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		86.90 1	88.67 1	81.57 20	91.50 1	63.30 104	84.80 24	87.77 7	86.18 1	96.26 2	96.06 2	90.32 1	84.49 49	68.08 82	97.05 3	96.93 1

MPTG		83.01 21	83.63 20	81.13 29	91.16 2	78.16 12	82.72 39	80.63 106	72.08 26	84.93 56	90.79 44	74.65 37	84.42 51	80.98 4	94.75 28	80.82 176

MTAPA		83.19 17	83.87 16	81.13 29	91.16 2	78.16 12	84.87 23	80.63 106	72.08 26	84.93 56	90.79 44	74.65 37	84.42 51	80.98 4	94.75 28	80.82 176

mPP-MVS		84.01 9	84.39 9	82.88 5	90.65 4	81.38 5	87.08 9	82.79 65	72.41 25	85.11 55	90.85 43	76.65 21	84.89 43	79.30 15	94.63 32	82.35 148

MP-MVS		83.19 17	83.54 21	82.14 18	90.54 5	79.00 9	86.42 18	83.59 54	71.31 30	81.26 94	90.96 40	74.57 39	84.69 47	78.41 19	94.78 27	82.74 139

PMVS	70.70 6	81.70 28	83.15 27	77.36 69	90.35 6	82.82 3	82.15 41	79.22 129	74.08 17	87.16 27	91.97 19	84.80 2	76.97 180	64.98 114	93.61 51	72.28 252
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
PS-CasMVS		80.41 40	82.86 30	73.07 127	89.93 7	39.21 264	77.15 90	81.28 90	79.74 4	90.87 6	92.73 11	75.03 35	84.93 42	63.83 121	95.19 17	95.07 3

DTE-MVSNet		80.35 41	82.89 29	72.74 137	89.84 8	37.34 281	77.16 89	81.81 78	80.45 2	90.92 5	92.95 8	74.57 39	86.12 24	63.65 122	94.68 31	94.76 6

PEN-MVS		80.46 39	82.91 28	73.11 126	89.83 9	39.02 267	77.06 92	82.61 68	80.04 3	90.60 8	92.85 9	74.93 36	85.21 38	63.15 124	95.15 19	95.09 2

region2R		83.54 13	83.86 17	82.58 13	89.82 10	77.53 16	87.06 10	84.23 43	70.19 38	83.86 68	90.72 48	75.20 30	86.27 17	79.41 13	94.25 43	83.95 115

ACMMPR		83.62 11	83.93 15	82.69 10	89.78 11	77.51 18	87.01 11	84.19 44	70.23 36	84.49 63	90.67 49	75.15 33	86.37 14	79.58 9	94.26 42	84.18 111

HSP-MVS		79.69 45	79.17 55	81.27 28	89.70 12	77.46 19	87.16 8	80.58 109	64.94 72	81.05 99	88.38 100	57.10 196	87.10 6	79.75 7	83.87 196	79.24 197

CP-MVSNet		79.48 48	81.65 38	72.98 130	89.66 13	39.06 266	76.76 94	80.46 111	78.91 6	90.32 9	91.70 25	68.49 78	84.89 43	63.40 123	95.12 20	95.01 4

PGM-MVS		83.07 19	83.25 26	82.54 15	89.57 14	77.21 20	82.04 43	85.40 23	67.96 47	84.91 58	90.88 41	75.59 27	86.57 12	78.16 20	94.71 30	83.82 116

WR-MVS_H		80.22 43	82.17 35	74.39 96	89.46 15	42.69 243	78.24 77	82.24 71	78.21 8	89.57 11	92.10 18	68.05 83	85.59 31	66.04 107	95.62 11	94.88 5

XVS		83.51 14	83.73 18	82.85 7	89.43 16	77.61 14	86.80 13	84.66 33	72.71 23	82.87 75	90.39 60	73.86 43	86.31 15	78.84 17	94.03 46	84.64 97

X-MVStestdata		76.81 72	74.79 97	82.85 7	89.43 16	77.61 14	86.80 13	84.66 33	72.71 23	82.87 75	9.95 350	73.86 43	86.31 15	78.84 17	94.03 46	84.64 97

CP-MVS		84.12 7	84.55 8	82.80 9	89.42 18	79.74 7	88.19 3	84.43 37	71.96 29	84.70 60	90.56 51	77.12 18	86.18 20	79.24 16	95.36 14	82.49 146

ACMMP_Plus		82.33 26	83.28 25	79.46 45	89.28 19	69.09 68	83.62 31	84.98 27	64.77 73	83.97 67	91.02 38	75.53 29	85.93 28	82.00 2	94.36 38	83.35 129

HFP-MVS		83.39 16	84.03 14	81.48 22	89.25 20	75.69 24	87.01 11	84.27 40	70.23 36	84.47 64	90.43 55	76.79 19	85.94 26	79.58 9	94.23 44	82.82 136

#test#		82.40 25	82.71 31	81.48 22	89.25 20	75.69 24	84.47 26	84.27 40	64.45 76	84.47 64	90.43 55	76.79 19	85.94 26	76.01 30	94.23 44	82.82 136

ACMMP		84.22 5	84.84 6	82.35 17	89.23 22	76.66 22	87.65 4	85.89 18	71.03 32	85.85 45	90.58 50	78.77 14	85.78 29	79.37 14	95.17 18	84.62 99
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
CPTT-MVS		81.51 31	81.76 36	80.76 34	89.20 23	78.75 10	86.48 17	82.03 74	68.80 43	80.92 102	88.52 96	72.00 54	82.39 79	74.80 31	93.04 57	81.14 169

HPM-MVS++		79.89 44	79.80 50	80.18 38	89.02 24	78.44 11	83.49 32	80.18 118	64.71 75	78.11 134	88.39 99	65.46 103	83.14 69	77.64 25	91.20 83	78.94 200

TSAR-MVS + MP.		79.05 51	78.81 56	79.74 41	88.94 25	67.52 75	86.61 15	81.38 89	51.71 207	77.15 141	91.42 33	65.49 102	87.20 5	79.44 12	87.17 152	84.51 104

UA-Net		81.56 30	82.28 34	79.40 46	88.91 26	69.16 66	84.67 25	80.01 121	75.34 13	79.80 114	94.91 3	69.79 69	80.25 133	72.63 43	94.46 36	88.78 51

MP-MVS-pluss		82.54 24	83.46 22	79.76 40	88.88 27	68.44 70	81.57 46	86.33 14	63.17 92	85.38 52	91.26 34	76.33 22	84.67 48	83.30 1	94.96 24	86.17 76
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
TDRefinement		86.32 2	86.33 2	86.29 1	88.64 28	81.19 6	88.84 2	90.72 1	78.27 7	87.95 17	92.53 13	79.37 12	84.79 46	74.51 34	96.15 4	92.88 9

HPM-MVS_fast		84.59 4	85.10 4	83.06 4	88.60 29	75.83 23	86.27 19	86.89 11	73.69 18	86.17 39	91.70 25	78.23 16	85.20 39	79.45 11	94.91 26	88.15 61

新几何1				69.99 173	88.37 30	71.34 47		62.08 250	43.85 270	74.99 170	86.11 146	52.85 213	70.57 241	50.99 200	83.23 202	68.05 289

1121		69.23 170	68.26 186	72.12 152	88.36 31	71.40 45	68.59 204	62.06 251	43.80 271	74.75 173	86.18 142	52.92 212	76.85 183	54.47 180	83.27 201	68.12 288

HPM-MVS		84.12 7	84.63 7	82.60 12	88.21 32	74.40 31	85.24 22	87.21 9	70.69 35	85.14 53	90.42 58	78.99 13	86.62 11	80.83 6	94.93 25	86.79 72

ACMM	69.25 9	82.11 27	83.31 24	78.49 57	88.17 33	73.96 34	83.11 36	84.52 36	66.40 56	87.45 23	89.16 84	81.02 7	80.52 129	74.27 36	95.73 9	80.98 173
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
test222					87.30 34	69.15 67	67.85 213	59.59 261	41.06 286	73.05 193	85.72 152	48.03 234			80.65 236	66.92 295

XVG-ACMP-BASELINE		80.54 38	81.06 41	78.98 51	87.01 35	72.91 41	80.23 57	85.56 20	66.56 55	85.64 46	89.57 74	69.12 73	80.55 128	72.51 47	93.37 53	83.48 122

LPG-MVS_test		83.47 15	84.33 10	80.90 32	87.00 36	70.41 56	82.04 43	86.35 12	69.77 40	87.75 18	91.13 35	81.83 3	86.20 18	77.13 26	95.96 7	86.08 77

LGP-MVS_train				80.90 32	87.00 36	70.41 56		86.35 12	69.77 40	87.75 18	91.13 35	81.83 3	86.20 18	77.13 26	95.96 7	86.08 77

OPM-MVS		80.99 36	81.63 39	79.07 50	86.86 38	69.39 63	79.41 65	84.00 49	65.64 60	85.54 50	89.28 77	76.32 23	83.47 64	74.03 37	93.57 52	84.35 109

abl_6		84.92 3	85.70 3	82.57 14	86.72 39	79.27 8	87.56 5	86.08 16	77.48 9	88.12 16	91.53 30	81.18 6	84.31 54	78.12 21	94.47 35	84.15 112

DeepC-MVS	72.44 4	81.00 35	80.83 43	81.50 21	86.70 40	70.03 60	82.06 42	87.00 10	59.89 120	80.91 103	90.53 52	72.19 50	88.56 1	73.67 39	94.52 34	85.92 82
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
ACMP	69.50 8	82.64 23	83.38 23	80.40 36	86.50 41	69.44 62	82.30 40	86.08 16	66.80 52	86.70 32	89.99 69	81.64 5	85.95 25	74.35 35	96.11 5	85.81 83
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
XVG-OURS-SEG-HR		79.62 46	79.99 48	78.49 57	86.46 42	74.79 30	77.15 90	85.39 24	66.73 53	80.39 109	88.85 94	74.43 41	78.33 167	74.73 33	85.79 164	82.35 148

VDDNet		71.60 149	73.13 124	67.02 204	86.29 43	41.11 252	69.97 186	66.50 234	68.72 44	74.74 174	91.70 25	59.90 147	75.81 191	48.58 218	91.72 70	84.15 112

XVG-OURS		79.51 47	79.82 49	78.58 56	86.11 44	74.96 29	76.33 102	84.95 28	66.89 49	82.75 77	88.99 90	66.82 93	78.37 166	74.80 31	90.76 97	82.40 147

test_part2					85.90 45	66.44 79				84.61 61

ESAPD		81.57 29	82.55 32	78.63 55	85.90 45	66.44 79	83.39 33	84.94 29	73.27 20	84.61 61	89.25 80	75.17 31	86.96 9	72.56 45	93.83 48	82.50 144

