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	72.28 253
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	79.24 198

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	9.95 351	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	78.94 201

TSAR-MVS + MP.		79.05 51	78.81 56	79.74 41	88.94 25	67.52 75	86.61 15	81.38 89	51.71 208	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	43.85 271	74.99 170	86.11 146	52.85 213	70.57 242	50.99 200	83.23 203	68.05 290

1121		69.23 170	68.26 186	72.12 152	88.36 31	71.40 45	68.59 204	62.06 251	43.80 272	74.75 173	86.18 142	52.92 212	76.85 183	54.47 180	83.27 202	68.12 289

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	41.06 287	73.05 193	85.72 152	48.03 234			80.65 237	66.92 296

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	50.97 223	74.25 180	86.16 144	61.60 131	83.54 62	56.75 158	91.08 87	73.00 244

testdata				64.13 223	85.87 48	63.34 103		61.80 254	47.83 246	76.42 158	86.60 132	48.83 230	62.31 292	54.46 182	81.26 229	66.74 300

agg_prior3		76.32 75	76.33 81	76.28 78	85.86 49	70.13 59	76.50 96	78.26 150	53.41 194	75.78 160	86.49 135	66.58 96	81.57 90	72.50 48	91.56 74	77.15 217

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	53.73 190	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	54.00 185	76.97 142	86.74 123	66.60 94	81.10 111	72.50 48	91.56 74	77.15 217

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	54.00 185	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	64.43 255	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	53.48 192	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	51.98 206	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	71.73 258

jajsoiax		78.51 59	78.16 63	79.59 44	84.65 64	73.83 37	80.42 53	76.12 170	51.33 213	87.19 26	91.51 31	73.79 45	78.44 161	68.27 80	90.13 108	86.49 75

pcd1.5k->3k		35.00 327	36.93 327	29.21 338	84.62 65	0.00 360	0.00 351	78.90 137	0.00 355	0.00 356	0.00 357	78.26 15	0.00 358	0.00 355	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			83.34 201	69.66 279

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	54.40 179	76.95 144	86.85 117	66.44 97	80.34 131	72.45 50	91.42 78	76.57 222

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	54.20 181	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	43.76 273	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	60.47 238	70.61 161	83.04 84	54.10 156	59.93 290	72.24 201	33.67 323	69.00 234	75.63 261	38.69 273	76.93 181	36.60 294	75.45 277	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	70.35 226	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	48.53 238	67.16 252	79.18 233	51.42 221	78.38 165	54.39 183	79.72 249	78.60 204

EG-PatchMatch MVS		70.70 156	70.88 159	70.16 169	82.64 90	58.80 134	71.48 165	73.64 188	54.98 168	76.55 152	81.77 203	61.10 139	78.94 148	54.87 177	80.84 235	72.74 248

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	45.20 264	69.79 230	79.37 229	47.42 237	72.17 227	34.48 304	85.15 183	77.99 213

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	73.37 241

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	81.66 220	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	78.92 202

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	82.92 204	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	52.27 201	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	45.50 260	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	58.59 271	81.26 107	37.73 278	64.06 258	57.62 268	57.02 145	78.40 129	90.75 46	62.65 118	58.10 302	41.77 264	89.58 115	79.95 193

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	76.32 223

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	65.26 248	65.66 236	44.36 269	67.30 251	75.54 262	43.27 252	71.77 234	37.68 287	84.44 189	78.01 212
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	54.57 177	67.09 253	80.63 212	59.44 153	81.74 89	46.91 231	84.17 191	78.63 203
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
CHOSEN 1792x2688		58.09 262	56.30 271	63.45 231	79.95 114	50.93 170	54.07 308	65.59 237	28.56 341	61.53 277	74.33 277	41.09 263	66.52 278	33.91 308	67.69 316	72.92 245

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	59.65 266	79.72 116	30.17 329	60.27 288	62.14 249	54.19 182	71.24 215	86.63 130	58.80 162	67.62 267	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	79.44 196
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	71.76 235	61.81 129	83.74 198	73.18 243

Test_1112_low_res		58.78 255	58.69 256	59.04 269	79.41 120	38.13 274	57.62 298	66.98 232	34.74 315	59.62 294	77.56 242	42.92 255	63.65 286	38.66 279	70.73 299	75.35 231

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	79.54 195

MVP-Stereo		61.56 234	59.22 244	68.58 193	79.28 122	60.44 120	69.20 196	71.57 204	43.58 276	56.42 307	78.37 238	39.57 270	76.46 187	34.86 303	60.16 330	68.86 287
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	68.74 237	88.14 106	56.08 203	83.69 59	59.90 140	81.71 219	79.43 197

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	47.98 243	67.70 241	74.19 281	61.31 134	72.62 221	56.51 160	78.26 261	76.27 224

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	47.98 243	67.70 241	74.19 281	61.31 134	72.62 221	56.51 160	78.26 261	76.27 224

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	47.98 243	67.70 241	74.19 281	61.31 134	72.62 221	56.51 160	78.26 261	76.27 224

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	78.44 237	57.72 188	82.78 74	60.16 137	89.60 114	79.11 200

wuykxyi23d		75.33 88	76.75 75	71.04 158	78.83 133	85.01 1	71.78 162	61.00 256	53.47 193	96.33 1	93.38 4	73.07 46	68.04 264	65.65 110	97.28 2	60.07 323

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	66.77 276	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	45.34 263	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	50.51 226	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	79.63 225	60.30 144	80.20 135	46.04 235	80.65 237	70.47 267

1112_ss		59.48 248	58.99 247	60.96 256	77.84 143	42.39 245	61.42 281	68.45 227	37.96 300	59.93 293	67.46 323	45.11 242	65.07 282	40.89 268	71.81 293	75.41 229

