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.

ESAPD		97.86 1	97.65 2	98.47 1	99.17 26	95.78 3	97.21 128	98.35 19	95.16 13	98.71 3	98.80 3	95.05 1	99.89 3	96.70 14	99.73 1	99.73 2

HPM-MVS++		97.34 9	96.97 13	98.47 1	99.08 29	96.16 1	97.55 95	97.97 81	95.59 4	96.61 39	97.89 54	92.57 19	99.84 14	95.95 35	99.51 19	99.40 36

CNVR-MVS		97.68 3	97.44 6	98.37 3	98.90 35	95.86 2	97.27 120	98.08 53	95.81 3	97.87 13	98.31 34	94.26 3	99.68 38	97.02 4	99.49 23	99.57 13

SMA-MVS		97.35 8	97.03 10	98.30 4	99.06 31	95.42 5	97.94 45	98.18 36	90.57 152	98.85 2	98.94 1	93.33 10	99.83 15	96.72 13	99.68 3	99.63 6

ACMMP_Plus		97.20 11	96.86 18	98.23 5	99.09 28	95.16 9	97.60 88	98.19 34	92.82 78	97.93 12	98.74 5	91.60 38	99.86 7	96.26 23	99.52 17	99.67 3

MCST-MVS		97.18 12	96.84 19	98.20 6	99.30 17	95.35 6	97.12 137	98.07 58	93.54 53	96.08 57	97.69 70	93.86 6	99.71 30	96.50 19	99.39 34	99.55 17

3Dnovator+	91.43 4	95.40 63	94.48 82	98.16 7	96.90 146	95.34 7	98.48 14	97.87 89	94.65 29	88.53 247	98.02 49	83.69 133	99.71 30	93.18 95	98.96 67	99.44 33

NCCC		97.30 10	97.03 10	98.11 8	98.77 38	95.06 11	97.34 114	98.04 67	95.96 2	97.09 29	97.88 56	93.18 11	99.71 30	95.84 38	99.17 54	99.56 15

APDe-MVS		97.82 2	97.73 1	98.08 9	99.15 27	94.82 13	98.81 2	98.30 23	94.76 25	98.30 6	98.90 2	93.77 7	99.68 38	97.93 1	99.69 2	99.75 1

APD-MVS		96.95 24	96.60 29	98.01 10	99.03 32	94.93 12	97.72 66	98.10 50	91.50 116	98.01 10	98.32 33	92.33 23	99.58 56	94.85 62	99.51 19	99.53 22
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
MP-MVS-pluss		96.70 34	96.27 41	97.98 11	99.23 24	94.71 14	96.96 147	98.06 60	90.67 142	95.55 80	98.78 4	91.07 45	99.86 7	96.58 17	99.55 14	99.38 40
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
zzz-MVS		97.07 18	96.77 25	97.97 12	99.37 10	94.42 19	97.15 135	98.08 53	95.07 15	96.11 55	98.59 7	90.88 50	99.90 1	96.18 30	99.50 21	99.58 11

MTAPA		97.08 17	96.78 24	97.97 12	99.37 10	94.42 19	97.24 122	98.08 53	95.07 15	96.11 55	98.59 7	90.88 50	99.90 1	96.18 30	99.50 21	99.58 11

SteuartSystems-ACMMP		97.62 4	97.53 3	97.87 14	98.39 62	94.25 23	98.43 16	98.27 25	95.34 9	98.11 7	98.56 9	94.53 2	99.71 30	96.57 18	99.62 7	99.65 4
Skip Steuart: Steuart Systems R&D Blog.
MVS_0304		96.05 52	95.45 55	97.85 15	97.75 108	94.50 16	96.87 157	97.95 84	95.46 6	95.60 78	98.01 50	80.96 197	99.83 15	97.23 2	99.25 47	99.23 50

HFP-MVS		97.14 15	96.92 16	97.83 16	99.42 3	94.12 29	98.52 10	98.32 20	93.21 60	97.18 22	98.29 37	92.08 28	99.83 15	95.63 43	99.59 9	99.54 19

#test#		97.02 21	96.75 26	97.83 16	99.42 3	94.12 29	98.15 30	98.32 20	92.57 85	97.18 22	98.29 37	92.08 28	99.83 15	95.12 53	99.59 9	99.54 19

GST-MVS		96.85 28	96.52 33	97.82 18	99.36 12	94.14 28	98.29 22	98.13 43	92.72 81	96.70 33	98.06 46	91.35 41	99.86 7	94.83 63	99.28 45	99.47 30

XVS		97.18 12	96.96 14	97.81 19	99.38 8	94.03 33	98.59 7	98.20 32	94.85 18	96.59 41	98.29 37	91.70 36	99.80 21	95.66 40	99.40 32	99.62 7

X-MVStestdata		91.71 188	89.67 245	97.81 19	99.38 8	94.03 33	98.59 7	98.20 32	94.85 18	96.59 41	32.69 365	91.70 36	99.80 21	95.66 40	99.40 32	99.62 7

ACMMPR		97.07 18	96.84 19	97.79 21	99.44 2	93.88 35	98.52 10	98.31 22	93.21 60	97.15 24	98.33 31	91.35 41	99.86 7	95.63 43	99.59 9	99.62 7

alignmvs		95.87 58	95.23 62	97.78 22	97.56 121	95.19 8	97.86 51	97.17 160	94.39 33	96.47 46	96.40 142	85.89 109	99.20 103	96.21 28	95.11 154	98.95 76

DeepC-MVS_fast	93.89 2	96.93 26	96.64 28	97.78 22	98.64 49	94.30 21	97.41 105	98.04 67	94.81 23	96.59 41	98.37 24	91.24 43	99.64 47	95.16 51	99.52 17	99.42 35
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
region2R		97.07 18	96.84 19	97.77 24	99.46 1	93.79 39	98.52 10	98.24 29	93.19 63	97.14 25	98.34 28	91.59 39	99.87 6	95.46 48	99.59 9	99.64 5

CDPH-MVS		95.97 55	95.38 58	97.77 24	98.93 34	94.44 18	96.35 213	97.88 87	86.98 256	96.65 37	97.89 54	91.99 32	99.47 81	92.26 104	99.46 25	99.39 37

canonicalmvs		96.02 54	95.45 55	97.75 26	97.59 119	95.15 10	98.28 23	97.60 113	94.52 30	96.27 51	96.12 153	87.65 87	99.18 106	96.20 29	94.82 158	98.91 80

train_agg		96.30 46	95.83 49	97.72 27	98.70 41	94.19 25	96.41 205	98.02 70	88.58 206	96.03 58	97.56 85	92.73 15	99.59 53	95.04 55	99.37 39	99.39 37

MP-MVS		96.77 32	96.45 37	97.72 27	99.39 7	93.80 38	98.41 17	98.06 60	93.37 55	95.54 81	98.34 28	90.59 53	99.88 4	94.83 63	99.54 15	99.49 26
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
PHI-MVS		96.77 32	96.46 36	97.71 29	98.40 60	94.07 31	98.21 29	98.45 15	89.86 163	97.11 28	98.01 50	92.52 21	99.69 36	96.03 34	99.53 16	99.36 42

TSAR-MVS + MP.		97.42 6	97.33 7	97.69 30	99.25 21	94.24 24	98.07 35	97.85 92	93.72 47	98.57 4	98.35 25	93.69 8	99.40 90	97.06 3	99.46 25	99.44 33

Regformer-2		97.16 14	96.99 12	97.67 31	98.32 68	93.84 37	96.83 162	98.10 50	95.24 10	97.49 15	98.25 40	92.57 19	99.61 48	96.80 9	99.29 43	99.56 15

PGM-MVS		96.81 30	96.53 32	97.65 32	99.35 14	93.53 47	97.65 75	98.98 1	92.22 91	97.14 25	98.44 17	91.17 44	99.85 11	94.35 71	99.46 25	99.57 13

test12				97.65 32	98.46 56	94.26 22		97.66 108		95.52 82		90.89 49	99.46 82		99.25 47	99.22 51

mPP-MVS		96.86 27	96.60 29	97.64 34	99.40 5	93.44 49	98.50 13	98.09 52	93.27 59	95.95 64	98.33 31	91.04 46	99.88 4	95.20 50	99.57 13	99.60 10

CP-MVS		97.02 21	96.81 22	97.64 34	99.33 15	93.54 46	98.80 3	98.28 24	92.99 69	96.45 48	98.30 36	91.90 33	99.85 11	95.61 45	99.68 3	99.54 19

HSP-MVS		97.53 5	97.49 5	97.63 36	99.40 5	93.77 42	98.53 9	97.85 92	95.55 5	98.56 5	97.81 63	93.90 5	99.65 42	96.62 15	99.21 51	99.48 28

agg_prior3		96.16 50	95.67 51	97.62 37	98.67 43	93.88 35	96.41 205	98.00 74	87.93 231	95.81 68	97.47 89	92.33 23	99.59 53	95.04 55	99.37 39	99.39 37

agg_prior1		96.22 49	95.77 50	97.56 38	98.67 43	93.79 39	96.28 221	98.00 74	88.76 203	95.68 74	97.55 87	92.70 17	99.57 64	95.01 57	99.32 41	99.32 44

Regformer-1		97.10 16	96.96 14	97.54 39	98.32 68	93.48 48	96.83 162	97.99 79	95.20 12	97.46 16	98.25 40	92.48 22	99.58 56	96.79 11	99.29 43	99.55 17

CANet		96.39 44	96.02 46	97.50 40	97.62 116	93.38 51	97.02 142	97.96 82	95.42 8	94.86 88	97.81 63	87.38 93	99.82 19	96.88 7	99.20 52	99.29 46

3Dnovator	91.36 5	95.19 72	94.44 84	97.44 41	96.56 162	93.36 53	98.65 6	98.36 16	94.12 38	89.25 237	98.06 46	82.20 179	99.77 23	93.41 92	99.32 41	99.18 53

HPM-MVS		96.69 35	96.45 37	97.40 42	99.36 12	93.11 57	98.87 1	98.06 60	91.17 130	96.40 49	97.99 52	90.99 47	99.58 56	95.61 45	99.61 8	99.49 26
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
DP-MVS Recon		95.68 60	95.12 65	97.37 43	99.19 25	94.19 25	97.03 140	98.08 53	88.35 220	95.09 86	97.65 74	89.97 60	99.48 80	92.08 113	98.59 76	98.44 119

1121		94.71 88	93.83 92	97.34 44	98.57 54	93.64 44	96.04 235	97.73 98	81.56 320	95.68 74	97.85 60	90.23 56	99.65 42	87.68 193	99.12 60	98.73 93

新几何1				97.32 45	98.60 50	93.59 45		97.75 96	81.58 318	95.75 71	97.85 60	90.04 59	99.67 40	86.50 216	99.13 57	98.69 98

DELS-MVS		96.61 38	96.38 39	97.30 46	97.79 105	93.19 55	95.96 240	98.18 36	95.23 11	95.87 65	97.65 74	91.45 40	99.70 35	95.87 36	99.44 29	99.00 72
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
DeepC-MVS	93.07 3	96.06 51	95.66 52	97.29 47	97.96 93	93.17 56	97.30 119	98.06 60	93.92 41	93.38 119	98.66 6	86.83 98	99.73 26	95.60 47	99.22 50	98.96 74
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
ACMMP		96.27 47	95.93 47	97.28 48	99.24 22	92.62 69	98.25 26	98.81 3	92.99 69	94.56 94	98.39 23	88.96 67	99.85 11	94.57 70	97.63 97	99.36 42
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
TSAR-MVS + GP.		96.69 35	96.49 34	97.27 49	98.31 70	93.39 50	96.79 169	96.72 206	94.17 37	97.44 17	97.66 73	92.76 13	99.33 95	96.86 8	97.76 96	99.08 63

