High-res multi-view results

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

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

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

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

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

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

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

LCM-MVSNet		86.90 1	88.67 1	81.57 25	91.50 1	63.30 118	84.80 33	87.77 10	86.18 1	96.26 1	96.06 1	90.32 1	84.49 66	68.08 90	97.05 1	96.93 1

test_part1		84.22 8	86.36 2	77.83 75	85.08 73	56.71 166	85.13 28	89.83 2	78.32 8	90.44 6	95.87 2	84.29 3	84.09 73	71.67 70	96.58 2	92.68 8

TDRefinement		86.32 2	86.33 3	86.29 1	88.64 32	81.19 5	88.84 2	90.72 1	78.27 9	87.95 15	92.53 14	79.37 14	84.79 63	74.51 48	96.15 3	92.88 7

ACMP	69.50 8	82.64 29	83.38 30	80.40 43	86.50 49	69.44 70	82.30 55	86.08 25	66.80 67	86.70 32	89.99 73	81.64 7	85.95 30	74.35 49	96.11 4	85.81 74
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
ACMH+	66.64 10	81.20 40	82.48 43	77.35 82	81.16 129	62.39 122	80.51 66	87.80 8	73.02 26	87.57 21	91.08 36	80.28 11	82.44 101	64.82 116	96.10 5	87.21 56

LPG-MVS_test		83.47 19	84.33 15	80.90 38	87.00 42	70.41 62	82.04 58	86.35 17	69.77 51	87.75 16	91.13 34	81.83 4	86.20 23	77.13 34	95.96 6	86.08 68

LGP-MVS_train				80.90 38	87.00 42	70.41 62		86.35 17	69.77 51	87.75 16	91.13 34	81.83 4	86.20 23	77.13 34	95.96 6	86.08 68

ACMM	69.25 9	82.11 34	83.31 31	78.49 66	88.17 39	73.96 37	83.11 50	84.52 59	66.40 71	87.45 23	89.16 91	81.02 10	80.52 137	74.27 50	95.73 8	80.98 186
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
NR-MVSNet		73.62 116	74.05 111	72.33 154	83.50 95	43.71 254	65.65 251	77.32 175	64.32 93	75.59 174	87.08 120	62.45 144	81.34 116	54.90 192	95.63 9	91.93 9

WR-MVS_H		80.22 56	82.17 45	74.39 112	89.46 15	42.69 265	78.24 97	82.24 93	78.21 10	89.57 9	92.10 19	68.05 100	85.59 45	66.04 108	95.62 10	94.88 5

TranMVSNet+NR-MVSNet		76.13 86	77.66 80	71.56 159	84.61 81	42.57 267	70.98 182	78.29 161	68.67 57	83.04 77	89.26 85	72.99 63	80.75 133	55.58 189	95.47 11	91.35 12

COLMAP_ROB	72.78 3	83.75 14	84.11 18	82.68 14	82.97 103	74.39 35	87.18 9	88.18 7	78.98 6	86.11 39	91.47 30	79.70 13	85.76 39	66.91 105	95.46 12	87.89 47
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
CP-MVS		84.12 11	84.55 13	82.80 12	89.42 18	79.74 7	88.19 3	84.43 60	71.96 40	84.70 59	90.56 50	77.12 27	86.18 25	79.24 18	95.36 13	82.49 161

Baseline_NR-MVSNet		70.62 157	73.19 127	62.92 257	76.97 176	34.44 324	68.84 204	70.88 237	60.25 125	79.50 119	90.53 51	61.82 151	69.11 269	54.67 195	95.27 14	85.22 83

UniMVSNet (Re)		75.00 100	75.48 98	73.56 125	83.14 100	47.92 221	70.41 189	81.04 117	63.67 99	79.54 118	86.37 148	62.83 139	81.82 110	57.10 172	95.25 15	90.94 16

PS-CasMVS		80.41 52	82.86 40	73.07 134	89.93 7	39.21 288	77.15 110	81.28 109	79.74 5	90.87 3	92.73 12	75.03 45	84.93 59	63.83 125	95.19 16	95.07 3

ACMMP		84.22 8	84.84 11	82.35 20	89.23 23	76.66 25	87.65 4	85.89 28	71.03 43	85.85 41	90.58 49	78.77 18	85.78 38	79.37 16	95.17 17	84.62 102
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
PEN-MVS		80.46 51	82.91 38	73.11 133	89.83 9	39.02 291	77.06 112	82.61 90	80.04 4	90.60 5	92.85 10	74.93 47	85.21 55	63.15 129	95.15 18	95.09 2

CP-MVSNet		79.48 60	81.65 50	72.98 137	89.66 13	39.06 290	76.76 113	80.46 126	78.91 7	90.32 7	91.70 25	68.49 95	84.89 60	63.40 128	95.12 19	95.01 4

SteuartSystems-ACMMP		83.07 24	83.64 25	81.35 31	85.14 71	71.00 56	85.53 25	84.78 48	70.91 44	85.64 44	90.41 58	75.55 40	87.69 3	79.75 8	95.08 20	85.36 82
Skip Steuart: Steuart Systems R&D Blog.
UniMVSNet_NR-MVSNet		74.90 103	75.65 95	72.64 148	83.04 101	45.79 241	69.26 200	78.81 149	66.66 69	81.74 93	86.88 126	63.26 137	81.07 124	56.21 181	94.98 21	91.05 14

DU-MVS		74.91 102	75.57 97	72.93 139	83.50 95	45.79 241	69.47 197	80.14 133	65.22 83	81.74 93	87.08 120	61.82 151	81.07 124	56.21 181	94.98 21	91.93 9

MP-MVS-pluss		82.54 30	83.46 29	79.76 47	88.88 31	68.44 79	81.57 61	86.33 19	63.17 106	85.38 50	91.26 33	76.33 32	84.67 65	83.30 1	94.96 23	86.17 67
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
HPM-MVS		84.12 11	84.63 12	82.60 15	88.21 38	74.40 34	85.24 27	87.21 14	70.69 46	85.14 52	90.42 57	78.99 17	86.62 14	80.83 6	94.93 24	86.79 62
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
HPM-MVS_fast		84.59 6	85.10 9	83.06 6	88.60 33	75.83 26	86.27 23	86.89 16	73.69 23	86.17 36	91.70 25	78.23 21	85.20 56	79.45 13	94.91 25	88.15 45

MP-MVS		83.19 21	83.54 27	82.14 22	90.54 5	79.00 11	86.42 21	83.59 78	71.31 41	81.26 99	90.96 38	74.57 51	84.69 64	78.41 22	94.78 26	82.74 155
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
zzz-MVS		83.01 26	83.63 26	81.13 35	91.16 2	78.16 14	82.72 54	80.63 121	72.08 37	84.93 54	90.79 42	74.65 49	84.42 68	80.98 4	94.75 27	80.82 189

MTAPA		83.19 21	83.87 21	81.13 35	91.16 2	78.16 14	84.87 31	80.63 121	72.08 37	84.93 54	90.79 42	74.65 49	84.42 68	80.98 4	94.75 27	80.82 189

test_0728_THIRD									74.03 22	85.83 42	90.41 58	75.58 39	85.69 41	77.43 33	94.74 29	84.31 116

PGM-MVS		83.07 24	83.25 34	82.54 18	89.57 14	77.21 23	82.04 58	85.40 35	67.96 60	84.91 57	90.88 39	75.59 38	86.57 15	78.16 23	94.71 30	83.82 124

DTE-MVSNet		80.35 53	82.89 39	72.74 145	89.84 8	37.34 306	77.16 109	81.81 99	80.45 3	90.92 2	92.95 8	74.57 51	86.12 28	63.65 126	94.68 31	94.76 6

mPP-MVS		84.01 13	84.39 14	82.88 8	90.65 4	81.38 4	87.08 11	82.79 87	72.41 34	85.11 53	90.85 41	76.65 30	84.89 60	79.30 17	94.63 32	82.35 164

FC-MVSNet-test		73.32 122	74.78 102	68.93 196	79.21 149	36.57 308	71.82 170	79.54 141	57.63 150	82.57 85	90.38 63	59.38 175	78.99 158	57.91 167	94.56 33	91.23 13

DeepC-MVS	72.44 4	81.00 45	80.83 56	81.50 26	86.70 48	70.03 66	82.06 57	87.00 15	59.89 128	80.91 105	90.53 51	72.19 65	88.56 1	73.67 54	94.52 34	85.92 73
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
abl_6		84.92 3	85.70 4	82.57 17	86.72 47	79.27 8	87.56 5	86.08 25	77.48 13	88.12 14	91.53 28	81.18 9	84.31 71	78.12 24	94.47 35	84.15 120

SR-MVS-dyc-post		84.75 5	85.26 8	83.21 3	86.19 53	79.18 9	87.23 7	86.27 20	77.51 11	87.65 19	90.73 45	79.20 15	85.58 46	78.11 25	94.46 36	84.89 92

RE-MVS-def			85.50 5		86.19 53	79.18 9	87.23 7	86.27 20	77.51 11	87.65 19	90.73 45	81.38 8		78.11 25	94.46 36	84.89 92

UA-Net		81.56 38	82.28 44	79.40 54	88.91 29	69.16 75	84.67 34	80.01 135	75.34 17	79.80 116	94.91 3	69.79 85	80.25 142	72.63 60	94.46 36	88.78 41

ACMH	63.62 14	77.50 79	80.11 60	69.68 183	79.61 141	56.28 167	78.81 88	83.62 77	63.41 104	87.14 31	90.23 69	76.11 34	73.32 231	67.58 97	94.44 39	79.44 211
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ZNCC-MVS		83.12 23	83.68 24	81.45 29	89.14 25	73.28 45	86.32 22	85.97 27	67.39 62	84.02 69	90.39 61	74.73 48	86.46 16	80.73 7	94.43 40	84.60 105

SED-MVS		81.78 36	83.48 28	76.67 85	86.12 57	61.06 131	83.62 43	84.72 51	72.61 30	87.38 25	89.70 78	77.48 25	85.89 36	75.29 42	94.39 41	83.08 144

IU-MVS					86.12 57	60.90 135		80.38 128	45.49 266	81.31 98				75.64 41	94.39 41	84.65 99

DVP-MVS		81.15 42	83.12 36	75.24 105	86.16 55	60.78 136	83.77 41	80.58 124	72.48 32	85.83 42	90.41 58	78.57 19	85.69 41	75.86 39	94.39 41	79.24 213

test_0728_SECOND				76.57 87	86.20 52	60.57 139	83.77 41	85.49 32					85.90 35	75.86 39	94.39 41	83.25 140

SMA-MVS		82.12 33	82.68 42	80.43 42	88.90 30	69.52 68	85.12 29	84.76 49	63.53 101	84.23 67	91.47 30	72.02 67	87.16 7	79.74 10	94.36 45	84.61 103
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
ACMMP_NAP		82.33 32	83.28 32	79.46 53	89.28 19	69.09 77	83.62 43	84.98 44	64.77 88	83.97 70	91.02 37	75.53 41	85.93 34	82.00 2	94.36 45	83.35 138

