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 8	85.07 36	99.27 3	99.54 1

XVG-OURS-SEG-HR		89.59 47	89.37 50	90.28 42	94.47 39	85.95 21	86.84 93	93.91 25	80.07 70	86.75 147	93.26 97	93.64 2	90.93 176	84.60 44	90.75 221	93.97 100

Anonymous20231211		90.14 31	91.88 12	84.92 117	94.75 35	64.47 177	90.13 38	92.97 56	91.68 3	95.35 12	98.79 2	93.19 3	91.76 157	71.67 170	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 41	87.54 18	98.02 39	93.71 110

ACMH+	77.89 11	90.73 25	91.50 19	88.44 62	93.00 63	76.26 97	89.65 49	95.55 3	87.72 18	93.89 27	94.94 50	91.62 5	93.44 107	78.35 115	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 46	93.67 30	94.82 54	91.18 6	95.52 25	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 46	93.67 30	94.82 54	91.18 6	95.52 25	85.36 34	98.73 8	95.23 72

PMVS	80.48 6	90.08 33	90.66 36	88.34 64	96.71 2	92.97 2	90.31 36	89.57 160	88.51 15	90.11 80	95.12 46	90.98 8	88.92 218	77.55 123	97.07 65	83.13 274
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 50	93.35 37	79.20 80	93.83 28	93.60 95	90.81 9	92.96 128	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 51	91.14 28	83.96 145	92.50 75	70.36 141	89.55 51	93.84 27	81.89 54	94.70 16	95.44 39	90.69 10	88.31 228	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 48	95.13 45	90.65 11	95.34 32	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 59	94.05 23	79.03 84	92.87 40	93.74 93	90.60 12	95.21 39	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 41	91.77 61	93.94 90	90.55 13	95.73 17	88.50 8	98.23 31	95.33 69

APD-MVS_3200maxsize		92.05 7	92.24 6	91.48 20	93.02 62	85.17 31	92.47 21	95.05 8	87.65 19	93.21 36	94.39 71	90.09 14	95.08 42	86.67 24	97.60 54	94.18 94

COLMAP_ROB	83.01 3	91.97 8	91.95 8	92.04 10	93.68 48	86.15 18	93.37 8	95.10 7	90.28 9	92.11 53	95.03 47	89.75 15	94.93 46	79.95 101	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		38.83 317	41.11 318	32.01 328	93.13 60	0.00 348	0.00 339	91.38 111	0.00 343	0.00 344	0.00 345	89.24 16	0.00 346	0.00 343	96.24 92	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 45	92.39 52	94.14 79	89.15 17	95.62 19	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 64	78.04 77	92.84 12	94.14 21	83.33 35	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 30	77.65 101	91.97 58	94.89 51	88.38 19	95.45 28	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 73	94.51 11	75.79 129	92.94 38	94.96 49	88.36 20	95.01 44	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 43	81.99 51	91.47 65	93.96 86	88.35 21	95.56 22	87.74 11	97.74 48	92.85 129

#test#		90.49 30	90.31 41	91.02 29	95.43 25	84.66 39	90.65 34	93.29 43	77.00 116	91.47 65	93.96 86	88.35 21	95.56 22	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 36	83.16 37	91.06 70	94.00 83	88.26 23	95.71 18	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 35	82.82 42	92.60 47	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 26	80.32 67	91.74 62	94.41 68	88.17 25	95.98 5	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 66	86.07 32	98.48 19	97.22 26

OPM-MVS		89.80 43	89.97 42	89.27 54	94.76 34	79.86 64	86.76 97	92.78 64	78.78 87	92.51 48	93.64 94	88.13 27	93.84 80	84.83 41	97.55 55	94.10 97

pmmvs6		86.52 87	88.06 64	81.90 185	92.22 85	62.28 216	84.66 126	89.15 165	83.54 34	89.85 91	97.32 4	88.08 28	86.80 243	70.43 179	97.30 60	96.62 37

mvs_tets		89.78 44	89.27 51	91.30 24	93.51 50	84.79 36	89.89 44	90.63 126	70.00 195	94.55 18	96.67 11	87.94 29	93.59 91	84.27 48	95.97 103	95.52 65

region2R		91.44 17	91.30 26	91.87 16	95.75 17	85.90 23	92.63 17	93.30 41	81.91 53	90.88 75	94.21 76	87.75 30	95.87 10	87.60 16	97.71 50	93.83 103

wuyk23d		75.13 234	79.30 197	62.63 308	75.56 312	75.18 102	80.89 220	73.10 286	75.06 137	94.76 15	95.32 40	87.73 31	52.85 338	34.16 332	97.11 64	59.85 331

wuykxyi23d		88.46 61	88.80 59	87.44 79	90.96 118	93.03 1	85.85 111	81.96 236	74.58 140	98.58 2	97.29 5	87.73 31	87.31 236	82.84 67	99.41 1	81.99 287

mPP-MVS		91.69 10	91.47 20	92.37 5	96.04 11	88.48 8	92.72 14	92.60 70	83.09 38	91.54 64	94.25 75	87.67 33	95.51 27	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 45	81.99 51	91.40 67	94.17 78	87.51 34	95.87 10	87.74 11	97.76 47	93.99 99

PS-CasMVS		90.06 34	91.92 9	84.47 131	96.56 6	58.83 248	89.04 60	92.74 65	91.40 5	96.12 4	96.06 24	87.23 35	95.57 21	79.42 109	98.74 7	99.00 2

PEN-MVS		90.03 35	91.88 12	84.48 130	96.57 5	58.88 247	88.95 61	93.19 46	91.62 4	96.01 6	96.16 22	87.02 36	95.60 20	78.69 113	98.72 10	98.97 3

DTE-MVSNet		89.98 37	91.91 11	84.21 139	96.51 7	57.84 251	88.93 63	92.84 62	91.92 2	96.16 3	96.23 20	86.95 37	95.99 4	79.05 110	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 54	82.59 44	88.52 122	94.37 72	86.74 38	95.41 30	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 62	91.81 89	84.07 30	92.00 56	94.40 69	86.63 39	95.28 35	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 89	84.07 30	92.00 56	94.40 69	86.63 39	95.28 35	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 38	85.07 25	89.99 84	94.03 82	86.57 41	95.80 13	87.35 19	97.62 52	94.20 92

X-MVStestdata		85.04 112	82.70 159	92.08 8	95.64 21	86.25 16	92.64 15	93.33 38	85.07 25	89.99 84	16.05 340	86.57 41	95.80 13	87.35 19	97.62 52	94.20 92

canonicalmvs		85.50 106	86.14 97	83.58 157	87.97 172	67.13 161	87.55 81	94.32 12	73.44 152	88.47 123	87.54 224	86.45 43	91.06 173	75.76 136	93.76 165	92.54 144

TranMVSNet+NR-MVSNet		87.86 67	88.76 60	85.18 114	94.02 43	64.13 179	84.38 131	91.29 113	84.88 27	92.06 55	93.84 92	86.45 43	93.73 81	73.22 156	98.66 12	97.69 12

test_0402		88.65 58	89.58 49	85.88 105	92.55 73	72.22 123	84.01 137	89.44 162	88.63 14	94.38 21	95.77 28	86.38 45	93.59 91	79.84 102	95.21 126	91.82 163

APD-MVS		89.54 48	89.63 48	89.26 55	92.57 72	81.34 56	90.19 37	93.08 50	80.87 62	91.13 69	93.19 98	86.22 46	95.97 6	82.23 73	97.18 63	90.45 192
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
SD-MVS		88.96 55	89.88 43	86.22 95	91.63 97	77.07 87	89.82 45	93.77 29	78.90 85	92.88 39	92.29 122	86.11 47	90.22 196	86.24 30	97.24 61	91.36 173

jajsoiax		89.41 49	88.81 58	91.19 28	93.38 54	84.72 37	89.70 46	90.29 142	69.27 199	94.39 20	96.38 15	86.02 48	93.52 101	83.96 51	95.92 106	95.34 68

nrg030		87.85 68	88.49 61	85.91 103	90.07 133	69.73 143	87.86 76	94.20 17	74.04 145	92.70 45	94.66 58	85.88 49	91.50 161	79.72 103	97.32 59	96.50 40

DeepC-MVS	82.31 4	89.15 54	89.08 52	89.37 53	93.64 49	79.07 70	88.54 69	94.20 17	73.53 150	89.71 95	94.82 54	85.09 50	95.77 15	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 39	90.60 37	88.07 68	88.69 153	72.01 125	91.35 30	92.64 68	82.22 48	95.97 8	96.31 16	84.82 51	93.98 73	88.59 4	94.83 140	98.23 8

LTVRE_ROB	86.10 1	93.04 3	93.44 2	91.82 19	93.73 47	85.72 28	96.79 1	95.51 4	88.86 13	95.63 10	96.99 8	84.81 52	93.16 120	91.10 1	97.53 56	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 39	90.60 37	88.07 68	88.69 153	72.01 125	91.35 30	92.64 68	82.22 48	95.98 7	96.31 16	84.80 53	93.98 73	88.59 4	94.83 140	98.23 8

DP-MVS		88.60 60	89.01 53	87.36 80	91.30 108	77.50 82	87.55 81	92.97 56	87.95 17	89.62 101	92.87 107	84.56 54	93.89 77	77.65 122	96.62 77	90.70 184

LS3D		90.60 28	90.34 40	91.38 23	89.03 147	84.23 42	93.58 4	94.68 10	90.65 7	90.33 79	93.95 89	84.50 55	95.37 31	80.87 86	95.50 118	94.53 85

anonymousdsp		89.73 45	88.88 56	92.27 7	89.82 138	86.67 12	90.51 35	90.20 147	69.87 196	95.06 14	96.14 23	84.28 56	93.07 127	87.68 13	96.34 87	97.09 30

OMC-MVS		88.19 63	87.52 72	90.19 44	91.94 93	81.68 53	87.49 83	93.17 47	76.02 125	88.64 119	91.22 145	84.24 57	93.37 110	77.97 121	97.03 66	95.52 65