PS-MVSNAJ		64.27 212	63.73 210	65.90 215	77.82 144	51.42 168	63.33 264	72.33 199	45.09 266	61.60 276	68.04 321	62.39 123	73.95 208	49.07 213	73.87 285	72.34 251

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	63.63 261	72.31 200	45.06 267	61.70 275	69.66 311	62.56 119	73.93 209	49.06 214	73.91 284	72.31 252

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		59.43 249	60.07 240	57.51 277	77.62 148	71.52 44	62.33 268	50.92 309	57.40 139	69.40 231	80.00 218	39.14 271	61.92 293	37.47 290	66.36 318	39.09 347

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	66.12 256	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	78.13 240	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	74.72 272	64.80 108	83.66 60	56.46 163	81.19 230	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	52.12 203	76.79 149	74.44 276	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	34.44 302	68.84 198	70.88 216	60.25 117	79.50 117	90.53 52	61.82 129	69.11 249	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	35.29 312	65.16 261	76.74 248	44.67 244	70.68 240	54.74 178	79.28 252	78.14 209
jason: jason.
PM-MVS		64.49 207	63.61 211	67.14 203	76.68 156	75.15 28	68.49 208	42.85 334	51.17 216	77.85 135	80.51 213	45.76 238	66.31 279	52.83 191	76.35 270	59.96 325

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	70.41 245	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	64.62 252	73.48 189	58.01 132	73.91 185	81.78 202	59.09 159	78.22 170	48.59 217	77.96 264	78.31 206

PHI-MVS		74.92 97	74.36 104	76.61 71	76.40 158	62.32 109	80.38 54	83.15 60	54.16 183	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	60.93 285	86.14 145	52.37 215	77.12 179	50.67 202	85.21 181	80.17 192
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		58.68 256	57.72 261	61.57 248	76.21 161	73.59 39	61.83 276	49.00 316	47.30 251	61.08 280	68.97 315	50.16 225	59.01 300	36.06 298	68.84 310	52.10 336

VPA-MVSNet		68.71 181	70.37 163	63.72 228	76.13 162	38.06 275	64.10 257	71.48 207	56.60 151	74.10 182	88.31 101	64.78 110	69.72 246	47.69 226	90.15 106	83.37 128

PAPM		61.79 233	60.37 239	66.05 213	76.09 163	41.87 247	69.30 194	76.79 167	40.64 290	53.80 320	79.62 226	44.38 246	82.92 73	29.64 324	73.11 288	73.36 242

BH-w/o		64.81 204	64.29 206	66.36 211	76.08 164	54.71 152	65.61 243	75.23 181	50.10 229	71.05 219	71.86 301	54.33 208	79.02 146	38.20 284	76.14 271	65.36 305

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	69.69 247	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	62.43 273	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	38.81 295	74.13 181	82.23 197	43.76 250	68.65 259	42.53 257	80.63 239	74.63 234

TR-MVS		64.59 205	63.54 212	67.73 199	75.75 168	50.83 171	63.39 263	70.29 220	49.33 233	71.55 212	74.55 274	50.94 222	78.46 160	40.43 271	75.69 273	73.89 239

cascas		64.59 205	62.77 223	70.05 172	75.27 169	50.02 175	61.79 277	71.61 203	42.46 281	63.68 268	68.89 317	49.33 228	80.35 130	47.82 225	84.05 193	79.78 194

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	77.51 243	61.51 133	80.96 120	52.04 192	85.76 165	71.22 262

PAPR		69.20 171	68.66 183	70.82 160	75.15 171	47.77 204	75.31 121	81.11 94	49.62 232	66.33 255	79.27 230	61.53 132	82.96 72	48.12 222	81.50 222	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	52.27 201	63.53 269	76.74 248	43.19 253	80.56 126	72.28 51	78.67 257	78.14 209

lupinMVS		63.36 216	61.49 232	68.97 184	74.93 172	59.19 129	65.80 240	64.52 243	34.68 317	63.53 269	74.25 279	43.19 253	70.62 241	53.88 186	78.67 257	77.10 219

nrg030		74.87 101	75.99 86	71.52 156	74.90 174	49.88 179	74.10 136	82.58 69	54.55 178	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	55.15 164	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	80.89 234	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	71.59 302	66.22 98	78.60 155	67.58 90	80.32 240	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	72.07 295	67.00 90	76.28 188	67.97 87	80.91 232	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	72.07 295	68.98 74	80.19 136	70.29 63	80.91 232	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	51.80 207	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	51.70 209	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	71.39 305	65.98 99	78.53 157	67.30 96	80.18 241	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	50.62 225	71.48 214	75.54 262	68.75 76	79.59 143	50.55 204	78.73 256	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	52.76 199	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	51.57 210	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	48.67 237	65.78 259	68.58 320	57.01 198	77.79 174	46.68 233	81.92 212	74.42 236
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	54.76 174	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	54.36 180	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	72.08 292	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	72.08 292	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	76.83 247	69.96 68	80.97 116	60.20 136	78.43 259	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	53.83 189	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	70.44 306	58.16 173	75.85 189	62.51 126	79.81 246	88.48 59

CVMVSNet		59.21 252	58.44 259	61.51 249	73.94 199	47.76 205	71.31 173	64.56 242	26.91 345	60.34 287	70.44 306	36.24 283	67.65 266	53.57 189	68.66 312	69.12 285