APD-MVS_3200maxsize		83.57 16	84.33 15	81.31 33	82.83 106	73.53 43	85.50 26	87.45 13	74.11 20	86.45 34	90.52 53	80.02 12	84.48 67	77.73 30	94.34 47	85.93 72

APDe-MVS		82.88 27	84.14 17	79.08 57	84.80 78	66.72 90	86.54 19	85.11 41	72.00 39	86.65 33	91.75 24	78.20 22	87.04 9	77.93 28	94.32 48	83.47 133

ACMMPR		83.62 15	83.93 20	82.69 13	89.78 11	77.51 21	87.01 13	84.19 69	70.23 47	84.49 61	90.67 48	75.15 43	86.37 19	79.58 11	94.26 49	84.18 119

region2R		83.54 17	83.86 22	82.58 16	89.82 10	77.53 19	87.06 12	84.23 68	70.19 49	83.86 71	90.72 47	75.20 42	86.27 22	79.41 15	94.25 50	83.95 123

HFP-MVS		83.39 20	84.03 19	81.48 27	89.25 21	75.69 27	87.01 13	84.27 64	70.23 47	84.47 62	90.43 55	76.79 28	85.94 31	79.58 11	94.23 51	82.82 152

#test#		82.40 31	82.71 41	81.48 27	89.25 21	75.69 27	84.47 35	84.27 64	64.45 91	84.47 62	90.43 55	76.79 28	85.94 31	76.01 36	94.23 51	82.82 152

test_241102_TWO								84.80 47	72.61 30	84.93 54	89.70 78	77.73 24	85.89 36	75.29 42	94.22 53	83.25 140

test1172		84.85 4	85.39 6	83.21 3	88.34 37	80.50 6	85.12 29	85.22 39	81.06 2	87.20 28	90.28 67	79.20 15	85.58 46	78.04 27	94.08 54	83.55 130

XVS		83.51 18	83.73 23	82.85 10	89.43 16	77.61 17	86.80 16	84.66 54	72.71 27	82.87 80	90.39 61	73.86 56	86.31 20	78.84 20	94.03 55	84.64 100

X-MVStestdata		76.81 82	74.79 101	82.85 10	89.43 16	77.61 17	86.80 16	84.66 54	72.71 27	82.87 80	9.95 354	73.86 56	86.31 20	78.84 20	94.03 55	84.64 100

DPE-MVS		82.00 35	83.02 37	78.95 60	85.36 68	67.25 86	82.91 51	84.98 44	73.52 24	85.43 49	90.03 72	76.37 31	86.97 11	74.56 47	94.02 57	82.62 157

GST-MVS		82.79 28	83.27 33	81.34 32	88.99 27	73.29 44	85.94 24	85.13 40	68.58 58	84.14 68	90.21 70	73.37 61	86.41 17	79.09 19	93.98 58	84.30 118

9.14			80.22 59		80.68 131		80.35 71	87.69 11	59.90 127	83.00 78	88.20 109	74.57 51	81.75 113	73.75 53	93.78 59

xxxxxxxxxxxxxcwj		80.31 54	80.94 54	78.42 68	87.00 42	67.23 87	79.24 83	88.61 5	56.65 161	84.29 65	89.18 88	73.73 59	83.22 88	76.01 36	93.77 60	84.81 96

SF-MVS		80.72 48	81.80 46	77.48 79	82.03 117	64.40 110	83.41 48	88.46 6	65.28 82	84.29 65	89.18 88	73.73 59	83.22 88	76.01 36	93.77 60	84.81 96

IS-MVSNet		75.10 98	75.42 99	74.15 117	79.23 148	48.05 219	79.43 80	78.04 165	70.09 50	79.17 123	88.02 115	53.04 218	83.60 80	58.05 166	93.76 62	90.79 18

ETH3D-3000-0.1		79.14 63	79.80 63	77.16 84	80.67 132	64.57 107	80.26 72	87.60 12	60.74 122	82.47 86	88.03 114	71.73 70	81.81 111	73.12 56	93.61 63	85.09 87

PMVS	70.70 6	81.70 37	83.15 35	77.36 81	90.35 6	82.82 2	82.15 56	79.22 143	74.08 21	87.16 30	91.97 20	84.80 2	76.97 196	64.98 115	93.61 63	72.28 271
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
SR-MVS		84.51 7	85.27 7	82.25 21	88.52 34	77.71 16	86.81 15	85.25 38	77.42 15	86.15 37	90.24 68	81.69 6	85.94 31	77.77 29	93.58 65	83.09 143

OPM-MVS		80.99 46	81.63 51	79.07 58	86.86 46	69.39 71	79.41 82	84.00 74	65.64 75	85.54 47	89.28 84	76.32 33	83.47 83	74.03 51	93.57 66	84.35 115

XVG-ACMP-BASELINE		80.54 49	81.06 53	78.98 59	87.01 41	72.91 46	80.23 74	85.56 30	66.56 70	85.64 44	89.57 80	69.12 89	80.55 136	72.51 62	93.37 67	83.48 132

FIs		72.56 139	73.80 115	68.84 199	78.74 157	37.74 302	71.02 181	79.83 136	56.12 164	80.88 106	89.45 82	58.18 184	78.28 178	56.63 174	93.36 68	90.51 20

WR-MVS		71.20 151	72.48 138	67.36 216	84.98 74	35.70 316	64.43 265	68.66 248	65.05 86	81.49 96	86.43 147	57.57 196	76.48 204	50.36 225	93.32 69	89.90 22

CLD-MVS		72.88 133	72.36 140	74.43 111	77.03 175	54.30 179	68.77 209	83.43 80	52.12 210	76.79 159	74.44 279	69.54 86	83.91 75	55.88 184	93.25 70	85.09 87
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
CPTT-MVS		81.51 39	81.76 47	80.76 40	89.20 24	78.75 12	86.48 20	82.03 96	68.80 54	80.92 104	88.52 102	72.00 68	82.39 102	74.80 44	93.04 71	81.14 181

APD-MVS		81.13 43	81.73 48	79.36 55	84.47 83	70.53 61	83.85 39	83.70 76	69.43 53	83.67 73	88.96 98	75.89 36	86.41 17	72.62 61	92.95 72	81.14 181
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
ZD-MVS					83.91 92	69.36 72		81.09 115	58.91 138	82.73 84	89.11 93	75.77 37	86.63 13	72.73 59	92.93 73

testtj		81.19 41	81.70 49	79.67 51	83.95 91	69.77 67	83.58 46	84.63 56	72.13 36	82.85 82	88.36 106	75.00 46	86.79 12	71.99 69	92.84 74	82.44 162

ETH3D cwj APD-0.16		78.38 74	78.72 72	77.38 80	80.09 137	66.16 95	79.08 86	86.13 24	57.55 151	80.93 103	87.76 117	71.98 69	82.73 98	72.11 68	92.83 75	83.25 140

OurMVSNet-221017-0		78.57 69	78.53 75	78.67 63	80.48 134	64.16 111	80.24 73	82.06 95	61.89 114	88.77 12	93.32 5	57.15 198	82.60 100	70.08 78	92.80 76	89.25 27

Anonymous20231211		75.54 92	77.19 83	70.59 168	77.67 170	45.70 244	74.73 139	80.19 131	68.80 54	82.95 79	92.91 9	66.26 116	76.76 202	58.41 164	92.77 77	89.30 26

test_prior3		76.71 84	77.19 83	75.27 103	82.15 115	59.85 145	75.57 126	84.33 62	58.92 136	76.53 165	86.78 130	67.83 103	83.39 85	69.81 80	92.76 78	82.58 158

test_prior2							75.57 126		58.92 136	76.53 165	86.78 130	67.83 103		69.81 80	92.76 78

CDPH-MVS		77.33 80	77.06 86	78.14 72	84.21 88	63.98 113	76.07 122	83.45 79	54.20 188	77.68 142	87.18 118	69.98 83	85.37 49	68.01 92	92.72 80	85.08 89

EPP-MVSNet		73.86 112	73.38 123	75.31 102	78.19 161	53.35 186	80.45 67	77.32 175	65.11 85	76.47 167	86.80 128	49.47 236	83.77 77	53.89 204	92.72 80	88.81 40

OMC-MVS		79.41 61	78.79 70	81.28 34	80.62 133	70.71 60	80.91 64	84.76 49	62.54 110	81.77 91	86.65 139	71.46 73	83.53 82	67.95 95	92.44 82	89.60 23

DP-MVS		78.44 73	79.29 67	75.90 96	81.86 120	65.33 100	79.05 87	84.63 56	74.83 19	80.41 110	86.27 149	71.68 71	83.45 84	62.45 133	92.40 83	78.92 217

nrg030		74.87 104	75.99 93	71.52 160	74.90 202	49.88 205	74.10 146	82.58 91	54.55 185	83.50 75	89.21 87	71.51 72	75.74 211	61.24 140	92.34 84	88.94 36

SD-MVS		80.28 55	81.55 52	76.47 90	83.57 94	67.83 83	83.39 49	85.35 37	64.42 92	86.14 38	87.07 122	74.02 55	80.97 127	77.70 31	92.32 85	80.62 196
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
LTVRE_ROB	75.46 1	84.22 8	84.98 10	81.94 23	84.82 76	75.40 29	91.60 1	87.80 8	73.52 24	88.90 11	93.06 7	71.39 75	81.53 115	81.53 3	92.15 86	88.91 37
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
ACMMP++															91.96 87

v7n		79.37 62	80.41 58	76.28 92	78.67 158	55.81 170	79.22 85	82.51 92	70.72 45	87.54 22	92.44 15	68.00 102	81.34 116	72.84 58	91.72 88	91.69 11

VDDNet		71.60 148	73.13 129	67.02 221	86.29 51	41.11 274	69.97 191	66.50 256	68.72 56	74.74 185	91.70 25	59.90 169	75.81 209	48.58 239	91.72 88	84.15 120

RRT_MVS		73.80 114	71.19 158	81.60 24	71.04 248	70.33 64	78.78 89	74.91 198	56.96 155	77.83 138	85.56 165	32.82 313	87.39 5	71.16 71	91.68 90	87.07 60

UniMVSNet_ETH3D		76.74 83	79.02 68	69.92 182	89.27 20	43.81 253	74.47 143	71.70 221	72.33 35	85.50 48	93.65 4	77.98 23	76.88 199	54.60 196	91.64 91	89.08 31

wuyk23d		61.97 245	66.25 210	49.12 311	58.19 337	60.77 138	66.32 242	52.97 316	55.93 167	90.62 4	86.91 125	73.07 62	35.98 350	20.63 352	91.63 92	50.62 341

CNVR-MVS		78.49 71	78.59 74	78.16 71	85.86 64	67.40 85	78.12 100	81.50 103	63.92 96	77.51 143	86.56 143	68.43 97	84.82 62	73.83 52	91.61 93	82.26 167

train_agg		76.38 85	76.55 87	75.86 97	85.47 66	69.32 73	76.42 115	78.69 152	54.00 192	76.97 149	86.74 133	66.60 113	81.10 122	72.50 63	91.56 94	77.15 233