XVG-OURS		89.18 53	88.83 57	90.23 43	94.28 41	86.11 20	85.91 109	93.60 33	80.16 69	89.13 112	93.44 96	83.82 58	90.98 174	83.86 54	95.30 125	93.60 114

XVG-ACMP-BASELINE		89.98 37	89.84 44	90.41 39	94.91 33	84.50 41	89.49 55	93.98 24	79.68 73	92.09 54	93.89 91	83.80 59	93.10 123	82.67 69	98.04 36	93.64 112

CDPH-MVS		86.17 96	85.54 106	88.05 71	92.25 83	75.45 100	83.85 143	92.01 82	65.91 225	86.19 157	91.75 135	83.77 60	94.98 45	77.43 126	96.71 75	93.73 109

Effi-MVS+		83.90 148	84.01 144	83.57 158	87.22 200	65.61 171	86.55 105	92.40 73	78.64 90	81.34 224	84.18 266	83.65 61	92.93 130	74.22 144	87.87 252	92.17 157

MVS_111021_HR		84.63 120	84.34 140	85.49 111	90.18 131	75.86 99	79.23 242	87.13 195	73.35 153	85.56 167	89.34 195	83.60 62	90.50 190	76.64 132	94.05 157	90.09 201

UA-Net		91.49 14	91.53 18	91.39 22	94.98 31	82.95 50	93.52 5	92.79 63	88.22 16	88.53 121	97.64 3	83.45 63	94.55 57	86.02 33	98.60 14	96.67 36

AdaColmap		83.66 151	83.69 149	83.57 158	90.05 135	72.26 122	86.29 108	90.00 151	78.19 96	81.65 219	87.16 227	83.40 64	94.24 62	61.69 235	94.76 145	84.21 259

LCM-MVSNet-Re		83.48 155	85.06 112	78.75 223	85.94 226	55.75 266	80.05 227	94.27 13	76.47 120	96.09 5	94.54 63	83.31 65	89.75 205	59.95 246	94.89 136	90.75 183

Regformer-2		86.74 83	86.08 98	88.73 59	84.18 248	79.20 69	83.52 155	89.33 163	83.33 35	89.92 90	85.07 255	83.23 66	93.16 120	83.39 57	92.72 191	93.83 103

TransMVSNet (Re)		84.02 144	85.74 102	78.85 221	91.00 117	55.20 271	82.29 190	87.26 190	79.65 74	88.38 126	95.52 37	83.00 67	86.88 241	67.97 199	96.60 78	94.45 88

CNVR-MVS		87.81 69	87.68 71	88.21 65	92.87 66	77.30 86	85.25 118	91.23 115	77.31 111	87.07 143	91.47 141	82.94 68	94.71 51	84.67 43	96.27 91	92.62 142

DeepPCF-MVS	81.24 5	87.28 73	86.21 96	90.49 38	91.48 105	84.90 34	83.41 160	92.38 75	70.25 193	89.35 109	90.68 170	82.85 69	94.57 55	79.55 105	95.95 104	92.00 159

v7n		90.13 32	90.96 32	87.65 75	91.95 91	71.06 137	89.99 41	93.05 51	86.53 21	94.29 22	96.27 19	82.69 70	94.08 69	86.25 29	97.63 51	97.82 11

AllTest		87.97 66	87.40 76	89.68 47	91.59 98	83.40 45	89.50 54	95.44 5	79.47 75	88.00 129	93.03 101	82.66 71	91.47 162	70.81 172	96.14 96	94.16 95

TestCases				89.68 47	91.59 98	83.40 45		95.44 5	79.47 75	88.00 129	93.03 101	82.66 71	91.47 162	70.81 172	96.14 96	94.16 95

v748		88.91 57	89.82 45	86.19 99	90.06 134	68.53 153	88.81 65	91.48 98	84.36 28	94.19 24	95.98 25	82.52 73	92.67 137	84.30 47	96.67 76	97.37 20

RPSCF		88.00 65	86.93 84	91.22 27	90.08 132	89.30 6	89.68 48	91.11 118	79.26 79	89.68 96	94.81 57	82.44 74	87.74 233	76.54 133	88.74 242	96.61 38

ITE_SJBPF				90.11 45	90.72 123	84.97 33		90.30 139	81.56 57	90.02 83	91.20 148	82.40 75	90.81 181	73.58 152	94.66 147	94.56 81

Fast-Effi-MVS+		81.04 184	80.57 185	82.46 180	87.50 191	63.22 194	78.37 249	89.63 158	68.01 208	81.87 214	82.08 290	82.31 76	92.65 138	67.10 202	88.30 248	91.51 171

Regformer-1		86.00 97	85.50 107	87.49 77	84.18 248	76.90 89	83.52 155	87.94 183	82.18 50	89.19 110	85.07 255	82.28 77	91.89 152	82.40 71	92.72 191	93.69 111

agg_prior1		85.72 104	85.20 111	87.28 81	91.58 101	77.69 79	83.69 150	90.30 139	66.29 221	84.32 188	91.07 156	82.13 78	93.18 118	81.02 83	96.36 86	90.98 174

Regformer-4		86.41 88	85.71 103	88.52 61	84.27 244	77.57 81	84.07 135	88.00 181	82.82 42	89.84 92	85.48 245	82.06 79	92.77 134	83.83 55	91.04 210	95.22 74

CLD-MVS		83.18 159	82.64 161	84.79 121	89.05 146	67.82 159	77.93 253	92.52 71	68.33 206	85.07 170	81.54 295	82.06 79	92.96 128	69.35 185	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 80	79.70 66	83.94 139	90.32 135	65.41 233	84.49 184	90.97 159	82.03 81	93.63 86

segment_acmp												81.94 82

train_agg		85.98 100	85.28 110	88.07 68	92.34 80	79.70 66	83.94 139	90.32 135	65.79 226	84.49 184	90.97 159	81.93 83	93.63 86	81.21 80	96.54 80	90.88 179

test_8					92.09 87	78.87 72	83.82 144	90.31 137	65.79 226	84.36 187	90.96 161	81.93 83	93.44 107

test_prior3		86.31 91	86.31 93	86.32 91	90.59 125	71.99 127	83.37 161	92.85 60	75.43 133	84.58 182	91.57 137	81.92 85	94.17 66	79.54 106	96.97 67	92.80 131

test_prior2							83.37 161		75.43 133	84.58 182	91.57 137	81.92 85		79.54 106	96.97 67

CP-MVSNet		89.27 52	90.91 34	84.37 134	96.34 8	58.61 250	88.66 68	92.06 81	90.78 6	95.67 9	95.17 44	81.80 87	95.54 24	79.00 111	98.69 11	98.95 4

MVS_111021_LR		84.28 134	83.76 148	85.83 107	89.23 144	83.07 48	80.99 219	83.56 227	72.71 166	86.07 158	89.07 198	81.75 88	86.19 250	77.11 129	93.36 174	88.24 216

test_djsdf		89.62 46	89.01 53	91.45 21	92.36 79	82.98 49	91.98 25	90.08 148	71.54 182	94.28 23	96.54 13	81.57 89	94.27 59	86.26 27	96.49 83	97.09 30

cdsmvs_eth3d_5k		20.81 318	27.75 319	0.00 334	0.00 347	0.00 348	0.00 339	85.44 213	0.00 343	0.00 344	82.82 282	81.46 90	0.00 346	0.00 343	0.00 344	0.00 342

WR-MVS_H		89.91 42	91.31 25	85.71 108	96.32 9	62.39 210	89.54 53	93.31 40	90.21 10	95.57 11	95.66 31	81.42 91	95.90 9	80.94 85	98.80 4	98.84 5

CPTT-MVS		89.39 50	88.98 55	90.63 36	95.09 29	86.95 10	92.09 23	92.30 76	79.74 72	87.50 136	92.38 118	81.42 91	93.28 115	83.07 62	97.24 61	91.67 166

pm-mvs1		83.69 150	84.95 116	79.91 209	90.04 136	59.66 241	82.43 185	87.44 187	75.52 132	87.85 131	95.26 42	81.25 93	85.65 257	68.74 193	96.04 101	94.42 89

agg_prior3		85.76 103	84.95 116	88.16 66	92.43 77	79.92 62	83.98 138	90.03 150	65.11 235	83.66 196	90.64 174	81.00 94	93.67 83	81.21 80	96.54 80	90.88 179

NCCC		87.36 71	86.87 85	88.83 58	92.32 82	78.84 73	86.58 104	91.09 119	78.77 88	84.85 175	90.89 163	80.85 95	95.29 33	81.14 82	95.32 122	92.34 150

TAPA-MVS	77.73 12	85.71 105	84.83 118	88.37 63	88.78 152	79.72 65	87.15 89	93.50 34	69.17 200	85.80 163	89.56 192	80.76 96	92.13 146	73.21 160	95.51 117	93.25 122
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
Fast-Effi-MVS+-dtu		82.54 166	81.41 177	85.90 104	85.60 227	76.53 94	83.07 169	89.62 159	73.02 163	79.11 245	83.51 272	80.74 97	90.24 195	68.76 192	89.29 234	90.94 176

VPA-MVSNet		83.47 156	84.73 119	79.69 213	90.29 129	57.52 254	81.30 215	88.69 169	76.29 121	87.58 134	94.44 66	80.60 98	87.20 237	66.60 208	96.82 73	94.34 91

Regformer-3		85.06 111	84.67 124	86.22 95	84.27 244	73.43 111	84.07 135	85.26 215	80.77 63	88.62 120	85.48 245	80.56 99	90.39 192	81.99 75	91.04 210	94.85 79

HPM-MVS++		88.93 56	88.45 62	90.38 40	94.92 32	85.85 25	89.70 46	91.27 114	78.20 95	86.69 148	92.28 123	80.36 100	95.06 43	86.17 31	96.49 83	90.22 196

ANet_high		83.17 160	85.68 104	75.65 260	81.24 271	45.26 320	79.94 229	92.91 58	83.83 33	91.33 68	96.88 10	80.25 101	85.92 253	68.89 191	95.89 107	95.76 53

EI-MVSNet-Vis-set		85.12 110	84.53 131	86.88 82	84.01 250	72.76 115	83.91 142	85.18 217	80.44 64	88.75 117	85.49 244	80.08 102	91.92 150	82.02 74	90.85 219	95.97 49

