High-res multi-view results

This table lists the benchmark results for the high-res multi-view scenario. The following metrics are evaluated:

Accuracy [%]: The fraction of the reconstruction which is closer to the ground truth than the evaluation threshold distance (*). Larger is better.
Completeness [%]: The fraction of the ground truth which is closer to the reconstruction than the evaluation threshold distance (*). Larger is better.
F₁ score [%]: The harmonic mean of accuracy and completeness, used to rank methods based on both metrics. Larger is better.
Time [s]: The runtime of the method. Smaller is better.

(*) For exact definitions, detailing how potentially incomplete ground truth is taken into account, see our paper.

The datasets are grouped into different categories, and result averages are computed for a category and method if results of the method are available for all datasets within the category. Note that the category "all" includes both the high-res multi-view and the low-res many-view scenarios.

Methods with suffix _ROB may participate in the Robust Vision Challenge.

Click a dataset result cell to show a visualization of the reconstruction. For training datasets, ground truth and accuracy / completeness visualizations are also available. The visualizations may not work with mobile browsers.

Method	all	high-res multi-view	indoor	outdoor	botani.	boulde.	bridge	door	exhibi.	lectur.	living.	lounge	observ.	old co.	statue	terrac.

LCM-MVSNet		95.70 1	96.40 1	93.61 2	98.67 1	85.39 32	95.54 3	97.36 1	96.97 1	99.04 1	99.05 1	96.61 1	95.92 13	85.07 50	99.27 2	99.54 1

test_part1		93.54 2	94.85 2	89.62 58	94.83 39	74.49 121	94.02 4	97.02 2	92.06 2	96.94 2	98.50 2	95.09 2	96.03 8	85.83 44	99.33 1	98.33 7

DTE-MVSNet		89.98 47	91.91 16	84.21 148	96.51 8	57.84 276	88.93 78	92.84 88	91.92 3	96.16 3	96.23 19	86.95 50	95.99 9	79.05 113	98.57 15	98.80 6

PS-CasMVS		90.06 43	91.92 14	84.47 142	96.56 7	58.83 271	89.04 75	92.74 91	91.40 5	96.12 4	96.06 23	87.23 47	95.57 33	79.42 111	98.74 6	99.00 2

LCM-MVSNet-Re		83.48 154	85.06 120	78.75 242	85.94 235	55.75 292	80.05 231	94.27 20	76.47 123	96.09 5	94.54 57	83.31 83	89.75 228	59.95 272	94.89 156	90.75 199

PEN-MVS		90.03 45	91.88 17	84.48 141	96.57 6	58.88 268	88.95 76	93.19 72	91.62 4	96.01 6	96.16 21	87.02 49	95.60 32	78.69 116	98.72 9	98.97 3

CP-MVSNet		89.27 61	90.91 43	84.37 143	96.34 9	58.61 273	88.66 85	92.06 105	90.78 6	95.67 7	95.17 39	81.80 108	95.54 38	79.00 114	98.69 10	98.95 4

LTVRE_ROB	86.10 1	93.04 4	93.44 3	91.82 23	93.73 64	85.72 31	96.79 1	95.51 6	88.86 14	95.63 8	96.99 9	84.81 69	93.16 133	91.10 1	97.53 70	96.58 29
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
WR-MVS_H		89.91 50	91.31 32	85.71 121	96.32 10	62.39 230	89.54 67	93.31 65	90.21 10	95.57 9	95.66 28	81.42 112	95.90 14	80.94 93	98.80 3	98.84 5

OurMVSNet-221017-0		90.01 46	89.74 55	90.83 37	93.16 77	80.37 66	91.91 33	93.11 74	81.10 74	95.32 10	97.24 6	72.94 197	94.85 68	85.07 50	97.78 53	97.26 16

anonymousdsp		89.73 53	88.88 70	92.27 9	89.82 163	86.67 15	90.51 46	90.20 160	69.87 207	95.06 11	96.14 22	84.28 73	93.07 138	87.68 13	96.34 107	97.09 20

wuyk23d		75.13 248	79.30 207	62.63 322	75.56 331	75.18 118	80.89 223	73.10 310	75.06 146	94.76 12	95.32 34	87.73 42	52.85 350	34.16 349	97.11 82	59.85 346

ACMH	76.49 14	89.34 60	91.14 35	83.96 154	92.50 93	70.36 162	89.55 65	93.84 45	81.89 66	94.70 13	95.44 33	90.69 10	88.31 247	83.33 69	98.30 26	93.20 125
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
SixPastTwentyTwo		87.20 85	87.45 83	86.45 101	92.52 92	69.19 174	87.84 94	88.05 195	81.66 68	94.64 14	96.53 15	65.94 235	94.75 70	83.02 73	96.83 91	95.41 50

mvs_tets		89.78 52	89.27 63	91.30 28	93.51 67	84.79 39	89.89 57	90.63 142	70.00 206	94.55 15	96.67 12	87.94 39	93.59 115	84.27 60	95.97 119	95.52 48

abl_6		93.02 5	93.16 5	92.60 4	94.73 43	88.99 7	93.26 11	94.19 27	89.11 12	94.43 16	95.27 36	91.86 4	95.09 60	87.54 18	98.02 38	93.71 108

jajsoiax		89.41 58	88.81 72	91.19 32	93.38 71	84.72 40	89.70 59	90.29 157	69.27 210	94.39 17	96.38 16	86.02 64	93.52 119	83.96 62	95.92 121	95.34 51

test_0402		88.65 70	89.58 59	85.88 117	92.55 91	72.22 145	84.01 153	89.44 175	88.63 18	94.38 18	95.77 25	86.38 60	93.59 115	79.84 104	95.21 144	91.82 177

UniMVSNet_ETH3D		89.12 64	90.72 46	84.31 146	97.00 2	64.33 208	89.67 62	88.38 189	88.84 15	94.29 19	97.57 4	90.48 15	91.26 183	72.57 181	97.65 60	97.34 15

v7n		90.13 41	90.96 41	87.65 87	91.95 110	71.06 157	89.99 54	93.05 77	86.53 27	94.29 19	96.27 18	82.69 88	94.08 95	86.25 37	97.63 61	97.82 9

test_djsdf		89.62 54	89.01 66	91.45 25	92.36 96	82.98 53	91.98 30	90.08 163	71.54 190	94.28 21	96.54 14	81.57 110	94.27 82	86.26 35	96.49 102	97.09 20

PS-MVSNAJss		88.31 73	87.90 78	89.56 60	93.31 73	77.96 88	87.94 92	91.97 108	70.73 198	94.19 22	96.67 12	76.94 155	94.57 78	83.07 71	96.28 109	96.15 32

SED-MVS		90.46 38	91.64 20	86.93 92	94.18 49	72.65 132	90.47 47	93.69 49	83.77 42	94.11 23	94.27 70	90.28 16	95.84 20	86.03 41	97.92 46	92.29 159

test_241102_ONE					94.18 49	72.65 132		93.69 49	83.62 44	94.11 23	93.78 98	90.28 16	95.50 45

DPE-MVS		90.53 36	91.08 36	88.88 67	93.38 71	78.65 83	89.15 74	94.05 35	84.68 36	93.90 25	94.11 80	88.13 36	96.30 3	84.51 58	97.81 52	91.70 180

APDe-MVS		91.22 25	91.92 14	89.14 65	92.97 81	78.04 87	92.84 15	94.14 32	83.33 49	93.90 25	95.73 26	88.77 27	96.41 1	87.60 16	97.98 42	92.98 132

ACMH+	77.89 11	90.73 31	91.50 24	88.44 76	93.00 80	76.26 113	89.65 63	95.55 5	87.72 23	93.89 27	94.94 44	91.62 5	93.44 123	78.35 119	98.76 4	95.61 47

ACMM	79.39 9	90.65 32	90.99 40	89.63 56	95.03 34	83.53 47	89.62 64	93.35 61	79.20 96	93.83 28	93.60 101	90.81 9	92.96 139	85.02 52	98.45 19	92.41 153
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
SR-MVS-dyc-post		92.41 7	92.41 8	92.39 5	94.13 54	88.95 8	92.87 13	94.16 28	88.75 16	93.79 29	94.43 62	88.83 25	95.51 42	87.16 25	97.60 64	92.73 141

RE-MVS-def			92.61 6		94.13 54	88.95 8	92.87 13	94.16 28	88.75 16	93.79 29	94.43 62	90.64 12		87.16 25	97.60 64	92.73 141

DVP-MVS		90.06 43	91.32 31	86.29 105	94.16 52	72.56 137	90.54 44	91.01 134	83.61 45	93.75 31	94.65 53	89.76 20	95.78 25	86.42 31	97.97 43	90.55 205

test_0728_THIRD									85.33 31	93.75 31	94.65 53	87.44 45	95.78 25	87.41 19	98.21 30	92.98 132

test0726					94.16 52	72.56 137	90.63 43	93.90 41	83.61 45	93.75 31	94.49 59	89.76 20

LPG-MVS_test		91.47 20	91.68 19	90.82 38	94.75 41	81.69 56	90.00 52	94.27 20	82.35 60	93.67 34	94.82 48	91.18 6	95.52 39	85.36 48	98.73 7	95.23 57

LGP-MVS_train				90.82 38	94.75 41	81.69 56		94.27 20	82.35 60	93.67 34	94.82 48	91.18 6	95.52 39	85.36 48	98.73 7	95.23 57

test1172		92.40 8	92.41 8	92.37 6	94.68 44	89.04 6	91.98 30	93.62 52	90.14 11	93.63 36	94.16 77	88.83 25	95.51 42	87.11 27	97.54 69	92.54 150

test_241102_TWO								93.71 48	83.77 42	93.49 37	94.27 70	89.27 23	95.84 20	86.03 41	97.82 51	92.04 168

SR-MVS		92.23 9	92.34 10	91.91 17	94.89 38	87.85 11	92.51 23	93.87 44	88.20 21	93.24 38	94.02 83	90.15 18	95.67 30	86.82 29	97.34 76	92.19 165

APD-MVS_3200maxsize		92.05 11	92.24 11	91.48 24	93.02 79	85.17 34	92.47 25	95.05 13	87.65 24	93.21 39	94.39 68	90.09 19	95.08 61	86.67 30	97.60 64	94.18 88

Anonymous20231211		88.40 72	89.62 58	84.73 136	90.46 152	65.27 199	88.86 79	93.02 81	87.15 25	93.05 40	97.10 7	82.28 96	92.02 162	76.70 139	97.99 40	96.88 24

MP-MVS-pluss		90.81 30	91.08 36	89.99 51	95.97 13	79.88 69	88.13 90	94.51 18	75.79 136	92.94 41	94.96 43	88.36 29	95.01 63	90.70 2	98.40 20	95.09 60
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
SD-MVS		88.96 67	89.88 53	86.22 108	91.63 119	77.07 103	89.82 58	93.77 46	78.90 100	92.88 42	92.29 134	86.11 62	90.22 215	86.24 38	97.24 79	91.36 189
Zhenlong Yuan, Jiakai Cao, Zhaoxin Li, Hao Jiang and Zhaoqi Wang: SD-MVS: Segmentation-driven Deformation Multi-View Stereo with Spherical Refinement and EM optimization. AAAI2024
ACMP	79.16 10	90.54 35	90.60 48	90.35 46	94.36 46	80.98 62	89.16 73	94.05 35	79.03 99	92.87 43	93.74 99	90.60 13	95.21 57	82.87 74	98.76 4	94.87 62
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
TDRefinement		93.52 3	93.39 4	93.88 1	95.94 14	90.26 3	95.70 2	96.46 3	90.58 8	92.86 44	96.29 17	88.16 35	94.17 91	86.07 40	98.48 18	97.22 18

