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.

LCM-MVSNet		95.70 1	96.40 1	93.61 2	98.67 1	85.39 29	95.54 3	97.36 1	96.97 1	99.04 1	99.05 1	96.61 1	95.92 10	85.07 36	99.27 3	99.54 1

XVG-OURS-SEG-HR		89.59 48	89.37 51	90.28 42	94.47 39	85.95 21	86.84 95	93.91 27	80.07 72	86.75 149	93.26 99	93.64 2	90.93 178	84.60 44	90.75 232	93.97 100

Anonymous20231211		90.14 31	91.88 12	84.92 118	94.75 35	64.47 179	90.13 38	92.97 57	91.68 3	95.35 12	98.79 2	93.19 3	91.76 159	71.67 172	98.40 21	98.52 7

abl_6		93.02 4	93.16 4	92.60 4	94.73 38	88.99 7	93.26 10	94.19 19	89.11 11	94.43 19	95.27 41	91.86 4	95.09 43	87.54 18	98.02 39	93.71 110

ACMH+	77.89 11	90.73 25	91.50 19	88.44 63	93.00 65	76.26 99	89.65 49	95.55 3	87.72 18	93.89 27	94.94 50	91.62 5	93.44 109	78.35 117	98.76 5	95.61 64

LPG-MVS_test		91.47 16	91.68 15	90.82 34	94.75 35	81.69 51	90.00 39	94.27 13	82.35 48	93.67 30	94.82 54	91.18 6	95.52 27	85.36 34	98.73 8	95.23 72

LGP-MVS_train				90.82 34	94.75 35	81.69 51		94.27 13	82.35 48	93.67 30	94.82 54	91.18 6	95.52 27	85.36 34	98.73 8	95.23 72

PMVS	80.48 6	90.08 33	90.66 36	88.34 65	96.71 2	92.97 2	90.31 36	89.57 161	88.51 15	90.11 82	95.12 46	90.98 8	88.92 221	77.55 125	97.07 67	83.13 287
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
ACMM	79.39 9	90.65 26	90.99 31	89.63 49	95.03 30	83.53 44	89.62 52	93.35 38	79.20 82	93.83 28	93.60 95	90.81 9	92.96 130	85.02 38	98.45 20	92.41 146
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ACMH	76.49 14	89.34 52	91.14 28	83.96 146	92.50 77	70.36 143	89.55 53	93.84 29	81.89 56	94.70 16	95.44 39	90.69 10	88.31 232	83.33 58	98.30 28	93.20 123
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
HPM-MVS_fast		92.50 5	92.54 5	92.37 5	95.93 14	85.81 27	92.99 11	94.23 16	85.21 24	92.51 50	95.13 45	90.65 11	95.34 34	88.06 10	98.15 34	95.95 51

ACMP	79.16 10	90.54 29	90.60 37	90.35 41	94.36 40	80.98 57	89.16 61	94.05 23	79.03 86	92.87 40	93.74 93	90.60 12	95.21 41	82.87 65	98.76 5	94.87 77
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
HPM-MVS		92.13 6	92.20 7	91.91 15	95.58 23	84.67 38	93.51 6	94.85 9	82.88 43	91.77 63	93.94 90	90.55 13	95.73 19	88.50 8	98.23 31	95.33 69

APD-MVS_3200maxsize		92.05 7	92.24 6	91.48 20	93.02 64	85.17 31	92.47 21	95.05 8	87.65 19	93.21 36	94.39 71	90.09 14	95.08 44	86.67 24	97.60 56	94.18 94

COLMAP_ROB	83.01 3	91.97 8	91.95 8	92.04 10	93.68 50	86.15 18	93.37 8	95.10 7	90.28 9	92.11 55	95.03 47	89.75 15	94.93 48	79.95 103	98.27 29	95.04 76
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
pcd1.5k->3k		38.83 328	41.11 329	32.01 339	93.13 62	0.00 360	0.00 351	91.38 112	0.00 355	0.00 356	0.00 357	89.24 16	0.00 358	0.00 355	96.24 94	96.02 48

ACMMP		91.91 9	91.87 14	92.03 11	95.53 24	85.91 22	93.35 9	94.16 20	82.52 47	92.39 54	94.14 79	89.15 17	95.62 21	87.35 19	98.24 30	94.56 81
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
APDe-MVS		91.22 20	91.92 9	89.14 56	92.97 66	78.04 77	92.84 12	94.14 21	83.33 37	93.90 26	95.73 29	88.77 18	96.41 1	87.60 16	97.98 42	92.98 128

ACMMP_Plus		90.65 26	91.07 30	89.42 52	95.93 14	79.54 68	89.95 42	93.68 31	77.65 103	91.97 60	94.89 51	88.38 19	95.45 30	89.27 3	97.87 45	93.27 120

MP-MVS-pluss		90.81 24	91.08 29	89.99 46	95.97 12	79.88 63	88.13 75	94.51 11	75.79 131	92.94 38	94.96 49	88.36 20	95.01 46	90.70 2	98.40 21	95.09 75
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
HFP-MVS		91.30 18	91.39 21	91.02 29	95.43 25	84.66 39	92.58 18	93.29 44	81.99 53	91.47 67	93.96 86	88.35 21	95.56 24	87.74 11	97.74 48	92.85 129

#test#		90.49 30	90.31 42	91.02 29	95.43 25	84.66 39	90.65 34	93.29 44	77.00 118	91.47 67	93.96 86	88.35 21	95.56 24	84.88 39	97.74 48	92.85 129

CP-MVS		91.67 11	91.58 17	91.96 12	95.29 27	87.62 9	93.38 7	93.36 37	83.16 39	91.06 72	94.00 83	88.26 23	95.71 20	87.28 22	98.39 23	92.55 143

SteuartSystems-ACMMP		91.16 22	91.36 22	90.55 37	93.91 46	80.97 58	91.49 29	93.48 36	82.82 44	92.60 49	93.97 84	88.19 24	96.29 3	87.61 15	98.20 33	94.39 90
Skip Steuart: Steuart Systems R&D Blog.
PGM-MVS		91.20 21	90.95 33	91.93 13	95.67 20	85.85 25	90.00 39	93.90 28	80.32 69	91.74 64	94.41 68	88.17 25	95.98 7	86.37 25	97.99 41	93.96 101

TDRefinement		93.52 2	93.39 3	93.88 1	95.94 13	90.26 4	95.70 2	96.46 2	90.58 8	92.86 41	96.29 18	88.16 26	94.17 68	86.07 32	98.48 19	97.22 26

OPM-MVS		89.80 44	89.97 43	89.27 54	94.76 34	79.86 64	86.76 99	92.78 65	78.78 89	92.51 50	93.64 94	88.13 27	93.84 82	84.83 41	97.55 57	94.10 97

pmmvs6		86.52 88	88.06 65	81.90 186	92.22 87	62.28 218	84.66 128	89.15 166	83.54 36	89.85 93	97.32 4	88.08 28	86.80 253	70.43 181	97.30 62	96.62 37

mvs_tets		89.78 45	89.27 52	91.30 24	93.51 52	84.79 36	89.89 44	90.63 127	70.00 206	94.55 18	96.67 11	87.94 29	93.59 93	84.27 48	95.97 105	95.52 65

region2R		91.44 17	91.30 26	91.87 16	95.75 17	85.90 23	92.63 17	93.30 42	81.91 55	90.88 77	94.21 76	87.75 30	95.87 12	87.60 16	97.71 50	93.83 103

wuyk23d		75.13 237	79.30 198	62.63 319	75.56 324	75.18 104	80.89 224	73.10 289	75.06 141	94.76 15	95.32 40	87.73 31	52.85 350	34.16 344	97.11 66	59.85 345

wuykxyi23d		88.46 62	88.80 60	87.44 80	90.96 120	93.03 1	85.85 113	81.96 237	74.58 144	98.58 2	97.29 5	87.73 31	87.31 240	82.84 67	99.41 1	81.99 300

mPP-MVS		91.69 10	91.47 20	92.37 5	96.04 11	88.48 8	92.72 14	92.60 71	83.09 40	91.54 66	94.25 75	87.67 33	95.51 29	87.21 23	98.11 35	93.12 125

ACMMPR		91.49 14	91.35 24	91.92 14	95.74 18	85.88 24	92.58 18	93.25 46	81.99 53	91.40 69	94.17 78	87.51 34	95.87 12	87.74 11	97.76 47	93.99 99

PS-CasMVS		90.06 34	91.92 9	84.47 132	96.56 6	58.83 250	89.04 62	92.74 66	91.40 5	96.12 4	96.06 24	87.23 35	95.57 23	79.42 111	98.74 7	99.00 2

test_part1								93.93 25				87.19 36			97.61 54	91.48 172

ESAPD		90.05 35	90.56 40	88.50 62	93.86 47	77.77 79	89.63 50	93.93 25	84.39 28	92.84 42	93.43 97	87.19 36	96.26 4	82.18 74	97.61 54	91.48 172

PEN-MVS		90.03 36	91.88 12	84.48 131	96.57 5	58.88 249	88.95 63	93.19 47	91.62 4	96.01 6	96.16 22	87.02 38	95.60 22	78.69 115	98.72 10	98.97 3

DTE-MVSNet		89.98 38	91.91 11	84.21 140	96.51 7	57.84 253	88.93 65	92.84 63	91.92 2	96.16 3	96.23 20	86.95 39	95.99 6	79.05 112	98.57 16	98.80 6

MP-MVS		91.14 23	90.91 34	91.83 18	96.18 10	86.88 11	92.20 22	93.03 55	82.59 46	88.52 124	94.37 72	86.74 40	95.41 32	86.32 26	98.21 32	93.19 124

MPTG		91.27 19	91.26 27	91.29 25	96.59 3	86.29 14	88.94 64	91.81 90	84.07 32	92.00 58	94.40 69	86.63 41	95.28 37	88.59 4	98.31 26	92.30 151

MTAPA		91.52 13	91.60 16	91.29 25	96.59 3	86.29 14	92.02 24	91.81 90	84.07 32	92.00 58	94.40 69	86.63 41	95.28 37	88.59 4	98.31 26	92.30 151

XVS		91.54 12	91.36 22	92.08 8	95.64 21	86.25 16	92.64 15	93.33 39	85.07 25	89.99 86	94.03 82	86.57 43	95.80 15	87.35 19	97.62 52	94.20 92

X-MVStestdata		85.04 113	82.70 160	92.08 8	95.64 21	86.25 16	92.64 15	93.33 39	85.07 25	89.99 86	16.05 352	86.57 43	95.80 15	87.35 19	97.62 52	94.20 92

canonicalmvs		85.50 107	86.14 98	83.58 158	87.97 174	67.13 163	87.55 83	94.32 12	73.44 156	88.47 125	87.54 226	86.45 45	91.06 175	75.76 138	93.76 167	92.54 144