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	53.94 188	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	68.84 236	74.71 273	65.36 104	75.75 192	52.00 193	79.00 253	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	62.16 245	73.40 212	36.65 282	63.45 262	64.99 241	55.97 153	72.82 195	87.80 108	57.06 197	69.10 250	48.31 221	87.54 140	80.72 180

semantic-postprocess				72.49 141	73.34 213	58.20 139		65.55 238	48.10 242	76.91 146	82.64 190	42.25 258	78.84 150	61.20 133	77.89 265	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	60.57 258	73.28 215	35.61 293	57.60 299	67.08 231	54.61 176	66.76 254	83.37 179	56.28 201	66.87 272	42.19 259	85.20 182	79.23 199

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	81.07 231	75.45 228

GBi-Net		68.30 184	68.79 179	66.81 206	73.14 217	40.68 254	71.96 155	73.03 190	54.81 169	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	54.81 169	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	54.81 169	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	55.01 165	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	55.01 165	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	64.61 253	70.50 218	55.52 159	73.97 184	89.33 76	63.91 114	68.38 261	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	55.01 165	81.23 96	84.95 161	57.45 191	80.89 122	66.58 100	85.67 166	88.68 56

pmmvs4		60.78 241	59.04 246	66.00 214	73.06 224	57.67 140	64.53 254	60.22 258	36.91 305	65.96 257	77.27 244	39.66 269	68.54 260	38.87 276	74.89 280	71.80 257

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	33.66 308	66.10 233	55.01 285	57.05 141	72.66 196	82.56 191	31.60 305	72.78 215	42.64 253	85.55 170	82.02 153

view800		62.88 223	62.90 218	62.82 236	72.97 227	33.66 308	66.10 233	55.01 285	57.05 141	72.66 196	82.56 191	31.60 305	72.78 215	42.64 253	85.55 170	82.02 153

conf0.05thres1000		62.88 223	62.90 218	62.82 236	72.97 227	33.66 308	66.10 233	55.01 285	57.05 141	72.66 196	82.56 191	31.60 305	72.78 215	42.64 253	85.55 170	82.02 153

tfpn		62.88 223	62.90 218	62.82 236	72.97 227	33.66 308	66.10 233	55.01 285	57.05 141	72.66 196	82.56 191	31.60 305	72.78 215	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		54.39 278	56.12 273	49.20 302	72.57 232	30.91 328	59.98 289	48.43 318	41.66 285	55.94 309	83.86 174	41.19 262	50.42 311	26.05 331	75.38 278	66.27 301

Patchmatch-RL test		59.95 245	59.12 245	62.44 243	72.46 233	54.61 154	59.63 291	47.51 321	41.05 288	74.58 178	74.30 278	31.06 314	65.31 280	51.61 195	79.85 245	67.39 293

Vis-MVSNet (Re-imp)		62.74 228	63.21 215	61.34 252	72.19 234	31.56 327	67.31 222	53.87 292	53.60 191	69.88 229	83.37 179	40.52 267	70.98 239	41.40 265	86.78 156	81.48 164

tfpn111		61.91 231	61.65 228	62.68 241	72.14 235	35.01 296	65.42 245	56.99 275	55.23 161	70.71 221	79.90 219	32.07 300	72.85 214	38.80 277	83.61 199	80.18 188

conf200view11		61.42 236	61.09 234	62.43 244	72.14 235	35.01 296	65.42 245	56.99 275	55.23 161	70.71 221	79.90 219	32.07 300	72.09 228	35.61 299	81.73 215	80.18 188

thres100view900		61.17 238	61.09 234	61.39 251	72.14 235	35.01 296	65.42 245	56.99 275	55.23 161	70.71 221	79.90 219	32.07 300	72.09 228	35.61 299	81.73 215	77.08 220

testmv		52.91 287	54.31 282	48.71 306	72.13 238	36.18 286	50.26 316	47.78 319	44.15 270	64.61 263	79.78 223	38.18 275	50.20 313	21.96 342	69.93 304	59.75 326

ab-mvs		64.11 213	65.13 204	61.05 254	71.99 239	38.03 276	67.59 215	68.79 225	49.08 234	65.32 260	86.26 140	58.02 180	66.85 274	39.33 273	79.79 248	78.27 207

thres600view7		61.82 232	61.38 233	63.12 233	71.81 240	34.93 299	64.64 251	56.99 275	54.78 173	70.33 227	79.74 224	32.07 300	72.42 225	38.61 280	83.46 200	82.02 153

QAPM		69.18 172	69.26 170	68.94 185	71.61 241	52.58 164	80.37 55	78.79 139	49.63 231	73.51 187	85.14 156	53.66 210	79.12 145	55.11 175	75.54 275	75.11 232

Anonymous20231206		54.13 279	55.82 274	49.04 305	70.89 242	35.96 289	51.73 313	50.87 310	34.86 313	62.49 272	79.22 231	42.52 257	44.29 331	27.95 329	81.88 214	66.88 297

testing_2		72.01 146	72.36 141	70.95 159	70.79 243	48.70 185	72.81 142	78.09 154	48.79 236	84.46 66	89.15 85	57.90 186	78.55 156	61.55 130	87.74 138	85.61 87

tfpn200view9		60.35 244	59.97 241	61.51 249	70.78 244	35.35 294	63.27 265	57.47 269	53.00 197	68.31 238	77.09 245	32.45 297	72.09 228	35.61 299	81.73 215	77.08 220

thres400		60.77 242	59.97 241	63.15 232	70.78 244	35.35 294	63.27 265	57.47 269	53.00 197	68.31 238	77.09 245	32.45 297	72.09 228	35.61 299	81.73 215	82.02 153

MSDG		67.47 193	67.48 192	67.46 200	70.70 246	54.69 153	66.90 226	78.17 152	60.88 111	70.41 225	74.76 270	61.22 138	73.18 211	47.38 227	76.87 268	74.49 235

OpenMVS	62.51 15	68.76 180	68.75 181	68.78 191	70.56 247	53.91 158	78.29 76	77.35 162	48.85 235	70.22 228	83.52 176	52.65 214	76.93 181	55.31 174	81.99 211	75.49 227

