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.

DVP-MVS		89.60 1	90.35 1	87.33 34	95.27 2	71.25 51	93.49 5	92.73 49	77.33 45	92.12 4	95.78 4	80.98 3	97.40 2	89.08 4	96.41 4	93.33 65

MSP-MVS		89.51 2	89.91 3	88.30 6	94.28 21	73.46 15	92.90 11	94.11 3	80.27 12	91.35 7	94.16 29	78.35 6	96.77 15	89.59 1	94.22 50	94.67 9

DPE-MVS		89.48 3	89.98 2	88.01 10	94.80 6	72.69 28	91.59 32	94.10 5	75.90 77	92.29 2	95.66 6	81.67 1	97.38 4	87.44 14	96.34 7	93.95 35

APDe-MVS		89.15 4	89.63 4	87.73 23	94.49 14	71.69 47	93.83 2	93.96 8	75.70 80	91.06 8	96.03 1	76.84 7	97.03 9	89.09 3	95.65 19	94.47 16

SMA-MVS		89.08 5	89.23 5	88.61 2	94.25 22	73.73 7	92.40 17	93.63 13	74.77 96	92.29 2	95.97 2	74.28 22	97.24 5	88.58 7	96.91 1	94.87 7

HPM-MVS++		89.02 6	89.15 6	88.63 1	95.01 5	76.03 1	92.38 19	92.85 44	80.26 13	87.78 20	94.27 25	75.89 11	96.81 14	87.45 13	96.44 3	93.05 76

CNVR-MVS		88.93 7	89.13 7	88.33 4	94.77 7	73.82 6	90.51 49	93.00 35	80.90 9	88.06 18	94.06 33	76.43 8	96.84 12	88.48 8	95.99 9	94.34 20

SteuartSystems-ACMMP		88.72 8	88.86 8	88.32 5	92.14 62	72.96 22	93.73 3	93.67 12	80.19 14	88.10 17	94.80 9	73.76 26	97.11 7	87.51 12	95.82 14	94.90 6
Skip Steuart: Steuart Systems R&D Blog.
DeepPCF-MVS	80.84 1	88.10 9	88.56 9	86.73 45	92.24 60	69.03 89	89.57 74	93.39 23	77.53 42	89.79 10	94.12 31	78.98 5	96.58 26	85.66 19	95.72 16	94.58 12

SD-MVS		88.06 10	88.50 10	86.71 46	92.60 58	72.71 26	91.81 31	93.19 28	77.87 33	90.32 9	94.00 34	74.83 15	93.78 125	87.63 11	94.27 49	93.65 52
Zhenlong Yuan, Jiakai Cao, Zhaoxin Li, Hao Jiang and Zhaoqi Wang: SD-MVS: Segmentation-driven Deformation Multi-View Stereo with Spherical Refinement and EM optimization. AAAI2024
NCCC		88.06 10	88.01 14	88.24 7	94.41 18	73.62 8	91.22 40	92.83 45	81.50 6	85.79 31	93.47 43	73.02 32	97.00 11	84.90 23	94.94 31	94.10 26

ACMMP_NAP		88.05 12	88.08 13	87.94 13	93.70 33	73.05 19	90.86 43	93.59 14	76.27 73	88.14 16	95.09 8	71.06 46	96.67 19	87.67 10	96.37 6	94.09 27

TSAR-MVS + MP.		88.02 13	88.11 12	87.72 25	93.68 35	72.13 42	91.41 36	92.35 63	74.62 100	88.90 11	93.85 37	75.75 12	96.00 40	87.80 9	94.63 39	95.04 3
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
testtj		87.78 14	87.78 15	87.77 21	94.55 12	72.47 35	92.23 24	93.49 18	74.75 97	88.33 15	94.43 22	73.27 29	97.02 10	84.18 36	94.84 35	93.82 43

MP-MVS		87.71 15	87.64 17	87.93 16	94.36 20	73.88 4	92.71 16	92.65 53	77.57 38	83.84 60	94.40 24	72.24 38	96.28 31	85.65 20	95.30 27	93.62 54
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
MP-MVS-pluss		87.67 16	87.72 16	87.54 30	93.64 36	72.04 44	89.80 68	93.50 17	75.17 91	86.34 26	95.29 7	70.86 47	96.00 40	88.78 6	96.04 8	94.58 12
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
HFP-MVS		87.58 17	87.47 19	87.94 13	94.58 10	73.54 12	93.04 8	93.24 25	76.78 58	84.91 39	94.44 20	70.78 48	96.61 22	84.53 29	94.89 33	93.66 47

zzz-MVS		87.53 18	87.41 20	87.90 17	94.18 26	74.25 2	90.23 58	92.02 73	79.45 19	85.88 28	94.80 9	68.07 71	96.21 33	86.69 16	95.34 23	93.23 68

ACMMPR		87.44 19	87.23 23	88.08 9	94.64 8	73.59 9	93.04 8	93.20 27	76.78 58	84.66 46	94.52 15	68.81 69	96.65 20	84.53 29	94.90 32	94.00 33

APD-MVS		87.44 19	87.52 18	87.19 36	94.24 23	72.39 37	91.86 30	92.83 45	73.01 129	88.58 12	94.52 15	73.36 27	96.49 27	84.26 33	95.01 29	92.70 85
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
GST-MVS		87.42 21	87.26 21	87.89 20	94.12 28	72.97 21	92.39 18	93.43 21	76.89 55	84.68 45	93.99 35	70.67 51	96.82 13	84.18 36	95.01 29	93.90 38

region2R		87.42 21	87.20 24	88.09 8	94.63 9	73.55 10	93.03 10	93.12 30	76.73 61	84.45 49	94.52 15	69.09 66	96.70 18	84.37 32	94.83 36	94.03 30

MCST-MVS		87.37 23	87.25 22	87.73 23	94.53 13	72.46 36	89.82 66	93.82 10	73.07 127	84.86 44	92.89 55	76.22 9	96.33 29	84.89 25	95.13 28	94.40 17

#test#		87.33 24	87.13 25	87.94 13	94.58 10	73.54 12	92.34 21	93.24 25	75.23 88	84.91 39	94.44 20	70.78 48	96.61 22	83.75 39	94.89 33	93.66 47

MTAPA		87.23 25	87.00 26	87.90 17	94.18 26	74.25 2	86.58 163	92.02 73	79.45 19	85.88 28	94.80 9	68.07 71	96.21 33	86.69 16	95.34 23	93.23 68

XVS		87.18 26	86.91 29	88.00 11	94.42 16	73.33 17	92.78 12	92.99 37	79.14 21	83.67 63	94.17 28	67.45 78	96.60 24	83.06 44	94.50 42	94.07 28

HPM-MVS		87.11 27	86.98 27	87.50 32	93.88 31	72.16 41	92.19 25	93.33 24	76.07 76	83.81 61	93.95 36	69.77 60	96.01 39	85.15 21	94.66 38	94.32 21
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
CP-MVS		87.11 27	86.92 28	87.68 29	94.20 25	73.86 5	93.98 1	92.82 48	76.62 63	83.68 62	94.46 19	67.93 73	95.95 42	84.20 35	94.39 45	93.23 68

DeepC-MVS	79.81 2	87.08 29	86.88 30	87.69 28	91.16 72	72.32 40	90.31 56	93.94 9	77.12 49	82.82 73	94.23 27	72.13 40	97.09 8	84.83 26	95.37 22	93.65 52
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
DeepC-MVS_fast	79.65 3	86.91 30	86.62 33	87.76 22	93.52 38	72.37 38	91.26 37	93.04 31	76.62 63	84.22 54	93.36 45	71.44 44	96.76 16	80.82 62	95.33 25	94.16 24
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
SR-MVS		86.73 31	86.67 32	86.91 41	94.11 29	72.11 43	92.37 20	92.56 56	74.50 101	86.84 24	94.65 14	67.31 80	95.77 46	84.80 27	92.85 58	92.84 83

test_prior3		86.73 31	86.86 31	86.33 52	92.61 56	69.59 80	88.85 92	92.97 40	75.41 84	84.91 39	93.54 39	74.28 22	95.48 53	83.31 40	95.86 11	93.91 36

PGM-MVS		86.68 33	86.27 37	87.90 17	94.22 24	73.38 16	90.22 60	93.04 31	75.53 82	83.86 59	94.42 23	67.87 75	96.64 21	82.70 49	94.57 41	93.66 47

mPP-MVS		86.67 34	86.32 36	87.72 25	94.41 18	73.55 10	92.74 14	92.22 66	76.87 56	82.81 74	94.25 26	66.44 86	96.24 32	82.88 48	94.28 48	93.38 62

Regformer-2		86.63 35	86.53 34	86.95 40	89.33 110	71.24 53	88.43 105	92.05 72	82.50 1	86.88 23	90.09 108	74.45 17	95.61 49	84.38 31	90.63 79	94.01 32

CANet		86.45 36	86.10 41	87.51 31	90.09 90	70.94 58	89.70 72	92.59 55	81.78 4	81.32 90	91.43 81	70.34 53	97.23 6	84.26 33	93.36 54	94.37 18

train_agg		86.43 37	86.20 38	87.13 38	93.26 42	72.96 22	88.75 96	91.89 82	68.69 199	85.00 37	93.10 49	74.43 18	95.41 57	84.97 22	95.71 17	93.02 78

PHI-MVS		86.43 37	86.17 40	87.24 35	90.88 78	70.96 56	92.27 23	94.07 7	72.45 132	85.22 35	91.90 68	69.47 62	96.42 28	83.28 42	95.94 10	94.35 19

Regformer-1		86.41 39	86.33 35	86.64 47	89.33 110	70.93 59	88.43 105	91.39 101	82.14 3	86.65 25	90.09 108	74.39 20	95.01 75	83.97 38	90.63 79	93.97 34

