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.

wuykxyi23d		75.33 90	76.75 77	71.04 164	78.83 135	85.01 1	71.78 167	61.00 269	53.47 202	96.33 1	93.38 3	73.07 45	68.04 275	65.65 113	97.28 2	60.07 336

LCM-MVSNet		86.90 1	88.67 1	81.57 20	91.50 1	63.30 106	84.80 27	87.77 7	86.18 1	96.26 2	96.06 1	90.32 1	84.49 50	68.08 84	97.05 3	96.93 1

V4		78.96 54	79.79 52	76.46 77	73.02 235	54.90 151	78.48 76	83.47 59	64.43 79	91.20 3	91.54 28	72.08 50	81.11 112	76.45 30	87.46 147	93.38 7

v52		78.96 54	79.79 52	76.46 77	73.03 234	54.90 151	78.48 76	83.48 58	64.43 79	91.19 4	91.54 28	72.08 50	81.11 112	76.45 30	87.47 145	93.38 7

DTE-MVSNet		80.35 43	82.89 31	72.74 142	89.84 8	37.34 295	77.16 93	81.81 82	80.45 2	90.92 5	92.95 7	74.57 37	86.12 25	63.65 127	94.68 31	94.76 6

PS-CasMVS		80.41 42	82.86 32	73.07 131	89.93 7	39.21 277	77.15 94	81.28 94	79.74 4	90.87 6	92.73 11	75.03 33	84.93 43	63.83 126	95.19 17	95.07 3

wuyk23d		61.97 241	66.25 207	49.12 318	58.19 345	60.77 121	66.32 239	52.97 312	55.93 161	90.62 7	86.91 121	73.07 45	35.98 359	20.63 358	91.63 73	50.62 350

PEN-MVS		80.46 41	82.91 30	73.11 130	89.83 9	39.02 280	77.06 96	82.61 71	80.04 3	90.60 8	92.85 9	74.93 34	85.21 39	63.15 129	95.15 19	95.09 2

CP-MVSNet		79.48 50	81.65 40	72.98 135	89.66 13	39.06 279	76.76 98	80.46 116	78.91 6	90.32 9	91.70 25	68.49 78	84.89 44	63.40 128	95.12 20	95.01 4

LCM-MVSNet-Re		69.10 181	71.57 159	61.70 260	70.37 260	34.30 317	61.45 290	79.62 130	56.81 154	89.59 10	88.16 109	68.44 79	72.94 224	42.30 271	87.33 151	77.85 226

WR-MVS_H		80.22 45	82.17 37	74.39 99	89.46 15	42.69 253	78.24 81	82.24 75	78.21 8	89.57 11	92.10 18	68.05 83	85.59 32	66.04 110	95.62 11	94.88 5

anonymousdsp		78.60 59	77.80 67	81.00 32	78.01 143	74.34 33	80.09 61	76.12 178	50.51 235	89.19 12	90.88 42	71.45 58	77.78 181	73.38 43	90.60 100	90.90 26

LTVRE_ROB	75.46 1	84.22 5	84.98 5	81.94 19	84.82 63	75.40 26	91.60 1	87.80 5	73.52 18	88.90 13	93.06 6	71.39 59	81.53 93	81.53 3	92.15 69	88.91 48
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
OurMVSNet-221017-0		78.57 60	78.53 62	78.67 57	80.48 113	64.16 98	80.24 59	82.06 77	61.89 105	88.77 14	93.32 4	57.15 199	82.60 78	70.08 68	92.80 59	89.25 38

test_0402		78.17 66	79.48 54	74.24 101	83.50 81	59.15 134	72.52 151	74.60 192	75.34 12	88.69 15	91.81 22	75.06 32	82.37 81	65.10 116	88.68 128	81.20 174

abl_6		84.92 3	85.70 3	82.57 14	86.72 41	79.27 8	87.56 5	86.08 16	77.48 9	88.12 16	91.53 30	81.18 6	84.31 55	78.12 23	94.47 35	84.15 118

TDRefinement		86.32 2	86.33 2	86.29 1	88.64 30	81.19 6	88.84 2	90.72 1	78.27 7	87.95 17	92.53 13	79.37 11	84.79 47	74.51 37	96.15 4	92.88 9

LPG-MVS_test		83.47 15	84.33 10	80.90 33	87.00 38	70.41 57	82.04 45	86.35 12	69.77 39	87.75 18	91.13 36	81.83 3	86.20 19	77.13 28	95.96 7	86.08 80

LGP-MVS_train				80.90 33	87.00 38	70.41 57		86.35 12	69.77 39	87.75 18	91.13 36	81.83 3	86.20 19	77.13 28	95.96 7	86.08 80

SixPastTwentyTwo		75.77 82	76.34 82	74.06 104	81.69 104	54.84 153	76.47 101	75.49 184	64.10 84	87.73 20	92.24 17	50.45 230	81.30 105	67.41 95	91.46 78	86.04 82

ACMH+	66.64 10	81.20 34	82.48 35	77.35 72	81.16 110	62.39 110	80.51 54	87.80 5	73.02 21	87.57 21	91.08 38	80.28 8	82.44 79	64.82 119	96.10 6	87.21 72

v7n		79.37 52	80.41 47	76.28 80	78.67 137	55.81 148	79.22 70	82.51 74	70.72 33	87.54 22	92.44 14	68.00 85	81.34 103	72.84 45	91.72 71	91.69 12

ACMM	69.25 9	82.11 29	83.31 24	78.49 59	88.17 35	73.96 34	83.11 37	84.52 39	66.40 58	87.45 23	89.16 87	81.02 7	80.52 133	74.27 39	95.73 9	80.98 181
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
mvs_tets		78.93 56	78.67 60	79.72 44	84.81 64	73.93 35	80.65 53	76.50 176	51.98 215	87.40 24	91.86 21	76.09 25	78.53 162	68.58 79	90.20 105	86.69 77

test_djsdf		78.88 57	78.27 63	80.70 36	81.42 106	71.24 49	83.98 31	75.72 182	52.27 210	87.37 25	92.25 16	68.04 84	80.56 130	72.28 52	91.15 85	90.32 32

jajsoiax		78.51 61	78.16 64	79.59 46	84.65 67	73.83 37	80.42 56	76.12 178	51.33 222	87.19 26	91.51 31	73.79 43	78.44 166	68.27 82	90.13 109	86.49 78

PMVS	70.70 6	81.70 31	83.15 28	77.36 71	90.35 6	82.82 3	82.15 43	79.22 135	74.08 16	87.16 27	91.97 19	84.80 2	76.97 186	64.98 118	93.61 51	72.28 265
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
ACMH	63.62 14	77.50 69	80.11 48	69.68 181	79.61 119	56.28 146	78.81 72	83.62 56	63.41 94	87.14 28	90.23 68	76.11 24	73.32 221	67.58 93	94.44 37	79.44 207
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
v13		76.23 77	77.02 75	73.86 108	74.61 192	48.80 190	76.91 97	81.10 101	62.66 99	87.02 29	91.01 40	59.76 157	81.41 98	71.29 55	88.78 127	91.38 13

v748		76.93 72	77.95 66	73.87 106	73.94 207	52.44 170	75.90 114	79.98 128	65.34 69	86.97 30	91.77 23	67.40 89	78.40 169	70.23 65	90.01 110	90.76 30

v12		76.03 79	76.79 76	73.76 110	74.45 194	48.60 196	76.59 99	81.11 98	62.22 104	86.79 31	90.74 48	59.51 158	81.40 100	71.01 58	88.67 129	91.29 15

ACMP	69.50 8	82.64 24	83.38 23	80.40 38	86.50 43	69.44 64	82.30 42	86.08 16	66.80 54	86.70 32	89.99 73	81.64 5	85.95 26	74.35 38	96.11 5	85.81 86
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
APDe-MVS		82.88 22	84.14 12	79.08 51	84.80 65	66.72 80	86.54 16	85.11 27	72.00 27	86.65 33	91.75 24	78.20 16	87.04 8	77.93 24	94.32 41	83.47 131

V9		75.82 81	76.53 79	73.66 111	74.28 198	48.37 197	76.26 107	81.10 101	61.73 107	86.59 34	90.43 57	59.16 164	81.42 97	70.71 61	88.56 130	91.21 18

APD-MVS_3200maxsize		83.57 12	84.33 10	81.31 27	82.83 90	73.53 40	85.50 22	87.45 8	74.11 15	86.45 35	90.52 55	80.02 9	84.48 51	77.73 25	94.34 40	85.93 84

v11		75.76 83	76.51 80	73.48 118	74.28 198	47.81 209	76.16 109	81.28 94	61.56 108	86.39 36	90.38 63	59.32 162	81.41 98	70.85 59	88.41 132	91.23 16

V14		75.58 86	76.26 85	73.55 116	74.10 206	48.13 202	75.91 113	81.07 104	61.19 111	86.34 37	90.11 71	58.80 168	81.40 100	70.40 63	88.43 131	91.12 19

PS-MVSNAJss		77.54 68	77.35 70	78.13 65	84.88 62	66.37 83	78.55 75	79.59 132	53.48 201	86.29 38	92.43 15	62.39 127	80.25 137	67.90 92	90.61 99	87.77 65

HPM-MVS_fast		84.59 4	85.10 4	83.06 4	88.60 31	75.83 23	86.27 19	86.89 11	73.69 17	86.17 39	91.70 25	78.23 15	85.20 40	79.45 12	94.91 26	88.15 62

SD-MVS		80.28 44	81.55 42	76.47 76	83.57 80	67.83 76	83.39 36	85.35 25	64.42 81	86.14 40	87.07 118	74.02 40	80.97 119	77.70 26	92.32 68	80.62 189

v15		75.37 89	76.01 87	73.44 120	73.91 210	47.87 208	75.55 120	81.04 105	60.76 116	86.11 41	89.76 77	58.53 174	81.40 100	70.11 66	88.32 133	91.04 22

COLMAP_ROB	72.78 3	83.75 10	84.11 13	82.68 11	82.97 88	74.39 32	87.18 7	88.18 4	78.98 5	86.11 41	91.47 32	79.70 10	85.76 31	66.91 101	95.46 13	87.89 64
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
v10		75.69 85	76.20 86	74.16 102	74.44 196	48.69 192	75.84 116	82.93 67	59.02 130	85.92 43	89.17 86	58.56 173	82.74 76	70.73 60	89.14 123	91.05 20

ACMMP		84.22 5	84.84 6	82.35 17	89.23 22	76.66 22	87.65 4	85.89 18	71.03 31	85.85 44	90.58 51	78.77 13	85.78 30	79.37 15	95.17 18	84.62 104
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
v8		75.07 96	75.64 91	73.35 122	73.42 216	47.46 218	75.20 125	81.45 89	60.05 122	85.64 45	89.26 82	58.08 183	81.80 90	69.71 72	87.97 139	90.79 28

XVG-ACMP-BASELINE		80.54 40	81.06 43	78.98 53	87.01 37	72.91 42	80.23 60	85.56 20	66.56 57	85.64 45	89.57 78	69.12 73	80.55 132	72.51 48	93.37 53	83.48 130

SteuartSystems-ACMMP		83.07 19	83.64 19	81.35 25	85.14 59	71.00 51	85.53 21	84.78 33	70.91 32	85.64 45	90.41 61	75.55 28	87.69 3	79.75 7	95.08 21	85.36 91
Skip Steuart: Steuart Systems R&D Blog.
v17		75.03 97	75.59 92	73.36 121	73.56 212	47.66 213	75.48 121	81.45 89	60.58 118	85.55 48	89.02 91	58.36 176	81.47 94	69.69 73	86.59 162	90.96 23

