High-res multi-view results

This table lists the benchmark results for the high-res multi-view scenario. The following metrics are evaluated:

Accuracy [%]: The fraction of the reconstruction which is closer to the ground truth than the evaluation threshold distance (*). Larger is better.
Completeness [%]: The fraction of the ground truth which is closer to the reconstruction than the evaluation threshold distance (*). Larger is better.
F₁ score [%]: The harmonic mean of accuracy and completeness, used to rank methods based on both metrics. Larger is better.
Time [s]: The runtime of the method. Smaller is better.

(*) For exact definitions, detailing how potentially incomplete ground truth is taken into account, see our paper.

The datasets are grouped into different categories, and result averages are computed for a category and method if results of the method are available for all datasets within the category. Note that the category "all" includes both the high-res multi-view and the low-res many-view scenarios.

Methods with suffix _ROB may participate in the Robust Vision Challenge.

Click a dataset result cell to show a visualization of the reconstruction. For training datasets, ground truth and accuracy / completeness visualizations are also available. The visualizations may not work with mobile browsers.

Method	all	high-res multi-view	indoor	outdoor	botani.	boulde.	bridge	door	exhibi.	lectur.	living.	lounge	observ.	old co.	statue	terrac.

APDe-MVS		97.82 1	97.73 1	98.08 9	99.15 25	94.82 13	98.81 2	98.30 22	94.76 24	98.30 5	98.90 2	93.77 8	99.68 38	97.93 1	99.69 1	99.75 1

ACMMP_Plus		97.20 11	96.86 18	98.23 5	99.09 26	95.16 9	97.60 84	98.19 33	92.82 78	97.93 11	98.74 4	91.60 39	99.86 8	96.26 21	99.52 18	99.67 2

SteuartSystems-ACMMP		97.62 3	97.53 2	97.87 14	98.39 60	94.25 23	98.43 16	98.27 24	95.34 9	98.11 6	98.56 8	94.53 3	99.71 30	96.57 17	99.62 8	99.65 3
Skip Steuart: Steuart Systems R&D Blog.
region2R		97.07 18	96.84 19	97.77 23	99.46 1	93.79 38	98.52 10	98.24 28	93.19 63	97.14 24	98.34 28	91.59 40	99.87 5	95.46 45	99.59 10	99.64 4

SMA-MVS		97.36 8	97.06 9	98.25 4	99.06 29	95.30 7	97.94 42	98.19 33	90.66 137	99.06 1	98.94 1	93.33 11	99.83 15	96.72 13	99.68 2	99.63 5

test_part1								98.26 25				95.31 1			99.63 5	99.63 5

ESAPD		97.57 4	97.29 7	98.41 2	99.28 17	95.74 3	97.50 92	98.26 25	93.81 45	98.10 7	98.53 12	95.31 1	99.87 5	95.19 48	99.63 5	99.63 5

XVS		97.18 12	96.96 14	97.81 18	99.38 8	94.03 32	98.59 7	98.20 31	94.85 17	96.59 38	98.29 37	91.70 37	99.80 21	95.66 38	99.40 33	99.62 8

X-MVStestdata		91.71 177	89.67 235	97.81 18	99.38 8	94.03 32	98.59 7	98.20 31	94.85 17	96.59 38	32.69 355	91.70 37	99.80 21	95.66 38	99.40 33	99.62 8

ACMMPR		97.07 18	96.84 19	97.79 20	99.44 2	93.88 34	98.52 10	98.31 21	93.21 60	97.15 23	98.33 31	91.35 42	99.86 8	95.63 40	99.59 10	99.62 8

mPP-MVS		96.86 27	96.60 29	97.64 33	99.40 5	93.44 48	98.50 13	98.09 50	93.27 59	95.95 61	98.33 31	91.04 46	99.88 3	95.20 47	99.57 14	99.60 11

zzz-MVS		97.07 18	96.77 25	97.97 12	99.37 10	94.42 19	97.15 128	98.08 51	95.07 14	96.11 52	98.59 6	90.88 50	99.90 1	96.18 28	99.50 22	99.58 12

MTAPA		97.08 17	96.78 24	97.97 12	99.37 10	94.42 19	97.24 116	98.08 51	95.07 14	96.11 52	98.59 6	90.88 50	99.90 1	96.18 28	99.50 22	99.58 12

PGM-MVS		96.81 29	96.53 32	97.65 31	99.35 13	93.53 46	97.65 71	98.98 1	92.22 88	97.14 24	98.44 17	91.17 44	99.85 11	94.35 69	99.46 26	99.57 14

CNVR-MVS		97.68 2	97.44 5	98.37 3	98.90 33	95.86 2	97.27 114	98.08 51	95.81 3	97.87 12	98.31 34	94.26 4	99.68 38	97.02 4	99.49 24	99.57 14

Regformer-2		97.16 14	96.99 12	97.67 30	98.32 66	93.84 36	96.83 153	98.10 48	95.24 10	97.49 14	98.25 40	92.57 20	99.61 48	96.80 9	99.29 44	99.56 16

NCCC		97.30 10	97.03 11	98.11 8	98.77 36	95.06 11	97.34 108	98.04 65	95.96 2	97.09 28	97.88 55	93.18 12	99.71 30	95.84 36	99.17 54	99.56 16

Regformer-1		97.10 16	96.96 14	97.54 38	98.32 66	93.48 47	96.83 153	97.99 77	95.20 12	97.46 15	98.25 40	92.48 23	99.58 56	96.79 11	99.29 44	99.55 18

MCST-MVS		97.18 12	96.84 19	98.20 6	99.30 16	95.35 5	97.12 130	98.07 56	93.54 53	96.08 54	97.69 69	93.86 7	99.71 30	96.50 18	99.39 35	99.55 18

HFP-MVS		97.14 15	96.92 16	97.83 16	99.42 3	94.12 28	98.52 10	98.32 19	93.21 60	97.18 21	98.29 37	92.08 29	99.83 15	95.63 40	99.59 10	99.54 20

#test#		97.02 21	96.75 26	97.83 16	99.42 3	94.12 28	98.15 29	98.32 19	92.57 83	97.18 21	98.29 37	92.08 29	99.83 15	95.12 52	99.59 10	99.54 20

CP-MVS		97.02 21	96.81 22	97.64 33	99.33 14	93.54 45	98.80 3	98.28 23	92.99 69	96.45 45	98.30 36	91.90 34	99.85 11	95.61 42	99.68 2	99.54 20

APD-MVS		96.95 24	96.60 29	98.01 10	99.03 30	94.93 12	97.72 62	98.10 48	91.50 113	98.01 9	98.32 33	92.33 24	99.58 56	94.85 61	99.51 20	99.53 23
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
APD-MVS_3200maxsize		96.81 29	96.71 27	97.12 56	99.01 31	92.31 74	97.98 41	98.06 58	93.11 66	97.44 16	98.55 10	90.93 48	99.55 66	96.06 30	99.25 47	99.51 24

agg_prior2														93.94 75	99.38 36	99.50 25

Regformer-4		96.97 23	96.87 17	97.25 49	98.34 63	92.66 67	96.96 140	98.01 70	95.12 13	97.14 24	98.42 19	91.82 35	99.61 48	96.90 6	99.13 57	99.50 25

MP-MVS		96.77 31	96.45 36	97.72 26	99.39 7	93.80 37	98.41 17	98.06 58	93.37 55	95.54 76	98.34 28	90.59 53	99.88 3	94.83 62	99.54 16	99.49 27
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
HPM-MVS		96.69 34	96.45 36	97.40 41	99.36 12	93.11 56	98.87 1	98.06 58	91.17 124	96.40 46	97.99 51	90.99 47	99.58 56	95.61 42	99.61 9	99.49 27
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
HSP-MVS		97.53 5	97.49 4	97.63 35	99.40 5	93.77 41	98.53 9	97.85 89	95.55 5	98.56 4	97.81 62	93.90 6	99.65 42	96.62 14	99.21 51	99.48 29

Regformer-3		96.85 28	96.80 23	97.01 58	98.34 63	92.02 85	96.96 140	97.76 92	95.01 16	97.08 29	98.42 19	91.71 36	99.54 68	96.80 9	99.13 57	99.48 29

test9_res														94.81 63	99.38 36	99.45 31

DeepPCF-MVS	93.97 1	96.61 37	97.09 8	95.15 136	98.09 81	86.63 256	96.00 229	98.15 39	95.43 7	97.95 10	98.56 8	93.40 10	99.36 92	96.77 12	99.48 25	99.45 31

TSAR-MVS + MP.		97.42 6	97.33 6	97.69 29	99.25 20	94.24 24	98.07 34	97.85 89	93.72 47	98.57 3	98.35 25	93.69 9	99.40 88	97.06 3	99.46 26	99.44 33

3Dnovator+	91.43 4	95.40 61	94.48 78	98.16 7	96.90 136	95.34 6	98.48 14	97.87 86	94.65 28	88.53 235	98.02 48	83.69 128	99.71 30	93.18 92	98.96 67	99.44 33

DeepC-MVS_fast	93.89 2	96.93 26	96.64 28	97.78 21	98.64 47	94.30 21	97.41 100	98.04 65	94.81 22	96.59 38	98.37 24	91.24 43	99.64 47	95.16 50	99.52 18	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
HPM-MVS++		97.34 9	96.97 13	98.47 1	99.08 27	96.16 1	97.55 89	97.97 79	95.59 4	96.61 36	97.89 53	92.57 20	99.84 14	95.95 33	99.51 20	99.40 36

train_agg		96.30 45	95.83 48	97.72 26	98.70 39	94.19 25	96.41 195	98.02 68	88.58 198	96.03 55	97.56 84	92.73 16	99.59 53	95.04 54	99.37 40	99.39 37

agg_prior3		96.16 49	95.67 50	97.62 36	98.67 41	93.88 34	96.41 195	98.00 72	87.93 223	95.81 65	97.47 88	92.33 24	99.59 53	95.04 54	99.37 40	99.39 37

CDPH-MVS		95.97 54	95.38 56	97.77 23	98.93 32	94.44 18	96.35 203	97.88 84	86.98 247	96.65 35	97.89 53	91.99 33	99.47 79	92.26 98	99.46 26	99.39 37

MP-MVS-pluss		96.70 33	96.27 40	97.98 11	99.23 23	94.71 14	96.96 140	98.06 58	90.67 135	95.55 75	98.78 3	91.07 45	99.86 8	96.58 16	99.55 15	99.38 40
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
HPM-MVS_fast		96.51 39	96.27 40	97.22 52	99.32 15	92.74 64	98.74 4	98.06 58	90.57 145	96.77 31	98.35 25	90.21 57	99.53 71	94.80 64	99.63 5	99.38 40

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 87	98.39 23	88.96 66	99.85 11	94.57 68	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
PHI-MVS		96.77 31	96.46 35	97.71 28	98.40 58	94.07 30	98.21 28	98.45 15	89.86 154	97.11 27	98.01 49	92.52 22	99.69 36	96.03 32	99.53 17	99.36 42

agg_prior1		96.22 48	95.77 49	97.56 37	98.67 41	93.79 38	96.28 211	98.00 72	88.76 195	95.68 69	97.55 86	92.70 18	99.57 64	95.01 56	99.32 42	99.32 44