CSCG		86.41 39	86.19 39	87.07 39	92.91 49	72.48 34	90.81 44	93.56 15	73.95 111	83.16 68	91.07 89	75.94 10	95.19 66	79.94 70	94.38 46	93.55 57

agg_prior1		86.22 41	86.09 42	86.62 48	92.85 50	71.94 45	88.59 102	91.78 88	68.96 196	84.41 50	93.18 48	74.94 14	94.93 76	84.75 28	95.33 25	93.01 79

APD-MVS_3200maxsize		85.97 42	85.88 43	86.22 55	92.69 54	69.53 82	91.93 29	92.99 37	73.54 121	85.94 27	94.51 18	65.80 95	95.61 49	83.04 46	92.51 63	93.53 59

canonicalmvs		85.91 43	85.87 44	86.04 59	89.84 96	69.44 87	90.45 54	93.00 35	76.70 62	88.01 19	91.23 83	73.28 28	93.91 119	81.50 56	88.80 100	94.77 8

ACMMP		85.89 44	85.39 47	87.38 33	93.59 37	72.63 30	92.74 14	93.18 29	76.78 58	80.73 99	93.82 38	64.33 103	96.29 30	82.67 50	90.69 78	93.23 68
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
CDPH-MVS		85.76 45	85.29 51	87.17 37	93.49 39	71.08 54	88.58 103	92.42 61	68.32 204	84.61 47	93.48 41	72.32 37	96.15 36	79.00 73	95.43 21	94.28 22

TSAR-MVS + GP.		85.71 46	85.33 48	86.84 42	91.34 70	72.50 33	89.07 86	87.28 206	76.41 66	85.80 30	90.22 106	74.15 25	95.37 62	81.82 54	91.88 65	92.65 89

Regformer-4		85.68 47	85.45 46	86.35 51	88.95 127	69.67 79	88.29 115	91.29 103	81.73 5	85.36 33	90.01 110	72.62 34	95.35 63	83.28 42	87.57 113	94.03 30

alignmvs		85.48 48	85.32 49	85.96 60	89.51 104	69.47 84	89.74 70	92.47 57	76.17 74	87.73 21	91.46 80	70.32 54	93.78 125	81.51 55	88.95 97	94.63 11

3Dnovator+	77.84 4	85.48 48	84.47 61	88.51 3	91.08 73	73.49 14	93.18 7	93.78 11	80.79 10	76.66 159	93.37 44	60.40 163	96.75 17	77.20 91	93.73 53	95.29 2

MSLP-MVS++		85.43 50	85.76 45	84.45 93	91.93 65	70.24 67	90.71 46	92.86 43	77.46 44	84.22 54	92.81 59	67.16 82	92.94 162	80.36 66	94.35 47	90.16 161

DELS-MVS		85.41 51	85.30 50	85.77 61	88.49 143	67.93 118	85.52 193	93.44 20	78.70 28	83.63 65	89.03 135	74.57 16	95.71 48	80.26 68	94.04 51	93.66 47
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
HPM-MVS_fast		85.35 52	84.95 56	86.57 50	93.69 34	70.58 65	92.15 27	91.62 92	73.89 114	82.67 76	94.09 32	62.60 123	95.54 52	80.93 60	92.93 56	93.57 56

Regformer-3		85.23 53	85.07 53	85.70 62	88.95 127	69.01 91	88.29 115	89.91 142	80.95 8	85.01 36	90.01 110	72.45 36	94.19 105	82.50 51	87.57 113	93.90 38

abl_6		85.23 53	84.95 56	86.07 58	92.23 61	70.48 66	90.80 45	92.08 71	73.51 122	85.26 34	94.16 29	62.75 122	95.92 43	82.46 52	91.30 73	91.81 114

MVS_111021_HR		85.14 55	84.75 58	86.32 54	91.65 68	72.70 27	85.98 178	90.33 130	76.11 75	82.08 80	91.61 75	71.36 45	94.17 107	81.02 59	92.58 62	92.08 106

casdiffmvs		85.11 56	85.14 52	85.01 76	87.20 183	65.77 153	87.75 130	92.83 45	77.84 34	84.36 53	92.38 61	72.15 39	93.93 118	81.27 58	90.48 81	95.33 1

UA-Net		85.08 57	84.96 55	85.45 64	92.07 63	68.07 116	89.78 69	90.86 115	82.48 2	84.60 48	93.20 47	69.35 63	95.22 65	71.39 138	90.88 77	93.07 75

DPM-MVS		84.93 58	84.29 62	86.84 42	90.20 88	73.04 20	87.12 145	93.04 31	69.80 174	82.85 72	91.22 84	73.06 31	96.02 38	76.72 98	94.63 39	91.46 123

baseline		84.93 58	84.98 54	84.80 85	87.30 181	65.39 160	87.30 141	92.88 42	77.62 36	84.04 58	92.26 62	71.81 41	93.96 112	81.31 57	90.30 83	95.03 4

EIA-MVS		84.90 60	84.67 59	85.59 63	89.39 108	68.66 105	88.74 98	92.64 54	79.97 17	84.10 56	85.71 221	69.32 64	95.38 59	80.82 62	91.37 71	92.72 84

CS-MVS		84.76 61	84.61 60	85.22 71	89.66 98	66.43 140	90.23 58	93.56 15	76.52 65	82.59 77	85.93 215	70.41 52	95.80 44	79.93 71	92.68 61	93.42 61

EI-MVSNet-Vis-set		84.19 62	83.81 63	85.31 66	88.18 152	67.85 119	87.66 132	89.73 147	80.05 16	82.95 69	89.59 120	70.74 50	94.82 84	80.66 65	84.72 148	93.28 67

nrg030		83.88 63	83.53 64	84.96 78	86.77 191	69.28 88	90.46 53	92.67 51	74.79 95	82.95 69	91.33 82	72.70 33	93.09 156	80.79 64	79.28 214	92.50 92

EI-MVSNet-UG-set		83.81 64	83.38 66	85.09 74	87.87 161	67.53 123	87.44 138	89.66 148	79.74 18	82.23 79	89.41 129	70.24 55	94.74 87	79.95 69	83.92 156	92.99 80

CPTT-MVS		83.73 65	83.33 67	84.92 81	93.28 41	70.86 61	92.09 28	90.38 126	68.75 198	79.57 106	92.83 57	60.60 159	93.04 160	80.92 61	91.56 69	90.86 137

EPNet		83.72 66	82.92 73	86.14 57	84.22 224	69.48 83	91.05 42	85.27 227	81.30 7	76.83 154	91.65 72	66.09 90	95.56 51	76.00 103	93.85 52	93.38 62
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
HQP_MVS		83.64 67	83.14 68	85.14 72	90.08 91	68.71 101	91.25 38	92.44 58	79.12 23	78.92 114	91.00 93	60.42 161	95.38 59	78.71 76	86.32 134	91.33 124

Effi-MVS+		83.62 68	83.08 69	85.24 69	88.38 148	67.45 124	88.89 90	89.15 164	75.50 83	82.27 78	88.28 154	69.61 61	94.45 95	77.81 85	87.84 111	93.84 42

OPM-MVS		83.50 69	82.95 72	85.14 72	88.79 135	70.95 57	89.13 85	91.52 95	77.55 41	80.96 97	91.75 70	60.71 155	94.50 94	79.67 72	86.51 132	89.97 176

Vis-MVSNet		83.46 70	82.80 75	85.43 65	90.25 87	68.74 99	90.30 57	90.13 136	76.33 72	80.87 98	92.89 55	61.00 152	94.20 104	72.45 131	90.97 75	93.35 64
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
MG-MVS		83.41 71	83.45 65	83.28 130	92.74 53	62.28 218	88.17 120	89.50 152	75.22 89	81.49 89	92.74 60	66.75 83	95.11 69	72.85 127	91.58 68	92.45 93

EPP-MVSNet		83.40 72	83.02 71	84.57 89	90.13 89	64.47 178	92.32 22	90.73 117	74.45 103	79.35 109	91.10 87	69.05 68	95.12 68	72.78 128	87.22 121	94.13 25

3Dnovator	76.31 5	83.38 73	82.31 81	86.59 49	87.94 160	72.94 25	90.64 47	92.14 70	77.21 47	75.47 181	92.83 57	58.56 170	94.72 88	73.24 125	92.71 60	92.13 105

ETV-MVS		83.31 74	82.80 75	84.82 83	89.59 100	65.59 155	88.21 118	92.68 50	74.66 99	78.96 112	86.42 207	69.06 67	95.26 64	75.54 108	90.09 87	93.62 54

MVS_Test		83.15 75	83.06 70	83.41 127	86.86 187	63.21 204	86.11 176	92.00 76	74.31 104	82.87 71	89.44 128	70.03 56	93.21 147	77.39 90	88.50 107	93.81 44

IS-MVSNet		83.15 75	82.81 74	84.18 103	89.94 94	63.30 202	91.59 32	88.46 184	79.04 25	79.49 107	92.16 63	65.10 99	94.28 98	67.71 168	91.86 66	94.95 5

DP-MVS Recon		83.11 77	82.09 83	86.15 56	94.44 15	70.92 60	88.79 94	92.20 67	70.53 162	79.17 110	91.03 92	64.12 105	96.03 37	68.39 165	90.14 86	91.50 120

PAPM_NR		83.02 78	82.41 78	84.82 83	92.47 59	66.37 142	87.93 127	91.80 86	73.82 115	77.32 145	90.66 98	67.90 74	94.90 80	70.37 146	89.48 94	93.19 72

VDD-MVS		83.01 79	82.36 80	84.96 78	91.02 75	66.40 141	88.91 89	88.11 187	77.57 38	84.39 52	93.29 46	52.19 218	93.91 119	77.05 93	88.70 102	94.57 14

MVSFormer		82.85 80	82.05 84	85.24 69	87.35 176	70.21 68	90.50 50	90.38 126	68.55 201	81.32 90	89.47 123	61.68 138	93.46 140	78.98 74	90.26 84	92.05 107

