High-res multi-view results

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

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

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

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

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

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

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

LCM-MVSNet		95.70 1	96.40 1	93.61 2	98.67 1	85.39 29	95.54 3	97.36 1	96.97 1	99.04 1	99.05 1	96.61 1	95.92 10	85.07 37	99.27 3	99.54 1

PMVS	80.48 6	90.08 34	90.66 36	88.34 66	96.71 2	92.97 2	90.31 38	89.57 167	88.51 14	90.11 85	95.12 49	90.98 7	88.92 226	77.55 127	97.07 68	83.13 295
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
zzz-MVS		91.27 19	91.26 26	91.29 25	96.59 3	86.29 14	88.94 65	91.81 94	84.07 33	92.00 61	94.40 72	86.63 39	95.28 37	88.59 4	98.31 25	92.30 154

MTAPA		91.52 13	91.60 15	91.29 25	96.59 3	86.29 14	92.02 24	91.81 94	84.07 33	92.00 61	94.40 72	86.63 39	95.28 37	88.59 4	98.31 25	92.30 154

PEN-MVS		90.03 36	91.88 12	84.48 133	96.57 5	58.88 254	88.95 64	93.19 49	91.62 3	96.01 6	96.16 22	87.02 36	95.60 22	78.69 117	98.72 10	98.97 3

PS-CasMVS		90.06 35	91.92 9	84.47 134	96.56 6	58.83 256	89.04 63	92.74 68	91.40 4	96.12 4	96.06 25	87.23 35	95.57 23	79.42 112	98.74 7	99.00 2

DTE-MVSNet		89.98 38	91.91 11	84.21 143	96.51 7	57.84 259	88.93 66	92.84 65	91.92 2	96.16 3	96.23 20	86.95 37	95.99 6	79.05 114	98.57 16	98.80 6

CP-MVSNet		89.27 53	90.91 34	84.37 138	96.34 8	58.61 258	88.66 72	92.06 85	90.78 5	95.67 9	95.17 47	81.80 90	95.54 26	79.00 115	98.69 11	98.95 4

WR-MVS_H		89.91 43	91.31 24	85.71 110	96.32 9	62.39 217	89.54 54	93.31 42	90.21 9	95.57 11	95.66 32	81.42 94	95.90 11	80.94 87	98.80 4	98.84 5

MP-MVS		91.14 23	90.91 34	91.83 18	96.18 10	86.88 11	92.20 22	93.03 57	82.59 48	88.52 128	94.37 75	86.74 38	95.41 32	86.32 27	98.21 31	93.19 127
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
mPP-MVS		91.69 10	91.47 19	92.37 5	96.04 11	88.48 8	92.72 14	92.60 73	83.09 41	91.54 69	94.25 78	87.67 33	95.51 29	87.21 24	98.11 34	93.12 128

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 40	94.96 52	88.36 19	95.01 46	90.70 2	98.40 21	95.09 77
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
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 43	96.29 18	88.16 25	94.17 68	86.07 33	98.48 19	97.22 25

ACMMP_Plus		90.65 26	91.07 30	89.42 53	95.93 14	79.54 68	89.95 43	93.68 32	77.65 106	91.97 63	94.89 54	88.38 18	95.45 30	89.27 3	97.87 45	93.27 123

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 53	95.13 48	90.65 10	95.34 34	88.06 10	98.15 33	95.95 52

HSP-MVS		88.63 60	87.84 70	91.02 29	95.76 16	86.14 19	92.75 13	91.01 127	78.43 97	89.16 117	92.25 131	72.03 209	96.36 2	88.21 9	90.93 233	90.55 201

region2R		91.44 17	91.30 25	91.87 16	95.75 17	85.90 23	92.63 17	93.30 43	81.91 57	90.88 80	94.21 79	87.75 30	95.87 12	87.60 17	97.71 53	93.83 106

ACMMPR		91.49 14	91.35 23	91.92 14	95.74 18	85.88 24	92.58 18	93.25 47	81.99 55	91.40 72	94.17 81	87.51 34	95.87 12	87.74 12	97.76 48	93.99 101

TSAR-MVS + MP.		88.14 66	87.82 71	89.09 58	95.72 19	76.74 94	92.49 20	91.19 123	67.85 231	86.63 155	94.84 56	79.58 109	95.96 9	87.62 15	94.50 155	94.56 83

PGM-MVS		91.20 21	90.95 33	91.93 13	95.67 20	85.85 25	90.00 40	93.90 29	80.32 72	91.74 67	94.41 71	88.17 24	95.98 7	86.37 26	97.99 40	93.96 103

XVS		91.54 12	91.36 21	92.08 8	95.64 21	86.25 16	92.64 15	93.33 40	85.07 26	89.99 89	94.03 86	86.57 41	95.80 15	87.35 20	97.62 55	94.20 94

X-MVStestdata		85.04 116	82.70 164	92.08 8	95.64 21	86.25 16	92.64 15	93.33 40	85.07 26	89.99 89	16.05 361	86.57 41	95.80 15	87.35 20	97.62 55	94.20 94

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 66	93.94 94	90.55 12	95.73 19	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
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 57	94.14 82	89.15 16	95.62 21	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
HFP-MVS		91.30 18	91.39 20	91.02 29	95.43 25	84.66 39	92.58 18	93.29 45	81.99 55	91.47 70	93.96 90	88.35 20	95.56 24	87.74 12	97.74 51	92.85 133

#test#		90.49 31	90.31 42	91.02 29	95.43 25	84.66 39	90.65 36	93.29 45	77.00 121	91.47 70	93.96 90	88.35 20	95.56 24	84.88 40	97.74 51	92.85 133

SMA-MVS		90.31 32	90.48 40	89.83 47	95.31 27	79.52 69	90.98 34	93.24 48	75.37 144	92.84 44	95.28 42	85.58 50	96.09 5	87.92 11	97.76 48	93.88 105

CP-MVS		91.67 11	91.58 16	91.96 12	95.29 28	87.62 9	93.38 7	93.36 38	83.16 40	91.06 75	94.00 87	88.26 22	95.71 20	87.28 23	98.39 22	92.55 147

VDDNet		84.35 136	85.39 113	81.25 203	95.13 29	59.32 252	85.42 123	81.11 251	86.41 22	87.41 143	96.21 21	73.61 180	90.61 193	66.33 215	96.85 73	93.81 111

CPTT-MVS		89.39 51	88.98 57	90.63 36	95.09 30	86.95 10	92.09 23	92.30 79	79.74 77	87.50 142	92.38 125	81.42 94	93.28 118	83.07 64	97.24 64	91.67 173

ACMM	79.39 9	90.65 26	90.99 31	89.63 50	95.03 31	83.53 44	89.62 51	93.35 39	79.20 85	93.83 28	93.60 100	90.81 8	92.96 132	85.02 39	98.45 20	92.41 150
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
UA-Net		91.49 14	91.53 17	91.39 22	94.98 32	82.95 50	93.52 5	92.79 66	88.22 15	88.53 127	97.64 2	83.45 64	94.55 59	86.02 34	98.60 14	96.67 37

HPM-MVS++		88.93 57	88.45 65	90.38 40	94.92 33	85.85 25	89.70 47	91.27 120	78.20 100	86.69 154	92.28 130	80.36 103	95.06 45	86.17 32	96.49 86	90.22 207

XVG-ACMP-BASELINE		89.98 38	89.84 45	90.41 39	94.91 34	84.50 41	89.49 56	93.98 25	79.68 78	92.09 59	93.89 95	83.80 60	93.10 126	82.67 71	98.04 35	93.64 115

OPM-MVS		89.80 44	89.97 43	89.27 55	94.76 35	79.86 64	86.76 103	92.78 67	78.78 92	92.51 53	93.64 99	88.13 26	93.84 82	84.83 42	97.55 58	94.10 99

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 30	94.82 57	91.18 5	95.52 27	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 30	94.82 57	91.18 5	95.52 27	85.36 35	98.73 8	95.23 74

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 43	91.86 3	95.09 43	87.54 19	98.02 38	93.71 113

XVG-OURS-SEG-HR		89.59 48	89.37 52	90.28 42	94.47 39	85.95 21	86.84 99	93.91 28	80.07 75	86.75 153	93.26 103	93.64 2	90.93 181	84.60 45	90.75 238	93.97 102

ACMP	79.16 10	90.54 29	90.60 37	90.35 41	94.36 40	80.98 57	89.16 61	94.05 23	79.03 89	92.87 42	93.74 98	90.60 11	95.21 41	82.87 67	98.76 5	94.87 79
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
XVG-OURS		89.18 54	88.83 59	90.23 43	94.28 41	86.11 20	85.91 115	93.60 35	80.16 74	89.13 118	93.44 101	83.82 59	90.98 179	83.86 56	95.30 129	93.60 117

MIMVSNet1		83.63 156	84.59 133	80.74 208	94.06 42	62.77 208	82.72 187	84.53 233	77.57 108	90.34 83	95.92 27	76.88 141	85.83 273	61.88 243	97.42 60	93.62 116

TranMVSNet+NR-MVSNet		87.86 69	88.76 62	85.18 116	94.02 43	64.13 186	84.38 138	91.29 119	84.88 28	92.06 60	93.84 96	86.45 43	93.73 83	73.22 162	98.66 12	97.69 11

新几何1				82.95 176	93.96 44	78.56 77		80.24 255	55.45 304	83.93 200	91.08 164	71.19 214	88.33 237	65.84 220	93.07 188	81.95 310

1121		80.86 193	79.81 204	84.02 147	93.93 45	78.70 75	81.64 215	80.18 256	55.43 305	83.67 202	91.15 159	71.29 213	91.41 171	67.95 206	93.06 189	81.96 309

SteuartSystems-ACMMP		91.16 22	91.36 21	90.55 37	93.91 46	80.97 58	91.49 29	93.48 37	82.82 46	92.60 52	93.97 88	88.19 23	96.29 4	87.61 16	98.20 32	94.39 92
Skip Steuart: Steuart Systems R&D Blog.
test_part2					93.86 47	77.77 81				92.84 44

v1.0		38.20 338	50.94 337	0.00 354	93.86 47	0.00 369	0.00 360	93.93 26	84.39 30	92.84 44	93.43 102	0.00 371	0.00 366	0.00 363	0.00 364	0.00 364

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 53	93.16 123	91.10 1	97.53 59	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
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 58	95.03 50	89.75 14	94.93 48	79.95 104	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
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	89.71 101	94.82 57	85.09 51	95.77 17	84.17 52	98.03 37	93.26 124
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
mvs_tets		89.78 45	89.27 53	91.30 24	93.51 52	84.79 36	89.89 45	90.63 133	70.00 212	94.55 17	96.67 11	87.94 29	93.59 93	84.27 50	95.97 107	95.52 66