TranMVSNet+NR-MVSNet		87.86 68	88.76 61	85.18 115	94.02 43	64.13 181	84.38 133	91.29 114	84.88 27	92.06 57	93.84 92	86.45 45	93.73 83	73.22 158	98.66 12	97.69 12

test_0402		88.65 59	89.58 50	85.88 106	92.55 75	72.22 125	84.01 139	89.44 163	88.63 14	94.38 21	95.77 28	86.38 47	93.59 93	79.84 104	95.21 128	91.82 163

APD-MVS		89.54 49	89.63 49	89.26 55	92.57 74	81.34 56	90.19 37	93.08 51	80.87 64	91.13 71	93.19 100	86.22 48	95.97 8	82.23 73	97.18 65	90.45 198
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
SD-MVS		88.96 56	89.88 44	86.22 96	91.63 99	77.07 89	89.82 45	93.77 30	78.90 87	92.88 39	92.29 124	86.11 49	90.22 198	86.24 30	97.24 63	91.36 175

jajsoiax		89.41 50	88.81 59	91.19 28	93.38 56	84.72 37	89.70 46	90.29 143	69.27 210	94.39 20	96.38 15	86.02 50	93.52 103	83.96 51	95.92 108	95.34 68

nrg030		87.85 69	88.49 62	85.91 104	90.07 135	69.73 145	87.86 78	94.20 17	74.04 149	92.70 47	94.66 58	85.88 51	91.50 163	79.72 105	97.32 61	96.50 40

DeepC-MVS	82.31 4	89.15 55	89.08 53	89.37 53	93.64 51	79.07 70	88.54 71	94.20 17	73.53 154	89.71 97	94.82 54	85.09 52	95.77 17	84.17 50	98.03 38	93.26 121
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
v52		89.97 40	90.60 37	88.07 69	88.69 155	72.01 127	91.35 30	92.64 69	82.22 50	95.97 8	96.31 16	84.82 53	93.98 75	88.59 4	94.83 142	98.23 8

LTVRE_ROB	86.10 1	93.04 3	93.44 2	91.82 19	93.73 49	85.72 28	96.79 1	95.51 4	88.86 13	95.63 10	96.99 8	84.81 54	93.16 122	91.10 1	97.53 58	96.58 39
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
V4		89.97 40	90.60 37	88.07 69	88.69 155	72.01 127	91.35 30	92.64 69	82.22 50	95.98 7	96.31 16	84.80 55	93.98 75	88.59 4	94.83 142	98.23 8

DP-MVS		88.60 61	89.01 54	87.36 81	91.30 110	77.50 84	87.55 83	92.97 57	87.95 17	89.62 103	92.87 109	84.56 56	93.89 79	77.65 124	96.62 79	90.70 190

LS3D		90.60 28	90.34 41	91.38 23	89.03 149	84.23 42	93.58 4	94.68 10	90.65 7	90.33 81	93.95 89	84.50 57	95.37 33	80.87 88	95.50 120	94.53 85

anonymousdsp		89.73 46	88.88 57	92.27 7	89.82 140	86.67 12	90.51 35	90.20 148	69.87 207	95.06 14	96.14 23	84.28 58	93.07 129	87.68 13	96.34 89	97.09 30

OMC-MVS		88.19 64	87.52 73	90.19 44	91.94 95	81.68 53	87.49 85	93.17 48	76.02 127	88.64 121	91.22 147	84.24 59	93.37 112	77.97 123	97.03 68	95.52 65

XVG-OURS		89.18 54	88.83 58	90.23 43	94.28 41	86.11 20	85.91 111	93.60 34	80.16 71	89.13 114	93.44 96	83.82 60	90.98 176	83.86 54	95.30 127	93.60 114

XVG-ACMP-BASELINE		89.98 38	89.84 45	90.41 39	94.91 33	84.50 41	89.49 57	93.98 24	79.68 75	92.09 56	93.89 91	83.80 61	93.10 125	82.67 69	98.04 36	93.64 112

CDPH-MVS		86.17 97	85.54 107	88.05 72	92.25 85	75.45 102	83.85 145	92.01 83	65.91 237	86.19 159	91.75 137	83.77 62	94.98 47	77.43 128	96.71 77	93.73 109

Effi-MVS+		83.90 149	84.01 145	83.57 159	87.22 204	65.61 173	86.55 107	92.40 74	78.64 92	81.34 228	84.18 270	83.65 63	92.93 132	74.22 146	87.87 264	92.17 157

MVS_111021_HR		84.63 121	84.34 141	85.49 112	90.18 133	75.86 101	79.23 254	87.13 196	73.35 157	85.56 169	89.34 197	83.60 64	90.50 192	76.64 134	94.05 159	90.09 207

UA-Net		91.49 14	91.53 18	91.39 22	94.98 31	82.95 50	93.52 5	92.79 64	88.22 16	88.53 123	97.64 3	83.45 65	94.55 59	86.02 33	98.60 14	96.67 36

AdaColmap		83.66 152	83.69 150	83.57 159	90.05 137	72.26 124	86.29 110	90.00 152	78.19 98	81.65 223	87.16 229	83.40 66	94.24 64	61.69 237	94.76 147	84.21 270

LCM-MVSNet-Re		83.48 156	85.06 113	78.75 224	85.94 236	55.75 268	80.05 231	94.27 13	76.47 122	96.09 5	94.54 63	83.31 67	89.75 207	59.95 249	94.89 138	90.75 189

Regformer-2		86.74 84	86.08 99	88.73 59	84.18 260	79.20 69	83.52 157	89.33 164	83.33 37	89.92 92	85.07 259	83.23 68	93.16 122	83.39 57	92.72 193	93.83 103

TransMVSNet (Re)		84.02 145	85.74 103	78.85 222	91.00 119	55.20 273	82.29 192	87.26 191	79.65 76	88.38 128	95.52 37	83.00 69	86.88 245	67.97 201	96.60 80	94.45 88

CNVR-MVS		87.81 70	87.68 72	88.21 66	92.87 68	77.30 88	85.25 120	91.23 116	77.31 113	87.07 145	91.47 143	82.94 70	94.71 53	84.67 43	96.27 93	92.62 142

DeepPCF-MVS	81.24 5	87.28 74	86.21 97	90.49 38	91.48 107	84.90 34	83.41 162	92.38 76	70.25 204	89.35 111	90.68 172	82.85 71	94.57 57	79.55 107	95.95 106	92.00 159

v7n		90.13 32	90.96 32	87.65 76	91.95 93	71.06 139	89.99 41	93.05 52	86.53 21	94.29 22	96.27 19	82.69 72	94.08 71	86.25 29	97.63 51	97.82 11

AllTest		87.97 67	87.40 77	89.68 47	91.59 100	83.40 45	89.50 56	95.44 5	79.47 77	88.00 131	93.03 103	82.66 73	91.47 164	70.81 174	96.14 98	94.16 95

TestCases				89.68 47	91.59 100	83.40 45		95.44 5	79.47 77	88.00 131	93.03 103	82.66 73	91.47 164	70.81 174	96.14 98	94.16 95

v748		88.91 58	89.82 46	86.19 100	90.06 136	68.53 155	88.81 67	91.48 99	84.36 30	94.19 24	95.98 25	82.52 75	92.67 139	84.30 47	96.67 78	97.37 20

RPSCF		88.00 66	86.93 85	91.22 27	90.08 134	89.30 6	89.68 48	91.11 119	79.26 81	89.68 98	94.81 57	82.44 76	87.74 237	76.54 135	88.74 254	96.61 38

ITE_SJBPF				90.11 45	90.72 125	84.97 33		90.30 140	81.56 59	90.02 85	91.20 150	82.40 77	90.81 183	73.58 154	94.66 149	94.56 81

Fast-Effi-MVS+		81.04 185	80.57 186	82.46 181	87.50 193	63.22 196	78.37 261	89.63 159	68.01 220	81.87 218	82.08 301	82.31 78	92.65 140	67.10 204	88.30 260	91.51 171

Regformer-1		86.00 98	85.50 108	87.49 78	84.18 260	76.90 91	83.52 157	87.94 184	82.18 52	89.19 112	85.07 259	82.28 79	91.89 154	82.40 71	92.72 193	93.69 111

agg_prior1		85.72 105	85.20 112	87.28 82	91.58 103	77.69 81	83.69 152	90.30 140	66.29 233	84.32 190	91.07 158	82.13 80	93.18 120	81.02 85	96.36 88	90.98 180

Regformer-4		86.41 89	85.71 104	88.52 61	84.27 256	77.57 83	84.07 137	88.00 182	82.82 44	89.84 94	85.48 249	82.06 81	92.77 136	83.83 55	91.04 220	95.22 74

CLD-MVS		83.18 160	82.64 162	84.79 122	89.05 148	67.82 161	77.93 265	92.52 72	68.33 218	85.07 172	81.54 306	82.06 81	92.96 130	69.35 187	97.91 43	93.57 115
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
TEST9					92.34 82	79.70 66	83.94 141	90.32 136	65.41 245	84.49 186	90.97 161	82.03 83	93.63 88

segment_acmp												81.94 84

train_agg		85.98 101	85.28 111	88.07 69	92.34 82	79.70 66	83.94 141	90.32 136	65.79 238	84.49 186	90.97 161	81.93 85	93.63 88	81.21 82	96.54 82	90.88 185

test_8					92.09 89	78.87 72	83.82 146	90.31 138	65.79 238	84.36 189	90.96 163	81.93 85	93.44 109

test_prior3		86.31 92	86.31 94	86.32 92	90.59 127	71.99 129	83.37 163	92.85 61	75.43 137	84.58 184	91.57 139	81.92 87	94.17 68	79.54 108	96.97 69	92.80 131

test_prior2							83.37 163		75.43 137	84.58 184	91.57 139	81.92 87		79.54 108	96.97 69

CP-MVSNet		89.27 53	90.91 34	84.37 135	96.34 8	58.61 252	88.66 70	92.06 82	90.78 6	95.67 9	95.17 44	81.80 89	95.54 26	79.00 113	98.69 11	98.95 4

MVS_111021_LR		84.28 135	83.76 149	85.83 108	89.23 146	83.07 48	80.99 223	83.56 228	72.71 170	86.07 160	89.07 200	81.75 90	86.19 262	77.11 131	93.36 176	88.24 222

test_djsdf		89.62 47	89.01 54	91.45 21	92.36 81	82.98 49	91.98 25	90.08 149	71.54 193	94.28 23	96.54 13	81.57 91	94.27 61	86.26 27	96.49 85	97.09 30

cdsmvs_eth3d_5k		20.81 329	27.75 330	0.00 345	0.00 359	0.00 360	0.00 351	85.44 214	0.00 355	0.00 356	82.82 287	81.46 92	0.00 358	0.00 355	0.00 356	0.00 356

WR-MVS_H		89.91 43	91.31 25	85.71 109	96.32 9	62.39 212	89.54 55	93.31 41	90.21 10	95.57 11	95.66 31	81.42 93	95.90 11	80.94 87	98.80 4	98.84 5