SMA-MVS		90.31 39	90.48 49	89.83 52	95.31 30	79.52 75	90.98 40	93.24 71	75.37 143	92.84 45	95.28 35	85.58 66	96.09 7	87.92 10	97.76 54	93.88 99
Yufeng Yin; Xiaoyan Liu; Zichao Zhang: SMA-MVS: Segmentation-Guided Multi-Scale Anchor Deformation Patch Multi-View Stereo. IEEE Transactions on Circuits and Systems for Video Technology
test_part2					93.86 61	77.77 90				92.84 45

v10		86.54 92	87.10 87	84.84 133	88.16 189	63.28 218	86.64 114	92.20 102	75.42 142	92.81 47	94.50 58	74.05 182	94.06 96	83.88 63	96.28 109	97.17 19

v8		86.22 99	86.83 94	84.36 144	87.82 194	62.35 232	86.42 117	91.33 125	76.78 122	92.73 48	94.48 60	73.41 191	93.72 108	83.10 70	95.41 136	97.01 22

nrg030		87.85 79	88.49 74	85.91 115	90.07 159	69.73 165	87.86 93	94.20 25	74.04 155	92.70 49	94.66 52	85.88 65	91.50 174	79.72 105	97.32 77	96.50 30

SteuartSystems-ACMMP		91.16 27	91.36 27	90.55 42	93.91 60	80.97 63	91.49 36	93.48 58	82.82 56	92.60 50	93.97 85	88.19 33	96.29 4	87.61 15	98.20 32	94.39 81
Skip Steuart: Steuart Systems R&D Blog.
OPM-MVS		89.80 51	89.97 52	89.27 63	94.76 40	79.86 70	86.76 111	92.78 90	78.78 102	92.51 51	93.64 100	88.13 36	93.84 104	84.83 55	97.55 67	94.10 92

HPM-MVS_fast		92.50 6	92.54 7	92.37 6	95.93 15	85.81 30	92.99 12	94.23 23	85.21 32	92.51 51	95.13 40	90.65 11	95.34 50	88.06 9	98.15 33	95.95 40

K. test v3		85.14 114	84.73 126	86.37 102	91.13 138	69.63 167	85.45 130	76.68 285	84.06 40	92.44 53	96.99 9	62.03 252	94.65 73	80.58 99	93.24 190	94.83 66

ACMMP		91.91 13	91.87 18	92.03 13	95.53 26	85.91 25	93.35 10	94.16 28	82.52 59	92.39 54	94.14 78	89.15 24	95.62 31	87.35 20	98.24 28	94.56 71
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
xxxxxxxxxxxxxcwj		89.04 66	89.13 64	88.79 69	93.75 62	77.44 95	86.31 119	95.27 10	70.80 196	92.28 55	93.80 95	86.89 51	94.64 74	85.52 46	97.51 71	94.30 84

SF-MVS		90.27 40	90.80 45	88.68 72	92.86 85	77.09 102	91.19 39	95.74 4	81.38 71	92.28 55	93.80 95	86.89 51	94.64 74	85.52 46	97.51 71	94.30 84

COLMAP_ROB	83.01 3	91.97 12	91.95 13	92.04 12	93.68 65	86.15 21	93.37 9	95.10 12	90.28 9	92.11 57	95.03 42	89.75 22	94.93 65	79.95 103	98.27 27	95.04 61
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
XVG-ACMP-BASELINE		89.98 47	89.84 54	90.41 44	94.91 37	84.50 44	89.49 69	93.98 37	79.68 89	92.09 58	93.89 93	83.80 77	93.10 137	82.67 76	98.04 35	93.64 112

TranMVSNet+NR-MVSNet		87.86 78	88.76 73	85.18 129	94.02 57	64.13 209	84.38 148	91.29 126	84.88 35	92.06 59	93.84 94	86.45 58	93.73 107	73.22 171	98.66 11	97.69 10

zzz-MVS		91.27 23	91.26 34	91.29 29	96.59 4	86.29 17	88.94 77	91.81 114	84.07 38	92.00 60	94.40 66	86.63 54	95.28 53	88.59 5	98.31 24	92.30 157

MTAPA		91.52 17	91.60 21	91.29 29	96.59 4	86.29 17	92.02 29	91.81 114	84.07 38	92.00 60	94.40 66	86.63 54	95.28 53	88.59 5	98.31 24	92.30 157

ACMMP_NAP		90.65 32	91.07 38	89.42 61	95.93 15	79.54 74	89.95 55	93.68 51	77.65 113	91.97 62	94.89 45	88.38 28	95.45 46	89.27 3	97.87 50	93.27 122

FC-MVSNet-test		85.93 105	87.05 89	82.58 185	92.25 101	56.44 287	85.75 125	93.09 75	77.33 116	91.94 63	94.65 53	74.78 175	93.41 125	75.11 155	98.58 14	97.88 8

lessismore_v0				85.95 114	91.10 139	70.99 158		70.91 323		91.79 64	94.42 64	61.76 253	92.93 141	79.52 110	93.03 196	93.93 97

HPM-MVS		92.13 10	92.20 12	91.91 17	95.58 25	84.67 41	93.51 7	94.85 15	82.88 55	91.77 65	93.94 92	90.55 14	95.73 28	88.50 7	98.23 29	95.33 52
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
PGM-MVS		91.20 26	90.95 42	91.93 15	95.67 22	85.85 28	90.00 52	93.90 41	80.32 82	91.74 66	94.41 65	88.17 34	95.98 10	86.37 33	97.99 40	93.96 96

testing_2		84.36 131	84.64 132	83.50 166	86.74 219	63.97 212	84.56 142	90.31 152	66.22 239	91.62 67	94.55 56	75.88 165	91.95 163	77.02 138	94.89 156	94.56 71

mPP-MVS		91.69 14	91.47 25	92.37 6	96.04 12	88.48 10	92.72 17	92.60 94	83.09 52	91.54 68	94.25 74	87.67 43	95.51 42	87.21 24	98.11 34	93.12 128

HFP-MVS		91.30 22	91.39 26	91.02 33	95.43 28	84.66 42	92.58 21	93.29 68	81.99 63	91.47 69	93.96 88	88.35 30	95.56 34	87.74 11	97.74 56	92.85 136

#test#		90.49 37	90.31 51	91.02 33	95.43 28	84.66 42	90.65 42	93.29 68	77.00 120	91.47 69	93.96 88	88.35 30	95.56 34	84.88 53	97.74 56	92.85 136

ACMMPR		91.49 18	91.35 29	91.92 16	95.74 19	85.88 27	92.58 21	93.25 70	81.99 63	91.40 71	94.17 76	87.51 44	95.87 16	87.74 11	97.76 54	93.99 94

GST-MVS		90.96 29	91.01 39	90.82 38	95.45 27	82.73 55	91.75 34	93.74 47	80.98 76	91.38 72	93.80 95	87.20 48	95.80 22	87.10 28	97.69 59	93.93 97

ANet_high		83.17 161	85.68 112	75.65 277	81.24 283	45.26 341	79.94 233	92.91 84	83.83 41	91.33 73	96.88 11	80.25 124	85.92 275	68.89 212	95.89 122	95.76 42

ZNCC-MVS		91.26 24	91.34 30	91.01 35	95.73 20	83.05 52	92.18 27	94.22 24	80.14 85	91.29 74	93.97 85	87.93 40	95.87 16	88.65 4	97.96 45	94.12 91

APD-MVS		89.54 56	89.63 57	89.26 64	92.57 90	81.34 61	90.19 50	93.08 76	80.87 77	91.13 75	93.19 104	86.22 61	95.97 11	82.23 80	97.18 81	90.45 207
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
9.14			89.29 62		91.84 116		88.80 81	95.32 9	75.14 145	91.07 76	92.89 114	87.27 46	93.78 106	83.69 66	97.55 67

CP-MVS		91.67 15	91.58 22	91.96 14	95.29 31	87.62 12	93.38 8	93.36 60	83.16 51	91.06 77	94.00 84	88.26 32	95.71 29	87.28 23	98.39 21	92.55 149

FIs		85.35 111	86.27 100	82.60 184	91.86 115	57.31 280	85.10 134	93.05 77	75.83 135	91.02 78	93.97 85	73.57 187	92.91 143	73.97 163	98.02 38	97.58 13

UniMVSNet_NR-MVSNet		86.84 88	87.06 88	86.17 112	92.86 85	67.02 187	82.55 194	91.56 118	83.08 53	90.92 79	91.82 146	78.25 139	93.99 97	74.16 160	98.35 22	97.49 14

DU-MVS		86.80 89	86.99 90	86.21 110	93.24 75	67.02 187	83.16 181	92.21 101	81.73 67	90.92 79	91.97 140	77.20 149	93.99 97	74.16 160	98.35 22	97.61 11

V42		83.47 155	83.37 153	83.75 159	83.16 269	63.33 217	81.31 216	90.23 159	69.51 209	90.91 81	90.81 175	74.16 180	92.29 155	80.06 101	90.22 248	95.62 46

region2R		91.44 21	91.30 33	91.87 19	95.75 18	85.90 26	92.63 20	93.30 67	81.91 65	90.88 82	94.21 75	87.75 41	95.87 16	87.60 16	97.71 58	93.83 101

WR-MVS		83.56 152	84.40 139	81.06 209	93.43 70	54.88 298	78.67 254	85.02 241	81.24 72	90.74 83	91.56 153	72.85 198	91.08 189	68.00 219	98.04 35	97.23 17

v1240		84.30 135	84.51 136	83.65 161	87.65 199	61.26 241	82.85 188	91.54 119	67.94 224	90.68 84	90.65 181	71.71 211	93.64 110	82.84 75	94.78 159	96.07 35

ETH3D-3000-0.1		88.85 69	88.96 69	88.52 73	91.94 112	77.27 101	88.71 83	95.26 11	76.08 127	90.66 85	92.69 121	84.48 72	93.83 105	83.38 68	97.48 73	94.47 76

testtj		89.51 57	89.48 60	89.59 59	92.26 100	80.80 64	90.14 51	93.54 56	83.37 48	90.57 86	92.55 126	84.99 68	96.15 5	81.26 88	96.61 97	91.83 176

ZD-MVS					92.22 103	80.48 65		91.85 111	71.22 193	90.38 87	92.98 109	86.06 63	96.11 6	81.99 82	96.75 94

MIMVSNet1		83.63 151	84.59 133	80.74 214	94.06 56	62.77 224	82.72 190	84.53 246	77.57 115	90.34 88	95.92 24	76.88 161	85.83 277	61.88 258	97.42 74	93.62 113

LS3D		90.60 34	90.34 50	91.38 27	89.03 172	84.23 45	93.58 5	94.68 16	90.65 7	90.33 89	93.95 91	84.50 71	95.37 49	80.87 94	95.50 135	94.53 75