原ACMM1				73.90 102	85.90 45	65.15 91		81.67 80	50.97 222	74.25 180	86.16 144	61.60 131	83.54 62	56.75 158	91.08 87	73.00 243

testdata				64.13 223	85.87 48	63.34 103		61.80 254	47.83 245	76.42 158	86.60 132	48.83 230	62.31 291	54.46 182	81.26 228	66.74 299

agg_prior3		76.32 75	76.33 81	76.28 78	85.86 49	70.13 59	76.50 96	78.26 150	53.41 193	75.78 160	86.49 135	66.58 96	81.57 90	72.50 48	91.56 74	77.15 216

CNVR-MVS		78.49 60	78.59 60	78.16 61	85.86 49	67.40 76	78.12 80	81.50 82	63.92 83	77.51 139	86.56 133	68.43 80	84.82 45	73.83 38	91.61 73	82.26 151

NCCC		78.25 63	78.04 64	78.89 52	85.61 51	69.45 61	79.80 62	80.99 103	65.77 59	75.55 164	86.25 141	67.42 88	85.42 32	70.10 66	90.88 95	81.81 160

TEST9					85.47 52	69.32 64	76.42 98	78.69 140	53.73 189	76.97 142	86.74 123	66.84 92	81.10 111

train_agg		76.38 74	76.55 76	75.86 84	85.47 52	69.32 64	76.42 98	78.69 140	54.00 184	76.97 142	86.74 123	66.60 94	81.10 111	72.50 48	91.56 74	77.15 216

HQP_MVS		78.77 56	78.78 58	78.72 53	85.18 54	65.18 89	82.74 37	85.49 21	65.45 62	78.23 132	89.11 86	60.83 141	86.15 21	71.09 55	90.94 89	84.82 94

plane_prior7					85.18 54	66.21 82

SteuartSystems-ACMMP		83.07 19	83.64 19	81.35 25	85.14 56	71.00 50	85.53 20	84.78 31	70.91 33	85.64 46	90.41 59	75.55 28	87.69 3	79.75 7	95.08 21	85.36 88
Skip Steuart: Steuart Systems R&D Blog.
test_8					85.09 57	67.89 73	76.26 103	78.66 142	54.00 184	76.89 147	86.72 125	66.60 94	80.89 122

WR-MVS		71.20 151	72.48 139	67.36 201	84.98 58	35.70 292	64.43 254	68.66 226	65.05 71	81.49 92	86.43 137	57.57 189	76.48 186	50.36 205	93.32 55	89.90 34

PS-MVSNAJss		77.54 67	77.35 69	78.13 63	84.88 59	66.37 81	78.55 71	79.59 126	53.48 191	86.29 38	92.43 15	62.39 123	80.25 133	67.90 89	90.61 98	87.77 64

LTVRE_ROB	75.46 1	84.22 5	84.98 5	81.94 19	84.82 60	75.40 26	91.60 1	87.80 5	73.52 19	88.90 13	93.06 7	71.39 59	81.53 91	81.53 3	92.15 68	88.91 47
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
mvs_tets		78.93 54	78.67 59	79.72 42	84.81 61	73.93 35	80.65 50	76.50 169	51.98 205	87.40 24	91.86 21	76.09 25	78.53 157	68.58 77	90.20 104	86.69 74

APDe-MVS		82.88 22	84.14 12	79.08 49	84.80 62	66.72 77	86.54 16	85.11 26	72.00 28	86.65 33	91.75 24	78.20 17	87.04 7	77.93 22	94.32 40	83.47 123

MSLP-MVS++		74.48 106	75.78 87	70.59 162	84.66 63	62.40 107	78.65 69	84.24 42	60.55 116	77.71 137	81.98 200	63.12 116	77.64 176	62.95 125	88.14 133	71.73 257

jajsoiax		78.51 59	78.16 63	79.59 44	84.65 64	73.83 37	80.42 53	76.12 170	51.33 212	87.19 26	91.51 31	73.79 45	78.44 161	68.27 80	90.13 108	86.49 75

pcd1.5k->3k		35.00 326	36.93 326	29.21 337	84.62 65	0.00 359	0.00 350	78.90 137	0.00 354	0.00 355	0.00 356	78.26 15	0.00 357	0.00 354	90.55 100	87.62 65

TranMVSNet+NR-MVSNet		76.13 77	77.66 67	71.56 155	84.61 66	42.57 244	70.98 177	78.29 149	68.67 45	83.04 74	89.26 78	72.99 48	80.75 125	55.58 173	95.47 12	91.35 15

旧先验1					84.55 67	60.36 121		63.69 244			87.05 113	54.65 207			83.34 200	69.66 278

APD-MVS		81.13 33	81.73 37	79.36 47	84.47 68	70.53 55	83.85 30	83.70 51	69.43 42	83.67 70	88.96 92	75.89 26	86.41 13	72.62 44	92.95 58	81.14 169
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
plane_prior1					84.46 69

agg_prior1		75.89 79	76.41 79	74.31 97	84.44 70	66.02 83	76.12 106	78.62 143	54.40 178	76.95 144	86.85 117	66.44 97	80.34 131	72.45 50	91.42 78	76.57 221

agg_prior					84.44 70	66.02 83		78.62 143		76.95 144			80.34 131

DeepPCF-MVS	71.07 5	78.48 61	77.14 71	82.52 16	84.39 72	77.04 21	76.35 100	84.05 47	56.66 150	80.27 110	85.31 154	68.56 77	87.03 8	67.39 93	91.26 81	83.50 121

CDPH-MVS		77.33 69	77.06 72	78.14 62	84.21 73	63.98 98	76.07 107	83.45 57	54.20 180	77.68 138	87.18 109	69.98 67	85.37 33	68.01 84	92.72 62	85.08 92

plane_prior6					84.18 74	65.31 88						60.83 141

114514_t		73.40 119	73.33 121	73.64 110	84.15 75	57.11 141	78.20 78	80.02 120	43.76 272	72.55 200	86.07 148	64.00 113	83.35 68	60.14 138	91.03 88	80.45 184

DeepC-MVS_fast	69.89 7	77.17 70	76.33 81	79.70 43	83.90 76	67.94 72	80.06 60	83.75 50	56.73 149	74.88 172	85.32 153	65.54 101	87.79 2	65.61 111	91.14 85	83.35 129
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
SD-MVS		80.28 42	81.55 40	76.47 74	83.57 77	67.83 74	83.39 33	85.35 25	64.42 79	86.14 40	87.07 112	74.02 42	80.97 116	77.70 24	92.32 67	80.62 181

DU-MVS		74.91 98	75.57 91	72.93 132	83.50 78	45.79 229	69.47 193	80.14 119	65.22 68	81.74 86	87.08 110	61.82 129	81.07 113	56.21 166	94.98 22	91.93 10

NR-MVSNet		73.62 114	74.05 107	72.33 148	83.50 78	43.71 236	65.65 242	77.32 163	64.32 80	75.59 163	87.08 110	62.45 122	81.34 101	54.90 176	95.63 10	91.93 10

test_0402		78.17 64	79.48 53	74.24 98	83.50 78	59.15 132	72.52 146	74.60 184	75.34 13	88.69 15	91.81 22	75.06 34	82.37 80	65.10 113	88.68 126	81.20 166

NP-MVS					83.34 81	63.07 106					85.97 149

UniMVSNet (Re)		75.00 96	75.48 92	73.56 112	83.14 82	47.92 201	70.41 183	81.04 101	63.67 86	79.54 116	86.37 138	62.83 117	81.82 87	57.10 157	95.25 16	90.94 26

Anonymous20231211		77.74 65	80.26 46	70.19 168	83.05 83	43.39 237	75.86 112	76.74 168	75.91 12	85.92 43	96.14 1	80.85 8	75.59 195	53.58 188	94.27 41	91.58 13

UniMVSNet_NR-MVSNet		74.90 99	75.65 88	72.64 139	83.04 84	45.79 229	69.26 195	78.81 138	66.66 54	81.74 86	86.88 116	63.26 115	81.07 113	56.21 166	94.98 22	91.05 21

HyFIR lowres test		63.01 220	60.47 237	70.61 161	83.04 84	54.10 156	59.93 289	72.24 201	33.67 322	69.00 233	75.63 260	38.69 273	76.93 181	36.60 293	75.45 276	80.81 178

COLMAP_ROB	72.78 3	83.75 10	84.11 13	82.68 11	82.97 86	74.39 32	87.18 7	88.18 4	78.98 5	86.11 41	91.47 32	79.70 11	85.76 30	66.91 98	95.46 13	87.89 63
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
DP-MVS Recon		73.57 116	72.69 137	76.23 80	82.85 87	63.39 102	74.32 134	82.96 63	57.75 133	70.35 225	81.98 200	64.34 112	84.41 53	49.69 209	89.95 111	80.89 174

APD-MVS_3200maxsize		83.57 12	84.33 10	81.31 26	82.83 88	73.53 40	85.50 21	87.45 8	74.11 16	86.45 35	90.52 54	80.02 10	84.48 50	77.73 23	94.34 39	85.93 81

PVSNet_Blended_VisFu		70.04 161	68.88 177	73.53 114	82.71 89	63.62 101	74.81 127	81.95 77	48.53 237	67.16 251	79.18 232	51.42 221	78.38 165	54.39 183	79.72 248	78.60 203

EG-PatchMatch MVS		70.70 156	70.88 159	70.16 169	82.64 90	58.80 134	71.48 165	73.64 188	54.98 167	76.55 152	81.77 203	61.10 139	78.94 148	54.87 177	80.84 234	72.74 247

HQP-NCC					82.37 91		77.32 86		59.08 123	71.58 208

ACMP_Plane					82.37 91		77.32 86		59.08 123	71.58 208

HQP-MVS		75.24 91	75.01 96	75.94 82	82.37 91	58.80 134	77.32 86	84.12 45	59.08 123	71.58 208	85.96 150	58.09 175	85.30 35	67.38 94	89.16 119	83.73 119

test12				76.51 72	82.28 94	60.94 118		81.64 81		73.60 186		64.88 107	85.19 40		90.42 102	83.38 126

TAMVS		65.31 201	63.75 209	69.97 174	82.23 95	59.76 127	66.78 227	63.37 246	45.20 263	69.79 229	79.37 228	47.42 237	72.17 226	34.48 303	85.15 183	77.99 212

test_prior3		76.71 73	77.19 70	75.27 90	82.15 96	59.85 125	75.57 115	84.33 38	58.92 127	76.53 154	86.78 120	67.83 86	83.39 66	69.81 69	92.76 60	82.58 141

test_prior				75.27 90	82.15 96	59.85 125		84.33 38					83.39 66			82.58 141

AdaColmap		74.22 108	74.56 99	73.20 124	81.95 98	60.97 117	79.43 63	80.90 104	65.57 61	72.54 201	81.76 204	70.98 62	85.26 36	47.88 224	90.00 110	73.37 240

PAPM_NR		73.91 110	74.16 106	73.16 125	81.90 99	53.50 160	81.28 47	81.40 88	66.17 57	73.30 191	83.31 182	59.96 146	83.10 70	58.45 149	81.66 219	82.87 134

