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.

PC_three_1452									82.47 312	97.09 19	97.07 72	92.72 1	98.04 202	92.70 81	99.02 12	98.86 16

DVP-MVS++		95.98 1	96.36 1	94.82 35	97.78 61	86.00 55	98.29 1	97.49 11	90.75 31	97.62 8	98.06 24	92.59 2	99.61 7	95.64 33	99.02 12	98.86 16

OPU-MVS				96.21 3	98.00 49	90.85 3	97.13 19				97.08 70	92.59 2	98.94 93	92.25 94	98.99 14	98.84 19

MED-MVS		95.95 2	96.31 2	94.90 25	98.88 1	85.89 66	97.32 10	97.86 1	90.76 29	97.21 14	98.09 18	92.42 4	99.67 1	95.27 41	98.95 15	99.14 2

SED-MVS		95.91 3	96.28 3	94.80 38	98.77 8	85.99 57	97.13 19	97.44 20	90.31 44	97.71 2	98.07 22	92.31 5	99.58 14	95.66 31	99.13 3	98.84 19

test_241102_ONE					98.77 8	85.99 57		97.44 20	90.26 50	97.71 2	97.96 33	92.31 5	99.38 36

test_0728_THIRD									90.75 31	97.04 21	98.05 27	92.09 7	99.55 21	95.64 33	99.13 3	99.13 4

DPE-MVS		95.57 5	95.67 5	95.25 12	98.36 32	87.28 19	95.56 119	97.51 10	89.13 91	97.14 17	97.91 34	91.64 8	99.62 5	94.61 50	99.17 2	98.86 16
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
DVP-MVS		95.67 4	96.02 4	94.64 44	98.78 6	85.93 60	97.09 21	96.73 99	90.27 48	97.04 21	98.05 27	91.47 9	99.55 21	95.62 35	99.08 7	98.45 42
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
test0726					98.78 6	85.93 60	97.19 16	97.47 16	90.27 48	97.64 6	98.13 7	91.47 9

test_241102_TWO								97.44 20	90.31 44	97.62 8	98.07 22	91.46 11	99.58 14	95.66 31	99.12 6	98.98 12

test_one_0601					98.58 14	85.83 69		97.44 20	91.05 23	96.78 27	98.06 24	91.45 12

MSP-MVS		95.42 7	95.56 7	94.98 21	98.49 20	86.52 38	96.91 30	97.47 16	91.73 14	96.10 36	96.69 87	89.90 13	99.30 49	94.70 48	98.04 80	99.13 4
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
DeepPCF-MVS	89.96 1	94.20 47	94.77 31	92.49 152	96.52 99	80.00 278	94.00 241	97.08 60	90.05 52	95.65 48	97.29 57	89.66 14	98.97 88	93.95 56	98.71 36	98.50 32

test-260524					98.47 21	86.91 23		97.38 27		95.81 44		89.60 15	99.63 4	95.95 29	98.95 15

SD-MVS		94.96 18	95.33 12	93.88 71	97.25 80	86.69 30	96.19 57	97.11 59	90.42 40	96.95 23	97.27 58	89.53 16	96.91 325	94.38 52	98.85 22	98.03 92
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
CNVR-MVS		95.40 8	95.37 11	95.50 8	98.11 43	88.51 8	95.29 132	96.96 69	92.09 10	95.32 51	97.08 70	89.49 17	99.33 46	95.10 44	98.85 22	98.66 26

APDe-MVS		95.46 6	95.64 6	94.91 23	98.26 35	86.29 48	97.46 7	97.40 26	89.03 97	96.20 35	98.10 14	89.39 18	99.34 43	95.88 30	99.03 11	99.10 6
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
TestfortrainingZip				95.40 9	97.32 75	88.97 6	97.32 10	96.82 86	89.07 92	95.69 46	96.49 100	89.27 19	99.29 51		95.80 144	97.95 98

aaEdge-Enhanced		95.17 12	95.29 14	94.81 36	98.39 29	85.89 66	95.91 88	97.55 8	89.01 99	95.86 42	97.54 46	89.24 20	99.59 11	95.27 41	98.85 22	98.95 13

MCST-MVS		94.45 34	94.20 51	95.19 14	98.46 23	87.50 17	95.00 156	97.12 56	87.13 177	92.51 114	96.30 106	89.24 20	99.34 43	93.46 64	98.62 50	98.73 23

TSAR-MVS + MP.		94.85 19	94.94 24	94.58 47	98.25 36	86.33 44	96.11 67	96.62 109	88.14 132	96.10 36	96.96 76	89.09 22	98.94 93	94.48 51	98.68 41	98.48 35
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
TestfortrainingZip a		95.33 9	95.44 10	94.99 20	98.88 1	86.26 49	97.32 10	97.43 25	90.76 29	96.80 26	98.09 18	89.00 23	99.58 14	93.66 61	96.99 113	99.14 2

SteuartSystems-ACMMP		95.20 10	95.32 13	94.85 28	96.99 83	86.33 44	97.33 8	97.30 38	91.38 19	95.39 50	97.46 50	88.98 24	99.40 35	94.12 54	98.89 20	98.82 21
Skip Steuart: Steuart Systems R&D Blog.
SMA-MVS		95.20 10	95.07 20	95.59 6	98.14 42	88.48 9	96.26 54	97.28 41	85.90 213	97.67 4	98.10 14	88.41 25	99.56 17	94.66 49	99.19 1	98.71 25
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
patch_mono-2		93.74 65	94.32 41	92.01 184	97.54 67	78.37 331	93.40 276	97.19 45	88.02 140	94.99 58	97.21 62	88.35 26	98.44 155	94.07 55	98.09 77	99.23 1

9.14			94.47 35		97.79 59		96.08 69	97.44 20	86.13 211	95.10 56	97.40 53	88.34 27	99.22 54	93.25 69	98.70 38

SF-MVS		94.97 17	94.90 28	95.20 13	97.84 57	87.76 11	96.65 39	97.48 15	87.76 156	95.71 45	97.70 42	88.28 28	99.35 42	93.89 58	98.78 30	98.48 35

HPM-MVS++		95.14 13	94.91 26	95.83 4	98.25 36	89.65 4	95.92 87	96.96 69	91.75 13	94.02 73	96.83 82	88.12 29	99.55 21	93.41 67	98.94 18	98.28 62

dcpmvs_2		93.49 70	94.19 52	91.38 226	97.69 64	76.78 375	94.25 216	96.29 133	88.33 122	94.46 61	96.88 79	88.07 30	98.64 131	93.62 63	98.09 77	98.73 23

CSCG		93.23 85	93.05 86	93.76 78	98.04 47	84.07 114	96.22 56	97.37 28	84.15 269	90.05 190	95.66 157	87.77 31	99.15 62	89.91 153	98.27 62	98.07 84

NCCC		94.81 22	94.69 32	95.17 15	97.83 58	87.46 18	95.66 110	96.93 73	92.34 7	93.94 74	96.58 97	87.74 32	99.44 34	92.83 76	98.40 58	98.62 27

TEST9					97.53 68	86.49 39	94.07 232	96.78 91	81.61 342	92.77 102	96.20 110	87.71 33	99.12 64

train_agg		93.44 75	93.08 85	94.52 49	97.53 68	86.49 39	94.07 232	96.78 91	81.86 333	92.77 102	96.20 110	87.63 34	99.12 64	92.14 100	98.69 39	97.94 99

test_8					97.49 70	86.30 47	94.02 238	96.76 94	81.86 333	92.70 106	96.20 110	87.63 34	99.02 74

ZD-MVS					98.15 41	86.62 35		97.07 61	83.63 282	94.19 66	96.91 78	87.57 36	99.26 52	91.99 107	98.44 57

fmvsm_l_conf0.5_n		94.29 41	94.46 36	93.79 77	95.28 160	85.43 78	95.68 107	96.43 122	86.56 196	96.84 25	97.81 39	87.56 37	98.77 116	97.14 15	96.82 121	97.16 175

fmvsm_l_conf0.5_n_a		94.20 47	94.40 38	93.60 83	95.29 159	84.98 85	95.61 115	96.28 136	86.31 203	96.75 28	97.86 37	87.40 38	98.74 120	97.07 17	97.02 112	97.07 180

TSAR-MVS + GP.		93.66 67	93.41 78	94.41 54	96.59 93	86.78 28	94.40 203	93.93 326	89.77 67	94.21 65	95.59 161	87.35 39	98.61 137	92.72 79	96.15 138	97.83 119

APD-MVS		94.24 43	94.07 56	94.75 41	98.06 46	86.90 25	95.88 90	96.94 72	85.68 220	95.05 57	97.18 66	87.31 40	99.07 66	91.90 113	98.61 52	98.28 62
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
segment_acmp												87.16 41

reproduce-ours		94.82 20	94.97 22	94.38 55	97.91 54	85.46 76	95.86 91	97.15 52	89.82 60	95.23 54	98.10 14	87.09 42	99.37 38	95.30 39	98.25 67	98.30 56

our_new_method		94.82 20	94.97 22	94.38 55	97.91 54	85.46 76	95.86 91	97.15 52	89.82 60	95.23 54	98.10 14	87.09 42	99.37 38	95.30 39	98.25 67	98.30 56

旧先验1					96.79 87	81.81 200		95.67 211			96.81 84	86.69 44			97.66 98	96.97 190

lecture		95.10 14	95.46 9	94.01 66	98.40 27	84.36 108	97.70 3	97.78 3	91.19 20	96.22 34	98.08 21	86.64 45	99.37 38	94.91 46	98.26 63	98.29 61

test_prior2							94.12 224		87.67 160	92.63 110	96.39 105	86.62 46		91.50 121	98.67 44

CDPH-MVS		92.83 94	92.30 103	94.44 50	97.79 59	86.11 54	94.06 234	96.66 106	80.09 364	92.77 102	96.63 94	86.62 46	99.04 70	87.40 196	98.66 45	98.17 75

DPM-MVS		92.58 100	91.74 111	95.08 16	96.19 108	89.31 5	92.66 317	96.56 114	83.44 288	91.68 141	95.04 193	86.60 48	98.99 83	85.60 223	97.92 85	96.93 194

reproduce_model		94.76 24	94.92 25	94.29 61	97.92 50	85.18 82	95.95 85	97.19 45	89.67 70	95.27 53	98.16 6	86.53 49	99.36 41	95.42 38	98.15 73	98.33 51

test_fmvsmconf_n		94.60 28	94.81 30	93.98 67	94.62 208	84.96 86	96.15 62	97.35 30	89.37 80	96.03 39	98.11 11	86.36 50	99.01 76	97.45 10	97.83 90	97.96 97

DELS-MVS		93.43 79	93.25 81	93.97 68	95.42 154	85.04 84	93.06 298	97.13 55	90.74 33	91.84 133	95.09 192	86.32 51	99.21 56	91.22 125	98.45 56	97.65 133
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
test_fmvsm_n_1920		94.71 26	95.11 19	93.50 85	95.79 134	84.62 93	96.15 62	97.64 5	89.85 59	97.19 16	97.89 35	86.28 52	98.71 123	97.11 16	98.08 79	97.17 168

ZNCC-MVS		94.47 33	94.28 45	95.03 17	98.52 18	86.96 21	96.85 33	97.32 35	88.24 127	93.15 89	97.04 73	86.17 53	99.62 5	92.40 88	98.81 27	98.52 31

HFP-MVS		94.52 31	94.40 38	94.86 27	98.61 13	86.81 27	96.94 25	97.34 31	88.63 112	93.65 79	97.21 62	86.10 54	99.49 31	92.35 91	98.77 32	98.30 56

MVS_111021_HR		93.45 74	93.31 79	93.84 73	96.99 83	84.84 87	93.24 289	97.24 42	88.76 107	91.60 142	95.85 143	86.07 55	98.66 126	91.91 111	98.16 71	98.03 92

ACMMP_NAP		94.74 25	94.56 33	95.28 11	98.02 48	87.70 12	95.68 107	97.34 31	88.28 126	95.30 52	97.67 43	85.90 56	99.54 25	93.91 57	98.95 15	98.60 28

fmvsm_l_conf0.5_n_9		94.65 27	95.28 15	92.77 126	95.95 130	81.83 199	95.53 120	97.12 56	91.68 16	97.89 1	98.06 24	85.71 57	98.65 128	97.32 12	98.26 63	97.83 119

CS-MVS		94.12 51	94.44 37	93.17 99	96.55 96	83.08 154	97.63 4	96.95 71	91.71 15	93.50 85	96.21 109	85.61 58	98.24 172	93.64 62	98.17 70	98.19 73

PHI-MVS		93.89 60	93.65 74	94.62 46	96.84 86	86.43 41	96.69 37	97.49 11	85.15 244	93.56 83	96.28 107	85.60 59	99.31 48	92.45 85	98.79 28	98.12 82

MP-MVS-pluss		94.21 45	94.00 59	94.85 28	98.17 40	86.65 33	94.82 169	97.17 50	86.26 205	92.83 99	97.87 36	85.57 60	99.56 17	94.37 53	98.92 19	98.34 49
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
GST-MVS		94.21 45	93.97 60	94.90 25	98.41 26	86.82 26	96.54 41	97.19 45	88.24 127	93.26 86	96.83 82	85.48 61	99.59 11	91.43 123	98.40 58	98.30 56

MP-MVS		94.25 42	94.07 56	94.77 40	98.47 21	86.31 46	96.71 36	96.98 65	89.04 95	91.98 125	97.19 65	85.43 62	99.56 17	92.06 105	98.79 28	98.44 43
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
DeepC-MVS_fast	89.43 2	94.04 53	93.79 65	94.80 38	97.48 71	86.78 28	95.65 112	96.89 78	89.40 79	92.81 100	96.97 75	85.37 63	99.24 53	90.87 134	98.69 39	98.38 48
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
fmvsm_s_conf0.5_n_11		94.60 28	95.23 16	92.69 138	96.05 121	82.00 192	96.31 46	96.71 102	92.27 8	96.68 30	98.39 2	85.32 64	98.92 96	97.20 14	98.16 71	97.17 168

region2R		94.43 36	94.27 47	94.92 22	98.65 11	86.67 32	96.92 29	97.23 44	88.60 115	93.58 81	97.27 58	85.22 65	99.54 25	92.21 96	98.74 35	98.56 30

CP-MVS		94.34 40	94.21 50	94.74 42	98.39 29	86.64 34	97.60 5	97.24 42	88.53 117	92.73 105	97.23 61	85.20 66	99.32 47	92.15 99	98.83 26	98.25 70

test_fmvsmconf0.1_n		94.20 47	94.31 43	93.88 71	92.46 334	84.80 89	96.18 59	96.82 86	89.29 85	95.68 47	98.11 11	85.10 67	98.99 83	97.38 11	97.75 96	97.86 114

test12				94.34 58	97.13 81	86.15 53		96.29 133		91.04 164		85.08 68	99.01 76		98.13 75	97.86 114

ACMMPR		94.43 36	94.28 45	94.91 23	98.63 12	86.69 30	96.94 25	97.32 35	88.63 112	93.53 84	97.26 60	85.04 69	99.54 25	92.35 91	98.78 30	98.50 32

SPE-MVS-test		94.02 54	94.29 44	93.24 93	96.69 89	83.24 142	97.49 6	96.92 74	92.14 9	92.90 95	95.77 152	85.02 70	98.33 167	93.03 73	98.62 50	98.13 79

XVS		94.45 34	94.32 41	94.85 28	98.54 16	86.60 36	96.93 27	97.19 45	90.66 36	92.85 97	97.16 68	85.02 70	99.49 31	91.99 107	98.56 54	98.47 38

X-MVStestdata		88.31 242	86.13 291	94.85 28	98.54 16	86.60 36	96.93 27	97.19 45	90.66 36	92.85 97	23.41 537	85.02 70	99.49 31	91.99 107	98.56 54	98.47 38

MVSMamba_PlusPlus		93.44 75	93.54 76	93.14 101	96.58 95	83.05 155	96.06 73	96.50 119	84.42 266	94.09 69	95.56 163	85.01 73	98.69 125	94.96 45	98.66 45	97.67 132

fmvsm_l_conf0.5_n_3		94.80 23	95.01 21	94.15 64	95.64 143	85.08 83	96.09 68	97.36 29	90.98 24	97.09 19	98.12 10	84.98 74	98.94 93	97.07 17	97.80 92	98.43 44

MSLP-MVS++		93.72 66	94.08 55	92.65 141	97.31 76	83.43 135	95.79 98	97.33 33	90.03 53	93.58 81	96.96 76	84.87 75	97.76 233	92.19 98	98.66 45	96.76 205

HPM-MVS		94.02 54	93.88 61	94.43 52	98.39 29	85.78 71	97.25 15	97.07 61	86.90 188	92.62 111	96.80 86	84.85 76	99.17 58	92.43 86	98.65 48	98.33 51
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
fmvsm_s_conf0.5_n_9		94.99 16	95.50 8	93.44 86	96.51 101	82.25 187	95.76 102	96.92 74	93.37 3	97.63 7	98.43 1	84.82 77	99.16 61	98.15 1	97.92 85	98.90 15

