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 27	96.53 30	97.65 29	99.35 13	93.53 44	97.65 68	98.98 1	92.22 86	97.14 22	98.44 14	91.17 42	99.85 9	94.35 66	99.46 24	99.57 12

MVS_111021_HR		96.68 34	96.58 29	96.99 57	98.46 52	92.31 72	96.20 208	98.90 2	94.30 35	95.86 61	97.74 64	92.33 22	99.38 88	96.04 30	99.42 29	99.28 47

ACMMP		96.27 44	95.93 44	97.28 45	99.24 20	92.62 66	98.25 25	98.81 3	92.99 67	94.56 85	98.39 20	88.96 64	99.85 9	94.57 65	97.63 95	99.36 40
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 45	96.19 42	96.39 79	98.23 73	91.35 101	96.24 206	98.79 4	93.99 39	95.80 64	97.65 70	89.92 59	99.24 95	95.87 33	99.20 50	98.58 93

FC-MVSNet-test		93.94 98	93.57 90	95.04 139	95.48 185	91.45 99	98.12 30	98.71 5	93.37 53	90.23 173	96.70 110	87.66 81	97.85 236	91.49 119	90.39 217	95.83 199

UniMVSNet (Re)		93.31 117	92.55 123	95.61 112	95.39 188	93.34 51	97.39 94	98.71 5	93.14 63	90.10 182	94.83 202	87.71 80	98.03 209	91.67 117	83.99 279	95.46 215

FIs		94.09 92	93.70 86	95.27 127	95.70 179	92.03 82	98.10 31	98.68 7	93.36 55	90.39 170	96.70 110	87.63 83	97.94 226	92.25 97	90.50 216	95.84 198

WR-MVS_H		92.00 166	91.35 161	93.95 187	95.09 209	89.47 159	98.04 35	98.68 7	91.46 112	88.34 229	94.68 208	85.86 104	97.56 258	85.77 215	84.24 277	94.82 259

VPA-MVSNet		93.24 119	92.48 128	95.51 117	95.70 179	92.39 71	97.86 46	98.66 9	92.30 85	92.09 137	95.37 181	80.49 202	98.40 166	93.95 71	85.86 252	95.75 206

UniMVSNet_NR-MVSNet		93.37 115	92.67 118	95.47 122	95.34 191	92.83 60	97.17 116	98.58 10	92.98 72	90.13 178	95.80 155	88.37 74	97.85 236	91.71 113	83.93 280	95.73 208

CSCG		96.05 49	95.91 45	96.46 77	99.24 20	90.47 129	98.30 21	98.57 11	89.01 175	93.97 96	97.57 79	92.62 17	99.76 21	94.66 64	99.27 44	99.15 54

MSLP-MVS++		96.94 23	97.06 8	96.59 67	98.72 36	91.86 87	97.67 65	98.49 12	94.66 27	97.24 17	98.41 19	92.31 25	98.94 124	96.61 14	99.46 24	98.96 70

HyFIR lowres test		93.66 106	92.92 109	95.87 101	98.24 70	89.88 143	94.58 262	98.49 12	85.06 264	93.78 97	95.78 159	82.86 154	98.67 145	91.77 111	95.71 138	99.07 62

CHOSEN 1792x2688		94.15 88	93.51 94	96.06 94	98.27 67	89.38 167	95.18 255	98.48 14	85.60 257	93.76 98	97.11 97	83.15 133	99.61 45	91.33 122	98.72 71	99.19 50

PHI-MVS		96.77 29	96.46 33	97.71 26	98.40 56	94.07 28	98.21 28	98.45 15	89.86 150	97.11 25	98.01 46	92.52 20	99.69 33	96.03 31	99.53 15	99.36 40

PVSNet_BlendedMVS		94.06 93	93.92 81	94.47 165	98.27 67	89.46 161	96.73 155	98.36 16	90.17 145	94.36 88	95.24 187	88.02 75	99.58 53	93.44 84	90.72 212	94.36 276

PVSNet_Blended		94.87 77	94.56 71	95.81 103	98.27 67	89.46 161	95.47 243	98.36 16	88.84 184	94.36 88	96.09 144	88.02 75	99.58 53	93.44 84	98.18 82	98.40 111

3Dnovator	91.36 5	95.19 67	94.44 78	97.44 38	96.56 142	93.36 50	98.65 6	98.36 16	94.12 37	89.25 217	98.06 43	82.20 172	99.77 20	93.41 86	99.32 40	99.18 51

HFP-MVS		97.14 13	96.92 14	97.83 14	99.42 3	94.12 26	98.52 10	98.32 19	93.21 58	97.18 19	98.29 34	92.08 27	99.83 13	95.63 39	99.59 8	99.54 18

#test#		97.02 19	96.75 24	97.83 14	99.42 3	94.12 26	98.15 29	98.32 19	92.57 81	97.18 19	98.29 34	92.08 27	99.83 13	95.12 49	99.59 8	99.54 18

ACMMPR		97.07 16	96.84 17	97.79 18	99.44 2	93.88 32	98.52 10	98.31 21	93.21 58	97.15 21	98.33 28	91.35 40	99.86 6	95.63 39	99.59 8	99.62 6

APDe-MVS		97.82 1	97.73 1	98.08 7	99.15 24	94.82 11	98.81 2	98.30 22	94.76 24	98.30 4	98.90 1	93.77 7	99.68 35	97.93 1	99.69 1	99.75 1

CP-MVS		97.02 19	96.81 20	97.64 31	99.33 14	93.54 43	98.80 3	98.28 23	92.99 67	96.45 43	98.30 33	91.90 32	99.85 9	95.61 41	99.68 2	99.54 18

SteuartSystems-ACMMP		97.62 3	97.53 2	97.87 12	98.39 58	94.25 21	98.43 16	98.27 24	95.34 9	98.11 5	98.56 7	94.53 2	99.71 27	96.57 16	99.62 6	99.65 3
Skip Steuart: Steuart Systems R&D Blog.
test_part1								98.26 25				95.31 1			99.63 4	99.63 5

test11111								98.25 26

PVSNet_Blended_VisFu		95.27 63	94.91 63	96.38 80	98.20 74	90.86 119	97.27 104	98.25 26	90.21 144	94.18 92	97.27 89	87.48 86	99.73 23	93.53 80	97.77 93	98.55 94

region2R		97.07 16	96.84 17	97.77 21	99.46 1	93.79 36	98.52 10	98.24 28	93.19 61	97.14 22	98.34 25	91.59 38	99.87 5	95.46 44	99.59 8	99.64 4

PS-CasMVS		91.55 186	90.84 184	93.69 206	94.96 214	88.28 193	97.84 49	98.24 28	91.46 112	88.04 236	95.80 155	79.67 215	97.48 263	87.02 196	84.54 275	95.31 228

DU-MVS		92.90 131	92.04 134	95.49 119	94.95 215	92.83 60	97.16 117	98.24 28	93.02 66	90.13 178	95.71 163	83.47 128	97.85 236	91.71 113	83.93 280	95.78 202

XVS		97.18 10	96.96 12	97.81 16	99.38 8	94.03 30	98.59 7	98.20 31	94.85 17	96.59 36	98.29 34	91.70 35	99.80 18	95.66 37	99.40 31	99.62 6

X-MVStestdata		91.71 172	89.67 226	97.81 16	99.38 8	94.03 30	98.59 7	98.20 31	94.85 17	96.59 36	32.69 343	91.70 35	99.80 18	95.66 37	99.40 31	99.62 6

ACMMP_Plus		97.20 9	96.86 16	98.23 3	99.09 25	95.16 7	97.60 76	98.19 33	92.82 76	97.93 9	98.74 3	91.60 37	99.86 6	96.26 20	99.52 16	99.67 2

CP-MVSNet		91.89 169	91.24 167	93.82 193	95.05 210	88.57 187	97.82 50	98.19 33	91.70 106	88.21 234	95.76 160	81.96 176	97.52 261	87.86 173	84.65 273	95.37 225

PEN-MVS		91.20 201	90.44 196	93.48 217	94.49 233	87.91 221	97.76 53	98.18 35	91.29 117	87.78 239	95.74 162	80.35 205	97.33 274	85.46 220	82.96 291	95.19 237

DELS-MVS		96.61 35	96.38 36	97.30 43	97.79 97	93.19 52	95.96 220	98.18 35	95.23 11	95.87 60	97.65 70	91.45 39	99.70 32	95.87 33	99.44 28	99.00 68
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		89.70 241	88.40 245	93.60 210	95.15 205	90.10 132	97.56 80	98.16 37	87.28 229	86.16 264	94.63 211	77.57 253	98.05 204	74.48 304	84.59 274	92.65 298

VNet		95.89 54	95.45 51	97.21 51	98.07 80	92.94 59	97.50 84	98.15 38	93.87 41	97.52 11	97.61 76	85.29 109	99.53 68	95.81 36	95.27 142	99.16 52

DeepPCF-MVS	93.97 1	96.61 35	97.09 7	95.15 134	98.09 79	86.63 247	96.00 219	98.15 38	95.43 7	97.95 8	98.56 7	93.40 9	99.36 89	96.77 12	99.48 23	99.45 29

SD-MVS		97.41 6	97.53 2	97.06 55	98.57 50	94.46 15	97.92 42	98.14 40	94.82 21	99.01 1	98.55 9	94.18 4	97.41 269	96.94 5	99.64 3	99.32 42

UA-Net		95.95 53	95.53 50	97.20 52	97.67 103	92.98 58	97.65 68	98.13 41	94.81 22	96.61 34	98.35 22	88.87 65	99.51 72	90.36 131	97.35 105	99.11 59

QAPM		93.45 113	92.27 131	96.98 58	96.77 134	92.62 66	98.39 18	98.12 42	84.50 272	88.27 233	97.77 62	82.39 168	99.81 17	85.40 221	98.81 68	98.51 99

Vis-MVSNet		95.23 64	94.81 64	96.51 72	97.18 118	91.58 95	98.26 24	98.12 42	94.38 33	94.90 80	98.15 39	82.28 169	98.92 125	91.45 121	98.58 75	99.01 67
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
OpenMVS	89.19 12	92.86 133	91.68 146	96.40 78	95.34 191	92.73 63	98.27 23	98.12 42	84.86 267	85.78 266	97.75 63	78.89 232	99.74 22	87.50 186	98.65 72	96.73 169

TranMVSNet+NR-MVSNet		92.50 144	91.63 151	95.14 135	94.76 224	92.07 80	97.53 82	98.11 45	92.90 75	89.56 205	96.12 141	83.16 132	97.60 257	89.30 145	83.20 290	95.75 206

CPTT-MVS		95.57 58	95.19 59	96.70 61	99.27 18	91.48 96	98.33 20	98.11 45	87.79 216	95.17 78	98.03 44	87.09 91	99.61 45	93.51 81	99.42 29	99.02 63