Gipuma		69.55 170	72.83 135	59.70 280	63.63 309	53.97 181	80.08 76	75.93 186	64.24 94	73.49 201	88.93 99	57.89 194	62.46 301	59.75 156	91.55 95	62.67 327
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
SixPastTwentyTwo		75.77 88	76.34 89	74.06 118	81.69 123	54.84 175	76.47 114	75.49 190	64.10 95	87.73 18	92.24 18	50.45 232	81.30 118	67.41 99	91.46 96	86.04 70

agg_prior1		75.89 87	76.41 88	74.31 114	84.44 85	66.02 96	76.12 121	78.62 155	54.40 186	76.95 151	86.85 127	66.44 115	80.34 139	72.45 64	91.42 97	76.57 237

test9_res														72.12 67	91.37 98	77.40 232

3Dnovator+	73.19 2	81.08 44	80.48 57	82.87 9	81.41 126	72.03 47	84.38 36	86.23 23	77.28 16	80.65 107	90.18 71	59.80 172	87.58 4	73.06 57	91.34 99	89.01 33

DeepPCF-MVS	71.07 5	78.48 72	77.14 85	82.52 19	84.39 87	77.04 24	76.35 117	84.05 72	56.66 160	80.27 112	85.31 168	68.56 94	87.03 10	67.39 100	91.26 100	83.50 131

LS3D		80.99 46	80.85 55	81.41 30	78.37 159	71.37 52	87.45 6	85.87 29	77.48 13	81.98 89	89.95 75	69.14 88	85.26 52	66.15 106	91.24 101	87.61 51

HPM-MVS++		79.89 57	79.80 63	80.18 45	89.02 26	78.44 13	83.49 47	80.18 132	64.71 90	78.11 136	88.39 104	65.46 124	83.14 90	77.64 32	91.20 102	78.94 216

CL-MVSNet_2432*1600		66.38 208	67.51 199	62.97 256	61.76 316	34.39 325	58.11 307	75.30 194	50.84 226	77.12 148	85.42 166	56.84 203	69.44 266	51.07 219	91.16 103	85.08 89

test_djsdf		78.88 66	78.27 76	80.70 41	81.42 125	71.24 54	83.98 37	75.72 188	52.27 208	87.37 27	92.25 17	68.04 101	80.56 134	72.28 65	91.15 104	90.32 21

DeepC-MVS_fast	69.89 7	77.17 81	76.33 90	79.70 50	83.90 93	67.94 81	80.06 77	83.75 75	56.73 159	74.88 183	85.32 167	65.54 122	87.79 2	65.61 111	91.14 105	83.35 138
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
ambc				70.10 178	77.74 168	50.21 200	74.28 145	77.93 168		79.26 122	88.29 108	54.11 215	79.77 150	64.43 118	91.10 106	80.30 201

原ACMM1				73.90 119	85.90 61	65.15 104		81.67 101	50.97 224	74.25 193	86.16 154	61.60 153	83.54 81	56.75 173	91.08 107	73.00 262

114514_t		73.40 120	73.33 126	73.64 123	84.15 90	57.11 162	78.20 98	80.02 134	43.76 278	72.55 212	86.07 159	64.00 135	83.35 87	60.14 151	91.03 108	80.45 199

HQP_MVS		78.77 67	78.78 71	78.72 62	85.18 69	65.18 102	82.74 52	85.49 32	65.45 77	78.23 133	89.11 93	60.83 163	86.15 26	71.09 72	90.94 109	84.82 94

plane_prior5								85.49 32					86.15 26	71.09 72	90.94 109	84.82 94

agg_prior2														70.70 75	90.93 111	78.55 220

PHI-MVS		74.92 101	74.36 108	76.61 86	76.40 183	62.32 123	80.38 69	83.15 82	54.16 190	73.23 206	80.75 214	62.19 148	83.86 76	68.02 91	90.92 112	83.65 129

AllTest		77.66 77	77.43 81	78.35 69	79.19 150	70.81 57	78.60 91	88.64 3	65.37 80	80.09 114	88.17 110	70.33 80	78.43 172	55.60 186	90.90 113	85.81 74

TestCases				78.35 69	79.19 150	70.81 57		88.64 3	65.37 80	80.09 114	88.17 110	70.33 80	78.43 172	55.60 186	90.90 113	85.81 74

NCCC		78.25 75	78.04 78	78.89 61	85.61 65	69.45 69	79.80 79	80.99 118	65.77 74	75.55 175	86.25 151	67.42 105	85.42 48	70.10 77	90.88 115	81.81 174

VPNet		65.58 213	67.56 198	59.65 281	79.72 140	30.17 339	60.27 296	62.14 276	54.19 189	71.24 230	86.63 140	58.80 180	67.62 278	44.17 265	90.87 116	81.18 180

XVG-OURS		79.51 59	79.82 62	78.58 65	86.11 60	74.96 32	76.33 119	84.95 46	66.89 64	82.75 83	88.99 97	66.82 111	78.37 175	74.80 44	90.76 117	82.40 163

PS-MVSNAJss		77.54 78	77.35 82	78.13 73	84.88 75	66.37 93	78.55 92	79.59 140	53.48 199	86.29 35	92.43 16	62.39 145	80.25 142	67.90 96	90.61 118	87.77 48

anonymousdsp		78.60 68	77.80 79	81.00 37	78.01 164	74.34 36	80.09 75	76.12 184	50.51 229	89.19 10	90.88 39	71.45 74	77.78 188	73.38 55	90.60 119	90.90 17

pmmvs6		71.82 146	73.66 118	66.31 228	75.94 190	42.01 269	66.99 234	72.53 215	63.45 103	76.43 168	92.78 11	72.95 64	69.69 265	51.41 216	90.46 120	87.22 55

test12				76.51 88	82.28 113	60.94 134		81.64 102		73.60 199		64.88 128	85.19 57		90.42 121	83.38 136

VDD-MVS		70.81 155	71.44 155	68.91 197	79.07 155	46.51 236	67.82 221	70.83 238	61.23 117	74.07 196	88.69 101	59.86 170	75.62 212	51.11 218	90.28 122	84.61 103

ETH3 D test6400		75.73 90	76.00 92	74.92 106	81.75 121	56.93 163	78.31 95	84.60 58	52.83 204	77.15 146	85.14 170	68.59 93	84.03 74	65.44 112	90.20 123	83.82 124

mvs_tets		78.93 65	78.67 73	79.72 49	84.81 77	73.93 38	80.65 65	76.50 182	51.98 213	87.40 24	91.86 22	76.09 35	78.53 167	68.58 85	90.20 123	86.69 64

EPNet		69.10 178	67.32 202	74.46 109	68.33 272	61.27 130	77.56 103	63.57 270	60.95 120	56.62 314	82.75 195	51.53 227	81.24 119	54.36 201	90.20 123	80.88 188
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
VPA-MVSNet		68.71 183	70.37 165	63.72 246	76.13 187	38.06 300	64.10 267	71.48 226	56.60 163	74.10 195	88.31 107	64.78 131	69.72 264	47.69 247	90.15 126	83.37 137

TAPA-MVS	65.27 12	75.16 97	74.29 109	77.77 76	74.86 203	68.08 80	77.89 101	84.04 73	55.15 174	76.19 171	83.39 186	66.91 109	80.11 147	60.04 153	90.14 127	85.13 86
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
jajsoiax		78.51 70	78.16 77	79.59 52	84.65 80	73.83 40	80.42 68	76.12 184	51.33 221	87.19 29	91.51 29	73.79 58	78.44 171	68.27 88	90.13 128	86.49 65

Anonymous20240529		72.56 139	73.79 116	68.86 198	76.89 180	45.21 246	68.80 208	77.25 177	67.16 63	76.89 154	90.44 54	65.95 119	74.19 228	50.75 221	90.00 129	87.18 58

AdaColmap		74.22 109	74.56 103	73.20 131	81.95 118	60.97 133	79.43 80	80.90 119	65.57 76	72.54 213	81.76 206	70.98 78	85.26 52	47.88 245	90.00 129	73.37 259

DP-MVS Recon		73.57 117	72.69 136	76.23 93	82.85 105	63.39 116	74.32 144	82.96 85	57.75 145	70.35 239	81.98 202	64.34 134	84.41 70	49.69 229	89.95 131	80.89 187

plane_prior						65.18 102	80.06 77		61.88 115						89.91 132

cl-mvsnet_		68.26 191	68.26 188	68.29 205	64.98 301	43.67 255	65.89 246	74.67 199	50.04 234	76.86 156	82.42 198	48.74 241	75.38 213	60.92 145	89.81 133	85.80 78

cl-mvsnet1		68.27 190	68.26 188	68.29 205	64.98 301	43.67 255	65.89 246	74.67 199	50.04 234	76.86 156	82.43 197	48.74 241	75.38 213	60.94 144	89.81 133	85.81 74

OPU-MVS				78.65 64	83.44 98	66.85 89	83.62 43				86.12 156	66.82 111	86.01 29	61.72 136	89.79 135	83.08 144

LFMVS		67.06 204	67.89 195	64.56 238	78.02 163	38.25 297	70.81 186	59.60 287	65.18 84	71.06 233	86.56 143	43.85 264	75.22 216	46.35 255	89.63 136	80.21 203

TSAR-MVS + GP.		73.08 125	71.60 152	77.54 78	78.99 156	70.73 59	74.96 132	69.38 245	60.73 123	74.39 192	78.44 246	57.72 195	82.78 96	60.16 150	89.60 137	79.11 215

MIMVSNet1		66.57 206	69.23 174	58.59 286	81.26 128	37.73 303	64.06 268	57.62 293	57.02 154	78.40 132	90.75 44	62.65 140	58.10 314	41.77 277	89.58 138	79.95 205

TransMVSNet (Re)		69.62 168	71.63 151	63.57 247	76.51 182	35.93 314	65.75 250	71.29 230	61.05 119	75.02 180	89.90 76	65.88 121	70.41 263	49.79 228	89.48 139	84.38 114

ACMMP++_ref															89.47 140

PCF-MVS	63.80 13	72.70 137	71.69 149	75.72 98	78.10 162	60.01 144	73.04 151	81.50 103	45.34 268	79.66 117	84.35 177	65.15 126	82.65 99	48.70 237	89.38 141	84.50 111
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
HQP3-MVS								84.12 70							89.16 142

HQP-MVS		75.24 96	75.01 100	75.94 95	82.37 110	58.80 155	77.32 106	84.12 70	59.08 132	71.58 223	85.96 161	58.09 187	85.30 51	67.38 101	89.16 142	83.73 128

AUN-MVS		70.22 160	67.88 196	77.22 83	82.96 104	71.61 49	69.08 202	71.39 227	49.17 242	71.70 221	78.07 251	37.62 300	79.21 155	61.81 134	89.15 144	80.82 189

v10		75.69 91	76.20 91	74.16 116	74.44 215	48.69 210	75.84 125	82.93 86	59.02 135	85.92 40	89.17 90	58.56 182	82.74 97	70.73 74	89.14 145	91.05 14

MCST-MVS		73.42 119	73.34 125	73.63 124	81.28 127	59.17 151	74.80 137	83.13 83	45.50 264	72.84 209	83.78 184	65.15 126	80.99 126	64.54 117	89.09 146	80.73 194