SR-MVS		94.23 44	94.17 54	94.43 52	98.21 39	85.78 71	96.40 43	96.90 77	88.20 130	94.33 63	97.40 53	84.75 78	99.03 71	93.35 68	97.99 82	98.48 35

PGM-MVS		93.96 58	93.72 70	94.68 43	98.43 24	86.22 50	95.30 130	97.78 3	87.45 167	93.26 86	97.33 56	84.62 79	99.51 29	90.75 137	98.57 53	98.32 55

BridgeMVS		93.98 57	94.22 48	93.26 92	96.13 111	83.29 141	96.27 53	96.52 117	89.82 60	95.56 49	95.51 166	84.50 80	98.79 114	94.83 47	98.86 21	97.72 129

EI-MVSNet-Vis-set		93.01 92	92.92 89	93.29 90	95.01 174	83.51 134	94.48 191	95.77 199	90.87 25	92.52 113	96.67 89	84.50 80	99.00 81	91.99 107	94.44 185	97.36 152

MTAPA		94.42 39	94.22 48	95.00 19	98.42 25	86.95 22	94.36 211	96.97 66	91.07 22	93.14 90	97.56 45	84.30 82	99.56 17	93.43 65	98.75 34	98.47 38

SR-MVS-dyc-post		93.82 62	93.82 63	93.82 74	97.92 50	84.57 95	96.28 51	96.76 94	87.46 165	93.75 77	97.43 51	84.24 83	99.01 76	92.73 77	97.80 92	97.88 112

ETV-MVS		92.74 98	92.66 95	92.97 113	95.20 166	84.04 118	95.07 151	96.51 118	90.73 34	92.96 94	91.19 348	84.06 84	98.34 165	91.72 116	96.54 128	96.54 217

EI-MVSNet-UG-set		92.74 98	92.62 97	93.12 102	94.86 188	83.20 144	94.40 203	95.74 202	90.71 35	92.05 123	96.60 96	84.00 85	98.99 83	91.55 119	93.63 213	97.17 168

mPP-MVS		93.99 56	93.78 66	94.63 45	98.50 19	85.90 65	96.87 31	96.91 76	88.70 110	91.83 135	97.17 67	83.96 86	99.55 21	91.44 122	98.64 49	98.43 44

APD-MVS_3200maxsize		93.78 63	93.77 67	93.80 76	97.92 50	84.19 112	96.30 47	96.87 80	86.96 184	93.92 75	97.47 49	83.88 87	98.96 90	92.71 80	97.87 88	98.26 69

EIA-MVS		91.95 111	91.94 108	91.98 188	95.16 168	80.01 277	95.36 125	96.73 99	88.44 118	89.34 203	92.16 311	83.82 88	98.45 153	89.35 163	97.06 110	97.48 147

fmvsm_s_conf0.5_n_3		94.49 32	95.13 17	92.56 146	95.49 152	81.10 227	95.93 86	97.16 51	92.96 4	97.39 12	98.13 7	83.63 89	98.80 112	97.89 3	97.61 99	97.78 125

fmvsm_s_conf0.5_n		93.76 64	94.06 58	92.86 120	95.62 145	83.17 146	96.14 64	96.12 166	88.13 133	95.82 43	98.04 30	83.43 90	98.48 145	96.97 21	96.23 135	96.92 195

casdiffmvs_mvg		92.96 93	92.83 91	93.35 88	94.59 212	83.40 137	95.00 156	96.34 130	90.30 46	92.05 123	96.05 125	83.43 90	98.15 180	92.07 102	95.67 148	98.49 34
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
EPP-MVSNet		91.70 128	91.56 118	92.13 183	95.88 131	80.50 257	97.33 8	95.25 250	86.15 208	89.76 196	95.60 160	83.42 92	98.32 169	87.37 198	93.25 228	97.56 141

test_fmvsmvis_n_1920		93.44 75	93.55 75	93.10 103	93.67 285	84.26 110	95.83 95	96.14 162	89.00 100	92.43 116	97.50 48	83.37 93	98.72 121	96.61 24	97.44 102	96.32 222

fmvsm_s_conf0.5_n_5		93.96 58	94.18 53	93.30 89	94.79 192	83.81 123	95.77 100	96.74 98	88.02 140	96.23 33	97.84 38	83.36 94	98.83 110	97.49 8	97.34 106	97.25 160

fmvsm_s_conf0.5_n_6		94.11 52	94.56 33	92.76 129	94.98 178	81.96 196	95.79 98	97.29 40	89.31 83	97.52 11	97.61 44	83.25 95	98.88 100	97.05 19	98.22 69	97.43 151

UA-Net		92.83 94	92.54 98	93.68 82	96.10 116	84.71 91	95.66 110	96.39 126	91.92 11	93.22 88	96.49 100	83.16 96	98.87 101	84.47 244	95.47 155	97.45 149

UniMVSNet_NR-MVSNet		89.92 187	89.29 190	91.81 205	93.39 294	83.72 125	94.43 197	97.12 56	89.80 63	86.46 264	93.32 271	83.16 96	97.23 299	84.92 232	81.02 407	94.49 308

EC-MVSNet		93.44 75	93.71 71	92.63 142	95.21 165	82.43 180	97.27 14	96.71 102	90.57 39	92.88 96	95.80 148	83.16 96	98.16 179	93.68 60	98.14 74	97.31 153

fmvsm_s_conf0.5_n_4		93.86 61	94.37 40	92.33 165	95.13 171	80.95 234	95.64 113	96.97 66	89.60 72	96.85 24	97.77 40	83.08 99	98.92 96	97.49 8	96.78 122	97.13 176

fmvsm_s_conf0.5_n_a		93.57 68	93.76 68	93.00 111	95.02 173	83.67 127	96.19 57	96.10 168	87.27 171	95.98 40	98.05 27	83.07 100	98.45 153	96.68 23	95.51 152	96.88 198

MM		95.10 14	94.91 26	95.68 5	96.09 117	88.34 10	96.68 38	94.37 308	95.08 1	94.68 59	97.72 41	82.94 101	99.64 3	97.85 5	98.76 33	99.06 9

RE-MVS-def			93.68 72		97.92 50	84.57 95	96.28 51	96.76 94	87.46 165	93.75 77	97.43 51	82.94 101		92.73 77	97.80 92	97.88 112

新几何1				93.10 103	97.30 77	84.35 109		95.56 221	71.09 470	91.26 152	96.24 108	82.87 103	98.86 103	79.19 346	98.10 76	96.07 238

fmvsm_s_conf0.1_n		93.46 72	93.66 73	92.85 121	93.75 277	83.13 148	96.02 77	95.74 202	87.68 159	95.89 41	98.17 5	82.78 104	98.46 149	96.71 22	96.17 137	96.98 189

Casviewmamba		92.82 96	92.75 92	93.03 108	94.79 192	82.44 179	95.39 124	96.24 144	90.58 38	91.79 137	96.43 104	82.73 105	98.19 177	91.31 124	95.54 150	98.46 41

原ACMM1				92.01 184	97.34 74	81.05 229		96.81 89	78.89 380	90.45 174	95.92 136	82.65 106	98.84 107	80.68 314	98.26 63	96.14 232

casdiffmvs		92.51 101	92.43 100	92.74 133	94.41 232	81.98 194	94.54 188	96.23 146	89.57 74	91.96 127	96.17 115	82.58 107	98.01 209	90.95 132	95.45 157	98.23 71
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
DeepC-MVS	88.79 3	93.31 81	92.99 88	94.26 62	96.07 119	85.83 69	94.89 162	96.99 64	89.02 98	89.56 198	97.37 55	82.51 108	99.38 36	92.20 97	98.30 61	97.57 140
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
HPM-MVS_fast		93.40 80	93.22 82	93.94 70	98.36 32	84.83 88	97.15 18	96.80 90	85.77 217	92.47 115	97.13 69	82.38 109	99.07 66	90.51 142	98.40 58	97.92 108

baseline		92.39 106	92.29 104	92.69 138	94.46 227	81.77 204	94.14 223	96.27 137	89.22 87	91.88 131	96.00 129	82.35 110	97.99 211	91.05 127	95.27 163	98.30 56

sasdasda		93.27 82	92.75 92	94.85 28	95.70 140	87.66 13	96.33 44	96.41 124	90.00 54	94.09 69	94.60 219	82.33 111	98.62 134	92.40 88	92.86 240	98.27 65

canonicalmvs		93.27 82	92.75 92	94.85 28	95.70 140	87.66 13	96.33 44	96.41 124	90.00 54	94.09 69	94.60 219	82.33 111	98.62 134	92.40 88	92.86 240	98.27 65

fmvsm_s_conf0.1_n_a		93.19 86	93.26 80	92.97 113	92.49 332	83.62 130	96.02 77	95.72 206	86.78 190	96.04 38	98.19 4	82.30 113	98.43 157	96.38 25	95.42 158	96.86 199

DP-MVS Recon		91.95 111	91.28 132	93.96 69	98.33 34	85.92 62	94.66 182	96.66 106	82.69 310	90.03 191	95.82 146	82.30 113	99.03 71	84.57 242	96.48 131	96.91 196

hybridcas		92.43 104	92.33 101	92.74 133	94.51 220	81.84 198	95.05 154	96.16 160	89.60 72	91.40 149	96.20 110	82.23 115	98.09 191	89.95 152	95.87 142	98.28 62

PAPR		90.02 181	89.27 192	92.29 172	95.78 135	80.95 234	92.68 316	96.22 147	81.91 329	86.66 261	93.75 261	82.23 115	98.44 155	79.40 345	94.79 171	97.48 147

MVS_Test		91.31 137	91.11 135	91.93 193	94.37 233	80.14 266	93.46 274	95.80 197	86.46 199	91.35 151	93.77 259	82.21 117	98.09 191	87.57 193	94.95 167	97.55 143

nrg030		91.08 148	90.39 154	93.17 99	93.07 306	86.91 23	96.41 42	96.26 141	88.30 124	88.37 223	94.85 206	82.19 118	97.64 244	91.09 126	82.95 377	94.96 282

MGCFI-Net		93.03 91	92.63 96	94.23 63	95.62 145	85.92 62	96.08 69	96.33 131	89.86 58	93.89 76	94.66 216	82.11 119	98.50 143	92.33 93	92.82 243	98.27 65

UniMVSNet (Re)		89.80 191	89.07 196	92.01 184	93.60 288	84.52 98	94.78 173	97.47 16	89.26 86	86.44 267	92.32 306	82.10 120	97.39 283	84.81 235	80.84 411	94.12 322

testdata				90.49 272	96.40 102	77.89 347		95.37 241	72.51 462	93.63 80	96.69 87	82.08 121	97.65 242	83.08 263	97.39 103	95.94 243

PAPM_NR		91.22 140	90.78 146	92.52 150	97.60 66	81.46 213	94.37 209	96.24 144	86.39 202	87.41 244	94.80 208	82.06 122	98.48 145	82.80 271	95.37 159	97.61 136

MG-MVS		91.77 119	91.70 112	92.00 187	97.08 82	80.03 276	93.60 269	95.18 256	87.85 152	90.89 167	96.47 102	82.06 122	98.36 162	85.07 230	97.04 111	97.62 134

CANet		93.54 69	93.20 83	94.55 48	95.65 142	85.73 73	94.94 159	96.69 105	91.89 12	90.69 169	95.88 139	81.99 124	99.54 25	93.14 71	97.95 84	98.39 46

FC-MVSNet-test		90.27 172	90.18 160	90.53 265	93.71 282	79.85 285	95.77 100	97.59 6	89.31 83	86.27 271	94.67 215	81.93 125	97.01 318	84.26 246	88.09 322	94.71 294

fmvsm_s_conf0.5_n_10		94.43 36	94.84 29	93.20 95	95.73 137	83.19 145	95.99 79	97.31 37	91.08 21	97.67 4	98.11 11	81.87 126	99.22 54	97.86 4	97.91 87	97.20 166

fmvsm_s_conf0.5_n_7		93.15 89	93.76 68	91.31 229	94.42 231	79.48 297	94.52 189	97.14 54	89.33 82	94.17 67	98.09 18	81.83 127	97.49 260	96.33 26	98.02 81	96.95 191

FIs		90.51 168	90.35 155	90.99 247	93.99 264	80.98 232	95.73 104	97.54 9	89.15 90	86.72 260	94.68 212	81.83 127	97.24 298	85.18 228	88.31 319	94.76 293

fmvsm_s_conf0.5_n_8		94.56 30	95.12 18	92.87 119	95.96 129	81.32 217	95.76 102	97.57 7	93.48 2	97.53 10	98.32 3	81.78 129	99.13 63	97.91 2	97.81 91	98.16 76

E3new		91.76 120	91.58 116	92.28 176	94.69 205	80.90 237	93.68 267	96.17 158	87.15 175	91.09 163	95.70 156	81.75 130	98.05 201	89.67 160	94.35 187	97.90 111

viewcassd2359sk11		91.79 114	91.62 113	92.29 172	94.62 208	80.88 238	93.70 264	96.18 157	87.38 169	91.13 156	95.85 143	81.62 131	98.06 197	89.71 157	94.40 186	97.94 99

ACMMP		93.24 84	92.88 90	94.30 60	98.09 45	85.33 80	96.86 32	97.45 19	88.33 122	90.15 189	97.03 74	81.44 132	99.51 29	90.85 135	95.74 147	98.04 91
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
Effi-MVS+		91.59 131	91.11 135	93.01 110	94.35 237	83.39 138	94.60 184	95.10 260	87.10 178	90.57 173	93.10 282	81.43 133	98.07 196	89.29 165	94.48 183	97.59 139

MVS_111021_LR		92.47 103	92.29 104	92.98 112	95.99 126	84.43 104	93.08 295	96.09 169	88.20 130	91.12 157	95.72 155	81.33 134	97.76 233	91.74 115	97.37 104	96.75 206

E2		91.79 114	91.61 114	92.31 167	94.49 223	80.86 240	93.74 259	96.19 151	87.63 162	91.16 153	95.94 134	81.31 135	98.06 197	89.76 155	94.29 190	97.99 94

E3		91.78 117	91.61 114	92.30 170	94.48 224	80.86 240	93.73 260	96.19 151	87.63 162	91.16 153	95.95 133	81.30 136	98.06 197	89.76 155	94.29 190	97.99 94

mvs_anonymous		89.37 210	89.32 189	89.51 333	93.47 291	74.22 407	91.65 357	94.83 285	82.91 305	85.45 296	93.79 257	81.23 137	96.36 371	86.47 210	94.09 195	97.94 99

PVSNet_BlendedMVS		89.98 182	89.70 175	90.82 256	96.12 112	81.25 219	93.92 247	96.83 84	83.49 287	89.10 207	92.26 309	81.04 138	98.85 105	86.72 208	87.86 326	92.35 414

PVSNet_Blended		90.73 156	90.32 156	91.98 188	96.12 112	81.25 219	92.55 321	96.83 84	82.04 325	89.10 207	92.56 299	81.04 138	98.85 105	86.72 208	95.91 141	95.84 249

viewmanbaseed2359cas		91.78 117	91.58 116	92.37 160	94.32 240	81.07 228	93.76 257	95.96 182	87.26 172	91.50 144	95.88 139	80.92 140	97.97 216	89.70 158	94.92 168	98.07 84

E4		91.74 122	91.55 119	92.31 167	94.27 245	80.80 244	93.81 254	96.17 158	87.97 142	91.11 158	96.05 125	80.75 141	98.08 194	89.78 154	94.02 197	98.06 89

viewmamba		91.38 134	91.32 129	91.58 214	93.02 313	79.63 294	92.83 309	95.38 238	88.29 125	90.66 170	95.81 147	80.63 142	97.50 259	91.52 120	93.71 211	97.62 134

MGCNet		94.18 50	93.80 64	95.34 10	94.91 185	87.62 15	95.97 82	93.01 359	92.58 6	94.22 64	97.20 64	80.56 143	99.59 11	97.04 20	98.68 41	98.81 22

alignmvs		93.08 90	92.50 99	94.81 36	95.62 145	87.61 16	95.99 79	96.07 171	89.77 67	94.12 68	94.87 203	80.56 143	98.66 126	92.42 87	93.10 235	98.15 77

API-MVS		90.66 162	90.07 164	92.45 155	96.36 104	84.57 95	96.06 73	95.22 253	82.39 313	89.13 206	94.27 236	80.32 145	98.46 149	80.16 324	96.71 124	94.33 314

PVSNet_Blended_VisFu		91.38 134	90.91 142	92.80 124	96.39 103	83.17 146	94.87 164	96.66 106	83.29 293	89.27 205	94.46 228	80.29 146	99.17 58	87.57 193	95.37 159	96.05 241

viewdifsd2359ckpt07		91.11 146	91.02 139	91.41 224	94.21 250	78.37 331	92.91 305	95.71 207	87.50 164	90.32 179	95.88 139	80.27 147	97.99 211	88.78 176	93.55 215	97.86 114

test222					96.55 96	81.70 205	92.22 339	95.01 264	68.36 479	90.20 182	96.14 120	80.26 148			97.80 92	96.05 241

diffmvs		91.37 136	91.23 133	91.77 206	93.09 304	80.27 261	92.36 327	95.52 226	87.03 181	91.40 149	94.93 199	80.08 149	97.44 269	92.13 101	94.56 180	97.61 136
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
Test By Simon												80.02 150

onestephybrid01		91.23 138	91.10 137	91.61 212	93.07 306	79.86 283	92.83 309	95.34 244	87.07 179	91.04 164	95.53 164	80.01 151	97.43 270	90.96 131	94.08 196	97.56 141

diffmvs_AUTHOR		91.51 132	91.44 125	91.73 207	93.09 304	80.27 261	92.51 322	95.58 220	87.22 173	91.80 136	95.57 162	79.96 152	97.48 261	92.23 95	94.97 166	97.45 149

