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.

APDe-MVS		89.15 3	89.63 3	87.73 22	94.49 10	71.69 44	93.83 2	93.96 4	75.70 72	91.06 5	96.03 1	76.84 5	97.03 8	89.09 2	95.65 16	94.47 11

SMA-MVS		89.01 5	89.19 4	88.46 3	94.19 21	73.73 7	92.40 14	93.63 9	74.77 91	92.29 1	95.82 2	74.28 19	97.22 5	88.44 5	96.91 1	94.44 12

test_part3							92.22 19		75.63 74		95.29 3		97.56 1	86.60 13

ESAPD		89.40 1	89.87 1	87.98 12	95.06 1	72.65 27	92.22 19	94.09 1	75.63 74	91.80 2	95.29 3	81.79 1	97.56 1	86.60 13	96.38 3	93.74 40

MP-MVS-pluss		87.67 14	87.72 13	87.54 28	93.64 29	72.04 41	89.80 60	93.50 12	75.17 85	86.34 19	95.29 3	70.86 40	96.00 36	88.78 3	96.04 6	94.58 7
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
ACMMP_Plus		88.05 11	88.08 11	87.94 13	93.70 26	73.05 19	90.86 37	93.59 10	76.27 66	88.14 10	95.09 6	71.06 39	96.67 16	87.67 7	96.37 5	94.09 23

zzz-MVS		87.53 16	87.41 17	87.90 17	94.18 22	74.25 2	90.23 51	92.02 62	79.45 19	85.88 21	94.80 7	68.07 61	96.21 30	86.69 11	95.34 20	93.23 60

MTAPA		87.23 22	87.00 22	87.90 17	94.18 22	74.25 2	86.58 168	92.02 62	79.45 19	85.88 21	94.80 7	68.07 61	96.21 30	86.69 11	95.34 20	93.23 60

SteuartSystems-ACMMP		88.72 7	88.86 7	88.32 5	92.14 55	72.96 20	93.73 3	93.67 8	80.19 15	88.10 11	94.80 7	73.76 23	97.11 6	87.51 9	95.82 11	94.90 4
Skip Steuart: Steuart Systems R&D Blog.
region2R		87.42 19	87.20 20	88.09 7	94.63 6	73.55 10	93.03 7	93.12 23	76.73 55	84.45 41	94.52 10	69.09 57	96.70 15	84.37 29	94.83 31	94.03 26

ACMMPR		87.44 17	87.23 19	88.08 8	94.64 5	73.59 9	93.04 5	93.20 20	76.78 52	84.66 38	94.52 10	68.81 59	96.65 17	84.53 26	94.90 28	94.00 29

APD-MVS		87.44 17	87.52 15	87.19 33	94.24 18	72.39 35	91.86 26	92.83 36	73.01 130	88.58 9	94.52 10	73.36 24	96.49 24	84.26 30	95.01 26	92.70 75
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
APD-MVS_3200maxsize		85.97 40	85.88 38	86.22 51	92.69 47	69.53 77	91.93 25	92.99 29	73.54 116	85.94 20	94.51 13	65.80 82	95.61 42	83.04 41	92.51 56	93.53 53

CP-MVS		87.11 24	86.92 24	87.68 27	94.20 20	73.86 5	93.98 1	92.82 38	76.62 57	83.68 52	94.46 14	67.93 63	95.95 38	84.20 32	94.39 39	93.23 60

HFP-MVS		87.58 15	87.47 16	87.94 13	94.58 7	73.54 12	93.04 5	93.24 18	76.78 52	84.91 32	94.44 15	70.78 41	96.61 19	84.53 26	94.89 29	93.66 42

#test#		87.33 21	87.13 21	87.94 13	94.58 7	73.54 12	92.34 16	93.24 18	75.23 82	84.91 32	94.44 15	70.78 41	96.61 19	83.75 34	94.89 29	93.66 42

PGM-MVS		86.68 29	86.27 32	87.90 17	94.22 19	73.38 16	90.22 52	93.04 24	75.53 76	83.86 49	94.42 17	67.87 65	96.64 18	82.70 44	94.57 35	93.66 42

MP-MVS		87.71 13	87.64 14	87.93 16	94.36 16	73.88 4	92.71 13	92.65 43	77.57 35	83.84 50	94.40 18	72.24 33	96.28 28	85.65 16	95.30 24	93.62 49
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
HPM-MVS++		89.02 4	89.15 5	88.63 1	95.01 3	76.03 1	92.38 15	92.85 35	80.26 14	87.78 14	94.27 19	75.89 9	96.81 11	87.45 10	96.44 2	93.05 68

mPP-MVS		86.67 30	86.32 31	87.72 24	94.41 14	73.55 10	92.74 11	92.22 55	76.87 50	82.81 64	94.25 20	66.44 75	96.24 29	82.88 43	94.28 42	93.38 55

DeepC-MVS	79.81 2	87.08 26	86.88 26	87.69 26	91.16 65	72.32 38	90.31 49	93.94 5	77.12 44	82.82 62	94.23 21	72.13 34	97.09 7	84.83 23	95.37 19	93.65 47
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
XVS		87.18 23	86.91 25	88.00 10	94.42 12	73.33 17	92.78 9	92.99 29	79.14 21	83.67 53	94.17 22	67.45 68	96.60 21	83.06 39	94.50 36	94.07 24

HSP-MVS		89.28 2	89.76 2	87.85 20	94.28 17	73.46 15	92.90 8	92.73 40	80.27 13	91.35 4	94.16 23	78.35 4	96.77 12	89.59 1	94.22 45	93.33 58

abl_6		85.23 51	84.95 51	86.07 54	92.23 54	70.48 60	90.80 39	92.08 60	73.51 117	85.26 27	94.16 23	62.75 117	95.92 39	82.46 47	91.30 65	91.81 103

DeepPCF-MVS	80.84 1	88.10 8	88.56 8	86.73 41	92.24 53	69.03 83	89.57 67	93.39 16	77.53 39	89.79 7	94.12 25	78.98 3	96.58 23	85.66 15	95.72 12	94.58 7

HPM-MVS_fast		85.35 50	84.95 51	86.57 46	93.69 27	70.58 59	92.15 23	91.62 82	73.89 105	82.67 66	94.09 26	62.60 124	95.54 45	80.93 53	92.93 51	93.57 50

CNVR-MVS		88.93 6	89.13 6	88.33 4	94.77 4	73.82 6	90.51 43	93.00 27	80.90 10	88.06 12	94.06 27	76.43 6	96.84 10	88.48 4	95.99 7	94.34 16

SD-MVS		88.06 9	88.50 9	86.71 42	92.60 51	72.71 25	91.81 27	93.19 21	77.87 32	90.32 6	94.00 28	74.83 12	93.78 116	87.63 8	94.27 43	93.65 47

HPM-MVS		87.11 24	86.98 23	87.50 30	93.88 25	72.16 39	92.19 21	93.33 17	76.07 69	83.81 51	93.95 29	69.77 51	96.01 35	85.15 17	94.66 33	94.32 18
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
TSAR-MVS + MP.		88.02 12	88.11 10	87.72 24	93.68 28	72.13 40	91.41 30	92.35 52	74.62 93	88.90 8	93.85 30	75.75 10	96.00 36	87.80 6	94.63 34	95.04 2

ACMMP		85.89 42	85.39 44	87.38 31	93.59 30	72.63 29	92.74 11	93.18 22	76.78 52	80.73 87	93.82 31	64.33 92	96.29 27	82.67 45	90.69 70	93.23 60
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
test_prior3		86.73 28	86.86 27	86.33 48	92.61 49	69.59 75	88.85 86	92.97 32	75.41 78	84.91 32	93.54 32	74.28 19	95.48 46	83.31 35	95.86 9	93.91 31

test_prior2							88.85 86		75.41 78	84.91 32	93.54 32	74.28 19		83.31 35	95.86 9

VDDNet		81.52 91	80.67 93	84.05 98	90.44 77	64.13 189	89.73 63	85.91 226	71.11 162	83.18 57	93.48 34	50.54 258	93.49 133	73.40 122	88.25 100	94.54 10

CDPH-MVS		85.76 43	85.29 48	87.17 34	93.49 32	71.08 48	88.58 97	92.42 49	68.32 215	84.61 39	93.48 34	72.32 32	96.15 33	79.00 65	95.43 18	94.28 19

NCCC		88.06 9	88.01 12	88.24 6	94.41 14	73.62 8	91.22 34	92.83 36	81.50 7	85.79 24	93.47 36	73.02 27	97.00 9	84.90 20	94.94 27	94.10 22

3Dnovator+	77.84 4	85.48 46	84.47 54	88.51 2	91.08 66	73.49 14	93.18 4	93.78 7	80.79 11	76.66 150	93.37 37	60.40 164	96.75 14	77.20 83	93.73 48	95.29 1

DeepC-MVS_fast	79.65 3	86.91 27	86.62 28	87.76 21	93.52 31	72.37 37	91.26 31	93.04 24	76.62 57	84.22 46	93.36 38	71.44 37	96.76 13	80.82 55	95.33 22	94.16 20
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
VDD-MVS		83.01 72	82.36 72	84.96 72	91.02 68	66.40 133	88.91 83	88.11 194	77.57 35	84.39 44	93.29 39	52.19 222	93.91 107	77.05 86	88.70 91	94.57 9

UA-Net		85.08 54	84.96 50	85.45 60	92.07 56	68.07 109	89.78 61	90.86 106	82.48 2	84.60 40	93.20 40	69.35 55	95.22 57	71.39 144	90.88 69	93.07 67

agg_prior1		86.22 38	86.09 37	86.62 44	92.85 44	71.94 42	88.59 96	91.78 77	68.96 201	84.41 42	93.18 41	74.94 11	94.93 68	84.75 25	95.33 22	93.01 71

TEST9					93.26 36	72.96 20	88.75 91	91.89 71	68.44 208	85.00 30	93.10 42	74.36 18	95.41 51

train_agg		86.43 33	86.20 33	87.13 35	93.26 36	72.96 20	88.75 91	91.89 71	68.69 204	85.00 30	93.10 42	74.43 15	95.41 51	84.97 18	95.71 13	93.02 69

test_8					93.13 38	72.57 31	88.68 94	91.84 74	68.69 204	84.87 36	93.10 42	74.43 15	95.16 59

LFMVS		81.82 86	81.23 85	83.57 113	91.89 59	63.43 204	89.84 57	81.85 274	77.04 47	83.21 56	93.10 42	52.26 221	93.43 138	71.98 139	89.95 79	93.85 34

旧先验1					91.96 57	65.79 144		86.37 220			93.08 46	69.31 56			92.74 53	88.74 214

testdata				79.97 213	90.90 70	64.21 187		84.71 234	59.27 297	85.40 25	92.91 47	62.02 137	89.08 260	68.95 161	91.37 64	86.63 269

MCST-MVS		87.37 20	87.25 18	87.73 22	94.53 9	72.46 34	89.82 58	93.82 6	73.07 129	84.86 37	92.89 48	76.22 7	96.33 26	84.89 22	95.13 25	94.40 13