OPM-MVS		80.99 38	81.63 41	79.07 52	86.86 40	69.39 65	79.41 69	84.00 52	65.64 62	85.54 49	89.28 81	76.32 23	83.47 65	74.03 40	93.57 52	84.35 115

v16		74.89 102	75.41 96	73.35 122	73.54 213	47.62 214	75.47 122	81.45 89	60.58 118	85.46 50	88.97 94	58.27 177	81.47 94	69.66 74	85.25 185	90.95 24

ESAPD		82.00 30	83.02 29	78.95 54	85.36 56	67.25 79	82.91 38	84.98 28	73.52 18	85.43 51	90.03 72	76.37 21	86.97 10	74.56 36	94.02 48	82.62 149

MP-MVS-pluss		82.54 25	83.46 22	79.76 42	88.88 29	68.44 72	81.57 48	86.33 14	63.17 96	85.38 52	91.26 35	76.33 22	84.67 49	83.30 1	94.96 24	86.17 79
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
HPM-MVS		84.12 7	84.63 7	82.60 12	88.21 34	74.40 31	85.24 23	87.21 9	70.69 34	85.14 53	90.42 60	78.99 12	86.62 11	80.83 6	94.93 25	86.79 75
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
v18		74.60 106	75.06 97	73.22 127	73.29 222	47.36 222	75.02 127	81.47 88	60.01 123	85.13 54	88.44 101	57.93 190	81.47 94	69.26 76	85.02 189	90.84 27

mPP-MVS		84.01 9	84.39 9	82.88 5	90.65 4	81.38 5	87.08 9	82.79 68	72.41 24	85.11 55	90.85 44	76.65 20	84.89 44	79.30 16	94.63 32	82.35 155

zzz-MVS		83.01 21	83.63 20	81.13 30	91.16 2	78.16 12	82.72 41	80.63 111	72.08 25	84.93 56	90.79 45	74.65 35	84.42 52	80.98 4	94.75 28	80.82 184

MTAPA		83.19 17	83.87 16	81.13 30	91.16 2	78.16 12	84.87 25	80.63 111	72.08 25	84.93 56	90.79 45	74.65 35	84.42 52	80.98 4	94.75 28	80.82 184

PGM-MVS		83.07 19	83.25 27	82.54 15	89.57 14	77.21 20	82.04 45	85.40 23	67.96 48	84.91 58	90.88 42	75.59 27	86.57 12	78.16 22	94.71 30	83.82 123

K. test v3		73.67 115	73.61 117	73.87 106	79.78 117	55.62 149	74.69 137	62.04 266	66.16 60	84.76 59	93.23 5	49.47 232	80.97 119	65.66 112	86.67 161	85.02 97

CP-MVS		84.12 7	84.55 8	82.80 9	89.42 18	79.74 7	88.19 3	84.43 40	71.96 28	84.70 60	90.56 52	77.12 17	86.18 21	79.24 17	95.36 14	82.49 153

test_part2					85.90 47	66.44 82				84.61 61

v1.0		34.83 342	46.44 326	0.00 359	85.90 47	0.00 374	0.00 365	84.94 31	73.27 20	84.61 61	89.25 84	0.00 376	0.00 371	0.00 368	0.00 369	0.00 369

ACMMPR		83.62 11	83.93 15	82.69 10	89.78 11	77.51 18	87.01 11	84.19 47	70.23 35	84.49 63	90.67 50	75.15 31	86.37 15	79.58 10	94.26 42	84.18 117

HFP-MVS		83.39 16	84.03 14	81.48 22	89.25 20	75.69 24	87.01 11	84.27 43	70.23 35	84.47 64	90.43 57	76.79 18	85.94 27	79.58 10	94.23 44	82.82 144

#test#		82.40 26	82.71 33	81.48 22	89.25 20	75.69 24	84.47 29	84.27 43	64.45 78	84.47 64	90.43 57	76.79 18	85.94 27	76.01 32	94.23 44	82.82 144

testing_2		72.01 151	72.36 145	70.95 165	70.79 252	48.70 191	72.81 147	78.09 160	48.79 246	84.46 66	89.15 88	57.90 191	78.55 161	61.55 136	87.74 141	85.61 90

SMA-MVS		82.12 28	82.68 34	80.43 37	88.90 28	69.52 62	85.12 24	84.76 34	63.53 91	84.23 67	91.47 32	72.02 53	87.16 6	79.74 9	94.36 38	84.61 105

GST-MVS		82.79 23	83.27 26	81.34 26	88.99 25	73.29 41	85.94 20	85.13 26	68.58 46	84.14 68	90.21 69	73.37 44	86.41 13	79.09 18	93.98 49	84.30 116

ACMMP_Plus		82.33 27	83.28 25	79.46 47	89.28 19	69.09 70	83.62 34	84.98 28	64.77 75	83.97 69	91.02 39	75.53 29	85.93 29	82.00 2	94.36 38	83.35 137

region2R		83.54 13	83.86 17	82.58 13	89.82 10	77.53 16	87.06 10	84.23 46	70.19 37	83.86 70	90.72 49	75.20 30	86.27 18	79.41 14	94.25 43	83.95 122

lessismore_v0				72.75 141	79.60 120	56.83 145		57.37 285		83.80 71	89.01 92	47.45 242	78.74 158	64.39 122	86.49 164	82.69 148

APD-MVS		81.13 35	81.73 39	79.36 49	84.47 71	70.53 56	83.85 33	83.70 54	69.43 41	83.67 72	88.96 95	75.89 26	86.41 13	72.62 47	92.95 58	81.14 177
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
ITE_SJBPF				80.35 39	76.94 158	73.60 38		80.48 115	66.87 52	83.64 73	86.18 149	70.25 66	79.90 144	61.12 140	88.95 125	87.56 68

nrg030		74.87 103	75.99 88	71.52 162	74.90 181	49.88 185	74.10 141	82.58 73	54.55 187	83.50 74	89.21 85	71.51 56	75.74 200	61.24 138	92.34 67	88.94 47

V42		71.06 157	70.83 166	71.72 159	67.25 291	47.14 225	65.94 246	80.35 119	51.35 221	83.40 75	83.23 193	59.25 163	78.80 156	65.91 111	80.81 248	89.23 39

TranMVSNet+NR-MVSNet		76.13 78	77.66 68	71.56 161	84.61 69	42.57 254	70.98 185	78.29 155	68.67 45	83.04 76	89.26 82	72.99 47	80.75 129	55.58 180	95.47 12	91.35 14

Anonymous20231211		75.54 87	77.19 71	70.59 168	77.67 150	45.70 239	74.73 135	80.19 123	68.80 42	82.95 77	92.91 8	66.26 99	76.76 191	58.41 156	92.77 60	89.30 37

XVS		83.51 14	83.73 18	82.85 7	89.43 16	77.61 14	86.80 13	84.66 36	72.71 22	82.87 78	90.39 62	73.86 41	86.31 16	78.84 19	94.03 46	84.64 102

X-MVStestdata		76.81 73	74.79 99	82.85 7	89.43 16	77.61 14	86.80 13	84.66 36	72.71 22	82.87 78	9.95 365	73.86 41	86.31 16	78.84 19	94.03 46	84.64 102

XVG-OURS		79.51 49	79.82 50	78.58 58	86.11 46	74.96 29	76.33 106	84.95 30	66.89 51	82.75 80	88.99 93	66.82 93	78.37 171	74.80 33	90.76 98	82.40 154

FC-MVSNet-test		73.32 123	74.78 100	68.93 192	79.21 127	36.57 297	71.82 166	79.54 133	57.63 144	82.57 81	90.38 63	59.38 161	78.99 152	57.91 158	94.56 33	91.23 16

ANet_high		67.08 202	69.94 172	58.51 285	57.55 347	27.09 350	58.43 309	76.80 174	63.56 89	82.40 82	91.93 20	59.82 155	64.98 294	50.10 214	88.86 126	83.46 132

v1240		73.06 126	73.14 126	72.84 139	74.74 185	47.27 224	71.88 165	81.11 98	51.80 216	82.28 83	84.21 178	56.22 207	82.34 82	68.82 77	87.17 156	88.91 48

v7		73.59 117	73.69 113	73.28 126	74.42 197	48.68 193	72.74 150	81.98 79	54.76 182	82.07 84	85.05 167	58.53 174	82.22 86	67.99 88	85.66 173	88.95 46

LS3D		80.99 38	80.85 44	81.41 24	78.37 138	71.37 47	87.45 6	85.87 19	77.48 9	81.98 85	89.95 74	69.14 72	85.26 37	66.15 108	91.24 83	87.61 67

v1192		73.40 121	73.42 119	73.32 125	74.65 191	48.67 194	72.21 154	81.73 83	52.76 208	81.85 86	84.56 174	57.12 200	82.24 85	68.58 79	87.33 151	89.06 42

v1144		73.29 124	73.39 120	73.01 133	74.12 205	48.11 203	72.01 159	81.08 103	53.83 198	81.77 87	84.68 172	58.07 184	81.91 88	68.10 83	86.86 159	88.99 45

OMC-MVS		79.41 51	78.79 58	81.28 28	80.62 112	70.71 55	80.91 51	84.76 34	62.54 101	81.77 87	86.65 135	71.46 57	83.53 64	67.95 91	92.44 65	89.60 34

UniMVSNet_NR-MVSNet		74.90 101	75.65 90	72.64 145	83.04 86	45.79 237	69.26 203	78.81 144	66.66 56	81.74 89	86.88 122	63.26 119	81.07 116	56.21 173	94.98 22	91.05 20

DU-MVS		74.91 100	75.57 93	72.93 137	83.50 81	45.79 237	69.47 201	80.14 125	65.22 70	81.74 89	87.08 116	61.82 133	81.07 116	56.21 173	94.98 22	91.93 10

v1921920		72.96 131	72.98 134	72.89 138	74.67 188	47.58 215	71.92 163	80.69 110	51.70 218	81.69 91	83.89 182	56.58 205	82.25 84	68.34 81	87.36 149	88.82 50

v6		72.93 132	73.08 129	72.48 148	73.42 216	47.47 217	72.17 155	80.25 122	55.63 164	81.65 92	85.04 168	57.95 189	81.28 106	66.56 105	85.01 190	88.70 53

v1neww		72.93 132	73.07 130	72.48 148	73.41 218	47.46 218	72.17 155	80.26 120	55.63 164	81.63 93	85.07 165	57.97 187	81.28 106	66.55 106	84.98 191	88.70 53

v7new		72.93 132	73.07 130	72.48 148	73.41 218	47.46 218	72.17 155	80.26 120	55.63 164	81.63 93	85.07 165	57.97 187	81.28 106	66.55 106	84.98 191	88.70 53

WR-MVS		71.20 156	72.48 143	67.36 211	84.98 61	35.70 306	64.43 263	68.66 235	65.05 73	81.49 95	86.43 143	57.57 194	76.48 193	50.36 212	93.32 55	89.90 33

v144192		72.99 129	73.06 132	72.77 140	74.58 193	47.48 216	71.90 164	80.44 117	51.57 219	81.46 96	84.11 180	58.04 185	82.12 87	67.98 89	87.47 145	88.70 53