SD-MVS		97.41 7	97.53 2	97.06 57	98.57 52	94.46 17	97.92 44	98.14 41	94.82 21	99.01 2	98.55 10	94.18 5	97.41 279	96.94 5	99.64 4	99.32 44

CANet		96.39 43	96.02 45	97.50 39	97.62 109	93.38 50	97.02 135	97.96 80	95.42 8	94.86 83	97.81 62	87.38 90	99.82 19	96.88 7	99.20 52	99.29 46

test_prior3		96.46 41	96.20 43	97.23 50	98.67 41	92.99 58	96.35 203	98.00 72	92.80 79	96.03 55	97.59 80	92.01 31	99.41 86	95.01 56	99.38 36	99.29 46

test_prior				97.23 50	98.67 41	92.99 58		98.00 72					99.41 86			99.29 46

MVS_111021_HR		96.68 36	96.58 31	96.99 59	98.46 54	92.31 74	96.20 218	98.90 2	94.30 35	95.86 63	97.74 67	92.33 24	99.38 91	96.04 31	99.42 31	99.28 49

MVS_0304		96.05 51	95.45 53	97.85 15	97.75 103	94.50 16	96.87 150	97.95 82	95.46 6	95.60 73	98.01 49	80.96 193	99.83 15	97.23 2	99.25 47	99.23 50

test12				97.65 31	98.46 54	94.26 22		97.66 104		95.52 77		90.89 49	99.46 80		99.25 47	99.22 51

CHOSEN 1792x2688		94.15 90	93.51 96	96.06 96	98.27 69	89.38 174	95.18 265	98.48 14	85.60 268	93.76 100	97.11 100	83.15 135	99.61 48	91.33 125	98.72 73	99.19 52

3Dnovator	91.36 5	95.19 69	94.44 80	97.44 40	96.56 150	93.36 52	98.65 6	98.36 16	94.12 37	89.25 225	98.06 46	82.20 174	99.77 23	93.41 89	99.32 42	99.18 53

旧先验1					98.38 61	93.38 50		97.75 93			98.09 44	92.30 28			99.01 65	99.16 54

VNet		95.89 56	95.45 53	97.21 53	98.07 82	92.94 61	97.50 92	98.15 39	93.87 41	97.52 13	97.61 79	85.29 111	99.53 71	95.81 37	95.27 144	99.16 54

CSCG		96.05 51	95.91 47	96.46 79	99.24 21	90.47 131	98.30 21	98.57 11	89.01 180	93.97 98	97.57 82	92.62 19	99.76 24	94.66 67	99.27 46	99.15 56

IS-MVSNet		94.90 77	94.52 76	96.05 97	97.67 106	90.56 128	98.44 15	96.22 221	93.21 60	93.99 96	97.74 67	85.55 109	98.45 167	89.98 135	97.86 91	99.14 57

EI-MVSNet-Vis-set		96.51 39	96.47 34	96.63 66	98.24 72	91.20 108	96.89 149	97.73 95	94.74 25	96.49 42	98.49 14	90.88 50	99.58 56	96.44 19	98.32 81	99.13 58

MG-MVS		95.61 59	95.38 56	96.31 86	98.42 57	90.53 129	96.04 225	97.48 121	93.47 54	95.67 72	98.10 43	89.17 64	99.25 97	91.27 127	98.77 71	99.13 58

LFMVS		93.60 110	92.63 121	96.52 71	98.13 80	91.27 105	97.94 42	93.39 319	90.57 145	96.29 47	98.31 34	69.00 309	99.16 104	94.18 70	95.87 136	99.12 60

UA-Net		95.95 55	95.53 52	97.20 54	97.67 106	92.98 60	97.65 71	98.13 42	94.81 22	96.61 36	98.35 25	88.87 67	99.51 75	90.36 134	97.35 107	99.11 61

EPNet		95.20 68	94.56 73	97.14 55	92.80 307	92.68 66	97.85 50	94.87 286	96.64 1	92.46 129	97.80 64	86.23 100	99.65 42	93.72 81	98.62 75	99.10 62
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
TSAR-MVS + GP.		96.69 34	96.49 33	97.27 48	98.31 68	93.39 49	96.79 160	96.72 198	94.17 36	97.44 16	97.66 72	92.76 14	99.33 93	96.86 8	97.76 96	99.08 63

HyFIR lowres test		93.66 108	92.92 111	95.87 103	98.24 72	89.88 145	94.58 272	98.49 12	85.06 275	93.78 99	95.78 162	82.86 156	98.67 149	91.77 114	95.71 140	99.07 64

mvs_anonymous		93.82 103	93.74 87	94.06 187	96.44 159	85.41 268	95.81 237	97.05 170	89.85 156	90.09 191	96.36 136	87.44 89	97.75 256	93.97 73	96.69 123	99.02 65

abl_6		96.40 42	96.21 42	96.98 60	98.89 34	92.20 79	97.89 46	98.03 67	93.34 58	97.22 20	98.42 19	87.93 80	99.72 29	95.10 53	99.07 62	99.02 65

CPTT-MVS		95.57 60	95.19 61	96.70 63	99.27 19	91.48 98	98.33 20	98.11 46	87.79 226	95.17 80	98.03 47	87.09 93	99.61 48	93.51 84	99.42 31	99.02 65

Vis-MVSNet (Re-imp)		94.15 90	93.88 84	94.95 149	97.61 110	87.92 228	98.10 31	95.80 243	92.22 88	93.02 120	97.45 89	84.53 122	97.91 243	88.24 169	97.97 89	99.02 65

Vis-MVSNet		95.23 66	94.81 66	96.51 74	97.18 126	91.58 97	98.26 24	98.12 43	94.38 33	94.90 82	98.15 42	82.28 171	98.92 129	91.45 124	98.58 77	99.01 69
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
DELS-MVS		96.61 37	96.38 38	97.30 45	97.79 100	93.19 54	95.96 230	98.18 36	95.23 11	95.87 62	97.65 73	91.45 41	99.70 35	95.87 34	99.44 30	99.00 70
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
PAPM_NR		95.01 71	94.59 72	96.26 91	98.89 34	90.68 126	97.24 116	97.73 95	91.80 107	92.93 126	96.62 125	89.13 65	99.14 107	89.21 152	97.78 94	98.97 71

MSLP-MVS++		96.94 25	97.06 9	96.59 69	98.72 38	91.86 89	97.67 68	98.49 12	94.66 27	97.24 19	98.41 22	92.31 27	98.94 128	96.61 15	99.46 26	98.96 72

DeepC-MVS	93.07 3	96.06 50	95.66 51	97.29 46	97.96 88	93.17 55	97.30 113	98.06 58	93.92 40	93.38 106	98.66 5	86.83 95	99.73 26	95.60 44	99.22 50	98.96 72
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
alignmvs		95.87 57	95.23 60	97.78 21	97.56 114	95.19 8	97.86 48	97.17 154	94.39 32	96.47 43	96.40 134	85.89 105	99.20 99	96.21 26	95.11 146	98.95 74

114514_t		93.95 99	93.06 108	96.63 66	99.07 28	91.61 94	97.46 99	97.96 80	77.99 327	93.00 121	97.57 82	86.14 104	99.33 93	89.22 151	99.15 55	98.94 75

WTY-MVS		94.71 82	94.02 82	96.79 62	97.71 105	92.05 83	96.59 186	97.35 144	90.61 142	94.64 86	96.93 104	86.41 99	99.39 89	91.20 129	94.71 154	98.94 75

EPP-MVSNet		95.22 67	95.04 64	95.76 107	97.49 121	89.56 161	98.67 5	97.00 177	90.69 134	94.24 93	97.62 78	89.79 62	98.81 139	93.39 90	96.49 127	98.92 77

canonicalmvs		96.02 53	95.45 53	97.75 25	97.59 112	95.15 10	98.28 22	97.60 109	94.52 29	96.27 48	96.12 144	87.65 84	99.18 102	96.20 27	94.82 150	98.91 78

EI-MVSNet-UG-set		96.34 44	96.30 39	96.47 77	98.20 76	90.93 119	96.86 151	97.72 98	94.67 26	96.16 51	98.46 15	90.43 54	99.58 56	96.23 22	97.96 90	98.90 79

PAPR		94.18 89	93.42 102	96.48 76	97.64 108	91.42 102	95.55 248	97.71 101	88.99 181	92.34 134	95.82 157	89.19 63	99.11 109	86.14 210	97.38 105	98.90 79

无先验							95.79 238	97.87 86	83.87 291				99.65 42	87.68 182		98.89 81

DP-MVS		92.76 139	91.51 162	96.52 71	98.77 36	90.99 115	97.38 106	96.08 226	82.38 302	89.29 222	97.87 56	83.77 127	99.69 36	81.37 282	96.69 123	98.89 81

MVSFormer		95.37 62	95.16 62	95.99 100	96.34 162	91.21 106	98.22 26	97.57 112	91.42 117	96.22 49	97.32 90	86.20 102	97.92 240	94.07 71	99.05 63	98.85 83

jason		94.84 80	94.39 81	96.18 94	95.52 191	90.93 119	96.09 222	96.52 210	89.28 167	96.01 59	97.32 90	84.70 119	98.77 143	95.15 51	98.91 69	98.85 83
jason: jason.
Effi-MVS+		94.93 76	94.45 79	96.36 84	96.61 145	91.47 99	96.41 195	97.41 137	91.02 129	94.50 88	95.92 151	87.53 87	98.78 141	93.89 77	96.81 118	98.84 85

lupinMVS		94.99 75	94.56 73	96.29 89	96.34 162	91.21 106	95.83 236	96.27 217	88.93 186	96.22 49	96.88 106	86.20 102	98.85 136	95.27 46	99.05 63	98.82 86

CVMVSNet		91.23 209	91.75 145	89.67 310	95.77 185	74.69 330	96.44 191	94.88 283	85.81 265	92.18 137	97.64 76	79.07 226	95.58 322	88.06 172	95.86 137	98.74 87

1121		94.71 82	93.83 85	97.34 43	98.57 52	93.64 43	96.04 225	97.73 95	81.56 311	95.68 69	97.85 59	90.23 56	99.65 42	87.68 182	99.12 60	98.73 88

test222					98.24 72	92.21 77	95.33 257	97.60 109	79.22 322	95.25 78	97.84 61	88.80 69			99.15 55	98.72 89

MVS_Test		94.89 78	94.62 71	95.68 112	96.83 140	89.55 162	96.70 173	97.17 154	91.17 124	95.60 73	96.11 146	87.87 81	98.76 144	93.01 95	97.17 111	98.72 89

VDD-MVS		93.82 103	93.08 107	96.02 98	97.88 97	89.96 143	97.72 62	95.85 240	92.43 85	95.86 63	98.44 17	68.42 313	99.39 89	96.31 20	94.85 148	98.71 91

新几何1				97.32 44	98.60 48	93.59 44		97.75 93	81.58 309	95.75 68	97.85 59	90.04 59	99.67 40	86.50 205	99.13 57	98.69 92

sss		94.51 84	93.80 86	96.64 64	97.07 130	91.97 87	96.32 207	98.06 58	88.94 185	94.50 88	96.78 108	84.60 120	99.27 96	91.90 110	96.02 132	98.68 93

testdata				95.46 125	98.18 79	88.90 192		97.66 104	82.73 300	97.03 30	98.07 45	90.06 58	98.85 136	89.67 141	98.98 66	98.64 94