HQP_MVS		87.75 72	87.43 78	88.70 62	93.45 53	76.42 98	89.45 57	93.61 33	79.44 82	86.55 156	92.95 112	74.84 158	95.22 39	80.78 90	95.83 114	94.46 88

plane_prior7					93.45 53	77.31 88

WR-MVS		83.56 158	84.40 139	81.06 206	93.43 55	54.88 282	78.67 265	85.02 229	81.24 63	90.74 81	91.56 148	72.85 195	91.08 177	68.00 204	98.04 35	97.23 24

ESAPD		90.53 30	91.08 28	88.88 59	93.38 56	78.65 76	89.15 62	94.05 23	84.68 29	93.90 25	94.11 84	88.13 26	96.30 3	84.51 46	97.81 46	91.70 172

jajsoiax		89.41 50	88.81 60	91.19 28	93.38 56	84.72 37	89.70 47	90.29 149	69.27 216	94.39 19	96.38 15	86.02 48	93.52 103	83.96 53	95.92 110	95.34 69

PS-MVSNAJss		88.31 64	87.90 69	89.56 52	93.31 58	77.96 80	87.94 80	91.97 89	70.73 205	94.19 23	96.67 11	76.94 135	94.57 57	83.07 64	96.28 93	96.15 43

test222					93.31 58	76.54 95	79.38 253	77.79 265	52.59 318	82.36 219	90.84 174	66.83 229			91.69 211	81.25 322

DU-MVS		86.80 84	86.99 85	86.21 99	93.24 60	67.02 166	83.16 176	92.21 81	81.73 59	90.92 77	91.97 135	77.20 129	93.99 73	74.16 151	98.35 23	97.61 12

NR-MVSNet		86.00 101	86.22 99	85.34 114	93.24 60	64.56 183	82.21 202	90.46 137	80.99 66	88.42 130	91.97 135	77.56 125	93.85 80	72.46 170	98.65 13	97.61 12

OurMVSNet-221017-0		90.01 37	89.74 47	90.83 33	93.16 62	80.37 59	91.91 27	93.11 51	81.10 65	95.32 12	97.24 5	72.94 194	94.85 50	85.07 37	97.78 47	97.26 22

pcd1.5k->3k		38.83 337	41.11 339	32.01 348	93.13 63	0.00 369	0.00 360	91.38 117	0.00 364	0.00 366	0.00 366	89.24 15	0.00 366	0.00 363	96.24 96	96.02 49

UniMVSNet (Re)		86.87 81	86.98 86	86.55 89	93.11 64	68.48 157	83.80 153	92.87 62	80.37 70	89.61 109	91.81 142	77.72 123	94.18 66	75.00 148	98.53 17	96.99 33

APD-MVS_3200maxsize		92.05 7	92.24 6	91.48 20	93.02 65	85.17 31	92.47 21	95.05 8	87.65 18	93.21 37	94.39 74	90.09 13	95.08 44	86.67 25	97.60 57	94.18 96

ACMH+	77.89 11	90.73 25	91.50 18	88.44 64	93.00 66	76.26 100	89.65 50	95.55 3	87.72 17	93.89 27	94.94 53	91.62 4	93.44 109	78.35 119	98.76 5	95.61 65

APDe-MVS		91.22 20	91.92 9	89.14 57	92.97 67	78.04 79	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

114514_t		83.10 166	82.54 169	84.77 124	92.90 68	69.10 155	86.65 108	90.62 134	54.66 308	81.46 232	90.81 175	76.98 134	94.38 60	72.62 169	96.18 97	90.82 193

testdata				79.54 223	92.87 69	72.34 123		80.14 257	59.91 286	85.47 175	91.75 144	67.96 225	85.24 277	68.57 202	92.18 208	81.06 327

CNVR-MVS		87.81 71	87.68 74	88.21 67	92.87 69	77.30 89	85.25 124	91.23 121	77.31 116	87.07 149	91.47 150	82.94 69	94.71 53	84.67 44	96.27 95	92.62 146

UniMVSNet_NR-MVSNet		86.84 83	87.06 83	86.17 102	92.86 71	67.02 166	82.55 191	91.56 100	83.08 42	90.92 77	91.82 141	78.25 119	93.99 73	74.16 151	98.35 23	97.49 15

plane_prior1					92.83 72

原ACMM1				84.60 130	92.81 73	74.01 111		91.50 102	62.59 267	82.73 217	90.67 180	76.53 142	94.25 63	69.24 191	95.69 119	85.55 258

plane_prior6					92.61 74	76.54 95						74.84 158

APD-MVS		89.54 49	89.63 49	89.26 56	92.57 75	81.34 56	90.19 39	93.08 53	80.87 67	91.13 74	93.19 104	86.22 46	95.97 8	82.23 75	97.18 66	90.45 203
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
test_0402		88.65 59	89.58 51	85.88 107	92.55 76	72.22 126	84.01 144	89.44 169	88.63 13	94.38 20	95.77 29	86.38 45	93.59 93	79.84 105	95.21 130	91.82 168

SixPastTwentyTwo		87.20 77	87.45 77	86.45 91	92.52 77	69.19 154	87.84 84	88.05 187	81.66 60	94.64 16	96.53 14	65.94 233	94.75 52	83.02 66	96.83 75	95.41 68

ACMH	76.49 14	89.34 52	91.14 27	83.96 150	92.50 78	70.36 144	89.55 52	93.84 30	81.89 58	94.70 15	95.44 40	90.69 9	88.31 238	83.33 60	98.30 27	93.20 126
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
VPNet		80.25 201	81.68 180	75.94 271	92.46 79	47.98 336	76.70 281	81.67 249	73.45 161	84.87 180	92.82 115	74.66 163	86.51 263	61.66 246	96.85 73	93.33 122

agg_prior3		85.76 107	84.95 120	88.16 68	92.43 80	79.92 62	83.98 145	90.03 157	65.11 253	83.66 203	90.64 183	81.00 97	93.67 85	81.21 82	96.54 83	90.88 190

F-COLMAP		84.97 119	83.42 155	89.63 50	92.39 81	83.40 45	88.83 68	91.92 91	73.19 170	80.18 257	89.15 205	77.04 133	93.28 118	65.82 221	92.28 203	92.21 159

test_djsdf		89.62 47	89.01 55	91.45 21	92.36 82	82.98 49	91.98 25	90.08 155	71.54 199	94.28 22	96.54 13	81.57 92	94.27 61	86.26 28	96.49 86	97.09 29

TEST9					92.34 83	79.70 66	83.94 146	90.32 142	65.41 251	84.49 190	90.97 168	82.03 84	93.63 88

train_agg		85.98 104	85.28 114	88.07 70	92.34 83	79.70 66	83.94 146	90.32 142	65.79 244	84.49 190	90.97 168	81.93 86	93.63 88	81.21 82	96.54 83	90.88 190

NCCC		87.36 74	86.87 89	88.83 60	92.32 85	78.84 74	86.58 110	91.09 125	78.77 93	84.85 181	90.89 172	80.85 98	95.29 35	81.14 84	95.32 126	92.34 153

FC-MVSNet-test		85.93 105	87.05 84	82.58 182	92.25 86	56.44 271	85.75 118	93.09 52	77.33 115	91.94 64	94.65 62	74.78 160	93.41 112	75.11 147	98.58 15	97.88 9

CDPH-MVS		86.17 100	85.54 110	88.05 73	92.25 86	75.45 103	83.85 150	92.01 87	65.91 243	86.19 163	91.75 144	83.77 61	94.98 47	77.43 130	96.71 78	93.73 112

pmmvs6		86.52 90	88.06 67	81.90 191	92.22 88	62.28 223	84.66 132	89.15 172	83.54 37	89.85 97	97.32 3	88.08 28	86.80 258	70.43 184	97.30 63	96.62 38

EG-PatchMatch MVS		84.08 146	84.11 146	83.98 149	92.22 88	72.61 120	82.20 204	87.02 206	72.63 177	88.86 120	91.02 166	78.52 115	91.11 176	73.41 161	91.09 224	88.21 230

test_8					92.09 90	78.87 73	83.82 151	90.31 144	65.79 244	84.36 193	90.96 170	81.93 86	93.44 109

Vis-MVSNet		86.86 82	86.58 93	87.72 75	92.09 90	77.43 86	87.35 90	92.09 84	78.87 91	84.27 198	94.05 85	78.35 118	93.65 86	80.54 95	91.58 213	92.08 161
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
IS-MVSNet		86.66 87	86.82 92	86.17 102	92.05 92	66.87 168	91.21 33	88.64 177	86.30 23	89.60 110	92.59 120	69.22 219	94.91 49	73.89 155	97.89 44	96.72 36

旧先验1					91.97 93	71.77 132		81.78 248			91.84 139	73.92 173			93.65 174	83.61 285

v7n		90.13 33	90.96 32	87.65 77	91.95 94	71.06 140	89.99 42	93.05 54	86.53 21	94.29 21	96.27 19	82.69 71	94.08 71	86.25 30	97.63 54	97.82 10

NP-MVS					91.95 94	74.55 108					90.17 192

OMC-MVS		88.19 65	87.52 75	90.19 44	91.94 96	81.68 53	87.49 89	93.17 50	76.02 130	88.64 125	91.22 154	84.24 58	93.37 113	77.97 125	97.03 69	95.52 66

FIs		85.35 112	86.27 98	82.60 181	91.86 97	57.31 263	85.10 126	93.05 54	75.83 134	91.02 76	93.97 88	73.57 181	92.91 136	73.97 154	98.02 38	97.58 14

MSLP-MVS++		85.00 118	86.03 103	81.90 191	91.84 98	71.56 138	86.75 104	93.02 58	75.95 132	87.12 146	89.39 202	77.98 120	89.40 217	77.46 128	94.78 147	84.75 271

Anonymous20240521		87.68 73	88.61 63	84.87 120	91.76 99	64.76 181	89.28 60	91.66 98	83.02 43	93.29 35	96.10 24	77.37 128	92.89 137	77.27 133	97.75 50	96.97 34

DP-MVS Recon		84.05 147	83.22 158	86.52 90	91.73 100	75.27 104	83.23 175	92.40 76	72.04 187	82.04 223	88.33 219	77.91 122	93.95 78	66.17 216	95.12 134	90.34 206

SD-MVS		88.96 56	89.88 44	86.22 97	91.63 101	77.07 90	89.82 46	93.77 31	78.90 90	92.88 41	92.29 129	86.11 47	90.22 202	86.24 31	97.24 64	91.36 180

AllTest		87.97 68	87.40 79	89.68 48	91.59 102	83.40 45	89.50 55	95.44 5	79.47 80	88.00 135	93.03 108	82.66 72	91.47 167	70.81 177	96.14 100	94.16 97

TestCases				89.68 48	91.59 102	83.40 45		95.44 5	79.47 80	88.00 135	93.03 108	82.66 72	91.47 167	70.81 177	96.14 100	94.16 97

