.14 157	85.97 263	93.09 119	95.35 185	84.87 117	98.55 159	89.51 145	96.26 131	98.28 121

Fast-Effi-MVS+		93.46 114	92.75 116	95.59 115	96.77 142	90.03 135	96.81 156	97.13 159	88.19 216	91.30 157	94.27 244	86.21 101	98.63 151	87.66 184	96.46 129	98.12 125

EI-MVSNet		93.03 128	92.88 112	93.48 225	95.77 185	86.98 247	96.44 190	97.12 160	90.66 137	91.30 157	97.64 76	86.56 97	98.05 213	89.91 136	90.55 222	95.41 228

MVSTER		93.20 122	92.81 113	94.37 177	96.56 150	89.59 160	97.06 131	97.12 160	91.24 123	91.30 157	95.96 149	82.02 177	98.05 213	93.48 86	90.55 222	95.47 224

testing_2		87.33 282	85.03 289	94.22 181	87.77 333	89.32 180	94.97 266	97.11 162	89.22 168	71.64 335	88.73 321	55.16 340	97.94 235	91.95 108	88.73 241	95.41 228

Test4		89.48 251	87.50 261	95.44 126	90.76 321	89.72 149	95.78 239	97.09 163	90.28 147	77.67 326	91.74 300	55.42 339	98.08 201	91.92 109	96.83 117	98.52 99

LTVRE_ROB	88.41 13	90.99 216	89.92 224	94.19 182	96.18 169	89.55 162	96.31 207	97.09 163	87.88 224	85.67 275	95.91 152	78.79 241	98.57 157	81.50 275	89.98 228	94.44 284
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
v10		91.04 215	90.23 213	93.49 224	94.12 259	88.16 211	97.32 110	97.08 165	88.26 213	88.29 240	94.22 247	82.17 175	97.97 229	86.45 206	84.12 287	94.33 287

v144192		91.06 214	90.28 209	93.39 229	93.66 282	87.23 241	96.83 152	97.07 166	87.43 233	89.69 208	94.28 242	81.48 185	98.00 225	87.18 197	84.92 280	94.93 261

v1192		91.07 213	90.23 213	93.58 221	93.70 280	87.82 230	96.73 164	97.07 166	87.77 226	89.58 211	94.32 232	80.90 199	97.97 229	86.52 204	85.48 262	94.95 257

v8		91.29 207	90.53 203	93.57 222	94.15 255	88.12 215	97.34 107	97.06 168	88.99 180	88.32 238	94.26 246	83.08 141	98.01 222	87.62 186	83.92 291	94.57 280

v7		91.47 198	90.73 191	93.68 215	94.13 257	88.16 211	97.09 130	97.05 169	88.38 209	89.80 201	94.52 217	82.21 173	98.01 222	88.00 173	85.42 264	94.87 263

mvs_anonymous		93.82 103	93.74 87	94.06 187	96.44 159	85.41 267	95.81 236	97.05 169	89.85 156	90.09 191	96.36 136	87.44 89	97.75 255	93.97 73	96.69 123	99.02 65

IterMVS-LS		92.29 159	91.94 141	93.34 232	96.25 165	86.97 248	96.57 188	97.05 169	90.67 135	89.50 216	94.80 208	86.59 96	97.64 263	89.91 136	86.11 259	95.40 232
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v1921920		90.85 220	90.03 220	93.29 234	93.55 283	86.96 249	96.74 163	97.04 172	87.36 235	89.52 215	94.34 230	80.23 210	97.97 229	86.27 207	85.21 268	94.94 259

CDS-MVSNet		94.14 92	93.54 94	95.93 101	96.18 169	91.46 100	96.33 205	97.04 172	88.97 183	93.56 101	96.51 129	87.55 86	97.89 243	89.80 138	95.95 134	98.44 112
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
v1144		91.37 203	90.60 201	93.68 215	93.89 275	88.23 205	96.84 151	97.03 174	88.37 210	89.69 208	94.39 227	82.04 176	97.98 226	87.80 178	85.37 265	94.84 265

v1240		90.70 228	89.85 227	93.23 236	93.51 286	86.80 250	96.61 182	97.02 175	87.16 240	89.58 211	94.31 233	79.55 219	97.98 226	85.52 222	85.44 263	94.90 262

EPP-MVSNet		95.22 67	95.04 64	95.76 107	97.49 121	89.56 161	98.67 5	97.00 176	90.69 134	94.24 93	97.62 78	89.79 62	98.81 139	93.39 90	96.49 127	98.92 77

v1neww		91.70 180	91.01 176	93.75 207	94.19 251	88.14 213	97.20 121	96.98 177	89.18 171	89.87 198	94.44 224	83.10 139	98.06 210	89.06 156	85.09 271	95.06 254

v7new		91.70 180	91.01 176	93.75 207	94.19 251	88.14 213	97.20 121	96.98 177	89.18 171	89.87 198	94.44 224	83.10 139	98.06 210	89.06 156	85.09 271	95.06 254

v6		91.69 182	91.00 178	93.75 207	94.14 256	88.12 215	97.20 121	96.98 177	89.19 169	89.90 195	94.42 226	83.04 145	98.07 205	89.07 155	85.10 270	95.07 251

V42		91.58 192	90.87 183	93.73 210	94.05 268	88.50 197	97.32 110	96.97 180	88.80 193	89.71 206	94.33 231	82.54 164	98.05 213	89.01 158	85.07 273	94.64 279

v1141		91.61 188	90.89 180	93.78 204	94.01 269	88.24 204	96.96 139	96.96 181	89.17 173	89.75 204	94.29 240	82.99 149	98.03 218	88.85 162	85.00 276	95.07 251

divwei89l23v2f112		91.61 188	90.89 180	93.78 204	94.01 269	88.22 206	96.96 139	96.96 181	89.17 173	89.75 204	94.28 242	83.02 147	98.03 218	88.86 161	84.98 279	95.08 249

v1		91.61 188	90.89 180	93.78 204	94.01 269	88.21 207	96.96 139	96.96 181	89.17 173	89.78 203	94.29 240	82.97 151	98.05 213	88.85 162	84.99 277	95.08 249

FMVSNet2		91.31 206	90.08 217	94.99 144	96.51 153	92.21 77	97.41 99	96.95 184	88.82 190	88.62 232	94.75 210	73.87 284	97.42 277	85.20 227	88.55 243	95.35 236