CPTT-MVS		89.39 51	88.98 56	90.63 36	95.09 29	86.95 10	92.09 23	92.30 77	79.74 74	87.50 138	92.38 120	81.42 93	93.28 117	83.07 62	97.24 63	91.67 166

pm-mvs1		83.69 151	84.95 117	79.91 210	90.04 138	59.66 243	82.43 187	87.44 188	75.52 136	87.85 133	95.26 42	81.25 95	85.65 269	68.74 195	96.04 103	94.42 89

agg_prior3		85.76 104	84.95 117	88.16 67	92.43 79	79.92 62	83.98 140	90.03 151	65.11 247	83.66 198	90.64 176	81.00 96	93.67 85	81.21 82	96.54 82	90.88 185

NCCC		87.36 72	86.87 86	88.83 58	92.32 84	78.84 73	86.58 106	91.09 120	78.77 90	84.85 177	90.89 165	80.85 97	95.29 35	81.14 84	95.32 124	92.34 150

TAPA-MVS	77.73 12	85.71 106	84.83 119	88.37 64	88.78 154	79.72 65	87.15 91	93.50 35	69.17 212	85.80 165	89.56 194	80.76 98	92.13 148	73.21 162	95.51 119	93.25 122
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
Fast-Effi-MVS+-dtu		82.54 167	81.41 178	85.90 105	85.60 237	76.53 96	83.07 171	89.62 160	73.02 167	79.11 256	83.51 276	80.74 99	90.24 197	68.76 194	89.29 246	90.94 182

VPA-MVSNet		83.47 157	84.73 120	79.69 214	90.29 131	57.52 256	81.30 217	88.69 170	76.29 123	87.58 136	94.44 66	80.60 100	87.20 241	66.60 210	96.82 75	94.34 91

Regformer-3		85.06 112	84.67 125	86.22 96	84.27 256	73.43 113	84.07 137	85.26 216	80.77 65	88.62 122	85.48 249	80.56 101	90.39 194	81.99 77	91.04 220	94.85 79

HPM-MVS++		88.93 57	88.45 63	90.38 40	94.92 32	85.85 25	89.70 46	91.27 115	78.20 97	86.69 150	92.28 125	80.36 102	95.06 45	86.17 31	96.49 85	90.22 202

ANet_high		83.17 161	85.68 105	75.65 265	81.24 283	45.26 332	79.94 234	92.91 59	83.83 35	91.33 70	96.88 10	80.25 103	85.92 265	68.89 193	95.89 109	95.76 53

EI-MVSNet-Vis-set		85.12 111	84.53 132	86.88 83	84.01 262	72.76 117	83.91 144	85.18 218	80.44 66	88.75 119	85.49 248	80.08 104	91.92 152	82.02 76	90.85 230	95.97 49

DeepC-MVS_fast	80.27 8	86.23 95	85.65 106	87.96 73	91.30 110	76.92 90	87.19 89	91.99 84	70.56 200	84.96 173	90.69 171	80.01 105	95.14 42	78.37 116	95.78 114	91.82 163
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
EI-MVSNet-UG-set		85.04 113	84.44 134	86.85 84	83.87 265	72.52 120	83.82 146	85.15 219	80.27 70	88.75 119	85.45 252	79.95 106	91.90 153	81.92 78	90.80 231	96.13 43

MCST-MVS		84.36 131	83.93 148	85.63 110	91.59 100	71.58 136	83.52 157	92.13 80	61.82 268	83.96 194	89.75 191	79.93 107	93.46 108	78.33 118	94.34 155	91.87 162

TSAR-MVS + MP.		88.14 65	87.82 69	89.09 57	95.72 19	76.74 93	92.49 20	91.19 118	67.85 225	86.63 151	94.84 53	79.58 108	95.96 9	87.62 14	94.50 152	94.56 81

test12				86.57 87	90.74 124	72.63 118		90.69 125		82.76 210		79.20 109	94.80 51		95.32 124	92.27 153

CSCG		86.26 93	86.47 92	85.60 111	90.87 122	74.26 109	87.98 76	91.85 88	80.35 68	89.54 109	88.01 217	79.09 110	92.13 148	75.51 139	95.06 133	90.41 199

Test By Simon												79.09 110

PHI-MVS		86.38 90	85.81 102	88.08 68	88.44 163	77.34 86	89.35 60	93.05 52	73.15 165	84.76 178	87.70 223	78.87 112	94.18 66	80.67 92	96.29 90	92.73 133

EG-PatchMatch MVS		84.08 143	84.11 143	83.98 145	92.22 87	72.61 119	82.20 198	87.02 200	72.63 171	88.86 116	91.02 159	78.52 113	91.11 173	73.41 157	91.09 218	88.21 223

Effi-MVS+-dtu		85.82 103	83.38 152	93.14 3	87.13 206	91.15 3	87.70 81	88.42 173	74.57 145	83.56 200	85.65 246	78.49 114	94.21 65	72.04 169	92.88 190	94.05 98

mvs-test1		84.55 124	82.12 169	91.84 17	87.13 206	89.54 5	85.05 123	88.42 173	74.57 145	80.60 241	82.98 283	78.49 114	93.98 75	72.04 169	89.77 243	92.00 159

Vis-MVSNet		86.86 80	86.58 90	87.72 74	92.09 89	77.43 85	87.35 86	92.09 81	78.87 88	84.27 193	94.05 81	78.35 116	93.65 86	80.54 94	91.58 209	92.08 158
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
UniMVSNet_NR-MVSNet		86.84 81	87.06 80	86.17 101	92.86 70	67.02 164	82.55 185	91.56 95	83.08 41	90.92 74	91.82 134	78.25 117	93.99 73	74.16 147	98.35 24	97.49 16

MSLP-MVS++		85.00 115	86.03 100	81.90 186	91.84 97	71.56 137	86.75 100	93.02 56	75.95 128	87.12 142	89.39 196	77.98 118	89.40 212	77.46 126	94.78 144	84.75 263

API-MVS		82.28 170	82.61 163	81.30 196	86.29 223	69.79 144	88.71 69	87.67 186	78.42 95	82.15 215	84.15 272	77.98 118	91.59 162	65.39 218	92.75 192	82.51 294

DP-MVS Recon		84.05 144	83.22 154	86.52 89	91.73 98	75.27 103	83.23 169	92.40 74	72.04 181	82.04 216	88.33 213	77.91 120	93.95 78	66.17 212	95.12 131	90.34 201

UniMVSNet (Re)		86.87 79	86.98 83	86.55 88	93.11 63	68.48 156	83.80 148	92.87 60	80.37 67	89.61 105	91.81 135	77.72 121	94.18 66	75.00 144	98.53 17	96.99 34

PCF-MVS	74.62 15	82.15 172	80.92 185	85.84 107	89.43 142	72.30 123	80.53 227	91.82 89	57.36 287	87.81 134	89.92 188	77.67 122	93.63 88	58.69 261	95.08 132	91.58 169
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
NR-MVSNet		86.00 98	86.22 96	85.34 113	93.24 59	64.56 178	82.21 196	90.46 131	80.99 63	88.42 126	91.97 128	77.56 123	93.85 80	72.46 166	98.65 13	97.61 13

3Dnovator+	83.92 2	89.97 40	89.66 48	90.92 32	91.27 112	81.66 54	91.25 32	94.13 22	88.89 12	88.83 118	94.26 74	77.55 124	95.86 14	84.88 39	95.87 110	95.24 71

MVS_Test		82.47 168	83.22 154	80.22 208	82.62 275	57.75 255	82.54 186	91.96 86	71.16 196	82.89 209	92.52 119	77.41 125	90.50 192	80.04 102	87.84 265	92.40 147

xiu_mvs_v2_base		77.19 214	76.75 213	78.52 228	87.01 211	61.30 229	75.55 287	87.12 198	61.24 274	74.45 288	78.79 319	77.20 126	90.93 178	64.62 223	84.80 295	83.32 283

DU-MVS		86.80 82	86.99 82	86.21 98	93.24 59	67.02 164	83.16 170	92.21 78	81.73 57	90.92 74	91.97 128	77.20 126	93.99 73	74.16 147	98.35 24	97.61 13

Baseline_NR-MVSNet		84.00 146	85.90 101	78.29 231	91.47 108	53.44 282	82.29 192	87.00 201	79.06 85	89.55 107	95.72 30	77.20 126	86.14 263	72.30 167	98.51 18	95.28 70

TinyColmap		81.25 183	82.34 168	77.99 236	85.33 242	60.68 237	82.32 191	88.33 175	71.26 195	86.97 147	92.22 127	77.10 129	86.98 244	62.37 232	95.17 130	86.31 244

F-COLMAP		84.97 116	83.42 151	89.63 49	92.39 80	83.40 45	88.83 66	91.92 87	73.19 164	80.18 249	89.15 199	77.04 130	93.28 117	65.82 217	92.28 199	92.21 156

114514_t		83.10 162	82.54 165	84.77 123	92.90 67	69.10 154	86.65 104	90.62 128	54.66 299	81.46 225	90.81 168	76.98 131	94.38 60	72.62 165	96.18 95	90.82 188

xiu_mvs_v1_base_debu		80.84 188	80.14 193	82.93 172	88.31 165	71.73 132	79.53 238	87.17 193	65.43 242	79.59 251	82.73 289	76.94 132	90.14 201	73.22 158	88.33 256	86.90 239

xiu_mvs_v1_base		80.84 188	80.14 193	82.93 172	88.31 165	71.73 132	79.53 238	87.17 193	65.43 242	79.59 251	82.73 289	76.94 132	90.14 201	73.22 158	88.33 256	86.90 239

xiu_mvs_v1_base_debi		80.84 188	80.14 193	82.93 172	88.31 165	71.73 132	79.53 238	87.17 193	65.43 242	79.59 251	82.73 289	76.94 132	90.14 201	73.22 158	88.33 256	86.90 239

pcd_1.5k_mvsjas		6.41 332	8.55 333	0.00 345	0.00 359	0.00 360	0.00 351	0.00 361	0.00 355	0.00 356	0.00 357	76.94 132	0.00 358	0.00 355	0.00 356	0.00 356

PS-MVSNAJss		88.31 63	87.90 67	89.56 51	93.31 57	77.96 78	87.94 77	91.97 85	70.73 199	94.19 24	96.67 11	76.94 132	94.57 57	83.07 62	96.28 91	96.15 42

PS-MVSNAJ		77.04 216	76.53 219	78.56 227	87.09 210	61.40 227	75.26 288	87.13 196	61.25 273	74.38 290	77.22 325	76.94 132	90.94 177	64.63 222	84.83 294	83.35 282

MIMVSNet1		83.63 153	84.59 130	80.74 203	94.06 42	62.77 203	82.72 181	84.53 226	77.57 105	90.34 80	95.92 26	76.88 138	85.83 267	61.88 235	97.42 59	93.62 113

原ACMM1				84.60 128	92.81 72	74.01 110		91.50 97	62.59 262	82.73 211	90.67 173	76.53 139	94.25 63	69.24 188	95.69 117	85.55 251

