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 23	91.50 1	63.30 113	84.80 29	87.77 9	86.18 1	96.26 1	96.06 1	90.32 1	84.49 62	68.08 85	97.05 1	96.93 1

PMVS	70.70 6	81.70 34	83.15 31	77.36 78	90.35 6	82.82 2	82.15 52	79.22 138	74.08 17	87.16 26	91.97 19	84.80 2	76.97 190	64.98 110	93.61 59	72.28 265
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
LPG-MVS_test		83.47 16	84.33 11	80.90 36	87.00 41	70.41 58	82.04 54	86.35 16	69.77 47	87.75 15	91.13 33	81.83 3	86.20 22	77.13 31	95.96 5	86.08 67

LGP-MVS_train				80.90 36	87.00 41	70.41 58		86.35 16	69.77 47	87.75 15	91.13 33	81.83 3	86.20 22	77.13 31	95.96 5	86.08 67

SR-MVS		84.51 5	85.27 4	82.25 19	88.52 34	77.71 13	86.81 13	85.25 34	77.42 11	86.15 33	90.24 64	81.69 5	85.94 30	77.77 26	93.58 61	83.09 138

ACMP	69.50 8	82.64 26	83.38 26	80.40 41	86.50 48	69.44 66	82.30 51	86.08 21	66.80 63	86.70 28	89.99 69	81.64 6	85.95 29	74.35 46	96.11 3	85.81 73
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
abl_6		84.92 3	85.70 3	82.57 15	86.72 46	79.27 7	87.56 5	86.08 21	77.48 9	88.12 13	91.53 27	81.18 7	84.31 67	78.12 24	94.47 34	84.15 116

ACMM	69.25 9	82.11 31	83.31 27	78.49 64	88.17 38	73.96 34	83.11 46	84.52 55	66.40 67	87.45 20	89.16 87	81.02 8	80.52 132	74.27 47	95.73 7	80.98 181
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ACMH+	66.64 10	81.20 37	82.48 39	77.35 79	81.16 123	62.39 117	80.51 62	87.80 7	73.02 22	87.57 18	91.08 35	80.28 9	82.44 96	64.82 111	96.10 4	87.21 55

APD-MVS_3200maxsize		83.57 13	84.33 11	81.31 31	82.83 100	73.53 40	85.50 24	87.45 12	74.11 16	86.45 30	90.52 50	80.02 10	84.48 63	77.73 27	94.34 44	85.93 71

COLMAP_ROB	72.78 3	83.75 11	84.11 14	82.68 12	82.97 98	74.39 32	87.18 7	88.18 6	78.98 5	86.11 35	91.47 29	79.70 11	85.76 38	66.91 100	95.46 11	87.89 46
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
TDRefinement		86.32 2	86.33 2	86.29 1	88.64 32	81.19 5	88.84 2	90.72 1	78.27 7	87.95 14	92.53 13	79.37 12	84.79 59	74.51 45	96.15 2	92.88 7

HPM-MVS		84.12 8	84.63 8	82.60 13	88.21 37	74.40 31	85.24 25	87.21 13	70.69 42	85.14 48	90.42 54	78.99 13	86.62 13	80.83 6	94.93 23	86.79 61
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
ACMMP		84.22 6	84.84 7	82.35 18	89.23 23	76.66 22	87.65 4	85.89 24	71.03 39	85.85 37	90.58 46	78.77 14	85.78 37	79.37 16	95.17 16	84.62 98
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
MSP-MVS		81.15 39	83.12 32	75.24 101	86.16 52	60.78 131	83.77 37	80.58 119	72.48 28	85.83 38	90.41 55	78.57 15	85.69 40	75.86 36	94.39 38	79.24 207

test0726					86.16 52	60.78 131	83.81 36	85.10 37	72.48 28	85.27 47	89.96 70	78.57 15

HPM-MVS_fast		84.59 4	85.10 5	83.06 4	88.60 33	75.83 23	86.27 21	86.89 15	73.69 19	86.17 32	91.70 24	78.23 17	85.20 52	79.45 13	94.91 24	88.15 44

APDe-MVS		82.88 24	84.14 13	79.08 55	84.80 74	66.72 85	86.54 17	85.11 36	72.00 35	86.65 29	91.75 23	78.20 18	87.04 9	77.93 25	94.32 45	83.47 128

UniMVSNet_ETH3D		76.74 80	79.02 64	69.92 178	89.27 20	43.81 247	74.47 139	71.70 215	72.33 31	85.50 44	93.65 3	77.98 19	76.88 193	54.60 190	91.64 86	89.08 30

test_241102_TWO								84.80 43	72.61 26	84.93 50	89.70 74	77.73 20	85.89 35	75.29 39	94.22 50	83.25 135

SED-MVS		81.78 33	83.48 24	76.67 81	86.12 54	61.06 126	83.62 39	84.72 47	72.61 26	87.38 22	89.70 74	77.48 21	85.89 35	75.29 39	94.39 38	83.08 139

test_241102_ONE					86.12 54	61.06 126		84.72 47	72.64 25	87.38 22	89.47 77	77.48 21	85.74 39

CP-MVS		84.12 8	84.55 9	82.80 10	89.42 18	79.74 6	88.19 3	84.43 56	71.96 36	84.70 55	90.56 47	77.12 23	86.18 24	79.24 18	95.36 12	82.49 156

HFP-MVS		83.39 17	84.03 15	81.48 25	89.25 21	75.69 24	87.01 11	84.27 60	70.23 43	84.47 58	90.43 52	76.79 24	85.94 30	79.58 11	94.23 48	82.82 147

#test#		82.40 28	82.71 37	81.48 25	89.25 21	75.69 24	84.47 31	84.27 60	64.45 87	84.47 58	90.43 52	76.79 24	85.94 30	76.01 33	94.23 48	82.82 147

mPP-MVS		84.01 10	84.39 10	82.88 6	90.65 4	81.38 4	87.08 9	82.79 83	72.41 30	85.11 49	90.85 40	76.65 26	84.89 56	79.30 17	94.63 31	82.35 159

DPE-MVS		82.00 32	83.02 33	78.95 58	85.36 65	67.25 81	82.91 47	84.98 39	73.52 20	85.43 45	90.03 68	76.37 27	86.97 11	74.56 44	94.02 53	82.62 152

MP-MVS-pluss		82.54 27	83.46 25	79.76 45	88.88 31	68.44 74	81.57 57	86.33 18	63.17 102	85.38 46	91.26 32	76.33 28	84.67 61	83.30 1	94.96 22	86.17 66
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
OPM-MVS		80.99 43	81.63 47	79.07 56	86.86 45	69.39 67	79.41 78	84.00 70	65.64 71	85.54 43	89.28 80	76.32 29	83.47 78	74.03 48	93.57 62	84.35 111

ACMH	63.62 14	77.50 76	80.11 56	69.68 179	79.61 135	56.28 161	78.81 84	83.62 73	63.41 100	87.14 27	90.23 65	76.11 30	73.32 225	67.58 92	94.44 36	79.44 205
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
mvs_tets		78.93 62	78.67 69	79.72 47	84.81 73	73.93 35	80.65 61	76.50 177	51.98 208	87.40 21	91.86 21	76.09 31	78.53 161	68.58 80	90.20 117	86.69 63

APD-MVS		81.13 40	81.73 44	79.36 53	84.47 79	70.53 57	83.85 35	83.70 72	69.43 49	83.67 69	88.96 93	75.89 32	86.41 16	72.62 57	92.95 68	81.14 176
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
PGM-MVS		83.07 21	83.25 30	82.54 16	89.57 14	77.21 20	82.04 54	85.40 31	67.96 56	84.91 53	90.88 38	75.59 33	86.57 14	78.16 23	94.71 29	83.82 120

test_0728_THIRD									74.03 18	85.83 38	90.41 55	75.58 34	85.69 40	77.43 30	94.74 28	84.31 112

SteuartSystems-ACMMP		83.07 21	83.64 21	81.35 29	85.14 68	71.00 52	85.53 23	84.78 44	70.91 40	85.64 40	90.41 55	75.55 35	87.69 3	79.75 8	95.08 19	85.36 81
Skip Steuart: Steuart Systems R&D Blog.
ACMMP_NAP		82.33 29	83.28 28	79.46 51	89.28 19	69.09 72	83.62 39	84.98 39	64.77 84	83.97 66	91.02 36	75.53 36	85.93 33	82.00 2	94.36 42	83.35 133

region2R		83.54 14	83.86 18	82.58 14	89.82 10	77.53 16	87.06 10	84.23 64	70.19 45	83.86 67	90.72 44	75.20 37	86.27 21	79.41 15	94.25 47	83.95 119

ACMMPR		83.62 12	83.93 16	82.69 11	89.78 11	77.51 18	87.01 11	84.19 65	70.23 43	84.49 57	90.67 45	75.15 38	86.37 18	79.58 11	94.26 46	84.18 115

test_0402		78.17 73	79.48 62	74.24 111	83.50 90	59.15 147	72.52 153	74.60 195	75.34 13	88.69 12	91.81 22	75.06 39	82.37 98	65.10 108	88.68 142	81.20 174

PS-CasMVS		80.41 49	82.86 36	73.07 130	89.93 7	39.21 282	77.15 106	81.28 105	79.74 4	90.87 3	92.73 11	75.03 40	84.93 55	63.83 120	95.19 15	95.07 3

testtj		81.19 38	81.70 45	79.67 49	83.95 87	69.77 63	83.58 42	84.63 52	72.13 32	82.85 78	88.36 101	75.00 41	86.79 12	71.99 65	92.84 69	82.44 157

PEN-MVS		80.46 48	82.91 34	73.11 129	89.83 9	39.02 285	77.06 108	82.61 86	80.04 3	90.60 5	92.85 9	74.93 42	85.21 51	63.15 124	95.15 17	95.09 2

ZNCC-MVS		83.12 20	83.68 20	81.45 27	89.14 25	73.28 42	86.32 20	85.97 23	67.39 58	84.02 65	90.39 58	74.73 43	86.46 15	80.73 7	94.43 37	84.60 101

zzz-MVS		83.01 23	83.63 22	81.13 33	91.16 2	78.16 11	82.72 50	80.63 116	72.08 33	84.93 50	90.79 41	74.65 44	84.42 64	80.98 4	94.75 26	80.82 184

MTAPA		83.19 18	83.87 17	81.13 33	91.16 2	78.16 11	84.87 27	80.63 116	72.08 33	84.93 50	90.79 41	74.65 44	84.42 64	80.98 4	94.75 26	80.82 184

9.14			80.22 55		80.68 125		80.35 67	87.69 10	59.90 123	83.00 74	88.20 104	74.57 46	81.75 108	73.75 50	93.78 55

MP-MVS		83.19 18	83.54 23	82.14 20	90.54 5	79.00 8	86.42 19	83.59 74	71.31 37	81.26 94	90.96 37	74.57 46	84.69 60	78.41 22	94.78 25	82.74 150
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
DTE-MVSNet		80.35 50	82.89 35	72.74 141	89.84 8	37.34 300	77.16 105	81.81 95	80.45 2	90.92 2	92.95 7	74.57 46	86.12 27	63.65 121	94.68 30	94.76 6

