High-res multi-view results

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

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

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

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

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

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

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

wuykxyi23d		88.46 62	88.80 61	87.44 81	90.96 121	93.03 1	85.85 117	81.96 243	74.58 149	98.58 2	97.29 4	87.73 30	87.31 245	82.84 68	99.41 1	81.99 308

no-one		71.52 280	70.43 285	74.81 275	78.45 314	63.41 193	57.73 349	77.03 267	51.46 328	77.17 276	90.33 186	54.96 285	80.35 306	47.41 327	99.29 2	80.68 329

LCM-MVSNet		95.70 1	96.40 1	93.61 2	98.67 1	85.39 29	95.54 3	97.36 1	96.97 1	99.04 1	99.05 1	96.61 1	95.92 11	85.07 37	99.27 3	99.54 1

WR-MVS_H		89.91 43	91.31 24	85.71 110	96.32 9	62.39 216	89.54 56	93.31 41	90.21 9	95.57 11	95.66 32	81.42 95	95.90 12	80.94 88	98.80 4	98.84 5

ACMP	79.16 10	90.54 29	90.60 36	90.35 41	94.36 40	80.98 57	89.16 63	94.05 23	79.03 89	92.87 41	93.74 96	90.60 11	95.21 42	82.87 66	98.76 5	94.87 79
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
ACMH+	77.89 11	90.73 25	91.50 18	88.44 64	93.00 65	76.26 100	89.65 50	95.55 3	87.72 17	93.89 26	94.94 52	91.62 4	93.44 110	78.35 120	98.76 5	95.61 65

PS-CasMVS		90.06 34	91.92 9	84.47 134	96.56 6	58.83 255	89.04 64	92.74 67	91.40 4	96.12 4	96.06 25	87.23 34	95.57 24	79.42 113	98.74 7	99.00 2

LPG-MVS_test		91.47 16	91.68 14	90.82 34	94.75 36	81.69 51	90.00 40	94.27 13	82.35 50	93.67 29	94.82 57	91.18 5	95.52 28	85.36 35	98.73 8	95.23 74

LGP-MVS_train				90.82 34	94.75 36	81.69 51		94.27 13	82.35 50	93.67 29	94.82 57	91.18 5	95.52 28	85.36 35	98.73 8	95.23 74

PEN-MVS		90.03 36	91.88 12	84.48 133	96.57 5	58.88 253	88.95 65	93.19 48	91.62 3	96.01 6	96.16 22	87.02 37	95.60 23	78.69 118	98.72 10	98.97 3

CP-MVSNet		89.27 53	90.91 33	84.37 138	96.34 8	58.61 257	88.66 73	92.06 84	90.78 5	95.67 9	95.17 46	81.80 91	95.54 27	79.00 116	98.69 11	98.95 4

TranMVSNet+NR-MVSNet		87.86 69	88.76 62	85.18 116	94.02 43	64.13 185	84.38 138	91.29 117	84.88 28	92.06 59	93.84 95	86.45 44	93.73 84	73.22 162	98.66 12	97.69 11

NR-MVSNet		86.00 101	86.22 99	85.34 114	93.24 59	64.56 182	82.21 202	90.46 135	80.99 66	88.42 129	91.97 134	77.56 125	93.85 81	72.46 170	98.65 13	97.61 12

UA-Net		91.49 14	91.53 17	91.39 22	94.98 32	82.95 50	93.52 5	92.79 65	88.22 15	88.53 126	97.64 2	83.45 65	94.55 60	86.02 34	98.60 14	96.67 37

FC-MVSNet-test		85.93 105	87.05 84	82.58 181	92.25 85	56.44 270	85.75 118	93.09 51	77.33 115	91.94 63	94.65 62	74.78 160	93.41 113	75.11 147	98.58 15	97.88 9

DTE-MVSNet		89.98 38	91.91 11	84.21 143	96.51 7	57.84 258	88.93 67	92.84 64	91.92 2	96.16 3	96.23 20	86.95 38	95.99 7	79.05 115	98.57 16	98.80 6

UniMVSNet (Re)		86.87 81	86.98 86	86.55 89	93.11 63	68.48 157	83.80 153	92.87 61	80.37 70	89.61 108	91.81 141	77.72 123	94.18 67	75.00 148	98.53 17	96.99 33

Baseline_NR-MVSNet		84.00 149	85.90 104	78.29 237	91.47 109	53.44 290	82.29 198	87.00 205	79.06 88	89.55 110	95.72 31	77.20 129	86.14 269	72.30 171	98.51 18	95.28 72

TDRefinement		93.52 2	93.39 3	93.88 1	95.94 13	90.26 4	95.70 2	96.46 2	90.58 7	92.86 42	96.29 18	88.16 25	94.17 69	86.07 33	98.48 19	97.22 25

ACMM	79.39 9	90.65 26	90.99 30	89.63 49	95.03 31	83.53 44	89.62 53	93.35 38	79.20 85	93.83 27	93.60 98	90.81 8	92.96 132	85.02 39	98.45 20	92.41 149
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
MP-MVS-pluss		90.81 24	91.08 28	89.99 46	95.97 12	79.88 63	88.13 78	94.51 11	75.79 135	92.94 39	94.96 51	88.36 19	95.01 47	90.70 2	98.40 21	95.09 77
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
CP-MVS		91.67 11	91.58 16	91.96 12	95.29 27	87.62 9	93.38 7	93.36 37	83.16 40	91.06 74	94.00 86	88.26 22	95.71 21	87.28 23	98.39 22	92.55 146

UniMVSNet_NR-MVSNet		86.84 83	87.06 83	86.17 102	92.86 70	67.02 166	82.55 191	91.56 98	83.08 42	90.92 76	91.82 140	78.25 119	93.99 74	74.16 151	98.35 23	97.49 15

DU-MVS		86.80 84	86.99 85	86.21 99	93.24 59	67.02 166	83.16 176	92.21 80	81.73 59	90.92 76	91.97 134	77.20 129	93.99 74	74.16 151	98.35 23	97.61 12

zzz-MVS		91.27 19	91.26 26	91.29 25	96.59 3	86.29 14	88.94 66	91.81 92	84.07 33	92.00 60	94.40 72	86.63 40	95.28 38	88.59 4	98.31 25	92.30 153

MTAPA		91.52 13	91.60 15	91.29 25	96.59 3	86.29 14	92.02 24	91.81 92	84.07 33	92.00 60	94.40 72	86.63 40	95.28 38	88.59 4	98.31 25	92.30 153

ACMH	76.49 14	89.34 52	91.14 27	83.96 149	92.50 77	70.36 144	89.55 54	93.84 29	81.89 58	94.70 15	95.44 40	90.69 9	88.31 238	83.33 59	98.30 27	93.20 125
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
COLMAP_ROB	83.01 3	91.97 8	91.95 8	92.04 10	93.68 50	86.15 18	93.37 8	95.10 7	90.28 8	92.11 57	95.03 49	89.75 14	94.93 49	79.95 105	98.27 28	95.04 78
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
ACMMP		91.91 9	91.87 13	92.03 11	95.53 24	85.91 22	93.35 9	94.16 20	82.52 49	92.39 56	94.14 82	89.15 16	95.62 22	87.35 20	98.24 29	94.56 83
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
HPM-MVS		92.13 6	92.20 7	91.91 15	95.58 23	84.67 38	93.51 6	94.85 9	82.88 45	91.77 65	93.94 93	90.55 12	95.73 20	88.50 8	98.23 30	95.33 70
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
MP-MVS		91.14 23	90.91 33	91.83 18	96.18 10	86.88 11	92.20 22	93.03 56	82.59 48	88.52 127	94.37 75	86.74 39	95.41 33	86.32 27	98.21 31	93.19 126
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
SteuartSystems-ACMMP		91.16 22	91.36 21	90.55 37	93.91 46	80.97 58	91.49 29	93.48 36	82.82 46	92.60 51	93.97 87	88.19 23	96.29 3	87.61 15	98.20 32	94.39 92
Skip Steuart: Steuart Systems R&D Blog.
HPM-MVS_fast		92.50 5	92.54 5	92.37 5	95.93 14	85.81 27	92.99 11	94.23 16	85.21 25	92.51 52	95.13 47	90.65 10	95.34 35	88.06 10	98.15 33	95.95 52

mPP-MVS		91.69 10	91.47 19	92.37 5	96.04 11	88.48 8	92.72 14	92.60 72	83.09 41	91.54 68	94.25 78	87.67 32	95.51 30	87.21 24	98.11 34	93.12 127

WR-MVS		83.56 157	84.40 139	81.06 205	93.43 55	54.88 281	78.67 265	85.02 227	81.24 63	90.74 80	91.56 147	72.85 195	91.08 177	68.00 204	98.04 35	97.23 24

XVG-ACMP-BASELINE		89.98 38	89.84 45	90.41 39	94.91 34	84.50 41	89.49 58	93.98 24	79.68 78	92.09 58	93.89 94	83.80 61	93.10 127	82.67 70	98.04 35	93.64 114

DeepC-MVS	82.31 4	89.15 55	89.08 54	89.37 54	93.64 51	79.07 71	88.54 74	94.20 17	73.53 159	89.71 100	94.82 57	85.09 52	95.77 18	84.17 51	98.03 37	93.26 123
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
FIs		85.35 112	86.27 98	82.60 180	91.86 96	57.31 262	85.10 126	93.05 53	75.83 134	91.02 75	93.97 87	73.57 181	92.91 136	73.97 154	98.02 38	97.58 14

abl_6		93.02 4	93.16 4	92.60 4	94.73 38	88.99 7	93.26 10	94.19 19	89.11 10	94.43 18	95.27 42	91.86 3	95.09 44	87.54 19	98.02 38	93.71 112

Anonymous20231211		88.40 63	89.62 50	84.73 126	90.46 131	65.27 176	88.86 68	93.02 57	87.15 19	93.05 38	97.10 6	82.28 79	92.02 154	76.70 138	97.99 40	96.88 35

PGM-MVS		91.20 21	90.95 32	91.93 13	95.67 20	85.85 25	90.00 40	93.90 28	80.32 72	91.74 66	94.41 71	88.17 24	95.98 8	86.37 26	97.99 40	93.96 103

APDe-MVS		91.22 20	91.92 9	89.14 57	92.97 66	78.04 78	92.84 12	94.14 21	83.33 38	93.90 25	95.73 30	88.77 17	96.41 1	87.60 17	97.98 42	92.98 131

CLD-MVS		83.18 163	82.64 165	84.79 123	89.05 151	67.82 163	77.93 271	92.52 73	68.33 224	85.07 175	81.54 313	82.06 83	92.96 132	69.35 190	97.91 43	93.57 117
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
IS-MVSNet		86.66 87	86.82 92	86.17 102	92.05 91	66.87 168	91.21 33	88.64 175	86.30 23	89.60 109	92.59 119	69.22 219	94.91 50	73.89 155	97.89 44	96.72 36

ACMMP_Plus		90.65 26	91.07 29	89.42 53	95.93 14	79.54 68	89.95 43	93.68 31	77.65 106	91.97 62	94.89 54	88.38 18	95.45 31	89.27 3	97.87 45	93.27 122

