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.

OPU-MVS				99.49 2	99.64 20	98.51 2	99.77 9				99.19 32	95.12 6	99.97 20	99.90 1	99.92 3	99.99 1

SED-MVS		98.18 2	98.10 4	98.41 14	99.63 21	95.24 21	99.77 9	97.72 73	94.17 24	99.30 4	99.54 3	93.32 15	99.98 10	99.70 2	99.81 19	99.99 1

test_241102_TWO								97.72 73	94.17 24	99.23 6	99.54 3	93.14 20	99.98 10	99.70 2	99.82 15	99.99 1

IU-MVS					99.63 21	95.38 19		97.73 71	95.54 15	99.54 1				99.69 4	99.81 19	99.99 1

DVP-MVS		98.07 6	98.00 5	98.29 15	99.66 15	95.20 26	99.72 14	97.47 126	93.95 29	99.07 8	99.46 11	93.18 18	99.97 20	99.64 5	99.82 15	99.69 60
Zhenlong Yuan, Jinguo Luo, Fei Shen, Zhaoxin Li, Cong Liu, Tianlu Mao, Zhaoqi Wang: DVP-MVS: Synergize Depth-Edge and Visibility Prior for Multi-View Stereo. AAAI2025
test_0728_SECOND				98.77 5	99.66 15	96.37 11	99.72 14	97.68 80					99.98 10	99.64 5	99.82 15	99.96 8

DPE-MVS		98.11 5	98.00 5	98.44 13	99.50 43	95.39 18	99.29 66	97.72 73	94.50 20	98.64 20	99.54 3	93.32 15	99.97 20	99.58 7	99.90 5	99.95 11

ETH3 D test6400		97.67 10	97.33 16	98.69 7	99.69 9	96.43 9	99.63 25	97.73 71	91.05 90	98.66 19	99.53 7	90.59 38	99.71 73	99.32 8	99.80 23	99.91 18

DeepPCF-MVS	93.56 1	96.55 41	97.84 8	92.68 207	98.71 92	78.11 318	99.70 17	97.71 77	98.18 1	97.36 52	99.76 1	90.37 45	99.94 33	99.27 9	99.54 57	99.99 1

APDe-MVS		97.53 11	97.47 10	97.70 36	99.58 29	93.63 62	99.56 32	97.52 115	93.59 41	98.01 39	99.12 46	90.80 35	99.55 94	99.26 10	99.79 25	99.93 17

test_0728_THIRD									93.01 47	99.07 8	99.46 11	94.66 10	99.97 20	99.25 11	99.82 15	99.95 11

TSAR-MVS + GP.		96.95 29	96.91 25	97.07 59	98.88 86	91.62 100	99.58 29	96.54 197	95.09 18	96.84 65	98.63 98	91.16 26	99.77 67	99.04 12	96.42 131	99.81 31

MCST-MVS		98.18 2	97.95 7	98.86 3	99.85 3	96.60 7	99.70 17	97.98 43	97.18 2	95.96 82	99.33 21	92.62 22	100.00 1	98.99 13	99.93 1	99.98 6

CNVR-MVS		98.46 1	98.38 1	98.72 6	99.80 4	96.19 12	99.80 8	97.99 42	97.05 3	99.41 2	99.59 2	92.89 21	100.00 1	98.99 13	99.90 5	99.96 8

ETH3D-3000-0.1		97.29 16	97.01 22	98.12 22	99.18 69	94.97 30	99.47 40	97.52 115	89.85 120	98.79 16	99.46 11	90.41 44	99.69 75	98.78 15	99.67 38	99.70 57

CANet		97.00 27	96.49 37	98.55 9	98.86 88	96.10 13	99.83 5	97.52 115	95.90 9	97.21 53	98.90 76	82.66 167	99.93 35	98.71 16	98.80 94	99.63 69

9.14			96.87 26		99.34 53		99.50 38	97.49 123	89.41 136	98.59 22	99.43 16	89.78 51	99.69 75	98.69 17	99.62 47

SD-MVS		97.51 12	97.40 14	97.81 32	99.01 79	93.79 61	99.33 64	97.38 139	93.73 38	98.83 15	99.02 57	90.87 33	99.88 44	98.69 17	99.74 28	99.77 44
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
test9_res														98.60 19	99.87 7	99.90 20

PS-MVSNAJ		96.87 33	96.40 39	98.29 15	97.35 128	97.29 3	99.03 96	97.11 164	95.83 10	98.97 11	99.14 43	82.48 170	99.60 91	98.60 19	99.08 80	98.00 175

xiu_mvs_v2_base		96.66 37	96.17 47	98.11 24	97.11 137	96.96 4	99.01 99	97.04 171	95.51 16	98.86 13	99.11 50	82.19 176	99.36 120	98.59 21	98.14 107	98.00 175

train_agg		97.20 22	97.08 19	97.57 42	99.57 33	93.17 72	99.38 56	97.66 82	90.18 111	98.39 27	99.18 35	90.94 30	99.66 80	98.58 22	99.85 11	99.88 24

agg_prior1		97.12 24	97.03 21	97.38 50	99.54 36	92.66 84	99.35 61	97.64 88	90.38 105	97.98 40	99.17 37	90.84 34	99.61 89	98.57 23	99.78 27	99.87 27

TSAR-MVS + MP.		97.44 15	97.46 11	97.39 49	99.12 72	93.49 67	98.52 153	97.50 121	94.46 21	98.99 10	98.64 96	91.58 25	99.08 138	98.49 24	99.83 13	99.60 73
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
xxxxxxxxxxxxxcwj		97.51 12	97.42 13	97.78 34	99.34 53	93.85 59	99.65 23	95.45 270	95.69 11	98.70 17	99.42 17	90.42 42	99.72 71	98.47 25	99.65 40	99.77 44

SF-MVS		97.22 21	96.92 24	98.12 22	99.11 73	94.88 32	99.44 48	97.45 128	89.60 129	98.70 17	99.42 17	90.42 42	99.72 71	98.47 25	99.65 40	99.77 44

ETH3D cwj APD-0.16		96.94 31	96.58 36	98.01 26	98.62 95	94.73 41	99.13 87	97.38 139	88.44 166	98.53 24	99.39 19	89.66 55	99.69 75	98.43 27	99.61 51	99.61 72

PHI-MVS		96.65 38	96.46 38	97.21 56	99.34 53	91.77 95	99.70 17	98.05 38	86.48 215	98.05 36	99.20 31	89.33 57	99.96 27	98.38 28	99.62 47	99.90 20

ZD-MVS					99.67 13	93.28 70		97.61 95	87.78 186	97.41 50	99.16 39	90.15 47	99.56 93	98.35 29	99.70 35

test_prior3		97.07 26	97.09 18	97.01 62	99.58 29	91.77 95	99.57 30	97.57 106	91.43 83	98.12 34	98.97 63	90.43 40	99.49 104	98.33 30	99.81 19	99.79 34

test_prior2							99.57 30		91.43 83	98.12 34	98.97 63	90.43 40		98.33 30	99.81 19

SMA-MVS		97.24 18	96.99 23	98.00 27	99.30 60	94.20 53	99.16 75	97.65 87	89.55 133	99.22 7	99.52 9	90.34 46	99.99 5	98.32 32	99.83 13	99.82 30
Yufeng Yin; Xiaoyan Liu; Zichao Zhang: SMA-MVS: Segmentation-Guided Multi-Scale Anchor Deformation Patch Multi-View Stereo. IEEE Transactions on Circuits and Systems for Video Technology
CHOSEN 280x420		96.80 35	96.85 27	96.66 91	97.85 113	94.42 49	94.76 296	98.36 23	92.50 59	95.62 93	97.52 140	97.92 1	97.38 212	98.31 33	98.80 94	98.20 171

NCCC		98.12 4	98.11 3	98.13 20	99.76 6	94.46 46	99.81 6	97.88 48	96.54 5	98.84 14	99.46 11	92.55 23	99.98 10	98.25 34	99.93 1	99.94 14

testtj		97.23 20	97.05 20	97.75 35	99.75 7	93.34 69	99.16 75	97.74 67	91.28 87	98.40 26	99.29 22	89.95 49	99.98 10	98.20 35	99.70 35	99.94 14

MSP-MVS		97.77 8	98.18 2	96.53 97	99.54 36	90.14 138	99.41 54	97.70 78	95.46 17	98.60 21	99.19 32	95.71 4	99.49 104	98.15 36	99.85 11	99.95 11
Zhenlong Yuan, Cong Liu, Fei Shen, Zhaoxin Li, Jingguo luo, Tianlu Mao and Zhaoqi Wang: MSP-MVS: Multi-granularity Segmentation Prior Guided Multi-View Stereo. AAAI2025
ETV-MVS		96.00 57	96.00 53	96.00 116	96.56 154	91.05 118	99.63 25	96.61 187	93.26 46	97.39 51	98.30 114	86.62 108	98.13 165	98.07 37	97.57 115	98.82 136

MSLP-MVS++		97.50 14	97.45 12	97.63 38	99.65 19	93.21 71	99.70 17	98.13 36	94.61 19	97.78 45	99.46 11	89.85 50	99.81 62	97.97 38	99.91 4	99.88 24

APD-MVS		96.95 29	96.72 32	97.63 38	99.51 42	93.58 63	99.16 75	97.44 132	90.08 116	98.59 22	99.07 51	89.06 59	99.42 114	97.92 39	99.66 39	99.88 24
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
SteuartSystems-ACMMP		97.25 17	97.34 15	97.01 62	97.38 127	91.46 104	99.75 13	97.66 82	94.14 28	98.13 32	99.26 24	92.16 24	99.66 80	97.91 40	99.64 43	99.90 20
Skip Steuart: Steuart Systems R&D Blog.
CS-MVS		95.85 64	95.86 58	95.82 122	96.80 147	89.78 152	99.84 3	96.60 188	92.60 55	96.81 69	98.70 92	85.04 134	98.25 161	97.90 41	98.43 104	99.42 89

agg_prior2														97.84 42	99.87 7	99.91 18

HPM-MVS++		97.72 9	97.59 9	98.14 19	99.53 41	94.76 39	99.19 70	97.75 65	95.66 13	98.21 30	99.29 22	91.10 28	99.99 5	97.68 43	99.87 7	99.68 61

SR-MVS		96.13 54	96.16 49	96.07 114	99.42 49	89.04 164	98.59 148	97.33 144	90.44 103	96.84 65	99.12 46	86.75 103	99.41 116	97.47 44	99.44 63	99.76 47

PVSNet_BlendedMVS		93.36 126	93.20 115	93.84 184	98.77 90	91.61 101	99.47 40	98.04 39	91.44 82	94.21 114	92.63 246	83.50 150	99.87 47	97.41 45	83.37 241	90.05 305

PVSNet_Blended		95.94 61	95.66 66	96.75 83	98.77 90	91.61 101	99.88 1	98.04 39	93.64 40	94.21 114	97.76 128	83.50 150	99.87 47	97.41 45	97.75 114	98.79 139

test1172		95.92 62	96.07 52	95.46 133	99.42 49	87.24 207	98.51 156	97.24 148	90.29 108	96.56 76	99.12 46	86.73 105	99.36 120	97.33 47	99.42 67	99.78 38