XVG-OURS-SEG-HR		79.62 55	79.99 57	78.49 64	86.46 49	74.79 30	77.15 106	85.39 32	66.73 64	80.39 106	88.85 95	74.43 49	78.33 171	74.73 43	85.79 178	82.35 159

SD-MVS		80.28 52	81.55 48	76.47 86	83.57 89	67.83 78	83.39 45	85.35 33	64.42 88	86.14 34	87.07 117	74.02 50	80.97 122	77.70 28	92.32 80	80.62 190
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
XVS		83.51 15	83.73 19	82.85 8	89.43 16	77.61 14	86.80 14	84.66 50	72.71 23	82.87 76	90.39 58	73.86 51	86.31 19	78.84 20	94.03 51	84.64 96

X-MVStestdata		76.81 79	74.79 97	82.85 8	89.43 16	77.61 14	86.80 14	84.66 50	72.71 23	82.87 76	9.95 347	73.86 51	86.31 19	78.84 20	94.03 51	84.64 96

jajsoiax		78.51 67	78.16 73	79.59 50	84.65 76	73.83 37	80.42 64	76.12 179	51.33 216	87.19 25	91.51 28	73.79 53	78.44 165	68.27 83	90.13 122	86.49 64

xxxxxxxxxxxxxcwj		80.31 51	80.94 50	78.42 66	87.00 41	67.23 82	79.24 79	88.61 4	56.65 156	84.29 61	89.18 84	73.73 54	83.22 83	76.01 33	93.77 56	84.81 92

SF-MVS		80.72 45	81.80 42	77.48 76	82.03 111	64.40 105	83.41 44	88.46 5	65.28 78	84.29 61	89.18 84	73.73 54	83.22 83	76.01 33	93.77 56	84.81 92

GST-MVS		82.79 25	83.27 29	81.34 30	88.99 27	73.29 41	85.94 22	85.13 35	68.58 54	84.14 64	90.21 66	73.37 56	86.41 16	79.09 19	93.98 54	84.30 114

wuyk23d		61.97 239	66.25 204	49.12 306	58.19 330	60.77 133	66.32 237	52.97 310	55.93 162	90.62 4	86.91 120	73.07 57	35.98 344	20.63 345	91.63 87	50.62 335

TranMVSNet+NR-MVSNet		76.13 83	77.66 76	71.56 155	84.61 77	42.57 261	70.98 178	78.29 156	68.67 53	83.04 73	89.26 81	72.99 58	80.75 128	55.58 183	95.47 10	91.35 11

pmmvs6		71.82 143	73.66 114	66.31 223	75.94 184	42.01 263	66.99 229	72.53 209	63.45 99	76.43 162	92.78 10	72.95 59	69.69 259	51.41 210	90.46 114	87.22 54

DeepC-MVS	72.44 4	81.00 42	80.83 52	81.50 24	86.70 47	70.03 62	82.06 53	87.00 14	59.89 124	80.91 100	90.53 48	72.19 60	88.56 1	73.67 51	94.52 33	85.92 72
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
canonicalmvs		72.29 139	73.38 119	69.04 187	74.23 210	47.37 223	73.93 143	83.18 77	54.36 182	76.61 156	81.64 202	72.03 61	75.34 209	57.12 165	87.28 161	84.40 109

SMA-MVS		82.12 30	82.68 38	80.43 40	88.90 30	69.52 64	85.12 26	84.76 45	63.53 97	84.23 63	91.47 29	72.02 62	87.16 7	79.74 10	94.36 42	84.61 99

CPTT-MVS		81.51 36	81.76 43	80.76 38	89.20 24	78.75 9	86.48 18	82.03 92	68.80 50	80.92 99	88.52 97	72.00 63	82.39 97	74.80 41	93.04 67	81.14 176

ETH3D cwj APD-0.16		78.38 71	78.72 68	77.38 77	80.09 131	66.16 90	79.08 82	86.13 20	57.55 146	80.93 98	87.76 112	71.98 64	82.73 93	72.11 64	92.83 70	83.25 135

ETH3D-3000-0.1		79.14 60	79.80 59	77.16 80	80.67 126	64.57 102	80.26 68	87.60 11	60.74 118	82.47 81	88.03 109	71.73 65	81.81 106	73.12 53	93.61 59	85.09 86

DP-MVS		78.44 70	79.29 63	75.90 92	81.86 114	65.33 95	79.05 83	84.63 52	74.83 15	80.41 105	86.27 144	71.68 66	83.45 79	62.45 128	92.40 78	78.92 211

nrg030		74.87 101	75.99 89	71.52 156	74.90 196	49.88 199	74.10 142	82.58 87	54.55 180	83.50 71	89.21 83	71.51 67	75.74 205	61.24 134	92.34 79	88.94 35

OMC-MVS		79.41 58	78.79 66	81.28 32	80.62 127	70.71 56	80.91 60	84.76 45	62.54 106	81.77 86	86.65 134	71.46 68	83.53 77	67.95 90	92.44 77	89.60 22

anonymousdsp		78.60 65	77.80 75	81.00 35	78.01 158	74.34 33	80.09 71	76.12 179	50.51 223	89.19 9	90.88 38	71.45 69	77.78 182	73.38 52	90.60 113	90.90 16

LTVRE_ROB	75.46 1	84.22 6	84.98 6	81.94 21	84.82 72	75.40 26	91.60 1	87.80 7	73.52 20	88.90 10	93.06 6	71.39 70	81.53 110	81.53 3	92.15 81	88.91 36
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
RPSCF		75.76 86	74.37 103	79.93 44	74.81 198	77.53 16	77.53 100	79.30 137	59.44 127	78.88 120	89.80 73	71.26 71	73.09 227	57.45 162	80.89 238	89.17 29

MVS_111021_HR		72.98 128	72.97 130	72.99 132	80.82 124	65.47 94	68.81 201	72.77 206	57.67 143	75.76 166	82.38 193	71.01 72	77.17 188	61.38 133	86.15 174	76.32 232

AdaColmap		74.22 106	74.56 99	73.20 127	81.95 112	60.97 128	79.43 76	80.90 114	65.57 72	72.54 207	81.76 200	70.98 73	85.26 48	47.88 238	90.00 123	73.37 253

Regformer-2		75.32 91	74.47 101	77.88 72	74.22 211	66.65 86	72.77 151	77.54 166	68.47 55	80.44 104	72.08 290	70.60 74	80.97 122	70.08 73	84.02 205	86.01 70

AllTest		77.66 74	77.43 77	78.35 67	79.19 144	70.81 53	78.60 87	88.64 2	65.37 76	80.09 109	88.17 105	70.33 75	78.43 166	55.60 180	90.90 107	85.81 73

TestCases				78.35 67	79.19 144	70.81 53		88.64 2	65.37 76	80.09 109	88.17 105	70.33 75	78.43 166	55.60 180	90.90 107	85.81 73

ITE_SJBPF				80.35 42	76.94 171	73.60 38		80.48 120	66.87 61	83.64 70	86.18 147	70.25 77	79.90 144	61.12 137	88.95 140	87.56 51

CDPH-MVS		77.33 77	77.06 82	78.14 70	84.21 84	63.98 108	76.07 118	83.45 75	54.20 183	77.68 137	87.18 113	69.98 78	85.37 45	68.01 87	92.72 75	85.08 88

Effi-MVS+		72.10 140	72.28 138	71.58 154	74.21 214	50.33 192	74.72 136	82.73 84	62.62 105	70.77 228	76.83 253	69.96 79	80.97 122	60.20 142	78.43 263	83.45 130

UA-Net		81.56 35	82.28 40	79.40 52	88.91 29	69.16 70	84.67 30	80.01 130	75.34 13	79.80 111	94.91 2	69.79 80	80.25 137	72.63 56	94.46 35	88.78 40

CLD-MVS		72.88 130	72.36 136	74.43 107	77.03 169	54.30 173	68.77 204	83.43 76	52.12 205	76.79 153	74.44 272	69.54 81	83.91 70	55.88 178	93.25 66	85.09 86
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
Regformer-1		74.28 105	73.63 115	76.21 90	74.22 211	64.12 107	72.77 151	75.46 186	66.86 62	79.27 116	72.08 290	69.29 82	78.74 157	68.73 79	84.02 205	85.77 78

LS3D		80.99 43	80.85 51	81.41 28	78.37 153	71.37 48	87.45 6	85.87 25	77.48 9	81.98 84	89.95 71	69.14 83	85.26 48	66.15 101	91.24 96	87.61 50

XVG-ACMP-BASELINE		80.54 46	81.06 49	78.98 57	87.01 40	72.91 43	80.23 70	85.56 26	66.56 66	85.64 40	89.57 76	69.12 84	80.55 131	72.51 58	93.37 63	83.48 127

Regformer-4		74.64 103	73.67 113	77.55 74	74.74 200	64.49 104	72.91 148	75.42 187	67.45 57	80.24 108	72.07 292	68.98 85	80.19 141	70.29 71	80.91 236	87.98 45

MVS_111021_LR		72.10 140	71.82 144	72.95 134	79.53 137	73.90 36	70.45 184	66.64 248	56.87 151	76.81 152	81.76 200	68.78 86	71.76 245	61.81 129	83.74 209	73.18 255

Fast-Effi-MVS+		68.81 177	68.30 183	70.35 168	74.66 205	48.61 206	66.06 239	78.32 154	50.62 222	71.48 222	75.54 260	68.75 87	79.59 148	50.55 217	78.73 260	82.86 146

ETH3 D test6400		75.73 87	76.00 88	74.92 102	81.75 115	56.93 158	78.31 91	84.60 54	52.83 199	77.15 141	85.14 164	68.59 88	84.03 69	65.44 107	90.20 117	83.82 120

DeepPCF-MVS	71.07 5	78.48 69	77.14 81	82.52 17	84.39 83	77.04 21	76.35 113	84.05 68	56.66 155	80.27 107	85.31 162	68.56 89	87.03 10	67.39 95	91.26 95	83.50 126

CP-MVSNet		79.48 57	81.65 46	72.98 133	89.66 13	39.06 284	76.76 109	80.46 121	78.91 6	90.32 6	91.70 24	68.49 90	84.89 56	63.40 123	95.12 18	95.01 4

LCM-MVSNet-Re		69.10 174	71.57 149	61.70 258	70.37 252	34.30 319	61.45 282	79.62 132	56.81 152	89.59 7	88.16 107	68.44 91	72.94 228	42.30 265	87.33 159	77.85 224

