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 25	95.78 3	97.21 126	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 28	96.16 1	97.55 94	97.97 80	95.59 4	96.61 38	97.89 53	92.57 19	99.84 13	95.95 35	99.51 19	99.40 35

CNVR-MVS		97.68 3	97.44 6	98.37 3	98.90 34	95.86 2	97.27 118	98.08 52	95.81 3	97.87 13	98.31 34	94.26 3	99.68 37	97.02 4	99.49 23	99.57 13

SMA-MVS		97.35 8	97.03 10	98.30 4	99.06 30	95.42 5	97.94 44	98.18 36	90.57 149	98.85 2	98.94 1	93.33 10	99.83 14	96.72 13	99.68 3	99.63 6

ACMMP_Plus		97.20 11	96.86 18	98.23 5	99.09 27	95.16 9	97.60 87	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 16	95.35 6	97.12 135	98.07 57	93.54 53	96.08 56	97.69 69	93.86 6	99.71 29	96.50 19	99.39 34	99.55 17

3Dnovator+	91.43 4	95.40 62	94.48 81	98.16 7	96.90 142	95.34 7	98.48 14	97.87 88	94.65 29	88.53 242	98.02 48	83.69 131	99.71 29	93.18 94	98.96 66	99.44 32

NCCC		97.30 10	97.03 10	98.11 8	98.77 37	95.06 11	97.34 112	98.04 66	95.96 2	97.09 29	97.88 55	93.18 11	99.71 29	95.84 38	99.17 53	99.56 15

APDe-MVS		97.82 2	97.73 1	98.08 9	99.15 26	94.82 13	98.81 2	98.30 23	94.76 25	98.30 6	98.90 2	93.77 7	99.68 37	97.93 1	99.69 2	99.75 1

APD-MVS		96.95 24	96.60 29	98.01 10	99.03 31	94.93 12	97.72 65	98.10 49	91.50 115	98.01 10	98.32 33	92.33 23	99.58 55	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 33	96.27 40	97.98 11	99.23 23	94.71 14	96.96 145	98.06 59	90.67 140	95.55 78	98.78 4	91.07 44	99.86 7	96.58 17	99.55 14	99.38 39
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	97.15 133	98.08 52	95.07 15	96.11 54	98.59 7	90.88 49	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	97.24 120	98.08 52	95.07 15	96.11 54	98.59 7	90.88 49	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 61	94.25 23	98.43 16	98.27 25	95.34 9	98.11 7	98.56 9	94.53 2	99.71 29	96.57 18	99.62 7	99.65 4
Skip Steuart: Steuart Systems R&D Blog.
MVS_0304		96.05 51	95.45 54	97.85 15	97.75 107	94.50 16	96.87 155	97.95 83	95.46 6	95.60 76	98.01 49	80.96 196	99.83 14	97.23 2	99.25 46	99.23 49

HFP-MVS		97.14 15	96.92 16	97.83 16	99.42 3	94.12 28	98.52 10	98.32 20	93.21 60	97.18 22	98.29 37	92.08 28	99.83 14	95.63 43	99.59 9	99.54 19

#test#		97.02 21	96.75 26	97.83 16	99.42 3	94.12 28	98.15 29	98.32 20	92.57 84	97.18 22	98.29 37	92.08 28	99.83 14	95.12 53	99.59 9	99.54 19

XVS		97.18 12	96.96 14	97.81 18	99.38 8	94.03 32	98.59 7	98.20 32	94.85 18	96.59 40	98.29 37	91.70 36	99.80 20	95.66 40	99.40 32	99.62 7

X-MVStestdata		91.71 183	89.67 241	97.81 18	99.38 8	94.03 32	98.59 7	98.20 32	94.85 18	96.59 40	32.69 361	91.70 36	99.80 20	95.66 40	99.40 32	99.62 7

ACMMPR		97.07 18	96.84 19	97.79 20	99.44 2	93.88 34	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 57	95.23 61	97.78 21	97.56 119	95.19 8	97.86 50	97.17 160	94.39 33	96.47 45	96.40 138	85.89 108	99.20 101	96.21 28	95.11 149	98.95 75

DeepC-MVS_fast	93.89 2	96.93 26	96.64 28	97.78 21	98.64 48	94.30 21	97.41 103	98.04 66	94.81 23	96.59 40	98.37 24	91.24 42	99.64 46	95.16 51	99.52 17	99.42 34
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 23	99.46 1	93.79 38	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 54	95.38 57	97.77 23	98.93 33	94.44 18	96.35 210	97.88 86	86.98 252	96.65 36	97.89 53	91.99 32	99.47 80	92.26 102	99.46 25	99.39 36

canonicalmvs		96.02 53	95.45 54	97.75 25	97.59 117	95.15 10	98.28 22	97.60 112	94.52 30	96.27 50	96.12 149	87.65 86	99.18 104	96.20 29	94.82 153	98.91 79

train_agg		96.30 45	95.83 48	97.72 26	98.70 40	94.19 25	96.41 202	98.02 69	88.58 203	96.03 57	97.56 84	92.73 15	99.59 52	95.04 55	99.37 39	99.39 36

MP-MVS		96.77 31	96.45 36	97.72 26	99.39 7	93.80 37	98.41 17	98.06 59	93.37 55	95.54 79	98.34 28	90.59 52	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 31	96.46 35	97.71 28	98.40 59	94.07 30	98.21 28	98.45 15	89.86 159	97.11 28	98.01 49	92.52 21	99.69 35	96.03 34	99.53 16	99.36 41

TSAR-MVS + MP.		97.42 6	97.33 7	97.69 29	99.25 20	94.24 24	98.07 34	97.85 91	93.72 47	98.57 4	98.35 25	93.69 8	99.40 89	97.06 3	99.46 25	99.44 32

Regformer-2		97.16 14	96.99 12	97.67 30	98.32 67	93.84 36	96.83 159	98.10 49	95.24 10	97.49 15	98.25 40	92.57 19	99.61 47	96.80 9	99.29 43	99.56 15

PGM-MVS		96.81 29	96.53 32	97.65 31	99.35 13	93.53 46	97.65 74	98.98 1	92.22 90	97.14 25	98.44 17	91.17 43	99.85 10	94.35 70	99.46 25	99.57 13

test12				97.65 31	98.46 55	94.26 22		97.66 107		95.52 80		90.89 48	99.46 81		99.25 46	99.22 50

mPP-MVS		96.86 27	96.60 29	97.64 33	99.40 5	93.44 48	98.50 13	98.09 51	93.27 59	95.95 63	98.33 31	91.04 45	99.88 4	95.20 50	99.57 13	99.60 10

CP-MVS		97.02 21	96.81 22	97.64 33	99.33 14	93.54 45	98.80 3	98.28 24	92.99 69	96.45 47	98.30 36	91.90 33	99.85 10	95.61 45	99.68 3	99.54 19

HSP-MVS		97.53 5	97.49 5	97.63 35	99.40 5	93.77 41	98.53 9	97.85 91	95.55 5	98.56 5	97.81 62	93.90 5	99.65 41	96.62 15	99.21 50	99.48 28

agg_prior3		96.16 49	95.67 50	97.62 36	98.67 42	93.88 34	96.41 202	98.00 73	87.93 228	95.81 67	97.47 88	92.33 23	99.59 52	95.04 55	99.37 39	99.39 36

agg_prior1		96.22 48	95.77 49	97.56 37	98.67 42	93.79 38	96.28 218	98.00 73	88.76 200	95.68 72	97.55 86	92.70 17	99.57 63	95.01 57	99.32 41	99.32 43

Regformer-1		97.10 16	96.96 14	97.54 38	98.32 67	93.48 47	96.83 159	97.99 78	95.20 12	97.46 16	98.25 40	92.48 22	99.58 55	96.79 11	99.29 43	99.55 17

CANet		96.39 43	96.02 45	97.50 39	97.62 114	93.38 50	97.02 140	97.96 81	95.42 8	94.86 86	97.81 62	87.38 92	99.82 18	96.88 7	99.20 51	99.29 45

3Dnovator	91.36 5	95.19 71	94.44 83	97.44 40	96.56 157	93.36 52	98.65 6	98.36 16	94.12 38	89.25 232	98.06 46	82.20 177	99.77 22	93.41 91	99.32 41	99.18 52

HPM-MVS		96.69 34	96.45 36	97.40 41	99.36 12	93.11 56	98.87 1	98.06 59	91.17 128	96.40 48	97.99 51	90.99 46	99.58 55	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 59	95.12 64	97.37 42	99.19 24	94.19 25	97.03 138	98.08 52	88.35 217	95.09 84	97.65 73	89.97 59	99.48 79	92.08 111	98.59 75	98.44 117

1121		94.71 86	93.83 90	97.34 43	98.57 53	93.64 43	96.04 232	97.73 97	81.56 316	95.68 72	97.85 59	90.23 55	99.65 41	87.68 188	99.12 59	98.73 91

新几何1				97.32 44	98.60 49	93.59 44		97.75 95	81.58 314	95.75 70	97.85 59	90.04 58	99.67 39	86.50 211	99.13 56	98.69 96

DELS-MVS		96.61 37	96.38 38	97.30 45	97.79 104	93.19 54	95.96 237	98.18 36	95.23 11	95.87 64	97.65 73	91.45 40	99.70 34	95.87 36	99.44 29	99.00 71
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 50	95.66 51	97.29 46	97.96 92	93.17 55	97.30 117	98.06 59	93.92 41	93.38 114	98.66 6	86.83 97	99.73 25	95.60 47	99.22 49	98.96 73
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 46	95.93 46	97.28 47	99.24 21	92.62 68	98.25 25	98.81 3	92.99 69	94.56 92	98.39 23	88.96 66	99.85 10	94.57 69	97.63 96	99.36 41
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 34	96.49 33	97.27 48	98.31 69	93.39 49	96.79 166	96.72 205	94.17 37	97.44 17	97.66 72	92.76 13	99.33 94	96.86 8	97.76 95	99.08 62

Regformer-4		96.97 23	96.87 17	97.25 49	98.34 64	92.66 67	96.96 145	98.01 71	95.12 14	97.14 25	98.42 19	91.82 34	99.61 47	96.90 6	99.13 56	99.50 24

test_prior3		96.46 41	96.20 43	97.23 50	98.67 42	92.99 58	96.35 210	98.00 73	92.80 79	96.03 57	97.59 80	92.01 30	99.41 87	95.01 57	99.38 35	99.29 45

test_prior				97.23 50	98.67 42	92.99 58		98.00 73					99.41 87			99.29 45

HPM-MVS_fast		96.51 39	96.27 40	97.22 52	99.32 15	92.74 64	98.74 4	98.06 59	90.57 149	96.77 32	98.35 25	90.21 56	99.53 71	94.80 65	99.63 6	99.38 39

VNet		95.89 56	95.45 54	97.21 53	98.07 86	92.94 61	97.50 97	98.15 40	93.87 42	97.52 14	97.61 79	85.29 114	99.53 71	95.81 39	95.27 146	99.16 53