MVS_111021_HR		96.69 36	96.69 33	96.72 87	98.58 97	91.00 120	99.14 84	99.45 1	93.86 35	95.15 100	98.73 87	88.48 68	99.76 68	97.23 48	99.56 55	99.40 90

Regformer-1		96.97 28	96.80 30	97.47 44	99.46 47	93.11 74	98.89 111	97.94 44	92.89 52	96.90 58	99.02 57	89.78 51	99.53 97	97.06 49	99.26 76	99.75 48

xiu_mvs_v1_base_debu		94.73 89	93.98 97	96.99 65	95.19 197	95.24 21	98.62 142	96.50 199	92.99 48	97.52 47	98.83 80	72.37 245	99.15 132	97.03 50	96.74 126	96.58 204

xiu_mvs_v1_base		94.73 89	93.98 97	96.99 65	95.19 197	95.24 21	98.62 142	96.50 199	92.99 48	97.52 47	98.83 80	72.37 245	99.15 132	97.03 50	96.74 126	96.58 204

xiu_mvs_v1_base_debi		94.73 89	93.98 97	96.99 65	95.19 197	95.24 21	98.62 142	96.50 199	92.99 48	97.52 47	98.83 80	72.37 245	99.15 132	97.03 50	96.74 126	96.58 204

Regformer-2		96.94 31	96.78 31	97.42 46	99.46 47	92.97 81	98.89 111	97.93 45	92.86 54	96.88 59	99.02 57	89.74 53	99.53 97	97.03 50	99.26 76	99.75 48

lupinMVS		96.32 49	95.94 55	97.44 45	95.05 209	94.87 33	99.86 2	96.50 199	93.82 36	98.04 37	98.77 83	85.52 125	98.09 168	96.98 54	98.97 85	99.37 91

MVS_111021_LR		95.78 68	95.94 55	95.28 140	98.19 106	87.69 189	98.80 119	99.26 7	93.39 43	95.04 102	98.69 94	84.09 145	99.76 68	96.96 55	99.06 81	98.38 160

VNet		95.08 82	94.26 88	97.55 43	98.07 109	93.88 58	98.68 133	98.73 15	90.33 107	97.16 55	97.43 144	79.19 199	99.53 97	96.91 56	91.85 185	99.24 103

APD-MVS_3200maxsize		95.64 72	95.65 68	95.62 128	99.24 65	87.80 188	98.42 166	97.22 151	88.93 150	96.64 75	98.98 62	85.49 128	99.36 120	96.68 57	99.27 75	99.70 57

SR-MVS-dyc-post		95.75 71	95.86 58	95.41 136	99.22 66	87.26 205	98.40 171	97.21 152	89.63 127	96.67 73	98.97 63	86.73 105	99.36 120	96.62 58	99.31 72	99.60 73

RE-MVS-def			95.70 65		99.22 66	87.26 205	98.40 171	97.21 152	89.63 127	96.67 73	98.97 63	85.24 133		96.62 58	99.31 72	99.60 73

DeepC-MVS_fast	93.52 2	97.16 23	96.84 28	98.13 20	99.61 27	94.45 47	98.85 114	97.64 88	96.51 7	95.88 85	99.39 19	87.35 93	99.99 5	96.61 60	99.69 37	99.96 8
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
VDD-MVS		91.24 165	90.18 171	94.45 164	97.08 138	85.84 240	98.40 171	96.10 224	86.99 201	93.36 126	98.16 120	54.27 329	99.20 129	96.59 61	90.63 201	98.31 166

MP-MVS-pluss		95.80 67	95.30 71	97.29 52	98.95 83	92.66 84	98.59 148	97.14 160	88.95 148	93.12 129	99.25 25	85.62 124	99.94 33	96.56 62	99.48 59	99.28 100
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
diffmvs		94.59 97	94.19 90	95.81 123	95.54 187	90.69 127	98.70 130	95.68 256	91.61 78	95.96 82	97.81 125	80.11 191	98.06 172	96.52 63	95.76 145	98.67 147

ACMMP_NAP		96.59 39	96.18 45	97.81 32	98.82 89	93.55 64	98.88 113	97.59 101	90.66 96	97.98 40	99.14 43	86.59 109	100.00 1	96.47 64	99.46 60	99.89 23

Regformer-3		96.50 42	96.36 41	96.91 73	99.34 53	91.72 98	98.71 126	97.90 47	92.48 60	96.00 79	98.95 70	88.60 65	99.52 100	96.44 65	98.83 91	99.49 83

Regformer-4		96.45 45	96.33 43	96.81 80	99.34 53	91.44 105	98.71 126	97.88 48	92.43 61	95.97 81	98.95 70	88.42 69	99.51 101	96.40 66	98.83 91	99.49 83

PAPM		96.35 47	95.94 55	97.58 40	94.10 231	95.25 20	98.93 106	98.17 31	94.26 23	93.94 119	98.72 89	89.68 54	97.88 181	96.36 67	99.29 74	99.62 71

zzz-MVS		96.21 53	95.96 54	96.96 70	99.29 61	91.19 109	98.69 131	97.45 128	92.58 56	94.39 111	99.24 27	86.43 115	99.99 5	96.22 68	99.40 68	99.71 55

MTAPA		96.09 55	95.80 63	96.96 70	99.29 61	91.19 109	97.23 237	97.45 128	92.58 56	94.39 111	99.24 27	86.43 115	99.99 5	96.22 68	99.40 68	99.71 55

alignmvs		95.77 69	95.00 78	98.06 25	97.35 128	95.68 16	99.71 16	97.50 121	91.50 81	96.16 78	98.61 99	86.28 118	99.00 140	96.19 70	91.74 187	99.51 81

canonicalmvs		95.02 83	93.96 100	98.20 17	97.53 125	95.92 14	98.71 126	96.19 220	91.78 76	95.86 87	98.49 107	79.53 196	99.03 139	96.12 71	91.42 193	99.66 65

DELS-MVS		97.12 24	96.60 35	98.68 8	98.03 110	96.57 8	99.84 3	97.84 52	96.36 8	95.20 99	98.24 116	88.17 73	99.83 57	96.11 72	99.60 52	99.64 67
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
jason		95.40 76	94.86 79	97.03 61	92.91 262	94.23 52	99.70 17	96.30 210	93.56 42	96.73 71	98.52 103	81.46 185	97.91 178	96.08 73	98.47 103	98.96 121
jason: jason.
CP-MVS		96.22 52	96.15 50	96.42 102	99.67 13	89.62 157	99.70 17	97.61 95	90.07 117	96.00 79	99.16 39	87.43 87	99.92 36	96.03 74	99.72 30	99.70 57

MP-MVS		96.00 57	95.82 60	96.54 96	99.47 46	90.13 140	99.36 60	97.41 136	90.64 99	95.49 94	98.95 70	85.51 127	99.98 10	96.00 75	99.59 54	99.52 79
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
#test#		96.48 43	96.34 42	96.90 74	99.69 9	90.96 121	99.53 36	97.81 57	90.94 94	96.88 59	99.05 54	87.57 83	99.96 27	95.87 76	99.72 30	99.78 38

HFP-MVS		96.42 46	96.26 44	96.90 74	99.69 9	90.96 121	99.47 40	97.81 57	90.54 101	96.88 59	99.05 54	87.57 83	99.96 27	95.65 77	99.72 30	99.78 38

XVS		96.47 44	96.37 40	96.77 81	99.62 25	90.66 129	99.43 51	97.58 103	92.41 65	96.86 62	98.96 68	87.37 89	99.87 47	95.65 77	99.43 64	99.78 38

X-MVStestdata		90.69 175	88.66 194	96.77 81	99.62 25	90.66 129	99.43 51	97.58 103	92.41 65	96.86 62	29.59 361	87.37 89	99.87 47	95.65 77	99.43 64	99.78 38

ACMMPR		96.28 51	96.14 51	96.73 85	99.68 12	90.47 132	99.47 40	97.80 59	90.54 101	96.83 67	99.03 56	86.51 113	99.95 30	95.65 77	99.72 30	99.75 48

HPM-MVS		95.41 75	95.22 74	95.99 117	99.29 61	89.14 162	99.17 74	97.09 168	87.28 199	95.40 95	98.48 108	84.93 136	99.38 118	95.64 81	99.65 40	99.47 86
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
test_yl		95.27 78	94.60 82	97.28 53	98.53 98	92.98 79	99.05 94	98.70 16	86.76 209	94.65 108	97.74 130	87.78 79	99.44 112	95.57 82	92.61 171	99.44 87

DCV-MVSNet		95.27 78	94.60 82	97.28 53	98.53 98	92.98 79	99.05 94	98.70 16	86.76 209	94.65 108	97.74 130	87.78 79	99.44 112	95.57 82	92.61 171	99.44 87

region2R		96.30 50	96.17 47	96.70 88	99.70 8	90.31 134	99.46 45	97.66 82	90.55 100	97.07 56	99.07 51	86.85 101	99.97 20	95.43 84	99.74 28	99.81 31

EI-MVSNet-Vis-set		95.76 70	95.63 70	96.17 111	99.14 71	90.33 133	98.49 160	97.82 54	91.92 73	94.75 105	98.88 78	87.06 97	99.48 109	95.40 85	97.17 124	98.70 146

EPNet		96.82 34	96.68 34	97.25 55	98.65 93	93.10 75	99.48 39	98.76 12	96.54 5	97.84 44	98.22 117	87.49 86	99.66 80	95.35 86	97.78 113	99.00 117
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
MG-MVS		97.24 18	96.83 29	98.47 12	99.79 5	95.71 15	99.07 91	99.06 9	94.45 22	96.42 77	98.70 92	88.81 63	99.74 70	95.35 86	99.86 10	99.97 7

HY-MVS	88.56 7	95.29 77	94.23 89	98.48 11	97.72 115	96.41 10	94.03 303	98.74 13	92.42 64	95.65 92	94.76 204	86.52 112	99.49 104	95.29 88	92.97 166	99.53 78

mPP-MVS		95.90 63	95.75 64	96.38 104	99.58 29	89.41 161	99.26 67	97.41 136	90.66 96	94.82 104	98.95 70	86.15 120	99.98 10	95.24 89	99.64 43	99.74 51

ZNCC-MVS		96.09 55	95.81 62	96.95 72	99.42 49	91.19 109	99.55 33	97.53 113	89.72 124	95.86 87	98.94 75	86.59 109	99.97 20	95.13 90	99.56 55	99.68 61

GG-mvs-BLEND				96.98 68	96.53 155	94.81 38	87.20 333	97.74 67		93.91 120	96.40 179	96.56 2	96.94 225	95.08 91	98.95 88	99.20 107

EIA-MVS		95.11 81	95.27 73	94.64 158	96.34 161	86.51 216	99.59 28	96.62 186	92.51 58	94.08 117	98.64 96	86.05 121	98.24 162	95.07 92	98.50 102	99.18 108

