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.

MSP-MVS		95.62 6	96.54 1	92.86 92	98.31 49	80.10 164	97.42 88	96.78 44	92.20 13	97.11 8	98.29 28	93.46 1	99.10 95	96.01 20	99.30 3	99.38 10

OPU-MVS				97.30 2	99.19 8	92.31 3	99.12 6				98.54 20	92.06 2	99.84 12	99.11 1	99.37 1	99.74 1

GG-mvs-BLEND				93.49 66	94.94 141	86.26 29	81.62 330	97.00 27		88.32 120	94.30 164	91.23 3	96.21 219	88.49 113	97.43 73	98.00 86

gg-mvs-nofinetune		85.48 184	82.90 204	93.24 74	94.51 155	85.82 37	79.22 334	96.97 31	61.19 333	87.33 128	53.01 346	90.58 4	96.07 220	86.07 130	97.23 78	97.81 100

baseline2		90.39 102	90.21 93	90.93 152	90.86 242	80.99 139	95.20 212	97.41 15	86.03 95	80.07 204	94.61 158	90.58 4	97.47 167	87.29 123	89.86 153	94.35 198

CHOSEN 280x420		91.71 73	91.85 67	91.29 143	94.94 141	82.69 100	87.89 309	96.17 123	85.94 96	87.27 129	94.31 163	90.27 6	95.65 246	94.04 46	95.86 103	95.53 180

DPM-MVS		96.21 2	95.53 9	98.26 1	96.26 105	95.09 1	99.15 4	96.98 29	93.39 9	96.45 14	98.79 10	90.17 7	99.99 1	89.33 106	99.25 4	99.70 3

ET-MVSNet_ETH3D		90.01 108	89.03 113	92.95 88	94.38 158	86.77 26	98.14 34	96.31 114	89.30 40	63.33 318	96.72 114	90.09 8	93.63 304	90.70 86	82.29 217	98.46 47

MVSTER		89.25 120	88.92 117	90.24 169	95.98 113	84.66 65	96.79 136	95.36 167	87.19 82	80.33 199	90.61 213	90.02 9	95.97 224	85.38 135	78.64 236	90.09 236

test_0728_THIRD									88.38 56	96.69 10	98.76 12	89.64 10	99.76 20	97.47 10	98.84 22	99.38 10

tttt0517		88.57 137	88.19 124	89.71 187	93.00 188	75.99 255	95.67 196	96.67 63	80.78 204	81.82 186	94.40 162	88.97 11	97.58 157	76.05 222	86.31 181	95.57 179

thisisatest0530		89.65 113	89.02 114	91.53 138	93.46 179	80.78 145	96.52 149	96.67 63	81.69 194	83.79 161	94.90 154	88.85 12	97.68 152	77.80 198	87.49 175	96.14 167

thisisatest0515		90.95 90	90.26 91	93.01 85	94.03 168	84.27 71	97.91 47	96.67 63	83.18 167	86.87 133	95.51 137	88.66 13	97.85 147	80.46 176	89.01 158	96.92 144

SED-MVS		95.88 4	96.22 3	94.87 19	99.03 13	85.03 58	99.12 6	96.78 44	88.72 49	97.79 3	98.91 3	88.48 14	99.82 16	98.15 2	98.97 15	99.74 1

test_241102_ONE					99.03 13	85.03 58		96.78 44	88.72 49	97.79 3	98.90 6	88.48 14	99.82 16

DPE-MVS		95.32 9	95.55 8	94.64 25	98.79 21	84.87 63	97.77 56	96.74 54	86.11 91	96.54 13	98.89 7	88.39 16	99.74 28	97.67 8	99.05 10	99.31 14

test_241102_TWO								96.78 44	88.72 49	97.70 5	98.91 3	87.86 17	99.82 16	98.15 2	99.00 13	99.47 7

DVP-MVS		95.58 7	95.91 7	94.57 26	99.05 10	85.18 51	99.06 9	96.46 94	88.75 47	96.69 10	98.76 12	87.69 18	99.76 20	97.90 5	98.85 20	98.77 30

test0726					99.05 10	85.18 51	99.11 8	96.78 44	88.75 47	97.65 6	98.91 3	87.69 18

MCST-MVS		96.17 3	96.12 5	96.32 5	99.42 2	89.36 8	98.94 15	97.10 23	95.17 2	92.11 64	98.46 24	87.33 20	99.97 2	97.21 12	99.31 2	99.63 5

TSAR-MVS + GP.		94.35 21	94.50 18	93.89 46	97.38 91	83.04 96	98.10 37	95.29 173	91.57 16	93.81 46	97.45 82	86.64 21	99.43 61	96.28 17	94.01 119	99.20 18

DWT-MVSNet_test		90.52 101	89.80 105	92.70 100	95.73 119	82.20 110	93.69 248	96.55 83	88.34 57	87.04 132	95.34 140	86.53 22	97.55 159	76.32 219	88.66 163	98.34 53

TSAR-MVS + MP.		94.79 14	95.17 12	93.64 57	97.66 75	84.10 73	95.85 191	96.42 99	91.26 20	97.49 7	96.80 111	86.50 23	98.49 126	95.54 28	99.03 11	98.33 54
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
CNVR-MVS		96.30 1	96.54 1	95.55 12	99.31 5	87.69 19	99.06 9	97.12 22	94.66 3	96.79 9	98.78 11	86.42 24	99.95 3	97.59 9	99.18 5	99.00 23

DeepPCF-MVS	89.82 1	94.61 16	96.17 4	89.91 181	97.09 97	70.21 304	98.99 14	96.69 61	95.57 1	95.08 28	99.23 1	86.40 25	99.87 8	97.84 7	98.66 30	99.65 4

ETH3 D test6400		95.56 8	95.41 11	96.00 7	99.02 16	89.42 7	98.75 18	96.80 43	87.28 77	95.88 19	98.95 2	85.92 26	99.41 62	97.15 13	98.95 18	99.18 20

HPM-MVS++		95.32 9	95.48 10	94.85 20	98.62 34	86.04 32	97.81 54	96.93 35	92.45 11	95.69 20	98.50 22	85.38 27	99.85 10	94.75 38	99.18 5	98.65 38

NCCC		95.63 5	95.94 6	94.69 24	99.21 7	85.15 56	99.16 3	96.96 32	94.11 6	95.59 21	98.64 19	85.07 28	99.91 4	95.61 27	99.10 7	99.00 23

EPP-MVSNet		89.76 111	89.72 106	89.87 182	93.78 170	76.02 254	97.22 95	96.51 88	79.35 236	85.11 143	95.01 152	84.82 29	97.10 186	87.46 122	88.21 169	96.50 157

agg_prior1		94.10 28	94.31 25	93.48 67	98.59 35	83.13 93	97.77 56	96.56 81	84.38 137	94.19 41	98.13 38	84.66 30	99.16 89	95.74 25	98.74 27	98.15 71

TEST9					98.64 31	83.71 80	97.82 52	96.65 67	84.29 141	95.16 25	98.09 43	84.39 31	99.36 70

train_agg		94.28 22	94.45 20	93.74 51	98.64 31	83.71 80	97.82 52	96.65 67	84.50 133	95.16 25	98.09 43	84.33 32	99.36 70	95.91 23	98.96 17	98.16 69

test_8					98.63 33	83.64 83	97.81 54	96.63 72	84.50 133	95.10 27	98.11 42	84.33 32	99.23 76

SD-MVS		94.84 13	95.02 13	94.29 34	97.87 70	84.61 66	97.76 60	96.19 122	89.59 36	96.66 12	98.17 36	84.33 32	99.60 46	96.09 18	98.50 37	98.66 37
Zhenlong Yuan, Jiakai Cao, Zhaoxin Li, Hao Jiang and Zhaoqi Wang: SD-MVS: Segmentation-driven Deformation Multi-View Stereo with Spherical Refinement and EM optimization. AAAI2024
APDe-MVS		94.56 17	94.75 14	93.96 45	98.84 20	83.40 87	98.04 42	96.41 100	85.79 99	95.00 31	98.28 29	84.32 35	99.18 87	97.35 11	98.77 25	99.28 15

旧先验1					97.39 88	79.58 177		96.54 84			98.08 46	84.00 36			97.42 74	97.62 114

CSCG		92.02 67	91.65 72	93.12 79	98.53 37	80.59 150	97.47 80	97.18 20	77.06 269	84.64 150	97.98 54	83.98 37	99.52 53	90.72 85	97.33 76	99.23 17

IB-MVS	85.34 4	88.67 133	87.14 150	93.26 73	93.12 186	84.32 68	98.76 17	97.27 17	87.19 82	79.36 208	90.45 216	83.92 38	98.53 124	84.41 141	69.79 284	96.93 142
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
CostFormer		89.08 121	88.39 123	91.15 147	93.13 185	79.15 185	88.61 305	96.11 126	83.14 168	89.58 103	86.93 262	83.83 39	96.87 196	88.22 117	85.92 187	97.42 125

SteuartSystems-ACMMP		94.13 27	94.44 21	93.20 76	95.41 125	81.35 132	99.02 13	96.59 77	89.50 37	94.18 43	98.36 27	83.68 40	99.45 60	94.77 37	98.45 40	98.81 29
Skip Steuart: Steuart Systems R&D Blog.
RRT_test8_iter05		87.14 157	86.41 158	89.32 191	94.41 157	81.10 137	97.06 118	95.33 171	84.67 128	76.27 240	90.48 214	83.60 41	96.33 213	85.10 136	70.78 273	90.53 226

ETH3D-3000-0.1		94.43 19	94.42 22	94.45 28	97.78 71	85.78 38	97.98 44	96.53 86	85.29 112	95.45 22	98.81 8	83.36 42	99.38 64	96.07 19	98.53 33	98.19 66

DELS-MVS		94.98 11	94.49 19	96.44 4	96.42 103	90.59 5	99.21 2	97.02 26	94.40 5	91.46 73	97.08 100	83.32 43	99.69 36	92.83 61	98.70 29	99.04 21
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
test_prior3		94.03 31	94.34 24	93.09 81	98.68 25	81.91 115	98.37 27	96.40 103	86.08 93	94.57 38	98.02 49	83.14 44	99.06 97	95.05 35	98.79 23	98.29 60

test_prior2							98.37 27		86.08 93	94.57 38	98.02 49	83.14 44		95.05 35	98.79 23

ETH3D cwj APD-0.16		93.91 35	93.76 33	94.36 31	96.70 101	85.74 39	97.22 95	96.41 100	83.94 150	94.13 44	98.69 18	83.13 46	99.37 68	95.25 34	98.39 47	97.97 89