DeepC-MVS_fast	80.27 8	86.23 94	85.65 105	87.96 72	91.30 108	76.92 88	87.19 87	91.99 83	70.56 189	84.96 171	90.69 169	80.01 103	95.14 40	78.37 114	95.78 112	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		85.04 112	84.44 133	86.85 83	83.87 253	72.52 118	83.82 144	85.15 218	80.27 68	88.75 117	85.45 248	79.95 104	91.90 151	81.92 76	90.80 220	96.13 43

MCST-MVS		84.36 130	83.93 147	85.63 109	91.59 98	71.58 134	83.52 155	92.13 79	61.82 256	83.96 192	89.75 189	79.93 105	93.46 106	78.33 116	94.34 153	91.87 162

TSAR-MVS + MP.		88.14 64	87.82 68	89.09 57	95.72 19	76.74 91	92.49 20	91.19 117	67.85 213	86.63 149	94.84 53	79.58 106	95.96 7	87.62 14	94.50 150	94.56 81

test12				86.57 86	90.74 122	72.63 116		90.69 124		82.76 206		79.20 107	94.80 49		95.32 122	92.27 153

CSCG		86.26 92	86.47 91	85.60 110	90.87 120	74.26 107	87.98 74	91.85 87	80.35 66	89.54 107	88.01 215	79.09 108	92.13 146	75.51 137	95.06 131	90.41 193

Test By Simon												79.09 108

PHI-MVS		86.38 89	85.81 101	88.08 67	88.44 161	77.34 84	89.35 58	93.05 51	73.15 161	84.76 176	87.70 221	78.87 110	94.18 64	80.67 90	96.29 88	92.73 133

EG-PatchMatch MVS		84.08 142	84.11 142	83.98 144	92.22 85	72.61 117	82.20 196	87.02 199	72.63 167	88.86 114	91.02 157	78.52 111	91.11 171	73.41 155	91.09 208	88.21 217

Effi-MVS+-dtu		85.82 102	83.38 151	93.14 3	87.13 202	91.15 3	87.70 79	88.42 172	74.57 141	83.56 198	85.65 242	78.49 112	94.21 63	72.04 167	92.88 188	94.05 98

mvs-test1		84.55 123	82.12 168	91.84 17	87.13 202	89.54 5	85.05 121	88.42 172	74.57 141	80.60 230	82.98 278	78.49 112	93.98 73	72.04 167	89.77 231	92.00 159

Vis-MVSNet		86.86 79	86.58 89	87.72 73	92.09 87	77.43 83	87.35 84	92.09 80	78.87 86	84.27 191	94.05 81	78.35 114	93.65 84	80.54 92	91.58 205	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 80	87.06 79	86.17 100	92.86 68	67.02 162	82.55 183	91.56 94	83.08 39	90.92 72	91.82 132	78.25 115	93.99 71	74.16 145	98.35 24	97.49 16

MSLP-MVS++		85.00 114	86.03 99	81.90 185	91.84 95	71.56 135	86.75 98	93.02 55	75.95 126	87.12 140	89.39 194	77.98 116	89.40 210	77.46 124	94.78 142	84.75 253

API-MVS		82.28 169	82.61 162	81.30 195	86.29 219	69.79 142	88.71 67	87.67 185	78.42 93	82.15 211	84.15 268	77.98 116	91.59 160	65.39 216	92.75 190	82.51 281

DP-MVS Recon		84.05 143	83.22 153	86.52 88	91.73 96	75.27 101	83.23 167	92.40 73	72.04 176	82.04 212	88.33 211	77.91 118	93.95 76	66.17 210	95.12 129	90.34 195

UniMVSNet (Re)		86.87 78	86.98 82	86.55 87	93.11 61	68.48 154	83.80 146	92.87 59	80.37 65	89.61 103	91.81 133	77.72 119	94.18 64	75.00 142	98.53 17	96.99 34

PCF-MVS	74.62 15	82.15 171	80.92 184	85.84 106	89.43 140	72.30 121	80.53 223	91.82 88	57.36 275	87.81 132	89.92 186	77.67 120	93.63 86	58.69 252	95.08 130	91.58 169
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
NR-MVSNet		86.00 97	86.22 95	85.34 112	93.24 57	64.56 176	82.21 194	90.46 130	80.99 61	88.42 124	91.97 126	77.56 121	93.85 78	72.46 164	98.65 13	97.61 13

3Dnovator+	83.92 2	89.97 39	89.66 47	90.92 32	91.27 110	81.66 54	91.25 32	94.13 22	88.89 12	88.83 116	94.26 74	77.55 122	95.86 12	84.88 39	95.87 108	95.24 71

MVS_Test		82.47 167	83.22 153	80.22 207	82.62 263	57.75 253	82.54 184	91.96 85	71.16 185	82.89 205	92.52 117	77.41 123	90.50 190	80.04 100	87.84 253	92.40 147

xiu_mvs_v2_base		77.19 213	76.75 212	78.52 227	87.01 207	61.30 227	75.55 275	87.12 197	61.24 262	74.45 276	78.79 307	77.20 124	90.93 176	64.62 221	84.80 283	83.32 270

DU-MVS		86.80 81	86.99 81	86.21 97	93.24 57	67.02 162	83.16 168	92.21 77	81.73 55	90.92 72	91.97 126	77.20 124	93.99 71	74.16 145	98.35 24	97.61 13

Baseline_NR-MVSNet		84.00 145	85.90 100	78.29 230	91.47 106	53.44 280	82.29 190	87.00 200	79.06 83	89.55 105	95.72 30	77.20 124	86.14 251	72.30 165	98.51 18	95.28 70

TinyColmap		81.25 182	82.34 167	77.99 235	85.33 231	60.68 235	82.32 189	88.33 174	71.26 184	86.97 145	92.22 125	77.10 127	86.98 240	62.37 230	95.17 128	86.31 238

F-COLMAP		84.97 115	83.42 150	89.63 49	92.39 78	83.40 45	88.83 64	91.92 86	73.19 160	80.18 238	89.15 197	77.04 128	93.28 115	65.82 215	92.28 197	92.21 156

114514_t		83.10 161	82.54 164	84.77 122	92.90 65	69.10 152	86.65 102	90.62 127	54.66 287	81.46 221	90.81 166	76.98 129	94.38 58	72.62 163	96.18 93	90.82 182

xiu_mvs_v1_base_debu		80.84 187	80.14 192	82.93 171	88.31 163	71.73 130	79.53 233	87.17 192	65.43 230	79.59 240	82.73 284	76.94 130	90.14 199	73.22 156	88.33 244	86.90 233

xiu_mvs_v1_base		80.84 187	80.14 192	82.93 171	88.31 163	71.73 130	79.53 233	87.17 192	65.43 230	79.59 240	82.73 284	76.94 130	90.14 199	73.22 156	88.33 244	86.90 233

xiu_mvs_v1_base_debi		80.84 187	80.14 192	82.93 171	88.31 163	71.73 130	79.53 233	87.17 192	65.43 230	79.59 240	82.73 284	76.94 130	90.14 199	73.22 156	88.33 244	86.90 233

pcd_1.5k_mvsjas		6.41 321	8.55 322	0.00 334	0.00 347	0.00 348	0.00 339	0.00 350	0.00 343	0.00 344	0.00 345	76.94 130	0.00 346	0.00 343	0.00 344	0.00 342

PS-MVSNAJss		88.31 62	87.90 66	89.56 51	93.31 55	77.96 78	87.94 75	91.97 84	70.73 188	94.19 24	96.67 11	76.94 130	94.57 55	83.07 62	96.28 89	96.15 42

PS-MVSNAJ		77.04 215	76.53 218	78.56 226	87.09 206	61.40 225	75.26 276	87.13 195	61.25 261	74.38 278	77.22 313	76.94 130	90.94 175	64.63 220	84.83 282	83.35 269

MIMVSNet1		83.63 152	84.59 129	80.74 202	94.06 42	62.77 201	82.72 179	84.53 225	77.57 103	90.34 78	95.92 26	76.88 136	85.83 255	61.88 233	97.42 57	93.62 113

原ACMM1				84.60 127	92.81 70	74.01 108		91.50 96	62.59 250	82.73 207	90.67 171	76.53 137	94.25 61	69.24 186	95.69 115	85.55 245

MSDG		80.06 197	79.99 195	80.25 206	83.91 252	68.04 157	77.51 258	89.19 164	77.65 101	81.94 213	83.45 274	76.37 138	86.31 249	63.31 228	86.59 264	86.41 236

Gipuma		84.44 126	86.33 92	78.78 222	84.20 247	73.57 110	89.55 51	90.44 131	84.24 29	84.38 186	94.89 51	76.35 139	80.40 286	76.14 134	96.80 74	82.36 282
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
testing_2		84.36 130	84.64 128	83.50 163	86.74 211	63.97 182	84.56 128	90.31 137	66.22 222	91.62 63	94.55 61	75.88 140	91.95 149	77.02 131	94.89 136	94.56 81

XXY-MVS		74.44 243	76.19 221	69.21 288	84.61 238	52.43 288	71.70 294	77.18 258	60.73 265	80.60 230	90.96 161	75.44 141	69.35 312	56.13 268	88.33 244	85.86 243

FMVSNet1		84.55 123	85.45 108	81.85 188	90.27 130	61.05 231	86.83 94	88.27 176	78.57 91	89.66 97	95.64 33	75.43 142	90.68 185	69.09 189	95.33 121	93.82 105

CANet		83.79 149	82.85 158	86.63 85	86.17 224	72.21 124	83.76 148	91.43 105	77.24 112	74.39 277	87.45 225	75.36 143	95.42 29	77.03 130	92.83 189	92.25 155

ab-mvs		79.67 198	80.56 186	76.99 246	88.48 160	56.93 257	84.70 125	86.06 207	68.95 204	80.78 229	93.08 100	75.30 144	84.62 266	56.78 265	90.90 217	89.43 205

v13		87.31 72	88.10 63	84.94 116	88.84 150	63.75 183	87.85 77	91.47 101	79.12 81	93.72 29	95.82 27	75.20 145	93.58 94	84.76 42	96.16 94	97.48 17

v11		86.96 76	87.78 69	84.51 128	88.50 159	62.60 206	87.21 86	91.63 93	78.08 98	93.40 34	95.56 36	75.07 146	93.57 95	84.46 46	96.08 99	97.36 21

