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.

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

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

MVS		90.60 97	88.64 119	96.50 3	94.25 161	90.53 6	93.33 259	97.21 19	77.59 262	78.88 211	97.31 89	71.52 187	99.69 36	89.60 101	98.03 60	99.27 16

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
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

PS-MVSNAJ		94.17 25	93.52 38	96.10 6	95.65 121	92.35 2	98.21 32	95.79 143	92.42 12	96.24 15	98.18 32	71.04 192	99.17 88	96.77 15	97.39 75	96.79 148

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

xiu_mvs_v2_base		93.92 33	93.26 40	95.91 8	95.07 138	92.02 4	98.19 33	95.68 148	92.06 14	96.01 18	98.14 37	70.83 195	98.96 103	96.74 16	96.57 94	96.76 151

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

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

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

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

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

HY-MVS	84.06 6	91.63 75	90.37 90	95.39 14	96.12 109	88.25 12	90.22 295	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

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

alignmvs		92.97 46	92.26 61	95.12 16	95.54 123	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

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
3Dnovator+	82.88 8	89.63 114	87.85 129	94.99 18	94.49 157	86.76 27	97.84 51	95.74 145	86.10 92	75.47 255	96.02 124	65.00 228	99.51 56	82.91 165	97.07 81	98.72 36

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

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

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	70.16 200	98.95 106	93.39 52	88.87 160	98.43 50

SMA-MVS		94.70 15	94.68 15	94.76 22	98.02 64	85.94 35	97.47 80	96.77 50	85.32 110	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
lupinMVS		93.87 36	93.58 37	94.75 23	93.00 189	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

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

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

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
Zhenlong Yuan, Jinguo Luo, Fei Shen, Zhaoxin Li, Cong Liu, Tianlu Mao, Zhaoqi Wang: DVP-MVS: Synergize Depth-Edge and Visibility Prior for Multi-View Stereo. AAAI2025
SF-MVS		94.17 25	94.05 30	94.55 27	97.56 80	85.95 33	97.73 62	96.43 98	84.02 148	95.07 29	98.74 14	82.93 48	99.38 64	95.42 30	98.51 34	98.32 55

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	85.29 113	95.45 22	98.81 8	83.36 42	99.38 64	96.07 19	98.53 33	98.19 66

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

3Dnovator	82.32 10	89.33 118	87.64 134	94.42 30	93.73 174	85.70 41	97.73 62	96.75 53	86.73 87	76.21 243	95.93 125	62.17 242	99.68 38	81.67 170	97.81 65	97.88 93

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	83.94 151	94.13 44	98.69 18	83.13 46	99.37 68	95.25 34	98.39 47	97.97 89

DP-MVS Recon		91.72 72	90.85 82	94.34 32	99.50 1	85.00 60	98.51 25	95.96 134	80.57 210	88.08 123	97.63 74	76.84 113	99.89 7	85.67 133	94.88 113	98.13 73

PAPM		92.87 49	92.40 57	94.30 33	92.25 210	87.85 16	96.40 162	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

SD-MVS		94.84 13	95.02 13	94.29 34	97.87 70	84.61 67	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
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

test_yl		91.46 79	90.53 87	94.24 36	97.41 86	85.18 51	98.08 38	97.72 10	80.94 202	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	80.94 202	89.85 96	96.14 121	75.61 135	98.81 113	90.42 92	88.56 165	98.74 31

jason		92.73 53	92.23 62	94.21 38	90.50 249	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.
ACMMP_NAP		93.46 39	93.23 41	94.17 39	97.16 95	84.28 71	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

1314		88.94 124	87.20 146	94.17 39	93.21 183	85.73 40	93.33 259	96.64 70	82.89 175	75.98 246	96.36 118	66.83 217	99.39 63	83.52 158	96.02 101	97.39 128

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	55.35 291	98.22 135	89.52 104	89.54 154	98.74 31

testtj		94.09 29	94.08 29	94.09 42	99.28 6	83.32 91	97.59 71	96.61 73	83.60 163	94.77 36	98.46 24	82.72 52	99.64 42	95.29 33	98.42 42	99.32 13

QAPM		86.88 162	84.51 180	93.98 43	94.04 167	85.89 36	97.19 100	96.05 130	73.62 292	75.12 258	95.62 134	62.02 245	99.74 28	70.88 262	96.06 100	96.30 166

MSLP-MVS++		94.28 22	94.39 23	93.97 44	98.30 50	84.06 75	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

APDe-MVS		94.56 17	94.75 14	93.96 45	98.84 20	83.40 88	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

TSAR-MVS + GP.		94.35 21	94.50 18	93.89 46	97.38 91	83.04 97	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

Regformer-1		94.00 32	94.04 31	93.87 47	98.41 43	84.29 70	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

CANet_DTU		90.98 89	90.04 97	93.83 48	94.76 147	86.23 30	96.32 167	93.12 280	93.11 10	93.71 47	96.82 110	63.08 237	99.48 58	84.29 143	95.12 112	95.77 174

API-MVS		90.18 106	88.97 115	93.80 49	98.66 28	82.95 99	97.50 79	95.63 152	75.16 281	86.31 136	97.69 68	72.49 176	99.90 5	81.26 172	96.07 99	98.56 42

EPNet		94.06 30	94.15 28	93.76 50	97.27 94	84.35 68	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
train_agg		94.28 22	94.45 20	93.74 51	98.64 31	83.71 81	97.82 52	96.65 67	84.50 134	95.16 25	98.09 43	84.33 32	99.36 70	95.91 23	98.96 17	98.16 69

CDPH-MVS		93.12 43	92.91 47	93.74 51	98.65 30	83.88 76	97.67 66	96.26 116	83.00 173	93.22 53	98.24 30	81.31 58	99.21 80	89.12 107	98.74 27	98.14 72

MVSFormer		91.36 82	90.57 86	93.73 53	93.00 189	88.08 14	94.80 227	94.48 214	80.74 206	94.90 32	97.13 98	78.84 83	95.10 275	83.77 148	97.46 70	98.02 81

APD-MVS		93.61 37	93.59 36	93.69 54	98.76 22	83.26 92	97.21 97	96.09 127	82.41 184	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
xxxxxxxxxxxxxcwj		94.38 20	94.62 17	93.68 55	98.24 52	83.34 89	98.61 23	92.69 287	91.32 18	95.07 29	98.74 14	82.93 48	99.38 64	95.42 30	98.51 34	98.32 55

Regformer-2		93.92 33	94.01 32	93.67 56	98.41 43	83.75 80	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

TSAR-MVS + MP.		94.79 14	95.17 12	93.64 57	97.66 75	84.10 74	95.85 192	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
CHOSEN 1792x2688		91.07 88	90.21 93	93.64 57	95.18 134	83.53 85	96.26 170	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

MVS_Test		90.29 105	89.18 112	93.62 59	95.23 130	84.93 61	94.41 233	94.66 204	84.31 140	90.37 93	91.02 206	75.13 150	97.82 148	83.11 163	94.42 115	98.12 74

sss		90.87 92	89.96 99	93.60 60	94.15 163	83.84 79	97.14 107	98.13 7	85.93 97	89.68 100	96.09 123	71.67 184	99.30 72	87.69 119	89.16 156	97.66 110

PVSNet_Blended		93.13 42	92.98 46	93.57 61	97.47 82	83.86 77	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

xiu_mvs_v1_base_debu		90.54 98	89.54 108	93.55 62	92.31 203	87.58 20	96.99 121	94.87 191	87.23 79	93.27 50	97.56 76	57.43 276	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	92.31 203	87.58 20	96.99 121	94.87 191	87.23 79	93.27 50	97.56 76	57.43 276	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	92.31 203	87.58 20	96.99 121	94.87 191	87.23 79	93.27 50	97.56 76	57.43 276	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	90.95 240	86.71 28	97.44 82	95.83 141	75.35 278	72.64 277	95.72 129	57.42 279	99.64 42	71.41 256	95.85 104	94.13 202

GG-mvs-BLEND				93.49 66	94.94 142	86.26 29	81.62 333	97.00 27		88.32 120	94.30 164	91.23 3	96.21 219	88.49 113	97.43 73	98.00 86

agg_prior1		94.10 28	94.31 25	93.48 67	98.59 35	83.13 94	97.77 56	96.56 81	84.38 138	94.19 41	98.13 38	84.66 30	99.16 89	95.74 25	98.74 27	98.15 71

ab-mvs		87.08 158	84.94 176	93.48 67	93.34 182	83.67 83	88.82 303	95.70 147	81.18 199	84.55 151	90.14 223	62.72 238	98.94 108	85.49 135	82.54 216	97.85 96

PHI-MVS		93.59 38	93.63 35	93.48 67	98.05 63	81.76 125	98.64 21	97.13 21	82.60 182	94.09 45	98.49 23	80.35 65	99.85 10	94.74 39	98.62 31	98.83 28

MVS_111021_HR		93.41 40	93.39 39	93.47 70	97.34 92	82.83 100	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

PAPM_NR		91.46 79	90.82 83	93.37 71	98.50 40	81.81 124	95.03 222	96.13 124	84.65 130	86.10 139	97.65 73	79.24 79	99.75 26	83.20 161	96.88 87	98.56 42

MP-MVS-pluss		92.58 60	92.35 58	93.29 72	97.30 93	82.53 104	96.44 158	96.04 131	84.68 128	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
IB-MVS	85.34 4	88.67 133	87.14 150	93.26 73	93.12 187	84.32 69	98.76 17	97.27 17	87.19 82	79.36 208	90.45 216	83.92 38	98.53 124	84.41 142	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
gg-mvs-nofinetune		85.48 184	82.90 204	93.24 74	94.51 156	85.82 37	79.22 337	96.97 31	61.19 337	87.33 128	53.01 349	90.58 4	96.07 220	86.07 131	97.23 78	97.81 100