Regformer-2		97.16 12	96.99 10	97.67 28	98.32 64	93.84 34	96.83 143	98.10 47	95.24 10	97.49 12	98.25 37	92.57 18	99.61 45	96.80 9	99.29 42	99.56 14

APD-MVS		96.95 22	96.60 27	98.01 8	99.03 28	94.93 10	97.72 60	98.10 47	91.50 110	98.01 7	98.32 30	92.33 22	99.58 53	94.85 58	99.51 18	99.53 21
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
mPP-MVS		96.86 25	96.60 27	97.64 31	99.40 5	93.44 46	98.50 13	98.09 49	93.27 57	95.95 59	98.33 28	91.04 44	99.88 3	95.20 46	99.57 12	99.60 9

MPTG		97.07 16	96.77 23	97.97 10	99.37 10	94.42 17	97.15 118	98.08 50	95.07 14	96.11 50	98.59 5	90.88 48	99.90 1	96.18 27	99.50 20	99.58 10

MTGPA								98.08 50

MTAPA		97.08 15	96.78 22	97.97 10	99.37 10	94.42 17	97.24 106	98.08 50	95.07 14	96.11 50	98.59 5	90.88 48	99.90 1	96.18 27	99.50 20	99.58 10

CNVR-MVS		97.68 2	97.44 5	98.37 2	98.90 31	95.86 2	97.27 104	98.08 50	95.81 3	97.87 10	98.31 31	94.26 3	99.68 35	97.02 4	99.49 22	99.57 12

DP-MVS Recon		95.68 56	95.12 61	97.37 40	99.19 23	94.19 23	97.03 123	98.08 50	88.35 202	95.09 79	97.65 70	89.97 58	99.48 75	92.08 104	98.59 74	98.44 108

MCST-MVS		97.18 10	96.84 17	98.20 4	99.30 16	95.35 4	97.12 120	98.07 55	93.54 51	96.08 52	97.69 66	93.86 6	99.71 27	96.50 17	99.39 33	99.55 16

NR-MVSNet		92.34 152	91.27 166	95.53 116	94.95 215	93.05 55	97.39 94	98.07 55	92.65 80	84.46 274	95.71 163	85.00 113	97.77 245	89.71 137	83.52 287	95.78 202

MP-MVS-pluss		96.70 31	96.27 38	97.98 9	99.23 22	94.71 12	96.96 130	98.06 57	90.67 132	95.55 73	98.78 2	91.07 43	99.86 6	96.58 15	99.55 13	99.38 38
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
APD-MVS_3200maxsize		96.81 27	96.71 25	97.12 54	99.01 29	92.31 72	97.98 40	98.06 57	93.11 64	97.44 14	98.55 9	90.93 46	99.55 63	96.06 29	99.25 45	99.51 22

MP-MVS		96.77 29	96.45 34	97.72 24	99.39 7	93.80 35	98.41 17	98.06 57	93.37 53	95.54 74	98.34 25	90.59 51	99.88 3	94.83 59	99.54 14	99.49 25

HPM-MVS_fast		96.51 37	96.27 38	97.22 50	99.32 15	92.74 62	98.74 4	98.06 57	90.57 141	96.77 29	98.35 22	90.21 55	99.53 68	94.80 61	99.63 4	99.38 38

HPM-MVS		96.69 32	96.45 34	97.40 39	99.36 12	93.11 54	98.87 1	98.06 57	91.17 121	96.40 44	97.99 48	90.99 45	99.58 53	95.61 41	99.61 7	99.49 25

sss		94.51 82	93.80 84	96.64 62	97.07 122	91.97 85	96.32 197	98.06 57	88.94 180	94.50 86	96.78 105	84.60 118	99.27 93	91.90 107	96.02 130	98.68 91

DeepC-MVS	93.07 3	96.06 48	95.66 49	97.29 44	97.96 86	93.17 53	97.30 103	98.06 57	93.92 40	93.38 104	98.66 4	86.83 93	99.73 23	95.60 43	99.22 48	98.96 70
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 8	97.03 9	98.11 6	98.77 34	95.06 9	97.34 98	98.04 64	95.96 2	97.09 26	97.88 52	93.18 10	99.71 27	95.84 35	99.17 52	99.56 14

DeepC-MVS_fast	93.89 2	96.93 24	96.64 26	97.78 19	98.64 45	94.30 19	97.41 90	98.04 64	94.81 22	96.59 36	98.37 21	91.24 41	99.64 44	95.16 47	99.52 16	99.42 33
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 40	96.21 40	96.98 58	98.89 32	92.20 77	97.89 44	98.03 66	93.34 56	97.22 18	98.42 16	87.93 78	99.72 26	95.10 50	99.07 60	99.02 63

TEST9					98.70 37	94.19 23	96.41 185	98.02 67	88.17 209	96.03 53	97.56 81	92.74 13	99.59 50

train_agg		96.30 43	95.83 46	97.72 24	98.70 37	94.19 23	96.41 185	98.02 67	88.58 193	96.03 53	97.56 81	92.73 14	99.59 50	95.04 51	99.37 38	99.39 35

test_8					98.67 39	94.06 29	96.37 192	98.01 69	88.58 193	95.98 58	97.55 83	92.73 14	99.58 53

Regformer-4		96.97 21	96.87 15	97.25 47	98.34 61	92.66 65	96.96 130	98.01 69	95.12 13	97.14 22	98.42 16	91.82 33	99.61 45	96.90 6	99.13 55	99.50 23

agg_prior3		96.16 47	95.67 48	97.62 34	98.67 39	93.88 32	96.41 185	98.00 71	87.93 213	95.81 63	97.47 85	92.33 22	99.59 50	95.04 51	99.37 38	99.39 35

agg_prior1		96.22 46	95.77 47	97.56 35	98.67 39	93.79 36	96.28 201	98.00 71	88.76 190	95.68 67	97.55 83	92.70 16	99.57 61	95.01 53	99.32 40	99.32 42

agg_prior					98.67 39	93.79 36		98.00 71		95.68 67			99.57 61

test_prior3		96.46 39	96.20 41	97.23 48	98.67 39	92.99 56	96.35 193	98.00 71	92.80 77	96.03 53	97.59 77	92.01 29	99.41 83	95.01 53	99.38 34	99.29 44

test_prior				97.23 48	98.67 39	92.99 56		98.00 71					99.41 83			99.29 44

Regformer-1		97.10 14	96.96 12	97.54 36	98.32 64	93.48 45	96.83 143	97.99 76	95.20 12	97.46 13	98.25 37	92.48 21	99.58 53	96.79 11	99.29 42	99.55 16

WR-MVS		92.34 152	91.53 156	94.77 155	95.13 207	90.83 120	96.40 189	97.98 77	91.88 103	89.29 214	95.54 173	82.50 163	97.80 241	89.79 136	85.27 259	95.69 209

HPM-MVS++		97.34 7	96.97 11	98.47 1	99.08 26	96.16 1	97.55 81	97.97 78	95.59 4	96.61 34	97.89 50	92.57 18	99.84 12	95.95 32	99.51 18	99.40 34

CANet		96.39 41	96.02 43	97.50 37	97.62 106	93.38 48	97.02 125	97.96 79	95.42 8	94.86 81	97.81 59	87.38 88	99.82 16	96.88 7	99.20 50	99.29 44

114514_t		93.95 97	93.06 106	96.63 64	99.07 27	91.61 92	97.46 89	97.96 79	77.99 315	93.00 118	97.57 79	86.14 102	99.33 90	89.22 148	99.15 53	98.94 73

MVS_0304		96.05 49	95.45 51	97.85 13	97.75 100	94.50 14	96.87 140	97.95 81	95.46 6	95.60 71	98.01 46	80.96 190	99.83 13	97.23 2	99.25 45	99.23 48

原ACMM1				96.38 80	98.59 47	91.09 112		97.89 82	87.41 225	95.22 77	97.68 67	90.25 53	99.54 65	87.95 172	99.12 58	98.49 103

CDPH-MVS		95.97 52	95.38 54	97.77 21	98.93 30	94.44 16	96.35 193	97.88 83	86.98 237	96.65 33	97.89 50	91.99 31	99.47 76	92.26 95	99.46 24	99.39 35

test11								97.88 83

无先验							95.79 228	97.87 85	83.87 279				99.65 39	87.68 179		98.89 79

3Dnovator+	91.43 4	95.40 59	94.48 76	98.16 5	96.90 128	95.34 5	98.48 14	97.87 85	94.65 28	88.53 227	98.02 45	83.69 126	99.71 27	93.18 89	98.96 65	99.44 31

VPNet		92.23 159	91.31 164	94.99 141	95.56 182	90.96 115	97.22 111	97.86 87	92.96 73	90.96 162	96.62 122	75.06 267	98.20 179	91.90 107	83.65 286	95.80 201

HSP-MVS		97.53 4	97.49 4	97.63 33	99.40 5	93.77 39	98.53 9	97.85 88	95.55 5	98.56 3	97.81 59	93.90 5	99.65 39	96.62 13	99.21 49	99.48 27

TSAR-MVS + MP.		97.42 5	97.33 6	97.69 27	99.25 19	94.24 22	98.07 34	97.85 88	93.72 45	98.57 2	98.35 22	93.69 8	99.40 85	97.06 3	99.46 24	99.44 31

AdaColmap		94.34 84	93.68 88	96.31 84	98.59 47	91.68 91	96.59 176	97.81 90	89.87 149	92.15 135	97.06 99	83.62 127	99.54 65	89.34 144	98.07 85	97.70 139

Regformer-3		96.85 26	96.80 21	97.01 56	98.34 61	92.02 83	96.96 130	97.76 91	95.01 16	97.08 27	98.42 16	91.71 34	99.54 65	96.80 9	99.13 55	99.48 27

新几何1				97.32 42	98.60 46	93.59 42		97.75 92	81.58 297	95.75 66	97.85 56	90.04 57	99.67 37	86.50 202	99.13 55	98.69 90

旧先验1					98.38 59	93.38 48		97.75 92			98.09 41	92.30 26			99.01 63	99.16 52

EI-MVSNet-Vis-set		96.51 37	96.47 32	96.63 64	98.24 70	91.20 106	96.89 139	97.73 94	94.74 25	96.49 40	98.49 11	90.88 48	99.58 53	96.44 18	98.32 79	99.13 56

1121		94.71 80	93.83 83	97.34 41	98.57 50	93.64 41	96.04 215	97.73 94	81.56 299	95.68 67	97.85 56	90.23 54	99.65 39	87.68 179	99.12 58	98.73 86