UA-Net		95.95 55	95.53 53	97.20 54	97.67 111	92.98 60	97.65 74	98.13 43	94.81 23	96.61 38	98.35 25	88.87 67	99.51 76	90.36 140	97.35 108	99.11 60

EPNet		95.20 70	94.56 76	97.14 55	92.80 315	92.68 66	97.85 53	94.87 294	96.64 1	92.46 136	97.80 64	86.23 103	99.65 41	93.72 83	98.62 74	99.10 61
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
APD-MVS_3200maxsize		96.81 29	96.71 27	97.12 56	99.01 32	92.31 74	97.98 43	98.06 59	93.11 66	97.44 17	98.55 11	90.93 47	99.55 66	96.06 32	99.25 46	99.51 23

SD-MVS		97.41 7	97.53 3	97.06 57	98.57 53	94.46 17	97.92 46	98.14 42	94.82 22	99.01 1	98.55 11	94.18 4	97.41 285	96.94 5	99.64 5	99.32 43

Regformer-3		96.85 28	96.80 23	97.01 58	98.34 64	92.02 85	96.96 145	97.76 94	95.01 17	97.08 30	98.42 19	91.71 35	99.54 68	96.80 9	99.13 56	99.48 28

MVS_111021_HR		96.68 36	96.58 31	96.99 59	98.46 55	92.31 74	96.20 226	98.90 2	94.30 36	95.86 65	97.74 67	92.33 23	99.38 92	96.04 33	99.42 30	99.28 48

abl_6		96.40 42	96.21 42	96.98 60	98.89 35	92.20 79	97.89 48	98.03 68	93.34 58	97.22 21	98.42 19	87.93 80	99.72 28	95.10 54	99.07 61	99.02 65

QAPM		93.45 120	92.27 137	96.98 60	96.77 149	92.62 68	98.39 18	98.12 44	84.50 288	88.27 248	97.77 65	82.39 173	99.81 19	85.40 230	98.81 69	98.51 106

WTY-MVS		94.71 86	94.02 87	96.79 62	97.71 110	92.05 83	96.59 192	97.35 149	90.61 146	94.64 91	96.93 106	86.41 102	99.39 90	91.20 133	94.71 157	98.94 76

CPTT-MVS		95.57 61	95.19 62	96.70 63	99.27 19	91.48 99	98.33 20	98.11 47	87.79 231	95.17 83	98.03 47	87.09 95	99.61 47	93.51 86	99.42 30	99.02 65

sss		94.51 88	93.80 91	96.64 64	97.07 136	91.97 87	96.32 214	98.06 59	88.94 190	94.50 93	96.78 111	84.60 122	99.27 98	91.90 114	96.02 134	98.68 97

ab-mvs		93.57 117	92.55 129	96.64 64	97.28 129	91.96 88	95.40 261	97.45 134	89.81 163	93.22 122	96.28 142	79.62 223	99.46 81	90.74 136	93.11 187	98.50 108

EI-MVSNet-Vis-set		96.51 39	96.47 34	96.63 66	98.24 73	91.20 111	96.89 154	97.73 97	94.74 26	96.49 44	98.49 14	90.88 49	99.58 55	96.44 20	98.32 80	99.13 57

114514_t		93.95 104	93.06 113	96.63 66	99.07 29	91.61 95	97.46 102	97.96 81	77.99 334	93.00 128	97.57 82	86.14 107	99.33 94	89.22 157	99.15 54	98.94 76

HY-MVS	89.66 9	93.87 106	92.95 115	96.63 66	97.10 135	92.49 72	95.64 252	96.64 213	89.05 184	93.00 128	95.79 168	85.77 111	99.45 83	89.16 160	94.35 158	97.96 135

MSLP-MVS++		96.94 25	97.06 9	96.59 69	98.72 39	91.86 89	97.67 71	98.49 12	94.66 28	97.24 20	98.41 22	92.31 26	98.94 130	96.61 16	99.46 25	98.96 73

CANet_DTU		94.37 90	93.65 96	96.55 70	96.46 165	92.13 81	96.21 225	96.67 212	94.38 34	93.53 111	97.03 105	79.34 226	99.71 29	90.76 135	98.45 78	97.82 144

LFMVS		93.60 115	92.63 125	96.52 71	98.13 84	91.27 107	97.94 44	93.39 326	90.57 149	96.29 49	98.31 34	69.00 314	99.16 106	94.18 71	95.87 138	99.12 59

DP-MVS		92.76 143	91.51 166	96.52 71	98.77 37	90.99 118	97.38 109	96.08 233	82.38 307	89.29 229	97.87 56	83.77 130	99.69 35	81.37 288	96.69 124	98.89 82

CNLPA		94.28 92	93.53 100	96.52 71	98.38 62	92.55 70	96.59 192	96.88 199	90.13 155	91.91 149	97.24 96	85.21 115	99.09 120	87.64 191	97.83 91	97.92 137

Vis-MVSNet		95.23 67	94.81 68	96.51 74	97.18 132	91.58 98	98.26 24	98.12 44	94.38 34	94.90 85	98.15 42	82.28 174	98.92 131	91.45 128	98.58 76	99.01 69
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
MAR-MVS		94.22 93	93.46 103	96.51 74	98.00 87	92.19 80	97.67 71	97.47 128	88.13 226	93.00 128	95.84 162	84.86 120	99.51 76	87.99 180	98.17 84	97.83 143
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 94	93.42 107	96.48 76	97.64 113	91.42 104	95.55 254	97.71 104	88.99 186	92.34 141	95.82 164	89.19 62	99.11 111	86.14 216	97.38 106	98.90 80

EI-MVSNet-UG-set		96.34 44	96.30 39	96.47 77	98.20 78	90.93 122	96.86 156	97.72 100	94.67 27	96.16 53	98.46 15	90.43 53	99.58 55	96.23 24	97.96 89	98.90 80

LS3D		93.57 117	92.61 127	96.47 77	97.59 117	91.61 95	97.67 71	97.72 100	85.17 278	90.29 187	98.34 28	84.60 122	99.73 25	83.85 257	98.27 81	98.06 134

CSCG		96.05 51	95.91 47	96.46 79	99.24 21	90.47 134	98.30 21	98.57 11	89.01 185	93.97 103	97.57 82	92.62 18	99.76 23	94.66 68	99.27 45	99.15 55

casdiffmvs1		95.77 58	95.55 52	96.44 80	97.30 128	91.43 103	97.57 92	97.58 115	91.21 127	96.65 36	96.60 130	89.18 63	98.83 140	96.27 22	97.60 97	99.05 64

0601test		94.78 85	94.23 85	96.43 81	97.74 108	91.22 108	96.85 157	97.10 169	91.23 126	95.71 71	96.93 106	84.30 126	99.31 96	93.10 95	95.12 148	98.75 89

casdiffmvs		95.23 67	94.84 67	96.40 82	96.90 142	91.71 90	97.36 110	97.30 153	91.02 133	94.81 88	96.18 145	87.74 83	98.77 146	95.65 42	96.55 128	98.71 94

OpenMVS	89.19 12	92.86 139	91.68 152	96.40 82	95.34 207	92.73 65	98.27 23	98.12 44	84.86 283	85.78 283	97.75 66	78.89 245	99.74 24	87.50 195	98.65 73	96.73 178

MVS_111021_LR		96.24 47	96.19 44	96.39 84	98.23 77	91.35 105	96.24 224	98.79 4	93.99 40	95.80 68	97.65 73	89.92 60	99.24 100	95.87 36	99.20 51	98.58 100

原ACMM1				96.38 85	98.59 50	91.09 117		97.89 84	87.41 240	95.22 82	97.68 70	90.25 54	99.54 68	87.95 181	99.12 59	98.49 110

PVSNet_Blended_VisFu		95.27 66	94.91 66	96.38 85	98.20 78	90.86 124	97.27 118	98.25 28	90.21 153	94.18 99	97.27 94	87.48 90	99.73 25	93.53 85	97.77 94	98.55 101

Effi-MVS+		94.93 79	94.45 82	96.36 87	96.61 152	91.47 100	96.41 202	97.41 141	91.02 133	94.50 93	95.92 158	87.53 89	98.78 144	93.89 79	96.81 119	98.84 87

PCF-MVS	89.48 11	91.56 199	89.95 230	96.36 87	96.60 153	92.52 71	92.51 319	97.26 155	79.41 327	88.90 234	96.56 131	84.04 128	99.55 66	77.01 317	97.30 109	97.01 164
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
UGNet		94.04 102	93.28 110	96.31 89	96.85 144	91.19 112	97.88 49	97.68 106	94.40 32	93.00 128	96.18 145	73.39 295	99.61 47	91.72 119	98.46 77	98.13 129
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 60	95.38 57	96.31 89	98.42 58	90.53 132	96.04 232	97.48 125	93.47 54	95.67 75	98.10 43	89.17 64	99.25 99	91.27 131	98.77 70	99.13 57

AdaColmap		94.34 91	93.68 95	96.31 89	98.59 50	91.68 94	96.59 192	97.81 93	89.87 158	92.15 145	97.06 104	83.62 132	99.54 68	89.34 153	98.07 86	97.70 148

lupinMVS		94.99 77	94.56 76	96.29 92	96.34 169	91.21 109	95.83 243	96.27 224	88.93 191	96.22 51	96.88 109	86.20 105	98.85 138	95.27 49	99.05 62	98.82 88

nrg030		94.05 101	93.31 109	96.27 93	95.22 217	94.59 15	98.34 19	97.46 130	92.93 76	91.21 175	96.64 121	87.23 94	98.22 192	94.99 60	85.80 267	95.98 208

PAPM_NR		95.01 73	94.59 75	96.26 94	98.89 35	90.68 129	97.24 120	97.73 97	91.80 109	92.93 133	96.62 128	89.13 65	99.14 109	89.21 158	97.78 93	98.97 72

OMC-MVS		95.09 72	94.70 73	96.25 95	98.46 55	91.28 106	96.43 199	97.57 116	92.04 104	94.77 90	97.96 52	87.01 96	99.09 120	91.31 130	96.77 120	98.36 124

1112_ss		93.37 121	92.42 135	96.21 96	97.05 139	90.99 118	96.31 215	96.72 205	86.87 258	89.83 207	96.69 118	86.51 101	99.14 109	88.12 177	93.67 175	98.50 108

jason		94.84 83	94.39 84	96.18 97	95.52 199	90.93 122	96.09 230	96.52 217	89.28 172	96.01 61	97.32 92	84.70 121	98.77 146	95.15 52	98.91 68	98.85 85
jason: jason.
PLC	91.00 6	94.11 98	93.43 105	96.13 98	98.58 52	91.15 116	96.69 181	97.39 142	87.29 243	91.37 159	96.71 114	88.39 75	99.52 75	87.33 199	97.13 113	97.73 146
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
CHOSEN 1792x2688		94.15 95	93.51 101	96.06 99	98.27 70	89.38 177	95.18 271	98.48 14	85.60 273	93.76 105	97.11 102	83.15 138	99.61 47	91.33 129	98.72 72	99.19 51

IS-MVSNet		94.90 80	94.52 79	96.05 100	97.67 111	90.56 131	98.44 15	96.22 228	93.21 60	93.99 101	97.74 67	85.55 112	98.45 172	89.98 141	97.86 90	99.14 56