SMA-MVS		94.70 15	94.68 15	94.76 22	98.02 64	85.94 35	97.47 80	96.77 50	85.32 109	97.92 2	98.70 16	83.09 47	99.84 12	95.79 24	99.08 8	98.49 46
Yufeng Yin; Xiaoyan Liu; Zichao Zhang: SMA-MVS: Segmentation-Guided Multi-Scale Anchor Deformation Patch Multi-View Stereo. IEEE Transactions on Circuits and Systems for Video Technology
ZD-MVS					99.09 9	83.22 92		96.60 76	82.88 175	93.61 49	98.06 48	82.93 48	99.14 91	95.51 29	98.49 38

xxxxxxxxxxxxxcwj		94.38 20	94.62 17	93.68 55	98.24 52	83.34 88	98.61 23	92.69 284	91.32 18	95.07 29	98.74 14	82.93 48	99.38 64	95.42 30	98.51 34	98.32 55

SF-MVS		94.17 25	94.05 30	94.55 27	97.56 80	85.95 33	97.73 62	96.43 98	84.02 147	95.07 29	98.74 14	82.93 48	99.38 64	95.42 30	98.51 34	98.32 55

9.14			94.26 27		98.10 60		98.14 34	96.52 87	84.74 124	94.83 34	98.80 9	82.80 51	99.37 68	95.95 22	98.42 42

testtj		94.09 29	94.08 29	94.09 42	99.28 6	83.32 90	97.59 71	96.61 73	83.60 162	94.77 36	98.46 24	82.72 52	99.64 42	95.29 33	98.42 42	99.32 13

segment_acmp												82.69 53

Regformer-1		94.00 32	94.04 31	93.87 47	98.41 43	84.29 69	97.43 86	97.04 25	89.50 37	92.75 59	98.13 38	82.60 54	99.26 75	93.55 50	96.99 82	98.06 78

Regformer-2		93.92 33	94.01 32	93.67 56	98.41 43	83.75 79	97.43 86	97.00 27	89.43 39	92.69 60	98.13 38	82.48 55	99.22 78	93.51 51	96.99 82	98.04 79

PAPM		92.87 49	92.40 57	94.30 33	92.25 209	87.85 16	96.40 161	96.38 107	91.07 21	88.72 114	96.90 104	82.11 56	97.37 171	90.05 96	97.70 67	97.67 109

APD-MVS		93.61 37	93.59 36	93.69 54	98.76 22	83.26 91	97.21 97	96.09 127	82.41 183	94.65 37	98.21 31	81.96 57	98.81 113	94.65 40	98.36 50	99.01 22
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
CDPH-MVS		93.12 43	92.91 47	93.74 51	98.65 30	83.88 75	97.67 66	96.26 116	83.00 172	93.22 53	98.24 30	81.31 58	99.21 80	89.12 107	98.74 27	98.14 72

MG-MVS		94.25 24	93.72 34	95.85 9	99.38 3	89.35 9	97.98 44	98.09 8	89.99 32	92.34 63	96.97 103	81.30 59	98.99 101	88.54 111	98.88 19	99.20 18

test12				94.25 35	98.34 47	85.55 44		96.35 110		92.36 62		80.84 60	99.22 78		98.31 52	97.98 88

Regformer-3		93.19 41	93.19 42	93.19 77	98.10 60	83.01 97	97.08 116	96.98 29	88.98 43	91.35 78	97.89 59	80.80 61	99.23 76	92.30 67	95.20 109	97.32 130

baseline1		88.85 128	87.49 140	92.93 90	95.21 131	86.85 25	95.47 203	94.61 209	87.29 76	83.11 169	94.99 153	80.70 62	96.89 194	82.28 166	73.72 258	95.05 187

tpmrst		88.36 142	87.38 144	91.31 141	94.36 159	79.92 166	87.32 313	95.26 175	85.32 109	88.34 119	86.13 278	80.60 63	96.70 203	83.78 146	85.34 195	97.30 133

Regformer-4		93.06 44	93.12 43	92.89 91	98.10 60	82.20 110	97.08 116	96.92 37	88.87 45	91.23 80	97.89 59	80.57 64	99.19 85	92.21 69	95.20 109	97.29 134

PHI-MVS		93.59 38	93.63 35	93.48 67	98.05 63	81.76 123	98.64 21	97.13 21	82.60 181	94.09 45	98.49 23	80.35 65	99.85 10	94.74 39	98.62 31	98.83 28

CDS-MVSNet		89.50 115	88.96 116	91.14 148	91.94 225	80.93 141	97.09 114	95.81 142	84.26 142	84.72 148	94.20 167	80.31 66	95.64 247	83.37 158	88.96 159	96.85 147
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
tpm2		87.35 156	86.26 159	90.62 161	92.93 191	78.67 196	88.06 308	95.99 132	79.33 237	87.40 126	86.43 273	80.28 67	96.40 210	80.23 179	85.73 191	96.79 148

1112_ss		88.60 136	87.47 142	92.00 124	93.21 182	80.97 140	96.47 152	92.46 286	83.64 160	80.86 192	97.30 91	80.24 68	97.62 155	77.60 203	85.49 192	97.40 127

Test_1112_low_res		88.03 147	86.73 155	91.94 126	93.15 184	80.88 142	96.44 157	92.41 287	83.59 163	80.74 194	91.16 204	80.18 69	97.59 156	77.48 206	85.40 193	97.36 129

DeepC-MVS_fast	89.06 2	94.48 18	94.30 26	95.02 17	98.86 19	85.68 42	98.06 40	96.64 70	93.64 8	91.74 70	98.54 20	80.17 70	99.90 5	92.28 68	98.75 26	99.49 6
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
MSLP-MVS++		94.28 22	94.39 23	93.97 44	98.30 50	84.06 74	98.64 21	96.93 35	90.71 25	93.08 55	98.70 16	79.98 71	99.21 80	94.12 45	99.07 9	98.63 39

EPNet		94.06 30	94.15 28	93.76 50	97.27 94	84.35 67	98.29 29	97.64 13	94.57 4	95.36 23	96.88 106	79.96 72	99.12 94	91.30 76	96.11 98	97.82 99
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
MVS_111021_HR		93.41 40	93.39 39	93.47 70	97.34 92	82.83 99	97.56 74	98.27 6	89.16 42	89.71 99	97.14 97	79.77 73	99.56 51	93.65 48	97.94 62	98.02 81

miper_enhance_ethall		85.95 176	85.20 170	88.19 216	94.85 144	79.76 169	96.00 180	94.06 235	82.98 173	77.74 220	88.76 235	79.42 74	95.46 256	80.58 175	72.42 265	89.36 251

TESTMET0.1,1		89.83 109	89.34 111	91.31 141	92.54 200	80.19 162	97.11 110	96.57 79	86.15 90	86.85 134	91.83 197	79.32 75	96.95 190	81.30 171	92.35 138	96.77 150

WTY-MVS		92.65 58	91.68 71	95.56 11	96.00 112	88.90 10	98.23 31	97.65 12	88.57 52	89.82 98	97.22 95	79.29 76	99.06 97	89.57 102	88.73 162	98.73 35

1121		90.66 95	89.82 104	93.16 78	97.39 88	81.71 126	93.33 257	96.66 66	74.45 283	91.38 74	97.55 79	79.27 77	99.52 53	79.95 182	98.43 41	98.26 63

HY-MVS	84.06 6	91.63 75	90.37 90	95.39 14	96.12 109	88.25 12	90.22 293	97.58 14	88.33 58	90.50 90	91.96 193	79.26 78	99.06 97	90.29 94	89.07 157	98.88 27

PAPM_NR		91.46 79	90.82 83	93.37 71	98.50 40	81.81 122	95.03 221	96.13 124	84.65 129	86.10 139	97.65 73	79.24 79	99.75 26	83.20 160	96.88 87	98.56 42

alignmvs		92.97 46	92.26 61	95.12 16	95.54 122	87.77 17	98.67 19	96.38 107	88.04 62	93.01 56	97.45 82	79.20 80	98.60 120	93.25 57	88.76 161	98.99 25

新几何1				93.12 79	97.44 84	81.60 129		96.71 58	74.54 282	91.22 81	97.57 75	79.13 81	99.51 56	77.40 207	98.46 39	98.26 63

JIA-IIPM		79.00 265	77.20 263	84.40 280	89.74 259	64.06 327	75.30 342	95.44 162	62.15 328	81.90 184	59.08 344	78.92 82	95.59 251	66.51 283	85.78 190	93.54 209

MVSFormer		91.36 82	90.57 86	93.73 53	93.00 188	88.08 14	94.80 226	94.48 214	80.74 205	94.90 32	97.13 98	78.84 83	95.10 275	83.77 147	97.46 70	98.02 81

lupinMVS		93.87 36	93.58 37	94.75 23	93.00 188	88.08 14	99.15 4	95.50 158	91.03 22	94.90 32	97.66 69	78.84 83	97.56 158	94.64 41	97.46 70	98.62 40

testdata				90.13 172	95.92 114	74.17 272		96.49 93	73.49 291	94.82 35	97.99 52	78.80 85	97.93 141	83.53 156	97.52 69	98.29 60

PAPR		92.74 51	92.17 64	94.45 28	98.89 18	84.87 63	97.20 99	96.20 120	87.73 70	88.40 118	98.12 41	78.71 86	99.76 20	87.99 118	96.28 96	98.74 31

EI-MVSNet-Vis-set		91.84 70	91.77 70	92.04 123	97.60 77	81.17 134	96.61 146	96.87 39	88.20 60	89.19 108	97.55 79	78.69 87	99.14 91	90.29 94	90.94 148	95.80 173

HFP-MVS		92.89 47	92.86 49	92.98 86	98.71 23	81.12 135	97.58 72	96.70 59	85.20 115	91.75 68	97.97 56	78.47 88	99.71 32	90.95 79	98.41 44	98.12 74

#test#		92.99 45	92.99 45	92.98 86	98.71 23	81.12 135	97.77 56	96.70 59	85.75 100	91.75 68	97.97 56	78.47 88	99.71 32	91.36 75	98.41 44	98.12 74

ZNCC-MVS		92.75 50	92.60 55	93.23 75	98.24 52	81.82 121	97.63 67	96.50 90	85.00 120	91.05 83	97.74 67	78.38 90	99.80 19	90.48 88	98.34 51	98.07 77

Patchmatch-test		78.25 269	74.72 281	88.83 200	91.20 234	74.10 273	73.91 345	88.70 325	59.89 339	66.82 303	85.12 294	78.38 90	94.54 288	48.84 338	79.58 228	97.86 95

Vis-MVSNet (Re-imp)		88.88 127	88.87 118	88.91 198	93.89 169	74.43 270	96.93 129	94.19 227	84.39 136	83.22 167	95.67 132	78.24 92	94.70 285	78.88 194	94.40 116	97.61 115

tpm		85.55 182	84.47 183	88.80 201	90.19 251	75.39 262	88.79 303	94.69 200	84.83 122	83.96 158	85.21 290	78.22 93	94.68 286	76.32 219	78.02 243	96.34 162

MP-MVS		92.61 59	92.67 53	92.42 109	98.13 59	79.73 173	97.33 93	96.20 120	85.63 102	90.53 89	97.66 69	78.14 94	99.70 35	92.12 70	98.30 53	97.85 96
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
HyFIR lowres test		89.36 117	88.60 120	91.63 136	94.91 143	80.76 146	95.60 199	95.53 155	82.56 182	84.03 155	91.24 203	78.03 95	96.81 199	87.07 126	88.41 167	97.32 130