ZNCC-MVS		92.75 50	92.60 55	93.23 75	98.24 52	81.82 123	97.63 67	96.50 90	85.00 121	91.05 83	97.74 67	78.38 90	99.80 19	90.48 88	98.34 51	98.07 77

SteuartSystems-ACMMP		94.13 27	94.44 21	93.20 76	95.41 126	81.35 134	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.
Regformer-3		93.19 41	93.19 42	93.19 77	98.10 60	83.01 98	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

1121		90.66 95	89.82 104	93.16 78	97.39 88	81.71 128	93.33 259	96.66 66	74.45 287	91.38 74	97.55 79	79.27 77	99.52 53	79.95 182	98.43 41	98.26 63

新几何1				93.12 79	97.44 84	81.60 131		96.71 58	74.54 286	91.22 81	97.57 75	79.13 81	99.51 56	77.40 208	98.46 39	98.26 63

CSCG		92.02 67	91.65 72	93.12 79	98.53 37	80.59 152	97.47 80	97.18 20	77.06 271	84.64 150	97.98 54	83.98 37	99.52 53	90.72 85	97.33 76	99.23 17

Effi-MVS+		90.70 94	89.90 102	93.09 81	93.61 175	83.48 86	95.20 213	92.79 285	83.22 167	91.82 67	95.70 130	71.82 183	97.48 166	91.25 77	93.67 124	98.32 55

test_prior3		94.03 31	94.34 24	93.09 81	98.68 25	81.91 117	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_prior				93.09 81	98.68 25	81.91 117		96.40 103					99.06 97			98.29 60

GST-MVS		92.43 63	92.22 63	93.04 84	98.17 57	81.64 130	97.40 90	96.38 107	84.71 127	90.90 85	97.40 87	77.55 102	99.76 20	89.75 100	97.74 66	97.72 105

thisisatest0515		90.95 90	90.26 91	93.01 85	94.03 169	84.27 72	97.91 47	96.67 63	83.18 168	86.87 133	95.51 137	88.66 13	97.85 147	80.46 176	89.01 158	96.92 144

HFP-MVS		92.89 47	92.86 49	92.98 86	98.71 23	81.12 137	97.58 72	96.70 59	85.20 116	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	81.12 137	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

ET-MVSNet_ETH3D		90.01 108	89.03 113	92.95 88	94.38 159	86.77 26	98.14 34	96.31 114	89.30 40	63.33 322	96.72 114	90.09 8	93.63 304	90.70 86	82.29 217	98.46 47

DeepC-MVS	86.58 3	91.53 78	91.06 81	92.94 89	94.52 153	81.89 119	95.95 184	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
baseline1		88.85 128	87.49 140	92.93 90	95.21 132	86.85 25	95.47 204	94.61 209	87.29 76	83.11 169	94.99 153	80.70 62	96.89 194	82.28 167	73.72 257	95.05 187

Regformer-4		93.06 44	93.12 43	92.89 91	98.10 60	82.20 111	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

MSP-MVS		95.62 6	96.54 1	92.86 92	98.31 49	80.10 166	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
Zhenlong Yuan, Cong Liu, Fei Shen, Zhaoxin Li, Jingguo luo, Tianlu Mao and Zhaoqi Wang: MSP-MVS: Multi-granularity Segmentation Prior Guided Multi-View Stereo. AAAI2025
zzz-MVS		92.74 51	92.71 50	92.86 92	97.90 66	80.85 145	96.47 153	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	80.85 145	92.88 272	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

region2R		92.72 54	92.70 52	92.79 95	98.68 25	80.53 156	97.53 76	96.51 88	85.22 114	91.94 66	97.98 54	77.26 106	99.67 40	90.83 83	98.37 49	98.18 67

ACMMPR		92.69 56	92.67 53	92.75 96	98.66 28	80.57 153	97.58 72	96.69 61	85.20 116	91.57 72	97.92 58	77.01 111	99.67 40	90.95 79	98.41 44	98.00 86

baseline		90.76 93	90.10 96	92.74 97	92.90 193	82.56 103	94.60 229	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

thres200		88.92 125	87.65 133	92.73 98	96.30 104	85.62 43	97.85 50	98.86 1	84.38 138	84.82 146	93.99 171	75.12 151	98.01 139	70.86 263	86.67 178	94.56 197

PVSNet	82.34 9	89.02 122	87.79 131	92.71 99	95.49 124	81.50 132	97.70 64	97.29 16	87.76 69	85.47 141	95.12 149	56.90 280	98.90 110	80.33 177	94.02 118	97.71 107

DWT-MVSNet_test		90.52 101	89.80 105	92.70 100	95.73 119	82.20 111	93.69 250	96.55 83	88.34 57	87.04 132	95.34 140	86.53 22	97.55 159	76.32 220	88.66 163	98.34 53

PVSNet_Blended_VisFu		91.24 85	90.77 84	92.66 101	95.09 136	82.40 107	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

XVS		92.69 56	92.71 50	92.63 102	98.52 38	80.29 159	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	80.29 159	97.37 91	96.44 96	87.04 84	91.38 74	20.73 359	77.24 108	99.59 47	90.46 89	98.07 58	98.02 81

casdiffmvs		90.95 90	90.39 89	92.63 102	92.82 194	82.53 104	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

cascas		86.50 169	84.48 182	92.55 105	92.64 199	85.95 33	97.04 120	95.07 182	75.32 279	80.50 195	91.02 206	54.33 298	97.98 140	86.79 128	87.62 172	93.71 208

tfpn200view9		88.48 138	87.15 148	92.47 106	96.21 106	85.30 49	97.44 82	98.85 2	83.37 165	83.99 156	93.82 174	75.36 145	97.93 141	69.04 269	86.24 184	94.17 199

114514_t		88.79 131	87.57 138	92.45 107	98.21 55	81.74 126	96.99 121	95.45 161	75.16 281	82.48 173	95.69 131	68.59 206	98.50 125	80.33 177	95.18 111	97.10 138

diffmvs		91.17 87	90.74 85	92.44 108	93.11 188	82.50 106	96.25 171	93.62 257	87.79 68	90.40 92	95.93 125	73.44 169	97.42 168	93.62 49	92.55 134	97.41 126

MP-MVS		92.61 59	92.67 53	92.42 109	98.13 59	79.73 175	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.
AdaColmap		88.81 129	87.61 137	92.39 110	99.33 4	79.95 167	96.70 145	95.58 153	77.51 263	83.05 170	96.69 115	61.90 249	99.72 31	84.29 143	93.47 126	97.50 122

CP-MVS		92.54 61	92.60 55	92.34 111	98.50 40	79.90 169	98.40 26	96.40 103	84.75 124	90.48 91	98.09 43	77.40 105	99.21 80	91.15 78	98.23 55	97.92 92

thres100view900		88.30 143	86.95 153	92.33 112	96.10 110	84.90 62	97.14 107	98.85 2	82.69 180	83.41 164	93.66 177	75.43 142	97.93 141	69.04 269	86.24 184	94.17 199

PGM-MVS		91.93 68	91.80 69	92.32 113	98.27 51	79.74 174	95.28 208	97.27 17	83.83 156	90.89 86	97.78 66	76.12 127	99.56 51	88.82 109	97.93 64	97.66 110

ETV-MVS		92.72 54	92.87 48	92.28 114	94.54 152	81.89 119	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

CS-MVS		92.88 48	93.09 44	92.26 115	95.21 132	80.70 149	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

thres400		88.42 141	87.15 148	92.23 116	96.21 106	85.30 49	97.44 82	98.85 2	83.37 165	83.99 156	93.82 174	75.36 145	97.93 141	69.04 269	86.24 184	93.45 212

VDDNet		86.44 170	84.51 180	92.22 117	91.56 230	81.83 122	97.10 113	94.64 207	69.50 317	87.84 124	95.19 143	48.01 312	97.92 146	89.82 99	86.92 176	96.89 145

EPMVS		87.47 155	85.90 163	92.18 118	95.41 126	82.26 110	87.00 318	96.28 115	85.88 98	84.23 153	85.57 286	75.07 152	96.26 216	71.14 261	92.50 135	98.03 80

thres600view7		88.06 146	86.70 156	92.15 119	96.10 110	85.17 55	97.14 107	98.85 2	82.70 179	83.41 164	93.66 177	75.43 142	97.82 148	67.13 278	85.88 188	93.45 212

PCF-MVS	84.09 5	86.77 167	85.00 175	92.08 120	92.06 220	83.07 96	92.14 280	94.47 216	79.63 233	76.90 230	94.78 155	71.15 190	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
mPP-MVS		91.88 69	91.82 68	92.07 121	98.38 45	78.63 200	97.29 94	96.09 127	85.12 118	88.45 117	97.66 69	75.53 138	99.68 38	89.83 98	98.02 61	97.88 93

VDD-MVS		88.28 144	87.02 152	92.06 122	95.09 136	80.18 165	97.55 75	94.45 218	83.09 170	89.10 110	95.92 127	47.97 313	98.49 126	93.08 60	86.91 177	97.52 121

EI-MVSNet-Vis-set		91.84 70	91.77 70	92.04 123	97.60 77	81.17 136	96.61 147	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

1112_ss		88.60 136	87.47 142	92.00 124	93.21 183	80.97 142	96.47 153	92.46 289	83.64 161	80.86 192	97.30 91	80.24 68	97.62 155	77.60 204	85.49 192	97.40 127