MVS_111021_LR		96.24 47	96.19 44	96.39 81	98.23 75	91.35 103	96.24 216	98.79 4	93.99 39	95.80 66	97.65 73	89.92 61	99.24 98	95.87 34	99.20 52	98.58 95

test_normal		92.01 167	90.75 190	95.80 106	93.24 296	89.97 141	95.93 232	96.24 220	90.62 140	81.63 303	93.45 274	74.98 279	98.89 133	93.61 82	97.04 114	98.55 96

PVSNet_Blended_VisFu		95.27 65	94.91 65	96.38 82	98.20 76	90.86 121	97.27 114	98.25 27	90.21 148	94.18 94	97.27 92	87.48 88	99.73 26	93.53 83	97.77 95	98.55 96

TAMVS		94.01 98	93.46 98	95.64 113	96.16 171	90.45 132	96.71 170	96.89 191	89.27 168	93.46 105	96.92 105	87.29 91	97.94 236	88.70 166	95.74 138	98.53 98

Test4		89.48 252	87.50 263	95.44 126	90.76 323	89.72 149	95.78 240	97.09 164	90.28 147	77.67 328	91.74 302	55.42 341	98.08 201	91.92 109	96.83 117	98.52 99

PatchmatchNet		91.91 171	91.35 164	93.59 220	95.38 197	84.11 282	93.15 304	95.39 256	89.54 160	92.10 139	93.68 264	82.82 158	98.13 194	84.81 231	95.32 143	98.52 99
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
DI_MVS_plusplus_test		92.01 167	90.77 188	95.73 111	93.34 292	89.78 148	96.14 220	96.18 223	90.58 144	81.80 302	93.50 271	74.95 280	98.90 131	93.51 84	96.94 115	98.51 101

QAPM		93.45 115	92.27 133	96.98 60	96.77 142	92.62 68	98.39 18	98.12 43	84.50 283	88.27 241	97.77 65	82.39 170	99.81 20	85.40 224	98.81 70	98.51 101

1112_ss		93.37 117	92.42 131	96.21 93	97.05 133	90.99 115	96.31 208	96.72 198	86.87 253	89.83 200	96.69 115	86.51 98	99.14 107	88.12 171	93.67 172	98.50 103

ab-mvs		93.57 112	92.55 125	96.64 64	97.28 123	91.96 88	95.40 255	97.45 130	89.81 158	93.22 114	96.28 138	79.62 220	99.46 80	90.74 131	93.11 184	98.50 103

原ACMM1				96.38 82	98.59 49	91.09 114		97.89 83	87.41 235	95.22 79	97.68 70	90.25 55	99.54 68	87.95 175	99.12 60	98.49 105

Test_1112_low_res		92.84 137	91.84 143	95.85 104	97.04 134	89.97 141	95.53 250	96.64 206	85.38 269	89.65 210	95.18 191	85.86 106	99.10 115	87.70 180	93.58 177	98.49 105

Patchmatch-test		89.42 254	87.99 258	93.70 214	95.27 204	85.11 270	88.98 336	94.37 301	81.11 312	87.10 263	93.69 263	82.28 171	97.50 272	74.37 318	94.76 151	98.48 107

VDDNet		93.05 127	92.07 135	96.02 98	96.84 138	90.39 133	98.08 33	95.85 240	86.22 261	95.79 67	98.46 15	67.59 316	99.19 100	94.92 60	94.85 148	98.47 108

PVSNet	86.66 18	92.24 161	91.74 147	93.73 211	97.77 102	83.69 287	92.88 308	96.72 198	87.91 224	93.00 121	94.86 203	78.51 245	99.05 124	86.53 203	97.45 104	98.47 108

GSMVS																98.45 110

sam_mvs1												82.76 159				98.45 110

CDS-MVSNet		94.14 92	93.54 94	95.93 101	96.18 169	91.46 100	96.33 206	97.04 173	88.97 184	93.56 101	96.51 129	87.55 86	97.89 244	89.80 138	95.95 134	98.44 112
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
DP-MVS Recon		95.68 58	95.12 63	97.37 42	99.19 24	94.19 25	97.03 133	98.08 51	88.35 212	95.09 81	97.65 73	89.97 60	99.48 78	92.08 107	98.59 76	98.44 112

Patchmatch-RL test		87.38 283	86.24 282	90.81 297	88.74 331	78.40 325	88.12 339	93.17 320	87.11 242	82.17 298	89.29 320	81.95 179	95.60 321	88.64 167	77.02 320	98.41 114

Patchmatch-test1		91.54 195	90.85 185	93.59 220	95.59 189	84.95 274	94.72 270	95.58 251	90.82 130	92.25 136	93.58 268	75.80 272	97.41 279	83.35 253	95.98 133	98.40 115

LCM-MVSNet-Re		92.50 146	92.52 128	92.44 257	96.82 141	81.89 300	96.92 147	93.71 314	92.41 86	84.30 287	94.60 217	85.08 114	97.03 292	91.51 121	97.36 106	98.40 115

PVSNet_Blended		94.87 79	94.56 73	95.81 105	98.27 69	89.46 168	95.47 253	98.36 16	88.84 189	94.36 90	96.09 147	88.02 77	99.58 56	93.44 87	98.18 84	98.40 115

MDTV_nov1_ep13_2view						70.35 338	93.10 306		83.88 290	93.55 102		82.47 168		86.25 208		98.38 118

BH-RMVSNet		92.72 140	91.97 140	94.97 147	97.16 127	87.99 223	96.15 219	95.60 249	90.62 140	91.87 143	97.15 99	78.41 247	98.57 157	83.16 256	97.60 98	98.36 119

OMC-MVS		95.09 70	94.70 70	96.25 92	98.46 54	91.28 104	96.43 193	97.57 112	92.04 102	94.77 85	97.96 52	87.01 94	99.09 118	91.31 126	96.77 119	98.36 119

diffmvs		93.43 116	92.75 116	95.48 123	96.47 157	89.61 158	96.09 222	97.14 158	85.97 264	93.09 119	95.35 185	84.87 117	98.55 159	89.51 145	96.26 131	98.28 121

GA-MVS		91.38 202	90.31 208	94.59 167	94.65 237	87.62 235	94.34 277	96.19 222	90.73 133	90.35 179	93.83 258	71.84 295	97.96 234	87.22 195	93.61 175	98.21 122

TAPA-MVS	90.10 7	92.30 158	91.22 172	95.56 116	98.33 65	89.60 159	96.79 160	97.65 106	81.83 306	91.52 149	97.23 95	87.94 79	98.91 130	71.31 327	98.37 80	98.17 123
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
UGNet		94.04 97	93.28 105	96.31 86	96.85 137	91.19 109	97.88 47	97.68 103	94.40 31	93.00 121	96.18 141	73.39 292	99.61 48	91.72 115	98.46 78	98.13 124
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
Fast-Effi-MVS+		93.46 114	92.75 116	95.59 115	96.77 142	90.03 135	96.81 157	97.13 160	88.19 217	91.30 157	94.27 245	86.21 101	98.63 151	87.66 184	96.46 129	98.12 125

tpm		90.25 238	89.74 234	91.76 282	93.92 274	79.73 318	93.98 286	93.54 318	88.28 213	91.99 141	93.25 278	77.51 265	97.44 276	87.30 194	87.94 246	98.12 125

PMMVS		92.86 135	92.34 132	94.42 176	94.92 226	86.73 252	94.53 274	96.38 213	84.78 280	94.27 92	95.12 195	83.13 137	98.40 175	91.47 123	96.49 127	98.12 125

EPMVS		90.70 229	89.81 230	93.37 232	94.73 235	84.21 280	93.67 293	88.02 346	89.50 162	92.38 132	93.49 272	77.82 263	97.78 253	86.03 214	92.68 188	98.11 128

LS3D		93.57 112	92.61 123	96.47 77	97.59 112	91.61 94	97.67 68	97.72 98	85.17 273	90.29 180	98.34 28	84.60 120	99.73 26	83.85 251	98.27 82	98.06 129

HY-MVS	89.66 9	93.87 101	92.95 110	96.63 66	97.10 129	92.49 72	95.64 245	96.64 206	89.05 179	93.00 121	95.79 161	85.77 108	99.45 82	89.16 154	94.35 155	97.96 130

DWT-MVSNet_test		90.76 223	89.89 226	93.38 231	95.04 219	83.70 286	95.85 235	94.30 304	88.19 217	90.46 176	92.80 282	73.61 290	98.50 163	88.16 170	90.58 221	97.95 131

CNLPA		94.28 87	93.53 95	96.52 71	98.38 61	92.55 70	96.59 186	96.88 192	90.13 150	91.91 142	97.24 94	85.21 112	99.09 118	87.64 185	97.83 92	97.92 132

CostFormer		91.18 213	90.70 192	92.62 255	94.84 230	81.76 301	94.09 285	94.43 298	84.15 286	92.72 128	93.77 261	79.43 222	98.20 188	90.70 132	92.18 196	97.90 133

tpmrst		91.44 199	91.32 166	91.79 279	95.15 213	79.20 322	93.42 298	95.37 258	88.55 200	93.49 104	93.67 265	82.49 166	98.27 185	90.41 133	89.34 234	97.90 133

EPNet_dtu		91.71 177	91.28 168	92.99 244	93.76 280	83.71 285	96.69 175	95.28 263	93.15 64	87.02 265	95.95 150	83.37 132	97.38 282	79.46 300	96.84 116	97.88 135
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
ADS-MVSNet2		89.45 253	88.59 251	92.03 272	95.86 180	82.26 298	90.93 325	94.32 303	83.23 297	91.28 160	91.81 300	79.01 231	95.99 314	79.52 297	91.39 210	97.84 136

ADS-MVSNet		89.89 246	88.68 250	93.53 224	95.86 180	84.89 275	90.93 325	95.07 275	83.23 297	91.28 160	91.81 300	79.01 231	97.85 246	79.52 297	91.39 210	97.84 136

MAR-MVS		94.22 88	93.46 98	96.51 74	98.00 83	92.19 80	97.67 68	97.47 124	88.13 221	93.00 121	95.84 155	84.86 118	99.51 75	87.99 174	98.17 85	97.83 138
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
CANet_DTU		94.37 85	93.65 91	96.55 70	96.46 158	92.13 81	96.21 217	96.67 205	94.38 33	93.53 103	97.03 103	79.34 223	99.71 30	90.76 130	98.45 79	97.82 139

tpmp4_e23		89.58 251	88.59 251	92.54 256	95.16 212	81.53 302	94.11 284	95.09 273	81.66 307	88.60 233	93.44 275	75.11 277	98.33 182	82.45 266	91.72 203	97.75 140

PLC	91.00 6	94.11 93	93.43 100	96.13 95	98.58 51	91.15 113	96.69 175	97.39 138	87.29 238	91.37 152	96.71 111	88.39 75	99.52 74	87.33 193	97.13 112	97.73 141
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
dp		88.90 259	88.26 257	90.81 297	94.58 241	76.62 327	92.85 309	94.93 281	85.12 274	90.07 193	93.07 279	75.81 271	98.12 196	80.53 293	87.42 252	97.71 142