Regformer-4		96.97 23	96.87 17	97.25 50	98.34 65	92.66 68	96.96 147	98.01 72	95.12 14	97.14 25	98.42 19	91.82 34	99.61 48	96.90 6	99.13 57	99.50 24

test_prior3		96.46 42	96.20 44	97.23 51	98.67 43	92.99 59	96.35 213	98.00 74	92.80 79	96.03 58	97.59 81	92.01 30	99.41 88	95.01 57	99.38 35	99.29 46

test_prior				97.23 51	98.67 43	92.99 59		98.00 74					99.41 88			99.29 46

HPM-MVS_fast		96.51 40	96.27 41	97.22 53	99.32 16	92.74 65	98.74 4	98.06 60	90.57 152	96.77 32	98.35 25	90.21 57	99.53 72	94.80 66	99.63 6	99.38 40

VNet		95.89 57	95.45 55	97.21 54	98.07 87	92.94 62	97.50 98	98.15 40	93.87 42	97.52 14	97.61 80	85.29 115	99.53 72	95.81 39	95.27 150	99.16 54

UA-Net		95.95 56	95.53 54	97.20 55	97.67 113	92.98 61	97.65 75	98.13 43	94.81 23	96.61 39	98.35 25	88.87 68	99.51 77	90.36 143	97.35 109	99.11 61

EPNet		95.20 71	94.56 77	97.14 56	92.80 319	92.68 67	97.85 54	94.87 295	96.64 1	92.46 141	97.80 65	86.23 104	99.65 42	93.72 84	98.62 75	99.10 62
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
APD-MVS_3200maxsize		96.81 30	96.71 27	97.12 57	99.01 33	92.31 75	97.98 43	98.06 60	93.11 66	97.44 17	98.55 11	90.93 48	99.55 67	96.06 32	99.25 47	99.51 23

SD-MVS		97.41 7	97.53 3	97.06 58	98.57 54	94.46 17	97.92 47	98.14 42	94.82 22	99.01 1	98.55 11	94.18 4	97.41 289	96.94 5	99.64 5	99.32 44

Regformer-3		96.85 28	96.80 23	97.01 59	98.34 65	92.02 86	96.96 147	97.76 95	95.01 17	97.08 30	98.42 19	91.71 35	99.54 69	96.80 9	99.13 57	99.48 28

MVS_111021_HR		96.68 37	96.58 31	96.99 60	98.46 56	92.31 75	96.20 229	98.90 2	94.30 36	95.86 66	97.74 68	92.33 23	99.38 93	96.04 33	99.42 30	99.28 49

abl_6		96.40 43	96.21 43	96.98 61	98.89 36	92.20 80	97.89 49	98.03 69	93.34 58	97.22 21	98.42 19	87.93 81	99.72 29	95.10 54	99.07 62	99.02 66

QAPM		93.45 122	92.27 140	96.98 61	96.77 153	92.62 69	98.39 18	98.12 45	84.50 292	88.27 253	97.77 66	82.39 175	99.81 20	85.40 235	98.81 70	98.51 108

WTY-MVS		94.71 88	94.02 89	96.79 63	97.71 112	92.05 84	96.59 195	97.35 149	90.61 149	94.64 93	96.93 108	86.41 103	99.39 91	91.20 135	94.71 162	98.94 77

CPTT-MVS		95.57 62	95.19 63	96.70 64	99.27 20	91.48 100	98.33 20	98.11 48	87.79 235	95.17 85	98.03 48	87.09 96	99.61 48	93.51 87	99.42 30	99.02 66

sss		94.51 90	93.80 93	96.64 65	97.07 139	91.97 88	96.32 217	98.06 60	88.94 193	94.50 95	96.78 114	84.60 123	99.27 100	91.90 116	96.02 135	98.68 99

ab-mvs		93.57 119	92.55 131	96.64 65	97.28 131	91.96 89	95.40 264	97.45 134	89.81 167	93.22 127	96.28 146	79.62 224	99.46 82	90.74 138	93.11 192	98.50 110

EI-MVSNet-Vis-set		96.51 40	96.47 35	96.63 67	98.24 74	91.20 113	96.89 156	97.73 98	94.74 26	96.49 45	98.49 14	90.88 50	99.58 56	96.44 20	98.32 81	99.13 58

114514_t		93.95 106	93.06 115	96.63 67	99.07 30	91.61 96	97.46 104	97.96 82	77.99 338	93.00 133	97.57 83	86.14 108	99.33 95	89.22 162	99.15 55	98.94 77

HY-MVS	89.66 9	93.87 108	92.95 117	96.63 67	97.10 138	92.49 73	95.64 255	96.64 214	89.05 187	93.00 133	95.79 172	85.77 112	99.45 84	89.16 165	94.35 163	97.96 139

MSLP-MVS++		96.94 25	97.06 9	96.59 70	98.72 40	91.86 90	97.67 72	98.49 12	94.66 28	97.24 20	98.41 22	92.31 26	98.94 135	96.61 16	99.46 25	98.96 74

CANet_DTU		94.37 92	93.65 98	96.55 71	96.46 170	92.13 82	96.21 228	96.67 213	94.38 34	93.53 115	97.03 106	79.34 227	99.71 30	90.76 137	98.45 79	97.82 149

LFMVS		93.60 117	92.63 127	96.52 72	98.13 85	91.27 108	97.94 45	93.39 327	90.57 152	96.29 50	98.31 34	69.00 318	99.16 108	94.18 72	95.87 139	99.12 60

DP-MVS		92.76 147	91.51 170	96.52 72	98.77 38	90.99 120	97.38 111	96.08 234	82.38 311	89.29 234	97.87 57	83.77 132	99.69 36	81.37 292	96.69 125	98.89 83

CNLPA		94.28 94	93.53 102	96.52 72	98.38 63	92.55 71	96.59 195	96.88 200	90.13 159	91.91 154	97.24 97	85.21 116	99.09 122	87.64 196	97.83 92	97.92 141

Vis-MVSNet		95.23 68	94.81 69	96.51 75	97.18 134	91.58 99	98.26 25	98.12 45	94.38 34	94.90 87	98.15 42	82.28 176	98.92 136	91.45 130	98.58 77	99.01 70
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
MAR-MVS		94.22 95	93.46 105	96.51 75	98.00 88	92.19 81	97.67 72	97.47 128	88.13 229	93.00 133	95.84 166	84.86 121	99.51 77	87.99 185	98.17 85	97.83 148
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
PAPR		94.18 96	93.42 109	96.48 77	97.64 115	91.42 105	95.55 257	97.71 105	88.99 189	92.34 146	95.82 168	89.19 63	99.11 113	86.14 221	97.38 107	98.90 81

EI-MVSNet-UG-set		96.34 45	96.30 40	96.47 78	98.20 79	90.93 124	96.86 158	97.72 101	94.67 27	96.16 54	98.46 15	90.43 54	99.58 56	96.23 24	97.96 90	98.90 81

LS3D		93.57 119	92.61 129	96.47 78	97.59 119	91.61 96	97.67 72	97.72 101	85.17 282	90.29 192	98.34 28	84.60 123	99.73 26	83.85 261	98.27 82	98.06 138

CSCG		96.05 52	95.91 48	96.46 80	99.24 22	90.47 136	98.30 21	98.57 11	89.01 188	93.97 106	97.57 83	92.62 18	99.76 24	94.66 69	99.27 46	99.15 56

casdiffmvs1		95.77 59	95.55 53	96.44 81	97.30 130	91.43 104	97.57 93	97.58 116	91.21 129	96.65 37	96.60 133	89.18 64	98.83 145	96.27 22	97.60 98	99.05 65

0601test		94.78 86	94.23 86	96.43 82	97.74 109	91.22 109	96.85 159	97.10 169	91.23 127	95.71 72	96.93 108	84.30 127	99.31 97	93.10 96	95.12 152	98.75 90

Anonymous20240521		94.78 86	94.23 86	96.43 82	97.74 109	91.22 109	96.85 159	97.10 169	91.23 127	95.71 72	96.93 108	84.30 127	99.31 97	93.10 96	95.12 152	98.75 90

casdiffmvs		95.23 68	94.84 68	96.40 84	96.90 146	91.71 91	97.36 112	97.30 153	91.02 135	94.81 90	96.18 149	87.74 84	98.77 151	95.65 42	96.55 129	98.71 96

OpenMVS	89.19 12	92.86 143	91.68 156	96.40 84	95.34 212	92.73 66	98.27 24	98.12 45	84.86 287	85.78 287	97.75 67	78.89 246	99.74 25	87.50 200	98.65 74	96.73 183

MVS_111021_LR		96.24 48	96.19 45	96.39 86	98.23 78	91.35 106	96.24 227	98.79 4	93.99 40	95.80 69	97.65 74	89.92 61	99.24 102	95.87 36	99.20 52	98.58 102

原ACMM1				96.38 87	98.59 51	91.09 119		97.89 85	87.41 244	95.22 84	97.68 71	90.25 55	99.54 69	87.95 186	99.12 60	98.49 112

PVSNet_Blended_VisFu		95.27 67	94.91 67	96.38 87	98.20 79	90.86 126	97.27 120	98.25 28	90.21 156	94.18 101	97.27 95	87.48 91	99.73 26	93.53 86	97.77 95	98.55 103

Effi-MVS+		94.93 80	94.45 83	96.36 89	96.61 156	91.47 101	96.41 205	97.41 141	91.02 135	94.50 95	95.92 162	87.53 90	98.78 149	93.89 80	96.81 120	98.84 88

PCF-MVS	89.48 11	91.56 204	89.95 234	96.36 89	96.60 157	92.52 72	92.51 323	97.26 155	79.41 331	88.90 239	96.56 134	84.04 130	99.55 67	77.01 321	97.30 110	97.01 169
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
UGNet		94.04 104	93.28 112	96.31 91	96.85 148	91.19 114	97.88 50	97.68 107	94.40 32	93.00 133	96.18 149	73.39 299	99.61 48	91.72 121	98.46 78	98.13 133
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
MG-MVS		95.61 61	95.38 58	96.31 91	98.42 59	90.53 134	96.04 235	97.48 125	93.47 54	95.67 77	98.10 43	89.17 65	99.25 101	91.27 133	98.77 71	99.13 58

AdaColmap		94.34 93	93.68 97	96.31 91	98.59 51	91.68 95	96.59 195	97.81 94	89.87 162	92.15 150	97.06 105	83.62 134	99.54 69	89.34 158	98.07 87	97.70 153

lupinMVS		94.99 78	94.56 77	96.29 94	96.34 174	91.21 111	95.83 246	96.27 225	88.93 194	96.22 52	96.88 112	86.20 106	98.85 143	95.27 49	99.05 63	98.82 89

nrg030		94.05 103	93.31 111	96.27 95	95.22 222	94.59 15	98.34 19	97.46 130	92.93 76	91.21 180	96.64 124	87.23 95	98.22 197	94.99 60	85.80 272	95.98 213

PAPM_NR		95.01 74	94.59 76	96.26 96	98.89 36	90.68 131	97.24 122	97.73 98	91.80 110	92.93 138	96.62 131	89.13 66	99.14 111	89.21 163	97.78 94	98.97 73