Vis-MVSNet		83.46 64	82.80 68	85.43 61	90.25 80	68.74 93	90.30 50	90.13 132	76.33 65	80.87 86	92.89 48	61.00 153	94.20 93	72.45 133	90.97 67	93.35 57
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
agg_prior3		86.16 39	85.85 40	87.10 36	93.31 33	72.86 24	88.77 89	91.68 81	68.29 216	84.26 45	92.83 50	72.83 28	95.42 50	84.97 18	95.71 13	93.02 69

CPTT-MVS		83.73 59	83.33 59	84.92 75	93.28 35	70.86 55	92.09 24	90.38 118	68.75 203	79.57 94	92.83 50	60.60 160	93.04 156	80.92 54	91.56 62	90.86 125

3Dnovator	76.31 5	83.38 67	82.31 73	86.59 45	87.94 154	72.94 23	90.64 41	92.14 59	77.21 42	75.47 176	92.83 50	58.56 171	94.72 79	73.24 124	92.71 54	92.13 95

MSLP-MVS++		85.43 48	85.76 42	84.45 84	91.93 58	70.24 61	90.71 40	92.86 34	77.46 41	84.22 46	92.81 53	67.16 71	92.94 159	80.36 58	94.35 41	90.16 155

MG-MVS		83.41 65	83.45 57	83.28 121	92.74 46	62.28 223	88.17 112	89.50 151	75.22 83	81.49 77	92.74 54	66.75 72	95.11 61	72.85 126	91.58 61	92.45 83

QAPM		80.88 100	79.50 117	85.03 70	88.01 153	68.97 87	91.59 28	92.00 65	66.63 231	75.15 189	92.16 55	57.70 176	95.45 48	63.52 199	88.76 90	90.66 134

IS-MVSNet		83.15 68	82.81 67	84.18 93	89.94 87	63.30 206	91.59 28	88.46 191	79.04 25	79.49 95	92.16 55	65.10 87	94.28 88	67.71 167	91.86 59	94.95 3

MVS_0304		86.37 37	85.81 41	88.02 9	90.13 81	72.39 35	89.66 65	92.75 39	81.64 6	82.66 67	92.04 57	64.44 91	97.35 3	84.76 24	94.25 44	94.33 17

1121		80.84 102	79.77 107	84.05 98	93.11 40	70.78 56	84.66 219	85.42 229	57.37 312	81.76 76	92.02 58	63.41 98	94.12 97	67.28 172	92.93 51	87.26 254

新几何1				83.42 116	93.13 38	70.71 57		85.48 228	57.43 311	81.80 74	91.98 59	63.28 100	92.27 178	64.60 196	92.99 50	87.27 253

OpenMVS	72.83 10	79.77 137	78.33 148	84.09 96	85.17 207	69.91 68	90.57 42	90.97 102	66.70 227	72.17 224	91.91 60	54.70 201	93.96 102	61.81 216	90.95 68	88.41 231

PHI-MVS		86.43 33	86.17 35	87.24 32	90.88 71	70.96 50	92.27 18	94.07 3	72.45 141	85.22 28	91.90 61	69.47 54	96.42 25	83.28 37	95.94 8	94.35 15

VNet		82.21 79	82.41 70	81.62 184	90.82 72	60.93 231	84.47 225	89.78 144	76.36 64	84.07 48	91.88 62	64.71 90	90.26 234	70.68 146	88.89 87	93.66 42

OPM-MVS		83.50 63	82.95 65	85.14 67	88.79 130	70.95 51	89.13 79	91.52 86	77.55 38	80.96 85	91.75 63	60.71 156	94.50 84	79.67 63	86.51 123	89.97 173

XVG-OURS-SEG-HR		80.81 105	79.76 108	83.96 105	85.60 203	68.78 90	83.54 245	90.50 115	70.66 170	76.71 149	91.66 64	60.69 157	91.26 215	76.94 87	81.58 180	91.83 101

EPNet		83.72 60	82.92 66	86.14 53	84.22 223	69.48 78	91.05 36	85.27 230	81.30 8	76.83 147	91.65 65	66.09 78	95.56 44	76.00 94	93.85 47	93.38 55
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
OMC-MVS		82.69 74	81.97 79	84.85 76	88.75 132	67.42 118	87.98 115	90.87 105	74.92 89	79.72 93	91.65 65	62.19 135	93.96 102	75.26 106	86.42 124	93.16 65

test222					91.50 62	68.26 105	84.16 235	83.20 256	54.63 323	79.74 92	91.63 67	58.97 169			91.42 63	86.77 265

MVS_111021_HR		85.14 53	84.75 53	86.32 50	91.65 61	72.70 26	85.98 184	90.33 123	76.11 68	82.08 70	91.61 68	71.36 38	94.17 96	81.02 52	92.58 55	92.08 96

原ACMM1				84.35 88	93.01 42	68.79 89		92.44 46	63.96 260	81.09 83	91.57 69	66.06 79	95.45 48	67.19 174	94.82 32	88.81 211

LPG-MVS_test		82.08 80	81.27 84	84.50 82	89.23 114	68.76 91	90.22 52	91.94 69	75.37 80	76.64 151	91.51 70	54.29 205	94.91 70	78.44 70	83.78 145	89.83 177

LGP-MVS_train				84.50 82	89.23 114	68.76 91		91.94 69	75.37 80	76.64 151	91.51 70	54.29 205	94.91 70	78.44 70	83.78 145	89.83 177

XVG-OURS		80.41 117	79.23 128	83.97 104	85.64 202	69.02 84	83.03 255	90.39 117	71.09 163	77.63 134	91.49 72	54.62 203	91.35 213	75.71 99	83.47 154	91.54 107

alignmvs		85.48 46	85.32 46	85.96 57	89.51 101	69.47 79	89.74 62	92.47 45	76.17 67	87.73 15	91.46 73	70.32 45	93.78 116	81.51 50	88.95 86	94.63 6

CANet		86.45 32	86.10 36	87.51 29	90.09 83	70.94 52	89.70 64	92.59 44	81.78 4	81.32 78	91.43 74	70.34 44	97.23 4	84.26 30	93.36 49	94.37 14

nrg030		83.88 57	83.53 56	84.96 72	86.77 190	69.28 82	90.46 46	92.67 41	74.79 90	82.95 59	91.33 75	72.70 29	93.09 152	80.79 56	79.28 212	92.50 82

canonicalmvs		85.91 41	85.87 39	86.04 56	89.84 89	69.44 81	90.45 47	93.00 27	76.70 56	88.01 13	91.23 76	73.28 25	93.91 107	81.50 51	88.80 89	94.77 5

Anonymous202405211		78.25 164	77.01 171	81.99 170	91.03 67	60.67 235	84.77 217	83.90 242	70.65 171	80.00 91	91.20 77	41.08 314	91.43 210	65.21 189	85.26 133	93.85 34

Anonymous20240529		80.19 127	78.89 134	84.10 95	90.60 74	64.75 169	88.95 82	90.90 104	65.97 239	80.59 88	91.17 78	49.97 263	93.73 125	69.16 160	82.70 169	93.81 37

EPP-MVSNet		83.40 66	83.02 64	84.57 80	90.13 81	64.47 182	92.32 17	90.73 107	74.45 95	79.35 97	91.10 79	69.05 58	95.12 60	72.78 127	87.22 111	94.13 21

TAPA-MVS	73.13 9	79.15 149	77.94 154	82.79 154	89.59 96	62.99 216	88.16 113	91.51 87	65.77 240	77.14 145	91.09 80	60.91 154	93.21 143	50.26 285	87.05 113	92.17 94
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
CSCG		86.41 35	86.19 34	87.07 37	92.91 43	72.48 33	90.81 38	93.56 11	73.95 101	83.16 58	91.07 81	75.94 8	95.19 58	79.94 62	94.38 40	93.55 51

FIs		82.07 81	82.42 69	81.04 197	88.80 129	58.34 252	88.26 110	93.49 13	76.93 49	78.47 109	91.04 82	69.92 49	92.34 177	69.87 153	84.97 135	92.44 84

MVS_111021_LR		82.61 76	82.11 74	84.11 94	88.82 127	71.58 45	85.15 210	86.16 223	74.69 92	80.47 89	91.04 82	62.29 132	90.55 232	80.33 59	90.08 77	90.20 154

DP-MVS Recon		83.11 70	82.09 75	86.15 52	94.44 11	70.92 54	88.79 88	92.20 56	70.53 172	79.17 98	91.03 84	64.12 94	96.03 34	68.39 166	90.14 76	91.50 109

HQP_MVS		83.64 61	83.14 61	85.14 67	90.08 84	68.71 95	91.25 32	92.44 46	79.12 23	78.92 101	91.00 85	60.42 162	95.38 53	78.71 68	86.32 125	91.33 112

plane_prior4											91.00 85

FC-MVSNet-test		81.52 91	82.02 77	80.03 212	88.42 143	55.97 291	87.95 117	93.42 15	77.10 45	77.38 137	90.98 87	69.96 48	91.79 191	68.46 165	84.50 139	92.33 86

Vis-MVSNet (Re-imp)		78.36 163	78.45 142	78.07 252	88.64 134	51.78 320	86.70 165	79.63 295	74.14 99	75.11 190	90.83 88	61.29 147	89.75 241	58.10 248	91.60 60	92.69 77

114514_t		80.68 111	79.51 116	84.20 92	94.09 24	67.27 122	89.64 66	91.11 99	58.75 302	74.08 199	90.72 89	58.10 174	95.04 66	69.70 154	89.42 84	90.30 152

PAPM_NR		83.02 71	82.41 70	84.82 77	92.47 52	66.37 134	87.93 119	91.80 75	73.82 110	77.32 139	90.66 90	67.90 64	94.90 72	70.37 149	89.48 83	93.19 64

LS3D		76.95 200	74.82 216	83.37 119	90.45 76	67.36 121	89.15 78	86.94 213	61.87 278	69.52 260	90.61 91	51.71 238	94.53 82	46.38 311	86.71 119	88.21 233

VPNet		78.69 158	78.66 137	78.76 240	88.31 146	55.72 297	84.45 228	86.63 216	76.79 51	78.26 121	90.55 92	59.30 167	89.70 243	66.63 178	77.05 231	90.88 124

ACMP	74.13 6	81.51 93	80.57 94	84.36 87	89.42 103	68.69 98	89.97 56	91.50 90	74.46 94	75.04 192	90.41 93	53.82 210	94.54 81	77.56 79	82.91 164	89.86 176
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
PCF-MVS	73.52 7	80.38 121	78.84 135	85.01 71	87.71 170	68.99 86	83.65 242	91.46 91	63.00 265	77.77 132	90.28 94	66.10 77	95.09 65	61.40 219	88.22 101	90.94 123
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
NP-MVS					89.62 95	68.32 103					90.24 95

HQP-MVS		82.61 76	82.02 77	84.37 86	89.33 106	66.98 126	89.17 74	92.19 57	76.41 59	77.23 142	90.23 96	60.17 165	95.11 61	77.47 80	85.99 129	91.03 119