MSDG		80.06 198	79.99 196	80.25 207	83.91 264	68.04 159	77.51 270	89.19 165	77.65 103	81.94 217	83.45 278	76.37 140	86.31 261	63.31 230	86.59 276	86.41 242

Gipuma		84.44 127	86.33 93	78.78 223	84.20 259	73.57 112	89.55 53	90.44 132	84.24 31	84.38 188	94.89 51	76.35 141	80.40 298	76.14 136	96.80 76	82.36 295
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
testing_2		84.36 131	84.64 129	83.50 164	86.74 215	63.97 184	84.56 130	90.31 138	66.22 234	91.62 65	94.55 61	75.88 142	91.95 151	77.02 133	94.89 138	94.56 81

XXY-MVS		74.44 246	76.19 222	69.21 299	84.61 250	52.43 290	71.70 306	77.18 259	60.73 277	80.60 241	90.96 163	75.44 143	69.35 324	56.13 278	88.33 256	85.86 249

FMVSNet1		84.55 124	85.45 109	81.85 189	90.27 132	61.05 233	86.83 96	88.27 177	78.57 93	89.66 99	95.64 33	75.43 144	90.68 187	69.09 191	95.33 123	93.82 105

CANet		83.79 150	82.85 159	86.63 86	86.17 228	72.21 126	83.76 150	91.43 106	77.24 114	74.39 289	87.45 227	75.36 145	95.42 31	77.03 132	92.83 191	92.25 155

ab-mvs		79.67 199	80.56 187	76.99 249	88.48 162	56.93 259	84.70 127	86.06 208	68.95 216	80.78 233	93.08 102	75.30 146	84.62 278	56.78 275	90.90 228	89.43 211

v13		87.31 73	88.10 64	84.94 117	88.84 152	63.75 185	87.85 79	91.47 102	79.12 83	93.72 29	95.82 27	75.20 147	93.58 96	84.76 42	96.16 96	97.48 17

v11		86.96 77	87.78 70	84.51 129	88.50 161	62.60 208	87.21 88	91.63 94	78.08 100	93.40 34	95.56 36	75.07 148	93.57 97	84.46 46	96.08 101	97.36 21

DELS-MVS		81.44 179	81.25 180	82.03 184	84.27 256	62.87 202	76.47 279	92.49 73	70.97 198	81.64 224	83.83 273	75.03 149	92.70 137	74.29 145	92.22 203	90.51 197
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
PAPR		78.84 202	78.10 205	81.07 200	85.17 243	60.22 240	82.21 196	90.57 129	62.51 263	75.32 282	84.61 266	74.99 150	92.30 145	59.48 259	88.04 262	90.68 191

v12		87.15 76	87.91 66	84.84 120	88.69 155	63.52 188	87.58 82	91.46 103	78.74 91	93.57 32	95.66 31	74.94 151	93.57 97	84.50 45	96.08 101	97.43 18

CNLPA		83.55 155	83.10 157	84.90 119	89.34 144	83.87 43	84.54 131	88.77 168	79.09 84	83.54 201	88.66 206	74.87 152	81.73 294	66.84 208	92.29 198	89.11 215

HQP_MVS		87.75 71	87.43 76	88.70 60	93.45 53	76.42 97	89.45 58	93.61 32	79.44 79	86.55 152	92.95 107	74.84 153	95.22 39	80.78 90	95.83 112	94.46 86

plane_prior6					92.61 73	76.54 94						74.84 153

FC-MVSNet-test		85.93 102	87.05 81	82.58 177	92.25 85	56.44 263	85.75 114	93.09 50	77.33 112	91.94 61	94.65 59	74.78 155	93.41 111	75.11 142	98.58 15	97.88 10

VDD-MVS		84.23 137	84.58 131	83.20 167	91.17 116	65.16 175	83.25 167	84.97 224	79.79 73	87.18 141	94.27 73	74.77 156	90.89 181	69.24 188	96.54 82	93.55 118

BH-untuned		80.96 186	80.99 183	80.84 202	88.55 159	68.23 157	80.33 229	88.46 172	72.79 169	86.55 152	86.76 233	74.72 157	91.77 158	61.79 236	88.99 250	82.52 293

VPNet		80.25 194	81.68 174	75.94 263	92.46 78	47.98 327	76.70 275	81.67 241	73.45 155	84.87 176	92.82 110	74.66 158	86.51 258	61.66 238	96.85 72	93.33 119

V9		86.96 77	87.70 71	84.74 124	88.52 160	63.27 194	87.31 87	91.45 105	78.28 96	93.43 33	95.45 38	74.59 159	93.57 97	84.23 49	96.01 104	97.38 19

tfpnnormal		81.79 177	82.95 158	78.31 230	88.93 151	55.40 269	80.83 226	82.85 232	76.81 119	85.90 164	94.14 79	74.58 160	86.51 258	66.82 209	95.68 118	93.01 127

MVS_0304		84.88 117	83.96 147	87.64 77	87.43 195	74.83 105	84.18 135	93.30 42	77.48 106	77.39 266	88.46 208	74.53 161	95.74 18	78.09 122	94.75 148	92.36 149

V14		86.75 83	87.46 74	84.62 127	88.35 164	63.00 199	87.02 93	91.42 108	77.78 102	93.27 35	95.23 43	74.22 162	93.56 100	83.95 52	95.93 107	97.31 22

V42		83.47 157	83.37 153	83.75 151	83.16 271	63.33 192	81.31 215	90.23 147	69.51 209	90.91 76	90.81 168	74.16 163	92.29 146	80.06 101	90.22 240	95.62 60

3Dnovator	80.37 7	84.80 119	84.71 123	85.06 116	86.36 221	74.71 106	88.77 68	90.00 152	75.65 135	84.96 173	93.17 101	74.06 164	91.19 171	78.28 119	91.09 218	89.29 214

v10		86.54 87	87.10 79	84.84 120	88.16 170	63.28 193	86.64 105	92.20 79	75.42 139	92.81 44	94.50 64	74.05 165	94.06 72	83.88 53	96.28 91	97.17 28

v7		84.81 118	85.00 115	84.23 139	88.15 171	63.27 194	83.79 149	91.39 111	71.10 197	90.07 83	91.28 145	74.04 166	93.63 88	81.48 81	93.67 170	95.79 52

v15		86.56 86	87.25 78	84.51 129	88.15 171	62.72 204	86.72 103	91.40 110	77.38 107	93.11 37	95.00 48	73.93 167	93.55 101	83.67 56	95.86 111	97.26 23

旧先验1					91.97 92	71.77 131		81.78 240			91.84 132	73.92 168			93.65 171	83.61 277

mvs_anonymous		78.13 206	78.76 201	76.23 260	79.24 298	50.31 314	78.69 259	84.82 225	61.60 272	83.09 208	92.82 110	73.89 169	87.01 242	68.33 199	86.41 278	91.37 174

MAR-MVS		80.24 195	78.74 202	84.73 125	86.87 214	78.18 76	85.75 114	87.81 185	65.67 241	77.84 261	78.50 320	73.79 170	90.53 191	61.59 240	90.87 229	85.49 253
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
v17		86.32 91	86.95 84	84.44 133	88.00 173	62.62 207	86.74 101	91.48 99	77.17 115	92.74 45	94.56 60	73.74 171	93.53 102	83.27 59	94.87 141	97.18 27

v16		86.24 94	86.85 87	84.43 134	87.96 175	62.59 209	86.73 102	91.48 99	77.17 115	92.67 48	94.55 61	73.63 172	93.52 103	83.26 60	94.16 156	97.17 28

test_normal		81.23 184	81.16 181	81.43 195	84.77 249	61.99 220	81.46 214	86.95 202	63.16 258	87.22 140	89.63 192	73.62 173	91.65 161	72.92 163	90.70 233	90.65 193

DI_MVS_plusplus_test		81.27 182	81.26 179	81.29 197	84.98 244	61.65 225	81.98 201	87.25 192	63.56 253	87.56 137	89.60 193	73.62 173	91.83 156	72.20 168	90.59 238	90.38 200

VDDNet		84.35 133	85.39 110	81.25 198	95.13 28	59.32 246	85.42 119	81.11 243	86.41 22	87.41 139	96.21 21	73.61 175	90.61 190	66.33 211	96.85 72	93.81 108

FIs		85.35 109	86.27 95	82.60 176	91.86 96	57.31 257	85.10 122	93.05 52	75.83 130	91.02 73	93.97 84	73.57 176	92.91 134	73.97 150	98.02 39	97.58 15

v1144		84.54 126	84.72 122	84.00 144	87.67 189	62.55 210	82.97 173	90.93 123	70.32 203	89.80 95	90.99 160	73.50 177	93.48 107	81.69 80	94.65 150	95.97 49

PAPM_NR		83.23 159	83.19 156	83.33 165	90.90 121	65.98 170	88.19 74	90.78 124	78.13 99	80.87 232	87.92 221	73.49 178	92.42 143	70.07 182	88.40 255	91.60 168

v8		86.22 96	86.83 88	84.36 136	87.82 181	62.35 213	86.42 108	91.33 113	76.78 120	92.73 46	94.48 65	73.41 179	93.72 84	83.10 61	95.41 121	97.01 33

EI-MVSNet		82.61 165	82.42 167	83.20 167	83.25 269	63.66 186	83.50 160	85.07 220	76.06 125	86.55 152	85.10 257	73.41 179	90.25 195	78.15 121	90.67 234	95.68 58

IterMVS-LS		84.73 120	84.98 116	83.96 146	87.35 196	63.66 186	83.25 167	89.88 154	76.06 125	89.62 103	92.37 123	73.40 181	92.52 142	78.16 120	94.77 146	95.69 57
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v6		84.43 128	84.66 126	83.75 151	87.81 182	62.34 214	83.59 153	90.26 146	72.33 176	89.94 89	91.19 151	73.30 182	93.29 114	80.26 98	93.26 180	95.62 60

v144192		84.24 136	84.41 135	83.71 155	87.59 192	61.57 226	82.95 174	91.03 121	67.82 226	89.80 95	90.49 177	73.28 183	93.51 106	81.88 79	94.89 138	96.04 47

v1neww		84.43 128	84.66 126	83.75 151	87.81 182	62.34 214	83.59 153	90.27 144	72.33 176	89.93 90	91.22 147	73.28 183	93.29 114	80.25 99	93.25 181	95.62 60

v7new		84.43 128	84.66 126	83.75 151	87.81 182	62.34 214	83.59 153	90.27 144	72.33 176	89.93 90	91.22 147	73.28 183	93.29 114	80.25 99	93.25 181	95.62 60

v18		85.99 100	86.55 91	84.30 138	87.73 187	62.29 217	86.40 109	91.49 98	76.64 121	92.40 53	94.20 77	73.28 183	93.52 103	82.87 65	93.99 160	97.09 30

BH-RMVSNet		80.53 191	80.22 192	81.49 194	87.19 205	66.21 169	77.79 267	86.23 206	74.21 148	83.69 196	88.50 207	73.25 187	90.75 184	63.18 231	87.90 263	87.52 232