DELS-MVS		81.44 178	81.25 179	82.03 183	84.27 244	62.87 200	76.47 267	92.49 72	70.97 187	81.64 220	83.83 269	75.03 147	92.70 135	74.29 143	92.22 200	90.51 191
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		78.84 201	78.10 204	81.07 199	85.17 232	60.22 238	82.21 194	90.57 128	62.51 251	75.32 270	84.61 262	74.99 148	92.30 143	59.48 250	88.04 250	90.68 185

v12		87.15 75	87.91 65	84.84 119	88.69 153	63.52 186	87.58 80	91.46 102	78.74 89	93.57 32	95.66 31	74.94 149	93.57 95	84.50 45	96.08 99	97.43 18

CNLPA		83.55 154	83.10 156	84.90 118	89.34 142	83.87 43	84.54 129	88.77 167	79.09 82	83.54 199	88.66 204	74.87 150	81.73 282	66.84 206	92.29 196	89.11 209

HQP_MVS		87.75 70	87.43 75	88.70 60	93.45 51	76.42 95	89.45 56	93.61 31	79.44 77	86.55 150	92.95 105	74.84 151	95.22 37	80.78 88	95.83 110	94.46 86

plane_prior6					92.61 71	76.54 92						74.84 151

FC-MVSNet-test		85.93 101	87.05 80	82.58 176	92.25 83	56.44 261	85.75 112	93.09 49	77.33 110	91.94 59	94.65 59	74.78 153	93.41 109	75.11 140	98.58 15	97.88 10

VDD-MVS		84.23 136	84.58 130	83.20 166	91.17 114	65.16 173	83.25 165	84.97 223	79.79 71	87.18 139	94.27 73	74.77 154	90.89 179	69.24 186	96.54 80	93.55 118

BH-untuned		80.96 185	80.99 182	80.84 201	88.55 157	68.23 155	80.33 225	88.46 171	72.79 165	86.55 150	86.76 231	74.72 155	91.77 156	61.79 234	88.99 238	82.52 280

VPNet		80.25 193	81.68 173	75.94 258	92.46 76	47.98 315	76.70 263	81.67 240	73.45 151	84.87 174	92.82 108	74.66 156	86.51 246	61.66 236	96.85 70	93.33 119

V9		86.96 76	87.70 70	84.74 123	88.52 158	63.27 192	87.31 85	91.45 104	78.28 94	93.43 33	95.45 38	74.59 157	93.57 95	84.23 49	96.01 102	97.38 19

tfpnnormal		81.79 176	82.95 157	78.31 229	88.93 149	55.40 267	80.83 222	82.85 231	76.81 117	85.90 162	94.14 79	74.58 158	86.51 246	66.82 207	95.68 116	93.01 127

MVS_0304		84.88 116	83.96 146	87.64 76	87.43 193	74.83 103	84.18 133	93.30 41	77.48 104	77.39 254	88.46 206	74.53 159	95.74 16	78.09 120	94.75 146	92.36 149

V14		86.75 82	87.46 73	84.62 126	88.35 162	63.00 197	87.02 91	91.42 107	77.78 100	93.27 35	95.23 43	74.22 160	93.56 98	83.95 52	95.93 105	97.31 22

V42		83.47 156	83.37 152	83.75 150	83.16 259	63.33 190	81.31 213	90.23 146	69.51 198	90.91 74	90.81 166	74.16 161	92.29 144	80.06 99	90.22 228	95.62 60

3Dnovator	80.37 7	84.80 118	84.71 122	85.06 115	86.36 217	74.71 104	88.77 66	90.00 151	75.65 131	84.96 171	93.17 99	74.06 162	91.19 169	78.28 117	91.09 208	89.29 208

v10		86.54 86	87.10 78	84.84 119	88.16 168	63.28 191	86.64 103	92.20 78	75.42 135	92.81 42	94.50 64	74.05 163	94.06 70	83.88 53	96.28 89	97.17 28

v7		84.81 117	85.00 114	84.23 138	88.15 169	63.27 192	83.79 147	91.39 110	71.10 186	90.07 81	91.28 143	74.04 164	93.63 86	81.48 79	93.67 168	95.79 52

v15		86.56 85	87.25 77	84.51 128	88.15 169	62.72 202	86.72 101	91.40 109	77.38 105	93.11 37	95.00 48	73.93 165	93.55 99	83.67 56	95.86 109	97.26 23

旧先验1					91.97 90	71.77 129		81.78 239			91.84 130	73.92 166			93.65 169	83.61 264

mvs_anonymous		78.13 205	78.76 200	76.23 257	79.24 286	50.31 309	78.69 247	84.82 224	61.60 260	83.09 204	92.82 108	73.89 167	87.01 238	68.33 197	86.41 266	91.37 172

MAR-MVS		80.24 194	78.74 201	84.73 124	86.87 210	78.18 76	85.75 112	87.81 184	65.67 229	77.84 249	78.50 308	73.79 168	90.53 189	61.59 238	90.87 218	85.49 247
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 90	86.95 83	84.44 132	88.00 171	62.62 205	86.74 99	91.48 98	77.17 113	92.74 43	94.56 60	73.74 169	93.53 100	83.27 59	94.87 139	97.18 27

v16		86.24 93	86.85 86	84.43 133	87.96 173	62.59 207	86.73 100	91.48 98	77.17 113	92.67 46	94.55 61	73.63 170	93.52 101	83.26 60	94.16 154	97.17 28

test_normal		81.23 183	81.16 180	81.43 194	84.77 237	61.99 218	81.46 212	86.95 201	63.16 246	87.22 138	89.63 190	73.62 171	91.65 159	72.92 161	90.70 222	90.65 187

DI_MVS_plusplus_test		81.27 181	81.26 178	81.29 196	84.98 233	61.65 223	81.98 199	87.25 191	63.56 241	87.56 135	89.60 191	73.62 171	91.83 154	72.20 166	90.59 227	90.38 194

VDDNet		84.35 132	85.39 109	81.25 197	95.13 28	59.32 244	85.42 117	81.11 242	86.41 22	87.41 137	96.21 21	73.61 173	90.61 188	66.33 209	96.85 70	93.81 108

FIs		85.35 108	86.27 94	82.60 175	91.86 94	57.31 255	85.10 120	93.05 51	75.83 128	91.02 71	93.97 84	73.57 174	92.91 132	73.97 148	98.02 39	97.58 15

v1144		84.54 125	84.72 121	84.00 143	87.67 187	62.55 208	82.97 171	90.93 122	70.32 192	89.80 93	90.99 158	73.50 175	93.48 105	81.69 78	94.65 148	95.97 49

PAPM_NR		83.23 158	83.19 155	83.33 164	90.90 119	65.98 168	88.19 72	90.78 123	78.13 97	80.87 228	87.92 219	73.49 176	92.42 141	70.07 180	88.40 243	91.60 168

v8		86.22 95	86.83 87	84.36 135	87.82 179	62.35 211	86.42 106	91.33 112	76.78 118	92.73 44	94.48 65	73.41 177	93.72 82	83.10 61	95.41 119	97.01 33

EI-MVSNet		82.61 164	82.42 166	83.20 166	83.25 257	63.66 184	83.50 158	85.07 219	76.06 123	86.55 150	85.10 253	73.41 177	90.25 193	78.15 119	90.67 223	95.68 58

IterMVS-LS		84.73 119	84.98 115	83.96 145	87.35 194	63.66 184	83.25 165	89.88 153	76.06 123	89.62 101	92.37 121	73.40 179	92.52 140	78.16 118	94.77 144	95.69 57
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v6		84.43 127	84.66 125	83.75 150	87.81 180	62.34 212	83.59 151	90.26 145	72.33 172	89.94 87	91.19 149	73.30 180	93.29 112	80.26 96	93.26 178	95.62 60

v144192		84.24 135	84.41 134	83.71 154	87.59 190	61.57 224	82.95 172	91.03 120	67.82 214	89.80 93	90.49 175	73.28 181	93.51 104	81.88 77	94.89 136	96.04 47

v1neww		84.43 127	84.66 125	83.75 150	87.81 180	62.34 212	83.59 151	90.27 143	72.33 172	89.93 88	91.22 145	73.28 181	93.29 112	80.25 97	93.25 179	95.62 60

v7new		84.43 127	84.66 125	83.75 150	87.81 180	62.34 212	83.59 151	90.27 143	72.33 172	89.93 88	91.22 145	73.28 181	93.29 112	80.25 97	93.25 179	95.62 60

v18		85.99 99	86.55 90	84.30 137	87.73 185	62.29 215	86.40 107	91.49 97	76.64 119	92.40 51	94.20 77	73.28 181	93.52 101	82.87 65	93.99 158	97.09 30

BH-RMVSNet		80.53 190	80.22 191	81.49 193	87.19 201	66.21 167	77.79 255	86.23 205	74.21 144	83.69 194	88.50 205	73.25 185	90.75 182	63.18 229	87.90 251	87.52 226

PLC	73.85 16	82.09 172	80.31 189	87.45 78	90.86 121	80.29 60	85.88 110	90.65 125	68.17 207	76.32 260	86.33 236	73.12 186	92.61 139	61.40 239	90.02 230	89.44 204
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
diffmvs		79.20 200	79.04 198	79.69 213	78.64 292	58.90 246	81.79 204	87.61 186	65.07 236	73.65 282	89.80 187	73.10 187	87.79 232	75.02 141	86.63 263	92.38 148

OurMVSNet-221017-0		90.01 36	89.74 46	90.83 33	93.16 59	80.37 59	91.91 27	93.11 48	81.10 60	95.32 13	97.24 6	72.94 188	94.85 48	85.07 36	97.78 46	97.26 23

WR-MVS		83.56 153	84.40 135	81.06 200	93.43 53	54.88 272	78.67 248	85.02 221	81.24 59	90.74 76	91.56 139	72.85 189	91.08 172	68.00 198	98.04 36	97.23 25