DP-MVS		78.44 62	79.29 54	75.90 83	81.86 100	65.33 87	79.05 67	84.63 35	74.83 15	80.41 108	86.27 139	71.68 55	83.45 65	62.45 128	92.40 65	78.92 201

F-COLMAP		75.29 90	73.99 108	79.18 48	81.73 101	71.90 43	81.86 45	82.98 62	59.86 121	72.27 203	84.00 172	64.56 111	83.07 71	51.48 196	87.19 151	82.56 143

SixPastTwentyTwo		75.77 81	76.34 80	74.06 101	81.69 102	54.84 151	76.47 97	75.49 176	64.10 82	87.73 20	92.24 17	50.45 224	81.30 103	67.41 92	91.46 77	86.04 79

Vis-MVSNet		74.85 102	74.56 99	75.72 85	81.63 103	64.64 93	76.35 100	79.06 134	62.85 94	73.33 190	88.41 98	62.54 121	79.59 143	63.94 120	82.92 203	82.94 133
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
test_djsdf		78.88 55	78.27 62	80.70 35	81.42 104	71.24 48	83.98 28	75.72 174	52.27 200	87.37 25	92.25 16	68.04 84	80.56 126	72.28 51	91.15 84	90.32 33

3Dnovator+	73.19 2	81.08 34	80.48 44	82.87 6	81.41 105	72.03 42	84.38 27	86.23 15	77.28 11	80.65 105	90.18 67	59.80 150	87.58 4	73.06 41	91.34 80	89.01 42

MCST-MVS		73.42 118	73.34 120	73.63 111	81.28 106	59.17 131	74.80 129	83.13 61	45.50 259	72.84 194	83.78 175	65.15 105	80.99 115	64.54 116	89.09 122	80.73 179

MIMVSNet1		66.57 196	69.23 171	58.59 270	81.26 107	37.73 278	64.06 257	57.62 268	57.02 145	78.40 129	90.75 46	62.65 118	58.10 301	41.77 264	89.58 115	79.95 192

ACMH+	66.64 10	81.20 32	82.48 33	77.35 70	81.16 108	62.39 108	80.51 51	87.80 5	73.02 22	87.57 21	91.08 37	80.28 9	82.44 78	64.82 115	96.10 6	87.21 70

MVS_111021_HR		72.98 128	72.97 132	72.99 129	80.82 109	65.47 86	68.81 200	72.77 194	57.67 136	75.76 161	82.38 195	71.01 61	77.17 178	61.38 131	86.15 160	76.32 222

OMC-MVS		79.41 49	78.79 57	81.28 27	80.62 110	70.71 54	80.91 49	84.76 32	62.54 97	81.77 84	86.65 129	71.46 57	83.53 63	67.95 88	92.44 64	89.60 35

OurMVSNet-221017-0		78.57 58	78.53 61	78.67 54	80.48 111	64.16 96	80.24 56	82.06 73	61.89 101	88.77 14	93.32 5	57.15 194	82.60 77	70.08 67	92.80 59	89.25 37

CDS-MVSNet		64.33 211	62.66 224	69.35 178	80.44 112	58.28 138	65.26 247	65.66 236	44.36 268	67.30 250	75.54 261	43.27 252	71.77 233	37.68 286	84.44 189	78.01 211
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
PLC	62.01 16	71.79 148	70.28 164	76.33 77	80.31 113	68.63 69	78.18 79	81.24 92	54.57 176	67.09 252	80.63 212	59.44 153	81.74 89	46.91 231	84.17 191	78.63 202
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
CHOSEN 1792x2688		58.09 261	56.30 270	63.45 231	79.95 114	50.93 170	54.07 307	65.59 237	28.56 340	61.53 276	74.33 276	41.09 263	66.52 277	33.91 307	67.69 315	72.92 244

K. test v3		73.67 113	73.61 114	73.87 103	79.78 115	55.62 147	74.69 132	62.04 253	66.16 58	84.76 59	93.23 6	49.47 226	80.97 116	65.66 109	86.67 157	85.02 93

VPNet		65.58 199	67.56 191	59.65 265	79.72 116	30.17 328	60.27 287	62.14 249	54.19 181	71.24 215	86.63 130	58.80 162	67.62 266	44.17 241	90.87 96	81.18 167

ACMH	63.62 14	77.50 68	80.11 47	69.68 175	79.61 117	56.28 144	78.81 68	83.62 53	63.41 91	87.14 28	90.23 66	76.11 24	73.32 210	67.58 90	94.44 37	79.44 195
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
lessismore_v0				72.75 136	79.60 118	56.83 143		57.37 271		83.80 69	89.01 89	47.45 236	78.74 153	64.39 118	86.49 159	82.69 140

MVS_111021_LR		72.10 144	71.82 148	72.95 131	79.53 119	73.90 36	70.45 182	66.64 233	56.87 146	76.81 148	81.76 204	68.78 75	71.76 234	61.81 129	83.74 198	73.18 242

Test_1112_low_res		58.78 254	58.69 255	59.04 268	79.41 120	38.13 274	57.62 297	66.98 232	34.74 314	59.62 293	77.56 241	42.92 255	63.65 285	38.66 278	70.73 298	75.35 230

CSCG		74.12 109	74.39 102	73.33 120	79.35 121	61.66 114	77.45 85	81.98 75	62.47 99	79.06 122	80.19 217	61.83 128	78.79 152	59.83 141	87.35 146	79.54 194

MVP-Stereo		61.56 233	59.22 243	68.58 193	79.28 122	60.44 120	69.20 196	71.57 204	43.58 275	56.42 306	78.37 237	39.57 270	76.46 187	34.86 302	60.16 329	68.86 286
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
MG-MVS		70.47 159	71.34 156	67.85 197	79.26 123	40.42 259	74.67 133	75.15 182	58.41 129	68.74 236	88.14 106	56.08 203	83.69 59	59.90 140	81.71 218	79.43 196

IS-MVSNet		75.10 93	75.42 93	74.15 100	79.23 124	48.05 199	79.43 63	78.04 155	70.09 39	79.17 121	88.02 107	53.04 211	83.60 61	58.05 150	93.76 50	90.79 29

FC-MVSNet-test		73.32 121	74.78 98	68.93 186	79.21 125	36.57 283	71.82 161	79.54 127	57.63 138	82.57 78	90.38 61	59.38 155	78.99 147	57.91 151	94.56 33	91.23 17

AllTest		77.66 66	77.43 68	78.35 59	79.19 126	70.81 51	78.60 70	88.64 2	65.37 65	80.09 112	88.17 103	70.33 64	78.43 162	55.60 170	90.90 93	85.81 83

TestCases				78.35 59	79.19 126	70.81 51		88.64 2	65.37 65	80.09 112	88.17 103	70.33 64	78.43 162	55.60 170	90.90 93	85.81 83

xiu_mvs_v1_base_debu		67.87 187	67.07 195	70.26 165	79.13 128	61.90 111	67.34 219	71.25 211	47.98 242	67.70 240	74.19 280	61.31 134	72.62 220	56.51 160	78.26 260	76.27 223

xiu_mvs_v1_base		67.87 187	67.07 195	70.26 165	79.13 128	61.90 111	67.34 219	71.25 211	47.98 242	67.70 240	74.19 280	61.31 134	72.62 220	56.51 160	78.26 260	76.27 223

xiu_mvs_v1_base_debi		67.87 187	67.07 195	70.26 165	79.13 128	61.90 111	67.34 219	71.25 211	47.98 242	67.70 240	74.19 280	61.31 134	72.62 220	56.51 160	78.26 260	76.27 223

VDD-MVS		70.81 155	71.44 155	68.91 188	79.07 131	46.51 227	67.82 214	70.83 217	61.23 106	74.07 183	88.69 95	59.86 148	75.62 194	51.11 199	90.28 103	84.61 100

TSAR-MVS + GP.		73.08 123	71.60 152	77.54 68	78.99 132	70.73 53	74.96 124	69.38 223	60.73 113	74.39 179	78.44 236	57.72 188	82.78 74	60.16 137	89.60 114	79.11 199

wuykxyi23d		75.33 88	76.75 75	71.04 158	78.83 133	85.01 1	71.78 162	61.00 256	53.47 192	96.33 1	93.38 4	73.07 46	68.04 263	65.65 110	97.28 2	60.07 322

FIs		72.56 141	73.80 109	68.84 190	78.74 134	37.74 277	71.02 176	79.83 123	56.12 152	80.88 104	89.45 75	58.18 172	78.28 168	56.63 159	93.36 54	90.51 32

v7n		79.37 50	80.41 45	76.28 78	78.67 135	55.81 146	79.22 66	82.51 70	70.72 34	87.54 22	92.44 14	68.00 85	81.34 101	72.84 42	91.72 70	91.69 12

LS3D		80.99 36	80.85 42	81.41 24	78.37 136	71.37 46	87.45 6	85.87 19	77.48 9	81.98 82	89.95 70	69.14 72	85.26 36	66.15 105	91.24 82	87.61 66

CNLPA		73.44 117	73.03 130	74.66 92	78.27 137	75.29 27	75.99 108	78.49 145	65.39 64	75.67 162	83.22 187	61.23 137	66.77 275	53.70 187	85.33 178	81.92 159

EPP-MVSNet		73.86 111	73.38 118	75.31 89	78.19 138	53.35 162	80.45 52	77.32 163	65.11 70	76.47 156	86.80 118	49.47 226	83.77 58	53.89 185	92.72 62	88.81 50

PCF-MVS	63.80 13	72.70 136	71.69 149	75.72 85	78.10 139	60.01 124	73.04 139	81.50 82	45.34 262	79.66 115	84.35 168	65.15 105	82.65 76	48.70 216	89.38 118	84.50 105
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
LFMVS		67.06 195	67.89 190	64.56 220	78.02 140	38.25 273	70.81 180	59.60 260	65.18 69	71.06 218	86.56 133	43.85 249	75.22 198	46.35 234	89.63 113	80.21 187

anonymousdsp		78.60 57	77.80 66	81.00 31	78.01 141	74.34 33	80.09 58	76.12 170	50.51 225	89.19 12	90.88 41	71.45 58	77.78 175	73.38 40	90.60 99	90.90 27

BH-untuned		69.39 168	69.46 167	69.18 181	77.96 142	56.88 142	68.47 209	77.53 160	56.77 148	77.79 136	79.63 224	60.30 144	80.20 135	46.04 235	80.65 236	70.47 266

1112_ss		59.48 247	58.99 246	60.96 255	77.84 143	42.39 245	61.42 280	68.45 227	37.96 299	59.93 292	67.46 322	45.11 242	65.07 281	40.89 268	71.81 292	75.41 228