MCST-MVS		84.36 134	83.93 151	85.63 111	91.59 102	71.58 137	83.52 162	92.13 83	61.82 273	83.96 199	89.75 197	79.93 108	93.46 108	78.33 120	94.34 158	91.87 167

agg_prior1		85.72 108	85.20 115	87.28 83	91.58 105	77.69 82	83.69 157	90.30 146	66.29 239	84.32 194	91.07 165	82.13 80	93.18 121	81.02 85	96.36 90	90.98 185

agg_prior					91.58 105	77.69 82		90.30 146		84.32 194			93.18 121

PVSNet_Blended_VisFu		81.55 184	80.49 196	84.70 128	91.58 105	73.24 116	84.21 139	91.67 97	62.86 265	80.94 238	87.16 235	67.27 227	92.87 138	69.82 187	88.94 258	87.99 234

EPP-MVSNet		85.47 111	85.04 117	86.77 86	91.52 108	69.37 149	91.63 28	87.98 190	81.51 62	87.05 150	91.83 140	66.18 232	95.29 35	70.75 179	96.89 72	95.64 60

DeepPCF-MVS	81.24 5	87.28 76	86.21 100	90.49 38	91.48 109	84.90 34	83.41 167	92.38 78	70.25 210	89.35 115	90.68 179	82.85 70	94.57 57	79.55 108	95.95 108	92.00 162

Baseline_NR-MVSNet		84.00 149	85.90 104	78.29 238	91.47 110	53.44 291	82.29 198	87.00 207	79.06 88	89.55 111	95.72 31	77.20 129	86.14 269	72.30 171	98.51 18	95.28 72

HyFIR lowres test		75.12 245	72.66 267	82.50 185	91.44 111	65.19 178	72.47 310	87.31 196	46.79 344	80.29 255	84.30 276	52.70 289	92.10 153	51.88 312	86.73 281	90.22 207

DP-MVS		88.60 61	89.01 55	87.36 82	91.30 112	77.50 85	87.55 87	92.97 60	87.95 16	89.62 107	92.87 114	84.56 55	93.89 79	77.65 126	96.62 80	90.70 195

DeepC-MVS_fast	80.27 8	86.23 97	85.65 109	87.96 74	91.30 112	76.92 91	87.19 93	91.99 88	70.56 206	84.96 177	90.69 178	80.01 106	95.14 42	78.37 118	95.78 116	91.82 168
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
3Dnovator+	83.92 2	89.97 40	89.66 48	90.92 32	91.27 114	81.66 54	91.25 32	94.13 22	88.89 11	88.83 122	94.26 77	77.55 126	95.86 14	84.88 40	95.87 112	95.24 73

HQP-NCC					91.19 115		84.77 128		73.30 166	80.55 252

ACMP_Plane					91.19 115		84.77 128		73.30 166	80.55 252

HQP-MVS		84.61 125	84.06 147	86.27 95	91.19 115	70.66 142	84.77 128	92.68 69	73.30 166	80.55 252	90.17 192	72.10 205	94.61 55	77.30 131	94.47 156	93.56 119

VDD-MVS		84.23 140	84.58 134	83.20 172	91.17 118	65.16 179	83.25 173	84.97 231	79.79 76	87.18 145	94.27 76	74.77 161	90.89 184	69.24 191	96.54 83	93.55 121

K. test v3		85.14 113	84.73 123	86.37 92	91.13 119	69.63 148	85.45 122	76.68 273	84.06 35	92.44 55	96.99 8	62.03 245	94.65 54	80.58 94	93.24 186	94.83 82

lessismore_v0				85.95 104	91.10 120	70.99 141		70.91 317		91.79 65	94.42 70	61.76 246	92.93 134	79.52 111	93.03 190	93.93 104

TransMVSNet (Re)		84.02 148	85.74 106	78.85 228	91.00 121	55.20 281	82.29 198	87.26 197	79.65 79	88.38 132	95.52 38	83.00 68	86.88 250	67.97 205	96.60 81	94.45 90

wuykxyi23d		88.46 62	88.80 61	87.44 81	90.96 122	93.03 1	85.85 117	81.96 245	74.58 149	98.58 2	97.29 4	87.73 31	87.31 245	82.84 69	99.41 1	81.99 308

PAPM_NR		83.23 163	83.19 160	83.33 170	90.90 123	65.98 173	88.19 77	90.78 129	78.13 102	80.87 240	87.92 227	73.49 183	92.42 146	70.07 185	88.40 262	91.60 175

CSCG		86.26 95	86.47 95	85.60 112	90.87 124	74.26 110	87.98 79	91.85 92	80.35 71	89.54 113	88.01 223	79.09 111	92.13 151	75.51 144	95.06 136	90.41 204

PLC	73.85 16	82.09 178	80.31 197	87.45 80	90.86 125	80.29 60	85.88 116	90.65 132	68.17 225	76.32 283	86.33 247	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
test12				86.57 88	90.74 126	72.63 119		90.69 131		82.76 216		79.20 110	94.80 51		95.32 126	92.27 156

ITE_SJBPF				90.11 45	90.72 127	84.97 33		90.30 146	81.56 61	90.02 88	91.20 157	82.40 76	90.81 186	73.58 158	94.66 152	94.56 83

TAMVS		78.08 214	76.36 227	83.23 171	90.62 128	72.87 117	79.08 260	80.01 258	61.72 275	81.35 234	86.92 238	63.96 239	88.78 231	50.61 313	93.01 191	88.04 233

test_prior3		86.31 94	86.31 97	86.32 93	90.59 129	71.99 130	83.37 168	92.85 63	75.43 141	84.58 188	91.57 146	81.92 88	94.17 68	79.54 109	96.97 70	92.80 135

test_prior				86.32 93	90.59 129	71.99 130		92.85 63					94.17 68			92.80 135

ambc				82.98 175	90.55 131	64.86 180	88.20 76	89.15 172		89.40 114	93.96 90	71.67 212	91.38 173	78.83 116	96.55 82	92.71 138

Anonymous20231211		88.40 63	89.62 50	84.73 126	90.46 132	65.27 177	88.86 67	93.02 58	87.15 19	93.05 39	97.10 6	82.28 78	92.02 154	76.70 138	97.99 40	96.88 35

Test_1112_low_res		73.90 258	73.08 263	76.35 265	90.35 133	55.95 272	73.40 309	86.17 213	50.70 334	73.14 305	85.94 252	58.31 265	85.90 272	56.51 284	83.22 310	87.20 243

VPA-MVSNet		83.47 161	84.73 123	79.69 220	90.29 134	57.52 262	81.30 222	88.69 176	76.29 126	87.58 140	94.44 69	80.60 101	87.20 246	66.60 214	96.82 76	94.34 93

FMVSNet1		84.55 127	85.45 112	81.85 194	90.27 135	61.05 238	86.83 100	88.27 184	78.57 96	89.66 103	95.64 34	75.43 149	90.68 190	69.09 195	95.33 125	93.82 108

Anonymous20240529		86.20 99	87.13 81	83.42 169	90.19 136	64.55 184	84.55 135	90.71 130	85.85 24	89.94 92	95.24 45	82.13 80	90.40 197	69.19 194	96.40 89	95.31 71

MVS_111021_HR		84.63 124	84.34 144	85.49 113	90.18 137	75.86 102	79.23 259	87.13 202	73.35 163	85.56 173	89.34 203	83.60 63	90.50 195	76.64 139	94.05 162	90.09 212

RPSCF		88.00 67	86.93 88	91.22 27	90.08 138	89.30 6	89.68 49	91.11 124	79.26 84	89.68 102	94.81 60	82.44 75	87.74 242	76.54 140	88.74 261	96.61 39

nrg030		87.85 70	88.49 64	85.91 105	90.07 139	69.73 146	87.86 81	94.20 17	74.04 154	92.70 50	94.66 61	85.88 49	91.50 166	79.72 106	97.32 62	96.50 41

v748		88.91 58	89.82 46	86.19 101	90.06 140	68.53 156	88.81 69	91.48 104	84.36 31	94.19 23	95.98 26	82.52 74	92.67 142	84.30 49	96.67 79	97.37 19

AdaColmap		83.66 155	83.69 153	83.57 163	90.05 141	72.26 125	86.29 114	90.00 158	78.19 101	81.65 230	87.16 235	83.40 65	94.24 64	61.69 245	94.76 150	84.21 278

pm-mvs1		83.69 154	84.95 120	79.91 216	90.04 142	59.66 249	82.43 193	87.44 194	75.52 140	87.85 137	95.26 44	81.25 96	85.65 275	68.74 199	96.04 105	94.42 91

CHOSEN 1792x2688		72.45 272	70.56 283	78.13 240	90.02 143	63.08 202	68.72 323	83.16 236	42.99 353	75.92 287	85.46 258	57.22 274	85.18 279	49.87 317	81.67 319	86.14 252

anonymousdsp		89.73 46	88.88 58	92.27 7	89.82 144	86.67 12	90.51 37	90.20 154	69.87 213	95.06 13	96.14 23	84.28 57	93.07 131	87.68 14	96.34 91	97.09 29

1112_ss		74.82 250	73.74 250	78.04 242	89.57 145	60.04 246	76.49 285	87.09 205	54.31 309	73.66 304	79.80 324	60.25 253	86.76 260	58.37 274	84.15 306	87.32 242

PCF-MVS	74.62 15	82.15 177	80.92 192	85.84 108	89.43 146	72.30 124	80.53 232	91.82 93	57.36 296	87.81 138	89.92 195	77.67 124	93.63 88	58.69 269	95.08 135	91.58 176
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
MVP-Stereo		75.81 240	73.51 260	82.71 180	89.35 147	73.62 112	80.06 235	85.20 224	60.30 284	73.96 302	87.94 225	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.
CNLPA		83.55 159	83.10 161	84.90 119	89.34 148	83.87 43	84.54 136	88.77 174	79.09 87	83.54 206	88.66 212	74.87 157	81.73 301	66.84 212	92.29 202	89.11 221

TSAR-MVS + GP.		83.95 150	82.69 165	87.72 75	89.27 149	81.45 55	83.72 156	81.58 250	74.73 148	85.66 170	86.06 251	72.56 203	92.69 141	75.44 145	95.21 130	89.01 226

MVS_111021_LR		84.28 138	83.76 152	85.83 109	89.23 150	83.07 48	80.99 228	83.56 235	72.71 176	86.07 164	89.07 206	81.75 91	86.19 268	77.11 134	93.36 179	88.24 229

LFMVS		80.15 204	80.56 194	78.89 227	89.19 151	55.93 273	85.22 125	73.78 289	82.96 44	84.28 197	92.72 119	57.38 272	90.07 209	63.80 231	95.75 117	90.68 196

CLD-MVS		83.18 164	82.64 166	84.79 123	89.05 152	67.82 163	77.93 271	92.52 74	68.33 224	85.07 176	81.54 314	82.06 82	92.96 132	69.35 190	97.91 43	93.57 118
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
LS3D		90.60 28	90.34 41	91.38 23	89.03 153	84.23 42	93.58 4	94.68 10	90.65 6	90.33 84	93.95 93	84.50 56	95.37 33	80.87 88	95.50 122	94.53 87