E5new		91.71 124	91.55 119	92.20 178	94.33 238	80.62 250	94.41 199	96.19 151	88.06 136	91.11 158	96.16 116	79.92 153	98.03 205	90.00 147	93.80 206	97.94 99

E6new		91.71 124	91.55 119	92.20 178	94.32 240	80.62 250	94.41 199	96.19 151	88.06 136	91.11 158	96.16 116	79.92 153	98.03 205	90.00 147	93.80 206	97.94 99

E6		91.71 124	91.55 119	92.20 178	94.32 240	80.62 250	94.41 199	96.19 151	88.06 136	91.11 158	96.16 116	79.92 153	98.03 205	90.00 147	93.80 206	97.94 99

E5		91.71 124	91.55 119	92.20 178	94.33 238	80.62 250	94.41 199	96.19 151	88.06 136	91.11 158	96.16 116	79.92 153	98.03 205	90.00 147	93.80 206	97.94 99

viewmacassd2359aftdt		91.67 130	91.43 126	92.37 160	93.95 268	81.00 231	93.90 251	95.97 181	87.75 157	91.45 147	96.04 127	79.92 153	97.97 216	89.26 166	94.67 174	98.14 78

IterMVS-LS		88.36 241	87.91 235	89.70 317	93.80 274	78.29 335	93.73 260	95.08 262	85.73 218	84.75 317	91.90 327	79.88 158	96.92 324	83.83 253	82.51 383	93.89 334
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
EI-MVSNet		89.10 215	88.86 207	89.80 311	91.84 354	78.30 334	93.70 264	95.01 264	85.73 218	87.15 248	95.28 179	79.87 159	97.21 301	83.81 254	87.36 334	93.88 337

TAPA-MVS	84.62 6	88.16 246	87.01 256	91.62 211	96.64 91	80.65 247	94.39 205	96.21 150	76.38 421	86.19 274	95.44 169	79.75 160	98.08 194	62.75 471	95.29 161	96.13 233
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
Fast-Effi-MVS+		89.41 206	88.64 210	91.71 209	94.74 197	80.81 243	93.54 270	95.10 260	83.11 297	86.82 259	90.67 371	79.74 161	97.75 237	80.51 317	93.55 215	96.57 215

pcd_1.5k_mvsjas		6.64 523	8.86 524	0.00 539	0.00 563	0.00 566	0.00 551	0.00 565	0.00 558	0.00 559	0.00 557	79.70 162	0.00 559	0.00 558	0.00 558	0.00 555

PS-MVSNAJss		89.97 183	89.62 178	91.02 244	91.90 352	80.85 242	95.26 136	95.98 178	86.26 205	86.21 273	94.29 233	79.70 162	97.65 242	88.87 175	88.10 320	94.57 300

PS-MVSNAJ		91.18 142	90.92 141	91.96 190	95.26 163	82.60 177	92.09 344	95.70 208	86.27 204	91.84 133	92.46 301	79.70 162	98.99 83	89.08 168	95.86 143	94.29 315

viewdifsd2359ckpt09		91.18 142	90.65 150	92.75 131	94.61 211	82.36 185	94.32 212	95.74 202	84.72 259	89.66 197	95.15 190	79.69 165	98.04 202	87.70 190	94.27 192	97.85 117

viewdifsd2359ckpt13		91.20 141	90.75 147	92.54 148	94.30 243	82.13 189	94.03 236	95.89 190	85.60 223	90.20 182	95.36 175	79.69 165	97.90 226	87.85 188	93.86 202	97.61 136

xiu_mvs_v2_base		91.13 144	90.89 143	91.86 199	94.97 179	82.42 181	92.24 337	95.64 216	86.11 212	91.74 140	93.14 280	79.67 167	98.89 99	89.06 169	95.46 156	94.28 316

WR-MVS_H		87.80 255	87.37 246	89.10 342	93.23 297	78.12 338	95.61 115	97.30 38	87.90 148	83.72 348	92.01 322	79.65 168	96.01 386	76.36 376	80.54 415	93.16 377

viewmambaseed2359dif		90.04 180	89.78 174	90.83 254	92.85 321	77.92 343	92.23 338	95.01 264	81.90 330	90.20 182	95.45 168	79.64 169	97.34 287	87.52 195	93.17 230	97.23 165

EPNet		91.79 114	91.02 139	94.10 65	90.10 420	85.25 81	96.03 76	92.05 386	92.83 5	87.39 247	95.78 151	79.39 170	99.01 76	88.13 183	97.48 101	98.05 90
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
miper_ehance_all_eth		87.22 286	86.62 272	89.02 346	92.13 343	77.40 364	90.91 381	94.81 287	81.28 349	84.32 334	90.08 389	79.26 171	96.62 342	83.81 254	82.94 378	93.04 384

test_fmvsmconf0.01_n		93.19 86	93.02 87	93.71 81	89.25 433	84.42 106	96.06 73	96.29 133	89.06 93	94.68 59	98.13 7	79.22 172	98.98 87	97.22 13	97.24 107	97.74 127

miper_enhance_ethall		86.90 298	86.18 289	89.06 344	91.66 363	77.58 362	90.22 400	94.82 286	79.16 376	84.48 325	89.10 408	79.19 173	96.66 335	84.06 249	82.94 378	92.94 387

hybridnocas07		90.93 149	90.72 148	91.54 216	92.75 326	79.72 291	92.35 329	95.21 254	86.41 201	90.44 177	95.40 172	79.17 174	97.39 283	90.83 136	93.94 200	97.50 146

NR-MVSNet		88.58 235	87.47 244	91.93 193	93.04 310	84.16 113	94.77 174	96.25 143	89.05 94	80.04 406	93.29 274	79.02 175	97.05 315	81.71 297	80.05 421	94.59 298

hybrid		90.69 158	90.45 153	91.43 223	92.67 330	79.42 302	92.28 336	95.21 254	85.15 244	90.39 178	95.37 174	78.93 176	97.32 289	90.27 145	93.74 210	97.55 143

TAMVS		89.21 212	88.29 223	91.96 190	93.71 282	82.62 176	93.30 284	94.19 316	82.22 319	87.78 238	93.94 249	78.83 177	96.95 322	77.70 362	92.98 237	96.32 222

c3_l		87.14 291	86.50 278	89.04 345	92.20 340	77.26 366	91.22 373	94.70 293	82.01 326	84.34 333	90.43 376	78.81 178	96.61 345	83.70 258	81.09 404	93.25 371

1112_ss		88.42 237	87.33 247	91.72 208	94.92 183	80.98 232	92.97 303	94.54 299	78.16 398	83.82 345	93.88 254	78.78 179	97.91 224	79.45 341	89.41 299	96.26 226

CDS-MVSNet		89.45 202	88.51 214	92.29 172	93.62 287	83.61 132	93.01 299	94.68 294	81.95 327	87.82 237	93.24 276	78.69 180	96.99 319	80.34 320	93.23 229	96.28 225
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
WTY-MVS		89.60 196	88.92 203	91.67 210	95.47 153	81.15 224	92.38 326	94.78 289	83.11 297	89.06 209	94.32 231	78.67 181	96.61 345	81.57 298	90.89 273	97.24 161

CPTT-MVS		91.99 110	91.80 110	92.55 147	98.24 38	81.98 194	96.76 35	96.49 120	81.89 332	90.24 180	96.44 103	78.59 182	98.61 137	89.68 159	97.85 89	97.06 181

IS-MVSNet		91.43 133	91.09 138	92.46 153	95.87 133	81.38 216	96.95 24	93.69 343	89.72 69	89.50 201	95.98 131	78.57 183	97.77 232	83.02 265	96.50 130	98.22 72

dtuplus		89.78 193	89.43 184	90.85 253	92.83 322	77.91 344	92.32 334	94.97 270	82.33 317	90.20 182	95.53 164	78.56 184	97.38 285	85.15 229	92.95 238	97.24 161

casdiffseed414692147		91.11 146	90.55 152	92.81 122	94.27 245	82.58 178	94.81 170	96.03 176	87.93 146	90.17 187	95.62 159	78.51 185	97.90 226	84.18 248	93.45 222	97.94 99

OMC-MVS		91.23 138	90.62 151	93.08 105	96.27 106	84.07 114	93.52 271	95.93 184	86.95 185	89.51 199	96.13 121	78.50 186	98.35 164	85.84 221	92.90 239	96.83 204

IMVS_0403		89.97 183	89.64 177	90.96 250	93.72 278	77.75 355	93.00 300	95.34 244	85.53 227	88.77 216	94.49 224	78.49 187	97.84 229	84.75 236	92.65 245	97.28 156

PCF-MVS	84.11 10	87.74 257	86.08 295	92.70 137	94.02 259	84.43 104	89.27 420	95.87 193	73.62 452	84.43 328	94.33 230	78.48 188	98.86 103	70.27 427	94.45 184	94.81 291
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
LCM-MVSNet-Re		88.30 243	88.32 222	88.27 367	94.71 202	72.41 434	93.15 290	90.98 418	87.77 155	79.25 421	91.96 324	78.35 189	95.75 400	83.04 264	95.62 149	96.65 211

HY-MVS	83.01 12	89.03 221	87.94 232	92.29 172	94.86 188	82.77 163	92.08 345	94.49 302	81.52 345	86.93 251	92.79 293	78.32 190	98.23 173	79.93 327	90.55 277	95.88 247

GeoE		90.05 179	89.43 184	91.90 198	95.16 168	80.37 260	95.80 96	94.65 295	83.90 274	87.55 243	94.75 209	78.18 191	97.62 246	81.28 302	93.63 213	97.71 130

NormalMVS		93.46 72	93.16 84	94.37 57	98.40 27	86.20 51	96.30 47	96.27 137	91.65 17	92.68 107	96.13 121	77.97 192	98.84 107	90.75 137	98.26 63	98.07 84

SymmetryMVS		92.81 97	92.31 102	94.32 59	96.15 109	86.20 51	96.30 47	94.43 304	91.65 17	92.68 107	96.13 121	77.97 192	98.84 107	90.75 137	94.72 172	97.92 108

MVS		87.44 274	86.10 294	91.44 222	92.61 331	83.62 130	92.63 318	95.66 213	67.26 482	81.47 384	92.15 312	77.95 194	98.22 175	79.71 330	95.48 154	92.47 407

MVSFormer		91.68 129	91.30 130	92.80 124	93.86 270	83.88 121	95.96 83	95.90 188	84.66 262	91.76 138	94.91 200	77.92 195	97.30 290	89.64 161	97.11 108	97.24 161

lupinMVS		90.92 150	90.21 158	93.03 108	93.86 270	83.88 121	92.81 311	93.86 330	79.84 367	91.76 138	94.29 233	77.92 195	98.04 202	90.48 143	97.11 108	97.17 168

Test_1112_low_res		87.65 260	86.51 277	91.08 240	94.94 182	79.28 311	91.77 352	94.30 311	76.04 427	83.51 355	92.37 304	77.86 197	97.73 238	78.69 352	89.13 306	96.22 227

VNet		92.24 107	91.91 109	93.24 93	96.59 93	83.43 135	94.84 168	96.44 121	89.19 89	94.08 72	95.90 137	77.85 198	98.17 178	88.90 173	93.38 224	98.13 79

icg_test_0407_2		89.15 213	88.97 200	89.68 323	93.72 278	77.75 355	88.26 439	95.34 244	85.53 227	88.34 224	94.49 224	77.69 199	93.99 439	84.75 236	92.65 245	97.28 156

IMVS_0407		89.85 190	89.51 181	90.88 252	93.72 278	77.75 355	93.07 297	95.34 244	85.53 227	88.34 224	94.49 224	77.69 199	97.60 247	84.75 236	92.65 245	97.28 156

fmvsm_s_conf0.5_n_2		93.47 71	93.83 62	92.39 159	95.36 156	81.19 223	95.20 144	96.56 114	90.37 42	97.13 18	98.03 31	77.47 201	98.96 90	97.79 6	96.58 127	97.03 184

mvsany_test1		85.42 339	85.30 324	85.77 426	87.95 452	75.41 395	87.61 453	80.97 493	76.82 417	88.68 217	95.83 145	77.44 202	90.82 479	85.90 219	86.51 341	91.08 446

DU-MVS		89.34 211	88.50 215	91.85 201	93.04 310	83.72 125	94.47 194	96.59 111	89.50 75	86.46 264	93.29 274	77.25 203	97.23 299	84.92 232	81.02 407	94.59 298

Baseline_NR-MVSNet		87.07 293	86.63 271	88.40 361	91.44 367	77.87 348	94.23 219	92.57 371	84.12 270	85.74 284	92.08 318	77.25 203	96.04 382	82.29 280	79.94 422	91.30 438

jason		90.80 152	90.10 162	92.90 117	93.04 310	83.53 133	93.08 295	94.15 319	80.22 361	91.41 148	94.91 200	76.87 205	97.93 222	90.28 144	96.90 117	97.24 161
jason: jason.
PAPM		86.68 310	85.39 320	90.53 265	93.05 309	79.33 310	89.79 410	94.77 290	78.82 383	81.95 380	93.24 276	76.81 206	97.30 290	66.94 451	93.16 231	94.95 286

Vis-MVSNet (Re-imp)		89.59 197	89.44 183	90.03 297	95.74 136	75.85 389	95.61 115	90.80 425	87.66 161	87.83 236	95.40 172	76.79 207	96.46 363	78.37 353	96.73 123	97.80 123

baseline1		88.10 247	87.28 249	90.57 261	94.96 180	80.07 271	94.27 215	91.29 411	86.74 191	87.41 244	94.00 246	76.77 208	96.20 377	80.77 311	79.31 430	95.44 263

114514_t		89.51 199	88.50 215	92.54 148	98.11 43	81.99 193	95.16 147	96.36 129	70.19 474	85.81 281	95.25 181	76.70 209	98.63 133	82.07 286	96.86 120	97.00 188

PLC	84.53 7	89.06 219	88.03 228	92.15 182	97.27 79	82.69 170	94.29 214	95.44 234	79.71 369	84.01 342	94.18 239	76.68 210	98.75 117	77.28 366	93.41 223	95.02 278
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
TranMVSNet+NR-MVSNet		88.84 225	87.95 231	91.49 219	92.68 329	83.01 158	94.92 161	96.31 132	89.88 57	85.53 290	93.85 256	76.63 211	96.96 321	81.91 290	79.87 424	94.50 306

MAR-MVS		90.30 171	89.37 187	93.07 107	96.61 92	84.48 100	95.68 107	95.67 211	82.36 315	87.85 234	92.85 287	76.63 211	98.80 112	80.01 326	96.68 125	95.91 244
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
fmvsm_s_conf0.1_n_2		93.16 88	93.42 77	92.37 160	94.62 208	81.13 225	95.23 137	95.89 190	90.30 46	96.74 29	98.02 32	76.14 213	98.95 92	97.64 7	96.21 136	97.03 184

SSM_0407		90.47 169	89.80 173	92.46 153	94.76 194	82.66 171	93.98 243	95.00 268	85.41 232	88.96 211	95.35 176	76.13 214	97.88 228	85.46 226	93.15 232	96.85 200

SSM_0404		90.73 156	90.08 163	92.69 138	95.00 177	83.13 148	94.32 212	95.00 268	85.41 232	89.84 192	95.35 176	76.13 214	97.98 214	85.46 226	94.18 194	96.95 191

WR-MVS		88.38 239	87.67 239	90.52 269	93.30 296	80.18 264	93.26 287	95.96 182	88.57 116	85.47 295	92.81 291	76.12 216	96.91 325	81.24 303	82.29 387	94.47 311

v8		87.50 273	86.71 265	89.89 304	91.37 372	79.40 303	94.50 190	95.38 238	84.81 256	83.60 353	91.33 343	76.05 217	97.42 272	82.84 269	80.51 418	92.84 391

v148		87.04 294	86.32 284	89.21 338	90.94 392	77.26 366	93.71 263	94.43 304	84.84 255	84.36 332	90.80 365	76.04 218	97.05 315	82.12 283	79.60 427	93.31 368

mamba_0408		89.06 219	87.92 233	92.50 151	94.76 194	82.66 171	79.84 497	94.64 296	85.18 237	88.96 211	95.00 195	76.00 219	97.98 214	83.74 256	93.15 232	96.85 200

SSM_04072		88.57 236	87.92 233	90.51 270	94.76 194	82.66 171	79.84 497	94.64 296	85.18 237	88.96 211	95.00 195	76.00 219	92.03 464	83.74 256	93.15 232	96.85 200

eth_miper_zixun_eth		86.50 317	85.77 309	88.68 355	91.94 349	75.81 390	90.47 392	94.89 279	82.05 323	84.05 340	90.46 375	75.96 221	96.77 329	82.76 272	79.36 429	93.46 364