PMVS	80.48 6	90.08 42	90.66 47	88.34 78	96.71 3	92.97 1	90.31 49	89.57 173	88.51 19	90.11 90	95.12 41	90.98 8	88.92 237	77.55 130	97.07 83	83.13 299
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
v1921920		84.23 138	84.37 140	83.79 157	87.64 200	61.71 236	82.91 187	91.20 129	67.94 224	90.06 91	90.34 185	72.04 208	93.59 115	82.32 79	94.91 155	96.07 35

ITE_SJBPF				90.11 50	90.72 146	84.97 36		90.30 154	81.56 69	90.02 92	91.20 160	82.40 92	90.81 199	73.58 168	94.66 163	94.56 71

XVS		91.54 16	91.36 27	92.08 10	95.64 23	86.25 19	92.64 18	93.33 62	85.07 33	89.99 93	94.03 82	86.57 56	95.80 22	87.35 20	97.62 62	94.20 86

X-MVStestdata		85.04 118	82.70 161	92.08 10	95.64 23	86.25 19	92.64 18	93.33 62	85.07 33	89.99 93	16.05 353	86.57 56	95.80 22	87.35 20	97.62 62	94.20 86

v1192		84.57 126	84.69 130	84.21 148	87.75 196	62.88 222	83.02 184	91.43 122	69.08 213	89.98 95	90.89 172	72.70 201	93.62 114	82.41 77	94.97 154	96.13 33

Anonymous20240529		86.20 100	87.13 86	83.42 167	90.19 156	64.55 206	84.55 143	90.71 139	85.85 30	89.94 96	95.24 38	82.13 98	90.40 210	69.19 209	96.40 105	95.31 53

Regformer-2		86.74 90	86.08 104	88.73 70	84.18 257	79.20 77	83.52 167	89.33 176	83.33 49	89.92 97	85.07 268	83.23 84	93.16 133	83.39 67	92.72 205	93.83 101

pmmvs6		86.52 93	88.06 77	81.90 194	92.22 103	62.28 233	84.66 140	89.15 178	83.54 47	89.85 98	97.32 5	88.08 38	86.80 263	70.43 198	97.30 78	96.62 27

Regformer-4		86.41 94	85.71 111	88.52 73	84.27 253	77.57 93	84.07 151	88.00 197	82.82 56	89.84 99	85.48 256	82.06 100	92.77 145	83.83 65	91.04 231	95.22 59

v144192		84.24 137	84.41 138	83.71 160	87.59 201	61.57 237	82.95 186	91.03 133	67.82 227	89.80 100	90.49 183	73.28 194	93.51 120	81.88 85	94.89 156	96.04 37

v1144		84.54 129	84.72 128	84.00 152	87.67 198	62.55 228	82.97 185	90.93 135	70.32 203	89.80 100	90.99 167	73.50 188	93.48 121	81.69 86	94.65 164	95.97 38

DeepC-MVS	82.31 4	89.15 63	89.08 65	89.37 62	93.64 66	79.07 78	88.54 86	94.20 25	73.53 161	89.71 102	94.82 48	85.09 67	95.77 27	84.17 61	98.03 37	93.26 123
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
RPSCF		88.00 76	86.93 92	91.22 31	90.08 158	89.30 5	89.68 61	91.11 131	79.26 95	89.68 103	94.81 51	82.44 91	87.74 251	76.54 141	88.74 264	96.61 28

IU-MVS					94.18 49	72.64 134		90.82 137	56.98 297	89.67 104				85.78 45	97.92 46	93.28 121

FMVSNet1		84.55 127	85.45 116	81.85 196	90.27 155	61.05 244	86.83 108	88.27 192	78.57 106	89.66 105	95.64 29	75.43 167	90.68 203	69.09 210	95.33 139	93.82 103

IterMVS-LS		84.73 123	84.98 122	83.96 154	87.35 204	63.66 213	83.25 177	89.88 167	76.06 128	89.62 106	92.37 132	73.40 193	92.52 150	78.16 122	94.77 161	95.69 43
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
DP-MVS		88.60 71	89.01 66	87.36 90	91.30 130	77.50 94	87.55 96	92.97 83	87.95 22	89.62 106	92.87 115	84.56 70	93.89 101	77.65 128	96.62 96	90.70 200

UniMVSNet (Re)		86.87 86	86.98 91	86.55 99	93.11 78	68.48 178	83.80 161	92.87 85	80.37 80	89.61 108	91.81 147	77.72 143	94.18 89	75.00 156	98.53 16	96.99 23

IS-MVSNet		86.66 91	86.82 95	86.17 112	92.05 108	66.87 189	91.21 38	88.64 185	86.30 29	89.60 109	92.59 123	69.22 220	94.91 66	73.89 164	97.89 49	96.72 25

v2v482		84.09 140	84.24 142	83.62 162	87.13 209	61.40 238	82.71 191	89.71 169	72.19 185	89.55 110	91.41 156	70.70 216	93.20 130	81.02 91	93.76 180	96.25 31

Baseline_NR-MVSNet		84.00 144	85.90 107	78.29 251	91.47 128	53.44 306	82.29 200	87.00 217	79.06 98	89.55 110	95.72 27	77.20 149	86.14 273	72.30 183	98.51 17	95.28 54

CSCG		86.26 97	86.47 97	85.60 123	90.87 142	74.26 123	87.98 91	91.85 111	80.35 81	89.54 112	88.01 219	79.09 132	92.13 157	75.51 149	95.06 151	90.41 208

ambc				82.98 175	90.55 151	64.86 202	88.20 88	89.15 178		89.40 113	93.96 88	71.67 212	91.38 182	78.83 115	96.55 99	92.71 144

DeepPCF-MVS	81.24 5	87.28 84	86.21 102	90.49 43	91.48 127	84.90 37	83.41 172	92.38 99	70.25 204	89.35 114	90.68 179	82.85 87	94.57 78	79.55 107	95.95 120	92.00 170

Regformer-1		86.00 102	85.50 115	87.49 88	84.18 257	76.90 105	83.52 167	87.94 199	82.18 62	89.19 115	85.07 268	82.28 96	91.89 167	82.40 78	92.72 205	93.69 109

MSP-MVS		89.08 65	88.16 76	91.83 21	95.76 17	86.14 22	92.75 16	93.90 41	78.43 107	89.16 116	92.25 136	72.03 209	96.36 2	88.21 8	90.93 237	92.98 132

XVG-OURS		89.18 62	88.83 71	90.23 48	94.28 47	86.11 23	85.91 122	93.60 55	80.16 84	89.13 117	93.44 102	83.82 76	90.98 191	83.86 64	95.30 143	93.60 114

ETH3D cwj APD-0.16		87.83 80	87.62 81	88.47 75	91.21 133	78.20 85	87.26 100	94.54 17	72.05 186	88.89 118	92.31 133	83.86 75	94.24 85	81.59 87	96.87 88	92.97 135

MDA-MVSNet-bldmvs		77.47 226	76.90 229	79.16 238	79.03 310	64.59 203	66.58 331	75.67 291	73.15 172	88.86 119	88.99 208	66.94 230	81.23 304	64.71 240	88.22 271	91.64 182

EG-PatchMatch MVS		84.08 141	84.11 143	83.98 153	92.22 103	72.61 136	82.20 206	87.02 214	72.63 179	88.86 119	91.02 166	78.52 135	91.11 188	73.41 170	91.09 229	88.21 237

3Dnovator+	83.92 2	89.97 49	89.66 56	90.92 36	91.27 132	81.66 59	91.25 37	94.13 33	88.89 13	88.83 121	94.26 73	77.55 146	95.86 19	84.88 53	95.87 123	95.24 56

EI-MVSNet-UG-set		85.04 118	84.44 137	86.85 94	83.87 263	72.52 139	83.82 159	85.15 237	80.27 83	88.75 122	85.45 259	79.95 128	91.90 166	81.92 84	90.80 241	96.13 33

EI-MVSNet-Vis-set		85.12 115	84.53 135	86.88 93	84.01 260	72.76 131	83.91 157	85.18 236	80.44 79	88.75 122	85.49 255	80.08 125	91.92 165	82.02 81	90.85 240	95.97 38

OMC-MVS		88.19 74	87.52 82	90.19 49	91.94 112	81.68 58	87.49 98	93.17 73	76.02 130	88.64 124	91.22 158	84.24 74	93.37 126	77.97 126	97.03 84	95.52 48

Regformer-3		85.06 117	84.67 131	86.22 108	84.27 253	73.43 127	84.07 151	85.26 234	80.77 78	88.62 125	85.48 256	80.56 121	90.39 211	81.99 82	91.04 231	94.85 64

UA-Net		91.49 18	91.53 23	91.39 26	94.98 35	82.95 54	93.52 6	92.79 89	88.22 20	88.53 126	97.64 3	83.45 81	94.55 80	86.02 43	98.60 13	96.67 26

MP-MVS		91.14 28	90.91 43	91.83 21	96.18 11	86.88 14	92.20 26	93.03 80	82.59 58	88.52 127	94.37 69	86.74 53	95.41 48	86.32 34	98.21 30	93.19 126
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
canonicalmvs		85.50 109	86.14 103	83.58 163	87.97 190	67.13 186	87.55 96	94.32 19	73.44 163	88.47 128	87.54 228	86.45 58	91.06 190	75.76 148	93.76 180	92.54 150

NR-MVSNet		86.00 102	86.22 101	85.34 127	93.24 75	64.56 205	82.21 204	90.46 145	80.99 75	88.42 129	91.97 140	77.56 145	93.85 102	72.46 182	98.65 12	97.61 11

alignmvs		83.94 146	83.98 146	83.80 156	87.80 195	67.88 183	84.54 145	91.42 124	73.27 170	88.41 130	87.96 220	72.33 204	90.83 198	76.02 146	94.11 174	92.69 145

TransMVSNet (Re)		84.02 143	85.74 110	78.85 240	91.00 140	55.20 297	82.29 200	87.26 205	79.65 90	88.38 131	95.52 32	83.00 85	86.88 261	67.97 220	96.60 98	94.45 79

PM-MVS		80.20 203	79.00 209	83.78 158	88.17 188	86.66 16	81.31 216	66.81 335	69.64 208	88.33 132	90.19 189	64.58 238	83.63 296	71.99 186	90.03 249	81.06 325

tttt0517		81.07 184	79.58 205	85.52 124	88.99 174	66.45 193	87.03 105	75.51 293	73.76 159	88.32 133	90.20 188	37.96 346	94.16 94	79.36 112	95.13 147	95.93 41

casdiffmvs		85.21 112	85.85 108	83.31 169	86.17 232	62.77 224	83.03 183	93.93 39	74.69 149	88.21 134	92.68 122	82.29 95	91.89 167	77.87 127	93.75 182	95.27 55

v148		82.31 169	82.48 167	81.81 199	85.59 237	59.66 258	81.47 214	86.02 225	72.85 176	88.05 135	90.65 181	70.73 215	90.91 195	75.15 154	91.79 220	94.87 62

AllTest		87.97 77	87.40 85	89.68 54	91.59 120	83.40 48	89.50 68	95.44 7	79.47 91	88.00 136	93.03 107	82.66 89	91.47 175	70.81 190	96.14 115	94.16 89