ACMH	87.59 16	90.53 232	89.42 239	93.87 200	96.21 166	87.92 227	97.24 115	96.94 185	88.45 201	83.91 291	96.27 139	71.92 292	98.62 153	84.43 238	89.43 233	95.05 256
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
GBi-Net		91.35 204	90.27 210	94.59 167	96.51 153	91.18 110	97.50 91	96.93 186	88.82 190	89.35 219	94.51 218	73.87 284	97.29 285	86.12 211	88.82 237	95.31 238

test1		91.35 204	90.27 210	94.59 167	96.51 153	91.18 110	97.50 91	96.93 186	88.82 190	89.35 219	94.51 218	73.87 284	97.29 285	86.12 211	88.82 237	95.31 238

FMVSNet3		91.78 174	90.69 193	95.03 143	96.53 152	92.27 76	97.02 134	96.93 186	89.79 159	89.35 219	94.65 214	77.01 264	97.47 273	86.12 211	88.82 237	95.35 236

FMVSNet1		89.88 246	88.31 254	94.59 167	95.41 195	91.18 110	97.50 91	96.93 186	86.62 255	87.41 255	94.51 218	65.94 321	97.29 285	83.04 257	87.43 251	95.31 238

TAMVS		94.01 98	93.46 98	95.64 113	96.16 171	90.45 132	96.71 169	96.89 190	89.27 167	93.46 105	96.92 105	87.29 91	97.94 235	88.70 166	95.74 138	98.53 98

v2v482		91.59 191	90.85 185	93.80 202	93.87 276	88.17 210	96.94 145	96.88 191	89.54 160	89.53 214	94.90 199	81.70 184	98.02 221	89.25 150	85.04 275	95.20 246

CNLPA		94.28 87	93.53 95	96.52 71	98.38 61	92.55 70	96.59 185	96.88 191	90.13 150	91.91 142	97.24 94	85.21 112	99.09 118	87.64 185	97.83 92	97.92 132

PAPM		91.52 196	90.30 208	95.20 130	95.30 203	89.83 146	93.38 297	96.85 193	86.26 259	88.59 234	95.80 158	84.88 116	98.15 193	75.67 313	95.93 135	97.63 144

pm-mvs1		90.72 226	89.65 236	93.96 194	94.29 249	89.63 157	97.79 53	96.82 194	89.07 177	86.12 273	95.48 182	78.61 242	97.78 252	86.97 200	81.67 306	94.46 283

CMPMVS	62.92 21	85.62 295	84.92 290	87.74 313	89.14 328	73.12 332	94.17 281	96.80 195	73.98 335	73.65 331	94.93 197	66.36 318	97.61 265	83.95 248	91.28 212	92.48 315
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
MS-PatchMatch		90.27 236	89.77 230	91.78 279	94.33 247	84.72 276	95.55 247	96.73 196	86.17 261	86.36 270	95.28 189	71.28 297	97.80 250	84.09 243	98.14 86	92.81 308

Effi-MVS+-dtu		93.08 125	93.21 106	92.68 253	96.02 177	83.25 290	97.14 128	96.72 197	93.85 42	91.20 169	93.44 273	83.08 141	98.30 184	91.69 118	95.73 139	96.50 182

mvs-test1		93.63 109	93.69 89	93.46 227	96.02 177	84.61 277	97.24 115	96.72 197	93.85 42	92.30 135	95.76 163	83.08 141	98.89 133	91.69 118	96.54 126	96.87 170

TSAR-MVS + GP.		96.69 34	96.49 33	97.27 48	98.31 68	93.39 49	96.79 159	96.72 197	94.17 36	97.44 16	97.66 72	92.76 14	99.33 93	96.86 8	97.76 96	99.08 63

1112_ss		93.37 117	92.42 131	96.21 93	97.05 133	90.99 115	96.31 207	96.72 197	86.87 252	89.83 200	96.69 115	86.51 98	99.14 107	88.12 171	93.67 172	98.50 103

PVSNet	86.66 18	92.24 161	91.74 147	93.73 210	97.77 102	83.69 286	92.88 306	96.72 197	87.91 223	93.00 121	94.86 203	78.51 243	99.05 124	86.53 203	97.45 104	98.47 108

v148		90.99 216	90.38 206	92.81 248	93.83 277	85.80 261	96.78 161	96.68 202	89.45 163	88.75 231	93.93 255	82.96 153	97.82 249	87.83 177	83.25 297	94.80 271

ACMH+	87.92 14	90.20 239	89.18 243	93.25 235	96.48 156	86.45 256	96.99 137	96.68 202	88.83 189	84.79 282	96.22 140	70.16 305	98.53 160	84.42 239	88.04 245	94.77 275

CANet_DTU		94.37 85	93.65 91	96.55 70	96.46 158	92.13 81	96.21 216	96.67 204	94.38 33	93.53 103	97.03 103	79.34 221	99.71 30	90.76 130	98.45 79	97.82 139

HY-MVS	89.66 9	93.87 101	92.95 110	96.63 66	97.10 129	92.49 72	95.64 244	96.64 205	89.05 178	93.00 121	95.79 161	85.77 108	99.45 82	89.16 154	94.35 155	97.96 130

Test_1112_low_res		92.84 137	91.84 143	95.85 104	97.04 134	89.97 141	95.53 249	96.64 205	85.38 267	89.65 210	95.18 191	85.86 106	99.10 115	87.70 180	93.58 177	98.49 105

Fast-Effi-MVS+-dtu		92.29 159	91.99 139	93.21 238	95.27 204	85.52 266	97.03 132	96.63 207	92.09 96	89.11 226	95.14 193	80.33 208	98.08 201	87.54 188	94.74 153	96.03 202

UnsupCasMVSNet_bld		82.13 307	79.46 309	90.14 305	88.00 331	82.47 293	90.89 325	96.62 208	78.94 321	75.61 328	84.40 337	56.63 336	96.31 301	77.30 308	66.77 343	91.63 329

jason		94.84 80	94.39 81	96.18 94	95.52 191	90.93 119	96.09 221	96.52 209	89.28 166	96.01 59	97.32 90	84.70 119	98.77 143	95.15 51	98.91 69	98.85 83
jason: jason.
EG-PatchMatch MVS		87.02 285	85.44 286	91.76 281	92.67 308	85.00 271	96.08 223	96.45 210	83.41 294	79.52 322	93.49 270	57.10 335	97.72 257	79.34 300	90.87 218	92.56 311

pmmvs6		87.81 279	86.19 281	92.69 252	91.32 318	86.30 257	97.34 107	96.41 211	80.59 316	84.05 290	94.37 229	67.37 316	97.67 260	84.75 231	79.51 314	94.09 293