VDD-MVS		93.82 108	93.08 112	96.02 101	97.88 101	89.96 146	97.72 65	95.85 247	92.43 87	95.86 65	98.44 17	68.42 318	99.39 90	96.31 21	94.85 151	98.71 94

VDDNet		93.05 131	92.07 139	96.02 101	96.84 145	90.39 136	98.08 33	95.85 247	86.22 266	95.79 69	98.46 15	67.59 321	99.19 102	94.92 61	94.85 151	98.47 113

MVSFormer		95.37 63	95.16 63	95.99 103	96.34 169	91.21 109	98.22 26	97.57 116	91.42 119	96.22 51	97.32 92	86.20 105	97.92 246	94.07 72	99.05 62	98.85 85

CDS-MVSNet		94.14 97	93.54 99	95.93 104	96.18 177	91.46 101	96.33 213	97.04 179	88.97 189	93.56 108	96.51 133	87.55 88	97.89 250	89.80 144	95.95 136	98.44 117
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
API-MVS		94.84 83	94.49 80	95.90 105	97.90 100	92.00 86	97.80 56	97.48 125	89.19 175	94.81 88	96.71 114	88.84 68	99.17 105	88.91 166	98.76 71	96.53 185

HyFIR lowres test		93.66 113	92.92 116	95.87 106	98.24 73	89.88 148	94.58 278	98.49 12	85.06 280	93.78 104	95.78 169	82.86 159	98.67 155	91.77 118	95.71 142	99.07 63

Test_1112_low_res		92.84 141	91.84 147	95.85 107	97.04 140	89.97 144	95.53 256	96.64 213	85.38 274	89.65 217	95.18 197	85.86 109	99.10 117	87.70 186	93.58 180	98.49 110

PVSNet_Blended		94.87 82	94.56 76	95.81 108	98.27 70	89.46 170	95.47 259	98.36 16	88.84 194	94.36 95	96.09 153	88.02 77	99.58 55	93.44 89	98.18 83	98.40 120

test_normal		92.01 172	90.75 195	95.80 109	93.24 304	89.97 144	95.93 239	96.24 227	90.62 144	81.63 311	93.45 281	74.98 282	98.89 135	93.61 84	97.04 115	98.55 101

Anonymous202405211		92.07 171	90.83 192	95.76 110	98.19 80	88.75 198	97.58 90	95.00 284	86.00 269	93.64 106	97.45 89	66.24 327	99.53 71	90.68 138	92.71 191	99.01 69

EPP-MVSNet		95.22 69	95.04 65	95.76 110	97.49 126	89.56 163	98.67 5	97.00 184	90.69 139	94.24 98	97.62 78	89.79 61	98.81 142	93.39 92	96.49 130	98.92 78

xiu_mvs_v1_base_debu		95.01 73	94.76 70	95.75 112	96.58 154	91.71 90	96.25 221	97.35 149	92.99 69	96.70 33	96.63 125	82.67 163	99.44 84	96.22 25	97.46 100	96.11 200

xiu_mvs_v1_base		95.01 73	94.76 70	95.75 112	96.58 154	91.71 90	96.25 221	97.35 149	92.99 69	96.70 33	96.63 125	82.67 163	99.44 84	96.22 25	97.46 100	96.11 200

xiu_mvs_v1_base_debi		95.01 73	94.76 70	95.75 112	96.58 154	91.71 90	96.25 221	97.35 149	92.99 69	96.70 33	96.63 125	82.67 163	99.44 84	96.22 25	97.46 100	96.11 200

Anonymous20240529		91.98 176	90.73 196	95.73 115	98.14 83	89.40 176	97.99 42	97.72 100	79.63 326	93.54 110	97.41 91	69.94 312	99.56 65	91.04 134	91.11 218	98.22 126

DI_MVS_plusplus_test		92.01 172	90.77 193	95.73 115	93.34 300	89.78 151	96.14 228	96.18 230	90.58 148	81.80 310	93.50 278	74.95 283	98.90 133	93.51 86	96.94 116	98.51 106

MVS_Test		94.89 81	94.62 74	95.68 117	96.83 147	89.55 164	96.70 179	97.17 160	91.17 128	95.60 76	96.11 151	87.87 81	98.76 148	93.01 98	97.17 112	98.72 92

TAMVS		94.01 103	93.46 103	95.64 118	96.16 179	90.45 135	96.71 176	96.89 198	89.27 173	93.46 113	96.92 108	87.29 93	97.94 242	88.70 172	95.74 140	98.53 103

UniMVSNet (Re)		93.31 123	92.55 129	95.61 119	95.39 204	93.34 53	97.39 107	98.71 5	93.14 65	90.10 197	94.83 213	87.71 84	98.03 225	91.67 124	83.99 296	95.46 231

diffmvs1		94.99 77	94.79 69	95.60 120	96.52 160	89.20 189	96.43 199	97.36 147	92.59 83	94.85 87	96.10 152	87.85 82	98.74 150	93.99 74	97.41 105	98.86 84

Fast-Effi-MVS+		93.46 119	92.75 121	95.59 121	96.77 149	90.03 138	96.81 163	97.13 165	88.19 222	91.30 164	94.27 252	86.21 104	98.63 157	87.66 190	96.46 132	98.12 130

PatchMatch-RL		92.90 137	92.02 142	95.56 122	98.19 80	90.80 126	95.27 268	97.18 158	87.96 227	91.86 151	95.68 176	80.44 209	98.99 128	84.01 253	97.54 99	96.89 174

TAPA-MVS	90.10 7	92.30 162	91.22 176	95.56 122	98.33 66	89.60 161	96.79 166	97.65 109	81.83 311	91.52 156	97.23 97	87.94 79	98.91 132	71.31 333	98.37 79	98.17 128
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
NR-MVSNet		92.34 159	91.27 173	95.53 124	94.95 231	93.05 57	97.39 107	98.07 57	92.65 82	84.46 293	95.71 173	85.00 118	97.77 261	89.71 146	83.52 304	95.78 217

MVS		91.71 183	90.44 210	95.51 125	95.20 219	91.59 97	96.04 232	97.45 134	73.44 346	87.36 265	95.60 179	85.42 113	99.10 117	85.97 221	97.46 100	95.83 214

VPA-MVSNet		93.24 125	92.48 134	95.51 125	95.70 195	92.39 73	97.86 50	98.66 9	92.30 89	92.09 147	95.37 191	80.49 208	98.40 180	93.95 76	85.86 266	95.75 221

PS-MVSNAJ		95.37 63	95.33 59	95.49 127	97.35 127	90.66 130	95.31 265	97.48 125	93.85 43	96.51 43	95.70 175	88.65 71	99.65 41	94.80 65	98.27 81	96.17 195

DU-MVS		92.90 137	92.04 140	95.49 127	94.95 231	92.83 62	97.16 132	98.24 29	93.02 68	90.13 193	95.71 173	83.47 133	97.85 252	91.71 120	83.93 297	95.78 217

UniMVSNet_NR-MVSNet		93.37 121	92.67 124	95.47 129	95.34 207	92.83 62	97.17 131	98.58 10	92.98 74	90.13 193	95.80 165	88.37 76	97.85 252	91.71 120	83.93 297	95.73 223

testdata				95.46 130	98.18 82	88.90 197		97.66 107	82.73 305	97.03 31	98.07 45	90.06 57	98.85 138	89.67 148	98.98 65	98.64 98

Test4		89.48 259	87.50 270	95.44 131	90.76 331	89.72 152	95.78 247	97.09 170	90.28 152	77.67 336	91.74 309	55.42 347	98.08 207	91.92 113	96.83 118	98.52 104

xiu_mvs_v2_base		95.32 65	95.29 60	95.40 132	97.22 130	90.50 133	95.44 260	97.44 137	93.70 49	96.46 46	96.18 145	88.59 74	99.53 71	94.79 67	97.81 92	96.17 195

F-COLMAP		93.58 116	92.98 114	95.37 133	98.40 59	88.98 195	97.18 130	97.29 154	87.75 233	90.49 182	97.10 103	85.21 115	99.50 78	86.70 208	96.72 123	97.63 149

FIs		94.09 99	93.70 93	95.27 134	95.70 195	92.03 84	98.10 31	98.68 7	93.36 57	90.39 185	96.70 116	87.63 87	97.94 242	92.25 104	90.50 229	95.84 213

PAPM		91.52 202	90.30 215	95.20 135	95.30 211	89.83 149	93.38 305	96.85 201	86.26 265	88.59 241	95.80 165	84.88 119	98.15 198	75.67 321	95.93 137	97.63 149

view600		92.55 146	91.68 152	95.18 136	97.98 88	89.44 172	98.00 38	94.57 301	92.09 98	93.17 123	95.52 184	78.14 256	99.11 111	81.61 277	94.04 167	96.98 165

view800		92.55 146	91.68 152	95.18 136	97.98 88	89.44 172	98.00 38	94.57 301	92.09 98	93.17 123	95.52 184	78.14 256	99.11 111	81.61 277	94.04 167	96.98 165

conf0.05thres1000		92.55 146	91.68 152	95.18 136	97.98 88	89.44 172	98.00 38	94.57 301	92.09 98	93.17 123	95.52 184	78.14 256	99.11 111	81.61 277	94.04 167	96.98 165

tfpn		92.55 146	91.68 152	95.18 136	97.98 88	89.44 172	98.00 38	94.57 301	92.09 98	93.17 123	95.52 184	78.14 256	99.11 111	81.61 277	94.04 167	96.98 165

thres600view7		92.49 152	91.60 158	95.18 136	97.91 99	89.47 168	97.65 74	94.66 296	92.18 97	93.33 115	94.91 205	78.06 260	99.10 117	81.61 277	94.06 165	96.98 165

diffmvs		94.47 89	94.23 85	95.18 136	96.32 171	88.22 213	96.27 219	97.04 179	92.55 85	93.60 107	95.94 157	86.79 98	98.70 154	92.98 99	96.61 126	98.63 99

DeepPCF-MVS	93.97 1	96.61 37	97.09 8	95.15 142	98.09 85	86.63 263	96.00 236	98.15 40	95.43 7	97.95 11	98.56 9	93.40 9	99.36 93	96.77 12	99.48 24	99.45 30

1314		92.81 142	92.03 141	95.14 143	95.33 210	89.52 167	96.04 232	97.44 137	87.72 234	86.25 280	95.33 192	83.84 129	98.79 143	89.26 155	97.05 114	97.11 163

TranMVSNet+NR-MVSNet		92.50 150	91.63 157	95.14 143	94.76 241	92.07 82	97.53 95	98.11 47	92.90 77	89.56 220	96.12 149	83.16 137	97.60 273	89.30 154	83.20 307	95.75 221

thres400		92.42 156	91.52 164	95.12 145	97.85 102	89.29 184	97.41 103	94.88 291	92.19 95	93.27 120	94.46 230	78.17 253	99.08 122	81.40 284	94.08 161	96.98 165

tfpn111		92.45 153	91.58 159	95.06 146	97.92 96	89.37 178	97.71 67	94.66 296	92.20 92	93.31 116	94.90 206	78.06 260	99.11 111	81.37 288	94.06 165	96.70 180

conf200view11		92.45 153	91.58 159	95.05 147	97.92 96	89.37 178	97.71 67	94.66 296	92.20 92	93.31 116	94.90 206	78.06 260	99.08 122	81.40 284	94.08 161	96.70 180