PatchmatchNet		86.83 164	85.12 173	91.95 125	94.12 164	82.27 109	86.55 322	95.64 151	84.59 132	82.98 171	84.99 298	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.
Test_1112_low_res		88.03 147	86.73 155	91.94 126	93.15 185	80.88 144	96.44 158	92.41 290	83.59 164	80.74 194	91.16 204	80.18 69	97.59 156	77.48 207	85.40 193	97.36 129

HPM-MVS		91.62 76	91.53 74	91.89 127	97.88 69	79.22 185	96.99 121	95.73 146	82.07 189	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
mvs_anonymous		88.68 132	87.62 136	91.86 128	94.80 146	81.69 129	93.53 255	94.92 188	82.03 190	78.87 212	90.43 217	75.77 132	95.34 260	85.04 139	93.16 130	98.55 44

MAR-MVS		90.63 96	90.22 92	91.86 128	98.47 42	78.20 216	97.18 101	96.61 73	83.87 155	88.18 122	98.18 32	68.71 205	99.75 26	83.66 153	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
Anonymous202405211		84.41 200	81.93 219	91.85 130	96.78 100	78.41 206	97.44 82	91.34 303	70.29 313	84.06 154	94.26 165	41.09 335	98.96 103	79.46 187	82.65 215	98.17 68

SR-MVS		92.16 65	92.27 60	91.83 131	98.37 46	78.41 206	96.67 146	95.76 144	82.19 188	91.97 65	98.07 47	76.44 120	98.64 117	93.71 47	97.27 77	98.45 49

EI-MVSNet-UG-set		91.35 83	91.22 77	91.73 132	97.39 88	80.68 150	96.47 153	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

CNLPA		86.96 159	85.37 168	91.72 133	97.59 78	79.34 183	97.21 97	91.05 308	74.22 288	78.90 210	96.75 113	67.21 214	98.95 106	74.68 234	90.77 149	96.88 146

RPMNet		79.85 258	75.92 276	91.64 134	90.16 255	79.75 172	79.02 339	95.44 162	58.43 346	82.27 180	72.55 342	73.03 172	98.41 130	46.10 345	86.25 182	96.75 152

ACMMP		90.39 102	89.97 98	91.64 134	97.58 79	78.21 215	96.78 137	96.72 57	84.73 126	84.72 148	97.23 94	71.22 189	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
HyFIR lowres test		89.36 117	88.60 120	91.63 136	94.91 144	80.76 148	95.60 200	95.53 155	82.56 183	84.03 155	91.24 203	78.03 95	96.81 199	87.07 126	88.41 167	97.32 130

SCA		85.63 181	83.64 194	91.60 137	92.30 206	81.86 121	92.88 272	95.56 154	84.85 122	82.52 172	85.12 296	58.04 271	95.39 257	73.89 242	87.58 174	97.54 117

thisisatest0530		89.65 113	89.02 114	91.53 138	93.46 180	80.78 147	96.52 150	96.67 63	81.69 195	83.79 161	94.90 154	88.85 12	97.68 152	77.80 199	87.49 175	96.14 167

BH-RMVSNet		86.84 163	85.28 169	91.49 139	95.35 128	80.26 162	96.95 127	92.21 291	82.86 177	81.77 187	95.46 138	59.34 262	97.64 153	69.79 267	93.81 123	96.57 156

MVS_111021_LR		91.60 77	91.64 73	91.47 140	95.74 117	78.79 198	96.15 176	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

TESTMET0.1,1		89.83 109	89.34 111	91.31 141	92.54 201	80.19 164	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

tpmrst		88.36 142	87.38 144	91.31 141	94.36 160	79.92 168	87.32 316	95.26 175	85.32 110	88.34 119	86.13 280	80.60 63	96.70 203	83.78 147	85.34 195	97.30 133

CHOSEN 280x420		91.71 73	91.85 67	91.29 143	94.94 142	82.69 101	87.89 312	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

UGNet		87.73 152	86.55 157	91.27 144	95.16 135	79.11 189	96.35 164	96.23 118	88.14 61	87.83 125	90.48 214	50.65 303	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
Vis-MVSNet		88.67 133	87.82 130	91.24 145	92.68 195	78.82 195	96.95 127	93.85 243	87.55 73	87.07 131	95.13 148	63.43 235	97.21 179	77.58 205	96.15 97	97.70 108
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
原ACMM1				91.22 146	97.77 72	78.10 218		96.61 73	81.05 201	91.28 79	97.42 86	77.92 97	98.98 102	79.85 185	98.51 34	96.59 155

CostFormer		89.08 121	88.39 123	91.15 147	93.13 186	79.15 188	88.61 306	96.11 126	83.14 169	89.58 103	86.93 264	83.83 39	96.87 196	88.22 117	85.92 187	97.42 125

CDS-MVSNet		89.50 115	88.96 116	91.14 148	91.94 226	80.93 143	97.09 114	95.81 142	84.26 143	84.72 148	94.20 167	80.31 66	95.64 247	83.37 159	88.96 159	96.85 147
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
DP-MVS		81.47 244	78.28 258	91.04 149	98.14 58	78.48 202	95.09 221	86.97 332	61.14 338	71.12 286	92.78 186	59.59 258	99.38 64	53.11 331	86.61 179	95.27 186

HPM-MVS_fast		90.38 104	90.17 95	91.03 150	97.61 76	77.35 236	97.15 106	95.48 159	79.51 234	88.79 113	96.90 104	71.64 186	98.81 113	87.01 127	97.44 72	96.94 141

GA-MVS		85.79 179	84.04 189	91.02 151	89.47 267	80.27 161	96.90 130	94.84 194	85.57 103	80.88 191	89.08 231	56.56 284	96.47 209	77.72 202	85.35 194	96.34 162

baseline2		90.39 102	90.21 93	90.93 152	90.86 243	80.99 141	95.20 213	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

Fast-Effi-MVS+		87.93 150	86.94 154	90.92 153	94.04 167	79.16 187	98.26 30	93.72 253	81.29 198	83.94 159	92.90 184	69.83 201	96.68 204	76.70 214	91.74 144	96.93 142

APD-MVS_3200maxsize		91.23 86	91.35 76	90.89 154	97.89 68	76.35 251	96.30 168	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

nrg030		86.79 166	85.43 166	90.87 155	88.76 272	85.34 47	97.06 118	94.33 221	84.31 140	80.45 197	91.98 192	72.36 177	96.36 212	88.48 114	71.13 270	90.93 223

SR-MVS-dyc-post		91.29 84	91.45 75	90.80 156	97.76 73	76.03 254	96.20 174	95.44 162	80.56 211	90.72 87	97.84 62	75.76 133	98.61 118	91.99 72	96.79 91	97.75 103

Anonymous20240529		83.15 219	80.60 237	90.80 156	95.74 117	78.27 210	96.81 135	94.92 188	60.10 342	81.89 185	92.54 187	45.82 320	98.82 112	79.25 191	78.32 241	95.31 185

EIA-MVS		91.73 71	92.05 65	90.78 158	94.52 153	76.40 250	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

OMC-MVS		88.80 130	88.16 125	90.72 159	95.30 129	77.92 224	94.81 226	94.51 213	86.80 86	84.97 144	96.85 107	67.53 210	98.60 120	85.08 138	87.62 172	95.63 177

FMVSNet3		84.71 194	82.71 209	90.70 160	94.55 151	87.71 18	95.92 186	94.67 203	81.73 194	75.82 250	88.08 248	66.99 215	94.47 289	71.23 258	75.38 251	89.91 241

tpm2		87.35 156	86.26 159	90.62 161	92.93 192	78.67 199	88.06 311	95.99 132	79.33 237	87.40 126	86.43 275	80.28 67	96.40 210	80.23 179	85.73 191	96.79 148

TAMVS		88.48 138	87.79 131	90.56 162	91.09 238	79.18 186	96.45 156	95.88 138	83.64 161	83.12 168	93.33 179	75.94 130	95.74 242	82.40 166	88.27 168	96.75 152

test1172		91.64 74	92.00 66	90.54 163	98.20 56	74.48 271	96.45 156	95.65 149	81.97 192	91.63 71	98.02 49	75.76 133	98.61 118	93.16 58	97.17 79	98.52 45

BH-w/o		88.24 145	87.47 142	90.54 163	95.03 140	78.54 201	97.41 89	93.82 244	84.08 146	78.23 218	94.51 161	69.34 203	97.21 179	80.21 180	94.58 114	95.87 172

TR-MVS		86.30 171	84.93 177	90.42 165	94.63 149	77.58 231	96.57 149	93.82 244	80.30 219	82.42 175	95.16 145	58.74 266	97.55 159	74.88 232	87.82 171	96.13 168

tpm cat1		83.63 211	81.38 227	90.39 166	93.53 179	78.19 217	85.56 328	95.09 180	70.78 311	78.51 215	83.28 311	74.80 154	97.03 187	66.77 279	84.05 200	95.95 169

test_part1		84.72 193	82.85 205	90.34 167	95.73 119	84.79 65	96.75 140	94.10 232	79.05 248	75.97 247	89.51 228	67.69 207	95.94 228	79.34 188	67.50 306	90.30 232

PVSNet_BlendedMVS		90.05 107	89.96 99	90.33 168	97.47 82	83.86 77	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

dp		84.30 203	82.31 214	90.28 169	94.24 162	77.97 220	86.57 321	95.53 155	79.94 228	80.75 193	85.16 294	71.49 188	96.39 211	63.73 295	83.36 205	96.48 158