OMC-MVS		95.09 73	94.70 74	96.25 97	98.46 56	91.28 107	96.43 202	97.57 117	92.04 105	94.77 92	97.96 53	87.01 97	99.09 122	91.31 132	96.77 121	98.36 127

1112_ss		93.37 123	92.42 137	96.21 98	97.05 143	90.99 120	96.31 218	96.72 206	86.87 262	89.83 212	96.69 121	86.51 102	99.14 111	88.12 182	93.67 180	98.50 110

jason		94.84 84	94.39 85	96.18 99	95.52 204	90.93 124	96.09 233	96.52 218	89.28 175	96.01 62	97.32 93	84.70 122	98.77 151	95.15 52	98.91 69	98.85 86
jason: jason.
PLC	91.00 6	94.11 100	93.43 107	96.13 100	98.58 53	91.15 118	96.69 184	97.39 142	87.29 247	91.37 164	96.71 117	88.39 76	99.52 76	87.33 204	97.13 114	97.73 151
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
CHOSEN 1792x2688		94.15 97	93.51 103	96.06 101	98.27 71	89.38 179	95.18 274	98.48 14	85.60 277	93.76 109	97.11 103	83.15 140	99.61 48	91.33 131	98.72 73	99.19 52

IS-MVSNet		94.90 81	94.52 80	96.05 102	97.67 113	90.56 133	98.44 15	96.22 229	93.21 60	93.99 104	97.74 68	85.55 113	98.45 177	89.98 145	97.86 91	99.14 57

VDD-MVS		93.82 110	93.08 114	96.02 103	97.88 102	89.96 148	97.72 66	95.85 248	92.43 88	95.86 66	98.44 17	68.42 322	99.39 91	96.31 21	94.85 156	98.71 96

VDDNet		93.05 133	92.07 143	96.02 103	96.84 149	90.39 138	98.08 34	95.85 248	86.22 270	95.79 70	98.46 15	67.59 325	99.19 104	94.92 61	94.85 156	98.47 115

MVSFormer		95.37 64	95.16 64	95.99 105	96.34 174	91.21 111	98.22 27	97.57 117	91.42 120	96.22 52	97.32 93	86.20 106	97.92 250	94.07 73	99.05 63	98.85 86

CDS-MVSNet		94.14 99	93.54 101	95.93 106	96.18 182	91.46 102	96.33 216	97.04 180	88.97 192	93.56 112	96.51 136	87.55 89	97.89 254	89.80 148	95.95 137	98.44 119
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
API-MVS		94.84 84	94.49 81	95.90 107	97.90 101	92.00 87	97.80 57	97.48 125	89.19 178	94.81 90	96.71 117	88.84 69	99.17 107	88.91 171	98.76 72	96.53 190

HyFIR lowres test		93.66 115	92.92 118	95.87 108	98.24 74	89.88 150	94.58 281	98.49 12	85.06 284	93.78 108	95.78 173	82.86 161	98.67 160	91.77 120	95.71 144	99.07 64

Test_1112_low_res		92.84 145	91.84 151	95.85 109	97.04 144	89.97 146	95.53 259	96.64 214	85.38 278	89.65 222	95.18 201	85.86 110	99.10 119	87.70 191	93.58 185	98.49 112

PVSNet_Blended		94.87 83	94.56 77	95.81 110	98.27 71	89.46 172	95.47 262	98.36 16	88.84 197	94.36 97	96.09 157	88.02 78	99.58 56	93.44 90	98.18 84	98.40 122

test_normal		92.01 177	90.75 200	95.80 111	93.24 308	89.97 146	95.93 242	96.24 228	90.62 147	81.63 315	93.45 285	74.98 286	98.89 140	93.61 85	97.04 116	98.55 103

Anonymous202405211		92.07 176	90.83 197	95.76 112	98.19 81	88.75 201	97.58 91	95.00 285	86.00 273	93.64 110	97.45 90	66.24 331	99.53 72	90.68 140	92.71 196	99.01 70

EPP-MVSNet		95.22 70	95.04 66	95.76 112	97.49 128	89.56 165	98.67 5	97.00 185	90.69 141	94.24 100	97.62 79	89.79 62	98.81 147	93.39 93	96.49 131	98.92 79

xiu_mvs_v1_base_debu		95.01 74	94.76 71	95.75 114	96.58 159	91.71 91	96.25 224	97.35 149	92.99 69	96.70 33	96.63 128	82.67 165	99.44 85	96.22 25	97.46 101	96.11 205

xiu_mvs_v1_base		95.01 74	94.76 71	95.75 114	96.58 159	91.71 91	96.25 224	97.35 149	92.99 69	96.70 33	96.63 128	82.67 165	99.44 85	96.22 25	97.46 101	96.11 205

xiu_mvs_v1_base_debi		95.01 74	94.76 71	95.75 114	96.58 159	91.71 91	96.25 224	97.35 149	92.99 69	96.70 33	96.63 128	82.67 165	99.44 85	96.22 25	97.46 101	96.11 205

Anonymous20240529		91.98 181	90.73 201	95.73 117	98.14 84	89.40 178	97.99 42	97.72 101	79.63 330	93.54 114	97.41 92	69.94 316	99.56 66	91.04 136	91.11 223	98.22 130

DI_MVS_plusplus_test		92.01 177	90.77 198	95.73 117	93.34 304	89.78 153	96.14 231	96.18 231	90.58 151	81.80 314	93.50 282	74.95 287	98.90 138	93.51 87	96.94 117	98.51 108

MVS_Test		94.89 82	94.62 75	95.68 119	96.83 151	89.55 166	96.70 182	97.17 160	91.17 130	95.60 78	96.11 155	87.87 82	98.76 153	93.01 100	97.17 113	98.72 94

TAMVS		94.01 105	93.46 105	95.64 120	96.16 184	90.45 137	96.71 179	96.89 199	89.27 176	93.46 117	96.92 111	87.29 94	97.94 246	88.70 177	95.74 142	98.53 105

UniMVSNet (Re)		93.31 125	92.55 131	95.61 121	95.39 209	93.34 54	97.39 109	98.71 5	93.14 65	90.10 202	94.83 217	87.71 85	98.03 229	91.67 126	83.99 300	95.46 236

diffmvs1		94.99 78	94.79 70	95.60 122	96.52 165	89.20 190	96.43 202	97.36 147	92.59 84	94.85 89	96.10 156	87.85 83	98.74 155	93.99 75	97.41 106	98.86 85

Fast-Effi-MVS+		93.46 121	92.75 123	95.59 123	96.77 153	90.03 140	96.81 166	97.13 165	88.19 225	91.30 169	94.27 256	86.21 105	98.63 162	87.66 195	96.46 133	98.12 134

PatchMatch-RL		92.90 141	92.02 146	95.56 124	98.19 81	90.80 128	95.27 271	97.18 158	87.96 230	91.86 156	95.68 180	80.44 210	98.99 133	84.01 257	97.54 100	96.89 179

TAPA-MVS	90.10 7	92.30 166	91.22 181	95.56 124	98.33 67	89.60 163	96.79 169	97.65 110	81.83 315	91.52 161	97.23 98	87.94 80	98.91 137	71.31 337	98.37 80	98.17 132
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
NR-MVSNet		92.34 163	91.27 178	95.53 126	94.95 236	93.05 58	97.39 109	98.07 58	92.65 83	84.46 297	95.71 177	85.00 119	97.77 265	89.71 150	83.52 308	95.78 222

MVS		91.71 188	90.44 215	95.51 127	95.20 224	91.59 98	96.04 235	97.45 134	73.44 350	87.36 270	95.60 183	85.42 114	99.10 119	85.97 226	97.46 101	95.83 219

VPA-MVSNet		93.24 127	92.48 136	95.51 127	95.70 200	92.39 74	97.86 51	98.66 9	92.30 90	92.09 152	95.37 195	80.49 209	98.40 185	93.95 77	85.86 271	95.75 226

thisisatest0530		93.03 134	92.21 141	95.49 129	97.07 139	89.11 195	97.49 102	92.19 344	90.16 158	94.09 102	96.41 141	76.43 276	99.05 129	90.38 142	95.68 145	98.31 129

PS-MVSNAJ		95.37 64	95.33 60	95.49 129	97.35 129	90.66 132	95.31 268	97.48 125	93.85 43	96.51 44	95.70 179	88.65 72	99.65 42	94.80 66	98.27 82	96.17 200

DU-MVS		92.90 141	92.04 144	95.49 129	94.95 236	92.83 63	97.16 134	98.24 29	93.02 68	90.13 198	95.71 177	83.47 135	97.85 256	91.71 122	83.93 301	95.78 222

UniMVSNet_NR-MVSNet		93.37 123	92.67 126	95.47 132	95.34 212	92.83 63	97.17 133	98.58 10	92.98 74	90.13 198	95.80 169	88.37 77	97.85 256	91.71 122	83.93 301	95.73 228

testdata				95.46 133	98.18 83	88.90 199		97.66 108	82.73 309	97.03 31	98.07 45	90.06 58	98.85 143	89.67 152	98.98 66	98.64 100

Test4		89.48 263	87.50 274	95.44 134	90.76 335	89.72 154	95.78 250	97.09 171	90.28 155	77.67 340	91.74 313	55.42 351	98.08 212	91.92 115	96.83 119	98.52 106

xiu_mvs_v2_base		95.32 66	95.29 61	95.40 135	97.22 132	90.50 135	95.44 263	97.44 137	93.70 49	96.46 47	96.18 149	88.59 75	99.53 72	94.79 68	97.81 93	96.17 200

F-COLMAP		93.58 118	92.98 116	95.37 136	98.40 60	88.98 197	97.18 132	97.29 154	87.75 237	90.49 187	97.10 104	85.21 116	99.50 79	86.70 213	96.72 124	97.63 154

FIs		94.09 101	93.70 95	95.27 137	95.70 200	92.03 85	98.10 32	98.68 7	93.36 57	90.39 190	96.70 119	87.63 88	97.94 246	92.25 106	90.50 234	95.84 218

thisisatest0515		92.29 167	91.30 176	95.25 138	96.60 157	88.90 199	94.36 286	92.32 342	87.92 232	93.43 118	94.57 228	77.28 270	99.00 132	89.42 157	95.86 140	97.86 145

PAPM		91.52 207	90.30 219	95.20 139	95.30 216	89.83 151	93.38 309	96.85 202	86.26 269	88.59 246	95.80 169	84.88 120	98.15 203	75.67 325	95.93 138	97.63 154

view600		92.55 150	91.68 156	95.18 140	97.98 89	89.44 174	98.00 38	94.57 302	92.09 99	93.17 128	95.52 188	78.14 257	99.11 113	81.61 281	94.04 172	96.98 170

view800		92.55 150	91.68 156	95.18 140	97.98 89	89.44 174	98.00 38	94.57 302	92.09 99	93.17 128	95.52 188	78.14 257	99.11 113	81.61 281	94.04 172	96.98 170

conf0.05thres1000		92.55 150	91.68 156	95.18 140	97.98 89	89.44 174	98.00 38	94.57 302	92.09 99	93.17 128	95.52 188	78.14 257	99.11 113	81.61 281	94.04 172	96.98 170

tfpn		92.55 150	91.68 156	95.18 140	97.98 89	89.44 174	98.00 38	94.57 302	92.09 99	93.17 128	95.52 188	78.14 257	99.11 113	81.61 281	94.04 172	96.98 170

thres600view7		92.49 156	91.60 162	95.18 140	97.91 100	89.47 170	97.65 75	94.66 297	92.18 98	93.33 120	94.91 209	78.06 261	99.10 119	81.61 281	94.06 170	96.98 170

diffmvs		94.47 91	94.23 86	95.18 140	96.32 176	88.22 216	96.27 222	97.04 180	92.55 86	93.60 111	95.94 161	86.79 99	98.70 159	92.98 101	96.61 127	98.63 101