OurMVSNet-221017-0		90.01 37	89.74 47	90.83 33	93.16 61	80.37 59	91.91 27	93.11 50	81.10 65	95.32 12	97.24 5	72.94 194	94.85 51	85.07 37	97.78 46	97.26 22

SMA-MVS		90.18 31	90.38 40	89.55 52	95.15 28	79.52 69	90.98 34	93.24 47	75.37 144	92.84 43	94.93 53	85.58 51	96.00 6	87.61 15	97.76 47	93.12 127

ACMMPR		91.49 14	91.35 23	91.92 14	95.74 18	85.88 24	92.58 18	93.25 46	81.99 55	91.40 71	94.17 81	87.51 33	95.87 13	87.74 11	97.76 47	93.99 101

Anonymous20240521		87.68 73	88.61 63	84.87 120	91.76 98	64.76 180	89.28 62	91.66 96	83.02 43	93.29 34	96.10 24	77.37 128	92.89 137	77.27 134	97.75 49	96.97 34

HFP-MVS		91.30 18	91.39 20	91.02 29	95.43 25	84.66 39	92.58 18	93.29 44	81.99 55	91.47 69	93.96 89	88.35 20	95.56 25	87.74 11	97.74 50	92.85 132

#test#		90.49 30	90.31 42	91.02 29	95.43 25	84.66 39	90.65 36	93.29 44	77.00 121	91.47 69	93.96 89	88.35 20	95.56 25	84.88 40	97.74 50	92.85 132

region2R		91.44 17	91.30 25	91.87 16	95.75 17	85.90 23	92.63 17	93.30 42	81.91 57	90.88 79	94.21 79	87.75 29	95.87 13	87.60 17	97.71 52	93.83 105

v7n		90.13 32	90.96 31	87.65 77	91.95 93	71.06 140	89.99 42	93.05 53	86.53 21	94.29 21	96.27 19	82.69 72	94.08 72	86.25 30	97.63 53	97.82 10

XVS		91.54 12	91.36 21	92.08 8	95.64 21	86.25 16	92.64 15	93.33 39	85.07 26	89.99 88	94.03 85	86.57 42	95.80 16	87.35 20	97.62 54	94.20 94

X-MVStestdata		85.04 116	82.70 163	92.08 8	95.64 21	86.25 16	92.64 15	93.33 39	85.07 26	89.99 88	16.05 360	86.57 42	95.80 16	87.35 20	97.62 54	94.20 94

test_part1								93.93 25				87.19 35			97.61 56	91.48 177

ESAPD		90.05 35	90.56 39	88.50 63	93.86 47	77.77 80	89.63 51	93.93 25	84.39 29	92.84 43	93.43 100	87.19 35	96.26 4	82.18 75	97.61 56	91.48 177

APD-MVS_3200maxsize		92.05 7	92.24 6	91.48 20	93.02 64	85.17 31	92.47 21	95.05 8	87.65 18	93.21 36	94.39 74	90.09 13	95.08 45	86.67 25	97.60 58	94.18 96

OPM-MVS		89.80 44	89.97 43	89.27 55	94.76 35	79.86 64	86.76 103	92.78 66	78.78 92	92.51 52	93.64 97	88.13 26	93.84 83	84.83 42	97.55 59	94.10 99

LTVRE_ROB	86.10 1	93.04 3	93.44 2	91.82 19	93.73 49	85.72 28	96.79 1	95.51 4	88.86 12	95.63 10	96.99 8	84.81 54	93.16 124	91.10 1	97.53 60	96.58 40
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
MIMVSNet1		83.63 156	84.59 133	80.74 207	94.06 42	62.77 207	82.72 187	84.53 231	77.57 108	90.34 82	95.92 27	76.88 141	85.83 273	61.88 243	97.42 61	93.62 115

ACMMP++															97.35 62

nrg030		87.85 70	88.49 64	85.91 105	90.07 138	69.73 146	87.86 81	94.20 17	74.04 154	92.70 49	94.66 61	85.88 50	91.50 166	79.72 107	97.32 63	96.50 41

pmmvs6		86.52 90	88.06 67	81.90 190	92.22 87	62.28 222	84.66 132	89.15 170	83.54 37	89.85 96	97.32 3	88.08 27	86.80 258	70.43 184	97.30 64	96.62 38

SD-MVS		88.96 56	89.88 44	86.22 97	91.63 100	77.07 90	89.82 46	93.77 30	78.90 90	92.88 40	92.29 128	86.11 48	90.22 202	86.24 31	97.24 65	91.36 180

CPTT-MVS		89.39 51	88.98 57	90.63 36	95.09 30	86.95 10	92.09 23	92.30 78	79.74 77	87.50 141	92.38 124	81.42 95	93.28 119	83.07 63	97.24 65	91.67 171

APD-MVS		89.54 49	89.63 49	89.26 56	92.57 74	81.34 56	90.19 39	93.08 52	80.87 67	91.13 73	93.19 103	86.22 47	95.97 9	82.23 74	97.18 67	90.45 203
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
wuyk23d		75.13 243	79.30 204	62.63 327	75.56 332	75.18 105	80.89 229	73.10 296	75.06 146	94.76 14	95.32 41	87.73 30	52.85 358	34.16 352	97.11 68	59.85 353

PMVS	80.48 6	90.08 33	90.66 35	88.34 66	96.71 2	92.97 2	90.31 38	89.57 165	88.51 14	90.11 84	95.12 48	90.98 7	88.92 226	77.55 128	97.07 69	83.13 295
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
OMC-MVS		88.19 65	87.52 75	90.19 44	91.94 95	81.68 53	87.49 89	93.17 49	76.02 130	88.64 124	91.22 153	84.24 59	93.37 114	77.97 126	97.03 70	95.52 66

test_prior3		86.31 94	86.31 97	86.32 93	90.59 128	71.99 130	83.37 168	92.85 62	75.43 141	84.58 187	91.57 145	81.92 89	94.17 69	79.54 110	96.97 71	92.80 134

test_prior2							83.37 168		75.43 141	84.58 187	91.57 145	81.92 89		79.54 110	96.97 71

EPP-MVSNet		85.47 111	85.04 117	86.77 86	91.52 107	69.37 149	91.63 28	87.98 188	81.51 62	87.05 149	91.83 139	66.18 232	95.29 36	70.75 179	96.89 73	95.64 60

VDDNet		84.35 136	85.39 113	81.25 202	95.13 29	59.32 251	85.42 123	81.11 249	86.41 22	87.41 142	96.21 21	73.61 180	90.61 193	66.33 215	96.85 74	93.81 110

VPNet		80.25 200	81.68 179	75.94 270	92.46 78	47.98 335	76.70 281	81.67 247	73.45 161	84.87 179	92.82 114	74.66 163	86.51 263	61.66 246	96.85 74	93.33 121

SixPastTwentyTwo		87.20 77	87.45 77	86.45 91	92.52 76	69.19 154	87.84 84	88.05 185	81.66 60	94.64 16	96.53 14	65.94 233	94.75 53	83.02 65	96.83 76	95.41 68

VPA-MVSNet		83.47 160	84.73 123	79.69 219	90.29 133	57.52 261	81.30 222	88.69 174	76.29 126	87.58 139	94.44 69	80.60 102	87.20 246	66.60 214	96.82 77	94.34 93

Gipuma		84.44 130	86.33 96	78.78 228	84.20 265	73.57 113	89.55 54	90.44 136	84.24 32	84.38 191	94.89 54	76.35 144	80.40 305	76.14 141	96.80 78	82.36 303
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
CDPH-MVS		86.17 100	85.54 110	88.05 73	92.25 85	75.45 103	83.85 150	92.01 85	65.91 243	86.19 162	91.75 143	83.77 62	94.98 48	77.43 131	96.71 79	93.73 111

v748		88.91 58	89.82 46	86.19 101	90.06 139	68.53 156	88.81 70	91.48 102	84.36 31	94.19 23	95.98 26	82.52 75	92.67 142	84.30 48	96.67 80	97.37 19

DP-MVS		88.60 61	89.01 55	87.36 82	91.30 111	77.50 85	87.55 87	92.97 59	87.95 16	89.62 106	92.87 113	84.56 56	93.89 80	77.65 127	96.62 81	90.70 195

TransMVSNet (Re)		84.02 148	85.74 106	78.85 227	91.00 120	55.20 280	82.29 198	87.26 195	79.65 79	88.38 131	95.52 38	83.00 69	86.88 250	67.97 205	96.60 82	94.45 90

ambc				82.98 174	90.55 130	64.86 179	88.20 76	89.15 170		89.40 113	93.96 89	71.67 212	91.38 173	78.83 117	96.55 83	92.71 137

train_agg		85.98 104	85.28 114	88.07 70	92.34 82	79.70 66	83.94 146	90.32 140	65.79 244	84.49 189	90.97 167	81.93 87	93.63 89	81.21 83	96.54 84	90.88 190

agg_prior3		85.76 107	84.95 120	88.16 68	92.43 79	79.92 62	83.98 145	90.03 155	65.11 253	83.66 202	90.64 182	81.00 98	93.67 86	81.21 83	96.54 84	90.88 190

VDD-MVS		84.23 140	84.58 134	83.20 171	91.17 117	65.16 178	83.25 173	84.97 229	79.79 76	87.18 144	94.27 76	74.77 161	90.89 184	69.24 191	96.54 84	93.55 120

HPM-MVS++		88.93 57	88.45 65	90.38 40	94.92 33	85.85 25	89.70 47	91.27 118	78.20 100	86.69 153	92.28 129	80.36 104	95.06 46	86.17 32	96.49 87	90.22 207

test_djsdf		89.62 47	89.01 55	91.45 21	92.36 81	82.98 49	91.98 25	90.08 153	71.54 199	94.28 22	96.54 13	81.57 93	94.27 62	86.26 28	96.49 87	97.09 29

test9_res														80.83 90	96.45 89	90.57 199

Anonymous20240529		86.20 99	87.13 81	83.42 168	90.19 135	64.55 183	84.55 135	90.71 128	85.85 24	89.94 91	95.24 44	82.13 81	90.40 197	69.19 194	96.40 90	95.31 71

agg_prior1		85.72 108	85.20 115	87.28 83	91.58 104	77.69 82	83.69 157	90.30 144	66.29 239	84.32 193	91.07 164	82.13 81	93.18 122	81.02 86	96.36 91	90.98 185

anonymousdsp		89.73 46	88.88 58	92.27 7	89.82 143	86.67 12	90.51 37	90.20 152	69.87 213	95.06 13	96.14 23	84.28 58	93.07 131	87.68 13	96.34 92	97.09 29

PHI-MVS		86.38 92	85.81 105	88.08 69	88.44 167	77.34 87	89.35 61	93.05 53	73.15 171	84.76 181	87.70 228	78.87 114	94.18 67	80.67 94	96.29 93	92.73 136

PS-MVSNAJss		88.31 64	87.90 69	89.56 51	93.31 57	77.96 79	87.94 80	91.97 87	70.73 205	94.19 23	96.67 11	76.94 135	94.57 58	83.07 63	96.28 94	96.15 43

v10		86.54 89	87.10 82	84.84 121	88.16 174	63.28 197	86.64 109	92.20 81	75.42 143	92.81 46	94.50 67	74.05 170	94.06 73	83.88 54	96.28 94	97.17 27