ITE_SJBPF				80.35 44	76.94 177	73.60 41		80.48 125	66.87 65	83.64 74	86.18 152	70.25 82	79.90 149	61.12 143	88.95 147	87.56 52

ANet_high		67.08 203	69.94 167	58.51 287	57.55 338	27.09 345	58.43 305	76.80 180	63.56 100	82.40 87	91.93 21	59.82 171	64.98 295	50.10 227	88.86 148	83.46 134

test_0402		78.17 76	79.48 66	74.24 115	83.50 95	59.15 152	72.52 157	74.60 201	75.34 17	88.69 13	91.81 23	75.06 44	82.37 103	65.10 113	88.68 149	81.20 179

IterMVS-LS		73.01 128	73.12 130	72.66 147	73.79 224	49.90 202	71.63 171	78.44 158	58.22 140	80.51 108	86.63 140	58.15 186	79.62 151	62.51 131	88.20 150	88.48 43
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
MSLP-MVS++		74.48 107	75.78 94	70.59 168	84.66 79	62.40 121	78.65 90	84.24 67	60.55 124	77.71 141	81.98 202	63.12 138	77.64 190	62.95 130	88.14 151	71.73 276

FMVSNet1		71.06 152	72.48 138	66.81 222	77.65 171	40.68 279	71.96 164	73.03 207	61.14 118	79.45 120	90.36 64	60.44 165	75.20 217	50.20 226	88.05 152	84.54 106

pm-mvs1		68.40 186	69.85 169	64.04 244	73.10 235	39.94 285	64.61 263	70.50 239	55.52 171	73.97 197	89.33 83	63.91 136	68.38 273	49.68 230	88.02 153	83.81 126

TinyColmap		67.98 192	69.28 172	64.08 242	67.98 276	46.82 233	70.04 190	75.26 195	53.05 201	77.36 145	86.79 129	59.39 174	72.59 241	45.64 258	88.01 154	72.83 264

v8		75.07 99	75.64 96	73.35 127	73.42 227	47.46 228	75.20 130	81.45 105	60.05 126	85.64 44	89.26 85	58.08 189	81.80 112	69.71 82	87.97 155	90.79 18

tttt0517		69.46 171	67.79 197	74.46 109	75.34 194	52.72 188	75.05 131	63.27 272	54.69 181	78.87 126	84.37 176	26.63 342	81.15 120	63.95 122	87.93 156	89.51 24

testing_2		72.01 145	72.36 140	70.95 164	70.79 250	48.70 209	72.81 154	78.09 164	48.79 246	84.46 64	89.15 92	57.90 193	78.55 166	61.55 138	87.74 157	85.61 81

new-patchmatchnet		52.89 293	55.76 286	44.26 326	59.94 328	6.31 359	37.36 349	50.76 323	41.10 292	64.28 276	79.82 227	44.77 258	48.43 325	36.24 310	87.61 158	78.03 227

tfpnnormal		66.48 207	67.93 194	62.16 262	73.40 228	36.65 307	63.45 274	64.99 264	55.97 165	72.82 210	87.80 116	57.06 201	69.10 270	48.31 242	87.54 159	80.72 195

Anonymous202405211		66.02 209	66.89 208	63.43 250	74.22 217	38.14 298	59.00 302	66.13 257	63.33 105	69.76 245	85.95 162	51.88 223	70.50 260	44.23 264	87.52 160	81.64 176

cl_fuxian		69.82 167	69.89 168	69.61 184	66.24 290	43.48 257	68.12 218	79.61 139	51.43 219	77.72 140	80.18 224	54.61 213	78.15 183	63.62 127	87.50 161	87.20 57

v144192		72.99 130	73.06 131	72.77 143	74.58 213	47.48 227	71.90 168	80.44 127	51.57 217	81.46 97	84.11 180	58.04 191	82.12 108	67.98 93	87.47 162	88.70 42

Patchmtry		60.91 252	63.01 235	54.62 296	66.10 292	26.27 348	67.47 225	56.40 303	54.05 191	72.04 219	86.66 137	33.19 311	60.17 308	43.69 266	87.45 163	77.42 231

v1921920		72.96 132	72.98 133	72.89 141	74.67 209	47.58 226	71.92 167	80.69 120	51.70 216	81.69 95	83.89 182	56.58 205	82.25 106	68.34 87	87.36 164	88.82 39

CSCG		74.12 110	74.39 106	73.33 128	79.35 145	61.66 128	77.45 105	81.98 97	62.47 112	79.06 124	80.19 223	61.83 150	78.79 162	59.83 155	87.35 165	79.54 210

v1192		73.40 120	73.42 121	73.32 129	74.65 212	48.67 211	72.21 160	81.73 100	52.76 205	81.85 90	84.56 174	57.12 199	82.24 107	68.58 85	87.33 166	89.06 32

LCM-MVSNet-Re		69.10 178	71.57 153	61.70 264	70.37 258	34.30 326	61.45 287	79.62 137	56.81 157	89.59 8	88.16 112	68.44 96	72.94 234	42.30 272	87.33 166	77.85 230

canonicalmvs		72.29 142	73.38 123	69.04 191	74.23 216	47.37 229	73.93 147	83.18 81	54.36 187	76.61 162	81.64 208	72.03 66	75.34 215	57.12 171	87.28 168	84.40 113

baseline		73.10 124	73.96 113	70.51 170	71.46 246	46.39 239	72.08 161	84.40 61	55.95 166	76.62 161	86.46 146	67.20 106	78.03 184	64.22 120	87.27 169	87.11 59

alignmvs		70.54 158	71.00 159	69.15 190	73.50 225	48.04 220	69.85 194	79.62 137	53.94 195	76.54 164	82.00 201	59.00 178	74.68 223	57.32 169	87.21 170	84.72 98

F-COLMAP		75.29 95	73.99 112	79.18 56	81.73 122	71.90 48	81.86 60	82.98 84	59.86 129	72.27 215	84.00 181	64.56 132	83.07 93	51.48 215	87.19 171	82.56 160

TSAR-MVS + MP.		79.05 64	78.81 69	79.74 48	88.94 28	67.52 84	86.61 18	81.38 107	51.71 215	77.15 146	91.42 32	65.49 123	87.20 6	79.44 14	87.17 172	84.51 110
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
v1240		73.06 126	73.14 128	72.84 142	74.74 206	47.27 231	71.88 169	81.11 113	51.80 214	82.28 88	84.21 178	56.22 207	82.34 104	68.82 83	87.17 172	88.91 37

v1144		73.29 123	73.39 122	73.01 135	74.12 221	48.11 218	72.01 163	81.08 116	53.83 196	81.77 91	84.68 172	58.07 190	81.91 109	68.10 89	86.86 174	88.99 35

casdiffmvs		73.06 126	73.84 114	70.72 166	71.32 247	46.71 235	70.93 183	84.26 66	55.62 169	77.46 144	87.10 119	67.09 107	77.81 186	63.95 122	86.83 175	87.64 50

RRT_test8_iter05		65.80 211	65.13 217	67.80 213	67.02 285	40.85 278	67.13 232	75.33 193	49.73 236	72.69 211	81.32 209	24.45 352	77.37 193	61.69 137	86.82 176	85.18 85

Vis-MVSNet (Re-imp)		62.74 241	63.21 233	61.34 269	72.19 241	31.56 336	67.31 230	53.87 310	53.60 198	69.88 243	83.37 188	40.52 285	70.98 256	41.40 278	86.78 177	81.48 178

K. test v3		73.67 115	73.61 120	73.87 120	79.78 139	55.62 173	74.69 141	62.04 281	66.16 73	84.76 58	93.23 6	49.47 236	80.97 127	65.66 110	86.67 178	85.02 91

thisisatest0530		67.05 205	65.16 214	72.73 146	73.10 235	50.55 197	71.26 179	63.91 268	50.22 231	74.46 191	80.75 214	26.81 341	80.25 142	59.43 157	86.50 179	87.37 54

lessismore_v0				72.75 144	79.60 142	56.83 165		57.37 296		83.80 72	89.01 96	47.45 248	78.74 163	64.39 119	86.49 180	82.69 156

MVS_111021_HR		72.98 131	72.97 134	72.99 136	80.82 130	65.47 99	68.81 206	72.77 212	57.67 148	75.76 172	82.38 199	71.01 77	77.17 194	61.38 139	86.15 181	76.32 238

LF4IMVS		67.50 198	67.31 203	68.08 208	58.86 333	61.93 124	71.43 173	75.90 187	44.67 273	72.42 214	80.20 222	57.16 197	70.44 261	58.99 160	86.12 182	71.88 274

FMVSNet2		67.48 199	68.21 191	65.29 233	73.14 232	38.94 292	68.81 206	71.21 234	54.81 176	76.73 160	86.48 145	48.63 243	74.60 224	47.98 244	86.11 183	82.35 164

EPNet_dtu		58.93 267	58.52 266	60.16 279	67.91 277	47.70 225	69.97 191	58.02 291	49.73 236	47.28 342	73.02 293	38.14 295	62.34 302	36.57 309	85.99 184	70.43 286
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
XVG-OURS-SEG-HR		79.62 58	79.99 61	78.49 66	86.46 50	74.79 33	77.15 110	85.39 36	66.73 68	80.39 111	88.85 100	74.43 54	78.33 177	74.73 46	85.79 185	82.35 164

API-MVS		70.97 154	71.51 154	69.37 185	75.20 196	55.94 169	80.99 63	76.84 179	62.48 111	71.24 230	77.51 256	61.51 155	80.96 131	52.04 211	85.76 186	71.22 280

v2v482		72.55 141	72.58 137	72.43 151	72.92 238	46.72 234	71.41 174	79.13 144	55.27 172	81.17 100	85.25 169	55.41 209	81.13 121	67.25 104	85.46 187	89.43 25

GBi-Net		68.30 188	68.79 180	66.81 222	73.14 232	40.68 279	71.96 164	73.03 207	54.81 176	74.72 186	90.36 64	48.63 243	75.20 217	47.12 249	85.37 188	84.54 106

test1		68.30 188	68.79 180	66.81 222	73.14 232	40.68 279	71.96 164	73.03 207	54.81 176	74.72 186	90.36 64	48.63 243	75.20 217	47.12 249	85.37 188	84.54 106

FMVSNet3		65.00 219	65.16 214	64.52 239	69.47 264	37.56 305	66.63 239	70.38 240	51.55 218	74.72 186	83.27 191	37.89 299	74.44 226	47.12 249	85.37 188	81.57 177

CNLPA		73.44 118	73.03 132	74.66 107	78.27 160	75.29 30	75.99 123	78.49 157	65.39 79	75.67 173	83.22 194	61.23 159	66.77 287	53.70 206	85.33 191	81.92 173

Effi-MVS+-dtu		75.43 93	72.28 142	84.91 2	77.05 173	83.58 1	78.47 93	77.70 169	57.68 146	74.89 182	78.13 250	64.80 129	84.26 72	56.46 178	85.32 192	86.88 61