FC-MVSNet-test		93.94 105	93.57 97	95.04 148	95.48 201	91.45 102	98.12 30	98.71 5	93.37 55	90.23 188	96.70 116	87.66 85	97.85 252	91.49 126	90.39 230	95.83 214

FMVSNet3		91.78 180	90.69 199	95.03 149	96.53 159	92.27 76	97.02 140	96.93 194	89.79 164	89.35 226	94.65 222	77.01 269	97.47 280	86.12 217	88.82 242	95.35 242

VPNet		92.23 166	91.31 171	94.99 150	95.56 198	90.96 120	97.22 125	97.86 90	92.96 75	90.96 177	96.62 128	75.06 281	98.20 193	91.90 114	83.65 303	95.80 216

FMVSNet2		91.31 213	90.08 224	94.99 150	96.51 161	92.21 77	97.41 103	96.95 192	88.82 196	88.62 239	94.75 218	73.87 289	97.42 284	85.20 234	88.55 248	95.35 242

thres100view900		92.43 155	91.58 159	94.98 152	97.92 96	89.37 178	97.71 67	94.66 296	92.20 92	93.31 116	94.90 206	78.06 260	99.08 122	81.40 284	94.08 161	96.48 188

BH-RMVSNet		92.72 144	91.97 144	94.97 153	97.16 133	87.99 230	96.15 227	95.60 256	90.62 144	91.87 150	97.15 101	78.41 250	98.57 163	83.16 262	97.60 97	98.36 124

MSDG		91.42 206	90.24 219	94.96 154	97.15 134	88.91 196	93.69 298	96.32 222	85.72 272	86.93 274	96.47 135	80.24 213	98.98 129	80.57 298	95.05 150	96.98 165

tfpn200view9		92.38 158	91.52 164	94.95 155	97.85 102	89.29 184	97.41 103	94.88 291	92.19 95	93.27 120	94.46 230	78.17 253	99.08 122	81.40 284	94.08 161	96.48 188

XXY-MVS		92.16 168	91.23 175	94.95 155	94.75 242	90.94 121	97.47 101	97.43 139	89.14 182	88.90 234	96.43 137	79.71 221	98.24 191	89.56 151	87.68 253	95.67 225

Vis-MVSNet (Re-imp)		94.15 95	93.88 89	94.95 155	97.61 115	87.92 235	98.10 31	95.80 250	92.22 90	93.02 127	97.45 89	84.53 124	97.91 249	88.24 175	97.97 88	99.02 65

conf0.01		91.74 181	90.67 200	94.94 158	97.55 120	89.68 153	97.64 78	93.14 328	88.43 208	91.24 169	94.30 242	78.91 238	98.45 172	81.28 290	93.57 181	96.70 180

conf0.002		91.74 181	90.67 200	94.94 158	97.55 120	89.68 153	97.64 78	93.14 328	88.43 208	91.24 169	94.30 242	78.91 238	98.45 172	81.28 290	93.57 181	96.70 180

OPM-MVS		93.28 124	92.76 119	94.82 160	94.63 246	90.77 128	96.65 184	97.18 158	93.72 47	91.68 154	97.26 95	79.33 227	98.63 157	92.13 108	92.28 196	95.07 257

HQP_MVS		93.78 110	93.43 105	94.82 160	96.21 174	89.99 141	97.74 61	97.51 123	94.85 18	91.34 161	96.64 121	81.32 192	98.60 160	93.02 96	92.23 197	95.86 210

XVG-OURS-SEG-HR		93.86 107	93.55 98	94.81 162	97.06 138	88.53 203	95.28 266	97.45 134	91.68 112	94.08 100	97.68 70	82.41 172	98.90 133	93.84 81	92.47 194	96.98 165

tfpn1000		91.99 175	91.05 179	94.80 163	97.78 105	89.66 159	97.91 47	92.90 337	88.99 186	91.73 152	94.84 211	78.99 237	98.33 187	82.41 273	93.91 173	96.40 190

XVG-OURS		93.72 112	93.35 108	94.80 163	97.07 136	88.61 201	94.79 275	97.46 130	91.97 107	93.99 101	97.86 58	81.74 186	98.88 137	92.64 101	92.67 193	96.92 173

IB-MVS	87.33 17	89.91 252	88.28 263	94.79 165	95.26 215	87.70 241	95.12 272	93.95 320	89.35 171	87.03 272	92.49 295	70.74 307	99.19 102	89.18 159	81.37 316	97.49 158
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		92.34 159	91.53 163	94.77 166	95.13 223	90.83 125	96.40 206	97.98 79	91.88 108	89.29 229	95.54 183	82.50 168	97.80 257	89.79 145	85.27 274	95.69 224

thresconf0.02		91.69 188	90.67 200	94.75 167	97.55 120	89.68 153	97.64 78	93.14 328	88.43 208	91.24 169	94.30 242	78.91 238	98.45 172	81.28 290	93.57 181	96.11 200

tfpn_n400		91.69 188	90.67 200	94.75 167	97.55 120	89.68 153	97.64 78	93.14 328	88.43 208	91.24 169	94.30 242	78.91 238	98.45 172	81.28 290	93.57 181	96.11 200

tfpnconf		91.69 188	90.67 200	94.75 167	97.55 120	89.68 153	97.64 78	93.14 328	88.43 208	91.24 169	94.30 242	78.91 238	98.45 172	81.28 290	93.57 181	96.11 200

tfpnview11		91.69 188	90.67 200	94.75 167	97.55 120	89.68 153	97.64 78	93.14 328	88.43 208	91.24 169	94.30 242	78.91 238	98.45 172	81.28 290	93.57 181	96.11 200

thres200		92.23 166	91.39 167	94.75 167	97.61 115	89.03 194	96.60 191	95.09 280	92.08 103	93.28 119	94.00 260	78.39 251	99.04 127	81.26 296	94.18 160	96.19 194

tfpn_ndepth		91.88 179	90.96 183	94.62 172	97.73 109	89.93 147	97.75 59	92.92 336	88.93 191	91.73 152	93.80 267	78.91 238	98.49 171	83.02 265	93.86 174	95.45 232

GA-MVS		91.38 208	90.31 214	94.59 173	94.65 245	87.62 242	94.34 283	96.19 229	90.73 138	90.35 186	93.83 265	71.84 299	97.96 240	87.22 201	93.61 178	98.21 127

GBi-Net		91.35 210	90.27 217	94.59 173	96.51 161	91.18 113	97.50 97	96.93 194	88.82 196	89.35 226	94.51 226	73.87 289	97.29 292	86.12 217	88.82 242	95.31 244

test1		91.35 210	90.27 217	94.59 173	96.51 161	91.18 113	97.50 97	96.93 194	88.82 196	89.35 226	94.51 226	73.87 289	97.29 292	86.12 217	88.82 242	95.31 244

FMVSNet1		89.88 254	88.31 262	94.59 173	95.41 203	91.18 113	97.50 97	96.93 194	86.62 261	87.41 263	94.51 226	65.94 329	97.29 292	83.04 264	87.43 256	95.31 244

cascas		91.20 216	90.08 224	94.58 177	94.97 229	89.16 193	93.65 300	97.59 114	79.90 325	89.40 224	92.92 288	75.36 279	98.36 183	92.14 107	94.75 155	96.23 192

HQP-MVS		93.19 127	92.74 122	94.54 178	95.86 188	89.33 181	96.65 184	97.39 142	93.55 50	90.14 189	95.87 160	80.95 197	98.50 168	92.13 108	92.10 202	95.78 217

PVSNet_BlendedMVS		94.06 100	93.92 88	94.47 179	98.27 70	89.46 170	96.73 171	98.36 16	90.17 154	94.36 95	95.24 196	88.02 77	99.58 55	93.44 89	90.72 225	94.36 293

gg-mvs-nofinetune		87.82 287	85.61 294	94.44 180	94.46 251	89.27 188	91.21 330	84.61 359	80.88 319	89.89 204	74.98 350	71.50 301	97.53 276	85.75 225	97.21 111	96.51 186

PS-MVSNAJss		93.74 111	93.51 101	94.44 180	93.91 283	89.28 186	97.75 59	97.56 119	92.50 86	89.94 201	96.54 132	88.65 71	98.18 196	93.83 82	90.90 222	95.86 210

PMMVS		92.86 139	92.34 136	94.42 182	94.92 234	86.73 259	94.53 280	96.38 220	84.78 285	94.27 97	95.12 201	83.13 140	98.40 180	91.47 127	96.49 130	98.12 130

MVSTER		93.20 126	92.81 118	94.37 183	96.56 157	89.59 162	97.06 137	97.12 166	91.24 125	91.30 164	95.96 155	82.02 180	98.05 219	93.48 88	90.55 227	95.47 230

ACMM	89.79 8	92.96 134	92.50 133	94.35 184	96.30 172	88.71 199	97.58 90	97.36 147	91.40 121	90.53 181	96.65 120	79.77 220	98.75 149	91.24 132	91.64 208	95.59 226
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CHOSEN 280x420		93.12 128	92.72 123	94.34 185	96.71 151	87.27 246	90.29 335	97.72 100	86.61 262	91.34 161	95.29 193	84.29 127	98.41 179	93.25 93	98.94 67	97.35 161

CLD-MVS		92.98 133	92.53 131	94.32 186	96.12 183	89.20 189	95.28 266	97.47 128	92.66 81	89.90 202	95.62 178	80.58 206	98.40 180	92.73 100	92.40 195	95.38 240
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
Anonymous20231211		90.63 238	89.42 246	94.27 187	98.24 73	89.19 192	98.05 35	97.89 84	79.95 324	88.25 249	94.96 202	72.56 297	98.13 199	89.70 147	85.14 276	95.49 227

testing_2		87.33 291	85.03 298	94.22 188	87.77 343	89.32 183	94.97 273	97.11 168	89.22 174	71.64 344	88.73 330	55.16 348	97.94 242	91.95 112	88.73 246	95.41 234

LTVRE_ROB	88.41 13	90.99 223	89.92 231	94.19 189	96.18 177	89.55 164	96.31 215	97.09 170	87.88 230	85.67 284	95.91 159	78.79 246	98.57 163	81.50 282	89.98 233	94.44 291
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		90.93 225	89.85 234	94.17 190	93.34 300	90.79 127	94.60 277	96.02 234	84.62 286	87.45 261	95.15 198	81.88 184	97.45 281	87.70 186	87.87 252	94.27 298

TR-MVS		91.48 203	90.59 208	94.16 191	96.40 167	87.33 244	95.67 249	95.34 269	87.68 235	91.46 157	95.52 184	76.77 270	98.35 184	82.85 267	93.61 178	96.79 177