PAPM_NR		95.01 69	94.59 70	96.26 89	98.89 32	90.68 124	97.24 106	97.73 94	91.80 104	92.93 123	96.62 122	89.13 63	99.14 104	89.21 149	97.78 92	98.97 69

CHOSEN 280x420		93.12 122	92.72 117	94.34 171	96.71 136	87.27 230	90.29 317	97.72 97	86.61 247	91.34 151	95.29 184	84.29 122	98.41 165	93.25 88	98.94 66	97.35 152

EI-MVSNet-UG-set		96.34 42	96.30 37	96.47 75	98.20 74	90.93 117	96.86 141	97.72 97	94.67 26	96.16 49	98.46 12	90.43 52	99.58 53	96.23 21	97.96 88	98.90 77

LS3D		93.57 110	92.61 121	96.47 75	97.59 109	91.61 92	97.67 65	97.72 97	85.17 262	90.29 172	98.34 25	84.60 118	99.73 23	83.85 247	98.27 80	98.06 125

PAPR		94.18 87	93.42 100	96.48 74	97.64 105	91.42 100	95.55 238	97.71 100	88.99 176	92.34 131	95.82 154	89.19 61	99.11 106	86.14 207	97.38 103	98.90 77

pcd1.5k->3k		38.37 321	40.51 322	31.96 334	94.29 241	0.00 353	0.00 343	97.69 101	0.00 347	0.00 349	0.00 349	81.45 184	0.00 350	0.00 347	91.11 206	95.89 194

UGNet		94.04 95	93.28 103	96.31 84	96.85 129	91.19 107	97.88 45	97.68 102	94.40 31	93.00 118	96.18 138	73.39 281	99.61 45	91.72 112	98.46 76	98.13 120
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				95.46 123	98.18 77	88.90 183		97.66 103	82.73 288	97.03 28	98.07 42	90.06 56	98.85 132	89.67 138	98.98 64	98.64 92

test12				97.65 29	98.46 52	94.26 20		97.66 103		95.52 75		90.89 47	99.46 77		99.25 45	99.22 49

DTE-MVSNet		90.56 223	89.75 224	93.01 234	93.95 264	87.25 231	97.64 72	97.65 105	90.74 129	87.12 253	95.68 166	79.97 211	97.00 285	83.33 251	81.66 298	94.78 264

TAPA-MVS	90.10 7	92.30 155	91.22 169	95.56 114	98.33 63	89.60 152	96.79 150	97.65 105	81.83 294	91.52 146	97.23 92	87.94 77	98.91 126	71.31 315	98.37 78	98.17 119
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
cdsmvs_eth3d_5k		23.24 323	30.99 323	0.00 338	0.00 352	0.00 353	0.00 343	97.63 107	0.00 347	0.00 349	96.88 103	84.38 121	0.00 350	0.00 347	0.00 349	0.00 347

canonicalmvs		96.02 51	95.45 51	97.75 23	97.59 109	95.15 8	98.28 22	97.60 108	94.52 29	96.27 46	96.12 141	87.65 82	99.18 99	96.20 26	94.82 148	98.91 76

test222					98.24 70	92.21 75	95.33 247	97.60 108	79.22 310	95.25 76	97.84 58	88.80 67			99.15 53	98.72 87

cascas		91.20 201	90.08 209	94.58 163	94.97 213	89.16 179	93.65 283	97.59 110	79.90 307	89.40 209	92.92 269	75.36 265	98.36 169	92.14 100	94.75 150	96.23 180

MVSFormer		95.37 60	95.16 60	95.99 98	96.34 154	91.21 104	98.22 26	97.57 111	91.42 114	96.22 47	97.32 87	86.20 100	97.92 230	94.07 68	99.05 61	98.85 81

test_djsdf		93.07 124	92.76 112	94.00 182	93.49 279	88.70 185	98.22 26	97.57 111	91.42 114	90.08 184	95.55 172	82.85 155	97.92 230	94.07 68	91.58 198	95.40 222

OMC-MVS		95.09 68	94.70 68	96.25 90	98.46 52	91.28 102	96.43 183	97.57 111	92.04 99	94.77 83	97.96 49	87.01 92	99.09 114	91.31 123	96.77 117	98.36 115

PS-MVSNAJss		93.74 104	93.51 94	94.44 166	93.91 266	89.28 175	97.75 54	97.56 114	92.50 82	89.94 186	96.54 125	88.65 69	98.18 182	93.83 77	90.90 209	95.86 195

jajsoiax		92.42 149	91.89 140	94.03 181	93.33 285	88.50 189	97.73 58	97.53 115	92.00 101	88.85 221	96.50 127	75.62 264	98.11 188	93.88 75	91.56 199	95.48 212

mvs_tets		92.31 154	91.76 142	93.94 190	93.41 281	88.29 192	97.63 74	97.53 115	92.04 99	88.76 222	96.45 129	74.62 271	98.09 191	93.91 73	91.48 200	95.45 216

HQP_MVS		93.78 103	93.43 98	94.82 149	96.21 158	89.99 136	97.74 56	97.51 117	94.85 17	91.34 151	96.64 115	81.32 186	98.60 150	93.02 90	92.23 185	95.86 195

plane_prior5								97.51 117					98.60 150	93.02 90	92.23 185	95.86 195

PS-MVSNAJ		95.37 60	95.33 56	95.49 119	97.35 114	90.66 125	95.31 249	97.48 119	93.85 42	96.51 39	95.70 165	88.65 69	99.65 39	94.80 61	98.27 80	96.17 183

API-MVS		94.84 78	94.49 75	95.90 100	97.90 93	92.00 84	97.80 51	97.48 119	89.19 165	94.81 82	96.71 108	88.84 66	99.17 100	88.91 157	98.76 70	96.53 173

MG-MVS		95.61 57	95.38 54	96.31 84	98.42 55	90.53 127	96.04 215	97.48 119	93.47 52	95.67 70	98.10 40	89.17 62	99.25 94	91.27 124	98.77 69	99.13 56

MAR-MVS		94.22 86	93.46 96	96.51 72	98.00 81	92.19 78	97.67 65	97.47 122	88.13 211	93.00 118	95.84 152	84.86 116	99.51 72	87.99 171	98.17 83	97.83 134
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		92.98 127	92.53 125	94.32 172	96.12 167	89.20 177	95.28 250	97.47 122	92.66 79	89.90 187	95.62 168	80.58 200	98.40 166	92.73 93	92.40 183	95.38 224
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 94	93.31 102	96.27 88	95.22 201	94.59 13	98.34 19	97.46 124	92.93 74	91.21 160	96.64 115	87.23 90	98.22 178	94.99 56	85.80 253	95.98 193

XVG-OURS		93.72 105	93.35 101	94.80 152	97.07 122	88.61 186	94.79 259	97.46 124	91.97 102	93.99 94	97.86 55	81.74 181	98.88 131	92.64 94	92.67 181	96.92 164

LPG-MVS_test		92.94 129	92.56 122	94.10 177	96.16 163	88.26 194	97.65 68	97.46 124	91.29 117	90.12 180	97.16 94	79.05 223	98.73 142	92.25 97	91.89 193	95.31 228

LGP-MVS_train				94.10 177	96.16 163	88.26 194		97.46 124	91.29 117	90.12 180	97.16 94	79.05 223	98.73 142	92.25 97	91.89 193	95.31 228

MVS		91.71 172	90.44 196	95.51 117	95.20 203	91.59 94	96.04 215	97.45 128	73.44 327	87.36 249	95.60 169	85.42 108	99.10 111	85.97 212	97.46 98	95.83 199

XVG-OURS-SEG-HR		93.86 100	93.55 91	94.81 151	97.06 124	88.53 188	95.28 250	97.45 128	91.68 107	94.08 93	97.68 67	82.41 167	98.90 127	93.84 76	92.47 182	96.98 156

ab-mvs		93.57 110	92.55 123	96.64 62	97.28 115	91.96 86	95.40 245	97.45 128	89.81 154	93.22 111	96.28 135	79.62 216	99.46 77	90.74 128	93.11 176	98.50 101

xiu_mvs_v2_base		95.32 62	95.29 57	95.40 125	97.22 116	90.50 128	95.44 244	97.44 131	93.70 47	96.46 42	96.18 138	88.59 72	99.53 68	94.79 63	97.81 91	96.17 183

1314		92.81 136	92.03 135	95.14 135	95.33 194	89.52 158	96.04 215	97.44 131	87.72 219	86.25 263	95.33 183	83.84 124	98.79 136	89.26 146	97.05 111	97.11 154

XXY-MVS		92.16 161	91.23 168	94.95 146	94.75 225	90.94 116	97.47 88	97.43 133	89.14 172	88.90 219	96.43 130	79.71 214	98.24 177	89.56 141	87.68 240	95.67 210

anonymousdsp		92.16 161	91.55 155	93.97 185	92.58 302	89.55 155	97.51 83	97.42 134	89.42 160	88.40 228	94.84 200	80.66 199	97.88 235	91.87 109	91.28 204	94.48 272

Effi-MVS+		94.93 74	94.45 77	96.36 82	96.61 137	91.47 97	96.41 185	97.41 135	91.02 126	94.50 86	95.92 148	87.53 85	98.78 137	93.89 74	96.81 116	98.84 83

HQP3-MVS								97.39 136							92.10 190

HQP-MVS		93.19 121	92.74 116	94.54 164	95.86 172	89.33 170	96.65 168	97.39 136	93.55 48	90.14 174	95.87 150	80.95 191	98.50 159	92.13 101	92.10 190	95.78 202

PLC	91.00 6	94.11 91	93.43 98	96.13 93	98.58 49	91.15 111	96.69 165	97.39 136	87.29 228	91.37 149	96.71 108	88.39 73	99.52 71	87.33 190	97.13 110	97.73 137
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
v7n		90.76 214	89.86 218	93.45 220	93.54 276	87.60 227	97.70 64	97.37 139	88.85 183	87.65 243	94.08 241	81.08 188	98.10 189	84.68 230	83.79 285	94.66 268

UnsupCasMVSNet_eth		85.99 283	84.45 284	90.62 290	89.97 315	82.40 286	93.62 284	97.37 139	89.86 150	78.59 316	92.37 278	65.25 314	95.35 313	82.27 265	70.75 328	94.10 281

ACMM	89.79 8	92.96 128	92.50 127	94.35 170	96.30 156	88.71 184	97.58 79	97.36 141	91.40 116	90.53 166	96.65 114	79.77 213	98.75 141	91.24 125	91.64 196	95.59 211
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
xiu_mvs_v1_base_debu		95.01 69	94.76 65	95.75 106	96.58 139	91.71 88	96.25 203	97.35 142	92.99 67	96.70 30	96.63 119	82.67 158	99.44 80	96.22 22	97.46 98	96.11 188

xiu_mvs_v1_base		95.01 69	94.76 65	95.75 106	96.58 139	91.71 88	96.25 203	97.35 142	92.99 67	96.70 30	96.63 119	82.67 158	99.44 80	96.22 22	97.46 98	96.11 188

xiu_mvs_v1_base_debi		95.01 69	94.76 65	95.75 106	96.58 139	91.71 88	96.25 203	97.35 142	92.99 67	96.70 30	96.63 119	82.67 158	99.44 80	96.22 22	97.46 98	96.11 188