MP-MVS		83.19 17	83.54 21	82.14 18	90.54 5	79.00 9	86.42 18	83.59 57	71.31 29	81.26 97	90.96 41	74.57 37	84.69 48	78.41 21	94.78 27	82.74 147
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
v1		72.60 141	72.73 138	72.19 155	73.12 228	47.01 227	71.48 170	79.10 139	55.01 173	81.24 98	84.92 171	57.46 195	80.90 125	66.59 102	85.67 171	88.68 57

v1141		72.59 143	72.73 138	72.19 155	73.10 229	47.00 228	71.48 170	79.11 137	55.01 173	81.23 99	84.94 170	57.45 196	80.89 126	66.58 103	85.65 174	88.68 57

divwei89l23v2f112		72.60 141	72.73 138	72.19 155	73.10 229	47.00 228	71.48 170	79.11 137	55.01 173	81.23 99	84.95 169	57.45 196	80.89 126	66.58 103	85.67 171	88.68 57

v2v482		72.55 146	72.58 142	72.43 151	72.92 240	46.72 232	71.41 175	79.13 136	55.27 168	81.17 101	85.25 163	55.41 209	81.13 111	67.25 100	85.46 179	89.43 36

HSP-MVS		79.69 47	79.17 56	81.27 29	89.70 12	77.46 19	87.16 8	80.58 114	64.94 74	81.05 102	88.38 104	57.10 201	87.10 7	79.75 7	83.87 203	79.24 209

Test4		69.04 183	68.95 183	69.32 186	69.52 266	48.10 204	70.69 189	78.25 157	45.90 266	80.99 103	82.24 204	51.91 221	78.11 179	58.46 154	82.58 215	81.74 168

MDA-MVSNet-bldmvs		62.34 240	61.73 237	64.16 233	61.64 323	49.90 182	48.11 336	57.24 288	53.31 204	80.95 104	79.39 237	49.00 235	61.55 306	45.92 245	80.05 255	81.03 179

CPTT-MVS		81.51 33	81.76 38	80.76 35	89.20 23	78.75 10	86.48 17	82.03 78	68.80 42	80.92 105	88.52 100	72.00 54	82.39 80	74.80 33	93.04 57	81.14 177

DeepC-MVS	72.44 4	81.00 37	80.83 45	81.50 21	86.70 42	70.03 61	82.06 44	87.00 10	59.89 124	80.91 106	90.53 53	72.19 49	88.56 1	73.67 42	94.52 34	85.92 85
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
FIs		72.56 144	73.80 111	68.84 197	78.74 136	37.74 291	71.02 184	79.83 129	56.12 159	80.88 107	89.45 79	58.18 178	78.28 174	56.63 166	93.36 54	90.51 31

3Dnovator+	73.19 2	81.08 36	80.48 46	82.87 6	81.41 107	72.03 43	84.38 30	86.23 15	77.28 11	80.65 108	90.18 70	59.80 156	87.58 4	73.06 44	91.34 81	89.01 43

IterMVS-LS		73.01 127	73.12 128	72.66 144	73.79 211	49.90 182	71.63 169	78.44 152	58.22 134	80.51 109	86.63 136	58.15 180	79.62 146	62.51 131	88.20 134	88.48 60
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Regformer-2		75.32 91	74.47 103	77.88 66	74.22 201	66.65 81	72.77 148	77.54 166	68.47 47	80.44 110	72.08 304	70.60 63	80.97 119	70.08 68	84.02 201	86.01 83

DP-MVS		78.44 64	79.29 55	75.90 85	81.86 102	65.33 89	79.05 71	84.63 38	74.83 14	80.41 111	86.27 146	71.68 55	83.45 66	62.45 133	92.40 66	78.92 213

XVG-OURS-SEG-HR		79.62 48	79.99 49	78.49 59	86.46 44	74.79 30	77.15 94	85.39 24	66.73 55	80.39 112	88.85 97	74.43 39	78.33 173	74.73 35	85.79 169	82.35 155

DeepPCF-MVS	71.07 5	78.48 63	77.14 73	82.52 16	84.39 75	77.04 21	76.35 104	84.05 50	56.66 157	80.27 113	85.31 162	68.56 77	87.03 9	67.39 96	91.26 82	83.50 129

Regformer-4		74.64 105	73.67 114	77.55 69	74.74 185	64.49 97	72.91 145	75.42 187	67.45 49	80.24 114	72.07 307	68.98 74	80.19 141	70.29 64	80.91 244	87.98 63

AllTest		77.66 67	77.43 69	78.35 61	79.19 128	70.81 52	78.60 74	88.64 2	65.37 67	80.09 115	88.17 107	70.33 64	78.43 167	55.60 177	90.90 94	85.81 86

TestCases				78.35 61	79.19 128	70.81 52		88.64 2	65.37 67	80.09 115	88.17 107	70.33 64	78.43 167	55.60 177	90.90 94	85.81 86

UA-Net		81.56 32	82.28 36	79.40 48	88.91 27	69.16 68	84.67 28	80.01 127	75.34 12	79.80 117	94.91 2	69.79 69	80.25 137	72.63 46	94.46 36	88.78 52

PCF-MVS	63.80 13	72.70 139	71.69 154	75.72 87	78.10 141	60.01 126	73.04 144	81.50 86	45.34 273	79.66 118	84.35 177	65.15 108	82.65 77	48.70 224	89.38 120	84.50 111
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
UniMVSNet (Re)		75.00 98	75.48 94	73.56 115	83.14 85	47.92 207	70.41 191	81.04 105	63.67 88	79.54 119	86.37 145	62.83 121	81.82 89	57.10 164	95.25 16	90.94 25

Baseline_NR-MVSNet		70.62 163	73.19 125	62.92 247	76.97 157	34.44 316	68.84 206	70.88 225	60.25 121	79.50 120	90.53 53	61.82 133	69.11 260	54.67 186	95.27 15	85.22 93

FMVSNet1		71.06 157	72.48 143	66.81 215	77.65 151	40.68 266	71.96 160	73.03 198	61.14 112	79.45 121	90.36 65	60.44 149	75.20 205	50.20 213	88.05 136	84.54 107

Regformer-1		74.28 109	73.63 116	76.21 83	74.22 201	64.12 99	72.77 148	75.46 186	66.86 53	79.27 122	72.08 304	69.29 71	78.74 158	68.73 78	84.02 201	85.77 89

ambc				70.10 177	77.74 147	50.21 180	74.28 140	77.93 162		79.26 123	88.29 106	54.11 214	79.77 145	64.43 121	91.10 87	80.30 194

IS-MVSNet		75.10 95	75.42 95	74.15 103	79.23 126	48.05 205	79.43 67	78.04 161	70.09 38	79.17 124	88.02 111	53.04 216	83.60 62	58.05 157	93.76 50	90.79 28

CSCG		74.12 111	74.39 104	73.33 124	79.35 123	61.66 116	77.45 89	81.98 79	62.47 103	79.06 125	80.19 226	61.83 132	78.79 157	59.83 147	87.35 150	79.54 206

RPSCF		75.76 83	74.37 105	79.93 41	74.81 183	77.53 16	77.53 88	79.30 134	59.44 126	78.88 126	89.80 76	71.26 60	73.09 223	57.45 160	80.89 246	89.17 41

tttt0517		69.46 175	67.79 198	74.46 96	75.34 174	52.72 168	75.05 126	63.27 258	54.69 183	78.87 127	84.37 176	26.63 349	81.15 110	63.95 124	87.93 140	89.51 35

v148		69.38 177	69.39 175	69.36 183	69.14 270	44.56 244	68.83 207	72.70 204	54.79 180	78.59 128	84.12 179	54.69 211	76.74 192	59.40 150	82.20 217	86.79 75

EI-MVSNet-Vis-set		72.78 138	71.87 150	75.54 89	74.77 184	59.02 135	72.24 153	71.56 214	63.92 85	78.59 128	71.59 314	66.22 100	78.60 160	67.58 93	80.32 252	89.00 44

EI-MVSNet-UG-set		72.63 140	71.68 155	75.47 90	74.67 188	58.64 139	72.02 158	71.50 215	63.53 91	78.58 130	71.39 317	65.98 101	78.53 162	67.30 99	80.18 253	89.23 39

旧先验2							71.17 182		45.11 275	78.54 131			61.28 307	59.19 151

Regformer-3		72.86 137	72.28 147	74.62 95	74.74 185	60.18 124	72.91 145	71.76 211	64.74 76	78.42 132	72.07 307	67.00 90	76.28 195	67.97 90	80.91 244	87.39 69

MIMVSNet1		66.57 205	69.23 178	58.59 284	81.26 109	37.73 292	64.06 266	57.62 282	57.02 151	78.40 133	90.75 47	62.65 122	58.10 315	41.77 277	89.58 117	79.95 202

test_normal		68.88 185	68.88 184	68.88 195	69.43 268	47.03 226	69.85 197	74.83 191	46.06 265	78.30 134	83.29 191	58.76 172	78.23 175	57.51 159	81.90 224	81.36 173

DI_MVS_plusplus_test		69.01 184	69.04 180	68.93 192	69.54 265	46.74 231	70.14 192	75.49 184	46.64 262	78.30 134	83.18 196	58.80 168	78.86 154	57.14 162	82.15 219	81.18 175

HQP_MVS		78.77 58	78.78 59	78.72 56	85.18 57	65.18 91	82.74 39	85.49 21	65.45 64	78.23 136	89.11 89	60.83 147	86.15 22	71.09 56	90.94 90	84.82 99

plane_prior3						65.67 87			63.82 87	78.23 136

HPM-MVS++		79.89 46	79.80 51	80.18 40	89.02 24	78.44 11	83.49 35	80.18 124	64.71 77	78.11 138	88.39 103	65.46 106	83.14 70	77.64 27	91.20 84	78.94 212

PM-MVS		64.49 218	63.61 222	67.14 213	76.68 161	75.15 28	68.49 216	42.85 349	51.17 225	77.85 139	80.51 222	45.76 244	66.31 290	52.83 197	76.35 282	59.96 338

BH-untuned		69.39 176	69.46 174	69.18 187	77.96 144	56.88 144	68.47 217	77.53 167	56.77 155	77.79 140	79.63 234	60.30 150	80.20 140	46.04 244	80.65 249	70.47 280

MSLP-MVS++		74.48 108	75.78 89	70.59 168	84.66 66	62.40 109	78.65 73	84.24 45	60.55 120	77.71 141	81.98 208	63.12 120	77.64 182	62.95 130	88.14 135	71.73 270

CDPH-MVS		77.33 70	77.06 74	78.14 64	84.21 76	63.98 100	76.07 111	83.45 60	54.20 190	77.68 142	87.18 115	69.98 67	85.37 34	68.01 87	92.72 63	85.08 96

CNVR-MVS		78.49 62	78.59 61	78.16 63	85.86 51	67.40 78	78.12 84	81.50 86	63.92 85	77.51 143	86.56 139	68.43 80	84.82 46	73.83 41	91.61 74	82.26 158

TinyColmap		67.98 194	69.28 176	64.08 235	67.98 285	46.82 230	70.04 193	75.26 188	53.05 205	77.36 144	86.79 125	59.39 160	72.59 235	45.64 246	88.01 138	72.83 258

TSAR-MVS + MP.		79.05 53	78.81 57	79.74 43	88.94 26	67.52 77	86.61 15	81.38 93	51.71 217	77.15 145	91.42 34	65.49 105	87.20 5	79.44 13	87.17 156	84.51 110