CNVR-MVS		78.49 68	78.59 70	78.16 69	85.86 61	67.40 80	78.12 96	81.50 99	63.92 92	77.51 138	86.56 138	68.43 92	84.82 58	73.83 49	91.61 88	82.26 162

segment_acmp												68.30 93

cdsmvs_eth3d_5k		17.71 320	23.62 322	0.00 337	0.00 356	0.00 357	0.00 348	70.17 235	0.00 352	0.00 353	74.25 275	68.16 94	0.00 354	0.00 351	0.00 351	0.00 350

WR-MVS_H		80.22 53	82.17 41	74.39 108	89.46 15	42.69 259	78.24 93	82.24 89	78.21 8	89.57 8	92.10 18	68.05 95	85.59 43	66.04 103	95.62 9	94.88 5

test_djsdf		78.88 63	78.27 72	80.70 39	81.42 119	71.24 50	83.98 33	75.72 183	52.27 203	87.37 24	92.25 16	68.04 96	80.56 129	72.28 61	91.15 98	90.32 20

v7n		79.37 59	80.41 54	76.28 88	78.67 152	55.81 164	79.22 81	82.51 88	70.72 41	87.54 19	92.44 14	68.00 97	81.34 111	72.84 55	91.72 83	91.69 10

test_prior3		76.71 81	77.19 79	75.27 99	82.15 109	59.85 140	75.57 122	84.33 58	58.92 132	76.53 159	86.78 125	67.83 98	83.39 80	69.81 75	92.76 73	82.58 153

test_prior2							75.57 122		58.92 132	76.53 159	86.78 125	67.83 98		69.81 75	92.76 73

NCCC		78.25 72	78.04 74	78.89 59	85.61 62	69.45 65	79.80 75	80.99 113	65.77 70	75.55 169	86.25 146	67.42 100	85.42 44	70.10 72	90.88 109	81.81 169

baseline		73.10 121	73.96 109	70.51 166	71.46 240	46.39 233	72.08 157	84.40 57	55.95 161	76.62 155	86.46 141	67.20 101	78.03 178	64.22 115	87.27 162	87.11 58

casdiffmvs		73.06 123	73.84 110	70.72 162	71.32 241	46.71 229	70.93 179	84.26 62	55.62 164	77.46 139	87.10 114	67.09 102	77.81 180	63.95 117	86.83 168	87.64 49

Regformer-3		72.86 131	72.28 138	74.62 104	74.74 200	60.18 137	72.91 148	71.76 214	64.74 85	78.42 126	72.07 292	67.00 103	76.28 200	67.97 89	80.91 236	87.39 52

TAPA-MVS	65.27 12	75.16 94	74.29 105	77.77 73	74.86 197	68.08 75	77.89 97	84.04 69	55.15 169	76.19 165	83.39 180	66.91 104	80.11 142	60.04 147	90.14 121	85.13 85
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
TEST9					85.47 63	69.32 68	76.42 111	78.69 147	53.73 192	76.97 143	86.74 128	66.84 105	81.10 117

OPU-MVS				78.65 62	83.44 93	66.85 84	83.62 39				86.12 151	66.82 106	86.01 28	61.72 130	89.79 129	83.08 139

XVG-OURS		79.51 56	79.82 58	78.58 63	86.11 57	74.96 29	76.33 115	84.95 41	66.89 60	82.75 79	88.99 92	66.82 106	78.37 169	74.80 41	90.76 111	82.40 158

train_agg		76.38 82	76.55 83	75.86 93	85.47 63	69.32 68	76.42 111	78.69 147	54.00 187	76.97 143	86.74 128	66.60 108	81.10 117	72.50 59	91.56 89	77.15 227

test_8					85.09 69	67.89 77	76.26 116	78.66 149	54.00 187	76.89 148	86.72 130	66.60 108	80.89 127

agg_prior1		75.89 84	76.41 84	74.31 110	84.44 81	66.02 91	76.12 117	78.62 150	54.40 181	76.95 145	86.85 122	66.44 110	80.34 134	72.45 60	91.42 92	76.57 231

Anonymous20231211		75.54 89	77.19 79	70.59 164	77.67 164	45.70 238	74.73 135	80.19 126	68.80 50	82.95 75	92.91 8	66.26 111	76.76 196	58.41 158	92.77 72	89.30 25

EI-MVSNet-Vis-set		72.78 132	71.87 142	75.54 96	74.77 199	59.02 148	72.24 155	71.56 218	63.92 92	78.59 122	71.59 298	66.22 112	78.60 159	67.58 92	80.32 244	89.00 33

EI-MVSNet-UG-set		72.63 135	71.68 146	75.47 97	74.67 203	58.64 153	72.02 158	71.50 219	63.53 97	78.58 124	71.39 301	65.98 113	78.53 161	67.30 98	80.18 246	89.23 27

Anonymous20240529		72.56 136	73.79 112	68.86 194	76.89 174	45.21 240	68.80 203	77.25 172	67.16 59	76.89 148	90.44 51	65.95 114	74.19 222	50.75 214	90.00 123	87.18 57

ETV-MVS		72.72 133	72.16 141	74.38 109	76.90 173	55.95 162	73.34 146	84.67 49	62.04 109	72.19 212	70.81 302	65.90 115	85.24 50	58.64 155	84.96 192	81.95 167

TransMVSNet (Re)		69.62 164	71.63 147	63.57 242	76.51 176	35.93 308	65.75 245	71.29 223	61.05 115	75.02 174	89.90 72	65.88 116	70.41 257	49.79 221	89.48 133	84.38 110

DeepC-MVS_fast	69.89 7	77.17 78	76.33 86	79.70 48	83.90 88	67.94 76	80.06 73	83.75 71	56.73 154	74.88 177	85.32 161	65.54 117	87.79 2	65.61 106	91.14 99	83.35 133
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
TSAR-MVS + MP.		79.05 61	78.81 65	79.74 46	88.94 28	67.52 79	86.61 16	81.38 103	51.71 210	77.15 141	91.42 31	65.49 118	87.20 6	79.44 14	87.17 165	84.51 106
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
HPM-MVS++		79.89 54	79.80 59	80.18 43	89.02 26	78.44 10	83.49 43	80.18 127	64.71 86	78.11 131	88.39 99	65.46 119	83.14 85	77.64 29	91.20 97	78.94 210

Fast-Effi-MVS+-dtu		70.00 159	68.74 179	73.77 117	73.47 220	64.53 103	71.36 171	78.14 158	55.81 163	68.84 248	74.71 269	65.36 120	75.75 204	52.00 206	79.00 257	81.03 178

MCST-MVS		73.42 116	73.34 121	73.63 120	81.28 121	59.17 146	74.80 133	83.13 79	45.50 257	72.84 203	83.78 178	65.15 121	80.99 121	64.54 112	89.09 139	80.73 188

PCF-MVS	63.80 13	72.70 134	71.69 145	75.72 94	78.10 156	60.01 139	73.04 147	81.50 99	45.34 261	79.66 112	84.35 171	65.15 121	82.65 94	48.70 230	89.38 135	84.50 107
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
test12				76.51 84	82.28 107	60.94 129		81.64 98		73.60 193		64.88 123	85.19 53		90.42 115	83.38 131

Effi-MVS+-dtu		75.43 90	72.28 138	84.91 2	77.05 167	83.58 1	78.47 89	77.70 164	57.68 141	74.89 176	78.13 244	64.80 124	84.26 68	56.46 172	85.32 185	86.88 60

mvs-test1		73.81 110	70.69 159	83.18 3	77.05 167	81.39 3	75.39 124	77.70 164	57.68 141	71.19 225	74.72 268	64.80 124	83.66 74	56.46 172	81.19 234	84.50 107

VPA-MVSNet		68.71 179	70.37 161	63.72 241	76.13 181	38.06 294	64.10 262	71.48 220	56.60 158	74.10 189	88.31 102	64.78 126	69.72 258	47.69 240	90.15 120	83.37 132

F-COLMAP		75.29 92	73.99 108	79.18 54	81.73 116	71.90 45	81.86 56	82.98 80	59.86 125	72.27 209	84.00 175	64.56 127	83.07 88	51.48 209	87.19 164	82.56 155

CS-MVS		71.24 147	70.57 160	73.26 126	74.93 193	52.00 184	73.59 144	85.55 27	55.58 165	68.88 245	70.17 309	64.37 128	85.62 42	57.19 164	84.83 194	82.17 163

DP-MVS Recon		73.57 114	72.69 132	76.23 89	82.85 99	63.39 111	74.32 140	82.96 81	57.75 140	70.35 232	81.98 196	64.34 129	84.41 66	49.69 222	89.95 125	80.89 182

114514_t		73.40 117	73.33 122	73.64 119	84.15 86	57.11 157	78.20 94	80.02 129	43.76 271	72.55 206	86.07 154	64.00 130	83.35 82	60.14 145	91.03 102	80.45 193

pm-mvs1		68.40 182	69.85 165	64.04 239	73.10 229	39.94 279	64.61 258	70.50 232	55.52 166	73.97 191	89.33 79	63.91 131	68.38 266	49.68 223	88.02 146	83.81 122

UniMVSNet_NR-MVSNet		74.90 100	75.65 91	72.64 144	83.04 96	45.79 235	69.26 196	78.81 144	66.66 65	81.74 88	86.88 121	63.26 132	81.07 119	56.21 175	94.98 20	91.05 13

MSLP-MVS++		74.48 104	75.78 90	70.59 164	84.66 75	62.40 116	78.65 86	84.24 63	60.55 120	77.71 136	81.98 196	63.12 133	77.64 184	62.95 125	88.14 144	71.73 270

UniMVSNet (Re)		75.00 97	75.48 94	73.56 121	83.14 95	47.92 215	70.41 185	81.04 112	63.67 95	79.54 113	86.37 143	62.83 134	81.82 105	57.10 166	95.25 14	90.94 15

MIMVSNet1		66.57 202	69.23 170	58.59 281	81.26 122	37.73 297	64.06 263	57.62 286	57.02 149	78.40 127	90.75 43	62.65 135	58.10 308	41.77 270	89.58 132	79.95 199

xiu_mvs_v2_base		64.43 219	63.96 218	65.85 227	77.72 163	51.32 188	63.63 268	72.31 212	45.06 265	61.70 282	69.66 311	62.56 136	73.93 224	49.06 227	73.91 289	72.31 264

Test By Simon												62.56 136

Vis-MVSNet		74.85 102	74.56 99	75.72 94	81.63 118	64.64 101	76.35 113	79.06 140	62.85 104	73.33 198	88.41 98	62.54 138	79.59 148	63.94 119	82.92 215	82.94 143
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
NR-MVSNet		73.62 113	74.05 107	72.33 150	83.50 90	43.71 248	65.65 246	77.32 170	64.32 89	75.59 168	87.08 115	62.45 139	81.34 111	54.90 186	95.63 8	91.93 8

pcd_1.5k_mvsjas		5.20 323	6.93 325	0.00 337	0.00 356	0.00 357	0.00 348	0.00 358	0.00 352	0.00 353	0.00 353	62.39 140	0.00 354	0.00 351	0.00 351	0.00 350