3Dnovator+	87.14 4	92.42 105	91.37 127	95.55 7	95.63 144	88.73 7	97.07 23	96.77 93	90.84 26	84.02 341	96.62 95	75.95 222	99.34 43	87.77 189	97.68 97	98.59 29

balanced_ft_v1		92.23 108	92.05 107	92.77 126	95.40 155	81.78 203	95.80 96	95.69 210	87.94 144	91.92 130	95.04 193	75.91 223	98.71 123	93.83 59	96.94 114	97.82 121

h-mvs33		90.80 152	90.15 161	92.75 131	96.01 122	82.66 171	95.43 123	95.53 225	89.80 63	93.08 91	95.64 158	75.77 224	99.00 81	92.07 102	78.05 434	96.60 212

hse-mvs2		89.88 189	89.34 188	91.51 218	94.83 190	81.12 226	93.94 245	93.91 329	89.80 63	93.08 91	93.60 264	75.77 224	97.66 241	92.07 102	77.07 442	95.74 254

BH-untuned		88.60 233	88.13 227	90.01 300	95.24 164	78.50 327	93.29 285	94.15 319	84.75 258	84.46 326	93.40 268	75.76 226	97.40 280	77.59 363	94.52 182	94.12 322

DIV-MVS_self_test		86.53 315	85.78 307	88.75 352	92.02 348	76.45 381	90.74 383	94.30 311	81.83 335	83.34 361	90.82 364	75.75 227	96.57 352	81.73 296	81.52 399	93.24 372

BH-w/o		87.57 269	87.05 254	89.12 341	94.90 186	77.90 346	92.41 324	93.51 346	82.89 306	83.70 349	91.34 342	75.75 227	97.07 312	75.49 384	93.49 219	92.39 412

cl____		86.52 316	85.78 307	88.75 352	92.03 347	76.46 380	90.74 383	94.30 311	81.83 335	83.34 361	90.78 366	75.74 229	96.57 352	81.74 295	81.54 398	93.22 373

cdsmvs_eth3d_5k		22.14 495	29.52 491	0.00 539	0.00 563	0.00 566	0.00 551	95.76 200	0.00 558	0.00 559	94.29 233	75.66 230	0.00 559	0.00 558	0.00 558	0.00 555

CNLPA		89.07 218	87.98 230	92.34 164	96.87 85	84.78 90	94.08 231	93.24 351	81.41 346	84.46 326	95.13 191	75.57 231	96.62 342	77.21 367	93.84 204	95.61 261

CHOSEN 1792x2688		88.84 225	87.69 238	92.30 170	96.14 110	81.42 215	90.01 407	95.86 194	74.52 442	87.41 244	93.94 249	75.46 232	98.36 162	80.36 319	95.53 151	97.12 177

CP-MVSNet		87.63 263	87.26 251	88.74 354	93.12 302	76.59 379	95.29 132	96.58 112	88.43 119	83.49 358	92.98 285	75.28 233	95.83 395	78.97 348	81.15 403	93.79 344

v10		87.25 283	86.38 280	89.85 306	91.19 378	79.50 296	94.48 191	95.45 232	83.79 279	83.62 352	91.19 348	75.13 234	97.42 272	81.94 289	80.60 413	92.63 398

Vis-MVSNet		91.75 121	91.23 133	93.29 90	95.32 158	83.78 124	96.14 64	95.98 178	89.89 56	90.45 174	96.58 97	75.09 235	98.31 170	84.75 236	96.90 117	97.78 125
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
PRO-TEST		90.79 154	91.35 128	89.09 343	95.56 150	70.84 452	94.18 221	95.64 216	88.41 121	88.10 226	94.99 198	75.04 236	98.62 134	92.70 81	97.56 100	97.81 122

sss		88.93 224	88.26 225	90.94 251	94.05 258	80.78 245	91.71 354	95.38 238	81.55 344	88.63 218	93.91 253	75.04 236	95.47 413	82.47 275	91.61 261	96.57 215

v1144		87.61 266	86.79 263	90.06 295	91.01 387	79.34 307	93.95 244	95.42 237	83.36 292	85.66 286	91.31 346	74.98 238	97.42 272	83.37 260	82.06 389	93.42 365

miper_lstm_enhance		85.27 344	84.59 342	87.31 394	91.28 376	74.63 402	87.69 450	94.09 323	81.20 353	81.36 387	89.85 397	74.97 239	94.30 433	81.03 307	79.84 425	93.01 385

test_yl		90.69 158	90.02 168	92.71 135	95.72 138	82.41 183	94.11 226	95.12 258	85.63 221	91.49 145	94.70 210	74.75 240	98.42 158	86.13 216	92.53 252	97.31 153

DCV-MVSNet		90.69 158	90.02 168	92.71 135	95.72 138	82.41 183	94.11 226	95.12 258	85.63 221	91.49 145	94.70 210	74.75 240	98.42 158	86.13 216	92.53 252	97.31 153

V42		87.68 258	86.86 258	90.15 289	90.58 408	80.14 266	94.24 218	95.28 249	83.66 281	85.67 285	91.33 343	74.73 242	97.41 278	84.43 245	81.83 393	92.89 389

FA-MVS(test-final)		89.66 194	88.91 204	91.93 193	94.57 216	80.27 261	91.36 365	94.74 291	84.87 253	89.82 193	92.61 298	74.72 243	98.47 148	83.97 251	93.53 217	97.04 183

viewdifsd2359ckpt11		89.43 204	89.05 198	90.56 263	92.89 319	77.00 370	92.81 311	94.52 300	87.03 181	89.77 194	95.79 149	74.67 244	97.51 255	88.97 171	84.98 354	97.17 168

viewmsd2359difaftdt		89.43 204	89.05 198	90.56 263	92.89 319	77.00 370	92.81 311	94.52 300	87.03 181	89.77 194	95.79 149	74.67 244	97.51 255	88.97 171	84.98 354	97.17 168

XVG-OURS-SEG-HR		89.95 185	89.45 182	91.47 221	94.00 263	81.21 222	91.87 349	96.06 173	85.78 216	88.55 219	95.73 154	74.67 244	97.27 294	88.71 177	89.64 297	95.91 244

BP-MVS1		92.48 102	92.07 106	93.72 80	94.50 222	84.39 107	95.90 89	94.30 311	90.39 41	92.67 109	95.94 134	74.46 247	98.65 128	93.14 71	97.35 105	98.13 79

v2v482		87.84 253	87.06 253	90.17 287	90.99 388	79.23 314	94.00 241	95.13 257	84.87 253	85.53 290	92.07 320	74.45 248	97.45 266	84.71 241	81.75 395	93.85 341

CLD-MVS		89.47 201	88.90 205	91.18 235	94.22 249	82.07 191	92.13 342	96.09 169	87.90 148	85.37 305	92.45 302	74.38 249	97.56 251	87.15 201	90.43 279	93.93 333
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
XXY-MVS		87.65 260	86.85 259	90.03 297	92.14 342	80.60 254	93.76 257	95.23 251	82.94 304	84.60 320	94.02 244	74.27 250	95.49 412	81.04 305	83.68 369	94.01 330

HQP_MVS		90.60 166	90.19 159	91.82 203	94.70 203	82.73 167	95.85 93	96.22 147	90.81 27	86.91 253	94.86 204	74.23 251	98.12 181	88.15 181	89.99 286	94.63 295

plane_prior6					94.52 219	82.75 164						74.23 251

v144192		87.19 289	86.35 282	89.74 314	90.64 406	78.24 336	93.92 247	95.43 235	81.93 328	85.51 292	91.05 357	74.21 253	97.45 266	82.86 268	81.56 397	93.53 359

VPA-MVSNet		89.62 195	88.96 201	91.60 213	93.86 270	82.89 162	95.46 121	97.33 33	87.91 147	88.43 222	93.31 272	74.17 254	97.40 280	87.32 199	82.86 382	94.52 303

ab-mvs		89.41 206	88.35 219	92.60 143	95.15 170	82.65 175	92.20 340	95.60 219	83.97 273	88.55 219	93.70 263	74.16 255	98.21 176	82.46 276	89.37 300	96.94 193

1314		87.51 271	86.57 274	90.34 283	92.42 336	79.74 290	92.63 318	95.35 243	78.35 393	80.14 403	91.62 337	74.05 256	97.15 303	81.05 304	93.53 217	94.12 322

test_djsdf		89.03 221	88.64 210	90.21 286	90.74 403	79.28 311	95.96 83	95.90 188	84.66 262	85.33 307	92.94 286	74.02 257	97.30 290	89.64 161	88.53 312	94.05 328

cl22		86.78 304	85.98 299	89.18 340	92.34 337	77.62 361	90.84 382	94.13 321	81.33 348	83.97 343	90.15 386	73.96 258	96.60 349	84.19 247	82.94 378	93.33 367

SD_0403		84.71 357	84.65 339	84.92 437	92.95 316	65.95 475	92.07 346	93.23 352	83.82 278	79.03 422	93.73 262	73.90 259	92.91 457	63.02 470	90.05 285	95.89 246

AdaColmap		89.89 188	89.07 196	92.37 160	97.41 72	83.03 156	94.42 198	95.92 185	82.81 307	86.34 270	94.65 217	73.89 260	99.02 74	80.69 313	95.51 152	95.05 277

HyFIR lowres test		88.09 248	86.81 261	91.93 193	96.00 123	80.63 248	90.01 407	95.79 198	73.42 454	87.68 240	92.10 317	73.86 261	97.96 218	80.75 312	91.70 260	97.19 167

HQP2-MVS												73.83 262

HQP-MVS		89.80 191	89.28 191	91.34 228	94.17 252	81.56 207	94.39 205	96.04 174	88.81 104	85.43 299	93.97 248	73.83 262	97.96 218	87.11 203	89.77 295	94.50 306

3Dnovator	86.66 5	91.73 123	90.82 145	94.44 50	94.59 212	86.37 43	97.18 17	97.02 63	89.20 88	84.31 336	96.66 90	73.74 264	99.17 58	86.74 206	97.96 83	97.79 124

EPNet_dtu		86.49 319	85.94 302	88.14 372	90.24 418	72.82 424	94.11 226	92.20 382	86.66 195	79.42 417	92.36 305	73.52 265	95.81 397	71.26 417	93.66 212	95.80 252
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
TransMVSNet (Re)		84.43 361	83.06 369	88.54 358	91.72 359	78.44 328	95.18 145	92.82 365	82.73 309	79.67 414	92.12 314	73.49 266	95.96 388	71.10 422	68.73 479	91.21 440

Effi-MVS+-dtu		88.65 231	88.35 219	89.54 328	93.33 295	76.39 382	94.47 194	94.36 309	87.70 158	85.43 299	89.56 403	73.45 267	97.26 296	85.57 224	91.28 265	94.97 279

GDP-MVS		92.04 109	91.46 124	93.75 79	94.55 218	84.69 92	95.60 118	96.56 114	87.83 153	93.07 93	95.89 138	73.44 268	98.65 128	90.22 146	96.03 140	97.91 110

baseline2		86.50 317	85.39 320	89.84 307	91.12 383	76.70 377	91.88 348	88.58 462	82.35 316	79.95 408	90.95 359	73.42 269	97.63 245	80.27 322	89.95 289	95.19 272

PEN-MVS		86.80 303	86.27 287	88.40 361	92.32 338	75.71 392	95.18 145	96.38 127	87.97 142	82.82 368	93.15 279	73.39 270	95.92 390	76.15 380	79.03 432	93.59 357

v1192		87.25 283	86.33 283	90.00 301	90.76 402	79.04 315	93.80 255	95.48 227	82.57 311	85.48 294	91.18 350	73.38 271	97.42 272	82.30 279	82.06 389	93.53 359

guyue		91.12 145	90.84 144	91.96 190	94.59 212	80.57 255	94.87 164	93.71 342	88.96 101	91.14 155	95.22 182	73.22 272	97.76 233	92.01 106	93.81 205	97.54 145

QAPM		89.51 199	88.15 226	93.59 84	94.92 183	84.58 94	96.82 34	96.70 104	78.43 392	83.41 359	96.19 114	73.18 273	99.30 49	77.11 369	96.54 128	96.89 197

tpmrst		85.35 341	84.99 330	86.43 417	90.88 397	67.88 469	88.71 430	91.43 408	80.13 363	86.08 276	88.80 416	73.05 274	96.02 384	82.48 274	83.40 375	95.40 265

PS-CasMVS		87.32 280	86.88 257	88.63 357	92.99 314	76.33 384	95.33 127	96.61 110	88.22 129	83.30 363	93.07 283	73.03 275	95.79 399	78.36 354	81.00 409	93.75 351

DTE-MVSNet		86.11 325	85.48 318	87.98 375	91.65 364	74.92 399	94.93 160	95.75 201	87.36 170	82.26 374	93.04 284	72.85 276	95.82 396	74.04 400	77.46 438	93.20 375

MVSTER		88.84 225	88.29 223	90.51 270	92.95 316	80.44 258	93.73 260	95.01 264	84.66 262	87.15 248	93.12 281	72.79 277	97.21 301	87.86 187	87.36 334	93.87 338

v1921920		86.97 296	86.06 296	89.69 319	90.53 411	78.11 339	93.80 255	95.43 235	81.90 330	85.33 307	91.05 357	72.66 278	97.41 278	82.05 287	81.80 394	93.53 359

DP-MVS		87.25 283	85.36 322	92.90 117	97.65 65	83.24 142	94.81 170	92.00 388	74.99 437	81.92 381	95.00 195	72.66 278	99.05 68	66.92 453	92.33 255	96.40 219

VortexMVS		88.42 237	88.01 229	89.63 325	93.89 269	78.82 317	93.82 253	95.47 228	86.67 194	84.53 324	91.99 323	72.62 280	96.65 336	89.02 170	84.09 363	93.41 366

v7n		86.81 302	85.76 310	89.95 302	90.72 404	79.25 313	95.07 151	95.92 185	84.45 265	82.29 373	90.86 361	72.60 281	97.53 253	79.42 344	80.52 417	93.08 383

OPM-MVS		90.12 175	89.56 180	91.82 203	93.14 301	83.90 120	94.16 222	95.74 202	88.96 101	87.86 233	95.43 171	72.48 282	97.91 224	88.10 185	90.18 284	93.65 356
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
LS3D		87.89 252	86.32 284	92.59 144	96.07 119	82.92 161	95.23 137	94.92 278	75.66 429	82.89 367	95.98 131	72.48 282	99.21 56	68.43 441	95.23 164	95.64 258

pm-mvs1		86.61 311	85.54 316	89.82 308	91.44 367	80.18 264	95.28 134	94.85 283	83.84 276	81.66 382	92.62 297	72.45 284	96.48 360	79.67 332	78.06 433	92.82 392

KinetiMVS		91.82 113	91.30 130	93.39 87	94.72 200	83.36 139	95.45 122	96.37 128	90.33 43	92.17 120	96.03 128	72.32 285	98.75 117	87.94 186	96.34 133	98.07 84

usedtu_dtu_shiyan1		86.84 300	85.61 314	90.53 265	90.50 412	81.80 201	90.97 378	94.96 271	83.05 299	83.50 356	90.32 378	72.15 286	96.65 336	79.49 338	85.55 348	93.15 379

FE-MVSNET3		86.84 300	85.61 314	90.53 265	90.50 412	81.80 201	90.97 378	94.96 271	83.05 299	83.50 356	90.32 378	72.15 286	96.65 336	79.49 338	85.55 348	93.15 379

PMMVS		85.71 334	84.96 332	87.95 376	88.90 437	77.09 368	88.68 431	90.06 441	72.32 464	86.47 263	90.76 367	72.15 286	94.40 430	81.78 294	93.49 219	92.36 413

SDMVSNet		90.19 174	89.61 179	91.93 193	96.00 123	83.09 153	92.89 306	95.98 178	88.73 108	86.85 257	95.20 186	72.09 289	97.08 310	88.90 173	89.85 292	95.63 259

PatchmatchNet		85.85 330	84.70 338	89.29 337	91.76 358	75.54 393	88.49 435	91.30 410	81.63 341	85.05 312	88.70 418	71.71 290	96.24 376	74.61 397	89.05 307	96.08 237
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
sam_mvs1												71.70 291				96.12 234

patchmatchnet-post											83.76 466	71.53 292	96.48 360

v1240		86.78 304	85.85 305	89.56 327	90.45 415	77.79 352	93.61 268	95.37 241	81.65 339	85.43 299	91.15 352	71.50 293	97.43 270	81.47 300	82.05 391	93.47 363

anonymousdsp		87.84 253	87.09 252	90.12 291	89.13 434	80.54 256	94.67 181	95.55 222	82.05 323	83.82 345	92.12 314	71.47 294	97.15 303	87.15 201	87.80 329	92.67 396

Patchmatch-test		81.37 405	79.30 411	87.58 385	90.92 394	74.16 409	80.99 492	87.68 469	70.52 472	76.63 447	88.81 414	71.21 295	92.76 459	60.01 480	86.93 340	95.83 250