UGNet		70.20 161	69.05 176	73.65 122	76.24 185	63.64 114	75.87 124	72.53 215	61.48 116	60.93 298	86.14 155	52.37 222	77.12 195	50.67 222	85.21 193	80.17 204
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
VNet		64.01 231	65.15 216	60.57 275	73.28 230	35.61 317	57.60 309	67.08 253	54.61 183	66.76 266	83.37 188	56.28 206	66.87 283	42.19 273	85.20 194	79.23 214

TAMVS		65.31 215	63.75 227	69.97 181	82.23 114	59.76 147	66.78 238	63.37 271	45.20 269	69.79 244	79.37 234	47.42 249	72.17 244	34.48 317	85.15 195	77.99 229

test_yl		65.11 216	65.09 219	65.18 234	70.59 254	40.86 276	63.22 279	72.79 210	57.91 143	68.88 252	79.07 241	42.85 271	74.89 221	45.50 259	84.97 196	79.81 206

DCV-MVSNet		65.11 216	65.09 219	65.18 234	70.59 254	40.86 276	63.22 279	72.79 210	57.91 143	68.88 252	79.07 241	42.85 271	74.89 221	45.50 259	84.97 196	79.81 206

USDC		62.80 240	63.10 234	61.89 263	65.19 297	43.30 260	67.42 226	74.20 203	35.80 319	72.25 216	84.48 175	45.67 252	71.95 249	37.95 299	84.97 196	70.42 287

ETV-MVS		72.72 136	72.16 145	74.38 113	76.90 179	55.95 168	73.34 150	84.67 53	62.04 113	72.19 218	70.81 309	65.90 120	85.24 54	58.64 161	84.96 199	81.95 172

DPM-MVS		69.98 164	69.22 175	72.26 155	82.69 108	58.82 154	70.53 187	81.23 111	47.79 255	64.16 277	80.21 221	51.32 229	83.12 91	60.14 151	84.95 200	74.83 250

CS-MVS		71.24 150	70.57 164	73.26 130	74.93 199	52.00 190	73.59 148	85.55 31	55.58 170	68.88 252	70.17 316	64.37 133	85.62 44	57.19 170	84.83 201	82.17 168

eth_miper_zixun_eth		69.42 172	68.73 184	71.50 161	67.99 275	46.42 237	67.58 223	78.81 149	50.72 227	78.13 135	80.34 220	50.15 234	80.34 139	60.18 149	84.65 202	87.74 49

miper_lstm_enhance		61.97 245	61.63 244	62.98 255	60.04 326	45.74 243	47.53 334	70.95 235	44.04 275	73.06 207	78.84 243	39.72 288	60.33 307	55.82 185	84.64 203	82.88 149

cl-mvsnet2		67.14 202	66.51 209	69.03 192	63.20 310	43.46 258	66.88 237	76.25 183	49.22 241	74.48 190	77.88 252	45.49 254	77.40 192	60.64 147	84.59 204	86.24 66

miper_ehance_all_eth		68.36 187	68.16 193	68.98 193	65.14 300	43.34 259	67.07 233	78.92 148	49.11 243	76.21 170	77.72 253	53.48 217	77.92 185	61.16 142	84.59 204	85.68 80

miper_enhance_ethall		65.86 210	65.05 222	68.28 207	61.62 318	42.62 266	64.74 260	77.97 166	42.52 287	73.42 203	72.79 294	49.66 235	77.68 189	58.12 165	84.59 204	84.54 106

CDS-MVSNet		64.33 227	62.66 238	69.35 187	80.44 135	58.28 159	65.26 255	65.66 259	44.36 274	67.30 263	75.54 267	43.27 267	71.77 250	37.68 300	84.44 207	78.01 228
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
CANet		73.00 129	71.84 147	76.48 89	75.82 191	61.28 129	74.81 135	80.37 129	63.17 106	62.43 288	80.50 218	61.10 161	85.16 58	64.00 121	84.34 208	83.01 147

PLC	62.01 16	71.79 147	70.28 166	76.33 91	80.31 136	68.63 78	78.18 99	81.24 110	54.57 184	67.09 265	80.63 216	59.44 173	81.74 114	46.91 252	84.17 209	78.63 218
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
PVSNet_BlendedMVS		65.38 214	64.30 223	68.61 201	69.81 261	49.36 206	65.60 253	78.96 146	45.50 264	59.98 301	78.61 244	51.82 224	78.20 180	44.30 262	84.11 210	78.27 223

cascas		64.59 221	62.77 237	70.05 179	75.27 195	50.02 201	61.79 286	71.61 222	42.46 288	63.68 282	68.89 325	49.33 238	80.35 138	47.82 246	84.05 211	79.78 208

Regformer-1		74.28 108	73.63 119	76.21 94	74.22 217	64.12 112	72.77 155	75.46 191	66.86 66	79.27 121	72.08 297	69.29 87	78.74 163	68.73 84	84.02 212	85.77 79

Regformer-2		75.32 94	74.47 105	77.88 74	74.22 217	66.65 91	72.77 155	77.54 171	68.47 59	80.44 109	72.08 297	70.60 79	80.97 127	70.08 78	84.02 212	86.01 71

MSP-MVS		80.49 50	79.67 65	82.96 7	89.70 12	77.46 22	87.16 10	85.10 42	64.94 87	81.05 101	88.38 105	57.10 200	87.10 8	79.75 8	83.87 214	84.31 116

test20.03		55.74 280	57.51 274	50.42 304	59.89 329	32.09 334	50.63 326	49.01 327	50.11 232	65.07 274	83.23 192	45.61 253	48.11 326	30.22 331	83.82 215	71.07 283

D2MVS		62.58 242	61.05 249	67.20 218	63.85 306	47.92 221	56.29 311	69.58 244	39.32 300	70.07 242	78.19 248	34.93 306	72.68 236	53.44 209	83.74 216	81.00 185

MVS_111021_LR		72.10 143	71.82 148	72.95 138	79.53 143	73.90 39	70.45 188	66.64 255	56.87 156	76.81 158	81.76 206	68.78 91	71.76 251	61.81 134	83.74 216	73.18 261

thres600view7		61.82 247	61.38 247	63.12 253	71.81 244	34.93 321	64.64 261	56.99 300	54.78 180	70.33 240	79.74 228	32.07 320	72.42 243	38.61 294	83.46 218	82.02 170

旧先验1					84.55 82	60.36 141		63.69 269			87.05 123	54.65 212			83.34 219	69.66 293

1121		69.23 175	68.26 188	72.12 156	88.36 36	71.40 51	68.59 210	62.06 279	43.80 277	74.75 184	86.18 152	52.92 219	76.85 200	54.47 197	83.27 220	68.12 302

新几何1				69.99 180	88.37 35	71.34 53		62.08 278	43.85 276	74.99 181	86.11 157	52.85 220	70.57 259	50.99 220	83.23 221	68.05 303

Vis-MVSNet		74.85 105	74.56 103	75.72 98	81.63 124	64.64 106	76.35 117	79.06 145	62.85 108	73.33 204	88.41 103	62.54 143	79.59 153	63.94 124	82.92 222	82.94 148
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
ET-MVSNet_ETH3D		63.32 233	60.69 252	71.20 163	70.15 260	55.66 171	65.02 258	64.32 267	43.28 286	68.99 250	72.05 303	25.46 348	78.19 182	54.16 203	82.80 223	79.74 209

DELS-MVS		68.83 180	68.31 186	70.38 171	70.55 257	48.31 214	63.78 272	82.13 94	54.00 192	68.96 251	75.17 271	58.95 179	80.06 148	58.55 162	82.74 224	82.76 154
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
CMPMVS	48.73 20	61.54 250	60.89 250	63.52 248	61.08 321	51.55 192	68.07 219	68.00 251	33.88 327	65.87 269	81.25 211	37.91 298	67.71 276	49.32 232	82.60 225	71.31 279
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
MVS_0304		62.51 243	62.27 240	63.25 251	69.39 265	48.47 213	64.05 269	62.48 274	59.69 130	54.10 326	81.04 213	45.71 251	66.31 290	41.38 279	82.58 226	74.96 249

ppachtmachnet_test		60.26 259	59.61 259	62.20 261	67.70 279	44.33 251	58.18 306	60.96 284	40.75 296	65.80 270	72.57 295	41.23 278	63.92 297	46.87 253	82.42 227	78.33 221

v148		69.38 174	69.39 171	69.36 186	69.14 268	44.56 249	68.83 205	72.70 213	54.79 179	78.59 127	84.12 179	54.69 211	76.74 203	59.40 158	82.20 228	86.79 62

thisisatest0515		60.48 257	57.86 271	68.34 204	67.25 282	46.42 237	60.58 294	62.14 276	40.82 295	63.58 283	69.12 321	26.28 344	78.34 176	48.83 235	82.13 229	80.26 202

OpenMVS	62.51 15	68.76 182	68.75 182	68.78 200	70.56 256	53.91 182	78.29 96	77.35 174	48.85 245	70.22 241	83.52 185	52.65 221	76.93 197	55.31 190	81.99 230	75.49 243

MAR-MVS		67.72 196	66.16 211	72.40 152	74.45 214	64.99 105	74.87 133	77.50 173	48.67 247	65.78 271	68.58 328	57.01 202	77.79 187	46.68 254	81.92 231	74.42 253
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
Anonymous20231206		54.13 287	55.82 285	49.04 312	70.89 249	35.96 313	51.73 324	50.87 322	34.86 321	62.49 287	79.22 236	42.52 274	44.29 338	27.95 340	81.88 232	66.88 309

thres100view900		61.17 251	61.09 248	61.39 268	72.14 242	35.01 320	65.42 254	56.99 300	55.23 173	70.71 236	79.90 226	32.07 320	72.09 245	35.61 313	81.73 233	77.08 235

tfpn200view9		60.35 258	59.97 256	61.51 266	70.78 251	35.35 318	63.27 277	57.47 294	53.00 202	68.31 257	77.09 258	32.45 317	72.09 245	35.61 313	81.73 233	77.08 235

thres400		60.77 255	59.97 256	63.15 252	70.78 251	35.35 318	63.27 277	57.47 294	53.00 202	68.31 257	77.09 258	32.45 317	72.09 245	35.61 313	81.73 233	82.02 170

MG-MVS		70.47 159	71.34 156	67.85 210	79.26 147	40.42 283	74.67 142	75.15 197	58.41 139	68.74 256	88.14 113	56.08 208	83.69 78	59.90 154	81.71 236	79.43 212

PAPM_NR		73.91 111	74.16 110	73.16 132	81.90 119	53.50 184	81.28 62	81.40 106	66.17 72	73.30 205	83.31 190	59.96 168	83.10 92	58.45 163	81.66 237	82.87 150

FMVSNet5		55.08 284	55.54 287	53.71 297	65.80 293	33.50 330	56.22 312	52.50 318	43.72 280	61.06 295	83.38 187	25.46 348	54.87 317	30.11 332	81.64 238	72.75 265