PS-MVSNAJss		82.07 81	81.31 83	84.34 89	86.51 193	67.27 122	89.27 71	91.51 87	71.75 153	79.37 96	90.22 97	63.15 105	94.27 89	77.69 78	82.36 173	91.49 110

TSAR-MVS + GP.		85.71 44	85.33 45	86.84 39	91.34 63	72.50 32	89.07 80	87.28 210	76.41 59	85.80 23	90.22 97	74.15 22	95.37 55	81.82 48	91.88 58	92.65 78

Regformer-1		86.41 35	86.33 30	86.64 43	89.33 106	70.93 53	88.43 99	91.39 92	82.14 3	86.65 18	90.09 99	74.39 17	95.01 67	83.97 33	90.63 71	93.97 30

Regformer-2		86.63 31	86.53 29	86.95 38	89.33 106	71.24 47	88.43 99	92.05 61	82.50 1	86.88 17	90.09 99	74.45 14	95.61 42	84.38 28	90.63 71	94.01 28

casdiffmvs		83.96 56	83.25 60	86.07 54	88.48 139	69.60 74	89.26 72	92.40 50	68.07 217	82.82 62	90.03 101	69.77 51	94.86 75	81.79 49	86.64 120	93.75 39

Regformer-3		85.23 51	85.07 49	85.70 59	88.95 122	69.01 85	88.29 108	89.91 142	80.95 9	85.01 29	90.01 102	72.45 31	94.19 94	82.50 46	87.57 104	93.90 33

Regformer-4		85.68 45	85.45 43	86.35 47	88.95 122	69.67 73	88.29 108	91.29 94	81.73 5	85.36 26	90.01 102	72.62 30	95.35 56	83.28 37	87.57 104	94.03 26

TranMVSNet+NR-MVSNet		80.84 102	80.31 99	82.42 162	87.85 156	62.33 221	87.74 122	91.33 93	80.55 12	77.99 128	89.86 104	65.23 86	92.62 167	67.05 176	75.24 265	92.30 88

BH-RMVSNet		79.61 139	78.44 144	83.14 128	89.38 105	65.93 140	84.95 214	87.15 211	73.56 115	78.19 123	89.79 105	56.67 187	93.36 139	59.53 234	86.74 118	90.13 157

AdaColmap		80.58 115	79.42 118	84.06 97	93.09 41	68.91 88	89.36 69	88.97 173	69.27 191	75.70 175	89.69 106	57.20 184	95.77 40	63.06 203	88.41 99	87.50 248

ACMM	73.20 8	80.78 110	79.84 106	83.58 112	89.31 111	68.37 102	89.99 55	91.60 83	70.28 176	77.25 140	89.66 107	53.37 213	93.53 132	74.24 113	82.85 165	88.85 209
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CNLPA		78.08 170	76.79 176	81.97 171	90.40 78	71.07 49	87.59 125	84.55 236	66.03 238	72.38 222	89.64 108	57.56 178	86.04 289	59.61 232	83.35 159	88.79 212

0601test		81.17 97	80.47 97	83.24 124	89.13 118	63.62 196	86.21 179	89.95 139	72.43 144	81.78 75	89.61 109	57.50 179	93.58 128	70.75 145	86.90 116	92.52 81

EI-MVSNet-Vis-set		84.19 55	83.81 55	85.31 62	88.18 148	67.85 112	87.66 123	89.73 146	80.05 17	82.95 59	89.59 110	70.74 43	94.82 76	80.66 57	84.72 138	93.28 59

PAPR		81.66 89	80.89 91	83.99 103	90.27 79	64.00 193	86.76 164	91.77 79	68.84 202	77.13 146	89.50 111	67.63 66	94.88 73	67.55 169	88.52 97	93.09 66

jajsoiax		79.29 147	77.96 153	83.27 122	84.68 216	66.57 132	89.25 73	90.16 131	69.20 193	75.46 177	89.49 112	45.75 291	93.13 150	76.84 89	80.80 188	90.11 158

MVSFormer		82.85 73	82.05 76	85.24 65	87.35 177	70.21 62	90.50 44	90.38 118	68.55 206	81.32 78	89.47 113	61.68 138	93.46 134	78.98 66	90.26 74	92.05 97

jason		81.39 95	80.29 100	84.70 79	86.63 191	69.90 69	85.95 185	86.77 214	63.24 262	81.07 84	89.47 113	61.08 152	92.15 181	78.33 73	90.07 78	92.05 97
jason: jason.
mvs_tets		79.13 150	77.77 159	83.22 125	84.70 215	66.37 134	89.17 74	90.19 130	69.38 189	75.40 180	89.46 115	44.17 297	93.15 148	76.78 90	80.70 190	90.14 156

UGNet		80.83 104	79.59 112	84.54 81	88.04 151	68.09 108	89.42 68	88.16 193	76.95 48	76.22 162	89.46 115	49.30 269	93.94 104	68.48 164	90.31 73	91.60 105
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
VPA-MVSNet		80.60 113	80.55 95	80.76 201	88.07 150	60.80 234	86.86 158	91.58 84	75.67 73	80.24 90	89.45 117	63.34 99	90.25 235	70.51 148	79.22 213	91.23 115

MVS_Test		83.15 68	83.06 63	83.41 118	86.86 187	63.21 209	86.11 182	92.00 65	74.31 96	82.87 61	89.44 118	70.03 47	93.21 143	77.39 82	88.50 98	93.81 37

EI-MVSNet-UG-set		83.81 58	83.38 58	85.09 69	87.87 155	67.53 116	87.44 135	89.66 147	79.74 18	82.23 69	89.41 119	70.24 46	94.74 78	79.95 61	83.92 144	92.99 72

RPSCF		73.23 254	71.46 253	78.54 244	82.50 276	59.85 240	82.18 259	82.84 260	58.96 299	71.15 239	89.41 119	45.48 293	84.77 298	58.82 240	71.83 291	91.02 121

UniMVSNet_NR-MVSNet		81.88 84	81.54 82	82.92 142	88.46 141	63.46 202	87.13 148	92.37 51	80.19 15	78.38 113	89.14 121	71.66 36	93.05 154	70.05 150	76.46 247	92.25 90

DU-MVS		81.12 98	80.52 96	82.90 143	87.80 165	63.46 202	87.02 153	91.87 73	79.01 26	78.38 113	89.07 122	65.02 88	93.05 154	70.05 150	76.46 247	92.20 92

NR-MVSNet		80.23 125	79.38 120	82.78 155	87.80 165	63.34 205	86.31 176	91.09 100	79.01 26	72.17 224	89.07 122	67.20 70	92.81 165	66.08 183	75.65 256	92.20 92

DELS-MVS		85.41 49	85.30 47	85.77 58	88.49 138	67.93 111	85.52 206	93.44 14	78.70 28	83.63 55	89.03 124	74.57 13	95.71 41	80.26 60	94.04 46	93.66 42
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
DP-MVS		76.78 202	74.57 218	83.42 116	93.29 34	69.46 80	88.55 98	83.70 244	63.98 259	70.20 247	88.89 125	54.01 209	94.80 77	46.66 308	81.88 177	86.01 283

ab-mvs		79.51 141	78.97 133	81.14 195	88.46 141	60.91 232	83.84 240	89.24 161	70.36 174	79.03 99	88.87 126	63.23 103	90.21 236	65.12 190	82.57 171	92.28 89

PEN-MVS		77.73 180	77.69 161	77.84 254	87.07 185	53.91 306	87.91 120	91.18 97	77.56 37	73.14 206	88.82 127	61.23 148	89.17 258	59.95 229	72.37 286	90.43 148

diffmvs		81.48 94	81.21 87	82.31 165	83.28 256	62.72 218	85.09 211	88.63 188	74.99 86	78.31 116	88.81 128	65.80 82	91.36 212	79.03 64	86.95 115	92.84 74

test_djsdf		80.30 123	79.32 122	83.27 122	83.98 240	65.37 153	90.50 44	90.38 118	68.55 206	76.19 163	88.70 129	56.44 188	93.46 134	78.98 66	80.14 199	90.97 122

PAPM		77.68 182	76.40 181	81.51 187	87.29 182	61.85 227	83.78 241	89.59 148	64.74 250	71.23 236	88.70 129	62.59 125	93.66 127	52.66 275	87.03 114	89.01 203

DTE-MVSNet		76.99 198	76.80 175	77.54 260	86.24 195	53.06 317	87.52 132	90.66 110	77.08 46	72.50 212	88.67 131	60.48 161	89.52 245	57.33 255	70.74 297	90.05 165

PS-CasMVS		78.01 173	78.09 151	77.77 256	87.71 170	54.39 304	88.02 114	91.22 95	77.50 40	73.26 204	88.64 132	60.73 155	88.41 272	61.88 214	73.88 277	90.53 144

cdsmvs_eth3d_5k		19.96 336	26.61 336	0.00 353	0.00 367	0.00 368	0.00 360	89.26 160	0.00 363	0.00 364	88.61 133	61.62 140	0.00 366	0.00 363	0.00 364	0.00 364

lupinMVS		81.39 95	80.27 101	84.76 78	87.35 177	70.21 62	85.55 202	86.41 218	62.85 268	81.32 78	88.61 133	61.68 138	92.24 180	78.41 72	90.26 74	91.83 101

F-COLMAP		76.38 212	74.33 223	82.50 161	89.28 112	66.95 129	88.41 102	89.03 165	64.05 257	66.83 288	88.61 133	46.78 282	92.89 160	57.48 252	78.55 214	87.67 243

mvs_anonymous		79.42 145	79.11 130	80.34 206	84.45 219	57.97 258	82.59 256	87.62 204	67.40 226	76.17 166	88.56 136	68.47 60	89.59 244	70.65 147	86.05 128	93.47 54

CP-MVSNet		78.22 165	78.34 147	77.84 254	87.83 163	54.54 302	87.94 118	91.17 98	77.65 33	73.48 202	88.49 137	62.24 134	88.43 271	62.19 210	74.07 273	90.55 143

PVSNet_Blended_VisFu		82.62 75	81.83 80	84.96 72	90.80 73	69.76 71	88.74 93	91.70 80	69.39 187	78.96 100	88.46 138	65.47 84	94.87 74	74.42 110	88.57 94	90.24 153

CANet_DTU		80.61 112	79.87 105	82.83 149	85.60 203	63.17 212	87.36 136	88.65 186	76.37 63	75.88 168	88.44 139	53.51 212	93.07 153	73.30 123	89.74 81	92.25 90

PLC	70.83 11	78.05 171	76.37 182	83.08 131	91.88 60	67.80 113	88.19 111	89.46 153	64.33 255	69.87 257	88.38 140	53.66 211	93.58 128	58.86 239	82.73 167	87.86 240
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
WR-MVS		79.49 142	79.22 129	80.27 209	88.79 130	58.35 251	85.06 212	88.61 189	78.56 29	77.65 133	88.34 141	63.81 97	90.66 231	64.98 193	77.22 229	91.80 104

XXY-MVS		75.41 227	75.56 200	74.96 288	83.59 249	57.82 262	80.59 273	83.87 243	66.54 232	74.93 194	88.31 142	63.24 102	80.09 316	62.16 211	76.85 239	86.97 261