F-COLMAP		87.95 251	86.80 262	91.40 225	96.35 105	80.88 238	94.73 177	95.45 232	79.65 370	82.04 379	94.61 218	71.13 296	98.50 143	76.24 379	91.05 271	94.80 292

pmmvs4		85.43 338	83.86 356	90.16 288	90.02 423	82.97 160	90.27 394	92.67 369	75.93 428	80.73 394	91.74 331	71.05 297	95.73 402	78.85 351	83.46 373	91.78 424

CR-MVSNet		85.35 341	83.76 357	90.12 291	90.58 408	79.34 307	85.24 473	91.96 392	78.27 395	85.55 288	87.87 431	71.03 298	95.61 405	73.96 402	89.36 301	95.40 265

Patchmtry		82.71 381	80.93 385	88.06 373	90.05 422	76.37 383	84.74 479	91.96 392	72.28 465	81.32 388	87.87 431	71.03 298	95.50 411	68.97 437	80.15 420	92.32 415

CL-MVSNet_self_test		81.74 396	80.53 386	85.36 430	85.96 465	72.45 433	90.25 396	93.07 357	81.24 351	79.85 411	87.29 437	70.93 300	92.52 460	66.95 450	69.23 469	91.11 444

RPMNet		83.95 369	81.53 380	91.21 233	90.58 408	79.34 307	85.24 473	96.76 94	71.44 468	85.55 288	82.97 474	70.87 301	98.91 98	61.01 475	89.36 301	95.40 265

Patchmatch-RL test		81.67 397	79.96 401	86.81 411	85.42 473	71.23 445	82.17 490	87.50 471	78.47 390	77.19 442	82.50 481	70.81 302	93.48 448	82.66 273	72.89 453	95.71 257

CostFormer		85.77 333	84.94 333	88.26 368	91.16 381	72.58 432	89.47 418	91.04 417	76.26 424	86.45 266	89.97 393	70.74 303	96.86 328	82.35 278	87.07 339	95.34 269

AstraMVS		90.69 158	90.30 157	91.84 202	93.81 273	79.85 285	94.76 175	92.39 374	88.96 101	91.01 166	95.87 142	70.69 304	97.94 221	92.49 84	92.70 244	97.73 128

sam_mvs												70.60 305

xiu_mvs_v1_base_debu		90.64 163	90.05 165	92.40 156	93.97 265	84.46 101	93.32 280	95.46 229	85.17 239	92.25 117	94.03 241	70.59 306	98.57 140	90.97 128	94.67 174	94.18 318

xiu_mvs_v1_base		90.64 163	90.05 165	92.40 156	93.97 265	84.46 101	93.32 280	95.46 229	85.17 239	92.25 117	94.03 241	70.59 306	98.57 140	90.97 128	94.67 174	94.18 318

xiu_mvs_v1_base_debi		90.64 163	90.05 165	92.40 156	93.97 265	84.46 101	93.32 280	95.46 229	85.17 239	92.25 117	94.03 241	70.59 306	98.57 140	90.97 128	94.67 174	94.18 318

test_post											10.29 552	70.57 309	95.91 392

CANet_DTU		90.26 173	89.41 186	92.81 122	93.46 292	83.01 158	93.48 272	94.47 303	89.43 78	87.76 239	94.23 238	70.54 310	99.03 71	84.97 231	96.39 132	96.38 220

BH-RMVSNet		88.37 240	87.48 243	91.02 244	95.28 160	79.45 299	92.89 306	93.07 357	85.45 231	86.91 253	94.84 207	70.35 311	97.76 233	73.97 401	94.59 179	95.85 248

Fast-Effi-MVS+-dtu		87.44 274	86.72 264	89.63 325	92.04 346	77.68 360	94.03 236	93.94 325	85.81 215	82.42 372	91.32 345	70.33 312	97.06 313	80.33 321	90.23 283	94.14 321

MDTV_nov1_ep13_2view						55.91 507	87.62 452		73.32 455	84.59 321		70.33 312		74.65 395		95.50 262

ACMM	84.12 9	89.14 214	88.48 218	91.12 236	94.65 207	81.22 221	95.31 128	96.12 166	85.31 236	85.92 279	94.34 229	70.19 314	98.06 197	85.65 222	88.86 309	94.08 326
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
RRT-MVS		90.85 151	90.70 149	91.30 230	94.25 247	76.83 374	94.85 167	96.13 165	89.04 95	90.23 181	94.88 202	70.15 315	98.72 121	91.86 114	94.88 169	98.34 49

LuminaMVS		90.55 167	89.81 172	92.77 126	92.78 325	84.21 111	94.09 230	94.17 318	85.82 214	91.54 143	94.14 240	69.93 316	97.92 223	91.62 118	94.21 193	96.18 230

ET-MVSNet_ETH3D		87.51 271	85.91 303	92.32 166	93.70 284	83.93 119	92.33 332	90.94 421	84.16 268	72.09 473	92.52 300	69.90 317	95.85 394	89.20 167	88.36 318	97.17 168

LPG-MVS_test		89.45 202	88.90 205	91.12 236	94.47 225	81.49 211	95.30 130	96.14 162	86.73 192	85.45 296	95.16 188	69.89 318	98.10 183	87.70 190	89.23 304	93.77 349

LGP-MVS_train				91.12 236	94.47 225	81.49 211		96.14 162	86.73 192	85.45 296	95.16 188	69.89 318	98.10 183	87.70 190	89.23 304	93.77 349

CHOSEN 280x420		85.15 346	83.99 354	88.65 356	92.47 333	78.40 330	79.68 499	92.76 366	74.90 439	81.41 386	89.59 401	69.85 320	95.51 409	79.92 328	95.29 161	92.03 420

LTVRE_ROB	82.13 13	86.26 324	84.90 334	90.34 283	94.44 229	81.50 209	92.31 335	94.89 279	83.03 301	79.63 415	92.67 295	69.69 321	97.79 231	71.20 418	86.26 343	91.72 425
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
OpenMVS	83.78 11	88.74 229	87.29 248	93.08 105	92.70 328	85.39 79	96.57 40	96.43 122	78.74 386	80.85 392	96.07 124	69.64 322	99.01 76	78.01 360	96.65 126	94.83 290

MonoMVSNet		86.89 299	86.55 275	87.92 378	89.46 432	73.75 411	94.12 224	93.10 355	87.82 154	85.10 310	90.76 367	69.59 323	94.94 424	86.47 210	82.50 384	95.07 276

MDTV_nov1_ep13			83.56 360		91.69 362	69.93 459	87.75 449	91.54 403	78.60 388	84.86 315	88.90 413	69.54 324	96.03 383	70.25 428	88.93 308

dtuonly		84.33 363	84.48 345	83.87 447	86.63 459	63.54 487	86.79 459	91.48 406	78.02 400	83.20 364	93.56 265	69.53 325	94.11 436	79.08 347	92.02 259	93.97 332

AUN-MVS		87.78 256	86.54 276	91.48 220	94.82 191	81.05 229	93.91 249	93.93 326	83.00 302	86.93 251	93.53 266	69.50 326	97.67 239	86.14 214	77.12 441	95.73 256

PatchT		82.68 382	81.27 382	86.89 409	90.09 421	70.94 451	84.06 482	90.15 438	74.91 438	85.63 287	83.57 469	69.37 327	94.87 425	65.19 459	88.50 314	94.84 289

VPNet		88.20 245	87.47 244	90.39 279	93.56 289	79.46 298	94.04 235	95.54 224	88.67 111	86.96 250	94.58 222	69.33 328	97.15 303	84.05 250	80.53 416	94.56 301

ACMP	84.23 8	89.01 223	88.35 219	90.99 247	94.73 198	81.27 218	95.07 151	95.89 190	86.48 197	83.67 350	94.30 232	69.33 328	97.99 211	87.10 205	88.55 311	93.72 354
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
test_post1							88.00 444				9.81 553	69.31 330	95.53 407	76.65 372

tpmvs		83.35 377	82.07 376	87.20 401	91.07 385	71.00 450	88.31 438	91.70 396	78.91 378	80.49 399	87.18 440	69.30 331	97.08 310	68.12 445	83.56 371	93.51 362

mvsmamba		90.33 170	89.69 176	92.25 177	95.17 167	81.64 206	95.27 135	93.36 349	84.88 252	89.51 199	94.27 236	69.29 332	97.42 272	89.34 164	96.12 139	97.68 131

thres200		87.21 287	86.24 288	90.12 291	95.36 156	78.53 325	93.26 287	92.10 384	86.42 200	88.00 232	91.11 354	69.24 333	98.00 210	69.58 435	91.04 272	93.83 343

tfpn200view9		87.58 268	86.64 269	90.41 278	95.99 126	78.64 321	94.58 185	91.98 390	86.94 186	88.09 227	91.77 329	69.18 334	98.10 183	70.13 431	91.10 266	94.48 309

thres400		87.62 265	86.64 269	90.57 261	95.99 126	78.64 321	94.58 185	91.98 390	86.94 186	88.09 227	91.77 329	69.18 334	98.10 183	70.13 431	91.10 266	94.96 282

WB-MVSnew		83.77 372	83.28 363	85.26 433	91.48 366	71.03 448	91.89 347	87.98 465	78.91 378	84.78 316	90.22 382	69.11 336	94.02 438	64.70 463	90.44 278	90.71 448

tfpnnormal		84.72 356	83.23 365	89.20 339	92.79 324	80.05 273	94.48 191	95.81 196	82.38 314	81.08 390	91.21 347	69.01 337	96.95 322	61.69 473	80.59 414	90.58 453

thres100view900		87.63 263	86.71 265	90.38 281	96.12 112	78.55 324	95.03 155	91.58 401	87.15 175	88.06 230	92.29 308	68.91 338	98.10 183	70.13 431	91.10 266	94.48 309

thres600view7		87.65 260	86.67 268	90.59 260	96.08 118	78.72 318	94.88 163	91.58 401	87.06 180	88.08 229	92.30 307	68.91 338	98.10 183	70.05 434	91.10 266	94.96 282

PatchMatch-RL		86.77 307	85.54 316	90.47 276	95.88 131	82.71 169	90.54 389	92.31 378	79.82 368	84.32 334	91.57 341	68.77 340	96.39 368	73.16 407	93.48 221	92.32 415

XVG-OURS		89.40 208	88.70 209	91.52 217	94.06 257	81.46 213	91.27 370	96.07 171	86.14 209	88.89 214	95.77 152	68.73 341	97.26 296	87.39 197	89.96 288	95.83 250

TR-MVS		86.78 304	85.76 310	89.82 308	94.37 233	78.41 329	92.47 323	92.83 363	81.11 354	86.36 268	92.40 303	68.73 341	97.48 261	73.75 405	89.85 292	93.57 358

tpm		84.73 355	84.02 353	86.87 410	90.33 416	68.90 463	89.06 425	89.94 445	80.85 356	85.75 283	89.86 396	68.54 343	95.97 387	77.76 361	84.05 364	95.75 253

FMVSNet3		87.40 276	86.11 293	91.30 230	93.79 276	83.64 129	94.20 220	94.81 287	83.89 275	84.37 329	91.87 328	68.45 344	96.56 354	78.23 357	85.36 350	93.70 355

MVP-Stereo		85.97 327	84.86 335	89.32 336	90.92 394	82.19 188	92.11 343	94.19 316	78.76 385	78.77 430	91.63 336	68.38 345	96.56 354	75.01 391	93.95 199	89.20 468
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
tpm cat1		81.96 390	80.27 392	87.01 404	91.09 384	71.02 449	87.38 455	91.53 404	66.25 486	80.17 401	86.35 452	68.22 346	96.15 380	69.16 436	82.29 387	93.86 340

dmvs_testset		74.57 448	75.81 445	70.86 479	87.72 454	40.47 521	87.05 458	77.90 503	82.75 308	71.15 479	85.47 459	67.98 347	84.12 502	45.26 503	76.98 443	88.00 480

sd_testset		88.59 234	87.85 236	90.83 254	96.00 123	80.42 259	92.35 329	94.71 292	88.73 108	86.85 257	95.20 186	67.31 348	96.43 366	79.64 333	89.85 292	95.63 259

tpm2		84.08 366	82.94 370	87.48 389	91.39 371	71.27 444	89.23 422	90.37 433	71.95 466	84.64 319	89.33 405	67.30 349	96.55 356	75.17 388	87.09 338	94.63 295

test-LLR		85.87 329	85.41 319	87.25 397	90.95 390	71.67 441	89.55 414	89.88 448	83.41 289	84.54 322	87.95 428	67.25 350	95.11 420	81.82 292	93.37 225	94.97 279

test0.0.03 1		82.41 387	81.69 378	84.59 440	88.23 446	72.89 423	90.24 398	87.83 467	83.41 289	79.86 410	89.78 398	67.25 350	88.99 489	65.18 460	83.42 374	91.90 423

CVMVSNet		84.69 358	84.79 337	84.37 442	91.84 354	64.92 482	93.70 264	91.47 407	66.19 487	86.16 275	95.28 179	67.18 352	93.33 450	80.89 310	90.42 280	94.88 288

thisisatest0515		87.33 279	85.99 298	91.37 227	93.49 290	79.55 295	90.63 386	89.56 455	80.17 362	87.56 242	90.86 361	67.07 353	98.28 171	81.50 299	93.02 236	96.29 224

tttt0517		88.61 232	87.78 237	91.11 239	94.96 180	77.81 350	95.35 126	89.69 450	85.09 247	88.05 231	94.59 221	66.93 354	98.48 145	83.27 262	92.13 257	97.03 184

our_test_3		81.93 392	80.46 390	86.33 419	88.46 442	73.48 416	88.46 436	91.11 413	76.46 418	76.69 446	88.25 424	66.89 355	94.36 431	68.75 438	79.08 431	91.14 442

thisisatest0530		88.67 230	87.61 240	91.86 199	94.87 187	80.07 271	94.63 183	89.90 447	84.00 272	88.46 221	93.78 258	66.88 356	98.46 149	83.30 261	92.65 245	97.06 181

IterMVS-SCA-FT		85.45 337	84.53 344	88.18 371	91.71 360	76.87 373	90.19 402	92.65 370	85.40 234	81.44 385	90.54 372	66.79 357	95.00 423	81.04 305	81.05 405	92.66 397

SCA		86.32 323	85.18 327	89.73 316	92.15 341	76.60 378	91.12 374	91.69 397	83.53 286	85.50 293	88.81 414	66.79 357	96.48 360	76.65 372	90.35 281	96.12 234

D2MVS		85.90 328	85.09 329	88.35 363	90.79 399	77.42 363	91.83 351	95.70 208	80.77 357	80.08 405	90.02 391	66.74 359	96.37 369	81.88 291	87.97 324	91.26 439

IterMVS		84.88 352	83.98 355	87.60 384	91.44 367	76.03 386	90.18 403	92.41 373	83.24 295	81.06 391	90.42 377	66.60 360	94.28 434	79.46 340	80.98 410	92.48 406
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
GBi-Net		87.26 281	85.98 299	91.08 240	94.01 260	83.10 150	95.14 148	94.94 273	83.57 283	84.37 329	91.64 333	66.59 361	96.34 372	78.23 357	85.36 350	93.79 344

test1		87.26 281	85.98 299	91.08 240	94.01 260	83.10 150	95.14 148	94.94 273	83.57 283	84.37 329	91.64 333	66.59 361	96.34 372	78.23 357	85.36 350	93.79 344

FMVSNet2		87.19 289	85.82 306	91.30 230	94.01 260	83.67 127	94.79 172	94.94 273	83.57 283	83.88 344	92.05 321	66.59 361	96.51 358	77.56 364	85.01 353	93.73 353

IMVS_0404		87.60 267	86.84 260	89.89 304	93.72 278	77.75 355	88.56 433	95.34 244	85.53 227	79.98 407	94.49 224	66.54 364	94.64 426	84.75 236	92.65 245	97.28 156

EPMVS		83.90 371	82.70 375	87.51 386	90.23 419	72.67 427	88.62 432	81.96 491	81.37 347	85.01 313	88.34 422	66.31 365	94.45 427	75.30 387	87.12 337	95.43 264

Syy-MVS		80.07 421	79.78 403	80.94 461	91.92 350	59.93 497	89.75 412	87.40 472	81.72 337	78.82 427	87.20 438	66.29 366	91.29 474	47.06 502	87.84 327	91.60 428

ppachtmachnet_test		81.84 393	80.07 397	87.15 402	88.46 442	74.43 406	89.04 426	92.16 383	75.33 433	77.75 438	88.99 411	66.20 367	95.37 415	65.12 461	77.60 436	91.65 426

MDA-MVSNet_test_wron		79.21 432	77.19 434	85.29 431	88.22 447	72.77 425	85.87 467	90.06 441	74.34 443	62.62 493	87.56 434	66.14 368	91.99 467	66.90 454	73.01 451	91.10 445

YYNet1		79.22 431	77.20 433	85.28 432	88.20 448	72.66 428	85.87 467	90.05 443	74.33 444	62.70 491	87.61 433	66.09 369	92.03 464	66.94 451	72.97 452	91.15 441

JIA-IIPM		81.04 408	78.98 420	87.25 397	88.64 438	73.48 416	81.75 491	89.61 454	73.19 456	82.05 378	73.71 501	66.07 370	95.87 393	71.18 420	84.60 358	92.41 410