ACMMP_NAP		93.46 39	93.23 41	94.17 39	97.16 95	84.28 70	96.82 134	96.65 67	86.24 89	94.27 40	97.99 52	77.94 96	99.83 15	93.39 52	98.57 32	98.39 52

原ACMM1				91.22 146	97.77 72	78.10 215		96.61 73	81.05 200	91.28 79	97.42 86	77.92 97	98.98 102	79.85 185	98.51 34	96.59 155

EI-MVSNet-UG-set		91.35 83	91.22 77	91.73 132	97.39 88	80.68 148	96.47 152	96.83 42	87.92 64	88.30 121	97.36 88	77.84 98	99.13 93	89.43 105	89.45 155	95.37 183

patchmatchnet-post											77.09 331	77.78 99	95.39 257

sam_mvs1												77.59 100				97.54 117

EIA-MVS		91.73 71	92.05 65	90.78 158	94.52 152	76.40 247	98.06 40	95.34 170	89.19 41	88.90 112	97.28 93	77.56 101	97.73 151	90.77 84	96.86 90	98.20 65

GST-MVS		92.43 63	92.22 63	93.04 84	98.17 57	81.64 128	97.40 90	96.38 107	84.71 126	90.90 85	97.40 87	77.55 102	99.76 20	89.75 100	97.74 66	97.72 105

CS-MVS		92.88 48	93.09 44	92.26 115	95.21 131	80.70 147	98.84 16	95.26 175	88.83 46	92.50 61	97.48 81	77.49 103	97.63 154	95.34 32	96.88 87	98.46 47

MP-MVS-pluss		92.58 60	92.35 58	93.29 72	97.30 93	82.53 103	96.44 157	96.04 131	84.68 127	89.12 109	98.37 26	77.48 104	99.74 28	93.31 56	98.38 48	97.59 116
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
CP-MVS		92.54 61	92.60 55	92.34 111	98.50 40	79.90 167	98.40 26	96.40 103	84.75 123	90.48 91	98.09 43	77.40 105	99.21 80	91.15 78	98.23 55	97.92 92

region2R		92.72 54	92.70 52	92.79 95	98.68 25	80.53 154	97.53 76	96.51 88	85.22 113	91.94 66	97.98 54	77.26 106	99.67 40	90.83 83	98.37 49	98.18 67

PatchmatchNet		86.83 164	85.12 173	91.95 125	94.12 163	82.27 108	86.55 319	95.64 151	84.59 131	82.98 171	84.99 296	77.26 106	95.96 227	68.61 273	91.34 146	97.64 112
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
XVS		92.69 56	92.71 50	92.63 102	98.52 38	80.29 157	97.37 91	96.44 96	87.04 84	91.38 74	97.83 64	77.24 108	99.59 47	90.46 89	98.07 58	98.02 81

X-MVStestdata		86.26 172	84.14 188	92.63 102	98.52 38	80.29 157	97.37 91	96.44 96	87.04 84	91.38 74	20.73 356	77.24 108	99.59 47	90.46 89	98.07 58	98.02 81

ETV-MVS		92.72 54	92.87 48	92.28 114	94.54 151	81.89 117	97.98 44	95.21 177	89.77 35	93.11 54	96.83 108	77.23 110	97.50 165	95.74 25	95.38 107	97.44 124

ACMMPR		92.69 56	92.67 53	92.75 96	98.66 28	80.57 151	97.58 72	96.69 61	85.20 115	91.57 72	97.92 58	77.01 111	99.67 40	90.95 79	98.41 44	98.00 86

UniMVSNet_NR-MVSNet		85.49 183	84.59 179	88.21 215	89.44 265	79.36 178	96.71 142	96.41 100	85.22 113	78.11 218	90.98 208	76.97 112	95.14 272	79.14 191	68.30 297	90.12 234

DP-MVS Recon		91.72 72	90.85 82	94.34 32	99.50 1	85.00 60	98.51 25	95.96 134	80.57 209	88.08 123	97.63 74	76.84 113	99.89 7	85.67 132	94.88 113	98.13 73

CANet		94.89 12	94.64 16	95.63 10	97.55 81	88.12 13	99.06 9	96.39 106	94.07 7	95.34 24	97.80 65	76.83 114	99.87 8	97.08 14	97.64 68	98.89 26

PVSNet_Blended_VisFu		91.24 85	90.77 84	92.66 101	95.09 135	82.40 106	97.77 56	95.87 140	88.26 59	86.39 135	93.94 172	76.77 115	99.27 73	88.80 110	94.00 120	96.31 165

FIs		86.73 168	86.10 160	88.61 204	90.05 254	80.21 161	96.14 176	96.95 33	85.56 106	78.37 216	92.30 188	76.73 116	95.28 264	79.51 186	79.27 230	90.35 229

zzz-MVS		92.74 51	92.71 50	92.86 92	97.90 66	80.85 143	96.47 152	96.33 111	87.92 64	90.20 94	98.18 32	76.71 117	99.76 20	92.57 65	98.09 56	97.96 90

MTAPA		92.45 62	92.31 59	92.86 92	97.90 66	80.85 143	92.88 270	96.33 111	87.92 64	90.20 94	98.18 32	76.71 117	99.76 20	92.57 65	98.09 56	97.96 90

miper_ehance_all_eth		84.57 196	83.60 196	87.50 230	92.64 198	78.25 208	95.40 206	93.47 259	79.28 240	76.41 236	87.64 251	76.53 119	95.24 267	78.58 195	72.42 265	89.01 263

SR-MVS		92.16 65	92.27 60	91.83 131	98.37 46	78.41 203	96.67 145	95.76 144	82.19 187	91.97 65	98.07 47	76.44 120	98.64 117	93.71 47	97.27 77	98.45 49

PVSNet_BlendedMVS		90.05 107	89.96 99	90.33 167	97.47 82	83.86 76	98.02 43	96.73 55	87.98 63	89.53 104	89.61 227	76.42 121	99.57 49	94.29 43	79.59 227	87.57 294

PVSNet_Blended		93.13 42	92.98 46	93.57 61	97.47 82	83.86 76	99.32 1	96.73 55	91.02 23	89.53 104	96.21 120	76.42 121	99.57 49	94.29 43	95.81 105	97.29 134

test-mter		88.95 123	88.60 120	89.98 177	92.26 207	77.23 235	97.11 110	95.96 134	85.32 109	86.30 137	91.38 200	76.37 123	96.78 201	80.82 173	91.92 142	95.94 170

test222					96.15 108	78.41 203	95.87 189	96.46 94	71.97 302	89.66 101	97.45 82	76.33 124			98.24 54	98.30 59

FC-MVSNet-test		85.96 175	85.39 167	87.66 224	89.38 266	78.02 216	95.65 198	96.87 39	85.12 117	77.34 222	91.94 195	76.28 125	94.74 284	77.09 208	78.82 234	90.21 232

test_post											33.80 352	76.17 126	95.97 224

PGM-MVS		91.93 68	91.80 69	92.32 113	98.27 51	79.74 172	95.28 207	97.27 17	83.83 155	90.89 86	97.78 66	76.12 127	99.56 51	88.82 109	97.93 64	97.66 110

Patchmatch-RL test		76.65 283	74.01 288	84.55 276	77.37 339	64.23 325	78.49 338	82.84 345	78.48 252	64.63 313	73.40 337	76.05 128	91.70 322	76.99 209	57.84 330	97.72 105

cl-mvsnet2		85.11 188	84.17 187	87.92 219	95.06 138	78.82 192	95.51 201	94.22 224	79.74 231	76.77 230	87.92 248	75.96 129	95.68 243	79.93 184	72.42 265	89.27 252

TAMVS		88.48 138	87.79 131	90.56 162	91.09 237	79.18 183	96.45 155	95.88 138	83.64 160	83.12 168	93.33 179	75.94 130	95.74 242	82.40 165	88.27 168	96.75 152

EPNet_dtu		87.65 153	87.89 128	86.93 242	94.57 149	71.37 298	96.72 140	96.50 90	88.56 53	87.12 130	95.02 151	75.91 131	94.01 297	66.62 280	90.00 152	95.42 182
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
mvs_anonymous		88.68 132	87.62 136	91.86 128	94.80 145	81.69 127	93.53 253	94.92 188	82.03 189	78.87 212	90.43 217	75.77 132	95.34 260	85.04 138	93.16 130	98.55 44

test1172		91.64 74	92.00 66	90.54 163	98.20 56	74.48 269	96.45 155	95.65 149	81.97 191	91.63 71	98.02 49	75.76 133	98.61 118	93.16 58	97.17 79	98.52 45

SR-MVS-dyc-post		91.29 84	91.45 75	90.80 156	97.76 73	76.03 252	96.20 173	95.44 162	80.56 210	90.72 87	97.84 62	75.76 133	98.61 118	91.99 72	96.79 91	97.75 103

test_yl		91.46 79	90.53 87	94.24 36	97.41 86	85.18 51	98.08 38	97.72 10	80.94 201	89.85 96	96.14 121	75.61 135	98.81 113	90.42 92	88.56 165	98.74 31

DCV-MVSNet		91.46 79	90.53 87	94.24 36	97.41 86	85.18 51	98.08 38	97.72 10	80.94 201	89.85 96	96.14 121	75.61 135	98.81 113	90.42 92	88.56 165	98.74 31

HPM-MVS		91.62 76	91.53 74	91.89 127	97.88 69	79.22 182	96.99 121	95.73 146	82.07 188	89.50 106	97.19 96	75.59 137	98.93 109	90.91 81	97.94 62	97.54 117
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
mPP-MVS		91.88 69	91.82 68	92.07 121	98.38 45	78.63 197	97.29 94	96.09 127	85.12 117	88.45 117	97.66 69	75.53 138	99.68 38	89.83 98	98.02 61	97.88 93

PatchT		79.75 257	76.85 267	88.42 206	89.55 262	75.49 261	77.37 340	94.61 209	63.07 325	82.46 174	73.32 338	75.52 139	93.41 307	51.36 331	84.43 198	96.36 160

CR-MVSNet		83.53 210	81.36 226	90.06 174	90.16 252	79.75 170	79.02 336	91.12 302	84.24 143	82.27 180	80.35 321	75.45 140	93.67 303	63.37 298	86.25 182	96.75 152

Patchmtry		77.36 277	74.59 282	85.67 261	89.75 257	75.75 259	77.85 339	91.12 302	60.28 336	71.23 282	80.35 321	75.45 140	93.56 305	57.94 313	67.34 308	87.68 290

thres100view900		88.30 143	86.95 153	92.33 112	96.10 110	84.90 62	97.14 107	98.85 2	82.69 179	83.41 164	93.66 177	75.43 142	97.93 141	69.04 269	86.24 184	94.17 199

thres600view7		88.06 146	86.70 156	92.15 119	96.10 110	85.17 55	97.14 107	98.85 2	82.70 178	83.41 164	93.66 177	75.43 142	97.82 148	67.13 278	85.88 188	93.45 212