PS-MVSNAJ		64.27 212	63.73 210	65.90 215	77.82 144	51.42 168	63.33 263	72.33 199	45.09 265	61.60 275	68.04 320	62.39 123	73.95 208	49.07 213	73.87 284	72.34 250

ambc				70.10 171	77.74 145	50.21 174	74.28 135	77.93 156		79.26 120	88.29 102	54.11 209	79.77 140	64.43 117	91.10 86	80.30 186

xiu_mvs_v2_base		64.43 209	63.96 207	65.85 216	77.72 146	51.32 169	63.63 260	72.31 200	45.06 266	61.70 274	69.66 310	62.56 119	73.93 209	49.06 214	73.91 283	72.31 251

FMVSNet1		71.06 152	72.48 139	66.81 206	77.65 147	40.68 254	71.96 155	73.03 190	61.14 108	79.45 118	90.36 63	60.44 143	75.20 199	50.20 206	88.05 134	84.54 101

FPMVS		59.43 248	60.07 239	57.51 276	77.62 148	71.52 44	62.33 267	50.92 308	57.40 139	69.40 230	80.00 218	39.14 271	61.92 292	37.47 289	66.36 317	39.09 346

MVS_0304		74.55 105	73.47 115	77.80 65	77.41 149	63.88 99	75.75 114	83.67 52	63.55 88	66.12 255	82.16 198	60.20 145	86.15 21	65.37 112	86.98 154	83.38 126

Effi-MVS+-dtu		75.43 86	72.28 143	84.91 2	77.05 150	83.58 2	78.47 74	77.70 157	57.68 134	74.89 171	78.13 239	64.80 108	84.26 55	56.46 163	85.32 179	86.88 71

mvs-test1		73.81 112	70.69 162	83.18 3	77.05 150	81.39 4	75.39 120	77.70 157	57.68 134	71.19 217	74.72 271	64.80 108	83.66 60	56.46 163	81.19 229	84.50 105

CLD-MVS		72.88 133	72.36 141	74.43 95	77.03 152	54.30 155	68.77 203	83.43 58	52.12 202	76.79 149	74.44 275	69.54 70	83.91 56	55.88 169	93.25 56	85.09 91
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
Baseline_NR-MVSNet		70.62 157	73.19 122	62.92 235	76.97 153	34.44 301	68.84 198	70.88 216	60.25 117	79.50 117	90.53 52	61.82 129	69.11 248	54.67 179	95.27 15	85.22 89

ITE_SJBPF				80.35 37	76.94 154	73.60 38		80.48 110	66.87 50	83.64 71	86.18 142	70.25 66	79.90 139	61.12 134	88.95 123	87.56 67

jason		64.47 208	62.84 222	69.34 179	76.91 155	59.20 128	67.15 223	65.67 235	35.29 311	65.16 260	76.74 247	44.67 244	70.68 239	54.74 178	79.28 251	78.14 208
jason: jason.
PM-MVS		64.49 207	63.61 211	67.14 203	76.68 156	75.15 28	68.49 208	42.85 333	51.17 215	77.85 135	80.51 213	45.76 238	66.31 278	52.83 191	76.35 269	59.96 324

TransMVSNet (Re)		69.62 165	71.63 151	63.57 229	76.51 157	35.93 290	65.75 241	71.29 210	61.05 109	75.02 169	89.90 71	65.88 100	70.41 244	49.79 208	89.48 116	84.38 108

BH-RMVSNet		68.69 182	68.20 188	70.14 170	76.40 158	53.90 159	64.62 251	73.48 189	58.01 132	73.91 185	81.78 202	59.09 159	78.22 170	48.59 217	77.96 263	78.31 205

PHI-MVS		74.92 97	74.36 104	76.61 71	76.40 158	62.32 109	80.38 54	83.15 60	54.16 182	73.23 192	80.75 211	62.19 126	83.86 57	68.02 83	90.92 92	83.65 120

UGNet		70.20 160	69.05 172	73.65 109	76.24 160	63.64 100	75.87 111	72.53 197	61.48 105	60.93 284	86.14 145	52.37 215	77.12 179	50.67 202	85.21 181	80.17 191
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
PatchMatch-RL		58.68 255	57.72 260	61.57 247	76.21 161	73.59 39	61.83 275	49.00 315	47.30 250	61.08 279	68.97 314	50.16 225	59.01 299	36.06 297	68.84 309	52.10 335

VPA-MVSNet		68.71 181	70.37 163	63.72 228	76.13 162	38.06 275	64.10 256	71.48 207	56.60 151	74.10 182	88.31 101	64.78 110	69.72 245	47.69 226	90.15 106	83.37 128

PAPM		61.79 232	60.37 238	66.05 213	76.09 163	41.87 247	69.30 194	76.79 167	40.64 289	53.80 319	79.62 225	44.38 246	82.92 73	29.64 323	73.11 287	73.36 241

BH-w/o		64.81 204	64.29 206	66.36 211	76.08 164	54.71 152	65.61 243	75.23 181	50.10 228	71.05 219	71.86 300	54.33 208	79.02 146	38.20 283	76.14 270	65.36 304

pmmvs6		71.82 147	73.66 112	66.31 212	75.94 165	42.01 246	66.99 224	72.53 197	63.45 90	76.43 157	92.78 10	72.95 49	69.69 246	51.41 197	90.46 101	87.22 69

CANet		73.00 126	71.84 147	76.48 73	75.82 166	61.28 115	74.81 127	80.37 113	63.17 92	62.43 272	80.50 214	61.10 139	85.16 41	64.00 119	84.34 190	83.01 132

pmmvs-eth3d		64.41 210	63.27 214	67.82 198	75.81 167	60.18 122	69.49 192	62.05 252	38.81 294	74.13 181	82.23 197	43.76 250	68.65 258	42.53 257	80.63 238	74.63 233

TR-MVS		64.59 205	63.54 212	67.73 199	75.75 168	50.83 171	63.39 262	70.29 220	49.33 232	71.55 212	74.55 273	50.94 222	78.46 160	40.43 271	75.69 272	73.89 238

cascas		64.59 205	62.77 223	70.05 172	75.27 169	50.02 175	61.79 276	71.61 203	42.46 280	63.68 267	68.89 316	49.33 228	80.35 130	47.82 225	84.05 193	79.78 193

API-MVS		70.97 154	71.51 154	69.37 176	75.20 170	55.94 145	80.99 48	76.84 165	62.48 98	71.24 215	77.51 242	61.51 133	80.96 120	52.04 192	85.76 165	71.22 261

PAPR		69.20 171	68.66 183	70.82 160	75.15 171	47.77 204	75.31 121	81.11 94	49.62 231	66.33 254	79.27 229	61.53 132	82.96 72	48.12 222	81.50 221	81.74 161

MVSFormer		69.93 163	69.03 174	72.63 140	74.93 172	59.19 129	83.98 28	75.72 174	52.27 200	63.53 268	76.74 247	43.19 253	80.56 126	72.28 51	78.67 256	78.14 208

lupinMVS		63.36 216	61.49 231	68.97 184	74.93 172	59.19 129	65.80 240	64.52 243	34.68 316	63.53 268	74.25 278	43.19 253	70.62 240	53.88 186	78.67 256	77.10 218

nrg030		74.87 101	75.99 86	71.52 156	74.90 174	49.88 179	74.10 136	82.58 69	54.55 177	83.50 72	89.21 82	71.51 56	75.74 193	61.24 132	92.34 66	88.94 46

TAPA-MVS	65.27 12	75.16 92	74.29 105	77.77 66	74.86 175	68.08 71	77.89 81	84.04 48	55.15 163	76.19 159	83.39 177	66.91 91	80.11 137	60.04 139	90.14 107	85.13 90
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
RPSCF		75.76 82	74.37 103	79.93 39	74.81 176	77.53 16	77.53 84	79.30 128	59.44 122	78.88 123	89.80 72	71.26 60	73.09 212	57.45 153	80.89 233	89.17 40

EI-MVSNet-Vis-set		72.78 135	71.87 146	75.54 87	74.77 177	59.02 133	72.24 148	71.56 205	63.92 83	78.59 124	71.59 301	66.22 98	78.60 155	67.58 90	80.32 239	89.00 43

Regformer-3		72.86 134	72.28 143	74.62 93	74.74 178	60.18 122	72.91 140	71.76 202	64.74 74	78.42 128	72.07 294	67.00 90	76.28 188	67.97 87	80.91 231	87.39 68

Regformer-4		74.64 103	73.67 111	77.55 67	74.74 178	64.49 95	72.91 140	75.42 179	67.45 48	80.24 111	72.07 294	68.98 74	80.19 136	70.29 63	80.91 231	87.98 62

v1240		73.06 124	73.14 123	72.84 134	74.74 178	47.27 218	71.88 160	81.11 94	51.80 206	82.28 80	84.21 169	56.22 202	82.34 81	68.82 75	87.17 152	88.91 47

v1921920		72.96 129	72.98 131	72.89 133	74.67 181	47.58 209	71.92 158	80.69 105	51.70 208	81.69 88	83.89 173	56.58 200	82.25 82	68.34 79	87.36 145	88.82 49

EI-MVSNet-UG-set		72.63 137	71.68 150	75.47 88	74.67 181	58.64 137	72.02 153	71.50 206	63.53 89	78.58 126	71.39 304	65.98 99	78.53 157	67.30 96	80.18 240	89.23 38

Fast-Effi-MVS+		68.81 179	68.30 185	70.35 164	74.66 183	48.61 189	66.06 237	78.32 147	50.62 224	71.48 214	75.54 261	68.75 76	79.59 143	50.55 204	78.73 255	82.86 135

v1192		73.40 119	73.42 116	73.32 121	74.65 184	48.67 188	72.21 149	81.73 79	52.76 198	81.85 83	84.56 166	57.12 195	82.24 83	68.58 77	87.33 147	89.06 41

v13		76.23 76	77.02 73	73.86 105	74.61 185	48.80 184	76.91 93	81.10 97	62.66 95	87.02 29	91.01 39	59.76 151	81.41 96	71.29 54	88.78 125	91.38 14

v144192		72.99 127	73.06 129	72.77 135	74.58 186	47.48 210	71.90 159	80.44 112	51.57 209	81.46 93	84.11 171	58.04 179	82.12 85	67.98 86	87.47 141	88.70 52

v12		76.03 78	76.79 74	73.76 107	74.45 187	48.60 190	76.59 95	81.11 94	62.22 100	86.79 31	90.74 47	59.51 152	81.40 98	71.01 57	88.67 127	91.29 16

MAR-MVS		67.72 190	66.16 199	72.40 146	74.45 187	64.99 92	74.87 125	77.50 161	48.67 236	65.78 258	68.58 319	57.01 198	77.79 174	46.68 233	81.92 211	74.42 235
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
v10		75.69 84	76.20 84	74.16 99	74.44 189	48.69 186	75.84 113	82.93 64	59.02 126	85.92 43	89.17 83	58.56 167	82.74 75	70.73 59	89.14 121	91.05 21

v7		73.59 115	73.69 110	73.28 122	74.42 190	48.68 187	72.74 145	81.98 75	54.76 173	82.07 81	85.05 159	58.53 168	82.22 84	67.99 85	85.66 168	88.95 45

v11		75.76 82	76.51 78	73.48 115	74.28 191	47.81 203	76.16 105	81.28 90	61.56 104	86.39 36	90.38 61	59.32 156	81.41 96	70.85 58	88.41 130	91.23 17