MSDG		84.86 353	83.09 367	90.14 290	93.80 274	80.05 273	89.18 423	93.09 356	78.89 380	78.19 432	91.91 326	65.86 371	97.27 294	68.47 440	88.45 315	93.11 381

FE-MVS		87.40 276	86.02 297	91.57 215	94.56 217	79.69 293	90.27 394	93.72 341	80.57 358	88.80 215	91.62 337	65.32 372	98.59 139	74.97 392	94.33 189	96.44 218

jajsoiax		88.24 244	87.50 242	90.48 273	90.89 396	80.14 266	95.31 128	95.65 215	84.97 250	84.24 337	94.02 244	65.31 373	97.42 272	88.56 178	88.52 313	93.89 334

cascas		86.43 321	84.98 331	90.80 257	92.10 345	80.92 236	90.24 398	95.91 187	73.10 457	83.57 354	88.39 421	65.15 374	97.46 265	84.90 234	91.43 263	94.03 329

ADS-MVSNet2		81.66 398	79.71 406	87.50 387	91.35 373	74.19 408	83.33 485	88.48 463	72.90 459	82.24 375	85.77 457	64.98 375	93.20 453	64.57 464	83.74 367	95.12 274

ADS-MVSNet		81.56 400	79.78 403	86.90 408	91.35 373	71.82 437	83.33 485	89.16 461	72.90 459	82.24 375	85.77 457	64.98 375	93.76 444	64.57 464	83.74 367	95.12 274

blended_shiyan6		82.78 379	80.48 389	89.67 324	85.53 470	79.76 288	91.37 364	93.82 333	77.14 408	79.30 420	83.73 467	64.96 377	96.63 339	79.68 331	68.75 475	92.63 398

blended_shiyan8		82.79 378	80.49 388	89.69 319	85.50 472	79.83 287	91.38 363	93.82 333	77.14 408	79.39 418	83.73 467	64.95 378	96.63 339	79.75 329	68.77 474	92.62 400

Elysia		90.12 175	89.10 194	93.18 97	93.16 299	84.05 116	95.22 139	96.27 137	85.16 242	90.59 171	94.68 212	64.64 379	98.37 160	86.38 212	95.77 145	97.12 177

StellarMVS		90.12 175	89.10 194	93.18 97	93.16 299	84.05 116	95.22 139	96.27 137	85.16 242	90.59 171	94.68 212	64.64 379	98.37 160	86.38 212	95.77 145	97.12 177

pmmvs5		84.21 364	82.84 374	88.34 365	88.95 436	76.94 372	92.41 324	91.91 394	75.63 430	80.28 400	91.18 350	64.59 381	95.57 406	77.09 370	83.47 372	92.53 405

PVSNet	78.82 18	85.55 335	84.65 339	88.23 370	94.72 200	71.93 435	87.12 457	92.75 367	78.80 384	84.95 314	90.53 373	64.43 382	96.71 333	74.74 394	93.86 202	96.06 240

dmvs_re		84.20 365	83.22 366	87.14 403	91.83 356	77.81 350	90.04 406	90.19 437	84.70 261	81.49 383	89.17 407	64.37 383	91.13 476	71.58 415	85.65 347	92.46 408

wanda-best-256-512		82.44 385	80.07 397	89.53 329	85.12 476	79.44 300	90.49 390	93.75 339	76.97 414	79.00 423	82.72 477	64.29 384	96.61 345	79.56 336	68.75 475	92.55 401

FE-blended-shiyan7		82.44 385	80.07 397	89.53 329	85.12 476	79.44 300	90.49 390	93.75 339	76.97 414	79.00 423	82.72 477	64.29 384	96.61 345	79.56 336	68.75 475	92.55 401

usedtu_blend_shiyan5		82.39 388	79.93 402	89.75 313	85.12 476	80.08 269	92.36 327	93.26 350	74.29 445	79.00 423	82.72 477	64.29 384	96.60 349	79.60 334	68.75 475	92.55 401

UGNet		89.95 185	88.95 202	92.95 115	94.51 220	83.31 140	95.70 106	95.23 251	89.37 80	87.58 241	93.94 249	64.00 387	98.78 115	83.92 252	96.31 134	96.74 207
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
WB-MVS		67.92 458	67.49 459	69.21 484	81.09 493	41.17 520	88.03 443	78.00 502	73.50 453	62.63 492	83.11 473	63.94 388	86.52 493	25.66 522	51.45 502	79.94 495

RPSCF		85.07 347	84.27 346	87.48 389	92.91 318	70.62 454	91.69 356	92.46 372	76.20 426	82.67 370	95.22 182	63.94 388	97.29 293	77.51 365	85.80 345	94.53 302

mvs_tets		88.06 250	87.28 249	90.38 281	90.94 392	79.88 282	95.22 139	95.66 213	85.10 246	84.21 338	93.94 249	63.53 390	97.40 280	88.50 179	88.40 317	93.87 338

SSC-MVS		67.06 459	66.56 461	68.56 486	80.54 494	40.06 522	87.77 448	77.37 505	72.38 463	61.75 494	82.66 480	63.37 391	86.45 494	24.48 524	48.69 505	79.16 498

test1111		89.10 215	88.64 210	90.48 273	95.53 151	74.97 398	96.08 69	84.89 483	88.13 133	90.16 188	96.65 91	63.29 392	98.10 183	86.14 214	96.90 117	98.39 46

gbinet_0.2-2-1-0.02		82.59 383	80.19 395	89.77 312	85.23 475	80.05 273	91.59 359	93.52 345	77.60 402	79.78 412	82.87 476	63.26 393	96.45 364	78.93 349	68.97 471	92.81 393

Anonymous20231211		86.59 313	85.13 328	90.98 249	96.52 99	81.50 209	96.14 64	96.16 160	73.78 450	83.65 351	92.15 312	63.26 393	97.37 286	82.82 270	81.74 396	94.06 327

ECVR-MVS		89.09 217	88.53 213	90.77 258	95.62 145	75.89 388	96.16 60	84.22 485	87.89 150	90.20 182	96.65 91	63.19 395	98.10 183	85.90 219	96.94 114	98.33 51

dtuonlycased		79.67 426	79.05 419	81.54 459	88.34 445	68.44 465	88.96 428	90.65 430	78.48 389	73.21 470	85.88 456	63.18 396	91.00 478	70.40 426	72.32 454	85.19 485

SSC-MVS3.2		84.60 359	84.19 347	85.85 425	92.74 327	68.07 466	88.15 441	93.81 336	87.42 168	83.76 347	91.07 356	62.91 397	95.73 402	74.56 398	83.24 376	93.75 351

dp		81.47 404	80.23 393	85.17 434	89.92 425	65.49 479	86.74 461	90.10 440	76.30 423	81.10 389	87.12 441	62.81 398	95.92 390	68.13 444	79.88 423	94.09 325

LFMVS		90.08 178	89.13 193	92.95 115	96.71 88	82.32 186	96.08 69	89.91 446	86.79 189	92.15 122	96.81 84	62.60 399	98.34 165	87.18 200	93.90 201	98.19 73

Anonymous20231206		81.03 409	79.77 405	84.82 438	87.85 453	70.26 457	91.42 362	92.08 385	73.67 451	77.75 438	89.25 406	62.43 400	93.08 454	61.50 474	82.00 392	91.12 443

VDD-MVS		90.74 155	89.92 170	93.20 95	96.27 106	83.02 157	95.73 104	93.86 330	88.42 120	92.53 112	96.84 81	62.09 401	98.64 131	90.95 132	92.62 250	97.93 107

MS-PatchMatch		85.05 348	84.16 349	87.73 381	91.42 370	78.51 326	91.25 371	93.53 344	77.50 403	80.15 402	91.58 339	61.99 402	95.51 409	75.69 383	94.35 187	89.16 469

OurMVSNet-221017-0		85.35 341	84.64 341	87.49 388	90.77 401	72.59 431	94.01 239	94.40 307	84.72 259	79.62 416	93.17 278	61.91 403	96.72 331	81.99 288	81.16 401	93.16 377

WBMVS		84.97 351	84.18 348	87.34 392	94.14 256	71.62 443	90.20 401	92.35 375	81.61 342	84.06 339	90.76 367	61.82 404	96.52 357	78.93 349	83.81 365	93.89 334

test_vis1_n_1920		89.39 209	89.84 171	88.04 374	92.97 315	72.64 429	94.71 179	96.03 176	86.18 207	91.94 129	96.56 99	61.63 405	95.74 401	93.42 66	95.11 165	95.74 254

test20.03		79.95 423	79.08 417	82.55 453	85.79 467	67.74 471	91.09 375	91.08 414	81.23 352	74.48 463	89.96 394	61.63 405	90.15 481	60.08 478	76.38 444	89.76 460

mmtdpeth		85.04 350	84.15 350	87.72 382	93.11 303	75.74 391	94.37 209	92.83 363	84.98 249	89.31 204	86.41 450	61.61 407	97.14 306	92.63 83	62.11 491	90.29 454

DSMNet-mixed		76.94 442	76.29 440	78.89 466	83.10 487	56.11 506	87.78 447	79.77 495	60.65 495	75.64 455	88.71 417	61.56 408	88.34 490	60.07 479	89.29 303	92.21 418

Anonymous20240529		88.09 248	86.59 273	92.58 145	96.53 98	81.92 197	95.99 79	95.84 195	74.11 447	89.06 209	95.21 185	61.44 409	98.81 111	83.67 259	87.47 331	97.01 187

UBG		85.51 336	84.57 343	88.35 363	94.21 250	71.78 439	90.07 405	89.66 452	82.28 318	85.91 280	89.01 410	61.30 410	97.06 313	76.58 375	92.06 258	96.22 227

IB-MVS	80.51 15	85.24 345	83.26 364	91.19 234	92.13 343	79.86 283	91.75 353	91.29 411	83.28 294	80.66 396	88.49 420	61.28 411	98.46 149	80.99 308	79.46 428	95.25 271
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
GA-MVS		86.61 311	85.27 325	90.66 259	91.33 375	78.71 320	90.40 393	93.81 336	85.34 235	85.12 309	89.57 402	61.25 412	97.11 308	80.99 308	89.59 298	96.15 231

N_pmnet		68.89 457	68.44 457	70.23 481	89.07 435	28.79 532	88.06 442	19.50 533	69.47 475	71.86 476	84.93 461	61.24 413	91.75 470	54.70 490	77.15 440	90.15 457

EU-MVSNet		81.32 406	80.95 384	82.42 456	88.50 441	63.67 486	93.32 280	91.33 409	64.02 491	80.57 398	92.83 289	61.21 414	92.27 463	76.34 377	80.38 419	91.32 437

testing91		87.11 292	86.18 289	89.92 303	94.43 230	75.38 397	91.53 360	92.27 380	86.48 197	86.50 262	90.24 381	61.19 415	97.53 253	82.10 284	90.88 274	96.84 203

test_cas_vis1_n_1920		88.83 228	88.85 208	88.78 350	91.15 382	76.72 376	93.85 252	94.93 277	83.23 296	92.81 100	96.00 129	61.17 416	94.45 427	91.67 117	94.84 170	95.17 273

VDDNet		89.56 198	88.49 217	92.76 129	95.07 172	82.09 190	96.30 47	93.19 354	81.05 355	91.88 131	96.86 80	61.16 417	98.33 167	88.43 180	92.49 254	97.84 118

PVSNet_0	73.20 20	77.22 441	74.83 447	84.37 442	90.70 405	71.10 447	83.09 487	89.67 451	72.81 461	73.93 465	83.13 471	60.79 418	93.70 446	68.54 439	50.84 503	88.30 479

SixPastTwentyTwo		83.91 370	82.90 372	86.92 407	90.99 388	70.67 453	93.48 272	91.99 389	85.54 225	77.62 440	92.11 316	60.59 419	96.87 327	76.05 381	77.75 435	93.20 375

gg-mvs-nofinetune		81.77 395	79.37 409	88.99 347	90.85 398	77.73 359	86.29 464	79.63 496	74.88 440	83.19 365	69.05 508	60.34 420	96.11 381	75.46 385	94.64 178	93.11 381

MDA-MVSNet-bldmvs		78.85 434	76.31 439	86.46 415	89.76 427	73.88 410	88.79 429	90.42 432	79.16 376	59.18 496	88.33 423	60.20 421	94.04 437	62.00 472	68.96 472	91.48 434

pmmvs6		83.42 375	81.60 379	88.87 349	88.01 450	77.87 348	94.96 158	94.24 315	74.67 441	78.80 429	91.09 355	60.17 422	96.49 359	77.06 371	75.40 448	92.23 417

ACMH	80.38 17	85.36 340	83.68 358	90.39 279	94.45 228	80.63 248	94.73 177	94.85 283	82.09 321	77.24 441	92.65 296	60.01 423	97.58 249	72.25 412	84.87 356	92.96 386
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
GG-mvs-BLEND				87.94 377	89.73 429	77.91 344	87.80 445	78.23 501		80.58 397	83.86 465	59.88 424	95.33 416	71.20 418	92.22 256	90.60 452

UniMVSNet_ETH3D		87.53 270	86.37 281	91.00 246	92.44 335	78.96 316	94.74 176	95.61 218	84.07 271	85.36 306	94.52 223	59.78 425	97.34 287	82.93 266	87.88 325	96.71 208

myMVS_eth3d28		85.80 332	85.26 326	87.42 391	94.73 198	69.92 460	90.60 387	90.95 420	87.21 174	86.06 277	90.04 390	59.47 426	96.02 384	74.89 393	93.35 227	96.33 221

pmmvs-eth3d		80.97 411	78.72 422	87.74 380	84.99 479	79.97 280	90.11 404	91.65 399	75.36 432	73.51 467	86.03 453	59.45 427	93.96 442	75.17 388	72.21 455	89.29 467

testing99		86.72 308	85.73 313	89.69 319	94.23 248	74.91 400	91.35 366	90.97 419	86.14 209	86.36 268	90.22 382	59.41 428	97.48 261	82.24 281	90.66 276	96.69 210

test_0402		81.30 407	79.17 415	87.67 383	93.19 298	78.17 337	92.98 302	91.71 395	75.25 434	76.02 453	90.31 380	59.23 429	96.37 369	50.22 497	83.63 370	88.47 478

KD-MVS_self_test		80.20 419	79.24 412	83.07 450	85.64 469	65.29 480	91.01 377	93.93 326	78.71 387	76.32 448	86.40 451	59.20 430	92.93 456	72.59 410	69.35 468	91.00 447

testing3-2		86.72 308	86.71 265	86.74 413	96.11 115	65.92 476	93.39 277	89.65 453	89.46 76	87.84 235	92.79 293	59.17 431	97.60 247	81.31 301	90.72 275	96.70 209

UWE-MVS-28		78.98 433	78.38 425	80.80 462	88.18 449	60.66 496	90.65 385	78.51 498	78.84 382	77.93 436	90.93 360	59.08 432	89.02 488	50.96 495	90.33 282	92.72 395

FMVSNet1		85.85 330	84.11 351	91.08 240	92.81 323	83.10 150	95.14 148	94.94 273	81.64 340	82.68 369	91.64 333	59.01 433	96.34 372	75.37 386	83.78 366	93.79 344

testing11		86.44 320	85.35 323	89.69 319	94.29 244	75.40 396	91.30 367	90.53 431	84.76 257	85.06 311	90.13 387	58.95 434	97.45 266	82.08 285	91.09 270	96.21 229

COLMAP_ROB	80.39 16	83.96 368	82.04 377	89.74 314	95.28 160	79.75 289	94.25 216	92.28 379	75.17 435	78.02 435	93.77 259	58.60 435	97.84 229	65.06 462	85.92 344	91.63 427
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
ACMH+	81.04 14	85.05 348	83.46 361	89.82 308	94.66 206	79.37 304	94.44 196	94.12 322	82.19 320	78.04 434	92.82 290	58.23 436	97.54 252	73.77 404	82.90 381	92.54 404

CMPMVS	59.16 21	80.52 415	79.20 414	84.48 441	83.98 482	67.63 472	89.95 409	93.84 332	64.79 490	66.81 487	91.14 353	57.93 437	95.17 418	76.25 378	88.10 320	90.65 449
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
reproduce_monomvs		86.37 322	85.87 304	87.87 379	93.66 286	73.71 412	93.44 275	95.02 263	88.61 114	82.64 371	91.94 325	57.88 438	96.68 334	89.96 151	79.71 426	93.22 373

FE-MVSNET2		81.82 394	79.99 400	87.34 392	84.74 480	77.36 365	92.72 315	94.55 298	82.09 321	73.79 466	86.46 447	57.80 439	94.45 427	74.65 395	73.10 450	90.20 455

tt0805		86.92 297	85.74 312	90.48 273	92.22 339	79.98 279	95.63 114	94.88 281	83.83 277	84.74 318	92.80 292	57.61 440	97.67 239	85.48 225	84.42 359	93.79 344

ITE_SJBPF				88.24 369	91.88 353	77.05 369		92.92 360	85.54 225	80.13 404	93.30 273	57.29 441	96.20 377	72.46 411	84.71 357	91.49 433

UWE-MVS		83.69 374	83.09 367	85.48 428	93.06 308	65.27 481	90.92 380	86.14 475	79.90 366	86.26 272	90.72 370	57.17 442	95.81 397	71.03 423	92.62 250	95.35 268