PAPR		69.20 176	68.66 185	70.82 165	75.15 198	47.77 223	75.31 129	81.11 113	49.62 239	66.33 267	79.27 235	61.53 154	82.96 94	48.12 243	81.50 239	81.74 175

testdata				64.13 241	85.87 63	63.34 117		61.80 282	47.83 254	76.42 169	86.60 142	48.83 240	62.31 303	54.46 199	81.26 240	66.74 312

mvs-test1		73.81 113	70.69 163	83.18 5	77.05 173	81.39 3	75.39 128	77.70 169	57.68 146	71.19 232	74.72 275	64.80 129	83.66 79	56.46 178	81.19 241	84.50 111

3Dnovator	65.95 11	71.50 149	71.22 157	72.34 153	73.16 231	63.09 119	78.37 94	78.32 159	57.67 148	72.22 217	84.61 173	54.77 210	78.47 169	60.82 146	81.07 242	75.45 244

Regformer-3		72.86 134	72.28 142	74.62 108	74.74 206	60.18 142	72.91 152	71.76 220	64.74 89	78.42 131	72.07 299	67.00 108	76.28 206	67.97 94	80.91 243	87.39 53

Regformer-4		74.64 106	73.67 117	77.55 77	74.74 206	64.49 109	72.91 152	75.42 192	67.45 61	80.24 113	72.07 299	68.98 90	80.19 146	70.29 76	80.91 243	87.98 46

RPSCF		75.76 89	74.37 107	79.93 46	74.81 204	77.53 19	77.53 104	79.30 142	59.44 131	78.88 125	89.80 77	71.26 76	73.09 233	57.45 168	80.89 245	89.17 30

EG-PatchMatch MVS		70.70 156	70.88 160	70.16 176	82.64 109	58.80 155	71.48 172	73.64 205	54.98 175	76.55 163	81.77 205	61.10 161	78.94 159	54.87 193	80.84 246	72.74 266

V42		71.06 152	70.83 161	71.72 157	67.25 282	47.14 232	65.94 245	80.35 130	51.35 220	83.40 76	83.23 192	59.25 176	78.80 161	65.91 109	80.81 247	89.23 28

test222					87.30 40	69.15 76	67.85 220	59.59 288	41.06 293	73.05 208	85.72 164	48.03 246			80.65 248	66.92 308

BH-untuned		69.39 173	69.46 170	69.18 189	77.96 165	56.88 164	68.47 215	77.53 172	56.77 158	77.79 139	79.63 230	60.30 167	80.20 145	46.04 256	80.65 248	70.47 285

pmmvs-eth3d		64.41 226	63.27 232	67.82 212	75.81 192	60.18 142	69.49 196	62.05 280	38.81 305	74.13 194	82.23 200	43.76 265	68.65 271	42.53 271	80.63 250	74.63 251

EI-MVSNet-Vis-set		72.78 135	71.87 146	75.54 100	74.77 205	59.02 153	72.24 159	71.56 224	63.92 96	78.59 127	71.59 305	66.22 117	78.60 165	67.58 97	80.32 251	89.00 34

diffmvs		67.42 201	67.50 200	67.20 218	62.26 314	45.21 246	64.87 259	77.04 178	48.21 249	71.74 220	79.70 229	58.40 183	71.17 255	64.99 114	80.27 252	85.22 83

EI-MVSNet-UG-set		72.63 138	71.68 150	75.47 101	74.67 209	58.64 158	72.02 162	71.50 225	63.53 101	78.58 129	71.39 308	65.98 118	78.53 167	67.30 103	80.18 253	89.23 28

MDA-MVSNet-bldmvs		62.34 244	61.73 241	64.16 240	61.64 317	49.90 202	48.11 332	57.24 299	53.31 200	80.95 102	79.39 233	49.00 239	61.55 305	45.92 257	80.05 254	81.03 183

IB-MVS	49.67 18	59.69 262	56.96 277	67.90 209	68.19 273	50.30 199	61.42 288	65.18 263	47.57 257	55.83 318	67.15 335	23.77 353	79.60 152	43.56 268	79.97 255	73.79 258
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
Patchmatch-RL test		59.95 260	59.12 261	62.44 260	72.46 240	54.61 178	59.63 299	47.51 332	41.05 294	74.58 189	74.30 281	31.06 328	65.31 292	51.61 214	79.85 256	67.39 305

EI-MVSNet		69.61 169	69.01 178	71.41 162	73.94 222	49.90 202	71.31 177	71.32 228	58.22 140	75.40 178	70.44 311	58.16 185	75.85 207	62.51 131	79.81 257	88.48 43

MVSTER		63.29 234	61.60 245	68.36 203	59.77 330	46.21 240	60.62 293	71.32 228	41.83 290	75.40 178	79.12 239	30.25 334	75.85 207	56.30 180	79.81 257	83.03 146

ab-mvs		64.11 229	65.13 217	61.05 271	71.99 243	38.03 301	67.59 222	68.79 247	49.08 244	65.32 272	86.26 150	58.02 192	66.85 285	39.33 288	79.79 259	78.27 223

PVSNet_Blended_VisFu		70.04 162	68.88 179	73.53 126	82.71 107	63.62 115	74.81 135	81.95 98	48.53 248	67.16 264	79.18 238	51.42 228	78.38 174	54.39 200	79.72 260	78.60 219

thres200		57.55 274	57.02 276	59.17 282	67.89 278	34.93 321	58.91 304	57.25 298	50.24 230	64.01 278	71.46 307	32.49 316	71.39 253	31.31 327	79.57 261	71.19 282

testgi		54.00 290	56.86 278	45.45 321	58.20 336	25.81 349	49.05 328	49.50 326	45.43 267	67.84 259	81.17 212	51.81 226	43.20 342	29.30 336	79.41 262	67.34 307

jason		64.47 224	62.84 236	69.34 188	76.91 178	59.20 148	67.15 231	65.67 258	35.29 320	65.16 273	76.74 261	44.67 259	70.68 257	54.74 194	79.28 263	78.14 225
jason: jason.
Fast-Effi-MVS+-dtu		70.00 163	68.74 183	73.77 121	73.47 226	64.53 108	71.36 175	78.14 163	55.81 168	68.84 255	74.71 276	65.36 125	75.75 210	52.00 212	79.00 264	81.03 183

EU-MVSNet		60.82 253	60.80 251	60.86 274	68.37 270	41.16 273	72.27 158	68.27 250	26.96 348	69.08 248	75.71 265	32.09 319	67.44 279	55.59 188	78.90 265	73.97 255

MVS_Test		69.84 166	70.71 162	67.24 217	67.49 281	43.25 261	69.87 193	81.22 112	52.69 206	71.57 226	86.68 136	62.09 149	74.51 225	66.05 107	78.74 266	83.96 122

Fast-Effi-MVS+		68.81 181	68.30 187	70.35 172	74.66 211	48.61 212	66.06 244	78.32 159	50.62 228	71.48 229	75.54 267	68.75 92	79.59 153	50.55 224	78.73 267	82.86 151

MVSFormer		69.93 165	69.03 177	72.63 149	74.93 199	59.19 149	83.98 37	75.72 188	52.27 208	63.53 284	76.74 261	43.19 268	80.56 134	72.28 65	78.67 268	78.14 225

lupinMVS		63.36 232	61.49 246	68.97 194	74.93 199	59.19 149	65.80 249	64.52 266	34.68 325	63.53 284	74.25 282	43.19 268	70.62 258	53.88 205	78.67 268	77.10 234

Effi-MVS+		72.10 143	72.28 142	71.58 158	74.21 220	50.33 198	74.72 140	82.73 88	62.62 109	70.77 235	76.83 260	69.96 84	80.97 127	60.20 148	78.43 270	83.45 135

CANet_DTU		64.04 230	63.83 226	64.66 237	68.39 269	42.97 263	73.45 149	74.50 202	52.05 212	54.78 321	75.44 270	43.99 263	70.42 262	53.49 208	78.41 271	80.59 197

xiu_mvs_v1_base_debu		67.87 193	67.07 205	70.26 173	79.13 152	61.90 125	67.34 227	71.25 231	47.98 251	67.70 260	74.19 284	61.31 156	72.62 238	56.51 175	78.26 272	76.27 239

xiu_mvs_v1_base		67.87 193	67.07 205	70.26 173	79.13 152	61.90 125	67.34 227	71.25 231	47.98 251	67.70 260	74.19 284	61.31 156	72.62 238	56.51 175	78.26 272	76.27 239

xiu_mvs_v1_base_debi		67.87 193	67.07 205	70.26 173	79.13 152	61.90 125	67.34 227	71.25 231	47.98 251	67.70 260	74.19 284	61.31 156	72.62 238	56.51 175	78.26 272	76.27 239

BH-RMVSNet		68.69 184	68.20 192	70.14 177	76.40 183	53.90 183	64.62 262	73.48 206	58.01 142	73.91 198	81.78 204	59.09 177	78.22 179	48.59 238	77.96 275	78.31 222

IterMVS-SCA-FT		67.68 197	66.07 212	72.49 150	73.34 229	58.20 160	63.80 271	65.55 261	48.10 250	76.91 153	82.64 196	45.20 255	78.84 160	61.20 141	77.89 276	80.44 200

PVSNet_Blended		62.90 239	61.64 243	66.69 225	69.81 261	49.36 206	61.23 290	78.96 146	42.04 289	59.98 301	68.86 326	51.82 224	78.20 180	44.30 262	77.77 277	72.52 267

MSDG		67.47 200	67.48 201	67.46 215	70.70 253	54.69 177	66.90 236	78.17 162	60.88 121	70.41 238	74.76 273	61.22 160	73.18 232	47.38 248	76.87 278	74.49 252

E-PMN		45.17 313	45.36 316	44.60 325	50.07 354	42.75 264	38.66 346	42.29 343	46.39 262	39.55 352	51.15 350	26.00 345	45.37 333	37.68 300	76.41 279	45.69 347

PM-MVS		64.49 223	63.61 229	67.14 220	76.68 181	75.15 31	68.49 214	42.85 339	51.17 223	77.85 137	80.51 217	45.76 250	66.31 290	52.83 210	76.35 280	59.96 333

EIA-MVS		68.59 185	67.16 204	72.90 140	75.18 197	55.64 172	69.39 198	81.29 108	52.44 207	64.53 275	70.69 310	60.33 166	82.30 105	54.27 202	76.31 281	80.75 193

BH-w/o		64.81 220	64.29 224	66.36 227	76.08 189	54.71 176	65.61 252	75.23 196	50.10 233	71.05 234	71.86 304	54.33 214	79.02 157	38.20 297	76.14 282	65.36 317

MVS		60.62 256	59.97 256	62.58 259	68.13 274	47.28 230	68.59 210	73.96 204	32.19 334	59.94 303	68.86 326	50.48 231	77.64 190	41.85 276	75.74 283	62.83 325

TR-MVS		64.59 221	63.54 230	67.73 214	75.75 193	50.83 196	63.39 275	70.29 241	49.33 240	71.55 227	74.55 277	50.94 230	78.46 170	40.43 284	75.69 284	73.89 257

mvs_anonymous		65.08 218	65.49 213	63.83 245	63.79 307	37.60 304	66.52 241	69.82 243	43.44 282	73.46 202	86.08 158	58.79 181	71.75 252	51.90 213	75.63 285	82.15 169