CNVR-MVS		87.81 71	87.68 74	88.21 67	92.87 68	77.30 89	85.25 124	91.23 119	77.31 116	87.07 148	91.47 149	82.94 70	94.71 54	84.67 44	96.27 96	92.62 145

pcd1.5k->3k		38.83 336	41.11 337	32.01 347	93.13 62	0.00 368	0.00 360	91.38 115	0.00 363	0.00 364	0.00 365	89.24 15	0.00 366	0.00 363	96.24 97	96.02 49

114514_t		83.10 165	82.54 168	84.77 124	92.90 67	69.10 155	86.65 108	90.62 132	54.66 307	81.46 230	90.81 174	76.98 134	94.38 61	72.62 169	96.18 98	90.82 193

agg_prior2														79.68 108	96.16 99	90.22 207

v13		87.31 75	88.10 66	84.94 118	88.84 155	63.75 189	87.85 83	91.47 105	79.12 86	93.72 28	95.82 28	75.20 152	93.58 97	84.76 43	96.16 99	97.48 16

AllTest		87.97 68	87.40 79	89.68 47	91.59 101	83.40 45	89.50 57	95.44 5	79.47 80	88.00 134	93.03 107	82.66 73	91.47 167	70.81 177	96.14 101	94.16 97

TestCases				89.68 47	91.59 101	83.40 45		95.44 5	79.47 80	88.00 134	93.03 107	82.66 73	91.47 167	70.81 177	96.14 101	94.16 97

EPNet		80.37 198	78.41 210	86.23 96	76.75 324	73.28 115	87.18 94	77.45 265	76.24 127	68.14 325	88.93 207	65.41 235	93.85 81	69.47 189	96.12 103	91.55 175
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
v12		87.15 78	87.91 68	84.84 121	88.69 158	63.52 192	87.58 86	91.46 106	78.74 94	93.57 31	95.66 32	74.94 156	93.57 98	84.50 46	96.08 104	97.43 17

v11		86.96 79	87.78 72	84.51 131	88.50 164	62.60 212	87.21 92	91.63 97	78.08 103	93.40 33	95.56 37	75.07 153	93.57 98	84.46 47	96.08 104	97.36 20

pm-mvs1		83.69 154	84.95 120	79.91 215	90.04 141	59.66 248	82.43 193	87.44 192	75.52 140	87.85 136	95.26 43	81.25 97	85.65 275	68.74 199	96.04 106	94.42 91

V9		86.96 79	87.70 73	84.74 125	88.52 163	63.27 198	87.31 91	91.45 108	78.28 99	93.43 32	95.45 39	74.59 164	93.57 98	84.23 50	96.01 107	97.38 18

mvs_tets		89.78 45	89.27 53	91.30 24	93.51 52	84.79 36	89.89 45	90.63 131	70.00 212	94.55 17	96.67 11	87.94 28	93.59 94	84.27 49	95.97 108	95.52 66

DeepPCF-MVS	81.24 5	87.28 76	86.21 100	90.49 38	91.48 108	84.90 34	83.41 167	92.38 77	70.25 210	89.35 114	90.68 178	82.85 71	94.57 58	79.55 109	95.95 109	92.00 161

V14		86.75 85	87.46 76	84.62 129	88.35 168	63.00 203	87.02 97	91.42 111	77.78 105	93.27 35	95.23 45	74.22 167	93.56 101	83.95 53	95.93 110	97.31 21

jajsoiax		89.41 50	88.81 60	91.19 28	93.38 56	84.72 37	89.70 47	90.29 147	69.27 216	94.39 19	96.38 15	86.02 49	93.52 104	83.96 52	95.92 111	95.34 69

ANet_high		83.17 164	85.68 108	75.65 272	81.24 289	45.26 340	79.94 239	92.91 60	83.83 36	91.33 72	96.88 10	80.25 105	85.92 271	68.89 197	95.89 112	95.76 54

3Dnovator+	83.92 2	89.97 40	89.66 48	90.92 32	91.27 113	81.66 54	91.25 32	94.13 22	88.89 11	88.83 121	94.26 77	77.55 126	95.86 15	84.88 40	95.87 113	95.24 73

v15		86.56 88	87.25 80	84.51 131	88.15 175	62.72 208	86.72 107	91.40 113	77.38 110	93.11 37	95.00 50	73.93 172	93.55 102	83.67 57	95.86 114	97.26 22

HQP_MVS		87.75 72	87.43 78	88.70 61	93.45 53	76.42 98	89.45 59	93.61 32	79.44 82	86.55 155	92.95 111	74.84 158	95.22 40	80.78 91	95.83 115	94.46 88

plane_prior5								93.61 32					95.22 40	80.78 91	95.83 115	94.46 88

DeepC-MVS_fast	80.27 8	86.23 97	85.65 109	87.96 74	91.30 111	76.92 91	87.19 93	91.99 86	70.56 206	84.96 176	90.69 177	80.01 107	95.14 43	78.37 119	95.78 117	91.82 167
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
LFMVS		80.15 203	80.56 193	78.89 226	89.19 150	55.93 272	85.22 125	73.78 287	82.96 44	84.28 196	92.72 118	57.38 272	90.07 209	63.80 231	95.75 118	90.68 196

ACMMP++_ref															95.74 119

原ACMM1				84.60 130	92.81 72	74.01 111		91.50 100	62.59 267	82.73 215	90.67 179	76.53 142	94.25 64	69.24 191	95.69 120	85.55 258

tfpnnormal		81.79 181	82.95 161	78.31 236	88.93 154	55.40 276	80.83 231	82.85 237	76.81 122	85.90 167	94.14 82	74.58 165	86.51 263	66.82 213	95.68 121	93.01 130

TAPA-MVS	77.73 12	85.71 109	84.83 122	88.37 65	88.78 157	79.72 65	87.15 95	93.50 35	69.17 218	85.80 168	89.56 199	80.76 100	92.13 151	73.21 166	95.51 122	93.25 124
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
LS3D		90.60 28	90.34 41	91.38 23	89.03 152	84.23 42	93.58 4	94.68 10	90.65 6	90.33 83	93.95 92	84.50 57	95.37 34	80.87 89	95.50 123	94.53 87

v8		86.22 98	86.83 91	84.36 139	87.82 185	62.35 217	86.42 112	91.33 116	76.78 123	92.73 48	94.48 68	73.41 184	93.72 85	83.10 62	95.41 124	97.01 32

Vis-MVSNet (Re-imp)		77.82 214	77.79 213	77.92 243	88.82 156	51.29 310	83.28 171	71.97 303	74.04 154	82.23 218	89.78 195	57.38 272	89.41 216	57.22 281	95.41 124	93.05 129

FMVSNet1		84.55 127	85.45 112	81.85 193	90.27 134	61.05 237	86.83 100	88.27 182	78.57 96	89.66 102	95.64 34	75.43 149	90.68 190	69.09 195	95.33 126	93.82 107

test12				86.57 88	90.74 125	72.63 119		90.69 129		82.76 214		79.20 111	94.80 52		95.32 127	92.27 155

NCCC		87.36 74	86.87 89	88.83 59	92.32 84	78.84 74	86.58 110	91.09 123	78.77 93	84.85 180	90.89 171	80.85 99	95.29 36	81.14 85	95.32 127	92.34 152

Patchmtry		76.56 231	77.46 214	73.83 284	79.37 305	46.60 337	82.41 194	76.90 268	73.81 157	85.56 172	92.38 124	48.07 300	83.98 290	63.36 234	95.31 129	90.92 188

XVG-OURS		89.18 54	88.83 59	90.23 43	94.28 41	86.11 20	85.91 115	93.60 34	80.16 74	89.13 117	93.44 99	83.82 60	90.98 179	83.86 55	95.30 130	93.60 116

TSAR-MVS + GP.		83.95 150	82.69 164	87.72 75	89.27 148	81.45 55	83.72 156	81.58 248	74.73 148	85.66 169	86.06 250	72.56 203	92.69 141	75.44 145	95.21 131	89.01 226

test_0402		88.65 59	89.58 51	85.88 107	92.55 75	72.22 126	84.01 144	89.44 167	88.63 13	94.38 20	95.77 29	86.38 46	93.59 94	79.84 106	95.21 131	91.82 167

TinyColmap		81.25 188	82.34 173	77.99 242	85.33 247	60.68 241	82.32 197	88.33 180	71.26 201	86.97 150	92.22 132	77.10 132	86.98 249	62.37 238	95.17 133	86.31 251

Anonymous202405211		80.51 197	81.19 186	78.49 234	88.48 165	57.26 263	76.63 282	82.49 239	81.21 64	84.30 195	92.24 131	67.99 224	86.24 267	62.22 239	95.13 134	91.98 164

DP-MVS Recon		84.05 147	83.22 157	86.52 90	91.73 99	75.27 104	83.23 175	92.40 75	72.04 187	82.04 221	88.33 218	77.91 122	93.95 79	66.17 216	95.12 135	90.34 206

PCF-MVS	74.62 15	82.15 176	80.92 191	85.84 108	89.43 145	72.30 124	80.53 232	91.82 91	57.36 295	87.81 137	89.92 194	77.67 124	93.63 89	58.69 269	95.08 136	91.58 174
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
CSCG		86.26 95	86.47 95	85.60 112	90.87 123	74.26 110	87.98 79	91.85 90	80.35 71	89.54 112	88.01 222	79.09 112	92.13 151	75.51 144	95.06 137	90.41 204

plane_prior						76.42 98	87.15 95		75.94 133						95.03 138

new-patchmatchnet		70.10 289	73.37 260	60.29 335	81.23 290	16.95 363	59.54 343	74.62 281	62.93 264	80.97 235	87.93 225	62.83 244	71.90 326	55.24 293	95.01 139	92.00 161

v1192		84.57 126	84.69 127	84.21 143	87.75 190	62.88 205	83.02 178	91.43 109	69.08 220	89.98 90	90.89 171	72.70 199	93.62 93	82.41 71	94.97 140	96.13 44

v1921920		84.23 140	84.37 143	83.79 152	87.64 195	61.71 225	82.91 181	91.20 120	67.94 229	90.06 86	90.34 185	72.04 208	93.59 94	82.32 73	94.91 141	96.07 46

v144192		84.24 139	84.41 138	83.71 158	87.59 196	61.57 230	82.95 180	91.03 124	67.82 232	89.80 98	90.49 183	73.28 188	93.51 107	81.88 80	94.89 142	96.04 48

testing_2		84.36 134	84.64 132	83.50 167	86.74 219	63.97 188	84.56 134	90.31 142	66.22 240	91.62 67	94.55 64	75.88 146	91.95 155	77.02 137	94.89 142	94.56 83

LCM-MVSNet-Re		83.48 159	85.06 116	78.75 229	85.94 241	55.75 275	80.05 236	94.27 13	76.47 125	96.09 5	94.54 66	83.31 67	89.75 212	59.95 257	94.89 142	90.75 194

v17		86.32 93	86.95 87	84.44 135	88.00 177	62.62 211	86.74 105	91.48 102	77.17 118	92.74 47	94.56 63	73.74 176	93.53 103	83.27 60	94.87 145	97.18 26

v52		89.97 40	90.60 36	88.07 70	88.69 158	72.01 128	91.35 30	92.64 70	82.22 52	95.97 8	96.31 16	84.82 53	93.98 76	88.59 4	94.83 146	98.23 7