TestCases				89.68 54	91.59 120	83.40 48		95.44 7	79.47 91	88.00 136	93.03 107	82.66 89	91.47 175	70.81 190	96.14 115	94.16 89

pm-mvs1		83.69 149	84.95 123	79.91 226	90.04 161	59.66 258	82.43 196	87.44 202	75.52 139	87.85 138	95.26 37	81.25 114	85.65 279	68.74 214	96.04 118	94.42 80

PCF-MVS	74.62 15	82.15 173	80.92 187	85.84 118	89.43 165	72.30 143	80.53 227	91.82 113	57.36 295	87.81 139	89.92 195	77.67 144	93.63 111	58.69 277	95.08 150	91.58 185
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
FMVSNet2		81.31 182	81.61 177	80.41 220	86.38 222	58.75 272	83.93 156	86.58 220	72.43 180	87.65 140	92.98 109	63.78 244	90.22 215	66.86 224	93.92 178	92.27 161

VPA-MVSNet		83.47 155	84.73 126	79.69 231	90.29 154	57.52 279	81.30 218	88.69 184	76.29 124	87.58 141	94.44 61	80.60 120	87.20 256	66.60 228	96.82 92	94.34 83

CPTT-MVS		89.39 59	88.98 68	90.63 41	95.09 33	86.95 13	92.09 28	92.30 100	79.74 88	87.50 142	92.38 129	81.42 112	93.28 128	83.07 71	97.24 79	91.67 181

VDDNet		84.35 133	85.39 117	81.25 204	95.13 32	59.32 261	85.42 131	81.11 263	86.41 28	87.41 143	96.21 20	73.61 186	90.61 206	66.33 229	96.85 89	93.81 106

cl_fuxian		81.64 179	81.59 178	81.79 200	80.86 289	59.15 265	78.61 255	90.18 161	68.36 218	87.20 144	87.11 237	69.39 218	91.62 172	78.16 122	94.43 169	94.60 70

VDD-MVS		84.23 138	84.58 134	83.20 171	91.17 137	65.16 201	83.25 177	84.97 243	79.79 87	87.18 145	94.27 70	74.77 176	90.89 196	69.24 206	96.54 100	93.55 118

MSLP-MVS++		85.00 120	86.03 105	81.90 194	91.84 116	71.56 155	86.75 112	93.02 81	75.95 133	87.12 146	89.39 199	77.98 140	89.40 233	77.46 131	94.78 159	84.75 276

baseline		85.20 113	85.93 106	83.02 174	86.30 227	62.37 231	84.55 143	93.96 38	74.48 152	87.12 146	92.03 139	82.30 94	91.94 164	78.39 117	94.21 172	94.74 67

YYNet1		70.06 287	70.44 283	68.90 304	73.76 341	53.42 307	58.99 343	67.20 331	58.42 286	87.10 148	85.39 261	59.82 265	67.32 336	59.79 273	83.50 308	85.96 262

MDA-MVSNet_test_wron		70.05 288	70.44 283	68.88 305	73.84 340	53.47 305	58.93 344	67.28 330	58.43 285	87.09 149	85.40 260	59.80 266	67.25 337	59.66 274	83.54 307	85.92 264

CNVR-MVS		87.81 81	87.68 80	88.21 80	92.87 83	77.30 100	85.25 132	91.23 128	77.31 117	87.07 150	91.47 155	82.94 86	94.71 71	84.67 56	96.27 111	92.62 148

EPP-MVSNet		85.47 110	85.04 121	86.77 96	91.52 126	69.37 168	91.63 35	87.98 198	81.51 70	87.05 151	91.83 145	66.18 234	95.29 51	70.75 193	96.89 87	95.64 45

RRT_MVS		83.25 158	81.08 184	89.74 53	80.55 296	79.32 76	86.41 118	86.69 218	72.33 184	87.00 152	91.08 163	44.98 330	95.55 37	84.47 59	96.24 112	94.36 82

TinyColmap		81.25 183	82.34 169	77.99 256	85.33 240	60.68 250	82.32 199	88.33 190	71.26 192	86.97 153	92.22 138	77.10 152	86.98 260	62.37 253	95.17 146	86.31 260

eth_miper_zixun_eth		80.84 188	80.22 197	82.71 182	81.41 281	60.98 247	77.81 264	90.14 162	67.31 231	86.95 154	87.24 234	64.26 240	92.31 153	75.23 153	91.61 223	94.85 64

Patchmatch-RL test		74.48 256	73.68 256	76.89 267	84.83 244	66.54 192	72.29 311	69.16 328	57.70 291	86.76 155	86.33 245	45.79 320	82.59 299	69.63 203	90.65 246	81.54 316

XVG-OURS-SEG-HR		89.59 55	89.37 61	90.28 47	94.47 45	85.95 24	86.84 107	93.91 40	80.07 86	86.75 156	93.26 103	93.64 3	90.93 193	84.60 57	90.75 242	93.97 95

HPM-MVS++		88.93 68	88.45 75	90.38 45	94.92 36	85.85 28	89.70 59	91.27 127	78.20 109	86.69 157	92.28 135	80.36 123	95.06 62	86.17 39	96.49 102	90.22 210

TSAR-MVS + MP.		88.14 75	87.82 79	89.09 66	95.72 21	76.74 107	92.49 24	91.19 130	67.85 226	86.63 158	94.84 47	79.58 130	95.96 12	87.62 14	94.50 166	94.56 71
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
EI-MVSNet		82.61 165	82.42 168	83.20 171	83.25 267	63.66 213	83.50 170	85.07 238	76.06 128	86.55 159	85.10 265	73.41 191	90.25 212	78.15 124	90.67 244	95.68 44

HQP_MVS		87.75 82	87.43 84	88.70 71	93.45 68	76.42 111	89.45 70	93.61 53	79.44 93	86.55 159	92.95 112	74.84 173	95.22 55	80.78 96	95.83 124	94.46 77

plane_prior3						76.85 106			77.79 112	86.55 159

BH-untuned		80.96 186	80.99 185	80.84 213	88.55 180	68.23 179	80.33 229	88.46 186	72.79 177	86.55 159	86.76 240	74.72 177	91.77 171	61.79 259	88.99 259	82.52 305

MVSTER		77.09 230	75.70 240	81.25 204	75.27 335	61.08 243	77.49 271	85.07 238	60.78 276	86.55 159	88.68 212	43.14 336	90.25 212	73.69 167	90.67 244	92.42 152

旧先验2							81.73 209		56.88 298	86.54 164			84.90 285	72.81 179

IterMVS-SCA-FT		80.64 192	79.41 206	84.34 145	83.93 261	69.66 166	76.28 286	81.09 264	72.43 180	86.47 165	90.19 189	60.46 258	93.15 135	77.45 132	86.39 287	90.22 210

cl-mvsnet1		80.43 195	80.23 195	81.02 210	79.99 299	59.25 262	77.07 275	87.02 214	67.38 229	86.19 166	89.22 202	63.09 248	90.16 217	76.32 142	95.80 126	93.66 110

CDPH-MVS		86.17 101	85.54 114	88.05 83	92.25 101	75.45 116	83.85 158	92.01 106	65.91 241	86.19 166	91.75 149	83.77 78	94.98 64	77.43 133	96.71 95	93.73 107

cl-mvsnet_		80.42 196	80.23 195	81.02 210	79.99 299	59.25 262	77.07 275	87.02 214	67.37 230	86.18 168	89.21 203	63.08 249	90.16 217	76.31 143	95.80 126	93.65 111

ETH3 D test6400		85.09 116	84.87 124	85.75 120	90.80 144	69.34 169	85.90 123	93.31 65	65.43 248	86.11 169	89.95 193	80.92 116	94.86 67	75.90 147	95.57 133	93.05 129

MVS_111021_LR		84.28 136	83.76 149	85.83 119	89.23 169	83.07 51	80.99 222	83.56 248	72.71 178	86.07 170	89.07 207	81.75 109	86.19 272	77.11 136	93.36 185	88.24 236

GBi-Net		82.02 175	82.07 171	81.85 196	86.38 222	61.05 244	86.83 108	88.27 192	72.43 180	86.00 171	95.64 29	63.78 244	90.68 203	65.95 231	93.34 186	93.82 103

test1		82.02 175	82.07 171	81.85 196	86.38 222	61.05 244	86.83 108	88.27 192	72.43 180	86.00 171	95.64 29	63.78 244	90.68 203	65.95 231	93.34 186	93.82 103

FMVSNet3		78.80 213	78.55 212	79.57 233	82.89 272	56.89 285	81.76 208	85.77 227	69.04 214	86.00 171	90.44 184	51.75 299	90.09 223	65.95 231	93.34 186	91.72 179

miper_ehance_all_eth		80.34 199	80.04 202	81.24 206	79.82 301	58.95 267	77.66 266	89.66 170	65.75 245	85.99 174	85.11 264	68.29 225	91.42 179	76.03 145	92.03 216	93.33 119

tfpnnormal		81.79 178	82.95 159	78.31 250	88.93 175	55.40 293	80.83 225	82.85 252	76.81 121	85.90 175	94.14 78	74.58 179	86.51 267	66.82 227	95.68 132	93.01 131

TAPA-MVS	77.73 12	85.71 108	84.83 125	88.37 77	88.78 177	79.72 71	87.15 103	93.50 57	69.17 211	85.80 176	89.56 198	80.76 118	92.13 157	73.21 176	95.51 134	93.25 124
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
TSAR-MVS + GP.		83.95 145	82.69 162	87.72 85	89.27 168	81.45 60	83.72 163	81.58 262	74.73 148	85.66 177	86.06 250	72.56 203	92.69 147	75.44 151	95.21 144	89.01 232

EU-MVSNet		75.12 249	74.43 251	77.18 263	83.11 270	59.48 260	85.71 127	82.43 255	39.76 349	85.64 178	88.76 210	44.71 332	87.88 250	73.86 165	85.88 291	84.16 282

LF4IMVS		82.75 164	81.93 174	85.19 128	82.08 274	80.15 68	85.53 129	88.76 183	68.01 221	85.58 179	87.75 224	71.80 210	86.85 262	74.02 162	93.87 179	88.58 235

Patchmtry		76.56 237	77.46 222	73.83 285	79.37 307	46.60 338	82.41 197	76.90 282	73.81 158	85.56 180	92.38 129	48.07 308	83.98 293	63.36 248	95.31 142	90.92 195

MVS_111021_HR		84.63 124	84.34 141	85.49 126	90.18 157	75.86 115	79.23 247	87.13 209	73.35 164	85.56 180	89.34 200	83.60 80	90.50 208	76.64 140	94.05 176	90.09 215

testdata				79.54 234	92.87 83	72.34 142		80.14 270	59.91 282	85.47 182	91.75 149	67.96 227	85.24 281	68.57 217	92.18 215	81.06 325

thisisatest0530		79.07 209	77.33 224	84.26 147	87.13 209	64.58 204	83.66 165	75.95 288	68.86 216	85.22 183	87.36 232	38.10 344	93.57 118	75.47 150	94.28 171	94.62 68