TESTMET0.1,1		83.74 373	82.85 373	86.42 418	89.96 424	71.21 446	89.55 414	87.88 466	77.41 404	83.37 360	87.31 436	56.71 443	93.65 447	80.62 315	92.85 242	94.40 312

UnsupCasMVSNet_eth		80.07 421	78.27 426	85.46 429	85.24 474	72.63 430	88.45 437	94.87 282	82.99 303	71.64 477	88.07 427	56.34 444	91.75 470	73.48 406	63.36 489	92.01 421

test_fmvs1		87.34 278	87.56 241	86.68 414	90.59 407	71.80 438	94.01 239	94.04 324	78.30 394	91.97 126	95.22 182	56.28 445	93.71 445	92.89 75	94.71 173	94.52 303

K. test v3		81.59 399	80.15 396	85.91 424	89.89 426	69.42 462	92.57 320	87.71 468	85.56 224	73.44 468	89.71 400	55.58 446	95.52 408	77.17 368	69.76 467	92.78 394

test-mter		84.54 360	83.64 359	87.25 397	90.95 390	71.67 441	89.55 414	89.88 448	79.17 375	84.54 322	87.95 428	55.56 447	95.11 420	81.82 292	93.37 225	94.97 279

FE-MVSNET		78.19 437	76.03 442	84.69 439	83.70 484	73.31 418	90.58 388	90.00 444	77.11 412	71.91 475	85.47 459	55.53 448	91.94 469	59.69 481	70.24 464	88.83 473

lessismore_v0				86.04 420	88.46 442	68.78 464		80.59 494		73.01 471	90.11 388	55.39 449	96.43 366	75.06 390	65.06 486	92.90 388

ETVMVS		84.43 361	82.92 371	88.97 348	94.37 233	74.67 401	91.23 372	88.35 464	83.37 291	86.06 277	89.04 409	55.38 450	95.67 404	67.12 449	91.34 264	96.58 214

MVS-HIRNet		73.70 449	72.20 451	78.18 470	91.81 357	56.42 505	82.94 488	82.58 489	55.24 498	68.88 482	66.48 510	55.32 451	95.13 419	58.12 485	88.42 316	83.01 489

test2506		87.21 287	86.28 286	90.02 299	95.62 145	73.64 414	96.25 55	71.38 510	87.89 150	90.45 174	96.65 91	55.29 452	98.09 191	86.03 218	96.94 114	98.33 51

mvs5depth		80.98 410	79.15 416	86.45 416	84.57 481	73.29 419	87.79 446	91.67 398	80.52 359	82.20 377	89.72 399	55.14 453	95.93 389	73.93 403	66.83 482	90.12 458

0.4-1-1-0.2		80.84 413	77.77 427	90.06 295	86.18 464	79.35 305	86.75 460	89.54 456	76.23 425	78.59 431	75.46 496	55.03 454	96.99 319	80.11 325	72.05 458	93.85 341

0.4-1-1-0.1		81.55 401	78.59 424	90.42 277	87.55 455	79.90 281	88.56 433	89.19 460	77.01 413	79.72 413	77.71 490	54.84 455	97.11 308	80.50 318	72.20 456	94.26 317

blend_shiyan4		81.94 391	79.35 410	89.70 317	85.52 471	80.08 269	91.29 368	93.82 333	77.12 411	79.31 419	82.94 475	54.81 456	96.60 349	79.60 334	69.78 466	92.41 410

new-patchmatchnet		76.41 444	75.17 446	80.13 463	82.65 489	59.61 498	87.66 451	91.08 414	78.23 397	69.85 481	83.22 470	54.76 457	91.63 473	64.14 466	64.89 487	89.16 469

Anonymous202405211		87.68 258	86.13 291	92.31 167	96.66 90	80.74 246	94.87 164	91.49 405	80.47 360	89.46 202	95.44 169	54.72 458	98.23 173	82.19 282	89.89 290	97.97 96

XVG-ACMP-BASELINE		86.00 326	84.84 336	89.45 334	91.20 377	78.00 341	91.70 355	95.55 222	85.05 248	82.97 366	92.25 310	54.49 459	97.48 261	82.93 266	87.45 333	92.89 389

USDC		82.76 380	81.26 383	87.26 396	91.17 379	74.55 403	89.27 420	93.39 348	78.26 396	75.30 457	92.08 318	54.43 460	96.63 339	71.64 414	85.79 346	90.61 450

AllTest		83.42 375	81.39 381	89.52 331	95.01 174	77.79 352	93.12 291	90.89 423	77.41 404	76.12 450	93.34 269	54.08 461	97.51 255	68.31 442	84.27 361	93.26 369

TestCases				89.52 331	95.01 174	77.79 352		90.89 423	77.41 404	76.12 450	93.34 269	54.08 461	97.51 255	68.31 442	84.27 361	93.26 369

KD-MVS_2432*1600		78.50 435	76.02 443	85.93 422	86.22 462	74.47 404	84.80 477	92.33 376	79.29 373	76.98 443	85.92 454	53.81 463	93.97 440	67.39 447	57.42 496	89.36 463

miper_refine_blended		78.50 435	76.02 443	85.93 422	86.22 462	74.47 404	84.80 477	92.33 376	79.29 373	76.98 443	85.92 454	53.81 463	93.97 440	67.39 447	57.42 496	89.36 463

MIMVSNet		82.59 383	80.53 386	88.76 351	91.51 365	78.32 333	86.57 463	90.13 439	79.32 372	80.70 395	88.69 419	52.98 465	93.07 455	66.03 457	88.86 309	94.90 287

testing222		84.84 354	83.32 362	89.43 335	94.15 255	75.94 387	91.09 375	89.41 459	84.90 251	85.78 282	89.44 404	52.70 466	96.28 375	70.80 425	91.57 262	96.07 238

0.3-1-1-0.015		80.75 414	77.58 429	90.25 285	86.55 460	79.72 291	87.46 454	89.48 458	76.43 420	77.93 436	75.94 493	52.31 467	97.05 315	80.25 323	71.85 460	93.99 331

FMVSNet5		81.52 403	79.60 407	87.27 395	91.17 379	77.95 342	91.49 361	92.26 381	76.87 416	76.16 449	87.91 430	51.67 468	92.34 462	67.74 446	81.16 401	91.52 431

testgi		80.94 412	80.20 394	83.18 449	87.96 451	66.29 474	91.28 369	90.70 429	83.70 280	78.12 433	92.84 288	51.37 469	90.82 479	63.34 467	82.46 385	92.43 409

test_fmvs1_n		87.03 295	87.04 255	86.97 405	89.74 428	71.86 436	94.55 187	94.43 304	78.47 390	91.95 128	95.50 167	51.16 470	93.81 443	93.02 74	94.56 180	95.26 270

Anonymous20240521		80.44 417	79.21 413	84.11 445	85.75 468	67.89 468	92.86 308	93.23 352	75.61 431	75.59 456	87.47 435	50.03 471	94.33 432	71.14 421	81.21 400	90.12 458

UnsupCasMVSNet_bld		76.23 445	73.27 449	85.09 435	83.79 483	72.92 422	85.65 470	93.47 347	71.52 467	68.84 483	79.08 488	49.77 472	93.21 452	66.81 455	60.52 493	89.13 471

OpenMVS_ROB	74.94 19	79.51 429	77.03 436	86.93 406	87.00 457	76.23 385	92.33 332	90.74 427	68.93 476	74.52 462	88.23 425	49.58 473	96.62 342	57.64 486	84.29 360	87.94 481

tt0320		80.13 420	77.41 430	88.29 366	90.50 412	78.02 340	93.10 294	90.71 428	66.06 488	76.75 445	86.97 443	49.56 474	95.40 414	71.65 413	71.41 462	91.46 435

TDRefinement		79.81 424	77.34 431	87.22 400	79.24 498	75.48 394	93.12 291	92.03 387	76.45 419	75.01 458	91.58 339	49.19 475	96.44 365	70.22 430	69.18 470	89.75 461

test_vis1_n		86.56 314	86.49 279	86.78 412	88.51 439	72.69 426	94.68 180	93.78 338	79.55 371	90.70 168	95.31 178	48.75 476	93.28 451	93.15 70	93.99 198	94.38 313

MIMVSNet1		79.38 430	77.28 432	85.69 427	86.35 461	73.67 413	91.61 358	92.75 367	78.11 399	72.64 472	88.12 426	48.16 477	91.97 468	60.32 477	77.49 437	91.43 436

LF4IMVS		80.37 418	79.07 418	84.27 444	86.64 458	69.87 461	89.39 419	91.05 416	76.38 421	74.97 459	90.00 392	47.85 478	94.25 435	74.55 399	80.82 412	88.69 475

EG-PatchMatch MVS		82.37 389	80.34 391	88.46 360	90.27 417	79.35 305	92.80 314	94.33 310	77.14 408	73.26 469	90.18 385	47.47 479	96.72 331	70.25 428	87.32 336	89.30 465

test_fmvs2		83.98 367	84.03 352	83.83 448	87.16 456	67.53 473	93.93 246	92.89 361	77.62 401	86.89 256	93.53 266	47.18 480	92.02 466	90.54 140	86.51 341	91.93 422

ttmdpeth		76.55 443	74.64 448	82.29 458	82.25 490	67.81 470	89.76 411	85.69 478	70.35 473	75.76 454	91.69 332	46.88 481	89.77 483	66.16 456	63.23 490	89.30 465

sc_t1		81.53 402	78.67 423	90.12 291	90.78 400	78.64 321	93.91 249	90.20 436	68.42 478	80.82 393	89.88 395	46.48 482	96.76 330	76.03 382	71.47 461	94.96 282

MVStest1		72.91 450	69.70 455	82.54 454	78.14 499	73.05 421	88.21 440	86.21 474	60.69 494	64.70 489	90.53 373	46.44 483	85.70 497	58.78 484	53.62 499	88.87 472

tt0320-xc		79.63 428	76.66 437	88.52 359	91.03 386	78.72 318	93.00 300	89.53 457	66.37 485	76.11 452	87.11 442	46.36 484	95.32 417	72.78 409	67.67 480	91.51 432

TinyColmap		79.76 425	77.69 428	85.97 421	91.71 360	73.12 420	89.55 414	90.36 434	75.03 436	72.03 474	90.19 384	46.22 485	96.19 379	63.11 468	81.03 406	88.59 477

myMVS_eth3d		79.67 426	78.79 421	82.32 457	91.92 350	64.08 484	89.75 412	87.40 472	81.72 337	78.82 427	87.20 438	45.33 486	91.29 474	59.09 483	87.84 327	91.60 428

tmp_tt		35.64 487	39.24 487	24.84 510	14.87 558	23.90 536	62.71 513	51.51 519	6.58 537	36.66 519	62.08 518	44.37 487	30.34 533	52.40 494	22.00 527	20.27 533

testing3		80.46 416	79.59 408	83.06 451	93.44 293	64.64 483	93.33 279	85.47 480	84.34 267	79.93 409	90.84 363	44.35 488	92.39 461	57.06 488	87.56 330	92.16 419

new_pmnet		72.15 451	70.13 454	78.20 469	82.95 488	65.68 477	83.91 483	82.40 490	62.94 493	64.47 490	79.82 487	42.85 489	86.26 495	57.41 487	74.44 449	82.65 491

test_vis1_rt		77.96 439	76.46 438	82.48 455	85.89 466	71.74 440	90.25 396	78.89 497	71.03 471	71.30 478	81.35 484	42.49 490	91.05 477	84.55 243	82.37 386	84.65 486

EGC-MVSNET		61.97 463	56.37 468	78.77 467	89.63 430	73.50 415	89.12 424	82.79 488	0.21 556	1.24 558	84.80 462	39.48 491	90.04 482	44.13 504	75.94 447	72.79 502

dongtai		58.82 468	58.24 466	60.56 492	83.13 486	45.09 517	82.32 489	48.22 522	67.61 481	61.70 495	69.15 507	38.75 492	76.05 512	32.01 517	41.31 508	60.55 515

kuosan		53.51 473	53.30 473	54.13 500	76.06 501	45.36 516	80.11 496	48.36 521	59.63 496	54.84 498	63.43 516	37.41 493	62.07 522	20.73 526	39.10 510	54.96 519

usedtu_dtu_shiyan2		74.72 447	71.30 452	84.98 436	77.78 500	70.58 455	91.85 350	90.76 426	67.24 483	68.06 485	82.17 482	37.13 494	92.78 458	60.69 476	66.03 483	91.59 430

pmmvs3		71.81 453	68.71 456	81.11 460	75.86 502	70.42 456	86.74 461	83.66 486	58.95 497	68.64 484	80.89 486	36.93 495	89.52 485	63.10 469	63.59 488	83.39 487

mvsany_test3		74.95 446	73.26 450	80.02 464	74.61 503	63.16 489	85.53 471	78.42 499	74.16 446	74.89 460	86.46 447	36.02 496	89.09 487	82.39 277	66.91 481	87.82 482

PM-MVS		78.11 438	76.12 441	84.09 446	83.54 485	70.08 458	88.97 427	85.27 482	79.93 365	74.73 461	86.43 449	34.70 497	93.48 448	79.43 343	72.06 457	88.72 474

ambc				83.06 451	79.99 496	63.51 488	77.47 500	92.86 362		74.34 464	84.45 464	28.74 498	95.06 422	73.06 408	68.89 473	90.61 450

test_method		50.52 477	48.47 477	56.66 497	52.26 528	18.98 538	41.51 524	81.40 492	10.10 527	44.59 509	75.01 499	28.51 499	68.16 515	53.54 492	49.31 504	82.83 490

DeepMVS_CX				56.31 498	74.23 505	51.81 509		56.67 518	44.85 507	48.54 503	75.16 498	27.87 500	58.74 523	40.92 511	52.22 501	58.39 518

test_fmvs3		77.67 440	77.16 435	79.22 465	79.52 497	61.14 493	92.34 331	91.64 400	73.98 448	78.86 426	86.59 446	27.38 501	87.03 491	88.12 184	75.97 446	89.50 462

test_f		71.95 452	70.87 453	75.21 474	74.21 506	59.37 499	85.07 475	85.82 477	65.25 489	70.42 480	83.13 471	23.62 502	82.93 504	78.32 355	71.94 459	83.33 488

FPMVS		64.63 462	62.55 464	70.88 478	70.80 509	56.71 501	84.42 481	84.42 484	51.78 501	49.57 501	81.61 483	23.49 503	81.48 506	40.61 512	76.25 445	74.46 501

APD_test1		69.04 456	66.26 462	77.36 473	80.51 495	62.79 490	85.46 472	83.51 487	54.11 500	59.14 497	84.79 463	23.40 504	89.61 484	55.22 489	70.24 464	79.68 496

ANet_high		58.88 467	54.22 472	72.86 475	56.50 525	56.67 502	80.75 493	86.00 476	73.09 458	37.39 517	64.63 514	22.17 505	79.49 508	43.51 506	23.96 522	82.43 492

EMVS		42.07 484	41.12 486	44.92 506	63.45 519	35.56 527	73.65 503	63.48 515	33.05 517	26.88 527	45.45 524	21.27 506	67.14 517	19.80 527	23.02 524	32.06 527

ArgMatch-SfM		70.39 454	67.69 458	78.49 468	81.44 492	60.73 494	84.71 480	75.65 508	68.09 480	66.71 488	86.79 444	20.42 507	86.05 496	71.50 416	53.87 498	88.67 476

Gipuma		57.99 469	54.91 471	67.24 487	88.51 439	65.59 478	52.21 517	90.33 435	43.58 509	42.84 510	51.18 521	20.29 508	85.07 498	34.77 514	70.45 463	51.05 520
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
E-PMN		43.23 483	42.29 483	46.03 504	65.58 517	37.41 525	73.51 504	64.62 513	33.99 515	28.47 525	47.87 523	19.90 509	67.91 516	22.23 525	24.45 520	32.77 526

ArgMatch-Sym		69.79 455	67.05 460	77.99 471	81.59 491	61.16 492	84.99 476	71.84 509	67.17 484	67.90 486	86.60 445	19.89 510	85.00 499	70.93 424	52.57 500	87.82 482

PMMVS2		59.60 464	56.40 467	69.21 484	68.83 512	46.58 513	73.02 507	77.48 504	55.07 499	49.21 502	72.95 503	17.43 511	80.04 507	49.32 499	44.33 507	80.99 494

LCM-MVSNet		66.00 460	62.16 465	77.51 472	64.51 518	58.29 500	83.87 484	90.90 422	48.17 503	54.69 499	73.31 502	16.83 512	86.75 492	65.47 458	61.67 492	87.48 484

test_vis3_rt		65.12 461	62.60 463	72.69 476	71.44 508	60.71 495	87.17 456	65.55 512	63.80 492	53.22 500	65.65 513	14.54 513	89.44 486	76.65 372	65.38 485	67.91 511

testf1		59.54 465	56.11 469	69.85 482	69.28 510	56.61 503	80.37 494	76.55 506	42.58 510	45.68 507	75.61 494	11.26 514	84.18 500	43.20 508	60.44 494	68.75 508