V4		89.97 40	90.60 36	88.07 70	88.69 158	72.01 128	91.35 30	92.64 70	82.22 52	95.98 7	96.31 16	84.80 55	93.98 76	88.59 4	94.83 146	98.23 7

v1240		84.30 137	84.51 136	83.65 159	87.65 194	61.26 234	82.85 182	91.54 99	67.94 229	90.68 81	90.65 180	71.71 211	93.64 88	82.84 68	94.78 148	96.07 46

MSLP-MVS++		85.00 118	86.03 103	81.90 190	91.84 97	71.56 138	86.75 104	93.02 57	75.95 132	87.12 145	89.39 201	77.98 120	89.40 217	77.46 129	94.78 148	84.75 271

IterMVS-LS		84.73 123	84.98 119	83.96 149	87.35 200	63.66 190	83.25 173	89.88 158	76.06 128	89.62 106	92.37 127	73.40 186	92.52 145	78.16 123	94.77 150	95.69 58
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
AdaColmap		83.66 155	83.69 153	83.57 162	90.05 140	72.26 125	86.29 114	90.00 156	78.19 101	81.65 228	87.16 234	83.40 66	94.24 65	61.69 245	94.76 151	84.21 278

MVS_0304		84.88 120	83.96 150	87.64 78	87.43 199	74.83 106	84.18 140	93.30 42	77.48 109	77.39 275	88.46 213	74.53 166	95.74 19	78.09 125	94.75 152	92.36 151

ITE_SJBPF				90.11 45	90.72 126	84.97 33		90.30 144	81.56 61	90.02 87	91.20 156	82.40 77	90.81 186	73.58 158	94.66 153	94.56 83

v1144		84.54 129	84.72 125	84.00 147	87.67 193	62.55 214	82.97 179	90.93 126	70.32 209	89.80 98	90.99 166	73.50 182	93.48 108	81.69 81	94.65 154	95.97 50

test20.03		73.75 258	74.59 243	71.22 301	81.11 291	51.12 312	70.15 319	72.10 302	70.42 207	80.28 254	91.50 148	64.21 237	74.72 322	46.96 331	94.58 155	87.82 238

TSAR-MVS + MP.		88.14 66	87.82 71	89.09 58	95.72 19	76.74 94	92.49 20	91.19 121	67.85 231	86.63 154	94.84 56	79.58 110	95.96 10	87.62 14	94.50 156	94.56 83

HQP3-MVS								92.68 68							94.47 157

HQP-MVS		84.61 125	84.06 147	86.27 95	91.19 114	70.66 142	84.77 128	92.68 68	73.30 166	80.55 250	90.17 191	72.10 205	94.61 56	77.30 132	94.47 157	93.56 118

MCST-MVS		84.36 134	83.93 151	85.63 111	91.59 101	71.58 137	83.52 162	92.13 82	61.82 273	83.96 198	89.75 196	79.93 109	93.46 109	78.33 121	94.34 159	91.87 166

v16		86.24 96	86.85 90	84.43 136	87.96 179	62.59 213	86.73 106	91.48 102	77.17 118	92.67 50	94.55 64	73.63 177	93.52 104	83.26 61	94.16 160	97.17 27

alignmvs		83.94 151	83.98 149	83.80 151	87.80 189	67.88 162	84.54 136	91.42 111	73.27 169	88.41 130	87.96 223	72.33 204	90.83 185	76.02 142	94.11 161	92.69 138

USDC		76.63 229	76.73 220	76.34 265	83.46 273	57.20 264	80.02 237	88.04 186	52.14 322	83.65 203	91.25 152	63.24 243	86.65 262	54.66 298	94.11 161	85.17 262

MVS_111021_HR		84.63 124	84.34 144	85.49 113	90.18 136	75.86 102	79.23 259	87.13 200	73.35 163	85.56 172	89.34 202	83.60 64	90.50 195	76.64 139	94.05 163	90.09 212

v18		85.99 103	86.55 94	84.30 141	87.73 191	62.29 221	86.40 113	91.49 101	76.64 124	92.40 55	94.20 80	73.28 188	93.52 104	82.87 66	93.99 164	97.09 29

VNet		79.31 206	80.27 197	76.44 263	87.92 180	53.95 285	75.58 293	84.35 232	74.39 152	82.23 218	90.72 176	72.84 196	84.39 286	60.38 256	93.98 165	90.97 186

FMVSNet2		81.31 185	81.61 181	80.41 210	86.38 222	58.75 256	83.93 148	86.58 208	72.43 178	87.65 138	92.98 109	63.78 240	90.22 202	66.86 210	93.92 166	92.27 155

LF4IMVS		82.75 168	81.93 178	85.19 115	82.08 282	80.15 61	85.53 121	88.76 173	68.01 226	85.58 171	87.75 227	71.80 210	86.85 251	74.02 153	93.87 167	88.58 228

divwei89l23v2f112		84.16 142	84.38 140	83.52 164	87.32 202	61.70 227	82.79 184	89.74 159	71.90 196	89.64 103	91.12 160	72.68 200	93.10 127	80.40 97	93.81 168	95.75 55

v1141		84.16 142	84.38 140	83.52 164	87.32 202	61.70 227	82.79 184	89.74 159	71.90 196	89.64 103	91.12 160	72.68 200	93.10 127	80.39 99	93.80 169	95.75 55

v1		84.16 142	84.38 140	83.52 164	87.33 201	61.71 225	82.79 184	89.73 161	71.89 198	89.64 103	91.11 162	72.72 197	93.10 127	80.40 97	93.79 170	95.75 55

canonicalmvs		85.50 110	86.14 101	83.58 161	87.97 178	67.13 165	87.55 87	94.32 12	73.44 162	88.47 128	87.54 231	86.45 44	91.06 178	75.76 143	93.76 171	92.54 147

v2v482		84.09 145	84.24 145	83.62 160	87.13 210	61.40 231	82.71 188	89.71 162	72.19 186	89.55 110	91.41 150	70.70 216	93.20 121	81.02 86	93.76 171	96.25 42

UGNet		82.78 167	81.64 180	86.21 99	86.20 232	76.24 101	86.86 98	85.68 216	77.07 120	73.76 301	92.82 114	69.64 217	91.82 161	69.04 196	93.69 173	90.56 200
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
v7		84.81 121	85.00 118	84.23 142	88.15 175	63.27 198	83.79 154	91.39 114	71.10 203	90.07 85	91.28 151	74.04 171	93.63 89	81.48 82	93.67 174	95.79 53

旧先验1					91.97 92	71.77 132		81.78 246			91.84 138	73.92 173			93.65 175	83.61 285

view600		76.79 224	76.54 221	77.56 247	87.91 181	50.77 316	81.92 208	71.35 311	77.38 110	84.62 183	88.40 214	45.18 323	89.26 219	58.58 270	93.49 176	92.66 139

view800		76.79 224	76.54 221	77.56 247	87.91 181	50.77 316	81.92 208	71.35 311	77.38 110	84.62 183	88.40 214	45.18 323	89.26 219	58.58 270	93.49 176	92.66 139

conf0.05thres1000		76.79 224	76.54 221	77.56 247	87.91 181	50.77 316	81.92 208	71.35 311	77.38 110	84.62 183	88.40 214	45.18 323	89.26 219	58.58 270	93.49 176	92.66 139

tfpn		76.79 224	76.54 221	77.56 247	87.91 181	50.77 316	81.92 208	71.35 311	77.38 110	84.62 183	88.40 214	45.18 323	89.26 219	58.58 270	93.49 176	92.66 139

MVS_111021_LR		84.28 138	83.76 152	85.83 109	89.23 149	83.07 48	80.99 228	83.56 233	72.71 176	86.07 163	89.07 205	81.75 92	86.19 268	77.11 135	93.36 180	88.24 229

GBi-Net		82.02 178	82.07 175	81.85 193	86.38 222	61.05 237	86.83 100	88.27 182	72.43 178	86.00 164	95.64 34	63.78 240	90.68 190	65.95 217	93.34 181	93.82 107

test1		82.02 178	82.07 175	81.85 193	86.38 222	61.05 237	86.83 100	88.27 182	72.43 178	86.00 164	95.64 34	63.78 240	90.68 190	65.95 217	93.34 181	93.82 107

FMVSNet3		78.80 208	78.55 208	79.57 221	82.89 279	56.89 267	81.76 212	85.77 215	69.04 221	86.00 164	90.44 184	51.75 291	90.09 208	65.95 217	93.34 181	91.72 170

v6		84.43 131	84.66 129	83.75 154	87.81 186	62.34 218	83.59 158	90.26 150	72.33 182	89.94 91	91.19 157	73.30 187	93.29 116	80.26 100	93.26 184	95.62 61

v1neww		84.43 131	84.66 129	83.75 154	87.81 186	62.34 218	83.59 158	90.27 148	72.33 182	89.93 93	91.22 153	73.28 188	93.29 116	80.25 101	93.25 185	95.62 61

v7new		84.43 131	84.66 129	83.75 154	87.81 186	62.34 218	83.59 158	90.27 148	72.33 182	89.93 93	91.22 153	73.28 188	93.29 116	80.25 101	93.25 185	95.62 61

K. test v3		85.14 113	84.73 123	86.37 92	91.13 118	69.63 148	85.45 122	76.68 271	84.06 35	92.44 54	96.99 8	62.03 245	94.65 55	80.58 95	93.24 187	94.83 82

Anonymous20231206		71.38 281	71.88 274	69.88 302	86.31 227	54.37 282	70.39 318	74.62 281	52.57 318	76.73 277	88.76 208	59.94 255	72.06 325	44.35 336	93.23 188	83.23 293

新几何1				82.95 175	93.96 44	78.56 76		80.24 253	55.45 303	83.93 199	91.08 163	71.19 214	88.33 237	65.84 220	93.07 189	81.95 310

1121		80.86 192	79.81 203	84.02 146	93.93 45	78.70 75	81.64 215	80.18 254	55.43 304	83.67 201	91.15 158	71.29 213	91.41 171	67.95 206	93.06 190	81.96 309

lessismore_v0				85.95 104	91.10 119	70.99 141		70.91 315		91.79 64	94.42 70	61.76 246	92.93 134	79.52 112	93.03 191	93.93 104

TAMVS		78.08 213	76.36 226	83.23 170	90.62 127	72.87 117	79.08 260	80.01 256	61.72 275	81.35 232	86.92 237	63.96 239	88.78 231	50.61 313	93.01 192	88.04 233

EPNet_dtu		72.87 267	71.33 280	77.49 252	77.72 318	60.55 242	82.35 196	75.79 273	66.49 238	58.39 355	81.06 316	53.68 287	85.98 270	53.55 302	92.97 193	85.95 254
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
Effi-MVS+-dtu		85.82 106	83.38 155	93.14 3	87.13 210	91.15 3	87.70 85	88.42 177	74.57 150	83.56 204	85.65 252	78.49 116	94.21 66	72.04 173	92.88 194	94.05 100

CANet		83.79 153	82.85 162	86.63 87	86.17 233	72.21 127	83.76 155	91.43 109	77.24 117	74.39 298	87.45 232	75.36 150	95.42 32	77.03 136	92.83 195	92.25 157