PS-MVSNAJss		77.54 75	77.35 78	78.13 71	84.88 71	66.37 88	78.55 88	79.59 135	53.48 194	86.29 31	92.43 15	62.39 140	80.25 137	67.90 91	90.61 112	87.77 47

PS-MVSNAJ		64.27 222	63.73 221	65.90 226	77.82 161	51.42 187	63.33 271	72.33 211	45.09 264	61.60 283	68.04 322	62.39 140	73.95 223	49.07 226	73.87 290	72.34 263

PHI-MVS		74.92 98	74.36 104	76.61 82	76.40 177	62.32 118	80.38 65	83.15 78	54.16 185	73.23 200	80.75 208	62.19 143	83.86 71	68.02 86	90.92 106	83.65 125

MVS_Test		69.84 162	70.71 158	67.24 212	67.49 275	43.25 255	69.87 189	81.22 108	52.69 201	71.57 219	86.68 131	62.09 144	74.51 219	66.05 102	78.74 259	83.96 118

CSCG		74.12 107	74.39 102	73.33 124	79.35 139	61.66 123	77.45 101	81.98 93	62.47 108	79.06 119	80.19 217	61.83 145	78.79 156	59.83 149	87.35 158	79.54 204

DU-MVS		74.91 99	75.57 93	72.93 135	83.50 90	45.79 235	69.47 193	80.14 128	65.22 79	81.74 88	87.08 115	61.82 146	81.07 119	56.21 175	94.98 20	91.93 8

Baseline_NR-MVSNet		70.62 154	73.19 123	62.92 251	76.97 170	34.44 318	68.84 199	70.88 230	60.25 121	79.50 114	90.53 48	61.82 146	69.11 262	54.67 189	95.27 13	85.22 82

原ACMM1				73.90 115	85.90 58	65.15 99		81.67 97	50.97 219	74.25 187	86.16 149	61.60 148	83.54 76	56.75 167	91.08 101	73.00 256

PAPR		69.20 172	68.66 181	70.82 161	75.15 192	47.77 217	75.31 125	81.11 109	49.62 233	66.33 260	79.27 229	61.53 149	82.96 89	48.12 236	81.50 232	81.74 170

API-MVS		70.97 151	71.51 150	69.37 181	75.20 190	55.94 163	80.99 59	76.84 174	62.48 107	71.24 223	77.51 249	61.51 150	80.96 126	52.04 205	85.76 179	71.22 274

xiu_mvs_v1_base_debu		67.87 189	67.07 199	70.26 169	79.13 146	61.90 120	67.34 222	71.25 224	47.98 244	67.70 253	74.19 277	61.31 151	72.62 232	56.51 169	78.26 265	76.27 233

xiu_mvs_v1_base		67.87 189	67.07 199	70.26 169	79.13 146	61.90 120	67.34 222	71.25 224	47.98 244	67.70 253	74.19 277	61.31 151	72.62 232	56.51 169	78.26 265	76.27 233

xiu_mvs_v1_base_debi		67.87 189	67.07 199	70.26 169	79.13 146	61.90 120	67.34 222	71.25 224	47.98 244	67.70 253	74.19 277	61.31 151	72.62 232	56.51 169	78.26 265	76.27 233

CNLPA		73.44 115	73.03 128	74.66 103	78.27 154	75.29 27	75.99 119	78.49 152	65.39 75	75.67 167	83.22 188	61.23 154	66.77 280	53.70 200	85.33 184	81.92 168

MSDG		67.47 196	67.48 195	67.46 210	70.70 247	54.69 171	66.90 231	78.17 157	60.88 117	70.41 231	74.76 266	61.22 155	73.18 226	47.38 241	76.87 271	74.49 246

CANet		73.00 126	71.84 143	76.48 85	75.82 185	61.28 124	74.81 131	80.37 124	63.17 102	62.43 281	80.50 212	61.10 156	85.16 54	64.00 116	84.34 201	83.01 142

EG-PatchMatch MVS		70.70 153	70.88 156	70.16 172	82.64 103	58.80 150	71.48 168	73.64 199	54.98 170	76.55 157	81.77 199	61.10 156	78.94 153	54.87 187	80.84 239	72.74 260

HQP_MVS		78.77 64	78.78 67	78.72 60	85.18 66	65.18 97	82.74 48	85.49 28	65.45 73	78.23 128	89.11 89	60.83 158	86.15 25	71.09 67	90.94 103	84.82 90

plane_prior6					84.18 85	65.31 96						60.83 158

FMVSNet1		71.06 149	72.48 134	66.81 217	77.65 165	40.68 273	71.96 160	73.03 201	61.14 114	79.45 115	90.36 61	60.44 160	75.20 211	50.20 219	88.05 145	84.54 102

EIA-MVS		68.59 181	67.16 198	72.90 136	75.18 191	55.64 166	69.39 194	81.29 104	52.44 202	64.53 268	70.69 303	60.33 161	82.30 100	54.27 196	76.31 274	80.75 187

BH-untuned		69.39 169	69.46 166	69.18 185	77.96 159	56.88 159	68.47 210	77.53 167	56.77 153	77.79 134	79.63 224	60.30 162	80.20 140	46.04 249	80.65 241	70.47 279

PAPM_NR		73.91 108	74.16 106	73.16 128	81.90 113	53.50 178	81.28 58	81.40 102	66.17 68	73.30 199	83.31 184	59.96 163	83.10 87	58.45 157	81.66 230	82.87 145

VDDNet		71.60 145	73.13 125	67.02 216	86.29 50	41.11 268	69.97 187	66.50 249	68.72 52	74.74 179	91.70 24	59.90 164	75.81 203	48.58 232	91.72 83	84.15 116

VDD-MVS		70.81 152	71.44 151	68.91 193	79.07 149	46.51 230	67.82 216	70.83 231	61.23 113	74.07 190	88.69 96	59.86 165	75.62 206	51.11 212	90.28 116	84.61 99

ANet_high		67.08 199	69.94 163	58.51 282	57.55 331	27.09 338	58.43 300	76.80 175	63.56 96	82.40 82	91.93 20	59.82 166	64.98 288	50.10 220	88.86 141	83.46 129

3Dnovator+	73.19 2	81.08 41	80.48 53	82.87 7	81.41 120	72.03 44	84.38 32	86.23 19	77.28 12	80.65 102	90.18 67	59.80 167	87.58 4	73.06 54	91.34 94	89.01 32

PLC	62.01 16	71.79 144	70.28 162	76.33 87	80.31 130	68.63 73	78.18 95	81.24 106	54.57 179	67.09 258	80.63 210	59.44 168	81.74 109	46.91 245	84.17 202	78.63 212
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
TinyColmap		67.98 188	69.28 168	64.08 237	67.98 270	46.82 227	70.04 186	75.26 189	53.05 196	77.36 140	86.79 124	59.39 169	72.59 235	45.64 251	88.01 147	72.83 258

FC-MVSNet-test		73.32 119	74.78 98	68.93 192	79.21 143	36.57 302	71.82 166	79.54 136	57.63 145	82.57 80	90.38 60	59.38 170	78.99 152	57.91 161	94.56 32	91.23 12

V42		71.06 149	70.83 157	71.72 153	67.25 276	47.14 226	65.94 240	80.35 125	51.35 215	83.40 72	83.23 186	59.25 171	78.80 155	65.91 104	80.81 240	89.23 27

BH-RMVSNet		68.69 180	68.20 188	70.14 173	76.40 177	53.90 177	64.62 257	73.48 200	58.01 137	73.91 192	81.78 198	59.09 172	78.22 173	48.59 231	77.96 268	78.31 216

alignmvs		70.54 155	71.00 155	69.15 186	73.50 219	48.04 214	69.85 190	79.62 132	53.94 190	76.54 158	82.00 195	59.00 173	74.68 217	57.32 163	87.21 163	84.72 94

DELS-MVS		68.83 176	68.31 182	70.38 167	70.55 251	48.31 208	63.78 267	82.13 90	54.00 187	68.96 244	75.17 264	58.95 174	80.06 143	58.55 156	82.74 217	82.76 149
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
VPNet		65.58 207	67.56 193	59.65 276	79.72 134	30.17 332	60.27 291	62.14 269	54.19 184	71.24 223	86.63 135	58.80 175	67.62 271	44.17 258	90.87 110	81.18 175

mvs_anonymous		65.08 212	65.49 207	63.83 240	63.79 301	37.60 298	66.52 236	69.82 236	43.44 275	73.46 196	86.08 153	58.79 176	71.75 246	51.90 207	75.63 278	82.15 164

v10		75.69 88	76.20 87	74.16 112	74.44 209	48.69 204	75.84 121	82.93 82	59.02 131	85.92 36	89.17 86	58.56 177	82.74 92	70.73 69	89.14 138	91.05 13

diffmvs		67.42 197	67.50 194	67.20 213	62.26 308	45.21 240	64.87 254	77.04 173	48.21 242	71.74 214	79.70 223	58.40 178	71.17 249	64.99 109	80.27 245	85.22 82

FIs		72.56 136	73.80 111	68.84 195	78.74 151	37.74 296	71.02 177	79.83 131	56.12 159	80.88 101	89.45 78	58.18 179	78.28 172	56.63 168	93.36 64	90.51 19

EI-MVSNet		69.61 165	69.01 174	71.41 158	73.94 216	49.90 196	71.31 173	71.32 221	58.22 135	75.40 172	70.44 304	58.16 180	75.85 201	62.51 126	79.81 250	88.48 42

IterMVS-LS		73.01 125	73.12 126	72.66 143	73.79 218	49.90 196	71.63 167	78.44 153	58.22 135	80.51 103	86.63 135	58.15 181	79.62 146	62.51 126	88.20 143	88.48 42
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
HQP2-MVS												58.09 182

HQP-MVS		75.24 93	75.01 96	75.94 91	82.37 104	58.80 150	77.32 102	84.12 66	59.08 128	71.58 216	85.96 156	58.09 182	85.30 47	67.38 96	89.16 136	83.73 124

v8		75.07 96	75.64 92	73.35 123	73.42 221	47.46 222	75.20 126	81.45 101	60.05 122	85.64 40	89.26 81	58.08 184	81.80 107	69.71 77	87.97 148	90.79 17

v1144		73.29 120	73.39 118	73.01 131	74.12 215	48.11 212	72.01 159	81.08 111	53.83 191	81.77 86	84.68 166	58.07 185	81.91 104	68.10 84	86.86 167	88.99 34

v144192		72.99 127	73.06 127	72.77 139	74.58 207	47.48 221	71.90 164	80.44 122	51.57 212	81.46 92	84.11 174	58.04 186	82.12 103	67.98 88	87.47 155	88.70 41

ab-mvs		64.11 223	65.13 211	61.05 265	71.99 237	38.03 295	67.59 217	68.79 240	49.08 237	65.32 265	86.26 145	58.02 187	66.85 278	39.33 281	79.79 252	78.27 217

testing_2		72.01 142	72.36 136	70.95 160	70.79 244	48.70 203	72.81 150	78.09 159	48.79 239	84.46 60	89.15 88	57.90 188	78.55 160	61.55 132	87.74 150	85.61 80