Effi-MVS+		83.62 62	83.08 62	85.24 65	88.38 144	67.45 117	88.89 84	89.15 163	75.50 77	82.27 68	88.28 143	69.61 53	94.45 85	77.81 77	87.84 102	93.84 36

API-MVS		81.99 83	81.23 85	84.26 91	90.94 69	70.18 67	91.10 35	89.32 156	71.51 159	78.66 105	88.28 143	65.26 85	95.10 64	64.74 195	91.23 66	87.51 247

xiu_mvs_v1_base_debu		80.80 107	79.72 109	84.03 100	87.35 177	70.19 64	85.56 199	88.77 182	69.06 196	81.83 71	88.16 145	50.91 245	92.85 161	78.29 74	87.56 106	89.06 196

xiu_mvs_v1_base		80.80 107	79.72 109	84.03 100	87.35 177	70.19 64	85.56 199	88.77 182	69.06 196	81.83 71	88.16 145	50.91 245	92.85 161	78.29 74	87.56 106	89.06 196

xiu_mvs_v1_base_debi		80.80 107	79.72 109	84.03 100	87.35 177	70.19 64	85.56 199	88.77 182	69.06 196	81.83 71	88.16 145	50.91 245	92.85 161	78.29 74	87.56 106	89.06 196

UniMVSNet (Re)		81.60 90	81.11 88	83.09 130	88.38 144	64.41 184	87.60 124	93.02 26	78.42 31	78.56 106	88.16 145	69.78 50	93.26 142	69.58 156	76.49 246	91.60 105

Anonymous20231211		78.97 154	77.69 161	82.81 151	90.54 75	64.29 186	90.11 54	91.51 87	65.01 248	76.16 167	88.13 149	50.56 257	93.03 157	69.68 155	77.56 224	91.11 118

Anonymous20240521		76.96 199	76.26 188	79.07 235	86.63 191	56.37 286	87.57 126	91.09 100	72.19 148	71.23 236	88.10 150	54.30 204	91.20 218	58.34 245	76.89 236	89.65 187

pm-mvs1		77.25 196	76.68 177	78.93 237	84.22 223	58.62 249	86.41 173	88.36 192	71.37 160	73.31 203	88.01 151	61.22 149	89.15 259	64.24 197	73.01 282	89.03 202

LTVRE_ROB	69.57 13	76.25 213	74.54 220	81.41 189	88.60 135	64.38 185	79.24 284	89.12 164	70.76 168	69.79 259	87.86 152	49.09 271	93.20 145	56.21 261	80.16 197	86.65 268
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
view600		76.20 214	75.21 210	79.16 231	89.64 91	55.82 292	85.74 194	82.06 269	73.88 106	75.74 171	87.85 153	51.84 233	91.66 202	46.75 304	83.42 155	90.00 166

view800		76.20 214	75.21 210	79.16 231	89.64 91	55.82 292	85.74 194	82.06 269	73.88 106	75.74 171	87.85 153	51.84 233	91.66 202	46.75 304	83.42 155	90.00 166

conf0.05thres1000		76.20 214	75.21 210	79.16 231	89.64 91	55.82 292	85.74 194	82.06 269	73.88 106	75.74 171	87.85 153	51.84 233	91.66 202	46.75 304	83.42 155	90.00 166

tfpn		76.20 214	75.21 210	79.16 231	89.64 91	55.82 292	85.74 194	82.06 269	73.88 106	75.74 171	87.85 153	51.84 233	91.66 202	46.75 304	83.42 155	90.00 166

WTY-MVS		75.65 224	75.68 199	75.57 283	86.40 194	56.82 276	77.92 298	82.40 263	65.10 246	76.18 164	87.72 157	63.13 108	80.90 312	60.31 227	81.96 175	89.00 205

TAMVS		78.89 156	77.51 164	83.03 134	87.80 165	67.79 114	84.72 218	85.05 233	67.63 220	76.75 148	87.70 158	62.25 133	90.82 228	58.53 243	87.13 112	90.49 145

BH-untuned		79.47 143	78.60 138	82.05 168	89.19 116	65.91 141	86.07 183	88.52 190	72.18 149	75.42 179	87.69 159	61.15 150	93.54 131	60.38 226	86.83 117	86.70 267

COLMAP_ROB	66.92 17	73.01 256	70.41 263	80.81 200	87.13 184	65.63 145	88.30 107	84.19 240	62.96 266	63.80 309	87.69 159	38.04 325	92.56 170	46.66 308	74.91 267	84.24 301
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
OurMVSNet-221017-0		74.26 234	72.42 240	79.80 216	83.76 247	59.59 241	85.92 187	86.64 215	66.39 233	66.96 287	87.58 161	39.46 319	91.60 207	65.76 186	69.27 301	88.22 232

Baseline_NR-MVSNet		78.15 169	78.33 148	77.61 258	85.79 199	56.21 289	86.78 162	85.76 227	73.60 114	77.93 129	87.57 162	65.02 88	88.99 262	67.14 175	75.33 262	87.63 244

WR-MVS_H		78.51 160	78.49 141	78.56 243	88.02 152	56.38 285	88.43 99	92.67 41	77.14 43	73.89 200	87.55 163	66.25 76	89.24 251	58.92 238	73.55 280	90.06 164

EI-MVSNet		80.52 116	79.98 103	82.12 166	84.28 220	63.19 211	86.41 173	88.95 175	74.18 98	78.69 103	87.54 164	66.62 73	92.43 172	72.57 132	80.57 192	90.74 129

CVMVSNet		72.99 257	72.58 237	74.25 295	84.28 220	50.85 326	86.41 173	83.45 250	44.56 342	73.23 205	87.54 164	49.38 267	85.70 291	65.90 184	78.44 217	86.19 277

ACMH+	68.96 14	76.01 220	74.01 225	82.03 169	88.60 135	65.31 155	88.86 85	87.55 205	70.25 177	67.75 279	87.47 166	41.27 312	93.19 146	58.37 244	75.94 252	87.60 245

TransMVSNet (Re)		75.39 228	74.56 219	77.86 253	85.50 205	57.10 271	86.78 162	86.09 225	72.17 150	71.53 234	87.34 167	63.01 109	89.31 250	56.84 258	61.83 329	87.17 256

GBi-Net		78.40 161	77.40 165	81.40 190	87.60 172	63.01 213	88.39 103	89.28 157	71.63 155	75.34 182	87.28 168	54.80 197	91.11 219	62.72 204	79.57 207	90.09 160

test1		78.40 161	77.40 165	81.40 190	87.60 172	63.01 213	88.39 103	89.28 157	71.63 155	75.34 182	87.28 168	54.80 197	91.11 219	62.72 204	79.57 207	90.09 160

FMVSNet2		78.20 167	77.21 168	81.20 193	87.60 172	62.89 217	87.47 134	89.02 166	71.63 155	75.29 186	87.28 168	54.80 197	91.10 222	62.38 208	79.38 210	89.61 188

FMVSNet1		77.44 193	76.12 190	81.40 190	86.81 189	63.01 213	88.39 103	89.28 157	70.49 173	74.39 198	87.28 168	49.06 272	91.11 219	60.91 223	78.52 215	90.09 160

v2v482		80.23 125	79.29 126	83.05 133	83.62 248	64.14 188	87.04 152	89.97 138	73.61 113	78.18 124	87.22 172	61.10 151	93.82 113	76.11 92	76.78 244	91.18 116

ITE_SJBPF				78.22 249	81.77 283	60.57 236		83.30 251	69.25 192	67.54 281	87.20 173	36.33 331	87.28 281	54.34 267	74.62 270	86.80 264

anonymousdsp		78.60 159	77.15 169	82.98 137	80.51 300	67.08 124	87.24 143	89.53 150	65.66 242	75.16 188	87.19 174	52.52 215	92.25 179	77.17 84	79.34 211	89.61 188

MVSTER		79.01 152	77.88 155	82.38 163	83.07 262	64.80 167	84.08 238	88.95 175	69.01 200	78.69 103	87.17 175	54.70 201	92.43 172	74.69 109	80.57 192	89.89 175

tfpn111		76.54 205	75.51 203	79.61 221	89.52 98	56.99 272	85.83 189	83.23 252	73.94 102	76.32 158	87.12 176	51.89 229	92.06 183	48.04 300	83.73 151	89.78 180

conf200view11		76.55 204	75.55 201	79.57 224	89.52 98	56.99 272	85.83 189	83.23 252	73.94 102	76.32 158	87.12 176	51.89 229	91.95 185	48.33 293	83.75 147	89.78 180

thres100view900		76.50 207	75.55 201	79.33 226	89.52 98	56.99 272	85.83 189	83.23 252	73.94 102	76.32 158	87.12 176	51.89 229	91.95 185	48.33 293	83.75 147	89.07 194

thres600view7		76.50 207	75.44 204	79.68 218	89.40 104	57.16 269	85.53 204	83.23 252	73.79 111	76.26 161	87.09 179	51.89 229	91.89 189	48.05 299	83.72 152	90.00 166

XVG-ACMP-BASELINE		76.11 219	74.27 224	81.62 184	83.20 258	64.67 170	83.60 244	89.75 145	69.75 183	71.85 230	87.09 179	32.78 335	92.11 182	69.99 152	80.43 195	88.09 235

HY-MVS	69.67 12	77.95 175	77.15 169	80.36 205	87.57 176	60.21 239	83.37 253	87.78 202	66.11 235	75.37 181	87.06 181	63.27 101	90.48 233	61.38 220	82.43 172	90.40 150

CHOSEN 1792x2688		77.63 185	75.69 198	83.44 115	89.98 86	68.58 100	78.70 291	87.50 207	56.38 317	75.80 170	86.84 182	58.67 170	91.40 211	61.58 218	85.75 132	90.34 151

v8		79.97 134	79.02 132	82.80 152	84.09 233	64.50 180	87.96 116	90.29 126	74.13 100	75.24 187	86.81 183	62.88 110	93.89 109	74.39 111	75.40 261	90.00 166

AllTest		70.96 269	68.09 279	79.58 222	85.15 208	63.62 196	84.58 224	79.83 293	62.31 274	60.32 319	86.73 184	32.02 336	88.96 265	50.28 283	71.57 293	86.15 278

TestCases				79.58 222	85.15 208	63.62 196		79.83 293	62.31 274	60.32 319	86.73 184	32.02 336	88.96 265	50.28 283	71.57 293	86.15 278

mvs-test1		80.88 100	79.40 119	85.29 63	85.13 210	69.75 72	89.28 70	88.10 195	74.99 86	76.44 156	86.72 186	57.27 181	94.26 92	73.53 120	83.18 162	91.87 100

LCM-MVSNet-Re		77.05 197	76.94 173	77.36 263	87.20 183	51.60 321	80.06 276	80.46 286	75.20 84	67.69 280	86.72 186	62.48 129	88.98 263	63.44 200	89.25 85	91.51 108