DeepPCF-MVS	93.97 1	96.61 38	97.09 8	95.15 146	98.09 86	86.63 267	96.00 239	98.15 40	95.43 7	97.95 11	98.56 9	93.40 9	99.36 94	96.77 12	99.48 24	99.45 31

1314		92.81 146	92.03 145	95.14 147	95.33 215	89.52 169	96.04 235	97.44 137	87.72 238	86.25 284	95.33 196	83.84 131	98.79 148	89.26 160	97.05 115	97.11 168

TranMVSNet+NR-MVSNet		92.50 154	91.63 161	95.14 147	94.76 245	92.07 83	97.53 96	98.11 48	92.90 77	89.56 225	96.12 153	83.16 139	97.60 277	89.30 159	83.20 311	95.75 226

thres400		92.42 160	91.52 168	95.12 149	97.85 103	89.29 186	97.41 105	94.88 292	92.19 96	93.27 125	94.46 234	78.17 254	99.08 124	81.40 288	94.08 166	96.98 170

tfpn111		92.45 157	91.58 163	95.06 150	97.92 97	89.37 180	97.71 68	94.66 297	92.20 93	93.31 121	94.90 210	78.06 261	99.11 113	81.37 292	94.06 170	96.70 185

conf200view11		92.45 157	91.58 163	95.05 151	97.92 97	89.37 180	97.71 68	94.66 297	92.20 93	93.31 121	94.90 210	78.06 261	99.08 124	81.40 288	94.08 166	96.70 185

FC-MVSNet-test		93.94 107	93.57 99	95.04 152	95.48 206	91.45 103	98.12 31	98.71 5	93.37 55	90.23 193	96.70 119	87.66 86	97.85 256	91.49 128	90.39 235	95.83 219

FMVSNet3		91.78 185	90.69 204	95.03 153	96.53 164	92.27 77	97.02 142	96.93 195	89.79 168	89.35 231	94.65 225	77.01 271	97.47 284	86.12 222	88.82 247	95.35 247

VPNet		92.23 171	91.31 175	94.99 154	95.56 203	90.96 122	97.22 127	97.86 91	92.96 75	90.96 182	96.62 131	75.06 285	98.20 198	91.90 116	83.65 307	95.80 221

FMVSNet2		91.31 217	90.08 228	94.99 154	96.51 166	92.21 78	97.41 105	96.95 193	88.82 199	88.62 244	94.75 221	73.87 293	97.42 288	85.20 238	88.55 253	95.35 247

thres100view900		92.43 159	91.58 163	94.98 156	97.92 97	89.37 180	97.71 68	94.66 297	92.20 93	93.31 121	94.90 210	78.06 261	99.08 124	81.40 288	94.08 166	96.48 193

BH-RMVSNet		92.72 148	91.97 148	94.97 157	97.16 135	87.99 233	96.15 230	95.60 257	90.62 147	91.87 155	97.15 102	78.41 251	98.57 168	83.16 266	97.60 98	98.36 127

MSDG		91.42 211	90.24 223	94.96 158	97.15 136	88.91 198	93.69 302	96.32 223	85.72 276	86.93 279	96.47 138	80.24 214	98.98 134	80.57 302	95.05 155	96.98 170

tfpn200view9		92.38 162	91.52 168	94.95 159	97.85 103	89.29 186	97.41 105	94.88 292	92.19 96	93.27 125	94.46 234	78.17 254	99.08 124	81.40 288	94.08 166	96.48 193

XXY-MVS		92.16 173	91.23 180	94.95 159	94.75 246	90.94 123	97.47 103	97.43 139	89.14 185	88.90 239	96.43 140	79.71 222	98.24 196	89.56 155	87.68 258	95.67 230

Vis-MVSNet (Re-imp)		94.15 97	93.88 91	94.95 159	97.61 117	87.92 238	98.10 32	95.80 251	92.22 91	93.02 132	97.45 90	84.53 125	97.91 253	88.24 180	97.97 89	99.02 66

conf0.01		91.74 186	90.67 205	94.94 162	97.55 122	89.68 155	97.64 79	93.14 329	88.43 211	91.24 174	94.30 246	78.91 239	98.45 177	81.28 294	93.57 186	96.70 185

conf0.002		91.74 186	90.67 205	94.94 162	97.55 122	89.68 155	97.64 79	93.14 329	88.43 211	91.24 174	94.30 246	78.91 239	98.45 177	81.28 294	93.57 186	96.70 185

tttt0517		92.96 137	92.33 139	94.87 164	97.11 137	87.16 255	97.97 44	92.09 345	90.63 146	93.88 107	97.01 107	76.50 273	99.06 128	90.29 144	95.45 147	98.38 125

OPM-MVS		93.28 126	92.76 121	94.82 165	94.63 250	90.77 130	96.65 187	97.18 158	93.72 47	91.68 159	97.26 96	79.33 228	98.63 162	92.13 110	92.28 201	95.07 262

HQP_MVS		93.78 112	93.43 107	94.82 165	96.21 179	89.99 143	97.74 62	97.51 123	94.85 18	91.34 166	96.64 124	81.32 193	98.60 165	93.02 98	92.23 202	95.86 215

XVG-OURS-SEG-HR		93.86 109	93.55 100	94.81 167	97.06 142	88.53 206	95.28 269	97.45 134	91.68 113	94.08 103	97.68 71	82.41 174	98.90 138	93.84 82	92.47 199	96.98 170

tfpn1000		91.99 180	91.05 184	94.80 168	97.78 106	89.66 161	97.91 48	92.90 338	88.99 189	91.73 157	94.84 215	78.99 238	98.33 192	82.41 277	93.91 178	96.40 195

XVG-OURS		93.72 114	93.35 110	94.80 168	97.07 139	88.61 204	94.79 278	97.46 130	91.97 108	93.99 104	97.86 59	81.74 188	98.88 142	92.64 103	92.67 198	96.92 178

IB-MVS	87.33 17	89.91 256	88.28 267	94.79 170	95.26 220	87.70 244	95.12 275	93.95 321	89.35 174	87.03 277	92.49 299	70.74 311	99.19 104	89.18 164	81.37 320	97.49 163
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
WR-MVS		92.34 163	91.53 167	94.77 171	95.13 228	90.83 127	96.40 209	97.98 80	91.88 109	89.29 234	95.54 187	82.50 170	97.80 261	89.79 149	85.27 278	95.69 229

thresconf0.02		91.69 193	90.67 205	94.75 172	97.55 122	89.68 155	97.64 79	93.14 329	88.43 211	91.24 174	94.30 246	78.91 239	98.45 177	81.28 294	93.57 186	96.11 205

tfpn_n400		91.69 193	90.67 205	94.75 172	97.55 122	89.68 155	97.64 79	93.14 329	88.43 211	91.24 174	94.30 246	78.91 239	98.45 177	81.28 294	93.57 186	96.11 205

tfpnconf		91.69 193	90.67 205	94.75 172	97.55 122	89.68 155	97.64 79	93.14 329	88.43 211	91.24 174	94.30 246	78.91 239	98.45 177	81.28 294	93.57 186	96.11 205

tfpnview11		91.69 193	90.67 205	94.75 172	97.55 122	89.68 155	97.64 79	93.14 329	88.43 211	91.24 174	94.30 246	78.91 239	98.45 177	81.28 294	93.57 186	96.11 205

thres200		92.23 171	91.39 171	94.75 172	97.61 117	89.03 196	96.60 194	95.09 281	92.08 104	93.28 124	94.00 264	78.39 252	99.04 131	81.26 300	94.18 165	96.19 199

tfpn_ndepth		91.88 184	90.96 188	94.62 177	97.73 111	89.93 149	97.75 60	92.92 337	88.93 194	91.73 157	93.80 271	78.91 239	98.49 176	83.02 269	93.86 179	95.45 237

GA-MVS		91.38 213	90.31 218	94.59 178	94.65 249	87.62 245	94.34 287	96.19 230	90.73 140	90.35 191	93.83 269	71.84 303	97.96 244	87.22 206	93.61 183	98.21 131

GBi-Net		91.35 215	90.27 221	94.59 178	96.51 166	91.18 115	97.50 98	96.93 195	88.82 199	89.35 231	94.51 230	73.87 293	97.29 296	86.12 222	88.82 247	95.31 249

test1		91.35 215	90.27 221	94.59 178	96.51 166	91.18 115	97.50 98	96.93 195	88.82 199	89.35 231	94.51 230	73.87 293	97.29 296	86.12 222	88.82 247	95.31 249

FMVSNet1		89.88 258	88.31 266	94.59 178	95.41 208	91.18 115	97.50 98	96.93 195	86.62 265	87.41 268	94.51 230	65.94 333	97.29 296	83.04 268	87.43 261	95.31 249

cascas		91.20 220	90.08 228	94.58 182	94.97 234	89.16 194	93.65 304	97.59 115	79.90 329	89.40 229	92.92 292	75.36 283	98.36 188	92.14 109	94.75 160	96.23 197

HQP-MVS		93.19 129	92.74 124	94.54 183	95.86 193	89.33 183	96.65 187	97.39 142	93.55 50	90.14 194	95.87 164	80.95 198	98.50 173	92.13 110	92.10 207	95.78 222

PVSNet_BlendedMVS		94.06 102	93.92 90	94.47 184	98.27 71	89.46 172	96.73 174	98.36 16	90.17 157	94.36 97	95.24 200	88.02 78	99.58 56	93.44 90	90.72 230	94.36 297

gg-mvs-nofinetune		87.82 291	85.61 298	94.44 185	94.46 255	89.27 189	91.21 334	84.61 363	80.88 323	89.89 209	74.98 354	71.50 305	97.53 280	85.75 230	97.21 112	96.51 191

PS-MVSNAJss		93.74 113	93.51 103	94.44 185	93.91 287	89.28 188	97.75 60	97.56 120	92.50 87	89.94 206	96.54 135	88.65 72	98.18 201	93.83 83	90.90 227	95.86 215

PMMVS		92.86 143	92.34 138	94.42 187	94.92 238	86.73 263	94.53 283	96.38 221	84.78 289	94.27 99	95.12 205	83.13 142	98.40 185	91.47 129	96.49 131	98.12 134

MVSTER		93.20 128	92.81 120	94.37 188	96.56 162	89.59 164	97.06 139	97.12 166	91.24 126	91.30 169	95.96 159	82.02 182	98.05 224	93.48 89	90.55 232	95.47 235

ACMM	89.79 8	92.96 137	92.50 135	94.35 189	96.30 177	88.71 202	97.58 91	97.36 147	91.40 122	90.53 186	96.65 123	79.77 221	98.75 154	91.24 134	91.64 213	95.59 231
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CHOSEN 280x420		93.12 130	92.72 125	94.34 190	96.71 155	87.27 249	90.29 339	97.72 101	86.61 266	91.34 166	95.29 197	84.29 129	98.41 184	93.25 94	98.94 68	97.35 166