UA-Net		88.92 125	88.48 122	90.24 170	94.06 166	77.18 240	93.04 268	94.66 204	87.39 75	91.09 82	93.89 173	74.92 153	98.18 138	75.83 225	91.43 145	95.35 184

MVSTER		89.25 120	88.92 117	90.24 170	95.98 113	84.66 66	96.79 136	95.36 167	87.19 82	80.33 199	90.61 213	90.02 9	95.97 224	85.38 136	78.64 236	90.09 237

IS-MVSNet		88.67 133	88.16 125	90.20 172	93.61 175	76.86 243	96.77 139	93.07 281	84.02 148	83.62 163	95.60 135	74.69 156	96.24 218	78.43 198	93.66 125	97.49 123

testdata				90.13 173	95.92 114	74.17 274		96.49 93	73.49 295	94.82 35	97.99 52	78.80 85	97.93 141	83.53 157	97.52 69	98.29 60

abl_6		89.80 110	89.71 107	90.07 174	96.53 102	75.52 262	94.48 230	95.04 183	81.12 200	89.22 107	97.00 102	68.83 204	98.96 103	89.86 97	95.27 108	95.73 175

CR-MVSNet		83.53 212	81.36 228	90.06 175	90.16 255	79.75 172	79.02 339	91.12 305	84.24 144	82.27 180	80.35 325	75.45 140	93.67 303	63.37 298	86.25 182	96.75 152

VPNet		84.69 195	82.92 203	90.01 176	89.01 271	83.45 87	96.71 143	95.46 160	85.71 101	79.65 206	92.18 190	56.66 283	96.01 223	83.05 164	67.84 303	90.56 225

BH-untuned		86.95 160	85.94 162	89.99 177	94.52 153	77.46 233	96.78 137	93.37 270	81.80 193	76.62 234	93.81 176	66.64 218	97.02 188	76.06 222	93.88 122	95.48 181

test-LLR		88.48 138	87.98 127	89.98 178	92.26 208	77.23 238	97.11 110	95.96 134	83.76 158	86.30 137	91.38 200	72.30 179	96.78 201	80.82 173	91.92 142	95.94 170

test-mter		88.95 123	88.60 120	89.98 178	92.26 208	77.23 238	97.11 110	95.96 134	85.32 110	86.30 137	91.38 200	76.37 123	96.78 201	80.82 173	91.92 142	95.94 170

ADS-MVSNet		81.26 247	78.36 257	89.96 180	93.78 171	79.78 170	79.48 335	93.60 258	73.09 298	80.14 201	79.99 328	62.15 243	95.24 266	59.49 310	83.52 202	94.85 190

PVSNet_0	77.72 15	81.70 241	78.95 255	89.94 181	90.77 246	76.72 246	95.96 183	96.95 33	85.01 120	70.24 293	88.53 242	52.32 300	98.20 136	86.68 130	44.08 348	94.89 189

DeepPCF-MVS	89.82 1	94.61 16	96.17 4	89.91 182	97.09 97	70.21 306	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

EPP-MVSNet		89.76 111	89.72 106	89.87 183	93.78 171	76.02 256	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

tpmvs		83.04 222	80.77 233	89.84 184	95.43 125	77.96 221	85.59 327	95.32 172	75.31 280	76.27 241	83.70 308	73.89 162	97.41 169	59.53 309	81.93 218	94.14 201

FMVSNet2		82.79 226	80.44 239	89.83 185	92.66 196	85.43 46	95.42 206	94.35 220	79.06 245	74.46 262	87.28 256	56.38 286	94.31 292	69.72 268	74.68 254	89.76 243

PLC	83.97 7	88.00 148	87.38 144	89.83 185	98.02 64	76.46 248	97.16 105	94.43 219	79.26 241	81.98 183	96.28 119	69.36 202	99.27 73	77.71 203	92.25 139	93.77 207
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
VPA-MVSNet		85.32 185	83.83 190	89.77 187	90.25 252	82.63 102	96.36 163	97.07 24	83.03 172	81.21 190	89.02 233	61.58 250	96.31 215	85.02 140	70.95 272	90.36 228

tttt0517		88.57 137	88.19 124	89.71 188	93.00 189	75.99 257	95.67 197	96.67 63	80.78 205	81.82 186	94.40 162	88.97 11	97.58 157	76.05 223	86.31 181	95.57 179

RRT_MVS		86.89 161	85.96 161	89.68 189	95.01 141	84.13 73	96.33 166	94.98 186	84.20 145	80.10 203	92.07 191	70.52 196	95.01 279	83.30 160	77.14 245	89.91 241

CLD-MVS		87.97 149	87.48 141	89.44 190	92.16 215	80.54 155	98.14 34	94.92 188	91.41 17	79.43 207	95.40 139	62.34 240	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
XXY-MVS		83.84 207	82.00 218	89.35 191	87.13 289	81.38 133	95.72 195	94.26 223	80.15 223	75.92 249	90.63 212	61.96 248	96.52 207	78.98 194	73.28 263	90.14 234

RRT_test8_iter05		87.14 157	86.41 158	89.32 192	94.41 158	81.10 139	97.06 118	95.33 171	84.67 129	76.27 241	90.48 214	83.60 41	96.33 213	85.10 137	70.78 273	90.53 226

CPTT-MVS		89.72 112	89.87 103	89.29 193	98.33 48	73.30 279	97.70 64	95.35 169	75.68 277	87.40 126	97.44 85	70.43 197	98.25 134	89.56 103	96.90 85	96.33 164

MSDG		80.62 254	77.77 262	89.14 194	93.43 181	77.24 237	91.89 283	90.18 314	69.86 316	68.02 300	91.94 195	52.21 301	98.84 111	59.32 312	83.12 206	91.35 218

TAPA-MVS	81.61 12	85.02 189	83.67 192	89.06 195	96.79 99	73.27 281	95.92 186	94.79 198	74.81 284	80.47 196	96.83 108	71.07 191	98.19 137	49.82 339	92.57 133	95.71 176
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
LS3D		82.22 236	79.94 248	89.06 195	97.43 85	74.06 276	93.20 266	92.05 292	61.90 333	73.33 270	95.21 142	59.35 261	99.21 80	54.54 327	92.48 136	93.90 206

PatchMatch-RL		85.00 190	83.66 193	89.02 197	95.86 115	74.55 270	92.49 276	93.60 258	79.30 239	79.29 209	91.47 198	58.53 268	98.45 128	70.22 266	92.17 141	94.07 203

HQP-MVS		87.91 151	87.55 139	88.98 198	92.08 217	78.48 202	97.63 67	94.80 196	90.52 27	82.30 176	94.56 159	65.40 224	97.32 172	87.67 120	83.01 208	91.13 219

bset_n11_16_dypcd		84.35 201	82.83 207	88.91 199	82.54 328	82.07 113	94.12 244	93.47 262	85.39 109	78.55 214	88.98 234	62.23 241	95.11 273	86.75 129	73.42 259	89.55 246

Vis-MVSNet (Re-imp)		88.88 127	88.87 118	88.91 199	93.89 170	74.43 272	96.93 129	94.19 226	84.39 137	83.22 167	95.67 132	78.24 92	94.70 285	78.88 195	94.40 116	97.61 115

NR-MVSNet		83.35 214	81.52 226	88.84 201	88.76 272	81.31 135	94.45 232	95.16 178	84.65 130	67.81 301	90.82 209	70.36 198	94.87 281	74.75 233	66.89 313	90.33 230

Patchmatch-test		78.25 271	74.72 284	88.83 202	91.20 235	74.10 275	73.91 348	88.70 328	59.89 343	66.82 307	85.12 296	78.38 90	94.54 288	48.84 341	79.58 228	97.86 95

tpm		85.55 182	84.47 183	88.80 203	90.19 254	75.39 264	88.79 304	94.69 200	84.83 123	83.96 158	85.21 292	78.22 93	94.68 286	76.32 220	78.02 243	96.34 162

HQP_MVS		87.50 154	87.09 151	88.74 204	91.86 227	77.96 221	97.18 101	94.69 200	89.89 33	81.33 188	94.15 168	64.77 229	97.30 174	87.08 124	82.82 212	90.96 221

MIMVSNet		79.18 266	75.99 275	88.72 205	87.37 288	80.66 151	79.96 334	91.82 295	77.38 265	74.33 263	81.87 317	41.78 332	90.74 331	66.36 285	83.10 207	94.76 192

FIs		86.73 168	86.10 160	88.61 206	90.05 257	80.21 163	96.14 177	96.95 33	85.56 106	78.37 217	92.30 188	76.73 116	95.28 264	79.51 186	79.27 230	90.35 229

UniMVSNet (Re)		85.31 186	84.23 186	88.55 207	89.75 260	80.55 154	96.72 141	96.89 38	85.42 107	78.40 216	88.93 235	75.38 144	95.52 254	78.58 196	68.02 300	89.57 245

PatchT		79.75 259	76.85 269	88.42 208	89.55 265	75.49 263	77.37 343	94.61 209	63.07 329	82.46 174	73.32 341	75.52 139	93.41 307	51.36 334	84.43 198	96.36 160

WR-MVS		84.32 202	82.96 202	88.41 209	89.38 269	80.32 158	96.59 148	96.25 117	83.97 150	76.63 233	90.36 218	67.53 210	94.86 282	75.82 226	70.09 282	90.06 239