UniMVSNet (Re)		85.31 186	84.23 186	88.55 205	89.75 257	80.55 152	96.72 140	96.89 38	85.42 107	78.40 215	88.93 233	75.38 144	95.52 254	78.58 195	68.02 300	89.57 244

tfpn200view9		88.48 138	87.15 148	92.47 106	96.21 106	85.30 49	97.44 82	98.85 2	83.37 164	83.99 156	93.82 174	75.36 145	97.93 141	69.04 269	86.24 184	94.17 199

thres400		88.42 141	87.15 148	92.23 116	96.21 106	85.30 49	97.44 82	98.85 2	83.37 164	83.99 156	93.82 174	75.36 145	97.93 141	69.04 269	86.24 184	93.45 212

sam_mvs												75.35 147

jason		92.73 53	92.23 62	94.21 38	90.50 246	87.30 23	98.65 20	95.09 180	90.61 26	92.76 58	97.13 98	75.28 148	97.30 174	93.32 55	96.75 93	98.02 81
jason: jason.
cl_fuxian		83.80 206	82.65 208	87.25 237	92.10 215	77.74 226	95.25 210	93.04 278	78.58 251	76.01 244	87.21 258	75.25 149	95.11 274	77.54 205	68.89 292	88.91 269

MVS_Test		90.29 105	89.18 112	93.62 59	95.23 129	84.93 61	94.41 232	94.66 204	84.31 139	90.37 93	91.02 206	75.13 150	97.82 148	83.11 162	94.42 115	98.12 74

thres200		88.92 125	87.65 133	92.73 98	96.30 104	85.62 43	97.85 50	98.86 1	84.38 137	84.82 146	93.99 171	75.12 151	98.01 139	70.86 263	86.67 178	94.56 197

EPMVS		87.47 155	85.90 163	92.18 118	95.41 125	82.26 109	87.00 315	96.28 115	85.88 98	84.23 153	85.57 284	75.07 152	96.26 216	71.14 261	92.50 135	98.03 80

UA-Net		88.92 125	88.48 122	90.24 169	94.06 165	77.18 237	93.04 266	94.66 204	87.39 75	91.09 82	93.89 173	74.92 153	98.18 138	75.83 224	91.43 145	95.35 184

tpm cat1		83.63 209	81.38 225	90.39 166	93.53 178	78.19 214	85.56 325	95.09 180	70.78 307	78.51 214	83.28 310	74.80 154	97.03 187	66.77 279	84.05 200	95.95 169

APD-MVS_3200maxsize		91.23 86	91.35 76	90.89 154	97.89 68	76.35 248	96.30 167	95.52 157	79.82 229	91.03 84	97.88 61	74.70 155	98.54 123	92.11 71	96.89 86	97.77 102

IS-MVSNet		88.67 133	88.16 125	90.20 171	93.61 174	76.86 240	96.77 139	93.07 277	84.02 147	83.62 163	95.60 135	74.69 156	96.24 218	78.43 197	93.66 125	97.49 123

MDTV_nov1_ep13			83.69 191		94.09 164	81.01 138	86.78 317	96.09 127	83.81 156	84.75 147	84.32 302	74.44 157	96.54 206	63.88 294	85.07 196

MDTV_nov1_ep13_2view						81.74 124	86.80 316		80.65 207	85.65 140		74.26 158		76.52 215		96.98 140

cl-mvsnet_		83.27 214	82.12 213	86.74 244	92.20 210	75.95 256	95.11 217	93.27 270	78.44 254	74.82 258	87.02 261	74.19 159	95.19 269	74.67 235	69.32 288	89.09 257

cl-mvsnet1		83.27 214	82.12 213	86.74 244	92.19 211	75.92 257	95.11 217	93.26 271	78.44 254	74.81 259	87.08 260	74.19 159	95.19 269	74.66 236	69.30 289	89.11 256

casdiffmvs		90.95 90	90.39 89	92.63 102	92.82 193	82.53 103	96.83 133	94.47 216	87.69 71	88.47 116	95.56 136	74.04 161	97.54 162	90.90 82	92.74 132	97.83 98

tpmvs		83.04 220	80.77 231	89.84 183	95.43 124	77.96 218	85.59 324	95.32 172	75.31 276	76.27 240	83.70 307	73.89 162	97.41 169	59.53 307	81.93 218	94.14 201

test_post1							85.88 323				30.24 355	73.77 163	95.07 277	73.89 242

baseline		90.76 93	90.10 96	92.74 97	92.90 192	82.56 102	94.60 228	94.56 212	87.69 71	89.06 111	95.67 132	73.76 164	97.51 164	90.43 91	92.23 140	98.16 69

EI-MVSNet		85.80 178	85.20 170	87.59 226	91.55 230	77.41 231	95.13 215	95.36 167	80.43 215	80.33 199	94.71 156	73.72 165	95.97 224	76.96 211	78.64 236	89.39 246

IterMVS-LS		83.93 204	82.80 206	87.31 235	91.46 233	77.39 232	95.66 197	93.43 261	80.44 213	75.51 252	87.26 256	73.72 165	95.16 271	76.99 209	70.72 275	89.39 246
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
AUN-MVS		86.25 173	85.57 164	88.26 213	93.57 176	73.38 275	95.45 204	95.88 138	83.94 150	85.47 141	94.21 166	73.70 167	96.67 205	83.54 155	64.41 319	94.73 196

miper_lstm_enhance		81.66 241	80.66 234	84.67 273	91.19 235	71.97 290	91.94 280	93.19 272	77.86 258	72.27 278	85.26 288	73.46 168	93.42 306	73.71 245	67.05 310	88.61 271

diffmvs		91.17 87	90.74 85	92.44 108	93.11 187	82.50 105	96.25 170	93.62 255	87.79 68	90.40 92	95.93 125	73.44 169	97.42 168	93.62 49	92.55 134	97.41 126

RE-MVS-def			91.18 80		97.76 73	76.03 252	96.20 173	95.44 162	80.56 210	90.72 87	97.84 62	73.36 170		91.99 72	96.79 91	97.75 103

DeepC-MVS	86.58 3	91.53 78	91.06 81	92.94 89	94.52 152	81.89 117	95.95 183	95.98 133	90.76 24	83.76 162	96.76 112	73.24 171	99.71 32	91.67 74	96.96 84	97.22 137
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
RPMNet		79.85 256	75.92 274	91.64 134	90.16 252	79.75 170	79.02 336	95.44 162	58.43 342	82.27 180	72.55 339	73.03 172	98.41 130	46.10 342	86.25 182	96.75 152

CHOSEN 1792x2688		91.07 88	90.21 93	93.64 57	95.18 133	83.53 84	96.26 169	96.13 124	88.92 44	84.90 145	93.10 183	72.86 173	99.62 45	88.86 108	95.67 106	97.79 101

eth_miper_zixun_eth		83.12 218	82.01 215	86.47 249	91.85 228	74.80 265	94.33 235	93.18 273	79.11 243	75.74 251	87.25 257	72.71 174	95.32 262	76.78 212	67.13 309	89.27 252

canonicalmvs		92.27 64	91.22 77	95.41 13	95.80 116	88.31 11	97.09 114	94.64 207	88.49 54	92.99 57	97.31 89	72.68 175	98.57 122	93.38 54	88.58 164	99.36 12

API-MVS		90.18 106	88.97 115	93.80 49	98.66 28	82.95 98	97.50 79	95.63 152	75.16 277	86.31 136	97.69 68	72.49 176	99.90 5	81.26 172	96.07 99	98.56 42

nrg030		86.79 166	85.43 166	90.87 155	88.76 269	85.34 47	97.06 118	94.33 221	84.31 139	80.45 197	91.98 192	72.36 177	96.36 212	88.48 114	71.13 270	90.93 223

MVS_111021_LR		91.60 77	91.64 73	91.47 140	95.74 117	78.79 195	96.15 175	96.77 50	88.49 54	88.64 115	97.07 101	72.33 178	99.19 85	93.13 59	96.48 95	96.43 159

test-LLR		88.48 138	87.98 127	89.98 177	92.26 207	77.23 235	97.11 110	95.96 134	83.76 157	86.30 137	91.38 200	72.30 179	96.78 201	80.82 173	91.92 142	95.94 170

test0.0.03 1		82.79 224	82.48 210	83.74 287	86.81 289	72.22 284	96.52 149	95.03 184	83.76 157	73.00 271	93.20 180	72.30 179	88.88 335	64.15 293	77.52 244	90.12 234

Effi-MVS+		90.70 94	89.90 102	93.09 81	93.61 174	83.48 85	95.20 212	92.79 282	83.22 166	91.82 67	95.70 130	71.82 181	97.48 166	91.25 77	93.67 124	98.32 55

sss		90.87 92	89.96 99	93.60 60	94.15 162	83.84 78	97.14 107	98.13 7	85.93 97	89.68 100	96.09 123	71.67 182	99.30 72	87.69 119	89.16 156	97.66 110

Test By Simon												71.65 183

HPM-MVS_fast		90.38 104	90.17 95	91.03 150	97.61 76	77.35 233	97.15 106	95.48 159	79.51 234	88.79 113	96.90 104	71.64 184	98.81 113	87.01 127	97.44 72	96.94 141

MVS		90.60 97	88.64 119	96.50 3	94.25 160	90.53 6	93.33 257	97.21 19	77.59 261	78.88 211	97.31 89	71.52 185	99.69 36	89.60 101	98.03 60	99.27 16

dp		84.30 201	82.31 212	90.28 168	94.24 161	77.97 217	86.57 318	95.53 155	79.94 228	80.75 193	85.16 292	71.49 186	96.39 211	63.73 295	83.36 205	96.48 158

ACMMP		90.39 102	89.97 98	91.64 134	97.58 79	78.21 212	96.78 137	96.72 57	84.73 125	84.72 148	97.23 94	71.22 187	99.63 44	88.37 116	92.41 137	97.08 139
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
PCF-MVS	84.09 5	86.77 167	85.00 175	92.08 120	92.06 219	83.07 95	92.14 278	94.47 216	79.63 233	76.90 229	94.78 155	71.15 188	99.20 84	72.87 247	91.05 147	93.98 204
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
TAPA-MVS	81.61 12	85.02 189	83.67 192	89.06 194	96.79 99	73.27 279	95.92 185	94.79 198	74.81 280	80.47 196	96.83 108	71.07 189	98.19 137	49.82 336	92.57 133	95.71 176
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
pcd_1.5k_mvsjas		5.92 330	7.89 333	0.00 342	0.00 363	0.00 364	0.00 354	0.00 364	0.00 359	0.00 360	0.00 360	71.04 190	0.00 360	0.00 358	0.00 358	0.00 356

PS-MVSNAJss		84.91 191	84.30 185	86.74 244	85.89 302	74.40 271	94.95 222	94.16 229	83.93 152	76.45 235	90.11 224	71.04 190	95.77 237	83.16 161	79.02 233	90.06 238