CLD-MVS		92.98 136	92.53 133	94.32 191	96.12 188	89.20 190	95.28 269	97.47 128	92.66 82	89.90 207	95.62 182	80.58 207	98.40 185	92.73 102	92.40 200	95.38 245
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
Anonymous20231211		90.63 242	89.42 250	94.27 192	98.24 74	89.19 193	98.05 36	97.89 85	79.95 328	88.25 254	94.96 206	72.56 301	98.13 204	89.70 151	85.14 280	95.49 232

testing_2		87.33 295	85.03 302	94.22 193	87.77 347	89.32 185	94.97 276	97.11 168	89.22 177	71.64 348	88.73 334	55.16 352	97.94 246	91.95 114	88.73 251	95.41 239

LTVRE_ROB	88.41 13	90.99 227	89.92 235	94.19 194	96.18 182	89.55 166	96.31 218	97.09 171	87.88 234	85.67 288	95.91 163	78.79 247	98.57 168	81.50 286	89.98 238	94.44 295
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
pmmvs4		90.93 229	89.85 238	94.17 195	93.34 304	90.79 129	94.60 280	96.02 235	84.62 290	87.45 266	95.15 202	81.88 186	97.45 285	87.70 191	87.87 257	94.27 302

TR-MVS		91.48 208	90.59 213	94.16 196	96.40 172	87.33 247	95.67 252	95.34 270	87.68 239	91.46 162	95.52 188	76.77 272	98.35 189	82.85 271	93.61 183	96.79 182

LPG-MVS_test		92.94 139	92.56 130	94.10 197	96.16 184	88.26 212	97.65 75	97.46 130	91.29 123	90.12 200	97.16 100	79.05 231	98.73 156	92.25 106	91.89 210	95.31 249

LGP-MVS_train				94.10 197	96.16 184	88.26 212		97.46 130	91.29 123	90.12 200	97.16 100	79.05 231	98.73 156	92.25 106	91.89 210	95.31 249

mvs_anonymous		93.82 110	93.74 94	94.06 199	96.44 171	85.41 279	95.81 247	97.05 177	89.85 165	90.09 203	96.36 144	87.44 92	97.75 266	93.97 76	96.69 125	99.02 66

ACMP	89.59 10	92.62 149	92.14 142	94.05 200	96.40 172	88.20 219	97.36 112	97.25 157	91.52 115	88.30 251	96.64 124	78.46 250	98.72 158	91.86 119	91.48 217	95.23 256
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
jajsoiax		92.42 160	91.89 150	94.03 201	93.33 306	88.50 207	97.73 64	97.53 121	92.00 107	88.85 241	96.50 137	75.62 282	98.11 208	93.88 81	91.56 216	95.48 233

test_djsdf		93.07 132	92.76 121	94.00 202	93.49 300	88.70 203	98.22 27	97.57 117	91.42 120	90.08 204	95.55 186	82.85 162	97.92 250	94.07 73	91.58 215	95.40 243

AllTest		90.23 250	88.98 257	93.98 203	97.94 95	86.64 264	96.51 199	95.54 260	85.38 278	85.49 290	96.77 115	70.28 313	99.15 109	80.02 305	92.87 193	96.15 202

TestCases				93.98 203	97.94 95	86.64 264		95.54 260	85.38 278	85.49 290	96.77 115	70.28 313	99.15 109	80.02 305	92.87 193	96.15 202

anonymousdsp		92.16 173	91.55 166	93.97 205	92.58 323	89.55 166	97.51 97	97.42 140	89.42 173	88.40 248	94.84 215	80.66 206	97.88 255	91.87 118	91.28 221	94.48 293

pm-mvs1		90.72 237	89.65 247	93.96 206	94.29 262	89.63 162	97.79 58	96.82 203	89.07 186	86.12 286	95.48 193	78.61 248	97.78 263	86.97 211	81.67 318	94.46 294

WR-MVS_H		92.00 179	91.35 172	93.95 207	95.09 230	89.47 170	98.04 37	98.68 7	91.46 118	88.34 249	94.68 223	85.86 110	97.56 278	85.77 229	84.24 298	94.82 280

CR-MVSNet		90.82 232	89.77 241	93.95 207	94.45 256	87.19 253	90.23 340	95.68 255	86.89 261	92.40 142	92.36 304	80.91 201	97.05 300	81.09 301	93.95 176	97.60 159

RPMNet		88.52 279	86.72 292	93.95 207	94.45 256	87.19 253	90.23 340	94.99 287	77.87 340	92.40 142	87.55 344	80.17 216	97.05 300	68.84 341	93.95 176	97.60 159

mvs_tets		92.31 165	91.76 152	93.94 210	93.41 302	88.29 210	97.63 86	97.53 121	92.04 105	88.76 242	96.45 139	74.62 289	98.09 211	93.91 79	91.48 217	95.45 237

BH-untuned		92.94 139	92.62 128	93.92 211	97.22 132	86.16 271	96.40 209	96.25 227	90.06 160	89.79 214	96.17 152	83.19 138	98.35 189	87.19 207	97.27 111	97.24 167

ACMH	87.59 16	90.53 244	89.42 250	93.87 212	96.21 179	87.92 238	97.24 122	96.94 194	88.45 210	83.91 305	96.27 147	71.92 302	98.62 164	84.43 249	89.43 243	95.05 267
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CP-MVSNet		91.89 183	91.24 179	93.82 213	95.05 231	88.57 205	97.82 56	98.19 34	91.70 112	88.21 255	95.76 174	81.96 183	97.52 281	87.86 187	84.65 294	95.37 246

v2v482		91.59 202	90.85 194	93.80 214	93.87 289	88.17 221	96.94 153	96.88 200	89.54 169	89.53 226	94.90 210	81.70 189	98.02 232	89.25 161	85.04 287	95.20 257

COLMAP_ROB	87.81 15	90.40 246	89.28 253	93.79 215	97.95 94	87.13 256	96.92 154	95.89 247	82.83 308	86.88 281	97.18 99	73.77 296	99.29 99	78.44 315	93.62 182	94.95 268
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
v1141		91.61 199	90.89 189	93.78 216	94.01 282	88.24 214	96.96 147	96.96 190	89.17 182	89.75 216	94.29 252	82.99 154	98.03 229	88.85 173	85.00 288	95.07 262

divwei89l23v2f112		91.61 199	90.89 189	93.78 216	94.01 282	88.22 216	96.96 147	96.96 190	89.17 182	89.75 216	94.28 254	83.02 152	98.03 229	88.86 172	84.98 291	95.08 260

v1		91.61 199	90.89 189	93.78 216	94.01 282	88.21 218	96.96 147	96.96 190	89.17 182	89.78 215	94.29 252	82.97 156	98.05 224	88.85 173	84.99 289	95.08 260

v1neww		91.70 191	91.01 185	93.75 219	94.19 264	88.14 224	97.20 129	96.98 186	89.18 180	89.87 210	94.44 236	83.10 144	98.06 221	89.06 167	85.09 283	95.06 265

v7new		91.70 191	91.01 185	93.75 219	94.19 264	88.14 224	97.20 129	96.98 186	89.18 180	89.87 210	94.44 236	83.10 144	98.06 221	89.06 167	85.09 283	95.06 265

v6		91.69 193	91.00 187	93.75 219	94.14 269	88.12 226	97.20 129	96.98 186	89.19 178	89.90 207	94.42 238	83.04 150	98.07 216	89.07 166	85.10 282	95.07 262

V42		91.58 203	90.87 192	93.73 222	94.05 281	88.50 207	97.32 117	96.97 189	88.80 202	89.71 218	94.33 243	82.54 169	98.05 224	89.01 169	85.07 285	94.64 290

PVSNet	86.66 18	92.24 170	91.74 155	93.73 222	97.77 107	83.69 298	92.88 318	96.72 206	87.91 233	93.00 133	94.86 214	78.51 249	99.05 129	86.53 214	97.45 105	98.47 115

MIMVSNet		88.50 281	86.76 290	93.72 224	94.84 242	87.77 242	91.39 330	94.05 318	86.41 267	87.99 258	92.59 297	63.27 337	95.82 328	77.44 317	92.84 195	97.57 161

Patchmatch-test		89.42 265	87.99 269	93.70 225	95.27 217	85.11 281	88.98 346	94.37 310	81.11 321	87.10 276	93.69 274	82.28 176	97.50 282	74.37 328	94.76 159	98.48 114

PS-CasMVS		91.55 205	90.84 196	93.69 226	94.96 235	88.28 211	97.84 55	98.24 29	91.46 118	88.04 257	95.80 169	79.67 223	97.48 283	87.02 210	84.54 296	95.31 249

v1144		91.37 214	90.60 212	93.68 227	93.89 288	88.23 215	96.84 161	97.03 183	88.37 219	89.69 220	94.39 239	82.04 181	97.98 237	87.80 189	85.37 276	94.84 276

v7		91.47 209	90.73 201	93.68 227	94.13 270	88.16 222	97.09 138	97.05 177	88.38 218	89.80 213	94.52 229	82.21 178	98.01 233	88.00 184	85.42 275	94.87 274

GG-mvs-BLEND				93.62 229	93.69 294	89.20 190	92.39 326	83.33 364		87.98 259	89.84 320	71.00 309	96.87 307	82.08 280	95.40 148	94.80 282

tfpnnormal		89.70 261	88.40 265	93.60 230	95.15 226	90.10 139	97.56 94	98.16 39	87.28 248	86.16 285	94.63 226	77.57 268	98.05 224	74.48 326	84.59 295	92.65 321

Patchmatch-test1		91.54 206	90.85 194	93.59 231	95.59 202	84.95 285	94.72 279	95.58 259	90.82 137	92.25 148	93.58 279	75.80 279	97.41 289	83.35 263	95.98 136	98.40 122

PatchmatchNet		91.91 182	91.35 172	93.59 231	95.38 210	84.11 293	93.15 314	95.39 264	89.54 169	92.10 151	93.68 275	82.82 163	98.13 204	84.81 241	95.32 149	98.52 106
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
v1192		91.07 224	90.23 224	93.58 233	93.70 293	87.82 241	96.73 174	97.07 174	87.77 236	89.58 223	94.32 244	80.90 204	97.97 240	86.52 215	85.48 273	94.95 268

v8		91.29 218	90.53 214	93.57 234	94.15 268	88.12 226	97.34 114	97.06 176	88.99 189	88.32 250	94.26 258	83.08 146	98.01 233	87.62 197	83.92 303	94.57 291

ADS-MVSNet		89.89 257	88.68 261	93.53 235	95.86 193	84.89 286	90.93 335	95.07 283	83.23 306	91.28 172	91.81 311	79.01 235	97.85 256	79.52 307	91.39 219	97.84 146

v10		91.04 226	90.23 224	93.49 236	94.12 272	88.16 222	97.32 117	97.08 173	88.26 222	88.29 252	94.22 259	82.17 180	97.97 240	86.45 217	84.12 299	94.33 298

EI-MVSNet		93.03 134	92.88 119	93.48 237	95.77 198	86.98 259	96.44 200	97.12 166	90.66 144	91.30 169	97.64 77	86.56 101	98.05 224	89.91 146	90.55 232	95.41 239