GBi-Net		82.42 232	80.43 240	88.39 210	92.66 196	81.95 114	94.30 238	93.38 267	79.06 245	75.82 250	85.66 282	56.38 286	93.84 299	71.23 258	75.38 251	89.38 249

test1		82.42 232	80.43 240	88.39 210	92.66 196	81.95 114	94.30 238	93.38 267	79.06 245	75.82 250	85.66 282	56.38 286	93.84 299	71.23 258	75.38 251	89.38 249

FMVSNet1		79.50 262	76.54 272	88.39 210	88.47 277	81.95 114	94.30 238	93.38 267	73.14 297	72.04 282	85.66 282	43.86 323	93.84 299	65.48 287	72.53 264	89.38 249

DU-MVS		84.57 197	83.33 200	88.28 213	88.76 272	79.36 181	96.43 160	95.41 166	85.42 107	78.11 219	90.82 209	67.61 208	95.14 271	79.14 192	68.30 297	90.33 230

AUN-MVS		86.25 173	85.57 164	88.26 214	93.57 177	73.38 277	95.45 205	95.88 138	83.94 151	85.47 141	94.21 166	73.70 167	96.67 205	83.54 156	64.41 319	94.73 196

v2v482		83.46 213	81.86 220	88.25 215	86.19 298	79.65 176	96.34 165	94.02 236	81.56 196	77.32 224	88.23 245	65.62 221	96.03 221	77.77 200	69.72 286	89.09 258

UniMVSNet_NR-MVSNet		85.49 183	84.59 179	88.21 216	89.44 268	79.36 181	96.71 143	96.41 100	85.22 114	78.11 219	90.98 208	76.97 112	95.14 271	79.14 192	68.30 297	90.12 235

miper_enhance_ethall		85.95 176	85.20 170	88.19 217	94.85 145	79.76 171	96.00 181	94.06 235	82.98 174	77.74 221	88.76 237	79.42 74	95.46 256	80.58 175	72.42 265	89.36 252

OPM-MVS		85.84 177	85.10 174	88.06 218	88.34 278	77.83 227	95.72 195	94.20 225	87.89 67	80.45 197	94.05 170	58.57 267	97.26 178	83.88 146	82.76 214	89.09 258

PMMVS		89.46 116	89.92 101	88.06 218	94.64 148	69.57 313	96.22 172	94.95 187	87.27 78	91.37 77	96.54 117	65.88 220	97.39 170	88.54 111	93.89 121	97.23 136

cl-mvsnet2		85.11 188	84.17 187	87.92 220	95.06 139	78.82 195	95.51 202	94.22 224	79.74 231	76.77 231	87.92 250	75.96 129	95.68 243	79.93 184	72.42 265	89.27 253

TranMVSNet+NR-MVSNet		83.24 218	81.71 222	87.83 221	87.71 285	78.81 197	96.13 179	94.82 195	84.52 133	76.18 244	90.78 211	64.07 232	94.60 287	74.60 237	66.59 315	90.09 237

pmmvs4		82.54 230	80.79 232	87.79 222	86.11 300	80.49 157	93.55 254	93.18 277	77.29 266	73.35 269	89.40 230	65.26 227	95.05 278	75.32 229	73.61 258	87.83 287

v1144		82.90 225	81.27 229	87.78 223	86.29 296	79.07 192	96.14 177	93.93 238	80.05 225	77.38 222	86.80 266	65.50 222	95.93 230	75.21 230	70.13 279	88.33 279

F-COLMAP		84.50 199	83.44 199	87.67 224	95.22 131	72.22 286	95.95 184	93.78 249	75.74 276	76.30 240	95.18 144	59.50 260	98.45 128	72.67 249	86.59 180	92.35 216

FC-MVSNet-test		85.96 175	85.39 167	87.66 225	89.38 269	78.02 219	95.65 199	96.87 39	85.12 118	77.34 223	91.94 195	76.28 125	94.74 284	77.09 209	78.82 234	90.21 233

v1192		82.31 235	80.55 238	87.60 226	85.94 302	78.47 205	95.85 192	93.80 247	79.33 237	76.97 229	86.51 270	63.33 236	95.87 232	73.11 246	70.13 279	88.46 275

EI-MVSNet		85.80 178	85.20 170	87.59 227	91.55 231	77.41 234	95.13 216	95.36 167	80.43 216	80.33 199	94.71 156	73.72 165	95.97 224	76.96 212	78.64 236	89.39 247

XVG-OURS		85.18 187	84.38 184	87.59 227	90.42 251	71.73 296	91.06 292	94.07 234	82.00 191	83.29 166	95.08 150	56.42 285	97.55 159	83.70 152	83.42 204	93.49 211

V42		83.04 222	81.53 225	87.57 229	86.27 297	79.09 191	95.87 190	94.11 231	80.35 218	77.22 226	86.79 267	65.32 226	96.02 222	77.74 201	70.14 278	87.61 293

v144192		82.43 231	80.73 234	87.54 230	85.81 305	78.22 212	95.98 182	93.78 249	79.09 244	77.11 227	86.49 271	64.66 231	95.91 231	74.20 240	69.42 287	88.49 273

miper_ehance_all_eth		84.57 197	83.60 196	87.50 231	92.64 199	78.25 211	95.40 207	93.47 262	79.28 240	76.41 237	87.64 253	76.53 119	95.24 266	78.58 196	72.42 265	89.01 263

XVG-OURS-SEG-HR		85.74 180	85.16 172	87.49 232	90.22 253	71.45 299	91.29 289	94.09 233	81.37 197	83.90 160	95.22 141	60.30 255	97.53 163	85.58 134	84.42 199	93.50 210

v1921920		82.02 238	80.23 242	87.41 233	85.62 306	77.92 224	95.79 194	93.69 254	78.86 249	76.67 232	86.44 273	62.50 239	95.83 234	72.69 248	69.77 285	88.47 274

Anonymous20231211		79.72 260	77.19 266	87.33 234	95.59 122	77.16 241	95.18 215	94.18 227	59.31 344	72.57 278	86.20 279	47.89 314	95.66 244	74.53 238	69.24 290	89.18 255

v8		81.88 239	80.06 246	87.32 235	86.63 292	79.04 193	94.41 233	93.65 256	78.77 250	73.19 272	85.57 286	66.87 216	95.81 235	73.84 244	67.61 305	87.11 301

IterMVS-LS		83.93 206	82.80 208	87.31 236	91.46 234	77.39 235	95.66 198	93.43 265	80.44 214	75.51 254	87.26 258	73.72 165	95.16 270	76.99 210	70.72 275	89.39 247
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v1240		81.70 241	79.83 249	87.30 237	85.50 307	77.70 230	95.48 203	93.44 264	78.46 254	76.53 235	86.44 273	60.85 253	95.84 233	71.59 255	70.17 277	88.35 278

cl_fuxian		83.80 208	82.65 210	87.25 238	92.10 216	77.74 229	95.25 211	93.04 282	78.58 252	76.01 245	87.21 260	75.25 149	95.11 273	77.54 206	68.89 292	88.91 269

UniMVSNet_ETH3D		80.86 252	78.75 256	87.22 239	86.31 295	72.02 290	91.95 281	93.76 252	73.51 293	75.06 259	90.16 222	43.04 329	95.66 244	76.37 219	78.55 239	93.98 204

v10		81.43 245	79.53 251	87.11 240	86.38 293	78.87 194	94.31 237	93.43 265	77.88 258	73.24 271	85.26 290	65.44 223	95.75 239	72.14 252	67.71 304	86.72 305

ACMH	75.40 17	77.99 273	74.96 280	87.10 241	90.67 247	76.41 249	93.19 267	91.64 299	72.47 304	63.44 321	87.61 254	43.34 326	97.16 182	58.34 314	73.94 256	87.72 288
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
v148		82.41 234	80.89 231	86.99 242	86.18 299	76.81 244	96.27 169	93.82 244	80.49 213	75.28 257	86.11 281	67.32 213	95.75 239	75.48 228	67.03 312	88.42 277

EPNet_dtu		87.65 153	87.89 128	86.93 243	94.57 150	71.37 300	96.72 141	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
cl-mvsnet_		83.27 216	82.12 215	86.74 244	92.20 211	75.95 258	95.11 218	93.27 274	78.44 255	74.82 260	87.02 263	74.19 159	95.19 268	74.67 235	69.32 288	89.09 258

cl-mvsnet1		83.27 216	82.12 215	86.74 244	92.19 212	75.92 259	95.11 218	93.26 275	78.44 255	74.81 261	87.08 262	74.19 159	95.19 268	74.66 236	69.30 289	89.11 257

PS-MVSNAJss		84.91 191	84.30 185	86.74 244	85.89 304	74.40 273	94.95 223	94.16 228	83.93 153	76.45 236	90.11 224	71.04 192	95.77 237	83.16 162	79.02 233	90.06 239

pmmvs5		81.34 246	79.54 250	86.73 247	85.02 314	76.91 242	96.22 172	91.65 298	77.65 261	73.55 266	88.61 239	55.70 289	94.43 290	74.12 241	73.35 262	88.86 270

MS-PatchMatch		83.05 221	81.82 221	86.72 248	89.64 263	79.10 190	94.88 225	94.59 211	79.70 232	70.67 289	89.65 226	50.43 305	96.82 198	70.82 265	95.99 102	84.25 328

eth_miper_zixun_eth		83.12 220	82.01 217	86.47 249	91.85 229	74.80 267	94.33 236	93.18 277	79.11 243	75.74 253	87.25 259	72.71 174	95.32 262	76.78 213	67.13 310	89.27 253

LPG-MVS_test		84.20 204	83.49 198	86.33 250	90.88 241	73.06 282	95.28 208	94.13 229	82.20 186	76.31 238	93.20 180	54.83 296	96.95 190	83.72 150	80.83 220	88.98 264