TEST9					85.47 54	69.32 66	76.42 102	78.69 146	53.73 199	76.97 146	86.74 129	66.84 92	81.10 114

train_agg		76.38 75	76.55 78	75.86 86	85.47 54	69.32 66	76.42 102	78.69 146	54.00 194	76.97 146	86.74 129	66.60 94	81.10 114	72.50 49	91.56 75	77.15 229

agg_prior1		75.89 80	76.41 81	74.31 100	84.44 73	66.02 85	76.12 110	78.62 149	54.40 188	76.95 148	86.85 123	66.44 98	80.34 135	72.45 51	91.42 79	76.57 234

agg_prior					84.44 73	66.02 85		78.62 149		76.95 148			80.34 135

semantic-postprocess				72.49 147	73.34 221	58.20 141		65.55 248	48.10 251	76.91 150	82.64 198	42.25 266	78.84 155	61.20 139	77.89 277	80.44 193

Anonymous20240529		72.56 144	73.79 112	68.86 196	76.89 160	45.21 242	68.80 210	77.25 172	67.16 50	76.89 151	90.44 56	65.95 102	74.19 216	50.75 208	90.00 111	87.18 73

test_8					85.09 60	67.89 75	76.26 107	78.66 148	54.00 194	76.89 151	86.72 131	66.60 94	80.89 126

MVS_111021_LR		72.10 149	71.82 153	72.95 136	79.53 121	73.90 36	70.45 190	66.64 242	56.87 153	76.81 153	81.76 212	68.78 75	71.76 245	61.81 134	83.74 205	73.18 255

CLD-MVS		72.88 136	72.36 145	74.43 98	77.03 156	54.30 158	68.77 211	83.43 61	52.12 212	76.79 154	74.44 287	69.54 70	83.91 57	55.88 176	93.25 56	85.09 95
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
FMVSNet2		67.48 200	68.21 194	65.29 226	73.14 225	38.94 281	68.81 208	71.21 223	54.81 177	76.73 155	86.48 142	48.63 237	74.60 212	47.98 231	86.11 167	82.35 155

canonicalmvs		72.29 147	73.38 121	69.04 189	74.23 200	47.37 221	73.93 142	83.18 62	54.36 189	76.61 156	81.64 214	72.03 52	75.34 203	57.12 163	87.28 153	84.40 113

EG-PatchMatch MVS		70.70 162	70.88 165	70.16 175	82.64 92	58.80 136	71.48 170	73.64 196	54.98 176	76.55 157	81.77 211	61.10 144	78.94 153	54.87 184	80.84 247	72.74 260

alignmvs		70.54 164	71.00 164	69.15 188	73.50 214	48.04 206	69.85 197	79.62 130	53.94 197	76.54 158	82.00 207	59.00 166	74.68 211	57.32 161	87.21 154	84.72 101

test_prior3		76.71 74	77.19 71	75.27 92	82.15 98	59.85 127	75.57 118	84.33 41	58.92 131	76.53 159	86.78 126	67.83 86	83.39 67	69.81 70	92.76 61	82.58 150

test_prior2							75.57 118		58.92 131	76.53 159	86.78 126	67.83 86		69.81 70	92.76 61

EPP-MVSNet		73.86 113	73.38 121	75.31 91	78.19 140	53.35 166	80.45 55	77.32 170	65.11 72	76.47 161	86.80 124	49.47 232	83.77 59	53.89 192	92.72 63	88.81 51

pmmvs6		71.82 152	73.66 115	66.31 221	75.94 170	42.01 256	66.99 232	72.53 206	63.45 93	76.43 162	92.78 10	72.95 48	69.69 258	51.41 203	90.46 102	87.22 71

testdata				64.13 234	85.87 50	63.34 105		61.80 267	47.83 255	76.42 163	86.60 138	48.83 236	62.31 304	54.46 189	81.26 241	66.74 313

TAPA-MVS	65.27 12	75.16 94	74.29 107	77.77 68	74.86 182	68.08 73	77.89 85	84.04 51	55.15 172	76.19 164	83.39 186	66.91 91	80.11 142	60.04 145	90.14 108	85.13 94
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
agg_prior3		76.32 76	76.33 83	76.28 80	85.86 51	70.13 60	76.50 100	78.26 156	53.41 203	75.78 165	86.49 141	66.58 96	81.57 92	72.50 49	91.56 75	77.15 229

MVS_111021_HR		72.98 130	72.97 135	72.99 134	80.82 111	65.47 88	68.81 208	72.77 203	57.67 142	75.76 166	82.38 203	71.01 61	77.17 184	61.38 137	86.15 165	76.32 235

CNLPA		73.44 119	73.03 133	74.66 94	78.27 139	75.29 27	75.99 112	78.49 151	65.39 66	75.67 167	83.22 195	61.23 142	66.77 287	53.70 194	85.33 183	81.92 166

NR-MVSNet		73.62 116	74.05 109	72.33 154	83.50 81	43.71 247	65.65 250	77.32 170	64.32 82	75.59 168	87.08 116	62.45 126	81.34 103	54.90 183	95.63 10	91.93 10

NCCC		78.25 65	78.04 65	78.89 55	85.61 53	69.45 63	79.80 65	80.99 107	65.77 61	75.55 169	86.25 148	67.42 88	85.42 33	70.10 67	90.88 96	81.81 167

YYNet1		52.58 303	53.50 299	49.85 312	54.15 362	36.45 299	40.53 350	46.55 339	38.09 313	75.52 170	73.31 298	41.08 273	43.88 345	41.10 279	71.14 310	69.21 297

MDA-MVSNet_test_wron		52.57 304	53.49 300	49.81 313	54.24 361	36.47 298	40.48 351	46.58 338	38.13 312	75.47 171	73.32 297	41.05 274	43.85 346	40.98 280	71.20 309	69.10 299

EI-MVSNet		69.61 172	69.01 182	71.41 163	73.94 207	49.90 182	71.31 178	71.32 217	58.22 134	75.40 172	70.44 318	58.16 179	75.85 196	62.51 131	79.81 258	88.48 60

MVSTER		63.29 228	61.60 241	68.36 202	59.77 334	46.21 235	60.62 296	71.32 217	41.83 295	75.40 172	79.12 243	30.25 330	75.85 196	56.30 172	79.81 258	83.03 139

TransMVSNet (Re)		69.62 171	71.63 156	63.57 240	76.51 162	35.93 304	65.75 249	71.29 219	61.05 113	75.02 174	89.90 75	65.88 103	70.41 256	49.79 215	89.48 118	84.38 114

新几何1				69.99 179	88.37 32	71.34 48		62.08 263	43.85 282	74.99 175	86.11 153	52.85 218	70.57 252	50.99 206	83.23 210	68.05 303

Effi-MVS+-dtu		75.43 88	72.28 147	84.91 2	77.05 154	83.58 2	78.47 78	77.70 164	57.68 140	74.89 176	78.13 251	64.80 111	84.26 56	56.46 170	85.32 184	86.88 74

DeepC-MVS_fast	69.89 7	77.17 71	76.33 83	79.70 45	83.90 79	67.94 74	80.06 63	83.75 53	56.73 156	74.88 177	85.32 161	65.54 104	87.79 2	65.61 114	91.14 86	83.35 137
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
1121		69.23 178	68.26 193	72.12 158	88.36 33	71.40 46	68.59 212	62.06 264	43.80 283	74.75 178	86.18 149	52.92 217	76.85 189	54.47 187	83.27 209	68.12 302

VDDNet		71.60 154	73.13 127	67.02 214	86.29 45	41.11 262	69.97 194	66.50 243	68.72 44	74.74 179	91.70 25	59.90 153	75.81 198	48.58 226	91.72 71	84.15 118

GBi-Net		68.30 192	68.79 186	66.81 215	73.14 225	40.68 266	71.96 160	73.03 198	54.81 177	74.72 180	90.36 65	48.63 237	75.20 205	47.12 236	85.37 180	84.54 107

test1		68.30 192	68.79 186	66.81 215	73.14 225	40.68 266	71.96 160	73.03 198	54.81 177	74.72 180	90.36 65	48.63 237	75.20 205	47.12 236	85.37 180	84.54 107

FMVSNet3		65.00 214	65.16 210	64.52 232	69.47 267	37.56 294	66.63 236	70.38 228	51.55 220	74.72 180	83.27 192	37.89 290	74.44 214	47.12 236	85.37 180	81.57 171

Patchmatch-RL test		59.95 258	59.12 257	62.44 255	72.46 242	54.61 156	59.63 302	47.51 336	41.05 299	74.58 183	74.30 289	31.06 324	65.31 291	51.61 201	79.85 257	67.39 306

thisisatest0530		67.05 204	65.16 210	72.73 143	73.10 229	50.55 177	71.26 180	63.91 254	50.22 237	74.46 184	80.75 219	26.81 348	80.25 137	59.43 149	86.50 163	87.37 70

TSAR-MVS + GP.		73.08 125	71.60 158	77.54 70	78.99 134	70.73 54	74.96 128	69.38 232	60.73 117	74.39 185	78.44 248	57.72 193	82.78 75	60.16 143	89.60 116	79.11 211

原ACMM1				73.90 105	85.90 47	65.15 93		81.67 84	50.97 232	74.25 186	86.16 151	61.60 135	83.54 63	56.75 165	91.08 88	73.00 256

pmmvs-eth3d		64.41 221	63.27 225	67.82 207	75.81 172	60.18 124	69.49 200	62.05 265	38.81 309	74.13 187	82.23 205	43.76 256	68.65 270	42.53 270	80.63 251	74.63 246

VPA-MVSNet		68.71 189	70.37 169	63.72 239	76.13 167	38.06 289	64.10 265	71.48 216	56.60 158	74.10 188	88.31 105	64.78 113	69.72 257	47.69 234	90.15 107	83.37 136

VDD-MVS		70.81 161	71.44 161	68.91 194	79.07 133	46.51 233	67.82 222	70.83 226	61.23 110	74.07 189	88.69 98	59.86 154	75.62 201	51.11 205	90.28 104	84.61 105

pm-mvs1		68.40 191	69.85 173	64.04 236	73.10 229	39.94 273	64.61 261	70.50 227	55.52 167	73.97 190	89.33 80	63.91 117	68.38 272	49.68 217	88.02 137	83.81 124

BH-RMVSNet		68.69 190	68.20 195	70.14 176	76.40 163	53.90 162	64.62 260	73.48 197	58.01 136	73.91 191	81.78 210	59.09 165	78.22 176	48.59 225	77.96 276	78.31 218

test12				76.51 74	82.28 96	60.94 120		81.64 85		73.60 192		64.88 110	85.19 41		90.42 103	83.38 134

QAPM		69.18 180	69.26 177	68.94 191	71.61 250	52.58 169	80.37 58	78.79 145	49.63 241	73.51 193	85.14 164	53.66 215	79.12 150	55.11 182	75.54 287	75.11 244

Gipuma		69.55 173	72.83 137	59.70 278	63.63 314	53.97 160	80.08 62	75.93 180	64.24 83	73.49 194	88.93 96	57.89 192	62.46 302	59.75 148	91.55 77	62.67 330
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
mvs_anonymous		65.08 213	65.49 209	63.83 238	63.79 312	37.60 293	66.52 238	69.82 231	43.44 288	73.46 195	86.08 154	58.79 171	71.75 246	51.90 200	75.63 286	82.15 159