OMC-MVS		82.69 81	81.97 87	84.85 82	88.75 137	67.42 125	87.98 123	90.87 114	74.92 94	79.72 105	91.65 72	62.19 133	93.96 112	75.26 109	86.42 133	93.16 73

PVSNet_Blended_VisFu		82.62 82	81.83 89	84.96 78	90.80 80	69.76 77	88.74 98	91.70 91	69.39 182	78.96 112	88.46 149	65.47 96	94.87 83	74.42 112	88.57 103	90.24 159

MVS_111021_LR		82.61 83	82.11 82	84.11 104	88.82 132	71.58 48	85.15 196	86.16 220	74.69 98	80.47 101	91.04 90	62.29 130	90.55 225	80.33 67	90.08 88	90.20 160

HQP-MVS		82.61 83	82.02 85	84.37 95	89.33 110	66.98 133	89.17 80	92.19 68	76.41 66	77.23 148	90.23 105	60.17 164	95.11 69	77.47 88	85.99 139	91.03 131

CLD-MVS		82.31 85	81.65 90	84.29 100	88.47 144	67.73 122	85.81 184	92.35 63	75.78 78	78.33 125	86.58 202	64.01 106	94.35 96	76.05 102	87.48 118	90.79 138
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
VNet		82.21 86	82.41 78	81.62 178	90.82 79	60.93 229	84.47 212	89.78 144	76.36 71	84.07 57	91.88 69	64.71 102	90.26 227	70.68 143	88.89 98	93.66 47

diffmvs		82.10 87	81.88 88	82.76 161	83.00 250	63.78 190	83.68 229	89.76 145	72.94 130	82.02 81	89.85 113	65.96 94	90.79 221	82.38 53	87.30 120	93.71 46

LPG-MVS_test		82.08 88	81.27 93	84.50 91	89.23 118	68.76 97	90.22 60	91.94 80	75.37 86	76.64 160	91.51 77	54.29 201	94.91 78	78.44 78	83.78 157	89.83 181

FIs		82.07 89	82.42 77	81.04 193	88.80 134	58.34 253	88.26 117	93.49 18	76.93 54	78.47 122	91.04 90	69.92 58	92.34 180	69.87 152	84.97 145	92.44 94

PS-MVSNAJss		82.07 89	81.31 92	84.34 98	86.51 194	67.27 129	89.27 78	91.51 96	71.75 142	79.37 108	90.22 106	63.15 116	94.27 99	77.69 86	82.36 179	91.49 121

API-MVS		81.99 91	81.23 94	84.26 101	90.94 76	70.18 73	91.10 41	89.32 156	71.51 148	78.66 118	88.28 154	65.26 97	95.10 72	64.74 197	91.23 74	87.51 238

UniMVSNet_NR-MVSNet		81.88 92	81.54 91	82.92 149	88.46 145	63.46 198	87.13 144	92.37 62	80.19 14	78.38 123	89.14 131	71.66 43	93.05 158	70.05 149	76.46 241	92.25 100

MAR-MVS		81.84 93	80.70 100	85.27 68	91.32 71	71.53 49	89.82 66	90.92 112	69.77 175	78.50 120	86.21 211	62.36 129	94.52 93	65.36 190	92.05 64	89.77 184
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
LFMVS		81.82 94	81.23 94	83.57 122	91.89 66	63.43 200	89.84 65	81.85 270	77.04 52	83.21 66	93.10 49	52.26 217	93.43 142	71.98 133	89.95 90	93.85 40

xiu_mvs_v2_base		81.69 95	81.05 97	83.60 120	89.15 121	68.03 117	84.46 214	90.02 138	70.67 159	81.30 93	86.53 205	63.17 115	94.19 105	75.60 107	88.54 105	88.57 219

PS-MVSNAJ		81.69 95	81.02 98	83.70 119	89.51 104	68.21 114	84.28 220	90.09 137	70.79 156	81.26 94	85.62 225	63.15 116	94.29 97	75.62 106	88.87 99	88.59 218

PAPR		81.66 97	80.89 99	83.99 113	90.27 86	64.00 185	86.76 159	91.77 90	68.84 197	77.13 152	89.50 121	67.63 76	94.88 82	67.55 170	88.52 106	93.09 74

UniMVSNet (Re)		81.60 98	81.11 96	83.09 140	88.38 148	64.41 179	87.60 133	93.02 34	78.42 31	78.56 119	88.16 157	69.78 59	93.26 146	69.58 155	76.49 240	91.60 116

FC-MVSNet-test		81.52 99	82.02 85	80.03 209	88.42 147	55.97 289	87.95 125	93.42 22	77.10 50	77.38 143	90.98 95	69.96 57	91.79 194	68.46 164	84.50 150	92.33 96

VDDNet		81.52 99	80.67 101	84.05 108	90.44 84	64.13 184	89.73 71	85.91 223	71.11 152	83.18 67	93.48 41	50.54 241	93.49 139	73.40 123	88.25 109	94.54 15

ACMP	74.13 6	81.51 101	80.57 102	84.36 96	89.42 106	68.69 104	89.97 64	91.50 99	74.46 102	75.04 197	90.41 102	53.82 206	94.54 91	77.56 87	82.91 171	89.86 180
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
jason		81.39 102	80.29 109	84.70 87	86.63 193	69.90 75	85.95 179	86.77 211	63.24 252	81.07 96	89.47 123	61.08 151	92.15 186	78.33 81	90.07 89	92.05 107
jason: jason.
lupinMVS		81.39 102	80.27 110	84.76 86	87.35 176	70.21 68	85.55 189	86.41 215	62.85 258	81.32 90	88.61 144	61.68 138	92.24 184	78.41 80	90.26 84	91.83 112

test_yl		81.17 104	80.47 105	83.24 133	89.13 122	63.62 191	86.21 172	89.95 140	72.43 135	81.78 86	89.61 118	57.50 178	93.58 133	70.75 141	86.90 125	92.52 90

DCV-MVSNet		81.17 104	80.47 105	83.24 133	89.13 122	63.62 191	86.21 172	89.95 140	72.43 135	81.78 86	89.61 118	57.50 178	93.58 133	70.75 141	86.90 125	92.52 90

DU-MVS		81.12 106	80.52 104	82.90 150	87.80 164	63.46 198	87.02 148	91.87 84	79.01 26	78.38 123	89.07 133	65.02 100	93.05 158	70.05 149	76.46 241	92.20 102

PVSNet_Blended		80.98 107	80.34 107	82.90 150	88.85 129	65.40 158	84.43 216	92.00 76	67.62 207	78.11 130	85.05 238	66.02 92	94.27 99	71.52 135	89.50 93	89.01 202

mvs-test1		80.88 108	79.40 125	85.29 67	85.13 212	69.75 78	89.28 77	88.10 188	74.99 92	76.44 165	86.72 191	57.27 181	94.26 103	73.53 120	83.18 168	91.87 111

QAPM		80.88 108	79.50 123	85.03 75	88.01 159	68.97 93	91.59 32	92.00 76	66.63 219	75.15 194	92.16 63	57.70 175	95.45 55	63.52 201	88.76 101	90.66 143

1121		80.84 110	79.77 116	84.05 108	93.11 46	70.78 62	84.66 206	85.42 226	57.37 300	81.76 88	92.02 65	63.41 109	94.12 108	67.28 173	92.93 56	87.26 245

TranMVSNet+NR-MVSNet		80.84 110	80.31 108	82.42 165	87.85 162	62.33 216	87.74 131	91.33 102	80.55 11	77.99 133	89.86 112	65.23 98	92.62 169	67.05 178	75.24 262	92.30 98

UGNet		80.83 112	79.59 121	84.54 90	88.04 157	68.09 115	89.42 75	88.16 186	76.95 53	76.22 169	89.46 125	49.30 255	93.94 115	68.48 163	90.31 82	91.60 116
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+		80.81 113	79.92 113	83.47 123	88.85 129	64.51 175	85.53 191	89.39 154	70.79 156	78.49 121	85.06 237	67.54 77	93.58 133	67.03 179	86.58 130	92.32 97

XVG-OURS-SEG-HR		80.81 113	79.76 117	83.96 115	85.60 204	68.78 96	83.54 233	90.50 123	70.66 160	76.71 158	91.66 71	60.69 156	91.26 208	76.94 94	81.58 186	91.83 112

xiu_mvs_v1_base_debu		80.80 115	79.72 118	84.03 110	87.35 176	70.19 70	85.56 186	88.77 176	69.06 192	81.83 82	88.16 157	50.91 235	92.85 164	78.29 82	87.56 115	89.06 197

xiu_mvs_v1_base		80.80 115	79.72 118	84.03 110	87.35 176	70.19 70	85.56 186	88.77 176	69.06 192	81.83 82	88.16 157	50.91 235	92.85 164	78.29 82	87.56 115	89.06 197

xiu_mvs_v1_base_debi		80.80 115	79.72 118	84.03 110	87.35 176	70.19 70	85.56 186	88.77 176	69.06 192	81.83 82	88.16 157	50.91 235	92.85 164	78.29 82	87.56 115	89.06 197

ACMM	73.20 8	80.78 118	79.84 115	83.58 121	89.31 115	68.37 109	89.99 63	91.60 93	70.28 166	77.25 146	89.66 116	53.37 209	93.53 138	74.24 115	82.85 172	88.85 210
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
114514_t		80.68 119	79.51 122	84.20 102	94.09 30	67.27 129	89.64 73	91.11 109	58.75 292	74.08 207	90.72 97	58.10 172	95.04 74	69.70 153	89.42 95	90.30 157

CANet_DTU		80.61 120	79.87 114	82.83 153	85.60 204	63.17 207	87.36 139	88.65 180	76.37 70	75.88 176	88.44 150	53.51 208	93.07 157	73.30 124	89.74 92	92.25 100

VPA-MVSNet		80.60 121	80.55 103	80.76 197	88.07 156	60.80 232	86.86 153	91.58 94	75.67 81	80.24 102	89.45 127	63.34 110	90.25 228	70.51 145	79.22 215	91.23 127