AdaColmap		94.34 86	93.68 90	96.31 86	98.59 49	91.68 93	96.59 186	97.81 91	89.87 153	92.15 138	97.06 102	83.62 129	99.54 68	89.34 147	98.07 87	97.70 143

test-LLR		91.42 200	91.19 173	92.12 269	94.59 239	80.66 307	94.29 279	92.98 327	91.11 126	90.76 172	92.37 290	79.02 229	98.07 205	88.81 164	96.74 120	97.63 144

test-mter		90.19 241	89.54 238	92.12 269	94.59 239	80.66 307	94.29 279	92.98 327	87.68 230	90.76 172	92.37 290	67.67 315	98.07 205	88.81 164	96.74 120	97.63 144

PAPM		91.52 196	90.30 209	95.20 130	95.30 203	89.83 146	93.38 299	96.85 194	86.26 260	88.59 234	95.80 158	84.88 116	98.15 193	75.67 315	95.93 135	97.63 144

F-COLMAP		93.58 111	92.98 109	95.37 128	98.40 58	88.98 190	97.18 125	97.29 148	87.75 228	90.49 175	97.10 101	85.21 112	99.50 77	86.70 202	96.72 122	97.63 144

TESTMET0.1,1		90.06 243	89.42 240	91.97 273	94.41 246	80.62 309	94.29 279	91.97 336	87.28 239	90.44 177	92.47 289	68.79 310	97.67 261	88.50 168	96.60 125	97.61 148

CR-MVSNet		90.82 222	89.77 231	93.95 196	94.45 244	87.19 243	90.23 330	95.68 247	86.89 252	92.40 130	92.36 293	80.91 197	97.05 290	81.09 291	93.95 168	97.60 149

RPMNet		88.52 268	86.72 281	93.95 196	94.45 244	87.19 243	90.23 330	94.99 278	77.87 329	92.40 130	87.55 334	80.17 212	97.05 290	68.84 331	93.95 168	97.60 149

MIMVSNet		88.50 270	86.76 279	93.72 213	94.84 230	87.77 232	91.39 320	94.05 309	86.41 258	87.99 245	92.59 286	63.27 327	95.82 318	77.44 307	92.84 187	97.57 151

PatchT		88.87 260	87.42 266	93.22 238	94.08 266	85.10 271	89.51 334	94.64 292	81.92 305	92.36 133	88.15 329	80.05 213	97.01 294	72.43 323	93.65 173	97.54 152

tpm2		89.96 244	89.21 243	92.23 263	94.91 228	81.25 304	93.78 289	94.42 299	80.62 317	91.56 148	93.44 275	76.44 269	97.94 236	85.60 221	92.08 200	97.49 153

IB-MVS	87.33 17	89.91 245	88.28 256	94.79 159	95.26 207	87.70 234	95.12 266	93.95 312	89.35 166	87.03 264	92.49 288	70.74 303	99.19 100	89.18 153	81.37 310	97.49 153
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
PatchFormer-LS_test		91.68 187	91.18 174	93.19 240	95.24 208	83.63 288	95.53 250	95.44 255	89.82 157	91.37 152	92.58 287	80.85 201	98.52 161	89.65 143	90.16 227	97.42 155

CHOSEN 280x420		93.12 124	92.72 119	94.34 179	96.71 144	87.27 239	90.29 329	97.72 98	86.61 257	91.34 154	95.29 187	84.29 124	98.41 174	93.25 91	98.94 68	97.35 156

BH-untuned		92.94 131	92.62 122	93.92 200	97.22 124	86.16 260	96.40 199	96.25 219	90.06 151	89.79 202	96.17 143	83.19 133	98.35 179	87.19 196	97.27 109	97.24 157

1314		92.81 138	92.03 137	95.14 137	95.33 202	89.52 165	96.04 225	97.44 133	87.72 229	86.25 272	95.33 186	83.84 126	98.79 140	89.26 149	97.05 113	97.11 158

PCF-MVS	89.48 11	91.56 193	89.95 224	96.36 84	96.60 146	92.52 71	92.51 313	97.26 149	79.41 320	88.90 227	96.56 127	84.04 125	99.55 66	77.01 311	97.30 108	97.01 159
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
view600		92.55 142	91.68 148	95.18 131	97.98 84	89.44 170	98.00 37	94.57 293	92.09 96	93.17 115	95.52 177	78.14 253	99.11 109	81.61 271	94.04 164	96.98 160

view800		92.55 142	91.68 148	95.18 131	97.98 84	89.44 170	98.00 37	94.57 293	92.09 96	93.17 115	95.52 177	78.14 253	99.11 109	81.61 271	94.04 164	96.98 160

conf0.05thres1000		92.55 142	91.68 148	95.18 131	97.98 84	89.44 170	98.00 37	94.57 293	92.09 96	93.17 115	95.52 177	78.14 253	99.11 109	81.61 271	94.04 164	96.98 160

tfpn		92.55 142	91.68 148	95.18 131	97.98 84	89.44 170	98.00 37	94.57 293	92.09 96	93.17 115	95.52 177	78.14 253	99.11 109	81.61 271	94.04 164	96.98 160

thres600view7		92.49 148	91.60 154	95.18 131	97.91 95	89.47 166	97.65 71	94.66 288	92.18 95	93.33 107	94.91 198	78.06 257	99.10 115	81.61 271	94.06 162	96.98 160

thres400		92.42 152	91.52 160	95.12 139	97.85 98	89.29 181	97.41 100	94.88 283	92.19 93	93.27 112	94.46 223	78.17 250	99.08 120	81.40 278	94.08 158	96.98 160

XVG-OURS-SEG-HR		93.86 102	93.55 93	94.81 156	97.06 132	88.53 197	95.28 260	97.45 130	91.68 110	94.08 95	97.68 70	82.41 169	98.90 131	93.84 79	92.47 190	96.98 160

MSDG		91.42 200	90.24 213	94.96 148	97.15 128	88.91 191	93.69 292	96.32 215	85.72 267	86.93 266	96.47 131	80.24 210	98.98 127	80.57 292	95.05 147	96.98 160

XVG-OURS		93.72 107	93.35 103	94.80 157	97.07 130	88.61 195	94.79 269	97.46 126	91.97 105	93.99 96	97.86 58	81.74 183	98.88 135	92.64 97	92.67 189	96.92 168

PatchMatch-RL		92.90 133	92.02 138	95.56 116	98.19 78	90.80 123	95.27 262	97.18 152	87.96 222	91.86 144	95.68 169	80.44 206	98.99 126	84.01 247	97.54 99	96.89 169

mvs-test1		93.63 109	93.69 89	93.46 228	96.02 177	84.61 278	97.24 116	96.72 198	93.85 42	92.30 135	95.76 163	83.08 141	98.89 133	91.69 118	96.54 126	96.87 170

tpmvs		89.83 249	89.15 245	91.89 275	94.92 226	80.30 313	93.11 305	95.46 254	86.28 259	88.08 243	92.65 284	80.44 206	98.52 161	81.47 277	89.92 230	96.84 171

TR-MVS		91.48 197	90.59 202	94.16 184	96.40 160	87.33 237	95.67 242	95.34 262	87.68 230	91.46 150	95.52 177	76.77 267	98.35 179	82.85 261	93.61 175	96.79 172

OpenMVS	89.19 12	92.86 135	91.68 148	96.40 80	95.34 199	92.73 65	98.27 23	98.12 43	84.86 278	85.78 275	97.75 66	78.89 242	99.74 25	87.50 189	98.65 74	96.73 173

tpm cat1		88.36 275	87.21 275	91.81 278	95.13 215	80.55 310	92.58 312	95.70 244	74.97 335	87.45 253	91.96 298	78.01 261	98.17 192	80.39 294	88.74 240	96.72 174

tfpn111		92.45 149	91.58 155	95.06 140	97.92 92	89.37 175	97.71 64	94.66 288	92.20 90	93.31 108	94.90 199	78.06 257	99.11 109	81.37 282	94.06 162	96.70 175

conf0.01		91.74 175	90.67 194	94.94 152	97.55 115	89.68 150	97.64 75	93.14 321	88.43 203	91.24 162	94.30 235	78.91 235	98.45 167	81.28 284	93.57 178	96.70 175

conf0.002		91.74 175	90.67 194	94.94 152	97.55 115	89.68 150	97.64 75	93.14 321	88.43 203	91.24 162	94.30 235	78.91 235	98.45 167	81.28 284	93.57 178	96.70 175

conf200view11		92.45 149	91.58 155	95.05 141	97.92 92	89.37 175	97.71 64	94.66 288	92.20 90	93.31 108	94.90 199	78.06 257	99.08 120	81.40 278	94.08 158	96.70 175

DSMNet-mixed		86.34 291	86.12 285	87.00 318	89.88 327	70.43 336	94.93 268	90.08 343	77.97 328	85.42 280	92.78 283	74.44 283	93.96 331	74.43 317	95.14 145	96.62 179

API-MVS		94.84 80	94.49 77	95.90 102	97.90 96	92.00 86	97.80 53	97.48 121	89.19 170	94.81 84	96.71 111	88.84 68	99.17 103	88.91 160	98.76 72	96.53 180

gg-mvs-nofinetune		87.82 280	85.61 287	94.44 174	94.46 243	89.27 185	91.21 324	84.61 352	80.88 314	89.89 197	74.98 344	71.50 297	97.53 270	85.75 219	97.21 110	96.51 181

Effi-MVS+-dtu		93.08 125	93.21 106	92.68 254	96.02 177	83.25 291	97.14 129	96.72 198	93.85 42	91.20 169	93.44 275	83.08 141	98.30 184	91.69 118	95.73 139	96.50 182

thres100view900		92.43 151	91.58 155	94.98 146	97.92 92	89.37 175	97.71 64	94.66 288	92.20 90	93.31 108	94.90 199	78.06 257	99.08 120	81.40 278	94.08 158	96.48 183

tfpn200view9		92.38 154	91.52 160	94.95 149	97.85 98	89.29 181	97.41 100	94.88 283	92.19 93	93.27 112	94.46 223	78.17 250	99.08 120	81.40 278	94.08 158	96.48 183

tfpn1000		91.99 170	91.05 175	94.80 157	97.78 101	89.66 156	97.91 45	92.90 330	88.99 181	91.73 145	94.84 204	78.99 234	98.33 182	82.41 267	93.91 170	96.40 185

JIA-IIPM		88.26 277	87.04 278	91.91 274	93.52 286	81.42 303	89.38 335	94.38 300	80.84 315	90.93 171	80.74 341	79.22 225	97.92 240	82.76 262	91.62 205	96.38 186

cascas		91.20 210	90.08 218	94.58 171	94.97 221	89.16 188	93.65 294	97.59 111	79.90 319	89.40 217	92.92 281	75.36 276	98.36 178	92.14 103	94.75 152	96.23 187