CDS-MVSNet		77.32 220	75.40 236	83.06 174	89.00 154	72.48 122	77.90 272	82.17 244	60.81 281	78.94 266	83.49 284	59.30 260	88.76 232	54.64 299	92.37 201	87.93 236
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
tfpnnormal		81.79 182	82.95 162	78.31 237	88.93 155	55.40 277	80.83 231	82.85 239	76.81 122	85.90 168	94.14 82	74.58 165	86.51 263	66.82 213	95.68 120	93.01 130

v13		87.31 75	88.10 66	84.94 118	88.84 156	63.75 190	87.85 83	91.47 107	79.12 86	93.72 29	95.82 28	75.20 152	93.58 96	84.76 43	96.16 98	97.48 16

Vis-MVSNet (Re-imp)		77.82 215	77.79 214	77.92 244	88.82 157	51.29 311	83.28 171	71.97 305	74.04 154	82.23 220	89.78 196	57.38 272	89.41 216	57.22 281	95.41 123	93.05 129

TAPA-MVS	77.73 12	85.71 109	84.83 122	88.37 65	88.78 158	79.72 65	87.15 95	93.50 36	69.17 218	85.80 169	89.56 200	80.76 99	92.13 151	73.21 166	95.51 121	93.25 125
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
v12		87.15 78	87.91 68	84.84 121	88.69 159	63.52 193	87.58 86	91.46 108	78.74 94	93.57 32	95.66 32	74.94 156	93.57 97	84.50 47	96.08 103	97.43 17

v52		89.97 40	90.60 37	88.07 70	88.69 159	72.01 128	91.35 30	92.64 71	82.22 52	95.97 8	96.31 16	84.82 52	93.98 75	88.59 4	94.83 145	98.23 7

V4		89.97 40	90.60 37	88.07 70	88.69 159	72.01 128	91.35 30	92.64 71	82.22 52	95.98 7	96.31 16	84.80 54	93.98 75	88.59 4	94.83 145	98.23 7

FPMVS		72.29 275	72.00 274	73.14 291	88.63 162	85.00 32	74.65 300	67.39 335	71.94 195	77.80 274	87.66 230	50.48 294	75.83 318	49.95 315	79.51 327	58.58 355

BH-untuned		80.96 192	80.99 190	80.84 207	88.55 163	68.23 158	80.33 234	88.46 178	72.79 175	86.55 156	86.76 239	74.72 162	91.77 162	61.79 244	88.99 257	82.52 301

V9		86.96 79	87.70 73	84.74 125	88.52 164	63.27 199	87.31 91	91.45 110	78.28 99	93.43 33	95.45 39	74.59 164	93.57 97	84.23 51	96.01 106	97.38 18

v11		86.96 79	87.78 72	84.51 131	88.50 165	62.60 213	87.21 92	91.63 99	78.08 103	93.40 34	95.56 37	75.07 153	93.57 97	84.46 48	96.08 103	97.36 20

Anonymous202405211		80.51 198	81.19 187	78.49 235	88.48 166	57.26 264	76.63 282	82.49 241	81.21 64	84.30 196	92.24 132	67.99 224	86.24 267	62.22 239	95.13 133	91.98 165

ab-mvs		79.67 206	80.56 194	76.99 256	88.48 166	56.93 266	84.70 131	86.06 214	68.95 222	80.78 241	93.08 106	75.30 151	84.62 284	56.78 283	90.90 234	89.43 217

PHI-MVS		86.38 92	85.81 105	88.08 69	88.44 168	77.34 87	89.35 59	93.05 54	73.15 171	84.76 182	87.70 229	78.87 113	94.18 66	80.67 93	96.29 92	92.73 137

V14		86.75 85	87.46 76	84.62 129	88.35 169	63.00 204	87.02 97	91.42 113	77.78 105	93.27 36	95.23 46	74.22 167	93.56 100	83.95 54	95.93 109	97.31 21

xiu_mvs_v1_base_debu		80.84 194	80.14 200	82.93 177	88.31 170	71.73 133	79.53 243	87.17 199	65.43 248	79.59 259	82.73 297	76.94 135	90.14 205	73.22 162	88.33 263	86.90 246

xiu_mvs_v1_base		80.84 194	80.14 200	82.93 177	88.31 170	71.73 133	79.53 243	87.17 199	65.43 248	79.59 259	82.73 297	76.94 135	90.14 205	73.22 162	88.33 263	86.90 246

xiu_mvs_v1_base_debi		80.84 194	80.14 200	82.93 177	88.31 170	71.73 133	79.53 243	87.17 199	65.43 248	79.59 259	82.73 297	76.94 135	90.14 205	73.22 162	88.33 263	86.90 246

MG-MVS		80.32 200	80.94 191	78.47 236	88.18 173	52.62 298	82.29 198	85.01 230	72.01 188	79.24 264	92.54 123	69.36 218	93.36 114	70.65 181	89.19 256	89.45 215

PM-MVS		80.20 203	79.00 206	83.78 154	88.17 174	86.66 13	81.31 220	66.81 341	69.64 214	88.33 133	90.19 190	64.58 236	83.63 293	71.99 175	90.03 247	81.06 327

v10		86.54 89	87.10 82	84.84 121	88.16 175	63.28 198	86.64 109	92.20 82	75.42 143	92.81 47	94.50 67	74.05 170	94.06 72	83.88 55	96.28 93	97.17 27

v15		86.56 88	87.25 80	84.51 131	88.15 176	62.72 209	86.72 107	91.40 115	77.38 110	93.11 38	95.00 51	73.93 172	93.55 101	83.67 58	95.86 113	97.26 22

v7		84.81 121	85.00 118	84.23 142	88.15 176	63.27 199	83.79 154	91.39 116	71.10 203	90.07 86	91.28 152	74.04 171	93.63 88	81.48 81	93.67 173	95.79 53

casdiffmvs1		83.63 156	83.44 154	84.20 145	88.08 178	66.53 171	88.62 73	92.02 86	58.66 289	82.94 214	93.84 96	78.76 114	93.10 126	76.73 137	91.29 221	92.96 132

v17		86.32 93	86.95 87	84.44 135	88.00 179	62.62 212	86.74 105	91.48 104	77.17 118	92.74 48	94.56 63	73.74 176	93.53 102	83.27 61	94.87 144	97.18 26

canonicalmvs		85.50 110	86.14 101	83.58 162	87.97 180	67.13 165	87.55 87	94.32 12	73.44 162	88.47 129	87.54 232	86.45 43	91.06 178	75.76 143	93.76 170	92.54 148

v16		86.24 96	86.85 90	84.43 136	87.96 181	62.59 214	86.73 106	91.48 104	77.17 118	92.67 51	94.55 64	73.63 177	93.52 103	83.26 62	94.16 159	97.17 27

VNet		79.31 207	80.27 198	76.44 264	87.92 182	53.95 286	75.58 293	84.35 234	74.39 152	82.23 220	90.72 177	72.84 196	84.39 286	60.38 256	93.98 164	90.97 186

view600		76.79 225	76.54 222	77.56 248	87.91 183	50.77 317	81.92 208	71.35 313	77.38 110	84.62 184	88.40 215	45.18 323	89.26 219	58.58 270	93.49 175	92.66 140

view800		76.79 225	76.54 222	77.56 248	87.91 183	50.77 317	81.92 208	71.35 313	77.38 110	84.62 184	88.40 215	45.18 323	89.26 219	58.58 270	93.49 175	92.66 140

conf0.05thres1000		76.79 225	76.54 222	77.56 248	87.91 183	50.77 317	81.92 208	71.35 313	77.38 110	84.62 184	88.40 215	45.18 323	89.26 219	58.58 270	93.49 175	92.66 140

tfpn		76.79 225	76.54 222	77.56 248	87.91 183	50.77 317	81.92 208	71.35 313	77.38 110	84.62 184	88.40 215	45.18 323	89.26 219	58.58 270	93.49 175	92.66 140

v8		86.22 98	86.83 91	84.36 139	87.82 187	62.35 218	86.42 112	91.33 118	76.78 123	92.73 49	94.48 68	73.41 184	93.72 84	83.10 63	95.41 123	97.01 32

v1neww		84.43 131	84.66 129	83.75 155	87.81 188	62.34 219	83.59 158	90.27 150	72.33 182	89.93 94	91.22 154	73.28 188	93.29 115	80.25 100	93.25 184	95.62 61

v7new		84.43 131	84.66 129	83.75 155	87.81 188	62.34 219	83.59 158	90.27 150	72.33 182	89.93 94	91.22 154	73.28 188	93.29 115	80.25 100	93.25 184	95.62 61

v6		84.43 131	84.66 129	83.75 155	87.81 188	62.34 219	83.59 158	90.26 152	72.33 182	89.94 92	91.19 158	73.30 187	93.29 115	80.26 99	93.26 183	95.62 61

alignmvs		83.94 151	83.98 149	83.80 152	87.80 191	67.88 162	84.54 136	91.42 113	73.27 169	88.41 131	87.96 224	72.33 204	90.83 185	76.02 142	94.11 160	92.69 139

v1192		84.57 126	84.69 127	84.21 143	87.75 192	62.88 206	83.02 178	91.43 111	69.08 220	89.98 91	90.89 172	72.70 199	93.62 92	82.41 72	94.97 139	96.13 44

v18		85.99 103	86.55 94	84.30 141	87.73 193	62.29 222	86.40 113	91.49 103	76.64 124	92.40 56	94.20 80	73.28 188	93.52 103	82.87 67	93.99 163	97.09 29

PatchMatch-RL		74.48 252	73.22 262	78.27 239	87.70 194	85.26 30	75.92 289	70.09 320	64.34 257	76.09 286	81.25 316	65.87 234	78.07 311	53.86 301	83.82 307	71.48 343

v1144		84.54 129	84.72 125	84.00 148	87.67 195	62.55 215	82.97 179	90.93 128	70.32 209	89.80 99	90.99 167	73.50 182	93.48 107	81.69 80	94.65 153	95.97 50

v1240		84.30 137	84.51 136	83.65 160	87.65 196	61.26 235	82.85 182	91.54 101	67.94 229	90.68 82	90.65 181	71.71 211	93.64 87	82.84 69	94.78 147	96.07 46

v1921920		84.23 140	84.37 143	83.79 153	87.64 197	61.71 226	82.91 181	91.20 122	67.94 229	90.06 87	90.34 186	72.04 208	93.59 93	82.32 74	94.91 140	96.07 46

v144192		84.24 139	84.41 138	83.71 159	87.59 198	61.57 231	82.95 180	91.03 126	67.82 232	89.80 99	90.49 184	73.28 188	93.51 106	81.88 79	94.89 141	96.04 48

Fast-Effi-MVS+		81.04 191	80.57 193	82.46 186	87.50 199	63.22 201	78.37 267	89.63 165	68.01 226	81.87 225	82.08 309	82.31 77	92.65 143	67.10 208	88.30 267	91.51 178

pmmvs-eth3d		78.42 212	77.04 218	82.57 184	87.44 200	74.41 109	80.86 230	79.67 259	55.68 303	84.69 183	90.31 189	60.91 249	85.42 276	62.20 240	91.59 212	87.88 237