V9		75.82 80	76.53 77	73.66 108	74.28 191	48.37 191	76.26 103	81.10 97	61.73 103	86.59 34	90.43 55	59.16 158	81.42 95	70.71 60	88.56 128	91.21 19

canonicalmvs		72.29 143	73.38 118	69.04 183	74.23 193	47.37 215	73.93 137	83.18 59	54.36 179	76.61 151	81.64 206	72.03 53	75.34 197	57.12 156	87.28 149	84.40 107

Regformer-1		74.28 107	73.63 113	76.21 81	74.22 194	64.12 97	72.77 143	75.46 178	66.86 51	79.27 119	72.08 291	69.29 71	78.74 153	68.73 76	84.02 194	85.77 86

Regformer-2		75.32 89	74.47 101	77.88 64	74.22 194	66.65 78	72.77 143	77.54 159	68.47 46	80.44 107	72.08 291	70.60 63	80.97 116	70.08 67	84.02 194	86.01 80

Effi-MVS+		72.10 144	72.28 143	71.58 154	74.21 196	50.33 172	74.72 131	82.73 66	62.62 96	70.77 220	76.83 246	69.96 68	80.97 116	60.20 136	78.43 258	83.45 125

v1144		73.29 122	73.39 117	73.01 128	74.12 197	48.11 197	72.01 154	81.08 99	53.83 188	81.77 84	84.68 164	58.07 178	81.91 86	68.10 81	86.86 155	88.99 44

V14		75.58 85	76.26 83	73.55 113	74.10 198	48.13 196	75.91 109	81.07 100	61.19 107	86.34 37	90.11 68	58.80 162	81.40 98	70.40 62	88.43 129	91.12 20

v748		76.93 71	77.95 65	73.87 103	73.94 199	52.44 165	75.90 110	79.98 122	65.34 67	86.97 30	91.77 23	67.40 89	78.40 164	70.23 64	90.01 109	90.76 31

EI-MVSNet		69.61 166	69.01 175	71.41 157	73.94 199	49.90 176	71.31 173	71.32 208	58.22 130	75.40 167	70.44 305	58.16 173	75.85 189	62.51 126	79.81 245	88.48 59

CVMVSNet		59.21 251	58.44 258	61.51 248	73.94 199	47.76 205	71.31 173	64.56 242	26.91 344	60.34 286	70.44 305	36.24 283	67.65 265	53.57 189	68.66 311	69.12 284

v15		75.37 87	76.01 85	73.44 116	73.91 202	47.87 202	75.55 117	81.04 101	60.76 112	86.11 41	89.76 73	58.53 168	81.40 98	70.11 65	88.32 131	91.04 23

IterMVS-LS		73.01 125	73.12 125	72.66 138	73.79 203	49.90 176	71.63 164	78.44 146	58.22 130	80.51 106	86.63 130	58.15 174	79.62 141	62.51 126	88.20 132	88.48 59
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v17		75.03 95	75.59 90	73.36 117	73.56 204	47.66 207	75.48 118	81.45 85	60.58 114	85.55 49	89.02 88	58.36 170	81.47 92	69.69 72	86.59 158	90.96 24

v16		74.89 100	75.41 94	73.35 118	73.54 205	47.62 208	75.47 119	81.45 85	60.58 114	85.46 51	88.97 91	58.27 171	81.47 92	69.66 73	85.25 180	90.95 25

alignmvs		70.54 158	71.00 158	69.15 182	73.50 206	48.04 200	69.85 189	79.62 124	53.94 187	76.54 153	82.00 199	59.00 160	74.68 203	57.32 154	87.21 150	84.72 96

Fast-Effi-MVS+-dtu		70.00 162	68.74 182	73.77 106	73.47 207	64.53 94	71.36 171	78.14 153	55.81 155	68.84 235	74.71 272	65.36 104	75.75 192	52.00 193	79.00 252	81.03 171

v8		75.07 94	75.64 89	73.35 118	73.42 208	47.46 212	75.20 122	81.45 85	60.05 118	85.64 46	89.26 78	58.08 177	81.80 88	69.71 71	87.97 137	90.79 29

v6		72.93 130	73.08 126	72.48 142	73.42 208	47.47 211	72.17 150	80.25 117	55.63 156	81.65 89	85.04 160	57.95 184	81.28 104	66.56 102	85.01 185	88.70 52

v1neww		72.93 130	73.07 127	72.48 142	73.41 210	47.46 212	72.17 150	80.26 115	55.63 156	81.63 90	85.07 157	57.97 181	81.28 104	66.55 103	84.98 186	88.70 52

v7new		72.93 130	73.07 127	72.48 142	73.41 210	47.46 212	72.17 150	80.26 115	55.63 156	81.63 90	85.07 157	57.97 181	81.28 104	66.55 103	84.98 186	88.70 52

tfpnnormal		66.48 197	67.93 189	62.16 244	73.40 212	36.65 282	63.45 261	64.99 241	55.97 153	72.82 195	87.80 108	57.06 197	69.10 249	48.31 221	87.54 140	80.72 180

semantic-postprocess				72.49 141	73.34 213	58.20 139		65.55 238	48.10 241	76.91 146	82.64 190	42.25 258	78.84 150	61.20 133	77.89 264	80.44 185

v18		74.60 104	75.06 95	73.22 123	73.29 214	47.36 216	75.02 123	81.47 84	60.01 119	85.13 54	88.44 97	57.93 185	81.47 92	69.26 74	85.02 184	90.84 28

VNet		64.01 215	65.15 203	60.57 257	73.28 215	35.61 293	57.60 298	67.08 231	54.61 175	66.76 253	83.37 179	56.28 201	66.87 271	42.19 259	85.20 182	79.23 198

3Dnovator	65.95 11	71.50 150	71.22 157	72.34 147	73.16 216	63.09 105	78.37 75	78.32 147	57.67 136	72.22 205	84.61 165	54.77 205	78.47 159	60.82 135	81.07 230	75.45 227

GBi-Net		68.30 184	68.79 179	66.81 206	73.14 217	40.68 254	71.96 155	73.03 190	54.81 168	74.72 175	90.36 63	48.63 231	75.20 199	47.12 228	85.37 175	84.54 101

test1		68.30 184	68.79 179	66.81 206	73.14 217	40.68 254	71.96 155	73.03 190	54.81 168	74.72 175	90.36 63	48.63 231	75.20 199	47.12 228	85.37 175	84.54 101

FMVSNet2		67.48 192	68.21 187	65.29 217	73.14 217	38.94 268	68.81 200	71.21 214	54.81 168	76.73 150	86.48 136	48.63 231	74.60 204	47.98 223	86.11 162	82.35 148

v1		72.60 138	72.73 134	72.19 149	73.12 220	47.01 221	71.48 165	79.10 133	55.01 164	81.24 95	84.92 163	57.46 190	80.90 121	66.59 99	85.67 166	88.68 56

v1141		72.59 140	72.73 134	72.19 149	73.10 221	47.00 222	71.48 165	79.11 131	55.01 164	81.23 96	84.94 162	57.45 191	80.89 122	66.58 100	85.65 169	88.68 56

pm-mvs1		68.40 183	69.85 166	64.04 225	73.10 221	39.94 261	64.61 252	70.50 218	55.52 159	73.97 184	89.33 76	63.91 114	68.38 260	49.68 210	88.02 135	83.81 117

divwei89l23v2f112		72.60 138	72.73 134	72.19 149	73.10 221	47.00 222	71.48 165	79.11 131	55.01 164	81.23 96	84.95 161	57.45 191	80.89 122	66.58 100	85.67 166	88.68 56

pmmvs4		60.78 240	59.04 245	66.00 214	73.06 224	57.67 140	64.53 253	60.22 258	36.91 304	65.96 256	77.27 243	39.66 269	68.54 259	38.87 276	74.89 279	71.80 256

v52		78.96 52	79.79 51	76.46 75	73.03 225	54.90 149	78.48 72	83.48 55	64.43 77	91.19 4	91.54 28	72.08 51	81.11 109	76.45 28	87.47 141	93.38 7

V4		78.96 52	79.79 51	76.46 75	73.02 226	54.90 149	78.48 72	83.47 56	64.43 77	91.20 3	91.54 28	72.08 51	81.11 109	76.45 28	87.46 143	93.38 7

view600		62.88 223	62.90 218	62.82 236	72.97 227	33.66 307	66.10 233	55.01 284	57.05 141	72.66 196	82.56 191	31.60 304	72.78 214	42.64 253	85.55 170	82.02 153

view800		62.88 223	62.90 218	62.82 236	72.97 227	33.66 307	66.10 233	55.01 284	57.05 141	72.66 196	82.56 191	31.60 304	72.78 214	42.64 253	85.55 170	82.02 153

conf0.05thres1000		62.88 223	62.90 218	62.82 236	72.97 227	33.66 307	66.10 233	55.01 284	57.05 141	72.66 196	82.56 191	31.60 304	72.78 214	42.64 253	85.55 170	82.02 153

tfpn		62.88 223	62.90 218	62.82 236	72.97 227	33.66 307	66.10 233	55.01 284	57.05 141	72.66 196	82.56 191	31.60 304	72.78 214	42.64 253	85.55 170	82.02 153

v2v482		72.55 142	72.58 138	72.43 145	72.92 231	46.72 226	71.41 170	79.13 130	55.27 160	81.17 98	85.25 155	55.41 204	81.13 108	67.25 97	85.46 174	89.43 36

MIMVSNet		54.39 277	56.12 272	49.20 301	72.57 232	30.91 327	59.98 288	48.43 317	41.66 284	55.94 308	83.86 174	41.19 262	50.42 310	26.05 330	75.38 277	66.27 300

Patchmatch-RL test		59.95 244	59.12 244	62.44 242	72.46 233	54.61 154	59.63 290	47.51 320	41.05 287	74.58 178	74.30 277	31.06 313	65.31 279	51.61 195	79.85 244	67.39 292

Vis-MVSNet (Re-imp)		62.74 228	63.21 215	61.34 251	72.19 234	31.56 326	67.31 222	53.87 291	53.60 190	69.88 228	83.37 179	40.52 267	70.98 238	41.40 265	86.78 156	81.48 164

conf200view11		61.42 235	61.09 233	62.43 243	72.14 235	35.01 296	65.42 245	56.99 275	55.23 161	70.71 221	79.90 219	32.07 300	72.09 227	35.61 298	81.73 214	80.18 188

thres100view900		61.17 237	61.09 233	61.39 250	72.14 235	35.01 296	65.42 245	56.99 275	55.23 161	70.71 221	79.90 219	32.07 300	72.09 227	35.61 298	81.73 214	77.08 219

testmv		52.91 286	54.31 281	48.71 305	72.13 237	36.18 286	50.26 315	47.78 318	44.15 269	64.61 262	79.78 222	38.18 275	50.20 312	21.96 341	69.93 303	59.75 325

ab-mvs		64.11 213	65.13 204	61.05 253	71.99 238	38.03 276	67.59 215	68.79 225	49.08 233	65.32 259	86.26 140	58.02 180	66.85 273	39.33 273	79.79 247	78.27 206

thres600view7		61.82 231	61.38 232	63.12 233	71.81 239	34.93 298	64.64 250	56.99 275	54.78 172	70.33 226	79.74 223	32.07 300	72.42 224	38.61 279	83.46 199	82.02 153