RPSCF		90.75 225	90.86 184	90.42 304	96.84 138	76.29 328	95.61 247	96.34 214	83.89 289	91.38 151	97.87 56	76.45 268	98.78 141	87.16 198	92.23 193	96.20 188

thres200		92.23 162	91.39 163	94.75 161	97.61 110	89.03 189	96.60 185	95.09 273	92.08 101	93.28 111	94.00 253	78.39 248	99.04 125	81.26 290	94.18 157	96.19 189

xiu_mvs_v2_base		95.32 64	95.29 59	95.40 127	97.22 124	90.50 130	95.44 254	97.44 133	93.70 49	96.46 44	96.18 141	88.59 74	99.53 71	94.79 66	97.81 93	96.17 190

PS-MVSNAJ		95.37 62	95.33 58	95.49 121	97.35 122	90.66 127	95.31 259	97.48 121	93.85 42	96.51 41	95.70 168	88.65 71	99.65 42	94.80 64	98.27 82	96.17 190

AllTest		90.23 239	88.98 246	93.98 191	97.94 90	86.64 253	96.51 190	95.54 252	85.38 269	85.49 278	96.77 109	70.28 305	99.15 105	80.02 295	92.87 185	96.15 192

TestCases				93.98 191	97.94 90	86.64 253		95.54 252	85.38 269	85.49 278	96.77 109	70.28 305	99.15 105	80.02 295	92.87 185	96.15 192

BH-w/o		92.14 166	91.75 145	93.31 234	96.99 135	85.73 263	95.67 242	95.69 245	88.73 196	89.26 224	94.82 207	82.97 151	98.07 205	85.26 227	96.32 130	96.13 194

thresconf0.02		91.69 182	90.67 194	94.75 161	97.55 115	89.68 150	97.64 75	93.14 321	88.43 203	91.24 162	94.30 235	78.91 235	98.45 167	81.28 284	93.57 178	96.11 195

tfpn_n400		91.69 182	90.67 194	94.75 161	97.55 115	89.68 150	97.64 75	93.14 321	88.43 203	91.24 162	94.30 235	78.91 235	98.45 167	81.28 284	93.57 178	96.11 195

tfpnconf		91.69 182	90.67 194	94.75 161	97.55 115	89.68 150	97.64 75	93.14 321	88.43 203	91.24 162	94.30 235	78.91 235	98.45 167	81.28 284	93.57 178	96.11 195

tfpnview11		91.69 182	90.67 194	94.75 161	97.55 115	89.68 150	97.64 75	93.14 321	88.43 203	91.24 162	94.30 235	78.91 235	98.45 167	81.28 284	93.57 178	96.11 195

xiu_mvs_v1_base_debu		95.01 71	94.76 67	95.75 108	96.58 147	91.71 90	96.25 213	97.35 144	92.99 69	96.70 32	96.63 122	82.67 160	99.44 83	96.22 23	97.46 100	96.11 195

xiu_mvs_v1_base		95.01 71	94.76 67	95.75 108	96.58 147	91.71 90	96.25 213	97.35 144	92.99 69	96.70 32	96.63 122	82.67 160	99.44 83	96.22 23	97.46 100	96.11 195

xiu_mvs_v1_base_debi		95.01 71	94.76 67	95.75 108	96.58 147	91.71 90	96.25 213	97.35 144	92.99 69	96.70 32	96.63 122	82.67 160	99.44 83	96.22 23	97.46 100	96.11 195

Fast-Effi-MVS+-dtu		92.29 159	91.99 139	93.21 239	95.27 204	85.52 267	97.03 133	96.63 208	92.09 96	89.11 226	95.14 193	80.33 209	98.08 201	87.54 188	94.74 153	96.03 202

nrg030		94.05 96	93.31 104	96.27 90	95.22 209	94.59 15	98.34 19	97.46 126	92.93 76	91.21 168	96.64 118	87.23 92	98.22 187	94.99 59	85.80 262	95.98 203

pcd1.5k->3k		38.37 332	40.51 333	31.96 345	94.29 250	0.00 364	0.00 355	97.69 102	0.00 359	0.00 360	0.00 361	81.45 187	0.00 362	0.00 359	91.11 214	95.89 204

PS-MVSNAJss		93.74 106	93.51 96	94.44 174	93.91 275	89.28 183	97.75 56	97.56 115	92.50 84	89.94 194	96.54 128	88.65 71	98.18 191	93.83 80	90.90 217	95.86 205

HQP_MVS		93.78 105	93.43 100	94.82 154	96.21 166	89.99 138	97.74 58	97.51 119	94.85 17	91.34 154	96.64 118	81.32 189	98.60 154	93.02 93	92.23 193	95.86 205

plane_prior5								97.51 119					98.60 154	93.02 93	92.23 193	95.86 205

FIs		94.09 94	93.70 88	95.27 129	95.70 187	92.03 84	98.10 31	98.68 7	93.36 57	90.39 178	96.70 113	87.63 85	97.94 236	92.25 100	90.50 224	95.84 208

FC-MVSNet-test		93.94 100	93.57 92	95.04 142	95.48 193	91.45 101	98.12 30	98.71 5	93.37 55	90.23 181	96.70 113	87.66 83	97.85 246	91.49 122	90.39 225	95.83 209

MVS		91.71 177	90.44 204	95.51 119	95.20 211	91.59 96	96.04 225	97.45 130	73.44 339	87.36 257	95.60 172	85.42 110	99.10 115	85.97 215	97.46 100	95.83 209

VPNet		92.23 162	91.31 167	94.99 144	95.56 190	90.96 117	97.22 121	97.86 88	92.96 75	90.96 170	96.62 125	75.06 278	98.20 188	91.90 110	83.65 297	95.80 211

DU-MVS		92.90 133	92.04 136	95.49 121	94.95 223	92.83 62	97.16 127	98.24 28	93.02 68	90.13 186	95.71 166	83.47 130	97.85 246	91.71 116	83.93 291	95.78 212

NR-MVSNet		92.34 155	91.27 169	95.53 118	94.95 223	93.05 57	97.39 104	98.07 56	92.65 82	84.46 285	95.71 166	85.00 115	97.77 255	89.71 140	83.52 298	95.78 212

HQP4-MVS										90.14 182			98.50 163			95.78 212

HQP-MVS		93.19 123	92.74 118	94.54 172	95.86 180	89.33 178	96.65 178	97.39 138	93.55 50	90.14 182	95.87 153	80.95 194	98.50 163	92.13 104	92.10 198	95.78 212

VPA-MVSNet		93.24 121	92.48 130	95.51 119	95.70 187	92.39 73	97.86 48	98.66 9	92.30 87	92.09 140	95.37 184	80.49 205	98.40 175	93.95 74	85.86 261	95.75 216

TranMVSNet+NR-MVSNet		92.50 146	91.63 153	95.14 137	94.76 233	92.07 82	97.53 90	98.11 46	92.90 77	89.56 213	96.12 144	83.16 134	97.60 267	89.30 148	83.20 301	95.75 216

UniMVSNet_NR-MVSNet		93.37 117	92.67 120	95.47 124	95.34 199	92.83 62	97.17 126	98.58 10	92.98 74	90.13 186	95.80 158	88.37 76	97.85 246	91.71 116	83.93 291	95.73 218

WR-MVS		92.34 155	91.53 159	94.77 160	95.13 215	90.83 122	96.40 199	97.98 78	91.88 106	89.29 222	95.54 176	82.50 165	97.80 251	89.79 139	85.27 269	95.69 219

XXY-MVS		92.16 164	91.23 171	94.95 149	94.75 234	90.94 118	97.47 98	97.43 135	89.14 177	88.90 227	96.43 133	79.71 218	98.24 186	89.56 144	87.68 248	95.67 220

ACMM	89.79 8	92.96 130	92.50 129	94.35 178	96.30 164	88.71 193	97.58 87	97.36 143	91.40 119	90.53 174	96.65 117	79.77 217	98.75 145	91.24 128	91.64 204	95.59 221
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
jajsoiax		92.42 152	91.89 142	94.03 189	93.33 294	88.50 198	97.73 60	97.53 116	92.00 104	88.85 229	96.50 130	75.62 275	98.11 197	93.88 78	91.56 207	95.48 222

testgi		87.97 278	87.21 275	90.24 306	92.86 305	80.76 306	96.67 177	94.97 279	91.74 108	85.52 277	95.83 156	62.66 329	94.47 329	76.25 312	88.36 244	95.48 222

MVSTER		93.20 122	92.81 113	94.37 177	96.56 150	89.59 160	97.06 132	97.12 161	91.24 123	91.30 157	95.96 149	82.02 177	98.05 213	93.48 86	90.55 222	95.47 224

UniMVSNet (Re)		93.31 119	92.55 125	95.61 114	95.39 196	93.34 53	97.39 104	98.71 5	93.14 65	90.10 190	94.83 206	87.71 82	98.03 219	91.67 120	83.99 290	95.46 225

tfpn_ndepth		91.88 173	90.96 179	94.62 166	97.73 104	89.93 144	97.75 56	92.92 329	88.93 186	91.73 145	93.80 260	78.91 235	98.49 166	83.02 259	93.86 171	95.45 226

mvs_tets		92.31 157	91.76 144	93.94 199	93.41 290	88.29 201	97.63 82	97.53 116	92.04 102	88.76 230	96.45 132	74.62 282	98.09 200	93.91 76	91.48 208	95.45 226

testing_2		87.33 284	85.03 291	94.22 181	87.77 335	89.32 180	94.97 267	97.11 163	89.22 169	71.64 337	88.73 323	55.16 342	97.94 236	91.95 108	88.73 241	95.41 228

EI-MVSNet		93.03 128	92.88 112	93.48 226	95.77 185	86.98 248	96.44 191	97.12 161	90.66 137	91.30 157	97.64 76	86.56 97	98.05 213	89.91 136	90.55 222	95.41 228

EU-MVSNet		88.72 262	88.90 247	88.20 313	93.15 302	74.21 331	96.63 182	94.22 307	85.18 272	87.32 258	95.97 148	76.16 270	94.98 327	85.27 226	86.17 258	95.41 228

test0.0.03 1		89.37 255	88.70 249	91.41 289	92.47 312	85.63 265	95.22 264	92.70 332	91.11 126	86.91 267	93.65 266	79.02 229	93.19 335	78.00 306	89.18 235	95.41 228

test_djsdf		93.07 126	92.76 114	94.00 190	93.49 288	88.70 194	98.22 26	97.57 112	91.42 117	90.08 192	95.55 175	82.85 157	97.92 240	94.07 71	91.58 206	95.40 232

IterMVS-LS		92.29 159	91.94 141	93.34 233	96.25 165	86.97 249	96.57 189	97.05 170	90.67 135	89.50 216	94.80 209	86.59 96	97.64 264	89.91 136	86.11 260	95.40 232
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
CLD-MVS		92.98 129	92.53 127	94.32 180	96.12 175	89.20 186	95.28 260	97.47 124	92.66 81	89.90 195	95.62 171	80.58 203	98.40 175	92.73 96	92.40 191	95.38 234
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
CP-MVSNet		91.89 172	91.24 170	93.82 202	95.05 218	88.57 196	97.82 52	98.19 33	91.70 109	88.21 242	95.76 163	81.96 178	97.52 271	87.86 176	84.65 284	95.37 235

FMVSNet3		91.78 174	90.69 193	95.03 143	96.53 152	92.27 76	97.02 135	96.93 187	89.79 159	89.35 219	94.65 215	77.01 266	97.47 274	86.12 211	88.82 237	95.35 236