DeepC-MVS	91.02 4	94.56 98	93.92 103	96.46 99	97.16 134	90.76 125	98.39 174	97.11 164	93.92 31	88.66 183	98.33 112	78.14 207	99.85 54	95.02 93	98.57 100	98.78 141
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
WTY-MVS		95.97 59	95.11 76	98.54 10	97.62 119	96.65 6	99.44 48	98.74 13	92.25 68	95.21 98	98.46 111	86.56 111	99.46 111	95.00 94	92.69 170	99.50 82

CSCG		94.87 85	94.71 80	95.36 137	99.54 36	86.49 217	99.34 63	98.15 34	82.71 272	90.15 170	99.25 25	89.48 56	99.86 52	94.97 95	98.82 93	99.72 54

EI-MVSNet-UG-set		95.43 73	95.29 72	95.86 121	99.07 77	89.87 149	98.43 165	97.80 59	91.78 76	94.11 116	98.77 83	86.25 119	99.48 109	94.95 96	96.45 130	98.22 169

CPTT-MVS		94.60 96	94.43 86	95.09 143	99.66 15	86.85 212	99.44 48	97.47 126	83.22 262	94.34 113	98.96 68	82.50 168	99.55 94	94.81 97	99.50 58	98.88 129

PVSNet_0	83.28 16	87.31 231	85.16 245	93.74 187	94.78 219	84.59 258	98.91 109	98.69 18	89.81 122	78.59 291	93.23 234	61.95 304	99.34 125	94.75 98	55.72 345	97.30 189

CLD-MVS		91.06 166	90.71 164	92.10 216	94.05 234	86.10 231	99.55 33	96.29 213	94.16 26	84.70 213	97.17 155	69.62 264	97.82 185	94.74 99	86.08 221	92.39 229
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
casdiffmvs		93.98 107	93.43 110	95.61 130	95.07 208	89.86 150	98.80 119	95.84 248	90.98 92	92.74 134	97.66 135	79.71 193	98.10 167	94.72 100	95.37 149	98.87 131

VDDNet		90.08 186	88.54 199	94.69 156	94.41 226	87.68 190	98.21 187	96.40 204	76.21 319	93.33 127	97.75 129	54.93 327	98.77 145	94.71 101	90.96 196	97.61 184

CDPH-MVS		96.56 40	96.18 45	97.70 36	99.59 28	93.92 57	99.13 87	97.44 132	89.02 145	97.90 43	99.22 29	88.90 62	99.49 104	94.63 102	99.79 25	99.68 61

GST-MVS		95.97 59	95.66 66	96.90 74	99.49 45	91.22 107	99.45 47	97.48 124	89.69 125	95.89 84	98.72 89	86.37 117	99.95 30	94.62 103	99.22 79	99.52 79

Effi-MVS+		93.87 111	93.15 116	96.02 115	95.79 178	90.76 125	96.70 259	95.78 249	86.98 203	95.71 90	97.17 155	79.58 194	98.01 176	94.57 104	96.09 139	99.31 96

abl_6		94.63 95	94.48 84	95.09 143	98.61 96	86.96 210	98.06 201	96.97 177	89.31 137	95.86 87	98.56 101	79.82 192	99.64 86	94.53 105	98.65 99	98.66 149

LFMVS		92.23 149	90.84 160	96.42 102	98.24 103	91.08 117	98.24 184	96.22 217	83.39 260	94.74 106	98.31 113	61.12 308	98.85 142	94.45 106	92.82 167	99.32 95

ET-MVSNet_ETH3D		92.56 144	91.45 150	95.88 120	96.39 159	94.13 55	99.46 45	96.97 177	92.18 70	66.94 338	98.29 115	94.65 11	94.28 317	94.34 107	83.82 237	99.24 103

baseline		93.91 109	93.30 112	95.72 126	95.10 207	90.07 142	97.48 226	95.91 240	91.03 91	93.54 125	97.68 133	79.58 194	98.02 175	94.27 108	95.14 150	99.08 114

PAPR		96.35 47	95.82 60	97.94 29	99.63 21	94.19 54	99.42 53	97.55 109	92.43 61	93.82 123	99.12 46	87.30 94	99.91 38	94.02 109	99.06 81	99.74 51

PGM-MVS		95.85 64	95.65 68	96.45 100	99.50 43	89.77 153	98.22 185	98.90 11	89.19 139	96.74 70	98.95 70	85.91 123	99.92 36	93.94 110	99.46 60	99.66 65

gg-mvs-nofinetune		90.00 187	87.71 208	96.89 79	96.15 170	94.69 43	85.15 339	97.74 67	68.32 341	92.97 133	60.16 350	96.10 3	96.84 227	93.89 111	98.87 89	99.14 110

MVS		93.92 108	92.28 132	98.83 4	95.69 182	96.82 5	96.22 274	98.17 31	84.89 239	84.34 217	98.61 99	79.32 198	99.83 57	93.88 112	99.43 64	99.86 28

旧先验2							98.67 135		85.75 223	98.96 12			98.97 141	93.84 113

ACMMP		94.67 93	94.30 87	95.79 124	99.25 64	88.13 183	98.41 168	98.67 19	90.38 105	91.43 149	98.72 89	82.22 175	99.95 30	93.83 114	95.76 145	99.29 98
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
BP-MVS														93.82 115

HQP-MVS		91.50 158	91.23 152	92.29 211	93.95 235	86.39 221	99.16 75	96.37 206	93.92 31	87.57 190	96.67 173	73.34 236	97.77 189	93.82 115	86.29 216	92.72 224

DP-MVS Recon		95.85 64	95.15 75	97.95 28	99.87 2	94.38 50	99.60 27	97.48 124	86.58 212	94.42 110	99.13 45	87.36 92	99.98 10	93.64 117	98.33 106	99.48 85

CHOSEN 1792x2688		94.35 101	93.82 105	95.95 119	97.40 126	88.74 174	98.41 168	98.27 25	92.18 70	91.43 149	96.40 179	78.88 200	99.81 62	93.59 118	97.81 110	99.30 97

cascas		90.93 170	89.33 181	95.76 125	95.69 182	93.03 78	98.99 101	96.59 190	80.49 298	86.79 202	94.45 207	65.23 293	98.60 154	93.52 119	92.18 180	95.66 214

HQP_MVS		91.26 162	90.95 157	92.16 215	93.84 242	86.07 233	99.02 97	96.30 210	93.38 44	86.99 196	96.52 175	72.92 240	97.75 194	93.46 120	86.17 219	92.67 226

plane_prior5								96.30 210					97.75 194	93.46 120	86.17 219	92.67 226

PVSNet_Blended_VisFu		94.67 93	94.11 93	96.34 106	97.14 135	91.10 115	99.32 65	97.43 134	92.10 72	91.53 148	96.38 182	83.29 156	99.68 78	93.42 122	96.37 132	98.25 167

AdaColmap		93.82 112	93.06 117	96.10 113	99.88 1	89.07 163	98.33 177	97.55 109	86.81 208	90.39 167	98.65 95	75.09 219	99.98 10	93.32 123	97.53 118	99.26 102

HyFIR lowres test		93.68 117	93.29 113	94.87 149	97.57 123	88.04 185	98.18 189	98.47 21	87.57 194	91.24 153	95.05 200	85.49 128	97.46 209	93.22 124	92.82 167	99.10 112

HPM-MVS_fast		94.89 84	94.62 81	95.70 127	99.11 73	88.44 180	99.14 84	97.11 164	85.82 222	95.69 91	98.47 109	83.46 152	99.32 126	93.16 125	99.63 46	99.35 92

PMMVS		93.62 120	93.90 104	92.79 202	96.79 148	81.40 292	98.85 114	96.81 181	91.25 88	96.82 68	98.15 121	77.02 213	98.13 165	93.15 126	96.30 135	98.83 135

LCM-MVSNet-Re		88.59 212	88.61 195	88.51 291	95.53 188	72.68 335	96.85 251	88.43 350	88.45 163	73.14 319	90.63 283	75.82 215	94.38 316	92.95 127	95.71 147	98.48 155

EPP-MVSNet		93.75 114	93.67 107	94.01 179	95.86 177	85.70 242	98.67 135	97.66 82	84.46 244	91.36 151	97.18 154	91.16 26	97.79 187	92.93 128	93.75 160	98.53 152

CostFormer		92.89 136	92.48 130	94.12 175	94.99 211	85.89 237	92.89 312	97.00 176	86.98 203	95.00 103	90.78 275	90.05 48	97.51 207	92.92 129	91.73 188	98.96 121

XVG-OURS-SEG-HR		90.95 169	90.66 166	91.83 220	95.18 200	81.14 299	95.92 281	95.92 236	88.40 168	90.33 168	97.85 123	70.66 260	99.38 118	92.83 130	88.83 207	94.98 215

sss		94.85 86	93.94 102	97.58 40	96.43 158	94.09 56	98.93 106	99.16 8	89.50 134	95.27 97	97.85 123	81.50 183	99.65 84	92.79 131	94.02 159	98.99 118

MAR-MVS		94.43 99	94.09 94	95.45 134	99.10 75	87.47 196	98.39 174	97.79 61	88.37 169	94.02 118	99.17 37	78.64 205	99.91 38	92.48 132	98.85 90	98.96 121
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
API-MVS		94.78 87	94.18 92	96.59 93	99.21 68	90.06 145	98.80 119	97.78 62	83.59 257	93.85 121	99.21 30	83.79 147	99.97 20	92.37 133	99.00 84	99.74 51

nrg030		90.23 180	88.87 188	94.32 168	91.53 280	93.54 65	98.79 123	95.89 243	88.12 177	84.55 215	94.61 206	78.80 203	96.88 226	92.35 134	75.21 282	92.53 228

OMC-MVS		93.90 110	93.62 108	94.73 155	98.63 94	87.00 209	98.04 202	96.56 194	92.19 69	92.46 136	98.73 87	79.49 197	99.14 135	92.16 135	94.34 157	98.03 174

1314		93.44 122	91.98 141	97.84 30	95.24 194	94.38 50	96.22 274	97.92 46	90.18 111	82.28 242	97.71 132	77.63 210	99.80 64	91.94 136	98.67 98	99.34 94

DPM-MVS		97.86 7	97.25 17	99.68 1	98.25 102	99.10 1	99.76 12	97.78 62	96.61 4	98.15 31	99.53 7	93.62 14	100.00 1	91.79 137	99.80 23	99.94 14

mvs_anonymous		92.50 145	91.65 147	95.06 145	96.60 153	89.64 156	97.06 243	96.44 203	86.64 211	84.14 218	93.93 216	82.49 169	96.17 268	91.47 138	96.08 140	99.35 92

baseline2		94.04 105	93.80 106	94.74 154	93.07 260	90.25 135	98.12 194	98.16 33	89.86 119	86.53 203	96.95 164	95.56 5	98.05 173	91.44 139	94.53 154	95.93 212

bset_n11_16_dypcd		89.07 198	87.85 205	92.76 204	86.16 333	90.66 129	97.30 231	95.62 259	89.78 123	83.94 221	93.15 238	74.85 220	95.89 283	91.34 140	78.48 264	91.74 250