1112_ss		77.40 195	76.43 180	80.32 207	89.11 121	60.41 238	83.65 242	87.72 203	62.13 276	73.05 207	86.72 186	62.58 126	89.97 238	62.11 213	80.80 188	90.59 139

ab-mvs-re		7.23 339	9.64 340	0.00 353	0.00 367	0.00 368	0.00 360	0.00 369	0.00 363	0.00 364	86.72 186	0.00 371	0.00 366	0.00 363	0.00 364	0.00 364

IterMVS-LS		80.06 131	79.38 120	82.11 167	85.89 198	63.20 210	86.79 161	89.34 155	74.19 97	75.45 178	86.72 186	66.62 73	92.39 174	72.58 131	76.86 238	90.75 128
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
ACMH	67.68 16	75.89 221	73.93 226	81.77 175	88.71 133	66.61 131	88.62 95	89.01 168	69.81 181	66.78 289	86.70 191	41.95 311	91.51 209	55.64 262	78.14 220	87.17 256
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
Test_1112_low_res		76.40 211	75.44 204	79.27 227	89.28 112	58.09 254	81.69 264	87.07 212	59.53 295	72.48 214	86.67 192	61.30 146	89.33 249	60.81 225	80.15 198	90.41 149

FMVSNet3		77.88 178	76.85 174	80.97 198	86.84 188	62.36 220	86.52 170	88.77 182	71.13 161	75.34 182	86.66 193	54.07 208	91.10 222	62.72 204	79.57 207	89.45 190

v1neww		80.40 118	79.54 113	82.98 137	84.10 231	64.51 176	87.57 126	90.22 127	73.25 122	78.47 109	86.65 194	62.83 113	93.86 110	75.72 97	77.02 232	90.58 140

v7new		80.40 118	79.54 113	82.98 137	84.10 231	64.51 176	87.57 126	90.22 127	73.25 122	78.47 109	86.65 194	62.83 113	93.86 110	75.72 97	77.02 232	90.58 140

v6		80.40 118	79.54 113	82.98 137	84.09 233	64.50 180	87.57 126	90.22 127	73.25 122	78.47 109	86.63 196	62.84 112	93.86 110	75.73 96	77.02 232	90.58 140

pmmvs6		74.69 230	73.39 229	78.61 242	81.38 289	57.48 267	86.64 166	87.95 199	64.99 249	70.18 248	86.61 197	50.43 259	89.52 245	62.12 212	70.18 299	88.83 210

testgi		66.67 296	66.53 292	67.08 322	75.62 329	41.69 345	75.93 305	76.50 317	66.11 235	65.20 302	86.59 198	35.72 333	74.71 337	43.71 326	73.38 281	84.84 296

CLD-MVS		82.31 78	81.65 81	84.29 90	88.47 140	67.73 115	85.81 193	92.35 52	75.78 70	78.33 115	86.58 199	64.01 95	94.35 86	76.05 93	87.48 109	90.79 126
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
v10		79.74 138	78.67 136	82.97 141	84.06 238	64.95 163	87.88 121	90.62 111	73.11 128	75.11 190	86.56 200	61.46 142	94.05 100	73.68 116	75.55 258	89.90 174

CDS-MVSNet		79.07 151	77.70 160	83.17 126	87.60 172	68.23 106	84.40 231	86.20 222	67.49 224	76.36 157	86.54 201	61.54 141	90.79 229	61.86 215	87.33 110	90.49 145
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
xiu_mvs_v2_base		81.69 87	81.05 89	83.60 111	89.15 117	68.03 110	84.46 227	90.02 137	70.67 169	81.30 81	86.53 202	63.17 104	94.19 94	75.60 102	88.54 96	88.57 225

v7		80.24 124	79.26 127	83.15 127	84.07 237	64.94 164	87.56 130	90.67 108	72.26 147	78.28 117	86.51 203	61.45 143	94.03 101	75.14 107	77.41 226	90.49 145

TR-MVS		77.44 193	76.18 189	81.20 193	88.24 147	63.24 208	84.61 223	86.40 219	67.55 223	77.81 130	86.48 204	54.10 207	93.15 148	57.75 251	82.72 168	87.20 255

tfpn200view9		76.42 210	75.37 207	79.55 225	89.13 118	57.65 264	85.17 208	83.60 245	73.41 119	76.45 153	86.39 205	52.12 223	91.95 185	48.33 293	83.75 147	89.07 194

v1141		80.19 127	79.31 123	82.85 146	83.84 243	64.12 190	87.14 145	90.08 134	73.13 125	78.27 118	86.39 205	62.67 122	93.75 120	75.40 104	76.83 241	90.68 131

divwei89l23v2f112		80.19 127	79.31 123	82.85 146	83.84 243	64.11 192	87.13 148	90.08 134	73.13 125	78.27 118	86.39 205	62.69 120	93.75 120	75.40 104	76.82 242	90.68 131

thres400		76.50 207	75.37 207	79.86 214	89.13 118	57.65 264	85.17 208	83.60 245	73.41 119	76.45 153	86.39 205	52.12 223	91.95 185	48.33 293	83.75 147	90.00 166

v1		80.19 127	79.31 123	82.85 146	83.83 245	64.12 190	87.14 145	90.07 136	73.13 125	78.27 118	86.38 209	62.72 119	93.75 120	75.41 103	76.82 242	90.68 131

v7n		78.97 154	77.58 163	83.14 128	83.45 252	65.51 148	88.32 106	91.21 96	73.69 112	72.41 221	86.32 210	57.93 175	93.81 114	69.18 159	75.65 256	90.11 158

tfpn1000		73.44 245	72.49 238	76.29 276	87.81 164	53.69 308	84.05 239	78.81 305	67.99 219	72.09 228	86.27 211	49.95 264	89.04 261	44.09 325	81.38 181	86.15 278

MAR-MVS		81.84 85	80.70 92	85.27 64	91.32 64	71.53 46	89.82 58	90.92 103	69.77 182	78.50 107	86.21 212	62.36 131	94.52 83	65.36 188	92.05 57	89.77 184
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
v1144		80.03 132	79.03 131	83.01 135	83.78 246	64.51 176	87.11 150	90.57 113	71.96 152	78.08 127	86.20 213	61.41 144	93.94 104	74.93 108	77.23 228	90.60 137

V42		79.38 146	78.24 150	82.83 149	81.10 294	65.50 149	85.55 202	89.82 143	71.57 158	78.21 122	86.12 214	60.66 158	93.18 147	75.64 100	75.46 260	89.81 179

PVSNet_BlendedMVS		80.60 113	80.02 102	82.36 164	88.85 124	65.40 150	86.16 180	92.00 65	69.34 190	78.11 125	86.09 215	66.02 80	94.27 89	71.52 142	82.06 174	87.39 249

conf0.01		73.67 240	72.42 240	77.42 261	87.85 156	53.28 311	83.38 246	79.08 298	68.40 209	72.45 215	86.08 216	50.60 251	89.19 252	44.25 319	79.66 201	89.78 180

conf0.002		73.67 240	72.42 240	77.42 261	87.85 156	53.28 311	83.38 246	79.08 298	68.40 209	72.45 215	86.08 216	50.60 251	89.19 252	44.25 319	79.66 201	89.78 180

thresconf0.02		73.39 246	72.42 240	76.31 272	87.85 156	53.28 311	83.38 246	79.08 298	68.40 209	72.45 215	86.08 216	50.60 251	89.19 252	44.25 319	79.66 201	86.48 270

tfpn_n400		73.39 246	72.42 240	76.31 272	87.85 156	53.28 311	83.38 246	79.08 298	68.40 209	72.45 215	86.08 216	50.60 251	89.19 252	44.25 319	79.66 201	86.48 270

tfpnconf		73.39 246	72.42 240	76.31 272	87.85 156	53.28 311	83.38 246	79.08 298	68.40 209	72.45 215	86.08 216	50.60 251	89.19 252	44.25 319	79.66 201	86.48 270

tfpnview11		73.39 246	72.42 240	76.31 272	87.85 156	53.28 311	83.38 246	79.08 298	68.40 209	72.45 215	86.08 216	50.60 251	89.19 252	44.25 319	79.66 201	86.48 270

v1192		79.59 140	78.43 145	83.07 132	83.55 250	64.52 174	86.93 156	90.58 112	70.83 165	77.78 131	85.90 222	59.15 168	93.94 104	73.96 115	77.19 230	90.76 127

SixPastTwentyTwo		73.37 250	71.26 257	79.70 217	85.08 212	57.89 260	85.57 198	83.56 247	71.03 164	65.66 296	85.88 223	42.10 309	92.57 169	59.11 237	63.34 326	88.65 216

EPNet_dtu		75.46 226	74.86 215	77.23 266	82.57 275	54.60 301	86.89 157	83.09 257	71.64 154	66.25 294	85.86 224	55.99 190	88.04 276	54.92 265	86.55 122	89.05 200
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
sss		73.60 242	73.64 228	73.51 298	82.80 269	55.01 299	76.12 304	81.69 275	62.47 273	74.68 196	85.85 225	57.32 180	78.11 324	60.86 224	80.93 185	87.39 249

DI_MVS_plusplus_test		79.89 135	78.58 139	83.85 108	82.89 268	65.32 154	86.12 181	89.55 149	69.64 186	70.55 242	85.82 226	57.24 183	93.81 114	76.85 88	88.55 95	92.41 85

v1240		78.99 153	77.78 158	82.64 159	83.21 257	63.54 199	86.62 167	90.30 125	69.74 185	77.33 138	85.68 227	57.04 186	93.76 119	73.13 125	76.92 235	90.62 135

v144192		79.47 143	78.37 146	82.78 155	83.35 253	63.96 194	86.96 154	90.36 121	69.99 179	77.50 135	85.67 228	60.66 158	93.77 118	74.27 112	76.58 245	90.62 135

test_normal		79.81 136	78.45 142	83.89 107	82.70 272	65.40 150	85.82 192	89.48 152	69.39 187	70.12 251	85.66 229	57.15 185	93.71 126	77.08 85	88.62 93	92.56 80

tfpnnormal		74.39 232	73.16 232	78.08 251	86.10 197	58.05 255	84.65 222	87.53 206	70.32 175	71.22 238	85.63 230	54.97 196	89.86 239	43.03 328	75.02 266	86.32 275

PS-MVSNAJ		81.69 87	81.02 90	83.70 109	89.51 101	68.21 107	84.28 234	90.09 133	70.79 166	81.26 82	85.62 231	63.15 105	94.29 87	75.62 101	88.87 88	88.59 223

v748		77.97 174	76.65 178	81.92 173	82.29 278	63.28 207	87.53 131	90.35 122	73.50 118	70.76 241	85.55 232	58.28 173	92.81 165	68.81 163	72.76 285	89.67 186

v1921920		79.22 148	78.03 152	82.80 152	83.30 255	63.94 195	86.80 160	90.33 123	69.91 180	77.48 136	85.53 233	58.44 172	93.75 120	73.60 119	76.85 239	90.71 130

test_0402		72.79 259	70.44 262	79.84 215	88.13 149	65.99 139	85.93 186	84.29 238	65.57 243	67.40 284	85.49 234	46.92 281	92.61 168	35.88 338	74.38 272	80.94 323

v148		78.72 157	77.80 157	81.47 188	82.73 271	61.96 226	86.30 177	88.08 197	73.26 121	76.18 164	85.47 235	62.46 130	92.36 176	71.92 141	73.82 278	90.09 160