Vis-MVSNet		74.85 104	74.56 101	75.72 87	81.63 105	64.64 95	76.35 104	79.06 140	62.85 98	73.33 196	88.41 102	62.54 125	79.59 148	63.94 125	82.92 212	82.94 141
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
PAPM_NR		73.91 112	74.16 108	73.16 129	81.90 101	53.50 163	81.28 49	81.40 92	66.17 59	73.30 197	83.31 190	59.96 152	83.10 71	58.45 155	81.66 231	82.87 142

PHI-MVS		74.92 99	74.36 106	76.61 73	76.40 163	62.32 111	80.38 57	83.15 63	54.16 192	73.23 198	80.75 219	62.19 130	83.86 58	68.02 86	90.92 93	83.65 127

test222					87.30 36	69.15 69	67.85 221	59.59 275	41.06 298	73.05 199	85.72 160	48.03 240			80.65 249	66.92 309

casdiffmvs1		72.89 135	72.85 136	73.04 132	77.69 149	53.36 165	80.89 52	80.76 109	44.66 279	72.86 200	88.56 99	66.45 97	80.91 124	61.58 135	82.17 218	84.84 98

MCST-MVS		73.42 120	73.34 123	73.63 114	81.28 108	59.17 133	74.80 133	83.13 64	45.50 270	72.84 201	83.78 184	65.15 108	80.99 118	64.54 120	89.09 124	80.73 187

tfpnnormal		66.48 206	67.93 196	62.16 258	73.40 220	36.65 296	63.45 270	64.99 251	55.97 160	72.82 202	87.80 112	57.06 202	69.10 261	48.31 229	87.54 143	80.72 188

view600		62.88 234	62.90 229	62.82 248	72.97 236	33.66 322	66.10 241	55.01 299	57.05 147	72.66 203	82.56 199	31.60 315	72.78 226	42.64 266	85.55 175	82.02 160

view800		62.88 234	62.90 229	62.82 248	72.97 236	33.66 322	66.10 241	55.01 299	57.05 147	72.66 203	82.56 199	31.60 315	72.78 226	42.64 266	85.55 175	82.02 160

conf0.05thres1000		62.88 234	62.90 229	62.82 248	72.97 236	33.66 322	66.10 241	55.01 299	57.05 147	72.66 203	82.56 199	31.60 315	72.78 226	42.64 266	85.55 175	82.02 160

tfpn		62.88 234	62.90 229	62.82 248	72.97 236	33.66 322	66.10 241	55.01 299	57.05 147	72.66 203	82.56 199	31.60 315	72.78 226	42.64 266	85.55 175	82.02 160

114514_t		73.40 121	73.33 124	73.64 113	84.15 78	57.11 143	78.20 82	80.02 126	43.76 284	72.55 207	86.07 155	64.00 116	83.35 69	60.14 144	91.03 89	80.45 192

AdaColmap		74.22 110	74.56 101	73.20 128	81.95 100	60.97 119	79.43 67	80.90 108	65.57 63	72.54 208	81.76 212	70.98 62	85.26 37	47.88 232	90.00 111	73.37 253

diffmvs1		70.85 160	71.63 156	68.50 201	64.78 309	46.14 236	71.03 183	77.76 163	57.00 152	72.44 209	87.61 114	61.32 138	74.11 217	69.58 75	83.16 211	85.26 92

LF4IMVS		67.50 199	67.31 202	68.08 204	58.86 339	61.93 112	71.43 174	75.90 181	44.67 278	72.42 210	80.20 225	57.16 198	70.44 254	58.99 152	86.12 166	71.88 268

F-COLMAP		75.29 92	73.99 110	79.18 50	81.73 103	71.90 44	81.86 47	82.98 65	59.86 125	72.27 211	84.00 181	64.56 114	83.07 72	51.48 202	87.19 155	82.56 152

USDC		62.80 238	63.10 227	61.89 259	65.19 305	43.30 248	67.42 226	74.20 194	35.80 324	72.25 212	84.48 175	45.67 245	71.95 243	37.95 299	84.97 193	70.42 282

3Dnovator	65.95 11	71.50 155	71.22 163	72.34 153	73.16 224	63.09 107	78.37 79	78.32 153	57.67 142	72.22 213	84.61 173	54.77 210	78.47 164	60.82 141	81.07 243	75.45 240

Patchmtry		60.91 250	63.01 228	54.62 303	66.10 301	26.27 354	67.47 225	56.40 294	54.05 193	72.04 214	86.66 133	33.19 302	60.17 309	43.69 254	87.45 148	77.42 227

HQP4-MVS										71.59 215			85.31 35			83.74 125

HQP-NCC					82.37 93		77.32 90		59.08 127	71.58 216

ACMP_Plane					82.37 93		77.32 90		59.08 127	71.58 216

HQP-MVS		75.24 93	75.01 98	75.94 84	82.37 93	58.80 136	77.32 90	84.12 48	59.08 127	71.58 216	85.96 157	58.09 181	85.30 36	67.38 97	89.16 121	83.73 126

MVS_Test		69.84 170	70.71 167	67.24 212	67.49 290	43.25 249	69.87 196	81.22 97	52.69 209	71.57 219	86.68 132	62.09 131	74.51 213	66.05 109	78.74 267	83.96 121

TR-MVS		64.59 216	63.54 223	67.73 208	75.75 173	50.83 176	63.39 271	70.29 229	49.33 243	71.55 220	74.55 285	50.94 228	78.46 165	40.43 284	75.69 285	73.89 251

IterMVS		63.12 230	62.48 236	65.02 229	66.34 299	52.86 167	63.81 267	62.25 260	46.57 263	71.51 221	80.40 224	44.60 251	66.82 286	51.38 204	75.47 288	75.38 242
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Fast-Effi-MVS+		68.81 187	68.30 192	70.35 171	74.66 190	48.61 195	66.06 245	78.32 153	50.62 234	71.48 222	75.54 273	68.75 76	79.59 148	50.55 211	78.73 268	82.86 143

VPNet		65.58 208	67.56 199	59.65 279	79.72 118	30.17 343	60.27 299	62.14 261	54.19 191	71.24 223	86.63 136	58.80 168	67.62 278	44.17 253	90.87 97	81.18 175

API-MVS		70.97 159	71.51 160	69.37 182	75.20 176	55.94 147	80.99 50	76.84 173	62.48 102	71.24 223	77.51 254	61.51 137	80.96 123	52.04 198	85.76 170	71.22 274

mvs-test1		73.81 114	70.69 168	83.18 3	77.05 154	81.39 4	75.39 123	77.70 164	57.68 140	71.19 225	74.72 283	64.80 111	83.66 61	56.46 170	81.19 242	84.50 111

LFMVS		67.06 203	67.89 197	64.56 231	78.02 142	38.25 286	70.81 188	59.60 274	65.18 71	71.06 226	86.56 139	43.85 255	75.22 204	46.35 243	89.63 115	80.21 196

BH-w/o		64.81 215	64.29 217	66.36 220	76.08 169	54.71 154	65.61 251	75.23 189	50.10 239	71.05 227	71.86 313	54.33 213	79.02 151	38.20 297	76.14 283	65.36 318

casdiffmvs		72.24 148	71.83 152	73.47 119	75.01 178	54.46 157	79.73 66	82.60 72	45.66 267	70.90 228	87.73 113	63.41 118	82.32 83	65.09 117	76.36 281	83.64 128

Effi-MVS+		72.10 149	72.28 147	71.58 160	74.21 204	50.33 178	74.72 136	82.73 69	62.62 100	70.77 229	76.83 258	69.96 68	80.97 119	60.20 142	78.43 271	83.45 133

tfpn111		61.91 242	61.65 239	62.68 253	72.14 244	35.01 310	65.42 253	56.99 289	55.23 169	70.71 230	79.90 228	32.07 310	72.85 225	38.80 290	83.61 206	80.18 197

conf200view11		61.42 247	61.09 245	62.43 256	72.14 244	35.01 310	65.42 253	56.99 289	55.23 169	70.71 230	79.90 228	32.07 310	72.09 238	35.61 312	81.73 226	80.18 197

thres100view900		61.17 249	61.09 245	61.39 264	72.14 244	35.01 310	65.42 253	56.99 289	55.23 169	70.71 230	79.90 228	32.07 310	72.09 238	35.61 312	81.73 226	77.08 232

OpenMVS_ROB	54.93 17	63.23 229	63.28 224	63.07 246	69.81 262	45.34 240	68.52 215	67.14 239	43.74 285	70.61 233	79.22 240	47.90 241	72.66 231	48.75 223	73.84 299	71.21 275

MSDG		67.47 201	67.48 200	67.46 210	70.70 255	54.69 155	66.90 234	78.17 158	60.88 115	70.41 234	74.76 281	61.22 143	73.18 222	47.38 235	76.87 279	74.49 247

DP-MVS Recon		73.57 118	72.69 141	76.23 82	82.85 89	63.39 104	74.32 139	82.96 66	57.75 139	70.35 235	81.98 208	64.34 115	84.41 54	49.69 216	89.95 113	80.89 182

thres600view7		61.82 243	61.38 244	63.12 245	71.81 249	34.93 313	64.64 259	56.99 289	54.78 181	70.33 236	79.74 233	32.07 310	72.42 236	38.61 293	83.46 207	82.02 160

OpenMVS	62.51 15	68.76 188	68.75 188	68.78 198	70.56 258	53.91 161	78.29 80	77.35 169	48.85 245	70.22 237	83.52 185	52.65 219	76.93 187	55.31 181	81.99 222	75.49 239

Vis-MVSNet (Re-imp)		62.74 239	63.21 226	61.34 265	72.19 243	31.56 341	67.31 230	53.87 306	53.60 200	69.88 238	83.37 188	40.52 276	70.98 249	41.40 278	86.78 160	81.48 172

TAMVS		65.31 210	63.75 220	69.97 180	82.23 97	59.76 129	66.78 235	63.37 257	45.20 274	69.79 239	79.37 238	47.42 243	72.17 237	34.48 317	85.15 188	77.99 225

Anonymous202405211		66.02 207	66.89 206	63.43 243	74.22 201	38.14 287	59.00 305	66.13 244	63.33 95	69.76 240	85.95 158	51.88 222	70.50 253	44.23 252	87.52 144	81.64 170

FPMVS		59.43 262	60.07 251	57.51 290	77.62 152	71.52 45	62.33 278	50.92 324	57.40 145	69.40 241	80.00 227	39.14 281	61.92 305	37.47 303	66.36 331	39.09 360

GA-MVS		62.91 232	61.66 238	66.66 219	67.09 294	44.49 245	61.18 294	69.36 233	51.33 222	69.33 242	74.47 286	36.83 291	74.94 208	50.60 210	74.72 294	80.57 191

EU-MVSNet		60.82 251	60.80 248	60.86 270	68.37 279	41.16 261	72.27 152	68.27 237	26.96 358	69.08 243	75.71 271	32.09 309	67.44 279	55.59 179	78.90 266	73.97 249

HyFIR lowres test		63.01 231	60.47 249	70.61 167	83.04 86	54.10 159	59.93 301	72.24 210	33.67 337	69.00 244	75.63 272	38.69 283	76.93 187	36.60 307	75.45 289	80.81 186

DELS-MVS		68.83 186	68.31 191	70.38 170	70.55 259	48.31 198	63.78 268	82.13 76	54.00 194	68.96 245	75.17 279	58.95 167	80.06 143	58.55 153	82.74 213	82.76 146
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
0601test		65.11 211	65.09 214	65.18 227	70.59 256	40.86 264	63.22 275	72.79 201	57.91 137	68.88 246	79.07 245	42.85 262	74.89 209	45.50 247	84.97 193	79.81 203