MVS_0304		84.88 120	83.96 150	87.64 78	87.43 201	74.83 106	84.18 140	93.30 43	77.48 109	77.39 277	88.46 214	74.53 166	95.74 18	78.09 124	94.75 151	92.36 152

IterMVS-LS		84.73 123	84.98 119	83.96 150	87.35 202	63.66 191	83.25 173	89.88 160	76.06 128	89.62 107	92.37 128	73.40 186	92.52 145	78.16 122	94.77 149	95.69 58
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v1		84.16 142	84.38 140	83.52 165	87.33 203	61.71 226	82.79 184	89.73 163	71.89 198	89.64 104	91.11 163	72.72 197	93.10 126	80.40 96	93.79 169	95.75 55

v1141		84.16 142	84.38 140	83.52 165	87.32 204	61.70 228	82.79 184	89.74 161	71.90 196	89.64 104	91.12 161	72.68 200	93.10 126	80.39 98	93.80 168	95.75 55

divwei89l23v2f112		84.16 142	84.38 140	83.52 165	87.32 204	61.70 228	82.79 184	89.74 161	71.90 196	89.64 104	91.12 161	72.68 200	93.10 126	80.40 96	93.81 167	95.75 55

tfpn111		76.03 237	75.53 235	77.53 252	87.27 206	51.88 303	81.07 225	73.26 294	75.68 136	83.25 208	86.37 244	45.54 313	89.38 218	55.07 295	92.26 204	91.34 181

conf200view11		75.62 241	75.05 239	77.34 254	87.27 206	51.88 303	81.07 225	73.26 294	75.68 136	83.25 208	86.37 244	45.54 313	88.80 227	51.98 308	90.99 228	91.34 181

thres100view900		75.45 242	75.05 239	76.66 263	87.27 206	51.88 303	81.07 225	73.26 294	75.68 136	83.25 208	86.37 244	45.54 313	88.80 227	51.98 308	90.99 228	89.31 218

MIMVSNet		71.09 283	71.59 277	69.57 307	87.23 209	50.07 325	78.91 261	71.83 307	60.20 285	71.26 315	91.76 143	55.08 284	76.09 316	41.06 341	87.02 280	82.54 300

Effi-MVS+		83.90 152	84.01 148	83.57 163	87.22 210	65.61 176	86.55 111	92.40 76	78.64 95	81.34 235	84.18 277	83.65 62	92.93 134	74.22 150	87.87 271	92.17 160

BH-RMVSNet		80.53 197	80.22 199	81.49 199	87.19 211	66.21 172	77.79 273	86.23 212	74.21 153	83.69 201	88.50 213	73.25 192	90.75 187	63.18 236	87.90 270	87.52 239

Effi-MVS+-dtu		85.82 106	83.38 156	93.14 3	87.13 212	91.15 3	87.70 85	88.42 179	74.57 150	83.56 205	85.65 253	78.49 116	94.21 65	72.04 173	92.88 193	94.05 100

mvs-test1		84.55 127	82.12 175	91.84 17	87.13 212	89.54 5	85.05 127	88.42 179	74.57 150	80.60 249	82.98 290	78.49 116	93.98 75	72.04 173	89.77 250	92.00 162

v2v482		84.09 145	84.24 145	83.62 161	87.13 212	61.40 232	82.71 188	89.71 164	72.19 186	89.55 111	91.41 151	70.70 216	93.20 120	81.02 85	93.76 170	96.25 42

jason		77.42 219	75.75 233	82.43 187	87.10 215	69.27 150	77.99 270	81.94 247	51.47 328	77.84 272	85.07 266	60.32 252	89.00 224	70.74 180	89.27 255	89.03 224
jason: jason.
PS-MVSNAJ		77.04 223	76.53 226	78.56 233	87.09 216	61.40 232	75.26 295	87.13 202	61.25 278	74.38 301	77.22 334	76.94 135	90.94 180	64.63 227	84.83 302	83.35 290

xiu_mvs_v2_base		77.19 221	76.75 220	78.52 234	87.01 217	61.30 234	75.55 294	87.12 204	61.24 279	74.45 299	78.79 328	77.20 129	90.93 181	64.62 228	84.80 303	83.32 291

thres600view7		75.97 238	75.35 238	77.85 246	87.01 217	51.84 307	80.45 233	73.26 294	75.20 145	83.10 212	86.31 249	45.54 313	89.05 223	55.03 296	92.24 205	92.66 140

BH-w/o		76.57 231	76.07 230	78.10 241	86.88 219	65.92 174	77.63 274	86.33 211	65.69 246	80.89 239	79.95 323	68.97 222	90.74 188	53.01 304	85.25 296	77.62 332

MAR-MVS		80.24 202	78.74 208	84.73 126	86.87 220	78.18 78	85.75 118	87.81 192	65.67 247	77.84 272	78.50 329	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
testing_2		84.36 134	84.64 132	83.50 168	86.74 221	63.97 189	84.56 134	90.31 144	66.22 240	91.62 68	94.55 64	75.88 146	91.95 155	77.02 136	94.89 141	94.56 83

QAPM		82.59 171	82.59 168	82.58 182	86.44 222	66.69 169	89.94 44	90.36 141	67.97 228	84.94 179	92.58 122	72.71 198	92.18 150	70.63 182	87.73 273	88.85 227

PAPM		71.77 278	70.06 289	76.92 257	86.39 223	53.97 285	76.62 283	86.62 209	53.44 314	63.97 344	84.73 272	57.79 270	92.34 147	39.65 343	81.33 322	84.45 273

GBi-Net		82.02 179	82.07 176	81.85 194	86.38 224	61.05 238	86.83 100	88.27 184	72.43 178	86.00 165	95.64 34	63.78 240	90.68 190	65.95 217	93.34 180	93.82 108

test1		82.02 179	82.07 176	81.85 194	86.38 224	61.05 238	86.83 100	88.27 184	72.43 178	86.00 165	95.64 34	63.78 240	90.68 190	65.95 217	93.34 180	93.82 108

FMVSNet2		81.31 186	81.61 182	80.41 211	86.38 224	58.75 257	83.93 148	86.58 210	72.43 178	87.65 139	92.98 110	63.78 240	90.22 202	66.86 210	93.92 165	92.27 156

3Dnovator	80.37 7	84.80 122	84.71 126	85.06 117	86.36 227	74.71 107	88.77 70	90.00 158	75.65 139	84.96 177	93.17 105	74.06 169	91.19 174	78.28 121	91.09 224	89.29 220

casdiffmvs		82.99 167	82.51 170	84.42 137	86.34 228	67.92 161	87.86 81	92.28 80	60.95 280	81.12 236	93.08 106	76.07 145	93.43 111	79.41 113	85.45 292	91.93 166

Anonymous20231206		71.38 282	71.88 275	69.88 303	86.31 229	54.37 283	70.39 318	74.62 283	52.57 319	76.73 279	88.76 209	59.94 255	72.06 325	44.35 336	93.23 187	83.23 293

API-MVS		82.28 175	82.61 167	81.30 201	86.29 230	69.79 145	88.71 71	87.67 193	78.42 98	82.15 222	84.15 279	77.98 120	91.59 165	65.39 223	92.75 195	82.51 302

tfpn200view9		74.86 249	74.23 247	76.74 262	86.24 231	52.12 300	79.24 256	73.87 287	73.34 164	81.82 227	84.60 274	46.02 307	88.80 227	51.98 308	90.99 228	89.31 218

thres400		75.14 243	74.23 247	77.86 245	86.24 231	52.12 300	79.24 256	73.87 287	73.34 164	81.82 227	84.60 274	46.02 307	88.80 227	51.98 308	90.99 228	92.66 140

testmv		70.47 287	70.70 282	69.77 305	86.22 233	53.89 287	67.32 329	71.91 306	63.32 260	78.16 270	89.47 201	56.12 278	73.10 323	36.43 349	87.33 276	82.33 304

UGNet		82.78 168	81.64 181	86.21 99	86.20 234	76.24 101	86.86 98	85.68 218	77.07 120	73.76 303	92.82 115	69.64 217	91.82 161	69.04 196	93.69 172	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
CANet		83.79 153	82.85 163	86.63 87	86.17 235	72.21 127	83.76 155	91.43 111	77.24 117	74.39 300	87.45 233	75.36 150	95.42 31	77.03 135	92.83 194	92.25 158

conf0.01		74.17 255	73.53 254	76.08 269	86.13 236	50.06 326	79.45 247	68.54 326	72.01 188	80.76 242	82.50 300	41.39 337	86.83 252	59.66 260	91.36 214	91.34 181

conf0.002		74.17 255	73.53 254	76.08 269	86.13 236	50.06 326	79.45 247	68.54 326	72.01 188	80.76 242	82.50 300	41.39 337	86.83 252	59.66 260	91.36 214	91.34 181

thresconf0.02		73.65 260	73.53 254	73.98 280	86.13 236	50.06 326	79.45 247	68.54 326	72.01 188	80.76 242	82.50 300	41.39 337	86.83 252	59.66 260	91.36 214	85.06 264

tfpn_n400		73.65 260	73.53 254	73.98 280	86.13 236	50.06 326	79.45 247	68.54 326	72.01 188	80.76 242	82.50 300	41.39 337	86.83 252	59.66 260	91.36 214	85.06 264

tfpnconf		73.65 260	73.53 254	73.98 280	86.13 236	50.06 326	79.45 247	68.54 326	72.01 188	80.76 242	82.50 300	41.39 337	86.83 252	59.66 260	91.36 214	85.06 264

tfpnview11		73.65 260	73.53 254	73.98 280	86.13 236	50.06 326	79.45 247	68.54 326	72.01 188	80.76 242	82.50 300	41.39 337	86.83 252	59.66 260	91.36 214	85.06 264

TR-MVS		76.77 229	75.79 231	79.72 219	86.10 242	65.79 175	77.14 278	83.02 237	65.20 252	81.40 233	82.10 308	66.30 230	90.73 189	55.57 290	85.27 295	82.65 297

LCM-MVSNet-Re		83.48 160	85.06 116	78.75 230	85.94 243	55.75 276	80.05 236	94.27 13	76.47 125	96.09 5	94.54 66	83.31 66	89.75 212	59.95 257	94.89 141	90.75 194

Fast-Effi-MVS+-dtu		82.54 172	81.41 184	85.90 106	85.60 244	76.53 97	83.07 177	89.62 166	73.02 173	79.11 265	83.51 283	80.74 100	90.24 201	68.76 198	89.29 253	90.94 187

v148		82.31 174	82.48 171	81.81 197	85.59 245	59.66 249	81.47 218	86.02 215	72.85 174	88.05 134	90.65 181	70.73 215	90.91 183	75.15 146	91.79 209	94.87 79