API-MVS		82.28 174	82.61 166	81.30 200	86.29 228	69.79 145	88.71 72	87.67 191	78.42 98	82.15 220	84.15 278	77.98 120	91.59 165	65.39 223	92.75 196	82.51 302

0601test		78.71 210	78.51 209	79.32 224	84.32 261	58.84 254	78.38 266	85.33 220	75.99 131	82.49 216	86.57 239	58.01 266	90.02 210	62.74 237	92.73 197	89.10 222

Regformer-1		86.00 101	85.50 111	87.49 79	84.18 266	76.90 92	83.52 162	87.94 189	82.18 54	89.19 115	85.07 265	82.28 79	91.89 158	82.40 72	92.72 198	93.69 113

Regformer-2		86.74 86	86.08 102	88.73 60	84.18 266	79.20 70	83.52 162	89.33 168	83.33 38	89.92 95	85.07 265	83.23 68	93.16 124	83.39 58	92.72 198	93.83 105

testgi		72.36 272	74.61 241	65.59 320	80.56 299	42.82 348	68.29 324	73.35 291	66.87 236	81.84 224	89.93 193	72.08 207	66.92 340	46.05 333	92.54 200	87.01 245

FMVSNet5		72.10 275	71.69 275	73.32 286	81.57 287	53.02 293	76.77 280	78.37 261	63.31 261	76.37 279	91.85 137	36.68 351	78.98 309	47.87 326	92.45 201	87.95 235

CDS-MVSNet		77.32 219	75.40 235	83.06 173	89.00 153	72.48 122	77.90 272	82.17 242	60.81 281	78.94 264	83.49 283	59.30 260	88.76 232	54.64 299	92.37 202	87.93 236
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
CNLPA		83.55 158	83.10 160	84.90 119	89.34 147	83.87 43	84.54 136	88.77 172	79.09 87	83.54 205	88.66 211	74.87 157	81.73 301	66.84 212	92.29 203	89.11 221

F-COLMAP		84.97 119	83.42 154	89.63 49	92.39 80	83.40 45	88.83 69	91.92 89	73.19 170	80.18 255	89.15 204	77.04 133	93.28 119	65.82 221	92.28 204	92.21 158

tfpn111		76.03 236	75.53 234	77.53 251	87.27 204	51.88 302	81.07 225	73.26 292	75.68 136	83.25 207	86.37 243	45.54 313	89.38 218	55.07 295	92.26 205	91.34 181

thres600view7		75.97 237	75.35 237	77.85 245	87.01 215	51.84 306	80.45 233	73.26 292	75.20 145	83.10 211	86.31 248	45.54 313	89.05 223	55.03 296	92.24 206	92.66 139

PVSNet_BlendedMVS		78.80 208	77.84 212	81.65 197	84.43 257	63.41 193	79.49 246	90.44 136	61.70 276	75.43 289	87.07 236	69.11 220	91.44 169	60.68 254	92.24 206	90.11 211

DELS-MVS		81.44 184	81.25 185	82.03 188	84.27 262	62.87 206	76.47 286	92.49 74	70.97 204	81.64 229	83.83 279	75.03 154	92.70 140	74.29 149	92.22 208	90.51 202
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
testdata				79.54 222	92.87 68	72.34 123		80.14 255	59.91 286	85.47 174	91.75 143	67.96 225	85.24 277	68.57 202	92.18 209	81.06 327

v148		82.31 173	82.48 170	81.81 196	85.59 243	59.66 248	81.47 218	86.02 213	72.85 174	88.05 133	90.65 180	70.73 215	90.91 183	75.15 146	91.79 210	94.87 79

tfpn1000		73.63 263	73.58 251	73.79 285	85.46 246	50.31 322	79.99 238	68.18 330	72.33 182	80.66 246	83.05 287	39.80 348	86.74 261	60.96 252	91.78 211	84.32 276

test222					93.31 57	76.54 95	79.38 253	77.79 263	52.59 317	82.36 217	90.84 173	66.83 229			91.69 212	81.25 322

pmmvs-eth3d		78.42 211	77.04 217	82.57 183	87.44 198	74.41 109	80.86 230	79.67 257	55.68 302	84.69 182	90.31 188	60.91 249	85.42 276	62.20 240	91.59 213	87.88 237

Vis-MVSNet		86.86 82	86.58 93	87.72 75	92.09 89	77.43 86	87.35 90	92.09 83	78.87 91	84.27 197	94.05 84	78.35 118	93.65 87	80.54 96	91.58 214	92.08 160
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
ppachtmachnet_test		74.73 250	74.00 248	76.90 257	80.71 297	56.89 267	71.53 314	78.42 260	58.24 291	79.32 261	82.92 293	57.91 268	84.26 287	65.60 222	91.36 215	89.56 214

conf0.01		74.17 254	73.53 253	76.08 268	86.13 234	50.06 325	79.45 247	68.54 324	72.01 188	80.76 240	82.50 299	41.39 337	86.83 252	59.66 260	91.36 215	91.34 181

conf0.002		74.17 254	73.53 253	76.08 268	86.13 234	50.06 325	79.45 247	68.54 324	72.01 188	80.76 240	82.50 299	41.39 337	86.83 252	59.66 260	91.36 215	91.34 181

thresconf0.02		73.65 259	73.53 253	73.98 279	86.13 234	50.06 325	79.45 247	68.54 324	72.01 188	80.76 240	82.50 299	41.39 337	86.83 252	59.66 260	91.36 215	85.06 264

tfpn_n400		73.65 259	73.53 253	73.98 279	86.13 234	50.06 325	79.45 247	68.54 324	72.01 188	80.76 240	82.50 299	41.39 337	86.83 252	59.66 260	91.36 215	85.06 264

tfpnconf		73.65 259	73.53 253	73.98 279	86.13 234	50.06 325	79.45 247	68.54 324	72.01 188	80.76 240	82.50 299	41.39 337	86.83 252	59.66 260	91.36 215	85.06 264

tfpnview11		73.65 259	73.53 253	73.98 279	86.13 234	50.06 325	79.45 247	68.54 324	72.01 188	80.76 240	82.50 299	41.39 337	86.83 252	59.66 260	91.36 215	85.06 264

OpenMVS	76.72 13	81.98 180	82.00 177	81.93 189	84.42 259	68.22 159	88.50 75	89.48 166	66.92 235	81.80 227	91.86 136	72.59 202	90.16 204	71.19 176	91.25 222	87.40 241

Test4		81.31 185	81.13 188	81.88 192	84.89 251	63.05 202	82.37 195	90.50 134	62.75 266	89.00 118	88.29 219	67.55 226	91.68 163	73.55 159	91.24 223	90.89 189

EG-PatchMatch MVS		84.08 146	84.11 146	83.98 148	92.22 87	72.61 120	82.20 204	87.02 204	72.63 177	88.86 119	91.02 165	78.52 115	91.11 176	73.41 161	91.09 224	88.21 230

3Dnovator	80.37 7	84.80 122	84.71 126	85.06 117	86.36 225	74.71 107	88.77 71	90.00 156	75.65 139	84.96 176	93.17 104	74.06 169	91.19 174	78.28 122	91.09 224	89.29 220

Regformer-3		85.06 115	84.67 128	86.22 97	84.27 262	73.43 114	84.07 142	85.26 221	80.77 68	88.62 125	85.48 255	80.56 103	90.39 198	81.99 78	91.04 226	94.85 81

Regformer-4		86.41 91	85.71 107	88.52 62	84.27 262	77.57 84	84.07 142	88.00 187	82.82 46	89.84 97	85.48 255	82.06 83	92.77 139	83.83 56	91.04 226	95.22 76

conf200view11		75.62 240	75.05 238	77.34 253	87.27 204	51.88 302	81.07 225	73.26 292	75.68 136	83.25 207	86.37 243	45.54 313	88.80 227	51.98 308	90.99 228	91.34 181

thres100view900		75.45 241	75.05 238	76.66 262	87.27 204	51.88 302	81.07 225	73.26 292	75.68 136	83.25 207	86.37 243	45.54 313	88.80 227	51.98 308	90.99 228	89.31 218

tfpn200view9		74.86 248	74.23 246	76.74 261	86.24 229	52.12 299	79.24 256	73.87 285	73.34 164	81.82 225	84.60 273	46.02 307	88.80 227	51.98 308	90.99 228	89.31 218

thres400		75.14 242	74.23 246	77.86 244	86.24 229	52.12 299	79.24 256	73.87 285	73.34 164	81.82 225	84.60 273	46.02 307	88.80 227	51.98 308	90.99 228	92.66 139

cascas		76.29 234	74.81 240	80.72 209	84.47 256	62.94 204	73.89 305	87.34 193	55.94 301	75.16 293	76.53 336	63.97 238	91.16 175	65.00 224	90.97 232	88.06 232

HSP-MVS		88.63 60	87.84 70	91.02 29	95.76 16	86.14 19	92.75 13	91.01 125	78.43 97	89.16 116	92.25 130	72.03 209	96.36 2	88.21 9	90.93 233	90.55 201

ab-mvs		79.67 205	80.56 193	76.99 255	88.48 165	56.93 265	84.70 131	86.06 212	68.95 222	80.78 239	93.08 105	75.30 151	84.62 284	56.78 283	90.90 234	89.43 217

MAR-MVS		80.24 201	78.74 207	84.73 126	86.87 218	78.18 77	85.75 118	87.81 190	65.67 247	77.84 270	78.50 328	73.79 175	90.53 194	61.59 248	90.87 235	85.49 260
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
EI-MVSNet-Vis-set		85.12 114	84.53 135	86.88 84	84.01 268	72.76 118	83.91 149	85.18 223	80.44 69	88.75 122	85.49 254	80.08 106	91.92 156	82.02 77	90.85 236	95.97 50

EI-MVSNet-UG-set		85.04 116	84.44 137	86.85 85	83.87 271	72.52 121	83.82 151	85.15 224	80.27 73	88.75 122	85.45 258	79.95 108	91.90 157	81.92 79	90.80 237	96.13 44

XVG-OURS-SEG-HR		89.59 48	89.37 52	90.28 42	94.47 39	85.95 21	86.84 99	93.91 27	80.07 75	86.75 152	93.26 102	93.64 2	90.93 181	84.60 45	90.75 238	93.97 102

test_normal		81.23 189	81.16 187	81.43 199	84.77 254	61.99 224	81.46 219	86.95 206	63.16 263	87.22 143	89.63 197	73.62 178	91.65 164	72.92 167	90.70 239	90.65 198

EI-MVSNet		82.61 169	82.42 171	83.20 171	83.25 275	63.66 190	83.50 165	85.07 225	76.06 128	86.55 155	85.10 263	73.41 184	90.25 199	78.15 124	90.67 240	95.68 59

MVSTER		77.09 221	75.70 233	81.25 202	75.27 338	61.08 236	77.49 277	85.07 225	60.78 282	86.55 155	88.68 210	43.14 333	90.25 199	73.69 157	90.67 240	92.42 148

Patchmatch-RL test		74.48 251	73.68 250	76.89 258	84.83 252	66.54 170	72.29 311	69.16 323	57.70 293	86.76 151	86.33 246	45.79 312	82.59 297	69.63 188	90.65 242	81.54 317