PVSNet_BlendedMVS		80.60 121	80.02 111	82.36 167	88.85 129	65.40 158	86.16 174	92.00 76	69.34 184	78.11 130	86.09 214	66.02 92	94.27 99	71.52 135	82.06 181	87.39 240

AdaColmap		80.58 123	79.42 124	84.06 107	93.09 47	68.91 94	89.36 76	88.97 171	69.27 185	75.70 179	89.69 115	57.20 184	95.77 46	63.06 207	88.41 108	87.50 239

EI-MVSNet		80.52 124	79.98 112	82.12 168	84.28 222	63.19 206	86.41 166	88.95 172	74.18 108	78.69 116	87.54 171	66.62 84	92.43 175	72.57 130	80.57 198	90.74 141

XVG-OURS		80.41 125	79.23 130	83.97 114	85.64 203	69.02 90	83.03 240	90.39 125	71.09 153	77.63 139	91.49 79	54.62 200	91.35 206	75.71 104	83.47 164	91.54 118

PCF-MVS	73.52 7	80.38 126	78.84 137	85.01 76	87.71 168	68.99 92	83.65 230	91.46 100	63.00 255	77.77 137	90.28 103	66.10 89	95.09 73	61.40 223	88.22 110	90.94 135
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
X-MVStestdata		80.37 127	77.83 160	88.00 11	94.42 16	73.33 17	92.78 12	92.99 37	79.14 21	83.67 63	12.47 336	67.45 78	96.60 24	83.06 44	94.50 42	94.07 28

test_djsdf		80.30 128	79.32 128	83.27 131	83.98 229	65.37 161	90.50 50	90.38 126	68.55 201	76.19 170	88.70 140	56.44 188	93.46 140	78.98 74	80.14 205	90.97 134

v2v482		80.23 129	79.29 129	83.05 143	83.62 234	64.14 183	87.04 147	89.97 139	73.61 118	78.18 129	87.22 179	61.10 150	93.82 122	76.11 101	76.78 238	91.18 128

NR-MVSNet		80.23 129	79.38 126	82.78 159	87.80 164	63.34 201	86.31 169	91.09 110	79.01 26	72.17 225	89.07 133	67.20 81	92.81 168	66.08 185	75.65 250	92.20 102

Anonymous20240529		80.19 131	78.89 136	84.10 105	90.60 81	64.75 172	88.95 88	90.90 113	65.97 227	80.59 100	91.17 86	49.97 246	93.73 131	69.16 159	82.70 176	93.81 44

IterMVS-LS		80.06 132	79.38 126	82.11 169	85.89 199	63.20 205	86.79 156	89.34 155	74.19 107	75.45 183	86.72 191	66.62 84	92.39 177	72.58 129	76.86 234	90.75 140
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Effi-MVS+-dtu		80.03 133	78.57 142	84.42 94	85.13 212	68.74 99	88.77 95	88.10 188	74.99 92	74.97 198	83.49 255	57.27 181	93.36 143	73.53 120	80.88 192	91.18 128

v1144		80.03 133	79.03 133	83.01 145	83.78 232	64.51 175	87.11 146	90.57 121	71.96 141	78.08 132	86.20 212	61.41 143	93.94 115	74.93 110	77.23 228	90.60 146

v8		79.97 135	79.02 134	82.80 156	84.09 226	64.50 177	87.96 124	90.29 133	74.13 110	75.24 192	86.81 188	62.88 121	93.89 121	74.39 113	75.40 257	90.00 173

DI_MVS_plusplus_test		79.89 136	78.58 141	83.85 118	82.89 255	65.32 162	86.12 175	89.55 150	69.64 179	70.55 238	85.82 220	57.24 183	93.81 123	76.85 95	88.55 104	92.41 95

OpenMVS	72.83 10	79.77 137	78.33 149	84.09 106	85.17 209	69.91 74	90.57 48	90.97 111	66.70 215	72.17 225	91.91 67	54.70 198	93.96 112	61.81 220	90.95 76	88.41 223

v10		79.74 138	78.67 138	82.97 148	84.06 227	64.95 169	87.88 129	90.62 119	73.11 126	75.11 195	86.56 203	61.46 142	94.05 111	73.68 118	75.55 252	89.90 178

BH-RMVSNet		79.61 139	78.44 145	83.14 138	89.38 109	65.93 148	84.95 201	87.15 207	73.56 120	78.19 128	89.79 114	56.67 187	93.36 143	59.53 237	86.74 128	90.13 163

v1192		79.59 140	78.43 146	83.07 142	83.55 236	64.52 174	86.93 151	90.58 120	70.83 155	77.78 136	85.90 216	59.15 167	93.94 115	73.96 117	77.19 230	90.76 139

ab-mvs		79.51 141	78.97 135	81.14 191	88.46 145	60.91 230	83.84 227	89.24 161	70.36 164	79.03 111	88.87 138	63.23 114	90.21 229	65.12 192	82.57 177	92.28 99

WR-MVS		79.49 142	79.22 131	80.27 206	88.79 135	58.35 252	85.06 198	88.61 182	78.56 29	77.65 138	88.34 152	63.81 108	90.66 224	64.98 195	77.22 229	91.80 115

v144192		79.47 143	78.37 147	82.78 159	83.35 238	63.96 186	86.96 149	90.36 129	69.99 170	77.50 140	85.67 223	60.66 157	93.77 127	74.27 114	76.58 239	90.62 144

BH-untuned		79.47 143	78.60 140	82.05 170	89.19 120	65.91 149	86.07 177	88.52 183	72.18 137	75.42 184	87.69 166	61.15 149	93.54 137	60.38 230	86.83 127	86.70 258

mvs_anonymous		79.42 145	79.11 132	80.34 204	84.45 221	57.97 259	82.59 241	87.62 199	67.40 211	76.17 173	88.56 147	68.47 70	89.59 238	70.65 144	86.05 138	93.47 60

thisisatest0530		79.40 146	77.76 164	84.31 99	87.69 170	65.10 168	87.36 139	84.26 238	70.04 169	77.42 142	88.26 156	49.94 247	94.79 86	70.20 147	84.70 149	93.03 77

tttt0517		79.40 146	77.91 157	83.90 117	88.10 155	63.84 188	88.37 112	84.05 240	71.45 149	76.78 156	89.12 132	49.93 249	94.89 81	70.18 148	83.18 168	92.96 81

V42		79.38 148	78.24 151	82.83 153	81.10 283	65.50 157	85.55 189	89.82 143	71.57 147	78.21 127	86.12 213	60.66 157	93.18 151	75.64 105	75.46 255	89.81 183

jajsoiax		79.29 149	77.96 155	83.27 131	84.68 218	66.57 139	89.25 79	90.16 135	69.20 189	75.46 182	89.49 122	45.75 278	93.13 154	76.84 96	80.80 194	90.11 165

v1921920		79.22 150	78.03 154	82.80 156	83.30 240	63.94 187	86.80 155	90.33 130	69.91 172	77.48 141	85.53 226	58.44 171	93.75 129	73.60 119	76.85 235	90.71 142

TAPA-MVS	73.13 9	79.15 151	77.94 156	82.79 158	89.59 100	62.99 211	88.16 121	91.51 96	65.77 228	77.14 151	91.09 88	60.91 153	93.21 147	50.26 287	87.05 123	92.17 104
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
mvs_tets		79.13 152	77.77 163	83.22 135	84.70 217	66.37 142	89.17 80	90.19 134	69.38 183	75.40 185	89.46 125	44.17 284	93.15 152	76.78 97	80.70 196	90.14 162

UniMVSNet_ETH3D		79.10 153	78.24 151	81.70 177	86.85 188	60.24 239	87.28 142	88.79 175	74.25 106	76.84 153	90.53 101	49.48 252	91.56 200	67.98 166	82.15 180	93.29 66

CDS-MVSNet		79.07 154	77.70 165	83.17 137	87.60 171	68.23 113	84.40 218	86.20 219	67.49 209	76.36 166	86.54 204	61.54 141	90.79 221	61.86 219	87.33 119	90.49 151
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
MVSTER		79.01 155	77.88 159	82.38 166	83.07 247	64.80 171	84.08 226	88.95 172	69.01 195	78.69 116	87.17 182	54.70 198	92.43 175	74.69 111	80.57 198	89.89 179

v1240		78.99 156	77.78 162	82.64 162	83.21 242	63.54 195	86.62 162	90.30 132	69.74 178	77.33 144	85.68 222	57.04 185	93.76 128	73.13 126	76.92 232	90.62 144

Anonymous20231211		78.97 157	77.69 166	82.81 155	90.54 82	64.29 181	90.11 62	91.51 96	65.01 237	76.16 174	88.13 161	50.56 240	93.03 161	69.68 154	77.56 226	91.11 130

v7n		78.97 157	77.58 168	83.14 138	83.45 237	65.51 156	88.32 113	91.21 105	73.69 117	72.41 222	86.32 210	57.93 173	93.81 123	69.18 158	75.65 250	90.11 165

TAMVS		78.89 159	77.51 169	83.03 144	87.80 164	67.79 121	84.72 205	85.05 230	67.63 206	76.75 157	87.70 165	62.25 131	90.82 220	58.53 248	87.13 122	90.49 151

cl_fuxian		78.75 160	77.91 157	81.26 188	82.89 255	61.56 226	84.09 225	89.13 165	69.97 171	75.56 180	84.29 245	66.36 87	92.09 188	73.47 122	75.48 254	90.12 164

v148		78.72 161	77.80 161	81.47 182	82.73 259	61.96 222	86.30 170	88.08 190	73.26 125	76.18 171	85.47 228	62.46 127	92.36 179	71.92 134	73.82 275	90.09 167