VNet		79.31 199	80.27 190	76.44 253	87.92 174	53.95 276	75.58 274	84.35 226	74.39 143	82.23 209	90.72 168	72.84 190	84.39 268	60.38 245	93.98 159	90.97 175

v1		84.16 138	84.38 136	83.52 160	87.33 195	61.71 219	82.79 176	89.73 156	71.89 181	89.64 98	91.11 154	72.72 191	93.10 123	80.40 93	93.79 164	95.75 54

QAPM		82.59 165	82.59 163	82.58 176	86.44 212	66.69 165	89.94 43	90.36 134	67.97 210	84.94 173	92.58 115	72.71 192	92.18 145	70.63 177	87.73 254	88.85 214

v1192		84.57 122	84.69 123	84.21 139	87.75 184	62.88 199	83.02 170	91.43 105	69.08 202	89.98 86	90.89 163	72.70 193	93.62 90	82.41 70	94.97 134	96.13 43

v1141		84.16 138	84.38 136	83.52 160	87.32 196	61.70 221	82.79 176	89.74 154	71.90 179	89.64 98	91.12 152	72.68 194	93.10 123	80.39 95	93.80 163	95.75 54

divwei89l23v2f112		84.16 138	84.38 136	83.52 160	87.32 196	61.70 221	82.79 176	89.74 154	71.90 179	89.64 98	91.12 152	72.68 194	93.10 123	80.40 93	93.81 162	95.75 54

OpenMVS	76.72 13	81.98 175	82.00 171	81.93 184	84.42 242	68.22 156	88.50 70	89.48 161	66.92 217	81.80 218	91.86 128	72.59 196	90.16 198	71.19 171	91.25 206	87.40 228

TSAR-MVS + GP.		83.95 146	82.69 160	87.72 73	89.27 143	81.45 55	83.72 149	81.58 241	74.73 139	85.66 164	86.06 240	72.56 197	92.69 136	75.44 138	95.21 126	89.01 213

alignmvs		83.94 147	83.98 145	83.80 147	87.80 183	67.88 158	84.54 129	91.42 107	73.27 159	88.41 125	87.96 216	72.33 198	90.83 180	76.02 135	94.11 155	92.69 135

HQP2-MVS												72.10 199

HQP-MVS		84.61 121	84.06 143	86.27 93	91.19 111	70.66 139	84.77 122	92.68 66	73.30 156	80.55 233	90.17 183	72.10 199	94.61 53	77.30 127	94.47 151	93.56 116

testgi		72.36 254	74.61 232	65.59 301	80.56 279	42.82 328	68.29 303	73.35 283	66.87 218	81.84 215	89.93 185	72.08 201	66.92 320	46.05 313	92.54 193	87.01 232

v1921920		84.23 136	84.37 139	83.79 148	87.64 189	61.71 219	82.91 173	91.20 116	67.94 211	90.06 82	90.34 177	72.04 202	93.59 91	82.32 72	94.91 135	96.07 45

HSP-MVS		88.63 59	87.84 67	91.02 29	95.76 16	86.14 19	92.75 13	91.01 121	78.43 92	89.16 111	92.25 124	72.03 203	96.36 2	88.21 9	90.93 216	90.55 190

LF4IMVS		82.75 163	81.93 172	85.19 113	82.08 264	80.15 61	85.53 115	88.76 168	68.01 208	85.58 166	87.75 220	71.80 204	86.85 242	74.02 147	93.87 161	88.58 215

v1240		84.30 133	84.51 132	83.65 155	87.65 188	61.26 228	82.85 174	91.54 95	67.94 211	90.68 77	90.65 172	71.71 205	93.64 85	82.84 67	94.78 142	96.07 45

ambc				82.98 169	90.55 127	64.86 174	88.20 71	89.15 165		89.40 108	93.96 86	71.67 206	91.38 168	78.83 112	96.55 79	92.71 134

1121		80.86 186	79.81 196	84.02 142	93.93 45	78.70 74	81.64 208	80.18 247	55.43 284	83.67 195	91.15 150	71.29 207	91.41 166	67.95 200	93.06 184	81.96 288

新几何1				82.95 170	93.96 44	78.56 75		80.24 246	55.45 283	83.93 193	91.08 155	71.19 208	88.33 227	65.84 214	93.07 183	81.95 289

v148		82.31 168	82.48 165	81.81 191	85.59 228	59.66 241	81.47 211	86.02 208	72.85 164	88.05 128	90.65 172	70.73 209	90.91 178	75.15 139	91.79 202	94.87 77

v2v482		84.09 141	84.24 141	83.62 156	87.13 202	61.40 225	82.71 180	89.71 157	72.19 175	89.55 105	91.41 142	70.70 210	93.20 117	81.02 83	93.76 165	96.25 41

UGNet		82.78 162	81.64 174	86.21 97	86.20 223	76.24 98	86.86 92	85.68 211	77.07 115	73.76 280	92.82 108	69.64 211	91.82 155	69.04 190	93.69 167	90.56 189
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		80.32 192	80.94 183	78.47 228	88.18 166	52.62 287	82.29 190	85.01 222	72.01 177	79.24 244	92.54 116	69.36 212	93.36 111	70.65 176	89.19 237	89.45 203

IS-MVSNet		86.66 84	86.82 88	86.17 100	92.05 89	66.87 164	91.21 33	88.64 170	86.30 23	89.60 104	92.59 113	69.22 213	94.91 47	73.89 149	97.89 44	96.72 35

PVSNet_BlendedMVS		78.80 202	77.84 205	81.65 192	84.43 240	63.41 187	79.49 236	90.44 131	61.70 259	75.43 268	87.07 229	69.11 214	91.44 164	60.68 243	92.24 198	90.11 200

PVSNet_Blended		76.49 225	75.40 227	79.76 211	84.43 240	63.41 187	75.14 277	90.44 131	57.36 275	75.43 268	78.30 309	69.11 214	91.44 164	60.68 243	87.70 255	84.42 256

BH-w/o		76.57 223	76.07 222	78.10 233	86.88 209	65.92 169	77.63 256	86.33 204	65.69 228	80.89 227	79.95 303	68.97 216	90.74 183	53.01 285	85.25 276	77.62 310

MVS		73.21 248	72.59 251	75.06 262	80.97 274	60.81 234	81.64 208	85.92 209	46.03 326	71.68 291	77.54 310	68.47 217	89.77 204	55.70 271	85.39 273	74.60 316

testdata				79.54 217	92.87 66	72.34 120		80.14 248	59.91 268	85.47 169	91.75 135	67.96 218	85.24 259	68.57 196	92.18 201	81.06 305

Test4		81.31 179	81.13 181	81.88 187	84.89 235	63.05 196	82.37 187	90.50 129	62.75 249	89.00 113	88.29 212	67.55 219	91.68 158	73.55 153	91.24 207	90.89 178

PVSNet_Blended_VisFu		81.55 177	80.49 188	84.70 125	91.58 101	73.24 113	84.21 132	91.67 92	62.86 248	80.94 226	87.16 227	67.27 220	92.87 133	69.82 182	88.94 239	87.99 221

MDA-MVSNet-bldmvs		77.47 210	76.90 211	79.16 219	79.03 288	64.59 175	66.58 311	75.67 267	73.15 161	88.86 114	88.99 199	66.94 221	81.23 283	64.71 218	88.22 249	91.64 167

test222					93.31 55	76.54 92	79.38 237	77.79 255	52.59 297	82.36 208	90.84 165	66.83 222			91.69 203	81.25 300

TR-MVS		76.77 221	75.79 223	79.72 212	86.10 225	65.79 170	77.14 260	83.02 229	65.20 234	81.40 222	82.10 289	66.30 223	90.73 184	55.57 272	85.27 275	82.65 276

OpenMVS_ROB	70.19 17	77.77 209	77.46 207	78.71 224	84.39 243	61.15 229	81.18 217	82.52 232	62.45 253	83.34 200	87.37 226	66.20 224	88.66 225	64.69 219	85.02 279	86.32 237

EPP-MVSNet		85.47 107	85.04 113	86.77 84	91.52 104	69.37 146	91.63 28	87.98 182	81.51 58	87.05 144	91.83 131	66.18 225	95.29 33	70.75 174	96.89 69	95.64 59

SixPastTwentyTwo		87.20 74	87.45 74	86.45 89	92.52 74	69.19 151	87.84 78	88.05 179	81.66 56	94.64 17	96.53 14	65.94 226	94.75 50	83.02 64	96.83 72	95.41 67

PatchMatch-RL		74.48 241	73.22 244	78.27 231	87.70 186	85.26 30	75.92 270	70.09 306	64.34 240	76.09 263	81.25 297	65.87 227	78.07 291	53.86 282	83.82 287	71.48 321

EPNet		80.37 191	78.41 203	86.23 94	76.75 304	73.28 112	87.18 88	77.45 257	76.24 122	68.14 305	88.93 200	65.41 228	93.85 78	69.47 184	96.12 98	91.55 170
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
PM-MVS		80.20 195	79.00 199	83.78 149	88.17 167	86.66 13	81.31 213	66.81 320	69.64 197	88.33 127	90.19 181	64.58 229	83.63 274	71.99 169	90.03 229	81.06 305

test20.03		73.75 246	74.59 234	71.22 282	81.11 273	51.12 299	70.15 299	72.10 292	70.42 190	80.28 237	91.50 140	64.21 230	74.72 302	46.96 311	94.58 149	87.82 225

cascas		76.29 227	74.81 231	80.72 204	84.47 239	62.94 198	73.89 286	87.34 188	55.94 281	75.16 272	76.53 316	63.97 231	91.16 170	65.00 217	90.97 215	88.06 219

TAMVS		78.08 206	76.36 219	83.23 165	90.62 124	72.87 114	79.08 243	80.01 249	61.72 258	81.35 223	86.92 230	63.96 232	88.78 222	50.61 293	93.01 186	88.04 220