CMPMVS	59.41 20	75.12 244	73.57 252	79.77 216	75.84 330	67.22 164	81.21 223	82.18 241	50.78 332	76.50 278	87.66 229	55.20 283	82.99 295	62.17 242	90.64 243	89.09 223
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
DI_MVS_plusplus_test		81.27 187	81.26 184	81.29 201	84.98 249	61.65 229	81.98 207	87.25 196	63.56 258	87.56 140	89.60 198	73.62 178	91.83 160	72.20 172	90.59 244	90.38 205

tfpn_ndepth		72.54 270	72.30 271	73.24 288	84.81 253	51.42 308	79.24 256	70.49 317	69.26 217	78.48 266	79.80 323	40.16 347	86.77 259	58.08 279	90.43 245	81.53 318

V42		83.47 160	83.37 156	83.75 154	83.16 277	63.33 196	81.31 220	90.23 151	69.51 215	90.91 78	90.81 174	74.16 168	92.29 149	80.06 103	90.22 246	95.62 61

PM-MVS		80.20 202	79.00 205	83.78 153	88.17 173	86.66 13	81.31 220	66.81 339	69.64 214	88.33 132	90.19 189	64.58 236	83.63 293	71.99 175	90.03 247	81.06 327

PLC	73.85 16	82.09 177	80.31 196	87.45 80	90.86 124	80.29 60	85.88 116	90.65 130	68.17 225	76.32 281	86.33 246	73.12 193	92.61 144	61.40 249	90.02 248	89.44 216
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
diffmvs		81.78 182	82.36 172	80.02 214	79.06 308	59.93 246	83.30 170	88.41 179	73.47 160	78.38 267	92.05 133	75.85 147	88.38 236	80.73 93	89.98 249	91.76 169

mvs-test1		84.55 127	82.12 174	91.84 17	87.13 210	89.54 5	85.05 127	88.42 177	74.57 150	80.60 247	82.98 289	78.49 116	93.98 76	72.04 173	89.77 250	92.00 161

CANet_DTU		77.81 215	77.05 216	80.09 213	81.37 288	59.90 247	83.26 172	88.29 181	69.16 219	67.83 328	83.72 280	60.93 248	89.47 213	69.22 193	89.70 251	90.88 190

PMMVS2		55.64 331	59.27 328	44.74 346	64.30 361	12.32 364	40.60 357	49.79 361	53.19 314	65.06 340	84.81 269	53.60 288	49.76 359	32.68 355	89.41 252	72.15 342

Fast-Effi-MVS+-dtu		82.54 171	81.41 183	85.90 106	85.60 242	76.53 97	83.07 177	89.62 164	73.02 173	79.11 263	83.51 282	80.74 101	90.24 201	68.76 198	89.29 253	90.94 187

thres200		72.34 273	71.55 278	74.70 277	83.48 272	51.60 307	75.02 297	73.71 288	70.14 211	78.56 265	80.57 317	46.20 305	88.20 239	46.99 330	89.29 253	84.32 276

jason		77.42 218	75.75 232	82.43 186	87.10 213	69.27 150	77.99 270	81.94 245	51.47 327	77.84 270	85.07 265	60.32 252	89.00 224	70.74 180	89.27 255	89.03 224
jason: jason.
MG-MVS		80.32 199	80.94 190	78.47 235	88.18 172	52.62 297	82.29 198	85.01 228	72.01 188	79.24 262	92.54 122	69.36 218	93.36 115	70.65 181	89.19 256	89.45 215

BH-untuned		80.96 191	80.99 189	80.84 206	88.55 162	68.23 158	80.33 234	88.46 176	72.79 175	86.55 155	86.76 238	74.72 162	91.77 162	61.79 244	88.99 257	82.52 301

PVSNet_Blended_VisFu		81.55 183	80.49 195	84.70 128	91.58 104	73.24 116	84.21 139	91.67 95	62.86 265	80.94 236	87.16 234	67.27 227	92.87 138	69.82 187	88.94 258	87.99 234

MVSFormer		82.23 175	81.57 182	84.19 145	85.54 244	69.26 151	91.98 25	90.08 153	71.54 199	76.23 282	85.07 265	58.69 263	94.27 62	86.26 28	88.77 259	89.03 224

lupinMVS		76.37 233	74.46 244	82.09 187	85.54 244	69.26 151	76.79 279	80.77 252	50.68 334	76.23 282	82.82 294	58.69 263	88.94 225	69.85 186	88.77 259	88.07 231

RPSCF		88.00 67	86.93 88	91.22 27	90.08 137	89.30 6	89.68 49	91.11 122	79.26 84	89.68 101	94.81 60	82.44 76	87.74 242	76.54 140	88.74 261	96.61 39

PAPM_NR		83.23 162	83.19 159	83.33 169	90.90 122	65.98 172	88.19 77	90.78 127	78.13 102	80.87 238	87.92 226	73.49 183	92.42 146	70.07 185	88.40 262	91.60 173

xiu_mvs_v1_base_debu		80.84 193	80.14 199	82.93 176	88.31 169	71.73 133	79.53 243	87.17 197	65.43 248	79.59 257	82.73 296	76.94 135	90.14 205	73.22 162	88.33 263	86.90 246

xiu_mvs_v1_base		80.84 193	80.14 199	82.93 176	88.31 169	71.73 133	79.53 243	87.17 197	65.43 248	79.59 257	82.73 296	76.94 135	90.14 205	73.22 162	88.33 263	86.90 246

xiu_mvs_v1_base_debi		80.84 193	80.14 199	82.93 176	88.31 169	71.73 133	79.53 243	87.17 197	65.43 248	79.59 257	82.73 296	76.94 135	90.14 205	73.22 162	88.33 263	86.90 246

XXY-MVS		74.44 253	76.19 228	69.21 307	84.61 255	52.43 298	71.70 313	77.18 266	60.73 283	80.60 247	90.96 169	75.44 148	69.35 332	56.13 286	88.33 263	85.86 256

Fast-Effi-MVS+		81.04 190	80.57 192	82.46 185	87.50 197	63.22 200	78.37 267	89.63 163	68.01 226	81.87 223	82.08 308	82.31 78	92.65 143	67.10 208	88.30 267	91.51 176

MDA-MVSNet-bldmvs		77.47 217	76.90 218	79.16 225	79.03 309	64.59 181	66.58 332	75.67 275	73.15 171	88.86 119	88.99 206	66.94 228	81.23 302	64.71 225	88.22 268	91.64 172

PAPR		78.84 207	78.10 211	81.07 204	85.17 248	60.22 244	82.21 202	90.57 133	62.51 268	75.32 291	84.61 272	74.99 155	92.30 148	59.48 267	88.04 269	90.68 196

BH-RMVSNet		80.53 196	80.22 198	81.49 198	87.19 209	66.21 171	77.79 273	86.23 210	74.21 153	83.69 200	88.50 212	73.25 192	90.75 187	63.18 236	87.90 270	87.52 239

Effi-MVS+		83.90 152	84.01 148	83.57 162	87.22 208	65.61 175	86.55 111	92.40 75	78.64 95	81.34 233	84.18 276	83.65 63	92.93 134	74.22 150	87.87 271	92.17 159

MVS_Test		82.47 172	83.22 157	80.22 212	82.62 281	57.75 260	82.54 192	91.96 88	71.16 202	82.89 213	92.52 123	77.41 127	90.50 195	80.04 104	87.84 272	92.40 150

QAPM		82.59 170	82.59 167	82.58 181	86.44 220	66.69 169	89.94 44	90.36 139	67.97 228	84.94 178	92.58 121	72.71 198	92.18 150	70.63 182	87.73 273	88.85 227

PVSNet_Blended		76.49 232	75.40 235	79.76 217	84.43 257	63.41 193	75.14 296	90.44 136	57.36 295	75.43 289	78.30 329	69.11 220	91.44 169	60.68 254	87.70 274	84.42 274

pmmvs5		70.73 284	70.07 287	72.72 293	77.03 323	52.73 295	74.14 302	75.65 276	50.36 336	72.17 309	85.37 261	55.42 282	80.67 304	52.86 305	87.59 275	84.77 270

testmv		70.47 286	70.70 281	69.77 304	86.22 231	53.89 286	67.32 329	71.91 304	63.32 260	78.16 268	89.47 200	56.12 278	73.10 323	36.43 349	87.33 276	82.33 304

IB-MVS	62.13 19	71.64 278	68.97 292	79.66 220	80.80 296	62.26 223	73.94 304	76.90 268	63.27 262	68.63 324	76.79 334	33.83 354	91.84 159	59.28 268	87.26 277	84.88 269
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
N_pmnet		70.20 287	68.80 294	74.38 278	80.91 293	84.81 35	59.12 346	76.45 272	55.06 305	75.31 292	82.36 305	55.74 279	54.82 357	47.02 329	87.24 278	83.52 286

pmmvs4		74.92 247	72.98 264	80.73 208	84.95 250	71.71 136	76.23 288	77.59 264	52.83 316	77.73 273	86.38 242	56.35 276	84.97 280	57.72 280	87.05 279	85.51 259

MIMVSNet		71.09 282	71.59 276	69.57 306	87.23 207	50.07 324	78.91 261	71.83 305	60.20 285	71.26 313	91.76 142	55.08 284	76.09 316	41.06 341	87.02 280	82.54 300

HyFIR lowres test		75.12 244	72.66 266	82.50 184	91.44 110	65.19 177	72.47 310	87.31 194	46.79 343	80.29 253	84.30 275	52.70 289	92.10 153	51.88 312	86.73 281	90.22 207

MSDG		80.06 204	79.99 202	80.25 211	83.91 270	68.04 160	77.51 276	89.19 169	77.65 106	81.94 222	83.45 284	76.37 143	86.31 266	63.31 235	86.59 282	86.41 249

Patchmatch-test		65.91 309	67.38 300	61.48 332	75.51 334	43.21 347	68.84 322	63.79 343	62.48 269	72.80 305	83.42 285	44.89 328	59.52 354	48.27 325	86.45 283	81.70 313

mvs_anonymous		78.13 212	78.76 206	76.23 267	79.24 306	50.31 322	78.69 264	84.82 230	61.60 277	83.09 212	92.82 114	73.89 174	87.01 247	68.33 203	86.41 284	91.37 179

semantic-postprocess				84.34 140	83.93 269	69.66 147		81.09 250	72.43 178	86.47 161	90.19 189	57.56 271	93.15 126	77.45 130	86.39 285	90.22 207

E-PMN		61.59 320	61.62 320	61.49 331	66.81 357	55.40 276	53.77 353	60.34 350	66.80 237	58.90 353	65.50 354	40.48 346	66.12 344	55.72 288	86.25 286	62.95 351

EMVS		61.10 323	60.81 323	61.99 329	65.96 359	55.86 273	53.10 354	58.97 352	67.06 233	56.89 357	63.33 355	40.98 344	67.03 339	54.79 297	86.18 287	63.08 350

our_test_3		71.85 276	71.59 276	72.62 295	80.71 297	53.78 287	69.72 321	71.71 309	58.80 288	78.03 269	80.51 318	56.61 275	78.84 310	62.20 240	86.04 288	85.23 261