MVSFormer		82.23 176	81.57 183	84.19 146	85.54 246	69.26 151	91.98 25	90.08 155	71.54 199	76.23 284	85.07 266	58.69 263	94.27 61	86.26 28	88.77 259	89.03 224

lupinMVS		76.37 234	74.46 245	82.09 188	85.54 246	69.26 151	76.79 279	80.77 254	50.68 335	76.23 284	82.82 295	58.69 263	88.94 225	69.85 186	88.77 259	88.07 231

tfpn1000		73.63 264	73.58 252	73.79 286	85.46 248	50.31 323	79.99 238	68.18 332	72.33 182	80.66 248	83.05 288	39.80 348	86.74 261	60.96 252	91.78 210	84.32 276

TinyColmap		81.25 189	82.34 174	77.99 243	85.33 249	60.68 242	82.32 197	88.33 182	71.26 201	86.97 151	92.22 133	77.10 132	86.98 249	62.37 238	95.17 132	86.31 251

PAPR		78.84 208	78.10 212	81.07 205	85.17 250	60.22 245	82.21 202	90.57 135	62.51 268	75.32 293	84.61 273	74.99 155	92.30 148	59.48 267	88.04 269	90.68 196

DI_MVS_plusplus_test		81.27 188	81.26 185	81.29 202	84.98 251	61.65 230	81.98 207	87.25 198	63.56 258	87.56 141	89.60 199	73.62 178	91.83 160	72.20 172	90.59 244	90.38 205

pmmvs4		74.92 248	72.98 265	80.73 209	84.95 252	71.71 136	76.23 288	77.59 266	52.83 317	77.73 275	86.38 243	56.35 276	84.97 280	57.72 280	87.05 279	85.51 259

Test4		81.31 186	81.13 189	81.88 193	84.89 253	63.05 203	82.37 195	90.50 136	62.75 266	89.00 119	88.29 220	67.55 226	91.68 163	73.55 159	91.24 223	90.89 189

Patchmatch-RL test		74.48 252	73.68 251	76.89 259	84.83 254	66.54 170	72.29 311	69.16 325	57.70 294	86.76 152	86.33 247	45.79 312	82.59 297	69.63 188	90.65 242	81.54 317

tfpn_ndepth		72.54 271	72.30 272	73.24 289	84.81 255	51.42 309	79.24 256	70.49 319	69.26 217	78.48 268	79.80 324	40.16 347	86.77 259	58.08 279	90.43 245	81.53 318

test_normal		81.23 190	81.16 188	81.43 200	84.77 256	61.99 225	81.46 219	86.95 208	63.16 263	87.22 144	89.63 198	73.62 178	91.65 164	72.92 167	90.70 239	90.65 198

XXY-MVS		74.44 254	76.19 229	69.21 308	84.61 257	52.43 299	71.70 313	77.18 268	60.73 283	80.60 249	90.96 170	75.44 148	69.35 332	56.13 286	88.33 263	85.86 256

cascas		76.29 235	74.81 241	80.72 210	84.47 258	62.94 205	73.89 305	87.34 195	55.94 302	75.16 295	76.53 337	63.97 238	91.16 175	65.00 224	90.97 232	88.06 232

PVSNet_BlendedMVS		78.80 209	77.84 213	81.65 198	84.43 259	63.41 194	79.49 246	90.44 138	61.70 276	75.43 291	87.07 237	69.11 220	91.44 169	60.68 254	92.24 205	90.11 211

PVSNet_Blended		76.49 233	75.40 236	79.76 218	84.43 259	63.41 194	75.14 296	90.44 138	57.36 296	75.43 291	78.30 330	69.11 220	91.44 169	60.68 254	87.70 274	84.42 274

OpenMVS	76.72 13	81.98 181	82.00 178	81.93 190	84.42 261	68.22 159	88.50 75	89.48 168	66.92 235	81.80 229	91.86 137	72.59 202	90.16 204	71.19 176	91.25 222	87.40 241

OpenMVS_ROB	70.19 17	77.77 217	77.46 215	78.71 231	84.39 262	61.15 236	81.18 224	82.52 240	62.45 270	83.34 207	87.37 234	66.20 231	88.66 234	64.69 226	85.02 299	86.32 250

0601test		78.71 211	78.51 210	79.32 225	84.32 263	58.84 255	78.38 266	85.33 222	75.99 131	82.49 218	86.57 240	58.01 266	90.02 210	62.74 237	92.73 196	89.10 222

Regformer-3		85.06 115	84.67 128	86.22 97	84.27 264	73.43 114	84.07 142	85.26 223	80.77 68	88.62 126	85.48 256	80.56 102	90.39 198	81.99 77	91.04 226	94.85 81

Regformer-4		86.41 91	85.71 107	88.52 63	84.27 264	77.57 84	84.07 142	88.00 189	82.82 46	89.84 98	85.48 256	82.06 82	92.77 139	83.83 57	91.04 226	95.22 76

DELS-MVS		81.44 185	81.25 186	82.03 189	84.27 264	62.87 207	76.47 286	92.49 75	70.97 204	81.64 231	83.83 280	75.03 154	92.70 140	74.29 149	92.22 207	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
Gipuma		84.44 130	86.33 96	78.78 229	84.20 267	73.57 113	89.55 52	90.44 138	84.24 32	84.38 192	94.89 54	76.35 144	80.40 305	76.14 141	96.80 77	82.36 303
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
Regformer-1		86.00 101	85.50 111	87.49 79	84.18 268	76.90 92	83.52 162	87.94 191	82.18 54	89.19 116	85.07 266	82.28 78	91.89 158	82.40 73	92.72 197	93.69 114

Regformer-2		86.74 86	86.08 102	88.73 61	84.18 268	79.20 70	83.52 162	89.33 170	83.33 38	89.92 96	85.07 266	83.23 67	93.16 123	83.39 59	92.72 197	93.83 106

EI-MVSNet-Vis-set		85.12 114	84.53 135	86.88 84	84.01 270	72.76 118	83.91 149	85.18 225	80.44 69	88.75 123	85.49 255	80.08 105	91.92 156	82.02 76	90.85 236	95.97 50

semantic-postprocess				84.34 140	83.93 271	69.66 147		81.09 252	72.43 178	86.47 162	90.19 190	57.56 271	93.15 125	77.45 129	86.39 285	90.22 207

MSDG		80.06 205	79.99 203	80.25 212	83.91 272	68.04 160	77.51 276	89.19 171	77.65 106	81.94 224	83.45 285	76.37 143	86.31 266	63.31 235	86.59 282	86.41 249

EI-MVSNet-UG-set		85.04 116	84.44 137	86.85 85	83.87 273	72.52 121	83.82 151	85.15 226	80.27 73	88.75 123	85.45 259	79.95 107	91.90 157	81.92 78	90.80 237	96.13 44

thres200		72.34 274	71.55 279	74.70 278	83.48 274	51.60 308	75.02 297	73.71 290	70.14 211	78.56 267	80.57 318	46.20 305	88.20 239	46.99 330	89.29 253	84.32 276

USDC		76.63 230	76.73 221	76.34 266	83.46 275	57.20 265	80.02 237	88.04 188	52.14 323	83.65 204	91.25 153	63.24 243	86.65 262	54.66 298	94.11 160	85.17 262

HY-MVS	64.64 18	73.03 267	72.47 271	74.71 277	83.36 276	54.19 284	82.14 205	81.96 245	56.76 301	69.57 323	86.21 250	60.03 254	84.83 283	49.58 319	82.65 315	85.11 263

EI-MVSNet		82.61 170	82.42 172	83.20 172	83.25 277	63.66 191	83.50 165	85.07 227	76.06 128	86.55 156	85.10 264	73.41 184	90.25 199	78.15 123	90.67 240	95.68 59

CVMVSNet		72.62 270	71.41 280	76.28 267	83.25 277	60.34 244	83.50 165	79.02 261	37.77 357	76.33 282	85.10 264	49.60 296	87.41 244	70.54 183	77.54 336	81.08 325

V42		83.47 161	83.37 157	83.75 155	83.16 279	63.33 197	81.31 220	90.23 153	69.51 215	90.91 79	90.81 175	74.16 168	92.29 149	80.06 102	90.22 246	95.62 61

EU-MVSNet		75.12 245	74.43 246	77.18 255	83.11 280	59.48 251	85.71 120	82.43 242	39.76 356	85.64 171	88.76 209	44.71 329	87.88 241	73.86 156	85.88 289	84.16 279

FMVSNet3		78.80 209	78.55 209	79.57 222	82.89 281	56.89 268	81.76 212	85.77 217	69.04 221	86.00 165	90.44 185	51.75 291	90.09 208	65.95 217	93.34 180	91.72 171

test1235678		65.57 312	65.73 309	65.06 324	82.84 282	50.90 315	62.90 337	69.26 323	57.17 299	72.36 309	83.04 289	46.02 307	70.10 329	32.79 354	85.24 297	74.19 339

MVS_Test		82.47 173	83.22 158	80.22 213	82.62 283	57.75 261	82.54 192	91.96 90	71.16 202	82.89 215	92.52 124	77.41 127	90.50 195	80.04 103	87.84 272	92.40 151

LF4IMVS		82.75 169	81.93 179	85.19 115	82.08 284	80.15 61	85.53 121	88.76 175	68.01 226	85.58 172	87.75 228	71.80 210	86.85 251	74.02 153	93.87 166	88.58 228

PVSNet	58.17 21	66.41 308	65.63 310	68.75 311	81.96 285	49.88 332	62.19 339	72.51 302	51.03 331	68.04 328	75.34 341	50.84 292	74.77 320	45.82 334	82.96 311	81.60 316

GA-MVS		75.83 239	74.61 242	79.48 224	81.87 286	59.25 253	73.42 308	82.88 238	68.68 223	79.75 258	81.80 311	50.62 293	89.46 214	66.85 211	85.64 291	89.72 213

MS-PatchMatch		70.93 284	70.22 287	73.06 292	81.85 287	62.50 216	73.82 306	77.90 264	52.44 320	75.92 287	81.27 315	55.67 280	81.75 300	55.37 292	77.70 334	74.94 337

Patchmatch-test1		72.75 269	72.61 268	73.19 290	81.62 288	55.86 274	78.89 262	71.37 312	61.73 274	74.93 296	82.15 307	60.46 251	81.80 299	59.68 259	82.63 317	81.92 311

FMVSNet5		72.10 276	71.69 276	73.32 287	81.57 289	53.02 294	76.77 280	78.37 263	63.31 261	76.37 281	91.85 138	36.68 351	78.98 309	47.87 326	92.45 200	87.95 235

CANet_DTU		77.81 216	77.05 217	80.09 214	81.37 290	59.90 248	83.26 172	88.29 183	69.16 219	67.83 330	83.72 281	60.93 248	89.47 213	69.22 193	89.70 251	90.88 190