QAPM		69.18 172	69.26 170	68.94 185	71.61 240	52.58 164	80.37 55	78.79 139	49.63 230	73.51 187	85.14 156	53.66 210	79.12 145	55.11 175	75.54 274	75.11 231

Anonymous20231206		54.13 278	55.82 273	49.04 304	70.89 241	35.96 289	51.73 312	50.87 309	34.86 312	62.49 271	79.22 230	42.52 257	44.29 330	27.95 328	81.88 213	66.88 296

testing_2		72.01 146	72.36 141	70.95 159	70.79 242	48.70 185	72.81 142	78.09 154	48.79 235	84.46 66	89.15 85	57.90 186	78.55 156	61.55 130	87.74 138	85.61 87

tfpn200view9		60.35 243	59.97 240	61.51 248	70.78 243	35.35 294	63.27 264	57.47 269	53.00 196	68.31 237	77.09 244	32.45 297	72.09 227	35.61 298	81.73 214	77.08 219

thres400		60.77 241	59.97 240	63.15 232	70.78 243	35.35 294	63.27 264	57.47 269	53.00 196	68.31 237	77.09 244	32.45 297	72.09 227	35.61 298	81.73 214	82.02 153

MSDG		67.47 193	67.48 192	67.46 200	70.70 245	54.69 153	66.90 226	78.17 152	60.88 111	70.41 224	74.76 269	61.22 138	73.18 211	47.38 227	76.87 267	74.49 234

OpenMVS	62.51 15	68.76 180	68.75 181	68.78 191	70.56 246	53.91 158	78.29 76	77.35 162	48.85 234	70.22 227	83.52 176	52.65 214	76.93 181	55.31 174	81.99 210	75.49 226

DELS-MVS		68.83 178	68.31 184	70.38 163	70.55 247	48.31 192	63.78 259	82.13 72	54.00 184	68.96 234	75.17 267	58.95 161	80.06 138	58.55 147	82.74 204	82.76 138
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
LCM-MVSNet-Re		69.10 173	71.57 153	61.70 246	70.37 248	34.30 302	61.45 279	79.62 124	56.81 147	89.59 10	88.16 105	68.44 79	72.94 213	42.30 258	87.33 147	77.85 213

Patchmatch-test1		57.81 263	58.04 259	57.13 277	70.17 249	41.07 253	65.19 248	53.38 295	43.34 279	61.00 282	71.94 298	45.20 241	62.69 288	41.81 263	70.31 300	67.63 291

PVSNet_BlendedMVS		65.38 200	64.30 205	68.61 192	69.81 250	49.36 180	65.60 244	78.96 135	45.50 259	59.98 289	78.61 235	51.82 217	78.20 171	44.30 239	84.11 192	78.27 206

PVSNet_Blended		62.90 222	61.64 228	66.69 209	69.81 250	49.36 180	61.23 282	78.96 135	42.04 282	59.98 289	68.86 317	51.82 217	78.20 171	44.30 239	77.77 265	72.52 248

OpenMVS_ROB	54.93 17	63.23 218	63.28 213	63.07 234	69.81 250	45.34 231	68.52 207	67.14 230	43.74 273	70.61 223	79.22 230	47.90 235	72.66 219	48.75 215	73.84 285	71.21 262

DI_MVS_plusplus_test		69.01 176	69.04 173	68.93 186	69.54 253	46.74 225	70.14 184	75.49 176	46.64 252	78.30 130	83.18 188	58.80 162	78.86 149	57.14 155	82.15 208	81.18 167

Test4		69.04 175	68.95 176	69.32 180	69.52 254	48.10 198	70.69 181	78.25 151	45.90 256	80.99 100	82.24 196	51.91 216	78.11 173	58.46 148	82.58 206	81.74 161

FMVSNet3		65.00 203	65.16 202	64.52 221	69.47 255	37.56 280	66.63 228	70.38 219	51.55 210	74.72 175	83.27 184	37.89 280	74.44 206	47.12 228	85.37 175	81.57 163

test_normal		68.88 177	68.88 177	68.88 189	69.43 256	47.03 220	69.85 189	74.83 183	46.06 255	78.30 130	83.29 183	58.76 166	78.23 169	57.51 152	81.90 212	81.36 165

MS-PatchMatch		55.59 273	54.89 279	57.68 274	69.18 257	49.05 183	61.00 284	62.93 247	35.98 307	58.36 297	68.93 315	36.71 282	66.59 276	37.62 288	63.30 323	57.39 328

v148		69.38 169	69.39 168	69.36 177	69.14 258	44.56 233	68.83 199	72.70 195	54.79 171	78.59 124	84.12 170	54.69 206	76.74 185	59.40 144	82.20 207	86.79 72

conf0.01		59.26 249	58.88 247	60.40 259	68.66 259	31.96 320	62.04 269	51.95 300	50.99 216	67.57 243	75.91 253	28.59 329	69.07 250	42.77 247	81.40 222	80.18 188

conf0.002		59.26 249	58.88 247	60.40 259	68.66 259	31.96 320	62.04 269	51.95 300	50.99 216	67.57 243	75.91 253	28.59 329	69.07 250	42.77 247	81.40 222	80.18 188

thresconf0.02		58.38 256	58.88 247	56.91 279	68.66 259	31.96 320	62.04 269	51.95 300	50.99 216	67.57 243	75.91 253	28.59 329	69.07 250	42.77 247	81.40 222	69.70 274

tfpn_n400		58.38 256	58.88 247	56.91 279	68.66 259	31.96 320	62.04 269	51.95 300	50.99 216	67.57 243	75.91 253	28.59 329	69.07 250	42.77 247	81.40 222	69.70 274

tfpnconf		58.38 256	58.88 247	56.91 279	68.66 259	31.96 320	62.04 269	51.95 300	50.99 216	67.57 243	75.91 253	28.59 329	69.07 250	42.77 247	81.40 222	69.70 274

tfpnview11		58.38 256	58.88 247	56.91 279	68.66 259	31.96 320	62.04 269	51.95 300	50.99 216	67.57 243	75.91 253	28.59 329	69.07 250	42.77 247	81.40 222	69.70 274

test1235678		48.41 301	49.60 301	44.83 318	68.52 265	33.81 305	46.33 327	45.89 325	38.72 295	58.46 295	72.08 291	29.85 324	47.82 317	19.67 345	66.91 316	52.88 333

CANet_DTU		64.04 214	63.83 208	64.66 219	68.39 266	42.97 241	73.45 138	74.50 185	52.05 204	54.78 313	75.44 266	43.99 248	70.42 243	53.49 190	78.41 259	80.59 182

EU-MVSNet		60.82 239	60.80 236	60.86 256	68.37 267	41.16 251	72.27 147	68.27 228	26.96 343	69.08 232	75.71 259	32.09 299	67.44 267	55.59 172	78.90 253	73.97 236

PVSNet	43.83 21	51.56 293	51.17 294	52.73 291	68.34 268	38.27 272	48.22 320	53.56 294	36.41 305	54.29 317	64.94 328	34.60 287	54.20 307	30.34 318	69.87 304	65.71 303

EPNet		69.10 173	67.32 193	74.46 94	68.33 269	61.27 116	77.56 83	63.57 245	60.95 110	56.62 305	82.75 189	51.53 220	81.24 107	54.36 184	90.20 104	80.88 175
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
IB-MVS	49.67 18	59.69 246	56.96 266	67.90 196	68.19 270	50.30 173	61.42 280	65.18 240	47.57 248	55.83 309	67.15 325	23.77 344	79.60 142	43.56 244	79.97 243	73.79 239
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
MVS		60.62 242	59.97 240	62.58 241	68.13 271	47.28 217	68.59 204	73.96 187	32.19 327	59.94 291	68.86 317	50.48 223	77.64 176	41.85 262	75.74 271	62.83 314

tfpn1000		58.28 260	58.86 253	56.53 283	68.05 272	32.26 317	62.58 266	51.67 307	51.25 214	67.38 249	75.95 252	27.24 336	68.83 256	43.51 245	82.11 209	68.49 287

TinyColmap		67.98 186	69.28 169	64.08 224	67.98 273	46.82 224	70.04 185	75.26 180	53.05 195	77.36 140	86.79 119	59.39 154	72.59 223	45.64 237	88.01 136	72.83 245

EPNet_dtu		58.93 253	58.52 256	60.16 263	67.91 274	47.70 206	69.97 186	58.02 265	49.73 229	47.28 334	73.02 289	38.14 276	62.34 290	36.57 294	85.99 163	70.43 267
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
thres200		57.55 265	57.02 265	59.17 266	67.89 275	34.93 298	58.91 294	57.25 273	50.24 226	64.01 265	71.46 303	32.49 296	71.39 236	31.31 314	79.57 249	71.19 263

MVS_Test		69.84 164	70.71 161	67.24 202	67.49 276	43.25 239	69.87 188	81.22 93	52.69 199	71.57 211	86.68 126	62.09 127	74.51 205	66.05 106	78.74 254	83.96 114

V42		71.06 152	70.83 160	71.72 153	67.25 277	47.14 219	65.94 238	80.35 114	51.35 211	83.40 73	83.23 185	59.25 157	78.80 151	65.91 108	80.81 235	89.23 38

tfpn_ndepth		56.91 267	57.30 264	55.71 284	67.22 278	33.26 312	61.72 277	53.98 290	48.49 238	64.16 264	71.94 298	27.65 335	68.71 257	40.49 270	80.08 241	65.17 306

GA-MVS		62.91 221	61.66 227	66.66 210	67.09 279	44.49 234	61.18 283	69.36 224	51.33 212	69.33 231	74.47 274	36.83 281	74.94 202	50.60 203	74.72 280	80.57 183

HY-MVS	49.31 19	57.96 262	57.59 261	59.10 267	66.85 280	36.17 287	65.13 249	65.39 239	39.24 292	54.69 315	78.14 238	44.28 247	67.18 270	33.75 308	70.79 297	73.95 237

tpmp4_e23		57.57 264	55.46 277	63.93 226	66.48 281	41.56 250	71.68 163	60.65 257	35.64 310	55.35 312	76.25 250	29.53 325	75.41 196	34.40 304	69.12 308	74.83 232

CR-MVSNet		58.96 252	58.49 257	60.36 261	66.37 282	48.24 194	70.93 178	56.40 279	32.87 326	61.35 277	86.66 127	33.19 292	63.22 286	48.50 219	70.17 301	69.62 279