WTY-MVS		94.71 80	94.02 80	96.79 60	97.71 102	92.05 81	96.59 176	97.35 142	90.61 138	94.64 84	96.93 101	86.41 97	99.39 86	91.20 126	94.71 152	98.94 73

F-COLMAP		93.58 109	92.98 107	95.37 126	98.40 56	88.98 181	97.18 115	97.29 146	87.75 218	90.49 167	97.10 98	85.21 110	99.50 74	86.70 199	96.72 120	97.63 140

XVG-ACMP-BASELINE		90.93 210	90.21 207	93.09 232	94.31 240	85.89 252	95.33 247	97.26 147	91.06 125	89.38 210	95.44 180	68.61 300	98.60 150	89.46 143	91.05 207	94.79 263

PCF-MVS	89.48 11	91.56 185	89.95 215	96.36 82	96.60 138	92.52 69	92.51 301	97.26 147	79.41 308	88.90 219	96.56 124	84.04 123	99.55 63	77.01 300	97.30 106	97.01 155
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
ACMP	89.59 10	92.62 139	92.14 132	94.05 180	96.40 152	88.20 200	97.36 97	97.25 149	91.52 109	88.30 231	96.64 115	78.46 236	98.72 144	91.86 110	91.48 200	95.23 235
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
OPM-MVS		93.28 118	92.76 112	94.82 149	94.63 229	90.77 123	96.65 168	97.18 150	93.72 45	91.68 144	97.26 90	79.33 220	98.63 147	92.13 101	92.28 184	95.07 241

PatchMatch-RL		92.90 131	92.02 136	95.56 114	98.19 76	90.80 121	95.27 252	97.18 150	87.96 212	91.86 141	95.68 166	80.44 203	98.99 122	84.01 243	97.54 97	96.89 165

alignmvs		95.87 55	95.23 58	97.78 19	97.56 111	95.19 6	97.86 46	97.17 152	94.39 32	96.47 41	96.40 131	85.89 103	99.20 96	96.21 25	95.11 144	98.95 72

v748		90.34 227	89.54 229	92.75 242	93.25 286	85.71 255	97.61 75	97.17 152	88.54 196	87.20 252	93.54 257	81.02 189	98.01 213	85.73 217	81.80 295	94.52 271

MVS_Test		94.89 76	94.62 69	95.68 110	96.83 132	89.55 155	96.70 163	97.17 152	91.17 121	95.60 71	96.11 143	87.87 79	98.76 140	93.01 92	97.17 109	98.72 87

V4		90.71 219	90.00 213	92.82 237	93.21 290	87.03 237	97.59 78	97.16 155	88.21 205	87.69 241	93.92 246	80.93 193	98.06 201	87.39 187	83.90 283	93.39 290

v52		90.70 220	90.00 213	92.82 237	93.24 287	87.03 237	97.60 76	97.14 156	88.21 205	87.69 241	93.94 244	80.91 194	98.07 196	87.39 187	83.87 284	93.36 292

diffmvs		93.43 114	92.75 114	95.48 121	96.47 149	89.61 151	96.09 212	97.14 156	85.97 254	93.09 116	95.35 182	84.87 115	98.55 155	89.51 142	96.26 129	98.28 117

Fast-Effi-MVS+		93.46 112	92.75 114	95.59 113	96.77 134	90.03 133	96.81 147	97.13 158	88.19 207	91.30 154	94.27 235	86.21 99	98.63 147	87.66 181	96.46 127	98.12 121

EI-MVSNet		93.03 126	92.88 110	93.48 217	95.77 177	86.98 239	96.44 181	97.12 159	90.66 134	91.30 154	97.64 73	86.56 95	98.05 204	89.91 133	90.55 214	95.41 218

MVSTER		93.20 120	92.81 111	94.37 169	96.56 142	89.59 153	97.06 122	97.12 159	91.24 120	91.30 154	95.96 146	82.02 175	98.05 204	93.48 83	90.55 214	95.47 214

testing_2		87.33 273	85.03 280	94.22 173	87.77 324	89.32 172	94.97 257	97.11 161	89.22 164	71.64 326	88.73 311	55.16 331	97.94 226	91.95 105	88.73 233	95.41 218

Test4		89.48 243	87.50 253	95.44 124	90.76 312	89.72 147	95.78 230	97.09 162	90.28 143	77.67 317	91.74 290	55.42 330	98.08 192	91.92 106	96.83 115	98.52 97

LTVRE_ROB	88.41 13	90.99 208	89.92 216	94.19 174	96.18 161	89.55 155	96.31 198	97.09 162	87.88 215	85.67 267	95.91 149	78.79 233	98.57 153	81.50 272	89.98 220	94.44 274
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		91.04 207	90.23 205	93.49 216	94.12 251	88.16 203	97.32 101	97.08 164	88.26 204	88.29 232	94.22 238	82.17 173	97.97 220	86.45 203	84.12 278	94.33 277

v144192		91.06 206	90.28 201	93.39 221	93.66 274	87.23 233	96.83 143	97.07 165	87.43 224	89.69 200	94.28 233	81.48 183	98.00 216	87.18 194	84.92 271	94.93 251

v1192		91.07 205	90.23 205	93.58 213	93.70 272	87.82 222	96.73 155	97.07 165	87.77 217	89.58 203	94.32 228	80.90 197	97.97 220	86.52 201	85.48 254	94.95 247

v8		91.29 199	90.53 195	93.57 214	94.15 247	88.12 207	97.34 98	97.06 167	88.99 176	88.32 230	94.26 237	83.08 139	98.01 213	87.62 183	83.92 282	94.57 270

v7		91.47 190	90.73 188	93.68 207	94.13 249	88.16 203	97.09 121	97.05 168	88.38 200	89.80 193	94.52 213	82.21 171	98.01 213	88.00 170	85.42 256	94.87 253

mvs_anonymous		93.82 101	93.74 85	94.06 179	96.44 151	85.41 259	95.81 227	97.05 168	89.85 152	90.09 183	96.36 133	87.44 87	97.75 246	93.97 70	96.69 121	99.02 63

IterMVS-LS		92.29 156	91.94 139	93.34 224	96.25 157	86.97 240	96.57 179	97.05 168	90.67 132	89.50 208	94.80 204	86.59 94	97.64 254	89.91 133	86.11 251	95.40 222
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v1921920		90.85 212	90.03 212	93.29 226	93.55 275	86.96 241	96.74 154	97.04 171	87.36 226	89.52 207	94.34 226	80.23 208	97.97 220	86.27 204	85.21 260	94.94 249

CDS-MVSNet		94.14 90	93.54 92	95.93 99	96.18 161	91.46 98	96.33 196	97.04 171	88.97 179	93.56 99	96.51 126	87.55 84	97.89 234	89.80 135	95.95 132	98.44 108
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
v1144		91.37 195	90.60 193	93.68 207	93.89 267	88.23 197	96.84 142	97.03 173	88.37 201	89.69 200	94.39 223	82.04 174	97.98 217	87.80 175	85.37 257	94.84 255

v1240		90.70 220	89.85 219	93.23 228	93.51 278	86.80 242	96.61 173	97.02 174	87.16 231	89.58 203	94.31 229	79.55 217	97.98 217	85.52 219	85.44 255	94.90 252

EPP-MVSNet		95.22 65	95.04 62	95.76 105	97.49 113	89.56 154	98.67 5	97.00 175	90.69 131	94.24 91	97.62 75	89.79 60	98.81 135	93.39 87	96.49 125	98.92 75

v1neww		91.70 175	91.01 173	93.75 199	94.19 243	88.14 205	97.20 112	96.98 176	89.18 167	89.87 190	94.44 220	83.10 137	98.06 201	89.06 153	85.09 263	95.06 244

v7new		91.70 175	91.01 173	93.75 199	94.19 243	88.14 205	97.20 112	96.98 176	89.18 167	89.87 190	94.44 220	83.10 137	98.06 201	89.06 153	85.09 263	95.06 244

v6		91.69 177	91.00 175	93.75 199	94.14 248	88.12 207	97.20 112	96.98 176	89.19 165	89.90 187	94.42 222	83.04 143	98.07 196	89.07 152	85.10 262	95.07 241

V42		91.58 184	90.87 180	93.73 202	94.05 260	88.50 189	97.32 101	96.97 179	88.80 189	89.71 198	94.33 227	82.54 162	98.05 204	89.01 155	85.07 265	94.64 269

v1141		91.61 180	90.89 177	93.78 196	94.01 261	88.24 196	96.96 130	96.96 180	89.17 169	89.75 196	94.29 231	82.99 147	98.03 209	88.85 159	85.00 268	95.07 241

divwei89l23v2f112		91.61 180	90.89 177	93.78 196	94.01 261	88.22 198	96.96 130	96.96 180	89.17 169	89.75 196	94.28 233	83.02 145	98.03 209	88.86 158	84.98 270	95.08 239

v1		91.61 180	90.89 177	93.78 196	94.01 261	88.21 199	96.96 130	96.96 180	89.17 169	89.78 195	94.29 231	82.97 149	98.05 204	88.85 159	84.99 269	95.08 239

FMVSNet2		91.31 198	90.08 209	94.99 141	96.51 145	92.21 75	97.41 90	96.95 183	88.82 186	88.62 224	94.75 206	73.87 275	97.42 268	85.20 224	88.55 235	95.35 226

ACMH	87.59 16	90.53 224	89.42 231	93.87 192	96.21 158	87.92 219	97.24 106	96.94 184	88.45 197	83.91 282	96.27 136	71.92 283	98.62 149	84.43 235	89.43 225	95.05 246
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
GBi-Net		91.35 196	90.27 202	94.59 159	96.51 145	91.18 108	97.50 84	96.93 185	88.82 186	89.35 211	94.51 214	73.87 275	97.29 276	86.12 208	88.82 229	95.31 228

test1		91.35 196	90.27 202	94.59 159	96.51 145	91.18 108	97.50 84	96.93 185	88.82 186	89.35 211	94.51 214	73.87 275	97.29 276	86.12 208	88.82 229	95.31 228

FMVSNet3		91.78 171	90.69 190	95.03 140	96.53 144	92.27 74	97.02 125	96.93 185	89.79 155	89.35 211	94.65 210	77.01 255	97.47 264	86.12 208	88.82 229	95.35 226