IB-MVS	89.43 6	92.12 150	90.83 162	95.98 118	95.40 192	90.78 124	99.81 6	98.06 37	91.23 89	85.63 207	93.66 224	90.63 37	98.78 144	91.22 141	71.85 316	98.36 163
Christian Sormann, Mattia Rossi, Andreas Kuhn and Friedrich Fraundorfer: IB-MVS: An Iterative Algorithm for Deep Multi-View Stereo based on Binary Decisions. BMVC 2021
ab-mvs		91.05 167	89.17 183	96.69 89	95.96 175	91.72 98	92.62 316	97.23 150	85.61 224	89.74 175	93.89 218	68.55 269	99.42 114	91.09 142	87.84 210	98.92 127

XVG-OURS		90.83 171	90.49 168	91.86 219	95.23 195	81.25 296	95.79 289	95.92 236	88.96 147	90.02 172	98.03 122	71.60 254	99.35 124	91.06 143	87.78 211	94.98 215

3Dnovator	87.35 11	93.17 133	91.77 145	97.37 51	95.41 191	93.07 76	98.82 117	97.85 51	91.53 80	82.56 236	97.58 139	71.97 249	99.82 60	91.01 144	99.23 78	99.22 106

VPA-MVSNet		89.10 197	87.66 209	93.45 190	92.56 264	91.02 119	97.97 205	98.32 24	86.92 205	86.03 205	92.01 252	68.84 268	97.10 220	90.92 145	75.34 281	92.23 236

PAPM_NR		95.43 73	95.05 77	96.57 95	99.42 49	90.14 138	98.58 150	97.51 118	90.65 98	92.44 137	98.90 76	87.77 81	99.90 40	90.88 146	99.32 71	99.68 61

3Dnovator+	87.72 8	93.43 123	91.84 143	98.17 18	95.73 181	95.08 29	98.92 108	97.04 171	91.42 85	81.48 260	97.60 137	74.60 223	99.79 65	90.84 147	98.97 85	99.64 67

gm-plane-assit					94.69 221	88.14 182			88.22 174		97.20 152		98.29 159	90.79 148

MVSTER		92.71 138	92.32 131	93.86 183	97.29 130	92.95 82	99.01 99	96.59 190	90.09 115	85.51 208	94.00 214	94.61 12	96.56 239	90.77 149	83.03 243	92.08 242

ACMP	87.39 10	88.71 211	88.24 202	90.12 258	93.91 240	81.06 300	98.50 158	95.67 257	89.43 135	80.37 268	95.55 192	65.67 290	97.83 184	90.55 150	84.51 229	91.47 262
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
RRT_test8_iter05		91.04 168	90.40 170	92.95 199	96.20 168	89.75 154	98.97 103	96.38 205	88.52 159	82.00 250	93.51 229	90.69 36	96.73 233	90.43 151	76.91 276	92.38 230

testdata				95.26 141	98.20 104	87.28 202		97.60 97	85.21 230	98.48 25	99.15 41	88.15 74	98.72 150	90.29 152	99.45 62	99.78 38

LPG-MVS_test		88.86 203	88.47 200	90.06 259	93.35 255	80.95 301	98.22 185	95.94 233	87.73 190	83.17 228	96.11 185	66.28 288	97.77 189	90.19 153	85.19 225	91.46 263

LGP-MVS_train				90.06 259	93.35 255	80.95 301		95.94 233	87.73 190	83.17 228	96.11 185	66.28 288	97.77 189	90.19 153	85.19 225	91.46 263

MVSFormer		94.71 92	94.08 95	96.61 92	95.05 209	94.87 33	97.77 215	96.17 221	86.84 206	98.04 37	98.52 103	85.52 125	95.99 274	89.83 155	98.97 85	98.96 121

test_djsdf		88.26 218	87.73 207	89.84 265	88.05 321	82.21 286	97.77 215	96.17 221	86.84 206	82.41 240	91.95 255	72.07 248	95.99 274	89.83 155	84.50 230	91.32 270

tpmrst		92.78 137	92.16 136	94.65 157	96.27 163	87.45 197	91.83 320	97.10 167	89.10 143	94.68 107	90.69 279	88.22 72	97.73 196	89.78 157	91.80 186	98.77 142

PLC	91.07 3	94.23 103	94.01 96	94.87 149	99.17 70	87.49 195	99.25 68	96.55 195	88.43 167	91.26 152	98.21 119	85.92 122	99.86 52	89.77 158	97.57 115	97.24 191
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
CDS-MVSNet		93.47 121	93.04 119	94.76 152	94.75 220	89.45 160	98.82 117	97.03 173	87.91 183	90.97 156	96.48 177	89.06 59	96.36 253	89.50 159	92.81 169	98.49 154
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
Effi-MVS+-dtu		89.97 188	90.68 165	87.81 296	95.15 201	71.98 337	97.87 210	95.40 274	91.92 73	87.57 190	91.44 263	74.27 229	96.84 227	89.45 160	93.10 165	94.60 217

mvs-test1		91.57 157	92.20 135	89.70 269	95.15 201	74.34 328	99.51 37	95.40 274	91.92 73	91.02 155	97.25 148	74.27 229	98.08 171	89.45 160	95.83 144	96.67 201

jajsoiax		87.35 230	86.51 227	89.87 263	87.75 326	81.74 289	97.03 244	95.98 227	88.47 160	80.15 272	93.80 220	61.47 305	96.36 253	89.44 162	84.47 231	91.50 261

mvs_tets		87.09 233	86.22 230	89.71 268	87.87 322	81.39 293	96.73 258	95.90 241	88.19 175	79.99 274	93.61 225	59.96 311	96.31 261	89.40 163	84.34 232	91.43 265

PS-MVSNAJss		89.54 194	89.05 185	91.00 236	88.77 312	84.36 261	97.39 227	95.97 228	88.47 160	81.88 253	93.80 220	82.48 170	96.50 243	89.34 164	83.34 242	92.15 239

VPNet		88.30 216	86.57 225	93.49 189	91.95 273	91.35 106	98.18 189	97.20 156	88.61 156	84.52 216	94.89 201	62.21 303	96.76 232	89.34 164	72.26 313	92.36 231

114514_t		94.06 104	93.05 118	97.06 60	99.08 76	92.26 93	98.97 103	97.01 175	82.58 274	92.57 135	98.22 117	80.68 189	99.30 127	89.34 164	99.02 83	99.63 69

OPM-MVS		89.76 190	89.15 184	91.57 227	90.53 291	85.58 244	98.11 196	95.93 235	92.88 53	86.05 204	96.47 178	67.06 283	97.87 182	89.29 167	86.08 221	91.26 273

MVS_Test		93.67 118	92.67 126	96.69 89	96.72 150	92.66 84	97.22 238	96.03 226	87.69 192	95.12 101	94.03 212	81.55 182	98.28 160	89.17 168	96.46 129	99.14 110

BH-w/o		92.32 146	91.79 144	93.91 182	96.85 144	86.18 228	99.11 89	95.74 252	88.13 176	84.81 212	97.00 162	77.26 212	97.91 178	89.16 169	98.03 108	97.64 180

RRT_MVS		91.95 153	91.09 153	94.53 161	96.71 152	95.12 28	98.64 139	96.23 216	89.04 144	85.24 210	95.06 199	87.71 82	96.43 249	89.10 170	82.06 250	92.05 244

TAMVS		92.62 141	92.09 139	94.20 172	94.10 231	87.68 190	98.41 168	96.97 177	87.53 196	89.74 175	96.04 187	84.77 140	96.49 245	88.97 171	92.31 177	98.42 156

CNLPA		93.64 119	92.74 124	96.36 105	98.96 82	90.01 148	99.19 70	95.89 243	86.22 218	89.40 178	98.85 79	80.66 190	99.84 55	88.57 172	96.92 125	99.24 103

baseline1		92.61 142	91.28 151	96.58 94	97.05 140	94.63 44	97.72 218	96.20 218	89.82 121	88.56 184	96.85 169	86.85 101	97.82 185	88.42 173	80.10 258	97.30 189

CANet_DTU		94.31 102	93.35 111	97.20 57	97.03 141	94.71 42	98.62 142	95.54 265	95.61 14	97.21 53	98.47 109	71.88 250	99.84 55	88.38 174	97.46 120	97.04 198

thisisatest0515		94.75 88	94.19 90	96.43 101	96.13 174	92.64 88	99.47 40	97.60 97	87.55 195	93.17 128	97.59 138	94.71 9	98.42 155	88.28 175	93.20 163	98.24 168

原ACMM1				96.18 109	99.03 78	90.08 141		97.63 92	88.98 146	97.00 57	98.97 63	88.14 75	99.71 73	88.23 176	99.62 47	98.76 143

UGNet		91.91 154	90.85 159	95.10 142	97.06 139	88.69 175	98.01 203	98.24 27	92.41 65	92.39 138	93.61 225	60.52 309	99.68 78	88.14 177	97.25 122	96.92 200
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
AUN-MVS		90.17 183	89.50 176	92.19 214	96.21 165	82.67 283	97.76 217	97.53 113	88.05 178	91.67 143	96.15 184	83.10 159	97.47 208	88.11 178	66.91 329	96.43 208

Vis-MVSNet (Re-imp)		93.26 131	93.00 121	94.06 177	96.14 171	86.71 215	98.68 133	96.70 184	88.30 171	89.71 177	97.64 136	85.43 131	96.39 251	88.06 179	96.32 133	99.08 114

PVSNet	87.13 12	93.69 115	92.83 123	96.28 107	97.99 111	90.22 137	99.38 56	98.93 10	91.42 85	93.66 124	97.68 133	71.29 257	99.64 86	87.94 180	97.20 123	98.98 119

FIs		90.70 174	89.87 173	93.18 194	92.29 267	91.12 113	98.17 191	98.25 26	89.11 142	83.44 224	94.82 203	82.26 174	96.17 268	87.76 181	82.76 245	92.25 234

tpm2		91.77 155	91.09 153	93.82 185	94.83 218	85.56 245	92.51 317	97.16 159	84.00 250	93.83 122	90.66 281	87.54 85	97.17 216	87.73 182	91.55 191	98.72 144

无先验							98.52 153	97.82 54	87.20 200				99.90 40	87.64 183		99.85 29

1121		95.19 80	94.45 85	97.42 46	98.88 86	92.58 89	96.22 274	97.75 65	85.50 227	96.86 62	99.01 61	88.59 67	99.90 40	87.64 183	99.60 52	99.79 34

Anonymous202405211		88.84 204	87.03 219	94.27 169	98.14 108	84.18 263	98.44 164	95.58 263	76.79 318	89.34 179	96.88 168	53.42 332	99.54 96	87.53 185	87.12 214	99.09 113

IS-MVSNet		93.00 135	92.51 129	94.49 162	96.14 171	87.36 200	98.31 180	95.70 254	88.58 158	90.17 169	97.50 141	83.02 160	97.22 215	87.06 186	96.07 141	98.90 128

MDTV_nov1_ep13_2view						91.17 112	91.38 323		87.45 197	93.08 130		86.67 107		87.02 187		98.95 125

Anonymous20240529		87.66 227	85.58 240	93.92 181	97.59 122	85.01 254	98.13 192	97.13 162	66.69 345	88.47 185	96.01 188	55.09 326	99.51 101	87.00 188	84.12 233	97.23 192