PEN-MVS		91.20 220	90.44 215	93.48 237	94.49 254	87.91 240	97.76 59	98.18 36	91.29 123	87.78 260	95.74 176	80.35 212	97.33 294	85.46 234	82.96 312	95.19 258

mvs-test1		93.63 116	93.69 96	93.46 239	96.02 190	84.61 289	97.24 122	96.72 206	93.85 43	92.30 147	95.76 174	83.08 146	98.89 140	91.69 124	96.54 130	96.87 180

v7n		90.76 233	89.86 237	93.45 240	93.54 297	87.60 246	97.70 71	97.37 145	88.85 196	87.65 264	94.08 263	81.08 195	98.10 209	84.68 244	83.79 306	94.66 289

v144192		91.06 225	90.28 220	93.39 241	93.66 295	87.23 252	96.83 162	97.07 174	87.43 243	89.69 220	94.28 254	81.48 190	98.00 236	87.18 208	84.92 292	94.93 272

DWT-MVSNet_test		90.76 233	89.89 236	93.38 242	95.04 232	83.70 297	95.85 245	94.30 313	88.19 225	90.46 188	92.80 293	73.61 297	98.50 173	88.16 181	90.58 231	97.95 140

EPMVS		90.70 239	89.81 240	93.37 243	94.73 247	84.21 291	93.67 303	88.02 357	89.50 171	92.38 144	93.49 283	77.82 267	97.78 263	86.03 225	92.68 197	98.11 137

IterMVS-LS		92.29 167	91.94 149	93.34 244	96.25 178	86.97 260	96.57 198	97.05 177	90.67 142	89.50 228	94.80 219	86.59 100	97.64 274	89.91 146	86.11 270	95.40 243
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
BH-w/o		92.14 175	91.75 153	93.31 245	96.99 145	85.73 274	95.67 252	95.69 253	88.73 204	89.26 236	94.82 218	82.97 156	98.07 216	85.26 237	96.32 134	96.13 204

v1921920		90.85 231	90.03 231	93.29 246	93.55 296	86.96 261	96.74 173	97.04 180	87.36 245	89.52 227	94.34 242	80.23 215	97.97 240	86.27 218	85.21 279	94.94 270

ACMH+	87.92 14	90.20 251	89.18 255	93.25 247	96.48 169	86.45 268	96.99 145	96.68 211	88.83 198	84.79 296	96.22 148	70.16 315	98.53 170	84.42 250	88.04 255	94.77 286

v1240		90.70 239	89.85 238	93.23 248	93.51 299	86.80 262	96.61 192	97.02 184	87.16 250	89.58 223	94.31 245	79.55 225	97.98 237	85.52 233	85.44 274	94.90 273

PatchT		88.87 271	87.42 277	93.22 249	94.08 278	85.10 282	89.51 344	94.64 301	81.92 314	92.36 145	88.15 340	80.05 217	97.01 304	72.43 333	93.65 181	97.54 162

Fast-Effi-MVS+-dtu		92.29 167	91.99 147	93.21 250	95.27 217	85.52 278	97.03 140	96.63 216	92.09 99	89.11 238	95.14 203	80.33 213	98.08 212	87.54 199	94.74 161	96.03 212

PatchFormer-LS_test		91.68 198	91.18 183	93.19 251	95.24 221	83.63 299	95.53 259	95.44 263	89.82 166	91.37 164	92.58 298	80.85 205	98.52 171	89.65 154	90.16 237	97.42 165

XVG-ACMP-BASELINE		90.93 229	90.21 226	93.09 252	94.31 261	85.89 272	95.33 266	97.26 155	91.06 134	89.38 230	95.44 194	68.61 320	98.60 165	89.46 156	91.05 225	94.79 284

TransMVSNet (Re)		88.94 268	87.56 272	93.08 253	94.35 259	88.45 209	97.73 64	95.23 275	87.47 242	84.26 300	95.29 197	79.86 220	97.33 294	79.44 311	74.44 346	93.45 312

DTE-MVSNet		90.56 243	89.75 243	93.01 254	93.95 285	87.25 250	97.64 79	97.65 110	90.74 139	87.12 274	95.68 180	79.97 219	97.00 305	83.33 265	81.66 319	94.78 285

EPNet_dtu		91.71 188	91.28 177	92.99 255	93.76 292	83.71 296	96.69 184	95.28 271	93.15 64	87.02 278	95.95 160	83.37 137	97.38 292	79.46 310	96.84 118	97.88 144
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
Baseline_NR-MVSNet		91.20 220	90.62 211	92.95 256	93.83 290	88.03 232	97.01 144	95.12 280	88.42 217	89.70 219	95.13 204	83.47 135	97.44 286	89.66 153	83.24 310	93.37 314

pmmvs5		89.86 259	88.87 259	92.82 257	92.86 317	86.23 270	96.26 223	95.39 264	84.24 294	87.12 274	94.51 230	74.27 291	97.36 293	87.61 198	87.57 259	94.86 275

v52		90.70 239	90.00 232	92.82 257	93.24 308	87.03 257	97.60 88	97.14 164	88.21 223	87.69 262	93.94 266	80.91 201	98.07 216	87.39 201	83.87 305	93.36 315

V4		90.71 238	90.00 232	92.82 257	93.21 311	87.03 257	97.59 90	97.16 163	88.21 223	87.69 262	93.92 268	80.93 200	98.06 221	87.39 201	83.90 304	93.39 313

v148		90.99 227	90.38 217	92.81 260	93.83 290	85.80 273	96.78 171	96.68 211	89.45 172	88.75 243	93.93 267	82.96 158	97.82 260	87.83 188	83.25 309	94.80 282

Patchmtry		88.64 277	87.25 282	92.78 261	94.09 276	86.64 264	89.82 343	95.68 255	80.81 325	87.63 265	92.36 304	80.91 201	97.03 302	78.86 313	85.12 281	94.67 288

v748		90.34 247	89.54 248	92.75 262	93.25 307	85.71 275	97.61 87	97.17 160	88.54 209	87.20 273	93.54 280	81.02 196	98.01 233	85.73 231	81.80 316	94.52 292

MVP-Stereo		90.74 236	90.08 228	92.71 263	93.19 313	88.20 219	95.86 244	96.27 225	86.07 272	84.86 295	94.76 220	77.84 266	97.75 266	83.88 260	98.01 88	92.17 338
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
pmmvs6		87.81 292	86.19 294	92.69 264	91.32 332	86.30 269	97.34 114	96.41 220	80.59 327	84.05 304	94.37 241	67.37 327	97.67 271	84.75 242	79.51 326	94.09 305

Effi-MVS+-dtu		93.08 131	93.21 113	92.68 265	96.02 190	83.25 302	97.14 136	96.72 206	93.85 43	91.20 181	93.44 286	83.08 146	98.30 194	91.69 124	95.73 143	96.50 192

CostFormer		91.18 223	90.70 203	92.62 266	94.84 242	81.76 312	94.09 295	94.43 307	84.15 295	92.72 140	93.77 272	79.43 226	98.20 198	90.70 139	92.18 205	97.90 142

tpmp4_e23		89.58 262	88.59 262	92.54 267	95.16 225	81.53 313	94.11 294	95.09 281	81.66 316	88.60 245	93.44 286	75.11 284	98.33 192	82.45 276	91.72 212	97.75 150

LCM-MVSNet-Re		92.50 154	92.52 134	92.44 268	96.82 152	81.89 311	96.92 154	93.71 322	92.41 89	84.30 299	94.60 227	85.08 118	97.03 302	91.51 127	97.36 108	98.40 122

ITE_SJBPF				92.43 269	95.34 212	85.37 280		95.92 240	91.47 117	87.75 261	96.39 143	71.00 309	97.96 244	82.36 278	89.86 241	93.97 306

v18		88.71 274	87.52 273	92.27 270	94.16 267	88.11 228	96.82 165	95.96 237	87.03 252	80.76 321	89.81 321	83.15 140	96.22 315	84.69 243	75.31 337	92.49 325

USDC		88.94 268	87.83 271	92.27 270	94.66 248	84.96 284	93.86 298	95.90 242	87.34 246	83.40 307	95.56 185	67.43 326	98.19 200	82.64 275	89.67 242	93.66 309

v17		88.67 276	87.47 276	92.26 272	94.13 270	88.09 230	96.81 166	95.95 238	87.02 253	80.72 322	89.75 323	83.11 143	96.20 316	84.61 246	75.15 339	92.49 325

v16		88.69 275	87.50 274	92.26 272	94.19 264	88.11 228	96.81 166	95.95 238	87.01 254	80.71 323	89.80 322	83.08 146	96.20 316	84.61 246	75.34 336	92.48 327

tpm2		89.96 255	89.21 254	92.23 274	94.91 240	81.25 315	93.78 299	94.42 308	80.62 326	91.56 160	93.44 286	76.44 275	97.94 246	85.60 232	92.08 209	97.49 163

v15		88.53 278	87.31 278	92.20 275	94.09 276	88.05 231	96.72 177	95.90 242	87.01 254	80.53 326	89.60 327	83.02 152	96.13 318	84.29 251	74.64 340	92.41 331

V9		88.49 282	87.26 281	92.18 276	94.12 272	87.97 236	96.73 174	95.90 242	86.95 258	80.40 329	89.61 325	82.98 155	96.13 318	84.14 253	74.55 343	92.44 329

v12		88.46 283	87.23 284	92.17 277	94.10 275	87.99 233	96.71 179	95.90 242	86.91 259	80.34 331	89.58 328	82.92 159	96.11 322	84.09 254	74.50 345	92.42 330

V14		88.52 279	87.30 279	92.17 277	94.12 272	87.99 233	96.72 177	95.91 241	86.98 256	80.50 327	89.63 324	83.03 151	96.12 320	84.23 252	74.60 342	92.40 332

v13		88.45 284	87.22 285	92.16 279	94.08 278	87.95 237	96.71 179	95.90 242	86.86 263	80.27 333	89.55 329	82.92 159	96.12 320	84.02 256	74.63 341	92.40 332

test-LLR		91.42 211	91.19 182	92.12 280	94.59 251	80.66 318	94.29 289	92.98 335	91.11 132	90.76 184	92.37 301	79.02 233	98.07 216	88.81 175	96.74 122	97.63 154

test-mter		90.19 252	89.54 248	92.12 280	94.59 251	80.66 318	94.29 289	92.98 335	87.68 239	90.76 184	92.37 301	67.67 324	98.07 216	88.81 175	96.74 122	97.63 154

v11		88.41 285	87.19 288	92.08 282	94.08 278	87.77 242	96.75 172	95.85 248	86.74 264	80.50 327	89.50 330	82.49 171	96.08 323	83.55 262	75.20 338	92.38 334

ADS-MVSNet2		89.45 264	88.59 262	92.03 283	95.86 193	82.26 309	90.93 335	94.32 312	83.23 306	91.28 172	91.81 311	79.01 235	95.99 324	79.52 307	91.39 219	97.84 146

TESTMET0.1,1		90.06 254	89.42 250	91.97 284	94.41 258	80.62 320	94.29 289	91.97 347	87.28 248	90.44 189	92.47 300	68.79 319	97.67 271	88.50 179	96.60 128	97.61 158

JIA-IIPM		88.26 288	87.04 289	91.91 285	93.52 298	81.42 314	89.38 345	94.38 309	80.84 324	90.93 183	80.74 351	79.22 229	97.92 250	82.76 272	91.62 214	96.38 196

tpmvs		89.83 260	89.15 256	91.89 286	94.92 238	80.30 324	93.11 315	95.46 262	86.28 268	88.08 256	92.65 295	80.44 210	98.52 171	81.47 287	89.92 240	96.84 181