CLD-MVS		83.18 160	82.64 163	84.79 134	89.05 171	67.82 184	77.93 262	92.52 95	68.33 219	85.07 184	81.54 306	82.06 100	92.96 139	69.35 205	97.91 48	93.57 115
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
DeepC-MVS_fast	80.27 8	86.23 98	85.65 113	87.96 84	91.30 130	76.92 104	87.19 101	91.99 107	70.56 199	84.96 185	90.69 178	80.01 126	95.14 58	78.37 118	95.78 128	91.82 177
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
3Dnovator	80.37 7	84.80 122	84.71 129	85.06 131	86.36 225	74.71 119	88.77 82	90.00 165	75.65 138	84.96 185	93.17 105	74.06 181	91.19 185	78.28 121	91.09 229	89.29 224

QAPM		82.59 166	82.59 165	82.58 185	86.44 220	66.69 190	89.94 56	90.36 149	67.97 223	84.94 187	92.58 125	72.71 200	92.18 156	70.63 196	87.73 276	88.85 233

VPNet		80.25 201	81.68 175	75.94 276	92.46 94	47.98 335	76.70 279	81.67 261	73.45 162	84.87 188	92.82 116	74.66 178	86.51 267	61.66 261	96.85 89	93.33 119

NCCC		87.36 83	86.87 93	88.83 68	92.32 99	78.84 81	86.58 115	91.09 132	78.77 103	84.85 189	90.89 172	80.85 117	95.29 51	81.14 90	95.32 140	92.34 155

PHI-MVS		86.38 95	85.81 109	88.08 81	88.44 183	77.34 98	89.35 72	93.05 77	73.15 172	84.76 190	87.70 225	78.87 134	94.18 89	80.67 98	96.29 108	92.73 141

pmmvs-eth3d		78.42 217	77.04 227	82.57 187	87.44 203	74.41 122	80.86 224	79.67 272	55.68 301	84.69 191	90.31 187	60.91 256	85.42 280	62.20 255	91.59 224	87.88 244

RRT_test8_iter05		78.08 220	77.52 221	79.75 229	80.84 290	52.54 313	80.61 226	88.96 180	67.77 228	84.62 192	89.29 201	33.89 351	92.10 160	77.59 129	94.15 173	94.62 68

test_prior3		86.31 96	86.31 99	86.32 103	90.59 149	71.99 147	83.37 173	92.85 86	75.43 140	84.58 193	91.57 151	81.92 106	94.17 91	79.54 108	96.97 85	92.80 138

test_prior2							83.37 173		75.43 140	84.58 193	91.57 151	81.92 106		79.54 108	96.97 85

TEST9					92.34 97	79.70 72	83.94 154	90.32 150	65.41 252	84.49 195	90.97 168	82.03 102	93.63 111

train_agg		85.98 104	85.28 118	88.07 82	92.34 97	79.70 72	83.94 154	90.32 150	65.79 242	84.49 195	90.97 168	81.93 104	93.63 111	81.21 89	96.54 100	90.88 196

Gipuma		84.44 130	86.33 98	78.78 241	84.20 256	73.57 126	89.55 65	90.44 146	84.24 37	84.38 197	94.89 45	76.35 164	80.40 307	76.14 144	96.80 93	82.36 307
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
test_8					92.09 106	78.87 80	83.82 159	90.31 152	65.79 242	84.36 198	90.96 170	81.93 104	93.44 123

cl-mvsnet2		78.97 210	78.21 217	81.24 206	77.74 315	59.01 266	77.46 272	87.13 209	65.79 242	84.32 199	85.10 265	58.96 271	90.88 197	75.36 152	92.03 216	93.84 100

agg_prior1		85.72 107	85.20 119	87.28 91	91.58 123	77.69 91	83.69 164	90.30 154	66.29 238	84.32 199	91.07 165	82.13 98	93.18 131	81.02 91	96.36 106	90.98 192

agg_prior					91.58 123	77.69 91		90.30 154		84.32 199			93.18 131

Anonymous202405211		80.51 194	81.19 183	78.49 247	88.48 181	57.26 281	76.63 280	82.49 254	81.21 73	84.30 202	92.24 137	67.99 226	86.24 271	62.22 254	95.13 147	91.98 173

LFMVS		80.15 204	80.56 189	78.89 239	89.19 170	55.93 289	85.22 133	73.78 304	82.96 54	84.28 203	92.72 120	57.38 281	90.07 224	63.80 245	95.75 129	90.68 201

Vis-MVSNet		86.86 87	86.58 96	87.72 85	92.09 106	77.43 97	87.35 99	92.09 104	78.87 101	84.27 204	94.05 81	78.35 138	93.65 109	80.54 100	91.58 225	92.08 167
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
ETV-MVS		84.31 134	83.91 148	85.52 124	88.58 179	70.40 161	84.50 147	93.37 59	78.76 104	84.07 205	78.72 323	80.39 122	95.13 59	73.82 166	92.98 198	91.04 191

MCST-MVS		84.36 131	83.93 147	85.63 122	91.59 120	71.58 154	83.52 167	92.13 103	61.82 268	83.96 206	89.75 197	79.93 129	93.46 122	78.33 120	94.34 170	91.87 175

新几何1				82.95 176	93.96 58	78.56 84		80.24 268	55.45 302	83.93 207	91.08 163	71.19 214	88.33 246	65.84 234	93.07 194	81.95 312

BH-RMVSNet		80.53 193	80.22 197	81.49 202	87.19 208	66.21 194	77.79 265	86.23 222	74.21 154	83.69 208	88.50 214	73.25 195	90.75 200	63.18 250	87.90 273	87.52 247

1121		80.86 187	79.81 204	84.02 151	93.93 59	78.70 82	81.64 211	80.18 269	55.43 303	83.67 209	91.15 161	71.29 213	91.41 180	67.95 221	93.06 195	81.96 311

USDC		76.63 235	76.73 231	76.34 273	83.46 265	57.20 282	80.02 232	88.04 196	52.14 321	83.65 210	91.25 157	63.24 247	86.65 266	54.66 301	94.11 174	85.17 271

miper_enhance_ethall		77.83 222	76.93 228	80.51 218	76.15 328	58.01 275	75.47 294	88.82 181	58.05 289	83.59 211	80.69 310	64.41 239	91.20 184	73.16 177	92.03 216	92.33 156

Effi-MVS+-dtu		85.82 106	83.38 152	93.14 3	87.13 209	91.15 2	87.70 95	88.42 187	74.57 150	83.56 212	85.65 253	78.49 136	94.21 87	72.04 184	92.88 200	94.05 93

CNLPA		83.55 153	83.10 157	84.90 132	89.34 167	83.87 46	84.54 145	88.77 182	79.09 97	83.54 213	88.66 213	74.87 172	81.73 302	66.84 226	92.29 210	89.11 226

OpenMVS_ROB	70.19 17	77.77 225	77.46 222	78.71 243	84.39 250	61.15 242	81.18 220	82.52 253	62.45 265	83.34 214	87.37 231	66.20 233	88.66 243	64.69 241	85.02 298	86.32 259

thres100view900		75.45 245	75.05 245	76.66 270	87.27 205	51.88 318	81.07 221	73.26 308	75.68 137	83.25 215	86.37 244	45.54 321	88.80 238	51.98 312	90.99 233	89.31 222

miper_lstm_enhance		76.45 239	76.10 236	77.51 261	76.72 323	60.97 248	64.69 334	85.04 240	63.98 257	83.20 216	88.22 216	56.67 284	78.79 312	73.22 171	93.12 193	92.78 140

IterMVS		76.91 232	76.34 234	78.64 244	80.91 287	64.03 210	76.30 285	79.03 273	64.88 255	83.11 217	89.16 204	59.90 264	84.46 289	68.61 216	85.15 297	87.42 248
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
thres600view7		75.97 242	75.35 244	77.85 259	87.01 215	51.84 319	80.45 228	73.26 308	75.20 144	83.10 218	86.31 247	45.54 321	89.05 234	55.03 299	92.24 212	92.66 146

mvs_anonymous		78.13 219	78.76 210	76.23 275	79.24 308	50.31 329	78.69 253	84.82 244	61.60 272	83.09 219	92.82 116	73.89 184	87.01 257	68.33 218	86.41 286	91.37 188

MVS_Test		82.47 168	83.22 154	80.22 223	82.62 273	57.75 278	82.54 195	91.96 109	71.16 194	82.89 220	92.52 128	77.41 147	90.50 208	80.04 102	87.84 275	92.40 154

test12				86.57 98	90.74 145	72.63 135		90.69 140		82.76 221		79.20 131	94.80 69		95.32 140	92.27 161

原ACMM1				84.60 139	92.81 88	74.01 124		91.50 120	62.59 262	82.73 222	90.67 180	76.53 162	94.25 84	69.24 206	95.69 131	85.55 267

test_yl		78.71 215	78.51 213	79.32 236	84.32 251	58.84 269	78.38 256	85.33 232	75.99 131	82.49 223	86.57 241	58.01 275	90.02 225	62.74 251	92.73 203	89.10 227

DCV-MVSNet		78.71 215	78.51 213	79.32 236	84.32 251	58.84 269	78.38 256	85.33 232	75.99 131	82.49 223	86.57 241	58.01 275	90.02 225	62.74 251	92.73 203	89.10 227

diffmvs		80.40 197	80.48 192	80.17 224	79.02 311	60.04 254	77.54 269	90.28 158	66.65 236	82.40 225	87.33 233	73.50 188	87.35 255	77.98 125	89.62 253	93.13 127

test222					93.31 73	76.54 108	79.38 242	77.79 278	52.59 316	82.36 226	90.84 174	66.83 231			91.69 222	81.25 320

D2MVS		76.84 233	75.67 241	80.34 221	80.48 297	62.16 235	73.50 306	84.80 245	57.61 293	82.24 227	87.54 228	51.31 300	87.65 252	70.40 199	93.19 192	91.23 190

VNet		79.31 208	80.27 194	76.44 271	87.92 192	53.95 302	75.58 292	84.35 247	74.39 153	82.23 228	90.72 177	72.84 199	84.39 290	60.38 271	93.98 177	90.97 193

Vis-MVSNet (Re-imp)		77.82 223	77.79 220	77.92 257	88.82 176	51.29 322	83.28 175	71.97 317	74.04 155	82.23 228	89.78 196	57.38 281	89.41 232	57.22 285	95.41 136	93.05 129

API-MVS		82.28 170	82.61 164	81.30 203	86.29 228	69.79 163	88.71 83	87.67 201	78.42 108	82.15 230	84.15 280	77.98 140	91.59 173	65.39 237	92.75 202	82.51 306

DP-MVS Recon		84.05 142	83.22 154	86.52 100	91.73 118	75.27 117	83.23 179	92.40 97	72.04 187	82.04 231	88.33 215	77.91 142	93.95 100	66.17 230	95.12 149	90.34 209

MSDG		80.06 206	79.99 203	80.25 222	83.91 262	68.04 182	77.51 270	89.19 177	77.65 113	81.94 232	83.45 286	76.37 163	86.31 270	63.31 249	86.59 284	86.41 258