PMMVS		92.86 135	92.34 132	94.42 176	94.92 225	86.73 251	94.53 273	96.38 212	84.78 278	94.27 92	95.12 195	83.13 137	98.40 175	91.47 123	96.49 127	98.12 125

RPSCF		90.75 224	90.86 184	90.42 302	96.84 138	76.29 326	95.61 246	96.34 213	83.89 287	91.38 151	97.87 56	76.45 266	98.78 141	87.16 198	92.23 193	96.20 188

MSDG		91.42 200	90.24 212	94.96 148	97.15 128	88.91 190	93.69 291	96.32 214	85.72 265	86.93 266	96.47 131	80.24 209	98.98 127	80.57 291	95.05 147	96.98 160

OurMVSNet-221017-0		90.51 233	90.19 216	91.44 287	93.41 289	81.25 302	96.98 138	96.28 215	91.68 110	86.55 269	96.30 137	74.20 283	97.98 226	88.96 159	87.40 253	95.09 248

MVP-Stereo		90.74 225	90.08 217	92.71 251	93.19 300	88.20 208	95.86 233	96.27 216	86.07 262	84.86 281	94.76 209	77.84 260	97.75 255	83.88 249	98.01 88	92.17 326
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
lupinMVS		94.99 75	94.56 73	96.29 89	96.34 162	91.21 106	95.83 235	96.27 216	88.93 185	96.22 49	96.88 106	86.20 102	98.85 136	95.27 46	99.05 63	98.82 86

BH-untuned		92.94 131	92.62 122	93.92 199	97.22 124	86.16 259	96.40 198	96.25 218	90.06 151	89.79 202	96.17 143	83.19 133	98.35 179	87.19 196	97.27 109	97.24 157

test_normal		92.01 167	90.75 190	95.80 106	93.24 295	89.97 141	95.93 231	96.24 219	90.62 140	81.63 301	93.45 272	74.98 277	98.89 133	93.61 82	97.04 114	98.55 96

IS-MVSNet		94.90 77	94.52 76	96.05 97	97.67 106	90.56 128	98.44 15	96.22 220	93.21 60	93.99 96	97.74 67	85.55 109	98.45 167	89.98 135	97.86 91	99.14 57

GA-MVS		91.38 202	90.31 207	94.59 167	94.65 236	87.62 234	94.34 276	96.19 221	90.73 133	90.35 179	93.83 257	71.84 293	97.96 233	87.22 195	93.61 175	98.21 122

DI_MVS_plusplus_test		92.01 167	90.77 188	95.73 111	93.34 291	89.78 148	96.14 219	96.18 222	90.58 144	81.80 300	93.50 269	74.95 278	98.90 131	93.51 84	96.94 115	98.51 101

semantic-postprocess				91.82 276	95.52 191	84.20 280		96.15 223	90.61 142	87.39 256	94.27 244	75.63 272	96.44 299	87.34 192	86.88 256	94.82 269

IterMVS		90.15 241	89.67 234	91.61 283	95.48 193	83.72 283	94.33 277	96.12 224	89.99 152	87.31 259	94.15 249	75.78 271	96.27 302	86.97 200	86.89 255	94.83 267
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
DP-MVS		92.76 139	91.51 162	96.52 71	98.77 36	90.99 115	97.38 105	96.08 225	82.38 300	89.29 222	97.87 56	83.77 127	99.69 36	81.37 281	96.69 123	98.89 81

pmmvs4		90.93 218	89.85 227	94.17 183	93.34 291	90.79 124	94.60 270	96.02 226	84.62 279	87.45 253	95.15 192	81.88 181	97.45 274	87.70 180	87.87 247	94.27 290

ppachtmachnet_test		88.35 274	87.29 267	91.53 284	92.45 312	83.57 288	93.75 289	95.97 227	84.28 282	85.32 280	94.18 248	79.00 231	96.93 295	75.71 312	84.99 277	94.10 291

v18		88.71 261	87.52 260	92.27 258	94.16 254	88.11 217	96.82 155	95.96 228	87.03 242	80.76 307	89.81 308	83.15 135	96.22 303	84.69 232	75.31 325	92.49 313

v17		88.67 263	87.47 263	92.26 260	94.13 257	88.09 219	96.81 156	95.95 229	87.02 243	80.72 308	89.75 310	83.11 138	96.20 304	84.61 235	75.15 327	92.49 313

v16		88.69 262	87.50 261	92.26 260	94.19 251	88.11 217	96.81 156	95.95 229	87.01 244	80.71 309	89.80 309	83.08 141	96.20 304	84.61 235	75.34 324	92.48 315

ITE_SJBPF				92.43 257	95.34 199	85.37 268		95.92 231	91.47 114	87.75 248	96.39 135	71.00 299	97.96 233	82.36 267	89.86 231	93.97 294

V14		88.52 266	87.30 266	92.17 265	94.12 259	87.99 222	96.72 167	95.91 232	86.98 246	80.50 313	89.63 311	83.03 146	96.12 308	84.23 241	74.60 330	92.40 320

v15		88.53 265	87.31 265	92.20 263	94.09 263	88.05 220	96.72 167	95.90 233	87.01 244	80.53 312	89.60 314	83.02 147	96.13 306	84.29 240	74.64 328	92.41 319

v13		88.45 271	87.22 272	92.16 267	94.08 265	87.95 226	96.71 169	95.90 233	86.86 253	80.27 319	89.55 316	82.92 154	96.12 308	84.02 245	74.63 329	92.40 320

v12		88.46 270	87.23 271	92.17 265	94.10 262	87.99 222	96.71 169	95.90 233	86.91 249	80.34 317	89.58 315	82.92 154	96.11 310	84.09 243	74.50 333	92.42 318

V9		88.49 269	87.26 268	92.18 264	94.12 259	87.97 225	96.73 164	95.90 233	86.95 248	80.40 315	89.61 312	82.98 150	96.13 306	84.14 242	74.55 331	92.44 317

USDC		88.94 256	87.83 258	92.27 258	94.66 235	84.96 272	93.86 287	95.90 233	87.34 236	83.40 293	95.56 174	67.43 315	98.19 190	82.64 264	89.67 232	93.66 297

COLMAP_ROB	87.81 15	90.40 234	89.28 241	93.79 203	97.95 89	87.13 244	96.92 146	95.89 238	82.83 297	86.88 268	97.18 96	73.77 287	99.29 95	78.44 303	93.62 174	94.95 257
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
VDD-MVS		93.82 103	93.08 107	96.02 98	97.88 97	89.96 143	97.72 61	95.85 239	92.43 85	95.86 63	98.44 17	68.42 311	99.39 89	96.31 20	94.85 148	98.71 91

v11		88.41 272	87.19 275	92.08 270	94.08 265	87.77 231	96.75 162	95.85 239	86.74 254	80.50 313	89.50 317	82.49 166	96.08 311	83.55 251	75.20 326	92.38 322