VPNet		78.69 162	78.66 139	78.76 233	88.31 150	55.72 291	84.45 215	86.63 213	76.79 57	78.26 126	90.55 100	59.30 166	89.70 237	66.63 180	77.05 231	90.88 136

ET-MVSNet_ETH3D		78.63 163	76.63 187	84.64 88	86.73 192	69.47 84	85.01 199	84.61 233	69.54 180	66.51 281	86.59 200	50.16 244	91.75 195	76.26 100	84.24 154	92.69 87

anonymousdsp		78.60 164	77.15 174	82.98 147	80.51 289	67.08 131	87.24 143	89.53 151	65.66 230	75.16 193	87.19 181	52.52 211	92.25 182	77.17 92	79.34 213	89.61 187

WR-MVS_H		78.51 165	78.49 143	78.56 236	88.02 158	56.38 284	88.43 105	92.67 51	77.14 48	73.89 208	87.55 170	66.25 88	89.24 244	58.92 243	73.55 277	90.06 171

GBi-Net		78.40 166	77.40 170	81.40 184	87.60 171	63.01 208	88.39 109	89.28 157	71.63 144	75.34 187	87.28 175	54.80 194	91.11 211	62.72 208	79.57 208	90.09 167

test1		78.40 166	77.40 170	81.40 184	87.60 171	63.01 208	88.39 109	89.28 157	71.63 144	75.34 187	87.28 175	54.80 194	91.11 211	62.72 208	79.57 208	90.09 167

Vis-MVSNet (Re-imp)		78.36 168	78.45 144	78.07 244	88.64 139	51.78 308	86.70 160	79.63 291	74.14 109	75.11 195	90.83 96	61.29 146	89.75 235	58.10 252	91.60 67	92.69 87

Anonymous202405211		78.25 169	77.01 176	81.99 172	91.03 74	60.67 233	84.77 204	83.90 242	70.65 161	80.00 103	91.20 85	41.08 300	91.43 204	65.21 191	85.26 143	93.85 40

CP-MVSNet		78.22 170	78.34 148	77.84 246	87.83 163	54.54 297	87.94 126	91.17 107	77.65 35	73.48 210	88.49 148	62.24 132	88.43 258	62.19 214	74.07 270	90.55 149

BH-w/o		78.21 171	77.33 172	80.84 195	88.81 133	65.13 167	84.87 202	87.85 196	69.75 176	74.52 203	84.74 241	61.34 144	93.11 155	58.24 251	85.84 141	84.27 287

FMVSNet2		78.20 172	77.21 173	81.20 189	87.60 171	62.89 212	87.47 137	89.02 168	71.63 144	75.29 191	87.28 175	54.80 194	91.10 214	62.38 212	79.38 212	89.61 187

MVS		78.19 173	76.99 177	81.78 175	85.66 202	66.99 132	84.66 206	90.47 124	55.08 310	72.02 227	85.27 231	63.83 107	94.11 110	66.10 184	89.80 91	84.24 288

Baseline_NR-MVSNet		78.15 174	78.33 149	77.61 251	85.79 200	56.21 287	86.78 157	85.76 224	73.60 119	77.93 134	87.57 169	65.02 100	88.99 248	67.14 177	75.33 259	87.63 235

CNLPA		78.08 175	76.79 181	81.97 173	90.40 85	71.07 55	87.59 134	84.55 234	66.03 226	72.38 223	89.64 117	57.56 177	86.04 278	59.61 236	83.35 165	88.79 213

PLC	70.83 11	78.05 176	76.37 191	83.08 141	91.88 67	67.80 120	88.19 119	89.46 153	64.33 245	69.87 252	88.38 151	53.66 207	93.58 133	58.86 244	82.73 174	87.86 231
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
Fast-Effi-MVS+-dtu		78.02 177	76.49 188	82.62 163	83.16 246	66.96 135	86.94 150	87.45 204	72.45 132	71.49 233	84.17 246	54.79 197	91.58 199	67.61 169	80.31 202	89.30 193

PS-CasMVS		78.01 178	78.09 153	77.77 248	87.71 168	54.39 299	88.02 122	91.22 104	77.50 43	73.26 212	88.64 143	60.73 154	88.41 259	61.88 218	73.88 274	90.53 150

HY-MVS	69.67 12	77.95 179	77.15 174	80.36 203	87.57 175	60.21 240	83.37 235	87.78 197	66.11 223	75.37 186	87.06 186	63.27 112	90.48 226	61.38 224	82.43 178	90.40 155

eth_miper_zixun_eth		77.92 180	76.69 185	81.61 180	83.00 250	61.98 221	83.15 236	89.20 163	69.52 181	74.86 200	84.35 244	61.76 137	92.56 172	71.50 137	72.89 280	90.28 158

FMVSNet3		77.88 181	76.85 179	80.97 194	86.84 189	62.36 215	86.52 165	88.77 176	71.13 151	75.34 187	86.66 198	54.07 204	91.10 214	62.72 208	79.57 208	89.45 191

PEN-MVS		77.73 182	77.69 166	77.84 246	87.07 186	53.91 301	87.91 128	91.18 106	77.56 40	73.14 214	88.82 139	61.23 147	89.17 245	59.95 233	72.37 282	90.43 153

cl-mvsnet_		77.72 183	76.76 182	80.58 199	82.49 264	60.48 236	83.09 237	87.87 194	69.22 187	74.38 205	85.22 233	62.10 134	91.53 201	71.09 139	75.41 256	89.73 186

cl-mvsnet1		77.72 183	76.76 182	80.58 199	82.48 265	60.48 236	83.09 237	87.86 195	69.22 187	74.38 205	85.24 232	62.10 134	91.53 201	71.09 139	75.40 257	89.74 185

PAPM		77.68 185	76.40 190	81.51 181	87.29 182	61.85 223	83.78 228	89.59 149	64.74 239	71.23 234	88.70 140	62.59 124	93.66 132	52.66 277	87.03 124	89.01 202

CHOSEN 1792x2688		77.63 186	75.69 194	83.44 124	89.98 93	68.58 107	78.70 279	87.50 202	56.38 305	75.80 178	86.84 187	58.67 169	91.40 205	61.58 222	85.75 142	90.34 156

HyFIR lowres test		77.53 187	75.40 200	83.94 116	89.59 100	66.62 137	80.36 261	88.64 181	56.29 306	76.45 162	85.17 234	57.64 176	93.28 145	61.34 225	83.10 170	91.91 110

FMVSNet1		77.44 188	76.12 193	81.40 184	86.81 190	63.01 208	88.39 109	89.28 157	70.49 163	74.39 204	87.28 175	49.06 258	91.11 211	60.91 227	78.52 217	90.09 167

TR-MVS		77.44 188	76.18 192	81.20 189	88.24 151	63.24 203	84.61 210	86.40 216	67.55 208	77.81 135	86.48 206	54.10 203	93.15 152	57.75 255	82.72 175	87.20 246

1112_ss		77.40 190	76.43 189	80.32 205	89.11 126	60.41 238	83.65 230	87.72 198	62.13 266	73.05 215	86.72 191	62.58 125	89.97 232	62.11 217	80.80 194	90.59 148

thisisatest0515		77.33 191	75.38 201	83.18 136	85.27 208	63.80 189	82.11 246	83.27 253	65.06 235	75.91 175	83.84 250	49.54 251	94.27 99	67.24 175	86.19 136	91.48 122

pm-mvs1		77.25 192	76.68 186	78.93 230	84.22 224	58.62 251	86.41 166	88.36 185	71.37 150	73.31 211	88.01 162	61.22 148	89.15 246	64.24 199	73.01 279	89.03 201

LCM-MVSNet-Re		77.05 193	76.94 178	77.36 254	87.20 183	51.60 309	80.06 264	80.46 283	75.20 90	67.69 267	86.72 191	62.48 126	88.98 249	63.44 203	89.25 96	91.51 119

DTE-MVSNet		76.99 194	76.80 180	77.54 253	86.24 196	53.06 305	87.52 135	90.66 118	77.08 51	72.50 220	88.67 142	60.48 160	89.52 239	57.33 259	70.74 293	90.05 172

baseline1		76.98 195	76.75 184	77.66 249	88.13 153	55.66 293	85.12 197	81.89 268	73.04 128	76.79 155	88.90 136	62.43 128	87.78 266	63.30 205	71.18 291	89.55 190

LS3D		76.95 196	74.82 207	83.37 128	90.45 83	67.36 128	89.15 84	86.94 209	61.87 268	69.52 255	90.61 99	51.71 229	94.53 92	46.38 307	86.71 129	88.21 225

GA-MVS		76.87 197	75.17 205	81.97 173	82.75 258	62.58 213	81.44 255	86.35 218	72.16 139	74.74 201	82.89 260	46.20 273	92.02 189	68.85 162	81.09 190	91.30 126

DP-MVS		76.78 198	74.57 209	83.42 125	93.29 40	69.46 86	88.55 104	83.70 244	63.98 249	70.20 243	88.89 137	54.01 205	94.80 85	46.66 304	81.88 184	86.01 270

cascas		76.72 199	74.64 208	82.99 146	85.78 201	65.88 150	82.33 244	89.21 162	60.85 274	72.74 217	81.02 278	47.28 265	93.75 129	67.48 171	85.02 144	89.34 192

1314		76.53 200	75.30 204	80.21 207	83.93 230	62.32 217	84.66 206	88.81 174	60.23 278	70.16 246	84.07 248	55.30 192	90.73 223	67.37 172	83.21 167	87.59 237

thres100view900		76.50 201	75.55 197	79.33 223	89.52 103	56.99 273	85.83 183	83.23 254	73.94 112	76.32 167	87.12 183	51.89 226	91.95 190	48.33 295	83.75 159	89.07 195

thres600view7		76.50 201	75.44 198	79.68 216	89.40 107	57.16 270	85.53 191	83.23 254	73.79 116	76.26 168	87.09 184	51.89 226	91.89 193	48.05 300	83.72 162	90.00 173

thres400		76.50 201	75.37 202	79.86 212	89.13 122	57.65 265	85.17 194	83.60 245	73.41 123	76.45 162	86.39 208	52.12 219	91.95 190	48.33 295	83.75 159	90.00 173

tfpn200view9		76.42 204	75.37 202	79.55 222	89.13 122	57.65 265	85.17 194	83.60 245	73.41 123	76.45 162	86.39 208	52.12 219	91.95 190	48.33 295	83.75 159	89.07 195