USDC		70.33 275	68.37 274	76.21 278	80.60 298	56.23 288	79.19 286	86.49 217	60.89 283	61.29 315	85.47 235	31.78 338	89.47 247	53.37 272	76.21 250	82.94 316

MVP-Stereo		76.12 218	74.46 222	81.13 196	85.37 206	69.79 70	84.42 230	87.95 199	65.03 247	67.46 282	85.33 237	53.28 214	91.73 195	58.01 249	83.27 160	81.85 320
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
MVS		78.19 168	76.99 172	81.78 174	85.66 201	66.99 125	84.66 219	90.47 116	55.08 322	72.02 229	85.27 238	63.83 96	94.11 99	66.10 182	89.80 80	84.24 301

HyFIR lowres test		77.53 186	75.40 206	83.94 106	89.59 96	66.62 130	80.36 274	88.64 187	56.29 318	76.45 153	85.17 239	57.64 177	93.28 141	61.34 221	83.10 163	91.91 99

pmmvs4		74.03 236	71.91 248	80.39 204	81.96 281	68.32 103	81.45 267	82.14 267	59.32 296	69.87 257	85.13 240	52.40 218	88.13 275	60.21 228	74.74 269	84.73 298

TDRefinement		67.49 290	64.34 297	76.92 267	73.47 335	61.07 230	84.86 216	82.98 258	59.77 292	58.30 325	85.13 240	26.06 342	87.89 277	47.92 301	60.59 334	81.81 321

Fast-Effi-MVS+		80.81 105	79.92 104	83.47 114	88.85 124	64.51 176	85.53 204	89.39 154	70.79 166	78.49 108	85.06 242	67.54 67	93.58 128	67.03 177	86.58 121	92.32 87

PVSNet_Blended		80.98 99	80.34 98	82.90 143	88.85 124	65.40 150	84.43 229	92.00 65	67.62 221	78.11 125	85.05 243	66.02 80	94.27 89	71.52 142	89.50 82	89.01 203

V4		77.95 175	76.37 182	82.67 157	79.40 313	65.52 146	86.43 171	89.94 140	72.28 145	72.14 227	84.95 244	55.72 191	93.44 136	73.64 117	72.86 283	89.05 200

v52		77.94 177	76.37 182	82.67 157	79.39 314	65.52 146	86.43 171	89.94 140	72.28 145	72.15 226	84.94 245	55.70 192	93.44 136	73.64 117	72.84 284	89.06 196

tfpn_ndepth		73.70 238	72.75 235	76.52 270	87.78 168	54.92 300	84.32 233	80.28 290	67.57 222	72.50 212	84.82 246	50.12 261	89.44 248	45.73 314	81.66 179	85.20 290

CMPMVS	51.72 21	70.19 277	68.16 277	76.28 277	73.15 337	57.55 266	79.47 282	83.92 241	48.02 340	56.48 332	84.81 247	43.13 301	86.42 287	62.67 207	81.81 178	84.89 295
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
EU-MVSNet		68.53 287	67.61 287	71.31 309	78.51 318	47.01 336	84.47 225	84.27 239	42.27 343	66.44 293	84.79 248	40.44 317	83.76 301	58.76 241	68.54 307	83.17 310

BH-w/o		78.21 166	77.33 167	80.84 199	88.81 128	65.13 160	84.87 215	87.85 201	69.75 183	74.52 197	84.74 249	61.34 145	93.11 151	58.24 247	85.84 131	84.27 300

pmmvs5		71.55 265	70.20 265	75.61 282	77.83 319	56.39 284	81.74 263	80.89 279	57.76 308	67.46 282	84.49 250	49.26 270	85.32 295	57.08 257	75.29 263	85.11 294

thres200		75.55 225	74.47 221	78.82 239	87.78 168	57.85 261	83.07 254	83.51 248	72.44 143	75.84 169	84.42 251	52.08 225	91.75 194	47.41 302	83.64 153	86.86 263

Fast-Effi-MVS+-dtu		78.02 172	76.49 179	82.62 160	83.16 261	66.96 128	86.94 155	87.45 209	72.45 141	71.49 235	84.17 252	54.79 200	91.58 208	67.61 168	80.31 196	89.30 192

semantic-postprocess				80.11 211	82.69 273	64.85 166		83.47 249	69.16 194	70.49 245	84.15 253	50.83 249	88.15 274	69.23 158	72.14 289	87.34 251

1314		76.53 206	75.30 209	80.21 210	83.93 241	62.32 222	84.66 219	88.81 181	60.23 288	70.16 250	84.07 254	55.30 195	90.73 230	67.37 171	83.21 161	87.59 246

EG-PatchMatch MVS		74.04 235	71.82 251	80.71 202	84.92 213	67.42 118	85.86 188	88.08 197	66.04 237	64.22 306	83.85 255	35.10 334	92.56 170	57.44 253	80.83 187	82.16 319

test20.03		67.45 291	66.95 290	68.94 316	75.48 331	44.84 338	77.50 299	77.67 311	66.66 228	63.01 311	83.80 256	47.02 280	78.40 322	42.53 330	68.86 305	83.58 307

Test4		77.83 179	75.90 197	83.62 110	80.24 302	65.25 156	85.27 207	90.67 108	69.03 199	66.48 292	83.75 257	43.07 302	93.00 158	75.93 95	88.66 92	92.62 79

MSDG		73.36 252	70.99 259	80.49 203	84.51 218	65.80 143	80.71 271	86.13 224	65.70 241	65.46 297	83.74 258	44.60 294	90.91 227	51.13 280	76.89 236	84.74 297

IterMVS		74.29 233	72.94 234	78.35 248	81.53 286	63.49 201	81.58 266	82.49 262	68.06 218	69.99 254	83.69 259	51.66 239	85.54 292	65.85 185	71.64 292	86.01 283
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
tpm		72.37 262	71.71 252	74.35 294	82.19 279	52.00 318	79.22 285	77.29 314	64.56 252	72.95 208	83.68 260	51.35 240	83.26 306	58.33 246	75.80 254	87.81 241

Effi-MVS+-dtu		80.03 132	78.57 140	84.42 85	85.13 210	68.74 93	88.77 89	88.10 195	74.99 86	74.97 193	83.49 261	57.27 181	93.36 139	73.53 120	80.88 186	91.18 116

TinyColmap		67.30 293	64.81 295	74.76 291	81.92 282	56.68 280	80.29 275	81.49 277	60.33 286	56.27 333	83.22 262	24.77 344	87.66 279	45.52 315	69.47 300	79.95 327

CostFormer		75.24 229	73.90 227	79.27 227	82.65 274	58.27 253	80.80 269	82.73 261	61.57 279	75.33 185	83.13 263	55.52 193	91.07 225	64.98 193	78.34 219	88.45 229

GA-MVS		76.87 201	75.17 214	81.97 171	82.75 270	62.58 219	81.44 268	86.35 221	72.16 151	74.74 195	82.89 264	46.20 286	92.02 184	68.85 162	81.09 184	91.30 114

K. test v3		71.19 267	68.51 273	79.21 229	83.04 264	57.78 263	84.35 232	76.91 316	72.90 133	62.99 312	82.86 265	39.27 320	91.09 224	61.65 217	52.66 343	88.75 213

MS-PatchMatch		73.83 237	72.67 236	77.30 265	83.87 242	66.02 138	81.82 261	84.66 235	61.37 282	68.61 273	82.82 266	47.29 278	88.21 273	59.27 235	84.32 142	77.68 333

lessismore_v0				78.97 236	81.01 295	57.15 270		65.99 350		61.16 316	82.82 266	39.12 321	91.34 214	59.67 231	46.92 347	88.43 230

Anonymous20231206		68.60 285	67.80 284	71.02 310	80.23 303	50.75 327	78.30 295	80.47 285	56.79 315	66.11 295	82.63 268	46.35 284	78.95 320	43.62 327	75.70 255	83.36 309

MIMVSNet		70.69 271	69.30 267	74.88 289	84.52 217	56.35 287	75.87 308	79.42 296	64.59 251	67.76 278	82.41 269	41.10 313	81.54 311	46.64 310	81.34 182	86.75 266

OpenMVS_ROB	64.09 19	70.56 273	68.19 276	77.65 257	80.26 301	59.41 245	85.01 213	82.96 259	58.76 301	65.43 298	82.33 270	37.63 328	91.23 217	45.34 317	76.03 251	82.32 317

test0.0.03 1		68.00 289	67.69 286	68.90 317	77.55 320	47.43 334	75.70 309	72.95 335	66.66 228	66.56 290	82.29 271	48.06 275	75.87 333	44.97 318	74.51 271	83.41 308

PVSNet	64.34 18	72.08 263	70.87 261	75.69 281	86.21 196	56.44 283	74.37 316	80.73 282	62.06 277	70.17 249	82.23 272	42.86 304	83.31 305	54.77 266	84.45 141	87.32 252

MIMVSNet1		68.58 286	66.78 291	73.98 297	80.07 304	51.82 319	80.77 270	84.37 237	64.40 254	59.75 322	82.16 273	36.47 330	83.63 303	42.73 329	70.33 298	86.48 270

v16		77.69 181	76.36 185	81.68 181	84.15 228	64.63 173	87.33 138	88.99 170	72.69 139	69.31 265	82.08 274	62.80 116	91.79 191	72.70 129	67.23 309	88.63 217

v17		77.68 182	76.35 186	81.69 180	84.15 228	64.65 171	87.33 138	88.99 170	72.70 138	69.25 266	82.07 275	62.82 115	91.79 191	72.69 130	67.15 311	88.63 217

v18		77.67 184	76.35 186	81.64 183	84.09 233	64.47 182	87.27 141	89.01 168	72.59 140	69.39 262	82.04 276	62.85 111	91.80 190	72.72 128	67.20 310	88.63 217

tpm2		73.26 253	71.46 253	78.63 241	83.34 254	56.71 279	80.65 272	80.40 287	56.63 316	73.55 201	82.02 277	51.80 237	91.24 216	56.35 260	78.42 218	87.95 237

PatchMatch-RL		72.38 261	70.90 260	76.80 269	88.60 135	67.38 120	79.53 281	76.17 318	62.75 270	69.36 263	82.00 278	45.51 292	84.89 297	53.62 271	80.58 191	78.12 331

v13		77.50 191	76.07 195	81.77 175	84.23 222	65.07 161	87.34 137	88.91 180	72.92 131	68.35 276	81.97 279	62.53 128	91.69 201	72.20 138	66.22 320	88.56 226

v12		77.51 189	76.09 194	81.76 177	84.22 223	64.99 162	87.30 140	88.93 179	72.92 131	68.48 275	81.97 279	62.54 127	91.70 200	72.24 137	66.21 321	88.58 224