Gipuma		69.55 166	72.83 131	59.70 275	63.63 303	53.97 175	80.08 72	75.93 181	64.24 90	73.49 195	88.93 94	57.89 189	62.46 295	59.75 150	91.55 90	62.67 321
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
TSAR-MVS + GP.		73.08 122	71.60 148	77.54 75	78.99 150	70.73 55	74.96 128	69.38 238	60.73 119	74.39 186	78.44 240	57.72 190	82.78 91	60.16 144	89.60 131	79.11 209

WR-MVS		71.20 148	72.48 134	67.36 211	84.98 70	35.70 310	64.43 260	68.66 241	65.05 82	81.49 91	86.43 142	57.57 191	76.48 198	50.36 218	93.32 65	89.90 21

LF4IMVS		67.50 194	67.31 197	68.08 204	58.86 326	61.93 119	71.43 169	75.90 182	44.67 266	72.42 208	80.20 216	57.16 192	70.44 255	58.99 154	86.12 175	71.88 268

OurMVSNet-221017-0		78.57 66	78.53 71	78.67 61	80.48 128	64.16 106	80.24 69	82.06 91	61.89 110	88.77 11	93.32 4	57.15 193	82.60 95	70.08 73	92.80 71	89.25 26

v1192		73.40 117	73.42 117	73.32 125	74.65 206	48.67 205	72.21 156	81.73 96	52.76 200	81.85 85	84.56 168	57.12 194	82.24 102	68.58 80	87.33 159	89.06 31

DVP-MVS		80.49 47	79.67 61	82.96 5	89.70 12	77.46 19	87.16 8	85.10 37	64.94 83	81.05 96	88.38 100	57.10 195	87.10 8	79.75 8	83.87 207	84.31 112

tfpnnormal		66.48 203	67.93 190	62.16 256	73.40 222	36.65 301	63.45 269	64.99 257	55.97 160	72.82 204	87.80 111	57.06 196	69.10 263	48.31 235	87.54 152	80.72 189

MAR-MVS		67.72 192	66.16 205	72.40 148	74.45 208	64.99 100	74.87 129	77.50 168	48.67 240	65.78 264	68.58 321	57.01 197	77.79 181	46.68 247	81.92 224	74.42 247
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
XXY-MVS		55.19 278	57.40 269	48.56 308	64.45 298	34.84 317	51.54 319	53.59 306	38.99 297	63.79 274	79.43 226	56.59 198	45.57 324	36.92 300	71.29 300	65.25 312

v1921920		72.96 129	72.98 129	72.89 137	74.67 203	47.58 220	71.92 163	80.69 115	51.70 211	81.69 90	83.89 176	56.58 199	82.25 101	68.34 82	87.36 157	88.82 38

VNet		64.01 225	65.15 210	60.57 269	73.28 224	35.61 311	57.60 303	67.08 246	54.61 178	66.76 259	83.37 182	56.28 200	66.87 276	42.19 266	85.20 187	79.23 208

v1240		73.06 123	73.14 124	72.84 138	74.74 200	47.27 225	71.88 165	81.11 109	51.80 209	82.28 83	84.21 172	56.22 201	82.34 99	68.82 78	87.17 165	88.91 36

MG-MVS		70.47 156	71.34 152	67.85 206	79.26 141	40.42 277	74.67 138	75.15 191	58.41 134	68.74 249	88.14 108	56.08 202	83.69 73	59.90 148	81.71 229	79.43 206

v2v482		72.55 138	72.58 133	72.43 147	72.92 232	46.72 228	71.41 170	79.13 139	55.27 167	81.17 95	85.25 163	55.41 203	81.13 116	67.25 99	85.46 180	89.43 24

3Dnovator	65.95 11	71.50 146	71.22 153	72.34 149	73.16 225	63.09 114	78.37 90	78.32 154	57.67 143	72.22 211	84.61 167	54.77 204	78.47 163	60.82 140	81.07 235	75.45 238

v148		69.38 170	69.39 167	69.36 182	69.14 262	44.56 243	68.83 200	72.70 207	54.79 174	78.59 122	84.12 173	54.69 205	76.74 197	59.40 152	82.20 221	86.79 61

旧先验1					84.55 78	60.36 136		63.69 262			87.05 118	54.65 206			83.34 212	69.66 287

cl_fuxian		69.82 163	69.89 164	69.61 180	66.24 284	43.48 251	68.12 213	79.61 134	51.43 214	77.72 135	80.18 218	54.61 207	78.15 177	63.62 122	87.50 154	87.20 56

BH-w/o		64.81 214	64.29 217	66.36 222	76.08 183	54.71 170	65.61 247	75.23 190	50.10 227	71.05 227	71.86 297	54.33 208	79.02 151	38.20 290	76.14 275	65.36 311

ambc				70.10 174	77.74 162	50.21 194	74.28 141	77.93 163		79.26 117	88.29 103	54.11 209	79.77 145	64.43 113	91.10 100	80.30 195

QAPM		69.18 173	69.26 169	68.94 191	71.61 239	52.58 183	80.37 66	78.79 146	49.63 232	73.51 194	85.14 164	53.66 210	79.12 150	55.11 185	75.54 279	75.11 242

miper_ehance_all_eth		68.36 183	68.16 189	68.98 189	65.14 294	43.34 253	67.07 228	78.92 143	49.11 236	76.21 164	77.72 246	53.48 211	77.92 179	61.16 136	84.59 197	85.68 79

IS-MVSNet		75.10 95	75.42 95	74.15 113	79.23 142	48.05 213	79.43 76	78.04 160	70.09 46	79.17 118	88.02 110	53.04 212	83.60 75	58.05 160	93.76 58	90.79 17

1121		69.23 171	68.26 184	72.12 152	88.36 36	71.40 47	68.59 205	62.06 272	43.80 270	74.75 178	86.18 147	52.92 213	76.85 194	54.47 191	83.27 213	68.12 296

新几何1				69.99 176	88.37 35	71.34 49		62.08 271	43.85 269	74.99 175	86.11 152	52.85 214	70.57 253	50.99 213	83.23 214	68.05 297

OpenMVS	62.51 15	68.76 178	68.75 178	68.78 196	70.56 250	53.91 176	78.29 92	77.35 169	48.85 238	70.22 234	83.52 179	52.65 215	76.93 191	55.31 184	81.99 223	75.49 237

UGNet		70.20 157	69.05 172	73.65 118	76.24 179	63.64 109	75.87 120	72.53 209	61.48 112	60.93 291	86.14 150	52.37 216	77.12 189	50.67 215	85.21 186	80.17 198
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
Anonymous202405211		66.02 204	66.89 202	63.43 245	74.22 211	38.14 292	59.00 297	66.13 250	63.33 101	69.76 238	85.95 157	51.88 217	70.50 254	44.23 257	87.52 153	81.64 171

PVSNet_BlendedMVS		65.38 208	64.30 216	68.61 197	69.81 255	49.36 200	65.60 248	78.96 141	45.50 257	59.98 294	78.61 238	51.82 218	78.20 174	44.30 255	84.11 203	78.27 217

PVSNet_Blended		62.90 233	61.64 236	66.69 220	69.81 255	49.36 200	61.23 285	78.96 141	42.04 282	59.98 294	68.86 319	51.82 218	78.20 174	44.30 255	77.77 270	72.52 261

testgi		54.00 285	56.86 272	45.45 316	58.20 329	25.81 342	49.05 322	49.50 320	45.43 260	67.84 252	81.17 206	51.81 220	43.20 336	29.30 329	79.41 255	67.34 301

EPNet		69.10 174	67.32 196	74.46 105	68.33 266	61.27 125	77.56 99	63.57 263	60.95 116	56.62 307	82.75 189	51.53 221	81.24 114	54.36 195	90.20 117	80.88 183
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
PVSNet_Blended_VisFu		70.04 158	68.88 175	73.53 122	82.71 101	63.62 110	74.81 131	81.95 94	48.53 241	67.16 257	79.18 232	51.42 222	78.38 168	54.39 194	79.72 253	78.60 213

DPM-MVS		69.98 160	69.22 171	72.26 151	82.69 102	58.82 149	70.53 183	81.23 107	47.79 248	64.16 270	80.21 215	51.32 223	83.12 86	60.14 145	84.95 193	74.83 244

TR-MVS		64.59 215	63.54 223	67.73 209	75.75 187	50.83 190	63.39 270	70.29 234	49.33 234	71.55 220	74.55 270	50.94 224	78.46 164	40.43 277	75.69 277	73.89 251

MVS		60.62 251	59.97 250	62.58 253	68.13 268	47.28 224	68.59 205	73.96 198	32.19 327	59.94 296	68.86 319	50.48 225	77.64 184	41.85 269	75.74 276	62.83 319

SixPastTwentyTwo		75.77 85	76.34 85	74.06 114	81.69 117	54.84 169	76.47 110	75.49 185	64.10 91	87.73 17	92.24 17	50.45 226	81.30 113	67.41 94	91.46 91	86.04 69

PatchMatch-RL		58.68 264	57.72 266	61.57 259	76.21 180	73.59 39	61.83 280	49.00 322	47.30 252	61.08 287	68.97 316	50.16 227	59.01 305	36.06 305	68.84 314	52.10 334

eth_miper_zixun_eth		69.42 168	68.73 180	71.50 157	67.99 269	46.42 231	67.58 218	78.81 144	50.72 221	78.13 130	80.34 214	50.15 228	80.34 134	60.18 143	84.65 195	87.74 48

miper_enhance_ethall		65.86 205	65.05 215	68.28 203	61.62 311	42.62 260	64.74 255	77.97 161	42.52 280	73.42 197	72.79 287	49.66 229	77.68 183	58.12 159	84.59 197	84.54 102

K. test v3		73.67 112	73.61 116	73.87 116	79.78 133	55.62 167	74.69 137	62.04 274	66.16 69	84.76 54	93.23 5	49.47 230	80.97 122	65.66 105	86.67 171	85.02 89

EPP-MVSNet		73.86 109	73.38 119	75.31 98	78.19 155	53.35 180	80.45 63	77.32 170	65.11 81	76.47 161	86.80 123	49.47 230	83.77 72	53.89 198	92.72 75	88.81 39

cascas		64.59 215	62.77 230	70.05 175	75.27 189	50.02 195	61.79 281	71.61 216	42.46 281	63.68 275	68.89 318	49.33 232	80.35 133	47.82 239	84.05 204	79.78 202