VDDNet		93.05 127	92.07 135	96.02 98	96.84 138	90.39 133	98.08 33	95.85 239	86.22 260	95.79 67	98.46 15	67.59 314	99.19 100	94.92 60	94.85 148	98.47 108

Vis-MVSNet (Re-imp)		94.15 90	93.88 84	94.95 149	97.61 110	87.92 227	98.10 31	95.80 242	92.22 88	93.02 120	97.45 89	84.53 122	97.91 242	88.24 169	97.97 89	99.02 65

tpm cat1		88.36 273	87.21 273	91.81 277	95.13 215	80.55 308	92.58 310	95.70 243	74.97 333	87.45 253	91.96 296	78.01 259	98.17 192	80.39 293	88.74 240	96.72 174

BH-w/o		92.14 166	91.75 145	93.31 233	96.99 135	85.73 262	95.67 241	95.69 244	88.73 195	89.26 224	94.82 207	82.97 151	98.07 205	85.26 226	96.32 130	96.13 194

CR-MVSNet		90.82 221	89.77 230	93.95 195	94.45 243	87.19 242	90.23 328	95.68 245	86.89 251	92.40 130	92.36 291	80.91 196	97.05 289	81.09 290	93.95 168	97.60 149

Patchmtry		88.64 264	87.25 269	92.78 249	94.09 263	86.64 252	89.82 331	95.68 245	80.81 314	87.63 252	92.36 291	80.91 196	97.03 291	78.86 301	85.12 269	94.67 277

BH-RMVSNet		92.72 140	91.97 140	94.97 147	97.16 127	87.99 222	96.15 218	95.60 247	90.62 140	91.87 143	97.15 99	78.41 245	98.57 157	83.16 255	97.60 98	98.36 119

PVSNet_0	82.17 19	85.46 296	83.64 297	90.92 293	95.27 204	79.49 317	90.55 326	95.60 247	83.76 290	83.00 294	89.95 305	71.09 298	97.97 229	82.75 262	60.79 344	95.31 238

Patchmatch-test1		91.54 195	90.85 185	93.59 219	95.59 189	84.95 273	94.72 269	95.58 249	90.82 130	92.25 136	93.58 266	75.80 270	97.41 278	83.35 252	95.98 133	98.40 115

AllTest		90.23 238	88.98 245	93.98 191	97.94 90	86.64 252	96.51 189	95.54 250	85.38 267	85.49 277	96.77 109	70.28 303	99.15 105	80.02 294	92.87 185	96.15 192

TestCases				93.98 191	97.94 90	86.64 252		95.54 250	85.38 267	85.49 277	96.77 109	70.28 303	99.15 105	80.02 294	92.87 185	96.15 192

tpmvs		89.83 248	89.15 244	91.89 274	94.92 225	80.30 311	93.11 303	95.46 252	86.28 258	88.08 243	92.65 282	80.44 205	98.52 161	81.47 276	89.92 230	96.84 171

PatchFormer-LS_test		91.68 187	91.18 174	93.19 239	95.24 208	83.63 287	95.53 249	95.44 253	89.82 157	91.37 152	92.58 285	80.85 200	98.52 161	89.65 143	90.16 227	97.42 155

pmmvs5		89.86 247	88.87 247	92.82 245	92.86 304	86.23 258	96.26 211	95.39 254	84.24 283	87.12 261	94.51 218	74.27 282	97.36 282	87.61 187	87.57 249	94.86 264

PatchmatchNet		91.91 171	91.35 164	93.59 219	95.38 197	84.11 281	93.15 302	95.39 254	89.54 160	92.10 139	93.68 262	82.82 158	98.13 194	84.81 230	95.32 143	98.52 99
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
tpmrst		91.44 199	91.32 166	91.79 278	95.15 213	79.20 320	93.42 296	95.37 256	88.55 199	93.49 104	93.67 263	82.49 166	98.27 185	90.41 133	89.34 234	97.90 133

Anonymous20231206		87.09 284	86.14 282	89.93 307	91.22 319	80.35 309	96.11 220	95.35 257	83.57 292	84.16 287	93.02 278	73.54 289	95.61 318	72.16 322	86.14 258	93.84 296

MIMVSNet1		84.93 298	83.05 298	90.56 300	89.56 327	84.84 275	95.40 254	95.35 257	83.91 286	80.38 316	92.21 295	57.23 334	93.34 332	70.69 328	82.75 303	93.50 298

TDRefinement		86.53 287	84.76 292	91.85 275	82.23 343	84.25 278	96.38 200	95.35 257	84.97 275	84.09 289	94.94 196	65.76 322	98.34 181	84.60 237	74.52 332	92.97 304

TR-MVS		91.48 197	90.59 202	94.16 184	96.40 160	87.33 236	95.67 241	95.34 260	87.68 229	91.46 150	95.52 177	76.77 265	98.35 179	82.85 260	93.61 175	96.79 172

EPNet_dtu		91.71 177	91.28 168	92.99 243	93.76 279	83.71 284	96.69 174	95.28 261	93.15 64	87.02 265	95.95 150	83.37 132	97.38 281	79.46 298	96.84 116	97.88 135
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
FMVSNet5		87.29 283	85.79 284	91.78 279	94.80 231	87.28 237	95.49 251	95.28 261	84.09 285	83.85 292	91.82 297	62.95 326	94.17 328	78.48 302	85.34 266	93.91 295

MDTV_nov1_ep13			90.76 189		95.22 209	80.33 310	93.03 305	95.28 261	88.14 219	92.84 127	93.83 257	81.34 187	98.08 201	82.86 259	94.34 156

LF4IMVS		87.94 277	87.25 269	89.98 306	92.38 313	80.05 315	94.38 275	95.25 264	87.59 231	84.34 284	94.74 211	64.31 324	97.66 262	84.83 229	87.45 250	92.23 324

TransMVSNet (Re)		88.94 256	87.56 259	93.08 241	94.35 246	88.45 199	97.73 59	95.23 265	87.47 232	84.26 286	95.29 187	79.86 214	97.33 283	79.44 299	74.44 334	93.45 300

test20.03		86.14 291	85.40 287	88.35 309	90.12 322	80.06 314	95.90 232	95.20 266	88.59 196	81.29 303	93.62 265	71.43 296	92.65 334	71.26 326	81.17 309	92.34 323

new-patchmatchnet		83.18 302	81.87 303	87.11 315	86.88 335	75.99 327	93.70 290	95.18 267	85.02 274	77.30 327	88.40 324	65.99 320	93.88 330	74.19 318	70.18 338	91.47 332