V9		77.52 187	76.11 193	81.73 178	84.19 227	64.89 165	87.26 142	88.94 178	72.87 134	68.65 271	81.96 281	62.65 123	91.72 197	72.27 136	66.24 319	88.60 221

V14		77.52 187	76.12 190	81.70 179	84.15 228	64.77 168	87.21 144	88.95 175	72.80 135	68.79 268	81.94 282	62.69 120	91.72 197	72.31 135	66.27 318	88.60 221

v15		77.51 189	76.12 190	81.66 182	84.09 233	64.65 171	87.14 145	88.96 174	72.76 136	68.90 267	81.91 283	62.74 118	91.73 195	72.32 134	66.29 317	88.61 220

v11		77.45 192	76.06 196	81.59 186	84.22 223	64.52 174	87.11 150	89.02 166	72.76 136	68.76 269	81.90 284	62.09 136	91.71 199	71.98 139	66.73 312	88.56 226

tpmp4_e23		73.45 244	71.17 258	80.31 208	83.55 250	59.56 243	81.88 260	82.33 264	57.94 307	70.51 244	81.62 285	51.19 243	91.63 206	53.96 269	77.51 225	89.75 185

FMVSNet5		69.50 281	67.96 280	74.15 296	82.97 265	55.35 298	80.01 277	82.12 268	62.56 272	63.02 310	81.53 286	36.92 329	81.92 309	48.42 292	74.06 274	85.17 293

CR-MVSNet		73.37 250	71.27 256	79.67 219	81.32 292	65.19 158	75.92 306	80.30 288	59.92 291	72.73 210	81.19 287	52.50 216	86.69 283	59.84 230	77.71 221	87.11 259

Patchmtry		70.74 270	69.16 269	75.49 285	80.72 296	54.07 305	74.94 315	80.30 288	58.34 303	70.01 252	81.19 287	52.50 216	86.54 285	53.37 272	71.09 295	85.87 286

testing_2		75.73 223	73.34 231	82.89 145	77.37 322	65.22 157	84.10 237	90.54 114	69.09 195	60.46 318	81.15 289	40.48 316	92.84 164	76.36 91	80.54 194	90.60 137

IB-MVS	68.01 15	75.85 222	73.36 230	83.31 120	84.76 214	66.03 137	83.38 246	85.06 232	70.21 178	69.40 261	81.05 290	45.76 290	94.66 80	65.10 191	75.49 259	89.25 193
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
cascas		76.72 203	74.64 217	82.99 136	85.78 200	65.88 142	82.33 258	89.21 162	60.85 284	72.74 209	81.02 291	47.28 279	93.75 120	67.48 170	85.02 134	89.34 191

LF4IMVS		64.02 305	62.19 306	69.50 315	70.90 342	53.29 310	76.13 303	77.18 315	52.65 332	58.59 323	80.98 292	23.55 345	76.52 330	53.06 274	66.66 314	78.68 330

gm-plane-assit					81.40 288	53.83 307			62.72 271		80.94 293		92.39 174	63.40 201

UnsupCasMVSNet_eth		67.33 292	65.99 293	71.37 306	73.48 334	51.47 323	75.16 311	85.19 231	65.20 245	60.78 317	80.93 294	42.35 306	77.20 328	57.12 256	53.69 342	85.44 288

MDTV_nov1_ep13			69.97 266		83.18 259	53.48 309	77.10 302	80.18 292	60.45 285	69.33 264	80.44 295	48.89 273	86.90 282	51.60 278	78.51 216

pmmvs-eth3d		70.50 274	67.83 283	78.52 245	77.37 322	66.18 136	81.82 261	81.51 276	58.90 300	63.90 308	80.42 296	42.69 305	86.28 288	58.56 242	65.30 323	83.11 312

PM-MVS		66.41 298	64.14 298	73.20 299	73.92 332	56.45 282	78.97 288	64.96 353	63.88 261	64.72 303	80.24 297	19.84 350	83.44 304	66.24 179	64.52 325	79.71 328

Patchmatch-test		64.82 303	63.24 301	69.57 314	79.42 312	49.82 331	63.49 345	69.05 346	51.98 334	59.95 321	80.13 298	50.91 245	70.98 347	40.66 333	73.57 279	87.90 239

tpmrst		72.39 260	72.13 247	73.18 300	80.54 299	49.91 330	79.91 279	79.08 298	63.11 263	71.69 232	79.95 299	55.32 194	82.77 307	65.66 187	73.89 276	86.87 262

DSMNet-mixed		57.77 315	56.90 315	60.38 331	67.70 347	35.61 351	69.18 330	53.97 356	32.30 353	57.49 328	79.88 300	40.39 318	68.57 351	38.78 335	72.37 286	76.97 336

MDA-MVSNet-bldmvs		66.68 295	63.66 299	75.75 280	79.28 315	60.56 237	73.92 317	78.35 307	64.43 253	50.13 343	79.87 301	44.02 298	83.67 302	46.10 312	56.86 337	83.03 314

PatchmatchNet		73.12 255	71.33 255	78.49 246	83.18 259	60.85 233	79.63 280	78.57 306	64.13 256	71.73 231	79.81 302	51.20 242	85.97 290	57.40 254	76.36 249	88.66 215
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
Patchmatch-test1		73.49 243	71.85 250	78.41 247	84.05 239	62.17 224	79.96 278	79.29 297	66.30 234	72.38 222	79.58 303	51.95 228	85.08 296	55.46 263	77.67 223	87.99 236

ppachtmachnet_test		70.04 278	67.34 288	78.14 250	79.80 307	61.13 229	79.19 286	80.59 283	59.16 298	65.27 299	79.29 304	46.75 283	87.29 280	49.33 289	66.72 313	86.00 285

EPMVS		69.02 284	68.16 277	71.59 304	79.61 310	49.80 332	77.40 300	66.93 349	62.82 269	70.01 252	79.05 305	45.79 289	77.86 326	56.58 259	75.26 264	87.13 258

PMMVS		69.34 282	68.67 272	71.35 308	75.67 328	62.03 225	75.17 310	73.46 333	50.00 338	68.68 270	79.05 305	52.07 226	78.13 323	61.16 222	82.77 166	73.90 341

test-LLR		72.94 258	72.43 239	74.48 292	81.35 290	58.04 256	78.38 292	77.46 312	66.66 228	69.95 255	79.00 307	48.06 275	79.24 318	66.13 180	84.83 136	86.15 278

test-mter		71.41 266	70.39 264	74.48 292	81.35 290	58.04 256	78.38 292	77.46 312	60.32 287	69.95 255	79.00 307	36.08 332	79.24 318	66.13 180	84.83 136	86.15 278

DWT-MVSNet_test		73.70 238	71.86 249	79.21 229	82.91 267	58.94 246	82.34 257	82.17 266	65.21 244	71.05 240	78.31 309	44.21 296	90.17 237	63.29 202	77.28 227	88.53 228

tpmvs		71.09 268	69.29 268	76.49 271	82.04 280	56.04 290	78.92 289	81.37 278	64.05 257	67.18 286	78.28 310	49.74 266	89.77 240	49.67 288	72.37 286	83.67 306

our_test_3		69.14 283	67.00 289	75.57 283	79.80 307	58.80 247	77.96 297	77.81 310	59.55 294	62.90 313	78.25 311	47.43 277	83.97 300	51.71 277	67.58 308	83.93 305

PatchFormer-LS_test		74.50 231	73.05 233	78.86 238	82.95 266	59.55 244	81.65 265	82.30 265	67.44 225	71.62 233	78.15 312	52.34 219	88.92 267	65.05 192	75.90 253	88.12 234

MDA-MVSNet_test_wron		65.03 301	62.92 302	71.37 306	75.93 326	56.73 277	69.09 333	74.73 327	57.28 313	54.03 336	77.89 313	45.88 287	74.39 339	49.89 287	61.55 330	82.99 315

YYNet1		65.03 301	62.91 303	71.38 305	75.85 327	56.60 281	69.12 332	74.66 330	57.28 313	54.12 335	77.87 314	45.85 288	74.48 338	49.95 286	61.52 331	83.05 313

ambc				75.24 287	73.16 336	50.51 328	63.05 346	87.47 208		64.28 305	77.81 315	17.80 353	89.73 242	57.88 250	60.64 333	85.49 287

tpm cat1		70.57 272	68.31 275	77.35 264	82.41 277	57.95 259	78.08 296	80.22 291	52.04 333	68.54 274	77.66 316	52.00 227	87.84 278	51.77 276	72.07 290	86.25 276

dp		66.80 294	65.43 294	70.90 311	79.74 309	48.82 333	75.12 313	74.77 326	59.61 293	64.08 307	77.23 317	42.89 303	80.72 313	48.86 291	66.58 315	83.16 311

TESTMET0.1,1		69.89 280	69.00 270	72.55 301	79.27 316	56.85 275	78.38 292	74.71 328	57.64 309	68.09 277	77.19 318	37.75 326	76.70 329	63.92 198	84.09 143	84.10 304

CHOSEN 280x420		66.51 297	64.71 296	71.90 303	81.45 287	63.52 200	57.98 350	68.95 347	53.57 328	62.59 314	76.70 319	46.22 285	75.29 336	55.25 264	79.68 200	76.88 339

PatchT		68.46 288	67.85 282	70.29 312	80.70 297	43.93 340	72.47 319	74.88 324	60.15 289	70.55 242	76.57 320	49.94 265	81.59 310	50.58 281	74.83 268	85.34 289

RPMNet		71.62 264	68.94 271	79.67 219	81.32 292	65.19 158	75.92 306	78.30 308	57.60 310	72.73 210	76.45 321	52.30 220	86.69 283	48.14 298	77.71 221	87.11 259

ADS-MVSNet2		66.20 300	63.33 300	74.82 290	79.92 305	58.75 248	67.55 338	75.19 322	53.37 329	65.25 300	75.86 322	42.32 307	80.53 314	41.57 331	68.91 303	85.18 291

ADS-MVSNet		64.36 304	62.88 304	68.78 319	79.92 305	47.17 335	67.55 338	71.18 338	53.37 329	65.25 300	75.86 322	42.32 307	73.99 341	41.57 331	68.91 303	85.18 291

new-patchmatchnet		61.73 307	61.73 307	61.70 330	72.74 338	24.50 361	69.16 331	78.03 309	61.40 280	56.72 331	75.53 324	38.42 323	76.48 331	45.95 313	57.67 336	84.13 303

N_pmnet		52.79 321	53.26 319	51.40 340	78.99 317	7.68 365	69.52 328	3.89 365	51.63 336	57.01 330	74.98 325	40.83 315	65.96 354	37.78 336	64.67 324	80.56 326

1111		57.11 316	56.82 317	57.97 334	69.10 344	28.28 356	68.90 334	74.54 331	54.01 325	53.71 337	74.51 326	23.09 346	67.90 352	32.28 343	61.26 332	77.73 332

.test1245		45.55 326	50.02 323	32.14 346	69.10 344	28.28 356	68.90 334	74.54 331	54.01 325	53.71 337	74.51 326	23.09 346	67.90 352	32.28 343	0.02 361	0.25 362

patchmatchnet-post											74.00 328	51.12 244	88.60 270

testus		59.00 312	57.91 311	62.25 329	72.25 339	39.09 348	69.74 326	75.02 323	53.04 331	57.21 329	73.72 329	18.76 352	70.33 348	32.86 341	68.57 306	77.35 334