EU-MVSNet		75.12 244	74.43 245	77.18 254	83.11 278	59.48 250	85.71 120	82.43 240	39.76 355	85.64 170	88.76 208	44.71 329	87.88 241	73.86 156	85.88 289	84.16 279

1111		61.71 318	63.77 314	55.55 341	78.05 315	25.74 360	60.62 340	67.52 331	66.09 241	74.68 295	86.50 240	16.00 366	59.22 355	38.79 344	85.65 290	81.70 313

GA-MVS		75.83 238	74.61 241	79.48 223	81.87 284	59.25 252	73.42 308	82.88 236	68.68 223	79.75 256	81.80 310	50.62 293	89.46 214	66.85 211	85.64 291	89.72 213

casdiffmvs		82.99 166	82.51 169	84.42 137	86.34 226	67.92 161	87.86 81	92.28 79	60.95 280	81.12 234	93.08 105	76.07 145	93.43 112	79.41 114	85.45 292	91.93 165

MVS		73.21 265	72.59 268	75.06 274	80.97 292	60.81 240	81.64 215	85.92 214	46.03 346	71.68 311	77.54 330	68.47 223	89.77 211	55.70 289	85.39 293	74.60 338

PatchT		70.52 285	72.76 265	63.79 326	79.38 304	33.53 356	77.63 274	65.37 341	73.61 158	71.77 310	92.79 117	44.38 330	75.65 319	64.53 229	85.37 294	82.18 306

TR-MVS		76.77 228	75.79 230	79.72 218	86.10 240	65.79 174	77.14 278	83.02 235	65.20 252	81.40 231	82.10 307	66.30 230	90.73 189	55.57 290	85.27 295	82.65 297

BH-w/o		76.57 230	76.07 229	78.10 240	86.88 217	65.92 173	77.63 274	86.33 209	65.69 246	80.89 237	79.95 322	68.97 222	90.74 188	53.01 304	85.25 296	77.62 332

test1235678		65.57 311	65.73 308	65.06 323	82.84 280	50.90 314	62.90 337	69.26 321	57.17 298	72.36 307	83.04 288	46.02 307	70.10 329	32.79 354	85.24 297	74.19 339

IterMVS		76.91 223	76.34 227	78.64 231	80.91 293	64.03 186	76.30 287	79.03 258	64.88 255	83.11 210	89.16 203	59.90 256	84.46 285	68.61 201	85.15 298	87.42 240
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
OpenMVS_ROB	70.19 17	77.77 216	77.46 214	78.71 230	84.39 260	61.15 235	81.18 224	82.52 238	62.45 270	83.34 206	87.37 233	66.20 231	88.66 234	64.69 226	85.02 299	86.32 250

test-LLR		67.21 303	66.74 304	68.63 311	76.45 327	55.21 278	67.89 325	67.14 336	62.43 271	65.08 338	72.39 344	43.41 331	69.37 330	61.00 250	84.89 300	81.31 320

test-mter		65.00 312	63.79 313	68.63 311	76.45 327	55.21 278	67.89 325	67.14 336	50.98 331	65.08 338	72.39 344	28.27 361	69.37 330	61.00 250	84.89 300	81.31 320

PS-MVSNAJ		77.04 222	76.53 225	78.56 232	87.09 214	61.40 231	75.26 295	87.13 200	61.25 278	74.38 299	77.22 333	76.94 135	90.94 180	64.63 227	84.83 302	83.35 290

xiu_mvs_v2_base		77.19 220	76.75 219	78.52 233	87.01 215	61.30 233	75.55 294	87.12 202	61.24 279	74.45 297	78.79 327	77.20 129	90.93 181	64.62 228	84.80 303	83.32 291

pmmvs3		62.47 314	60.02 327	69.80 303	71.58 354	64.00 187	70.52 317	58.44 353	39.77 354	66.05 332	75.84 337	27.10 363	72.28 324	46.15 332	84.77 304	73.11 341

MDTV_nov1_ep13			68.29 298		78.03 317	43.87 345	74.12 303	72.22 301	52.17 320	67.02 330	85.54 253	45.36 319	80.85 303	55.73 287	84.42 305

1112_ss		74.82 249	73.74 249	78.04 241	89.57 144	60.04 245	76.49 285	87.09 203	54.31 308	73.66 302	79.80 323	60.25 253	86.76 260	58.37 274	84.15 306	87.32 242

PatchMatch-RL		74.48 251	73.22 261	78.27 238	87.70 192	85.26 30	75.92 289	70.09 318	64.34 257	76.09 284	81.25 315	65.87 234	78.07 311	53.86 301	83.82 307	71.48 343

MDA-MVSNet_test_wron		70.05 291	70.44 283	68.88 309	73.84 343	53.47 289	58.93 348	67.28 334	58.43 289	87.09 147	85.40 259	59.80 258	67.25 338	59.66 260	83.54 308	85.92 255

YYNet1		70.06 290	70.44 283	68.90 308	73.76 344	53.42 291	58.99 347	67.20 335	58.42 290	87.10 146	85.39 260	59.82 257	67.32 337	59.79 258	83.50 309	85.96 253

Test_1112_low_res		73.90 257	73.08 262	76.35 264	90.35 132	55.95 271	73.40 309	86.17 211	50.70 333	73.14 303	85.94 251	58.31 265	85.90 272	56.51 284	83.22 310	87.20 243

PVSNet	58.17 21	66.41 307	65.63 309	68.75 310	81.96 283	49.88 331	62.19 339	72.51 300	51.03 330	68.04 326	75.34 340	50.84 292	74.77 320	45.82 334	82.96 311	81.60 316

gg-mvs-nofinetune		68.96 298	69.11 291	68.52 313	76.12 329	45.32 339	83.59 158	55.88 356	86.68 20	64.62 341	97.01 7	30.36 358	83.97 291	44.78 335	82.94 312	76.26 335

CR-MVSNet		74.00 256	73.04 263	76.85 259	79.58 301	62.64 209	82.58 189	76.90 268	50.50 335	75.72 287	92.38 124	48.07 300	84.07 288	68.72 200	82.91 313	83.85 282

RPMNet		76.06 235	75.79 230	76.85 259	79.58 301	62.64 209	82.58 189	71.75 307	74.80 147	75.72 287	92.59 119	48.69 298	84.07 288	73.48 160	82.91 313	83.85 282

test0.0.03 1		64.66 313	64.36 312	65.57 321	75.03 340	46.89 336	64.69 335	61.58 349	62.43 271	71.18 315	77.54 330	43.41 331	68.47 335	40.75 342	82.65 315	81.35 319

HY-MVS	64.64 18	73.03 266	72.47 270	74.71 276	83.36 274	54.19 283	82.14 205	81.96 243	56.76 300	69.57 321	86.21 249	60.03 254	84.83 283	49.58 319	82.65 315	85.11 263

Patchmatch-test1		72.75 268	72.61 267	73.19 289	81.62 286	55.86 273	78.89 262	71.37 310	61.73 274	74.93 294	82.15 306	60.46 251	81.80 299	59.68 259	82.63 317	81.92 311

testus		62.33 316	63.03 316	60.20 336	78.78 311	40.74 349	59.14 344	69.80 320	49.26 339	71.41 312	74.72 342	52.33 290	63.52 349	29.84 356	82.01 318	76.36 334

CHOSEN 1792x2688		72.45 271	70.56 282	78.13 239	90.02 142	63.08 201	68.72 323	83.16 234	42.99 352	75.92 285	85.46 257	57.22 274	85.18 279	49.87 317	81.67 319	86.14 252

WTY-MVS		67.91 301	68.35 296	66.58 318	80.82 295	48.12 334	65.96 333	72.60 298	53.67 312	71.20 314	81.68 312	58.97 262	69.06 334	48.57 322	81.67 319	82.55 299

TESTMET0.1,1		61.29 321	60.32 325	64.19 325	72.06 352	51.30 309	67.89 325	62.09 345	45.27 347	60.65 347	69.01 347	27.93 362	64.74 348	56.31 285	81.65 321	76.53 333

PAPM		71.77 277	70.06 288	76.92 256	86.39 221	53.97 284	76.62 283	86.62 207	53.44 313	63.97 342	84.73 271	57.79 270	92.34 147	39.65 343	81.33 322	84.45 273

DSMNet-mixed		60.98 324	61.61 321	59.09 338	72.88 350	45.05 342	74.70 299	46.61 362	26.20 358	65.34 336	90.32 187	55.46 281	63.12 352	41.72 340	81.30 323	69.09 347

sss		66.92 304	67.26 302	65.90 319	77.23 320	51.10 313	64.79 334	71.72 308	52.12 323	70.13 319	80.18 320	57.96 267	65.36 347	50.21 314	81.01 324	81.25 322

tpm		67.95 300	68.08 299	67.55 315	78.74 312	43.53 346	75.60 292	67.10 338	54.92 306	72.23 308	88.10 221	42.87 334	75.97 317	52.21 306	80.95 325	83.15 294

tpmp4_e23		69.43 294	67.33 301	75.72 271	78.53 313	52.75 294	82.13 206	74.91 278	49.23 340	66.37 331	84.17 277	41.28 343	88.67 233	49.73 318	79.63 326	85.75 257

tpm2		68.45 299	66.83 303	73.30 287	78.93 310	48.50 332	79.76 240	71.76 306	47.50 342	69.92 320	83.60 281	42.07 336	88.40 235	48.44 324	79.51 327	83.01 296

FPMVS		72.29 274	72.00 273	73.14 290	88.63 161	85.00 32	74.65 300	67.39 333	71.94 195	77.80 272	87.66 229	50.48 294	75.83 318	49.95 315	79.51 327	58.58 355

UnsupCasMVSNet_bld		69.21 297	69.68 289	67.82 314	79.42 303	51.15 311	67.82 328	75.79 273	54.15 309	77.47 274	85.36 262	59.26 261	70.64 328	48.46 323	79.35 329	81.66 315

CostFormer		69.98 292	68.68 295	73.87 283	77.14 321	50.72 320	79.26 255	74.51 283	51.94 324	70.97 316	84.75 270	45.16 327	87.49 243	55.16 294	79.23 330	83.40 289

1314		73.22 264	72.56 269	75.20 273	80.41 300	57.84 258	81.64 215	85.36 219	51.68 325	73.10 304	76.65 335	61.45 247	85.19 278	63.54 232	79.21 331	82.59 298

PMMVS		61.65 319	60.38 324	65.47 322	65.40 360	69.26 151	63.97 336	61.73 348	36.80 357	60.11 348	68.43 348	59.42 259	66.35 343	48.97 321	78.57 332	60.81 352

test2356		56.69 328	55.15 332	61.32 333	73.20 346	44.11 344	54.95 351	62.52 344	48.75 341	62.45 344	68.42 349	21.10 365	65.67 346	26.86 358	78.08 333	74.19 339

MS-PatchMatch		70.93 283	70.22 286	73.06 291	81.85 285	62.50 215	73.82 306	77.90 262	52.44 319	75.92 285	81.27 314	55.67 280	81.75 300	55.37 292	77.70 334	74.94 337

UnsupCasMVSNet_eth		71.63 279	72.30 271	69.62 305	76.47 326	52.70 296	70.03 320	80.97 251	59.18 287	79.36 260	88.21 220	60.50 250	69.12 333	58.33 276	77.62 335	87.04 244