MDA-MVSNet_test_wron		85.87 293	84.23 295	90.80 297	92.38 313	82.57 292	93.17 300	95.15 268	82.15 301	67.65 337	92.33 294	78.20 247	95.51 321	77.33 306	79.74 312	94.31 289

YYNet1		85.87 293	84.23 295	90.78 298	92.38 313	82.46 294	93.17 300	95.14 269	82.12 302	67.69 336	92.36 291	78.16 250	95.50 322	77.31 307	79.73 313	94.39 285

Baseline_NR-MVSNet		91.20 209	90.62 200	92.95 244	93.83 277	88.03 221	97.01 136	95.12 270	88.42 208	89.70 207	95.13 194	83.47 130	97.44 275	89.66 142	83.24 298	93.37 302

thres200		92.23 162	91.39 163	94.75 161	97.61 110	89.03 188	96.60 184	95.09 271	92.08 101	93.28 111	94.00 252	78.39 246	99.04 125	81.26 289	94.18 157	96.19 189

tpmp4_e23		89.58 250	88.59 250	92.54 255	95.16 212	81.53 300	94.11 283	95.09 271	81.66 305	88.60 233	93.44 273	75.11 275	98.33 182	82.45 265	91.72 203	97.75 140

ADS-MVSNet		89.89 245	88.68 249	93.53 223	95.86 180	84.89 274	90.93 323	95.07 273	83.23 295	91.28 160	91.81 298	79.01 229	97.85 245	79.52 296	91.39 210	97.84 136

pmmvs-eth3d		86.22 290	84.45 293	91.53 284	88.34 330	87.25 239	94.47 274	95.01 274	83.47 293	79.51 323	89.61 312	69.75 306	95.71 317	83.13 256	76.73 320	91.64 328

MDA-MVSNet-bldmvs		85.00 297	82.95 299	91.17 290	93.13 302	83.33 289	94.56 272	95.00 275	84.57 280	65.13 341	92.65 282	70.45 302	95.85 314	73.57 319	77.49 317	94.33 287

RPMNet		88.52 266	86.72 279	93.95 195	94.45 243	87.19 242	90.23 328	94.99 276	77.87 327	92.40 130	87.55 332	80.17 211	97.05 289	68.84 329	93.95 168	97.60 149

ambc				86.56 318	83.60 340	70.00 338	85.69 341	94.97 277		80.60 311	88.45 323	37.42 348	96.84 297	82.69 263	75.44 323	92.86 305

testgi		87.97 276	87.21 273	90.24 304	92.86 304	80.76 304	96.67 176	94.97 277	91.74 108	85.52 276	95.83 156	62.66 327	94.47 327	76.25 310	88.36 244	95.48 222

dp		88.90 258	88.26 256	90.81 295	94.58 240	76.62 325	92.85 307	94.93 279	85.12 272	90.07 193	93.07 277	75.81 269	98.12 196	80.53 292	87.42 252	97.71 142

test_0402		86.46 288	84.79 291	91.45 286	95.02 220	85.55 265	96.29 209	94.89 280	80.90 311	82.21 295	93.97 253	68.21 312	97.29 285	62.98 335	88.68 242	91.51 330

tfpn200view9		92.38 154	91.52 160	94.95 149	97.85 98	89.29 181	97.41 99	94.88 281	92.19 93	93.27 112	94.46 222	78.17 248	99.08 120	81.40 277	94.08 158	96.48 183

CVMVSNet		91.23 208	91.75 145	89.67 308	95.77 185	74.69 328	96.44 190	94.88 281	85.81 264	92.18 137	97.64 76	79.07 224	95.58 320	88.06 172	95.86 137	98.74 87

thres400		92.42 152	91.52 160	95.12 139	97.85 98	89.29 181	97.41 99	94.88 281	92.19 93	93.27 112	94.46 222	78.17 248	99.08 120	81.40 277	94.08 158	96.98 160

EPNet		95.20 68	94.56 73	97.14 55	92.80 306	92.68 66	97.85 49	94.87 284	96.64 1	92.46 129	97.80 64	86.23 100	99.65 42	93.72 81	98.62 75	99.10 62
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
SixPastTwentyTwo		89.15 255	88.54 252	90.98 291	93.49 287	80.28 312	96.70 172	94.70 285	90.78 131	84.15 288	95.57 173	71.78 294	97.71 258	84.63 234	85.07 273	94.94 259

tfpn111		92.45 149	91.58 155	95.06 140	97.92 92	89.37 175	97.71 63	94.66 286	92.20 90	93.31 108	94.90 199	78.06 255	99.11 109	81.37 281	94.06 162	96.70 175

conf200view11		92.45 149	91.58 155	95.05 141	97.92 92	89.37 175	97.71 63	94.66 286	92.20 90	93.31 108	94.90 199	78.06 255	99.08 120	81.40 277	94.08 158	96.70 175

thres100view900		92.43 151	91.58 155	94.98 146	97.92 92	89.37 175	97.71 63	94.66 286	92.20 90	93.31 108	94.90 199	78.06 255	99.08 120	81.40 277	94.08 158	96.48 183

thres600view7		92.49 148	91.60 154	95.18 131	97.91 95	89.47 166	97.65 70	94.66 286	92.18 95	93.33 107	94.91 198	78.06 255	99.10 115	81.61 270	94.06 162	96.98 160

PatchT		88.87 259	87.42 264	93.22 237	94.08 265	85.10 270	89.51 332	94.64 290	81.92 303	92.36 133	88.15 327	80.05 212	97.01 293	72.43 321	93.65 173	97.54 152

view600		92.55 142	91.68 148	95.18 131	97.98 84	89.44 170	98.00 36	94.57 291	92.09 96	93.17 115	95.52 177	78.14 251	99.11 109	81.61 270	94.04 164	96.98 160

view800		92.55 142	91.68 148	95.18 131	97.98 84	89.44 170	98.00 36	94.57 291	92.09 96	93.17 115	95.52 177	78.14 251	99.11 109	81.61 270	94.04 164	96.98 160

conf0.05thres1000		92.55 142	91.68 148	95.18 131	97.98 84	89.44 170	98.00 36	94.57 291	92.09 96	93.17 115	95.52 177	78.14 251	99.11 109	81.61 270	94.04 164	96.98 160

tfpn		92.55 142	91.68 148	95.18 131	97.98 84	89.44 170	98.00 36	94.57 291	92.09 96	93.17 115	95.52 177	78.14 251	99.11 109	81.61 270	94.04 164	96.98 160