MDA-MVSNet-bldmvs		62.34 238	61.73 234	64.16 235	61.64 310	49.90 196	48.11 326	57.24 292	53.31 195	80.95 97	79.39 227	49.00 233	61.55 299	45.92 250	80.05 247	81.03 178

testdata				64.13 236	85.87 60	63.34 112		61.80 275	47.83 247	76.42 163	86.60 137	48.83 234	62.31 297	54.46 193	81.26 233	66.74 306

cl-mvsnet_		68.26 187	68.26 184	68.29 201	64.98 295	43.67 249	65.89 241	74.67 193	50.04 228	76.86 150	82.42 192	48.74 235	75.38 207	60.92 139	89.81 127	85.80 77

cl-mvsnet1		68.27 186	68.26 184	68.29 201	64.98 295	43.67 249	65.89 241	74.67 193	50.04 228	76.86 150	82.43 191	48.74 235	75.38 207	60.94 138	89.81 127	85.81 73

GBi-Net		68.30 184	68.79 176	66.81 217	73.14 226	40.68 273	71.96 160	73.03 201	54.81 171	74.72 180	90.36 61	48.63 237	75.20 211	47.12 242	85.37 181	84.54 102

test1		68.30 184	68.79 176	66.81 217	73.14 226	40.68 273	71.96 160	73.03 201	54.81 171	74.72 180	90.36 61	48.63 237	75.20 211	47.12 242	85.37 181	84.54 102

FMVSNet2		67.48 195	68.21 187	65.29 228	73.14 226	38.94 286	68.81 201	71.21 227	54.81 171	76.73 154	86.48 140	48.63 237	74.60 218	47.98 237	86.11 176	82.35 159

test222					87.30 39	69.15 71	67.85 215	59.59 281	41.06 286	73.05 202	85.72 159	48.03 240			80.65 241	66.92 302

OpenMVS_ROB	54.93 17	63.23 229	63.28 224	63.07 249	69.81 255	45.34 239	68.52 208	67.14 245	43.74 272	70.61 230	79.22 230	47.90 241	72.66 231	48.75 229	73.84 291	71.21 275

lessismore_v0				72.75 140	79.60 136	56.83 160		57.37 289		83.80 68	89.01 91	47.45 242	78.74 157	64.39 114	86.49 173	82.69 151

TAMVS		65.31 209	63.75 220	69.97 177	82.23 108	59.76 142	66.78 233	63.37 264	45.20 262	69.79 237	79.37 228	47.42 243	72.17 238	34.48 310	85.15 188	77.99 223

PM-MVS		64.49 217	63.61 222	67.14 215	76.68 175	75.15 28	68.49 209	42.85 333	51.17 218	77.85 132	80.51 211	45.76 244	66.31 283	52.83 204	76.35 273	59.96 327

MVS_0304		62.51 237	62.27 233	63.25 246	69.39 259	48.47 207	64.05 264	62.48 267	59.69 126	54.10 319	81.04 207	45.71 245	66.31 283	41.38 272	82.58 219	74.96 243

USDC		62.80 234	63.10 227	61.89 257	65.19 291	43.30 254	67.42 221	74.20 197	35.80 312	72.25 210	84.48 169	45.67 246	71.95 243	37.95 292	84.97 189	70.42 281

test20.03		55.74 275	57.51 268	50.42 299	59.89 322	32.09 327	50.63 320	49.01 321	50.11 226	65.07 267	83.23 186	45.61 247	48.11 320	30.22 324	83.82 208	71.07 277

cl-mvsnet2		67.14 198	66.51 203	69.03 188	63.20 304	43.46 252	66.88 232	76.25 178	49.22 235	74.48 184	77.88 245	45.49 248	77.40 186	60.64 141	84.59 197	86.24 65

IterMVS-SCA-FT		67.68 193	66.07 206	72.49 146	73.34 223	58.20 155	63.80 266	65.55 254	48.10 243	76.91 147	82.64 190	45.20 249	78.84 154	61.20 135	77.89 269	80.44 194

SCA		58.57 265	58.04 264	60.17 273	70.17 253	41.07 269	65.19 251	53.38 308	43.34 278	61.00 290	73.48 281	45.20 249	69.38 260	40.34 278	70.31 306	70.05 283

1112_ss		59.48 258	58.99 257	60.96 267	77.84 160	42.39 262	61.42 283	68.45 242	37.96 302	59.93 297	67.46 324	45.11 251	65.07 287	40.89 275	71.81 298	75.41 239

new-patchmatchnet		52.89 288	55.76 280	44.26 321	59.94 321	6.31 352	37.36 343	50.76 317	41.10 285	64.28 269	79.82 221	44.77 252	48.43 319	36.24 303	87.61 151	78.03 221

jason		64.47 218	62.84 229	69.34 184	76.91 172	59.20 143	67.15 226	65.67 251	35.29 313	65.16 266	76.74 254	44.67 253	70.68 251	54.74 188	79.28 256	78.14 219
jason: jason.
IterMVS		63.12 230	62.48 232	65.02 231	66.34 283	52.86 181	63.81 265	62.25 268	46.57 254	71.51 221	80.40 213	44.60 254	66.82 279	51.38 211	75.47 280	75.38 240
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
PAPM		61.79 242	60.37 248	66.05 224	76.09 182	41.87 264	69.30 195	76.79 176	40.64 290	53.80 320	79.62 225	44.38 255	82.92 90	29.64 328	73.11 293	73.36 254

HY-MVS	49.31 19	57.96 267	57.59 267	59.10 278	66.85 280	36.17 305	65.13 252	65.39 255	39.24 295	54.69 316	78.14 243	44.28 256	67.18 275	33.75 314	70.79 303	73.95 250

CANet_DTU		64.04 224	63.83 219	64.66 232	68.39 263	42.97 257	73.45 145	74.50 196	52.05 207	54.78 314	75.44 263	43.99 257	70.42 256	53.49 202	78.41 264	80.59 191

LFMVS		67.06 200	67.89 191	64.56 233	78.02 157	38.25 291	70.81 182	59.60 280	65.18 80	71.06 226	86.56 138	43.85 258	75.22 210	46.35 248	89.63 130	80.21 197

pmmvs-eth3d		64.41 220	63.27 225	67.82 207	75.81 186	60.18 137	69.49 192	62.05 273	38.81 298	74.13 188	82.23 194	43.76 259	68.65 264	42.53 264	80.63 243	74.63 245

1314		59.83 256	58.86 258	62.74 252	65.71 288	44.78 242	68.59 205	72.63 208	33.54 325	61.05 289	67.29 327	43.62 260	71.26 248	49.49 224	67.84 318	72.19 266

CDS-MVSNet		64.33 221	62.66 231	69.35 183	80.44 129	58.28 154	65.26 250	65.66 252	44.36 267	67.30 256	75.54 260	43.27 261	71.77 244	37.68 293	84.44 200	78.01 222
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
MVSFormer		69.93 161	69.03 173	72.63 145	74.93 193	59.19 144	83.98 33	75.72 183	52.27 203	63.53 277	76.74 254	43.19 262	80.56 129	72.28 61	78.67 261	78.14 219

lupinMVS		63.36 226	61.49 240	68.97 190	74.93 193	59.19 144	65.80 244	64.52 259	34.68 318	63.53 277	74.25 275	43.19 262	70.62 252	53.88 199	78.67 261	77.10 228

Test_1112_low_res		58.78 263	58.69 259	59.04 279	79.41 138	38.13 293	57.62 302	66.98 247	34.74 316	59.62 298	77.56 248	42.92 264	63.65 292	38.66 286	70.73 304	75.35 241

test_yl		65.11 210	65.09 213	65.18 229	70.59 248	40.86 270	63.22 274	72.79 204	57.91 138	68.88 245	79.07 235	42.85 265	74.89 215	45.50 252	84.97 189	79.81 200

DCV-MVSNet		65.11 210	65.09 213	65.18 229	70.59 248	40.86 270	63.22 274	72.79 204	57.91 138	68.88 245	79.07 235	42.85 265	74.89 215	45.50 252	84.97 189	79.81 200

PMMVS		44.69 310	43.95 316	46.92 310	50.05 348	53.47 179	48.08 327	42.40 335	22.36 346	44.01 343	53.05 341	42.60 267	45.49 325	31.69 319	61.36 329	41.79 342

Anonymous20231206		54.13 282	55.82 279	49.04 307	70.89 243	35.96 307	51.73 318	50.87 316	34.86 314	62.49 280	79.22 230	42.52 268	44.29 332	27.95 333	81.88 225	66.88 303

WTY-MVS		49.39 299	50.31 301	46.62 312	61.22 313	32.00 328	46.61 331	49.77 319	33.87 321	54.12 318	69.55 313	41.96 269	45.40 326	31.28 321	64.42 324	62.47 322

UnsupCasMVSNet_eth		52.26 292	53.29 289	49.16 305	55.08 341	33.67 322	50.03 321	58.79 283	37.67 303	63.43 279	74.75 267	41.82 270	45.83 323	38.59 288	59.42 333	67.98 298

UnsupCasMVSNet_bld		50.01 298	51.03 299	46.95 309	58.61 327	32.64 325	48.31 324	53.27 309	34.27 319	60.47 292	71.53 299	41.40 271	47.07 321	30.68 322	60.78 330	61.13 325

ppachtmachnet_test		60.26 254	59.61 253	62.20 255	67.70 273	44.33 245	58.18 301	60.96 277	40.75 289	65.80 263	72.57 288	41.23 272	63.92 290	46.87 246	82.42 220	78.33 215

baseline1		57.82 268	58.36 263	56.19 288	69.17 261	30.76 331	62.94 276	55.21 299	46.04 256	63.83 273	78.47 239	41.20 273	63.68 291	39.44 280	68.99 313	74.13 248

MIMVSNet		54.39 281	56.12 278	49.20 304	72.57 233	30.91 330	59.98 292	48.43 324	41.66 284	55.94 310	83.86 177	41.19 274	50.42 316	26.05 335	75.38 282	66.27 307

CHOSEN 1792x2688		58.09 266	56.30 276	63.45 244	79.95 132	50.93 189	54.07 313	65.59 253	28.56 338	61.53 284	74.33 273	41.09 275	66.52 282	33.91 313	67.69 319	72.92 257

YYNet1		52.58 289	53.50 287	49.85 300	54.15 345	36.45 304	40.53 337	46.55 328	38.09 301	75.52 170	73.31 284	41.08 276	43.88 333	41.10 273	71.14 302	69.21 292

MDA-MVSNet_test_wron		52.57 290	53.49 288	49.81 301	54.24 344	36.47 303	40.48 338	46.58 327	38.13 300	75.47 171	73.32 283	41.05 277	43.85 334	40.98 274	71.20 301	69.10 294

PVSNet_0	36.71 22	41.12 316	40.78 318	42.14 324	59.97 320	40.13 278	40.97 336	42.24 338	30.81 336	44.86 340	49.41 344	40.70 278	45.12 328	23.15 342	34.96 346	41.16 343