RPMNet		61.25 236	61.55 230	60.36 261	66.37 282	48.24 194	70.93 178	54.45 289	54.66 174	61.35 277	86.77 122	33.29 291	63.22 286	55.93 168	70.17 301	69.62 279

IterMVS		63.12 219	62.48 225	65.02 218	66.34 284	52.86 163	63.81 258	62.25 248	46.57 253	71.51 213	80.40 215	44.60 245	66.82 274	51.38 198	75.47 275	75.38 229
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
tpm2		56.12 268	54.64 280	60.55 258	66.24 285	36.01 288	68.14 211	56.77 278	33.60 324	58.25 298	75.52 263	30.25 319	74.33 207	33.27 309	69.76 306	71.32 259

Patchmtry		60.91 238	63.01 217	54.62 288	66.10 286	26.27 339	67.47 217	56.40 279	54.05 183	72.04 206	86.66 127	33.19 292	60.17 296	43.69 242	87.45 144	77.42 214

FMVSNet5		55.08 275	55.54 276	53.71 289	65.80 287	33.50 311	56.22 300	52.50 299	43.72 274	61.06 280	83.38 178	25.46 341	54.87 304	30.11 320	81.64 220	72.75 246

1314		59.83 245	58.86 253	62.74 240	65.71 288	44.78 232	68.59 204	72.63 196	33.54 325	61.05 281	67.29 324	43.62 251	71.26 237	49.49 211	67.84 314	72.19 253

MDTV_nov1_ep13			54.05 283		65.54 289	29.30 330	59.00 293	55.22 281	35.96 308	52.44 321	75.98 251	30.77 316	59.62 297	38.21 282	73.33 286

USDC		62.80 227	63.10 216	61.89 245	65.19 290	43.30 238	67.42 218	74.20 186	35.80 309	72.25 204	84.48 167	45.67 239	71.95 232	37.95 285	84.97 188	70.42 268

tpm		50.60 294	52.42 290	45.14 316	65.18 291	26.29 338	60.30 286	43.50 330	37.41 301	57.01 301	79.09 234	30.20 321	42.32 336	32.77 311	66.36 317	66.81 298

PatchmatchNet		54.60 276	54.27 282	55.59 285	65.17 292	39.08 265	66.92 225	51.80 306	39.89 290	58.39 296	73.12 288	31.69 303	58.33 300	43.01 246	58.38 338	69.38 282
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
tpm cat1		54.02 280	52.63 288	58.19 272	64.85 293	39.86 262	66.26 232	57.28 272	32.16 328	56.90 303	70.39 307	32.75 295	65.30 280	34.29 305	58.79 334	69.41 281

XXY-MVS		55.19 274	57.40 263	48.56 306	64.45 294	34.84 300	51.54 313	53.59 293	38.99 293	63.79 266	79.43 226	56.59 199	45.57 321	36.92 292	71.29 294	65.25 305

PatchT		53.35 283	56.47 269	43.99 321	64.19 295	17.46 348	59.15 291	43.10 331	52.11 203	54.74 314	86.95 114	29.97 322	49.98 313	43.62 243	74.40 281	64.53 311

mvs_anonymous		65.08 202	65.49 201	63.83 227	63.79 296	37.60 279	66.52 230	69.82 222	43.44 276	73.46 189	86.08 147	58.79 165	71.75 235	51.90 194	75.63 273	82.15 152

CostFormer		57.35 266	56.14 271	60.97 254	63.76 297	38.43 270	67.50 216	60.22 258	37.14 303	59.12 294	76.34 249	32.78 294	71.99 231	39.12 275	69.27 307	72.47 249

Gipuma		69.55 167	72.83 133	59.70 264	63.63 298	53.97 157	80.08 59	75.93 172	64.24 81	73.49 188	88.93 93	57.89 187	62.46 289	59.75 143	91.55 76	62.67 316
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
gg-mvs-nofinetune		55.75 271	56.75 268	52.72 292	62.87 299	28.04 334	68.92 197	41.36 341	71.09 31	50.80 325	92.63 12	20.74 347	66.86 272	29.97 321	72.41 289	63.25 312

no-one		56.11 269	55.62 275	57.60 275	62.68 300	49.23 182	39.12 340	58.99 263	33.72 320	60.98 283	80.90 210	36.07 284	60.36 295	30.68 316	97.40 1	63.22 313

testus		45.03 312	46.49 310	40.65 328	62.53 301	25.24 341	42.54 332	46.23 324	31.16 337	57.69 299	62.90 331	34.60 287	42.33 335	17.72 347	63.01 324	54.37 332

gm-plane-assit					62.51 302	33.91 304			37.25 302		62.71 332		72.74 218	38.70 277

diffmvs		66.15 198	65.86 200	67.01 205	62.31 303	44.43 235	68.81 200	72.93 193	48.13 240	62.12 273	83.33 181	57.96 183	72.29 225	59.83 141	77.31 266	84.33 110

MVS-HIRNet		45.53 307	47.29 307	40.24 329	62.29 304	26.82 337	56.02 301	37.41 347	29.74 339	43.69 345	81.27 207	33.96 289	55.48 303	24.46 336	56.79 339	38.43 347

PatchFormer-LS_test		53.94 282	52.64 287	57.85 273	61.87 305	39.59 263	61.60 278	57.63 267	40.65 288	54.52 316	58.64 339	29.07 328	64.18 283	46.78 232	62.98 325	69.78 272

CHOSEN 280x420		41.62 318	39.89 324	46.80 309	61.81 306	51.59 166	33.56 346	35.74 348	27.48 342	37.64 350	53.53 342	23.24 345	42.09 337	27.39 329	58.64 335	46.72 340

MDA-MVSNet-bldmvs		62.34 229	61.73 226	64.16 222	61.64 307	49.90 176	48.11 321	57.24 274	53.31 194	80.95 101	79.39 227	49.00 229	61.55 293	45.92 236	80.05 242	81.03 171

DWT-MVSNet_test		53.04 284	51.12 295	58.77 269	61.23 308	38.67 269	62.16 268	57.74 266	38.24 296	51.76 323	59.07 338	21.36 346	67.40 268	44.80 238	63.76 322	70.25 269

WTY-MVS		49.39 298	50.31 299	46.62 310	61.22 309	32.00 319	46.61 325	49.77 312	33.87 319	54.12 318	69.55 312	41.96 259	45.40 323	31.28 315	64.42 321	62.47 317

CMPMVS	48.73 20	61.54 234	60.89 235	63.52 230	61.08 310	51.55 167	68.07 212	68.00 229	33.88 318	65.87 257	81.25 208	37.91 279	67.71 264	49.32 212	82.60 205	71.31 260
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
test-LLR		50.43 295	50.69 298	49.64 299	60.76 311	41.87 247	53.18 309	45.48 328	43.41 277	49.41 330	60.47 336	29.22 326	44.73 327	42.09 260	72.14 290	62.33 318

test-mter		48.56 300	48.20 305	49.64 299	60.76 311	41.87 247	53.18 309	45.48 328	31.91 333	49.41 330	60.47 336	18.34 350	44.73 327	42.09 260	72.14 290	62.33 318

GG-mvs-BLEND				52.24 293	60.64 313	29.21 331	69.73 191	42.41 334		45.47 337	52.33 344	20.43 348	68.16 261	25.52 334	65.42 319	59.36 326

tpmvs		55.84 270	55.45 278	57.01 278	60.33 314	33.20 313	65.89 239	59.29 262	47.52 249	56.04 307	73.60 283	31.05 314	68.06 262	40.64 269	64.64 320	69.77 273

PVSNet_0	36.71 22	41.12 319	40.78 321	42.14 323	59.97 315	40.13 260	40.97 334	42.24 338	30.81 338	44.86 340	49.41 347	40.70 266	45.12 325	23.15 339	34.96 348	41.16 345

new-patchmatchnet		52.89 287	55.76 274	44.26 320	59.94 316	6.31 354	37.36 344	50.76 310	41.10 285	64.28 263	79.82 221	44.77 243	48.43 315	36.24 295	87.61 139	78.03 210

test20.03		55.74 272	57.51 262	50.42 296	59.89 317	32.09 318	50.63 314	49.01 314	50.11 227	65.07 261	83.23 185	45.61 240	48.11 316	30.22 319	83.82 197	71.07 265

MVSTER		63.29 217	61.60 229	68.36 194	59.77 318	46.21 228	60.62 285	71.32 208	41.83 283	75.40 167	79.12 233	30.25 319	75.85 189	56.30 165	79.81 245	83.03 131

N_pmnet		52.06 292	51.11 296	54.92 287	59.64 319	71.03 49	37.42 343	61.62 255	33.68 321	57.12 300	72.10 290	37.94 278	31.03 349	29.13 327	71.35 293	62.70 315

1111		45.08 311	47.96 306	36.43 333	59.56 320	14.82 350	43.56 330	45.65 326	45.60 257	60.04 287	75.47 264	9.31 357	34.46 346	23.66 337	68.76 310	60.02 323

.test1245		34.47 327	40.38 323	16.73 339	59.56 320	14.82 350	43.56 330	45.65 326	45.60 257	60.04 287	75.47 264	9.31 357	34.46 346	23.66 337	0.55 353	0.90 352

JIA-IIPM		54.03 279	51.62 292	61.25 252	59.14 322	55.21 148	59.10 292	47.72 319	50.85 223	50.31 329	85.81 151	20.10 349	63.97 284	36.16 296	55.41 343	64.55 310

LF4IMVS		67.50 191	67.31 194	68.08 195	58.86 323	61.93 110	71.43 169	75.90 173	44.67 267	72.42 202	80.20 216	57.16 193	70.44 242	58.99 146	86.12 161	71.88 255

test2356		40.85 320	40.47 322	41.98 324	58.78 324	28.65 333	39.45 338	40.98 344	31.95 332	48.47 332	56.63 340	12.54 356	44.41 329	15.84 349	59.58 331	52.88 333

UnsupCasMVSNet_bld		50.01 297	51.03 297	46.95 307	58.61 325	32.64 315	48.31 319	53.27 296	34.27 317	60.47 285	71.53 302	41.40 261	47.07 318	30.68 316	60.78 328	61.13 320

dp		44.09 316	44.88 315	41.72 327	58.53 326	23.18 344	54.70 306	42.38 336	34.80 313	44.25 343	65.61 327	24.48 343	44.80 326	29.77 322	49.42 346	57.18 329

test12356		38.35 321	40.80 320	31.01 335	58.31 327	9.09 353	36.67 345	46.65 321	33.65 323	44.39 342	60.94 335	17.56 352	39.23 344	16.01 348	53.03 344	44.72 343

testgi		54.00 281	56.86 267	45.45 314	58.20 328	25.81 340	49.05 317	49.50 313	45.43 261	67.84 239	81.17 209	51.81 219	43.20 334	29.30 324	79.41 250	67.34 294