Gipuma		67.86 320	65.41 321	75.18 333	92.66 309	73.45 331	66.50 351	94.52 295	53.33 346	57.80 345	66.07 348	30.81 350	89.20 344	48.15 349	78.88 315	62.90 350
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
CostFormer		91.18 212	90.70 192	92.62 254	94.84 229	81.76 299	94.09 284	94.43 296	84.15 284	92.72 128	93.77 260	79.43 220	98.20 188	90.70 132	92.18 196	97.90 133

tpm2		89.96 243	89.21 242	92.23 262	94.91 227	81.25 302	93.78 288	94.42 297	80.62 315	91.56 148	93.44 273	76.44 267	97.94 235	85.60 221	92.08 200	97.49 153

JIA-IIPM		88.26 275	87.04 276	91.91 273	93.52 285	81.42 301	89.38 333	94.38 298	80.84 313	90.93 171	80.74 339	79.22 223	97.92 239	82.76 261	91.62 205	96.38 186

Patchmatch-test		89.42 253	87.99 257	93.70 213	95.27 204	85.11 269	88.98 334	94.37 299	81.11 310	87.10 263	93.69 261	82.28 171	97.50 271	74.37 316	94.76 151	98.48 107

LCM-MVSNet		72.55 315	69.39 318	82.03 323	70.81 353	65.42 344	90.12 330	94.36 300	55.02 345	65.88 340	81.72 338	24.16 357	89.96 342	74.32 317	68.10 341	90.71 334

ADS-MVSNet2		89.45 252	88.59 250	92.03 271	95.86 180	82.26 296	90.93 323	94.32 301	83.23 295	91.28 160	91.81 298	79.01 229	95.99 312	79.52 296	91.39 210	97.84 136

DWT-MVSNet_test		90.76 222	89.89 225	93.38 230	95.04 219	83.70 285	95.85 234	94.30 302	88.19 216	90.46 176	92.80 280	73.61 288	98.50 163	88.16 170	90.58 221	97.95 131

testus		82.63 305	82.15 301	84.07 321	87.31 334	67.67 340	93.18 298	94.29 303	82.47 299	82.14 297	90.69 303	53.01 341	91.94 337	66.30 332	89.96 229	92.62 310

LP		84.13 300	81.85 305	90.97 292	93.20 299	82.12 297	87.68 338	94.27 304	76.80 328	81.93 298	88.52 322	72.97 291	95.95 313	59.53 340	81.73 305	94.84 265

EU-MVSNet		88.72 260	88.90 246	88.20 311	93.15 301	74.21 329	96.63 181	94.22 305	85.18 270	87.32 258	95.97 148	76.16 268	94.98 325	85.27 225	86.17 257	95.41 228

test1235678		79.82 310	78.53 311	83.69 322	82.55 342	67.55 341	92.50 312	94.13 306	79.28 319	72.10 334	86.45 335	57.27 333	90.68 341	61.60 338	80.90 310	92.82 306

MIMVSNet		88.50 268	86.76 277	93.72 212	94.84 229	87.77 231	91.39 318	94.05 307	86.41 257	87.99 245	92.59 284	63.27 325	95.82 316	77.44 305	92.84 187	97.57 151

OpenMVS_ROB	81.14 20	84.42 299	82.28 300	90.83 294	90.06 323	84.05 282	95.73 240	94.04 308	73.89 336	80.17 321	91.53 302	59.15 332	97.64 263	66.92 331	89.05 236	90.80 333

TinyColmap		86.82 286	85.35 288	91.21 289	94.91 227	82.99 291	93.94 286	94.02 309	83.58 291	81.56 302	94.68 212	62.34 328	98.13 194	75.78 311	87.35 254	92.52 312

IB-MVS	87.33 17	89.91 244	88.28 255	94.79 159	95.26 207	87.70 233	95.12 265	93.95 310	89.35 165	87.03 264	92.49 286	70.74 301	99.19 100	89.18 153	81.37 308	97.49 153
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
Anonymous20231211		78.22 313	75.30 314	86.99 317	86.14 336	74.16 330	95.62 245	93.88 311	66.43 340	74.44 330	87.86 329	41.39 347	95.11 324	62.49 336	69.46 340	91.71 327

1111		78.29 312	77.55 312	80.50 325	83.89 338	59.98 348	91.89 315	93.71 312	75.06 331	73.60 332	87.67 330	55.66 337	92.60 335	58.54 342	77.92 316	88.93 337

.test1245		65.38 321	69.22 319	53.86 341	83.89 338	59.98 348	91.89 315	93.71 312	75.06 331	73.60 332	87.67 330	55.66 337	92.60 335	58.54 342	2.96 355	9.00 355

LCM-MVSNet-Re		92.50 146	92.52 128	92.44 256	96.82 141	81.89 298	96.92 146	93.71 312	92.41 86	84.30 285	94.60 216	85.08 114	97.03 291	91.51 121	97.36 106	98.40 115

test2356		82.77 304	82.14 302	84.65 320	85.77 337	70.36 335	91.22 321	93.69 315	81.58 307	81.82 299	89.00 320	60.63 331	90.77 340	64.74 333	90.80 219	92.82 306

tpm		90.25 237	89.74 233	91.76 281	93.92 273	79.73 316	93.98 285	93.54 316	88.28 212	91.99 141	93.25 276	77.51 263	97.44 275	87.30 194	87.94 246	98.12 125

LFMVS		93.60 110	92.63 121	96.52 71	98.13 80	91.27 105	97.94 41	93.39 317	90.57 145	96.29 47	98.31 34	69.00 307	99.16 104	94.18 70	95.87 136	99.12 60