FMVSNet2		91.31 207	90.08 218	94.99 144	96.51 153	92.21 77	97.41 100	96.95 185	88.82 191	88.62 232	94.75 211	73.87 286	97.42 278	85.20 228	88.55 243	95.35 236

PS-CasMVS		91.55 194	90.84 187	93.69 215	94.96 222	88.28 202	97.84 51	98.24 28	91.46 115	88.04 244	95.80 158	79.67 219	97.48 273	87.02 199	84.54 286	95.31 238

LPG-MVS_test		92.94 131	92.56 124	94.10 185	96.16 171	88.26 203	97.65 71	97.46 126	91.29 120	90.12 188	97.16 97	79.05 227	98.73 146	92.25 100	91.89 201	95.31 238

LGP-MVS_train				94.10 185	96.16 171	88.26 203		97.46 126	91.29 120	90.12 188	97.16 97	79.05 227	98.73 146	92.25 100	91.89 201	95.31 238

GBi-Net		91.35 204	90.27 211	94.59 167	96.51 153	91.18 110	97.50 92	96.93 187	88.82 191	89.35 219	94.51 219	73.87 286	97.29 286	86.12 211	88.82 237	95.31 238

test1		91.35 204	90.27 211	94.59 167	96.51 153	91.18 110	97.50 92	96.93 187	88.82 191	89.35 219	94.51 219	73.87 286	97.29 286	86.12 211	88.82 237	95.31 238

FMVSNet1		89.88 247	88.31 255	94.59 167	95.41 195	91.18 110	97.50 92	96.93 187	86.62 256	87.41 255	94.51 219	65.94 323	97.29 286	83.04 258	87.43 251	95.31 238

PVSNet_0	82.17 19	85.46 298	83.64 299	90.92 295	95.27 204	79.49 319	90.55 328	95.60 249	83.76 292	83.00 296	89.95 307	71.09 300	97.97 230	82.75 263	60.79 346	95.31 238

ACMP	89.59 10	92.62 141	92.14 134	94.05 188	96.40 160	88.20 209	97.36 107	97.25 151	91.52 112	88.30 239	96.64 118	78.46 246	98.72 148	91.86 113	91.48 208	95.23 245
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
v2v482		91.59 191	90.85 185	93.80 203	93.87 277	88.17 211	96.94 146	96.88 192	89.54 160	89.53 214	94.90 199	81.70 185	98.02 222	89.25 150	85.04 277	95.20 246

PEN-MVS		91.20 210	90.44 204	93.48 226	94.49 242	87.91 230	97.76 55	98.18 36	91.29 120	87.78 247	95.74 165	80.35 208	97.33 284	85.46 223	82.96 302	95.19 247

OurMVSNet-221017-0		90.51 234	90.19 217	91.44 288	93.41 290	81.25 304	96.98 139	96.28 216	91.68 110	86.55 270	96.30 137	74.20 285	97.98 227	88.96 159	87.40 253	95.09 248

divwei89l23v2f112		91.61 188	90.89 180	93.78 205	94.01 270	88.22 207	96.96 140	96.96 182	89.17 174	89.75 204	94.28 243	83.02 147	98.03 219	88.86 161	84.98 281	95.08 249

v1		91.61 188	90.89 180	93.78 205	94.01 270	88.21 208	96.96 140	96.96 182	89.17 174	89.78 203	94.29 241	82.97 151	98.05 213	88.85 162	84.99 279	95.08 249

OPM-MVS		93.28 120	92.76 114	94.82 154	94.63 238	90.77 125	96.65 178	97.18 152	93.72 47	91.68 147	97.26 93	79.33 224	98.63 151	92.13 104	92.28 192	95.07 251

v1141		91.61 188	90.89 180	93.78 205	94.01 270	88.24 205	96.96 140	96.96 182	89.17 174	89.75 204	94.29 241	82.99 149	98.03 219	88.85 162	85.00 278	95.07 251

v6		91.69 182	91.00 178	93.75 208	94.14 257	88.12 216	97.20 122	96.98 178	89.19 170	89.90 195	94.42 227	83.04 145	98.07 205	89.07 155	85.10 272	95.07 251

v1neww		91.70 180	91.01 176	93.75 208	94.19 252	88.14 214	97.20 122	96.98 178	89.18 172	89.87 198	94.44 225	83.10 139	98.06 210	89.06 156	85.09 273	95.06 254

v7new		91.70 180	91.01 176	93.75 208	94.19 252	88.14 214	97.20 122	96.98 178	89.18 172	89.87 198	94.44 225	83.10 139	98.06 210	89.06 156	85.09 273	95.06 254

ACMH	87.59 16	90.53 233	89.42 240	93.87 201	96.21 166	87.92 228	97.24 116	96.94 186	88.45 202	83.91 293	96.27 139	71.92 294	98.62 153	84.43 239	89.43 233	95.05 256
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
v1192		91.07 214	90.23 214	93.58 222	93.70 281	87.82 231	96.73 165	97.07 167	87.77 227	89.58 211	94.32 233	80.90 200	97.97 230	86.52 204	85.48 263	94.95 257

COLMAP_ROB	87.81 15	90.40 235	89.28 242	93.79 204	97.95 89	87.13 245	96.92 147	95.89 239	82.83 299	86.88 268	97.18 96	73.77 289	99.29 95	78.44 305	93.62 174	94.95 257
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
v1921920		90.85 221	90.03 221	93.29 235	93.55 284	86.96 250	96.74 164	97.04 173	87.36 236	89.52 215	94.34 231	80.23 211	97.97 230	86.27 207	85.21 270	94.94 259

SixPastTwentyTwo		89.15 256	88.54 253	90.98 293	93.49 288	80.28 314	96.70 173	94.70 287	90.78 131	84.15 290	95.57 173	71.78 296	97.71 259	84.63 235	85.07 275	94.94 259

v144192		91.06 215	90.28 210	93.39 230	93.66 283	87.23 242	96.83 153	97.07 167	87.43 234	89.69 208	94.28 243	81.48 186	98.00 226	87.18 197	84.92 282	94.93 261

v1240		90.70 229	89.85 228	93.23 237	93.51 287	86.80 251	96.61 183	97.02 176	87.16 241	89.58 211	94.31 234	79.55 221	97.98 227	85.52 222	85.44 264	94.90 262

v7		91.47 198	90.73 191	93.68 216	94.13 258	88.16 212	97.09 131	97.05 170	88.38 210	89.80 201	94.52 218	82.21 173	98.01 223	88.00 173	85.42 265	94.87 263

pmmvs5		89.86 248	88.87 248	92.82 246	92.86 305	86.23 259	96.26 212	95.39 256	84.24 285	87.12 261	94.51 219	74.27 284	97.36 283	87.61 187	87.57 249	94.86 264

v1144		91.37 203	90.60 201	93.68 216	93.89 276	88.23 206	96.84 152	97.03 175	88.37 211	89.69 208	94.39 228	82.04 176	97.98 227	87.80 178	85.37 267	94.84 265

LP		84.13 302	81.85 307	90.97 294	93.20 300	82.12 299	87.68 340	94.27 306	76.80 330	81.93 300	88.52 324	72.97 293	95.95 315	59.53 342	81.73 307	94.84 265

K. test v3		87.64 282	86.75 280	90.32 305	93.02 304	79.48 320	96.61 183	92.08 335	90.66 137	80.25 322	94.09 251	67.21 319	96.65 300	85.96 216	80.83 313	94.83 267

IterMVS		90.15 242	89.67 235	91.61 284	95.48 193	83.72 284	94.33 278	96.12 225	89.99 152	87.31 259	94.15 250	75.78 273	96.27 304	86.97 200	86.89 255	94.83 267
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
semantic-postprocess				91.82 277	95.52 191	84.20 281		96.15 224	90.61 142	87.39 256	94.27 245	75.63 274	96.44 301	87.34 192	86.88 256	94.82 269

WR-MVS_H		92.00 169	91.35 164	93.95 196	95.09 217	89.47 166	98.04 35	98.68 7	91.46 115	88.34 237	94.68 213	85.86 106	97.56 268	85.77 218	84.24 288	94.82 269

GG-mvs-BLEND				93.62 218	93.69 282	89.20 186	92.39 316	83.33 353		87.98 246	89.84 309	71.00 301	96.87 297	82.08 270	95.40 142	94.80 271

v148		90.99 217	90.38 207	92.81 249	93.83 278	85.80 262	96.78 162	96.68 203	89.45 164	88.75 231	93.93 256	82.96 153	97.82 250	87.83 177	83.25 299	94.80 271

XVG-ACMP-BASELINE		90.93 219	90.21 216	93.09 241	94.31 249	85.89 261	95.33 257	97.26 149	91.06 128	89.38 218	95.44 183	68.61 311	98.60 154	89.46 146	91.05 215	94.79 273

DTE-MVSNet		90.56 232	89.75 233	93.01 243	93.95 273	87.25 240	97.64 75	97.65 106	90.74 132	87.12 261	95.68 169	79.97 215	97.00 295	83.33 255	81.66 309	94.78 274

ACMH+	87.92 14	90.20 240	89.18 244	93.25 236	96.48 156	86.45 257	96.99 138	96.68 203	88.83 190	84.79 284	96.22 140	70.16 307	98.53 160	84.42 240	88.04 245	94.77 275

lessismore_v0				90.45 303	91.96 318	79.09 323		87.19 349		80.32 320	94.39 228	66.31 321	97.55 269	84.00 248	76.84 321	94.70 276

Patchmtry		88.64 266	87.25 271	92.78 250	94.09 264	86.64 253	89.82 333	95.68 247	80.81 316	87.63 252	92.36 293	80.91 197	97.03 292	78.86 303	85.12 271	94.67 277

v7n		90.76 223	89.86 227	93.45 229	93.54 285	87.60 236	97.70 67	97.37 141	88.85 188	87.65 251	94.08 252	81.08 191	98.10 198	84.68 234	83.79 296	94.66 278

V42		91.58 192	90.87 183	93.73 211	94.05 269	88.50 198	97.32 111	96.97 181	88.80 194	89.71 206	94.33 232	82.54 164	98.05 213	89.01 158	85.07 275	94.64 279

Anonymous20240521		91.32 206	90.43 206	93.98 191	94.93 225	89.28 183	98.04 35	97.53 116	89.49 163	86.68 269	94.82 207	81.72 184	98.05 213	85.31 225	85.39 266	94.61 280

v8		91.29 208	90.53 203	93.57 223	94.15 256	88.12 216	97.34 108	97.06 169	88.99 181	88.32 238	94.26 247	83.08 141	98.01 223	87.62 186	83.92 293	94.57 281

v748		90.34 236	89.54 238	92.75 251	93.25 295	85.71 264	97.61 83	97.17 154	88.54 201	87.20 260	93.54 269	81.02 192	98.01 223	85.73 220	81.80 306	94.52 282

anonymousdsp		92.16 164	91.55 158	93.97 194	92.58 311	89.55 162	97.51 91	97.42 136	89.42 165	88.40 236	94.84 204	80.66 202	97.88 245	91.87 112	91.28 212	94.48 283

pm-mvs1		90.72 227	89.65 237	93.96 195	94.29 250	89.63 157	97.79 54	96.82 195	89.07 178	86.12 274	95.48 182	78.61 244	97.78 253	86.97 200	81.67 308	94.46 284