Test_1112_low_res		76.40 205	75.44 198	79.27 224	89.28 116	58.09 255	81.69 250	87.07 208	59.53 285	72.48 221	86.67 197	61.30 145	89.33 242	60.81 229	80.15 204	90.41 154

F-COLMAP		76.38 206	74.33 214	82.50 164	89.28 116	66.95 136	88.41 108	89.03 167	64.05 247	66.83 277	88.61 144	46.78 268	92.89 163	57.48 256	78.55 216	87.67 234

LTVRE_ROB	69.57 13	76.25 207	74.54 211	81.41 183	88.60 140	64.38 180	79.24 272	89.12 166	70.76 158	69.79 254	87.86 163	49.09 257	93.20 149	56.21 265	80.16 203	86.65 259
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
MVP-Stereo		76.12 208	74.46 213	81.13 192	85.37 207	69.79 76	84.42 217	87.95 192	65.03 236	67.46 269	85.33 230	53.28 210	91.73 197	58.01 253	83.27 166	81.85 307
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
XVG-ACMP-BASELINE		76.11 209	74.27 215	81.62 178	83.20 243	64.67 173	83.60 232	89.75 146	69.75 176	71.85 228	87.09 184	32.78 321	92.11 187	69.99 151	80.43 201	88.09 227

ACMH+	68.96 14	76.01 210	74.01 216	82.03 171	88.60 140	65.31 163	88.86 91	87.55 200	70.25 167	67.75 266	87.47 173	41.27 298	93.19 150	58.37 249	75.94 246	87.60 236

ACMH	67.68 16	75.89 211	73.93 217	81.77 176	88.71 138	66.61 138	88.62 101	89.01 169	69.81 173	66.78 278	86.70 196	41.95 297	91.51 203	55.64 266	78.14 222	87.17 247
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
IB-MVS	68.01 15	75.85 212	73.36 223	83.31 129	84.76 216	66.03 145	83.38 234	85.06 229	70.21 168	69.40 256	81.05 277	45.76 277	94.66 89	65.10 193	75.49 253	89.25 194
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
testing_2		75.73 213	73.34 224	82.89 152	77.37 310	65.22 164	84.10 224	90.54 122	69.09 191	60.46 307	81.15 276	40.48 302	92.84 167	76.36 99	80.54 200	90.60 146

baseline2		75.70 214	73.83 220	81.30 187	83.26 241	61.79 224	82.57 242	80.65 279	66.81 213	66.88 275	83.42 256	57.86 174	92.19 185	63.47 202	79.57 208	89.91 177

WTY-MVS		75.65 215	75.68 195	75.57 270	86.40 195	56.82 275	77.92 286	82.40 263	65.10 234	76.18 171	87.72 164	63.13 119	80.90 301	60.31 231	81.96 182	89.00 204

thres200		75.55 216	74.47 212	78.82 232	87.78 167	57.85 262	83.07 239	83.51 248	72.44 134	75.84 177	84.42 243	52.08 221	91.75 195	47.41 302	83.64 163	86.86 254

EPNet_dtu		75.46 217	74.86 206	77.23 258	82.57 262	54.60 296	86.89 152	83.09 257	71.64 143	66.25 283	85.86 218	55.99 189	88.04 263	54.92 268	86.55 131	89.05 200
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
IterMVS-SCA-FT		75.43 218	73.87 219	80.11 208	82.69 260	64.85 170	81.57 253	83.47 250	69.16 190	70.49 240	84.15 247	51.95 224	88.15 261	69.23 157	72.14 285	87.34 242

XXY-MVS		75.41 219	75.56 196	74.96 275	83.59 235	57.82 263	80.59 260	83.87 243	66.54 220	74.93 199	88.31 153	63.24 113	80.09 304	62.16 215	76.85 235	86.97 252

TransMVSNet (Re)		75.39 220	74.56 210	77.86 245	85.50 206	57.10 272	86.78 157	86.09 222	72.17 138	71.53 232	87.34 174	63.01 120	89.31 243	56.84 262	61.83 313	87.17 247

CostFormer		75.24 221	73.90 218	79.27 224	82.65 261	58.27 254	80.80 256	82.73 261	61.57 269	75.33 190	83.13 258	55.52 190	91.07 217	64.98 195	78.34 221	88.45 221

D2MVS		74.82 222	73.21 225	79.64 219	79.81 296	62.56 214	80.34 262	87.35 205	64.37 244	68.86 259	82.66 264	46.37 270	90.10 231	67.91 167	81.24 189	86.25 263

pmmvs6		74.69 223	73.39 222	78.61 235	81.38 278	57.48 268	86.64 161	87.95 192	64.99 238	70.18 244	86.61 199	50.43 242	89.52 239	62.12 216	70.18 295	88.83 211

PatchFormer-LS_test		74.50 224	73.05 228	78.86 231	82.95 253	59.55 246	81.65 251	82.30 264	67.44 210	71.62 231	78.15 300	52.34 215	88.92 253	65.05 194	75.90 247	88.12 226

tfpnnormal		74.39 225	73.16 226	78.08 243	86.10 198	58.05 256	84.65 209	87.53 201	70.32 165	71.22 235	85.63 224	54.97 193	89.86 233	43.03 316	75.02 263	86.32 262

IterMVS		74.29 226	72.94 229	78.35 240	81.53 275	63.49 197	81.58 252	82.49 262	68.06 205	69.99 249	83.69 253	51.66 230	85.54 281	65.85 187	71.64 288	86.01 270
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
OurMVSNet-221017-0		74.26 227	72.42 233	79.80 214	83.76 233	59.59 244	85.92 181	86.64 212	66.39 221	66.96 274	87.58 168	39.46 305	91.60 198	65.76 188	69.27 297	88.22 224

SCA		74.22 228	72.33 234	79.91 211	84.05 228	62.17 219	79.96 266	79.29 293	66.30 222	72.38 223	80.13 286	51.95 224	88.60 256	59.25 239	77.67 225	88.96 206

miper_lstm_enhance		74.11 229	73.11 227	77.13 259	80.11 292	59.62 243	72.23 308	86.92 210	66.76 214	70.40 241	82.92 259	56.93 186	82.92 295	69.06 160	72.63 281	88.87 209

EG-PatchMatch MVS		74.04 230	71.82 238	80.71 198	84.92 215	67.42 125	85.86 182	88.08 190	66.04 225	64.22 295	83.85 249	35.10 319	92.56 172	57.44 257	80.83 193	82.16 306

pmmvs4		74.03 231	71.91 236	80.39 202	81.96 270	68.32 110	81.45 254	82.14 266	59.32 286	69.87 252	85.13 235	52.40 214	88.13 262	60.21 232	74.74 266	84.73 284

MS-PatchMatch		73.83 232	72.67 230	77.30 256	83.87 231	66.02 146	81.82 247	84.66 232	61.37 272	68.61 262	82.82 262	47.29 264	88.21 260	59.27 238	84.32 153	77.68 319

DWT-MVSNet_test		73.70 233	71.86 237	79.21 226	82.91 254	58.94 248	82.34 243	82.17 265	65.21 232	71.05 237	78.31 297	44.21 283	90.17 230	63.29 206	77.28 227	88.53 220

sss		73.60 234	73.64 221	73.51 285	82.80 257	55.01 295	76.12 292	81.69 271	62.47 263	74.68 202	85.85 219	57.32 180	78.11 311	60.86 228	80.93 191	87.39 240

SixPastTwentyTwo		73.37 235	71.26 244	79.70 215	85.08 214	57.89 261	85.57 185	83.56 247	71.03 154	65.66 285	85.88 217	42.10 295	92.57 171	59.11 241	63.34 312	88.65 217

CR-MVSNet		73.37 235	71.27 243	79.67 217	81.32 281	65.19 165	75.92 294	80.30 285	59.92 281	72.73 218	81.19 274	52.50 212	86.69 272	59.84 234	77.71 223	87.11 250

MSDG		73.36 237	70.99 245	80.49 201	84.51 220	65.80 151	80.71 258	86.13 221	65.70 229	65.46 286	83.74 252	44.60 281	90.91 219	51.13 282	76.89 233	84.74 283

tpm2		73.26 238	71.46 240	78.63 234	83.34 239	56.71 278	80.65 259	80.40 284	56.63 304	73.55 209	82.02 271	51.80 228	91.24 209	56.35 264	78.42 220	87.95 228

RPSCF		73.23 239	71.46 240	78.54 237	82.50 263	59.85 241	82.18 245	82.84 260	58.96 289	71.15 236	89.41 129	45.48 280	84.77 286	58.82 245	71.83 287	91.02 133

PatchmatchNet		73.12 240	71.33 242	78.49 239	83.18 244	60.85 231	79.63 268	78.57 295	64.13 246	71.73 229	79.81 291	51.20 233	85.97 279	57.40 258	76.36 243	88.66 216
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
COLMAP_ROB	66.92 17	73.01 241	70.41 250	80.81 196	87.13 185	65.63 154	88.30 114	84.19 239	62.96 256	63.80 298	87.69 166	38.04 311	92.56 172	46.66 304	74.91 264	84.24 288
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
CVMVSNet		72.99 242	72.58 231	74.25 282	84.28 222	50.85 314	86.41 166	83.45 251	44.56 321	73.23 213	87.54 171	49.38 253	85.70 280	65.90 186	78.44 219	86.19 265

test-LLR		72.94 243	72.43 232	74.48 279	81.35 279	58.04 257	78.38 280	77.46 301	66.66 216	69.95 250	79.00 295	48.06 261	79.24 305	66.13 182	84.83 146	86.15 266

test_0402		72.79 244	70.44 249	79.84 213	88.13 153	65.99 147	85.93 180	84.29 236	65.57 231	67.40 271	85.49 227	46.92 267	92.61 170	35.88 325	74.38 269	80.94 310