LGP-MVS_train				86.33 250	90.88 241	73.06 282		94.13 229	82.20 186	76.31 238	93.20 180	54.83 296	96.95 190	83.72 150	80.83 220	88.98 264

ACMP	81.66 11	84.00 205	83.22 201	86.33 250	91.53 233	72.95 284	95.91 188	93.79 248	83.70 160	73.79 265	92.22 189	54.31 299	96.89 194	83.98 145	79.74 226	89.16 256
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
tfpnnormal		78.14 272	75.42 278	86.31 253	88.33 279	79.24 184	94.41 233	96.22 119	73.51 293	69.81 295	85.52 288	55.43 290	95.75 239	47.65 343	67.86 302	83.95 330

ACMM	80.70 13	83.72 210	82.85 205	86.31 253	91.19 236	72.12 289	95.88 189	94.29 222	80.44 214	77.02 228	91.96 193	55.24 292	97.14 185	79.30 190	80.38 222	89.67 244
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
pm-mvs1		80.05 257	78.02 260	86.15 255	85.42 308	75.81 260	95.11 218	92.69 287	77.13 268	70.36 291	87.43 255	58.44 269	95.27 265	71.36 257	64.25 320	87.36 299

ppachtmachnet_test		77.19 281	74.22 289	86.13 256	85.39 309	78.22 212	93.98 246	91.36 302	71.74 308	67.11 304	84.87 299	56.67 282	93.37 308	52.21 332	64.59 318	86.80 304

D2MVS		82.67 228	81.55 224	86.04 257	87.77 284	76.47 247	95.21 212	96.58 78	82.66 181	70.26 292	85.46 289	60.39 254	95.80 236	76.40 218	79.18 231	85.83 318

USDC		78.65 268	76.25 273	85.85 258	87.58 286	74.60 269	89.58 298	90.58 313	84.05 147	63.13 323	88.23 245	40.69 337	96.86 197	66.57 282	75.81 249	86.09 314

mvs-test1		86.83 164	87.17 147	85.81 259	91.96 223	65.24 326	97.90 49	93.34 271	85.57 103	84.51 152	95.14 147	61.99 246	97.19 181	83.55 154	90.55 150	95.00 188

KD-MVS_2432*1600		77.63 277	74.92 282	85.77 260	90.86 243	79.44 179	88.08 309	93.92 239	76.26 273	67.05 305	82.78 313	72.15 181	91.92 320	61.53 302	41.62 349	85.94 316

miper_refine_blended		77.63 277	74.92 282	85.77 260	90.86 243	79.44 179	88.08 309	93.92 239	76.26 273	67.05 305	82.78 313	72.15 181	91.92 320	61.53 302	41.62 349	85.94 316

ADS-MVSNet2		79.57 261	77.53 263	85.71 262	93.78 171	72.13 288	79.48 335	86.11 337	73.09 298	80.14 201	79.99 328	62.15 243	90.14 336	59.49 310	83.52 202	94.85 190

Patchmtry		77.36 280	74.59 285	85.67 263	89.75 260	75.75 261	77.85 342	91.12 305	60.28 340	71.23 284	80.35 325	75.45 140	93.56 305	57.94 315	67.34 309	87.68 290

MVP-Stereo		82.65 229	81.67 223	85.59 264	86.10 301	78.29 209	93.33 259	92.82 284	77.75 260	69.17 299	87.98 249	59.28 263	95.76 238	71.77 253	96.88 87	82.73 334
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
Fast-Effi-MVS+-dtu		83.33 215	82.60 211	85.50 265	89.55 265	69.38 314	96.09 180	91.38 300	82.30 185	75.96 248	91.41 199	56.71 281	95.58 252	75.13 231	84.90 197	91.54 217

our_test_3		77.90 275	75.37 279	85.48 266	85.39 309	76.74 245	93.63 251	91.67 297	73.39 296	65.72 313	84.65 301	58.20 270	93.13 310	57.82 316	67.87 301	86.57 307

v7n		79.32 265	77.34 264	85.28 267	84.05 324	72.89 285	93.38 257	93.87 242	75.02 283	70.68 288	84.37 302	59.58 259	95.62 249	67.60 275	67.50 306	87.32 300

IterMVS		80.67 253	79.16 253	85.20 268	89.79 259	76.08 253	92.97 270	91.86 294	80.28 220	71.20 285	85.14 295	57.93 274	91.34 326	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.
ACMH+	76.62 16	77.47 279	74.94 281	85.05 269	91.07 239	71.58 298	93.26 264	90.01 315	71.80 307	64.76 316	88.55 240	41.62 333	96.48 208	62.35 301	71.00 271	87.09 302

jajsoiax		82.12 237	81.15 230	85.03 270	84.19 321	70.70 302	94.22 242	93.95 237	83.07 171	73.48 267	89.75 225	49.66 308	95.37 259	82.24 168	79.76 224	89.02 262

mvs_tets		81.74 240	80.71 235	84.84 271	84.22 320	70.29 305	93.91 247	93.78 249	82.77 178	73.37 268	89.46 229	47.36 317	95.31 263	81.99 169	79.55 229	88.92 268

LTVRE_ROB	73.68 18	77.99 273	75.74 277	84.74 272	90.45 250	72.02 290	86.41 323	91.12 305	72.57 303	66.63 308	87.27 257	54.95 295	96.98 189	56.29 322	75.98 247	85.21 322
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
IterMVS-SCA-FT		80.51 255	79.10 254	84.73 273	89.63 264	74.66 268	92.98 269	91.81 296	80.05 225	71.06 287	85.18 293	58.04 271	91.40 325	72.48 251	70.70 276	88.12 283

Baseline_NR-MVSNet		81.22 248	80.07 245	84.68 274	85.32 312	75.12 266	96.48 152	88.80 325	76.24 275	77.28 225	86.40 276	67.61 208	94.39 291	75.73 227	66.73 314	84.54 325

miper_lstm_enhance		81.66 243	80.66 236	84.67 275	91.19 236	71.97 292	91.94 282	93.19 276	77.86 259	72.27 280	85.26 290	73.46 168	93.42 306	73.71 245	67.05 311	88.61 271

test_djsdf		83.00 224	82.45 213	84.64 276	84.07 323	69.78 310	94.80 227	94.48 214	80.74 206	75.41 256	87.70 252	61.32 252	95.10 275	83.77 148	79.76 224	89.04 261

TransMVSNet (Re)		76.94 283	74.38 287	84.62 277	85.92 303	75.25 265	95.28 208	89.18 322	73.88 291	67.22 302	86.46 272	59.64 257	94.10 295	59.24 313	52.57 340	84.50 326

Patchmatch-RL test		76.65 285	74.01 292	84.55 278	77.37 343	64.23 328	78.49 341	82.84 348	78.48 253	64.63 317	73.40 340	76.05 128	91.70 324	76.99 210	57.84 330	97.72 105

AllTest		75.92 288	73.06 295	84.47 279	92.18 213	67.29 320	91.07 291	84.43 342	67.63 320	63.48 319	90.18 220	38.20 339	97.16 182	57.04 318	73.37 260	88.97 266

TestCases				84.47 279	92.18 213	67.29 320		84.43 342	67.63 320	63.48 319	90.18 220	38.20 339	97.16 182	57.04 318	73.37 260	88.97 266

MVS-HIRNet		71.36 308	67.00 312	84.46 281	90.58 248	69.74 311	79.15 338	87.74 331	46.09 349	61.96 328	50.50 350	45.14 321	95.64 247	53.74 329	88.11 170	88.00 285

JIA-IIPM		79.00 267	77.20 265	84.40 282	89.74 262	64.06 330	75.30 345	95.44 162	62.15 332	81.90 184	59.08 347	78.92 82	95.59 251	66.51 283	85.78 190	93.54 209

LCM-MVSNet-Re		83.75 209	83.54 197	84.39 283	93.54 178	64.14 329	92.51 275	84.03 344	83.90 154	66.14 311	86.59 269	67.36 212	92.68 311	84.89 141	92.87 131	96.35 161

anonymousdsp		80.98 251	79.97 247	84.01 284	81.73 329	70.44 304	92.49 276	93.58 260	77.10 270	72.98 274	86.31 277	57.58 275	94.90 280	79.32 189	78.63 238	86.69 306

COLMAP_ROB	73.24 19	75.74 290	73.00 296	83.94 285	92.38 202	69.08 315	91.85 284	86.93 333	61.48 336	65.32 314	90.27 219	42.27 331	96.93 193	50.91 336	75.63 250	85.80 319
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
XVG-ACMP-BASELINE		79.38 264	77.90 261	83.81 286	84.98 315	67.14 323	89.03 302	93.18 277	80.26 222	72.87 275	88.15 247	38.55 338	96.26 216	76.05 223	78.05 242	88.02 284

CP-MVSNet		81.01 250	80.08 244	83.79 287	87.91 283	70.51 303	94.29 241	95.65 149	80.83 204	72.54 279	88.84 236	63.71 233	92.32 315	68.58 274	68.36 296	88.55 272

WR-MVS_H		81.02 249	80.09 243	83.79 287	88.08 282	71.26 301	94.46 231	96.54 84	80.08 224	72.81 276	86.82 265	70.36 198	92.65 312	64.18 292	67.50 306	87.46 298

test0.0.03 1		82.79 226	82.48 212	83.74 289	86.81 291	72.22 286	96.52 150	95.03 184	83.76 158	73.00 273	93.20 180	72.30 179	88.88 338	64.15 293	77.52 244	90.12 235