LPG-MVS_test		92.94 135	92.56 128	94.10 192	96.16 179	88.26 209	97.65 74	97.46 130	91.29 122	90.12 195	97.16 99	79.05 230	98.73 151	92.25 104	91.89 205	95.31 244

LGP-MVS_train				94.10 192	96.16 179	88.26 209		97.46 130	91.29 122	90.12 195	97.16 99	79.05 230	98.73 151	92.25 104	91.89 205	95.31 244

mvs_anonymous		93.82 108	93.74 92	94.06 194	96.44 166	85.41 275	95.81 244	97.05 176	89.85 161	90.09 198	96.36 140	87.44 91	97.75 262	93.97 75	96.69 124	99.02 65

ACMP	89.59 10	92.62 145	92.14 138	94.05 195	96.40 167	88.20 216	97.36 110	97.25 157	91.52 114	88.30 246	96.64 121	78.46 249	98.72 153	91.86 117	91.48 212	95.23 251
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
jajsoiax		92.42 156	91.89 146	94.03 196	93.33 302	88.50 204	97.73 63	97.53 120	92.00 106	88.85 236	96.50 134	75.62 278	98.11 203	93.88 80	91.56 211	95.48 228

test_djsdf		93.07 130	92.76 119	94.00 197	93.49 296	88.70 200	98.22 26	97.57 116	91.42 119	90.08 199	95.55 182	82.85 160	97.92 246	94.07 72	91.58 210	95.40 238

Anonymous20240521		91.32 212	90.43 212	93.98 198	94.93 233	89.28 186	98.04 36	97.53 120	89.49 168	86.68 277	94.82 214	81.72 187	98.05 219	85.31 231	85.39 271	94.61 286

AllTest		90.23 246	88.98 253	93.98 198	97.94 94	86.64 260	96.51 196	95.54 259	85.38 274	85.49 286	96.77 112	70.28 309	99.15 107	80.02 301	92.87 188	96.15 197

TestCases				93.98 198	97.94 94	86.64 260		95.54 259	85.38 274	85.49 286	96.77 112	70.28 309	99.15 107	80.02 301	92.87 188	96.15 197

anonymousdsp		92.16 168	91.55 162	93.97 201	92.58 319	89.55 164	97.51 96	97.42 140	89.42 170	88.40 243	94.84 211	80.66 205	97.88 251	91.87 116	91.28 216	94.48 289

pm-mvs1		90.72 233	89.65 243	93.96 202	94.29 258	89.63 160	97.79 57	96.82 202	89.07 183	86.12 282	95.48 189	78.61 247	97.78 259	86.97 206	81.67 314	94.46 290

WR-MVS_H		92.00 174	91.35 168	93.95 203	95.09 225	89.47 168	98.04 36	98.68 7	91.46 117	88.34 244	94.68 220	85.86 109	97.56 274	85.77 224	84.24 294	94.82 275

CR-MVSNet		90.82 228	89.77 237	93.95 203	94.45 252	87.19 250	90.23 336	95.68 254	86.89 257	92.40 137	92.36 300	80.91 200	97.05 296	81.09 297	93.95 171	97.60 154

RPMNet		88.52 275	86.72 288	93.95 203	94.45 252	87.19 250	90.23 336	94.99 286	77.87 336	92.40 137	87.55 340	80.17 215	97.05 296	68.84 337	93.95 171	97.60 154

mvs_tets		92.31 161	91.76 148	93.94 206	93.41 298	88.29 207	97.63 85	97.53 120	92.04 104	88.76 237	96.45 136	74.62 285	98.09 206	93.91 78	91.48 212	95.45 232

BH-untuned		92.94 135	92.62 126	93.92 207	97.22 130	86.16 267	96.40 206	96.25 226	90.06 156	89.79 209	96.17 148	83.19 136	98.35 184	87.19 202	97.27 110	97.24 162

ACMH	87.59 16	90.53 240	89.42 246	93.87 208	96.21 174	87.92 235	97.24 120	96.94 193	88.45 207	83.91 301	96.27 143	71.92 298	98.62 159	84.43 245	89.43 238	95.05 262
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CP-MVSNet		91.89 178	91.24 174	93.82 209	95.05 226	88.57 202	97.82 55	98.19 34	91.70 111	88.21 250	95.76 170	81.96 181	97.52 277	87.86 182	84.65 290	95.37 241

v2v482		91.59 197	90.85 189	93.80 210	93.87 285	88.17 218	96.94 151	96.88 199	89.54 165	89.53 221	94.90 206	81.70 188	98.02 228	89.25 156	85.04 283	95.20 252

COLMAP_ROB	87.81 15	90.40 242	89.28 249	93.79 211	97.95 93	87.13 252	96.92 152	95.89 246	82.83 304	86.88 276	97.18 98	73.77 292	99.29 97	78.44 311	93.62 177	94.95 263
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
v1141		91.61 194	90.89 184	93.78 212	94.01 278	88.24 211	96.96 145	96.96 189	89.17 179	89.75 211	94.29 248	82.99 152	98.03 225	88.85 168	85.00 284	95.07 257

divwei89l23v2f112		91.61 194	90.89 184	93.78 212	94.01 278	88.22 213	96.96 145	96.96 189	89.17 179	89.75 211	94.28 250	83.02 150	98.03 225	88.86 167	84.98 287	95.08 255

v1		91.61 194	90.89 184	93.78 212	94.01 278	88.21 215	96.96 145	96.96 189	89.17 179	89.78 210	94.29 248	82.97 154	98.05 219	88.85 168	84.99 285	95.08 255

v1neww		91.70 186	91.01 180	93.75 215	94.19 260	88.14 221	97.20 127	96.98 185	89.18 177	89.87 205	94.44 232	83.10 142	98.06 216	89.06 162	85.09 279	95.06 260

v7new		91.70 186	91.01 180	93.75 215	94.19 260	88.14 221	97.20 127	96.98 185	89.18 177	89.87 205	94.44 232	83.10 142	98.06 216	89.06 162	85.09 279	95.06 260

v6		91.69 188	91.00 182	93.75 215	94.14 265	88.12 223	97.20 127	96.98 185	89.19 175	89.90 202	94.42 234	83.04 148	98.07 211	89.07 161	85.10 278	95.07 257

V42		91.58 198	90.87 187	93.73 218	94.05 277	88.50 204	97.32 115	96.97 188	88.80 199	89.71 213	94.33 239	82.54 167	98.05 219	89.01 164	85.07 281	94.64 285

PVSNet	86.66 18	92.24 165	91.74 151	93.73 218	97.77 106	83.69 294	92.88 314	96.72 205	87.91 229	93.00 128	94.86 210	78.51 248	99.05 126	86.53 209	97.45 104	98.47 113

MIMVSNet		88.50 277	86.76 286	93.72 220	94.84 238	87.77 239	91.39 326	94.05 317	86.41 263	87.99 253	92.59 293	63.27 333	95.82 324	77.44 313	92.84 190	97.57 156

Patchmatch-test		89.42 261	87.99 265	93.70 221	95.27 212	85.11 277	88.98 342	94.37 309	81.11 317	87.10 271	93.69 270	82.28 174	97.50 278	74.37 324	94.76 154	98.48 112

PS-CasMVS		91.55 200	90.84 191	93.69 222	94.96 230	88.28 208	97.84 54	98.24 29	91.46 117	88.04 252	95.80 165	79.67 222	97.48 279	87.02 205	84.54 292	95.31 244

v1144		91.37 209	90.60 207	93.68 223	93.89 284	88.23 212	96.84 158	97.03 182	88.37 216	89.69 215	94.39 235	82.04 179	97.98 233	87.80 184	85.37 272	94.84 271

v7		91.47 204	90.73 196	93.68 223	94.13 266	88.16 219	97.09 136	97.05 176	88.38 215	89.80 208	94.52 225	82.21 176	98.01 229	88.00 179	85.42 270	94.87 269

GG-mvs-BLEND				93.62 225	93.69 290	89.20 189	92.39 322	83.33 360		87.98 254	89.84 316	71.00 305	96.87 303	82.08 276	95.40 144	94.80 277

tfpnnormal		89.70 257	88.40 261	93.60 226	95.15 221	90.10 137	97.56 93	98.16 39	87.28 244	86.16 281	94.63 223	77.57 267	98.05 219	74.48 322	84.59 291	92.65 317

Patchmatch-test1		91.54 201	90.85 189	93.59 227	95.59 197	84.95 281	94.72 276	95.58 258	90.82 135	92.25 143	93.58 275	75.80 275	97.41 285	83.35 259	95.98 135	98.40 120

PatchmatchNet		91.91 177	91.35 168	93.59 227	95.38 205	84.11 289	93.15 310	95.39 263	89.54 165	92.10 146	93.68 271	82.82 161	98.13 199	84.81 237	95.32 145	98.52 104
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
v1192		91.07 220	90.23 220	93.58 229	93.70 289	87.82 238	96.73 171	97.07 173	87.77 232	89.58 218	94.32 240	80.90 203	97.97 236	86.52 210	85.48 268	94.95 263

v8		91.29 214	90.53 209	93.57 230	94.15 264	88.12 223	97.34 112	97.06 175	88.99 186	88.32 245	94.26 254	83.08 144	98.01 229	87.62 192	83.92 299	94.57 287

ADS-MVSNet		89.89 253	88.68 257	93.53 231	95.86 188	84.89 282	90.93 331	95.07 282	83.23 302	91.28 167	91.81 307	79.01 234	97.85 252	79.52 303	91.39 214	97.84 141

v10		91.04 222	90.23 220	93.49 232	94.12 268	88.16 219	97.32 115	97.08 172	88.26 219	88.29 247	94.22 255	82.17 178	97.97 236	86.45 212	84.12 295	94.33 294

EI-MVSNet		93.03 132	92.88 117	93.48 233	95.77 193	86.98 255	96.44 197	97.12 166	90.66 142	91.30 164	97.64 76	86.56 100	98.05 219	89.91 142	90.55 227	95.41 234

PEN-MVS		91.20 216	90.44 210	93.48 233	94.49 250	87.91 237	97.76 58	98.18 36	91.29 122	87.78 255	95.74 172	80.35 211	97.33 290	85.46 229	82.96 308	95.19 253

mvs-test1		93.63 114	93.69 94	93.46 235	96.02 185	84.61 285	97.24 120	96.72 205	93.85 43	92.30 142	95.76 170	83.08 144	98.89 135	91.69 122	96.54 129	96.87 175

v7n		90.76 229	89.86 233	93.45 236	93.54 293	87.60 243	97.70 70	97.37 145	88.85 193	87.65 259	94.08 259	81.08 194	98.10 204	84.68 240	83.79 302	94.66 284

v144192		91.06 221	90.28 216	93.39 237	93.66 291	87.23 249	96.83 159	97.07 173	87.43 239	89.69 215	94.28 250	81.48 189	98.00 232	87.18 203	84.92 288	94.93 267

DWT-MVSNet_test		90.76 229	89.89 232	93.38 238	95.04 227	83.70 293	95.85 242	94.30 312	88.19 222	90.46 183	92.80 289	73.61 293	98.50 168	88.16 176	90.58 226	97.95 136