Fast-Effi-MVS+		81.04 185	80.57 188	82.46 189	87.50 202	63.22 219	78.37 258	89.63 171	68.01 221	81.87 233	82.08 301	82.31 93	92.65 148	67.10 223	88.30 270	91.51 187

testgi		72.36 271	74.61 247	65.59 316	80.56 295	42.82 347	68.29 324	73.35 307	66.87 234	81.84 234	89.93 194	72.08 207	66.92 339	46.05 334	92.54 207	87.01 254

tfpn200view9		74.86 253	74.23 252	76.74 269	86.24 229	52.12 315	79.24 245	73.87 302	73.34 165	81.82 235	84.60 276	46.02 315	88.80 238	51.98 312	90.99 233	89.31 222

thres400		75.14 247	74.23 252	77.86 258	86.24 229	52.12 315	79.24 245	73.87 302	73.34 165	81.82 235	84.60 276	46.02 315	88.80 238	51.98 312	90.99 233	92.66 146

OpenMVS	76.72 13	81.98 177	82.00 173	81.93 193	84.42 249	68.22 180	88.50 87	89.48 174	66.92 233	81.80 237	91.86 142	72.59 202	90.16 217	71.19 189	91.25 228	87.40 249

AdaColmap		83.66 150	83.69 150	83.57 164	90.05 160	72.26 144	86.29 121	90.00 165	78.19 110	81.65 238	87.16 235	83.40 82	94.24 85	61.69 260	94.76 162	84.21 281

DELS-MVS		81.44 181	81.25 181	82.03 192	84.27 253	62.87 223	76.47 284	92.49 96	70.97 195	81.64 239	83.83 281	75.03 170	92.70 146	74.29 158	92.22 214	90.51 206
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
114514_t		83.10 162	82.54 166	84.77 135	92.90 82	69.10 176	86.65 113	90.62 143	54.66 306	81.46 240	90.81 175	76.98 154	94.38 81	72.62 180	96.18 113	90.82 198

TR-MVS		76.77 234	75.79 238	79.72 230	86.10 234	65.79 197	77.14 273	83.02 250	65.20 253	81.40 241	82.10 300	66.30 232	90.73 202	55.57 294	85.27 295	82.65 301

TAMVS		78.08 220	76.36 233	83.23 170	90.62 148	72.87 130	79.08 248	80.01 271	61.72 270	81.35 242	86.92 239	63.96 243	88.78 241	50.61 316	93.01 197	88.04 240

Effi-MVS+		83.90 147	84.01 145	83.57 164	87.22 207	65.61 198	86.55 116	92.40 97	78.64 105	81.34 243	84.18 279	83.65 79	92.93 141	74.22 159	87.87 274	92.17 166

CS-MVS		83.43 157	83.04 158	84.59 140	87.87 193	66.61 191	85.57 128	94.90 14	73.02 174	81.12 244	78.56 324	80.00 127	95.52 39	73.04 178	93.29 189	91.62 183

new-patchmatchnet		70.10 286	73.37 260	60.29 329	81.23 284	16.95 357	59.54 340	74.62 296	62.93 260	80.97 245	87.93 222	62.83 251	71.90 326	55.24 297	95.01 153	92.00 170

PVSNet_Blended_VisFu		81.55 180	80.49 191	84.70 138	91.58 123	73.24 129	84.21 149	91.67 117	62.86 261	80.94 246	87.16 235	67.27 229	92.87 144	69.82 202	88.94 261	87.99 241

BH-w/o		76.57 236	76.07 237	78.10 254	86.88 217	65.92 196	77.63 267	86.33 221	65.69 246	80.89 247	79.95 317	68.97 223	90.74 201	53.01 308	85.25 296	77.62 329

PAPM_NR		83.23 159	83.19 156	83.33 168	90.90 141	65.98 195	88.19 89	90.78 138	78.13 111	80.87 248	87.92 223	73.49 190	92.42 151	70.07 200	88.40 265	91.60 184

ab-mvs		79.67 207	80.56 189	76.99 264	88.48 181	56.93 283	84.70 139	86.06 224	68.95 215	80.78 249	93.08 106	75.30 169	84.62 288	56.78 286	90.90 238	89.43 220

mvs-test1		84.55 127	82.12 170	91.84 20	87.13 209	89.54 4	85.05 135	88.42 187	74.57 150	80.60 250	82.98 289	78.49 136	93.98 99	72.04 184	89.77 251	92.00 170

XXY-MVS		74.44 258	76.19 235	69.21 303	84.61 245	52.43 314	71.70 313	77.18 281	60.73 277	80.60 250	90.96 170	75.44 166	69.35 331	56.13 290	88.33 266	85.86 265

HQP4-MVS										80.56 252			94.61 76			93.56 116

HQP-NCC					91.19 134		84.77 136		73.30 167	80.55 253

ACMP_Plane					91.19 134		84.77 136		73.30 167	80.55 253

HQP-MVS		84.61 125	84.06 144	86.27 106	91.19 134	70.66 159	84.77 136	92.68 92	73.30 167	80.55 253	90.17 191	72.10 205	94.61 76	77.30 134	94.47 167	93.56 116

HyFIR lowres test		75.12 249	72.66 267	82.50 188	91.44 129	65.19 200	72.47 310	87.31 204	46.79 337	80.29 256	84.30 278	52.70 298	92.10 160	51.88 315	86.73 283	90.22 210

test20.03		73.75 261	74.59 249	71.22 298	81.11 285	51.12 324	70.15 319	72.10 316	70.42 200	80.28 257	91.50 154	64.21 241	74.72 323	46.96 332	94.58 165	87.82 246

F-COLMAP		84.97 121	83.42 151	89.63 56	92.39 95	83.40 48	88.83 80	91.92 110	73.19 171	80.18 258	89.15 205	77.04 153	93.28 128	65.82 235	92.28 211	92.21 164

GA-MVS		75.83 243	74.61 247	79.48 235	81.87 276	59.25 262	73.42 307	82.88 251	68.68 217	79.75 259	81.80 303	50.62 302	89.46 230	66.85 225	85.64 292	89.72 216

xiu_mvs_v1_base_debu		80.84 188	80.14 199	82.93 177	88.31 184	71.73 150	79.53 238	87.17 206	65.43 248	79.59 260	82.73 296	76.94 155	90.14 220	73.22 171	88.33 266	86.90 255

xiu_mvs_v1_base		80.84 188	80.14 199	82.93 177	88.31 184	71.73 150	79.53 238	87.17 206	65.43 248	79.59 260	82.73 296	76.94 155	90.14 220	73.22 171	88.33 266	86.90 255

xiu_mvs_v1_base_debi		80.84 188	80.14 199	82.93 177	88.31 184	71.73 150	79.53 238	87.17 206	65.43 248	79.59 260	82.73 296	76.94 155	90.14 220	73.22 171	88.33 266	86.90 255

UnsupCasMVSNet_eth		71.63 278	72.30 272	69.62 301	76.47 325	52.70 311	70.03 320	80.97 265	59.18 283	79.36 263	88.21 217	60.50 257	69.12 332	58.33 280	77.62 333	87.04 253

ppachtmachnet_test		74.73 255	74.00 254	76.90 266	80.71 293	56.89 285	71.53 314	78.42 275	58.24 287	79.32 264	82.92 293	57.91 278	84.26 291	65.60 236	91.36 227	89.56 217

MG-MVS		80.32 200	80.94 186	78.47 248	88.18 187	52.62 312	82.29 200	85.01 242	72.01 188	79.24 265	92.54 127	69.36 219	93.36 127	70.65 195	89.19 258	89.45 218

Fast-Effi-MVS+-dtu		82.54 167	81.41 180	85.90 116	85.60 236	76.53 110	83.07 182	89.62 172	73.02 174	79.11 266	83.51 284	80.74 119	90.24 214	68.76 213	89.29 255	90.94 194

CDS-MVSNet		77.32 228	75.40 242	83.06 173	89.00 173	72.48 140	77.90 263	82.17 257	60.81 275	78.94 267	83.49 285	59.30 268	88.76 242	54.64 302	92.37 209	87.93 243
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
baseline1		73.26 263	73.54 258	72.43 294	84.92 243	47.79 336	79.89 234	74.00 301	65.93 240	78.81 268	86.28 248	56.36 286	81.63 303	56.63 287	79.04 329	87.87 245

EIA-MVS		82.19 172	81.23 182	85.10 130	87.95 191	69.17 175	83.22 180	93.33 62	70.42 200	78.58 269	79.77 320	77.29 148	94.20 88	71.51 187	88.96 260	91.93 174

thres200		72.34 272	71.55 278	74.70 282	83.48 264	51.60 320	75.02 297	73.71 305	70.14 205	78.56 270	80.57 311	46.20 313	88.20 248	46.99 331	89.29 255	84.32 280

our_test_3		71.85 275	71.59 276	72.62 292	80.71 293	53.78 303	69.72 321	71.71 321	58.80 284	78.03 271	80.51 313	56.61 285	78.84 311	62.20 255	86.04 290	85.23 270

jason		77.42 227	75.75 239	82.43 190	87.10 213	69.27 170	77.99 261	81.94 259	51.47 325	77.84 272	85.07 268	60.32 260	89.00 235	70.74 194	89.27 257	89.03 230
jason: jason.
MAR-MVS		80.24 202	78.74 211	84.73 136	86.87 218	78.18 86	85.75 125	87.81 200	65.67 247	77.84 272	78.50 325	73.79 185	90.53 207	61.59 263	90.87 239	85.49 269
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
FPMVS		72.29 273	72.00 273	73.14 288	88.63 178	85.00 35	74.65 300	67.39 329	71.94 189	77.80 274	87.66 226	50.48 303	75.83 319	49.95 318	79.51 324	58.58 348

pmmvs4		74.92 252	72.98 264	80.73 215	84.95 242	71.71 153	76.23 287	77.59 279	52.83 315	77.73 275	86.38 243	56.35 287	84.97 284	57.72 284	87.05 281	85.51 268

ET-MVSNet_ETH3D		75.28 246	72.77 265	82.81 181	83.03 271	68.11 181	77.09 274	76.51 286	60.67 278	77.60 276	80.52 312	38.04 345	91.15 187	70.78 192	90.68 243	89.17 225

UnsupCasMVSNet_bld		69.21 293	69.68 288	67.82 310	79.42 305	51.15 323	67.82 328	75.79 289	54.15 308	77.47 277	85.36 263	59.26 269	70.64 328	48.46 325	79.35 326	81.66 314

Anonymous20231206		71.38 279	71.88 274	69.88 299	86.31 226	54.37 299	70.39 318	74.62 296	52.57 317	76.73 278	88.76 210	59.94 263	72.06 325	44.35 337	93.23 191	83.23 297

CMPMVS	59.41 20	75.12 249	73.57 257	79.77 227	75.84 330	67.22 185	81.21 219	82.18 256	50.78 329	76.50 279	87.66 226	55.20 293	82.99 298	62.17 257	90.64 247	89.09 229
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
FMVSNet5		72.10 274	71.69 275	73.32 286	81.57 279	53.02 309	76.77 278	78.37 276	63.31 258	76.37 280	91.85 143	36.68 348	78.98 310	47.87 328	92.45 208	87.95 242

CVMVSNet		72.62 269	71.41 279	76.28 274	83.25 267	60.34 252	83.50 170	79.02 274	37.77 350	76.33 281	85.10 265	49.60 305	87.41 254	70.54 197	77.54 334	81.08 323