PLC	73.85 16	82.09 173	80.31 190	87.45 79	90.86 123	80.29 60	85.88 112	90.65 126	68.17 219	76.32 272	86.33 240	73.12 188	92.61 141	61.40 241	90.02 242	89.44 210
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
diffmvs		79.20 201	79.04 199	79.69 214	78.64 304	58.90 248	81.79 206	87.61 187	65.07 248	73.65 294	89.80 189	73.10 189	87.79 236	75.02 143	86.63 275	92.38 148

OurMVSNet-221017-0		90.01 37	89.74 47	90.83 33	93.16 61	80.37 59	91.91 27	93.11 49	81.10 62	95.32 13	97.24 6	72.94 190	94.85 50	85.07 36	97.78 46	97.26 23

WR-MVS		83.56 154	84.40 136	81.06 201	93.43 55	54.88 274	78.67 260	85.02 222	81.24 61	90.74 78	91.56 141	72.85 191	91.08 174	68.00 200	98.04 36	97.23 25

VNet		79.31 200	80.27 191	76.44 256	87.92 176	53.95 278	75.58 286	84.35 227	74.39 147	82.23 213	90.72 170	72.84 192	84.39 280	60.38 248	93.98 161	90.97 181

v1		84.16 139	84.38 137	83.52 161	87.33 197	61.71 221	82.79 178	89.73 157	71.89 192	89.64 100	91.11 156	72.72 193	93.10 125	80.40 95	93.79 166	95.75 54

QAPM		82.59 166	82.59 164	82.58 177	86.44 216	66.69 167	89.94 43	90.36 135	67.97 222	84.94 175	92.58 117	72.71 194	92.18 147	70.63 179	87.73 266	88.85 220

v1192		84.57 123	84.69 124	84.21 140	87.75 186	62.88 201	83.02 172	91.43 106	69.08 214	89.98 88	90.89 165	72.70 195	93.62 92	82.41 70	94.97 136	96.13 43

v1141		84.16 139	84.38 137	83.52 161	87.32 198	61.70 223	82.79 178	89.74 155	71.90 190	89.64 100	91.12 154	72.68 196	93.10 125	80.39 97	93.80 165	95.75 54

divwei89l23v2f112		84.16 139	84.38 137	83.52 161	87.32 198	61.70 223	82.79 178	89.74 155	71.90 190	89.64 100	91.12 154	72.68 196	93.10 125	80.40 95	93.81 164	95.75 54

OpenMVS	76.72 13	81.98 176	82.00 172	81.93 185	84.42 254	68.22 158	88.50 72	89.48 162	66.92 229	81.80 222	91.86 130	72.59 198	90.16 200	71.19 173	91.25 216	87.40 234

TSAR-MVS + GP.		83.95 147	82.69 161	87.72 74	89.27 145	81.45 55	83.72 151	81.58 242	74.73 143	85.66 166	86.06 244	72.56 199	92.69 138	75.44 140	95.21 128	89.01 219

alignmvs		83.94 148	83.98 146	83.80 148	87.80 185	67.88 160	84.54 131	91.42 108	73.27 163	88.41 127	87.96 218	72.33 200	90.83 182	76.02 137	94.11 157	92.69 135

HQP2-MVS												72.10 201

HQP-MVS		84.61 122	84.06 144	86.27 94	91.19 113	70.66 141	84.77 124	92.68 67	73.30 160	80.55 244	90.17 185	72.10 201	94.61 55	77.30 129	94.47 153	93.56 116

testgi		72.36 265	74.61 235	65.59 312	80.56 291	42.82 340	68.29 315	73.35 284	66.87 230	81.84 219	89.93 187	72.08 203	66.92 332	46.05 325	92.54 195	87.01 238

v1921920		84.23 137	84.37 140	83.79 149	87.64 191	61.71 221	82.91 175	91.20 117	67.94 223	90.06 84	90.34 179	72.04 204	93.59 93	82.32 72	94.91 137	96.07 45

HSP-MVS		88.63 60	87.84 68	91.02 29	95.76 16	86.14 19	92.75 13	91.01 122	78.43 94	89.16 113	92.25 126	72.03 205	96.36 2	88.21 9	90.93 227	90.55 196

LF4IMVS		82.75 164	81.93 173	85.19 114	82.08 276	80.15 61	85.53 117	88.76 169	68.01 220	85.58 168	87.75 222	71.80 206	86.85 246	74.02 149	93.87 163	88.58 221

v1240		84.30 134	84.51 133	83.65 156	87.65 190	61.26 230	82.85 176	91.54 96	67.94 223	90.68 79	90.65 174	71.71 207	93.64 87	82.84 67	94.78 144	96.07 45

ambc				82.98 170	90.55 129	64.86 176	88.20 73	89.15 166		89.40 110	93.96 86	71.67 208	91.38 170	78.83 114	96.55 81	92.71 134

1121		80.86 187	79.81 197	84.02 143	93.93 45	78.70 74	81.64 210	80.18 248	55.43 296	83.67 197	91.15 152	71.29 209	91.41 168	67.95 202	93.06 186	81.96 301

新几何1				82.95 171	93.96 44	78.56 75		80.24 247	55.45 295	83.93 195	91.08 157	71.19 210	88.33 231	65.84 216	93.07 185	81.95 302

v148		82.31 169	82.48 166	81.81 192	85.59 238	59.66 243	81.47 213	86.02 209	72.85 168	88.05 130	90.65 174	70.73 211	90.91 180	75.15 141	91.79 205	94.87 77

v2v482		84.09 142	84.24 142	83.62 157	87.13 206	61.40 227	82.71 182	89.71 158	72.19 180	89.55 107	91.41 144	70.70 212	93.20 119	81.02 85	93.76 167	96.25 41

UGNet		82.78 163	81.64 175	86.21 98	86.20 227	76.24 100	86.86 94	85.68 212	77.07 117	73.76 292	92.82 110	69.64 213	91.82 157	69.04 192	93.69 169	90.56 195
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
MG-MVS		80.32 193	80.94 184	78.47 229	88.18 168	52.62 289	82.29 192	85.01 223	72.01 182	79.24 255	92.54 118	69.36 214	93.36 113	70.65 178	89.19 249	89.45 209

IS-MVSNet		86.66 85	86.82 89	86.17 101	92.05 91	66.87 166	91.21 33	88.64 171	86.30 23	89.60 106	92.59 115	69.22 215	94.91 49	73.89 151	97.89 44	96.72 35

PVSNet_BlendedMVS		78.80 203	77.84 206	81.65 193	84.43 252	63.41 189	79.49 241	90.44 132	61.70 271	75.43 280	87.07 231	69.11 216	91.44 166	60.68 246	92.24 201	90.11 206

PVSNet_Blended		76.49 226	75.40 229	79.76 212	84.43 252	63.41 189	75.14 289	90.44 132	57.36 287	75.43 280	78.30 321	69.11 216	91.44 166	60.68 246	87.70 267	84.42 266

BH-w/o		76.57 224	76.07 223	78.10 234	86.88 213	65.92 171	77.63 268	86.33 205	65.69 240	80.89 231	79.95 314	68.97 218	90.74 185	53.01 296	85.25 288	77.62 324

MVS		73.21 258	72.59 261	75.06 267	80.97 286	60.81 236	81.64 210	85.92 210	46.03 338	71.68 303	77.54 322	68.47 219	89.77 206	55.70 281	85.39 285	74.60 330

testdata				79.54 218	92.87 68	72.34 122		80.14 249	59.91 280	85.47 171	91.75 137	67.96 220	85.24 271	68.57 198	92.18 204	81.06 319

Test4		81.31 180	81.13 182	81.88 188	84.89 246	63.05 198	82.37 189	90.50 130	62.75 261	89.00 115	88.29 214	67.55 221	91.68 160	73.55 155	91.24 217	90.89 184

PVSNet_Blended_VisFu		81.55 178	80.49 189	84.70 126	91.58 103	73.24 115	84.21 134	91.67 93	62.86 260	80.94 230	87.16 229	67.27 222	92.87 135	69.82 184	88.94 251	87.99 227

MDA-MVSNet-bldmvs		77.47 211	76.90 212	79.16 220	79.03 300	64.59 177	66.58 323	75.67 268	73.15 165	88.86 116	88.99 201	66.94 223	81.23 295	64.71 220	88.22 261	91.64 167

test222					93.31 57	76.54 94	79.38 248	77.79 256	52.59 309	82.36 212	90.84 167	66.83 224			91.69 207	81.25 314

TR-MVS		76.77 222	75.79 224	79.72 213	86.10 235	65.79 172	77.14 272	83.02 230	65.20 246	81.40 226	82.10 300	66.30 225	90.73 186	55.57 282	85.27 287	82.65 289

OpenMVS_ROB	70.19 17	77.77 210	77.46 208	78.71 225	84.39 255	61.15 231	81.18 219	82.52 233	62.45 265	83.34 202	87.37 228	66.20 226	88.66 229	64.69 221	85.02 291	86.32 243

EPP-MVSNet		85.47 108	85.04 114	86.77 85	91.52 106	69.37 148	91.63 28	87.98 183	81.51 60	87.05 146	91.83 133	66.18 227	95.29 35	70.75 176	96.89 71	95.64 59

SixPastTwentyTwo		87.20 75	87.45 75	86.45 90	92.52 76	69.19 153	87.84 80	88.05 180	81.66 58	94.64 17	96.53 14	65.94 228	94.75 52	83.02 64	96.83 74	95.41 67

PatchMatch-RL		74.48 244	73.22 254	78.27 232	87.70 188	85.26 30	75.92 282	70.09 310	64.34 252	76.09 275	81.25 308	65.87 229	78.07 303	53.86 293	83.82 299	71.48 335

EPNet		80.37 192	78.41 204	86.23 95	76.75 316	73.28 114	87.18 90	77.45 258	76.24 124	68.14 317	88.93 202	65.41 230	93.85 80	69.47 186	96.12 100	91.55 170
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
PM-MVS		80.20 196	79.00 200	83.78 150	88.17 169	86.66 13	81.31 215	66.81 331	69.64 208	88.33 129	90.19 183	64.58 231	83.63 286	71.99 171	90.03 241	81.06 319

test20.03		73.75 251	74.59 237	71.22 293	81.11 285	51.12 304	70.15 311	72.10 295	70.42 201	80.28 248	91.50 142	64.21 232	74.72 314	46.96 323	94.58 151	87.82 231

cascas		76.29 228	74.81 234	80.72 205	84.47 251	62.94 200	73.89 298	87.34 189	55.94 293	75.16 284	76.53 328	63.97 233	91.16 172	65.00 219	90.97 226	88.06 225

TAMVS		78.08 207	76.36 220	83.23 166	90.62 126	72.87 116	79.08 255	80.01 250	61.72 270	81.35 227	86.92 232	63.96 234	88.78 226	50.61 305	93.01 188	88.04 226