DELS-MVS		68.83 178	68.31 184	70.38 163	70.55 248	48.31 192	63.78 260	82.13 72	54.00 185	68.96 235	75.17 268	58.95 161	80.06 138	58.55 147	82.74 205	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	61.70 247	70.37 249	34.30 303	61.45 280	79.62 124	56.81 147	89.59 10	88.16 105	68.44 79	72.94 213	42.30 258	87.33 147	77.85 214

Patchmatch-test1		57.81 264	58.04 260	57.13 278	70.17 250	41.07 253	65.19 249	53.38 296	43.34 280	61.00 283	71.94 299	45.20 241	62.69 289	41.81 263	70.31 301	67.63 292

PVSNet_BlendedMVS		65.38 200	64.30 205	68.61 192	69.81 251	49.36 180	65.60 244	78.96 135	45.50 260	59.98 290	78.61 236	51.82 217	78.20 171	44.30 239	84.11 192	78.27 207

PVSNet_Blended		62.90 222	61.64 229	66.69 209	69.81 251	49.36 180	61.23 283	78.96 135	42.04 283	59.98 290	68.86 318	51.82 217	78.20 171	44.30 239	77.77 266	72.52 249

OpenMVS_ROB	54.93 17	63.23 218	63.28 213	63.07 234	69.81 251	45.34 231	68.52 207	67.14 230	43.74 274	70.61 224	79.22 231	47.90 235	72.66 220	48.75 215	73.84 286	71.21 263

DI_MVS_plusplus_test		69.01 176	69.04 173	68.93 186	69.54 254	46.74 225	70.14 184	75.49 176	46.64 253	78.30 130	83.18 188	58.80 162	78.86 149	57.14 155	82.15 209	81.18 167

Test4		69.04 175	68.95 176	69.32 180	69.52 255	48.10 198	70.69 181	78.25 151	45.90 257	80.99 100	82.24 196	51.91 216	78.11 173	58.46 148	82.58 207	81.74 161

FMVSNet3		65.00 203	65.16 202	64.52 221	69.47 256	37.56 280	66.63 228	70.38 219	51.55 211	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	69.43 257	47.03 220	69.85 189	74.83 183	46.06 256	78.30 130	83.29 183	58.76 166	78.23 169	57.51 152	81.90 213	81.36 165

MS-PatchMatch		55.59 274	54.89 280	57.68 275	69.18 258	49.05 183	61.00 285	62.93 247	35.98 308	58.36 298	68.93 316	36.71 282	66.59 277	37.62 289	63.30 324	57.39 329

v148		69.38 169	69.39 168	69.36 177	69.14 259	44.56 233	68.83 199	72.70 195	54.79 172	78.59 124	84.12 170	54.69 206	76.74 185	59.40 144	82.20 208	86.79 72

conf0.01		59.26 250	58.88 248	60.40 260	68.66 260	31.96 321	62.04 270	51.95 301	50.99 217	67.57 244	75.91 254	28.59 330	69.07 251	42.77 247	81.40 223	80.18 188

conf0.002		59.26 250	58.88 248	60.40 260	68.66 260	31.96 321	62.04 270	51.95 301	50.99 217	67.57 244	75.91 254	28.59 330	69.07 251	42.77 247	81.40 223	80.18 188

thresconf0.02		58.38 257	58.88 248	56.91 280	68.66 260	31.96 321	62.04 270	51.95 301	50.99 217	67.57 244	75.91 254	28.59 330	69.07 251	42.77 247	81.40 223	69.70 275

tfpn_n400		58.38 257	58.88 248	56.91 280	68.66 260	31.96 321	62.04 270	51.95 301	50.99 217	67.57 244	75.91 254	28.59 330	69.07 251	42.77 247	81.40 223	69.70 275

tfpnconf		58.38 257	58.88 248	56.91 280	68.66 260	31.96 321	62.04 270	51.95 301	50.99 217	67.57 244	75.91 254	28.59 330	69.07 251	42.77 247	81.40 223	69.70 275

tfpnview11		58.38 257	58.88 248	56.91 280	68.66 260	31.96 321	62.04 270	51.95 301	50.99 217	67.57 244	75.91 254	28.59 330	69.07 251	42.77 247	81.40 223	69.70 275

test1235678		48.41 302	49.60 302	44.83 319	68.52 266	33.81 306	46.33 328	45.89 326	38.72 296	58.46 296	72.08 292	29.85 325	47.82 318	19.67 346	66.91 317	52.88 334

CANet_DTU		64.04 214	63.83 208	64.66 219	68.39 267	42.97 241	73.45 138	74.50 185	52.05 205	54.78 314	75.44 267	43.99 248	70.42 244	53.49 190	78.41 260	80.59 182

EU-MVSNet		60.82 240	60.80 237	60.86 257	68.37 268	41.16 251	72.27 147	68.27 228	26.96 344	69.08 233	75.71 260	32.09 299	67.44 268	55.59 172	78.90 254	73.97 237

PVSNet	43.83 21	51.56 294	51.17 295	52.73 292	68.34 269	38.27 272	48.22 321	53.56 295	36.41 306	54.29 318	64.94 329	34.60 287	54.20 308	30.34 319	69.87 305	65.71 304