PS-MVSNAJ		94.17 25	93.52 38	96.10 6	95.65 120	92.35 2	98.21 32	95.79 143	92.42 12	96.24 15	98.18 32	71.04 190	99.17 88	96.77 15	97.39 75	96.79 148

xiu_mvs_v2_base		93.92 33	93.26 40	95.91 8	95.07 137	92.02 4	98.19 33	95.68 148	92.06 14	96.01 18	98.14 37	70.83 193	98.96 103	96.74 16	96.57 94	96.76 151

RRT_MVS		86.89 161	85.96 161	89.68 188	95.01 140	84.13 72	96.33 165	94.98 186	84.20 144	80.10 203	92.07 191	70.52 194	95.01 279	83.30 159	77.14 245	89.91 240

CPTT-MVS		89.72 112	89.87 103	89.29 192	98.33 48	73.30 277	97.70 64	95.35 169	75.68 273	87.40 126	97.44 85	70.43 195	98.25 134	89.56 103	96.90 85	96.33 164

WR-MVS_H		81.02 247	80.09 241	83.79 285	88.08 279	71.26 299	94.46 230	96.54 84	80.08 224	72.81 274	86.82 263	70.36 196	92.65 312	64.18 292	67.50 306	87.46 298

NR-MVSNet		83.35 212	81.52 224	88.84 199	88.76 269	81.31 133	94.45 231	95.16 178	84.65 129	67.81 299	90.82 209	70.36 196	94.87 281	74.75 233	66.89 312	90.33 230

VNet		92.11 66	91.22 77	94.79 21	96.91 98	86.98 24	97.91 47	97.96 9	86.38 88	93.65 48	95.74 128	70.16 198	98.95 106	93.39 52	88.87 160	98.43 50

Fast-Effi-MVS+		87.93 150	86.94 154	90.92 153	94.04 166	79.16 184	98.26 30	93.72 251	81.29 197	83.94 159	92.90 184	69.83 199	96.68 204	76.70 213	91.74 144	96.93 142

PLC	83.97 7	88.00 148	87.38 144	89.83 184	98.02 64	76.46 245	97.16 105	94.43 219	79.26 241	81.98 183	96.28 119	69.36 200	99.27 73	77.71 202	92.25 139	93.77 207
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
BH-w/o		88.24 145	87.47 142	90.54 163	95.03 139	78.54 198	97.41 89	93.82 242	84.08 145	78.23 217	94.51 161	69.34 201	97.21 179	80.21 180	94.58 114	95.87 172

abl_6		89.80 110	89.71 107	90.07 173	96.53 102	75.52 260	94.48 229	95.04 183	81.12 199	89.22 107	97.00 102	68.83 202	98.96 103	89.86 97	95.27 108	95.73 175

MAR-MVS		90.63 96	90.22 92	91.86 128	98.47 42	78.20 213	97.18 101	96.61 73	83.87 154	88.18 122	98.18 32	68.71 203	99.75 26	83.66 152	97.15 80	97.63 113
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
114514_t		88.79 131	87.57 138	92.45 107	98.21 55	81.74 124	96.99 121	95.45 161	75.16 277	82.48 173	95.69 131	68.59 204	98.50 125	80.33 177	95.18 111	97.10 138

DU-MVS		84.57 196	83.33 200	88.28 212	88.76 269	79.36 178	96.43 159	95.41 166	85.42 107	78.11 218	90.82 209	67.61 205	95.14 272	79.14 191	68.30 297	90.33 230

Baseline_NR-MVSNet		81.22 246	80.07 243	84.68 272	85.32 310	75.12 264	96.48 151	88.80 322	76.24 271	77.28 224	86.40 274	67.61 205	94.39 291	75.73 226	66.73 313	84.54 322

WR-MVS		84.32 200	82.96 202	88.41 207	89.38 266	80.32 156	96.59 147	96.25 117	83.97 149	76.63 232	90.36 218	67.53 207	94.86 282	75.82 225	70.09 282	90.06 238

OMC-MVS		88.80 130	88.16 125	90.72 159	95.30 128	77.92 221	94.81 225	94.51 213	86.80 86	84.97 144	96.85 107	67.53 207	98.60 120	85.08 137	87.62 172	95.63 177

LCM-MVSNet-Re		83.75 207	83.54 197	84.39 281	93.54 177	64.14 326	92.51 273	84.03 341	83.90 153	66.14 307	86.59 267	67.36 209	92.68 311	84.89 140	92.87 131	96.35 161

v148		82.41 232	80.89 229	86.99 241	86.18 297	76.81 241	96.27 168	93.82 242	80.49 212	75.28 255	86.11 279	67.32 210	95.75 239	75.48 227	67.03 311	88.42 277

CNLPA		86.96 159	85.37 168	91.72 133	97.59 78	79.34 180	97.21 97	91.05 305	74.22 284	78.90 210	96.75 113	67.21 211	98.95 106	74.68 234	90.77 149	96.88 146

FMVSNet3		84.71 193	82.71 207	90.70 160	94.55 150	87.71 18	95.92 185	94.67 203	81.73 193	75.82 248	88.08 246	66.99 212	94.47 289	71.23 258	75.38 251	89.91 240

v8		81.88 237	80.06 244	87.32 234	86.63 290	79.04 190	94.41 232	93.65 254	78.77 249	73.19 270	85.57 284	66.87 213	95.81 234	73.84 244	67.61 305	87.11 301

1314		88.94 124	87.20 146	94.17 39	93.21 182	85.73 40	93.33 257	96.64 70	82.89 174	75.98 245	96.36 118	66.83 214	99.39 63	83.52 157	96.02 101	97.39 128

BH-untuned		86.95 160	85.94 162	89.99 176	94.52 152	77.46 230	96.78 137	93.37 266	81.80 192	76.62 233	93.81 176	66.64 215	97.02 188	76.06 221	93.88 122	95.48 181

CVMVSNet		84.83 192	85.57 164	82.63 299	91.55 230	60.38 336	95.13 215	95.03 184	80.60 208	82.10 182	94.71 156	66.40 216	90.19 333	74.30 239	90.32 151	97.31 132

PMMVS		89.46 116	89.92 101	88.06 217	94.64 147	69.57 311	96.22 171	94.95 187	87.27 78	91.37 77	96.54 117	65.88 217	97.39 170	88.54 111	93.89 121	97.23 136

v2v482		83.46 211	81.86 218	88.25 214	86.19 296	79.65 174	96.34 164	94.02 236	81.56 195	77.32 223	88.23 243	65.62 218	96.03 221	77.77 199	69.72 286	89.09 257

v1144		82.90 223	81.27 227	87.78 222	86.29 294	79.07 189	96.14 176	93.93 238	80.05 225	77.38 221	86.80 264	65.50 219	95.93 229	75.21 230	70.13 279	88.33 279

v10		81.43 243	79.53 249	87.11 239	86.38 291	78.87 191	94.31 236	93.43 261	77.88 257	73.24 269	85.26 288	65.44 220	95.75 239	72.14 252	67.71 304	86.72 305

HQP2-MVS												65.40 221

HQP-MVS		87.91 151	87.55 139	88.98 197	92.08 216	78.48 199	97.63 67	94.80 196	90.52 27	82.30 176	94.56 159	65.40 221	97.32 172	87.67 120	83.01 208	91.13 219

V42		83.04 220	81.53 223	87.57 228	86.27 295	79.09 188	95.87 189	94.11 232	80.35 217	77.22 225	86.79 265	65.32 223	96.02 222	77.74 200	70.14 278	87.61 293

pmmvs4		82.54 228	80.79 230	87.79 221	86.11 298	80.49 155	93.55 252	93.18 273	77.29 265	73.35 267	89.40 229	65.26 224	95.05 278	75.32 228	73.61 259	87.83 287

3Dnovator+	82.88 8	89.63 114	87.85 129	94.99 18	94.49 156	86.76 27	97.84 51	95.74 145	86.10 92	75.47 253	96.02 124	65.00 225	99.51 56	82.91 164	97.07 81	98.72 36

HQP_MVS		87.50 154	87.09 151	88.74 202	91.86 226	77.96 218	97.18 101	94.69 200	89.89 33	81.33 188	94.15 168	64.77 226	97.30 174	87.08 124	82.82 212	90.96 221

plane_prior6					91.98 221	77.92 221						64.77 226

v144192		82.43 229	80.73 232	87.54 229	85.81 303	78.22 209	95.98 181	93.78 247	79.09 244	77.11 226	86.49 269	64.66 228	95.91 230	74.20 240	69.42 287	88.49 273

TranMVSNet+NR-MVSNet		83.24 216	81.71 220	87.83 220	87.71 282	78.81 194	96.13 178	94.82 195	84.52 132	76.18 243	90.78 211	64.07 229	94.60 287	74.60 237	66.59 314	90.09 236

CP-MVSNet		81.01 248	80.08 242	83.79 285	87.91 280	70.51 301	94.29 241	95.65 149	80.83 203	72.54 277	88.84 234	63.71 230	92.32 315	68.58 274	68.36 296	88.55 272

cdsmvs_eth3d_5k		21.43 325	28.57 328	0.00 342	0.00 363	0.00 364	0.00 354	95.93 137	0.00 359	0.00 360	97.66 69	63.57 231	0.00 360	0.00 358	0.00 358	0.00 356

Vis-MVSNet		88.67 133	87.82 130	91.24 145	92.68 194	78.82 192	96.95 127	93.85 241	87.55 73	87.07 131	95.13 148	63.43 232	97.21 179	77.58 204	96.15 97	97.70 108
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
v1192		82.31 233	80.55 236	87.60 225	85.94 300	78.47 202	95.85 191	93.80 245	79.33 237	76.97 228	86.51 268	63.33 233	95.87 231	73.11 246	70.13 279	88.46 275

CANet_DTU		90.98 89	90.04 97	93.83 48	94.76 146	86.23 30	96.32 166	93.12 276	93.11 10	93.71 47	96.82 110	63.08 234	99.48 58	84.29 142	95.12 112	95.77 174

ab-mvs		87.08 158	84.94 176	93.48 67	93.34 181	83.67 82	88.82 302	95.70 147	81.18 198	84.55 151	90.14 223	62.72 235	98.94 108	85.49 134	82.54 216	97.85 96

v1921920		82.02 236	80.23 240	87.41 232	85.62 304	77.92 221	95.79 193	93.69 252	78.86 248	76.67 231	86.44 271	62.50 236	95.83 233	72.69 248	69.77 285	88.47 274

CLD-MVS		87.97 149	87.48 141	89.44 189	92.16 214	80.54 153	98.14 34	94.92 188	91.41 17	79.43 207	95.40 139	62.34 237	97.27 177	90.60 87	82.90 211	90.50 227
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
3Dnovator	82.32 10	89.33 118	87.64 134	94.42 30	93.73 173	85.70 41	97.73 62	96.75 53	86.73 87	76.21 242	95.93 125	62.17 238	99.68 38	81.67 170	97.81 65	97.88 93