FMVSNet1		89.88 238	88.31 246	94.59 159	95.41 187	91.18 108	97.50 84	96.93 185	86.62 246	87.41 247	94.51 214	65.94 312	97.29 276	83.04 254	87.43 243	95.31 228

TAMVS		94.01 96	93.46 96	95.64 111	96.16 163	90.45 130	96.71 160	96.89 189	89.27 163	93.46 103	96.92 102	87.29 89	97.94 226	88.70 163	95.74 136	98.53 96

v2v482		91.59 183	90.85 182	93.80 194	93.87 268	88.17 202	96.94 136	96.88 190	89.54 156	89.53 206	94.90 196	81.70 182	98.02 212	89.25 147	85.04 267	95.20 236

CNLPA		94.28 85	93.53 93	96.52 69	98.38 59	92.55 68	96.59 176	96.88 190	90.13 146	91.91 139	97.24 91	85.21 110	99.09 114	87.64 182	97.83 90	97.92 128

PAPM		91.52 188	90.30 200	95.20 128	95.30 195	89.83 144	93.38 287	96.85 192	86.26 250	88.59 226	95.80 155	84.88 114	98.15 184	75.67 303	95.93 133	97.63 140

pm-mvs1		90.72 218	89.65 228	93.96 186	94.29 241	89.63 150	97.79 52	96.82 193	89.07 173	86.12 265	95.48 179	78.61 234	97.78 243	86.97 197	81.67 297	94.46 273

CMPMVS	62.92 21	85.62 286	84.92 281	87.74 304	89.14 319	73.12 323	94.17 272	96.80 194	73.98 325	73.65 322	94.93 194	66.36 309	97.61 256	83.95 245	91.28 204	92.48 304
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
MS-PatchMatch		90.27 228	89.77 222	91.78 271	94.33 239	84.72 268	95.55 238	96.73 195	86.17 252	86.36 262	95.28 186	71.28 288	97.80 241	84.09 240	98.14 84	92.81 297

Effi-MVS+-dtu		93.08 123	93.21 104	92.68 245	96.02 169	83.25 281	97.14 119	96.72 196	93.85 42	91.20 161	93.44 263	83.08 139	98.30 175	91.69 115	95.73 137	96.50 175

mvs-test1		93.63 107	93.69 87	93.46 219	96.02 169	84.61 269	97.24 106	96.72 196	93.85 42	92.30 132	95.76 160	83.08 139	98.89 129	91.69 115	96.54 124	96.87 166

TSAR-MVS + GP.		96.69 32	96.49 31	97.27 46	98.31 66	93.39 47	96.79 150	96.72 196	94.17 36	97.44 14	97.66 69	92.76 12	99.33 90	96.86 8	97.76 94	99.08 61

1112_ss		93.37 115	92.42 129	96.21 91	97.05 125	90.99 113	96.31 198	96.72 196	86.87 243	89.83 192	96.69 112	86.51 96	99.14 104	88.12 168	93.67 169	98.50 101

PVSNet	86.66 18	92.24 158	91.74 145	93.73 202	97.77 99	83.69 278	92.88 296	96.72 196	87.91 214	93.00 118	94.86 199	78.51 235	99.05 120	86.53 200	97.45 102	98.47 106

v148		90.99 208	90.38 198	92.81 240	93.83 269	85.80 253	96.78 152	96.68 201	89.45 159	88.75 223	93.93 245	82.96 151	97.82 240	87.83 174	83.25 288	94.80 261

ACMH+	87.92 14	90.20 231	89.18 235	93.25 227	96.48 148	86.45 248	96.99 128	96.68 201	88.83 185	84.79 273	96.22 137	70.16 296	98.53 156	84.42 236	88.04 237	94.77 265

CANet_DTU		94.37 83	93.65 89	96.55 68	96.46 150	92.13 79	96.21 207	96.67 203	94.38 33	93.53 101	97.03 100	79.34 219	99.71 27	90.76 127	98.45 77	97.82 135

HY-MVS	89.66 9	93.87 99	92.95 108	96.63 64	97.10 121	92.49 70	95.64 235	96.64 204	89.05 174	93.00 118	95.79 158	85.77 106	99.45 79	89.16 151	94.35 153	97.96 126

Test_1112_low_res		92.84 135	91.84 141	95.85 102	97.04 126	89.97 139	95.53 240	96.64 204	85.38 258	89.65 202	95.18 188	85.86 104	99.10 111	87.70 177	93.58 174	98.49 103

Fast-Effi-MVS+-dtu		92.29 156	91.99 137	93.21 230	95.27 196	85.52 258	97.03 123	96.63 206	92.09 93	89.11 218	95.14 190	80.33 206	98.08 192	87.54 185	94.74 151	96.03 192

UnsupCasMVSNet_bld		82.13 298	79.46 300	90.14 296	88.00 322	82.47 284	90.89 315	96.62 207	78.94 311	75.61 319	84.40 327	56.63 327	96.31 291	77.30 299	66.77 334	91.63 318

jason		94.84 78	94.39 79	96.18 92	95.52 183	90.93 117	96.09 212	96.52 208	89.28 162	96.01 57	97.32 87	84.70 117	98.77 139	95.15 48	98.91 67	98.85 81
jason: jason.
EG-PatchMatch MVS		87.02 276	85.44 277	91.76 273	92.67 300	85.00 263	96.08 214	96.45 209	83.41 284	79.52 313	93.49 260	57.10 326	97.72 248	79.34 291	90.87 210	92.56 300

pmmvs6		87.81 270	86.19 272	92.69 244	91.32 309	86.30 249	97.34 98	96.41 210	80.59 306	84.05 281	94.37 225	67.37 307	97.67 251	84.75 228	79.51 305	94.09 282

PMMVS		92.86 133	92.34 130	94.42 168	94.92 217	86.73 243	94.53 264	96.38 211	84.78 269	94.27 90	95.12 192	83.13 135	98.40 166	91.47 120	96.49 125	98.12 121

RPSCF		90.75 216	90.86 181	90.42 293	96.84 130	76.29 317	95.61 237	96.34 212	83.89 277	91.38 148	97.87 53	76.45 257	98.78 137	87.16 195	92.23 185	96.20 181

MSDG		91.42 192	90.24 204	94.96 145	97.15 120	88.91 182	93.69 281	96.32 213	85.72 256	86.93 258	96.47 128	80.24 207	98.98 123	80.57 282	95.05 145	96.98 156

OurMVSNet-221017-0		90.51 225	90.19 208	91.44 278	93.41 281	81.25 293	96.98 129	96.28 214	91.68 107	86.55 261	96.30 134	74.20 274	97.98 217	88.96 156	87.40 245	95.09 238

MVP-Stereo		90.74 217	90.08 209	92.71 243	93.19 292	88.20 200	95.86 224	96.27 215	86.07 253	84.86 272	94.76 205	77.84 251	97.75 246	83.88 246	98.01 86	92.17 315
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
lupinMVS		94.99 73	94.56 71	96.29 87	96.34 154	91.21 104	95.83 226	96.27 215	88.93 181	96.22 47	96.88 103	86.20 100	98.85 132	95.27 45	99.05 61	98.82 84

BH-untuned		92.94 129	92.62 120	93.92 191	97.22 116	86.16 251	96.40 189	96.25 217	90.06 147	89.79 194	96.17 140	83.19 131	98.35 170	87.19 193	97.27 107	97.24 153

test_normal		92.01 164	90.75 187	95.80 104	93.24 287	89.97 139	95.93 222	96.24 218	90.62 136	81.63 292	93.45 262	74.98 268	98.89 129	93.61 79	97.04 112	98.55 94

IS-MVSNet		94.90 75	94.52 74	96.05 95	97.67 103	90.56 126	98.44 15	96.22 219	93.21 58	93.99 94	97.74 64	85.55 107	98.45 163	89.98 132	97.86 89	99.14 55

GA-MVS		91.38 194	90.31 199	94.59 159	94.65 228	87.62 226	94.34 267	96.19 220	90.73 130	90.35 171	93.83 247	71.84 284	97.96 224	87.22 192	93.61 172	98.21 118

DI_MVS_plusplus_test		92.01 164	90.77 185	95.73 109	93.34 283	89.78 146	96.14 210	96.18 221	90.58 140	81.80 291	93.50 259	74.95 269	98.90 127	93.51 81	96.94 113	98.51 99

semantic-postprocess				91.82 268	95.52 183	84.20 272		96.15 222	90.61 138	87.39 248	94.27 235	75.63 263	96.44 289	87.34 189	86.88 248	94.82 259

IterMVS		90.15 233	89.67 226	91.61 275	95.48 185	83.72 275	94.33 268	96.12 223	89.99 148	87.31 251	94.15 239	75.78 262	96.27 292	86.97 197	86.89 247	94.83 257
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
DP-MVS		92.76 137	91.51 159	96.52 69	98.77 34	90.99 113	97.38 96	96.08 224	82.38 290	89.29 214	97.87 53	83.77 125	99.69 33	81.37 278	96.69 121	98.89 79

pmmvs4		90.93 210	89.85 219	94.17 175	93.34 283	90.79 122	94.60 261	96.02 225	84.62 270	87.45 245	95.15 189	81.88 179	97.45 265	87.70 177	87.87 239	94.27 280

v18		88.71 253	87.52 252	92.27 250	94.16 246	88.11 209	96.82 146	95.96 226	87.03 233	80.76 298	89.81 298	83.15 133	96.22 293	84.69 229	75.31 316	92.49 302

v17		88.67 255	87.47 255	92.26 252	94.13 249	88.09 211	96.81 147	95.95 227	87.02 234	80.72 299	89.75 300	83.11 136	96.20 294	84.61 232	75.15 318	92.49 302

v16		88.69 254	87.50 253	92.26 252	94.19 243	88.11 209	96.81 147	95.95 227	87.01 235	80.71 300	89.80 299	83.08 139	96.20 294	84.61 232	75.34 315	92.48 304

ITE_SJBPF				92.43 249	95.34 191	85.37 260		95.92 229	91.47 111	87.75 240	96.39 132	71.00 290	97.96 224	82.36 264	89.86 223	93.97 283

V14		88.52 258	87.30 258	92.17 257	94.12 251	87.99 214	96.72 158	95.91 230	86.98 237	80.50 304	89.63 301	83.03 144	96.12 298	84.23 238	74.60 321	92.40 309

v15		88.53 257	87.31 257	92.20 255	94.09 255	88.05 212	96.72 158	95.90 231	87.01 235	80.53 303	89.60 304	83.02 145	96.13 296	84.29 237	74.64 319	92.41 308

v13		88.45 263	87.22 263	92.16 259	94.08 257	87.95 218	96.71 160	95.90 231	86.86 244	80.27 310	89.55 306	82.92 152	96.12 298	84.02 242	74.63 320	92.40 309

v12		88.46 262	87.23 262	92.17 257	94.10 254	87.99 214	96.71 160	95.90 231	86.91 240	80.34 308	89.58 305	82.92 152	96.11 300	84.09 240	74.50 324	92.42 307