EPNet		69.10 173	67.32 193	74.46 94	68.33 270	61.27 116	77.56 83	63.57 245	60.95 110	56.62 306	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	59.69 247	56.96 267	67.90 196	68.19 271	50.30 173	61.42 281	65.18 240	47.57 249	55.83 310	67.15 326	23.77 345	79.60 142	43.56 244	79.97 244	73.79 240
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		60.62 243	59.97 241	62.58 242	68.13 272	47.28 217	68.59 204	73.96 187	32.19 328	59.94 292	68.86 318	50.48 223	77.64 176	41.85 262	75.74 272	62.83 315

tfpn1000		58.28 261	58.86 254	56.53 284	68.05 273	32.26 318	62.58 267	51.67 308	51.25 215	67.38 250	75.95 253	27.24 337	68.83 257	43.51 245	82.11 210	68.49 288

TinyColmap		67.98 186	69.28 169	64.08 224	67.98 274	46.82 224	70.04 185	75.26 180	53.05 196	77.36 140	86.79 119	59.39 154	72.59 224	45.64 237	88.01 136	72.83 246

EPNet_dtu		58.93 254	58.52 257	60.16 264	67.91 275	47.70 206	69.97 186	58.02 265	49.73 230	47.28 335	73.02 290	38.14 276	62.34 291	36.57 295	85.99 163	70.43 268
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
thres200		57.55 266	57.02 266	59.17 267	67.89 276	34.93 299	58.91 295	57.25 273	50.24 227	64.01 266	71.46 304	32.49 296	71.39 237	31.31 315	79.57 250	71.19 264

MVS_Test		69.84 164	70.71 161	67.24 202	67.49 277	43.25 239	69.87 188	81.22 93	52.69 200	71.57 211	86.68 126	62.09 127	74.51 205	66.05 106	78.74 255	83.96 114

V42		71.06 152	70.83 160	71.72 153	67.25 278	47.14 219	65.94 238	80.35 114	51.35 212	83.40 73	83.23 185	59.25 157	78.80 151	65.91 108	80.81 236	89.23 38

tfpn_ndepth		56.91 268	57.30 265	55.71 285	67.22 279	33.26 313	61.72 278	53.98 291	48.49 239	64.16 265	71.94 299	27.65 336	68.71 258	40.49 270	80.08 242	65.17 307

GA-MVS		62.91 221	61.66 227	66.66 210	67.09 280	44.49 234	61.18 284	69.36 224	51.33 213	69.33 232	74.47 275	36.83 281	74.94 202	50.60 203	74.72 281	80.57 183

HY-MVS	49.31 19	57.96 263	57.59 262	59.10 268	66.85 281	36.17 287	65.13 250	65.39 239	39.24 293	54.69 316	78.14 239	44.28 247	67.18 271	33.75 309	70.79 298	73.95 238

tpmp4_e23		57.57 265	55.46 278	63.93 226	66.48 282	41.56 250	71.68 163	60.65 257	35.64 311	55.35 313	76.25 251	29.53 326	75.41 196	34.40 305	69.12 309	74.83 233

CR-MVSNet		58.96 253	58.49 258	60.36 262	66.37 283	48.24 194	70.93 178	56.40 280	32.87 327	61.35 278	86.66 127	33.19 292	63.22 287	48.50 219	70.17 302	69.62 280

RPMNet		61.25 237	61.55 231	60.36 262	66.37 283	48.24 194	70.93 178	54.45 290	54.66 175	61.35 278	86.77 122	33.29 291	63.22 287	55.93 168	70.17 302	69.62 280

IterMVS		63.12 219	62.48 225	65.02 218	66.34 285	52.86 163	63.81 259	62.25 248	46.57 254	71.51 213	80.40 215	44.60 245	66.82 275	51.38 198	75.47 276	75.38 230
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
tpm2		56.12 269	54.64 281	60.55 259	66.24 286	36.01 288	68.14 211	56.77 279	33.60 325	58.25 299	75.52 264	30.25 320	74.33 207	33.27 310	69.76 307	71.32 260

Patchmtry		60.91 239	63.01 217	54.62 289	66.10 287	26.27 340	67.47 217	56.40 280	54.05 184	72.04 206	86.66 127	33.19 292	60.17 297	43.69 242	87.45 144	77.42 215

FMVSNet5		55.08 276	55.54 277	53.71 290	65.80 288	33.50 312	56.22 301	52.50 300	43.72 275	61.06 281	83.38 178	25.46 342	54.87 305	30.11 321	81.64 221	72.75 247

1314		59.83 246	58.86 254	62.74 240	65.71 289	44.78 232	68.59 204	72.63 196	33.54 326	61.05 282	67.29 325	43.62 251	71.26 238	49.49 211	67.84 315	72.19 254

MDTV_nov1_ep13			54.05 284		65.54 290	29.30 331	59.00 294	55.22 282	35.96 309	52.44 322	75.98 252	30.77 317	59.62 298	38.21 283	73.33 287

USDC		62.80 227	63.10 216	61.89 246	65.19 291	43.30 238	67.42 218	74.20 186	35.80 310	72.25 204	84.48 167	45.67 239	71.95 233	37.95 286	84.97 188	70.42 269

tpm		50.60 295	52.42 291	45.14 317	65.18 292	26.29 339	60.30 287	43.50 331	37.41 302	57.01 302	79.09 235	30.20 322	42.32 337	32.77 312	66.36 318	66.81 299

PatchmatchNet		54.60 277	54.27 283	55.59 286	65.17 293	39.08 265	66.92 225	51.80 307	39.89 291	58.39 297	73.12 289	31.69 304	58.33 301	43.01 246	58.38 339	69.38 283
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
tpm cat1		54.02 281	52.63 289	58.19 273	64.85 294	39.86 262	66.26 232	57.28 272	32.16 329	56.90 304	70.39 308	32.75 295	65.30 281	34.29 306	58.79 335	69.41 282

XXY-MVS		55.19 275	57.40 264	48.56 307	64.45 295	34.84 301	51.54 314	53.59 294	38.99 294	63.79 267	79.43 227	56.59 199	45.57 322	36.92 293	71.29 295	65.25 306