Effi-MVS+-dtu		84.61 196	84.90 178	83.72 290	91.96 223	63.14 333	94.95 223	93.34 271	85.57 103	79.79 205	87.12 261	61.99 246	95.61 250	83.55 154	85.83 189	92.41 215

EG-PatchMatch MVS		74.92 293	72.02 298	83.62 291	83.76 326	73.28 280	93.62 252	92.04 293	68.57 319	58.88 335	83.80 307	31.87 349	95.57 253	56.97 320	78.67 235	82.00 340

MVS_0304		78.43 269	76.70 270	83.60 292	88.22 280	69.81 309	92.91 271	95.10 179	72.32 305	78.71 213	80.29 327	33.78 345	93.37 308	68.77 272	80.23 223	87.63 291

pmmvs6		74.65 295	71.67 299	83.60 292	79.13 337	69.94 307	93.31 263	90.88 311	61.05 339	65.83 312	84.15 305	43.43 325	94.83 283	66.62 280	60.63 327	86.02 315

PS-CasMVS		80.27 256	79.18 252	83.52 294	87.56 287	69.88 308	94.08 245	95.29 173	80.27 221	72.08 281	88.51 243	59.22 264	92.23 317	67.49 276	68.15 299	88.45 276

OpenMVS_ROB	68.52 20	73.02 302	69.57 307	83.37 295	80.54 333	71.82 294	93.60 253	88.22 329	62.37 331	61.98 327	83.15 312	35.31 344	95.47 255	45.08 346	75.88 248	82.82 332

FMVSNet5		76.46 286	74.16 290	83.35 296	90.05 257	76.17 252	89.58 298	89.85 316	71.39 310	65.29 315	80.42 324	50.61 304	87.70 342	61.05 307	69.24 290	86.18 312

PEN-MVS		79.47 263	78.26 259	83.08 297	86.36 294	68.58 316	93.85 248	94.77 199	79.76 230	71.37 283	88.55 240	59.79 256	92.46 313	64.50 291	65.40 316	88.19 281

MDA-MVSNet_test_wron		73.54 298	70.43 305	82.86 298	84.55 316	71.85 293	91.74 286	91.32 304	67.63 320	46.73 347	81.09 322	55.11 293	90.42 334	55.91 324	59.76 328	86.31 310

YYNet1		73.53 299	70.43 305	82.85 299	84.52 318	71.73 296	91.69 287	91.37 301	67.63 320	46.79 346	81.21 321	55.04 294	90.43 333	55.93 323	59.70 329	86.38 309

TinyColmap		72.41 304	68.99 310	82.68 300	88.11 281	69.59 312	88.41 307	85.20 339	65.55 326	57.91 338	84.82 300	30.80 351	95.94 228	51.38 333	68.70 293	82.49 337

CVMVSNet		84.83 192	85.57 164	82.63 301	91.55 231	60.38 339	95.13 216	95.03 184	80.60 209	82.10 182	94.71 156	66.40 219	90.19 335	74.30 239	90.32 151	97.31 132

pmmvs-eth3d		73.59 297	70.66 303	82.38 302	76.40 345	73.38 277	89.39 301	89.43 319	72.69 302	60.34 333	77.79 333	46.43 319	91.26 328	66.42 284	57.06 331	82.51 335

ITE_SJBPF				82.38 302	87.00 290	65.59 325		89.55 318	79.99 227	69.37 297	91.30 202	41.60 334	95.33 261	62.86 300	74.63 255	86.24 311

DTE-MVSNet		78.37 270	77.06 267	82.32 304	85.22 313	67.17 322	93.40 256	93.66 255	78.71 251	70.53 290	88.29 244	59.06 265	92.23 317	61.38 305	63.28 324	87.56 295

test_0402		72.68 303	69.54 308	82.09 305	88.67 275	71.81 295	92.72 274	86.77 334	61.52 335	62.21 326	83.91 306	43.22 327	93.76 302	34.60 350	72.23 268	80.72 342

MDA-MVSNet-bldmvs		71.45 307	67.94 311	81.98 306	85.33 311	68.50 317	92.35 279	88.76 326	70.40 312	42.99 348	81.96 316	46.57 318	91.31 327	48.75 342	54.39 334	86.11 313

UnsupCasMVSNet_eth		73.25 300	70.57 304	81.30 307	77.53 341	66.33 324	87.24 317	93.89 241	80.38 217	57.90 339	81.59 318	42.91 330	90.56 332	65.18 289	48.51 343	87.01 303

SixPastTwentyTwo		76.04 287	74.32 288	81.22 308	84.54 317	61.43 338	91.16 290	89.30 321	77.89 257	64.04 318	86.31 277	48.23 310	94.29 293	63.54 297	63.84 322	87.93 286

RPSCF		77.73 276	76.63 271	81.06 309	88.66 276	55.76 347	87.77 313	87.88 330	64.82 328	74.14 264	92.79 185	49.22 309	96.81 199	67.47 277	76.88 246	90.62 224

UnsupCasMVSNet_bld		68.60 314	64.50 317	80.92 310	74.63 348	67.80 318	83.97 330	92.94 283	65.12 327	54.63 343	68.23 345	35.97 341	92.17 319	60.13 308	44.83 346	82.78 333

CL-MVSNet_2432*1600		75.81 289	74.14 291	80.83 311	78.33 339	67.79 319	94.22 242	93.52 261	77.28 267	69.82 294	81.54 319	61.47 251	89.22 337	57.59 317	53.51 336	85.48 320

OurMVSNet-221017-0		77.18 282	76.06 274	80.55 312	83.78 325	60.00 340	90.35 294	91.05 308	77.01 272	66.62 309	87.92 250	47.73 315	94.03 296	71.63 254	68.44 295	87.62 292

Anonymous20231206		75.29 292	73.64 293	80.22 313	80.75 330	63.38 332	93.36 258	90.71 312	73.09 298	67.12 303	83.70 308	50.33 306	90.85 330	53.63 330	70.10 281	86.44 308

lessismore_v0				79.98 314	80.59 332	58.34 343		80.87 350		58.49 336	83.46 310	43.10 328	93.89 298	63.11 299	48.68 342	87.72 288

K. test v3		73.62 296	71.59 300	79.69 315	82.98 327	59.85 341	90.85 293	88.83 324	77.13 268	58.90 334	82.11 315	43.62 324	91.72 323	65.83 286	54.10 335	87.50 297

TDRefinement		69.20 312	65.78 316	79.48 316	66.04 353	62.21 335	88.21 308	86.12 336	62.92 330	61.03 331	85.61 285	33.23 346	94.16 294	55.82 325	53.02 338	82.08 339

testgi		74.88 294	73.40 294	79.32 317	80.13 334	61.75 336	93.21 265	86.64 335	79.49 235	66.56 310	91.06 205	35.51 343	88.67 339	56.79 321	71.25 269	87.56 295

CMPMVS	54.94 21	75.71 291	74.56 286	79.17 318	79.69 335	55.98 345	89.59 297	93.30 273	60.28 340	53.85 344	89.07 232	47.68 316	96.33 213	76.55 215	81.02 219	85.22 321
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
MIMVSNet1		69.44 310	66.65 314	77.84 319	76.48 344	62.84 334	87.42 315	88.97 323	66.96 325	57.75 340	79.72 330	32.77 348	85.83 347	46.32 344	63.42 323	84.85 324

new-patchmatchnet		68.85 313	65.93 315	77.61 320	73.57 350	63.94 331	90.11 296	88.73 327	71.62 309	55.08 342	73.60 339	40.84 336	87.22 344	51.35 335	48.49 344	81.67 341

LF4IMVS		72.36 305	70.82 302	76.95 321	79.18 336	56.33 344	86.12 324	86.11 337	69.30 318	63.06 324	86.66 268	33.03 347	92.25 316	65.33 288	68.64 294	82.28 338

EU-MVSNet		76.92 284	76.95 268	76.83 322	84.10 322	54.73 349	91.77 285	92.71 286	72.74 301	69.57 296	88.69 238	58.03 273	87.43 343	64.91 290	70.00 283	88.33 279

PM-MVS		69.32 311	66.93 313	76.49 323	73.60 349	55.84 346	85.91 325	79.32 353	74.72 285	61.09 330	78.18 332	21.76 353	91.10 329	70.86 263	56.90 332	82.51 335

pmmvs3		65.75 316	62.18 319	76.45 324	67.12 352	64.54 327	88.68 305	85.05 340	54.77 348	57.54 341	73.79 338	29.40 352	86.21 346	55.49 326	47.77 345	78.62 343

ambc				76.02 325	68.11 351	51.43 350	64.97 351	89.59 317		60.49 332	74.49 337	17.17 355	92.46 313	61.50 304	52.85 339	84.17 329

test20.03		72.36 305	71.15 301	75.98 326	77.79 340	59.16 342	92.40 278	89.35 320	74.09 289	61.50 329	84.32 303	48.09 311	85.54 348	50.63 337	62.15 326	83.24 331

DIV-MVS_2432*1600		70.97 309	69.31 309	75.95 327	76.24 347	55.39 348	87.45 314	90.94 310	70.20 314	62.96 325	77.48 334	44.01 322	88.09 340	61.25 306	53.26 337	84.37 327

DSMNet-mixed		73.13 301	72.45 297	75.19 328	77.51 342	46.82 352	85.09 329	82.01 349	67.61 324	69.27 298	81.33 320	50.89 302	86.28 345	54.54 327	83.80 201	92.46 214

new_pmnet		66.18 315	63.18 318	75.18 329	76.27 346	61.74 337	83.79 331	84.66 341	56.64 347	51.57 345	71.85 344	31.29 350	87.93 341	49.98 338	62.55 325	75.86 345