GBi-Net		82.02 174	82.07 170	81.85 189	86.38 218	61.05 233	86.83 96	88.27 177	72.43 172	86.00 161	95.64 33	63.78 235	90.68 187	65.95 213	93.34 177	93.82 105

test1		82.02 174	82.07 170	81.85 189	86.38 218	61.05 233	86.83 96	88.27 177	72.43 172	86.00 161	95.64 33	63.78 235	90.68 187	65.95 213	93.34 177	93.82 105

FMVSNet2		81.31 180	81.61 176	80.41 206	86.38 218	58.75 251	83.93 143	86.58 204	72.43 172	87.65 135	92.98 105	63.78 235	90.22 198	66.86 206	93.92 162	92.27 153

USDC		76.63 223	76.73 214	76.34 258	83.46 267	57.20 258	80.02 232	88.04 181	52.14 314	83.65 199	91.25 146	63.24 238	86.65 257	54.66 290	94.11 157	85.17 254

new-patchmatchnet		70.10 281	73.37 253	60.29 327	81.23 284	16.95 355	59.54 334	74.62 274	62.93 259	80.97 229	87.93 220	62.83 239	71.90 318	55.24 285	95.01 135	92.00 159

K. test v3		85.14 110	84.73 120	86.37 91	91.13 117	69.63 147	85.45 118	76.68 264	84.06 34	92.44 52	96.99 8	62.03 240	94.65 54	80.58 93	93.24 183	94.83 80

lessismore_v0				85.95 103	91.10 118	70.99 140		70.91 307		91.79 62	94.42 67	61.76 241	92.93 132	79.52 110	93.03 187	93.93 102

1314		73.22 257	72.56 262	75.20 266	80.41 292	57.84 253	81.64 210	85.36 215	51.68 317	73.10 296	76.65 327	61.45 242	85.19 272	63.54 227	79.21 323	82.59 290

CANet_DTU		77.81 209	77.05 210	80.09 209	81.37 282	59.90 242	83.26 166	88.29 176	69.16 213	67.83 320	83.72 274	60.93 243	89.47 208	69.22 190	89.70 244	90.88 185

pmmvs-eth3d		78.42 205	77.04 211	82.57 179	87.44 194	74.41 108	80.86 225	79.67 251	55.68 294	84.69 179	90.31 182	60.91 244	85.42 270	62.20 233	91.59 208	87.88 230

UnsupCasMVSNet_eth		71.63 271	72.30 264	69.62 297	76.47 318	52.70 288	70.03 312	80.97 245	59.18 281	79.36 254	88.21 215	60.50 245	69.12 325	58.33 268	77.62 327	87.04 237

Patchmatch-test1		72.75 261	72.61 260	73.19 282	81.62 280	55.86 266	78.89 257	71.37 302	61.73 269	74.93 285	82.15 299	60.46 246	81.80 292	59.68 251	82.63 309	81.92 303

jason		77.42 212	75.75 226	82.43 182	87.10 209	69.27 149	77.99 264	81.94 239	51.47 319	77.84 261	85.07 259	60.32 247	89.00 219	70.74 177	89.27 248	89.03 217
jason: jason.
1112_ss		74.82 243	73.74 242	78.04 235	89.57 141	60.04 241	76.49 278	87.09 199	54.31 300	73.66 293	79.80 315	60.25 248	86.76 255	58.37 266	84.15 298	87.32 235

HY-MVS	64.64 18	73.03 259	72.47 263	74.71 269	83.36 268	54.19 276	82.14 199	81.96 237	56.76 292	69.57 313	86.21 243	60.03 249	84.83 277	49.58 311	82.65 307	85.11 255

Anonymous20231206		71.38 273	71.88 267	69.88 294	86.31 222	54.37 275	70.39 310	74.62 274	52.57 310	76.73 268	88.76 203	59.94 250	72.06 317	44.35 328	93.23 184	83.23 285

IterMVS		76.91 217	76.34 221	78.64 226	80.91 287	64.03 182	76.30 280	79.03 252	64.88 250	83.11 206	89.16 198	59.90 251	84.46 279	68.61 197	85.15 290	87.42 233
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
YYNet1		70.06 282	70.44 275	68.90 300	73.76 336	53.42 283	58.99 338	67.20 327	58.42 283	87.10 143	85.39 254	59.82 252	67.32 329	59.79 250	83.50 301	85.96 246

MDA-MVSNet_test_wron		70.05 283	70.44 275	68.88 301	73.84 335	53.47 281	58.93 339	67.28 326	58.43 282	87.09 144	85.40 253	59.80 253	67.25 330	59.66 252	83.54 300	85.92 248

PMMVS		61.65 311	60.38 316	65.47 314	65.40 352	69.26 150	63.97 327	61.73 340	36.80 349	60.11 340	68.43 340	59.42 254	66.35 335	48.97 313	78.57 324	60.81 344

CDS-MVSNet		77.32 213	75.40 229	83.06 169	89.00 150	72.48 121	77.90 266	82.17 236	60.81 275	78.94 257	83.49 277	59.30 255	88.76 227	54.64 291	92.37 197	87.93 229
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
UnsupCasMVSNet_bld		69.21 289	69.68 281	67.82 306	79.42 295	51.15 303	67.82 319	75.79 266	54.15 301	77.47 265	85.36 256	59.26 256	70.64 320	48.46 315	79.35 321	81.66 307

WTY-MVS		67.91 293	68.35 288	66.58 310	80.82 289	48.12 326	65.96 324	72.60 291	53.67 304	71.20 306	81.68 305	58.97 257	69.06 326	48.57 314	81.67 311	82.55 291

MVSFormer		82.23 171	81.57 177	84.19 142	85.54 239	69.26 150	91.98 25	90.08 149	71.54 193	76.23 273	85.07 259	58.69 258	94.27 61	86.26 27	88.77 252	89.03 217

lupinMVS		76.37 227	74.46 238	82.09 183	85.54 239	69.26 150	76.79 273	80.77 246	50.68 326	76.23 273	82.82 287	58.69 258	88.94 220	69.85 183	88.77 252	88.07 224

Test_1112_low_res		73.90 250	73.08 255	76.35 257	90.35 130	55.95 264	73.40 302	86.17 207	50.70 325	73.14 295	85.94 245	58.31 260	85.90 266	56.51 276	83.22 302	87.20 236

sss		66.92 296	67.26 294	65.90 311	77.23 312	51.10 305	64.79 325	71.72 301	52.12 315	70.13 311	80.18 312	57.96 261	65.36 339	50.21 306	81.01 316	81.25 314

MVP-Stereo		75.81 233	73.51 252	82.71 175	89.35 143	73.62 111	80.06 230	85.20 217	60.30 278	73.96 291	87.94 219	57.89 262	89.45 210	52.02 299	74.87 332	85.06 256
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
PAPM		71.77 269	70.06 280	76.92 250	86.39 217	53.97 277	76.62 276	86.62 203	53.44 305	63.97 334	84.73 265	57.79 263	92.34 144	39.65 335	81.33 314	84.45 265

semantic-postprocess				84.34 137	83.93 263	69.66 146		81.09 244	72.43 172	86.47 158	90.19 183	57.56 264	93.15 124	77.45 127	86.39 279	90.22 202

LFMVS		80.15 197	80.56 187	78.89 221	89.19 147	55.93 265	85.22 121	73.78 280	82.96 42	84.28 192	92.72 114	57.38 265	90.07 205	63.80 226	95.75 115	90.68 191

Vis-MVSNet (Re-imp)		77.82 208	77.79 207	77.92 237	88.82 153	51.29 302	83.28 165	71.97 296	74.04 149	82.23 213	89.78 190	57.38 265	89.41 211	57.22 273	95.41 121	93.05 126

CHOSEN 1792x2688		72.45 264	70.56 274	78.13 233	90.02 139	63.08 197	68.72 314	83.16 229	42.99 344	75.92 276	85.46 251	57.22 267	85.18 273	49.87 309	81.67 311	86.14 245

pmmvs4		74.92 241	72.98 257	80.73 204	84.95 245	71.71 135	76.23 281	77.59 257	52.83 308	77.73 264	86.38 236	56.35 268	84.97 274	57.72 272	87.05 272	85.51 252

MVE	40.22 23	51.82 326	50.47 328	55.87 331	62.66 354	51.91 293	31.61 350	39.28 355	40.65 345	50.76 351	74.98 333	56.24 269	44.67 353	33.94 345	64.11 347	71.04 337
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
testmv		70.47 278	70.70 273	69.77 296	86.22 226	53.89 279	67.32 320	71.91 297	63.32 255	78.16 260	89.47 195	56.12 270	73.10 315	36.43 341	87.33 269	82.33 296

N_pmnet		70.20 279	68.80 286	74.38 271	80.91 287	84.81 35	59.12 337	76.45 265	55.06 297	75.31 283	82.36 298	55.74 271	54.82 349	47.02 321	87.24 271	83.52 278

MS-PatchMatch		70.93 275	70.22 278	73.06 284	81.85 279	62.50 211	73.82 299	77.90 255	52.44 311	75.92 276	81.27 307	55.67 272	81.75 293	55.37 284	77.70 326	74.94 329

DSMNet-mixed		60.98 316	61.61 313	59.09 330	72.88 342	45.05 334	74.70 292	46.61 354	26.20 350	65.34 328	90.32 181	55.46 273	63.12 344	41.72 332	81.30 315	69.09 339

pmmvs5		70.73 276	70.07 279	72.72 286	77.03 315	52.73 287	74.14 295	75.65 269	50.36 328	72.17 301	85.37 255	55.42 274	80.67 297	52.86 297	87.59 268	84.77 262

CMPMVS	59.41 20	75.12 238	73.57 245	79.77 211	75.84 322	67.22 162	81.21 218	82.18 235	50.78 324	76.50 269	87.66 224	55.20 275	82.99 288	62.17 234	90.64 237	89.09 216
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
MIMVSNet		71.09 274	71.59 269	69.57 298	87.23 203	50.07 316	78.91 256	71.83 298	60.20 279	71.26 305	91.76 136	55.08 276	76.09 308	41.06 333	87.02 273	82.54 292

no-one		71.52 272	70.43 277	74.81 268	78.45 306	63.41 189	57.73 340	77.03 260	51.46 320	77.17 267	90.33 180	54.96 277	80.35 299	47.41 319	99.29 2	80.68 321

PVSNet_0	51.08 22	56.10 321	54.97 325	59.48 329	75.12 331	53.28 284	55.16 341	61.89 338	44.30 341	59.16 343	62.48 348	54.22 278	65.91 337	35.40 343	47.01 349	59.25 346

EPNet_dtu		72.87 260	71.33 272	77.49 246	77.72 310	60.55 238	82.35 190	75.79 266	66.49 232	58.39 347	81.06 309	53.68 279	85.98 264	53.55 294	92.97 189	85.95 247
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
PMMVS2		55.64 323	59.27 320	44.74 338	64.30 353	12.32 356	40.60 348	49.79 353	53.19 306	65.06 332	84.81 263	53.60 280	49.76 351	32.68 347	89.41 245	72.15 334