UniMVSNet_NR-MVSNet		89.60 192	88.55 198	92.75 205	92.17 270	90.07 142	98.74 125	98.15 34	88.37 169	83.21 226	93.98 215	82.86 162	95.93 278	86.95 189	72.47 310	92.25 234

DU-MVS		88.83 206	87.51 210	92.79 202	91.46 281	90.07 142	98.71 126	97.62 94	88.87 152	83.21 226	93.68 222	74.63 221	95.93 278	86.95 189	72.47 310	92.36 231

ACMM	86.95 13	88.77 209	88.22 203	90.43 250	93.61 247	81.34 294	98.50 158	95.92 236	87.88 184	83.85 222	95.20 198	67.20 281	97.89 180	86.90 191	84.90 227	92.06 243
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
UniMVSNet (Re)		89.50 195	88.32 201	93.03 196	92.21 269	90.96 121	98.90 110	98.39 22	89.13 141	83.22 225	92.03 250	81.69 181	96.34 259	86.79 192	72.53 309	91.81 249

BH-untuned		91.46 160	90.84 160	93.33 192	96.51 157	84.83 256	98.84 116	95.50 267	86.44 217	83.50 223	96.70 172	75.49 218	97.77 189	86.78 193	97.81 110	97.40 186

miper_enhance_ethall		90.33 178	89.70 174	92.22 212	97.12 136	88.93 168	98.35 176	95.96 230	88.60 157	83.14 230	92.33 248	87.38 88	96.18 267	86.49 194	77.89 268	91.55 260

thisisatest0530		94.00 106	93.52 109	95.43 135	95.76 180	90.02 147	98.99 101	97.60 97	86.58 212	91.74 142	97.36 146	94.78 8	98.34 156	86.37 195	92.48 174	97.94 177

TESTMET0.1,1		93.82 112	93.26 114	95.49 132	95.21 196	90.25 135	99.15 81	97.54 112	89.18 140	91.79 141	94.87 202	89.13 58	97.63 200	86.21 196	96.29 136	98.60 150

anonymousdsp		86.69 239	85.75 238	89.53 274	86.46 332	82.94 276	96.39 265	95.71 253	83.97 251	79.63 279	90.70 278	68.85 267	95.94 277	86.01 197	84.02 234	89.72 310

F-COLMAP		92.07 151	91.75 146	93.02 197	98.16 107	82.89 279	98.79 123	95.97 228	86.54 214	87.92 188	97.80 126	78.69 204	99.65 84	85.97 198	95.93 143	96.53 207

cl-mvsnet2		89.57 193	88.79 191	91.91 218	97.94 112	87.62 192	97.98 204	96.51 198	85.03 235	82.37 241	91.79 256	83.65 148	96.50 243	85.96 199	77.89 268	91.61 257

test-LLR		93.11 134	92.68 125	94.40 165	94.94 214	87.27 203	99.15 81	97.25 146	90.21 109	91.57 145	94.04 210	84.89 137	97.58 203	85.94 200	96.13 137	98.36 163

test-mter		93.27 130	92.89 122	94.40 165	94.94 214	87.27 203	99.15 81	97.25 146	88.95 148	91.57 145	94.04 210	88.03 77	97.58 203	85.94 200	96.13 137	98.36 163

FC-MVSNet-test		90.22 181	89.40 179	92.67 208	91.78 277	89.86 150	97.89 207	98.22 28	88.81 153	82.96 231	94.66 205	81.90 180	95.96 276	85.89 202	82.52 248	92.20 238

DWT-MVSNet_test		94.36 100	93.95 101	95.62 128	96.99 142	89.47 159	96.62 261	97.38 139	90.96 93	93.07 131	97.27 147	93.73 13	98.09 168	85.86 203	93.65 161	99.29 98

Vis-MVSNet		92.64 140	91.85 142	95.03 147	95.12 203	88.23 181	98.48 161	96.81 181	91.61 78	92.16 140	97.22 151	71.58 255	98.00 177	85.85 204	97.81 110	98.88 129
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
WR-MVS		88.54 213	87.22 217	92.52 209	91.93 275	89.50 158	98.56 151	97.84 52	86.99 201	81.87 254	93.81 219	74.25 231	95.92 280	85.29 205	74.43 290	92.12 240

XXY-MVS		87.75 224	86.02 233	92.95 199	90.46 292	89.70 155	97.71 220	95.90 241	84.02 249	80.95 262	94.05 209	67.51 279	97.10 220	85.16 206	78.41 265	92.04 245

thres200		93.69 115	92.59 128	96.97 69	97.76 114	94.74 40	99.35 61	99.36 2	89.23 138	91.21 154	96.97 163	83.42 153	98.77 145	85.08 207	90.96 196	97.39 187

tttt0517		93.30 128	93.01 120	94.17 173	95.57 185	86.47 218	98.51 156	97.60 97	85.99 220	90.55 162	97.19 153	94.80 7	98.31 157	85.06 208	91.86 184	97.74 179

XVG-ACMP-BASELINE		85.86 253	84.95 249	88.57 289	89.90 297	77.12 321	94.30 299	95.60 262	87.40 198	82.12 245	92.99 242	53.42 332	97.66 198	85.02 209	83.83 235	90.92 281

test_part1		88.43 214	86.68 224	93.67 188	97.56 124	92.40 92	98.12 194	96.55 195	82.26 280	80.31 269	93.16 237	74.59 225	96.62 236	85.00 210	72.61 308	91.99 246

新几何1				97.40 48	98.92 84	92.51 91		97.77 64	85.52 225	96.69 72	99.06 53	88.08 76	99.89 43	84.88 211	99.62 47	99.79 34

1112_ss		92.71 138	91.55 149	96.20 108	95.56 186	91.12 113	98.48 161	94.69 296	88.29 172	86.89 199	98.50 105	87.02 98	98.66 152	84.75 212	89.77 205	98.81 137

miper_ehance_all_eth		88.94 201	88.12 204	91.40 228	95.32 193	86.93 211	97.85 211	95.55 264	84.19 247	81.97 251	91.50 262	84.16 144	95.91 281	84.69 213	77.89 268	91.36 268

Test_1112_low_res		92.27 148	90.97 156	96.18 109	95.53 188	91.10 115	98.47 163	94.66 297	88.28 173	86.83 201	93.50 230	87.00 99	98.65 153	84.69 213	89.74 206	98.80 138

TR-MVS		90.77 172	89.44 178	94.76 152	96.31 162	88.02 186	97.92 206	95.96 230	85.52 225	88.22 187	97.23 150	66.80 284	98.09 168	84.58 215	92.38 175	98.17 172

OpenMVS	85.28 14	90.75 173	88.84 189	96.48 98	93.58 248	93.51 66	98.80 119	97.41 136	82.59 273	78.62 289	97.49 142	68.00 275	99.82 60	84.52 216	98.55 101	96.11 211

UniMVSNet_ETH3D		85.65 260	83.79 266	91.21 231	90.41 293	80.75 303	95.36 291	95.78 249	78.76 308	81.83 257	94.33 208	49.86 340	96.66 234	84.30 217	83.52 240	96.22 210

NR-MVSNet		87.74 226	86.00 234	92.96 198	91.46 281	90.68 128	96.65 260	97.42 135	88.02 180	73.42 317	93.68 222	77.31 211	95.83 285	84.26 218	71.82 317	92.36 231

D2MVS		87.96 220	87.39 212	89.70 269	91.84 276	83.40 271	98.31 180	98.49 20	88.04 179	78.23 295	90.26 294	73.57 234	96.79 231	84.21 219	83.53 239	88.90 319

testdata2													99.88 44	84.16 220

Baseline_NR-MVSNet		85.83 254	84.82 252	88.87 288	88.73 313	83.34 272	98.63 141	91.66 337	80.41 301	82.44 238	91.35 265	74.63 221	95.42 296	84.13 221	71.39 319	87.84 325

thres100view900		93.34 127	92.15 137	96.90 74	97.62 119	94.84 35	99.06 93	99.36 2	87.96 181	90.47 165	96.78 170	83.29 156	98.75 147	84.11 222	90.69 198	97.12 193

tfpn200view9		93.43 123	92.27 133	96.90 74	97.68 117	94.84 35	99.18 72	99.36 2	88.45 163	90.79 157	96.90 166	83.31 154	98.75 147	84.11 222	90.69 198	97.12 193

thres400		93.39 125	92.27 133	96.73 85	97.68 117	94.84 35	99.18 72	99.36 2	88.45 163	90.79 157	96.90 166	83.31 154	98.75 147	84.11 222	90.69 198	96.61 202

cl_fuxian		88.19 219	87.23 216	91.06 234	94.97 212	86.17 229	97.72 218	95.38 276	83.43 259	81.68 258	91.37 264	82.81 163	95.72 288	84.04 225	73.70 298	91.29 272

UA-Net		93.30 128	92.62 127	95.34 138	96.27 163	88.53 179	95.88 284	96.97 177	90.90 95	95.37 96	97.07 159	82.38 173	99.10 137	83.91 226	94.86 153	98.38 160

IterMVS-LS		88.34 215	87.44 211	91.04 235	94.10 231	85.85 239	98.10 197	95.48 268	85.12 231	82.03 249	91.21 268	81.35 186	95.63 291	83.86 227	75.73 280	91.63 253
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
EI-MVSNet		89.87 189	89.38 180	91.36 230	94.32 227	85.87 238	97.61 223	96.59 190	85.10 232	85.51 208	97.10 157	81.30 187	96.56 239	83.85 228	83.03 243	91.64 252

tpm		89.67 191	88.95 187	91.82 221	92.54 265	81.43 291	92.95 311	95.92 236	87.81 185	90.50 164	89.44 307	84.99 135	95.65 290	83.67 229	82.71 246	98.38 160

eth_miper_zixun_eth		87.76 223	87.00 220	90.06 259	94.67 222	82.65 284	97.02 246	95.37 277	84.19 247	81.86 256	91.58 261	81.47 184	95.90 282	83.24 230	73.61 299	91.61 257

Fast-Effi-MVS+		91.72 156	90.79 163	94.49 162	95.89 176	87.40 199	99.54 35	95.70 254	85.01 237	89.28 180	95.68 191	77.75 209	97.57 206	83.22 231	95.06 151	98.51 153

test_post1							90.74 330				41.37 360	85.38 132	96.36 253	83.16 232

SCA		90.64 176	89.25 182	94.83 151	94.95 213	88.83 170	96.26 271	97.21 152	90.06 118	90.03 171	90.62 284	66.61 285	96.81 229	83.16 232	94.36 156	98.84 132

TranMVSNet+NR-MVSNet		87.75 224	86.31 229	92.07 217	90.81 288	88.56 176	98.33 177	97.18 157	87.76 187	81.87 254	93.90 217	72.45 244	95.43 295	83.13 234	71.30 320	92.23 236

CMPMVS	58.40 21	80.48 295	80.11 295	81.59 327	85.10 335	59.56 350	94.14 302	95.95 232	68.54 340	60.71 345	93.31 231	55.35 325	97.87 182	83.06 235	84.85 228	87.33 330
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
thres600view7		93.18 132	92.00 140	96.75 83	97.62 119	94.92 31	99.07 91	99.36 2	87.96 181	90.47 165	96.78 170	83.29 156	98.71 151	82.93 236	90.47 202	96.61 202

pmmvs4		87.58 229	86.17 232	91.80 222	89.58 302	88.92 169	97.25 235	95.28 280	82.54 275	80.49 267	93.17 236	75.62 217	96.05 273	82.75 237	78.90 262	90.42 296