MVS_0304		72.48 245	70.89 247	77.24 257	82.20 267	59.68 242	84.11 223	83.49 249	67.10 212	66.87 276	80.59 282	35.00 320	87.40 268	59.07 242	79.58 207	84.63 285

tpmrst		72.39 246	72.13 235	73.18 287	80.54 288	49.91 317	79.91 267	79.08 294	63.11 253	71.69 230	79.95 288	55.32 191	82.77 296	65.66 189	73.89 273	86.87 253

PatchMatch-RL		72.38 247	70.90 246	76.80 262	88.60 140	67.38 127	79.53 269	76.17 307	62.75 260	69.36 257	82.00 272	45.51 279	84.89 285	53.62 273	80.58 197	78.12 318

tpm		72.37 248	71.71 239	74.35 281	82.19 268	52.00 306	79.22 273	77.29 303	64.56 241	72.95 216	83.68 254	51.35 231	83.26 294	58.33 250	75.80 248	87.81 232

PVSNet	64.34 18	72.08 249	70.87 248	75.69 268	86.21 197	56.44 282	74.37 304	80.73 278	62.06 267	70.17 245	82.23 269	42.86 290	83.31 293	54.77 269	84.45 152	87.32 243

RPMNet		71.62 250	68.94 258	79.67 217	81.32 281	65.19 165	75.92 294	78.30 297	57.60 298	72.73 218	76.45 309	52.30 216	86.69 272	48.14 299	77.71 223	87.11 250

pmmvs5		71.55 251	70.20 252	75.61 269	77.83 307	56.39 283	81.74 249	80.89 275	57.76 296	67.46 269	84.49 242	49.26 256	85.32 284	57.08 261	75.29 260	85.11 280

test-mter		71.41 252	70.39 251	74.48 279	81.35 279	58.04 257	78.38 280	77.46 301	60.32 277	69.95 250	79.00 295	36.08 317	79.24 305	66.13 182	84.83 146	86.15 266

K. test v3		71.19 253	68.51 260	79.21 226	83.04 249	57.78 264	84.35 219	76.91 305	72.90 131	62.99 301	82.86 261	39.27 306	91.09 216	61.65 221	52.66 325	88.75 214

tpmvs		71.09 254	69.29 255	76.49 263	82.04 269	56.04 288	78.92 277	81.37 274	64.05 247	67.18 273	78.28 298	49.74 250	89.77 234	49.67 290	72.37 282	83.67 293

AllTest		70.96 255	68.09 266	79.58 220	85.15 210	63.62 191	84.58 211	79.83 289	62.31 264	60.32 308	86.73 189	32.02 323	88.96 251	50.28 285	71.57 289	86.15 266

Patchmtry		70.74 256	69.16 256	75.49 272	80.72 285	54.07 300	74.94 303	80.30 285	58.34 293	70.01 247	81.19 274	52.50 212	86.54 274	53.37 274	71.09 292	85.87 273

MIMVSNet		70.69 257	69.30 254	74.88 276	84.52 219	56.35 285	75.87 296	79.42 292	64.59 240	67.76 265	82.41 266	41.10 299	81.54 300	46.64 306	81.34 187	86.75 257

tpm cat1		70.57 258	68.31 262	77.35 255	82.41 266	57.95 260	78.08 284	80.22 287	52.04 316	68.54 263	77.66 304	52.00 223	87.84 265	51.77 278	72.07 286	86.25 263

OpenMVS_ROB	64.09 19	70.56 259	68.19 263	77.65 250	80.26 290	59.41 247	85.01 199	82.96 259	58.76 291	65.43 287	82.33 267	37.63 313	91.23 210	45.34 312	76.03 245	82.32 304

pmmvs-eth3d		70.50 260	67.83 270	78.52 238	77.37 310	66.18 144	81.82 247	81.51 272	58.90 290	63.90 297	80.42 284	42.69 291	86.28 277	58.56 247	65.30 309	83.11 299

USDC		70.33 261	68.37 261	76.21 265	80.60 287	56.23 286	79.19 274	86.49 214	60.89 273	61.29 304	85.47 228	31.78 325	89.47 241	53.37 274	76.21 244	82.94 303

Patchmatch-RL test		70.24 262	67.78 272	77.61 251	77.43 309	59.57 245	71.16 310	70.33 319	62.94 257	68.65 261	72.77 316	50.62 239	85.49 282	69.58 155	66.58 306	87.77 233

CMPMVS	51.72 21	70.19 263	68.16 264	76.28 264	73.15 324	57.55 267	79.47 270	83.92 241	48.02 320	56.48 319	84.81 239	43.13 288	86.42 276	62.67 211	81.81 185	84.89 281
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
ppachtmachnet_test		70.04 264	67.34 275	78.14 242	79.80 297	61.13 227	79.19 274	80.59 280	59.16 288	65.27 288	79.29 292	46.75 269	87.29 269	49.33 291	66.72 304	86.00 272

gg-mvs-nofinetune		69.95 265	67.96 267	75.94 266	83.07 247	54.51 298	77.23 289	70.29 320	63.11 253	70.32 242	62.33 323	43.62 286	88.69 255	53.88 272	87.76 112	84.62 286

TESTMET0.1,1		69.89 266	69.00 257	72.55 288	79.27 304	56.85 274	78.38 280	74.71 313	57.64 297	68.09 264	77.19 306	37.75 312	76.70 316	63.92 200	84.09 155	84.10 291

FMVSNet5		69.50 267	67.96 267	74.15 283	82.97 252	55.35 294	80.01 265	82.12 267	62.56 262	63.02 299	81.53 273	36.92 314	81.92 298	48.42 294	74.06 271	85.17 279

PMMVS		69.34 268	68.67 259	71.35 294	75.67 316	62.03 220	75.17 298	73.46 315	50.00 319	68.68 260	79.05 293	52.07 222	78.13 310	61.16 226	82.77 173	73.90 322

our_test_3		69.14 269	67.00 276	75.57 270	79.80 297	58.80 249	77.96 285	77.81 299	59.55 284	62.90 302	78.25 299	47.43 263	83.97 288	51.71 279	67.58 303	83.93 292

EPMVS		69.02 270	68.16 264	71.59 290	79.61 300	49.80 319	77.40 288	66.93 327	62.82 259	70.01 247	79.05 293	45.79 276	77.86 313	56.58 263	75.26 261	87.13 249

Anonymous20231206		68.60 271	67.80 271	71.02 296	80.23 291	50.75 315	78.30 283	80.47 282	56.79 303	66.11 284	82.63 265	46.35 271	78.95 307	43.62 315	75.70 249	83.36 296

MIMVSNet1		68.58 272	66.78 278	73.98 284	80.07 293	51.82 307	80.77 257	84.37 235	64.40 243	59.75 311	82.16 270	36.47 315	83.63 291	42.73 317	70.33 294	86.48 261

EU-MVSNet		68.53 273	67.61 274	71.31 295	78.51 306	47.01 323	84.47 212	84.27 237	42.27 322	66.44 282	84.79 240	40.44 303	83.76 289	58.76 246	68.54 302	83.17 297

PatchT		68.46 274	67.85 269	70.29 298	80.70 286	43.93 326	72.47 307	74.88 310	60.15 279	70.55 238	76.57 308	49.94 247	81.59 299	50.58 283	74.83 265	85.34 276

test0.0.03 1		68.00 275	67.69 273	68.90 303	77.55 308	47.43 321	75.70 297	72.95 317	66.66 216	66.56 279	82.29 268	48.06 261	75.87 320	44.97 313	74.51 268	83.41 295

TDRefinement		67.49 276	64.34 284	76.92 260	73.47 322	61.07 228	84.86 203	82.98 258	59.77 282	58.30 314	85.13 235	26.06 327	87.89 264	47.92 301	60.59 317	81.81 308

test_normal		67.47 277	63.56 287	79.18 228	72.78 325	55.71 292	40.72 333	90.78 116	72.12 140	48.43 326	65.82 321	32.32 322	92.25 182	72.25 132	76.85 235	89.59 189

test20.03		67.45 278	66.95 277	68.94 302	75.48 318	44.84 325	77.50 287	77.67 300	66.66 216	63.01 300	83.80 251	47.02 266	78.40 309	42.53 318	68.86 301	83.58 294

UnsupCasMVSNet_eth		67.33 279	65.99 280	71.37 292	73.48 321	51.47 311	75.16 299	85.19 228	65.20 233	60.78 306	80.93 281	42.35 292	77.20 315	57.12 260	53.69 324	85.44 275

TinyColmap		67.30 280	64.81 282	74.76 278	81.92 271	56.68 279	80.29 263	81.49 273	60.33 276	56.27 320	83.22 257	24.77 328	87.66 267	45.52 310	69.47 296	79.95 314

dp		66.80 281	65.43 281	70.90 297	79.74 299	48.82 320	75.12 301	74.77 311	59.61 283	64.08 296	77.23 305	42.89 289	80.72 302	48.86 293	66.58 306	83.16 298

MDA-MVSNet-bldmvs		66.68 282	63.66 286	75.75 267	79.28 303	60.56 235	73.92 305	78.35 296	64.43 242	50.13 325	79.87 290	44.02 285	83.67 290	46.10 308	56.86 320	83.03 301

testgi		66.67 283	66.53 279	67.08 308	75.62 317	41.69 329	75.93 293	76.50 306	66.11 223	65.20 291	86.59 200	35.72 318	74.71 324	43.71 314	73.38 278	84.84 282

CHOSEN 280x420		66.51 284	64.71 283	71.90 289	81.45 276	63.52 196	57.98 329	68.95 326	53.57 312	62.59 303	76.70 307	46.22 272	75.29 323	55.25 267	79.68 206	76.88 321