TDRefinement		86.53 300	84.76 305	91.85 287	82.23 356	84.25 290	96.38 211	95.35 267	84.97 286	84.09 303	94.94 207	65.76 334	98.34 191	84.60 248	74.52 344	92.97 316

semantic-postprocess				91.82 288	95.52 204	84.20 292		96.15 232	90.61 149	87.39 269	94.27 256	75.63 281	96.44 311	87.34 203	86.88 266	94.82 280

tpm cat1		88.36 286	87.21 286	91.81 289	95.13 228	80.55 321	92.58 322	95.70 252	74.97 346	87.45 266	91.96 309	78.01 265	98.17 202	80.39 304	88.74 250	96.72 184

tpmrst		91.44 210	91.32 174	91.79 290	95.15 226	79.20 333	93.42 308	95.37 266	88.55 208	93.49 116	93.67 276	82.49 171	98.27 195	90.41 141	89.34 244	97.90 142

MS-PatchMatch		90.27 248	89.77 241	91.78 291	94.33 260	84.72 288	95.55 257	96.73 205	86.17 271	86.36 283	95.28 199	71.28 307	97.80 261	84.09 254	98.14 86	92.81 320

FMVSNet5		87.29 296	85.79 297	91.78 291	94.80 244	87.28 248	95.49 261	95.28 271	84.09 296	83.85 306	91.82 310	62.95 338	94.17 339	78.48 314	85.34 277	93.91 307

EG-PatchMatch MVS		87.02 298	85.44 299	91.76 293	92.67 321	85.00 283	96.08 234	96.45 219	83.41 305	79.52 336	93.49 283	57.10 347	97.72 268	79.34 312	90.87 228	92.56 323

tpm		90.25 249	89.74 244	91.76 293	93.92 286	79.73 329	93.98 296	93.54 326	88.28 221	91.99 153	93.25 289	77.51 269	97.44 286	87.30 205	87.94 256	98.12 134

IterMVS		90.15 253	89.67 245	91.61 295	95.48 206	83.72 295	94.33 288	96.12 233	89.99 161	87.31 272	94.15 261	75.78 280	96.27 314	86.97 211	86.89 265	94.83 278
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
ppachtmachnet_test		88.35 287	87.29 280	91.53 296	92.45 325	83.57 300	93.75 300	95.97 236	84.28 293	85.32 293	94.18 260	79.00 237	96.93 306	75.71 324	84.99 289	94.10 303

pmmvs-eth3d		86.22 303	84.45 306	91.53 296	88.34 344	87.25 250	94.47 284	95.01 284	83.47 304	79.51 337	89.61 325	69.75 317	95.71 329	83.13 267	76.73 332	91.64 339

test_0402		86.46 301	84.79 304	91.45 298	95.02 233	85.55 277	96.29 220	94.89 291	80.90 322	82.21 309	93.97 265	68.21 323	97.29 296	62.98 347	88.68 252	91.51 341

OurMVSNet-221017-0		90.51 245	90.19 227	91.44 299	93.41 302	81.25 315	96.98 146	96.28 224	91.68 113	86.55 282	96.30 145	74.20 292	97.98 237	88.96 170	87.40 263	95.09 259

test0.0.03 1		89.37 266	88.70 260	91.41 300	92.47 324	85.63 276	95.22 273	92.70 340	91.11 132	86.91 280	93.65 277	79.02 233	93.19 344	78.00 316	89.18 245	95.41 239

TinyColmap		86.82 299	85.35 301	91.21 301	94.91 240	82.99 303	93.94 297	94.02 320	83.58 302	81.56 316	94.68 223	62.34 340	98.13 204	75.78 323	87.35 264	92.52 324

our_test_3		88.78 272	87.98 270	91.20 302	92.45 325	82.53 305	93.61 306	95.69 253	85.77 275	84.88 294	93.71 273	79.99 218	96.78 309	79.47 309	86.24 267	94.28 301

MDA-MVSNet-bldmvs		85.00 310	82.95 312	91.17 303	93.13 315	83.33 301	94.56 282	95.00 285	84.57 291	65.13 354	92.65 295	70.45 312	95.85 326	73.57 331	77.49 329	94.33 298

SixPastTwentyTwo		89.15 267	88.54 264	90.98 304	93.49 300	80.28 325	96.70 182	94.70 296	90.78 138	84.15 302	95.57 184	71.78 304	97.71 269	84.63 245	85.07 285	94.94 270

LP		84.13 313	81.85 318	90.97 305	93.20 312	82.12 310	87.68 350	94.27 315	76.80 341	81.93 312	88.52 335	72.97 300	95.95 325	59.53 351	81.73 317	94.84 276

PVSNet_0	82.17 19	85.46 309	83.64 310	90.92 306	95.27 217	79.49 330	90.55 338	95.60 257	83.76 301	83.00 308	89.95 318	71.09 308	97.97 240	82.75 273	60.79 355	95.31 249

OpenMVS_ROB	81.14 20	84.42 312	82.28 313	90.83 307	90.06 337	84.05 294	95.73 251	94.04 319	73.89 349	80.17 335	91.53 315	59.15 344	97.64 274	66.92 343	89.05 246	90.80 344

Patchmatch-RL test		87.38 294	86.24 293	90.81 308	88.74 343	78.40 336	88.12 349	93.17 328	87.11 251	82.17 310	89.29 331	81.95 184	95.60 331	88.64 178	77.02 330	98.41 121

dp		88.90 270	88.26 268	90.81 308	94.58 253	76.62 338	92.85 319	94.93 290	85.12 283	90.07 205	93.07 290	75.81 278	98.12 207	80.53 303	87.42 262	97.71 152

MDA-MVSNet_test_wron		85.87 306	84.23 308	90.80 310	92.38 327	82.57 304	93.17 312	95.15 278	82.15 312	67.65 350	92.33 307	78.20 253	95.51 333	77.33 318	79.74 324	94.31 300

YYNet1		85.87 306	84.23 308	90.78 311	92.38 327	82.46 307	93.17 312	95.14 279	82.12 313	67.69 349	92.36 304	78.16 256	95.50 334	77.31 319	79.73 325	94.39 296

UnsupCasMVSNet_eth		85.99 305	84.45 306	90.62 312	89.97 338	82.40 308	93.62 305	97.37 145	89.86 163	78.59 339	92.37 301	65.25 335	95.35 335	82.27 279	70.75 349	94.10 303

MIMVSNet1		84.93 311	83.05 311	90.56 313	89.56 341	84.84 287	95.40 264	95.35 267	83.91 297	80.38 330	92.21 308	57.23 346	93.34 343	70.69 340	82.75 315	93.50 310

lessismore_v0				90.45 314	91.96 330	79.09 334		87.19 360		80.32 332	94.39 239	66.31 330	97.55 279	84.00 258	76.84 331	94.70 287

RPSCF		90.75 235	90.86 193	90.42 315	96.84 149	76.29 339	95.61 256	96.34 222	83.89 298	91.38 163	97.87 57	76.45 274	98.78 149	87.16 209	92.23 202	96.20 198

K. test v3		87.64 293	86.75 291	90.32 316	93.02 316	79.48 331	96.61 192	92.08 346	90.66 144	80.25 334	94.09 262	67.21 328	96.65 310	85.96 227	80.83 323	94.83 278

testgi		87.97 289	87.21 286	90.24 317	92.86 317	80.76 317	96.67 186	94.97 288	91.74 111	85.52 289	95.83 167	62.66 339	94.47 338	76.25 322	88.36 254	95.48 233

UnsupCasMVSNet_bld		82.13 320	79.46 322	90.14 318	88.00 345	82.47 306	90.89 337	96.62 217	78.94 334	75.61 342	84.40 349	56.63 348	96.31 313	77.30 320	66.77 354	91.63 340

LF4IMVS		87.94 290	87.25 282	89.98 319	92.38 327	80.05 328	94.38 285	95.25 274	87.59 241	84.34 298	94.74 222	64.31 336	97.66 273	84.83 240	87.45 260	92.23 336

Anonymous20231206		87.09 297	86.14 295	89.93 320	91.22 333	80.35 322	96.11 232	95.35 267	83.57 303	84.16 301	93.02 291	73.54 298	95.61 330	72.16 334	86.14 269	93.84 308

CVMVSNet		91.23 219	91.75 153	89.67 321	95.77 198	74.69 341	96.44 200	94.88 292	85.81 274	92.18 149	97.64 77	79.07 230	95.58 332	88.06 183	95.86 140	98.74 92

test20.03		86.14 304	85.40 300	88.35 322	90.12 336	80.06 327	95.90 243	95.20 276	88.59 205	81.29 317	93.62 278	71.43 306	92.65 345	71.26 338	81.17 321	92.34 335

PM-MVS		83.48 314	81.86 317	88.31 323	87.83 346	77.59 337	93.43 307	91.75 348	86.91 259	80.63 324	89.91 319	44.42 358	95.84 327	85.17 239	76.73 332	91.50 342

EU-MVSNet		88.72 273	88.90 258	88.20 324	93.15 314	74.21 342	96.63 191	94.22 316	85.18 281	87.32 271	95.97 158	76.16 277	94.98 336	85.27 236	86.17 268	95.41 239

new_pmnet		82.89 316	81.12 321	88.18 325	89.63 340	80.18 326	91.77 329	92.57 341	76.79 342	75.56 343	88.23 339	61.22 342	94.48 337	71.43 336	82.92 313	89.87 346

CMPMVS	62.92 21	85.62 308	84.92 303	87.74 326	89.14 342	73.12 344	94.17 292	96.80 204	73.98 348	73.65 344	94.93 208	66.36 329	97.61 276	83.95 259	91.28 221	92.48 327
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
pmmvs3		79.97 322	77.50 326	87.39 327	82.80 354	79.38 332	92.70 321	90.75 352	70.69 352	78.66 338	87.47 345	51.34 355	93.40 342	73.39 332	69.65 351	89.38 347

new-patchmatchnet		83.18 315	81.87 316	87.11 328	86.88 349	75.99 340	93.70 301	95.18 277	85.02 285	77.30 341	88.40 337	65.99 332	93.88 341	74.19 330	70.18 350	91.47 343

DSMNet-mixed		86.34 302	86.12 296	87.00 329	89.88 339	70.43 346	94.93 277	90.08 354	77.97 339	85.42 292	92.78 294	74.44 290	93.96 340	74.43 327	95.14 151	96.62 189

ambc				86.56 330	83.60 353	70.00 350	85.69 353	94.97 288		80.60 325	88.45 336	37.42 359	96.84 308	82.69 274	75.44 335	92.86 317

MVS-HIRNet		82.47 319	81.21 320	86.26 331	95.38 210	69.21 351	88.96 347	89.49 356	66.28 353	80.79 320	74.08 356	68.48 321	97.39 291	71.93 335	95.47 146	92.18 337

test2356		82.77 317	82.14 315	84.65 332	85.77 350	70.36 347	91.22 333	93.69 325	81.58 318	81.82 313	89.00 333	60.63 343	90.77 351	64.74 345	90.80 229	92.82 318

testus		82.63 318	82.15 314	84.07 333	87.31 348	67.67 352	93.18 310	94.29 314	82.47 310	82.14 311	90.69 316	53.01 353	91.94 348	66.30 344	89.96 239	92.62 322

test1235678		79.82 323	78.53 324	83.69 334	82.55 355	67.55 353	92.50 324	94.13 317	79.28 332	72.10 347	86.45 347	57.27 345	90.68 352	61.60 349	80.90 322	92.82 318