Vis-MVSNet (Re-imp)		62.74 235	63.21 226	61.34 263	72.19 235	31.56 329	67.31 225	53.87 304	53.60 193	69.88 236	83.37 182	40.52 279	70.98 250	41.40 271	86.78 170	81.48 173

sss		47.59 304	48.32 303	45.40 317	56.73 335	33.96 320	45.17 334	48.51 323	32.11 331	52.37 323	65.79 329	40.39 280	41.91 340	31.85 318	61.97 328	60.35 326

our_test_3		56.46 271	56.51 274	56.30 287	67.70 273	39.66 281	55.36 310	52.34 313	40.57 291	63.85 272	69.91 310	40.04 281	58.22 307	43.49 262	75.29 284	71.03 278

miper_lstm_enhance		61.97 239	61.63 237	62.98 250	60.04 319	45.74 237	47.53 328	70.95 228	44.04 268	73.06 201	78.84 237	39.72 282	60.33 301	55.82 179	84.64 196	82.88 144

pmmvs4		60.78 249	59.04 256	66.00 225	73.06 231	57.67 156	64.53 259	60.22 278	36.91 307	65.96 261	77.27 250	39.66 283	68.54 265	38.87 284	74.89 285	71.80 269

MVP-Stereo		61.56 243	59.22 254	68.58 198	79.28 140	60.44 135	69.20 197	71.57 217	43.58 274	56.42 308	78.37 241	39.57 284	76.46 199	34.86 309	60.16 331	68.86 295
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
FPMVS		59.43 259	60.07 249	57.51 285	77.62 166	71.52 46	62.33 278	50.92 315	57.40 147	69.40 239	80.00 219	39.14 285	61.92 298	37.47 296	66.36 320	39.09 344

DSMNet-mixed		43.18 314	44.66 314	38.75 330	54.75 343	28.88 336	57.06 304	27.42 351	13.47 347	47.27 336	77.67 247	38.83 286	39.29 343	25.32 340	60.12 332	48.08 337

HyFIR lowres test		63.01 231	60.47 247	70.61 163	83.04 96	54.10 174	59.93 293	72.24 213	33.67 323	69.00 242	75.63 259	38.69 287	76.93 191	36.60 301	75.45 281	80.81 186

MVE	27.91 23	36.69 319	35.64 321	39.84 329	43.37 350	35.85 309	19.49 346	24.61 352	24.68 344	39.05 346	62.63 334	38.67 288	27.10 349	21.04 344	47.25 345	56.56 332
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
EPNet_dtu		58.93 262	58.52 260	60.16 274	67.91 271	47.70 219	69.97 187	58.02 284	49.73 230	47.28 335	73.02 286	38.14 289	62.34 296	36.57 302	85.99 177	70.43 280
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
pmmvs5		52.49 291	52.58 292	52.21 297	54.99 342	32.38 326	55.45 309	53.84 305	32.15 329	55.49 313	74.81 265	38.08 290	57.37 309	34.02 312	74.40 287	66.88 303

N_pmnet		52.06 293	51.11 298	54.92 290	59.64 324	71.03 51	37.42 342	61.62 276	33.68 322	57.12 303	72.10 289	37.94 291	31.03 346	29.13 332	71.35 299	62.70 320

CMPMVS	48.73 20	61.54 244	60.89 244	63.52 243	61.08 314	51.55 186	68.07 214	68.00 244	33.88 320	65.87 262	81.25 205	37.91 292	67.71 269	49.32 225	82.60 218	71.31 273
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
FMVSNet3		65.00 213	65.16 208	64.52 234	69.47 258	37.56 299	66.63 234	70.38 233	51.55 213	74.72 180	83.27 185	37.89 293	74.44 220	47.12 242	85.37 181	81.57 172

GA-MVS		62.91 232	61.66 235	66.66 221	67.09 278	44.49 244	61.18 286	69.36 239	51.33 216	69.33 240	74.47 271	36.83 294	74.94 214	50.60 216	74.72 286	80.57 192

MS-PatchMatch		55.59 276	54.89 283	57.68 284	69.18 260	49.05 202	61.00 287	62.93 266	35.98 310	58.36 301	68.93 317	36.71 295	66.59 281	37.62 295	63.30 327	57.39 330

CVMVSNet		59.21 260	58.44 262	61.51 260	73.94 216	47.76 218	71.31 173	64.56 258	26.91 342	60.34 293	70.44 304	36.24 296	67.65 270	53.57 201	68.66 315	69.12 293

PMMVS2		37.74 317	40.87 317	28.36 332	42.41 351	5.35 353	24.61 345	27.75 350	32.15 329	47.85 334	70.27 307	35.85 297	29.51 347	19.08 346	67.85 317	50.22 336

tpmrst		50.15 297	51.38 295	46.45 313	56.05 336	24.77 343	64.40 261	49.98 318	36.14 309	53.32 321	69.59 312	35.16 298	48.69 318	39.24 282	58.51 336	65.89 308

D2MVS		62.58 236	61.05 243	67.20 213	63.85 300	47.92 215	56.29 305	69.58 237	39.32 293	70.07 235	78.19 242	34.93 299	72.68 230	53.44 203	83.74 209	81.00 180

PVSNet	43.83 21	51.56 294	51.17 296	52.73 294	68.34 265	38.27 290	48.22 325	53.56 307	36.41 308	54.29 317	64.94 331	34.60 300	54.20 314	30.34 323	69.87 309	65.71 310

MVS-HIRNet		45.53 307	47.29 306	40.24 328	62.29 307	26.82 339	56.02 307	37.41 346	29.74 337	43.69 344	81.27 204	33.96 301	55.48 310	24.46 341	56.79 338	38.43 345

baseline2		55.57 277	52.74 290	64.05 238	65.26 290	44.11 246	62.38 277	54.43 303	39.03 296	51.21 325	67.35 326	33.66 302	72.45 236	37.14 298	64.22 325	75.60 236

RPMNet		61.25 245	61.55 239	60.36 271	66.37 281	48.24 210	70.93 179	54.45 302	54.66 177	61.35 285	86.77 127	33.29 303	63.22 293	55.93 177	70.17 307	69.62 288

CR-MVSNet		58.96 261	58.49 261	60.36 271	66.37 281	48.24 210	70.93 179	56.40 296	32.87 326	61.35 285	86.66 132	33.19 304	63.22 293	48.50 233	70.17 307	69.62 288

Patchmtry		60.91 247	63.01 228	54.62 291	66.10 286	26.27 341	67.47 220	56.40 296	54.05 186	72.04 213	86.66 132	33.19 304	60.17 302	43.69 259	87.45 156	77.42 225

RRT_MVS		73.80 111	71.19 154	81.60 22	71.04 242	70.33 60	78.78 85	74.91 192	56.96 150	77.83 133	85.56 160	32.82 306	87.39 5	71.16 66	91.68 85	87.07 59

CostFormer		57.35 270	56.14 277	60.97 266	63.76 302	38.43 288	67.50 219	60.22 278	37.14 306	59.12 299	76.34 256	32.78 307	71.99 242	39.12 283	69.27 312	72.47 262

tpm cat1		54.02 284	52.63 291	58.19 283	64.85 297	39.86 280	66.26 238	57.28 290	32.16 328	56.90 305	70.39 306	32.75 308	65.30 286	34.29 311	58.79 334	69.41 290

thres200		57.55 269	57.02 270	59.17 277	67.89 272	34.93 315	58.91 299	57.25 291	50.24 224	64.01 271	71.46 300	32.49 309	71.39 247	31.31 320	79.57 254	71.19 276

tfpn200view9		60.35 253	59.97 250	61.51 260	70.78 245	35.35 312	63.27 272	57.47 287	53.00 197	68.31 250	77.09 251	32.45 310	72.09 239	35.61 306	81.73 226	77.08 229

thres400		60.77 250	59.97 250	63.15 247	70.78 245	35.35 312	63.27 272	57.47 287	53.00 197	68.31 250	77.09 251	32.45 310	72.09 239	35.61 306	81.73 226	82.02 165

EU-MVSNet		60.82 248	60.80 245	60.86 268	68.37 264	41.16 267	72.27 154	68.27 243	26.96 341	69.08 241	75.71 258	32.09 312	67.44 272	55.59 182	78.90 258	73.97 249

thres100view900		61.17 246	61.09 242	61.39 262	72.14 236	35.01 314	65.42 249	56.99 293	55.23 168	70.71 229	79.90 220	32.07 313	72.09 239	35.61 306	81.73 226	77.08 229

thres600view7		61.82 241	61.38 241	63.12 248	71.81 238	34.93 315	64.64 256	56.99 293	54.78 175	70.33 233	79.74 222	32.07 313	72.42 237	38.61 287	83.46 211	82.02 165

PatchmatchNet		54.60 280	54.27 285	55.59 289	65.17 293	39.08 283	66.92 230	51.80 314	39.89 292	58.39 300	73.12 285	31.69 315	58.33 306	43.01 263	58.38 337	69.38 291
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
sam_mvs1												31.41 316				70.05 283

patchmatchnet-post											68.99 315	31.32 317	69.38 260

ADS-MVSNet2		48.76 300	47.25 307	53.29 293	55.90 338	40.54 276	47.34 329	54.99 301	31.41 334	50.48 328	72.06 294	31.23 318	54.26 313	25.93 336	55.93 339	65.07 313

ADS-MVSNet		44.62 311	45.58 309	41.73 326	55.90 338	20.83 347	47.34 329	39.94 344	31.41 334	50.48 328	72.06 294	31.23 318	39.31 342	25.93 336	55.93 339	65.07 313

sam_mvs												31.21 320

Patchmatch-RL test		59.95 255	59.12 255	62.44 254	72.46 234	54.61 172	59.63 294	47.51 326	41.05 287	74.58 183	74.30 274	31.06 321	65.31 285	51.61 208	79.85 249	67.39 299

tpmvs		55.84 273	55.45 282	57.01 286	60.33 318	33.20 324	65.89 241	59.29 282	47.52 251	56.04 309	73.60 280	31.05 322	68.06 268	40.64 276	64.64 323	69.77 286

test_post											1.99 350	30.91 323	54.76 312

MDTV_nov1_ep13			54.05 286		65.54 289	29.30 334	59.00 297	55.22 298	35.96 311	52.44 322	75.98 257	30.77 324	59.62 303	38.21 289	73.33 292

test_post1							66.63 234				2.08 349	30.66 325	59.33 304	40.34 278

Patchmatch-test		47.93 302	49.96 302	41.84 325	57.42 332	24.26 344	48.75 323	41.49 340	39.30 294	56.79 306	73.48 281	30.48 326	33.87 345	29.29 330	72.61 294	67.39 299

tpm2		56.12 272	54.64 284	60.55 270	66.24 284	36.01 306	68.14 212	56.77 295	33.60 324	58.25 302	75.52 262	30.25 327	74.33 221	33.27 315	69.76 311	71.32 272

MVSTER		63.29 228	61.60 238	68.36 199	59.77 323	46.21 234	60.62 288	71.32 221	41.83 283	75.40 172	79.12 233	30.25 327	75.85 201	56.30 174	79.81 250	83.03 141

tpm		50.60 295	52.42 293	45.14 318	65.18 292	26.29 340	60.30 290	43.50 331	37.41 304	57.01 304	79.09 234	30.20 329	42.32 337	32.77 317	66.36 320	66.81 305