PM-MVS		66.41 285	64.14 285	73.20 286	73.92 319	56.45 281	78.97 276	64.96 331	63.88 251	64.72 292	80.24 285	19.84 332	83.44 292	66.24 181	64.52 311	79.71 315

JIA-IIPM		66.32 286	62.82 293	76.82 261	77.09 312	61.72 225	65.34 324	75.38 308	58.04 295	64.51 293	62.32 324	42.05 296	86.51 275	51.45 281	69.22 298	82.21 305

ADS-MVSNet2		66.20 287	63.33 288	74.82 277	79.92 294	58.75 250	67.55 321	75.19 309	53.37 313	65.25 289	75.86 310	42.32 293	80.53 303	41.57 319	68.91 299	85.18 277

YYNet1		65.03 288	62.91 291	71.38 291	75.85 315	56.60 280	69.12 318	74.66 314	57.28 301	54.12 321	77.87 302	45.85 275	74.48 325	49.95 288	61.52 315	83.05 300

MDA-MVSNet_test_wron		65.03 288	62.92 290	71.37 292	75.93 314	56.73 276	69.09 319	74.73 312	57.28 301	54.03 322	77.89 301	45.88 274	74.39 326	49.89 289	61.55 314	82.99 302

Patchmatch-test		64.82 290	63.24 289	69.57 300	79.42 302	49.82 318	63.49 327	69.05 325	51.98 317	59.95 310	80.13 286	50.91 235	70.98 329	40.66 321	73.57 276	87.90 230

ADS-MVSNet		64.36 291	62.88 292	68.78 305	79.92 294	47.17 322	67.55 321	71.18 318	53.37 313	65.25 289	75.86 310	42.32 293	73.99 327	41.57 319	68.91 299	85.18 277

LF4IMVS		64.02 292	62.19 294	69.50 301	70.90 327	53.29 304	76.13 291	77.18 304	52.65 315	58.59 312	80.98 279	23.55 329	76.52 317	53.06 276	66.66 305	78.68 317

UnsupCasMVSNet_bld		63.70 293	61.53 296	70.21 299	73.69 320	51.39 312	72.82 306	81.89 268	55.63 308	57.81 315	71.80 318	38.67 308	78.61 308	49.26 292	52.21 326	80.63 311

new-patchmatchnet		61.73 294	61.73 295	61.70 311	72.74 326	24.50 339	69.16 317	78.03 298	61.40 270	56.72 318	75.53 312	38.42 309	76.48 318	45.95 309	57.67 319	84.13 290

PVSNet_0	57.27 20	61.67 295	59.27 297	68.85 304	79.61 300	57.44 269	68.01 320	73.44 316	55.93 307	58.54 313	70.41 319	44.58 282	77.55 314	47.01 303	35.91 329	71.55 324

MVS-HIRNet		59.14 296	57.67 298	63.57 310	81.65 273	43.50 327	71.73 309	65.06 330	39.59 326	51.43 324	57.73 327	38.34 310	82.58 297	39.53 322	73.95 272	64.62 327

pmmvs3		57.79 297	54.26 300	68.37 306	64.02 331	56.72 277	75.12 301	65.17 329	40.20 324	52.93 323	69.86 320	20.36 331	75.48 322	45.45 311	55.25 323	72.90 323

DSMNet-mixed		57.77 298	56.90 299	60.38 312	67.70 329	35.61 332	69.18 316	53.97 334	32.30 331	57.49 316	79.88 289	40.39 304	68.57 331	38.78 323	72.37 282	76.97 320

LCM-MVSNet		54.25 299	49.68 304	67.97 307	53.73 334	45.28 324	66.85 323	80.78 277	35.96 328	39.45 329	62.23 325	8.70 340	78.06 312	48.24 298	51.20 327	80.57 312

FPMVS		53.68 300	51.64 302	59.81 313	65.08 330	51.03 313	69.48 315	69.58 323	41.46 323	40.67 328	72.32 317	16.46 335	70.00 330	24.24 330	65.42 308	58.40 328

N_pmnet		52.79 301	53.26 301	51.40 317	78.99 305	7.68 342	69.52 314	3.89 342	51.63 318	57.01 317	74.98 313	40.83 301	65.96 332	37.78 324	64.67 310	80.56 313

new_pmnet		50.91 302	50.29 303	52.78 316	68.58 328	34.94 334	63.71 326	56.63 333	39.73 325	44.95 327	65.47 322	21.93 330	58.48 333	34.98 326	56.62 321	64.92 326

ANet_high		50.57 303	46.10 305	63.99 309	48.67 337	39.13 330	70.99 312	80.85 276	61.39 271	31.18 331	57.70 328	17.02 334	73.65 328	31.22 327	15.89 335	79.18 316

Gipuma		45.18 304	41.86 306	55.16 315	77.03 313	51.52 310	32.50 336	80.52 281	32.46 330	27.12 332	35.02 332	9.52 339	75.50 321	22.31 331	60.21 318	38.45 331
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PMVS	37.38 22	44.16 305	40.28 307	55.82 314	40.82 339	42.54 328	65.12 325	63.99 332	34.43 329	24.48 333	57.12 329	3.92 342	76.17 319	17.10 333	55.52 322	48.75 329
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
PMMVS2		40.82 306	38.86 308	46.69 318	53.84 333	16.45 340	48.61 332	49.92 335	37.49 327	31.67 330	60.97 326	8.14 341	56.42 334	28.42 328	30.72 330	67.19 325

E-PMN		31.77 307	30.64 309	35.15 320	52.87 335	27.67 336	57.09 330	47.86 336	24.64 332	16.40 337	33.05 333	11.23 337	54.90 335	14.46 335	18.15 333	22.87 333

EMVS		30.81 308	29.65 310	34.27 321	50.96 336	25.95 338	56.58 331	46.80 337	24.01 333	15.53 338	30.68 334	12.47 336	54.43 336	12.81 336	17.05 334	22.43 334

MVE	26.22 23	30.37 309	25.89 312	43.81 319	44.55 338	35.46 333	28.87 337	39.07 338	18.20 334	18.58 336	40.18 331	2.68 343	47.37 337	17.07 334	23.78 332	48.60 330
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
cdsmvs_eth3d_5k		19.96 310	26.61 311	0.00 327	0.00 344	0.00 345	0.00 338	89.26 160	0.00 340	0.00 342	88.61 144	61.62 140	0.00 343	0.00 340	0.00 340	0.00 339

tmp_tt		18.61 311	21.40 313	10.23 324	4.82 341	10.11 341	34.70 335	30.74 340	1.48 337	23.91 335	26.07 335	28.42 326	13.41 340	27.12 329	15.35 336	7.17 335

wuyk23d		16.82 312	15.94 314	19.46 323	58.74 332	31.45 335	39.22 334	3.74 343	6.84 336	6.04 339	2.70 339	1.27 344	24.29 339	10.54 337	14.40 337	2.63 336

ab-mvs-re		7.23 313	9.64 315	0.00 327	0.00 344	0.00 345	0.00 338	0.00 346	0.00 340	0.00 342	86.72 191	0.00 347	0.00 343	0.00 340	0.00 340	0.00 339

test123		6.12 314	8.11 316	0.14 325	0.06 343	0.09 343	71.05 311	0.03 345	0.04 339	0.25 341	1.30 341	0.05 345	0.03 342	0.21 339	0.01 339	0.29 337

testmvs		6.04 315	8.02 317	0.10 326	0.08 342	0.03 344	69.74 313	0.04 344	0.05 338	0.31 340	1.68 340	0.02 346	0.04 341	0.24 338	0.02 338	0.25 338

pcd_1.5k_mvsjas		5.26 316	7.02 318	0.00 327	0.00 344	0.00 345	0.00 338	0.00 346	0.00 340	0.00 342	0.00 342	63.15 116	0.00 343	0.00 340	0.00 340	0.00 339

uanet_test		0.00 317	0.00 319	0.00 327	0.00 344	0.00 345	0.00 338	0.00 346	0.00 340	0.00 342	0.00 342	0.00 347	0.00 343	0.00 340	0.00 340	0.00 339

sosnet-low-res		0.00 317	0.00 319	0.00 327	0.00 344	0.00 345	0.00 338	0.00 346	0.00 340	0.00 342	0.00 342	0.00 347	0.00 343	0.00 340	0.00 340	0.00 339

sosnet		0.00 317	0.00 319	0.00 327	0.00 344	0.00 345	0.00 338	0.00 346	0.00 340	0.00 342	0.00 342	0.00 347	0.00 343	0.00 340	0.00 340	0.00 339

uncertanet		0.00 317	0.00 319	0.00 327	0.00 344	0.00 345	0.00 338	0.00 346	0.00 340	0.00 342	0.00 342	0.00 347	0.00 343	0.00 340	0.00 340	0.00 339

Regformer		0.00 317	0.00 319	0.00 327	0.00 344	0.00 345	0.00 338	0.00 346	0.00 340	0.00 342	0.00 342	0.00 347	0.00 343	0.00 340	0.00 340	0.00 339

uanet		0.00 317	0.00 319	0.00 327	0.00 344	0.00 345	0.00 338	0.00 346	0.00 340	0.00 342	0.00 342	0.00 347	0.00 343	0.00 340	0.00 340	0.00 339

9.14			88.26 11		92.84 52		91.52 35	94.75 1	73.93 113	88.57 13	94.67 12	75.57 13	95.79 45	86.77 15	95.76 15

save filter2										88.41 14	94.67 12	72.46 35	94.59 90	86.67 18	95.86 11	91.94 109

save fliter					93.80 32	72.35 39	90.47 52	91.17 107	74.31 104

test_0728_THIRD									78.38 32	92.12 4	95.78 4	81.46 2	97.40 2	89.42 2	96.57 2	94.67

High-resolution multi-view benchmark