Anonymous20240521		65.11 211	65.09 214	65.18 227	70.59 256	40.86 264	63.22 275	72.79 201	57.91 137	68.88 246	79.07 245	42.85 262	74.89 209	45.50 247	84.97 193	79.81 203

Fast-Effi-MVS+-dtu		70.00 168	68.74 189	73.77 109	73.47 215	64.53 96	71.36 176	78.14 159	55.81 162	68.84 248	74.71 284	65.36 107	75.75 199	52.00 199	79.00 265	81.03 179

MG-MVS		70.47 165	71.34 162	67.85 206	79.26 125	40.42 271	74.67 138	75.15 190	58.41 133	68.74 249	88.14 110	56.08 208	83.69 60	59.90 146	81.71 230	79.43 208

tfpn200view9		60.35 256	59.97 252	61.51 262	70.78 253	35.35 308	63.27 273	57.47 283	53.00 206	68.31 250	77.09 256	32.45 307	72.09 238	35.61 312	81.73 226	77.08 232

thres400		60.77 253	59.97 252	63.15 244	70.78 253	35.35 308	63.27 273	57.47 283	53.00 206	68.31 250	77.09 256	32.45 307	72.09 238	35.61 312	81.73 226	82.02 160

diffmvs		69.55 173	70.18 171	67.66 209	63.63 314	45.24 241	71.26 180	76.21 177	55.79 163	67.89 252	86.41 144	61.00 146	73.76 220	68.03 85	81.40 234	83.98 120

testgi		54.00 296	56.86 281	45.45 329	58.20 344	25.81 355	49.05 332	49.50 329	45.43 272	67.84 253	81.17 217	51.81 225	43.20 348	29.30 338	79.41 263	67.34 308

xiu_mvs_v1_base_debu		67.87 195	67.07 203	70.26 172	79.13 130	61.90 113	67.34 227	71.25 220	47.98 252	67.70 254	74.19 292	61.31 139	72.62 232	56.51 167	78.26 273	76.27 236

xiu_mvs_v1_base		67.87 195	67.07 203	70.26 172	79.13 130	61.90 113	67.34 227	71.25 220	47.98 252	67.70 254	74.19 292	61.31 139	72.62 232	56.51 167	78.26 273	76.27 236

xiu_mvs_v1_base_debi		67.87 195	67.07 203	70.26 172	79.13 130	61.90 113	67.34 227	71.25 220	47.98 252	67.70 254	74.19 292	61.31 139	72.62 232	56.51 167	78.26 273	76.27 236

conf0.01		59.26 263	58.88 260	60.40 273	68.66 271	31.96 335	62.04 280	51.95 316	50.99 226	67.57 257	75.91 265	28.59 340	69.07 262	42.77 260	81.40 234	80.18 197

conf0.002		59.26 263	58.88 260	60.40 273	68.66 271	31.96 335	62.04 280	51.95 316	50.99 226	67.57 257	75.91 265	28.59 340	69.07 262	42.77 260	81.40 234	80.18 197

thresconf0.02		58.38 270	58.88 260	56.91 293	68.66 271	31.96 335	62.04 280	51.95 316	50.99 226	67.57 257	75.91 265	28.59 340	69.07 262	42.77 260	81.40 234	69.70 288

tfpn_n400		58.38 270	58.88 260	56.91 293	68.66 271	31.96 335	62.04 280	51.95 316	50.99 226	67.57 257	75.91 265	28.59 340	69.07 262	42.77 260	81.40 234	69.70 288

tfpnconf		58.38 270	58.88 260	56.91 293	68.66 271	31.96 335	62.04 280	51.95 316	50.99 226	67.57 257	75.91 265	28.59 340	69.07 262	42.77 260	81.40 234	69.70 288

tfpnview11		58.38 270	58.88 260	56.91 293	68.66 271	31.96 335	62.04 280	51.95 316	50.99 226	67.57 257	75.91 265	28.59 340	69.07 262	42.77 260	81.40 234	69.70 288

tfpn1000		58.28 274	58.86 266	56.53 297	68.05 284	32.26 332	62.58 277	51.67 323	51.25 224	67.38 263	75.95 264	27.24 347	68.83 268	43.51 257	82.11 221	68.49 301

CDS-MVSNet		64.33 222	62.66 235	69.35 184	80.44 114	58.28 140	65.26 256	65.66 246	44.36 280	67.30 264	75.54 273	43.27 258	71.77 244	37.68 300	84.44 196	78.01 224
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
PVSNet_Blended_VisFu		70.04 167	68.88 184	73.53 117	82.71 91	63.62 103	74.81 131	81.95 81	48.53 248	67.16 265	79.18 242	51.42 227	78.38 170	54.39 190	79.72 261	78.60 215

PLC	62.01 16	71.79 153	70.28 170	76.33 79	80.31 115	68.63 71	78.18 83	81.24 96	54.57 186	67.09 266	80.63 221	59.44 159	81.74 91	46.91 239	84.17 198	78.63 214
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
VNet		64.01 226	65.15 212	60.57 271	73.28 223	35.61 307	57.60 312	67.08 240	54.61 185	66.76 267	83.37 188	56.28 206	66.87 283	42.19 272	85.20 187	79.23 210

PAPR		69.20 179	68.66 190	70.82 166	75.15 177	47.77 210	75.31 124	81.11 98	49.62 242	66.33 268	79.27 239	61.53 136	82.96 73	48.12 230	81.50 233	81.74 168

MVS_0304		74.55 107	73.47 118	77.80 67	77.41 153	63.88 101	75.75 117	83.67 55	63.55 90	66.12 269	82.16 206	60.20 151	86.15 22	65.37 115	86.98 158	83.38 134

pmmvs4		60.78 252	59.04 258	66.00 223	73.06 233	57.67 142	64.53 262	60.22 272	36.91 319	65.96 270	77.27 255	39.66 279	68.54 271	38.87 289	74.89 293	71.80 269

CMPMVS	48.73 20	61.54 246	60.89 247	63.52 241	61.08 326	51.55 172	68.07 220	68.00 238	33.88 333	65.87 271	81.25 216	37.91 289	67.71 276	49.32 219	82.60 214	71.31 273
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
ppachtmachnet_test		60.26 257	59.61 255	62.20 257	67.70 288	44.33 246	58.18 310	60.96 270	40.75 301	65.80 272	72.57 302	41.23 270	63.92 297	46.87 240	82.42 216	78.33 217

MAR-MVS		67.72 198	66.16 208	72.40 152	74.45 194	64.99 94	74.87 129	77.50 168	48.67 247	65.78 273	68.58 334	57.01 203	77.79 180	46.68 242	81.92 223	74.42 248
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
ab-mvs		64.11 224	65.13 213	61.05 267	71.99 248	38.03 290	67.59 223	68.79 234	49.08 244	65.32 274	86.26 147	58.02 186	66.85 285	39.33 286	79.79 260	78.27 219

jason		64.47 219	62.84 233	69.34 185	76.91 159	59.20 130	67.15 231	65.67 245	35.29 326	65.16 275	76.74 259	44.67 250	70.68 250	54.74 185	79.28 264	78.14 221
jason: jason.
test20.03		55.74 287	57.51 276	50.42 311	59.89 333	32.09 333	50.63 329	49.01 330	50.11 238	65.07 276	83.23 193	45.61 246	48.11 330	30.22 333	83.82 204	71.07 278

testmv		52.91 301	54.31 296	48.71 320	72.13 247	36.18 300	50.26 330	47.78 334	44.15 281	64.61 277	79.78 232	38.18 285	50.20 326	21.96 355	69.93 317	59.75 339

new-patchmatchnet		52.89 302	55.76 289	44.26 335	59.94 332	6.31 369	37.36 359	50.76 326	41.10 297	64.28 278	79.82 231	44.77 249	48.43 329	36.24 309	87.61 142	78.03 223

tfpn_ndepth		56.91 281	57.30 278	55.71 299	67.22 293	33.26 327	61.72 288	53.98 305	48.49 249	64.16 279	71.94 311	27.65 346	68.71 269	40.49 283	80.08 254	65.17 320

thres200		57.55 279	57.02 279	59.17 280	67.89 287	34.93 313	58.91 307	57.25 287	50.24 236	64.01 280	71.46 316	32.49 306	71.39 247	31.31 328	79.57 262	71.19 276

our_test_3		56.46 282	56.51 283	56.30 298	67.70 288	39.66 275	55.36 319	52.34 315	40.57 304	63.85 281	69.91 323	40.04 278	58.22 314	43.49 258	75.29 292	71.03 279

XXY-MVS		55.19 289	57.40 277	48.56 321	64.45 310	34.84 315	51.54 328	53.59 308	38.99 308	63.79 282	79.43 236	56.59 204	45.57 335	36.92 306	71.29 308	65.25 319

cascas		64.59 216	62.77 234	70.05 178	75.27 175	50.02 181	61.79 287	71.61 212	42.46 292	63.68 283	68.89 331	49.33 234	80.35 134	47.82 233	84.05 200	79.78 205

thisisatest0515		60.48 255	57.86 273	68.34 203	67.25 291	46.42 234	60.58 297	62.14 261	40.82 300	63.58 284	69.12 327	26.28 351	78.34 172	48.83 222	82.13 220	80.26 195

MVSFormer		69.93 169	69.03 181	72.63 146	74.93 179	59.19 131	83.98 31	75.72 182	52.27 210	63.53 285	76.74 259	43.19 259	80.56 130	72.28 52	78.67 269	78.14 221

lupinMVS		63.36 227	61.49 243	68.97 190	74.93 179	59.19 131	65.80 248	64.52 253	34.68 331	63.53 285	74.25 290	43.19 259	70.62 251	53.88 193	78.67 269	77.10 231

UnsupCasMVSNet_eth		52.26 306	53.29 301	49.16 317	55.08 358	33.67 321	50.03 331	58.79 278	37.67 315	63.43 287	74.75 282	41.82 268	45.83 334	38.59 294	59.42 346	67.98 304

Anonymous20231206		54.13 293	55.82 288	49.04 319	70.89 251	35.96 303	51.73 327	50.87 325	34.86 327	62.49 288	79.22 240	42.52 265	44.29 344	27.95 342	81.88 225	66.88 310

CANet		73.00 128	71.84 151	76.48 75	75.82 171	61.28 117	74.81 131	80.37 118	63.17 96	62.43 289	80.50 223	61.10 144	85.16 42	64.00 123	84.34 197	83.01 140

xiu_mvs_v2_base		64.43 220	63.96 218	65.85 225	77.72 148	51.32 174	63.63 269	72.31 209	45.06 277	61.70 290	69.66 324	62.56 123	73.93 219	49.06 221	73.91 297	72.31 264

PS-MVSNAJ		64.27 223	63.73 221	65.90 224	77.82 146	51.42 173	63.33 272	72.33 208	45.09 276	61.60 291	68.04 335	62.39 127	73.95 218	49.07 220	73.87 298	72.34 263

CHOSEN 1792x2688		58.09 275	56.30 285	63.45 242	79.95 116	50.93 175	54.07 322	65.59 247	28.56 355	61.53 292	74.33 288	41.09 272	66.52 289	33.91 321	67.69 329	72.92 257