QAPM		69.18 177	69.26 173	68.94 195	71.61 245	52.58 189	80.37 70	78.79 151	49.63 238	73.51 200	85.14 170	53.66 216	79.12 156	55.11 191	75.54 286	75.11 248

IterMVS		63.12 236	62.48 239	65.02 236	66.34 289	52.86 187	63.81 270	62.25 275	46.57 261	71.51 228	80.40 219	44.60 260	66.82 286	51.38 217	75.47 287	75.38 246
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
HyFIR lowres test		63.01 237	60.47 253	70.61 167	83.04 101	54.10 180	59.93 298	72.24 219	33.67 330	69.00 249	75.63 266	38.69 293	76.93 197	36.60 308	75.45 288	80.81 192

EMVS		44.61 317	44.45 321	45.10 324	48.91 356	43.00 262	37.92 347	41.10 349	46.75 260	38.00 354	48.43 352	26.42 343	46.27 328	37.11 306	75.38 289	46.03 346

MIMVSNet		54.39 286	56.12 284	49.20 309	72.57 239	30.91 337	59.98 297	48.43 330	41.66 291	55.94 317	83.86 183	41.19 280	50.42 322	26.05 342	75.38 289	66.27 313

our_test_3		56.46 276	56.51 280	56.30 292	67.70 279	39.66 287	55.36 316	52.34 319	40.57 298	63.85 279	69.91 317	40.04 287	58.22 313	43.49 269	75.29 291	71.03 284

pmmvs4		60.78 254	59.04 262	66.00 230	73.06 237	57.67 161	64.53 264	60.22 285	36.91 314	65.96 268	77.27 257	39.66 289	68.54 272	38.87 291	74.89 292	71.80 275

GA-MVS		62.91 238	61.66 242	66.66 226	67.09 284	44.49 250	61.18 291	69.36 246	51.33 221	69.33 247	74.47 278	36.83 301	74.94 220	50.60 223	74.72 293	80.57 198

pmmvs5		52.49 296	52.58 298	52.21 302	54.99 349	32.38 333	55.45 315	53.84 311	32.15 336	55.49 320	74.81 272	38.08 296	57.37 315	34.02 319	74.40 294	66.88 309

PatchT		53.35 291	56.47 281	43.99 327	64.19 305	17.46 356	59.15 300	43.10 338	52.11 211	54.74 322	86.95 124	29.97 337	49.98 323	43.62 267	74.40 294	64.53 323

xiu_mvs_v2_base		64.43 225	63.96 225	65.85 232	77.72 169	51.32 194	63.63 273	72.31 218	45.06 272	61.70 289	69.66 318	62.56 141	73.93 230	49.06 234	73.91 296	72.31 270

PS-MVSNAJ		64.27 228	63.73 228	65.90 231	77.82 167	51.42 193	63.33 276	72.33 217	45.09 271	61.60 290	68.04 329	62.39 145	73.95 229	49.07 233	73.87 297	72.34 269

OpenMVS_ROB	54.93 17	63.23 235	63.28 231	63.07 254	69.81 261	45.34 245	68.52 213	67.14 252	43.74 279	70.61 237	79.22 236	47.90 247	72.66 237	48.75 236	73.84 298	71.21 281

MDTV_nov1_ep13			54.05 292		65.54 295	29.30 341	59.00 302	55.22 305	35.96 318	52.44 329	75.98 264	30.77 331	59.62 309	38.21 296	73.33 299

PAPM		61.79 248	60.37 254	66.05 229	76.09 188	41.87 270	69.30 199	76.79 181	40.64 297	53.80 327	79.62 231	44.38 261	82.92 95	29.64 335	73.11 300	73.36 260

Patchmatch-test		47.93 307	49.96 308	41.84 330	57.42 339	24.26 351	48.75 329	41.49 346	39.30 301	56.79 313	73.48 288	30.48 333	33.87 351	29.29 337	72.61 301	67.39 305

gg-mvs-nofinetune		55.75 279	56.75 279	52.72 300	62.87 311	28.04 344	68.92 203	41.36 347	71.09 42	50.80 334	92.63 13	20.74 356	66.86 284	29.97 333	72.41 302	63.25 324

test-LLR		50.43 301	50.69 306	49.64 307	60.76 322	41.87 270	53.18 321	45.48 335	43.41 283	49.41 339	60.47 343	29.22 339	44.73 336	42.09 274	72.14 303	62.33 329

test-mter		48.56 306	48.20 311	49.64 307	60.76 322	41.87 270	53.18 321	45.48 335	31.91 339	49.41 339	60.47 343	18.34 359	44.73 336	42.09 274	72.14 303	62.33 329

1112_ss		59.48 263	58.99 263	60.96 273	77.84 166	42.39 268	61.42 288	68.45 249	37.96 309	59.93 304	67.46 331	45.11 257	65.07 294	40.89 282	71.81 305	75.41 245

N_pmnet		52.06 298	51.11 304	54.92 295	59.64 331	71.03 55	37.42 348	61.62 283	33.68 329	57.12 310	72.10 296	37.94 297	31.03 352	29.13 339	71.35 306	62.70 326

XXY-MVS		55.19 283	57.40 275	48.56 313	64.45 304	34.84 323	51.54 325	53.59 312	38.99 304	63.79 281	79.43 232	56.59 204	45.57 330	36.92 307	71.29 307	65.25 318

MDA-MVSNet_test_wron		52.57 295	53.49 294	49.81 306	54.24 351	36.47 309	40.48 344	46.58 333	38.13 307	75.47 177	73.32 290	41.05 283	43.85 340	40.98 281	71.20 308	69.10 300

YYNet1		52.58 294	53.50 293	49.85 305	54.15 352	36.45 310	40.53 343	46.55 334	38.09 308	75.52 176	73.31 291	41.08 282	43.88 339	41.10 280	71.14 309	69.21 298

HY-MVS	49.31 19	57.96 272	57.59 273	59.10 283	66.85 286	36.17 311	65.13 257	65.39 262	39.24 302	54.69 323	78.14 249	44.28 262	67.18 282	33.75 321	70.79 310	73.95 256

Test_1112_low_res		58.78 268	58.69 265	59.04 284	79.41 144	38.13 299	57.62 308	66.98 254	34.74 323	59.62 305	77.56 255	42.92 270	63.65 299	38.66 293	70.73 311	75.35 247

pmmvs3		46.71 310	45.09 317	51.55 303	56.76 341	48.25 215	55.78 314	39.53 351	24.13 352	50.35 337	63.40 339	15.90 362	51.08 321	29.29 337	70.69 312	55.33 339

SCA		58.57 270	58.04 270	60.17 278	70.17 259	41.07 275	65.19 256	53.38 314	43.34 285	61.00 297	73.48 288	45.20 255	69.38 267	40.34 285	70.31 313	70.05 289

CR-MVSNet		58.96 266	58.49 267	60.36 277	66.37 287	48.24 216	70.93 183	56.40 303	32.87 333	61.35 292	86.66 137	33.19 311	63.22 300	48.50 240	70.17 314	69.62 294

RPMNet		65.77 212	65.08 221	67.84 211	66.37 287	48.24 216	70.93 183	86.27 20	54.66 182	61.35 292	86.77 132	33.29 310	85.67 43	55.93 183	70.17 314	69.62 294

test0.0.03 1		47.72 308	48.31 310	45.93 319	55.53 347	29.39 340	46.40 338	41.21 348	43.41 283	55.81 319	67.65 330	29.22 339	43.77 341	25.73 345	69.87 316	64.62 321

PVSNet	43.83 21	51.56 299	51.17 302	52.73 299	68.34 271	38.27 296	48.22 331	53.56 313	36.41 315	54.29 324	64.94 338	34.60 307	54.20 320	30.34 330	69.87 316	65.71 316

tpm2		56.12 277	54.64 290	60.55 276	66.24 290	36.01 312	68.14 217	56.77 302	33.60 331	58.25 309	75.52 269	30.25 334	74.33 227	33.27 322	69.76 318	71.32 278

CostFormer		57.35 275	56.14 283	60.97 272	63.76 308	38.43 294	67.50 224	60.22 285	37.14 313	59.12 306	76.34 263	32.78 314	71.99 248	39.12 290	69.27 319	72.47 268

baseline1		57.82 273	58.36 269	56.19 293	69.17 267	30.76 338	62.94 281	55.21 306	46.04 263	63.83 280	78.47 245	41.20 279	63.68 298	39.44 287	68.99 320	74.13 254

PatchMatch-RL		58.68 269	57.72 272	61.57 265	76.21 186	73.59 42	61.83 285	49.00 328	47.30 259	61.08 294	68.97 323	50.16 233	59.01 311	36.06 312	68.84 321	52.10 340

CVMVSNet		59.21 265	58.44 268	61.51 266	73.94 222	47.76 224	71.31 177	64.56 265	26.91 349	60.34 300	70.44 311	36.24 303	67.65 277	53.57 207	68.66 322	69.12 299

TESTMET0.1,1		45.17 313	44.93 318	45.89 320	56.02 344	38.31 295	53.18 321	41.94 345	27.85 346	44.86 347	56.47 346	17.93 360	41.50 347	38.08 298	68.06 323	57.85 335

PMMVS2		37.74 322	40.87 323	28.36 337	42.41 358	5.35 360	24.61 351	27.75 356	32.15 336	47.85 341	70.27 314	35.85 304	29.51 353	19.08 353	67.85 324	50.22 342

1314		59.83 261	58.86 264	62.74 258	65.71 294	44.78 248	68.59 210	72.63 214	33.54 332	61.05 296	67.29 334	43.62 266	71.26 254	49.49 231	67.84 325	72.19 272

CHOSEN 1792x2688		58.09 271	56.30 282	63.45 249	79.95 138	50.93 195	54.07 319	65.59 260	28.56 345	61.53 291	74.33 280	41.09 281	66.52 289	33.91 320	67.69 326	72.92 263

tpm		50.60 300	52.42 299	45.14 323	65.18 298	26.29 347	60.30 295	43.50 337	37.41 311	57.01 311	79.09 240	30.20 336	42.32 343	32.77 324	66.36 327	66.81 311

FPMVS		59.43 264	60.07 255	57.51 290	77.62 172	71.52 50	62.33 283	50.92 321	57.40 152	69.40 246	80.00 225	39.14 291	61.92 304	37.47 303	66.36 327	39.09 350

GG-mvs-BLEND				52.24 301	60.64 324	29.21 342	69.73 195	42.41 340		45.47 344	52.33 349	20.43 357	68.16 274	25.52 346	65.42 329	59.36 334

tpmvs		55.84 278	55.45 288	57.01 291	60.33 325	33.20 331	65.89 246	59.29 289	47.52 258	56.04 316	73.60 287	31.05 329	68.06 275	40.64 283	64.64 330	69.77 292

WTY-MVS		49.39 304	50.31 307	46.62 317	61.22 320	32.00 335	46.61 337	49.77 325	33.87 328	54.12 325	69.55 320	41.96 275	45.40 332	31.28 328	64.42 331	62.47 328

baseline2		55.57 282	52.74 296	64.05 243	65.26 296	44.11 252	62.38 282	54.43 309	39.03 303	51.21 332	67.35 333	33.66 309	72.45 242	37.14 305	64.22 332	75.60 242