ADS-MVSNet2		79.57 259	77.53 261	85.71 260	93.78 170	72.13 286	79.48 332	86.11 334	73.09 294	80.14 201	79.99 324	62.15 239	90.14 334	59.49 308	83.52 202	94.85 190

ADS-MVSNet		81.26 245	78.36 255	89.96 179	93.78 170	79.78 168	79.48 332	93.60 256	73.09 294	80.14 201	79.99 324	62.15 239	95.24 267	59.49 308	83.52 202	94.85 190

QAPM		86.88 162	84.51 180	93.98 43	94.04 166	85.89 36	97.19 100	96.05 130	73.62 288	75.12 256	95.62 134	62.02 241	99.74 28	70.88 262	96.06 100	96.30 166

Effi-MVS+-dtu		84.61 195	84.90 178	83.72 288	91.96 222	63.14 330	94.95 222	93.34 267	85.57 103	79.79 205	87.12 259	61.99 242	95.61 250	83.55 153	85.83 189	92.41 215

mvs-test1		86.83 164	87.17 147	85.81 259	91.96 222	65.24 323	97.90 49	93.34 267	85.57 103	84.51 152	95.14 147	61.99 242	97.19 181	83.55 153	90.55 150	95.00 188

XXY-MVS		83.84 205	82.00 216	89.35 190	87.13 286	81.38 131	95.72 194	94.26 223	80.15 223	75.92 247	90.63 212	61.96 244	96.52 207	78.98 193	73.28 263	90.14 233

AdaColmap		88.81 129	87.61 137	92.39 110	99.33 4	79.95 165	96.70 144	95.58 153	77.51 262	83.05 170	96.69 115	61.90 245	99.72 31	84.29 142	93.47 126	97.50 122

VPA-MVSNet		85.32 185	83.83 190	89.77 186	90.25 249	82.63 101	96.36 162	97.07 24	83.03 171	81.21 190	89.02 232	61.58 246	96.31 215	85.02 139	70.95 272	90.36 228

test_djsdf		83.00 222	82.45 211	84.64 274	84.07 321	69.78 308	94.80 226	94.48 214	80.74 205	75.41 254	87.70 250	61.32 247	95.10 275	83.77 147	79.76 224	89.04 261

v1240		81.70 239	79.83 247	87.30 236	85.50 305	77.70 227	95.48 202	93.44 260	78.46 253	76.53 234	86.44 271	60.85 248	95.84 232	71.59 255	70.17 277	88.35 278

D2MVS		82.67 226	81.55 222	86.04 257	87.77 281	76.47 244	95.21 211	96.58 78	82.66 180	70.26 290	85.46 287	60.39 249	95.80 236	76.40 217	79.18 231	85.83 316

XVG-OURS-SEG-HR		85.74 180	85.16 172	87.49 231	90.22 250	71.45 297	91.29 287	94.09 233	81.37 196	83.90 160	95.22 141	60.30 250	97.53 163	85.58 133	84.42 199	93.50 210

PEN-MVS		79.47 261	78.26 257	83.08 295	86.36 292	68.58 314	93.85 246	94.77 199	79.76 230	71.37 281	88.55 238	59.79 251	92.46 313	64.50 291	65.40 316	88.19 281

TransMVSNet (Re)		76.94 281	74.38 284	84.62 275	85.92 301	75.25 263	95.28 207	89.18 319	73.88 287	67.22 300	86.46 270	59.64 252	94.10 295	59.24 311	52.57 339	84.50 323

DP-MVS		81.47 242	78.28 256	91.04 149	98.14 58	78.48 199	95.09 220	86.97 329	61.14 334	71.12 284	92.78 186	59.59 253	99.38 64	53.11 328	86.61 179	95.27 186

v7n		79.32 263	77.34 262	85.28 265	84.05 322	72.89 283	93.38 255	93.87 240	75.02 279	70.68 286	84.37 301	59.58 254	95.62 249	67.60 275	67.50 306	87.32 300

F-COLMAP		84.50 198	83.44 199	87.67 223	95.22 130	72.22 284	95.95 183	93.78 247	75.74 272	76.30 239	95.18 144	59.50 255	98.45 128	72.67 249	86.59 180	92.35 216

LS3D		82.22 234	79.94 246	89.06 194	97.43 85	74.06 274	93.20 264	92.05 289	61.90 329	73.33 268	95.21 142	59.35 256	99.21 80	54.54 324	92.48 136	93.90 206

BH-RMVSNet		86.84 163	85.28 169	91.49 139	95.35 127	80.26 160	96.95 127	92.21 288	82.86 176	81.77 187	95.46 138	59.34 257	97.64 153	69.79 267	93.81 123	96.57 156

MVP-Stereo		82.65 227	81.67 221	85.59 262	86.10 299	78.29 206	93.33 257	92.82 281	77.75 259	69.17 297	87.98 247	59.28 258	95.76 238	71.77 253	96.88 87	82.73 331
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
PS-CasMVS		80.27 254	79.18 250	83.52 292	87.56 284	69.88 306	94.08 243	95.29 173	80.27 220	72.08 279	88.51 241	59.22 259	92.23 317	67.49 276	68.15 299	88.45 276

DTE-MVSNet		78.37 268	77.06 265	82.32 302	85.22 311	67.17 319	93.40 254	93.66 253	78.71 250	70.53 288	88.29 242	59.06 260	92.23 317	61.38 303	63.28 324	87.56 295

TR-MVS		86.30 171	84.93 177	90.42 165	94.63 148	77.58 228	96.57 148	93.82 242	80.30 218	82.42 175	95.16 145	58.74 261	97.55 159	74.88 232	87.82 171	96.13 168

OPM-MVS		85.84 177	85.10 174	88.06 217	88.34 275	77.83 224	95.72 194	94.20 226	87.89 67	80.45 197	94.05 170	58.57 262	97.26 178	83.88 145	82.76 214	89.09 257

PatchMatch-RL		85.00 190	83.66 193	89.02 196	95.86 115	74.55 268	92.49 274	93.60 256	79.30 239	79.29 209	91.47 198	58.53 263	98.45 128	70.22 266	92.17 141	94.07 203

pm-mvs1		80.05 255	78.02 258	86.15 255	85.42 306	75.81 258	95.11 217	92.69 284	77.13 266	70.36 289	87.43 253	58.44 264	95.27 265	71.36 257	64.25 320	87.36 299

our_test_3		77.90 274	75.37 277	85.48 264	85.39 307	76.74 242	93.63 249	91.67 294	73.39 292	65.72 309	84.65 299	58.20 265	93.13 310	57.82 314	67.87 301	86.57 307

IterMVS-SCA-FT		80.51 253	79.10 252	84.73 271	89.63 261	74.66 266	92.98 267	91.81 293	80.05 225	71.06 285	85.18 291	58.04 266	91.40 323	72.48 251	70.70 276	88.12 283

SCA		85.63 181	83.64 194	91.60 137	92.30 205	81.86 119	92.88 270	95.56 154	84.85 121	82.52 172	85.12 294	58.04 266	95.39 257	73.89 242	87.58 174	97.54 117

EU-MVSNet		76.92 282	76.95 266	76.83 319	84.10 320	54.73 346	91.77 283	92.71 283	72.74 297	69.57 294	88.69 236	58.03 268	87.43 340	64.91 290	70.00 283	88.33 279

IterMVS		80.67 251	79.16 251	85.20 266	89.79 256	76.08 251	92.97 268	91.86 291	80.28 219	71.20 283	85.14 293	57.93 269	91.34 324	72.52 250	70.74 274	88.18 282
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
anonymousdsp		80.98 249	79.97 245	84.01 282	81.73 326	70.44 302	92.49 274	93.58 258	77.10 268	72.98 272	86.31 275	57.58 270	94.90 280	79.32 188	78.63 238	86.69 306

xiu_mvs_v1_base_debu		90.54 98	89.54 108	93.55 62	92.31 202	87.58 20	96.99 121	94.87 191	87.23 79	93.27 50	97.56 76	57.43 271	98.32 131	92.72 62	93.46 127	94.74 193

xiu_mvs_v1_base		90.54 98	89.54 108	93.55 62	92.31 202	87.58 20	96.99 121	94.87 191	87.23 79	93.27 50	97.56 76	57.43 271	98.32 131	92.72 62	93.46 127	94.74 193

xiu_mvs_v1_base_debi		90.54 98	89.54 108	93.55 62	92.31 202	87.58 20	96.99 121	94.87 191	87.23 79	93.27 50	97.56 76	57.43 271	98.32 131	92.72 62	93.46 127	94.74 193

OpenMVS	79.58 14	86.09 174	83.62 195	93.50 65	90.95 239	86.71 28	97.44 82	95.83 141	75.35 274	72.64 275	95.72 129	57.42 274	99.64 42	71.41 256	95.85 104	94.13 202

PVSNet	82.34 9	89.02 122	87.79 131	92.71 99	95.49 123	81.50 130	97.70 64	97.29 16	87.76 69	85.47 141	95.12 149	56.90 275	98.90 110	80.33 177	94.02 118	97.71 107

Fast-Effi-MVS+-dtu		83.33 213	82.60 209	85.50 263	89.55 262	69.38 312	96.09 179	91.38 297	82.30 184	75.96 246	91.41 199	56.71 276	95.58 252	75.13 231	84.90 197	91.54 217

ppachtmachnet_test		77.19 278	74.22 286	86.13 256	85.39 307	78.22 209	93.98 244	91.36 299	71.74 304	67.11 302	84.87 297	56.67 277	93.37 308	52.21 329	64.59 318	86.80 304

VPNet		84.69 194	82.92 203	90.01 175	89.01 268	83.45 86	96.71 142	95.46 160	85.71 101	79.65 206	92.18 190	56.66 278	96.01 223	83.05 163	67.84 303	90.56 225

GA-MVS		85.79 179	84.04 189	91.02 151	89.47 264	80.27 159	96.90 130	94.84 194	85.57 103	80.88 191	89.08 230	56.56 279	96.47 209	77.72 201	85.35 194	96.34 162

XVG-OURS		85.18 187	84.38 184	87.59 226	90.42 248	71.73 294	91.06 290	94.07 234	82.00 190	83.29 166	95.08 150	56.42 280	97.55 159	83.70 151	83.42 204	93.49 211

GBi-Net		82.42 230	80.43 238	88.39 208	92.66 195	81.95 112	94.30 238	93.38 263	79.06 245	75.82 248	85.66 280	56.38 281	93.84 299	71.23 258	75.38 251	89.38 248

test1		82.42 230	80.43 238	88.39 208	92.66 195	81.95 112	94.30 238	93.38 263	79.06 245	75.82 248	85.66 280	56.38 281	93.84 299	71.23 258	75.38 251	89.38 248