CR-MVSNet		58.96 266	58.49 270	60.36 275	66.37 297	48.24 200	70.93 186	56.40 294	32.87 341	61.35 293	86.66 133	33.19 302	63.22 299	48.50 227	70.17 315	69.62 293

RPMNet		61.25 248	61.55 242	60.36 275	66.37 297	48.24 200	70.93 186	54.45 304	54.66 184	61.35 293	86.77 128	33.29 301	63.22 299	55.93 175	70.17 315	69.62 293

PatchMatch-RL		58.68 269	57.72 274	61.57 261	76.21 166	73.59 39	61.83 286	49.00 331	47.30 260	61.08 295	68.97 329	50.16 231	59.01 312	36.06 311	68.84 323	52.10 349

FMVSNet5		55.08 290	55.54 291	53.71 304	65.80 302	33.50 326	56.22 314	52.50 314	43.72 286	61.06 296	83.38 187	25.46 355	54.87 318	30.11 334	81.64 232	72.75 259

1314		59.83 259	58.86 266	62.74 252	65.71 303	44.78 243	68.59 212	72.63 205	33.54 340	61.05 297	67.29 339	43.62 257	71.26 248	49.49 218	67.84 328	72.19 266

Patchmatch-test1		57.81 277	58.04 272	57.13 291	70.17 261	41.07 263	65.19 257	53.38 310	43.34 291	61.00 298	71.94 311	45.20 247	62.69 301	41.81 276	70.31 314	67.63 305

no-one		56.11 284	55.62 290	57.60 289	62.68 317	49.23 188	39.12 355	58.99 277	33.72 335	60.98 299	80.90 218	36.07 294	60.36 308	30.68 330	97.40 1	63.22 327

UGNet		70.20 166	69.05 179	73.65 112	76.24 165	63.64 102	75.87 115	72.53 206	61.48 109	60.93 300	86.14 152	52.37 220	77.12 185	50.67 209	85.21 186	80.17 201
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
UnsupCasMVSNet_bld		50.01 312	51.03 312	46.95 322	58.61 341	32.64 330	48.31 334	53.27 311	34.27 332	60.47 301	71.53 315	41.40 269	47.07 332	30.68 330	60.78 342	61.13 334

CVMVSNet		59.21 265	58.44 271	61.51 262	73.94 207	47.76 211	71.31 178	64.56 252	26.91 359	60.34 302	70.44 318	36.24 293	67.65 277	53.57 195	68.66 325	69.12 298

1111		45.08 326	47.96 321	36.43 348	59.56 336	14.82 365	43.56 345	45.65 342	45.60 268	60.04 303	75.47 276	9.31 371	34.46 360	23.66 351	68.76 324	60.02 337

.test1245		34.47 343	40.38 339	16.73 354	59.56 336	14.82 365	43.56 345	45.65 342	45.60 268	60.04 303	75.47 276	9.31 371	34.46 360	23.66 351	0.55 367	0.90 366

PVSNet_BlendedMVS		65.38 209	64.30 216	68.61 199	69.81 262	49.36 186	65.60 252	78.96 141	45.50 270	59.98 305	78.61 247	51.82 223	78.20 177	44.30 250	84.11 199	78.27 219

PVSNet_Blended		62.90 233	61.64 240	66.69 218	69.81 262	49.36 186	61.23 293	78.96 141	42.04 294	59.98 305	68.86 332	51.82 223	78.20 177	44.30 250	77.77 278	72.52 261

MVS		60.62 254	59.97 252	62.58 254	68.13 283	47.28 223	68.59 212	73.96 195	32.19 342	59.94 307	68.86 332	50.48 229	77.64 182	41.85 275	75.74 284	62.83 328

1112_ss		59.48 261	58.99 259	60.96 269	77.84 145	42.39 255	61.42 291	68.45 236	37.96 314	59.93 308	67.46 337	45.11 248	65.07 293	40.89 281	71.81 306	75.41 241

Test_1112_low_res		58.78 268	58.69 268	59.04 282	79.41 122	38.13 288	57.62 311	66.98 241	34.74 329	59.62 309	77.56 253	42.92 261	63.65 298	38.66 292	70.73 312	75.35 243

CostFormer		57.35 280	56.14 286	60.97 268	63.76 313	38.43 283	67.50 224	60.22 272	37.14 318	59.12 310	76.34 261	32.78 304	71.99 242	39.12 288	69.27 321	72.47 262

test1235678		48.41 316	49.60 316	44.83 333	68.52 277	33.81 320	46.33 342	45.89 341	38.72 310	58.46 311	72.08 304	29.85 335	47.82 331	19.67 359	66.91 330	52.88 347

PatchmatchNet		54.60 291	54.27 297	55.59 300	65.17 307	39.08 278	66.92 233	51.80 322	39.89 305	58.39 312	73.12 300	31.69 314	58.33 313	43.01 259	58.38 352	69.38 296
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
MS-PatchMatch		55.59 288	54.89 294	57.68 288	69.18 269	49.05 189	61.00 295	62.93 259	35.98 322	58.36 313	68.93 330	36.71 292	66.59 288	37.62 302	63.30 337	57.39 342

tpm2		56.12 283	54.64 295	60.55 272	66.24 300	36.01 302	68.14 219	56.77 293	33.60 339	58.25 314	75.52 275	30.25 330	74.33 215	33.27 323	69.76 320	71.32 272

testus		45.03 327	46.49 325	40.65 343	62.53 318	25.24 356	42.54 347	46.23 340	31.16 352	57.69 315	62.90 346	34.60 297	42.33 349	17.72 361	63.01 338	54.37 346

N_pmnet		52.06 307	51.11 311	54.92 302	59.64 335	71.03 50	37.42 358	61.62 268	33.68 336	57.12 316	72.10 303	37.94 288	31.03 363	29.13 341	71.35 307	62.70 329

tpm		50.60 309	52.42 305	45.14 331	65.18 306	26.29 353	60.30 298	43.50 346	37.41 316	57.01 317	79.09 244	30.20 332	42.32 350	32.77 325	66.36 331	66.81 312

LP		53.02 300	52.27 306	55.27 301	55.76 356	40.55 269	55.64 317	55.07 297	42.46 292	56.95 318	73.21 299	33.67 300	54.18 322	38.41 295	59.29 347	71.08 277

tpm cat1		54.02 295	52.63 303	58.19 286	64.85 308	39.86 274	66.26 240	57.28 286	32.16 343	56.90 319	70.39 320	32.75 305	65.30 292	34.29 319	58.79 348	69.41 295

Patchmatch-test		47.93 317	49.96 315	41.84 340	57.42 348	24.26 358	48.75 333	41.49 356	39.30 306	56.79 320	73.48 296	30.48 329	33.87 362	29.29 339	72.61 302	67.39 306

EPNet		69.10 181	67.32 201	74.46 96	68.33 281	61.27 118	77.56 87	63.57 256	60.95 114	56.62 321	82.75 197	51.53 226	81.24 109	54.36 191	90.20 105	80.88 183
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
MVP-Stereo		61.56 245	59.22 256	68.58 200	79.28 124	60.44 122	69.20 204	71.57 213	43.58 287	56.42 322	78.37 249	39.57 280	76.46 194	34.86 316	60.16 343	68.86 300
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
tpmvs		55.84 285	55.45 293	57.01 292	60.33 330	33.20 328	65.89 247	59.29 276	47.52 259	56.04 323	73.60 295	31.05 325	68.06 274	40.64 282	64.64 334	69.77 287

MIMVSNet		54.39 292	56.12 287	49.20 316	72.57 241	30.91 342	59.98 300	48.43 333	41.66 296	55.94 324	83.86 183	41.19 271	50.42 324	26.05 344	75.38 290	66.27 314

IB-MVS	49.67 18	59.69 260	56.96 280	67.90 205	68.19 282	50.30 179	61.42 291	65.18 250	47.57 258	55.83 325	67.15 340	23.77 358	79.60 147	43.56 256	79.97 256	73.79 252
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
test0.0.03 1		47.72 318	48.31 319	45.93 327	55.53 357	29.39 344	46.40 341	41.21 358	43.41 289	55.81 326	67.65 336	29.22 337	43.77 347	25.73 347	69.87 318	64.62 323

pmmvs5		52.49 305	52.58 304	52.21 309	54.99 359	32.38 331	55.45 318	53.84 307	32.15 344	55.49 327	74.81 280	38.08 287	57.37 316	34.02 320	74.40 295	66.88 310

tpmp4_e23		57.57 278	55.46 292	63.93 237	66.48 296	41.56 260	71.68 168	60.65 271	35.64 325	55.35 328	76.25 262	29.53 336	75.41 202	34.40 318	69.12 322	74.83 245

CANet_DTU		64.04 225	63.83 219	64.66 230	68.39 278	42.97 251	73.45 143	74.50 193	52.05 214	54.78 329	75.44 278	43.99 254	70.42 255	53.49 196	78.41 272	80.59 190

PatchT		53.35 298	56.47 284	43.99 336	64.19 311	17.46 363	59.15 303	43.10 347	52.11 213	54.74 330	86.95 120	29.97 333	49.98 327	43.62 255	74.40 295	64.53 325

HY-MVS	49.31 19	57.96 276	57.59 275	59.10 281	66.85 295	36.17 301	65.13 258	65.39 249	39.24 307	54.69 331	78.14 250	44.28 253	67.18 282	33.75 322	70.79 311	73.95 250

PatchFormer-LS_test		53.94 297	52.64 302	57.85 287	61.87 321	39.59 276	61.60 289	57.63 281	40.65 302	54.52 332	58.64 354	29.07 339	64.18 295	46.78 241	62.98 339	69.78 286

PVSNet	43.83 21	51.56 308	51.17 309	52.73 306	68.34 280	38.27 285	48.22 335	53.56 309	36.41 320	54.29 333	64.94 343	34.60 297	54.20 321	30.34 332	69.87 318	65.71 317

WTY-MVS		49.39 313	50.31 314	46.62 325	61.22 325	32.00 334	46.61 340	49.77 328	33.87 334	54.12 334	69.55 326	41.96 267	45.40 337	31.28 329	64.42 335	62.47 331

PAPM		61.79 244	60.37 250	66.05 222	76.09 168	41.87 257	69.30 202	76.79 175	40.64 303	53.80 335	79.62 235	44.38 252	82.92 74	29.64 337	73.11 301	73.36 254

tpmrst		50.15 311	51.38 308	46.45 326	56.05 352	24.77 357	64.40 264	49.98 327	36.14 321	53.32 336	69.59 325	35.16 296	48.69 328	39.24 287	58.51 351	65.89 315

MDTV_nov1_ep13			54.05 298		65.54 304	29.30 345	59.00 305	55.22 296	35.96 323	52.44 337	75.98 263	30.77 327	59.62 310	38.21 296	73.33 300

sss		47.59 319	48.32 318	45.40 330	56.73 351	33.96 318	45.17 344	48.51 332	32.11 346	52.37 338	65.79 341	40.39 277	41.91 353	31.85 326	61.97 340	60.35 335

DWT-MVSNet_test		53.04 299	51.12 310	58.77 283	61.23 324	38.67 282	62.16 279	57.74 280	38.24 311	51.76 339	59.07 353	21.36 360	67.40 280	44.80 249	63.76 336	70.25 283