V9		88.49 261	87.26 259	92.18 256	94.12 251	87.97 217	96.73 155	95.90 231	86.95 239	80.40 306	89.61 302	82.98 148	96.13 296	84.14 239	74.55 322	92.44 306

USDC		88.94 248	87.83 250	92.27 250	94.66 227	84.96 264	93.86 278	95.90 231	87.34 227	83.40 284	95.56 171	67.43 306	98.19 181	82.64 261	89.67 224	93.66 286

COLMAP_ROB	87.81 15	90.40 226	89.28 233	93.79 195	97.95 87	87.13 236	96.92 137	95.89 236	82.83 287	86.88 260	97.18 93	73.77 278	99.29 92	78.44 294	93.62 171	94.95 247
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
VDD-MVS		93.82 101	93.08 105	96.02 96	97.88 94	89.96 141	97.72 60	95.85 237	92.43 83	95.86 61	98.44 14	68.42 302	99.39 86	96.31 19	94.85 146	98.71 89

v11		88.41 264	87.19 266	92.08 262	94.08 257	87.77 223	96.75 153	95.85 237	86.74 245	80.50 304	89.50 307	82.49 164	96.08 301	83.55 248	75.20 317	92.38 311

VDDNet		93.05 125	92.07 133	96.02 96	96.84 130	90.39 131	98.08 33	95.85 237	86.22 251	95.79 65	98.46 12	67.59 305	99.19 97	94.92 57	94.85 146	98.47 106

Vis-MVSNet (Re-imp)		94.15 88	93.88 82	94.95 146	97.61 107	87.92 219	98.10 31	95.80 240	92.22 86	93.02 117	97.45 86	84.53 120	97.91 233	88.24 166	97.97 87	99.02 63

tpm cat1		88.36 265	87.21 264	91.81 269	95.13 207	80.55 299	92.58 300	95.70 241	74.97 323	87.45 245	91.96 286	78.01 250	98.17 183	80.39 284	88.74 232	96.72 170

BH-w/o		92.14 163	91.75 143	93.31 225	96.99 127	85.73 254	95.67 232	95.69 242	88.73 191	89.26 216	94.82 203	82.97 149	98.07 196	85.26 223	96.32 128	96.13 187

CR-MVSNet		90.82 213	89.77 222	93.95 187	94.45 235	87.19 234	90.23 318	95.68 243	86.89 242	92.40 127	92.36 281	80.91 194	97.05 280	81.09 281	93.95 165	97.60 145

Patchmtry		88.64 256	87.25 260	92.78 241	94.09 255	86.64 244	89.82 321	95.68 243	80.81 304	87.63 244	92.36 281	80.91 194	97.03 282	78.86 292	85.12 261	94.67 267

BH-RMVSNet		92.72 138	91.97 138	94.97 144	97.16 119	87.99 214	96.15 209	95.60 245	90.62 136	91.87 140	97.15 96	78.41 237	98.57 153	83.16 252	97.60 96	98.36 115

PVSNet_0	82.17 19	85.46 287	83.64 288	90.92 284	95.27 196	79.49 308	90.55 316	95.60 245	83.76 280	83.00 285	89.95 295	71.09 289	97.97 220	82.75 259	60.79 335	95.31 228

Patchmatch-test1		91.54 187	90.85 182	93.59 211	95.59 181	84.95 265	94.72 260	95.58 247	90.82 127	92.25 133	93.58 256	75.80 261	97.41 269	83.35 249	95.98 131	98.40 111

AllTest		90.23 230	88.98 237	93.98 183	97.94 88	86.64 244	96.51 180	95.54 248	85.38 258	85.49 269	96.77 106	70.28 294	99.15 102	80.02 285	92.87 177	96.15 185

TestCases				93.98 183	97.94 88	86.64 244		95.54 248	85.38 258	85.49 269	96.77 106	70.28 294	99.15 102	80.02 285	92.87 177	96.15 185

tpmvs		89.83 240	89.15 236	91.89 266	94.92 217	80.30 302	93.11 293	95.46 250	86.28 249	88.08 235	92.65 272	80.44 203	98.52 157	81.47 273	89.92 222	96.84 167

PatchFormer-LS_test		91.68 179	91.18 171	93.19 231	95.24 200	83.63 279	95.53 240	95.44 251	89.82 153	91.37 149	92.58 275	80.85 198	98.52 157	89.65 140	90.16 219	97.42 151

pmmvs5		89.86 239	88.87 239	92.82 237	92.86 296	86.23 250	96.26 202	95.39 252	84.24 273	87.12 253	94.51 214	74.27 273	97.36 273	87.61 184	87.57 241	94.86 254

PatchmatchNet		91.91 168	91.35 161	93.59 211	95.38 189	84.11 273	93.15 292	95.39 252	89.54 156	92.10 136	93.68 252	82.82 156	98.13 185	84.81 227	95.32 141	98.52 97
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
tpmrst		91.44 191	91.32 163	91.79 270	95.15 205	79.20 311	93.42 286	95.37 254	88.55 195	93.49 102	93.67 253	82.49 164	98.27 176	90.41 130	89.34 226	97.90 129

Anonymous20231206		87.09 275	86.14 273	89.93 298	91.22 310	80.35 300	96.11 211	95.35 255	83.57 282	84.16 278	93.02 268	73.54 280	95.61 308	72.16 312	86.14 250	93.84 285

MIMVSNet1		84.93 289	83.05 289	90.56 291	89.56 318	84.84 267	95.40 245	95.35 255	83.91 276	80.38 307	92.21 285	57.23 325	93.34 322	70.69 318	82.75 294	93.50 287

TDRefinement		86.53 278	84.76 283	91.85 267	82.23 334	84.25 270	96.38 191	95.35 255	84.97 266	84.09 280	94.94 193	65.76 313	98.34 172	84.60 234	74.52 323	92.97 293

TR-MVS		91.48 189	90.59 194	94.16 176	96.40 152	87.33 228	95.67 232	95.34 258	87.68 220	91.46 147	95.52 174	76.77 256	98.35 170	82.85 257	93.61 172	96.79 168

EPNet_dtu		91.71 172	91.28 165	92.99 235	93.76 271	83.71 276	96.69 165	95.28 259	93.15 62	87.02 257	95.95 147	83.37 130	97.38 272	79.46 289	96.84 114	97.88 131
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
FMVSNet5		87.29 274	85.79 275	91.78 271	94.80 223	87.28 229	95.49 242	95.28 259	84.09 275	83.85 283	91.82 287	62.95 317	94.17 318	78.48 293	85.34 258	93.91 284

MDTV_nov1_ep13			90.76 186		95.22 201	80.33 301	93.03 295	95.28 259	88.14 210	92.84 124	93.83 247	81.34 185	98.08 192	82.86 256	94.34 154

LF4IMVS		87.94 268	87.25 260	89.98 297	92.38 304	80.05 306	94.38 266	95.25 262	87.59 222	84.34 275	94.74 207	64.31 315	97.66 253	84.83 226	87.45 242	92.23 313

TransMVSNet (Re)		88.94 248	87.56 251	93.08 233	94.35 238	88.45 191	97.73 58	95.23 263	87.47 223	84.26 277	95.29 184	79.86 212	97.33 274	79.44 290	74.44 325	93.45 289

test20.03		86.14 282	85.40 278	88.35 300	90.12 313	80.06 305	95.90 223	95.20 264	88.59 192	81.29 294	93.62 255	71.43 287	92.65 324	71.26 316	81.17 300	92.34 312

new-patchmatchnet		83.18 293	81.87 294	87.11 306	86.88 326	75.99 318	93.70 280	95.18 265	85.02 265	77.30 318	88.40 314	65.99 311	93.88 320	74.19 308	70.18 329	91.47 321

MDA-MVSNet_test_wron		85.87 284	84.23 286	90.80 288	92.38 304	82.57 283	93.17 290	95.15 266	82.15 291	67.65 328	92.33 284	78.20 239	95.51 311	77.33 297	79.74 303	94.31 279

YYNet1		85.87 284	84.23 286	90.78 289	92.38 304	82.46 285	93.17 290	95.14 267	82.12 292	67.69 327	92.36 281	78.16 242	95.50 312	77.31 298	79.73 304	94.39 275

Baseline_NR-MVSNet		91.20 201	90.62 192	92.95 236	93.83 269	88.03 213	97.01 127	95.12 268	88.42 199	89.70 199	95.13 191	83.47 128	97.44 266	89.66 139	83.24 289	93.37 291

thres200		92.23 159	91.39 160	94.75 156	97.61 107	89.03 180	96.60 175	95.09 269	92.08 98	93.28 108	94.00 242	78.39 238	99.04 121	81.26 280	94.18 155	96.19 182

tpmp4_e23		89.58 242	88.59 242	92.54 247	95.16 204	81.53 291	94.11 274	95.09 269	81.66 295	88.60 225	93.44 263	75.11 266	98.33 173	82.45 262	91.72 195	97.75 136

ADS-MVSNet		89.89 237	88.68 241	93.53 215	95.86 172	84.89 266	90.93 313	95.07 271	83.23 285	91.28 157	91.81 288	79.01 227	97.85 236	79.52 287	91.39 202	97.84 132

pmmvs-eth3d		86.22 281	84.45 284	91.53 276	88.34 321	87.25 231	94.47 265	95.01 272	83.47 283	79.51 314	89.61 302	69.75 297	95.71 307	83.13 253	76.73 311	91.64 317

MDA-MVSNet-bldmvs		85.00 288	82.95 290	91.17 281	93.13 294	83.33 280	94.56 263	95.00 273	84.57 271	65.13 332	92.65 272	70.45 293	95.85 304	73.57 309	77.49 308	94.33 277

RPMNet		88.52 258	86.72 270	93.95 187	94.45 235	87.19 234	90.23 318	94.99 274	77.87 317	92.40 127	87.55 322	80.17 209	97.05 280	68.84 319	93.95 165	97.60 145

ambc				86.56 309	83.60 331	70.00 329	85.69 331	94.97 275		80.60 302	88.45 313	37.42 339	96.84 287	82.69 260	75.44 314	92.86 294

testgi		87.97 267	87.21 264	90.24 295	92.86 296	80.76 295	96.67 167	94.97 275	91.74 105	85.52 268	95.83 153	62.66 318	94.47 317	76.25 301	88.36 236	95.48 212

dp		88.90 250	88.26 248	90.81 286	94.58 232	76.62 316	92.85 297	94.93 277	85.12 263	90.07 185	93.07 267	75.81 260	98.12 187	80.53 283	87.42 244	97.71 138

test_0402		86.46 279	84.79 282	91.45 277	95.02 212	85.55 257	96.29 200	94.89 278	80.90 301	82.21 286	93.97 243	68.21 303	97.29 276	62.98 325	88.68 234	91.51 319

tfpn200view9		92.38 151	91.52 157	94.95 146	97.85 95	89.29 173	97.41 90	94.88 279	92.19 90	93.27 109	94.46 218	78.17 240	99.08 116	81.40 274	94.08 156	96.48 176