FMVSNet2		82.79 224	80.44 237	89.83 184	92.66 195	85.43 46	95.42 205	94.35 220	79.06 245	74.46 260	87.28 254	56.38 281	94.31 292	69.72 268	74.68 255	89.76 242

pmmvs5		81.34 244	79.54 248	86.73 247	85.02 312	76.91 239	96.22 171	91.65 295	77.65 260	73.55 264	88.61 237	55.70 284	94.43 290	74.12 241	73.35 262	88.86 270

tfpnnormal		78.14 270	75.42 276	86.31 253	88.33 276	79.24 181	94.41 232	96.22 119	73.51 289	69.81 292	85.52 286	55.43 285	95.75 239	47.65 340	67.86 302	83.95 327

LFMVS		89.27 119	87.64 134	94.16 41	97.16 95	85.52 45	97.18 101	94.66 204	79.17 242	89.63 102	96.57 116	55.35 286	98.22 135	89.52 104	89.54 154	98.74 31

ACMM	80.70 13	83.72 208	82.85 205	86.31 253	91.19 235	72.12 287	95.88 188	94.29 222	80.44 213	77.02 227	91.96 193	55.24 287	97.14 185	79.30 189	80.38 222	89.67 243
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
MDA-MVSNet_test_wron		73.54 295	70.43 302	82.86 296	84.55 314	71.85 291	91.74 284	91.32 301	67.63 316	46.73 344	81.09 318	55.11 288	90.42 332	55.91 321	59.76 328	86.31 310

YYNet1		73.53 296	70.43 302	82.85 297	84.52 316	71.73 294	91.69 285	91.37 298	67.63 316	46.79 343	81.21 317	55.04 289	90.43 331	55.93 320	59.70 329	86.38 309

LTVRE_ROB	73.68 18	77.99 271	75.74 275	84.74 270	90.45 247	72.02 288	86.41 320	91.12 302	72.57 299	66.63 304	87.27 255	54.95 290	96.98 189	56.29 319	75.98 247	85.21 319
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
LPG-MVS_test		84.20 202	83.49 198	86.33 250	90.88 240	73.06 280	95.28 207	94.13 230	82.20 185	76.31 237	93.20 180	54.83 291	96.95 190	83.72 149	80.83 220	88.98 264

LGP-MVS_train				86.33 250	90.88 240	73.06 280		94.13 230	82.20 185	76.31 237	93.20 180	54.83 291	96.95 190	83.72 149	80.83 220	88.98 264

cascas		86.50 169	84.48 182	92.55 105	92.64 198	85.95 33	97.04 120	95.07 182	75.32 275	80.50 195	91.02 206	54.33 293	97.98 140	86.79 128	87.62 172	93.71 208

ACMP	81.66 11	84.00 203	83.22 201	86.33 250	91.53 232	72.95 282	95.91 187	93.79 246	83.70 159	73.79 263	92.22 189	54.31 294	96.89 194	83.98 144	79.74 226	89.16 255
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
PVSNet_0	77.72 15	81.70 239	78.95 253	89.94 180	90.77 243	76.72 243	95.96 182	96.95 33	85.01 119	70.24 291	88.53 240	52.32 295	98.20 136	86.68 129	44.08 347	94.89 189

MSDG		80.62 252	77.77 260	89.14 193	93.43 180	77.24 234	91.89 281	90.18 311	69.86 312	68.02 298	91.94 195	52.21 296	98.84 111	59.32 310	83.12 206	91.35 218

DSMNet-mixed		73.13 298	72.45 294	75.19 325	77.51 338	46.82 349	85.09 326	82.01 346	67.61 320	69.27 296	81.33 316	50.89 297	86.28 342	54.54 324	83.80 201	92.46 214

UGNet		87.73 152	86.55 157	91.27 144	95.16 134	79.11 186	96.35 163	96.23 118	88.14 61	87.83 125	90.48 214	50.65 298	99.09 96	80.13 181	94.03 117	95.60 178
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
FMVSNet5		76.46 284	74.16 287	83.35 294	90.05 254	76.17 249	89.58 297	89.85 313	71.39 306	65.29 311	80.42 320	50.61 299	87.70 339	61.05 305	69.24 290	86.18 312

MS-PatchMatch		83.05 219	81.82 219	86.72 248	89.64 260	79.10 187	94.88 224	94.59 211	79.70 232	70.67 287	89.65 226	50.43 300	96.82 198	70.82 265	95.99 102	84.25 325

Anonymous20231206		75.29 289	73.64 289	80.22 310	80.75 327	63.38 329	93.36 256	90.71 309	73.09 294	67.12 301	83.70 307	50.33 301	90.85 328	53.63 327	70.10 281	86.44 308

N_pmnet		61.30 314	60.20 317	64.60 329	84.32 317	17.00 361	91.67 286	10.98 360	61.77 330	58.45 333	78.55 327	49.89 302	91.83 320	42.27 345	63.94 321	84.97 320

jajsoiax		82.12 235	81.15 228	85.03 268	84.19 319	70.70 300	94.22 242	93.95 237	83.07 170	73.48 265	89.75 225	49.66 303	95.37 259	82.24 168	79.76 224	89.02 262

RPSCF		77.73 275	76.63 269	81.06 307	88.66 273	55.76 344	87.77 310	87.88 327	64.82 324	74.14 262	92.79 185	49.22 304	96.81 199	67.47 277	76.88 246	90.62 224

SixPastTwentyTwo		76.04 285	74.32 285	81.22 306	84.54 315	61.43 335	91.16 288	89.30 318	77.89 256	64.04 314	86.31 275	48.23 305	94.29 293	63.54 297	63.84 322	87.93 286

test20.03		72.36 302	71.15 298	75.98 323	77.79 336	59.16 339	92.40 276	89.35 317	74.09 285	61.50 325	84.32 302	48.09 306	85.54 345	50.63 334	62.15 326	83.24 328

VDDNet		86.44 170	84.51 180	92.22 117	91.56 229	81.83 120	97.10 113	94.64 207	69.50 313	87.84 124	95.19 143	48.01 307	97.92 146	89.82 99	86.92 176	96.89 145

VDD-MVS		88.28 144	87.02 152	92.06 122	95.09 135	80.18 163	97.55 75	94.45 218	83.09 169	89.10 110	95.92 127	47.97 308	98.49 126	93.08 60	86.91 177	97.52 121

Anonymous20231211		79.72 258	77.19 264	87.33 233	95.59 121	77.16 238	95.18 214	94.18 228	59.31 340	72.57 276	86.20 277	47.89 309	95.66 244	74.53 238	69.24 290	89.18 254

OurMVSNet-221017-0		77.18 279	76.06 272	80.55 309	83.78 323	60.00 337	90.35 292	91.05 305	77.01 270	66.62 305	87.92 248	47.73 310	94.03 296	71.63 254	68.44 295	87.62 292

CMPMVS	54.94 21	75.71 288	74.56 283	79.17 315	79.69 332	55.98 342	89.59 296	93.30 269	60.28 336	53.85 340	89.07 231	47.68 311	96.33 213	76.55 214	81.02 219	85.22 318
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
mvs_tets		81.74 238	80.71 233	84.84 269	84.22 318	70.29 303	93.91 245	93.78 247	82.77 177	73.37 266	89.46 228	47.36 312	95.31 263	81.99 169	79.55 229	88.92 268

test_part1		77.94 273	74.98 278	86.83 243	86.85 288	76.14 250	94.31 236	93.03 279	58.41 343	69.77 293	84.53 300	47.22 313	95.27 265	75.23 229	65.46 315	89.06 260

MDA-MVSNet-bldmvs		71.45 304	67.94 308	81.98 304	85.33 309	68.50 315	92.35 277	88.76 323	70.40 308	42.99 345	81.96 313	46.57 314	91.31 325	48.75 339	54.39 334	86.11 313

pmmvs-eth3d		73.59 294	70.66 300	82.38 300	76.40 341	73.38 275	89.39 300	89.43 316	72.69 298	60.34 329	77.79 329	46.43 315	91.26 326	66.42 284	57.06 331	82.51 332

Anonymous20240529		83.15 217	80.60 235	90.80 156	95.74 117	78.27 207	96.81 135	94.92 188	60.10 338	81.89 185	92.54 187	45.82 316	98.82 112	79.25 190	78.32 241	95.31 185

MVS-HIRNet		71.36 305	67.00 309	84.46 279	90.58 245	69.74 309	79.15 335	87.74 328	46.09 346	61.96 324	50.50 347	45.14 317	95.64 247	53.74 326	88.11 170	88.00 285

CL-MVSNet_2432*1600		70.97 306	69.31 306	75.95 324	76.24 343	55.39 345	87.45 311	90.94 307	70.20 310	62.96 321	77.48 330	44.01 318	88.09 337	61.25 304	53.26 336	84.37 324

FMVSNet1		79.50 260	76.54 270	88.39 208	88.47 274	81.95 112	94.30 238	93.38 263	73.14 293	72.04 280	85.66 280	43.86 319	93.84 299	65.48 287	72.53 264	89.38 248

K. test v3		73.62 293	71.59 297	79.69 312	82.98 325	59.85 338	90.85 291	88.83 321	77.13 266	58.90 330	82.11 312	43.62 320	91.72 321	65.83 286	54.10 335	87.50 297

pmmvs6		74.65 292	71.67 296	83.60 290	79.13 334	69.94 305	93.31 261	90.88 308	61.05 335	65.83 308	84.15 304	43.43 321	94.83 283	66.62 280	60.63 327	86.02 315

ACMH	75.40 17	77.99 271	74.96 279	87.10 240	90.67 244	76.41 246	93.19 265	91.64 296	72.47 300	63.44 317	87.61 252	43.34 322	97.16 182	58.34 312	73.94 257	87.72 288
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
test_0402		72.68 300	69.54 305	82.09 303	88.67 272	71.81 293	92.72 272	86.77 331	61.52 331	62.21 322	83.91 305	43.22 323	93.76 302	34.60 347	72.23 268	80.72 339

lessismore_v0				79.98 311	80.59 329	58.34 340		80.87 347		58.49 332	83.46 309	43.10 324	93.89 298	63.11 299	48.68 341	87.72 288

UniMVSNet_ETH3D		80.86 250	78.75 254	87.22 238	86.31 293	72.02 288	91.95 279	93.76 250	73.51 289	75.06 257	90.16 222	43.04 325	95.66 244	76.37 218	78.55 239	93.98 204

UnsupCasMVSNet_eth		73.25 297	70.57 301	81.30 305	77.53 337	66.33 321	87.24 314	93.89 239	80.38 216	57.90 335	81.59 315	42.91 326	90.56 330	65.18 289	48.51 342	87.01 303

COLMAP_ROB	73.24 19	75.74 287	73.00 293	83.94 283	92.38 201	69.08 313	91.85 282	86.93 330	61.48 332	65.32 310	90.27 219	42.27 327	96.93 193	50.91 333	75.63 250	85.80 317
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
MIMVSNet		79.18 264	75.99 273	88.72 203	87.37 285	80.66 149	79.96 331	91.82 292	77.38 264	74.33 261	81.87 314	41.78 328	90.74 329	66.36 285	83.10 207	94.76 192