HyFIR lowres test		75.12 238	72.66 259	82.50 180	91.44 109	65.19 174	72.47 303	87.31 190	46.79 335	80.29 247	84.30 269	52.70 281	92.10 150	51.88 304	86.73 274	90.22 202

testus		62.33 308	63.03 308	60.20 328	78.78 302	40.74 341	59.14 335	69.80 312	49.26 331	71.41 304	74.72 334	52.33 282	63.52 341	29.84 348	82.01 310	76.36 326

FMVSNet3		78.80 203	78.55 203	79.57 217	82.89 273	56.89 261	81.76 207	85.77 211	69.04 215	86.00 161	90.44 178	51.75 283	90.09 204	65.95 213	93.34 177	91.72 165

PVSNet	58.17 21	66.41 299	65.63 301	68.75 302	81.96 277	49.88 323	62.19 330	72.51 293	51.03 322	68.04 318	75.34 332	50.84 284	74.77 312	45.82 326	82.96 303	81.60 308

GA-MVS		75.83 232	74.61 235	79.48 219	81.87 278	59.25 247	73.42 301	82.88 231	68.68 217	79.75 250	81.80 303	50.62 285	89.46 209	66.85 207	85.64 284	89.72 208

FPMVS		72.29 267	72.00 266	73.14 283	88.63 158	85.00 32	74.65 293	67.39 325	71.94 189	77.80 263	87.66 224	50.48 286	75.83 310	49.95 307	79.51 319	58.58 347

MVS-HIRNet		61.16 314	62.92 309	55.87 331	79.09 299	35.34 347	71.83 305	57.98 347	46.56 336	59.05 344	91.14 153	49.95 287	76.43 307	38.74 338	71.92 338	55.84 348

CVMVSNet		72.62 262	71.41 271	76.28 259	83.25 269	60.34 239	83.50 160	79.02 253	37.77 348	76.33 271	85.10 257	49.60 288	87.41 239	70.54 180	77.54 328	81.08 317

LP		69.42 287	68.30 289	72.77 285	71.48 347	56.84 262	73.66 300	74.84 272	63.52 254	70.95 309	83.35 280	49.55 289	77.15 306	57.13 274	70.21 340	84.33 267

RPMNet		76.06 229	75.79 224	76.85 252	79.58 293	62.64 205	82.58 183	71.75 300	74.80 142	75.72 278	92.59 115	48.69 290	84.07 281	73.48 156	82.91 305	83.85 274

tpmrst		66.28 300	66.69 297	65.05 316	72.82 343	39.33 343	78.20 262	70.69 308	53.16 307	67.88 319	80.36 311	48.18 291	74.75 313	58.13 269	70.79 339	81.08 317

CR-MVSNet		74.00 249	73.04 256	76.85 252	79.58 293	62.64 205	82.58 183	76.90 261	50.50 327	75.72 278	92.38 120	48.07 292	84.07 281	68.72 196	82.91 305	83.85 274

Patchmtry		76.56 225	77.46 208	73.83 277	79.37 297	46.60 329	82.41 188	76.90 261	73.81 152	85.56 169	92.38 120	48.07 292	83.98 283	63.36 229	95.31 126	90.92 183

ADS-MVSNet2		65.87 302	63.64 307	72.55 288	73.16 340	56.92 260	67.10 321	74.81 273	49.74 329	66.04 325	82.97 284	46.71 294	77.26 304	42.29 330	69.96 342	83.46 279

ADS-MVSNet		61.90 309	62.19 311	61.03 326	73.16 340	36.42 346	67.10 321	61.75 339	49.74 329	66.04 325	82.97 284	46.71 294	63.21 343	42.29 330	69.96 342	83.46 279

PatchmatchNet		69.71 285	68.83 285	72.33 289	77.66 311	53.60 280	79.29 249	69.99 311	57.66 286	72.53 298	82.93 286	46.45 296	80.08 301	60.91 245	72.09 337	83.31 284
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
thres200		72.34 266	71.55 270	74.70 270	83.48 266	51.60 299	75.02 290	73.71 281	70.14 205	78.56 258	80.57 310	46.20 297	88.20 233	46.99 322	89.29 246	84.32 268

sam_mvs1												46.11 298				83.88 272

tfpn200view9		74.86 242	74.23 240	76.74 254	86.24 224	52.12 291	79.24 251	73.87 278	73.34 158	81.82 220	84.60 267	46.02 299	88.80 222	51.98 300	90.99 222	89.31 212

thres400		75.14 236	74.23 240	77.86 238	86.24 224	52.12 291	79.24 251	73.87 278	73.34 158	81.82 220	84.60 267	46.02 299	88.80 222	51.98 300	90.99 222	92.66 136

test1235678		65.57 303	65.73 300	65.06 315	82.84 274	50.90 306	62.90 328	69.26 313	57.17 290	72.36 299	83.04 282	46.02 299	70.10 321	32.79 346	85.24 289	74.19 331

patchmatchnet-post											81.71 304	45.93 302	87.01 242

sam_mvs												45.92 303

Patchmatch-RL test		74.48 244	73.68 243	76.89 251	84.83 247	66.54 168	72.29 304	69.16 315	57.70 285	86.76 148	86.33 240	45.79 304	82.59 290	69.63 185	90.65 236	81.54 309

tfpn111		76.03 230	75.53 228	77.53 245	87.27 200	51.88 294	81.07 220	73.26 285	75.68 132	83.25 203	86.37 237	45.54 305	89.38 213	55.07 287	92.26 200	91.34 176

conf200view11		75.62 234	75.05 232	77.34 247	87.27 200	51.88 294	81.07 220	73.26 285	75.68 132	83.25 203	86.37 237	45.54 305	88.80 222	51.98 300	90.99 222	91.34 176

thres100view900		75.45 235	75.05 232	76.66 255	87.27 200	51.88 294	81.07 220	73.26 285	75.68 132	83.25 203	86.37 237	45.54 305	88.80 222	51.98 300	90.99 222	89.31 212

thres600view7		75.97 231	75.35 231	77.85 239	87.01 211	51.84 298	80.45 228	73.26 285	75.20 140	83.10 207	86.31 242	45.54 305	89.05 218	55.03 288	92.24 201	92.66 136

tpm cat1		66.76 297	65.21 302	71.42 292	77.09 314	50.62 313	78.01 263	73.68 282	44.89 340	68.64 314	79.00 318	45.51 309	82.42 291	49.91 308	70.15 341	81.23 316

test_post											3.10 354	45.43 310	77.22 305

MDTV_nov1_ep13			68.29 290		78.03 309	43.87 337	74.12 296	72.22 294	52.17 312	67.02 322	85.54 247	45.36 311	80.85 296	55.73 279	84.42 297

tpmvs		70.16 280	69.56 282	71.96 291	74.71 334	48.13 325	79.63 236	75.45 270	65.02 249	70.26 310	81.88 302	45.34 312	85.68 268	58.34 267	75.39 331	82.08 299

MDTV_nov1_ep13_2view						27.60 351	70.76 308		46.47 337	61.27 337		45.20 313		49.18 312		83.75 276

test_post1							78.85 258				3.13 353	45.19 314	80.13 300	58.11 270

view600		76.79 218	76.54 215	77.56 241	87.91 177	50.77 308	81.92 202	71.35 303	77.38 107	84.62 180	88.40 209	45.18 315	89.26 214	58.58 262	93.49 172	92.66 136

view800		76.79 218	76.54 215	77.56 241	87.91 177	50.77 308	81.92 202	71.35 303	77.38 107	84.62 180	88.40 209	45.18 315	89.26 214	58.58 262	93.49 172	92.66 136

conf0.05thres1000		76.79 218	76.54 215	77.56 241	87.91 177	50.77 308	81.92 202	71.35 303	77.38 107	84.62 180	88.40 209	45.18 315	89.26 214	58.58 262	93.49 172	92.66 136

tfpn		76.79 218	76.54 215	77.56 241	87.91 177	50.77 308	81.92 202	71.35 303	77.38 107	84.62 180	88.40 209	45.18 315	89.26 214	58.58 262	93.49 172	92.66 136

CostFormer		69.98 284	68.68 287	73.87 276	77.14 313	50.72 312	79.26 250	74.51 276	51.94 316	70.97 308	84.75 264	45.16 319	87.49 238	55.16 286	79.23 322	83.40 281

Patchmatch-test		65.91 301	67.38 292	61.48 324	75.51 326	43.21 339	68.84 313	63.79 335	62.48 264	72.80 297	83.42 279	44.89 320	59.52 346	48.27 317	86.45 277	81.70 305

EU-MVSNet		75.12 238	74.43 239	77.18 248	83.11 272	59.48 245	85.71 116	82.43 234	39.76 347	85.64 167	88.76 203	44.71 321	87.88 235	73.86 152	85.88 282	84.16 271

PatchT		70.52 277	72.76 258	63.79 318	79.38 296	33.53 348	77.63 268	65.37 333	73.61 153	71.77 302	92.79 113	44.38 322	75.65 311	64.53 224	85.37 286	82.18 298

test-LLR		67.21 295	66.74 296	68.63 303	76.45 319	55.21 271	67.89 316	67.14 328	62.43 266	65.08 330	72.39 336	43.41 323	69.37 322	61.00 242	84.89 292	81.31 312

test0.0.03 1		64.66 305	64.36 304	65.57 313	75.03 332	46.89 328	64.69 326	61.58 341	62.43 266	71.18 307	77.54 322	43.41 323	68.47 327	40.75 334	82.65 307	81.35 311

MVSTER		77.09 215	75.70 227	81.25 198	75.27 330	61.08 232	77.49 271	85.07 220	60.78 276	86.55 152	88.68 205	43.14 325	90.25 195	73.69 153	90.67 234	92.42 145

tpm		67.95 292	68.08 291	67.55 307	78.74 303	43.53 338	75.60 285	67.10 330	54.92 298	72.23 300	88.10 216	42.87 326	75.97 309	52.21 298	80.95 317	83.15 286