PatchT		53.35 284	56.47 270	43.99 322	64.19 296	17.46 349	59.15 292	43.10 332	52.11 204	54.74 315	86.95 114	29.97 323	49.98 314	43.62 243	74.40 282	64.53 312

mvs_anonymous		65.08 202	65.49 201	63.83 227	63.79 297	37.60 279	66.52 230	69.82 222	43.44 277	73.46 189	86.08 147	58.79 165	71.75 236	51.90 194	75.63 274	82.15 152

CostFormer		57.35 267	56.14 272	60.97 255	63.76 298	38.43 270	67.50 216	60.22 258	37.14 304	59.12 295	76.34 250	32.78 294	71.99 232	39.12 275	69.27 308	72.47 250

Gipuma		69.55 167	72.83 133	59.70 265	63.63 299	53.97 157	80.08 59	75.93 172	64.24 81	73.49 188	88.93 93	57.89 187	62.46 290	59.75 143	91.55 76	62.67 317
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
gg-mvs-nofinetune		55.75 272	56.75 269	52.72 293	62.87 300	28.04 335	68.92 197	41.36 342	71.09 31	50.80 326	92.63 12	20.74 348	66.86 273	29.97 322	72.41 290	63.25 313

no-one		56.11 270	55.62 276	57.60 276	62.68 301	49.23 182	39.12 341	58.99 263	33.72 321	60.98 284	80.90 210	36.07 284	60.36 296	30.68 317	97.40 1	63.22 314

testus		45.03 313	46.49 311	40.65 329	62.53 302	25.24 342	42.54 333	46.23 325	31.16 338	57.69 300	62.90 332	34.60 287	42.33 336	17.72 348	63.01 325	54.37 333

gm-plane-assit					62.51 303	33.91 305			37.25 303		62.71 333		72.74 219	38.70 278

diffmvs		66.15 198	65.86 200	67.01 205	62.31 304	44.43 235	68.81 200	72.93 193	48.13 241	62.12 274	83.33 181	57.96 183	72.29 226	59.83 141	77.31 267	84.33 110

MVS-HIRNet		45.53 308	47.29 308	40.24 330	62.29 305	26.82 338	56.02 302	37.41 348	29.74 340	43.69 346	81.27 207	33.96 289	55.48 304	24.46 337	56.79 340	38.43 348

PatchFormer-LS_test		53.94 283	52.64 288	57.85 274	61.87 306	39.59 263	61.60 279	57.63 267	40.65 289	54.52 317	58.64 340	29.07 329	64.18 284	46.78 232	62.98 326	69.78 273

CHOSEN 280x420		41.62 319	39.89 325	46.80 310	61.81 307	51.59 166	33.56 347	35.74 349	27.48 343	37.64 351	53.53 343	23.24 346	42.09 338	27.39 330	58.64 336	46.72 341

MDA-MVSNet-bldmvs		62.34 229	61.73 226	64.16 222	61.64 308	49.90 176	48.11 322	57.24 274	53.31 195	80.95 101	79.39 228	49.00 229	61.55 294	45.92 236	80.05 243	81.03 171

DWT-MVSNet_test		53.04 285	51.12 296	58.77 270	61.23 309	38.67 269	62.16 269	57.74 266	38.24 297	51.76 324	59.07 339	21.36 347	67.40 269	44.80 238	63.76 323	70.25 270

WTY-MVS		49.39 299	50.31 300	46.62 311	61.22 310	32.00 320	46.61 326	49.77 313	33.87 320	54.12 319	69.55 313	41.96 259	45.40 324	31.28 316	64.42 322	62.47 318

CMPMVS	48.73 20	61.54 235	60.89 236	63.52 230	61.08 311	51.55 167	68.07 212	68.00 229	33.88 319	65.87 258	81.25 208	37.91 279	67.71 265	49.32 212	82.60 206	71.31 261
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
test-LLR		50.43 296	50.69 299	49.64 300	60.76 312	41.87 247	53.18 310	45.48 329	43.41 278	49.41 331	60.47 337	29.22 327	44.73 328	42.09 260	72.14 291	62.33 319

test-mter		48.56 301	48.20 306	49.64 300	60.76 312	41.87 247	53.18 310	45.48 329	31.91 334	49.41 331	60.47 337	18.34 351	44.73 328	42.09 260	72.14 291	62.33 319

GG-mvs-BLEND				52.24 294	60.64 314	29.21 332	69.73 191	42.41 335		45.47 338	52.33 345	20.43 349	68.16 262	25.52 335	65.42 320	59.36 327

tpmvs		55.84 271	55.45 279	57.01 279	60.33 315	33.20 314	65.89 239	59.29 262	47.52 250	56.04 308	73.60 284	31.05 315	68.06 263	40.64 269	64.64 321	69.77 274

PVSNet_0	36.71 22	41.12 320	40.78 322	42.14 324	59.97 316	40.13 260	40.97 335	42.24 339	30.81 339	44.86 341	49.41 348	40.70 266	45.12 326	23.15 340	34.96 349	41.16 346

new-patchmatchnet		52.89 288	55.76 275	44.26 321	59.94 317	6.31 355	37.36 345	50.76 311	41.10 286	64.28 264	79.82 222	44.77 243	48.43 316	36.24 296	87.61 139	78.03 211

test20.03		55.74 273	57.51 263	50.42 297	59.89 318	32.09 319	50.63 315	49.01 315	50.11 228	65.07 262	83.23 185	45.61 240	48.11 317	30.22 320	83.82 197	71.07 266

MVSTER		63.29 217	61.60 230	68.36 194	59.77 319	46.21 228	60.62 286	71.32 208	41.83 284	75.40 167	79.12 234	30.25 320	75.85 189	56.30 165	79.81 246	83.03 131