PLC	73.85 16	82.09 174	80.31 193	87.45 89	90.86 143	80.29 67	85.88 124	90.65 141	68.17 220	76.32 282	86.33 245	73.12 196	92.61 149	61.40 264	90.02 250	89.44 219
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
MVSFormer		82.23 171	81.57 179	84.19 150	85.54 238	69.26 171	91.98 30	90.08 163	71.54 190	76.23 283	85.07 268	58.69 272	94.27 82	86.26 35	88.77 262	89.03 230

lupinMVS		76.37 240	74.46 250	82.09 191	85.54 238	69.26 171	76.79 277	80.77 267	50.68 331	76.23 283	82.82 294	58.69 272	88.94 236	69.85 201	88.77 262	88.07 238

PatchMatch-RL		74.48 256	73.22 261	78.27 252	87.70 197	85.26 33	75.92 289	70.09 325	64.34 256	76.09 285	81.25 308	65.87 236	78.07 313	53.86 304	83.82 306	71.48 337

thisisatest0515		73.00 267	70.52 282	80.46 219	81.45 280	59.90 256	73.16 309	74.31 300	57.86 290	76.08 286	77.78 327	37.60 347	92.12 159	65.00 238	91.45 226	89.35 221

MS-PatchMatch		70.93 281	70.22 285	73.06 289	81.85 277	62.50 229	73.82 305	77.90 277	52.44 318	75.92 287	81.27 307	55.67 290	81.75 301	55.37 296	77.70 332	74.94 333

CHOSEN 1792x2688		72.45 270	70.56 281	78.13 253	90.02 162	63.08 220	68.72 323	83.16 249	42.99 346	75.92 287	85.46 258	57.22 283	85.18 283	49.87 320	81.67 317	86.14 261

CR-MVSNet		74.00 259	73.04 263	76.85 268	79.58 302	62.64 226	82.58 192	76.90 282	50.50 332	75.72 289	92.38 129	48.07 308	84.07 292	68.72 215	82.91 312	83.85 286

RPMNet		78.88 211	78.28 216	80.68 217	79.58 302	62.64 226	82.58 192	94.16 28	74.80 147	75.72 289	92.59 123	48.69 306	95.56 34	73.48 169	82.91 312	83.85 286

DPM-MVS		80.10 205	79.18 208	82.88 180	90.71 147	69.74 164	78.87 251	90.84 136	60.29 280	75.64 291	85.92 252	67.28 228	93.11 136	71.24 188	91.79 220	85.77 266

PVSNet_BlendedMVS		78.80 213	77.84 219	81.65 201	84.43 247	63.41 215	79.49 241	90.44 146	61.70 271	75.43 292	87.07 238	69.11 221	91.44 177	60.68 269	92.24 212	90.11 214

PVSNet_Blended		76.49 238	75.40 242	79.76 228	84.43 247	63.41 215	75.14 296	90.44 146	57.36 295	75.43 292	78.30 326	69.11 221	91.44 177	60.68 269	87.70 277	84.42 279

PAPR		78.84 212	78.10 218	81.07 208	85.17 241	60.22 253	82.21 204	90.57 144	62.51 263	75.32 294	84.61 275	74.99 171	92.30 154	59.48 275	88.04 272	90.68 201

N_pmnet		70.20 284	68.80 293	74.38 283	80.91 287	84.81 38	59.12 342	76.45 287	55.06 304	75.31 295	82.36 299	55.74 289	54.82 349	47.02 330	87.24 280	83.52 290

cascas		76.29 241	74.81 246	80.72 216	84.47 246	62.94 221	73.89 304	87.34 203	55.94 300	75.16 296	76.53 335	63.97 242	91.16 186	65.00 238	90.97 236	88.06 239

SCA		73.32 262	72.57 269	75.58 278	81.62 278	55.86 290	78.89 250	71.37 322	61.73 269	74.93 297	83.42 287	60.46 258	87.01 257	58.11 282	82.63 316	83.88 283

xiu_mvs_v2_base		77.19 229	76.75 230	78.52 246	87.01 215	61.30 240	75.55 293	87.12 212	61.24 274	74.45 298	78.79 322	77.20 149	90.93 193	64.62 243	84.80 302	83.32 295

CANet		83.79 148	82.85 160	86.63 97	86.17 232	72.21 146	83.76 162	91.43 122	77.24 118	74.39 299	87.45 230	75.36 168	95.42 47	77.03 137	92.83 201	92.25 163

PS-MVSNAJ		77.04 231	76.53 232	78.56 245	87.09 214	61.40 238	75.26 295	87.13 209	61.25 273	74.38 300	77.22 332	76.94 155	90.94 192	64.63 242	84.83 301	83.35 294

MVP-Stereo		75.81 244	73.51 259	82.71 182	89.35 166	73.62 125	80.06 230	85.20 235	60.30 279	73.96 301	87.94 221	57.89 279	89.45 231	52.02 311	74.87 338	85.06 273
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
UGNet		82.78 163	81.64 176	86.21 110	86.20 231	76.24 114	86.86 106	85.68 228	77.07 119	73.76 302	92.82 116	69.64 217	91.82 170	69.04 211	93.69 183	90.56 204
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
1112_ss		74.82 254	73.74 255	78.04 255	89.57 164	60.04 254	76.49 283	87.09 213	54.31 307	73.66 303	79.80 318	60.25 261	86.76 265	58.37 278	84.15 305	87.32 250

Test_1112_low_res		73.90 260	73.08 262	76.35 272	90.35 153	55.95 288	73.40 308	86.17 223	50.70 330	73.14 304	85.94 251	58.31 274	85.90 276	56.51 288	83.22 309	87.20 251

1314		73.22 264	72.56 270	75.20 279	80.41 298	57.84 276	81.64 211	85.36 231	51.68 324	73.10 305	76.65 334	61.45 254	85.19 282	63.54 246	79.21 328	82.59 302

Patchmatch-test		65.91 304	67.38 298	61.48 327	75.51 332	43.21 346	68.84 322	63.79 339	62.48 264	72.80 306	83.42 287	44.89 331	59.52 348	48.27 327	86.45 285	81.70 313

PatchmatchNet		69.71 291	68.83 292	72.33 295	77.66 317	53.60 304	79.29 243	69.99 326	57.66 292	72.53 307	82.93 292	46.45 312	80.08 309	60.91 267	72.09 341	83.31 296
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
tpm		67.95 296	68.08 297	67.55 311	78.74 313	43.53 345	75.60 291	67.10 334	54.92 305	72.23 308	88.10 218	42.87 337	75.97 318	52.21 310	80.95 323	83.15 298

pmmvs5		70.73 282	70.07 286	72.72 290	77.03 321	52.73 310	74.14 301	75.65 292	50.36 333	72.17 309	85.37 262	55.42 292	80.67 306	52.86 309	87.59 278	84.77 275

PatchT		70.52 283	72.76 266	63.79 321	79.38 306	33.53 353	77.63 267	65.37 337	73.61 160	71.77 310	92.79 119	44.38 333	75.65 320	64.53 244	85.37 294	82.18 309

MVS		73.21 265	72.59 268	75.06 280	80.97 286	60.81 249	81.64 211	85.92 226	46.03 340	71.68 311	77.54 328	68.47 224	89.77 227	55.70 293	85.39 293	74.60 334

MIMVSNet		71.09 280	71.59 276	69.57 302	87.23 206	50.07 330	78.91 249	71.83 318	60.20 281	71.26 312	91.76 148	55.08 294	76.09 317	41.06 342	87.02 282	82.54 304

WTY-MVS		67.91 297	68.35 295	66.58 314	80.82 291	48.12 334	65.96 332	72.60 312	53.67 311	71.20 313	81.68 305	58.97 270	69.06 333	48.57 324	81.67 317	82.55 303

test0.0.03 1		64.66 307	64.36 308	65.57 317	75.03 337	46.89 337	64.69 334	61.58 344	62.43 266	71.18 314	77.54 328	43.41 334	68.47 334	40.75 343	82.65 314	81.35 317

CostFormer		69.98 289	68.68 294	73.87 284	77.14 319	50.72 327	79.26 244	74.51 298	51.94 323	70.97 315	84.75 273	45.16 329	87.49 253	55.16 298	79.23 327	83.40 293

tpmvs		70.16 285	69.56 289	71.96 296	74.71 339	48.13 333	79.63 236	75.45 294	65.02 254	70.26 316	81.88 302	45.34 326	85.68 278	58.34 279	75.39 337	82.08 310

sss		66.92 299	67.26 299	65.90 315	77.23 318	51.10 325	64.79 333	71.72 320	52.12 322	70.13 317	80.18 315	57.96 277	65.36 344	50.21 317	81.01 322	81.25 320

tpm2		68.45 295	66.83 301	73.30 287	78.93 312	48.50 332	79.76 235	71.76 319	47.50 336	69.92 318	83.60 283	42.07 338	88.40 245	48.44 326	79.51 324	83.01 300

HY-MVS	64.64 18	73.03 266	72.47 271	74.71 281	83.36 266	54.19 300	82.14 207	81.96 258	56.76 299	69.57 319	86.21 249	60.03 262	84.83 287	49.58 321	82.65 314	85.11 272

tpm cat1		66.76 300	65.21 306	71.42 297	77.09 320	50.62 328	78.01 260	73.68 306	44.89 342	68.64 320	79.00 321	45.51 323	82.42 300	49.91 319	70.15 344	81.23 322

IB-MVS	62.13 19	71.64 277	68.97 291	79.66 232	80.80 292	62.26 234	73.94 303	76.90 282	63.27 259	68.63 321	76.79 333	33.83 352	91.84 169	59.28 276	87.26 279	84.88 274
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
EPNet		80.37 198	78.41 215	86.23 107	76.75 322	73.28 128	87.18 102	77.45 280	76.24 126	68.14 322	88.93 209	65.41 237	93.85 102	69.47 204	96.12 117	91.55 186
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
PVSNet	58.17 21	66.41 302	65.63 305	68.75 306	81.96 275	49.88 331	62.19 338	72.51 314	51.03 327	68.04 323	75.34 338	50.84 301	74.77 321	45.82 335	82.96 310	81.60 315

tpmrst		66.28 303	66.69 303	65.05 319	72.82 347	39.33 348	78.20 259	70.69 324	53.16 314	67.88 324	80.36 314	48.18 307	74.75 322	58.13 281	70.79 343	81.08 323

CANet_DTU		77.81 224	77.05 226	80.09 225	81.37 282	59.90 256	83.26 176	88.29 191	69.16 212	67.83 325	83.72 282	60.93 255	89.47 229	69.22 208	89.70 252	90.88 196

EPMVS		62.47 308	62.63 312	62.01 323	70.63 351	38.74 349	74.76 298	52.86 352	53.91 310	67.71 326	80.01 316	39.40 342	66.60 340	55.54 295	68.81 348	80.68 327

MVS_0304		78.17 218	77.23 225	80.99 212	84.13 259	69.07 177	81.39 215	80.81 266	76.28 125	67.53 327	89.11 206	62.87 250	86.77 264	60.90 268	92.01 219	87.13 252

MDTV_nov1_ep13			68.29 296		78.03 314	43.87 344	74.12 302	72.22 315	52.17 319	67.02 328	85.54 254	45.36 325	80.85 305	55.73 291	84.42 304

pmmvs3		62.47 308	60.02 320	69.80 300	71.58 350	64.00 211	70.52 317	58.44 348	39.77 348	66.05 329	75.84 336	27.10 360	72.28 324	46.15 333	84.77 303	73.11 335