PatchT		53.35 286	56.47 275	43.99 322	64.19 299	17.46 349	59.15 295	43.10 332	52.11 206	54.74 315	86.95 119	29.97 330	49.98 317	43.62 260	74.40 287	64.53 317

MDTV_nov1_ep13_2view						18.41 348	53.74 314		31.57 333	44.89 339		29.90 331		32.93 316		71.48 271

test-LLR		50.43 296	50.69 300	49.64 302	60.76 315	41.87 264	53.18 315	45.48 329	43.41 276	49.41 332	60.47 336	29.22 332	44.73 330	42.09 267	72.14 296	62.33 323

test0.0.03 1		47.72 303	48.31 304	45.93 314	55.53 340	29.39 333	46.40 332	41.21 342	43.41 276	55.81 312	67.65 323	29.22 332	43.77 335	25.73 338	69.87 309	64.62 315

thisisatest0530		67.05 201	65.16 208	72.73 142	73.10 229	50.55 191	71.26 175	63.91 261	50.22 225	74.46 185	80.75 208	26.81 334	80.25 137	59.43 151	86.50 172	87.37 53

tttt0517		69.46 167	67.79 192	74.46 105	75.34 188	52.72 182	75.05 127	63.27 265	54.69 176	78.87 121	84.37 170	26.63 335	81.15 115	63.95 117	87.93 149	89.51 23

EMVS		44.61 312	44.45 315	45.10 319	48.91 349	43.00 256	37.92 341	41.10 343	46.75 253	38.00 347	48.43 345	26.42 336	46.27 322	37.11 299	75.38 282	46.03 340

thisisatest0515		60.48 252	57.86 265	68.34 200	67.25 276	46.42 231	60.58 289	62.14 269	40.82 288	63.58 276	69.12 314	26.28 337	78.34 170	48.83 228	82.13 222	80.26 196

E-PMN		45.17 308	45.36 310	44.60 320	50.07 347	42.75 258	38.66 340	42.29 337	46.39 255	39.55 345	51.15 343	26.00 338	45.37 327	37.68 293	76.41 272	45.69 341

EPMVS		45.74 306	46.53 308	43.39 323	54.14 346	22.33 346	55.02 311	35.00 348	34.69 317	51.09 326	70.20 308	25.92 339	42.04 339	37.19 297	55.50 341	65.78 309

tmp_tt		11.98 321	14.73 323	3.72 334	2.28 353	4.62 354	19.44 347	14.50 354	0.47 349	21.55 349	9.58 348	25.78 340	4.57 351	11.61 347	27.37 347	1.96 347

ET-MVSNet_ETH3D		63.32 227	60.69 246	71.20 159	70.15 254	55.66 165	65.02 253	64.32 260	43.28 279	68.99 243	72.05 296	25.46 341	78.19 176	54.16 197	82.80 216	79.74 203

FMVSNet5		55.08 279	55.54 281	53.71 292	65.80 287	33.50 323	56.22 306	52.50 312	43.72 273	61.06 288	83.38 181	25.46 341	54.87 311	30.11 325	81.64 231	72.75 259

new_pmnet		37.55 318	39.80 320	30.79 331	56.83 333	16.46 350	39.35 339	30.65 349	25.59 343	45.26 338	61.60 335	24.54 343	28.02 348	21.60 343	52.80 343	47.90 338

dp		44.09 313	44.88 313	41.72 327	58.53 328	23.18 345	54.70 312	42.38 336	34.80 315	44.25 342	65.61 330	24.48 344	44.80 329	29.77 327	49.42 344	57.18 331

RRT_test8_iter05		65.80 206	65.13 211	67.80 208	67.02 279	40.85 272	67.13 227	75.33 188	49.73 230	72.69 205	81.32 203	24.45 345	77.37 187	61.69 131	86.82 169	85.18 84

IB-MVS	49.67 18	59.69 257	56.96 271	67.90 205	68.19 267	50.30 193	61.42 283	65.18 256	47.57 250	55.83 311	67.15 328	23.77 346	79.60 147	43.56 261	79.97 248	73.79 252
Christian Sormann, Mattia Rossi, Andreas Kuhn and Friedrich Fraundorfer: IB-MVS: An Iterative Algorithm for Deep Multi-View Stereo based on Binary Decisions. BMVC 2021
CHOSEN 280x420		41.62 315	39.89 319	46.80 311	61.81 309	51.59 185	33.56 344	35.74 347	27.48 340	37.64 348	53.53 340	23.24 347	42.09 338	27.39 334	58.64 335	46.72 339

DWT-MVSNet_test		53.04 287	51.12 297	58.77 280	61.23 312	38.67 287	62.16 279	57.74 285	38.24 299	51.76 324	59.07 338	21.36 348	67.40 273	44.80 254	63.76 326	70.25 282

gg-mvs-nofinetune		55.75 274	56.75 273	52.72 295	62.87 305	28.04 337	68.92 198	41.36 341	71.09 38	50.80 327	92.63 12	20.74 349	66.86 277	29.97 326	72.41 295	63.25 318

GG-mvs-BLEND				52.24 296	60.64 317	29.21 335	69.73 191	42.41 334		45.47 337	52.33 342	20.43 350	68.16 267	25.52 339	65.42 322	59.36 328

JIA-IIPM		54.03 283	51.62 294	61.25 264	59.14 325	55.21 168	59.10 296	47.72 325	50.85 220	50.31 331	85.81 158	20.10 351	63.97 289	36.16 304	55.41 342	64.55 316

test-mter		48.56 301	48.20 305	49.64 302	60.76 315	41.87 264	53.18 315	45.48 329	31.91 332	49.41 332	60.47 336	18.34 352	44.73 330	42.09 267	72.14 296	62.33 323

TESTMET0.1,1		45.17 308	44.93 312	45.89 315	56.02 337	38.31 289	53.18 315	41.94 339	27.85 339	44.86 340	56.47 339	17.93 353	41.50 341	38.08 291	68.06 316	57.85 329

DeepMVS_CX				11.83 333	15.51 352	13.86 351		11.25 355	5.76 348	20.85 350	26.46 346	17.06 354	9.22 350	9.69 348	13.82 348	12.42 346

pmmvs3		46.71 305	45.09 311	51.55 298	56.76 334	48.25 209	55.78 308	39.53 345	24.13 345	50.35 330	63.40 332	15.90 355	51.08 315	29.29 330	70.69 305	55.33 333

testmvs		4.06 325	5.28 327	0.41 335	0.64 355	0.16 356	42.54 335	0.31 357	0.26 351	0.50 352	1.40 352	0.77 356	0.17 352	0.56 349	0.55 350	0.90 348

test123		4.43 324	5.78 326	0.39 336	0.97 354	0.28 355	46.33 333	0.45 356	0.31 350	0.62 351	1.50 351	0.61 357	0.11 353	0.56 349	0.63 349	0.77 349

uanet_test		0.00 326	0.00 328	0.00 337	0.00 356	0.00 357	0.00 348	0.00 358	0.00 352	0.00 353	0.00 353	0.00 358	0.00 354	0.00 351	0.00 351	0.00 350

test_part1				0.00 337		0.00 357	0.00 348	84.94 42				0.00 358	0.00 354	0.00 351	0.00 351	0.00 350

sosnet-low-res		0.00 326	0.00 328	0.00 337	0.00 356	0.00 357	0.00 348	0.00 358	0.00 352	0.00 353	0.00 353	0.00 358	0.00 354	0.00 351	0.00 351	0.00 350

sosnet		0.00 326	0.00 328	0.00 337	0.00 356	0.00 357	0.00 348	0.00 358	0.00 352	0.00 353	0.00 353	0.00 358	0.00 354	0.00 351	0.00 351	0.00 350

uncertanet		0.00 326	0.00 328	0.00 337	0.00 356	0.00 357	0.00 348	0.00 358	0.00 352	0.00 353	0.00 353	0.00 358	0.00 354	0.00 351	0.00 351	0.00 350

Regformer		0.00 326	0.00 328	0.00 337	0.00 356	0.00 357	0.00 348	0.00 358	0.00 352	0.00 353	0.00 353	0.00 358	0.00 354	0.00 351	0.00 351	0.00 350

ab-mvs-re		5.62 322	7.50 324	0.00 337	0.00 356	0.00 357	0.00 348	0.00 358	0.00 352	0.00 353	67.46 324	0.00 358	0.00 354	0.00 351	0.00 351	0.00 350

uanet		0.00 326	0.00 328	0.00 337	0.00 356	0.00 357	0.00 348	0.00 358	0.00 352	0.00 353	0.00 353	0.00 358	0.00 354	0.00 351	0.00 351	0.00 350

IU-MVS					86.12 54	60.90 130		80.38 123	45.49 259	81.31 93				75.64 38	94.39 38	84.65 95

save fliter					87.00 41	67.23 82	79.24 79	77.94 162	56.65 156

test_0728_SECOND				76.57 83	86.20 51	60.57 134	83.77 37	85.49 28					85.90 34	75.86 36	94.39 38	83.25 135

GSMVS																70.05 283

test_part2					85.90 58	66.44 87				84.61 56

MTGPA								80.63 116

MTMP							84.83 28	19.26 353

gm-plane-assit					62.51 306	33.91 321			37.25 305		62.71 333		72.74 229	38.70 285

test9_res														72.12 63	91.37 93	77.40 226

agg_prior2														70.70 70	90.93 105	78.55 214

agg_prior					84.44 81	66.02 91		78.62 150		76.95 145			80.34 134

test_prior4						70.14 61	77.57 98

test_prior				75.27 99	82.15 109	59.85 140		84.33 58					83.39 80			82.58 153

旧先验2							71.17 176		45.11 263	78.54 125			61.28 300	59.19 153

新几何2							71.33 172

无先验							74.82 130	70.94 229	47.75 249				76.85 194	54.47 191		72.09 267

原ACMM2							74.78 134

testdata2													67.30 274	48.34 234

testdata1							68.34 211		57.24 148

plane_prior7					85.18 66	66.21 89

plane_prior5								85.49 28					86.15 25	71.09 67	90.94 103	84.82 90

plane_prior4											89.11 89

plane_prior3						65.67 93			63.82 94	78.23 128

plane_prior2							82.74 48		65.45 73

plane_prior1					84.46 80

plane_prior						65.18 97	80.06 73		61.88 111						89.91 126

n2								0.00 358

nn								0.00 358

door-mid								55.02 300

test11								82.71 85

door								52.91 311

HQP5-MVS						58.80 150

HQP-NCC					82.37 104		77.32 102		59.08 128	71.58 216

ACMP_Plane					82.37 104		77.32 102		59.08 128	71.58 216

BP-MVS														67.38 96

HQP4-MVS										71.59 215			85.31 46			83.74 123

HQP3-MVS								84.12 66							89.16 136

NP-MVS					83.34 94	63.07 115					85.97 155

ACMMP++_ref															89.47 134

ACMMP++															91.96 82

High-resolution multi-view benchmark