GBi-Net		82.02 173	82.07 169	81.85 188	86.38 214	61.05 231	86.83 94	88.27 176	72.43 168	86.00 159	95.64 33	63.78 233	90.68 185	65.95 211	93.34 175	93.82 105

test1		82.02 173	82.07 169	81.85 188	86.38 214	61.05 231	86.83 94	88.27 176	72.43 168	86.00 159	95.64 33	63.78 233	90.68 185	65.95 211	93.34 175	93.82 105

FMVSNet2		81.31 179	81.61 175	80.41 205	86.38 214	58.75 249	83.93 141	86.58 203	72.43 168	87.65 133	92.98 103	63.78 233	90.22 196	66.86 204	93.92 160	92.27 153

USDC		76.63 222	76.73 213	76.34 255	83.46 255	57.20 256	80.02 228	88.04 180	52.14 302	83.65 197	91.25 144	63.24 236	86.65 245	54.66 279	94.11 155	85.17 248

new-patchmatchnet		70.10 270	73.37 243	60.29 316	81.23 272	16.95 343	59.54 322	74.62 273	62.93 247	80.97 225	87.93 218	62.83 237	71.90 306	55.24 275	95.01 133	92.00 159

K. test v3		85.14 109	84.73 119	86.37 90	91.13 115	69.63 145	85.45 116	76.68 263	84.06 32	92.44 50	96.99 8	62.03 238	94.65 52	80.58 91	93.24 181	94.83 80

lessismore_v0				85.95 102	91.10 116	70.99 138		70.91 304		91.79 60	94.42 67	61.76 239	92.93 130	79.52 108	93.03 185	93.93 102

1314		73.22 247	72.56 252	75.20 261	80.41 280	57.84 251	81.64 208	85.36 214	51.68 305	73.10 284	76.65 315	61.45 240	85.19 260	63.54 225	79.21 311	82.59 277

CANet_DTU		77.81 208	77.05 209	80.09 208	81.37 270	59.90 240	83.26 164	88.29 175	69.16 201	67.83 308	83.72 270	60.93 241	89.47 206	69.22 188	89.70 232	90.88 179

pmmvs-eth3d		78.42 204	77.04 210	82.57 178	87.44 192	74.41 106	80.86 221	79.67 250	55.68 282	84.69 177	90.31 180	60.91 242	85.42 258	62.20 231	91.59 204	87.88 224

UnsupCasMVSNet_eth		71.63 260	72.30 254	69.62 286	76.47 306	52.70 286	70.03 300	80.97 244	59.18 269	79.36 243	88.21 213	60.50 243	69.12 313	58.33 259	77.62 315	87.04 231

Patchmatch-test1		72.75 251	72.61 250	73.19 271	81.62 268	55.86 264	78.89 245	71.37 299	61.73 257	74.93 273	82.15 288	60.46 244	81.80 280	59.68 248	82.63 297	81.92 290

jason		77.42 211	75.75 225	82.43 181	87.10 205	69.27 147	77.99 252	81.94 238	51.47 307	77.84 249	85.07 255	60.32 245	89.00 216	70.74 175	89.27 236	89.03 211
jason: jason.
1112_ss		74.82 240	73.74 239	78.04 234	89.57 139	60.04 239	76.49 266	87.09 198	54.31 288	73.66 281	79.80 304	60.25 246	86.76 244	58.37 257	84.15 286	87.32 229

HY-MVS	64.64 18	73.03 249	72.47 253	74.71 264	83.36 256	54.19 274	82.14 197	81.96 236	56.76 280	69.57 301	86.21 239	60.03 247	84.83 265	49.58 299	82.65 295	85.11 249

Anonymous20231206		71.38 262	71.88 256	69.88 283	86.31 218	54.37 273	70.39 298	74.62 273	52.57 298	76.73 256	88.76 201	59.94 248	72.06 305	44.35 316	93.23 182	83.23 272

IterMVS		76.91 216	76.34 220	78.64 225	80.91 275	64.03 180	76.30 268	79.03 251	64.88 238	83.11 202	89.16 196	59.90 249	84.46 267	68.61 195	85.15 278	87.42 227
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
YYNet1		70.06 271	70.44 264	68.90 289	73.76 324	53.42 281	58.99 326	67.20 316	58.42 271	87.10 141	85.39 250	59.82 250	67.32 317	59.79 247	83.50 289	85.96 240

MDA-MVSNet_test_wron		70.05 272	70.44 264	68.88 290	73.84 323	53.47 279	58.93 327	67.28 315	58.43 270	87.09 142	85.40 249	59.80 251	67.25 318	59.66 249	83.54 288	85.92 242

PMMVS		61.65 300	60.38 305	65.47 303	65.40 340	69.26 148	63.97 315	61.73 329	36.80 337	60.11 328	68.43 328	59.42 252	66.35 323	48.97 301	78.57 312	60.81 330

CDS-MVSNet		77.32 212	75.40 227	83.06 168	89.00 148	72.48 119	77.90 254	82.17 235	60.81 263	78.94 246	83.49 273	59.30 253	88.76 223	54.64 280	92.37 195	87.93 223
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
UnsupCasMVSNet_bld		69.21 278	69.68 270	67.82 295	79.42 283	51.15 298	67.82 307	75.79 265	54.15 289	77.47 253	85.36 252	59.26 254	70.64 308	48.46 303	79.35 309	81.66 294

WTY-MVS		67.91 282	68.35 277	66.58 299	80.82 277	48.12 314	65.96 312	72.60 288	53.67 292	71.20 294	81.68 294	58.97 255	69.06 314	48.57 302	81.67 299	82.55 278

MVSFormer		82.23 170	81.57 176	84.19 141	85.54 229	69.26 148	91.98 25	90.08 148	71.54 182	76.23 261	85.07 255	58.69 256	94.27 59	86.26 27	88.77 240	89.03 211

lupinMVS		76.37 226	74.46 235	82.09 182	85.54 229	69.26 148	76.79 261	80.77 245	50.68 314	76.23 261	82.82 282	58.69 256	88.94 217	69.85 181	88.77 240	88.07 218

Test_1112_low_res		73.90 245	73.08 245	76.35 254	90.35 128	55.95 262	73.40 290	86.17 206	50.70 313	73.14 283	85.94 241	58.31 258	85.90 254	56.51 266	83.22 290	87.20 230

sss		66.92 285	67.26 283	65.90 300	77.23 300	51.10 300	64.79 313	71.72 298	52.12 303	70.13 299	80.18 301	57.96 259	65.36 327	50.21 294	81.01 304	81.25 300

MVP-Stereo		75.81 231	73.51 242	82.71 174	89.35 141	73.62 109	80.06 226	85.20 216	60.30 266	73.96 279	87.94 217	57.89 260	89.45 208	52.02 288	74.87 320	85.06 250
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
PAPM		71.77 258	70.06 269	76.92 247	86.39 213	53.97 275	76.62 264	86.62 202	53.44 293	63.97 322	84.73 261	57.79 261	92.34 142	39.65 323	81.33 302	84.45 255

semantic-postprocess				84.34 136	83.93 251	69.66 144		81.09 243	72.43 168	86.47 156	90.19 181	57.56 262	93.15 122	77.45 125	86.39 267	90.22 196

LFMVS		80.15 196	80.56 186	78.89 220	89.19 145	55.93 263	85.22 119	73.78 279	82.96 40	84.28 190	92.72 112	57.38 263	90.07 203	63.80 224	95.75 113	90.68 185

Vis-MVSNet (Re-imp)		77.82 207	77.79 206	77.92 236	88.82 151	51.29 297	83.28 163	71.97 293	74.04 145	82.23 209	89.78 188	57.38 263	89.41 209	57.22 263	95.41 119	93.05 126

CHOSEN 1792x2688		72.45 253	70.56 263	78.13 232	90.02 137	63.08 195	68.72 302	83.16 228	42.99 332	75.92 264	85.46 247	57.22 265	85.18 261	49.87 297	81.67 299	86.14 239

pmmvs4		74.92 238	72.98 247	80.73 203	84.95 234	71.71 133	76.23 269	77.59 256	52.83 296	77.73 252	86.38 234	56.35 266	84.97 262	57.72 262	87.05 260	85.51 246

MVE	40.22 23	51.82 315	50.47 317	55.87 320	62.66 342	51.91 291	31.61 338	39.28 344	40.65 333	50.76 339	74.98 321	56.24 267	44.67 341	33.94 333	64.11 335	71.04 323
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
testmv		70.47 267	70.70 262	69.77 285	86.22 222	53.89 277	67.32 308	71.91 294	63.32 243	78.16 248	89.47 193	56.12 268	73.10 303	36.43 329	87.33 257	82.33 283

N_pmnet		70.20 268	68.80 275	74.38 266	80.91 275	84.81 35	59.12 325	76.45 264	55.06 285	75.31 271	82.36 287	55.74 269	54.82 337	47.02 309	87.24 259	83.52 265

MS-PatchMatch		70.93 264	70.22 267	73.06 273	81.85 267	62.50 209	73.82 287	77.90 254	52.44 299	75.92 264	81.27 296	55.67 270	81.75 281	55.37 274	77.70 314	74.94 315

DSMNet-mixed		60.98 305	61.61 302	59.09 319	72.88 330	45.05 322	74.70 280	46.61 343	26.20 338	65.34 316	90.32 179	55.46 271	63.12 332	41.72 320	81.30 303	69.09 325

pmmvs5		70.73 265	70.07 268	72.72 275	77.03 303	52.73 285	74.14 283	75.65 268	50.36 316	72.17 289	85.37 251	55.42 272	80.67 285	52.86 286	87.59 256	84.77 252

CMPMVS	59.41 20	75.12 235	73.57 241	79.77 210	75.84 310	67.22 160	81.21 216	82.18 234	50.78 312	76.50 257	87.66 222	55.20 273	82.99 276	62.17 232	90.64 226	89.09 210
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
MIMVSNet		71.09 263	71.59 258	69.57 287	87.23 199	50.07 310	78.91 244	71.83 295	60.20 267	71.26 293	91.76 134	55.08 274	76.09 296	41.06 321	87.02 261	82.54 279

no-one		71.52 261	70.43 266	74.81 263	78.45 294	63.41 187	57.73 328	77.03 259	51.46 308	77.17 255	90.33 178	54.96 275	80.35 287	47.41 307	99.29 2	80.68 307