ADS-MVSNet2		65.87 305	63.64 310	72.55 293	73.16 344	56.92 284	67.10 329	74.81 295	49.74 334	66.04 330	82.97 290	46.71 310	77.26 314	42.29 339	69.96 345	83.46 291

ADS-MVSNet		61.90 310	62.19 313	61.03 328	73.16 344	36.42 351	67.10 329	61.75 342	49.74 334	66.04 330	82.97 290	46.71 310	63.21 346	42.29 339	69.96 345	83.46 291

DWT-MVSNet_test		66.43 301	64.37 307	72.63 291	74.86 338	50.86 326	76.52 282	72.74 311	54.06 309	65.50 332	68.30 346	32.13 354	84.84 286	61.63 262	73.59 339	82.19 308

DSMNet-mixed		60.98 316	61.61 315	59.09 331	72.88 346	45.05 342	74.70 299	46.61 355	26.20 352	65.34 333	90.32 186	55.46 291	63.12 347	41.72 341	81.30 321	69.09 341

JIA-IIPM		69.41 292	66.64 304	77.70 260	73.19 343	71.24 156	75.67 290	65.56 336	70.42 200	65.18 334	92.97 111	33.64 353	83.06 297	53.52 306	69.61 347	78.79 328

test-LLR		67.21 298	66.74 302	68.63 307	76.45 326	55.21 295	67.89 325	67.14 332	62.43 266	65.08 335	72.39 340	43.41 334	69.37 329	61.00 265	84.89 299	81.31 318

test-mter		65.00 306	63.79 309	68.63 307	76.45 326	55.21 295	67.89 325	67.14 332	50.98 328	65.08 335	72.39 340	28.27 358	69.37 329	61.00 265	84.89 299	81.31 318

PMMVS2		55.64 321	59.27 321	44.74 335	64.30 355	12.32 358	40.60 349	49.79 354	53.19 313	65.06 337	84.81 272	53.60 297	49.76 351	32.68 351	89.41 254	72.15 336

baseline2		69.77 290	66.89 300	78.41 249	79.51 304	58.09 274	76.23 287	69.57 327	57.50 294	64.82 338	77.45 330	46.02 315	88.44 244	53.08 307	77.83 331	88.70 234

gg-mvs-nofinetune		68.96 294	69.11 290	68.52 309	76.12 329	45.32 340	83.59 166	55.88 350	86.68 26	64.62 339	97.01 8	30.36 356	83.97 294	44.78 336	82.94 311	76.26 331

PAPM		71.77 276	70.06 287	76.92 265	86.39 221	53.97 301	76.62 281	86.62 219	53.44 312	63.97 340	84.73 274	57.79 280	92.34 152	39.65 344	81.33 320	84.45 278

new_pmnet		55.69 320	57.66 322	49.76 334	75.47 333	30.59 354	59.56 339	51.45 353	43.62 345	62.49 341	75.48 337	40.96 340	49.15 352	37.39 347	72.52 340	69.55 340

MDTV_nov1_ep13_2view						27.60 356	70.76 316		46.47 339	61.27 342		45.20 327		49.18 322		83.75 288

dp		60.70 317	60.29 319	61.92 325	72.04 349	38.67 350	70.83 315	64.08 338	51.28 326	60.75 343	77.28 331	36.59 349	71.58 327	47.41 329	62.34 350	75.52 332

TESTMET0.1,1		61.29 313	60.32 318	64.19 320	72.06 348	51.30 321	67.89 325	62.09 340	45.27 341	60.65 344	69.01 343	27.93 359	64.74 345	56.31 289	81.65 319	76.53 330

PMMVS		61.65 311	60.38 317	65.47 318	65.40 354	69.26 171	63.97 336	61.73 343	36.80 351	60.11 345	68.43 344	59.42 267	66.35 341	48.97 323	78.57 330	60.81 345

PVSNet_0	51.08 22	56.10 319	54.97 324	59.48 330	75.12 336	53.28 308	55.16 345	61.89 341	44.30 343	59.16 346	62.48 349	54.22 295	65.91 343	35.40 348	47.01 351	59.25 347

MVS-HIRNet		61.16 314	62.92 311	55.87 332	79.09 309	35.34 352	71.83 312	57.98 349	46.56 338	59.05 347	91.14 162	49.95 304	76.43 316	38.74 345	71.92 342	55.84 349

E-PMN		61.59 312	61.62 314	61.49 326	66.81 352	55.40 293	53.77 346	60.34 345	66.80 235	58.90 348	65.50 347	40.48 341	66.12 342	55.72 292	86.25 288	62.95 344

GG-mvs-BLEND				67.16 312	73.36 342	46.54 339	84.15 150	55.04 351		58.64 349	61.95 350	29.93 357	83.87 295	38.71 346	76.92 335	71.07 338

EPNet_dtu		72.87 268	71.33 280	77.49 262	77.72 316	60.55 251	82.35 198	75.79 289	66.49 237	58.39 350	81.06 309	53.68 296	85.98 274	53.55 305	92.97 199	85.95 263
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
EMVS		61.10 315	60.81 316	61.99 324	65.96 353	55.86 290	53.10 347	58.97 347	67.06 232	56.89 351	63.33 348	40.98 339	67.03 338	54.79 300	86.18 289	63.08 343

CHOSEN 280x420		59.08 318	56.52 323	66.76 313	76.51 324	64.39 207	49.62 348	59.00 346	43.86 344	55.66 352	68.41 345	35.55 350	68.21 335	43.25 338	76.78 336	67.69 342

MVE	40.22 23	51.82 322	50.47 325	55.87 332	62.66 356	51.91 317	31.61 351	39.28 356	40.65 347	50.76 353	74.98 339	56.24 288	44.67 353	33.94 350	64.11 349	71.04 339
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
DeepMVS_CX				24.13 336	32.95 357	29.49 355		21.63 359	12.07 353	37.95 354	45.07 351	30.84 355	19.21 354	17.94 353	33.06 353	23.69 350

tmp_tt		20.25 324	24.50 327	7.49 337	4.47 358	8.70 359	34.17 350	25.16 358	1.00 354	32.43 355	18.49 352	39.37 343	9.21 355	21.64 352	43.75 352	4.57 351

testmvs		5.91 328	7.65 331	0.72 339	1.20 359	0.37 361	59.14 341	0.67 361	0.49 356	1.11 356	2.76 356	0.94 362	0.24 357	1.02 355	1.47 354	1.55 353

test123		6.27 327	8.08 330	0.84 338	1.11 360	0.57 360	62.90 337	0.82 360	0.54 355	1.07 357	2.75 357	1.26 361	0.30 356	1.04 354	1.26 355	1.66 352

uanet_test		0.00 329	0.00 332	0.00 340	0.00 361	0.00 362	0.00 352	0.00 362	0.00 357	0.00 358	0.00 358	0.00 363	0.00 358	0.00 356	0.00 356	0.00 354

cdsmvs_eth3d_5k		20.81 323	27.75 326	0.00 340	0.00 361	0.00 362	0.00 352	85.44 230	0.00 357	0.00 358	82.82 294	81.46 111	0.00 358	0.00 356	0.00 356	0.00 354

pcd_1.5k_mvsjas		6.41 326	8.55 329	0.00 340	0.00 361	0.00 362	0.00 352	0.00 362	0.00 357	0.00 358	0.00 358	76.94 155	0.00 358	0.00 356	0.00 356	0.00 354

sosnet-low-res		0.00 329	0.00 332	0.00 340	0.00 361	0.00 362	0.00 352	0.00 362	0.00 357	0.00 358	0.00 358	0.00 363	0.00 358	0.00 356	0.00 356	0.00 354

sosnet		0.00 329	0.00 332	0.00 340	0.00 361	0.00 362	0.00 352	0.00 362	0.00 357	0.00 358	0.00 358	0.00 363	0.00 358	0.00 356	0.00 356	0.00 354

uncertanet		0.00 329	0.00 332	0.00 340	0.00 361	0.00 362	0.00 352	0.00 362	0.00 357	0.00 358	0.00 358	0.00 363	0.00 358	0.00 356	0.00 356	0.00 354

Regformer		0.00 329	0.00 332	0.00 340	0.00 361	0.00 362	0.00 352	0.00 362	0.00 357	0.00 358	0.00 358	0.00 363	0.00 358	0.00 356	0.00 356	0.00 354

ab-mvs-re		6.65 325	8.87 328	0.00 340	0.00 361	0.00 362	0.00 352	0.00 362	0.00 357	0.00 358	79.80 318	0.00 363	0.00 358	0.00 356	0.00 356	0.00 354

uanet		0.00 329	0.00 332	0.00 340	0.00 361	0.00 362	0.00 352	0.00 362	0.00 357	0.00 358	0.00 358	0.00 363	0.00 358	0.00 356	0.00 356	0.00 354

OPU-MVS				88.27 79	91.89 114	77.83 89	90.47 47				91.22 158	81.12 115	94.68 72	74.48 157	95.35 138	92.29 159

save fliter					93.75 62	77.44 95	86.31 119	89.72 168	70.80 196

test_0728_SECOND				86.79 95	94.25 48	72.45 141	90.54 44	94.10 34					95.88 15	86.42 31	97.97 43	92.02 169

GSMVS																83.88 283

sam_mvs1												46.11 314				83.88 283

sam_mvs												45.92 319

MTGPA								91.81 114

test_post1							78.85 252				3.13 354	45.19 328	80.13 308	58.11 282

test_post											3.10 355	45.43 324	77.22 315

patchmatchnet-post											81.71 304	45.93 318	87.01 257

MTMP							90.66 41	33.14 357

gm-plane-assit					75.42 334	44.97 343			52.17 319		72.36 342		87.90 249	54.10 303

test9_res														80.83 95	96.45 104	90.57 203

agg_prior2														79.68 106	96.16 114	90.22 210

test_prior4						78.97 79	84.59 141

test_prior				86.32 103	90.59 149	71.99 147		92.85 86					94.17 91			92.80 138

新几何2							81.72 210

旧先验1					91.97 109	71.77 149		81.78 260			91.84 144	73.92 183			93.65 184	83.61 289

无先验							82.81 189	85.62 229	58.09 288				91.41 180	67.95 221		84.48 277

原ACMM2							82.26 203

testdata2													86.43 269	63.52 247

segment_acmp												81.94 103

testdata1							79.62 237		73.95 157

plane_prior7					93.45 68	77.31 99

plane_prior6					92.61 89	76.54 108						74.84 173

plane_prior5								93.61 53					95.22 55	80.78 96	95.83 124	94.46 77

plane_prior4											92.95 112

plane_prior2							89.45 70		79.44 93

plane_prior1					92.83 87

plane_prior						76.42 111	87.15 103		75.94 134						95.03 152

n2								0.00 362

nn								0.00 362

door-mid								74.45 299

test11								91.46 121

door								72.57 313

HQP5-MVS						70.66 159

BP-MVS														77.30 134

HQP3-MVS								92.68 92							94.47 167

HQP2-MVS												72.10 205

NP-MVS					91.95 110	74.55 120					90.17 191

ACMMP++_ref															95.74 130

ACMMP++															97.35 75

Test By Simon												79.09 132

High-resolution multi-view benchmark