ACMH+	76.62 16	77.47 276	74.94 280	85.05 267	91.07 238	71.58 296	93.26 262	90.01 312	71.80 303	64.76 312	88.55 238	41.62 329	96.48 208	62.35 301	71.00 271	87.09 302

ITE_SJBPF				82.38 300	87.00 287	65.59 322		89.55 315	79.99 227	69.37 295	91.30 202	41.60 330	95.33 261	62.86 300	74.63 256	86.24 311

Anonymous202405211		84.41 199	81.93 217	91.85 130	96.78 100	78.41 203	97.44 82	91.34 300	70.29 309	84.06 154	94.26 165	41.09 331	98.96 103	79.46 187	82.65 215	98.17 68

new-patchmatchnet		68.85 310	65.93 312	77.61 317	73.57 347	63.94 328	90.11 294	88.73 324	71.62 305	55.08 338	73.60 336	40.84 332	87.22 341	51.35 332	48.49 343	81.67 338

USDC		78.65 266	76.25 271	85.85 258	87.58 283	74.60 267	89.58 297	90.58 310	84.05 146	63.13 319	88.23 243	40.69 333	96.86 197	66.57 282	75.81 249	86.09 314

XVG-ACMP-BASELINE		79.38 262	77.90 259	83.81 284	84.98 313	67.14 320	89.03 301	93.18 273	80.26 221	72.87 273	88.15 245	38.55 334	96.26 216	76.05 222	78.05 242	88.02 284

AllTest		75.92 286	73.06 292	84.47 277	92.18 212	67.29 317	91.07 289	84.43 339	67.63 316	63.48 315	90.18 220	38.20 335	97.16 182	57.04 315	73.37 260	88.97 266

TestCases				84.47 277	92.18 212	67.29 317		84.43 339	67.63 316	63.48 315	90.18 220	38.20 335	97.16 182	57.04 315	73.37 260	88.97 266

UnsupCasMVSNet_bld		68.60 311	64.50 314	80.92 308	74.63 345	67.80 316	83.97 327	92.94 280	65.12 323	54.63 339	68.23 342	35.97 337	92.17 319	60.13 306	44.83 345	82.78 330

tmp_tt		41.54 320	41.93 322	40.38 336	20.10 360	26.84 357	61.93 349	59.09 356	14.81 355	28.51 350	80.58 319	35.53 338	48.33 356	63.70 296	13.11 353	45.96 349

testgi		74.88 291	73.40 290	79.32 314	80.13 331	61.75 333	93.21 263	86.64 332	79.49 235	66.56 306	91.06 205	35.51 339	88.67 336	56.79 318	71.25 269	87.56 295

OpenMVS_ROB	68.52 20	73.02 299	69.57 304	83.37 293	80.54 330	71.82 292	93.60 251	88.22 326	62.37 327	61.98 323	83.15 311	35.31 340	95.47 255	45.08 343	75.88 248	82.82 329

testing_2		76.96 280	73.18 291	88.30 211	75.87 344	79.64 175	89.92 295	94.21 225	80.16 222	51.23 342	75.94 333	33.94 341	95.81 234	82.28 166	75.12 254	89.46 245

MVS_0304		78.43 267	76.70 268	83.60 290	88.22 277	69.81 307	92.91 269	95.10 179	72.32 301	78.71 213	80.29 323	33.78 342	93.37 308	68.77 272	80.23 223	87.63 291

TDRefinement		69.20 309	65.78 313	79.48 313	66.04 350	62.21 332	88.21 307	86.12 333	62.92 326	61.03 327	85.61 283	33.23 343	94.16 294	55.82 322	53.02 337	82.08 336

LF4IMVS		72.36 302	70.82 299	76.95 318	79.18 333	56.33 341	86.12 321	86.11 334	69.30 314	63.06 320	86.66 266	33.03 344	92.25 316	65.33 288	68.64 294	82.28 335

MIMVSNet1		69.44 307	66.65 311	77.84 316	76.48 340	62.84 331	87.42 312	88.97 320	66.96 321	57.75 336	79.72 326	32.77 345	85.83 344	46.32 341	63.42 323	84.85 321

EG-PatchMatch MVS		74.92 290	72.02 295	83.62 289	83.76 324	73.28 278	93.62 250	92.04 290	68.57 315	58.88 331	83.80 306	31.87 346	95.57 253	56.97 317	78.67 235	82.00 337

new_pmnet		66.18 312	63.18 315	75.18 326	76.27 342	61.74 334	83.79 328	84.66 338	56.64 344	51.57 341	71.85 341	31.29 347	87.93 338	49.98 335	62.55 325	75.86 342

TinyColmap		72.41 301	68.99 307	82.68 298	88.11 278	69.59 310	88.41 306	85.20 336	65.55 322	57.91 334	84.82 298	30.80 348	95.94 228	51.38 330	68.70 293	82.49 334

pmmvs3		65.75 313	62.18 316	76.45 321	67.12 349	64.54 324	88.68 304	85.05 337	54.77 345	57.54 337	73.79 335	29.40 349	86.21 343	55.49 323	47.77 344	78.62 340

PM-MVS		69.32 308	66.93 310	76.49 320	73.60 346	55.84 343	85.91 322	79.32 350	74.72 281	61.09 326	78.18 328	21.76 350	91.10 327	70.86 263	56.90 332	82.51 332

DeepMVS_CX				64.06 330	78.53 335	43.26 352		68.11 354	69.94 311	38.55 346	76.14 332	18.53 351	79.34 346	43.72 344	41.62 348	69.57 345

ambc				76.02 322	68.11 348	51.43 347	64.97 348	89.59 314		60.49 328	74.49 334	17.17 352	92.46 313	61.50 302	52.85 338	84.17 326

FPMVS		55.09 315	52.93 318	61.57 331	55.98 351	40.51 354	83.11 329	83.41 344	37.61 348	34.95 348	71.95 340	14.40 353	76.95 347	29.81 348	65.16 317	67.25 346

EMVS		31.70 324	31.45 326	32.48 338	50.72 355	23.95 359	74.78 343	52.30 359	20.36 353	16.08 356	31.48 354	12.80 354	53.60 355	11.39 354	13.10 354	19.88 352

ANet_high		46.22 318	41.28 323	61.04 332	39.91 358	46.25 351	70.59 347	76.18 351	58.87 341	23.09 352	48.00 349	12.58 355	66.54 352	28.65 349	13.62 352	70.35 344

E-PMN		32.70 323	32.39 325	33.65 337	53.35 354	25.70 358	74.07 344	53.33 358	21.08 352	17.17 355	33.63 353	11.85 356	54.84 354	12.98 353	14.04 351	20.42 351

Gipuma		45.11 319	42.05 321	54.30 333	80.69 328	51.30 348	35.80 352	83.81 342	28.13 350	27.94 351	34.53 351	11.41 357	76.70 349	21.45 350	54.65 333	34.90 350
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PMMVS2		50.90 317	46.31 320	64.67 328	55.53 352	46.67 350	77.30 341	71.02 352	40.89 347	34.16 349	59.32 343	9.83 358	76.14 350	40.09 346	28.63 349	71.21 343

LCM-MVSNet		52.52 316	48.24 319	65.35 327	47.63 356	41.45 353	72.55 346	83.62 343	31.75 349	37.66 347	57.92 345	9.19 359	76.76 348	49.26 337	44.60 346	77.84 341

PMVS	34.80 23	39.19 321	35.53 324	50.18 334	29.72 359	30.30 356	59.60 350	66.20 355	26.06 351	17.91 354	49.53 348	3.12 360	74.09 351	18.19 352	49.40 340	46.14 347
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MVE	35.65 22	33.85 322	29.49 327	46.92 335	41.86 357	36.28 355	50.45 351	56.52 357	18.75 354	18.28 353	37.84 350	2.41 361	58.41 353	18.71 351	20.62 350	46.06 348
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
wuyk23d		14.10 326	13.89 329	14.72 339	55.23 353	22.91 360	33.83 353	3.56 361	4.94 356	4.11 357	2.28 359	2.06 362	19.66 357	10.23 355	8.74 355	1.59 355

test123		9.07 328	11.73 331	1.11 340	0.50 362	0.77 362	89.44 299	0.20 363	0.34 358	2.15 359	10.72 358	0.34 363	0.32 358	1.79 357	0.08 357	2.23 353

testmvs		9.92 327	12.94 330	0.84 341	0.65 361	0.29 363	93.78 247	0.39 362	0.42 357	2.85 358	15.84 357	0.17 364	0.30 359	2.18 356	0.21 356	1.91 354

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

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

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

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

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

ab-mvs-re		8.11 329	10.81 332	0.00 342	0.00 363	0.00 364	0.00 354	0.00 364	0.00 359	0.00 360	97.30 91	0.00 365	0.00 360	0.00 358	0.00 358	0.00 356

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

IU-MVS					99.03 13	85.34 47		96.86 41	92.05 15	98.74 1				98.15 2	98.97 15	99.42 9

save fliter					98.24 52	83.34 88	98.61 23	96.57 79	91.32 18

test_0728_SECOND				95.14 15	99.04 12	86.14 31	99.06 9	96.77 50					99.84 12	97.90 5	98.85 20	99.45 8

GSMVS																97.54 117

test_part2					98.90 17	85.14 57				96.07 17

MTGPA								96.33 111

MTMP							97.53 76	68.16 353

gm-plane-assit					92.27 206	79.64 175			84.47 135		95.15 146		97.93 141	85.81 131

test9_res														96.00 21	99.03 11	98.31 58

agg_prior2														94.30 42	99.00 13	98.57 41

agg_prior					98.59 35	83.13 93		96.56 81		94.19 41			99.16 89

test_prior4						82.34 107	97.75 61

test_prior				93.09 81	98.68 25	81.91 115		96.40 103					99.06 97			98.29 60

旧先验2							96.97 126		74.06 286	96.10 16			97.76 150	88.38 115

新几何2							96.42 160

无先验							96.87 131	96.78 44	77.39 263				99.52 53	79.95 182		98.43 50

原ACMM2							96.84 132

testdata2													99.48 58	76.45 216

testdata1							95.57 200		87.44 74

plane_prior7					91.86 226	77.55 229

plane_prior5								94.69 200					97.30 174	87.08 124	82.82 212	90.96 221

plane_prior4											94.15 168

plane_prior3						77.75 225			90.17 31	81.33 188

plane_prior2							97.18 101		89.89 33

plane_prior1					91.95 224

plane_prior						77.96 218	97.52 78		90.36 30						82.96 210

n2								0.00 364

nn								0.00 364

door-mid								79.75 349

test11								96.50 90

door								80.13 348

HQP5-MVS						78.48 199

HQP-NCC					92.08 216		97.63 67		90.52 27	82.30 176

ACMP_Plane					92.08 216		97.63 67		90.52 27	82.30 176

BP-MVS														87.67 120

HQP4-MVS										82.30 176			97.32 172			91.13 219

HQP3-MVS								94.80 196							83.01 208

NP-MVS					92.04 220	78.22 209					94.56 159

ACMMP++_ref															78.45 240

ACMMP++															79.05 232

High-resolution multi-view benchmark