PVSNet_0	51.08 22	56.10 310	54.97 314	59.48 318	75.12 319	53.28 282	55.16 329	61.89 327	44.30 329	59.16 331	62.48 336	54.22 276	65.91 325	35.40 331	47.01 337	59.25 332

EPNet_dtu		72.87 250	71.33 261	77.49 244	77.72 298	60.55 236	82.35 188	75.79 265	66.49 220	58.39 335	81.06 298	53.68 277	85.98 252	53.55 283	92.97 187	85.95 241
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
PMMVS2		55.64 312	59.27 309	44.74 327	64.30 341	12.32 344	40.60 336	49.79 342	53.19 294	65.06 320	84.81 259	53.60 278	49.76 339	32.68 335	89.41 233	72.15 320

HyFIR lowres test		75.12 235	72.66 249	82.50 179	91.44 107	65.19 172	72.47 291	87.31 189	46.79 323	80.29 236	84.30 265	52.70 279	92.10 148	51.88 292	86.73 262	90.22 196

testus		62.33 297	63.03 297	60.20 317	78.78 290	40.74 329	59.14 323	69.80 308	49.26 319	71.41 292	74.72 322	52.33 280	63.52 329	29.84 336	82.01 298	76.36 312

FMVSNet3		78.80 202	78.55 202	79.57 216	82.89 261	56.89 259	81.76 205	85.77 210	69.04 203	86.00 159	90.44 176	51.75 281	90.09 202	65.95 211	93.34 175	91.72 165

PVSNet	58.17 21	66.41 288	65.63 290	68.75 291	81.96 265	49.88 311	62.19 318	72.51 290	51.03 310	68.04 306	75.34 320	50.84 282	74.77 300	45.82 314	82.96 291	81.60 295

GA-MVS		75.83 230	74.61 232	79.48 218	81.87 266	59.25 245	73.42 289	82.88 230	68.68 205	79.75 239	81.80 292	50.62 283	89.46 207	66.85 205	85.64 272	89.72 202

FPMVS		72.29 256	72.00 255	73.14 272	88.63 156	85.00 32	74.65 281	67.39 314	71.94 178	77.80 251	87.66 222	50.48 284	75.83 298	49.95 295	79.51 307	58.58 333

MVS-HIRNet		61.16 303	62.92 298	55.87 320	79.09 287	35.34 335	71.83 293	57.98 336	46.56 324	59.05 332	91.14 151	49.95 285	76.43 295	38.74 326	71.92 326	55.84 334

CVMVSNet		72.62 252	71.41 260	76.28 256	83.25 257	60.34 237	83.50 158	79.02 252	37.77 336	76.33 259	85.10 253	49.60 286	87.41 235	70.54 178	77.54 316	81.08 303

LP		69.42 276	68.30 278	72.77 274	71.48 335	56.84 260	73.66 288	74.84 271	63.52 242	70.95 297	83.35 276	49.55 287	77.15 294	57.13 264	70.21 328	84.33 257

RPMNet		76.06 228	75.79 223	76.85 249	79.58 281	62.64 203	82.58 181	71.75 297	74.80 138	75.72 266	92.59 113	48.69 288	84.07 269	73.48 154	82.91 293	83.85 261

tpmrst		66.28 289	66.69 286	65.05 305	72.82 331	39.33 331	78.20 250	70.69 305	53.16 295	67.88 307	80.36 300	48.18 289	74.75 301	58.13 260	70.79 327	81.08 303

CR-MVSNet		74.00 244	73.04 246	76.85 249	79.58 281	62.64 203	82.58 181	76.90 260	50.50 315	75.72 266	92.38 118	48.07 290	84.07 269	68.72 194	82.91 293	83.85 261

Patchmtry		76.56 224	77.46 207	73.83 268	79.37 285	46.60 317	82.41 186	76.90 260	73.81 148	85.56 167	92.38 118	48.07 290	83.98 271	63.36 227	95.31 124	90.92 177

ADS-MVSNet2		65.87 291	63.64 296	72.55 277	73.16 328	56.92 258	67.10 309	74.81 272	49.74 317	66.04 313	82.97 279	46.71 292	77.26 292	42.29 318	69.96 330	83.46 266

ADS-MVSNet		61.90 298	62.19 300	61.03 315	73.16 328	36.42 334	67.10 309	61.75 328	49.74 317	66.04 313	82.97 279	46.71 292	63.21 331	42.29 318	69.96 330	83.46 266

PatchmatchNet		69.71 274	68.83 274	72.33 278	77.66 299	53.60 278	79.29 238	69.99 307	57.66 274	72.53 286	82.93 281	46.45 294	80.08 289	60.91 242	72.09 325	83.31 271
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
thres200		72.34 255	71.55 259	74.70 265	83.48 254	51.60 295	75.02 278	73.71 280	70.14 194	78.56 247	80.57 299	46.20 295	88.20 229	46.99 310	89.29 234	84.32 258

sam_mvs1												46.11 296

tfpn200view9		74.86 239	74.23 237	76.74 251	86.24 220	52.12 289	79.24 240	73.87 277	73.34 154	81.82 216	84.60 263	46.02 297	88.80 219	51.98 289	90.99 212	89.31 206

thres400		75.14 233	74.23 237	77.86 237	86.24 220	52.12 289	79.24 240	73.87 277	73.34 154	81.82 216	84.60 263	46.02 297	88.80 219	51.98 289	90.99 212	92.66 136

test1235678		65.57 292	65.73 289	65.06 304	82.84 262	50.90 301	62.90 316	69.26 309	57.17 278	72.36 287	83.04 277	46.02 297	70.10 309	32.79 334	85.24 277	74.19 317

patchmatchnet-post											81.71 293	45.93 300	87.01 238

sam_mvs												45.92 301

Patchmatch-RL test		74.48 241	73.68 240	76.89 248	84.83 236	66.54 166	72.29 292	69.16 311	57.70 273	86.76 146	86.33 236	45.79 302	82.59 278	69.63 183	90.65 225	81.54 296

thres100view900		75.45 232	75.05 230	76.66 252	87.27 198	51.88 292	81.07 218	73.26 284	75.68 130	83.25 201	86.37 235	45.54 303	88.80 219	51.98 289	90.99 212	89.31 206

thres600view7		75.97 229	75.35 229	77.85 238	87.01 207	51.84 294	80.45 224	73.26 284	75.20 136	83.10 203	86.31 238	45.54 303	89.05 215	55.03 277	92.24 198	92.66 136

tpm cat1		66.76 286	65.21 291	71.42 281	77.09 302	50.62 308	78.01 251	73.68 281	44.89 328	68.64 302	79.00 306	45.51 305	82.42 279	49.91 296	70.15 329	81.23 302

test_post											3.10 342	45.43 306	77.22 293

MDTV_nov1_ep13			68.29 279		78.03 297	43.87 325	74.12 284	72.22 291	52.17 300	67.02 310	85.54 243	45.36 307	80.85 284	55.73 269	84.42 285

tpmvs		70.16 269	69.56 271	71.96 280	74.71 322	48.13 313	79.63 231	75.45 269	65.02 237	70.26 298	81.88 291	45.34 308	85.68 256	58.34 258	75.39 319	82.08 286

MDTV_nov1_ep13_2view						27.60 339	70.76 296		46.47 325	61.27 325		45.20 309		49.18 300		83.75 263

test_post1							78.85 246				3.13 341	45.19 310	80.13 288	58.11 261

view600		76.79 217	76.54 214	77.56 240	87.91 175	50.77 303	81.92 200	71.35 300	77.38 105	84.62 178	88.40 207	45.18 311	89.26 211	58.58 253	93.49 170	92.66 136

view800		76.79 217	76.54 214	77.56 240	87.91 175	50.77 303	81.92 200	71.35 300	77.38 105	84.62 178	88.40 207	45.18 311	89.26 211	58.58 253	93.49 170	92.66 136

conf0.05thres1000		76.79 217	76.54 214	77.56 240	87.91 175	50.77 303	81.92 200	71.35 300	77.38 105	84.62 178	88.40 207	45.18 311	89.26 211	58.58 253	93.49 170	92.66 136

tfpn		76.79 217	76.54 214	77.56 240	87.91 175	50.77 303	81.92 200	71.35 300	77.38 105	84.62 178	88.40 207	45.18 311	89.26 211	58.58 253	93.49 170	92.66 136

CostFormer		69.98 273	68.68 276	73.87 267	77.14 301	50.72 307	79.26 239	74.51 275	51.94 304	70.97 296	84.75 260	45.16 315	87.49 234	55.16 276	79.23 310	83.40 268

Patchmatch-test		65.91 290	67.38 281	61.48 313	75.51 314	43.21 327	68.84 301	63.79 324	62.48 252	72.80 285	83.42 275	44.89 316	59.52 334	48.27 305	86.45 265	81.70 292

EU-MVSNet		75.12 235	74.43 236	77.18 245	83.11 260	59.48 243	85.71 114	82.43 233	39.76 335	85.64 165	88.76 201	44.71 317	87.88 231	73.86 150	85.88 270	84.16 260

PatchT		70.52 266	72.76 248	63.79 307	79.38 284	33.53 336	77.63 256	65.37 322	73.61 149	71.77 290	92.79 111	44.38 318	75.65 299	64.53 222	85.37 274	82.18 285

test-LLR		67.21 284	66.74 285	68.63 292	76.45 307	55.21 269	67.89 304	67.14 317	62.43 254	65.08 318	72.39 324	43.41 319	69.37 310	61.00 240	84.89 280	81.31 298

test0.0.03 1		64.66 294	64.36 293	65.57 302	75.03 320	46.89 316	64.69 314	61.58 330	62.43 254	71.18 295	77.54 310	43.41 319	68.47 315	40.75 322	82.65 295	81.35 297

MVSTER		77.09 214	75.70 226	81.25 197	75.27 318	61.08 230	77.49 259	85.07 219	60.78 264	86.55 150	88.68 203	43.14 321	90.25 193	73.69 151	90.67 223	92.42 145

tpm		67.95 281	68.08 280	67.55 296	78.74 291	43.53 326	75.60 273	67.10 319	54.92 286	72.23 288	88.10 214	42.87 322	75.97 297	52.21 287	80.95 305	83.15 273