LCM-MVSNet		52.52 319	48.24 322	65.35 330	47.63 359	41.45 356	72.55 349	83.62 346	31.75 352	37.66 350	57.92 348	9.19 362	76.76 351	49.26 340	44.60 347	77.84 344

PMMVS2		50.90 320	46.31 323	64.67 331	55.53 355	46.67 353	77.30 344	71.02 355	40.89 350	34.16 352	59.32 346	9.83 361	76.14 353	40.09 349	28.63 352	71.21 346

N_pmnet		61.30 317	60.20 320	64.60 332	84.32 319	17.00 364	91.67 288	10.98 363	61.77 334	58.45 337	78.55 331	49.89 307	91.83 322	42.27 348	63.94 321	84.97 323

DeepMVS_CX				64.06 333	78.53 338	43.26 355		68.11 357	69.94 315	38.55 349	76.14 336	18.53 354	79.34 349	43.72 347	41.62 349	69.57 348

FPMVS		55.09 318	52.93 321	61.57 334	55.98 354	40.51 357	83.11 332	83.41 347	37.61 351	34.95 351	71.95 343	14.40 356	76.95 350	29.81 351	65.16 317	67.25 349

ANet_high		46.22 321	41.28 326	61.04 335	39.91 361	46.25 354	70.59 350	76.18 354	58.87 345	23.09 355	48.00 352	12.58 358	66.54 355	28.65 352	13.62 355	70.35 347

Gipuma		45.11 322	42.05 324	54.30 336	80.69 331	51.30 351	35.80 355	83.81 345	28.13 353	27.94 354	34.53 354	11.41 360	76.70 352	21.45 353	54.65 333	34.90 353
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PMVS	34.80 23	39.19 324	35.53 327	50.18 337	29.72 362	30.30 359	59.60 353	66.20 358	26.06 354	17.91 357	49.53 351	3.12 363	74.09 354	18.19 355	49.40 341	46.14 350
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MVE	35.65 22	33.85 325	29.49 330	46.92 338	41.86 360	36.28 358	50.45 354	56.52 360	18.75 357	18.28 356	37.84 353	2.41 364	58.41 356	18.71 354	20.62 353	46.06 351
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
tmp_tt		41.54 323	41.93 325	40.38 339	20.10 363	26.84 360	61.93 352	59.09 359	14.81 358	28.51 353	80.58 323	35.53 342	48.33 359	63.70 296	13.11 356	45.96 352

E-PMN		32.70 326	32.39 328	33.65 340	53.35 357	25.70 361	74.07 347	53.33 361	21.08 355	17.17 358	33.63 356	11.85 359	54.84 357	12.98 356	14.04 354	20.42 354

EMVS		31.70 327	31.45 329	32.48 341	50.72 358	23.95 362	74.78 346	52.30 362	20.36 356	16.08 359	31.48 357	12.80 357	53.60 358	11.39 357	13.10 357	19.88 355

wuyk23d		14.10 329	13.89 332	14.72 342	55.23 356	22.91 363	33.83 356	3.56 364	4.94 359	4.11 360	2.28 362	2.06 365	19.66 360	10.23 358	8.74 358	1.59 358

test123		9.07 331	11.73 334	1.11 343	0.50 365	0.77 365	89.44 300	0.20 366	0.34 361	2.15 362	10.72 361	0.34 366	0.32 361	1.79 360	0.08 360	2.23 356

testmvs		9.92 330	12.94 333	0.84 344	0.65 364	0.29 366	93.78 249	0.39 365	0.42 360	2.85 361	15.84 360	0.17 367	0.30 362	2.18 359	0.21 359	1.91 357

uanet_test		0.00 334	0.00 337	0.00 345	0.00 366	0.00 367	0.00 357	0.00 367	0.00 362	0.00 363	0.00 363	0.00 368	0.00 363	0.00 361	0.00 361	0.00 359

cdsmvs_eth3d_5k		21.43 328	28.57 331	0.00 345	0.00 366	0.00 367	0.00 357	95.93 137	0.00 362	0.00 363	97.66 69	63.57 234	0.00 363	0.00 361	0.00 361	0.00 359

pcd_1.5k_mvsjas		5.92 333	7.89 336	0.00 345	0.00 366	0.00 367	0.00 357	0.00 367	0.00 362	0.00 363	0.00 363	71.04 192	0.00 363	0.00 361	0.00 361	0.00 359

sosnet-low-res		0.00 334	0.00 337	0.00 345	0.00 366	0.00 367	0.00 357	0.00 367	0.00 362	0.00 363	0.00 363	0.00 368	0.00 363	0.00 361	0.00 361	0.00 359

sosnet		0.00 334	0.00 337	0.00 345	0.00 366	0.00 367	0.00 357	0.00 367	0.00 362	0.00 363	0.00 363	0.00 368	0.00 363	0.00 361	0.00 361	0.00 359

uncertanet		0.00 334	0.00 337	0.00 345	0.00 366	0.00 367	0.00 357	0.00 367	0.00 362	0.00 363	0.00 363	0.00 368	0.00 363	0.00 361	0.00 361	0.00 359

Regformer		0.00 334	0.00 337	0.00 345	0.00 366	0.00 367	0.00 357	0.00 367	0.00 362	0.00 363	0.00 363	0.00 368	0.00 363	0.00 361	0.00 361	0.00 359

ab-mvs-re		8.11 332	10.81 335	0.00 345	0.00 366	0.00 367	0.00 357	0.00 367	0.00 362	0.00 363	97.30 91	0.00 368	0.00 363	0.00 361	0.00 361	0.00 359

uanet		0.00 334	0.00 337	0.00 345	0.00 366	0.00 367	0.00 357	0.00 367	0.00 362	0.00 363	0.00 363	0.00 368	0.00 363	0.00 361	0.00 361	0.00 359

ZD-MVS					99.09 9	83.22 93		96.60 76	82.88 176	93.61 49	98.06 48	82.93 48	99.14 91	95.51 29	98.49 38

RE-MVS-def			91.18 80		97.76 73	76.03 254	96.20 174	95.44 162	80.56 211	90.72 87	97.84 62	73.36 170		91.99 72	96.79 91	97.75 103

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

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

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

9.14			94.26 27		98.10 60		98.14 34	96.52 87	84.74 125	94.83 34	98.80 9	82.80 51	99.37 68	95.95 22	98.42 42

save fliter					98.24 52	83.34 89	98.61 23	96.57 79	91.32 18

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

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

GSMVS																97.54 117

test_part2					98.90 17	85.14 57				96.07 17

sam_mvs1												77.59 100				97.54 117

sam_mvs												75.35 147

MTGPA								96.33 111

test_post1							85.88 326				30.24 358	73.77 163	95.07 277	73.89 242

test_post											33.80 355	76.17 126	95.97 224

patchmatchnet-post											77.09 335	77.78 99	95.39 257

MTMP							97.53 76	68.16 356

gm-plane-assit					92.27 207	79.64 177			84.47 136		95.15 146		97.93 141	85.81 132

test9_res														96.00 21	99.03 11	98.31 58

TEST9					98.64 31	83.71 81	97.82 52	96.65 67	84.29 142	95.16 25	98.09 43	84.39 31	99.36 70

test_8					98.63 33	83.64 84	97.81 54	96.63 72	84.50 134	95.10 27	98.11 42	84.33 32	99.23 76

agg_prior2														94.30 42	99.00 13	98.57 41

agg_prior					98.59 35	83.13 94		96.56 81		94.19 41			99.16 89

test_prior4						82.34 108	97.75 61

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

旧先验2							96.97 126		74.06 290	96.10 16			97.76 150	88.38 115

新几何2							96.42 161

旧先验1					97.39 88	79.58 178		96.54 84			98.08 46	84.00 36			97.42 74	97.62 114

无先验							96.87 131	96.78 44	77.39 264				99.52 53	79.95 182		98.43 50

原ACMM2							96.84 132

test222					96.15 108	78.41 206	95.87 190	96.46 94	71.97 306	89.66 101	97.45 82	76.33 124			98.24 54	98.30 59

testdata2													99.48 58	76.45 217

segment_acmp												82.69 53

testdata1							95.57 201		87.44 74

plane_prior7					91.86 227	77.55 232

plane_prior6					91.98 222	77.92 224						64.77 229

plane_prior5								94.69 200					97.30 174	87.08 124	82.82 212	90.96 221

plane_prior4											94.15 168

plane_prior3						77.75 228			90.17 31	81.33 188

plane_prior2							97.18 101		89.89 33

plane_prior1					91.95 225

plane_prior						77.96 221	97.52 78		90.36 30						82.96 210

n2								0.00 367

nn								0.00 367

door-mid								79.75 352

test11								96.50 90

door								80.13 351

HQP5-MVS						78.48 202

HQP-NCC					92.08 217		97.63 67		90.52 27	82.30 176

ACMP_Plane					92.08 217		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

HQP2-MVS												65.40 224

NP-MVS					92.04 221	78.22 212					94.56 159

MDTV_nov1_ep13_2view						81.74 126	86.80 319		80.65 208	85.65 140		74.26 158		76.52 216		96.98 140

MDTV_nov1_ep13			83.69 191		94.09 165	81.01 140	86.78 320	96.09 127	83.81 157	84.75 147	84.32 303	74.44 157	96.54 206	63.88 294	85.07 196

ACMMP++_ref															78.45 240

ACMMP++															79.05 232

Test By Simon												71.65 185

High-resolution multi-view benchmark