DWT-MVSNet_test		53.04 292	51.12 303	58.77 285	61.23 319	38.67 293	62.16 284	57.74 292	38.24 306	51.76 331	59.07 345	21.36 355	67.40 280	44.80 261	63.76 333	70.25 288

MS-PatchMatch		55.59 281	54.89 289	57.68 289	69.18 266	49.05 208	61.00 292	62.93 273	35.98 317	58.36 308	68.93 324	36.71 302	66.59 288	37.62 302	63.30 334	57.39 336

sss		47.59 309	48.32 309	45.40 322	56.73 342	33.96 327	45.17 340	48.51 329	32.11 338	52.37 330	65.79 336	40.39 286	41.91 346	31.85 325	61.97 335	60.35 332

PMMVS		44.69 315	43.95 322	46.92 315	50.05 355	53.47 185	48.08 333	42.40 341	22.36 353	44.01 350	53.05 348	42.60 273	45.49 331	31.69 326	61.36 336	41.79 348

UnsupCasMVSNet_bld		50.01 303	51.03 305	46.95 314	58.61 334	32.64 332	48.31 330	53.27 315	34.27 326	60.47 299	71.53 306	41.40 277	47.07 327	30.68 329	60.78 337	61.13 331

MVP-Stereo		61.56 249	59.22 260	68.58 202	79.28 146	60.44 140	69.20 201	71.57 223	43.58 281	56.42 315	78.37 247	39.57 290	76.46 205	34.86 316	60.16 338	68.86 301
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
DSMNet-mixed		43.18 319	44.66 320	38.75 335	54.75 350	28.88 343	57.06 310	27.42 357	13.47 354	47.27 343	77.67 254	38.83 292	39.29 349	25.32 347	60.12 339	48.08 343

UnsupCasMVSNet_eth		52.26 297	53.29 295	49.16 310	55.08 348	33.67 329	50.03 327	58.79 290	37.67 310	63.43 286	74.75 274	41.82 276	45.83 329	38.59 295	59.42 340	67.98 304

tpm cat1		54.02 289	52.63 297	58.19 288	64.85 303	39.86 286	66.26 243	57.28 297	32.16 335	56.90 312	70.39 313	32.75 315	65.30 293	34.29 318	58.79 341	69.41 296

CHOSEN 280x420		41.62 320	39.89 325	46.80 316	61.81 315	51.59 191	33.56 350	35.74 353	27.48 347	37.64 355	53.53 347	23.24 354	42.09 344	27.39 341	58.64 342	46.72 345

tpmrst		50.15 302	51.38 301	46.45 318	56.05 343	24.77 350	64.40 266	49.98 324	36.14 316	53.32 328	69.59 319	35.16 305	48.69 324	39.24 289	58.51 343	65.89 314

PatchmatchNet		54.60 285	54.27 291	55.59 294	65.17 299	39.08 289	66.92 235	51.80 320	39.89 299	58.39 307	73.12 292	31.69 322	58.33 312	43.01 270	58.38 344	69.38 297
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
MVS-HIRNet		45.53 312	47.29 312	40.24 333	62.29 313	26.82 346	56.02 313	37.41 352	29.74 344	43.69 351	81.27 210	33.96 308	55.48 316	24.46 348	56.79 345	38.43 351

ADS-MVSNet2		48.76 305	47.25 313	53.29 298	55.90 345	40.54 282	47.34 335	54.99 308	31.41 341	50.48 335	72.06 301	31.23 325	54.26 319	25.93 343	55.93 346	65.07 319

ADS-MVSNet		44.62 316	45.58 315	41.73 331	55.90 345	20.83 354	47.34 335	39.94 350	31.41 341	50.48 335	72.06 301	31.23 325	39.31 348	25.93 343	55.93 346	65.07 319

EPMVS		45.74 311	46.53 314	43.39 328	54.14 353	22.33 353	55.02 317	35.00 354	34.69 324	51.09 333	70.20 315	25.92 346	42.04 345	37.19 304	55.50 348	65.78 315

JIA-IIPM		54.03 288	51.62 300	61.25 270	59.14 332	55.21 174	59.10 301	47.72 331	50.85 225	50.31 338	85.81 163	20.10 358	63.97 296	36.16 311	55.41 349	64.55 322

new_pmnet		37.55 323	39.80 326	30.79 336	56.83 340	16.46 357	39.35 345	30.65 355	25.59 350	45.26 345	61.60 342	24.54 350	28.02 354	21.60 350	52.80 350	47.90 344

dp		44.09 318	44.88 319	41.72 332	58.53 335	23.18 352	54.70 318	42.38 342	34.80 322	44.25 349	65.61 337	24.48 351	44.80 335	29.77 334	49.42 351	57.18 337

MVE	27.91 23	36.69 324	35.64 327	39.84 334	43.37 357	35.85 315	19.49 352	24.61 358	24.68 351	39.05 353	62.63 341	38.67 294	27.10 355	21.04 351	47.25 352	56.56 338
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
PVSNet_0	36.71 22	41.12 321	40.78 324	42.14 329	59.97 327	40.13 284	40.97 342	42.24 344	30.81 343	44.86 347	49.41 351	40.70 284	45.12 334	23.15 349	34.96 353	41.16 349

tmp_tt		11.98 326	14.73 329	3.72 339	2.28 360	4.62 361	19.44 353	14.50 360	0.47 356	21.55 356	9.58 355	25.78 347	4.57 357	11.61 354	27.37 354	1.96 353

DeepMVS_CX				11.83 338	15.51 359	13.86 358		11.25 361	5.76 355	20.85 357	26.46 353	17.06 361	9.22 356	9.69 355	13.82 355	12.42 352

test123		4.43 329	5.78 332	0.39 341	0.97 361	0.28 362	46.33 339	0.45 362	0.31 357	0.62 358	1.50 358	0.61 364	0.11 359	0.56 356	0.63 356	0.77 355

testmvs		4.06 330	5.28 333	0.41 340	0.64 362	0.16 363	42.54 341	0.31 363	0.26 358	0.50 359	1.40 359	0.77 363	0.17 358	0.56 356	0.55 357	0.90 354

uanet_test		0.00 331	0.00 334	0.00 342	0.00 363	0.00 364	0.00 354	0.00 364	0.00 359	0.00 360	0.00 360	0.00 365	0.00 360	0.00 358	0.00 358	0.00 356

cdsmvs_eth3d_5k		17.71 325	23.62 328	0.00 342	0.00 363	0.00 364	0.00 354	70.17 242	0.00 359	0.00 360	74.25 282	68.16 99	0.00 360	0.00 358	0.00 358	0.00 356

pcd_1.5k_mvsjas		5.20 328	6.93 331	0.00 342	0.00 363	0.00 364	0.00 354	0.00 364	0.00 359	0.00 360	0.00 360	62.39 145	0.00 360	0.00 358	0.00 358	0.00 356

sosnet-low-res		0.00 331	0.00 334	0.00 342	0.00 363	0.00 364	0.00 354	0.00 364	0.00 359	0.00 360	0.00 360	0.00 365	0.00 360	0.00 358	0.00 358	0.00 356

sosnet		0.00 331	0.00 334	0.00 342	0.00 363	0.00 364	0.00 354	0.00 364	0.00 359	0.00 360	0.00 360	0.00 365	0.00 360	0.00 358	0.00 358	0.00 356

uncertanet		0.00 331	0.00 334	0.00 342	0.00 363	0.00 364	0.00 354	0.00 364	0.00 359	0.00 360	0.00 360	0.00 365	0.00 360	0.00 358	0.00 358	0.00 356

Regformer		0.00 331	0.00 334	0.00 342	0.00 363	0.00 364	0.00 354	0.00 364	0.00 359	0.00 360	0.00 360	0.00 365	0.00 360	0.00 358	0.00 358	0.00 356

ab-mvs-re		5.62 327	7.50 330	0.00 342	0.00 363	0.00 364	0.00 354	0.00 364	0.00 359	0.00 360	67.46 331	0.00 365	0.00 360	0.00 358	0.00 358	0.00 356

uanet		0.00 331	0.00 334	0.00 342	0.00 363	0.00 364	0.00 354	0.00 364	0.00 359	0.00 360	0.00 360	0.00 365	0.00 360	0.00 358	0.00 358	0.00 356

test_241102_ONE					86.12 57	61.06 131		84.72 51	72.64 29	87.38 25	89.47 81	77.48 25	85.74 40

save fliter					87.00 42	67.23 87	79.24 83	77.94 167	56.65 161

test0726					86.16 55	60.78 136	83.81 40	85.10 42	72.48 32	85.27 51	89.96 74	78.57 19

GSMVS																70.05 289

test_part2					85.90 61	66.44 92				84.61 60

sam_mvs1												31.41 323				70.05 289

sam_mvs												31.21 327

MTGPA								80.63 121

test_post1							66.63 239				2.08 356	30.66 332	59.33 310	40.34 285

test_post											1.99 357	30.91 330	54.76 318

patchmatchnet-post											68.99 322	31.32 324	69.38 267

MTMP							84.83 32	19.26 359

gm-plane-assit					62.51 312	33.91 328			37.25 312		62.71 340		72.74 235	38.70 292

TEST9					85.47 66	69.32 73	76.42 115	78.69 152	53.73 197	76.97 149	86.74 133	66.84 110	81.10 122

test_8					85.09 72	67.89 82	76.26 120	78.66 154	54.00 192	76.89 154	86.72 135	66.60 113	80.89 132

agg_prior					84.44 85	66.02 96		78.62 155		76.95 151			80.34 139

test_prior4						70.14 65	77.57 102

test_prior				75.27 103	82.15 115	59.85 145		84.33 62					83.39 85			82.58 158

旧先验2							71.17 180		45.11 270	78.54 130			61.28 306	59.19 159

新几何2							71.33 176

无先验							74.82 134	70.94 236	47.75 256				76.85 200	54.47 197		72.09 273

原ACMM2							74.78 138

testdata2													67.30 281	48.34 241

segment_acmp												68.30 98

testdata1							68.34 216		57.24 153

plane_prior7					85.18 69	66.21 94

plane_prior6					84.18 89	65.31 101						60.83 163

plane_prior4											89.11 93

plane_prior3						65.67 98			63.82 98	78.23 133

plane_prior2							82.74 52		65.45 77

plane_prior1					84.46 84

n2								0.00 364

nn								0.00 364

door-mid								55.02 307

test11								82.71 89

door								52.91 317

HQP5-MVS						58.80 155

HQP-NCC					82.37 110		77.32 106		59.08 132	71.58 223

ACMP_Plane					82.37 110		77.32 106		59.08 132	71.58 223

BP-MVS														67.38 101

HQP4-MVS										71.59 222			85.31 50			83.74 127

HQP2-MVS												58.09 187

NP-MVS					83.34 99	63.07 120					85.97 160

MDTV_nov1_ep13_2view						18.41 355	53.74 320		31.57 340	44.89 346		29.90 338		32.93 323		71.48 277

Test By Simon												62.56 141

High-resolution multi-view benchmark