PatchFormer-LS_test		67.91 282	66.49 288	72.17 279	75.29 317	51.85 293	75.68 271	73.62 282	57.23 277	68.64 302	68.13 332	42.19 323	82.76 277	64.06 223	73.51 322	81.89 291

tpm2		68.45 280	66.83 284	73.30 270	78.93 289	48.50 312	79.76 230	71.76 296	47.50 322	69.92 300	83.60 271	42.07 324	88.40 226	48.44 304	79.51 307	83.01 275

tpmp4_e23		69.43 275	67.33 282	75.72 259	78.53 293	52.75 284	82.13 198	74.91 270	49.23 320	66.37 311	84.17 267	41.28 325	88.67 224	49.73 298	79.63 306	85.75 244

EMVS		61.10 304	60.81 304	61.99 310	65.96 339	55.86 264	53.10 333	58.97 333	67.06 215	56.89 337	63.33 335	40.98 326	67.03 319	54.79 278	86.18 269	63.08 328

new_pmnet		55.69 311	57.66 310	49.76 324	75.47 315	30.59 337	59.56 321	51.45 341	43.62 331	62.49 323	75.48 318	40.96 327	49.15 340	37.39 328	72.52 323	69.55 324

E-PMN		61.59 301	61.62 301	61.49 312	66.81 337	55.40 267	53.77 332	60.34 331	66.80 219	58.90 333	65.50 334	40.48 328	66.12 324	55.72 270	86.25 268	62.95 329

EPMVS		62.47 295	62.63 299	62.01 309	70.63 336	38.74 332	74.76 279	52.86 339	53.91 291	67.71 309	80.01 302	39.40 329	66.60 322	55.54 273	68.81 334	80.68 307

tmp_tt		20.25 319	24.50 320	7.49 331	4.47 344	8.70 345	34.17 337	25.16 346	1.00 340	32.43 341	18.49 339	39.37 330	9.21 343	21.64 339	43.75 338	4.57 338

FMVSNet5		72.10 257	71.69 257	73.32 269	81.57 269	53.02 283	76.77 262	78.37 253	63.31 244	76.37 258	91.85 129	36.68 331	78.98 290	47.87 306	92.45 194	87.95 222

dp		60.70 306	60.29 307	61.92 311	72.04 333	38.67 333	70.83 295	64.08 323	51.28 309	60.75 326	77.28 312	36.59 332	71.58 307	47.41 307	62.34 336	75.52 314

CHOSEN 280x420		59.08 307	56.52 312	66.76 298	76.51 305	64.39 178	49.62 335	59.00 332	43.86 330	55.66 338	68.41 330	35.55 333	68.21 316	43.25 317	76.78 318	67.69 326

IB-MVS	62.13 19	71.64 259	68.97 273	79.66 215	80.80 278	62.26 217	73.94 285	76.90 260	63.27 245	68.63 304	76.79 314	33.83 334	91.84 153	59.28 251	87.26 258	84.88 251
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		69.41 277	66.64 287	77.70 239	73.19 327	71.24 136	75.67 272	65.56 321	70.42 190	65.18 317	92.97 104	33.64 335	83.06 275	53.52 284	69.61 332	78.79 309

DWT-MVSNet_test		66.43 287	64.37 292	72.63 276	74.86 321	50.86 302	76.52 265	72.74 287	54.06 290	65.50 315	68.30 331	32.13 336	84.84 264	61.63 237	73.59 321	82.19 284

DeepMVS_CX				24.13 330	32.95 343	29.49 338		21.63 347	12.07 339	37.95 340	45.07 338	30.84 337	19.21 342	17.94 340	33.06 340	23.69 337

gg-mvs-nofinetune		68.96 279	69.11 272	68.52 294	76.12 309	45.32 319	83.59 151	55.88 337	86.68 20	64.62 321	97.01 7	30.36 338	83.97 272	44.78 315	82.94 292	76.26 313

GG-mvs-BLEND				67.16 297	73.36 325	46.54 318	84.15 134	55.04 338		58.64 334	61.95 337	29.93 339	83.87 273	38.71 327	76.92 317	71.07 322

test12356		54.91 313	57.14 311	48.22 326	75.83 311	17.47 342	52.31 334	69.20 310	51.66 306	60.11 328	75.40 319	29.77 340	62.62 333	27.64 337	72.37 324	64.59 327

test-mter		65.00 293	63.79 294	68.63 292	76.45 307	55.21 269	67.89 304	67.14 317	50.98 311	65.08 318	72.39 324	28.27 341	69.37 310	61.00 240	84.89 280	81.31 298

TESTMET0.1,1		61.29 302	60.32 306	64.19 306	72.06 332	51.30 296	67.89 304	62.09 326	45.27 327	60.65 327	69.01 327	27.93 342	64.74 328	56.31 267	81.65 301	76.53 311

pmmvs3		62.47 295	60.02 308	69.80 284	71.58 334	64.00 181	70.52 297	58.44 334	39.77 334	66.05 312	75.84 317	27.10 343	72.28 304	46.15 312	84.77 284	73.11 319

testpf		58.55 308	61.58 303	49.48 325	66.03 338	40.05 330	74.40 282	58.07 335	64.72 239	59.36 330	72.67 323	22.76 344	66.92 320	67.07 203	69.15 333	41.46 336

test2356		56.69 309	55.15 313	61.32 314	73.20 326	44.11 324	54.95 330	62.52 325	48.75 321	62.45 324	68.42 329	21.10 345	65.67 326	26.86 338	78.08 313	74.19 317

1111		61.71 299	63.77 295	55.55 322	78.05 295	25.74 340	60.62 319	67.52 312	66.09 223	74.68 274	86.50 232	16.00 346	59.22 335	38.79 324	85.65 271	81.70 292

.test1245		48.02 316	54.41 315	28.84 329	78.05 295	25.74 340	60.62 319	67.52 312	66.09 223	74.68 274	86.50 232	16.00 346	59.22 335	38.79 324	1.47 341	1.55 340

PNet_i23d		52.13 314	51.24 316	54.79 323	75.56 312	45.26 320	54.54 331	52.55 340	66.95 216	57.19 336	65.82 333	13.15 348	63.40 330	36.39 330	39.04 339	55.71 335

test123		6.27 322	8.08 323	0.84 332	1.11 346	0.57 346	62.90 316	0.82 348	0.54 341	1.07 343	2.75 344	1.26 349	0.30 344	1.04 341	1.26 343	1.66 339

testmvs		5.91 323	7.65 324	0.72 333	1.20 345	0.37 347	59.14 323	0.67 349	0.49 342	1.11 342	2.76 343	0.94 350	0.24 345	1.02 342	1.47 341	1.55 340

sosnet-low-res		0.00 324	0.00 325	0.00 334	0.00 347	0.00 348	0.00 339	0.00 350	0.00 343	0.00 344	0.00 345	0.00 351	0.00 346	0.00 343	0.00 344	0.00 342

sosnet		0.00 324	0.00 325	0.00 334	0.00 347	0.00 348	0.00 339	0.00 350	0.00 343	0.00 344	0.00 345	0.00 351	0.00 346	0.00 343	0.00 344	0.00 342

uncertanet		0.00 324	0.00 325	0.00 334	0.00 347	0.00 348	0.00 339	0.00 350	0.00 343	0.00 344	0.00 345	0.00 351	0.00 346	0.00 343	0.00 344	0.00 342

Regformer		0.00 324	0.00 325	0.00 334	0.00 347	0.00 348	0.00 339	0.00 350	0.00 343	0.00 344	0.00 345	0.00 351	0.00 346	0.00 343	0.00 344	0.00 342

ab-mvs-re		6.65 320	8.87 321	0.00 334	0.00 347	0.00 348	0.00 339	0.00 350	0.00 343	0.00 344	79.80 304	0.00 351	0.00 346	0.00 343	0.00 344	0.00 342

uanet		0.00 324	0.00 325	0.00 334	0.00 347	0.00 348	0.00 339	0.00 350	0.00 343	0.00 344	0.00 345	0.00 351	0.00 346	0.00 343	0.00 344	0.00 342

ESAPD								93.79 28

MTGPA								91.81 89

MTMP								33.14 345

gm-plane-assit					75.42 316	44.97 323			52.17 300		72.36 326		87.90 230	54.10 281

test9_res														80.83 87	96.45 85	90.57 188

agg_prior2														79.68 104	96.16 94	90.22 196

agg_prior					91.58 101	77.69 79		90.30 139		84.32 188			93.18 118

test_prior4						78.97 71	84.59 127

test_prior				86.32 91	90.59 125	71.99 127		92.85 60					94.17 66			92.80 131

旧先验2							81.73 206		56.88 279	86.54 155			84.90 263	72.81 162

新几何2							81.72 207

无先验							82.81 175	85.62 212	58.09 272				91.41 166	67.95 200		84.48 254

原ACMM2							82.26 193

testdata2													86.43 248	63.52 226

testdata1							79.62 232		73.95 147

plane_prior7					93.45 51	77.31 85

plane_prior5								93.61 31					95.22 37	80.78 88	95.83 110	94.46 86

plane_prior4											92.95 105

plane_prior3						76.85 90			77.79 99	86.55 150

plane_prior2							89.45 56		79.44 77

plane_prior1					92.83 69

plane_prior						76.42 95	87.15 89		75.94 127						95.03 132

n2								0.00 350

nn								0.00 350

door-mid								74.45 276

test11								91.46 102

door								72.57 289

HQP5-MVS						70.66 139

HQP-NCC					91.19 111		84.77 122		73.30 156	80.55 233

ACMP_Plane					91.19 111		84.77 122		73.30 156	80.55 233

BP-MVS														77.30 127

HQP4-MVS										80.56 232			94.61 53			93.56 116

HQP3-MVS								92.68 66							94.47 151

NP-MVS					91.95 91	74.55 105					90.17 183

ACMMP++_ref															95.74 114

ACMMP++															97.35 58

High-resolution multi-view benchmark