EPMVS		90.70 235	89.81 236	93.37 239	94.73 243	84.21 287	93.67 299	88.02 353	89.50 167	92.38 139	93.49 279	77.82 266	97.78 259	86.03 220	92.68 192	98.11 133

IterMVS-LS		92.29 163	91.94 145	93.34 240	96.25 173	86.97 256	96.57 195	97.05 176	90.67 140	89.50 223	94.80 216	86.59 99	97.64 270	89.91 142	86.11 265	95.40 238
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
BH-w/o		92.14 170	91.75 149	93.31 241	96.99 141	85.73 270	95.67 249	95.69 252	88.73 201	89.26 231	94.82 214	82.97 154	98.07 211	85.26 233	96.32 133	96.13 199

v1921920		90.85 227	90.03 227	93.29 242	93.55 292	86.96 257	96.74 170	97.04 179	87.36 241	89.52 222	94.34 238	80.23 214	97.97 236	86.27 213	85.21 275	94.94 265

ACMH+	87.92 14	90.20 247	89.18 251	93.25 243	96.48 164	86.45 264	96.99 143	96.68 210	88.83 195	84.79 292	96.22 144	70.16 311	98.53 165	84.42 246	88.04 250	94.77 281

v1240		90.70 235	89.85 234	93.23 244	93.51 295	86.80 258	96.61 189	97.02 183	87.16 246	89.58 218	94.31 241	79.55 224	97.98 233	85.52 228	85.44 269	94.90 268

PatchT		88.87 267	87.42 273	93.22 245	94.08 274	85.10 278	89.51 340	94.64 300	81.92 310	92.36 140	88.15 336	80.05 216	97.01 300	72.43 329	93.65 176	97.54 157

Fast-Effi-MVS+-dtu		92.29 163	91.99 143	93.21 246	95.27 212	85.52 274	97.03 138	96.63 215	92.09 98	89.11 233	95.14 199	80.33 212	98.08 207	87.54 194	94.74 156	96.03 207

PatchFormer-LS_test		91.68 193	91.18 178	93.19 247	95.24 216	83.63 295	95.53 256	95.44 262	89.82 162	91.37 159	92.58 294	80.85 204	98.52 166	89.65 150	90.16 232	97.42 160

XVG-ACMP-BASELINE		90.93 225	90.21 222	93.09 248	94.31 257	85.89 268	95.33 263	97.26 155	91.06 132	89.38 225	95.44 190	68.61 316	98.60 160	89.46 152	91.05 220	94.79 279

TransMVSNet (Re)		88.94 264	87.56 268	93.08 249	94.35 255	88.45 206	97.73 63	95.23 274	87.47 238	84.26 296	95.29 193	79.86 219	97.33 290	79.44 307	74.44 342	93.45 308

DTE-MVSNet		90.56 239	89.75 239	93.01 250	93.95 281	87.25 247	97.64 78	97.65 109	90.74 137	87.12 269	95.68 176	79.97 218	97.00 301	83.33 261	81.66 315	94.78 280

EPNet_dtu		91.71 183	91.28 172	92.99 251	93.76 288	83.71 292	96.69 181	95.28 270	93.15 64	87.02 273	95.95 156	83.37 135	97.38 288	79.46 306	96.84 117	97.88 140
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
Baseline_NR-MVSNet		91.20 216	90.62 206	92.95 252	93.83 286	88.03 229	97.01 142	95.12 279	88.42 214	89.70 214	95.13 200	83.47 133	97.44 282	89.66 149	83.24 306	93.37 310

pmmvs5		89.86 255	88.87 255	92.82 253	92.86 313	86.23 266	96.26 220	95.39 263	84.24 290	87.12 269	94.51 226	74.27 287	97.36 289	87.61 193	87.57 254	94.86 270

v52		90.70 235	90.00 228	92.82 253	93.24 304	87.03 253	97.60 87	97.14 164	88.21 220	87.69 257	93.94 262	80.91 200	98.07 211	87.39 196	83.87 301	93.36 311

V4		90.71 234	90.00 228	92.82 253	93.21 307	87.03 253	97.59 89	97.16 163	88.21 220	87.69 257	93.92 264	80.93 199	98.06 216	87.39 196	83.90 300	93.39 309

v148		90.99 223	90.38 213	92.81 256	93.83 286	85.80 269	96.78 168	96.68 210	89.45 169	88.75 238	93.93 263	82.96 156	97.82 256	87.83 183	83.25 305	94.80 277

Patchmtry		88.64 273	87.25 278	92.78 257	94.09 272	86.64 260	89.82 339	95.68 254	80.81 321	87.63 260	92.36 300	80.91 200	97.03 298	78.86 309	85.12 277	94.67 283

v748		90.34 243	89.54 244	92.75 258	93.25 303	85.71 271	97.61 86	97.17 160	88.54 206	87.20 268	93.54 276	81.02 195	98.01 229	85.73 226	81.80 312	94.52 288

MVP-Stereo		90.74 232	90.08 224	92.71 259	93.19 309	88.20 216	95.86 241	96.27 224	86.07 268	84.86 291	94.76 217	77.84 265	97.75 262	83.88 256	98.01 87	92.17 334
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
pmmvs6		87.81 288	86.19 290	92.69 260	91.32 328	86.30 265	97.34 112	96.41 219	80.59 323	84.05 300	94.37 237	67.37 323	97.67 267	84.75 238	79.51 322	94.09 301

Effi-MVS+-dtu		93.08 129	93.21 111	92.68 261	96.02 185	83.25 298	97.14 134	96.72 205	93.85 43	91.20 176	93.44 282	83.08 144	98.30 189	91.69 122	95.73 141	96.50 187

CostFormer		91.18 219	90.70 198	92.62 262	94.84 238	81.76 308	94.09 291	94.43 306	84.15 291	92.72 135	93.77 268	79.43 225	98.20 193	90.70 137	92.18 200	97.90 138

tpmp4_e23		89.58 258	88.59 258	92.54 263	95.16 220	81.53 309	94.11 290	95.09 280	81.66 312	88.60 240	93.44 282	75.11 280	98.33 187	82.45 272	91.72 207	97.75 145

LCM-MVSNet-Re		92.50 150	92.52 132	92.44 264	96.82 148	81.89 307	96.92 152	93.71 321	92.41 88	84.30 295	94.60 224	85.08 117	97.03 298	91.51 125	97.36 107	98.40 120

ITE_SJBPF				92.43 265	95.34 207	85.37 276		95.92 239	91.47 116	87.75 256	96.39 139	71.00 305	97.96 240	82.36 274	89.86 236	93.97 302

v18		88.71 270	87.52 269	92.27 266	94.16 263	88.11 225	96.82 162	95.96 236	87.03 248	80.76 317	89.81 317	83.15 138	96.22 311	84.69 239	75.31 333	92.49 321

USDC		88.94 264	87.83 267	92.27 266	94.66 244	84.96 280	93.86 294	95.90 241	87.34 242	83.40 303	95.56 181	67.43 322	98.19 195	82.64 271	89.67 237	93.66 305

v17		88.67 272	87.47 272	92.26 268	94.13 266	88.09 227	96.81 163	95.95 237	87.02 249	80.72 318	89.75 319	83.11 141	96.20 312	84.61 242	75.15 335	92.49 321

v16		88.69 271	87.50 270	92.26 268	94.19 260	88.11 225	96.81 163	95.95 237	87.01 250	80.71 319	89.80 318	83.08 144	96.20 312	84.61 242	75.34 332	92.48 323

tpm2		89.96 251	89.21 250	92.23 270	94.91 236	81.25 311	93.78 295	94.42 307	80.62 322	91.56 155	93.44 282	76.44 272	97.94 242	85.60 227	92.08 204	97.49 158

v15		88.53 274	87.31 274	92.20 271	94.09 272	88.05 228	96.72 174	95.90 241	87.01 250	80.53 322	89.60 323	83.02 150	96.13 314	84.29 247	74.64 336	92.41 327

V9		88.49 278	87.26 277	92.18 272	94.12 268	87.97 233	96.73 171	95.90 241	86.95 254	80.40 325	89.61 321	82.98 153	96.13 314	84.14 249	74.55 339	92.44 325

v12		88.46 279	87.23 280	92.17 273	94.10 271	87.99 230	96.71 176	95.90 241	86.91 255	80.34 327	89.58 324	82.92 157	96.11 318	84.09 250	74.50 341	92.42 326

V14		88.52 275	87.30 275	92.17 273	94.12 268	87.99 230	96.72 174	95.91 240	86.98 252	80.50 323	89.63 320	83.03 149	96.12 316	84.23 248	74.60 338	92.40 328

v13		88.45 280	87.22 281	92.16 275	94.08 274	87.95 234	96.71 176	95.90 241	86.86 259	80.27 329	89.55 325	82.92 157	96.12 316	84.02 252	74.63 337	92.40 328

test-LLR		91.42 206	91.19 177	92.12 276	94.59 247	80.66 314	94.29 285	92.98 334	91.11 130	90.76 179	92.37 297	79.02 232	98.07 211	88.81 170	96.74 121	97.63 149

test-mter		90.19 248	89.54 244	92.12 276	94.59 247	80.66 314	94.29 285	92.98 334	87.68 235	90.76 179	92.37 297	67.67 320	98.07 211	88.81 170	96.74 121	97.63 149

v11		88.41 281	87.19 284	92.08 278	94.08 274	87.77 239	96.75 169	95.85 247	86.74 260	80.50 323	89.50 326	82.49 169	96.08 319	83.55 258	75.20 334	92.38 330

ADS-MVSNet2		89.45 260	88.59 258	92.03 279	95.86 188	82.26 305	90.93 331	94.32 311	83.23 302	91.28 167	91.81 307	79.01 234	95.99 320	79.52 303	91.39 214	97.84 141

TESTMET0.1,1		90.06 250	89.42 246	91.97 280	94.41 254	80.62 316	94.29 285	91.97 343	87.28 244	90.44 184	92.47 296	68.79 315	97.67 267	88.50 174	96.60 127	97.61 153

JIA-IIPM		88.26 284	87.04 285	91.91 281	93.52 294	81.42 310	89.38 341	94.38 308	80.84 320	90.93 178	80.74 347	79.22 228	97.92 246	82.76 268	91.62 209	96.38 191

tpmvs		89.83 256	89.15 252	91.89 282	94.92 234	80.30 320	93.11 311	95.46 261	86.28 264	88.08 251	92.65 291	80.44 209	98.52 166	81.47 283	89.92 235	96.84 176

TDRefinement		86.53 296	84.76 301	91.85 283	82.23 352	84.25 286	96.38 208	95.35 266	84.97 282	84.09 299	94.94 203	65.76 330	98.34 186	84.60 244	74.52 340	92.97 312

semantic-postprocess				91.82 284	95.52 199	84.20 288		96.15 231	90.61 146	87.39 264	94.27 252	75.63 277	96.44 307	87.34 198	86.88 261	94.82 275

tpm cat1		88.36 282	87.21 282	91.81 285	95.13 223	80.55 317	92.58 318	95.70 251	74.97 342	87.45 261	91.96 305	78.01 264	98.17 197	80.39 300	88.74 245	96.72 179

tpmrst		91.44 205	91.32 170	91.79 286	95.15 221	79.20 329	93.42 304	95.37 265	88.55 205	93.49 112	93.67 272	82.49 169	98.27 190	90.41 139	89.34 239	97.90 138