PatchFormer-LS_test		67.91 293	66.49 299	72.17 290	75.29 329	51.85 297	75.68 283	73.62 283	57.23 289	68.64 314	68.13 344	42.19 327	82.76 289	64.06 225	73.51 334	81.89 304

tpm2		68.45 291	66.83 295	73.30 280	78.93 301	48.50 324	79.76 235	71.76 299	47.50 334	69.92 312	83.60 275	42.07 328	88.40 230	48.44 316	79.51 319	83.01 288

conf0.01		74.17 247	73.53 246	76.08 261	86.13 229	50.06 317	79.45 242	68.54 316	72.01 182	80.76 234	82.50 292	41.39 329	86.83 247	59.66 252	91.36 210	91.34 176

conf0.002		74.17 247	73.53 246	76.08 261	86.13 229	50.06 317	79.45 242	68.54 316	72.01 182	80.76 234	82.50 292	41.39 329	86.83 247	59.66 252	91.36 210	91.34 176

thresconf0.02		73.65 252	73.53 246	73.98 272	86.13 229	50.06 317	79.45 242	68.54 316	72.01 182	80.76 234	82.50 292	41.39 329	86.83 247	59.66 252	91.36 210	85.06 256

tfpn_n400		73.65 252	73.53 246	73.98 272	86.13 229	50.06 317	79.45 242	68.54 316	72.01 182	80.76 234	82.50 292	41.39 329	86.83 247	59.66 252	91.36 210	85.06 256

tfpnconf		73.65 252	73.53 246	73.98 272	86.13 229	50.06 317	79.45 242	68.54 316	72.01 182	80.76 234	82.50 292	41.39 329	86.83 247	59.66 252	91.36 210	85.06 256

tfpnview11		73.65 252	73.53 246	73.98 272	86.13 229	50.06 317	79.45 242	68.54 316	72.01 182	80.76 234	82.50 292	41.39 329	86.83 247	59.66 252	91.36 210	85.06 256

tpmp4_e23		69.43 286	67.33 293	75.72 264	78.53 305	52.75 286	82.13 200	74.91 271	49.23 332	66.37 323	84.17 271	41.28 335	88.67 228	49.73 310	79.63 318	85.75 250

EMVS		61.10 315	60.81 315	61.99 321	65.96 351	55.86 266	53.10 345	58.97 344	67.06 227	56.89 349	63.33 347	40.98 336	67.03 331	54.79 289	86.18 281	63.08 342

new_pmnet		55.69 322	57.66 321	49.76 335	75.47 327	30.59 349	59.56 333	51.45 352	43.62 343	62.49 335	75.48 330	40.96 337	49.15 352	37.39 340	72.52 335	69.55 338

E-PMN		61.59 312	61.62 312	61.49 323	66.81 349	55.40 269	53.77 344	60.34 342	66.80 231	58.90 345	65.50 346	40.48 338	66.12 336	55.72 280	86.25 280	62.95 343

tfpn_ndepth		72.54 263	72.30 264	73.24 281	84.81 248	51.42 300	79.24 251	70.49 309	69.26 211	78.48 259	79.80 315	40.16 339	86.77 254	58.08 271	90.43 239	81.53 310

tfpn1000		73.63 256	73.58 244	73.79 278	85.46 241	50.31 314	79.99 233	68.18 322	72.33 176	80.66 240	83.05 281	39.80 340	86.74 256	60.96 244	91.78 206	84.32 268

EPMVS		62.47 306	62.63 310	62.01 320	70.63 348	38.74 344	74.76 291	52.86 350	53.91 303	67.71 321	80.01 313	39.40 341	66.60 334	55.54 283	68.81 346	80.68 321

tmp_tt		20.25 330	24.50 331	7.49 342	4.47 356	8.70 357	34.17 349	25.16 357	1.00 352	32.43 353	18.49 351	39.37 342	9.21 355	21.64 351	43.75 350	4.57 352

FMVSNet5		72.10 268	71.69 268	73.32 279	81.57 281	53.02 285	76.77 274	78.37 254	63.31 256	76.37 270	91.85 131	36.68 343	78.98 302	47.87 318	92.45 196	87.95 228

dp		60.70 317	60.29 318	61.92 322	72.04 345	38.67 345	70.83 307	64.08 334	51.28 321	60.75 338	77.28 324	36.59 344	71.58 319	47.41 319	62.34 348	75.52 328

CHOSEN 280x420		59.08 318	56.52 323	66.76 309	76.51 317	64.39 180	49.62 347	59.00 343	43.86 342	55.66 350	68.41 342	35.55 345	68.21 328	43.25 329	76.78 330	67.69 340

IB-MVS	62.13 19	71.64 270	68.97 284	79.66 216	80.80 290	62.26 219	73.94 297	76.90 261	63.27 257	68.63 316	76.79 326	33.83 346	91.84 155	59.28 260	87.26 270	84.88 261
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
JIA-IIPM		69.41 288	66.64 298	77.70 240	73.19 339	71.24 138	75.67 284	65.56 332	70.42 201	65.18 329	92.97 106	33.64 347	83.06 287	53.52 295	69.61 344	78.79 323

DWT-MVSNet_test		66.43 298	64.37 303	72.63 287	74.86 333	50.86 307	76.52 277	72.74 290	54.06 302	65.50 327	68.30 343	32.13 348	84.84 276	61.63 239	73.59 333	82.19 297

DeepMVS_CX				24.13 341	32.95 355	29.49 350		21.63 358	12.07 351	37.95 352	45.07 350	30.84 349	19.21 354	17.94 352	33.06 352	23.69 351

gg-mvs-nofinetune		68.96 290	69.11 283	68.52 305	76.12 321	45.32 331	83.59 153	55.88 348	86.68 20	64.62 333	97.01 7	30.36 350	83.97 284	44.78 327	82.94 304	76.26 327

GG-mvs-BLEND				67.16 308	73.36 337	46.54 330	84.15 136	55.04 349		58.64 346	61.95 349	29.93 351	83.87 285	38.71 339	76.92 329	71.07 336

test12356		54.91 324	57.14 322	48.22 337	75.83 323	17.47 354	52.31 346	69.20 314	51.66 318	60.11 340	75.40 331	29.77 352	62.62 345	27.64 349	72.37 336	64.59 341

test-mter		65.00 304	63.79 305	68.63 303	76.45 319	55.21 271	67.89 316	67.14 328	50.98 323	65.08 330	72.39 336	28.27 353	69.37 322	61.00 242	84.89 292	81.31 312

TESTMET0.1,1		61.29 313	60.32 317	64.19 317	72.06 344	51.30 301	67.89 316	62.09 337	45.27 339	60.65 339	69.01 339	27.93 354	64.74 340	56.31 277	81.65 313	76.53 325

pmmvs3		62.47 306	60.02 319	69.80 295	71.58 346	64.00 183	70.52 309	58.44 345	39.77 346	66.05 324	75.84 329	27.10 355	72.28 316	46.15 324	84.77 296	73.11 333

testpf		58.55 319	61.58 314	49.48 336	66.03 350	40.05 342	74.40 294	58.07 346	64.72 251	59.36 342	72.67 335	22.76 356	66.92 332	67.07 205	69.15 345	41.46 350

test2356		56.69 320	55.15 324	61.32 325	73.20 338	44.11 336	54.95 342	62.52 336	48.75 333	62.45 336	68.42 341	21.10 357	65.67 338	26.86 350	78.08 325	74.19 331

1111		61.71 310	63.77 306	55.55 333	78.05 307	25.74 352	60.62 331	67.52 323	66.09 235	74.68 286	86.50 234	16.00 358	59.22 347	38.79 336	85.65 283	81.70 305

.test1245		48.02 327	54.41 326	28.84 340	78.05 307	25.74 352	60.62 331	67.52 323	66.09 235	74.68 286	86.50 234	16.00 358	59.22 347	38.79 336	1.47 353	1.55 354

PNet_i23d		52.13 325	51.24 327	54.79 334	75.56 324	45.26 332	54.54 343	52.55 351	66.95 228	57.19 348	65.82 345	13.15 360	63.40 342	36.39 342	39.04 351	55.71 349

test123		6.27 333	8.08 334	0.84 343	1.11 358	0.57 358	62.90 328	0.82 359	0.54 353	1.07 355	2.75 356	1.26 361	0.30 356	1.04 353	1.26 355	1.66 353

testmvs		5.91 334	7.65 335	0.72 344	1.20 357	0.37 359	59.14 335	0.67 360	0.49 354	1.11 354	2.76 355	0.94 362	0.24 357	1.02 354	1.47 353	1.55 354

sosnet-low-res		0.00 335	0.00 336	0.00 345	0.00 359	0.00 360	0.00 351	0.00 361	0.00 355	0.00 356	0.00 357	0.00 363	0.00 358	0.00 355	0.00 356	0.00 356

sosnet		0.00 335	0.00 336	0.00 345	0.00 359	0.00 360	0.00 351	0.00 361	0.00 355	0.00 356	0.00 357	0.00 363	0.00 358	0.00 355	0.00 356	0.00 356

uncertanet		0.00 335	0.00 336	0.00 345	0.00 359	0.00 360	0.00 351	0.00 361	0.00 355	0.00 356	0.00 357	0.00 363	0.00 358	0.00 355	0.00 356	0.00 356

Regformer		0.00 335	0.00 336	0.00 345	0.00 359	0.00 360	0.00 351	0.00 361	0.00 355	0.00 356	0.00 357	0.00 363	0.00 358	0.00 355	0.00 356	0.00 356

ab-mvs-re		6.65 331	8.87 332	0.00 345	0.00 359	0.00 360	0.00 351	0.00 361	0.00 355	0.00 356	79.80 315	0.00 363	0.00 358	0.00 355	0.00 356	0.00 356

uanet		0.00 335	0.00 336	0.00 345	0.00 359	0.00 360	0.00 351	0.00 361	0.00 355	0.00 356	0.00 357	0.00 363	0.00 358	0.00 355	0.00 356	0.00 356

GSMVS																83.88 272

test_part3							89.63 50		84.39 28		93.43 97		96.26 4	82.18 74

test_part2					93.86 47	77.77 79				92.84 42

MTGPA								91.81 90

MTMP								33.14 356

gm-plane-assit					75.42 328	44.97 335			52.17 312		72.36 338		87.90 234	54.10 292

test9_res														80.83 89	96.45 87	90.57 194

agg_prior2														79.68 106	96.16 96	90.22 202

agg_prior					91.58 103	77.69 81		90.30 140		84.32 190			93.18 120

test_prior4						78.97 71	84.59 129

test_prior				86.32 92	90.59 127	71.99 129		92.85 61					94.17 68			92.80 131

旧先验2							81.73 208		56.88 291	86.54 157			84.90 275	72.81 164

新几何2							81.72 209

无先验							82.81 177	85.62 213	58.09 284				91.41 168	67.95 202		84.48 264

原ACMM2							82.26 195

testdata2													86.43 260	63.52 228

testdata1							79.62 237		73.95 151

plane_prior7					93.45 53	77.31 87

plane_prior5								93.61 32					95.22 39	80.78 90	95.83 112	94.46 86

plane_prior4											92.95 107

plane_prior3						76.85 92			77.79 101	86.55 152

plane_prior2							89.45 58		79.44 79

plane_prior1					92.83 71

plane_prior						76.42 97	87.15 91		75.94 129						95.03 134

n2								0.00 361

nn								0.00 361

door-mid								74.45 277

test11								91.46 103

door								72.57 292

HQP5-MVS						70.66 141

HQP-NCC					91.19 113		84.77 124		73.30 160	80.55 244

ACMP_Plane					91.19 113		84.77 124		73.30 160	80.55 244

BP-MVS														77.30 129

HQP4-MVS										80.56 243			94.61 55			93.56 116

HQP3-MVS								92.68 67							94.47 153

NP-MVS					91.95 93	74.55 107					90.17 185

ACMMP++_ref															95.74 116

ACMMP++															97.35 60

High-resolution multi-view benchmark