ANet_high		83.17 165	85.68 108	75.65 273	81.24 291	45.26 341	79.94 239	92.91 61	83.83 36	91.33 73	96.88 10	80.25 104	85.92 271	68.89 197	95.89 111	95.76 54

new-patchmatchnet		70.10 290	73.37 261	60.29 336	81.23 292	16.95 364	59.54 343	74.62 283	62.93 264	80.97 237	87.93 226	62.83 244	71.90 326	55.24 293	95.01 138	92.00 162

test20.03		73.75 259	74.59 244	71.22 302	81.11 293	51.12 313	70.15 319	72.10 304	70.42 207	80.28 256	91.50 149	64.21 237	74.72 322	46.96 331	94.58 154	87.82 238

MVS		73.21 266	72.59 269	75.06 275	80.97 294	60.81 241	81.64 215	85.92 216	46.03 347	71.68 313	77.54 331	68.47 223	89.77 211	55.70 289	85.39 293	74.60 338

N_pmnet		70.20 288	68.80 295	74.38 279	80.91 295	84.81 35	59.12 346	76.45 274	55.06 306	75.31 294	82.36 306	55.74 279	54.82 357	47.02 329	87.24 278	83.52 286

IterMVS		76.91 224	76.34 228	78.64 232	80.91 295	64.03 187	76.30 287	79.03 260	64.88 255	83.11 211	89.16 204	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.
WTY-MVS		67.91 302	68.35 297	66.58 319	80.82 297	48.12 335	65.96 333	72.60 300	53.67 313	71.20 316	81.68 313	58.97 262	69.06 334	48.57 322	81.67 319	82.55 299

IB-MVS	62.13 19	71.64 279	68.97 293	79.66 221	80.80 298	62.26 224	73.94 304	76.90 270	63.27 262	68.63 326	76.79 335	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
our_test_3		71.85 277	71.59 277	72.62 296	80.71 299	53.78 288	69.72 321	71.71 311	58.80 288	78.03 271	80.51 319	56.61 275	78.84 310	62.20 240	86.04 288	85.23 261

ppachtmachnet_test		74.73 251	74.00 249	76.90 258	80.71 299	56.89 268	71.53 314	78.42 262	58.24 292	79.32 263	82.92 294	57.91 268	84.26 287	65.60 222	91.36 214	89.56 214

testgi		72.36 273	74.61 242	65.59 321	80.56 301	42.82 349	68.29 324	73.35 293	66.87 236	81.84 226	89.93 194	72.08 207	66.92 340	46.05 333	92.54 199	87.01 245

1314		73.22 265	72.56 270	75.20 274	80.41 302	57.84 259	81.64 215	85.36 221	51.68 326	73.10 306	76.65 336	61.45 247	85.19 278	63.54 232	79.21 331	82.59 298

CR-MVSNet		74.00 257	73.04 264	76.85 260	79.58 303	62.64 210	82.58 189	76.90 270	50.50 336	75.72 289	92.38 125	48.07 300	84.07 288	68.72 200	82.91 313	83.85 282

RPMNet		76.06 236	75.79 231	76.85 260	79.58 303	62.64 210	82.58 189	71.75 309	74.80 147	75.72 289	92.59 120	48.69 298	84.07 288	73.48 160	82.91 313	83.85 282

UnsupCasMVSNet_bld		69.21 298	69.68 290	67.82 315	79.42 305	51.15 312	67.82 328	75.79 275	54.15 310	77.47 276	85.36 263	59.26 261	70.64 328	48.46 323	79.35 329	81.66 315

PatchT		70.52 286	72.76 266	63.79 327	79.38 306	33.53 357	77.63 274	65.37 343	73.61 158	71.77 312	92.79 118	44.38 330	75.65 319	64.53 229	85.37 294	82.18 306

Patchmtry		76.56 232	77.46 215	73.83 285	79.37 307	46.60 338	82.41 194	76.90 270	73.81 157	85.56 173	92.38 125	48.07 300	83.98 290	63.36 234	95.31 128	90.92 188

mvs_anonymous		78.13 213	78.76 207	76.23 268	79.24 308	50.31 323	78.69 264	84.82 232	61.60 277	83.09 213	92.82 115	73.89 174	87.01 247	68.33 203	86.41 284	91.37 179

MVS-HIRNet		61.16 323	62.92 318	55.87 340	79.09 309	35.34 356	71.83 312	57.98 357	46.56 345	59.05 354	91.14 160	49.95 295	76.43 315	38.74 346	71.92 346	55.84 356

diffmvs		81.78 183	82.36 173	80.02 215	79.06 310	59.93 247	83.30 170	88.41 181	73.47 160	78.38 269	92.05 134	75.85 147	88.38 236	80.73 92	89.98 249	91.76 170

MDA-MVSNet-bldmvs		77.47 218	76.90 219	79.16 226	79.03 311	64.59 182	66.58 332	75.67 277	73.15 171	88.86 120	88.99 207	66.94 228	81.23 302	64.71 225	88.22 268	91.64 174

tpm2		68.45 300	66.83 304	73.30 288	78.93 312	48.50 333	79.76 240	71.76 308	47.50 343	69.92 322	83.60 282	42.07 336	88.40 235	48.44 324	79.51 327	83.01 296

testus		62.33 317	63.03 317	60.20 337	78.78 313	40.74 350	59.14 344	69.80 322	49.26 340	71.41 314	74.72 343	52.33 290	63.52 349	29.84 356	82.01 318	76.36 334

tpm		67.95 301	68.08 300	67.55 316	78.74 314	43.53 347	75.60 292	67.10 340	54.92 307	72.23 310	88.10 222	42.87 334	75.97 317	52.21 306	80.95 325	83.15 294

tpmp4_e23		69.43 295	67.33 302	75.72 272	78.53 315	52.75 295	82.13 206	74.91 280	49.23 341	66.37 333	84.17 278	41.28 343	88.67 233	49.73 318	79.63 326	85.75 257

no-one		71.52 281	70.43 286	74.81 276	78.45 316	63.41 194	57.73 349	77.03 269	51.46 329	77.17 278	90.33 187	54.96 285	80.35 306	47.41 327	99.29 2	80.68 329

1111		61.71 319	63.77 315	55.55 342	78.05 317	25.74 361	60.62 340	67.52 333	66.09 241	74.68 297	86.50 241	16.00 366	59.22 355	38.79 344	85.65 290	81.70 313

.test1245		48.02 336	54.41 335	28.84 349	78.05 317	25.74 361	60.62 340	67.52 333	66.09 241	74.68 297	86.50 241	16.00 366	59.22 355	38.79 344	1.47 361	1.55 362

MDTV_nov1_ep13			68.29 299		78.03 319	43.87 346	74.12 303	72.22 303	52.17 321	67.02 332	85.54 254	45.36 319	80.85 303	55.73 287	84.42 305

EPNet_dtu		72.87 268	71.33 281	77.49 253	77.72 320	60.55 243	82.35 196	75.79 275	66.49 238	58.39 357	81.06 317	53.68 287	85.98 270	53.55 302	92.97 192	85.95 254
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
PatchmatchNet		69.71 294	68.83 294	72.33 298	77.66 321	53.60 289	79.29 254	69.99 321	57.66 295	72.53 308	82.93 293	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.
sss		66.92 305	67.26 303	65.90 320	77.23 322	51.10 314	64.79 334	71.72 310	52.12 324	70.13 321	80.18 321	57.96 267	65.36 347	50.21 314	81.01 324	81.25 322

CostFormer		69.98 293	68.68 296	73.87 284	77.14 323	50.72 321	79.26 255	74.51 285	51.94 325	70.97 318	84.75 271	45.16 327	87.49 243	55.16 294	79.23 330	83.40 289

tpm cat1		66.76 306	65.21 311	71.42 301	77.09 324	50.62 322	78.01 269	73.68 291	44.89 349	68.64 324	79.00 327	45.51 317	82.42 298	49.91 316	70.15 349	81.23 324

pmmvs5		70.73 285	70.07 288	72.72 294	77.03 325	52.73 296	74.14 302	75.65 278	50.36 337	72.17 311	85.37 262	55.42 282	80.67 304	52.86 305	87.59 275	84.77 270

EPNet		80.37 199	78.41 211	86.23 96	76.75 326	73.28 115	87.18 94	77.45 267	76.24 127	68.14 327	88.93 208	65.41 235	93.85 80	69.47 189	96.12 102	91.55 177
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CHOSEN 280x420		59.08 327	56.52 332	66.76 318	76.51 327	64.39 185	49.62 356	59.00 353	43.86 351	55.66 360	68.41 351	35.55 353	68.21 336	43.25 337	76.78 338	67.69 348

UnsupCasMVSNet_eth		71.63 280	72.30 272	69.62 306	76.47 328	52.70 297	70.03 320	80.97 253	59.18 287	79.36 262	88.21 221	60.50 250	69.12 333	58.33 276	77.62 335	87.04 244

test-LLR		67.21 304	66.74 305	68.63 312	76.45 329	55.21 279	67.89 325	67.14 338	62.43 271	65.08 340	72.39 345	43.41 331	69.37 330	61.00 250	84.89 300	81.31 320

test-mter		65.00 313	63.79 314	68.63 312	76.45 329	55.21 279	67.89 325	67.14 338	50.98 332	65.08 340	72.39 345	28.27 361	69.37 330	61.00 250	84.89 300	81.31 320

gg-mvs-nofinetune		68.96 299	69.11 292	68.52 314	76.12 331	45.32 340	83.59 158	55.88 358	86.68 20	64.62 343	97.01 7	30.36 358	83.97 291	44.78 335	82.94 312	76.26 335

CMPMVS	59.41 20	75.12 245	73.57 253	79.77 217	75.84 332	67.22 164	81.21 223	82.18 243	50.78 333	76.50 280	87.66 230	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
test12356		54.91 333	57.14 331	48.22 346	75.83 333	17.47 363	52.31 355	69.20 324	51.66 327	60.11 350	75.40 340	29.77 360	62.62 353	27.64 357	72.37 344	64.59 349

PNet_i23d		52.13 334	51.24 336	54.79 343	75.56 334	45.26 341	54.54 352	52.55 361	66.95 234	57.19 358	65.82 354	13.15 368	63.40 350	36.39 350	39.04 359	55.71 357

wuyk23d		75.13 244	79.30 205	62.63 328	75.56 334	75.18 105	80.89 229	73.10 298	75.06 146	94.76 14	95.32 41	87.73 31	52.85 358	34.16 352	97.11 67	59.85 353

Patchmatch-test		65.91 310	67.38 301	61.48 333	75.51 336	43.21 348	68.84 322	63.79 345	62.48 269	72.80 307	83.42 286	44.89 328	59.52 354	48.27 325	86.45 283	81.70 313

new_pmnet		55.69 331	57.66 330	49.76 344	75.47 337	30.59 358	59.56 342	51.45 362	43.62 352	62.49 345	75.48 339	40.96 345	49.15 360	37.39 348	72.52 343	69.55 346

gm-plane-assit					75.42 338	44.97 344			52.17 321		72.36 347		87.90 240	54.10 300