CVMVSNet		90.30 179	90.91 158	88.46 292	94.32 227	73.58 332	97.61 223	97.59 101	90.16 114	88.43 186	97.10 157	76.83 214	92.86 326	82.64 238	93.54 162	98.93 126

Anonymous20231211		84.72 267	82.65 278	90.91 238	97.71 116	84.55 259	97.28 233	96.67 185	66.88 344	79.18 285	90.87 274	58.47 313	96.60 237	82.61 239	74.20 294	91.59 259

GA-MVS		90.10 185	88.69 193	94.33 167	92.44 266	87.97 187	99.08 90	96.26 214	89.65 126	86.92 198	93.11 239	68.09 273	96.96 223	82.54 240	90.15 203	98.05 173

QAPM		91.41 161	89.49 177	97.17 58	95.66 184	93.42 68	98.60 146	97.51 118	80.92 296	81.39 261	97.41 145	72.89 242	99.87 47	82.33 241	98.68 97	98.21 170

Patchmatch-RL test		81.90 291	80.13 294	87.23 301	80.71 346	70.12 342	84.07 345	88.19 351	83.16 264	70.57 326	82.18 337	87.18 95	92.59 331	82.28 242	62.78 334	98.98 119

v2v482		87.27 232	85.76 237	91.78 226	89.59 301	87.58 193	98.56 151	95.54 265	84.53 243	82.51 237	91.78 257	73.11 239	96.47 246	82.07 243	74.14 296	91.30 271

Fast-Effi-MVS+-dtu		88.84 204	88.59 197	89.58 273	93.44 253	78.18 316	98.65 137	94.62 298	88.46 162	84.12 219	95.37 197	68.91 266	96.52 242	82.06 244	91.70 189	94.06 218

pmmvs5		85.87 252	84.40 261	90.30 255	88.53 316	84.23 262	98.60 146	93.71 315	81.53 288	80.29 270	92.02 251	64.51 295	95.52 293	82.04 245	78.34 266	91.15 275

V42		87.00 234	85.68 239	90.98 237	89.91 296	86.08 232	98.32 179	95.61 261	83.67 256	82.72 233	90.67 280	74.00 233	96.53 241	81.94 246	74.28 293	90.32 298

EPMVS		92.59 143	91.59 148	95.59 131	97.22 132	90.03 146	91.78 321	98.04 39	90.42 104	91.66 144	90.65 282	86.49 114	97.46 209	81.78 247	96.31 134	99.28 100

cl-mvsnet1		87.82 221	86.81 222	90.87 241	94.87 217	85.39 247	97.81 212	95.22 288	82.92 270	80.76 264	91.31 266	81.99 177	95.81 286	81.36 248	75.04 284	91.42 266

cl-mvsnet_		87.82 221	86.79 223	90.89 240	94.88 216	85.43 246	97.81 212	95.24 284	82.91 271	80.71 265	91.22 267	81.97 179	95.84 284	81.34 249	75.06 283	91.40 267

RPSCF		85.33 262	85.55 241	84.67 316	94.63 223	62.28 348	93.73 305	93.76 313	74.38 326	85.23 211	97.06 160	64.09 296	98.31 157	80.98 250	86.08 221	93.41 223

OurMVSNet-221017-0		84.13 278	83.59 267	85.77 310	87.81 323	70.24 340	94.89 295	93.65 317	86.08 219	76.53 299	93.28 233	61.41 306	96.14 270	80.95 251	77.69 273	90.93 280

v148		86.38 246	85.06 246	90.37 254	89.47 306	84.10 264	98.52 153	95.48 268	83.80 252	80.93 263	90.22 298	74.60 223	96.31 261	80.92 252	71.55 318	90.69 291

PatchMatch-RL		91.47 159	90.54 167	94.26 170	98.20 104	86.36 223	96.94 247	97.14 160	87.75 188	88.98 181	95.75 190	71.80 252	99.40 117	80.92 252	97.39 121	97.02 199

miper_lstm_enhance		86.90 235	86.20 231	89.00 285	94.53 224	81.19 297	96.74 257	95.24 284	82.33 279	80.15 272	90.51 291	81.99 177	94.68 313	80.71 254	73.58 300	91.12 276

PCF-MVS	89.78 5	91.26 162	89.63 175	96.16 112	95.44 190	91.58 103	95.29 292	96.10 224	85.07 234	82.75 232	97.45 143	78.28 206	99.78 66	80.60 255	95.65 148	97.12 193
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
BH-RMVSNet		91.25 164	89.99 172	95.03 147	96.75 149	88.55 177	98.65 137	94.95 290	87.74 189	87.74 189	97.80 126	68.27 272	98.14 164	80.53 256	97.49 119	98.41 157

CP-MVSNet		86.54 243	85.45 243	89.79 267	91.02 287	82.78 282	97.38 229	97.56 108	85.37 228	79.53 281	93.03 240	71.86 251	95.25 300	79.92 257	73.43 303	91.34 269

PatchmatchNet		92.05 152	91.04 155	95.06 145	96.17 169	89.04 164	91.26 325	97.26 145	89.56 132	90.64 161	90.56 288	88.35 71	97.11 218	79.53 258	96.07 141	99.03 116
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
v1144		86.83 237	85.31 244	91.40 228	89.75 299	87.21 208	98.31 180	95.45 270	83.22 262	82.70 234	90.78 275	73.36 235	96.36 253	79.49 259	74.69 288	90.63 293

IterMVS		85.81 255	84.67 255	89.22 280	93.51 249	83.67 269	96.32 268	94.80 292	85.09 233	78.69 287	90.17 301	66.57 287	93.17 325	79.48 260	77.42 274	90.81 283
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
IterMVS-SCA-FT		85.73 258	84.64 256	89.00 285	93.46 252	82.90 278	96.27 269	94.70 295	85.02 236	78.62 289	90.35 293	66.61 285	93.33 322	79.38 261	77.36 275	90.76 287

GBi-Net		86.67 240	84.96 247	91.80 222	95.11 204	88.81 171	96.77 253	95.25 281	82.94 267	82.12 245	90.25 295	62.89 300	94.97 304	79.04 262	80.24 255	91.62 254

test1		86.67 240	84.96 247	91.80 222	95.11 204	88.81 171	96.77 253	95.25 281	82.94 267	82.12 245	90.25 295	62.89 300	94.97 304	79.04 262	80.24 255	91.62 254

FMVSNet3		88.81 208	87.08 218	93.99 180	96.52 156	94.59 45	98.08 199	96.20 218	85.85 221	82.12 245	91.60 260	74.05 232	95.40 297	79.04 262	80.24 255	91.99 246

LF4IMVS		81.94 290	81.17 289	84.25 318	87.23 329	68.87 345	93.35 309	91.93 335	83.35 261	75.40 308	93.00 241	49.25 342	96.65 235	78.88 265	78.11 267	87.22 332

v8		86.11 249	84.45 258	91.10 233	89.99 295	86.85 212	97.24 236	95.36 278	81.99 283	79.89 276	89.86 303	74.53 226	96.39 251	78.83 266	72.32 312	90.05 305

pm-mvs1		84.68 268	82.78 274	90.40 251	89.58 302	85.18 250	97.31 230	94.73 294	81.93 285	76.05 302	92.01 252	65.48 292	96.11 271	78.75 267	69.14 323	89.91 308

v144192		86.40 245	84.89 250	90.91 238	89.48 305	85.59 243	98.21 187	95.43 273	82.45 277	82.62 235	90.58 287	72.79 243	96.36 253	78.45 268	74.04 297	90.79 285

PS-CasMVS		85.81 255	84.58 257	89.49 277	90.77 289	82.11 287	97.20 239	97.36 142	84.83 240	79.12 286	92.84 243	67.42 280	95.16 302	78.39 269	73.25 304	91.21 274

tmp_tt		53.66 322	52.86 324	56.05 337	32.75 363	41.97 359	73.42 352	76.12 359	21.91 358	39.68 354	96.39 181	42.59 347	65.10 356	78.00 270	14.92 357	61.08 350

JIA-IIPM		85.97 251	84.85 251	89.33 279	93.23 257	73.68 331	85.05 340	97.13 162	69.62 337	91.56 147	68.03 348	88.03 77	96.96 223	77.89 271	93.12 164	97.34 188

MDTV_nov1_ep13			90.47 169		96.14 171	88.55 177	91.34 324	97.51 118	89.58 130	92.24 139	90.50 292	86.99 100	97.61 202	77.64 272	92.34 176

v1192		86.32 247	84.71 254	91.17 232	89.53 304	86.40 220	98.13 192	95.44 272	82.52 276	82.42 239	90.62 284	71.58 255	96.33 260	77.23 273	74.88 285	90.79 285

FMVSNet2		86.90 235	84.79 253	93.24 193	95.11 204	92.54 90	97.67 221	95.86 247	82.94 267	80.55 266	91.17 269	62.89 300	95.29 299	77.23 273	79.71 261	91.90 248

MVP-Stereo		86.61 242	85.83 236	88.93 287	88.70 314	83.85 268	96.07 279	94.41 304	82.15 282	75.64 307	91.96 254	67.65 278	96.45 248	77.20 275	98.72 96	86.51 336
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
tpm cat1		88.89 202	87.27 215	93.76 186	95.79 178	85.32 248	90.76 329	97.09 168	76.14 320	85.72 206	88.59 313	82.92 161	98.04 174	76.96 276	91.43 192	97.90 178

v10		85.73 258	84.01 264	90.87 241	90.03 294	86.73 214	97.20 239	95.22 288	81.25 291	79.85 277	89.75 304	73.30 238	96.28 265	76.87 277	72.64 307	89.61 312

v1921920		86.02 250	84.44 259	90.77 243	89.32 307	85.20 249	98.10 197	95.35 279	82.19 281	82.25 243	90.71 277	70.73 258	96.30 264	76.85 278	74.49 289	90.80 284

MS-PatchMatch		86.75 238	85.92 235	89.22 280	91.97 272	82.47 285	96.91 248	96.14 223	83.74 253	77.73 296	93.53 228	58.19 314	97.37 214	76.75 279	98.35 105	87.84 325

K. test v3		81.04 293	79.77 296	84.83 314	87.41 327	70.23 341	95.60 290	93.93 312	83.70 255	67.51 336	89.35 309	55.76 320	93.58 321	76.67 280	68.03 326	90.67 292

PM-MVS		74.88 313	72.85 316	80.98 328	78.98 349	64.75 347	90.81 328	85.77 353	80.95 295	68.23 333	82.81 335	29.08 354	92.84 327	76.54 281	62.46 336	85.36 340

MVS_0304		84.13 278	82.66 277	88.52 290	93.07 260	80.15 304	95.81 288	98.21 29	79.27 303	86.85 200	86.40 329	41.33 350	94.69 312	76.36 282	86.69 215	90.73 289