N_pmnet		52.06 293	51.11 297	54.92 288	59.64 320	71.03 49	37.42 344	61.62 255	33.68 322	57.12 301	72.10 291	37.94 278	31.03 350	29.13 328	71.35 294	62.70 316

1111		45.08 312	47.96 307	36.43 334	59.56 321	14.82 351	43.56 331	45.65 327	45.60 258	60.04 288	75.47 265	9.31 358	34.46 347	23.66 338	68.76 311	60.02 324

.test1245		34.47 328	40.38 324	16.73 340	59.56 321	14.82 351	43.56 331	45.65 327	45.60 258	60.04 288	75.47 265	9.31 358	34.46 347	23.66 338	0.55 354	0.90 353

JIA-IIPM		54.03 280	51.62 293	61.25 253	59.14 323	55.21 148	59.10 293	47.72 320	50.85 224	50.31 330	85.81 151	20.10 350	63.97 285	36.16 297	55.41 344	64.55 311

LF4IMVS		67.50 191	67.31 194	68.08 195	58.86 324	61.93 110	71.43 169	75.90 173	44.67 268	72.42 202	80.20 216	57.16 193	70.44 243	58.99 146	86.12 161	71.88 256

test2356		40.85 321	40.47 323	41.98 325	58.78 325	28.65 334	39.45 339	40.98 345	31.95 333	48.47 333	56.63 341	12.54 357	44.41 330	15.84 350	59.58 332	52.88 334

UnsupCasMVSNet_bld		50.01 298	51.03 298	46.95 308	58.61 326	32.64 316	48.31 320	53.27 297	34.27 318	60.47 286	71.53 303	41.40 261	47.07 319	30.68 317	60.78 329	61.13 321

dp		44.09 317	44.88 316	41.72 328	58.53 327	23.18 345	54.70 307	42.38 337	34.80 314	44.25 344	65.61 328	24.48 344	44.80 327	29.77 323	49.42 347	57.18 330

test12356		38.35 322	40.80 321	31.01 336	58.31 328	9.09 354	36.67 346	46.65 322	33.65 324	44.39 343	60.94 336	17.56 353	39.23 345	16.01 349	53.03 345	44.72 344

testgi		54.00 282	56.86 268	45.45 315	58.20 329	25.81 341	49.05 318	49.50 314	45.43 262	67.84 240	81.17 209	51.81 219	43.20 335	29.30 325	79.41 251	67.34 295

PNet_i23d		36.76 325	36.63 328	37.12 333	58.19 330	33.00 315	39.86 338	32.55 351	48.44 240	39.64 347	51.31 346	6.89 360	41.83 341	22.29 341	30.55 350	36.54 349

wuyk23d		61.97 230	66.25 198	49.12 304	58.19 330	60.77 119	66.32 231	52.97 298	55.93 154	90.62 7	86.91 115	73.07 46	35.98 346	20.63 345	91.63 72	50.62 337

ANet_high		67.08 194	69.94 165	58.51 272	57.55 332	27.09 336	58.43 297	76.80 166	63.56 87	82.40 79	91.93 20	59.82 149	64.98 283	50.10 207	88.86 124	83.46 124

Patchmatch-test		47.93 303	49.96 301	41.84 326	57.42 333	24.26 344	48.75 319	41.49 341	39.30 292	56.79 305	73.48 285	30.48 319	33.87 349	29.29 326	72.61 289	67.39 293

new_pmnet		37.55 324	39.80 326	30.79 337	56.83 334	16.46 350	39.35 340	30.65 352	25.59 346	45.26 339	61.60 335	24.54 343	28.02 352	21.60 343	52.80 346	47.90 340

pmmvs3		46.71 306	45.09 314	51.55 296	56.76 335	48.25 193	55.78 303	39.53 347	24.13 348	50.35 329	63.40 331	15.90 355	51.08 310	29.29 326	70.69 300	55.33 332

sss		47.59 305	48.32 304	45.40 316	56.73 336	33.96 304	45.17 330	48.51 317	32.11 332	52.37 323	65.79 327	40.39 268	41.91 340	31.85 313	61.97 327	60.35 322

tpmrst		50.15 297	51.38 294	46.45 312	56.05 337	24.77 343	64.40 256	49.98 312	36.14 307	53.32 321	69.59 312	35.16 286	48.69 315	39.24 274	58.51 338	65.89 302

TESTMET0.1,1		45.17 310	44.93 315	45.89 314	56.02 338	38.31 271	53.18 310	41.94 340	27.85 342	44.86 341	56.47 342	17.93 352	41.50 342	38.08 285	68.06 313	57.85 328

ADS-MVSNet2		48.76 300	47.25 309	53.29 291	55.90 339	40.54 258	47.34 324	54.99 289	31.41 336	50.48 327	72.06 297	31.23 311	54.26 307	25.93 332	55.93 341	65.07 308

ADS-MVSNet		44.62 315	45.58 312	41.73 327	55.90 339	20.83 347	47.34 324	39.94 346	31.41 336	50.48 327	72.06 297	31.23 311	39.31 343	25.93 332	55.93 341	65.07 308

LP		53.02 286	52.27 292	55.27 287	55.76 341	40.55 257	55.64 304	55.07 283	42.46 281	56.95 303	73.21 288	33.67 290	54.18 309	38.41 282	59.29 334	71.08 265

test0.0.03 1		47.72 304	48.31 305	45.93 313	55.53 342	29.39 330	46.40 327	41.21 343	43.41 278	55.81 311	67.65 322	29.22 327	43.77 334	25.73 334	69.87 305	64.62 310