Patchmatch-RL test		87.38 281	86.24 280	90.81 295	88.74 329	78.40 323	88.12 337	93.17 318	87.11 241	82.17 296	89.29 318	81.95 179	95.60 319	88.64 167	77.02 318	98.41 114

conf0.01		91.74 175	90.67 194	94.94 152	97.55 115	89.68 150	97.64 74	93.14 319	88.43 202	91.24 162	94.30 234	78.91 233	98.45 167	81.28 283	93.57 178	96.70 175

conf0.002		91.74 175	90.67 194	94.94 152	97.55 115	89.68 150	97.64 74	93.14 319	88.43 202	91.24 162	94.30 234	78.91 233	98.45 167	81.28 283	93.57 178	96.70 175

thresconf0.02		91.69 182	90.67 194	94.75 161	97.55 115	89.68 150	97.64 74	93.14 319	88.43 202	91.24 162	94.30 234	78.91 233	98.45 167	81.28 283	93.57 178	96.11 195

tfpn_n400		91.69 182	90.67 194	94.75 161	97.55 115	89.68 150	97.64 74	93.14 319	88.43 202	91.24 162	94.30 234	78.91 233	98.45 167	81.28 283	93.57 178	96.11 195

tfpnconf		91.69 182	90.67 194	94.75 161	97.55 115	89.68 150	97.64 74	93.14 319	88.43 202	91.24 162	94.30 234	78.91 233	98.45 167	81.28 283	93.57 178	96.11 195

tfpnview11		91.69 182	90.67 194	94.75 161	97.55 115	89.68 150	97.64 74	93.14 319	88.43 202	91.24 162	94.30 234	78.91 233	98.45 167	81.28 283	93.57 178	96.11 195

test-LLR		91.42 200	91.19 173	92.12 268	94.59 238	80.66 305	94.29 278	92.98 325	91.11 126	90.76 172	92.37 288	79.02 227	98.07 205	88.81 164	96.74 120	97.63 144

test-mter		90.19 240	89.54 237	92.12 268	94.59 238	80.66 305	94.29 278	92.98 325	87.68 229	90.76 172	92.37 288	67.67 313	98.07 205	88.81 164	96.74 120	97.63 144

tfpn_ndepth		91.88 173	90.96 179	94.62 166	97.73 104	89.93 144	97.75 55	92.92 327	88.93 185	91.73 145	93.80 259	78.91 233	98.49 166	83.02 258	93.86 171	95.45 226

tfpn1000		91.99 170	91.05 175	94.80 157	97.78 101	89.66 156	97.91 44	92.90 328	88.99 180	91.73 145	94.84 204	78.99 232	98.33 182	82.41 266	93.91 170	96.40 185

test12356		74.97 314	74.13 315	77.49 330	78.81 344	56.23 352	88.53 336	92.75 329	75.14 330	67.50 338	85.07 336	44.88 345	89.96 342	58.71 341	75.75 322	86.26 338

test0.0.03 1		89.37 254	88.70 248	91.41 288	92.47 311	85.63 264	95.22 263	92.70 330	91.11 126	86.91 267	93.65 264	79.02 227	93.19 333	78.00 304	89.18 235	95.41 228

new_pmnet		82.89 303	81.12 308	88.18 312	89.63 326	80.18 313	91.77 317	92.57 331	76.79 329	75.56 329	88.23 326	61.22 330	94.48 326	71.43 324	82.92 301	89.87 335

testmv		72.22 316	70.02 316	78.82 328	73.06 351	61.75 346	91.24 320	92.31 332	74.45 334	61.06 343	80.51 340	34.21 349	88.63 345	55.31 345	68.07 342	86.06 339

K. test v3		87.64 280	86.75 278	90.32 303	93.02 303	79.48 318	96.61 182	92.08 333	90.66 137	80.25 320	94.09 250	67.21 317	96.65 298	85.96 216	80.83 311	94.83 267

TESTMET0.1,1		90.06 242	89.42 239	91.97 272	94.41 245	80.62 307	94.29 278	91.97 334	87.28 238	90.44 177	92.47 287	68.79 308	97.67 260	88.50 168	96.60 125	97.61 148

PM-MVS		83.48 301	81.86 304	88.31 310	87.83 332	77.59 324	93.43 295	91.75 335	86.91 249	80.63 310	89.91 306	44.42 346	95.84 315	85.17 228	76.73 320	91.50 331

FPMVS		71.27 317	69.85 317	75.50 332	74.64 346	59.03 350	91.30 319	91.50 336	58.80 344	57.92 344	88.28 325	29.98 353	85.53 348	53.43 346	82.84 302	81.95 342

door								91.13 337

door-mid								91.06 338

pmmvs3		79.97 309	77.50 313	87.39 314	82.80 341	79.38 319	92.70 309	90.75 339	70.69 339	78.66 324	87.47 333	51.34 343	93.40 331	73.39 320	69.65 339	89.38 336

no-one		68.12 319	63.78 322	81.13 324	74.01 348	70.22 337	87.61 339	90.71 340	72.63 338	53.13 346	71.89 345	30.29 351	91.45 338	61.53 339	32.21 349	81.72 343

DSMNet-mixed		86.34 289	86.12 283	87.00 316	89.88 325	70.43 334	94.93 267	90.08 341	77.97 326	85.42 279	92.78 281	74.44 281	93.96 329	74.43 315	95.14 145	96.62 179

testpf		80.97 308	81.40 306	79.65 327	91.53 317	72.43 333	73.47 349	89.55 342	78.63 322	80.81 305	89.06 319	61.36 329	91.36 339	83.34 253	84.89 281	75.15 346

MVS-HIRNet		82.47 306	81.21 307	86.26 319	95.38 197	69.21 339	88.96 335	89.49 343	66.28 341	80.79 306	74.08 344	68.48 310	97.39 280	71.93 323	95.47 141	92.18 325

EPMVS		90.70 228	89.81 229	93.37 231	94.73 234	84.21 279	93.67 292	88.02 344	89.50 162	92.38 132	93.49 270	77.82 261	97.78 252	86.03 214	92.68 188	98.11 128

ANet_high		63.94 322	59.58 323	77.02 331	61.24 356	66.06 342	85.66 342	87.93 345	78.53 324	42.94 348	71.04 346	25.42 356	80.71 350	52.60 347	30.83 351	84.28 341

PMMVS2		70.19 318	66.92 320	80.01 326	76.35 345	65.67 343	86.22 340	87.58 346	64.83 343	62.38 342	80.29 341	26.78 355	88.49 346	63.79 334	54.07 345	85.88 340

lessismore_v0				90.45 301	91.96 316	79.09 321		87.19 347		80.32 318	94.39 227	66.31 319	97.55 268	84.00 247	76.84 319	94.70 276

PMVS	53.92 22	58.58 324	55.40 325	68.12 337	51.00 357	48.64 354	78.86 347	87.10 348	46.77 349	35.84 353	74.28 343	8.76 359	86.34 347	42.07 350	73.91 335	69.38 348
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
wuykxyi23d		56.92 325	51.11 329	74.38 335	62.30 355	61.47 347	80.09 346	84.87 349	49.62 348	30.80 354	57.20 352	7.03 360	82.94 349	55.69 344	32.36 348	78.72 345

gg-mvs-nofinetune		87.82 278	85.61 285	94.44 174	94.46 242	89.27 184	91.21 322	84.61 350	80.88 312	89.89 197	74.98 342	71.50 295	97.53 269	85.75 219	97.21 110	96.51 181