WR-MVS_H		86.53 244	85.49 242	89.66 272	91.04 286	83.31 273	97.53 225	98.20 30	84.95 238	79.64 278	90.90 273	78.01 208	95.33 298	76.29 283	72.81 305	90.35 297

ACMH+	83.78 15	84.21 275	82.56 280	89.15 282	93.73 246	79.16 308	96.43 264	94.28 306	81.09 293	74.00 314	94.03 212	54.58 328	97.67 197	76.10 284	78.81 263	90.63 293

PEN-MVS		85.21 263	83.93 265	89.07 284	89.89 298	81.31 295	97.09 242	97.24 148	84.45 245	78.66 288	92.68 245	68.44 271	94.87 307	75.98 285	70.92 321	91.04 278

USDC		84.74 266	82.93 270	90.16 257	91.73 278	83.54 270	95.00 294	93.30 320	88.77 154	73.19 318	93.30 232	53.62 331	97.65 199	75.88 286	81.54 253	89.30 314

EU-MVSNet		84.19 276	84.42 260	83.52 320	88.64 315	67.37 346	96.04 280	95.76 251	85.29 229	78.44 292	93.18 235	70.67 259	91.48 341	75.79 287	75.98 278	91.70 251

v1240		85.77 257	84.11 262	90.73 244	89.26 308	85.15 252	97.88 209	95.23 287	81.89 286	82.16 244	90.55 289	69.60 265	96.31 261	75.59 288	74.87 286	90.72 290

ITE_SJBPF				87.93 294	92.26 268	76.44 322		93.47 319	87.67 193	79.95 275	95.49 195	56.50 319	97.38 212	75.24 289	82.33 249	89.98 307

dp		90.16 184	88.83 190	94.14 174	96.38 160	86.42 219	91.57 322	97.06 170	84.76 241	88.81 182	90.19 300	84.29 143	97.43 211	75.05 290	91.35 195	98.56 151

LS3D		90.19 182	88.72 192	94.59 160	98.97 80	86.33 224	96.90 249	96.60 188	74.96 323	84.06 220	98.74 86	75.78 216	99.83 57	74.93 291	97.57 115	97.62 183

TDRefinement		78.01 307	75.31 310	86.10 308	70.06 353	73.84 330	93.59 308	91.58 339	74.51 325	73.08 321	91.04 270	49.63 341	97.12 217	74.88 292	59.47 339	87.33 330

tpmvs		89.16 196	87.76 206	93.35 191	97.19 133	84.75 257	90.58 331	97.36 142	81.99 283	84.56 214	89.31 310	83.98 146	98.17 163	74.85 293	90.00 204	97.12 193

pmmvs6		79.90 298	77.31 303	87.67 297	84.17 338	78.13 317	95.86 286	93.68 316	67.94 342	72.67 324	89.62 306	50.98 338	95.75 287	74.80 294	66.04 330	89.14 317

SixPastTwentyTwo		82.63 286	81.58 284	85.79 309	88.12 320	71.01 339	95.17 293	92.54 326	84.33 246	72.93 323	92.08 249	60.41 310	95.61 292	74.47 295	74.15 295	90.75 288

ACMH	83.09 17	84.60 269	82.61 279	90.57 246	93.18 258	82.94 276	96.27 269	94.92 291	81.01 294	72.61 325	93.61 225	56.54 318	97.79 187	74.31 296	81.07 254	90.99 279
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ADS-MVSNet2		87.62 228	86.88 221	89.86 264	96.21 165	79.14 309	87.15 334	92.99 321	83.01 265	89.91 173	87.27 322	78.87 201	92.80 329	74.20 297	92.27 178	97.64 180

ADS-MVSNet		88.99 199	87.30 214	94.07 176	96.21 165	87.56 194	87.15 334	96.78 183	83.01 265	89.91 173	87.27 322	78.87 201	97.01 222	74.20 297	92.27 178	97.64 180

lessismore_v0				85.08 312	85.59 334	69.28 343		90.56 344		67.68 335	90.21 299	54.21 330	95.46 294	73.88 299	62.64 335	90.50 295

MIMVSNet		84.48 272	81.83 282	92.42 210	91.73 278	87.36 200	85.52 337	94.42 303	81.40 289	81.91 252	87.58 317	51.92 335	92.81 328	73.84 300	88.15 209	97.08 197

v7n		84.42 274	82.75 275	89.43 278	88.15 319	81.86 288	96.75 256	95.67 257	80.53 297	78.38 293	89.43 308	69.89 261	96.35 258	73.83 301	72.13 314	90.07 303

ambc				79.60 329	72.76 352	56.61 352	76.20 350	92.01 334		68.25 332	80.23 341	23.34 355	94.73 311	73.78 302	60.81 338	87.48 327

pmmvs-eth3d		78.71 305	76.16 308	86.38 305	80.25 347	81.19 297	94.17 301	92.13 332	77.97 311	66.90 339	82.31 336	55.76 320	92.56 332	73.63 303	62.31 337	85.38 339

FMVSNet1		83.94 280	81.32 288	91.80 222	91.94 274	88.81 171	96.77 253	95.25 281	77.98 310	78.25 294	90.25 295	50.37 339	94.97 304	73.27 304	77.81 272	91.62 254

MSDG		88.29 217	86.37 228	94.04 178	96.90 143	86.15 230	96.52 263	94.36 305	77.89 314	79.22 284	96.95 164	69.72 263	99.59 92	73.20 305	92.58 173	96.37 209

test0.0.03 1		88.96 200	88.61 195	90.03 262	91.09 285	84.43 260	98.97 103	97.02 174	90.21 109	80.29 270	96.31 183	84.89 137	91.93 339	72.98 306	85.70 224	93.73 219

UnsupCasMVSNet_eth		78.90 303	76.67 306	85.58 311	82.81 342	74.94 326	91.98 319	96.31 209	84.64 242	65.84 341	87.71 316	51.33 336	92.23 335	72.89 307	56.50 344	89.56 313

DTE-MVSNet		84.14 277	82.80 272	88.14 293	88.95 311	79.87 307	96.81 252	96.24 215	83.50 258	77.60 297	92.52 247	67.89 277	94.24 318	72.64 308	69.05 324	90.32 298

EPNet_dtu		92.28 147	92.15 137	92.70 206	97.29 130	84.84 255	98.64 139	97.82 54	92.91 51	93.02 132	97.02 161	85.48 130	95.70 289	72.25 309	94.89 152	97.55 185
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
AllTest		84.97 265	83.12 269	90.52 248	96.82 145	78.84 311	95.89 282	92.17 330	77.96 312	75.94 303	95.50 193	55.48 322	99.18 130	71.15 310	87.14 212	93.55 221

TestCases				90.52 248	96.82 145	78.84 311		92.17 330	77.96 312	75.94 303	95.50 193	55.48 322	99.18 130	71.15 310	87.14 212	93.55 221

DP-MVS		88.75 210	86.56 226	95.34 138	98.92 84	87.45 197	97.64 222	93.52 318	70.55 333	81.49 259	97.25 148	74.43 227	99.88 44	71.14 312	94.09 158	98.67 147

CR-MVSNet		88.83 206	87.38 213	93.16 195	93.47 250	86.24 225	84.97 341	94.20 308	88.92 151	90.76 159	86.88 326	84.43 141	94.82 309	70.64 313	92.17 181	98.41 157

KD-MVS_2432*1600		82.98 284	80.52 292	90.38 252	94.32 227	88.98 166	92.87 313	95.87 245	80.46 299	73.79 315	87.49 319	82.76 165	93.29 323	70.56 314	46.53 350	88.87 320

miper_refine_blended		82.98 284	80.52 292	90.38 252	94.32 227	88.98 166	92.87 313	95.87 245	80.46 299	73.79 315	87.49 319	82.76 165	93.29 323	70.56 314	46.53 350	88.87 320

LTVRE_ROB	81.71 19	84.59 270	82.72 276	90.18 256	92.89 263	83.18 274	93.15 310	94.74 293	78.99 305	75.14 310	92.69 244	65.64 291	97.63 200	69.46 316	81.82 252	89.74 309
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
FMVSNet5		82.29 287	80.54 291	87.52 298	93.79 245	84.01 265	93.73 305	92.47 327	76.92 317	74.27 312	86.15 331	63.69 299	89.24 345	69.07 317	74.79 287	89.29 315

our_test_3		84.47 273	82.80 272	89.50 275	89.01 309	83.90 267	97.03 244	94.56 299	81.33 290	75.36 309	90.52 290	71.69 253	94.54 315	68.81 318	76.84 277	90.07 303

UnsupCasMVSNet_bld		73.85 315	70.14 318	84.99 313	79.44 348	75.73 323	88.53 332	95.24 284	70.12 336	61.94 344	74.81 345	41.41 349	93.62 320	68.65 319	51.13 349	85.62 338

Patchmtry		83.61 283	81.64 283	89.50 275	93.36 254	82.84 281	84.10 344	94.20 308	69.47 338	79.57 280	86.88 326	84.43 141	94.78 310	68.48 320	74.30 292	90.88 282

DIV-MVS_2432*1600		77.47 310	75.88 309	82.24 322	81.59 343	68.93 344	92.83 315	94.02 311	77.03 316	73.14 319	83.39 334	55.44 324	90.42 342	67.95 321	57.53 342	87.38 328

TransMVSNet (Re)		81.97 289	79.61 297	89.08 283	89.70 300	84.01 265	97.26 234	91.85 336	78.84 306	73.07 322	91.62 259	67.17 282	95.21 301	67.50 322	59.46 340	88.02 324

COLMAP_ROB	82.69 18	84.54 271	82.82 271	89.70 269	96.72 150	78.85 310	95.89 282	92.83 324	71.55 331	77.54 298	95.89 189	59.40 312	99.14 135	67.26 323	88.26 208	91.11 277
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
EG-PatchMatch MVS		79.92 297	77.59 301	86.90 303	87.06 330	77.90 320	96.20 277	94.06 310	74.61 324	66.53 340	88.76 312	40.40 352	96.20 266	67.02 324	83.66 238	86.61 334

DSMNet-mixed		81.60 292	81.43 286	82.10 324	84.36 337	60.79 349	93.63 307	86.74 352	79.00 304	79.32 283	87.15 324	63.87 298	89.78 344	66.89 325	91.92 183	95.73 213

testgi		82.29 287	81.00 290	86.17 307	87.24 328	74.84 327	97.39 227	91.62 338	88.63 155	75.85 306	95.42 196	46.07 345	91.55 340	66.87 326	79.94 259	92.12 240

MDA-MVSNet_test_wron		79.65 300	77.05 304	87.45 299	87.79 325	80.13 305	96.25 272	94.44 301	73.87 327	51.80 348	87.47 321	68.04 274	92.12 337	66.02 327	67.79 327	90.09 301

YYNet1		79.64 301	77.04 305	87.43 300	87.80 324	79.98 306	96.23 273	94.44 301	73.83 328	51.83 347	87.53 318	67.96 276	92.07 338	66.00 328	67.75 328	90.23 300

DeepMVS_CX				76.08 330	90.74 290	51.65 355		90.84 343	86.47 216	57.89 346	87.98 314	35.88 353	92.60 330	65.77 329	65.06 332	83.97 343