LCM-MVSNet		72.55 327	69.39 330	82.03 335	70.81 366	65.42 356	90.12 342	94.36 311	55.02 357	65.88 353	81.72 350	24.16 368	89.96 353	74.32 329	68.10 352	90.71 345

no-one		68.12 331	63.78 334	81.13 336	74.01 361	70.22 349	87.61 351	90.71 353	72.63 351	53.13 359	71.89 357	30.29 362	91.45 349	61.53 350	32.21 360	81.72 354

1111		78.29 325	77.55 325	80.50 337	83.89 351	59.98 360	91.89 327	93.71 322	75.06 344	73.60 345	87.67 342	55.66 349	92.60 346	58.54 353	77.92 328	88.93 348

PMMVS2		70.19 330	66.92 332	80.01 338	76.35 358	65.67 355	86.22 352	87.58 359	64.83 355	62.38 355	80.29 353	26.78 366	88.49 357	63.79 346	54.07 356	85.88 351

testpf		80.97 321	81.40 319	79.65 339	91.53 331	72.43 345	73.47 361	89.55 355	78.63 335	80.81 319	89.06 332	61.36 341	91.36 350	83.34 264	84.89 293	75.15 357

testmv		72.22 328	70.02 328	78.82 340	73.06 364	61.75 358	91.24 332	92.31 343	74.45 347	61.06 356	80.51 352	34.21 360	88.63 356	55.31 356	68.07 353	86.06 350

N_pmnet		78.73 324	78.71 323	78.79 341	92.80 319	46.50 368	94.14 293	43.71 372	78.61 336	80.83 318	91.66 314	74.94 288	96.36 312	67.24 342	84.45 297	93.50 310

test12356		74.97 326	74.13 327	77.49 342	78.81 357	56.23 364	88.53 348	92.75 339	75.14 343	67.50 351	85.07 348	44.88 357	89.96 353	58.71 352	75.75 334	86.26 349

ANet_high		63.94 334	59.58 335	77.02 343	61.24 369	66.06 354	85.66 354	87.93 358	78.53 337	42.94 361	71.04 358	25.42 367	80.71 361	52.60 358	30.83 362	84.28 352

FPMVS		71.27 329	69.85 329	75.50 344	74.64 359	59.03 362	91.30 331	91.50 349	58.80 356	57.92 357	88.28 338	29.98 364	85.53 359	53.43 357	82.84 314	81.95 353

Gipuma		67.86 332	65.41 333	75.18 345	92.66 322	73.45 343	66.50 363	94.52 306	53.33 358	57.80 358	66.07 360	30.81 361	89.20 355	48.15 360	78.88 327	62.90 361
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
DeepMVS_CX				74.68 346	90.84 334	64.34 357		81.61 367	65.34 354	67.47 352	88.01 341	48.60 356	80.13 362	62.33 348	73.68 348	79.58 355

wuykxyi23d		56.92 337	51.11 342	74.38 347	62.30 368	61.47 359	80.09 358	84.87 362	49.62 360	30.80 367	57.20 364	7.03 371	82.94 360	55.69 355	32.36 359	78.72 356

PNet_i23d		59.01 335	55.87 336	68.44 348	73.98 362	51.37 365	81.36 357	82.41 365	52.37 359	42.49 363	70.39 359	11.39 369	79.99 363	49.77 359	38.71 358	73.97 358

PMVS	53.92 22	58.58 336	55.40 337	68.12 349	51.00 370	48.64 366	78.86 359	87.10 361	46.77 361	35.84 366	74.28 355	8.76 370	86.34 358	42.07 361	73.91 347	69.38 359
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MVE	50.73 23	53.25 339	48.81 343	66.58 350	65.34 367	57.50 363	72.49 362	70.94 370	40.15 364	39.28 365	63.51 361	6.89 373	73.48 366	38.29 362	42.38 357	68.76 360
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
E-PMN		53.28 338	52.56 340	55.43 351	74.43 360	47.13 367	83.63 356	76.30 368	42.23 362	42.59 362	62.22 362	28.57 365	74.40 364	31.53 363	31.51 361	44.78 362

EMVS		52.08 340	51.31 341	54.39 352	72.62 365	45.39 369	83.84 355	75.51 369	41.13 363	40.77 364	59.65 363	30.08 363	73.60 365	28.31 364	29.90 363	44.18 363

.test1245		65.38 333	69.22 331	53.86 353	83.89 351	59.98 360	91.89 327	93.71 322	75.06 344	73.60 345	87.67 342	55.66 349	92.60 346	58.54 353	2.96 366	9.00 366

tmp_tt		51.94 341	53.82 339	46.29 354	33.73 371	45.30 370	78.32 360	67.24 371	18.02 365	50.93 360	87.05 346	52.99 354	53.11 367	70.76 339	25.29 364	40.46 364

pcd1.5k->3k		38.37 343	40.51 344	31.96 355	94.29 262	0.00 374	0.00 365	97.69 106	0.00 369	0.00 371	0.00 371	81.45 191	0.00 371	0.00 368	91.11 223	95.89 214

wuyk23d		25.11 344	24.57 346	26.74 356	73.98 362	39.89 371	57.88 364	9.80 373	12.27 366	10.39 368	6.97 370	7.03 371	36.44 368	25.43 365	17.39 365	3.89 368

test123		13.04 347	15.66 348	5.18 357	4.51 373	3.45 372	92.50 324	1.81 375	2.50 368	7.58 370	20.15 367	3.67 374	2.18 370	7.13 367	1.07 368	9.90 365

testmvs		13.36 346	16.33 347	4.48 358	5.04 372	2.26 373	93.18 310	3.28 374	2.70 367	8.24 369	21.66 366	2.29 375	2.19 369	7.58 366	2.96 366	9.00 366

test_part1				0.00 359		0.00 374	0.00 365	98.26 26				0.00 376	0.00 371	0.00 368	0.00 369	0.00 369

v1.0		40.67 342	54.22 338	0.00 359	99.28 18	0.00 374	0.00 365	98.26 26	93.81 46	98.10 8	98.53 13	0.00 376	0.00 371	0.00 368	0.00 369	0.00 369

cdsmvs_eth3d_5k		23.24 345	30.99 345	0.00 359	0.00 374	0.00 374	0.00 365	97.63 112	0.00 369	0.00 371	96.88 112	84.38 126	0.00 371	0.00 368	0.00 369	0.00 369

pcd_1.5k_mvsjas		7.39 349	9.85 350	0.00 359	0.00 374	0.00 374	0.00 365	0.00 376	0.00 369	0.00 371	0.00 371	88.65 72	0.00 371	0.00 368	0.00 369	0.00 369

sosnet-low-res		0.00 350	0.00 351	0.00 359	0.00 374	0.00 374	0.00 365	0.00 376	0.00 369	0.00 371	0.00 371	0.00 376	0.00 371	0.00 368	0.00 369	0.00 369

sosnet		0.00 350	0.00 351	0.00 359	0.00 374	0.00 374	0.00 365	0.00 376	0.00 369	0.00 371	0.00 371	0.00 376	0.00 371	0.00 368	0.00 369	0.00 369

uncertanet		0.00 350	0.00 351	0.00 359	0.00 374	0.00 374	0.00 365	0.00 376	0.00 369	0.00 371	0.00 371	0.00 376	0.00 371	0.00 368	0.00 369	0.00 369

Regformer		0.00 350	0.00 351	0.00 359	0.00 374	0.00 374	0.00 365	0.00 376	0.00 369	0.00 371	0.00 371	0.00 376	0.00 371	0.00 368	0.00 369	0.00 369

ab-mvs-re		8.06 348	10.74 349	0.00 359	0.00 374	0.00 374	0.00 365	0.00 376	0.00 369	0.00 371	96.69 121	0.00 376	0.00 371	0.00 368	0.00 369	0.00 369

uanet		0.00 350	0.00 351	0.00 359	0.00 374	0.00 374	0.00 365	0.00 376	0.00 369	0.00 371	0.00 371	0.00 376	0.00 371	0.00 368	0.00 369	0.00 369

GSMVS																98.45 117

test_part2					99.28 18	95.74 4				98.10 8

sam_mvs1												82.76 164				98.45 117

sam_mvs												81.94 185

MTGPA								98.08 53

test_post1							92.81 320				16.58 369	80.53 208	97.68 270	86.20 220

test_post											17.58 368	81.76 187	98.08 212

patchmatchnet-post											90.45 317	82.65 168	98.10 209

MTMP							97.86 51	82.03 366

gm-plane-assit					93.22 310	78.89 335			84.82 288		93.52 281		98.64 161	87.72 190

test9_res														94.81 65	99.38 35	99.45 31

TEST9					98.70 41	94.19 25	96.41 205	98.02 70	88.17 227	96.03 58	97.56 85	92.74 14	99.59 53

test_8					98.67 43	94.06 32	96.37 212	98.01 72	88.58 206	95.98 63	97.55 87	92.73 15	99.58 56

agg_prior2														93.94 78	99.38 35	99.50 24

agg_prior					98.67 43	93.79 39		98.00 74		95.68 74			99.57 64

test_prior4						93.66 43	96.42 204

test_prior2							96.35 213		92.80 79	96.03 58	97.59 81	92.01 30		95.01 57	99.38 35

旧先验2							95.94 241		81.66 316	97.34 19			98.82 146	92.26 104

新几何2							95.79 248

旧先验1					98.38 63	93.38 51		97.75 96			98.09 44	92.30 27			99.01 65	99.16 54

无先验							95.79 248	97.87 89	83.87 300				99.65 42	87.68 193		98.89 83

原ACMM2							95.67 252

test222					98.24 74	92.21 78	95.33 266	97.60 113	79.22 333	95.25 83	97.84 62	88.80 70			99.15 55	98.72 94

testdata2													99.67 40	85.96 227

segment_acmp												92.89 12

testdata1							95.26 272		93.10 67

plane_prior7					96.21 179	89.98 145

plane_prior6					96.10 189	90.00 141						81.32 193

plane_prior5								97.51 123					98.60 165	93.02 98	92.23 202	95.86 215

plane_prior4											96.64 124

plane_prior3						90.00 141			94.46 31	91.34 166

plane_prior2							97.74 62		94.85 18

plane_prior1					96.14 187

plane_prior						89.99 143	97.24 122		94.06 39						92.16 206

n2								0.00 376

nn								0.00 376

door-mid								91.06 351

test11								97.88 87

door								91.13 350

HQP5-MVS						89.33 183

HQP-NCC					95.86 193		96.65 187		93.55 50	90.14 194

ACMP_Plane					95.86 193		96.65 187		93.55 50	90.14 194

BP-MVS														92.13 110

HQP4-MVS										90.14 194			98.50 173			95.78 222

HQP3-MVS								97.39 142							92.10 207

HQP2-MVS												80.95 198

NP-MVS					95.99 192	89.81 152					95.87 164

MDTV_nov1_ep13_2view						70.35 348	93.10 316		83.88 299	93.55 113		82.47 173		86.25 219		98.38 125

MDTV_nov1_ep13			90.76 199		95.22 222	80.33 323	93.03 317	95.28 271	88.14 228	92.84 139	93.83 269	81.34 192	98.08 212	82.86 270	94.34 164

ACMMP++_ref															90.30 236

ACMMP++															91.02 226

Test By Simon												88.73 71

High-resolution multi-view benchmark