UnsupCasMVSNet_eth		52.26 292	53.29 287	49.16 303	55.08 343	33.67 307	50.03 317	58.79 264	37.67 301	63.43 271	74.75 271	41.82 260	45.83 321	38.59 281	59.42 333	67.98 291

pmmvs5		52.49 291	52.58 290	52.21 295	54.99 344	32.38 317	55.45 305	53.84 293	32.15 330	55.49 312	74.81 269	38.08 277	57.37 303	34.02 307	74.40 282	66.88 297

DSMNet-mixed		43.18 318	44.66 317	38.75 332	54.75 345	28.88 333	57.06 300	27.42 354	13.47 350	47.27 336	77.67 241	38.83 272	39.29 344	25.32 336	60.12 331	48.08 339

MDA-MVSNet_test_wron		52.57 290	53.49 286	49.81 299	54.24 346	36.47 284	40.48 337	46.58 323	38.13 298	75.47 166	73.32 286	41.05 265	43.85 333	40.98 267	71.20 296	69.10 286

YYNet1		52.58 289	53.50 285	49.85 298	54.15 347	36.45 285	40.53 336	46.55 324	38.09 299	75.52 165	73.31 287	41.08 264	43.88 332	41.10 266	71.14 297	69.21 284

EPMVS		45.74 307	46.53 310	43.39 323	54.14 348	22.33 346	55.02 306	35.00 350	34.69 316	51.09 325	70.20 310	25.92 340	42.04 339	37.19 291	55.50 343	65.78 303

testpf		45.32 309	48.47 303	35.88 335	53.56 349	26.84 337	58.86 296	42.95 333	47.78 247	46.18 337	63.70 330	13.73 356	50.29 312	50.81 201	58.61 337	30.51 350

E-PMN		45.17 310	45.36 313	44.60 320	50.07 350	42.75 242	38.66 342	42.29 338	46.39 255	39.55 348	51.15 347	26.00 339	45.37 325	37.68 287	76.41 269	45.69 343

PMMVS		44.69 314	43.95 319	46.92 309	50.05 351	53.47 161	48.08 323	42.40 336	22.36 349	44.01 345	53.05 344	42.60 256	45.49 323	31.69 314	61.36 328	41.79 345

EMVS		44.61 316	44.45 318	45.10 318	48.91 352	43.00 240	37.92 343	41.10 344	46.75 252	38.00 350	48.43 349	26.42 338	46.27 320	37.11 292	75.38 278	46.03 342

MVE	27.91 23	36.69 326	35.64 329	39.84 331	43.37 353	35.85 291	19.49 349	24.61 355	24.68 347	39.05 349	62.63 334	38.67 274	27.10 353	21.04 344	47.25 348	56.56 331
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
PMMVS2		37.74 323	40.87 320	28.36 339	42.41 354	5.35 356	24.61 348	27.75 353	32.15 330	47.85 334	70.27 309	35.85 285	29.51 351	19.08 347	67.85 314	50.22 338

DeepMVS_CX				11.83 341	15.51 355	13.86 353		11.25 358	5.76 351	20.85 353	26.46 350	17.06 354	9.22 354	9.69 352	13.82 352	12.42 351

tmp_tt		11.98 330	14.73 331	3.72 342	2.28 356	4.62 357	19.44 350	14.50 357	0.47 352	21.55 352	9.58 352	25.78 341	4.57 355	11.61 351	27.37 351	1.96 352

test123		4.43 333	5.78 334	0.39 344	0.97 357	0.28 358	46.33 328	0.45 359	0.31 353	0.62 354	1.50 355	0.61 362	0.11 357	0.56 353	0.63 353	0.77 355

testmvs		4.06 334	5.28 335	0.41 343	0.64 358	0.16 359	42.54 333	0.31 360	0.26 354	0.50 355	1.40 356	0.77 361	0.17 356	0.56 353	0.55 354	0.90 353

cdsmvs_eth3d_5k		17.71 329	23.62 330	0.00 345	0.00 359	0.00 360	0.00 351	70.17 221	0.00 355	0.00 356	74.25 279	68.16 82	0.00 358	0.00 355	0.00 356	0.00 356

pcd_1.5k_mvsjas		5.20 332	6.93 333	0.00 345	0.00 359	0.00 360	0.00 351	0.00 361	0.00 355	0.00 356	0.00 357	62.39 123	0.00 358	0.00 355	0.00 356	0.00 356

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

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

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

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

ab-mvs-re		5.62 331	7.50 332	0.00 345	0.00 359	0.00 360	0.00 351	0.00 361	0.00 355	0.00 356	67.46 323	0.00 363	0.00 358	0.00 355	0.00 356	0.00 356

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

GSMVS																70.05 271

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												31.41 309				70.05 271

sam_mvs												31.21 313

MTGPA								80.63 106

test_post1							66.63 228				2.08 353	30.66 318	59.33 299	40.34 272

test_post											1.99 354	30.91 316	54.76 306

patchmatchnet-post											68.99 314	31.32 310	69.38 248

MTMP								19.26 356

test9_res														72.12 53	91.37 79	77.40 216

agg_prior2														70.70 61	90.93 91	78.55 205

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		45.11 265	78.54 127			61.28 295	59.19 145

新几何2							71.33 172

无先验							74.82 126	70.94 215	47.75 248				76.85 183	54.47 180		72.09 255

原ACMM2							74.78 130

testdata2													67.30 270	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								0.00 361

nn								0.00 361

door-mid								55.02 284

test11								82.71 67

door								52.91 299

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						18.41 348	53.74 309		31.57 335	44.89 340		29.90 324		32.93 311		71.48 259

ACMMP++_ref															89.47 117

ACMMP++															91.96 69

Test By Simon												62.56 119

High-resolution multi-view benchmark