LTVRE_ROB	88.41 13	90.99 217	89.92 225	94.19 182	96.18 169	89.55 162	96.31 208	97.09 164	87.88 225	85.67 276	95.91 152	78.79 243	98.57 157	81.50 276	89.98 228	94.44 285
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
YYNet1		85.87 295	84.23 297	90.78 300	92.38 315	82.46 296	93.17 302	95.14 271	82.12 304	67.69 338	92.36 293	78.16 252	95.50 324	77.31 309	79.73 315	94.39 286

PVSNet_BlendedMVS		94.06 95	93.92 83	94.47 173	98.27 69	89.46 168	96.73 165	98.36 16	90.17 149	94.36 90	95.24 190	88.02 77	99.58 56	93.44 87	90.72 220	94.36 287

v10		91.04 216	90.23 214	93.49 225	94.12 260	88.16 212	97.32 111	97.08 166	88.26 214	88.29 240	94.22 248	82.17 175	97.97 230	86.45 206	84.12 289	94.33 288

MDA-MVSNet-bldmvs		85.00 299	82.95 301	91.17 292	93.13 303	83.33 290	94.56 273	95.00 277	84.57 282	65.13 343	92.65 284	70.45 304	95.85 316	73.57 321	77.49 319	94.33 288

MDA-MVSNet_test_wron		85.87 295	84.23 297	90.80 299	92.38 315	82.57 293	93.17 302	95.15 270	82.15 303	67.65 339	92.33 296	78.20 249	95.51 323	77.33 308	79.74 314	94.31 290

our_test_3		88.78 261	87.98 259	91.20 291	92.45 313	82.53 294	93.61 296	95.69 245	85.77 266	84.88 282	93.71 262	79.99 214	96.78 299	79.47 299	86.24 257	94.28 291

pmmvs4		90.93 219	89.85 228	94.17 183	93.34 292	90.79 124	94.60 271	96.02 227	84.62 281	87.45 253	95.15 192	81.88 181	97.45 275	87.70 180	87.87 247	94.27 292

ppachtmachnet_test		88.35 276	87.29 269	91.53 285	92.45 313	83.57 289	93.75 290	95.97 228	84.28 284	85.32 281	94.18 249	79.00 233	96.93 296	75.71 314	84.99 279	94.10 293

UnsupCasMVSNet_eth		85.99 294	84.45 295	90.62 301	89.97 326	82.40 297	93.62 295	97.37 141	89.86 154	78.59 327	92.37 290	65.25 325	95.35 325	82.27 269	70.75 339	94.10 293

pmmvs6		87.81 281	86.19 283	92.69 253	91.32 320	86.30 258	97.34 108	96.41 212	80.59 318	84.05 292	94.37 230	67.37 318	97.67 261	84.75 232	79.51 316	94.09 295

ITE_SJBPF				92.43 258	95.34 199	85.37 269		95.92 232	91.47 114	87.75 248	96.39 135	71.00 301	97.96 234	82.36 268	89.86 231	93.97 296

FMVSNet5		87.29 285	85.79 286	91.78 280	94.80 232	87.28 238	95.49 252	95.28 263	84.09 287	83.85 294	91.82 299	62.95 328	94.17 330	78.48 304	85.34 268	93.91 297

Anonymous20231206		87.09 286	86.14 284	89.93 309	91.22 321	80.35 311	96.11 221	95.35 259	83.57 294	84.16 289	93.02 280	73.54 291	95.61 320	72.16 324	86.14 259	93.84 298

USDC		88.94 257	87.83 260	92.27 259	94.66 236	84.96 273	93.86 288	95.90 234	87.34 237	83.40 295	95.56 174	67.43 317	98.19 190	82.64 265	89.67 232	93.66 299

N_pmnet		78.73 313	78.71 312	78.79 331	92.80 307	46.50 358	94.14 283	43.71 361	78.61 325	80.83 306	91.66 303	74.94 281	96.36 302	67.24 332	84.45 287	93.50 300

MIMVSNet1		84.93 300	83.05 300	90.56 302	89.56 329	84.84 276	95.40 255	95.35 259	83.91 288	80.38 318	92.21 297	57.23 336	93.34 334	70.69 330	82.75 305	93.50 300

TransMVSNet (Re)		88.94 257	87.56 261	93.08 242	94.35 247	88.45 200	97.73 60	95.23 267	87.47 233	84.26 288	95.29 187	79.86 216	97.33 284	79.44 301	74.44 336	93.45 302

V4		90.71 228	90.00 222	92.82 246	93.21 299	87.03 246	97.59 86	97.16 157	88.21 215	87.69 249	93.92 257	80.93 196	98.06 210	87.39 190	83.90 294	93.39 303

Baseline_NR-MVSNet		91.20 210	90.62 200	92.95 245	93.83 278	88.03 222	97.01 137	95.12 272	88.42 209	89.70 207	95.13 194	83.47 130	97.44 276	89.66 142	83.24 300	93.37 304

v52		90.70 229	90.00 222	92.82 246	93.24 296	87.03 246	97.60 84	97.14 158	88.21 215	87.69 249	93.94 255	80.91 197	98.07 205	87.39 190	83.87 295	93.36 305

TDRefinement		86.53 289	84.76 294	91.85 276	82.23 345	84.25 279	96.38 201	95.35 259	84.97 277	84.09 291	94.94 196	65.76 324	98.34 181	84.60 238	74.52 334	92.97 306

ambc				86.56 320	83.60 342	70.00 340	85.69 343	94.97 279		80.60 313	88.45 325	37.42 350	96.84 298	82.69 264	75.44 325	92.86 307

test2356		82.77 306	82.14 304	84.65 322	85.77 339	70.36 337	91.22 323	93.69 317	81.58 309	81.82 301	89.00 322	60.63 333	90.77 342	64.74 335	90.80 219	92.82 308

test1235678		79.82 312	78.53 313	83.69 324	82.55 344	67.55 343	92.50 314	94.13 308	79.28 321	72.10 336	86.45 337	57.27 335	90.68 343	61.60 340	80.90 312	92.82 308

MS-PatchMatch		90.27 237	89.77 231	91.78 280	94.33 248	84.72 277	95.55 248	96.73 197	86.17 262	86.36 271	95.28 189	71.28 299	97.80 251	84.09 244	98.14 86	92.81 310

tfpnnormal		89.70 250	88.40 254	93.60 219	95.15 213	90.10 134	97.56 88	98.16 38	87.28 239	86.16 273	94.63 216	77.57 264	98.05 213	74.48 316	84.59 285	92.65 311

testus		82.63 307	82.15 303	84.07 323	87.31 336	67.67 342	93.18 300	94.29 305	82.47 301	82.14 299	90.69 305	53.01 343	91.94 339	66.30 334	89.96 229	92.62 312

EG-PatchMatch MVS		87.02 287	85.44 288	91.76 282	92.67 309	85.00 272	96.08 224	96.45 211	83.41 296	79.52 324	93.49 272	57.10 337	97.72 258	79.34 302	90.87 218	92.56 313

TinyColmap		86.82 288	85.35 290	91.21 290	94.91 228	82.99 292	93.94 287	94.02 311	83.58 293	81.56 304	94.68 213	62.34 330	98.13 194	75.78 313	87.35 254	92.52 314

v18		88.71 263	87.52 262	92.27 259	94.16 255	88.11 218	96.82 156	95.96 229	87.03 243	80.76 309	89.81 310	83.15 135	96.22 305	84.69 233	75.31 327	92.49 315

v17		88.67 265	87.47 265	92.26 261	94.13 258	88.09 220	96.81 157	95.95 230	87.02 244	80.72 310	89.75 312	83.11 138	96.20 306	84.61 236	75.15 329	92.49 315

v16		88.69 264	87.50 263	92.26 261	94.19 252	88.11 218	96.81 157	95.95 230	87.01 245	80.71 311	89.80 311	83.08 141	96.20 306	84.61 236	75.34 326	92.48 317

CMPMVS	62.92 21	85.62 297	84.92 292	87.74 315	89.14 330	73.12 334	94.17 282	96.80 196	73.98 337	73.65 333	94.93 197	66.36 320	97.61 266	83.95 249	91.28 212	92.48 317
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
V9		88.49 271	87.26 270	92.18 265	94.12 260	87.97 226	96.73 165	95.90 234	86.95 249	80.40 317	89.61 314	82.98 150	96.13 308	84.14 243	74.55 333	92.44 319

v12		88.46 272	87.23 273	92.17 266	94.10 263	87.99 223	96.71 170	95.90 234	86.91 250	80.34 319	89.58 317	82.92 154	96.11 312	84.09 244	74.50 335	92.42 320

v15		88.53 267	87.31 267	92.20 264	94.09 264	88.05 221	96.72 168	95.90 234	87.01 245	80.53 314	89.60 316	83.02 147	96.13 308	84.29 241	74.64 330	92.41 321

v13		88.45 273	87.22 274	92.16 268	94.08 266	87.95 227	96.71 170	95.90 234	86.86 254	80.27 321	89.55 318	82.92 154	96.12 310	84.02 246	74.63 331	92.40 322

V14		88.52 268	87.30 268	92.17 266	94.12 260	87.99 223	96.72 168	95.91 233	86.98 247	80.50 315	89.63 313	83.03 146	96.12 310	84.23 242	74.60 332	92.40 322

v11		88.41 274	87.19 277	92.08 271	94.08 266	87.77 232	96.75 163	95.85 240	86.74 255	80.50 315	89.50 319	82.49 166	96.08 313	83.55 252	75.20 328	92.38 324

test20.03		86.14 293	85.40 289	88.35 311	90.12 324	80.06 316	95.90 233	95.20 268	88.59 197	81.29 305	93.62 267	71.43 298	92.65 336	71.26 328	81.17 311	92.34 325

LF4IMVS		87.94 279	87.25 271	89.98 308	92.38 315	80.05 317	94.38 276	95.25 266	87.59 232	84.34 286	94.74 212	64.31 326	97.66 263	84.83 230	87.45 250	92.23 326

MVS-HIRNet		82.47 308	81.21 309	86.26 321	95.38 197	69.21 341	88.96 337	89.49 345	66.28 343	80.79 308	74.08 346	68.48 312	97.39 281	71.93 325	95.47 141	92.18 327

MVP-Stereo		90.74 226	90.08 218	92.71 252	93.19 301	88.20 209	95.86 234	96.27 217	86.07 263	84.86 283	94.76 210	77.84 262	97.75 256	83.88 250	98.01 88	92.17 328
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
Anonymous20231211		78.22 315	75.30 316	86.99 319	86.14 338	74.16 332	95.62 246	93.88 313	66.43 342	74.44 332	87.86 331	41.39 349	95.11 326	62.49 338	69.46 342	91.71 329

pmmvs-eth3d		86.22 292	84.45 295	91.53 285	88.34 332	87.25 240	94.47 275	95.01 276	83.47 295	79.51 325	89.61 314	69.75 308	95.71 319	83.13 257	76.73 322	91.64 330