EPMVS		45.74 321	46.53 324	43.39 337	54.14 363	22.33 360	55.02 320	35.00 365	34.69 330	51.09 340	70.20 322	25.92 353	42.04 352	37.19 304	55.50 356	65.78 316

gg-mvs-nofinetune		55.75 286	56.75 282	52.72 307	62.87 316	28.04 349	68.92 205	41.36 357	71.09 30	50.80 341	92.63 12	20.74 361	66.86 284	29.97 335	72.41 303	63.25 326

ADS-MVSNet2		48.76 314	47.25 323	53.29 305	55.90 354	40.54 270	47.34 338	54.99 303	31.41 350	50.48 342	72.06 309	31.23 321	54.26 320	25.93 345	55.93 354	65.07 321

ADS-MVSNet		44.62 329	45.58 327	41.73 341	55.90 354	20.83 361	47.34 338	39.94 361	31.41 350	50.48 342	72.06 309	31.23 321	39.31 356	25.93 345	55.93 354	65.07 321

pmmvs3		46.71 320	45.09 329	51.55 310	56.76 350	48.25 199	55.78 316	39.53 362	24.13 362	50.35 344	63.40 345	15.90 368	51.08 323	29.29 339	70.69 313	55.33 345

JIA-IIPM		54.03 294	51.62 307	61.25 266	59.14 338	55.21 150	59.10 304	47.72 335	50.85 233	50.31 345	85.81 159	20.10 363	63.97 296	36.16 310	55.41 357	64.55 324

test-LLR		50.43 310	50.69 313	49.64 314	60.76 327	41.87 257	53.18 324	45.48 344	43.41 289	49.41 346	60.47 351	29.22 337	44.73 341	42.09 273	72.14 304	62.33 332

test-mter		48.56 315	48.20 320	49.64 314	60.76 327	41.87 257	53.18 324	45.48 344	31.91 348	49.41 346	60.47 351	18.34 364	44.73 341	42.09 273	72.14 304	62.33 332

test2356		40.85 335	40.47 338	41.98 339	58.78 340	28.65 348	39.45 353	40.98 360	31.95 347	48.47 348	56.63 355	12.54 370	44.41 343	15.84 363	59.58 345	52.88 347

PMMVS2		37.74 337	40.87 335	28.36 353	42.41 369	5.35 370	24.61 362	27.75 368	32.15 344	47.85 349	70.27 321	35.85 295	29.51 364	19.08 360	67.85 327	50.22 351

EPNet_dtu		58.93 267	58.52 269	60.16 277	67.91 286	47.70 212	69.97 194	58.02 279	49.73 240	47.28 350	73.02 301	38.14 286	62.34 303	36.57 308	85.99 168	70.43 281
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
DSMNet-mixed		43.18 332	44.66 332	38.75 346	54.75 360	28.88 347	57.06 313	27.42 369	13.47 364	47.27 351	77.67 252	38.83 282	39.29 357	25.32 349	60.12 344	48.08 352

testpf		45.32 323	48.47 317	35.88 349	53.56 364	26.84 351	58.86 308	42.95 348	47.78 256	46.18 352	63.70 344	13.73 369	50.29 325	50.81 207	58.61 350	30.51 363

GG-mvs-BLEND				52.24 308	60.64 329	29.21 346	69.73 199	42.41 350		45.47 353	52.33 359	20.43 362	68.16 273	25.52 348	65.42 333	59.36 340

new_pmnet		37.55 338	39.80 341	30.79 351	56.83 349	16.46 364	39.35 354	30.65 367	25.59 360	45.26 354	61.60 349	24.54 356	28.02 365	21.60 356	52.80 359	47.90 353

MDTV_nov1_ep13_2view						18.41 362	53.74 323		31.57 349	44.89 355		29.90 334		32.93 324		71.48 271

TESTMET0.1,1		45.17 324	44.93 330	45.89 328	56.02 353	38.31 284	53.18 324	41.94 355	27.85 356	44.86 356	56.47 356	17.93 365	41.50 355	38.08 298	68.06 326	57.85 341

PVSNet_0	36.71 22	41.12 334	40.78 337	42.14 338	59.97 331	40.13 272	40.97 349	42.24 354	30.81 353	44.86 356	49.41 362	40.70 275	45.12 339	23.15 353	34.96 362	41.16 359

test12356		38.35 336	40.80 336	31.01 350	58.31 343	9.09 368	36.67 360	46.65 337	33.65 338	44.39 358	60.94 350	17.56 366	39.23 358	16.01 362	53.03 358	44.72 357

dp		44.09 331	44.88 331	41.72 342	58.53 342	23.18 359	54.70 321	42.38 352	34.80 328	44.25 359	65.61 342	24.48 357	44.80 340	29.77 336	49.42 360	57.18 343

PMMVS		44.69 328	43.95 334	46.92 323	50.05 366	53.47 164	48.08 337	42.40 351	22.36 363	44.01 360	53.05 358	42.60 264	45.49 336	31.69 327	61.36 341	41.79 358

MVS-HIRNet		45.53 322	47.29 322	40.24 344	62.29 320	26.82 352	56.02 315	37.41 363	29.74 354	43.69 361	81.27 215	33.96 299	55.48 317	24.46 350	56.79 353	38.43 361

PNet_i23d		36.76 339	36.63 343	37.12 347	58.19 345	33.00 329	39.86 352	32.55 366	48.44 250	39.64 362	51.31 360	6.89 373	41.83 354	22.29 354	30.55 363	36.54 362

E-PMN		45.17 324	45.36 328	44.60 334	50.07 365	42.75 252	38.66 356	42.29 353	46.39 264	39.55 363	51.15 361	26.00 352	45.37 338	37.68 300	76.41 280	45.69 356

MVE	27.91 23	36.69 340	35.64 344	39.84 345	43.37 368	35.85 305	19.49 363	24.61 370	24.68 361	39.05 364	62.63 348	38.67 284	27.10 366	21.04 357	47.25 361	56.56 344
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
EMVS		44.61 330	44.45 333	45.10 332	48.91 367	43.00 250	37.92 357	41.10 359	46.75 261	38.00 365	48.43 363	26.42 350	46.27 333	37.11 305	75.38 290	46.03 355

CHOSEN 280x420		41.62 333	39.89 340	46.80 324	61.81 322	51.59 171	33.56 361	35.74 364	27.48 357	37.64 366	53.53 357	23.24 359	42.09 351	27.39 343	58.64 349	46.72 354

tmp_tt		11.98 345	14.73 346	3.72 356	2.28 371	4.62 371	19.44 364	14.50 372	0.47 366	21.55 367	9.58 366	25.78 354	4.57 368	11.61 364	27.37 364	1.96 365

DeepMVS_CX				11.83 355	15.51 370	13.86 367		11.25 373	5.76 365	20.85 368	26.46 364	17.06 367	9.22 367	9.69 365	13.82 365	12.42 364

test123		4.43 348	5.78 349	0.39 358	0.97 372	0.28 372	46.33 342	0.45 374	0.31 367	0.62 369	1.50 369	0.61 375	0.11 370	0.56 366	0.63 366	0.77 368

testmvs		4.06 349	5.28 350	0.41 357	0.64 373	0.16 373	42.54 347	0.31 375	0.26 368	0.50 370	1.40 370	0.77 374	0.17 369	0.56 366	0.55 367	0.90 366

cdsmvs_eth3d_5k		17.71 344	23.62 345	0.00 359	0.00 374	0.00 374	0.00 365	70.17 230	0.00 369	0.00 371	74.25 290	68.16 82	0.00 371	0.00 368	0.00 369	0.00 369

pcd_1.5k_mvsjas		5.20 347	6.93 348	0.00 359	0.00 374	0.00 374	0.00 365	0.00 376	0.00 369	0.00 371	0.00 371	62.39 127	0.00 371	0.00 368	0.00 369	0.00 369

pcd1.5k->3k		35.00 341	36.93 342	29.21 352	84.62 68	0.00 374	0.00 365	78.90 143	0.00 369	0.00 371	0.00 371	78.26 14	0.00 371	0.00 368	90.55 101	87.62 66

sosnet-low-res		0.00 350	0.00 351	0.00 359	0.00 374	0.00 374	0.00 365	0.00 376	0.00 369	0.00 371	0.00 371	0.00 376	0.00 371	0.00 368	0.00 369	0.00 369

sosnet		0.00 350	0.00 351	0.00 359	0.00 374	0.00 374	0.00 365	0.00 376	0.00 369	0.00 371	0.00 371	0.00 376	0.00 371	0.00 368	0.00 369	0.00 369

uncertanet		0.00 350	0.00 351	0.00 359	0.00 374	0.00 374	0.00 365	0.00 376	0.00 369	0.00 371	0.00 371	0.00 376	0.00 371	0.00 368	0.00 369	0.00 369

Regformer		0.00 350	0.00 351	0.00 359	0.00 374	0.00 374	0.00 365	0.00 376	0.00 369	0.00 371	0.00 371	0.00 376	0.00 371	0.00 368	0.00 369	0.00 369

ab-mvs-re		5.62 346	7.50 347	0.00 359	0.00 374	0.00 374	0.00 365	0.00 376	0.00 369	0.00 371	67.46 337	0.00 376	0.00 371	0.00 368	0.00 369	0.00 369

uanet		0.00 350	0.00 351	0.00 359	0.00 374	0.00 374	0.00 365	0.00 376	0.00 369	0.00 371	0.00 371	0.00 376	0.00 371	0.00 368	0.00 369	0.00 369

GSMVS																70.05 284

test_part1				0.00 359		0.00 374	0.00 365	84.94 31				0.00 376	0.00 371	0.00 368	0.00 369	0.00 369

sam_mvs1												31.41 319				70.05 284

sam_mvs												31.21 323

MTGPA								80.63 111

test_post1							66.63 236				2.08 367	30.66 328	59.33 311	40.34 285

test_post											1.99 368	30.91 326	54.76 319

patchmatchnet-post											68.99 328	31.32 320	69.38 259

MTMP							84.83 26	19.26 371

gm-plane-assit					62.51 319	33.91 319			37.25 317		62.71 347		72.74 230	38.70 291

test9_res														72.12 54	91.37 80	77.40 228

agg_prior2														70.70 62	90.93 92	78.55 216

test_prior4						70.14 59	77.57 86

test_prior				75.27 92	82.15 98	59.85 127		84.33 41					83.39 67			82.58 150

新几何2							71.33 177

旧先验1					84.55 70	60.36 123		63.69 255			87.05 119	54.65 212			83.34 208	69.66 292

无先验							74.82 130	70.94 224	47.75 257				76.85 189	54.47 187		72.09 267

原ACMM2							74.78 134

testdata2													67.30 281	48.34 228

segment_acmp												68.30 81

testdata1							68.34 218		57.24 146

plane_prior7					85.18 57	66.21 84

plane_prior6					84.18 77	65.31 90						60.83 147

plane_prior5								85.49 21					86.15 22	71.09 56	90.94 90	84.82 99

plane_prior4											89.11 89

plane_prior2							82.74 39		65.45 64

plane_prior1					84.46 72

plane_prior						65.18 91	80.06 63		61.88 106						89.91 114

n2								0.00 376

nn								0.00 376

door-mid								55.02 298

test11								82.71 70

door								52.91 313

HQP5-MVS						58.80 136

BP-MVS														67.38 97

HQP3-MVS								84.12 48							89.16 121

HQP2-MVS												58.09 181

NP-MVS					83.34 84	63.07 108					85.97 156

ACMMP++_ref															89.47 119

ACMMP++															91.96 70

Test By Simon												62.56 123

High-resolution multi-view benchmark