MS-PatchMatch		90.27 244	89.77 237	91.78 287	94.33 256	84.72 284	95.55 254	96.73 204	86.17 267	86.36 279	95.28 195	71.28 303	97.80 257	84.09 250	98.14 85	92.81 316

FMVSNet5		87.29 292	85.79 293	91.78 287	94.80 240	87.28 245	95.49 258	95.28 270	84.09 292	83.85 302	91.82 306	62.95 334	94.17 335	78.48 310	85.34 273	93.91 303

EG-PatchMatch MVS		87.02 294	85.44 295	91.76 289	92.67 317	85.00 279	96.08 231	96.45 218	83.41 301	79.52 332	93.49 279	57.10 343	97.72 264	79.34 308	90.87 223	92.56 319

tpm		90.25 245	89.74 240	91.76 289	93.92 282	79.73 325	93.98 292	93.54 325	88.28 218	91.99 148	93.25 285	77.51 268	97.44 282	87.30 200	87.94 251	98.12 130

IterMVS		90.15 249	89.67 241	91.61 291	95.48 201	83.72 291	94.33 284	96.12 232	89.99 157	87.31 267	94.15 257	75.78 276	96.27 310	86.97 206	86.89 260	94.83 273
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
ppachtmachnet_test		88.35 283	87.29 276	91.53 292	92.45 321	83.57 296	93.75 296	95.97 235	84.28 289	85.32 289	94.18 256	79.00 236	96.93 302	75.71 320	84.99 285	94.10 299

pmmvs-eth3d		86.22 299	84.45 302	91.53 292	88.34 340	87.25 247	94.47 281	95.01 283	83.47 300	79.51 333	89.61 321	69.75 313	95.71 325	83.13 263	76.73 328	91.64 335

test_0402		86.46 297	84.79 300	91.45 294	95.02 228	85.55 273	96.29 217	94.89 290	80.90 318	82.21 305	93.97 261	68.21 319	97.29 292	62.98 343	88.68 247	91.51 337

OurMVSNet-221017-0		90.51 241	90.19 223	91.44 295	93.41 298	81.25 311	96.98 144	96.28 223	91.68 112	86.55 278	96.30 141	74.20 288	97.98 233	88.96 165	87.40 258	95.09 254

test0.0.03 1		89.37 262	88.70 256	91.41 296	92.47 320	85.63 272	95.22 270	92.70 339	91.11 130	86.91 275	93.65 273	79.02 232	93.19 340	78.00 312	89.18 240	95.41 234

TinyColmap		86.82 295	85.35 297	91.21 297	94.91 236	82.99 299	93.94 293	94.02 319	83.58 298	81.56 312	94.68 220	62.34 336	98.13 199	75.78 319	87.35 259	92.52 320

our_test_3		88.78 268	87.98 266	91.20 298	92.45 321	82.53 301	93.61 302	95.69 252	85.77 271	84.88 290	93.71 269	79.99 217	96.78 305	79.47 305	86.24 262	94.28 297

MDA-MVSNet-bldmvs		85.00 306	82.95 308	91.17 299	93.13 311	83.33 297	94.56 279	95.00 284	84.57 287	65.13 350	92.65 291	70.45 308	95.85 322	73.57 327	77.49 325	94.33 294

SixPastTwentyTwo		89.15 263	88.54 260	90.98 300	93.49 296	80.28 321	96.70 179	94.70 295	90.78 136	84.15 298	95.57 180	71.78 300	97.71 265	84.63 241	85.07 281	94.94 265

LP		84.13 309	81.85 314	90.97 301	93.20 308	82.12 306	87.68 346	94.27 314	76.80 337	81.93 308	88.52 331	72.97 296	95.95 321	59.53 347	81.73 313	94.84 271

PVSNet_0	82.17 19	85.46 305	83.64 306	90.92 302	95.27 212	79.49 326	90.55 334	95.60 256	83.76 297	83.00 304	89.95 314	71.09 304	97.97 236	82.75 269	60.79 351	95.31 244

OpenMVS_ROB	81.14 20	84.42 308	82.28 309	90.83 303	90.06 333	84.05 290	95.73 248	94.04 318	73.89 345	80.17 331	91.53 311	59.15 340	97.64 270	66.92 339	89.05 241	90.80 340

Patchmatch-RL test		87.38 290	86.24 289	90.81 304	88.74 339	78.40 332	88.12 345	93.17 327	87.11 247	82.17 306	89.29 327	81.95 182	95.60 327	88.64 173	77.02 326	98.41 119

dp		88.90 266	88.26 264	90.81 304	94.58 249	76.62 334	92.85 315	94.93 289	85.12 279	90.07 200	93.07 286	75.81 274	98.12 202	80.53 299	87.42 257	97.71 147

MDA-MVSNet_test_wron		85.87 302	84.23 304	90.80 306	92.38 323	82.57 300	93.17 308	95.15 277	82.15 308	67.65 346	92.33 303	78.20 252	95.51 329	77.33 314	79.74 320	94.31 296

YYNet1		85.87 302	84.23 304	90.78 307	92.38 323	82.46 303	93.17 308	95.14 278	82.12 309	67.69 345	92.36 300	78.16 255	95.50 330	77.31 315	79.73 321	94.39 292

UnsupCasMVSNet_eth		85.99 301	84.45 302	90.62 308	89.97 334	82.40 304	93.62 301	97.37 145	89.86 159	78.59 335	92.37 297	65.25 331	95.35 331	82.27 275	70.75 345	94.10 299

MIMVSNet1		84.93 307	83.05 307	90.56 309	89.56 337	84.84 283	95.40 261	95.35 266	83.91 293	80.38 326	92.21 304	57.23 342	93.34 339	70.69 336	82.75 311	93.50 306

lessismore_v0				90.45 310	91.96 326	79.09 330		87.19 356		80.32 328	94.39 235	66.31 326	97.55 275	84.00 254	76.84 327	94.70 282

RPSCF		90.75 231	90.86 188	90.42 311	96.84 145	76.29 335	95.61 253	96.34 221	83.89 294	91.38 158	97.87 56	76.45 271	98.78 144	87.16 204	92.23 197	96.20 193

K. test v3		87.64 289	86.75 287	90.32 312	93.02 312	79.48 327	96.61 189	92.08 342	90.66 142	80.25 330	94.09 258	67.21 324	96.65 306	85.96 222	80.83 319	94.83 273

testgi		87.97 285	87.21 282	90.24 313	92.86 313	80.76 313	96.67 183	94.97 287	91.74 110	85.52 285	95.83 163	62.66 335	94.47 334	76.25 318	88.36 249	95.48 228

UnsupCasMVSNet_bld		82.13 316	79.46 318	90.14 314	88.00 341	82.47 302	90.89 333	96.62 216	78.94 330	75.61 338	84.40 345	56.63 344	96.31 309	77.30 316	66.77 350	91.63 336

LF4IMVS		87.94 286	87.25 278	89.98 315	92.38 323	80.05 324	94.38 282	95.25 273	87.59 237	84.34 294	94.74 219	64.31 332	97.66 269	84.83 236	87.45 255	92.23 332

Anonymous20231206		87.09 293	86.14 291	89.93 316	91.22 329	80.35 318	96.11 229	95.35 266	83.57 299	84.16 297	93.02 287	73.54 294	95.61 326	72.16 330	86.14 264	93.84 304

CVMVSNet		91.23 215	91.75 149	89.67 317	95.77 193	74.69 337	96.44 197	94.88 291	85.81 270	92.18 144	97.64 76	79.07 229	95.58 328	88.06 178	95.86 139	98.74 90

test20.03		86.14 300	85.40 296	88.35 318	90.12 332	80.06 323	95.90 240	95.20 275	88.59 202	81.29 313	93.62 274	71.43 302	92.65 341	71.26 334	81.17 317	92.34 331

PM-MVS		83.48 310	81.86 313	88.31 319	87.83 342	77.59 333	93.43 303	91.75 344	86.91 255	80.63 320	89.91 315	44.42 354	95.84 323	85.17 235	76.73 328	91.50 338

EU-MVSNet		88.72 269	88.90 254	88.20 320	93.15 310	74.21 338	96.63 188	94.22 315	85.18 277	87.32 266	95.97 154	76.16 273	94.98 332	85.27 232	86.17 263	95.41 234

new_pmnet		82.89 312	81.12 317	88.18 321	89.63 336	80.18 322	91.77 325	92.57 340	76.79 338	75.56 339	88.23 335	61.22 338	94.48 333	71.43 332	82.92 309	89.87 342

CMPMVS	62.92 21	85.62 304	84.92 299	87.74 322	89.14 338	73.12 340	94.17 288	96.80 203	73.98 344	73.65 340	94.93 204	66.36 325	97.61 272	83.95 255	91.28 216	92.48 323
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
pmmvs3		79.97 318	77.50 322	87.39 323	82.80 350	79.38 328	92.70 317	90.75 348	70.69 348	78.66 334	87.47 341	51.34 351	93.40 338	73.39 328	69.65 347	89.38 343

new-patchmatchnet		83.18 311	81.87 312	87.11 324	86.88 345	75.99 336	93.70 297	95.18 276	85.02 281	77.30 337	88.40 333	65.99 328	93.88 337	74.19 326	70.18 346	91.47 339

DSMNet-mixed		86.34 298	86.12 292	87.00 325	89.88 335	70.43 342	94.93 274	90.08 350	77.97 335	85.42 288	92.78 290	74.44 286	93.96 336	74.43 323	95.14 147	96.62 184

ambc				86.56 326	83.60 349	70.00 346	85.69 349	94.97 287		80.60 321	88.45 332	37.42 355	96.84 304	82.69 270	75.44 331	92.86 313

MVS-HIRNet		82.47 315	81.21 316	86.26 327	95.38 205	69.21 347	88.96 343	89.49 352	66.28 349	80.79 316	74.08 352	68.48 317	97.39 287	71.93 331	95.47 143	92.18 333

test2356		82.77 313	82.14 311	84.65 328	85.77 346	70.36 343	91.22 329	93.69 324	81.58 314	81.82 309	89.00 329	60.63 339	90.77 347	64.74 341	90.80 224	92.82 314

testus		82.63 314	82.15 310	84.07 329	87.31 344	67.67 348	93.18 306	94.29 313	82.47 306	82.14 307	90.69 312	53.01 349	91.94 344	66.30 340	89.96 234	92.62 318

test1235678		79.82 319	78.53 320	83.69 330	82.55 351	67.55 349	92.50 320	94.13 316	79.28 328	72.10 343	86.45 343	57.27 341	90.68 348	61.60 345	80.90 318	92.82 314