PNet_i23d		36.76 324	36.63 327	37.12 332	58.19 329	33.00 314	39.86 337	32.55 350	48.44 239	39.64 346	51.31 345	6.89 359	41.83 340	22.29 340	30.55 349	36.54 348

wuyk23d		61.97 230	66.25 198	49.12 303	58.19 329	60.77 119	66.32 231	52.97 297	55.93 154	90.62 7	86.91 115	73.07 46	35.98 345	20.63 344	91.63 72	50.62 336

ANet_high		67.08 194	69.94 165	58.51 271	57.55 331	27.09 335	58.43 296	76.80 166	63.56 87	82.40 79	91.93 20	59.82 149	64.98 282	50.10 207	88.86 124	83.46 124

Patchmatch-test		47.93 302	49.96 300	41.84 325	57.42 332	24.26 343	48.75 318	41.49 340	39.30 291	56.79 304	73.48 284	30.48 318	33.87 348	29.29 325	72.61 288	67.39 292

new_pmnet		37.55 323	39.80 325	30.79 336	56.83 333	16.46 349	39.35 339	30.65 351	25.59 345	45.26 338	61.60 334	24.54 342	28.02 351	21.60 342	52.80 345	47.90 339

pmmvs3		46.71 305	45.09 313	51.55 295	56.76 334	48.25 193	55.78 302	39.53 346	24.13 347	50.35 328	63.40 330	15.90 354	51.08 309	29.29 325	70.69 299	55.33 331

sss		47.59 304	48.32 303	45.40 315	56.73 335	33.96 303	45.17 329	48.51 316	32.11 331	52.37 322	65.79 326	40.39 268	41.91 339	31.85 312	61.97 326	60.35 321

tpmrst		50.15 296	51.38 293	46.45 311	56.05 336	24.77 342	64.40 255	49.98 311	36.14 306	53.32 320	69.59 311	35.16 286	48.69 314	39.24 274	58.51 337	65.89 301

TESTMET0.1,1		45.17 309	44.93 314	45.89 313	56.02 337	38.31 271	53.18 309	41.94 339	27.85 341	44.86 340	56.47 341	17.93 351	41.50 341	38.08 284	68.06 312	57.85 327

ADS-MVSNet2		48.76 299	47.25 308	53.29 290	55.90 338	40.54 258	47.34 323	54.99 288	31.41 335	50.48 326	72.06 296	31.23 310	54.26 306	25.93 331	55.93 340	65.07 307

ADS-MVSNet		44.62 314	45.58 311	41.73 326	55.90 338	20.83 346	47.34 323	39.94 345	31.41 335	50.48 326	72.06 296	31.23 310	39.31 342	25.93 331	55.93 340	65.07 307

LP		53.02 285	52.27 291	55.27 286	55.76 340	40.55 257	55.64 303	55.07 282	42.46 280	56.95 302	73.21 287	33.67 290	54.18 308	38.41 281	59.29 333	71.08 264

test0.0.03 1		47.72 303	48.31 304	45.93 312	55.53 341	29.39 329	46.40 326	41.21 342	43.41 277	55.81 310	67.65 321	29.22 326	43.77 333	25.73 333	69.87 304	64.62 309

UnsupCasMVSNet_eth		52.26 291	53.29 286	49.16 302	55.08 342	33.67 306	50.03 316	58.79 264	37.67 300	63.43 270	74.75 270	41.82 260	45.83 320	38.59 280	59.42 332	67.98 290

pmmvs5		52.49 290	52.58 289	52.21 294	54.99 343	32.38 316	55.45 304	53.84 292	32.15 329	55.49 311	74.81 268	38.08 277	57.37 302	34.02 306	74.40 281	66.88 296

DSMNet-mixed		43.18 317	44.66 316	38.75 331	54.75 344	28.88 332	57.06 299	27.42 353	13.47 349	47.27 335	77.67 240	38.83 272	39.29 343	25.32 335	60.12 330	48.08 338

MDA-MVSNet_test_wron		52.57 289	53.49 285	49.81 298	54.24 345	36.47 284	40.48 336	46.58 322	38.13 297	75.47 166	73.32 285	41.05 265	43.85 332	40.98 267	71.20 295	69.10 285

YYNet1		52.58 288	53.50 284	49.85 297	54.15 346	36.45 285	40.53 335	46.55 323	38.09 298	75.52 165	73.31 286	41.08 264	43.88 331	41.10 266	71.14 296	69.21 283

EPMVS		45.74 306	46.53 309	43.39 322	54.14 347	22.33 345	55.02 305	35.00 349	34.69 315	51.09 324	70.20 309	25.92 339	42.04 338	37.19 290	55.50 342	65.78 302

testpf		45.32 308	48.47 302	35.88 334	53.56 348	26.84 336	58.86 295	42.95 332	47.78 246	46.18 336	63.70 329	13.73 355	50.29 311	50.81 201	58.61 336	30.51 349

E-PMN		45.17 309	45.36 312	44.60 319	50.07 349	42.75 242	38.66 341	42.29 337	46.39 254	39.55 347	51.15 346	26.00 338	45.37 324	37.68 286	76.41 268	45.69 342

PMMVS		44.69 313	43.95 318	46.92 308	50.05 350	53.47 161	48.08 322	42.40 335	22.36 348	44.01 344	53.05 343	42.60 256	45.49 322	31.69 313	61.36 327	41.79 344

EMVS		44.61 315	44.45 317	45.10 317	48.91 351	43.00 240	37.92 342	41.10 343	46.75 251	38.00 349	48.43 348	26.42 337	46.27 319	37.11 291	75.38 277	46.03 341

MVE	27.91 23	36.69 325	35.64 328	39.84 330	43.37 352	35.85 291	19.49 348	24.61 354	24.68 346	39.05 348	62.63 333	38.67 274	27.10 352	21.04 343	47.25 347	56.56 330
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
PMMVS2		37.74 322	40.87 319	28.36 338	42.41 353	5.35 355	24.61 347	27.75 352	32.15 329	47.85 333	70.27 308	35.85 285	29.51 350	19.08 346	67.85 313	50.22 337

DeepMVS_CX				11.83 340	15.51 354	13.86 352		11.25 357	5.76 350	20.85 352	26.46 349	17.06 353	9.22 353	9.69 351	13.82 351	12.42 350

tmp_tt		11.98 329	14.73 330	3.72 341	2.28 355	4.62 356	19.44 349	14.50 356	0.47 351	21.55 351	9.58 351	25.78 340	4.57 354	11.61 350	27.37 350	1.96 351

test123		4.43 332	5.78 333	0.39 343	0.97 356	0.28 357	46.33 327	0.45 358	0.31 352	0.62 353	1.50 354	0.61 361	0.11 356	0.56 352	0.63 352	0.77 354

testmvs		4.06 333	5.28 334	0.41 342	0.64 357	0.16 358	42.54 332	0.31 359	0.26 353	0.50 354	1.40 355	0.77 360	0.17 355	0.56 352	0.55 353	0.90 352

cdsmvs_eth3d_5k		17.71 328	23.62 329	0.00 344	0.00 358	0.00 359	0.00 350	70.17 221	0.00 354	0.00 355	74.25 278	68.16 82	0.00 357	0.00 354	0.00 355	0.00 355

pcd_1.5k_mvsjas		5.20 331	6.93 332	0.00 344	0.00 358	0.00 359	0.00 350	0.00 360	0.00 354	0.00 355	0.00 356	62.39 123	0.00 357	0.00 354	0.00 355	0.00 355

sosnet-low-res		0.00 334	0.00 335	0.00 344	0.00 358	0.00 359	0.00 350	0.00 360	0.00 354	0.00 355	0.00 356	0.00 362	0.00 357	0.00 354	0.00 355	0.00 355

sosnet		0.00 334	0.00 335	0.00 344	0.00 358	0.00 359	0.00 350	0.00 360	0.00 354	0.00 355	0.00 356	0.00 362	0.00 357	0.00 354	0.00 355	0.00 355

uncertanet		0.00 334	0.00 335	0.00 344	0.00 358	0.00 359	0.00 350	0.00 360	0.00 354	0.00 355	0.00 356	0.00 362	0.00 357	0.00 354	0.00 355	0.00 355

Regformer		0.00 334	0.00 335	0.00 344	0.00 358	0.00 359	0.00 350	0.00 360	0.00 354	0.00 355	0.00 356	0.00 362	0.00 357	0.00 354	0.00 355	0.00 355

ab-mvs-re		5.62 330	7.50 331	0.00 344	0.00 358	0.00 359	0.00 350	0.00 360	0.00 354	0.00 355	67.46 322	0.00 362	0.00 357	0.00 354	0.00 355	0.00 355

uanet		0.00 334	0.00 335	0.00 344	0.00 358	0.00 359	0.00 350	0.00 360	0.00 354	0.00 355	0.00 356	0.00 362	0.00 357	0.00 354	0.00 355	0.00 355

GSMVS																70.05 270

test_part3							83.39 33		73.27 20		89.25 80		86.96 9	72.56 45

test_part1								84.94 29				75.17 31			93.83 48	82.50 144

sam_mvs1												31.41 308				70.05 270

sam_mvs												31.21 312

MTGPA								80.63 106

test_post1							66.63 228				2.08 352	30.66 317	59.33 298	40.34 272

test_post											1.99 353	30.91 315	54.76 305

patchmatchnet-post											68.99 313	31.32 309	69.38 247

MTMP								19.26 355

test9_res														72.12 53	91.37 79	77.40 215

agg_prior2														70.70 61	90.93 91	78.55 204

test_prior4						70.14 58	77.57 82

test_prior2							75.57 115		58.92 127	76.53 154	86.78 120	67.83 86		69.81 69	92.76 60

旧先验2							71.17 175		45.11 264	78.54 127			61.28 294	59.19 145

新几何2							71.33 172

无先验							74.82 126	70.94 215	47.75 247				76.85 183	54.47 180		72.09 254

原ACMM2							74.78 130

testdata2													67.30 269	48.34 220

segment_acmp												68.30 81

testdata1							68.34 210		57.24 140

plane_prior5								85.49 21					86.15 21	71.09 55	90.94 89	84.82 94

plane_prior4											89.11 86

plane_prior3						65.67 85			63.82 85	78.23 132

plane_prior2							82.74 37		65.45 62

plane_prior						65.18 89	80.06 60		61.88 102						89.91 112

n2								0.00 360

nn								0.00 360

door-mid								55.02 283

test11								82.71 67

door								52.91 298

HQP5-MVS						58.80 134

BP-MVS														67.38 94

HQP4-MVS										71.59 207			85.31 34			83.74 118

HQP3-MVS								84.12 45							89.16 119

HQP2-MVS												58.09 175

MDTV_nov1_ep13_2view						18.41 347	53.74 308		31.57 334	44.89 339		29.90 323		32.93 310		71.48 258

ACMMP++_ref															89.47 117

ACMMP++															91.96 69

Test By Simon												62.56 119

High-resolution multi-view benchmark