GG-mvs-BLEND				93.62 217	93.69 281	89.20 185	92.39 314	83.33 351		87.98 246	89.84 307	71.00 299	96.87 296	82.08 269	95.40 142	94.80 271

PNet_i23d		59.01 323	55.87 324	68.44 336	73.98 349	51.37 353	81.36 345	82.41 352	52.37 347	42.49 350	70.39 347	11.39 358	79.99 352	49.77 348	38.71 347	73.97 347

MTMP								82.03 353

DeepMVS_CX				74.68 334	90.84 320	64.34 345		81.61 354	65.34 342	67.47 339	88.01 328	48.60 344	80.13 351	62.33 337	73.68 336	79.58 344

E-PMN		53.28 326	52.56 327	55.43 339	74.43 347	47.13 355	83.63 344	76.30 355	42.23 350	42.59 349	62.22 350	28.57 354	74.40 353	31.53 352	31.51 350	44.78 351

EMVS		52.08 328	51.31 328	54.39 340	72.62 352	45.39 357	83.84 343	75.51 356	41.13 351	40.77 351	59.65 351	30.08 352	73.60 354	28.31 353	29.90 352	44.18 352

MVE	50.73 23	53.25 327	48.81 330	66.58 338	65.34 354	57.50 351	72.49 350	70.94 357	40.15 352	39.28 352	63.51 349	6.89 362	73.48 355	38.29 351	42.38 346	68.76 349
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
tmp_tt		51.94 329	53.82 326	46.29 342	33.73 358	45.30 358	78.32 348	67.24 358	18.02 353	50.93 347	87.05 334	52.99 342	53.11 356	70.76 327	25.29 353	40.46 353

N_pmnet		78.73 311	78.71 310	78.79 329	92.80 306	46.50 356	94.14 282	43.71 359	78.61 323	80.83 304	91.66 301	74.94 279	96.36 300	67.24 330	84.45 285	93.50 298

wuyk23d		25.11 331	24.57 333	26.74 344	73.98 349	39.89 359	57.88 352	9.80 360	12.27 354	10.39 355	6.97 358	7.03 360	36.44 357	25.43 354	17.39 354	3.89 357

testmvs		13.36 333	16.33 334	4.48 346	5.04 359	2.26 361	93.18 298	3.28 361	2.70 355	8.24 356	21.66 354	2.29 364	2.19 358	7.58 355	2.96 355	9.00 355

test123		13.04 334	15.66 335	5.18 345	4.51 360	3.45 360	92.50 312	1.81 362	2.50 356	7.58 357	20.15 355	3.67 363	2.18 359	7.13 356	1.07 357	9.90 354

pcd_1.5k_mvsjas		7.39 336	9.85 337	0.00 347	0.00 361	0.00 362	0.00 353	0.00 363	0.00 357	0.00 358	0.00 359	88.65 71	0.00 360	0.00 357	0.00 358	0.00 358

sosnet-low-res		0.00 337	0.00 338	0.00 347	0.00 361	0.00 362	0.00 353	0.00 363	0.00 357	0.00 358	0.00 359	0.00 365	0.00 360	0.00 357	0.00 358	0.00 358

sosnet		0.00 337	0.00 338	0.00 347	0.00 361	0.00 362	0.00 353	0.00 363	0.00 357	0.00 358	0.00 359	0.00 365	0.00 360	0.00 357	0.00 358	0.00 358

uncertanet		0.00 337	0.00 338	0.00 347	0.00 361	0.00 362	0.00 353	0.00 363	0.00 357	0.00 358	0.00 359	0.00 365	0.00 360	0.00 357	0.00 358	0.00 358

Regformer		0.00 337	0.00 338	0.00 347	0.00 361	0.00 362	0.00 353	0.00 363	0.00 357	0.00 358	0.00 359	0.00 365	0.00 360	0.00 357	0.00 358	0.00 358

n2								0.00 363

nn								0.00 363

ab-mvs-re		8.06 335	10.74 336	0.00 347	0.00 361	0.00 362	0.00 353	0.00 363	0.00 357	0.00 358	96.69 115	0.00 365	0.00 360	0.00 357	0.00 358	0.00 358

uanet		0.00 337	0.00 338	0.00 347	0.00 361	0.00 362	0.00 353	0.00 363	0.00 357	0.00 358	0.00 359	0.00 365	0.00 360	0.00 357	0.00 358	0.00 358

GSMVS																98.45 110

test_part3							97.50 91		93.81 45		98.53 12		99.87 5	95.19 48

test_part2					99.28 17	95.74 3				98.10 7

sam_mvs1												82.76 159				98.45 110

sam_mvs												81.94 180

test_post1							92.81 308				16.58 357	80.53 203	97.68 259	86.20 209

test_post											17.58 356	81.76 182	98.08 201

patchmatchnet-post											90.45 304	82.65 163	98.10 198

gm-plane-assit					93.22 297	78.89 322			84.82 277		93.52 268		98.64 150	87.72 179

test9_res														94.81 63	99.38 36	99.45 31

agg_prior2														93.94 75	99.38 36	99.50 25

test_prior4						93.66 42	96.42 193

test_prior2							96.35 202		92.80 79	96.03 55	97.59 80	92.01 31		95.01 56	99.38 36

旧先验2							95.94 230		81.66 305	97.34 18			98.82 138	92.26 98

新几何2							95.79 237

原ACMM2							95.67 241

testdata2													99.67 40	85.96 216

segment_acmp												92.89 13

testdata1							95.26 262		93.10 67

plane_prior7					96.21 166	89.98 140

plane_prior6					96.10 176	90.00 136						81.32 188

plane_prior4											96.64 118

plane_prior3						90.00 136			94.46 30	91.34 154

plane_prior2							97.74 57		94.85 17

plane_prior1					96.14 174

plane_prior						89.99 138	97.24 115		94.06 38						92.16 197

HQP5-MVS						89.33 178

HQP-NCC					95.86 180		96.65 177		93.55 50	90.14 182

ACMP_Plane					95.86 180		96.65 177		93.55 50	90.14 182

BP-MVS														92.13 104

HQP4-MVS										90.14 182			98.50 163			95.78 212

HQP2-MVS												80.95 193

NP-MVS					95.99 179	89.81 147					95.87 153

MDTV_nov1_ep13_2view						70.35 336	93.10 304		83.88 288	93.55 102		82.47 168		86.25 208		98.38 118

ACMMP++_ref															90.30 226

ACMMP++															91.02 216

Test By Simon												88.73 70

High-resolution multi-view benchmark