APD_test2		59.54 465	56.11 469	69.85 482	69.28 510	56.61 503	80.37 494	76.55 506	42.58 510	45.68 507	75.61 494	11.26 514	84.18 500	43.20 508	60.44 494	68.75 508

MVS_clip		24.79 494	27.71 494	16.02 520	35.36 540	15.85 540	27.38 532	5.39 556	6.70 536	40.04 514	63.09 517	10.55 516	8.72 554	27.86 521	33.03 516	23.49 531

PDCNetPlus		48.34 478	45.15 481	57.91 495	61.43 520	41.85 519	65.98 512	38.30 526	47.59 504	37.96 516	71.85 504	10.18 517	66.85 519	52.94 493	20.14 533	65.03 513

VLMVS		10.93 510	11.73 510	8.51 532	11.99 559	6.47 562	9.10 549	5.11 557	0.73 553	17.62 532	25.59 536	9.61 518	6.56 556	6.19 537	19.64 534	12.50 536

VLMVS_CLIP		27.58 491	28.97 492	23.41 512	23.47 554	13.17 546	30.64 530	40.90 524	9.21 529	36.34 520	50.75 522	8.75 519	38.05 528	25.18 523	35.53 512	19.03 535

LoFTR		57.22 470	52.62 474	71.00 477	72.03 507	48.57 512	72.00 508	70.08 511	44.40 508	40.92 513	76.42 492	8.12 520	82.76 505	42.28 510	47.33 506	81.66 493

DenseAffine		56.77 471	52.17 475	70.54 480	74.27 504	53.25 508	77.23 501	50.43 520	49.87 502	47.26 506	77.37 491	7.99 521	79.10 509	50.35 496	34.79 513	79.28 497

RoMa-SfM		53.80 472	49.39 476	67.06 488	67.87 514	48.86 510	75.04 502	38.06 527	47.23 505	47.40 505	78.96 489	7.40 522	76.66 511	48.89 500	33.62 514	75.64 500

MASt3R-SfM		45.78 481	43.96 482	51.24 502	45.04 530	29.83 531	57.88 514	38.83 525	31.88 518	47.48 504	81.30 485	7.16 523	51.15 526	49.56 498	36.51 511	72.74 503

ALIKED-LG		28.00 490	26.54 495	32.41 507	58.12 522	31.80 528	47.26 520	21.21 532	14.15 524	19.16 530	41.93 526	6.72 524	35.73 529	5.96 538	24.32 521	29.69 528

MatchFormer		51.11 475	46.66 479	64.46 490	67.11 515	43.39 518	70.54 509	63.67 514	33.19 516	37.22 518	70.30 506	6.67 525	78.17 510	30.29 518	40.94 509	71.81 505

PMVS	47.18 22	52.22 474	48.46 478	63.48 491	45.72 529	46.20 514	73.41 505	78.31 500	41.03 512	30.06 523	65.68 512	6.05 526	83.43 503	30.04 519	65.86 484	60.80 514
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
SP-DiffGlue		20.02 499	19.96 502	20.21 515	19.64 555	13.14 547	30.51 531	15.49 538	8.39 530	19.98 529	43.75 525	5.48 527	13.72 540	13.75 530	22.65 525	33.78 524

RoMa-HiRes		46.47 479	42.20 484	59.28 494	57.74 523	39.86 524	66.76 511	24.64 530	39.96 513	41.50 512	75.37 497	5.40 528	69.26 514	43.35 507	25.09 518	68.71 510

ALIKED-NN		26.07 493	24.75 497	30.02 509	55.08 527	30.61 530	44.20 523	19.22 534	10.98 526	17.98 531	40.71 527	5.39 529	32.83 531	5.59 539	23.63 523	26.63 530

ALIKED-MNN		26.28 492	24.57 498	31.39 508	56.22 526	31.73 529	45.54 521	19.13 535	11.12 525	17.11 533	39.35 528	5.01 530	34.53 530	5.54 540	22.12 526	27.92 529

DKM		50.92 476	46.13 480	65.30 489	66.27 516	45.98 515	73.05 506	31.91 529	45.08 506	42.04 511	75.01 499	4.95 531	73.81 513	47.90 501	28.96 517	76.09 499

SP-LightGlue		20.24 497	20.15 501	20.49 513	43.51 532	12.27 548	38.68 526	14.56 540	7.54 533	12.90 538	30.07 533	4.75 532	14.38 537	7.60 533	21.75 528	34.82 521

SP-SuperGlue		20.22 498	20.18 500	20.36 514	43.26 533	12.27 548	38.71 525	14.77 539	7.64 532	13.04 537	30.21 532	4.73 533	14.21 539	7.59 534	21.65 529	34.59 522

MVE	39.65 23	43.39 482	38.59 488	57.77 496	56.52 524	48.77 511	55.38 515	58.64 517	29.33 520	28.96 524	52.65 520	4.68 534	64.62 521	28.11 520	33.07 515	59.93 516
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
SP-NN		19.44 501	19.37 504	19.67 517	41.70 535	11.48 553	37.75 528	13.72 543	6.86 534	11.86 539	29.97 534	4.23 535	14.25 538	7.13 535	21.07 530	33.30 525

SP-MNN		19.61 500	19.42 503	20.19 516	42.15 534	11.42 554	38.15 527	14.24 541	6.55 538	11.64 540	29.88 535	4.16 536	14.56 536	7.09 536	20.92 531	34.58 523

XFeat-MNN		17.43 502	16.95 505	18.86 518	16.90 556	11.28 555	27.31 533	17.08 536	8.08 531	15.61 535	35.73 529	4.06 537	22.95 534	10.20 531	17.59 537	22.35 532

wuyk23d		21.27 496	20.48 499	23.63 511	68.59 513	36.41 526	49.57 518	6.85 550	9.37 528	7.89 541	4.46 556	4.03 538	31.37 532	17.47 529	16.07 538	3.12 552

GLUNet-SfM		31.36 489	26.25 496	46.70 503	35.51 539	24.89 535	33.71 529	36.36 528	19.08 523	23.78 528	52.69 519	3.82 539	56.26 525	19.75 528	11.56 547	58.95 517

DKM-HiRes		45.90 480	41.41 485	59.36 493	59.55 521	39.90 523	67.13 510	23.25 531	39.95 514	38.74 515	71.81 505	3.67 540	66.42 520	43.82 505	24.82 519	71.77 506

XFeat-NN		15.96 503	15.86 506	16.25 519	15.78 557	9.87 558	25.17 534	13.83 542	6.76 535	15.68 534	34.83 530	3.61 541	19.28 535	9.22 532	17.90 535	19.58 534

ELoFTR		40.15 485	35.08 489	55.36 499	41.27 536	28.17 534	47.70 519	43.76 523	29.15 521	30.35 522	65.97 511	2.17 542	66.90 518	34.51 515	20.83 532	71.00 507

SIFT-NN		12.98 504	13.18 507	12.37 521	36.49 538	16.03 539	22.41 535	7.69 546	4.89 539	7.41 542	20.48 538	1.69 543	11.46 542	1.88 544	15.70 539	9.61 538

SIFT-MNN		12.44 505	12.55 508	12.11 522	34.55 541	15.21 541	20.91 536	7.74 545	4.86 540	6.54 544	20.09 539	1.51 544	11.47 541	1.88 544	14.87 541	9.64 537

SIFT-NN-NCMNet		12.12 506	12.25 509	11.75 523	32.82 543	14.83 542	20.73 537	7.58 547	4.72 542	6.60 543	19.53 540	1.49 545	11.15 544	1.74 546	15.02 540	9.28 539

SIFT-NN-UMatch		11.06 509	11.19 515	10.66 527	28.66 549	12.16 550	19.79 538	6.86 549	4.73 541	5.21 547	19.47 542	1.46 546	10.70 547	1.71 547	12.79 545	9.13 541

SIFT-NN-CMatch		11.26 508	11.31 513	11.13 525	30.21 547	13.40 545	18.43 540	6.79 551	4.71 543	6.47 545	19.53 540	1.43 547	10.72 546	1.71 547	12.49 546	9.26 540

SIFT-NN-PointCN		10.26 513	10.46 518	9.65 530	27.18 550	9.89 557	17.89 542	6.17 553	4.40 549	5.65 546	18.29 546	1.43 547	10.09 550	1.61 551	11.55 548	8.99 543

SIFT-NCM-Cal		11.58 507	11.64 511	11.40 524	33.45 542	14.10 543	19.75 539	6.89 548	4.68 545	4.55 551	18.60 545	1.34 549	11.28 543	1.53 552	13.95 542	8.82 544

SIFT-ConvMatch		10.91 511	10.94 516	10.84 526	32.07 544	13.57 544	17.23 543	6.35 552	4.71 543	5.18 548	18.94 543	1.30 550	10.76 545	1.65 550	11.02 549	8.19 545

SIFT-CM-Cal		10.08 514	10.13 520	9.92 529	30.71 546	11.88 551	15.35 545	5.44 555	4.59 547	4.72 549	18.04 548	1.26 551	10.19 549	1.46 554	9.60 551	7.69 547

SIFT-UMatch		10.58 512	10.73 517	10.15 528	31.05 545	11.65 552	18.01 541	5.92 554	4.65 546	4.72 549	18.93 544	1.25 552	10.62 548	1.66 549	10.39 550	8.16 546

SIFT-UM-Cal		9.80 515	10.00 521	9.22 531	30.05 548	10.15 556	16.31 544	4.85 559	4.54 548	4.19 552	18.23 547	1.19 553	9.95 551	1.52 553	9.11 553	7.57 548

MVS_baseline		7.30 522	8.69 525	3.12 536	8.45 560	0.31 565	3.27 550	0.80 562	0.16 557	14.50 536	32.51 531	1.15 554	0.00 559	4.24 541	13.11 544	9.06 542

PMatch-SfM		38.18 486	33.34 490	52.72 501	43.67 531	28.18 533	52.96 516	16.29 537	29.70 519	31.24 521	68.56 509	1.08 555	57.70 524	38.73 513	17.80 536	72.30 504

SIFT-PCN-Cal		8.65 519	8.88 523	7.98 533	26.74 551	7.47 560	13.90 547	4.61 560	4.09 551	3.82 553	15.86 549	1.01 556	8.94 552	1.34 555	8.52 554	7.53 549

SIFT-PointCN		8.76 517	9.03 522	7.96 534	26.50 552	7.60 559	14.94 546	5.08 558	4.10 550	3.74 554	15.46 550	0.94 557	8.92 553	1.33 556	9.14 552	7.37 550

SIFT-NCMNet		7.46 521	7.71 526	6.72 535	25.03 553	6.86 561	11.42 548	2.98 561	4.05 552	3.38 555	13.68 551	0.84 558	7.65 555	1.13 557	6.87 555	5.66 551

PMatch-Up-SfM		32.59 488	28.46 493	44.98 505	37.19 537	22.27 537	44.73 522	10.63 544	23.85 522	27.52 526	64.10 515	0.78 559	47.14 527	34.15 516	13.22 543	65.53 512

test123		8.76 517	11.22 514	1.39 537	0.85 562	0.97 563	85.76 469	0.35 564	0.54 555	2.45 557	8.14 555	0.60 560	0.48 557	2.16 543	0.17 557	2.71 553

testmvs		8.92 516	11.52 512	1.12 538	1.06 561	0.46 564	86.02 465	0.65 563	0.62 554	2.74 556	9.52 554	0.31 561	0.45 558	2.38 542	0.39 556	2.46 554

mmdepth		0.00 524	0.00 527	0.00 539	0.00 563	0.00 566	0.00 551	0.00 565	0.00 558	0.00 559	0.00 557	0.00 562	0.00 559	0.00 558	0.00 558	0.00 555

monomultidepth		0.00 524	0.00 527	0.00 539	0.00 563	0.00 566	0.00 551	0.00 565	0.00 558	0.00 559	0.00 557	0.00 562	0.00 559	0.00 558	0.00 558	0.00 555

test_blank		0.00 524	0.00 527	0.00 539	0.00 563	0.00 566	0.00 551	0.00 565	0.00 558	0.00 559	0.00 557	0.00 562	0.00 559	0.00 558	0.00 558	0.00 555

uanet_test		0.00 524	0.00 527	0.00 539	0.00 563	0.00 566	0.00 551	0.00 565	0.00 558	0.00 559	0.00 557	0.00 562	0.00 559	0.00 558	0.00 558	0.00 555

DCPMVS		0.00 524	0.00 527	0.00 539	0.00 563	0.00 566	0.00 551	0.00 565	0.00 558	0.00 559	0.00 557	0.00 562	0.00 559	0.00 558	0.00 558	0.00 555

sosnet-low-res		0.00 524	0.00 527	0.00 539	0.00 563	0.00 566	0.00 551	0.00 565	0.00 558	0.00 559	0.00 557	0.00 562	0.00 559	0.00 558	0.00 558	0.00 555

sosnet		0.00 524	0.00 527	0.00 539	0.00 563	0.00 566	0.00 551	0.00 565	0.00 558	0.00 559	0.00 557	0.00 562	0.00 559	0.00 558	0.00 558	0.00 555

uncertanet		0.00 524	0.00 527	0.00 539	0.00 563	0.00 566	0.00 551	0.00 565	0.00 558	0.00 559	0.00 557	0.00 562	0.00 559	0.00 558	0.00 558	0.00 555

Regformer		0.00 524	0.00 527	0.00 539	0.00 563	0.00 566	0.00 551	0.00 565	0.00 558	0.00 559	0.00 557	0.00 562	0.00 559	0.00 558	0.00 558	0.00 555

ab-mvs-re		7.82 520	10.43 519	0.00 539	0.00 563	0.00 566	0.00 551	0.00 565	0.00 558	0.00 559	93.88 254	0.00 562	0.00 559	0.00 558	0.00 558	0.00 555

uanet		0.00 524	0.00 527	0.00 539	0.00 563	0.00 566	0.00 551	0.00 565	0.00 558	0.00 559	0.00 557	0.00 562	0.00 559	0.00 558	0.00 558	0.00 555

PatchmatchNet2					0.00 563	62.07 491	85.98 466	87.63 470	68.79 477
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. CVPR 2021
PatchmatchNet1														54.59 491	77.20 439	90.17 456
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. CVPR 2021
PatchmatchNet3													91.68 472
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. CVPR 2021
aaatest				94.84 34	98.88 1	85.89 66	97.32 10	97.86 1	88.11 135	97.21 14	97.54 46		99.67 1	95.27 41	98.85 22	98.95 13

WAC-MVS						64.08 484								59.14 482

FOURS1					98.86 4	85.54 75	98.29 1	97.49 11	89.79 66	96.29 32

MSC_two_6792asdad				96.52 1	97.78 61	90.86 1		96.85 81					99.61 7	96.03 27	99.06 9	99.07 7

No_MVS				96.52 1	97.78 61	90.86 1		96.85 81					99.61 7	96.03 27	99.06 9	99.07 7

eth-test2					0.00 563

eth-test					0.00 563

IU-MVS					98.77 8	86.00 55		96.84 83	81.26 350	97.26 13				95.50 37	99.13 3	99.03 10

save fliter					97.85 56	85.63 74	95.21 142	96.82 86	89.44 77

test_0728_SECOND				95.01 18	98.79 5	86.43 41	97.09 21	97.49 11					99.61 7	95.62 35	99.08 7	98.99 11

GSMVS																96.12 234

test_part2					98.55 15	87.22 20				96.40 31

MTGPA								96.97 66

MTMP							96.16 60	60.64 516

gm-plane-assit					89.60 431	68.00 467			77.28 407		88.99 411		97.57 250	79.44 342

test9_res														91.91 111	98.71 36	98.07 84

agg_prior2														90.54 140	98.68 41	98.27 65

agg_prior					97.38 73	85.92 62		96.72 101		92.16 121			98.97 88

test_prior4						85.96 59	94.11 226

test_prior				93.82 74	97.29 78	84.49 99		96.88 79					98.87 101			98.11 83

旧先验2							93.36 278		71.25 469	94.37 62			97.13 307	86.74 206

新几何2							93.11 293

无先验							93.28 286	96.26 141	73.95 449				99.05 68	80.56 316		96.59 213

原ACMM2							92.94 304

testdata2													98.75 117	78.30 356

testdata1							92.15 341		87.94 144

plane_prior7					94.70 203	82.74 166

plane_prior5								96.22 147					98.12 181	88.15 181	89.99 286	94.63 295

plane_prior4											94.86 204

plane_prior3						82.75 164			90.26 50	86.91 253

plane_prior2							95.85 93		90.81 27

plane_prior1					94.59 212

plane_prior						82.73 167	95.21 142		89.66 71						89.88 291

n2								0.00 565

nn								0.00 565

door-mid								85.49 479

test11								96.57 113

door								85.33 481

HQP5-MVS						81.56 207

HQP-NCC					94.17 252		94.39 205		88.81 104	85.43 299

ACMP_Plane					94.17 252		94.39 205		88.81 104	85.43 299

BP-MVS														87.11 203

HQP4-MVS										85.43 299			97.96 218			94.51 305

HQP3-MVS								96.04 174							89.77 295

NP-MVS					94.37 233	82.42 181					93.98 247

ACMMP++_ref															87.47 331

ACMMP++															88.01 323

High-resolution multi-view benchmark