CVMVSNet		72.62 269	71.41 279	76.28 266	83.25 275	60.34 243	83.50 165	79.02 259	37.77 356	76.33 280	85.10 263	49.60 296	87.41 244	70.54 183	77.54 336	81.08 325

GG-mvs-BLEND				67.16 316	73.36 345	46.54 338	84.15 141	55.04 357		58.64 354	61.95 357	29.93 359	83.87 292	38.71 347	76.92 337	71.07 344

CHOSEN 280x420		59.08 326	56.52 331	66.76 317	76.51 325	64.39 184	49.62 356	59.00 351	43.86 350	55.66 358	68.41 350	35.55 353	68.21 336	43.25 337	76.78 338	67.69 348

tpmvs		70.16 288	69.56 290	71.96 299	74.71 342	48.13 333	79.63 241	75.45 277	65.02 254	70.26 318	81.88 309	45.34 320	85.68 274	58.34 275	75.39 339	82.08 307

MVP-Stereo		75.81 239	73.51 259	82.71 179	89.35 146	73.62 112	80.06 235	85.20 222	60.30 284	73.96 300	87.94 224	57.89 269	89.45 215	52.02 307	74.87 340	85.06 264
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
DWT-MVSNet_test		66.43 306	64.37 311	72.63 294	74.86 341	50.86 315	76.52 284	72.74 297	54.06 310	65.50 335	68.30 351	32.13 356	84.84 282	61.63 247	73.59 341	82.19 305

PatchFormer-LS_test		67.91 301	66.49 307	72.17 298	75.29 337	51.85 305	75.68 290	73.62 290	57.23 297	68.64 322	68.13 352	42.19 335	82.76 296	64.06 230	73.51 342	81.89 312

new_pmnet		55.69 330	57.66 329	49.76 343	75.47 335	30.59 357	59.56 342	51.45 360	43.62 351	62.49 343	75.48 338	40.96 345	49.15 360	37.39 348	72.52 343	69.55 346

test12356		54.91 332	57.14 330	48.22 345	75.83 331	17.47 362	52.31 355	69.20 322	51.66 326	60.11 348	75.40 339	29.77 360	62.62 353	27.64 357	72.37 344	64.59 349

PatchmatchNet		69.71 293	68.83 293	72.33 297	77.66 319	53.60 288	79.29 254	69.99 319	57.66 294	72.53 306	82.93 292	46.45 304	80.08 308	60.91 253	72.09 345	83.31 292
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
MVS-HIRNet		61.16 322	62.92 317	55.87 339	79.09 307	35.34 355	71.83 312	57.98 355	46.56 344	59.05 352	91.14 159	49.95 295	76.43 315	38.74 346	71.92 346	55.84 356

tpmrst		66.28 308	66.69 305	65.05 324	72.82 351	39.33 351	78.20 268	70.69 316	53.16 315	67.88 327	80.36 319	48.18 299	74.75 321	58.13 277	70.79 347	81.08 325

LP		69.42 295	68.30 297	72.77 292	71.48 355	56.84 269	73.66 307	74.84 279	63.52 259	70.95 317	83.35 286	49.55 297	77.15 314	57.13 282	70.21 348	84.33 275

tpm cat1		66.76 305	65.21 310	71.42 300	77.09 322	50.62 321	78.01 269	73.68 289	44.89 348	68.64 322	79.00 326	45.51 317	82.42 298	49.91 316	70.15 349	81.23 324

ADS-MVSNet2		65.87 310	63.64 315	72.55 296	73.16 348	56.92 266	67.10 330	74.81 280	49.74 337	66.04 333	82.97 290	46.71 302	77.26 312	42.29 338	69.96 350	83.46 287

ADS-MVSNet		61.90 317	62.19 319	61.03 334	73.16 348	36.42 354	67.10 330	61.75 347	49.74 337	66.04 333	82.97 290	46.71 302	63.21 351	42.29 338	69.96 350	83.46 287

JIA-IIPM		69.41 296	66.64 306	77.70 246	73.19 347	71.24 139	75.67 291	65.56 340	70.42 207	65.18 337	92.97 110	33.64 355	83.06 294	53.52 303	69.61 352	78.79 331

testpf		58.55 327	61.58 322	49.48 344	66.03 358	40.05 350	74.40 301	58.07 354	64.72 256	59.36 350	72.67 343	22.76 364	66.92 340	67.07 209	69.15 353	41.46 358

EPMVS		62.47 314	62.63 318	62.01 328	70.63 356	38.74 352	74.76 298	52.86 358	53.91 311	67.71 329	80.01 321	39.40 349	66.60 342	55.54 291	68.81 354	80.68 329

MVE	40.22 23	51.82 334	50.47 336	55.87 339	62.66 362	51.91 301	31.61 359	39.28 363	40.65 353	50.76 359	74.98 341	56.24 277	44.67 361	33.94 353	64.11 355	71.04 345
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
dp		60.70 325	60.29 326	61.92 330	72.04 353	38.67 353	70.83 315	64.08 342	51.28 329	60.75 346	77.28 332	36.59 352	71.58 327	47.41 327	62.34 356	75.52 336

PVSNet_0	51.08 22	56.10 329	54.97 333	59.48 337	75.12 339	53.28 292	55.16 350	61.89 346	44.30 349	59.16 351	62.48 356	54.22 286	65.91 345	35.40 351	47.01 357	59.25 354

tmp_tt		20.25 338	24.50 339	7.49 350	4.47 364	8.70 365	34.17 358	25.16 365	1.00 360	32.43 361	18.49 359	39.37 350	9.21 363	21.64 359	43.75 358	4.57 360

PNet_i23d		52.13 333	51.24 335	54.79 342	75.56 332	45.26 340	54.54 352	52.55 359	66.95 234	57.19 356	65.82 353	13.15 368	63.40 350	36.39 350	39.04 359	55.71 357

DeepMVS_CX				24.13 349	32.95 363	29.49 358		21.63 366	12.07 359	37.95 360	45.07 358	30.84 357	19.21 362	17.94 360	33.06 360	23.69 359

.test1245		48.02 335	54.41 334	28.84 348	78.05 315	25.74 360	60.62 340	67.52 331	66.09 241	74.68 295	86.50 240	16.00 366	59.22 355	38.79 344	1.47 361	1.55 362

testmvs		5.91 342	7.65 343	0.72 352	1.20 365	0.37 367	59.14 344	0.67 368	0.49 362	1.11 362	2.76 363	0.94 370	0.24 365	1.02 362	1.47 361	1.55 362

test123		6.27 341	8.08 342	0.84 351	1.11 366	0.57 366	62.90 337	0.82 367	0.54 361	1.07 363	2.75 364	1.26 369	0.30 364	1.04 361	1.26 363	1.66 361

cdsmvs_eth3d_5k		20.81 337	27.75 338	0.00 353	0.00 367	0.00 368	0.00 360	85.44 218	0.00 363	0.00 364	82.82 294	81.46 94	0.00 366	0.00 363	0.00 364	0.00 364

pcd_1.5k_mvsjas		6.41 340	8.55 341	0.00 353	0.00 367	0.00 368	0.00 360	0.00 369	0.00 363	0.00 364	0.00 365	76.94 135	0.00 366	0.00 363	0.00 364	0.00 364

sosnet-low-res		0.00 343	0.00 344	0.00 353	0.00 367	0.00 368	0.00 360	0.00 369	0.00 363	0.00 364	0.00 365	0.00 371	0.00 366	0.00 363	0.00 364	0.00 364

sosnet		0.00 343	0.00 344	0.00 353	0.00 367	0.00 368	0.00 360	0.00 369	0.00 363	0.00 364	0.00 365	0.00 371	0.00 366	0.00 363	0.00 364	0.00 364

uncertanet		0.00 343	0.00 344	0.00 353	0.00 367	0.00 368	0.00 360	0.00 369	0.00 363	0.00 364	0.00 365	0.00 371	0.00 366	0.00 363	0.00 364	0.00 364

Regformer		0.00 343	0.00 344	0.00 353	0.00 367	0.00 368	0.00 360	0.00 369	0.00 363	0.00 364	0.00 365	0.00 371	0.00 366	0.00 363	0.00 364	0.00 364

ab-mvs-re		6.65 339	8.87 340	0.00 353	0.00 367	0.00 368	0.00 360	0.00 369	0.00 363	0.00 364	79.80 323	0.00 371	0.00 366	0.00 363	0.00 364	0.00 364

uanet		0.00 343	0.00 344	0.00 353	0.00 367	0.00 368	0.00 360	0.00 369	0.00 363	0.00 364	0.00 365	0.00 371	0.00 366	0.00 363	0.00 364	0.00 364

GSMVS																83.88 280

test_part3							89.63 51		84.39 29		93.43 100		96.26 4	82.18 75

test_part2					93.86 47	77.77 80				92.84 43

sam_mvs1												46.11 306				83.88 280

sam_mvs												45.92 311

MTGPA								91.81 92

test_post1							78.85 263				3.13 361	45.19 322	80.13 307	58.11 278

test_post											3.10 362	45.43 318	77.22 313

patchmatchnet-post											81.71 311	45.93 310	87.01 247

MTMP							90.66 35	33.14 364

gm-plane-assit					75.42 336	44.97 343			52.17 320		72.36 346		87.90 240	54.10 300

TEST9					92.34 82	79.70 66	83.94 146	90.32 140	65.41 251	84.49 189	90.97 167	82.03 85	93.63 89

test_8					92.09 89	78.87 73	83.82 151	90.31 142	65.79 244	84.36 192	90.96 169	81.93 87	93.44 110

agg_prior					91.58 104	77.69 82		90.30 144		84.32 193			93.18 122

test_prior4						78.97 72	84.59 133

test_prior				86.32 93	90.59 128	71.99 130		92.85 62					94.17 69			92.80 134

旧先验2							81.73 213		56.88 299	86.54 160			84.90 281	72.81 168

新几何2							81.72 214

无先验							82.81 183	85.62 217	58.09 292				91.41 171	67.95 206		84.48 272

原ACMM2							82.26 201

testdata2													86.43 265	63.52 233

segment_acmp												81.94 86

testdata1							79.62 242		73.95 156

plane_prior7					93.45 53	77.31 88

plane_prior6					92.61 73	76.54 95						74.84 158

plane_prior4											92.95 111

plane_prior3						76.85 93			77.79 104	86.55 155

plane_prior2							89.45 59		79.44 82

plane_prior1					92.83 71

n2								0.00 369

nn								0.00 369

door-mid								74.45 284

test11								91.46 106

door								72.57 299

HQP5-MVS						70.66 142

HQP-NCC					91.19 114		84.77 128		73.30 166	80.55 250

ACMP_Plane					91.19 114		84.77 128		73.30 166	80.55 250

BP-MVS														77.30 132

HQP4-MVS										80.56 249			94.61 56			93.56 118

HQP2-MVS												72.10 205

NP-MVS					91.95 93	74.55 108					90.17 191

MDTV_nov1_ep13_2view						27.60 359	70.76 316		46.47 345	61.27 345		45.20 321		49.18 320		83.75 284

Test By Simon												79.09 112

High-resolution multi-view benchmark