CVMVSNet		91.23 200	91.75 143	89.67 299	95.77 177	74.69 319	96.44 181	94.88 279	85.81 255	92.18 134	97.64 73	79.07 222	95.58 310	88.06 169	95.86 135	98.74 85

thres400		92.42 149	91.52 157	95.12 137	97.85 95	89.29 173	97.41 90	94.88 279	92.19 90	93.27 109	94.46 218	78.17 240	99.08 116	81.40 274	94.08 156	96.98 156

EPNet		95.20 66	94.56 71	97.14 53	92.80 298	92.68 64	97.85 48	94.87 282	96.64 1	92.46 126	97.80 61	86.23 98	99.65 39	93.72 78	98.62 73	99.10 60
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
SixPastTwentyTwo		89.15 247	88.54 244	90.98 282	93.49 279	80.28 303	96.70 163	94.70 283	90.78 128	84.15 279	95.57 170	71.78 285	97.71 249	84.63 231	85.07 265	94.94 249

conf200view11		92.45 147	91.58 153	95.05 138	97.92 90	89.37 168	97.71 62	94.66 284	92.20 88	93.31 106	94.90 196	78.06 247	99.08 116	81.40 274	94.08 156	96.70 171

thres100view900		92.43 148	91.58 153	94.98 143	97.92 90	89.37 168	97.71 62	94.66 284	92.20 88	93.31 106	94.90 196	78.06 247	99.08 116	81.40 274	94.08 156	96.48 176

thres600view7		92.49 146	91.60 152	95.18 129	97.91 92	89.47 159	97.65 68	94.66 284	92.18 92	93.33 105	94.91 195	78.06 247	99.10 111	81.61 267	94.06 160	96.98 156

PatchT		88.87 251	87.42 256	93.22 229	94.08 257	85.10 262	89.51 322	94.64 287	81.92 293	92.36 130	88.15 317	80.05 210	97.01 284	72.43 311	93.65 170	97.54 148

view600		92.55 140	91.68 146	95.18 129	97.98 82	89.44 163	98.00 36	94.57 288	92.09 93	93.17 112	95.52 174	78.14 243	99.11 106	81.61 267	94.04 161	96.98 156

view800		92.55 140	91.68 146	95.18 129	97.98 82	89.44 163	98.00 36	94.57 288	92.09 93	93.17 112	95.52 174	78.14 243	99.11 106	81.61 267	94.04 161	96.98 156

conf0.05thres1000		92.55 140	91.68 146	95.18 129	97.98 82	89.44 163	98.00 36	94.57 288	92.09 93	93.17 112	95.52 174	78.14 243	99.11 106	81.61 267	94.04 161	96.98 156

tfpn		92.55 140	91.68 146	95.18 129	97.98 82	89.44 163	98.00 36	94.57 288	92.09 93	93.17 112	95.52 174	78.14 243	99.11 106	81.61 267	94.04 161	96.98 156

Gipuma		67.86 311	65.41 312	75.18 324	92.66 301	73.45 322	66.50 341	94.52 292	53.33 336	57.80 336	66.07 338	30.81 341	89.20 334	48.15 339	78.88 306	62.90 339
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
CostFormer		91.18 204	90.70 189	92.62 246	94.84 221	81.76 290	94.09 275	94.43 293	84.15 274	92.72 125	93.77 250	79.43 218	98.20 179	90.70 129	92.18 188	97.90 129

tpm2		89.96 235	89.21 234	92.23 254	94.91 219	81.25 293	93.78 279	94.42 294	80.62 305	91.56 145	93.44 263	76.44 258	97.94 226	85.60 218	92.08 192	97.49 149

JIA-IIPM		88.26 266	87.04 267	91.91 265	93.52 277	81.42 292	89.38 323	94.38 295	80.84 303	90.93 163	80.74 329	79.22 221	97.92 230	82.76 258	91.62 197	96.38 179

Patchmatch-test		89.42 245	87.99 249	93.70 205	95.27 196	85.11 261	88.98 324	94.37 296	81.11 300	87.10 255	93.69 251	82.28 169	97.50 262	74.37 306	94.76 149	98.48 105

LCM-MVSNet		72.55 306	69.39 309	82.03 314	70.81 344	65.42 335	90.12 320	94.36 297	55.02 335	65.88 331	81.72 328	24.16 348	89.96 332	74.32 307	68.10 332	90.71 323

ADS-MVSNet2		89.45 244	88.59 242	92.03 263	95.86 172	82.26 287	90.93 313	94.32 298	83.23 285	91.28 157	91.81 288	79.01 227	95.99 302	79.52 287	91.39 202	97.84 132

DWT-MVSNet_test		90.76 214	89.89 217	93.38 222	95.04 211	83.70 277	95.85 225	94.30 299	88.19 207	90.46 168	92.80 270	73.61 279	98.50 159	88.16 167	90.58 213	97.95 127

testus		82.63 296	82.15 292	84.07 312	87.31 325	67.67 331	93.18 288	94.29 300	82.47 289	82.14 288	90.69 293	53.01 332	91.94 327	66.30 322	89.96 221	92.62 299

LP		84.13 291	81.85 296	90.97 283	93.20 291	82.12 288	87.68 328	94.27 301	76.80 318	81.93 289	88.52 312	72.97 282	95.95 303	59.53 330	81.73 296	94.84 255

EU-MVSNet		88.72 252	88.90 238	88.20 302	93.15 293	74.21 320	96.63 172	94.22 302	85.18 261	87.32 250	95.97 145	76.16 259	94.98 315	85.27 222	86.17 249	95.41 218

test1235678		79.82 301	78.53 302	83.69 313	82.55 333	67.55 332	92.50 302	94.13 303	79.28 309	72.10 325	86.45 325	57.27 324	90.68 331	61.60 328	80.90 301	92.82 295

MIMVSNet		88.50 260	86.76 268	93.72 204	94.84 221	87.77 223	91.39 308	94.05 304	86.41 248	87.99 237	92.59 274	63.27 316	95.82 306	77.44 296	92.84 179	97.57 147

OpenMVS_ROB	81.14 20	84.42 290	82.28 291	90.83 285	90.06 314	84.05 274	95.73 231	94.04 305	73.89 326	80.17 312	91.53 292	59.15 323	97.64 254	66.92 321	89.05 228	90.80 322

TinyColmap		86.82 277	85.35 279	91.21 280	94.91 219	82.99 282	93.94 277	94.02 306	83.58 281	81.56 293	94.68 208	62.34 319	98.13 185	75.78 302	87.35 246	92.52 301

IB-MVS	87.33 17	89.91 236	88.28 247	94.79 154	95.26 199	87.70 225	95.12 256	93.95 307	89.35 161	87.03 256	92.49 276	70.74 292	99.19 97	89.18 150	81.37 299	97.49 149
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		78.22 304	75.30 305	86.99 308	86.14 327	74.16 321	95.62 236	93.88 308	66.43 330	74.44 321	87.86 319	41.39 338	95.11 314	62.49 326	69.46 331	91.71 316

1111		78.29 303	77.55 303	80.50 316	83.89 329	59.98 339	91.89 305	93.71 309	75.06 321	73.60 323	87.67 320	55.66 328	92.60 325	58.54 332	77.92 307	88.93 326

.test1245		65.38 312	69.22 310	53.86 332	83.89 329	59.98 339	91.89 305	93.71 309	75.06 321	73.60 323	87.67 320	55.66 328	92.60 325	58.54 332	2.96 346	9.00 344

LCM-MVSNet-Re		92.50 144	92.52 126	92.44 248	96.82 133	81.89 289	96.92 137	93.71 309	92.41 84	84.30 276	94.60 212	85.08 112	97.03 282	91.51 118	97.36 104	98.40 111

test2356		82.77 295	82.14 293	84.65 311	85.77 328	70.36 326	91.22 311	93.69 312	81.58 297	81.82 290	89.00 310	60.63 322	90.77 330	64.74 323	90.80 211	92.82 295

tpm		90.25 229	89.74 225	91.76 273	93.92 265	79.73 307	93.98 276	93.54 313	88.28 203	91.99 138	93.25 266	77.51 254	97.44 266	87.30 191	87.94 238	98.12 121

LFMVS		93.60 108	92.63 119	96.52 69	98.13 78	91.27 103	97.94 41	93.39 314	90.57 141	96.29 45	98.31 31	69.00 298	99.16 101	94.18 67	95.87 134	99.12 58

Patchmatch-RL test		87.38 272	86.24 271	90.81 286	88.74 320	78.40 314	88.12 327	93.17 315	87.11 232	82.17 287	89.29 308	81.95 177	95.60 309	88.64 164	77.02 309	98.41 110

tfpnview11		91.69 177	90.67 191	94.75 156	97.55 112	89.68 148	97.64 72	93.14 316	88.43 198	91.24 159	94.30 230	78.91 230	98.45 163	81.28 279	93.57 175	96.11 188

test-LLR		91.42 192	91.19 170	92.12 260	94.59 230	80.66 296	94.29 269	92.98 317	91.11 123	90.76 164	92.37 278	79.02 225	98.07 196	88.81 161	96.74 118	97.63 140

test-mter		90.19 232	89.54 229	92.12 260	94.59 230	80.66 296	94.29 269	92.98 317	87.68 220	90.76 164	92.37 278	67.67 304	98.07 196	88.81 161	96.74 118	97.63 140

tfpn_ndepth		91.88 170	90.96 176	94.62 158	97.73 101	89.93 142	97.75 54	92.92 319	88.93 181	91.73 142	93.80 249	78.91 230	98.49 162	83.02 255	93.86 168	95.45 216

tfpn1000		91.99 167	91.05 172	94.80 152	97.78 98	89.66 149	97.91 43	92.90 320	88.99 176	91.73 142	94.84 200	78.99 229	98.33 173	82.41 263	93.91 167	96.40 178

test12356		74.97 305	74.13 306	77.49 321	78.81 335	56.23 343	88.53 326	92.75 321	75.14 320	67.50 329	85.07 326	44.88 336	89.96 332	58.71 331	75.75 313	86.26 327

test0.0.03 1		89.37 246	88.70 240	91.41 279	92.47 303	85.63 256	95.22 254	92.70 322	91.11 123	86.91 259	93.65 254	79.02 225	93.19 323	78.00 295	89.18 227	95.41 218

new_pmnet		82.89 294	81.12 299	88.18 303	89.63 317	80.18 304	91.77 307	92.57 323	76.79 319	75.56 320	88.23 316	61.22 321	94.48 316	71.43 314	82.92 292	89.87 324

testmv		72.22 307	70.02 307	78.82 319	73.06 342	61.75 337	91.24 310	92.31 324	74.45 324	61.06 334	80.51 330	34.21 340	88.63 335	55.31 335	68.07 333	86.06 328