GG-mvs-BLEND				75.38 286	81.59 285	55.80 296	79.32 283	69.63 342		67.19 285	73.67 330	43.24 300	88.90 268	50.41 282	84.50 139	81.45 322

Patchmatch-RL test		70.24 276	67.78 285	77.61 258	77.43 321	59.57 242	71.16 321	70.33 339	62.94 267	68.65 271	72.77 331	50.62 250	85.49 293	69.58 156	66.58 315	87.77 242

FPMVS		53.68 320	51.64 320	59.81 332	65.08 348	51.03 325	69.48 329	69.58 343	41.46 344	40.67 346	72.32 332	16.46 355	70.00 349	24.24 353	65.42 322	58.40 350

UnsupCasMVSNet_bld		63.70 306	61.53 308	70.21 313	73.69 333	51.39 324	72.82 318	81.89 273	55.63 320	57.81 326	71.80 333	38.67 322	78.61 321	49.26 290	52.21 344	80.63 324

test1235678		58.74 313	56.89 316	64.30 324	69.70 343	41.87 344	71.05 322	74.87 325	54.06 324	50.63 342	71.53 334	25.30 343	74.10 340	31.80 346	63.10 327	76.93 337

test2356		59.50 310	58.08 310	63.74 326	71.23 341	41.88 343	67.59 337	72.42 337	53.72 327	57.65 327	70.74 335	26.31 341	72.40 344	32.03 345	71.06 296	76.93 337

PVSNet_0	57.27 20	61.67 308	59.27 309	68.85 318	79.61 310	57.44 268	68.01 336	73.44 334	55.93 319	58.54 324	70.41 336	44.58 295	77.55 327	47.01 303	35.91 349	71.55 343

LP		61.36 309	57.78 312	72.09 302	75.54 330	58.53 250	67.16 340	75.22 321	51.90 335	54.13 334	69.97 337	37.73 327	80.45 315	32.74 342	55.63 339	77.29 335

pmmvs3		57.79 314	54.26 318	68.37 320	64.02 349	56.72 278	75.12 313	65.17 351	40.20 345	52.93 339	69.86 338	20.36 349	75.48 335	45.45 316	55.25 341	72.90 342

testpf		56.51 317	57.58 314	53.30 337	71.99 340	41.19 346	46.89 355	69.32 345	58.06 305	52.87 340	69.45 339	27.99 340	72.73 343	59.59 233	62.07 328	45.98 353

testmv		53.85 319	51.03 321	62.31 328	61.46 351	38.88 349	70.95 325	74.69 329	51.11 337	41.26 345	66.85 340	14.28 356	72.13 345	29.19 348	49.51 346	75.93 340

test12356		49.28 325	48.51 325	51.59 339	62.06 350	19.11 362	60.40 347	72.45 336	47.60 341	40.64 347	65.68 341	13.84 357	68.72 350	27.29 350	46.67 348	66.94 346

new_pmnet		50.91 323	50.29 322	52.78 338	68.58 346	34.94 354	63.71 344	56.63 355	39.73 346	44.95 344	65.47 342	21.93 348	58.48 356	34.98 339	56.62 338	64.92 347

gg-mvs-nofinetune		69.95 279	67.96 280	75.94 279	83.07 262	54.51 303	77.23 301	70.29 340	63.11 263	70.32 246	62.33 343	43.62 299	88.69 269	53.88 270	87.76 103	84.62 299

JIA-IIPM		66.32 299	62.82 305	76.82 268	77.09 324	61.72 228	65.34 342	75.38 320	58.04 306	64.51 304	62.32 344	42.05 310	86.51 286	51.45 279	69.22 302	82.21 318

LCM-MVSNet		54.25 318	49.68 324	67.97 321	53.73 356	45.28 337	66.85 341	80.78 281	35.96 349	39.45 348	62.23 345	8.70 362	78.06 325	48.24 297	51.20 345	80.57 325

PMMVS2		40.82 329	38.86 330	46.69 342	53.84 355	16.45 363	48.61 354	49.92 358	37.49 348	31.67 350	60.97 346	8.14 363	56.42 357	28.42 349	30.72 350	67.19 345

MVS-HIRNet		59.14 311	57.67 313	63.57 327	81.65 284	43.50 341	71.73 320	65.06 352	39.59 347	51.43 341	57.73 347	38.34 324	82.58 308	39.53 334	73.95 275	64.62 348

ANet_high		50.57 324	46.10 326	63.99 325	48.67 359	39.13 347	70.99 324	80.85 280	61.39 281	31.18 351	57.70 348	17.02 354	73.65 342	31.22 347	15.89 358	79.18 329

PMVS	37.38 22	44.16 328	40.28 329	55.82 335	40.82 362	42.54 342	65.12 343	63.99 354	34.43 350	24.48 353	57.12 349	3.92 364	76.17 332	17.10 356	55.52 340	48.75 351
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
no-one		51.08 322	45.79 327	66.95 323	57.92 354	50.49 329	59.63 349	76.04 319	48.04 339	31.85 349	56.10 350	19.12 351	80.08 317	36.89 337	26.52 351	70.29 344

DeepMVS_CX				27.40 348	40.17 363	26.90 359		24.59 364	17.44 358	23.95 354	48.61 351	9.77 360	26.48 361	18.06 355	24.47 352	28.83 356

PNet_i23d		38.26 331	35.42 331	46.79 341	58.74 352	35.48 352	59.65 348	51.25 357	32.45 352	23.44 356	47.53 352	2.04 366	58.96 355	25.60 352	18.09 356	45.92 354

MVE	26.22 23	30.37 335	25.89 337	43.81 343	44.55 361	35.46 353	28.87 359	39.07 361	18.20 357	18.58 357	40.18 353	2.68 365	47.37 360	17.07 357	23.78 353	48.60 352
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
Gipuma		45.18 327	41.86 328	55.16 336	77.03 325	51.52 322	32.50 358	80.52 284	32.46 351	27.12 352	35.02 354	9.52 361	75.50 334	22.31 354	60.21 335	38.45 355
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
wuykxyi23d		39.76 330	33.18 333	59.51 333	46.98 360	44.01 339	57.70 351	67.74 348	24.13 355	13.98 360	34.33 355	1.27 367	71.33 346	34.23 340	18.23 354	63.18 349

E-PMN		31.77 333	30.64 334	35.15 344	52.87 357	27.67 358	57.09 352	47.86 359	24.64 354	16.40 358	33.05 356	11.23 359	54.90 358	14.46 358	18.15 355	22.87 357

EMVS		30.81 334	29.65 335	34.27 345	50.96 358	25.95 360	56.58 353	46.80 360	24.01 356	15.53 359	30.68 357	12.47 358	54.43 359	12.81 359	17.05 357	22.43 358

tmp_tt		18.61 337	21.40 338	10.23 350	4.82 364	10.11 364	34.70 357	30.74 363	1.48 360	23.91 355	26.07 358	28.42 339	13.41 363	27.12 351	15.35 359	7.17 359

X-MVStestdata		80.37 122	77.83 156	88.00 10	94.42 12	73.33 17	92.78 9	92.99 29	79.14 21	83.67 53	12.47 359	67.45 68	96.60 21	83.06 39	94.50 36	94.07 24

test_post											5.46 360	50.36 260	84.24 299

test_post1							78.90 290				5.43 361	48.81 274	85.44 294	59.25 236

wuyk23d		16.82 338	15.94 339	19.46 349	58.74 352	31.45 355	39.22 356	3.74 366	6.84 359	6.04 361	2.70 362	1.27 367	24.29 362	10.54 360	14.40 360	2.63 360

testmvs		6.04 341	8.02 342	0.10 352	0.08 365	0.03 367	69.74 326	0.04 367	0.05 361	0.31 362	1.68 363	0.02 370	0.04 364	0.24 361	0.02 361	0.25 362

test123		6.12 340	8.11 341	0.14 351	0.06 366	0.09 366	71.05 322	0.03 368	0.04 362	0.25 363	1.30 364	0.05 369	0.03 365	0.21 362	0.01 363	0.29 361

pcd_1.5k_mvsjas		5.26 342	7.02 343	0.00 353	0.00 367	0.00 368	0.00 360	0.00 369	0.00 363	0.00 364	0.00 365	63.15 105	0.00 366	0.00 363	0.00 364	0.00 364

pcd1.5k->3k		34.07 332	35.26 332	30.50 347	86.92 186	0.00 368	0.00 360	91.58 84	0.00 363	0.00 364	0.00 365	56.23 189	0.00 366	0.00 363	82.60 170	91.49 110

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

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																88.96 207

test_part2					95.06 1	72.65 27				91.80 2

test_part1								94.09 1				81.79 1			96.38 3	93.74 40

sam_mvs1												51.32 241				88.96 207

sam_mvs												50.01 262

MTGPA								92.02 62

MTMP							92.18 22	32.83 362

test9_res														84.90 20	95.70 15	92.87 73

agg_prior2														82.91 42	95.45 17	92.70 75

agg_prior					92.85 44	71.94 42		91.78 77		84.41 42			94.93 68

test_prior4						72.60 30	89.01 81

test_prior				86.33 48	92.61 49	69.59 75		92.97 32					95.48 46			93.91 31

旧先验2							86.56 169		58.10 304	87.04 16			88.98 263	74.07 114

新几何2							86.29 178

无先验							87.48 133	88.98 172	60.00 290				94.12 97	67.28 172		88.97 206

原ACMM2							86.86 158

testdata2													91.01 226	62.37 209

segment_acmp												73.08 26

testdata1							84.14 236		75.71 71

test12				86.80 40	92.63 48	70.70 58		91.79 76		82.71 65		71.67 35	96.16 32		94.50 36	93.54 52

plane_prior7					90.08 84	68.51 101

plane_prior6					89.84 89	68.70 97						60.42 162

plane_prior5								92.44 46					95.38 53	78.71 68	86.32 125	91.33 112

plane_prior3						68.60 99			78.44 30	78.92 101

plane_prior2							91.25 32		79.12 23

plane_prior1					89.90 88

plane_prior						68.71 95	90.38 48		77.62 34						86.16 127

n2								0.00 369

nn								0.00 369

door-mid								69.98 341

test11								92.23 54

door								69.44 344

HQP5-MVS						66.98 126

HQP-NCC					89.33 106		89.17 74		76.41 59	77.23 142

ACMP_Plane					89.33 106		89.17 74		76.41 59	77.23 142

BP-MVS														77.47 80

HQP4-MVS										77.24 141			95.11 61			91.03 119

HQP3-MVS								92.19 57							85.99 129

HQP2-MVS												60.17 165

MDTV_nov1_ep13_2view						37.79 350	75.16 311		55.10 321	66.53 291		49.34 268		53.98 268		87.94 238

ACMMP++_ref															81.95 176

ACMMP++															81.25 183

Test By Simon												64.33 92

High-resolution multi-view benchmark