TinyColmap		80.42 296	77.94 300	87.85 295	92.09 271	78.58 313	93.74 304	89.94 346	74.99 322	69.77 328	91.78 257	46.09 344	97.58 203	65.17 330	77.89 268	87.38 328

MVS-HIRNet		79.01 302	75.13 311	90.66 245	93.82 244	81.69 290	85.16 338	93.75 314	54.54 349	74.17 313	59.15 352	57.46 316	96.58 238	63.74 331	94.38 155	93.72 220

ppachtmachnet_test		83.63 282	81.57 285	89.80 266	89.01 309	85.09 253	97.13 241	94.50 300	78.84 306	76.14 301	91.00 271	69.78 262	94.61 314	63.40 332	74.36 291	89.71 311

CL-MVSNet_2432*1600		79.89 299	78.34 299	84.54 317	81.56 344	75.01 325	96.88 250	95.62 259	81.10 292	75.86 305	85.81 332	68.49 270	90.26 343	63.21 333	56.51 343	88.35 322

Patchmatch-test		86.25 248	84.06 263	92.82 201	94.42 225	82.88 280	82.88 348	94.23 307	71.58 330	79.39 282	90.62 284	89.00 61	96.42 250	63.03 334	91.37 194	99.16 109

pmmvs3		72.86 316	69.76 320	82.17 323	73.86 351	74.19 329	94.20 300	89.01 349	64.23 348	67.72 334	80.91 340	41.48 348	88.65 347	62.40 335	54.02 347	83.68 344

new_pmnet		76.02 311	73.71 314	82.95 321	83.88 339	72.85 334	91.26 325	92.26 329	70.44 334	62.60 343	81.37 338	47.64 343	92.32 334	61.85 336	72.10 315	83.68 344

tfpnnormal		83.65 281	81.35 287	90.56 247	91.37 283	88.06 184	97.29 232	97.87 50	78.51 309	76.20 300	90.91 272	64.78 294	96.47 246	61.71 337	73.50 301	87.13 333

MDA-MVSNet-bldmvs		77.82 309	74.75 313	87.03 302	88.33 317	78.52 314	96.34 267	92.85 323	75.57 321	48.87 350	87.89 315	57.32 317	92.49 333	60.79 338	64.80 333	90.08 302

Anonymous20231206		80.76 294	79.42 298	84.79 315	84.78 336	72.98 333	96.53 262	92.97 322	79.56 302	74.33 311	88.83 311	61.27 307	92.15 336	60.59 339	75.92 279	89.24 316

new-patchmatchnet		74.80 314	72.40 317	81.99 325	78.36 350	72.20 336	94.44 297	92.36 328	77.06 315	63.47 342	79.98 342	51.04 337	88.85 346	60.53 340	54.35 346	84.92 342

LCM-MVSNet		60.07 319	56.37 322	71.18 331	54.81 359	48.67 356	82.17 349	89.48 348	37.95 352	49.13 349	69.12 346	13.75 362	81.76 350	59.28 341	51.63 348	83.10 346

TAPA-MVS	87.50 9	90.35 177	89.05 185	94.25 171	98.48 100	85.17 251	98.42 166	96.58 193	82.44 278	87.24 195	98.53 102	82.77 164	98.84 143	59.09 342	97.88 109	98.72 144
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
test20.03		78.51 306	77.48 302	81.62 326	83.07 341	71.03 338	96.11 278	92.83 324	81.66 287	69.31 329	89.68 305	57.53 315	87.29 349	58.65 343	68.47 325	86.53 335

PatchT		85.44 261	83.19 268	92.22 212	93.13 259	83.00 275	83.80 347	96.37 206	70.62 332	90.55 162	79.63 343	84.81 139	94.87 307	58.18 344	91.59 190	98.79 139

MIMVSNet1		75.92 312	73.30 315	83.81 319	81.29 345	75.57 324	92.26 318	92.05 333	73.09 329	67.48 337	86.18 330	40.87 351	87.64 348	55.78 345	70.68 322	88.21 323

OpenMVS_ROB	73.86 20	77.99 308	75.06 312	86.77 304	83.81 340	77.94 319	96.38 266	91.53 340	67.54 343	68.38 331	87.13 325	43.94 346	96.08 272	55.03 346	81.83 251	86.29 337

RPMNet		85.07 264	81.88 281	94.64 158	93.47 250	86.24 225	84.97 341	97.21 152	64.85 347	90.76 159	78.80 344	80.95 188	99.27 128	53.76 347	92.17 181	98.41 157

N_pmnet		70.19 317	69.87 319	71.12 332	88.24 318	30.63 363	95.85 287	28.70 363	70.18 335	68.73 330	86.55 328	64.04 297	93.81 319	53.12 348	73.46 302	88.94 318

PMMVS2		58.97 320	55.07 323	70.69 333	62.72 354	55.37 353	85.97 336	80.52 356	49.48 350	45.94 351	68.31 347	15.73 360	80.78 352	49.79 349	37.12 352	75.91 347

test_0402		78.81 304	76.33 307	86.26 306	91.18 284	78.44 315	95.88 284	91.34 341	68.55 339	70.51 327	89.91 302	52.65 334	94.99 303	47.14 350	79.78 260	85.34 341

FPMVS		61.57 318	60.32 321	65.34 334	60.14 357	42.44 358	91.02 327	89.72 347	44.15 351	42.63 352	80.93 339	19.02 356	80.59 353	42.50 351	72.76 306	73.00 348

ANet_high		50.71 323	46.17 326	64.33 335	44.27 361	52.30 354	76.13 351	78.73 357	64.95 346	27.37 356	55.23 353	14.61 361	67.74 355	36.01 352	18.23 355	72.95 349

Gipuma		54.77 321	52.22 325	62.40 336	86.50 331	59.37 351	50.20 355	90.35 345	36.52 353	41.20 353	49.49 354	18.33 358	81.29 351	32.10 353	65.34 331	46.54 353
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PMVS	41.42 23	45.67 324	42.50 327	55.17 338	34.28 362	32.37 361	66.24 353	78.71 358	30.72 354	22.04 359	59.59 351	4.59 363	77.85 354	27.49 354	58.84 341	55.29 351
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MVE	44.00 22	41.70 325	37.64 330	53.90 339	49.46 360	43.37 357	65.09 354	66.66 360	26.19 357	25.77 358	48.53 355	3.58 365	63.35 357	26.15 355	27.28 353	54.97 352
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
E-PMN		41.02 326	40.93 328	41.29 340	61.97 355	33.83 360	84.00 346	65.17 361	27.17 355	27.56 355	46.72 356	17.63 359	60.41 358	19.32 356	18.82 354	29.61 354

EMVS		39.96 327	39.88 329	40.18 341	59.57 358	32.12 362	84.79 343	64.57 362	26.27 356	26.14 357	44.18 359	18.73 357	59.29 359	17.03 357	17.67 356	29.12 355

wuyk23d		16.71 330	16.73 334	16.65 342	60.15 356	25.22 364	41.24 356	5.17 364	6.56 359	5.48 362	3.61 362	3.64 364	22.72 360	15.20 358	9.52 358	1.99 358

testmvs		18.81 329	23.05 332	6.10 344	4.48 364	2.29 366	97.78 214	3.00 365	3.27 360	18.60 360	62.71 349	1.53 367	2.49 362	14.26 359	1.80 359	13.50 357

test123		16.58 331	19.47 333	7.91 343	3.59 365	5.37 365	94.32 298	1.39 366	2.49 361	13.98 361	44.60 358	2.91 366	2.65 361	11.35 360	0.57 360	15.70 356

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

cdsmvs_eth3d_5k		22.52 328	30.03 331	0.00 345	0.00 366	0.00 367	0.00 357	97.17 158	0.00 362	0.00 363	98.77 83	74.35 228	0.00 363	0.00 361	0.00 361	0.00 359

pcd_1.5k_mvsjas		6.87 333	9.16 336	0.00 345	0.00 366	0.00 367	0.00 357	0.00 367	0.00 362	0.00 363	0.00 363	82.48 170	0.00 363	0.00 361	0.00 361	0.00 359

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

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

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

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

ab-mvs-re		8.21 332	10.94 335	0.00 345	0.00 366	0.00 367	0.00 357	0.00 367	0.00 362	0.00 363	98.50 105	0.00 368	0.00 363	0.00 361	0.00 361	0.00 359

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

test_241102_ONE					99.63 21	95.24 21		97.72 73	94.16 26	99.30 4	99.49 10	93.32 15	99.98 10

save fliter					99.34 53	93.85 59	99.65 23	97.63 92	95.69 11

test0726					99.66 15	95.20 26	99.77 9	97.70 78	93.95 29	99.35 3	99.54 3	93.18 18

GSMVS																98.84 132

test_part2					99.54 36	95.42 17				98.13 32

sam_mvs1												88.39 70				98.84 132

sam_mvs												87.08 96

MTGPA								97.45 128

test_post											46.00 357	87.37 89	97.11 218

patchmatchnet-post											84.86 333	88.73 64	96.81 229

MTMP							99.21 69	91.09 342

TEST9					99.57 33	93.17 72	99.38 56	97.66 82	89.57 131	98.39 27	99.18 35	90.88 32	99.66 80

test_8					99.55 35	93.07 76	99.37 59	97.64 88	90.18 111	98.36 29	99.19 32	90.94 30	99.64 86

agg_prior					99.54 36	92.66 84		97.64 88		97.98 40			99.61 89

test_prior4						92.00 94	99.41 54

test_prior				97.01 62	99.58 29	91.77 95		97.57 106					99.49 104			99.79 34

新几何2							98.26 183

旧先验1					98.97 80	92.90 83		97.74 67			99.15 41	91.05 29			99.33 70	99.60 73

原ACMM2							98.69 131

test222					98.32 101	91.21 108	98.08 199	97.58 103	83.74 253	95.87 86	99.02 57	86.74 104			99.64 43	99.81 31

segment_acmp												90.56 39

testdata1							97.89 207		92.43 61

test12				97.83 31	99.33 59	94.45 47		97.55 109		97.56 46		88.60 65	99.50 103		99.71 34	99.55 77

plane_prior7					93.84 242	85.73 241

plane_prior6					93.92 239	86.02 235						72.92 240

plane_prior4											96.52 175

plane_prior3						85.91 236			93.65 39	86.99 196

plane_prior2							99.02 97		93.38 44

plane_prior1					93.90 241

plane_prior						86.07 233	99.14 84		93.81 37						86.26 218

n2								0.00 367

nn								0.00 367

door-mid								84.90 355

test11								97.68 80

door								85.30 354

HQP5-MVS						86.39 221

HQP-NCC					93.95 235		99.16 75		93.92 31	87.57 190

ACMP_Plane					93.95 235		99.16 75		93.92 31	87.57 190

HQP4-MVS										87.57 190			97.77 189			92.72 224

HQP3-MVS								96.37 206							86.29 216

HQP2-MVS												73.34 236

NP-MVS					93.94 238	86.22 227					96.67 173

ACMMP++_ref															82.64 247

ACMMP++															83.83 235

Test By Simon												83.62 149

High-resolution multi-view benchmark