K. test v3		87.64 271	86.75 269	90.32 294	93.02 295	79.48 309	96.61 173	92.08 325	90.66 134	80.25 311	94.09 240	67.21 308	96.65 288	85.96 213	80.83 302	94.83 257

TESTMET0.1,1		90.06 234	89.42 231	91.97 264	94.41 237	80.62 298	94.29 269	91.97 326	87.28 229	90.44 169	92.47 277	68.79 299	97.67 251	88.50 165	96.60 123	97.61 144

PM-MVS		83.48 292	81.86 295	88.31 301	87.83 323	77.59 315	93.43 285	91.75 327	86.91 240	80.63 301	89.91 296	44.42 337	95.84 305	85.17 225	76.73 311	91.50 320

FPMVS		71.27 308	69.85 308	75.50 323	74.64 337	59.03 341	91.30 309	91.50 328	58.80 334	57.92 335	88.28 315	29.98 344	85.53 338	53.43 336	82.84 293	81.95 331

door								91.13 329

door-mid								91.06 330

pmmvs3		79.97 300	77.50 304	87.39 305	82.80 332	79.38 310	92.70 299	90.75 331	70.69 329	78.66 315	87.47 323	51.34 334	93.40 321	73.39 310	69.65 330	89.38 325

no-one		68.12 310	63.78 313	81.13 315	74.01 339	70.22 328	87.61 329	90.71 332	72.63 328	53.13 337	71.89 335	30.29 342	91.45 328	61.53 329	32.21 340	81.72 332

DSMNet-mixed		86.34 280	86.12 274	87.00 307	89.88 316	70.43 325	94.93 258	90.08 333	77.97 316	85.42 271	92.78 271	74.44 272	93.96 319	74.43 305	95.14 143	96.62 172

testpf		80.97 299	81.40 297	79.65 318	91.53 308	72.43 324	73.47 339	89.55 334	78.63 312	80.81 296	89.06 309	61.36 320	91.36 329	83.34 250	84.89 272	75.15 335

MVS-HIRNet		82.47 297	81.21 298	86.26 310	95.38 189	69.21 330	88.96 325	89.49 335	66.28 331	80.79 297	74.08 334	68.48 301	97.39 271	71.93 313	95.47 139	92.18 314

EPMVS		90.70 220	89.81 221	93.37 223	94.73 226	84.21 271	93.67 282	88.02 336	89.50 158	92.38 129	93.49 260	77.82 252	97.78 243	86.03 211	92.68 180	98.11 124

ANet_high		63.94 313	59.58 314	77.02 322	61.24 347	66.06 333	85.66 332	87.93 337	78.53 314	42.94 339	71.04 336	25.42 347	80.71 340	52.60 337	30.83 342	84.28 330

PMMVS2		70.19 309	66.92 311	80.01 317	76.35 336	65.67 334	86.22 330	87.58 338	64.83 333	62.38 333	80.29 331	26.78 346	88.49 336	63.79 324	54.07 336	85.88 329

lessismore_v0				90.45 292	91.96 307	79.09 312		87.19 339		80.32 309	94.39 223	66.31 310	97.55 259	84.00 244	76.84 310	94.70 266

PMVS	53.92 22	58.58 315	55.40 316	68.12 328	51.00 348	48.64 345	78.86 337	87.10 340	46.77 339	35.84 344	74.28 333	8.76 350	86.34 337	42.07 340	73.91 326	69.38 337
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
wuykxyi23d		56.92 316	51.11 320	74.38 326	62.30 346	61.47 338	80.09 336	84.87 341	49.62 338	30.80 345	57.20 342	7.03 351	82.94 339	55.69 334	32.36 339	78.72 334

gg-mvs-nofinetune		87.82 269	85.61 276	94.44 166	94.46 234	89.27 176	91.21 312	84.61 342	80.88 302	89.89 189	74.98 332	71.50 286	97.53 260	85.75 216	97.21 108	96.51 174

GG-mvs-BLEND				93.62 209	93.69 273	89.20 177	92.39 304	83.33 343		87.98 238	89.84 297	71.00 290	96.87 286	82.08 266	95.40 140	94.80 261

PNet_i23d		59.01 314	55.87 315	68.44 327	73.98 340	51.37 344	81.36 335	82.41 344	52.37 337	42.49 341	70.39 337	11.39 349	79.99 342	49.77 338	38.71 338	73.97 336

MTMP								82.03 345

DeepMVS_CX				74.68 325	90.84 311	64.34 336		81.61 346	65.34 332	67.47 330	88.01 318	48.60 335	80.13 341	62.33 327	73.68 327	79.58 333

E-PMN		53.28 317	52.56 318	55.43 330	74.43 338	47.13 346	83.63 334	76.30 347	42.23 340	42.59 340	62.22 340	28.57 345	74.40 343	31.53 342	31.51 341	44.78 340

EMVS		52.08 319	51.31 319	54.39 331	72.62 343	45.39 348	83.84 333	75.51 348	41.13 341	40.77 342	59.65 341	30.08 343	73.60 344	28.31 343	29.90 343	44.18 341

MVE	50.73 23	53.25 318	48.81 321	66.58 329	65.34 345	57.50 342	72.49 340	70.94 349	40.15 342	39.28 343	63.51 339	6.89 353	73.48 345	38.29 341	42.38 337	68.76 338
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
tmp_tt		51.94 320	53.82 317	46.29 333	33.73 349	45.30 349	78.32 338	67.24 350	18.02 343	50.93 338	87.05 324	52.99 333	53.11 346	70.76 317	25.29 344	40.46 342

N_pmnet		78.73 302	78.71 301	78.79 320	92.80 298	46.50 347	94.14 273	43.71 351	78.61 313	80.83 295	91.66 291	74.94 270	96.36 290	67.24 320	84.45 276	93.50 287

wuyk23d		25.11 322	24.57 324	26.74 335	73.98 340	39.89 350	57.88 342	9.80 352	12.27 344	10.39 346	6.97 348	7.03 351	36.44 347	25.43 344	17.39 345	3.89 346

testmvs		13.36 324	16.33 325	4.48 337	5.04 350	2.26 352	93.18 288	3.28 353	2.70 345	8.24 347	21.66 344	2.29 355	2.19 348	7.58 345	2.96 346	9.00 344

test123		13.04 325	15.66 326	5.18 336	4.51 351	3.45 351	92.50 302	1.81 354	2.50 346	7.58 348	20.15 345	3.67 354	2.18 349	7.13 346	1.07 348	9.90 343

pcd_1.5k_mvsjas		7.39 327	9.85 328	0.00 338	0.00 352	0.00 353	0.00 343	0.00 355	0.00 347	0.00 349	0.00 349	88.65 69	0.00 350	0.00 347	0.00 349	0.00 347

sosnet-low-res		0.00 328	0.00 329	0.00 338	0.00 352	0.00 353	0.00 343	0.00 355	0.00 347	0.00 349	0.00 349	0.00 356	0.00 350	0.00 347	0.00 349	0.00 347

sosnet		0.00 328	0.00 329	0.00 338	0.00 352	0.00 353	0.00 343	0.00 355	0.00 347	0.00 349	0.00 349	0.00 356	0.00 350	0.00 347	0.00 349	0.00 347

uncertanet		0.00 328	0.00 329	0.00 338	0.00 352	0.00 353	0.00 343	0.00 355	0.00 347	0.00 349	0.00 349	0.00 356	0.00 350	0.00 347	0.00 349	0.00 347

Regformer		0.00 328	0.00 329	0.00 338	0.00 352	0.00 353	0.00 343	0.00 355	0.00 347	0.00 349	0.00 349	0.00 356	0.00 350	0.00 347	0.00 349	0.00 347

n2								0.00 355

nn								0.00 355

ab-mvs-re		8.06 326	10.74 327	0.00 338	0.00 352	0.00 353	0.00 343	0.00 355	0.00 347	0.00 349	96.69 112	0.00 356	0.00 350	0.00 347	0.00 349	0.00 347

uanet		0.00 328	0.00 329	0.00 338	0.00 352	0.00 353	0.00 343	0.00 355	0.00 347	0.00 349	0.00 349	0.00 356	0.00 350	0.00 347	0.00 349	0.00 347

test_part2					99.28 17	95.74 3				98.10 6

sam_mvs1												82.76 157

sam_mvs												81.94 178

test_post1							92.81 298				16.58 347	80.53 201	97.68 250	86.20 206

test_post											17.58 346	81.76 180	98.08 192

patchmatchnet-post											90.45 294	82.65 161	98.10 189

gm-plane-assit					93.22 289	78.89 313			84.82 268		93.52 258		98.64 146	87.72 176

test9_res														94.81 60	99.38 34	99.45 29

agg_prior2														93.94 72	99.38 34	99.50 23

test_prior4						93.66 40	96.42 184

test_prior2							96.35 193		92.80 77	96.03 53	97.59 77	92.01 29		95.01 53	99.38 34

旧先验2							95.94 221		81.66 295	97.34 16			98.82 134	92.26 95

新几何2							95.79 228

原ACMM2							95.67 232

testdata2													99.67 37	85.96 213

segment_acmp												92.89 11

testdata1							95.26 253		93.10 65

plane_prior7					96.21 158	89.98 138

plane_prior6					96.10 168	90.00 134						81.32 186

plane_prior4											96.64 115

plane_prior3						90.00 134			94.46 30	91.34 151

plane_prior2							97.74 56		94.85 17

plane_prior1					96.14 166

plane_prior						89.99 136	97.24 106		94.06 38						92.16 189

HQP5-MVS						89.33 170

HQP-NCC					95.86 172		96.65 168		93.55 48	90.14 174

ACMP_Plane					95.86 172		96.65 168		93.55 48	90.14 174

BP-MVS														92.13 101

HQP4-MVS										90.14 174			98.50 159			95.78 202

HQP2-MVS												80.95 191

NP-MVS					95.99 171	89.81 145					95.87 150

MDTV_nov1_ep13_2view						70.35 327	93.10 294		83.88 278	93.55 100		82.47 166		86.25 205		98.38 114

ACMMP++_ref															90.30 218

ACMMP++															91.02 208

Test By Simon												88.73 68

High-resolution multi-view benchmark