PatchFormer-LS_test		67.91 302	66.49 308	72.17 299	75.29 339	51.85 306	75.68 290	73.62 292	57.23 298	68.64 324	68.13 353	42.19 335	82.76 296	64.06 230	73.51 342	81.89 312

MVSTER		77.09 222	75.70 234	81.25 203	75.27 340	61.08 237	77.49 277	85.07 227	60.78 282	86.55 156	88.68 211	43.14 333	90.25 199	73.69 157	90.67 240	92.42 149

PVSNet_0	51.08 22	56.10 330	54.97 334	59.48 338	75.12 341	53.28 293	55.16 350	61.89 348	44.30 350	59.16 353	62.48 357	54.22 286	65.91 345	35.40 351	47.01 357	59.25 354

test0.0.03 1		64.66 314	64.36 313	65.57 322	75.03 342	46.89 337	64.69 335	61.58 351	62.43 271	71.18 317	77.54 331	43.41 331	68.47 335	40.75 342	82.65 315	81.35 319

DWT-MVSNet_test		66.43 307	64.37 312	72.63 295	74.86 343	50.86 316	76.52 284	72.74 299	54.06 311	65.50 337	68.30 352	32.13 356	84.84 282	61.63 247	73.59 341	82.19 305

tpmvs		70.16 289	69.56 291	71.96 300	74.71 344	48.13 334	79.63 241	75.45 279	65.02 254	70.26 320	81.88 310	45.34 320	85.68 274	58.34 275	75.39 339	82.08 307

MDA-MVSNet_test_wron		70.05 292	70.44 284	68.88 310	73.84 345	53.47 290	58.93 348	67.28 336	58.43 290	87.09 148	85.40 260	59.80 258	67.25 338	59.66 260	83.54 308	85.92 255

YYNet1		70.06 291	70.44 284	68.90 309	73.76 346	53.42 292	58.99 347	67.20 337	58.42 291	87.10 147	85.39 261	59.82 257	67.32 337	59.79 258	83.50 309	85.96 253

GG-mvs-BLEND				67.16 317	73.36 347	46.54 339	84.15 141	55.04 359		58.64 356	61.95 358	29.93 359	83.87 292	38.71 347	76.92 337	71.07 344

test2356		56.69 329	55.15 333	61.32 334	73.20 348	44.11 345	54.95 351	62.52 346	48.75 342	62.45 346	68.42 350	21.10 365	65.67 346	26.86 358	78.08 333	74.19 339

JIA-IIPM		69.41 297	66.64 307	77.70 247	73.19 349	71.24 139	75.67 291	65.56 342	70.42 207	65.18 339	92.97 111	33.64 355	83.06 294	53.52 303	69.61 352	78.79 331

ADS-MVSNet2		65.87 311	63.64 316	72.55 297	73.16 350	56.92 267	67.10 330	74.81 282	49.74 338	66.04 335	82.97 291	46.71 302	77.26 312	42.29 338	69.96 350	83.46 287

ADS-MVSNet		61.90 318	62.19 320	61.03 335	73.16 350	36.42 355	67.10 330	61.75 349	49.74 338	66.04 335	82.97 291	46.71 302	63.21 351	42.29 338	69.96 350	83.46 287

DSMNet-mixed		60.98 325	61.61 322	59.09 339	72.88 352	45.05 343	74.70 299	46.61 364	26.20 359	65.34 338	90.32 188	55.46 281	63.12 352	41.72 340	81.30 323	69.09 347

tpmrst		66.28 309	66.69 306	65.05 325	72.82 353	39.33 352	78.20 268	70.69 318	53.16 316	67.88 329	80.36 320	48.18 299	74.75 321	58.13 277	70.79 347	81.08 325

TESTMET0.1,1		61.29 322	60.32 326	64.19 326	72.06 354	51.30 310	67.89 325	62.09 347	45.27 348	60.65 349	69.01 348	27.93 362	64.74 348	56.31 285	81.65 321	76.53 333

dp		60.70 326	60.29 327	61.92 331	72.04 355	38.67 354	70.83 315	64.08 344	51.28 330	60.75 348	77.28 333	36.59 352	71.58 327	47.41 327	62.34 356	75.52 336

pmmvs3		62.47 315	60.02 328	69.80 304	71.58 356	64.00 188	70.52 317	58.44 355	39.77 355	66.05 334	75.84 338	27.10 363	72.28 324	46.15 332	84.77 304	73.11 341

LP		69.42 296	68.30 298	72.77 293	71.48 357	56.84 270	73.66 307	74.84 281	63.52 259	70.95 319	83.35 287	49.55 297	77.15 314	57.13 282	70.21 348	84.33 275

EPMVS		62.47 315	62.63 319	62.01 329	70.63 358	38.74 353	74.76 298	52.86 360	53.91 312	67.71 331	80.01 322	39.40 349	66.60 342	55.54 291	68.81 354	80.68 329

E-PMN		61.59 321	61.62 321	61.49 332	66.81 359	55.40 277	53.77 353	60.34 352	66.80 237	58.90 355	65.50 355	40.48 346	66.12 344	55.72 288	86.25 286	62.95 351

testpf		58.55 328	61.58 323	49.48 345	66.03 360	40.05 351	74.40 301	58.07 356	64.72 256	59.36 352	72.67 344	22.76 364	66.92 340	67.07 209	69.15 353	41.46 358

EMVS		61.10 324	60.81 324	61.99 330	65.96 361	55.86 274	53.10 354	58.97 354	67.06 233	56.89 359	63.33 356	40.98 344	67.03 339	54.79 297	86.18 287	63.08 350

PMMVS		61.65 320	60.38 325	65.47 323	65.40 362	69.26 151	63.97 336	61.73 350	36.80 358	60.11 350	68.43 349	59.42 259	66.35 343	48.97 321	78.57 332	60.81 352

PMMVS2		55.64 332	59.27 329	44.74 347	64.30 363	12.32 365	40.60 357	49.79 363	53.19 315	65.06 342	84.81 270	53.60 288	49.76 359	32.68 355	89.41 252	72.15 342

MVE	40.22 23	51.82 335	50.47 338	55.87 340	62.66 364	51.91 302	31.61 359	39.28 365	40.65 354	50.76 361	74.98 342	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)
DeepMVS_CX				24.13 350	32.95 365	29.49 359		21.63 368	12.07 360	37.95 362	45.07 359	30.84 357	19.21 362	17.94 360	33.06 360	23.69 359

tmp_tt		20.25 340	24.50 341	7.49 351	4.47 366	8.70 366	34.17 358	25.16 367	1.00 361	32.43 363	18.49 360	39.37 350	9.21 363	21.64 359	43.75 358	4.57 360

testmvs		5.91 344	7.65 345	0.72 353	1.20 367	0.37 368	59.14 344	0.67 370	0.49 363	1.11 364	2.76 364	0.94 370	0.24 365	1.02 362	1.47 361	1.55 362

test123		6.27 343	8.08 344	0.84 352	1.11 368	0.57 367	62.90 337	0.82 369	0.54 362	1.07 365	2.75 365	1.26 369	0.30 364	1.04 361	1.26 363	1.66 361

cdsmvs_eth3d_5k		20.81 339	27.75 340	0.00 354	0.00 369	0.00 369	0.00 360	85.44 220	0.00 364	0.00 366	82.82 295	81.46 93	0.00 366	0.00 363	0.00 364	0.00 364

pcd_1.5k_mvsjas		6.41 342	8.55 343	0.00 354	0.00 369	0.00 369	0.00 360	0.00 371	0.00 364	0.00 366	0.00 366	76.94 135	0.00 366	0.00 363	0.00 364	0.00 364

sosnet-low-res		0.00 345	0.00 346	0.00 354	0.00 369	0.00 369	0.00 360	0.00 371	0.00 364	0.00 366	0.00 366	0.00 371	0.00 366	0.00 363	0.00 364	0.00 364

sosnet		0.00 345	0.00 346	0.00 354	0.00 369	0.00 369	0.00 360	0.00 371	0.00 364	0.00 366	0.00 366	0.00 371	0.00 366	0.00 363	0.00 364	0.00 364

uncertanet		0.00 345	0.00 346	0.00 354	0.00 369	0.00 369	0.00 360	0.00 371	0.00 364	0.00 366	0.00 366	0.00 371	0.00 366	0.00 363	0.00 364	0.00 364

Regformer		0.00 345	0.00 346	0.00 354	0.00 369	0.00 369	0.00 360	0.00 371	0.00 364	0.00 366	0.00 366	0.00 371	0.00 366	0.00 363	0.00 364	0.00 364

ab-mvs-re		6.65 341	8.87 342	0.00 354	0.00 369	0.00 369	0.00 360	0.00 371	0.00 364	0.00 366	79.80 324	0.00 371	0.00 366	0.00 363	0.00 364	0.00 364

uanet		0.00 345	0.00 346	0.00 354	0.00 369	0.00 369	0.00 360	0.00 371	0.00 364	0.00 366	0.00 366	0.00 371	0.00 366	0.00 363	0.00 364	0.00 364

GSMVS																83.88 280

test_part1				0.00 354		0.00 369	0.00 360	93.93 26				0.00 371	0.00 366	0.00 363	0.00 364	0.00 364

sam_mvs1												46.11 306				83.88 280

sam_mvs												45.92 311

MTGPA								91.81 94

test_post1							78.85 263				3.13 362	45.19 322	80.13 307	58.11 278

test_post											3.10 363	45.43 318	77.22 313

patchmatchnet-post											81.71 312	45.93 310	87.01 247

MTMP							90.66 35	33.14 366

test9_res														80.83 89	96.45 88	90.57 199

agg_prior2														79.68 107	96.16 98	90.22 207

test_prior4						78.97 72	84.59 133

test_prior2							83.37 168		75.43 141	84.58 188	91.57 146	81.92 88		79.54 109	96.97 70

旧先验2							81.73 213		56.88 300	86.54 161			84.90 281	72.81 168

新几何2							81.72 214

无先验							82.81 183	85.62 219	58.09 293				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 85

testdata1							79.62 242		73.95 156

plane_prior5								93.61 33					95.22 39	80.78 90	95.83 114	94.46 88

plane_prior4											92.95 112

plane_prior3						76.85 93			77.79 104	86.55 156

plane_prior2							89.45 57		79.44 82

plane_prior						76.42 98	87.15 95		75.94 133						95.03 137

n2								0.00 371

nn								0.00 371

door-mid								74.45 286

test11								91.46 108

door								72.57 301

HQP5-MVS						70.66 142

BP-MVS														77.30 131

HQP4-MVS										80.56 251			94.61 55			93.56 119

HQP3-MVS								92.68 69							94.47 156

HQP2-MVS												72.10 205

MDTV_nov1_ep13_2view						27.60 360	70.76 316		46.47 346	61.27 347		45.20 321		49.18 320		83.75 284

ACMMP++_ref															95.74 118

ACMMP++															97.35 61

Test By Simon												79.09 111

High-resolution multi-view benchmark