LCM-MVSNet		72.55 323	69.39 326	82.03 331	70.81 362	65.42 352	90.12 338	94.36 310	55.02 353	65.88 349	81.72 346	24.16 364	89.96 349	74.32 325	68.10 348	90.71 341

no-one		68.12 327	63.78 330	81.13 332	74.01 357	70.22 345	87.61 347	90.71 349	72.63 347	53.13 355	71.89 353	30.29 358	91.45 345	61.53 346	32.21 356	81.72 350

1111		78.29 321	77.55 321	80.50 333	83.89 347	59.98 356	91.89 323	93.71 321	75.06 340	73.60 341	87.67 338	55.66 345	92.60 342	58.54 349	77.92 324	88.93 344

PMMVS2		70.19 326	66.92 328	80.01 334	76.35 354	65.67 351	86.22 348	87.58 355	64.83 351	62.38 351	80.29 349	26.78 362	88.49 353	63.79 342	54.07 352	85.88 347

testpf		80.97 317	81.40 315	79.65 335	91.53 327	72.43 341	73.47 357	89.55 351	78.63 331	80.81 315	89.06 328	61.36 337	91.36 346	83.34 260	84.89 289	75.15 353

testmv		72.22 324	70.02 324	78.82 336	73.06 360	61.75 354	91.24 328	92.31 341	74.45 343	61.06 352	80.51 348	34.21 356	88.63 352	55.31 352	68.07 349	86.06 346

N_pmnet		78.73 320	78.71 319	78.79 337	92.80 315	46.50 364	94.14 289	43.71 368	78.61 332	80.83 314	91.66 310	74.94 284	96.36 308	67.24 338	84.45 293	93.50 306

test12356		74.97 322	74.13 323	77.49 338	78.81 353	56.23 360	88.53 344	92.75 338	75.14 339	67.50 347	85.07 344	44.88 353	89.96 349	58.71 348	75.75 330	86.26 345

ANet_high		63.94 330	59.58 331	77.02 339	61.24 365	66.06 350	85.66 350	87.93 354	78.53 333	42.94 357	71.04 354	25.42 363	80.71 357	52.60 354	30.83 358	84.28 348

FPMVS		71.27 325	69.85 325	75.50 340	74.64 355	59.03 358	91.30 327	91.50 345	58.80 352	57.92 353	88.28 334	29.98 360	85.53 355	53.43 353	82.84 310	81.95 349

Gipuma		67.86 328	65.41 329	75.18 341	92.66 318	73.45 339	66.50 359	94.52 305	53.33 354	57.80 354	66.07 356	30.81 357	89.20 351	48.15 356	78.88 323	62.90 357
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
DeepMVS_CX				74.68 342	90.84 330	64.34 353		81.61 363	65.34 350	67.47 348	88.01 337	48.60 352	80.13 358	62.33 344	73.68 344	79.58 351

wuykxyi23d		56.92 333	51.11 338	74.38 343	62.30 364	61.47 355	80.09 354	84.87 358	49.62 356	30.80 363	57.20 360	7.03 367	82.94 356	55.69 351	32.36 355	78.72 352

PNet_i23d		59.01 331	55.87 332	68.44 344	73.98 358	51.37 361	81.36 353	82.41 361	52.37 355	42.49 359	70.39 355	11.39 365	79.99 359	49.77 355	38.71 354	73.97 354

PMVS	53.92 22	58.58 332	55.40 333	68.12 345	51.00 366	48.64 362	78.86 355	87.10 357	46.77 357	35.84 362	74.28 351	8.76 366	86.34 354	42.07 357	73.91 343	69.38 355
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MVE	50.73 23	53.25 335	48.81 339	66.58 346	65.34 363	57.50 359	72.49 358	70.94 366	40.15 360	39.28 361	63.51 357	6.89 369	73.48 362	38.29 358	42.38 353	68.76 356
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
E-PMN		53.28 334	52.56 336	55.43 347	74.43 356	47.13 363	83.63 352	76.30 364	42.23 358	42.59 358	62.22 358	28.57 361	74.40 360	31.53 359	31.51 357	44.78 358

EMVS		52.08 336	51.31 337	54.39 348	72.62 361	45.39 365	83.84 351	75.51 365	41.13 359	40.77 360	59.65 359	30.08 359	73.60 361	28.31 360	29.90 359	44.18 359

.test1245		65.38 329	69.22 327	53.86 349	83.89 347	59.98 356	91.89 323	93.71 321	75.06 340	73.60 341	87.67 338	55.66 345	92.60 342	58.54 349	2.96 362	9.00 362

tmp_tt		51.94 337	53.82 335	46.29 350	33.73 367	45.30 366	78.32 356	67.24 367	18.02 361	50.93 356	87.05 342	52.99 350	53.11 363	70.76 335	25.29 360	40.46 360

pcd1.5k->3k		38.37 339	40.51 340	31.96 351	94.29 258	0.00 370	0.00 361	97.69 105	0.00 365	0.00 367	0.00 367	81.45 190	0.00 367	0.00 364	91.11 218	95.89 209

wuyk23d		25.11 340	24.57 342	26.74 352	73.98 358	39.89 367	57.88 360	9.80 369	12.27 362	10.39 364	6.97 366	7.03 367	36.44 364	25.43 361	17.39 361	3.89 364

test123		13.04 343	15.66 344	5.18 353	4.51 369	3.45 368	92.50 320	1.81 371	2.50 364	7.58 366	20.15 363	3.67 370	2.18 366	7.13 363	1.07 364	9.90 361

testmvs		13.36 342	16.33 343	4.48 354	5.04 368	2.26 369	93.18 306	3.28 370	2.70 363	8.24 365	21.66 362	2.29 371	2.19 365	7.58 362	2.96 362	9.00 362

test_part1				0.00 355		0.00 370	0.00 361	98.26 26				0.00 372	0.00 367	0.00 364	0.00 365	0.00 365

v1.0		40.67 338	54.22 334	0.00 355	99.28 17	0.00 370	0.00 361	98.26 26	93.81 46	98.10 8	98.53 13	0.00 372	0.00 367	0.00 364	0.00 365	0.00 365

cdsmvs_eth3d_5k		23.24 341	30.99 341	0.00 355	0.00 370	0.00 370	0.00 361	97.63 111	0.00 365	0.00 367	96.88 109	84.38 125	0.00 367	0.00 364	0.00 365	0.00 365

pcd_1.5k_mvsjas		7.39 345	9.85 346	0.00 355	0.00 370	0.00 370	0.00 361	0.00 372	0.00 365	0.00 367	0.00 367	88.65 71	0.00 367	0.00 364	0.00 365	0.00 365

sosnet-low-res		0.00 346	0.00 347	0.00 355	0.00 370	0.00 370	0.00 361	0.00 372	0.00 365	0.00 367	0.00 367	0.00 372	0.00 367	0.00 364	0.00 365	0.00 365

sosnet		0.00 346	0.00 347	0.00 355	0.00 370	0.00 370	0.00 361	0.00 372	0.00 365	0.00 367	0.00 367	0.00 372	0.00 367	0.00 364	0.00 365	0.00 365

uncertanet		0.00 346	0.00 347	0.00 355	0.00 370	0.00 370	0.00 361	0.00 372	0.00 365	0.00 367	0.00 367	0.00 372	0.00 367	0.00 364	0.00 365	0.00 365

Regformer		0.00 346	0.00 347	0.00 355	0.00 370	0.00 370	0.00 361	0.00 372	0.00 365	0.00 367	0.00 367	0.00 372	0.00 367	0.00 364	0.00 365	0.00 365

ab-mvs-re		8.06 344	10.74 345	0.00 355	0.00 370	0.00 370	0.00 361	0.00 372	0.00 365	0.00 367	96.69 118	0.00 372	0.00 367	0.00 364	0.00 365	0.00 365

uanet		0.00 346	0.00 347	0.00 355	0.00 370	0.00 370	0.00 361	0.00 372	0.00 365	0.00 367	0.00 367	0.00 372	0.00 367	0.00 364	0.00 365	0.00 365

GSMVS																98.45 115

test_part2					99.28 17	95.74 4				98.10 8

sam_mvs1												82.76 162				98.45 115

sam_mvs												81.94 183

MTGPA								98.08 52

test_post1							92.81 316				16.58 365	80.53 207	97.68 266	86.20 215

test_post											17.58 364	81.76 185	98.08 207

patchmatchnet-post											90.45 313	82.65 166	98.10 204

MTMP							97.86 50	82.03 362

gm-plane-assit					93.22 306	78.89 331			84.82 284		93.52 277		98.64 156	87.72 185

test9_res														94.81 64	99.38 35	99.45 30

TEST9					98.70 40	94.19 25	96.41 202	98.02 69	88.17 224	96.03 57	97.56 84	92.74 14	99.59 52

test_8					98.67 42	94.06 31	96.37 209	98.01 71	88.58 203	95.98 62	97.55 86	92.73 15	99.58 55

agg_prior2														93.94 77	99.38 35	99.50 24

agg_prior					98.67 42	93.79 38		98.00 73		95.68 72			99.57 63

test_prior4						93.66 42	96.42 201

test_prior2							96.35 210		92.80 79	96.03 57	97.59 80	92.01 30		95.01 57	99.38 35

旧先验2							95.94 238		81.66 312	97.34 19			98.82 141	92.26 102

新几何2							95.79 245

旧先验1					98.38 62	93.38 50		97.75 95			98.09 44	92.30 27			99.01 64	99.16 53

无先验							95.79 245	97.87 88	83.87 296				99.65 41	87.68 188		98.89 82

原ACMM2							95.67 249

test222					98.24 73	92.21 77	95.33 263	97.60 112	79.22 329	95.25 81	97.84 61	88.80 69			99.15 54	98.72 92

testdata2													99.67 39	85.96 222

segment_acmp												92.89 12

testdata1							95.26 269		93.10 67

plane_prior7					96.21 174	89.98 143

plane_prior6					96.10 184	90.00 139						81.32 192

plane_prior5								97.51 123					98.60 160	93.02 96	92.23 197	95.86 210

plane_prior4											96.64 121

plane_prior3						90.00 139			94.46 31	91.34 161

plane_prior2							97.74 61		94.85 18

plane_prior1					96.14 182

plane_prior						89.99 141	97.24 120		94.06 39						92.16 201

n2								0.00 372

nn								0.00 372

door-mid								91.06 347

test11								97.88 86

door								91.13 346

HQP5-MVS						89.33 181

HQP-NCC					95.86 188		96.65 184		93.55 50	90.14 189

ACMP_Plane					95.86 188		96.65 184		93.55 50	90.14 189

BP-MVS														92.13 108

HQP4-MVS										90.14 189			98.50 168			95.78 217

HQP3-MVS								97.39 142							92.10 202

HQP2-MVS												80.95 197

NP-MVS					95.99 187	89.81 150					95.87 160

MDTV_nov1_ep13_2view						70.35 344	93.10 312		83.88 295	93.55 109		82.47 171		86.25 214		98.38 123

MDTV_nov1_ep13			90.76 194		95.22 217	80.33 319	93.03 313	95.28 270	88.14 225	92.84 134	93.83 265	81.34 191	98.08 207	82.86 266	94.34 159

ACMMP++_ref															90.30 231

ACMMP++															91.02 221

Test By Simon												88.73 70

High-resolution multi-view benchmark