UnsupCasMVSNet_bld		82.13 309	79.46 311	90.14 307	88.00 333	82.47 295	90.89 327	96.62 209	78.94 323	75.61 330	84.40 339	56.63 338	96.31 303	77.30 310	66.77 345	91.63 331

test_0402		86.46 290	84.79 293	91.45 287	95.02 220	85.55 266	96.29 210	94.89 282	80.90 313	82.21 297	93.97 254	68.21 314	97.29 286	62.98 337	88.68 242	91.51 332

PM-MVS		83.48 303	81.86 306	88.31 312	87.83 334	77.59 326	93.43 297	91.75 337	86.91 250	80.63 312	89.91 308	44.42 348	95.84 317	85.17 229	76.73 322	91.50 333

new-patchmatchnet		83.18 304	81.87 305	87.11 317	86.88 337	75.99 329	93.70 291	95.18 269	85.02 276	77.30 329	88.40 326	65.99 322	93.88 332	74.19 320	70.18 340	91.47 334

OpenMVS_ROB	81.14 20	84.42 301	82.28 302	90.83 296	90.06 325	84.05 283	95.73 241	94.04 310	73.89 338	80.17 323	91.53 304	59.15 334	97.64 264	66.92 333	89.05 236	90.80 335

LCM-MVSNet		72.55 317	69.39 320	82.03 325	70.81 355	65.42 346	90.12 332	94.36 302	55.02 347	65.88 342	81.72 340	24.16 359	89.96 344	74.32 319	68.10 343	90.71 336

new_pmnet		82.89 305	81.12 310	88.18 314	89.63 328	80.18 315	91.77 319	92.57 333	76.79 331	75.56 331	88.23 328	61.22 332	94.48 328	71.43 326	82.92 303	89.87 337

pmmvs3		79.97 311	77.50 315	87.39 316	82.80 343	79.38 321	92.70 311	90.75 341	70.69 341	78.66 326	87.47 335	51.34 345	93.40 333	73.39 322	69.65 341	89.38 338

1111		78.29 314	77.55 314	80.50 327	83.89 340	59.98 350	91.89 317	93.71 314	75.06 333	73.60 334	87.67 332	55.66 339	92.60 337	58.54 344	77.92 318	88.93 339

test12356		74.97 316	74.13 317	77.49 332	78.81 346	56.23 354	88.53 338	92.75 331	75.14 332	67.50 340	85.07 338	44.88 347	89.96 344	58.71 343	75.75 324	86.26 340

testmv		72.22 318	70.02 318	78.82 330	73.06 353	61.75 348	91.24 322	92.31 334	74.45 336	61.06 345	80.51 342	34.21 351	88.63 347	55.31 347	68.07 344	86.06 341

PMMVS2		70.19 320	66.92 322	80.01 328	76.35 347	65.67 345	86.22 342	87.58 348	64.83 345	62.38 344	80.29 343	26.78 357	88.49 348	63.79 336	54.07 347	85.88 342

ANet_high		63.94 324	59.58 325	77.02 333	61.24 358	66.06 344	85.66 344	87.93 347	78.53 326	42.94 350	71.04 348	25.42 358	80.71 352	52.60 349	30.83 353	84.28 343

FPMVS		71.27 319	69.85 319	75.50 334	74.64 348	59.03 352	91.30 321	91.50 338	58.80 346	57.92 346	88.28 327	29.98 355	85.53 350	53.43 348	82.84 304	81.95 344

no-one		68.12 321	63.78 324	81.13 326	74.01 350	70.22 339	87.61 341	90.71 342	72.63 340	53.13 348	71.89 347	30.29 353	91.45 340	61.53 341	32.21 351	81.72 345

DeepMVS_CX				74.68 336	90.84 322	64.34 347		81.61 356	65.34 344	67.47 341	88.01 330	48.60 346	80.13 353	62.33 339	73.68 338	79.58 346

wuykxyi23d		56.92 327	51.11 331	74.38 337	62.30 357	61.47 349	80.09 348	84.87 351	49.62 350	30.80 356	57.20 354	7.03 362	82.94 351	55.69 346	32.36 350	78.72 347

testpf		80.97 310	81.40 308	79.65 329	91.53 319	72.43 335	73.47 351	89.55 344	78.63 324	80.81 307	89.06 321	61.36 331	91.36 341	83.34 254	84.89 283	75.15 348

PNet_i23d		59.01 325	55.87 326	68.44 338	73.98 351	51.37 355	81.36 347	82.41 354	52.37 349	42.49 352	70.39 349	11.39 360	79.99 354	49.77 350	38.71 349	73.97 349

PMVS	53.92 22	58.58 326	55.40 327	68.12 339	51.00 359	48.64 356	78.86 349	87.10 350	46.77 351	35.84 355	74.28 345	8.76 361	86.34 349	42.07 352	73.91 337	69.38 350
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MVE	50.73 23	53.25 329	48.81 332	66.58 340	65.34 356	57.50 353	72.49 352	70.94 359	40.15 354	39.28 354	63.51 351	6.89 364	73.48 357	38.29 353	42.38 348	68.76 351
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
Gipuma		67.86 322	65.41 323	75.18 335	92.66 310	73.45 333	66.50 353	94.52 297	53.33 348	57.80 347	66.07 350	30.81 352	89.20 346	48.15 351	78.88 317	62.90 352
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
E-PMN		53.28 328	52.56 329	55.43 341	74.43 349	47.13 357	83.63 346	76.30 357	42.23 352	42.59 351	62.22 352	28.57 356	74.40 355	31.53 354	31.51 352	44.78 353

EMVS		52.08 330	51.31 330	54.39 342	72.62 354	45.39 359	83.84 345	75.51 358	41.13 353	40.77 353	59.65 353	30.08 354	73.60 356	28.31 355	29.90 354	44.18 354

tmp_tt		51.94 331	53.82 328	46.29 344	33.73 360	45.30 360	78.32 350	67.24 360	18.02 355	50.93 349	87.05 336	52.99 344	53.11 358	70.76 329	25.29 355	40.46 355

test123		13.04 336	15.66 337	5.18 347	4.51 362	3.45 362	92.50 314	1.81 364	2.50 358	7.58 359	20.15 357	3.67 365	2.18 361	7.13 358	1.07 359	9.90 356

.test1245		65.38 323	69.22 321	53.86 343	83.89 340	59.98 350	91.89 317	93.71 314	75.06 333	73.60 334	87.67 332	55.66 339	92.60 337	58.54 344	2.96 357	9.00 357

testmvs		13.36 335	16.33 336	4.48 348	5.04 361	2.26 363	93.18 300	3.28 363	2.70 357	8.24 358	21.66 356	2.29 366	2.19 360	7.58 357	2.96 357	9.00 357

wuyk23d		25.11 333	24.57 335	26.74 346	73.98 351	39.89 361	57.88 354	9.80 362	12.27 356	10.39 357	6.97 360	7.03 362	36.44 359	25.43 356	17.39 356	3.89 359

cdsmvs_eth3d_5k		23.24 334	30.99 334	0.00 349	0.00 363	0.00 364	0.00 355	97.63 108	0.00 359	0.00 360	96.88 106	84.38 123	0.00 362	0.00 359	0.00 360	0.00 360

pcd_1.5k_mvsjas		7.39 338	9.85 339	0.00 349	0.00 363	0.00 364	0.00 355	0.00 365	0.00 359	0.00 360	0.00 361	88.65 71	0.00 362	0.00 359	0.00 360	0.00 360

sosnet-low-res		0.00 339	0.00 340	0.00 349	0.00 363	0.00 364	0.00 355	0.00 365	0.00 359	0.00 360	0.00 361	0.00 367	0.00 362	0.00 359	0.00 360	0.00 360

sosnet		0.00 339	0.00 340	0.00 349	0.00 363	0.00 364	0.00 355	0.00 365	0.00 359	0.00 360	0.00 361	0.00 367	0.00 362	0.00 359	0.00 360	0.00 360

uncertanet		0.00 339	0.00 340	0.00 349	0.00 363	0.00 364	0.00 355	0.00 365	0.00 359	0.00 360	0.00 361	0.00 367	0.00 362	0.00 359	0.00 360	0.00 360

Regformer		0.00 339	0.00 340	0.00 349	0.00 363	0.00 364	0.00 355	0.00 365	0.00 359	0.00 360	0.00 361	0.00 367	0.00 362	0.00 359	0.00 360	0.00 360

ab-mvs-re		8.06 337	10.74 338	0.00 349	0.00 363	0.00 364	0.00 355	0.00 365	0.00 359	0.00 360	96.69 115	0.00 367	0.00 362	0.00 359	0.00 360	0.00 360

uanet		0.00 339	0.00 340	0.00 349	0.00 363	0.00 364	0.00 355	0.00 365	0.00 359	0.00 360	0.00 361	0.00 367	0.00 362	0.00 359	0.00 360	0.00 360

test_part3							97.50 92		93.81 45		98.53 12		99.87 5	95.19 48

test_part2					99.28 17	95.74 3				98.10 7

sam_mvs												81.94 180

MTGPA								98.08 51

test_post1							92.81 310				16.58 359	80.53 204	97.68 260	86.20 209

test_post											17.58 358	81.76 182	98.08 201

patchmatchnet-post											90.45 306	82.65 163	98.10 198

MTMP								82.03 355

gm-plane-assit					93.22 298	78.89 324			84.82 279		93.52 270		98.64 150	87.72 179

TEST9					98.70 39	94.19 25	96.41 195	98.02 68	88.17 219	96.03 55	97.56 84	92.74 15	99.59 53

test_8					98.67 41	94.06 31	96.37 202	98.01 70	88.58 198	95.98 60	97.55 86	92.73 16	99.58 56

agg_prior					98.67 41	93.79 38		98.00 72		95.68 69			99.57 64

test_prior4						93.66 42	96.42 194

test_prior2							96.35 203		92.80 79	96.03 55	97.59 80	92.01 31		95.01 56	99.38 36

旧先验2							95.94 231		81.66 307	97.34 18			98.82 138	92.26 98

新几何2							95.79 238

原ACMM2							95.67 242

testdata2													99.67 40	85.96 216

segment_acmp												92.89 13

testdata1							95.26 263		93.10 67

plane_prior7					96.21 166	89.98 140

plane_prior6					96.10 176	90.00 136						81.32 189

plane_prior4											96.64 118

plane_prior3						90.00 136			94.46 30	91.34 154

plane_prior2							97.74 58		94.85 17

plane_prior1					96.14 174

plane_prior						89.99 138	97.24 116		94.06 38						92.16 197

n2								0.00 365

nn								0.00 365

door-mid								91.06 340

test11								97.88 84

door								91.13 339

HQP5-MVS						89.33 178

HQP-NCC					95.86 180		96.65 178		93.55 50	90.14 182

ACMP_Plane					95.86 180		96.65 178		93.55 50	90.14 182

BP-MVS														92.13 104

HQP3-MVS								97.39 138							92.10 198

HQP2-MVS												80.95 194

NP-MVS					95.99 179	89.81 147					95.87 153

MDTV_nov1_ep13			90.76 189		95.22 209	80.33 312	93.03 307	95.28 263	88.14 220	92.84 127	93.83 258	81.34 188	98.08 201	82.86 260	94.34 156

ACMMP++_ref															90.30 226

ACMMP++															91.02 216

Test By Simon												88.73 70

High-resolution multi-view benchmark